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https://github.com/huggingface/pytorch-image-models.git
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Support features_only
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Ryan
parent
b37f0f7a76
commit
848b8c3e57
@ -10,7 +10,7 @@ The official pytorch code is released and available at
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https://github.com/huawei-noah/Efficient-AI-Backbones/tree/master/tnt_pytorch
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"""
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import math
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from typing import Optional
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from typing import List, Optional, Tuple, Union
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import torch
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import torch.nn as nn
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@ -18,6 +18,7 @@ import torch.nn as nn
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from timm.data import IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
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from timm.layers import Mlp, DropPath, trunc_normal_, _assert, to_2tuple, resample_abs_pos_embed
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from ._builder import build_model_with_cfg
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from ._features import feature_take_indices
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from ._manipulate import checkpoint
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from ._registry import register_model
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@ -172,7 +173,16 @@ class PixelEmbed(nn.Module):
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else:
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self.unfold = nn.Unfold(kernel_size=patch_size, stride=patch_size)
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def forward(self, x, pixel_pos):
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def feat_ratio(self, as_scalar=True) -> Union[Tuple[int, int], int]:
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if as_scalar:
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return max(self.patch_size)
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else:
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return self.patch_size
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def dynamic_feat_size(self, img_size: Tuple[int, int]) -> Tuple[int, int]:
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return img_size[0] // self.patch_size[0], img_size[1] // self.patch_size[1]
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def forward(self, x: torch.Tensor, pixel_pos: torch.Tensor) -> torch.Tensor:
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B, C, H, W = x.shape
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_assert(H == self.img_size[0],
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f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]}).")
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@ -222,6 +232,7 @@ class TNT(nn.Module):
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self.num_classes = num_classes
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self.global_pool = global_pool
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self.num_features = self.head_hidden_size = self.embed_dim = embed_dim # for consistency with other models
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self.num_prefix_tokens = 1
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self.grad_checkpointing = False
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self.pixel_embed = PixelEmbed(
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@ -233,6 +244,7 @@ class TNT(nn.Module):
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legacy=legacy,
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)
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num_patches = self.pixel_embed.num_patches
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r = self.pixel_embed.feat_ratio() if hasattr(self.pixel_embed, 'feat_ratio') else patch_size
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self.num_patches = num_patches
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new_patch_size = self.pixel_embed.new_patch_size
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num_pixel = new_patch_size[0] * new_patch_size[1]
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@ -264,8 +276,10 @@ class TNT(nn.Module):
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legacy=legacy,
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))
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self.blocks = nn.ModuleList(blocks)
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self.feature_info = [
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dict(module=f'blocks.{i}', num_chs=embed_dim, reduction=r) for i in range(depth)]
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self.norm = norm_layer(embed_dim)
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self.head_drop = nn.Dropout(drop_rate)
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self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()
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@ -313,6 +327,92 @@ class TNT(nn.Module):
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self.global_pool = global_pool
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self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
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def forward_intermediates(
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self,
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x: torch.Tensor,
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indices: Optional[Union[int, List[int]]] = None,
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return_prefix_tokens: bool = False,
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norm: bool = False,
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stop_early: bool = False,
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output_fmt: str = 'NCHW',
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intermediates_only: bool = False,
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) -> Union[List[torch.Tensor], Tuple[torch.Tensor, List[torch.Tensor]]]:
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""" Forward features that returns intermediates.
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Args:
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x: Input image tensor
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indices: Take last n blocks if an int, if is a sequence, select by matching indices
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return_prefix_tokens: Return both prefix and spatial intermediate tokens
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norm: Apply norm layer to all intermediates
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stop_early: Stop iterating over blocks when last desired intermediate hit
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output_fmt: Shape of intermediate feature outputs
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intermediates_only: Only return intermediate features
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Returns:
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"""
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assert output_fmt in ('NCHW', 'NLC'), 'Output format must be one of NCHW or NLC.'
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reshape = output_fmt == 'NCHW'
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intermediates = []
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take_indices, max_index = feature_take_indices(len(self.blocks), indices)
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# forward pass
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B, _, height, width = x.shape
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pixel_embed = self.pixel_embed(x, self.pixel_pos)
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patch_embed = self.norm2_proj(self.proj(self.norm1_proj(pixel_embed.reshape(B, self.num_patches, -1))))
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patch_embed = torch.cat((self.cls_token.expand(B, -1, -1), patch_embed), dim=1)
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patch_embed = patch_embed + self.patch_pos
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patch_embed = self.pos_drop(patch_embed)
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if torch.jit.is_scripting() or not stop_early: # can't slice blocks in torchscript
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blocks = self.blocks
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else:
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blocks = self.blocks[:max_index + 1]
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for i, blk in enumerate(blocks):
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pixel_embed, patch_embed = blk(pixel_embed, patch_embed)
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if i in take_indices:
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# normalize intermediates with final norm layer if enabled
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intermediates.append(self.norm(patch_embed) if norm else patch_embed)
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# process intermediates
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if self.num_prefix_tokens:
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# split prefix (e.g. class, distill) and spatial feature tokens
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prefix_tokens = [y[:, 0:self.num_prefix_tokens] for y in intermediates]
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intermediates = [y[:, self.num_prefix_tokens:] for y in intermediates]
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if reshape:
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# reshape to BCHW output format
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H, W = self.pixel_embed.dynamic_feat_size((height, width))
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intermediates = [y.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous() for y in intermediates]
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if not torch.jit.is_scripting() and return_prefix_tokens:
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# return_prefix not support in torchscript due to poor type handling
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intermediates = list(zip(intermediates, prefix_tokens))
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if intermediates_only:
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return intermediates
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patch_embed = self.norm(patch_embed)
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return patch_embed, intermediates
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def prune_intermediate_layers(
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self,
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indices: Union[int, List[int]] = 1,
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prune_norm: bool = False,
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prune_head: bool = True,
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):
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""" Prune layers not required for specified intermediates.
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"""
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take_indices, max_index = feature_take_indices(len(self.blocks), indices)
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self.blocks = self.blocks[:max_index + 1] # truncate blocks
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if prune_norm:
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self.norm = nn.Identity()
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if prune_head:
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self.reset_classifier(0, '')
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return take_indices
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def forward_features(self, x):
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B = x.shape[0]
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pixel_embed = self.pixel_embed(x, self.pixel_pos)
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@ -322,11 +422,10 @@ class TNT(nn.Module):
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patch_embed = patch_embed + self.patch_pos
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patch_embed = self.pos_drop(patch_embed)
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if self.grad_checkpointing and not torch.jit.is_scripting():
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for blk in self.blocks:
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for blk in self.blocks:
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if self.grad_checkpointing and not torch.jit.is_scripting():
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pixel_embed, patch_embed = checkpoint(blk, pixel_embed, patch_embed)
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else:
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for blk in self.blocks:
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else:
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pixel_embed, patch_embed = blk(pixel_embed, patch_embed)
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patch_embed = self.norm(patch_embed)
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@ -334,7 +433,7 @@ class TNT(nn.Module):
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def forward_head(self, x, pre_logits: bool = False):
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if self.global_pool:
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x = x[:, 1:].mean(dim=1) if self.global_pool == 'avg' else x[:, 0]
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x = x[:, self.num_prefix_tokens:].mean(dim=1) if self.global_pool == 'avg' else x[:, 0]
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x = self.head_drop(x)
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return x if pre_logits else self.head(x)
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@ -344,6 +443,30 @@ class TNT(nn.Module):
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return x
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def _cfg(url='', **kwargs):
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return {
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'url': url,
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'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
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'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True,
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'mean': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD,
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'first_conv': 'pixel_embed.proj', 'classifier': 'head',
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**kwargs
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}
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default_cfgs = {
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'tnt_s_patch16_224.in1k': _cfg(
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# hf_hub_id='timm/',
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# url='https://github.com/contrastive/pytorch-image-models/releases/download/TNT/tnt_s_patch16_224.pth.tar',
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url='https://github.com/huawei-noah/Efficient-AI-Backbones/releases/download/tnt/tnt_s_81.5.pth.tar',
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),
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'tnt_b_patch16_224.in1k': _cfg(
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# hf_hub_id='timm/',
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url='https://github.com/huawei-noah/Efficient-AI-Backbones/releases/download/tnt/tnt_b_82.9.pth.tar',
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),
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}
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def checkpoint_filter_fn(state_dict, model):
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state_dict.pop('outer_tokens', None)
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@ -380,40 +503,15 @@ def checkpoint_filter_fn(state_dict, model):
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def _create_tnt(variant, pretrained=False, **kwargs):
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if kwargs.get('features_only', None):
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raise RuntimeError('features_only not implemented for Vision Transformer models.')
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out_indices = kwargs.pop('out_indices', 3)
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model = build_model_with_cfg(
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TNT, variant, pretrained,
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pretrained_filter_fn=checkpoint_filter_fn,
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feature_cfg=dict(out_indices=out_indices, feature_cls='getter'),
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**kwargs)
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return model
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def _cfg(url='', **kwargs):
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return {
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'url': url,
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'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
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'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True,
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'mean': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD,
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'first_conv': 'pixel_embed.proj', 'classifier': 'head',
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**kwargs
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}
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default_cfgs = {
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'tnt_s_patch16_224': _cfg(
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# hf_hub_id='timm/',
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# url='https://github.com/contrastive/pytorch-image-models/releases/download/TNT/tnt_s_patch16_224.pth.tar',
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url='https://github.com/huawei-noah/Efficient-AI-Backbones/releases/download/tnt/tnt_s_81.5.pth.tar',
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),
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'tnt_b_patch16_224': _cfg(
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# hf_hub_id='timm/',
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url='https://github.com/huawei-noah/Efficient-AI-Backbones/releases/download/tnt/tnt_b_82.9.pth.tar',
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),
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}
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@register_model
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def tnt_s_patch16_224(pretrained=False, **kwargs) -> TNT:
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model_cfg = dict(
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