Closed
Description
Issue: Image Classification with Resnet will only infer category 3 (cat)
The training code as follow. The resnet_cifar10 is based on the image_classification_test with some minor change.
After changing training with about 10 epochs, the accuracy during training is about 95%. When test against with the test_suit using trainer.test, the accuracy is about 79%
However, when using the Inferencer to load the saved params to do infer. The result is always showing category 3 (cat). I even use the the original train dataset for inferring, but the result is always 3.
Could this mean that there might be a bug within the inferencer or saving the params?
Note: The test from image_classification_test does provide correct inferring.
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License
from __future__ import print_function
import paddle
import paddle.fluid as fluid
import numpy
import sys
from vgg import vgg_bn_drop
from resnet import resnet_cifar10
def inference_network():
# The image is 32 * 32 with RGB representation.
data_shape = [3, 32, 32]
images = fluid.layers.data(name='pixel', shape=data_shape, dtype='float32')
predict = resnet_cifar10(images, 32)
# predict = vgg_bn_drop(images) # un-comment to use vgg net
return predict
def train_network():
predict = inference_network()
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(cost)
accuracy = fluid.layers.accuracy(input=predict, label=label)
return [avg_cost, accuracy]
def optimizer_program():
return fluid.optimizer.Adam(learning_rate=0.001)
def train(use_cuda, train_program, params_dirname):
BATCH_SIZE = 128
EPOCH_NUM = 2
train_reader = paddle.batch(
paddle.reader.shuffle(paddle.dataset.cifar.train10(), buf_size=50000),
batch_size=BATCH_SIZE)
test_reader = paddle.batch(
paddle.dataset.cifar.test10(), batch_size=BATCH_SIZE)
def event_handler(event):
if isinstance(event, fluid.EndStepEvent):
if event.step % 100 == 0:
print("\nPass %d, Batch %d, Cost %f, Acc %f" %
(event.step, event.epoch, event.metrics[0],
event.metrics[1]))
else:
sys.stdout.write('.')
sys.stdout.flush()
if isinstance(event, fluid.EndEpochEvent):
avg_cost, accuracy = trainer.test(
reader=test_reader, feed_order=['pixel', 'label'])
print('\nTest with Pass {0}, Loss {1:2.2}, Acc {2:2.2}'.format(
event.epoch, avg_cost, accuracy))
if params_dirname is not None:
trainer.save_params(params_dirname)
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
trainer = fluid.Trainer(
train_func=train_program, optimizer_func=optimizer_program, place=place)
trainer.train(
reader=train_reader,
num_epochs=EPOCH_NUM,
event_handler=event_handler,
feed_order=['pixel', 'label'])
def infer(use_cuda, inference_program, params_dirname=None):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
inferencer = fluid.Inferencer(
infer_func=inference_program, param_path=params_dirname, place=place)
train_reader = paddle.batch(
paddle.reader.shuffle(paddle.dataset.cifar.train10(), buf_size=50000),
batch_size=1000)
for batch in train_reader():
image = batch[0][0]
label = batch[0][1]
image_array = image.reshape(1, 3, 32, 32)
results = inferencer.infer({'pixel': image_array})
lab = results[0].argsort()
print("Label--------: ", label)
print("infer results: ", lab[0][-1])
def main(use_cuda):
if use_cuda and not fluid.core.is_compiled_with_cuda():
return
save_path = "image_classification_resnet.inference.model"
train(
use_cuda=use_cuda,
train_program=train_network,
params_dirname=save_path)
infer(
use_cuda=use_cuda,
inference_program=inference_network,
params_dirname=save_path)
if __name__ == '__main__':
# For demo purpose, the training runs on CPU
# Please change accordingly.
main(use_cuda=False)