arif-ariff

Finalized training parameters

55da669 almost 3 years ago

4.84 kB

from datasets import load_dataset
from transformers import AutoImageProcessor, create_optimizer, TFAutoModelForImageClassification, KerasMetricCallback, \
    PushToHubCallback, pipeline
import tensorflow as tf
from tensorflow.python import keras
from keras import layers, losses
import numpy as np
from PIL import Image
from transformers import DefaultDataCollator
import evaluate


def convert_to_tf_tensor(image: Image):
    np_image = np.array(image)
    tf_image = tf.convert_to_tensor(np_image)

    # `expand_dims()` is used to add a batch dimension since
    # the TF augmentation layers operates on batched inputs.
    return tf.expand_dims(tf_image, 0)


def preprocess_train(example_batch):
    """Apply train_transforms across a batch."""
    images = [
        train_data_augmentation(convert_to_tf_tensor(image.convert("RGB"))) for image in example_batch["image"]
    ]
    example_batch["pixel_values"] = [tf.transpose(tf.squeeze(image)) for image in images]
    return example_batch


def preprocess_val(example_batch):
    """Apply val_transforms across a batch."""
    images = [
        val_data_augmentation(convert_to_tf_tensor(image.convert("RGB"))) for image in example_batch["image"]
    ]
    example_batch["pixel_values"] = [tf.transpose(tf.squeeze(image)) for image in images]
    return example_batch


def compute_metrics(eval_pred):
    predictions, labels = eval_pred
    predictions = np.argmax(predictions, axis=1)
    return accuracy.compute(predictions=predictions, references=labels)


# load dataset
fashion = load_dataset("fashion_mnist", split="train[:4000]")
# Split into train/test sets
fashion = fashion.train_test_split(test_size=0.2)
# an example
print(fashion["train"][0])

# Map label names to an integer and vice-versa
labels = fashion["train"].features["label"].names
label2id, id2label = dict(), dict()
for i, label in enumerate(labels):
    label2id[label] = str(i)
    id2label[str(i)] = label

# Should convert label id into a name
# print(label2id)
# print(id2label)

# Pre-processing with ViT
# Load image processor to process image into tensor
checkpoint = "google/vit-base-patch16-224-in21k"
image_processor = AutoImageProcessor.from_pretrained(checkpoint)

# To avoid overfitting and make the model more robust, add data augmentation to the training set.
# User Keras preprocessing layers to define transformations for the training set.
size = (image_processor.size["height"], image_processor.size["width"])

train_data_augmentation = keras.Sequential(
    [
        layers.RandomCrop(size[0], size[1]),
        layers.Rescaling(scale=1.0 / 127.5, offset=-1),
        layers.RandomFlip("horizontal"),
        layers.RandomRotation(factor=0.02),
        layers.RandomZoom(height_factor=0.2, width_factor=0.2),
    ],
    name="train_data_augmentation",
)

val_data_augmentation = keras.Sequential(
    [
        layers.CenterCrop(size[0], size[1]),
        layers.Rescaling(scale=1.0 / 127.5, offset=-1),
    ],
    name="val_data_augmentation",
)

fashion["train"].set_transform(preprocess_train)
fashion["test"].set_transform(preprocess_val)

data_collator = DefaultDataCollator(return_tensors="tf")

accuracy = evaluate.load("accuracy")

# Set hyperparameters
batch_size = 16
num_epochs = 4
num_train_steps = len(fashion["train"]) * num_epochs
learning_rate = 3e-5
weight_decay_rate = 0.01

# define optimizer, learning rate schedule
optimizer, lr_schedule = create_optimizer(
    init_lr=learning_rate,
    num_train_steps=num_train_steps,
    weight_decay_rate=weight_decay_rate,
    num_warmup_steps=0,
)

# Load ViT along with label mappings
model = TFAutoModelForImageClassification.from_pretrained(
    checkpoint,
    id2label=id2label,
    label2id=label2id,
)

# converting datasets to tf.data.Dataset
tf_train_dataset = fashion["train"].to_tf_dataset(
    columns="pixel_values", label_cols="label", shuffle=True, batch_size=batch_size, collate_fn=data_collator
)

tf_eval_dataset = fashion["test"].to_tf_dataset(
    columns="pixel_values", label_cols="label", shuffle=True, batch_size=batch_size, collate_fn=data_collator
)

# Configure model for training
loss = losses.SparseCategoricalCrossentropy(from_logits=True)
model.compile(optimizer=optimizer, loss=loss)


metric_callback = KerasMetricCallback(metric_fn=compute_metrics, eval_dataset=tf_eval_dataset)
push_to_hub_callback = PushToHubCallback(
    output_dir="../fashion_classifier",
    tokenizer=image_processor,
    save_strategy="no",
)

callbacks = [metric_callback, push_to_hub_callback]

model.fit(tf_train_dataset, validation_data=tf_eval_dataset, epochs=num_epochs, callbacks=callbacks)

# model.push_to_hub()

# ds = load_dataset("fashion_mnist", split="test[:10]")
# image = ds["image"][0]
# classifier = pipeline("image-classification", model="my_awesome_fashion_model")
# print(classifier(image))

	from datasets import load_dataset
	from transformers import AutoImageProcessor, create_optimizer, TFAutoModelForImageClassification, KerasMetricCallback, \
	PushToHubCallback, pipeline
	import tensorflow as tf
	from tensorflow.python import keras
	from keras import layers, losses
	import numpy as np
	from PIL import Image
	from transformers import DefaultDataCollator
	import evaluate


	def convert_to_tf_tensor(image: Image):
	np_image = np.array(image)
	tf_image = tf.convert_to_tensor(np_image)

	# `expand_dims()` is used to add a batch dimension since
	# the TF augmentation layers operates on batched inputs.
	return tf.expand_dims(tf_image, 0)


	def preprocess_train(example_batch):
	"""Apply train_transforms across a batch."""
	images = [
	train_data_augmentation(convert_to_tf_tensor(image.convert("RGB"))) for image in example_batch["image"]
	]
	example_batch["pixel_values"] = [tf.transpose(tf.squeeze(image)) for image in images]
	return example_batch


	def preprocess_val(example_batch):
	"""Apply val_transforms across a batch."""
	images = [
	val_data_augmentation(convert_to_tf_tensor(image.convert("RGB"))) for image in example_batch["image"]
	]
	example_batch["pixel_values"] = [tf.transpose(tf.squeeze(image)) for image in images]
	return example_batch


	def compute_metrics(eval_pred):
	predictions, labels = eval_pred
	predictions = np.argmax(predictions, axis=1)
	return accuracy.compute(predictions=predictions, references=labels)


	# load dataset
	fashion = load_dataset("fashion_mnist", split="train[:4000]")
	# Split into train/test sets
	fashion = fashion.train_test_split(test_size=0.2)
	# an example
	print(fashion["train"][0])

	# Map label names to an integer and vice-versa
	labels = fashion["train"].features["label"].names
	label2id, id2label = dict(), dict()
	for i, label in enumerate(labels):
	label2id[label] = str(i)
	id2label[str(i)] = label

	# Should convert label id into a name
	# print(label2id)
	# print(id2label)

	# Pre-processing with ViT
	# Load image processor to process image into tensor
	checkpoint = "google/vit-base-patch16-224-in21k"
	image_processor = AutoImageProcessor.from_pretrained(checkpoint)

	# To avoid overfitting and make the model more robust, add data augmentation to the training set.
	# User Keras preprocessing layers to define transformations for the training set.
	size = (image_processor.size["height"], image_processor.size["width"])

	train_data_augmentation = keras.Sequential(
	[
	layers.RandomCrop(size[0], size[1]),
	layers.Rescaling(scale=1.0 / 127.5, offset=-1),
	layers.RandomFlip("horizontal"),
	layers.RandomRotation(factor=0.02),
	layers.RandomZoom(height_factor=0.2, width_factor=0.2),
	],
	name="train_data_augmentation",
	)

	val_data_augmentation = keras.Sequential(
	[
	layers.CenterCrop(size[0], size[1]),
	layers.Rescaling(scale=1.0 / 127.5, offset=-1),
	],
	name="val_data_augmentation",
	)

	fashion["train"].set_transform(preprocess_train)
	fashion["test"].set_transform(preprocess_val)

	data_collator = DefaultDataCollator(return_tensors="tf")

	accuracy = evaluate.load("accuracy")

	# Set hyperparameters
	batch_size = 16
	num_epochs = 4
	num_train_steps = len(fashion["train"]) * num_epochs
	learning_rate = 3e-5
	weight_decay_rate = 0.01

	# define optimizer, learning rate schedule
	optimizer, lr_schedule = create_optimizer(
	init_lr=learning_rate,
	num_train_steps=num_train_steps,
	weight_decay_rate=weight_decay_rate,
	num_warmup_steps=0,
	)

	# Load ViT along with label mappings
	model = TFAutoModelForImageClassification.from_pretrained(
	checkpoint,
	id2label=id2label,
	label2id=label2id,
	)

	# converting datasets to tf.data.Dataset
	tf_train_dataset = fashion["train"].to_tf_dataset(
	columns="pixel_values", label_cols="label", shuffle=True, batch_size=batch_size, collate_fn=data_collator
	)

	tf_eval_dataset = fashion["test"].to_tf_dataset(
	columns="pixel_values", label_cols="label", shuffle=True, batch_size=batch_size, collate_fn=data_collator
	)

	# Configure model for training
	loss = losses.SparseCategoricalCrossentropy(from_logits=True)
	model.compile(optimizer=optimizer, loss=loss)


	metric_callback = KerasMetricCallback(metric_fn=compute_metrics, eval_dataset=tf_eval_dataset)
	push_to_hub_callback = PushToHubCallback(
	output_dir="../fashion_classifier",
	tokenizer=image_processor,
	save_strategy="no",
	)

	callbacks = [metric_callback, push_to_hub_callback]

	model.fit(tf_train_dataset, validation_data=tf_eval_dataset, epochs=num_epochs, callbacks=callbacks)

	# model.push_to_hub()

	# ds = load_dataset("fashion_mnist", split="test[:10]")
	# image = ds["image"][0]
	# classifier = pipeline("image-classification", model="my_awesome_fashion_model")
	# print(classifier(image))