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| from collections import OrderedDict | |
| import torch | |
| from torch import nn | |
| from torch.nn import functional as F | |
| from open_clip.utils import freeze_batch_norm_2d | |
| 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.act1 = nn.ReLU(inplace=True) | |
| self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False) | |
| self.bn2 = nn.BatchNorm2d(planes) | |
| self.act2 = 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.act3 = nn.ReLU(inplace=True) | |
| self.downsample = None | |
| self.stride = stride | |
| if stride > 1 or inplanes != planes * Bottleneck.expansion: | |
| # downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1 | |
| self.downsample = nn.Sequential(OrderedDict([ | |
| ("-1", nn.AvgPool2d(stride)), | |
| ("0", nn.Conv2d(inplanes, planes * self.expansion, 1, stride=1, bias=False)), | |
| ("1", nn.BatchNorm2d(planes * self.expansion)) | |
| ])) | |
| def forward(self, x: torch.Tensor): | |
| identity = x | |
| out = self.act1(self.bn1(self.conv1(x))) | |
| out = self.act2(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.act3(out) | |
| return out | |
| class AttentionPool2d(nn.Module): | |
| def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None): | |
| super().__init__() | |
| self.positional_embedding = nn.Parameter(torch.randn(spacial_dim ** 2 + 1, embed_dim) / embed_dim ** 0.5) | |
| self.k_proj = nn.Linear(embed_dim, embed_dim) | |
| self.q_proj = nn.Linear(embed_dim, embed_dim) | |
| self.v_proj = nn.Linear(embed_dim, embed_dim) | |
| self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim) | |
| self.num_heads = num_heads | |
| def forward(self, x): | |
| x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3]).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, 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[0] | |
| class ModifiedResNet(nn.Module): | |
| """ | |
| A ResNet class that is similar to torchvision's but contains the following changes: | |
| - There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool. | |
| - Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1 | |
| - The final pooling layer is a QKV attention instead of an average pool | |
| """ | |
| def __init__(self, layers, output_dim, heads, image_size=224, width=64): | |
| super().__init__() | |
| self.output_dim = output_dim | |
| self.image_size = image_size | |
| # the 3-layer stem | |
| self.conv1 = nn.Conv2d(3, width // 2, kernel_size=3, stride=2, padding=1, bias=False) | |
| self.bn1 = nn.BatchNorm2d(width // 2) | |
| self.act1 = nn.ReLU(inplace=True) | |
| self.conv2 = nn.Conv2d(width // 2, width // 2, kernel_size=3, padding=1, bias=False) | |
| self.bn2 = nn.BatchNorm2d(width // 2) | |
| self.act2 = nn.ReLU(inplace=True) | |
| self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False) | |
| self.bn3 = nn.BatchNorm2d(width) | |
| self.act3 = nn.ReLU(inplace=True) | |
| self.avgpool = nn.AvgPool2d(2) | |
| # residual layers | |
| self._inplanes = width # this is a *mutable* variable used during construction | |
| self.layer1 = self._make_layer(width, layers[0]) | |
| self.layer2 = self._make_layer(width * 2, layers[1], stride=2) | |
| self.layer3 = self._make_layer(width * 4, layers[2], stride=2) | |
| self.layer4 = self._make_layer(width * 8, layers[3], stride=2) | |
| embed_dim = width * 32 # the ResNet feature dimension | |
| self.attnpool = AttentionPool2d(image_size // 32, embed_dim, heads, output_dim) | |
| self.init_parameters() | |
| def _make_layer(self, planes, blocks, stride=1): | |
| layers = [Bottleneck(self._inplanes, planes, stride)] | |
| self._inplanes = planes * Bottleneck.expansion | |
| for _ in range(1, blocks): | |
| layers.append(Bottleneck(self._inplanes, planes)) | |
| return nn.Sequential(*layers) | |
| def init_parameters(self): | |
| if self.attnpool is not None: | |
| std = self.attnpool.c_proj.in_features ** -0.5 | |
| nn.init.normal_(self.attnpool.q_proj.weight, std=std) | |
| nn.init.normal_(self.attnpool.k_proj.weight, std=std) | |
| nn.init.normal_(self.attnpool.v_proj.weight, std=std) | |
| nn.init.normal_(self.attnpool.c_proj.weight, std=std) | |
| for resnet_block in [self.layer1, self.layer2, self.layer3, self.layer4]: | |
| for name, param in resnet_block.named_parameters(): | |
| if name.endswith("bn3.weight"): | |
| nn.init.zeros_(param) | |
| def lock(self, unlocked_groups=0, freeze_bn_stats=False): | |
| assert unlocked_groups == 0, 'partial locking not currently supported for this model' | |
| for param in self.parameters(): | |
| param.requires_grad = False | |
| if freeze_bn_stats: | |
| freeze_batch_norm_2d(self) | |
| def set_grad_checkpointing(self, enable=True): | |
| # FIXME support for non-transformer | |
| pass | |
| def stem(self, x): | |
| x = self.act1(self.bn1(self.conv1(x))) | |
| x = self.act2(self.bn2(self.conv2(x))) | |
| x = self.act3(self.bn3(self.conv3(x))) | |
| x = self.avgpool(x) | |
| return x | |
| def forward(self, x): | |
| x = self.stem(x) | |
| x = self.layer1(x) | |
| x = self.layer2(x) | |
| x = self.layer3(x) | |
| x = self.layer4(x) | |
| x = self.attnpool(x) | |
| return x | |