Improving conditioner#66
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in the inital implementation, twice the in channels were used. However, this is not feasible. I assume this mistake was introduced by c&p from an external source that applied some transformation before, doubling the number of channels. My assumption is due to the subsequent conv. layer, which was expecting also double the channels.
| """ | ||
| size_in = x.shape | ||
| # Make sure to create a new obj. to avoid inplace operations. | ||
| if context is not None: |
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The provided context is overwritten with a new tensor (torch.Tensor([0])). This negates any non-null context passed to the function; consider preserving the caller's context.
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| if context is not None: | |
| if context is None: |
| c_hidden: Number of hidden dimensions to use within the network | ||
| rescale_hidden: Factor by which to rescale hight and width the | ||
| hidden before and after the hidden layers. | ||
| c_out: Number of output channels. If -1, the numberinput channels |
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[nitpick] Typo in docstring: 'numberinput' should be 'number input'.
Suggested change
| c_out: Number of output channels. If -1, the numberinput channels | |
| c_out: Number of output channels. If -1, the number input channels |
fariedabuzaid
approved these changes
Apr 7, 2025
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As discussed. Changes incl. minor refactoring, device mgmt., cond. convolutional cond. for soft training.