851 lines
25 KiB
Python
851 lines
25 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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# Taken from https://github.com/facebookresearch/deit with slight modifications
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from loguru import logger
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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 functools import partial
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from timm.models.vision_transformer import Mlp, PatchEmbed, _cfg
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from timm.models.layers import DropPath, to_2tuple, trunc_normal_
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from timm.models.registry import register_model
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from resizing_interface import ResizingInterface
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class Attention(nn.Module):
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# taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
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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=0.0,
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proj_drop=0.0,
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):
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super().__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)
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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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def forward(self, x):
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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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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 Block(nn.Module):
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# taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
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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=0.0,
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attn_drop=0.0,
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drop_path=0.0,
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act_layer=nn.GELU,
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norm_layer=nn.LayerNorm,
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Attention_block=Attention,
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Mlp_block=Mlp,
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init_values=1e-4,
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):
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super().__init__()
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self.norm1 = norm_layer(dim)
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self.attn = Attention_block(
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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=attn_drop,
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proj_drop=drop,
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)
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# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
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self.drop_path = DropPath(drop_path) if drop_path > 0.0 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_block(
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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,
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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 Layer_scale_init_Block(nn.Module):
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# taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
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# with slight modifications
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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=0.0,
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attn_drop=0.0,
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drop_path=0.0,
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act_layer=nn.GELU,
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norm_layer=nn.LayerNorm,
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Attention_block=Attention,
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Mlp_block=Mlp,
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init_values=1e-4,
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):
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super().__init__()
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self.norm1 = norm_layer(dim)
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self.attn = Attention_block(
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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=attn_drop,
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proj_drop=drop,
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)
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# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
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self.drop_path = DropPath(drop_path) if drop_path > 0.0 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_block(
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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,
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)
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self.gamma_1 = nn.Parameter(init_values * torch.ones((dim)), requires_grad=True)
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self.gamma_2 = nn.Parameter(init_values * torch.ones((dim)), requires_grad=True)
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def forward(self, x):
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x = x + self.drop_path(self.gamma_1 * self.attn(self.norm1(x)))
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x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x)))
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return x
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class Layer_scale_init_Block_paralx2(nn.Module):
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# taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
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# with slight modifications
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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=0.0,
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attn_drop=0.0,
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drop_path=0.0,
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act_layer=nn.GELU,
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norm_layer=nn.LayerNorm,
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Attention_block=Attention,
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Mlp_block=Mlp,
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init_values=1e-4,
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):
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super().__init__()
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self.norm1 = norm_layer(dim)
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self.norm11 = norm_layer(dim)
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self.attn = Attention_block(
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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=attn_drop,
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proj_drop=drop,
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)
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# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
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self.attn1 = Attention_block(
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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=attn_drop,
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proj_drop=drop,
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)
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self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
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self.norm2 = norm_layer(dim)
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self.norm21 = norm_layer(dim)
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mlp_hidden_dim = int(dim * mlp_ratio)
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self.mlp = Mlp_block(
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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,
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)
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self.mlp1 = Mlp_block(
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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,
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)
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self.gamma_1 = nn.Parameter(init_values * torch.ones((dim)), requires_grad=True)
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self.gamma_1_1 = nn.Parameter(init_values * torch.ones((dim)), requires_grad=True)
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self.gamma_2 = nn.Parameter(init_values * torch.ones((dim)), requires_grad=True)
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self.gamma_2_1 = nn.Parameter(init_values * torch.ones((dim)), requires_grad=True)
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def forward(self, x):
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x = (
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x
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+ self.drop_path(self.gamma_1 * self.attn(self.norm1(x)))
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+ self.drop_path(self.gamma_1_1 * self.attn1(self.norm11(x)))
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)
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x = (
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x
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+ self.drop_path(self.gamma_2 * self.mlp(self.norm2(x)))
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+ self.drop_path(self.gamma_2_1 * self.mlp1(self.norm21(x)))
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)
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return x
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class Block_paralx2(nn.Module):
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# taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
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# with slight modifications
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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=0.0,
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attn_drop=0.0,
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drop_path=0.0,
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act_layer=nn.GELU,
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norm_layer=nn.LayerNorm,
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Attention_block=Attention,
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Mlp_block=Mlp,
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init_values=1e-4,
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):
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super().__init__()
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self.norm1 = norm_layer(dim)
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self.norm11 = norm_layer(dim)
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self.attn = Attention_block(
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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=attn_drop,
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proj_drop=drop,
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)
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# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
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self.attn1 = Attention_block(
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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=attn_drop,
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proj_drop=drop,
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)
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self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
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self.norm2 = norm_layer(dim)
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self.norm21 = norm_layer(dim)
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mlp_hidden_dim = int(dim * mlp_ratio)
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self.mlp = Mlp_block(
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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,
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)
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self.mlp1 = Mlp_block(
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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,
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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))) + self.drop_path(self.attn1(self.norm11(x)))
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x = x + self.drop_path(self.mlp(self.norm2(x))) + self.drop_path(self.mlp1(self.norm21(x)))
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return x
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class hMLP_stem(nn.Module):
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"""hMLP_stem: https://arxiv.org/pdf/2203.09795.pdf
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taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
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with slight modifications
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"""
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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_chans=3,
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embed_dim=768,
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norm_layer=nn.SyncBatchNorm,
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):
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super().__init__()
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img_size = to_2tuple(img_size)
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patch_size = to_2tuple(patch_size)
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num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
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self.img_size = img_size
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self.patch_size = patch_size
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self.num_patches = num_patches
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self.proj = torch.nn.Sequential(
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*[
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nn.Conv2d(in_chans, embed_dim // 4, kernel_size=4, stride=4),
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norm_layer(embed_dim // 4),
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nn.GELU(),
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nn.Conv2d(embed_dim // 4, embed_dim // 4, kernel_size=2, stride=2),
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norm_layer(embed_dim // 4),
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nn.GELU(),
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nn.Conv2d(embed_dim // 4, embed_dim, kernel_size=2, stride=2),
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norm_layer(embed_dim),
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]
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)
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def forward(self, x):
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B, C, H, W = x.shape
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x = self.proj(x).flatten(2).transpose(1, 2)
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return x
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class vit_models(nn.Module, ResizingInterface):
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"""Vision Transformer with LayerScale (https://arxiv.org/abs/2103.17239) support
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taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
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with slight modifications
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"""
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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_chans=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=False,
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qk_scale=None,
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drop_rate=0.0,
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attn_drop_rate=0.0,
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drop_path_rate=0.0,
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norm_layer=nn.LayerNorm,
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global_pool=None,
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block_layers=Block,
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Patch_layer=PatchEmbed,
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act_layer=nn.GELU,
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Attention_block=Attention,
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Mlp_block=Mlp,
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dpr_constant=True,
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init_scale=1e-4,
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mlp_ratio_clstk=4.0,
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**kwargs,
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):
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super().__init__()
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self.dropout_rate = drop_rate
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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.img_size = img_size
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self.patch_embed = Patch_layer(
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img_size=img_size,
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patch_size=patch_size,
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in_chans=in_chans,
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embed_dim=embed_dim,
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)
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num_patches = self.patch_embed.num_patches
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self.patch_size = patch_size
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self.in_chans = in_chans
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self.embed_dim = embed_dim
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self.embed_layer = Patch_layer
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self.pre_norm = False
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self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
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self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
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self.no_embed_class = True
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dpr = [drop_path_rate for i in range(depth)]
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self.blocks = nn.ModuleList(
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[
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block_layers(
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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=0.0,
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attn_drop=attn_drop_rate,
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drop_path=dpr[i],
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norm_layer=norm_layer,
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act_layer=act_layer,
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Attention_block=Attention_block,
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Mlp_block=Mlp_block,
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init_values=init_scale,
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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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self.feature_info = [dict(num_chs=embed_dim, reduction=0, module="head")]
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self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()
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trunc_normal_(self.pos_embed, std=0.02)
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trunc_normal_(self.cls_token, std=0.02)
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self.apply(self._init_weights)
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def set_num_classes(self, n_classes):
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super().set_num_classes(n_classes)
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self._init_weights(self.head)
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def _init_weights(self, m):
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if isinstance(m, nn.Linear):
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trunc_normal_(m.weight, std=0.02)
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if isinstance(m, nn.Linear) and m.bias is not None:
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.LayerNorm):
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nn.init.constant_(m.bias, 0)
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nn.init.constant_(m.weight, 1.0)
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@torch.jit.ignore
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def no_weight_decay(self):
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return {"pos_embed", "cls_token"}
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def get_classifier(self):
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return self.head
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def get_num_layers(self):
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return len(self.blocks)
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def reset_classifier(self, num_classes, global_pool=""):
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self.num_classes = num_classes
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self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
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def forward_features(self, x, test=False):
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B = x.shape[0]
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x = self.patch_embed(x)
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if test and x.isnan().any().item():
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logger.error("patch embedded input has nan value")
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cls_tokens = self.cls_token.expand(B, -1, -1)
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x = x + self.pos_embed
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if test and x.isnan().any().item():
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logger.error("position embedded input has a nan value")
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x = torch.cat((cls_tokens, x), dim=1)
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if test and x.isnan().any().item():
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logger.error("input with [CLS] has a nan value")
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for i, blk in enumerate(self.blocks):
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x = blk(x)
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if test and x.isnan().any().item():
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logger.error(f"output of block {i} has a nan value")
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x = self.norm(x)
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return x[:, 0]
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def forward(self, x, test=False):
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x = self.forward_features(x, test=test)
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if self.dropout_rate:
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x = F.dropout(x, p=float(self.dropout_rate), training=self.training)
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x = self.head(x)
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return x
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# DeiT III: Revenge of the ViT (https://arxiv.org/abs/2204.07118)
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@register_model
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def deit_tiny_patch16_LS(pretrained=False, img_size=224, pretrained_21k=False, **kwargs):
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model = vit_models(
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img_size=img_size,
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patch_size=16,
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embed_dim=192,
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depth=12,
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num_heads=3,
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mlp_ratio=4,
|
|
norm_layer=partial(nn.LayerNorm, eps=1e-6),
|
|
block_layers=Layer_scale_init_Block,
|
|
**kwargs,
|
|
)
|
|
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def deit_small_patch16_LS(pretrained=False, img_size=224, pretrained_21k=False, **kwargs):
|
|
if "pretrained_cfg" in kwargs:
|
|
kwargs.pop("pretrained_cfg")
|
|
model = vit_models(
|
|
img_size=img_size,
|
|
patch_size=16,
|
|
embed_dim=384,
|
|
depth=12,
|
|
num_heads=6,
|
|
mlp_ratio=4,
|
|
norm_layer=partial(nn.LayerNorm, eps=1e-6),
|
|
block_layers=Layer_scale_init_Block,
|
|
**kwargs,
|
|
)
|
|
model.default_cfg = _cfg()
|
|
if pretrained:
|
|
name = "https://dl.fbaipublicfiles.com/deit/deit_3_small_" + str(img_size) + "_"
|
|
if pretrained_21k:
|
|
name += "21k.pth"
|
|
else:
|
|
name += "1k.pth"
|
|
|
|
checkpoint = torch.hub.load_state_dict_from_url(url=name, map_location="cpu", check_hash=True)
|
|
model.load_state_dict(checkpoint["model"])
|
|
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def deit_medium_patch16_LS(pretrained=False, img_size=224, pretrained_21k=False, **kwargs):
|
|
model = vit_models(
|
|
patch_size=16,
|
|
embed_dim=512,
|
|
depth=12,
|
|
num_heads=8,
|
|
mlp_ratio=4,
|
|
norm_layer=partial(nn.LayerNorm, eps=1e-6),
|
|
block_layers=Layer_scale_init_Block,
|
|
**kwargs,
|
|
)
|
|
model.default_cfg = _cfg()
|
|
if pretrained:
|
|
name = "https://dl.fbaipublicfiles.com/deit/deit_3_medium_" + str(img_size) + "_"
|
|
if pretrained_21k:
|
|
name += "21k.pth"
|
|
else:
|
|
name += "1k.pth"
|
|
|
|
checkpoint = torch.hub.load_state_dict_from_url(url=name, map_location="cpu", check_hash=True)
|
|
model.load_state_dict(checkpoint["model"])
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def deit_base_patch16_LS(pretrained=False, img_size=224, pretrained_21k=False, **kwargs):
|
|
model = vit_models(
|
|
img_size=img_size,
|
|
patch_size=16,
|
|
embed_dim=768,
|
|
depth=12,
|
|
num_heads=12,
|
|
mlp_ratio=4,
|
|
norm_layer=partial(nn.LayerNorm, eps=1e-6),
|
|
block_layers=Layer_scale_init_Block,
|
|
**kwargs,
|
|
)
|
|
if pretrained:
|
|
name = "https://dl.fbaipublicfiles.com/deit/deit_3_base_" + str(img_size) + "_"
|
|
if pretrained_21k:
|
|
name += "21k.pth"
|
|
else:
|
|
name += "1k.pth"
|
|
|
|
checkpoint = torch.hub.load_state_dict_from_url(url=name, map_location="cpu", check_hash=True)
|
|
model.load_state_dict(checkpoint["model"])
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def deit_large_patch16_LS(pretrained=False, img_size=224, pretrained_21k=False, **kwargs):
|
|
model = vit_models(
|
|
img_size=img_size,
|
|
patch_size=16,
|
|
embed_dim=1024,
|
|
depth=24,
|
|
num_heads=16,
|
|
mlp_ratio=4,
|
|
norm_layer=partial(nn.LayerNorm, eps=1e-6),
|
|
block_layers=Layer_scale_init_Block,
|
|
**kwargs,
|
|
)
|
|
if pretrained:
|
|
name = "https://dl.fbaipublicfiles.com/deit/deit_3_large_" + str(img_size) + "_"
|
|
if pretrained_21k:
|
|
name += "21k.pth"
|
|
else:
|
|
name += "1k.pth"
|
|
|
|
checkpoint = torch.hub.load_state_dict_from_url(url=name, map_location="cpu", check_hash=True)
|
|
model.load_state_dict(checkpoint["model"])
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def deit_huge_patch14_LS(pretrained=False, img_size=224, pretrained_21k=False, **kwargs):
|
|
model = vit_models(
|
|
img_size=img_size,
|
|
patch_size=14,
|
|
embed_dim=1280,
|
|
depth=32,
|
|
num_heads=16,
|
|
mlp_ratio=4,
|
|
norm_layer=partial(nn.LayerNorm, eps=1e-6),
|
|
block_layers=Layer_scale_init_Block,
|
|
**kwargs,
|
|
)
|
|
if pretrained:
|
|
name = "https://dl.fbaipublicfiles.com/deit/deit_3_huge_" + str(img_size) + "_"
|
|
if pretrained_21k:
|
|
name += "21k_v1.pth"
|
|
else:
|
|
name += "1k_v1.pth"
|
|
|
|
checkpoint = torch.hub.load_state_dict_from_url(url=name, map_location="cpu", check_hash=True)
|
|
model.load_state_dict(checkpoint["model"])
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def deit_huge_patch14_52_LS(pretrained=False, img_size=224, pretrained_21k=False, **kwargs):
|
|
model = vit_models(
|
|
img_size=img_size,
|
|
patch_size=14,
|
|
embed_dim=1280,
|
|
depth=52,
|
|
num_heads=16,
|
|
mlp_ratio=4,
|
|
norm_layer=partial(nn.LayerNorm, eps=1e-6),
|
|
block_layers=Layer_scale_init_Block,
|
|
**kwargs,
|
|
)
|
|
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def deit_huge_patch14_26x2_LS(pretrained=False, img_size=224, pretrained_21k=False, **kwargs):
|
|
model = vit_models(
|
|
img_size=img_size,
|
|
patch_size=14,
|
|
embed_dim=1280,
|
|
depth=26,
|
|
num_heads=16,
|
|
mlp_ratio=4,
|
|
norm_layer=partial(nn.LayerNorm, eps=1e-6),
|
|
block_layers=Layer_scale_init_Block_paralx2,
|
|
**kwargs,
|
|
)
|
|
|
|
return model
|
|
|
|
|
|
# @register_model
|
|
# def deit_Giant_48x2_patch14_LS(pretrained=False, img_size=224, pretrained_21k=False, **kwargs):
|
|
# model = vit_models(
|
|
# img_size=img_size, patch_size=14, embed_dim=1664, depth=48, num_heads=16, mlp_ratio=4,
|
|
# norm_layer=partial(nn.LayerNorm, eps=1e-6), block_layers=Block_paral_LS, **kwargs)
|
|
#
|
|
# return model
|
|
|
|
|
|
# @register_model
|
|
# def deit_giant_40x2_patch14_LS(pretrained=False, img_size=224, pretrained_21k=False, **kwargs):
|
|
# model = vit_models(
|
|
# img_size=img_size, patch_size=14, embed_dim=1408, depth=40, num_heads=16, mlp_ratio=4,
|
|
# norm_layer=partial(nn.LayerNorm, eps=1e-6), block_layers=Block_paral_LS, **kwargs)
|
|
# return model
|
|
|
|
|
|
@register_model
|
|
def deit_Giant_48_patch14_LS(pretrained=False, img_size=224, pretrained_21k=False, **kwargs):
|
|
model = vit_models(
|
|
img_size=img_size,
|
|
patch_size=14,
|
|
embed_dim=1664,
|
|
depth=48,
|
|
num_heads=16,
|
|
mlp_ratio=4,
|
|
norm_layer=partial(nn.LayerNorm, eps=1e-6),
|
|
block_layers=Layer_scale_init_Block,
|
|
**kwargs,
|
|
)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def deit_giant_40_patch14_LS(pretrained=False, img_size=224, pretrained_21k=False, **kwargs):
|
|
model = vit_models(
|
|
img_size=img_size,
|
|
patch_size=14,
|
|
embed_dim=1408,
|
|
depth=40,
|
|
num_heads=16,
|
|
mlp_ratio=4,
|
|
norm_layer=partial(nn.LayerNorm, eps=1e-6),
|
|
block_layers=Layer_scale_init_Block,
|
|
**kwargs,
|
|
)
|
|
# model.default_cfg = _cfg()
|
|
|
|
return model
|
|
|
|
|
|
# Models from Three things everyone should know about Vision Transformers (https://arxiv.org/pdf/2203.09795.pdf)
|
|
|
|
|
|
@register_model
|
|
def deit_small_patch16_36_LS(pretrained=False, img_size=224, pretrained_21k=False, **kwargs):
|
|
model = vit_models(
|
|
img_size=img_size,
|
|
patch_size=16,
|
|
embed_dim=384,
|
|
depth=36,
|
|
num_heads=6,
|
|
mlp_ratio=4,
|
|
norm_layer=partial(nn.LayerNorm, eps=1e-6),
|
|
block_layers=Layer_scale_init_Block,
|
|
**kwargs,
|
|
)
|
|
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def deit_small_patch16_36(pretrained=False, img_size=224, pretrained_21k=False, **kwargs):
|
|
model = vit_models(
|
|
img_size=img_size,
|
|
patch_size=16,
|
|
embed_dim=384,
|
|
depth=36,
|
|
num_heads=6,
|
|
mlp_ratio=4,
|
|
norm_layer=partial(nn.LayerNorm, eps=1e-6),
|
|
**kwargs,
|
|
)
|
|
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def deit_small_patch16_18x2_LS(pretrained=False, img_size=224, pretrained_21k=False, **kwargs):
|
|
model = vit_models(
|
|
img_size=img_size,
|
|
patch_size=16,
|
|
embed_dim=384,
|
|
depth=18,
|
|
num_heads=6,
|
|
mlp_ratio=4,
|
|
norm_layer=partial(nn.LayerNorm, eps=1e-6),
|
|
block_layers=Layer_scale_init_Block_paralx2,
|
|
**kwargs,
|
|
)
|
|
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def deit_small_patch16_18x2(pretrained=False, img_size=224, pretrained_21k=False, **kwargs):
|
|
model = vit_models(
|
|
img_size=img_size,
|
|
patch_size=16,
|
|
embed_dim=384,
|
|
depth=18,
|
|
num_heads=6,
|
|
mlp_ratio=4,
|
|
norm_layer=partial(nn.LayerNorm, eps=1e-6),
|
|
block_layers=Block_paralx2,
|
|
**kwargs,
|
|
)
|
|
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def deit_base_patch16_18x2_LS(pretrained=False, img_size=224, pretrained_21k=False, **kwargs):
|
|
model = vit_models(
|
|
img_size=img_size,
|
|
patch_size=16,
|
|
embed_dim=768,
|
|
depth=18,
|
|
num_heads=12,
|
|
mlp_ratio=4,
|
|
norm_layer=partial(nn.LayerNorm, eps=1e-6),
|
|
block_layers=Layer_scale_init_Block_paralx2,
|
|
**kwargs,
|
|
)
|
|
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def deit_base_patch16_18x2(pretrained=False, img_size=224, pretrained_21k=False, **kwargs):
|
|
model = vit_models(
|
|
img_size=img_size,
|
|
patch_size=16,
|
|
embed_dim=768,
|
|
depth=18,
|
|
num_heads=12,
|
|
mlp_ratio=4,
|
|
norm_layer=partial(nn.LayerNorm, eps=1e-6),
|
|
block_layers=Block_paralx2,
|
|
**kwargs,
|
|
)
|
|
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def deit_base_patch16_36x1_LS(pretrained=False, img_size=224, pretrained_21k=False, **kwargs):
|
|
model = vit_models(
|
|
img_size=img_size,
|
|
patch_size=16,
|
|
embed_dim=768,
|
|
depth=36,
|
|
num_heads=12,
|
|
mlp_ratio=4,
|
|
norm_layer=partial(nn.LayerNorm, eps=1e-6),
|
|
block_layers=Layer_scale_init_Block,
|
|
**kwargs,
|
|
)
|
|
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def deit_base_patch16_36x1(pretrained=False, img_size=224, pretrained_21k=False, **kwargs):
|
|
model = vit_models(
|
|
img_size=img_size,
|
|
patch_size=16,
|
|
embed_dim=768,
|
|
depth=36,
|
|
num_heads=12,
|
|
mlp_ratio=4,
|
|
norm_layer=partial(nn.LayerNorm, eps=1e-6),
|
|
**kwargs,
|
|
)
|
|
|
|
return model
|