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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2017/11/17 07:43:46 UTC
[GitHub] qingzhouzhen closed pull request #7957: add densenet
qingzhouzhen closed pull request #7957: add densenet
URL: https://github.com/apache/incubator-mxnet/pull/7957
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diff --git a/example/image-classification/symbols/densenet.py b/example/image-classification/symbols/densenet.py
new file mode 100644
index 0000000000..13332d148f
--- /dev/null
+++ b/example/image-classification/symbols/densenet.py
@@ -0,0 +1,184 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you 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.
+
+"""
+Adapted from https://github.com/bruinxiong/densenet.mxnet/blob/master/symbol_densenet.py
+Implemented the following paper:
+Gao Huang, Zhang Liu, Laurend van der Maaten. "Densely Connected Convolutional Networks"
+"""
+import mxnet as mx
+import math
+
+def BasicBlock(data, growth_rate, stride, name, bottle_neck=True, drop_out=0.0, bn_mom=0.9, workspace=512):
+ """Return BaiscBlock Unit symbol for building DenseBlock
+ Parameters
+ ----------
+ data : str
+ Input data
+ growth_rate : int
+ Number of output channels
+ stride : tupe
+ Stride used in convolution
+ drop_out : float
+ Probability of an element to be zeroed. Default = 0.2
+ name : str
+ Base name of the operators
+ workspace : int
+ Workspace used in convolution operator
+ """
+
+ if bottle_neck:
+ # the same as https://github.com/facebook/fb.resnet.torch#notes, a bit difference with origin paper
+ bn1 = mx.sym.BatchNorm(data=data, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn1')
+ act1 = mx.sym.Activation(data=bn1, act_type='relu', name=name + '_relu1')
+ conv1 = mx.sym.Convolution(data=act1, num_filter=int(growth_rate * 4), kernel=(1, 1), stride=(1, 1), pad=(0, 0),
+ no_bias=True, workspace=workspace, name=name + '_conv1')
+ if drop_out > 0:
+ conv1 = mx.symbol.Dropout(data=conv1, p=drop_out, name=name + '_dp1')
+ bn2 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn2')
+ act2 = mx.sym.Activation(data=bn2, act_type='relu', name=name + '_relu2')
+ conv2 = mx.sym.Convolution(data=act2, num_filter=int(growth_rate), kernel=(3, 3), stride=stride, pad=(1, 1),
+ no_bias=True, workspace=workspace, name=name + '_conv2')
+ if drop_out > 0:
+ conv2 = mx.symbol.Dropout(data=conv2, p=drop_out, name=name + '_dp2')
+ # return mx.symbol.Concat(data, conv2, name=name + '_concat0')
+ return conv2
+ else:
+ bn1 = mx.sym.BatchNorm(data=data, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn1')
+ act1 = mx.sym.Activation(data=bn1, act_type='relu', name=name + '_relu1')
+ conv1 = mx.sym.Convolution(data=act1, num_filter=int(growth_rate), kernel=(3, 3), stride=(1, 1), pad=(1, 1),
+ no_bias=True, workspace=workspace, name=name + '_conv1')
+ if drop_out > 0:
+ conv1 = mx.symbol.Dropout(data=conv1, p=drop_out, name=name + '_dp1')
+ # return mx.symbol.Concat(data, conv1, name=name + '_concat0')
+ return conv1
+
+
+def DenseBlock(units_num, data, growth_rate, name, bottle_neck=True, drop_out=0.0, bn_mom=0.9, workspace=512):
+ """Return DenseBlock Unit symbol for building DenseNet
+ Parameters
+ ----------
+ units_num : int
+ the number of BasicBlock in each DenseBlock
+ data : str
+ Input data
+ growth_rate : int
+ Number of output channels
+ drop_out : float
+ Probability of an element to be zeroed. Default = 0.2
+ workspace : int
+ Workspace used in convolution operator
+ """
+
+ for i in range(units_num):
+ Block = BasicBlock(data, growth_rate=growth_rate, stride=(1, 1), name=name + '_unit%d' % (i + 1),
+ bottle_neck=bottle_neck, drop_out=drop_out,
+ bn_mom=bn_mom, workspace=workspace)
+ data = mx.symbol.Concat(data, Block, name=name + '_concat%d' % (i + 1))
+ return data
+
+def TransitionBlock(num_stage, data, num_filter, stride, name, drop_out=0.0, bn_mom=0.9, workspace=512):
+ """Return TransitionBlock Unit symbol for building DenseNet
+ Parameters
+ ----------
+ num_stage : int
+ Number of stage
+ data : str
+ Input data
+ num : int
+ Number of output channels
+ stride : tupe
+ Stride used in convolution
+ name : str
+ Base name of the operators
+ drop_out : float
+ Probability of an element to be zeroed. Default = 0.2
+ workspace : int
+ Workspace used in convolution operator
+ """
+ bn1 = mx.sym.BatchNorm(data=data, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn1')
+ act1 = mx.sym.Activation(data=bn1, act_type='relu', name=name + '_relu1')
+ conv1 = mx.sym.Convolution(data=act1, num_filter=num_filter,
+ kernel=(1, 1), stride=stride, pad=(0, 0), no_bias=True,
+ workspace=workspace, name=name + '_conv1')
+ if drop_out > 0:
+ conv1 = mx.symbol.Dropout(data=conv1, p=drop_out, name=name + '_dp1')
+ return mx.symbol.Pooling(conv1, global_pool=False, kernel=(2, 2), stride=(2, 2), pool_type='avg',
+ name=name + '_pool%d' % (num_stage + 1))
+
+def get_symbol(num_classes, num_layers=121, reduction=0.5, drop_out=0.2, bottle_neck=True, bn_mom=0.9,
+ workspace=512, num_stage=4, growth_rate=32, **kwargs):
+ """
+ Adapted from https://github.com/bruinxiong/densenet.mxnet/blob/master/symbol_densenet.py
+ Return DenseNet symbol of imagenet
+ Parameters
+ ----------
+ num_class : int
+ Ouput size of symbol
+ num_layers : int
+ Number of layers of the whole net
+ reduction : float
+ Compression ratio. Default = 0.5
+ drop_out : float
+ Probability of an element to be zeroed. Default = 0.2
+ workspace : int
+ Workspace used in convolution operator
+ num_stage : int
+ Number of stage
+ growth_rate : int
+ Number of output channels
+ """
+
+ if num_layers == 121:
+ units = [6, 12, 24, 16]
+ elif num_layers == 161:
+ units = [6, 12, 36, 24]
+ elif num_layers == 169:
+ units = [6, 12, 32, 32]
+ elif num_layers == 201:
+ units = [6, 12, 48, 32]
+ elif num_layers == 264:
+ units = [6, 12, 64, 48]
+ else:
+ raise ValueError('please specify num-layers, 121,169, 201, or 264')
+
+ num_unit = len(units)
+ assert (num_unit == num_stage)
+ init_channels = 2 * growth_rate
+ n_channels = init_channels
+ data = mx.sym.Variable(name='data')
+ data = mx.sym.BatchNorm(data=data, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='bn_data')
+ body = mx.sym.Convolution(data=data, num_filter=growth_rate * 2, kernel=(7, 7), stride=(2, 2), pad=(3, 3),
+ no_bias=True, name="conv0", workspace=workspace)
+ body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn0')
+ body = mx.sym.Activation(data=body, act_type='relu', name='relu0')
+ body = mx.symbol.Pooling(data=body, kernel=(3, 3), stride=(2, 2), pad=(1, 1), pool_type='max')
+ for i in range(num_stage - 1):
+ body = DenseBlock(units[i], body, growth_rate=growth_rate, name='DBstage%d' % (i + 1), bottle_neck=bottle_neck,
+ drop_out=drop_out, bn_mom=bn_mom, workspace=workspace)
+ n_channels += units[i] * growth_rate
+ n_channels = int(math.floor(n_channels * reduction))
+ body = TransitionBlock(i, body, n_channels, stride=(1, 1), name='TBstage%d' % (i + 1), drop_out=drop_out,
+ bn_mom=bn_mom, workspace=workspace)
+ body = DenseBlock(units[num_stage - 1], body, growth_rate=growth_rate, name='DBstage%d' % (num_stage),
+ bottle_neck=bottle_neck, drop_out=drop_out, bn_mom=bn_mom, workspace=workspace)
+ bn1 = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn1')
+ relu1 = mx.sym.Activation(data=bn1, act_type='relu', name='relu1')
+ pool1 = mx.symbol.Pooling(data=relu1, global_pool=True, kernel=(7, 7), pool_type='avg', name='pool1')
+ flat = mx.symbol.Flatten(data=pool1)
+ fc1 = mx.symbol.FullyConnected(data=flat, num_hidden=num_classes, name='fc1')
+ return mx.symbol.SoftmaxOutput(data=fc1, name='softmax')
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