def vae_generation():
target_shape = (64, 64)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# model_path = "useful_models/vae_5_layers_mnist_max_lr_0.01_24122020_115212_050.tar"
# model_path = "useful_models/vae_5_layers_mnist_max_lr_0.01_24122020_141519_050.tar"
model_path = "useful_models/squeeze_vae_mountain_car_v0_max_lr_0.005_29122020_143138_050.tar"
input_shape = (1, ) + target_shape
# model = AutoEncoder.get_basic_ae(input_shape=input_shape).to(device)
# model = VariationalAutoEncoder.get_basic_vae(input_shape=input_shape).to(device)
model = VariationalAutoEncoder.get_squeeze_vae(
input_shape=input_shape).to(device)
load_checkpoint(model_path, model)
mnist_preprocessor = CNNPreProcessor(bgr_mean=0.1307,
bgr_std=0.3081,
target_shape=target_shape)
mountain_car_64g_preprocessor = CNNPreProcessor(bgr_mean=0.9857,
bgr_std=0.1056)
preprocessor = mountain_car_64g_preprocessor
for _ in range(100):
generated = model.generate(device)
im_generated = preprocessor.reverse_preprocess(generated)
opencv_show(im_generated)
print("")
def evaluation():
# cart_pole_v0_bgr_mean = (0.9890, 0.9898, 0.9908)
# cart_pole_v0_bgr_std = (0.0977, 0.0936, 0.0906)
# target_shape = (64, 64)
cart_pole_v0_bgr_mean = (0.9922, 0.9931, 0.9940)
cart_pole_v0_bgr_std = (0.0791, 0.0741, 0.0703)
target_shape = (64, 64)
latent_dim = 512
preprocessor = CNNPreProcessor(bgr_mean=cart_pole_v0_bgr_mean,
bgr_std=cart_pole_v0_bgr_std,
target_shape=target_shape)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# model_path = "model_checkpoints/cartpoleV0_autoencoder_2_200.tar"
# model_path = "model_checkpoints/cartpoleV0_autoencoder_3_032.tar"
# model_path = "useful_models/cartpoleV0_basic_autoencoder_mnist_14122020_026.tar"
# model_path = "useful_models/no_out_activation_autoencoder_5_layers_mnist_max_lr_0.01_23122020_160428_049.tar"
# model_path = "useful_models/squeeze_autoencoder_mnist_max_lr_0.001_26122020_122631_050.tar"
model_path = "useful_models/squeeze_vae_mnist_max_lr_0.001_26122020_142330_050.tar"
# model_path = "useful_models/vae_5_layers_mnist_max_lr_0.01_24122020_115212_050.tar"
input_shape = (1, ) + target_shape
# model = AutoEncoder.get_basic_ae(input_shape=input_shape).to(device)
# model = AutoEncoder.get_squeeze_ae(input_shape=input_shape).to(device)
# model = VariationalAutoEncoder.get_basic_vae(input_shape=input_shape).to(device)
model = VariationalAutoEncoder.get_squeeze_vae(
input_shape=input_shape).to(device)
load_checkpoint(model_path, model)
ims_path = "agent_frames/cartpoleV0"
f_names = [
join(ims_path, e) for e in listdir(ims_path) if e.endswith(".jpg")
]
mnist_test_ims = [el for el in mnist.test_images()]
mnist_preprocessor = CNNPreProcessor(bgr_mean=0.1307,
bgr_std=0.3081,
target_shape=target_shape)
preprocessor = mnist_preprocessor
for _ in range(100):
# random_im_path = np.random.choice(f_names)
#
# im = cv.imread(random_im_path)
im = mnist_test_ims[np.random.randint(len(mnist_test_ims))]
im_target = cv.resize(im, target_shape)
orig_shape = im.shape[:2][::-1]
in_t = preprocessor.preprocess(im).to(device)
# out_t, embedding = model(in_t)
out_t, mu, log_var = model(in_t)
loss = F.mse_loss(in_t, out_t)
out_im_target = preprocessor.reverse_preprocess(out_t)
out_im = cv.resize(out_im_target, orig_shape)
sbs = put_side_by_side([im_target, out_im_target])
print(f"Loss: {loss.item()}")
opencv_show(sbs)
print("")
pass