def _build_samples(self, split, day, label_resource_id):
assert day > 0
samples = []
for (category, wordnet_synset) in _icubworld28_labels_to_wordnet:
for individual in range(1, 4 + 1):
sample_dir = os.path.join(
self.base_path,
split,
f"day{day}",
category,
f"{category}{individual}",
)
for filename in sorted(os.listdir(sample_dir)):
samples += [
sample.Sample(
source=self.__class__.__name__,
uid=f"{_namespace_uid}::{split}:{day}:" +
f"{category}{individual}:{filename}",
).add_resource(
self.__class__.__name__,
label_resource_id,
f"{_namespace_uid}::{category}{individual}",
).add_resource(
self.__class__.__name__,
"image_input_np",
imageio.imread(os.path.join(sample_dir, filename)),
)
]
return samples
def _build_sample(self, image_id, label_id, label_resource_id, split):
sample_ = sample.Sample(
source=self.__class__.__name__,
uid=f"{_namespace_uid}::{split}:{image_id}").add_resource(
self.__class__.__name__,
label_resource_id,
self.uid_for_label_id[label_id],
)
if self.use_lazy_mode:
sample_ = sample_.add_resource(
self.__class__.__name__,
"image_location",
os.path.join(self.base_path, "images",
self.image_location_for_image_id[image_id]),
).add_lazy_resource(self.__class__.__name__, "input_img_np",
self._load_from_location)
else:
sample_ = sample_.add_resource(
self.__class__.__name__,
"image_location",
os.path.join(self.base_path, "images",
self.image_location_for_image_id[image_id]),
)
sample_ = sample_.add_resource(
self.__class__.__name__,
"input_img_np",
self._load_from_location(sample_),
)
return sample_
def restore_inner(self, path):
if not os.path.exists(path + "_dfnpool.pkl"):
print("Falling back to dfnstate.pkl")
with open(path + "_dfnstate.pkl", "rb") as target:
(X, y) = pkl.load(target)
for i, (Xi, yi) in enumerate(zip(X, y)):
self.rehearsal_pool.append(
sample.Sample(self.__class__.__name__,
uid=f"DFNImport_{i:05d}").add_resource(
self.__class__.__name__,
"input_img_np", Xi).add_resource(
self.__class__.__name__,
"zDFN.label", yi))
else:
with open(path + "_dfnpool.pkl", "rb") as target:
self.rehearsal_pool = pkl.load(target)
def build_sample(self, image_dict, label_resource_id, annotations):
image_filename = image_dict["file_name"]
image_id = image_dict["id"]
sample_ = sample.Sample(source=self.__class__.__name__,
uid=f"{_namespace_uid}::{image_filename}")
sample_ = sample_.add_resource(
self.__class__.__name__,
label_resource_id,
self._id_to_class[annotations[image_id]],
)
sample_ = sample_.add_resource(
self.__class__.__name__,
"image_location",
os.path.join(self.base_path, image_filename),
).add_lazy_resource(self.__class__.__name__, "input_img_np",
self._load_from_location)
return sample_
def _build_samples(self, X, y, data_range, label_resource_id, prefix):
assert X.shape[0] == len(data_range)
samples = []
for i, data_id in enumerate(data_range):
class_label = y[i, 0]
np_image = X[i]
samples += [
sample.Sample(
source=self.__class__.__name__,
uid=f"{_namespace_uid}::{prefix}.{data_id}",
).add_resource(
self.__class__.__name__, "input_img_np",
np_image).add_resource(
self.__class__.__name__,
label_resource_id,
f"{_namespace_uid}::{_label_names[int(class_label)]}",
)
]
return samples
def get_pool_for(self, scenario, run, batch, label_resource_id):
# Find the data
scenario = str(scenario).lower()
assert scenario in ["ni", "nc", "nic"]
filelist_path = os.path.join(
self.base_path,
"batches_filelists",
f"{scenario.upper()}_inc",
f"run{run:d}",
f"{batch}_filelist.txt",
)
# Find appropriate label map
label_map = self.labels_to_names[scenario][run]
samples = []
with open(filelist_path) as filelist:
for line in filelist:
path, class_id = line.strip().split(" ")
samples += [
sample.Sample(
source=self.__class__.__name__,
uid=f"{_namespace_uid}::{path}").add_resource(
self.__class__.__name__,
"input_img_np",
self.imgs[self.path_to_index[path]],
).add_resource(
self.__class__.__name__,
label_resource_id,
f"{_namespace_uid}::{label_map[int(class_id)]}",
)
]
return samples
def _build_sample(self, class_, filename, label_resource_id, individuals):
# Open and resize image
the_image = Image.open(
os.path.join(self.base_path, f"{int(class_['folder']):02d}",
filename))
the_image = np.asarray(
the_image.resize((self.side_length, self.side_length),
Image.ANTIALIAS))
if individuals:
label_string = (f"{_namespace_uid}::{class_['class_name']}" +
f"{int(class_['individual_id']):02d}")
else:
label_string = f"{_namespace_uid}::{class_['class_name']}"
# Build sample
the_sample = (sample.Sample(
source=self.__class__.__name__,
uid=f"{_namespace_uid}::{class_}.{filename}",
).add_resource(self.__class__.__name__, "input_img_np",
the_image).add_resource(self.__class__.__name__,
label_resource_id,
label_string))
return the_sample