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import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from functools import partial
from .modeling_finetune import Block, DropPath, Mlp, _cfg, PatchEmbed
from timm.models.registry import register_model
from timm.models.layers import trunc_normal_ as __call_trunc_normal_
def trunc_normal_(tensor, mean=0., std=1.):
__call_trunc_normal_(tensor, mean=mean, std=std, a=-std, b=std)
# sin-cos position encoding
# https://github.com/jadore801120/attention-is-all-you-need-pytorch/blob/master/transformer/Models.py#L31
def get_sinusoid_encoding_table(n_position, d_hid):
''' Sinusoid position encoding table '''
# TODO: make it with torch instead of numpy
def get_position_angle_vec(position):
return [position / np.power(10000, 2 * (hid_j // 2) / d_hid) for hid_j in range(d_hid)]
sinusoid_table = np.array([get_position_angle_vec(pos_i) for pos_i in range(n_position)])
sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) # dim 2i
sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1
return torch.tensor(sinusoid_table, dtype=torch.float, requires_grad=False).unsqueeze(0)
class PretrainVisionTransformerEncoder(nn.Module):
""" Vision Transformer with support for patch or hybrid CNN input stage
"""
def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=0, embed_dim=768, depth=12,
num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0.,
drop_path_rate=0., norm_layer=nn.LayerNorm, init_values=None, num_frames=16, tubelet_size=2,
use_checkpoint=False, checkpoint_num=0, use_learnable_pos_emb=False, clip_return_layer=1,
clip_student_return_interval=1):
super().__init__()
self.num_classes = num_classes
self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models
self.patch_embed = PatchEmbed(
img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim,
num_frames=num_frames, tubelet_size=tubelet_size
)
num_patches = self.patch_embed.num_patches
self.use_checkpoint = use_checkpoint
self.checkpoint_num = checkpoint_num
print(f'Use checkpoint: {use_checkpoint}')
print(f'Checkpoint number: {checkpoint_num}')
self.return_index = []
for i in range(clip_return_layer):
self.return_index.append(depth - int(i * clip_student_return_interval) - 1)
print(f'Student return index: {self.return_index}')
self.use_learnable_pos_emb = use_learnable_pos_emb
if use_learnable_pos_emb:
print('Use learnable position embedding')
self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
else:
# sine-cosine positional embeddings
self.pos_embed = get_sinusoid_encoding_table(num_patches, embed_dim)
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
self.blocks = nn.ModuleList([
Block(
dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer,
init_values=init_values)
for i in range(depth)])
self.norm = norm_layer(embed_dim)
self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()
if use_learnable_pos_emb:
trunc_normal_(self.pos_embed, std=.02)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
nn.init.xavier_uniform_(m.weight)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
def get_num_layers(self):
return len(self.blocks)
@torch.jit.ignore
def no_weight_decay(self):
return {'pos_embed', 'cls_token'}
def get_classifier(self):
return self.head
def reset_classifier(self, num_classes, global_pool=''):
self.num_classes = num_classes
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
def forward_features(self, x, mask):
x = self.patch_embed(x)
if self.use_learnable_pos_emb:
x = x + self.pos_embed.type_as(x).to(x.device)
else:
x = x + self.pos_embed.type_as(x).to(x.device).clone().detach()
B, _, C = x.shape
x_vis = x[~mask].reshape(B, -1, C) # ~mask means visible
x_clip_vis = []
for idx, blk in enumerate(self.blocks):
if self.use_checkpoint and idx < self.checkpoint_num:
x_vis = checkpoint.checkpoint(blk, x_vis)
else:
x_vis = blk(x_vis)
if idx in self.return_index:
x_clip_vis.append(x_vis)
x_vis = self.norm(x_vis)
x_clip_vis = self.norm(torch.stack(x_clip_vis))
return x_vis, x_clip_vis
def forward(self, x, mask):
x, x_clip_vis = self.forward_features(x, mask)
x = self.head(x)
x_clip_vis = self.head(x_clip_vis)
return x_clip_vis
class Linear_Decoder(nn.Module):
def __init__(self, num_classes=768, embed_dim=768,
norm_layer=nn.LayerNorm, clip_norm_type='l2'):
super().__init__()
self.clip_norm_type = clip_norm_type
print(f'Normalization Type: {clip_norm_type}')
self.head = nn.Linear(embed_dim, num_classes)
self.norm = norm_layer(num_classes)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
nn.init.xavier_uniform_(m.weight)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
def forward(self, x):
x = self.norm(self.head(x))
if self.clip_norm_type == 'l2':
x = x / x.norm(dim=-1, keepdim=True)
elif self.clip_norm_type == 'none':
pass
else:
raise NotImplementedError
return x
class PretrainVisionTransformer(nn.Module):
""" Vision Transformer with support for patch or hybrid CNN input stage
"""
def __init__(self,
img_size=224,
patch_size=16,
encoder_in_chans=3,
encoder_num_classes=0,
encoder_embed_dim=768,
encoder_depth=12,
encoder_num_heads=12,
mlp_ratio=4.,
qkv_bias=False,
qk_scale=None,
drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.,
norm_layer=nn.LayerNorm,
init_values=0.,
use_learnable_pos_emb=False,
use_checkpoint=False,
checkpoint_num=0,
num_frames=16,
tubelet_size=2,
# clip,
clip_decoder_embed_dim=768,
clip_output_dim=512,
clip_norm_type='l2',
clip_return_layer=1,
clip_student_return_interval=1,
):
super().__init__()
self.encoder = PretrainVisionTransformerEncoder(
img_size=img_size,
patch_size=patch_size,
in_chans=encoder_in_chans,
num_classes=encoder_num_classes,
embed_dim=encoder_embed_dim,
depth=encoder_depth,
num_heads=encoder_num_heads,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
qk_scale=qk_scale,
drop_rate=drop_rate,
attn_drop_rate=attn_drop_rate,
drop_path_rate=drop_path_rate,
norm_layer=norm_layer,
init_values=init_values,
num_frames=num_frames,
tubelet_size=tubelet_size,
use_checkpoint=use_checkpoint,
checkpoint_num=checkpoint_num,
use_learnable_pos_emb=use_learnable_pos_emb,
clip_return_layer=clip_return_layer,
clip_student_return_interval=clip_student_return_interval
)
# CLIP decoder
self.clip_decoder = nn.ModuleList([
Linear_Decoder(
num_classes=clip_output_dim,
embed_dim=clip_decoder_embed_dim,
norm_layer=norm_layer,
clip_norm_type=clip_norm_type
) for _ in range(clip_return_layer)
])
self.clip_pos_embed = get_sinusoid_encoding_table(self.encoder.patch_embed.num_patches, clip_decoder_embed_dim)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
nn.init.xavier_uniform_(m.weight)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
def get_num_layers(self):
return len(self.blocks)
@torch.jit.ignore
def no_weight_decay(self):
return {'pos_embed', 'cls_token', 'mask_token', 'clip_mask_token', 'clip_pos_embed'}
def forward(self, x, mask):
x_clip_vis = self.encoder(x, mask) # [B, N_vis, C_e]
# align CLIP
K, B, _, C_CLIP = x_clip_vis.shape
expand_clip_pos_embed = self.clip_pos_embed.repeat(B, 1, 1).type_as(x).to(x.device).clone().detach()
clip_pos_emd_vis = expand_clip_pos_embed[~mask].view(B, -1, C_CLIP).unsqueeze(0).repeat(K, 1, 1, 1)
x_clip_full = x_clip_vis + clip_pos_emd_vis # [K, B, N, C_d_clip]
x_clip = []
for idx, clip_decoder in enumerate(self.clip_decoder):
x_clip.append(clip_decoder(x_clip_full[idx]))
x_clip = torch.stack(x_clip) # align and normalize
return x_clip
@register_model
def pretrain_umt_base_patch16_224(pretrained=False, **kwargs):
model = PretrainVisionTransformer(
img_size=224,
patch_size=16,
encoder_embed_dim=768,
encoder_depth=12,
encoder_num_heads=12,
encoder_num_classes=0,
mlp_ratio=4,
qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
**kwargs)
model.default_cfg = _cfg()
if pretrained:
checkpoint = torch.load(
kwargs["init_ckpt"], map_location="cpu"
)
model.load_state_dict(checkpoint["model"])
return model
@register_model
def pretrain_umt_large_patch16_224(pretrained=False, **kwargs):
model = PretrainVisionTransformer(
img_size=224,
patch_size=16,
encoder_embed_dim=1024,
encoder_depth=24,
encoder_num_heads=16,
encoder_num_classes=0,
mlp_ratio=4,
qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
**kwargs)
model.default_cfg = _cfg()
if pretrained:
checkpoint = torch.load(
kwargs["init_ckpt"], map_location="cpu"
)
model.load_state_dict(checkpoint["model"])
return model
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)
model = pretrain_umt_base_patch16_224()
# flops = FlopCountAnalysis(model, torch.rand(1, 3, 16, 224, 224))
# s = time.time()
# print(flop_count_table(flops, max_depth=1))
# print(time.time()-s)
mask = torch.cat([
torch.ones(1, 8 * int(14 * 14 * 0.75)),
torch.zeros(1, 8 * int(14 * 14 * 0.25)),
], dim=-1).to(torch.bool)
print(model(torch.rand(1, 3, 16, 224, 224), mask)[1].shape) |