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| """Layers used by the OpenHTR encoder.""" | |
| from torch import nn | |
| def drop_path(x, | |
| drop_prob: float = 0.0, | |
| training: bool = False, | |
| scale_by_keep: bool = True): | |
| """Drop paths (Stochastic Depth) per sample (when applied in main path of | |
| residual blocks). | |
| This is the same as the DropConnect impl I created for EfficientNet, etc networks, however, | |
| the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... | |
| See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for | |
| changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use | |
| 'survival rate' as the argument. | |
| """ | |
| if drop_prob == 0.0 or not training: | |
| return x | |
| keep_prob = 1 - drop_prob | |
| shape = (x.shape[0], ) + (1, ) * ( | |
| x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets | |
| random_tensor = x.new_empty(shape).bernoulli_(keep_prob) | |
| if keep_prob > 0.0 and scale_by_keep: | |
| random_tensor.div_(keep_prob) | |
| return x * random_tensor | |
| class DropPath(nn.Module): | |
| """Drop paths (Stochastic Depth) per sample (when applied in main path of | |
| residual blocks).""" | |
| def __init__(self, drop_prob: float = 0.0, scale_by_keep: bool = True): | |
| super(DropPath, self).__init__() | |
| self.drop_prob = drop_prob | |
| self.scale_by_keep = scale_by_keep | |
| def forward(self, x): | |
| return drop_path(x, self.drop_prob, self.training, self.scale_by_keep) | |
| def extra_repr(self): | |
| return f'drop_prob={round(self.drop_prob,3):0.3f}' | |
| class Identity(nn.Module): | |
| def __init__(self): | |
| super(Identity, self).__init__() | |
| def forward(self, input): | |
| return input | |