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370 lines
14 KiB
370 lines
14 KiB
""" ConViT Model
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@article{d2021convit,
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title={ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases},
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author={d'Ascoli, St{\'e}phane and Touvron, Hugo and Leavitt, Matthew and Morcos, Ari and Biroli, Giulio and Sagun, Levent},
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journal={arXiv preprint arXiv:2103.10697},
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year={2021}
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}
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Paper link: https://arxiv.org/abs/2103.10697
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Original code: https://github.com/facebookresearch/convit, original copyright below
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Modifications and additions for timm hacked together by / Copyright 2021, Ross Wightman
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"""
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# Copyright (c) 2015-present, Facebook, Inc.
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# All rights reserved.
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#
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# This source code is licensed under the CC-by-NC license found in the
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# LICENSE file in the root directory of this source tree.
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#
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'''These modules are adapted from those of timm, see
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https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
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'''
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from functools import partial
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import torch
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import torch.nn as nn
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from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
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from timm.layers import DropPath, trunc_normal_, PatchEmbed, Mlp
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from ._builder import build_model_with_cfg
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from ._features_fx import register_notrace_module
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from ._registry import register_model
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from .vision_transformer_hybrid import HybridEmbed
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__all__ = ['ConViT']
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def _cfg(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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'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, 'fixed_input_size': True,
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'first_conv': 'patch_embed.proj', 'classifier': 'head',
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**kwargs
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}
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default_cfgs = {
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# ConViT
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'convit_tiny': _cfg(
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url="https://dl.fbaipublicfiles.com/convit/convit_tiny.pth"),
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'convit_small': _cfg(
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url="https://dl.fbaipublicfiles.com/convit/convit_small.pth"),
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'convit_base': _cfg(
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url="https://dl.fbaipublicfiles.com/convit/convit_base.pth")
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}
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@register_notrace_module # reason: FX can't symbolically trace control flow in forward method
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class GPSA(nn.Module):
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def __init__(
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self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0., locality_strength=1.):
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super().__init__()
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self.num_heads = num_heads
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self.dim = dim
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head_dim = dim // num_heads
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self.scale = head_dim ** -0.5
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self.locality_strength = locality_strength
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self.qk = nn.Linear(dim, dim * 2, bias=qkv_bias)
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self.v = nn.Linear(dim, dim, 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.pos_proj = nn.Linear(3, num_heads)
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self.proj_drop = nn.Dropout(proj_drop)
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self.gating_param = nn.Parameter(torch.ones(self.num_heads))
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self.rel_indices: torch.Tensor = torch.zeros(1, 1, 1, 3) # silly torchscript hack, won't work with None
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def forward(self, x):
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B, N, C = x.shape
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if self.rel_indices is None or self.rel_indices.shape[1] != N:
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self.rel_indices = self.get_rel_indices(N)
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attn = self.get_attention(x)
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v = self.v(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
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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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def get_attention(self, x):
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B, N, C = x.shape
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qk = self.qk(x).reshape(B, N, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
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q, k = qk[0], qk[1]
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pos_score = self.rel_indices.expand(B, -1, -1, -1)
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pos_score = self.pos_proj(pos_score).permute(0, 3, 1, 2)
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patch_score = (q @ k.transpose(-2, -1)) * self.scale
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patch_score = patch_score.softmax(dim=-1)
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pos_score = pos_score.softmax(dim=-1)
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gating = self.gating_param.view(1, -1, 1, 1)
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attn = (1. - torch.sigmoid(gating)) * patch_score + torch.sigmoid(gating) * pos_score
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attn /= attn.sum(dim=-1).unsqueeze(-1)
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attn = self.attn_drop(attn)
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return attn
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def get_attention_map(self, x, return_map=False):
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attn_map = self.get_attention(x).mean(0) # average over batch
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distances = self.rel_indices.squeeze()[:, :, -1] ** .5
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dist = torch.einsum('nm,hnm->h', (distances, attn_map)) / distances.size(0)
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if return_map:
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return dist, attn_map
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else:
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return dist
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def local_init(self):
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self.v.weight.data.copy_(torch.eye(self.dim))
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locality_distance = 1 # max(1,1/locality_strength**.5)
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kernel_size = int(self.num_heads ** .5)
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center = (kernel_size - 1) / 2 if kernel_size % 2 == 0 else kernel_size // 2
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for h1 in range(kernel_size):
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for h2 in range(kernel_size):
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position = h1 + kernel_size * h2
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self.pos_proj.weight.data[position, 2] = -1
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self.pos_proj.weight.data[position, 1] = 2 * (h1 - center) * locality_distance
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self.pos_proj.weight.data[position, 0] = 2 * (h2 - center) * locality_distance
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self.pos_proj.weight.data *= self.locality_strength
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def get_rel_indices(self, num_patches: int) -> torch.Tensor:
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img_size = int(num_patches ** .5)
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rel_indices = torch.zeros(1, num_patches, num_patches, 3)
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ind = torch.arange(img_size).view(1, -1) - torch.arange(img_size).view(-1, 1)
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indx = ind.repeat(img_size, img_size)
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indy = ind.repeat_interleave(img_size, dim=0).repeat_interleave(img_size, dim=1)
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indd = indx ** 2 + indy ** 2
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rel_indices[:, :, :, 2] = indd.unsqueeze(0)
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rel_indices[:, :, :, 1] = indy.unsqueeze(0)
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rel_indices[:, :, :, 0] = indx.unsqueeze(0)
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device = self.qk.weight.device
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return rel_indices.to(device)
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class MHSA(nn.Module):
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def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., 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 = 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 get_attention_map(self, x, return_map=False):
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B, N, C = x.shape
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qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
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q, k, v = qkv[0], qkv[1], qkv[2]
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attn_map = (q @ k.transpose(-2, -1)) * self.scale
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attn_map = attn_map.softmax(dim=-1).mean(0)
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img_size = int(N ** .5)
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ind = torch.arange(img_size).view(1, -1) - torch.arange(img_size).view(-1, 1)
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indx = ind.repeat(img_size, img_size)
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indy = ind.repeat_interleave(img_size, dim=0).repeat_interleave(img_size, dim=1)
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indd = indx ** 2 + indy ** 2
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distances = indd ** .5
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distances = distances.to(x.device)
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dist = torch.einsum('nm,hnm->h', (distances, attn_map)) / N
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if return_map:
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return dist, attn_map
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else:
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return dist
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def forward(self, x):
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B, N, C = x.shape
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qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
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q, k, v = qkv.unbind(0)
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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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def __init__(
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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, use_gpsa=True, **kwargs):
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super().__init__()
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self.norm1 = norm_layer(dim)
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self.use_gpsa = use_gpsa
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if self.use_gpsa:
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self.attn = GPSA(
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dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop, **kwargs)
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else:
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self.attn = MHSA(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop)
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self.drop_path = DropPath(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(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
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def forward(self, x):
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x = x + self.drop_path(self.attn(self.norm1(x)))
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x = x + self.drop_path(self.mlp(self.norm2(x)))
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return x
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class ConViT(nn.Module):
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""" Vision Transformer with support for patch or hybrid CNN input stage
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"""
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def __init__(
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self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, global_pool='token',
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embed_dim=768, depth=12, num_heads=12, mlp_ratio=4., qkv_bias=False, drop_rate=0., attn_drop_rate=0.,
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drop_path_rate=0., hybrid_backbone=None, norm_layer=nn.LayerNorm,
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local_up_to_layer=3, locality_strength=1., use_pos_embed=True):
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super().__init__()
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assert global_pool in ('', 'avg', 'token')
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embed_dim *= num_heads
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self.num_classes = num_classes
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self.global_pool = global_pool
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self.local_up_to_layer = local_up_to_layer
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self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models
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self.locality_strength = locality_strength
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self.use_pos_embed = use_pos_embed
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if hybrid_backbone is not None:
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self.patch_embed = HybridEmbed(
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hybrid_backbone, img_size=img_size, in_chans=in_chans, embed_dim=embed_dim)
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else:
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self.patch_embed = PatchEmbed(
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img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
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num_patches = self.patch_embed.num_patches
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self.num_patches = num_patches
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self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
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self.pos_drop = nn.Dropout(p=drop_rate)
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if self.use_pos_embed:
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self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
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trunc_normal_(self.pos_embed, std=.02)
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dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
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self.blocks = nn.ModuleList([
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Block(
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dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias,
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drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer,
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use_gpsa=True,
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locality_strength=locality_strength)
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if i < local_up_to_layer else
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Block(
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dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias,
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drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer,
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use_gpsa=False)
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for i in range(depth)])
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self.norm = norm_layer(embed_dim)
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# Classifier head
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self.feature_info = [dict(num_chs=embed_dim, reduction=0, module='head')]
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self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()
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trunc_normal_(self.cls_token, std=.02)
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self.apply(self._init_weights)
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for n, m in self.named_modules():
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if hasattr(m, 'local_init'):
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m.local_init()
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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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@torch.jit.ignore
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def no_weight_decay(self):
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return {'pos_embed', 'cls_token'}
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@torch.jit.ignore
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def group_matcher(self, coarse=False):
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return dict(
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stem=r'^cls_token|pos_embed|patch_embed', # stem and embed
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blocks=[(r'^blocks\.(\d+)', None), (r'^norm', (99999,))]
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)
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@torch.jit.ignore
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def set_grad_checkpointing(self, enable=True):
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assert not enable, 'gradient checkpointing not supported'
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@torch.jit.ignore
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def get_classifier(self):
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return self.head
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def reset_classifier(self, num_classes, global_pool=None):
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self.num_classes = num_classes
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if global_pool is not None:
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assert global_pool in ('', 'token', 'avg')
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self.global_pool = global_pool
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self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
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def forward_features(self, x):
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x = self.patch_embed(x)
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if self.use_pos_embed:
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x = x + self.pos_embed
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x = self.pos_drop(x)
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cls_tokens = self.cls_token.expand(x.shape[0], -1, -1)
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for u, blk in enumerate(self.blocks):
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if u == self.local_up_to_layer:
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x = torch.cat((cls_tokens, x), dim=1)
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x = blk(x)
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x = self.norm(x)
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return x
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def forward_head(self, x, pre_logits: bool = False):
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if self.global_pool:
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x = x[:, 1:].mean(dim=1) if self.global_pool == 'avg' else x[:, 0]
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return x if pre_logits else self.head(x)
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def forward(self, x):
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x = self.forward_features(x)
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x = self.forward_head(x)
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return x
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def _create_convit(variant, pretrained=False, **kwargs):
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if kwargs.get('features_only', None):
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raise RuntimeError('features_only not implemented for Vision Transformer models.')
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return build_model_with_cfg(ConViT, variant, pretrained, **kwargs)
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@register_model
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def convit_tiny(pretrained=False, **kwargs):
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model_args = dict(
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local_up_to_layer=10, locality_strength=1.0, embed_dim=48,
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num_heads=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
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model = _create_convit(variant='convit_tiny', pretrained=pretrained, **model_args)
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return model
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@register_model
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def convit_small(pretrained=False, **kwargs):
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model_args = dict(
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local_up_to_layer=10, locality_strength=1.0, embed_dim=48,
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num_heads=9, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
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model = _create_convit(variant='convit_small', pretrained=pretrained, **model_args)
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return model
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@register_model
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def convit_base(pretrained=False, **kwargs):
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model_args = dict(
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local_up_to_layer=10, locality_strength=1.0, embed_dim=48,
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num_heads=16, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
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model = _create_convit(variant='convit_base', pretrained=pretrained, **model_args)
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return model
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