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model.py
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import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
import functools
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)
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)
assert out_w % x.shape[3] == 0
factor = out_w // x.shape[3]
x = torch.cat([x[..., -1:], x, x[..., :1]], 3)
x = F.interpolate(x, size=(x.shape[2], out_w + 2 * factor), mode='bilinear', align_corners=False)
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)
return feature
'''
HorizonNet
'''
class HorizonNet(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, use_rnn):
super(HorizonNet, self).__init__()
self.backbone = backbone
self.use_rnn = use_rnn
self.out_scale = 8
self.step_cols = 4
self.rnn_hidden_size = 512
# 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)]
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)
# 1D prediction
if self.use_rnn:
self.bi_rnn = nn.LSTM(input_size=c_last,
hidden_size=self.rnn_hidden_size,
num_layers=2,
dropout=0.5,
batch_first=False,
bidirectional=True)
self.drop_out = nn.Dropout(0.5)
self.linear = nn.Linear(in_features=2 * self.rnn_hidden_size,
out_features=3 * self.step_cols)
self.linear.bias.data[0*self.step_cols:1*self.step_cols].fill_(-1)
self.linear.bias.data[1*self.step_cols:2*self.step_cols].fill_(-0.478)
self.linear.bias.data[2*self.step_cols:3*self.step_cols].fill_(0.425)
else:
self.linear = nn.Sequential(
nn.Linear(c_last, self.rnn_hidden_size),
nn.ReLU(inplace=True),
nn.Dropout(0.5),
nn.Linear(self.rnn_hidden_size, 3 * self.step_cols),
)
self.linear[-1].bias.data[0*self.step_cols:1*self.step_cols].fill_(-1)
self.linear[-1].bias.data[1*self.step_cols:2*self.step_cols].fill_(-0.478)
self.linear[-1].bias.data[2*self.step_cols:3*self.step_cols].fill_(0.425)
self.x_mean.requires_grad = False
self.x_std.requires_grad = False
wrap_lr_pad(self)
def _prepare_x(self, x):
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)
return (x[:, :3] - self.x_mean) / self.x_std
def forward(self, x):
if x.shape[2] != 512 or x.shape[3] != 1024:
raise NotImplementedError()
x = self._prepare_x(x)
conv_list = self.feature_extractor(x)
feature = self.reduce_height_module(conv_list, x.shape[3]//self.step_cols)
# rnn
if self.use_rnn:
feature = feature.permute(2, 0, 1) # [w, b, c*h]
output, hidden = self.bi_rnn(feature) # [seq_len, b, num_directions * hidden_size]
output = self.drop_out(output)
output = self.linear(output) # [seq_len, b, 3 * step_cols]
output = output.view(output.shape[0], output.shape[1], 3, self.step_cols) # [seq_len, b, 3, step_cols]
output = output.permute(1, 2, 0, 3) # [b, 3, seq_len, step_cols]
output = output.contiguous().view(output.shape[0], 3, -1) # [b, 3, seq_len*step_cols]
else:
feature = feature.permute(0, 2, 1) # [b, w, c*h]
output = self.linear(feature) # [b, w, 3 * step_cols]
output = output.view(output.shape[0], output.shape[1], 3, self.step_cols) # [b, w, 3, step_cols]
output = output.permute(0, 2, 1, 3) # [b, 3, w, step_cols]
output = output.contiguous().view(output.shape[0], 3, -1) # [b, 3, w*step_cols]
# output.shape => B x 3 x W
cor = output[:, :1] # B x 1 x W
bon = output[:, 1:] # B x 2 x W
return bon, cor