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提交 7caf3f53 编写于 作者: Y yankai
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fix export md

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tutorials/source_en/advanced_use/on_device_inference.md
浏览文件 @ 7caf3f53
......@@ -98,68 +98,76 @@ To perform on-device model inference using MindSpore, perform the following step
param_dict = load_checkpoint(ckpt_file_name=ckpt_file_path)
load_param_into_net(net, param_dict)
```
2. Call the `export` API to export the `.ms` model file on the device.
2. Call the `export` API to export the `.pb` model file on the device.
```python
export(net, input_data, file_name="./lenet.ms", file_format='BINARY')
export(net, input_data, file_name="./lenet.pb", file_format='BINARY')
```
Take the LeNet network as an example. The generated on-device model file is `lenet.ms`. The complete sample code `lenet.py` is as follows:
```python
import os
import numpy as np
import mindspore.nn as nn
import mindspore.ops.operations as P
import mindspore.context as context
from mindspore.common.tensor import Tensor
from mindspore.train.serialization import export, load_checkpoint, load_param_into_net
class LeNet(nn.Cell):
def __init__(self):
super(LeNet, self).__init__()
self.relu = P.ReLU()
self.batch_size = 32
self.conv1 = nn.Conv2d(1, 6, kernel_size=5, stride=1, padding=0, has_bias=False, pad_mode='valid')
self.conv2 = nn.Conv2d(6, 16, kernel_size=5, stride=1, padding=0, has_bias=False, pad_mode='valid')
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.reshape = P.Reshape()
self.fc1 = nn.Dense(400, 120)
self.fc2 = nn.Dense(120, 84)
self.fc3 = nn.Dense(84, 10)
def construct(self, input_x):
output = self.conv1(input_x)
output = self.relu(output)
output = self.pool(output)
output = self.conv2(output)
output = self.relu(output)
output = self.pool(output)
output = self.reshape(output, (self.batch_size, -1))
output = self.fc1(output)
output = self.relu(output)
output = self.fc2(output)
output = self.relu(output)
output = self.fc3(output)
return output
if __name__ == '__main__':
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
seed = 0
np.random.seed(seed)
origin_data = np.random.uniform(low=0, high=255, size=(32, 1, 32, 32)).astype(np.float32)
origin_data.tofile("lenet.bin")
input_data = Tensor(origin_data)
net = LeNet()
ckpt_file_path = "path_to/lenet.ckpt"
is_ckpt_exist = os.path.exists(ckpt_file_path)
if is_ckpt_exist:
param_dict = load_checkpoint(ckpt_file_name=ckpt_file_path)
load_param_into_net(net, param_dict)
export(net, input_data, file_name="./lenet.ms", file_format='BINARY')
print("export model success.")
else:
print("checkpoint file does not exist.")
```
Take the LeNet network as an example. The generated on-device model file is `lenet.pb`. The complete sample code `lenet.py` is as follows:
```python
import os
import numpy as np
import mindspore.nn as nn
import mindspore.ops.operations as P
import mindspore.context as context
from mindspore.common.tensor import Tensor
from mindspore.train.serialization import export, load_checkpoint, load_param_into_net
class LeNet(nn.Cell):
def __init__(self):
super(LeNet, self).__init__()
self.relu = P.ReLU()
self.batch_size = 32
self.conv1 = nn.Conv2d(1, 6, kernel_size=5, stride=1, padding=0, has_bias=False, pad_mode='valid')
self.conv2 = nn.Conv2d(6, 16, kernel_size=5, stride=1, padding=0, has_bias=False, pad_mode='valid')
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.reshape = P.Reshape()
self.fc1 = nn.Dense(400, 120)
self.fc2 = nn.Dense(120, 84)
self.fc3 = nn.Dense(84, 10)
def construct(self, input_x):
output = self.conv1(input_x)
output = self.relu(output)
output = self.pool(output)
output = self.conv2(output)
output = self.relu(output)
output = self.pool(output)
output = self.reshape(output, (self.batch_size, -1))
output = self.fc1(output)
output = self.relu(output)
output = self.fc2(output)
output = self.relu(output)
output = self.fc3(output)
return output
if __name__ == '__main__':
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
seed = 0
np.random.seed(seed)
origin_data = np.random.uniform(low=0, high=255, size=(32, 1, 32, 32)).astype(np.float32)
origin_data.tofile("lenet.bin")
input_data = Tensor(origin_data)
net = LeNet()
ckpt_file_path = "path_to/lenet.ckpt"
is_ckpt_exist = os.path.exists(ckpt_file_path)
if is_ckpt_exist:
param_dict = load_checkpoint(ckpt_file_name=ckpt_file_path)
load_param_into_net(net, param_dict)
export(net, input_data, file_name="./lenet.pb", file_format='BINARY')
print("export model success.")
else:
print("checkpoint file does not exist.")
```
3. Calling MindSpore convert tool named `converter_lite`, convert model file (`.pb`) to on_device inference model file (`.ms`).
```
./converter_lite --fmk=MS --modelFile=./lenet.pb --outputFile=lenet
```
Result:
```
INFO [converter/converter.cc:146] Runconverter] CONVERTER RESULT: SUCCESS!
```
This means that the model has been successfully converted to the mindspore on_device inference model.
### Implementing On-Device Inference
......
tutorials/source_zh_cn/advanced_use/on_device_inference.md
浏览文件 @ 7caf3f53
......@@ -97,69 +97,78 @@ MindSpore进行端侧模型推理的步骤如下。
param_dict = load_checkpoint(ckpt_file_name=ckpt_file_path)
load_param_into_net(net, param_dict)
```
2. 调用`export`接口，导出端侧模型文件(`.ms`)。
2. 调用`export`接口，导出模型文件(`.pb`)。
```python
export(net, input_data, file_name="./lenet.ms", file_format='BINARY')
export(net, input_data, file_name="./lenet.pb", file_format='BINARY')
```
以LeNet网络为例，生成的端侧模型文件为`lenet.ms`，完整示例代码`lenet.py`如下。
```python
import os
import numpy as np
import mindspore.nn as nn
import mindspore.ops.operations as P
import mindspore.context as context
from mindspore.common.tensor import Tensor
from mindspore.train.serialization import export, load_checkpoint, load_param_into_net
class LeNet(nn.Cell):
def __init__(self):
super(LeNet, self).__init__()
self.relu = P.ReLU()
self.batch_size = 32
self.conv1 = nn.Conv2d(1, 6, kernel_size=5, stride=1, padding=0, has_bias=False, pad_mode='valid')
self.conv2 = nn.Conv2d(6, 16, kernel_size=5, stride=1, padding=0, has_bias=False, pad_mode='valid')
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.reshape = P.Reshape()
self.fc1 = nn.Dense(400, 120)
self.fc2 = nn.Dense(120, 84)
self.fc3 = nn.Dense(84, 10)
def construct(self, input_x):
output = self.conv1(input_x)
output = self.relu(output)
output = self.pool(output)
output = self.conv2(output)
output = self.relu(output)
output = self.pool(output)
output = self.reshape(output, (self.batch_size, -1))
output = self.fc1(output)
output = self.relu(output)
output = self.fc2(output)
output = self.relu(output)
output = self.fc3(output)
return output
if __name__ == '__main__':
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
seed = 0
np.random.seed(seed)
origin_data = np.random.uniform(low=0, high=255, size=(32, 1, 32, 32)).astype(np.float32)
origin_data.tofile("lenet.bin")
input_data = Tensor(origin_data)
net = LeNet()
ckpt_file_path = "path_to/lenet.ckpt"
is_ckpt_exist = os.path.exists(ckpt_file_path)
if is_ckpt_exist:
param_dict = load_checkpoint(ckpt_file_name=ckpt_file_path)
load_param_into_net(net, param_dict)
export(net, input_data, file_name="./lenet.ms", file_format='BINARY')
print("export model success.")
else:
print("checkpoint file does not exist.")
```
以LeNet网络为例，生成的端侧模型文件为`lenet.pb`，完整示例代码`lenet.py`如下。
```python
import os
import numpy as np
import mindspore.nn as nn
import mindspore.ops.operations as P
import mindspore.context as context
from mindspore.common.tensor import Tensor
from mindspore.train.serialization import export, load_checkpoint, load_param_into_net
class LeNet(nn.Cell):
def __init__(self):
super(LeNet, self).__init__()
self.relu = P.ReLU()
self.batch_size = 32
self.conv1 = nn.Conv2d(1, 6, kernel_size=5, stride=1, padding=0, has_bias=False, pad_mode='valid')
self.conv2 = nn.Conv2d(6, 16, kernel_size=5, stride=1, padding=0, has_bias=False, pad_mode='valid')
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.reshape = P.Reshape()
self.fc1 = nn.Dense(400, 120)
self.fc2 = nn.Dense(120, 84)
self.fc3 = nn.Dense(84, 10)
def construct(self, input_x):
output = self.conv1(input_x)
output = self.relu(output)
output = self.pool(output)
output = self.conv2(output)
output = self.relu(output)
output = self.pool(output)
output = self.reshape(output, (self.batch_size, -1))
output = self.fc1(output)
output = self.relu(output)
output = self.fc2(output)
output = self.relu(output)
output = self.fc3(output)
return output
if __name__ == '__main__':
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
seed = 0
np.random.seed(seed)
origin_data = np.random.uniform(low=0, high=255, size=(32, 1, 32, 32)).astype(np.float32)
origin_data.tofile("lenet.bin")
input_data = Tensor(origin_data)
net = LeNet()
ckpt_file_path = "path_to/lenet.ckpt"
is_ckpt_exist = os.path.exists(ckpt_file_path)
if is_ckpt_exist:
param_dict = load_checkpoint(ckpt_file_name=ckpt_file_path)
load_param_into_net(net, param_dict)
export(net, input_data, file_name="./lenet.pb", file_format='BINARY')
print("export model success.")
else:
print("checkpoint file does not exist.")
```
3. 调用MindSpore端侧转化工具`converter_lite`工具，将模型文件(`.pb`)转换为端侧模型文件(`.ms`)。
```
./converter_lite --fmk=MS --modelFile=./lenet.pb --outputFile=lenet
```
结果显示为：
```
INFO [converter/converter.cc:146] Runconverter] CONVERTER RESULT: SUCCESS!
```
这表示已经成功将模型转化为MindSpore端侧模型。
### 在端侧实现推理
将`.ms`模型文件和图片数据作为输入，创建`session`在端侧实现推理。
......
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