from collections import OrderedDict from itertools import repeat import collections.abc import math import torch import torch.nn.functional as F from torch import nn from .eva_vit import convert_weights_to_fp16 from .utils import download_cached_file 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: torch.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**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 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, use_grad_checkpointing=False): 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 # if use_grad_checkpointing: # self.attn = checkpoint_wrapper(self.attn) # self.mlp = checkpoint_wrapper(self.mlp) # raise NotImplementedError 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, use_grad_checkpointing=False): super().__init__() self.width = width self.layers = layers self.resblocks = nn.Sequential( *[ResidualAttentionBlock(width, heads, attn_mask, use_grad_checkpointing and i > 12) for i 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, use_grad_checkpointing: bool): super().__init__() self.input_resolution = input_resolution self.num_features = width self.num_heads = heads self.num_patches = (input_resolution // patch_size)**2 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(self.num_patches + 1, width)) self.ln_pre = LayerNorm(width) self.transformer = Transformer(width, layers, heads, use_grad_checkpointing=use_grad_checkpointing) # self.ln_final = LayerNorm(width) 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) 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_final(x) return x # From PyTorch internals def _ntuple(n): def parse(x): if isinstance(x, collections.abc.Iterable): return x return tuple(repeat(x, n)) return parse to_2tuple = _ntuple(2) def interpolate_pos_embed(model, state_dict, interpolation: str = 'bicubic', seq_dim=1): # Rescale the grid of position embeddings when loading from state_dict old_pos_embed = state_dict.get('positional_embedding', None) grid_size = round((model.positional_embedding.shape[0] - 1)**0.5) if old_pos_embed is None: return grid_size = to_2tuple(grid_size) extra_tokens = 1 # FIXME detect different token configs (ie no class token, or more) new_seq_len = grid_size[0] * grid_size[1] + extra_tokens if new_seq_len == old_pos_embed.shape[0]: return if extra_tokens: pos_emb_tok, pos_emb_img = old_pos_embed[:extra_tokens], old_pos_embed[extra_tokens:] else: pos_emb_tok, pos_emb_img = None, old_pos_embed old_grid_size = to_2tuple(int(math.sqrt(len(pos_emb_img)))) print('Resizing position embedding grid-size from %s to %s', old_grid_size, grid_size) pos_emb_img = pos_emb_img.reshape(1, old_grid_size[0], old_grid_size[1], -1).permute(0, 3, 1, 2) pos_emb_img = F.interpolate( pos_emb_img, size=grid_size, mode=interpolation, align_corners=True, ) pos_emb_img = pos_emb_img.permute(0, 2, 3, 1).reshape(1, grid_size[0] * grid_size[1], -1)[0] if pos_emb_tok is not None: new_pos_embed = torch.cat([pos_emb_tok, pos_emb_img], dim=0) else: new_pos_embed = pos_emb_img state_dict['positional_embedding'] = new_pos_embed def create_clip_vit_L(img_size=224, use_checkpoint=False, precision="fp16"): model = VisionTransformer( input_resolution=img_size, patch_size=14, width=1024, layers=23, heads=16, use_grad_checkpointing=use_checkpoint, ) url = "https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/clip_vit_L.pth" cached_file = download_cached_file(url, check_hash=False, progress=True) state_dict = torch.load(cached_file, map_location="cpu") interpolate_pos_embed(model, state_dict) incompatible_keys = model.load_state_dict(state_dict, strict=False) # print(incompatible_keys) if precision == "fp16": convert_weights_to_fp16(model) return model