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

# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.

# Modified from
# https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
# Copyright 2020 Ross Wightman, Apache-2.0 License
import itertools

import torch

from timm.data import IMAGENET_DEFAULT_STD, IMAGENET_DEFAULT_MEAN
from .vision_transformer import trunc_normal_
from .registry import register_model


def _cfg(url='', **kwargs):
    return {
        'url': url,
        'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
        'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True,
        'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
        'first_conv': 'patch_embed.proj', 'classifier': 'head',
        **kwargs
    }


specification = {
    'levit_128s': {
        'C': '128_256_384', 'D': 16, 'N': '4_6_8', 'X': '2_3_4', 'drop_path': 0,
        'weights': 'https://dl.fbaipublicfiles.com/LeViT/LeViT-128S-96703c44.pth'},
    'levit_128': {
        'C': '128_256_384', 'D': 16, 'N': '4_8_12', 'X': '4_4_4', 'drop_path': 0,
        'weights': 'https://dl.fbaipublicfiles.com/LeViT/LeViT-128-b88c2750.pth'},
    'levit_192': {
        'C': '192_288_384', 'D': 32, 'N': '3_5_6', 'X': '4_4_4', 'drop_path': 0,
        'weights': 'https://dl.fbaipublicfiles.com/LeViT/LeViT-192-92712e41.pth'},
    'levit_256': {
        'C': '256_384_512', 'D': 32, 'N': '4_6_8', 'X': '4_4_4', 'drop_path': 0,
        'weights': 'https://dl.fbaipublicfiles.com/LeViT/LeViT-256-13b5763e.pth'},
    'levit_384': {
        'C': '384_512_768', 'D': 32, 'N': '6_9_12', 'X': '4_4_4', 'drop_path': 0.1,
        'weights': 'https://dl.fbaipublicfiles.com/LeViT/LeViT-384-9bdaf2e2.pth'},
}

__all__ = ['Levit']


@register_model
def levit_128s(num_classes=1000, distillation=True, pretrained=False, fuse=False, **kwargs):
    return model_factory(**specification['levit_128s'], num_classes=num_classes,
                         distillation=distillation, pretrained=pretrained, fuse=fuse)


@register_model
def levit_128(num_classes=1000, distillation=True, pretrained=False, fuse=False, **kwargs):
    return model_factory(**specification['levit_128'], num_classes=num_classes,
                         distillation=distillation, pretrained=pretrained, fuse=fuse)


@register_model
def levit_192(num_classes=1000, distillation=True, pretrained=False, fuse=False, **kwargs):
    return model_factory(**specification['levit_192'], num_classes=num_classes,
                         distillation=distillation, pretrained=pretrained, fuse=fuse)


@register_model
def levit_256(num_classes=1000, distillation=True, pretrained=False, fuse=False, **kwargs):
    return model_factory(**specification['levit_256'], num_classes=num_classes,
                         distillation=distillation, pretrained=pretrained, fuse=fuse)


@register_model
def levit_384(num_classes=1000, distillation=True, pretrained=False, fuse=False, **kwargs):
    return model_factory(**specification['levit_384'], num_classes=num_classes,
                         distillation=distillation, pretrained=pretrained, fuse=fuse)


class ConvNorm(torch.nn.Sequential):
    def __init__(
            self, a, b, ks=1, stride=1, pad=0, dilation=1, groups=1, bn_weight_init=1, resolution=-10000):
        super().__init__()
        self.add_module('c', torch.nn.Conv2d(a, b, ks, stride, pad, dilation, groups, bias=False))
        bn = torch.nn.BatchNorm2d(b)
        torch.nn.init.constant_(bn.weight, bn_weight_init)
        torch.nn.init.constant_(bn.bias, 0)
        self.add_module('bn', bn)

    @torch.no_grad()
    def fuse(self):
        c, bn = self._modules.values()
        w = bn.weight / (bn.running_var + bn.eps) ** 0.5
        w = c.weight * w[:, None, None, None]
        b = bn.bias - bn.running_mean * bn.weight / (bn.running_var + bn.eps) ** 0.5
        m = torch.nn.Conv2d(
            w.size(1), w.size(0), w.shape[2:], stride=self.c.stride,
            padding=self.c.padding, dilation=self.c.dilation, groups=self.c.groups)
        m.weight.data.copy_(w)
        m.bias.data.copy_(b)
        return m


class LinearNorm(torch.nn.Sequential):
    def __init__(self, a, b, bn_weight_init=1, resolution=-100000):
        super().__init__()
        self.add_module('c', torch.nn.Linear(a, b, bias=False))
        bn = torch.nn.BatchNorm1d(b)
        torch.nn.init.constant_(bn.weight, bn_weight_init)
        torch.nn.init.constant_(bn.bias, 0)
        self.add_module('bn', bn)

    @torch.no_grad()
    def fuse(self):
        l, bn = self._modules.values()
        w = bn.weight / (bn.running_var + bn.eps) ** 0.5
        w = l.weight * w[:, None]
        b = bn.bias - bn.running_mean * bn.weight / (bn.running_var + bn.eps) ** 0.5
        m = torch.nn.Linear(w.size(1), w.size(0))
        m.weight.data.copy_(w)
        m.bias.data.copy_(b)
        return m

    def forward(self, x):
        l, bn = self._modules.values()
        x = l(x)
        return bn(x.flatten(0, 1)).reshape_as(x)


class NormLinear(torch.nn.Sequential):
    def __init__(self, a, b, bias=True, std=0.02):
        super().__init__()
        self.add_module('bn', torch.nn.BatchNorm1d(a))
        l = torch.nn.Linear(a, b, bias=bias)
        trunc_normal_(l.weight, std=std)
        if bias:
            torch.nn.init.constant_(l.bias, 0)
        self.add_module('l', l)

    @torch.no_grad()
    def fuse(self):
        bn, l = self._modules.values()
        w = bn.weight / (bn.running_var + bn.eps) ** 0.5
        b = bn.bias - self.bn.running_mean * self.bn.weight / (bn.running_var + bn.eps) ** 0.5
        w = l.weight * w[None, :]
        if l.bias is None:
            b = b @ self.l.weight.T
        else:
            b = (l.weight @ b[:, None]).view(-1) + self.l.bias
        m = torch.nn.Linear(w.size(1), w.size(0))
        m.weight.data.copy_(w)
        m.bias.data.copy_(b)
        return m


def b16(n, activation, resolution=224):
    return torch.nn.Sequential(
        ConvNorm(3, n // 8, 3, 2, 1, resolution=resolution),
        activation(),
        ConvNorm(n // 8, n // 4, 3, 2, 1, resolution=resolution // 2),
        activation(),
        ConvNorm(n // 4, n // 2, 3, 2, 1, resolution=resolution // 4),
        activation(),
        ConvNorm(n // 2, n, 3, 2, 1, resolution=resolution // 8))


class Residual(torch.nn.Module):
    def __init__(self, m, drop):
        super().__init__()
        self.m = m
        self.drop = drop

    def forward(self, x):
        if self.training and self.drop > 0:
            return x + self.m(x) * torch.rand(
                x.size(0), 1, 1, device=x.device).ge_(self.drop).div(1 - self.drop).detach()
        else:
            return x + self.m(x)


class Attention(torch.nn.Module):
    def __init__(
            self, dim, key_dim, num_heads=8, attn_ratio=4, act_layer=None, resolution=14):
        super().__init__()
        self.num_heads = num_heads
        self.scale = key_dim ** -0.5
        self.key_dim = key_dim
        self.nh_kd = nh_kd = key_dim * num_heads
        self.d = int(attn_ratio * key_dim)
        self.dh = int(attn_ratio * key_dim) * num_heads
        self.attn_ratio = attn_ratio
        h = self.dh + nh_kd * 2
        self.qkv = LinearNorm(dim, h, resolution=resolution)
        self.proj = torch.nn.Sequential(
            act_layer(),
            LinearNorm(self.dh, dim, bn_weight_init=0, resolution=resolution))

        points = list(itertools.product(range(resolution), range(resolution)))
        N = len(points)
        attention_offsets = {}
        idxs = []
        for p1 in points:
            for p2 in points:
                offset = (abs(p1[0] - p2[0]), abs(p1[1] - p2[1]))
                if offset not in attention_offsets:
                    attention_offsets[offset] = len(attention_offsets)
                idxs.append(attention_offsets[offset])
        self.attention_biases = torch.nn.Parameter(torch.zeros(num_heads, len(attention_offsets)))
        self.register_buffer('attention_bias_idxs', torch.LongTensor(idxs).view(N, N))

    @torch.no_grad()
    def train(self, mode=True):
        super().train(mode)
        if mode and hasattr(self, 'ab'):
            del self.ab
        else:
            self.ab = self.attention_biases[:, self.attention_bias_idxs]

    def forward(self, x):  # x (B,N,C)
        B, N, C = x.shape
        qkv = self.qkv(x)
        q, k, v = qkv.view(B, N, self.num_heads, -1).split([self.key_dim, self.key_dim, self.d], dim=3)
        q = q.permute(0, 2, 1, 3)
        k = k.permute(0, 2, 1, 3)
        v = v.permute(0, 2, 1, 3)

        ab = self.attention_biases[:, self.attention_bias_idxs] if self.training else self.ab
        attn = q @ k.transpose(-2, -1) * self.scale + ab

        attn = attn.softmax(dim=-1)
        x = (attn @ v).transpose(1, 2).reshape(B, N, self.dh)
        x = self.proj(x)
        return x


class Subsample(torch.nn.Module):
    def __init__(self, stride, resolution):
        super().__init__()
        self.stride = stride
        self.resolution = resolution

    def forward(self, x):
        B, N, C = x.shape
        x = x.view(B, self.resolution, self.resolution, C)[:, ::self.stride, ::self.stride]
        return x.reshape(B, -1, C)


class AttentionSubsample(torch.nn.Module):
    def __init__(self, in_dim, out_dim, key_dim, num_heads=8,
                 attn_ratio=2, act_layer=None, stride=2, resolution=14, resolution_=7):
        super().__init__()
        self.num_heads = num_heads
        self.scale = key_dim ** -0.5
        self.key_dim = key_dim
        self.nh_kd = nh_kd = key_dim * num_heads
        self.d = int(attn_ratio * key_dim)
        self.dh = int(attn_ratio * key_dim) * self.num_heads
        self.attn_ratio = attn_ratio
        self.resolution_ = resolution_
        self.resolution_2 = resolution_ ** 2
        h = self.dh + nh_kd
        self.kv = LinearNorm(in_dim, h, resolution=resolution)

        self.q = torch.nn.Sequential(
            Subsample(stride, resolution),
            LinearNorm(in_dim, nh_kd, resolution=resolution_))
        self.proj = torch.nn.Sequential(
            act_layer(),
            LinearNorm(self.dh, out_dim, resolution=resolution_))

        self.stride = stride
        self.resolution = resolution
        points = list(itertools.product(range(resolution), range(resolution)))
        points_ = list(itertools.product(range(resolution_), range(resolution_)))
        N = len(points)
        N_ = len(points_)
        attention_offsets = {}
        idxs = []
        for p1 in points_:
            for p2 in points:
                size = 1
                offset = (
                    abs(p1[0] * stride - p2[0] + (size - 1) / 2),
                    abs(p1[1] * stride - p2[1] + (size - 1) / 2))
                if offset not in attention_offsets:
                    attention_offsets[offset] = len(attention_offsets)
                idxs.append(attention_offsets[offset])
        self.attention_biases = torch.nn.Parameter(torch.zeros(num_heads, len(attention_offsets)))
        self.register_buffer('attention_bias_idxs', torch.LongTensor(idxs).view(N_, N))


    @torch.no_grad()
    def train(self, mode=True):
        super().train(mode)
        if mode and hasattr(self, 'ab'):
            del self.ab
        else:
            self.ab = self.attention_biases[:, self.attention_bias_idxs]

    def forward(self, x):
        B, N, C = x.shape
        k, v = self.kv(x).view(B, N, self.num_heads, -1).split([self.key_dim, self.d], dim=3)
        k = k.permute(0, 2, 1, 3)  # BHNC
        v = v.permute(0, 2, 1, 3)  # BHNC
        q = self.q(x).view(B, self.resolution_2, self.num_heads, self.key_dim).permute(0, 2, 1, 3)

        ab = self.attention_biases[:, self.attention_bias_idxs] if self.training else self.ab
        attn = q @ k.transpose(-2, -1) * self.scale + ab
        attn = attn.softmax(dim=-1)

        x = (attn @ v).transpose(1, 2).reshape(B, -1, self.dh)
        x = self.proj(x)
        return x


class Levit(torch.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=[192],
            key_dim=[64],
            depth=[12],
            num_heads=[3],
            attn_ratio=[2],
            mlp_ratio=[2],
            hybrid_backbone=None,
            down_ops=[],
            attn_act_layer=torch.nn.Hardswish,
            mlp_act_layer=torch.nn.Hardswish,
            distillation=True,
            drop_path=0):
        super().__init__()
        global FLOPS_COUNTER

        self.num_classes = num_classes
        self.num_features = embed_dim[-1]
        self.embed_dim = embed_dim
        self.distillation = distillation

        self.patch_embed = hybrid_backbone

        self.blocks = []
        down_ops.append([''])
        resolution = img_size // patch_size
        for i, (ed, kd, dpth, nh, ar, mr, do) in enumerate(
                zip(embed_dim, key_dim, depth, num_heads, attn_ratio, mlp_ratio, down_ops)):
            for _ in range(dpth):
                self.blocks.append(
                    Residual(
                        Attention(ed, kd, nh, attn_ratio=ar, act_layer=attn_act_layer, resolution=resolution),
                        drop_path))
                if mr > 0:
                    h = int(ed * mr)
                    self.blocks.append(
                        Residual(torch.nn.Sequential(
                            LinearNorm(ed, h, resolution=resolution),
                            mlp_act_layer(),
                            LinearNorm(h, ed, bn_weight_init=0, resolution=resolution),
                        ), drop_path))
            if do[0] == 'Subsample':
                # ('Subsample',key_dim, num_heads, attn_ratio, mlp_ratio, stride)
                resolution_ = (resolution - 1) // do[5] + 1
                self.blocks.append(
                    AttentionSubsample(
                        *embed_dim[i:i + 2], key_dim=do[1], num_heads=do[2],
                        attn_ratio=do[3], act_layer=attn_act_layer, stride=do[5],
                        resolution=resolution, resolution_=resolution_))
                resolution = resolution_
                if do[4] > 0:  # mlp_ratio
                    h = int(embed_dim[i + 1] * do[4])
                    self.blocks.append(
                        Residual(torch.nn.Sequential(
                            LinearNorm(embed_dim[i + 1], h, resolution=resolution),
                            mlp_act_layer(),
                            LinearNorm(h, embed_dim[i + 1], bn_weight_init=0, resolution=resolution),
                        ), drop_path))
        self.blocks = torch.nn.Sequential(*self.blocks)

        # Classifier head
        self.head = NormLinear(embed_dim[-1], num_classes) if num_classes > 0 else torch.nn.Identity()
        if distillation:
            self.head_dist = NormLinear(embed_dim[-1], num_classes) if num_classes > 0 else torch.nn.Identity()
        else:
            self.head_dist = None

    @torch.jit.ignore
    def no_weight_decay(self):
        return {x for x in self.state_dict().keys() if 'attention_biases' in x}

    def forward(self, x):
        x = self.patch_embed(x)
        x = x.flatten(2).transpose(1, 2)
        x = self.blocks(x)
        x = x.mean(1)
        if self.distillation:
            x = self.head(x), self.head_dist(x)
            if not self.training:
                x = (x[0] + x[1]) / 2
        else:
            x = self.head(x)
        return x


def model_factory(C, D, X, N, drop_path, weights, num_classes, distillation, pretrained, fuse):
    embed_dim = [int(x) for x in C.split('_')]
    num_heads = [int(x) for x in N.split('_')]
    depth = [int(x) for x in X.split('_')]
    act = torch.nn.Hardswish
    model = Levit(
        patch_size=16,
        embed_dim=embed_dim,
        num_heads=num_heads,
        key_dim=[D] * 3,
        depth=depth,
        attn_ratio=[2, 2, 2],
        mlp_ratio=[2, 2, 2],
        down_ops=[
            # ('Subsample',key_dim, num_heads, attn_ratio, mlp_ratio, stride)
            ['Subsample', D, embed_dim[0] // D, 4, 2, 2],
            ['Subsample', D, embed_dim[1] // D, 4, 2, 2],
        ],
        attn_act_layer=act,
        mlp_act_layer=act,
        hybrid_backbone=b16(embed_dim[0], activation=act),
        num_classes=num_classes,
        drop_path=drop_path,
        distillation=distillation
    )
    model.default_cfg = _cfg()
    if pretrained:
        checkpoint = torch.hub.load_state_dict_from_url(weights, map_location='cpu')
        model.load_state_dict(checkpoint['model'])
    #if fuse:
    #    utils.replace_batchnorm(model)

    return model