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evaluate.py
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import numpy as np
import torch
from sklearn.metrics import roc_auc_score, roc_curve, auc
from tqdm import tqdm
from model import load_model, FeatureExtractor
import config as c
from utils import *
import matplotlib.pyplot as plt
import torch.nn.functional as F
import PIL
from os.path import join
import os
from copy import deepcopy
localize = True
upscale_mode = 'bilinear'
score_export_dir = join('./viz/scores/', c.modelname)
os.makedirs(score_export_dir, exist_ok=True)
map_export_dir = join('./viz/maps/', c.modelname)
os.makedirs(map_export_dir, exist_ok=True)
def compare_histogram(scores, classes, thresh=2.5, n_bins=64):
classes = deepcopy(classes)
scores = deepcopy(scores)
classes[classes > 0] = 1
scores[scores > thresh] = thresh
bins = np.linspace(np.min(scores), np.max(scores), n_bins)
scores_norm = scores[classes == 0]
scores_ano = scores[classes == 1]
plt.clf()
plt.hist(scores_norm, bins, alpha=0.5, density=True, label='non-defects', color='cyan', edgecolor="black")
plt.hist(scores_ano, bins, alpha=0.5, density=True, label='defects', color='crimson', edgecolor="black")
ticks = np.linspace(0.5, thresh, 5)
labels = [str(i) for i in ticks[:-1]] + ['>' + str(thresh)]
plt.xticks(ticks, labels=labels)
plt.xlabel(r'$-log(p(z))$')
plt.ylabel('Count (normalized)')
plt.legend()
plt.grid(axis='y')
plt.savefig(join(score_export_dir, 'score_histogram.png'), bbox_inches='tight', pad_inches=0)
def viz_roc(values, classes, class_names):
def export_roc(values, classes, export_name='all'):
# Compute ROC curve and ROC area for each class
classes = deepcopy(classes)
classes[classes > 0] = 1
fpr, tpr, _ = roc_curve(classes, values)
roc_auc = auc(fpr, tpr)
plt.clf()
lw = 2
plt.plot(fpr, tpr, color='darkorange',
lw=lw, label='ROC curve (area = %0.4f)' % roc_auc)
plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic for class ' + c.class_name)
plt.legend(loc="lower right")
plt.axis('equal')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.0])
plt.savefig(join(score_export_dir, export_name + '.png'))
export_roc(values, classes)
for cl in range(1, classes.max() + 1):
filtered_indices = np.concatenate([np.where(classes == 0)[0], np.where(classes == cl)[0]])
classes_filtered = classes[filtered_indices]
values_filtered = values[filtered_indices]
export_roc(values_filtered, classes_filtered, export_name=class_names[filtered_indices[-1]])
def viz_maps(maps, name, label):
img_path = img_paths[c.viz_sample_count]
image = PIL.Image.open(img_path).convert('RGB')
image = np.array(image)
map_to_viz = t2np(F.interpolate(maps[0][None, None], size=image.shape[:2], mode=upscale_mode, align_corners=False))[
0, 0]
plt.clf()
plt.imshow(map_to_viz)
plt.axis('off')
plt.savefig(join(map_export_dir, name + '_map.jpg'), bbox_inches='tight', pad_inches=0)
if label > 0:
plt.clf()
plt.imshow(image)
plt.axis('off')
plt.savefig(join(map_export_dir, name + '_orig.jpg'), bbox_inches='tight', pad_inches=0)
plt.imshow(map_to_viz, cmap='viridis', alpha=0.3)
plt.savefig(join(map_export_dir, name + '_overlay.jpg'), bbox_inches='tight', pad_inches=0)
return
def viz_map_array(maps, labels, n_col=8, subsample=4, max_figures=-1):
plt.clf()
fig, subplots = plt.subplots(3, n_col)
fig_count = -1
col_count = -1
for i in range(len(maps)):
if i % subsample != 0:
continue
if labels[i] == 0:
continue
col_count = (col_count + 1) % n_col
if col_count == 0:
if fig_count >= 0:
plt.savefig(join(map_export_dir, str(fig_count) + '.jpg'), bbox_inches='tight', pad_inches=0)
plt.close()
fig, subplots = plt.subplots(3, n_col, figsize=(22, 8))
fig_count += 1
if fig_count == max_figures:
return
anomaly_description = img_paths[i].split('/')[-2]
image = PIL.Image.open(img_paths[i]).convert('RGB')
image = np.array(image)
map = t2np(F.interpolate(maps[i][None, None], size=image.shape[:2], mode=upscale_mode, align_corners=False))[
0, 0]
subplots[1][col_count].imshow(map)
subplots[1][col_count].axis('off')
subplots[0][col_count].imshow(image)
subplots[0][col_count].axis('off')
subplots[0][col_count].set_title(c.class_name + ":\n" + anomaly_description)
subplots[2][col_count].imshow(image)
subplots[2][col_count].axis('off')
subplots[2][col_count].imshow(map, cmap='viridis', alpha=0.3)
for i in range(col_count, n_col):
subplots[0][i].axis('off')
subplots[1][i].axis('off')
subplots[2][i].axis('off')
if col_count > 0:
plt.savefig(join(map_export_dir, str(fig_count) + '.jpg'), bbox_inches='tight', pad_inches=0)
return
def evaluate(model, test_loader):
model.to(c.device)
model.eval()
if not c.pre_extracted:
fe = FeatureExtractor()
fe.eval()
fe.to(c.device)
for param in fe.parameters():
param.requires_grad = False
print('\nCompute maps, loss and scores on test set:')
anomaly_score = list()
test_labels = list()
c.viz_sample_count = 0
all_maps = list()
with torch.no_grad():
for i, data in enumerate(tqdm(test_loader, disable=c.hide_tqdm_bar)):
inputs, labels = preprocess_batch(data)
if not c.pre_extracted:
inputs = fe(inputs)
z = model(inputs)
z_concat = t2np(concat_maps(z))
nll_score = np.mean(z_concat ** 2 / 2, axis=(1, 2))
anomaly_score.append(nll_score)
test_labels.append(t2np(labels))
if localize:
z_grouped = list()
likelihood_grouped = list()
for i in range(len(z)):
z_grouped.append(z[i].view(-1, *z[i].shape[1:]))
likelihood_grouped.append(torch.mean(z_grouped[-1] ** 2, dim=(1,)))
all_maps.extend(likelihood_grouped[0])
for i_l, l in enumerate(t2np(labels)):
# viz_maps([lg[i_l] for lg in likelihood_grouped], c.modelname + '_' + str(c.viz_sample_count), label=l, show_scales = 1)
c.viz_sample_count += 1
anomaly_score = np.concatenate(anomaly_score)
test_labels = np.concatenate(test_labels)
compare_histogram(anomaly_score, test_labels)
class_names = [img_path.split('/')[-2] for img_path in img_paths]
viz_roc(anomaly_score, test_labels, class_names)
test_labels = np.array([1 if l > 0 else 0 for l in test_labels])
auc_score = roc_auc_score(test_labels, anomaly_score)
print('AUC:', auc_score)
if localize:
viz_map_array(all_maps, test_labels)
return
train_set, test_set = load_datasets(c.dataset_path, c.class_name)
img_paths = test_set.paths if c.pre_extracted else [p for p, l in test_set.samples]
_, test_loader = make_dataloaders(train_set, test_set)
mod = load_model(c.modelname)
evaluate(mod, test_loader)