import paddle import paddle.fluid as fluid OPS = { 'none' : lambda inputs, C_in, C_out, stride: ZERO(inputs, stride), 'avg_pool_3x3' : lambda inputs, C_in, C_out, stride: POOL_3x3(inputs, C_in, C_out, stride, 'avg'), 'max_pool_3x3' : lambda inputs, C_in, C_out, stride: POOL_3x3(inputs, C_in, C_out, stride, 'max'), 'skip_connect' : lambda inputs, C_in, C_out, stride: Identity(inputs, C_in, C_out, stride), 'sep_conv_3x3' : lambda inputs, C_in, C_out, stride: SepConv(inputs, C_in, C_out, 3, stride, 1), 'sep_conv_5x5' : lambda inputs, C_in, C_out, stride: SepConv(inputs, C_in, C_out, 5, stride, 2), 'sep_conv_7x7' : lambda inputs, C_in, C_out, stride: SepConv(inputs, C_in, C_out, 7, stride, 3), 'dil_conv_3x3' : lambda inputs, C_in, C_out, stride: DilConv(inputs, C_in, C_out, 3, stride, 2, 2), 'dil_conv_5x5' : lambda inputs, C_in, C_out, stride: DilConv(inputs, C_in, C_out, 5, stride, 4, 2), 'conv_3x1_1x3' : lambda inputs, C_in, C_out, stride: Conv313(inputs, C_in, C_out, stride), 'conv_7x1_1x7' : lambda inputs, C_in, C_out, stride: Conv717(inputs, C_in, C_out, stride), } def ReLUConvBN(inputs, C_in, C_out, kernel, stride, padding): temp = fluid.layers.relu(inputs) temp = fluid.layers.conv2d(temp, filter_size=kernel, num_filters=C_out, stride=stride, padding=padding, act=None, bias_attr=False) temp = fluid.layers.batch_norm(input=temp, act=None, bias_attr=None) return temp def ZERO(inputs, stride): if stride == 1: return inputs * 0 elif stride == 2: return fluid.layers.pool2d(inputs, filter_size=2, pool_stride=2, pool_padding=0, pool_type='avg') * 0 else: raise ValueError('invalid stride of {:} not [1, 2]'.format(stride)) def Identity(inputs, C_in, C_out, stride): if C_in == C_out and stride == 1: return inputs elif stride == 1: return ReLUConvBN(inputs, C_in, C_out, 1, 1, 0) else: temp1 = fluid.layers.relu(inputs) temp2 = fluid.layers.pad2d(input=temp1, paddings=[0, 1, 0, 1], mode='reflect') temp2 = fluid.layers.slice(temp2, axes=[0, 1, 2, 3], starts=[0, 0, 1, 1], ends=[999, 999, 999, 999]) temp1 = fluid.layers.conv2d(temp1, filter_size=1, num_filters=C_out//2, stride=stride, padding=0, act=None, bias_attr=False) temp2 = fluid.layers.conv2d(temp2, filter_size=1, num_filters=C_out-C_out//2, stride=stride, padding=0, act=None, bias_attr=False) temp = fluid.layers.concat([temp1,temp2], axis=1) return fluid.layers.batch_norm(input=temp, act=None, bias_attr=None) def POOL_3x3(inputs, C_in, C_out, stride, mode): if C_in == C_out: xinputs = inputs else: xinputs = ReLUConvBN(inputs, C_in, C_out, 1, 1, 0) return fluid.layers.pool2d(xinputs, pool_size=3, pool_stride=stride, pool_padding=1, pool_type=mode) def SepConv(inputs, C_in, C_out, kernel, stride, padding): temp = fluid.layers.relu(inputs) temp = fluid.layers.conv2d(temp, filter_size=kernel, num_filters=C_in , stride=stride, padding=padding, act=None, bias_attr=False) temp = fluid.layers.conv2d(temp, filter_size= 1, num_filters=C_in , stride= 1, padding= 0, act=None, bias_attr=False) temp = fluid.layers.batch_norm(input=temp, act='relu', bias_attr=None) temp = fluid.layers.conv2d(temp, filter_size=kernel, num_filters=C_in , stride= 1, padding=padding, act=None, bias_attr=False) temp = fluid.layers.conv2d(temp, filter_size= 1, num_filters=C_out, stride= 1, padding= 0, act=None, bias_attr=False) temp = fluid.layers.batch_norm(input=temp, act=None , bias_attr=None) return temp def DilConv(inputs, C_in, C_out, kernel, stride, padding, dilation): temp = fluid.layers.relu(inputs) temp = fluid.layers.conv2d(temp, filter_size=kernel, num_filters=C_in , stride=stride, padding=padding, dilation=dilation, act=None, bias_attr=False) temp = fluid.layers.conv2d(temp, filter_size= 1, num_filters=C_out, stride= 1, padding= 0, act=None, bias_attr=False) temp = fluid.layers.batch_norm(input=temp, act=None, bias_attr=None) return temp def Conv313(inputs, C_in, C_out, stride): temp = fluid.layers.relu(inputs) temp = fluid.layers.conv2d(temp, filter_size=(1,3), num_filters=C_out, stride=(1,stride), padding=(0,1), act=None, bias_attr=False) temp = fluid.layers.conv2d(temp, filter_size=(3,1), num_filters=C_out, stride=(stride,1), padding=(1,0), act=None, bias_attr=False) temp = fluid.layers.batch_norm(input=temp, act=None, bias_attr=None) return temp def Conv717(inputs, C_in, C_out, stride): temp = fluid.layers.relu(inputs) temp = fluid.layers.conv2d(temp, filter_size=(1,7), num_filters=C_out, stride=(1,stride), padding=(0,3), act=None, bias_attr=False) temp = fluid.layers.conv2d(temp, filter_size=(7,1), num_filters=C_out, stride=(stride,1), padding=(3,0), act=None, bias_attr=False) temp = fluid.layers.batch_norm(input=temp, act=None, bias_attr=None) return temp