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January 24, 2023 06:16
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""" | |
Usage: python run_semantic_segmentation.py | |
References: | |
* https://www.tensorflow.org/tutorials/images/segmentation#define_the_model | |
* https://huggingface.co/docs/transformers/main/en/tasks/semantic_segmentation | |
""" | |
import os | |
import uuid | |
from typing import Dict, List, Tuple | |
import tensorflow as tf | |
import tensorflow_datasets as tfds | |
from tensorflow.keras import backend | |
from transformers import AutoImageProcessor, TFSegformerForSemanticSegmentation | |
from transformers.keras_callbacks import PushToHubCallback | |
IMAGE_SIZE = 512 | |
MEAN = tf.constant([0.485, 0.456, 0.406]) | |
STD = tf.constant([0.229, 0.224, 0.225]) | |
BATCH_SIZE = 4 | |
AUTO = tf.data.AUTOTUNE | |
MODEL_CKPT = "nvidia/mit-b0" | |
LR = 0.00006 | |
EPOCHS = 10 | |
def load_dataset(): | |
dataset, info = tfds.load("oxford_iiit_pet:3.*.*", with_info=True) | |
return dataset, info | |
def normalize(input_image, input_mask) -> Tuple[tf.Tensor, tf.Tensor]: | |
input_image = tf.image.convert_image_dtype(input_image, tf.float32) | |
input_image = (input_image - MEAN) / tf.maximum(STD, backend.epsilon()) | |
input_mask -= 1 | |
return input_image, input_mask | |
def load_image(datapoint) -> Dict[str, tf.Tensor]: | |
input_image = tf.image.resize(datapoint["image"], (IMAGE_SIZE, IMAGE_SIZE)) | |
input_mask = tf.image.resize( | |
datapoint["segmentation_mask"], | |
(IMAGE_SIZE, IMAGE_SIZE), | |
method="bilinear", | |
) | |
input_image, input_mask = normalize(input_image, input_mask) | |
input_image = tf.transpose(input_image, (2, 0, 1)) | |
return {"pixel_values": input_image, "labels": tf.squeeze(input_mask)} | |
def prepare_datasets() -> Tuple[tf.data.Dataset, tf.data.Dataset]: | |
dataset, _ = load_dataset() | |
train_ds = ( | |
dataset["train"] | |
.cache() | |
.shuffle(BATCH_SIZE * 10) | |
.map(load_image, num_parallel_calls=AUTO) | |
.batch(BATCH_SIZE) | |
.prefetch(AUTO) | |
) | |
test_ds = ( | |
dataset["test"] | |
.map(load_image, num_parallel_calls=AUTO) | |
.batch(BATCH_SIZE) | |
.prefetch(AUTO) | |
) | |
print("Datasets prepared.") | |
return train_ds, test_ds | |
def load_and_compile_model() -> tf.keras.Model: | |
id2label = {0: "outer", 1: "inner", 2: "border"} | |
label2id = {label: id for id, label in id2label.items()} | |
num_labels = len(id2label) | |
model = TFSegformerForSemanticSegmentation.from_pretrained( | |
MODEL_CKPT, | |
num_labels=num_labels, | |
id2label=id2label, | |
label2id=label2id, | |
ignore_mismatched_sizes=True, # Will ensure the segmentation specific components are reinitialized. | |
) | |
optimizer = tf.keras.optimizers.Adam(learning_rate=LR) | |
model.compile(optimizer=optimizer) | |
print("Model initialized and compiled.") | |
return model | |
def prepare_callbacks(output_dir: str, dataset=None): | |
image_processor = AutoImageProcessor.from_pretrained(MODEL_CKPT) | |
model_name = MODEL_CKPT.split("/")[-1] | |
push_to_hub_model_id = f"{model_name}-finetuned-pets" | |
push_to_hub_callback = PushToHubCallback( | |
output_dir=output_dir, | |
hub_model_id=push_to_hub_model_id, | |
tokenizer=image_processor, | |
) | |
print("Callbacks prepared.") | |
return [push_to_hub_callback] | |
def train(): | |
train_ds, test_ds = prepare_datasets() | |
callbacks = prepare_callbacks(output_dir="finetuned-pets") | |
model = load_and_compile_model() | |
history = model.fit( | |
train_ds, | |
validation_data=test_ds, | |
callbacks=callbacks, | |
epochs=EPOCHS, | |
) | |
print("Model trained.") | |
return model, history | |
if __name__ == "__main__": | |
_, _ = train() |
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