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X1up*ez^&JVAh;f^2erTtoFMoQqwa{z literal 0 HcmV?d00001 diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/classifier.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/classifier.py new file mode 100644 index 00000000..e307d9b7 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/classifier.py @@ -0,0 +1,155 @@ +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np +import torch +import torch.nn as nn +from .utils import _tranpose_and_gather_feat, _get_wh_feat, _get_4ps_feat, _normalized_ps +import torch.nn.functional as F + +import json +import cv2 +import os +from .transformer import Transformer +import math +import time +import random +import imgaug.augmenters as iaa +import time +import copy + +class Stacker(nn.Module): + def __init__(self, input_size, hidden_size, output_size, layers, heads=8, dropout=0.1): + super(Stacker, self).__init__() + self.logi_encoder = nn.Sequential( + nn.Linear(input_size, hidden_size), + nn.ReLU(inplace=True), + nn.Linear(hidden_size, hidden_size), + nn.ReLU(inplace=True) #newly added + ) + self.tsfm = Transformer(2 * hidden_size, hidden_size, output_size, layers, heads, dropout) + + def forward(self, outputs, logi, mask = None, require_att = False): + logi_embeddings = self.logi_encoder(logi) + + cat_embeddings = torch.cat((logi_embeddings, outputs), dim=2) + + if mask is None: + if require_att: + stacked_axis, att = self.tsfm(cat_embeddings) + else: + stacked_axis = self.tsfm(cat_embeddings) + else: + stacked_axis = self.tsfm(cat_embeddings, mask=mask) + + if require_att: + return stacked_axis, att + else: + return stacked_axis + +class Processor(nn.Module): + def __init__(self, opt): + super(Processor, self).__init__() + + if opt.wiz_stacking: + self.stacker = Stacker(opt.output_size, opt.hidden_size, opt.output_size, opt.stacking_layers) + + #input_state, hidden_state, output_state, layers, heads, dropout + self.tsfm_axis = Transformer(opt.input_size, opt.hidden_size, opt.output_size, opt.tsfm_layers, opt.num_heads, opt.att_dropout) #original version + self.x_position_embeddings = nn.Embedding(opt.max_fmp_size, opt.hidden_size) + self.y_position_embeddings = nn.Embedding(opt.max_fmp_size, opt.hidden_size) + + self.opt = opt + + def forward(self, outputs, dets = None, batch = None, cc_match = None): #training version forward + # 'outputs' stands for the feature of cells + # mask = None + # att = None + + ''' + Constructing Features: + ''' + if batch is None: + # Inference Mode, the four corner features are gathered + # during bounding boxes decoding for simplicity (See ctdet_4ps_decode() in ./src/lib/model/decode.py). + + vis_feat = outputs + if dets is None: + feat = vis_feat + + else: + left_pe = self.x_position_embeddings(dets[:, :, 0]) + upper_pe = self.y_position_embeddings(dets[:, :, 1]) + right_pe = self.x_position_embeddings(dets[:, :, 2]) + lower_pe = self.y_position_embeddings(dets[:, :, 5]) + feat = vis_feat + left_pe + upper_pe + right_pe + lower_pe + + # !TODO: moving the processings here and uniform the feature construction code for training and inference. + + else: + #Training Mode + ind = batch['hm_ind'] + mask = batch['hm_mask'] #during training, the attention mask will be applied + output = outputs[-1] + pred = output['ax'] + ct_feat = _tranpose_and_gather_feat(pred, ind) + + if self.opt.wiz_2dpe: + cr_feat = _get_4ps_feat(batch['cc_match'], output) + cr_feat = cr_feat.sum(axis = 3) + vis_feat = ct_feat + cr_feat + + ps = _get_wh_feat(ind, batch, 'gt') + ps = _normalized_ps(ps, self.opt.max_fmp_size) + + left_pe = self.x_position_embeddings(ps[:, :, 0]) + upper_pe = self.y_position_embeddings(ps[:, :, 1]) + right_pe = self.x_position_embeddings(ps[:, :, 2]) + lower_pe = self.y_position_embeddings(ps[:, :, 5]) + + feat = vis_feat + left_pe + upper_pe + right_pe + lower_pe + + elif self.opt.wiz_4ps: + cr_feat = _get_4ps_feat(batch['cc_match'], output) + cr_feat = cr_feat.sum(axis = 3) + feat = ct_feat + cr_feat + + elif self.opt.wiz_vanilla: + feat = ct_feat + + ''' + Put Features into TSFM: + ''' + + if batch is None: + #Inference Mode + logic_axis = self.tsfm_axis(feat) + if self.opt.wiz_stacking: + stacked_axis = self.stacker(feat, logic_axis) + else: + #Training Mode + logic_axis = self.tsfm_axis(feat, mask = mask) + if self.opt.wiz_stacking: + stacked_axis = self.stacker(feat, logic_axis, mask = mask) + + if self.opt.wiz_stacking: + return logic_axis, stacked_axis + else: + return logic_axis + +def load_processor(model, model_path, optimizer=None, resume=False, lr=None, lr_step=None): + checkpoint = torch.load(model_path, map_location=lambda storage, loc: storage) + print('loaded {}, epoch {}'.format(model_path, checkpoint['epoch'])) + state_dict = checkpoint['state_dict'] + model.load_state_dict(state_dict) + return model + +def _judge(box): + countx = len(list(set([box[0],box[2],box[4],box[6]]))) + county = len(list(set([box[1],box[3],box[5],box[7]]))) + if countx<2 or county<2: + return False + + return True + diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/data_parallel.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/data_parallel.py new file mode 100644 index 00000000..1a96c0d2 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/data_parallel.py @@ -0,0 +1,128 @@ +import torch +from torch.nn.modules import Module +from torch.nn.parallel.scatter_gather import gather +from torch.nn.parallel.replicate import replicate +from torch.nn.parallel.parallel_apply import parallel_apply + + +from .scatter_gather import scatter_kwargs + +class _DataParallel(Module): + r"""Implements data parallelism at the module level. + + This container parallelizes the application of the given module by + splitting the input across the specified devices by chunking in the batch + dimension. In the forward pass, the module is replicated on each device, + and each replica handles a portion of the input. During the backwards + pass, gradients from each replica are summed into the original module. + + The batch size should be larger than the number of GPUs used. It should + also be an integer multiple of the number of GPUs so that each chunk is the + same size (so that each GPU processes the same number of samples). + + See also: :ref:`cuda-nn-dataparallel-instead` + + Arbitrary positional and keyword inputs are allowed to be passed into + DataParallel EXCEPT Tensors. All variables will be scattered on dim + specified (default 0). Primitive types will be broadcasted, but all + other types will be a shallow copy and can be corrupted if written to in + the model's forward pass. + + Args: + module: module to be parallelized + device_ids: CUDA devices (default: all devices) + output_device: device location of output (default: device_ids[0]) + + Example:: + + >>> net = torch.nn.DataParallel(model, device_ids=[0, 1, 2]) + >>> output = net(input_var) + """ + + # TODO: update notes/cuda.rst when this class handles 8+ GPUs well + + def __init__(self, module, device_ids=None, output_device=None, dim=0, chunk_sizes=None): + super(_DataParallel, self).__init__() + + if not torch.cuda.is_available(): + self.module = module + self.device_ids = [] + return + + if device_ids is None: + device_ids = list(range(torch.cuda.device_count())) + if output_device is None: + output_device = device_ids[0] + self.dim = dim + self.module = module + self.device_ids = device_ids + self.chunk_sizes = chunk_sizes + self.output_device = output_device + if len(self.device_ids) == 1: + self.module.cuda(device_ids[0]) + + def forward(self, *inputs, **kwargs): + if not self.device_ids: + return self.module(*inputs, **kwargs) + inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids, self.chunk_sizes) + if len(self.device_ids) == 1: + return self.module(*inputs[0], **kwargs[0]) + replicas = self.replicate(self.module, self.device_ids[:len(inputs)]) + outputs = self.parallel_apply(replicas, inputs, kwargs) + return self.gather(outputs, self.output_device) + + def replicate(self, module, device_ids): + return replicate(module, device_ids) + + def scatter(self, inputs, kwargs, device_ids, chunk_sizes): + return scatter_kwargs(inputs, kwargs, device_ids, dim=self.dim, chunk_sizes=self.chunk_sizes) + + def parallel_apply(self, replicas, inputs, kwargs): + return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)]) + + def gather(self, outputs, output_device): + return gather(outputs, output_device, dim=self.dim) + + +def data_parallel(module, inputs, device_ids=None, output_device=None, dim=0, module_kwargs=None): + r"""Evaluates module(input) in parallel across the GPUs given in device_ids. + + This is the functional version of the DataParallel module. + + Args: + module: the module to evaluate in parallel + inputs: inputs to the module + device_ids: GPU ids on which to replicate module + output_device: GPU location of the output Use -1 to indicate the CPU. + (default: device_ids[0]) + Returns: + a Variable containing the result of module(input) located on + output_device + """ + if not isinstance(inputs, tuple): + inputs = (inputs,) + + if device_ids is None: + device_ids = list(range(torch.cuda.device_count())) + + if output_device is None: + output_device = device_ids[0] + + inputs, module_kwargs = scatter_kwargs(inputs, module_kwargs, device_ids, dim) + if len(device_ids) == 1: + return module(*inputs[0], **module_kwargs[0]) + used_device_ids = device_ids[:len(inputs)] + replicas = replicate(module, used_device_ids) + outputs = parallel_apply(replicas, inputs, module_kwargs, used_device_ids) + return gather(outputs, output_device, dim) + +def DataParallel(module, device_ids=None, output_device=None, dim=0, chunk_sizes=None): + if chunk_sizes is None: + return torch.nn.DataParallel(module, device_ids, output_device, dim) + standard_size = True + for i in range(1, len(chunk_sizes)): + if chunk_sizes[i] != chunk_sizes[0]: + standard_size = False + if standard_size: + return torch.nn.DataParallel(module, device_ids, output_device, dim) + return _DataParallel(module, device_ids, output_device, dim, chunk_sizes) \ No newline at end of file diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/decode.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/decode.py new file mode 100644 index 00000000..e07da2fe --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/decode.py @@ -0,0 +1,383 @@ +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import torch +import torch.nn as nn +from .utils import _gather_feat, _tranpose_and_gather_feat, _get_4ps_feat +import numpy as np +import shapely +import time +from shapely.geometry import Polygon, MultiPoint, Point + +def _nms(heat, name, kernel=3): + pad = (kernel - 1) // 2 + + hmax = nn.functional.max_pool2d( + heat, (kernel, kernel), stride=1, padding=pad) + #save_map(hmax.cpu().numpy()[0],name) + keep = (hmax == heat).float() + return heat * keep,keep + +def _topk_channel(scores, K=40): + + batch, cat, height, width = scores.size() + + topk_scores, topk_inds = torch.topk(scores.view(batch, cat, -1), K) + + topk_inds = topk_inds % (height * width) + topk_ys = (topk_inds / width).int().float() + topk_xs = (topk_inds % width).int().float() + + return topk_scores, topk_inds, topk_ys, topk_xs + +def _topk(scores, K=40, device=None): + #import ipdb + #ipdb.set_trace() + batch, cat, height, width = scores.size() + + topk_scores, topk_inds = torch.topk(scores.view(batch, cat, -1), K) + + topk_inds = topk_inds % (torch.Tensor([height]).to(torch.int64).to(device) * torch.Tensor([width]).to(torch.int64).to(device)) + topk_ys = (topk_inds / torch.Tensor([width]).to(device)).int().float() + topk_xs = (topk_inds % torch.Tensor([width]).to(torch.int64).to(device)).int().float() + + topk_score, topk_ind = torch.topk(topk_scores.view(batch, -1), K) + topk_clses = (topk_ind // K).int() + topk_inds = _gather_feat( + topk_inds.view(batch, -1, 1), topk_ind).view(batch, K) + topk_ys = _gather_feat(topk_ys.view(batch, -1, 1), topk_ind).view(batch, K) + topk_xs = _gather_feat(topk_xs.view(batch, -1, 1), topk_ind).view(batch, K) + + return topk_score, topk_inds, topk_clses, topk_ys, topk_xs + +def corner_decode(mk, st_reg, mk_reg=None, K=400,device=None): + batch, cat, height, width = mk.size() + mk,keep = _nms(mk,'mk.0.maxpool') + scores, inds, clses, ys, xs = _topk(mk, K=K, device=device) + if mk_reg is not None: + reg = _tranpose_and_gather_feat(mk_reg, inds) + reg = reg.view(batch, K, 2) + xs = xs.view(batch, K, 1) + reg[:, :, 0:1] + ys = ys.view(batch, K, 1) + reg[:, :, 1:2] + else: + xs = xs.view(batch, K, 1) + 0.5 + ys = ys.view(batch, K, 1) + 0.5 + scores = scores.view(batch, K, 1) + st_Reg = _tranpose_and_gather_feat(st_reg, inds) + bboxes = torch.cat([xs - st_Reg[..., 0:1], + ys - st_Reg[..., 1:2], + xs - st_Reg[..., 2:3], + ys - st_Reg[..., 3:4], + xs - st_Reg[..., 4:5], + ys - st_Reg[..., 5:6], + xs - st_Reg[..., 6:7], + ys - st_Reg[..., 7:8]], dim=2) + corner_dict = {'scores': scores, 'inds': inds, 'ys': ys, 'xs': xs, 'gboxes': bboxes} + return scores, inds, ys, xs, bboxes, corner_dict + +def ctdet_4ps_decode(heat, wh, ax, cr, corner_dict=None, reg=None, cat_spec_wh=False, K=100, wiz_rev = False): + + # if wiz_rev : + # print('Grouping and Parsing ...') + batch, cat, height, width = heat.size() + device = heat.device + # heat = torch.sigmoid(heat) + # perform nms on heatmaps + heat,keep = _nms(heat,'hm.0.maxpool') + + scores, inds, clses, ys, xs = _topk(heat, K=K,device=device) + if reg is not None: + reg = _tranpose_and_gather_feat(reg, inds) + reg = reg.view(batch, K, 2) + xs = xs.view(batch, K, 1) + reg[:, :, 0:1] + ys = ys.view(batch, K, 1) + reg[:, :, 1:2] + else: + xs = xs.view(batch, K, 1) + 0.5 + ys = ys.view(batch, K, 1) + 0.5 + wh = _tranpose_and_gather_feat(wh, inds) + ax = _tranpose_and_gather_feat(ax, inds) + + if cat_spec_wh: + wh = wh.view(batch, K, cat, 8) + clses_ind = clses.view(batch, K, 1, 1).expand(batch, K, 1, 8).long() + wh = wh.gather(2, clses_ind).view(batch, K, 8) + else: + wh = wh.view(batch, K, 8) + clses = clses.view(batch, K, 1).float() + scores = scores.view(batch, K, 1) + + ''' + bboxes = torch.cat([xs - wh[..., 0:1], + ys - wh[..., 1:2], + xs + wh[..., 2:3], + ys - wh[..., 3:4], + xs + wh[..., 4:5], + ys + wh[..., 5:6], + xs - wh[..., 6:7], + ys + wh[..., 7:8]], dim=2) + ''' + + bboxes = torch.cat([xs - wh[..., 0:1], + ys - wh[..., 1:2], + xs - wh[..., 2:3], + ys - wh[..., 3:4], + xs - wh[..., 4:5], + ys - wh[..., 5:6], + xs - wh[..., 6:7], + ys - wh[..., 7:8]], dim=2) + + rev_time_s1 = time.time() + if wiz_rev : + bboxes_rev = bboxes.clone() + bboxes_cpu = bboxes.clone().cpu() + + gboxes = corner_dict['gboxes'] + gboxes_cpu = gboxes.cpu() + + num_bboxes = bboxes.shape[1] + num_gboxes = gboxes.shape[1] + + corner_xs = corner_dict['xs'] + corner_ys = corner_dict['ys'] + corner_scores = corner_dict['scores'] + + + for i in range(num_bboxes): + if scores[0,i,0] >= 0.2 : + count = 0 # counting the number of ends of st head in bbox i + for j in range(num_gboxes): + if corner_scores[0,j,0] >= 0.3: + #here comes to one pair of valid bbox and gbox + #step1 is there an overlap + + bbox = bboxes_cpu[0,i,:] + gbox = gboxes_cpu[0,j,:] + #rev_time_s3 = time.time() + if is_group_faster_faster(bbox, gbox): + #step2 find which corner point to refine, and do refine + cr_x = corner_xs[0,j,0] + cr_y = corner_ys[0,j,0] + + ind4ps = find4ps(bbox, cr_x, cr_y, device) + if bboxes_rev[0, i, 2*ind4ps] == bboxes[0, i, 2*ind4ps] and bboxes_rev[0, i, 2*ind4ps+1] == bboxes[0, i, 2*ind4ps+1]: + #first_shift + count = count + 1 + bboxes_rev[0, i, 2*ind4ps] = cr_x + bboxes_rev[0, i, 2*ind4ps + 1] = cr_y + else: + origin_x = bboxes[0, i, 2*ind4ps] + origin_y = bboxes[0, i, 2*ind4ps+1] + + old_x = bboxes_rev[0, i, 2*ind4ps] + old_y = bboxes_rev[0, i, 2*ind4ps+1] + + if dist(origin_x, origin_y, old_x, old_y) >= dist(origin_x, origin_y, cr_x, cr_y): + count = count + 1 + bboxes_rev[0, i, 2*ind4ps] = cr_x + bboxes_rev[0, i, 2*ind4ps + 1] = cr_y + else: + continue + else: + + continue + else: + break + if count <= 2: + scores[0,i,0] = scores[0,i,0] * 0.4 + else : + break + + if wiz_rev: + + cc_match = torch.cat([(bboxes_rev[:,:,0:1]) + width * torch.round(bboxes_rev[:,:,1:2]), + (bboxes_rev[:,:,2:3]) + width * torch.round(bboxes_rev[:,:,3:4]), + (bboxes_rev[:,:,4:5]) + width * torch.round(bboxes_rev[:,:,5:6]), + (bboxes_rev[:,:,6:7]) + width * torch.round(bboxes_rev[:,:,7:8])], dim=2) + + else: + cc_match = torch.cat([(xs - wh[..., 0:1]) + width * torch.round(ys - wh[..., 1:2]), + (xs - wh[..., 2:3]) + width * torch.round(ys - wh[..., 3:4]), + (xs - wh[..., 4:5]) + width * torch.round(ys - wh[..., 5:6]), + (xs - wh[..., 6:7]) + width * torch.round(ys - wh[..., 7:8])], dim=2) + + cc_match = torch.round(cc_match).to(torch.int64) + + cr_feat = _get_4ps_feat(cc_match, cr) + cr_feat = cr_feat.sum(axis = 3) + if wiz_rev: + detections = torch.cat([bboxes_rev, scores, clses], dim=2) + _, sorted_ind = torch.sort(scores, descending=True, dim=1) + sorted_inds = sorted_ind.expand(detections.size(0), detections.size(1), detections.size(2)) + detections = detections.gather(1, sorted_inds) + sorted_inds2 = sorted_ind.expand(detections.size(0), detections.size(1), ax.size(2)) + ax = ax.gather(1, sorted_inds2) + else: + + detections = torch.cat([bboxes, scores, clses], dim=2) + + return detections, keep, ax, cr_feat + +def wireless_decode(heat, wh, ax, cr, reg=None, cat_spec_wh=False, K=100): + + batch, cat, height, width = heat.size() + # heat = torch.sigmoid(heat) + # perform nms on heatmaps + heat,keep = _nms(heat,'hm.0.maxpool') + + scores, inds, clses, ys, xs = _topk(heat, K=K) + if reg is not None: + reg = _tranpose_and_gather_feat(reg, inds) + reg = reg.view(batch, K, 2) + xs = xs.view(batch, K, 1) + reg[:, :, 0:1] + ys = ys.view(batch, K, 1) + reg[:, :, 1:2] + else: + xs = xs.view(batch, K, 1) + 0.5 + ys = ys.view(batch, K, 1) + 0.5 + wh = _tranpose_and_gather_feat(wh, inds) + ax = _tranpose_and_gather_feat(ax, inds) + + if cat_spec_wh: + wh = wh.view(batch, K, cat, 8) + clses_ind = clses.view(batch, K, 1, 1).expand(batch, K, 1, 8).long() + wh = wh.gather(2, clses_ind).view(batch, K, 8) + else: + wh = wh.view(batch, K, 8) + clses = clses.view(batch, K, 1).float() + scores = scores.view(batch, K, 1) + ''' + bboxes = torch.cat([xs - wh[..., 0:1], + ys - wh[..., 1:2], + xs + wh[..., 2:3], + ys - wh[..., 3:4], + xs + wh[..., 4:5], + ys + wh[..., 5:6], + xs - wh[..., 6:7], + ys + wh[..., 7:8]], dim=2) + ''' + bboxes = torch.cat([xs - wh[..., 0:1], + ys - wh[..., 1:2], + xs - wh[..., 2:3], + ys - wh[..., 3:4], + xs - wh[..., 4:5], + ys - wh[..., 5:6], + xs - wh[..., 6:7], + ys - wh[..., 7:8]], dim=2) + + cc_match = torch.cat([(xs - wh[..., 0:1]) + width * torch.round(ys - wh[..., 1:2]), + (xs - wh[..., 2:3]) + width * torch.round(ys - wh[..., 3:4]), + (xs - wh[..., 4:5]) + width * torch.round(ys - wh[..., 5:6]), + (xs - wh[..., 6:7]) + width * torch.round(ys - wh[..., 7:8])], dim=2) + + cc_match = torch.round(cc_match).to(torch.int64) + + cr_feat = _get_4ps_feat(cc_match, cr) + cr_feat = cr_feat.sum(axis = 3) + + detections = torch.cat([bboxes, scores, clses], dim=2) + return detections, keep, ax, cr_feat + +def find4ps(bbox, x, y,device): + xs = torch.Tensor([bbox[0],bbox[2],bbox[4],bbox[6]]).to(device) + ys = torch.Tensor([bbox[1],bbox[3],bbox[5],bbox[7]]).to(device) + + dx = xs - x + dy = ys - y + + l = dx**2 + dy**2 + return torch.argmin(l) + +def dist(x1, y1, x2, y2): + dx = x1 - x2 + dy = y1 - y2 + l = dx**2 + dy**2 + return l + +def rect_inter(b1_x1, b1_y1, b1_x2, b1_y2, b2_x1, b2_y1, b2_x2, b2_y2): + if (b1_x1 <= b2_x1 and b2_x1 <= b1_x2) or (b1_x1 <= b2_x2 and b2_x2 <= b1_x2): + if (b1_y1 <= b2_y1 and b2_y1 <= b1_y2) or (b1_y1 <= b2_y2 and b2_y2 <= b1_y2): + return True + else: + return False + else: + return False + +def is_group_faster_faster(bbox, gbox): + bbox = bbox.view(4,2) + gbox = gbox.view(4,2) + + bbox_xmin, bbox_xmax, bbox_ymin, bbox_ymax = bbox[:,0].min(), bbox[:,0].max(), bbox[:,1].min(), bbox[:,1].max()#min(bbox_xs), max(bbox_xs), min(bbox_ys), max(bbox_ys) + gbox_xmin, gbox_xmax, gbox_ymin, gbox_ymax = gbox[:,0].min(), gbox[:,0].max(), gbox[:,1].min(), gbox[:,1].max() + + if bbox_xmin > gbox_xmax or gbox_xmin > bbox_xmax or bbox_ymin > gbox_ymax or gbox_ymin > bbox_ymax: + return False + else: + bpoly = Polygon(bbox) + + flag = 0 + for i in range(4): + p = Point(gbox[i]) + if p.within(bpoly): + flag = 1 + break + if flag == 0: + return False + else : + return True + +def ctdet_st_decode(heat, st, reg=None, cat_spec_wh=False, K=100): + batch, cat, height, width = heat.size() + heat,keep = _nms(heat,'hm.0.maxpool') + + scores, inds, clses, ys, xs = _topk(heat, K=K) + if reg is not None: + reg = _tranpose_and_gather_feat(reg, inds) + reg = reg.view(batch, K, 2) + xs = xs.view(batch, K, 1) + reg[:, :, 0:1] + ys = ys.view(batch, K, 1) + reg[:, :, 1:2] + else: + xs = xs.view(batch, K, 1) + 0.5 + ys = ys.view(batch, K, 1) + 0.5 + st = _tranpose_and_gather_feat(st, inds) + if cat_spec_wh: + st = st.view(batch, K, cat, 4) + clses_ind = clses.view(batch, K, 1, 1).expand(batch, K, 1, 4).long() + st = st.gather(2, clses_ind).view(batch, K, 4) + else: + st = st.view(batch, K, 4) + + return st + +def ctdet_decode(heat, wh, reg=None, cat_spec_wh=False, K=100): + batch, cat, height, width = heat.size() + + # heat = torch.sigmoid(heat) + # perform nms on heatmaps + heat = _nms(heat) + + scores, inds, clses, ys, xs = _topk(heat, K=K) + if reg is not None: + reg = _tranpose_and_gather_feat(reg, inds) + reg = reg.view(batch, K, 2) + xs = xs.view(batch, K, 1) + reg[:, :, 0:1] + ys = ys.view(batch, K, 1) + reg[:, :, 1:2] + else: + xs = xs.view(batch, K, 1) + 0.5 + ys = ys.view(batch, K, 1) + 0.5 + wh = _tranpose_and_gather_feat(wh, inds) + if cat_spec_wh: + wh = wh.view(batch, K, cat, 2) + clses_ind = clses.view(batch, K, 1, 1).expand(batch, K, 1, 2).long() + wh = wh.gather(2, clses_ind).view(batch, K, 2) + else: + wh = wh.view(batch, K, 2) + clses = clses.view(batch, K, 1).float() + scores = scores.view(batch, K, 1) + bboxes = torch.cat([xs - wh[..., 0:1] / 2, + ys - wh[..., 1:2] / 2, + xs + wh[..., 0:1] / 2, + ys + wh[..., 1:2] / 2], dim=2) + detections = torch.cat([bboxes, scores, clses], dim=2) + + return detections + diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/losses.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/losses.py new file mode 100644 index 00000000..ca0e35c5 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/losses.py @@ -0,0 +1,245 @@ +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import torch +import torch.nn as nn +from .utils import _flatten_and_gather_feat, _tranpose_and_gather_feat +import torch.nn.functional as F + +def _neg_loss(pred, gt): + ''' Modified focal loss. Exactly the same as CornerNet. + Runs faster and costs a little bit more memory + Arguments: + pred (batch x c x h x w) + gt_regr (batch x c x h x w) + ''' + pos_inds = gt.eq(1).float() + neg_inds = gt.lt(1).float() + + neg_weights = torch.pow(1 - gt, 4) + + loss = 0 + + pos_loss = torch.log(pred) * torch.pow(1 - pred, 2) * pos_inds + neg_loss = torch.log(1 - pred) * torch.pow(pred, 2) * neg_weights * neg_inds + + num_pos = pos_inds.float().sum() + pos_loss = pos_loss.sum() + neg_loss = neg_loss.sum() + + if num_pos == 0: + loss = loss - neg_loss + else: + loss = loss - (pos_loss + neg_loss) / num_pos + return loss + +def _reg_loss(regr, gt_regr, mask): + ''' L1 regression loss + Arguments: + regr (batch x max_objects x dim) + gt_regr (batch x max_objects x dim) + mask (batch x max_objects) + ''' + num = mask.float().sum() + mask = mask.unsqueeze(2).expand_as(gt_regr).float() + + regr = regr * mask + gt_regr = gt_regr * mask + + regr_loss = nn.functional.smooth_l1_loss(regr, gt_regr, size_average=False) + regr_loss = regr_loss / (num + 1e-4) + return regr_loss + +class AxisLoss(nn.Module): + def __init__(self): + super(AxisLoss, self).__init__() + + def forward(self, output, mask, ind, target, logi=None): + span_type = False + #computing vanilla axis loss + if logi is None: + pred = _tranpose_and_gather_feat(output, ind) + else: + pred = logi + + mask = mask.unsqueeze(2).float() + loss = F.l1_loss(pred * mask, target * mask, size_average=False) + loss = loss / (4*(mask.sum() + 1e-4)) + + return loss + +class FocalLoss(nn.Module): + '''nn.Module warpper for focal loss''' + def __init__(self): + super(FocalLoss, self).__init__() + self.neg_loss = _neg_loss + + def forward(self, out, target): + return self.neg_loss(out, target) + +class RegLoss(nn.Module): + '''Regression loss for an output tensor + Arguments: + output (batch x dim x h x w) + mask (batch x max_objects) + ind (batch x max_objects) + target (batch x max_objects x dim) + ''' + def __init__(self): + super(RegLoss, self).__init__() + + def forward(self, output, mask, ind, target): + pred = _tranpose_and_gather_feat(output, ind) + loss = _reg_loss(pred, target, mask) + return loss + +class RegL1Loss(nn.Module): + def __init__(self): + super(RegL1Loss, self).__init__() + + def forward(self, output, mask, ind, target): + pred = _tranpose_and_gather_feat(output, ind) + mask = mask.unsqueeze(2).expand_as(pred).float() + # loss = F.l1_loss(pred * mask, target * mask, reduction='elementwise_mean') + loss = F.l1_loss(pred * mask, target * mask, size_average=False) + loss = loss / (mask.sum() + 1e-4) + return loss + +class PairLoss(nn.Module): + def __init__(self): + super(PairLoss, self).__init__() + + def forward(self, output1, ind1, output2, ind2, mask, mask_cro, ctr_cro_ind, target1, target2, hm_ctxy): + + pred1 = _tranpose_and_gather_feat(output1, ind1) #bxmx8 + pred2 = _tranpose_and_gather_feat(output2, ind2) #bxnx8 + pred2_tmp = pred2 + target2_tmp = target2 + mask = mask.unsqueeze(2).expand_as(pred1).float() + + b = pred1.size(0) + m = pred1.size(1) + n = pred2.size(1) + pred2 = pred2.view(b,4*n,2) + ctr_cro_ind = ctr_cro_ind.unsqueeze(2).expand(b,4*m,2) + pred2 = pred2.gather(1,ctr_cro_ind).view(b,m,8) #bxmx8 + target2 = target2.view(b,4*n,2).gather(1,ctr_cro_ind).view(b,m,8) + + delta = (torch.abs(pred1-target1)+torch.abs(pred2-target2)) / (torch.abs(target1) + 1e-4) + delta = delta * delta + delta_mask = (~delta.gt(1.0))*1#1 - delta.gt(1.0) + delta = delta*(delta_mask.float())+(1-delta_mask).float() + delta = (-3.14)*delta + weight = 1 - torch.exp(delta) + + loss1 = F.l1_loss(pred1 * mask * weight, target1 * mask * weight, size_average=False) + loss2 = F.l1_loss(pred2 * mask * weight, target2 * mask * weight, size_average=False) + loss1 = loss1 / (mask.sum() + 1e-4) + loss2 = loss2 / (mask.sum() + 1e-4) + + mask1 = (target2_tmp==0) + mask_cro = mask_cro.unsqueeze(2).expand(b,n,8) + MASK = (mask1==mask_cro).float() + loss3 = F.l1_loss(pred2_tmp * MASK, target2_tmp * MASK, size_average=False) + loss3 = loss3 / (mask.sum() + 1e-4) + + return loss1, 0.5 * loss2 + 0.2 * loss3 + +class NormRegL1Loss(nn.Module): + def __init__(self): + super(NormRegL1Loss, self).__init__() + + def forward(self, output, mask, ind, target): + pred = _tranpose_and_gather_feat(output, ind) + mask = mask.unsqueeze(2).expand_as(pred).float() + # loss = F.l1_loss(pred * mask, target * mask, reduction='elementwise_mean') + pred = pred / (target + 1e-4) + target = target * 0 + 1 + loss = F.l1_loss(pred * mask, target * mask, size_average=False) + loss = loss / (mask.sum() + 1e-4) + return loss + +class RegWeightedL1Loss(nn.Module): + def __init__(self): + super(RegWeightedL1Loss, self).__init__() + + def forward(self, output, mask, ind, target): + pred = _tranpose_and_gather_feat(output, ind) + mask = mask.float() + # loss = F.l1_loss(pred * mask, target * mask, reduction='elementwise_mean') + loss = F.l1_loss(pred * mask, target * mask, size_average=False) + loss = loss / (mask.sum() + 1e-4) + return loss + +class L1Loss(nn.Module): + def __init__(self): + super(L1Loss, self).__init__() + + def forward(self, output, mask, ind, target): + pred = _tranpose_and_gather_feat(output, ind) + mask = mask.unsqueeze(2).expand_as(pred).float() + loss = F.l1_loss(pred * mask, target * mask, reduction='elementwise_mean') + return loss + + +def compute_res_loss(output, target): + return F.smooth_l1_loss(output, target, reduction='elementwise_mean') + + +def _axis_eval(output, mask, ind, target, logi=None, mode = None): + if logi is None: + pred = _tranpose_and_gather_feat(output, ind) + else: + pred = logi + + dev = pred - target + dev = torch.abs(dev) + + one = torch.ones(dev.shape).cuda() + zero = torch.zeros(dev.shape).cuda() + + true_vec = torch.where(dev < 0.5, one, zero) + #true = torch.sum((true_vec * mask.unsqueeze(2)) == 1) + true = torch.sum(true_vec.sum(axis=2)*mask == 4) + total = (mask == 1).sum() + true = true.to(torch.float32) + total = total.to(torch.float32) + + acc = true/total + + if acc is None: + acc = 0 + + if mode == 'full': + pred_int = process_logi(pred) + + pred_pair = _make_pair_feat(pred_int) + target_pair = _make_pair_feat(target) + mask_pair = _make_pair_feat(ind) + + #pred_int = pred_int.expand() + + mask_1 = mask_pair[:,:,:,0] + mask_2 = mask_pair[:,:,:,1] + + at_vec_h = (target_pair[:,:,:,2]<=target_pair[:,:,:,6]) & (target_pair[:,:,:,6]<=target_pair[:,:,:,3]) & (mask_1 != 0) & (mask_2 != 0) + at_vec_w = (target_pair[:,:,:,0]<=target_pair[:,:,:,4]) & (target_pair[:,:,:,4]<=target_pair[:,:,:,1]) & (mask_1 != 0) & (mask_2 != 0) + + ap_vec_h = (pred_pair[:,:,:,2]<=pred_pair[:,:,:,6]) & (pred_pair[:,:,:,6]<=pred_pair[:,:,:,3]) & (mask_1 != 0) & (mask_2 != 0) + ap_vec_w = (pred_pair[:,:,:,0]<=pred_pair[:,:,:,4]) & (pred_pair[:,:,:,4]<=pred_pair[:,:,:,1]) & (mask_1 != 0) & (mask_2 != 0) + + tp_h = at_vec_h & ap_vec_h + tp_w = at_vec_w & ap_vec_w + + ap = torch.sum(ap_vec_h) + torch.sum(ap_vec_w) + at = torch.sum(at_vec_h) + torch.sum(at_vec_w) + tp = torch.sum(tp_h) + torch.sum(tp_w) + + pre = tp/(ap + 1e-4) + rec = tp/(at + 1e-4) + + return acc , pre, rec + else: + return acc + diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/model.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/model.py new file mode 100644 index 00000000..dfef0dd1 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/model.py @@ -0,0 +1,151 @@ +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import torchvision.models as models +import torch +import torch.nn as nn +import os + +from .networks.fpn_resnet import get_pose_net_fpn +from .networks.fpn_resnet_half import get_pose_net_fpn_half + +from .networks.fpn_mask_resnet import get_pose_net_fpn_mask +from .networks.fpn_mask_resnet_half import get_pose_net_fpn_mask_half + +from .networks.pose_dla_dcn import get_pose_net as get_dla_dcn + + +_model_factory = { + 'dla': get_dla_dcn, + 'resfpn':get_pose_net_fpn, + 'resfpnhalf': get_pose_net_fpn_half, + 'resfpnmask':get_pose_net_fpn_mask, + 'resfpnmaskhalf':get_pose_net_fpn_mask_half +} + +def create_model(arch, heads, head_conv): + num_layers = int(arch[arch.find('_') + 1:]) if '_' in arch else 0 + arch = arch[:arch.find('_')] if '_' in arch else arch + get_model = _model_factory[arch] + model = get_model(num_layers=num_layers, heads=heads, head_conv=head_conv) + return model + +def load_model(model, model_path, optimizer=None, resume=False, + lr=None, lr_step=None): + start_epoch = 0 + + checkpoint = torch.load(model_path, map_location=lambda storage, loc: storage) + + print('loaded {}, epoch {}'.format(model_path, checkpoint['epoch'])) + state_dict_ = checkpoint['state_dict'] + state_dict = {} + + # convert data_parallal to model + for k in state_dict_: + if k.startswith('module') and not k.startswith('module_list'): + state_dict[k[7:]] = state_dict_[k] + else: + state_dict[k] = state_dict_[k] + print(model, file=open('model.txt', 'a')) + model_state_dict = model.state_dict() + + # check loaded parameters and created model parameters + for k in state_dict: + if k in model_state_dict: + if state_dict[k].shape != model_state_dict[k].shape: + print('Skip loading parameter {}, required shape{}, '\ + 'loaded shape{}.'.format( + k, model_state_dict[k].shape, state_dict[k].shape)) + state_dict[k] = model_state_dict[k] + else: + print('Drop parameter {}.'.format(k)) + for k in model_state_dict: + if not (k in state_dict): + print('No param {}.'.format(k)) + state_dict[k] = model_state_dict[k] + model.load_state_dict(state_dict, strict=False) + + # resume optimizer parameters + if optimizer is not None and resume: + if 'optimizer' in checkpoint: + optimizer.load_state_dict(checkpoint['optimizer']) + start_epoch = checkpoint['epoch'] + start_lr = lr + for step in lr_step: + if start_epoch >= step: + start_lr *= 0.1 + for param_group in optimizer.param_groups: + param_group['lr'] = start_lr + print('Resumed optimizer with start lr', start_lr) + else: + print('No optimizer parameters in checkpoint.') + if optimizer is not None: + return model, optimizer, start_epoch + else: + return model + +def load_multiple(model, model_path, optimizer=None, resume=False, + lr=None, lr_step=None): + start_epoch = 0 + + checkpoint = torch.load(model_path, map_location=lambda storage, loc: storage) + + print('loaded {}, epoch {}'.format(model_path, checkpoint['epoch'])) + state_dict_ = checkpoint['state_dict'] + state_dict = {} + + # convert data_parallal to model + for k in state_dict_: + if k.startswith('module') and not k.startswith('module_list'): + state_dict[k[7:]] = state_dict_[k] + else: + state_dict[k] = state_dict_[k] + model_state_dict = model.state_dict() + + # check loaded parameters and created model parameters + for k in state_dict: + if k in model_state_dict: + if state_dict[k].shape != model_state_dict[k].shape: + print('Skip loading parameter {}, required shape{}, '\ + 'loaded shape{}.'.format( + k, model_state_dict[k].shape, state_dict[k].shape)) + state_dict[k] = model_state_dict[k] + else: + print('Drop parameter {}.'.format(k)) + for k in model_state_dict: + if not (k in state_dict): + print('No param {}.'.format(k)) + state_dict[k] = model_state_dict[k] + model.load_state_dict(state_dict, strict=False) + + # resume optimizer parameters + if optimizer is not None and resume: + if 'optimizer' in checkpoint: + optimizer.load_state_dict(checkpoint['optimizer']) + start_epoch = checkpoint['epoch'] + start_lr = lr + for step in lr_step: + if start_epoch >= step: + start_lr *= 0.1 + for param_group in optimizer.param_groups: + param_group['lr'] = start_lr + print('Resumed optimizer with start lr', start_lr) + else: + print('No optimizer parameters in checkpoint.') + if optimizer is not None: + return model, optimizer, start_epoch + else: + return model + +def save_model(path, epoch, model, optimizer=None): + if isinstance(model, torch.nn.DataParallel): + state_dict = model.module.state_dict() + else: + state_dict = model.state_dict() + data = {'epoch': epoch, + 'state_dict': state_dict} + if not (optimizer is None): + data['optimizer'] = optimizer.state_dict() + torch.save(data, path) + diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/DCNv2.egg-info/PKG-INFO b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/DCNv2.egg-info/PKG-INFO new file mode 100644 index 00000000..0e4d91d8 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/DCNv2.egg-info/PKG-INFO @@ -0,0 +1,7 @@ +Metadata-Version: 2.1 +Name: DCNv2 +Version: 0.1 +Summary: deformable convolutional networks +Home-page: https://github.com/charlesshang/DCNv2 +Author: charlesshang +License-File: LICENSE diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/DCNv2.egg-info/SOURCES.txt b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/DCNv2.egg-info/SOURCES.txt new file mode 100644 index 00000000..f4eeb2b5 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/DCNv2.egg-info/SOURCES.txt @@ -0,0 +1,13 @@ +LICENSE +setup.py +/ssd8/exec/huangjy/AdvancedLiterateMachinery/DocumentUnderstanding/LORE-TSR/src/lib/models/networks/DCNv2/src/vision.cpp +/ssd8/exec/huangjy/AdvancedLiterateMachinery/DocumentUnderstanding/LORE-TSR/src/lib/models/networks/DCNv2/src/cpu/dcn_v2_cpu.cpp +/ssd8/exec/huangjy/AdvancedLiterateMachinery/DocumentUnderstanding/LORE-TSR/src/lib/models/networks/DCNv2/src/cpu/dcn_v2_im2col_cpu.cpp +/ssd8/exec/huangjy/AdvancedLiterateMachinery/DocumentUnderstanding/LORE-TSR/src/lib/models/networks/DCNv2/src/cpu/dcn_v2_psroi_pooling_cpu.cpp +/ssd8/exec/huangjy/AdvancedLiterateMachinery/DocumentUnderstanding/LORE-TSR/src/lib/models/networks/DCNv2/src/cuda/dcn_v2_cuda.cu +/ssd8/exec/huangjy/AdvancedLiterateMachinery/DocumentUnderstanding/LORE-TSR/src/lib/models/networks/DCNv2/src/cuda/dcn_v2_im2col_cuda.cu +/ssd8/exec/huangjy/AdvancedLiterateMachinery/DocumentUnderstanding/LORE-TSR/src/lib/models/networks/DCNv2/src/cuda/dcn_v2_psroi_pooling_cuda.cu +DCNv2.egg-info/PKG-INFO +DCNv2.egg-info/SOURCES.txt +DCNv2.egg-info/dependency_links.txt +DCNv2.egg-info/top_level.txt \ No newline at end of file diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/DCNv2.egg-info/dependency_links.txt b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/DCNv2.egg-info/dependency_links.txt new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/DCNv2.egg-info/dependency_links.txt @@ -0,0 +1 @@ + diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/DCNv2.egg-info/top_level.txt b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/DCNv2.egg-info/top_level.txt new file mode 100644 index 00000000..8eafb8a8 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/DCNv2.egg-info/top_level.txt @@ -0,0 +1 @@ +_ext diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/LICENSE b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/LICENSE new file mode 100644 index 00000000..b2e3b520 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/LICENSE @@ -0,0 +1,29 @@ +BSD 3-Clause License + +Copyright (c) 2019, Charles Shang +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +1. Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +2. Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +3. Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. 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b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/build/temp.linux-x86_64-3.9/build.ninja @@ -0,0 +1,34 @@ +ninja_required_version = 1.3 +cxx = c++ +nvcc = /usr/local/cuda/bin/nvcc + +cflags = -Wno-unused-result -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /ssd8/exec/huangjy/miniconda3/envs/vgt/include -I/ssd8/exec/huangjy/miniconda3/envs/vgt/include -fPIC -O2 -isystem /ssd8/exec/huangjy/miniconda3/envs/vgt/include -fPIC -DWITH_CUDA -I/ssd8/exec/huangjy/AdvancedLiterateMachinery/DocumentUnderstanding/LORE-TSR/src/lib/models/networks/DCNv2/src -I/ssd8/exec/huangjy/miniconda3/envs/vgt/lib/python3.9/site-packages/torch/include -I/ssd8/exec/huangjy/miniconda3/envs/vgt/lib/python3.9/site-packages/torch/include/torch/csrc/api/include -I/ssd8/exec/huangjy/miniconda3/envs/vgt/lib/python3.9/site-packages/torch/include/TH -I/ssd8/exec/huangjy/miniconda3/envs/vgt/lib/python3.9/site-packages/torch/include/THC -I/usr/local/cuda/include -I/ssd8/exec/huangjy/miniconda3/envs/vgt/include/python3.9 -c +post_cflags = -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_gcc"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1011"' -DTORCH_EXTENSION_NAME=_ext -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++14 +cuda_cflags = -DWITH_CUDA -I/ssd8/exec/huangjy/AdvancedLiterateMachinery/DocumentUnderstanding/LORE-TSR/src/lib/models/networks/DCNv2/src -I/ssd8/exec/huangjy/miniconda3/envs/vgt/lib/python3.9/site-packages/torch/include -I/ssd8/exec/huangjy/miniconda3/envs/vgt/lib/python3.9/site-packages/torch/include/torch/csrc/api/include -I/ssd8/exec/huangjy/miniconda3/envs/vgt/lib/python3.9/site-packages/torch/include/TH -I/ssd8/exec/huangjy/miniconda3/envs/vgt/lib/python3.9/site-packages/torch/include/THC -I/usr/local/cuda/include -I/ssd8/exec/huangjy/miniconda3/envs/vgt/include/python3.9 -c +cuda_post_cflags = -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options ''"'"'-fPIC'"'"'' -DCUDA_HAS_FP16=1 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_gcc"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1011"' -DTORCH_EXTENSION_NAME=_ext -D_GLIBCXX_USE_CXX11_ABI=0 -gencode=arch=compute_86,code=compute_86 -gencode=arch=compute_86,code=sm_86 -ccbin g++ -std=c++14 +ldflags = + +rule compile + command = $cxx -MMD -MF $out.d $cflags -c $in -o $out $post_cflags + depfile = $out.d + deps = gcc + +rule cuda_compile + depfile = $out.d + deps = gcc + command = $nvcc $cuda_cflags -c $in -o $out $cuda_post_cflags + + + +build /ssd8/exec/huangjy/AdvancedLiterateMachinery/DocumentUnderstanding/LORE-TSR/src/lib/models/networks/DCNv2/build/temp.linux-x86_64-3.9/ssd8/exec/huangjy/AdvancedLiterateMachinery/DocumentUnderstanding/LORE-TSR/src/lib/models/networks/DCNv2/src/cpu/dcn_v2_cpu.o: compile /ssd8/exec/huangjy/AdvancedLiterateMachinery/DocumentUnderstanding/LORE-TSR/src/lib/models/networks/DCNv2/src/cpu/dcn_v2_cpu.cpp +build /ssd8/exec/huangjy/AdvancedLiterateMachinery/DocumentUnderstanding/LORE-TSR/src/lib/models/networks/DCNv2/build/temp.linux-x86_64-3.9/ssd8/exec/huangjy/AdvancedLiterateMachinery/DocumentUnderstanding/LORE-TSR/src/lib/models/networks/DCNv2/src/cpu/dcn_v2_im2col_cpu.o: compile /ssd8/exec/huangjy/AdvancedLiterateMachinery/DocumentUnderstanding/LORE-TSR/src/lib/models/networks/DCNv2/src/cpu/dcn_v2_im2col_cpu.cpp +build /ssd8/exec/huangjy/AdvancedLiterateMachinery/DocumentUnderstanding/LORE-TSR/src/lib/models/networks/DCNv2/build/temp.linux-x86_64-3.9/ssd8/exec/huangjy/AdvancedLiterateMachinery/DocumentUnderstanding/LORE-TSR/src/lib/models/networks/DCNv2/src/cpu/dcn_v2_psroi_pooling_cpu.o: compile /ssd8/exec/huangjy/AdvancedLiterateMachinery/DocumentUnderstanding/LORE-TSR/src/lib/models/networks/DCNv2/src/cpu/dcn_v2_psroi_pooling_cpu.cpp +build /ssd8/exec/huangjy/AdvancedLiterateMachinery/DocumentUnderstanding/LORE-TSR/src/lib/models/networks/DCNv2/build/temp.linux-x86_64-3.9/ssd8/exec/huangjy/AdvancedLiterateMachinery/DocumentUnderstanding/LORE-TSR/src/lib/models/networks/DCNv2/src/cuda/dcn_v2_cuda.o: cuda_compile /ssd8/exec/huangjy/AdvancedLiterateMachinery/DocumentUnderstanding/LORE-TSR/src/lib/models/networks/DCNv2/src/cuda/dcn_v2_cuda.cu +build /ssd8/exec/huangjy/AdvancedLiterateMachinery/DocumentUnderstanding/LORE-TSR/src/lib/models/networks/DCNv2/build/temp.linux-x86_64-3.9/ssd8/exec/huangjy/AdvancedLiterateMachinery/DocumentUnderstanding/LORE-TSR/src/lib/models/networks/DCNv2/src/cuda/dcn_v2_im2col_cuda.o: cuda_compile /ssd8/exec/huangjy/AdvancedLiterateMachinery/DocumentUnderstanding/LORE-TSR/src/lib/models/networks/DCNv2/src/cuda/dcn_v2_im2col_cuda.cu +build /ssd8/exec/huangjy/AdvancedLiterateMachinery/DocumentUnderstanding/LORE-TSR/src/lib/models/networks/DCNv2/build/temp.linux-x86_64-3.9/ssd8/exec/huangjy/AdvancedLiterateMachinery/DocumentUnderstanding/LORE-TSR/src/lib/models/networks/DCNv2/src/cuda/dcn_v2_psroi_pooling_cuda.o: cuda_compile /ssd8/exec/huangjy/AdvancedLiterateMachinery/DocumentUnderstanding/LORE-TSR/src/lib/models/networks/DCNv2/src/cuda/dcn_v2_psroi_pooling_cuda.cu +build 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zf3~82Os{I3n@f0e=iR>uEC#{X{(`kPRHI`OZ{uXaCg&@X#cw%=AvD8^rt z82$a{^Yu51`e7x6uT=j%gZ=>O&(ZV`V)UyG`X!TP`zxkj0X)}!wxE8$rZ0ZeTlrV* zCuz{HL;dMR%HL|xuY1iO|H+L08q}xlw-fc1UuA=CJ57B5Gl2Y^3dGkkvi+H6o}YfG zj6c<*+pin=I^dG&Fbk(Next~5D58JS1w6m9Bv|=NTX=pw@~eyJ z{|0&5|JR8xTY#m6@O1k*;JNm{9^0R4=i>M;W!m307xMmNs6UzL)A~Ptk>siWOOOXi z&mv1z*mWWBB2UV&mDj38U524{RV^nU_kw-L4OmY zFYe{4{#5mgY1F6v_gd7a;~Ld(tJgl{RsXDB??qm1t5?OT?Jpw_);!A)@^g;%?9TgF zd#^Q=e+0|F^H7;moIhtV +#include "cpu/dcn_v2_im2col_cpu.h" + +#include +//#include + +#include +//#include +//#include + +//extern THCState *state; + +// author: Charles Shang +// https://github.com/torch/cunn/blob/master/lib/THCUNN/generic/SpatialConvolutionMM.cu +// modified from the CUDA version for CPU use by Daniel K. Suhendro + +at::Tensor +dcn_v2_cpu_forward(const at::Tensor &input, + const at::Tensor &weight, + const at::Tensor &bias, + const at::Tensor &offset, + const at::Tensor &mask, + const int kernel_h, + const int kernel_w, + const int stride_h, + const int stride_w, + const int pad_h, + const int pad_w, + const int dilation_h, + const int dilation_w, + const int deformable_group) +{ + // THCAssertSameGPU(THCudaTensor_checkGPU(state, 5, input, weight, bias, offset, mask)); + /*AT_ASSERTM(input.type().is_cuda(), "input must be a CUDA tensor"); + AT_ASSERTM(weight.type().is_cuda(), "weight must be a CUDA tensor"); + AT_ASSERTM(bias.type().is_cuda(), "bias must be a CUDA tensor"); + AT_ASSERTM(offset.type().is_cuda(), "offset must be a CUDA tensor"); + AT_ASSERTM(mask.type().is_cuda(), "mask must be a CUDA tensor");*/ + + const int batch = input.size(0); + const int channels = input.size(1); + const int height = input.size(2); + const int width = input.size(3); + + const int channels_out = weight.size(0); + const int channels_kernel = weight.size(1); + const int kernel_h_ = weight.size(2); + const int kernel_w_ = weight.size(3); + + // printf("Kernels: %d %d %d %d\n", kernel_h_, kernel_w_, kernel_w, kernel_h); + // printf("Channels: %d %d\n", channels, channels_kernel); + // printf("Channels: %d %d\n", channels_out, channels_kernel); + + AT_ASSERTM(kernel_h_ == kernel_h && kernel_w_ == kernel_w, + "Input shape and kernel shape wont match: (%d x %d vs %d x %d).", kernel_h_, kernel_w, kernel_h_, kernel_w_); + + AT_ASSERTM(channels == channels_kernel, + "Input shape and kernel channels wont match: (%d vs %d).", channels, channels_kernel); + + const int height_out = (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1; + const int width_out = (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1; + + auto ones = at::ones({height_out, width_out}, input.options()); + auto columns = at::empty({channels * kernel_h * kernel_w, 1 * height_out * width_out}, input.options()); + auto output = at::empty({batch, channels_out, height_out, width_out}, input.options()); + + using scalar_t = float; + for (int b = 0; b < batch; b++) + { + auto input_n = input.select(0, b); + auto offset_n = offset.select(0, b); + auto mask_n = mask.select(0, b); + auto output_n = output.select(0, b); + + // Do Bias first: + // M,N,K are dims of matrix A and B + // (see http://docs.nvidia.com/cuda/cublas/#cublas-lt-t-gt-gemm) + // (N x 1) (1 x M) + long m_ = channels_out; + long n_ = height_out * width_out; + long k_ = 1; + THFloatBlas_gemm('t', 'n', n_, m_, k_, 1.0f, + ones.contiguous().data(), k_, + bias.contiguous().data(), k_, 0.0f, + output_n.data(), n_); + + modulated_deformable_im2col_cpu(input_n.data(), + offset_n.data(), + mask_n.data(), + 1, channels, height, width, + height_out, width_out, kernel_h, kernel_w, + pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, + deformable_group, + columns.data()); + + //(k * m) x (m * n) + // Y = WC + long m = channels_out; + long n = height_out * width_out; + long k = channels * kernel_h * kernel_w; + THFloatBlas_gemm('n', 'n', n, m, k, 1.0f, + columns.data(), n, + weight.data(), k, 1.0f, + output_n.data(), n); + } + return output; +} + +std::vector dcn_v2_cpu_backward(const at::Tensor &input, + const at::Tensor &weight, + const at::Tensor &bias, + const at::Tensor &offset, + const at::Tensor &mask, + const at::Tensor &grad_output, + int kernel_h, int kernel_w, + int stride_h, int stride_w, + int pad_h, int pad_w, + int dilation_h, int dilation_w, + int deformable_group) +{ + + THArgCheck(input.is_contiguous(), 1, "input tensor has to be contiguous"); + THArgCheck(weight.is_contiguous(), 2, "weight tensor has to be contiguous"); + + /*AT_ASSERTM(input.type().is_cuda(), "input must be a CUDA tensor"); + AT_ASSERTM(weight.type().is_cuda(), "weight must be a CUDA tensor"); + AT_ASSERTM(bias.type().is_cuda(), "bias must be a CUDA tensor"); + AT_ASSERTM(offset.type().is_cuda(), "offset must be a CUDA tensor"); + AT_ASSERTM(mask.type().is_cuda(), "mask must be a CUDA tensor");*/ + + const int batch = input.size(0); + const int channels = input.size(1); + const int height = input.size(2); + const int width = input.size(3); + + const int channels_out = weight.size(0); + const int channels_kernel = weight.size(1); + const int kernel_h_ = weight.size(2); + const int kernel_w_ = weight.size(3); + + AT_ASSERTM(kernel_h_ == kernel_h && kernel_w_ == kernel_w, + "Input shape and kernel shape wont match: (%d x %d vs %d x %d).", kernel_h_, kernel_w, kernel_h_, kernel_w_); + + AT_ASSERTM(channels == channels_kernel, + "Input shape and kernel channels wont match: (%d vs %d).", channels, channels_kernel); + + const int height_out = (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1; + const int width_out = (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1; + + auto ones = at::ones({height_out, width_out}, input.options()); + auto columns = at::empty({channels * kernel_h * kernel_w, 1 * height_out * width_out}, input.options()); + auto output = at::empty({batch, channels_out, height_out, width_out}, input.options()); + + auto grad_input = at::zeros_like(input); + auto grad_weight = at::zeros_like(weight); + auto grad_bias = at::zeros_like(bias); + auto grad_offset = at::zeros_like(offset); + auto grad_mask = at::zeros_like(mask); + + using scalar_t = float; + + for (int b = 0; b < batch; b++) + { + auto input_n = input.select(0, b); + auto offset_n = offset.select(0, b); + auto mask_n = mask.select(0, b); + auto grad_output_n = grad_output.select(0, b); + auto grad_input_n = grad_input.select(0, b); + auto grad_offset_n = grad_offset.select(0, b); + auto grad_mask_n = grad_mask.select(0, b); + + long m = channels * kernel_h * kernel_w; + long n = height_out * width_out; + long k = channels_out; + + THFloatBlas_gemm('n', 't', n, m, k, 1.0f, + grad_output_n.data(), n, + weight.data(), m, 0.0f, + columns.data(), n); + + // gradient w.r.t. input coordinate data + modulated_deformable_col2im_coord_cpu(columns.data(), + input_n.data(), + offset_n.data(), + mask_n.data(), + 1, channels, height, width, + height_out, width_out, kernel_h, kernel_w, + pad_h, pad_w, stride_h, stride_w, + dilation_h, dilation_w, deformable_group, + grad_offset_n.data(), + grad_mask_n.data()); + // gradient w.r.t. input data + modulated_deformable_col2im_cpu(columns.data(), + offset_n.data(), + mask_n.data(), + 1, channels, height, width, + height_out, width_out, kernel_h, kernel_w, + pad_h, pad_w, stride_h, stride_w, + dilation_h, dilation_w, deformable_group, + grad_input_n.data()); + + // gradient w.r.t. weight, dWeight should accumulate across the batch and group + modulated_deformable_im2col_cpu(input_n.data(), + offset_n.data(), + mask_n.data(), + 1, channels, height, width, + height_out, width_out, kernel_h, kernel_w, + pad_h, pad_w, stride_h, stride_w, + dilation_h, dilation_w, deformable_group, + columns.data()); + + long m_ = channels_out; + long n_ = channels * kernel_h * kernel_w; + long k_ = height_out * width_out; + + THFloatBlas_gemm('t', 'n', n_, m_, k_, 1.0f, + columns.data(), k_, + grad_output_n.data(), k_, 1.0f, + grad_weight.data(), n_); + + // gradient w.r.t. bias + // long m_ = channels_out; + // long k__ = height_out * width_out; + // THFloatBlas_gemv('t', k_, m_, 1.0f, + // grad_output_n.data(), k_, + // ones.data(), 1, 1.0f, + // grad_bias.data(), 1); + } + + return { + grad_input, grad_offset, grad_mask, grad_weight, grad_bias + }; +} \ No newline at end of file diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/src/cpu/dcn_v2_im2col_cpu.cpp b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/src/cpu/dcn_v2_im2col_cpu.cpp new file mode 100644 index 00000000..1704a60d --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/src/cpu/dcn_v2_im2col_cpu.cpp @@ -0,0 +1,395 @@ +#include "dcn_v2_im2col_cpu.h" +#include +#include +#include + +#include +//#include + +#include +//#include +//#include + +// modified from the CUDA version for CPU use by Daniel K. Suhendro + +/*#define CUDA_KERNEL_LOOP(i, n) \ + for (int i = blockIdx.x * blockDim.x + threadIdx.x; \ + i < (n); \ + i += blockDim.x * gridDim.x) + +const int CUDA_NUM_THREADS = 1024; +inline int GET_BLOCKS(const int N) +{ + return (N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS; +}*/ + + +float dmcn_im2col_bilinear_cpu(const float *bottom_data, const int data_width, + const int height, const int width, float h, float w) +{ + int h_low = floor(h); + int w_low = floor(w); + int h_high = h_low + 1; + int w_high = w_low + 1; + + float lh = h - h_low; + float lw = w - w_low; + float hh = 1 - lh, hw = 1 - lw; + + float v1 = 0; + if (h_low >= 0 && w_low >= 0) + v1 = bottom_data[h_low * data_width + w_low]; + float v2 = 0; + if (h_low >= 0 && w_high <= width - 1) + v2 = bottom_data[h_low * data_width + w_high]; + float v3 = 0; + if (h_high <= height - 1 && w_low >= 0) + v3 = bottom_data[h_high * data_width + w_low]; + float v4 = 0; + if (h_high <= height - 1 && w_high <= width - 1) + v4 = bottom_data[h_high * data_width + w_high]; + + float w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; + + float val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); + return val; +} + +float dmcn_get_gradient_weight_cpu(float argmax_h, float argmax_w, + const int h, const int w, const int height, const int width) +{ + if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width) + { + //empty + return 0; + } + + int argmax_h_low = floor(argmax_h); + int argmax_w_low = floor(argmax_w); + int argmax_h_high = argmax_h_low + 1; + int argmax_w_high = argmax_w_low + 1; + + float weight = 0; + if (h == argmax_h_low && w == argmax_w_low) + weight = (h + 1 - argmax_h) * (w + 1 - argmax_w); + if (h == argmax_h_low && w == argmax_w_high) + weight = (h + 1 - argmax_h) * (argmax_w + 1 - w); + if (h == argmax_h_high && w == argmax_w_low) + weight = (argmax_h + 1 - h) * (w + 1 - argmax_w); + if (h == argmax_h_high && w == argmax_w_high) + weight = (argmax_h + 1 - h) * (argmax_w + 1 - w); + return weight; +} + +float dmcn_get_coordinate_weight_cpu(float argmax_h, float argmax_w, + const int height, const int width, const float *im_data, + const int data_width, const int bp_dir) +{ + if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width) + { + //empty + return 0; + } + + int argmax_h_low = floor(argmax_h); + int argmax_w_low = floor(argmax_w); + int argmax_h_high = argmax_h_low + 1; + int argmax_w_high = argmax_w_low + 1; + + float weight = 0; + + if (bp_dir == 0) + { + if (argmax_h_low >= 0 && argmax_w_low >= 0) + weight += -1 * (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_low * data_width + argmax_w_low]; + if (argmax_h_low >= 0 && argmax_w_high <= width - 1) + weight += -1 * (argmax_w - argmax_w_low) * im_data[argmax_h_low * data_width + argmax_w_high]; + if (argmax_h_high <= height - 1 && argmax_w_low >= 0) + weight += (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_high * data_width + argmax_w_low]; + if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) + weight += (argmax_w - argmax_w_low) * im_data[argmax_h_high * data_width + argmax_w_high]; + } + else if (bp_dir == 1) + { + if (argmax_h_low >= 0 && argmax_w_low >= 0) + weight += -1 * (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_low]; + if (argmax_h_low >= 0 && argmax_w_high <= width - 1) + weight += (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_high]; + if (argmax_h_high <= height - 1 && argmax_w_low >= 0) + weight += -1 * (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_low]; + if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) + weight += (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_high]; + } + + return weight; +} + +void modulated_deformable_im2col_cpu_kernel(const int n, const float *data_im, const float *data_offset, const float *data_mask, + const int height, const int width, const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, + const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int channel_per_deformable_group, + const int batch_size, const int num_channels, const int deformable_group, + const int height_col, const int width_col, + float *data_col) +{ + // launch channels * batch_size * height_col * width_col cores + for(int index=0; index(0); + const float h_im = h_in + i * dilation_h + offset_h; + const float w_im = w_in + j * dilation_w + offset_w; + //if (h_im >= 0 && w_im >= 0 && h_im < height && w_im < width) { + if (h_im > -1 && w_im > -1 && h_im < height && w_im < width) + { + //const float map_h = i * dilation_h + offset_h; + //const float map_w = j * dilation_w + offset_w; + //const int cur_height = height - h_in; + //const int cur_width = width - w_in; + //val = dmcn_im2col_bilinear_cpu(data_im_ptr, width, cur_height, cur_width, map_h, map_w); + val = dmcn_im2col_bilinear_cpu(data_im_ptr, width, height, width, h_im, w_im); + } + *data_col_ptr = val * mask; + // data_col_ptr += batch_size * height_col * width_col; + data_col_ptr += height_col * width_col; + } + } + } +} + +void modulated_deformable_col2im_cpu_kernel(const int n, const float *data_col, const float *data_offset, const float *data_mask, + const int channels, const int height, const int width, + const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, + const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int channel_per_deformable_group, + const int batch_size, const int deformable_group, + const int height_col, const int width_col, + float *grad_im) +{ + for(int index = 0; index < n; index++) + { + const int j = (index / width_col / height_col / batch_size) % kernel_w; + const int i = (index / width_col / height_col / batch_size / kernel_w) % kernel_h; + const int c = index / width_col / height_col / batch_size / kernel_w / kernel_h; + // compute the start and end of the output + + const int deformable_group_index = c / channel_per_deformable_group; + + int w_out = index % width_col; + int h_out = (index / width_col) % height_col; + int b = (index / width_col / height_col) % batch_size; + int w_in = w_out * stride_w - pad_w; + int h_in = h_out * stride_h - pad_h; + + const float *data_offset_ptr = data_offset + (b * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col; + const float *data_mask_ptr = data_mask + (b * deformable_group + deformable_group_index) * kernel_h * kernel_w * height_col * width_col; + const int data_offset_h_ptr = ((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out; + const int data_offset_w_ptr = ((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out; + const int data_mask_hw_ptr = ((i * kernel_w + j) * height_col + h_out) * width_col + w_out; + const float offset_h = data_offset_ptr[data_offset_h_ptr]; + const float offset_w = data_offset_ptr[data_offset_w_ptr]; + const float mask = data_mask_ptr[data_mask_hw_ptr]; + const float cur_inv_h_data = h_in + i * dilation_h + offset_h; + const float cur_inv_w_data = w_in + j * dilation_w + offset_w; + + const float cur_top_grad = data_col[index] * mask; + const int cur_h = (int)cur_inv_h_data; + const int cur_w = (int)cur_inv_w_data; + + for (int dy = -2; dy <= 2; dy++) + { + for (int dx = -2; dx <= 2; dx++) + { + if (cur_h + dy >= 0 && cur_h + dy < height && + cur_w + dx >= 0 && cur_w + dx < width && + abs(cur_inv_h_data - (cur_h + dy)) < 1 && + abs(cur_inv_w_data - (cur_w + dx)) < 1) + { + int cur_bottom_grad_pos = ((b * channels + c) * height + cur_h + dy) * width + cur_w + dx; + float weight = dmcn_get_gradient_weight_cpu(cur_inv_h_data, cur_inv_w_data, cur_h + dy, cur_w + dx, height, width); + //atomicAdd(grad_im + cur_bottom_grad_pos, weight * cur_top_grad); + *(grad_im + cur_bottom_grad_pos) += weight * cur_top_grad; + + } + } + } + } +} + +void modulated_deformable_col2im_coord_cpu_kernel(const int n, const float *data_col, const float *data_im, + const float *data_offset, const float *data_mask, + const int channels, const int height, const int width, + const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, + const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int channel_per_deformable_group, + const int batch_size, const int offset_channels, const int deformable_group, + const int height_col, const int width_col, + float *grad_offset, float *grad_mask) +{ + for(int index = 0; index < n; index++) + { + float val = 0, mval = 0; + int w = index % width_col; + int h = (index / width_col) % height_col; + int c = (index / width_col / height_col) % offset_channels; + int b = (index / width_col / height_col) / offset_channels; + // compute the start and end of the output + + const int deformable_group_index = c / (2 * kernel_h * kernel_w); + const int col_step = kernel_h * kernel_w; + int cnt = 0; + const float *data_col_ptr = data_col + deformable_group_index * channel_per_deformable_group * batch_size * width_col * height_col; + const float *data_im_ptr = data_im + (b * deformable_group + deformable_group_index) * channel_per_deformable_group / kernel_h / kernel_w * height * width; + const float *data_offset_ptr = data_offset + (b * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col; + const float *data_mask_ptr = data_mask + (b * deformable_group + deformable_group_index) * kernel_h * kernel_w * height_col * width_col; + + const int offset_c = c - deformable_group_index * 2 * kernel_h * kernel_w; + + for (int col_c = (offset_c / 2); col_c < channel_per_deformable_group; col_c += col_step) + { + const int col_pos = (((col_c * batch_size + b) * height_col) + h) * width_col + w; + const int bp_dir = offset_c % 2; + + int j = (col_pos / width_col / height_col / batch_size) % kernel_w; + int i = (col_pos / width_col / height_col / batch_size / kernel_w) % kernel_h; + int w_out = col_pos % width_col; + int h_out = (col_pos / width_col) % height_col; + int w_in = w_out * stride_w - pad_w; + int h_in = h_out * stride_h - pad_h; + const int data_offset_h_ptr = (((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out); + const int data_offset_w_ptr = (((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out); + const int data_mask_hw_ptr = (((i * kernel_w + j) * height_col + h_out) * width_col + w_out); + const float offset_h = data_offset_ptr[data_offset_h_ptr]; + const float offset_w = data_offset_ptr[data_offset_w_ptr]; + const float mask = data_mask_ptr[data_mask_hw_ptr]; + float inv_h = h_in + i * dilation_h + offset_h; + float inv_w = w_in + j * dilation_w + offset_w; + if (inv_h <= -1 || inv_w <= -1 || inv_h >= height || inv_w >= width) + { + inv_h = inv_w = -2; + } + else + { + mval += data_col_ptr[col_pos] * dmcn_im2col_bilinear_cpu(data_im_ptr + cnt * height * width, width, height, width, inv_h, inv_w); + } + const float weight = dmcn_get_coordinate_weight_cpu( + inv_h, inv_w, + height, width, data_im_ptr + cnt * height * width, width, bp_dir); + val += weight * data_col_ptr[col_pos] * mask; + cnt += 1; + } + // KERNEL_ASSIGN(grad_offset[index], offset_req, val); + grad_offset[index] = val; + if (offset_c % 2 == 0) + // KERNEL_ASSIGN(grad_mask[(((b * deformable_group + deformable_group_index) * kernel_h * kernel_w + offset_c / 2) * height_col + h) * width_col + w], mask_req, mval); + grad_mask[(((b * deformable_group + deformable_group_index) * kernel_h * kernel_w + offset_c / 2) * height_col + h) * width_col + w] = mval; + } +} + +void modulated_deformable_im2col_cpu(const float* data_im, const float* data_offset, const float* data_mask, + const int batch_size, const int channels, const int height_im, const int width_im, + const int height_col, const int width_col, const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int deformable_group, float* data_col) { + // num_axes should be smaller than block size + const int channel_per_deformable_group = channels / deformable_group; + const int num_kernels = channels * batch_size * height_col * width_col; + modulated_deformable_im2col_cpu_kernel( + num_kernels, data_im, data_offset, data_mask, height_im, width_im, kernel_h, kernel_w, + pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, channel_per_deformable_group, + batch_size, channels, deformable_group, height_col, width_col, data_col); + + /*cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) + { + printf("error in modulated_deformable_im2col_cuda: %s\n", cudaGetErrorString(err)); + }*/ + +} + +void modulated_deformable_col2im_cpu(const float* data_col, const float* data_offset, const float* data_mask, + const int batch_size, const int channels, const int height_im, const int width_im, + const int height_col, const int width_col, const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int deformable_group, float* grad_im){ + + const int channel_per_deformable_group = channels / deformable_group; + const int num_kernels = channels * kernel_h * kernel_w * batch_size * height_col * width_col; + modulated_deformable_col2im_cpu_kernel( + num_kernels, data_col, data_offset, data_mask, channels, height_im, width_im, + kernel_h, kernel_w, pad_h, pad_h, stride_h, stride_w, + dilation_h, dilation_w, channel_per_deformable_group, + batch_size, deformable_group, height_col, width_col, grad_im); + /*cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) + { + printf("error in modulated_deformable_col2im_cuda: %s\n", cudaGetErrorString(err)); + }*/ + +} + +void modulated_deformable_col2im_coord_cpu(const float* data_col, const float* data_im, const float* data_offset, const float* data_mask, + const int batch_size, const int channels, const int height_im, const int width_im, + const int height_col, const int width_col, const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int deformable_group, + float* grad_offset, float* grad_mask) { + const int num_kernels = batch_size * height_col * width_col * 2 * kernel_h * kernel_w * deformable_group; + const int channel_per_deformable_group = channels * kernel_h * kernel_w / deformable_group; + modulated_deformable_col2im_coord_cpu_kernel( + num_kernels, data_col, data_im, data_offset, data_mask, channels, height_im, width_im, + kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, + dilation_h, dilation_w, channel_per_deformable_group, + batch_size, 2 * kernel_h * kernel_w * deformable_group, deformable_group, height_col, width_col, + grad_offset, grad_mask); + /*cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) + { + printf("error in modulated_deformable_col2im_coord_cuda: %s\n", cudaGetErrorString(err)); + }*/ +} \ No newline at end of file diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/src/cpu/dcn_v2_im2col_cpu.h b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/src/cpu/dcn_v2_im2col_cpu.h new file mode 100644 index 00000000..bad5c528 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/src/cpu/dcn_v2_im2col_cpu.h @@ -0,0 +1,99 @@ + +/*! + ******************* BEGIN Caffe Copyright Notice and Disclaimer **************** + * + * COPYRIGHT + * + * All contributions by the University of California: + * Copyright (c) 2014-2017 The Regents of the University of California (Regents) + * All rights reserved. + * + * All other contributions: + * Copyright (c) 2014-2017, the respective contributors + * All rights reserved. + * + * Caffe uses a shared copyright model: each contributor holds copyright over + * their contributions to Caffe. The project versioning records all such + * contribution and copyright details. If a contributor wants to further mark + * their specific copyright on a particular contribution, they should indicate + * their copyright solely in the commit message of the change when it is + * committed. + * + * LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * + * 1. Redistributions of source code must retain the above copyright notice, this + * list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright notice, + * this list of conditions and the following disclaimer in the documentation + * and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND + * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED + * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR + * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES + * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; + * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND + * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + * + * CONTRIBUTION AGREEMENT + * + * By contributing to the BVLC/caffe repository through pull-request, comment, + * or otherwise, the contributor releases their content to the + * license and copyright terms herein. + * + ***************** END Caffe Copyright Notice and Disclaimer ******************** + * + * Copyright (c) 2018 Microsoft + * Licensed under The MIT License [see LICENSE for details] + * \file modulated_deformable_im2col.h + * \brief Function definitions of converting an image to + * column matrix based on kernel, padding, dilation, and offset. + * These functions are mainly used in deformable convolution operators. + * \ref: https://arxiv.org/abs/1811.11168 + * \author Yuwen Xiong, Haozhi Qi, Jifeng Dai, Xizhou Zhu, Han Hu + */ + +/***************** Adapted by Charles Shang *********************/ +// modified from the CUDA version for CPU use by Daniel K. Suhendro + +#ifndef DCN_V2_IM2COL_CPU +#define DCN_V2_IM2COL_CPU + +#ifdef __cplusplus +extern "C" +{ +#endif + + void modulated_deformable_im2col_cpu(const float *data_im, const float *data_offset, const float *data_mask, + const int batch_size, const int channels, const int height_im, const int width_im, + const int height_col, const int width_col, const int kernel_h, const int kenerl_w, + const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int deformable_group, float *data_col); + + void modulated_deformable_col2im_cpu(const float *data_col, const float *data_offset, const float *data_mask, + const int batch_size, const int channels, const int height_im, const int width_im, + const int height_col, const int width_col, const int kernel_h, const int kenerl_w, + const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int deformable_group, float *grad_im); + + void modulated_deformable_col2im_coord_cpu(const float *data_col, const float *data_im, const float *data_offset, const float *data_mask, + const int batch_size, const int channels, const int height_im, const int width_im, + const int height_col, const int width_col, const int kernel_h, const int kenerl_w, + const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int deformable_group, + float *grad_offset, float *grad_mask); + +#ifdef __cplusplus +} +#endif + +#endif \ No newline at end of file diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/src/cpu/dcn_v2_psroi_pooling_cpu.cpp b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/src/cpu/dcn_v2_psroi_pooling_cpu.cpp new file mode 100644 index 00000000..6e41aaed --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/src/cpu/dcn_v2_psroi_pooling_cpu.cpp @@ -0,0 +1,426 @@ +/*! + * Copyright (c) 2017 Microsoft + * Licensed under The MIT License [see LICENSE for details] + * \file deformable_psroi_pooling.cu + * \brief + * \author Yi Li, Guodong Zhang, Jifeng Dai +*/ +/***************** Adapted by Charles Shang *********************/ +// modified from the CUDA version for CPU use by Daniel K. Suhendro + +#include +#include +#include + +#include +//#include + +#include +//#include +//#include + +/*#define CUDA_KERNEL_LOOP(i, n) \ + for (int i = blockIdx.x * blockDim.x + threadIdx.x; \ + i < (n); \ + i += blockDim.x * gridDim.x) + +const int CUDA_NUM_THREADS = 1024; +inline int GET_BLOCKS(const int N) +{ + return (N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS; +}*/ + +template +T bilinear_interp_cpu( + const T *data, + const T x, + const T y, + const int width, + const int height) +{ + int x1 = floor(x); + int x2 = ceil(x); + int y1 = floor(y); + int y2 = ceil(y); + T dist_x = static_cast(x - x1); + T dist_y = static_cast(y - y1); + T value11 = data[y1 * width + x1]; + T value12 = data[y2 * width + x1]; + T value21 = data[y1 * width + x2]; + T value22 = data[y2 * width + x2]; + T value = (1 - dist_x) * (1 - dist_y) * value11 + + (1 - dist_x) * dist_y * value12 + + dist_x * (1 - dist_y) * value21 + + dist_x * dist_y * value22; + return value; +} + +template + void DeformablePSROIPoolForwardKernelCpu( + const int count, + const T *bottom_data, + const T spatial_scale, + const int channels, + const int height, const int width, + const int pooled_height, const int pooled_width, + const T *bottom_rois, const T *bottom_trans, + const int no_trans, + const T trans_std, + const int sample_per_part, + const int output_dim, + const int group_size, + const int part_size, + const int num_classes, + const int channels_each_class, + T *top_data, + T *top_count) +{ + for(int index = 0; index < count; index++) + { + // The output is in order (n, ctop, ph, pw) + int pw = index % pooled_width; + int ph = (index / pooled_width) % pooled_height; + int ctop = (index / pooled_width / pooled_height) % output_dim; + int n = index / pooled_width / pooled_height / output_dim; + + // [start, end) interval for spatial sampling + const T *offset_bottom_rois = bottom_rois + n * 5; + int roi_batch_ind = offset_bottom_rois[0]; + T roi_start_w = static_cast(round(offset_bottom_rois[1])) * spatial_scale - 0.5; + T roi_start_h = static_cast(round(offset_bottom_rois[2])) * spatial_scale - 0.5; + T roi_end_w = static_cast(round(offset_bottom_rois[3]) + 1.) * spatial_scale - 0.5; + T roi_end_h = static_cast(round(offset_bottom_rois[4]) + 1.) * spatial_scale - 0.5; + + // Force too small ROIs to be 1x1 + T roi_width = std::max(roi_end_w - roi_start_w, T(0.1)); //avoid 0 + T roi_height = std::max(roi_end_h - roi_start_h, T(0.1)); + + // Compute w and h at bottom + T bin_size_h = roi_height / static_cast(pooled_height); + T bin_size_w = roi_width / static_cast(pooled_width); + + T sub_bin_size_h = bin_size_h / static_cast(sample_per_part); + T sub_bin_size_w = bin_size_w / static_cast(sample_per_part); + + int part_h = floor(static_cast(ph) / pooled_height * part_size); + int part_w = floor(static_cast(pw) / pooled_width * part_size); + int class_id = ctop / channels_each_class; + T trans_x = no_trans ? static_cast(0) : bottom_trans[(((n * num_classes + class_id) * 2) * part_size + part_h) * part_size + part_w] * trans_std; + T trans_y = no_trans ? static_cast(0) : bottom_trans[(((n * num_classes + class_id) * 2 + 1) * part_size + part_h) * part_size + part_w] * trans_std; + + T wstart = static_cast(pw) * bin_size_w + roi_start_w; + wstart += trans_x * roi_width; + T hstart = static_cast(ph) * bin_size_h + roi_start_h; + hstart += trans_y * roi_height; + + T sum = 0; + int count = 0; + int gw = floor(static_cast(pw) * group_size / pooled_width); + int gh = floor(static_cast(ph) * group_size / pooled_height); + gw = std::min(std::max(gw, 0), group_size - 1); + gh = std::min(std::max(gh, 0), group_size - 1); + + const T *offset_bottom_data = bottom_data + (roi_batch_ind * channels) * height * width; + for (int ih = 0; ih < sample_per_part; ih++) + { + for (int iw = 0; iw < sample_per_part; iw++) + { + T w = wstart + iw * sub_bin_size_w; + T h = hstart + ih * sub_bin_size_h; + // bilinear interpolation + if (w < -0.5 || w > width - 0.5 || h < -0.5 || h > height - 0.5) + { + continue; + } + w = std::min(std::max(w, T(0.)), width - T(1.)); + h = std::min(std::max(h, T(0.)), height - T(1.)); + int c = (ctop * group_size + gh) * group_size + gw; + T val = bilinear_interp_cpu(offset_bottom_data + c * height * width, w, h, width, height); + sum += val; + count++; + } + } + top_data[index] = count == 0 ? static_cast(0) : sum / count; + top_count[index] = count; + } +} + +template +void DeformablePSROIPoolBackwardAccKernelCpu( + const int count, + const T *top_diff, + const T *top_count, + const int num_rois, + const T spatial_scale, + const int channels, + const int height, const int width, + const int pooled_height, const int pooled_width, + const int output_dim, + T *bottom_data_diff, T *bottom_trans_diff, + const T *bottom_data, + const T *bottom_rois, + const T *bottom_trans, + const int no_trans, + const T trans_std, + const int sample_per_part, + const int group_size, + const int part_size, + const int num_classes, + const int channels_each_class) +{ + for(int index = 0; index < count; index++) + { + // The output is in order (n, ctop, ph, pw) + int pw = index % pooled_width; + int ph = (index / pooled_width) % pooled_height; + int ctop = (index / pooled_width / pooled_height) % output_dim; + int n = index / pooled_width / pooled_height / output_dim; + + // [start, end) interval for spatial sampling + const T *offset_bottom_rois = bottom_rois + n * 5; + int roi_batch_ind = offset_bottom_rois[0]; + T roi_start_w = static_cast(round(offset_bottom_rois[1])) * spatial_scale - 0.5; + T roi_start_h = static_cast(round(offset_bottom_rois[2])) * spatial_scale - 0.5; + T roi_end_w = static_cast(round(offset_bottom_rois[3]) + 1.) * spatial_scale - 0.5; + T roi_end_h = static_cast(round(offset_bottom_rois[4]) + 1.) * spatial_scale - 0.5; + + // Force too small ROIs to be 1x1 + T roi_width = std::max(roi_end_w - roi_start_w, T(0.1)); //avoid 0 + T roi_height = std::max(roi_end_h - roi_start_h, T(0.1)); + + // Compute w and h at bottom + T bin_size_h = roi_height / static_cast(pooled_height); + T bin_size_w = roi_width / static_cast(pooled_width); + + T sub_bin_size_h = bin_size_h / static_cast(sample_per_part); + T sub_bin_size_w = bin_size_w / static_cast(sample_per_part); + + int part_h = floor(static_cast(ph) / pooled_height * part_size); + int part_w = floor(static_cast(pw) / pooled_width * part_size); + int class_id = ctop / channels_each_class; + T trans_x = no_trans ? static_cast(0) : bottom_trans[(((n * num_classes + class_id) * 2) * part_size + part_h) * part_size + part_w] * trans_std; + T trans_y = no_trans ? static_cast(0) : bottom_trans[(((n * num_classes + class_id) * 2 + 1) * part_size + part_h) * part_size + part_w] * trans_std; + + T wstart = static_cast(pw) * bin_size_w + roi_start_w; + wstart += trans_x * roi_width; + T hstart = static_cast(ph) * bin_size_h + roi_start_h; + hstart += trans_y * roi_height; + + if (top_count[index] <= 0) + { + continue; + } + T diff_val = top_diff[index] / top_count[index]; + const T *offset_bottom_data = bottom_data + roi_batch_ind * channels * height * width; + T *offset_bottom_data_diff = bottom_data_diff + roi_batch_ind * channels * height * width; + int gw = floor(static_cast(pw) * group_size / pooled_width); + int gh = floor(static_cast(ph) * group_size / pooled_height); + gw = std::min(std::max(gw, 0), group_size - 1); + gh = std::min(std::max(gh, 0), group_size - 1); + + for (int ih = 0; ih < sample_per_part; ih++) + { + for (int iw = 0; iw < sample_per_part; iw++) + { + T w = wstart + iw * sub_bin_size_w; + T h = hstart + ih * sub_bin_size_h; + // bilinear interpolation + if (w < -0.5 || w > width - 0.5 || h < -0.5 || h > height - 0.5) + { + continue; + } + w = std::min(std::max(w, T(0.)), width - T(1.)); + h = std::min(std::max(h, T(0.)), height - T(1.)); + int c = (ctop * group_size + gh) * group_size + gw; + // backward on feature + int x0 = floor(w); + int x1 = ceil(w); + int y0 = floor(h); + int y1 = ceil(h); + T dist_x = w - x0, dist_y = h - y0; + T q00 = (1 - dist_x) * (1 - dist_y); + T q01 = (1 - dist_x) * dist_y; + T q10 = dist_x * (1 - dist_y); + T q11 = dist_x * dist_y; + int bottom_index_base = c * height * width; + /*atomicAdd(offset_bottom_data_diff + bottom_index_base + y0 * width + x0, q00 * diff_val); + atomicAdd(offset_bottom_data_diff + bottom_index_base + y1 * width + x0, q01 * diff_val); + atomicAdd(offset_bottom_data_diff + bottom_index_base + y0 * width + x1, q10 * diff_val); + atomicAdd(offset_bottom_data_diff + bottom_index_base + y1 * width + x1, q11 * diff_val);*/ + *(offset_bottom_data_diff + bottom_index_base + y0 * width + x0) += q00 * diff_val; + *(offset_bottom_data_diff + bottom_index_base + y1 * width + x0) += q01 * diff_val; + *(offset_bottom_data_diff + bottom_index_base + y0 * width + x1) += q10 * diff_val; + *(offset_bottom_data_diff + bottom_index_base + y1 * width + x1) += q11 * diff_val; + + + if (no_trans) + { + continue; + } + T U00 = offset_bottom_data[bottom_index_base + y0 * width + x0]; + T U01 = offset_bottom_data[bottom_index_base + y1 * width + x0]; + T U10 = offset_bottom_data[bottom_index_base + y0 * width + x1]; + T U11 = offset_bottom_data[bottom_index_base + y1 * width + x1]; + T diff_x = (U11 * dist_y + U10 * (1 - dist_y) - U01 * dist_y - U00 * (1 - dist_y)) * trans_std * diff_val; + diff_x *= roi_width; + T diff_y = (U11 * dist_x + U01 * (1 - dist_x) - U10 * dist_x - U00 * (1 - dist_x)) * trans_std * diff_val; + diff_y *= roi_height; + + /*atomicAdd(bottom_trans_diff + (((n * num_classes + class_id) * 2) * part_size + part_h) * part_size + part_w, diff_x); + atomicAdd(bottom_trans_diff + (((n * num_classes + class_id) * 2 + 1) * part_size + part_h) * part_size + part_w, diff_y);*/ + *(bottom_trans_diff + (((n * num_classes + class_id) * 2) * part_size + part_h) * part_size + part_w) += diff_x; + *(bottom_trans_diff + (((n * num_classes + class_id) * 2 + 1) * part_size + part_h) * part_size + part_w) += diff_y; + } + } + } +} + +std::tuple +dcn_v2_psroi_pooling_cpu_forward(const at::Tensor &input, + const at::Tensor &bbox, + const at::Tensor &trans, + const int no_trans, + const float spatial_scale, + const int output_dim, + const int group_size, + const int pooled_size, + const int part_size, + const int sample_per_part, + const float trans_std) +{ + /*AT_ASSERTM(input.type().is_cuda(), "input must be a CUDA tensor"); + AT_ASSERTM(bbox.type().is_cuda(), "rois must be a CUDA tensor"); + AT_ASSERTM(trans.type().is_cuda(), "trans must be a CUDA tensor");*/ + + const int batch = input.size(0); + const int channels = input.size(1); + const int height = input.size(2); + const int width = input.size(3); + const int channels_trans = no_trans ? 2 : trans.size(1); + const int num_bbox = bbox.size(0); + + AT_ASSERTM(channels == output_dim, "input channels and output channels must equal"); + auto pooled_height = pooled_size; + auto pooled_width = pooled_size; + + auto out = at::empty({num_bbox, output_dim, pooled_height, pooled_width}, input.options()); + long out_size = num_bbox * output_dim * pooled_height * pooled_width; + auto top_count = at::zeros({num_bbox, output_dim, pooled_height, pooled_width}, input.options()); + + const int num_classes = no_trans ? 1 : channels_trans / 2; + const int channels_each_class = no_trans ? output_dim : output_dim / num_classes; + + //cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + if (out.numel() == 0) + { + //THCudaCheck(cudaGetLastError()); + return std::make_tuple(out, top_count); + } + + /*dim3 grid(std::min(THCCeilDiv(out_size, 512L), 4096L)); + dim3 block(512);*/ + + AT_DISPATCH_FLOATING_TYPES(input.type(), "dcn_v2_psroi_pooling_cpu_forward", [&] { + DeformablePSROIPoolForwardKernelCpu( + out_size, + input.contiguous().data(), + spatial_scale, + channels, + height, width, + pooled_height, + pooled_width, + bbox.contiguous().data(), + trans.contiguous().data(), + no_trans, + trans_std, + sample_per_part, + output_dim, + group_size, + part_size, + num_classes, + channels_each_class, + out.data(), + top_count.data()); + }); + //THCudaCheck(cudaGetLastError()); + return std::make_tuple(out, top_count); +} + +std::tuple +dcn_v2_psroi_pooling_cpu_backward(const at::Tensor &out_grad, + const at::Tensor &input, + const at::Tensor &bbox, + const at::Tensor &trans, + const at::Tensor &top_count, + const int no_trans, + const float spatial_scale, + const int output_dim, + const int group_size, + const int pooled_size, + const int part_size, + const int sample_per_part, + const float trans_std) +{ + /*AT_ASSERTM(out_grad.type().is_cuda(), "out_grad must be a CUDA tensor"); + AT_ASSERTM(input.type().is_cuda(), "input must be a CUDA tensor"); + AT_ASSERTM(bbox.type().is_cuda(), "bbox must be a CUDA tensor"); + AT_ASSERTM(trans.type().is_cuda(), "trans must be a CUDA tensor"); + AT_ASSERTM(top_count.type().is_cuda(), "top_count must be a CUDA tensor");*/ + + const int batch = input.size(0); + const int channels = input.size(1); + const int height = input.size(2); + const int width = input.size(3); + const int channels_trans = no_trans ? 2 : trans.size(1); + const int num_bbox = bbox.size(0); + + AT_ASSERTM(channels == output_dim, "input channels and output channels must equal"); + auto pooled_height = pooled_size; + auto pooled_width = pooled_size; + long out_size = num_bbox * output_dim * pooled_height * pooled_width; + const int num_classes = no_trans ? 1 : channels_trans / 2; + const int channels_each_class = no_trans ? output_dim : output_dim / num_classes; + + auto input_grad = at::zeros({batch, channels, height, width}, out_grad.options()); + auto trans_grad = at::zeros_like(trans); + + if (input_grad.numel() == 0) + { + //THCudaCheck(cudaGetLastError()); + return std::make_tuple(input_grad, trans_grad); + } + + /*dim3 grid(std::min(THCCeilDiv(out_size, 512L), 4096L)); + dim3 block(512); + cudaStream_t stream = at::cuda::getCurrentCUDAStream();*/ + + AT_DISPATCH_FLOATING_TYPES(out_grad.type(), "dcn_v2_psroi_pooling_cpu_backward", [&] { + DeformablePSROIPoolBackwardAccKernelCpu( + out_size, + out_grad.contiguous().data(), + top_count.contiguous().data(), + num_bbox, + spatial_scale, + channels, + height, + width, + pooled_height, + pooled_width, + output_dim, + input_grad.contiguous().data(), + trans_grad.contiguous().data(), + input.contiguous().data(), + bbox.contiguous().data(), + trans.contiguous().data(), + no_trans, + trans_std, + sample_per_part, + group_size, + part_size, + num_classes, + channels_each_class); + }); + //THCudaCheck(cudaGetLastError()); + return std::make_tuple(input_grad, trans_grad); +} \ No newline at end of file diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/src/cpu/vision.h b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/src/cpu/vision.h new file mode 100644 index 00000000..d5fbf1f0 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/src/cpu/vision.h @@ -0,0 +1,60 @@ +#pragma once +#include + +at::Tensor +dcn_v2_cpu_forward(const at::Tensor &input, + const at::Tensor &weight, + const at::Tensor &bias, + const at::Tensor &offset, + const at::Tensor &mask, + const int kernel_h, + const int kernel_w, + const int stride_h, + const int stride_w, + const int pad_h, + const int pad_w, + const int dilation_h, + const int dilation_w, + const int deformable_group); + +std::vector +dcn_v2_cpu_backward(const at::Tensor &input, + const at::Tensor &weight, + const at::Tensor &bias, + const at::Tensor &offset, + const at::Tensor &mask, + const at::Tensor &grad_output, + int kernel_h, int kernel_w, + int stride_h, int stride_w, + int pad_h, int pad_w, + int dilation_h, int dilation_w, + int deformable_group); + + +std::tuple +dcn_v2_psroi_pooling_cpu_forward(const at::Tensor &input, + const at::Tensor &bbox, + const at::Tensor &trans, + const int no_trans, + const float spatial_scale, + const int output_dim, + const int group_size, + const int pooled_size, + const int part_size, + const int sample_per_part, + const float trans_std); + +std::tuple +dcn_v2_psroi_pooling_cpu_backward(const at::Tensor &out_grad, + const at::Tensor &input, + const at::Tensor &bbox, + const at::Tensor &trans, + const at::Tensor &top_count, + const int no_trans, + const float spatial_scale, + const int output_dim, + const int group_size, + const int pooled_size, + const int part_size, + const int sample_per_part, + const float trans_std); \ No newline at end of file diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/src/cuda/dcn_v2_cuda.cu b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/src/cuda/dcn_v2_cuda.cu new file mode 100644 index 00000000..cef3068f --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/src/cuda/dcn_v2_cuda.cu @@ -0,0 +1,372 @@ +#include +#include "cuda/dcn_v2_im2col_cuda.h" + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +THCState *state = at::globalContext().lazyInitCUDA(); + +static cublasOperation_t _cublasOpFromChar(char op) { + switch (op) { + case 'n': + case 'N': + return CUBLAS_OP_N; + case 't': + case 'T': + return CUBLAS_OP_T; + case 'c': + case 'C': + return CUBLAS_OP_C; + } + AT_ERROR( + "_cublasOpFromChar input should be 't', 'n' or 'c' but got `", op, "`"); + } + + static void _cublasAdjustLdLevel2(int64_t m, int64_t n, int64_t* lda) { + // Note: leading dimensions generally are checked that they are > 0 + // and at least as big the result requires (even if the value won't + // be used). + + // Q: Why does Level3 check trans but this doesn't? + // A: In level 2, the sizes (m, n) specify the size of A + // (independent of trans value). In level 3. the sizes (m, n, k) + // specify the sizes of op(A), op(B) where op depend on trans + // values. + if (n <= 1) + *lda = std::max(m, 1); + } + + + +// author: Charles Shang +// https://github.com/torch/cunn/blob/master/lib/THCUNN/generic/SpatialConvolutionMM.cu + +// [batch gemm] +// https://github.com/pytorch/pytorch/blob/master/aten/src/THC/generic/THCTensorMathBlas.cu + +__global__ void createBatchGemmBuffer(const float **input_b, float **output_b, + float **columns_b, const float **ones_b, + const float **weight_b, const float **bias_b, + float *input, float *output, + float *columns, float *ones, + float *weight, float *bias, + const int input_stride, const int output_stride, + const int columns_stride, const int ones_stride, + const int num_batches) +{ + const int idx = blockIdx.x * blockDim.x + threadIdx.x; + if (idx < num_batches) + { + input_b[idx] = input + idx * input_stride; + output_b[idx] = output + idx * output_stride; + columns_b[idx] = columns + idx * columns_stride; + ones_b[idx] = ones + idx * ones_stride; + // share weights and bias within a Mini-Batch + weight_b[idx] = weight; + bias_b[idx] = bias; + } +} + +at::Tensor +dcn_v2_cuda_forward(const at::Tensor &input, + const at::Tensor &weight, + const at::Tensor &bias, + const at::Tensor &offset, + const at::Tensor &mask, + const int kernel_h, + const int kernel_w, + const int stride_h, + const int stride_w, + const int pad_h, + const int pad_w, + const int dilation_h, + const int dilation_w, + const int deformable_group) +{ + using scalar_t = float; + // THCAssertSameGPU(THCudaTensor_checkGPU(state, 5, input, weight, bias, offset, mask)); + AT_ASSERTM(input.type().is_cuda(), "input must be a CUDA tensor"); + AT_ASSERTM(weight.type().is_cuda(), "weight must be a CUDA tensor"); + AT_ASSERTM(bias.type().is_cuda(), "bias must be a CUDA tensor"); + AT_ASSERTM(offset.type().is_cuda(), "offset must be a CUDA tensor"); + AT_ASSERTM(mask.type().is_cuda(), "mask must be a CUDA tensor"); + + const int batch = input.size(0); + const int channels = input.size(1); + const int height = input.size(2); + const int width = input.size(3); + + const int channels_out = weight.size(0); + const int channels_kernel = weight.size(1); + const int kernel_h_ = weight.size(2); + const int kernel_w_ = weight.size(3); + + // printf("Kernels: %d %d %d %d\n", kernel_h_, kernel_w_, kernel_w, kernel_h); + // printf("Channels: %d %d\n", channels, channels_kernel); + // printf("Channels: %d %d\n", channels_out, channels_kernel); + + AT_ASSERTM(kernel_h_ == kernel_h && kernel_w_ == kernel_w, + "Input shape and kernel shape wont match: (%d x %d vs %d x %d).", kernel_h_, kernel_w, kernel_h_, kernel_w_); + + AT_ASSERTM(channels == channels_kernel, + "Input shape and kernel channels wont match: (%d vs %d).", channels, channels_kernel); + + const int height_out = (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1; + const int width_out = (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1; + + auto ones = at::ones({batch, height_out, width_out}, input.options()); + auto columns = at::empty({batch, channels * kernel_h * kernel_w, 1 * height_out * width_out}, input.options()); + auto output = at::empty({batch, channels_out, height_out, width_out}, input.options()); + + // prepare for batch-wise computing, which is significantly faster than instance-wise computing + // when batch size is large. + // launch batch threads + int matrices_size = batch * sizeof(float *); + auto input_b = static_cast(THCudaMalloc(state, matrices_size)); + auto output_b = static_cast(THCudaMalloc(state, matrices_size)); + auto columns_b = static_cast(THCudaMalloc(state, matrices_size)); + auto ones_b = static_cast(THCudaMalloc(state, matrices_size)); + auto weight_b = static_cast(THCudaMalloc(state, matrices_size)); + auto bias_b = static_cast(THCudaMalloc(state, matrices_size)); + + const int block = 128; + const int grid = (batch + block - 1) / block; + + createBatchGemmBuffer<<>>( + input_b, output_b, + columns_b, ones_b, + weight_b, bias_b, + input.data_ptr(), + output.data_ptr(), + columns.data_ptr(), + ones.data_ptr(), + weight.data_ptr(), + bias.data_ptr(), + channels * width * height, + channels_out * width_out * height_out, + channels * kernel_h * kernel_w * height_out * width_out, + height_out * width_out, + batch); + + long m_ = channels_out; + long n_ = height_out * width_out; + long k_ = 1; + THCudaBlas_SgemmBatched(state, + 't', + 'n', + n_, + m_, + k_, + 1.0f, + ones_b, k_, + bias_b, k_, + 0.0f, + output_b, n_, + batch); + + modulated_deformable_im2col_cuda(c10::cuda::getCurrentCUDAStream(), + input.data_ptr(), + offset.data_ptr(), + mask.data_ptr(), + batch, channels, height, width, + height_out, width_out, kernel_h, kernel_w, + pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, + deformable_group, + columns.data_ptr()); + + long m = channels_out; + long n = height_out * width_out; + long k = channels * kernel_h * kernel_w; + THCudaBlas_SgemmBatched(state, + 'n', + 'n', + n, + m, + k, + 1.0f, + (const float **)columns_b, n, + weight_b, k, + 1.0f, + output_b, n, + batch); + + THCudaFree(state, input_b); + THCudaFree(state, output_b); + THCudaFree(state, columns_b); + THCudaFree(state, ones_b); + THCudaFree(state, weight_b); + THCudaFree(state, bias_b); + return output; +} + +__global__ void createBatchGemmBufferBackward( + float **grad_output_b, + float **columns_b, + float **ones_b, + float **weight_b, + float **grad_weight_b, + float **grad_bias_b, + float *grad_output, + float *columns, + float *ones, + float *weight, + float *grad_weight, + float *grad_bias, + const int grad_output_stride, + const int columns_stride, + const int ones_stride, + const int num_batches) +{ + const int idx = blockIdx.x * blockDim.x + threadIdx.x; + if (idx < num_batches) + { + grad_output_b[idx] = grad_output + idx * grad_output_stride; + columns_b[idx] = columns + idx * columns_stride; + ones_b[idx] = ones + idx * ones_stride; + + // share weights and bias within a Mini-Batch + weight_b[idx] = weight; + grad_weight_b[idx] = grad_weight; + grad_bias_b[idx] = grad_bias; + } +} + +std::vector dcn_v2_cuda_backward(const at::Tensor &input, + const at::Tensor &weight, + const at::Tensor &bias, + const at::Tensor &offset, + const at::Tensor &mask, + const at::Tensor &grad_output, + int kernel_h, int kernel_w, + int stride_h, int stride_w, + int pad_h, int pad_w, + int dilation_h, int dilation_w, + int deformable_group) +{ + + THArgCheck(input.is_contiguous(), 1, "input tensor has to be contiguous"); + THArgCheck(weight.is_contiguous(), 2, "weight tensor has to be contiguous"); + + AT_ASSERTM(input.type().is_cuda(), "input must be a CUDA tensor"); + AT_ASSERTM(weight.type().is_cuda(), "weight must be a CUDA tensor"); + AT_ASSERTM(bias.type().is_cuda(), "bias must be a CUDA tensor"); + AT_ASSERTM(offset.type().is_cuda(), "offset must be a CUDA tensor"); + AT_ASSERTM(mask.type().is_cuda(), "mask must be a CUDA tensor"); + + const int batch = input.size(0); + const int channels = input.size(1); + const int height = input.size(2); + const int width = input.size(3); + + const int channels_out = weight.size(0); + const int channels_kernel = weight.size(1); + const int kernel_h_ = weight.size(2); + const int kernel_w_ = weight.size(3); + + AT_ASSERTM(kernel_h_ == kernel_h && kernel_w_ == kernel_w, + "Input shape and kernel shape wont match: (%d x %d vs %d x %d).", kernel_h_, kernel_w, kernel_h_, kernel_w_); + + AT_ASSERTM(channels == channels_kernel, + "Input shape and kernel channels wont match: (%d vs %d).", channels, channels_kernel); + + const int height_out = (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1; + const int width_out = (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1; + + auto ones = at::ones({height_out, width_out}, input.options()); + auto columns = at::empty({channels * kernel_h * kernel_w, 1 * height_out * width_out}, input.options()); + auto output = at::empty({batch, channels_out, height_out, width_out}, input.options()); + + auto grad_input = at::zeros_like(input); + auto grad_weight = at::zeros_like(weight); + auto grad_bias = at::zeros_like(bias); + auto grad_offset = at::zeros_like(offset); + auto grad_mask = at::zeros_like(mask); + + using scalar_t = float; + + for (int b = 0; b < batch; b++) + { + auto input_n = input.select(0, b); + auto offset_n = offset.select(0, b); + auto mask_n = mask.select(0, b); + auto grad_output_n = grad_output.select(0, b); + auto grad_input_n = grad_input.select(0, b); + auto grad_offset_n = grad_offset.select(0, b); + auto grad_mask_n = grad_mask.select(0, b); + + long m = channels * kernel_h * kernel_w; + long n = height_out * width_out; + long k = channels_out; + + THCudaBlas_Sgemm(state, 'n', 't', n, m, k, 1.0f, + grad_output_n.data_ptr(), n, + weight.data_ptr(), m, 0.0f, + columns.data_ptr(), n); + + // gradient w.r.t. input coordinate data + modulated_deformable_col2im_coord_cuda(c10::cuda::getCurrentCUDAStream(), + columns.data_ptr(), + input_n.data_ptr(), + offset_n.data_ptr(), + mask_n.data_ptr(), + 1, channels, height, width, + height_out, width_out, kernel_h, kernel_w, + pad_h, pad_w, stride_h, stride_w, + dilation_h, dilation_w, deformable_group, + grad_offset_n.data_ptr(), + grad_mask_n.data_ptr()); + // gradient w.r.t. input data + modulated_deformable_col2im_cuda(c10::cuda::getCurrentCUDAStream(), + columns.data_ptr(), + offset_n.data_ptr(), + mask_n.data_ptr(), + 1, channels, height, width, + height_out, width_out, kernel_h, kernel_w, + pad_h, pad_w, stride_h, stride_w, + dilation_h, dilation_w, deformable_group, + grad_input_n.data_ptr()); + + // gradient w.r.t. weight, dWeight should accumulate across the batch and group + modulated_deformable_im2col_cuda(c10::cuda::getCurrentCUDAStream(), + input_n.data_ptr(), + offset_n.data_ptr(), + mask_n.data_ptr(), + 1, channels, height, width, + height_out, width_out, kernel_h, kernel_w, + pad_h, pad_w, stride_h, stride_w, + dilation_h, dilation_w, deformable_group, + columns.data_ptr()); + + long m_ = channels_out; + long n_ = channels * kernel_h * kernel_w; + long k_ = height_out * width_out; + + THCudaBlas_Sgemm(state, 't', 'n', n_, m_, k_, 1.0f, + columns.data_ptr(), k_, + grad_output_n.data_ptr(), k_, 1.0f, + grad_weight.data_ptr(), n_); + + cublasHandle_t handle = at::cuda::getCurrentCUDABlasHandle(); + cublasOperation_t op = _cublasOpFromChar('t'); + _cublasAdjustLdLevel2(k_, m_, &k_); + scalar_t* grad_output_n_float = grad_output_n.data_ptr(); + scalar_t* one_float = ones.data_ptr(); + scalar_t alpha = 1.0; + scalar_t beta = 1.0; + cublasSgemv(handle, op, k_, m_, &alpha, grad_output_n_float,k_, one_float,1, &beta, grad_bias.data_ptr(), 1); + + } + + + return { + grad_input, grad_offset, grad_mask, grad_weight, grad_bias + }; +} diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/src/cuda/dcn_v2_im2col_cuda.cu b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/src/cuda/dcn_v2_im2col_cuda.cu new file mode 100644 index 00000000..4140eacf --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/src/cuda/dcn_v2_im2col_cuda.cu @@ -0,0 +1,402 @@ +#include "dcn_v2_im2col_cuda.h" +#include +#include +#include + +#include +#include + +#include +#include +#include + +#define CUDA_KERNEL_LOOP(i, n) \ + for (int i = blockIdx.x * blockDim.x + threadIdx.x; \ + i < (n); \ + i += blockDim.x * gridDim.x) + +const int CUDA_NUM_THREADS = 1024; +inline int GET_BLOCKS(const int N) +{ + return (N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS; +} + + +__device__ float dmcn_im2col_bilinear_cuda(const float *bottom_data, const int data_width, + const int height, const int width, float h, float w) +{ + int h_low = floor(h); + int w_low = floor(w); + int h_high = h_low + 1; + int w_high = w_low + 1; + + float lh = h - h_low; + float lw = w - w_low; + float hh = 1 - lh, hw = 1 - lw; + + float v1 = 0; + if (h_low >= 0 && w_low >= 0) + v1 = bottom_data[h_low * data_width + w_low]; + float v2 = 0; + if (h_low >= 0 && w_high <= width - 1) + v2 = bottom_data[h_low * data_width + w_high]; + float v3 = 0; + if (h_high <= height - 1 && w_low >= 0) + v3 = bottom_data[h_high * data_width + w_low]; + float v4 = 0; + if (h_high <= height - 1 && w_high <= width - 1) + v4 = bottom_data[h_high * data_width + w_high]; + + float w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; + + float val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); + return val; +} + +__device__ float dmcn_get_gradient_weight_cuda(float argmax_h, float argmax_w, + const int h, const int w, const int height, const int width) +{ + if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width) + { + //empty + return 0; + } + + int argmax_h_low = floor(argmax_h); + int argmax_w_low = floor(argmax_w); + int argmax_h_high = argmax_h_low + 1; + int argmax_w_high = argmax_w_low + 1; + + float weight = 0; + if (h == argmax_h_low && w == argmax_w_low) + weight = (h + 1 - argmax_h) * (w + 1 - argmax_w); + if (h == argmax_h_low && w == argmax_w_high) + weight = (h + 1 - argmax_h) * (argmax_w + 1 - w); + if (h == argmax_h_high && w == argmax_w_low) + weight = (argmax_h + 1 - h) * (w + 1 - argmax_w); + if (h == argmax_h_high && w == argmax_w_high) + weight = (argmax_h + 1 - h) * (argmax_w + 1 - w); + return weight; +} + +__device__ float dmcn_get_coordinate_weight_cuda(float argmax_h, float argmax_w, + const int height, const int width, const float *im_data, + const int data_width, const int bp_dir) +{ + if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width) + { + //empty + return 0; + } + + int argmax_h_low = floor(argmax_h); + int argmax_w_low = floor(argmax_w); + int argmax_h_high = argmax_h_low + 1; + int argmax_w_high = argmax_w_low + 1; + + float weight = 0; + + if (bp_dir == 0) + { + if (argmax_h_low >= 0 && argmax_w_low >= 0) + weight += -1 * (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_low * data_width + argmax_w_low]; + if (argmax_h_low >= 0 && argmax_w_high <= width - 1) + weight += -1 * (argmax_w - argmax_w_low) * im_data[argmax_h_low * data_width + argmax_w_high]; + if (argmax_h_high <= height - 1 && argmax_w_low >= 0) + weight += (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_high * data_width + argmax_w_low]; + if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) + weight += (argmax_w - argmax_w_low) * im_data[argmax_h_high * data_width + argmax_w_high]; + } + else if (bp_dir == 1) + { + if (argmax_h_low >= 0 && argmax_w_low >= 0) + weight += -1 * (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_low]; + if (argmax_h_low >= 0 && argmax_w_high <= width - 1) + weight += (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_high]; + if (argmax_h_high <= height - 1 && argmax_w_low >= 0) + weight += -1 * (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_low]; + if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) + weight += (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_high]; + } + + return weight; +} + +__global__ void modulated_deformable_im2col_gpu_kernel(const int n, + const float *data_im, const float *data_offset, const float *data_mask, + const int height, const int width, const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, + const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int channel_per_deformable_group, + const int batch_size, const int num_channels, const int deformable_group, + const int height_col, const int width_col, + float *data_col) +{ + // launch channels * batch_size * height_col * width_col cores + CUDA_KERNEL_LOOP(index, n) + { + // NOTE(CharlesShang): different from Dai Jifeng's MXNet implementation, col_buffer is of shape (c*kw*kh, N, oh, ow) + // here columns is of shape (N, c*kw*kh, oh * ow), need to adapt axis + + // index index of output matrix + const int w_col = index % width_col; + const int h_col = (index / width_col) % height_col; + // const int b_col = (index / width_col / height_col) % batch_size; + const int b_col = (index / width_col / height_col / num_channels) % batch_size; + // const int c_im = (index / width_col / height_col) / batch_size; + const int c_im = (index / width_col / height_col) % num_channels; + // const int c_col = c_im * kernel_h * kernel_w; + const int c_col = c_im * kernel_h * kernel_w; + + // compute deformable group index + const int deformable_group_index = c_im / channel_per_deformable_group; + + const int h_in = h_col * stride_h - pad_h; + const int w_in = w_col * stride_w - pad_w; + + // float *data_col_ptr = data_col + ((c_col * batch_size + b_col) * height_col + h_col) * width_col + w_col; + float *data_col_ptr = data_col + ((b_col * num_channels * kernel_w * kernel_h + c_col) * height_col + h_col) * width_col + w_col; + //const float* data_im_ptr = data_im + ((b_col * num_channels + c_im) * height + h_in) * width + w_in; + const float *data_im_ptr = data_im + (b_col * num_channels + c_im) * height * width; + const float *data_offset_ptr = data_offset + (b_col * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col; + + const float *data_mask_ptr = data_mask + (b_col * deformable_group + deformable_group_index) * kernel_h * kernel_w * height_col * width_col; + + for (int i = 0; i < kernel_h; ++i) + { + for (int j = 0; j < kernel_w; ++j) + { + const int data_offset_h_ptr = ((2 * (i * kernel_w + j)) * height_col + h_col) * width_col + w_col; + const int data_offset_w_ptr = ((2 * (i * kernel_w + j) + 1) * height_col + h_col) * width_col + w_col; + const int data_mask_hw_ptr = ((i * kernel_w + j) * height_col + h_col) * width_col + w_col; + const float offset_h = data_offset_ptr[data_offset_h_ptr]; + const float offset_w = data_offset_ptr[data_offset_w_ptr]; + const float mask = data_mask_ptr[data_mask_hw_ptr]; + float val = static_cast(0); + const float h_im = h_in + i * dilation_h + offset_h; + const float w_im = w_in + j * dilation_w + offset_w; + //if (h_im >= 0 && w_im >= 0 && h_im < height && w_im < width) { + if (h_im > -1 && w_im > -1 && h_im < height && w_im < width) + { + //const float map_h = i * dilation_h + offset_h; + //const float map_w = j * dilation_w + offset_w; + //const int cur_height = height - h_in; + //const int cur_width = width - w_in; + //val = dmcn_im2col_bilinear_cuda(data_im_ptr, width, cur_height, cur_width, map_h, map_w); + val = dmcn_im2col_bilinear_cuda(data_im_ptr, width, height, width, h_im, w_im); + } + *data_col_ptr = val * mask; + // data_col_ptr += batch_size * height_col * width_col; + data_col_ptr += height_col * width_col; + } + } + } +} + +__global__ void modulated_deformable_col2im_gpu_kernel(const int n, + const float *data_col, const float *data_offset, const float *data_mask, + const int channels, const int height, const int width, + const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, + const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int channel_per_deformable_group, + const int batch_size, const int deformable_group, + const int height_col, const int width_col, + float *grad_im) +{ + CUDA_KERNEL_LOOP(index, n) + { + const int j = (index / width_col / height_col / batch_size) % kernel_w; + const int i = (index / width_col / height_col / batch_size / kernel_w) % kernel_h; + const int c = index / width_col / height_col / batch_size / kernel_w / kernel_h; + // compute the start and end of the output + + const int deformable_group_index = c / channel_per_deformable_group; + + int w_out = index % width_col; + int h_out = (index / width_col) % height_col; + int b = (index / width_col / height_col) % batch_size; + int w_in = w_out * stride_w - pad_w; + int h_in = h_out * stride_h - pad_h; + + const float *data_offset_ptr = data_offset + (b * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col; + const float *data_mask_ptr = data_mask + (b * deformable_group + deformable_group_index) * kernel_h * kernel_w * height_col * width_col; + const int data_offset_h_ptr = ((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out; + const int data_offset_w_ptr = ((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out; + const int data_mask_hw_ptr = ((i * kernel_w + j) * height_col + h_out) * width_col + w_out; + const float offset_h = data_offset_ptr[data_offset_h_ptr]; + const float offset_w = data_offset_ptr[data_offset_w_ptr]; + const float mask = data_mask_ptr[data_mask_hw_ptr]; + const float cur_inv_h_data = h_in + i * dilation_h + offset_h; + const float cur_inv_w_data = w_in + j * dilation_w + offset_w; + + const float cur_top_grad = data_col[index] * mask; + const int cur_h = (int)cur_inv_h_data; + const int cur_w = (int)cur_inv_w_data; + for (int dy = -2; dy <= 2; dy++) + { + for (int dx = -2; dx <= 2; dx++) + { + if (cur_h + dy >= 0 && cur_h + dy < height && + cur_w + dx >= 0 && cur_w + dx < width && + abs(cur_inv_h_data - (cur_h + dy)) < 1 && + abs(cur_inv_w_data - (cur_w + dx)) < 1) + { + int cur_bottom_grad_pos = ((b * channels + c) * height + cur_h + dy) * width + cur_w + dx; + float weight = dmcn_get_gradient_weight_cuda(cur_inv_h_data, cur_inv_w_data, cur_h + dy, cur_w + dx, height, width); + atomicAdd(grad_im + cur_bottom_grad_pos, weight * cur_top_grad); + } + } + } + } +} + +__global__ void modulated_deformable_col2im_coord_gpu_kernel(const int n, + const float *data_col, const float *data_im, + const float *data_offset, const float *data_mask, + const int channels, const int height, const int width, + const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, + const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int channel_per_deformable_group, + const int batch_size, const int offset_channels, const int deformable_group, + const int height_col, const int width_col, + float *grad_offset, float *grad_mask) +{ + CUDA_KERNEL_LOOP(index, n) + { + float val = 0, mval = 0; + int w = index % width_col; + int h = (index / width_col) % height_col; + int c = (index / width_col / height_col) % offset_channels; + int b = (index / width_col / height_col) / offset_channels; + // compute the start and end of the output + + const int deformable_group_index = c / (2 * kernel_h * kernel_w); + const int col_step = kernel_h * kernel_w; + int cnt = 0; + const float *data_col_ptr = data_col + deformable_group_index * channel_per_deformable_group * batch_size * width_col * height_col; + const float *data_im_ptr = data_im + (b * deformable_group + deformable_group_index) * channel_per_deformable_group / kernel_h / kernel_w * height * width; + const float *data_offset_ptr = data_offset + (b * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col; + const float *data_mask_ptr = data_mask + (b * deformable_group + deformable_group_index) * kernel_h * kernel_w * height_col * width_col; + + const int offset_c = c - deformable_group_index * 2 * kernel_h * kernel_w; + + for (int col_c = (offset_c / 2); col_c < channel_per_deformable_group; col_c += col_step) + { + const int col_pos = (((col_c * batch_size + b) * height_col) + h) * width_col + w; + const int bp_dir = offset_c % 2; + + int j = (col_pos / width_col / height_col / batch_size) % kernel_w; + int i = (col_pos / width_col / height_col / batch_size / kernel_w) % kernel_h; + int w_out = col_pos % width_col; + int h_out = (col_pos / width_col) % height_col; + int w_in = w_out * stride_w - pad_w; + int h_in = h_out * stride_h - pad_h; + const int data_offset_h_ptr = (((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out); + const int data_offset_w_ptr = (((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out); + const int data_mask_hw_ptr = (((i * kernel_w + j) * height_col + h_out) * width_col + w_out); + const float offset_h = data_offset_ptr[data_offset_h_ptr]; + const float offset_w = data_offset_ptr[data_offset_w_ptr]; + const float mask = data_mask_ptr[data_mask_hw_ptr]; + float inv_h = h_in + i * dilation_h + offset_h; + float inv_w = w_in + j * dilation_w + offset_w; + if (inv_h <= -1 || inv_w <= -1 || inv_h >= height || inv_w >= width) + { + inv_h = inv_w = -2; + } + else + { + mval += data_col_ptr[col_pos] * dmcn_im2col_bilinear_cuda(data_im_ptr + cnt * height * width, width, height, width, inv_h, inv_w); + } + const float weight = dmcn_get_coordinate_weight_cuda( + inv_h, inv_w, + height, width, data_im_ptr + cnt * height * width, width, bp_dir); + val += weight * data_col_ptr[col_pos] * mask; + cnt += 1; + } + // KERNEL_ASSIGN(grad_offset[index], offset_req, val); + grad_offset[index] = val; + if (offset_c % 2 == 0) + // KERNEL_ASSIGN(grad_mask[(((b * deformable_group + deformable_group_index) * kernel_h * kernel_w + offset_c / 2) * height_col + h) * width_col + w], mask_req, mval); + grad_mask[(((b * deformable_group + deformable_group_index) * kernel_h * kernel_w + offset_c / 2) * height_col + h) * width_col + w] = mval; + } +} + +void modulated_deformable_im2col_cuda(cudaStream_t stream, + const float* data_im, const float* data_offset, const float* data_mask, + const int batch_size, const int channels, const int height_im, const int width_im, + const int height_col, const int width_col, const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int deformable_group, float* data_col) { + // num_axes should be smaller than block size + const int channel_per_deformable_group = channels / deformable_group; + const int num_kernels = channels * batch_size * height_col * width_col; + modulated_deformable_im2col_gpu_kernel + <<>>( + num_kernels, data_im, data_offset, data_mask, height_im, width_im, kernel_h, kernel_w, + pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, channel_per_deformable_group, + batch_size, channels, deformable_group, height_col, width_col, data_col); + + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) + { + printf("error in modulated_deformable_im2col_cuda: %s\n", cudaGetErrorString(err)); + } + +} + +void modulated_deformable_col2im_cuda(cudaStream_t stream, + const float* data_col, const float* data_offset, const float* data_mask, + const int batch_size, const int channels, const int height_im, const int width_im, + const int height_col, const int width_col, const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int deformable_group, float* grad_im){ + + const int channel_per_deformable_group = channels / deformable_group; + const int num_kernels = channels * kernel_h * kernel_w * batch_size * height_col * width_col; + modulated_deformable_col2im_gpu_kernel + <<>>( + num_kernels, data_col, data_offset, data_mask, channels, height_im, width_im, + kernel_h, kernel_w, pad_h, pad_h, stride_h, stride_w, + dilation_h, dilation_w, channel_per_deformable_group, + batch_size, deformable_group, height_col, width_col, grad_im); + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) + { + printf("error in modulated_deformable_col2im_cuda: %s\n", cudaGetErrorString(err)); + } + +} + +void modulated_deformable_col2im_coord_cuda(cudaStream_t stream, + const float* data_col, const float* data_im, const float* data_offset, const float* data_mask, + const int batch_size, const int channels, const int height_im, const int width_im, + const int height_col, const int width_col, const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int deformable_group, + float* grad_offset, float* grad_mask) { + const int num_kernels = batch_size * height_col * width_col * 2 * kernel_h * kernel_w * deformable_group; + const int channel_per_deformable_group = channels * kernel_h * kernel_w / deformable_group; + modulated_deformable_col2im_coord_gpu_kernel + <<>>( + num_kernels, data_col, data_im, data_offset, data_mask, channels, height_im, width_im, + kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, + dilation_h, dilation_w, channel_per_deformable_group, + batch_size, 2 * kernel_h * kernel_w * deformable_group, deformable_group, height_col, width_col, + grad_offset, grad_mask); + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) + { + printf("error in modulated_deformable_col2im_coord_cuda: %s\n", cudaGetErrorString(err)); + } +} \ No newline at end of file diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/src/cuda/dcn_v2_im2col_cuda.h b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/src/cuda/dcn_v2_im2col_cuda.h new file mode 100644 index 00000000..c8568319 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/src/cuda/dcn_v2_im2col_cuda.h @@ -0,0 +1,101 @@ + +/*! + ******************* BEGIN Caffe Copyright Notice and Disclaimer **************** + * + * COPYRIGHT + * + * All contributions by the University of California: + * Copyright (c) 2014-2017 The Regents of the University of California (Regents) + * All rights reserved. + * + * All other contributions: + * Copyright (c) 2014-2017, the respective contributors + * All rights reserved. + * + * Caffe uses a shared copyright model: each contributor holds copyright over + * their contributions to Caffe. The project versioning records all such + * contribution and copyright details. If a contributor wants to further mark + * their specific copyright on a particular contribution, they should indicate + * their copyright solely in the commit message of the change when it is + * committed. + * + * LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * + * 1. Redistributions of source code must retain the above copyright notice, this + * list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright notice, + * this list of conditions and the following disclaimer in the documentation + * and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND + * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED + * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR + * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES + * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; + * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND + * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + * + * CONTRIBUTION AGREEMENT + * + * By contributing to the BVLC/caffe repository through pull-request, comment, + * or otherwise, the contributor releases their content to the + * license and copyright terms herein. + * + ***************** END Caffe Copyright Notice and Disclaimer ******************** + * + * Copyright (c) 2018 Microsoft + * Licensed under The MIT License [see LICENSE for details] + * \file modulated_deformable_im2col.h + * \brief Function definitions of converting an image to + * column matrix based on kernel, padding, dilation, and offset. + * These functions are mainly used in deformable convolution operators. + * \ref: https://arxiv.org/abs/1811.11168 + * \author Yuwen Xiong, Haozhi Qi, Jifeng Dai, Xizhou Zhu, Han Hu + */ + +/***************** Adapted by Charles Shang *********************/ + +#ifndef DCN_V2_IM2COL_CUDA +#define DCN_V2_IM2COL_CUDA + +#ifdef __cplusplus +extern "C" +{ +#endif + + void modulated_deformable_im2col_cuda(cudaStream_t stream, + const float *data_im, const float *data_offset, const float *data_mask, + const int batch_size, const int channels, const int height_im, const int width_im, + const int height_col, const int width_col, const int kernel_h, const int kenerl_w, + const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int deformable_group, float *data_col); + + void modulated_deformable_col2im_cuda(cudaStream_t stream, + const float *data_col, const float *data_offset, const float *data_mask, + const int batch_size, const int channels, const int height_im, const int width_im, + const int height_col, const int width_col, const int kernel_h, const int kenerl_w, + const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int deformable_group, float *grad_im); + + void modulated_deformable_col2im_coord_cuda(cudaStream_t stream, + const float *data_col, const float *data_im, const float *data_offset, const float *data_mask, + const int batch_size, const int channels, const int height_im, const int width_im, + const int height_col, const int width_col, const int kernel_h, const int kenerl_w, + const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int deformable_group, + float *grad_offset, float *grad_mask); + +#ifdef __cplusplus +} +#endif + +#endif \ No newline at end of file diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/src/cuda/dcn_v2_psroi_pooling_cuda.cu b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/src/cuda/dcn_v2_psroi_pooling_cuda.cu new file mode 100644 index 00000000..bf99f0ca --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/src/cuda/dcn_v2_psroi_pooling_cuda.cu @@ -0,0 +1,419 @@ +/*! + * Copyright (c) 2017 Microsoft + * Licensed under The MIT License [see LICENSE for details] + * \file deformable_psroi_pooling.cu + * \brief + * \author Yi Li, Guodong Zhang, Jifeng Dai +*/ +/***************** Adapted by Charles Shang *********************/ + +#include +#include +#include +#include + +#include +#include + +#include +#include +#include + +#define CUDA_KERNEL_LOOP(i, n) \ + for (int i = blockIdx.x * blockDim.x + threadIdx.x; \ + i < (n); \ + i += blockDim.x * gridDim.x) + +const int CUDA_NUM_THREADS = 1024; +inline int GET_BLOCKS(const int N) +{ + return (N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS; +} + +template +__device__ T bilinear_interp_cuda( + const T *data, + const T x, + const T y, + const int width, + const int height) +{ + int x1 = floor(x); + int x2 = ceil(x); + int y1 = floor(y); + int y2 = ceil(y); + T dist_x = static_cast(x - x1); + T dist_y = static_cast(y - y1); + T value11 = data[y1 * width + x1]; + T value12 = data[y2 * width + x1]; + T value21 = data[y1 * width + x2]; + T value22 = data[y2 * width + x2]; + T value = (1 - dist_x) * (1 - dist_y) * value11 + + (1 - dist_x) * dist_y * value12 + + dist_x * (1 - dist_y) * value21 + + dist_x * dist_y * value22; + return value; +} + +template +__global__ void DeformablePSROIPoolForwardKernelCuda( + const int count, + const T *bottom_data, + const T spatial_scale, + const int channels, + const int height, const int width, + const int pooled_height, const int pooled_width, + const T *bottom_rois, const T *bottom_trans, + const int no_trans, + const T trans_std, + const int sample_per_part, + const int output_dim, + const int group_size, + const int part_size, + const int num_classes, + const int channels_each_class, + T *top_data, + T *top_count) +{ + CUDA_KERNEL_LOOP(index, count) + { + // The output is in order (n, ctop, ph, pw) + int pw = index % pooled_width; + int ph = (index / pooled_width) % pooled_height; + int ctop = (index / pooled_width / pooled_height) % output_dim; + int n = index / pooled_width / pooled_height / output_dim; + + // [start, end) interval for spatial sampling + const T *offset_bottom_rois = bottom_rois + n * 5; + int roi_batch_ind = offset_bottom_rois[0]; + T roi_start_w = static_cast(round(offset_bottom_rois[1])) * spatial_scale - 0.5; + T roi_start_h = static_cast(round(offset_bottom_rois[2])) * spatial_scale - 0.5; + T roi_end_w = static_cast(round(offset_bottom_rois[3]) + 1.) * spatial_scale - 0.5; + T roi_end_h = static_cast(round(offset_bottom_rois[4]) + 1.) * spatial_scale - 0.5; + + // Force too small ROIs to be 1x1 + T roi_width = max(roi_end_w - roi_start_w, 0.1); //avoid 0 + T roi_height = max(roi_end_h - roi_start_h, 0.1); + + // Compute w and h at bottom + T bin_size_h = roi_height / static_cast(pooled_height); + T bin_size_w = roi_width / static_cast(pooled_width); + + T sub_bin_size_h = bin_size_h / static_cast(sample_per_part); + T sub_bin_size_w = bin_size_w / static_cast(sample_per_part); + + int part_h = floor(static_cast(ph) / pooled_height * part_size); + int part_w = floor(static_cast(pw) / pooled_width * part_size); + int class_id = ctop / channels_each_class; + T trans_x = no_trans ? static_cast(0) : bottom_trans[(((n * num_classes + class_id) * 2) * part_size + part_h) * part_size + part_w] * trans_std; + T trans_y = no_trans ? static_cast(0) : bottom_trans[(((n * num_classes + class_id) * 2 + 1) * part_size + part_h) * part_size + part_w] * trans_std; + + T wstart = static_cast(pw) * bin_size_w + roi_start_w; + wstart += trans_x * roi_width; + T hstart = static_cast(ph) * bin_size_h + roi_start_h; + hstart += trans_y * roi_height; + + T sum = 0; + int count = 0; + int gw = floor(static_cast(pw) * group_size / pooled_width); + int gh = floor(static_cast(ph) * group_size / pooled_height); + gw = min(max(gw, 0), group_size - 1); + gh = min(max(gh, 0), group_size - 1); + + const T *offset_bottom_data = bottom_data + (roi_batch_ind * channels) * height * width; + for (int ih = 0; ih < sample_per_part; ih++) + { + for (int iw = 0; iw < sample_per_part; iw++) + { + T w = wstart + iw * sub_bin_size_w; + T h = hstart + ih * sub_bin_size_h; + // bilinear interpolation + if (w < -0.5 || w > width - 0.5 || h < -0.5 || h > height - 0.5) + { + continue; + } + w = min(max(w, 0.), width - 1.); + h = min(max(h, 0.), height - 1.); + int c = (ctop * group_size + gh) * group_size + gw; + T val = bilinear_interp_cuda(offset_bottom_data + c * height * width, w, h, width, height); + sum += val; + count++; + } + } + top_data[index] = count == 0 ? static_cast(0) : sum / count; + top_count[index] = count; + } +} + +template +__global__ void DeformablePSROIPoolBackwardAccKernelCuda( + const int count, + const T *top_diff, + const T *top_count, + const int num_rois, + const T spatial_scale, + const int channels, + const int height, const int width, + const int pooled_height, const int pooled_width, + const int output_dim, + T *bottom_data_diff, T *bottom_trans_diff, + const T *bottom_data, + const T *bottom_rois, + const T *bottom_trans, + const int no_trans, + const T trans_std, + const int sample_per_part, + const int group_size, + const int part_size, + const int num_classes, + const int channels_each_class) +{ + CUDA_KERNEL_LOOP(index, count) + { + // The output is in order (n, ctop, ph, pw) + int pw = index % pooled_width; + int ph = (index / pooled_width) % pooled_height; + int ctop = (index / pooled_width / pooled_height) % output_dim; + int n = index / pooled_width / pooled_height / output_dim; + + // [start, end) interval for spatial sampling + const T *offset_bottom_rois = bottom_rois + n * 5; + int roi_batch_ind = offset_bottom_rois[0]; + T roi_start_w = static_cast(round(offset_bottom_rois[1])) * spatial_scale - 0.5; + T roi_start_h = static_cast(round(offset_bottom_rois[2])) * spatial_scale - 0.5; + T roi_end_w = static_cast(round(offset_bottom_rois[3]) + 1.) * spatial_scale - 0.5; + T roi_end_h = static_cast(round(offset_bottom_rois[4]) + 1.) * spatial_scale - 0.5; + + // Force too small ROIs to be 1x1 + T roi_width = max(roi_end_w - roi_start_w, 0.1); //avoid 0 + T roi_height = max(roi_end_h - roi_start_h, 0.1); + + // Compute w and h at bottom + T bin_size_h = roi_height / static_cast(pooled_height); + T bin_size_w = roi_width / static_cast(pooled_width); + + T sub_bin_size_h = bin_size_h / static_cast(sample_per_part); + T sub_bin_size_w = bin_size_w / static_cast(sample_per_part); + + int part_h = floor(static_cast(ph) / pooled_height * part_size); + int part_w = floor(static_cast(pw) / pooled_width * part_size); + int class_id = ctop / channels_each_class; + T trans_x = no_trans ? static_cast(0) : bottom_trans[(((n * num_classes + class_id) * 2) * part_size + part_h) * part_size + part_w] * trans_std; + T trans_y = no_trans ? static_cast(0) : bottom_trans[(((n * num_classes + class_id) * 2 + 1) * part_size + part_h) * part_size + part_w] * trans_std; + + T wstart = static_cast(pw) * bin_size_w + roi_start_w; + wstart += trans_x * roi_width; + T hstart = static_cast(ph) * bin_size_h + roi_start_h; + hstart += trans_y * roi_height; + + if (top_count[index] <= 0) + { + continue; + } + T diff_val = top_diff[index] / top_count[index]; + const T *offset_bottom_data = bottom_data + roi_batch_ind * channels * height * width; + T *offset_bottom_data_diff = bottom_data_diff + roi_batch_ind * channels * height * width; + int gw = floor(static_cast(pw) * group_size / pooled_width); + int gh = floor(static_cast(ph) * group_size / pooled_height); + gw = min(max(gw, 0), group_size - 1); + gh = min(max(gh, 0), group_size - 1); + + for (int ih = 0; ih < sample_per_part; ih++) + { + for (int iw = 0; iw < sample_per_part; iw++) + { + T w = wstart + iw * sub_bin_size_w; + T h = hstart + ih * sub_bin_size_h; + // bilinear interpolation + if (w < -0.5 || w > width - 0.5 || h < -0.5 || h > height - 0.5) + { + continue; + } + w = min(max(w, 0.), width - 1.); + h = min(max(h, 0.), height - 1.); + int c = (ctop * group_size + gh) * group_size + gw; + // backward on feature + int x0 = floor(w); + int x1 = ceil(w); + int y0 = floor(h); + int y1 = ceil(h); + T dist_x = w - x0, dist_y = h - y0; + T q00 = (1 - dist_x) * (1 - dist_y); + T q01 = (1 - dist_x) * dist_y; + T q10 = dist_x * (1 - dist_y); + T q11 = dist_x * dist_y; + int bottom_index_base = c * height * width; + atomicAdd(offset_bottom_data_diff + bottom_index_base + y0 * width + x0, q00 * diff_val); + atomicAdd(offset_bottom_data_diff + bottom_index_base + y1 * width + x0, q01 * diff_val); + atomicAdd(offset_bottom_data_diff + bottom_index_base + y0 * width + x1, q10 * diff_val); + atomicAdd(offset_bottom_data_diff + bottom_index_base + y1 * width + x1, q11 * diff_val); + + if (no_trans) + { + continue; + } + T U00 = offset_bottom_data[bottom_index_base + y0 * width + x0]; + T U01 = offset_bottom_data[bottom_index_base + y1 * width + x0]; + T U10 = offset_bottom_data[bottom_index_base + y0 * width + x1]; + T U11 = offset_bottom_data[bottom_index_base + y1 * width + x1]; + T diff_x = (U11 * dist_y + U10 * (1 - dist_y) - U01 * dist_y - U00 * (1 - dist_y)) * trans_std * diff_val; + diff_x *= roi_width; + T diff_y = (U11 * dist_x + U01 * (1 - dist_x) - U10 * dist_x - U00 * (1 - dist_x)) * trans_std * diff_val; + diff_y *= roi_height; + + atomicAdd(bottom_trans_diff + (((n * num_classes + class_id) * 2) * part_size + part_h) * part_size + part_w, diff_x); + atomicAdd(bottom_trans_diff + (((n * num_classes + class_id) * 2 + 1) * part_size + part_h) * part_size + part_w, diff_y); + } + } + } +} + +std::tuple +dcn_v2_psroi_pooling_cuda_forward(const at::Tensor &input, + const at::Tensor &bbox, + const at::Tensor &trans, + const int no_trans, + const float spatial_scale, + const int output_dim, + const int group_size, + const int pooled_size, + const int part_size, + const int sample_per_part, + const float trans_std) +{ + AT_ASSERTM(input.type().is_cuda(), "input must be a CUDA tensor"); + AT_ASSERTM(bbox.type().is_cuda(), "rois must be a CUDA tensor"); + AT_ASSERTM(trans.type().is_cuda(), "trans must be a CUDA tensor"); + + const int batch = input.size(0); + const int channels = input.size(1); + const int height = input.size(2); + const int width = input.size(3); + const int channels_trans = no_trans ? 2 : trans.size(1); + const int num_bbox = bbox.size(0); + + AT_ASSERTM(channels == output_dim, "input channels and output channels must equal"); + auto pooled_height = pooled_size; + auto pooled_width = pooled_size; + + auto out = at::empty({num_bbox, output_dim, pooled_height, pooled_width}, input.options()); + long out_size = num_bbox * output_dim * pooled_height * pooled_width; + auto top_count = at::zeros({num_bbox, output_dim, pooled_height, pooled_width}, input.options()); + + const int num_classes = no_trans ? 1 : channels_trans / 2; + const int channels_each_class = no_trans ? output_dim : output_dim / num_classes; + + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + if (out.numel() == 0) + { + THCudaCheck(cudaGetLastError()); + return std::make_tuple(out, top_count); + } + + dim3 grid(std::min(THCCeilDiv(out_size, 512L), 4096L)); + dim3 block(512); + + AT_DISPATCH_FLOATING_TYPES(input.type(), "dcn_v2_psroi_pooling_cuda_forward", [&] { + DeformablePSROIPoolForwardKernelCuda<<>>( + out_size, + input.contiguous().data_ptr(), + spatial_scale, + channels, + height, width, + pooled_height, + pooled_width, + bbox.contiguous().data_ptr(), + trans.contiguous().data_ptr(), + no_trans, + trans_std, + sample_per_part, + output_dim, + group_size, + part_size, + num_classes, + channels_each_class, + out.data_ptr(), + top_count.data_ptr()); + }); + THCudaCheck(cudaGetLastError()); + return std::make_tuple(out, top_count); +} + +std::tuple +dcn_v2_psroi_pooling_cuda_backward(const at::Tensor &out_grad, + const at::Tensor &input, + const at::Tensor &bbox, + const at::Tensor &trans, + const at::Tensor &top_count, + const int no_trans, + const float spatial_scale, + const int output_dim, + const int group_size, + const int pooled_size, + const int part_size, + const int sample_per_part, + const float trans_std) +{ + AT_ASSERTM(out_grad.type().is_cuda(), "out_grad must be a CUDA tensor"); + AT_ASSERTM(input.type().is_cuda(), "input must be a CUDA tensor"); + AT_ASSERTM(bbox.type().is_cuda(), "bbox must be a CUDA tensor"); + AT_ASSERTM(trans.type().is_cuda(), "trans must be a CUDA tensor"); + AT_ASSERTM(top_count.type().is_cuda(), "top_count must be a CUDA tensor"); + + const int batch = input.size(0); + const int channels = input.size(1); + const int height = input.size(2); + const int width = input.size(3); + const int channels_trans = no_trans ? 2 : trans.size(1); + const int num_bbox = bbox.size(0); + + AT_ASSERTM(channels == output_dim, "input channels and output channels must equal"); + auto pooled_height = pooled_size; + auto pooled_width = pooled_size; + long out_size = num_bbox * output_dim * pooled_height * pooled_width; + const int num_classes = no_trans ? 1 : channels_trans / 2; + const int channels_each_class = no_trans ? output_dim : output_dim / num_classes; + + auto input_grad = at::zeros({batch, channels, height, width}, out_grad.options()); + auto trans_grad = at::zeros_like(trans); + + if (input_grad.numel() == 0) + { + THCudaCheck(cudaGetLastError()); + return std::make_tuple(input_grad, trans_grad); + } + + dim3 grid(std::min(THCCeilDiv(out_size, 512L), 4096L)); + dim3 block(512); + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + AT_DISPATCH_FLOATING_TYPES(out_grad.type(), "dcn_v2_psroi_pooling_cuda_backward", [&] { + DeformablePSROIPoolBackwardAccKernelCuda<<>>( + out_size, + out_grad.contiguous().data_ptr(), + top_count.contiguous().data_ptr(), + num_bbox, + spatial_scale, + channels, + height, + width, + pooled_height, + pooled_width, + output_dim, + input_grad.contiguous().data_ptr(), + trans_grad.contiguous().data_ptr(), + input.contiguous().data_ptr(), + bbox.contiguous().data_ptr(), + trans.contiguous().data_ptr(), + no_trans, + trans_std, + sample_per_part, + group_size, + part_size, + num_classes, + channels_each_class); + }); + THCudaCheck(cudaGetLastError()); + return std::make_tuple(input_grad, trans_grad); +} \ No newline at end of file diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/src/cuda/vision.h b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/src/cuda/vision.h new file mode 100644 index 00000000..f3672b11 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/src/cuda/vision.h @@ -0,0 +1,60 @@ +#pragma once +#include +#include +at::Tensor +dcn_v2_cuda_forward(const at::Tensor &input, + const at::Tensor &weight, + const at::Tensor &bias, + const at::Tensor &offset, + const at::Tensor &mask, + const int kernel_h, + const int kernel_w, + const int stride_h, + const int stride_w, + const int pad_h, + const int pad_w, + const int dilation_h, + const int dilation_w, + const int deformable_group); + +std::vector +dcn_v2_cuda_backward(const at::Tensor &input, + const at::Tensor &weight, + const at::Tensor &bias, + const at::Tensor &offset, + const at::Tensor &mask, + const at::Tensor &grad_output, + int kernel_h, int kernel_w, + int stride_h, int stride_w, + int pad_h, int pad_w, + int dilation_h, int dilation_w, + int deformable_group); + + +std::tuple +dcn_v2_psroi_pooling_cuda_forward(const at::Tensor &input, + const at::Tensor &bbox, + const at::Tensor &trans, + const int no_trans, + const float spatial_scale, + const int output_dim, + const int group_size, + const int pooled_size, + const int part_size, + const int sample_per_part, + const float trans_std); + +std::tuple +dcn_v2_psroi_pooling_cuda_backward(const at::Tensor &out_grad, + const at::Tensor &input, + const at::Tensor &bbox, + const at::Tensor &trans, + const at::Tensor &top_count, + const int no_trans, + const float spatial_scale, + const int output_dim, + const int group_size, + const int pooled_size, + const int part_size, + const int sample_per_part, + const float trans_std); \ No newline at end of file diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/src/dcn_v2.h b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/src/dcn_v2.h new file mode 100644 index 00000000..de670bf9 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/src/dcn_v2.h @@ -0,0 +1,190 @@ +#pragma once + +#include "cpu/vision.h" + +#ifdef WITH_CUDA +#include "cuda/vision.h" +#endif + +at::Tensor +dcn_v2_forward(const at::Tensor &input, + const at::Tensor &weight, + const at::Tensor &bias, + const at::Tensor &offset, + const at::Tensor &mask, + const int kernel_h, + const int kernel_w, + const int stride_h, + const int stride_w, + const int pad_h, + const int pad_w, + const int dilation_h, + const int dilation_w, + const int deformable_group) +{ + if (input.type().is_cuda()) + { +#ifdef WITH_CUDA + return dcn_v2_cuda_forward(input, weight, bias, offset, mask, + kernel_h, kernel_w, + stride_h, stride_w, + pad_h, pad_w, + dilation_h, dilation_w, + deformable_group); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + else{ + return dcn_v2_cpu_forward(input, weight, bias, offset, mask, + kernel_h, kernel_w, + stride_h, stride_w, + pad_h, pad_w, + dilation_h, dilation_w, + deformable_group); + } +} + +std::vector +dcn_v2_backward(const at::Tensor &input, + const at::Tensor &weight, + const at::Tensor &bias, + const at::Tensor &offset, + const at::Tensor &mask, + const at::Tensor &grad_output, + int kernel_h, int kernel_w, + int stride_h, int stride_w, + int pad_h, int pad_w, + int dilation_h, int dilation_w, + int deformable_group) +{ + if (input.type().is_cuda()) + { +#ifdef WITH_CUDA + return dcn_v2_cuda_backward(input, + weight, + bias, + offset, + mask, + grad_output, + kernel_h, kernel_w, + stride_h, stride_w, + pad_h, pad_w, + dilation_h, dilation_w, + deformable_group); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + else{ + return dcn_v2_cpu_backward(input, + weight, + bias, + offset, + mask, + grad_output, + kernel_h, kernel_w, + stride_h, stride_w, + pad_h, pad_w, + dilation_h, dilation_w, + deformable_group); + } +} + +std::tuple +dcn_v2_psroi_pooling_forward(const at::Tensor &input, + const at::Tensor &bbox, + const at::Tensor &trans, + const int no_trans, + const float spatial_scale, + const int output_dim, + const int group_size, + const int pooled_size, + const int part_size, + const int sample_per_part, + const float trans_std) +{ + if (input.type().is_cuda()) + { +#ifdef WITH_CUDA + return dcn_v2_psroi_pooling_cuda_forward(input, + bbox, + trans, + no_trans, + spatial_scale, + output_dim, + group_size, + pooled_size, + part_size, + sample_per_part, + trans_std); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + else{ + return dcn_v2_psroi_pooling_cpu_forward(input, + bbox, + trans, + no_trans, + spatial_scale, + output_dim, + group_size, + pooled_size, + part_size, + sample_per_part, + trans_std); + } +} + +std::tuple +dcn_v2_psroi_pooling_backward(const at::Tensor &out_grad, + const at::Tensor &input, + const at::Tensor &bbox, + const at::Tensor &trans, + const at::Tensor &top_count, + const int no_trans, + const float spatial_scale, + const int output_dim, + const int group_size, + const int pooled_size, + const int part_size, + const int sample_per_part, + const float trans_std) +{ + if (input.type().is_cuda()) + { +#ifdef WITH_CUDA + return dcn_v2_psroi_pooling_cuda_backward(out_grad, + input, + bbox, + trans, + top_count, + no_trans, + spatial_scale, + output_dim, + group_size, + pooled_size, + part_size, + sample_per_part, + trans_std); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + else{ + return dcn_v2_psroi_pooling_cpu_backward(out_grad, + input, + bbox, + trans, + top_count, + no_trans, + spatial_scale, + output_dim, + group_size, + pooled_size, + part_size, + sample_per_part, + trans_std); + } +} \ No newline at end of file diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/src/vision.cpp b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/src/vision.cpp new file mode 100644 index 00000000..ff54233e --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/src/vision.cpp @@ -0,0 +1,9 @@ + +#include "dcn_v2.h" + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("dcn_v2_forward", &dcn_v2_forward, "dcn_v2_forward"); + m.def("dcn_v2_backward", &dcn_v2_backward, "dcn_v2_backward"); + m.def("dcn_v2_psroi_pooling_forward", &dcn_v2_psroi_pooling_forward, "dcn_v2_psroi_pooling_forward"); + m.def("dcn_v2_psroi_pooling_backward", &dcn_v2_psroi_pooling_backward, "dcn_v2_psroi_pooling_backward"); +} diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/testcpu.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/testcpu.py new file mode 100644 index 00000000..0a0b36eb --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/testcpu.py @@ -0,0 +1,270 @@ +#!/usr/bin/env python +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import time +import torch +import torch.nn as nn +from torch.autograd import gradcheck + +from dcn_v2 import dcn_v2_conv, DCNv2, DCN +from dcn_v2 import dcn_v2_pooling, DCNv2Pooling, DCNPooling + +deformable_groups = 1 +N, inC, inH, inW = 2, 2, 4, 4 +outC = 2 +kH, kW = 3, 3 + + +def conv_identify(weight, bias): + weight.data.zero_() + bias.data.zero_() + o, i, h, w = weight.shape + y = h//2 + x = w//2 + for p in range(i): + for q in range(o): + if p == q: + weight.data[q, p, y, x] = 1.0 + + +def check_zero_offset(): + conv_offset = nn.Conv2d(inC, deformable_groups * 2 * kH * kW, + kernel_size=(kH, kW), + stride=(1, 1), + padding=(1, 1), + bias=True) + + conv_mask = nn.Conv2d(inC, deformable_groups * 1 * kH * kW, + kernel_size=(kH, kW), + stride=(1, 1), + padding=(1, 1), + bias=True) + + dcn_v2 = DCNv2(inC, outC, (kH, kW), + stride=1, padding=1, dilation=1, + deformable_groups=deformable_groups) + + conv_offset.weight.data.zero_() + conv_offset.bias.data.zero_() + conv_mask.weight.data.zero_() + conv_mask.bias.data.zero_() + conv_identify(dcn_v2.weight, dcn_v2.bias) + + input = torch.randn(N, inC, inH, inW) + offset = conv_offset(input) + mask = conv_mask(input) + mask = torch.sigmoid(mask) + output = dcn_v2(input, offset, mask) + output *= 2 + d = (input - output).abs().max() + if d < 1e-10: + print('Zero offset passed') + else: + print('Zero offset failed') + print(input) + print(output) + +def check_gradient_dconv(): + + input = torch.rand(N, inC, inH, inW) * 0.01 + input.requires_grad = True + + offset = torch.randn(N, deformable_groups * 2 * kW * kH, inH, inW) * 2 + # offset.data.zero_() + # offset.data -= 0.5 + offset.requires_grad = True + + mask = torch.rand(N, deformable_groups * 1 * kW * kH, inH, inW) + # mask.data.zero_() + mask.requires_grad = True + mask = torch.sigmoid(mask) + + weight = torch.randn(outC, inC, kH, kW) + weight.requires_grad = True + + bias = torch.rand(outC) + bias.requires_grad = True + + stride = 1 + padding = 1 + dilation = 1 + + print('check_gradient_dconv: ', + gradcheck(dcn_v2_conv, (input, offset, mask, weight, bias, + stride, padding, dilation, deformable_groups), + eps=1e-3, atol=1e-4, rtol=1e-2)) + + +def check_pooling_zero_offset(): + + input = torch.randn(2, 16, 64, 64).zero_() + input[0, :, 16:26, 16:26] = 1. + input[1, :, 10:20, 20:30] = 2. + rois = torch.tensor([ + [0, 65, 65, 103, 103], + [1, 81, 41, 119, 79], + ]).float() + pooling = DCNv2Pooling(spatial_scale=1.0 / 4, + pooled_size=7, + output_dim=16, + no_trans=True, + group_size=1, + trans_std=0.0) + + out = pooling(input, rois, input.new()) + s = ', '.join(['%f' % out[i, :, :, :].mean().item() + for i in range(rois.shape[0])]) + print(s) + + dpooling = DCNv2Pooling(spatial_scale=1.0 / 4, + pooled_size=7, + output_dim=16, + no_trans=False, + group_size=1, + trans_std=0.0) + offset = torch.randn(20, 2, 7, 7).zero_() + dout = dpooling(input, rois, offset) + s = ', '.join(['%f' % dout[i, :, :, :].mean().item() + for i in range(rois.shape[0])]) + print(s) + + +def check_gradient_dpooling(): + input = torch.randn(2, 3, 5, 5) * 0.01 + N = 4 + batch_inds = torch.randint(2, (N, 1)).float() + x = torch.rand((N, 1)).float() * 15 + y = torch.rand((N, 1)).float() * 15 + w = torch.rand((N, 1)).float() * 10 + h = torch.rand((N, 1)).float() * 10 + rois = torch.cat((batch_inds, x, y, x + w, y + h), dim=1) + offset = torch.randn(N, 2, 3, 3) + input.requires_grad = True + offset.requires_grad = True + + spatial_scale = 1.0 / 4 + pooled_size = 3 + output_dim = 3 + no_trans = 0 + group_size = 1 + trans_std = 0.0 + sample_per_part = 4 + part_size = pooled_size + + print('check_gradient_dpooling:', + gradcheck(dcn_v2_pooling, (input, rois, offset, + spatial_scale, + pooled_size, + output_dim, + no_trans, + group_size, + part_size, + sample_per_part, + trans_std), + eps=1e-4)) + + +def example_dconv(): + input = torch.randn(2, 64, 128, 128) + # wrap all things (offset and mask) in DCN + dcn = DCN(64, 64, kernel_size=(3, 3), stride=1, + padding=1, deformable_groups=2) + # print(dcn.weight.shape, input.shape) + output = dcn(input) + targert = output.new(*output.size()) + targert.data.uniform_(-0.01, 0.01) + error = (targert - output).mean() + error.backward() + print(output.shape) + + +def example_dpooling(): + input = torch.randn(2, 32, 64, 64) + batch_inds = torch.randint(2, (20, 1)).float() + x = torch.randint(256, (20, 1)).float() + y = torch.randint(256, (20, 1)).float() + w = torch.randint(64, (20, 1)).float() + h = torch.randint(64, (20, 1)).float() + rois = torch.cat((batch_inds, x, y, x + w, y + h), dim=1) + offset = torch.randn(20, 2, 7, 7) + input.requires_grad = True + offset.requires_grad = True + + # normal roi_align + pooling = DCNv2Pooling(spatial_scale=1.0 / 4, + pooled_size=7, + output_dim=32, + no_trans=True, + group_size=1, + trans_std=0.1) + + # deformable pooling + dpooling = DCNv2Pooling(spatial_scale=1.0 / 4, + pooled_size=7, + output_dim=32, + no_trans=False, + group_size=1, + trans_std=0.1) + + out = pooling(input, rois, offset) + dout = dpooling(input, rois, offset) + print(out.shape) + print(dout.shape) + + target_out = out.new(*out.size()) + target_out.data.uniform_(-0.01, 0.01) + target_dout = dout.new(*dout.size()) + target_dout.data.uniform_(-0.01, 0.01) + e = (target_out - out).mean() + e.backward() + e = (target_dout - dout).mean() + e.backward() + + +def example_mdpooling(): + input = torch.randn(2, 32, 64, 64) + input.requires_grad = True + batch_inds = torch.randint(2, (20, 1)).float() + x = torch.randint(256, (20, 1)).float() + y = torch.randint(256, (20, 1)).float() + w = torch.randint(64, (20, 1)).float() + h = torch.randint(64, (20, 1)).float() + rois = torch.cat((batch_inds, x, y, x + w, y + h), dim=1) + + # mdformable pooling (V2) + dpooling = DCNPooling(spatial_scale=1.0 / 4, + pooled_size=7, + output_dim=32, + no_trans=False, + group_size=1, + trans_std=0.1, + deform_fc_dim=1024) + + dout = dpooling(input, rois) + target = dout.new(*dout.size()) + target.data.uniform_(-0.1, 0.1) + error = (target - dout).mean() + error.backward() + print(dout.shape) + + +if __name__ == '__main__': + + example_dconv() + example_dpooling() + example_mdpooling() + + check_pooling_zero_offset() + # zero offset check + if inC == outC: + check_zero_offset() + + check_gradient_dpooling() + check_gradient_dconv() + # """ + # ****** Note: backward is not reentrant error may not be a serious problem, + # ****** since the max error is less than 1e-7, + # ****** Still looking for what trigger this problem + # """ diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/testcuda.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/testcuda.py new file mode 100644 index 00000000..3bd5bd22 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2/testcuda.py @@ -0,0 +1,270 @@ +#!/usr/bin/env python +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import time +import torch +import torch.nn as nn +from torch.autograd import gradcheck + +from dcn_v2 import dcn_v2_conv, DCNv2, DCN +from dcn_v2 import dcn_v2_pooling, DCNv2Pooling, DCNPooling + +deformable_groups = 1 +N, inC, inH, inW = 2, 2, 4, 4 +outC = 2 +kH, kW = 3, 3 + + +def conv_identify(weight, bias): + weight.data.zero_() + bias.data.zero_() + o, i, h, w = weight.shape + y = h//2 + x = w//2 + for p in range(i): + for q in range(o): + if p == q: + weight.data[q, p, y, x] = 1.0 + + +def check_zero_offset(): + conv_offset = nn.Conv2d(inC, deformable_groups * 2 * kH * kW, + kernel_size=(kH, kW), + stride=(1, 1), + padding=(1, 1), + bias=True).cuda() + + conv_mask = nn.Conv2d(inC, deformable_groups * 1 * kH * kW, + kernel_size=(kH, kW), + stride=(1, 1), + padding=(1, 1), + bias=True).cuda() + + dcn_v2 = DCNv2(inC, outC, (kH, kW), + stride=1, padding=1, dilation=1, + deformable_groups=deformable_groups).cuda() + + conv_offset.weight.data.zero_() + conv_offset.bias.data.zero_() + conv_mask.weight.data.zero_() + conv_mask.bias.data.zero_() + conv_identify(dcn_v2.weight, dcn_v2.bias) + + input = torch.randn(N, inC, inH, inW).cuda() + offset = conv_offset(input) + mask = conv_mask(input) + mask = torch.sigmoid(mask) + output = dcn_v2(input, offset, mask) + output *= 2 + d = (input - output).abs().max() + if d < 1e-10: + print('Zero offset passed') + else: + print('Zero offset failed') + print(input) + print(output) + +def check_gradient_dconv(): + + input = torch.rand(N, inC, inH, inW).cuda() * 0.01 + input.requires_grad = True + + offset = torch.randn(N, deformable_groups * 2 * kW * kH, inH, inW).cuda() * 2 + # offset.data.zero_() + # offset.data -= 0.5 + offset.requires_grad = True + + mask = torch.rand(N, deformable_groups * 1 * kW * kH, inH, inW).cuda() + # mask.data.zero_() + mask.requires_grad = True + mask = torch.sigmoid(mask) + + weight = torch.randn(outC, inC, kH, kW).cuda() + weight.requires_grad = True + + bias = torch.rand(outC).cuda() + bias.requires_grad = True + + stride = 1 + padding = 1 + dilation = 1 + + print('check_gradient_dconv: ', + gradcheck(dcn_v2_conv, (input, offset, mask, weight, bias, + stride, padding, dilation, deformable_groups), + eps=1e-3, atol=1e-4, rtol=1e-2)) + + +def check_pooling_zero_offset(): + + input = torch.randn(2, 16, 64, 64).cuda().zero_() + input[0, :, 16:26, 16:26] = 1. + input[1, :, 10:20, 20:30] = 2. + rois = torch.tensor([ + [0, 65, 65, 103, 103], + [1, 81, 41, 119, 79], + ]).cuda().float() + pooling = DCNv2Pooling(spatial_scale=1.0 / 4, + pooled_size=7, + output_dim=16, + no_trans=True, + group_size=1, + trans_std=0.0).cuda() + + out = pooling(input, rois, input.new()) + s = ', '.join(['%f' % out[i, :, :, :].mean().item() + for i in range(rois.shape[0])]) + print(s) + + dpooling = DCNv2Pooling(spatial_scale=1.0 / 4, + pooled_size=7, + output_dim=16, + no_trans=False, + group_size=1, + trans_std=0.0).cuda() + offset = torch.randn(20, 2, 7, 7).cuda().zero_() + dout = dpooling(input, rois, offset) + s = ', '.join(['%f' % dout[i, :, :, :].mean().item() + for i in range(rois.shape[0])]) + print(s) + + +def check_gradient_dpooling(): + input = torch.randn(2, 3, 5, 5).cuda() * 0.01 + N = 4 + batch_inds = torch.randint(2, (N, 1)).cuda().float() + x = torch.rand((N, 1)).cuda().float() * 15 + y = torch.rand((N, 1)).cuda().float() * 15 + w = torch.rand((N, 1)).cuda().float() * 10 + h = torch.rand((N, 1)).cuda().float() * 10 + rois = torch.cat((batch_inds, x, y, x + w, y + h), dim=1) + offset = torch.randn(N, 2, 3, 3).cuda() + input.requires_grad = True + offset.requires_grad = True + + spatial_scale = 1.0 / 4 + pooled_size = 3 + output_dim = 3 + no_trans = 0 + group_size = 1 + trans_std = 0.0 + sample_per_part = 4 + part_size = pooled_size + + print('check_gradient_dpooling:', + gradcheck(dcn_v2_pooling, (input, rois, offset, + spatial_scale, + pooled_size, + output_dim, + no_trans, + group_size, + part_size, + sample_per_part, + trans_std), + eps=1e-4)) + + +def example_dconv(): + input = torch.randn(2, 64, 128, 128).cuda() + # wrap all things (offset and mask) in DCN + dcn = DCN(64, 64, kernel_size=(3, 3), stride=1, + padding=1, deformable_groups=2).cuda() + # print(dcn.weight.shape, input.shape) + output = dcn(input) + targert = output.new(*output.size()) + targert.data.uniform_(-0.01, 0.01) + error = (targert - output).mean() + error.backward() + print(output.shape) + + +def example_dpooling(): + input = torch.randn(2, 32, 64, 64).cuda() + batch_inds = torch.randint(2, (20, 1)).cuda().float() + x = torch.randint(256, (20, 1)).cuda().float() + y = torch.randint(256, (20, 1)).cuda().float() + w = torch.randint(64, (20, 1)).cuda().float() + h = torch.randint(64, (20, 1)).cuda().float() + rois = torch.cat((batch_inds, x, y, x + w, y + h), dim=1) + offset = torch.randn(20, 2, 7, 7).cuda() + input.requires_grad = True + offset.requires_grad = True + + # normal roi_align + pooling = DCNv2Pooling(spatial_scale=1.0 / 4, + pooled_size=7, + output_dim=32, + no_trans=True, + group_size=1, + trans_std=0.1).cuda() + + # deformable pooling + dpooling = DCNv2Pooling(spatial_scale=1.0 / 4, + pooled_size=7, + output_dim=32, + no_trans=False, + group_size=1, + trans_std=0.1).cuda() + + out = pooling(input, rois, offset) + dout = dpooling(input, rois, offset) + print(out.shape) + print(dout.shape) + + target_out = out.new(*out.size()) + target_out.data.uniform_(-0.01, 0.01) + target_dout = dout.new(*dout.size()) + target_dout.data.uniform_(-0.01, 0.01) + e = (target_out - out).mean() + e.backward() + e = (target_dout - dout).mean() + e.backward() + + +def example_mdpooling(): + input = torch.randn(2, 32, 64, 64).cuda() + input.requires_grad = True + batch_inds = torch.randint(2, (20, 1)).cuda().float() + x = torch.randint(256, (20, 1)).cuda().float() + y = torch.randint(256, (20, 1)).cuda().float() + w = torch.randint(64, (20, 1)).cuda().float() + h = torch.randint(64, (20, 1)).cuda().float() + rois = torch.cat((batch_inds, x, y, x + w, y + h), dim=1) + + # mdformable pooling (V2) + dpooling = DCNPooling(spatial_scale=1.0 / 4, + pooled_size=7, + output_dim=32, + no_trans=False, + group_size=1, + trans_std=0.1, + deform_fc_dim=1024).cuda() + + dout = dpooling(input, rois) + target = dout.new(*dout.size()) + target.data.uniform_(-0.1, 0.1) + error = (target - dout).mean() + error.backward() + print(dout.shape) + + +if __name__ == '__main__': + + example_dconv() + example_dpooling() + example_mdpooling() + + check_pooling_zero_offset() + # zero offset check + if inC == outC: + check_zero_offset() + + check_gradient_dpooling() + check_gradient_dconv() + # """ + # ****** Note: backward is not reentrant error may not be a serious problem, + # ****** since the max error is less than 1e-7, + # ****** Still looking for what trigger this problem + # """ diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/LICENSE b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/LICENSE new file mode 100644 index 00000000..b2e3b520 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/LICENSE @@ -0,0 +1,29 @@ +BSD 3-Clause License + +Copyright (c) 2019, Charles Shang +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +1. Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +2. Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +3. Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. \ No newline at end of file diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/__init__.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/build/temp.linux-x86_64-cpython-37/build.ninja b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/build/temp.linux-x86_64-cpython-37/build.ninja new file mode 100644 index 00000000..d05f7a42 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/build/temp.linux-x86_64-cpython-37/build.ninja @@ -0,0 +1,23 @@ +ninja_required_version = 1.3 +cxx = c++ + +cflags = -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -I/ssd8/exec/huangjy/AdvancedLiterateMachinery/DocumentUnderstanding/LORE-TSR/src/lib/models/networks/DCNv2_1.4/src -I/ssd8/exec/huangjy/miniconda3/envs/lore/lib/python3.7/site-packages/torch/include -I/ssd8/exec/huangjy/miniconda3/envs/lore/lib/python3.7/site-packages/torch/include/torch/csrc/api/include -I/ssd8/exec/huangjy/miniconda3/envs/lore/lib/python3.7/site-packages/torch/include/TH -I/ssd8/exec/huangjy/miniconda3/envs/lore/lib/python3.7/site-packages/torch/include/THC -I/ssd8/exec/huangjy/miniconda3/envs/lore/include/python3.7m -c +post_cflags = -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_gcc"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1011"' -DTORCH_EXTENSION_NAME=_ext -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++14 +ldflags = + +rule compile + command = $cxx -MMD -MF $out.d $cflags -c $in -o $out $post_cflags + depfile = $out.d + deps = gcc + + + +build /ssd8/exec/huangjy/AdvancedLiterateMachinery/DocumentUnderstanding/LORE-TSR/src/lib/models/networks/DCNv2_1.4/build/temp.linux-x86_64-cpython-37/ssd8/exec/huangjy/AdvancedLiterateMachinery/DocumentUnderstanding/LORE-TSR/src/lib/models/networks/DCNv2_1.4/src/cpu/dcn_v2_cpu.o: compile /ssd8/exec/huangjy/AdvancedLiterateMachinery/DocumentUnderstanding/LORE-TSR/src/lib/models/networks/DCNv2_1.4/src/cpu/dcn_v2_cpu.cpp +build /ssd8/exec/huangjy/AdvancedLiterateMachinery/DocumentUnderstanding/LORE-TSR/src/lib/models/networks/DCNv2_1.4/build/temp.linux-x86_64-cpython-37/ssd8/exec/huangjy/AdvancedLiterateMachinery/DocumentUnderstanding/LORE-TSR/src/lib/models/networks/DCNv2_1.4/src/cpu/dcn_v2_im2col_cpu.o: compile /ssd8/exec/huangjy/AdvancedLiterateMachinery/DocumentUnderstanding/LORE-TSR/src/lib/models/networks/DCNv2_1.4/src/cpu/dcn_v2_im2col_cpu.cpp +build /ssd8/exec/huangjy/AdvancedLiterateMachinery/DocumentUnderstanding/LORE-TSR/src/lib/models/networks/DCNv2_1.4/build/temp.linux-x86_64-cpython-37/ssd8/exec/huangjy/AdvancedLiterateMachinery/DocumentUnderstanding/LORE-TSR/src/lib/models/networks/DCNv2_1.4/src/cpu/dcn_v2_psroi_pooling_cpu.o: compile /ssd8/exec/huangjy/AdvancedLiterateMachinery/DocumentUnderstanding/LORE-TSR/src/lib/models/networks/DCNv2_1.4/src/cpu/dcn_v2_psroi_pooling_cpu.cpp +build /ssd8/exec/huangjy/AdvancedLiterateMachinery/DocumentUnderstanding/LORE-TSR/src/lib/models/networks/DCNv2_1.4/build/temp.linux-x86_64-cpython-37/ssd8/exec/huangjy/AdvancedLiterateMachinery/DocumentUnderstanding/LORE-TSR/src/lib/models/networks/DCNv2_1.4/src/vision.o: compile /ssd8/exec/huangjy/AdvancedLiterateMachinery/DocumentUnderstanding/LORE-TSR/src/lib/models/networks/DCNv2_1.4/src/vision.cpp + + + + + diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/dcn_v2.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/dcn_v2.py new file mode 100644 index 00000000..d3f44c08 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/dcn_v2.py @@ -0,0 +1,319 @@ +#!/usr/bin/env python +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import math +import torch +from torch import nn +from torch.autograd import Function +from torch.nn.modules.utils import _pair +from torch.autograd.function import once_differentiable +from torch.onnx.symbolic_helper import parse_args +import _ext as _backend + + +class _DCNv2(Function): + #@parse_args('v', 'v', "v", 'v', 'v', "i", "i", "i", "i", "i") + #def symbolic(g, input, offset, weight, stride=1, padding=0,\ + # dilation=1, groups=1,\ + # deformable_groups=1, im2col_step=64): + # return g.op("DeformConv", input, offset, weight,strides_i=stride, \ + # padding_i = padding, dilation_i=dilation, groups_i=groups, \ + # deformable_group_i=deformable_groups, im2col_step_i = im2col_step, auto_pad_s="None") + + @staticmethod + def forward(ctx, input, offset, mask, weight, bias, + stride, padding, dilation, deformable_groups): + ctx.stride = _pair(stride) + ctx.padding = _pair(padding) + ctx.dilation = _pair(dilation) + ctx.kernel_size = _pair(weight.shape[2:4]) + ctx.deformable_groups = deformable_groups + output = _backend.dcn_v2_forward(input, weight, bias, + offset, mask, + ctx.kernel_size[0], ctx.kernel_size[1], + ctx.stride[0], ctx.stride[1], + ctx.padding[0], ctx.padding[1], + ctx.dilation[0], ctx.dilation[1], + ctx.deformable_groups) + ctx.save_for_backward(input, offset, mask, weight, bias) + return output + + @staticmethod + @once_differentiable + def backward(ctx, grad_output): + input, offset, mask, weight, bias = ctx.saved_tensors + grad_input, grad_offset, grad_mask, grad_weight, grad_bias = \ + _backend.dcn_v2_backward(input, weight, + bias, + offset, mask, + grad_output, + ctx.kernel_size[0], ctx.kernel_size[1], + ctx.stride[0], ctx.stride[1], + ctx.padding[0], ctx.padding[1], + ctx.dilation[0], ctx.dilation[1], + ctx.deformable_groups) + + return grad_input, grad_offset, grad_mask, grad_weight, grad_bias,\ + None, None, None, None, + + +dcn_v2_conv = _DCNv2.apply + + +class DCNv2(nn.Module): + + def __init__(self, in_channels, out_channels, + kernel_size, stride, padding, dilation=1, deformable_groups=1): + super(DCNv2, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = _pair(kernel_size) + self.stride = _pair(stride) + self.padding = _pair(padding) + self.dilation = _pair(dilation) + self.deformable_groups = deformable_groups + + self.weight = nn.Parameter(torch.Tensor( + out_channels, in_channels, *self.kernel_size)) + self.bias = nn.Parameter(torch.Tensor(out_channels)) + self.reset_parameters() + + def reset_parameters(self): + n = self.in_channels + for k in self.kernel_size: + n *= k + stdv = 1. / math.sqrt(n) + self.weight.data.uniform_(-stdv, stdv) + self.bias.data.zero_() + + def forward(self, input, offset, mask): + assert 2 * self.deformable_groups * self.kernel_size[0] * self.kernel_size[1] == \ + offset.shape[1] + assert self.deformable_groups * self.kernel_size[0] * self.kernel_size[1] == \ + mask.shape[1] + return dcn_v2_conv(input, offset, mask, + self.weight, + self.bias, + self.stride, + self.padding, + self.dilation, + self.deformable_groups) + + +class DCN(DCNv2): + + def __init__(self, in_channels, out_channels, + kernel_size, stride, padding, + dilation=1, deformable_groups=1): + super(DCN, self).__init__(in_channels, out_channels, + kernel_size, stride, padding, dilation, deformable_groups) + + channels_ = self.deformable_groups * 3 * self.kernel_size[0] * self.kernel_size[1] + self.conv_offset_mask = nn.Conv2d(self.in_channels, + channels_, + kernel_size=self.kernel_size, + stride=self.stride, + padding=self.padding, + bias=True) + self.init_offset() + + def init_offset(self): + self.conv_offset_mask.weight.data.zero_() + self.conv_offset_mask.bias.data.zero_() + + #@parse_args('v', 'v', "v", 'v', 'v', "i", "i", "i", "i", "i") + #def symbolic(g, input, offset, weight, stride=1, padding=0, + # dilation=1, groups=1, + # deformable_groups=1, im2col_step=64): + # return g.op("DeformConv", input, offset, weight,strides_i=stride, + # padding_i = padding, dilation_i=dilation, groups_i=groups, + # deformable_group_i=deformable_groups, im2col_step_i = im2col_step, auto_pad_s="None") + + def forward(self, input): + out = self.conv_offset_mask(input) + o1, o2, mask = torch.chunk(out, 3, dim=1) + offset = torch.cat((o1, o2), dim=1) + mask = torch.sigmoid(mask) + return dcn_v2_conv(input, offset, mask, + self.weight, self.bias, + self.stride, + self.padding, + self.dilation, + self.deformable_groups) + + + +class _DCNv2Pooling(Function): + @staticmethod + def forward(ctx, input, rois, offset, + spatial_scale, + pooled_size, + output_dim, + no_trans, + group_size=1, + part_size=None, + sample_per_part=4, + trans_std=.0): + ctx.spatial_scale = spatial_scale + ctx.no_trans = int(no_trans) + ctx.output_dim = output_dim + ctx.group_size = group_size + ctx.pooled_size = pooled_size + ctx.part_size = pooled_size if part_size is None else part_size + ctx.sample_per_part = sample_per_part + ctx.trans_std = trans_std + + output, output_count = \ + _backend.dcn_v2_psroi_pooling_forward(input, rois, offset, + ctx.no_trans, ctx.spatial_scale, + ctx.output_dim, ctx.group_size, + ctx.pooled_size, ctx.part_size, + ctx.sample_per_part, ctx.trans_std) + ctx.save_for_backward(input, rois, offset, output_count) + return output + + @staticmethod + @once_differentiable + def backward(ctx, grad_output): + input, rois, offset, output_count = ctx.saved_tensors + grad_input, grad_offset = \ + _backend.dcn_v2_psroi_pooling_backward(grad_output, + input, + rois, + offset, + output_count, + ctx.no_trans, + ctx.spatial_scale, + ctx.output_dim, + ctx.group_size, + ctx.pooled_size, + ctx.part_size, + ctx.sample_per_part, + ctx.trans_std) + + return grad_input, None, grad_offset, \ + None, None, None, None, None, None, None, None + + +dcn_v2_pooling = _DCNv2Pooling.apply + + +class DCNv2Pooling(nn.Module): + + def __init__(self, + spatial_scale, + pooled_size, + output_dim, + no_trans, + group_size=1, + part_size=None, + sample_per_part=4, + trans_std=.0): + super(DCNv2Pooling, self).__init__() + self.spatial_scale = spatial_scale + self.pooled_size = pooled_size + self.output_dim = output_dim + self.no_trans = no_trans + self.group_size = group_size + self.part_size = pooled_size if part_size is None else part_size + self.sample_per_part = sample_per_part + self.trans_std = trans_std + + def forward(self, input, rois, offset): + assert input.shape[1] == self.output_dim + if self.no_trans: + offset = input.new() + return dcn_v2_pooling(input, rois, offset, + self.spatial_scale, + self.pooled_size, + self.output_dim, + self.no_trans, + self.group_size, + self.part_size, + self.sample_per_part, + self.trans_std) + + +class DCNPooling(DCNv2Pooling): + + def __init__(self, + spatial_scale, + pooled_size, + output_dim, + no_trans, + group_size=1, + part_size=None, + sample_per_part=4, + trans_std=.0, + deform_fc_dim=1024): + super(DCNPooling, self).__init__(spatial_scale, + pooled_size, + output_dim, + no_trans, + group_size, + part_size, + sample_per_part, + trans_std) + + self.deform_fc_dim = deform_fc_dim + + if not no_trans: + self.offset_mask_fc = nn.Sequential( + nn.Linear(self.pooled_size * self.pooled_size * + self.output_dim, self.deform_fc_dim), + nn.ReLU(inplace=True), + nn.Linear(self.deform_fc_dim, self.deform_fc_dim), + nn.ReLU(inplace=True), + nn.Linear(self.deform_fc_dim, self.pooled_size * + self.pooled_size * 3) + ) + self.offset_mask_fc[4].weight.data.zero_() + self.offset_mask_fc[4].bias.data.zero_() + + def forward(self, input, rois): + offset = input.new() + + if not self.no_trans: + + # do roi_align first + n = rois.shape[0] + roi = dcn_v2_pooling(input, rois, offset, + self.spatial_scale, + self.pooled_size, + self.output_dim, + True, # no trans + self.group_size, + self.part_size, + self.sample_per_part, + self.trans_std) + + # build mask and offset + offset_mask = self.offset_mask_fc(roi.view(n, -1)) + offset_mask = offset_mask.view( + n, 3, self.pooled_size, self.pooled_size) + o1, o2, mask = torch.chunk(offset_mask, 3, dim=1) + offset = torch.cat((o1, o2), dim=1) + mask = torch.sigmoid(mask) + + # do pooling with offset and mask + return dcn_v2_pooling(input, rois, offset, + self.spatial_scale, + self.pooled_size, + self.output_dim, + self.no_trans, + self.group_size, + self.part_size, + self.sample_per_part, + self.trans_std) * mask + # only roi_align + return dcn_v2_pooling(input, rois, offset, + self.spatial_scale, + self.pooled_size, + self.output_dim, + self.no_trans, + self.group_size, + self.part_size, + self.sample_per_part, + self.trans_std) diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/make.sh b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/make.sh new file mode 100755 index 00000000..f1f15c0e --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/make.sh @@ -0,0 +1,2 @@ +#!/usr/bin/env bash +python setup.py build develop diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/setup.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/setup.py new file mode 100644 index 00000000..0e0dc964 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/setup.py @@ -0,0 +1,71 @@ +#!/usr/bin/env python + +import os +import glob + +import torch + +from torch.utils.cpp_extension import CUDA_HOME +from torch.utils.cpp_extension import CppExtension +from torch.utils.cpp_extension import CUDAExtension + +from setuptools import find_packages +from setuptools import setup + +requirements = ["torch", "torchvision"] + + +def get_extensions(): + this_dir = os.path.dirname(os.path.abspath(__file__)) + extensions_dir = os.path.join(this_dir, "src") + + main_file = glob.glob(os.path.join(extensions_dir, "*.cpp")) + source_cpu = glob.glob(os.path.join(extensions_dir, "cpu", "*.cpp")) + source_cuda = glob.glob(os.path.join(extensions_dir, "cuda", "*.cu")) + + os.environ["CC"] = "g++" + sources = main_file + source_cpu + extension = CppExtension + extra_compile_args = {"cxx": []} + define_macros = [] + + + if torch.cuda.is_available() and CUDA_HOME is not None: + extension = CUDAExtension + sources += source_cuda + define_macros += [("WITH_CUDA", None)] + extra_compile_args["nvcc"] = [ + "-DCUDA_HAS_FP16=1", + "-D__CUDA_NO_HALF_OPERATORS__", + "-D__CUDA_NO_HALF_CONVERSIONS__", + "-D__CUDA_NO_HALF2_OPERATORS__", + ] + else: + #raise NotImplementedError('Cuda is not available') + pass + + + sources = [os.path.join(extensions_dir, s) for s in sources] + include_dirs = [extensions_dir] + ext_modules = [ + extension( + "_ext", + sources, + include_dirs=include_dirs, + define_macros=define_macros, + extra_compile_args=extra_compile_args, + ) + ] + return ext_modules + +setup( + name="DCNv2", + version="0.1", + author="charlesshang", + url="https://github.com/charlesshang/DCNv2", + description="deformable convolutional networks", + packages=find_packages(exclude=("configs", "tests",)), + # install_requires=requirements, + ext_modules=get_extensions(), + cmdclass={"build_ext": torch.utils.cpp_extension.BuildExtension}, +) \ No newline at end of file diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/src/cpu/dcn_v2_cpu.cpp b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/src/cpu/dcn_v2_cpu.cpp new file mode 100644 index 00000000..a94273e2 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/src/cpu/dcn_v2_cpu.cpp @@ -0,0 +1,233 @@ +#include +#include "cpu/dcn_v2_im2col_cpu.h" + +#include +//#include + +#include +//#include +//#include + +//extern THCState *state; + +// author: Charles Shang +// https://github.com/torch/cunn/blob/master/lib/THCUNN/generic/SpatialConvolutionMM.cu +// modified from the CUDA version for CPU use by Daniel K. Suhendro + +at::Tensor +dcn_v2_cpu_forward(const at::Tensor &input, + const at::Tensor &weight, + const at::Tensor &bias, + const at::Tensor &offset, + const at::Tensor &mask, + const int kernel_h, + const int kernel_w, + const int stride_h, + const int stride_w, + const int pad_h, + const int pad_w, + const int dilation_h, + const int dilation_w, + const int deformable_group) +{ + // THCAssertSameGPU(THCudaTensor_checkGPU(state, 5, input, weight, bias, offset, mask)); + /*AT_ASSERTM(input.type().is_cuda(), "input must be a CUDA tensor"); + AT_ASSERTM(weight.type().is_cuda(), "weight must be a CUDA tensor"); + AT_ASSERTM(bias.type().is_cuda(), "bias must be a CUDA tensor"); + AT_ASSERTM(offset.type().is_cuda(), "offset must be a CUDA tensor"); + AT_ASSERTM(mask.type().is_cuda(), "mask must be a CUDA tensor");*/ + + const int batch = input.size(0); + const int channels = input.size(1); + const int height = input.size(2); + const int width = input.size(3); + + const int channels_out = weight.size(0); + const int channels_kernel = weight.size(1); + const int kernel_h_ = weight.size(2); + const int kernel_w_ = weight.size(3); + + // printf("Kernels: %d %d %d %d\n", kernel_h_, kernel_w_, kernel_w, kernel_h); + // printf("Channels: %d %d\n", channels, channels_kernel); + // printf("Channels: %d %d\n", channels_out, channels_kernel); + + AT_ASSERTM(kernel_h_ == kernel_h && kernel_w_ == kernel_w, + "Input shape and kernel shape wont match: (%d x %d vs %d x %d).", kernel_h_, kernel_w, kernel_h_, kernel_w_); + + AT_ASSERTM(channels == channels_kernel, + "Input shape and kernel channels wont match: (%d vs %d).", channels, channels_kernel); + + const int height_out = (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1; + const int width_out = (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1; + + auto ones = at::ones({height_out, width_out}, input.options()); + auto columns = at::empty({channels * kernel_h * kernel_w, 1 * height_out * width_out}, input.options()); + auto output = at::empty({batch, channels_out, height_out, width_out}, input.options()); + + using scalar_t = float; + for (int b = 0; b < batch; b++) + { + auto input_n = input.select(0, b); + auto offset_n = offset.select(0, b); + auto mask_n = mask.select(0, b); + auto output_n = output.select(0, b); + + // Do Bias first: + // M,N,K are dims of matrix A and B + // (see http://docs.nvidia.com/cuda/cublas/#cublas-lt-t-gt-gemm) + // (N x 1) (1 x M) + long m_ = channels_out; + long n_ = height_out * width_out; + long k_ = 1; + THFloatBlas_gemm('t', 'n', n_, m_, k_, 1.0f, + ones.contiguous().data(), k_, + bias.contiguous().data(), k_, 0.0f, + output_n.data(), n_); + + modulated_deformable_im2col_cpu(input_n.data(), + offset_n.data(), + mask_n.data(), + 1, channels, height, width, + height_out, width_out, kernel_h, kernel_w, + pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, + deformable_group, + columns.data()); + + //(k * m) x (m * n) + // Y = WC + long m = channels_out; + long n = height_out * width_out; + long k = channels * kernel_h * kernel_w; + THFloatBlas_gemm('n', 'n', n, m, k, 1.0f, + columns.data(), n, + weight.data(), k, 1.0f, + output_n.data(), n); + } + return output; +} + +std::vector dcn_v2_cpu_backward(const at::Tensor &input, + const at::Tensor &weight, + const at::Tensor &bias, + const at::Tensor &offset, + const at::Tensor &mask, + const at::Tensor &grad_output, + int kernel_h, int kernel_w, + int stride_h, int stride_w, + int pad_h, int pad_w, + int dilation_h, int dilation_w, + int deformable_group) +{ + + THArgCheck(input.is_contiguous(), 1, "input tensor has to be contiguous"); + THArgCheck(weight.is_contiguous(), 2, "weight tensor has to be contiguous"); + + /*AT_ASSERTM(input.type().is_cuda(), "input must be a CUDA tensor"); + AT_ASSERTM(weight.type().is_cuda(), "weight must be a CUDA tensor"); + AT_ASSERTM(bias.type().is_cuda(), "bias must be a CUDA tensor"); + AT_ASSERTM(offset.type().is_cuda(), "offset must be a CUDA tensor"); + AT_ASSERTM(mask.type().is_cuda(), "mask must be a CUDA tensor");*/ + + const int batch = input.size(0); + const int channels = input.size(1); + const int height = input.size(2); + const int width = input.size(3); + + const int channels_out = weight.size(0); + const int channels_kernel = weight.size(1); + const int kernel_h_ = weight.size(2); + const int kernel_w_ = weight.size(3); + + AT_ASSERTM(kernel_h_ == kernel_h && kernel_w_ == kernel_w, + "Input shape and kernel shape wont match: (%d x %d vs %d x %d).", kernel_h_, kernel_w, kernel_h_, kernel_w_); + + AT_ASSERTM(channels == channels_kernel, + "Input shape and kernel channels wont match: (%d vs %d).", channels, channels_kernel); + + const int height_out = (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1; + const int width_out = (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1; + + auto ones = at::ones({height_out, width_out}, input.options()); + auto columns = at::empty({channels * kernel_h * kernel_w, 1 * height_out * width_out}, input.options()); + auto output = at::empty({batch, channels_out, height_out, width_out}, input.options()); + + auto grad_input = at::zeros_like(input); + auto grad_weight = at::zeros_like(weight); + auto grad_bias = at::zeros_like(bias); + auto grad_offset = at::zeros_like(offset); + auto grad_mask = at::zeros_like(mask); + + using scalar_t = float; + + for (int b = 0; b < batch; b++) + { + auto input_n = input.select(0, b); + auto offset_n = offset.select(0, b); + auto mask_n = mask.select(0, b); + auto grad_output_n = grad_output.select(0, b); + auto grad_input_n = grad_input.select(0, b); + auto grad_offset_n = grad_offset.select(0, b); + auto grad_mask_n = grad_mask.select(0, b); + + long m = channels * kernel_h * kernel_w; + long n = height_out * width_out; + long k = channels_out; + + THFloatBlas_gemm('n', 't', n, m, k, 1.0f, + grad_output_n.data(), n, + weight.data(), m, 0.0f, + columns.data(), n); + + // gradient w.r.t. input coordinate data + modulated_deformable_col2im_coord_cpu(columns.data(), + input_n.data(), + offset_n.data(), + mask_n.data(), + 1, channels, height, width, + height_out, width_out, kernel_h, kernel_w, + pad_h, pad_w, stride_h, stride_w, + dilation_h, dilation_w, deformable_group, + grad_offset_n.data(), + grad_mask_n.data()); + // gradient w.r.t. input data + modulated_deformable_col2im_cpu(columns.data(), + offset_n.data(), + mask_n.data(), + 1, channels, height, width, + height_out, width_out, kernel_h, kernel_w, + pad_h, pad_w, stride_h, stride_w, + dilation_h, dilation_w, deformable_group, + grad_input_n.data()); + + // gradient w.r.t. weight, dWeight should accumulate across the batch and group + modulated_deformable_im2col_cpu(input_n.data(), + offset_n.data(), + mask_n.data(), + 1, channels, height, width, + height_out, width_out, kernel_h, kernel_w, + pad_h, pad_w, stride_h, stride_w, + dilation_h, dilation_w, deformable_group, + columns.data()); + + long m_ = channels_out; + long n_ = channels * kernel_h * kernel_w; + long k_ = height_out * width_out; + + THFloatBlas_gemm('t', 'n', n_, m_, k_, 1.0f, + columns.data(), k_, + grad_output_n.data(), k_, 1.0f, + grad_weight.data(), n_); + + // gradient w.r.t. bias + // long m_ = channels_out; + // long k__ = height_out * width_out; + THFloatBlas_gemv('t', k_, m_, 1.0f, + grad_output_n.data(), k_, + ones.data(), 1, 1.0f, + grad_bias.data(), 1); + } + + return { + grad_input, grad_offset, grad_mask, grad_weight, grad_bias + }; +} \ No newline at end of file diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/src/cpu/dcn_v2_im2col_cpu.cpp b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/src/cpu/dcn_v2_im2col_cpu.cpp new file mode 100644 index 00000000..1704a60d --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/src/cpu/dcn_v2_im2col_cpu.cpp @@ -0,0 +1,395 @@ +#include "dcn_v2_im2col_cpu.h" +#include +#include +#include + +#include +//#include + +#include +//#include +//#include + +// modified from the CUDA version for CPU use by Daniel K. Suhendro + +/*#define CUDA_KERNEL_LOOP(i, n) \ + for (int i = blockIdx.x * blockDim.x + threadIdx.x; \ + i < (n); \ + i += blockDim.x * gridDim.x) + +const int CUDA_NUM_THREADS = 1024; +inline int GET_BLOCKS(const int N) +{ + return (N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS; +}*/ + + +float dmcn_im2col_bilinear_cpu(const float *bottom_data, const int data_width, + const int height, const int width, float h, float w) +{ + int h_low = floor(h); + int w_low = floor(w); + int h_high = h_low + 1; + int w_high = w_low + 1; + + float lh = h - h_low; + float lw = w - w_low; + float hh = 1 - lh, hw = 1 - lw; + + float v1 = 0; + if (h_low >= 0 && w_low >= 0) + v1 = bottom_data[h_low * data_width + w_low]; + float v2 = 0; + if (h_low >= 0 && w_high <= width - 1) + v2 = bottom_data[h_low * data_width + w_high]; + float v3 = 0; + if (h_high <= height - 1 && w_low >= 0) + v3 = bottom_data[h_high * data_width + w_low]; + float v4 = 0; + if (h_high <= height - 1 && w_high <= width - 1) + v4 = bottom_data[h_high * data_width + w_high]; + + float w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; + + float val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); + return val; +} + +float dmcn_get_gradient_weight_cpu(float argmax_h, float argmax_w, + const int h, const int w, const int height, const int width) +{ + if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width) + { + //empty + return 0; + } + + int argmax_h_low = floor(argmax_h); + int argmax_w_low = floor(argmax_w); + int argmax_h_high = argmax_h_low + 1; + int argmax_w_high = argmax_w_low + 1; + + float weight = 0; + if (h == argmax_h_low && w == argmax_w_low) + weight = (h + 1 - argmax_h) * (w + 1 - argmax_w); + if (h == argmax_h_low && w == argmax_w_high) + weight = (h + 1 - argmax_h) * (argmax_w + 1 - w); + if (h == argmax_h_high && w == argmax_w_low) + weight = (argmax_h + 1 - h) * (w + 1 - argmax_w); + if (h == argmax_h_high && w == argmax_w_high) + weight = (argmax_h + 1 - h) * (argmax_w + 1 - w); + return weight; +} + +float dmcn_get_coordinate_weight_cpu(float argmax_h, float argmax_w, + const int height, const int width, const float *im_data, + const int data_width, const int bp_dir) +{ + if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width) + { + //empty + return 0; + } + + int argmax_h_low = floor(argmax_h); + int argmax_w_low = floor(argmax_w); + int argmax_h_high = argmax_h_low + 1; + int argmax_w_high = argmax_w_low + 1; + + float weight = 0; + + if (bp_dir == 0) + { + if (argmax_h_low >= 0 && argmax_w_low >= 0) + weight += -1 * (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_low * data_width + argmax_w_low]; + if (argmax_h_low >= 0 && argmax_w_high <= width - 1) + weight += -1 * (argmax_w - argmax_w_low) * im_data[argmax_h_low * data_width + argmax_w_high]; + if (argmax_h_high <= height - 1 && argmax_w_low >= 0) + weight += (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_high * data_width + argmax_w_low]; + if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) + weight += (argmax_w - argmax_w_low) * im_data[argmax_h_high * data_width + argmax_w_high]; + } + else if (bp_dir == 1) + { + if (argmax_h_low >= 0 && argmax_w_low >= 0) + weight += -1 * (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_low]; + if (argmax_h_low >= 0 && argmax_w_high <= width - 1) + weight += (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_high]; + if (argmax_h_high <= height - 1 && argmax_w_low >= 0) + weight += -1 * (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_low]; + if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) + weight += (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_high]; + } + + return weight; +} + +void modulated_deformable_im2col_cpu_kernel(const int n, const float *data_im, const float *data_offset, const float *data_mask, + const int height, const int width, const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, + const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int channel_per_deformable_group, + const int batch_size, const int num_channels, const int deformable_group, + const int height_col, const int width_col, + float *data_col) +{ + // launch channels * batch_size * height_col * width_col cores + for(int index=0; index(0); + const float h_im = h_in + i * dilation_h + offset_h; + const float w_im = w_in + j * dilation_w + offset_w; + //if (h_im >= 0 && w_im >= 0 && h_im < height && w_im < width) { + if (h_im > -1 && w_im > -1 && h_im < height && w_im < width) + { + //const float map_h = i * dilation_h + offset_h; + //const float map_w = j * dilation_w + offset_w; + //const int cur_height = height - h_in; + //const int cur_width = width - w_in; + //val = dmcn_im2col_bilinear_cpu(data_im_ptr, width, cur_height, cur_width, map_h, map_w); + val = dmcn_im2col_bilinear_cpu(data_im_ptr, width, height, width, h_im, w_im); + } + *data_col_ptr = val * mask; + // data_col_ptr += batch_size * height_col * width_col; + data_col_ptr += height_col * width_col; + } + } + } +} + +void modulated_deformable_col2im_cpu_kernel(const int n, const float *data_col, const float *data_offset, const float *data_mask, + const int channels, const int height, const int width, + const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, + const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int channel_per_deformable_group, + const int batch_size, const int deformable_group, + const int height_col, const int width_col, + float *grad_im) +{ + for(int index = 0; index < n; index++) + { + const int j = (index / width_col / height_col / batch_size) % kernel_w; + const int i = (index / width_col / height_col / batch_size / kernel_w) % kernel_h; + const int c = index / width_col / height_col / batch_size / kernel_w / kernel_h; + // compute the start and end of the output + + const int deformable_group_index = c / channel_per_deformable_group; + + int w_out = index % width_col; + int h_out = (index / width_col) % height_col; + int b = (index / width_col / height_col) % batch_size; + int w_in = w_out * stride_w - pad_w; + int h_in = h_out * stride_h - pad_h; + + const float *data_offset_ptr = data_offset + (b * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col; + const float *data_mask_ptr = data_mask + (b * deformable_group + deformable_group_index) * kernel_h * kernel_w * height_col * width_col; + const int data_offset_h_ptr = ((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out; + const int data_offset_w_ptr = ((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out; + const int data_mask_hw_ptr = ((i * kernel_w + j) * height_col + h_out) * width_col + w_out; + const float offset_h = data_offset_ptr[data_offset_h_ptr]; + const float offset_w = data_offset_ptr[data_offset_w_ptr]; + const float mask = data_mask_ptr[data_mask_hw_ptr]; + const float cur_inv_h_data = h_in + i * dilation_h + offset_h; + const float cur_inv_w_data = w_in + j * dilation_w + offset_w; + + const float cur_top_grad = data_col[index] * mask; + const int cur_h = (int)cur_inv_h_data; + const int cur_w = (int)cur_inv_w_data; + + for (int dy = -2; dy <= 2; dy++) + { + for (int dx = -2; dx <= 2; dx++) + { + if (cur_h + dy >= 0 && cur_h + dy < height && + cur_w + dx >= 0 && cur_w + dx < width && + abs(cur_inv_h_data - (cur_h + dy)) < 1 && + abs(cur_inv_w_data - (cur_w + dx)) < 1) + { + int cur_bottom_grad_pos = ((b * channels + c) * height + cur_h + dy) * width + cur_w + dx; + float weight = dmcn_get_gradient_weight_cpu(cur_inv_h_data, cur_inv_w_data, cur_h + dy, cur_w + dx, height, width); + //atomicAdd(grad_im + cur_bottom_grad_pos, weight * cur_top_grad); + *(grad_im + cur_bottom_grad_pos) += weight * cur_top_grad; + + } + } + } + } +} + +void modulated_deformable_col2im_coord_cpu_kernel(const int n, const float *data_col, const float *data_im, + const float *data_offset, const float *data_mask, + const int channels, const int height, const int width, + const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, + const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int channel_per_deformable_group, + const int batch_size, const int offset_channels, const int deformable_group, + const int height_col, const int width_col, + float *grad_offset, float *grad_mask) +{ + for(int index = 0; index < n; index++) + { + float val = 0, mval = 0; + int w = index % width_col; + int h = (index / width_col) % height_col; + int c = (index / width_col / height_col) % offset_channels; + int b = (index / width_col / height_col) / offset_channels; + // compute the start and end of the output + + const int deformable_group_index = c / (2 * kernel_h * kernel_w); + const int col_step = kernel_h * kernel_w; + int cnt = 0; + const float *data_col_ptr = data_col + deformable_group_index * channel_per_deformable_group * batch_size * width_col * height_col; + const float *data_im_ptr = data_im + (b * deformable_group + deformable_group_index) * channel_per_deformable_group / kernel_h / kernel_w * height * width; + const float *data_offset_ptr = data_offset + (b * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col; + const float *data_mask_ptr = data_mask + (b * deformable_group + deformable_group_index) * kernel_h * kernel_w * height_col * width_col; + + const int offset_c = c - deformable_group_index * 2 * kernel_h * kernel_w; + + for (int col_c = (offset_c / 2); col_c < channel_per_deformable_group; col_c += col_step) + { + const int col_pos = (((col_c * batch_size + b) * height_col) + h) * width_col + w; + const int bp_dir = offset_c % 2; + + int j = (col_pos / width_col / height_col / batch_size) % kernel_w; + int i = (col_pos / width_col / height_col / batch_size / kernel_w) % kernel_h; + int w_out = col_pos % width_col; + int h_out = (col_pos / width_col) % height_col; + int w_in = w_out * stride_w - pad_w; + int h_in = h_out * stride_h - pad_h; + const int data_offset_h_ptr = (((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out); + const int data_offset_w_ptr = (((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out); + const int data_mask_hw_ptr = (((i * kernel_w + j) * height_col + h_out) * width_col + w_out); + const float offset_h = data_offset_ptr[data_offset_h_ptr]; + const float offset_w = data_offset_ptr[data_offset_w_ptr]; + const float mask = data_mask_ptr[data_mask_hw_ptr]; + float inv_h = h_in + i * dilation_h + offset_h; + float inv_w = w_in + j * dilation_w + offset_w; + if (inv_h <= -1 || inv_w <= -1 || inv_h >= height || inv_w >= width) + { + inv_h = inv_w = -2; + } + else + { + mval += data_col_ptr[col_pos] * dmcn_im2col_bilinear_cpu(data_im_ptr + cnt * height * width, width, height, width, inv_h, inv_w); + } + const float weight = dmcn_get_coordinate_weight_cpu( + inv_h, inv_w, + height, width, data_im_ptr + cnt * height * width, width, bp_dir); + val += weight * data_col_ptr[col_pos] * mask; + cnt += 1; + } + // KERNEL_ASSIGN(grad_offset[index], offset_req, val); + grad_offset[index] = val; + if (offset_c % 2 == 0) + // KERNEL_ASSIGN(grad_mask[(((b * deformable_group + deformable_group_index) * kernel_h * kernel_w + offset_c / 2) * height_col + h) * width_col + w], mask_req, mval); + grad_mask[(((b * deformable_group + deformable_group_index) * kernel_h * kernel_w + offset_c / 2) * height_col + h) * width_col + w] = mval; + } +} + +void modulated_deformable_im2col_cpu(const float* data_im, const float* data_offset, const float* data_mask, + const int batch_size, const int channels, const int height_im, const int width_im, + const int height_col, const int width_col, const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int deformable_group, float* data_col) { + // num_axes should be smaller than block size + const int channel_per_deformable_group = channels / deformable_group; + const int num_kernels = channels * batch_size * height_col * width_col; + modulated_deformable_im2col_cpu_kernel( + num_kernels, data_im, data_offset, data_mask, height_im, width_im, kernel_h, kernel_w, + pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, channel_per_deformable_group, + batch_size, channels, deformable_group, height_col, width_col, data_col); + + /*cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) + { + printf("error in modulated_deformable_im2col_cuda: %s\n", cudaGetErrorString(err)); + }*/ + +} + +void modulated_deformable_col2im_cpu(const float* data_col, const float* data_offset, const float* data_mask, + const int batch_size, const int channels, const int height_im, const int width_im, + const int height_col, const int width_col, const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int deformable_group, float* grad_im){ + + const int channel_per_deformable_group = channels / deformable_group; + const int num_kernels = channels * kernel_h * kernel_w * batch_size * height_col * width_col; + modulated_deformable_col2im_cpu_kernel( + num_kernels, data_col, data_offset, data_mask, channels, height_im, width_im, + kernel_h, kernel_w, pad_h, pad_h, stride_h, stride_w, + dilation_h, dilation_w, channel_per_deformable_group, + batch_size, deformable_group, height_col, width_col, grad_im); + /*cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) + { + printf("error in modulated_deformable_col2im_cuda: %s\n", cudaGetErrorString(err)); + }*/ + +} + +void modulated_deformable_col2im_coord_cpu(const float* data_col, const float* data_im, const float* data_offset, const float* data_mask, + const int batch_size, const int channels, const int height_im, const int width_im, + const int height_col, const int width_col, const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int deformable_group, + float* grad_offset, float* grad_mask) { + const int num_kernels = batch_size * height_col * width_col * 2 * kernel_h * kernel_w * deformable_group; + const int channel_per_deformable_group = channels * kernel_h * kernel_w / deformable_group; + modulated_deformable_col2im_coord_cpu_kernel( + num_kernels, data_col, data_im, data_offset, data_mask, channels, height_im, width_im, + kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, + dilation_h, dilation_w, channel_per_deformable_group, + batch_size, 2 * kernel_h * kernel_w * deformable_group, deformable_group, height_col, width_col, + grad_offset, grad_mask); + /*cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) + { + printf("error in modulated_deformable_col2im_coord_cuda: %s\n", cudaGetErrorString(err)); + }*/ +} \ No newline at end of file diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/src/cpu/dcn_v2_im2col_cpu.h b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/src/cpu/dcn_v2_im2col_cpu.h new file mode 100644 index 00000000..bad5c528 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/src/cpu/dcn_v2_im2col_cpu.h @@ -0,0 +1,99 @@ + +/*! + ******************* BEGIN Caffe Copyright Notice and Disclaimer **************** + * + * COPYRIGHT + * + * All contributions by the University of California: + * Copyright (c) 2014-2017 The Regents of the University of California (Regents) + * All rights reserved. + * + * All other contributions: + * Copyright (c) 2014-2017, the respective contributors + * All rights reserved. + * + * Caffe uses a shared copyright model: each contributor holds copyright over + * their contributions to Caffe. The project versioning records all such + * contribution and copyright details. If a contributor wants to further mark + * their specific copyright on a particular contribution, they should indicate + * their copyright solely in the commit message of the change when it is + * committed. + * + * LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * + * 1. Redistributions of source code must retain the above copyright notice, this + * list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright notice, + * this list of conditions and the following disclaimer in the documentation + * and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND + * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED + * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR + * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES + * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; + * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND + * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + * + * CONTRIBUTION AGREEMENT + * + * By contributing to the BVLC/caffe repository through pull-request, comment, + * or otherwise, the contributor releases their content to the + * license and copyright terms herein. + * + ***************** END Caffe Copyright Notice and Disclaimer ******************** + * + * Copyright (c) 2018 Microsoft + * Licensed under The MIT License [see LICENSE for details] + * \file modulated_deformable_im2col.h + * \brief Function definitions of converting an image to + * column matrix based on kernel, padding, dilation, and offset. + * These functions are mainly used in deformable convolution operators. + * \ref: https://arxiv.org/abs/1811.11168 + * \author Yuwen Xiong, Haozhi Qi, Jifeng Dai, Xizhou Zhu, Han Hu + */ + +/***************** Adapted by Charles Shang *********************/ +// modified from the CUDA version for CPU use by Daniel K. Suhendro + +#ifndef DCN_V2_IM2COL_CPU +#define DCN_V2_IM2COL_CPU + +#ifdef __cplusplus +extern "C" +{ +#endif + + void modulated_deformable_im2col_cpu(const float *data_im, const float *data_offset, const float *data_mask, + const int batch_size, const int channels, const int height_im, const int width_im, + const int height_col, const int width_col, const int kernel_h, const int kenerl_w, + const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int deformable_group, float *data_col); + + void modulated_deformable_col2im_cpu(const float *data_col, const float *data_offset, const float *data_mask, + const int batch_size, const int channels, const int height_im, const int width_im, + const int height_col, const int width_col, const int kernel_h, const int kenerl_w, + const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int deformable_group, float *grad_im); + + void modulated_deformable_col2im_coord_cpu(const float *data_col, const float *data_im, const float *data_offset, const float *data_mask, + const int batch_size, const int channels, const int height_im, const int width_im, + const int height_col, const int width_col, const int kernel_h, const int kenerl_w, + const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int deformable_group, + float *grad_offset, float *grad_mask); + +#ifdef __cplusplus +} +#endif + +#endif \ No newline at end of file diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/src/cpu/dcn_v2_psroi_pooling_cpu.cpp b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/src/cpu/dcn_v2_psroi_pooling_cpu.cpp new file mode 100644 index 00000000..6e41aaed --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/src/cpu/dcn_v2_psroi_pooling_cpu.cpp @@ -0,0 +1,426 @@ +/*! + * Copyright (c) 2017 Microsoft + * Licensed under The MIT License [see LICENSE for details] + * \file deformable_psroi_pooling.cu + * \brief + * \author Yi Li, Guodong Zhang, Jifeng Dai +*/ +/***************** Adapted by Charles Shang *********************/ +// modified from the CUDA version for CPU use by Daniel K. Suhendro + +#include +#include +#include + +#include +//#include + +#include +//#include +//#include + +/*#define CUDA_KERNEL_LOOP(i, n) \ + for (int i = blockIdx.x * blockDim.x + threadIdx.x; \ + i < (n); \ + i += blockDim.x * gridDim.x) + +const int CUDA_NUM_THREADS = 1024; +inline int GET_BLOCKS(const int N) +{ + return (N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS; +}*/ + +template +T bilinear_interp_cpu( + const T *data, + const T x, + const T y, + const int width, + const int height) +{ + int x1 = floor(x); + int x2 = ceil(x); + int y1 = floor(y); + int y2 = ceil(y); + T dist_x = static_cast(x - x1); + T dist_y = static_cast(y - y1); + T value11 = data[y1 * width + x1]; + T value12 = data[y2 * width + x1]; + T value21 = data[y1 * width + x2]; + T value22 = data[y2 * width + x2]; + T value = (1 - dist_x) * (1 - dist_y) * value11 + + (1 - dist_x) * dist_y * value12 + + dist_x * (1 - dist_y) * value21 + + dist_x * dist_y * value22; + return value; +} + +template + void DeformablePSROIPoolForwardKernelCpu( + const int count, + const T *bottom_data, + const T spatial_scale, + const int channels, + const int height, const int width, + const int pooled_height, const int pooled_width, + const T *bottom_rois, const T *bottom_trans, + const int no_trans, + const T trans_std, + const int sample_per_part, + const int output_dim, + const int group_size, + const int part_size, + const int num_classes, + const int channels_each_class, + T *top_data, + T *top_count) +{ + for(int index = 0; index < count; index++) + { + // The output is in order (n, ctop, ph, pw) + int pw = index % pooled_width; + int ph = (index / pooled_width) % pooled_height; + int ctop = (index / pooled_width / pooled_height) % output_dim; + int n = index / pooled_width / pooled_height / output_dim; + + // [start, end) interval for spatial sampling + const T *offset_bottom_rois = bottom_rois + n * 5; + int roi_batch_ind = offset_bottom_rois[0]; + T roi_start_w = static_cast(round(offset_bottom_rois[1])) * spatial_scale - 0.5; + T roi_start_h = static_cast(round(offset_bottom_rois[2])) * spatial_scale - 0.5; + T roi_end_w = static_cast(round(offset_bottom_rois[3]) + 1.) * spatial_scale - 0.5; + T roi_end_h = static_cast(round(offset_bottom_rois[4]) + 1.) * spatial_scale - 0.5; + + // Force too small ROIs to be 1x1 + T roi_width = std::max(roi_end_w - roi_start_w, T(0.1)); //avoid 0 + T roi_height = std::max(roi_end_h - roi_start_h, T(0.1)); + + // Compute w and h at bottom + T bin_size_h = roi_height / static_cast(pooled_height); + T bin_size_w = roi_width / static_cast(pooled_width); + + T sub_bin_size_h = bin_size_h / static_cast(sample_per_part); + T sub_bin_size_w = bin_size_w / static_cast(sample_per_part); + + int part_h = floor(static_cast(ph) / pooled_height * part_size); + int part_w = floor(static_cast(pw) / pooled_width * part_size); + int class_id = ctop / channels_each_class; + T trans_x = no_trans ? static_cast(0) : bottom_trans[(((n * num_classes + class_id) * 2) * part_size + part_h) * part_size + part_w] * trans_std; + T trans_y = no_trans ? static_cast(0) : bottom_trans[(((n * num_classes + class_id) * 2 + 1) * part_size + part_h) * part_size + part_w] * trans_std; + + T wstart = static_cast(pw) * bin_size_w + roi_start_w; + wstart += trans_x * roi_width; + T hstart = static_cast(ph) * bin_size_h + roi_start_h; + hstart += trans_y * roi_height; + + T sum = 0; + int count = 0; + int gw = floor(static_cast(pw) * group_size / pooled_width); + int gh = floor(static_cast(ph) * group_size / pooled_height); + gw = std::min(std::max(gw, 0), group_size - 1); + gh = std::min(std::max(gh, 0), group_size - 1); + + const T *offset_bottom_data = bottom_data + (roi_batch_ind * channels) * height * width; + for (int ih = 0; ih < sample_per_part; ih++) + { + for (int iw = 0; iw < sample_per_part; iw++) + { + T w = wstart + iw * sub_bin_size_w; + T h = hstart + ih * sub_bin_size_h; + // bilinear interpolation + if (w < -0.5 || w > width - 0.5 || h < -0.5 || h > height - 0.5) + { + continue; + } + w = std::min(std::max(w, T(0.)), width - T(1.)); + h = std::min(std::max(h, T(0.)), height - T(1.)); + int c = (ctop * group_size + gh) * group_size + gw; + T val = bilinear_interp_cpu(offset_bottom_data + c * height * width, w, h, width, height); + sum += val; + count++; + } + } + top_data[index] = count == 0 ? static_cast(0) : sum / count; + top_count[index] = count; + } +} + +template +void DeformablePSROIPoolBackwardAccKernelCpu( + const int count, + const T *top_diff, + const T *top_count, + const int num_rois, + const T spatial_scale, + const int channels, + const int height, const int width, + const int pooled_height, const int pooled_width, + const int output_dim, + T *bottom_data_diff, T *bottom_trans_diff, + const T *bottom_data, + const T *bottom_rois, + const T *bottom_trans, + const int no_trans, + const T trans_std, + const int sample_per_part, + const int group_size, + const int part_size, + const int num_classes, + const int channels_each_class) +{ + for(int index = 0; index < count; index++) + { + // The output is in order (n, ctop, ph, pw) + int pw = index % pooled_width; + int ph = (index / pooled_width) % pooled_height; + int ctop = (index / pooled_width / pooled_height) % output_dim; + int n = index / pooled_width / pooled_height / output_dim; + + // [start, end) interval for spatial sampling + const T *offset_bottom_rois = bottom_rois + n * 5; + int roi_batch_ind = offset_bottom_rois[0]; + T roi_start_w = static_cast(round(offset_bottom_rois[1])) * spatial_scale - 0.5; + T roi_start_h = static_cast(round(offset_bottom_rois[2])) * spatial_scale - 0.5; + T roi_end_w = static_cast(round(offset_bottom_rois[3]) + 1.) * spatial_scale - 0.5; + T roi_end_h = static_cast(round(offset_bottom_rois[4]) + 1.) * spatial_scale - 0.5; + + // Force too small ROIs to be 1x1 + T roi_width = std::max(roi_end_w - roi_start_w, T(0.1)); //avoid 0 + T roi_height = std::max(roi_end_h - roi_start_h, T(0.1)); + + // Compute w and h at bottom + T bin_size_h = roi_height / static_cast(pooled_height); + T bin_size_w = roi_width / static_cast(pooled_width); + + T sub_bin_size_h = bin_size_h / static_cast(sample_per_part); + T sub_bin_size_w = bin_size_w / static_cast(sample_per_part); + + int part_h = floor(static_cast(ph) / pooled_height * part_size); + int part_w = floor(static_cast(pw) / pooled_width * part_size); + int class_id = ctop / channels_each_class; + T trans_x = no_trans ? static_cast(0) : bottom_trans[(((n * num_classes + class_id) * 2) * part_size + part_h) * part_size + part_w] * trans_std; + T trans_y = no_trans ? static_cast(0) : bottom_trans[(((n * num_classes + class_id) * 2 + 1) * part_size + part_h) * part_size + part_w] * trans_std; + + T wstart = static_cast(pw) * bin_size_w + roi_start_w; + wstart += trans_x * roi_width; + T hstart = static_cast(ph) * bin_size_h + roi_start_h; + hstart += trans_y * roi_height; + + if (top_count[index] <= 0) + { + continue; + } + T diff_val = top_diff[index] / top_count[index]; + const T *offset_bottom_data = bottom_data + roi_batch_ind * channels * height * width; + T *offset_bottom_data_diff = bottom_data_diff + roi_batch_ind * channels * height * width; + int gw = floor(static_cast(pw) * group_size / pooled_width); + int gh = floor(static_cast(ph) * group_size / pooled_height); + gw = std::min(std::max(gw, 0), group_size - 1); + gh = std::min(std::max(gh, 0), group_size - 1); + + for (int ih = 0; ih < sample_per_part; ih++) + { + for (int iw = 0; iw < sample_per_part; iw++) + { + T w = wstart + iw * sub_bin_size_w; + T h = hstart + ih * sub_bin_size_h; + // bilinear interpolation + if (w < -0.5 || w > width - 0.5 || h < -0.5 || h > height - 0.5) + { + continue; + } + w = std::min(std::max(w, T(0.)), width - T(1.)); + h = std::min(std::max(h, T(0.)), height - T(1.)); + int c = (ctop * group_size + gh) * group_size + gw; + // backward on feature + int x0 = floor(w); + int x1 = ceil(w); + int y0 = floor(h); + int y1 = ceil(h); + T dist_x = w - x0, dist_y = h - y0; + T q00 = (1 - dist_x) * (1 - dist_y); + T q01 = (1 - dist_x) * dist_y; + T q10 = dist_x * (1 - dist_y); + T q11 = dist_x * dist_y; + int bottom_index_base = c * height * width; + /*atomicAdd(offset_bottom_data_diff + bottom_index_base + y0 * width + x0, q00 * diff_val); + atomicAdd(offset_bottom_data_diff + bottom_index_base + y1 * width + x0, q01 * diff_val); + atomicAdd(offset_bottom_data_diff + bottom_index_base + y0 * width + x1, q10 * diff_val); + atomicAdd(offset_bottom_data_diff + bottom_index_base + y1 * width + x1, q11 * diff_val);*/ + *(offset_bottom_data_diff + bottom_index_base + y0 * width + x0) += q00 * diff_val; + *(offset_bottom_data_diff + bottom_index_base + y1 * width + x0) += q01 * diff_val; + *(offset_bottom_data_diff + bottom_index_base + y0 * width + x1) += q10 * diff_val; + *(offset_bottom_data_diff + bottom_index_base + y1 * width + x1) += q11 * diff_val; + + + if (no_trans) + { + continue; + } + T U00 = offset_bottom_data[bottom_index_base + y0 * width + x0]; + T U01 = offset_bottom_data[bottom_index_base + y1 * width + x0]; + T U10 = offset_bottom_data[bottom_index_base + y0 * width + x1]; + T U11 = offset_bottom_data[bottom_index_base + y1 * width + x1]; + T diff_x = (U11 * dist_y + U10 * (1 - dist_y) - U01 * dist_y - U00 * (1 - dist_y)) * trans_std * diff_val; + diff_x *= roi_width; + T diff_y = (U11 * dist_x + U01 * (1 - dist_x) - U10 * dist_x - U00 * (1 - dist_x)) * trans_std * diff_val; + diff_y *= roi_height; + + /*atomicAdd(bottom_trans_diff + (((n * num_classes + class_id) * 2) * part_size + part_h) * part_size + part_w, diff_x); + atomicAdd(bottom_trans_diff + (((n * num_classes + class_id) * 2 + 1) * part_size + part_h) * part_size + part_w, diff_y);*/ + *(bottom_trans_diff + (((n * num_classes + class_id) * 2) * part_size + part_h) * part_size + part_w) += diff_x; + *(bottom_trans_diff + (((n * num_classes + class_id) * 2 + 1) * part_size + part_h) * part_size + part_w) += diff_y; + } + } + } +} + +std::tuple +dcn_v2_psroi_pooling_cpu_forward(const at::Tensor &input, + const at::Tensor &bbox, + const at::Tensor &trans, + const int no_trans, + const float spatial_scale, + const int output_dim, + const int group_size, + const int pooled_size, + const int part_size, + const int sample_per_part, + const float trans_std) +{ + /*AT_ASSERTM(input.type().is_cuda(), "input must be a CUDA tensor"); + AT_ASSERTM(bbox.type().is_cuda(), "rois must be a CUDA tensor"); + AT_ASSERTM(trans.type().is_cuda(), "trans must be a CUDA tensor");*/ + + const int batch = input.size(0); + const int channels = input.size(1); + const int height = input.size(2); + const int width = input.size(3); + const int channels_trans = no_trans ? 2 : trans.size(1); + const int num_bbox = bbox.size(0); + + AT_ASSERTM(channels == output_dim, "input channels and output channels must equal"); + auto pooled_height = pooled_size; + auto pooled_width = pooled_size; + + auto out = at::empty({num_bbox, output_dim, pooled_height, pooled_width}, input.options()); + long out_size = num_bbox * output_dim * pooled_height * pooled_width; + auto top_count = at::zeros({num_bbox, output_dim, pooled_height, pooled_width}, input.options()); + + const int num_classes = no_trans ? 1 : channels_trans / 2; + const int channels_each_class = no_trans ? output_dim : output_dim / num_classes; + + //cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + if (out.numel() == 0) + { + //THCudaCheck(cudaGetLastError()); + return std::make_tuple(out, top_count); + } + + /*dim3 grid(std::min(THCCeilDiv(out_size, 512L), 4096L)); + dim3 block(512);*/ + + AT_DISPATCH_FLOATING_TYPES(input.type(), "dcn_v2_psroi_pooling_cpu_forward", [&] { + DeformablePSROIPoolForwardKernelCpu( + out_size, + input.contiguous().data(), + spatial_scale, + channels, + height, width, + pooled_height, + pooled_width, + bbox.contiguous().data(), + trans.contiguous().data(), + no_trans, + trans_std, + sample_per_part, + output_dim, + group_size, + part_size, + num_classes, + channels_each_class, + out.data(), + top_count.data()); + }); + //THCudaCheck(cudaGetLastError()); + return std::make_tuple(out, top_count); +} + +std::tuple +dcn_v2_psroi_pooling_cpu_backward(const at::Tensor &out_grad, + const at::Tensor &input, + const at::Tensor &bbox, + const at::Tensor &trans, + const at::Tensor &top_count, + const int no_trans, + const float spatial_scale, + const int output_dim, + const int group_size, + const int pooled_size, + const int part_size, + const int sample_per_part, + const float trans_std) +{ + /*AT_ASSERTM(out_grad.type().is_cuda(), "out_grad must be a CUDA tensor"); + AT_ASSERTM(input.type().is_cuda(), "input must be a CUDA tensor"); + AT_ASSERTM(bbox.type().is_cuda(), "bbox must be a CUDA tensor"); + AT_ASSERTM(trans.type().is_cuda(), "trans must be a CUDA tensor"); + AT_ASSERTM(top_count.type().is_cuda(), "top_count must be a CUDA tensor");*/ + + const int batch = input.size(0); + const int channels = input.size(1); + const int height = input.size(2); + const int width = input.size(3); + const int channels_trans = no_trans ? 2 : trans.size(1); + const int num_bbox = bbox.size(0); + + AT_ASSERTM(channels == output_dim, "input channels and output channels must equal"); + auto pooled_height = pooled_size; + auto pooled_width = pooled_size; + long out_size = num_bbox * output_dim * pooled_height * pooled_width; + const int num_classes = no_trans ? 1 : channels_trans / 2; + const int channels_each_class = no_trans ? output_dim : output_dim / num_classes; + + auto input_grad = at::zeros({batch, channels, height, width}, out_grad.options()); + auto trans_grad = at::zeros_like(trans); + + if (input_grad.numel() == 0) + { + //THCudaCheck(cudaGetLastError()); + return std::make_tuple(input_grad, trans_grad); + } + + /*dim3 grid(std::min(THCCeilDiv(out_size, 512L), 4096L)); + dim3 block(512); + cudaStream_t stream = at::cuda::getCurrentCUDAStream();*/ + + AT_DISPATCH_FLOATING_TYPES(out_grad.type(), "dcn_v2_psroi_pooling_cpu_backward", [&] { + DeformablePSROIPoolBackwardAccKernelCpu( + out_size, + out_grad.contiguous().data(), + top_count.contiguous().data(), + num_bbox, + spatial_scale, + channels, + height, + width, + pooled_height, + pooled_width, + output_dim, + input_grad.contiguous().data(), + trans_grad.contiguous().data(), + input.contiguous().data(), + bbox.contiguous().data(), + trans.contiguous().data(), + no_trans, + trans_std, + sample_per_part, + group_size, + part_size, + num_classes, + channels_each_class); + }); + //THCudaCheck(cudaGetLastError()); + return std::make_tuple(input_grad, trans_grad); +} \ No newline at end of file diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/src/cpu/vision.h b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/src/cpu/vision.h new file mode 100644 index 00000000..d5fbf1f0 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/src/cpu/vision.h @@ -0,0 +1,60 @@ +#pragma once +#include + +at::Tensor +dcn_v2_cpu_forward(const at::Tensor &input, + const at::Tensor &weight, + const at::Tensor &bias, + const at::Tensor &offset, + const at::Tensor &mask, + const int kernel_h, + const int kernel_w, + const int stride_h, + const int stride_w, + const int pad_h, + const int pad_w, + const int dilation_h, + const int dilation_w, + const int deformable_group); + +std::vector +dcn_v2_cpu_backward(const at::Tensor &input, + const at::Tensor &weight, + const at::Tensor &bias, + const at::Tensor &offset, + const at::Tensor &mask, + const at::Tensor &grad_output, + int kernel_h, int kernel_w, + int stride_h, int stride_w, + int pad_h, int pad_w, + int dilation_h, int dilation_w, + int deformable_group); + + +std::tuple +dcn_v2_psroi_pooling_cpu_forward(const at::Tensor &input, + const at::Tensor &bbox, + const at::Tensor &trans, + const int no_trans, + const float spatial_scale, + const int output_dim, + const int group_size, + const int pooled_size, + const int part_size, + const int sample_per_part, + const float trans_std); + +std::tuple +dcn_v2_psroi_pooling_cpu_backward(const at::Tensor &out_grad, + const at::Tensor &input, + const at::Tensor &bbox, + const at::Tensor &trans, + const at::Tensor &top_count, + const int no_trans, + const float spatial_scale, + const int output_dim, + const int group_size, + const int pooled_size, + const int part_size, + const int sample_per_part, + const float trans_std); \ No newline at end of file diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/src/cuda/dcn_v2_cuda.cu b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/src/cuda/dcn_v2_cuda.cu new file mode 100644 index 00000000..9711a497 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/src/cuda/dcn_v2_cuda.cu @@ -0,0 +1,335 @@ +#include +#include "cuda/dcn_v2_im2col_cuda.h" + +#include +#include + +#include +#include +#include + +THCState *state = at::globalContext().lazyInitCUDA(); + +// author: Charles Shang +// https://github.com/torch/cunn/blob/master/lib/THCUNN/generic/SpatialConvolutionMM.cu + +// [batch gemm] +// https://github.com/pytorch/pytorch/blob/master/aten/src/THC/generic/THCTensorMathBlas.cu + +__global__ void createBatchGemmBuffer(const float **input_b, float **output_b, + float **columns_b, const float **ones_b, + const float **weight_b, const float **bias_b, + float *input, float *output, + float *columns, float *ones, + float *weight, float *bias, + const int input_stride, const int output_stride, + const int columns_stride, const int ones_stride, + const int num_batches) +{ + const int idx = blockIdx.x * blockDim.x + threadIdx.x; + if (idx < num_batches) + { + input_b[idx] = input + idx * input_stride; + output_b[idx] = output + idx * output_stride; + columns_b[idx] = columns + idx * columns_stride; + ones_b[idx] = ones + idx * ones_stride; + // share weights and bias within a Mini-Batch + weight_b[idx] = weight; + bias_b[idx] = bias; + } +} + +at::Tensor +dcn_v2_cuda_forward(const at::Tensor &input, + const at::Tensor &weight, + const at::Tensor &bias, + const at::Tensor &offset, + const at::Tensor &mask, + const int kernel_h, + const int kernel_w, + const int stride_h, + const int stride_w, + const int pad_h, + const int pad_w, + const int dilation_h, + const int dilation_w, + const int deformable_group) +{ + using scalar_t = float; + // THCAssertSameGPU(THCudaTensor_checkGPU(state, 5, input, weight, bias, offset, mask)); + AT_ASSERTM(input.type().is_cuda(), "input must be a CUDA tensor"); + AT_ASSERTM(weight.type().is_cuda(), "weight must be a CUDA tensor"); + AT_ASSERTM(bias.type().is_cuda(), "bias must be a CUDA tensor"); + AT_ASSERTM(offset.type().is_cuda(), "offset must be a CUDA tensor"); + AT_ASSERTM(mask.type().is_cuda(), "mask must be a CUDA tensor"); + + const int batch = input.size(0); + const int channels = input.size(1); + const int height = input.size(2); + const int width = input.size(3); + + const int channels_out = weight.size(0); + const int channels_kernel = weight.size(1); + const int kernel_h_ = weight.size(2); + const int kernel_w_ = weight.size(3); + + // printf("Kernels: %d %d %d %d\n", kernel_h_, kernel_w_, kernel_w, kernel_h); + // printf("Channels: %d %d\n", channels, channels_kernel); + // printf("Channels: %d %d\n", channels_out, channels_kernel); + + AT_ASSERTM(kernel_h_ == kernel_h && kernel_w_ == kernel_w, + "Input shape and kernel shape wont match: (%d x %d vs %d x %d).", kernel_h_, kernel_w, kernel_h_, kernel_w_); + + AT_ASSERTM(channels == channels_kernel, + "Input shape and kernel channels wont match: (%d vs %d).", channels, channels_kernel); + + const int height_out = (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1; + const int width_out = (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1; + + auto ones = at::ones({batch, height_out, width_out}, input.options()); + auto columns = at::empty({batch, channels * kernel_h * kernel_w, 1 * height_out * width_out}, input.options()); + auto output = at::empty({batch, channels_out, height_out, width_out}, input.options()); + + // prepare for batch-wise computing, which is significantly faster than instance-wise computing + // when batch size is large. + // launch batch threads + int matrices_size = batch * sizeof(float *); + auto input_b = static_cast(THCudaMalloc(state, matrices_size)); + auto output_b = static_cast(THCudaMalloc(state, matrices_size)); + auto columns_b = static_cast(THCudaMalloc(state, matrices_size)); + auto ones_b = static_cast(THCudaMalloc(state, matrices_size)); + auto weight_b = static_cast(THCudaMalloc(state, matrices_size)); + auto bias_b = static_cast(THCudaMalloc(state, matrices_size)); + + const int block = 128; + const int grid = (batch + block - 1) / block; + + createBatchGemmBuffer<<>>( + input_b, output_b, + columns_b, ones_b, + weight_b, bias_b, + input.data(), + output.data(), + columns.data(), + ones.data(), + weight.data(), + bias.data(), + channels * width * height, + channels_out * width_out * height_out, + channels * kernel_h * kernel_w * height_out * width_out, + height_out * width_out, + batch); + + long m_ = channels_out; + long n_ = height_out * width_out; + long k_ = 1; + THCudaBlas_SgemmBatched(state, + 't', + 'n', + n_, + m_, + k_, + 1.0f, + ones_b, k_, + bias_b, k_, + 0.0f, + output_b, n_, + batch); + + modulated_deformable_im2col_cuda(THCState_getCurrentStream(state), + input.data(), + offset.data(), + mask.data(), + batch, channels, height, width, + height_out, width_out, kernel_h, kernel_w, + pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, + deformable_group, + columns.data()); + + long m = channels_out; + long n = height_out * width_out; + long k = channels * kernel_h * kernel_w; + THCudaBlas_SgemmBatched(state, + 'n', + 'n', + n, + m, + k, + 1.0f, + (const float **)columns_b, n, + weight_b, k, + 1.0f, + output_b, n, + batch); + + THCudaFree(state, input_b); + THCudaFree(state, output_b); + THCudaFree(state, columns_b); + THCudaFree(state, ones_b); + THCudaFree(state, weight_b); + THCudaFree(state, bias_b); + return output; +} + +__global__ void createBatchGemmBufferBackward( + float **grad_output_b, + float **columns_b, + float **ones_b, + float **weight_b, + float **grad_weight_b, + float **grad_bias_b, + float *grad_output, + float *columns, + float *ones, + float *weight, + float *grad_weight, + float *grad_bias, + const int grad_output_stride, + const int columns_stride, + const int ones_stride, + const int num_batches) +{ + const int idx = blockIdx.x * blockDim.x + threadIdx.x; + if (idx < num_batches) + { + grad_output_b[idx] = grad_output + idx * grad_output_stride; + columns_b[idx] = columns + idx * columns_stride; + ones_b[idx] = ones + idx * ones_stride; + + // share weights and bias within a Mini-Batch + weight_b[idx] = weight; + grad_weight_b[idx] = grad_weight; + grad_bias_b[idx] = grad_bias; + } +} + +std::vector dcn_v2_cuda_backward(const at::Tensor &input, + const at::Tensor &weight, + const at::Tensor &bias, + const at::Tensor &offset, + const at::Tensor &mask, + const at::Tensor &grad_output, + int kernel_h, int kernel_w, + int stride_h, int stride_w, + int pad_h, int pad_w, + int dilation_h, int dilation_w, + int deformable_group) +{ + + THArgCheck(input.is_contiguous(), 1, "input tensor has to be contiguous"); + THArgCheck(weight.is_contiguous(), 2, "weight tensor has to be contiguous"); + + AT_ASSERTM(input.type().is_cuda(), "input must be a CUDA tensor"); + AT_ASSERTM(weight.type().is_cuda(), "weight must be a CUDA tensor"); + AT_ASSERTM(bias.type().is_cuda(), "bias must be a CUDA tensor"); + AT_ASSERTM(offset.type().is_cuda(), "offset must be a CUDA tensor"); + AT_ASSERTM(mask.type().is_cuda(), "mask must be a CUDA tensor"); + + const int batch = input.size(0); + const int channels = input.size(1); + const int height = input.size(2); + const int width = input.size(3); + + const int channels_out = weight.size(0); + const int channels_kernel = weight.size(1); + const int kernel_h_ = weight.size(2); + const int kernel_w_ = weight.size(3); + + AT_ASSERTM(kernel_h_ == kernel_h && kernel_w_ == kernel_w, + "Input shape and kernel shape wont match: (%d x %d vs %d x %d).", kernel_h_, kernel_w, kernel_h_, kernel_w_); + + AT_ASSERTM(channels == channels_kernel, + "Input shape and kernel channels wont match: (%d vs %d).", channels, channels_kernel); + + const int height_out = (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1; + const int width_out = (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1; + + auto ones = at::ones({height_out, width_out}, input.options()); + auto columns = at::empty({channels * kernel_h * kernel_w, 1 * height_out * width_out}, input.options()); + auto output = at::empty({batch, channels_out, height_out, width_out}, input.options()); + + auto grad_input = at::zeros_like(input); + auto grad_weight = at::zeros_like(weight); + auto grad_bias = at::zeros_like(bias); + auto grad_offset = at::zeros_like(offset); + auto grad_mask = at::zeros_like(mask); + + using scalar_t = float; + + for (int b = 0; b < batch; b++) + { + auto input_n = input.select(0, b); + auto offset_n = offset.select(0, b); + auto mask_n = mask.select(0, b); + auto grad_output_n = grad_output.select(0, b); + auto grad_input_n = grad_input.select(0, b); + auto grad_offset_n = grad_offset.select(0, b); + auto grad_mask_n = grad_mask.select(0, b); + + long m = channels * kernel_h * kernel_w; + long n = height_out * width_out; + long k = channels_out; + + THCudaBlas_Sgemm(state, 'n', 't', n, m, k, 1.0f, + grad_output_n.data(), n, + weight.data(), m, 0.0f, + columns.data(), n); + + // gradient w.r.t. input coordinate data + modulated_deformable_col2im_coord_cuda(THCState_getCurrentStream(state), + columns.data(), + input_n.data(), + offset_n.data(), + mask_n.data(), + 1, channels, height, width, + height_out, width_out, kernel_h, kernel_w, + pad_h, pad_w, stride_h, stride_w, + dilation_h, dilation_w, deformable_group, + grad_offset_n.data(), + grad_mask_n.data()); + // gradient w.r.t. input data + modulated_deformable_col2im_cuda(THCState_getCurrentStream(state), + columns.data(), + offset_n.data(), + mask_n.data(), + 1, channels, height, width, + height_out, width_out, kernel_h, kernel_w, + pad_h, pad_w, stride_h, stride_w, + dilation_h, dilation_w, deformable_group, + grad_input_n.data()); + + // gradient w.r.t. weight, dWeight should accumulate across the batch and group + modulated_deformable_im2col_cuda(THCState_getCurrentStream(state), + input_n.data(), + offset_n.data(), + mask_n.data(), + 1, channels, height, width, + height_out, width_out, kernel_h, kernel_w, + pad_h, pad_w, stride_h, stride_w, + dilation_h, dilation_w, deformable_group, + columns.data()); + + long m_ = channels_out; + long n_ = channels * kernel_h * kernel_w; + long k_ = height_out * width_out; + + THCudaBlas_Sgemm(state, 't', 'n', n_, m_, k_, 1.0f, + columns.data(), k_, + grad_output_n.data(), k_, 1.0f, + grad_weight.data(), n_); + + // gradient w.r.t. bias + // long m_ = channels_out; + // long k__ = height_out * width_out; + THCudaBlas_Sgemv(state, + 't', + k_, m_, 1.0f, + grad_output_n.data(), k_, + ones.data(), 1, 1.0f, + grad_bias.data(), 1); + } + + return { + grad_input, grad_offset, grad_mask, grad_weight, grad_bias + }; +} \ No newline at end of file diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/src/cuda/dcn_v2_im2col_cuda.cu b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/src/cuda/dcn_v2_im2col_cuda.cu new file mode 100644 index 00000000..4140eacf --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/src/cuda/dcn_v2_im2col_cuda.cu @@ -0,0 +1,402 @@ +#include "dcn_v2_im2col_cuda.h" +#include +#include +#include + +#include +#include + +#include +#include +#include + +#define CUDA_KERNEL_LOOP(i, n) \ + for (int i = blockIdx.x * blockDim.x + threadIdx.x; \ + i < (n); \ + i += blockDim.x * gridDim.x) + +const int CUDA_NUM_THREADS = 1024; +inline int GET_BLOCKS(const int N) +{ + return (N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS; +} + + +__device__ float dmcn_im2col_bilinear_cuda(const float *bottom_data, const int data_width, + const int height, const int width, float h, float w) +{ + int h_low = floor(h); + int w_low = floor(w); + int h_high = h_low + 1; + int w_high = w_low + 1; + + float lh = h - h_low; + float lw = w - w_low; + float hh = 1 - lh, hw = 1 - lw; + + float v1 = 0; + if (h_low >= 0 && w_low >= 0) + v1 = bottom_data[h_low * data_width + w_low]; + float v2 = 0; + if (h_low >= 0 && w_high <= width - 1) + v2 = bottom_data[h_low * data_width + w_high]; + float v3 = 0; + if (h_high <= height - 1 && w_low >= 0) + v3 = bottom_data[h_high * data_width + w_low]; + float v4 = 0; + if (h_high <= height - 1 && w_high <= width - 1) + v4 = bottom_data[h_high * data_width + w_high]; + + float w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; + + float val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); + return val; +} + +__device__ float dmcn_get_gradient_weight_cuda(float argmax_h, float argmax_w, + const int h, const int w, const int height, const int width) +{ + if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width) + { + //empty + return 0; + } + + int argmax_h_low = floor(argmax_h); + int argmax_w_low = floor(argmax_w); + int argmax_h_high = argmax_h_low + 1; + int argmax_w_high = argmax_w_low + 1; + + float weight = 0; + if (h == argmax_h_low && w == argmax_w_low) + weight = (h + 1 - argmax_h) * (w + 1 - argmax_w); + if (h == argmax_h_low && w == argmax_w_high) + weight = (h + 1 - argmax_h) * (argmax_w + 1 - w); + if (h == argmax_h_high && w == argmax_w_low) + weight = (argmax_h + 1 - h) * (w + 1 - argmax_w); + if (h == argmax_h_high && w == argmax_w_high) + weight = (argmax_h + 1 - h) * (argmax_w + 1 - w); + return weight; +} + +__device__ float dmcn_get_coordinate_weight_cuda(float argmax_h, float argmax_w, + const int height, const int width, const float *im_data, + const int data_width, const int bp_dir) +{ + if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width) + { + //empty + return 0; + } + + int argmax_h_low = floor(argmax_h); + int argmax_w_low = floor(argmax_w); + int argmax_h_high = argmax_h_low + 1; + int argmax_w_high = argmax_w_low + 1; + + float weight = 0; + + if (bp_dir == 0) + { + if (argmax_h_low >= 0 && argmax_w_low >= 0) + weight += -1 * (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_low * data_width + argmax_w_low]; + if (argmax_h_low >= 0 && argmax_w_high <= width - 1) + weight += -1 * (argmax_w - argmax_w_low) * im_data[argmax_h_low * data_width + argmax_w_high]; + if (argmax_h_high <= height - 1 && argmax_w_low >= 0) + weight += (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_high * data_width + argmax_w_low]; + if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) + weight += (argmax_w - argmax_w_low) * im_data[argmax_h_high * data_width + argmax_w_high]; + } + else if (bp_dir == 1) + { + if (argmax_h_low >= 0 && argmax_w_low >= 0) + weight += -1 * (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_low]; + if (argmax_h_low >= 0 && argmax_w_high <= width - 1) + weight += (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_high]; + if (argmax_h_high <= height - 1 && argmax_w_low >= 0) + weight += -1 * (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_low]; + if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) + weight += (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_high]; + } + + return weight; +} + +__global__ void modulated_deformable_im2col_gpu_kernel(const int n, + const float *data_im, const float *data_offset, const float *data_mask, + const int height, const int width, const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, + const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int channel_per_deformable_group, + const int batch_size, const int num_channels, const int deformable_group, + const int height_col, const int width_col, + float *data_col) +{ + // launch channels * batch_size * height_col * width_col cores + CUDA_KERNEL_LOOP(index, n) + { + // NOTE(CharlesShang): different from Dai Jifeng's MXNet implementation, col_buffer is of shape (c*kw*kh, N, oh, ow) + // here columns is of shape (N, c*kw*kh, oh * ow), need to adapt axis + + // index index of output matrix + const int w_col = index % width_col; + const int h_col = (index / width_col) % height_col; + // const int b_col = (index / width_col / height_col) % batch_size; + const int b_col = (index / width_col / height_col / num_channels) % batch_size; + // const int c_im = (index / width_col / height_col) / batch_size; + const int c_im = (index / width_col / height_col) % num_channels; + // const int c_col = c_im * kernel_h * kernel_w; + const int c_col = c_im * kernel_h * kernel_w; + + // compute deformable group index + const int deformable_group_index = c_im / channel_per_deformable_group; + + const int h_in = h_col * stride_h - pad_h; + const int w_in = w_col * stride_w - pad_w; + + // float *data_col_ptr = data_col + ((c_col * batch_size + b_col) * height_col + h_col) * width_col + w_col; + float *data_col_ptr = data_col + ((b_col * num_channels * kernel_w * kernel_h + c_col) * height_col + h_col) * width_col + w_col; + //const float* data_im_ptr = data_im + ((b_col * num_channels + c_im) * height + h_in) * width + w_in; + const float *data_im_ptr = data_im + (b_col * num_channels + c_im) * height * width; + const float *data_offset_ptr = data_offset + (b_col * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col; + + const float *data_mask_ptr = data_mask + (b_col * deformable_group + deformable_group_index) * kernel_h * kernel_w * height_col * width_col; + + for (int i = 0; i < kernel_h; ++i) + { + for (int j = 0; j < kernel_w; ++j) + { + const int data_offset_h_ptr = ((2 * (i * kernel_w + j)) * height_col + h_col) * width_col + w_col; + const int data_offset_w_ptr = ((2 * (i * kernel_w + j) + 1) * height_col + h_col) * width_col + w_col; + const int data_mask_hw_ptr = ((i * kernel_w + j) * height_col + h_col) * width_col + w_col; + const float offset_h = data_offset_ptr[data_offset_h_ptr]; + const float offset_w = data_offset_ptr[data_offset_w_ptr]; + const float mask = data_mask_ptr[data_mask_hw_ptr]; + float val = static_cast(0); + const float h_im = h_in + i * dilation_h + offset_h; + const float w_im = w_in + j * dilation_w + offset_w; + //if (h_im >= 0 && w_im >= 0 && h_im < height && w_im < width) { + if (h_im > -1 && w_im > -1 && h_im < height && w_im < width) + { + //const float map_h = i * dilation_h + offset_h; + //const float map_w = j * dilation_w + offset_w; + //const int cur_height = height - h_in; + //const int cur_width = width - w_in; + //val = dmcn_im2col_bilinear_cuda(data_im_ptr, width, cur_height, cur_width, map_h, map_w); + val = dmcn_im2col_bilinear_cuda(data_im_ptr, width, height, width, h_im, w_im); + } + *data_col_ptr = val * mask; + // data_col_ptr += batch_size * height_col * width_col; + data_col_ptr += height_col * width_col; + } + } + } +} + +__global__ void modulated_deformable_col2im_gpu_kernel(const int n, + const float *data_col, const float *data_offset, const float *data_mask, + const int channels, const int height, const int width, + const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, + const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int channel_per_deformable_group, + const int batch_size, const int deformable_group, + const int height_col, const int width_col, + float *grad_im) +{ + CUDA_KERNEL_LOOP(index, n) + { + const int j = (index / width_col / height_col / batch_size) % kernel_w; + const int i = (index / width_col / height_col / batch_size / kernel_w) % kernel_h; + const int c = index / width_col / height_col / batch_size / kernel_w / kernel_h; + // compute the start and end of the output + + const int deformable_group_index = c / channel_per_deformable_group; + + int w_out = index % width_col; + int h_out = (index / width_col) % height_col; + int b = (index / width_col / height_col) % batch_size; + int w_in = w_out * stride_w - pad_w; + int h_in = h_out * stride_h - pad_h; + + const float *data_offset_ptr = data_offset + (b * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col; + const float *data_mask_ptr = data_mask + (b * deformable_group + deformable_group_index) * kernel_h * kernel_w * height_col * width_col; + const int data_offset_h_ptr = ((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out; + const int data_offset_w_ptr = ((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out; + const int data_mask_hw_ptr = ((i * kernel_w + j) * height_col + h_out) * width_col + w_out; + const float offset_h = data_offset_ptr[data_offset_h_ptr]; + const float offset_w = data_offset_ptr[data_offset_w_ptr]; + const float mask = data_mask_ptr[data_mask_hw_ptr]; + const float cur_inv_h_data = h_in + i * dilation_h + offset_h; + const float cur_inv_w_data = w_in + j * dilation_w + offset_w; + + const float cur_top_grad = data_col[index] * mask; + const int cur_h = (int)cur_inv_h_data; + const int cur_w = (int)cur_inv_w_data; + for (int dy = -2; dy <= 2; dy++) + { + for (int dx = -2; dx <= 2; dx++) + { + if (cur_h + dy >= 0 && cur_h + dy < height && + cur_w + dx >= 0 && cur_w + dx < width && + abs(cur_inv_h_data - (cur_h + dy)) < 1 && + abs(cur_inv_w_data - (cur_w + dx)) < 1) + { + int cur_bottom_grad_pos = ((b * channels + c) * height + cur_h + dy) * width + cur_w + dx; + float weight = dmcn_get_gradient_weight_cuda(cur_inv_h_data, cur_inv_w_data, cur_h + dy, cur_w + dx, height, width); + atomicAdd(grad_im + cur_bottom_grad_pos, weight * cur_top_grad); + } + } + } + } +} + +__global__ void modulated_deformable_col2im_coord_gpu_kernel(const int n, + const float *data_col, const float *data_im, + const float *data_offset, const float *data_mask, + const int channels, const int height, const int width, + const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, + const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int channel_per_deformable_group, + const int batch_size, const int offset_channels, const int deformable_group, + const int height_col, const int width_col, + float *grad_offset, float *grad_mask) +{ + CUDA_KERNEL_LOOP(index, n) + { + float val = 0, mval = 0; + int w = index % width_col; + int h = (index / width_col) % height_col; + int c = (index / width_col / height_col) % offset_channels; + int b = (index / width_col / height_col) / offset_channels; + // compute the start and end of the output + + const int deformable_group_index = c / (2 * kernel_h * kernel_w); + const int col_step = kernel_h * kernel_w; + int cnt = 0; + const float *data_col_ptr = data_col + deformable_group_index * channel_per_deformable_group * batch_size * width_col * height_col; + const float *data_im_ptr = data_im + (b * deformable_group + deformable_group_index) * channel_per_deformable_group / kernel_h / kernel_w * height * width; + const float *data_offset_ptr = data_offset + (b * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col; + const float *data_mask_ptr = data_mask + (b * deformable_group + deformable_group_index) * kernel_h * kernel_w * height_col * width_col; + + const int offset_c = c - deformable_group_index * 2 * kernel_h * kernel_w; + + for (int col_c = (offset_c / 2); col_c < channel_per_deformable_group; col_c += col_step) + { + const int col_pos = (((col_c * batch_size + b) * height_col) + h) * width_col + w; + const int bp_dir = offset_c % 2; + + int j = (col_pos / width_col / height_col / batch_size) % kernel_w; + int i = (col_pos / width_col / height_col / batch_size / kernel_w) % kernel_h; + int w_out = col_pos % width_col; + int h_out = (col_pos / width_col) % height_col; + int w_in = w_out * stride_w - pad_w; + int h_in = h_out * stride_h - pad_h; + const int data_offset_h_ptr = (((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out); + const int data_offset_w_ptr = (((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out); + const int data_mask_hw_ptr = (((i * kernel_w + j) * height_col + h_out) * width_col + w_out); + const float offset_h = data_offset_ptr[data_offset_h_ptr]; + const float offset_w = data_offset_ptr[data_offset_w_ptr]; + const float mask = data_mask_ptr[data_mask_hw_ptr]; + float inv_h = h_in + i * dilation_h + offset_h; + float inv_w = w_in + j * dilation_w + offset_w; + if (inv_h <= -1 || inv_w <= -1 || inv_h >= height || inv_w >= width) + { + inv_h = inv_w = -2; + } + else + { + mval += data_col_ptr[col_pos] * dmcn_im2col_bilinear_cuda(data_im_ptr + cnt * height * width, width, height, width, inv_h, inv_w); + } + const float weight = dmcn_get_coordinate_weight_cuda( + inv_h, inv_w, + height, width, data_im_ptr + cnt * height * width, width, bp_dir); + val += weight * data_col_ptr[col_pos] * mask; + cnt += 1; + } + // KERNEL_ASSIGN(grad_offset[index], offset_req, val); + grad_offset[index] = val; + if (offset_c % 2 == 0) + // KERNEL_ASSIGN(grad_mask[(((b * deformable_group + deformable_group_index) * kernel_h * kernel_w + offset_c / 2) * height_col + h) * width_col + w], mask_req, mval); + grad_mask[(((b * deformable_group + deformable_group_index) * kernel_h * kernel_w + offset_c / 2) * height_col + h) * width_col + w] = mval; + } +} + +void modulated_deformable_im2col_cuda(cudaStream_t stream, + const float* data_im, const float* data_offset, const float* data_mask, + const int batch_size, const int channels, const int height_im, const int width_im, + const int height_col, const int width_col, const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int deformable_group, float* data_col) { + // num_axes should be smaller than block size + const int channel_per_deformable_group = channels / deformable_group; + const int num_kernels = channels * batch_size * height_col * width_col; + modulated_deformable_im2col_gpu_kernel + <<>>( + num_kernels, data_im, data_offset, data_mask, height_im, width_im, kernel_h, kernel_w, + pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, channel_per_deformable_group, + batch_size, channels, deformable_group, height_col, width_col, data_col); + + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) + { + printf("error in modulated_deformable_im2col_cuda: %s\n", cudaGetErrorString(err)); + } + +} + +void modulated_deformable_col2im_cuda(cudaStream_t stream, + const float* data_col, const float* data_offset, const float* data_mask, + const int batch_size, const int channels, const int height_im, const int width_im, + const int height_col, const int width_col, const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int deformable_group, float* grad_im){ + + const int channel_per_deformable_group = channels / deformable_group; + const int num_kernels = channels * kernel_h * kernel_w * batch_size * height_col * width_col; + modulated_deformable_col2im_gpu_kernel + <<>>( + num_kernels, data_col, data_offset, data_mask, channels, height_im, width_im, + kernel_h, kernel_w, pad_h, pad_h, stride_h, stride_w, + dilation_h, dilation_w, channel_per_deformable_group, + batch_size, deformable_group, height_col, width_col, grad_im); + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) + { + printf("error in modulated_deformable_col2im_cuda: %s\n", cudaGetErrorString(err)); + } + +} + +void modulated_deformable_col2im_coord_cuda(cudaStream_t stream, + const float* data_col, const float* data_im, const float* data_offset, const float* data_mask, + const int batch_size, const int channels, const int height_im, const int width_im, + const int height_col, const int width_col, const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int deformable_group, + float* grad_offset, float* grad_mask) { + const int num_kernels = batch_size * height_col * width_col * 2 * kernel_h * kernel_w * deformable_group; + const int channel_per_deformable_group = channels * kernel_h * kernel_w / deformable_group; + modulated_deformable_col2im_coord_gpu_kernel + <<>>( + num_kernels, data_col, data_im, data_offset, data_mask, channels, height_im, width_im, + kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, + dilation_h, dilation_w, channel_per_deformable_group, + batch_size, 2 * kernel_h * kernel_w * deformable_group, deformable_group, height_col, width_col, + grad_offset, grad_mask); + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) + { + printf("error in modulated_deformable_col2im_coord_cuda: %s\n", cudaGetErrorString(err)); + } +} \ No newline at end of file diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/src/cuda/dcn_v2_im2col_cuda.h b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/src/cuda/dcn_v2_im2col_cuda.h new file mode 100644 index 00000000..c8568319 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/src/cuda/dcn_v2_im2col_cuda.h @@ -0,0 +1,101 @@ + +/*! + ******************* BEGIN Caffe Copyright Notice and Disclaimer **************** + * + * COPYRIGHT + * + * All contributions by the University of California: + * Copyright (c) 2014-2017 The Regents of the University of California (Regents) + * All rights reserved. + * + * All other contributions: + * Copyright (c) 2014-2017, the respective contributors + * All rights reserved. + * + * Caffe uses a shared copyright model: each contributor holds copyright over + * their contributions to Caffe. The project versioning records all such + * contribution and copyright details. If a contributor wants to further mark + * their specific copyright on a particular contribution, they should indicate + * their copyright solely in the commit message of the change when it is + * committed. + * + * LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * + * 1. Redistributions of source code must retain the above copyright notice, this + * list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright notice, + * this list of conditions and the following disclaimer in the documentation + * and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND + * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED + * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR + * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES + * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; + * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND + * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + * + * CONTRIBUTION AGREEMENT + * + * By contributing to the BVLC/caffe repository through pull-request, comment, + * or otherwise, the contributor releases their content to the + * license and copyright terms herein. + * + ***************** END Caffe Copyright Notice and Disclaimer ******************** + * + * Copyright (c) 2018 Microsoft + * Licensed under The MIT License [see LICENSE for details] + * \file modulated_deformable_im2col.h + * \brief Function definitions of converting an image to + * column matrix based on kernel, padding, dilation, and offset. + * These functions are mainly used in deformable convolution operators. + * \ref: https://arxiv.org/abs/1811.11168 + * \author Yuwen Xiong, Haozhi Qi, Jifeng Dai, Xizhou Zhu, Han Hu + */ + +/***************** Adapted by Charles Shang *********************/ + +#ifndef DCN_V2_IM2COL_CUDA +#define DCN_V2_IM2COL_CUDA + +#ifdef __cplusplus +extern "C" +{ +#endif + + void modulated_deformable_im2col_cuda(cudaStream_t stream, + const float *data_im, const float *data_offset, const float *data_mask, + const int batch_size, const int channels, const int height_im, const int width_im, + const int height_col, const int width_col, const int kernel_h, const int kenerl_w, + const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int deformable_group, float *data_col); + + void modulated_deformable_col2im_cuda(cudaStream_t stream, + const float *data_col, const float *data_offset, const float *data_mask, + const int batch_size, const int channels, const int height_im, const int width_im, + const int height_col, const int width_col, const int kernel_h, const int kenerl_w, + const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int deformable_group, float *grad_im); + + void modulated_deformable_col2im_coord_cuda(cudaStream_t stream, + const float *data_col, const float *data_im, const float *data_offset, const float *data_mask, + const int batch_size, const int channels, const int height_im, const int width_im, + const int height_col, const int width_col, const int kernel_h, const int kenerl_w, + const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int deformable_group, + float *grad_offset, float *grad_mask); + +#ifdef __cplusplus +} +#endif + +#endif \ No newline at end of file diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/src/cuda/dcn_v2_psroi_pooling_cuda.cu b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/src/cuda/dcn_v2_psroi_pooling_cuda.cu new file mode 100644 index 00000000..8f08f6a3 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/src/cuda/dcn_v2_psroi_pooling_cuda.cu @@ -0,0 +1,419 @@ +/*! + * Copyright (c) 2017 Microsoft + * Licensed under The MIT License [see LICENSE for details] + * \file deformable_psroi_pooling.cu + * \brief + * \author Yi Li, Guodong Zhang, Jifeng Dai +*/ +/***************** Adapted by Charles Shang *********************/ + +#include +#include +#include +#include + +#include +#include + +#include +#include +#include + +#define CUDA_KERNEL_LOOP(i, n) \ + for (int i = blockIdx.x * blockDim.x + threadIdx.x; \ + i < (n); \ + i += blockDim.x * gridDim.x) + +const int CUDA_NUM_THREADS = 1024; +inline int GET_BLOCKS(const int N) +{ + return (N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS; +} + +template +__device__ T bilinear_interp_cuda( + const T *data, + const T x, + const T y, + const int width, + const int height) +{ + int x1 = floor(x); + int x2 = ceil(x); + int y1 = floor(y); + int y2 = ceil(y); + T dist_x = static_cast(x - x1); + T dist_y = static_cast(y - y1); + T value11 = data[y1 * width + x1]; + T value12 = data[y2 * width + x1]; + T value21 = data[y1 * width + x2]; + T value22 = data[y2 * width + x2]; + T value = (1 - dist_x) * (1 - dist_y) * value11 + + (1 - dist_x) * dist_y * value12 + + dist_x * (1 - dist_y) * value21 + + dist_x * dist_y * value22; + return value; +} + +template +__global__ void DeformablePSROIPoolForwardKernelCuda( + const int count, + const T *bottom_data, + const T spatial_scale, + const int channels, + const int height, const int width, + const int pooled_height, const int pooled_width, + const T *bottom_rois, const T *bottom_trans, + const int no_trans, + const T trans_std, + const int sample_per_part, + const int output_dim, + const int group_size, + const int part_size, + const int num_classes, + const int channels_each_class, + T *top_data, + T *top_count) +{ + CUDA_KERNEL_LOOP(index, count) + { + // The output is in order (n, ctop, ph, pw) + int pw = index % pooled_width; + int ph = (index / pooled_width) % pooled_height; + int ctop = (index / pooled_width / pooled_height) % output_dim; + int n = index / pooled_width / pooled_height / output_dim; + + // [start, end) interval for spatial sampling + const T *offset_bottom_rois = bottom_rois + n * 5; + int roi_batch_ind = offset_bottom_rois[0]; + T roi_start_w = static_cast(round(offset_bottom_rois[1])) * spatial_scale - 0.5; + T roi_start_h = static_cast(round(offset_bottom_rois[2])) * spatial_scale - 0.5; + T roi_end_w = static_cast(round(offset_bottom_rois[3]) + 1.) * spatial_scale - 0.5; + T roi_end_h = static_cast(round(offset_bottom_rois[4]) + 1.) * spatial_scale - 0.5; + + // Force too small ROIs to be 1x1 + T roi_width = max(roi_end_w - roi_start_w, 0.1); //avoid 0 + T roi_height = max(roi_end_h - roi_start_h, 0.1); + + // Compute w and h at bottom + T bin_size_h = roi_height / static_cast(pooled_height); + T bin_size_w = roi_width / static_cast(pooled_width); + + T sub_bin_size_h = bin_size_h / static_cast(sample_per_part); + T sub_bin_size_w = bin_size_w / static_cast(sample_per_part); + + int part_h = floor(static_cast(ph) / pooled_height * part_size); + int part_w = floor(static_cast(pw) / pooled_width * part_size); + int class_id = ctop / channels_each_class; + T trans_x = no_trans ? static_cast(0) : bottom_trans[(((n * num_classes + class_id) * 2) * part_size + part_h) * part_size + part_w] * trans_std; + T trans_y = no_trans ? static_cast(0) : bottom_trans[(((n * num_classes + class_id) * 2 + 1) * part_size + part_h) * part_size + part_w] * trans_std; + + T wstart = static_cast(pw) * bin_size_w + roi_start_w; + wstart += trans_x * roi_width; + T hstart = static_cast(ph) * bin_size_h + roi_start_h; + hstart += trans_y * roi_height; + + T sum = 0; + int count = 0; + int gw = floor(static_cast(pw) * group_size / pooled_width); + int gh = floor(static_cast(ph) * group_size / pooled_height); + gw = min(max(gw, 0), group_size - 1); + gh = min(max(gh, 0), group_size - 1); + + const T *offset_bottom_data = bottom_data + (roi_batch_ind * channels) * height * width; + for (int ih = 0; ih < sample_per_part; ih++) + { + for (int iw = 0; iw < sample_per_part; iw++) + { + T w = wstart + iw * sub_bin_size_w; + T h = hstart + ih * sub_bin_size_h; + // bilinear interpolation + if (w < -0.5 || w > width - 0.5 || h < -0.5 || h > height - 0.5) + { + continue; + } + w = min(max(w, 0.), width - 1.); + h = min(max(h, 0.), height - 1.); + int c = (ctop * group_size + gh) * group_size + gw; + T val = bilinear_interp_cuda(offset_bottom_data + c * height * width, w, h, width, height); + sum += val; + count++; + } + } + top_data[index] = count == 0 ? static_cast(0) : sum / count; + top_count[index] = count; + } +} + +template +__global__ void DeformablePSROIPoolBackwardAccKernelCuda( + const int count, + const T *top_diff, + const T *top_count, + const int num_rois, + const T spatial_scale, + const int channels, + const int height, const int width, + const int pooled_height, const int pooled_width, + const int output_dim, + T *bottom_data_diff, T *bottom_trans_diff, + const T *bottom_data, + const T *bottom_rois, + const T *bottom_trans, + const int no_trans, + const T trans_std, + const int sample_per_part, + const int group_size, + const int part_size, + const int num_classes, + const int channels_each_class) +{ + CUDA_KERNEL_LOOP(index, count) + { + // The output is in order (n, ctop, ph, pw) + int pw = index % pooled_width; + int ph = (index / pooled_width) % pooled_height; + int ctop = (index / pooled_width / pooled_height) % output_dim; + int n = index / pooled_width / pooled_height / output_dim; + + // [start, end) interval for spatial sampling + const T *offset_bottom_rois = bottom_rois + n * 5; + int roi_batch_ind = offset_bottom_rois[0]; + T roi_start_w = static_cast(round(offset_bottom_rois[1])) * spatial_scale - 0.5; + T roi_start_h = static_cast(round(offset_bottom_rois[2])) * spatial_scale - 0.5; + T roi_end_w = static_cast(round(offset_bottom_rois[3]) + 1.) * spatial_scale - 0.5; + T roi_end_h = static_cast(round(offset_bottom_rois[4]) + 1.) * spatial_scale - 0.5; + + // Force too small ROIs to be 1x1 + T roi_width = max(roi_end_w - roi_start_w, 0.1); //avoid 0 + T roi_height = max(roi_end_h - roi_start_h, 0.1); + + // Compute w and h at bottom + T bin_size_h = roi_height / static_cast(pooled_height); + T bin_size_w = roi_width / static_cast(pooled_width); + + T sub_bin_size_h = bin_size_h / static_cast(sample_per_part); + T sub_bin_size_w = bin_size_w / static_cast(sample_per_part); + + int part_h = floor(static_cast(ph) / pooled_height * part_size); + int part_w = floor(static_cast(pw) / pooled_width * part_size); + int class_id = ctop / channels_each_class; + T trans_x = no_trans ? static_cast(0) : bottom_trans[(((n * num_classes + class_id) * 2) * part_size + part_h) * part_size + part_w] * trans_std; + T trans_y = no_trans ? static_cast(0) : bottom_trans[(((n * num_classes + class_id) * 2 + 1) * part_size + part_h) * part_size + part_w] * trans_std; + + T wstart = static_cast(pw) * bin_size_w + roi_start_w; + wstart += trans_x * roi_width; + T hstart = static_cast(ph) * bin_size_h + roi_start_h; + hstart += trans_y * roi_height; + + if (top_count[index] <= 0) + { + continue; + } + T diff_val = top_diff[index] / top_count[index]; + const T *offset_bottom_data = bottom_data + roi_batch_ind * channels * height * width; + T *offset_bottom_data_diff = bottom_data_diff + roi_batch_ind * channels * height * width; + int gw = floor(static_cast(pw) * group_size / pooled_width); + int gh = floor(static_cast(ph) * group_size / pooled_height); + gw = min(max(gw, 0), group_size - 1); + gh = min(max(gh, 0), group_size - 1); + + for (int ih = 0; ih < sample_per_part; ih++) + { + for (int iw = 0; iw < sample_per_part; iw++) + { + T w = wstart + iw * sub_bin_size_w; + T h = hstart + ih * sub_bin_size_h; + // bilinear interpolation + if (w < -0.5 || w > width - 0.5 || h < -0.5 || h > height - 0.5) + { + continue; + } + w = min(max(w, 0.), width - 1.); + h = min(max(h, 0.), height - 1.); + int c = (ctop * group_size + gh) * group_size + gw; + // backward on feature + int x0 = floor(w); + int x1 = ceil(w); + int y0 = floor(h); + int y1 = ceil(h); + T dist_x = w - x0, dist_y = h - y0; + T q00 = (1 - dist_x) * (1 - dist_y); + T q01 = (1 - dist_x) * dist_y; + T q10 = dist_x * (1 - dist_y); + T q11 = dist_x * dist_y; + int bottom_index_base = c * height * width; + atomicAdd(offset_bottom_data_diff + bottom_index_base + y0 * width + x0, q00 * diff_val); + atomicAdd(offset_bottom_data_diff + bottom_index_base + y1 * width + x0, q01 * diff_val); + atomicAdd(offset_bottom_data_diff + bottom_index_base + y0 * width + x1, q10 * diff_val); + atomicAdd(offset_bottom_data_diff + bottom_index_base + y1 * width + x1, q11 * diff_val); + + if (no_trans) + { + continue; + } + T U00 = offset_bottom_data[bottom_index_base + y0 * width + x0]; + T U01 = offset_bottom_data[bottom_index_base + y1 * width + x0]; + T U10 = offset_bottom_data[bottom_index_base + y0 * width + x1]; + T U11 = offset_bottom_data[bottom_index_base + y1 * width + x1]; + T diff_x = (U11 * dist_y + U10 * (1 - dist_y) - U01 * dist_y - U00 * (1 - dist_y)) * trans_std * diff_val; + diff_x *= roi_width; + T diff_y = (U11 * dist_x + U01 * (1 - dist_x) - U10 * dist_x - U00 * (1 - dist_x)) * trans_std * diff_val; + diff_y *= roi_height; + + atomicAdd(bottom_trans_diff + (((n * num_classes + class_id) * 2) * part_size + part_h) * part_size + part_w, diff_x); + atomicAdd(bottom_trans_diff + (((n * num_classes + class_id) * 2 + 1) * part_size + part_h) * part_size + part_w, diff_y); + } + } + } +} + +std::tuple +dcn_v2_psroi_pooling_cuda_forward(const at::Tensor &input, + const at::Tensor &bbox, + const at::Tensor &trans, + const int no_trans, + const float spatial_scale, + const int output_dim, + const int group_size, + const int pooled_size, + const int part_size, + const int sample_per_part, + const float trans_std) +{ + AT_ASSERTM(input.type().is_cuda(), "input must be a CUDA tensor"); + AT_ASSERTM(bbox.type().is_cuda(), "rois must be a CUDA tensor"); + AT_ASSERTM(trans.type().is_cuda(), "trans must be a CUDA tensor"); + + const int batch = input.size(0); + const int channels = input.size(1); + const int height = input.size(2); + const int width = input.size(3); + const int channels_trans = no_trans ? 2 : trans.size(1); + const int num_bbox = bbox.size(0); + + AT_ASSERTM(channels == output_dim, "input channels and output channels must equal"); + auto pooled_height = pooled_size; + auto pooled_width = pooled_size; + + auto out = at::empty({num_bbox, output_dim, pooled_height, pooled_width}, input.options()); + long out_size = num_bbox * output_dim * pooled_height * pooled_width; + auto top_count = at::zeros({num_bbox, output_dim, pooled_height, pooled_width}, input.options()); + + const int num_classes = no_trans ? 1 : channels_trans / 2; + const int channels_each_class = no_trans ? output_dim : output_dim / num_classes; + + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + if (out.numel() == 0) + { + THCudaCheck(cudaGetLastError()); + return std::make_tuple(out, top_count); + } + + dim3 grid(std::min(THCCeilDiv(out_size, 512L), 4096L)); + dim3 block(512); + + AT_DISPATCH_FLOATING_TYPES(input.type(), "dcn_v2_psroi_pooling_cuda_forward", [&] { + DeformablePSROIPoolForwardKernelCuda<<>>( + out_size, + input.contiguous().data(), + spatial_scale, + channels, + height, width, + pooled_height, + pooled_width, + bbox.contiguous().data(), + trans.contiguous().data(), + no_trans, + trans_std, + sample_per_part, + output_dim, + group_size, + part_size, + num_classes, + channels_each_class, + out.data(), + top_count.data()); + }); + THCudaCheck(cudaGetLastError()); + return std::make_tuple(out, top_count); +} + +std::tuple +dcn_v2_psroi_pooling_cuda_backward(const at::Tensor &out_grad, + const at::Tensor &input, + const at::Tensor &bbox, + const at::Tensor &trans, + const at::Tensor &top_count, + const int no_trans, + const float spatial_scale, + const int output_dim, + const int group_size, + const int pooled_size, + const int part_size, + const int sample_per_part, + const float trans_std) +{ + AT_ASSERTM(out_grad.type().is_cuda(), "out_grad must be a CUDA tensor"); + AT_ASSERTM(input.type().is_cuda(), "input must be a CUDA tensor"); + AT_ASSERTM(bbox.type().is_cuda(), "bbox must be a CUDA tensor"); + AT_ASSERTM(trans.type().is_cuda(), "trans must be a CUDA tensor"); + AT_ASSERTM(top_count.type().is_cuda(), "top_count must be a CUDA tensor"); + + const int batch = input.size(0); + const int channels = input.size(1); + const int height = input.size(2); + const int width = input.size(3); + const int channels_trans = no_trans ? 2 : trans.size(1); + const int num_bbox = bbox.size(0); + + AT_ASSERTM(channels == output_dim, "input channels and output channels must equal"); + auto pooled_height = pooled_size; + auto pooled_width = pooled_size; + long out_size = num_bbox * output_dim * pooled_height * pooled_width; + const int num_classes = no_trans ? 1 : channels_trans / 2; + const int channels_each_class = no_trans ? output_dim : output_dim / num_classes; + + auto input_grad = at::zeros({batch, channels, height, width}, out_grad.options()); + auto trans_grad = at::zeros_like(trans); + + if (input_grad.numel() == 0) + { + THCudaCheck(cudaGetLastError()); + return std::make_tuple(input_grad, trans_grad); + } + + dim3 grid(std::min(THCCeilDiv(out_size, 512L), 4096L)); + dim3 block(512); + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + AT_DISPATCH_FLOATING_TYPES(out_grad.type(), "dcn_v2_psroi_pooling_cuda_backward", [&] { + DeformablePSROIPoolBackwardAccKernelCuda<<>>( + out_size, + out_grad.contiguous().data(), + top_count.contiguous().data(), + num_bbox, + spatial_scale, + channels, + height, + width, + pooled_height, + pooled_width, + output_dim, + input_grad.contiguous().data(), + trans_grad.contiguous().data(), + input.contiguous().data(), + bbox.contiguous().data(), + trans.contiguous().data(), + no_trans, + trans_std, + sample_per_part, + group_size, + part_size, + num_classes, + channels_each_class); + }); + THCudaCheck(cudaGetLastError()); + return std::make_tuple(input_grad, trans_grad); +} \ No newline at end of file diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/src/cuda/vision.h b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/src/cuda/vision.h new file mode 100644 index 00000000..e42a2a79 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/src/cuda/vision.h @@ -0,0 +1,60 @@ +#pragma once +#include + +at::Tensor +dcn_v2_cuda_forward(const at::Tensor &input, + const at::Tensor &weight, + const at::Tensor &bias, + const at::Tensor &offset, + const at::Tensor &mask, + const int kernel_h, + const int kernel_w, + const int stride_h, + const int stride_w, + const int pad_h, + const int pad_w, + const int dilation_h, + const int dilation_w, + const int deformable_group); + +std::vector +dcn_v2_cuda_backward(const at::Tensor &input, + const at::Tensor &weight, + const at::Tensor &bias, + const at::Tensor &offset, + const at::Tensor &mask, + const at::Tensor &grad_output, + int kernel_h, int kernel_w, + int stride_h, int stride_w, + int pad_h, int pad_w, + int dilation_h, int dilation_w, + int deformable_group); + + +std::tuple +dcn_v2_psroi_pooling_cuda_forward(const at::Tensor &input, + const at::Tensor &bbox, + const at::Tensor &trans, + const int no_trans, + const float spatial_scale, + const int output_dim, + const int group_size, + const int pooled_size, + const int part_size, + const int sample_per_part, + const float trans_std); + +std::tuple +dcn_v2_psroi_pooling_cuda_backward(const at::Tensor &out_grad, + const at::Tensor &input, + const at::Tensor &bbox, + const at::Tensor &trans, + const at::Tensor &top_count, + const int no_trans, + const float spatial_scale, + const int output_dim, + const int group_size, + const int pooled_size, + const int part_size, + const int sample_per_part, + const float trans_std); \ No newline at end of file diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/src/dcn_v2.h b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/src/dcn_v2.h new file mode 100644 index 00000000..de670bf9 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/src/dcn_v2.h @@ -0,0 +1,190 @@ +#pragma once + +#include "cpu/vision.h" + +#ifdef WITH_CUDA +#include "cuda/vision.h" +#endif + +at::Tensor +dcn_v2_forward(const at::Tensor &input, + const at::Tensor &weight, + const at::Tensor &bias, + const at::Tensor &offset, + const at::Tensor &mask, + const int kernel_h, + const int kernel_w, + const int stride_h, + const int stride_w, + const int pad_h, + const int pad_w, + const int dilation_h, + const int dilation_w, + const int deformable_group) +{ + if (input.type().is_cuda()) + { +#ifdef WITH_CUDA + return dcn_v2_cuda_forward(input, weight, bias, offset, mask, + kernel_h, kernel_w, + stride_h, stride_w, + pad_h, pad_w, + dilation_h, dilation_w, + deformable_group); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + else{ + return dcn_v2_cpu_forward(input, weight, bias, offset, mask, + kernel_h, kernel_w, + stride_h, stride_w, + pad_h, pad_w, + dilation_h, dilation_w, + deformable_group); + } +} + +std::vector +dcn_v2_backward(const at::Tensor &input, + const at::Tensor &weight, + const at::Tensor &bias, + const at::Tensor &offset, + const at::Tensor &mask, + const at::Tensor &grad_output, + int kernel_h, int kernel_w, + int stride_h, int stride_w, + int pad_h, int pad_w, + int dilation_h, int dilation_w, + int deformable_group) +{ + if (input.type().is_cuda()) + { +#ifdef WITH_CUDA + return dcn_v2_cuda_backward(input, + weight, + bias, + offset, + mask, + grad_output, + kernel_h, kernel_w, + stride_h, stride_w, + pad_h, pad_w, + dilation_h, dilation_w, + deformable_group); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + else{ + return dcn_v2_cpu_backward(input, + weight, + bias, + offset, + mask, + grad_output, + kernel_h, kernel_w, + stride_h, stride_w, + pad_h, pad_w, + dilation_h, dilation_w, + deformable_group); + } +} + +std::tuple +dcn_v2_psroi_pooling_forward(const at::Tensor &input, + const at::Tensor &bbox, + const at::Tensor &trans, + const int no_trans, + const float spatial_scale, + const int output_dim, + const int group_size, + const int pooled_size, + const int part_size, + const int sample_per_part, + const float trans_std) +{ + if (input.type().is_cuda()) + { +#ifdef WITH_CUDA + return dcn_v2_psroi_pooling_cuda_forward(input, + bbox, + trans, + no_trans, + spatial_scale, + output_dim, + group_size, + pooled_size, + part_size, + sample_per_part, + trans_std); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + else{ + return dcn_v2_psroi_pooling_cpu_forward(input, + bbox, + trans, + no_trans, + spatial_scale, + output_dim, + group_size, + pooled_size, + part_size, + sample_per_part, + trans_std); + } +} + +std::tuple +dcn_v2_psroi_pooling_backward(const at::Tensor &out_grad, + const at::Tensor &input, + const at::Tensor &bbox, + const at::Tensor &trans, + const at::Tensor &top_count, + const int no_trans, + const float spatial_scale, + const int output_dim, + const int group_size, + const int pooled_size, + const int part_size, + const int sample_per_part, + const float trans_std) +{ + if (input.type().is_cuda()) + { +#ifdef WITH_CUDA + return dcn_v2_psroi_pooling_cuda_backward(out_grad, + input, + bbox, + trans, + top_count, + no_trans, + spatial_scale, + output_dim, + group_size, + pooled_size, + part_size, + sample_per_part, + trans_std); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + else{ + return dcn_v2_psroi_pooling_cpu_backward(out_grad, + input, + bbox, + trans, + top_count, + no_trans, + spatial_scale, + output_dim, + group_size, + pooled_size, + part_size, + sample_per_part, + trans_std); + } +} \ No newline at end of file diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/src/vision.cpp b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/src/vision.cpp new file mode 100644 index 00000000..ff54233e --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/src/vision.cpp @@ -0,0 +1,9 @@ + +#include "dcn_v2.h" + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("dcn_v2_forward", &dcn_v2_forward, "dcn_v2_forward"); + m.def("dcn_v2_backward", &dcn_v2_backward, "dcn_v2_backward"); + m.def("dcn_v2_psroi_pooling_forward", &dcn_v2_psroi_pooling_forward, "dcn_v2_psroi_pooling_forward"); + m.def("dcn_v2_psroi_pooling_backward", &dcn_v2_psroi_pooling_backward, "dcn_v2_psroi_pooling_backward"); +} diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/testcpu.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/testcpu.py new file mode 100644 index 00000000..0a0b36eb --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/testcpu.py @@ -0,0 +1,270 @@ +#!/usr/bin/env python +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import time +import torch +import torch.nn as nn +from torch.autograd import gradcheck + +from dcn_v2 import dcn_v2_conv, DCNv2, DCN +from dcn_v2 import dcn_v2_pooling, DCNv2Pooling, DCNPooling + +deformable_groups = 1 +N, inC, inH, inW = 2, 2, 4, 4 +outC = 2 +kH, kW = 3, 3 + + +def conv_identify(weight, bias): + weight.data.zero_() + bias.data.zero_() + o, i, h, w = weight.shape + y = h//2 + x = w//2 + for p in range(i): + for q in range(o): + if p == q: + weight.data[q, p, y, x] = 1.0 + + +def check_zero_offset(): + conv_offset = nn.Conv2d(inC, deformable_groups * 2 * kH * kW, + kernel_size=(kH, kW), + stride=(1, 1), + padding=(1, 1), + bias=True) + + conv_mask = nn.Conv2d(inC, deformable_groups * 1 * kH * kW, + kernel_size=(kH, kW), + stride=(1, 1), + padding=(1, 1), + bias=True) + + dcn_v2 = DCNv2(inC, outC, (kH, kW), + stride=1, padding=1, dilation=1, + deformable_groups=deformable_groups) + + conv_offset.weight.data.zero_() + conv_offset.bias.data.zero_() + conv_mask.weight.data.zero_() + conv_mask.bias.data.zero_() + conv_identify(dcn_v2.weight, dcn_v2.bias) + + input = torch.randn(N, inC, inH, inW) + offset = conv_offset(input) + mask = conv_mask(input) + mask = torch.sigmoid(mask) + output = dcn_v2(input, offset, mask) + output *= 2 + d = (input - output).abs().max() + if d < 1e-10: + print('Zero offset passed') + else: + print('Zero offset failed') + print(input) + print(output) + +def check_gradient_dconv(): + + input = torch.rand(N, inC, inH, inW) * 0.01 + input.requires_grad = True + + offset = torch.randn(N, deformable_groups * 2 * kW * kH, inH, inW) * 2 + # offset.data.zero_() + # offset.data -= 0.5 + offset.requires_grad = True + + mask = torch.rand(N, deformable_groups * 1 * kW * kH, inH, inW) + # mask.data.zero_() + mask.requires_grad = True + mask = torch.sigmoid(mask) + + weight = torch.randn(outC, inC, kH, kW) + weight.requires_grad = True + + bias = torch.rand(outC) + bias.requires_grad = True + + stride = 1 + padding = 1 + dilation = 1 + + print('check_gradient_dconv: ', + gradcheck(dcn_v2_conv, (input, offset, mask, weight, bias, + stride, padding, dilation, deformable_groups), + eps=1e-3, atol=1e-4, rtol=1e-2)) + + +def check_pooling_zero_offset(): + + input = torch.randn(2, 16, 64, 64).zero_() + input[0, :, 16:26, 16:26] = 1. + input[1, :, 10:20, 20:30] = 2. + rois = torch.tensor([ + [0, 65, 65, 103, 103], + [1, 81, 41, 119, 79], + ]).float() + pooling = DCNv2Pooling(spatial_scale=1.0 / 4, + pooled_size=7, + output_dim=16, + no_trans=True, + group_size=1, + trans_std=0.0) + + out = pooling(input, rois, input.new()) + s = ', '.join(['%f' % out[i, :, :, :].mean().item() + for i in range(rois.shape[0])]) + print(s) + + dpooling = DCNv2Pooling(spatial_scale=1.0 / 4, + pooled_size=7, + output_dim=16, + no_trans=False, + group_size=1, + trans_std=0.0) + offset = torch.randn(20, 2, 7, 7).zero_() + dout = dpooling(input, rois, offset) + s = ', '.join(['%f' % dout[i, :, :, :].mean().item() + for i in range(rois.shape[0])]) + print(s) + + +def check_gradient_dpooling(): + input = torch.randn(2, 3, 5, 5) * 0.01 + N = 4 + batch_inds = torch.randint(2, (N, 1)).float() + x = torch.rand((N, 1)).float() * 15 + y = torch.rand((N, 1)).float() * 15 + w = torch.rand((N, 1)).float() * 10 + h = torch.rand((N, 1)).float() * 10 + rois = torch.cat((batch_inds, x, y, x + w, y + h), dim=1) + offset = torch.randn(N, 2, 3, 3) + input.requires_grad = True + offset.requires_grad = True + + spatial_scale = 1.0 / 4 + pooled_size = 3 + output_dim = 3 + no_trans = 0 + group_size = 1 + trans_std = 0.0 + sample_per_part = 4 + part_size = pooled_size + + print('check_gradient_dpooling:', + gradcheck(dcn_v2_pooling, (input, rois, offset, + spatial_scale, + pooled_size, + output_dim, + no_trans, + group_size, + part_size, + sample_per_part, + trans_std), + eps=1e-4)) + + +def example_dconv(): + input = torch.randn(2, 64, 128, 128) + # wrap all things (offset and mask) in DCN + dcn = DCN(64, 64, kernel_size=(3, 3), stride=1, + padding=1, deformable_groups=2) + # print(dcn.weight.shape, input.shape) + output = dcn(input) + targert = output.new(*output.size()) + targert.data.uniform_(-0.01, 0.01) + error = (targert - output).mean() + error.backward() + print(output.shape) + + +def example_dpooling(): + input = torch.randn(2, 32, 64, 64) + batch_inds = torch.randint(2, (20, 1)).float() + x = torch.randint(256, (20, 1)).float() + y = torch.randint(256, (20, 1)).float() + w = torch.randint(64, (20, 1)).float() + h = torch.randint(64, (20, 1)).float() + rois = torch.cat((batch_inds, x, y, x + w, y + h), dim=1) + offset = torch.randn(20, 2, 7, 7) + input.requires_grad = True + offset.requires_grad = True + + # normal roi_align + pooling = DCNv2Pooling(spatial_scale=1.0 / 4, + pooled_size=7, + output_dim=32, + no_trans=True, + group_size=1, + trans_std=0.1) + + # deformable pooling + dpooling = DCNv2Pooling(spatial_scale=1.0 / 4, + pooled_size=7, + output_dim=32, + no_trans=False, + group_size=1, + trans_std=0.1) + + out = pooling(input, rois, offset) + dout = dpooling(input, rois, offset) + print(out.shape) + print(dout.shape) + + target_out = out.new(*out.size()) + target_out.data.uniform_(-0.01, 0.01) + target_dout = dout.new(*dout.size()) + target_dout.data.uniform_(-0.01, 0.01) + e = (target_out - out).mean() + e.backward() + e = (target_dout - dout).mean() + e.backward() + + +def example_mdpooling(): + input = torch.randn(2, 32, 64, 64) + input.requires_grad = True + batch_inds = torch.randint(2, (20, 1)).float() + x = torch.randint(256, (20, 1)).float() + y = torch.randint(256, (20, 1)).float() + w = torch.randint(64, (20, 1)).float() + h = torch.randint(64, (20, 1)).float() + rois = torch.cat((batch_inds, x, y, x + w, y + h), dim=1) + + # mdformable pooling (V2) + dpooling = DCNPooling(spatial_scale=1.0 / 4, + pooled_size=7, + output_dim=32, + no_trans=False, + group_size=1, + trans_std=0.1, + deform_fc_dim=1024) + + dout = dpooling(input, rois) + target = dout.new(*dout.size()) + target.data.uniform_(-0.1, 0.1) + error = (target - dout).mean() + error.backward() + print(dout.shape) + + +if __name__ == '__main__': + + example_dconv() + example_dpooling() + example_mdpooling() + + check_pooling_zero_offset() + # zero offset check + if inC == outC: + check_zero_offset() + + check_gradient_dpooling() + check_gradient_dconv() + # """ + # ****** Note: backward is not reentrant error may not be a serious problem, + # ****** since the max error is less than 1e-7, + # ****** Still looking for what trigger this problem + # """ diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/testcuda.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/testcuda.py new file mode 100644 index 00000000..3bd5bd22 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/DCNv2_1.4/testcuda.py @@ -0,0 +1,270 @@ +#!/usr/bin/env python +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import time +import torch +import torch.nn as nn +from torch.autograd import gradcheck + +from dcn_v2 import dcn_v2_conv, DCNv2, DCN +from dcn_v2 import dcn_v2_pooling, DCNv2Pooling, DCNPooling + +deformable_groups = 1 +N, inC, inH, inW = 2, 2, 4, 4 +outC = 2 +kH, kW = 3, 3 + + +def conv_identify(weight, bias): + weight.data.zero_() + bias.data.zero_() + o, i, h, w = weight.shape + y = h//2 + x = w//2 + for p in range(i): + for q in range(o): + if p == q: + weight.data[q, p, y, x] = 1.0 + + +def check_zero_offset(): + conv_offset = nn.Conv2d(inC, deformable_groups * 2 * kH * kW, + kernel_size=(kH, kW), + stride=(1, 1), + padding=(1, 1), + bias=True).cuda() + + conv_mask = nn.Conv2d(inC, deformable_groups * 1 * kH * kW, + kernel_size=(kH, kW), + stride=(1, 1), + padding=(1, 1), + bias=True).cuda() + + dcn_v2 = DCNv2(inC, outC, (kH, kW), + stride=1, padding=1, dilation=1, + deformable_groups=deformable_groups).cuda() + + conv_offset.weight.data.zero_() + conv_offset.bias.data.zero_() + conv_mask.weight.data.zero_() + conv_mask.bias.data.zero_() + conv_identify(dcn_v2.weight, dcn_v2.bias) + + input = torch.randn(N, inC, inH, inW).cuda() + offset = conv_offset(input) + mask = conv_mask(input) + mask = torch.sigmoid(mask) + output = dcn_v2(input, offset, mask) + output *= 2 + d = (input - output).abs().max() + if d < 1e-10: + print('Zero offset passed') + else: + print('Zero offset failed') + print(input) + print(output) + +def check_gradient_dconv(): + + input = torch.rand(N, inC, inH, inW).cuda() * 0.01 + input.requires_grad = True + + offset = torch.randn(N, deformable_groups * 2 * kW * kH, inH, inW).cuda() * 2 + # offset.data.zero_() + # offset.data -= 0.5 + offset.requires_grad = True + + mask = torch.rand(N, deformable_groups * 1 * kW * kH, inH, inW).cuda() + # mask.data.zero_() + mask.requires_grad = True + mask = torch.sigmoid(mask) + + weight = torch.randn(outC, inC, kH, kW).cuda() + weight.requires_grad = True + + bias = torch.rand(outC).cuda() + bias.requires_grad = True + + stride = 1 + padding = 1 + dilation = 1 + + print('check_gradient_dconv: ', + gradcheck(dcn_v2_conv, (input, offset, mask, weight, bias, + stride, padding, dilation, deformable_groups), + eps=1e-3, atol=1e-4, rtol=1e-2)) + + +def check_pooling_zero_offset(): + + input = torch.randn(2, 16, 64, 64).cuda().zero_() + input[0, :, 16:26, 16:26] = 1. + input[1, :, 10:20, 20:30] = 2. + rois = torch.tensor([ + [0, 65, 65, 103, 103], + [1, 81, 41, 119, 79], + ]).cuda().float() + pooling = DCNv2Pooling(spatial_scale=1.0 / 4, + pooled_size=7, + output_dim=16, + no_trans=True, + group_size=1, + trans_std=0.0).cuda() + + out = pooling(input, rois, input.new()) + s = ', '.join(['%f' % out[i, :, :, :].mean().item() + for i in range(rois.shape[0])]) + print(s) + + dpooling = DCNv2Pooling(spatial_scale=1.0 / 4, + pooled_size=7, + output_dim=16, + no_trans=False, + group_size=1, + trans_std=0.0).cuda() + offset = torch.randn(20, 2, 7, 7).cuda().zero_() + dout = dpooling(input, rois, offset) + s = ', '.join(['%f' % dout[i, :, :, :].mean().item() + for i in range(rois.shape[0])]) + print(s) + + +def check_gradient_dpooling(): + input = torch.randn(2, 3, 5, 5).cuda() * 0.01 + N = 4 + batch_inds = torch.randint(2, (N, 1)).cuda().float() + x = torch.rand((N, 1)).cuda().float() * 15 + y = torch.rand((N, 1)).cuda().float() * 15 + w = torch.rand((N, 1)).cuda().float() * 10 + h = torch.rand((N, 1)).cuda().float() * 10 + rois = torch.cat((batch_inds, x, y, x + w, y + h), dim=1) + offset = torch.randn(N, 2, 3, 3).cuda() + input.requires_grad = True + offset.requires_grad = True + + spatial_scale = 1.0 / 4 + pooled_size = 3 + output_dim = 3 + no_trans = 0 + group_size = 1 + trans_std = 0.0 + sample_per_part = 4 + part_size = pooled_size + + print('check_gradient_dpooling:', + gradcheck(dcn_v2_pooling, (input, rois, offset, + spatial_scale, + pooled_size, + output_dim, + no_trans, + group_size, + part_size, + sample_per_part, + trans_std), + eps=1e-4)) + + +def example_dconv(): + input = torch.randn(2, 64, 128, 128).cuda() + # wrap all things (offset and mask) in DCN + dcn = DCN(64, 64, kernel_size=(3, 3), stride=1, + padding=1, deformable_groups=2).cuda() + # print(dcn.weight.shape, input.shape) + output = dcn(input) + targert = output.new(*output.size()) + targert.data.uniform_(-0.01, 0.01) + error = (targert - output).mean() + error.backward() + print(output.shape) + + +def example_dpooling(): + input = torch.randn(2, 32, 64, 64).cuda() + batch_inds = torch.randint(2, (20, 1)).cuda().float() + x = torch.randint(256, (20, 1)).cuda().float() + y = torch.randint(256, (20, 1)).cuda().float() + w = torch.randint(64, (20, 1)).cuda().float() + h = torch.randint(64, (20, 1)).cuda().float() + rois = torch.cat((batch_inds, x, y, x + w, y + h), dim=1) + offset = torch.randn(20, 2, 7, 7).cuda() + input.requires_grad = True + offset.requires_grad = True + + # normal roi_align + pooling = DCNv2Pooling(spatial_scale=1.0 / 4, + pooled_size=7, + output_dim=32, + no_trans=True, + group_size=1, + trans_std=0.1).cuda() + + # deformable pooling + dpooling = DCNv2Pooling(spatial_scale=1.0 / 4, + pooled_size=7, + output_dim=32, + no_trans=False, + group_size=1, + trans_std=0.1).cuda() + + out = pooling(input, rois, offset) + dout = dpooling(input, rois, offset) + print(out.shape) + print(dout.shape) + + target_out = out.new(*out.size()) + target_out.data.uniform_(-0.01, 0.01) + target_dout = dout.new(*dout.size()) + target_dout.data.uniform_(-0.01, 0.01) + e = (target_out - out).mean() + e.backward() + e = (target_dout - dout).mean() + e.backward() + + +def example_mdpooling(): + input = torch.randn(2, 32, 64, 64).cuda() + input.requires_grad = True + batch_inds = torch.randint(2, (20, 1)).cuda().float() + x = torch.randint(256, (20, 1)).cuda().float() + y = torch.randint(256, (20, 1)).cuda().float() + w = torch.randint(64, (20, 1)).cuda().float() + h = torch.randint(64, (20, 1)).cuda().float() + rois = torch.cat((batch_inds, x, y, x + w, y + h), dim=1) + + # mdformable pooling (V2) + dpooling = DCNPooling(spatial_scale=1.0 / 4, + pooled_size=7, + output_dim=32, + no_trans=False, + group_size=1, + trans_std=0.1, + deform_fc_dim=1024).cuda() + + dout = dpooling(input, rois) + target = dout.new(*dout.size()) + target.data.uniform_(-0.1, 0.1) + error = (target - dout).mean() + error.backward() + print(dout.shape) + + +if __name__ == '__main__': + + example_dconv() + 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file mode 100644 index 00000000..165e6372 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/dcn/__init__.py @@ -0,0 +1,13 @@ +from .functions.deform_conv import deform_conv, modulated_deform_conv +from .functions.deform_pool import deform_roi_pooling +from .modules.deform_conv import (DeformConv, ModulatedDeformConv, + DeformConvPack, ModulatedDeformConvPack) +from .modules.deform_pool import (DeformRoIPooling, DeformRoIPoolingPack, + ModulatedDeformRoIPoolingPack) + +__all__ = [ + 'DeformConv', 'DeformConvPack', 'ModulatedDeformConv', + 'ModulatedDeformConvPack', 'DeformRoIPooling', 'DeformRoIPoolingPack', + 'ModulatedDeformRoIPoolingPack', 'deform_conv', 'modulated_deform_conv', + 'deform_roi_pooling' +] diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/models/networks/dcn/__pycache__/__init__.cpython-37.pyc 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