def main(_):
checkpoint_path=FLAGS.checkpoint_path
img = load_image(FLAGS.input)
labels = load_labels_from_file(FLAGS.dataset_dir)
# num_classes = len(labels) + FLAGS.label_offset
num_classes = len(labels)
network_fn = nets_factory.get_network_fn(
FLAGS.model_name,
num_classes=num_classes,
is_training=False)
eval_image_size = FLAGS.eval_image_size or network_fn.default_image_size
print("\nLoading Model")
imgs0 = tf.placeholder(tf.uint8, [None, None, 3])
imgs = preprocess_image(imgs0, eval_image_size)
imgs = tf.expand_dims(imgs, 0)
_,end_points = network_fn(imgs)
# print(end_points)
checkpoint_file = tf.train.latest_checkpoint(checkpoint_path)
init_fn = slim.assign_from_checkpoint_fn(checkpoint_file, slim.get_variables_to_restore())
print("\nFeedforwarding")
with tf.Session() as sess:
init_fn(sess)
# print(init_fn(sess))
ep = sess.run(end_points, feed_dict={imgs0: img})
pred_layer_name = _layer_names[FLAGS.model_name][1]
probs = ep[pred_layer_name][0]
preds = (np.argsort(probs)[::-1])[0:5]
print(preds)
print('\nTop 5 classes are')
for p in preds:
# print(p-FLAGS.label_offset, labels[p-FLAGS.label_offset], probs[p])
print(p, labels[p], probs[p])
for i in range(5):
# Target class
predicted_class = preds[i]
print(predicted_class)
# Target layer for visualization
layer_name = FLAGS.layer_name or _layer_names[FLAGS.model_name][0]
# Number of output classes of model being used
nb_classes = num_classes
cam3 = grad_cam(img, imgs0, end_points, sess, predicted_class, layer_name, nb_classes, eval_image_size)
img = cv2.resize(img, (eval_image_size, eval_image_size))
img = img.astype(float)
img /= img.max()
cam3 = cv2.applyColorMap(np.uint8(255*cam3), cv2.COLORMAP_JET)
cam3 = cv2.cvtColor(cam3, cv2.COLOR_BGR2RGB)
# Superimposing the visualization with the image.
alpha = 0.0025
new_img = img+alpha*cam3
new_img /= new_img.max()
# Display and save
io.imshow(new_img)
plt.axis('off')
plt.savefig(FLAGS.output, bbox_inches='tight')
plt.show()
def get_network_fn(model_name, num_classes):
return nets_factory.get_network_fn(model_name,
num_classes=num_classes,
is_training=False)