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@ -1,11 +1,11 @@
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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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# You may obtain a copy of the License at
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#
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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#
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# Unless required by applicable law or agreed to in writing, software
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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@ -106,10 +106,10 @@ def vgg16_bn_drop(input):
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conv5 = conv_block(conv4, 512, 3, [0.4, 0.4, 0])
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conv5 = conv_block(conv4, 512, 3, [0.4, 0.4, 0])
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drop = fluid.layers.dropout(x=conv5, dropout_prob=0.5)
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drop = fluid.layers.dropout(x=conv5, dropout_prob=0.5)
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fc1 = fluid.layers.fc(input=drop, size=512, act=None)
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fc1 = fluid.layers.fc(input=drop, size=4096, act=None)
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bn = fluid.layers.batch_norm(input=fc1, act='relu')
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bn = fluid.layers.batch_norm(input=fc1, act='relu')
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drop2 = fluid.layers.dropout(x=bn, dropout_prob=0.5)
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drop2 = fluid.layers.dropout(x=bn, dropout_prob=0.5)
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fc2 = fluid.layers.fc(input=drop2, size=512, act=None)
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fc2 = fluid.layers.fc(input=drop2, size=4096, act=None)
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return fc2
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return fc2
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@ -138,13 +138,14 @@ def main():
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avg_cost = fluid.layers.mean(x=cost)
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avg_cost = fluid.layers.mean(x=cost)
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# Evaluator
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# Evaluator
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accuracy = fluid.evaluator.Accuracy(input=predict, label=label)
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batch_size = fluid.layers.create_tensor(dtype='int64')
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batch_acc = fluid.layers.accuracy(
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input=predict, label=label, total=batch_size)
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# inference program
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# inference program
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inference_program = fluid.default_main_program().clone()
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inference_program = fluid.default_main_program().clone()
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with fluid.program_guard(inference_program):
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with fluid.program_guard(inference_program):
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test_target = accuracy.metrics + accuracy.states
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inference_program = fluid.io.get_inference_program(batch_acc)
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inference_program = fluid.io.get_inference_program(test_target)
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# Optimization
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# Optimization
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optimizer = fluid.optimizer.Adam(learning_rate=args.learning_rate)
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optimizer = fluid.optimizer.Adam(learning_rate=args.learning_rate)
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@ -157,27 +158,30 @@ def main():
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# test
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# test
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def test(exe):
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def test(exe):
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accuracy.reset(exe)
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test_pass_acc = fluid.average.WeightedAverage()
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for batch_id, data in enumerate(test_reader()):
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for batch_id, data in enumerate(test_reader()):
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img_data = np.array(map(lambda x: x[0].reshape(data_shape),
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img_data = np.array(map(lambda x: x[0].reshape(data_shape),
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data)).astype("float32")
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data)).astype("float32")
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y_data = np.array(map(lambda x: x[1], data)).astype("int64")
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y_data = np.array(map(lambda x: x[1], data)).astype("int64")
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y_data = y_data.reshape([-1, 1])
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y_data = y_data.reshape([-1, 1])
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exe.run(inference_program,
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outs = exe.run(inference_program,
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feed={"pixel": img_data,
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feed={"pixel": img_data,
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"label": y_data})
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"label": y_data},
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fetch_list=[batch_acc, batch_size])
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test_pass_acc.add(value=np.array(outs[0]), weight=np.array(outs[1]))
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return accuracy.eval(exe)
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return test_pass_acc.eval()
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def train_loop(exe, trainer_prog):
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def train_loop(exe, trainer_prog):
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iters = 0
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iters = 0
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ts = time.time()
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ts = time.time()
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train_pass_acc = fluid.average.WeightedAverage()
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for pass_id in range(args.num_passes):
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for pass_id in range(args.num_passes):
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# train
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# train
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start_time = time.time()
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start_time = time.time()
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num_samples = 0
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num_samples = 0
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accuracy.reset(exe)
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train_pass_acc.reset()
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with profiler.profiler("CPU", 'total') as prof:
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with profiler.profiler("CPU", 'total') as prof:
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for batch_id, data in enumerate(train_reader()):
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for batch_id, data in enumerate(train_reader()):
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ts = time.time()
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ts = time.time()
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@ -187,13 +191,14 @@ def main():
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y_data = np.array(map(lambda x: x[1], data)).astype("int64")
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y_data = np.array(map(lambda x: x[1], data)).astype("int64")
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y_data = y_data.reshape([-1, 1])
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y_data = y_data.reshape([-1, 1])
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loss, acc = exe.run(
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loss, acc, b_size = exe.run(
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trainer_prog,
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trainer_prog,
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feed={"pixel": img_data,
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feed={"pixel": img_data,
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"label": y_data},
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"label": y_data},
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fetch_list=[avg_cost] + accuracy.metrics)
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fetch_list=[avg_cost, batch_acc, batch_size])
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iters += 1
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iters += 1
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num_samples += len(data)
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num_samples += len(data)
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train_pass_acc.add(value=acc, weight=b_size)
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print(
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print(
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"Pass = %d, Iters = %d, Loss = %f, Accuracy = %f, Speed = %.2f img/s"
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"Pass = %d, Iters = %d, Loss = %f, Accuracy = %f, Speed = %.2f img/s"
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% (pass_id, iters, loss, acc,
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% (pass_id, iters, loss, acc,
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@ -201,7 +206,7 @@ def main():
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) # The accuracy is the accumulation of batches, but not the current batch.
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) # The accuracy is the accumulation of batches, but not the current batch.
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pass_elapsed = time.time() - start_time
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pass_elapsed = time.time() - start_time
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pass_train_acc = accuracy.eval(exe)
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pass_train_acc = train_pass_acc.eval()
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pass_test_acc = test(exe)
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pass_test_acc = test(exe)
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print(
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print(
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"Pass = %d, Training performance = %f imgs/s, Train accuracy = %f, Test accuracy = %f\n"
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"Pass = %d, Training performance = %f imgs/s, Train accuracy = %f, Test accuracy = %f\n"
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Luo Tao
7 years ago