np.min(image_s_trains), np.max(image_s_trains)))
print("[image_t_trains] shape={}, dtype={}, min={}, max={}".format(
image_t_trains.shape, image_t_trains.dtype,
np.min(image_t_trains), np.max(image_t_trains)))
print("[image_s_valids] shape={}, dtype={}, min={}, max={}".format(
image_s_valids.shape, image_s_valids.dtype,
np.min(image_s_valids), np.max(image_s_valids)))
print("[image_t_valids] shape={}, dtype={}, min={}, max={}".format(
image_t_valids.shape, image_t_valids.dtype,
np.min(image_t_valids), np.max(image_t_valids)))
#================================
# モデルの構造を定義する。
#================================
with mirrored_strategy.scope():
model_G = TempleteNetworks(out_dim=3)
model_G(
tf.zeros([args.batch_size, args.image_height, args.image_width, 3],
dtype=tf.float32))
#================================
# loss 設定
#================================
with mirrored_strategy.scope():
loss_mse = tf.keras.losses.MeanSquaredError()
#================================
# optimizer 設定
#================================
with mirrored_strategy.scope():
optimizer_G = tf.keras.optimizers.Adam(learning_rate=args.lr,
args.dataset_dir,
datamode="test",
image_height=args.image_height,
image_width=args.image_width,
data_augument=False,
debug=args.debug)
dloader_test = torch.utils.data.DataLoader(ds_test,
batch_size=args.batch_size_test,
shuffle=False,
num_workers=args.n_workers,
pin_memory=True)
#================================
# モデルの構造を定義する。
#================================
model_G = TempleteNetworks().to(device)
if (args.debug):
print("model_G\n", model_G)
# モデルを読み込む
if not args.load_checkpoints_path == '' and os.path.exists(
args.load_checkpoints_path):
load_checkpoint(model_G, device, args.load_checkpoints_path)
#================================
# モデルの推論
#================================
print("Starting Testing Loop...")
n_print = 1
model_G.eval()
for step, inputs in enumerate(tqdm(dloader_test, desc="Samplings")):