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registration.py
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registration.py
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#!/usr/bin/python
# -*- coding: utf-8 -*-
from __future__ import print_function
import os
import gc
import argparse
import torch
import torch.nn.functional as F
import torch.optim as optim
from torch.optim.lr_scheduler import MultiStepLR
import numpy as np
from tqdm import tqdm
from util import transform_point_cloud, npmat2euler
def test_one_epoch(args, net, test_loader):
net.eval()
mse_ab = 0
mae_ab = 0
mse_ba = 0
mae_ba = 0
total_loss = 0
total_cycle_loss = 0
num_examples = 0
rotations_ab = []
translations_ab = []
rotations_ab_pred = []
translations_ab_pred = []
rotations_ba = []
translations_ba = []
rotations_ba_pred = []
translations_ba_pred = []
eulers_ab = []
eulers_ba = []
for (
src,
target,
rotation_ab,
translation_ab,
rotation_ba,
translation_ba,
euler_ab,
euler_ba,
) in tqdm(test_loader):
src = src.cuda()
target = target.cuda()
rotation_ab = rotation_ab.cuda()
translation_ab = translation_ab.cuda()
rotation_ba = rotation_ba.cuda()
translation_ba = translation_ba.cuda()
batch_size = src.size(0)
num_examples += batch_size
(rotation_ab_pred, translation_ab_pred, rotation_ba_pred,
translation_ba_pred) = net(src, target)
# # save rotation and translation
rotations_ab.append(rotation_ab.detach().cpu().numpy())
translations_ab.append(translation_ab.detach().cpu().numpy())
rotations_ab_pred.append(rotation_ab_pred.detach().cpu().numpy())
translations_ab_pred.append(translation_ab_pred.detach().cpu().numpy())
eulers_ab.append(euler_ab.numpy())
# #
rotations_ba.append(rotation_ba.detach().cpu().numpy())
translations_ba.append(translation_ba.detach().cpu().numpy())
rotations_ba_pred.append(rotation_ba_pred.detach().cpu().numpy())
translations_ba_pred.append(translation_ba_pred.detach().cpu().numpy())
eulers_ba.append(euler_ba.numpy())
transformed_src = transform_point_cloud(src, rotation_ab_pred,
translation_ab_pred)
transformed_target = transform_point_cloud(target,
rotation_ba_pred, translation_ba_pred)
# ##########################
identity = torch.eye(3).cuda().unsqueeze(0).repeat(batch_size,
1, 1)
loss = F.mse_loss(torch.matmul(rotation_ab_pred.transpose(2,
1), rotation_ab), identity) \
+ F.mse_loss(translation_ab_pred, translation_ab)
if args.cycle:
rotation_loss = F.mse_loss(torch.matmul(rotation_ba_pred,
rotation_ab_pred), identity.clone())
translation_loss = \
torch.mean((torch.matmul(rotation_ba_pred.transpose(2,
1), translation_ab_pred.view(batch_size, 3,
1)).view(batch_size, 3)
+ translation_ba_pred) ** 2, dim=[0, 1])
cycle_loss = rotation_loss + translation_loss
loss = loss + cycle_loss * 0.1
total_loss += loss.item() * batch_size
if args.cycle:
total_cycle_loss = total_cycle_loss + cycle_loss.item() \
* 0.1 * batch_size
mse_ab += torch.mean((transformed_src - target) ** 2, dim=[0,
1, 2]).item() * batch_size
mae_ab += torch.mean(torch.abs(transformed_src - target),
dim=[0, 1, 2]).item() * batch_size
mse_ba += torch.mean((transformed_target - src) ** 2, dim=[0,
1, 2]).item() * batch_size
mae_ba += torch.mean(torch.abs(transformed_target - src),
dim=[0, 1, 2]).item() * batch_size
rotations_ab = np.concatenate(rotations_ab, axis=0)
translations_ab = np.concatenate(translations_ab, axis=0)
rotations_ab_pred = np.concatenate(rotations_ab_pred, axis=0)
translations_ab_pred = np.concatenate(translations_ab_pred, axis=0)
rotations_ba = np.concatenate(rotations_ba, axis=0)
translations_ba = np.concatenate(translations_ba, axis=0)
rotations_ba_pred = np.concatenate(rotations_ba_pred, axis=0)
translations_ba_pred = np.concatenate(translations_ba_pred, axis=0)
eulers_ab = np.concatenate(eulers_ab, axis=0)
eulers_ba = np.concatenate(eulers_ba, axis=0)
return (
total_loss * 1.0 / num_examples,
total_cycle_loss / num_examples,
mse_ab * 1.0 / num_examples,
mae_ab * 1.0 / num_examples,
mse_ba * 1.0 / num_examples,
mae_ba * 1.0 / num_examples,
rotations_ab,
translations_ab,
rotations_ab_pred,
translations_ab_pred,
rotations_ba,
translations_ba,
rotations_ba_pred,
translations_ba_pred,
eulers_ab,
eulers_ba,
)
def train_one_epoch(
args,
net,
train_loader,
opt,
):
net.train()
mse_ab = 0
mae_ab = 0
mse_ba = 0
mae_ba = 0
total_loss = 0
total_cycle_loss = 0
num_examples = 0
rotations_ab = []
translations_ab = []
rotations_ab_pred = []
translations_ab_pred = []
rotations_ba = []
translations_ba = []
rotations_ba_pred = []
translations_ba_pred = []
eulers_ab = []
eulers_ba = []
for (
src,
target,
rotation_ab,
translation_ab,
rotation_ba,
translation_ba,
euler_ab,
euler_ba,
) in tqdm(train_loader):
src = src.cuda()
target = target.cuda()
rotation_ab = rotation_ab.cuda()
translation_ab = translation_ab.cuda()
rotation_ba = rotation_ba.cuda()
translation_ba = translation_ba.cuda()
batch_size = src.size(0)
opt.zero_grad()
num_examples += batch_size
(rotation_ab_pred, translation_ab_pred, rotation_ba_pred,
translation_ba_pred) = net(src, target)
# # save rotation and translation
rotations_ab.append(rotation_ab.detach().cpu().numpy())
translations_ab.append(translation_ab.detach().cpu().numpy())
rotations_ab_pred.append(rotation_ab_pred.detach().cpu().numpy())
translations_ab_pred.append(translation_ab_pred.detach().cpu().numpy())
eulers_ab.append(euler_ab.numpy())
# #
rotations_ba.append(rotation_ba.detach().cpu().numpy())
translations_ba.append(translation_ba.detach().cpu().numpy())
rotations_ba_pred.append(rotation_ba_pred.detach().cpu().numpy())
translations_ba_pred.append(translation_ba_pred.detach().cpu().numpy())
eulers_ba.append(euler_ba.numpy())
transformed_src = transform_point_cloud(src, rotation_ab_pred,
translation_ab_pred)
transformed_target = transform_point_cloud(target,
rotation_ba_pred, translation_ba_pred)
# ##########################
identity = torch.eye(3).cuda().unsqueeze(0).repeat(batch_size,
1, 1)
loss = F.mse_loss(torch.matmul(rotation_ab_pred.transpose(2,
1), rotation_ab), identity) \
+ F.mse_loss(translation_ab_pred, translation_ab)
if args.cycle:
rotation_loss = F.mse_loss(torch.matmul(rotation_ba_pred,
rotation_ab_pred), identity.clone())
translation_loss = \
torch.mean((torch.matmul(rotation_ba_pred.transpose(2,
1), translation_ab_pred.view(batch_size, 3,
1)).view(batch_size, 3)
+ translation_ba_pred) ** 2, dim=[0, 1])
cycle_loss = rotation_loss + translation_loss
loss = loss + cycle_loss * 0.1
loss.backward()
opt.step()
total_loss += loss.item() * batch_size
if args.cycle:
total_cycle_loss = total_cycle_loss + cycle_loss.item() \
* 0.1 * batch_size
mse_ab += torch.mean((transformed_src - target) ** 2, dim=[0,
1, 2]).item() * batch_size
mae_ab += torch.mean(torch.abs(transformed_src - target),
dim=[0, 1, 2]).item() * batch_size
mse_ba += torch.mean((transformed_target - src) ** 2, dim=[0,
1, 2]).item() * batch_size
mae_ba += torch.mean(torch.abs(transformed_target - src),
dim=[0, 1, 2]).item() * batch_size
rotations_ab = np.concatenate(rotations_ab, axis=0)
translations_ab = np.concatenate(translations_ab, axis=0)
rotations_ab_pred = np.concatenate(rotations_ab_pred, axis=0)
translations_ab_pred = np.concatenate(translations_ab_pred, axis=0)
rotations_ba = np.concatenate(rotations_ba, axis=0)
translations_ba = np.concatenate(translations_ba, axis=0)
rotations_ba_pred = np.concatenate(rotations_ba_pred, axis=0)
translations_ba_pred = np.concatenate(translations_ba_pred, axis=0)
eulers_ab = np.concatenate(eulers_ab, axis=0)
eulers_ba = np.concatenate(eulers_ba, axis=0)
return (
total_loss * 1.0 / num_examples,
total_cycle_loss / num_examples,
mse_ab * 1.0 / num_examples,
mae_ab * 1.0 / num_examples,
mse_ba * 1.0 / num_examples,
mae_ba * 1.0 / num_examples,
rotations_ab,
translations_ab,
rotations_ab_pred,
translations_ab_pred,
rotations_ba,
translations_ba,
rotations_ba_pred,
translations_ba_pred,
eulers_ab,
eulers_ba,
)
def test(
args,
net,
test_loader,
boardio,
textio,
):
(
test_loss,
test_cycle_loss,
test_mse_ab,
test_mae_ab,
test_mse_ba,
test_mae_ba,
test_rotations_ab,
test_translations_ab,
test_rotations_ab_pred,
test_translations_ab_pred,
test_rotations_ba,
test_translations_ba,
test_rotations_ba_pred,
test_translations_ba_pred,
test_eulers_ab,
test_eulers_ba,
) = test_one_epoch(args, net, test_loader)
test_rmse_ab = np.sqrt(test_mse_ab)
test_rmse_ba = np.sqrt(test_mse_ba)
test_rotations_ab_pred_euler = npmat2euler(test_rotations_ab_pred)
test_r_mse_ab = np.mean((test_rotations_ab_pred_euler
- np.degrees(test_eulers_ab)) ** 2)
test_r_rmse_ab = np.sqrt(test_r_mse_ab)
test_r_mae_ab = np.mean(np.abs(test_rotations_ab_pred_euler
- np.degrees(test_eulers_ab)))
test_t_mse_ab = np.mean((test_translations_ab
- test_translations_ab_pred) ** 2)
test_t_rmse_ab = np.sqrt(test_t_mse_ab)
test_t_mae_ab = np.mean(np.abs(test_translations_ab
- test_translations_ab_pred))
test_rotations_ba_pred_euler = npmat2euler(test_rotations_ba_pred,
'xyz')
test_r_mse_ba = np.mean((test_rotations_ba_pred_euler
- np.degrees(test_eulers_ba)) ** 2)
test_r_rmse_ba = np.sqrt(test_r_mse_ba)
test_r_mae_ba = np.mean(np.abs(test_rotations_ba_pred_euler
- np.degrees(test_eulers_ba)))
test_t_mse_ba = np.mean((test_translations_ba
- test_translations_ba_pred) ** 2)
test_t_rmse_ba = np.sqrt(test_t_mse_ba)
test_t_mae_ba = np.mean(np.abs(test_translations_ba
- test_translations_ba_pred))
textio.cprint('==FINAL TEST==')
textio.cprint('A--------->B')
textio.cprint('EPOCH:: %d, Loss: %f, Cycle Loss: %f, MSE: %f, RMSE: %f, MAE: %f, rot_MSE: %f, rot_RMSE: %f, rot_MAE: %f, trans_MSE: %f, trans_RMSE: %f, trans_MAE: %f'
% (
-1,
test_loss,
test_cycle_loss,
test_mse_ab,
test_rmse_ab,
test_mae_ab,
test_r_mse_ab,
test_r_rmse_ab,
test_r_mae_ab,
test_t_mse_ab,
test_t_rmse_ab,
test_t_mae_ab,
))
textio.cprint('B--------->A')
textio.cprint('EPOCH:: %d, Loss: %f, MSE: %f, RMSE: %f, MAE: %f, rot_MSE: %f, rot_RMSE: %f, rot_MAE: %f, trans_MSE: %f, trans_RMSE: %f, trans_MAE: %f'
% (
-1,
test_loss,
test_mse_ba,
test_rmse_ba,
test_mae_ba,
test_r_mse_ba,
test_r_rmse_ba,
test_r_mae_ba,
test_t_mse_ba,
test_t_rmse_ba,
test_t_mae_ba,
))
def train(
args,
net,
train_loader,
test_loader,
boardio,
textio,
):
if args.use_sgd:
print('Use SGD')
opt = optim.SGD(net.parameters(), lr=args.lr * 100,
momentum=args.momentum, weight_decay=1e-4)
else:
print('Use Adam')
opt = optim.Adam(net.parameters(), lr=args.lr,
weight_decay=1e-4)
scheduler = MultiStepLR(opt, milestones=[75, 150, 200], gamma=0.1)
best_test_loss = np.inf
best_test_cycle_loss = np.inf
best_test_mse_ab = np.inf
best_test_rmse_ab = np.inf
best_test_mae_ab = np.inf
best_test_r_mse_ab = np.inf
best_test_r_rmse_ab = np.inf
best_test_r_mae_ab = np.inf
best_test_t_mse_ab = np.inf
best_test_t_rmse_ab = np.inf
best_test_t_mae_ab = np.inf
best_test_mse_ba = np.inf
best_test_rmse_ba = np.inf
best_test_mae_ba = np.inf
best_test_r_mse_ba = np.inf
best_test_r_rmse_ba = np.inf
best_test_r_mae_ba = np.inf
best_test_t_mse_ba = np.inf
best_test_t_rmse_ba = np.inf
best_test_t_mae_ba = np.inf
for epoch in range(args.epochs):
scheduler.step()
(
train_loss,
train_cycle_loss,
train_mse_ab,
train_mae_ab,
train_mse_ba,
train_mae_ba,
train_rotations_ab,
train_translations_ab,
train_rotations_ab_pred,
train_translations_ab_pred,
train_rotations_ba,
train_translations_ba,
train_rotations_ba_pred,
train_translations_ba_pred,
train_eulers_ab,
train_eulers_ba,
) = train_one_epoch(args, net, train_loader, opt)
(
test_loss,
test_cycle_loss,
test_mse_ab,
test_mae_ab,
test_mse_ba,
test_mae_ba,
test_rotations_ab,
test_translations_ab,
test_rotations_ab_pred,
test_translations_ab_pred,
test_rotations_ba,
test_translations_ba,
test_rotations_ba_pred,
test_translations_ba_pred,
test_eulers_ab,
test_eulers_ba,
) = test_one_epoch(args, net, test_loader)
train_rmse_ab = np.sqrt(train_mse_ab)
test_rmse_ab = np.sqrt(test_mse_ab)
train_rmse_ba = np.sqrt(train_mse_ba)
test_rmse_ba = np.sqrt(test_mse_ba)
train_rotations_ab_pred_euler = \
npmat2euler(train_rotations_ab_pred)
train_r_mse_ab = np.mean((train_rotations_ab_pred_euler
- np.degrees(train_eulers_ab)) ** 2)
train_r_rmse_ab = np.sqrt(train_r_mse_ab)
train_r_mae_ab = np.mean(np.abs(train_rotations_ab_pred_euler
- np.degrees(train_eulers_ab)))
train_t_mse_ab = np.mean((train_translations_ab
- train_translations_ab_pred) ** 2)
train_t_rmse_ab = np.sqrt(train_t_mse_ab)
train_t_mae_ab = np.mean(np.abs(train_translations_ab
- train_translations_ab_pred))
train_rotations_ba_pred_euler = \
npmat2euler(train_rotations_ba_pred, 'xyz')
train_r_mse_ba = np.mean((train_rotations_ba_pred_euler
- np.degrees(train_eulers_ba)) ** 2)
train_r_rmse_ba = np.sqrt(train_r_mse_ba)
train_r_mae_ba = np.mean(np.abs(train_rotations_ba_pred_euler
- np.degrees(train_eulers_ba)))
train_t_mse_ba = np.mean((train_translations_ba
- train_translations_ba_pred) ** 2)
train_t_rmse_ba = np.sqrt(train_t_mse_ba)
train_t_mae_ba = np.mean(np.abs(train_translations_ba
- train_translations_ba_pred))
test_rotations_ab_pred_euler = \
npmat2euler(test_rotations_ab_pred)
test_r_mse_ab = np.mean((test_rotations_ab_pred_euler
- np.degrees(test_eulers_ab)) ** 2)
test_r_rmse_ab = np.sqrt(test_r_mse_ab)
test_r_mae_ab = np.mean(np.abs(test_rotations_ab_pred_euler
- np.degrees(test_eulers_ab)))
test_t_mse_ab = np.mean((test_translations_ab
- test_translations_ab_pred) ** 2)
test_t_rmse_ab = np.sqrt(test_t_mse_ab)
test_t_mae_ab = np.mean(np.abs(test_translations_ab
- test_translations_ab_pred))
test_rotations_ba_pred_euler = \
npmat2euler(test_rotations_ba_pred, 'xyz')
test_r_mse_ba = np.mean((test_rotations_ba_pred_euler
- np.degrees(test_eulers_ba)) ** 2)
test_r_rmse_ba = np.sqrt(test_r_mse_ba)
test_r_mae_ba = np.mean(np.abs(test_rotations_ba_pred_euler
- np.degrees(test_eulers_ba)))
test_t_mse_ba = np.mean((test_translations_ba
- test_translations_ba_pred) ** 2)
test_t_rmse_ba = np.sqrt(test_t_mse_ba)
test_t_mae_ba = np.mean(np.abs(test_translations_ba
- test_translations_ba_pred))
if best_test_loss >= test_loss:
best_test_loss = test_loss
best_test_cycle_loss = test_cycle_loss
best_test_mse_ab = test_mse_ab
best_test_rmse_ab = test_rmse_ab
best_test_mae_ab = test_mae_ab
best_test_r_mse_ab = test_r_mse_ab
best_test_r_rmse_ab = test_r_rmse_ab
best_test_r_mae_ab = test_r_mae_ab
best_test_t_mse_ab = test_t_mse_ab
best_test_t_rmse_ab = test_t_rmse_ab
best_test_t_mae_ab = test_t_mae_ab
best_test_mse_ba = test_mse_ba
best_test_rmse_ba = test_rmse_ba
best_test_mae_ba = test_mae_ba
best_test_r_mse_ba = test_r_mse_ba
best_test_r_rmse_ba = test_r_rmse_ba
best_test_r_mae_ba = test_r_mae_ba
best_test_t_mse_ba = test_t_mse_ba
best_test_t_rmse_ba = test_t_rmse_ba
best_test_t_mae_ba = test_t_mae_ba
if torch.cuda.device_count() > 1:
torch.save(net.module.state_dict(),
'checkpoints/%s/models/model.best.t7'
% args.exp_name)
else:
torch.save(net.state_dict(),
'checkpoints/%s/models/model.best.t7'
% args.exp_name)
textio.cprint('==TRAIN==')
textio.cprint('A--------->B')
textio.cprint('EPOCH:: %d, Loss: %f, Cycle Loss:, %f, MSE: %f, RMSE: %f, MAE: %f, rot_MSE: %f, rot_RMSE: %f, rot_MAE: %f, trans_MSE: %f, trans_RMSE: %f, trans_MAE: %f'
% (
epoch,
train_loss,
train_cycle_loss,
train_mse_ab,
train_rmse_ab,
train_mae_ab,
train_r_mse_ab,
train_r_rmse_ab,
train_r_mae_ab,
train_t_mse_ab,
train_t_rmse_ab,
train_t_mae_ab,
))
textio.cprint('B--------->A')
textio.cprint('EPOCH:: %d, Loss: %f, MSE: %f, RMSE: %f, MAE: %f, rot_MSE: %f, rot_RMSE: %f, rot_MAE: %f, trans_MSE: %f, trans_RMSE: %f, trans_MAE: %f'
% (
epoch,
train_loss,
train_mse_ba,
train_rmse_ba,
train_mae_ba,
train_r_mse_ba,
train_r_rmse_ba,
train_r_mae_ba,
train_t_mse_ba,
train_t_rmse_ba,
train_t_mae_ba,
))
textio.cprint('==TEST==')
textio.cprint('A--------->B')
textio.cprint('EPOCH:: %d, Loss: %f, Cycle Loss: %f, MSE: %f, RMSE: %f, MAE: %f, rot_MSE: %f, rot_RMSE: %f, rot_MAE: %f, trans_MSE: %f, trans_RMSE: %f, trans_MAE: %f'
% (
epoch,
test_loss,
test_cycle_loss,
test_mse_ab,
test_rmse_ab,
test_mae_ab,
test_r_mse_ab,
test_r_rmse_ab,
test_r_mae_ab,
test_t_mse_ab,
test_t_rmse_ab,
test_t_mae_ab,
))
textio.cprint('B--------->A')
textio.cprint('EPOCH:: %d, Loss: %f, MSE: %f, RMSE: %f, MAE: %f, rot_MSE: %f, rot_RMSE: %f, rot_MAE: %f, trans_MSE: %f, trans_RMSE: %f, trans_MAE: %f'
% (
epoch,
test_loss,
test_mse_ba,
test_rmse_ba,
test_mae_ba,
test_r_mse_ba,
test_r_rmse_ba,
test_r_mae_ba,
test_t_mse_ba,
test_t_rmse_ba,
test_t_mae_ba,
))
textio.cprint('==BEST TEST==')
textio.cprint('A--------->B')
textio.cprint('EPOCH:: %d, Loss: %f, Cycle Loss: %f, MSE: %f, RMSE: %f, MAE: %f, rot_MSE: %f, rot_RMSE: %f, rot_MAE: %f, trans_MSE: %f, trans_RMSE: %f, trans_MAE: %f'
% (
epoch,
best_test_loss,
best_test_cycle_loss,
best_test_mse_ab,
best_test_rmse_ab,
best_test_mae_ab,
best_test_r_mse_ab,
best_test_r_rmse_ab,
best_test_r_mae_ab,
best_test_t_mse_ab,
best_test_t_rmse_ab,
best_test_t_mae_ab,
))
textio.cprint('B--------->A')
textio.cprint('EPOCH:: %d, Loss: %f, MSE: %f, RMSE: %f, MAE: %f, rot_MSE: %f, rot_RMSE: %f, rot_MAE: %f, trans_MSE: %f, trans_RMSE: %f, trans_MAE: %f'
% (
epoch,
best_test_loss,
best_test_mse_ba,
best_test_rmse_ba,
best_test_mae_ba,
best_test_r_mse_ba,
best_test_r_rmse_ba,
best_test_r_mae_ba,
best_test_t_mse_ba,
best_test_t_rmse_ba,
best_test_t_mae_ba,
))
boardio.add_scalar('A->B/train/loss', train_loss, epoch)
boardio.add_scalar('A->B/train/MSE', train_mse_ab, epoch)
boardio.add_scalar('A->B/train/RMSE', train_rmse_ab, epoch)
boardio.add_scalar('A->B/train/MAE', train_mae_ab, epoch)
boardio.add_scalar('A->B/train/rotation/MSE', train_r_mse_ab,
epoch)
boardio.add_scalar('A->B/train/rotation/RMSE', train_r_rmse_ab,
epoch)
boardio.add_scalar('A->B/train/rotation/MAE', train_r_mae_ab,
epoch)
boardio.add_scalar('A->B/train/translation/MSE',
train_t_mse_ab, epoch)
boardio.add_scalar('A->B/train/translation/RMSE',
train_t_rmse_ab, epoch)
boardio.add_scalar('A->B/train/translation/MAE',
train_t_mae_ab, epoch)
boardio.add_scalar('B->A/train/loss', train_loss, epoch)
boardio.add_scalar('B->A/train/MSE', train_mse_ba, epoch)
boardio.add_scalar('B->A/train/RMSE', train_rmse_ba, epoch)
boardio.add_scalar('B->A/train/MAE', train_mae_ba, epoch)
boardio.add_scalar('B->A/train/rotation/MSE', train_r_mse_ba,
epoch)
boardio.add_scalar('B->A/train/rotation/RMSE', train_r_rmse_ba,
epoch)
boardio.add_scalar('B->A/train/rotation/MAE', train_r_mae_ba,
epoch)
boardio.add_scalar('B->A/train/translation/MSE',
train_t_mse_ba, epoch)
boardio.add_scalar('B->A/train/translation/RMSE',
train_t_rmse_ba, epoch)
boardio.add_scalar('B->A/train/translation/MAE',
train_t_mae_ba, epoch)
# ###########TEST
boardio.add_scalar('A->B/test/loss', test_loss, epoch)
boardio.add_scalar('A->B/test/MSE', test_mse_ab, epoch)
boardio.add_scalar('A->B/test/RMSE', test_rmse_ab, epoch)
boardio.add_scalar('A->B/test/MAE', test_mae_ab, epoch)
boardio.add_scalar('A->B/test/rotation/MSE', test_r_mse_ab,
epoch)
boardio.add_scalar('A->B/test/rotation/RMSE', test_r_rmse_ab,
epoch)
boardio.add_scalar('A->B/test/rotation/MAE', test_r_mae_ab,
epoch)
boardio.add_scalar('A->B/test/translation/MSE', test_t_mse_ab,
epoch)
boardio.add_scalar('A->B/test/translation/RMSE',
test_t_rmse_ab, epoch)
boardio.add_scalar('A->B/test/translation/MAE', test_t_mae_ab,
epoch)
boardio.add_scalar('B->A/test/loss', test_loss, epoch)
boardio.add_scalar('B->A/test/MSE', test_mse_ba, epoch)
boardio.add_scalar('B->A/test/RMSE', test_rmse_ba, epoch)
boardio.add_scalar('B->A/test/MAE', test_mae_ba, epoch)
boardio.add_scalar('B->A/test/rotation/MSE', test_r_mse_ba,
epoch)
boardio.add_scalar('B->A/test/rotation/RMSE', test_r_rmse_ba,
epoch)
boardio.add_scalar('B->A/test/rotation/MAE', test_r_mae_ba,
epoch)
boardio.add_scalar('B->A/test/translation/MSE', test_t_mse_ba,
epoch)
boardio.add_scalar('B->A/test/translation/RMSE',
test_t_rmse_ba, epoch)
boardio.add_scalar('B->A/test/translation/MAE', test_t_mae_ba,
epoch)
# ###########BEST TEST
boardio.add_scalar('A->B/best_test/loss', best_test_loss, epoch)
boardio.add_scalar('A->B/best_test/MSE', best_test_mse_ab,
epoch)
boardio.add_scalar('A->B/best_test/RMSE', best_test_rmse_ab,
epoch)
boardio.add_scalar('A->B/best_test/MAE', best_test_mae_ab,
epoch)
boardio.add_scalar('A->B/best_test/rotation/MSE',
best_test_r_mse_ab, epoch)
boardio.add_scalar('A->B/best_test/rotation/RMSE',
best_test_r_rmse_ab, epoch)
boardio.add_scalar('A->B/best_test/rotation/MAE',
best_test_r_mae_ab, epoch)
boardio.add_scalar('A->B/best_test/translation/MSE',
best_test_t_mse_ab, epoch)
boardio.add_scalar('A->B/best_test/translation/RMSE',
best_test_t_rmse_ab, epoch)
boardio.add_scalar('A->B/best_test/translation/MAE',
best_test_t_mae_ab, epoch)
boardio.add_scalar('B->A/best_test/loss', best_test_loss, epoch)
boardio.add_scalar('B->A/best_test/MSE', best_test_mse_ba,
epoch)
boardio.add_scalar('B->A/best_test/RMSE', best_test_rmse_ba,
epoch)
boardio.add_scalar('B->A/best_test/MAE', best_test_mae_ba,
epoch)
boardio.add_scalar('B->A/best_test/rotation/MSE',
best_test_r_mse_ba, epoch)
boardio.add_scalar('B->A/best_test/rotation/RMSE',
best_test_r_rmse_ba, epoch)
boardio.add_scalar('B->A/best_test/rotation/MAE',
best_test_r_mae_ba, epoch)
boardio.add_scalar('B->A/best_test/translation/MSE',
best_test_t_mse_ba, epoch)
boardio.add_scalar('B->A/best_test/translation/RMSE',
best_test_t_rmse_ba, epoch)
boardio.add_scalar('B->A/best_test/translation/MAE',
best_test_t_mae_ba, epoch)
if torch.cuda.device_count() > 1:
torch.save(net.module.state_dict(),
'checkpoints/%s/models/model.%d.t7'
% (args.exp_name, epoch))
else:
torch.save(net.state_dict(),
'checkpoints/%s/models/model.%d.t7'
% (args.exp_name, epoch))
gc.collect()