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"""
@author:
@Date: 2021/07/17
@description: Use the feature extractor proposed by HorizonNet
"""
import numpy as np
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
import functools
from models.base_model import BaseModule
ENCODER_RESNET = [
'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152',
'resnext50_32x4d', 'resnext101_32x8d'
]
ENCODER_DENSENET = [
'densenet121', 'densenet169', 'densenet161', 'densenet201'
]
def lr_pad(x, padding=1):
''' Pad left/right-most to each other instead of zero padding '''
return torch.cat([x[..., -padding:], x, x[..., :padding]], dim=3)
class LR_PAD(nn.Module):
''' Pad left/right-most to each other instead of zero padding '''
def __init__(self, padding=1):
super(LR_PAD, self).__init__()
self.padding = padding
def forward(self, x):
return lr_pad(x, self.padding)
def wrap_lr_pad(net):
for name, m in net.named_modules():
if not isinstance(m, nn.Conv2d):
continue
if m.padding[1] == 0:
continue
w_pad = int(m.padding[1])
m.padding = (m.padding[0], 0) # weight padding is 0, LR_PAD then use valid padding will keep dim of weight
names = name.split('.')
root = functools.reduce(lambda o, i: getattr(o, i), [net] + names[:-1])
setattr(
root, names[-1],
nn.Sequential(LR_PAD(w_pad), m)
)
'''
Encoder
'''
class Resnet(nn.Module):
def __init__(self, backbone='resnet50', pretrained=True):
super(Resnet, self).__init__()
assert backbone in ENCODER_RESNET
self.encoder = getattr(models, backbone)(pretrained=pretrained)
del self.encoder.fc, self.encoder.avgpool
def forward(self, x):
features = []
x = self.encoder.conv1(x)
x = self.encoder.bn1(x)
x = self.encoder.relu(x)
x = self.encoder.maxpool(x)
x = self.encoder.layer1(x)
features.append(x) # 1/4
x = self.encoder.layer2(x)
features.append(x) # 1/8
x = self.encoder.layer3(x)
features.append(x) # 1/16
x = self.encoder.layer4(x)
features.append(x) # 1/32
return features
def list_blocks(self):
lst = [m for m in self.encoder.children()]
block0 = lst[:4]
block1 = lst[4:5]
block2 = lst[5:6]
block3 = lst[6:7]
block4 = lst[7:8]
return block0, block1, block2, block3, block4
class Densenet(nn.Module):
def __init__(self, backbone='densenet169', pretrained=True):
super(Densenet, self).__init__()
assert backbone in ENCODER_DENSENET
self.encoder = getattr(models, backbone)(pretrained=pretrained)
self.final_relu = nn.ReLU(inplace=True)
del self.encoder.classifier
def forward(self, x):
lst = []
for m in self.encoder.features.children():
x = m(x)
lst.append(x)
features = [lst[4], lst[6], lst[8], self.final_relu(lst[11])]
return features
def list_blocks(self):
lst = [m for m in self.encoder.features.children()]
block0 = lst[:4]
block1 = lst[4:6]
block2 = lst[6:8]
block3 = lst[8:10]
block4 = lst[10:]
return block0, block1, block2, block3, block4
'''
Decoder
'''
class ConvCompressH(nn.Module):
''' Reduce feature height by factor of two '''
def __init__(self, in_c, out_c, ks=3):
super(ConvCompressH, self).__init__()
assert ks % 2 == 1
self.layers = nn.Sequential(
nn.Conv2d(in_c, out_c, kernel_size=ks, stride=(2, 1), padding=ks // 2),
nn.BatchNorm2d(out_c),
nn.ReLU(inplace=True),
)
def forward(self, x):
return self.layers(x)
class GlobalHeightConv(nn.Module):
def __init__(self, in_c, out_c):
super(GlobalHeightConv, self).__init__()
self.layer = nn.Sequential(
ConvCompressH(in_c, in_c // 2),
ConvCompressH(in_c // 2, in_c // 2),
ConvCompressH(in_c // 2, in_c // 4),
ConvCompressH(in_c // 4, out_c),
)
def forward(self, x, out_w):
x = self.layer(x)
factor = out_w // x.shape[3]
x = torch.cat([x[..., -1:], x, x[..., :1]], 3) # 先补左右，相当于warp模式，然后进行插值
d_type = x.dtype
x = F.interpolate(x, size=(x.shape[2], out_w + 2 * factor), mode='bilinear', align_corners=False)
# if x.dtype != d_type:
# x = x.type(d_type)
x = x[..., factor:-factor]
return x
class GlobalHeightStage(nn.Module):
def __init__(self, c1, c2, c3, c4, out_scale=8):
''' Process 4 blocks from encoder to single multiscale features '''
super(GlobalHeightStage, self).__init__()
self.cs = c1, c2, c3, c4
self.out_scale = out_scale
self.ghc_lst = nn.ModuleList([
GlobalHeightConv(c1, c1 // out_scale),
GlobalHeightConv(c2, c2 // out_scale),
GlobalHeightConv(c3, c3 // out_scale),
GlobalHeightConv(c4, c4 // out_scale),
])
def forward(self, conv_list, out_w):
assert len(conv_list) == 4
bs = conv_list[0].shape[0]
feature = torch.cat([
f(x, out_w).reshape(bs, -1, out_w)
for f, x, out_c in zip(self.ghc_lst, conv_list, self.cs)
], dim=1)
# conv_list:
# 0 [b, 256(d), 128(h), 256(w)] ->(4*{conv3*3 step2*1} : d/8 h/16)-> [b 32(d) 8(h) 256(w)]
# 1 [b, 512(d), 64(h), 128(w)] ->(4*{conv3*3 step2*1} : d/8 h/16)-> [b 64(d) 4(h) 128(w)]
# 2 [b, 1024(d), 32(h), 64(w)] ->(4*{conv3*3 step2*1} : d/8 h/16)-> [b 128(d) 2(h) 64(w)]
# 3 [b, 2048(d), 16(h), 32(w)] ->(4*{conv3*3 step2*1} : d/8 h/16)-> [b 256(d) 1(h) 32(w)]
# 0 ->(unsampledW256} : w=256)-> [b 32(d) 8(h) 256(w)] ->(reshapeH1} : h=1)-> [b 256(d) 1(h) 256(w)]
# 1 ->(unsampledW256} : w=256)-> [b 64(d) 4(h) 256(w)] ->(reshapeH1} : h=1)-> [b 256(d) 1(h) 256(w)]
# 2 ->(unsampledW256} : w=256)-> [b 128(d) 2(h) 256(w)] ->(reshapeH1} : h=1)-> [b 256(d) 1(h) 256(w)]
# 3 ->(unsampledW256} : w=256)-> [b 256(d) 1(h) 256(w)] ->(reshapeH1} : h=1)-> [b 256(d) 1(h) 256(w)]
# 0 --\
# 1 -- \
# ---- cat [b 1024(d) 1(h) 256(w)]
# 2 -- /
# 3 --/
return feature # [b 1024(d) 256(w)]
class HorizonNetFeatureExtractor(nn.Module):
x_mean = torch.FloatTensor(np.array([0.485, 0.456, 0.406])[None, :, None, None])
x_std = torch.FloatTensor(np.array([0.229, 0.224, 0.225])[None, :, None, None])
def __init__(self, backbone='resnet50'):
super(HorizonNetFeatureExtractor, self).__init__()
self.out_scale = 8
self.step_cols = 4
# Encoder
if backbone.startswith('res'):
self.feature_extractor = Resnet(backbone, pretrained=True)
elif backbone.startswith('dense'):
self.feature_extractor = Densenet(backbone, pretrained=True)
else:
raise NotImplementedError()
# Inference channels number from each block of the encoder
with torch.no_grad():
dummy = torch.zeros(1, 3, 512, 1024)
c1, c2, c3, c4 = [b.shape[1] for b in self.feature_extractor(dummy)]
self.c_last = (c1 * 8 + c2 * 4 + c3 * 2 + c4 * 1) // self.out_scale
# Convert features from 4 blocks of the encoder into B x C x 1 x W'
self.reduce_height_module = GlobalHeightStage(c1, c2, c3, c4, self.out_scale)
self.x_mean.requires_grad = False
self.x_std.requires_grad = False
wrap_lr_pad(self)
def _prepare_x(self, x):
x = x.clone()
if self.x_mean.device != x.device:
self.x_mean = self.x_mean.to(x.device)
self.x_std = self.x_std.to(x.device)
x[:, :3] = (x[:, :3] - self.x_mean) / self.x_std
return x
def forward(self, x):
# x [b 3 512 1024]
x = self._prepare_x(x) # [b 3 512 1024]
conv_list = self.feature_extractor(x)
# conv_list:
# 0 [b, 256(d), 128(h), 256(w)]
# 1 [b, 512(d), 64(h), 128(w)]
# 2 [b, 1024(d), 32(h), 64(w)]
# 3 [b, 2048(d), 16(h), 32(w)]
x = self.reduce_height_module(conv_list, x.shape[3] // self.step_cols) # [b 1024(d) 1(h) 256(w)]
# After reduce_Height_module, h becomes 1, the information is compressed to d,
# and w contains different resolutions
# 0 [b, 256(d), 128(h), 256(w)] -> [b, 256/8(d) * 128/16(h') = 256(d), 1(h) 256(w)]
# 1 [b, 512(d), 64(h), 128(w)] -> [b, 512/8(d) * 64/16(h') = 256(d), 1(h) 256(w)]
# 2 [b, 1024(d), 32(h), 64(w)] -> [b, 1024/8(d) * 32/16(h') = 256(d), 1(h) 256(w)]
# 3 [b, 2048(d), 16(h), 32(w)] -> [b, 2048/8(d) * 16/16(h') = 256(d), 1(h) 256(w)]
return x # [b 1024(d) 1(h) 256(w)]
if __name__ == '__main__':
from PIL import Image
extractor = HorizonNetFeatureExtractor()
img = np.array(Image.open("../../src/demo.png")).transpose((2, 0, 1))
input = torch.Tensor([img]) # 1 3 512 1024
feature = extractor(input)
print(feature.shape) # 1, 1024, 256 | 1024 = (out_c_0*h_0 +... + out_c_3*h_3) = 256 * 4
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