import os
import numpy as np
import tensorflow as tf
from object_detection.utils import config_util
from object_detection.utils import label_map_util
from object_detection.builders import model_builder

# Set the paths to the necessary files and directories
pipeline_config_path = 'path/to/pipeline.config'
model_dir = 'path/to/model_directory'
checkpoint_dir = 'path/to/pretrained_model_directory'

# Load the pipeline config
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
model_config = configs['model']

# Build the model
detection_model = model_builder.build(model_config=model_config, is_training=True)

# Restore the checkpoint
ckpt = tf.compat.v2.train.Checkpoint(model=detection_model)
ckpt.restore(os.path.join(checkpoint_dir, 'ckpt-0')).expect_partial()

# Set up the training data
train_dataset = tf.data.TFRecordDataset('path/to/train.tfrecord')
train_dataset = train_dataset.shuffle(1000).batch(batch_size)

# Set up the optimizer
optimizer = tf.keras.optimizers.SGD(learning_rate=0.001, momentum=0.9)

# Set up the loss function
loss_fn = detection_model.loss_fn

@tf.function
def train_step(inputs):
    images, groundtruths = inputs
    with tf.GradientTape() as tape:
        prediction_dict = detection_model(images, training=True)
        losses_dict = loss_fn(groundtruths, prediction_dict)
        total_loss = losses_dict['loss']
    grads = tape.gradient(total_loss, detection_model.trainable_variables)
    optimizer.apply_gradients(zip(grads, detection_model.trainable_variables))
    return total_loss

# Training loop
num_epochs = 10
for epoch in range(num_epochs):
    total_loss = 0.0
    num_batches = 0
    for batch in train_dataset:
        total_loss += train_step(batch)
        num_batches += 1
    average_loss = total_loss / num_batches
    print(f"Epoch {epoch+1}/{num_epochs}, Average Loss: {average_loss.numpy():.4f}")

# Save the trained model
ckpt.save(os.path.join(model_dir, 'ckpt'))
