Comments (5)
mhsamavatian
commented on May 22, 2024
4
Modify sample function in model.py like below
I added 'inputs = inputs.unsqueeze(1)' in last like of for loop and changed sampled_ids = torch.cat(sampled_ids, 1) to sampled_ids = torch.cat(sampled_ids, 0)
`def sample(self, features, states=None):
"""Samples captions for given image features (Greedy search)."""
sampled_ids = []
inputs = features.unsqueeze(1)
for i in range(20): # maximum sampling length
hiddens, states = self.lstm(inputs, states) # (batch_size, 1, hidden_size),
outputs = self.linear(hiddens.squeeze(1)) # (batch_size, vocab_size)
predicted = outputs.max(1)[1]
sampled_ids.append(predicted)
inputs = self.embed(predicted)
inputs = inputs.unsqueeze(1)
sampled_ids = torch.cat(sampled_ids, 0) # (batch_size, 20)
return sampled_ids.squeeze()`
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jysenj
commented on May 22, 2024
i got this to, have you fixed it?
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yunjey
commented on May 22, 2024
@mhsamavatian Thanks, you are right. I updated the code :-)
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WangWenshan
commented on May 22, 2024
However, I have an error when I do that:
runtimeerror input must have 3 dimensions, got 4
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autogyro
commented on May 22, 2024
Hi there
I am also meet that problem, and then I add the 'inputs = inputs.unsqueeze(1)',
def sample(self, features, states=None):
"""Samples captions for given image features (Greedy search)."""
sampled_ids = []
inputs = features.unsqueeze(1)
for i in range(20): # maximum sampling length
hiddens, states = self.lstm(inputs, states) # (batch_size, 1, hidden_size),
outputs = self.linear(hiddens.squeeze(1)) # (batch_size, vocab_size)
predicted = outputs.max(1)[1]
sampled_ids.append(predicted)
inputs = self.embed(predicted)
inputs = inputs.unsqueeze(1)
sampled_ids = torch.cat(sampled_ids, 1) # (batch_size, 20)
return sampled_ids.squeeze()
but, I got the following:
File "D:\Dev\image_captioning\model.py", line 134, in sample
sampled_ids = torch.cat(sampled_ids, 1) # (batch_size, 20)
RuntimeError: Dimension out of range (expected to be in range of [-1, 0], but got 1)
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