pynative-add-lenet

tests/st/pynative/test_ascend_lenet.py

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+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+import pytest
+import numpy as np
+import time, math
+import mindspore.nn as nn
+from mindspore import context, Tensor, ParameterTuple
+from mindspore.ops import operations as P
+from mindspore.common.initializer import TruncatedNormal
+from mindspore.ops import functional as F
+from mindspore.ops import composite as C
+from mindspore.common import dtype as mstype
+
+from mindspore.nn.wrap.cell_wrapper import WithLossCell
+from mindspore.nn.optim import Momentum
+
+np.random.seed(1)
+
+def weight_variable():
+    """weight initial"""
+    return TruncatedNormal(0.02)
+
+
+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)
+
+
+class LeNet(nn.Cell):
+    """
+    Lenet network
+    Args:
+        num_class (int): Num classes, Default: 10.
+    Returns:
+        Tensor, output tensor
+    Examples:
+        >>> LeNet(num_class=10)
+    """
+    def __init__(self, num_class=10):
+        super(LeNet, self).__init__()
+        self.num_class = num_class
+        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, self.num_class)
+        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 CrossEntropyLoss(nn.Cell):
+    """
+    Define loss for network
+    """
+    def __init__(self):
+        super(CrossEntropyLoss, self).__init__()
+        self.cross_entropy = P.SoftmaxCrossEntropyWithLogits()
+        self.mean = P.ReduceMean()
+        self.one_hot = P.OneHot()
+        self.on_value = Tensor(1.0, mstype.float32)
+        self.off_value = Tensor(0.0, mstype.float32)
+        self.num = Tensor(32.0, mstype.float32)
+
+    def construct(self, logits, label):
+        label = self.one_hot(label, F.shape(logits)[1], self.on_value, self.off_value)
+        loss = self.cross_entropy(logits, label)[0]
+        loss = P.RealDiv()(P.ReduceSum()(loss, -1), self.num)
+        return loss
+
+
+class GradWrap(nn.Cell):
+    """
+    GradWrap definition
+    """
+    def __init__(self, network):
+        super(GradWrap, self).__init__()
+        self.network = network
+        self.weights = ParameterTuple(filter(lambda x: x.requires_grad, network.get_parameters()))
+
+    def construct(self, x, label):
+        weights = self.weights
+        return C.grad_by_list(self.network, weights)(x, label)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_single
+def test_ascend_pynative_lenet():
+    context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
+
+    epoch_size = 20
+    batch_size = 32
+    inputs = Tensor(np.ones([batch_size, 1, 32, 32]).astype(np.float32))
+    labels = Tensor(np.ones([batch_size]).astype(np.int32))
+
+    net = LeNet()
+    criterion = CrossEntropyLoss()
+    optimizer = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.1, 0.9)
+
+    net_with_criterion = WithLossCell(net, criterion)
+    train_network = GradWrap(net_with_criterion)
+    train_network.set_train()
+    total_time = 0
+
+    for epoch in range(0, epoch_size):
+        start_time = time.time()
+        fw_output = net(inputs)
+        loss_output = criterion(fw_output, labels)
+        grads = train_network(inputs, labels)
+        success = optimizer(grads)
+        end_time = time.time()
+        cost_time = end_time - start_time
+        total_time = total_time + cost_time
+
+        print("======epoch: ", epoch, " loss: ", loss_output.asnumpy(), " cost time: ", cost_time)
+    assert(total_time < 20.0)
+    assert(loss_output.asnumpy() < 0.01)
+