add export mindir lenet grad

tests/ut/python/utils/test_export.py

@@ -0,0 +1,97 @@
+import os
+import numpy as np
+
+import mindspore.nn as nn
+from mindspore import context
+from mindspore.common.tensor import Tensor
+from mindspore.common.initializer import TruncatedNormal
+from mindspore.common.parameter import ParameterTuple
+from mindspore.ops import operations as P
+from mindspore.ops import composite as C
+from mindspore.train.serialization import export
+
+
+def weight_variable():
+    return TruncatedNormal(0.02)
+
+
+def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):
+    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 = weight_variable()
+    bias = weight_variable()
+    return nn.Dense(input_channels, out_channels, weight, bias)
+
+
+class LeNet5(nn.Cell):
+    def __init__(self):
+        super(LeNet5, self).__init__()
+        self.batch_size = 32
+        self.conv1 = conv(1, 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, 10)
+        self.relu = nn.ReLU()
+        self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.reshape = P.Reshape()
+
+    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.reshape(x, (self.batch_size, -1))
+        x = self.fc1(x)
+        x = self.relu(x)
+        x = self.fc2(x)
+        x = self.relu(x)
+        x = self.fc3(x)
+        return x
+
+
+class WithLossCell(nn.Cell):
+    def __init__(self, network):
+        super(WithLossCell, self).__init__(auto_prefix=False)
+        self.loss = nn.SoftmaxCrossEntropyWithLogits()
+        self.network = network
+
+    def construct(self, x, label):
+        predict = self.network(x)
+        return self.loss(predict, label)
+
+
+class TrainOneStepCell(nn.Cell):
+    def __init__(self, network):
+        super(TrainOneStepCell, self).__init__(auto_prefix=False)
+        self.network = network
+        self.network.set_train()
+        self.weights = ParameterTuple(network.trainable_params())
+        self.optimizer = nn.Momentum(self.weights, 0.1, 0.9)
+        self.hyper_map = C.HyperMap()
+        self.grad = C.GradOperation(get_by_list=True)
+
+    def construct(self, x, label):
+        weights = self.weights
+        grads = self.grad(self.network, weights)(x, label)
+        return self.optimizer(grads)
+
+
+def test_export_lenet_grad_mindir():
+    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+    network = LeNet5()
+    network.set_train()
+    predict = Tensor(np.ones([32, 1, 32, 32]).astype(np.float32) * 0.01)
+    label = Tensor(np.zeros([32, 10]).astype(np.float32))
+    net = TrainOneStepCell(WithLossCell(network))
+    file_name = "lenet_grad.mindir"
+    export(net, predict, label, file_name=file_name, file_format='MINDIR')
+    assert os.path.exists(file_name)
+    os.remove(file_name)