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validation.py
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validation.py
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import cv2
from sklearn.metrics import accuracy_score, f1_score
from transforms import get_transforms
from datasets import make_loader
from utils.metrics import dice_score
from utils.functions import resize_batch_images
from utils.utils import post_process, dict_to_json
from utils.config import load_config
from utils import predict_batch, load_model
from models import CustomNet
import segmentation_models_pytorch as smp
from catalyst.dl.utils import load_checkpoint
import argparse
import os
import warnings
import pandas as pd
import numpy as np
import torch
from tqdm import tqdm
warnings.filterwarnings("ignore")
SUB_HEIGHT, SUB_WIDTH = 350, 525
def validation(config_file_seg):
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
config = load_config(config_file_seg)
if 'COLAB_GPU' in os.environ:
config.work_dir = '/content/drive/My Drive/kaggle_cloud/' + config.work_dir
elif 'KAGGLE_WORKING_DIR' in os.environ:
config.work_dir = '/kaggle/working/' + config.work_dir
validloader = make_loader(
data_folder=config.data.train_dir,
df_path=config.data.train_df_path,
phase='valid',
img_size=(config.data.height, config.data.width),
batch_size=config.test.batch_size,
num_workers=config.num_workers,
idx_fold=config.data.params.idx_fold,
transforms=get_transforms(config.transforms.test),
num_classes=config.data.num_classes,
)
model = load_model(config_file_seg)
min_sizes = np.arange(0, 20000, 5000)
label_thresholds = [0.6, 0.7, 0.8]
mask_thresholds = [0.2, 0.3, 0.4]
all_dice = np.zeros(
(4, len(label_thresholds), len(mask_thresholds), len(min_sizes)))
count = 0
with torch.no_grad():
for i, (batch_images, batch_masks) in enumerate(tqdm(validloader)):
batch_images = batch_images.to(config.device)
batch_preds = predict_batch(
model, batch_images, tta=config.test.tta)
batch_labels = torch.nn.functional.adaptive_max_pool2d(
torch.sigmoid(torch.Tensor(batch_preds)), 1).view(batch_preds.shape[0], -1)
batch_masks = batch_masks.cpu().numpy()
batch_labels = batch_labels.cpu().numpy()
batch_masks = resize_batch_images(
batch_masks, SUB_HEIGHT, SUB_WIDTH)
batch_preds = resize_batch_images(
batch_preds, SUB_HEIGHT, SUB_WIDTH)
for labels, masks, preds in zip(batch_labels, batch_masks, batch_preds):
for cls in range(config.data.num_classes):
for i, label_th in enumerate(label_thresholds):
for j, mask_th in enumerate(mask_thresholds):
for k, min_size in enumerate(min_sizes):
if labels[cls] <= label_th:
pred = np.zeros(preds[cls, :, :].shape)
else:
pred, _ = post_process(
preds[cls, :, :],
mask_th,
min_size,
height=SUB_HEIGHT,
width=SUB_WIDTH
)
mask = masks[cls, :, :]
dice = dice_score(pred, mask)
all_dice[cls, i, j, k] += dice
count += 1
all_dice = all_dice / (count)
np.save('all_dice', all_dice)
parameters = {}
parameters['label_thresholds'] = []
parameters['mask_thresholds'] = []
parameters['min_sizes'] = []
parameters['dice'] = []
cv_score = 0
for cls in range(4):
i, j, k = np.where((all_dice[cls] == all_dice[cls].max()))
parameters['label_thresholds'].append(float(label_thresholds[i[0]]))
parameters['mask_thresholds'].append(float(mask_thresholds[j[0]]))
parameters['min_sizes'].append(int(min_sizes[k[0]]))
parameters['dice'].append(float(all_dice[cls].max()))
cv_score += all_dice[cls].max() / 4
print('cv_score:', cv_score)
dict_to_json(parameters, config.work_dir + '/parameters.json')
print(pd.DataFrame(parameters))
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--config', dest='config_file',
help='configuration file path',
default=None, type=str)
return parser.parse_args()
def main():
args = parse_args()
print('segmentation validation.')
validation(args.config_file)
if __name__ == '__main__':
main()