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)
# print(config)
# print(spad_config)
del data_config["data_name"]
model = DORN_sinkhorn_opt(
sgd_iters=400,
sinkhorn_iters=40,
sigma=.5,
lam=1e-2,
kde_eps=1e-4,
sinkhorn_eps=1e-4,
remove_dc=spad_config["dc_count"] > 0.,
use_intensity=spad_config["use_intensity"],
use_squared_falloff=spad_config["use_squared_falloff"],
lr=1e3)
model.to(device)
_, _, test = load_data(**data_config, spad_config=spad_config)
dataloader = DataLoader(test, shuffle=True)
start = perf_counter()
init_randomness(95290421)
input_ = test.get_item_by_id("kitchen_0002/1121")
for key in [
"rgb", "rgb_orig", "rawdepth", "spad", "mask", "rawdepth_orig",
"mask_orig"
]:
input_[key] = input_[key].unsqueeze(0).to(device)
data_load_time = perf_counter() - start
print("dataloader: {}".format(data_load_time))
# print(input_["entry"])
# print(model.hints_extractor[0].weight)
spad_config = spad_cfg()
# print(config)
# print(spad_config)
del data_config["data_name"]
model = DORN_nyu_hints(
in_channels=3,
in_height=257,
in_width=353,
sid_bins=68,
offset=data_config["offset"],
min_depth=data_config["min_depth"],
max_depth=data_config["max_depth"],
alpha=data_config["alpha"],
beta=data_config["beta"],
frozen=True,
pretrained=True,
state_dict_file=os.path.join("models", "torch_params_nyuv2_BGR.pth.tar"),
hints_len=68,
spad_weight=1.
)
train, _, _ = load_data(**data_config, spad_config=spad_config)
dataloader = DataLoader(train)
start = perf_counter()
input_ = next(iter(dataloader))
data_load_time = perf_counter() - start
print("dataloader: {}".format(data_load_time))
# print(input_["entry"])
# print(model.hints_extractor[0].weight)
loss, output = model.get_loss(input_, "cpu")
print(loss)