195 lines
6.1 KiB
Python
195 lines
6.1 KiB
Python
#copyright (c) 2020 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 __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import math
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import paddle
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import paddle.fluid as fluid
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from paddle.fluid.param_attr import ParamAttr
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__all__ = ["ResNet", "ResNet50_vc", "ResNet101_vc", "ResNet152_vc"]
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train_parameters = {
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"input_size": [3, 224, 224],
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"input_mean": [0.485, 0.456, 0.406],
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"input_std": [0.229, 0.224, 0.225],
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"learning_strategy": {
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"name": "piecewise_decay",
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"batch_size": 256,
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"epochs": [30, 60, 90],
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"steps": [0.1, 0.01, 0.001, 0.0001]
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}
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}
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class ResNet():
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def __init__(self, layers=50):
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self.params = train_parameters
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self.layers = layers
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def net(self, input, class_dim=1000):
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layers = self.layers
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supported_layers = [50, 101, 152]
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assert layers in supported_layers, \
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"supported layers are {} but input layer is {}".format(supported_layers, layers)
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if layers == 50:
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depth = [3, 4, 6, 3]
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elif layers == 101:
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depth = [3, 4, 23, 3]
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elif layers == 152:
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depth = [3, 8, 36, 3]
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num_filters = [64, 128, 256, 512]
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conv = self.conv_bn_layer(
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input=input,
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num_filters=32,
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filter_size=3,
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stride=2,
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act='relu',
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name='conv1_1')
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conv = self.conv_bn_layer(
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input=conv,
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num_filters=32,
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filter_size=3,
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stride=1,
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act='relu',
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name='conv1_2')
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conv = self.conv_bn_layer(
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input=conv,
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num_filters=64,
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filter_size=3,
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stride=1,
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act='relu',
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name='conv1_3')
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conv = fluid.layers.pool2d(
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input=conv,
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pool_size=3,
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pool_stride=2,
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pool_padding=1,
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pool_type='max')
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for block in range(len(depth)):
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for i in range(depth[block]):
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if layers in [101, 152] and block == 2:
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if i == 0:
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conv_name = "res" + str(block + 2) + "a"
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else:
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conv_name = "res" + str(block + 2) + "b" + str(i)
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else:
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conv_name = "res" + str(block + 2) + chr(97 + i)
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conv = self.bottleneck_block(
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input=conv,
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num_filters=num_filters[block],
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stride=2 if i == 0 and block != 0 else 1,
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name=conv_name)
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pool = fluid.layers.pool2d(
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input=conv, pool_type='avg', global_pooling=True)
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stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
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out = fluid.layers.fc(input=pool,
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size=class_dim,
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param_attr=fluid.param_attr.ParamAttr(
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name="fc_0.w_0",
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initializer=fluid.initializer.Uniform(-stdv,
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stdv)),
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bias_attr=ParamAttr(name="fc_0.b_0"))
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return out
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def conv_bn_layer(self,
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input,
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num_filters,
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filter_size,
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stride=1,
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groups=1,
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act=None,
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name=None):
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conv = fluid.layers.conv2d(
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input=input,
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num_filters=num_filters,
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filter_size=filter_size,
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stride=stride,
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padding=(filter_size - 1) // 2,
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groups=groups,
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act=None,
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param_attr=ParamAttr(name=name + "_weights"),
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bias_attr=False,
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name=name + '.conv2d.output.1')
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if name == "conv1":
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bn_name = "bn_" + name
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else:
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bn_name = "bn" + name[3:]
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return fluid.layers.batch_norm(
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input=conv,
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act=act,
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name=bn_name + '.output.1',
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param_attr=ParamAttr(name=bn_name + '_scale'),
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bias_attr=ParamAttr(bn_name + '_offset'),
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moving_mean_name=bn_name + '_mean',
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moving_variance_name=bn_name + '_variance', )
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def shortcut(self, input, ch_out, stride, name):
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ch_in = input.shape[1]
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if ch_in != ch_out or stride != 1:
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return self.conv_bn_layer(input, ch_out, 1, stride, name=name)
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else:
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return input
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def bottleneck_block(self, input, num_filters, stride, name):
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conv0 = self.conv_bn_layer(
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input=input,
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num_filters=num_filters,
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filter_size=1,
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act='relu',
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name=name + "_branch2a")
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conv1 = self.conv_bn_layer(
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input=conv0,
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num_filters=num_filters,
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filter_size=3,
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stride=stride,
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act='relu',
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name=name + "_branch2b")
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conv2 = self.conv_bn_layer(
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input=conv1,
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num_filters=num_filters * 4,
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filter_size=1,
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act=None,
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name=name + "_branch2c")
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short = self.shortcut(
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input, num_filters * 4, stride, name=name + "_branch1")
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return fluid.layers.elementwise_add(
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x=short, y=conv2, act='relu', name=name + ".add.output.5")
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def ResNet50_vc():
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model = ResNet(layers=50)
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return model
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def ResNet101_vc():
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model = ResNet(layers=101)
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return model
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def ResNet152_vc():
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model = ResNet(layers=152)
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return model
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