EMCP
commented
5 years ago I have a tutorial to use this project with resnet 101 here https://mahr.io/tutorials/faster-r-cnn-pytorch-how-to
give it a try
Open kiosy opened 5 years ago
EMCP
commented
5 years ago I have a tutorial to use this project with resnet 101 here https://mahr.io/tutorials/faster-r-cnn-pytorch-how-to
give it a try
CUDA_VISIBLE_DEVICES=1 python trainval_net.py --dataset pascal_voc --net res101 --epochs 50 --bs 8 --nw 2 --lr 0.001 --lr_decay_step 5 --cuda Called with args: Namespace(batch_size=8, checkepoch=1, checkpoint=0, checkpoint_interval=10000, checksession=1, class_agnostic=False, cuda=True, dataset='pascal_voc', disp_interval=100, large_scale=False, lr=0.001, lr_decay_gamma=0.1, lr_decay_step=5, mGPUs=False, max_epochs=50, net='res101', num_workers=2, optimizer='sgd', resume=False, save_dir='models', session=1, start_epoch=1, use_tfboard=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/uestc/faster-rcnn.pytorch-pytorch-1.0/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/uestc/faster-rcnn.pytorch-pytorch-1.0', '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
fasterRCNN.create_architecture()
File "/home/uestc/faster-rcnn.pytorch-pytorch-1.0/lib/model/faster_rcnn/faster_rcnn.py", line 131, in create_architecture
self._init_modules()
File "/home/uestc/faster-rcnn.pytorch-pytorch-1.0/lib/model/faster_rcnn/resnet.py", line 236, in _init_modules
state_dict = torch.load(self.model_path)
File "/home/uestc/anaconda3/lib/python3.6/site-packages/torch/serialization.py", line 387, in load
return _load(f, map_location, pickle_module, **pickle_load_args)
File "/home/uestc/anaconda3/lib/python3.6/site-packages/torch/serialization.py", line 581, in _load
deserialized_objects[key]._set_from_file(f, offset, f_should_read_directly)
RuntimeError: storage has wrong size: expected 3101919920280910811 got 256
voc_2007_trainvalfor training Set proposal method: gt Appending horizontally-flipped training examples... voc_2007_trainval gt roidb loaded from /home/uestc/faster-rcnn.pytorch-pytorch-1.0/data/cache/voc_2007_trainval_gt_roidb.pkl done Preparing training data... done before filtering, there are 144066 images... after filtering, there are 144066 images... 144066 roidb entries Loading pretrained weights from data/pretrained_model/resnet101_caffe.pth Traceback (most recent call last): File "trainval_net.py", line 249, inwhen i use vgg it works,why？anyone can help？thanks！