def on_epoch_end(self, data: Data):
for _ in range(self.trials):
img_path = self.dataset.one_shot_trial(self.N)
input_img = (
np.array([
np.expand_dims(cv2.imread(i, cv2.IMREAD_GRAYSCALE), -1) /
255. for i in img_path[0]
],
dtype=np.float32),
np.array([
np.expand_dims(cv2.imread(i, cv2.IMREAD_GRAYSCALE), -1) /
255. for i in img_path[1]
],
dtype=np.float32))
prediction_score = feed_forward(self.model,
input_img,
training=False).numpy()
if np.argmax(
prediction_score) == 0 and prediction_score.std() > 0.01:
self.correct += 1
self.total += 1
data.write_with_log(self.outputs[0], self.correct / self.total)
def on_epoch_end(self, data: Data):
device = next(self.model.parameters()).device
for _ in range(self.trials):
img_path = self.dataset.one_shot_trial(self.N)
input_img = (
np.array([
np.expand_dims(cv2.imread(i, cv2.IMREAD_GRAYSCALE),
-1).reshape((1, 105, 105)) / 255.
for i in img_path[0]
],
dtype=np.float32),
np.array([
np.expand_dims(cv2.imread(i, cv2.IMREAD_GRAYSCALE),
-1).reshape((1, 105, 105)) / 255.
for i in img_path[1]
],
dtype=np.float32))
input_img = (to_tensor(input_img[0], "torch").to(device),
to_tensor(input_img[1], "torch").to(device))
model = self.model.module if torch.cuda.device_count(
) > 1 else self.model
prediction_score = feed_forward(
model, input_img, training=False).cpu().detach().numpy()
if np.argmax(
prediction_score) == 0 and prediction_score.std() > 0.01:
self.correct += 1
self.total += 1
data.write_with_log(self.outputs[0], self.correct / self.total)
def on_epoch_end(self, data: Data) -> None:
data.write_with_log(self.outputs[0], np.mean(self.dice))