def test_imagenet_heatmap():
print("Testing heatmap mode with imagenet head...")
model = build_resnet_50(fully_convolutional=True)
images = load_test_image_paths()
for image_path in images:
if "ipod" in image_path:
dim = int(224 * 1)
pred = predict(model, image_path, target_size=(dim, dim))
heatmap = pred[0, :, :, 605]
mean, std = np.mean(heatmap), np.std(heatmap)
threshold = mean + 2 * std
assert (threshold > 0)
heatmap[heatmap < threshold] = 0
heatmap[heatmap >= threshold] = 1
print("Binary ipod heatmap for ipod.jpg, scaled to {}x{}:".format(
dim, dim))
print(heatmap)
print("Compare ipod.jpg to see that it localizes the ipod!")
true_heatmap = np.array([[0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 1., 1., 0.],
[0., 0., 0., 0., 1., 1., 0.],
[0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0.]])
assert (np.allclose(true_heatmap, heatmap))
def test_custom_head_heatmap():
print("Testing fully convolutial mode with custom head...")
model = build_resnet_50(fully_convolutional=True, cls_head='custom')
images = load_test_image_paths()
for image_path in images:
if "cat3" in image_path:
dim = int(224 * 1.3)
pred = predict(model, image_path, target_size=(dim, dim))
heatmap = pred[0, :, :, 0]
mean, std = np.mean(heatmap), np.std(heatmap)
threshold = mean + 2 * std
assert (threshold > 0)
heatmap[heatmap < threshold] = 0
heatmap[heatmap >= threshold] = 1
print("Binary cat heatmap for cat3.jpg, scaled to {}x{}:".format(dim, dim))
print(heatmap)
print("Compare cat3.jpg to see that it localizes the cat!")
true_heatmap = np.array([[0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 1., 0., 0., 0., 0.],
[0., 0., 0., 1., 1., 1., 0., 0., 0.],
[0., 0., 0., 1., 1., 1., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0.]])
assert (np.allclose(true_heatmap, heatmap))
def extract_features(extension, img_size):
extractor = Extractor(extension)
image_files, features = extractor.load_latest_extractions()
if image_files is None or features is None:
name = "{}x{}_{}".format(img_size[0], img_size[1], "feature-maps")
model = build_resnet_50(cls_head=None)
image_files, features = extractor.extract(name, model, img_size)
print("Features extracted. Shape: {}".format(features.shape))
return image_files, features
def test_imagenet_classification():
print("Testing classification mode with imagenet head...")
model = build_resnet_50()
images = load_test_image_paths()
preds = []
for image_path in images:
preds.append(predict(model, image_path))
predictios = np.vstack(preds)
for i, top5 in enumerate(decode_predictions(predictios)):
current_image = images[i]
current_class_label = IMAGE_CLASS_LABELS[current_image]
class_labels_predictions = [pred[1] for pred in top5]
print("Image: {} | Actual: {} | Top5 predictions: {}".format(
current_image, current_class_label, class_labels_predictions))
assert (current_class_label in class_labels_predictions)
def test_custom_head_classification():
print("Testing classfication mode with custom head...")
model = build_resnet_50(cls_head='custom')
images = load_test_image_paths()
preds = []
for image_path in images:
preds.append(predict(model, image_path))
predictios = np.vstack(preds)
for i, pred in enumerate(predictios):
current_image = images[i]
is_cat = "cat" in current_image
print("Image: {} | Cat score: {:3.5f} | No-cat score {:3.5f}".format(
current_image, pred[0], pred[1]))
if is_cat:
assert (pred[0] > 0.95)
assert (pred[1] < 0.05)
else:
assert (pred[0] < 0.05)
assert (pred[1] > 0.95)
parser.add_argument("-ws",
"--width",
nargs="?",
type=int,
default=224,
help="Image width")
args = parser.parse_args()
return args
def extract(name, model, extension, img_size):
extractor = Extractor(extension)
image_files, features = extractor.extract(name, model, img_size)
def filename_base(args):
return "{}x{}-FCN_{}-{}" \
.format(args.height, args.width, args.fully_convolutional,
args.cls_head)
if __name__ == "__main__":
args = parse_args()
img_size = (args.height, args.width)
model = build_resnet_50(args.fully_convolutional, 'imagenet',
args.cls_head)
name = filename_base(args)
extract(name, model, args.extension, img_size)