|
- import network.mynn as mynn
- import mindspore.nn as nn
- import mindspore
- from mindspore.common.initializer import HeNormal, Constant
- import numpy as np
-
- def conv3x3(in_planes, out_planes, stride=1):
- """3x3 convolution with padding"""
- return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
- has_bias=False, pad_mode = "pad", padding=1,weight_init="HeNormal")
-
- class BasicBlock(nn.Cell):
- expansion = 1
-
- def __init__(self, inplanes, planes, stride=1, downsample=None):
- super(BasicBlock, self).__init__()
- self.conv1 = conv3x3(inplanes, planes, stride)
- self.bn1 = mynn.Norm2d(planes)
- self.relu = nn.ReLU()
- self.conv2 = conv3x3(planes, planes)
- self.bn2 = mynn.Norm2d(planes)
- #self.downsample = nn.SequentialCell()
- if downsample is not None:
- self.downsample = downsample
- else :
- self.downsample = nn.SequentialCell()
- self.stride = stride
- # for m in self.cells():
- # if isinstance(m, nn.Conv2d):
- # HeNormal(m.weight, mode='fan_out', nonlinearity='relu')
- # """batchnorm需不需要权重初始化"""
- # elif isinstance(m, nn.BatchNorm2d):
- # Constant(m.weight, 1)
- # Constant(m.bias, 0)
-
-
- def construct(self, x):
- residual = x
-
- out = self.conv1(x)
- out = self.bn1(out)
- out = self.relu(out)
-
- out = self.conv2(out)
- out = self.bn2(out)
-
-
- residual = self.downsample(residual)
-
- out += residual
- out = self.relu(out)
-
- return out
-
-
- # inputs = mindspore.Tensor(np.ones((2, 64, 24, 24)).astype("float32"))
- # resnet = BasicBlock(64, 64, stride=1, downsample=None)
- # #print(resnet)
- # outputs = resnet(inputs)
- #print(outputs.shape)
|