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LeViT-UNet-192.py
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LeViT-UNet-192.py
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# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
# Modified from
# https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
# Copyright 2020 Ross Wightman, Apache-2.0 License
from timm.models.layers import activations
import torch
import itertools
import utils
import torch.nn as nn
import torch.nn.functional as F
from timm.models.vision_transformer import trunc_normal_
from timm.models.registry import register_model
#__all__ = ["LeViT"]
specification = {
'LeViT_192': {
'C': '192_288_384', 'D': 32, 'N': '3_5_6', 'X': '4_4_4', 'drop_path': 0,
'weights': 'https://dl.fbaipublicfiles.com/LeViT/LeViT-192-92712e41.pth'},
}
__all__ = [specification.keys()]
@register_model ## No Mask
def Build_LeViT_UNet_192(num_classes=4, distillation=True,
pretrained=False, fuse=False):
return model_factory_v3_NM(**specification['LeViT_192'], num_classes=num_classes,
distillation=distillation, pretrained=pretrained, fuse=fuse)
FLOPS_COUNTER = 0
class Conv2d_BN(torch.nn.Sequential):
def __init__(self, a, b, ks=1, stride=1, pad=0, dilation=1,
groups=1, bn_weight_init=1, resolution=-10000):
super().__init__()
self.add_module('c', torch.nn.Conv2d(
a, b, ks, stride, pad, dilation, groups, bias=False))
bn = torch.nn.BatchNorm2d(b)
torch.nn.init.constant_(bn.weight, bn_weight_init)
torch.nn.init.constant_(bn.bias, 0)
self.add_module('bn', bn)
global FLOPS_COUNTER
output_points = ((resolution + 2 * pad - dilation *
(ks - 1) - 1) // stride + 1)**2
FLOPS_COUNTER += a * b * output_points * (ks**2) // groups
@torch.no_grad()
def fuse(self):
c, bn = self._modules.values()
w = bn.weight / (bn.running_var + bn.eps)**0.5
w = c.weight * w[:, None, None, None]
b = bn.bias - bn.running_mean * bn.weight / \
(bn.running_var + bn.eps)**0.5
m = torch.nn.Conv2d(w.size(1), w.size(
0), w.shape[2:], stride=self.c.stride, padding=self.c.padding, dilation=self.c.dilation, groups=self.c.groups)
m.weight.data.copy_(w)
m.bias.data.copy_(b)
return m
class Linear_BN(torch.nn.Sequential):
def __init__(self, a, b, bn_weight_init=1, resolution=-100000):
super().__init__()
self.add_module('c', torch.nn.Linear(a, b, bias=False))
bn = torch.nn.BatchNorm1d(b)
torch.nn.init.constant_(bn.weight, bn_weight_init)
torch.nn.init.constant_(bn.bias, 0)
self.add_module('bn', bn)
global FLOPS_COUNTER
output_points = resolution**2
FLOPS_COUNTER += a * b * output_points
@torch.no_grad()
def fuse(self):
l, bn = self._modules.values()
w = bn.weight / (bn.running_var + bn.eps)**0.5
w = l.weight * w[:, None]
b = bn.bias - bn.running_mean * bn.weight / \
(bn.running_var + bn.eps)**0.5
m = torch.nn.Linear(w.size(1), w.size(0))
m.weight.data.copy_(w)
m.bias.data.copy_(b)
return m
def forward(self, x):
l, bn = self._modules.values()
x = l(x)
return bn(x.flatten(0, 1)).reshape_as(x)
class BN_Linear(torch.nn.Sequential):
def __init__(self, a, b, bias=True, std=0.02):
super().__init__()
self.add_module('bn', torch.nn.BatchNorm1d(a))
l = torch.nn.Linear(a, b, bias=bias)
trunc_normal_(l.weight, std=std)
if bias:
torch.nn.init.constant_(l.bias, 0)
self.add_module('l', l)
global FLOPS_COUNTER
FLOPS_COUNTER += a * b
@torch.no_grad()
def fuse(self):
bn, l = self._modules.values()
w = bn.weight / (bn.running_var + bn.eps)**0.5
b = bn.bias - self.bn.running_mean * \
self.bn.weight / (bn.running_var + bn.eps)**0.5
w = l.weight * w[None, :]
if l.bias is None:
b = b @ self.l.weight.T
else:
b = (l.weight @ b[:, None]).view(-1) + self.l.bias
m = torch.nn.Linear(w.size(1), w.size(0))
m.weight.data.copy_(w)
m.bias.data.copy_(b)
return m
def b16(n, activation, resolution=224):
return torch.nn.Sequential(
Conv2d_BN(1, n // 8, 3, 2, 1, resolution=resolution), # aping num_channel
activation(),
Conv2d_BN(n // 8, n // 4, 3, 2, 1, resolution=resolution // 2),
activation(),
Conv2d_BN(n // 4, n // 2, 3, 2, 1, resolution=resolution // 4),
activation(),
Conv2d_BN(n // 2, n, 3, 2, 1, resolution=resolution // 8))
class Residual(torch.nn.Module):
def __init__(self, m, drop):
super().__init__()
self.m = m
self.drop = drop
def forward(self, x):
if self.training and self.drop > 0:
return x + self.m(x) * torch.rand(x.size(0), 1, 1,
device=x.device).ge_(self.drop).div(1 - self.drop).detach()
else:
return x + self.m(x)
class Attention(torch.nn.Module):
def __init__(self, dim, key_dim, num_heads=8,
attn_ratio=4,
activation=None,
resolution=14):
super().__init__()
self.num_heads = num_heads
self.scale = key_dim ** -0.5
self.key_dim = key_dim
self.nh_kd = nh_kd = key_dim * num_heads
self.d = int(attn_ratio * key_dim)
self.dh = int(attn_ratio * key_dim) * num_heads
self.attn_ratio = attn_ratio
h = self.dh + nh_kd * 2
self.qkv = Linear_BN(dim, h, resolution=resolution)
self.proj = torch.nn.Sequential(activation(), Linear_BN(
self.dh, dim, bn_weight_init=0, resolution=resolution))
points = list(itertools.product(range(resolution), range(resolution)))
N = len(points)
attention_offsets = {}
idxs = []
for p1 in points:
for p2 in points:
offset = (abs(p1[0] - p2[0]), abs(p1[1] - p2[1]))
if offset not in attention_offsets:
attention_offsets[offset] = len(attention_offsets)
idxs.append(attention_offsets[offset])
self.attention_biases = torch.nn.Parameter(
torch.zeros(num_heads, len(attention_offsets)))
self.register_buffer('attention_bias_idxs',
torch.LongTensor(idxs).view(N, N))
global FLOPS_COUNTER
#queries * keys
FLOPS_COUNTER += num_heads * (resolution**4) * key_dim
# softmax
FLOPS_COUNTER += num_heads * (resolution**4)
#attention * v
FLOPS_COUNTER += num_heads * self.d * (resolution**4)
@torch.no_grad()
def train(self, mode=True):
super().train(mode)
if mode and hasattr(self, 'ab'):
del self.ab
else:
self.ab = self.attention_biases[:, self.attention_bias_idxs]
def forward(self, x): # x (B,N,C)
B, N, C = x.shape
qkv = self.qkv(x)
q, k, v = qkv.view(B, N, self.num_heads, -
1).split([self.key_dim, self.key_dim, self.d], dim=3)
q = q.permute(0, 2, 1, 3)
k = k.permute(0, 2, 1, 3)
v = v.permute(0, 2, 1, 3)
attn = (
(q @ k.transpose(-2, -1)) * self.scale
+
(self.attention_biases[:, self.attention_bias_idxs]
if self.training else self.ab)
)
attn = attn.softmax(dim=-1)
x = (attn @ v).transpose(1, 2).reshape(B, N, self.dh)
x = self.proj(x)
return x
class Subsample(torch.nn.Module):
def __init__(self, stride, resolution):
super().__init__()
self.stride = stride
self.resolution = resolution
def forward(self, x):
B, N, C = x.shape
x = x.view(B, self.resolution, self.resolution, C)[
:, ::self.stride, ::self.stride].reshape(B, -1, C)
return x
class AttentionSubsample(torch.nn.Module):
def __init__(self, in_dim, out_dim, key_dim, num_heads=8,
attn_ratio=2,
activation=None,
stride=2,
resolution=14, resolution_=7):
super().__init__()
self.num_heads = num_heads
self.scale = key_dim ** -0.5
self.key_dim = key_dim
self.nh_kd = nh_kd = key_dim * num_heads
self.d = int(attn_ratio * key_dim)
self.dh = int(attn_ratio * key_dim) * self.num_heads
self.attn_ratio = attn_ratio
self.resolution_ = resolution_
self.resolution_2 = resolution_**2
h = self.dh + nh_kd
self.kv = Linear_BN(in_dim, h, resolution=resolution)
self.q = torch.nn.Sequential(
Subsample(stride, resolution),
Linear_BN(in_dim, nh_kd, resolution=resolution_))
self.proj = torch.nn.Sequential(activation(), Linear_BN(
self.dh, out_dim, resolution=resolution_))
self.stride = stride
self.resolution = resolution
points = list(itertools.product(range(resolution), range(resolution)))
points_ = list(itertools.product(
range(resolution_), range(resolution_)))
N = len(points)
N_ = len(points_)
attention_offsets = {}
idxs = []
for p1 in points_:
for p2 in points:
size = 1
offset = (
abs(p1[0] * stride - p2[0] + (size - 1) / 2),
abs(p1[1] * stride - p2[1] + (size - 1) / 2))
if offset not in attention_offsets:
attention_offsets[offset] = len(attention_offsets)
idxs.append(attention_offsets[offset])
self.attention_biases = torch.nn.Parameter(
torch.zeros(num_heads, len(attention_offsets)))
self.register_buffer('attention_bias_idxs',
torch.LongTensor(idxs).view(N_, N))
global FLOPS_COUNTER
#queries * keys
FLOPS_COUNTER += num_heads * \
(resolution**2) * (resolution_**2) * key_dim
# softmax
FLOPS_COUNTER += num_heads * (resolution**2) * (resolution_**2)
#attention * v
FLOPS_COUNTER += num_heads * \
(resolution**2) * (resolution_**2) * self.d
@torch.no_grad()
def train(self, mode=True):
super().train(mode)
if mode and hasattr(self, 'ab'):
del self.ab
else:
self.ab = self.attention_biases[:, self.attention_bias_idxs]
def forward(self, x):
B, N, C = x.shape
k, v = self.kv(x).view(B, N, self.num_heads, -
1).split([self.key_dim, self.d], dim=3)
k = k.permute(0, 2, 1, 3) # BHNC
v = v.permute(0, 2, 1, 3) # BHNC
q = self.q(x).view(B, self.resolution_2, self.num_heads,
self.key_dim).permute(0, 2, 1, 3)
attn = (q @ k.transpose(-2, -1)) * self.scale + \
(self.attention_biases[:, self.attention_bias_idxs]
if self.training else self.ab)
attn = attn.softmax(dim=-1)
x = (attn @ v).transpose(1, 2).reshape(B, -1, self.dh)
x = self.proj(x)
return x
class Conv2dReLU(nn.Sequential):
def __init__(
self,
in_channels,
out_channels,
kernel_size,
padding=0,
stride=1,
use_batchnorm=True,
):
conv = nn.Conv2d(
in_channels,
out_channels,
kernel_size,
stride=stride,
padding=padding,
bias=not (use_batchnorm),
)
relu = nn.ReLU(inplace=True)
bn = nn.BatchNorm2d(out_channels)
super(Conv2dReLU, self).__init__(conv, bn, relu)
class DecoderBlock(nn.Module):
def __init__(
self,
in_channels,
out_channels,
#skip_channels=0,
use_batchnorm=True,
):
super().__init__()
self.conv1 = Conv2dReLU(
#in_channels + skip_channels,
in_channels,
out_channels,
kernel_size=3,
padding=1,
use_batchnorm=use_batchnorm,
)
self.conv2 = Conv2dReLU(
out_channels,
out_channels,
kernel_size=3,
padding=1,
use_batchnorm=use_batchnorm,
)
self.up = nn.UpsamplingBilinear2d(scale_factor=2)
def forward(self, x, skip=None):
x = self.up(x)
if skip is not None:
x = torch.cat([x, skip], dim=1)
x = self.conv1(x)
x = self.conv2(x)
return x
class SegmentationHead(nn.Sequential):
def __init__(self, in_channels, out_channels, kernel_size=3, upsampling=1):
conv2d = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, padding=kernel_size // 2)
upsampling = nn.UpsamplingBilinear2d(scale_factor=upsampling) if upsampling > 1 else nn.Identity()
super().__init__(conv2d, upsampling)
##################################
##################################
def model_factory_v3_NM(C, D, X, N, drop_path, weights,
num_classes, distillation, pretrained, fuse):
embed_dim = [int(x) for x in C.split('_')]
num_heads = [int(x) for x in N.split('_')]
depth = [int(x) for x in X.split('_')]
act = torch.nn.Hardswish
model = LeViT_UNet_192(
patch_size=16,
embed_dim=embed_dim,
num_heads=num_heads,
key_dim=[D] * 3,
depth=depth,
attn_ratio=[2, 2, 2],
mlp_ratio=[2, 2, 2],
down_ops=[
#('Subsample',key_dim, num_heads, attn_ratio, mlp_ratio, stride)
['Subsample', D, embed_dim[0] // D, 4, 2, 2],
['Subsample', D, embed_dim[1] // D, 4, 2, 2],
],
attention_activation=act,
mlp_activation=act,
hybrid_backbone=None,
num_classes=num_classes, ### aping: set number class
drop_path=drop_path,
distillation=distillation
)
if pretrained:
checkpoint = torch.hub.load_state_dict_from_url(
weights, map_location='cpu')
## aping: load according the model items
checkpoint_model = checkpoint['model']
# checkpoint_model['cnn_b1']=checkpoint_model.pop('patch_embed.0.c.weight')
all_pre_keys = checkpoint_model.keys()
re_str = ['patch_embed.2', 'patch_embed.2', 'patch_embed.4', 'patch_embed.6']
new_str = ['cnn_b2.0', 'cnn_b2.0', 'cnn_b3.0', 'cnn_b4.0']
for i, search_str in enumerate(re_str):
for item in list(all_pre_keys):
if search_str in item:
replace_name = item.replace(re_str[i], new_str[i])
# print(replace_name)
checkpoint_model[replace_name]=checkpoint_model.pop(item)
## preposcess the pretrained model
model_dict = model.state_dict()
pretrained_dict = {k:v for k, v in checkpoint_model.items() if k in model_dict}
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict) ## model.blocks[0].m.qkv.c == model.block_1[0].m.qkv.c
if fuse:
utils.replace_batchnorm(model)
return model
class LeViT_UNet_192(torch.nn.Module):
""" Vision Transformer with support for patch or hybrid CNN input stage
"""
def __init__(self, img_size=224, ## aping: should change
patch_size=16,
in_chans=3,
num_classes=9,
embed_dim=[192],
key_dim=[64],
depth=[12],
num_heads=[3],
attn_ratio=[2],
mlp_ratio=[2],
hybrid_backbone=None,
down_ops=[],
attention_activation=torch.nn.Hardswish,
mlp_activation=torch.nn.Hardswish,
distillation=True,
drop_path=0):
super().__init__()
global FLOPS_COUNTER
self.num_classes = num_classes
self.num_features = embed_dim[-1]
self.embed_dim = embed_dim
self.distillation = distillation
# self.patch_embed = hybrid_backbone ## aping: CNN # aping num_channel
n = 192 ## cnn num channel
activation = torch.nn.Hardswish
self.cnn_b1 = torch.nn.Sequential(
Conv2d_BN(1, n // 8, 3, 2, 1, resolution=img_size), activation())
self.cnn_b2 = torch.nn.Sequential(
Conv2d_BN(n // 8, n // 4, 3, 2, 1, resolution=img_size // 2), activation())
self.cnn_b3 = torch.nn.Sequential(
Conv2d_BN(n // 4, n // 2, 3, 2, 1, resolution=img_size // 4), activation())
self.cnn_b4 = torch.nn.Sequential(
Conv2d_BN(n // 2, n, 3, 2, 1, resolution=img_size // 8))
self.decoderBlock_1 = DecoderBlock(n + (192+288+384), 512) #->att+cnn:128+768=896 '192_288_384'
self.decoderBlock_2 = DecoderBlock(n//2 + 512, 256) #->28->56, 512+9+192=713
self.decoderBlock_3 = DecoderBlock(n//4 + 256, 128) #->56->112, 256+9+96=361
self.segmentation_head = SegmentationHead(n//8 + 128, self.num_classes, kernel_size=3, upsampling=2)
self.blocks = [] ## attention block
down_ops.append([''])
resolution = img_size // patch_size
for i, (ed, kd, dpth, nh, ar, mr, do) in enumerate(
zip(embed_dim, key_dim, depth, num_heads, attn_ratio, mlp_ratio, down_ops)):
print(i)
for _ in range(dpth):
self.blocks.append(
Residual(Attention(
ed, kd, nh,
attn_ratio=ar,
activation=attention_activation,
resolution=resolution,
), drop_path))
if mr > 0:
h = int(ed * mr)
self.blocks.append(
Residual(torch.nn.Sequential(
Linear_BN(ed, h, resolution=resolution),
mlp_activation(),
Linear_BN(h, ed, bn_weight_init=0,
resolution=resolution),
), drop_path))
if do[0] == 'Subsample':
#('Subsample',key_dim, num_heads, attn_ratio, mlp_ratio, stride)
resolution_ = (resolution - 1) // do[5] + 1
self.blocks.append(
AttentionSubsample(
*embed_dim[i:i + 2], key_dim=do[1], num_heads=do[2],
attn_ratio=do[3],
activation=attention_activation,
stride=do[5],
resolution=resolution,
resolution_=resolution_))
resolution = resolution_
if do[4] > 0: # mlp_ratio
h = int(embed_dim[i + 1] * do[4])
self.blocks.append(
Residual(torch.nn.Sequential(
Linear_BN(embed_dim[i + 1], h,
resolution=resolution),
mlp_activation(),
Linear_BN(
h, embed_dim[i + 1], bn_weight_init=0, resolution=resolution),
), drop_path))
self.blocks = torch.nn.Sequential(*self.blocks)
## divid the blocks
self.block_1 = self.blocks[0:8]
self.block_2 = self.blocks[8:18]
self.block_3 = self.blocks[18:]
# del self.blocks
## aping: upsampling
self.up = nn.UpsamplingBilinear2d(scale_factor=2)
self.FLOPS = FLOPS_COUNTER
FLOPS_COUNTER = 0
@torch.jit.ignore
def no_weight_decay(self):
return {x for x in self.state_dict().keys() if 'attention_biases' in x}
def forward(self, x):
x_cnn_1 = self.cnn_b1(x) # channel num 1->16
x_cnn_2 = self.cnn_b2(x_cnn_1) #-> 16->32
x_cnn_3 = self.cnn_b3(x_cnn_2) # 32->64
x_cnn = self.cnn_b4(x_cnn_3) # 64->128 : torch.Size([1, 128, 14, 14])
x = x_cnn.flatten(2).transpose(1, 2) # torch.Size([4, 196, 256])
## aping
x = self.block_1(x) # torch.Size([1, 196, 128])--> torch.Size([1, 196, 128]) : CNN->Trans
x_num, x_len = x.shape[0], x.shape[1]
x_r_1 = x.reshape(x_num, int(x_len**0.5), int(x_len**0.5), -1)
x_r_1 = x_r_1.permute(0,3,1,2)
x = self.block_2(x)
x_num, x_len = x.shape[0], x.shape[1]
x_r_2 = x.reshape(x_num, int(x_len**0.5), int(x_len**0.5), -1)
x_r_2 = x_r_2.permute(0,3,1,2)
## upsampling
x_r_2_up = self.up(x_r_2)
x = self.block_3(x) # torch.Size([1, 49, 256])->torch.Size([1, 16, 384])
x_num, x_len = x.shape[0], x.shape[1]
x_r_3 = x.reshape(x_num, int(x_len**0.5), int(x_len**0.5), -1)
x_r_3 = x_r_3.permute(0,3,1,2)
## upsampling
x_r_3_up = self.up(x_r_3)
x_r_3_up = self.up(x_r_3_up)
## aping: resize the feature maps
if (x_r_2_up.shape != x_r_3_up.shape):
x_r_3_up = F.interpolate(x_r_3_up, size=x_r_2_up.shape[2:], mode="bilinear", align_corners=True)
att_all = torch.cat([ x_r_1, x_r_2_up, x_r_3_up], dim=1) # 128+256+384=768
x_att_all = torch.cat([x_cnn, att_all], dim=1) ## 768+128=896
decoder_feature = self.decoderBlock_1(x_att_all) # x_att_all: ([4, 896, 32, 32])->512
decoder_feature = torch.cat([decoder_feature, x_cnn_3], dim=1) #:(512+9)x64x64
decoder_feature = self.decoderBlock_2(decoder_feature)
decoder_feature = torch.cat([decoder_feature, x_cnn_2], dim=1) # ([4, (320+9), 128, 128])
decoder_feature = self.decoderBlock_3(decoder_feature)
decoder_feature = torch.cat([decoder_feature, x_cnn_1], dim=1) # ([4, 169, 256, 256])
logits = self.segmentation_head(decoder_feature) ## torch.Size([4, 2, 224, 224])
return logits
#################################
def netParams(model):
"""
computing total network parameters
args:
model: model
return: the number of parameters
"""
total_paramters = 0
for parameter in model.parameters():
i = len(parameter.size())
p = 1
for j in range(i):
p *= parameter.size(j)
total_paramters += p
return total_paramters
from ptflops import get_model_complexity_info
if __name__ == '__main__':
for name in specification:
# net = globals()[name](fuse=True, pretrained=False)
net = Build_LeViT_UNet_192(num_classes=9, pretrained=True)
print(net)
net.eval()
net(torch.randn(1, 1, 224, 224)) #NCHW
## cal para FLOPS
print(name,
net.FLOPS, 'FLOPs',
sum(p.numel() for p in net.parameters() if p.requires_grad), 'parameters')
total_paramters = netParams(net)
print("the number of parameters: %d ==> %.2f M" % (total_paramters, (total_paramters / 1e6)))
# print(net)