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train.py
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train.py
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import numpy as np
import torch
from sklearn.metrics import roc_auc_score, average_precision_score
from tqdm import tqdm
import config as c
from localization import export_gradient_maps
from model import FastFlow, save_model, save_weights
from utils import *
import neptune.new as neptune
import config as c
import neptuneparams as nep_params
# Neptune.ai set up, in order to keep track of your experiments
if c.neptune_activate:
run = neptune.init(
project = nep_params.project,
api_token = nep_params.api_token,
) # your credentials
run["name_dataset"] = [c.dataset_path]
run["img_dims"] = [c.img_dims]
run["device"] = c.device
run["n_scales"] = c.n_scales
run["class_name"] = [c.class_name]
run["meta_epochs"] = c.meta_epochs
run["sub_epochs"] = c.sub_epochs
run["batch_size"]= c.batch_size
run["n_coupling_blocks"] = c.n_coupling_blocks
run["n_transforms"] = c.n_transforms
run["n_transforms_test"] = c.n_transforms_test
run["dropout"] =c.dropout
run["learning_rate"] = c.lr_init
run["subnet_conv_dim"]= c.subnet_conv_dim
class Score_Observer:
'''Keeps an eye on the current and highest score so far'''
def __init__(self, name):
self.name = name
self.max_epoch = 0
self.max_score = None
self.last = None
def update(self, score, epoch, print_score=False):
self.last = score
if epoch == 0 or score > self.max_score:
self.max_score = score
self.max_epoch = epoch
if print_score:
self.print_score()
def print_score(self):
print('{:s}: \t last: {:.4f} \t max: {:.4f} \t epoch_max: {:d}'.format(self.name, self.last, self.max_score,
self.max_epoch))
def train(train_loader, test_loader):
model = FastFlow()
optimizer = torch.optim.Adam(model.nf.parameters(), lr=c.lr_init, betas=(0.8, 0.8), eps=1e-04, weight_decay=1e-5)
model.to(c.device)
score_obs_auroc = Score_Observer('AUROC')
score_obs_aucpr = Score_Observer('AUCPR')
for epoch in range(c.meta_epochs):
# train some epochs
model.train()
if c.verbose:
print(F'\nTrain epoch {epoch}')
for sub_epoch in range(c.sub_epochs):
train_loss = list()
for i, data in enumerate(tqdm(train_loader, disable=c.hide_tqdm_bar)):
optimizer.zero_grad()
inputs, labels = preprocess_batch(data) # move to device and reshape
# TODO inspect
# inputs += torch.randn(*inputs.shape).cuda() * c.add_img_noise
z, log_jac_det = model(inputs)
loss = get_loss(z, log_jac_det)
train_loss.append(t2np(loss))
loss.backward()
optimizer.step()
mean_train_loss = np.mean(train_loss)
if c.verbose:
print('Epoch: {:d}.{:d} \t train loss: {:.4f}'.format(epoch, sub_epoch, mean_train_loss))
if c.neptune_activate:
run["train/train_loss"].log(mean_train_loss)
# evaluate
model.eval()
if c.verbose:
print('\nCompute loss and scores on test set:')
test_loss = list()
test_z = list()
test_labels = list()
with torch.no_grad():
for i, data in enumerate(tqdm(test_loader, disable=c.hide_tqdm_bar)):
inputs, labels = preprocess_batch(data)
z, log_jac_det = model(inputs)
# Why do I compute the loss also for defective images, which will have great loss values?
loss = get_loss(z, log_jac_det)
test_z.append(z)
test_loss.append(t2np(loss))
test_labels.append(t2np(labels))
test_loss_good = list()
test_loss_defective = list()
for i in range(len(test_labels)):
if test_labels[i] == 0: # label value of good TODO eliminate magic numbers
test_loss_good.append(test_loss[i])
else:
test_loss_defective.append(-test_loss[i])
test_loss_good = np.mean(np.array(test_loss_good))
test_loss_defective = np.mean(np.array(test_loss_defective))
test_loss = np.mean(np.array(test_loss))
if c.verbose:
print('Epoch: {:d} \t test_loss: {:.4f} \t test_loss_good: {:.4f} \t test_loss_defective: {:.4f}'.format(epoch, test_loss, test_loss_good, test_loss_defective))
test_labels = np.concatenate(test_labels)
is_anomaly = np.array([0 if l == 0 else 1 for l in test_labels])
z_grouped = torch.cat(test_z, dim=0).view(-1, c.n_transforms_test, c.n_feat)
anomaly_score = t2np(torch.mean(z_grouped ** 2, dim=(-2, -1)))
score_obs_auroc.update(roc_auc_score(is_anomaly, anomaly_score), epoch,
print_score=c.verbose or epoch == c.meta_epochs - 1)
score_obs_aucpr.update(average_precision_score(is_anomaly, anomaly_score), epoch,
print_score=c.verbose or epoch == c.meta_epochs - 1)
if c.neptune_activate:
run["train/auroc"].log(score_obs_auroc.last)
run["train/aucpr"].log(score_obs_aucpr.last)
run["train/test_loss"].log(test_loss)
run["train/test_loss_good"].log(test_loss_good)
run["train/test_loss_defective"].log(test_loss_defective)
if c.grad_map_viz:
export_gradient_maps(model, test_loader, optimizer, -1)
if c.save_model:
model.to('cpu')
save_model(model, c.modelname)
save_weights(model, c.modelname)
return model