!3 简化experiment_1的训练代码，去掉部分高级功能，展示简介高效的效果

Merge pull request !3 from dyonghan/experiment_1

.gitignore

@@ -134,3 +134,4 @@ dmypy.json
 
 # IDE
 .idea/
+.vscode/

experiment_1/main.py

@@ -3,19 +3,14 @@
 import os
 # os.environ['DEVICE_ID'] = '0'
 
-import matplotlib.pyplot as plt
-import numpy as np
-
 import mindspore as ms
 import mindspore.context as context
 import mindspore.dataset.transforms.c_transforms as C
 import mindspore.dataset.transforms.vision.c_transforms as CV
 
-from mindspore.dataset.transforms.vision import Inter
-from mindspore import nn, Tensor
+from mindspore import nn
 from mindspore.train import Model
-from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor
-from mindspore.train.serialization import load_checkpoint, load_param_into_net
+from mindspore.train.callback import LossMonitor
 
 context.set_context(mode=context.GRAPH_MODE, device_target='Ascend')
 
@@ -26,26 +21,16 @@ DATA_DIR_TEST = "MNIST/test" # 测试集信息
 def create_dataset(training=True, num_epoch=1, batch_size=32, resize=(32, 32),
                    rescale=1/(255*0.3081), shift=-0.1307/0.3081, buffer_size=64):
     ds = ms.dataset.MnistDataset(DATA_DIR_TRAIN if training else DATA_DIR_TEST)
-    
-    # define map operations
-    resize_op = CV.Resize(resize)
-    rescale_op = CV.Rescale(rescale, shift)
-    hwc2chw_op = CV.HWC2CHW()
-    
-    # apply map operations on images
-    ds = ds.map(input_columns="image", operations=[resize_op, rescale_op, hwc2chw_op])
+    ds = ds.map(input_columns="image", operations=[CV.Resize(resize), CV.Rescale(rescale, shift), CV.HWC2CHW()])
     ds = ds.map(input_columns="label", operations=C.TypeCast(ms.int32))
-    
-    ds = ds.shuffle(buffer_size=buffer_size)
-    ds = ds.batch(batch_size, drop_remainder=True)
-    ds = ds.repeat(num_epoch)
+    ds = ds.shuffle(buffer_size=buffer_size).batch(batch_size, drop_remainder=True).repeat(num_epoch)
     
     return ds
 
 
-class LeNet(nn.Cell):
+class LeNet5(nn.Cell):
     def __init__(self):
-        super(LeNet, self).__init__()
+        super(LeNet5, self).__init__()
         self.relu = nn.ReLU()
         self.conv1 = nn.Conv2d(1, 6, 5, stride=1, pad_mode='valid')
         self.conv2 = nn.Conv2d(6, 16, 5, stride=1, pad_mode='valid')
@@ -70,26 +55,22 @@ class LeNet(nn.Cell):
         return output
 
 
-LOOP_SINK = context.get_context('enable_loop_sink')
-
 def test_train(lr=0.01, momentum=0.9, num_epoch=3, ckpt_name="a_lenet"):
     ds_train = create_dataset(num_epoch=num_epoch)
     ds_eval = create_dataset(training=False)
-    steps_per_epoch = ds_train.get_dataset_size()
     
-    net = LeNet()
+    net = LeNet5()
     loss = nn.loss.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction='mean')
     opt = nn.Momentum(net.trainable_params(), lr, momentum)
     
-    ckpt_cfg = CheckpointConfig(save_checkpoint_steps=steps_per_epoch, keep_checkpoint_max=5)
-    ckpt_cb = ModelCheckpoint(prefix=ckpt_name, config=ckpt_cfg)
-    loss_cb = LossMonitor(per_print_times=1 if LOOP_SINK else steps_per_epoch)
+    loss_cb = LossMonitor(per_print_times=1)
     
     model = Model(net, loss, opt, metrics={'acc', 'loss'})
-    model.train(num_epoch, ds_train, callbacks=[ckpt_cb, loss_cb], dataset_sink_mode=True)
+    model.train(num_epoch, ds_train, callbacks=[loss_cb])
     metrics = model.eval(ds_eval)
     print('Metrics:', metrics)
 
+
 if __name__ == "__main__":
     import argparse
     parser = argparse.ArgumentParser()
@@ -101,6 +82,4 @@ if __name__ == "__main__":
     import moxing as mox
     mox.file.copy_parallel(src_url=args.data_url, dst_url='MNIST/')
 
-    os.system('rm -f *.ckpt *.ir *.meta') # 清理旧的运行文件
-
     test_train()