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Holmeyoung / crnn-pytorch

Pytorch implementation of CRNN (CNN + RNN + CTCLoss) for all language OCR.
MIT License
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运行demo用cenn.pth预训练模型显示Expected 512, got 64 #71

Open whistbibi opened 3 years ago

whistbibi commented 3 years ago

我改了在cpu上运行和去掉covert（‘L’） model = model.cuda()

print('loading pretrained model from %s' % model_path)

if params.multi_gpu:

model = torch.nn.DataParallel(model) checkpoint =torch.load(model_path, map_location=torch.device('cpu')) model.load_state_dict(checkpoint,False)

converter = utils.strLabelConverter(params.alphabet)

transformer = dataset.resizeNormalize((100, 32))

image = Image.open(image_path).convert('L')

image = Image.open(image_path) image = transformer(image) if torch.cuda.is_available(): image = image.cuda()

image = image.view(1, *image.size()) image = Variable(image)

log：

`CRNN( (cnn): Sequential( (conv0): Conv2d(3, 64, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2)) (relu0): ReLU(inplace=True) (pooling0): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False) (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (relu1): ReLU(inplace=True) (conv2): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (batchnorm2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv3): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (pooling2): MaxPool2d(kernel_size=(2, 2), stride=(2, 1), padding=(0, 1), dilation=1, ceil_mode=False) (conv4): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (batchnorm4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv5): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (pooling3): MaxPool2d(kernel_size=(2, 2), stride=(2, 1), padding=(0, 1), dilation=1, ceil_mode=False) (conv6): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (batchnorm6): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (rnn): Sequential( (0): BidirectionalLSTM( (rnn): LSTM(512, 256, bidirectional=True) (embedding): Linear(in_features=512, out_features=256, bias=True) ) (1): BidirectionalLSTM( (rnn): LSTM(256, 256, bidirectional=True) (embedding): Linear(in_features=512, out_features=204, bias=True) ) ) ) loading pretrained model from ./path/to/crnn.pth

Traceback (most recent call last): File "demo.py", line 45, in preds = model(image) File "/usrhome/jingli/.conda/envs/202110-crnn-License-plate-env/lib/python3.6/site-packages/torch/nn/modules/module.py", line 532, in call result = self.forward(*input, kwargs) File "/usrhome/jingli/.conda/envs/202110-crnn-License-plate-env/lib/python3.6/site-packages/torch/nn/parallel/data_parallel.py", line 140, in forward return self.module(*inputs, *kwargs) File "/usrhome/jingli/.conda/envs/202110-crnn-License-plate-env/lib/python3.6/site-packages/torch/nn/modules/module.py", line 532, in call result = self.forward(input, kwargs) File "/dm10801/B1_tinyAI_project/202110-cruiser/1.1creat_pytorch_module/models/crnn.py", line 87, in forward output = self.rnn(conv) File "/usrhome/jingli/.conda/envs/202110-crnn-License-plate-env/lib/python3.6/site-packages/torch/nn/modules/module.py", line 532, in call result = self.forward(*input, kwargs) File "/usrhome/jingli/.conda/envs/202110-crnn-License-plate-env/lib/python3.6/site-packages/torch/nn/modules/container.py", line 100, in forward input = module(input) File "/usrhome/jingli/.conda/envs/202110-crnn-License-plate-env/lib/python3.6/site-packages/torch/nn/modules/module.py", line 532, in call result = self.forward(*input, *kwargs) File "/dm10801/B1_tinyAI_project/202110-cruiser/1.1creat_pytorchmodule/models/crnn.py", line 14, in forward recurrent, = self.rnn(input) File "/usrhome/jingli/.conda/envs/202110-crnn-License-plate-env/lib/python3.6/site-packages/torch/nn/modules/module.py", line 532, in call result = self.forward(input, kwargs) File "/usrhome/jingli/.conda/envs/202110-crnn-License-plate-env/lib/python3.6/site-packages/torch/nn/modules/rnn.py", line 556, in forward self.check_forward_args(input, hx, batch_sizes) File "/usrhome/jingli/.conda/envs/202110-crnn-License-plate-env/lib/python3.6/site-packages/torch/nn/modules/rnn.py", line 508, in check_forward_args self.check_input(input, batch_sizes) File "/usrhome/jingli/.conda/envs/202110-crnn-License-plate-env/lib/python3.6/site-packages/torch/nn/modules/rnn.py", line 159, in check_input self.input_size, input.size(-1))) RuntimeError: input.size(-1) must be equal to input_size. Expected 512, got 64`