timm/models/ghostnet.py [685:756]:
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        self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
        self.flatten = nn.Flatten(1) if global_pool else nn.Identity()  # don't flatten if pooling disabled
        self.classifier = Linear(self.head_hidden_size, num_classes) if num_classes > 0 else nn.Identity()

    def forward_intermediates(
            self,
            x: torch.Tensor,
            indices: Optional[Union[int, List[int]]] = None,
            norm: bool = False,
            stop_early: bool = False,
            output_fmt: str = 'NCHW',
            intermediates_only: bool = False,
    ) -> Union[List[torch.Tensor], Tuple[torch.Tensor, List[torch.Tensor]]]:
        """ Forward features that returns intermediates.

        Args:
            x: Input image tensor
            indices: Take last n blocks if int, all if None, select matching indices if sequence
            norm: Apply norm layer to compatible intermediates
            stop_early: Stop iterating over blocks when last desired intermediate hit
            output_fmt: Shape of intermediate feature outputs
            intermediates_only: Only return intermediate features
        Returns:

        """
        assert output_fmt in ('NCHW',), 'Output shape must be NCHW.'
        intermediates = []
        stage_ends = [-1] + [int(info['module'].split('.')[-1]) for info in self.feature_info[1:]]
        take_indices, max_index = feature_take_indices(len(stage_ends), indices)
        take_indices = [stage_ends[i]+1 for i in take_indices]
        max_index = stage_ends[max_index]

        # forward pass
        feat_idx = 0
        x = self.conv_stem(x)
        if feat_idx in take_indices:
            intermediates.append(x)
        x = self.bn1(x)
        x = self.act1(x)
        if torch.jit.is_scripting() or not stop_early:  # can't slice blocks in torchscript
            stages = self.blocks
        else:
            stages = self.blocks[:max_index + 1]

        for feat_idx, stage in enumerate(stages, start=1):
            if self.grad_checkpointing and not torch.jit.is_scripting():
                x = checkpoint_seq(stage, x)
            else:
                x = stage(x)
            if feat_idx in take_indices:
                intermediates.append(x)

        if intermediates_only:
            return intermediates

        return x, intermediates

    def prune_intermediate_layers(
            self,
            indices: Union[int, List[int]] = 1,
            prune_norm: bool = False,
            prune_head: bool = True,
    ):
        """ Prune layers not required for specified intermediates.
        """
        stage_ends = [-1] + [int(info['module'].split('.')[-1]) for info in self.feature_info[1:]]
        take_indices, max_index = feature_take_indices(len(stage_ends), indices)
        max_index = stage_ends[max_index]
        self.blocks = self.blocks[:max_index + 1]  # truncate blocks w/ stem as idx 0
        if prune_head:
            self.reset_classifier(0, '')
        return take_indices
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timm/models/repghost.py [294:365]:
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            self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
            self.flatten = nn.Flatten(1) if global_pool else nn.Identity()  # don't flatten if pooling disabled
        self.classifier = Linear(self.head_hidden_size, num_classes) if num_classes > 0 else nn.Identity()

    def forward_intermediates(
            self,
            x: torch.Tensor,
            indices: Optional[Union[int, List[int]]] = None,
            norm: bool = False,
            stop_early: bool = False,
            output_fmt: str = 'NCHW',
            intermediates_only: bool = False,
    ) -> Union[List[torch.Tensor], Tuple[torch.Tensor, List[torch.Tensor]]]:
        """ Forward features that returns intermediates.

        Args:
            x: Input image tensor
            indices: Take last n blocks if int, all if None, select matching indices if sequence
            norm: Apply norm layer to compatible intermediates
            stop_early: Stop iterating over blocks when last desired intermediate hit
            output_fmt: Shape of intermediate feature outputs
            intermediates_only: Only return intermediate features
        Returns:

        """
        assert output_fmt in ('NCHW',), 'Output shape must be NCHW.'
        intermediates = []
        stage_ends = [-1] + [int(info['module'].split('.')[-1]) for info in self.feature_info[1:]]
        take_indices, max_index = feature_take_indices(len(stage_ends), indices)
        take_indices = [stage_ends[i]+1 for i in take_indices]
        max_index = stage_ends[max_index]

        # forward pass
        feat_idx = 0
        x = self.conv_stem(x)
        if feat_idx in take_indices:
            intermediates.append(x)
        x = self.bn1(x)
        x = self.act1(x)
        if torch.jit.is_scripting() or not stop_early:  # can't slice blocks in torchscript
            stages = self.blocks
        else:
            stages = self.blocks[:max_index + 1]

        for feat_idx, stage in enumerate(stages, start=1):
            if self.grad_checkpointing and not torch.jit.is_scripting():
                x = checkpoint_seq(stage, x)
            else:
                x = stage(x)
            if feat_idx in take_indices:
                intermediates.append(x)

        if intermediates_only:
            return intermediates

        return x, intermediates

    def prune_intermediate_layers(
            self,
            indices: Union[int, List[int]] = 1,
            prune_norm: bool = False,
            prune_head: bool = True,
    ):
        """ Prune layers not required for specified intermediates.
        """
        stage_ends = [-1] + [int(info['module'].split('.')[-1]) for info in self.feature_info[1:]]
        take_indices, max_index = feature_take_indices(len(stage_ends), indices)
        max_index = stage_ends[max_index]
        self.blocks = self.blocks[:max_index + 1]  # truncate blocks w/ stem as idx 0
        if prune_head:
            self.reset_classifier(0, '')
        return take_indices
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