Testing error

/usr/bin/python2.7 /home/e/R-CNN/faster-rcnn.pytorch-master/test_net.py Called with args: Namespace(batch_size=1, cfg_file=‘cfgs/res101.yml’, checkepoch=1, checkpoint=10021, checksession=1, class_agnostic=False, cuda=‘cuda’, dataset=‘pascal_voc’, large_scale=False, load_dir=‘/home/e/R-CNN/faster-rcnn.pytorch-master/data/pretrained_model’, mGPUs=False, net=‘res101’, parallel_type=0, set_cfgs=None, vis=False) Using config: {‘ANCHOR_RATIOS’: [0.5, 1, 2], ‘ANCHOR_SCALES’: [8, 16, 32], ‘CROP_RESIZE_WITH_MAX_POOL’: False, ‘CUDA’: False, ‘DATA_DIR’: ‘/home/e/R-CNN/faster-rcnn.pytorch-master/data’, ‘DEDUP_BOXES’: 0.0625, ‘EPS’: 1e-14, ‘EXP_DIR’: ‘res101’, ‘FEAT_STRIDE’: [16], ‘GPU_ID’: 0, ‘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([[[102.9801, 115.9465, 122.7717]]]), ‘POOLING_MODE’: ‘align’, ‘POOLING_SIZE’: 7, ‘RESNET’: {‘FIXED_BLOCKS’: 1, ‘MAX_POOL’: False}, ‘RNG_SEED’: 3, ‘ROOT_DIR’: ‘/home/e/R-CNN/faster-rcnn.pytorch-master’, ‘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], ‘SVM’: False}, ‘TRAIN’: {‘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, ‘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} Loaded dataset voc_2007_test for training Set proposal method: gt Preparing training data… voc_2007_test gt roidb loaded from /home/e/R-CNN/faster-rcnn.pytorch-master/data/cache/voc_2007_test_gt_roidb.pkl done 4952 roidb entries load checkpoint /home/e/R-CNN/faster-rcnn.pytorch-master/data/pretrained_model/res101/pascal_voc/faster_rcnn_1_1_10021.pth load model successfully! /home/e/R-CNN/faster-rcnn.pytorch-master/test_net.py:190: UserWarning: volatile was removed and now has no effect. Use with torch.no_grad(): instead. im_data = Variable(im_data, volatile=True) /home/e/R-CNN/faster-rcnn.pytorch-master/test_net.py:191: UserWarning: volatile was removed and now has no effect. Use with torch.no_grad(): instead. im_info = Variable(im_info, volatile=True) /home/e/R-CNN/faster-rcnn.pytorch-master/test_net.py:192: UserWarning: volatile was removed and now has no effect. Use with torch.no_grad(): instead. num_boxes = Variable(num_boxes, volatile=True) /home/e/R-CNN/faster-rcnn.pytorch-master/test_net.py:193: UserWarning: volatile was removed and now has no effect. Use with torch.no_grad(): instead. gt_boxes = Variable(gt_boxes, volatile=True) /home/e/R-CNN/faster-rcnn.pytorch-master/lib/model/rpn/rpn.py:68: 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/e/R-CNN/faster-rcnn.pytorch-master/lib/model/faster_rcnn/faster_rcnn.py:98: 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 “/home/e/R-CNN/faster-rcnn.pytorch-master/test_net.py”, line 267, in <module> pred_boxes /= data[1][0][2] RuntimeError: Expected object of type torch.cuda.FloatTensor but found type torch.FloatTensor for argument #3 ‘other’

[ljtruong]

@EldorIbragimov change pred_boxes /= data[1][0][2] to pred_boxes /= data[1][0][2].cuda()

this was my quick fix. I’m not sure if it’s correct, but it runs. Can anyone confirm if this is the correct way to patch this error?

[ljtruong]

@wjx2 change lines in train and test files

loss_temp += loss.data[0] to loss_temp += loss.item() replacing data[0] with “item()”

do the same to these lines loss_rpn_cls = rpn_loss_cls.mean().data[0] loss_rpn_box = rpn_loss_box.mean().data[0] loss_rcnn_cls = RCNN_loss_cls.mean().data[0] loss_rcnn_box = RCNN_loss_bbox.mean().data[0]