def test_merge_w_channels(self): X_train, Y_train = get_ds_simple(cnt_samples=10) X_train = np.expand_dims(X_train, axis=1).astype(np.float32) / 255 Y_train = Y_train[:, np.newaxis] print(X_train.shape, Y_train.shape) im = merge_samples(X_train, Y_train) im.save("/tmp/simple_channel.png")
def test_simple_old(self): print("generating sample output") cnt_samples = 10 X_train = np.array([gen_item(i % 2) for i in range(cnt_samples)]) Y_train = np.array([i % 2 for i in range(cnt_samples)], dtype=np.int32) print(X_train.shape, Y_train.shape) im = merge_samples(X_train, Y_train) im.save("/tmp/test_legacy.png")
def main(): net = Net() model = Classifier(net) # res = net(X_train[0:1]) # print(res.shape) # generate examples before training print("image size : ", X_train[:1].size) im = merge_samples(X_train[:10], Y_train[:10]) im.save("/tmp/ae_0_original.png") noisy_X = make_noise(X_train) im = merge_samples(noisy_X, Y_train[:10]) im.save("/tmp/ae_1_noisy_input.png") encoded = net.encode(X_train[:1]) print("encoded size", encoded.shape) generated = net(X_train[:10]) im = merge_samples(generated.data, Y_train) im.save("/tmp/ae_2_untrained.png") ds_train = chainer.datasets.tuple_dataset.TupleDataset(noisy_X, X_train) if len(params["gpus"]) > 0: chainer.cuda.get_device(0).use() model.to_gpu() train(model, ds_train, None, params) if len(params["gpus"]) > 0: model.to_cpu() decoded = net(X_train[:10]) im = merge_samples(decoded.data, Y_train[:10]) im.save("/tmp/ae_3_trained.png") denoised = net(noisy_X[:10]) print(denoised.data.min(), denoised.data.max(), denoised.data.mean()) im = merge_samples(denoised.data, Y_train[:10]) im.save("/tmp/ae_4_trained_denoised.png")
def test_size(self): X_train, Y_train = get_ds_simple(dim_image=128, cnt_samples=10) print(X_train.shape, Y_train.shape) im = merge_samples(X_train, Y_train) im.save("/tmp/size128.png")
def test_naive(self): X_train, Y_train = get_ds_naive(cnt_samples=10) print(X_train.shape, Y_train.shape) im = merge_samples(X_train, Y_train) im.save("/tmp/naive.png")
def test_counting(self): X_train, Y_train = get_ds_counting() print(X_train.shape, Y_train.shape) im = merge_samples(X_train, Y_train) im.save("/tmp/counting.png")