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from collections import defaultdict
import os
import json
from pathlib import Path
import time
import numpy as np
import torch.distributed as dist
from evaluation.compute_metrics import (
compute_nlp_metrics,
compute_green_score,
compute_multilabel_ct_classification_metrics,
)
from constants_and_path_utils import CT_RATE_ROOT, PATHOLOGIES_LIST
import pandas as pd
def generate_gt_list(ground_truth_report_path):
"""
prepare ground truth list
:param ground_truth_report_path:
:return:
"""
test_ground_truth = {}
with open(ground_truth_report_path, "r") as file:
all_reports = json.load(file)
for sample in all_reports:
id = sample["image"].split(".nii")[0]
test_ground_truth[id] = sample["conversations"][1]["value"]
return test_ground_truth
def generate_test_list_from_agent_outputs(generated_report_root_path):
"""
prepare generated report list from agent outputs
e.g. path = radagent/outputs/v1b/Mistral-Small-24B-Instruct-2501/v1_chunked
has two subfolders 'trajectories' and 'fails'
:param generated_report_root_path:
:return:
"""
generated_result = {}
sucessful_reports_path = os.path.join(generated_report_root_path, "trajectory")
failed_reports_path = os.path.join(generated_report_root_path, "fails")
for path in os.listdir(sucessful_reports_path):
with open(os.path.join(sucessful_reports_path, path), "r") as file:
data = json.load(file)
id = (
path.replace(".json", "").split("_result")[0]
if "_result" in path
else path.replace(".json", "").split("_trajectory")[0]
)
try:
if data[-1].get("reward", None) is not None:
# New format with reward at the end
last_message = json.loads(data[-2]["content"])
else:
# old format without reward
last_message = json.loads(data[-1]["content"])
if (
last_message.get("action", "No") == "final_answer"
and "answer" in last_message
):
generated_result[id] = last_message["answer"]
if not isinstance(generated_result[id], str):
print(
f"Warning: The generated answer for ID {id} is not a string. Got {generated_result[id]}"
)
generated_result[id] = ""
else:
generated_result[id] = ""
except json.JSONDecodeError:
generated_result[id] = ""
for path in os.listdir(failed_reports_path):
id = (
path.replace(".json", "").split("_result")[0]
if "_result" in path
else path.replace(".json", "").split("_trajectory")[0]
)
generated_result[id] = "Failed"
return generated_result
def test_if_paired_data(test_ground_truth, generated_result):
missing_reports = set(test_ground_truth.keys()) - set(generated_result.keys())
missing_reports_id = list(missing_reports) if missing_reports is not None else None
print(len(missing_reports_id), "cases are missing!")
if missing_reports_id:
for id in missing_reports_id:
test_ground_truth.pop(id)
return test_ground_truth, generated_result
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--output_folder", type=str, default=0)
parser.add_argument(
"--is_ct_chat_eval",
action="store_true",
help="Whether to evaluate the CT-Chat outputs. If set, it will load the CT-Chat specific ground truth and predictions instead of the ones from the generated report folder.",
)
parser.add_argument(
"--ct_chat_predictions_path",
type=str,
default="",
help="Path to CT-Chat predictions JSON file (used only if --is_ct_chat_eval is set)",
)
parser.add_argument(
"--ct_chat_ground_truth_path",
type=str,
default="",
help="Path to CT-Chat ground truth JSON file (used only if --is_ct_chat_eval is set)",
)
parser.add_argument(
"--split",
choices=["val", "test"],
default="val",
help="Which split to evaluate on (default: val)",
)
args = parser.parse_args()
generated_report_root_path = args.output_folder
Path(args.output_folder).mkdir(parents=True, exist_ok=True)
match args.split:
case "val":
ground_truth_report_path = (
CT_RATE_ROOT / "labels/report_generation/report_generation_valid.json"
)
case "test":
ground_truth_report_path = (
CT_RATE_ROOT / "labels/report_generation/report_generation_test.json"
)
if not args.is_ct_chat_eval:
test_ground_truth = generate_gt_list(ground_truth_report_path)
test_generated_output = generate_test_list_from_agent_outputs(
generated_report_root_path
)
# For CT-Chat eval
else:
with open(
args.ct_chat_ground_truth_path,
"r",
) as f:
test_ground_truth = json.load(f)
with open(
args.ct_chat_predictions_path,
"r",
) as f:
test_generated_output = json.load(f)
print(len(test_ground_truth), len(test_generated_output))
# Drop all the unpaired data (to allow for computing metrics on partial outputs)
test_ground_truth, test_generated_output = test_if_paired_data(
test_ground_truth, test_generated_output
)
is_distributed = int(os.environ.get("WORLD_SIZE", 1)) > 1
print("Is distributed:", is_distributed, flush=True)
if is_distributed:
if not dist.is_initialized():
rank = int(os.environ.get("RANK", "0"))
world_size = int(os.environ.get("WORLD_SIZE", 1))
dist.init_process_group("nccl", rank=rank, world_size=world_size)
print(f"Rank {rank}: Process group initialized.")
if dist.get_rank() == 0:
print(
"Distributed training with",
int(os.environ.get("WORLD_SIZE", 1)),
"GPUs",
)
global_rank = dist.get_rank() # This is your device ID
else:
global_rank = 0
device_id = global_rank if is_distributed else 0
# Add barrier to ensure all processes are ready
if is_distributed:
dist.barrier()
print("##### GREEN SCORE #####")
all_green_results = compute_green_score(test_ground_truth, test_generated_output)
all_results = {}
if global_rank == 0:
all_classification_results = compute_multilabel_ct_classification_metrics(
test_ground_truth, test_generated_output, batch_size=16
)
classification_results, detailed_predictions_results = (
all_classification_results
)
average_green_score, green_score_list, green_df = all_green_results
all_results.update(classification_results)
all_results.update(average_green_score)
print("Starting NLP metrics computation")
average_nlp_metrics = compute_nlp_metrics(
test_ground_truth, test_generated_output
)[0]
all_results.update(average_nlp_metrics)
print("Finished NLP metrics computation")
print(average_nlp_metrics)
print(average_green_score)
print(classification_results)
t3 = time.time()
with open(os.path.join(args.output_folder, "results.json"), "w") as f:
json.dump(all_results, f, indent=4)
with open(
os.path.join(args.output_folder, "green_score_results.json"), "w"
) as f:
json.dump(green_score_list, f, indent=4)
green_df.to_csv(
os.path.join(args.output_folder, "green_score_detailed_results.csv"),
index=False,
)
# Save all_resutls to a json file
ids = []
gt_report = []
generated_report = []
image_id = []
df_classification_results = pd.DataFrame(
detailed_predictions_results["pred"], columns=PATHOLOGIES_LIST
)
result_dict = defaultdict(list)
for i, id in enumerate(test_ground_truth.keys()):
result_dict["id"].append(i)
result_dict["image_id"].append(id)
result_dict["gt_report"].append(test_ground_truth[id])
result_dict["generated_report"].append(test_generated_output[id])
idx = np.where(np.asarray(detailed_predictions_results["volume_id"]) == id)[
0
][0]
for j, p in enumerate(PATHOLOGIES_LIST):
result_dict[f"pred_{p}"].append(
int(detailed_predictions_results["pred"][idx, j])
)
result_dict[f"gt_{p}"].append(
int(detailed_predictions_results["gt"][idx, j])
)
result_dict[f"is_correct_{p}"].append(
int(detailed_predictions_results["equal"][idx, j])
)
result_dict["accuracy_18findings"].append(
sum(
[
int(detailed_predictions_results["equal"][idx][j])
for j in range(len(PATHOLOGIES_LIST))
]
)
/ len(PATHOLOGIES_LIST)
)
result_dict["green_score"].extend(green_score_list["GREEN"])
df = pd.DataFrame(result_dict)
df.to_csv(os.path.join(args.output_folder, "detailed_results.csv"), index=False)
print(f'Results saved to {os.path.join(args.output_folder, "results.json")}')
|