from keras import optimizers
import configparser as cp
from datetime import datetime as dt
import os
from shutil import copyfile
from plot.plot_autoencoder import plot_ae
if __name__ == "__main__":
config = cp.ConfigParser()
config.read("config.ini")
batch_size = config["general"].getint("batch_size")
ae_model = config["general"].get("ae_model")
color_mode = config["general"].get("color_mode")
noise_ratio = config["general"].getfloat("noise_ratio")
train_set = SVHNDataset.from_npy(config["general"].get("training_set"))
dev_set = SVHNDataset.from_npy(config["general"].get("dev_set"))
print(f"Training Set Color_Mode is {train_set.color_mode}")
print(f"Dev Set Color_Mode is {train_set.color_mode}")
if color_mode == "grayscale":
converter = ColorConverter(color_mode)
train_set = converter.transform(train_set)
dev_set = converter.transform(dev_set)
plotter = SVHNPlotter(output_dir=f"images/{train_set.name}")
plotter.save_images(train_set, n=10)
plotter.save_mosaic(train_set, row=10, col=10)
trn_gen = train_set.generator(batch_size=100, flatten=False, noise=0.05)
print(trn_gen[0][0].shape)
print(trn_gen[0][1].shape)
train_set_gen = SVHNDataset("trn_generator", images=trn_gen[0][0], labels=train_set.labels)
def test_from_npy(self):
ds = SVHNDataset.from_npy("dataset_split/arrays/training/rgb_all.npy")
assert ds.name == "rgb_all"
assert ds.labels.shape == (71791, )
assert ds.images.shape == (71791, 32, 32, 3)