cct-cnn_error.txt

AssertionError                            Traceback (most recent call last)
<ipython-input-2-8a242b2868b1> in <cell line: 28>()
     26 
     27 # inpaint image
---> 28 inpainted_tensor = model(image_tensor, mask)
     29 
     30 # convert tensors to numpy arrays and show images

9 frames
/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py in _call_impl(self, *args, **kwargs)
   1499                 or _global_backward_pre_hooks or _global_backward_hooks
   1500                 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1501             return forward_call(*args, **kwargs)
   1502         # Do not call functions when jit is used
   1503         full_backward_hooks, non_full_backward_hooks = [], []

<ipython-input-1-f52b433f2bef> in forward(self, x, mask)
     86 
     87     def forward(self, x, mask):
---> 88         encoded_x = self.encoder(x)
     89         batch_size, channels, height, width = encoded_x.size()
     90         mask = F.interpolate(mask, size=(height, width), mode='bilinear', align_corners=False)

/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py in _call_impl(self, *args, **kwargs)
   1499                 or _global_backward_pre_hooks or _global_backward_hooks
   1500                 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1501             return forward_call(*args, **kwargs)
   1502         # Do not call functions when jit is used
   1503         full_backward_hooks, non_full_backward_hooks = [], []

<ipython-input-1-f52b433f2bef> in forward(self, x)
     39         x = self.conv(x)
     40         for block in self.blocks:
---> 41             x = block(x)
     42         return x
     43 

/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py in _call_impl(self, *args, **kwargs)
   1499                 or _global_backward_pre_hooks or _global_backward_hooks
   1500                 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1501             return forward_call(*args, **kwargs)
   1502         # Do not call functions when jit is used
   1503         full_backward_hooks, non_full_backward_hooks = [], []

<ipython-input-1-f52b433f2bef> in forward(self, x)
     18     def forward(self, x):
     19         x_norm = self.norm1(x)
---> 20         attn_output, _ = self.attn(x_norm, x_norm, x_norm)
     21         x = x + attn_output
     22         x_norm = self.norm2(x)

/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py in _call_impl(self, *args, **kwargs)
   1499                 or _global_backward_pre_hooks or _global_backward_hooks
   1500                 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1501             return forward_call(*args, **kwargs)
   1502         # Do not call functions when jit is used
   1503         full_backward_hooks, non_full_backward_hooks = [], []

/usr/local/lib/python3.9/dist-packages/torch/nn/modules/activation.py in forward(self, query, key, value, key_padding_mask, need_weights, attn_mask, average_attn_weights, is_causal)
   1187                 is_causal=is_causal)
   1188         else:
-> 1189             attn_output, attn_output_weights = F.multi_head_attention_forward(
   1190                 query, key, value, self.embed_dim, self.num_heads,
   1191                 self.in_proj_weight, self.in_proj_bias,

/usr/local/lib/python3.9/dist-packages/torch/nn/functional.py in multi_head_attention_forward(query, key, value, embed_dim_to_check, num_heads, in_proj_weight, in_proj_bias, bias_k, bias_v, add_zero_attn, dropout_p, out_proj_weight, out_proj_bias, training, key_padding_mask, need_weights, attn_mask, use_separate_proj_weight, q_proj_weight, k_proj_weight, v_proj_weight, static_k, static_v, average_attn_weights, is_causal)
   5138         )
   5139 
-> 5140     is_batched = _mha_shape_check(query, key, value, key_padding_mask, attn_mask, num_heads)
   5141 
   5142     # For unbatched input, we unsqueeze at the expected batch-dim to pretend that the input

/usr/local/lib/python3.9/dist-packages/torch/nn/functional.py in _mha_shape_check(query, key, value, key_padding_mask, attn_mask, num_heads)
   4975                     (f"Expected `attn_mask` shape to be {expected_shape} but got {attn_mask.shape}")
   4976     else:
-> 4977         raise AssertionError(
   4978             f"query should be unbatched 2D or batched 3D tensor but received {query.dim()}-D query tensor")
   4979