vision_transformer.py

# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
import torch
import torch.nn as nn
from functools import partial

from timm.models.vision_transformer import VisionTransformer, _cfg
from timm.models.registry import register_model
from timm.models.layers import trunc_normal_
IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225)
# from timm.data import ImageNET_DEFAULT_MEAN, ImageNET_DEFAULT_STD


__all__ = [
    'deit_tiny_patch16_224', 'deit_small_patch16_224', 'deit_base_patch16_224',
    'deit_tiny_distilled_patch16_224', 'deit_small_distilled_patch16_224',
    'deit_base_distilled_patch16_224', 'deit_base_patch16_384',
    'deit_base_distilled_patch16_384',
]

def _get_pos_embed(self, pos_embed, patch_embed, H, W):
    if H * W == self.patch_embed1.num_patches:
        return pos_embed
    else:
        return F.interpolate(
            pos_embed.reshape(1, patch_embed.H, patch_embed.W, -1).permute(0, 3, 1, 2),
            size=(H, W), mode="bilinear").reshape(1, -1, H * W).permute(0, 2, 1)


class TeacherVisionTransformer(VisionTransformer):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        num_patches = self.patch_embed.num_patches
        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, self.embed_dim))

        trunc_normal_(self.pos_embed, std=.02)

    def forward_features(self, x):
        # taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
        # with slight modifications to add the feature output
        index = 0
        B = x.shape[0]
        x = self.patch_embed(x)
        x_interfeature_list = []

        cls_tokens = self.cls_token.expand(B, -1, -1)  # stole cls_tokens impl from Phil Wang, thanks
        x = torch.cat((cls_tokens, x), dim=1)

        x = x + self.pos_embed
        x = self.pos_drop(x)

        for blk in self.blocks:
            x = blk(x)
            if (index == 3 or index == 7 or index == 11):
                x_interfeature_list.append(x)
            index += 1

        x = self.norm(x)
        B, N, c = x.shape

        num_blocks = len(x_interfeature_list)
        x_interfeature = torch.cat(x_interfeature_list, dim=0).view(num_blocks, B, N, c)
        return x[:, 0], x_interfeature

    def forward(self, x):
        x, x_interfeature = self.forward_features(x)
        x = self.head(x)
        return x, x_interfeature


class DistilledVisionTransformer(VisionTransformer):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.dist_token = nn.Parameter(torch.zeros(1, 1, self.embed_dim))
        num_patches = self.patch_embed.num_patches
        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 2, self.embed_dim))
        self.head_dist = nn.Linear(self.embed_dim, self.num_classes) if self.num_classes > 0 else nn.Identity()

        trunc_normal_(self.dist_token, std=.02)
        trunc_normal_(self.pos_embed, std=.02)
        self.head_dist.apply(self._init_weights)

    def forward_features(self, x):
        # taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
        # with slight modifications to add the dist_token
        B = x.shape[0]
        x = self.patch_embed(x)

        cls_tokens = self.cls_token.expand(B, -1, -1)  # stole cls_tokens impl from Phil Wang, thanks
        dist_token = self.dist_token.expand(B, -1, -1)
        x = torch.cat((cls_tokens, dist_token, x), dim=1)

        x = x + self.pos_embed
        x = self.pos_drop(x)

        for blk in self.blocks:
            x = blk(x)

        x = self.norm(x)
        return x[:, 0], x[:, 1]

    def forward(self, x):
        x, x_dist = self.forward_features(x)
        x = self.head(x)
        x_dist = self.head_dist(x_dist)
        if self.training:
            return x, x_dist
        else:
            # during inference, return the average of both classifier predictions
            return (x + x_dist) / 2


@register_model
def deit_tiny_patch4_32_teacher(pretrained=False, **kwargs):
    model = TeacherVisionTransformer(
        img_size=32, patch_size=4, embed_dim=128, depth=12, num_heads=2, mlp_ratio=4, qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    model.default_cfg = _cfg()
    if pretrained:
        checkpoint = torch.hub.load_state_dict_from_url(
            url="https://dl.fbaipublicfiles.com/deit/deit_tiny_patch16_224-a1311bcf.pth",
            map_location="cpu", check_hash=True
        )
        model.load_state_dict(checkpoint["model"])
    return model


@register_model
def deit_small_patch4_32_teacher(pretrained=False, **kwargs):
    model = TeacherVisionTransformer(
        img_size=32, patch_size=4, embed_dim=192, depth=12, num_heads=2, mlp_ratio=4, qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    model.default_cfg = _cfg()
    if pretrained:
        checkpoint = torch.hub.load_state_dict_from_url(
            url="https://dl.fbaipublicfiles.com/deit/deit_small_patch16_224-cd65a155.pth",
            map_location="cpu", check_hash=True
        )
        model.load_state_dict(checkpoint["model"])
    return model


@register_model
def deit_base_patch4_32_teacher(pretrained=False, **kwargs):
    model = TeacherVisionTransformer(
        img_size=32, patch_size=4, embed_dim=384, depth=12, num_heads=3, mlp_ratio=4, qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    model.default_cfg = _cfg()
    if pretrained:
        checkpoint = torch.hub.load_state_dict_from_url(
            url="https://dl.fbaipublicfiles.com/deit/deit_base_patch16_224-b5f2ef4d.pth",
            map_location="cpu", check_hash=True
        )
        model.load_state_dict(checkpoint["model"])
    return model


@register_model
def deit_tiny_patch4_28_teacher(pretrained=False, **kwargs):
    del kwargs['in_chans']
    model = TeacherVisionTransformer(
        img_size=28, patch_size=4, in_chans=1, embed_dim=128, depth=12, num_heads=2, mlp_ratio=4, qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    model.default_cfg = _cfg()
    if pretrained:
        checkpoint = torch.hub.load_state_dict_from_url(
            url="https://dl.fbaipublicfiles.com/deit/deit_tiny_patch16_224-a1311bcf.pth",
            map_location="cpu", check_hash=True
        )
        model.load_state_dict(checkpoint["model"])
    return model


@register_model
def deit_small_patch4_28_teacher(pretrained=False, **kwargs):
    del kwargs['in_chans']
    model = TeacherVisionTransformer(
        img_size=28, patch_size=4, in_chans=1, embed_dim=192, depth=12, num_heads=2, mlp_ratio=4, qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    model.default_cfg = _cfg()
    if pretrained:
        checkpoint = torch.hub.load_state_dict_from_url(
            url="https://dl.fbaipublicfiles.com/deit/deit_small_patch16_224-cd65a155.pth",
            map_location="cpu", check_hash=True
        )
        model.load_state_dict(checkpoint["model"])
    return model


@register_model
def deit_base_patch4_28_teacher(pretrained=False, **kwargs):
    del kwargs['in_chans']
    model = TeacherVisionTransformer(
        img_size=28, patch_size=4, in_chans=1, embed_dim=384, depth=12, num_heads=3, mlp_ratio=4, qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    model.default_cfg = _cfg()
    if pretrained:
        checkpoint = torch.hub.load_state_dict_from_url(
            url="https://dl.fbaipublicfiles.com/deit/deit_base_patch16_224-b5f2ef4d.pth",
            map_location="cpu", check_hash=True
        )
        model.load_state_dict(checkpoint["model"])
    return model

    
@register_model
def deit_tiny_patch16_224(pretrained=False, **kwargs):
    model = TeacherVisionTransformer(
        patch_size=16, embed_dim=192, depth=12, num_heads=3, mlp_ratio=4, qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    model.default_cfg = _cfg()
    if pretrained:
        checkpoint = torch.hub.load_state_dict_from_url(
            url="https://dl.fbaipublicfiles.com/deit/deit_tiny_patch16_224-a1311bcf.pth",
            map_location="cpu", check_hash=True
        )
        model.load_state_dict(checkpoint["model"])
    return model


@register_model
def deit_small_patch16_224(pretrained=False, **kwargs):
    model = TeacherVisionTransformer(
        patch_size=16, embed_dim=384, depth=12, num_heads=6, mlp_ratio=4, qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    model.default_cfg = _cfg()
    if pretrained:
        checkpoint = torch.hub.load_state_dict_from_url(
            url="https://dl.fbaipublicfiles.com/deit/deit_small_patch16_224-cd65a155.pth",
            map_location="cpu", check_hash=True
        )
        model.load_state_dict(checkpoint["model"])
    return model


@register_model
def deit_base_patch16_224(pretrained=False, **kwargs):
    model = TeacherVisionTransformer(
        patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    model.default_cfg = _cfg()
    if pretrained:
        checkpoint = torch.hub.load_state_dict_from_url(
            url="https://dl.fbaipublicfiles.com/deit/deit_base_patch16_224-b5f2ef4d.pth",
            map_location="cpu", check_hash=True
        )
        model.load_state_dict(checkpoint["model"])
    return model


@register_model
def deit_tiny_distilled_patch16_224(pretrained=False, **kwargs):
    model = DistilledVisionTransformer(
        patch_size=16, embed_dim=192, depth=12, num_heads=3, mlp_ratio=4, qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    model.default_cfg = _cfg()
    if pretrained:
        checkpoint = torch.hub.load_state_dict_from_url(
            url="https://dl.fbaipublicfiles.com/deit/deit_tiny_distilled_patch16_224-b40b3cf7.pth",
            map_location="cpu", check_hash=True
        )
        model.load_state_dict(checkpoint["model"])
    return model


@register_model
def deit_small_distilled_patch16_224(pretrained=False, **kwargs):
    model = DistilledVisionTransformer(
        patch_size=16, embed_dim=384, depth=12, num_heads=6, mlp_ratio=4, qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    model.default_cfg = _cfg()
    if pretrained:
        checkpoint = torch.hub.load_state_dict_from_url(
            url="https://dl.fbaipublicfiles.com/deit/deit_small_distilled_patch16_224-649709d9.pth",
            map_location="cpu", check_hash=True
        )
        model.load_state_dict(checkpoint["model"])
    return model


@register_model
def deit_base_distilled_patch16_224(pretrained=False, **kwargs):
    model = DistilledVisionTransformer(
        patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    model.default_cfg = _cfg()
    if pretrained:
        checkpoint = torch.hub.load_state_dict_from_url(
            url="https://dl.fbaipublicfiles.com/deit/deit_base_distilled_patch16_224-df68dfff.pth",
            map_location="cpu", check_hash=True
        )
        model.load_state_dict(checkpoint["model"])
    return model