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train.py
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train.py
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# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
"""Training Loop script"""
import os
import glob
import torch
from torch.utils.data import DataLoader
from geneva.data.datasets import DATASETS
from geneva.evaluation.evaluate import Evaluator
from geneva.utils.config import keys, parse_config
from geneva.utils.visualize import VisdomPlotter
from geneva.models.models import MODELS
from geneva.data import codraw_dataset
from geneva.data import clevr_dataset
from tqdm import tqdm
from geneva.utils.logger import Logger
import numpy as np
import nltk
import torchvision as TV
class Trainer():
def __init__(self, cfg):
img_path = os.path.join(cfg.log_path,
cfg.exp_name,
'train_images_*')
if glob.glob(img_path):
raise Exception('all directories with name train_images_* under '
'the experiment directory need to be removed')
path = os.path.join(cfg.log_path, cfg.exp_name)
self.model = MODELS[cfg.gan_type](cfg)
if cfg.load_snapshot is not None:
self.model.load_model(cfg.load_snapshot)
print('Load model:', cfg.load_snapshot)
self.model.save_model(path, 0, 0)
shuffle = cfg.gan_type != 'recurrent_gan'
self.dataset = DATASETS[cfg.dataset](path=keys[cfg.dataset], cfg=cfg, img_size=cfg.img_size)
self.dataloader = DataLoader(self.dataset,
batch_size=cfg.batch_size,
shuffle=shuffle,
num_workers=cfg.num_workers,
pin_memory=True,
drop_last=True)
if cfg.dataset == 'codraw':
self.dataloader.collate_fn = codraw_dataset.collate_data
elif cfg.dataset == 'iclevr':
self.dataloader.collate_fn = clevr_dataset.collate_data
self.visualizer = VisdomPlotter(env_name=cfg.exp_name, server=cfg.vis_server)
self.logger = Logger(cfg.log_path, cfg.exp_name)
self.cfg = cfg
def train(self):
iteration_counter = 0
for epoch in tqdm(range(self.cfg.epochs), ascii=True):
if cfg.dataset == 'codraw':
self.dataset.shuffle()
for batch in self.dataloader:
if iteration_counter >= 0 and iteration_counter % self.cfg.save_rate == 0:
torch.cuda.empty_cache()
evaluator = Evaluator.factory(self.cfg, self.visualizer,
self.logger)
res = evaluator.evaluate(iteration_counter)
print('\nIter %d:' % (iteration_counter))
print(res)
self.logger.write_res(iteration_counter, res)
del evaluator
iteration_counter += 1
self.model.train_batch(batch,
epoch,
iteration_counter,
self.visualizer,
self.logger)
def train_with_ctr(self):
cfg = self.cfg
if cfg.dataset == 'codraw':
self.model.ctr.E.load_state_dict(torch.load('models/codraw_1.0_e.pt'))
elif cfg.dataset == 'iclevr':
self.model.ctr.E.load_state_dict(torch.load('models/iclevr_1.0_e.pt'))
iteration_counter = 0
for epoch in tqdm(range(self.cfg.epochs), ascii=True):
if cfg.dataset == 'codraw':
self.dataset.shuffle()
for batch in self.dataloader:
if iteration_counter >= 0 and iteration_counter % self.cfg.save_rate == 0:
torch.cuda.empty_cache()
evaluator = Evaluator.factory(self.cfg, self.visualizer,
self.logger)
res = evaluator.evaluate(iteration_counter)
print('\nIter %d:' % (iteration_counter))
print(res)
self.logger.write_res(iteration_counter, res)
del evaluator
iteration_counter += 1
self.model.train_batch_with_ctr(batch,
epoch,
iteration_counter,
self.visualizer,
self.logger)
def train_ctr(self):
iteration_counter = 0
with tqdm(range(self.cfg.epochs), ascii=True) as TQ:
for epoch in TQ:
if cfg.dataset == 'codraw':
self.dataset.shuffle()
for batch in self.dataloader:
loss = self.model.train_ctr(batch, epoch, iteration_counter, self.visualizer, self.logger)
TQ.set_postfix(ls_bh=loss)
if iteration_counter>0 and (iteration_counter%self.cfg.save_rate)==0:
torch.cuda.empty_cache()
print('Iter %d: %f' % (iteration_counter, loss))
loss = self.eval_ctr(epoch, iteration_counter)
print('Eval: %f' % (loss))
print('')
self.logger.write_res(iteration_counter, loss)
iteration_counter += 1
def eval_ctr(self, epoch, iteration_counter):
cfg = self.cfg
dataset = DATASETS[cfg.dataset](path=keys[cfg.val_dataset], cfg=cfg, img_size=cfg.img_size)
dataloader = DataLoader(dataset,
batch_size=cfg.batch_size, shuffle=False, num_workers=cfg.num_workers, pin_memory=True, drop_last=True)
if cfg.dataset == 'codraw':
dataloader.collate_fn = codraw_dataset.collate_data
elif cfg.dataset == 'iclevr':
dataloader.collate_fn = clevr_dataset.collate_data
if cfg.dataset == 'codraw':
dataset.shuffle()
rec_loss = []
for batch in dataloader:
loss = self.model.train_ctr(batch, epoch, iteration_counter, self.visualizer, self.logger, is_eval=True)
rec_loss.append(loss)
loss = np.average(rec_loss)
return loss
def infer_ctr(self):
cfg = self.cfg
if cfg.dataset == 'codraw':
self.model.ctr.E.load_state_dict(torch.load('models/codraw_1.0_e.pt'))
elif cfg.dataset == 'iclevr':
self.model.ctr.E.load_state_dict(torch.load('models/iclevr_1.0_e.pt'))
dataset = DATASETS[cfg.dataset](path=keys[cfg.val_dataset], cfg=cfg, img_size=cfg.img_size)
dataloader = DataLoader(dataset,
batch_size=cfg.batch_size, shuffle=True, num_workers=cfg.num_workers, pin_memory=True, drop_last=True)
if cfg.dataset == 'codraw':
dataloader.collate_fn = codraw_dataset.collate_data
elif cfg.dataset == 'iclevr':
dataloader.collate_fn = clevr_dataset.collate_data
glove_key = list(dataset.glove_key.keys())
for batch in dataloader:
rec_out, loss = self.model.train_ctr(batch, -1, -1, self.visualizer, self.logger,
is_eval=True, is_infer=True)
rec_out = np.argmax(rec_out, axis=3)
os.system('mkdir ins_result')
for i in range(30):
os.system('mkdir ins_result/%d' % (i))
F = open('ins_result/%d/ins.txt' % (i), 'w')
for j in range(rec_out.shape[1]):
print([glove_key[rec_out[i, j, k]] for k in range(rec_out.shape[2])])
print([glove_key[int(batch['turn_word'][i, j, k].detach().cpu().numpy())] for k in range(rec_out.shape[2])])
print()
F.write(' '.join([glove_key[rec_out[i, j, k]] for k in range(rec_out.shape[2])]))
F.write('\n')
F.write(' '.join([glove_key[int(batch['turn_word'][i, j, k].detach().cpu().numpy())] for k in range(rec_out.shape[2])]))
F.write('\n')
F.write('\n')
TV.utils.save_image(batch['image'][i, j].data, 'ins_result/%d/%d.png' % (i, j), normalize=True, range=(-1, 1))
print('\n----------------\n')
F.close()
break
os.system('tar zcvf ins_result.tar.gz ins_result')
def infer_gen(self):
cfg = self.cfg
if cfg.dataset == 'codraw':
self.model.ctr.E.load_state_dict(torch.load('models/codraw_1.0.pt'))
elif cfg.dataset == 'iclevr':
self.model.ctr.E.load_state_dict(torch.load('models/iclevr_1.0.pt'))
dataset = DATASETS[cfg.dataset](path=keys[cfg.val_dataset], cfg=cfg, img_size=cfg.img_size)
dataloader = DataLoader(dataset,
batch_size=cfg.batch_size, shuffle=True, num_workers=cfg.num_workers, pin_memory=True, drop_last=True)
if cfg.dataset == 'codraw':
dataloader.collate_fn = codraw_dataset.collate_data
elif cfg.dataset == 'iclevr':
dataloader.collate_fn = clevr_dataset.collate_data
glove_key = list(dataset.glove_key.keys())
for batch in dataloader:
rec_out = self.model.infer_gen(batch)
os.system('mkdir gen_result')
for i in range(30):
os.system('mkdir gen_result/%d' % (i))
F = open('gen_result/%d/ins.txt' % (i), 'w')
for j in range(rec_out.shape[1]):
F.write(' '.join([glove_key[int(batch['turn_word'][i, j, k].detach().cpu().numpy())] for k in range(batch['turn_word'].shape[2])]))
F.write('\n')
TV.utils.save_image(batch['image'][i, j].data, 'gen_result/%d/_%d.png' % (i, j), normalize=True, range=(-1, 1))
TV.utils.save_image(torch.from_numpy(rec_out[i, j]).data, 'gen_result/%d/%d.png' % (i, j), normalize=True, range=(-1, 1))
F.close()
break
os.system('tar zcvf gen_result.tar.gz gen_result')
if __name__ == '__main__':
cfg = parse_config()
trainer = Trainer(cfg)
# TRAIN
trainer.train() # train GeNeVA only
# trainer.train_ctr() # train E
# trainer.train_with_ctr() # train w/ CTC
# INFERENCE
# trainer.infer_ctr() # inference E
# trainer.infer_gen() # inference G