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valid_models.py
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valid_models.py
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import os
import gc
from functools import partial
from tqdm import tqdm
import cv2
import numpy as np
import logging
import albumentations as A
from albumentations.pytorch import ToTensorV2
from albumentations import ImageOnlyTransform
import segmentation_models_pytorch as smp
import torch
from torch.utils.data import DataLoader
from torch.cuda.amp import GradScaler
from torch.cuda.amp import autocast
from torch.optim import Adam, SGD, AdamW
from torchvision.transforms.functional import hflip, vflip, rotate
from utils.dataset_v import VesuviusDataset
from utils.image_loaders import get_train_valid_dataset_4_folds, read_images_mask_middle_layers
from utils.set_seed import set_seed
from utils.metrics import calc_cv
from torchvision.transforms.functional import hflip, vflip, rotate
from utils.triple_mit_unet_uneven import VesuviusModelTripleMIT_Uneven
from utils.gradual_warmup_scheduler_v2 import get_scheduler, scheduler_step
from utils.manet_meanpooled import MAnetMeanPooled
from utils.pooled_unet_smp import UnetMeanPooled
from torch.utils.tensorboard import SummaryWriter
class CFG:
device = 'cuda:1'
PATH_TO_DS = '../data_4_folds'
PATH_TO_SAVE_INF = './inference'
valid_batch_size = 1
num_workers = 4
criterion = smp.losses.SoftBCEWithLogitsLoss()
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def valid_fn(valid_loader, model, criterion, img_shape, device, _rotate=False):
mask_pred = np.zeros(img_shape)
mask_count = np.zeros(img_shape)
model = model.eval()
losses = AverageMeter()
for step, (images, labels, valid_xyxy) in tqdm(enumerate(valid_loader), total=len(valid_loader)):
images = images.to(device)
labels = labels.to(device)
batch_size = labels.size(0)
with torch.no_grad():
y_preds = model(images)
loss = criterion(y_preds, labels)
losses.update(loss.item(), batch_size)
y_preds = torch.sigmoid(y_preds).to('cpu').numpy()
for i in range(batch_size):
x1, y1, x2, y2 = valid_xyxy[i, 0].item(), valid_xyxy[i, 1].item(), valid_xyxy[i, 2].item(), valid_xyxy[i, 3].item()
mask_pred[y1:y2, x1:x2] += y_preds[i].squeeze(0)
mask_count[y1:y2, x1:x2] += 1
print(f'mask count min {mask_count.min()}')
mask_pred /= mask_count
return losses.avg, mask_pred
def criterion(y_pred, y_true):
return CFG.criterion(y_pred, y_true)
def valid_on_fold(valid_img, model, transformations, in_chans, tile_size, stride, rotate=False):
read_images_mask = partial(read_images_mask_middle_layers, PATH_TO_DS = CFG.PATH_TO_DS, chans_idxs=in_chans, tile_size=tile_size)
_, _, valid_images, valid_masks, valid_xyxys = get_train_valid_dataset_4_folds(valid_img, read_images_mask, tile_size, stride, stride)
valid_dataset = VesuviusDataset(valid_images, valid_masks, valid_xyxys, transformations)
valid_dataloader = DataLoader(valid_dataset,
batch_size=CFG.valid_batch_size,
shuffle=False,
num_workers=CFG.num_workers, pin_memory=True, drop_last=False)
valid_mask_gt = cv2.imread(os.path.join(CFG.PATH_TO_DS, f"train/{valid_img}/inklabels.png"), 0)
valid_mask_gt_shape = valid_mask_gt.shape
valid_mask_gt = valid_mask_gt / 255
pad0 = (tile_size - valid_mask_gt.shape[0] % tile_size)
pad1 = (tile_size - valid_mask_gt.shape[1] % tile_size)
valid_mask_gt = np.pad(valid_mask_gt, [(0, pad0), (0, pad1)], constant_values=0)
model.to(CFG.device)
avg_val_loss, mask_pred = valid_fn(
valid_dataloader, model, criterion, valid_mask_gt.shape, CFG.device, _rotate=rotate)
best_dice, best_th, ths = calc_cv(valid_mask_gt, mask_pred)
print(f"Avg val loss - {avg_val_loss}")
print("THs", ths)
print(f"best_th - {best_th}")
print(f'best dice - {best_dice}')
del model
gc.collect()
torch.cuda.empty_cache()
return best_th, best_dice, mask_pred
DATASET_PATH = '/home/dmitry/Documents/KaggleCompetitions/Vesuvius/'
model_paths = [#'models/mit_mean_pooling_32_ch_256/Dataset/mit_mean_pooling_32_ch_256_fold_1_epoch_12_model.pth',
#'models/mit_mean_pooling_32_ch_256/Dataset/mit_mean_pooling_32_ch_256_fold_1_epoch_21_model.pth',
#'models/mit_mean_pooling_32_ch_256/Dataset/mit_mean_pooling_32_ch_256_fold_2_epoch_10_model.pth',
#'models/mit_mean_pooling_32_ch_256/Dataset/mit_mean_pooling_32_ch_256_fold_2_epoch_21_model.pth',
#'models/mit_mean_pooling_32_ch_256/Dataset/mit_mean_pooling_32_ch_256_fold_3_epoch_12_model.pth',
#'models/mit_mean_pooling_32_ch_256/Dataset/mit_mean_pooling_32_ch_256_fold_3_epoch_19_model.pth',
#'models/mit_mean_pooling_32_ch_256/Dataset/mit_mean_pooling_32_ch_256_fold_4_epoch_13_model.pth',
#'models/mit_mean_pooling_32_ch_256/Dataset/mit_mean_pooling_32_ch_256_fold_4_epoch_13_model.pth',
#'models/mit_b1_manet_32_ch/Dataset/mit_b1_manet_32_ch_fold_1_epoch_12_model.pth',
#'models/mit_b1_manet_32_ch/Dataset/mit_b1_manet_32_ch_fold_1_epoch_23_model.pth',
#'models/mit_b1_manet_32_ch/Dataset/mit_b1_manet_32_ch_fold_2_epoch_16_model.pth',
#'models/mit_b1_manet_32_ch/Dataset/mit_b1_manet_32_ch_fold_2_epoch_25_model.pth',
#'models/mit_b1_manet_32_ch/Dataset/mit_b1_manet_32_ch_fold_3_epoch_22_model.pth',
#'models/mit_b1_manet_32_ch/Dataset/mit_b1_manet_32_ch_fold_3_epoch_31_model.pth',
#'models/mit_b1_manet_32_ch/Dataset/mit_b1_manet_32_ch_fold_4_epoch_20_model.pth',
#'models/mit_b1_manet_32_ch/Dataset/mit_b1_manet_32_ch_fold_4_epoch_30_model.pth',
'models/triple_mit_b1_b2_9_slices_from_the_middle_default_augmentations_4_folds_uneven/Dataset/triple_mit_b1_b2_9_slices_from_the_middle_default_augmentations_4_folds_uneven_fold_1_epoch_8_model.pth',
'models/triple_mit_b1_b2_9_slices_from_the_middle_default_augmentations_4_folds_uneven/Dataset/triple_mit_b1_b2_9_slices_from_the_middle_default_augmentations_4_folds_uneven_fold_1_epoch_30_model.pth',
'models/triple_mit_b1_b2_9_slices_from_the_middle_default_augmentations_4_folds_uneven/Dataset/triple_mit_b1_b2_9_slices_from_the_middle_default_augmentations_4_folds_uneven_fold_2_epoch_13_model.pth',
'models/triple_mit_b1_b2_9_slices_from_the_middle_default_augmentations_4_folds_uneven/Dataset/triple_mit_b1_b2_9_slices_from_the_middle_default_augmentations_4_folds_uneven_fold_2_epoch_20_model.pth',
'models/triple_mit_b1_b2_9_slices_from_the_middle_default_augmentations_4_folds_uneven/Dataset/triple_mit_b1_b2_9_slices_from_the_middle_default_augmentations_4_folds_uneven_fold_3_epoch_13_model.pth',
'models/triple_mit_b1_b2_9_slices_from_the_middle_default_augmentations_4_folds_uneven/Dataset/triple_mit_b1_b2_9_slices_from_the_middle_default_augmentations_4_folds_uneven_fold_3_epoch_16_model.pth',
'models/triple_mit_b1_b2_9_slices_from_the_middle_default_augmentations_4_folds_uneven/Dataset/triple_mit_b1_b2_9_slices_from_the_middle_default_augmentations_4_folds_uneven_fold_4_epoch_13_model.pth',
'models/triple_mit_b1_b2_9_slices_from_the_middle_default_augmentations_4_folds_uneven/Dataset/triple_mit_b1_b2_9_slices_from_the_middle_default_augmentations_4_folds_uneven_fold_4_epoch_20_model.pth',
#'models/mit_b1_max_pooling_32/Dataset/mit_b1_max_pooling_32_fold_1_epoch_10_model.pth',
#'models/mit_b1_max_pooling_32/Dataset/mit_b1_max_pooling_32_fold_1_epoch_21_model.pth',
#'models/mit_b1_max_pooling_32/Dataset/mit_b1_max_pooling_32_fold_2_epoch_17_model.pth',
#'models/mit_b1_max_pooling_32/Dataset/mit_b1_max_pooling_32_fold_2_epoch_22_model.pth',
#'models/mit_b1_max_pooling_32/Dataset/mit_b1_max_pooling_32_fold_3_epoch_25_model.pth',
#'models/mit_b1_max_pooling_32/Dataset/mit_b1_max_pooling_32_fold_3_epoch_30_model.pth',
#'models/mit_b1_max_pooling_32/Dataset/mit_b1_max_pooling_32_fold_4_epoch_15_model.pth',
#'models/mit_b1_max_pooling_32/Dataset/mit_b1_max_pooling_32_fold_4_epoch_33_model.pth'
]
folds = [#1, 1, 2, 2, 3, 3, 4, 4,
#1, 1, 2, 2, 3, 3, 4, 4,
1, 1, 2, 2, 3, 3, 4, 4,
#1, 1, 2, 2, 3, 3, 4, 4
]
model_names = [#'mit_b2_mean_pooling_1_12',
#'mit_b2_mean_pooling_1_21',
#'mit_b2_mean_pooling_2_10',
#'mit_b2_mean_pooling_2_21',
#'mit_b2_mean_pooling_3_12',
#'mit_b2_mean_pooling_3_19',
#'mit_b2_mean_pooling_4_13',
#'mit_b2_mean_pooling_4_13_',
#'mit_b2_manet_mean_pooling_1_12',
#'mit_b2_manet_mean_pooling_1_23',
#'mit_b2_manet_mean_pooling_2_16',
#'mit_b2_manet_mean_pooling_2_25',
#'mit_b2_manet_mean_pooling_3_22',
#'mit_b2_manet_mean_pooling_3_31',
#'mit_b2_manet_mean_pooling_4_20',
#'mit_b2_manet_mean_pooling_4_30',
'triple_mit_b1_b2_1_8',
'triple_mit_b1_b2_1_30',
'triple_mit_b1_b2_2_13',
'triple_mit_b1_b2_2_20',
'triple_mit_b1_b2_3_13',
'triple_mit_b1_b2_3_16',
'triple_mit_b1_b2_4_13',
'triple_mit_b1_b2_4_20',
#'mit_b1_max_pooling_1_10',
#'mit_b1_max_pooling_1_21',
#'mit_b1_max_pooling_2_17',
#'mit_b1_max_pooling_2_22',
#'mit_b1_max_pooling_3_25',
#'mit_b1_max_pooling_3_30',
#'mit_b1_max_pooling_4_15',
#'mit_b1_max_pooling_4_33'
]
in_chans = [#[16 + i for i in range(32)],
#[16 + i for i in range(32)],
#[16 + i for i in range(32)],
#[16 + i for i in range(32)],
#[16 + i for i in range(32)],
#[16 + i for i in range(32)],
#[16 + i for i in range(32)],
#[16 + i for i in range(32)],
#[16 + i for i in range(32)],
#[16 + i for i in range(32)],
#[16 + i for i in range(32)],
#[16 + i for i in range(32)],
#[16 + i for i in range(32)],
#[16 + i for i in range(32)],
#[16 + i for i in range(32)],
#[16 + i for i in range(32)],
[28, 29, 30, 31, 32, 33, 34, 35, 36],
[28, 29, 30, 31, 32, 33, 34, 35, 36],
[28, 29, 30, 31, 32, 33, 34, 35, 36],
[28, 29, 30, 31, 32, 33, 34, 35, 36],
[28, 29, 30, 31, 32, 33, 34, 35, 36],
[28, 29, 30, 31, 32, 33, 34, 35, 36],
[28, 29, 30, 31, 32, 33, 34, 35, 36],
[28, 29, 30, 31, 32, 33, 34, 35, 36],
#[i for i in range(20, 38)],
#[i for i in range(20, 38)],
#[i for i in range(20, 38)],
#[i for i in range(20, 38)],
#[i for i in range(20, 38)],
#[i for i in range(20, 38)],
#[i for i in range(20, 38)],
#[i for i in range(20, 38)]
]
tile_sizes = [#256,
#256,
#256,
#256,
#256,
#256,
#256,
#256,
#256,
#256,
#256,
#256,
#256,
#256,
#256,
#256,
#256,
#256,
448,
448,
448,
448,
448,
448,
448,
448,
#256,
#256,
#256,
#256,
#256,
#256,
#256,
#256,
#256,
]
stride_sizes = [#128,
#128,
#128,
#128,
#128,
#128,
#128,
#128,
#128,
#128,
#128,
#128,
#128,
#128,
#128,
#128,
#128,
#128,
224,
224,
224,
224,
224,
224,
224,
224,
224,
#128,
#128,
#128,
#128,
#128,
#128,
#128,
#128,
#128,
]
def load_model(model_path, model_name, in_chans):
best_th, best_dice = None, None
model_state_dict = torch.load(model_path, map_location=torch.device('cpu'))
if 'mit_b1_max_pooling' in model_name:
model = UnetMeanPooled(encoder_name = 'mit_b1', encoder_weights=None, pooling_type='max', crop_stride=2)
model.load_state_dict(model_state_dict['model'])
best_th = model_state_dict['best_th']
best_dice = model_state_dict['best_dice']
transformations = A.Compose([A.Normalize(
mean= [0] * len(in_chans),
std= [1] * len(in_chans)
),
ToTensorV2(transpose_mask=True)])
elif 'mit_b2_mean_pooling' in model_name:
model = UnetMeanPooled(encoder_name='mit_b2', encoder_weights=None, pooling_type='mean', crop_stride=2, decoder_attention_type='scse')
model.load_state_dict(model_state_dict['model'])
transformations = A.Compose([A.Normalize(
mean= [0] * len(in_chans),
std= [1] * len(in_chans)
),
ToTensorV2(transpose_mask=True)])
best_th = model_state_dict['best_th']
best_dice = model_state_dict['best_dice']
elif 'triple_mit' in model_name:
model = VesuviusModelTripleMIT_Uneven(backbone_small='mit_b1', backbone_name='mit_b2')
model.load_state_dict(model_state_dict['model'])
transformations = A.Compose([A.Normalize(
mean= [0] * len(in_chans),
std= [1] * len(in_chans)
),
ToTensorV2(transpose_mask=True)])
best_th = model_state_dict['best_th']
best_dice = model_state_dict['best_dice']
elif 'mit_b2_manet' in model_name:
model = MAnetMeanPooled(encoder_name='mit_b2')
model.load_state_dict(model_state_dict['model'])
transformations = A.Compose([A.Normalize(
mean= [0] * len(in_chans),
std= [1] * len(in_chans)
),
ToTensorV2(transpose_mask=True)])
best_th = model_state_dict['best_th']
best_dice = model_state_dict['best_dice']
del model_state_dict
gc.collect()
return model, transformations, best_th, best_dice
if __name__ == "__main__":
logging.basicConfig(filename='./valid_models.log',
filemode='a',
format='%(asctime)s,%(msecs)d %(name)s %(levelname)s %(message)s',
datefmt='%H:%M:%S',
level=logging.DEBUG)
logging.info("Valid Models logging")
logger = logging.getLogger('ValidModels')
for model_path, fold, model_name, in_chans, tile_size, stride_size in zip(model_paths, folds, model_names, in_chans, tile_sizes, stride_sizes):
model, transformations, best_th_, best_dice_ = load_model(os.path.join(DATASET_PATH, model_path), model_name, in_chans)
model.to(CFG.device)
best_th, best_dice, mask = valid_on_fold(fold, model, transformations, in_chans, tile_size, stride_size, rotate=False)
print(best_th, best_th_, best_dice, best_dice_)
logger.info(f'{model_name} - best_th : {best_th}, best_dice : {best_dice}, best_th_old : {best_th_}, best_dice_old : {best_dice_}')
np.save(os.path.join(CFG.PATH_TO_SAVE_INF, f'{model_name}_mask.npy'), mask)
del model
gc.collect()
torch.cuda.empty_cache()