Add MobileViT models (w/ ByobNet base). Close #1038.

pull/1014/head
Ross Wightman 3 years ago
parent 5f81d4de23
commit 58ba49c8ef

@ -24,6 +24,7 @@ from .inception_v4 import *
from .levit import * from .levit import *
from .mlp_mixer import * from .mlp_mixer import *
from .mobilenetv3 import * from .mobilenetv3 import *
from .mobilevit import *
from .nasnet import * from .nasnet import *
from .nest import * from .nest import *
from .nfnet import * from .nfnet import *

@ -0,0 +1,248 @@
""" MobileViT
Paper:
`MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer` - https://arxiv.org/abs/2110.02178
MobileVitBlock and checkpoints adapted from https://github.com/apple/ml-cvnets (original copyright below)
License: https://github.com/apple/ml-cvnets/blob/main/LICENSE (Apple open source)
Rest of code, ByobNet, and Transformer block hacked together by / Copyright 2022, Ross Wightman
"""
#
# For licensing see accompanying LICENSE file.
# Copyright (C) 2020 Apple Inc. All Rights Reserved.
#
import math
from typing import Union, Callable, Dict, Tuple, Optional
import torch
from torch import nn
import torch.nn.functional as F
from .byobnet import register_block, ByoBlockCfg, ByoModelCfg, ByobNet, LayerFn, num_groups
from .layers import to_2tuple, make_divisible
from .vision_transformer import Block as TransformerBlock
from .helpers import build_model_with_cfg
from .registry import register_model
__all__ = []
def _cfg(url='', **kwargs):
return {
'url': url, 'num_classes': 1000, 'input_size': (3, 256, 256), 'pool_size': (8, 8),
'crop_pct': 0.9, 'interpolation': 'bicubic',
'mean': (0, 0, 0), 'std': (1, 1, 1),
'first_conv': 'stem.conv', 'classifier': 'head.fc',
'fixed_input_size': False, 'min_input_size': (3, 256, 256),
**kwargs
}
default_cfgs = {
# GPU-Efficient (ResNet) weights
'mobilevit_xxs': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-mvit-weights/mobilevit_xxs-ad385b40.pth'),
'mobilevit_xs': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-mvit-weights/mobilevit_xs-8fbd6366.pth'),
'mobilevit_s': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-mvit-weights/mobilevit_s-38a5a959.pth'),
}
def _inverted_residual_block(d, c, s, br=4.0):
# inverted residual is a bottleneck block with bottle_ratio > 1 applied to in_chs, linear output, gs=1 (depthwise)
return ByoBlockCfg(
type='bottle', d=d, c=c, s=s, gs=1, br=br,
block_kwargs=dict(bottle_in=True, linear_out=True))
def _mobilevit_block(d, c, s, transformer_dim, transformer_depth, patch_size=4, br=4.0):
# inverted residual + mobilevit blocks as per MobileViT network
return (
_inverted_residual_block(d=d, c=c, s=s, br=br),
ByoBlockCfg(
type='mobilevit', d=1, c=c, s=1,
block_kwargs=dict(
transformer_dim=transformer_dim,
transformer_depth=transformer_depth,
patch_size=patch_size)
)
)
model_cfgs = dict(
mobilevit_xxs=ByoModelCfg(
blocks=(
_inverted_residual_block(d=1, c=16, s=1, br=2.0),
_inverted_residual_block(d=3, c=24, s=2, br=2.0),
_mobilevit_block(d=1, c=48, s=2, transformer_dim=64, transformer_depth=2, patch_size=2, br=2.0),
_mobilevit_block(d=1, c=64, s=2, transformer_dim=80, transformer_depth=4, patch_size=2, br=2.0),
_mobilevit_block(d=1, c=80, s=2, transformer_dim=96, transformer_depth=3, patch_size=2, br=2.0),
),
stem_chs=16,
stem_type='3x3',
stem_pool='',
downsample='',
act_layer='silu',
num_features=320,
),
mobilevit_xs=ByoModelCfg(
blocks=(
_inverted_residual_block(d=1, c=32, s=1),
_inverted_residual_block(d=3, c=48, s=2),
_mobilevit_block(d=1, c=64, s=2, transformer_dim=96, transformer_depth=2, patch_size=2),
_mobilevit_block(d=1, c=80, s=2, transformer_dim=120, transformer_depth=4, patch_size=2),
_mobilevit_block(d=1, c=96, s=2, transformer_dim=144, transformer_depth=3, patch_size=2),
),
stem_chs=16,
stem_type='3x3',
stem_pool='',
downsample='',
act_layer='silu',
num_features=384,
),
mobilevit_s=ByoModelCfg(
blocks=(
_inverted_residual_block(d=1, c=32, s=1),
_inverted_residual_block(d=3, c=64, s=2),
_mobilevit_block(d=1, c=96, s=2, transformer_dim=144, transformer_depth=2, patch_size=2),
_mobilevit_block(d=1, c=128, s=2, transformer_dim=192, transformer_depth=4, patch_size=2),
_mobilevit_block(d=1, c=160, s=2, transformer_dim=240, transformer_depth=3, patch_size=2),
),
stem_chs=16,
stem_type='3x3',
stem_pool='',
downsample='',
act_layer='silu',
num_features=640,
),
)
class MobileViTBlock(nn.Module):
""" MobileViT block
Paper: https://arxiv.org/abs/2110.02178?context=cs.LG
"""
def __init__(
self,
in_chs: int,
out_chs: Optional[int] = None,
kernel_size: int = 3,
stride: int = 1,
bottle_ratio: float = 1.0,
group_size: Optional[int] = None,
dilation: Tuple[int, int] = (1, 1),
mlp_ratio: float = 2.0,
transformer_dim: Optional[int] = None,
transformer_depth: int = 2,
patch_size: int = 8,
num_heads: int = 4,
attn_drop: float = 0.,
drop: int = 0.,
no_fusion: bool = False,
drop_path_rate: float = 0.,
layers: LayerFn = None,
transformer_norm_layer: Callable = nn.LayerNorm,
downsample: str = ''
):
super(MobileViTBlock, self).__init__()
layers = layers or LayerFn()
groups = num_groups(group_size, in_chs)
out_chs = out_chs or in_chs
transformer_dim = transformer_dim or make_divisible(bottle_ratio * in_chs)
self.conv_kxk = layers.conv_norm_act(
in_chs, in_chs, kernel_size=kernel_size,
stride=stride, groups=groups, dilation=dilation[0])
self.conv_1x1 = nn.Conv2d(in_chs, transformer_dim, kernel_size=1, bias=False)
self.transformer = nn.Sequential(*[
TransformerBlock(
transformer_dim, mlp_ratio=mlp_ratio, num_heads=num_heads, qkv_bias=True,
attn_drop=attn_drop, drop=drop, drop_path=drop_path_rate,
act_layer=layers.act, norm_layer=transformer_norm_layer)
for _ in range(transformer_depth)
])
self.norm = transformer_norm_layer(transformer_dim)
self.conv_proj = layers.conv_norm_act(transformer_dim, out_chs, kernel_size=1, stride=1)
if no_fusion:
self.conv_fusion = None
else:
self.conv_fusion = layers.conv_norm_act(in_chs + out_chs, out_chs, kernel_size=kernel_size, stride=1)
self.patch_size = to_2tuple(patch_size)
self.patch_area = self.patch_size[0] * self.patch_size[1]
def forward(self, x: torch.Tensor) -> torch.Tensor:
shortcut = x
# Local representation
x = self.conv_kxk(x)
x = self.conv_1x1(x)
# Unfold (feature map -> patches)
patch_h, patch_w = self.patch_size
B, C, H, W = x.shape
new_h, new_w = int(math.ceil(H / patch_h) * patch_h), int(math.ceil(W / patch_w) * patch_w)
num_patch_h, num_patch_w = new_h // patch_h, new_w // patch_w # n_h, n_w
num_patches = num_patch_h * num_patch_w # N
interpolate = False
if new_h != H or new_w != W:
# Note: Padding can be done, but then it needs to be handled in attention function.
x = F.interpolate(x, size=(new_h, new_w), mode="bilinear", align_corners=False)
interpolate = True
# [B, C, H, W] --> [B * C * n_h, n_w, p_h, p_w]
x = x.reshape(B * C * num_patch_h, patch_h, num_patch_w, patch_w).transpose(1, 2)
# [B * C * n_h, n_w, p_h, p_w] --> [BP, N, C] where P = p_h * p_w and N = n_h * n_w
x = x.reshape(B, C, num_patches, self.patch_area).transpose(1, 3).reshape(B * self.patch_area, num_patches, -1)
# Global representations
x = self.transformer(x)
x = self.norm(x)
# Fold (patch -> feature map)
# [B, P, N, C] --> [B*C*n_h, n_w, p_h, p_w]
x = x.contiguous().view(B, self.patch_area, num_patches, -1)
x = x.transpose(1, 3).reshape(B * C * num_patch_h, num_patch_w, patch_h, patch_w)
# [B*C*n_h, n_w, p_h, p_w] --> [B*C*n_h, p_h, n_w, p_w] --> [B, C, H, W]
x = x.transpose(1, 2).reshape(B, C, num_patch_h * patch_h, num_patch_w * patch_w)
if interpolate:
x = F.interpolate(x, size=(H, W), mode="bilinear", align_corners=False)
x = self.conv_proj(x)
if self.conv_fusion is not None:
x = self.conv_fusion(torch.cat((shortcut, x), dim=1))
return x
register_block('mobilevit', MobileViTBlock)
def _create_mobilevit(variant, cfg_variant=None, pretrained=False, **kwargs):
return build_model_with_cfg(
ByobNet, variant, pretrained,
model_cfg=model_cfgs[variant] if not cfg_variant else model_cfgs[cfg_variant],
feature_cfg=dict(flatten_sequential=True),
**kwargs)
@register_model
def mobilevit_xxs(pretrained=False, **kwargs):
return _create_mobilevit('mobilevit_xxs', pretrained=pretrained, **kwargs)
@register_model
def mobilevit_xs(pretrained=False, **kwargs):
return _create_mobilevit('mobilevit_xs', pretrained=pretrained, **kwargs)
@register_model
def mobilevit_s(pretrained=False, **kwargs):
return _create_mobilevit('mobilevit_s', pretrained=pretrained, **kwargs)
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