def __init__(self, networks, txt_emb):
# builds an evaluator for running tests on model
# most of this is not yet functional -- pulled from notebooks
image_encoder, image_generator, text_encoder, text_generator, disc_image, disc_latent = networks
self.txt_emb = txt_emb
self.text_encoder = text_encoder
self.text_generator = text_generator
self.image_encoder = image_encoder
self.image_generator = image_generator
# load fasttext embeddings (e.g., birds.en.vec)
path = os.path.join(cfg.DATA_DIR, cfg.DATASET_NAME + ".en.vec")
txt_dico, _txt_emb = load_external_embeddings(path)
txt_emb = nn.Embedding(len(txt_dico), 300, sparse=False)
txt_emb.weight.data.copy_(_txt_emb)
txt_emb.weight.requires_grad = False
self.txt_dico = txt_dico
self.txt_emb = txt_emb
image_transform = transforms.Compose([
transforms.RandomCrop(cfg.IMSIZE),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
dataset = TextDataset(cfg.DATA_DIR,
'test',
imsize=cfg.IMSIZE,
transform=image_transform)
assert dataset
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=10,
drop_last=True,
shuffle=True,
num_workers=int(cfg.WORKERS))
self.dataloader = dataloader
s_gpus = cfg.GPU_ID.split(',')
self.gpus = [int(ix) for ix in s_gpus] # 0
torch.cuda.manual_seed_all(args.manualSeed)
now = datetime.datetime.now(dateutil.tz.tzlocal())
timestamp = now.strftime('%Y_%m_%d_%H_%M_%S')
output_dir = '../output/%s_%s_%s' % \
(cfg.DATASET_NAME, cfg.CONFIG_NAME, timestamp)
num_gpu = len(cfg.GPU_ID.split(','))
if cfg.TRAIN.FLAG:
image_transform = transforms.Compose([
transforms.RandomCrop(cfg.IMSIZE),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
dataset = TextDataset(cfg.DATA_DIR,
'train',
imsize=cfg.IMSIZE,
transform=image_transform)
assert dataset
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=cfg.TRAIN.BATCH_SIZE * num_gpu,
drop_last=True,
shuffle=True,
num_workers=int(cfg.WORKERS))
algo = GANTrainer(output_dir)
algo.train(dataloader, cfg.STAGE)
else:
datapath = '%s/test/val_captions_custom.t7' % (cfg.DATA_DIR)
algo = GANTrainer(output_dir)
algo.sample(datapath, cfg.STAGE)
if exc.errno == errno.EEXIST and os.path.isdir(path):
pass
else:
raise
copyfile(sys.argv[0], output_dir + "/" + sys.argv[0])
copyfile("trainer.py", output_dir + "/" + "trainer.py")
copyfile("model.py", output_dir + "/" + "model.py")
copyfile("miscc/utils.py", output_dir + "/" + "utils.py")
copyfile("miscc/datasets.py", output_dir + "/" + "datasets.py")
copyfile(args.cfg_file, output_dir + "/" + "cfg_file.yml")
imsize=64
img_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
dataset = TextDataset(cfg.DATA_DIR, split="train", imsize=imsize, transform=img_transform,
crop=True)
assert dataset
dataloader = torch.utils.data.DataLoader(
dataset, batch_size=cfg.TRAIN.BATCH_SIZE,
drop_last=True, shuffle=True, num_workers=int(cfg.WORKERS))
algo = GANTrainer(output_dir)
algo.train(dataloader)
else:
datapath = os.path.join(cfg.DATA_DIR, "test")
algo = GANTrainer(output_dir)
algo.sample(datapath, num_samples=25, draw_bbox=True)
print(cfg.GPU_ID)
num_gpu = len(cfg.GPU_ID.split(','))
#num_gpu = 8
print(num_gpu)
if cfg.TRAIN.FLAG:
image_transform = transforms.Compose([
transforms.Resize(cfg.IMSIZE),
transforms.RandomCrop(cfg.IMSIZE),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
dataset = TextDataset(cfg.DATA_DIR,
num_classes=cfg.GAN.CONDITION_DIM,
csv_file='genre_train.csv',
split='train',
imsize=cfg.IMSIZE,
transform=image_transform)
assert dataset
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=cfg.TRAIN.BATCH_SIZE * num_gpu,
drop_last=True,
shuffle=True,
num_workers=int(cfg.WORKERS))
algo = GANTrainer(output_dir)
algo.train(dataloader, cfg.STAGE)
else:
datapath = '%s/test/val_captions.t7' % (cfg.DATA_DIR)
algo = GANTrainer(output_dir)
now = datetime.datetime.now(dateutil.tz.tzlocal())
timestamp = now.strftime('%Y_%m_%d_%H_%M_%S')
output_dir = '../output/%s_%s_%s' % \
(cfg.DATASET_NAME, cfg.CONFIG_NAME, timestamp)
num_gpu = len(cfg.GPU_ID.split(','))
if cfg.TRAIN.FLAG:
image_transform = transforms.Compose([
transforms.RandomCrop(cfg.IMSIZE),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
dataset = TextDataset(cfg.DATA_DIR,
'train',
imsize=cfg.IMSIZE,
transform=image_transform,
embedding_type=cfg.EMBEDDING_TYPE)
assert dataset
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=cfg.TRAIN.BATCH_SIZE * num_gpu,
drop_last=True,
shuffle=True,
num_workers=int(cfg.WORKERS))
algo = GANTrainer(output_dir)
algo.train(dataloader, cfg.STAGE)
else:
datapath = '%s/test/val_captions.t7' % (cfg.DATA_DIR)
algo = GANTrainer(output_dir)
pass
else:
raise
copyfile(sys.argv[0], output_dir + "/" + sys.argv[0])
copyfile("trainer.py", output_dir + "/" + "trainer.py")
copyfile("model.py", output_dir + "/" + "model.py")
copyfile("miscc/utils.py", output_dir + "/" + "utils.py")
copyfile("miscc/datasets.py", output_dir + "/" + "datasets.py")
copyfile(args.cfg_file, output_dir + "/" + "cfg_file.yml")
imsize=64
img_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
dataset = TextDataset(cfg.DATA_DIR, split="train", imsize=imsize, transform=img_transform)
assert dataset
dataloader = torch.utils.data.DataLoader(
dataset, batch_size=cfg.TRAIN.BATCH_SIZE,
drop_last=True, shuffle=True, num_workers=int(cfg.WORKERS))
algo = GANTrainer(output_dir)
algo.train(dataloader)
else:
imsize=64
datapath= '%s/test/' % (cfg.DATA_DIR)
img_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
dataset = TextDataset(cfg.DATA_DIR, split="test", imsize=imsize, transform=img_transform)
assert dataset
torch.cuda.manual_seed_all(args.manualSeed)
now = datetime.datetime.now(dateutil.tz.tzlocal())
timestamp = now.strftime('%Y_%m_%d_%H_%M_%S')
output_dir = '../output/%s_%s_%s' % \
(cfg.DATASET_NAME, cfg.CONFIG_NAME, timestamp)
num_gpu = len(cfg.GPU_ID.split(','))
if cfg.TRAIN.FLAG:
image_transform = transforms.Compose([
transforms.RandomCrop(cfg.IMSIZE),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
dataset = TextDataset(cfg.DATA_DIR,
'train',
embedding_type='lstm',
imsize=cfg.IMSIZE,
transform=image_transform)
assert dataset
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=cfg.TRAIN.BATCH_SIZE * num_gpu,
drop_last=True,
shuffle=True,
num_workers=int(cfg.WORKERS))
algo = GANTrainer(output_dir)
algo.train(dataloader, cfg.STAGE)
if cfg.STAGE == 1:
resize = 76
imsize = 64
elif cfg.STAGE == 2:
resize = 268
imsize = 256
img_transform = transforms.Compose([
transforms.Resize((resize, resize)),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
dataset = TextDataset(cfg.DATA_DIR,
cfg.IMG_DIR,
split="train",
imsize=imsize,
transform=img_transform,
crop=True,
stage=cfg.STAGE)
assert dataset
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=cfg.TRAIN.BATCH_SIZE,
drop_last=True,
shuffle=True,
num_workers=int(cfg.WORKERS))
algo = GANTrainer(output_dir)
algo.train(dataloader, cfg.STAGE)
else:
datapath = '%s/test/' % (cfg.DATA_DIR)
# train_loader = torch.utils.data.DataLoader(
# datasets.MNIST('../data', train=True, download=True,
# transform=transforms.ToTensor()),
# batch_size=args.batch_size, shuffle=True, **kwargs)
# test_loader = torch.utils.data.DataLoader(
# datasets.MNIST('../data', train=False, transform=transforms.ToTensor()),
# batch_size=args.batch_size, shuffle=True, **kwargs)
image_transform = transforms.Compose([
transforms.RandomCrop(64),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
train_dataset = TextDataset(DATA_DIR,
'train',
imsize=64,
transform=image_transform)
assert train_dataset
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
drop_last=True,
shuffle=True)
######
image_transform = transforms.Compose([
transforms.RandomCrop(64),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
