def load_model(
model_type,
max_filters,
num_layers,
image_size,
model_parameters,
small_conv,
model_path,
device,
):
if model_type == "vae":
model = vae.ConvolutionalVAE(
max_filters=max_filters,
num_layers=num_layers,
input_image_dimensions=image_size,
latent_dim=model_parameters,
small_conv=small_conv,
)
elif model_type == "dual_input_vae":
model = vae.FusionVAE(
max_filters=max_filters,
num_layers=num_layers,
input_image_dimensions=image_size,
latent_dim=model_parameters,
small_conv=small_conv,
)
elif model_type == "vq_vae":
num_embeddings = model_parameters["K"]
embedding_dim = model_parameters["D"]
commitment_cost = model_parameters["commitment_cost"]
model = vqvae.VQVAE(
num_layers=num_layers,
input_image_dimensions=image_size,
small_conv=small_conv,
embedding_dim=embedding_dim,
num_embeddings=num_embeddings,
commitment_cost=commitment_cost,
)
elif model_type == "dual_input_autoencoder":
model = autoencoder.FusionAE(
max_filters=max_filters,
num_layers=num_layers,
input_image_dimensions=image_size,
latent_dim=model_parameters,
small_conv=small_conv,
)
else:
model = autoencoder.ConvolutionalAE(
max_filters=max_filters,
num_layers=num_layers,
input_image_dimensions=image_size,
latent_dim=model_parameters,
small_conv=small_conv,
)
model.load_state_dict(torch.load(model_path, map_location=device))
model.eval()
return model
################################################################################
#################################### Setup $####################################
################################################################################
# Setup Device
gpu = torch.cuda.is_available()
device = torch.device("cuda" if gpu else "cpu")
print(gpu, device)
# Create Output Paths
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# Create & Load VQVAE Model
vq_vae = vqvae.VQVAE(**vq_vae_model_config)
vq_vae.load_state_dict(torch.load(vq_vae_model_path, map_location=device))
vq_vae.eval()
vq_vae.to(device)
checkpoint = torch.load(model_path, map_location=device)
label_handler = data.ConditioningLabelsHandlerFromSaved(
conditioning_info_file, conditioning_info_columns,
checkpoint["encoding_dict"])
model_config["conditioning_size"] = label_handler.get_size()
# Create Model
model = gated_pixelcnn.ConditionalPixelCNN(**model_config)
model.load_state_dict(checkpoint["model_state_dict"])
model.to(device)
model.eval()
prior_input_dim = image_size
prior_output_dim = prior_input_dim // np.power(2, vq_vae_num_layers)
## Setup Devices
gpu = torch.cuda.is_available()
device = torch.device("cuda" if gpu else "cpu")
## Setup Transform
transform = data.image2tensor_resize(image_size)
## Load VQVAE Model
model = vqvae.VQVAE(
num_layers=vq_vae_num_layers,
input_image_dimensions=image_size,
small_conv=vq_vae_small_conv,
embedding_dim=vq_vae_embedding_dim,
num_embeddings=vq_vae_num_embeddings,
commitment_cost=vq_vae_commitment_cost,
use_max_filters=vq_vae_use_max_filters,
max_filters=vq_vae_max_filters,
)
model_path = os.path.join(model_prefix, vq_vae_model_name, "model.pt")
model.load_state_dict(torch.load(model_path, map_location=device))
model.eval()
## Load Prior
prior = cnn_prior.CNNPrior(
input_channels=prior_input_channels,
output_channels=prior_output_channels,
input_dim=prior_input_dim,
output_dim=prior_output_dim,
)
