742 lines
33 KiB
Python
742 lines
33 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 ResidualAttentionBlock_IVLP(nn.Module):
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def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None, add_prompt=False,
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text_layer=False, i=0, design_details=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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# Only add learnable tokens if flag is set True
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# For the first iteration i, we should not add the learnable parameters
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# as it is already been taken care of in the very start, for both text
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# and the visual branch
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self.text_layer = text_layer
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self.attn_mask = attn_mask
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if i != 0:
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self.add_prompt = add_prompt
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if self.add_prompt:
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if self.text_layer:
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self.n_ctx_text = design_details["language_ctx"] # hyperparameter
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ctx_vectors = torch.empty(self.n_ctx_text, d_model)
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else:
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self.n_ctx_visual = design_details["vision_ctx"] # hyperparameter
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ctx_vectors = torch.empty(self.n_ctx_visual, d_model)
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# Code snippet for per layer visual prompts
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nn.init.normal_(ctx_vectors, std=0.02)
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self.VPT_shallow = nn.Parameter(ctx_vectors)
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else:
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self.add_prompt = False
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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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# Will need to append the learnable tokens for this layer here
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# Check if flag was set for this layer or not
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if self.add_prompt:
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# Also see if this is textual transformer layer or not
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if not self.text_layer:
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# Remove the outputs produced by learnable tokens of previous layer
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prefix = x[0:x.shape[0] - self.n_ctx_visual, :, :]
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# Create/configure learnable tokens of this layer
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visual_context = self.VPT_shallow.expand(x.shape[1], -1, -1).permute(1, 0, 2).half()
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# Add the learnable tokens of this layer with the input, by replacing the previous
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# layer learnable tokens
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x = torch.cat([prefix, visual_context], dim=0)
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else:
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# Appending the learnable tokens in different way
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# x -> [77, NCLS, DIM]
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# First remove the learnable tokens from previous layer
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prefix = x[:1, :, :]
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suffix = x[1 + self.n_ctx_text:, :, :]
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# Create/configure learnable tokens of this layer
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textual_context = self.VPT_shallow.expand(x.shape[1], -1, -1).permute(1, 0, 2).half()
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# Add the learnable tokens of this layer with the input, replaced by previous
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# layer learnable tokens
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x = torch.cat([prefix, textual_context, suffix], dim=0)
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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 ResidualAttentionBlock_MaPLe(nn.Module):
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def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None, design_details=None,
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text_layer=False, i=0):
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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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# For the first iteration i, we do not need to add the learnable parameters here
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# as it will be added in the beginning, for both text and the vision branch
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self.text_layer = text_layer
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self.attn_mask = attn_mask
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# This must be consistent with the config file prompt
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self.compound_prompt_nctx = design_details['maple_length']
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if i == 0:
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self.first_layer = True
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else:
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self.first_layer = False
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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=True, attn_mask=self.attn_mask)
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def forward(self, inputs):
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# For the first layer, we do not need to add any duplicate, as it is already added
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# as the shallow version
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x = inputs[0]
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compound_prompts_deeper = inputs[1]
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counter = inputs[2]
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if not self.first_layer:
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if len(compound_prompts_deeper) > 0:
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# This means that deeper compound prompts are turned on
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# Here it behaves differently for text and visual side
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# Forward function is same for both
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if not self.text_layer:
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# First check if the ith layer needs compound prompts or not
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if not (counter > len(compound_prompts_deeper) - 1):
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# Remove the outputs produced by learnable tokens of previous layer
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prefix = x[0:x.shape[0] - self.compound_prompt_nctx, :, :]
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# Create/configure learnable tokens of this layer
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visual_context = compound_prompts_deeper[counter] # extract the correct index
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visual_context = visual_context.expand(x.shape[1], -1, -1).permute(1, 0, 2).half()
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# Add the learnable tokens of this layer with the input, by replacing previous
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# layer learnable tokens
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x = torch.cat([prefix, visual_context], dim=0)
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# Once done, update the counter, so that the next time, it does not use same learnable tokens
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counter += 1
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else:
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# First check if the ith layer needs compound prompts or not
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if not (counter > len(compound_prompts_deeper) - 1):
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# Appending the learnable tokens in different way
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# x -> [77, NCLS, DIM]
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# First remove the learnable tokens from previous layer
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prefix = x[:1, :, :]
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suffix = x[1 + self.compound_prompt_nctx:, :, :]
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# Create/configure learnable tokens of this layer
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textual_context = compound_prompts_deeper[counter]
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textual_context = textual_context.expand(x.shape[1], -1, -1).permute(1, 0, 2).half()
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# Add the learnable tokens of this layer with the input, replaced by previous
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# layer learnable tokens
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x = torch.cat([prefix, textual_context, suffix], dim=0)
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# Once done, update the counter, so that the next time, it does not use same learnable tokens
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counter += 1
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inp,attn_mask = self.attention(self.ln_1(x))
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x = x + inp
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x = x + self.mlp(self.ln_2(x))
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if self.text_layer:
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return [x, compound_prompts_deeper, counter]
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else:
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return [x, compound_prompts_deeper, counter, attn_mask] # return again as a list, so that nn.seq can work
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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, prompts_needed=0,
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text_layer=False, design_details=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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# Implements respective encoder blocks for a given design choice
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current_trainer = design_details['trainer']
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if current_trainer == 'IVLP' or current_trainer == 'VPT':
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self.resblocks = nn.Sequential(*[ResidualAttentionBlock_IVLP(width, heads, attn_mask, True,
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text_layer, i,
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design_details) if prompts_needed > i
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else ResidualAttentionBlock_IVLP(width, heads, attn_mask, False,
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text_layer, i, design_details)
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for i in range(layers)])
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elif current_trainer == 'MaPLe':
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self.resblocks = nn.Sequential(
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*[ResidualAttentionBlock_MaPLe(width, heads, attn_mask, design_details, text_layer, i)
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for i in range(layers)])
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else:
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# Corresponds to default CoOp or CoCoOp
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assert current_trainer == 'CoOp' or current_trainer == 'CoCoOp'
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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 Transformer_normal(nn.Module):
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def __init__(self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None, prompts_needed=0,
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text_layer=False, design_details=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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# Implements respective encoder blocks for a given design choice
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# current_trainer = design_details['trainer']
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# if current_trainer == 'IVLP' or current_trainer == 'VPT':
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# self.resblocks = nn.Sequential(*[ResidualAttentionBlock_IVLP(width, heads, attn_mask, True,
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# text_layer, i,
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# design_details) if prompts_needed > i
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# else ResidualAttentionBlock_IVLP(width, heads, attn_mask, False,
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# text_layer, i, design_details)
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# for i in range(layers)])
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# elif current_trainer == 'MaPLe':
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# self.resblocks = nn.Sequential(
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# *[ResidualAttentionBlock_MaPLe(width, heads, attn_mask, design_details, text_layer, i)
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# for i in range(layers)])
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# else:
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# # Corresponds to default CoOp or CoCoOp
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# assert current_trainer == 'CoOp' or current_trainer == 'CoCoOp'
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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,
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output_dim: int, design_details):
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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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if design_details["vision_depth"] == 0:
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self.VPT_shallow = False
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else:
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self.VPT_shallow = True
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if self.VPT_shallow:
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# Add visual prompt tokens here
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n_ctx = design_details["vision_ctx"] # hyperparameter
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ctx_vectors = torch.empty(n_ctx, width)
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nn.init.normal_(ctx_vectors, std=0.02)
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self.VPT = nn.Parameter(ctx_vectors)
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# self.VPT.half()
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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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|
# hyper-parameter if need to add prompt embeddings inside to the input
|
|
# of transformer block or not:
|
|
self.prompt_till_layer_visual = design_details["vision_depth"]
|
|
self.transformer = Transformer_normal(width, layers, heads, prompts_needed=self.prompt_till_layer_visual,
|
|
design_details=design_details)
|
|
|
|
self.ln_post = LayerNorm(width)
|
|
self.proj = nn.Parameter(scale * torch.randn(width, output_dim))
|
|
|
|
def forward(self, x: torch.Tensor):
|
|
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]
|
|
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)
|
|
|
|
# After positional embeddings, we will attach prompts with the model, remember only those
|
|
# are trainable parameters here in whole image encoder.
|
|
if self.VPT_shallow:
|
|
visual_ctx = self.VPT.expand(x.shape[0], -1, -1).half()
|
|
x = torch.cat([x, visual_ctx], dim=1)
|
|
else:
|
|
assert self.prompt_till_layer_visual == 0
|
|
|
|
# Normal code as before
|
|
x = self.ln_pre(x)
|
|
|
|
x = x.permute(1, 0, 2) # NLD -> LND
|
|
x = self.transformer(x)
|
|
x = x.permute(1, 0, 2) # LND -> NLD
|
|
|
|
x = self.ln_post(x[:, 0, :])
|
|
|
|
if self.proj is not None:
|
|
x = x @ self.proj
|
|
|
|
return x
|
|
|
|
|
|
class VisionTransformer_MaPLe(nn.Module):
|
|
def __init__(self, input_resolution: int, patch_size: int, width: int, layers: int, heads: int, output_dim: int,
|
|
design_details):
|
|
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.VPT_shallow = True
|
|
scale = width ** -0.5
|
|
self.patch_num = self.input_resolution // patch_size
|
|
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)
|
|
# hyper-parameter if need to add prompt embeddings inside to the input
|
|
# of transformer block or not:
|
|
self.prompt_till_layer_visual = 0
|
|
self.transformer = Transformer(width, layers, heads, design_details=design_details)
|
|
|
|
self.ln_post = LayerNorm(width)
|
|
self.proj = nn.Parameter(scale * torch.randn(width, output_dim))
|
|
|
|
def forward(self, x: torch.Tensor, shared_ctx, compound_deeper_prompts):
|
|
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, output_size]
|
|
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)
|
|
|
|
# After positional embeddings, we will attach prompts with the model, remember only those
|
|
# are trainable parameters here in whole image encoder.
|
|
if self.VPT_shallow:
|
|
visual_ctx = shared_ctx.expand(x.shape[0], -1, -1).half()
|
|
x = torch.cat([x, visual_ctx], dim=1)
|
|
else:
|
|
assert self.prompt_till_layer_visual == 0
|
|
|
|
# Normal code as before
|
|
x = self.ln_pre(x)
|
|
|
|
x = x.permute(1, 0, 2) # NLD -> LND
|
|
# Again combine the inputs, so nn.sequential can work
|
|
outputs = self.transformer([x, compound_deeper_prompts, 0]) # third argument is counter
|
|
x = outputs[0]
|
|
mask = outputs[3]
|
|
x = x.permute(1, 0, 2) # LND -> NLD
|
|
visual_ctx = x[:,-shared_ctx.shape[0]:,:]
|
|
x = self.ln_post(x[:, 0, :]) #only cls embedding is selected
|
|
visual_ctx = self.ln_post(visual_ctx)
|
|
if self.proj is not None:
|
|
x = x @ self.proj
|
|
visual_ctx = visual_ctx @ self.proj
|
|
return x,visual_ctx,mask
|
|
|
|
|
|
class CLIP(nn.Module):
|
|
def __init__(self,
|
|
embed_dim: int,
|
|
# vision
|
|
image_resolution: int,
|
|
vision_layers: Union[Tuple[int, int, int, int], int],
|
|
vision_width: int,
|
|
vision_patch_size: int,
|
|
# text
|
|
context_length: int,
|
|
vocab_size: int,
|
|
transformer_width: int,
|
|
transformer_heads: int,
|
|
transformer_layers: int,
|
|
design_details
|
|
):
|
|
super().__init__()
|
|
|
|
self.context_length = context_length
|
|
trainer = design_details['trainer']
|
|
|
|
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
|
|
if trainer == "MaPLe":
|
|
self.visual = VisionTransformer_MaPLe(
|
|
input_resolution=image_resolution,
|
|
patch_size=vision_patch_size,
|
|
width=vision_width,
|
|
layers=vision_layers,
|
|
heads=vision_heads,
|
|
output_dim=embed_dim,
|
|
design_details=design_details
|
|
)
|
|
self.visual_ori = VisionTransformer(
|
|
input_resolution=image_resolution,
|
|
patch_size=vision_patch_size,
|
|
width=vision_width,
|
|
layers=vision_layers,
|
|
heads=vision_heads,
|
|
output_dim=embed_dim,
|
|
design_details=design_details
|
|
)
|
|
else:
|
|
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,
|
|
design_details=design_details
|
|
)
|
|
# hyper-parameter if need to add prompt embeddings inside to the input
|
|
# of transformer block or not:
|
|
prompt_till_layer_text = design_details['language_depth']
|
|
self.transformer = Transformer(
|
|
width=transformer_width,
|
|
layers=transformer_layers,
|
|
heads=transformer_heads,
|
|
attn_mask=self.build_attention_mask(),
|
|
prompts_needed=prompt_till_layer_text,
|
|
text_layer=True,
|
|
design_details=design_details
|
|
)
|
|
|
|
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.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):
|
|
# lazily create causal attention mask, with full attention between the vision tokens
|
|
# pytorch uses additive attention mask; fill with -inf
|
|
mask = torch.empty(self.context_length, self.context_length)
|
|
mask.fill_(float("-inf"))
|
|
mask.triu_(1) # zero out the lower diagonal
|
|
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) # [batch_size, n_ctx, d_model]
|
|
|
|
x = x + self.positional_embedding.type(self.dtype)
|
|
x = x.permute(1, 0, 2) # NLD -> LND
|
|
x = self.transformer(x)
|
|
x = x.permute(1, 0, 2) # LND -> NLD
|
|
x = self.ln_final(x).type(self.dtype)
|
|
|
|
# x.shape = [batch_size, n_ctx, transformer.width]
|
|
# take features from the eot embedding (eot_token is the highest number in each sequence)
|
|
x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection
|
|
|
|
return x
|
|
|
|
def forward(self, image, text):
|
|
image_features = self.encode_image(image)
|
|
text_features = self.encode_text(text)
|
|
|
|
# normalized features
|
|
image_features = image_features / image_features.norm(dim=-1, keepdim=True)
|
|
text_features = text_features / text_features.norm(dim=-1, keepdim=True)
|
|
|
|
# cosine similarity as logits
|
|
logit_scale = self.logit_scale.exp()
|
|
logits_per_image = logit_scale * image_features @ text_features.t()
|
|
logits_per_text = logit_scale * text_features @ image_features.t()
|
|
|
|
# shape = [global_batch_size, global_batch_size]
|
|
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)
|
|
|
|
|
|
def build_model(state_dict: dict, design_details):
|
|
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(f"transformer.resblocks")))
|
|
|
|
model = CLIP(
|
|
embed_dim,
|
|
image_resolution, vision_layers, vision_width, vision_patch_size,
|
|
context_length, vocab_size, transformer_width, transformer_heads, transformer_layers, design_details
|
|
)
|
|
|
|
for key in ["input_resolution", "context_length", "vocab_size"]:
|
|
if key in state_dict:
|
|
del state_dict[key]
|
|
|
|
convert_weights(model)
|
|
try:
|
|
model.load_state_dict(state_dict)
|
|
except:
|
|
missing_keys, _ = model.load_state_dict(state_dict, strict=False)
|
|
print('Weights not found for some missing keys: ', missing_keys)
|
|
return model.eval()
|