watermark_codec_pkg/tests/train.py

import mindspore
import mindspore.nn as nn
from mindspore.dataset import vision, Cifar10Dataset
from mindspore.dataset.vision import transforms

from tests.model.AlexNet import AlexNet

train_dataset = Cifar10Dataset(dataset_dir='data/cifar-10-batches-bin', usage='train', shuffle=True)
test_dataset = Cifar10Dataset(dataset_dir='data/cifar-10-batches-bin', usage='test')
batch_size = 32

def datapipe(dataset, batch_size):
    image_transforms = [
        vision.Rescale(1.0 / 255.0, 0),
        vision.Normalize(mean=(0.1307,), std=(0.3081,)),
        vision.HWC2CHW()
    ]
    label_transform = transforms.TypeCast(mindspore.int32)

    dataset = dataset.map(image_transforms, 'image')
    dataset = dataset.map(label_transform, 'label')
    dataset = dataset.batch(batch_size)
    return dataset

# Map vision transforms and batch dataset
train_dataset = datapipe(train_dataset, batch_size)
test_dataset = datapipe(test_dataset, batch_size)


# Define model
model = AlexNet(input_channels=3, output_num=10, input_size=32)
print(model)

# Instantiate loss function and optimizer
loss_fn = nn.CrossEntropyLoss()
optimizer = nn.Adam(model.trainable_params(), 1e-2)

# 1. Define forward function
def forward_fn(data, label):
    logits = model(data)
    loss = loss_fn(logits, label)
    return loss, logits

# 2. Get gradient function
grad_fn = mindspore.value_and_grad(forward_fn, None, optimizer.parameters, has_aux=True)

# 3. Define function of one-step training
def train_step(data, label):
    (loss, _), grads = grad_fn(data, label)
    optimizer(grads)
    return loss

def train(model, dataset):
    size = dataset.get_dataset_size()
    model.set_train()
    for batch, (data, label) in enumerate(dataset.create_tuple_iterator()):
        loss = train_step(data, label)

        if batch % 100 == 0:
            loss, current = loss.asnumpy(), batch
            print(f"loss: {loss:>7f}  [{current:>3d}/{size:>3d}]")

def test(model, dataset, loss_fn):
    num_batches = dataset.get_dataset_size()
    model.set_train(False)
    total, test_loss, correct = 0, 0, 0
    for data, label in dataset.create_tuple_iterator():
        pred = model(data)
        total += len(data)
        test_loss += loss_fn(pred, label).asnumpy()
        correct += (pred.argmax(1) == label).asnumpy().sum()
    test_loss /= num_batches
    correct /= total
    print(f"Test: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n")


if __name__ == '__main__':
    epochs = 10
    mindspore.set_context(device_target="GPU")
    for t in range(epochs):
        print(f"Epoch {t + 1}\n-------------------------------")
        train(model, train_dataset)
        test(model, test_dataset, loss_fn)
    print("Done!")