You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
pytorch-image-models/timm/models/efficientformer.py

552 lines
18 KiB

""" EfficientFormer
@article{li2022efficientformer,
title={EfficientFormer: Vision Transformers at MobileNet Speed},
author={Li, Yanyu and Yuan, Geng and Wen, Yang and Hu, Eric and Evangelidis, Georgios and Tulyakov,
Sergey and Wang, Yanzhi and Ren, Jian},
journal={arXiv preprint arXiv:2206.01191},
year={2022}
}
Based on Apache 2.0 licensed code at https://github.com/snap-research/EfficientFormer, Copyright (c) 2022 Snap Inc.
Modifications and timm support by / Copyright 2022, Ross Wightman
"""
from typing import Dict
import torch
import torch.nn as nn
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from .helpers import build_model_with_cfg
from .layers import DropPath, trunc_normal_, to_2tuple, Mlp
from .registry import register_model
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None, 'fixed_input_size': True,
'crop_pct': .95, 'interpolation': 'bicubic',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'stem.conv1', 'classifier': 'head',
**kwargs
}
default_cfgs = dict(
efficientformer_l1=_cfg(
url="https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-morevit/efficientformer_l1_1000d_224-5b08fab0.pth",
),
efficientformer_l3=_cfg(
url="https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-morevit/efficientformer_l3_300d_224-6816624f.pth",
),
efficientformer_l7=_cfg(
url="https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-morevit/efficientformer_l7_300d_224-e957ab75.pth",
),
)
EfficientFormer_width = {
'l1': (48, 96, 224, 448),
'l3': (64, 128, 320, 512),
'l7': (96, 192, 384, 768),
}
EfficientFormer_depth = {
'l1': (3, 2, 6, 4),
'l3': (4, 4, 12, 6),
'l7': (6, 6, 18, 8),
}
class Attention(torch.nn.Module):
attention_bias_cache: Dict[str, torch.Tensor]
def __init__(
self,
dim=384,
key_dim=32,
num_heads=8,
attn_ratio=4,
resolution=7
):
super().__init__()
self.num_heads = num_heads
self.scale = key_dim ** -0.5
self.key_dim = key_dim
self.key_attn_dim = key_dim * num_heads
self.val_dim = int(attn_ratio * key_dim)
self.val_attn_dim = self.val_dim * num_heads
self.attn_ratio = attn_ratio
self.qkv = nn.Linear(dim, self.key_attn_dim * 2 + self.val_attn_dim)
self.proj = nn.Linear(self.val_attn_dim, dim)
resolution = to_2tuple(resolution)
pos = torch.stack(torch.meshgrid(torch.arange(resolution[0]), torch.arange(resolution[1]))).flatten(1)
rel_pos = (pos[..., :, None] - pos[..., None, :]).abs()
rel_pos = (rel_pos[0] * resolution[1]) + rel_pos[1]
self.attention_biases = torch.nn.Parameter(torch.zeros(num_heads, resolution[0] * resolution[1]))
self.register_buffer('attention_bias_idxs', torch.LongTensor(rel_pos))
self.attention_bias_cache = {} # per-device attention_biases cache (data-parallel compat)
@torch.no_grad()
def train(self, mode=True):
super().train(mode)
if mode and self.attention_bias_cache:
self.attention_bias_cache = {} # clear ab cache
def get_attention_biases(self, device: torch.device) -> torch.Tensor:
if self.training:
return self.attention_biases[:, self.attention_bias_idxs]
else:
device_key = str(device)
if device_key not in self.attention_bias_cache:
self.attention_bias_cache[device_key] = self.attention_biases[:, self.attention_bias_idxs]
return self.attention_bias_cache[device_key]
def forward(self, x): # x (B,N,C)
B, N, C = x.shape
qkv = self.qkv(x)
qkv = qkv.reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3)
q, k, v = qkv.split([self.key_dim, self.key_dim, self.val_dim], dim=3)
attn = (q @ k.transpose(-2, -1)) * self.scale
attn = attn + self.get_attention_biases(x.device)
attn = attn.softmax(dim=-1)
x = (attn @ v).transpose(1, 2).reshape(B, N, self.val_attn_dim)
x = self.proj(x)
return x
class Stem4(nn.Sequential):
def __init__(self, in_chs, out_chs, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d):
super().__init__()
self.stride = 4
self.add_module('conv1', nn.Conv2d(in_chs, out_chs // 2, kernel_size=3, stride=2, padding=1))
self.add_module('norm1', norm_layer(out_chs // 2))
self.add_module('act1', act_layer())
self.add_module('conv2', nn.Conv2d(out_chs // 2, out_chs, kernel_size=3, stride=2, padding=1))
self.add_module('norm2', norm_layer(out_chs))
self.add_module('act2', act_layer())
class Downsample(nn.Module):
"""
Downsampling via strided conv w/ norm
Input: tensor in shape [B, C, H, W]
Output: tensor in shape [B, C, H/stride, W/stride]
"""
def __init__(self, in_chs, out_chs, kernel_size=3, stride=2, padding=None, norm_layer=nn.BatchNorm2d):
super().__init__()
if padding is None:
padding = kernel_size // 2
self.conv = nn.Conv2d(in_chs, out_chs, kernel_size=kernel_size, stride=stride, padding=padding)
self.norm = norm_layer(out_chs)
def forward(self, x):
x = self.conv(x)
x = self.norm(x)
return x
class Flat(nn.Module):
def __init__(self, ):
super().__init__()
def forward(self, x):
x = x.flatten(2).transpose(1, 2)
return x
class Pooling(nn.Module):
"""
Implementation of pooling for PoolFormer
--pool_size: pooling size
"""
def __init__(self, pool_size=3):
super().__init__()
self.pool = nn.AvgPool2d(pool_size, stride=1, padding=pool_size // 2, count_include_pad=False)
def forward(self, x):
return self.pool(x) - x
class ConvMlpWithNorm(nn.Module):
"""
Implementation of MLP with 1*1 convolutions.
Input: tensor with shape [B, C, H, W]
"""
def __init__(
self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.GELU,
norm_layer=nn.BatchNorm2d,
drop=0.
):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = nn.Conv2d(in_features, hidden_features, 1)
self.norm1 = norm_layer(hidden_features) if norm_layer is not None else nn.Identity()
self.act = act_layer()
self.fc2 = nn.Conv2d(hidden_features, out_features, 1)
self.norm2 = norm_layer(out_features) if norm_layer is not None else nn.Identity()
self.drop = nn.Dropout(drop)
def forward(self, x):
x = self.fc1(x)
x = self.norm1(x)
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
x = self.norm2(x)
x = self.drop(x)
return x
class LayerScale(nn.Module):
def __init__(self, dim, init_values=1e-5, inplace=False):
super().__init__()
self.inplace = inplace
self.gamma = nn.Parameter(init_values * torch.ones(dim))
def forward(self, x):
return x.mul_(self.gamma) if self.inplace else x * self.gamma
class MetaBlock1d(nn.Module):
def __init__(
self,
dim,
mlp_ratio=4.,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm,
drop=0.,
drop_path=0.,
layer_scale_init_value=1e-5
):
super().__init__()
self.norm1 = norm_layer(dim)
self.token_mixer = Attention(dim)
self.norm2 = norm_layer(dim)
self.mlp = Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer, drop=drop)
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.ls1 = LayerScale(dim, layer_scale_init_value)
self.ls2 = LayerScale(dim, layer_scale_init_value)
def forward(self, x):
x = x + self.drop_path(self.ls1(self.token_mixer(self.norm1(x))))
x = x + self.drop_path(self.ls2(self.mlp(self.norm2(x))))
return x
class LayerScale2d(nn.Module):
def __init__(self, dim, init_values=1e-5, inplace=False):
super().__init__()
self.inplace = inplace
self.gamma = nn.Parameter(init_values * torch.ones(dim))
def forward(self, x):
gamma = self.gamma.view(1, -1, 1, 1)
return x.mul_(gamma) if self.inplace else x * gamma
class MetaBlock2d(nn.Module):
def __init__(
self,
dim,
pool_size=3,
mlp_ratio=4.,
act_layer=nn.GELU,
norm_layer=nn.BatchNorm2d,
drop=0.,
drop_path=0.,
layer_scale_init_value=1e-5
):
super().__init__()
self.token_mixer = Pooling(pool_size=pool_size)
self.mlp = ConvMlpWithNorm(
dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer, norm_layer=norm_layer, drop=drop)
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.ls1 = LayerScale2d(dim, layer_scale_init_value)
self.ls2 = LayerScale2d(dim, layer_scale_init_value)
def forward(self, x):
x = x + self.drop_path(self.ls1(self.token_mixer(x)))
x = x + self.drop_path(self.ls2(self.mlp(x)))
return x
class EfficientFormerStage(nn.Module):
def __init__(
self,
dim,
dim_out,
depth,
downsample=True,
num_vit=1,
pool_size=3,
mlp_ratio=4.,
act_layer=nn.GELU,
norm_layer=nn.BatchNorm2d,
norm_layer_cl=nn.LayerNorm,
drop=.0,
drop_path=0.,
layer_scale_init_value=1e-5,
):
super().__init__()
self.grad_checkpointing = False
if downsample:
self.downsample = Downsample(in_chs=dim, out_chs=dim_out, norm_layer=norm_layer)
dim = dim_out
else:
assert dim == dim_out
self.downsample = nn.Identity()
blocks = []
if num_vit and num_vit >= depth:
blocks.append(Flat())
for block_idx in range(depth):
remain_idx = depth - block_idx - 1
if num_vit and num_vit > remain_idx:
blocks.append(
MetaBlock1d(
dim,
mlp_ratio=mlp_ratio,
act_layer=act_layer,
norm_layer=norm_layer_cl,
drop=drop,
drop_path=drop_path[block_idx],
layer_scale_init_value=layer_scale_init_value,
))
else:
blocks.append(
MetaBlock2d(
dim,
pool_size=pool_size,
mlp_ratio=mlp_ratio,
act_layer=act_layer,
norm_layer=norm_layer,
drop=drop,
drop_path=drop_path[block_idx],
layer_scale_init_value=layer_scale_init_value,
))
if num_vit and num_vit == remain_idx:
blocks.append(Flat())
self.blocks = nn.Sequential(*blocks)
def forward(self, x):
x = self.downsample(x)
x = self.blocks(x)
return x
class EfficientFormer(nn.Module):
def __init__(
self,
depths,
embed_dims=None,
in_chans=3,
num_classes=1000,
global_pool='avg',
downsamples=None,
num_vit=0,
mlp_ratios=4,
pool_size=3,
layer_scale_init_value=1e-5,
act_layer=nn.GELU,
norm_layer=nn.BatchNorm2d,
norm_layer_cl=nn.LayerNorm,
drop_rate=0.,
drop_path_rate=0.,
**kwargs
):
super().__init__()
self.num_classes = num_classes
self.global_pool = global_pool
self.stem = Stem4(in_chans, embed_dims[0], norm_layer=norm_layer)
prev_dim = embed_dims[0]
# stochastic depth decay rule
dpr = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(depths)).split(depths)]
downsamples = downsamples or (False,) + (True,) * (len(depths) - 1)
stages = []
for i in range(len(depths)):
stage = EfficientFormerStage(
prev_dim,
embed_dims[i],
depths[i],
downsample=downsamples[i],
num_vit=num_vit if i == 3 else 0,
pool_size=pool_size,
mlp_ratio=mlp_ratios,
act_layer=act_layer,
norm_layer_cl=norm_layer_cl,
norm_layer=norm_layer,
drop=drop_rate,
drop_path=dpr[i],
layer_scale_init_value=layer_scale_init_value,
)
prev_dim = embed_dims[i]
stages.append(stage)
self.stages = nn.Sequential(*stages)
# Classifier head
self.num_features = embed_dims[-1]
self.norm = norm_layer_cl(self.num_features)
self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
# assuming model is always distilled (valid for current checkpoints, will split def if that changes)
self.head_dist = nn.Linear(embed_dims[-1], num_classes) if num_classes > 0 else nn.Identity()
self.distilled_training = False # must set this True to train w/ distillation token
self.apply(self._init_weights)
# init for classification
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)
@torch.jit.ignore
def no_weight_decay(self):
return {k for k, _ in self.named_parameters() if 'attention_biases' in k}
@torch.jit.ignore
def group_matcher(self, coarse=False):
matcher = dict(
stem=r'^stem', # stem and embed
blocks=[(r'^stages\.(\d+)', None), (r'^norm', (99999,))]
)
return matcher
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
for s in self.stages:
s.grad_checkpointing = enable
@torch.jit.ignore
def get_classifier(self):
return self.head, self.head_dist
def reset_classifier(self, num_classes, global_pool=None):
self.num_classes = num_classes
if global_pool is not None:
self.global_pool = global_pool
self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
self.head_dist = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
@torch.jit.ignore
def set_distilled_training(self, enable=True):
self.distilled_training = enable
def forward_features(self, x):
x = self.stem(x)
x = self.stages(x)
x = self.norm(x)
return x
def forward_head(self, x, pre_logits: bool = False):
if self.global_pool == 'avg':
x = x.mean(dim=1)
if pre_logits:
return x
x, x_dist = self.head(x), self.head_dist(x)
if self.distilled_training and self.training and not torch.jit.is_scripting():
# only return separate classification predictions when training in distilled mode
return x, x_dist
else:
# during standard train/finetune, inference average the classifier predictions
return (x + x_dist) / 2
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def _checkpoint_filter_fn(state_dict, model):
""" Remap original checkpoints -> timm """
if 'stem.0.weight' in state_dict:
return state_dict # non-original checkpoint, no remapping needed
out_dict = {}
import re
stage_idx = 0
for k, v in state_dict.items():
if k.startswith('patch_embed'):
k = k.replace('patch_embed.0', 'stem.conv1')
k = k.replace('patch_embed.1', 'stem.norm1')
k = k.replace('patch_embed.3', 'stem.conv2')
k = k.replace('patch_embed.4', 'stem.norm2')
if re.match(r'network\.(\d+)\.proj\.weight', k):
stage_idx += 1
k = re.sub(r'network.(\d+).(\d+)', f'stages.{stage_idx}.blocks.\\2', k)
k = re.sub(r'network.(\d+).proj', f'stages.{stage_idx}.downsample.conv', k)
k = re.sub(r'network.(\d+).norm', f'stages.{stage_idx}.downsample.norm', k)
k = re.sub(r'layer_scale_([0-9])', r'ls\1.gamma', k)
k = k.replace('dist_head', 'head_dist')
out_dict[k] = v
return out_dict
def _create_efficientformer(variant, pretrained=False, **kwargs):
model = build_model_with_cfg(
EfficientFormer, variant, pretrained,
pretrained_filter_fn=_checkpoint_filter_fn,
**kwargs)
return model
@register_model
def efficientformer_l1(pretrained=False, **kwargs):
model_kwargs = dict(
depths=EfficientFormer_depth['l1'],
embed_dims=EfficientFormer_width['l1'],
num_vit=1,
**kwargs)
return _create_efficientformer('efficientformer_l1', pretrained=pretrained, **model_kwargs)
@register_model
def efficientformer_l3(pretrained=False, **kwargs):
model_kwargs = dict(
depths=EfficientFormer_depth['l3'],
embed_dims=EfficientFormer_width['l3'],
num_vit=4,
**kwargs)
return _create_efficientformer('efficientformer_l3', pretrained=pretrained, **model_kwargs)
@register_model
def efficientformer_l7(pretrained=False, **kwargs):
model_kwargs = dict(
depths=EfficientFormer_depth['l7'],
embed_dims=EfficientFormer_width['l7'],
num_vit=8,
**kwargs)
return _create_efficientformer('efficientformer_l7', pretrained=pretrained, **model_kwargs)