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# Choice of image classification model
img_cls_model_name = ['ResNet-50']
# Choice of object detection model
obj_det_model_name = ['Faster-RCNN']
# Choice of image classification saliency algorithm
img_cls_saliency_algo_name = ['RISE']
# Choice of object detection saliency algorithm
obj_det_saliency_algo_name = ['DRISE']
# Number of threads to utilize when generating masks
threads_state = [4]
# Window_size for SlidingWindowStack algorithm
window_size_state = ['(50,50)']
# Stride for SlidingWindowStack algorithm
stride_state = ['(20,20)']
# Number of random masks for RISEStack/DRISEStack algorithm
num_masks_state = [200]
# Spatial resolution of masking grid for RISEStack/DRISEStack algorithm
spatial_res_state = [8]
# Probability of the grid cell being set to 1 (otherwise 0)
p1_state = [0.5]
# Random seed to allow for reproducibility
seed_state = [0]
# Debiased option for RISEStack/DRISEStack saliency algorithm
debiased_state = [True]
# Occlusion grid cell size in pixels for RandomGridStack algorithm
occlusion_grid_state = ['(128,128)']
def select_img_cls_model(model_choice):
img_cls_model_name.append(model_choice)
return model_choice
def select_obj_det_model(model_choice):
obj_det_model_name.append(model_choice)
return model_choice
def select_img_cls_saliency_algo(sal_choice):
img_cls_saliency_algo_name.append(sal_choice)
return sal_choice
def select_obj_det_saliency_algo(sal_choice):
obj_det_saliency_algo_name.append(sal_choice)
return sal_choice
def select_threads(threads):
threads_state.append(threads)
return threads
def enter_window_size(val):
window_size_state.append(val)
return val
def enter_stride(val):
stride_state.append(val)
return val
def enter_num_masks(val):
num_masks_state.append(val)
return val
def enter_spatial_res(val):
spatial_res_state.append(val)
return val
def select_p1(prob):
p1_state.append(prob)
return prob
def enter_seed(seed):
seed_state.append(seed)
return seed
def check_debiased(debiased):
debiased_state.append(debiased)
return debiased
def enter_occlusion_grid_size(val):
occlusion_grid_state.append(val)
return val
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