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#!/usr/bin/env python
import os
from collections import OrderedDict
import torch
from torch import nn
MODEL_PATH = 'your_model_path/clip_visual_encoder'
_MODELS = {
# extracted from OpenAI, see extract_clip
"ViT-B/16": os.path.join(MODEL_PATH, "vit_b16.pth"),
"ViT-L/14": os.path.join(MODEL_PATH, "vit_l14.pth"),
"ViT-L/14_336": os.path.join(MODEL_PATH, "vit_l14_336.pth"),
}
class LayerNorm(nn.LayerNorm):
"""Subclass torch's LayerNorm to handle fp16."""
def forward(self, x):
orig_type = x.dtype
ret = super().forward(x.type(torch.float32))
return ret.type(orig_type)
class QuickGELU(nn.Module):
def forward(self, x):
return x * torch.sigmoid(1.702 * x)
class ResidualAttentionBlock(nn.Module):
def __init__(self, d_model, n_head, attn_mask=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, return_attn=False):
self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None
if return_attn:
return self.attn(x, x, x, need_weights=True, attn_mask=self.attn_mask)
else:
return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0]
def forward(self, x, return_attn=False):
if return_attn:
x_, attn = self.attention(self.ln_1(x), return_attn=True)
x = x + x_
x = x + self.mlp(self.ln_2(x))
return x, attn
else:
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, layers, heads, return_attn=False,
clip_return_layer=1, clip_return_interval=1,
):
super().__init__()
self.layers = layers
self.return_attn = return_attn
self.resblocks = nn.ModuleList()
for _ in range(layers):
self.resblocks.append(
ResidualAttentionBlock(
width, heads,
)
)
self.return_index = []
for i in range(clip_return_layer):
self.return_index.append(layers - int(i * clip_return_interval) - 1)
print(f'Teacher return index: {self.return_index}')
def forward(self, x):
attn = None
z = []
for idx, blk in enumerate(self.resblocks):
if idx == self.layers - 1 and self.return_attn:
x, attn = blk(x, return_attn=True)
else:
x = blk(x)
if idx in self.return_index:
z.append(x)
x = torch.stack(z)
return x, attn
class VisionTransformer(nn.Module):
def __init__(
self, input_resolution, patch_size, width, layers, heads, output_dim,
clip_norm_type='l2', kernel_size=1,
return_attn=False, clip_return_layer=1, clip_return_interval=1,
):
super().__init__()
self.clip_norm_type = clip_norm_type
self.return_attn = return_attn
print(f'Normalization Type: {clip_norm_type}')
print(f'Return Attention: {return_attn}')
print(f'Return Layer: {clip_return_layer}')
print(f'Return Interval: {clip_return_interval}')
self.output_dim = output_dim
self.conv1 = nn.Conv3d(
3, width,
(kernel_size, patch_size, patch_size),
(kernel_size, patch_size, patch_size),
(0, 0, 0), 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, return_attn=return_attn,
clip_return_layer=clip_return_layer,
clip_return_interval=clip_return_interval,
)
self.ln_post = LayerNorm(width)
self.proj = nn.Parameter(scale * torch.randn(width, output_dim))
def forward(self, x, mask=None):
x = self.conv1(x) # shape = [*, width, grid, grid]
N, C, T, H, W = x.shape
x = x.permute(0, 2, 3, 4, 1).reshape(N * T, H * W, C)
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)
if mask is not None:
cls_tokens = x[:, :1, :]
x = x[:, 1:]
x = x.reshape(N, T * H * W, C)
x = x[~mask].view(N * T, -1, C)
HW = x.shape[1]
x = torch.cat([cls_tokens, x], dim=1)
else:
HW = H * W
x = x.permute(1, 0, 2) # NLD -> LND
x, attn = self.transformer(x)
K = x.shape[0]
x = self.ln_post(x[:, 1:, :, :]) # [HW, NT, C]
x = x.view(K, HW, N, T, C).permute(0, 2, 3, 1, 4).reshape(K, N, T * HW, C) # [K, N, THW, C]
x = x @ self.proj
if self.clip_norm_type == 'l2':
x = x / x.norm(dim=-1, keepdim=True)
elif self.clip_norm_type == 'none':
pass
else:
raise NotImplementedError
if self.return_attn:
return x, attn[:, 0, 1:]
else:
return x
def inflate_weight(weight_2d, time_dim, center=True):
print(f'Init center: {center}')
if center:
weight_3d = torch.zeros(*weight_2d.shape)
weight_3d = weight_3d.unsqueeze(2).repeat(1, 1, time_dim, 1, 1)
middle_idx = time_dim // 2
weight_3d[:, :, middle_idx, :, :] = weight_2d
else:
weight_3d = weight_2d.unsqueeze(2).repeat(1, 1, time_dim, 1, 1)
weight_3d = weight_3d / time_dim
return weight_3d
def load_state_dict(model, state_dict, input_resolution=224, patch_size=16, center=True):
state_dict_3d = model.state_dict()
for k in state_dict.keys():
if k in state_dict_3d.keys() and state_dict[k].shape != state_dict_3d[k].shape:
if len(state_dict_3d[k].shape) <= 2:
print(f'Ignore: {k}')
continue
print(f'Inflate: {k}, {state_dict[k].shape} => {state_dict_3d[k].shape}')
time_dim = state_dict_3d[k].shape[2]
state_dict[k] = inflate_weight(state_dict[k], time_dim, center=center)
pos_embed_checkpoint = state_dict['positional_embedding']
embedding_size = pos_embed_checkpoint.shape[-1]
num_patches = (input_resolution // patch_size) ** 2
orig_size = int((pos_embed_checkpoint.shape[-2] - 1) ** 0.5)
new_size = int(num_patches ** 0.5)
if orig_size != new_size:
print(f'Pos_emb from {orig_size} to {new_size}')
extra_tokens = pos_embed_checkpoint[:1]
pos_tokens = pos_embed_checkpoint[1:]
pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2)
pos_tokens = torch.nn.functional.interpolate(
pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False)
pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(0, 2)
new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=0)
state_dict['positional_embedding'] = new_pos_embed
model.load_state_dict(state_dict, strict=True)
def clip_b16(
pretrained=True,
clip_norm_type='l2', input_resolution=224, kernel_size=1,
return_attn=False, center=True, clip_return_layer=1,
clip_return_interval=1
):
model = VisionTransformer(
input_resolution=input_resolution, patch_size=16,
width=768, layers=12, heads=12, output_dim=512,
clip_norm_type=clip_norm_type,
kernel_size=kernel_size, return_attn=return_attn,
clip_return_layer=clip_return_layer,
clip_return_interval=clip_return_interval
)
if pretrained:
print('load pretrained weights')
state_dict = torch.load(_MODELS["ViT-B/16"], map_location='cpu')
load_state_dict(model, state_dict, input_resolution=input_resolution, patch_size=16, center=center)
return model.eval()
def clip_l14(
pretrained=True,
clip_norm_type='l2', input_resolution=224, kernel_size=1,
return_attn=False, center=True, clip_return_layer=1,
clip_return_interval=1
):
model = VisionTransformer(
input_resolution=input_resolution, patch_size=14,
width=1024, layers=24, heads=16, output_dim=768,
clip_norm_type=clip_norm_type,
kernel_size=kernel_size, return_attn=return_attn,
clip_return_layer=clip_return_layer,
clip_return_interval=clip_return_interval
)
if pretrained:
print('load pretrained weights')
state_dict = torch.load(_MODELS["ViT-L/14"], map_location='cpu')
load_state_dict(model, state_dict, input_resolution=input_resolution, patch_size=14, center=center)
return model.eval()
def clip_l14_336(
pretrained=True,
clip_norm_type='l2', input_resolution=336, kernel_size=1,
return_attn=False, center=True, clip_return_layer=1,
clip_return_interval=1
):
model = VisionTransformer(
input_resolution=input_resolution, patch_size=14,
width=1024, layers=24, heads=16, output_dim=768,
clip_norm_type=clip_norm_type,
kernel_size=kernel_size, return_attn=return_attn,
clip_return_layer=clip_return_layer,
clip_return_interval=clip_return_interval,
)
if pretrained:
print('load pretrained weights')
state_dict = torch.load(_MODELS["ViT-L/14_336"], map_location='cpu')
load_state_dict(model, state_dict, input_resolution=input_resolution, patch_size=14, center=center)
return model.eval()
if __name__ == '__main__':
import time
from fvcore.nn import FlopCountAnalysis
from fvcore.nn import flop_count_table
import numpy as np
seed = 4217
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
num_frames = 8
model = clip_ml_b16(pretrained=True, kernel_size=1, return_attn=False, clip_return_layer=1)
# print(model)
# flops = FlopCountAnalysis(model, torch.rand(1, 3, num_frames, 224, 224))
# s = time.time()
# print(flop_count_table(flops, max_depth=1))
# print(time.time()-s)
print(model(torch.rand(1, 3, num_frames, 224, 224)).shape) |