crnn.pytorch
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meijieru
TypeError: function takes exactly 5 arguments (1 given)
pytorch1.01 python3.6
python train.py --adadelta --trainRoot /home/chase/crnn.pytorch-master/tool/tool/ --valRoot /home/chase/crnn.pytorch-master/tool/tool/ --cuda --random_sample
Namespace(adadelta=True, adam=False, alphabet='0123456789abcdefghijklmnopqrstuvwxyz', batchSize=64, beta1=0.5, cuda=True, displayInterval=500, expr_dir='expr', imgH=32, imgW=100, keep_ratio=False, lr=0.01, manualSeed=1234, n_test_disp=10, nepoch=25, ngpu=1, nh=256, pretrained='', random_sample=True, saveInterval=500, trainRoot='/home/chase/crnn.pytorch-master/tool/tool/', valInterval=500, valRoot='/home/chase/crnn.pytorch-master/tool/tool/', workers=2)
CRNN(
(cnn): Sequential(
(conv0): Conv2d(1, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(relu0): ReLU(inplace)
(pooling0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(relu1): ReLU(inplace)
(pooling1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(conv2): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(batchnorm2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(relu3): ReLU(inplace)
(pooling2): MaxPool2d(kernel_size=(2, 2), stride=(2, 1), padding=(0, 1), dilation=1, ceil_mode=False)
(conv4): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(batchnorm4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu4): ReLU(inplace)
(conv5): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(relu5): ReLU(inplace)
(pooling3): MaxPool2d(kernel_size=(2, 2), stride=(2, 1), padding=(0, 1), dilation=1, ceil_mode=False)
(conv6): Conv2d(512, 512, kernel_size=(2, 2), stride=(1, 1))
(batchnorm6): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu6): ReLU(inplace)
)
(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=37, bias=True)
)
)
)
Traceback (most recent call last):
File "train.py", line 198, in
