RuntimeError when resume a pretrained model.

I want to finetune a model, but when I resume a pretrained model ,it get error below: Called with args: Namespace(batch_size=1, checkepoch=20, checkpoint=3557, checkpoint_interval=10000, checksession=1, class_agnostic=False, cuda=‘–cuda’, dataset=‘pascal_voc’, disp_interval=100, large_scale=False, lr=0.0005, lr_decay_gamma=0.1, lr_decay_step=5, mGPUs=False, max_epochs=26, net=‘vgg16’, num_workers=0, optimizer=‘sgd’, resume=True, save_dir=‘/home/smartdsp/new_home/faster-rcnn.pytorch/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/smartdsp/new_home/faster-rcnn.pytorch/data’, ‘DEDUP_BOXES’: 0.0625, ‘EPS’: 1e-14, ‘EXP_DIR’: ‘vgg16’, ‘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/smartdsp/new_home/faster-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], ‘SVM’: False}, ‘TRAIN’: {‘ASPECT_GROUPING’: False, ‘BATCH_SIZE’: 256, ‘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’: 10, ‘DOUBLE_BIAS’: True, ‘FG_FRACTION’: 0.25, ‘FG_THRESH’: 0.5, ‘GAMMA’: 0.1, ‘HAS_RPN’: True, ‘IMS_PER_BATCH’: 1, ‘LEARNING_RATE’: 0.01, ‘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’: 0.0005}, ‘USE_GPU_NMS’: True} Loaded dataset voc_2007_trainval for training Set proposal method: gt Appending horizontally-flipped training examples… voc_2007_trainval gt roidb loaded from /home/smartdsp/new_home/faster-rcnn.pytorch/data/cache/voc_2007_trainval_gt_roidb.pkl done Preparing training data… done before filtering, there are 2372 images… after filtering, there are 2372 images… 2372 roidb entries Loading pretrained weights from data/pretrained_model/vgg16_caffe.pth loading checkpoint /home/smartdsp/new_home/faster-rcnn.pytorch/models/vgg16/pascal_voc/vgg16_baseline/faster_rcnn_1_20_3557.pth loaded checkpoint /home/smartdsp/new_home/faster-rcnn.pytorch/models/vgg16/pascal_voc/vgg16_baseline/faster_rcnn_1_20_3557.pth 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) 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) /home/smartdsp/new_home/faster-rcnn.pytorch/trainval_net_finetune.py:330: UserWarning: invalid index of a 0-dim tensor. This will be an error in PyTorch 0.5. Use tensor.item() to convert a 0-dim tensor to a Python number loss_temp += loss.data[0] Traceback (most recent call last):

File “<ipython-input-1-cbbe4ee4d4f2>”, line 1, in <module> runfile(‘/home/smartdsp/new_home/faster-rcnn.pytorch/trainval_net_finetune.py’, wdir=‘/home/smartdsp/new_home/faster-rcnn.pytorch’)

File “/home/smartdsp/anaconda2/lib/python2.7/site-packages/spyder/utils/site/sitecustomize.py”, line 705, in runfile execfile(filename, namespace)

File “/home/smartdsp/anaconda2/lib/python2.7/site-packages/spyder/utils/site/sitecustomize.py”, line 94, in execfile builtins.execfile(filename, *where)

File “/home/smartdsp/new_home/faster-rcnn.pytorch/trainval_net_finetune.py”, line 337, in <module> optimizer.step()

File “/home/smartdsp/anaconda2/lib/python2.7/site-packages/torch/optim/sgd.py”, line 101, in step buf.mul_(momentum).add_(1 - dampening, d_p)

RuntimeError: Expected object of type torch.FloatTensor but found type torch.cuda.FloatTensor for argument #4 ‘other’

[ljtruong]

@wjx2 @babyjie57 This update is due to the new pytorch 0.4.

you can re-initialise the weights manually using this

model.load_state_dict(checkpoint['model'])
model.cuda()
optimizer = optim.SGD(model.parameters(), momentum = 0.9, weight_decy = 0.0001)
optimizer.load_state_dict(checkpoint['optimizer'])
for state in optimizer.state.values():
    for k, v in state.items():
        if isinstance(v, torch.Tensor):
            state[k] = v.cuda()

[jwyang]

@wjx2 see the error in last row. it is because the mismatch of cpu data and cpu data. use cuda when you run the code.