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trainer_seenmask.py
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trainer_seenmask.py
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import datetime
import fcn
import math
import numpy as np
import os
import os.path as osp
import pickle
import pytz
import scipy.misc
import shutil
import torch
import torch.nn.functional as F
import tqdm
import utils
import vis_utils
from torch.autograd import Variable
class Trainer(object):
def __init__(self, cuda, model, optimizer, train_loader, val_loader, log_dir, dataset, max_epoch, tb_writer, checkpoint, unseen):
self.cuda = cuda
self.model = model
self.optim = optimizer
self.train_loader = train_loader
self.val_loader = val_loader
self.log_dir = log_dir
self.dataset = dataset
self.max_epoch = max_epoch
self.tb_writer = tb_writer
self.checkpoint = checkpoint
self.unseen = unseen
self.epoch = 0
self.iteration = 0
self.best_mean_iu = 0
self.n_class = len(self.train_loader.dataset.class_names)
self.timestamp_start = datetime.datetime.now(pytz.timezone('US/Eastern'))
self.train_log_headers = ['epoch', 'iteration', 'train/loss', 'train/pxl_acc', 'train/class_acc', 'train/mean_iu', 'train/fwavacc', 'elapsed_time']
if not osp.exists(osp.join(self.log_dir, 'seenmask_train_log.csv')):
with open(osp.join(self.log_dir, 'seenmask_train_log.csv'), 'w') as f:
f.write(','.join(self.train_log_headers) + '\n')
self.val_log_headers = ['epoch', 'iteration', 'val/loss', 'val/pxl_acc', 'val/class_acc', 'val/mean_iu', 'val/fwavacc', 'elapsed_time']
if not osp.exists(osp.join(self.log_dir, 'seenmask_val_log.csv')):
with open(osp.join(self.log_dir, 'seenmask_val_log.csv'), 'w') as f:
f.write(','.join(self.val_log_headers) + '\n')
def forward(self, data, target):
target, target_embed = target
target = target.numpy()
# reshape target into binary seenmask
seen = [x for x in range(self.n_class) if x not in self.unseen]
target = np.in1d(target.ravel(), seen).reshape(target.shape).astype(int)
target = torch.from_numpy(target)
if self.cuda:
data, target = data.cuda(), target.cuda()
data, target = Variable(data), Variable(target)
score = self.model(data, mode='seenmask')
loss = utils.cross_entropy2d(score, target, size_average=True)
lbl_pred = score.data.max(1)[1].cpu().numpy()[:, :, :]
lbl_true = target.data.cpu()
return score, loss, lbl_pred, lbl_true
def train_epoch(self):
self.model.train()
for batch_idx, (data, target) in enumerate(self.train_loader):
score, loss, lbl_pred, lbl_true = self.forward(data, target)
self.optim.zero_grad()
loss.backward()
self.optim.step()
print("Seenmask Train Epoch {:<5} | Iteration {:<5} | Loss {:5.5f} | seenmask_score grad sum {:7.8f} | seenmask_upscore grad sum {:7.8f} | score sum {:10.5f}".format(
int(self.epoch), int(batch_idx), float(loss.data[0]), float(self.model.seenmask_score.weight.grad.sum().data[0]),
float(self.model.seenmask_upscore.weight.grad.sum().data[0]), float(score.sum().data[0])))
metrics = utils.label_accuracy_score(lbl_true.numpy(), lbl_pred, self.n_class)
with open(osp.join(self.log_dir, 'seenmask_train_log.csv'), 'a') as f:
elapsed_time = (datetime.datetime.now(pytz.timezone('US/Eastern')) - self.timestamp_start).total_seconds()
log = [self.epoch, self.iteration] + [loss.data[0]] + list(metrics) + [elapsed_time]
log = map(str, log)
f.write(','.join(log) + '\n')
# write to tensorboard
self.tb_writer.add_scalar('seenmask/train/loss', loss.data[0], self.iteration)
self.tb_writer.add_scalar('seenmask/train/pxl_acc', metrics[0], self.iteration)
self.tb_writer.add_scalar('seenmask/train/class_acc', metrics[1], self.iteration)
self.tb_writer.add_scalar('seenmask/train/mean_iu', metrics[2], self.iteration)
self.tb_writer.add_scalar('seenmask/train/fwavacc', metrics[3], self.iteration)
self.iteration += 1
def validate(self):
self.model.eval()
val_loss = 0
lbl_trues, lbl_preds, visualizations = [], [], []
for batch_idx, (data, target) in enumerate(self.val_loader):
score, loss, lbl_pred, lbl_true = self.forward(data, target)
val_loss += float(loss.data[0])
print("Seenmask Test Epoch {:<5} | Iteration {:<5} | Loss {:5.5f} | Score Sum {:10.5f}".format(int(self.epoch), int(batch_idx), float(loss.data[0]), float(score.sum().data[0])))
img, lt, lp = data[0], lbl_true[0], lbl_pred[0] # eliminate first dimension (n=1) for visualization
img, lt = self.val_loader.dataset.untransform(img, lt)
lbl_trues.append(lt)
lbl_preds.append(lp)
# generate visualization for first few images of val_loader
if len(visualizations) < 25:
viz = vis_utils.visualize_seenmask(lbl_pred=lp, lbl_true=lt, img=img, n_class=self.n_class, unseen=self.unseen)
visualizations.append(viz)
# save the visualizaton image
out = osp.join(self.log_dir, 'seenmask_viz')
if not osp.exists(out):
os.makedirs(out)
out_file = osp.join(out, 'epoch%d.jpg' % self.epoch)
viz_img = fcn.utils.get_tile_image(visualizations)
scipy.misc.imsave(out_file, viz_img)
metrics = utils.label_accuracy_score(lbl_trues, lbl_preds, self.n_class)
val_loss /= len(self.val_loader) # val loss is averaged across all the images
with open(osp.join(self.log_dir, 'seenmask_val_log.csv'), 'a') as f:
elapsed_time = datetime.datetime.now(pytz.timezone('US/Eastern')) - self.timestamp_start
log = [self.epoch, self.iteration] + [val_loss] + list(metrics) + [elapsed_time]
log = map(str, log)
f.write(','.join(log) + '\n')
# write metrics to tensorboard
self.tb_writer.add_scalar('seenmask/val/loss', val_loss, self.epoch)
self.tb_writer.add_scalar('seenmask/val/pxl_acc', metrics[0], self.epoch)
self.tb_writer.add_scalar('seenmask/val/class_acc', metrics[1], self.epoch)
self.tb_writer.add_scalar('seenmask/val/mean_iu', metrics[2], self.epoch)
self.tb_writer.add_scalar('seenmask/val/fwavacc', metrics[3], self.epoch)
self.tb_writer.add_image('fcn/segmentations', viz_img, self.epoch)
print('pxl_acc: %.3f'%metrics[0])
print('class_acc: %.3f'%metrics[1])
print('mean_iu: %.3f'%metrics[2])
print('fwavacc: %.3f'%metrics[3])
# track and update the best mean intersection over union
mean_iu = metrics[2]
is_best = mean_iu > self.best_mean_iu
if is_best:
self.best_mean_iu = mean_iu
self.checkpoint['model_state_dict'] = self.model.state_dict() # TODO: verify
torch.save(self.checkpoint, osp.join(self.log_dir, 'best'))
def train(self):
for epoch in range(self.max_epoch):
self.epoch = epoch
self.train_epoch()
self.validate()