RuntimeError: The size of tensor a (900) must match the size of tensor b (300) at non-singleton dimension 1

[weixiangzhan]

(python36) pytorch@pytorch:~/cascade-rcnn_Pytorch$ CUDA_VISIBLE_DEVICES=0 python demo.py exp_name --dataset pascal_voc --net detnet59 --checksession 1 --checkepoch 19 --checkpoint 631 --cuda --soft_nms Called with args: Namespace(cascade=False, cfg_file='cfgs/detnet59.yml', checkepoch=19, checkpoint=631, checksession=1, class_agnostic=False, cuda=True, dataset='pascal_voc', exp_name='exp_name', image_dir='demo_images/', load_dir='models/', net='detnet59', result_dir='vis_results/', set_cfgs=None, soft_nms=True) /home/pytorch/cascade-rcnn_Pytorch/lib/model/utils/config.py:405: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details. yaml_cfg = edict(yaml.load(f)) Using config: {'ANCHOR_RATIOS': [0.5, 1, 2], 'ANCHOR_SCALES': [8, 16, 32], 'CROP_RESIZE_WITH_MAX_POOL': False, 'CUDA': False, 'DATA_DIR': '/home/pytorch/cascade-rcnn_Pytorch/data', 'DEDUP_BOXES': 0.0625, 'DETNET': {'FIXED_BLOCKS': 1, 'MAX_POOL': False}, 'EPS': 1e-14, 'EXP_DIR': 'res101', 'FEAT_STRIDE': [16], 'FPN_ANCHOR_SCALES': [32, 64, 128, 256, 512], 'FPN_ANCHOR_STRIDE': 1, 'FPN_FEAT_STRIDES': [4, 8, 16, 32, 64], 'GPU_ID': 0, 'HAS_MASK': True, 'MATLAB': 'matlab', 'MAX_NUM_GT_BOXES': 20, 'MOBILENET': {'DEPTH_MULTIPLIER': 1.0, 'FIXED_LAYERS': 5, 'REGU_DEPTH': False, 'WEIGHT_DECAY': 4e-05}, 'PIXEL_MEANS': array([[[0.485, 0.456, 0.406]]]), 'PIXEL_STDS': array([[[0.229, 0.224, 0.225]]]), 'POOLING_MODE': 'align', 'POOLING_SIZE': 14, 'RESNET': {'FIXED_BLOCKS': 1, 'MAX_POOL': False}, 'RNG_SEED': 3, 'ROOT_DIR': '/home/pytorch/cascade-rcnn_Pytorch', 'TEST': {'BBOX_REG': True, 'HAS_RPN': True, 'MAX_SIZE': 1000, 'MODE': 'nms', 'NMS': 0.3, 'PROPOSAL_METHOD': 'gt', 'RPN_MIN_SIZE': 16, 'RPN_NMS_THRESH': 0.7, 'RPN_POST_NMS_TOP_N': 300, 'RPN_PRE_NMS_TOP_N': 6000, 'RPN_TOP_N': 5000, 'SCALES': [600], 'SOFT_NMS_METHOD': 1, 'SVM': False}, 'TRAIN': {'ASPECT_CROPPING': False, 'ASPECT_GROUPING': False, 'BATCH_SIZE': 128, 'BBOX_INSIDE_WEIGHTS': [1.0, 1.0, 1.0, 1.0], 'BBOX_NORMALIZE_MEANS': [0.0, 0.0, 0.0, 0.0], 'BBOX_NORMALIZE_STDS': [0.1, 0.1, 0.2, 0.2], 'BBOX_NORMALIZE_TARGETS': True, 'BBOX_NORMALIZE_TARGETS_PRECOMPUTED': True, 'BBOX_REG': True, 'BBOX_THRESH': 0.5, 'BG_THRESH_HI': 0.5, 'BG_THRESH_LO': 0.0, 'BIAS_DECAY': False, 'BN_TRAIN': False, 'DISPLAY': 20, 'DOUBLE_BIAS': False, 'FG_FRACTION': 0.25, 'FG_THRESH': 0.5, 'FG_THRESH_2ND': 0.6, 'FG_THRESH_3RD': 0.7, 'GAMMA': 0.1, 'HAS_RPN': True, 'IMS_PER_BATCH': 1, 'LEARNING_RATE': 0.001, 'MAX_SIZE': 1000, 'MOMENTUM': 0.9, 'PROPOSAL_METHOD': 'gt', 'RPN_BATCHSIZE': 256, 'RPN_BBOX_INSIDE_WEIGHTS': [1.0, 1.0, 1.0, 1.0], 'RPN_CLOBBER_POSITIVES': False, 'RPN_FG_FRACTION': 0.5, 'RPN_MIN_SIZE': 8, 'RPN_NEGATIVE_OVERLAP': 0.3, 'RPN_NMS_THRESH': 0.7, 'RPN_POSITIVE_OVERLAP': 0.7, 'RPN_POSITIVE_WEIGHT': -1.0, 'RPN_POST_NMS_TOP_N': 2000, 'RPN_PRE_NMS_TOP_N': 12000, 'SCALES': [600], 'SNAPSHOT_ITERS': 5000, 'SNAPSHOT_KEPT': 3, 'SNAPSHOT_PREFIX': 'res101_faster_rcnn', 'STEPSIZE': [30000], 'SUMMARY_INTERVAL': 180, 'TRIM_HEIGHT': 600, 'TRIM_WIDTH': 600, 'TRUNCATED': False, 'USE_ALL_GT': True, 'USE_FLIPPED': True, 'USE_GT': False, 'WEIGHT_DECAY': 0.0001}, 'USE_GPU_NMS': True} load model successfully! load checkpoint models//detnet59/pascal_voc/exp_name/fpn_1_19_631.pth demo.py:199: UserWarning: volatile was removed and now has no effect. Use with torch.no_grad(): instead. im_data = Variable(im_data, volatile=True) demo.py:200: UserWarning: volatile was removed and now has no effect. Use with torch.no_grad(): instead. im_info = Variable(im_info, volatile=True) demo.py:201: UserWarning: volatile was removed and now has no effect. Use with torch.no_grad(): instead. num_boxes = Variable(num_boxes, volatile=True) demo.py:202: UserWarning: volatile was removed and now has no effect. Use with torch.no_grad(): instead. gt_boxes = Variable(gt_boxes, volatile=True) Loaded Photo: 5 images. /home/pytorch/anaconda3/envs/python36/lib/python3.6/site-packages/torch/nn/functional.py:1749: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details. "See the documentation of nn.Upsample for details.".format(mode)) /home/pytorch/cascade-rcnn_Pytorch/lib/model/rpn/rpn_fpn.py:79: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument. rpn_cls_prob_reshape = F.softmax(rpn_cls_score_reshape) /home/pytorch/cascade-rcnn_Pytorch/lib/model/fpn/non_cascade/fpn.py:263: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument. cls_prob = F.softmax(cls_score) Traceback (most recent call last): File "demo.py", line 310, in pred_boxes = bbox_transform_inv(boxes, box_deltas, 1) File "/home/pytorch/cascade-rcnn_Pytorch/lib/model/rpn/bbox_transform.py", line 118, in bbox_transform_inv pred_ctr_x = dx * widths.unsqueeze(2) + ctr_x.unsqueeze(2) RuntimeError: The size of tensor a (900) must match the size of tensor b (300) at non-singleton dimension 1