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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 | |