diff --git a/tutorials/source_en/advanced_use/quantization_aware.md b/tutorials/source_en/advanced_use/quantization_aware.md index 91e94d61367c98f97a5a36e71f44fda3cafa61df..25a7027be57310e75d9f6ff652b22e9243b00819 100644 --- a/tutorials/source_en/advanced_use/quantization_aware.md +++ b/tutorials/source_en/advanced_use/quantization_aware.md @@ -90,63 +90,36 @@ Define a fusion network and replace the specified operators. The definition of the original network model LeNet5 is as follows: ```python -def conv(in_channels, out_channels, kernel_size, stride=1, padding=0): - """weight initial for conv layer""" - weight = weight_variable() - return nn.Conv2d(in_channels, out_channels, - kernel_size=kernel_size, stride=stride, padding=padding, - weight_init=weight, has_bias=False, pad_mode="valid") - - -def fc_with_initialize(input_channels, out_channels): - """weight initial for fc layer""" - weight = weight_variable() - bias = weight_variable() - return nn.Dense(input_channels, out_channels, weight, bias) - - -def weight_variable(): - """weight initial""" - return TruncatedNormal(0.02) - - class LeNet5(nn.Cell): """ Lenet network Args: num_class (int): Num classes. Default: 10. - + num_channel (int): Num channel. Default: 1. Returns: Tensor, output tensor Examples: - >>> LeNet(num_class=10) - + >>> LeNet(num_class=10, num_channel=1) + """ - def __init__(self, num_class=10, channel=1): + def __init__(self, num_class=10, num_channel=1): super(LeNet5, self).__init__() - self.num_class = num_class - self.conv1 = conv(channel, 6, 5) - self.conv2 = conv(6, 16, 5) - self.fc1 = fc_with_initialize(16 * 5 * 5, 120) - self.fc2 = fc_with_initialize(120, 84) - self.fc3 = fc_with_initialize(84, self.num_class) + self.conv1 = nn.Conv2d(num_channel, 6, 5, pad_mode='valid') + self.conv2 = nn.Conv2d(6, 16, 5, pad_mode='valid') + self.fc1 = nn.Dense(16 * 5 * 5, 120, weight_init=Normal(0.02)) + self.fc2 = nn.Dense(120, 84, weight_init=Normal(0.02)) + self.fc3 = nn.Dense(84, num_class, weight_init=Normal(0.02)) self.relu = nn.ReLU() self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2) self.flatten = nn.Flatten() def construct(self, x): - x = self.conv1(x) - x = self.relu(x) - x = self.max_pool2d(x) - x = self.conv2(x) - x = self.relu(x) - x = self.max_pool2d(x) + x = self.max_pool2d(self.relu(self.conv1(x))) + x = self.max_pool2d(self.relu(self.conv2(x))) x = self.flatten(x) - x = self.fc1(x) - x = self.relu(x) - x = self.fc2(x) - x = self.relu(x) + x = self.relu(self.fc1(x)) + x = self.relu(self.fc2(x)) x = self.fc3(x) return x ``` @@ -168,10 +141,8 @@ class LeNet5(nn.Cell): self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2) def construct(self, x): - x = self.conv1(x) - x = self.max_pool2d(x) - x = self.conv2(x) - x = self.max_pool2d(x) + x = self.max_pool2d(self.conv1(x)) + x = self.max_pool2d(self.conv2(x)) x = self.flattern(x) x = self.fc1(x) x = self.fc2(x) diff --git a/tutorials/source_zh_cn/advanced_use/quantization_aware.md b/tutorials/source_zh_cn/advanced_use/quantization_aware.md index 6d4a06c6d7c3f96c6564632a5a5f8f6dd7f7bf6d..72782ed785658362020b2979d649a2dddae3e761 100644 --- a/tutorials/source_zh_cn/advanced_use/quantization_aware.md +++ b/tutorials/source_zh_cn/advanced_use/quantization_aware.md @@ -90,63 +90,36 @@ MindSpore的感知量化训练是在训练基础上，使用低精度数据替 原网络模型LeNet5的定义如下所示： ```python -def conv(in_channels, out_channels, kernel_size, stride=1, padding=0): - """weight initial for conv layer""" - weight = weight_variable() - return nn.Conv2d(in_channels, out_channels, - kernel_size=kernel_size, stride=stride, padding=padding, - weight_init=weight, has_bias=False, pad_mode="valid") - - -def fc_with_initialize(input_channels, out_channels): - """weight initial for fc layer""" - weight = weight_variable() - bias = weight_variable() - return nn.Dense(input_channels, out_channels, weight, bias) - - -def weight_variable(): - """weight initial""" - return TruncatedNormal(0.02) - - class LeNet5(nn.Cell): """ Lenet network Args: num_class (int): Num classes. Default: 10. - + num_channel (int): Num channel. Default: 1. Returns: Tensor, output tensor Examples: - >>> LeNet(num_class=10) - + >>> LeNet(num_class=10, num_channel=1) + """ - def __init__(self, num_class=10, channel=1): + def __init__(self, num_class=10, num_channel=1): super(LeNet5, self).__init__() - self.num_class = num_class - self.conv1 = conv(channel, 6, 5) - self.conv2 = conv(6, 16, 5) - self.fc1 = fc_with_initialize(16 * 5 * 5, 120) - self.fc2 = fc_with_initialize(120, 84) - self.fc3 = fc_with_initialize(84, self.num_class) + self.conv1 = nn.Conv2d(num_channel, 6, 5, pad_mode='valid') + self.conv2 = nn.Conv2d(6, 16, 5, pad_mode='valid') + self.fc1 = nn.Dense(16 * 5 * 5, 120, weight_init=Normal(0.02)) + self.fc2 = nn.Dense(120, 84, weight_init=Normal(0.02)) + self.fc3 = nn.Dense(84, num_class, weight_init=Normal(0.02)) self.relu = nn.ReLU() self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2) self.flatten = nn.Flatten() def construct(self, x): - x = self.conv1(x) - x = self.relu(x) - x = self.max_pool2d(x) - x = self.conv2(x) - x = self.relu(x) - x = self.max_pool2d(x) + x = self.max_pool2d(self.relu(self.conv1(x))) + x = self.max_pool2d(self.relu(self.conv2(x))) x = self.flatten(x) - x = self.fc1(x) - x = self.relu(x) - x = self.fc2(x) - x = self.relu(x) + x = self.relu(self.fc1(x)) + x = self.relu(self.fc2(x)) x = self.fc3(x) return x ``` @@ -168,10 +141,8 @@ class LeNet5(nn.Cell): self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2) def construct(self, x): - x = self.conv1(x) - x = self.max_pool2d(x) - x = self.conv2(x) - x = self.max_pool2d(x) + x = self.max_pool2d(self.conv1(x)) + x = self.max_pool2d(self.conv2(x)) x = self.flattern(x) x = self.fc1(x) x = self.fc2(x)