# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

import flash
from flash.core.data.utils import download_data
from flash.image import ObjectDetectionData, ObjectDetector

# 1. Create the DataModule
# Dataset Credit: https://www.kaggle.com/ultralytics/coco128
download_data("https://github.com/zhiqwang/yolov5-rt-stack/releases/download/v0.3.0/coco128.zip", "data/")

datamodule = ObjectDetectionData.from_coco(
    train_folder="data/coco128/images/train2017/",
    train_ann_file="data/coco128/annotations/instances_train2017.json",
    val_split=0.1,
    batch_size=2,
)

# 2. Build the task
model = ObjectDetector(model="retinanet", num_classes=datamodule.num_classes)

# 3. Create the trainer and finetune the model
trainer = flash.Trainer(max_epochs=3, gpus=torch.cuda.device_count())
trainer.finetune(model, datamodule=datamodule)

# 4. Detect objects in a few images!
predictions = model.predict(
    [
        "data/coco128/images/train2017/000000000625.jpg",
        "data/coco128/images/train2017/000000000626.jpg",
        "data/coco128/images/train2017/000000000629.jpg",
    ]
)
print(predictions)

# 5. Save the model!
trainer.save_checkpoint("object_detection_model.pt")
