# Copyright (c) 2021 Microsoft # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. # -------------------------------------------------------- # Modified by Chunyuan Li (chunyl@microsoft.com) # Swin Transformer # -------------------------------------------------------- import os import logging import torch import torch.nn as nn import torch.nn.functional as F from functools import partial import torch.distributed as dist from torch.nn.init import trunc_normal_ from megatron.model.transformer import DropPath from megatron import get_args from megatron.model import LayerNorm import numpy as np from math import sqrt class Mlp(nn.Module): def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): super(Mlp, self).__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features self.fc1 = nn.Linear(in_features, hidden_features) self.act = act_layer() self.fc2 = nn.Linear(hidden_features, out_features) self.drop = nn.Dropout(drop) def forward(self, x): x = self.fc1(x) x = self.act(x) x = self.drop(x) x = self.fc2(x) x = self.drop(x) return x 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 qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 proj_drop (float, optional): Dropout ratio of output. Default: 0.0 """ def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.): super(WindowAttention, self).__init__() self.dim = dim self.window_size = window_size # Wh, Ww self.num_heads = num_heads head_dim = dim // num_heads self.scale = qk_scale or head_dim ** -0.5 # define a parameter table of relative position bias self.relative_position_bias_table = nn.Parameter( torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH # 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=qkv_bias) self.attn_drop = nn.Dropout(attn_drop) self.proj = nn.Linear(dim, dim) self.proj_drop = nn.Dropout(proj_drop) trunc_normal_(self.relative_position_bias_table, std=.02) 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 = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) q = q * self.scale attn = (q @ k.transpose(-2, -1)) relative_position_bias = self.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 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).type(attn.type()) attn = attn.view(-1, self.num_heads, N, N) attn = self.softmax(attn) else: attn = self.softmax(attn) attn_out = 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, attn_out def extra_repr(self) -> str: return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}' def flops(self, N): # calculate flops for 1 window with token length of N flops = 0 # qkv = self.qkv(x) flops += N * self.dim * 3 * self.dim # attn = (q @ k.transpose(-2, -1)) flops += self.num_heads * N * (self.dim // self.num_heads) * N # x = (attn @ v) flops += self.num_heads * N * N * (self.dim // self.num_heads) # x = self.proj(x) flops += N * self.dim * self.dim return flops @staticmethod def compute_macs(module, input, output): B, N, C = input[0].shape module.__flops__ += module.flops(N) * B class SwinTransformerBlock(nn.Module): r"""Swin Transformer Block. Args: dim (int): Number of input channels. input_resolution (tuple[int]): Input resulotion. 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 qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. 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 """ def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0, mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm): 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.norm1 = norm_layer(dim) self.attn = WindowAttention( dim, window_size=(self.window_size, self.window_size), num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() self.norm2 = norm_layer(dim) mlp_hidden_dim = int(dim * mlp_ratio) self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) self.H = input_resolution[0] self.W = input_resolution[1] self.attn_mask_dict = {} def create_attn_mask(self, H, W): # calculate attention mask for SW-MSA Hp = int(np.ceil(H / self.window_size)) * self.window_size Wp = int(np.ceil(W / self.window_size)) * self.window_size img_mask = torch.zeros((1, Hp, Wp, 1)) # 1 Hp Wp 1 h_slices = (slice(0, -self.window_size), slice(-self.window_size, -self.shift_size), slice(-self.shift_size, None)) w_slices = (slice(0, -self.window_size), slice(-self.window_size, -self.shift_size), slice(-self.shift_size, None)) cnt = 0 for h in h_slices: for w in w_slices: 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)) return attn_mask def forward(self, x): B, L, C = x.shape H = int(sqrt(L)) W = H shortcut = x x = self.norm1(x) x = x.view(B, H, W, C) # pad feature maps to multiples of window size pad_l = pad_t = 0 pad_r = (self.window_size - W % self.window_size) % self.window_size pad_b = (self.window_size - H % self.window_size) % self.window_size x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b)) _, Hp, Wp, _ = x.shape # cyclic shift if self.shift_size > 0: shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) if H in self.attn_mask_dict.keys(): attn_mask = self.attn_mask_dict[H] else: self.attn_mask_dict[H] = self.create_attn_mask(self.H, self.W).to(x.device) attn_mask = self.attn_mask_dict[H] else: shifted_x = x attn_mask = None # 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, attn = self.attn(x_windows, 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, Hp, Wp) # 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 if pad_r > 0 or pad_b > 0: x = x[:, :H, :W, :].contiguous() x = x.view(B, H * W, C) # FFN x = shortcut + self.drop_path(x) x = x + self.drop_path(self.mlp(self.norm2(x))) return x, attn def extra_repr(self) -> str: return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \ f"window_size={self.window_size}, shift_size={self.shift_size} mlp_ratio={self.mlp_ratio}" def flops(self): flops = 0 H, W = self.input_resolution # norm1 flops += self.dim * H * W # W-MSA/SW-MSA nW = H * W / self.window_size / self.window_size flops += nW * self.attn.flops(self.window_size * self.window_size) # mlp flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio # norm2 flops += self.dim * H * W return flops 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(4 * dim) def forward(self, x): """ Forward function. Args: x: Input feature, tensor size (B, H*W, C). H, W: Spatial resolution of the input feature. """ B, L, C = x.shape H = int(sqrt(L)) W = H x = x.view(B, H, W, C) # padding pad_input = (H % 2 == 1) or (W % 2 == 1) if pad_input: x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2)) 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.norm(x) x = self.reduction(x) return x def extra_repr(self) -> str: return f"input_resolution={self.input_resolution}, dim={self.dim}" def flops(self): H, W = self.input_resolution flops = H * W * self.dim flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim return flops class BasicLayer(nn.Module): """A basic Swin Transformer layer for one stage. Args: dim (int): Number of input channels. input_resolution (tuple[int]): Input resulotion. depth (int): Number of blocks. num_heads (int): Number of attention heads. window_size (int): 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 qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. 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 """ def __init__(self, dim, input_resolution, depth, num_heads, window_size, mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0., norm_layer=nn.LayerNorm, downsample=None): super().__init__() self.dim = dim self.input_resolution = input_resolution self.depth = depth 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, qk_scale=qk_scale, drop=drop, attn_drop=attn_drop, drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, norm_layer=norm_layer) for i in range(depth)]) if downsample is not None: self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer) else: self.downsample = None def forward(self, x): for blk in self.blocks: x, _ = blk(x) if self.downsample is not None: x = self.downsample(x) return x def forward_with_features(self, x): fea = [] for blk in self.blocks: x, _ = blk(x) fea.append(x) if self.downsample is not None: x = self.downsample(x) return x, fea def forward_with_attention(self, x): attns = [] for blk in self.blocks: x, attn = blk(x) attns.append(attn) if self.downsample is not None: x = self.downsample(x) return x, attns def extra_repr(self) -> str: return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}" def flops(self): flops = 0 for blk in self.blocks: flops += blk.flops() if self.downsample is not None: flops += self.downsample.flops() return flops class PatchEmbed(nn.Module): """ Image to Patch Embedding """ def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, norm_layer=None): super().__init__() img_size = (img_size, img_size) patch_size = (patch_size, patch_size) patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]] self.img_size = img_size self.patch_size = patch_size self.patches_resolution = patches_resolution self.num_patches = patches_resolution[0] * patches_resolution[1] self.in_chans = in_chans self.embed_dim = embed_dim self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) if norm_layer is not None: self.norm = norm_layer(embed_dim) else: self.norm = None def forward(self, x): B, C, H, W = x.shape x = self.proj(x).flatten(2).transpose(1, 2) # B Ph*Pw C if self.norm is not None: x = self.norm(x) return x def flops(self): Ho, Wo = self.patches_resolution flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1]) if self.norm is not None: flops += Ho * Wo * self.embed_dim return flops class SwinTransformer(nn.Module): r""" Swin Transformer A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` - https://arxiv.org/pdf/2103.14030 Args: img_size (int | tuple(int)): Input image size. patch_size (int | tuple(int)): Patch size. in_chans (int): Number of input channels. num_classes (int): Number of classes for classification head. embed_dim (int): Embedding dimension. depths (tuple(int)): Depth of Swin Transformer layers. num_heads (tuple(int)): Number of attention heads in different layers. window_size (int): Window size. mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: Truee qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. drop_rate (float): Dropout rate. attn_drop_rate (float): Attention dropout rate. drop_path_rate (float): Stochastic depth rate. norm_layer (nn.Module): normalization layer. ape (bool): If True, add absolute position embedding to the patch embedding. patch_norm (bool): If True, add normalization after patch embedding. """ def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes=1000, embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24], window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None, drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1, norm_layer=nn.LayerNorm, ape=False, patch_norm=True, **kwargs): super().__init__() self.num_classes = num_classes self.num_layers = len(depths) self.embed_dim = embed_dim self.ape = ape self.patch_norm = patch_norm self.num_features = int(embed_dim * 2 ** (self.num_layers - 1)) self.mlp_ratio = mlp_ratio 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 patches_resolution = self.patch_embed.patches_resolution self.patches_resolution = patches_resolution if self.ape: self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim)) trunc_normal_(self.absolute_pos_embed, std=.02) self.pos_drop = nn.Dropout(p=drop_rate) dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule self.layers = nn.ModuleList() for i_layer in range(self.num_layers): layer = BasicLayer(dim=int(embed_dim * 2 ** i_layer), input_resolution=(patches_resolution[0] // (2 ** i_layer), patches_resolution[1] // (2 ** i_layer)), depth=depths[i_layer], num_heads=num_heads[i_layer], window_size=window_size, mlp_ratio=self.mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, 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) self.layers.append(layer) self.norm = norm_layer(self.num_features) self.avgpool = nn.AdaptiveAvgPool1d(1) self.apply(self._init_weights) def _init_weights(self, m): if isinstance(m, nn.Linear): trunc_normal_(m.weight, std=.02) if isinstance(m, nn.Linear) and m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.LayerNorm): nn.init.constant_(m.bias, 0) nn.init.constant_(m.weight, 1.0) @torch.jit.ignore def no_weight_decay(self): return {'absolute_pos_embed'} @torch.jit.ignore def no_weight_decay_keywords(self): # todo: to be implemented return {'relative_position_bias_table'} def forward(self, x): x = self.patch_embed(x) if self.ape: x = x + self.absolute_pos_embed x = self.pos_drop(x) for layer in self.layers: x = layer(x) x_region = self.norm(x) # B L C x = self.avgpool(x_region.transpose(1, 2)) # B C 1 x = torch.flatten(x, 1) return x def forward_feature_maps(self, x): x = self.patch_embed(x) if self.ape: x = x + self.absolute_pos_embed x = self.pos_drop(x) for layer in self.layers: x = layer(x) x_grid = self.norm(x) # B L C x = self.avgpool(x_grid.transpose(1, 2)) # B C 1 x = torch.flatten(x, 1) return x, x_grid def forward_selfattention(self, x, n=1): # n=1 return the last layer attn map; otherwise return attn maps in all layers x = self.patch_embed(x) if self.ape: x = x + self.absolute_pos_embed x = self.pos_drop(x) if n==1: return self.forward_last_selfattention(x) else: return self.forward_all_selfattention(x) def forward_last_selfattention(self, x): for i, layer in enumerate(self.layers): if i < len(self.layers) - 1: x = layer(x) else: x, attns = layer.forward_with_attention(x) return attns[-1] def forward_all_selfattention(self, x): attn_out = [] for layer in self.layers: x, attns = layer.forward_with_attention(x) attn_out += attns return attn_out def forward_return_n_last_blocks(self, x, n=1, return_patch_avgpool=False, depth=[]): num_blks = sum(depth) start_idx = num_blks - n sum_cur = 0 for i, d in enumerate(depth): sum_cur_new = sum_cur + d if start_idx >= sum_cur and start_idx < sum_cur_new: start_stage = i start_blk = start_idx - sum_cur sum_cur = sum_cur_new x = self.patch_embed(x) if self.ape: x = x + self.absolute_pos_embed x = self.pos_drop(x) # we will return the averaged token features from the `n` last blocks # note: there is no [CLS] token in Swin Transformer output = [] s = 0 for i, layer in enumerate(self.layers): x, fea = layer.forward_with_features(x) if i >= start_stage: for x_ in fea[start_blk:]: if i == len(self.layers)-1: # use the norm in the last stage x_ = self.norm(x_) x_avg = torch.flatten(self.avgpool(x_.transpose(1, 2)), 1) # B C # print(f'Stage {i}, x_avg {x_avg.shape}') output.append(x_avg) start_blk = 0 return torch.cat(output, dim=-1) def flops(self): flops = 0 flops += self.patch_embed.flops() for i, layer in enumerate(self.layers): flops += layer.flops() if dist.get_rank() == 0: print(f"GFLOPs layer_{i}: {layer.flops() / 1e9}") flops += self.num_features * self.patches_resolution[0] * self.patches_resolution[1] // (2 ** self.num_layers) flops += self.num_features * self.num_classes return flops def init_weights(self, pretrained='', pretrained_layers=[], verbose=True): if os.path.isfile(pretrained): pretrained_dict = torch.load(pretrained, map_location='cpu') logging.info(f'=> loading pretrained model {pretrained}') model_dict = self.state_dict() pretrained_dict = { k: v for k, v in pretrained_dict.items() if k in model_dict.keys() } need_init_state_dict = {} for k, v in pretrained_dict.items(): need_init = ( k.split('.')[0] in pretrained_layers or pretrained_layers[0] is '*' or 'relative_position_index' not in k or 'attn_mask' not in k ) if need_init: if verbose: logging.info(f'=> init {k} from {pretrained}') if 'relative_position_bias_table' in k and v.size() != model_dict[k].size(): relative_position_bias_table_pretrained = v relative_position_bias_table_current = model_dict[k] L1, nH1 = relative_position_bias_table_pretrained.size() L2, nH2 = relative_position_bias_table_current.size() if nH1 != nH2: logging.info(f"Error in loading {k}, passing") else: if L1 != L2: logging.info( '=> load_pretrained: resized variant: {} to {}' .format((L1, nH1), (L2, nH2)) ) S1 = int(L1 ** 0.5) S2 = int(L2 ** 0.5) relative_position_bias_table_pretrained_resized = torch.nn.functional.interpolate( relative_position_bias_table_pretrained.permute(1, 0).view(1, nH1, S1, S1), size=(S2, S2), mode='bicubic') v = relative_position_bias_table_pretrained_resized.view(nH2, L2).permute(1, 0) if 'absolute_pos_embed' in k and v.size() != model_dict[k].size(): absolute_pos_embed_pretrained = v absolute_pos_embed_current = model_dict[k] _, L1, C1 = absolute_pos_embed_pretrained.size() _, L2, C2 = absolute_pos_embed_current.size() if C1 != C1: logging.info(f"Error in loading {k}, passing") else: if L1 != L2: logging.info( '=> load_pretrained: resized variant: {} to {}' .format((1, L1, C1), (1, L2, C2)) ) S1 = int(L1 ** 0.5) S2 = int(L2 ** 0.5) absolute_pos_embed_pretrained = absolute_pos_embed_pretrained.reshape(-1, S1, S1, C1) absolute_pos_embed_pretrained = absolute_pos_embed_pretrained.permute(0, 3, 1, 2) absolute_pos_embed_pretrained_resized = torch.nn.functional.interpolate( absolute_pos_embed_pretrained, size=(S2, S2), mode='bicubic') v = absolute_pos_embed_pretrained_resized.permute(0, 2, 3, 1).flatten(1, 2) need_init_state_dict[k] = v self.load_state_dict(need_init_state_dict, strict=False) def freeze_pretrained_layers(self, frozen_layers=[]): for name, module in self.named_modules(): if ( name.split('.')[0] in frozen_layers or '.'.join(name.split('.')[0:2]) in frozen_layers or (len(frozen_layers) > 0 and frozen_layers[0] is '*') ): for _name, param in module.named_parameters(): param.requires_grad = False logging.info( '=> set param {} requires grad to False' .format(name) ) for name, param in self.named_parameters(): if ( name.split('.')[0] in frozen_layers or (len(frozen_layers) > 0 and frozen_layers[0] is '*') and param.requires_grad is True ): param.requires_grad = False logging.info( '=> set param {} requires grad to False' .format(name) ) return self def get_swin(is_teacher=False): args = get_args() if args.swin_backbone_type == "tiny": embed_dim = 96 depths = [2, 2, 6, 2] num_heads = [3, 6, 12, 24] drop_path_rate = 0.1 elif args.swin_backbone_type == 'h3': embed_dim = 384 depths = [2, 2, 18, 2] num_heads = [6, 12, 24, 48] drop_path_rate = 0.2 else: embed_dim = 128 depths = [2, 2, 18, 2] num_heads = [4, 8, 16, 32] drop_path_rate = 0.2 swin = SwinTransformer( img_size=224, in_chans=3, num_classes=1000, patch_size=4, embed_dim=embed_dim, depths=depths, num_heads=num_heads, window_size=7, mlp_ratio=4, qkv_bias=True, drop_rate=0, attn_drop_rate=0, drop_path_rate=(0.0 if is_teacher else drop_path_rate), norm_layer=partial(LayerNorm, eps=1e-6), ape=False, patch_norm=True, ) return swin