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"""
MedSedX internal evaluating script
"""
# setup environment
import argparse
import os
join = os.path.join
import time
import torch
import torch.nn as nn
from torch.nn import functional as F
from torch.utils.data import DataLoader
from torchvision.transforms import Resize
import pandas as pd
from tqdm import tqdm
from segment_anything import sam_model_registry, sam_model_checkpoint
from segment_anything.utils.transforms import ResizeLongestSide
from model import *
from data.dataset import TaskMedSegDB
from utils.metric import SegmentMetrics
# setup parser
parser = argparse.ArgumentParser("MedSedX internal evaluating", add_help=False)
# model
parser.add_argument("--checkpoint", type=str, default="./playground/SAM",
help="path to SAM checkpoint folder")
parser.add_argument("--model_type", type=str, default="vit_b",
help="SAM model scale (e.g vit_b, vit_l, vit_h)")
parser.add_argument("--model_weight", type=str, default="./playground/MedSegX/medsegx_vit_b.pth",
help="path to MedSegX model weight")
parser.add_argument("--method", type=str, default="medsegx")
parser.add_argument("--bottleneck_dim", type=int, default=16)
parser.add_argument("--embedding_dim", type=int, default=16)
parser.add_argument("--expert_num", type=int, default=4)
# data
parser.add_argument("--data_path", type=str, default="./playground/MedSegDB/eval/ID",
help="path to MedSegDB data folder")
parser.add_argument("--metric", type=str, default=["dsc", "hd"], nargs='+',
help="evaluation metrics (e.g dsc, hd)")
# eval
parser.add_argument("--device", type=str, default="cuda:0")
parser.add_argument("--device_ids", type=int, default=[0,1,2,3,4,5,6,7], nargs='+',
help="device ids assignment (e.g 0 1 2 3)")
parser.add_argument("--batch_size", type=int, default=32)
parser.add_argument("--num_workers", type=int, default=32)
def evaluate(model, metric, dataloader, img_size, img_transform, box_transform,
dataset, task, sequence=None, result_total=None, meta=None, args=None):
model.eval()
device = torch.device(args.device)
result_task = {}
for m in args.metric:
result_task[m] = []
pbar = tqdm(dataloader)
if sequence is not None:
pbar.set_description(f"Evaluating - {dataset} {task} {sequence}")
else:
pbar.set_description(f"Evaluating - {dataset} {task}")
with torch.no_grad():
for data, label in pbar:
if data["img"].shape[-1] != img_size:
data["box"] = box_transform.apply_boxes_torch((data["box"].reshape(-1, 2, 2)),
data["img"].shape[-2:]).reshape(-1, 4)
data["img"] = img_transform(data["img"])
data["img"] = data["img"].to(device, non_blocking=True)
data["box"] = data["box"].to(device, non_blocking=True)
label = label.to(device, non_blocking=True, dtype=torch.bool)
mask_pred = model(data)
if mask_pred.shape[-1] != label.shape[-1]:
mask_pred = F.interpolate(mask_pred, size=label.shape[-1], mode="bilinear", antialias=True)
mask_prob = torch.sigmoid(mask_pred)
mask = (mask_prob > 0.5).bool()
result_list = {}
metric_dict = {}
metric_list = {m: [] for m in args.metric}
for m in args.metric:
result_list[m] = []
# handle ambiguous segmentation
for idx in range(model.module.sam.mask_decoder.num_multimask_outputs):
result_batch = metric(mask[:, idx].unsqueeze(1), label)
for m in args.metric:
result_list[m].append(result_batch[m])
dsc, max_idx = torch.stack(result_list["dsc"], dim=0).max(dim=0)
for m in args.metric:
if m == "dsc":
result = dsc
else:
# select other metrics based on the best DSC
result = torch.stack(result_list[m], dim=0)
result = result[max_idx, torch.arange(result.shape[1])]
metric_dict[m] = result.mean().item()
metric_list[m].append(result)
result_task[m].append(result)
result_total[m].append(result)
pbar.set_postfix(metric_dict)
metric_list = {m: torch.cat(v) for m, v in metric_list.items()}
for idx, name in enumerate(data["name"]):
file = name.replace(f"{args.data_path}/", "").replace("npy_gts/", "")
meta["File"].append(file)
for m in args.metric:
meta[m.upper()].append(metric_list[m][idx].item())
result_task = {k: torch.cat(v).mean().item() for k, v in result_task.items()}
return result_task
def main(args):
device = torch.device(args.device)
checkpoint = join(args.checkpoint, sam_model_checkpoint[args.model_type])
sam_model = sam_model_registry[args.model_type](image_size=256, keep_resolution=True, checkpoint=checkpoint)
if args.method == "medsam":
model = MedSAM(sam_model).to(device)
elif args.method == "medsegx":
model = MedSegX(sam_model, args.bottleneck_dim, args.embedding_dim, args.expert_num).to(device)
else:
raise NotImplementedError("Method {} not implemented!".format(args.method))
seg_metric = SegmentMetrics(args.metric).to(device)
model = nn.DataParallel(model, device_ids=args.device_ids)
seg_metric = nn.DataParallel(seg_metric, device_ids=args.device_ids)
if os.path.isfile(args.model_weight):
## Map model to be loaded to specified single GPU
print(f"load model from {args.model_weight}")
checkpoint = torch.load(args.model_weight, map_location=device)
model.module.load_parameters(checkpoint["model"])
else:
raise FileNotFoundError(f"model weight {args.model_weight} not found!")
work_dir = os.path.dirname(args.model_weight)
work_dir = join(work_dir, "internal")
os.makedirs(work_dir, exist_ok=True)
img_size = model.module.sam.image_encoder.img_size
img_transform = Resize((img_size, img_size), antialias=True)
box_transform = ResizeLongestSide(img_size)
result_total = {}
meta = {"File": []}
for m in args.metric:
result_total[m] = []
meta[m.upper()] = []
time_start = time.time()
print(f"save evaluation result to {join(work_dir, 'ID.md')}")
with open(join(work_dir, 'ID.md'), mode="w") as f:
f.write("# internal evaluation\n\n")
data_path = args.data_path
# iterate over datasets
for dataset in sorted(os.listdir(data_path)):
f.write(f"- {dataset}\n")
dataset_path = join(data_path, dataset)
# iterate over tasks
for task in sorted(os.listdir(dataset_path)):
task_path = join(dataset_path, task)
# do not have different sequences
if 'npy_gts' in os.listdir(task_path):
test_dataset = TaskMedSegDB(task_path, train=False)
test_dataloader = DataLoader(
test_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
pin_memory=True,
)
if len(test_dataset) == 0:
continue
# evaluate
metric_task = evaluate(model, seg_metric, test_dataloader,
img_size, img_transform, box_transform,
dataset, task, result_total=result_total,
meta=meta, args=args)
result_task = ", ".join([f"{k.upper()} ({v:.4f})" for k, v in metric_task.items()])
f.write(f" - {task}: {result_task}\n")
# have different sequences
else:
f.write(f" - {task}\n")
for sequence in sorted(os.listdir(task_path)):
sequence_path = join(task_path, sequence)
test_dataset = TaskMedSegDB(sequence_path, train=False, sequence=True)
test_dataloader = DataLoader(
test_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
pin_memory=True,
)
if len(test_dataset) == 0:
continue
# evaluate
metric_task = evaluate(model, seg_metric, test_dataloader,
img_size, img_transform, box_transform,
dataset, task, sequence, result_total=result_total,
meta=meta, args=args)
result_task = ", ".join([f"{k.upper()} ({v:.4f})" for k, v in metric_task.items()])
f.write(f" - {sequence}: {result_task}\n")
result_total = {k: torch.cat(v).mean().item() for k, v in result_total.items()}
result_total = ", ".join([f"{k.upper()} ({v:.4f})" for k, v in result_total.items()])
f.write(f"- ALL\n")
f.write(f" - Mean: {result_total}\n")
time_end = time.time()
print(f"Time cost: {time_end - time_start:.0f} s")
# record instance-level results
df = pd.DataFrame(meta)
df.to_csv(f"{work_dir}/ID.csv", index=False)
if __name__ == "__main__":
args = parser.parse_args()
main(args)
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