def evaluate_top_down_pred():
P.initialize_experiment()
setup = P.get_current_parameters()["Setup"]
model, model_loaded = load_model()
eval_envs = get_correct_eval_env_id_list()
dataset_name = P.get_current_parameters().get("Data").get("dataset_name")
dataset = model.get_dataset(envs=eval_envs,
dataset_prefix=dataset_name,
dataset_prefix="supervised",
eval=eval)
dataloader = DataLoader(dataset,
collate_fn=dataset.collate_fn,
batch_size=1,
shuffle=False,
num_workers=1,
pin_memory=False)
total_loss = 0
count = 0
num_batches = len(dataloader)
for b, batch in enumerate(dataloader):
loss_var = model.sup_loss_on_batch(batch, eval=True, viz=True)
total_loss += loss_var.data[0]
count += 1
print("batch: " + str(b) + " / " + str(num_batches) + \
" loss: " + str(loss_var.data[0]))
avg_loss = total_loss / count
results_dir = get_results_dir(setup["run_name"])
results_json_path = get_results_path(setup["run_name"])
os.makedirs(results_dir, exist_ok=True)
viz = model.get_viz()
for key, lst in viz.items():
for i, img in enumerate(lst):
img_path = os.path.join(
results_dir, key + str(i) + "_" + setup["model"] + ".jpg")
sp.misc.imsave(img_path, img)
print("Saved image: " + img_path)
with open(results_json_path, "w") as fp:
json.dump({"loss": avg_loss}, fp)
def save_results(self):
# Write results dict
path = get_results_path(self.run_name, makedir=True)
with open(path, "w") as fp:
json.dump(self.get_results(), fp)