def update_prototypes_on_batch(search_batch_input,
start_index_of_search_batch,
prototype_network_parallel,
global_min_proto_dist, # this will be updated
global_min_fmap_patches, # this will be updated
proto_rf_boxes, # this will be updated
proto_bound_boxes, # this will be updated
class_specific=True,
search_y=None, # required if class_specific == True
num_classes=None, # required if class_specific == True
preprocess_input_function=None,
prototype_layer_stride=1,
dir_for_saving_prototypes=None,
prototype_img_filename_prefix=None,
prototype_self_act_filename_prefix=None,
prototype_activation_function_in_numpy=None):
prototype_network_parallel.eval()
if preprocess_input_function is not None:
# print('preprocessing input for pushing ...')
# search_batch = copy.deepcopy(search_batch_input)
search_batch = preprocess_input_function(search_batch_input)
else:
search_batch = search_batch_input
with torch.no_grad():
search_batch = search_batch.cuda()
# this computation currently is not parallelized
protoL_input_torch, proto_dist_torch = prototype_network_parallel.module.push_forward(search_batch)
protoL_input_ = np.copy(protoL_input_torch.detach().cpu().numpy())
proto_dist_ = np.copy(proto_dist_torch.detach().cpu().numpy())
del protoL_input_torch, proto_dist_torch
if class_specific:
class_to_img_index_dict = {key: [] for key in range(num_classes)}
# img_y is the image's integer label
for img_index, img_y in enumerate(search_y):
img_label = img_y.item()
class_to_img_index_dict[img_label].append(img_index)
prototype_shape = prototype_network_parallel.module.prototype_shape
n_prototypes = prototype_shape[0]
proto_h = prototype_shape[2]
proto_w = prototype_shape[3]
max_dist = prototype_shape[1] * prototype_shape[2] * prototype_shape[3]
for j in range(n_prototypes):
#if n_prototypes_per_class != None:
if class_specific:
# target_class is the class of the class_specific prototype
target_class = torch.argmax(prototype_network_parallel.module.prototype_class_identity[j]).item()
# if there is not images of the target_class from this batch
# we go on to the next prototype
if len(class_to_img_index_dict[target_class]) == 0:
continue
proto_dist_j = proto_dist_[class_to_img_index_dict[target_class]][:,j,:,:]
else:
# if it is not class specific, then we will search through
# every example
proto_dist_j = proto_dist_[:,j,:,:]
batch_min_proto_dist_j = np.amin(proto_dist_j)
if batch_min_proto_dist_j < global_min_proto_dist[j]:
batch_argmin_proto_dist_j = \
list(np.unravel_index(np.argmin(proto_dist_j, axis=None),
proto_dist_j.shape))
if class_specific:
'''
change the argmin index from the index among
images of the target class to the index in the entire search
batch
'''
batch_argmin_proto_dist_j[0] = class_to_img_index_dict[target_class][batch_argmin_proto_dist_j[0]]
# retrieve the corresponding feature map patch
img_index_in_batch = batch_argmin_proto_dist_j[0]
fmap_height_start_index = batch_argmin_proto_dist_j[1] * prototype_layer_stride
fmap_height_end_index = fmap_height_start_index + proto_h
fmap_width_start_index = batch_argmin_proto_dist_j[2] * prototype_layer_stride
fmap_width_end_index = fmap_width_start_index + proto_w
batch_min_fmap_patch_j = protoL_input_[img_index_in_batch,
:,
fmap_height_start_index:fmap_height_end_index,
fmap_width_start_index:fmap_width_end_index]
global_min_proto_dist[j] = batch_min_proto_dist_j
global_min_fmap_patches[j] = batch_min_fmap_patch_j
# get the receptive field boundary of the image patch
# that generates the representation
protoL_rf_info = prototype_network_parallel.module.proto_layer_rf_info
rf_prototype_j = compute_rf_prototype(search_batch.size(2), batch_argmin_proto_dist_j, protoL_rf_info)
# get the whole image
original_img_j = search_batch_input[rf_prototype_j[0]]
original_img_j = original_img_j.numpy()
original_img_j = np.transpose(original_img_j, (1, 2, 0))
original_img_size = original_img_j.shape[0]
# crop out the receptive field
rf_img_j = original_img_j[rf_prototype_j[1]:rf_prototype_j[2],
rf_prototype_j[3]:rf_prototype_j[4], :]
# save the prototype receptive field information
proto_rf_boxes[j, 0] = rf_prototype_j[0] + start_index_of_search_batch
proto_rf_boxes[j, 1] = rf_prototype_j[1]
proto_rf_boxes[j, 2] = rf_prototype_j[2]
proto_rf_boxes[j, 3] = rf_prototype_j[3]
proto_rf_boxes[j, 4] = rf_prototype_j[4]
if proto_rf_boxes.shape[1] == 6 and search_y is not None:
proto_rf_boxes[j, 5] = search_y[rf_prototype_j[0]].item()
# find the highly activated region of the original image
proto_dist_img_j = proto_dist_[img_index_in_batch, j, :, :]
if prototype_network_parallel.module.prototype_activation_function == 'log':
proto_act_img_j = np.log((proto_dist_img_j + 1) / (proto_dist_img_j + prototype_network_parallel.module.epsilon))
elif prototype_network_parallel.module.prototype_activation_function == 'linear':
proto_act_img_j = max_dist - proto_dist_img_j
else:
proto_act_img_j = prototype_activation_function_in_numpy(proto_dist_img_j)
upsampled_act_img_j = cv2.resize(proto_act_img_j, dsize=(original_img_size, original_img_size),
interpolation=cv2.INTER_CUBIC)
proto_bound_j = find_high_activation_crop(upsampled_act_img_j)
# crop out the image patch with high activation as prototype image
proto_img_j = original_img_j[proto_bound_j[0]:proto_bound_j[1],
proto_bound_j[2]:proto_bound_j[3], :]
# save the prototype boundary (rectangular boundary of highly activated region)
proto_bound_boxes[j, 0] = proto_rf_boxes[j, 0]
proto_bound_boxes[j, 1] = proto_bound_j[0]
proto_bound_boxes[j, 2] = proto_bound_j[1]
proto_bound_boxes[j, 3] = proto_bound_j[2]
proto_bound_boxes[j, 4] = proto_bound_j[3]
if proto_bound_boxes.shape[1] == 6 and search_y is not None:
proto_bound_boxes[j, 5] = search_y[rf_prototype_j[0]].item()
if dir_for_saving_prototypes is not None:
if prototype_self_act_filename_prefix is not None:
# save the numpy array of the prototype self activation
np.save(os.path.join(dir_for_saving_prototypes,
prototype_self_act_filename_prefix + str(j) + '.npy'),
proto_act_img_j)
if prototype_img_filename_prefix is not None:
# save the whole image containing the prototype as png
plt.imsave(os.path.join(dir_for_saving_prototypes,
prototype_img_filename_prefix + '-original' + str(j) + '.png'),
original_img_j,
vmin=0.0,
vmax=1.0)
# overlay (upsampled) self activation on original image and save the result
rescaled_act_img_j = upsampled_act_img_j - np.amin(upsampled_act_img_j)
rescaled_act_img_j = rescaled_act_img_j / np.amax(rescaled_act_img_j)
heatmap = cv2.applyColorMap(np.uint8(255*rescaled_act_img_j), cv2.COLORMAP_JET)
heatmap = np.float32(heatmap) / 255
heatmap = heatmap[...,::-1]
overlayed_original_img_j = 0.5 * original_img_j + 0.3 * heatmap
plt.imsave(os.path.join(dir_for_saving_prototypes,
prototype_img_filename_prefix + '-original_with_self_act' + str(j) + '.png'),
overlayed_original_img_j,
vmin=0.0,
vmax=1.0)
# if different from the original (whole) image, save the prototype receptive field as png
if rf_img_j.shape[0] != original_img_size or rf_img_j.shape[1] != original_img_size:
plt.imsave(os.path.join(dir_for_saving_prototypes,
prototype_img_filename_prefix + '-receptive_field' + str(j) + '.png'),
rf_img_j,
vmin=0.0,
vmax=1.0)
overlayed_rf_img_j = overlayed_original_img_j[rf_prototype_j[1]:rf_prototype_j[2],
rf_prototype_j[3]:rf_prototype_j[4]]
plt.imsave(os.path.join(dir_for_saving_prototypes,
prototype_img_filename_prefix + '-receptive_field_with_self_act' + str(j) + '.png'),
overlayed_rf_img_j,
vmin=0.0,
vmax=1.0)
# save the prototype image (highly activated region of the whole image)
plt.imsave(os.path.join(dir_for_saving_prototypes,
prototype_img_filename_prefix + str(j) + '.png'),
proto_img_j,
vmin=0.0,
vmax=1.0)
if class_specific:
del class_to_img_index_dict
if prototype_max_connection[sorted_indices_act[-i].item(
)] != prototype_img_identity[sorted_indices_act[-i].item()]:
log('prototype connection identity: {0}'.format(
prototype_max_connection[sorted_indices_act[-i].item()]))
log('activation value (similarity score): {0}'.format(array_act[-i]))
log('last layer connection with predicted class: {0}'.format(
ppnet.last_layer.weight[predicted_cls][sorted_indices_act[-i].item()]))
activation_pattern = prototype_activation_patterns[idx][
sorted_indices_act[-i].item()].detach().cpu().numpy()
upsampled_activation_pattern = cv2.resize(activation_pattern,
dsize=(img_size, img_size),
interpolation=cv2.INTER_CUBIC)
# show the most highly activated patch of the image by this prototype
high_act_patch_indices = find_high_activation_crop(
upsampled_activation_pattern)
high_act_patch = original_img[
high_act_patch_indices[0]:high_act_patch_indices[1],
high_act_patch_indices[2]:high_act_patch_indices[3], :]
log('most highly activated patch of the chosen image by this prototype:')
#plt.axis('off')
plt.imsave(
os.path.join(save_analysis_path, 'most_activated_prototypes',
'most_highly_activated_patch_by_top-%d_prototype.png' %
i), high_act_patch)
log('most highly activated patch by this prototype shown in the original image:'
)
imsave_with_bbox(fname=os.path.join(
save_analysis_path, 'most_activated_prototypes',
'most_highly_activated_patch_in_original_img_by_top-%d_prototype.png' %
i),
def find_k_nearest_patches_to_prototypes(dataloader, # pytorch dataloader (must be unnormalized in [0,1])
ppnet, # pytorch network with prototype_vectors
k=5,
preprocess_input_function=None, # normalize if needed
full_save=False, # save all the images
root_dir_for_saving_images='./nearest',
log=print,
prototype_activation_function_in_numpy=None,
only_n_most_activated=None):
ppnet.eval()
'''
full_save=False will only return the class identity of the closest
patches, but it will not save anything.
'''
print(' find nearest patches')
start = time.time()
n_prototypes = ppnet.num_prototypes
prototype_shape = ppnet.prototype_shape
max_dist = prototype_shape[1] * prototype_shape[2] * prototype_shape[3]
protoL_rf_info = ppnet.proto_layer_rf_info
heaps = []
# allocate an array of n_prototypes number of heaps
for _ in range(n_prototypes):
# a heap in python is just a maintained list
heaps.append([])
with tqdm(total=len(dataloader.dataset), unit='bag') as pbar:
for idx, (search_batch_raw, search_batch, search_y) in enumerate(dataloader):
with torch.no_grad():
search_batch = search_batch.cuda()
ppnet.forward(search_batch)
proto_dist_torch = ppnet.distances
attention = ppnet.A
raw_sample = search_batch_raw[0]
# protoL_input_ = np.copy(protoL_input_torch.detach().cpu().numpy())
proto_dist_ = np.copy(proto_dist_torch.detach().cpu().numpy())
if only_n_most_activated:
most_activates = list(
torch.topk(attention, min(only_n_most_activated, attention.shape[1]), dim=-1, largest=True)[1].detach().cpu().numpy()[0])
for img_idx, distance_map in enumerate(proto_dist_):
if only_n_most_activated:
if img_idx not in most_activates:
continue
for j in range(n_prototypes):
# find the closest patches in this batch to prototype j
closest_patch_distance_to_prototype_j = np.amin(distance_map[j])
if full_save:
closest_patch = ImagePatchLazy(
label=search_y[0],
distance=closest_patch_distance_to_prototype_j,
batch_idx=idx,
image_idx=img_idx,
dataset=dataloader.dataset,
protoL_rf_info=protoL_rf_info,
distance_map_j=distance_map[j],
prototype_activation_function_in_numpy=prototype_activation_function_in_numpy,
prototype_activation_function=ppnet.prototype_activation_function,
max_dist=max_dist,
epsilon=ppnet.epsilon
)
else:
closest_patch = ImagePatchInfo(label=search_y[0],
distance=closest_patch_distance_to_prototype_j)
# add to the j-th heap
if len(heaps[j]) < k:
heapq.heappush(heaps[j], closest_patch)
else:
# heappushpop runs more efficiently than heappush
# followed by heappop
heapq.heappushpop(heaps[j], closest_patch)
pbar.update(1)
# after looping through the dataset every heap will
# have the k closest prototypes
for j in range(n_prototypes):
# finally sort the heap; the heap only contains the k closest
# but they are not ranked yet
heaps[j].sort()
heaps[j] = heaps[j][::-1]
if full_save:
dir_for_saving_images = os.path.join(root_dir_for_saving_images,
str(j))
makedir(dir_for_saving_images)
labels = []
for i, patch in enumerate(heaps[j]):
# save the activation pattern of the original image where the patch comes from
np.save(os.path.join(dir_for_saving_images,
'nearest-' + str(i + 1) + '_act.npy'),
patch.act_pattern)
# save the original image where the patch comes from
plt.imsave(fname=os.path.join(dir_for_saving_images,
'nearest-' + str(i + 1) + '_original.png'),
arr=patch.original_img,
vmin=0.0,
vmax=1.0)
# overlay (upsampled) activation on original image and save the result
img_size = patch.original_img.shape[0]
upsampled_act_pattern = cv2.resize(patch.act_pattern,
dsize=(img_size, img_size),
interpolation=cv2.INTER_CUBIC)
rescaled_act_pattern = upsampled_act_pattern - np.amin(upsampled_act_pattern)
rescaled_act_pattern = rescaled_act_pattern / np.amax(rescaled_act_pattern)
heatmap = cv2.applyColorMap(np.uint8(255 * rescaled_act_pattern), cv2.COLORMAP_JET)
heatmap = np.float32(heatmap) / 255
heatmap = heatmap[..., ::-1]
overlayed_original_img = 0.5 * patch.original_img + 0.3 * heatmap
plt.imsave(fname=os.path.join(dir_for_saving_images,
'nearest-' + str(i + 1) + '_original_with_heatmap.png'),
arr=overlayed_original_img,
vmin=0.0,
vmax=1.0)
# if different from original image, save the patch (i.e. receptive field)
if patch.patch.shape[0] != img_size or patch.patch.shape[1] != img_size:
np.save(os.path.join(dir_for_saving_images,
'nearest-' + str(i + 1) + '_receptive_field_indices.npy'),
patch.patch_indices)
plt.imsave(fname=os.path.join(dir_for_saving_images,
'nearest-' + str(i + 1) + '_receptive_field.png'),
arr=patch.patch,
vmin=0.0,
vmax=1.0)
# save the receptive field patch with heatmap
overlayed_patch = overlayed_original_img[patch.patch_indices[0]:patch.patch_indices[1],
patch.patch_indices[2]:patch.patch_indices[3], :]
plt.imsave(fname=os.path.join(dir_for_saving_images,
'nearest-' + str(i + 1) + '_receptive_field_with_heatmap.png'),
arr=overlayed_patch,
vmin=0.0,
vmax=1.0)
# save the highly activated patch
high_act_patch_indices = find_high_activation_crop(upsampled_act_pattern)
high_act_patch = patch.original_img[high_act_patch_indices[0]:high_act_patch_indices[1],
high_act_patch_indices[2]:high_act_patch_indices[3], :]
np.save(os.path.join(dir_for_saving_images,
'nearest-' + str(i + 1) + '_high_act_patch_indices.npy'),
high_act_patch_indices)
plt.imsave(fname=os.path.join(dir_for_saving_images,
'nearest-' + str(i + 1) + '_high_act_patch.png'),
arr=high_act_patch,
vmin=0.0,
vmax=1.0)
# save the original image with bounding box showing high activation patch
imsave_with_bbox(fname=os.path.join(dir_for_saving_images,
'nearest-' + str(i + 1) + '_high_act_patch_in_original_img.png'),
img_rgb=patch.original_img,
bbox_height_start=high_act_patch_indices[0],
bbox_height_end=high_act_patch_indices[1],
bbox_width_start=high_act_patch_indices[2],
bbox_width_end=high_act_patch_indices[3], color=(0, 255, 255))
labels = np.array([patch.label for patch in heaps[j]])
np.save(os.path.join(dir_for_saving_images, 'class_id.npy'),
labels)
labels_all_prototype = np.array([[patch.label for patch in heaps[j]] for j in range(n_prototypes)])
if full_save:
np.save(os.path.join(root_dir_for_saving_images, 'full_class_id.npy'),
labels_all_prototype)
end = time.time()
log(' find nearest patches time: \t{0}'.format(end - start))
return labels_all_prototype
def find_k_nearest_patches_to_prototypes(
dataloader, # pytorch dataloader (must be unnormalized in [0,1])
prototype_network_parallel, # pytorch network with prototype_vectors
k=5,
preprocess_input_function=None, # normalize if needed
full_save=False, # save all the images
root_dir_for_saving_images='./nearest',
log=print,
prototype_activation_function_in_numpy=None):
prototype_network_parallel.eval()
'''
full_save=False will only return the class identity of the closest
patches, but it will not save anything.
'''
log('find nearest patches')
start = time.time()
n_prototypes = prototype_network_parallel.module.num_prototypes
prototype_shape = prototype_network_parallel.module.prototype_shape
max_dist = prototype_shape[1] * prototype_shape[2] * prototype_shape[3]
protoL_rf_info = prototype_network_parallel.module.proto_layer_rf_info
heaps = []
# allocate an array of n_prototypes number of heaps
for _ in range(n_prototypes):
# a heap in python is just a maintained list
heaps.append([])
for idx, (search_batch_input, search_y) in enumerate(dataloader):
print('batch {}'.format(idx))
if preprocess_input_function is not None:
# print('preprocessing input for pushing ...')
# search_batch = copy.deepcopy(search_batch_input)
search_batch = preprocess_input_function(search_batch_input)
else:
search_batch = search_batch_input
with torch.no_grad():
search_batch = search_batch.cuda()
protoL_input_torch, proto_dist_torch = \
prototype_network_parallel.module.push_forward(search_batch)
#protoL_input_ = np.copy(protoL_input_torch.detach().cpu().numpy())
proto_dist_ = np.copy(proto_dist_torch.detach().cpu().numpy())
for img_idx, distance_map in enumerate(proto_dist_):
for j in range(n_prototypes):
# find the closest patches in this batch to prototype j
closest_patch_distance_to_prototype_j = np.amin(
distance_map[j])
if full_save:
closest_patch_indices_in_distance_map_j = \
list(np.unravel_index(np.argmin(distance_map[j],axis=None),
distance_map[j].shape))
closest_patch_indices_in_distance_map_j = [
0
] + closest_patch_indices_in_distance_map_j
closest_patch_indices_in_img = \
compute_rf_prototype(search_batch.size(2),
closest_patch_indices_in_distance_map_j,
protoL_rf_info)
closest_patch = \
search_batch_input[img_idx, :,
closest_patch_indices_in_img[1]:closest_patch_indices_in_img[2],
closest_patch_indices_in_img[3]:closest_patch_indices_in_img[4]]
closest_patch = closest_patch.numpy()
closest_patch = np.transpose(closest_patch, (1, 2, 0))
original_img = search_batch_input[img_idx].numpy()
original_img = np.transpose(original_img, (1, 2, 0))
if prototype_network_parallel.module.prototype_activation_function == 'log':
act_pattern = np.log(
(distance_map[j] + 1) /
(distance_map[j] +
prototype_network_parallel.module.epsilon))
elif prototype_network_parallel.module.prototype_activation_function == 'linear':
act_pattern = max_dist - distance_map[j]
else:
act_pattern = prototype_activation_function_in_numpy(
distance_map[j])
# 4 numbers: height_start, height_end, width_start, width_end
patch_indices = closest_patch_indices_in_img[1:5]
# construct the closest patch object
closest_patch = ImagePatch(
patch=closest_patch,
label=search_y[img_idx],
distance=closest_patch_distance_to_prototype_j,
original_img=original_img,
act_pattern=act_pattern,
patch_indices=patch_indices)
else:
closest_patch = ImagePatchInfo(
label=search_y[img_idx],
distance=closest_patch_distance_to_prototype_j)
# add to the j-th heap
if len(heaps[j]) < k:
heapq.heappush(heaps[j], closest_patch)
else:
# heappushpop runs more efficiently than heappush
# followed by heappop
heapq.heappushpop(heaps[j], closest_patch)
# after looping through the dataset every heap will
# have the k closest prototypes
for j in range(n_prototypes):
# finally sort the heap; the heap only contains the k closest
# but they are not ranked yet
heaps[j].sort()
heaps[j] = heaps[j][::-1]
if full_save:
dir_for_saving_images = os.path.join(root_dir_for_saving_images,
str(j))
makedir(dir_for_saving_images)
labels = []
for i, patch in enumerate(heaps[j]):
# save the activation pattern of the original image where the patch comes from
np.save(
os.path.join(dir_for_saving_images,
'nearest-' + str(i + 1) + '_act.npy'),
patch.act_pattern)
# save the original image where the patch comes from
plt.imsave(fname=os.path.join(
dir_for_saving_images,
'nearest-' + str(i + 1) + '_original.png'),
arr=patch.original_img,
vmin=0.0,
vmax=1.0)
# overlay (upsampled) activation on original image and save the result
img_size = patch.original_img.shape[0]
upsampled_act_pattern = cv2.resize(
patch.act_pattern,
dsize=(img_size, img_size),
interpolation=cv2.INTER_CUBIC)
rescaled_act_pattern = upsampled_act_pattern - np.amin(
upsampled_act_pattern)
rescaled_act_pattern = rescaled_act_pattern / np.amax(
rescaled_act_pattern)
heatmap = cv2.applyColorMap(
np.uint8(255 * rescaled_act_pattern), cv2.COLORMAP_JET)
heatmap = np.float32(heatmap) / 255
heatmap = heatmap[..., ::-1]
overlayed_original_img = 0.5 * patch.original_img + 0.3 * heatmap
plt.imsave(fname=os.path.join(
dir_for_saving_images,
'nearest-' + str(i + 1) + '_original_with_heatmap.png'),
arr=overlayed_original_img,
vmin=0.0,
vmax=1.0)
# if different from original image, save the patch (i.e. receptive field)
if patch.patch.shape[0] != img_size or patch.patch.shape[
1] != img_size:
np.save(
os.path.join(
dir_for_saving_images, 'nearest-' + str(i + 1) +
'_receptive_field_indices.npy'),
patch.patch_indices)
plt.imsave(fname=os.path.join(
dir_for_saving_images,
'nearest-' + str(i + 1) + '_receptive_field.png'),
arr=patch.patch,
vmin=0.0,
vmax=1.0)
# save the receptive field patch with heatmap
overlayed_patch = overlayed_original_img[
patch.patch_indices[0]:patch.patch_indices[1],
patch.patch_indices[2]:patch.patch_indices[3], :]
plt.imsave(fname=os.path.join(
dir_for_saving_images, 'nearest-' + str(i + 1) +
'_receptive_field_with_heatmap.png'),
arr=overlayed_patch,
vmin=0.0,
vmax=1.0)
# save the highly activated patch
high_act_patch_indices = find_high_activation_crop(
upsampled_act_pattern)
high_act_patch = patch.original_img[
high_act_patch_indices[0]:high_act_patch_indices[1],
high_act_patch_indices[2]:high_act_patch_indices[3], :]
np.save(
os.path.join(
dir_for_saving_images, 'nearest-' + str(i + 1) +
'_high_act_patch_indices.npy'), high_act_patch_indices)
plt.imsave(fname=os.path.join(
dir_for_saving_images,
'nearest-' + str(i + 1) + '_high_act_patch.png'),
arr=high_act_patch,
vmin=0.0,
vmax=1.0)
# save the original image with bounding box showing high activation patch
imsave_with_bbox(fname=os.path.join(
dir_for_saving_images, 'nearest-' + str(i + 1) +
'_high_act_patch_in_original_img.png'),
img_rgb=patch.original_img,
bbox_height_start=high_act_patch_indices[0],
bbox_height_end=high_act_patch_indices[1],
bbox_width_start=high_act_patch_indices[2],
bbox_width_end=high_act_patch_indices[3],
color=(0, 255, 255))
labels = np.array([patch.label for patch in heaps[j]])
np.save(os.path.join(dir_for_saving_images, 'class_id.npy'),
labels)
labels_all_prototype = np.array([[patch.label for patch in heaps[j]]
for j in range(n_prototypes)])
if full_save:
np.save(os.path.join(root_dir_for_saving_images, 'full_class_id.npy'),
labels_all_prototype)
end = time.time()
log('\tfind nearest patches time: \t{0}'.format(end - start))
return labels_all_prototype