summary¶
- paddle. summary ( net, input_size=None, dtypes=None, input=None ) [源代码] ¶
-
summary函数通过input_size或input打印网络net的基础结构和参数信息。input_size指定网络net输入 Tensor 的大小，而input指定网络net的输入 Tensor；如果给出input，那么input_size和dtypes的输入将被忽略。
参数¶
net (Layer) - 网络实例，必须是
Layer的子类。input_size (tuple|InputSpec|list[tuple|InputSpec) - 输入张量的大小。如果网络只有一个输入，那么该值需要设定为tuple或InputSpec。如果模型有多个输入。那么该值需要设定为list[tuple|InputSpec]，包含每个输入的shape。默认值：None。
dtypes (str，可选) - 输入张量的数据类型，如果没有给定，默认使用
float32类型。默认值：None。input (tensor，可选) - 输入张量数据，如果给出
input，那么input_size和dtypes的输入将被忽略。默认值：None。
返回¶
字典，包含了总的参数量和总的可训练的参数量。
代码示例¶
import paddle
import paddle.nn as nn
class LeNet(nn.Layer):
def __init__(self, num_classes=10):
super(LeNet, self).__init__()
self.num_classes = num_classes
self.features = nn.Sequential(
nn.Conv2D(
1, 6, 3, stride=1, padding=1),
nn.ReLU(),
nn.MaxPool2D(2, 2),
nn.Conv2D(
6, 16, 5, stride=1, padding=0),
nn.ReLU(),
nn.MaxPool2D(2, 2))
if num_classes > 0:
self.fc = nn.Sequential(
nn.Linear(400, 120),
nn.Linear(120, 84),
nn.Linear(
84, 10))
def forward(self, inputs):
x = self.features(inputs)
if self.num_classes > 0:
x = paddle.flatten(x, 1)
x = self.fc(x)
return x
lenet = LeNet()
params_info = paddle.summary(lenet, (1, 1, 28, 28))
print(params_info)
# ---------------------------------------------------------------------------
# Layer (type) Input Shape Output Shape Param #
# ===========================================================================
# Conv2D-11 [[1, 1, 28, 28]] [1, 6, 28, 28] 60
# ReLU-11 [[1, 6, 28, 28]] [1, 6, 28, 28] 0
# MaxPool2D-11 [[1, 6, 28, 28]] [1, 6, 14, 14] 0
# Conv2D-12 [[1, 6, 14, 14]] [1, 16, 10, 10] 2,416
# ReLU-12 [[1, 16, 10, 10]] [1, 16, 10, 10] 0
# MaxPool2D-12 [[1, 16, 10, 10]] [1, 16, 5, 5] 0
# Linear-16 [[1, 400]] [1, 120] 48,120
# Linear-17 [[1, 120]] [1, 84] 10,164
# Linear-18 [[1, 84]] [1, 10] 850
# ===========================================================================
# Total params: 61,610
# Trainable params: 61,610
# Non-trainable params: 0
# ---------------------------------------------------------------------------
# Input size (MB): 0.00
# Forward/backward pass size (MB): 0.11
# Params size (MB): 0.24
# Estimated Total Size (MB): 0.35
# ---------------------------------------------------------------------------
# {'total_params': 61610, 'trainable_params': 61610}
# multi input demo
class LeNetMultiInput(LeNet):
def forward(self, inputs, y):
x = self.features(inputs)
if self.num_classes > 0:
x = paddle.flatten(x, 1)
x = self.fc(x + y)
return x
lenet_multi_input = LeNetMultiInput()
params_info = paddle.summary(lenet_multi_input, [(1, 1, 28, 28), (1, 400)],
['float32', 'float32'])
print(params_info)
# list input demo
class LeNetListInput(LeNet):
def forward(self, inputs):
x = self.features(inputs[0])
if self.num_classes > 0:
x = paddle.flatten(x, 1)
x = self.fc(x + inputs[1])
return x
lenet_list_input = LeNetListInput()
input_data = [paddle.rand([1, 1, 28, 28]), paddle.rand([1, 400])]
params_info = paddle.summary(lenet_list_input, input=input_data)
print(params_info)
# dict input demo
class LeNetDictInput(LeNet):
def forward(self, inputs):
x = self.features(inputs['x1'])
if self.num_classes > 0:
x = paddle.flatten(x, 1)
x = self.fc(x + inputs['x2'])
return x
lenet_dict_input = LeNetDictInput()
input_data = {'x1': paddle.rand([1, 1, 28, 28]),
'x2': paddle.rand([1, 400])}
params_info = paddle.summary(lenet_dict_input, input=input_data)
print(params_info)