429 lines
11 KiB
Python
429 lines
11 KiB
Python
from collections import OrderedDict
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from functools import partial
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import torch
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import torch.nn as nn
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def drop_path(x, drop_prob: float = 0.0, training: bool = False):
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if drop_prob == 0.0 or not training:
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return x
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keep_prob = 1 - drop_prob
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shape = (x.shape[0],) + (1,) * (x.ndim - 1)
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random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
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random_tensor.floor_()
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output = x.div(keep_prob) * random_tensor
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return output
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class DropPath(nn.Module):
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def __init__(self, drop_prob=None):
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super(DropPath, self).__init__()
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self.drop_prob = drop_prob
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def forward(self, x):
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return drop_path(x, self.drop_prob, self.training)
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class PatchEmbed(nn.Module):
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def __init__(
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self, img_size=224, patch_size=16, in_c=3, embed_dim=768, norm_layer=None
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):
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super().__init__()
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img_size = (img_size, img_size)
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patch_size = (patch_size, patch_size)
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self.img_size = img_size
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self.patch_size = patch_size
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self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])
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self.num_patches = self.grid_size[0] * self.grid_size[1]
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self.proj = nn.Conv2d(
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in_c, embed_dim, kernel_size=patch_size, stride=patch_size
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)
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self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
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def forward(self, x):
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B, C, H, W = x.shape
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assert (
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H == self.img_size[0] and W == self.img_size[1]
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), f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
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x = self.proj(x).flatten(2).transpose(1, 2)
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x = self.norm(x)
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return x
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class Attention(nn.Module):
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def __init__(
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self,
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dim,
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num_heads=8,
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qkv_bias=False,
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qk_scale=None,
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attn_drop_ratio=0.0,
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proj_drop_ratio=0.0,
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):
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super(Attention, self).__init__()
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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.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
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self.attn_drop = nn.Dropout(attn_drop_ratio)
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self.proj = nn.Linear(dim, dim)
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self.proj_drop = nn.Dropout(proj_drop_ratio)
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def forward(self, x):
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B, N, C = x.shape
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qkv = (
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self.qkv(x)
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.reshape(B, N, 3, self.num_heads, C // self.num_heads)
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.permute(2, 0, 3, 1, 4)
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)
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q, k, v = qkv[0], qkv[1], qkv[2]
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attn = (q @ k.transpose(-2, -1)) * self.scale
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attn = attn.softmax(dim=-1)
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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 Mlp(nn.Module):
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def __init__(
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self,
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in_features,
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hidden_features=None,
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out_features=None,
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act_layer=nn.GELU,
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drop=0.0,
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):
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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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class Block(nn.Module):
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def __init__(
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self,
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dim,
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num_heads,
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mlp_ratio=4.0,
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qkv_bias=False,
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qk_scale=None,
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drop_ratio=0.0,
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attn_drop_ratio=0.0,
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drop_path_ratio=0.0,
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act_layer=nn.GELU,
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norm_layer=nn.LayerNorm,
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):
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super(Block, self).__init__()
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self.norm1 = norm_layer(dim)
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self.attn = Attention(
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dim,
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num_heads=num_heads,
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qkv_bias=qkv_bias,
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qk_scale=qk_scale,
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attn_drop_ratio=attn_drop_ratio,
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proj_drop_ratio=drop_ratio,
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)
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self.drop_path = (
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DropPath(drop_path_ratio) if drop_path_ratio > 0.0 else nn.Identity()
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)
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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(
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in_features=dim,
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hidden_features=mlp_hidden_dim,
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act_layer=act_layer,
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drop=drop_ratio,
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)
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def forward(self, x):
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x = x + self.drop_path(self.attn(self.norm1(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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class VisionTransformer(nn.Module):
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def __init__(
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self,
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img_size=224,
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patch_size=16,
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in_c=3,
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num_classes=1000,
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embed_dim=768,
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depth=12,
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num_heads=12,
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mlp_ratio=4.0,
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qkv_bias=True,
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qk_scale=None,
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representation_size=None,
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distilled=False,
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drop_ratio=0.0,
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attn_drop_ratio=0.0,
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drop_path_ratio=0.0,
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embed_layer=PatchEmbed,
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norm_layer=None,
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act_layer=None,
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):
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super(VisionTransformer, self).__init__()
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self.num_classes = num_classes
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self.num_features = self.embed_dim = embed_dim
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self.num_tokens = 2 if distilled else 1
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norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
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act_layer = act_layer or nn.GELU
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self.patch_embed = embed_layer(
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img_size=img_size, patch_size=patch_size, in_c=in_c, embed_dim=embed_dim
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)
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num_patches = self.patch_embed.num_patches
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self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
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self.dist_token = (
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nn.Parameter(torch.zeros(1, 1, embed_dim)) if distilled else None
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)
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self.pos_embed = nn.Parameter(
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torch.zeros(1, num_patches + self.num_tokens, embed_dim)
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)
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self.pos_drop = nn.Dropout(p=drop_ratio)
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dpr = [x.item() for x in torch.linspace(0, drop_path_ratio, depth)]
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self.blocks = nn.Sequential(
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*[
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Block(
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dim=embed_dim,
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num_heads=num_heads,
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mlp_ratio=mlp_ratio,
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qkv_bias=qkv_bias,
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qk_scale=qk_scale,
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drop_ratio=drop_ratio,
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attn_drop_ratio=attn_drop_ratio,
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drop_path_ratio=dpr[i],
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norm_layer=norm_layer,
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act_layer=act_layer,
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)
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for i in range(depth)
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]
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)
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self.norm = norm_layer(embed_dim)
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if representation_size and not distilled:
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self.has_logits = True
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self.num_features = representation_size
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self.pre_logits = nn.Sequential(
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OrderedDict(
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[
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("fc", nn.Linear(embed_dim, representation_size)),
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("act", nn.Tanh()),
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]
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)
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)
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else:
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self.has_logits = False
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self.pre_logits = nn.Identity()
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self.head = (
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nn.Linear(self.num_features, num_classes)
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if num_classes > 0
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else nn.Identity()
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)
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self.head_dist = None
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if distilled:
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self.head_dist = (
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nn.Linear(self.embed_dim, self.num_classes)
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if num_classes > 0
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else nn.Identity()
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)
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# Weight init
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nn.init.trunc_normal_(self.pos_embed, std=0.02)
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if self.dist_token is not None:
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nn.init.trunc_normal_(self.dist_token, std=0.02)
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nn.init.trunc_normal_(self.cls_token, std=0.02)
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self.apply(_init_vit_weights)
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def forward_features(self, x):
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x = self.patch_embed(x)
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cls_token = self.cls_token.expand(x.shape[0], -1, -1)
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if self.dist_token is None:
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x = torch.cat((cls_token, x), dim=1)
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else:
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x = torch.cat(
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(cls_token, self.dist_token.expand(x.shape[0], -1, -1), x), dim=1
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)
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x = self.pos_drop(x + self.pos_embed)
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x = self.blocks(x)
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x = self.norm(x)
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if self.dist_token is None:
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return self.pre_logits(x[:, 0])
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else:
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return x[:, 0], x[:, 1]
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def forward(self, x):
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x = self.forward_features(x)
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if self.head_dist is not None:
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x, x_dist = self.head(x[0]), self.head_dist(x[1])
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if self.training and not torch.jit.is_scripting():
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return x, x_dist
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else:
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return (x + x_dist) / 2
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else:
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x = self.head(x)
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return x
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def _init_vit_weights(m):
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if isinstance(m, nn.Linear):
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nn.init.trunc_normal_(m.weight, std=0.01)
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if m.bias is not None:
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nn.init.zeros_(m.bias)
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elif isinstance(m, nn.Conv2d):
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nn.init.kaiming_normal_(m.weight, mode="fan_out")
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if m.bias is not None:
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nn.init.zeros_(m.bias)
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elif isinstance(m, nn.LayerNorm):
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nn.init.zeros_(m.bias)
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nn.init.ones_(m.weight)
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def vit_base_patch16_224(num_classes: int = 1000):
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model = VisionTransformer(
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img_size=224,
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patch_size=16,
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embed_dim=768,
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depth=12,
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num_heads=12,
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representation_size=None,
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num_classes=num_classes,
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)
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return model
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def vit_base_patch16_224_in21k(num_classes: int = 21843, has_logits: bool = True):
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model = VisionTransformer(
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img_size=224,
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patch_size=16,
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embed_dim=768,
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depth=12,
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num_heads=12,
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representation_size=768 if has_logits else None,
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num_classes=num_classes,
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)
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return model
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def vit_base_patch32_224(num_classes: int = 1000):
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model = VisionTransformer(
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img_size=224,
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patch_size=32,
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embed_dim=768,
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depth=12,
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num_heads=12,
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representation_size=None,
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num_classes=num_classes,
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)
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return model
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def vit_base_patch32_224_in21k(num_classes: int = 21843, has_logits: bool = True):
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model = VisionTransformer(
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img_size=224,
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patch_size=32,
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embed_dim=768,
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depth=12,
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num_heads=12,
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representation_size=768 if has_logits else None,
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num_classes=num_classes,
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)
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return model
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def vit_large_patch16_224(num_classes: int = 1000):
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model = VisionTransformer(
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img_size=224,
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patch_size=16,
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embed_dim=1024,
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depth=24,
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num_heads=16,
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representation_size=None,
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num_classes=num_classes,
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)
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return model
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def vit_large_patch16_224_in21k(num_classes: int = 21843, has_logits: bool = True):
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model = VisionTransformer(
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img_size=224,
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patch_size=16,
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embed_dim=1024,
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depth=24,
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num_heads=16,
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representation_size=1024 if has_logits else None,
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num_classes=num_classes,
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)
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return model
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def vit_large_patch32_224_in21k(num_classes: int = 21843, has_logits: bool = True):
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model = VisionTransformer(
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img_size=224,
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patch_size=32,
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embed_dim=1024,
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depth=24,
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num_heads=16,
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representation_size=1024 if has_logits else None,
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num_classes=num_classes,
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)
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return model
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def vit_huge_patch14_224_in21k(num_classes: int = 21843, has_logits: bool = True):
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model = VisionTransformer(
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img_size=224,
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patch_size=14,
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embed_dim=1280,
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depth=32,
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num_heads=16,
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representation_size=1280 if has_logits else None,
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num_classes=num_classes,
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)
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return model
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