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validation.py
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validation.py
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import argparse
import collections
import json
import pickle
import os
import random
import torch
import numpy as np
import pandas as pd
import torch.nn.functional as F
import torch.utils.data as module_data
import data_loader as module_dataset
import model as module_arch
import albumentations as A
from utils import IND2CLASS, encode_mask_to_rle, CLASSES
from parse_config import ConfigParser
from tqdm import tqdm
from sklearn.model_selection import GroupKFold
def dice_coef(outputs, masks):
y_true_f = masks.flatten(2)
y_pred_f = outputs.flatten(2)
intersection = torch.sum(y_true_f * y_pred_f, -1)
eps = 0.0001
return (2.0 * intersection + eps) / (
torch.sum(y_true_f, -1) + torch.sum(y_pred_f, -1) + eps
)
def set_seeds(seed=42):
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
np.random.seed(seed)
random.seed(seed)
os.environ["PYTHONHASHSEED"] = str(seed)
def main(config):
set_seeds()
molel_name = config["path"]["model_path"].split('/')[-2]
save_csv_path = config["path"]["save_csv_path"]
thresholds = config["thresholds"]
cfg_path = config["path"]
with open(cfg_path["image_name_pickle_path"], "rb") as f:
filenames = np.array(pickle.load(f))
with open(cfg_path["label_name_pickle_path"], "rb") as f:
labelnames = np.array(pickle.load(f))
with open(cfg_path["image_dict_pickle_path"], "rb") as f:
hash_dict = pickle.load(f)
valid_tf_list = []
for tf in config["valid_transforms"]:
valid_tf_list.append(
getattr(A, tf["name"])(*tf["args"], **tf["kwargs"])
)
# group k-fold
groups = [os.path.dirname(fname) for fname in filenames]
ys = [0 for _ in filenames]
gkf = GroupKFold(n_splits=config["kfold"]["n_splits"])
for fold, (x, y) in enumerate(gkf.split(filenames, ys, groups), start=1):
if fold != config["kfold"]["fold"]: continue
valid_filenames = list(filenames[y])
valid_labelnames = list(labelnames[y])
valid_dataset = config.init_obj(
"valid_dataset",
module_dataset,
filenames=valid_filenames,
labelnames=valid_labelnames,
hash_dict=hash_dict,
mmap_path=cfg_path["mmap_path"],
label_root=cfg_path["label_path"],
transforms=valid_tf_list,
)
valid_data_loader = config.init_obj(
"valid_data_loader", module_data, valid_dataset
)
# build model architecture
model = config.init_obj("arch", module_arch)
if config["n_gpu"] > 1:
model = torch.nn.DataParallel(model)
model.load_state_dict(
torch.load(config["path"]["model_path"])["state_dict"]
)
# prepare model for testing
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)
model.eval()
with torch.no_grad():
for threshold in thresholds:
dices = []
rles = []
filename_and_class = []
for step, (images, masks, image_names) in tqdm(enumerate(valid_data_loader), total=len(valid_data_loader)):
images, masks = images.cuda(), masks.cuda()
outputs = model(images)
outputs = F.interpolate(outputs, size=(2048, 2048), mode="bilinear")
outputs = torch.sigmoid(outputs)
outputs = (outputs > threshold).detach().cpu()
masks = masks.detach().cpu()
dice = dice_coef(outputs, masks)
dices.append(dice)
for output, image_name in zip(outputs, image_names):
for c, segm in enumerate(output):
rle = encode_mask_to_rle(segm)
rles.append(rle)
filename_and_class.append(f"{IND2CLASS[c]}_{image_name.replace('_','-')}")
dices = torch.cat(dices, 0)
dices_per_class = torch.mean(dices, 0)
dice_str = [
f"{d.item():.4f}"
for c, d in zip(CLASSES, dices_per_class)
]
dice_str = "\n".join(dice_str)
avg_dice = torch.mean(dices_per_class).item()
print(dice_str)
print(f'{avg_dice:.4f}')
classes, filename = zip(*[x.split("_") for x in filename_and_class])
image_name = [os.path.basename(f) for f in filename]
df = pd.DataFrame(
{
"image_name": image_name,
"class": classes,
"rle": rles,
}
)
df.to_csv(f'{save_csv_path}/{molel_name}_{threshold}.csv', index=False)
if __name__ == "__main__":
args = argparse.ArgumentParser(description="PyTorch Template")
args.add_argument(
"-c",
"--config",
default="/data/ephemeral/home/level2-cv-semanticsegmentation-cv-03/config_inference.json",
type=str,
help="config file path (default: None)",
)
args.add_argument(
"-r",
"--resume",
default=None,
type=str,
help="path to latest checkpoint (default: None)",
)
args.add_argument(
"-d",
"--device",
default=None,
type=str,
help="indices of GPUs to enable (default: all)",
)
CustomArgs = collections.namedtuple("CustomArgs", "flags type target")
config = ConfigParser.from_args(args, mode="inference")
main(config)