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main_mvtec.py
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main_mvtec.py
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import os
import sys
import glob
import random
import logging
import argparse
import numpy as np
import pandas as pd
from tqdm import tqdm
from os import makedirs
from os.path import exists
from prettytable import PrettyTable
import torch
from torch.utils.data import DataLoader
from tensorboardX import SummaryWriter
from datasets.data_manager import DataManager
from models.mvtec_model import MVTecNet_AutoEncoder
from trainers.trainer_mvtec import pretrain, train, test
from utils import set_seeds, get_out_dir, eval_spheres_centers, load_mvtec_model_from_checkpoint
def test_models(test_loader: DataLoader, net_cehckpoint: str, tables: tuple, out_df: pd.DataFrame, is_texture: bool, input_shape: tuple, idx_list_enc: list, boundary: str, normal_class: str, use_selectors: bool, device: str, debug: bool):
"""Test a single model.
Parameters
----------
test_loader : DataLoader
Test data loader
net_cehckpoint : str
Path to model checkpoint
tables : tuple
Tuple containing PrettyTabels for soft and hard boundary
out_df : DataFrame
Output dataframe
is_texture : bool
True if we are dealing with texture-type class
input_shape : tuple
Shape of the input data
idx_list_enc : list
List containing the index of layers from which extract features
boundary : str
Type of boundary
normal_class : str
Name of the normal class
use_selectors : bool
True if we want to use Selector modules
device : str
Device to be used
debug : bool
Activate debug mode
Returns
-------
out_df : DataFrame
Dataframe containing the test results
"""
logger = logging.getLogger()
if not os.path.exists(net_cehckpoint):
print(f"File not found at: {net_cehckpoint}")
return out_df
# Get latent code size from checkpoint name
code_length = int(net_cehckpoint.split('/')[-2].split('-')[3].split('_')[-1])
if net_cehckpoint.split('/')[-2].split('-')[-1].split('_')[-1].split('.')[0] == '':
idx_list_enc = [7]
idx_list_enc = [int(i) for i in net_cehckpoint.split('/')[-2].split('-')[-1].split('_')[-1].split('.')]
boundary = net_cehckpoint.split('/')[-2].split('-')[9].split('_')[-1]
normal_class = net_cehckpoint.split('/')[-2].split('-')[2].split('_')[-1]
logger.info(
f"Start test with params"
f"\n\t\t\t\tCode length : {code_length}"
f"\n\t\t\t\tEnc layer list : {idx_list_enc}"
f"\n\t\t\t\tBoundary : {boundary}"
f"\n\t\t\t\tObject class : {normal_class}"
)
# Init Encoder
net = load_mvtec_model_from_checkpoint(
input_shape=input_shape,
code_length=code_length,
idx_list_enc=idx_list_enc,
use_selectors=use_selectors,
net_cehckpoint=net_cehckpoint
)
st_dict = torch.load(net_cehckpoint)
net.load_state_dict(st_dict['net_state_dict'])
### TEST
test_auc, test_b_acc = test(
normal_class=normal_class,
is_texture=is_texture,
net=net,
test_loader=test_loader,
R=st_dict['R'],
c=st_dict['c'],
device=device,
boundary=boundary,
debug=debug
)
table = tables[0] if boundary == 'soft' else tables[1]
table.add_row([
net_cehckpoint.split('/')[-2],
code_length,
idx_list_enc,
net_cehckpoint.split('/')[-2].split('-')[7].split('_')[-1]+'-'+net_cehckpoint.split('/')[-2].split('-')[8],
normal_class,
boundary,
net_cehckpoint.split('/')[-2].split('-')[4].split('_')[-1],
net_cehckpoint.split('/')[-2].split('-')[5].split('_')[-1],
test_auc,
test_b_acc
])
out_df = out_df.append(dict(
path=net_cehckpoint.split('/')[-2],
code_length=code_length,
enc_l_list=idx_list_enc,
weight_decay=net_cehckpoint.split('/')[-2].split('-')[7].split('_')[-1]+'-'+net_cehckpoint.split('/')[-2].split('-')[8],
object_class=normal_class,
boundary=boundary,
batch_size=net_cehckpoint.split('/')[-2].split('-')[4].split('_')[-1],
nu=net_cehckpoint.split('/')[-2].split('-')[5].split('_')[-1],
auc=test_auc,
balanced_acc=test_b_acc
),
ignore_index=True
)
return out_df
def main(args):
# Set seed
set_seeds(args.seed)
# Get the device
device = "cuda" if torch.cuda.is_available() else "cpu"
if args.disable_logging:
logging.disable(level=logging.INFO)
## Init logger & print training/warm-up summary
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s | %(message)s",
handlers=[
logging.FileHandler('./training.log'),
logging.StreamHandler()
])
logger = logging.getLogger()
if args.train or args.pretrain:
# If the list of layers from which extract the features is empty, then use the last one (after the sigmoid)
if len(args.idx_list_enc) == 0: args.idx_list_enc = [7]
logger.info(
"Start run with params:"
f"\n\t\t\t\tPretrain model : {args.pretrain}"
f"\n\t\t\t\tTrain model : {args.train}"
f"\n\t\t\t\tTest model : {args.test}"
f"\n\t\t\t\tBoundary : {args.boundary}"
f"\n\t\t\t\tPretrain epochs : {args.ae_epochs}"
f"\n\t\t\t\tAE-Learning rate : {args.ae_learning_rate}"
f"\n\t\t\t\tAE-milestones : {args.ae_lr_milestones}"
f"\n\t\t\t\tAE-Weight decay : {args.ae_weight_decay}\n"
f"\n\t\t\t\tTrain epochs : {args.epochs}"
f"\n\t\t\t\tBatch size: : {args.batch_size}"
f"\n\t\t\t\tBatch acc. : {args.batch_accumulation}"
f"\n\t\t\t\tWarm up epochs : {args.warm_up_n_epochs}"
f"\n\t\t\t\tLearning rate : {args.learning_rate}"
f"\n\t\t\t\tMilestones : {args.lr_milestones}"
f"\n\t\t\t\tUse selectors : {args.use_selectors}"
f"\n\t\t\t\tWeight decay : {args.weight_decay}\n"
f"\n\t\t\t\tCode length : {args.code_length}"
f"\n\t\t\t\tNu : {args.nu}"
f"\n\t\t\t\tEncoder list : {args.idx_list_enc}\n"
f"\n\t\t\t\tTest metric : {args.metric}"
)
else:
if args.model_ckp is None:
logger.info("CANNOT TEST MODEL WITHOUT A VALID CHECKPOINT")
sys.exit(0)
if args.debug:
args.normal_class = 'carpet'
else:
if os.path.isfile(args.model_ckp):
args.normal_class = args.model_ckp.split('/')[-2].split('-')[2].split('_')[-1]
else:
args.normal_class = args.model_ckp.split('/')[-3]
# Init DataHolder class
data_holder = DataManager(
dataset_name='MVTec_Anomaly',
data_path=args.data_path,
normal_class=args.normal_class,
only_test=args.test
).get_data_holder()
# Load data
train_loader, test_loader = data_holder.get_loaders(
batch_size=args.batch_size,
shuffle_train=True,
pin_memory=device=="cuda",
num_workers=args.n_workers
)
# Print data infos
only_test = args.test and not args.train and not args.pretrain
logger.info("Dataset info:")
logger.info(
"\n"
f"\n\t\t\t\tNormal class : {args.normal_class}"
f"\n\t\t\t\tBatch size : {args.batch_size}"
)
if not only_test:
logger.info(
f"TRAIN:"
f"\n\t\t\t\tNumber of images : {len(train_loader.dataset)}"
f"\n\t\t\t\tNumber of batches : {len(train_loader.dataset)//args.batch_size}"
)
logger.info(
f"TEST:"
f"\n\t\t\t\tNumber of images : {len(test_loader.dataset)}"
)
is_texture = args.normal_class in tuple(["carpet", "grid", "leather", "tile", "wood"])
input_shape = (3, 64, 64) if is_texture else (3, 128, 128)
### PRETRAIN the full AutoEncoder
ae_net_cehckpoint = None
if args.pretrain:
pretrain_out_dir, tmp = get_out_dir(args, pretrain=True, aelr=None, dset_name='mvtec')
pretrain_tb_writer = SummaryWriter(os.path.join(args.output_path, 'mvtec', str(args.normal_class), 'tb_runs/pretrain', tmp))
# Init AutoEncoder
ae_net = MVTecNet_AutoEncoder(input_shape=input_shape, code_length=args.code_length, use_selectors=args.use_selectors)
# Start pretraining
ae_net_cehckpoint = pretrain(
ae_net=ae_net,
train_loader=train_loader,
out_dir=pretrain_out_dir,
tb_writer=pretrain_tb_writer,
device=device,
ae_learning_rate=args.ae_learning_rate,
ae_weight_decay=args.ae_weight_decay,
ae_lr_milestones=args.ae_lr_milestones,
ae_epochs=args.ae_epochs,
log_frequency=args.log_frequency,
batch_accumulation=args.batch_accumulation,
debug=args.debug
)
pretrain_tb_writer.close()
### TRAIN the Encoder
net_cehckpoint = None
if args.train:
if ae_net_cehckpoint is None:
if args.model_ckp is None:
logger.info("CANNOT TRAIN MODEL WITHOUT A VALID CHECKPOINT")
sys.exit(0)
ae_net_cehckpoint = args.model_ckp
aelr = float(ae_net_cehckpoint.split('/')[-2].split('-')[4].split('_')[-1])
train_out_dir, tmp = get_out_dir(args, pretrain=False, aelr=aelr, dset_name='mvtec')
train_tb_writer = SummaryWriter(os.path.join(args.output_path, 'mvtec', str(args.normal_class), 'tb_runs/train', tmp))
# Init the Encoder network
encoder_net = load_mvtec_model_from_checkpoint(
input_shape=input_shape,
code_length=args.code_length,
idx_list_enc=args.idx_list_enc,
use_selectors=args.use_selectors,
net_cehckpoint=ae_net_cehckpoint,
purge_ae_params=True
)
## Eval/Load hyperspeheres centers
encoder_net.set_idx_list_enc(range(8))
centers = eval_spheres_centers(train_loader=train_loader, encoder_net=encoder_net, ae_net_cehckpoint=ae_net_cehckpoint, use_selectors=args.use_selectors, device=device, debug=args.debug)
encoder_net.set_idx_list_enc(args.idx_list_enc)
# Start training
net_cehckpoint = train(
net=encoder_net,
train_loader=train_loader,
centers=centers,
out_dir=train_out_dir,
tb_writer=train_tb_writer,
device=device,
learning_rate=args.learning_rate,
weight_decay=args.weight_decay,
lr_milestones=args.lr_milestones,
epochs=args.epochs,
nu=args.nu,
boundary=args.boundary,
batch_accumulation=args.batch_accumulation,
warm_up_n_epochs=args.warm_up_n_epochs,
log_frequency=args.log_frequency,
debug=args.debug
)
train_tb_writer.close()
### TEST the Encoder
if args.test:
if net_cehckpoint is None:
net_cehckpoint = args.model_ckp
# Init table to print resutls on shell
# If we only test one model at a time, on the two tables will be empty
# If all the model checkpoints are in one folder then the two tables will be automatically filled
table_s = PrettyTable()
table_s.field_names = ['Path', 'Code length', 'Enc layer list', 'weight decay', 'Object class', 'Boundary', 'batch size', 'nu', 'AUC', 'Balanced acc']
table_s.float_format = '0.3'
table_h = PrettyTable()
table_h.field_names = ['Path', 'Code length', 'Enc layer list', 'weight decay', 'Object class', 'Boundary', 'batch size', 'nu', 'AUC', 'Balanced acc']
table_h.float_format = '0.3'
# Init dataframe to store results
out_df = pd.DataFrame()
is_file = os.path.isfile(net_cehckpoint)
if is_file:
out_df = test_models(
test_loader=test_loader,
net_cehckpoint=net_cehckpoint,
tables=(table_s, table_h),
out_df=out_df,
is_texture=is_texture,
input_shape=input_shape,
idx_list_enc=args.idx_list_enc,
boundary=args.boundary,
normal_class=args.normal_class,
use_selectors=args.use_selectors,
device=device,
debug=args.debug
)
else:
for model_ckp in tqdm(os.listdir(net_cehckpoint), total=len(os.listdir(net_cehckpoint)), desc="Running on models"):
out_df = test_models(
test_loader=test_loader,
net_cehckpoint=os.path.join(net_cehckpoint, model_ckp, 'best_oc_model_model.pth'),
tables=(table_s, table_h),
out_df=out_df,
is_texture=is_texture,
idx_list_enc=args.idx_list_enc,
boundary=args.boundary,
normal_class=args.normal_class,
use_selectors=args.use_selectors,
device=device,
debug=args.debug
)
print(table_s)
print(table_h)
b_path = "./output/mvtec_test_results/test_csv"
if not exists(b_path):
makedirs(b_path)
normal_class = net_cehckpoint.split('/')[-4]
ff = glob.glob(os.path.join(b_path, f'*{normal_class}*'))
if len(ff) == 0:
csv_out_name = os.path.join(b_path, f"test-results-{normal_class}_0.csv")
else:
ff.sort()
version = int(ff[-1].split('_')[-1].split('.')[0]) + 1
logger.info(f"Already found csv file for {normal_class} with latest version: {version-1} ==> creaing new csv file with version: {version}")
csv_out_name = os.path.join(b_path, f"test-results-{normal_class}_{version}.csv")
out_df.to_csv(csv_out_name)
if __name__ == '__main__':
parser = argparse.ArgumentParser('AD')
## General config
parser.add_argument('-s', '--seed', type=int, default=-1, help='Random seed (default: -1)')
parser.add_argument('--n_workers', type=int, default=8, help='Number of workers for data loading. 0 means that the data will be loaded in the main process. (default: 8)')
parser.add_argument('--output_path', default='./output')
parser.add_argument('-lf', '--log-frequency', type=int, default=5, help='Log frequency (default: 5)')
parser.add_argument('-dl', '--disable-logging', action="store_true", help='Disabel logging (default: False)')
parser.add_argument('-db', '--debug', action="store_true", help='Activate debug mode, i.e., only use the first three batches (default: False)')
## Model config
parser.add_argument('-zl', '--code-length', default=64, type=int, help='Code length (default: 64)')
parser.add_argument('-ck', '--model-ckp', help='Model checkpoint')
## Optimizer config
parser.add_argument('-alr', '--ae-learning-rate', type=float, default=1.e-4, help='Warm up learning rate (default: 1.e-4)')
parser.add_argument('-lr', '--learning-rate', type=float, default=1.e-4, help='Learning rate (default: 1.e-4)')
parser.add_argument('-awd', '--ae-weight-decay', type=float, default=0.5e-6, help='Warm up learning rate (default: 0.5e-4)')
parser.add_argument('-wd', '--weight-decay', type=float, default=0.5e-6, help='Learning rate (default: 0.5e-6)')
parser.add_argument('-aml', '--ae-lr-milestones', type=int, nargs='+', default=[], help='Pretrain milestone')
parser.add_argument('-ml', '--lr-milestones', type=int, nargs='+', default=[], help='Training milestone')
## Data
parser.add_argument('-dp', '--data-path', default='./MVTec_Anomaly', help='Dataset main path')
parser.add_argument('-nc', '--normal-class', choices=('bottle', 'capsule', 'grid', 'leather', 'metal_nut', 'screw', 'toothbrush', 'wood', 'cable', 'carpet', 'hazelnut', 'pill', 'tile', 'transistor', 'zipper'), default='cable', help='Category (default: cable)')
## Training config
parser.add_argument('-we', '--warm_up_n_epochs', type=int, default=5, help='Warm up epochs (default: 5)')
parser.add_argument('--use-selectors', action="store_true", help='Use features selector (default: False)')
parser.add_argument('-ba', '--batch-accumulation', type=int, default=-1, help='Batch accumulation (default: -1, i.e., None)')
parser.add_argument('-ptr', '--pretrain', action="store_true", help='Pretrain model (default: False)')
parser.add_argument('-tr', '--train', action="store_true", help='Train model (default: False)')
parser.add_argument('-tt', '--test', action="store_true", help='Test model (default: False)')
parser.add_argument('-tbc', '--train-best-conf', action="store_true", help='Train best configurations (default: False)')
parser.add_argument('-bs', '--batch-size', type=int, default=128, help='Batch size (default: 128)')
parser.add_argument('-bd', '--boundary', choices=("hard", "soft"), default="soft", help='Boundary (default: soft)')
parser.add_argument('-ile', '--idx-list-enc', type=int, nargs='+', default=[], help='List of indices of model encoder')
parser.add_argument('-e', '--epochs', type=int, default=1, help='Training epochs (default: 1)')
parser.add_argument('-ae', '--ae-epochs', type=int, default=1, help='Warmp up epochs (default: 1)')
parser.add_argument('-nu', '--nu', type=float, default=0.1)
## Test config
parser.add_argument('-mt', '--metric', choices=(1, 2), type=int, default=2, help="Metric to evaluate norms (default: 2, i.e., L2)")
args = parser.parse_args()
main(args)