# get a cropped image sample
image = Assistant.get_cropped_sample()[0].unsqueeze(0).unsqueeze(0).to(
device)
# pass image through first module of FasteNet and get feature map
F_map_1 = FasteNet.module_one(image)
# based on feature map, scale to get inputs for AC
AC_input = F.adaptive_max_pool2d(F_map_1[..., :32], 32)
# based on feature map get reward and actions
actions, estimated_reward = ActorCritic.forward(AC_input)
# crop the feature map
cropped_F_map = Assistant.crop_feature_map(actions[0].squeeze().item(),
actions[1].squeeze().item(),
F_map_1).to(device)
# passed cropped feature map to FasteNet to get saliency map
saliency_map = FasteNet.module_two(cropped_F_map).to(
'cpu').squeeze().numpy()
# calculate 'reward' from saliency map
Assistant.parse_saliency_map(saliency_map)
reward = Assistant.calculate_loss()
# zero the gradients of both actor and critic
Actor_optimizer.zero_grad()
Critic_optimizer.zero_grad()
# calculate loss
