def test(net,
test_data,
metric,
use_gpus,
calc_weight_count=False,
extended_log=False):
tic = time.time()
predictor = test_data["predictor_class"](base_model=net)
if use_gpus:
predictor.to_gpu()
if calc_weight_count:
weight_count = net.count_params()
logging.info("Model: {} trainable parameters".format(weight_count))
in_values, out_values, rest_values = apply_to_iterator(
predictor.predict,
test_data["iterator"],
hook=ProgressHook(test_data["ds_len"]))
del in_values
pred_labels, = out_values
gt_labels, = rest_values
labels = iter(gt_labels)
preds = iter(pred_labels)
for label, pred in zip(labels, preds):
metric.update(label, pred)
accuracy_msg = report_accuracy(metric=metric, extended_log=extended_log)
logging.info("Test: {}".format(accuracy_msg))
logging.info("Time cost: {:.4f} sec".format(time.time() - tic))
def test(net, test_data, metric, calc_weight_count=False, extended_log=False):
tic = time.time()
predictor = Predictor(model=net, transform=None)
if calc_weight_count:
weight_count = net.count_params()
logging.info("Model: {} trainable parameters".format(weight_count))
_, out_values, rest_values = apply_to_iterator(
func=predictor,
iterator=test_data["iterator"],
hook=ProgressHook(test_data["ds_len"]))
assert (len(rest_values) == 1)
assert (len(out_values) == 1)
if False:
labels = iter(rest_values[0])
preds = iter(out_values[0])
for label, pred in zip(labels, preds):
metric.update(label, pred)
else:
import numpy as np
metric.update(labels=np.array(list(rest_values[0])),
preds=np.array(list(out_values[0])))
accuracy_msg = report_accuracy(metric=metric, extended_log=extended_log)
logging.info("Test: {}".format(accuracy_msg))
logging.info("Time cost: {:.4f} sec".format(time.time() - tic))
def test(net, test_data, metric, calc_weight_count=False, extended_log=False):
"""
Main test routine.
Parameters:
----------
net : Chain
Model.
test_data : dict
Data loader.
metric : EvalMetric
Metric object instance.
calc_weight_count : bool, default False
Whether to calculate count of weights.
extended_log : bool, default False
Whether to log more precise accuracy values.
"""
tic = time.time()
predictor = Predictor(model=net, transform=None)
if calc_weight_count:
weight_count = net.count_params()
logging.info("Model: {} trainable parameters".format(weight_count))
in_values, out_values, rest_values = apply_to_iterator(
func=predictor,
iterator=test_data["iterator"],
hook=ProgressHook(test_data["ds_len"]))
assert (len(rest_values) == 1)
assert (len(out_values) == 1)
assert (len(in_values) == 1)
if True:
labels = iter(rest_values[0])
preds = iter(out_values[0])
inputs = iter(in_values[0])
for label, pred, inputi in zip(labels, preds, inputs):
metric.update(label, pred)
del label
del pred
del inputi
else:
import numpy as np
metric.update(labels=np.array(list(rest_values[0])),
preds=np.array(list(out_values[0])))
accuracy_msg = report_accuracy(metric=metric, extended_log=extended_log)
logging.info("Test: {}".format(accuracy_msg))
logging.info("Time cost: {:.4f} sec".format(time.time() - tic))