test_net

MindSpore/src/step4/test_net.py

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+"""Lenet Tutorial
+The sample can be run on CPU, GPU and Ascend 910 AI processor.
+"""
+import os
+import urllib.request
+from urllib.parse import urlparse
+import gzip
+import argparse
+import mindspore.dataset as ds
+import mindspore.nn as nn
+from mindspore import context
+from mindspore.train.serialization import load_checkpoint, load_param_into_net
+from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor
+from mindspore.train import Model
+from mindspore.common.initializer import TruncatedNormal
+import mindspore.dataset.transforms.vision.c_transforms as CV
+import mindspore.dataset.transforms.c_transforms as C
+from mindspore.dataset.transforms.vision import Inter
+from mindspore.nn.metrics import Accuracy
+from mindspore.common import dtype as mstype
+from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
+
+
+def unzipfile(gzip_path):
+    """unzip dataset file
+    Args:
+        gzip_path: dataset file path
+    """
+    open_file = open(gzip_path.replace('.gz',''), 'wb')
+    gz_file = gzip.GzipFile(gzip_path)
+    open_file.write(gz_file.read())
+    gz_file.close()
+
+
+def download_dataset():
+    """Download the dataset from http://yann.lecun.com/exdb/mnist/."""
+    print("******Downloading the MNIST dataset******")
+    train_path = "./MNIST_Data/train/"
+    test_path = "./MNIST_Data/test/"
+    train_path_check = os.path.exists(train_path)
+    test_path_check = os.path.exists(test_path)
+    if train_path_check == False and test_path_check ==False:
+        os.makedirs(train_path)
+        os.makedirs(test_path)
+    train_url = {"http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz", "http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz"}
+    test_url = {"http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz", "http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz"}
+    for url in train_url:
+        url_parse = urlparse(url)
+        # split the file name from url
+        file_name = os.path.join(train_path,url_parse.path.split('/')[-1])
+        if not os.path.exists(file_name.replace('.gz','')):
+            file = urllib.request.urlretrieve(url, file_name)
+            unzipfile(file_name)
+            os.remove(file_name)
+    for url in test_url:
+        url_parse = urlparse(url)
+        # split the file name from url
+        file_name = os.path.join(test_path,url_parse.path.split('/')[-1])
+        if not os.path.exists(file_name.replace('.gz','')):
+            file = urllib.request.urlretrieve(url, file_name)
+            unzipfile(file_name)
+            os.remove(file_name)
+
+
+def create_dataset(data_path, batch_size=32, repeat_size=1,
+                   num_parallel_workers=1):
+    """ create dataset for train or test
+    Args:
+        data_path: Data path
+        batch_size: The number of data records in each group
+        repeat_size: The number of replicated data records
+        num_parallel_workers: The number of parallel workers
+    """
+    # define dataset
+    mnist_ds = ds.MnistDataset(data_path)
+
+    # define operation parameters
+    resize_height, resize_width = 32, 32
+    rescale = 1.0 / 255.0
+    shift = 0.0
+    rescale_nml = 1 / 0.3081
+    shift_nml = -1 * 0.1307 / 0.3081
+
+    # define map operations
+    resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR)  # Resize images to (32, 32)
+    rescale_nml_op = CV.Rescale(rescale_nml, shift_nml) # normalize images
+    rescale_op = CV.Rescale(rescale, shift) # rescale images
+    hwc2chw_op = CV.HWC2CHW() # change shape from (height, width, channel) to (channel, height, width) to fit network.
+    type_cast_op = C.TypeCast(mstype.int32) # change data type of label to int32 to fit network
+
+    # apply map operations on images
+    mnist_ds = mnist_ds.map(input_columns="label", operations=type_cast_op, num_parallel_workers=num_parallel_workers)
+    mnist_ds = mnist_ds.map(input_columns="image", operations=resize_op, num_parallel_workers=num_parallel_workers)
+    mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_op, num_parallel_workers=num_parallel_workers)
+    mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_nml_op, num_parallel_workers=num_parallel_workers)
+    mnist_ds = mnist_ds.map(input_columns="image", operations=hwc2chw_op, num_parallel_workers=num_parallel_workers)
+
+    # apply DatasetOps
+    buffer_size = 10000
+    mnist_ds = mnist_ds.shuffle(buffer_size=buffer_size)  # 10000 as in LeNet train script
+    mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True)
+    mnist_ds = mnist_ds.repeat(repeat_size)
+
+    return mnist_ds
+
+
+def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):
+    """Conv layer weight initial."""
+    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):
+    """Fc layer weight initial."""
+    weight = weight_variable()
+    bias = weight_variable()
+    return nn.Dense(input_channels, out_channels, weight, bias)
+
+
+def weight_variable():
+    """Weight initial."""
+    return TruncatedNormal(0.02)
+
+
+class LeNet5(nn.Cell):
+    """Lenet network structure."""
+    # define the operator required
+    def __init__(self):
+        super(LeNet5, self).__init__()
+        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.flatten = nn.Flatten()
+
+    # use the preceding operators to construct networks
+    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.flatten(x)
+        x = self.fc1(x)
+        x = self.relu(x)
+        x = self.fc2(x)
+        x = self.relu(x)
+        x = self.fc3(x)
+        return x
+
+
+def train_net(args, model, epoch_size, mnist_path, repeat_size, ckpoint_cb):
+    """Define the training method."""
+    print("============== Starting Training ==============")
+    # load training dataset
+    ds_train = create_dataset(os.path.join(mnist_path, "train"), 32, repeat_size)
+    model.train(epoch_size, ds_train, callbacks=[ckpoint_cb, LossMonitor()], dataset_sink_mode=False)
+
+
+def test_net(args, network, model, mnist_path):
+    """Define the evaluation method."""
+    print("============== Starting Testing ==============")
+    # load the saved model for evaluation
+    # 请在此添加代码完成本关任务
+    # **********Begin*********#
+    ##提示:补全验证函数的代码
+
+    # **********End**********#
+    print("============== Accuracy:{} ==============".format(acc))
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description='MindSpore LeNet Example')
+    parser.add_argument('--device_target', type=str, default="CPU", choices=['Ascend', 'GPU', 'CPU'],
+                        help='device where the code will be implemented (default: CPU)')
+    args = parser.parse_args()
+    context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
+    # download mnist dataset
+    download_dataset()
+    # learning rate setting
+    lr = 0.01
+    momentum = 0.9
+    epoch_size = 1
+    mnist_path = "./MNIST_Data"
+    # define the loss function
+    net_loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction='mean')
+    repeat_size = epoch_size
+    # create the network
+    network = LeNet5()
+    # define the optimizer
+    net_opt = nn.Momentum(network.trainable_params(), lr, momentum)
+    config_ck = CheckpointConfig(save_checkpoint_steps=1875, keep_checkpoint_max=10)
+    # save the network model and parameters for subsequence fine-tuning
+    ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet", config=config_ck)
+    # group layers into an object with training and evaluation features
+    model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
+
+    train_net(args, model, epoch_size, mnist_path, repeat_size, ckpoint_cb)
+    test_net(args, network, model, mnist_path)