utils.py

# ---------------------------------------------------------------
# Copyright (c) 2021, NVIDIA Corporation. All rights reserved.
#
# This work is licensed under the NVIDIA Source Code License
# ---------------------------------------------------------------
# from mmseg.models.builder import BACKBONES
# from mmseg.utils import get_root_logger
# from mmcv.runner import load_checkpoint
import math
from functools import partial

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from timm.models.registry import register_model
from timm.models.vision_transformer import _cfg


class Mlp(nn.Module):
    def __init__(
        self,
        in_features,
        hidden_features=None,
        out_features=None,
        act_layer=nn.GELU,
        drop=0.0,
    ):
        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.dwconv = DWConv(hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)

        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=0.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)
        elif isinstance(m, nn.Conv2d):
            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
            fan_out //= m.groups
            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
            if m.bias is not None:
                m.bias.data.zero_()

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


class Attention(nn.Module):
    def __init__(
        self,
        dim,
        num_heads=8,
        qkv_bias=False,
        qk_scale=None,
        attn_drop=0.0,
        proj_drop=0.0,
        sr_ratio=1,
    ):
        super().__init__()
        assert (
            dim % num_heads == 0
        ), f"dim {dim} should be divided by num_heads {num_heads}."

        self.dim = dim
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim**-0.5

        self.q = nn.Linear(dim, dim, bias=qkv_bias)
        self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

        self.sr_ratio = sr_ratio
        if sr_ratio > 1:
            self.sr = nn.Conv2d(dim, dim, kernel_size=sr_ratio, stride=sr_ratio)
            self.norm = nn.LayerNorm(dim)

        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=0.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)
        elif isinstance(m, nn.Conv2d):
            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
            fan_out //= m.groups
            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
            if m.bias is not None:
                m.bias.data.zero_()

    def forward(self, x, H, W):
        B, N, C = x.shape
        q = (
            self.q(x)
            .reshape(B, N, self.num_heads, C // self.num_heads)
            .permute(0, 2, 1, 3)
        )

        if self.sr_ratio > 1:
            x_ = x.permute(0, 2, 1).reshape(B, C, H, W)
            x_ = self.sr(x_).reshape(B, C, -1).permute(0, 2, 1)
            x_ = self.norm(x_)
            kv = (
                self.kv(x_)
                .reshape(B, -1, 2, self.num_heads, C // self.num_heads)
                .permute(2, 0, 3, 1, 4)
            )
        else:
            kv = (
                self.kv(x)
                .reshape(B, -1, 2, self.num_heads, C // self.num_heads)
                .permute(2, 0, 3, 1, 4)
            )
        k, v = kv[0], kv[1]

        attn = (q @ k.transpose(-2, -1)) * self.scale
        attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)

        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)

        return x


class Block(nn.Module):
    def __init__(
        self,
        dim,
        num_heads,
        mlp_ratio=4.0,
        qkv_bias=False,
        qk_scale=None,
        drop=0.0,
        attn_drop=0.0,
        drop_path=0.0,
        act_layer=nn.GELU,
        norm_layer=nn.LayerNorm,
        sr_ratio=1,
    ):
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.attn = Attention(
            dim,
            num_heads=num_heads,
            qkv_bias=qkv_bias,
            qk_scale=qk_scale,
            attn_drop=attn_drop,
            proj_drop=drop,
            sr_ratio=sr_ratio,
        )
        # 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 > 0.0 else nn.Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(
            in_features=dim,
            hidden_features=mlp_hidden_dim,
            act_layer=act_layer,
            drop=drop,
        )

        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=0.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)
        elif isinstance(m, nn.Conv2d):
            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
            fan_out //= m.groups
            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
            if m.bias is not None:
                m.bias.data.zero_()

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

        return x


class OverlapPatchEmbed(nn.Module):
    """Image to Patch Embedding."""

    def __init__(self, img_size=224, patch_size=7, stride=4, in_chans=3, embed_dim=768):
        super().__init__()
        img_size = to_2tuple(img_size)
        patch_size = to_2tuple(patch_size)

        self.img_size = img_size
        self.patch_size = patch_size
        self.H, self.W = img_size[0] // patch_size[0], img_size[1] // patch_size[1]
        self.num_patches = self.H * self.W
        self.proj = nn.Conv2d(
            in_chans,
            embed_dim,
            kernel_size=patch_size,
            stride=stride,
            padding=(patch_size[0] // 2, patch_size[1] // 2),
        )
        self.norm = nn.LayerNorm(embed_dim)

        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=0.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)
        elif isinstance(m, nn.Conv2d):
            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
            fan_out //= m.groups
            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
            if m.bias is not None:
                m.bias.data.zero_()

    def forward(self, x):
        x = self.proj(x)
        _, _, H, W = x.shape
        x = x.flatten(2).transpose(1, 2)
        x = self.norm(x)

        return x, H, W


class MixVisionTransformer(nn.Module):
    def __init__(
        self,
        img_size=224,
        patch_size=16,
        in_chans=3,
        num_classes=1000,
        embed_dims=[64, 128, 256, 512],
        num_heads=[1, 2, 4, 8],
        mlp_ratios=[4, 4, 4, 4],
        qkv_bias=False,
        qk_scale=None,
        drop_rate=0.0,
        attn_drop_rate=0.0,
        drop_path_rate=0.0,
        norm_layer=nn.LayerNorm,
        depths=[3, 4, 6, 3],
        sr_ratios=[8, 4, 2, 1],
    ):
        super().__init__()
        self.num_classes = num_classes
        self.depths = depths

        # patch_embed
        self.patch_embed1 = OverlapPatchEmbed(
            img_size=img_size,
            patch_size=7,
            stride=4,
            in_chans=in_chans,
            embed_dim=embed_dims[0],
        )
        self.patch_embed2 = OverlapPatchEmbed(
            img_size=img_size // 4,
            patch_size=3,
            stride=2,
            in_chans=embed_dims[0],
            embed_dim=embed_dims[1],
        )
        self.patch_embed3 = OverlapPatchEmbed(
            img_size=img_size // 8,
            patch_size=3,
            stride=2,
            in_chans=embed_dims[1],
            embed_dim=embed_dims[2],
        )
        self.patch_embed4 = OverlapPatchEmbed(
            img_size=img_size // 16,
            patch_size=3,
            stride=2,
            in_chans=embed_dims[2],
            embed_dim=embed_dims[3],
        )

        # transformer encoder
        dpr = [
            x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))
        ]  # stochastic depth decay rule
        cur = 0
        self.block1 = nn.ModuleList(
            [
                Block(
                    dim=embed_dims[0],
                    num_heads=num_heads[0],
                    mlp_ratio=mlp_ratios[0],
                    qkv_bias=qkv_bias,
                    qk_scale=qk_scale,
                    drop=drop_rate,
                    attn_drop=attn_drop_rate,
                    drop_path=dpr[cur + i],
                    norm_layer=norm_layer,
                    sr_ratio=sr_ratios[0],
                )
                for i in range(depths[0])
            ]
        )
        self.norm1 = norm_layer(embed_dims[0])

        cur += depths[0]
        self.block2 = nn.ModuleList(
            [
                Block(
                    dim=embed_dims[1],
                    num_heads=num_heads[1],
                    mlp_ratio=mlp_ratios[1],
                    qkv_bias=qkv_bias,
                    qk_scale=qk_scale,
                    drop=drop_rate,
                    attn_drop=attn_drop_rate,
                    drop_path=dpr[cur + i],
                    norm_layer=norm_layer,
                    sr_ratio=sr_ratios[1],
                )
                for i in range(depths[1])
            ]
        )
        self.norm2 = norm_layer(embed_dims[1])

        cur += depths[1]
        self.block3 = nn.ModuleList(
            [
                Block(
                    dim=embed_dims[2],
                    num_heads=num_heads[2],
                    mlp_ratio=mlp_ratios[2],
                    qkv_bias=qkv_bias,
                    qk_scale=qk_scale,
                    drop=drop_rate,
                    attn_drop=attn_drop_rate,
                    drop_path=dpr[cur + i],
                    norm_layer=norm_layer,
                    sr_ratio=sr_ratios[2],
                )
                for i in range(depths[2])
            ]
        )
        self.norm3 = norm_layer(embed_dims[2])

        cur += depths[2]
        self.block4 = nn.ModuleList(
            [
                Block(
                    dim=embed_dims[3],
                    num_heads=num_heads[3],
                    mlp_ratio=mlp_ratios[3],
                    qkv_bias=qkv_bias,
                    qk_scale=qk_scale,
                    drop=drop_rate,
                    attn_drop=attn_drop_rate,
                    drop_path=dpr[cur + i],
                    norm_layer=norm_layer,
                    sr_ratio=sr_ratios[3],
                )
                for i in range(depths[3])
            ]
        )
        self.norm4 = norm_layer(embed_dims[3])

        # classification head
        # self.head = nn.Linear(embed_dims[3], num_classes)
        # if num_classes > 0 else nn.Identity()

        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=0.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)
        elif isinstance(m, nn.Conv2d):
            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
            fan_out //= m.groups
            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
            if m.bias is not None:
                m.bias.data.zero_()

    def init_weights(self, pretrained=None):
        if isinstance(pretrained, str):
            pass
            # logger = get_root_logger()
            # load_checkpoint(self, pretrained, map_location='cpu',
            #                 strict=False, logger=logger)

    def reset_drop_path(self, drop_path_rate):
        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(self.depths))]
        cur = 0
        for i in range(self.depths[0]):
            self.block1[i].drop_path.drop_prob = dpr[cur + i]

        cur += self.depths[0]
        for i in range(self.depths[1]):
            self.block2[i].drop_path.drop_prob = dpr[cur + i]

        cur += self.depths[1]
        for i in range(self.depths[2]):
            self.block3[i].drop_path.drop_prob = dpr[cur + i]

        cur += self.depths[2]
        for i in range(self.depths[3]):
            self.block4[i].drop_path.drop_prob = dpr[cur + i]

    def freeze_patch_emb(self):
        self.patch_embed1.requires_grad = False

    @torch.jit.ignore
    def no_weight_decay(self):
        # has pos_embed may be better
        return {"pos_embed1", "pos_embed2", "pos_embed3", "pos_embed4", "cls_token"}

    def get_classifier(self):
        return self.head

    def reset_classifier(self, num_classes, global_pool=""):
        self.num_classes = num_classes
        self.head = (
            nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
        )

    def forward_features(self, x):
        B = x.shape[0]
        outs = []

        # stage 1
        x, H, W = self.patch_embed1(x)
        for i, blk in enumerate(self.block1):
            x = blk(x, H, W)
        x = self.norm1(x)
        x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
        outs.append(x)

        # stage 2
        x, H, W = self.patch_embed2(x)
        for i, blk in enumerate(self.block2):
            x = blk(x, H, W)
        x = self.norm2(x)
        x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
        outs.append(x)

        # stage 3
        x, H, W = self.patch_embed3(x)
        for i, blk in enumerate(self.block3):
            x = blk(x, H, W)
        x = self.norm3(x)
        x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
        outs.append(x)

        # stage 4
        x, H, W = self.patch_embed4(x)
        for i, blk in enumerate(self.block4):
            x = blk(x, H, W)
        x = self.norm4(x)
        x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
        outs.append(x)

        return outs

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

        return x


class DWConv(nn.Module):
    def __init__(self, dim=768):
        super().__init__()
        self.dwconv = nn.Conv2d(dim, dim, 3, 1, 1, bias=True, groups=dim)

    def forward(self, x, H, W):
        B, N, C = x.shape
        x = x.transpose(1, 2).view(B, C, H, W)
        x = self.dwconv(x)
        x = x.flatten(2).transpose(1, 2)

        return x


class mit_b0(MixVisionTransformer):
    def __init__(self, **kwargs):
        super().__init__(
            patch_size=4,
            embed_dims=[32, 64, 160, 256],
            num_heads=[1, 2, 5, 8],
            mlp_ratios=[4, 4, 4, 4],
            qkv_bias=True,
            norm_layer=partial(nn.LayerNorm, eps=1e-6),
            depths=[2, 2, 2, 2],
            sr_ratios=[8, 4, 2, 1],
            drop_rate=0.0,
            drop_path_rate=0.1,
        )


class mit_b1(MixVisionTransformer):
    def __init__(self, **kwargs):
        super().__init__(
            patch_size=4,
            embed_dims=[64, 128, 320, 512],
            num_heads=[1, 2, 5, 8],
            mlp_ratios=[4, 4, 4, 4],
            qkv_bias=True,
            norm_layer=partial(nn.LayerNorm, eps=1e-6),
            depths=[2, 2, 2, 2],
            sr_ratios=[8, 4, 2, 1],
            drop_rate=0.0,
            drop_path_rate=0.1,
        )


class mit_b2(MixVisionTransformer):
    def __init__(self, **kwargs):
        super().__init__(
            patch_size=4,
            embed_dims=[64, 128, 320, 512],
            num_heads=[1, 2, 5, 8],
            mlp_ratios=[4, 4, 4, 4],
            qkv_bias=True,
            norm_layer=partial(nn.LayerNorm, eps=1e-6),
            depths=[3, 4, 6, 3],
            sr_ratios=[8, 4, 2, 1],
            drop_rate=0.0,
            drop_path_rate=0.1,
        )


class mit_b3(MixVisionTransformer):
    def __init__(self, **kwargs):
        super().__init__(
            patch_size=4,
            embed_dims=[64, 128, 320, 512],
            num_heads=[1, 2, 5, 8],
            mlp_ratios=[4, 4, 4, 4],
            qkv_bias=True,
            norm_layer=partial(nn.LayerNorm, eps=1e-6),
            depths=[3, 4, 18, 3],
            sr_ratios=[8, 4, 2, 1],
            drop_rate=0.0,
            drop_path_rate=0.1,
        )


class mit_b4(MixVisionTransformer):
    def __init__(self, **kwargs):
        super().__init__(
            patch_size=4,
            embed_dims=[64, 128, 320, 512],
            num_heads=[1, 2, 5, 8],
            mlp_ratios=[4, 4, 4, 4],
            qkv_bias=True,
            norm_layer=partial(nn.LayerNorm, eps=1e-6),
            depths=[3, 8, 27, 3],
            sr_ratios=[8, 4, 2, 1],
            drop_rate=0.0,
            drop_path_rate=0.1,
        )


class mit_b5(MixVisionTransformer):
    def __init__(self, **kwargs):
        super().__init__(
            patch_size=4,
            embed_dims=[64, 128, 320, 512],
            num_heads=[1, 2, 5, 8],
            mlp_ratios=[4, 4, 4, 4],
            qkv_bias=True,
            norm_layer=partial(nn.LayerNorm, eps=1e-6),
            depths=[3, 6, 40, 3],
            sr_ratios=[8, 4, 2, 1],
            drop_rate=0.0,
            drop_path_rate=0.1,
        )


class Cutout:
    """Randomly mask out one or more patches from an image.

    Args:
        n_holes (int): Number of patches to cut out of each image.
        length (int): The length (in pixels) of each square patch.
    """

    def __init__(self, n_holes, length):
        self.n_holes = n_holes
        self.length = length

    def __call__(self, img):
        """
        Args:
            img (Tensor): Tensor image of size (C, H, W).
        Returns:
            Tensor: Image with n_holes of dimension length x length cut out of it.
        """
        h = img.size(1)
        w = img.size(2)

        mask = np.ones((h, w), np.float32)

        for n in range(self.n_holes):
            y = np.random.randint(h)
            x = np.random.randint(w)

            y1 = np.clip(y - self.length // 2, 0, h)
            y2 = np.clip(y + self.length // 2, 0, h)
            x1 = np.clip(x - self.length // 2, 0, w)
            x2 = np.clip(x + self.length // 2, 0, w)

            mask[y1:y2, x1:x2] = 0.0

        mask = torch.from_numpy(mask)
        mask = mask.expand_as(img)
        img = img * mask

        return img


# if __name__ == "__main__":
#     import pdb
#     model = mit_b5()
#     pdb.set_trace()