#if read_lut_list == LUT_filelist:
mean_train_losses.append(np.mean(train_losses))
mean_valid_losses.append(np.mean(valid_losses))
#mean_test_losses.append(np.mean(test_losses))
print('len mean train losses ', len(mean_train_losses))
#test_loader
for batch_idx, (f_plot, r_plot) in enumerate(test_loader):
outputs = model(f_plot)
loss = msre(outputs, radiances)
#if i % 10 == 0:
#print(i, outputs)
filename = ('./plot/03_rtm_nn_' + lat + '_alltoz_epoch_' + str(epoch).zfill(5) +
'_index_' + str(batch_idx).zfill(5))
if batch_idx == 0:
test_plot300(real_epoch, batch_idx, f_plot, wav300, r_plot,
outputs, filename, lr)
if real_epoch % 5 == 0:
print(batch_idx)
test_plot300(real_epoch, batch_idx, f_plot, wav300, r_plot,
outputs, filename, lr)
outputs = None
del outputs
lossesfile = './result/03_rtm_nn_latL_tozall_mean_losses.txt'
print('lossesfile')
if os.path.exists(lossesfile):
with open(lossesfile, 'a') as f:
f.write(str(real_epoch).zfill(5) + ',' + str(np.mean(train_losses))
+ ',' + str(np.mean(valid_losses))+'\n')
else:
# plot radiances
with torch.no_grad():
for i, (features, radiances) in enumerate(test_loader):
outputs = model(features)
loss = msre(outputs, radiances)
test_losses.append(loss.item())
if (i * 128) % (128 * 10) == 0:
print(f'{i * 128} / ', len(test_loader)*128, time.time() - timestamp,
datetime.datetime.now(), 'test')
print(loss.item())
timestamp = time.time()
batch_idx = 0
filename = ('./plot/01_rtm_nn_' + lat + toz + '_epoch_' + str(real_epoch).zfill(5) +
'_index_' + str(batch_idx).zfill(5))
test_plot300(real_epoch, batch_idx, features, wav300, radiances,
outputs, filename, lr)
print('test plotting done', filename, lat, toz)
lossesfile = './result/01_rtm_nn_' + lat + toz + '_mean_losses.txt'
if os.path.exists(lossesfile):
with open(lossesfile, 'a') as f:
f.write(str(real_epoch).zfill(5) + ',' +
str(np.mean(train_losses)) + ',' +
str(np.mean(valid_losses)) + ',' +
str(np.mean(test_losses)) + '\n')
else:
with open(lossesfile, 'w') as f:
f.write('index,mean_train_losses,mean_valid_losses,mean_test_losses' + '\n')
f.write(str(real_epoch).zfill(5) + ',' +
str(np.mean(train_losses)) + ',' +
str(np.mean(valid_losses)) + ',' +
outputs = model(features)
loss = msre(outputs, radiances)
loss.backward()
optimizer.step()
#train_losses.append(loss.item())
train_losses = np.append(train_losses, loss.item())
#train_losses.append(loss.data)
if (i * 128) % (128 * 10) == 0:
print(f'{i * 128} / ', len(train_loader)*128, time.time() - timestamp,
datetime.datetime.now())
print(loss.item())
timestamp = time.time()
if epoch_local % 100 == 0:
filename = ('./plot/09_rtm_nn_' + lat + '_alltoz_epoch_' + str(epoch).zfill(8) +
'_index_' + str(i).zfill(8))
test_plot300(epoch_local, i, features, wav300, radiances,
outputs, filename, lr)
print('model.eval()')
model.eval()
correct = 0
total = 0
#with torch.no_grad():
#for i, (features, radiances) in enumerate(valid_loader):
#outputs = model(features)
#loss = msre(outputs, radiances)
##valid_losses.append(loss.item())
#valid_losses = np.append(valid_losses, loss.item())