def forward(self, inputs, outputs): """See modeling.detector.GenerateProposalLabels for inputs/outputs documentation. """ # During training we reuse the data loader code. We populate roidb # entries on the fly using the rois generated by RPN. # im_info: [[im_height, im_width, im_scale], ...] rois = inputs[0].data roidb = blob_utils.deserialize(inputs[1].data) im_info = inputs[2].data im_scales = im_info[:, 2] output_blob_names = cascade_rcnn_roi_data.get_cascade_rcnn_stage_3_blob_names( ) # For historical consistency with the original Faster R-CNN # implementation we are *not* filtering crowd proposals. # This choice should be investigated in the future (it likely does # not matter). json_dataset.add_proposals(roidb, rois, im_scales, crowd_thresh=0) roidb_utils.add_bbox_regression_targets(roidb) blobs = {k: [] for k in output_blob_names} cascade_rcnn_roi_data.add_cascade_rcnn_stage_3_blobs( blobs, im_scales, roidb) for i, k in enumerate(output_blob_names): blob_utils.py_op_copy_blob(blobs[k], outputs[i])
def forward(self, inputs, outputs): """See modeling.detector.DistributeCascadeProposals for inputs/outputs documentation. """ rois = inputs[0].data if self._train: # During training we reuse the data loader code. We populate roidb # entries on the fly using the rois generated by RPN. # im_info: [[im_height, im_width, im_scale], ...] roidb = blob_utils.deserialize(inputs[1].data) im_info = inputs[2].data im_scales = im_info[:, 2] # For historical consistency with the original Faster R-CNN # implementation we are *not* filtering crowd proposals. # This choice should be investigated in the future (it likely does # not matter). json_dataset.add_proposals(roidb, rois, im_scales, crowd_thresh=0) # Compute training labels for the RPN proposals; also handles # distributing the proposals over FPN levels output_blob_names = cascade_rcnn_roi_data.get_cascade_rcnn_blob_names( self._stage) blobs = {k: [] for k in output_blob_names} # 进行rois映射到了合适的fpn层, 并重新进行采样构成训练数据 cascade_rcnn_roi_data.add_cascade_rcnn_blobs( blobs, im_scales, roidb, self._stage) for i, k in enumerate(output_blob_names): blob_utils.py_op_copy_blob(blobs[k], outputs[i]) else: # For inference we have a special code path that avoids some data # loader overhead distribute(rois, None, outputs, self._train)
def forward(self, inputs, outputs): """See modeling.detector.CollectAndDistributeFpnRpnProposals for inputs/outputs documentation. """ # inputs is # [rpn_rois_fpn2, ..., rpn_rois_fpn6, # rpn_roi_probs_fpn2, ..., rpn_roi_probs_fpn6] # If training with Faster R-CNN, then inputs will additionally include # + [roidb, im_info] rois = collect(inputs, self._train) if self._train: # During training we reuse the data loader code. We populate roidb # entries on the fly using the rois generated by RPN. # im_info: [[im_height, im_width, im_scale], ...] im_info = inputs[-1].data im_scales = im_info[:, 2] roidb = blob_utils.deserialize(inputs[-2].data) # For historical consistency with the original Faster R-CNN # implementation we are *not* filtering crowd proposals. # This choice should be investigated in the future (it likely does # not matter). json_dataset.add_proposals(roidb, rois, im_scales, crowd_thresh=0) roidb_utils.add_bbox_regression_targets(roidb) # Compute training labels for the RPN proposals; also handles # distributing the proposals over FPN levels output_blob_names = fast_rcnn_roi_data.get_fast_rcnn_blob_names() blobs = {k: [] for k in output_blob_names} fast_rcnn_roi_data.add_fast_rcnn_blobs(blobs, im_info, roidb) for i, k in enumerate(output_blob_names): blob_utils.py_op_copy_blob(blobs[k], outputs[i]) else: # For inference we have a special code path that avoids some data # loader overhead distribute(rois, None, outputs, self._train)
def forward(self, inputs, outputs): """See modeling.detector.CollectAndDistributeFpnRpnProposals for inputs/outputs documentation. """ # inputs is # [rpn_rois_fpn2, ..., rpn_rois_fpn6, # rpn_roi_probs_fpn2, ..., rpn_roi_probs_fpn6] # If training with Faster R-CNN, then inputs will additionally include # + [roidb, im_info] rois = collect(inputs, self._train) if self._train: # During training we reuse the data loader code. We populate roidb # entries on the fly using the rois generated by RPN. # im_info: [[im_height, im_width, im_scale], ...] im_info = inputs[-1].data im_scales = im_info[:, 2] roidb = blob_utils.deserialize(inputs[-2].data) # For historical consistency with the original Faster R-CNN # implementation we are *not* filtering crowd proposals. # This choice should be investigated in the future (it likely does # not matter). json_dataset.add_proposals(roidb, rois, im_scales, crowd_thresh=0) # Compute training labels for the RPN proposals; also handles # distributing the proposals over FPN levels output_blob_names = fast_rcnn_roi_data.get_fast_rcnn_blob_names() blobs = {k: [] for k in output_blob_names} fast_rcnn_roi_data.add_fast_rcnn_blobs(blobs, im_scales, roidb) for i, k in enumerate(output_blob_names): blob_utils.py_op_copy_blob(blobs[k], outputs[i]) else: # For inference we have a special code path that avoids some data # loader overhead distribute(rois, None, outputs, self._train)
def forward(self, inputs, outputs): rois, transfer_rois = collect(inputs, self._train, self._mc) im_info = inputs[-1].data im_scales = im_info[:, 2] roidb = blob_utils.deserialize(inputs[-2].data) json_dataset.add_proposals(roidb, rois, im_scales, crowd_thresh=0) roidb_utils.add_bbox_regression_targets(roidb) output_blob_names = fast_rcnn_roi_data.get_fast_rcnn_blob_names() blobs = {k: [] for k in output_blob_names} fast_rcnn_roi_data.add_fast_rcnn_blobs(blobs, im_scales, roidb, transfer_rois) for i, k in enumerate(output_blob_names): blob_utils.py_op_copy_blob(blobs[k], outputs[i])
def forward(self, inputs, outputs): """See modeling.detector.GenerateProposalLabels for inputs/outputs documentation. """ # During training we reuse the data loader code. We populate roidb # entries on the fly using the rois generated by RPN. # im_info: [[im_height, im_width, im_scale], ...] rois = inputs[0].data roidb = blob_utils.deserialize(inputs[1].data) im_info = inputs[2].data im_scales = im_info[:, 2] output_blob_names = fast_rcnn_roi_data.get_fast_rcnn_blob_names() # For historical consistency with the original Faster R-CNN # implementation we are *not* filtering crowd proposals. # This choice should be investigated in the future (it likely does # not matter). json_dataset.add_proposals(roidb, rois, im_scales, crowd_thresh=0) blobs = {k: [] for k in output_blob_names} fast_rcnn_roi_data.add_fast_rcnn_blobs(blobs, im_scales, roidb) for i, k in enumerate(output_blob_names): blob_utils.py_op_copy_blob(blobs[k], outputs[i])
def forward(self, inputs, outputs): """See modeling.detector.DistributeFpnRpnProposals for inputs/outputs documentation. """ # inputs is [rois] out from decode_bbox operator # If training with Faster R-CNN, then inputs will additionally include # + [roidb, im_info] _rois = inputs[0].data rois = remove_invalid_boxes(_rois, self._stage_num) # print('++++++++++++++++ DFRP Op of RCNN stage {} ++++++++++++++++++'.format(self._stage_num)) if self._train: # During training we reuse the data loader code. We populate roidb # entries on the fly using the rois generated by RPN. # im_info: [[im_height, im_width, im_scale], ...] im_info = inputs[2].data im_scales = im_info[:, 2] roidb = blob_utils.deserialize(inputs[1].data) json_dataset.add_proposals(roidb, rois, im_scales, crowd_thresh=0) roidb_utils.add_bbox_regression_targets(roidb) # Compute training labels for the RPN proposals; also handles # distributing the proposals over FPN levels output_blob_names = fast_rcnn_roi_data.get_cascade_fast_rcnn_blob_names( is_training=True, stage_num=self._stage_num) blobs = {k: [] for k in output_blob_names} fast_rcnn_roi_data.add_fast_rcnn_blobs(blobs, im_scales, roidb, self._stage_num) for i, k in enumerate(output_blob_names): blob_utils.py_op_copy_blob(blobs[k], outputs[i]) # reset roidb for next rcnn stage, remove 'max_overlaps', 'max_classes', 'bbox_targets' in each roidb, # intialize 'boxes', 'seg_areas', 'gt_classes', 'gt_overlaps', 'box_to_gt_ind_map' only contain gt infos # if self._stage_num == 2: # json_dataset.reset_roidb_for_next_stage(roidb) else: # For inference we have a special code path that avoids some data # loader overhead distribute(rois, None, outputs, self._train, self._stage_num)
def train_model(): """Model training loop.""" logger = logging.getLogger(__name__) model, weights_file, start_iter, checkpoints, output_dir = create_model( ) #for create model if 'final' in checkpoints: # The final model was found in the output directory, so nothing to do return checkpoints if 0: output_dir = '/home/icubic/daily_work/code/Detectron/train/coco_2014_train_ET_PH_part/generalized_rcnn_multi/' #output_dir = output_dir + '_101' setup_model_for_training(model, weights_file, output_dir) training_stats = TrainingStats(model) uuuu = model.roi_data_loader._blobs_queue_name CHECKPOINT_PERIOD = int(cfg.TRAIN.SNAPSHOT_ITERS / cfg.NUM_GPUS) print('------------train.py') for cur_iter in range(start_iter, cfg.SOLVER.MAX_ITER): training_stats.IterTic() lr = model.UpdateWorkspaceLr(cur_iter, lr_policy.get_lr_at_iter(cur_iter)) #aaa_debug = workspace.FetchBlob('gpu_0/data') #bbb_debug = workspace.FetchBlob('gpu_0/conv1_w') #ccc_debug = workspace.FetchBlob('gpu_0/'+uuuu) try: workspace.RunNet(model.net.Proto().name) if 0: #import detectron.utils.blob as blob_utils inputs = [workspace.FetchBlob("gpu_0/rpn_rois_fpn2"),workspace.FetchBlob("gpu_0/rpn_rois_fpn3"),workspace.FetchBlob("gpu_0/rpn_rois_fpn4"),workspace.FetchBlob("gpu_0/rpn_rois_fpn5"), \ workspace.FetchBlob("gpu_0/rpn_rois_fpn6"),workspace.FetchBlob("gpu_0/rpn_roi_probs_fpn2"),workspace.FetchBlob("gpu_0/rpn_roi_probs_fpn3"),workspace.FetchBlob("gpu_0/rpn_roi_probs_fpn4"), \ workspace.FetchBlob("gpu_0/rpn_roi_probs_fpn5"),workspace.FetchBlob("gpu_0/rpn_roi_probs_fpn6"),workspace.FetchBlob("gpu_0/roidb"),workspace.FetchBlob("gpu_0/im_info"),\ ] rois = collect(inputs, True) #inputs.append(workspace.FetchBlob("gpu_0/rpn_rois_fpn2")) im_info = inputs[-1] im_scales = im_info[:, 2] roidb = blob_utils.deserialize(inputs[-2]) # For historical consistency with the original Faster R-CNN # implementation we are *not* filtering crowd proposals. # This choice should be investigated in the future (it likely does # not matter). json_dataset.add_proposals(roidb, rois, im_scales, crowd_thresh=0) roidb_utils.add_bbox_regression_targets(roidb) # Compute training labels for the RPN proposals; also handles # distributing the proposals over FPN levels output_blob_names = fast_rcnn_roi_data.get_fast_rcnn_blob_names( ) blobs = {k: [] for k in output_blob_names} fast_rcnn_roi_data.add_fast_rcnn_blobs(blobs, im_scales, roidb) for i, k in enumerate(output_blob_names): blob_utils.py_op_copy_blob(blobs[k], outputs[i]) #if (np.sum(bb == 1))>0: # print('cc') except: aa = workspace.FetchBlob("gpu_0/rpn_rois_fpn2") aaa_debug = workspace.FetchBlob('gpu_0/data') print('aaaaaerror') #print("blobs:\n{}".format(workspace.Blobs())) #print('train.py aaaaaaaa_debug') if 1: aaa = workspace.FetchBlob("gpu_0/data") # nchw #img = aaa[1].copy() # BGR HWC -> CHW 12 #transform_img = img.swapaxes(0, 1).swapaxes(1, 2) #cv2.imshow("image0 ", transform_img[:, :, (2, 1, 0)]) #cv2.waitKey(0) #cv2.destroyAllWindows() #cv2.imshow('/home/icubic/daily_work/code/Detectron/aaa.png', aaa[0]) aaa_debug = workspace.FetchBlob('gpu_0/data') bbb_debug = workspace.FetchBlob('gpu_0/conv1_w') ccc_debug = workspace.FetchBlob('gpu_0/' + uuuu) ddd_debug = workspace.FetchBlob('gpu_0/roidb') eee_debug = workspace.FetchBlob('gpu_0/im_info') #print("Fetched data:\n{}".format(workspace.FetchBlob("gpu_0/data"))) if cur_iter == start_iter: nu.print_net(model) training_stats.IterToc() training_stats.UpdateIterStats() training_stats.LogIterStats(cur_iter, lr) if (cur_iter + 1) % ( CHECKPOINT_PERIOD / 4 ) == 0 and cur_iter > start_iter: #((cur_iter + 1) % (CHECKPOINT_PERIOD/1) == 0 and (cur_iter > start_iter and cur_iter < 50000)) or ((cur_iter + 1) % (CHECKPOINT_PERIOD/8) == 0 and cur_iter > 50000): checkpoints[cur_iter] = os.path.join( output_dir, 'model_iter_50_{}.pkl'.format(cur_iter)) nu.save_model_to_weights_file(checkpoints[cur_iter], model) if cur_iter == start_iter + training_stats.LOG_PERIOD: # Reset the iteration timer to remove outliers from the first few # SGD iterations training_stats.ResetIterTimer() if np.isnan(training_stats.iter_total_loss): logger.critical('Loss is NaN, exiting...') model.roi_data_loader.shutdown() envu.exit_on_error() # Save the final model checkpoints['final'] = os.path.join(output_dir, 'model_final_50.pkl') nu.save_model_to_weights_file(checkpoints['final'], model) # Shutdown data loading threads model.roi_data_loader.shutdown() return checkpoints