Replace torch.flatten with nn.Flatten in inception.py #4096
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Insertion in line 119: self.flatten = nn.Flatten() Change in line 191: x = torch.flatten(x, 1) -> x = self.flatten(x) This change allows to override flattening before build-in dense classifier, therefore enabling non-dense custom processing heads(e.g. pseudo-embedders for features injection into transformers for image captioning). Before, flattening was inaccessible, forcing users to play with un-flattening, which is inconvenient.
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fmassa
changed the title
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Hi, Thanks for the PR! This PR is related to the discussion in #3331 As I mention in #3331 (comment) , I'm ok with merging this PR (after lint is fixed), but a full solution to the general problem of model surgery might require other tools, as I discussed in #3331 (comment) |
Insertion in line 119:
self.flatten = nn.Flatten()
Change in line 191:
x = torch.flatten(x, 1) -> x = self.flatten(x)
This change allows to override flattening before build-in dense classifier, therefore enabling non-dense custom processing heads(e.g. pseudo-embedders for features injection into transformers for image captioning).
Before, flattening was inaccessible, forcing users to play with un-flattening, which is inconvenient.