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model.py
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model.py
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import torch
import torch.nn as nn
from util import *
from loss import *
class conv_bn_relu(nn.Module):
def __init__(self, in_channels, channels, bn=True):
super(conv_bn_relu, self).__init__()
self.BN_ = bn
self.conv = nn.Conv2d(in_channels, channels, 3, padding=1)
if self.BN_:
self.bn = nn.BatchNorm2d(channels)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
x = self.conv(x)
if self.BN_:
x = self.bn(x)
x = self.relu(x)
return x
class cnn_module(nn.Module):
def __init__(self, out_channel=15):
super(cnn_module, self).__init__()
self.conv1 = conv_bn_relu(5, 64)
self.conv2 = conv_bn_relu(64, 64)
self.pool1 = nn.MaxPool2d(3, 2, 1)
self.conv3 = conv_bn_relu(64, 64)
self.conv4 = conv_bn_relu(64, 64)
self.pool2 = nn.MaxPool2d(3, 2, 1)
self.conv5 = conv_bn_relu(64, 64)
self.conv6 = conv_bn_relu(64, 64)
self.conv6_up = nn.Upsample(scale_factor=4)
self.conv4_up = nn.Upsample(scale_factor=2)
self.conv7 = conv_bn_relu(197, out_channel, False)
def forward(self, x):
conv1 = self.conv1(x)
conv2 = self.conv2(conv1)
pool1 = self.pool1(conv2)
conv3 = self.conv3(pool1)
conv4 = self.conv4(conv3)
pool2 = self.pool2(conv4)
conv5 = self.conv5(pool2)
conv6 = self.conv6(conv5)
conv6_up = self.conv6_up(conv6)
conv4_up = self.conv4_up(conv4)
conv_concat = torch.cat((x, conv2, conv4_up, conv6_up), 1)
conv7 = self.conv7(conv_concat)
conv_comb = torch.cat((x, conv7), 1)
return conv_comb
class create_ssn_net(nn.Module):
def __init__(self, num_spixels, num_iter, num_spixels_h, num_spixels_w, dtype='train', ssn=1):
super(create_ssn_net, self).__init__()
self.trans_features = cnn_module()
self.num_spixels = num_spixels
self.num_iter = num_iter
self.num_spixels_h = num_spixels_h
self.num_spixels_w = num_spixels_w
self.num_spixels = num_spixels_h * num_spixels_w
self.dtype = dtype
self.ssn = ssn
def forward(self, x, p2sp_index, invisible, init_index, cir_index, problabel, spixel_h, spixel_w, device):
if self.ssn:
trans_features = self.trans_features(x)
else:
trans_features = x
self.num_spixels_h = spixel_h[0]
self.num_spixels_w = spixel_w[0]
self.num_spixels = spixel_h[0] * spixel_w[0]
self.device = device
# init spixel feature
spixel_feature = SpixelFeature(trans_features, init_index, max_spixels=self.num_spixels)
for i in range(self.num_iter):
spixel_feature, _ = exec_iter(spixel_feature, trans_features, cir_index, p2sp_index,
invisible, self.num_spixels_h, self.num_spixels_w, self.device)
final_pixel_assoc = compute_assignments(spixel_feature, trans_features, p2sp_index, invisible, device) # out of memory
if self.dtype == 'train':
new_spixel_feat = SpixelFeature2(x, final_pixel_assoc, cir_index, invisible,
self.num_spixels_h, self.num_spixels_w)
new_spix_indices = compute_final_spixel_labels(final_pixel_assoc, p2sp_index,
self.num_spixels_h, self.num_spixels_w)
recon_feat2 = Semar(new_spixel_feat, new_spix_indices)
spixel_label = SpixelFeature2(problabel, final_pixel_assoc, cir_index, invisible,
self.num_spixels_h, self.num_spixels_w)
recon_label = decode_features(final_pixel_assoc, spixel_label, p2sp_index,
self.num_spixels_h, self.num_spixels_w, self.num_spixels, 50)
return recon_feat2, recon_label
elif self.dtype == 'test':
new_spixel_feat = SpixelFeature2(x, final_pixel_assoc, cir_index, invisible,
self.num_spixels_h, self.num_spixels_w)
new_spix_indices = compute_final_spixel_labels(final_pixel_assoc, p2sp_index,
self.num_spixels_h, self.num_spixels_w)
recon_feat2 = Semar(new_spixel_feat, new_spix_indices)
spixel_label = SpixelFeature2(problabel, final_pixel_assoc, cir_index, invisible,
self.num_spixels_h, self.num_spixels_w)
recon_label = decode_features(final_pixel_assoc, spixel_label, p2sp_index,
self.num_spixels_h, self.num_spixels_w, self.num_spixels, 50)
# import pdb
# pdb.set_trace()
return recon_feat2, recon_label, new_spix_indices
else:
pass
class Loss(nn.Module):
def __init__(self):
super(Loss, self).__init__()
self.loss1 = position_color_loss()
self.loss2 = LossWithoutSoftmax()
def forward(self, recon_feat2, pixel_feature, recon_label, label):
loss1 = self.loss1(recon_feat2, pixel_feature)
loss2 = self.loss2(recon_label, label)
return loss1 + loss2, loss1, loss2