mirror of https://github.com/Jittor/Jittor
146 lines
6.4 KiB
Python
146 lines
6.4 KiB
Python
# ***************************************************************
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# Copyright (c) 2022 Jittor. All Rights Reserved.
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# Maintainers:
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# Guowei Yang <471184555@qq.com>
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# Dun Liang <randonlang@gmail.com>.
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#
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# This file is subject to the terms and conditions defined in
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# file 'LICENSE.txt', which is part of this source code package.
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# ***************************************************************
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# This model is generated by pytorch converter.
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import jittor as jt
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from jittor import nn
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from jittor import init
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from collections import OrderedDict
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def densenet121(pretrained=False, **kwargs):
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'''Densenet-121 model from
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`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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'''
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model = DenseNet(num_init_features=64, growth_rate=32, block_config=(6, 12, 24, 16), **kwargs)
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if pretrained: model.load("jittorhub://densenet121.pkl")
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return model
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def densenet161(pretrained=False, **kwargs):
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'''Densenet-161 model from
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`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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'''
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model = DenseNet(num_init_features=96, growth_rate=48, block_config=(6, 12, 36, 24), **kwargs)
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if pretrained: model.load("jittorhub://densenet161.pkl")
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return model
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def densenet169(pretrained=False, **kwargs):
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'''Densenet-169 model from
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`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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'''
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model = DenseNet(num_init_features=64, growth_rate=32, block_config=(6, 12, 32, 32), **kwargs)
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if pretrained: model.load("jittorhub://densenet169.pkl")
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return model
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def densenet201(pretrained=False, **kwargs):
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'''Densenet-201 model from
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`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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'''
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model = DenseNet(num_init_features=64, growth_rate=32, block_config=(6, 12, 48, 32), **kwargs)
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if pretrained: model.load("jittorhub://densenet201.pkl")
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return model
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class _DenseLayer(nn.Sequential):
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def __init__(self, num_input_features, growth_rate, bn_size, drop_rate):
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super(_DenseLayer, self).__init__()
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self.add_module('norm1', nn.BatchNorm(num_input_features))
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self.add_module('relu1', nn.ReLU())
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self.add_module('conv1', nn.Conv(num_input_features, (bn_size * growth_rate), 1, stride=1, bias=False))
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self.add_module('norm2', nn.BatchNorm((bn_size * growth_rate)))
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self.add_module('relu2', nn.ReLU())
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self.add_module('conv2', nn.Conv((bn_size * growth_rate), growth_rate, 3, stride=1, padding=1, bias=False))
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self.drop_rate = drop_rate
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self.drop = nn.Dropout(self.drop_rate)
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def execute(self, x):
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new_features = super(_DenseLayer, self).execute(x)
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if (self.drop_rate > 0):
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new_features = self.drop(new_features)
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return jt.concat([x, new_features], dim=1)
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class _DenseBlock(nn.Sequential):
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def __init__(self, num_layers, num_input_features, bn_size, growth_rate, drop_rate):
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super(_DenseBlock, self).__init__()
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for i in range(num_layers):
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layer = _DenseLayer((num_input_features + (i * growth_rate)), growth_rate, bn_size, drop_rate)
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self.add_module('denselayer%d' % (i + 1), layer)
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class _Transition(nn.Sequential):
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def __init__(self, num_input_features, num_output_features):
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super(_Transition, self).__init__()
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self.add_module('norm', nn.BatchNorm(num_input_features))
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self.add_module('relu', nn.ReLU())
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self.add_module('conv', nn.Conv(num_input_features, num_output_features, 1, stride=1, bias=False))
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self.add_module('pool', nn.Pool(2, stride=2, op='mean'))
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class DenseNet(nn.Module):
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'''Densenet-BC model class, based on
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`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_
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Args:
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growth_rate (int) - how many filters to add each layer (`k` in paper)
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block_config (list of 4 ints) - how many layers in each pooling block
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num_init_features (int) - the number of filters to learn in the first convolution layer
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bn_size (int) - multiplicative factor for number of bottle neck layers
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(i.e. bn_size * k features in the bottleneck layer)
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drop_rate (float) - dropout rate after each dense layer
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num_classes (int) - number of classification classes
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'''
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def __init__(self, growth_rate=32, block_config=(6, 12, 24, 16), num_init_features=64, bn_size=4, drop_rate=0, num_classes=1000):
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super(DenseNet, self).__init__()
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self.features = nn.Sequential(OrderedDict([
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('conv0', nn.Conv(3, num_init_features, 7, stride=2, padding=3, bias=False)),
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('norm0', nn.BatchNorm(num_init_features)),
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('relu0', nn.ReLU()),
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('pool0', nn.Pool(3, stride=2, padding=1, op='maximum')),
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]))
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num_features = num_init_features
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for (i, num_layers) in enumerate(block_config):
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block = _DenseBlock(num_layers=num_layers, num_input_features=num_features, bn_size=bn_size, growth_rate=growth_rate, drop_rate=drop_rate)
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self.features.add_module('denseblock%d' % (i + 1), block)
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num_features = (num_features + (num_layers * growth_rate))
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if (i != (len(block_config) - 1)):
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trans = _Transition(num_input_features=num_features, num_output_features=(num_features // 2))
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self.features.add_module('transition%d' % (i + 1), trans)
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num_features = (num_features // 2)
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self.features.add_module('norm5', nn.BatchNorm(num_features))
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self.classifier = nn.Linear(num_features, num_classes)
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for m in self.modules():
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if isinstance(m, nn.Conv):
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nn.init.invariant_uniform_(m.weight)
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elif isinstance(m, nn.BatchNorm):
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nn.init.constant_(m.weight, 1)
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.Linear):
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nn.init.constant_(m.bias, 0)
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def execute(self, x):
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features = self.features(x)
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out = nn.relu(features)
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out = out.mean([2,3])
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out = self.classifier(out)
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return out
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