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find_nearest.py
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find_nearest.py
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import torch
import numpy as np
import heapq
import matplotlib.pyplot as plt
import os
import copy
import time
import cv2
from receptive_field import compute_rf_prototype
from helpers import makedir, find_high_activation_crop
def imsave_with_bbox(fname, img_rgb, bbox_height_start, bbox_height_end,
bbox_width_start, bbox_width_end, color=(0, 255, 255)):
img_bgr_uint8 = cv2.cvtColor(np.uint8(255*img_rgb), cv2.COLOR_RGB2BGR)
cv2.rectangle(img_bgr_uint8, (bbox_width_start, bbox_height_start), (bbox_width_end-1, bbox_height_end-1),
color, thickness=2)
img_rgb_uint8 = img_bgr_uint8[...,::-1]
img_rgb_float = np.float32(img_rgb_uint8) / 255
#plt.imshow(img_rgb_float)
#plt.axis('off')
plt.imsave(fname, img_rgb_float)
class ImagePatch:
def __init__(self, patch, label, distance,
original_img=None, act_pattern=None, patch_indices=None):
self.patch = patch
self.label = label
self.negative_distance = -distance
self.original_img = original_img
self.act_pattern = act_pattern
self.patch_indices = patch_indices
def __lt__(self, other):
return self.negative_distance < other.negative_distance
class ImagePatchInfo:
def __init__(self, label, distance):
self.label = label
self.negative_distance = -distance
def __lt__(self, other):
return self.negative_distance < other.negative_distance
# find the nearest patches in the dataset to each 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