def infer_net(test_reader, use_gpu, model_path=None):
"""
Inference function
"""
if model_path is None:
print(str(model_path) + "can not be found")
return
# set place, executor
place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
inference_scope = fluid.core.Scope()
with fluid.scope_guard(inference_scope):
# load the saved model
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(model_path, exe)
for data in test_reader():
# infer a batch
pred = exe.run(inference_program,
feed=utils.data2tensor(data, place),
fetch_list=fetch_targets,
return_numpy=True)
for i, val in enumerate(data):
class3_label, class2_label = utils.get_predict_label(
pred[0][i, 1])
pos_prob = pred[0][i, 1]
neg_prob = 1 - pos_prob
print("predict label: %d, pos_prob: %f, neg_prob: %f" %
(class3_label, pos_prob, neg_prob))
def eval_net(test_reader, use_gpu, model_path=None):
"""
Evaluation function
"""
if model_path is None:
print(str(model_path) + "can not be found")
return
# set place, executor
place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
inference_scope = fluid.core.Scope()
with fluid.scope_guard(inference_scope):
# load the saved model
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(model_path, exe)
# compute 2class and 3class accuracy
class2_acc, class3_acc = 0.0, 0.0
total_count, neu_count = 0, 0
for data in test_reader():
# infer a batch
pred = exe.run(inference_program,
feed=utils.data2tensor(data, place),
fetch_list=fetch_targets,
return_numpy=True)
for i, val in enumerate(data):
class3_label, class2_label = utils.get_predict_label(
pred[0][i, 1])
true_label = val[1]
if class2_label == true_label:
class2_acc += 1
if class3_label == true_label:
class3_acc += 1
if true_label == 1.0:
neu_count += 1
total_count += len(data)
class2_acc = class2_acc / (total_count - neu_count)
class3_acc = class3_acc / total_count
print("[test info] model_path: %s, class2_acc: %f, class3_acc: %f" %
(model_path, class2_acc, class3_acc))