# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Code was based on https://github.com/microsoft/Swin-Transformer # reference: https://arxiv.org/abs/2111.09883 import paddle import paddle.nn as nn import paddle.nn.functional as F from paddle.nn.initializer import TruncatedNormal, Constant, Normal import numpy as np import math from ..model_zoo.vision_transformer import trunc_normal_, zeros_, ones_, to_2tuple, DropPath, Identity from ..base.theseus_layer import TheseusLayer from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url MODEL_URLS = { "SwinTransformerV2_tiny_patch4_window8_256": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformerV2_tiny_patch4_window8_256_pretrained.pdparams", "SwinTransformerV2_tiny_patch4_window16_256": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformerV2_tiny_patch4_window16_256_pretrained.pdparams", "SwinTransformerV2_small_patch4_window8_256": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformerV2_small_patch4_window8_256_pretrained.pdparams", "SwinTransformerV2_small_patch4_window16_256": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformerV2_small_patch4_window16_256_pretrained.pdparams", "SwinTransformerV2_base_patch4_window8_256": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformerV2_base_patch4_window8_256_pretrained.pdparams", "SwinTransformerV2_base_patch4_window16_256": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformerV2_base_patch4_window16_256_pretrained.pdparams", "SwinTransformerV2_base_patch4_window12to16_256": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformerV2_base_patch4_window12to16_256_22kto1k_pretrained.pdparams", "SwinTransformerV2_base_patch4_window24_384": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformerV2_base_patch4_window24_384_22kto1k_pretrained.pdparams", "SwinTransformerV2_large_patch4_window16_256": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformerV2_large_patch4_window16_256_22kto1k_pretrained.pdparams", "SwinTransformerV2_large_patch4_window24_384": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformerV2_large_patch4_window24_384_22kto1k_pretrained.pdparams", } __all__ = list(MODEL_URLS.keys()) class RollWithIndexSelect(paddle.autograd.PyLayer): @staticmethod def forward(ctx, input1, index_fp, index_bp): N, H, W, C = input1.shape ctx.input1 = input1 ctx.index_bp = index_bp result = input1.reshape([N, H * W, C]).index_select( index_fp, 1).reshape([N, H, W, C]) return result @staticmethod def backward(ctx, grad): input1 = ctx.input1 N, H, W, C = input1.shape index_bp = ctx.index_bp grad_input = grad.reshape([N, H * W, C]).index_select( index_bp, 1).reshape([N, H, W, C]) return grad_input, None, None def get_roll_index(H, W, shifts, place): index = np.arange(0, H * W, dtype=np.int64).reshape([H, W]) index_fp = np.roll(index, shift=shifts, axis=(0, 1)).reshape([-1]) index_bp = {i: idx for idx, i in enumerate(index_fp.tolist())} index_bp = [index_bp[i] for i in range(H * W)] index_fp = paddle.to_tensor(index_fp, place=place) index_bp = paddle.to_tensor(index_fp, dtype='int64', place=place) return [index_fp, index_bp] class NpuRollWithIndexSelect(): def __init__(self): self.index_dict = {} self.roll_with_index_select = RollWithIndexSelect.apply def __call__(self, x, shifts, axis): assert x.dim() == 4 assert len(shifts) == 2 assert len(axis) == 2 N, H, W, C = x.shape key = (H, W, shifts, axis) if key not in self.index_dict: self.index_dict[key] = get_roll_index(H, W, shifts, x.place) index_fp, index_bp = self.index_dict[key] return self.roll_with_index_select(x, index_fp, index_bp) class RollWrapper(object): _roll = None @staticmethod def roll(x, shifts, axis): if RollWrapper._roll is None: RollWrapper._roll = NpuRollWithIndexSelect( ) if 'npu' in paddle.device.get_all_custom_device_type( ) else paddle.roll return RollWrapper._roll(x, shifts, axis) def masked_fill(x, mask, value): y = paddle.full(x.shape, value, x.dtype) return paddle.where(mask, y, x) class Mlp(nn.Layer): def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features self.fc1 = nn.Linear(in_features, hidden_features) self.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.reshape( [B, H // window_size, window_size, W // window_size, window_size, C]) windows = x.transpose(perm=[0, 1, 3, 2, 4, 5]).reshape( [-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) """ C = windows.shape[-1] B = int(windows.shape[0] / (H * W / window_size / window_size)) x = windows.reshape( [B, H // window_size, W // window_size, window_size, window_size, C]) x = x.transpose(perm=[0, 1, 3, 2, 4, 5]).reshape([B, H, W, C]) return x class WindowAttention(nn.Layer): 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 = self.create_parameter( [num_heads, 1, 1], dtype='float32', default_initializer=Constant(math.log(10.))) # mlp to generate continuous relative position bias self.cpb_mlp = nn.Sequential( nn.Linear( 2, 512, bias_attr=True), nn.ReLU(), nn.Linear( 512, num_heads, bias_attr=False)) # get relative_coords_table relative_coords_h = paddle.arange( -(self.window_size[0] - 1), self.window_size[0], dtype='float32') relative_coords_w = paddle.arange( -(self.window_size[1] - 1), self.window_size[1], dtype='float32') relative_coords_table = paddle.stack( paddle.meshgrid([relative_coords_h, relative_coords_w])).transpose( perm=[1, 2, 0]).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 = paddle.sign( relative_coords_table) * paddle.log2( paddle.abs(relative_coords_table) + 1.0) / np.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 = paddle.arange(self.window_size[0]) coords_w = paddle.arange(self.window_size[1]) coords = paddle.stack(paddle.meshgrid( [coords_h, coords_w])) # 2, Wh, Ww coords_flatten = paddle.flatten(coords, 1) # 2, Wh*Ww relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww relative_coords = relative_coords.transpose( perm=[1, 2, 0]) # 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_attr=False) if qkv_bias: self.q_bias = self.create_parameter( [dim], dtype='float32', default_initializer=zeros_) self.v_bias = self.create_parameter( [dim], dtype='float32', default_initializer=zeros_) 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(axis=-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 = paddle.concat( x=[self.q_bias, paddle.zeros_like(self.v_bias), self.v_bias]) qkv = F.linear(x=x, weight=self.qkv.weight, bias=qkv_bias) qkv = qkv.reshape(shape=[ B_, N, 3, self.num_heads, qkv.shape[-1] // (3 * self.num_heads) ]).transpose(perm=[2, 0, 3, 1, 4]) q, k, v = qkv[0], qkv[1], qkv[ 2] # make paddlescript happy (cannot use tensor as tuple) # cosine attention attn = (F.normalize( q, axis=-1) @F.normalize( k, axis=-1).transpose(perm=[0, 1, 3, 2])) logit_scale = paddle.clip( 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).reshape([-1, self.num_heads]) relative_position_bias = relative_position_bias_table[ self.relative_position_index.reshape([-1])].reshape([ 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.transpose( perm=[2, 0, 1]) # nH, Wh*Ww, Wh*Ww relative_position_bias = 16 * F.sigmoid(relative_position_bias) attn = attn + relative_position_bias.unsqueeze(0) if mask is not None: nW = mask.shape[0] attn = attn.reshape([B_ // nW, nW, self.num_heads, N, N ]) + mask.unsqueeze(1).unsqueeze(0) attn = attn.reshape([-1, self.num_heads, N, N]) attn = self.softmax(attn) else: attn = self.softmax(attn) attn = self.attn_drop(attn) x = (attn @v).transpose(perm=[0, 2, 1, 3]).reshape(shape=[B_, N, C]) x = self.proj(x) x = self.proj_drop(x) return x def extra_repr(self): return f'dim={self.dim}, window_size={self.window_size}, ' \ f'pretrained_window_size={self.pretrained_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 class SwinTransformerBlock(nn.Layer): 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 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 pre-training. """ def __init__(self, dim, input_resolution, num_heads, window_size=8, 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.norm1 = norm_layer(dim) 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.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) if self.shift_size > 0: # calculate attention mask for SW-MSA H, W = self.input_resolution img_mask = paddle.zeros([1, H, W, 1]) # 1 H W 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.reshape( shape=[-1, self.window_size * self.window_size]) attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) attn_mask = masked_fill(attn_mask, attn_mask != 0, float(-100.0)) attn_mask = masked_fill(attn_mask, attn_mask == 0, float(0.0)) else: attn_mask = None self.register_buffer("attn_mask", attn_mask) def forward(self, x): H, W = self.input_resolution B, L, C = x.shape assert L == H * W, "input feature has wrong size" shortcut = x x = x.reshape(shape=[B, H, W, C]) # cyclic shift if self.shift_size > 0: shifted_x = RollWrapper.roll( x, shifts=(-self.shift_size, -self.shift_size), axis=(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.reshape( [-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.reshape( [-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 = RollWrapper.roll( shifted_x, shifts=(self.shift_size, self.shift_size), axis=(1, 2)) else: x = shifted_x x = x.reshape([B, H * W, C]) x = shortcut + self.drop_path(self.norm1(x)) # FFN x = x + self.drop_path(self.norm2(self.mlp(x))) return x def extra_repr(self): 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.Layer): 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_attr=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 and W % 2 == 0, f"x size ({H}*{W}) are not even." x = x.reshape([B, H // 2, 2, W // 2, 2, C]) x = x.transpose((0, 1, 3, 4, 2, 5)) x = x.reshape([B, H * W // 4, 4 * C]) # B H/2*W/2 4*C x = self.reduction(x) x = self.norm(x) return x def extra_repr(self): return f"input_resolution={self.input_resolution}, dim={self.dim}" def flops(self): H, W = self.input_resolution flops = (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim flops += H * W * self.dim // 2 return flops class BasicLayer(nn.Layer): """ 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 # build blocks self.blocks = nn.LayerList([ 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 = 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 extra_repr(self): 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.Layer): r""" Image to Patch Embedding Args: img_size (int): Image size. Default: 256. patch_size (int): Patch token size. Default: 4. in_chans (int): Number of input image channels. Default: 3. embed_dim (int): Number of linear projection output channels. Default: 96. norm_layer (nn.Module, optional): Normalization layer. Default: None """ def __init__(self, img_size=256, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None): super().__init__() img_size = to_2tuple(img_size) patch_size = to_2tuple(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 # FIXME look at relaxing size constraints assert H == self.img_size[0] and W == self.img_size[1], \ f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})." x = self.proj(x).flatten(2).transpose([0, 2, 1]) # 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 SwinTransformerV2(nn.Layer): r""" Swin TransformerV2 A PaddlePaddle 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 256 patch_size (int | tuple(int)): Patch size. Default: 4 in_chans (int): Number of input image channels. Default: 3 class_num (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.Layer): 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 pretrained_window_sizes (tuple(int)): Pretrained window sizes of each layer. """ def __init__(self, img_size=256, patch_size=4, in_chans=3, class_num=1000, 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.class_num = class_num 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 # 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 patches_resolution = self.patch_embed.patches_resolution self.patches_resolution = patches_resolution # absolute position embedding if self.ape: self.absolute_pos_embed = self.create_parameter( shape=(1, num_patches, embed_dim), default_initializer=zeros_) trunc_normal_(self.absolute_pos_embed) self.pos_drop = nn.Dropout(p=drop_rate) # stochastic depth dpr = [ x.item() for x in paddle.linspace(0, drop_path_rate, sum(depths)) ] # stochastic depth decay rule # build layers self.layers = nn.LayerList() 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, 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.avgpool = nn.AdaptiveAvgPool1D(1) self.head = nn.Linear(self.num_features, class_num) if class_num > 0 else nn.Identity() self.apply(self._init_weights) def _init_weights(self, m): if isinstance(m, nn.Linear): trunc_normal_(m.weight) if isinstance(m, nn.Linear) and m.bias is not None: zeros_(m.bias) elif isinstance(m, nn.LayerNorm): zeros_(m.bias) ones_(m.weight) def forward_features(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 = self.norm(x) # B L C x = self.avgpool(x.transpose([0, 2, 1])) # B C 1 x = paddle.flatten(x, 1) return x def forward(self, x): x = self.forward_features(x) x = self.head(x) return x def flops(self): flops = 0 flops += self.patch_embed.flops() for i, layer in enumerate(self.layers): flops += layer.flops() flops += self.num_features * self.patches_resolution[ 0] * self.patches_resolution[1] // (2**self.num_layers) flops += self.num_features * self.class_num return flops def _load_pretrained(pretrained, model, model_url, use_ssld=False): if pretrained is False: pass elif pretrained is True: load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld) elif isinstance(pretrained, str): load_dygraph_pretrain(model, pretrained) else: raise RuntimeError( "pretrained type is not available. Please use `string` or `boolean` type." ) def SwinTransformerV2_tiny_patch4_window8_256(pretrained=False, use_ssld=False, **kwargs): model = SwinTransformerV2( img_size=256, embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24], window_size=8, drop_path_rate=0.2, **kwargs) _load_pretrained( pretrained, model, MODEL_URLS["SwinTransformerV2_tiny_patch4_window8_256"], use_ssld=use_ssld) return model def SwinTransformerV2_tiny_patch4_window16_256(pretrained=False, use_ssld=False, **kwargs): model = SwinTransformerV2( img_size=256, embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24], window_size=16, drop_path_rate=0.2, **kwargs) _load_pretrained( pretrained, model, MODEL_URLS["SwinTransformerV2_tiny_patch4_window16_256"], use_ssld=use_ssld) return model def SwinTransformerV2_small_patch4_window8_256(pretrained=False, use_ssld=False, **kwargs): model = SwinTransformerV2( img_size=256, embed_dim=96, depths=[2, 2, 18, 2], num_heads=[3, 6, 12, 24], window_size=8, drop_path_rate=0.3, **kwargs) _load_pretrained( pretrained, model, MODEL_URLS["SwinTransformerV2_small_patch4_window8_256"], use_ssld=use_ssld) return model def SwinTransformerV2_small_patch4_window16_256(pretrained=False, use_ssld=False, **kwargs): model = SwinTransformerV2( img_size=256, embed_dim=96, depths=[2, 2, 18, 2], num_heads=[3, 6, 12, 24], window_size=16, drop_path_rate=0.3, **kwargs) _load_pretrained( pretrained, model, MODEL_URLS["SwinTransformerV2_small_patch4_window16_256"], use_ssld=use_ssld) return model def SwinTransformerV2_base_patch4_window8_256(pretrained=False, use_ssld=False, **kwargs): model = SwinTransformerV2( img_size=256, embed_dim=128, depths=[2, 2, 18, 2], num_heads=[4, 8, 16, 32], window_size=8, drop_path_rate=0.5, **kwargs) _load_pretrained( pretrained, model, MODEL_URLS["SwinTransformerV2_base_patch4_window8_256"], use_ssld=use_ssld) return model def SwinTransformerV2_base_patch4_window16_256(pretrained=False, use_ssld=False, **kwargs): model = SwinTransformerV2( img_size=256, embed_dim=128, depths=[2, 2, 18, 2], num_heads=[4, 8, 16, 32], window_size=16, drop_path_rate=0.5, **kwargs) _load_pretrained( pretrained, model, MODEL_URLS["SwinTransformerV2_base_patch4_window16_256"], use_ssld=use_ssld) return model def SwinTransformerV2_base_patch4_window12to16_256(pretrained=False, use_ssld=False, **kwargs): model = SwinTransformerV2( img_size=256, embed_dim=128, depths=[2, 2, 18, 2], num_heads=[4, 8, 16, 32], window_size=16, drop_path_rate=0.2, **kwargs) _load_pretrained( pretrained, model, MODEL_URLS["SwinTransformerV2_base_patch4_window12to16_256"], use_ssld=use_ssld) return model def SwinTransformerV2_base_patch4_window24_384(pretrained=False, use_ssld=False, **kwargs): model = SwinTransformerV2( img_size=384, embed_dim=128, depths=[2, 2, 18, 2], num_heads=[4, 8, 16, 32], window_size=24, drop_path_rate=0.2, **kwargs) _load_pretrained( pretrained, model, MODEL_URLS["SwinTransformerV2_base_patch4_window24_384"], use_ssld=use_ssld) return model def SwinTransformerV2_large_patch4_window16_256(pretrained=False, use_ssld=False, **kwargs): model = SwinTransformerV2( img_size=256, embed_dim=192, depths=[2, 2, 18, 2], num_heads=[6, 12, 24, 48], window_size=16, drop_path_rate=0.2, **kwargs) _load_pretrained( pretrained, model, MODEL_URLS["SwinTransformerV2_large_patch4_window16_256"], use_ssld=use_ssld) return model def SwinTransformerV2_large_patch4_window24_384(pretrained=False, use_ssld=False, **kwargs): model = SwinTransformerV2( img_size=384, embed_dim=192, depths=[2, 2, 18, 2], num_heads=[6, 12, 24, 48], window_size=24, drop_path_rate=0.2, **kwargs) _load_pretrained( pretrained, model, MODEL_URLS["SwinTransformerV2_large_patch4_window24_384"], use_ssld=use_ssld) return model