def third_hop(response, energy_prev, test_input):
print("---- Hop 3 ----")
sec_hop_total = []
sec_hop_energy = []
sec_hop_test = []
for c in range(response.shape[-1]):
cur_response = response[:, :, :, c][:, :, :, None]
cur_test = test_input[:, :, :, c][:, :, :, None]
cur_attri_saab, cur_saab = PixelHop_Unit(cur_response, getK=True, idx_list=None,
dilate=2, window_size=window_rad, pad='reflect',
weight_root=os.path.join(save_dir_matFiles, 'weight'),
Pass_Ener_thrs=0.1, Leaf_Ener_thrs=0.6, num_kernels=None,
PCA_ener_percent=1,
useDC=False, stride=None, getcov=0, split_spec=0, hopidx=1)
cur_response = PixelHop_trans(weight_name=os.path.join(save_dir_matFiles, 'weight'),
feature_ori=cur_attri_saab,
split_spec=0, hopidx=1, Pass_Ener_thrs=0.1)
cur_test = Shrink(cur_test)
cur_test_context = PixelHop_trans(weight_name=os.path.join(save_dir_matFiles, 'weight'),
feature_ori=cur_test,
split_spec=0, hopidx=1, Pass_Ener_thrs=0.1)
print("sec hop attribute shape:", cur_response.shape)
energy = cur_saab['leaf0'].tree['ac_energy']
energy = energy / np.sum(energy) * energy_prev[c]
engr_th = 0.0005
high_engr_channels = np.where((energy > engr_th))
to_append = np.squeeze(cur_response[:, :, :, high_engr_channels])
if len(to_append.shape) == 3:
to_append = to_append[:, :, :, None]
if to_append.shape[-1] == 0:
to_append = np.squeeze(cur_response[:, :, :, :3])
sec_hop_total.append(to_append.copy())
sec_hop_energy.append(energy[high_engr_channels].copy() if len(high_engr_channels) != 0 else energy[:3].copy())
to_append_test = np.squeeze(cur_test_context[:, :, :, high_engr_channels])
if len(to_append_test.shape) == 3:
to_append_test = to_append_test[:, :, :, None]
if to_append_test.shape[-1] == 0:
to_append_test = np.squeeze(cur_test_context[:, :, :, :3])
sec_hop_test.append(to_append_test.copy())
print("CURRENT C:", c)
sec_hop_total = np.concatenate(sec_hop_total, axis=3)
sec_hop_energy = np.concatenate(sec_hop_energy, axis=0)
sec_hop_test = np.concatenate(sec_hop_test, axis=3)
return sec_hop_total, sec_hop_energy, sec_hop_test
def one_hop(response, test_response):
print("---- Hop 1 ----")
one_hop_attri_saab, all_saab = PixelHop_Unit(response, getK=True, idx_list=None,
dilate=2, window_size=window_rad, pad='reflect',
weight_root=os.path.join(save_dir_matFiles, 'weight'),
Pass_Ener_thrs=0.1, Leaf_Ener_thrs=0.6, num_kernels=None,
PCA_ener_percent=1,
useDC=False, stride=None, getcov=0, split_spec=0, hopidx=1)
print("Hop-1 feature ori shape:", one_hop_attri_saab.shape)
one_hop_attri_response = PixelHop_trans(weight_name=os.path.join(save_dir_matFiles, 'weight'),
feature_ori=one_hop_attri_saab,
split_spec=0, hopidx=1, Pass_Ener_thrs=0.1)
test_response = Shrink(test_response)
test_context = PixelHop_trans(weight_name=os.path.join(save_dir_matFiles, 'weight'),
feature_ori=test_response,
split_spec=0, hopidx=1, Pass_Ener_thrs=0.1)
print("one hop attribute shape:", one_hop_attri_response.shape)
kernels = all_saab['leaf0'].tree['kernel']
energy = all_saab['leaf0'].tree['ac_energy']
energy = energy / np.sum(energy)
kernel_5 = np.array(
[[0, 0, 0, 0, 0],
[0, 1, 1, 1, 0],
[0, 1, 1, 1, 0],
[0, 1, 1, 1, 0],
[0, 0, 0, 0, 0],
]
)
high_frez_pow = []
thres_high = 0.7
engr_th_high = 0.005
engr_th_low = 0.02
for each_kernel in kernels:
each_kernel = np.reshape(each_kernel, (5, 5))
each_fft = np.fft.fftshift(np.fft.fft2(each_kernel))
high_frez_pow.append(1 - np.sum(np.abs(each_fft * kernel_5)) / np.sum(np.abs(each_fft)))
high_frez_pow = np.array(high_frez_pow)
high_frez_channels = np.where((high_frez_pow > thres_high) & (energy > engr_th_high))
low_frez_channels = np.where((high_frez_pow < thres_high) & (energy > engr_th_low))
return (one_hop_attri_response[:, :, :, high_frez_channels], one_hop_attri_response[:, :, :, low_frez_channels],
energy[high_frez_channels], energy[low_frez_channels],
test_context[:, :, :, high_frez_channels], test_context[:, :, :, low_frez_channels])
def test_one_hop(ext_test_imgs):
test_one_hop_attri_saab = PixelHop_Unit(ext_test_imgs, dilate=1, window_size=window_rad,
idx_list=None, getK=False, pad='reflect',
weight_root=os.path.join(save_dir_matFiles, 'weight'),
Pass_Ener_thrs=0.1, Leaf_Ener_thrs=0.0001, num_kernels=None,
PCA_ener_percent=0.99,
useDC=False, stride=None, getcov=0, split_spec=0, hopidx=1)
test_one_hop_attri_biased = PixelHop_trans(weight_name=os.path.join(save_dir_matFiles, 'weight'),
feature_ori=test_one_hop_attri_saab,
split_spec=0, hopidx=1, Pass_Ener_thrs=0.1)
return test_one_hop_attri_biased
def test_forth_hop(test_third_hop_attri_response):
test_fourth_hop_attri_saab = PixelHop_Unit(test_third_hop_attri_response, dilate=1, window_size=window_rad,
idx_list=None, getK=False, pad='reflect',
weight_root=os.path.join(save_dir_matFiles, 'weight'),
Pass_Ener_thrs=0.1, Leaf_Ener_thrs=0.0001, num_kernels=None,
PCA_ener_percent=0.97,
useDC=False, stride=None, getcov=0, split_spec=1, hopidx=4)
# print("Hop-3 feature ori shape:", test_third_hop_attri_saab.shape)
test_fourth_hop_attri_response = PixelHop_trans(weight_name=os.path.join(save_dir_matFiles, 'weight'),
feature_ori=test_fourth_hop_attri_saab,
split_spec=1, hopidx=4, Pass_Ener_thrs=0.1)
# print("third hop attri shape:", test_third_hop_attri_response.shape)
return test_fourth_hop_attri_response