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import sys
sys.path.append(".")
import logging
import os
from typing import Optional, Dict
import hydra
import torch
from hydra.utils import instantiate
from datasets import DatasetDict, load_dataset, IterableDatasetDict
from omegaconf import DictConfig, OmegaConf
from src.data.transforms import SamCaptionerDataTransform
from src.data.collator import SamCaptionerDataCollator
from src.arguments import Arguments, global_setup, SAMCaptionerModelArguments, SCAModelArguments, SCAModelBaseArguments
from src.models.sam_captioner import SAMCaptionerConfig, SAMCaptionerModel, SAMCaptionerProcessor
from src.models.sca import ScaProcessor
from transformers.trainer_utils import get_last_checkpoint
from transformers import set_seed, Trainer
import gradio as gr
from dataclasses import dataclass
import numpy as np
from functools import partial
import pandas as pd
from src.train import prepare_datasets, prepare_data_transform, prepare_processor
import pycocotools.mask
from PIL import Image
import dotenv
logger = logging.getLogger(__name__)
@hydra.main(version_base="1.3", config_path="../../src/conf", config_name="conf")
def main(args: DictConfig) -> None:
# NOTE(xiaoke): follow https://github.com/huggingface/transformers/blob/main/examples/pytorch/image-classification/run_image_classification.py
logger.info(OmegaConf.to_yaml(args))
args, training_args, model_args = global_setup(args)
# Set seed before initializing model.
set_seed(args.training.seed)
# Initialize our dataset and prepare it
train_dataset, eval_dataset = prepare_datasets(args)
# NOTE(xiaoke): load sas_key from .env for huggingface model downloading.
logger.info(f"Try to load sas_key from .env file: {dotenv.load_dotenv('.env')}.")
use_auth_token = os.getenv("USE_AUTH_TOKEN", False)
processor = prepare_processor(model_args, use_auth_token)
train_dataset, eval_dataset = prepare_data_transform(
training_args, model_args, train_dataset, eval_dataset, processor
)
# [NOTE] Used to restore the image tensor after transformed
# Use global to avoid passing too many arguments
global image_mean, image_std
image_mean, image_std = (
processor.sam_processor.image_processor.image_mean,
processor.sam_processor.image_processor.image_std,
)
def view_one_batch(dataset_split, batch_idx, dataset_type):
if dataset_type == "before_transform":
return _view_one_batch_before_transform(dataset_split, batch_idx, dataset_type)
elif dataset_type == "after_transform":
return _view_one_batch_after_transform(dataset_split, batch_idx, dataset_type)
else:
raise ValueError(f"Unknown type of sample: {dataset_type}")
def _view_one_batch_before_transform(dataset_split, batch_idx, dataset_type):
sample = dataset_split[batch_idx]
image = sample["image"]
text = f"dataset_type: {dataset_type}\nsample_id: {batch_idx}\n"
for k, v in sample.items():
if isinstance(v, (int, str)):
text += f"{k}: {v}\n"
regions = sample["regions"]
regions = pd.DataFrame(regions)
regions.sort_values(by=["region_id"], ascending=True, inplace=True)
return image, text, regions
def _view_one_batch_after_transform(dataset_split, batch_idx, dataset_type):
sample = dataset_split[batch_idx]
image = sample["images"]
image = sample["pixel_values"]
image_mean_tensor = torch.tensor(image_mean).view(3, 1, 1)
image_std_tensor = torch.tensor(image_std).view(3, 1, 1)
image = image * image_std_tensor + image_mean_tensor
image = image.clamp(0, 1) * 255
image = image.permute(1, 2, 0).numpy().astype(np.uint8)
PRINT_VALUE_KEYS = ["original_sizes", "reshaped_input_sizes"]
text = f"dataset_type: {dataset_type}\nsample_id: {batch_idx}\n"
for k, v in sample.items():
text += f"{k}:\t{type(v)}\t"
if k in PRINT_VALUE_KEYS:
text += f"{v}\n"
elif isinstance(v, str):
text += f"{v}\n"
elif isinstance(v, torch.Tensor):
text += f"{v.shape}\n"
elif isinstance(v, list):
text += f"{len(v)}\n"
elif isinstance(v, np.ndarray):
text += f"{v.shape}\n"
else:
try:
text += f"{v.size}\n"
except AttributeError:
text += f"{v}\n"
REGION_KEYS = [
"input_boxes",
"metadata_input_boxes",
"metadata_image_id",
"metadata_region_id",
"metadata_captions",
]
pd_series = []
for region_tensor_key in REGION_KEYS:
region_tensor = sample[region_tensor_key]
# NOTE: cast the float to int in bbox.
if region_tensor_key == "input_boxes":
if isinstance(region_tensor, torch.Tensor):
region_tensor = region_tensor.long()
elif isinstance(region_tensor, np.ndarray):
region_tensor = region_tensor.astype(np.int64)
if isinstance(region_tensor, (torch.Tensor, np.ndarray)):
region_list = region_tensor.tolist()
elif isinstance(region_tensor, list):
region_list = region_tensor
else:
raise ValueError(f"Unknown type of region_tensor: {type(region_tensor)}")
pd_series.append(pd.Series(region_list, name=region_tensor_key))
regions = pd.concat(pd_series, axis=1)
regions.sort_values(by=["metadata_region_id"], ascending=True, inplace=True)
return image, text, regions
def view_one_region(image, data_frame, output_chioce_radio, dataset_type, evt: gr.SelectData):
if dataset_type == "before_transform":
return _view_one_region_before_transform(image, data_frame, output_chioce_radio, evt)
elif dataset_type == "after_transform":
return _view_one_region_after_transform(image, data_frame, output_chioce_radio, evt)
else:
raise ValueError(f"Unknown type of sample: {dataset_type}")
def _view_one_region_before_transform(image, data_frame, output_chioce_radio, evt):
row_id, _ = evt.index
region = data_frame.iloc[row_id]
if output_chioce_radio == "segmentation" and region.get("mask", None) is not None:
annot = region["mask"]
annot = pycocotools.mask.decode(annot)
elif output_chioce_radio == "segmentation" and region.get("mask", None) is None:
x, y, w, h = region["x"], region["y"], region["width"], region["height"]
x2, y2 = x + w, y + h
annot = [x, y, x2, y2]
elif output_chioce_radio == "bbox":
x, y, w, h = region["x"], region["y"], region["width"], region["height"]
x2, y2 = x + w, y + h
annot = [x, y, x2, y2]
else:
raise ValueError(f"Unknown output_chioce_radio: {output_chioce_radio}")
phrases = [f"{idx}: {phrase}" for idx, phrase in enumerate(region["phrases"])]
phrases = "; ".join(phrases)
return image, [[annot, phrases]]
def _view_one_region_after_transform(image, data_frame, output_chioce_radio, evt):
row_id, _ = evt.index
region = data_frame.iloc[row_id]
if output_chioce_radio == "segmentation" and region.get("mask", None) is not None:
raise NotImplementedError("TODO: implement segmentation for after_transform")
elif output_chioce_radio == "segmentation" and region.get("mask", None) is None:
annot = list(map(int, region["input_boxes"]))
elif output_chioce_radio == "bbox":
annot = list(map(int, region["input_boxes"]))
else:
raise ValueError(f"Unknown output_chioce_radio: {output_chioce_radio}")
phrases = region["metadata_captions"]
if not isinstance(phrases[0], list):
phrases = [phrases]
phrases = [f"{idx}: {phrase}" for idx, phrase in enumerate(phrases)]
phrases = "; ".join(phrases)
return image, [[annot, phrases]]
def get_gr_frame(frame_name, dataset_split):
dataset_type = "before_transform" if dataset_split[0].get("images", None) is None else "after_transform"
dataset_type = gr.Variable(dataset_type)
with gr.Accordion(label=frame_name) as frame:
batch_idx = gr.Slider(minimum=0, maximum=len(dataset_split), step=1, default=0)
button = gr.Button(text="View the batch")
output_chioce_radio = gr.Radio(["bbox", "segmentation"], value="bbox")
image = gr.Image(height=500)
text = gr.Textbox(lines=1)
data_frame = gr.DataFrame()
annotated_image = gr.AnnotatedImage(height=500)
dataset_split = gr.Variable(dataset_split)
button.click(
view_one_batch, inputs=[dataset_split, batch_idx, dataset_type], outputs=[image, text, data_frame]
)
data_frame.select(
view_one_region,
inputs=[image, data_frame, output_chioce_radio, dataset_type],
outputs=[annotated_image],
)
return frame
with gr.Blocks() as app:
get_gr_frame("train", train_dataset)
for eval_data_k, eval_data_v in eval_dataset.items():
get_gr_frame(f"validate-{eval_data_k}", eval_data_v)
app.launch()
if __name__ == "__main__":
main()
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