From 9a86b900fa6ea62d39ecd7c7a95e9523fe4cf264 Mon Sep 17 00:00:00 2001 From: Ross Wightman Date: Thu, 12 May 2022 15:01:23 -0700 Subject: [PATCH 1/7] Official SwinV2 models --- timm/models/__init__.py | 1 + timm/models/helpers.py | 2 +- timm/models/swin_transformer_v2.py | 736 ++++++++++++++++++++++++++ timm/models/swin_transformer_v2_cr.py | 90 ++-- 4 files changed, 783 insertions(+), 46 deletions(-) create mode 100644 timm/models/swin_transformer_v2.py diff --git a/timm/models/__init__.py b/timm/models/__init__.py index 32212fca..8cb6c70a 100644 --- a/timm/models/__init__.py +++ b/timm/models/__init__.py @@ -42,6 +42,7 @@ from .senet import * from .sequencer import * from .sknet import * from .swin_transformer import * +from .swin_transformer_v2 import * from .swin_transformer_v2_cr import * from .tnt import * from .tresnet import * diff --git a/timm/models/helpers.py b/timm/models/helpers.py index c4f48d6a..1276b68e 100644 --- a/timm/models/helpers.py +++ b/timm/models/helpers.py @@ -477,7 +477,7 @@ def build_model_with_cfg( pretrained_cfg: Optional[Dict] = None, model_cfg: Optional[Any] = None, feature_cfg: Optional[Dict] = None, - pretrained_strict: bool = False, + pretrained_strict: bool = True, pretrained_filter_fn: Optional[Callable] = None, pretrained_custom_load: bool = False, kwargs_filter: Optional[Tuple[str]] = None, diff --git a/timm/models/swin_transformer_v2.py b/timm/models/swin_transformer_v2.py new file mode 100644 index 00000000..29c0be9e --- /dev/null +++ b/timm/models/swin_transformer_v2.py @@ -0,0 +1,736 @@ +""" Swin Transformer V2 +A PyTorch impl of : `Swin Transformer V2: Scaling Up Capacity and Resolution` + - https://arxiv.org/abs/2111.09883 + +Code/weights from https://github.com/microsoft/Swin-Transformer, original copyright/license info below + +Modifications and additions for timm hacked together by / Copyright 2022, Ross Wightman +""" +# -------------------------------------------------------- +# Swin Transformer V2 +# Copyright (c) 2022 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ze Liu +# -------------------------------------------------------- +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as checkpoint + +from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD +from .fx_features import register_notrace_function +from .helpers import build_model_with_cfg, named_apply +from .layers import PatchEmbed, Mlp, DropPath, to_2tuple, to_ntuple, trunc_normal_, _assert +from .registry import register_model +from .vision_transformer import checkpoint_filter_fn, get_init_weights_vit + + +def _cfg(url='', **kwargs): + return { + 'url': url, + 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None, + 'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True, + 'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, + 'first_conv': 'patch_embed.proj', 'classifier': 'head', + **kwargs + } + + +default_cfgs = { + 'swinv2_tiny_window8_256.': _cfg( + url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_tiny_patch4_window8_256.pth', + input_size=(3, 256, 256) + ), + 'swinv2_tiny_window16_256': _cfg( + url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_tiny_patch4_window16_256.pth', + input_size=(3, 256, 256) + ), + 'swinv2_small_window8_256': _cfg( + url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_small_patch4_window8_256.pth', + input_size=(3, 256, 256) + ), + 'swinv2_small_window16_256': _cfg( + url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_small_patch4_window16_256.pth', + input_size=(3, 256, 256) + ), + 'swinv2_base_window8_256': _cfg( + url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window8_256.pth', + input_size=(3, 256, 256) + ), + 'swinv2_base_window16_256': _cfg( + url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window16_256.pth', + input_size=(3, 256, 256) + ), + + 'swinv2_base_window12_192_22k': _cfg( + url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window12_192_22k.pth', + num_classes=21841, input_size=(3, 192, 192) + ), + 'swinv2_base_window12to16_192to256_22kft1k': _cfg( + url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window12to16_192to256_22kto1k_ft.pth', + input_size=(3, 256, 256) + ), + 'swinv2_base_window12to24_192to384_22kft1k': _cfg( + url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window12to24_192to384_22kto1k_ft.pth', + input_size=(3, 384, 384) + ), + 'swinv2_large_window12_192_22k': _cfg( + url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_large_patch4_window12_192_22k.pth', + num_classes=21841, input_size=(3, 192, 192) + ), + 'swinv2_large_window12to16_192to256_22kft1k': _cfg( + url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_large_patch4_window12to16_192to256_22kto1k_ft.pth', + input_size=(3, 256, 256) + ), + 'swinv2_large_window12to24_192to384_22kft1k': _cfg( + url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_large_patch4_window12to24_192to384_22kto1k_ft.pth', + input_size=(3, 384, 384) + ), +} + + +def window_partition(x, window_size): + """ + Args: + x: (B, H, W, C) + window_size (int): window size + + Returns: + windows: (num_windows*B, window_size, window_size, C) + """ + B, H, W, C = x.shape + x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) + windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) + return windows + + +def window_reverse(windows, window_size, H, W): + """ + Args: + windows: (num_windows*B, window_size, window_size, C) + window_size (int): Window size + H (int): Height of image + W (int): Width of image + + Returns: + x: (B, H, W, C) + """ + B = int(windows.shape[0] / (H * W / window_size / window_size)) + x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) + return x + + +class WindowAttention(nn.Module): + r""" Window based multi-head self attention (W-MSA) module with relative position bias. + It supports both of shifted and non-shifted window. + + Args: + dim (int): Number of input channels. + window_size (tuple[int]): The height and width of the window. + num_heads (int): Number of attention heads. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 + proj_drop (float, optional): Dropout ratio of output. Default: 0.0 + pretrained_window_size (tuple[int]): The height and width of the window in pre-training. + """ + + def __init__( + self, dim, window_size, num_heads, qkv_bias=True, attn_drop=0., proj_drop=0., + pretrained_window_size=[0, 0]): + + super().__init__() + self.dim = dim + self.window_size = window_size # Wh, Ww + self.pretrained_window_size = pretrained_window_size + self.num_heads = num_heads + + self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))), requires_grad=True) + + # mlp to generate continuous relative position bias + self.cpb_mlp = nn.Sequential( + nn.Linear(2, 512, bias=True), + nn.ReLU(inplace=True), + nn.Linear(512, num_heads, bias=False) + ) + + # get relative_coords_table + relative_coords_h = torch.arange(-(self.window_size[0] - 1), self.window_size[0], dtype=torch.float32) + relative_coords_w = torch.arange(-(self.window_size[1] - 1), self.window_size[1], dtype=torch.float32) + relative_coords_table = torch.stack(torch.meshgrid([ + relative_coords_h, + relative_coords_w])).permute(1, 2, 0).contiguous().unsqueeze(0) # 1, 2*Wh-1, 2*Ww-1, 2 + if pretrained_window_size[0] > 0: + relative_coords_table[:, :, :, 0] /= (pretrained_window_size[0] - 1) + relative_coords_table[:, :, :, 1] /= (pretrained_window_size[1] - 1) + else: + relative_coords_table[:, :, :, 0] /= (self.window_size[0] - 1) + relative_coords_table[:, :, :, 1] /= (self.window_size[1] - 1) + relative_coords_table *= 8 # normalize to -8, 8 + relative_coords_table = torch.sign(relative_coords_table) * torch.log2( + torch.abs(relative_coords_table) + 1.0) / math.log2(8) + + self.register_buffer("relative_coords_table", relative_coords_table) + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(self.window_size[0]) + coords_w = torch.arange(self.window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += self.window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 + relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + self.register_buffer("relative_position_index", relative_position_index) + + self.qkv = nn.Linear(dim, dim * 3, bias=False) + if qkv_bias: + self.q_bias = nn.Parameter(torch.zeros(dim)) + self.v_bias = nn.Parameter(torch.zeros(dim)) + else: + self.q_bias = None + self.v_bias = None + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + self.softmax = nn.Softmax(dim=-1) + + def forward(self, x, mask=None): + """ + Args: + x: input features with shape of (num_windows*B, N, C) + mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None + """ + B_, N, C = x.shape + qkv_bias = None + if self.q_bias is not None: + qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias)) + qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias) + qkv = qkv.reshape(B_, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + + # cosine attention + attn = (F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1)) + logit_scale = torch.clamp(self.logit_scale, max=torch.log(torch.tensor(1. / 0.01))).exp() + attn = attn * logit_scale + + relative_position_bias_table = self.cpb_mlp(self.relative_coords_table).view(-1, self.num_heads) + relative_position_bias = relative_position_bias_table[self.relative_position_index.view(-1)].view( + self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH + relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww + relative_position_bias = 16 * torch.sigmoid(relative_position_bias) + attn = attn + relative_position_bias.unsqueeze(0) + + if mask is not None: + nW = mask.shape[0] + attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0) + attn = attn.view(-1, self.num_heads, N, N) + attn = self.softmax(attn) + else: + attn = self.softmax(attn) + + 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 SwinTransformerBlock(nn.Module): + r""" Swin Transformer Block. + + Args: + dim (int): Number of input channels. + input_resolution (tuple[int]): Input resolution. + num_heads (int): Number of attention heads. + window_size (int): Window size. + shift_size (int): Shift size for SW-MSA. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float, optional): Stochastic depth rate. Default: 0.0 + act_layer (nn.Module, optional): Activation layer. Default: nn.GELU + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + pretrained_window_size (int): Window size in pretraining. + """ + + def __init__( + self, dim, input_resolution, num_heads, window_size=7, shift_size=0, + mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0., drop_path=0., + act_layer=nn.GELU, norm_layer=nn.LayerNorm, pretrained_window_size=0): + super().__init__() + self.dim = dim + self.input_resolution = input_resolution + self.num_heads = num_heads + self.window_size = window_size + self.shift_size = shift_size + self.mlp_ratio = mlp_ratio + if min(self.input_resolution) <= self.window_size: + # if window size is larger than input resolution, we don't partition windows + self.shift_size = 0 + self.window_size = min(self.input_resolution) + _assert(0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size") + + self.attn = WindowAttention( + dim, window_size=to_2tuple(self.window_size), num_heads=num_heads, + qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop, + pretrained_window_size=to_2tuple(pretrained_window_size)) + self.norm1 = norm_layer(dim) + self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity() + + self.mlp = Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer, drop=drop) + self.norm2 = norm_layer(dim) + self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity() + + if self.shift_size > 0: + # calculate attention mask for SW-MSA + H, W = self.input_resolution + img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1 + cnt = 0 + for h in ( + slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None)): + for w in ( + slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None)): + img_mask[:, h, w, :] = cnt + cnt += 1 + mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1 + mask_windows = mask_windows.view(-1, self.window_size * self.window_size) + attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) + attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) + else: + attn_mask = None + + self.register_buffer("attn_mask", attn_mask) + + def _attn(self, x): + H, W = self.input_resolution + B, L, C = x.shape + _assert(L == H * W, "input feature has wrong size") + x = x.view(B, H, W, C) + + # cyclic shift + if self.shift_size > 0: + shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) + else: + shifted_x = x + + # partition windows + x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C + x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C + + # W-MSA/SW-MSA + attn_windows = self.attn(x_windows, mask=self.attn_mask) # nW*B, window_size*window_size, C + + # merge windows + attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) + shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C + + # reverse cyclic shift + if self.shift_size > 0: + x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) + else: + x = shifted_x + x = x.view(B, H * W, C) + return x + + def forward(self, x): + x = x + self.drop_path1(self.norm1(self._attn(x))) + x = x + self.drop_path2(self.norm2(self.mlp(x))) + return x + + +class PatchMerging(nn.Module): + r""" Patch Merging Layer. + + Args: + input_resolution (tuple[int]): Resolution of input feature. + dim (int): Number of input channels. + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm): + super().__init__() + self.input_resolution = input_resolution + self.dim = dim + self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) + self.norm = norm_layer(2 * dim) + + def forward(self, x): + """ + x: B, H*W, C + """ + H, W = self.input_resolution + B, L, C = x.shape + _assert(L == H * W, "input feature has wrong size") + _assert(H % 2 == 0, f"x size ({H}*{W}) are not even.") + _assert(W % 2 == 0, f"x size ({H}*{W}) are not even.") + + x = x.view(B, H, W, C) + + x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C + x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C + x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C + x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C + x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C + x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C + + x = self.reduction(x) + x = self.norm(x) + + return x + + +class BasicLayer(nn.Module): + """ A basic Swin Transformer layer for one stage. + + Args: + dim (int): Number of input channels. + input_resolution (tuple[int]): Input resolution. + depth (int): Number of blocks. + num_heads (int): Number of attention heads. + window_size (int): Local window size. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None + pretrained_window_size (int): Local window size in pre-training. + """ + + def __init__( + self, dim, input_resolution, depth, num_heads, window_size, + mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0., drop_path=0., + norm_layer=nn.LayerNorm, downsample=None, pretrained_window_size=0): + + super().__init__() + self.dim = dim + self.input_resolution = input_resolution + self.depth = depth + self.grad_checkpointing = False + + # build blocks + self.blocks = nn.ModuleList([ + SwinTransformerBlock( + dim=dim, input_resolution=input_resolution, + num_heads=num_heads, window_size=window_size, + shift_size=0 if (i % 2 == 0) else window_size // 2, + 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, + pretrained_window_size=pretrained_window_size) + for i in range(depth)]) + + # patch merging layer + if downsample is not None: + self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer) + else: + self.downsample = nn.Identity() + + def forward(self, x): + for blk in self.blocks: + if self.grad_checkpointing: + x = checkpoint.checkpoint(blk, x) + else: + x = blk(x) + x = self.downsample(x) + return x + + def _init_respostnorm(self): + for blk in self.blocks: + nn.init.constant_(blk.norm1.bias, 0) + nn.init.constant_(blk.norm1.weight, 0) + nn.init.constant_(blk.norm2.bias, 0) + nn.init.constant_(blk.norm2.weight, 0) + + +class SwinTransformerV2(nn.Module): + r""" Swin Transformer V2 + A PyTorch impl of : `Swin Transformer V2: Scaling Up Capacity and Resolution` + - https://arxiv.org/abs/2111.09883 + Args: + img_size (int | tuple(int)): Input image size. Default 224 + patch_size (int | tuple(int)): Patch size. Default: 4 + in_chans (int): Number of input image channels. Default: 3 + num_classes (int): Number of classes for classification head. Default: 1000 + embed_dim (int): Patch embedding dimension. Default: 96 + depths (tuple(int)): Depth of each Swin Transformer layer. + num_heads (tuple(int)): Number of attention heads in different layers. + window_size (int): Window size. Default: 7 + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4 + qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True + drop_rate (float): Dropout rate. Default: 0 + attn_drop_rate (float): Attention dropout rate. Default: 0 + drop_path_rate (float): Stochastic depth rate. Default: 0.1 + norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. + ape (bool): If True, add absolute position embedding to the patch embedding. Default: False + patch_norm (bool): If True, add normalization after patch embedding. Default: True + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False + pretrained_window_sizes (tuple(int)): Pretrained window sizes of each layer. + """ + + def __init__( + self, img_size=224, patch_size=4, in_chans=3, num_classes=1000, global_pool='avg', + embed_dim=96, depths=(2, 2, 6, 2), num_heads=(3, 6, 12, 24), + window_size=7, mlp_ratio=4., qkv_bias=True, + drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1, + norm_layer=nn.LayerNorm, ape=False, patch_norm=True, + pretrained_window_sizes=(0, 0, 0, 0), **kwargs): + super().__init__() + + self.num_classes = num_classes + assert global_pool in ('', 'avg') + self.global_pool = global_pool + self.num_layers = len(depths) + self.embed_dim = embed_dim + self.patch_norm = patch_norm + self.num_features = int(embed_dim * 2 ** (self.num_layers - 1)) + + # split image into non-overlapping patches + self.patch_embed = PatchEmbed( + img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim, + norm_layer=norm_layer if self.patch_norm else None) + num_patches = self.patch_embed.num_patches + + # absolute position embedding + if ape: + self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim)) + trunc_normal_(self.absolute_pos_embed, std=.02) + else: + self.absolute_pos_embed = None + + self.pos_drop = nn.Dropout(p=drop_rate) + + # stochastic depth + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule + + # build layers + self.layers = nn.ModuleList() + for i_layer in range(self.num_layers): + layer = BasicLayer( + dim=int(embed_dim * 2 ** i_layer), + input_resolution=( + self.patch_embed.grid_size[0] // (2 ** i_layer), + self.patch_embed.grid_size[1] // (2 ** i_layer)), + depth=depths[i_layer], + num_heads=num_heads[i_layer], + window_size=window_size, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + drop=drop_rate, attn_drop=attn_drop_rate, + drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], + norm_layer=norm_layer, + downsample=PatchMerging if (i_layer < self.num_layers - 1) else None, + pretrained_window_size=pretrained_window_sizes[i_layer] + ) + self.layers.append(layer) + + self.norm = norm_layer(self.num_features) + self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity() + + self.apply(self._init_weights) + for bly in self.layers: + bly._init_respostnorm() + + 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.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + @torch.jit.ignore + def no_weight_decay(self): + nod = {'absolute_pos_embed'} + for n, m in self.named_modules(): + if any([kw in n for kw in ("cpb_mlp", "logit_scale", 'relative_position_bias_table')]): + nod.add(n) + return nod + + @torch.jit.ignore + def group_matcher(self, coarse=False): + return dict( + stem=r'^absolute_pos_embed|patch_embed', # stem and embed + blocks=r'^layers\.(\d+)' if coarse else [ + (r'^layers\.(\d+).downsample', (0,)), + (r'^layers\.(\d+)\.\w+\.(\d+)', None), + (r'^norm', (99999,)), + ] + ) + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + for l in self.layers: + l.grad_checkpointing = enable + + @torch.jit.ignore + 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.num_features, num_classes) if num_classes > 0 else nn.Identity() + + def forward_features(self, x): + x = self.patch_embed(x) + if self.absolute_pos_embed is not None: + x = x + self.absolute_pos_embed + x = self.pos_drop(x) + + for layer in self.layers: + x = layer(x) + + x = self.norm(x) # B L C + return x + + def forward_head(self, x, pre_logits: bool = False): + if self.global_pool == 'avg': + x = x.mean(dim=1) + 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 _create_swin_transformer_v2(variant, pretrained=False, **kwargs): + model = build_model_with_cfg( + SwinTransformerV2, variant, pretrained, + pretrained_filter_fn=checkpoint_filter_fn, + **kwargs) + return model + + +@register_model +def swinv2_tiny_window16_256(pretrained=False, **kwargs): + """ + """ + model_kwargs = dict( + window_size=16, embed_dim=96, depths=(2, 2, 6, 2), num_heads=(3, 6, 12, 24), **kwargs) + return _create_swin_transformer_v2('swinv2_tiny_window16_256', pretrained=pretrained, **model_kwargs) + + +@register_model +def swinv2_tiny_window8_256(pretrained=False, **kwargs): + """ + """ + model_kwargs = dict( + window_size=8, embed_dim=96, depths=(2, 2, 6, 2), num_heads=(3, 6, 12, 24), **kwargs) + return _create_swin_transformer_v2('swinv2_tiny_window8_256', pretrained=pretrained, **model_kwargs) + + +@register_model +def swinv2_small_window16_256(pretrained=False, **kwargs): + """ + """ + model_kwargs = dict( + window_size=16, embed_dim=96, depths=(2, 2, 18, 2), num_heads=(3, 6, 12, 24), **kwargs) + return _create_swin_transformer_v2('swinv2_small_window16_256', pretrained=pretrained, **model_kwargs) + + +@register_model +def swinv2_small_window8_256(pretrained=False, **kwargs): + """ + """ + model_kwargs = dict( + window_size=8, embed_dim=96, depths=(2, 2, 18, 2), num_heads=(3, 6, 12, 24), **kwargs) + return _create_swin_transformer_v2('swinv2_small_window8_256', pretrained=pretrained, **model_kwargs) + + +@register_model +def swinv2_base_window16_256(pretrained=False, **kwargs): + """ + """ + model_kwargs = dict( + window_size=16, embed_dim=128, depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32), **kwargs) + return _create_swin_transformer_v2('swinv2_base_window16_256', pretrained=pretrained, **model_kwargs) + + +@register_model +def swinv2_base_window8_256(pretrained=False, **kwargs): + """ + """ + model_kwargs = dict( + window_size=8, embed_dim=128, depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32), **kwargs) + return _create_swin_transformer_v2('swinv2_base_window8_256', pretrained=pretrained, **model_kwargs) + + +@register_model +def swinv2_base_window12_192_22k(pretrained=False, **kwargs): + """ + """ + model_kwargs = dict( + window_size=12, embed_dim=128, depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32), **kwargs) + return _create_swin_transformer_v2('swinv2_base_window12_192_22k', pretrained=pretrained, **model_kwargs) + + +@register_model +def swinv2_base_window12to16_192to256_22kft1k(pretrained=False, **kwargs): + """ + """ + model_kwargs = dict( + window_size=16, embed_dim=128, depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32), + pretrained_window_sizes=(12, 12, 12, 6), **kwargs) + return _create_swin_transformer_v2( + 'swinv2_base_window12to16_192to256_22kft1k', pretrained=pretrained, **model_kwargs) + + +@register_model +def swinv2_base_window12to24_192to384_22kft1k(pretrained=False, **kwargs): + """ + """ + model_kwargs = dict( + window_size=24, embed_dim=128, depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32), + pretrained_window_sizes=(12, 12, 12, 6), **kwargs) + return _create_swin_transformer_v2( + 'swinv2_base_window12to24_192to384_22kft1k', pretrained=pretrained, **model_kwargs) + + +@register_model +def swinv2_large_window12_192_22k(pretrained=False, **kwargs): + """ + """ + model_kwargs = dict( + window_size=12, embed_dim=192, depths=(2, 2, 18, 2), num_heads=(6, 12, 24, 48), **kwargs) + return _create_swin_transformer_v2('swinv2_large_window12_192_22k', pretrained=pretrained, **model_kwargs) + + +@register_model +def swinv2_large_window12to16_192to256_22kft1k(pretrained=False, **kwargs): + """ + """ + model_kwargs = dict( + window_size=16, embed_dim=192, depths=(2, 2, 18, 2), num_heads=(6, 12, 24, 48), + pretrained_window_sizes=(12, 12, 12, 6), **kwargs) + return _create_swin_transformer_v2( + 'swinv2_large_window12to16_192to256_22kft1k', pretrained=pretrained, **model_kwargs) + + +@register_model +def swinv2_large_window12to24_192to384_22kft1k(pretrained=False, **kwargs): + """ + """ + model_kwargs = dict( + window_size=24, embed_dim=192, depths=(2, 2, 18, 2), num_heads=(6, 12, 24, 48), + pretrained_window_sizes=(12, 12, 12, 6), **kwargs) + return _create_swin_transformer_v2( + 'swinv2_large_window12to24_192to384_22kft1k', pretrained=pretrained, **model_kwargs) diff --git a/timm/models/swin_transformer_v2_cr.py b/timm/models/swin_transformer_v2_cr.py index 472ae205..596ee204 100644 --- a/timm/models/swin_transformer_v2_cr.py +++ b/timm/models/swin_transformer_v2_cr.py @@ -64,38 +64,38 @@ def _cfg(url='', **kwargs): default_cfgs = { - 'swin_v2_cr_tiny_384': _cfg( + 'swinv2_cr_tiny_384': _cfg( url="", input_size=(3, 384, 384), crop_pct=1.0), - 'swin_v2_cr_tiny_224': _cfg( + 'swinv2_cr_tiny_224': _cfg( url="", input_size=(3, 224, 224), crop_pct=0.9), - 'swin_v2_cr_tiny_ns_224': _cfg( + 'swinv2_cr_tiny_ns_224': _cfg( url="https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-swinv2/swin_v2_cr_tiny_ns_224-ba8166c6.pth", input_size=(3, 224, 224), crop_pct=0.9), - 'swin_v2_cr_small_384': _cfg( + 'swinv2_cr_small_384': _cfg( url="", input_size=(3, 384, 384), crop_pct=1.0), - 'swin_v2_cr_small_224': _cfg( + 'swinv2_cr_small_224': _cfg( url="https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-swinv2/swin_v2_cr_small_224-0813c165.pth", input_size=(3, 224, 224), crop_pct=0.9), - 'swin_v2_cr_small_ns_224': _cfg( + 'swinv2_cr_small_ns_224': _cfg( url="https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-swinv2/swin_v2_cr_small_ns_224_iv-2ce90f8e.pth", input_size=(3, 224, 224), crop_pct=0.9), - 'swin_v2_cr_base_384': _cfg( + 'swinv2_cr_base_384': _cfg( url="", input_size=(3, 384, 384), crop_pct=1.0), - 'swin_v2_cr_base_224': _cfg( + 'swinv2_cr_base_224': _cfg( url="", input_size=(3, 224, 224), crop_pct=0.9), - 'swin_v2_cr_base_ns_224': _cfg( + 'swinv2_cr_base_ns_224': _cfg( url="", input_size=(3, 224, 224), crop_pct=0.9), - 'swin_v2_cr_large_384': _cfg( + 'swinv2_cr_large_384': _cfg( url="", input_size=(3, 384, 384), crop_pct=1.0), - 'swin_v2_cr_large_224': _cfg( + 'swinv2_cr_large_224': _cfg( url="", input_size=(3, 224, 224), crop_pct=0.9), - 'swin_v2_cr_huge_384': _cfg( + 'swinv2_cr_huge_384': _cfg( url="", input_size=(3, 384, 384), crop_pct=1.0), - 'swin_v2_cr_huge_224': _cfg( + 'swinv2_cr_huge_224': _cfg( url="", input_size=(3, 224, 224), crop_pct=0.9), - 'swin_v2_cr_giant_384': _cfg( + 'swinv2_cr_giant_384': _cfg( url="", input_size=(3, 384, 384), crop_pct=1.0), - 'swin_v2_cr_giant_224': _cfg( + 'swinv2_cr_giant_224': _cfg( url="", input_size=(3, 224, 224), crop_pct=0.9), } @@ -820,7 +820,7 @@ def _create_swin_transformer_v2_cr(variant, pretrained=False, **kwargs): @register_model -def swin_v2_cr_tiny_384(pretrained=False, **kwargs): +def swinv2_cr_tiny_384(pretrained=False, **kwargs): """Swin-T V2 CR @ 384x384, trained ImageNet-1k""" model_kwargs = dict( embed_dim=96, @@ -828,11 +828,11 @@ def swin_v2_cr_tiny_384(pretrained=False, **kwargs): num_heads=(3, 6, 12, 24), **kwargs ) - return _create_swin_transformer_v2_cr('swin_v2_cr_tiny_384', pretrained=pretrained, **model_kwargs) + return _create_swin_transformer_v2_cr('swinv2_cr_tiny_384', pretrained=pretrained, **model_kwargs) @register_model -def swin_v2_cr_tiny_224(pretrained=False, **kwargs): +def swinv2_cr_tiny_224(pretrained=False, **kwargs): """Swin-T V2 CR @ 224x224, trained ImageNet-1k""" model_kwargs = dict( embed_dim=96, @@ -840,11 +840,11 @@ def swin_v2_cr_tiny_224(pretrained=False, **kwargs): num_heads=(3, 6, 12, 24), **kwargs ) - return _create_swin_transformer_v2_cr('swin_v2_cr_tiny_224', pretrained=pretrained, **model_kwargs) + return _create_swin_transformer_v2_cr('swinv2_cr_tiny_224', pretrained=pretrained, **model_kwargs) @register_model -def swin_v2_cr_tiny_ns_224(pretrained=False, **kwargs): +def swinv2_cr_tiny_ns_224(pretrained=False, **kwargs): """Swin-T V2 CR @ 224x224, trained ImageNet-1k w/ extra stage norms. ** Experimental, may make default if results are improved. ** """ @@ -855,11 +855,11 @@ def swin_v2_cr_tiny_ns_224(pretrained=False, **kwargs): extra_norm_stage=True, **kwargs ) - return _create_swin_transformer_v2_cr('swin_v2_cr_tiny_ns_224', pretrained=pretrained, **model_kwargs) + return _create_swin_transformer_v2_cr('swinv2_cr_tiny_ns_224', pretrained=pretrained, **model_kwargs) @register_model -def swin_v2_cr_small_384(pretrained=False, **kwargs): +def swinv2_cr_small_384(pretrained=False, **kwargs): """Swin-S V2 CR @ 384x384, trained ImageNet-1k""" model_kwargs = dict( embed_dim=96, @@ -867,12 +867,12 @@ def swin_v2_cr_small_384(pretrained=False, **kwargs): num_heads=(3, 6, 12, 24), **kwargs ) - return _create_swin_transformer_v2_cr('swin_v2_cr_small_384', pretrained=pretrained, **model_kwargs + return _create_swin_transformer_v2_cr('swinv2_cr_small_384', pretrained=pretrained, **model_kwargs ) @register_model -def swin_v2_cr_small_224(pretrained=False, **kwargs): +def swinv2_cr_small_224(pretrained=False, **kwargs): """Swin-S V2 CR @ 224x224, trained ImageNet-1k""" model_kwargs = dict( embed_dim=96, @@ -880,11 +880,11 @@ def swin_v2_cr_small_224(pretrained=False, **kwargs): num_heads=(3, 6, 12, 24), **kwargs ) - return _create_swin_transformer_v2_cr('swin_v2_cr_small_224', pretrained=pretrained, **model_kwargs) + return _create_swin_transformer_v2_cr('swinv2_cr_small_224', pretrained=pretrained, **model_kwargs) @register_model -def swin_v2_cr_small_ns_224(pretrained=False, **kwargs): +def swinv2_cr_small_ns_224(pretrained=False, **kwargs): """Swin-S V2 CR @ 224x224, trained ImageNet-1k""" model_kwargs = dict( embed_dim=96, @@ -894,11 +894,11 @@ def swin_v2_cr_small_ns_224(pretrained=False, **kwargs): extra_norm_stage=True, **kwargs ) - return _create_swin_transformer_v2_cr('swin_v2_cr_small_ns_224', pretrained=pretrained, **model_kwargs) + return _create_swin_transformer_v2_cr('swinv2_cr_small_ns_224', pretrained=pretrained, **model_kwargs) @register_model -def swin_v2_cr_base_384(pretrained=False, **kwargs): +def swinv2_cr_base_384(pretrained=False, **kwargs): """Swin-B V2 CR @ 384x384, trained ImageNet-1k""" model_kwargs = dict( embed_dim=128, @@ -906,11 +906,11 @@ def swin_v2_cr_base_384(pretrained=False, **kwargs): num_heads=(4, 8, 16, 32), **kwargs ) - return _create_swin_transformer_v2_cr('swin_v2_cr_base_384', pretrained=pretrained, **model_kwargs) + return _create_swin_transformer_v2_cr('swinv2_cr_base_384', pretrained=pretrained, **model_kwargs) @register_model -def swin_v2_cr_base_224(pretrained=False, **kwargs): +def swinv2_cr_base_224(pretrained=False, **kwargs): """Swin-B V2 CR @ 224x224, trained ImageNet-1k""" model_kwargs = dict( embed_dim=128, @@ -918,11 +918,11 @@ def swin_v2_cr_base_224(pretrained=False, **kwargs): num_heads=(4, 8, 16, 32), **kwargs ) - return _create_swin_transformer_v2_cr('swin_v2_cr_base_224', pretrained=pretrained, **model_kwargs) + return _create_swin_transformer_v2_cr('swinv2_cr_base_224', pretrained=pretrained, **model_kwargs) @register_model -def swin_v2_cr_base_ns_224(pretrained=False, **kwargs): +def swinv2_cr_base_ns_224(pretrained=False, **kwargs): """Swin-B V2 CR @ 224x224, trained ImageNet-1k""" model_kwargs = dict( embed_dim=128, @@ -932,11 +932,11 @@ def swin_v2_cr_base_ns_224(pretrained=False, **kwargs): extra_norm_stage=True, **kwargs ) - return _create_swin_transformer_v2_cr('swin_v2_cr_base_ns_224', pretrained=pretrained, **model_kwargs) + return _create_swin_transformer_v2_cr('swinv2_cr_base_ns_224', pretrained=pretrained, **model_kwargs) @register_model -def swin_v2_cr_large_384(pretrained=False, **kwargs): +def swinv2_cr_large_384(pretrained=False, **kwargs): """Swin-L V2 CR @ 384x384, trained ImageNet-1k""" model_kwargs = dict( embed_dim=192, @@ -944,12 +944,12 @@ def swin_v2_cr_large_384(pretrained=False, **kwargs): num_heads=(6, 12, 24, 48), **kwargs ) - return _create_swin_transformer_v2_cr('swin_v2_cr_large_384', pretrained=pretrained, **model_kwargs + return _create_swin_transformer_v2_cr('swinv2_cr_large_384', pretrained=pretrained, **model_kwargs ) @register_model -def swin_v2_cr_large_224(pretrained=False, **kwargs): +def swinv2_cr_large_224(pretrained=False, **kwargs): """Swin-L V2 CR @ 224x224, trained ImageNet-1k""" model_kwargs = dict( embed_dim=192, @@ -957,11 +957,11 @@ def swin_v2_cr_large_224(pretrained=False, **kwargs): num_heads=(6, 12, 24, 48), **kwargs ) - return _create_swin_transformer_v2_cr('swin_v2_cr_large_224', pretrained=pretrained, **model_kwargs) + return _create_swin_transformer_v2_cr('swinv2_cr_large_224', pretrained=pretrained, **model_kwargs) @register_model -def swin_v2_cr_huge_384(pretrained=False, **kwargs): +def swinv2_cr_huge_384(pretrained=False, **kwargs): """Swin-H V2 CR @ 384x384, trained ImageNet-1k""" model_kwargs = dict( embed_dim=352, @@ -970,11 +970,11 @@ def swin_v2_cr_huge_384(pretrained=False, **kwargs): extra_norm_period=6, **kwargs ) - return _create_swin_transformer_v2_cr('swin_v2_cr_huge_384', pretrained=pretrained, **model_kwargs) + return _create_swin_transformer_v2_cr('swinv2_cr_huge_384', pretrained=pretrained, **model_kwargs) @register_model -def swin_v2_cr_huge_224(pretrained=False, **kwargs): +def swinv2_cr_huge_224(pretrained=False, **kwargs): """Swin-H V2 CR @ 224x224, trained ImageNet-1k""" model_kwargs = dict( embed_dim=352, @@ -983,11 +983,11 @@ def swin_v2_cr_huge_224(pretrained=False, **kwargs): extra_norm_period=6, **kwargs ) - return _create_swin_transformer_v2_cr('swin_v2_cr_huge_224', pretrained=pretrained, **model_kwargs) + return _create_swin_transformer_v2_cr('swinv2_cr_huge_224', pretrained=pretrained, **model_kwargs) @register_model -def swin_v2_cr_giant_384(pretrained=False, **kwargs): +def swinv2_cr_giant_384(pretrained=False, **kwargs): """Swin-G V2 CR @ 384x384, trained ImageNet-1k""" model_kwargs = dict( embed_dim=512, @@ -996,12 +996,12 @@ def swin_v2_cr_giant_384(pretrained=False, **kwargs): extra_norm_period=6, **kwargs ) - return _create_swin_transformer_v2_cr('swin_v2_cr_giant_384', pretrained=pretrained, **model_kwargs + return _create_swin_transformer_v2_cr('swinv2_cr_giant_384', pretrained=pretrained, **model_kwargs ) @register_model -def swin_v2_cr_giant_224(pretrained=False, **kwargs): +def swinv2_cr_giant_224(pretrained=False, **kwargs): """Swin-G V2 CR @ 224x224, trained ImageNet-1k""" model_kwargs = dict( embed_dim=512, @@ -1010,4 +1010,4 @@ def swin_v2_cr_giant_224(pretrained=False, **kwargs): extra_norm_period=6, **kwargs ) - return _create_swin_transformer_v2_cr('swin_v2_cr_giant_224', pretrained=pretrained, **model_kwargs) + return _create_swin_transformer_v2_cr('swinv2_cr_giant_224', pretrained=pretrained, **model_kwargs) From c0211b0bf79ee7e1009d04f11d27a061caa670b6 Mon Sep 17 00:00:00 2001 From: Ross Wightman Date: Thu, 12 May 2022 22:31:55 -0700 Subject: [PATCH 2/7] Swin-V2 test fixes, typo --- tests/test_models.py | 2 +- timm/models/swin_transformer_v2.py | 7 +++++-- timm/models/swin_transformer_v2_cr.py | 14 +++++++------- 3 files changed, 13 insertions(+), 10 deletions(-) diff --git a/tests/test_models.py b/tests/test_models.py index 6489892c..7ea9af6e 100644 --- a/tests/test_models.py +++ b/tests/test_models.py @@ -25,7 +25,7 @@ if hasattr(torch._C, '_jit_set_profiling_executor'): NON_STD_FILTERS = [ 'vit_*', 'tnt_*', 'pit_*', 'swin_*', 'coat_*', 'cait_*', '*mixer_*', 'gmlp_*', 'resmlp_*', 'twins_*', 'convit_*', 'levit*', 'visformer*', 'deit*', 'jx_nest_*', 'nest_*', 'xcit_*', 'crossvit_*', 'beit_*', - 'poolformer_*', 'volo_*', 'sequencer2d_*'] + 'poolformer_*', 'volo_*', 'sequencer2d_*', 'swinv2_*'] NUM_NON_STD = len(NON_STD_FILTERS) # exclude models that cause specific test failures diff --git a/timm/models/swin_transformer_v2.py b/timm/models/swin_transformer_v2.py index 29c0be9e..700012fe 100644 --- a/timm/models/swin_transformer_v2.py +++ b/timm/models/swin_transformer_v2.py @@ -39,7 +39,7 @@ def _cfg(url='', **kwargs): default_cfgs = { - 'swinv2_tiny_window8_256.': _cfg( + 'swinv2_tiny_window8_256': _cfg( url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_tiny_patch4_window8_256.pth', input_size=(3, 256, 256) ), @@ -106,6 +106,7 @@ def window_partition(x, window_size): return windows +@register_notrace_function # reason: int argument is a Proxy def window_reverse(windows, window_size, H, W): """ Args: @@ -190,9 +191,11 @@ class WindowAttention(nn.Module): self.qkv = nn.Linear(dim, dim * 3, bias=False) if qkv_bias: self.q_bias = nn.Parameter(torch.zeros(dim)) + self.register_buffer('k_bias', torch.zeros(dim), persistent=False) self.v_bias = nn.Parameter(torch.zeros(dim)) else: self.q_bias = None + self.k_bias = None self.v_bias = None self.attn_drop = nn.Dropout(attn_drop) self.proj = nn.Linear(dim, dim) @@ -208,7 +211,7 @@ class WindowAttention(nn.Module): B_, N, C = x.shape qkv_bias = None if self.q_bias is not None: - qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias)) + qkv_bias = torch.cat((self.q_bias, self.k_bias, self.v_bias)) qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias) qkv = qkv.reshape(B_, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) diff --git a/timm/models/swin_transformer_v2_cr.py b/timm/models/swin_transformer_v2_cr.py index 596ee204..fcfa217e 100644 --- a/timm/models/swin_transformer_v2_cr.py +++ b/timm/models/swin_transformer_v2_cr.py @@ -51,7 +51,7 @@ def _cfg(url='', **kwargs): 'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), - 'pool_size': None, + 'pool_size': (7, 7), 'crop_pct': 0.9, 'interpolation': 'bicubic', 'fixed_input_size': True, @@ -65,14 +65,14 @@ def _cfg(url='', **kwargs): default_cfgs = { 'swinv2_cr_tiny_384': _cfg( - url="", input_size=(3, 384, 384), crop_pct=1.0), + url="", input_size=(3, 384, 384), crop_pct=1.0, pool_size=(12, 12)), 'swinv2_cr_tiny_224': _cfg( url="", input_size=(3, 224, 224), crop_pct=0.9), 'swinv2_cr_tiny_ns_224': _cfg( url="https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-swinv2/swin_v2_cr_tiny_ns_224-ba8166c6.pth", input_size=(3, 224, 224), crop_pct=0.9), 'swinv2_cr_small_384': _cfg( - url="", input_size=(3, 384, 384), crop_pct=1.0), + url="", input_size=(3, 384, 384), crop_pct=1.0, pool_size=(12, 12)), 'swinv2_cr_small_224': _cfg( url="https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-swinv2/swin_v2_cr_small_224-0813c165.pth", input_size=(3, 224, 224), crop_pct=0.9), @@ -80,21 +80,21 @@ default_cfgs = { url="https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-swinv2/swin_v2_cr_small_ns_224_iv-2ce90f8e.pth", input_size=(3, 224, 224), crop_pct=0.9), 'swinv2_cr_base_384': _cfg( - url="", input_size=(3, 384, 384), crop_pct=1.0), + url="", input_size=(3, 384, 384), crop_pct=1.0, pool_size=(12, 12)), 'swinv2_cr_base_224': _cfg( url="", input_size=(3, 224, 224), crop_pct=0.9), 'swinv2_cr_base_ns_224': _cfg( url="", input_size=(3, 224, 224), crop_pct=0.9), 'swinv2_cr_large_384': _cfg( - url="", input_size=(3, 384, 384), crop_pct=1.0), + url="", input_size=(3, 384, 384), crop_pct=1.0, pool_size=(12, 12)), 'swinv2_cr_large_224': _cfg( url="", input_size=(3, 224, 224), crop_pct=0.9), 'swinv2_cr_huge_384': _cfg( - url="", input_size=(3, 384, 384), crop_pct=1.0), + url="", input_size=(3, 384, 384), crop_pct=1.0, pool_size=(12, 12)), 'swinv2_cr_huge_224': _cfg( url="", input_size=(3, 224, 224), crop_pct=0.9), 'swinv2_cr_giant_384': _cfg( - url="", input_size=(3, 384, 384), crop_pct=1.0), + url="", input_size=(3, 384, 384), crop_pct=1.0, pool_size=(12, 12)), 'swinv2_cr_giant_224': _cfg( url="", input_size=(3, 224, 224), crop_pct=0.9), } From 2f2b22d8c7889174dbf11b92c2d72d8587f9164b Mon Sep 17 00:00:00 2001 From: Ross Wightman Date: Fri, 13 May 2022 09:26:16 -0700 Subject: [PATCH 3/7] Disable nvfuser fma / opt level overrides per #1244 --- timm/utils/jit.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/timm/utils/jit.py b/timm/utils/jit.py index 8ebfdbff..a32cbd40 100644 --- a/timm/utils/jit.py +++ b/timm/utils/jit.py @@ -35,8 +35,8 @@ def set_jit_fuser(fuser): torch._C._jit_set_texpr_fuser_enabled(False) elif fuser == "nvfuser" or fuser == "nvf": os.environ['PYTORCH_NVFUSER_DISABLE_FALLBACK'] = '1' - os.environ['PYTORCH_NVFUSER_DISABLE_FMA'] = '1' - os.environ['PYTORCH_NVFUSER_JIT_OPT_LEVEL'] = '0' + #os.environ['PYTORCH_NVFUSER_DISABLE_FMA'] = '1' + #os.environ['PYTORCH_NVFUSER_JIT_OPT_LEVEL'] = '0' torch._C._jit_set_texpr_fuser_enabled(False) torch._C._jit_set_profiling_executor(True) torch._C._jit_set_profiling_mode(True) From 27c42f0830afab4b2ff40b948cf612328ed26680 Mon Sep 17 00:00:00 2001 From: Ross Wightman Date: Fri, 13 May 2022 09:29:33 -0700 Subject: [PATCH 4/7] Fix torchscript use for offician Swin-V2, add support for non-square window/shift to WindowAttn/Block --- timm/models/swin_transformer_v2.py | 80 ++++++++++++++++-------------- 1 file changed, 43 insertions(+), 37 deletions(-) diff --git a/timm/models/swin_transformer_v2.py b/timm/models/swin_transformer_v2.py index 700012fe..8b4eff64 100644 --- a/timm/models/swin_transformer_v2.py +++ b/timm/models/swin_transformer_v2.py @@ -13,6 +13,7 @@ Modifications and additions for timm hacked together by / Copyright 2022, Ross W # Written by Ze Liu # -------------------------------------------------------- import math +from typing import Tuple, Optional import torch import torch.nn as nn @@ -91,7 +92,7 @@ default_cfgs = { } -def window_partition(x, window_size): +def window_partition(x, window_size: Tuple[int, int]): """ Args: x: (B, H, W, C) @@ -101,25 +102,25 @@ def window_partition(x, window_size): windows: (num_windows*B, window_size, window_size, C) """ B, H, W, C = x.shape - x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) - windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) + x = x.view(B, H // window_size[0], window_size[0], W // window_size[1], window_size[1], C) + windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size[0], window_size[1], C) return windows @register_notrace_function # reason: int argument is a Proxy -def window_reverse(windows, window_size, H, W): +def window_reverse(windows, window_size: Tuple[int, int], img_size: Tuple[int, int]): """ Args: - windows: (num_windows*B, window_size, window_size, C) - window_size (int): Window size - H (int): Height of image - W (int): Width of image + windows: (num_windows * B, window_size[0], window_size[1], C) + window_size (Tuple[int, int]): Window size + img_size (Tuple[int, int]): Image size Returns: x: (B, H, W, C) """ - B = int(windows.shape[0] / (H * W / window_size / window_size)) - x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) + H, W = img_size + B = int(windows.shape[0] / (H * W / window_size[0] / window_size[1])) + x = windows.view(B, H // window_size[0], W // window_size[1], window_size[0], window_size[1], -1) x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) return x @@ -148,7 +149,7 @@ class WindowAttention(nn.Module): self.pretrained_window_size = pretrained_window_size self.num_heads = num_heads - self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))), requires_grad=True) + self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1)))) # mlp to generate continuous relative position bias self.cpb_mlp = nn.Sequential( @@ -202,7 +203,7 @@ class WindowAttention(nn.Module): self.proj_drop = nn.Dropout(proj_drop) self.softmax = nn.Softmax(dim=-1) - def forward(self, x, mask=None): + def forward(self, x, mask: Optional[torch.Tensor] = None): """ Args: x: input features with shape of (num_windows*B, N, C) @@ -218,7 +219,7 @@ class WindowAttention(nn.Module): # cosine attention attn = (F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1)) - logit_scale = torch.clamp(self.logit_scale, max=torch.log(torch.tensor(1. / 0.01))).exp() + logit_scale = torch.clamp(self.logit_scale, max=math.log(1. / 0.01)).exp() attn = attn * logit_scale relative_position_bias_table = self.cpb_mlp(self.relative_coords_table).view(-1, self.num_heads) @@ -269,16 +270,13 @@ class SwinTransformerBlock(nn.Module): act_layer=nn.GELU, norm_layer=nn.LayerNorm, pretrained_window_size=0): super().__init__() self.dim = dim - self.input_resolution = input_resolution + self.input_resolution = to_2tuple(input_resolution) self.num_heads = num_heads - self.window_size = window_size - self.shift_size = shift_size + ws, ss = self._calc_window_shift(window_size, shift_size) + self.window_size: Tuple[int, int] = ws + self.shift_size: Tuple[int, int] = ss + self.window_area = self.window_size[0] * self.window_size[1] self.mlp_ratio = mlp_ratio - if min(self.input_resolution) <= self.window_size: - # if window size is larger than input resolution, we don't partition windows - self.shift_size = 0 - self.window_size = min(self.input_resolution) - _assert(0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size") self.attn = WindowAttention( dim, window_size=to_2tuple(self.window_size), num_heads=num_heads, @@ -291,23 +289,23 @@ class SwinTransformerBlock(nn.Module): self.norm2 = norm_layer(dim) self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity() - if self.shift_size > 0: + if any(self.shift_size): # calculate attention mask for SW-MSA H, W = self.input_resolution img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1 cnt = 0 for h in ( - slice(0, -self.window_size), - slice(-self.window_size, -self.shift_size), - slice(-self.shift_size, None)): + slice(0, -self.window_size[0]), + slice(-self.window_size[0], -self.shift_size[0]), + slice(-self.shift_size[0], None)): for w in ( - slice(0, -self.window_size), - slice(-self.window_size, -self.shift_size), - slice(-self.shift_size, None)): + slice(0, -self.window_size[1]), + slice(-self.window_size[1], -self.shift_size[1]), + slice(-self.shift_size[1], None)): img_mask[:, h, w, :] = cnt cnt += 1 mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1 - mask_windows = mask_windows.view(-1, self.window_size * self.window_size) + mask_windows = mask_windows.view(-1, self.window_area) attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) else: @@ -315,6 +313,13 @@ class SwinTransformerBlock(nn.Module): self.register_buffer("attn_mask", attn_mask) + def _calc_window_shift(self, target_window_size, target_shift_size) -> Tuple[Tuple[int, int], Tuple[int, int]]: + target_window_size = to_2tuple(target_window_size) + target_shift_size = to_2tuple(target_shift_size) + window_size = [r if r <= w else w for r, w in zip(self.input_resolution, target_window_size)] + shift_size = [0 if r <= w else s for r, w, s in zip(self.input_resolution, window_size, target_shift_size)] + return tuple(window_size), tuple(shift_size) + def _attn(self, x): H, W = self.input_resolution B, L, C = x.shape @@ -322,25 +327,26 @@ class SwinTransformerBlock(nn.Module): x = x.view(B, H, W, C) # cyclic shift - if self.shift_size > 0: - shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) + has_shift = any(self.shift_size) + if has_shift: + shifted_x = torch.roll(x, shifts=(-self.shift_size[0], -self.shift_size[1]), dims=(1, 2)) else: shifted_x = x # partition windows x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C - x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C + x_windows = x_windows.view(-1, self.window_area, C) # nW*B, window_size*window_size, C # W-MSA/SW-MSA attn_windows = self.attn(x_windows, mask=self.attn_mask) # nW*B, window_size*window_size, C # merge windows - attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) - shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C + attn_windows = attn_windows.view(-1, self.window_size[0], self.window_size[1], C) + shifted_x = window_reverse(attn_windows, self.window_size, self.input_resolution) # B H' W' C # reverse cyclic shift - if self.shift_size > 0: - x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) + if has_shift: + x = torch.roll(shifted_x, shifts=self.shift_size, dims=(1, 2)) else: x = shifted_x x = x.view(B, H * W, C) @@ -445,7 +451,7 @@ class BasicLayer(nn.Module): def forward(self, x): for blk in self.blocks: - if self.grad_checkpointing: + if not torch.jit.is_scripting() and self.grad_checkpointing: x = checkpoint.checkpoint(blk, x) else: x = blk(x) From d4c0588012a9b5d9fddd13035a9682acd9db0ad7 Mon Sep 17 00:00:00 2001 From: Ross Wightman Date: Fri, 13 May 2022 10:49:11 -0700 Subject: [PATCH 5/7] Remove persistent buffers from Swin-V2. Change SwinV2Cr cos attn + tau/logit_scale to match official, add ckpt convert, init_value zeros resid LN weight by default --- timm/models/swin_transformer_v2.py | 26 +++++++++++------ timm/models/swin_transformer_v2_cr.py | 42 ++++++++++++++++++--------- 2 files changed, 46 insertions(+), 22 deletions(-) diff --git a/timm/models/swin_transformer_v2.py b/timm/models/swin_transformer_v2.py index 8b4eff64..fe90144c 100644 --- a/timm/models/swin_transformer_v2.py +++ b/timm/models/swin_transformer_v2.py @@ -25,7 +25,6 @@ from .fx_features import register_notrace_function from .helpers import build_model_with_cfg, named_apply from .layers import PatchEmbed, Mlp, DropPath, to_2tuple, to_ntuple, trunc_normal_, _assert from .registry import register_model -from .vision_transformer import checkpoint_filter_fn, get_init_weights_vit def _cfg(url='', **kwargs): @@ -75,7 +74,7 @@ default_cfgs = { ), 'swinv2_base_window12to24_192to384_22kft1k': _cfg( url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window12to24_192to384_22kto1k_ft.pth', - input_size=(3, 384, 384) + input_size=(3, 384, 384), crop_pct=1.0, ), 'swinv2_large_window12_192_22k': _cfg( url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_large_patch4_window12_192_22k.pth', @@ -87,7 +86,7 @@ default_cfgs = { ), 'swinv2_large_window12to24_192to384_22kft1k': _cfg( url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_large_patch4_window12to24_192to384_22kto1k_ft.pth', - input_size=(3, 384, 384) + input_size=(3, 384, 384), crop_pct=1.0, ), } @@ -174,7 +173,7 @@ class WindowAttention(nn.Module): relative_coords_table = torch.sign(relative_coords_table) * torch.log2( torch.abs(relative_coords_table) + 1.0) / math.log2(8) - self.register_buffer("relative_coords_table", relative_coords_table) + self.register_buffer("relative_coords_table", relative_coords_table, persistent=False) # get pair-wise relative position index for each token inside the window coords_h = torch.arange(self.window_size[0]) @@ -187,7 +186,7 @@ class WindowAttention(nn.Module): relative_coords[:, :, 1] += self.window_size[1] - 1 relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww - self.register_buffer("relative_position_index", relative_position_index) + self.register_buffer("relative_position_index", relative_position_index, persistent=False) self.qkv = nn.Linear(dim, dim * 3, bias=False) if qkv_bias: @@ -215,7 +214,7 @@ class WindowAttention(nn.Module): qkv_bias = torch.cat((self.q_bias, self.k_bias, self.v_bias)) qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias) qkv = qkv.reshape(B_, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) - q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + q, k, v = qkv.unbind(0) # cosine attention attn = (F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1)) @@ -559,9 +558,6 @@ class SwinTransformerV2(nn.Module): 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.LayerNorm): - nn.init.constant_(m.bias, 0) - nn.init.constant_(m.weight, 1.0) @torch.jit.ignore def no_weight_decay(self): @@ -621,6 +617,18 @@ class SwinTransformerV2(nn.Module): return x +def checkpoint_filter_fn(state_dict, model): + out_dict = {} + if 'model' in state_dict: + # For deit models + state_dict = state_dict['model'] + for k, v in state_dict.items(): + if any([n in k for n in ('relative_position_index', 'relative_coords_table')]): + continue # skip buffers that should not be persistent + out_dict[k] = v + return out_dict + + def _create_swin_transformer_v2(variant, pretrained=False, **kwargs): model = build_model_with_cfg( SwinTransformerV2, variant, pretrained, diff --git a/timm/models/swin_transformer_v2_cr.py b/timm/models/swin_transformer_v2_cr.py index fcfa217e..d143c14c 100644 --- a/timm/models/swin_transformer_v2_cr.py +++ b/timm/models/swin_transformer_v2_cr.py @@ -34,6 +34,7 @@ from typing import Tuple, Optional, List, Union, Any, Type import torch import torch.nn as nn +import torch.nn.functional as F import torch.utils.checkpoint as checkpoint from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD @@ -41,7 +42,7 @@ from .fx_features import register_notrace_function from .helpers import build_model_with_cfg, named_apply from .layers import DropPath, Mlp, to_2tuple, _assert from .registry import register_model -from .vision_transformer import checkpoint_filter_fn + _logger = logging.getLogger(__name__) @@ -186,12 +187,13 @@ class WindowMultiHeadAttention(nn.Module): act_layer=nn.ReLU, drop=(0.125, 0.) # FIXME should there be stochasticity, appears to 'overfit' without? ) - self.register_parameter("tau", torch.nn.Parameter(torch.ones(num_heads))) + # NOTE old checkpoints used inverse of logit_scale ('tau') following the paper, see conversion fn + self.logit_scale = nn.Parameter(torch.log(10 * torch.ones(num_heads))) self._make_pair_wise_relative_positions() def _make_pair_wise_relative_positions(self) -> None: """Method initializes the pair-wise relative positions to compute the positional biases.""" - device = self.tau.device + device = self.logit_scale.device coordinates = torch.stack(torch.meshgrid([ torch.arange(self.window_size[0], device=device), torch.arange(self.window_size[1], device=device)]), dim=0).flatten(1) @@ -250,10 +252,11 @@ class WindowMultiHeadAttention(nn.Module): query, key, value = qkv.unbind(0) # compute attention map with scaled cosine attention - denom = torch.norm(query, dim=-1, keepdim=True) @ torch.norm(key, dim=-1, keepdim=True).transpose(-2, -1) - attn = query @ key.transpose(-2, -1) / denom.clamp(min=1e-6) - attn = attn / self.tau.clamp(min=0.01).reshape(1, self.num_heads, 1, 1) + attn = (F.normalize(query, dim=-1) @ F.normalize(key, dim=-1).transpose(-2, -1)) + logit_scale = torch.clamp(self.logit_scale.reshape(1, self.num_heads, 1, 1), max=math.log(1. / 0.01)).exp() + attn = attn * logit_scale attn = attn + self._relative_positional_encodings() + if mask is not None: # Apply mask if utilized num_win: int = mask.shape[0] @@ -309,7 +312,7 @@ class SwinTransformerBlock(nn.Module): window_size: Tuple[int, int], shift_size: Tuple[int, int] = (0, 0), mlp_ratio: float = 4.0, - init_values: float = 0, + init_values: Optional[float] = 0, drop: float = 0.0, drop_attn: float = 0.0, drop_path: float = 0.0, @@ -323,7 +326,7 @@ class SwinTransformerBlock(nn.Module): self.target_shift_size: Tuple[int, int] = to_2tuple(shift_size) self.window_size, self.shift_size = self._calc_window_shift(to_2tuple(window_size)) self.window_area = self.window_size[0] * self.window_size[1] - self.init_values: float = init_values + self.init_values: Optional[float] = init_values # attn branch self.attn = WindowMultiHeadAttention( @@ -387,7 +390,7 @@ class SwinTransformerBlock(nn.Module): def init_weights(self): # extra, module specific weight init - if self.init_values: + if self.init_values is not None: nn.init.constant_(self.norm1.weight, self.init_values) nn.init.constant_(self.norm2.weight, self.init_values) @@ -536,7 +539,7 @@ class SwinTransformerStage(nn.Module): feat_size: Tuple[int, int], window_size: Tuple[int, int], mlp_ratio: float = 4.0, - init_values: float = 0.0, + init_values: Optional[float] = 0.0, drop: float = 0.0, drop_attn: float = 0.0, drop_path: Union[List[float], float] = 0.0, @@ -650,7 +653,7 @@ class SwinTransformerV2Cr(nn.Module): depths: Tuple[int, ...] = (2, 2, 6, 2), num_heads: Tuple[int, ...] = (3, 6, 12, 24), mlp_ratio: float = 4.0, - init_values: float = 0.0, + init_values: Optional[float] = 0., drop_rate: float = 0.0, attn_drop_rate: float = 0.0, drop_path_rate: float = 0.0, @@ -808,6 +811,21 @@ def init_weights(module: nn.Module, name: str = ''): module.init_weights() +def checkpoint_filter_fn(state_dict, model): + """ convert patch embedding weight from manual patchify + linear proj to conv""" + out_dict = {} + if 'model' in state_dict: + # For deit models + state_dict = state_dict['model'] + for k, v in state_dict.items(): + if 'tau' in k: + # convert old tau based checkpoints -> logit_scale (inverse) + v = torch.log(1 / v) + k = k.replace('tau', 'logit_scale') + out_dict[k] = v + return out_dict + + def _create_swin_transformer_v2_cr(variant, pretrained=False, **kwargs): if kwargs.get('features_only', None): raise RuntimeError('features_only not implemented for Vision Transformer models.') @@ -890,7 +908,6 @@ def swinv2_cr_small_ns_224(pretrained=False, **kwargs): embed_dim=96, depths=(2, 2, 18, 2), num_heads=(3, 6, 12, 24), - init_values=1e-5, extra_norm_stage=True, **kwargs ) @@ -928,7 +945,6 @@ def swinv2_cr_base_ns_224(pretrained=False, **kwargs): embed_dim=128, depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32), - init_values=1e-6, extra_norm_stage=True, **kwargs ) From 4b30bae67b48a8e0e4b727c952bee33a4b52aa3d Mon Sep 17 00:00:00 2001 From: Ross Wightman Date: Fri, 13 May 2022 13:53:57 -0700 Subject: [PATCH 6/7] Add updated vit_relpos weights, and impl w/ support for official swin-v2 differences for relpos. Add bias control support for MLP layers --- timm/models/layers/mlp.py | 33 ++-- timm/models/swin_transformer_v2.py | 2 +- timm/models/vision_transformer_relpos.py | 184 +++++++++++++++++++---- 3 files changed, 178 insertions(+), 41 deletions(-) diff --git a/timm/models/layers/mlp.py b/timm/models/layers/mlp.py index a85e28d0..91e80a84 100644 --- a/timm/models/layers/mlp.py +++ b/timm/models/layers/mlp.py @@ -10,16 +10,17 @@ from .helpers import to_2tuple class Mlp(nn.Module): """ MLP as used in Vision Transformer, MLP-Mixer and related networks """ - def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, bias=True, drop=0.): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features + bias = to_2tuple(bias) drop_probs = to_2tuple(drop) - self.fc1 = nn.Linear(in_features, hidden_features) + self.fc1 = nn.Linear(in_features, hidden_features, bias=bias[0]) self.act = act_layer() self.drop1 = nn.Dropout(drop_probs[0]) - self.fc2 = nn.Linear(hidden_features, out_features) + self.fc2 = nn.Linear(hidden_features, out_features, bias=bias[1]) self.drop2 = nn.Dropout(drop_probs[1]) def forward(self, x): @@ -35,17 +36,18 @@ class GluMlp(nn.Module): """ MLP w/ GLU style gating See: https://arxiv.org/abs/1612.08083, https://arxiv.org/abs/2002.05202 """ - def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.Sigmoid, drop=0.): + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.Sigmoid, bias=True, drop=0.): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features assert hidden_features % 2 == 0 + bias = to_2tuple(bias) drop_probs = to_2tuple(drop) - self.fc1 = nn.Linear(in_features, hidden_features) + self.fc1 = nn.Linear(in_features, hidden_features, bias=bias[0]) self.act = act_layer() self.drop1 = nn.Dropout(drop_probs[0]) - self.fc2 = nn.Linear(hidden_features // 2, out_features) + self.fc2 = nn.Linear(hidden_features // 2, out_features, bias=bias[1]) self.drop2 = nn.Dropout(drop_probs[1]) def init_weights(self): @@ -67,14 +69,16 @@ class GluMlp(nn.Module): class GatedMlp(nn.Module): """ MLP as used in gMLP """ - def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, - gate_layer=None, drop=0.): + def __init__( + self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, + gate_layer=None, bias=True, drop=0.): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features + bias = to_2tuple(bias) drop_probs = to_2tuple(drop) - self.fc1 = nn.Linear(in_features, hidden_features) + self.fc1 = nn.Linear(in_features, hidden_features, bias=bias[0]) self.act = act_layer() self.drop1 = nn.Dropout(drop_probs[0]) if gate_layer is not None: @@ -83,7 +87,7 @@ class GatedMlp(nn.Module): hidden_features = hidden_features // 2 # FIXME base reduction on gate property? else: self.gate = nn.Identity() - self.fc2 = nn.Linear(hidden_features, out_features) + self.fc2 = nn.Linear(hidden_features, out_features, bias=bias[1]) self.drop2 = nn.Dropout(drop_probs[1]) def forward(self, x): @@ -100,15 +104,18 @@ class ConvMlp(nn.Module): """ MLP using 1x1 convs that keeps spatial dims """ def __init__( - self, in_features, hidden_features=None, out_features=None, act_layer=nn.ReLU, norm_layer=None, drop=0.): + self, in_features, hidden_features=None, out_features=None, act_layer=nn.ReLU, + norm_layer=None, bias=True, drop=0.): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features - self.fc1 = nn.Conv2d(in_features, hidden_features, kernel_size=1, bias=True) + bias = to_2tuple(bias) + + self.fc1 = nn.Conv2d(in_features, hidden_features, kernel_size=1, bias=bias[0]) self.norm = norm_layer(hidden_features) if norm_layer else nn.Identity() self.act = act_layer() - self.fc2 = nn.Conv2d(hidden_features, out_features, kernel_size=1, bias=True) self.drop = nn.Dropout(drop) + self.fc2 = nn.Conv2d(hidden_features, out_features, kernel_size=1, bias=bias[1]) def forward(self, x): x = self.fc1(x) diff --git a/timm/models/swin_transformer_v2.py b/timm/models/swin_transformer_v2.py index fe90144c..0c9db3dd 100644 --- a/timm/models/swin_transformer_v2.py +++ b/timm/models/swin_transformer_v2.py @@ -450,7 +450,7 @@ class BasicLayer(nn.Module): def forward(self, x): for blk in self.blocks: - if not torch.jit.is_scripting() and self.grad_checkpointing: + if self.grad_checkpointing and not torch.jit.is_scripting(): x = checkpoint.checkpoint(blk, x) else: x = blk(x) diff --git a/timm/models/vision_transformer_relpos.py b/timm/models/vision_transformer_relpos.py index 9ecfd473..0c2ac376 100644 --- a/timm/models/vision_transformer_relpos.py +++ b/timm/models/vision_transformer_relpos.py @@ -1,5 +1,7 @@ """ Relative Position Vision Transformer (ViT) in PyTorch +NOTE: these models are experimental / WIP, expect changes + Hacked together by / Copyright 2022, Ross Wightman """ import math @@ -37,9 +39,23 @@ default_cfgs = { url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_replos_base_patch32_plus_rpn_256-sw-dd486f51.pth', input_size=(3, 256, 256)), 'vit_relpos_base_patch16_plus_240': _cfg(url='', input_size=(3, 240, 240)), - 'vit_relpos_base_patch16_rpn_224': _cfg(url=''), + + 'vit_relpos_small_patch16_224': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_relpos_small_patch16_224-sw-ec2778b4.pth'), + 'vit_relpos_medium_patch16_224': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_relpos_medium_patch16_224-sw-11c174af.pth'), 'vit_relpos_base_patch16_224': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_relpos_base_patch16_224-sw-49049aed.pth'), + + 'vit_relpos_base_patch16_cls_224': _cfg( + url=''), + 'vit_relpos_base_patch16_gapcls_224': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_relpos_base_patch16_gapcls_224-sw-1a341d6c.pth'), + + 'vit_relpos_small_patch16_rpn_224': _cfg(url=''), + 'vit_relpos_medium_patch16_rpn_224': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_relpos_medium_patch16_rpn_224-sw-5d2befd8.pth'), + 'vit_relpos_base_patch16_rpn_224': _cfg(url=''), } @@ -66,43 +82,84 @@ def gen_relative_position_index(win_size: Tuple[int, int], class_token: int = 0) return relative_position_index -def gen_relative_position_log(win_size: Tuple[int, int]) -> torch.Tensor: - """Method initializes the pair-wise relative positions to compute the positional biases.""" - coordinates = torch.stack(torch.meshgrid([torch.arange(win_size[0]), torch.arange(win_size[1])])).flatten(1) - relative_coords = coordinates[:, :, None] - coordinates[:, None, :] - relative_coords = relative_coords.permute(1, 2, 0).float() - relative_coordinates_log = torch.sign(relative_coords) * torch.log(1.0 + relative_coords.abs()) - return relative_coordinates_log +def gen_relative_log_coords( + win_size: Tuple[int, int], + pretrained_win_size: Tuple[int, int] = (0, 0), + mode='swin' +): + # as per official swin-v2 impl, supporting timm swin-v2-cr coords as well + relative_coords_h = torch.arange(-(win_size[0] - 1), win_size[0], dtype=torch.float32) + relative_coords_w = torch.arange(-(win_size[1] - 1), win_size[1], dtype=torch.float32) + relative_coords_table = torch.stack(torch.meshgrid([relative_coords_h, relative_coords_w])) + relative_coords_table = relative_coords_table.permute(1, 2, 0).contiguous() # 2*Wh-1, 2*Ww-1, 2 + if mode == 'swin': + if pretrained_win_size[0] > 0: + relative_coords_table[:, :, 0] /= (pretrained_win_size[0] - 1) + relative_coords_table[:, :, 1] /= (pretrained_win_size[1] - 1) + else: + relative_coords_table[:, :, 0] /= (win_size[0] - 1) + relative_coords_table[:, :, 1] /= (win_size[1] - 1) + relative_coords_table *= 8 # normalize to -8, 8 + scale = math.log2(8) + else: + # FIXME we should support a form of normalization (to -1/1) for this mode? + scale = math.log2(math.e) + relative_coords_table = torch.sign(relative_coords_table) * torch.log2( + 1.0 + relative_coords_table.abs()) / scale + return relative_coords_table class RelPosMlp(nn.Module): - # based on timm swin-v2 impl - def __init__(self, window_size, num_heads=8, hidden_dim=32, class_token=False): + def __init__( + self, + window_size, + num_heads=8, + hidden_dim=128, + class_token=False, + mode='cr', + pretrained_window_size=(0, 0) + ): super().__init__() self.window_size = window_size self.window_area = self.window_size[0] * self.window_size[1] self.class_token = 1 if class_token else 0 self.num_heads = num_heads + self.bias_shape = (self.window_area,) * 2 + (num_heads,) + self.apply_sigmoid = mode == 'swin' + mlp_bias = (True, False) if mode == 'swin' else True self.mlp = Mlp( 2, # x, y - hidden_features=min(128, hidden_dim * num_heads), + hidden_features=hidden_dim, out_features=num_heads, act_layer=nn.ReLU, + bias=mlp_bias, drop=(0.125, 0.) ) self.register_buffer( - 'rel_coords_log', - gen_relative_position_log(window_size), - persistent=False - ) + "relative_position_index", + gen_relative_position_index(window_size), + persistent=False) + + # get relative_coords_table + self.register_buffer( + "rel_coords_log", + gen_relative_log_coords(window_size, pretrained_window_size, mode=mode), + persistent=False) def get_bias(self) -> torch.Tensor: - relative_position_bias = self.mlp(self.rel_coords_log).permute(2, 0, 1).unsqueeze(0) + relative_position_bias = self.mlp(self.rel_coords_log) + if self.relative_position_index is not None: + relative_position_bias = relative_position_bias.view(-1, self.num_heads)[ + self.relative_position_index.view(-1)] # Wh*Ww,Wh*Ww,nH + relative_position_bias = relative_position_bias.view(self.bias_shape) + relative_position_bias = relative_position_bias.permute(2, 0, 1) + if self.apply_sigmoid: + relative_position_bias = 16 * torch.sigmoid(relative_position_bias) if self.class_token: relative_position_bias = F.pad(relative_position_bias, [self.class_token, 0, self.class_token, 0]) - return relative_position_bias + return relative_position_bias.unsqueeze(0).contiguous() def forward(self, attn, shared_rel_pos: Optional[torch.Tensor] = None): return attn + self.get_bias() @@ -131,10 +188,10 @@ class RelPosBias(nn.Module): trunc_normal_(self.relative_position_bias_table, std=.02) def get_bias(self) -> torch.Tensor: - relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view( - self.bias_shape) # win_h * win_w, win_h * win_w, num_heads - relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() - return relative_position_bias + relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)] + # win_h * win_w, win_h * win_w, num_heads + relative_position_bias = relative_position_bias.view(self.bias_shape).permute(2, 0, 1) + return relative_position_bias.unsqueeze(0).contiguous() def forward(self, attn, shared_rel_pos: Optional[torch.Tensor] = None): return attn + self.get_bias() @@ -250,8 +307,8 @@ class VisionTransformerRelPos(nn.Module): def __init__( self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, global_pool='avg', - embed_dim=768, depth=12, num_heads=12, mlp_ratio=4., qkv_bias=True, init_values=1e-5, - class_token=False, rel_pos_type='mlp', shared_rel_pos=False, fc_norm=False, + embed_dim=768, depth=12, num_heads=12, mlp_ratio=4., qkv_bias=True, init_values=1e-6, + class_token=False, fc_norm=False, rel_pos_type='mlp', shared_rel_pos=False, rel_pos_dim=None, drop_rate=0., attn_drop_rate=0., drop_path_rate=0., weight_init='skip', embed_layer=PatchEmbed, norm_layer=None, act_layer=None, block_fn=RelPosBlock): """ @@ -268,9 +325,9 @@ class VisionTransformerRelPos(nn.Module): qkv_bias (bool): enable bias for qkv if True init_values: (float): layer-scale init values class_token (bool): use class token (default: False) + fc_norm (bool): use pre classifier norm instead of pre-pool rel_pos_ty pe (str): type of relative position shared_rel_pos (bool): share relative pos across all blocks - fc_norm (bool): use pre classifier norm instead of pre-pool drop_rate (float): dropout rate attn_drop_rate (float): attention dropout rate drop_path_rate (float): stochastic depth rate @@ -295,8 +352,15 @@ class VisionTransformerRelPos(nn.Module): img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) feat_size = self.patch_embed.grid_size - rel_pos_cls = RelPosMlp if rel_pos_type == 'mlp' else RelPosBias - rel_pos_cls = partial(rel_pos_cls, window_size=feat_size, class_token=class_token) + rel_pos_args = dict(window_size=feat_size, class_token=class_token) + if rel_pos_type.startswith('mlp'): + if rel_pos_dim: + rel_pos_args['hidden_dim'] = rel_pos_dim + if 'swin' in rel_pos_type: + rel_pos_args['mode'] = 'swin' + rel_pos_cls = partial(RelPosMlp, **rel_pos_args) + else: + rel_pos_cls = partial(RelPosBias, **rel_pos_args) self.shared_rel_pos = None if shared_rel_pos: self.shared_rel_pos = rel_pos_cls(num_heads=num_heads) @@ -408,6 +472,26 @@ def vit_relpos_base_patch16_plus_240(pretrained=False, **kwargs): return model +@register_model +def vit_relpos_small_patch16_224(pretrained=False, **kwargs): + """ ViT-Base (ViT-B/16) w/ relative log-coord position, no class token + """ + model_kwargs = dict( + patch_size=16, embed_dim=384, depth=12, num_heads=6, qkv_bias=False, fc_norm=True, **kwargs) + model = _create_vision_transformer_relpos('vit_relpos_small_patch16_224', pretrained=pretrained, **model_kwargs) + return model + + +@register_model +def vit_relpos_medium_patch16_224(pretrained=False, **kwargs): + """ ViT-Base (ViT-B/16) w/ relative log-coord position, no class token + """ + model_kwargs = dict( + patch_size=16, embed_dim=512, depth=12, num_heads=8, qkv_bias=False, fc_norm=True, **kwargs) + model = _create_vision_transformer_relpos('vit_relpos_medium_patch16_224', pretrained=pretrained, **model_kwargs) + return model + + @register_model def vit_relpos_base_patch16_224(pretrained=False, **kwargs): """ ViT-Base (ViT-B/16) w/ relative log-coord position, no class token @@ -418,11 +502,57 @@ def vit_relpos_base_patch16_224(pretrained=False, **kwargs): return model +@register_model +def vit_relpos_base_patch16_cls_224(pretrained=False, **kwargs): + """ ViT-Base (ViT-B/16) w/ relative log-coord position, class token present + """ + model_kwargs = dict( + patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, + class_token=True, global_pool='token', **kwargs) + model = _create_vision_transformer_relpos('vit_relpos_base_patch16_cls_224', pretrained=pretrained, **model_kwargs) + return model + + +@register_model +def vit_relpos_base_patch16_gapcls_224(pretrained=False, **kwargs): + """ ViT-Base (ViT-B/16) w/ relative log-coord position, class token present + NOTE this config is a bit of a mistake, class token was enabled but global avg-pool w/ fc-norm was not disabled + Leaving here for comparisons w/ a future re-train as it performs quite well. + """ + model_kwargs = dict( + patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, fc_norm=True, class_token=True, **kwargs) + model = _create_vision_transformer_relpos('vit_relpos_base_patch16_gapcls_224', pretrained=pretrained, **model_kwargs) + return model + + +@register_model +def vit_relpos_small_patch16_rpn_224(pretrained=False, **kwargs): + """ ViT-Base (ViT-B/16) w/ relative log-coord position and residual post-norm, no class token + """ + model_kwargs = dict( + patch_size=16, embed_dim=384, depth=12, num_heads=6, qkv_bias=False, block_fn=ResPostRelPosBlock, **kwargs) + model = _create_vision_transformer_relpos( + 'vit_relpos_small_patch16_rpn_224', pretrained=pretrained, **model_kwargs) + return model + + +@register_model +def vit_relpos_medium_patch16_rpn_224(pretrained=False, **kwargs): + """ ViT-Base (ViT-B/16) w/ relative log-coord position and residual post-norm, no class token + """ + model_kwargs = dict( + patch_size=16, embed_dim=512, depth=12, num_heads=8, qkv_bias=False, block_fn=ResPostRelPosBlock, **kwargs) + model = _create_vision_transformer_relpos( + 'vit_relpos_medium_patch16_rpn_224', pretrained=pretrained, **model_kwargs) + return model + + @register_model def vit_relpos_base_patch16_rpn_224(pretrained=False, **kwargs): """ ViT-Base (ViT-B/16) w/ relative log-coord position and residual post-norm, no class token """ model_kwargs = dict( patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, block_fn=ResPostRelPosBlock, **kwargs) - model = _create_vision_transformer_relpos('vit_relpos_base_patch16_rpn_224', pretrained=pretrained, **model_kwargs) + model = _create_vision_transformer_relpos( + 'vit_relpos_base_patch16_rpn_224', pretrained=pretrained, **model_kwargs) return model From 347308faadbe98a7e736371fffa013c28543530c Mon Sep 17 00:00:00 2001 From: Ross Wightman Date: Fri, 13 May 2022 13:54:41 -0700 Subject: [PATCH 7/7] Update README.md, version to 0.6.2 --- README.md | 12 ++++++++++++ timm/version.py | 2 +- 2 files changed, 13 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index df5fb968..f90c6abd 100644 --- a/README.md +++ b/README.md @@ -23,6 +23,17 @@ I'm fortunate to be able to dedicate significant time and money of my own suppor ## What's New +### May 13, 2022 +* Official Swin-V2 models and weights added from (https://github.com/microsoft/Swin-Transformer). Cleaned up to support torchscript. +* Some refactoring for existing `timm` Swin-V2-CR impl, will likely do a bit more to bring parts closer to official and decide whether to merge some aspects. +* More Vision Transformer relative position / residual post-norm experiments w/ 512 dim + * `vit_relpos_small_patch16_224` - 81.5 @ 224, 82.5 @ 320 -- rel pos, layer scale, no class token, avg pool + * `vit_relpos_medium_patch16_rpn_224` - 82.3 @ 224, 83.1 @ 320 -- rel pos + res-post-norm, no class token, avg pool + * `vit_relpos_medium_patch16_224` - 82.5 @ 224, 83.3 @ 320 -- rel pos, layer scale, no class token, avg pool + * `vit_relpos_base_patch16_gapcls_224` - 82.8 @ 224, 83.9 @ 320 -- rel pos, layer scale, class token, avg pool (by mistake) +* Bring 512 dim, 8-head 'medium' ViT model variant back to life (after using in a pre DeiT 'small' model for first ViT impl back in 2020) +* Add ViT relative position support for switching btw existing impl and some additions in official Swin-V2 impl for future trials +* Sequencer2D impl (https://arxiv.org/abs/2205.01972), added via PR from author (https://github.com/okojoalg) ### May 2, 2022 * Vision Transformer experiments adding Relative Position (Swin-V2 log-coord) (`vision_transformer_relpos.py`) and Residual Post-Norm branches (from Swin-V2) (`vision_transformer*.py`) @@ -390,6 +401,7 @@ A full version of the list below with source links can be found in the [document * ReXNet - https://arxiv.org/abs/2007.00992 * SelecSLS - https://arxiv.org/abs/1907.00837 * Selective Kernel Networks - https://arxiv.org/abs/1903.06586 +* Sequencer2D - https://arxiv.org/abs/2205.01972 * Swin S3 (AutoFormerV2) - https://arxiv.org/abs/2111.14725 * Swin Transformer - https://arxiv.org/abs/2103.14030 * Swin Transformer V2 - https://arxiv.org/abs/2111.09883 diff --git a/timm/version.py b/timm/version.py index 8411e551..aece342d 100644 --- a/timm/version.py +++ b/timm/version.py @@ -1 +1 @@ -__version__ = '0.6.1' +__version__ = '0.6.2'