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680 lines
27 KiB
680 lines
27 KiB
"""
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CoaT architecture.
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Paper: Co-Scale Conv-Attentional Image Transformers - https://arxiv.org/abs/2104.06399
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Official CoaT code at: https://github.com/mlpc-ucsd/CoaT
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Modified from timm/models/vision_transformer.py
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"""
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from functools import partial
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from typing import Tuple, List, Union
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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 timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
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from timm.layers import PatchEmbed, Mlp, DropPath, to_2tuple, trunc_normal_, _assert
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from ._builder import build_model_with_cfg
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from ._registry import register_model
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__all__ = ['CoaT']
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def _cfg_coat(url='', **kwargs):
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return {
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'url': url,
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'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
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'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True,
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'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
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'first_conv': 'patch_embed1.proj', 'classifier': 'head',
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**kwargs
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}
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default_cfgs = {
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'coat_tiny': _cfg_coat(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-coat-weights/coat_tiny-473c2a20.pth'
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),
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'coat_mini': _cfg_coat(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-coat-weights/coat_mini-2c6baf49.pth'
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),
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'coat_lite_tiny': _cfg_coat(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-coat-weights/coat_lite_tiny-461b07a7.pth'
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),
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'coat_lite_mini': _cfg_coat(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-coat-weights/coat_lite_mini-d7842000.pth'
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),
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'coat_lite_small': _cfg_coat(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-coat-weights/coat_lite_small-fea1d5a1.pth'
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),
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}
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class ConvRelPosEnc(nn.Module):
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""" Convolutional relative position encoding. """
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def __init__(self, Ch, h, window):
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"""
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Initialization.
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Ch: Channels per head.
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h: Number of heads.
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window: Window size(s) in convolutional relative positional encoding. It can have two forms:
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1. An integer of window size, which assigns all attention heads with the same window s
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size in ConvRelPosEnc.
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2. A dict mapping window size to #attention head splits (
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e.g. {window size 1: #attention head split 1, window size 2: #attention head split 2})
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It will apply different window size to the attention head splits.
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"""
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super().__init__()
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if isinstance(window, int):
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# Set the same window size for all attention heads.
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window = {window: h}
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self.window = window
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elif isinstance(window, dict):
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self.window = window
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else:
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raise ValueError()
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self.conv_list = nn.ModuleList()
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self.head_splits = []
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for cur_window, cur_head_split in window.items():
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dilation = 1
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# Determine padding size.
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# Ref: https://discuss.pytorch.org/t/how-to-keep-the-shape-of-input-and-output-same-when-dilation-conv/14338
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padding_size = (cur_window + (cur_window - 1) * (dilation - 1)) // 2
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cur_conv = nn.Conv2d(cur_head_split*Ch, cur_head_split*Ch,
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kernel_size=(cur_window, cur_window),
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padding=(padding_size, padding_size),
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dilation=(dilation, dilation),
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groups=cur_head_split*Ch,
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)
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self.conv_list.append(cur_conv)
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self.head_splits.append(cur_head_split)
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self.channel_splits = [x*Ch for x in self.head_splits]
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def forward(self, q, v, size: Tuple[int, int]):
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B, h, N, Ch = q.shape
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H, W = size
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_assert(N == 1 + H * W, '')
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# Convolutional relative position encoding.
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q_img = q[:, :, 1:, :] # [B, h, H*W, Ch]
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v_img = v[:, :, 1:, :] # [B, h, H*W, Ch]
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v_img = v_img.transpose(-1, -2).reshape(B, h * Ch, H, W)
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v_img_list = torch.split(v_img, self.channel_splits, dim=1) # Split according to channels
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conv_v_img_list = []
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for i, conv in enumerate(self.conv_list):
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conv_v_img_list.append(conv(v_img_list[i]))
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conv_v_img = torch.cat(conv_v_img_list, dim=1)
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conv_v_img = conv_v_img.reshape(B, h, Ch, H * W).transpose(-1, -2)
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EV_hat = q_img * conv_v_img
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EV_hat = F.pad(EV_hat, (0, 0, 1, 0, 0, 0)) # [B, h, N, Ch].
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return EV_hat
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class FactorAttnConvRelPosEnc(nn.Module):
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""" Factorized attention with convolutional relative position encoding class. """
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def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0., shared_crpe=None):
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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 = 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) # Note: attn_drop is actually not used.
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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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# Shared convolutional relative position encoding.
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self.crpe = shared_crpe
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def forward(self, x, size: Tuple[int, int]):
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B, N, C = x.shape
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# Generate Q, K, V.
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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] # [B, h, N, Ch]
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# Factorized attention.
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k_softmax = k.softmax(dim=2)
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factor_att = k_softmax.transpose(-1, -2) @ v
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factor_att = q @ factor_att
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# Convolutional relative position encoding.
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crpe = self.crpe(q, v, size=size) # [B, h, N, Ch]
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# Merge and reshape.
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x = self.scale * factor_att + crpe
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x = x.transpose(1, 2).reshape(B, N, C) # [B, h, N, Ch] -> [B, N, h, Ch] -> [B, N, C]
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# Output projection.
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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 ConvPosEnc(nn.Module):
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""" Convolutional Position Encoding.
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Note: This module is similar to the conditional position encoding in CPVT.
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"""
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def __init__(self, dim, k=3):
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super(ConvPosEnc, self).__init__()
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self.proj = nn.Conv2d(dim, dim, k, 1, k//2, groups=dim)
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def forward(self, x, size: Tuple[int, int]):
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B, N, C = x.shape
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H, W = size
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_assert(N == 1 + H * W, '')
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# Extract CLS token and image tokens.
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cls_token, img_tokens = x[:, :1], x[:, 1:] # [B, 1, C], [B, H*W, C]
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# Depthwise convolution.
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feat = img_tokens.transpose(1, 2).view(B, C, H, W)
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x = self.proj(feat) + feat
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x = x.flatten(2).transpose(1, 2)
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# Combine with CLS token.
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x = torch.cat((cls_token, x), dim=1)
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return x
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class SerialBlock(nn.Module):
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""" Serial block class.
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Note: In this implementation, each serial block only contains a conv-attention and a FFN (MLP) module. """
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def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0.,
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drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, shared_cpe=None, shared_crpe=None):
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super().__init__()
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# Conv-Attention.
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self.cpe = shared_cpe
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self.norm1 = norm_layer(dim)
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self.factoratt_crpe = FactorAttnConvRelPosEnc(
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dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop, shared_crpe=shared_crpe)
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self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
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# MLP.
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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(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
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def forward(self, x, size: Tuple[int, int]):
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# Conv-Attention.
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x = self.cpe(x, size)
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cur = self.norm1(x)
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cur = self.factoratt_crpe(cur, size)
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x = x + self.drop_path(cur)
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# MLP.
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cur = self.norm2(x)
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cur = self.mlp(cur)
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x = x + self.drop_path(cur)
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return x
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class ParallelBlock(nn.Module):
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""" Parallel block class. """
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def __init__(self, dims, num_heads, mlp_ratios=[], qkv_bias=False, drop=0., attn_drop=0.,
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drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, shared_crpes=None):
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super().__init__()
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# Conv-Attention.
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self.norm12 = norm_layer(dims[1])
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self.norm13 = norm_layer(dims[2])
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self.norm14 = norm_layer(dims[3])
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self.factoratt_crpe2 = FactorAttnConvRelPosEnc(
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dims[1], num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop,
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shared_crpe=shared_crpes[1]
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)
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self.factoratt_crpe3 = FactorAttnConvRelPosEnc(
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dims[2], num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop,
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shared_crpe=shared_crpes[2]
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)
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self.factoratt_crpe4 = FactorAttnConvRelPosEnc(
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dims[3], num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop,
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shared_crpe=shared_crpes[3]
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)
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self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
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# MLP.
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self.norm22 = norm_layer(dims[1])
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self.norm23 = norm_layer(dims[2])
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self.norm24 = norm_layer(dims[3])
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# In parallel block, we assume dimensions are the same and share the linear transformation.
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assert dims[1] == dims[2] == dims[3]
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assert mlp_ratios[1] == mlp_ratios[2] == mlp_ratios[3]
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mlp_hidden_dim = int(dims[1] * mlp_ratios[1])
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self.mlp2 = self.mlp3 = self.mlp4 = Mlp(
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in_features=dims[1], hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
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def upsample(self, x, factor: float, size: Tuple[int, int]):
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""" Feature map up-sampling. """
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return self.interpolate(x, scale_factor=factor, size=size)
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def downsample(self, x, factor: float, size: Tuple[int, int]):
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""" Feature map down-sampling. """
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return self.interpolate(x, scale_factor=1.0/factor, size=size)
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def interpolate(self, x, scale_factor: float, size: Tuple[int, int]):
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""" Feature map interpolation. """
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B, N, C = x.shape
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H, W = size
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_assert(N == 1 + H * W, '')
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cls_token = x[:, :1, :]
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img_tokens = x[:, 1:, :]
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img_tokens = img_tokens.transpose(1, 2).reshape(B, C, H, W)
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img_tokens = F.interpolate(
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img_tokens, scale_factor=scale_factor, recompute_scale_factor=False, mode='bilinear', align_corners=False)
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img_tokens = img_tokens.reshape(B, C, -1).transpose(1, 2)
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out = torch.cat((cls_token, img_tokens), dim=1)
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return out
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def forward(self, x1, x2, x3, x4, sizes: List[Tuple[int, int]]):
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_, S2, S3, S4 = sizes
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cur2 = self.norm12(x2)
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cur3 = self.norm13(x3)
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cur4 = self.norm14(x4)
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cur2 = self.factoratt_crpe2(cur2, size=S2)
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cur3 = self.factoratt_crpe3(cur3, size=S3)
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cur4 = self.factoratt_crpe4(cur4, size=S4)
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upsample3_2 = self.upsample(cur3, factor=2., size=S3)
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upsample4_3 = self.upsample(cur4, factor=2., size=S4)
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upsample4_2 = self.upsample(cur4, factor=4., size=S4)
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downsample2_3 = self.downsample(cur2, factor=2., size=S2)
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downsample3_4 = self.downsample(cur3, factor=2., size=S3)
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downsample2_4 = self.downsample(cur2, factor=4., size=S2)
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cur2 = cur2 + upsample3_2 + upsample4_2
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cur3 = cur3 + upsample4_3 + downsample2_3
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cur4 = cur4 + downsample3_4 + downsample2_4
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x2 = x2 + self.drop_path(cur2)
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x3 = x3 + self.drop_path(cur3)
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x4 = x4 + self.drop_path(cur4)
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# MLP.
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cur2 = self.norm22(x2)
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cur3 = self.norm23(x3)
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cur4 = self.norm24(x4)
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cur2 = self.mlp2(cur2)
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cur3 = self.mlp3(cur3)
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cur4 = self.mlp4(cur4)
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x2 = x2 + self.drop_path(cur2)
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x3 = x3 + self.drop_path(cur3)
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x4 = x4 + self.drop_path(cur4)
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return x1, x2, x3, x4
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class CoaT(nn.Module):
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""" CoaT class. """
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def __init__(
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self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dims=(0, 0, 0, 0),
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serial_depths=(0, 0, 0, 0), parallel_depth=0, num_heads=0, mlp_ratios=(0, 0, 0, 0), qkv_bias=True,
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drop_rate=0., attn_drop_rate=0., drop_path_rate=0., norm_layer=partial(nn.LayerNorm, eps=1e-6),
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return_interm_layers=False, out_features=None, crpe_window=None, global_pool='token'):
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super().__init__()
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assert global_pool in ('token', 'avg')
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crpe_window = crpe_window or {3: 2, 5: 3, 7: 3}
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self.return_interm_layers = return_interm_layers
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self.out_features = out_features
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self.embed_dims = embed_dims
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self.num_features = embed_dims[-1]
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self.num_classes = num_classes
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self.global_pool = global_pool
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# Patch embeddings.
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img_size = to_2tuple(img_size)
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self.patch_embed1 = PatchEmbed(
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img_size=img_size, patch_size=patch_size, in_chans=in_chans,
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embed_dim=embed_dims[0], norm_layer=nn.LayerNorm)
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self.patch_embed2 = PatchEmbed(
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img_size=[x // 4 for x in img_size], patch_size=2, in_chans=embed_dims[0],
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embed_dim=embed_dims[1], norm_layer=nn.LayerNorm)
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self.patch_embed3 = PatchEmbed(
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img_size=[x // 8 for x in img_size], patch_size=2, in_chans=embed_dims[1],
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embed_dim=embed_dims[2], norm_layer=nn.LayerNorm)
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self.patch_embed4 = PatchEmbed(
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img_size=[x // 16 for x in img_size], patch_size=2, in_chans=embed_dims[2],
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embed_dim=embed_dims[3], norm_layer=nn.LayerNorm)
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# Class tokens.
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self.cls_token1 = nn.Parameter(torch.zeros(1, 1, embed_dims[0]))
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self.cls_token2 = nn.Parameter(torch.zeros(1, 1, embed_dims[1]))
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self.cls_token3 = nn.Parameter(torch.zeros(1, 1, embed_dims[2]))
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self.cls_token4 = nn.Parameter(torch.zeros(1, 1, embed_dims[3]))
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# Convolutional position encodings.
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self.cpe1 = ConvPosEnc(dim=embed_dims[0], k=3)
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self.cpe2 = ConvPosEnc(dim=embed_dims[1], k=3)
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self.cpe3 = ConvPosEnc(dim=embed_dims[2], k=3)
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self.cpe4 = ConvPosEnc(dim=embed_dims[3], k=3)
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# Convolutional relative position encodings.
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self.crpe1 = ConvRelPosEnc(Ch=embed_dims[0] // num_heads, h=num_heads, window=crpe_window)
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self.crpe2 = ConvRelPosEnc(Ch=embed_dims[1] // num_heads, h=num_heads, window=crpe_window)
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self.crpe3 = ConvRelPosEnc(Ch=embed_dims[2] // num_heads, h=num_heads, window=crpe_window)
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self.crpe4 = ConvRelPosEnc(Ch=embed_dims[3] // num_heads, h=num_heads, window=crpe_window)
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# Disable stochastic depth.
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dpr = drop_path_rate
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assert dpr == 0.0
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# Serial blocks 1.
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self.serial_blocks1 = nn.ModuleList([
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SerialBlock(
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dim=embed_dims[0], num_heads=num_heads, mlp_ratio=mlp_ratios[0], qkv_bias=qkv_bias,
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drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr, norm_layer=norm_layer,
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shared_cpe=self.cpe1, shared_crpe=self.crpe1
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)
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for _ in range(serial_depths[0])]
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)
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# Serial blocks 2.
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self.serial_blocks2 = nn.ModuleList([
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SerialBlock(
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dim=embed_dims[1], num_heads=num_heads, mlp_ratio=mlp_ratios[1], qkv_bias=qkv_bias,
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drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr, norm_layer=norm_layer,
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shared_cpe=self.cpe2, shared_crpe=self.crpe2
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)
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for _ in range(serial_depths[1])]
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)
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# Serial blocks 3.
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self.serial_blocks3 = nn.ModuleList([
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SerialBlock(
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dim=embed_dims[2], num_heads=num_heads, mlp_ratio=mlp_ratios[2], qkv_bias=qkv_bias,
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drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr, norm_layer=norm_layer,
|
|
shared_cpe=self.cpe3, shared_crpe=self.crpe3
|
|
)
|
|
for _ in range(serial_depths[2])]
|
|
)
|
|
|
|
# Serial blocks 4.
|
|
self.serial_blocks4 = nn.ModuleList([
|
|
SerialBlock(
|
|
dim=embed_dims[3], num_heads=num_heads, mlp_ratio=mlp_ratios[3], qkv_bias=qkv_bias,
|
|
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr, norm_layer=norm_layer,
|
|
shared_cpe=self.cpe4, shared_crpe=self.crpe4
|
|
)
|
|
for _ in range(serial_depths[3])]
|
|
)
|
|
|
|
# Parallel blocks.
|
|
self.parallel_depth = parallel_depth
|
|
if self.parallel_depth > 0:
|
|
self.parallel_blocks = nn.ModuleList([
|
|
ParallelBlock(
|
|
dims=embed_dims, num_heads=num_heads, mlp_ratios=mlp_ratios, qkv_bias=qkv_bias,
|
|
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr, norm_layer=norm_layer,
|
|
shared_crpes=(self.crpe1, self.crpe2, self.crpe3, self.crpe4)
|
|
)
|
|
for _ in range(parallel_depth)]
|
|
)
|
|
else:
|
|
self.parallel_blocks = None
|
|
|
|
# Classification head(s).
|
|
if not self.return_interm_layers:
|
|
if self.parallel_blocks is not None:
|
|
self.norm2 = norm_layer(embed_dims[1])
|
|
self.norm3 = norm_layer(embed_dims[2])
|
|
else:
|
|
self.norm2 = self.norm3 = None
|
|
self.norm4 = norm_layer(embed_dims[3])
|
|
|
|
if self.parallel_depth > 0:
|
|
# CoaT series: Aggregate features of last three scales for classification.
|
|
assert embed_dims[1] == embed_dims[2] == embed_dims[3]
|
|
self.aggregate = torch.nn.Conv1d(in_channels=3, out_channels=1, kernel_size=1)
|
|
self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
|
|
else:
|
|
# CoaT-Lite series: Use feature of last scale for classification.
|
|
self.aggregate = None
|
|
self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
|
|
|
|
# Initialize weights.
|
|
trunc_normal_(self.cls_token1, std=.02)
|
|
trunc_normal_(self.cls_token2, std=.02)
|
|
trunc_normal_(self.cls_token3, std=.02)
|
|
trunc_normal_(self.cls_token4, std=.02)
|
|
self.apply(self._init_weights)
|
|
|
|
def _init_weights(self, m):
|
|
if isinstance(m, nn.Linear):
|
|
trunc_normal_(m.weight, std=.02)
|
|
if isinstance(m, nn.Linear) and m.bias is not None:
|
|
nn.init.constant_(m.bias, 0)
|
|
elif isinstance(m, nn.LayerNorm):
|
|
nn.init.constant_(m.bias, 0)
|
|
nn.init.constant_(m.weight, 1.0)
|
|
|
|
@torch.jit.ignore
|
|
def no_weight_decay(self):
|
|
return {'cls_token1', 'cls_token2', 'cls_token3', 'cls_token4'}
|
|
|
|
@torch.jit.ignore
|
|
def set_grad_checkpointing(self, enable=True):
|
|
assert not enable, 'gradient checkpointing not supported'
|
|
|
|
@torch.jit.ignore
|
|
def group_matcher(self, coarse=False):
|
|
matcher = dict(
|
|
stem1=r'^cls_token1|patch_embed1|crpe1|cpe1',
|
|
serial_blocks1=r'^serial_blocks1\.(\d+)',
|
|
stem2=r'^cls_token2|patch_embed2|crpe2|cpe2',
|
|
serial_blocks2=r'^serial_blocks2\.(\d+)',
|
|
stem3=r'^cls_token3|patch_embed3|crpe3|cpe3',
|
|
serial_blocks3=r'^serial_blocks3\.(\d+)',
|
|
stem4=r'^cls_token4|patch_embed4|crpe4|cpe4',
|
|
serial_blocks4=r'^serial_blocks4\.(\d+)',
|
|
parallel_blocks=[ # FIXME (partially?) overlap parallel w/ serial blocks??
|
|
(r'^parallel_blocks\.(\d+)', None),
|
|
(r'^norm|aggregate', (99999,)),
|
|
]
|
|
)
|
|
return matcher
|
|
|
|
@torch.jit.ignore
|
|
def get_classifier(self):
|
|
return self.head
|
|
|
|
def reset_classifier(self, num_classes, global_pool=None):
|
|
self.num_classes = num_classes
|
|
if global_pool is not None:
|
|
assert global_pool in ('token', 'avg')
|
|
self.global_pool = global_pool
|
|
self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
|
|
|
|
def forward_features(self, x0):
|
|
B = x0.shape[0]
|
|
|
|
# Serial blocks 1.
|
|
x1 = self.patch_embed1(x0)
|
|
H1, W1 = self.patch_embed1.grid_size
|
|
x1 = insert_cls(x1, self.cls_token1)
|
|
for blk in self.serial_blocks1:
|
|
x1 = blk(x1, size=(H1, W1))
|
|
x1_nocls = remove_cls(x1).reshape(B, H1, W1, -1).permute(0, 3, 1, 2).contiguous()
|
|
|
|
# Serial blocks 2.
|
|
x2 = self.patch_embed2(x1_nocls)
|
|
H2, W2 = self.patch_embed2.grid_size
|
|
x2 = insert_cls(x2, self.cls_token2)
|
|
for blk in self.serial_blocks2:
|
|
x2 = blk(x2, size=(H2, W2))
|
|
x2_nocls = remove_cls(x2).reshape(B, H2, W2, -1).permute(0, 3, 1, 2).contiguous()
|
|
|
|
# Serial blocks 3.
|
|
x3 = self.patch_embed3(x2_nocls)
|
|
H3, W3 = self.patch_embed3.grid_size
|
|
x3 = insert_cls(x3, self.cls_token3)
|
|
for blk in self.serial_blocks3:
|
|
x3 = blk(x3, size=(H3, W3))
|
|
x3_nocls = remove_cls(x3).reshape(B, H3, W3, -1).permute(0, 3, 1, 2).contiguous()
|
|
|
|
# Serial blocks 4.
|
|
x4 = self.patch_embed4(x3_nocls)
|
|
H4, W4 = self.patch_embed4.grid_size
|
|
x4 = insert_cls(x4, self.cls_token4)
|
|
for blk in self.serial_blocks4:
|
|
x4 = blk(x4, size=(H4, W4))
|
|
x4_nocls = remove_cls(x4).reshape(B, H4, W4, -1).permute(0, 3, 1, 2).contiguous()
|
|
|
|
# Only serial blocks: Early return.
|
|
if self.parallel_blocks is None:
|
|
if not torch.jit.is_scripting() and self.return_interm_layers:
|
|
# Return intermediate features for down-stream tasks (e.g. Deformable DETR and Detectron2).
|
|
feat_out = {}
|
|
if 'x1_nocls' in self.out_features:
|
|
feat_out['x1_nocls'] = x1_nocls
|
|
if 'x2_nocls' in self.out_features:
|
|
feat_out['x2_nocls'] = x2_nocls
|
|
if 'x3_nocls' in self.out_features:
|
|
feat_out['x3_nocls'] = x3_nocls
|
|
if 'x4_nocls' in self.out_features:
|
|
feat_out['x4_nocls'] = x4_nocls
|
|
return feat_out
|
|
else:
|
|
# Return features for classification.
|
|
x4 = self.norm4(x4)
|
|
return x4
|
|
|
|
# Parallel blocks.
|
|
for blk in self.parallel_blocks:
|
|
x2, x3, x4 = self.cpe2(x2, (H2, W2)), self.cpe3(x3, (H3, W3)), self.cpe4(x4, (H4, W4))
|
|
x1, x2, x3, x4 = blk(x1, x2, x3, x4, sizes=[(H1, W1), (H2, W2), (H3, W3), (H4, W4)])
|
|
|
|
if not torch.jit.is_scripting() and self.return_interm_layers:
|
|
# Return intermediate features for down-stream tasks (e.g. Deformable DETR and Detectron2).
|
|
feat_out = {}
|
|
if 'x1_nocls' in self.out_features:
|
|
x1_nocls = remove_cls(x1).reshape(B, H1, W1, -1).permute(0, 3, 1, 2).contiguous()
|
|
feat_out['x1_nocls'] = x1_nocls
|
|
if 'x2_nocls' in self.out_features:
|
|
x2_nocls = remove_cls(x2).reshape(B, H2, W2, -1).permute(0, 3, 1, 2).contiguous()
|
|
feat_out['x2_nocls'] = x2_nocls
|
|
if 'x3_nocls' in self.out_features:
|
|
x3_nocls = remove_cls(x3).reshape(B, H3, W3, -1).permute(0, 3, 1, 2).contiguous()
|
|
feat_out['x3_nocls'] = x3_nocls
|
|
if 'x4_nocls' in self.out_features:
|
|
x4_nocls = remove_cls(x4).reshape(B, H4, W4, -1).permute(0, 3, 1, 2).contiguous()
|
|
feat_out['x4_nocls'] = x4_nocls
|
|
return feat_out
|
|
else:
|
|
x2 = self.norm2(x2)
|
|
x3 = self.norm3(x3)
|
|
x4 = self.norm4(x4)
|
|
return [x2, x3, x4]
|
|
|
|
def forward_head(self, x_feat: Union[torch.Tensor, List[torch.Tensor]], pre_logits: bool = False):
|
|
if isinstance(x_feat, list):
|
|
assert self.aggregate is not None
|
|
if self.global_pool == 'avg':
|
|
x = torch.cat([xl[:, 1:].mean(dim=1, keepdim=True) for xl in x_feat], dim=1) # [B, 3, C]
|
|
else:
|
|
x = torch.stack([xl[:, 0] for xl in x_feat], dim=1) # [B, 3, C]
|
|
x = self.aggregate(x).squeeze(dim=1) # Shape: [B, C]
|
|
else:
|
|
x = x_feat[:, 1:].mean(dim=1) if self.global_pool == 'avg' else x_feat[:, 0]
|
|
return x if pre_logits else self.head(x)
|
|
|
|
def forward(self, x) -> torch.Tensor:
|
|
if not torch.jit.is_scripting() and self.return_interm_layers:
|
|
# Return intermediate features (for down-stream tasks).
|
|
return self.forward_features(x)
|
|
else:
|
|
# Return features for classification.
|
|
x_feat = self.forward_features(x)
|
|
x = self.forward_head(x_feat)
|
|
return x
|
|
|
|
|
|
def insert_cls(x, cls_token):
|
|
""" Insert CLS token. """
|
|
cls_tokens = cls_token.expand(x.shape[0], -1, -1)
|
|
x = torch.cat((cls_tokens, x), dim=1)
|
|
return x
|
|
|
|
|
|
def remove_cls(x):
|
|
""" Remove CLS token. """
|
|
return x[:, 1:, :]
|
|
|
|
|
|
def checkpoint_filter_fn(state_dict, model):
|
|
out_dict = {}
|
|
for k, v in state_dict.items():
|
|
# original model had unused norm layers, removing them requires filtering pretrained checkpoints
|
|
if k.startswith('norm1') or \
|
|
(model.norm2 is None and k.startswith('norm2')) or \
|
|
(model.norm3 is None and k.startswith('norm3')):
|
|
continue
|
|
out_dict[k] = v
|
|
return out_dict
|
|
|
|
|
|
def _create_coat(variant, pretrained=False, default_cfg=None, **kwargs):
|
|
if kwargs.get('features_only', None):
|
|
raise RuntimeError('features_only not implemented for Vision Transformer models.')
|
|
|
|
model = build_model_with_cfg(
|
|
CoaT, variant, pretrained,
|
|
pretrained_filter_fn=checkpoint_filter_fn,
|
|
**kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def coat_tiny(pretrained=False, **kwargs):
|
|
model_cfg = dict(
|
|
patch_size=4, embed_dims=[152, 152, 152, 152], serial_depths=[2, 2, 2, 2], parallel_depth=6,
|
|
num_heads=8, mlp_ratios=[4, 4, 4, 4], **kwargs)
|
|
model = _create_coat('coat_tiny', pretrained=pretrained, **model_cfg)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def coat_mini(pretrained=False, **kwargs):
|
|
model_cfg = dict(
|
|
patch_size=4, embed_dims=[152, 216, 216, 216], serial_depths=[2, 2, 2, 2], parallel_depth=6,
|
|
num_heads=8, mlp_ratios=[4, 4, 4, 4], **kwargs)
|
|
model = _create_coat('coat_mini', pretrained=pretrained, **model_cfg)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def coat_lite_tiny(pretrained=False, **kwargs):
|
|
model_cfg = dict(
|
|
patch_size=4, embed_dims=[64, 128, 256, 320], serial_depths=[2, 2, 2, 2], parallel_depth=0,
|
|
num_heads=8, mlp_ratios=[8, 8, 4, 4], **kwargs)
|
|
model = _create_coat('coat_lite_tiny', pretrained=pretrained, **model_cfg)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def coat_lite_mini(pretrained=False, **kwargs):
|
|
model_cfg = dict(
|
|
patch_size=4, embed_dims=[64, 128, 320, 512], serial_depths=[2, 2, 2, 2], parallel_depth=0,
|
|
num_heads=8, mlp_ratios=[8, 8, 4, 4], **kwargs)
|
|
model = _create_coat('coat_lite_mini', pretrained=pretrained, **model_cfg)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def coat_lite_small(pretrained=False, **kwargs):
|
|
model_cfg = dict(
|
|
patch_size=4, embed_dims=[64, 128, 320, 512], serial_depths=[3, 4, 6, 3], parallel_depth=0,
|
|
num_heads=8, mlp_ratios=[8, 8, 4, 4], **kwargs)
|
|
model = _create_coat('coat_lite_small', pretrained=pretrained, **model_cfg)
|
|
return model |