397 lines
15 KiB
Python
397 lines
15 KiB
Python
"""Modified from https://github.com/rwightman/pytorch-image-
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models/blob/master/timm/models/vision_transformer.py."""
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import math
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.utils.checkpoint as cp
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from mmcv.cnn import (Conv2d, Linear, build_activation_layer, build_norm_layer,
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constant_init, kaiming_init, normal_init, xavier_init)
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from mmcv.runner import _load_checkpoint
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from mmcv.utils.parrots_wrapper import _BatchNorm
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from mmseg.utils import get_root_logger
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from ..builder import BACKBONES
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class Mlp(nn.Module):
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"""MLP layer for Encoder block.
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Args:
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in_features(int): Input dimension for the first fully
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connected layer.
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hidden_features(int): Output dimension for the first fully
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connected layer.
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out_features(int): Output dementsion for the second fully
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connected layer.
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act_cfg(dict): Config dict for activation layer.
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Default: dict(type='GELU').
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drop(float): Drop rate for the dropout layer. Dropout rate has
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to be between 0 and 1. Default: 0.
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"""
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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_cfg=dict(type='GELU'),
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drop=0.):
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super(Mlp, self).__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 = Linear(in_features, hidden_features)
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self.act = build_activation_layer(act_cfg)
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self.fc2 = 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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"""Attention layer for Encoder block.
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Args:
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dim (int): Dimension for the input vector.
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num_heads (int): Number of parallel attention heads.
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qkv_bias (bool): Enable bias for qkv if True. Default: False.
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qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
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attn_drop (float): Drop rate for attention output weights.
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Default: 0.
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proj_drop (float): Drop rate for output weights. Default: 0.
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"""
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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(Attention, self).__init__()
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self.num_heads = num_heads
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head_dim = dim // num_heads
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self.scale = qk_scale or head_dim**-0.5
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self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
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self.attn_drop = nn.Dropout(attn_drop)
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self.proj = 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
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class Block(nn.Module):
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"""Implements encoder block with residual connection.
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Args:
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dim (int): The feature dimension.
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num_heads (int): Number of parallel attention heads.
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mlp_ratio (int): Ratio of mlp hidden dim to embedding dim.
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qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
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drop (float): Drop rate for mlp output weights. Default: 0.
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attn_drop (float): Drop rate for attention output weights.
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Default: 0.
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proj_drop (float): Drop rate for attn layer output weights.
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Default: 0.
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act_cfg (dict): Config dict for activation layer.
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Default: dict(type='GELU').
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norm_cfg (dict): Config dict for normalization layer.
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Default: dict(type='LN', requires_grad=True).
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"""
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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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proj_drop=0.,
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act_cfg=dict(type='GELU'),
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norm_cfg=dict(type='LN'),
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with_cp=False):
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super(Block, self).__init__()
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self.with_cp = with_cp
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_, self.norm1 = build_norm_layer(norm_cfg, dim)
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self.attn = Attention(dim, num_heads, qkv_bias, qk_scale, attn_drop,
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proj_drop)
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_, self.norm2 = build_norm_layer(norm_cfg, 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_cfg=act_cfg,
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drop=drop)
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def forward(self, x):
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def _inner_forward(x):
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out = x + self.attn(self.norm1(x))
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out = out + self.mlp(self.norm2(out))
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return out
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if self.with_cp and x.requires_grad:
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out = cp.checkpoint(_inner_forward, x)
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else:
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out = _inner_forward(x)
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return out
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class PatchEmbed(nn.Module):
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"""Image to Patch Embedding.
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Args:
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img_size (int， tuple): Input image size.
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default: 224.
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patch_size (int): Width and height for a patch.
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default: 16.
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in_channels (int): Input channels for images. Default: 3.
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embed_dim (int): The embedding dimension. Default: 768.
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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_channels=3,
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embed_dim=768):
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super(PatchEmbed, self).__init__()
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if isinstance(img_size, int):
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self.img_size = (img_size, img_size)
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elif isinstance(img_size, tuple):
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self.img_size = img_size
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else:
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raise TypeError('img_size must be type of int or tuple')
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h, w = self.img_size
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self.patch_size = (patch_size, patch_size)
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self.num_patches = (h // patch_size) * (w // patch_size)
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self.proj = Conv2d(
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in_channels, embed_dim, kernel_size=patch_size, stride=patch_size)
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def forward(self, x):
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return self.proj(x).flatten(2).transpose(1, 2)
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@BACKBONES.register_module()
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class VisionTransformer(nn.Module):
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"""Vision transformer backbone.
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A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for
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Image Recognition at Scale` - https://arxiv.org/abs/2010.11929
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Args:
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img_size (tuple): input image size. Default: (224, 224）.
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patch_size (int, tuple): patch size. Default: 16.
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in_channels (int): number of input channels. Default: 3.
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embed_dim (int): embedding dimension. Default: 768.
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depth (int): depth of transformer. Default: 12.
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num_heads (int): number of attention heads. Default: 12.
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mlp_ratio (int): ratio of mlp hidden dim to embedding dim. Default: 4.
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qkv_bias (bool): enable bias for qkv if True. Default: True.
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qk_scale (float): override default qk scale of head_dim ** -0.5 if set.
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drop_rate (float): dropout rate. Default: 0.
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attn_drop_rate (float): attention dropout rate. Default: 0.
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norm_cfg (dict): Config dict for normalization layer.
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Default: dict(type='LN', requires_grad=True).
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act_cfg (dict): Config dict for activation layer.
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Default: dict(type='GELU').
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norm_eval (bool): Whether to set norm layers to eval mode, namely,
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freeze running stats (mean and var). Note: Effect on Batch Norm
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and its variants only. Default: False.
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with_cp (bool): Use checkpoint or not. Using checkpoint
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will save some memory while slowing down the training speed.
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Default: False.
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"""
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def __init__(self,
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img_size=(224, 224),
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patch_size=16,
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in_channels=3,
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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=True,
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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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norm_cfg=dict(type='LN'),
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act_cfg=dict(type='GELU'),
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norm_eval=False,
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with_cp=False):
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super(VisionTransformer, self).__init__()
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self.img_size = img_size
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self.patch_size = patch_size
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self.features = self.embed_dim = embed_dim
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self.patch_embed = PatchEmbed(
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img_size=img_size,
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patch_size=patch_size,
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in_channels=in_channels,
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embed_dim=embed_dim)
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self.pos_embed = nn.Parameter(
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torch.zeros(1, self.patch_embed.num_patches, embed_dim))
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self.pos_drop = nn.Dropout(p=drop_rate)
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self.blocks = nn.Sequential(*[
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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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act_cfg=act_cfg,
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norm_cfg=norm_cfg,
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with_cp=with_cp) for i in range(depth)
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])
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_, self.norm = build_norm_layer(norm_cfg, embed_dim)
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self.norm_eval = norm_eval
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self.with_cp = with_cp
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def init_weights(self, pretrained=None):
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if isinstance(pretrained, str):
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logger = get_root_logger()
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checkpoint = _load_checkpoint(pretrained, logger=logger)
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if 'state_dict' in checkpoint:
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state_dict = checkpoint['state_dict']
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else:
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state_dict = checkpoint
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if 'pos_embed' in state_dict.keys():
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state_dict['pos_embed'] = state_dict['pos_embed'][:, 1:, :]
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logger.info(
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msg='Remove the "cls_token" dimension from the checkpoint')
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if self.pos_embed.shape != state_dict['pos_embed'].shape:
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logger.info(msg=f'Resize the pos_embed shape from \
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{state_dict["pos_embed"].shape} to \
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{self.pos_embed.shape}')
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h, w = self.img_size
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pos_size = int(math.sqrt(state_dict['pos_embed'].shape[1]))
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state_dict['pos_embed'] = self.resize_pos_embed(
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state_dict['pos_embed'], (h, w), (pos_size, pos_size),
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self.patch_size)
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self.load_state_dict(state_dict, False)
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elif pretrained is None:
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# We only implement the 'jax_impl' initialization implemented at
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# https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py#L353 # noqa: E501
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normal_init(self.pos_embed)
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for n, m in self.named_modules():
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if isinstance(m, Linear):
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xavier_init(m.weight, distribution='uniform')
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if m.bias is not None:
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if 'mlp' in n:
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normal_init(m.bias, std=1e-6)
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else:
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constant_init(m.bias, 0)
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elif isinstance(m, Conv2d):
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kaiming_init(m.weight, mode='fan_in')
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if m.bias is not None:
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constant_init(m.bias, 0)
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elif isinstance(m, (_BatchNorm, nn.GroupNorm, nn.LayerNorm)):
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constant_init(m.bias, 0)
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constant_init(m.weight, 1)
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else:
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raise TypeError('pretrained must be a str or None')
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def _pos_embeding(self, img, patched_img, pos_embed):
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"""Positiong embeding method.
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Resize the pos_embed, if the input image size doesn't match
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the training size.
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Args:
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img (torch.Tensor): The inference image tensor, the shape
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must be [B, C, H, W].
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patched_img (torch.Tensor): The patched image, it should be
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shape of [B, L1, C].
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pos_embed (torch.Tensor): The pos_embed weighs, it should be
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shape of [B, L2, c].
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Return:
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torch.Tensor: The pos encoded image feature.
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"""
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assert patched_img.ndim == 3 and pos_embed.ndim == 3, \
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'the shapes of patched_img and pos_embed must be [B, L, C]'
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x_len, pos_len = patched_img.shape[1], pos_embed.shape[1]
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if x_len != pos_len:
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if pos_len == (self.img_size[0] // self.patch_size) * (
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self.img_size[1] // self.patch_size):
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pos_h = self.img_size[0] // self.patch_size
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pos_w = self.img_size[1] // self.patch_size
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else:
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raise ValueError(
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'Unexpected shape of pos_embed, got {}.'.format(
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pos_embed.shape))
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pos_embed = self.resize_pos_embed(pos_embed, img.shape[2:],
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(pos_h, pos_w), self.patch_size)
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return patched_img + pos_embed
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@staticmethod
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def resize_pos_embed(pos_embed, input_shpae, pos_shape, patch_size):
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"""Resize pos_embed weights.
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Resize pos_embed using bicubic interpolate method.
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Args:
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pos_embed (torch.Tensor): pos_embed weights.
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input_shpae (tuple): Tuple for (input_h, intput_w).
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pos_shape (tuple): Tuple for (pos_h, pos_w).
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patch_size (int): Patch size.
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Return:
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torch.Tensor: The resized pos_embed of shape [B, L_new, C]
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"""
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assert pos_embed.ndim == 3, 'shape of pos_embed must be [B, L, C]'
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input_h, input_w = input_shpae
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pos_h, pos_w = pos_shape
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pos_embed = pos_embed.reshape(1, pos_h, pos_w,
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pos_embed.shape[2]).permute(0, 3, 1, 2)
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pos_embed = F.interpolate(
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pos_embed,
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size=[input_h // patch_size, input_w // patch_size],
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align_corners=False,
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mode='bicubic')
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pos_embed = torch.flatten(pos_embed, 2).transpose(1, 2)
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return pos_embed
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def forward(self, inputs):
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x = self.patch_embed(inputs)
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x = self._pos_embeding(inputs, x, self.pos_embed)
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x = self.blocks(x)
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x = self.norm(x)
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B, _, C = x.shape
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x = x.reshape(B, inputs.shape[2] // self.patch_size,
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inputs.shape[3] // self.patch_size,
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C).permute(0, 3, 1, 2)
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return [x]
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def train(self, mode=True):
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super(VisionTransformer, self).train(mode)
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if mode and self.norm_eval:
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for m in self.modules():
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if isinstance(m, nn.LayerNorm):
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m.eval()
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