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from collections import OrderedDict
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from typing import Tuple, Union
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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import numpy as np
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import torch
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import torch.nn.functional as F
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from torch import nn
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from ..utils.dataset import tokenize
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from ..utils.simple_tokenizer import SimpleTokenizer as _Tokenizer
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_tokenizer = _Tokenizer()
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class Bottleneck(nn.Module):
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expansion = 4
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def __init__(self, inplanes, planes, stride=1):
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super().__init__()
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self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False)
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self.bn1 = nn.BatchNorm2d(planes)
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self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False)
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self.bn2 = nn.BatchNorm2d(planes)
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self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity()
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self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False)
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self.bn3 = nn.BatchNorm2d(planes * self.expansion)
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self.relu = nn.ReLU(inplace=True)
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self.downsample = None
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self.stride = stride
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if stride > 1 or inplanes != planes * Bottleneck.expansion:
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self.downsample = nn.Sequential(
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OrderedDict([("-1", nn.AvgPool2d(stride)),
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("0",
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nn.Conv2d(inplanes,
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planes * self.expansion,
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1,
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stride=1,
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bias=False)),
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("1", nn.BatchNorm2d(planes * self.expansion))]))
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def forward(self, x: torch.Tensor):
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identity = x
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out = self.relu(self.bn1(self.conv1(x)))
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out = self.relu(self.bn2(self.conv2(out)))
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out = self.avgpool(out)
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out = self.bn3(self.conv3(out))
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if self.downsample is not None:
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identity = self.downsample(x)
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out += identity
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out = self.relu(out)
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return out
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"""
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attenpool used in CRIS (output: C1/C2/C3 3 deiffent feature maps)
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"""
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class ModifiedAttentionPool2d(nn.Module):
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def __init__(self,
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spacial_dim: int,
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embed_dim: int,
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num_heads: int,
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output_dim: int = None):
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super().__init__()
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self.spacial_dim = spacial_dim
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self.positional_embedding = nn.Parameter(
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torch.randn(spacial_dim**2 + 1, embed_dim) / embed_dim**0.5)
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self.k_proj = nn.Linear(embed_dim, embed_dim)
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self.q_proj = nn.Linear(embed_dim, embed_dim)
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self.v_proj = nn.Linear(embed_dim, embed_dim)
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self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim)
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self.num_heads = num_heads
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self.connect = nn.Sequential(
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nn.Conv2d(embed_dim, output_dim, 1, stride=1, bias=False),
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nn.BatchNorm2d(output_dim))
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def resize_pos_embed(self, pos_embed, input_shpae):
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"""Resize pos_embed weights.
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Resize pos_embed using bicubic interpolate method.
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Args:
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pos_embed (torch.Tensor): Position embedding weights.
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input_shpae (tuple): Tuple for (downsampled input image height,
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downsampled input image width).
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pos_shape (tuple): The resolution of downsampled origin training
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image.
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mode (str): Algorithm used for upsampling:
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``'nearest'`` | ``'linear'`` | ``'bilinear'`` | ``'bicubic'`` |
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``'trilinear'``. Default: ``'nearest'``
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Return:
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torch.Tensor: The resized pos_embed of shape [B, C, L_new]
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"""
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assert pos_embed.ndim == 3, 'shape of pos_embed must be [B, L, C]'
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pos_h = pos_w = self.spacial_dim
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cls_token_weight = pos_embed[:, 0]
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pos_embed_weight = pos_embed[:, (-1 * pos_h * pos_w):]
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pos_embed_weight = pos_embed_weight.reshape(
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1, pos_h, pos_w, pos_embed.shape[2]).permute(0, 3, 1, 2)
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pos_embed_weight = F.interpolate(pos_embed_weight,
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size=input_shpae,
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align_corners=False,
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mode='bicubic')
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cls_token_weight = cls_token_weight.unsqueeze(1)
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pos_embed_weight = torch.flatten(pos_embed_weight, 2).transpose(1, 2)
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return pos_embed_weight.transpose(-2, -1)
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def forward(self, x):
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B, C, H, W = x.size()
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res = self.connect(x)
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x = x.reshape(B, C, -1)
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pos_embed = self.positional_embedding.unsqueeze(0)
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pos_embed = self.resize_pos_embed(pos_embed, (H, W))
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x = x + pos_embed.to(x.dtype)
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x = x.permute(2, 0, 1)
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x, _ = F.multi_head_attention_forward(
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query=x,
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key=x,
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value=x,
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embed_dim_to_check=x.shape[-1],
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num_heads=self.num_heads,
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q_proj_weight=self.q_proj.weight,
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k_proj_weight=self.k_proj.weight,
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v_proj_weight=self.v_proj.weight,
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in_proj_weight=None,
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in_proj_bias=torch.cat(
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[self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]),
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bias_k=None,
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bias_v=None,
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add_zero_attn=False,
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dropout_p=0,
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out_proj_weight=self.c_proj.weight,
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out_proj_bias=self.c_proj.bias,
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use_separate_proj_weight=True,
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training=self.training,
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need_weights=False)
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xt = x[0]
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x = x.permute(1, 2, 0).reshape(B, -1, H, W)
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x = x + res
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x = F.relu(x, True)
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return x, xt
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"""
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attenpool used in Clip (output: a tensor (b, dim) image encoding)
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"""
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class AttentionPool2d(nn.Module):
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def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None):
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super().__init__()
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self.positional_embedding = nn.Parameter(torch.randn(spacial_dim ** 2 + 1, embed_dim) / embed_dim ** 0.5)
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self.k_proj = nn.Linear(embed_dim, embed_dim)
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self.q_proj = nn.Linear(embed_dim, embed_dim)
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self.v_proj = nn.Linear(embed_dim, embed_dim)
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self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim)
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self.num_heads = num_heads
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def forward(self, x):
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x = x.flatten(start_dim=2).permute(2, 0, 1)
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x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0)
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x = x + self.positional_embedding[:, None, :].to(x.dtype)
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x, _ = F.multi_head_attention_forward(
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query=x[:1], key=x, value=x,
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embed_dim_to_check=x.shape[-1],
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num_heads=self.num_heads,
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q_proj_weight=self.q_proj.weight,
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k_proj_weight=self.k_proj.weight,
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v_proj_weight=self.v_proj.weight,
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in_proj_weight=None,
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in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]),
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bias_k=None,
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bias_v=None,
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add_zero_attn=False,
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dropout_p=0,
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out_proj_weight=self.c_proj.weight,
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out_proj_bias=self.c_proj.bias,
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use_separate_proj_weight=True,
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training=self.training,
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need_weights=False
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)
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return x.squeeze(0)
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class ModifiedResNet(nn.Module):
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"""
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A ResNet class that is similar to torchvision's but contains the following changes:
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- There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool.
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- Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1
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- The final pooling layer is a QKV attention instead of an average pool
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"""
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def __init__(self,
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layers,
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output_dim,
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heads,
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input_resolution=224,
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width=64):
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super().__init__()
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self.output_dim = output_dim
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self.input_resolution = input_resolution
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self.conv1 = nn.Conv2d(3,
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width // 2,
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kernel_size=3,
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stride=2,
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padding=1,
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bias=False)
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self.bn1 = nn.BatchNorm2d(width // 2)
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self.conv2 = nn.Conv2d(width // 2,
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width // 2,
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kernel_size=3,
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padding=1,
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bias=False)
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self.bn2 = nn.BatchNorm2d(width // 2)
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self.conv3 = nn.Conv2d(width // 2,
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width,
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kernel_size=3,
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padding=1,
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bias=False)
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self.bn3 = nn.BatchNorm2d(width)
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self.avgpool = nn.AvgPool2d(2)
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self.relu = nn.ReLU(inplace=True)
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self._inplanes = width
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self.layer1 = self._make_layer(width, layers[0])
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self.layer2 = self._make_layer(width * 2, layers[1], stride=2)
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self.layer3 = self._make_layer(width * 4, layers[2], stride=2)
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self.layer4 = self._make_layer(width * 8, layers[3], stride=2)
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embed_dim = width * 32
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self.attnpool = AttentionPool2d(input_resolution // 32, embed_dim,
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heads, output_dim)
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|
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def _make_layer(self, planes, blocks, stride=1):
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layers = [Bottleneck(self._inplanes, planes, stride)]
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self._inplanes = planes * Bottleneck.expansion
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for _ in range(1, blocks):
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layers.append(Bottleneck(self._inplanes, planes))
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return nn.Sequential(*layers)
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def forward(self, x):
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def stem(x):
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for conv, bn in [(self.conv1, self.bn1), (self.conv2, self.bn2),
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(self.conv3, self.bn3)]:
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x = self.relu(bn(conv(x)))
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x = self.avgpool(x)
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return x
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x = x.type(self.conv1.weight.dtype)
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x = stem(x)
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x = self.layer1(x)
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x2 = self.layer2(x)
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x3 = self.layer3(x2)
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x4 = self.layer4(x3)
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x5 = self.attnpool(x4)
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return (x2, x3, x4), x5
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|
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|
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class LayerNorm(nn.LayerNorm):
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"""Subclass torch's LayerNorm to handle fp16."""
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def forward(self, x: torch.Tensor):
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orig_type = x.dtype
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ret = super().forward(x.type(torch.float32))
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return ret.type(orig_type)
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|
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|
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class QuickGELU(nn.Module):
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def forward(self, x: torch.Tensor):
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return x * torch.sigmoid(1.702 * x)
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|
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|
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class ResidualAttentionBlock(nn.Module):
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def __init__(self,
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d_model: int,
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n_head: int,
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attn_mask: torch.Tensor = None):
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super().__init__()
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self.attn = nn.MultiheadAttention(d_model, n_head)
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self.ln_1 = LayerNorm(d_model)
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self.mlp = nn.Sequential(
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OrderedDict([("c_fc", nn.Linear(d_model, d_model * 4)),
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("gelu", QuickGELU()),
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("c_proj", nn.Linear(d_model * 4, d_model))]))
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self.ln_2 = LayerNorm(d_model)
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self.attn_mask = attn_mask
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def attention(self, x: torch.Tensor):
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self.attn_mask = self.attn_mask.to(
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dtype=x.dtype,
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device=x.device) if self.attn_mask is not None else None
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res = self.attn(x, x, x, need_weights=False,
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attn_mask=self.attn_mask)[0]
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return res
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|
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def forward(self, x: torch.Tensor):
|
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|
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x = x + self.attention(self.ln_1(x))
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x = x + self.mlp(self.ln_2(x))
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return x
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|
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class Transformer(nn.Module):
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def __init__(self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None):
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super().__init__()
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self.width = width
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self.layers = layers
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self.resblocks = nn.Sequential(*[ResidualAttentionBlock(width, heads, attn_mask) for _ in range(layers)])
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def forward(self, x: torch.Tensor):
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return self.resblocks(x)
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class ViTTransformer(nn.Module):
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def __init__(self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None):
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super().__init__()
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self.width = width
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self.layers = layers
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self.resblocks = nn.Sequential(*[ResidualAttentionBlock(width, heads, attn_mask) for _ in range(layers)])
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|
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def forward(self, x: torch.Tensor):
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outputs = []
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i = 1
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for block in self.resblocks:
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x = block(x)
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if i > 7:
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outputs.append(x)
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i = i + 1
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return outputs
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|
|
|
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class VisionTransformer(nn.Module):
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def __init__(self, input_resolution: int, patch_size: int, width: int,
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layers: int, heads: int, output_dim: int):
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super().__init__()
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self.input_resolution = input_resolution
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self.output_dim = output_dim
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self.conv1 = nn.Conv2d(in_channels=3,
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out_channels=width,
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kernel_size=patch_size,
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stride=patch_size,
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bias=False)
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scale = width ** -0.5
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self.class_embedding = nn.Parameter(scale * torch.randn(width))
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self.positional_embedding = nn.Parameter(scale * torch.randn(
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(input_resolution // patch_size) ** 2 + 1, width))
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self.ln_pre = LayerNorm(width)
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self.transformer = ViTTransformer(width, layers, heads)
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|
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self.ln_post = LayerNorm(width)
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self.proj = nn.Parameter(scale * torch.randn(width, output_dim))
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def forward(self, x: torch.Tensor):
|
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|
|
|
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x = self.conv1(x)
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x = x.reshape(x.shape[0], x.shape[1],
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-1)
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|
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x = x.permute(0, 2, 1)
|
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|
|
x = torch.cat([
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self.class_embedding.to(x.dtype) + torch.zeros(
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x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x
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],
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dim=1)
|
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|
|
x = x + self.positional_embedding.to(x.dtype)
|
|
|
|
x = self.ln_pre(x)
|
|
|
|
x = x.permute(1, 0, 2)
|
|
|
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out = self.transformer(x)
|
|
|
|
x1, x2 ,x3, x4 = out[0], out[1], out[2], out[3]
|
|
x1 = x1.permute(1, 0, 2)
|
|
x2 = x2.permute(1, 0, 2)
|
|
x3 = x3.permute(1, 0, 2)
|
|
x4 = x4.permute(1, 0, 2)
|
|
|
|
|
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x = self.ln_post(x4[:, 0, :])
|
|
|
|
|
|
|
|
|
|
if self.proj is not None:
|
|
x = x @ self.proj
|
|
|
|
return (x1[:, 1:, :], x2[:, 1:, :], x3[:, 1:, :], x4[:, 1:, :]), x
|
|
|
|
class ModifiedVisionTransformer(nn.Module):
|
|
def __init__(self, input_resolution: int, patch_size: int, width: int,
|
|
layers: int, heads: int, output_dim: int):
|
|
super().__init__()
|
|
self.input_resolution = input_resolution
|
|
self.output_dim = output_dim
|
|
self.conv1 = nn.Conv2d(in_channels=3,
|
|
out_channels=width,
|
|
kernel_size=patch_size,
|
|
stride=patch_size,
|
|
bias=False)
|
|
|
|
self.conv2 = nn.Conv2d(in_channels=3,
|
|
out_channels=width // 2,
|
|
kernel_size=patch_size // 2,
|
|
stride=patch_size // 2,
|
|
bias=False)
|
|
|
|
self.conv3 = nn.Conv2d(in_channels=3,
|
|
out_channels=width,
|
|
kernel_size=patch_size * 2,
|
|
stride=patch_size * 2,
|
|
bias=False)
|
|
self.conv_layers = [self.conv1, self.conv2]
|
|
scale = width**-0.5
|
|
|
|
self.class_embedding1 = nn.Parameter(scale * torch.randn(width))
|
|
self.class_embedding2 = nn.Parameter(scale * torch.randn(width // 2))
|
|
self.cls_layers = [self.class_embedding1, self.class_embedding2]
|
|
|
|
self.positional_embedding1 = nn.Parameter(scale * torch.randn(
|
|
(input_resolution // patch_size)**2 + 1, width))
|
|
self.positional_embedding2 = nn.Parameter(scale * torch.randn(
|
|
(input_resolution // (patch_size // 2)) ** 2 + 1, width // 2))
|
|
self.pos_layers = [self.positional_embedding1, self.positional_embedding2]
|
|
|
|
self.ln_pre1 = LayerNorm(width)
|
|
self.ln_pre2 = LayerNorm(width // 2)
|
|
self.pre_layers = [self.ln_pre1, self.ln_pre2]
|
|
|
|
self.transformer1 = Transformer(width, layers, heads)
|
|
self.transformer2 = Transformer(width // 2, layers, heads)
|
|
self.tran_layers = [self.transformer1, self.transformer2]
|
|
|
|
self.ln_post1 = LayerNorm(width)
|
|
self.ln_post2 = LayerNorm(width // 2)
|
|
self.post_layers = [self.ln_post1, self.ln_post2]
|
|
|
|
self.proj1 = nn.Parameter(scale * torch.randn(width, output_dim * 2))
|
|
self.proj2 = nn.Parameter(scale * torch.randn(width // 2, output_dim))
|
|
self.proj_layers = [self.proj1, self.proj2]
|
|
|
|
|
|
def forward(self, x: torch.Tensor):
|
|
|
|
input = x
|
|
|
|
out = []
|
|
f = []
|
|
cl = []
|
|
for i in range(2):
|
|
x = self.conv_layers[i](input)
|
|
|
|
b, c, w, h = x.shape
|
|
|
|
x = x.reshape(x.shape[0], x.shape[1],
|
|
-1)
|
|
|
|
x = x.permute(0, 2, 1)
|
|
|
|
x = torch.cat([
|
|
self.cls_layers[i].to(x.dtype) + torch.zeros(
|
|
x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x
|
|
],
|
|
dim=1)
|
|
|
|
x = x + self.pos_layers[i].to(x.dtype)
|
|
|
|
x = self.pre_layers[i](x)
|
|
|
|
x = x.permute(1, 0, 2)
|
|
|
|
x, cls = self.tran_layers[i](x)
|
|
|
|
x = x.permute(1, 0, 2)
|
|
|
|
|
|
|
|
|
|
x = self.post_layers[i](x[:, 1:, :])
|
|
|
|
|
|
|
|
if self.proj_layers[i] is not None:
|
|
x = x @ self.proj_layers[i]
|
|
cls = [j @ self.proj_layers[i] for j in cls]
|
|
|
|
feat = x.permute(0,2,1).reshape(b, x.shape[2] , w, h)
|
|
out.append(x)
|
|
f.append(feat)
|
|
cl.append(cls)
|
|
return out, f, cl
|
|
|
|
"""
|
|
Long CLIP
|
|
"""
|
|
class LCLIP(nn.Module):
|
|
def __init__(self,
|
|
embed_dim: int,
|
|
|
|
image_resolution: int,
|
|
vision_layers: Union[Tuple[int, int, int, int], int],
|
|
vision_width: int,
|
|
vision_patch_size: int,
|
|
|
|
context_length: int,
|
|
vocab_size: int,
|
|
transformer_width: int,
|
|
transformer_heads: int,
|
|
transformer_layers: int,
|
|
load_from_clip: bool
|
|
):
|
|
super().__init__()
|
|
self.context_length = 248
|
|
|
|
if isinstance(vision_layers, (tuple, list)):
|
|
vision_heads = vision_width * 32 // 64
|
|
self.visual = ModifiedResNet(
|
|
layers=vision_layers,
|
|
output_dim=embed_dim,
|
|
heads=vision_heads,
|
|
input_resolution=image_resolution,
|
|
width=vision_width
|
|
)
|
|
else:
|
|
vision_heads = vision_width // 64
|
|
self.visual = VisionTransformer(
|
|
input_resolution=image_resolution,
|
|
patch_size=vision_patch_size,
|
|
width=vision_width,
|
|
layers=vision_layers,
|
|
heads=vision_heads,
|
|
output_dim=embed_dim
|
|
)
|
|
|
|
self.transformer = Transformer(
|
|
width=transformer_width,
|
|
layers=transformer_layers,
|
|
heads=transformer_heads,
|
|
attn_mask=self.build_attention_mask()
|
|
)
|
|
|
|
self.vocab_size = vocab_size
|
|
self.token_embedding = nn.Embedding(vocab_size, transformer_width)
|
|
|
|
|
|
if load_from_clip == False:
|
|
self.positional_embedding = nn.Parameter(torch.empty(248, transformer_width))
|
|
self.positional_embedding_res = nn.Parameter(torch.empty(248, transformer_width))
|
|
|
|
else:
|
|
self.positional_embedding = nn.Parameter(torch.empty(248, transformer_width))
|
|
|
|
self.ln_final = LayerNorm(transformer_width)
|
|
|
|
self.text_projection = nn.Parameter(torch.empty(transformer_width, embed_dim))
|
|
self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
|
|
|
|
self.initialize_parameters()
|
|
self.mask1 = torch.zeros([248, 1])
|
|
self.mask1[:20, :] = 1
|
|
self.mask2 = torch.zeros([248, 1])
|
|
self.mask2[20:, :] = 1
|
|
|
|
|
|
def initialize_parameters(self):
|
|
nn.init.normal_(self.token_embedding.weight, std=0.02)
|
|
nn.init.normal_(self.positional_embedding, std=0.01)
|
|
|
|
if isinstance(self.visual, ModifiedResNet):
|
|
if self.visual.attnpool is not None:
|
|
std = self.visual.attnpool.c_proj.in_features ** -0.5
|
|
nn.init.normal_(self.visual.attnpool.q_proj.weight, std=std)
|
|
nn.init.normal_(self.visual.attnpool.k_proj.weight, std=std)
|
|
nn.init.normal_(self.visual.attnpool.v_proj.weight, std=std)
|
|
nn.init.normal_(self.visual.attnpool.c_proj.weight, std=std)
|
|
|
|
for resnet_block in [self.visual.layer1, self.visual.layer2, self.visual.layer3, self.visual.layer4]:
|
|
for name, param in resnet_block.named_parameters():
|
|
if name.endswith("bn3.weight"):
|
|
nn.init.zeros_(param)
|
|
|
|
proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5)
|
|
attn_std = self.transformer.width ** -0.5
|
|
fc_std = (2 * self.transformer.width) ** -0.5
|
|
for block in self.transformer.resblocks:
|
|
nn.init.normal_(block.attn.in_proj_weight, std=attn_std)
|
|
nn.init.normal_(block.attn.out_proj.weight, std=proj_std)
|
|
nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)
|
|
nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)
|
|
|
|
if self.text_projection is not None:
|
|
nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5)
|
|
|
|
def build_attention_mask(self):
|
|
|
|
|
|
mask = torch.empty(self.context_length, self.context_length)
|
|
mask.fill_(float("-inf"))
|
|
mask.triu_(1)
|
|
return mask
|
|
|
|
@property
|
|
def dtype(self):
|
|
return self.visual.conv1.weight.dtype
|
|
|
|
def encode_image(self, image):
|
|
return self.visual(image.type(self.dtype))
|
|
|
|
def encode_text(self, text):
|
|
x = self.token_embedding(text).type(self.dtype)
|
|
|
|
|
|
x = x + (self.positional_embedding.to(x.device) * self.mask1.to(x.device)).type(self.dtype).to(x.device)
|
|
x = x.permute(1, 0, 2)
|
|
x = self.transformer(x)
|
|
x = x.permute(1, 0, 2)
|
|
x = self.ln_final(x).type(self.dtype)
|
|
|
|
|
|
|
|
x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection
|
|
|
|
return x
|
|
|
|
def encode_text_full(self, text):
|
|
x = self.token_embedding(text).type(self.dtype)
|
|
|
|
x = x + (self.positional_embedding.to(x.device) * self.mask1.to(x.device)).type(self.dtype).to(x.device) + (self.positional_embedding_res.to(x.device) * self.mask2.to(x.device)).type(self.dtype).to(x.device)
|
|
|
|
x = x.permute(1, 0, 2)
|
|
x = self.transformer(x)
|
|
x = x.permute(1, 0, 2)
|
|
x = self.ln_final(x).type(self.dtype)
|
|
|
|
|
|
|
|
|
|
|
|
return x
|
|
|
|
|
|
def forward(self, image, text):
|
|
image_features = self.encode_image(image)
|
|
text_features, _ = self.encode_text(text)
|
|
|
|
|
|
image_features = image_features / image_features.norm(dim=1, keepdim=True)
|
|
text_features = text_features / text_features.norm(dim=1, keepdim=True)
|
|
|
|
|
|
logit_scale = self.logit_scale.exp()
|
|
logits_per_image = logit_scale * image_features @ text_features.t()
|
|
logits_per_text = logits_per_image.t()
|
|
|
|
|
|
return logits_per_image, logits_per_text
|
|
"""
|
|
original CLIP
|
|
"""
|
|
class CLIP(nn.Module):
|
|
def __init__(
|
|
self,
|
|
embed_dim: int,
|
|
|
|
image_resolution: int,
|
|
vision_layers: Union[Tuple[int, int, int, int], int],
|
|
vision_width: int,
|
|
vision_patch_size: int,
|
|
|
|
context_length: int,
|
|
txt_length: int,
|
|
vocab_size: int,
|
|
transformer_width: int,
|
|
transformer_heads: int,
|
|
transformer_layers: int):
|
|
super().__init__()
|
|
|
|
self.context_length = context_length
|
|
|
|
if isinstance(vision_layers, (tuple, list)):
|
|
vision_heads = vision_width * 32 // 64
|
|
self.visual = ModifiedResNet(layers=vision_layers,
|
|
output_dim=embed_dim,
|
|
heads=vision_heads,
|
|
input_resolution=image_resolution,
|
|
width=vision_width)
|
|
|
|
|
|
else:
|
|
vision_heads = vision_width // 64
|
|
self.visual = VisionTransformer(input_resolution=image_resolution,
|
|
patch_size=vision_patch_size,
|
|
width=vision_width,
|
|
layers=vision_layers,
|
|
heads=vision_heads,
|
|
output_dim=embed_dim)
|
|
|
|
self.transformer = Transformer(
|
|
width=transformer_width,
|
|
layers=transformer_layers,
|
|
heads=transformer_heads,
|
|
attn_mask=self.build_attention_mask(txt_length))
|
|
|
|
self.vocab_size = vocab_size
|
|
self.token_embedding = nn.Embedding(vocab_size, transformer_width)
|
|
self.positional_embedding = nn.Parameter(
|
|
torch.empty(self.context_length, transformer_width))
|
|
self.ln_final = LayerNorm(transformer_width)
|
|
|
|
self.text_projection = nn.Parameter(
|
|
torch.empty(transformer_width, embed_dim))
|
|
self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
|
|
|
|
self.token_embedding.requires_grad_ = False
|
|
self.initialize_parameters()
|
|
|
|
def initialize_parameters(self):
|
|
nn.init.normal_(self.token_embedding.weight, std=0.02)
|
|
nn.init.normal_(self.positional_embedding, std=0.01)
|
|
|
|
if isinstance(self.visual, ModifiedResNet):
|
|
if self.visual.attnpool is not None:
|
|
std = self.visual.attnpool.c_proj.in_features**-0.5
|
|
nn.init.normal_(self.visual.attnpool.q_proj.weight, std=std)
|
|
nn.init.normal_(self.visual.attnpool.k_proj.weight, std=std)
|
|
nn.init.normal_(self.visual.attnpool.v_proj.weight, std=std)
|
|
nn.init.normal_(self.visual.attnpool.c_proj.weight, std=std)
|
|
|
|
for resnet_block in [
|
|
self.visual.layer1, self.visual.layer2, self.visual.layer3,
|
|
self.visual.layer4
|
|
]:
|
|
for name, param in resnet_block.named_parameters():
|
|
if name.endswith("bn3.weight"):
|
|
nn.init.zeros_(param)
|
|
|
|
proj_std = (self.transformer.width**-0.5) * (
|
|
(2 * self.transformer.layers)**-0.5)
|
|
attn_std = self.transformer.width**-0.5
|
|
fc_std = (2 * self.transformer.width)**-0.5
|
|
for block in self.transformer.resblocks:
|
|
nn.init.normal_(block.attn.in_proj_weight, std=attn_std)
|
|
nn.init.normal_(block.attn.out_proj.weight, std=proj_std)
|
|
nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)
|
|
nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)
|
|
|
|
if self.text_projection is not None:
|
|
nn.init.normal_(self.text_projection,
|
|
std=self.transformer.width**-0.5)
|
|
|
|
def build_attention_mask(self, context_length):
|
|
|
|
|
|
mask = torch.empty(context_length, context_length)
|
|
mask.fill_(float("-inf"))
|
|
mask.triu_(1)
|
|
return mask
|
|
|
|
@property
|
|
def dtype(self):
|
|
return self.visual.conv1.weight.dtype
|
|
|
|
def encode_image(self, image):
|
|
return self.visual(image.type(self.dtype))
|
|
|
|
def encode_fq(self, image):
|
|
return self.fq_attnpool(image.type(self.dtype))
|
|
|
|
def encode_text(self, text):
|
|
a = self.token_embedding
|
|
x = self.token_embedding(text).type(
|
|
self.dtype)
|
|
|
|
x = x + self.positional_embedding.type(self.dtype)[:x.size(1)]
|
|
|
|
|
|
|
|
x = x.permute(1, 0, 2)
|
|
x = self.transformer(x)
|
|
x = x.permute(1, 0, 2)
|
|
x = self.ln_final(x).type(self.dtype)
|
|
|
|
|
|
|
|
state = x[torch.arange(x.shape[0]),
|
|
text.argmax(dim=-1)] @ self.text_projection
|
|
|
|
|
|
|
|
return x, state
|
|
|
|
def forward(self, image, text):
|
|
image_features = self.encode_image(image)
|
|
text_features = self.encode_text(text)
|
|
|
|
|
|
image_features = image_features / image_features.norm(dim=-1,
|
|
keepdim=True)
|
|
text_features = text_features / text_features.norm(dim=-1,
|
|
keepdim=True)
|
|
|
|
|
|
logit_scale = self.logit_scale.exp()
|
|
logits_per_image = logit_scale * image_features @ text_features.t()
|
|
logits_per_text = logits_per_image.t()
|
|
|
|
|
|
return logits_per_image, logits_per_text
|
|
|
|
"""
|
|
modified CLIP : without text encoder
|
|
"""
|
|
|
|
class zhCLIP(nn.Module):
|
|
def __init__(self,
|
|
embed_dim,
|
|
|
|
image_resolution: int,
|
|
vision_layers: Union[Tuple[int, int, int, int], int],
|
|
vision_width: int,
|
|
vision_patch_size: int):
|
|
super().__init__()
|
|
|
|
|
|
|
|
if isinstance(vision_layers, (tuple, list)):
|
|
vision_heads = vision_width * 32 // 64
|
|
self.visual = ModifiedResNet(layers=vision_layers,
|
|
output_dim=embed_dim,
|
|
heads=vision_heads,
|
|
input_resolution=image_resolution,
|
|
width=vision_width)
|
|
self.fq_attnpool = AttentionPool2d(image_resolution // 32, vision_width* 32,
|
|
vision_heads, embed_dim)
|
|
else:
|
|
vision_heads = vision_width // 64
|
|
self.visual = ModifiedVisionTransformer(input_resolution=image_resolution,
|
|
patch_size=vision_patch_size,
|
|
width=vision_width,
|
|
layers=vision_layers,
|
|
heads=vision_heads,
|
|
output_dim=embed_dim)
|
|
|
|
self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
|
|
self.initialize_parameters()
|
|
|
|
def initialize_parameters(self):
|
|
|
|
if isinstance(self.visual, ModifiedResNet):
|
|
if self.visual.attnpool is not None:
|
|
std = self.visual.attnpool.c_proj.in_features**-0.5
|
|
nn.init.normal_(self.visual.attnpool.q_proj.weight, std=std)
|
|
nn.init.normal_(self.visual.attnpool.k_proj.weight, std=std)
|
|
nn.init.normal_(self.visual.attnpool.v_proj.weight, std=std)
|
|
nn.init.normal_(self.visual.attnpool.c_proj.weight, std=std)
|
|
|
|
for resnet_block in [
|
|
self.visual.layer1, self.visual.layer2, self.visual.layer3,
|
|
self.visual.layer4
|
|
]:
|
|
for name, param in resnet_block.named_parameters():
|
|
if name.endswith("bn3.weight"):
|
|
nn.init.zeros_(param)
|
|
|
|
|
|
def build_attention_mask(self, context_length):
|
|
|
|
|
|
mask = torch.empty(context_length, context_length)
|
|
mask.fill_(float("-inf"))
|
|
mask.triu_(1)
|
|
return mask
|
|
|
|
@property
|
|
def dtype(self):
|
|
return self.visual.conv1.weight.dtype
|
|
|
|
def encode_image(self, image):
|
|
return self.visual(image.type(self.dtype))
|
|
|
|
def encode_fq(self, image):
|
|
return self.fq_attnpool(image.type(self.dtype))
|
|
|
|
def forward(self, image, text):
|
|
image_features = self.encode_image(image)
|
|
text_features = self.encode_text(text)
|
|
|
|
|
|
image_features = image_features / image_features.norm(dim=-1,
|
|
keepdim=True)
|
|
text_features = text_features / text_features.norm(dim=-1,
|
|
keepdim=True)
|
|
|
|
|
|
logit_scale = self.logit_scale.exp()
|
|
logits_per_image = logit_scale * image_features @ text_features.t()
|
|
logits_per_text = logits_per_image.t()
|
|
|
|
|
|
return logits_per_image, logits_per_text
|
|
|
|
|
|
def convert_weights(model: nn.Module):
|
|
"""Convert applicable model parameters to fp16"""
|
|
def _convert_weights_to_fp16(l):
|
|
if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)):
|
|
l.weight.data = l.weight.data.half()
|
|
if l.bias is not None:
|
|
l.bias.data = l.bias.data.half()
|
|
|
|
if isinstance(l, nn.MultiheadAttention):
|
|
for attr in [
|
|
*[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]],
|
|
"in_proj_bias", "bias_k", "bias_v"
|
|
]:
|
|
tensor = getattr(l, attr)
|
|
if tensor is not None:
|
|
tensor.data = tensor.data.half()
|
|
|
|
for name in ["text_projection", "proj"]:
|
|
if hasattr(l, name):
|
|
attr = getattr(l, name)
|
|
if attr is not None:
|
|
attr.data = attr.data.half()
|
|
|
|
model.apply(_convert_weights_to_fp16)
|
|
|
|
class PromptLearner(nn.Module):
|
|
|
|
def __init__(self, transformer_width, context_length, vocab_size,
|
|
transformer_layers, transformer_heads, bert_embed_dim):
|
|
super().__init__()
|
|
|
|
self.transformer_width = transformer_width
|
|
self.context_length = context_length
|
|
self.vocab_size = vocab_size
|
|
self.token_embedding = nn.Embedding(self.vocab_size, self.transformer_width)
|
|
|
|
self.transformer = Transformer(
|
|
width=transformer_width,
|
|
layers=transformer_layers,
|
|
heads=transformer_heads,
|
|
attn_mask=self.build_attention_mask()
|
|
)
|
|
|
|
self.positional_embedding = nn.Parameter(torch.empty(self.context_length, transformer_width))
|
|
self.ln_final = LayerNorm(transformer_width)
|
|
|
|
self.text_projection = nn.Parameter(torch.empty(transformer_width, bert_embed_dim))
|
|
|
|
|
|
|
|
|
|
def build_attention_mask(self):
|
|
|
|
|
|
mask = torch.empty(self.context_length, self.context_length)
|
|
mask.fill_(float("-inf"))
|
|
mask.triu_(1)
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return mask
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def init_label_emb(self, labels_path):
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label = open(labels_path, 'r').readlines()
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self.name_lens = [len(_tokenizer.encode(name)) for name in label]
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self.label_token = torch.zeros((len(self.name_lens), self.context_length), dtype=torch.long)
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for i, c in enumerate(label):
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self.label_token[i] = tokenize(f"There is a {c.strip()} in the scene")
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self.label_emb = torch.zeros((len(self.name_lens), max(self.name_lens), self.transformer_width))
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for i, embed in enumerate(self.token_embedding(self.label_token)):
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self.label_emb[i][:self.name_lens[i]] = embed[4:4 + self.name_lens[i]].clone().detach()
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def load_label_emb(self, label=None):
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self.name_lens = [len(_tokenizer.encode(name.split("\t")[-1])) for name in label]
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self.label_token = torch.zeros((len(self.name_lens), self.context_length), dtype=torch.long).cuda()
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for i, c in enumerate(label):
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name = c.split("\t")[-1]
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self.label_token[i] = tokenize(f"There is a {name.strip()} in the scene")
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self.label_emb = torch.zeros((len(self.name_lens), max(self.name_lens), self.transformer_width)).cuda()
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for i, embed in enumerate(self.token_embedding(self.label_token)):
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self.label_emb[i][:self.name_lens[i]] = embed[4:4 + self.name_lens[i]].clone().detach()
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def forward(self, device):
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label_embeds = self.token_embedding(self.label_token.to(device))
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for i in range(label_embeds.shape[0]):
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label_embeds[i, 4:4 + self.name_lens[i], :] = self.label_emb[i][:self.name_lens[i]]
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x = label_embeds + self.positional_embedding
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x = x.permute(1, 0, 2)
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x = self.transformer(x)
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x = x.permute(1, 0, 2)
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x = self.ln_final(x)
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res = x[torch.arange(x.shape[0]), self.label_token.argmax(dim=-1)] @ self.text_projection
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return res
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def build_promptlearner(state_dict: dict):
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embed_dim = state_dict["text_projection"].shape[1]
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context_length = state_dict["positional_embedding"].shape[0]
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vocab_size = state_dict["token_embedding.weight"].shape[0]
|
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transformer_width = state_dict["ln_final.weight"].shape[0]
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transformer_heads = transformer_width // 64
|
|
transformer_layers = len(
|
|
set(
|
|
k.split(".")[2] for k in state_dict
|
|
if k.startswith(f"transformer.resblocks")))
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model = PromptLearner(transformer_width, context_length, vocab_size,
|
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transformer_layers, transformer_heads, embed_dim)
|
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load_dict = {}
|
|
for k, v in state_dict.items():
|
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if not k.startswith("visual") and (
|
|
k not in ["logit_scale", "input_resolution", "context_length", "vocab_size"]):
|
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load_dict[k] = v
|
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|
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convert_weights(model)
|
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model.load_state_dict(load_dict, False)
|
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return model
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def build_model(state_dict: dict, txt_length: int):
|
|
vit = "visual.proj" in state_dict
|
|
|
|
if vit:
|
|
vision_width = state_dict["visual.conv1.weight"].shape[0]
|
|
vision_layers = len([
|
|
k for k in state_dict.keys()
|
|
if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")
|
|
])
|
|
vision_patch_size = state_dict["visual.conv1.weight"].shape[-1]
|
|
grid_size = round(
|
|
(state_dict["visual.positional_embedding"].shape[0] - 1)**0.5)
|
|
image_resolution = vision_patch_size * grid_size
|
|
else:
|
|
counts: list = [
|
|
len(
|
|
set(
|
|
k.split(".")[2] for k in state_dict
|
|
if k.startswith(f"visual.layer{b}")))
|
|
for b in [1, 2, 3, 4]
|
|
]
|
|
vision_layers = tuple(counts)
|
|
vision_width = state_dict["visual.layer1.0.conv1.weight"].shape[0]
|
|
output_width = round(
|
|
(state_dict["visual.attnpool.positional_embedding"].shape[0] -
|
|
1)**0.5)
|
|
vision_patch_size = None
|
|
assert output_width**2 + 1 == state_dict[
|
|
"visual.attnpool.positional_embedding"].shape[0]
|
|
image_resolution = output_width * 32
|
|
|
|
vision_heads = vision_width * 32 // 64
|
|
embed_dim = state_dict["text_projection"].shape[1]
|
|
|
|
context_length = txt_length
|
|
vocab_size = state_dict["token_embedding.weight"].shape[0]
|
|
transformer_width = state_dict["ln_final.weight"].shape[0]
|
|
transformer_heads = transformer_width // 64
|
|
transformer_layers = len(
|
|
set(
|
|
k.split(".")[2] for k in state_dict
|
|
if k.startswith(f"transformer.resblocks")))
|
|
|
|
model = CLIP(embed_dim, image_resolution, vision_layers, vision_width,
|
|
vision_patch_size, context_length, txt_length, vocab_size,
|
|
transformer_width, transformer_heads, transformer_layers)
|
|
|
|
for key in ["input_resolution", "context_length", "vocab_size", 'positional_embedding']:
|
|
if key in state_dict:
|
|
del state_dict[key]
|
|
|
|
convert_weights(model)
|
|
model.load_state_dict(state_dict, False)
|
|
return model.eval(), image_resolution, vision_heads, embed_dim, vision_width, vision_patch_size
|
|
|
|
def build_lclip_model(state_dict: dict, load_from_clip: bool):
|
|
vit = "visual.proj" in state_dict
|
|
|
|
if vit:
|
|
vision_width = state_dict["visual.conv1.weight"].shape[0]
|
|
vision_layers = len([k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")])
|
|
vision_patch_size = state_dict["visual.conv1.weight"].shape[-1]
|
|
grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5)
|
|
image_resolution = vision_patch_size * grid_size
|
|
|
|
else:
|
|
counts: list = [len(set(k.split(".")[2] for k in state_dict if k.startswith(f"visual.layer{b}"))) for b in [1, 2, 3, 4]]
|
|
vision_layers = tuple(counts)
|
|
vision_width = state_dict["visual.layer1.0.conv1.weight"].shape[0]
|
|
output_width = round((state_dict["visual.attnpool.positional_embedding"].shape[0] - 1) ** 0.5)
|
|
vision_patch_size = None
|
|
assert output_width ** 2 + 1 == state_dict["visual.attnpool.positional_embedding"].shape[0]
|
|
image_resolution = output_width * 32
|
|
|
|
embed_dim = state_dict["text_projection"].shape[1]
|
|
|
|
context_length = state_dict["positional_embedding"].shape[0]
|
|
vocab_size = state_dict["token_embedding.weight"].shape[0]
|
|
transformer_width = state_dict["ln_final.weight"].shape[0]
|
|
transformer_heads = transformer_width // 64
|
|
transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith("transformer.resblocks")))
|
|
|
|
model = LCLIP(
|
|
embed_dim,
|
|
image_resolution, vision_layers, vision_width, vision_patch_size,
|
|
context_length, vocab_size, transformer_width, transformer_heads, transformer_layers, load_from_clip
|
|
)
|
|
|
|
for key in ["input_resolution", "context_length", "vocab_size"]:
|
|
if key in state_dict:
|
|
del state_dict[key]
|
|
|
|
convert_weights(model)
|
|
|
|
model.load_state_dict(state_dict, strict=False)
|
|
vision_heads = vision_width // 64
|
|
|
|
return model.eval(), image_resolution, vision_heads, embed_dim, vision_width, vision_patch_size
|
|
|
|
def build_modified_model(state_dict: dict, txt_length: int):
|
|
vit = "visual.proj" in state_dict
|
|
|
|
if vit:
|
|
vision_width = state_dict["visual.conv1.weight"].shape[0]
|
|
vision_layers = len([
|
|
k for k in state_dict.keys()
|
|
if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")
|
|
])
|
|
vision_patch_size = state_dict["visual.conv1.weight"].shape[-1]
|
|
grid_size = round(
|
|
(state_dict["visual.positional_embedding"].shape[0] - 1)**0.5)
|
|
image_resolution = vision_patch_size * grid_size
|
|
else:
|
|
counts: list = [
|
|
len(
|
|
set(
|
|
k.split(".")[2] for k in state_dict
|
|
if k.startswith(f"visual.layer{b}")))
|
|
for b in [1, 2, 3, 4]
|
|
]
|
|
vision_layers = tuple(counts)
|
|
vision_width = state_dict["visual.layer1.0.conv1.weight"].shape[0]
|
|
|
|
output_width = round(
|
|
(state_dict["visual.attnpool.positional_embedding"].shape[0] -
|
|
1)**0.5)
|
|
vision_patch_size = None
|
|
assert output_width**2 + 1 == state_dict[
|
|
"visual.attnpool.positional_embedding"].shape[0]
|
|
image_resolution = output_width * 32
|
|
embed_dim = state_dict["text_projection"].shape[1]
|
|
|
|
model = zhCLIP(embed_dim, image_resolution, vision_layers, vision_width,
|
|
vision_patch_size)
|
|
|
|
for key in ["input_resolution", "context_length", "vocab_size"]:
|
|
if key in state_dict:
|
|
del state_dict[key]
|
|
|
|
convert_weights(model)
|
|
model.load_state_dict(state_dict, False)
|
|
return model.eval()
|
|
|