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import torch
import torch.nn as nn
import torch.nn.functional as F
class ViTPatchEmbeddings(nn.Module):
def __init__(self, cfg):
super().__init__()
self.img_size = cfg.vit_img_size
self.patch_size = cfg.vit_patch_size
self.num_patches = (self.img_size // self.patch_size) ** 2
self.cls_flag = cfg.vit_cls_flag
self.embd_dim = cfg.vit_hidden_dim
self.conv = nn.Conv2d(
in_channels=3,
out_channels=self.embd_dim,
kernel_size=self.patch_size,
stride=self.patch_size,
padding="valid",
)
if self.cls_flag:
self.cls_token = nn.Parameter(torch.zeros(1, 1, self.embd_dim))
self.position_embedding = nn.Parameter(torch.rand(1, self.num_patches + 1, self.embd_dim))
else:
self.position_embedding = nn.Parameter(torch.rand(1, self.num_patches, self.embd_dim))
def forward(self, x):
x = self.conv(x)
x = x.flatten(2)
x = x.transpose(1, 2)
if self.cls_flag:
cls_token = self.cls_token.expand(x.shape[0], -1, -1)
x = torch.cat((cls_token, x), dim=1)
x = x + self.position_embedding
return x
class ViTMultiHeadAttention(nn.Module):
def __init__(self, cfg):
super().__init__()
self.n_heads = cfg.vit_n_heads
self.embd_dim = cfg.vit_hidden_dim
assert self.embd_dim % self.n_heads == 0, "embd_dim must be divisible by num_heads"
self.head_dim = self.embd_dim // self.n_heads
self.dropout = cfg.vit_dropout
self.qkv_proj = nn.Linear(self.embd_dim, 3 * self.embd_dim, bias=True)
self.out_proj = nn.Linear(self.embd_dim, self.embd_dim, bias=True)
self.attn_dropout = nn.Dropout(self.dropout)
self.resid_dropout = nn.Dropout(self.dropout)
self.sdpa = hasattr(torch.nn.functional, 'scaled_dot_product_attention')
if not self.sdpa:
print("Warning: scaled dot product attention not available. Using standard attention in ViT.")
def forward(self, x):
B, T, C = x.size()
qkv = self.qkv_proj(x)
q, k, v = qkv.split(C, dim=2)
q = q.view(B, T, self.n_heads, self.head_dim).transpose(1, 2)
k = k.view(B, T, self.n_heads, self.head_dim).transpose(1, 2)
v = v.view(B, T, self.n_heads, self.head_dim).transpose(1, 2)
if self.sdpa:
y = torch.nn.functional.scaled_dot_product_attention(
q, k, v,
attn_mask=None,
dropout_p=self.dropout if self.training else 0.0,
is_causal=False
)
else:
attn = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
attn = F.softmax(attn, dim=-1)
attn = self.attn_dropout(attn)
y = attn @ v
y = y.transpose(1, 2).contiguous().view(B, T, C)
y = self.out_proj(y)
y = self.resid_dropout(y)
return y
class ViTMLP(nn.Module):
def __init__(self, cfg):
super().__init__()
self.activation_fn = nn.GELU(approximate='tanh')
self.fc1 = nn.Linear(cfg.vit_hidden_dim, cfg.vit_inter_dim)
self.fc2 = nn.Linear(cfg.vit_inter_dim, cfg.vit_hidden_dim)
self.dropout = nn.Dropout(cfg.vit_dropout)
def forward(self, x):
x = self.fc1(x)
x = self.activation_fn(x)
x = self.fc2(x)
x = self.dropout(x)
return x
class ViTBlock(nn.Module):
def __init__(self, cfg):
super().__init__()
self.ln1 = nn.LayerNorm(cfg.vit_hidden_dim, eps=cfg.vit_ln_eps)
self.attn = ViTMultiHeadAttention(cfg)
self.ln2 = nn.LayerNorm(cfg.vit_hidden_dim, eps=cfg.vit_ln_eps)
self.mlp = ViTMLP(cfg)
def forward(self, x):
x = x + self.attn(self.ln1(x))
x = x + self.mlp(self.ln2(x))
return x
class ViT(nn.Module):
def __init__(self, cfg):
super().__init__()
self.cfg = cfg
self.patch_embedding = ViTPatchEmbeddings(cfg)
self.cls_flag = cfg.vit_cls_flag
self.dropout = nn.Dropout(cfg.vit_dropout)
self.blocks = nn.ModuleList([ViTBlock(cfg) for _ in range(cfg.vit_n_blocks)])
self.layer_norm = nn.LayerNorm(cfg.vit_hidden_dim, eps=cfg.vit_ln_eps)
self.apply(self._init_weights)
def _init_weights(self, module):
if isinstance(module, nn.Linear):
torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
if module.bias is not None:
torch.nn.init.zeros_(module.bias)
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
elif isinstance(module, nn.Conv2d):
torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
if module.bias is not None:
torch.nn.init.zeros_(module.bias)
def forward(self, x):
x = self.patch_embedding(x)
x = self.dropout(x)
for block in self.blocks:
x = block(x)
if self.cls_flag:
x = self.layer_norm(x[:, 0])
else:
x = self.layer_norm(x)
return x
@classmethod
def from_pretrained(cls, cfg):
from transformers import SiglipVisionConfig
from huggingface_hub import hf_hub_download
import safetensors
hf_config = SiglipVisionConfig.from_pretrained(cfg.vit_model_type)
cfg.vit_dropout=hf_config.attention_dropout
cfg.vit_hidden_dim=hf_config.hidden_size
cfg.vit_img_size=hf_config.image_size
cfg.vit_inter_dim=hf_config.intermediate_size
cfg.vit_ln_eps=hf_config.layer_norm_eps
cfg.vit_n_heads=hf_config.num_attention_heads
cfg.vit_n_blocks=hf_config.num_hidden_layers
cfg.vit_patch_size=hf_config.patch_size
model = cls(cfg)
safetensors_file = hf_hub_download(repo_id=cfg.vit_model_type, filename="model.safetensors")
sd = model.state_dict()
mapping = {
'vision_model.embeddings.patch_embedding.weight': 'patch_embedding.conv.weight',
'vision_model.embeddings.patch_embedding.bias': 'patch_embedding.conv.bias',
'vision_model.embeddings.position_embedding.weight': 'patch_embedding.position_embedding',
'vision_model.post_layernorm.weight': 'layer_norm.weight',
'vision_model.post_layernorm.bias': 'layer_norm.bias',
}
for i in range(cfg.vit_n_blocks):
mapping[f'vision_model.encoder.layers.{i}.layer_norm1.weight'] = f'blocks.{i}.ln1.weight'
mapping[f'vision_model.encoder.layers.{i}.layer_norm1.bias'] = f'blocks.{i}.ln1.bias'
mapping[f'vision_model.encoder.layers.{i}.layer_norm2.weight'] = f'blocks.{i}.ln2.weight'
mapping[f'vision_model.encoder.layers.{i}.layer_norm2.bias'] = f'blocks.{i}.ln2.bias'
mapping[f'vision_model.encoder.layers.{i}.mlp.fc1.weight'] = f'blocks.{i}.mlp.fc1.weight'
mapping[f'vision_model.encoder.layers.{i}.mlp.fc1.bias'] = f'blocks.{i}.mlp.fc1.bias'
mapping[f'vision_model.encoder.layers.{i}.mlp.fc2.weight'] = f'blocks.{i}.mlp.fc2.weight'
mapping[f'vision_model.encoder.layers.{i}.mlp.fc2.bias'] = f'blocks.{i}.mlp.fc2.bias'
mapping[f'vision_model.encoder.layers.{i}.self_attn.out_proj.weight'] = f'blocks.{i}.attn.out_proj.weight'
mapping[f'vision_model.encoder.layers.{i}.self_attn.out_proj.bias'] = f'blocks.{i}.attn.out_proj.bias'
with safetensors.safe_open(filename=safetensors_file, framework="pt", device="cpu") as f:
for hf_key, our_key in mapping.items():
if hf_key in f.keys() and our_key in sd:
tensor = f.get_tensor(hf_key)
if tensor.shape == sd[our_key].shape:
sd[our_key].copy_(tensor)
else:
if 'position_embedding' in hf_key:
sd[our_key].copy_(tensor.unsqueeze(0))
else:
print(f"Shape mismatch for {hf_key} -> {our_key}: {tensor.shape} vs {sd[our_key].shape}")
else:
if hf_key not in f.keys():
print(f"Warning: Key {hf_key} not found in safetensors file")
if our_key not in sd:
print(f"Warning: Key {our_key} not found in model state dict")
for i in range(model.cfg.vit_n_blocks):
q_weight = f.get_tensor(f'vision_model.encoder.layers.{i}.self_attn.q_proj.weight')
k_weight = f.get_tensor(f'vision_model.encoder.layers.{i}.self_attn.k_proj.weight')
v_weight = f.get_tensor(f'vision_model.encoder.layers.{i}.self_attn.v_proj.weight')
qkv_weight = torch.cat((q_weight, k_weight, v_weight), dim=0)
sd[f'blocks.{i}.attn.qkv_proj.weight'].copy_(qkv_weight)
q_bias = f.get_tensor(f'vision_model.encoder.layers.{i}.self_attn.q_proj.bias')
k_bias = f.get_tensor(f'vision_model.encoder.layers.{i}.self_attn.k_proj.bias')
v_bias = f.get_tensor(f'vision_model.encoder.layers.{i}.self_attn.v_proj.bias')
qkv_bias = torch.cat((q_bias, k_bias, v_bias), dim=0)
sd[f'blocks.{i}.attn.qkv_proj.bias'].copy_(qkv_bias)
model.load_state_dict(sd)
print(f"Successfully loaded {cfg.vit_model_type} weights from safetensors. Model has {sum(p.numel() for p in model.parameters()):,} parameters.")
return model
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