class RN18(Net):
'''
ResNet18 has a total of 18 layers:
Note that some parameters are predetermined. The parameters need to be specified are in ''.
For all ResNetBlock modules, the output sizes of stage 1 and stage 2 conv2D blocks equals to
1/4 of that of the final stage.
Conv1 - kernel:(3x3), pad:(1,1), stride:1, output: 'c1OutChannel'
Conv1 - kernel:(3x3), pad:(1,1), stride:2, output: 'c2OutChannel' # H and W reduced by half
RNB1 - skipMode:identity, output : 'rnb1OutChannel'
RNB2 - skipMode:identity, output : same as RNB1
RNB3 - skipMode:identity, output : same as RNB1
RNB4 - skipMode:conv, skipStride:2, output : 'rnb4OutChannel' # H and W reduced by half
RNB5 - skipMode:identity, output : same as RNB4
pool - average pooling of RNB5 per channel, reducing output to 'rnb4OutChannel', need to specify
'pSize', which is used to specify stride and kernel size
fc - outChannel: 'classNum'
softmax - final classification layer
'''
def __init__(self, para):
Net.__init__(self, para)
convPara1 = {
'instanceName': 'RN18' + '_Conv1',
'padding': True,
'padShape': (1, 1),
'stride': 1,
'outChannel': para['c1OutChannel'],
'kernelShape': (3, 3),
'bias': False
}
self.conv1 = Conv2D(convPara1)
self.norm1 = Normalize({'instanceName': 'RN18' + '_Norm1'})
self.scale1 = Scale({'instanceName': 'RN18' + '_Scale1'})
self.activation1 = Activation({
'instanceName': 'RN18' + '_Activation1',
'activationType': 'ReLU'
})
self.layerList.append(self.conv1)
self.layerList.append(self.norm1)
self.layerList.append(self.scale1)
self.layerList.append(self.activation1)
convPara2 = {
'instanceName': 'RN18' + '_Conv2',
'padding': True,
'padShape': (1, 1),
'stride': 2,
'outChannel': para['c2OutChannel'],
'kernelShape': (3, 3),
'bias': False
}
self.conv2 = Conv2D(convPara2)
self.norm2 = Normalize({'instanceName': 'RN18' + '_Norm2'})
self.scale2 = Scale({'instanceName': 'RN18' + '_Scale2'})
self.activation2 = Activation({
'instanceName': 'RN18' + '_Activation2',
'activationType': 'ReLU'
})
self.layerList.append(self.conv2)
self.layerList.append(self.norm2)
self.layerList.append(self.scale2)
self.layerList.append(self.activation2)
self.rnb1 = ResNetBlock({
'instanceName': 'RN18' + '_RNB1',
'skipMode': 'identity',
'skipStride': 0,
'stride1': 1,
'outChannel1': int(para['rnb1OutChannel'] / 4),
'outChannel2': int(para['rnb1OutChannel'] / 4),
'outChannel3': para['rnb1OutChannel'],
'activationType': 'ReLU'
})
self.layerList.append(self.rnb1)
self.rnb2 = ResNetBlock({
'instanceName': 'RN18' + '_RNB2',
'skipMode': 'identity',
'skipStride': 0,
'stride1': 1,
'outChannel1': int(para['rnb1OutChannel'] / 4),
'outChannel2': int(para['rnb1OutChannel'] / 4),
'outChannel3': para['rnb1OutChannel'],
'activationType': 'ReLU'
})
self.layerList.append(self.rnb2)
self.rnb3 = ResNetBlock({
'instanceName': 'RN18' + '_RNB3',
'skipMode': 'identity',
'skipStride': 0,
'stride1': 1,
'outChannel1': int(para['rnb1OutChannel'] / 4),
'outChannel2': int(para['rnb1OutChannel'] / 4),
'outChannel3': para['rnb1OutChannel'],
'activationType': 'ReLU'
})
self.layerList.append(self.rnb3)
self.rnb4 = ResNetBlock({
'instanceName': 'RN18' + '_RNB4',
'skipMode': 'conv',
'skipStride': 2,
'stride1': 2,
'outChannel1': int(para['rnb4OutChannel'] / 4),
'outChannel2': int(para['rnb4OutChannel'] / 4),
'outChannel3': para['rnb4OutChannel'],
'activationType': 'ReLU'
})
self.layerList.append(self.rnb4)
self.rnb5 = ResNetBlock({
'instanceName': 'RN18' + '_RNB5',
'skipMode': 'identity',
'skipStride': 1,
'stride1': 1,
'outChannel1': int(para['rnb4OutChannel'] / 4),
'outChannel2': int(para['rnb4OutChannel'] / 4),
'outChannel3': para['rnb4OutChannel'],
'activationType': 'ReLU'
})
self.layerList.append(self.rnb5)
self.pool1 = Pool({
'instanceName': 'RN18' + '_pool1',
'poolType': 'ave',
'stride': para['pSize'],
'kernelShape': (para['pSize'], para['pSize'])
})
self.layerList.append(self.pool1)
self.fc1 = FullyConnected({
'instanceName': 'RN18' + '_fc1',
'outChannel': para['classNum'],
'bias': True
})
self.layerList.append(self.fc1)
self.softmax = Softmax({'instanceName': 'RN18' + '_softmax'})
self.layerList.append(self.softmax)
self.bottomInterface = self.conv1
self.topInterface = self.softmax
self.softmax.setNet(self)
def stack(self, top, bottom):
self.top = top
self.bottom = bottom
self.conv1.stack(self.norm1, bottom)
self.norm1.stack(self.scale1, self.conv1)
self.scale1.stack(self.activation1, self.norm1)
self.activation1.stack(self.conv2, self.scale1)
self.conv2.stack(self.norm2, self.activation1)
self.norm2.stack(self.scale2, self.conv2)
self.scale2.stack(self.activation2, self.norm2)
self.activation2.stack(self.rnb1, self.scale2)
self.rnb1.stack(self.rnb2, self.activation2)
self.rnb2.stack(self.rnb3, self.rnb1)
self.rnb3.stack(self.rnb4, self.rnb2)
self.rnb4.stack(self.rnb5, self.rnb3)
self.rnb5.stack(self.pool1, self.rnb4)
self.pool1.stack(self.fc1, self.rnb5)
self.fc1.stack(self.softmax, self.pool1)
self.softmax.stack(top, self.fc1)
self.softmax.setSource(bottom)
