mirror of https://github.com/alibaba/EasyCV.git
528 lines
17 KiB
Python
528 lines
17 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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dynamic Input support borrow from
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https://github.com/microsoft/esvit/blob/main/models/vision_transformer.py
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"""
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import math
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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 utils import trunc_normal_
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from timm.models.layers import trunc_normal_
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from easycv.utils.checkpoint import load_checkpoint
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from easycv.utils.logger import get_root_logger
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def drop_path(x, drop_prob: float = 0., training: bool = False):
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if drop_prob == 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, ) * (
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x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
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random_tensor = keep_prob + torch.rand(
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shape, dtype=x.dtype, device=x.device)
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random_tensor.floor_() # binarize
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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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"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
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"""
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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 Mlp(nn.Module):
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def __init__(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.):
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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 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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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,
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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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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, 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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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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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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def forward(self, x, return_attention=False):
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y, attn = self.attn(self.norm1(x))
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if return_attention:
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return attn
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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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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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class VisionTransformer(nn.Module):
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""" Vision Transformer """
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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=0,
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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=nn.LayerNorm,
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use_dense_prediction=False,
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global_pool=False,
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**kwargs):
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super().__init__()
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self.num_features = self.embed_dim = embed_dim
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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(
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torch.zeros(1, num_patches + 1, embed_dim))
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self.pos_drop = nn.Dropout(p=drop_rate)
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dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)
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] # stochastic depth decay rule
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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=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=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) 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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# Dense prediction head
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self.use_dense_prediction = use_dense_prediction
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if self.use_dense_prediction:
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self.head_dense = None
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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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trunc_normal_(self.pos_embed, std=.02)
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trunc_normal_(self.cls_token, std=.02)
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self.apply(self._init_weights)
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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=.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 init_weights(self, pretrained=None):
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if isinstance(pretrained, str) or isinstance(pretrained, dict):
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logger = get_root_logger()
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load_checkpoint(
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self,
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pretrained,
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map_location='cpu',
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strict=False,
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logger=logger)
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elif pretrained is None:
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self.apply(self._init_weights)
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else:
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raise TypeError('pretrained must be a str or None')
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def forward(self, x):
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# convert to list
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if not isinstance(x, list):
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x = [x]
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# Perform forward pass separately on each resolution input.
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# The inputs corresponding to a single resolution are clubbed and single
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# forward is run on the same resolution inputs. Hence we do several
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# forward passes = number of different resolutions used. We then
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# concatenate all the output features.
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idx_crops = torch.cumsum(
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torch.unique_consecutive(
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torch.tensor([inp.shape[-1] for inp in x]),
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return_counts=True,
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)[1], 0)
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if self.use_dense_prediction:
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start_idx = 0
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for end_idx in idx_crops:
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_out_cls, _out_fea = self.forward_features(
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torch.cat(x[start_idx:end_idx]))
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B, N, C = _out_fea.shape
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if start_idx == 0:
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output_cls = _out_cls
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output_fea = _out_fea.reshape(B * N, C)
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npatch = [N]
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else:
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output_cls = torch.cat((output_cls, _out_cls))
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output_fea = torch.cat(
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(output_fea, _out_fea.reshape(B * N, C)))
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npatch.append(N)
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start_idx = end_idx
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return [
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self.head(output_cls),
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self.head_dense(output_fea), output_fea, npatch
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]
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else:
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start_idx = 0
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for end_idx in idx_crops:
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_out = self.forward_features(torch.cat(x[start_idx:end_idx]))
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# _out = self.forward_return_n_last_blocks(torch.cat(x[start_idx: end_idx]), 4, True)
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if start_idx == 0:
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output = _out
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else:
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output = torch.cat((output, _out))
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start_idx = end_idx
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# print(f'output[0] {output[0].shape}')
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# Run the head forward on the concatenated features.
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return [self.head(output)]
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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 = torch.cat((cls_tokens, x), dim=1)
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pos_embed = self.interpolate_pos_encoding(x, self.pos_embed)
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x = x + pos_embed
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x = self.pos_drop(x)
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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.use_dense_prediction:
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return x[:, 0], x[:, 1:]
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else:
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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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def forward_feature_maps(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 = torch.cat((cls_tokens, x), dim=1)
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pos_embed = self.interpolate_pos_encoding(x, self.pos_embed)
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x = x + pos_embed
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x = self.pos_drop(x)
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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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return x
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def interpolate_pos_encoding(self, x, pos_embed):
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npatch = x.shape[1] - 1
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N = pos_embed.shape[1] - 1
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if npatch == N:
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return pos_embed
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class_emb = pos_embed[:, 0]
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pos_embed = pos_embed[:, 1:]
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dim = x.shape[-1]
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pos_embed = nn.functional.interpolate(
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pos_embed.reshape(1, int(math.sqrt(N)), int(math.sqrt(N)),
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dim).permute(0, 3, 1, 2),
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scale_factor=math.sqrt(npatch / N),
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mode='bicubic',
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)
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pos_embed = pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
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return torch.cat((class_emb.unsqueeze(0), pos_embed), dim=1)
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def forward_selfattention(self, x, n=1):
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# n=1 return the last layer attn map; otherwise return attn maps in all layers
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B, nc, w, h = x.shape
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N = self.pos_embed.shape[1] - 1
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x = self.patch_embed(x)
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# interpolate patch embeddings
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dim = x.shape[-1]
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w0 = w // self.patch_embed.patch_size
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h0 = h // self.patch_embed.patch_size
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class_pos_embed = self.pos_embed[:, 0]
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patch_pos_embed = self.pos_embed[:, 1:]
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patch_pos_embed = nn.functional.interpolate(
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patch_pos_embed.reshape(1, int(math.sqrt(N)), int(math.sqrt(N)),
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dim).permute(0, 3, 1, 2),
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scale_factor=(w0 / math.sqrt(N), h0 / math.sqrt(N)),
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mode='bicubic',
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)
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if w0 != patch_pos_embed.shape[-2]:
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helper = torch.zeros(h0)[None, None, None, :].repeat(
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1, dim, w0 - patch_pos_embed.shape[-2], 1).to(x.device)
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patch_pos_embed = torch.cat((patch_pos_embed, helper), dim=-2)
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if h0 != patch_pos_embed.shape[-1]:
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helper = torch.zeros(w0)[None, None, :, None].repeat(
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1, dim, 1, h0 - patch_pos_embed.shape[-1]).to(x.device)
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pos_embed = torch.cat((patch_pos_embed, helper), dim=-1)
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patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
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pos_embed = torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed),
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dim=1)
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cls_tokens = self.cls_token.expand(B, -1, -1)
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x = torch.cat((cls_tokens, x), dim=1)
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x = x + pos_embed
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x = self.pos_drop(x)
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if n == 1:
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return self.forward_last_selfattention(x)
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else:
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return self.forward_all_selfattention(x)
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def forward_last_selfattention(self, x):
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for i, blk in enumerate(self.blocks):
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if i < len(self.blocks) - 1:
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x = blk(x)
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else:
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return blk(x, return_attention=True)
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def forward_all_selfattention(self, x):
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attn_out = []
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for i, blk in enumerate(self.blocks):
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x, attn = blk.forward_fea_and_attn(x)
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attn_out.append(attn)
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return attn_out
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def forward_return_n_last_blocks(self,
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x,
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n=1,
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return_patch_avgpool=False,
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depths=[]):
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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 = torch.cat((cls_tokens, x), dim=1)
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pos_embed = self.interpolate_pos_encoding(x, self.pos_embed)
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x = x + pos_embed
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x = self.pos_drop(x)
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# we will return the [CLS] tokens from the `n` last blocks
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output = []
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for i, blk in enumerate(self.blocks):
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x = blk(x)
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if len(self.blocks) - i <= n:
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output.append(self.norm(x)[:, 0])
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if return_patch_avgpool:
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x = self.norm(x)
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# In addition to the [CLS] tokens from the `n` last blocks, we also return
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# the patch tokens from the last block. This is useful for linear eval.
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output.append(torch.mean(x[:, 1:], dim=1))
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return torch.cat(output, dim=-1)
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def dynamic_deit_tiny_p16(patch_size=16, **kwargs):
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model = VisionTransformer(
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patch_size=patch_size,
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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,
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qkv_bias=True,
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norm_layer=partial(nn.LayerNorm, eps=1e-6),
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**kwargs)
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return model
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def dynamic_deit_small_p16(patch_size=16, **kwargs):
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model = VisionTransformer(
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patch_size=patch_size,
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embed_dim=384,
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depth=12,
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num_heads=6,
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mlp_ratio=4,
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qkv_bias=True,
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norm_layer=partial(nn.LayerNorm, eps=1e-6),
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**kwargs)
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return model
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def dynamic_vit_base_p16(patch_size=16, **kwargs):
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model = VisionTransformer(
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patch_size=patch_size,
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embed_dim=768,
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depth=12,
|
|
num_heads=12,
|
|
mlp_ratio=4,
|
|
qkv_bias=True,
|
|
norm_layer=partial(nn.LayerNorm, eps=1e-6),
|
|
**kwargs)
|
|
return model
|
|
|
|
|
|
def dynamic_vit_large_p16(patch_size=16, **kwargs):
|
|
model = VisionTransformer(
|
|
patch_size=patch_size,
|
|
embed_dim=1024,
|
|
depth=24,
|
|
num_heads=16,
|
|
mlp_ratio=4,
|
|
qkv_bias=True,
|
|
norm_layer=partial(nn.LayerNorm, eps=1e-6),
|
|
**kwargs)
|
|
return model
|
|
|
|
|
|
def dynamic_vit_huge_p14(patch_size=14, **kwargs):
|
|
model = VisionTransformer(
|
|
patch_size=patch_size,
|
|
embed_dim=1280,
|
|
depth=32,
|
|
num_heads=16,
|
|
mlp_ratio=4,
|
|
qkv_bias=True,
|
|
norm_layer=partial(nn.LayerNorm, eps=1e-6),
|
|
**kwargs)
|
|
return model
|