mirror of https://github.com/alibaba/EasyCV.git
362 lines
12 KiB
Python
362 lines
12 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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"""
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Mostly copy-paste from timm library.
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https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
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"""
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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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from timm.models.layers import trunc_normal_
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from easycv.models.utils import DropPath, Mlp
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from ..registry import BACKBONES
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def hydra(q, k, v):
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""" Hydra Attention
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Paper link: https://arxiv.org/pdf/2209.07484.pdf (Hydra Attention: Efficient Attention with Many Heads)
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Args:
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q, k, and v should all be tensors of shape
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[batch, tokens, features]
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"""
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q = q / q.norm(dim=-1, keepdim=True)
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k = k / k.norm(dim=-1, keepdim=True)
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kv = (k * v).sum(dim=-2, keepdim=True)
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out = q * kv
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return out
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class Attention(nn.Module):
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def __init__(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.,
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proj_drop=0.,
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hydra_attention=False):
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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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self.hydra_attention = hydra_attention
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def forward(self, x, rel_pos_bias=None):
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B, N, C = x.shape
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if self.hydra_attention:
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qkv = self.qkv(x).reshape(B, N, 3,
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self.num_heads).permute(2, 0, 1, 3)
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q, k, v = qkv[0], qkv[1], qkv[2]
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x = hydra(q, k, v)
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x = x.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, None
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else:
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qkv = self.qkv(x).reshape(B, N, 3, self.num_heads,
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C // self.num_heads).permute(
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2, 0, 3, 1, 4)
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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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if rel_pos_bias is not None:
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attn = attn + rel_pos_bias
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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, attn
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class Block(nn.Module):
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def __init__(self,
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dim,
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num_heads,
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mlp_ratio=4.,
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qkv_bias=False,
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qk_scale=None,
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drop=0.,
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attn_drop=0.,
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drop_path=0.,
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act_layer=nn.GELU,
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norm_layer=nn.LayerNorm,
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use_layer_scale=False,
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init_values=1e-4,
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hydra_attention=False):
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super().__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=attn_drop,
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proj_drop=drop,
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hydra_attention=hydra_attention)
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self.drop_path = DropPath(
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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(
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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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self.use_layer_scale = use_layer_scale
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if self.use_layer_scale:
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self.gamma_1 = nn.Parameter(
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init_values * torch.ones((dim)), requires_grad=True)
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self.gamma_2 = nn.Parameter(
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init_values * torch.ones((dim)), requires_grad=True)
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def forward(self, x, return_attention=False, rel_pos_bias=None):
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y, attn = self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias)
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if return_attention:
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return attn
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if self.use_layer_scale:
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x = x + self.drop_path(self.gamma_1 * y)
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x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x)))
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else:
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x = x + self.drop_path(y)
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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 forward_fea_and_attn(self, x):
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y, attn = self.attn(self.norm1(x))
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if self.use_layer_scale:
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x = x + self.drop_path(self.gamma_1 * y)
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x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x)))
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else:
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x = x + self.drop_path(y)
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x = x + self.drop_path(self.mlp(self.norm2(x)))
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return x, attn
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class PatchEmbed(nn.Module):
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""" Image to Patch Embedding
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"""
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def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
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super().__init__()
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num_patches = (img_size // patch_size) * (img_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.num_patches = num_patches
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self.proj = nn.Conv2d(
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in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
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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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@BACKBONES.register_module
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class VisionTransformer(nn.Module):
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""" DeiT III is based on ViT. It uses some strategies to make the vit model
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better, just like layer scale, stochastic depth, 3-Augment.
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Paper link: https://arxiv.org/pdf/2204.07118.pdf (DeiT III: Revenge of the ViT)
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Args:
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img_size (list): Input image size. img_size=[224] means the image size is
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224*224. img_size=[192, 224] means the image size is 192*224.
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patch_size (int): The patch size. Default: 16
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in_chans (int): The num of input channels. Default: 3
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num_classes (int): The num of picture classes. Default: 1000
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embed_dim (int): The dimensions of embedding. Default: 768
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depth (int): The num of blocks. Default: 12
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num_heads (int): Parallel attention heads. Default: 12
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mlp_ratio (float): Mlp expansion ratio. Default: 4.0
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qkv_bias (bool): Does kqv use bias. Default: False
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qk_scale (float | None): In the step of self-attention, if qk_scale is not
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None, it will use qk_scale to scale the q @ k. Otherwise it will use
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head_dim**-0.5 instead of qk_scale. Default: None
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drop_rate (float): Probability of an element to be zeroed after the feed
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forward layer. Default: 0.0
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drop_path_rate (float): Stochastic depth rate. Default: 0
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norm_layer (nn.Module): normalization layer
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global_pool (bool): Global pool before head. Default: False
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use_layer_scale (bool): If use_layer_scale is True, it will use layer
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scale. Default: False
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init_scale (float): It is used for layer scale in Block to scale the
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gamma_1 and gamma_2.
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hydra_attention (bool): If hydra_attention is True, it will use Hydra
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Attention. Default: False
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hydra_attention_layers (int | None): The number of layers that use Hydra Attention.
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If it is None and hydra_attention is True, it will be equal to depth.
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Default: None
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use_dpr_linspace (bool): If use_dpr_linspace is False, all block's drop_path_rate
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are the same. Otherwise, it will use "torch.linspace" on drop_path_rate.
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Default: True
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"""
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def __init__(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.,
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qkv_bias=False,
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qk_scale=None,
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drop_rate=0.,
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attn_drop_rate=0.,
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drop_path_rate=0.,
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norm_layer=partial(nn.LayerNorm, eps=1e-6),
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global_pool=False,
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use_layer_scale=False,
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init_scale=1e-4,
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hydra_attention=False,
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hydra_attention_layers=None,
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use_dpr_linspace=True,
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**kwargs):
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super().__init__()
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if hydra_attention:
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if hydra_attention_layers is None:
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hydra_attention_layers = depth
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elif hydra_attention_layers > depth:
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raise ValueError(
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'When using Hydra Attention, hydra_attention_Layers must be smaller than or equal to depth.'
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)
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self.num_features = self.embed_dim = embed_dim
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self.num_heads = num_heads
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self.mlp_ratio = mlp_ratio
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self.qkv_bias = qkv_bias
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self.qk_scale = qk_scale
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self.drop_rate = drop_rate
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self.attn_drop_rate = attn_drop_rate
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self.norm_layer = norm_layer
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self.use_layer_scale = use_layer_scale
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self.init_scale = init_scale
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self.hydra_attention = hydra_attention
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self.hydra_attention_layers = hydra_attention_layers
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self.drop_path_rate = drop_path_rate
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self.depth = depth
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self.patch_embed = PatchEmbed(
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img_size=img_size[0],
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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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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.pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
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self.pos_drop = nn.Dropout(p=drop_rate)
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if use_dpr_linspace:
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dpr = [
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x.item()
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for x in torch.linspace(0, self.drop_path_rate, self.depth)
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]
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else:
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dpr = [drop_path_rate for x in range(self.depth)]
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self.dpr = dpr
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if self.hydra_attention:
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hy = [
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x >= (self.depth - self.hydra_attention_layers)
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for x in range(self.depth)
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]
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head = [
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self.embed_dim if x >=
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(self.depth - self.hydra_attention_layers) else self.num_heads
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for x in range(self.depth)
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]
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else:
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hy = [False for x in range(self.depth)]
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head = [self.num_heads for x in range(self.depth)]
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self.blocks = nn.ModuleList([
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Block(
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dim=embed_dim,
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num_heads=head[i],
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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=drop_rate,
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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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use_layer_scale=use_layer_scale,
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init_values=init_scale,
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hydra_attention=hy[i]) for i in range(depth)
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])
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self.norm = norm_layer(embed_dim)
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# Classifier head
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self.head = nn.Linear(
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embed_dim, num_classes) if num_classes > 0 else nn.Identity()
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# Use global average pooling
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self.global_pool = global_pool
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if self.global_pool:
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self.fc_norm = norm_layer(embed_dim)
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self.norm = None
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def init_weights(self):
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trunc_normal_(self.pos_embed, std=.02)
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trunc_normal_(self.cls_token, std=.02)
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for m in self.modules():
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if isinstance(m, nn.Linear):
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trunc_normal_(m.weight, std=.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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def forward(self, x):
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x = self.forward_features(x)
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x = self.pos_drop(x)
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x = self.head(x)
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return [x]
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def forward_features(self, x):
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B = x.shape[0]
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x = self.patch_embed(x)
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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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x = torch.cat((cls_tokens, x), dim=1)
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for blk in self.blocks:
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x = blk(x)
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if self.norm is not None:
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x = self.norm(x)
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if self.global_pool:
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x = x[:, 1:, :].mean(dim=1)
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return self.fc_norm(x)
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else:
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return x[:, 0]
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