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encoder-decoder_L3.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import numpy as np
import glob as glob
from torch.autograd import Variable
from reader import get_depth_data
from PIL import Image
import sys
path = './checkpoints/EncoderDecoder/L3/'
Train = np.load('iccv_dataset_train.npy')
Val = np.load('iccv_dataset_val.npy')
Test = np.load('iccv_dataset_test.npy')
class EncoderDecoder(nn.Module):
def __init__(self):
super(EncoderDecoder, self).__init__()
self.cnn1 = nn.Conv2d(3,10,kernel_size=(8,8))
self.pool1 = nn.MaxPool2d(4,return_indices=True)
self.cnn2 = nn.Conv2d(10,25,kernel_size=(8,8))
self.pool2 = nn.MaxPool2d(4,return_indices=True)
self.cnn3 = nn.Conv2d(25,50,kernel_size=(5,5))
self.pool3 = nn.MaxPool2d(4,return_indices=True)
self.unpool1 = nn.MaxUnpool2d(4)
self.decnn1 = nn.ConvTranspose2d(50,25,kernel_size=(5,5))
self.unpool2 = nn.MaxUnpool2d(4)
self.decnn2 = nn.ConvTranspose2d(25,10,kernel_size=(8,8))
self.unpool3 = nn.MaxUnpool2d(4)
self.decnn3 = nn.ConvTranspose2d(10,1,kernel_size=(8,8))
def forward(self,x):
x = F.relu(self.cnn1(x))
s1 = x.size()
x, i1 = self.pool1(x)
x = F.relu(self.cnn2(x))
s2 = x.size()
x, i2 = self.pool2(x)
x = F.relu(self.cnn3(x))
s3 = x.size()
x, i3 = self.pool3(x)
x = self.unpool1(x,i3,s3)
x = F.relu(self.decnn1(x))
x = self.unpool2(x,i2,s2)
x = F.relu(self.decnn2(x))
x = self.unpool3(x,i1,s1)
x = F.relu(self.decnn3(x))
return x
model = EncoderDecoder().cuda()
learning_rate = 1e-3
loss_fn = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
max_epochs = 15
import time
candidate_models = []
validation_losses = []
# Training and Validation
for epoch in range(max_epochs):
epoch_loss = []
start = time.time()
for train in Train:
dirs = glob.glob(train)
gdata,data = get_depth_data(dirs)
x,y = np.array(Image.open(data[0][0])),data[0][1]
x,y = x.reshape(1,3,480,640),y.reshape(1,1,480,640)
x = Variable(torch.Tensor(x).cuda(), requires_grad=True)
y = Variable(torch.Tensor(y).cuda(), requires_grad=False)
pred = model(x)
loss = loss_fn(pred,y)
epoch_loss.append(loss.item())
optimizer.zero_grad()
loss.backward()
optimizer.step()
# Validation
validation_loss = []
for val in Val:
val_x,val_y = [],[]
dirs = glob.glob(val)
g_data, data = get_depth_data(dirs)
val_x, val_y = np.array(Image.open(data[0][0])), data[0][1]
val_x, val_y = val_x.reshape(1,3,480,640), val_y.reshape(1,1,480,640)
val_x = Variable(torch.Tensor(val_x).cuda(),requires_grad=False)
val_pred = model.forward(val_x)
val_y = Variable(torch.Tensor(val_y).cuda(),requires_grad=False)
val_loss = loss_fn(val_pred,val_y)
validation_loss.append(val_loss.item())
validation_losses.append(np.array(validation_loss).mean())
end = time.time()
model_path = path + 'EncoderDecoder-3_layer-epoch_'+str(epoch)+'.model'
torch.save(model.state_dict(), model_path)
candidate_models.append(model_path)
print('epoch loss: ' + str(np.array(epoch_loss).mean()) + ', Val loss: ' + str(np.array(validation_loss).mean()) + ', Time: ' + str((end-start)))
if len(validation_losses) > 1:
check = (((validation_losses[-2] - validation_losses[-1])/(validation_losses[-2])) * 100)
if check < 1.0 and check > 0:
break
if check < 0:
candidate_models.pop()
break
# Test
test_model = EncoderDecoder().cuda()
test_model.load_state_dict(torch.load(candidate_models[-1]))
test_loss = []
for test in Test:
test_x,test_y = [],[]
dirs = glob.glob(test)
g_data, data = get_depth_data(dirs)
test_x, test_y = np.array(Image.open(data[0][0])), data[0][1]
test_x, test_y = test_x.reshape(1,3,480,640), test_y.reshape(1,1,480,640)
test_x = Variable(torch.Tensor(test_x).cuda(),requires_grad=False)
test_pred = test_model.forward(test_x).detach().cpu().numpy()
test_pred = test_pred.reshape(480,640)
for box in g_data:
info,dis = box
info = [int(i) for i in info[:4]]
# str(startX),str(startY),str(endX),str(endY)
startX ,startY, endX, endY = info[0], info[1], info[2], info[3]
pred_dis = test_pred[startY:endY,startX:endX]/1000.0
pred_dis = pred_dis[pred_dis > 0]
dis_error = abs(pred_dis.mean() - dis)
test_loss.append(dis_error)
np.save(path+'test_loss.npy',test_loss)
print('Test Loss:',np.mean(test_loss))