From ff4a38e2c3d64353995b78732d3e3dec7e3df5dc Mon Sep 17 00:00:00 2001 From: Ross Wightman Date: Wed, 17 Aug 2022 12:06:05 -0700 Subject: [PATCH] Add PyramidVisionTransformerV2 --- timm/models/__init__.py | 1 + timm/models/pvt_v2.py | 476 ++++++++++++++++++++++++++++++++++++++++ 2 files changed, 477 insertions(+) create mode 100644 timm/models/pvt_v2.py diff --git a/timm/models/__init__.py b/timm/models/__init__.py index 195e451b..65b1d955 100644 --- a/timm/models/__init__.py +++ b/timm/models/__init__.py @@ -32,6 +32,7 @@ from .nfnet import * from .pit import * from .pnasnet import * from .poolformer import * +from .pvt_v2 import * from .regnet import * from .res2net import * from .resnest import * diff --git a/timm/models/pvt_v2.py b/timm/models/pvt_v2.py new file mode 100644 index 00000000..551a8325 --- /dev/null +++ b/timm/models/pvt_v2.py @@ -0,0 +1,476 @@ +""" Pyramid Vision Transformer v2 + +@misc{wang2021pvtv2, + title={PVTv2: Improved Baselines with Pyramid Vision Transformer}, + author={Wenhai Wang and Enze Xie and Xiang Li and Deng-Ping Fan and Kaitao Song and Ding Liang and + Tong Lu and Ping Luo and Ling Shao}, + year={2021}, + eprint={2106.13797}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} + +Based on Apache 2.0 licensed code at https://github.com/whai362/PVT + +Modifications and timm support by / Copyright 2022, Ross Wightman +""" + +import math +from functools import partial +from typing import Tuple, List, Callable, Union + +import torch +import torch.nn as nn +import torch.utils.checkpoint as checkpoint + +from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD +from .helpers import build_model_with_cfg +from .layers import DropPath, to_2tuple, to_ntuple, trunc_normal_ +from .registry import register_model + +__all__ = ['PyramidVisionTransformerV2'] + + +def _cfg(url='', **kwargs): + return { + 'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7), + 'crop_pct': 0.9, 'interpolation': 'bicubic', + 'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, + 'first_conv': 'patch_embed.conv', 'classifier': 'head', 'fixed_input_size': False, + **kwargs + } + + +default_cfgs = { + 'pvt_v2_b0': _cfg(url='https://github.com/whai362/PVT/releases/download/v2/pvt_v2_b0.pth'), + 'pvt_v2_b1': _cfg(url='https://github.com/whai362/PVT/releases/download/v2/pvt_v2_b1.pth'), + 'pvt_v2_b2': _cfg(url='https://github.com/whai362/PVT/releases/download/v2/pvt_v2_b2.pth'), + 'pvt_v2_b3': _cfg(url='https://github.com/whai362/PVT/releases/download/v2/pvt_v2_b3.pth'), + 'pvt_v2_b4': _cfg(url='https://github.com/whai362/PVT/releases/download/v2/pvt_v2_b4.pth'), + 'pvt_v2_b5': _cfg(url='https://github.com/whai362/PVT/releases/download/v2/pvt_v2_b5.pth'), + 'pvt_v2_b2_li': _cfg(url='https://github.com/whai362/PVT/releases/download/v2/pvt_v2_b2_li.pth') +} + + +class MlpWithDepthwiseConv(nn.Module): + def __init__( + self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, + drop=0., extra_relu=False): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.relu = nn.ReLU() if extra_relu else nn.Identity() + self.dwconv = nn.Conv2d(hidden_features, hidden_features, 3, 1, 1, bias=True, groups=hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x, feat_size: List[int]): + x = self.fc1(x) + B, N, C = x.shape + x = x.transpose(1, 2).view(B, C, feat_size[0], feat_size[1]) + x = self.relu(x) + x = self.dwconv(x) + x = x.flatten(2).transpose(1, 2) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +class Attention(nn.Module): + def __init__( + self, + dim, + num_heads=8, + sr_ratio=1, + linear_attn=False, + qkv_bias=True, + attn_drop=0., + proj_drop=0. + ): + super().__init__() + assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}." + + self.dim = dim + self.num_heads = num_heads + self.head_dim = dim // num_heads + self.scale = self.head_dim ** -0.5 + + self.q = nn.Linear(dim, dim, bias=qkv_bias) + self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + if not linear_attn: + self.pool = None + if sr_ratio > 1: + self.sr = nn.Conv2d(dim, dim, kernel_size=sr_ratio, stride=sr_ratio) + self.norm = nn.LayerNorm(dim) + else: + self.sr = None + self.norm = None + self.act = None + else: + self.pool = nn.AdaptiveAvgPool2d(7) + self.sr = nn.Conv2d(dim, dim, kernel_size=1, stride=1) + self.norm = nn.LayerNorm(dim) + self.act = nn.GELU() + + def forward(self, x, feat_size: List[int]): + B, N, C = x.shape + H, W = feat_size + q = self.q(x).reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3) + + if self.pool is not None: + x_ = x.permute(0, 2, 1).reshape(B, C, H, W) + x_ = self.sr(self.pool(x_)).reshape(B, C, -1).permute(0, 2, 1) + x_ = self.norm(x_) + x_ = self.act(x_) + kv = self.kv(x_).reshape(B, -1, 2, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4) + else: + if self.sr is not None: + x_ = x.permute(0, 2, 1).reshape(B, C, H, W) + x_ = self.sr(x_).reshape(B, C, -1).permute(0, 2, 1) + x_ = self.norm(x_) + kv = self.kv(x_).reshape(B, -1, 2, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4) + else: + kv = self.kv(x).reshape(B, -1, 2, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4) + k, v = kv.unbind(0) + + attn = (q @ k.transpose(-2, -1)) * self.scale + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class Block(nn.Module): + + def __init__( + self, dim, num_heads, mlp_ratio=4., sr_ratio=1, linear_attn=False, qkv_bias=False, + drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm): + super().__init__() + self.norm1 = norm_layer(dim) + self.attn = Attention( + dim, + num_heads=num_heads, + sr_ratio=sr_ratio, + linear_attn=linear_attn, + qkv_bias=qkv_bias, + attn_drop=attn_drop, + proj_drop=drop, + ) + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.norm2 = norm_layer(dim) + self.mlp = MlpWithDepthwiseConv( + in_features=dim, + hidden_features=int(dim * mlp_ratio), + act_layer=act_layer, + drop=drop, + extra_relu=linear_attn + ) + + def forward(self, x, feat_size: List[int]): + x = x + self.drop_path(self.attn(self.norm1(x), feat_size)) + x = x + self.drop_path(self.mlp(self.norm2(x), feat_size)) + + return x + + +class OverlapPatchEmbed(nn.Module): + """ Image to Patch Embedding + """ + def __init__(self, patch_size=7, stride=4, in_chans=3, embed_dim=768): + super().__init__() + patch_size = to_2tuple(patch_size) + assert max(patch_size) > stride, "Set larger patch_size than stride" + self.patch_size = patch_size + self.proj = nn.Conv2d( + in_chans, embed_dim, kernel_size=patch_size, stride=stride, + padding=(patch_size[0] // 2, patch_size[1] // 2)) + self.norm = nn.LayerNorm(embed_dim) + + def forward(self, x): + x = self.proj(x) + feat_size = x.shape[-2:] + x = x.flatten(2).transpose(1, 2) + x = self.norm(x) + return x, feat_size + + +class PyramidVisionTransformerStage(nn.Module): + def __init__( + self, + dim: int, + dim_out: int, + depth: int, + downsample: bool = True, + num_heads: int = 8, + sr_ratio: int = 1, + linear_attn: bool = False, + mlp_ratio: float = 4.0, + qkv_bias: bool = True, + drop: float = 0., + attn_drop: float = 0., + drop_path: Union[List[float], float] = 0.0, + norm_layer: Callable = nn.LayerNorm, + ): + super().__init__() + self.grad_checkpointing = False + + if downsample: + self.downsample = OverlapPatchEmbed( + patch_size=3, + stride=2, + in_chans=dim, + embed_dim=dim_out) + else: + assert dim == dim_out + self.downsample = None + + self.blocks = nn.ModuleList([Block( + dim=dim_out, + num_heads=num_heads, + sr_ratio=sr_ratio, + linear_attn=linear_attn, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + drop=drop, + attn_drop=attn_drop, + drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, + norm_layer=norm_layer, + ) for i in range(depth)]) + + self.norm = norm_layer(dim_out) + + def forward(self, x, feat_size: List[int]) -> Tuple[torch.Tensor, List[int]]: + if self.downsample is not None: + x, feat_size = self.downsample(x) + for blk in self.blocks: + if self.grad_checkpointing and not torch.jit.is_scripting(): + x = checkpoint.checkpoint(blk, x, feat_size) + else: + x = blk(x, feat_size) + x = self.norm(x) + x = x.reshape(x.shape[0], feat_size[0], feat_size[1], -1).permute(0, 3, 1, 2).contiguous() + return x, feat_size + + +class PyramidVisionTransformerV2(nn.Module): + def __init__( + self, + img_size=None, + in_chans=3, + num_classes=1000, + global_pool='avg', + depths=(3, 4, 6, 3), + embed_dims=(64, 128, 256, 512), + num_heads=(1, 2, 4, 8), + sr_ratios=(8, 4, 2, 1), + mlp_ratios=(8., 8., 4., 4.), + qkv_bias=True, + linear=False, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0., + norm_layer=nn.LayerNorm, + ): + super().__init__() + self.num_classes = num_classes + assert global_pool in ('avg', '') + self.global_pool = global_pool + self.img_size = to_2tuple(img_size) if img_size is not None else None + self.depths = depths + num_stages = len(depths) + mlp_ratios = to_ntuple(num_stages)(mlp_ratios) + num_heads = to_ntuple(num_stages)(num_heads) + sr_ratios = to_ntuple(num_stages)(sr_ratios) + assert(len(embed_dims)) == num_stages + + self.patch_embed = OverlapPatchEmbed( + patch_size=7, + stride=4, + in_chans=in_chans, + embed_dim=embed_dims[0]) + + dpr = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(depths)).split(depths)] + cur = 0 + prev_dim = embed_dims[0] + self.stages = nn.ModuleList() + for i in range(num_stages): + self.stages.append(PyramidVisionTransformerStage( + dim=prev_dim, + dim_out=embed_dims[i], + depth=depths[i], + downsample=i > 0, + num_heads=num_heads[i], + sr_ratio=sr_ratios[i], + mlp_ratio=mlp_ratios[i], + linear_attn=linear, + qkv_bias=qkv_bias, + drop=drop_rate, + attn_drop=attn_drop_rate, + drop_path=dpr[i], + norm_layer=norm_layer + )) + prev_dim = embed_dims[i] + cur += depths[i] + + # classification head + self.head = nn.Linear(embed_dims[3], num_classes) if num_classes > 0 else nn.Identity() + + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.Conv2d): + fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + fan_out //= m.groups + m.weight.data.normal_(0, math.sqrt(2.0 / fan_out)) + if m.bias is not None: + m.bias.data.zero_() + + def freeze_patch_emb(self): + self.patch_embed.requires_grad = False + + @torch.jit.ignore + def no_weight_decay(self): + return {} + + @torch.jit.ignore + def group_matcher(self, coarse=False): + matcher = dict( + stem=r'^patch_embed', # stem and embed + blocks=[(r'^stages\.(\d+)', None), (r'^norm', (99999,))] + ) + return matcher + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + for s in self.stages: + s.grad_checkpointing = enable + + def get_classifier(self): + return self.head + + def reset_classifier(self, num_classes, global_pool=None): + self.num_classes = num_classes + if global_pool is not None: + assert global_pool in ('avg', '') + self.global_pool = global_pool + self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + def forward_features(self, x): + x, feat_size = self.patch_embed(x) + for stage in self.stages: + x, feat_size = stage(x, feat_size=feat_size) + return x + + def forward_head(self, x, pre_logits: bool = False): + if self.global_pool: + x = x.mean(dim=(-1, -2)) + return x if pre_logits else self.head(x) + + def forward(self, x): + x = self.forward_features(x) + x = self.forward_head(x) + return x + + +def _checkpoint_filter_fn(state_dict, model): + """ Remap original checkpoints -> timm """ + if 'patch_embed.proj.weight' in state_dict: + return state_dict # non-original checkpoint, no remapping needed + + out_dict = {} + import re + for k, v in state_dict.items(): + if k.startswith('patch_embed'): + k = k.replace('patch_embed1', 'patch_embed') + k = k.replace('patch_embed2', 'stages.1.downsample') + k = k.replace('patch_embed3', 'stages.2.downsample') + k = k.replace('patch_embed4', 'stages.3.downsample') + k = k.replace('dwconv.dwconv', 'dwconv') + k = re.sub(r'block(\d+).(\d+)', lambda x: f'stages.{int(x.group(1)) - 1}.blocks.{x.group(2)}', k) + k = re.sub(r'^norm(\d+)', lambda x: f'stages.{int(x.group(1)) - 1}.norm', k) + out_dict[k] = v + return out_dict + + +def _create_pvt2(variant, pretrained=False, **kwargs): + if kwargs.get('features_only', None): + raise RuntimeError('features_only not implemented for Vision Transformer models.') + model = build_model_with_cfg( + PyramidVisionTransformerV2, variant, pretrained, + pretrained_filter_fn=_checkpoint_filter_fn, + **kwargs + ) + return model + + +@register_model +def pvt_v2_b0(pretrained=False, **kwargs): + model_kwargs = dict( + depths=(2, 2, 2, 2), embed_dims=(32, 64, 160, 256), num_heads=(1, 2, 5, 8), + norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) + return _create_pvt2('pvt_v2_b0', pretrained=pretrained, **model_kwargs) + + +@register_model +def pvt_v2_b1(pretrained=False, **kwargs): + model_kwargs = dict( + depths=(2, 2, 2, 2), embed_dims=(64, 128, 320, 512), num_heads=(1, 2, 5, 8), + norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) + return _create_pvt2('pvt_v2_b1', pretrained=pretrained, **model_kwargs) + + +@register_model +def pvt_v2_b2(pretrained=False, **kwargs): + model_kwargs = dict( + depths=(3, 4, 6, 3), embed_dims=(64, 128, 320, 512), num_heads=(1, 2, 5, 8), + norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) + return _create_pvt2('pvt_v2_b2', pretrained=pretrained, **model_kwargs) + + +@register_model +def pvt_v2_b3(pretrained=False, **kwargs): + model_kwargs = dict( + depths=(3, 4, 18, 3), embed_dims=(64, 128, 320, 512), num_heads=(1, 2, 5, 8), + norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) + return _create_pvt2('pvt_v2_b3', pretrained=pretrained, **model_kwargs) + + +@register_model +def pvt_v2_b4(pretrained=False, **kwargs): + model_kwargs = dict( + depths=(3, 8, 27, 3), embed_dims=(64, 128, 320, 512), num_heads=(1, 2, 5, 8), + norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) + return _create_pvt2('pvt_v2_b4', pretrained=pretrained, **model_kwargs) + + +@register_model +def pvt_v2_b5(pretrained=False, **kwargs): + model_kwargs = dict( + depths=(3, 6, 40, 3), embed_dims=(64, 128, 320, 512), num_heads=(1, 2, 5, 8), + mlp_ratios=(4, 4, 4, 4), norm_layer=partial(nn.LayerNorm, eps=1e-6), + **kwargs) + return _create_pvt2('pvt_v2_b5', pretrained=pretrained, **model_kwargs) + + +@register_model +def pvt_v2_b2_li(pretrained=False, **kwargs): + model_kwargs = dict( + depths=(3, 4, 6, 3), embed_dims=(64, 128, 320, 512), num_heads=(1, 2, 5, 8), + norm_layer=partial(nn.LayerNorm, eps=1e-6), linear=True, **kwargs) + return _create_pvt2('pvt_v2_b2_li', pretrained=pretrained, **model_kwargs) +