# Tensor value error with inception\_v3 model **URL:** \ **Category:** vision\ **Created:** [March 2, 2021, 5:03pm UTC](https://discuss.pytorch.org/t/tensor-value-error-with-inception-v3-model/113499 "2021-03-02T17:03:57Z")\ **Posts on this page:** 3\ **Page:** 1
**Author:** ![byeongsu\_KIM](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/byeongsu_kim/32/35290_2.png) [@byeongsu\_KIM](https://discuss.pytorch.org/u/byeongsu_KIM)\ **Post date:** [March 2, 2021, 5:03pm UTC](https://discuss.pytorch.org/t/tensor-value-error-with-inception-v3-model/113499/1 "2021-03-02T17:03:57Z")
I’m fixing a inception v3 model for image captioning. But after 1 epoch of training, i got value error. How can I fix this problem? ```auto Traceback (most recent call last): File "train.py", line 107, in main(args) File "train.py", line 61, in main features = encoder(images) File "/home/mango/anaconda3/envs/img/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl result = self.forward(*input, **kwargs) File "/home/mango/바탕화면/lab/pytorch-tutorial/tutorials/03-advanced/image_captioning/model.py", line 20, in forward embed = self.inception(images) File "/home/mango/anaconda3/envs/img/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl result = self.forward(*input, **kwargs) File "/home/mango/anaconda3/envs/img/lib/python3.7/site-packages/torchvision/models/inception.py", line 199, in forward x, aux = self._forward(x) File "/home/mango/anaconda3/envs/img/lib/python3.7/site-packages/torchvision/models/inception.py", line 169, in _forward aux = self.AuxLogits(x) File "/home/mango/anaconda3/envs/img/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl result = self.forward(*input, **kwargs) File "/home/mango/anaconda3/envs/img/lib/python3.7/site-packages/torchvision/models/inception.py", line 419, in forward x = self.conv1(x) File "/home/mango/anaconda3/envs/img/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl result = self.forward(*input, **kwargs) File "/home/mango/anaconda3/envs/img/lib/python3.7/site-packages/torchvision/models/inception.py", line 440, in forward x = self.bn(x) File "/home/mango/anaconda3/envs/img/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl result = self.forward(*input, **kwargs) File "/home/mango/anaconda3/envs/img/lib/python3.7/site-packages/torch/nn/modules/batchnorm.py", line 136, in forward self.weight, self.bias, bn_training, exponential_average_factor, self.eps) File "/home/mango/anaconda3/envs/img/lib/python3.7/site-packages/torch/nn/functional.py", line 2054, in batch_norm _verify_batch_size(input.size()) File "/home/mango/anaconda3/envs/img/lib/python3.7/site-packages/torch/nn/functional.py", line 2037, in _verify_batch_size raise ValueError('Expected more than 1 value per channel when training, got input size {}'.format(size)) ValueError: Expected more than 1 value per channel when training, got input size torch.Size([1, 768, 1, 1]) ``` ```auto class Inception(nn.Module): def __init__ (self, embed_size): super(Inception, self). __init__ () self.inception = models.inception_v3(pretrained=True) #in_features = self.inception.fc.in_features #self.linear = nn.Linear(in_features, embed_size) self.linear = nn.Linear(self.inception.fc.in_features, embed_size) self.bn = nn.BatchNorm1d(embed_size, momentum=0.01) #self.inception.fc = self.linear def forward(self, images): embed = self.inception(images) return embed class DecoderRNN(nn.Module): def __init__ (self, embed_size, hidden_size, vocab_size, num_layers, max_seq_length=20): """Set the hyper-parameters and build the layers.""" super(DecoderRNN, self). __init__ () self.embed = nn.Embedding(vocab_size, embed_size) self.lstm = nn.LSTM(embed_size, hidden_size, num_layers, batch_first=True) self.linear = nn.Linear(hidden_size, vocab_size) self.max_seg_length = max_seq_length def forward(self, features, captions, lengths): """Decode image feature vectors and generates captions.""" embeddings = self.embed(captions) #print(embeddings.size()) #print(features.logits.unsqueeze(1).size()) embeddings = torch.cat((features.logits.unsqueeze(1), embeddings), 1) packed = pack_padded_sequence(embeddings, lengths, batch_first=True) hiddens, _ = self.lstm(packed) outputs = self.linear(hiddens[0]) return outputs def sample(self, features, states=None): """Generate captions for given image features using greedy search.""" sampled_ids = [] inputs = features.unsqueeze(1) for i in range(self.max_seg_length): hiddens, states = self.lstm(inputs, states) # hiddens: (batch_size, 1, hidden_size) outputs = self.linear(hiddens.squeeze(1)) # outputs: (batch_size, vocab_size) _, predicted = outputs.max(1) # predicted: (batch_size) sampled_ids.append(predicted) inputs = self.embed(predicted) # inputs: (batch_size, embed_size) inputs = inputs.unsqueeze(1) # inputs: (batch_size, 1, embed_size) sampled_ids = torch.stack(sampled_ids, 1) # sampled_ids: (batch_size, max_seq_length) return sampled_ids ``` ---
**Author:** ![ptrblck](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/ptrblck/32/1823_2.png) [@ptrblck](https://discuss.pytorch.org/u/ptrblck)\ **Post date:** [March 2, 2021, 7:52pm UTC](https://discuss.pytorch.org/t/tensor-value-error-with-inception-v3-model/113499/2 "2021-03-02T19:52:09Z")
The error is raised by some batchnorm layers, which cannot calculate the stats from a single sample. If you leave the model in training mode, you would have to provide at least 2 samples in the batch. Assuming this error is only raised at the end of the epoch, I guess the last batch might be smaller than the rest and you could drop it via setting `drop_last=True` in the `DataLoader`. ---
**Author:** ![byeongsu\_KIM](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/byeongsu_kim/32/35290_2.png) [@byeongsu\_KIM](https://discuss.pytorch.org/u/byeongsu_KIM)\ **Post date:** [March 3, 2021, 6:54am UTC](https://discuss.pytorch.org/t/tensor-value-error-with-inception-v3-model/113499/3 "2021-03-03T06:54:48Z")
Thank you. I solved this problem with drop\_last=True.