vision_transformer.py

""" Vision Transformer (ViT) in Paddle
A Paddle implement of Vision Transformers as described in
'An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale' - https://arxiv.org/abs/2010.11929
The official jax code is released and available at https://github.com/google-research/vision_transformer
"""
import paddle
import paddle.nn as nn
from paddle.nn.initializer import TruncatedNormal, Constant


__all__ = [
    "VisionTransformer",
    "ViT_small_patch16_224",
    "ViT_base_patch16_224", "ViT_base_patch16_384", "ViT_base_patch32_384",
    "ViT_large_patch16_224", "ViT_large_patch16_384", "ViT_large_patch32_384",
    "ViT_huge_patch16_224", "ViT_huge_patch32_384"
]


trunc_normal_ = TruncatedNormal(std=.02)
zeros_ = Constant(value=0.)
ones_ = Constant(value=1.)


def to_2tuple(x):
    return tuple([x] * 2)


def drop_path(x, drop_prob=0., training=False):
    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
    the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ...
    """
    if drop_prob == 0. or not training:
        return x
    keep_prob = 1 - drop_prob
    shape = (x.shape[0],) + (1,) * (x.ndim - 1)
    random_tensor = keep_prob + paddle.rand(shape, dtype=x.dtype)
    random_tensor.floor_()  # binarize
    output = x.div(keep_prob) * random_tensor
    return output


class DropPath(nn.Layer):
    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
    """

    def __init__(self, drop_prob=None):
        super(DropPath, self).__init__()
        self.drop_prob = drop_prob

    def forward(self, x):
        return drop_path(x, self.drop_prob, self.training)


class Identity(nn.Layer):
    def __init__(self):
        super(Identity, self).__init__()

    def forward(self, input):
        return input


class Mlp(nn.Layer):
    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x


class Attention(nn.Layer):
    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim ** -0.5

        self.qkv = nn.Linear(dim, dim * 3, bias_attr=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

    def forward(self, x):
        B, N, C = x.shape
        qkv = self.qkv(x).reshape((B, N, 3, self.num_heads, C //
                                   self.num_heads)).transpose((2, 0, 3, 1, 4))
        q, k, v = qkv[0], qkv[1], qkv[2]

        attn = (q.matmul(k.transpose((0, 1, 3, 2)))) * self.scale
        attn = nn.functional.softmax(attn, axis=-1)
        attn = self.attn_drop(attn)

        x = (attn.matmul(v)).transpose((0, 2, 1, 3)).reshape((B, N, C))
        x = self.proj(x)
        x = self.proj_drop(x)
        return x


class Block(nn.Layer):

    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
                 drop_path=0., act_layer=nn.GELU, norm_layer='nn.LayerNorm', epsilon=1e-5):
        super().__init__()
        self.norm1 = eval(norm_layer)(dim, epsilon=epsilon)
        self.attn = Attention(
            dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
        self.drop_path = DropPath(drop_path) if drop_path > 0. else Identity()
        self.norm2 = eval(norm_layer)(dim, epsilon=epsilon)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim,
                       act_layer=act_layer, drop=drop)

    def forward(self, x):
        x = x + self.drop_path(self.attn(self.norm1(x)))
        x = x + self.drop_path(self.mlp(self.norm2(x)))
        return x


class PatchEmbed(nn.Layer):
    """ Image to Patch Embedding
    """

    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
        super().__init__()
        img_size = to_2tuple(img_size)
        patch_size = to_2tuple(patch_size)
        num_patches = (img_size[1] // patch_size[1]) * \
            (img_size[0] // patch_size[0])
        self.img_size = img_size
        self.patch_size = patch_size
        self.num_patches = num_patches

        self.proj = nn.Conv2D(in_chans, embed_dim,
                              kernel_size=patch_size, stride=patch_size)

    def forward(self, x):
        B, C, H, W = x.shape
        assert H == self.img_size[0] and W == self.img_size[1], \
            f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."

        x = self.proj(x).flatten(2).transpose((0, 2, 1))
        return x


class VisionTransformer(nn.Layer):
    """ Vision Transformer with support for patch input
    """

    def __init__(self, img_size=224, patch_size=16, in_chans=3, class_dim=1000, embed_dim=768, depth=12,
                 num_heads=12, mlp_ratio=4, qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0.,
                 drop_path_rate=0., norm_layer='nn.LayerNorm', epsilon=1e-5, **args):
        super().__init__()
        self.class_dim = class_dim

        self.num_features = self.embed_dim = embed_dim

        self.patch_embed = PatchEmbed(
            img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
        num_patches = self.patch_embed.num_patches

        self.pos_embed = self.create_parameter(
            shape=(1, num_patches + 1, embed_dim), default_initializer=zeros_)
        self.add_parameter("pos_embed", self.pos_embed)
        self.cls_token = self.create_parameter(
            shape=(1, 1, embed_dim), default_initializer=zeros_)
        self.add_parameter("cls_token", self.cls_token)
        self.pos_drop = nn.Dropout(p=drop_rate)

        dpr = [x for x in paddle.linspace(0, drop_path_rate, depth)]

        self.blocks = nn.LayerList([
            Block(
                dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, epsilon=epsilon)
            for i in range(depth)])

        self.norm = eval(norm_layer)(embed_dim, epsilon=epsilon)

        # Classifier head
        self.head = nn.Linear(
            embed_dim, class_dim) if class_dim > 0 else Identity()

        trunc_normal_(self.pos_embed)
        trunc_normal_(self.cls_token)
        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight)
            if isinstance(m, nn.Linear) and m.bias is not None:
                zeros_(m.bias)
        elif isinstance(m, nn.LayerNorm):
            zeros_(m.bias)
            ones_(m.weight)

    def forward_features(self, x):
        B = x.shape[0]
        x = self.patch_embed(x)
        cls_tokens = self.cls_token.expand((B, -1, -1))
        x = paddle.concat((cls_tokens, x), axis=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]

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


def ViT_small_patch16_224(**kwargs):
    model = VisionTransformer(
        patch_size=16, embed_dim=768, depth=8, num_heads=8, mlp_ratio=3, qk_scale=768**-0.5, **kwargs)
    return model


def ViT_base_patch16_224(**kwargs):
    model = VisionTransformer(
        patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True,
        epsilon=1e-6, **kwargs)
    return model


def ViT_base_patch16_384(**kwargs):
    model = VisionTransformer(
        img_size=384, patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4,
        qkv_bias=True, epsilon=1e-6, **kwargs)
    return model


def ViT_base_patch32_384(**kwargs):
    model = VisionTransformer(
        img_size=384, patch_size=32, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4,
        qkv_bias=True, epsilon=1e-6, **kwargs)
    return model


def ViT_large_patch16_224(**kwargs):
    model = VisionTransformer(
        patch_size=16, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, qkv_bias=True,
        epsilon=1e-6, **kwargs)
    return model


def ViT_large_patch16_384(**kwargs):
    model = VisionTransformer(
        img_size=384, patch_size=16, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4,
        qkv_bias=True, epsilon=1e-6, **kwargs)
    return model


def ViT_large_patch32_384(**kwargs):
    model = VisionTransformer(
        img_size=384, patch_size=32, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4,
        qkv_bias=True, epsilon=1e-6, **kwargs)
    return model


def ViT_huge_patch16_224(**kwargs):
    model = VisionTransformer(
        patch_size=16, embed_dim=1280, depth=32, num_heads=16, mlp_ratio=4, **kwargs)
    return model


def ViT_huge_patch32_384(**kwargs):
    model = VisionTransformer(
        img_size=384, patch_size=32, embed_dim=1280, depth=32, num_heads=16, mlp_ratio=4, **kwargs)
    return model