def main(model_config, dataset_type, save_outputs, output_dir, data_config, seed, small_run, entry, device): # Load the model model = make_model(**model_config) # model.sid_obj.to(device) # print(model) model.to(device) # Load the data _, val, test = load_data() dataset = test if dataset_type == "test" else val init_randomness(seed) if entry is None: print("Evaluating the model on {} ({})".format(data_config["data_name"], dataset_type)) evaluate_model_on_dataset(model, dataset, small_run, device, save_outputs, output_dir) else: print("Evaluating {}".format(entry)) evaluate_model_on_data_entry(model, dataset, entry, device)
def main(model_config, save_outputs, output_dir, data_config, seed, small_run, dataset_type, entry, device): # Load the model model = make_model(**model_config) model.sinkhorn_opt.to(device) from tensorboardX import SummaryWriter from datetime import datetime # Load the data train, test = load_data(dorn_mode=False) dataset = train if dataset_type == "train" else test eval_fn = lambda input_, device: model.evaluate( input_["rgb"], input_["crop"][0, :], input_["depth_cropped"], torch.ones_like(input_["depth_cropped"]), device) init_randomness(seed) if entry is None: print("Evaluating the model on {}.".format(data_config["data_name"])) evaluate_model_on_dataset(eval_fn, dataset, small_run, device, save_outputs, output_dir) else: print("Evaluating {}".format(entry)) model.sinkhorn_opt.writer = SummaryWriter(log_dir=os.path.join("runs", datetime.now().strftime('%b%d'), datetime.now().strftime('%H-%M-%S_') + \ "densedepth_hist_match_wass")) evaluate_model_on_data_entry(eval_fn, dataset, entry, device, save_outputs, output_dir)
def main(model_config, dataset_type, save_outputs, output_dir, data_config, seed, small_run, device): # Load the model model = make_model(**model_config) model.eval() model.to(device) model.sid_obj.to(device) # Load the data train, test = load_data(dorn_mode=True) dataset = test if dataset_type == "test" else train print( list((name, entry.shape) for name, entry in dataset[0].items() if isinstance(entry, torch.Tensor))) init_randomness(seed) eval_fn = lambda input_, device: model.evaluate( input_["bgr"].to(device), input_["bgr_orig"].to(device), input_["crop"] [0, :], input_["depth_cropped"].to(device), input_["depth"].to(device), torch.ones_like(input_["depth_cropped"]).to(device), device) print("Evaluating the model on {} ({})".format(data_config["data_name"], dataset_type)) evaluate_model_on_dataset(eval_fn, dataset, small_run, device, save_outputs, output_dir)
def main(model_config, save_outputs, output_dir, data_config, seed, small_run, entry, device): # Load the model model = make_model(**model_config) # model.sid_obj.to(device) # print(model) model.to(device) from tensorboardX import SummaryWriter from datetime import datetime # model.writer = SummaryWriter(log_dir=os.path.join("runs", # datetime.now().strftime('%b%d'), # datetime.now().strftime('%H-%M-%S_') + \ # "dorn_sinkhorn_opt")) # Load the data dataset = load_data(dorn_mode=True) eval_fn = lambda input_, device: model.evaluate( input_["rgb_cropped"].to(device), input_["rgb_cropped_orig"].to( device), input_["spad"].to(device), input_["mask_orig"].to(device), input_["depth_cropped_orig"].to(device), device) init_randomness(seed) if entry is None: print("Evaluating the model on {}.".format(data_config["data_name"])) evaluate_model_on_dataset(eval_fn, dataset, small_run, device, save_outputs, output_dir) else: print("Evaluating {}".format(entry)) evaluate_model_on_data_entry(eval_fn, dataset, entry, device, save_outputs, output_dir)
def main(model_config, save_outputs, output_dir, data_config, seed, small_run, entry, device): # Load the model model = make_model(**model_config) model.eval() model.to(device) model.sid_obj.to(device) # Load the data dataset = load_data(dorn_mode=True) eval_fn = lambda input_, device: model.evaluate( input_["rgb_cropped"].to(device), input_["rgb_cropped_orig"].to( device), input_["depth_cropped_orig"].to(device), input_[ "mask_orig"].to(device), device) init_randomness(seed) if entry is None: print("Evaluating the model on {}.".format(data_config["data_name"])) evaluate_model_on_dataset(eval_fn, dataset, small_run, device, save_outputs, output_dir) else: print("Evaluating {}".format(entry)) evaluate_model_on_data_entry(eval_fn, dataset, entry, device, save_outputs, output_dir)
def main(model_config, save_outputs, output_dir, data_config, seed, small_run, dataset_type, entry): # Load the model model = make_model(**model_config) # model.sid_obj.to(device) from tensorboardX import SummaryWriter from datetime import datetime # Load the data train, test = load_data(dorn_mode=False) dataset = train if dataset_type == "train" else test eval_fn = lambda input_, device: model.evaluate( input_["rgb"], input_["crop"][0, :], input_["depth_cropped"], input_[ "rawdepth_cropped"], input_["mask_cropped"], torch.ones_like(input_["depth_cropped"])) init_randomness(seed) if entry is None: print("Evaluating the model on {}.".format(data_config["data_name"])) evaluate_model_on_dataset(eval_fn, dataset, small_run, None, save_outputs, output_dir) else: print("Evaluating {}".format(entry)) evaluate_model_on_data_entry(eval_fn, dataset, entry, None, save_outputs, output_dir)
def main(model_config, save_outputs, output_dir, data_config, seed, small_run, device): # Load the model model = make_model(**model_config) model.eval() model.to(device) # model.sid_obj.to(device) # Load the data dataset = load_data() init_randomness(seed) print("Evaluating the model on {}".format(data_config["data_name"])) evaluate_model_on_dataset(model, dataset, small_run, device, save_outputs, output_dir)
def main(model_config, dataset_type, save_outputs, output_dir, data_config, seed, small_run, entry, device): # Load the model model = make_model(**model_config) model.eval() model.to(device) model.sid_obj.to(device) model.sinkhorn_opt.to(device) # Load the data train, test = load_data(dorn_mode=True) dataset = test if dataset_type == "test" else train from tensorboardX import SummaryWriter from datetime import datetime init_randomness(seed) eval_fn = lambda input_, device: model.evaluate( input_["bgr"].to(device), input_["bgr_orig"].to(device), input_["crop"] [0, :], input_["depth_cropped"].to(device), torch.ones_like(input_["depth_cropped"]).to(device), device) if entry is None: print("Evaluating the model on {} ({}).".format( data_config["data_name"], dataset_type)) ex.observers.append( FileStorageObserver.create(os.path.join(output_dir, "runs"))) evaluate_model_on_dataset(eval_fn, dataset, small_run, device, save_outputs, output_dir) else: print("Evaluating {}".format(entry)) model.sinkhorn_opt.writer = SummaryWriter(log_dir=os.path.join("runs", datetime.now().strftime('%b%d'), datetime.now().strftime('%H-%M-%S_') + \ "dorn_hist_match_wass")) evaluate_model_on_data_entry(eval_fn, dataset, entry, device, save_outputs, output_dir)
def main(model_config, save_outputs, output_dir, data_config, seed, small_run, entry, device): # Load the model model = make_model(**model_config) # model.sid_obj.to(device) # print(model) model.to(device) from tensorboardX import SummaryWriter from datetime import datetime model.writer = SummaryWriter(log_dir=os.path.join("runs", datetime.now().strftime('%b%d'), datetime.now().strftime('%H-%M-%S_') + \ "densedepth_sinkhorn_opt")) # Load the data dataset = load_data(dorn_mode=False) eval_fn = lambda input_, device: model.evaluate(input_["rgb"], # RGB input input_["rgb_cropped"], # rgb cropped for intensity scaling input_["crop"], # 4-tuple of crop parameters input_["spad"], # simulated SPAD input_["mask"], # Cropped mask input_["depth_cropped"], # Ground truth depth device) init_randomness(seed) if entry is None: print("Evaluating the model on {}.".format(data_config["data_name"])) evaluate_model_on_dataset(eval_fn, dataset, small_run, torch_cuda_device, save_outputs, output_dir) else: print("Evaluating {}".format(entry)) evaluate_model_on_data_entry(eval_fn, dataset, entry, torch_cuda_device, save_outputs, output_dir)