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ConViT cleanup, fix torchscript, bit of reformatting, reuse existing layers.

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pull/660/head
Ross Wightman 4 years ago
parent 306c86b668
commit b7de82e835
1 changed files with 98 additions and 192 deletions
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timm/models/convit.py
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@ -1,6 +1,24 @@
"""These modules are adapted from those of timm, see
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
""" ConViT Model
@article{d2021convit,
title={ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases},
author={d'Ascoli, St{\'e}phane and Touvron, Hugo and Leavitt, Matthew and Morcos, Ari and Biroli, Giulio and Sagun, Levent},
journal={arXiv preprint arXiv:2103.10697},
year={2021}
}
Paper link: https://arxiv.org/abs/2103.10697
Original code: https://github.com/facebookresearch/convit, original copyright below
"""
# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the CC-by-NC license found in the
# LICENSE file in the root directory of this source tree.
#
'''These modules are adapted from those of timm, see
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
'''
import torch
import torch.nn as nn
@ -9,8 +27,9 @@ import torch.nn.functional as F
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from .helpers import build_model_with_cfg
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from timm.models.registry import register_model
from .layers import DropPath, to_2tuple, trunc_normal_, PatchEmbed, Mlp
from .registry import register_model
from .vision_transformer_hybrid import HybridEmbed
import torch
import torch.nn as nn
@ -29,7 +48,7 @@ def _cfg(url='', **kwargs):
default_cfgs = {
# ConViT
'convit_tiny': _cfg(
url="https://dl.fbaipublicfiles.com/convit/convit_tiny.pth"),
url="https://dl.fbaipublicfiles.com/convit/convit_tiny.pth"),
'convit_small': _cfg(
url="https://dl.fbaipublicfiles.com/convit/convit_small.pth"),
'convit_base': _cfg(
@ -37,71 +56,31 @@ default_cfgs = {
}
class Mlp(nn.Module):
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = nn.Linear(in_features, hidden_features)
self.act = act_layer()
self.fc2 = nn.Linear(hidden_features, out_features)
self.drop = nn.Dropout(drop)
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)
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
x = self.drop(x)
return x
class GPSA(nn.Module):
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.,
locality_strength=1., use_local_init=True):
locality_strength=1.):
super().__init__()
self.num_heads = num_heads
self.dim = dim
head_dim = dim // num_heads
self.scale = qk_scale or head_dim ** -0.5
self.locality_strength = locality_strength
self.qk = nn.Linear(dim, dim * 2, bias=qkv_bias)
self.v = nn.Linear(dim, dim, bias=qkv_bias)
self.qk = nn.Linear(dim, dim * 2, bias=qkv_bias)
self.v = nn.Linear(dim, dim, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.pos_proj = nn.Linear(3, num_heads)
self.proj_drop = nn.Dropout(proj_drop)
self.locality_strength = locality_strength
self.gating_param = nn.Parameter(torch.ones(self.num_heads))
self.apply(self._init_weights)
if use_local_init:
self.local_init(locality_strength=locality_strength)
self.rel_indices: torch.Tensor = torch.zeros(1, 1, 1, 3) # silly torchscript hack, won't work with None
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)
def forward(self, x):
B, N, C = x.shape
if not hasattr(self, 'rel_indices') or self.rel_indices.size(1)!=N:
self.get_rel_indices(N)
if self.rel_indices is None or self.rel_indices.shape[1] != N:
self.rel_indices = self.get_rel_indices(N)
attn = self.get_attention(x)
v = self.v(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
@ -110,61 +89,58 @@ class GPSA(nn.Module):
return x
def get_attention(self, x):
B, N, C = x.shape
B, N, C = x.shape
qk = self.qk(x).reshape(B, N, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
q, k = qk[0], qk[1]
pos_score = self.rel_indices.expand(B, -1, -1,-1)
pos_score = self.pos_proj(pos_score).permute(0,3,1,2)
pos_score = self.rel_indices.expand(B, -1, -1, -1)
pos_score = self.pos_proj(pos_score).permute(0, 3, 1, 2)
patch_score = (q @ k.transpose(-2, -1)) * self.scale
patch_score = patch_score.softmax(dim=-1)
pos_score = pos_score.softmax(dim=-1)
gating = self.gating_param.view(1,-1,1,1)
attn = (1.-torch.sigmoid(gating)) * patch_score + torch.sigmoid(gating) * pos_score
gating = self.gating_param.view(1, -1, 1, 1)
attn = (1. - torch.sigmoid(gating)) * patch_score + torch.sigmoid(gating) * pos_score
attn /= attn.sum(dim=-1).unsqueeze(-1)
attn = self.attn_drop(attn)
return attn
def get_attention_map(self, x, return_map = False):
attn_map = self.get_attention(x).mean(0) # average over batch
distances = self.rel_indices.squeeze()[:,:,-1]**.5
dist = torch.einsum('nm,hnm->h', (distances, attn_map))
dist /= distances.size(0)
def get_attention_map(self, x, return_map=False):
attn_map = self.get_attention(x).mean(0) # average over batch
distances = self.rel_indices.squeeze()[:, :, -1] ** .5
dist = torch.einsum('nm,hnm->h', (distances, attn_map)) / distances.size(0)
if return_map:
return dist, attn_map
else:
return dist
def local_init(self, locality_strength=1.):
def local_init(self):
self.v.weight.data.copy_(torch.eye(self.dim))
locality_distance = 1 #max(1,1/locality_strength**.5)
kernel_size = int(self.num_heads**.5)
center = (kernel_size-1)/2 if kernel_size%2==0 else kernel_size//2
locality_distance = 1 # max(1,1/locality_strength**.5)
kernel_size = int(self.num_heads ** .5)
center = (kernel_size - 1) / 2 if kernel_size % 2 == 0 else kernel_size // 2
for h1 in range(kernel_size):
for h2 in range(kernel_size):
position = h1+kernel_size*h2
self.pos_proj.weight.data[position,2] = -1
self.pos_proj.weight.data[position,1] = 2*(h1-center)*locality_distance
self.pos_proj.weight.data[position,0] = 2*(h2-center)*locality_distance
self.pos_proj.weight.data *= locality_strength
def get_rel_indices(self, num_patches):
img_size = int(num_patches**.5)
rel_indices = torch.zeros(1, num_patches, num_patches, 3)
ind = torch.arange(img_size).view(1,-1) - torch.arange(img_size).view(-1, 1)
indx = ind.repeat(img_size,img_size)
indy = ind.repeat_interleave(img_size,dim=0).repeat_interleave(img_size,dim=1)
indd = indx**2 + indy**2
rel_indices[:,:,:,2] = indd.unsqueeze(0)
rel_indices[:,:,:,1] = indy.unsqueeze(0)
rel_indices[:,:,:,0] = indx.unsqueeze(0)
position = h1 + kernel_size * h2
self.pos_proj.weight.data[position, 2] = -1
self.pos_proj.weight.data[position, 1] = 2 * (h1 - center) * locality_distance
self.pos_proj.weight.data[position, 0] = 2 * (h2 - center) * locality_distance
self.pos_proj.weight.data *= self.locality_strength
def get_rel_indices(self, num_patches: int) -> torch.Tensor:
img_size = int(num_patches ** .5)
rel_indices = torch.zeros(1, num_patches, num_patches, 3)
ind = torch.arange(img_size).view(1, -1) - torch.arange(img_size).view(-1, 1)
indx = ind.repeat(img_size, img_size)
indy = ind.repeat_interleave(img_size, dim=0).repeat_interleave(img_size, dim=1)
indd = indx ** 2 + indy ** 2
rel_indices[:, :, :, 2] = indd.unsqueeze(0)
rel_indices[:, :, :, 1] = indy.unsqueeze(0)
rel_indices[:, :, :, 0] = indx.unsqueeze(0)
device = self.qk.weight.device
self.rel_indices = rel_indices.to(device)
return rel_indices.to(device)
class MHSA(nn.Module):
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
super().__init__()
@ -176,41 +152,28 @@ class MHSA(nn.Module):
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
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)
def get_attention_map(self, x, return_map = False):
def get_attention_map(self, x, return_map=False):
B, N, C = x.shape
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
q, k, v = qkv[0], qkv[1], qkv[2]
attn_map = (q @ k.transpose(-2, -1)) * self.scale
attn_map = attn_map.softmax(dim=-1).mean(0)
img_size = int(N**.5)
ind = torch.arange(img_size).view(1,-1) - torch.arange(img_size).view(-1, 1)
indx = ind.repeat(img_size,img_size)
indy = ind.repeat_interleave(img_size,dim=0).repeat_interleave(img_size,dim=1)
indd = indx**2 + indy**2
distances = indd**.5
img_size = int(N ** .5)
ind = torch.arange(img_size).view(1, -1) - torch.arange(img_size).view(-1, 1)
indx = ind.repeat(img_size, img_size)
indy = ind.repeat_interleave(img_size, dim=0).repeat_interleave(img_size, dim=1)
indd = indx ** 2 + indy ** 2
distances = indd ** .5
distances = distances.to('cuda')
dist = torch.einsum('nm,hnm->h', (distances, attn_map))
dist /= N
dist = torch.einsum('nm,hnm->h', (distances, attn_map)) / N
if return_map:
return dist, attn_map
else:
return dist
def forward(self, x):
B, N, C = x.shape
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
@ -228,15 +191,19 @@ class MHSA(nn.Module):
class Block(nn.Module):
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, use_gpsa=True, **kwargs):
super().__init__()
self.norm1 = norm_layer(dim)
self.use_gpsa = use_gpsa
if self.use_gpsa:
self.attn = GPSA(dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop, **kwargs)
self.attn = GPSA(
dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop,
proj_drop=drop, **kwargs)
else:
self.attn = MHSA(dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop, **kwargs)
self.attn = MHSA(
dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop,
proj_drop=drop, **kwargs)
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
@ -246,75 +213,12 @@ class Block(nn.Module):
x = x + self.drop_path(self.attn(self.norm1(x)))
x = x + self.drop_path(self.mlp(self.norm2(x)))
return x
class PatchEmbed(nn.Module):
""" Image to Patch Embedding, from timm
"""
def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
super().__init__()
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
self.img_size = img_size
self.patch_size = patch_size
self.num_patches = num_patches
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
self.apply(self._init_weights)
def forward(self, x):
B, C, H, W = x.shape
assert H == self.img_size[0] and W == self.img_size[1], \
f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
x = self.proj(x).flatten(2).transpose(1, 2)
return x
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)
class HybridEmbed(nn.Module):
""" CNN Feature Map Embedding, from timm
"""
def __init__(self, backbone, img_size=224, feature_size=None, in_chans=3, embed_dim=768):
super().__init__()
assert isinstance(backbone, nn.Module)
img_size = to_2tuple(img_size)
self.img_size = img_size
self.backbone = backbone
if feature_size is None:
with torch.no_grad():
training = backbone.training
if training:
backbone.eval()
o = self.backbone(torch.zeros(1, in_chans, img_size[0], img_size[1]))[-1]
feature_size = o.shape[-2:]
feature_dim = o.shape[1]
backbone.train(training)
else:
feature_size = to_2tuple(feature_size)
feature_dim = self.backbone.feature_info.channels()[-1]
self.num_patches = feature_size[0] * feature_size[1]
self.proj = nn.Linear(feature_dim, embed_dim)
self.apply(self._init_weights)
def forward(self, x):
x = self.backbone(x)[-1]
x = x.flatten(2).transpose(1, 2)
x = self.proj(x)
return x
class ConViT(nn.Module):
""" Vision Transformer with support for patch or hybrid CNN input stage
"""
def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,
num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0.,
drop_path_rate=0., hybrid_backbone=None, norm_layer=nn.LayerNorm, global_pool=None,
@ -335,7 +239,7 @@ class ConViT(nn.Module):
img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
num_patches = self.patch_embed.num_patches
self.num_patches = num_patches
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
self.pos_drop = nn.Dropout(p=drop_rate)
@ -350,7 +254,7 @@ class ConViT(nn.Module):
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer,
use_gpsa=True,
locality_strength=locality_strength)
if i<local_up_to_layer else
if i < local_up_to_layer else
Block(
dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer,
@ -363,7 +267,10 @@ class ConViT(nn.Module):
self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()
trunc_normal_(self.cls_token, std=.02)
self.head.apply(self._init_weights)
self.apply(self._init_weights)
for n, m in self.named_modules():
if hasattr(m, 'local_init'):
m.local_init()
def _init_weights(self, m):
if isinstance(m, nn.Linear):
@ -395,8 +302,8 @@ class ConViT(nn.Module):
x = x + self.pos_embed
x = self.pos_drop(x)
for u,blk in enumerate(self.blocks):
if u == self.local_up_to_layer :
for u, blk in enumerate(self.blocks):
if u == self.local_up_to_layer:
x = torch.cat((cls_tokens, x), dim=1)
x = blk(x)
@ -415,30 +322,29 @@ def _create_convit(variant, pretrained=False, **kwargs):
default_cfg=default_cfgs[variant],
**kwargs)
@register_model
def convit_tiny(pretrained=False, **kwargs):
model_args = dict(
local_up_to_layer=10, locality_strength=1.0, embed_dim=48,
local_up_to_layer=10, locality_strength=1.0, embed_dim=48,
num_heads=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
model = _create_convit(
variant='convit_tiny', pretrained=pretrained, **model_args)
model = _create_convit(variant='convit_tiny', pretrained=pretrained, **model_args)
return model
@register_model
def convit_small(pretrained=False, **kwargs):
model_args = dict(
local_up_to_layer=10, locality_strength=1.0, embed_dim=48,
local_up_to_layer=10, locality_strength=1.0, embed_dim=48,
num_heads=9, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
model = _create_convit(
variant='convit_small', pretrained=pretrained, **model_args)
model = _create_convit(variant='convit_small', pretrained=pretrained, **model_args)
return model
@register_model
def convit_base(pretrained=False, **kwargs):
model_args = dict(
local_up_to_layer=10, locality_strength=1.0, embed_dim=48,
local_up_to_layer=10, locality_strength=1.0, embed_dim=48,
num_heads=16, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
model = _create_convit(
variant='convit_base', pretrained=pretrained, **model_args)
model = _create_convit(variant='convit_base', pretrained=pretrained, **model_args)
return model

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