tensorlayer3/DenseNet100-cifar10

import time
import multiprocessing
import tensorflow as tf
import os
os.environ['TL_BACKEND'] = 'tensorflow'

import tensorlayer as tl
from .densenet import densenet

tl.logging.set_verbosity(tl.logging.DEBUG)
X_train, y_train, X_test, y_test = tl.files.load_cifar10_dataset(shape=(-1, 32, 32, 3), plotable=False)

# get the network
net = densenet("densenet-100")

# training settings
batch_size = 128
n_epoch = 500
learning_rate = 0.0001
print_freq = 5
n_step_epoch = int(len(y_train) / batch_size)
n_step = n_epoch * n_step_epoch
shuffle_buffer_size = 128

train_weights = net.trainable_weights
optimizer = tl.optimizers.Adam(learning_rate)
metrics = tl.metric.Accuracy()


def generator_train():
    inputs = X_train
    targets = y_train
    if len(inputs) != len(targets):
        raise AssertionError("The length of inputs and targets should be equal")
    for _input, _target in zip(inputs, targets):
        # yield _input.encode('utf-8'), _target.encode('utf-8')
        yield _input, _target


def generator_test():
    inputs = X_test
    targets = y_test
    if len(inputs) != len(targets):
        raise AssertionError("The length of inputs and targets should be equal")
    for _input, _target in zip(inputs, targets):
        # yield _input.encode('utf-8'), _target.encode('utf-8')
        yield _input, _target


def _map_fn_train(img, target):
    # 1. Randomly crop a [height, width] section of the image.
    img = tf.image.random_crop(img, [24, 24, 3])
    # 2. Randomly flip the image horizontally.
    img = tf.image.random_flip_left_right(img)
    # 3. Randomly change brightness.
    img = tf.image.random_brightness(img, max_delta=63)
    # 4. Randomly change contrast.
    img = tf.image.random_contrast(img, lower=0.2, upper=1.8)
    # 5. Subtract off the mean and divide by the variance of the pixels.
    img = tf.image.per_image_standardization(img)
    target = tf.reshape(target, ())
    return img, target


def _map_fn_test(img, target):
    # 1. Crop the central [height, width] of the image.
    img = tf.image.resize_with_pad(img, 24, 24)
    # 2. Subtract off the mean and divide by the variance of the pixels.
    img = tf.image.per_image_standardization(img)
    img = tf.reshape(img, (24, 24, 3))
    target = tf.reshape(target, ())
    return img, target


# dataset API and augmentation
train_ds = tf.data.Dataset.from_generator(
    generator_train, output_types=(tf.float32, tf.int32)
)  # , output_shapes=((24, 24, 3), (1)))
train_ds = train_ds.map(_map_fn_train,num_parallel_calls=multiprocessing.cpu_count())
# train_ds = train_ds.repeat(n_epoch)
train_ds = train_ds.shuffle(shuffle_buffer_size)
train_ds = train_ds.prefetch(buffer_size=4096)
train_ds = train_ds.batch(batch_size)
# value = train_ds.make_one_shot_iterator().get_next()

test_ds = tf.data.Dataset.from_generator(
    generator_test, output_types=(tf.float32, tf.int32)
)  # , output_shapes=((24, 24, 3), (1)))
# test_ds = test_ds.shuffle(shuffle_buffer_size)
test_ds = test_ds.map(_map_fn_test,num_parallel_calls=multiprocessing.cpu_count())
# test_ds = test_ds.repeat(n_epoch)
test_ds = test_ds.prefetch(buffer_size=4096)
test_ds = test_ds.batch(batch_size)
# value_test = test_ds.make_one_shot_iterator().get_next()


class WithLoss(tl.layers.Module):

    def __init__(self, net, loss_fn):
        super(WithLoss, self).__init__()
        self._net = net
        self._loss_fn = loss_fn

    def forward(self, data, label):
        out = self._net(data)
        loss = self._loss_fn(out, label)
        return loss


net_with_loss = WithLoss(net, loss_fn=tl.cost.softmax_cross_entropy_with_logits)
net_with_train = tl.models.TrainOneStep(net_with_loss, optimizer, train_weights)

for epoch in range(n_epoch):
    start_time = time.time()
    net.set_train()
    train_loss, train_acc, n_iter = 0, 0, 0
    for X_batch, y_batch in train_ds:

        X_batch = tl.ops.convert_to_tensor(X_batch.numpy(), dtype=tl.float32)
        y_batch = tl.ops.convert_to_tensor(y_batch.numpy(), dtype=tl.int64)

        _loss_ce = net_with_train(X_batch, y_batch)
        train_loss += _loss_ce

        n_iter += 1
        _logits = net(X_batch)
        metrics.update(_logits, y_batch)
        train_acc += metrics.result()
        metrics.reset()
        print("Epoch {} of {} took {}".format(epoch + 1, n_epoch, time.time() - start_time))
        print("   train loss: {}".format(train_loss / n_iter))
        print("   train acc:  {}".format(train_acc / n_iter))