PaddlePaddle复现ResNeXt，如何应用于识别？
摘要：import paddle.nn as nn import paddle class BN_Conv2D(nn.Layer): """ BN_CONV_RELU
import paddle.nn as nn
import paddle
class BN_Conv2D(nn.Layer):
"""
BN_CONV_RELU
"""
def __init__(self, in_channels, out_channels, kernel_size, stride, padding, dilation=1, groups=1, bias=False):
super(BN_Conv2D, self).__init__()
self.seq = nn.Sequential(
nn.Conv2D(in_channels, out_channels, kernel_size=kernel_size, stride=stride,
padding=padding, dilation=dilation, groups=groups, bias_attr=bias),
nn.BatchNorm2D(out_channels)
)
def forward(self, x):
return F.relu(self.seq(x))
class ResNeXt_Block(nn.Layer):
"""
ResNeXt block with group convolutions
"""
def __init__(self, in_chnls, cardinality, group_depth, stride):
super(ResNeXt_Block, self).__init__()
self.group_chnls = cardinality * group_depth
self.conv1 = BN_Conv2D(in_chnls, self.group_chnls, 1, stride=1, padding=0)
self.conv2 = BN_Conv2D(self.group_chnls, self.group_chnls, 3, stride=stride, padding=1, groups=cardinality)
self.conv3 = nn.Conv2D(self.group_chnls, self.group_chnls*2, 1, stride=1, padding=0)
self.bn = nn.BatchNorm2D(self.group_chnls*2)
self.short_cut = nn.Sequential(
nn.Conv2D(in_chnls, self.group_chnls*2, 1, stride, 0, bias_attr=False),
nn.BatchNorm2D(self.group_chnls*2)
)
def forward(self, x):
out = self.conv1(x)
out = self.conv2(out)
out = self.bn(self.conv3(out))
out += self.short_cut(x)
return F.relu(out)
class ResNeXt(nn.Layer):
"""
ResNeXt builder
"""
def __init__(self, layers: object, cardinality, group_depth, num_classes) -> object:
super(ResNeXt, self).__init__()
self.cardinality = cardinality
self.channels = 64
self.conv1 = BN_Conv2D(3, self.channels, 7, stride=2, padding=3)
d1 = group_depth
self.conv2 = self.___make_layers(d1, layers[0], stride=1)
d2 = d1 * 2