for idx in data_idxs
]
loss, pred_y, _ = model.predict(
tuple(map(Variable, batch_array)))
valid_eval.update(cuda.to_cpu(loss.data), pred_y, batch)
write_prediction(prediction_dict["predictions"], batch, pred_y)
message_str = "Iter {}: train loss {} / ADE {} / FDE {}, valid loss {} / " \
"ADE {} / FDE {}, elapsed time: {} (s)"
logger.info(
message_str.format(iter_cnt + 1, train_eval("loss"),
train_eval("ade"), train_eval("fde"),
valid_eval("loss"), valid_eval("ade"),
valid_eval("fde"),
time.time() - st))
train_eval.update_summary(summary, iter_cnt,
["loss", "ade", "fde"])
valid_eval.update_summary(summary, iter_cnt,
["loss", "ade", "fde"])
predictions = prediction_dict["predictions"]
pred_list = [[
pred for vk, v_dict in sorted(predictions.items())
for fk, f_dict in sorted(v_dict.items())
for pk, pred in sorted(f_dict.items()) if pred[8] == idx
] for idx in range(4)]
error_rates = [
np.mean([pred[7] for pred in preds]) for preds in pred_list
]
logger.info("Towards {} / Away {} / Across {} / Other {}".format(
*error_rates))
valid_eval = Evaluator("valid", args)
logger.info("Evaluation...")
chainer.config.train = False
chainer.config.enable_backprop = False
# Evaluation loop
for itr, batch in enumerate(valid_iterator):
batch_array = [convert.concat_examples([x[idx] for x in batch], args.gpu) for idx in data_idxs]
loss, pred_y, prob = model.predict(tuple(map(Variable, batch_array)))
valid_eval.update(cuda.to_cpu(loss.data), pred_y, batch)
write_prediction(prediction_dict["predictions"], batch, pred_y)
message_str = "Evaluation: valid loss {} / ADE {} / FDE {}"
logger.info(message_str.format(valid_eval("loss"), valid_eval("ade"), valid_eval("fde")))
valid_eval.update_summary(summary, -1, ["loss", "ade", "fde"])
predictions = prediction_dict["predictions"]
pred_list = [[pred for vk, v_dict in sorted(predictions.items())
for fk, f_dict in sorted(v_dict.items())
for pk, pred in sorted(f_dict.items()) if pred[8] == idx] for idx in range(4)]
logger.info([len(x) for x in pred_list])
error_rates = [np.mean([pred[7] for pred in preds]) for preds in pred_list]
logger.info("Towards {} / Away {} / Across {} / Other {}".format(*error_rates))
prediction_path = os.path.join(save_dir, "prediction.json")
with open(prediction_path, "w") as f:
json.dump(prediction_dict, f)
summary.update("finished", 1)
summary.write()
