def load(self, model_path=None):
for model_link, file_path in zip(
Constant.FACE_DETECTION_PRETRAINED['PRETRAINED_MODEL_LINKS'],
Constant.FACE_DETECTION_PRETRAINED['FILE_PATHS']):
download_file(model_link, file_path)
self.pnet, self.rnet, self.onet = Constant.FACE_DETECTION_PRETRAINED[
'FILE_PATHS']
def load(self, model_path=None):
# https://s3.amazonaws.com/amdegroot-models/ssd300_mAP_77.43_v2.pth
if model_path is None:
file_link = Constant.PRE_TRAIN_DETECTION_FILE_LINK
# model_path = os.path.join(temp_path_generator(), "object_detection_pretrained.pth")
model_path = temp_path_generator(
) + '_object_detection_pretrained.pth'
download_file(file_link, model_path)
# load net
num_classes = len(VOC_CLASSES) + 1 # +1 for background
self.model = self._build_ssd('test', 300,
num_classes) # initialize SSD
if self.device.startswith("cuda"):
self.model.load_state_dict(torch.load(model_path))
else:
self.model.load_state_dict(
torch.load(model_path,
map_location=lambda storage, loc: storage))
self.model.eval()
print('Finished loading model!')
self.model = self.model.to(self.device)
def load(self, model_path=None):
temp_path = temp_path_generator()
ensure_dir(temp_path)
for model_link, file_path in zip(Constant.FACE_DETECTION_PRETRAINED['PRETRAINED_MODEL_LINKS'],
Constant.FACE_DETECTION_PRETRAINED['FILE_NAMES']):
download_file(model_link, f'{temp_path}/{file_path}')
def test_fetch(_):
# Assert requests.get calls
clean_dir(TEST_TEMP_DIR)
mgc = download_file("dummy_url", TEST_TEMP_DIR + '/dummy_file')
clean_dir(TEST_TEMP_DIR)
def test_fetch(_):
# Assert requests.get calls
clean_dir(TEST_TEMP_DIR)
mgc = download_file("dummy_url", TEST_TEMP_DIR + '/dummy_file')
clean_dir(TEST_TEMP_DIR)