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import math |
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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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import torch.utils.checkpoint as checkpoint |
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from timm.models.layers import DropPath, to_2tuple, trunc_normal_ |
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class Mlp(nn.Module): |
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def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): |
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super().__init__() |
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out_features = out_features or in_features |
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hidden_features = hidden_features or in_features |
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self.fc1 = nn.Linear(in_features, hidden_features) |
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self.act = act_layer() |
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self.fc2 = nn.Linear(hidden_features, out_features) |
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self.drop = nn.Dropout(drop) |
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def forward(self, x): |
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x = self.fc1(x) |
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x = self.act(x) |
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x = self.drop(x) |
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x = self.fc2(x) |
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x = self.drop(x) |
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return x |
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def window_partition(x, window_size): |
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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, window_size, W // window_size, window_size, C) |
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windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) |
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return windows |
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def window_reverse(windows, window_size, H, W): |
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""" |
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Args: |
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windows: (num_windows*B, window_size, window_size, C) |
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window_size (int): Window size |
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H (int): Height of image |
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W (int): Width of image |
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Returns: |
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x: (B, H, W, C) |
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""" |
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B = int(windows.shape[0] / (H * W / window_size / window_size)) |
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x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) |
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x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) |
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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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qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set |
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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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""" |
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def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.): |
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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.num_heads = num_heads |
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head_dim = dim // num_heads |
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self.scale = qk_scale or head_dim ** -0.5 |
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self.relative_position_bias_table = nn.Parameter( |
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torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) |
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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(torch.meshgrid([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) |
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self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) |
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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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trunc_normal_(self.relative_position_bias_table, std=.02) |
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self.softmax = nn.Softmax(dim=-1) |
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def forward(self, x, mask=None): |
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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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qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) |
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q, k, v = qkv[0], qkv[1], qkv[2] |
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q = q * self.scale |
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attn = (q @ k.transpose(-2, -1)) |
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relative_position_bias = self.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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attn = attn + relative_position_bias.unsqueeze(0) |
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if mask is not None: |
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nW = mask.shape[0] |
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attn = attn.view(B_ // nW, nW, 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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def extra_repr(self) -> str: |
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return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}' |
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def flops(self, N): |
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flops = 0 |
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flops += N * self.dim * 3 * self.dim |
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flops += self.num_heads * N * (self.dim // self.num_heads) * N |
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flops += self.num_heads * N * N * (self.dim // self.num_heads) |
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flops += N * self.dim * self.dim |
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return flops |
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class SwinTransformerBlock(nn.Module): |
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r""" Swin Transformer Block. |
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Args: |
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dim (int): Number of input channels. |
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input_resolution (tuple[int]): Input resulotion. |
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num_heads (int): Number of attention heads. |
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window_size (int): Window size. |
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shift_size (int): Shift size for SW-MSA. |
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mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. |
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qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True |
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qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. |
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drop (float, optional): Dropout rate. Default: 0.0 |
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attn_drop (float, optional): Attention dropout rate. Default: 0.0 |
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drop_path (float, optional): Stochastic depth rate. Default: 0.0 |
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act_layer (nn.Module, optional): Activation layer. Default: nn.GELU |
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norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm |
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""" |
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def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0, |
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mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0., |
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act_layer=nn.GELU, norm_layer=nn.LayerNorm): |
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super().__init__() |
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self.dim = dim |
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self.input_resolution = input_resolution |
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self.num_heads = num_heads |
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self.window_size = window_size |
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self.shift_size = shift_size |
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self.mlp_ratio = mlp_ratio |
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if min(self.input_resolution) <= self.window_size: |
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self.shift_size = 0 |
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self.window_size = min(self.input_resolution) |
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assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size" |
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self.norm1 = norm_layer(dim) |
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self.attn = WindowAttention( |
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dim, window_size=to_2tuple(self.window_size), num_heads=num_heads, |
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qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) |
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self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() |
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self.norm2 = norm_layer(dim) |
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mlp_hidden_dim = int(dim * mlp_ratio) |
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self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) |
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if self.shift_size > 0: |
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attn_mask = self.calculate_mask(self.input_resolution) |
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else: |
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attn_mask = None |
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self.register_buffer("attn_mask", attn_mask) |
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def calculate_mask(self, x_size): |
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H, W = x_size |
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img_mask = torch.zeros((1, H, W, 1)) |
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h_slices = (slice(0, -self.window_size), |
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slice(-self.window_size, -self.shift_size), |
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slice(-self.shift_size, None)) |
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w_slices = (slice(0, -self.window_size), |
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slice(-self.window_size, -self.shift_size), |
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slice(-self.shift_size, None)) |
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cnt = 0 |
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for h in h_slices: |
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for w in w_slices: |
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img_mask[:, h, w, :] = 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_size * self.window_size) |
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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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return attn_mask |
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def forward(self, x, x_size): |
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H, W = x_size |
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B, L, C = x.shape |
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shortcut = x |
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x = self.norm1(x) |
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x = x.view(B, H, W, C) |
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if self.shift_size > 0: |
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shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) |
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else: |
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shifted_x = x |
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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_size * self.window_size, C) |
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if self.input_resolution == x_size: |
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attn_windows = self.attn(x_windows, mask=self.attn_mask) |
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else: |
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attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device)) |
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attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) |
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shifted_x = window_reverse(attn_windows, self.window_size, H, W) |
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if self.shift_size > 0: |
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x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) |
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else: |
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x = shifted_x |
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x = x.view(B, H * W, C) |
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x = shortcut + self.drop_path(x) |
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x = x + self.drop_path(self.mlp(self.norm2(x))) |
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return x |
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def extra_repr(self) -> str: |
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return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \ |
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f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}" |
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def flops(self): |
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flops = 0 |
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H, W = self.input_resolution |
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flops += self.dim * H * W |
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nW = H * W / self.window_size / self.window_size |
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flops += nW * self.attn.flops(self.window_size * self.window_size) |
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flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio |
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flops += self.dim * H * W |
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return flops |
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class PatchMerging(nn.Module): |
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r""" Patch Merging Layer. |
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Args: |
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input_resolution (tuple[int]): Resolution of input feature. |
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dim (int): Number of input channels. |
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norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm |
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""" |
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def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm): |
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super().__init__() |
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self.input_resolution = input_resolution |
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self.dim = dim |
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self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) |
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self.norm = norm_layer(4 * dim) |
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def forward(self, x): |
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""" |
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x: B, H*W, C |
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""" |
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H, W = self.input_resolution |
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B, L, C = x.shape |
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assert L == H * W, "input feature has wrong size" |
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assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even." |
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x = x.view(B, H, W, C) |
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x0 = x[:, 0::2, 0::2, :] |
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x1 = x[:, 1::2, 0::2, :] |
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x2 = x[:, 0::2, 1::2, :] |
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x3 = x[:, 1::2, 1::2, :] |
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x = torch.cat([x0, x1, x2, x3], -1) |
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x = x.view(B, -1, 4 * C) |
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x = self.norm(x) |
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x = self.reduction(x) |
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return x |
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def extra_repr(self) -> str: |
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return f"input_resolution={self.input_resolution}, dim={self.dim}" |
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def flops(self): |
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H, W = self.input_resolution |
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flops = H * W * self.dim |
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flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim |
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return flops |
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class BasicLayer(nn.Module): |
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""" A basic Swin Transformer layer for one stage. |
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Args: |
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dim (int): Number of input channels. |
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input_resolution (tuple[int]): Input resolution. |
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depth (int): Number of blocks. |
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num_heads (int): Number of attention heads. |
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window_size (int): Local window size. |
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mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. |
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qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True |
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qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. |
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drop (float, optional): Dropout rate. Default: 0.0 |
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attn_drop (float, optional): Attention dropout rate. Default: 0.0 |
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drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 |
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norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm |
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downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None |
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use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. |
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""" |
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def __init__(self, dim, input_resolution, depth, num_heads, window_size, |
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mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., |
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drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False): |
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super().__init__() |
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self.dim = dim |
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self.input_resolution = input_resolution |
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self.depth = depth |
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self.use_checkpoint = use_checkpoint |
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self.blocks = nn.ModuleList([ |
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SwinTransformerBlock(dim=dim, input_resolution=input_resolution, |
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num_heads=num_heads, window_size=window_size, |
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shift_size=0 if (i % 2 == 0) else window_size // 2, |
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mlp_ratio=mlp_ratio, |
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qkv_bias=qkv_bias, qk_scale=qk_scale, |
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drop=drop, attn_drop=attn_drop, |
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drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, |
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norm_layer=norm_layer) |
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for i in range(depth)]) |
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if downsample is not None: |
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self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer) |
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else: |
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self.downsample = None |
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def forward(self, x, x_size): |
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for blk in self.blocks: |
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if self.use_checkpoint: |
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x = checkpoint.checkpoint(blk, x, x_size) |
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else: |
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x = blk(x, x_size) |
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if self.downsample is not None: |
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x = self.downsample(x) |
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return x |
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def extra_repr(self) -> str: |
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return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}" |
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def flops(self): |
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flops = 0 |
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for blk in self.blocks: |
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flops += blk.flops() |
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if self.downsample is not None: |
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flops += self.downsample.flops() |
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return flops |
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class RSTB(nn.Module): |
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"""Residual Swin Transformer Block (RSTB). |
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Args: |
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dim (int): Number of input channels. |
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input_resolution (tuple[int]): Input resolution. |
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depth (int): Number of blocks. |
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num_heads (int): Number of attention heads. |
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window_size (int): Local window size. |
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mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. |
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qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True |
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qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. |
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drop (float, optional): Dropout rate. Default: 0.0 |
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attn_drop (float, optional): Attention dropout rate. Default: 0.0 |
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drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 |
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norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm |
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downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None |
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use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. |
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img_size: Input image size. |
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patch_size: Patch size. |
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resi_connection: The convolutional block before residual connection. |
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""" |
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def __init__(self, dim, input_resolution, depth, num_heads, window_size, |
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mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., |
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drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False, |
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img_size=224, patch_size=4, resi_connection='1conv'): |
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super(RSTB, self).__init__() |
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self.dim = dim |
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self.input_resolution = input_resolution |
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self.residual_group = BasicLayer(dim=dim, |
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input_resolution=input_resolution, |
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depth=depth, |
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num_heads=num_heads, |
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window_size=window_size, |
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mlp_ratio=mlp_ratio, |
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qkv_bias=qkv_bias, qk_scale=qk_scale, |
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drop=drop, attn_drop=attn_drop, |
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drop_path=drop_path, |
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norm_layer=norm_layer, |
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downsample=downsample, |
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use_checkpoint=use_checkpoint) |
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|
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if resi_connection == '1conv': |
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self.conv = nn.Conv2d(dim, dim, 3, 1, 1) |
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elif resi_connection == '3conv': |
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|
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self.conv = nn.Sequential(nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.GELU(), |
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nn.Conv2d(dim // 4, dim // 4, 1, 1, 0), |
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nn.GELU(), |
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nn.Conv2d(dim // 4, dim, 3, 1, 1)) |
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self.patch_embed = PatchEmbed( |
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img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim, |
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norm_layer=None) |
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|
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self.patch_unembed = PatchUnEmbed( |
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img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim, |
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norm_layer=None) |
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|
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def forward(self, x, x_size): |
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return self.patch_embed(self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))) + x |
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|
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def flops(self): |
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flops = 0 |
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flops += self.residual_group.flops() |
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H, W = self.input_resolution |
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flops += H * W * self.dim * self.dim * 9 |
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flops += self.patch_embed.flops() |
|
flops += self.patch_unembed.flops() |
|
|
|
return flops |
|
|
|
|
|
class PatchEmbed(nn.Module): |
|
r""" Image to Patch Embedding |
|
|
|
Args: |
|
img_size (int): Image size. Default: 224. |
|
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=224, 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 |
|
|
|
if norm_layer is not None: |
|
self.norm = norm_layer(embed_dim) |
|
else: |
|
self.norm = None |
|
|
|
def forward(self, x): |
|
x = x.flatten(2).transpose(1, 2) |
|
if self.norm is not None: |
|
x = self.norm(x) |
|
return x |
|
|
|
def flops(self): |
|
flops = 0 |
|
H, W = self.img_size |
|
if self.norm is not None: |
|
flops += H * W * self.embed_dim |
|
return flops |
|
|
|
|
|
class PatchUnEmbed(nn.Module): |
|
r""" Image to Patch Unembedding |
|
|
|
Args: |
|
img_size (int): Image size. Default: 224. |
|
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=224, 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 |
|
|
|
def forward(self, x, x_size): |
|
B, HW, C = x.shape |
|
x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1]) |
|
return x |
|
|
|
def flops(self): |
|
flops = 0 |
|
return flops |
|
|
|
|
|
class Upsample(nn.Sequential): |
|
"""Upsample module. |
|
|
|
Args: |
|
scale (int): Scale factor. Supported scales: 2^n and 3. |
|
num_feat (int): Channel number of intermediate features. |
|
""" |
|
|
|
def __init__(self, scale, num_feat): |
|
m = [] |
|
if (scale & (scale - 1)) == 0: |
|
for _ in range(int(math.log(scale, 2))): |
|
m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1)) |
|
m.append(nn.PixelShuffle(2)) |
|
elif scale == 3: |
|
m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1)) |
|
m.append(nn.PixelShuffle(3)) |
|
else: |
|
raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.') |
|
super(Upsample, self).__init__(*m) |
|
|
|
|
|
class UpsampleOneStep(nn.Sequential): |
|
"""UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle) |
|
Used in lightweight SR to save parameters. |
|
|
|
Args: |
|
scale (int): Scale factor. Supported scales: 2^n and 3. |
|
num_feat (int): Channel number of intermediate features. |
|
|
|
""" |
|
|
|
def __init__(self, scale, num_feat, num_out_ch, input_resolution=None): |
|
self.num_feat = num_feat |
|
self.input_resolution = input_resolution |
|
m = [] |
|
m.append(nn.Conv2d(num_feat, (scale ** 2) * num_out_ch, 3, 1, 1)) |
|
m.append(nn.PixelShuffle(scale)) |
|
super(UpsampleOneStep, self).__init__(*m) |
|
|
|
def flops(self): |
|
H, W = self.input_resolution |
|
flops = H * W * self.num_feat * 3 * 9 |
|
return flops |
|
|
|
|
|
class Generator(nn.Module): |
|
r""" SwinIR |
|
A PyTorch impl of : `SwinIR: Image Restoration Using Swin Transformer`, based on Swin Transformer. |
|
|
|
Args: |
|
img_size (int | tuple(int)): Input image size. Default 64 |
|
patch_size (int | tuple(int)): Patch size. Default: 1 |
|
in_chans (int): Number of input image channels. Default: 3 |
|
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 |
|
qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None |
|
drop_rate (float): Dropout rate. Default: 0 |
|
attn_drop_rate (float): Attention dropout rate. Default: 0 |
|
drop_path_rate (float): Stochastic depth rate. Default: 0.1 |
|
norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. |
|
ape (bool): If True, add absolute position embedding to the patch embedding. Default: False |
|
patch_norm (bool): If True, add normalization after patch embedding. Default: True |
|
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False |
|
upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction |
|
img_range: Image range. 1. or 255. |
|
upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None |
|
resi_connection: The convolutional block before residual connection. '1conv'/'3conv' |
|
""" |
|
|
|
def __init__(self, img_size=64, patch_size=1, in_chans=3, |
|
embed_dim=96, depths=[6, 6, 6, 6], num_heads=[6, 6, 6, 6], |
|
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, |
|
use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv', |
|
**kwargs): |
|
super(Generator, self).__init__() |
|
num_in_ch = in_chans |
|
num_out_ch = in_chans |
|
num_feat = 64 |
|
self.img_range = img_range |
|
if in_chans == 3: |
|
rgb_mean = (0.4488, 0.4371, 0.4040) |
|
self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1) |
|
else: |
|
self.mean = torch.zeros(1, 1, 1, 1) |
|
self.upscale = upscale |
|
self.upsampler = upsampler |
|
self.window_size = window_size |
|
|
|
|
|
|
|
self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1) |
|
|
|
|
|
|
|
self.num_layers = len(depths) |
|
self.embed_dim = embed_dim |
|
self.ape = ape |
|
self.patch_norm = patch_norm |
|
self.num_features = embed_dim |
|
self.mlp_ratio = mlp_ratio |
|
|
|
|
|
self.patch_embed = PatchEmbed( |
|
img_size=img_size, patch_size=patch_size, in_chans=embed_dim, 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 |
|
|
|
|
|
self.patch_unembed = PatchUnEmbed( |
|
img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim, |
|
norm_layer=norm_layer if self.patch_norm else None) |
|
|
|
|
|
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))] |
|
|
|
|
|
self.layers = nn.ModuleList() |
|
for i_layer in range(self.num_layers): |
|
layer = RSTB(dim=embed_dim, |
|
input_resolution=(patches_resolution[0], |
|
patches_resolution[1]), |
|
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=None, |
|
use_checkpoint=use_checkpoint, |
|
img_size=img_size, |
|
patch_size=patch_size, |
|
resi_connection=resi_connection |
|
|
|
) |
|
self.layers.append(layer) |
|
self.norm = norm_layer(self.num_features) |
|
|
|
|
|
if resi_connection == '1conv': |
|
self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1) |
|
elif resi_connection == '3conv': |
|
|
|
self.conv_after_body = nn.Sequential(nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1), |
|
nn.GELU(), |
|
nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0), |
|
nn.GELU(), |
|
nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1)) |
|
|
|
|
|
|
|
if self.upsampler == 'pixelshuffle': |
|
|
|
self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1), |
|
nn.GELU()) |
|
self.upsample = Upsample(upscale, num_feat) |
|
self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) |
|
elif self.upsampler == 'pixelshuffledirect': |
|
|
|
self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch, |
|
(patches_resolution[0], patches_resolution[1])) |
|
elif self.upsampler == 'nearest+conv': |
|
|
|
assert self.upscale == 4, 'only support x4 now.' |
|
self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1), |
|
nn.GELU()) |
|
self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) |
|
self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) |
|
self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1) |
|
self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) |
|
self.lrelu = nn.GELU() |
|
else: |
|
|
|
self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 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): |
|
return {'relative_position_bias_table'} |
|
|
|
def check_image_size(self, x): |
|
_, _, h, w = x.size() |
|
mod_pad_h = (self.window_size - h % self.window_size) % self.window_size |
|
mod_pad_w = (self.window_size - w % self.window_size) % self.window_size |
|
x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect') |
|
return x |
|
|
|
def forward_features(self, x): |
|
x_size = (x.shape[2], x.shape[3]) |
|
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_size) |
|
|
|
x = self.norm(x) |
|
x = self.patch_unembed(x, x_size) |
|
|
|
return x |
|
|
|
def forward(self, x): |
|
H, W = x.shape[2:] |
|
x = self.check_image_size(x) |
|
|
|
self.mean = self.mean.type_as(x) |
|
x = (x - self.mean) * self.img_range |
|
|
|
if self.upsampler == 'pixelshuffle': |
|
|
|
x = self.conv_first(x) |
|
x = self.conv_after_body(self.forward_features(x)) + x |
|
x = self.conv_before_upsample(x) |
|
x = self.conv_last(self.upsample(x)) |
|
elif self.upsampler == 'pixelshuffledirect': |
|
|
|
x = self.conv_first(x) |
|
x = self.conv_after_body(self.forward_features(x)) + x |
|
x = self.upsample(x) |
|
elif self.upsampler == 'nearest+conv': |
|
|
|
x = self.conv_first(x) |
|
x = self.conv_after_body(self.forward_features(x)) + x |
|
x = self.conv_before_upsample(x) |
|
x = self.lrelu(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest'))) |
|
x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest'))) |
|
x = self.conv_last(self.lrelu(self.conv_hr(x))) |
|
else: |
|
|
|
x_first = self.conv_first(x) |
|
res = self.conv_after_body(self.forward_features(x_first)) + x_first |
|
x = x + self.conv_last(res) |
|
|
|
x = x / self.img_range + self.mean |
|
|
|
return x[:, :, :H*self.upscale, :W*self.upscale] |
|
|
|
def flops(self): |
|
flops = 0 |
|
H, W = self.patches_resolution |
|
flops += H * W * 3 * self.embed_dim * 9 |
|
flops += self.patch_embed.flops() |
|
for i, layer in enumerate(self.layers): |
|
flops += layer.flops() |
|
flops += H * W * 3 * self.embed_dim * self.embed_dim |
|
flops += self.upsample.flops() |
|
return flops |
|
|
|
|
|
class Discriminator(nn.Module): |
|
def __init__(self): |
|
super(Discriminator, self).__init__() |
|
self.net = nn.Sequential( |
|
nn.Conv2d(3, 64, kernel_size=3, padding=1), |
|
nn.GELU(), |
|
|
|
nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1), |
|
nn.GELU(), |
|
|
|
nn.Conv2d(64, 128, kernel_size=3, padding=1), |
|
nn.GELU(), |
|
|
|
nn.Conv2d(128, 128, kernel_size=3, stride=2, padding=1), |
|
nn.GELU(), |
|
|
|
nn.Conv2d(128, 256, kernel_size=3, padding=1), |
|
nn.GELU(), |
|
|
|
nn.Conv2d(256, 256, kernel_size=3, stride=2, padding=1), |
|
nn.GELU(), |
|
|
|
nn.Conv2d(256, 512, kernel_size=3, padding=1), |
|
nn.GELU(), |
|
|
|
nn.Conv2d(512, 512, kernel_size=3, stride=2, padding=1), |
|
nn.GELU(), |
|
|
|
nn.AdaptiveAvgPool2d(1), |
|
nn.Conv2d(512, 1024, kernel_size=1), |
|
nn.GELU(), |
|
nn.Conv2d(1024, 1, kernel_size=1) |
|
) |
|
|
|
def forward(self, x): |
|
batch_size = x.size(0) |
|
return self.net(x).view(batch_size) |
|
|
|
def compute_gradient_penalty(D, real_samples, fake_samples): |
|
alpha = torch.randn(real_samples.size(0), 1, 1, 1) |
|
if torch.cuda.is_available(): |
|
alpha = alpha.cuda() |
|
|
|
interpolates = (alpha * real_samples + ((1 - alpha) * fake_samples)).requires_grad_(True) |
|
d_interpolates = D(interpolates) |
|
fake = torch.ones(d_interpolates.size()) |
|
if torch.cuda.is_available(): |
|
fake = fake.cuda() |
|
|
|
gradients = torch.autograd.grad( |
|
outputs=d_interpolates, |
|
inputs=interpolates, |
|
grad_outputs=fake, |
|
create_graph=True, |
|
retain_graph=True, |
|
only_inputs=True, |
|
)[0] |
|
gradients = gradients.view(gradients.size(0), -1) |
|
gradient_penalty = ((gradients.norm(2, dim=1) - 1) ** 2).mean() |
|
return gradient_penalty |
|
|
|
if __name__ == '__main__': |
|
upscale = 4 |
|
window_size = 7 |
|
height = (112 // upscale // window_size + 1) * window_size |
|
width = (112 // upscale // window_size + 1) * window_size |
|
model = Generator(upscale=upscale, img_size=(height, width), |
|
window_size=window_size, img_range=1., depths=[6, 6, 6, 6], |
|
embed_dim=60, num_heads=[6, 6, 6, 6], mlp_ratio=4, upsampler='nearest+conv') |
|
print(model) |
|
|
|
|
|
x = torch.randn((1, 3, height, width)) |
|
x = model(x) |
|
print(x.shape) |
|
|