models_clip.py

from collections import OrderedDict
from typing import Tuple, Union, Callable

import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from torch.nn.init import trunc_normal_


def named_apply(fn: Callable, module: nn.Module, name="", depth_first=True, include_root=False) -> nn.Module:
    if not depth_first and include_root:
        fn(module=module, name=name)
    for child_name, child_module in module.named_children():
        child_name = ".".join((name, child_name)) if name else child_name
        named_apply(fn=fn, module=child_module, name=child_name, depth_first=depth_first, include_root=True)
    if depth_first and include_root:
        fn(module=module, name=name)
    return module


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1):
        super().__init__()

        # all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1
        self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu1 = nn.ReLU(inplace=True)

        self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.relu2 = nn.ReLU(inplace=True)

        self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity()

        self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
        self.relu3 = nn.ReLU(inplace=True)

        self.downsample = None
        self.stride = stride

        if stride > 1 or inplanes != planes * Bottleneck.expansion:
            # downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1
            self.downsample = nn.Sequential(OrderedDict([
                ("-1", nn.AvgPool2d(stride)),
                ("0", nn.Conv2d(inplanes, planes * self.expansion, 1, stride=1, bias=False)),
                ("1", nn.BatchNorm2d(planes * self.expansion))
            ]))

    def forward(self, x: torch.Tensor):
        identity = x

        out = self.relu1(self.bn1(self.conv1(x)))
        out = self.relu2(self.bn2(self.conv2(out)))
        out = self.avgpool(out)
        out = self.bn3(self.conv3(out))

        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu3(out)
        return out


class AttentionPool2d(nn.Module):
    def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None):
        super().__init__()
        self.positional_embedding = nn.Parameter(torch.randn(spacial_dim ** 2 + 1, embed_dim) / embed_dim ** 0.5)
        self.k_proj = nn.Linear(embed_dim, embed_dim)
        self.q_proj = nn.Linear(embed_dim, embed_dim)
        self.v_proj = nn.Linear(embed_dim, embed_dim)
        self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim)
        self.num_heads = num_heads

    def forward(self, x):
        x = x.flatten(start_dim=2).permute(2, 0, 1)  # NCHW -> (HW)NC
        x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0)  # (HW+1)NC
        x = x + self.positional_embedding[:, None, :].to(x.dtype)  # (HW+1)NC
        x, _ = F.multi_head_attention_forward(
            query=x[:1], key=x, value=x,
            embed_dim_to_check=x.shape[-1],
            num_heads=self.num_heads,
            q_proj_weight=self.q_proj.weight,
            k_proj_weight=self.k_proj.weight,
            v_proj_weight=self.v_proj.weight,
            in_proj_weight=None,
            in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]),
            bias_k=None,
            bias_v=None,
            add_zero_attn=False,
            dropout_p=0,
            out_proj_weight=self.c_proj.weight,
            out_proj_bias=self.c_proj.bias,
            use_separate_proj_weight=True,
            training=self.training,
            need_weights=False
        )
        return x.squeeze(0)


class ModifiedResNet(nn.Module):
    """
    A ResNet class that is similar to torchvision's but contains the following changes:
    - There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool.
    - Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1
    - The final pooling layer is a QKV attention instead of an average pool
    """

    def __init__(self, layers, output_dim, heads, input_resolution=224, width=64):
        super().__init__()
        self.output_dim = output_dim
        self.input_resolution = input_resolution

        # the 3-layer stem
        self.conv1 = nn.Conv2d(3, width // 2, kernel_size=3, stride=2, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(width // 2)
        self.relu1 = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(width // 2, width // 2, kernel_size=3, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(width // 2)
        self.relu2 = nn.ReLU(inplace=True)
        self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False)
        self.bn3 = nn.BatchNorm2d(width)
        self.relu3 = nn.ReLU(inplace=True)
        self.avgpool = nn.AvgPool2d(2)

        # residual layers
        self._inplanes = width  # this is a *mutable* variable used during construction
        self.layer1 = self._make_layer(width, layers[0])
        self.layer2 = self._make_layer(width * 2, layers[1], stride=2)
        self.layer3 = self._make_layer(width * 4, layers[2], stride=2)
        self.layer4 = self._make_layer(width * 8, layers[3], stride=2)

        embed_dim = width * 32  # the ResNet feature dimension
        self.attnpool = AttentionPool2d(input_resolution // 32, embed_dim, heads, output_dim)

    def _make_layer(self, planes, blocks, stride=1):
        layers = [Bottleneck(self._inplanes, planes, stride)]

        self._inplanes = planes * Bottleneck.expansion
        for _ in range(1, blocks):
            layers.append(Bottleneck(self._inplanes, planes))

        return nn.Sequential(*layers)

    def forward(self, x):
        def stem(x):
            x = self.relu1(self.bn1(self.conv1(x)))
            x = self.relu2(self.bn2(self.conv2(x)))
            x = self.relu3(self.bn3(self.conv3(x)))
            x = self.avgpool(x)
            return x

        x = x.type(self.conv1.weight.dtype)
        x = stem(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.attnpool(x)

        return x


class LayerNorm(nn.LayerNorm):
    """Subclass torch's LayerNorm to handle fp16."""

    def forward(self, x: torch.Tensor):
        orig_type = x.dtype
        ret = super().forward(x.type(torch.float32))
        return ret.type(orig_type)


class QuickGELU(nn.Module):
    def forward(self, x: torch.Tensor):
        return x * torch.sigmoid(1.702 * x)


class ResidualAttentionBlock(nn.Module):
    def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None):
        super().__init__()

        self.attn = nn.MultiheadAttention(d_model, n_head)
        self.ln_1 = LayerNorm(d_model)
        self.mlp = nn.Sequential(OrderedDict([
            ("c_fc", nn.Linear(d_model, d_model * 4)),
            ("gelu", QuickGELU()),
            ("c_proj", nn.Linear(d_model * 4, d_model))
        ]))
        self.ln_2 = LayerNorm(d_model)
        self.attn_mask = attn_mask

    def attention(self, x: torch.Tensor):
        self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None
        return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0]

    def forward(self, x: torch.Tensor):
        x = x + self.attention(self.ln_1(x))
        x = x + self.mlp(self.ln_2(x))
        return x


class Transformer(nn.Module):
    def __init__(self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None):
        super().__init__()
        self.width = width
        self.layers = layers
        self.resblocks = nn.Sequential(*[ResidualAttentionBlock(width, heads, attn_mask) for _ in range(layers)])

    def forward(self, x: torch.Tensor):
        x = x.permute(1, 0, 2)  # NLD -> LND
        x = self.resblocks(x)
        x = x.permute(1, 0, 2)  # LND -> NLD

        return x


class VisionTransformer(nn.Module):
    def __init__(self, input_resolution: int, patch_size: int, width: int, layers: int, heads: int):
        super().__init__()
        self.input_resolution = input_resolution
        self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False)

        scale = width ** -0.5
        self.class_embedding = nn.Parameter(scale * torch.randn(width))
        self.positional_embedding = nn.Parameter(scale * torch.randn((input_resolution // patch_size) ** 2 + 1, width))
        self.ln_pre = LayerNorm(width)

        self.transformer = Transformer(width, layers, heads)

        self.mask_token = nn.Parameter(torch.zeros(1, width))

        self.ln_post = LayerNorm(width)

        self.embed_dim = width
        self.patch_size = patch_size

        self.init_weights()

    def init_weights(self):
        trunc_normal_(self.positional_embedding, std=0.02)
        nn.init.normal_(self.class_embedding, std=1e-6)
        named_apply(init_weights_vit_timm, self)

    def prepare_tokens_with_masks(self, x, masks=None):    
        x = self.conv1(x)  # shape = [*, width, grid, grid]
        x = x.reshape(x.shape[0], x.shape[1], -1)  # shape = [*, width, grid ** 2]
        x = x.permute(0, 2, 1)  # shape = [*, grid ** 2, width]

        if masks is not None:
            x = torch.where(masks.unsqueeze(-1), self.mask_token.to(x.dtype).unsqueeze(0), x)

        x = torch.cat([self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x], dim=1)  # shape = [*, grid ** 2 + 1, width]
        x = x + self.positional_embedding.to(x.dtype)
        x = self.ln_pre(x)

        return x

    def forward_features_list(self, x_list, masks_list):
        x = [self.prepare_tokens_with_masks(x, masks) for x, masks in zip(x_list, masks_list)]

        all_x = [self.transformer(t) for t in x]

        output = []
        for x, masks in zip(all_x, masks_list):
            output.append(
                {
                    "x_norm_clstoken": self.ln_post(x[:, 0]),
                    "x_norm_patchtokens": x[:, 1 :],
                    "x_prenorm": x,
                    "masks": masks,
                }
            )
        return output

    def forward(self, x: torch.Tensor, masks=None):
        if isinstance(x, list):
            return self.forward_features_list(x, masks)

        x = self.prepare_tokens_with_masks(x, masks)

        x = self.transformer(x)

        return {
            "x_norm_clstoken": self.ln_post(x[:, 0]),
            "x_norm_patchtokens": x[:, 1 :],
            "x_prenorm": x,
            "masks": masks,
        }


def init_weights_vit_timm(module: nn.Module, name: str = ""):
    """ViT weight initialization, original timm impl (for reproducibility)"""
    if isinstance(module, nn.Linear):
        trunc_normal_(module.weight, std=0.02)
        if module.bias is not None:
            nn.init.zeros_(module.bias)



def vit_small(patch_size=14, teacher_path=None):
    model = VisionTransformer(
                input_resolution=224,
                patch_size=patch_size,
                width=384,
                layers=12,
                heads=6
            )
    
    if teacher_path is not None:
        checkpoint = torch.load(teacher_path, map_location='cpu')

        if 'state_dict' in checkpoint:
            pretrained_dict = checkpoint['state_dict']
        elif 'model' in checkpoint:
            pretrained_dict = checkpoint['model']
        else:
            pretrained_dict = checkpoint

        missing_keys, unexpected_keys = model.load_state_dict(pretrained_dict, False)
        print('missing_keys: ', missing_keys)
        print('unexpected_keys: ', unexpected_keys)
    
    return model


def vit_base(patch_size=14, teacher_path=None):
    model = VisionTransformer(
                input_resolution=224,
                patch_size=patch_size,
                width=768,
                layers=12,
                heads=12
            )
    
    if teacher_path is not None:
        checkpoint = torch.load(teacher_path, map_location='cpu')

        if 'state_dict' in checkpoint:
            pretrained_dict = checkpoint['state_dict']
        elif 'model' in checkpoint:
            pretrained_dict = checkpoint['model']
        else:
            pretrained_dict = checkpoint

        missing_keys, unexpected_keys = model.load_state_dict(pretrained_dict, False)
        print('missing_keys: ', missing_keys)
        print('unexpected_keys: ', unexpected_keys)
    
    return model


def vit_large(patch_size=14, teacher_path=None):
    model = VisionTransformer(
                input_resolution=224,
                patch_size=patch_size,
                width=1024,
                layers=24,
                heads=16
            )

    if teacher_path is not None:
        checkpoint = torch.load(teacher_path, map_location='cpu')

        if 'state_dict' in checkpoint:
            pretrained_dict = checkpoint['state_dict']
        elif 'model' in checkpoint:
            pretrained_dict = checkpoint['model']
        else:
            pretrained_dict = checkpoint

        missing_keys, unexpected_keys = model.load_state_dict(pretrained_dict, False)
        print('missing_keys: ', missing_keys)
        print('unexpected_keys: ', unexpected_keys)

    return model



if __name__ == "__main__":
    import argparse
    import clip
    import open_clip
    from fvcore.nn import FlopCountAnalysis, parameter_count_table
    parser = argparse.ArgumentParser(description='PyTorch resnet Training')
    args = parser.parse_args()

    # with torch.no_grad():
        # print(clip.available_models())
        
        # device = "cuda" if torch.cuda.is_available() else "cpu"
        # model, preprocess = clip.load('ViT-L/14', device)
        # print(model.visual)

        # model, _, preprocess = open_clip.create_model_and_transforms('ViT-B-16', pretrained='laion400m_e32')
        # model = model.to('cuda')

        # for k,v in model.visual.named_parameters():
        #     print(k, v.shape)

        # self_model = VisionTransformer(
        #         input_resolution=224,
        #         patch_size=32,
        #         width=768,
        #         layers=12,
        #         heads=12
        #     )

        # print(self_model)

        # for k,v in self_model.named_parameters():
        #     print(k, v.shape)

        # new_ckpt = OrderedDict()
        # for k,v in model.visual.named_parameters():
        #     if 'proj' != k:
        #         print(k)
        #         new_ckpt[k] = v
            # new_ckpt[k] = v

        # torch.save(new_ckpt, '/home/qw/yitian/TA-KD/clip_model/clip_l_14_400m.pth')

        # model, _, preprocess = open_clip.create_model_and_transforms('ViT-B-32', pretrained='laion400m_e32')
        # print(model.visual)


        # model = clip_base_32()
        # model = clip_base_14()

        # print(parameter_count_table(model))

        # tensor = torch.rand(1, 3, 224, 224)
        # flops = FlopCountAnalysis(model, tensor)
        # print("FLOPs: ", flops.total()/1e9)