args.cache_dir = './cache/%s/' % expr_name
for arg in vars(args):
print('[%s] =' % arg, getattr(args, arg))
# create directories
rec_dir = os.path.join(args.cache_dir, 'rec')
model_dir = os.path.join(args.cache_dir, 'models')
log_dir = os.path.join(args.cache_dir, 'log')
web_dir = os.path.join(args.cache_dir, 'web_rec')
html = image_save.ImageSave(web_dir, expr_name, append=True)
utils.mkdirs([rec_dir, model_dir, log_dir, web_dir])
# load data
tr_data, te_data, tr_stream, te_stream, ntrain, ntest \
= load_imgs(ntrain=None, ntest=None, batch_size=args.batch_size, data_file=args.data_file)
te_handle = te_data.open()
ntest = int(np.floor(ntest / float(args.batch_size)) * args.batch_size)
# st()
test_x, = te_data.get_data(te_handle, slice(0, ntest))
test_x = train_dcgan_utils.transform(test_x, nc=nc)
predict_params = train_dcgan_utils.init_predict_params(nz=nz,
n_f=n_f,
n_layers=n_layers,
nc=nc)
# load modelG
gen_params = train_dcgan_utils.init_gen_params(nz=nz,
n_f=n_f,
n_layers=n_layers,
nc=nc)
import argparse
import load
parser = argparse.ArgumentParser(description='get #images in a hdf5 dataset.')
parser.add_argument('--data_file',
dest='data_file',
help='the location of dataset file',
default='../datasets/outdoor_64.hdf5',
type=str)
args = parser.parse_args()
_, _, _, _, ntrain, ntest = load.load_imgs(ntrain=None,
ntest=None,
batch_size=128,
data_file=args.data_file)
print('dataset: %s; #training images: %d; #test images: %d' %
(args.data_file, ntrain, ntest))
for arg in vars(args):
print('[%s] =' % arg, getattr(args, arg))
# create directories
sample_dir = os.path.join(args.cache_dir, 'samples')
model_dir = os.path.join(args.cache_dir, 'models')
log_dir = os.path.join(args.cache_dir, 'log')
web_dir = os.path.join(args.cache_dir, 'web_dcgan')
html = image_save.ImageSave(web_dir, expr_name, append=True)
utils.mkdirs([sample_dir, model_dir, log_dir, web_dir])
# load data from hdf5 file
tr_data, te_data, tr_stream, te_stream, ntrain, ntest = load.load_imgs(
ntrain=None,
ntest=None,
batch_size=args.batch_size,
data_file=args.data_file)
te_handle = te_data.open()
test_x, = te_data.get_data(te_handle, slice(0, ntest))
# generate real samples and test transform/inverse_transform
test_x = train_dcgan_utils.transform(test_x, nc=nc)
vis_idxs = py_rng.sample(np.arange(len(test_x)), n_vis)
vaX_vis = train_dcgan_utils.inverse_transform(test_x[vis_idxs], npx=npx, nc=nc)
# st()
n_grid = int(np.sqrt(n_vis))
grid_real = utils.grid_vis((vaX_vis * 255.0).astype(np.uint8), n_grid, n_grid)
train_dcgan_utils.save_image(grid_real,
os.path.join(sample_dir, 'real_samples.png'))
args.cache_dir = './cache/%s/' % expr_name
for arg in vars(args):
print('[%s] =' % arg, getattr(args, arg))
# create directories
rec_dir = os.path.join(args.cache_dir, 'rec')
model_dir = os.path.join(args.cache_dir, 'models')
log_dir = os.path.join(args.cache_dir, 'log')
web_dir = os.path.join(args.cache_dir, 'web_rec')
html = image_save.ImageSave(web_dir, expr_name, append=True)
utils.mkdirs([rec_dir, model_dir, log_dir, web_dir])
# load data
tr_data, te_data, tr_stream, te_stream, ntrain, ntest \
= load_imgs(ntrain=None, ntest=None, batch_size=args.batch_size, data_file=args.data_file)
te_handle = te_data.open()
ntest = int(np.floor(ntest/float(args.batch_size)) * args.batch_size)
# st()
test_x, = te_data.get_data(te_handle, slice(0, ntest))
test_x = train_dcgan_utils.transform(test_x, nc=nc)
predict_params = train_dcgan_utils.init_predict_params(nz=nz, n_f=n_f, n_layers=n_layers, nc=nc)
# load modelG
gen_params = train_dcgan_utils.init_gen_params(nz=nz, n_f=n_f, n_layers=n_layers, nc=nc)
train_dcgan_utils.load_model(gen_params, os.path.join(model_dir, 'gen_params'))
gen_batchnorm = train_dcgan_utils.load_batchnorm(os.path.join(model_dir, 'gen_batchnorm'))
# define the model
t= time()
x = T.tensor4()
if not args.cache_dir:
args.cache_dir = './cache/%s/' % expr_name
for arg in vars(args):
print('[%s] =' % arg, getattr(args, arg))
# create directories
sample_dir = os.path.join(args.cache_dir, 'samples')
model_dir = os.path.join(args.cache_dir, 'models')
log_dir = os.path.join(args.cache_dir, 'log')
web_dir = os.path.join(args.cache_dir, 'web_dcgan')
html = image_save.ImageSave(web_dir, expr_name, append=True)
utils.mkdirs([sample_dir, model_dir, log_dir, web_dir])
# load data from hdf5 file
tr_data, te_data, tr_stream, te_stream, ntrain, ntest = load.load_imgs(ntrain=None, ntest=None, batch_size=args.batch_size,data_file=args.data_file)
te_handle = te_data.open()
test_x, = te_data.get_data(te_handle, slice(0, ntest))
# generate real samples and test transform/inverse_transform
test_x = train_dcgan_utils.transform(test_x, nc=nc)
vis_idxs = py_rng.sample(np.arange(len(test_x)), n_vis)
vaX_vis = train_dcgan_utils.inverse_transform(test_x[vis_idxs], npx=npx, nc=nc)
# st()
n_grid = int(np.sqrt(n_vis))
grid_real = utils.grid_vis((vaX_vis*255.0).astype(np.uint8), n_grid, n_grid)
train_dcgan_utils.save_image(grid_real, os.path.join(sample_dir, 'real_samples.png'))
# define DCGAN model
disc_params = train_dcgan_utils.init_disc_params(n_f=n_f, n_layers=n_layers, nc=nc)
import argparse
import load
parser = argparse.ArgumentParser(description='get #images in a hdf5 dataset.')
parser.add_argument('--data_file', dest='data_file', help='the location of dataset file', default='../datasets/outdoor_64.hdf5', type=str)
args = parser.parse_args()
_, _, _, _, ntrain, ntest = load.load_imgs(ntrain=None, ntest=None, batch_size=128, data_file=args.data_file)
print('dataset: %s; #training images: %d; #test images: %d' % (args.data_file, ntrain, ntest))