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@ -19,8 +19,6 @@ from mindspore.dataset.transforms.vision import Inter
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from mindspore.nn.metrics import Accuracy
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from mindspore.common import dtype as mstype
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from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
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def unzipfile(gzip_path):
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"""unzip dataset file
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Args:
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@ -30,8 +28,6 @@ def unzipfile(gzip_path):
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gz_file = gzip.GzipFile(gzip_path)
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open_file.write(gz_file.read())
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gz_file.close()
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def download_dataset():
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"""Download the dataset from http://yann.lecun.com/exdb/mnist/."""
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print("******Downloading the MNIST dataset******")
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@ -60,8 +56,6 @@ def download_dataset():
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file = urllib.request.urlretrieve(url, file_name)
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unzipfile(file_name)
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os.remove(file_name)
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def create_dataset(data_path, batch_size=32, repeat_size=1,
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num_parallel_workers=1):
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""" create dataset for train or test
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@ -73,57 +67,44 @@ def create_dataset(data_path, batch_size=32, repeat_size=1,
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"""
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# define dataset
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mnist_ds = ds.MnistDataset(data_path)
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# define operation parameters
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resize_height, resize_width = 32, 32
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rescale = 1.0 / 255.0
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shift = 0.0
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rescale_nml = 1 / 0.3081
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shift_nml = -1 * 0.1307 / 0.3081
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# define map operations
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resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR) # Resize images to (32, 32)
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rescale_nml_op = CV.Rescale(rescale_nml, shift_nml) # normalize images
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rescale_op = CV.Rescale(rescale, shift) # rescale images
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hwc2chw_op = CV.HWC2CHW() # change shape from (height, width, channel) to (channel, height, width) to fit network.
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type_cast_op = C.TypeCast(mstype.int32) # change data type of label to int32 to fit network
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# apply map operations on images
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mnist_ds = mnist_ds.map(input_columns="label", operations=type_cast_op, num_parallel_workers=num_parallel_workers)
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mnist_ds = mnist_ds.map(input_columns="image", operations=resize_op, num_parallel_workers=num_parallel_workers)
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mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_op, num_parallel_workers=num_parallel_workers)
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mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_nml_op, num_parallel_workers=num_parallel_workers)
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mnist_ds = mnist_ds.map(input_columns="image", operations=hwc2chw_op, num_parallel_workers=num_parallel_workers)
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# apply DatasetOps
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buffer_size = 10000
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mnist_ds = mnist_ds.shuffle(buffer_size=buffer_size) # 10000 as in LeNet train script
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mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True)
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mnist_ds = mnist_ds.repeat(repeat_size)
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return mnist_ds
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def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):
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"""Conv layer weight initial."""
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weight = weight_variable()
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return nn.Conv2d(in_channels, out_channels,
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kernel_size=kernel_size, stride=stride, padding=padding,
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weight_init=weight, has_bias=False, pad_mode="valid")
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def fc_with_initialize(input_channels, out_channels):
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"""Fc layer weight initial."""
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weight = weight_variable()
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bias = weight_variable()
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return nn.Dense(input_channels, out_channels, weight, bias)
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def weight_variable():
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"""Weight initial."""
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return TruncatedNormal(0.02)
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class LeNet5(nn.Cell):
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"""Lenet network structure."""
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# define the operator required
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@ -137,7 +118,6 @@ class LeNet5(nn.Cell):
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self.relu = nn.ReLU()
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self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
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self.flatten = nn.Flatten()
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# use the preceding operators to construct networks
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def construct(self, x):
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x = self.conv1(x)
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@ -153,16 +133,12 @@ class LeNet5(nn.Cell):
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x = self.relu(x)
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x = self.fc3(x)
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return x
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def train_net(args, model, epoch_size, mnist_path, repeat_size, ckpoint_cb):
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"""Define the training method."""
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print("============== Starting Training ==============")
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# load training dataset
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ds_train = create_dataset(os.path.join(mnist_path, "train"), 32, repeat_size)
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model.train(epoch_size, ds_train, callbacks=[ckpoint_cb, LossMonitor()], dataset_sink_mode=False)
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def test_net(args, network, model, mnist_path):
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"""Define the evaluation method."""
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print("============== Starting Testing ==============")
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@ -170,11 +146,14 @@ def test_net(args, network, model, mnist_path):
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# 请在此添加代码完成本关任务
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#********** Begin *********#
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## 提示:补全验证函数的代码
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param_dict = load_checkpoint("checkpoint_lenet-1_1875.ckpt")
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# load parameter to the network
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load_param_into_net(network, param_dict)
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# load testing dataset
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ds_eval = create_dataset(os.path.join(mnist_path, "test"))
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acc = model.eval(ds_eval, dataset_sink_mode=False)
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#********** End **********#
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print("============== Accuracy:{} ==============".format(acc))
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description='MindSpore LeNet Example')
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parser.add_argument('--device_target', type=str, default="CPU", choices=['Ascend', 'GPU', 'CPU'],
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@ -200,6 +179,5 @@ if __name__ == "__main__":
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ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet", config=config_ck)
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# group layers into an object with training and evaluation features
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model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
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train_net(args, model, epoch_size, mnist_path, repeat_size, ckpoint_cb)
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test_net(args, network, model, mnist_path)
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test_net(args, network, model, mnist_path)
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