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| from collections import OrderedDict | |
| from typing import Tuple, Union | |
| import math | |
| import numpy as np | |
| import torch | |
| import torch.nn.functional as F | |
| from torch import nn | |
| 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.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False) | |
| self.bn2 = nn.BatchNorm2d(planes) | |
| 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.relu = 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.relu(self.bn1(self.conv1(x))) | |
| out = self.relu(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.relu(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 interpolate_pos_encoding(self, x, h0, w0): | |
| assert w0 == h0, f'{self} only support square images!' | |
| pos_embed = self.positional_embedding.unsqueeze(1).to(x.dtype) | |
| npatch = x.shape[0] - 1 | |
| N = pos_embed.shape[0] - 1 | |
| if npatch == N: | |
| return pos_embed | |
| class_pos_embed = pos_embed[0] | |
| patch_pos_embed = pos_embed[1:] | |
| dim = x.shape[-1] | |
| # we add a small number to avoid floating point error in the interpolation | |
| # see discussion at https://github.com/facebookresearch/dino/issues/8 | |
| w0, h0 = w0 + 0.1, h0 + 0.1 | |
| patch_pos_embed = nn.functional.interpolate( | |
| patch_pos_embed.reshape(int(math.sqrt(N)), int(math.sqrt(N)), 1, dim).permute(2, 3, 0, 1), | |
| scale_factor=(w0 / math.sqrt(N), h0 / math.sqrt(N)), | |
| mode='bicubic', | |
| align_corners=False, | |
| recompute_scale_factor=False | |
| ) | |
| assert int(w0) == patch_pos_embed.shape[-2] and int(h0) == patch_pos_embed.shape[-1] | |
| patch_pos_embed = patch_pos_embed.permute(2, 3, 0, 1).view(-1, 1, dim) | |
| return torch.cat((class_pos_embed.unsqueeze(1), patch_pos_embed), dim=0) | |
| def forward(self, x): | |
| B, C, H, W = x.shape | |
| 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.interpolate_pos_encoding(x, H, W) # (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, input_resolution=224, width=64): | |
| super().__init__() | |
| self.output_dim = output_dim | |
| self.input_resolution = input_resolution | |
| # 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.conv2 = nn.Conv2d(width // 2, width // 2, kernel_size=3, padding=1, bias=False) | |
| self.bn2 = nn.BatchNorm2d(width // 2) | |
| self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False) | |
| self.bn3 = nn.BatchNorm2d(width) | |
| self.avgpool = nn.AvgPool2d(2) | |
| self.relu = nn.ReLU(inplace=True) | |
| # 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(input_resolution // 32, embed_dim, heads, output_dim) | |
| #self.gap = nn.AdaptiveAvgPool2d((1, 1)) | |
| 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 forward(self, x): | |
| def stem(x): | |
| for conv, bn in [(self.conv1, self.bn1), (self.conv2, self.bn2), (self.conv3, self.bn3)]: | |
| x = self.relu(bn(conv(x))) | |
| x = self.avgpool(x) | |
| return x | |
| x = x.type(self.conv1.weight.dtype) | |
| x = stem(x) | |
| x = self.layer1(x) | |
| x = self.layer2(x) | |
| x = self.layer3(x) | |
| x = self.layer4(x) | |
| x = self.attnpool(x) | |
| #x = self.gap(x) | |
| return x | |
| class LayerNorm(nn.LayerNorm): | |
| """Subclass torch's LayerNorm to handle fp16.""" | |
| def forward(self, x: torch.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: torch.Tensor): | |
| return x * torch.sigmoid(1.702 * x) | |
| class ResidualAttentionBlock(nn.Module): | |
| def __init__(self, d_model: int, n_head: int, attn_mask: torch.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: torch.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: torch.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: torch.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: torch.Tensor): | |
| return self.resblocks(x) | |
| class VisionTransformer(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.patch_size = patch_size | |
| self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False) | |
| scale = width ** -0.5 | |
| 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) | |
| self.transformer = Transformer(width, layers, heads) | |
| self.ln_post = LayerNorm(width) | |
| self.proj = nn.Parameter(scale * torch.randn(width, output_dim)) | |
| def interpolate_pos_encoding(self, x, h, w): | |
| pos_embed = self.positional_embedding.unsqueeze(0).to(x.dtype) | |
| npatch = x.shape[1] - 1 | |
| N = pos_embed.shape[1] - 1 | |
| if npatch == N and w == h: | |
| return pos_embed | |
| class_pos_embed = pos_embed[:, 0] | |
| patch_pos_embed = pos_embed[:, 1:] | |
| dim = x.shape[-1] | |
| w0 = w // self.patch_size | |
| h0 = h // self.patch_size | |
| # we add a small number to avoid floating point error in the interpolation | |
| # see discussion at https://github.com/facebookresearch/dino/issues/8 | |
| w0, h0 = w0 + 0.1, h0 + 0.1 | |
| patch_pos_embed = nn.functional.interpolate( | |
| patch_pos_embed.reshape(1, int(math.sqrt(N)), int(math.sqrt(N)), dim).permute(0, 3, 1, 2), | |
| scale_factor=(w0 / math.sqrt(N), h0 / math.sqrt(N)), | |
| mode='bicubic', | |
| align_corners=False, | |
| recompute_scale_factor=False | |
| ) | |
| assert int(w0) == patch_pos_embed.shape[-2] and int(h0) == patch_pos_embed.shape[-1] | |
| patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim) | |
| return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1) | |
| def forward(self, x: torch.Tensor): | |
| B, C, H, W = x.shape | |
| 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.interpolate_pos_encoding(x, H, W) | |
| 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 VisionBackbone(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, | |
| ): | |
| 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 | |
| ) | |
| 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.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 dtype(self): | |
| return self.visual.conv1.weight.dtype | |
| def forward(self, image): | |
| return self.visual(image.type(self.dtype)) | |
| 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 | |
| ): | |
| 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() | |
| ) | |
| 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 | |
| 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 = logits_per_image.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): | |
| 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 | |
| ) | |
| 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) | |
| return model.eval() | |
| def build_vision_model(state_dict: dict): | |
| 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 = VisionBackbone( | |
| embed_dim, | |
| image_resolution, vision_layers, vision_width, vision_patch_size, | |
| ) | |
| convert_weights(model) | |
| msg = model.load_state_dict(state_dict, strict=False) | |
| print(f'clip.build_vision_model: pretrained weights loaded with message: {msg}') | |
| return model.eval() | |