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densenet_cifar.py

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+import os
+os.environ['TL_BACKEND'] = 'tensorflow'
+import time
+import multiprocessing
+import tensorflow as tf
+from tensorlayer.models import TrainOneStep
+from tensorlayer.layers import Module
+import tensorlayer as tl
+from .densenet import builddensenet
+
+
+# enable debug logging
+tl.logging.set_verbosity(tl.logging.DEBUG)
+
+# prepare cifar10 data
+X_train, y_train, X_test, y_test = tl.files.load_cifar10_dataset(shape=(-1, 32, 32, 3), plotable=False)
+
+
+# get the network
+net = builddensenet("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(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 = 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))