Hello, I have trained my model using this code
!python train_bap.py train\
--model-name inception \
--batch-size 12 \
--dataset car \
--image-size 512 \
--input-size 448 \
--checkpoint-path checkpoint/car \
--optim sgd \
--scheduler step \
--lr 0.001 \
--momentum 0.9 \
--weight-decay 1e-5 \
--workers 4 \
--parts 32 \
--epochs 80 \
--use-gpu \
--multi-gpu \
--gpu-ids 0 \
it takes much time so I wanted to test the model. I interrupted the model at epoch 11. it gave me the interruption error which I think is normal:
Epoch: [11][300/679] Time 1.259 (1.257) Data 0.000 (0.005) Loss 0.3933 (0.5248) Prec@1 100.000 (98.754) Prec@5 100.000 (100.000)
loss1,loss2,loss3,feature_center_loss 0.07012923806905746 0.05921419337391853 0.15795135498046875 0.2975512146949768
Traceback (most recent call last):
File "train_bap.py", line 210, in <module>
train()
File "train_bap.py", line 145, in train
train_prec, train_loss = engine.train(state, e)
File "/content/WS_DAN_PyTorch-master/utils/engine.py", line 73, in train
loss.backward()
File "/usr/local/lib/python3.6/site-packages/torch/tensor.py", line 198, in backward
torch.autograd.backward(self, gradient, retain_graph, create_graph)
File "/usr/local/lib/python3.6/site-packages/torch/autograd/__init__.py", line 100, in backward
allow_unreachable=True) # allow_unreachable flag
KeyboardInterrupt
When I tried to test the model using this code
%cd /content/WS_DAN_PyTorch-master/
!python train_bap.py test\
--model-name inception \
--batch-size 12 \
--dataset car \
--image-size 512 \
--input-size 448 \
--checkpoint-path checkpoint/car/model_best.pth.tar \
--workers 4 \
--parts 32 \
--use-gpu \
--multi-gpu \
--gpu-ids 0 \
it gave me that error:
Traceback (most recent call last):
File "train_bap.py", line 212, in <module>
test()
File "train_bap.py", line 196, in test
net.load_state_dict(torch.load(config.checkpoint_path)['state_dict'])
File "/usr/local/lib/python3.6/site-packages/torch/nn/modules/module.py", line 847, in load_state_dict
self.__class__.__name__, "\n\t".join(error_msgs)))
RuntimeError: Error(s) in loading state_dict for Inception3:
Missing key(s) in state_dict: "Conv2d_1a_3x3.conv.weight", "Conv2d_1a_3x3.bn.weight", "Conv2d_1a_3x3.bn.bias", "Conv2d_1a_3x3.bn.running_mean", "Conv2d_1a_3x3.bn.running_var", "Conv2d_2a_3x3.conv.weight", "Conv2d_2a_3x3.bn.weight", "Conv2d_2a_3x3.bn.bias", "Conv2d_2a_3x3.bn.running_mean", "Conv2d_2a_3x3.bn.running_var", "Conv2d_2b_3x3.conv.weight", "Conv2d_2b_3x3.bn.weight", "Conv2d_2b_3x3.bn.bias", "Conv2d_2b_3x3.bn.running_mean", "Conv2d_2b_3x3.bn.running_var", "Conv2d_3b_1x1.conv.weight", "Conv2d_3b_1x1.bn.weight", "Conv2d_3b_1x1.bn.bias", "Conv2d_3b_1x1.bn.running_mean", "Conv2d_3b_1x1.bn.running_var", "Conv2d_4a_3x3.conv.weight", "Conv2d_4a_3x3.bn.weight", "Conv2d_4a_3x3.bn.bias", "Conv2d_4a_3x3.bn.running_mean", "Conv2d_4a_3x3.bn.running_var", "Mixed_5b.branch1x1.conv.weight", "Mixed_5b.branch1x1.bn.weight", "Mixed_5b.branch1x1.bn.bias", "Mixed_5b.branch1x1.bn.running_mean", "Mixed_5b.branch1x1.bn.running_var", "Mixed_5b.branch5x5_1.conv.weight", "Mixed_5b.branch5x5_1.bn.weight", 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Unexpected key(s) in state_dict: "module.Conv2d_1a_3x3.conv.weight", "module.Conv2d_1a_3x3.bn.weight", "module.Conv2d_1a_3x3.bn.bias", "module.Conv2d_1a_3x3.bn.running_mean", "module.Conv2d_1a_3x3.bn.running_var", "module.Conv2d_1a_3x3.bn.num_batches_tracked", "module.Conv2d_2a_3x3.conv.weight", "module.Conv2d_2a_3x3.bn.weight", "module.Conv2d_2a_3x3.bn.bias", "module.Conv2d_2a_3x3.bn.running_mean", "module.Conv2d_2a_3x3.bn.running_var", "module.Conv2d_2a_3x3.bn.num_batches_tracked", "module.Conv2d_2b_3x3.conv.weight", "module.Conv2d_2b_3x3.bn.weight", "module.Conv2d_2b_3x3.bn.bias", "module.Conv2d_2b_3x3.bn.running_mean", "module.Conv2d_2b_3x3.bn.running_var", "module.Conv2d_2b_3x3.bn.num_batches_tracked", "module.Conv2d_3b_1x1.conv.weight", "module.Conv2d_3b_1x1.bn.weight", "module.Conv2d_3b_1x1.bn.bias", "module.Conv2d_3b_1x1.bn.running_mean", "module.Conv2d_3b_1x1.bn.running_var", "module.Conv2d_3b_1x1.bn.num_batches_tracked", "module.Conv2d_4a_3x3.conv.weight", "module.Conv2d_4a_3x3.bn.weight", "module.Conv2d_4a_3x3.bn.bias", "module.Conv2d_4a_3x3.bn.running_mean", "module.Conv2d_4a_3x3.bn.running_var", "module.Conv2d_4a_3x3.bn.num_batches_tracked", "module.Mixed_5b.branch1x1.conv.weight", "module.Mixed_5b.branch1x1.bn.weight", "module.Mixed_5b.branch1x1.bn.bias", "module.Mixed_5b.branch1x1.bn.running_mean", "module.Mixed_5b.branch1x1.bn.running_var", "module.Mixed_5b.branch1x1.bn.num_batches_tracked", "module.Mixed_5b.branch5x5_1.conv.weight", "module.Mixed_5b.branch5x5_1.bn.weight", "module.Mixed_5b.branch5x5_1.bn.bias", "module.Mixed_5b.branch5x5_1.bn.running_mean", "module.Mixed_5b.branch5x5_1.bn.running_var", "module.Mixed_5b.branch5x5_1.bn.num_batches_tracked", "module.Mixed_5b.branch5x5_2.conv.weight", "module.Mixed_5b.branch5x5_2.bn.weight", "module.Mixed_5b.branch5x5_2.bn.bias", "module.Mixed_5b.branch5x5_2.bn.running_mean", "module.Mixed_5b.branch5x5_2.bn.running_var", "module.Mixed_5b.branch5x5_2.bn.num_batches_tracked", "module.Mixed_5b.branch3x3dbl_1.conv.weight", "module.Mixed_5b.branch3x3dbl_1.bn.weight", "module.Mixed_5b.branch3x3dbl_1.bn.bias", "module.Mixed_5b.branch3x3dbl_1.bn.running_mean", "module.Mixed_5b.branch3x3dbl_1.bn.running_var", "module.Mixed_5b.branch3x3dbl_1.bn.num_batches_tracked", "module.Mixed_5b.branch3x3dbl_2.conv.weight", "module.Mixed_5b.branch3x3dbl_2.bn.weight", "module.Mixed_5b.branch3x3dbl_2.bn.bias", "module.Mixed_5b.branch3x3dbl_2.bn.running_mean", "module.Mixed_5b.branch3x3dbl_2.bn.running_var", "module.Mixed_5b.branch3x3dbl_2.bn.num_batches_tracked", "module.Mixed_5b.branch3x3dbl_3.conv.weight", "module.Mixed_5b.branch3x3dbl_3.bn.weight", "module.Mixed_5b.branch3x3dbl_3.bn.bias", "module.Mixed_5b.branch3x3dbl_3.bn.running_mean", "module.Mixed_5b.branch3x3dbl_3.bn.running_var", "module.Mixed_5b.branch3x3dbl_3.bn.num_batches_tracked", "module.Mixed_5b.branch_pool.conv.weight", "module.Mixed_5b.branch_pool.bn.weight", "module.Mixed_5b.branch_pool.bn.bias", 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"module.Mixed_5c.branch_pool.bn.running_var", "module.Mixed_5c.branch_pool.bn.num_batches_tracked", "module.Mixed_5d.branch1x1.conv.weight", "module.Mixed_5d.branch1x1.bn.weight", "module.Mixed_5d.branch1x1.bn.bias", "module.Mixed_5d.branch1x1.bn.running_mean", "module.Mixed_5d.branch1x1.bn.running_var", "module.Mixed_5d.branch1x1.bn.num_batches_tracked", "module.Mixed_5d.branch5x5_1.conv.weight", "module.Mixed_5d.branch5x5_1.bn.weight", "module.Mixed_5d.branch5x5_1.bn.bias", "module.Mixed_5d.branch5x5_1.bn.running_mean", "module.Mixed_5d.branch5x5_1.bn.running_var", "module.Mixed_5d.branch5x5_1.bn.num_batches_tracked", "module.Mixed_5d.branch5x5_2.conv.weight", "module.Mixed_5d.branch5x5_2.bn.weight", "module.Mixed_5d.branch5x5_2.bn.bias", "module.Mixed_5d.branch5x5_2.bn.running_mean", "module.Mixed_5d.branch5x5_2.bn.running_var", "module.Mixed_5d.branch5x5_2.bn.num_batches_tracked", "module.Mixed_5d.branch3x3dbl_1.conv.weight", "module.Mixed_5d.branch3x3dbl_1.bn.weight", 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def test():
##
engine = Engine()
config = getConfig()
data_config = getDatasetConfig(config.dataset)
# define dataset
transform_test = transforms.Compose([
transforms.Resize((config.image_size, config.image_size)),
transforms.CenterCrop(config.input_size),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])
val_dataset = CustomDataset(
data_config['val'], data_config['val_root'], transform=transform_test)
val_loader = DataLoader(
val_dataset, batch_size=config.batch_size, shuffle=False, num_workers=config.workers, pin_memory=True)
# define model
if config.model_name == 'inception':
net = inception_v3_bap(pretrained=True, aux_logits=False)
elif config.model_name == 'resnet50':
net = resnet50(pretrained=True)
in_features = net.fc_new.in_features
new_linear = torch.nn.Linear(
in_features=in_features, out_features=val_dataset.num_classes)
net.fc_new = new_linear
# load checkpoint
use_gpu = torch.cuda.is_available() and config.use_gpu
if use_gpu:
net = net.cuda()
gpu_ids = [int(r) for r in config.gpu_ids.split(',')]
if use_gpu and len(gpu_ids) > 1:
net = torch.nn.DataParallel(net, device_ids=gpu_ids)
#checkpoint_path = os.path.join(config.checkpoint_path,'model_best.pth.tar')
net.load_state_dict(torch.load(config.checkpoint_path)['state_dict'])
# define loss
# define loss
criterion = torch.nn.CrossEntropyLoss()
if use_gpu:
criterion = criterion.cuda()
prec1, prec5 = engine.test(val_loader, net, criterion)
Can anyone help me please?