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| import torch | |
| from torch import nn, Tensor | |
| import torch.nn.functional as F | |
| from collections import OrderedDict | |
| from typing import Optional, Iterable | |
| class LayerNorm(nn.LayerNorm): | |
| """Subclass torch's LayerNorm to handle fp16.""" | |
| def forward(self, x: Tensor): | |
| orig_type = x.dtype | |
| ret = super().forward(x.type(torch.float32)) | |
| return ret.type(orig_type) | |
| class QuickGELU(nn.Module): | |
| def forward(self, x: Tensor): | |
| return x * torch.sigmoid(1.702 * x) | |
| class ResidualAttentionBlock(nn.Module): | |
| def __init__(self, d_model: int, n_head: int, attn_mask: Tensor = None): | |
| super().__init__() | |
| self.attn = nn.MultiheadAttention(d_model, n_head) | |
| self.ln_1 = LayerNorm(d_model) | |
| self.mlp = nn.Sequential(OrderedDict([ | |
| ("c_fc", nn.Linear(d_model, d_model * 4)), | |
| ("gelu", QuickGELU()), | |
| ("c_proj", nn.Linear(d_model * 4, d_model)) | |
| ])) | |
| self.ln_2 = LayerNorm(d_model) | |
| self.attn_mask = attn_mask | |
| def attention(self, x: Tensor): | |
| self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None | |
| return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0] | |
| def forward(self, x: Tensor) -> Tensor: | |
| x = x + self.attention(self.ln_1(x)) | |
| x = x + self.mlp(self.ln_2(x)) | |
| return x | |
| class Transformer(nn.Module): | |
| def __init__(self, width: int, layers: int, heads: int, attn_mask: Tensor = None): | |
| super().__init__() | |
| self.width = width | |
| self.layers = layers | |
| self.resblocks = nn.Sequential(*[ResidualAttentionBlock(width, heads, attn_mask) for _ in range(layers)]) | |
| def forward(self, x: Tensor): | |
| return self.resblocks(x) | |
| class Bottleneck(nn.Module): | |
| expansion = 4 | |
| def __init__(self, inplanes, planes, stride=1): | |
| super().__init__() | |
| # all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1 | |
| self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False) | |
| self.bn1 = nn.BatchNorm2d(planes) | |
| self.relu1 = nn.ReLU(inplace=True) | |
| self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False) | |
| self.bn2 = nn.BatchNorm2d(planes) | |
| self.relu2 = nn.ReLU(inplace=True) | |
| self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity() | |
| self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False) | |
| self.bn3 = nn.BatchNorm2d(planes * self.expansion) | |
| self.relu3 = nn.ReLU(inplace=True) | |
| self.downsample = None | |
| self.stride = stride | |
| if stride > 1 or inplanes != planes * Bottleneck.expansion: | |
| # downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1 | |
| self.downsample = nn.Sequential(OrderedDict([ | |
| ("-1", nn.AvgPool2d(stride)), | |
| ("0", nn.Conv2d(inplanes, planes * self.expansion, 1, stride=1, bias=False)), | |
| ("1", nn.BatchNorm2d(planes * self.expansion)) | |
| ])) | |
| def forward(self, x: Tensor): | |
| identity = x | |
| out = self.relu1(self.bn1(self.conv1(x))) | |
| out = self.relu2(self.bn2(self.conv2(out))) | |
| out = self.avgpool(out) | |
| out = self.bn3(self.conv3(out)) | |
| if self.downsample is not None: | |
| identity = self.downsample(x) | |
| out += identity | |
| out = self.relu3(out) | |
| return out | |
| class AttentionPool2d(nn.Module): | |
| def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None): | |
| super().__init__() | |
| self.positional_embedding = nn.Parameter(torch.randn(spacial_dim + 1, embed_dim) / embed_dim ** 0.5) | |
| self.k_proj = nn.Linear(embed_dim, embed_dim) | |
| self.q_proj = nn.Linear(embed_dim, embed_dim) | |
| self.v_proj = nn.Linear(embed_dim, embed_dim) | |
| self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim) | |
| self.num_heads = num_heads | |
| def forward(self, x): | |
| x = x.flatten(start_dim=2).permute(2, 0, 1) # NCHW -> (HW)NC | |
| x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0) # (HW+1)NC | |
| x = x + self.positional_embedding[:, None, :].to(x.dtype) # (HW+1)NC | |
| x, _ = F.multi_head_attention_forward( | |
| query=x[:1], key=x, value=x, | |
| embed_dim_to_check=x.shape[-1], | |
| num_heads=self.num_heads, | |
| q_proj_weight=self.q_proj.weight, | |
| k_proj_weight=self.k_proj.weight, | |
| v_proj_weight=self.v_proj.weight, | |
| in_proj_weight=None, | |
| in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]), | |
| bias_k=None, | |
| bias_v=None, | |
| add_zero_attn=False, | |
| dropout_p=0, | |
| out_proj_weight=self.c_proj.weight, | |
| out_proj_bias=self.c_proj.bias, | |
| use_separate_proj_weight=True, | |
| training=self.training, | |
| need_weights=False | |
| ) | |
| return x.squeeze(0) | |