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)