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""" Swin Transformer V2 |
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A PyTorch impl of : `Swin Transformer V2: Scaling Up Capacity and Resolution` |
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- https://arxiv.org/abs/2111.09883 |
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Code/weights from https://github.com/microsoft/Swin-Transformer, original copyright/license info below |
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Modifications and additions for timm hacked together by / Copyright 2022, Ross Wightman |
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""" |
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import math |
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from typing import Callable, List, Optional, Tuple, Type, Union |
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import torch |
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import torch.nn as nn |
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import torch.nn.functional as F |
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from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD |
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from timm.layers import PatchEmbed, Mlp, DropPath, to_2tuple, trunc_normal_, ClassifierHead,\ |
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resample_patch_embed, ndgrid, get_act_layer, LayerType |
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from ._builder import build_model_with_cfg |
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from ._features import feature_take_indices |
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from ._features_fx import register_notrace_function |
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from ._manipulate import checkpoint |
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from ._registry import generate_default_cfgs, register_model, register_model_deprecations |
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__all__ = ['SwinTransformerV2'] |
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_int_or_tuple_2_t = Union[int, Tuple[int, int]] |
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def window_partition(x: torch.Tensor, window_size: Tuple[int, int]) -> torch.Tensor: |
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""" |
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Args: |
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x: (B, H, W, C) |
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window_size (int): window size |
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Returns: |
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windows: (num_windows*B, window_size, window_size, C) |
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""" |
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B, H, W, C = x.shape |
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x = x.view(B, H // window_size[0], window_size[0], W // window_size[1], window_size[1], C) |
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windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size[0], window_size[1], C) |
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return windows |
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@register_notrace_function |
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def window_reverse(windows: torch.Tensor, window_size: Tuple[int, int], img_size: Tuple[int, int]) -> torch.Tensor: |
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""" |
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Args: |
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windows: (num_windows * B, window_size[0], window_size[1], C) |
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window_size (Tuple[int, int]): Window size |
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img_size (Tuple[int, int]): Image size |
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Returns: |
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x: (B, H, W, C) |
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""" |
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H, W = img_size |
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C = windows.shape[-1] |
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x = windows.view(-1, H // window_size[0], W // window_size[1], window_size[0], window_size[1], C) |
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x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, H, W, C) |
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return x |
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class WindowAttention(nn.Module): |
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r""" Window based multi-head self attention (W-MSA) module with relative position bias. |
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It supports both of shifted and non-shifted window. |
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Args: |
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dim (int): Number of input channels. |
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window_size (tuple[int]): The height and width of the window. |
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num_heads (int): Number of attention heads. |
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qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True |
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attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 |
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proj_drop (float, optional): Dropout ratio of output. Default: 0.0 |
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pretrained_window_size (tuple[int]): The height and width of the window in pre-training. |
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""" |
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def __init__( |
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self, |
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dim: int, |
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window_size: Tuple[int, int], |
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num_heads: int, |
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qkv_bias: bool = True, |
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qkv_bias_separate: bool = False, |
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attn_drop: float = 0., |
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proj_drop: float = 0., |
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pretrained_window_size: Tuple[int, int] = (0, 0), |
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) -> None: |
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super().__init__() |
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self.dim = dim |
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self.window_size = window_size |
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self.pretrained_window_size = to_2tuple(pretrained_window_size) |
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self.num_heads = num_heads |
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self.qkv_bias_separate = qkv_bias_separate |
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self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1)))) |
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self.cpb_mlp = nn.Sequential( |
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nn.Linear(2, 512, bias=True), |
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nn.ReLU(inplace=True), |
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nn.Linear(512, num_heads, bias=False) |
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) |
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self.qkv = nn.Linear(dim, dim * 3, bias=False) |
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if qkv_bias: |
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self.q_bias = nn.Parameter(torch.zeros(dim)) |
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self.register_buffer('k_bias', torch.zeros(dim), persistent=False) |
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self.v_bias = nn.Parameter(torch.zeros(dim)) |
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else: |
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self.q_bias = None |
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self.k_bias = None |
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self.v_bias = None |
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self.attn_drop = nn.Dropout(attn_drop) |
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self.proj = nn.Linear(dim, dim) |
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self.proj_drop = nn.Dropout(proj_drop) |
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self.softmax = nn.Softmax(dim=-1) |
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self._make_pair_wise_relative_positions() |
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def _make_pair_wise_relative_positions(self): |
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relative_coords_h = torch.arange(-(self.window_size[0] - 1), self.window_size[0]).to(torch.float32) |
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relative_coords_w = torch.arange(-(self.window_size[1] - 1), self.window_size[1]).to(torch.float32) |
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relative_coords_table = torch.stack(ndgrid(relative_coords_h, relative_coords_w)) |
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relative_coords_table = relative_coords_table.permute(1, 2, 0).contiguous().unsqueeze(0) |
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if self.pretrained_window_size[0] > 0: |
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relative_coords_table[:, :, :, 0] /= (self.pretrained_window_size[0] - 1) |
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relative_coords_table[:, :, :, 1] /= (self.pretrained_window_size[1] - 1) |
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else: |
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relative_coords_table[:, :, :, 0] /= (self.window_size[0] - 1) |
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relative_coords_table[:, :, :, 1] /= (self.window_size[1] - 1) |
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relative_coords_table *= 8 |
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relative_coords_table = torch.sign(relative_coords_table) * torch.log2( |
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torch.abs(relative_coords_table) + 1.0) / math.log2(8) |
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self.register_buffer("relative_coords_table", relative_coords_table, persistent=False) |
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coords_h = torch.arange(self.window_size[0]) |
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coords_w = torch.arange(self.window_size[1]) |
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coords = torch.stack(ndgrid(coords_h, coords_w)) |
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coords_flatten = torch.flatten(coords, 1) |
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relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] |
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relative_coords = relative_coords.permute(1, 2, 0).contiguous() |
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relative_coords[:, :, 0] += self.window_size[0] - 1 |
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relative_coords[:, :, 1] += self.window_size[1] - 1 |
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relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 |
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relative_position_index = relative_coords.sum(-1) |
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self.register_buffer("relative_position_index", relative_position_index, persistent=False) |
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def set_window_size(self, window_size: Tuple[int, int]) -> None: |
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"""Update window size & interpolate position embeddings |
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Args: |
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window_size (int): New window size |
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""" |
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window_size = to_2tuple(window_size) |
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if window_size != self.window_size: |
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self.window_size = window_size |
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self._make_pair_wise_relative_positions() |
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def forward(self, x: torch.Tensor, mask: Optional[torch.Tensor] = None) -> torch.Tensor: |
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""" |
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Args: |
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x: input features with shape of (num_windows*B, N, C) |
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mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None |
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""" |
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B_, N, C = x.shape |
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if self.q_bias is None: |
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qkv = self.qkv(x) |
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else: |
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qkv_bias = torch.cat((self.q_bias, self.k_bias, self.v_bias)) |
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if self.qkv_bias_separate: |
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qkv = self.qkv(x) |
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qkv += qkv_bias |
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else: |
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qkv = F.linear(x, weight=self.qkv.weight, bias=qkv_bias) |
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qkv = qkv.reshape(B_, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) |
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q, k, v = qkv.unbind(0) |
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attn = (F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1)) |
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logit_scale = torch.clamp(self.logit_scale, max=math.log(1. / 0.01)).exp() |
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attn = attn * logit_scale |
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relative_position_bias_table = self.cpb_mlp(self.relative_coords_table).view(-1, self.num_heads) |
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relative_position_bias = relative_position_bias_table[self.relative_position_index.view(-1)].view( |
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self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) |
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relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() |
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relative_position_bias = 16 * torch.sigmoid(relative_position_bias) |
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attn = attn + relative_position_bias.unsqueeze(0) |
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if mask is not None: |
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num_win = mask.shape[0] |
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attn = attn.view(-1, num_win, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0) |
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attn = attn.view(-1, self.num_heads, N, N) |
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attn = self.softmax(attn) |
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else: |
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attn = self.softmax(attn) |
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attn = self.attn_drop(attn) |
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x = (attn @ v).transpose(1, 2).reshape(B_, N, C) |
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x = self.proj(x) |
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x = self.proj_drop(x) |
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return x |
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class SwinTransformerV2Block(nn.Module): |
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""" Swin Transformer Block. |
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""" |
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def __init__( |
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self, |
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dim: int, |
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input_resolution: _int_or_tuple_2_t, |
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num_heads: int, |
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window_size: _int_or_tuple_2_t = 7, |
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shift_size: _int_or_tuple_2_t = 0, |
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always_partition: bool = False, |
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dynamic_mask: bool = False, |
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mlp_ratio: float = 4., |
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qkv_bias: bool = True, |
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proj_drop: float = 0., |
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attn_drop: float = 0., |
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drop_path: float = 0., |
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act_layer: LayerType = "gelu", |
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norm_layer: Type[nn.Module] = nn.LayerNorm, |
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pretrained_window_size: _int_or_tuple_2_t = 0, |
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): |
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""" |
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Args: |
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dim: Number of input channels. |
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input_resolution: Input resolution. |
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num_heads: Number of attention heads. |
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window_size: Window size. |
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shift_size: Shift size for SW-MSA. |
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always_partition: Always partition into full windows and shift |
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mlp_ratio: Ratio of mlp hidden dim to embedding dim. |
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qkv_bias: If True, add a learnable bias to query, key, value. |
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proj_drop: Dropout rate. |
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attn_drop: Attention dropout rate. |
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drop_path: Stochastic depth rate. |
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act_layer: Activation layer. |
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norm_layer: Normalization layer. |
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pretrained_window_size: Window size in pretraining. |
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""" |
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super().__init__() |
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self.dim = dim |
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self.input_resolution = to_2tuple(input_resolution) |
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self.num_heads = num_heads |
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self.target_shift_size = to_2tuple(shift_size) |
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self.always_partition = always_partition |
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self.dynamic_mask = dynamic_mask |
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self.window_size, self.shift_size = self._calc_window_shift(window_size, shift_size) |
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self.window_area = self.window_size[0] * self.window_size[1] |
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self.mlp_ratio = mlp_ratio |
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act_layer = get_act_layer(act_layer) |
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self.attn = WindowAttention( |
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dim, |
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window_size=to_2tuple(self.window_size), |
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num_heads=num_heads, |
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qkv_bias=qkv_bias, |
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attn_drop=attn_drop, |
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proj_drop=proj_drop, |
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pretrained_window_size=to_2tuple(pretrained_window_size), |
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) |
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self.norm1 = norm_layer(dim) |
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self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity() |
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self.mlp = Mlp( |
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in_features=dim, |
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hidden_features=int(dim * mlp_ratio), |
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act_layer=act_layer, |
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drop=proj_drop, |
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) |
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self.norm2 = norm_layer(dim) |
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self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity() |
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self.register_buffer( |
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"attn_mask", |
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None if self.dynamic_mask else self.get_attn_mask(), |
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persistent=False, |
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) |
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def get_attn_mask(self, x: Optional[torch.Tensor] = None) -> Optional[torch.Tensor]: |
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if any(self.shift_size): |
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if x is None: |
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img_mask = torch.zeros((1, *self.input_resolution, 1)) |
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else: |
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img_mask = torch.zeros((1, x.shape[1], x.shape[2], 1), dtype=x.dtype, device=x.device) |
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cnt = 0 |
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for h in ( |
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(0, -self.window_size[0]), |
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(-self.window_size[0], -self.shift_size[0]), |
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(-self.shift_size[0], None), |
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): |
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for w in ( |
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(0, -self.window_size[1]), |
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(-self.window_size[1], -self.shift_size[1]), |
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(-self.shift_size[1], None), |
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): |
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img_mask[:, h[0]:h[1], w[0]:w[1], :] = cnt |
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cnt += 1 |
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mask_windows = window_partition(img_mask, self.window_size) |
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mask_windows = mask_windows.view(-1, self.window_area) |
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attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) |
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attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) |
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else: |
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attn_mask = None |
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return attn_mask |
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def _calc_window_shift( |
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self, |
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target_window_size: _int_or_tuple_2_t, |
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target_shift_size: Optional[_int_or_tuple_2_t] = None, |
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) -> Tuple[Tuple[int, int], Tuple[int, int]]: |
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target_window_size = to_2tuple(target_window_size) |
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if target_shift_size is None: |
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target_shift_size = self.target_shift_size |
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if any(target_shift_size): |
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target_shift_size = (target_window_size[0] // 2, target_window_size[1] // 2) |
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else: |
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target_shift_size = to_2tuple(target_shift_size) |
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if self.always_partition: |
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return target_window_size, target_shift_size |
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target_window_size = to_2tuple(target_window_size) |
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target_shift_size = to_2tuple(target_shift_size) |
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window_size = [r if r <= w else w for r, w in zip(self.input_resolution, target_window_size)] |
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shift_size = [0 if r <= w else s for r, w, s in zip(self.input_resolution, window_size, target_shift_size)] |
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return tuple(window_size), tuple(shift_size) |
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def set_input_size( |
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self, |
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feat_size: Tuple[int, int], |
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window_size: Tuple[int, int], |
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always_partition: Optional[bool] = None, |
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): |
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""" Updates the input resolution, window size. |
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Args: |
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feat_size (Tuple[int, int]): New input resolution |
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window_size (int): New window size |
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always_partition: Change always_partition attribute if not None |
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""" |
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self.input_resolution = feat_size |
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if always_partition is not None: |
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self.always_partition = always_partition |
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self.window_size, self.shift_size = self._calc_window_shift(to_2tuple(window_size)) |
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self.window_area = self.window_size[0] * self.window_size[1] |
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self.attn.set_window_size(self.window_size) |
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self.register_buffer( |
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"attn_mask", |
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None if self.dynamic_mask else self.get_attn_mask(), |
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persistent=False, |
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) |
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def _attn(self, x: torch.Tensor) -> torch.Tensor: |
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B, H, W, C = x.shape |
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has_shift = any(self.shift_size) |
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if has_shift: |
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shifted_x = torch.roll(x, shifts=(-self.shift_size[0], -self.shift_size[1]), dims=(1, 2)) |
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else: |
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shifted_x = x |
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pad_h = (self.window_size[0] - H % self.window_size[0]) % self.window_size[0] |
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pad_w = (self.window_size[1] - W % self.window_size[1]) % self.window_size[1] |
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shifted_x = torch.nn.functional.pad(shifted_x, (0, 0, 0, pad_w, 0, pad_h)) |
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_, Hp, Wp, _ = shifted_x.shape |
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x_windows = window_partition(shifted_x, self.window_size) |
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x_windows = x_windows.view(-1, self.window_area, C) |
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if getattr(self, 'dynamic_mask', False): |
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attn_mask = self.get_attn_mask(shifted_x) |
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else: |
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attn_mask = self.attn_mask |
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attn_windows = self.attn(x_windows, mask=attn_mask) |
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attn_windows = attn_windows.view(-1, self.window_size[0], self.window_size[1], C) |
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shifted_x = window_reverse(attn_windows, self.window_size, (Hp, Wp)) |
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shifted_x = shifted_x[:, :H, :W, :].contiguous() |
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if has_shift: |
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x = torch.roll(shifted_x, shifts=self.shift_size, dims=(1, 2)) |
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else: |
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x = shifted_x |
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return x |
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def forward(self, x: torch.Tensor) -> torch.Tensor: |
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B, H, W, C = x.shape |
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x = x + self.drop_path1(self.norm1(self._attn(x))) |
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x = x.reshape(B, -1, C) |
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x = x + self.drop_path2(self.norm2(self.mlp(x))) |
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x = x.reshape(B, H, W, C) |
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return x |
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class PatchMerging(nn.Module): |
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""" Patch Merging Layer. |
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""" |
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def __init__( |
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self, |
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dim: int, |
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out_dim: Optional[int] = None, |
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norm_layer: Type[nn.Module] = nn.LayerNorm |
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): |
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""" |
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Args: |
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dim (int): Number of input channels. |
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out_dim (int): Number of output channels (or 2 * dim if None) |
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norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm |
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""" |
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super().__init__() |
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self.dim = dim |
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self.out_dim = out_dim or 2 * dim |
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self.reduction = nn.Linear(4 * dim, self.out_dim, bias=False) |
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self.norm = norm_layer(self.out_dim) |
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def forward(self, x: torch.Tensor) -> torch.Tensor: |
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B, H, W, C = x.shape |
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pad_values = (0, 0, 0, W % 2, 0, H % 2) |
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x = nn.functional.pad(x, pad_values) |
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_, H, W, _ = x.shape |
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x = x.reshape(B, H // 2, 2, W // 2, 2, C).permute(0, 1, 3, 4, 2, 5).flatten(3) |
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x = self.reduction(x) |
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x = self.norm(x) |
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return x |
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class SwinTransformerV2Stage(nn.Module): |
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""" A Swin Transformer V2 Stage. |
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""" |
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def __init__( |
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self, |
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dim: int, |
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out_dim: int, |
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input_resolution: _int_or_tuple_2_t, |
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depth: int, |
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num_heads: int, |
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window_size: _int_or_tuple_2_t, |
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always_partition: bool = False, |
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dynamic_mask: bool = False, |
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downsample: bool = False, |
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mlp_ratio: float = 4., |
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qkv_bias: bool = True, |
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proj_drop: float = 0., |
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attn_drop: float = 0., |
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drop_path: float = 0., |
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act_layer: Union[str, Callable] = 'gelu', |
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norm_layer: Type[nn.Module] = nn.LayerNorm, |
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pretrained_window_size: _int_or_tuple_2_t = 0, |
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output_nchw: bool = False, |
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) -> None: |
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""" |
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Args: |
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dim: Number of input channels. |
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out_dim: Number of output channels. |
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input_resolution: Input resolution. |
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depth: Number of blocks. |
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num_heads: Number of attention heads. |
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window_size: Local window size. |
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always_partition: Always partition into full windows and shift |
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dynamic_mask: Create attention mask in forward based on current input size |
|
downsample: Use downsample layer at start of the block. |
|
mlp_ratio: Ratio of mlp hidden dim to embedding dim. |
|
qkv_bias: If True, add a learnable bias to query, key, value. |
|
proj_drop: Projection dropout rate |
|
attn_drop: Attention dropout rate. |
|
drop_path: Stochastic depth rate. |
|
act_layer: Activation layer type. |
|
norm_layer: Normalization layer. |
|
pretrained_window_size: Local window size in pretraining. |
|
output_nchw: Output tensors on NCHW format instead of NHWC. |
|
""" |
|
super().__init__() |
|
self.dim = dim |
|
self.input_resolution = input_resolution |
|
self.output_resolution = tuple(i // 2 for i in input_resolution) if downsample else input_resolution |
|
self.depth = depth |
|
self.output_nchw = output_nchw |
|
self.grad_checkpointing = False |
|
window_size = to_2tuple(window_size) |
|
shift_size = tuple([w // 2 for w in window_size]) |
|
|
|
|
|
if downsample: |
|
self.downsample = PatchMerging(dim=dim, out_dim=out_dim, norm_layer=norm_layer) |
|
else: |
|
assert dim == out_dim |
|
self.downsample = nn.Identity() |
|
|
|
|
|
self.blocks = nn.ModuleList([ |
|
SwinTransformerV2Block( |
|
dim=out_dim, |
|
input_resolution=self.output_resolution, |
|
num_heads=num_heads, |
|
window_size=window_size, |
|
shift_size=0 if (i % 2 == 0) else shift_size, |
|
always_partition=always_partition, |
|
dynamic_mask=dynamic_mask, |
|
mlp_ratio=mlp_ratio, |
|
qkv_bias=qkv_bias, |
|
proj_drop=proj_drop, |
|
attn_drop=attn_drop, |
|
drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, |
|
act_layer=act_layer, |
|
norm_layer=norm_layer, |
|
pretrained_window_size=pretrained_window_size, |
|
) |
|
for i in range(depth)]) |
|
|
|
def set_input_size( |
|
self, |
|
feat_size: Tuple[int, int], |
|
window_size: int, |
|
always_partition: Optional[bool] = None, |
|
): |
|
""" Updates the resolution, window size and so the pair-wise relative positions. |
|
|
|
Args: |
|
feat_size: New input (feature) resolution |
|
window_size: New window size |
|
always_partition: Always partition / shift the window |
|
""" |
|
self.input_resolution = feat_size |
|
if isinstance(self.downsample, nn.Identity): |
|
self.output_resolution = feat_size |
|
else: |
|
assert isinstance(self.downsample, PatchMerging) |
|
self.output_resolution = tuple(i // 2 for i in feat_size) |
|
for block in self.blocks: |
|
block.set_input_size( |
|
feat_size=self.output_resolution, |
|
window_size=window_size, |
|
always_partition=always_partition, |
|
) |
|
|
|
def forward(self, x: torch.Tensor) -> torch.Tensor: |
|
x = self.downsample(x) |
|
|
|
for blk in self.blocks: |
|
if self.grad_checkpointing and not torch.jit.is_scripting(): |
|
x = checkpoint.checkpoint(blk, x) |
|
else: |
|
x = blk(x) |
|
return x |
|
|
|
def _init_respostnorm(self) -> None: |
|
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): |
|
""" Swin Transformer V2 |
|
|
|
A PyTorch impl of : `Swin Transformer V2: Scaling Up Capacity and Resolution` |
|
- https://arxiv.org/abs/2111.09883 |
|
""" |
|
|
|
def __init__( |
|
self, |
|
img_size: _int_or_tuple_2_t = 224, |
|
patch_size: int = 4, |
|
in_chans: int = 3, |
|
num_classes: int = 1000, |
|
global_pool: str = 'avg', |
|
embed_dim: int = 96, |
|
depths: Tuple[int, ...] = (2, 2, 6, 2), |
|
num_heads: Tuple[int, ...] = (3, 6, 12, 24), |
|
window_size: _int_or_tuple_2_t = 7, |
|
always_partition: bool = False, |
|
strict_img_size: bool = True, |
|
mlp_ratio: float = 4., |
|
qkv_bias: bool = True, |
|
drop_rate: float = 0., |
|
proj_drop_rate: float = 0., |
|
attn_drop_rate: float = 0., |
|
drop_path_rate: float = 0.1, |
|
act_layer: Union[str, Callable] = 'gelu', |
|
norm_layer: Callable = nn.LayerNorm, |
|
pretrained_window_sizes: Tuple[int, ...] = (0, 0, 0, 0), |
|
**kwargs, |
|
): |
|
""" |
|
Args: |
|
img_size: Input image size. |
|
patch_size: Patch size. |
|
in_chans: Number of input image channels. |
|
num_classes: Number of classes for classification head. |
|
embed_dim: Patch embedding dimension. |
|
depths: Depth of each Swin Transformer stage (layer). |
|
num_heads: Number of attention heads in different layers. |
|
window_size: Window size. |
|
mlp_ratio: Ratio of mlp hidden dim to embedding dim. |
|
qkv_bias: If True, add a learnable bias to query, key, value. |
|
drop_rate: Head dropout rate. |
|
proj_drop_rate: Projection dropout rate. |
|
attn_drop_rate: Attention dropout rate. |
|
drop_path_rate: Stochastic depth rate. |
|
norm_layer: Normalization layer. |
|
act_layer: Activation layer type. |
|
patch_norm: If True, add normalization after patch embedding. |
|
pretrained_window_sizes: Pretrained window sizes of each layer. |
|
output_fmt: Output tensor format if not None, otherwise output 'NHWC' by default. |
|
""" |
|
super().__init__() |
|
|
|
self.num_classes = num_classes |
|
assert global_pool in ('', 'avg') |
|
self.global_pool = global_pool |
|
self.output_fmt = 'NHWC' |
|
self.num_layers = len(depths) |
|
self.embed_dim = embed_dim |
|
self.num_features = self.head_hidden_size = int(embed_dim * 2 ** (self.num_layers - 1)) |
|
self.feature_info = [] |
|
|
|
if not isinstance(embed_dim, (tuple, list)): |
|
embed_dim = [int(embed_dim * 2 ** i) for i in range(self.num_layers)] |
|
|
|
|
|
self.patch_embed = PatchEmbed( |
|
img_size=img_size, |
|
patch_size=patch_size, |
|
in_chans=in_chans, |
|
embed_dim=embed_dim[0], |
|
norm_layer=norm_layer, |
|
strict_img_size=strict_img_size, |
|
output_fmt='NHWC', |
|
) |
|
grid_size = self.patch_embed.grid_size |
|
|
|
dpr = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(depths)).split(depths)] |
|
layers = [] |
|
in_dim = embed_dim[0] |
|
scale = 1 |
|
for i in range(self.num_layers): |
|
out_dim = embed_dim[i] |
|
layers += [SwinTransformerV2Stage( |
|
dim=in_dim, |
|
out_dim=out_dim, |
|
input_resolution=(grid_size[0] // scale, grid_size[1] // scale), |
|
depth=depths[i], |
|
downsample=i > 0, |
|
num_heads=num_heads[i], |
|
window_size=window_size, |
|
always_partition=always_partition, |
|
dynamic_mask=not strict_img_size, |
|
mlp_ratio=mlp_ratio, |
|
qkv_bias=qkv_bias, |
|
proj_drop=proj_drop_rate, |
|
attn_drop=attn_drop_rate, |
|
drop_path=dpr[i], |
|
act_layer=act_layer, |
|
norm_layer=norm_layer, |
|
pretrained_window_size=pretrained_window_sizes[i], |
|
)] |
|
in_dim = out_dim |
|
if i > 0: |
|
scale *= 2 |
|
self.feature_info += [dict(num_chs=out_dim, reduction=4 * scale, module=f'layers.{i}')] |
|
|
|
self.layers = nn.Sequential(*layers) |
|
self.norm = norm_layer(self.num_features) |
|
self.head = ClassifierHead( |
|
self.num_features, |
|
num_classes, |
|
pool_type=global_pool, |
|
drop_rate=drop_rate, |
|
input_fmt=self.output_fmt, |
|
) |
|
|
|
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) |
|
|
|
def set_input_size( |
|
self, |
|
img_size: Optional[Tuple[int, int]] = None, |
|
patch_size: Optional[Tuple[int, int]] = None, |
|
window_size: Optional[Tuple[int, int]] = None, |
|
window_ratio: Optional[int] = 8, |
|
always_partition: Optional[bool] = None, |
|
): |
|
"""Updates the image resolution, window size, and so the pair-wise relative positions. |
|
|
|
Args: |
|
img_size (Optional[Tuple[int, int]]): New input resolution, if None current resolution is used |
|
patch_size (Optional[Tuple[int, int]): New patch size, if None use current patch size |
|
window_size (Optional[int]): New window size, if None based on new_img_size // window_div |
|
window_ratio (int): divisor for calculating window size from patch grid size |
|
always_partition: always partition / shift windows even if feat size is < window |
|
""" |
|
if img_size is not None or patch_size is not None: |
|
self.patch_embed.set_input_size(img_size=img_size, patch_size=patch_size) |
|
grid_size = self.patch_embed.grid_size |
|
|
|
if window_size is None and window_ratio is not None: |
|
window_size = tuple([s // window_ratio for s in grid_size]) |
|
|
|
for index, stage in enumerate(self.layers): |
|
stage_scale = 2 ** max(index - 1, 0) |
|
stage.set_input_size( |
|
feat_size=(grid_size[0] // stage_scale, grid_size[1] // stage_scale), |
|
window_size=window_size, |
|
always_partition=always_partition, |
|
) |
|
|
|
@torch.jit.ignore |
|
def no_weight_decay(self): |
|
nod = set() |
|
for n, m in self.named_modules(): |
|
if any([kw in n for kw in ("cpb_mlp", "logit_scale")]): |
|
nod.add(n) |
|
return nod |
|
|
|
@torch.jit.ignore |
|
def group_matcher(self, coarse=False): |
|
return dict( |
|
stem=r'^absolute_pos_embed|patch_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) -> nn.Module: |
|
return self.head.fc |
|
|
|
def reset_classifier(self, num_classes: int, global_pool: Optional[str] = None): |
|
self.num_classes = num_classes |
|
self.head.reset(num_classes, global_pool) |
|
|
|
def forward_intermediates( |
|
self, |
|
x: torch.Tensor, |
|
indices: Optional[Union[int, List[int]]] = None, |
|
norm: bool = False, |
|
stop_early: bool = False, |
|
output_fmt: str = 'NCHW', |
|
intermediates_only: bool = False, |
|
) -> Union[List[torch.Tensor], Tuple[torch.Tensor, List[torch.Tensor]]]: |
|
""" Forward features that returns intermediates. |
|
|
|
Args: |
|
x: Input image tensor |
|
indices: Take last n blocks if int, all if None, select matching indices if sequence |
|
norm: Apply norm layer to compatible intermediates |
|
stop_early: Stop iterating over blocks when last desired intermediate hit |
|
output_fmt: Shape of intermediate feature outputs |
|
intermediates_only: Only return intermediate features |
|
Returns: |
|
|
|
""" |
|
assert output_fmt in ('NCHW',), 'Output shape must be NCHW.' |
|
intermediates = [] |
|
take_indices, max_index = feature_take_indices(len(self.layers), indices) |
|
|
|
|
|
x = self.patch_embed(x) |
|
|
|
num_stages = len(self.layers) |
|
if torch.jit.is_scripting() or not stop_early: |
|
stages = self.layers |
|
else: |
|
stages = self.layers[:max_index + 1] |
|
for i, stage in enumerate(stages): |
|
x = stage(x) |
|
if i in take_indices: |
|
if norm and i == num_stages - 1: |
|
x_inter = self.norm(x) |
|
else: |
|
x_inter = x |
|
x_inter = x_inter.permute(0, 3, 1, 2).contiguous() |
|
intermediates.append(x_inter) |
|
|
|
if intermediates_only: |
|
return intermediates |
|
|
|
x = self.norm(x) |
|
|
|
return x, intermediates |
|
|
|
def prune_intermediate_layers( |
|
self, |
|
indices: Union[int, List[int]] = 1, |
|
prune_norm: bool = False, |
|
prune_head: bool = True, |
|
): |
|
""" Prune layers not required for specified intermediates. |
|
""" |
|
take_indices, max_index = feature_take_indices(len(self.layers), indices) |
|
self.layers = self.layers[:max_index + 1] |
|
if prune_norm: |
|
self.norm = nn.Identity() |
|
if prune_head: |
|
self.reset_classifier(0, '') |
|
return take_indices |
|
|
|
def forward_features(self, x): |
|
x = self.patch_embed(x) |
|
x = self.layers(x) |
|
x = self.norm(x) |
|
return x |
|
|
|
def forward_head(self, x, pre_logits: bool = False): |
|
return self.head(x, pre_logits=True) if pre_logits else self.head(x) |
|
|
|
def forward(self, x): |
|
x = self.forward_features(x) |
|
x = self.forward_head(x) |
|
return x |
|
|
|
|
|
def checkpoint_filter_fn(state_dict, model): |
|
state_dict = state_dict.get('model', state_dict) |
|
state_dict = state_dict.get('state_dict', state_dict) |
|
native_checkpoint = 'head.fc.weight' in state_dict |
|
out_dict = {} |
|
import re |
|
for k, v in state_dict.items(): |
|
if any([n in k for n in ('relative_position_index', 'relative_coords_table', 'attn_mask')]): |
|
continue |
|
|
|
if 'patch_embed.proj.weight' in k: |
|
_, _, H, W = model.patch_embed.proj.weight.shape |
|
if v.shape[-2] != H or v.shape[-1] != W: |
|
v = resample_patch_embed( |
|
v, |
|
(H, W), |
|
interpolation='bicubic', |
|
antialias=True, |
|
verbose=True, |
|
) |
|
|
|
if not native_checkpoint: |
|
|
|
k = re.sub(r'layers.(\d+).downsample', lambda x: f'layers.{int(x.group(1)) + 1}.downsample', k) |
|
k = k.replace('head.', 'head.fc.') |
|
out_dict[k] = v |
|
|
|
return out_dict |
|
|
|
|
|
def _create_swin_transformer_v2(variant, pretrained=False, **kwargs): |
|
default_out_indices = tuple(i for i, _ in enumerate(kwargs.get('depths', (1, 1, 1, 1)))) |
|
out_indices = kwargs.pop('out_indices', default_out_indices) |
|
|
|
model = build_model_with_cfg( |
|
SwinTransformerV2, variant, pretrained, |
|
pretrained_filter_fn=checkpoint_filter_fn, |
|
feature_cfg=dict(flatten_sequential=True, out_indices=out_indices), |
|
**kwargs) |
|
return model |
|
|
|
|
|
def _cfg(url='', **kwargs): |
|
return { |
|
'url': url, |
|
'num_classes': 1000, 'input_size': (3, 256, 256), 'pool_size': (8, 8), |
|
'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True, |
|
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, |
|
'first_conv': 'patch_embed.proj', 'classifier': 'head.fc', |
|
'license': 'mit', **kwargs |
|
} |
|
|
|
|
|
default_cfgs = generate_default_cfgs({ |
|
'swinv2_base_window12to16_192to256.ms_in22k_ft_in1k': _cfg( |
|
hf_hub_id='timm/', |
|
url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window12to16_192to256_22kto1k_ft.pth', |
|
), |
|
'swinv2_base_window12to24_192to384.ms_in22k_ft_in1k': _cfg( |
|
hf_hub_id='timm/', |
|
url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window12to24_192to384_22kto1k_ft.pth', |
|
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, |
|
), |
|
'swinv2_large_window12to16_192to256.ms_in22k_ft_in1k': _cfg( |
|
hf_hub_id='timm/', |
|
url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_large_patch4_window12to16_192to256_22kto1k_ft.pth', |
|
), |
|
'swinv2_large_window12to24_192to384.ms_in22k_ft_in1k': _cfg( |
|
hf_hub_id='timm/', |
|
url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_large_patch4_window12to24_192to384_22kto1k_ft.pth', |
|
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, |
|
), |
|
|
|
'swinv2_tiny_window8_256.ms_in1k': _cfg( |
|
hf_hub_id='timm/', |
|
url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_tiny_patch4_window8_256.pth', |
|
), |
|
'swinv2_tiny_window16_256.ms_in1k': _cfg( |
|
hf_hub_id='timm/', |
|
url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_tiny_patch4_window16_256.pth', |
|
), |
|
'swinv2_small_window8_256.ms_in1k': _cfg( |
|
hf_hub_id='timm/', |
|
url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_small_patch4_window8_256.pth', |
|
), |
|
'swinv2_small_window16_256.ms_in1k': _cfg( |
|
hf_hub_id='timm/', |
|
url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_small_patch4_window16_256.pth', |
|
), |
|
'swinv2_base_window8_256.ms_in1k': _cfg( |
|
hf_hub_id='timm/', |
|
url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window8_256.pth', |
|
), |
|
'swinv2_base_window16_256.ms_in1k': _cfg( |
|
hf_hub_id='timm/', |
|
url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window16_256.pth', |
|
), |
|
|
|
'swinv2_base_window12_192.ms_in22k': _cfg( |
|
hf_hub_id='timm/', |
|
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), pool_size=(6, 6) |
|
), |
|
'swinv2_large_window12_192.ms_in22k': _cfg( |
|
hf_hub_id='timm/', |
|
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), pool_size=(6, 6) |
|
), |
|
}) |
|
|
|
|
|
@register_model |
|
def swinv2_tiny_window16_256(pretrained=False, **kwargs) -> SwinTransformerV2: |
|
""" |
|
""" |
|
model_args = dict(window_size=16, embed_dim=96, depths=(2, 2, 6, 2), num_heads=(3, 6, 12, 24)) |
|
return _create_swin_transformer_v2( |
|
'swinv2_tiny_window16_256', pretrained=pretrained, **dict(model_args, **kwargs)) |
|
|
|
|
|
@register_model |
|
def swinv2_tiny_window8_256(pretrained=False, **kwargs) -> SwinTransformerV2: |
|
""" |
|
""" |
|
model_args = dict(window_size=8, embed_dim=96, depths=(2, 2, 6, 2), num_heads=(3, 6, 12, 24)) |
|
return _create_swin_transformer_v2( |
|
'swinv2_tiny_window8_256', pretrained=pretrained, **dict(model_args, **kwargs)) |
|
|
|
|
|
@register_model |
|
def swinv2_small_window16_256(pretrained=False, **kwargs) -> SwinTransformerV2: |
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""" |
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""" |
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model_args = dict(window_size=16, embed_dim=96, depths=(2, 2, 18, 2), num_heads=(3, 6, 12, 24)) |
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return _create_swin_transformer_v2( |
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'swinv2_small_window16_256', pretrained=pretrained, **dict(model_args, **kwargs)) |
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@register_model |
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def swinv2_small_window8_256(pretrained=False, **kwargs) -> SwinTransformerV2: |
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""" |
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""" |
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model_args = dict(window_size=8, embed_dim=96, depths=(2, 2, 18, 2), num_heads=(3, 6, 12, 24)) |
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return _create_swin_transformer_v2( |
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'swinv2_small_window8_256', pretrained=pretrained, **dict(model_args, **kwargs)) |
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@register_model |
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def swinv2_base_window16_256(pretrained=False, **kwargs) -> SwinTransformerV2: |
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""" |
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""" |
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model_args = dict(window_size=16, embed_dim=128, depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32)) |
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return _create_swin_transformer_v2( |
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'swinv2_base_window16_256', pretrained=pretrained, **dict(model_args, **kwargs)) |
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|
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@register_model |
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def swinv2_base_window8_256(pretrained=False, **kwargs) -> SwinTransformerV2: |
|
""" |
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""" |
|
model_args = dict(window_size=8, embed_dim=128, depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32)) |
|
return _create_swin_transformer_v2( |
|
'swinv2_base_window8_256', pretrained=pretrained, **dict(model_args, **kwargs)) |
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|
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|
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@register_model |
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def swinv2_base_window12_192(pretrained=False, **kwargs) -> SwinTransformerV2: |
|
""" |
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""" |
|
model_args = dict(window_size=12, embed_dim=128, depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32)) |
|
return _create_swin_transformer_v2( |
|
'swinv2_base_window12_192', pretrained=pretrained, **dict(model_args, **kwargs)) |
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|
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@register_model |
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def swinv2_base_window12to16_192to256(pretrained=False, **kwargs) -> SwinTransformerV2: |
|
""" |
|
""" |
|
model_args = 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)) |
|
return _create_swin_transformer_v2( |
|
'swinv2_base_window12to16_192to256', pretrained=pretrained, **dict(model_args, **kwargs)) |
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|
|
|
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@register_model |
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def swinv2_base_window12to24_192to384(pretrained=False, **kwargs) -> SwinTransformerV2: |
|
""" |
|
""" |
|
model_args = 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)) |
|
return _create_swin_transformer_v2( |
|
'swinv2_base_window12to24_192to384', pretrained=pretrained, **dict(model_args, **kwargs)) |
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|
|
|
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@register_model |
|
def swinv2_large_window12_192(pretrained=False, **kwargs) -> SwinTransformerV2: |
|
""" |
|
""" |
|
model_args = dict(window_size=12, embed_dim=192, depths=(2, 2, 18, 2), num_heads=(6, 12, 24, 48)) |
|
return _create_swin_transformer_v2( |
|
'swinv2_large_window12_192', pretrained=pretrained, **dict(model_args, **kwargs)) |
|
|
|
|
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@register_model |
|
def swinv2_large_window12to16_192to256(pretrained=False, **kwargs) -> SwinTransformerV2: |
|
""" |
|
""" |
|
model_args = 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)) |
|
return _create_swin_transformer_v2( |
|
'swinv2_large_window12to16_192to256', pretrained=pretrained, **dict(model_args, **kwargs)) |
|
|
|
|
|
@register_model |
|
def swinv2_large_window12to24_192to384(pretrained=False, **kwargs) -> SwinTransformerV2: |
|
""" |
|
""" |
|
model_args = 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)) |
|
return _create_swin_transformer_v2( |
|
'swinv2_large_window12to24_192to384', pretrained=pretrained, **dict(model_args, **kwargs)) |
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|
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register_model_deprecations(__name__, { |
|
'swinv2_base_window12_192_22k': 'swinv2_base_window12_192.ms_in22k', |
|
'swinv2_base_window12to16_192to256_22kft1k': 'swinv2_base_window12to16_192to256.ms_in22k_ft_in1k', |
|
'swinv2_base_window12to24_192to384_22kft1k': 'swinv2_base_window12to24_192to384.ms_in22k_ft_in1k', |
|
'swinv2_large_window12_192_22k': 'swinv2_large_window12_192.ms_in22k', |
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'swinv2_large_window12to16_192to256_22kft1k': 'swinv2_large_window12to16_192to256.ms_in22k_ft_in1k', |
|
'swinv2_large_window12to24_192to384_22kft1k': 'swinv2_large_window12to24_192to384.ms_in22k_ft_in1k', |
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}) |
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|