FileUtils.createDir(args.path_output)
logging_utils = LoggingUtils(
f"{args.path_output}/simpsons-{datetime.now().strftime('%y-%m-%d_%H-%M-%S')}.log"
)
class_names = []
last_class_name = None
mmap_shape = [0, 3, args.size_img, args.size_img]
logging_utils.info(
f'move test samples into train to change from classification to re-identification task'
)
paths_files = FileUtils.listSubFiles(args.path_input_test)
for path_file in paths_files:
base_path_file = os.path.basename(path_file)
base_path_file = base_path_file[:-4] # remove .jpg at the end
str_sample_idx = base_path_file[base_path_file.rindex('_') + 1:]
base_path_file = base_path_file[:base_path_file.rindex(
'_')] # remove sample idx like _22
if os.path.exists(f'{args.path_input_train}/{base_path_file}'):
os.rename(
path_file,
f'{args.path_input_train}/{base_path_file}/test_{str_sample_idx}.jpg'
)
else:
LoggingUtils.error(
f'not exiting, cannot move from test: {args.path_input_train}/{base_path_file}'
)
def calculate_accuracy(
path_embeddings,
meter_acc: tnt.meter.ClassErrorMeter,
meter_auc: tnt.meter.AUCMeter,
type='range',
norm='l2',
triplet_similarity='cos',
mode='cpu',
embedding_size=None,
class_max_dist=None, # precomputed
class_centroids=None,
y_list=None, #precumputed
sample_count=None, #precomputed
paths_embs_idx_path_pairs=None): # precomputed
paths_embs = FileUtils.listSubFiles(path_embeddings)
# calculate centroids first
if class_max_dist is None:
class_centroids = {}
class_max_dist = {}
y_list = []
paths_embs_idx_path_pairs = []
sample_count = 0
for path_emb in paths_embs:
if path_emb.endswith('.json'):
y_each = int(os.path.basename(path_emb).split('.')[0])
path_emb_json = f'{path_embeddings}/{y_each}.json'
path_emb_mem = f'{path_embeddings}/{y_each}.mmap'
emb_json = FileUtils.loadJSON(path_emb_json)
emb_mem = np.memmap(path_emb_mem,
mode='r',
dtype=np.float16,
shape=(emb_json['count'], embedding_size))
paths_embs_idx_path_pairs.append((sample_count, y_each))
sample_count += emb_json['count']
y_list += (np.ones(
(emb_json['count'], ), dtype=np.int) * y_each).tolist()
class_centroids[y_each] = np.average(emb_mem, axis=0)
if norm == 'l2':
class_centroids[y_each] = normalize_vec(
class_centroids[y_each])
np_class_centroids_tiled = np.tile(class_centroids[y_each],
(len(emb_mem), 1))
list_dists = get_distance(np_class_centroids_tiled, emb_mem,
triplet_similarity, mode).tolist()
list_dists = sorted(list_dists, reverse=False)
list_dists = list_dists[:max(
2, int(len(list_dists) * 0.9)
)] # drop 10 top percent embeddings as they could contain noise
class_max_dist[y_each] = list_dists[
-1] # last largest distance
classes_size = int(np.max(y_list)) + 1
# store distance matrix as memmap for optimization
path_dists_mem = f'{path_embeddings}/dists.mmap'
is_exist_dists_mem = os.path.exists(path_dists_mem)
dists_mem = np.memmap(path_dists_mem,
mode='r+' if is_exist_dists_mem else 'w+',
dtype=np.float16,
shape=(sample_count, classes_size))
#dists_mem.flush()
path_centroids_mem = f'{path_embeddings}/dists.mmap'
is_exist_centroids_mem = os.path.exists(path_centroids_mem)
centroids_mem = np.memmap(path_centroids_mem,
mode='r+' if is_exist_centroids_mem else 'w+',
dtype=np.float16,
shape=(classes_size, embedding_size))
for key, value in class_centroids.items():
centroids_mem[key] = value
#centroids_mem.flush()
if not is_exist_dists_mem:
Parallel(n_jobs=multiprocessing.cpu_count() * 2, backend='threading')(
delayed(process_dists)(idx_start, y_each, y_list, path_embeddings,
sample_count, classes_size, embedding_size,
triplet_similarity, mode)
for idx_start, y_each in paths_embs_idx_path_pairs)
dists_mem = np.memmap(path_dists_mem,
mode='r',
dtype=np.float16,
shape=(sample_count, classes_size))
# iterate through precomputed distances to add to data to meters for mem optimization
chunk_size = 1024
for idx_chunk_start in range(sample_count // chunk_size + 1):
idx_chunk_end = min(sample_count, idx_chunk_start + chunk_size)
chunk_each_size = idx_chunk_end - idx_chunk_start
if chunk_each_size == 0:
break
if type == 'range':
predicted = np.zeros((chunk_each_size, classes_size),
dtype=np.float)
else:
predicted = np.ones(
(chunk_each_size, classes_size), dtype=np.float) * 1e9
target = np.zeros((chunk_each_size, classes_size), dtype=np.float)
for idx_y in class_max_dist.keys():
max_dist = class_max_dist[idx_y]
for idx_class in range(chunk_each_size):
target[idx_class, y_list[idx_chunk_start + idx_class]] = 1.0
dists = dists_mem[idx_chunk_start:idx_chunk_end]
if type == 'range':
for idx_emb, dist in enumerate(dists):
if max_dist > dist[idx_y]:
predicted[idx_emb, idx_y] += 1.0
else:
predicted[:, idx_y] = np.minimum(
predicted[:, idx_y], dists[:, idx_y]
) # store for each class closest embedding with distance value
if type == 'range':
predicted = predicted / (np.sum(predicted, axis=1, keepdims=True) +
1e-18)
else:
# TODO softmax/hardmax based accuracy
idx_class = np.argmin(
predicted, axis=1) # for each sample select closest distance
predicted = np.zeros_like(predicted) # init probabilities vector
predicted[
np.arange(predicted.shape[0]),
idx_class] = 1.0 # for each sample set prob 100% by columns
y_chunk = np.array(y_list[idx_chunk_start:idx_chunk_end])
meter_acc.add(predicted, y_chunk)
# AssertionError: targets should be binary (0, 1)
idxes_classes = np.argmax(predicted, axis=1)
target_tp = np.array(np.equal(y_chunk, idxes_classes), dtype=np.int)
meter_auc.add(np.max(predicted, axis=1), target_tp)
return class_max_dist, class_centroids, y_list, sample_count, paths_embs_idx_path_pairs