pytorch-image-models/timm/models/mobilevit.py

""" 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 .fx_features import register_notrace_module
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,
        **kwargs
    }


default_cfgs = {
    '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'),
    'semobilevit_s': _cfg(),
}


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,
    ),

    semobilevit_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='',
        attn_layer='se',
        attn_kwargs=dict(rd_ratio=1/8),
        num_features=640,
    ),
)


@register_notrace_module
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 = math.ceil(H / patch_h) * patch_h, 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)


@register_model
def semobilevit_s(pretrained=False, **kwargs):
    return _create_mobilevit('semobilevit_s', pretrained=pretrained, **kwargs)