def test_eval(self, pkl=False):
print("Testing mode will begin NOW..")
# load the best model checkpoint to test on it
if not pkl:
self.load_best_model()
# init tqdm and get the epoch value
tt = tqdm(range(self.test_data_len))
# idx of image
idx = 0
# loop by the number of iterations
for cur_iteration in tt:
# load mini_batches
x_batch = self.test_data['X'][idx:idx + 1]
# Feed this variables to the network
if self.args.random_cropping:
feed_dict = {
self.test_model.x_pl_before: x_batch,
self.test_model.is_training: False,
}
else:
feed_dict = {
self.test_model.x_pl: x_batch,
self.test_model.is_training: False
}
# run the feed_forward
out_argmax, segmented_imgs = self.sess.run([
self.test_model.out_argmax, self.test_model.segmented_summary
],
feed_dict=feed_dict)
if pkl:
out_argmax[0] = self.linknet_postprocess(out_argmax[0])
segmented_imgs = decode_labels(out_argmax, 20)
# Colored results for visualization
colored_save_path = self.args.out_dir + 'imgs/' + str(
self.names_mapper['Y'][idx])
if not os.path.exists(os.path.dirname(colored_save_path)):
os.makedirs(os.path.dirname(colored_save_path))
plt.imsave(colored_save_path, segmented_imgs[0])
# Results for official evaluation
save_path = self.args.out_dir + 'results/' + str(
self.names_mapper['Y'][idx])
if not os.path.exists(os.path.dirname(save_path)):
os.makedirs(os.path.dirname(save_path))
output = postprocess(out_argmax[0])
misc.imsave(save_path,
misc.imresize(output, [1024, 2048], 'nearest'))
idx += 1
# print in console
tt.close()
def test(self, pkl=False):
print("Testing mode will begin NOW..")
# load the best model checkpoint to test on it
if not pkl:
self.load_best_model()
# init tqdm and get the epoch value
tt = tqdm(range(self.test_data_len))
# naming = np.load(self.args.data_dir + 'names_train.npy')
# init acc and loss lists
acc_list = []
img_list = []
# idx of image
idx = 0
# reset metrics
self.metrics.reset()
# loop by the number of iterations
for cur_iteration in tt:
# load mini_batches
x_batch = self.test_data['X'][idx:idx + 1]
y_batch = self.test_data['Y'][idx:idx + 1]
if self.args.data_mode == 'test_v2':
y_batch_large = self.test_data['Y_large'][idx:idx + 1]
idx += 1
# Feed this variables to the network
if self.args.random_cropping:
feed_dict = {self.test_model.x_pl_before: x_batch,
self.test_model.y_pl_before: y_batch,
self.test_model.is_training: False,
}
else:
feed_dict = {self.test_model.x_pl: x_batch,
self.test_model.y_pl: y_batch,
self.test_model.is_training: False
}
# run the feed_forward
if self.args.data_mode == 'test_v2':
out_argmax, acc = self.sess.run(
[self.test_model.out_argmax, self.test_model.accuracy],
feed_dict=feed_dict)
else:
out_argmax, acc, segmented_imgs = self.sess.run(
[self.test_model.out_argmax, self.test_model.accuracy,
# self.test_model.merged_summaries, self.test_model.segmented_summary],
self.test_model.segmented_summary],
feed_dict=feed_dict)
if self.args.data_mode == 'test_v2':
yy = np.zeros((out_argmax.shape[0], y_batch_large.shape[1], y_batch_large.shape[2]), dtype=np.uint32)
out_argmax = np.asarray(out_argmax, dtype=np.uint8)
for y in range(out_argmax.shape[0]):
yy[y, ...] = misc.imresize(out_argmax[y, ...], y_batch_large.shape[1:], interp='nearest')
y_batch = y_batch_large
out_argmax = yy
if pkl:
out_argmax[0] = self.linknet_postprocess(out_argmax[0])
segmented_imgs = decode_labels(out_argmax, 20)
# print('mean preds ', out_argmax.mean())
# np.save(self.args.out_dir + 'npy/' + str(cur_iteration) + '.npy', out_argmax[0])
if self.args.data_mode == 'test':
plt.imsave(self.args.out_dir + 'imgs/' + 'test_' + str(cur_iteration) + '.png', segmented_imgs[0])
# log loss and acc
acc_list += [acc]
# log metrics
if self.args.random_cropping:
y1 = np.expand_dims(y_batch[0, :, :512], axis=0)
y2 = np.expand_dims(y_batch[0, :, 512:], axis=0)
y_batch = np.concatenate((y1, y2), axis=0)
self.metrics.update_metrics(out_argmax, y_batch, 0, 0)
else:
self.metrics.update_metrics(out_argmax[0], y_batch[0], 0, 0)
# mean over batches
total_loss = 0
total_acc = np.mean(acc_list)
mean_iou = self.metrics.compute_final_metrics(self.test_data_len)
# print in console
tt.close()
print("Here the statistics")
print("Total_loss: " + str(total_loss))
print("Total_acc: " + str(total_acc)[:6])
print("mean_iou: " + str(mean_iou))
print("Plotting imgs")
for i in range(len(img_list)):
plt.imsave(self.args.imgs_dir + 'test_' + str(i) + '.png', img_list[i])
