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cvt_v4_transformer.py
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cvt_v4_transformer.py
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import logging
import os
import torch
import torch.nn.functional as F
from functools import partial
from torch import nn, einsum
from torch._six import container_abcs
import numpy as np
from einops import rearrange, repeat
from einops.layers.torch import Rearrange
from timm.models.layers import DropPath, trunc_normal_
# helper methods
from .registry import register_model
# From PyTorch internals
def _ntuple(n):
def parse(x):
if isinstance(x, container_abcs.Iterable):
return x
return tuple(repeat(x, n))
return parse
to_1tuple = _ntuple(1)
to_2tuple = _ntuple(2)
to_3tuple = _ntuple(3)
to_4tuple = _ntuple(4)
to_ntuple = _ntuple
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 PreNorm(nn.Module):
def __init__(self, norm, dim, fn):
super().__init__()
self.norm = norm(dim)
self.fn = fn
def forward(self, x, *args, **kwargs):
x = rearrange(x, 'b c h w -> b h w c')
x = self.norm(x)
x = rearrange(x, 'b h w c -> b c h w')
return self.fn(x, *args, **kwargs)
class FeedForward(nn.Module):
def __init__(self, dim, act_layer, mult=4):
super().__init__()
self.net = nn.Sequential(
nn.Conv2d(dim, int(dim * mult), 1),
act_layer(),
nn.Conv2d(int(dim * mult), dim, 1),
)
def forward(self, x):
return self.net(x)
class DepthWiseConv2d(nn.Module):
def __init__(
self,
dim_in,
dim_out,
kernel_size,
padding,
stride,
bias=True
):
super().__init__()
self.dw = nn.Conv2d(
dim_in, dim_in,
kernel_size=kernel_size,
padding=padding,
groups=dim_in,
stride=stride,
bias=False
)
self.bn = nn.BatchNorm2d(dim_in)
self.pw = nn.Conv2d(
dim_in, dim_out,
kernel_size=1,
bias=bias
)
def forward(self, x):
x = self.dw(x)
x = self.bn(x)
x = self.pw(x)
return x
class Attention(nn.Module):
def __init__(
self,
dim_in,
dim_out,
num_heads,
qkv_bias,
kernel_size,
padding,
window_size,
shift_size,
rel_pos_embed,
**kwargs
):
super().__init__()
self.heads = num_heads
self.window_size = window_size
self.shift_size = shift_size
self.scale = dim_out ** -0.5
self.attend = nn.Softmax(dim=-1)
self.qkv = DepthWiseConv2d(
dim_in, dim_out*3, kernel_size,
padding=padding, stride=1, bias=qkv_bias
)
self.proj_out = nn.Conv2d(dim_out, dim_in, 1)
self.rel_pos_embed = rel_pos_embed
if rel_pos_embed:
self.init_rel_pos_embed(window_size, num_heads)
def init_rel_pos_embed(self, window_size, num_heads):
# get pair-wise relative position index for each token inside the window
coords_h = torch.arange(self.window_size)
coords_w = torch.arange(self.window_size)
coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
relative_coords[:, :, 0] += self.window_size - 1 # shift to start from 0
relative_coords[:, :, 1] += self.window_size - 1
relative_coords[:, :, 0] *= 2 * self.window_size - 1
rel_pos_idx = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
self.register_buffer("rel_pos_idx", rel_pos_idx)
# define a parameter table of relative position bias
self.rel_pos_bias_table = nn.Parameter(
torch.zeros(
(2 * window_size - 1) * (2 * window_size - 1),
num_heads
)
) # 2*Wh-1 * 2*Ww-1, nH
trunc_normal_(self.rel_pos_bias_table, std=.02)
def forward(self, x, mask):
shape = x.shape
_, _, H, W, h = *shape, self.heads
w = min(self.window_size, min(H, W))
pad_l = pad_t = 0
pad_r = (w - W % w) % w
pad_b = (w - H % w) % w
if pad_r > 0 or pad_b > 0:
x = F.pad(x, (pad_l, pad_r, pad_t, pad_b))
_, _, Hp, Wp = x.shape
s_x, s_y = Hp // w, Wp // w
else:
s_x, s_y = H // w, W // w
q, k, v = self.qkv(x).chunk(3, dim=1)
q, k, v = map(
lambda t: rearrange(
t, 'b (h d) (s_x w_x) (s_y w_y) -> (b s_x s_y) h (w_x w_y) d',
h=h, s_x=s_x, s_y=s_y, w_x=w, w_y=w
),
(q, k, v)
)
dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale
if self.rel_pos_embed:
rel_pos_bias = self.rel_pos_bias_table[self.rel_pos_idx.view(-1)]\
.view(
self.window_size * self.window_size,
self.window_size * self.window_size,
-1
) # Wh*Ww,Wh*Ww,nH
rel_pos_bias = rel_pos_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
dots = dots + rel_pos_bias.unsqueeze(0)
if mask is not None:
nW = mask.shape[0]
B_, H, N, M = dots.shape
dots = dots.view(
B_ // nW, nW, self.heads, N, M
) + mask.unsqueeze(1).unsqueeze(0)
dots = dots.view(-1, self.heads, N, M)
attn = self.attend(dots)
out = einsum('b h i j, b h j d -> b h i d', attn, v)
out = rearrange(
out, '(b s_x s_y) h (w_x w_y) d -> b (h d) (s_x w_x) (s_y w_y)',
h=h, s_x=s_x, s_y=s_y, w_x=w, w_y=w
).contiguous()
if pad_r > 0 or pad_b > 0:
out = out[:, :, :H, :W].contiguous()
return self.proj_out(out)
@staticmethod
def compute_macs(module, input, output):
# T: num_token
# S: num_token
input = input[0]
B, C, H, W = input.shape
flops = 0
params = sum([p.numel() for p in module.qkv.dw.parameters()])
flops += params * H * W
params = sum([p.numel() for p in module.qkv.pw.parameters()])
flops += params * H * W
params = sum([p.numel() for p in module.proj_out.parameters()])
flops += params * H * W
flops += 2 * C * H * W * module.window_size ** 2
module.__flops__ += flops
class Transformer(nn.Module):
def __init__(
self,
embed_dim=768,
depth=12,
num_heads=12,
mlp_ratio=4.,
qkv_bias=False,
drop_path_rate=None,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm,
kernel_qkv=3,
padding_qkv=1,
window_size=-1,
shift=False,
rel_pos_embed=False,
**kwargs
):
super().__init__()
self.layers = nn.ModuleList([])
for i in range(depth):
shift_size = window_size//2 if shift and i % 2 == 1 else 0,
self.layers.append(nn.ModuleList([
PreNorm(
norm_layer, embed_dim,
Attention(
dim_in=embed_dim,
dim_out=embed_dim,
num_heads=num_heads,
qkv_bias=qkv_bias,
kernel_size=kernel_qkv,
padding=padding_qkv,
window_size=window_size,
shift_size=shift_size,
rel_pos_embed=rel_pos_embed,
*kwargs
)
),
PreNorm(
norm_layer, embed_dim,
FeedForward(embed_dim, act_layer, mlp_ratio)
),
DropPath(drop_path_rate[i])
if isinstance(drop_path_rate, list) else nn.Identity()
]))
self.window_size = window_size
self.shift = shift
def build_attn_mask(self, x):
_, _, H, W = x.shape
# calculate attention mask for SW-MSA
Hp = int(np.ceil(H / self.window_size)) * self.window_size
Wp = int(np.ceil(W / self.window_size)) * self.window_size
img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device) # 1 Hp Wp 1
shift_size = self.window_size//2
h_slices = (
slice(0, -self.window_size),
slice(-self.window_size, -shift_size),
slice(-shift_size, None)
)
w_slices = (
slice(0, -self.window_size),
slice(-self.window_size, -shift_size),
slice(-shift_size, None)
)
cnt = 0
for h in h_slices:
for w in w_slices:
img_mask[:, h, w, :] = cnt
cnt += 1
s_x = Hp // self.window_size
s_y = Wp // self.window_size
mask_windows = rearrange(
img_mask, 'i (s_x w_x) (s_y w_y) j -> (i s_x s_y) w_x w_y j',
s_x=s_x, s_y=s_y, w_y=self.window_size, w_x=self.window_size
)
# mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1
mask_windows = mask_windows.view(
-1, self.window_size * self.window_size
)
attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
attn_mask = attn_mask.masked_fill(
attn_mask != 0, float(-100.0)
).masked_fill(attn_mask == 0, float(0.0))
return attn_mask
def forward(self, x):
attn_mask = self.build_attn_mask(x) if self.shift else None
for attn, ff, drop_path in self.layers:
x = drop_path(attn(x, attn_mask)) + x
x = drop_path(ff(x)) + x
return x
def forward_with_features(self, x):
attn_mask = self.build_attn_mask(x) if self.shift else None
feats = []
for attn, ff, drop_path in self.layers:
x = drop_path(attn(x, attn_mask)) + x
x = drop_path(ff(x)) + x
feats.append(x)
return x, feats
class ConvEmbed(nn.Module):
""" Image to Patch Embedding
"""
def __init__(
self,
patch_size=7,
in_chans=3,
embed_dim=64,
stride=4,
padding=2,
norm_layer=None
):
super().__init__()
self.patch_size = patch_size
self.proj = nn.Conv2d(
in_chans, embed_dim,
kernel_size=patch_size,
stride=stride,
padding=padding
)
self.norm = norm_layer(embed_dim) if norm_layer else None
def forward(self, x):
x = self.proj(x)
B, C, H, W = x.shape
x = rearrange(x, 'b c h w -> b (h w) c').contiguous()
if self.norm:
x = self.norm(x)
x = rearrange(x, 'b (h w) c -> b c h w', h=H, w=W).contiguous()
return x
class ResStem(nn.Module):
def __init__(self, channels_stem, deep=False):
super().__init__()
if deep:
self.stem = nn.Sequential(
nn.Conv2d(
3, channels_stem, kernel_size=3, stride=2, padding=1,
bias=False
),
nn.BatchNorm2d(channels_stem),
nn.ReLU(inplace=True),
nn.Conv2d(
channels_stem, channels_stem,
kernel_size=3, stride=1,
padding=1, bias=False
),
nn.BatchNorm2d(channels_stem),
nn.ReLU(inplace=True),
nn.Conv2d(
channels_stem, channels_stem,
kernel_size=3, stride=2,
padding=1, bias=False
),
nn.BatchNorm2d(channels_stem),
nn.ReLU(inplace=True)
)
else:
self.stem = nn.Sequential(
nn.Conv2d(
3, channels_stem, kernel_size=3, stride=2,
padding=1, bias=False
),
nn.BatchNorm2d(channels_stem),
nn.ReLU(inplace=True),
nn.Conv2d(
channels_stem, channels_stem,
kernel_size=3, stride=2,
padding=1, bias=False
),
nn.BatchNorm2d(channels_stem),
nn.ReLU(inplace=True)
)
def forward(self, x):
x = self.stem(x)
return x
class CvT(nn.Module):
def __init__(
self,
*,
num_classes,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm,
init='trunc_norm',
use_dense_prediction=False,
spec=None
):
super().__init__()
self.num_stages = spec['NUM_STAGES']
total_depth = sum(spec['DEPTH'])
logging.info(f'=> total path: {total_depth}')
drop_path_rate = spec['DROP_PATH_RATE']
dpr = [
x.item() for x in torch.linspace(0, drop_path_rate, total_depth)
] # stochastic depth decay rule
in_chans = 3
depth_accum=0
for i in range(self.num_stages):
kwargs = {
'patch_size': spec['PATCH_SIZE'][i],
'patch_stride': spec['PATCH_STRIDE'][i],
'patch_padding': spec['PATCH_PADDING'][i],
'embed_dim': spec['DIM_EMBED'][i],
'depth': spec['DEPTH'][i],
'num_heads': spec['NUM_HEADS'][i],
'mlp_ratio': spec['MLP_RATIO'][i],
'qkv_bias': spec['QKV_BIAS'][i],
'kernel_qkv': spec['KERNEL_QKV'][i],
'padding_qkv': spec['PADDING_QKV'][i],
'window_size': spec['WINDOW_SIZE'][i],
'shift': spec['SHIFT'][i],
}
if i == 0 and getattr(spec, 'RES_STEM', False):
conv = ResStem(kwargs['embed_dim'], True)
else:
conv = ConvEmbed(
patch_size=kwargs['patch_size'],
in_chans=in_chans,
embed_dim=kwargs['embed_dim'],
stride=kwargs['patch_stride'],
padding=kwargs['patch_padding'],
norm_layer=norm_layer
)
stage = nn.Sequential(
conv,
Transformer(
embed_dim=kwargs['embed_dim'],
depth=kwargs['depth'],
num_heads=kwargs['num_heads'],
mlp_ratio=kwargs['mlp_ratio'],
qkv_bias=kwargs['qkv_bias'],
drop_path_rate=dpr[
depth_accum: depth_accum+kwargs['depth']
],
act_layer=act_layer,
norm_layer=norm_layer,
kernel_qkv=kwargs['kernel_qkv'],
padding_qkv=kwargs['padding_qkv'],
window_size=kwargs['window_size'],
shift=kwargs['shift'],
rel_pos_embed=spec['REL_POS_EMBED']
)
)
setattr(self, f'stage{i}', stage)
in_chans = spec['DIM_EMBED'][i]
depth_accum += kwargs['depth']
self.norm = norm_layer(in_chans)
self.avg_pool = nn.Sequential(
nn.AdaptiveAvgPool2d(1),
Rearrange('... () () -> ...')
)
self.head = nn.Linear(in_chans, num_classes) if num_classes > 0 else nn.Identity()
# Region prediction head
self.use_dense_prediction = use_dense_prediction
if self.use_dense_prediction: self.head_dense = None
if init == 'xavier':
self.apply(self._init_weights_xavier)
else:
self.apply(self._init_weights_trunc_normal)
def _init_weights_trunc_normal(self, m):
if isinstance(m, (nn.Linear, nn.Conv2d)):
logging.info('=> init weight from trunc norm')
trunc_normal_(m.weight, std=0.02)
if m.bias is not None:
logging.info('=> init bias to zeros')
nn.init.constant_(m.bias, 0)
elif isinstance(m, (nn.LayerNorm, nn.BatchNorm2d)):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
def _init_weights_xavier(self, m):
if isinstance(m, nn.Linear):
logging.info('=> init weight of Linear from xavier uniform')
nn.init.xavier_uniform_(m.weight)
if m.bias is not None:
logging.info('=> init bias of Linear to zeros')
nn.init.constant_(m.bias, 0)
elif isinstance(m, (nn.LayerNorm, nn.BatchNorm2d)):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
def forward_features(self, x):
for i in range(self.num_stages):
x = getattr(self, f'stage{i}')(x)
H, W = x.shape[-2], x.shape[-1]
x = rearrange(x, 'b c h w -> b (h w) c')
x_region = self.norm(x)
x = rearrange(x_region, 'b (h w) c -> b c h w', h=H, w=W)
x = self.avg_pool(x) # B C 1
if self.use_dense_prediction:
return x, x_region
else:
return x
def forward_return_n_last_blocks(self, x, n=1, return_patch_avgpool=False, depth=[]):
num_blks = sum(depth)
start_idx = num_blks - n
sum_cur = 0
for i, d in enumerate(depth):
sum_cur_new = sum_cur + d
if start_idx >= sum_cur and start_idx < sum_cur_new:
start_stage = i
start_blk = start_idx - sum_cur
sum_cur = sum_cur_new
# we will return the averaged token features from the `n` last blocks
# note: there is no [CLS] token in Swin Transformer
output = []
s = 0
for i in range(self.num_stages):
stage = getattr(self, f'stage{i}')
x = stage[0](x)
x, fea = stage[1].forward_with_features(x)
# x = getattr(self, f'stage{i}')(x)
# print(f'fea list length {len(fea)}')
# for i, layer in enumerate(self.layers):
# x, fea = layer.forward_with_features(x)
if i >= start_stage:
for x_ in fea[start_blk:]:
# print(f'x_ shape {x_.shape}')
if i == self.num_stages-1: # use the norm in the last stage
x_ = rearrange(x_, 'b c h w -> b h w c').contiguous()
x_ = self.norm(x_)
x_ = rearrange(x_, 'b h w c -> b c h w').contiguous()
x_avg = torch.flatten(self.avg_pool(x_), 1) # B C
# print(f'Stage {i}, x_avg {x_avg.shape}, x_ {x_.shape}')
output.append(x_avg)
start_blk = 0
return torch.cat(output, dim=-1)
def forward(self, x):
# convert to list
if not isinstance(x, list):
x = [x]
idx_crops = torch.cumsum(torch.unique_consecutive(
torch.tensor([inp.shape[-1] for inp in x]),
return_counts=True,
)[1], 0)
if self.use_dense_prediction:
start_idx = 0
for end_idx in idx_crops:
_out_cls, _out_fea = self.forward_features(torch.cat(x[start_idx: end_idx]))
B, N, C = _out_fea.shape
if start_idx == 0:
output_cls = _out_cls
output_fea = _out_fea.reshape(B * N, C)
npatch = [N]
else:
output_cls = torch.cat((output_cls, _out_cls))
output_fea = torch.cat((output_fea, _out_fea.reshape(B * N, C) ))
npatch.append(N)
start_idx = end_idx
return self.head(output_cls), self.head_dense(output_fea), output_fea, npatch
else:
start_idx = 0
for end_idx in idx_crops:
_out = self.forward_features(torch.cat(x[start_idx: end_idx]))
if start_idx == 0:
output = _out
else:
output = torch.cat((output, _out))
start_idx = end_idx
# Run the head forward on the concatenated features.
return self.head(output)
def init_weights(self, pretrained='', pretrained_layers=[], verbose=True):
if os.path.isfile(pretrained):
pretrained_dict = torch.load(pretrained, map_location='cpu')
logging.info(f'=> loading pretrained model {pretrained}')
model_dict = self.state_dict()
pretrained_dict = {
k: v for k, v in pretrained_dict.items()
if k in model_dict.keys()
}
need_init_state_dict = {}
for k, v in pretrained_dict.items():
need_init = (
k.split('.')[0] in pretrained_layers
or pretrained_layers[0] is '*'
)
if need_init:
if verbose:
logging.info(f'=> init {k} from {pretrained}')
need_init_state_dict[k] = v
self.load_state_dict(need_init_state_dict, strict=False)
@register_model
def get_cls_model(config, is_teacher=False, use_dense_prediction=False, **kwargs):
cvt_spec = config.MODEL.SPEC
if is_teacher: cvt_spec['DROP_PATH_RATE']=0.0
cvt = CvT(
num_classes=config.MODEL.NUM_CLASSES,
act_layer=QuickGELU,
norm_layer=partial(LayerNorm, eps=1e-5),
init='trunc_norm',
use_dense_prediction=use_dense_prediction,
spec=cvt_spec
)
if config.MODEL.INIT_WEIGHTS:
cvt.init_weights(
config.MODEL.PRETRAINED,
config.MODEL.PRETRAINED_LAYERS,
config.VERBOSE
)
return cvt