433 lines
17 KiB
Python
433 lines
17 KiB
Python
from collections import OrderedDict
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from typing import Tuple, Union
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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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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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# all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1
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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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# downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1
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self.downsample = nn.Sequential(OrderedDict([
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("-1", nn.AvgPool2d(stride)),
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("0", nn.Conv2d(inplanes, planes * self.expansion, 1, stride=1, bias=False)),
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("1", nn.BatchNorm2d(planes * self.expansion))
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]))
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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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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.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3]).permute(2, 0, 1) # NCHW -> (HW)NC
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x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0) # (HW+1)NC
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x = x + self.positional_embedding[:, None, :].to(x.dtype) # (HW+1)NC
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x, _ = F.multi_head_attention_forward(
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query=x, 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[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, layers, output_dim, heads, input_resolution=224, 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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# the 3-layer stem
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self.conv1 = nn.Conv2d(3, width // 2, kernel_size=3, stride=2, padding=1, bias=False)
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self.bn1 = nn.BatchNorm2d(width // 2)
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self.conv2 = nn.Conv2d(width // 2, width // 2, kernel_size=3, padding=1, bias=False)
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self.bn2 = nn.BatchNorm2d(width // 2)
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self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, 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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# residual layers
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self._inplanes = width # this is a *mutable* variable used during construction
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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 # the ResNet feature dimension
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self.attnpool = AttentionPool2d(input_resolution // 32, embed_dim, heads, output_dim)
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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), (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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x = self.layer2(x)
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x = self.layer3(x)
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x = self.layer4(x)
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x = self.attnpool(x)
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return x
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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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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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class ResidualAttentionBlock(nn.Module):
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def __init__(self, d_model: int, n_head: int, 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(OrderedDict([
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("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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]))
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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(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None
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return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0]
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def forward(self, x: torch.Tensor):
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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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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 VisionTransformer(nn.Module):
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def __init__(self, input_resolution: int, patch_size: int, width: int, 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, out_channels=width, kernel_size=patch_size, stride=patch_size, 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((input_resolution // patch_size) ** 2 + 1, width))
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self.ln_pre = LayerNorm(width)
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self.transformer = Transformer(width, layers, heads)
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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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x = self.conv1(x) # shape = [*, width, grid, grid]
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x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2]
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x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width]
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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]
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x = x + self.positional_embedding.to(x.dtype)
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x = self.ln_pre(x)
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x = x.permute(1, 0, 2) # NLD -> LND
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x = self.transformer(x)
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x = x.permute(1, 0, 2) # LND -> NLD
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x = self.ln_post(x[:, 0, :])
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if self.proj is not None:
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x = x @ self.proj
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return x
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class CLIP(nn.Module):
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def __init__(self,
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embed_dim: int,
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# vision
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image_resolution: int,
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vision_layers: Union[Tuple[int, int, int, int], int],
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vision_width: int,
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vision_patch_size: int,
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# text
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context_length: int,
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vocab_size: int,
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transformer_width: int,
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transformer_heads: int,
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transformer_layers: int
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):
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super().__init__()
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self.context_length = context_length
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if isinstance(vision_layers, (tuple, list)):
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vision_heads = vision_width * 32 // 64
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self.visual = ModifiedResNet(
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layers=vision_layers,
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output_dim=embed_dim,
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heads=vision_heads,
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input_resolution=image_resolution,
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width=vision_width
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)
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else:
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vision_heads = vision_width // 64
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self.visual = VisionTransformer(
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input_resolution=image_resolution,
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patch_size=vision_patch_size,
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width=vision_width,
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layers=vision_layers,
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heads=vision_heads,
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output_dim=embed_dim
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)
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self.transformer = Transformer(
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width=transformer_width,
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layers=transformer_layers,
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heads=transformer_heads,
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attn_mask=self.build_attention_mask()
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)
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self.vocab_size = vocab_size
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self.token_embedding = nn.Embedding(vocab_size, transformer_width)
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self.positional_embedding = nn.Parameter(torch.empty(self.context_length, transformer_width))
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self.ln_final = LayerNorm(transformer_width)
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self.text_projection = nn.Parameter(torch.empty(transformer_width, embed_dim))
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self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
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self.initialize_parameters()
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def initialize_parameters(self):
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nn.init.normal_(self.token_embedding.weight, std=0.02)
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nn.init.normal_(self.positional_embedding, std=0.01)
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if isinstance(self.visual, ModifiedResNet):
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if self.visual.attnpool is not None:
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std = self.visual.attnpool.c_proj.in_features ** -0.5
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nn.init.normal_(self.visual.attnpool.q_proj.weight, std=std)
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nn.init.normal_(self.visual.attnpool.k_proj.weight, std=std)
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nn.init.normal_(self.visual.attnpool.v_proj.weight, std=std)
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nn.init.normal_(self.visual.attnpool.c_proj.weight, std=std)
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for resnet_block in [self.visual.layer1, self.visual.layer2, self.visual.layer3, self.visual.layer4]:
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for name, param in resnet_block.named_parameters():
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if name.endswith("bn3.weight"):
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nn.init.zeros_(param)
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proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5)
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attn_std = self.transformer.width ** -0.5
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fc_std = (2 * self.transformer.width) ** -0.5
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for block in self.transformer.resblocks:
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nn.init.normal_(block.attn.in_proj_weight, std=attn_std)
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nn.init.normal_(block.attn.out_proj.weight, std=proj_std)
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nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)
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nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)
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if self.text_projection is not None:
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nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5)
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def build_attention_mask(self):
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# lazily create causal attention mask, with full attention between the vision tokens
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# pytorch uses additive attention mask; fill with -inf
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mask = torch.empty(self.context_length, self.context_length)
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mask.fill_(float("-inf"))
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mask.triu_(1) # zero out the lower diagonal
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return mask
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@property
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def dtype(self):
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return self.visual.conv1.weight.dtype
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def encode_image(self, image):
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return self.visual(image.type(self.dtype))
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def encode_text(self, text):
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x = self.token_embedding(text).type(self.dtype) # [batch_size, n_ctx, d_model]
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x = x + self.positional_embedding.type(self.dtype)
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x = x.permute(1, 0, 2) # NLD -> LND
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x = self.transformer(x)
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x = x.permute(1, 0, 2) # LND -> NLD
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x = self.ln_final(x).type(self.dtype)
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# x.shape = [batch_size, n_ctx, transformer.width]
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# take features from the eot embedding (eot_token is the highest number in each sequence)
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x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection
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return x
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def forward(self, image, text):
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image_features = self.encode_image(image)
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text_features = self.encode_text(text)
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# normalized features
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image_features = image_features / image_features.norm(dim=-1, keepdim=True)
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text_features = text_features / text_features.norm(dim=-1, keepdim=True)
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# cosine similarity as logits
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logit_scale = self.logit_scale.exp()
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logits_per_image = logit_scale * image_features @ text_features.t()
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logits_per_text = logits_per_image.t()
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# shape = [global_batch_size, global_batch_size]
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return logits_per_image, logits_per_text
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def convert_weights(model: nn.Module):
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"""Convert applicable model parameters to fp16"""
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def _convert_weights_to_fp16(l):
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if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)):
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l.weight.data = l.weight.data.half()
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if l.bias is not None:
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l.bias.data = l.bias.data.half()
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if isinstance(l, nn.MultiheadAttention):
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for attr in [*[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]], "in_proj_bias", "bias_k", "bias_v"]:
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tensor = getattr(l, attr)
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if tensor is not None:
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tensor.data = tensor.data.half()
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for name in ["text_projection", "proj"]:
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if hasattr(l, name):
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attr = getattr(l, name)
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if attr is not None:
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attr.data = attr.data.half()
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model.apply(_convert_weights_to_fp16)
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def build_model(state_dict: dict):
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vit = "visual.proj" in state_dict
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if vit:
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vision_width = state_dict["visual.conv1.weight"].shape[0]
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vision_layers = len([k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")])
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vision_patch_size = state_dict["visual.conv1.weight"].shape[-1]
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grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5)
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image_resolution = vision_patch_size * grid_size
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else:
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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]]
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vision_layers = tuple(counts)
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vision_width = state_dict["visual.layer1.0.conv1.weight"].shape[0]
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output_width = round((state_dict["visual.attnpool.positional_embedding"].shape[0] - 1) ** 0.5)
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vision_patch_size = None
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assert output_width ** 2 + 1 == state_dict["visual.attnpool.positional_embedding"].shape[0]
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image_resolution = output_width * 32
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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
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transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith(f"transformer.resblocks")))
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model = CLIP(
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embed_dim,
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image_resolution, vision_layers, vision_width, vision_patch_size,
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context_length, vocab_size, transformer_width, transformer_heads, transformer_layers
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)
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for key in ["input_resolution", "context_length", "vocab_size"]:
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if key in state_dict:
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del state_dict[key]
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convert_weights(model)
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model.load_state_dict(state_dict)
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return model.eval()
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