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94 lines
2.7 KiB
94 lines
2.7 KiB
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
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#
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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 obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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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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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from paddle.trainer_config_helpers import *
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height = 227
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width = 227
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num_class = 1000
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batch_size = get_config_arg('batch_size', int, 128)
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gp = get_config_arg('layer_num', int, 1)
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is_infer = get_config_arg("is_infer", bool, False)
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num_samples = get_config_arg('num_samples', int, 2560)
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args = {
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'height': height,
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'width': width,
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'color': True,
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'num_class': num_class,
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'is_infer': is_infer,
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'num_samples': num_samples
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}
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define_py_data_sources2(
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"train.list" if not is_infer else None,
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"test.list" if is_infer else None,
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module="provider",
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obj="process",
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args=args)
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settings(
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batch_size=batch_size,
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learning_rate=0.01 / batch_size,
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learning_method=MomentumOptimizer(0.9),
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regularization=L2Regularization(0.0005 * batch_size))
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# conv1
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net = data_layer('data', size=height * width * 3)
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net = img_conv_layer(
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input=net,
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filter_size=11,
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num_channels=3,
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num_filters=96,
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stride=4,
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padding=1)
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net = img_cmrnorm_layer(input=net, size=5, scale=0.0001, power=0.75)
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net = img_pool_layer(input=net, pool_size=3, stride=2)
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# conv2
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net = img_conv_layer(
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input=net, filter_size=5, num_filters=256, stride=1, padding=2, groups=gp)
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net = img_cmrnorm_layer(input=net, size=5, scale=0.0001, power=0.75)
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net = img_pool_layer(input=net, pool_size=3, stride=2)
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# conv3
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net = img_conv_layer(
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input=net, filter_size=3, num_filters=384, stride=1, padding=1)
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# conv4
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net = img_conv_layer(
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input=net, filter_size=3, num_filters=384, stride=1, padding=1, groups=gp)
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# conv5
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net = img_conv_layer(
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input=net, filter_size=3, num_filters=256, stride=1, padding=1, groups=gp)
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net = img_pool_layer(input=net, pool_size=3, stride=2)
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net = fc_layer(
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input=net,
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size=4096,
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act=ReluActivation(),
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layer_attr=ExtraAttr(drop_rate=0.5))
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net = fc_layer(
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input=net,
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size=4096,
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act=ReluActivation(),
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layer_attr=ExtraAttr(drop_rate=0.5))
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net = fc_layer(input=net, size=1000, act=SoftmaxActivation())
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if is_infer:
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outputs(net)
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else:
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lab = data_layer('label', num_class)
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loss = cross_entropy(input=net, label=lab)
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outputs(loss)
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