183 lines
5.6 KiB
Python
183 lines
5.6 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__ = [
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"ResNeXt101_32x8d_wsl", "ResNeXt101_32x16d_wsl", "ResNeXt101_32x32d_wsl",
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"ResNeXt101_32x48d_wsl", "Fix_ResNeXt101_32x48d_wsl"
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]
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class ResNeXt101_wsl():
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def __init__(self, layers=101, cardinality=32, width=48):
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self.layers = layers
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self.cardinality = cardinality
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self.width = width
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def net(self, input, class_dim=1000):
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layers = self.layers
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cardinality = self.cardinality
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width = self.width
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depth = [3, 4, 23, 3]
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base_width = cardinality * width
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num_filters = [base_width * i for i in [1, 2, 4, 8]]
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conv = self.conv_bn_layer(
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input=input,
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num_filters=64,
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filter_size=7,
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stride=2,
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act='relu',
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name="conv1") #debug
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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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conv_name = 'layer' + str(block + 1) + "." + str(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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cardinality=cardinality,
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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(
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input=pool,
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size=class_dim,
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param_attr=fluid.param_attr.ParamAttr(
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initializer=fluid.initializer.Uniform(-stdv, stdv),
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name='fc.weight'),
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bias_attr=fluid.param_attr.ParamAttr(name='fc.bias'))
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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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if "downsample" in name:
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conv_name = name + '.0'
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else:
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conv_name = name
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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=conv_name + ".weight"),
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bias_attr=False)
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if "downsample" in name:
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bn_name = name[:9] + 'downsample' + '.1'
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else:
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if "conv1" == name:
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bn_name = 'bn' + name[-1]
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else:
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bn_name = (name[:10] if name[7:9].isdigit() else name[:9]
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) + 'bn' + name[-1]
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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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param_attr=ParamAttr(name=bn_name + '.weight'),
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bias_attr=ParamAttr(bn_name + '.bias'),
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moving_mean_name=bn_name + '.running_mean',
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moving_variance_name=bn_name + '.running_var', )
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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, cardinality, name):
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cardinality = self.cardinality
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width = self.width
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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 + ".conv1")
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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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groups=cardinality,
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act='relu',
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name=name + ".conv2")
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conv2 = self.conv_bn_layer(
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input=conv1,
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num_filters=num_filters // (width // 8),
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filter_size=1,
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act=None,
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name=name + ".conv3")
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short = self.shortcut(
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input,
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num_filters // (width // 8),
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stride,
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name=name + ".downsample")
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return fluid.layers.elementwise_add(x=short, y=conv2, act='relu')
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def ResNeXt101_32x8d_wsl():
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model = ResNeXt101_wsl(cardinality=32, width=8)
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return model
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def ResNeXt101_32x16d_wsl():
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model = ResNeXt101_wsl(cardinality=32, width=16)
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return model
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def ResNeXt101_32x32d_wsl():
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model = ResNeXt101_wsl(cardinality=32, width=32)
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return model
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def ResNeXt101_32x48d_wsl():
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model = ResNeXt101_wsl(cardinality=32, width=48)
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return model
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def Fix_ResNeXt101_32x48d_wsl():
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model = ResNeXt101_wsl(cardinality=32, width=48)
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return model
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