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
import tqdm

from tests.model.AlexNet import AlexNet
from tests.secret_func import get_secret
from watermark_codec import ModelEncoder

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)

# init white_box watermark encoder
layers = []
secret = get_secret(512)
key_path = './run/train/key.ckpt'
for name, layer in model.cells_and_names():
    if isinstance (layer, nn.Conv2d):
        layers.append(layer)
model_encoder = ModelEncoder(layers=layers[0:2], secret=secret, key_path=key_path)

# 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_embed = model_encoder.get_embeder_loss()
    loss = loss_fn(logits, label) + loss_embed
    return loss, loss_embed, 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, loss_embed, _), grads = grad_fn(data, label)
    optimizer(grads)
    return loss, loss_embed


def train(model, dataset):
    num_batches = dataset.get_dataset_size()
    tqdm_show = tqdm.tqdm(total=num_batches, desc='train', unit='batch')
    model.set_train()
    for batch, (data, label) in enumerate(dataset.create_tuple_iterator()):
        loss, loss_embed = train_step(data, label)
        tqdm_show.set_postfix({'train_loss': loss.asnumpy(), 'wm_loss': loss_embed.asnumpy()})  # 添加显示
        tqdm_show.update(1)  # 更新进度条
    tqdm_show.close()


def test(model, dataset, loss_fn):
    num_batches = dataset.get_dataset_size()
    model.set_train(False)
    total, test_loss, correct = 0, 0, 0
    tqdm_show = tqdm.tqdm(total=num_batches, desc='test', unit='batch')
    for data, label in dataset.create_tuple_iterator():
        pred = model(data)
        total += len(data)
        loss_embed = model_encoder.get_embeder_loss().asnumpy()
        loss = loss_fn(pred, label).asnumpy() + loss_embed
        tqdm_show.set_postfix({'val_loss': loss, 'wm_loss': loss_embed.asnumpy()})  # 添加显示
        tqdm_show.update(1)  # 更新进度条
        test_loss += loss
        correct += (pred.argmax(1) == label).asnumpy().sum()
    test_loss /= num_batches
    correct /= total
    tqdm_show.write(f"Test: \n Accuracy: {(100 * correct):>0.1f}%, Avg loss: {test_loss:>8f} \n")
    tqdm_show.close()


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!")