add exoirt air test

mindspore/train/callback/_time_monitor.py

@@ -36,7 +36,7 @@ class TimeMonitor(Callback):
         self.epoch_time = time.time()
 
     def epoch_end(self, run_context):
-        epoch_seconds = (time.time() - self.epoch_time) * 1000
+        epoch_seconds = time.time() - self.epoch_time
         step_size = self.data_size
         cb_params = run_context.original_args()
         if hasattr(cb_params, "batch_num"):
@@ -49,4 +49,4 @@ class TimeMonitor(Callback):
             return
 
         step_seconds = epoch_seconds / step_size
-        print("Epoch time: {:5.3f}, per step time: {:5.3f}".format(epoch_seconds, step_seconds), flush=True)
+        print("Epoch time: {:5.3f}s, per step time: {:5.3f}s".format(epoch_seconds, step_seconds), flush=True)

mindspore/train/serialization.py

@@ -378,7 +378,8 @@ def load_param_into_net(net, parameter_dict, strict_load=False):
         logger.debug("%s", param_name)
 
     logger.info("Load parameter into net finish.")
-    logger.warning("{} parameters in the net are not loaded.".format(len(param_not_load)))
+    if param_not_load:
+        logger.warning("{} parameters in the net are not loaded.".format(len(param_not_load)))
     return param_not_load
 
 

tests/st/export/test_export.py

@@ -0,0 +1,335 @@
+# 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.
+"""Test network export."""
+import os
+import numpy as np
+import pytest
+
+import mindspore.context as context
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore.nn import Dense
+from mindspore.nn.cell import Cell
+from mindspore.nn.layer.basic import Flatten
+from mindspore.nn.layer.conv import Conv2d
+from mindspore.nn.layer.normalization import BatchNorm2d
+from mindspore.nn.layer.pooling import MaxPool2d
+from mindspore.ops import operations as P
+from mindspore.ops.operations import TensorAdd
+from mindspore.train.serialization import export
+
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+
+def random_normal_init(shape, mean=0.0, stddev=0.01, seed=None):
+    init_value = np.ones(shape).astype(np.float32) * 0.01
+    return Tensor(init_value)
+
+
+def variance_scaling_raw(shape):
+    variance_scaling_value = np.ones(shape).astype(np.float32) * 0.01
+    return Tensor(variance_scaling_value)
+
+
+def weight_variable_0(shape):
+    zeros = np.zeros(shape).astype(np.float32)
+    return Tensor(zeros)
+
+
+def weight_variable_1(shape):
+    ones = np.ones(shape).astype(np.float32)
+    return Tensor(ones)
+
+
+def conv3x3(in_channels, out_channels, stride=1, padding=1):
+    """3x3 convolution """
+    weight_shape = (out_channels, in_channels, 3, 3)
+    weight = variance_scaling_raw(weight_shape)
+    return Conv2d(in_channels, out_channels,
+                  kernel_size=3, stride=stride, weight_init=weight, has_bias=False, pad_mode="same")
+
+
+def conv1x1(in_channels, out_channels, stride=1, padding=0):
+    """1x1 convolution"""
+    weight_shape = (out_channels, in_channels, 1, 1)
+    weight = variance_scaling_raw(weight_shape)
+    return Conv2d(in_channels, out_channels,
+                  kernel_size=1, stride=stride, weight_init=weight, has_bias=False, pad_mode="same")
+
+
+def conv7x7(in_channels, out_channels, stride=1, padding=0):
+    """1x1 convolution"""
+    weight_shape = (out_channels, in_channels, 7, 7)
+    weight = variance_scaling_raw(weight_shape)
+    return Conv2d(in_channels, out_channels,
+                  kernel_size=7, stride=stride, weight_init=weight, has_bias=False, pad_mode="same")
+
+
+def bn_with_initialize(out_channels):
+    shape = (out_channels)
+    mean = weight_variable_0(shape)
+    var = weight_variable_1(shape)
+    beta = weight_variable_0(shape)
+    gamma = weight_variable_1(shape)
+    bn = BatchNorm2d(out_channels, momentum=0.1, eps=0.0001, gamma_init=gamma,
+                     beta_init=beta, moving_mean_init=mean, moving_var_init=var)
+    return bn
+
+
+def bn_with_initialize_last(out_channels):
+    shape = (out_channels)
+    mean = weight_variable_0(shape)
+    var = weight_variable_1(shape)
+    beta = weight_variable_0(shape)
+    gamma = weight_variable_0(shape)
+    bn = BatchNorm2d(out_channels, momentum=0.1, eps=0.0001, gamma_init=gamma,
+                     beta_init=beta, moving_mean_init=mean, moving_var_init=var)
+    return bn
+
+
+def fc_with_initialize(input_channels, out_channels):
+    weight_shape = (out_channels, input_channels)
+    bias_shape = (out_channels)
+    weight = random_normal_init(weight_shape)
+    bias = weight_variable_0(bias_shape)
+
+    return Dense(input_channels, out_channels, weight, bias)
+
+
+class ResidualBlock(Cell):
+    expansion = 4
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 stride=1,
+                 down_sample=False):
+        super(ResidualBlock, self).__init__()
+
+        out_chls = out_channels // self.expansion
+        self.conv1 = conv1x1(in_channels, out_chls, stride=1, padding=0)
+        self.bn1 = bn_with_initialize(out_chls)
+
+        self.conv2 = conv3x3(out_chls, out_chls, stride=stride, padding=1)
+        self.bn2 = bn_with_initialize(out_chls)
+
+        self.conv3 = conv1x1(out_chls, out_channels, stride=1, padding=0)
+        self.bn3 = bn_with_initialize_last(out_channels)
+
+        self.relu = P.ReLU()
+        self.add = TensorAdd()
+
+    def construct(self, x):
+        identity = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        out = self.add(out, identity)
+        out = self.relu(out)
+
+        return out
+
+
+class ResidualBlockWithDown(Cell):
+    expansion = 4
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 stride=1,
+                 down_sample=False):
+        super(ResidualBlockWithDown, self).__init__()
+
+        out_chls = out_channels // self.expansion
+        self.conv1 = conv1x1(in_channels, out_chls, stride=1, padding=0)
+        self.bn1 = bn_with_initialize(out_chls)
+
+        self.conv2 = conv3x3(out_chls, out_chls, stride=stride, padding=1)
+        self.bn2 = bn_with_initialize(out_chls)
+
+        self.conv3 = conv1x1(out_chls, out_channels, stride=1, padding=0)
+        self.bn3 = bn_with_initialize_last(out_channels)
+
+        self.relu = P.ReLU()
+        self.downSample = down_sample
+
+        self.conv_down_sample = conv1x1(
+            in_channels, out_channels, stride=stride, padding=0)
+        self.bn_down_sample = bn_with_initialize(out_channels)
+        self.add = TensorAdd()
+
+    def construct(self, x):
+        identity = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        identity = self.conv_down_sample(identity)
+        identity = self.bn_down_sample(identity)
+
+        out = self.add(out, identity)
+        out = self.relu(out)
+
+        return out
+
+
+class MakeLayer0(Cell):
+
+    def __init__(self, block, layer_num, in_channels, out_channels, stride):
+        super(MakeLayer0, self).__init__()
+        self.a = ResidualBlockWithDown(
+            in_channels, out_channels, stride=1, down_sample=True)
+        self.b = block(out_channels, out_channels, stride=stride)
+        self.c = block(out_channels, out_channels, stride=1)
+
+    def construct(self, x):
+        x = self.a(x)
+        x = self.b(x)
+        x = self.c(x)
+
+        return x
+
+
+class MakeLayer1(Cell):
+
+    def __init__(self, block, layer_num, in_channels, out_channels, stride):
+        super(MakeLayer1, self).__init__()
+        self.a = ResidualBlockWithDown(
+            in_channels, out_channels, stride=stride, down_sample=True)
+        self.b = block(out_channels, out_channels, stride=1)
+        self.c = block(out_channels, out_channels, stride=1)
+        self.d = block(out_channels, out_channels, stride=1)
+
+    def construct(self, x):
+        x = self.a(x)
+        x = self.b(x)
+        x = self.c(x)
+        x = self.d(x)
+
+        return x
+
+
+class MakeLayer2(Cell):
+
+    def __init__(self, block, layer_num, in_channels, out_channels, stride):
+        super(MakeLayer2, self).__init__()
+        self.a = ResidualBlockWithDown(
+            in_channels, out_channels, stride=stride, down_sample=True)
+        self.b = block(out_channels, out_channels, stride=1)
+        self.c = block(out_channels, out_channels, stride=1)
+        self.d = block(out_channels, out_channels, stride=1)
+        self.e = block(out_channels, out_channels, stride=1)
+        self.f = block(out_channels, out_channels, stride=1)
+
+    def construct(self, x):
+        x = self.a(x)
+        x = self.b(x)
+        x = self.c(x)
+        x = self.d(x)
+        x = self.e(x)
+        x = self.f(x)
+
+        return x
+
+
+class MakeLayer3(Cell):
+
+    def __init__(self, block, layer_num, in_channels, out_channels, stride):
+        super(MakeLayer3, self).__init__()
+        self.a = ResidualBlockWithDown(
+            in_channels, out_channels, stride=stride, down_sample=True)
+        self.b = block(out_channels, out_channels, stride=1)
+        self.c = block(out_channels, out_channels, stride=1)
+
+    def construct(self, x):
+        x = self.a(x)
+        x = self.b(x)
+        x = self.c(x)
+
+        return x
+
+
+class ResNet(Cell):
+
+    def __init__(self, block, layer_num, num_classes=100):
+        super(ResNet, self).__init__()
+
+        self.conv1 = conv7x7(3, 64, stride=2, padding=3)
+
+        self.bn1 = bn_with_initialize(64)
+        self.relu = P.ReLU()
+        self.maxpool = MaxPool2d(kernel_size=3, stride=2, pad_mode="same")
+
+        self.layer1 = MakeLayer0(
+            block, layer_num[0], in_channels=64, out_channels=256, stride=1)
+        self.layer2 = MakeLayer1(
+            block, layer_num[1], in_channels=256, out_channels=512, stride=2)
+        self.layer3 = MakeLayer2(
+            block, layer_num[2], in_channels=512, out_channels=1024, stride=2)
+        self.layer4 = MakeLayer3(
+            block, layer_num[3], in_channels=1024, out_channels=2048, stride=2)
+
+        self.pool = nn.AvgPool2d(7, 1)
+        self.fc = fc_with_initialize(512 * block.expansion, num_classes)
+        self.flatten = Flatten()
+
+    def construct(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        x = self.maxpool(x)
+
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+
+        x = self.pool(x)
+        x = self.flatten(x)
+        x = self.fc(x)
+        return x
+
+
+def resnet50(num_classes):
+    return ResNet(ResidualBlock, [3, 4, 6, 3], num_classes)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.env_onecard
+def test_export_resnet_air():
+    net = resnet50(10)
+    inputs = Tensor(np.ones([1, 3, 224, 224]).astype(np.float32) * 0.01)
+    file_name = "resnet.air"
+    export(net, inputs, file_name=file_name, file_format='AIR')
+    assert os.path.exists(file_name)
+    os.remove(file_name)