Hello @ptrblck,
x1 and x2 shapes are :
print(x1.shape, x2.shape)
=> torch.Size([2, 11, 2]), torch.Size([2, 11, 2])
- 1st dimension (2) represents the batch_size
- 2nd dimension (11) represents the number of classes.
- I don’t know what do represent the 3rd dimension.
Here is a reminder of the logits layer :
end_point = 'Logits'
self.avg_pool = nn.AvgPool3d(kernel_size=[2, 7, 7],
stride=(1, 1, 1))
self.dropout = nn.Dropout(dropout_keep_prob)
self.logits = Unit3D(in_channels=384+384+128+128, output_channels=self._num_classes,
kernel_shape=[1, 1, 1],
padding=0,
activation_fn=None,
use_batch_norm=False,
use_bias=True,
name='logits')
My inputs are the following :
rgb_clips : torch.Size([2, 3, 20, 224, 224])
flow_clipss : torch.Size([2, 2, 20, 224, 224])
- 1st dimension represents the batch_size
- 2nd dimension the channels (3 for rgb, 2 for optical flow)
- 3rd represents the number of frames that were selected from the clips
- 4th dimension the weight
- 5th the height
My problem seems to be similar to this one : How to Concatenate layers in PyTorch similar to tf.keras.layers.Concatenate