def test_dataset_by_frame_ids():
frame_ids = set([
724, 1532, 5037, 5992, 6040, 6723, 7088, 7386, 7977, 8762, 9769, 9891
])
if platform.system() == "Linux":
datalake_path = r"/data1/workspaces/aiftimie/tms/tms_data"
else:
datalake_path = r"D:\tms_data"
download_data_if_not_exists(datalake_path)
coco_dset = get_dataset(datalake_path)
g_tdp_fdp_1: Iterable[Tuple[FrameDatapoint, TargetDatapoint]]
g_tdp_fdp_2: Iterable[Tuple[FrameDatapoint, TargetDatapoint]]
g_tdp_fdp_1, g_tdp_fdp_2 = tee(
gen_cocoitem2datapoints(coco_dset, frame_ids))
g_tdp = gen_cocoitem2targetdp(g_tdp_fdp_1)
g_fdp_1, g_fdp_2, g_fdp_3 = tee(gen_cocoitem2framedp(g_tdp_fdp_2), 3)
model = create_model_efficient(
model_creation_func=partial(create_model, max_operating_res=800))
g_pred = compute(g_fdp_1,
model,
batch_size=5,
filter_classes=model_class_names)
for fdp_pred, fdp_gt in zip(plot_detections(g_fdp_2, g_pred),
plot_targets(g_fdp_3, g_tdp)):
cv2.imshow("image_pred", fdp_pred.image)
cv2.imshow("image_gt", fdp_gt.image)
cv2.waitKey(0)
def test_target_pred_iter_to_pandas(mock_some_obj_some_method):
mock_some_obj_some_method.return_value = 100
if platform.system() == "Linux":
datalake_path = r"/data1/workspaces/aiftimie/tms/tms_data"
else:
datalake_path = r"D:\tms_data"
download_data_if_not_exists(datalake_path)
coco_dset = get_dataset(datalake_path)
g_tdp_fdp_1: Iterable[Tuple[FrameDatapoint, TargetDatapoint]]
g_tdp_fdp_2: Iterable[Tuple[FrameDatapoint, TargetDatapoint]]
g_tdp_fdp_1, g_tdp_fdp_2 = tee(gen_cocoitem2datapoints(coco_dset))
g_tdp = gen_cocoitem2targetdp(g_tdp_fdp_1)
g_fdp = gen_cocoitem2framedp(g_tdp_fdp_2)
model = create_model_efficient(
model_creation_func=partial(create_model, max_operating_res=800))
g_pred = compute(g_fdp,
model,
batch_size=5,
filter_classes=model_class_names)
df_pred, df_target = target_pred_iter_to_pandas(g_tdp, g_pred)
if platform.system() == "Linux":
df_pred.to_csv(
"/data1/workspaces/aiftimie/tms/tms_experiments/pandas_dataframes/coco_pred.csv"
)
df_target.to_csv(
"/data1/workspaces/aiftimie/tms/tms_experiments/pandas_dataframes/coco_target.csv"
)
else:
pass
def test_get_dataset():
if platform.system() == "Linux":
datalake_path = r"D:\tms_data"
else:
datalake_path = r"/data1/workspaces/aiftimie/tms/tms_data"
download_data_if_not_exists(datalake_path)
# if error about some mask: https://stackoverflow.com/questions/49311195/how-to-install-coco-pythonapi-in-python3
coco_dset = get_dataset(datalake_path)
assert len(coco_dset) == 5000
def test_target_iter_to_pandas(mock_some_obj_some_method):
mock_some_obj_some_method.return_value = 100
if platform.system() == "Linux":
datalake_path = r"/data1/workspaces/aiftimie/tms/tms_data"
else:
datalake_path = r"D:\tms_data"
download_data_if_not_exists(datalake_path)
coco_dset = get_dataset(datalake_path)
g = gen_cocoitem2datapoints(coco_dset)
g2 = gen_cocoitem2targetdp(g)
df = target_iter_to_pandas(g2)
def get_dataframes(datalake_path, pred_csv_path, target_csv_path, max_operating_res):
download_data_if_not_exists(datalake_path)
coco_dset = get_dataset(datalake_path)
g_tdp_fdp_1, g_tdp_fdp_2 = tee(gen_cocoitem2datapoints(coco_dset))
g_tdp = gen_cocoitem2targetdp(g_tdp_fdp_1)
g_fdp = gen_cocoitem2framedp(g_tdp_fdp_2)
model = create_model_efficient(model_creation_func=partial(create_model, max_operating_res=max_operating_res, conf_thr=0.05))
g_pred = compute(g_fdp, model, batch_size=10, filter_classes=model_class_names)
df_pred, df_target = target_pred_iter_to_pandas(g_tdp, g_pred)
df_pred.to_csv(pred_csv_path)
df_target.to_csv(target_csv_path)
return df_pred, df_target
def evaluate_speed(mock_some_obj_some_method, datalake_path, max_operating_res):
num_eval_frames = 200
mock_some_obj_some_method.return_value = num_eval_frames
download_data_if_not_exists(datalake_path)
coco_dset = get_dataset(datalake_path)
g_tdp_fdp_1, g_tdp_fdp_2 = tee(gen_cocoitem2datapoints(coco_dset))
g_tdp = gen_cocoitem2targetdp(g_tdp_fdp_1)
g_fdp = gen_cocoitem2framedp(g_tdp_fdp_2)
model = create_model_efficient(
model_creation_func=partial(create_model, max_operating_res=max_operating_res, conf_thr=0.05))
g_pred = compute(g_fdp, model, batch_size=10, filter_classes=model_class_names)
start = time.time()
df_pred, df_target = target_pred_iter_to_pandas(g_tdp, g_pred)
end = time.time()
return (end - start) / num_eval_frames
def test_gen_cocoitem2datapoints(mock_some_obj_some_method):
mock_some_obj_some_method.return_value = 100
if platform.system() == "Linux":
datalake_path = r"/data1/workspaces/aiftimie/tms/tms_data"
else:
datalake_path = r"D:\tms_data"
download_data_if_not_exists(datalake_path)
coco_dset = get_dataset(datalake_path)
g = gen_cocoitem2datapoints(coco_dset)
for fdp, tdp in g:
dummy_pred = tdp.target
dummy_pred['scores'] = np.zeros_like(dummy_pred['labels'])
dummy_pred['obj_id'] = np.zeros_like(dummy_pred['labels'])
image = plot_over_image(fdp.image, dummy_pred)
cv2.imshow("image", image)
cv2.waitKey(0)
assert isinstance(fdp, FrameDatapoint)
assert isinstance(tdp, TargetDatapoint)
def test_download_data_if_not_exists(tmpdir):
download_data_if_not_exists(tmpdir)
assert os.path.exists(os.path.join(tmpdir, "coco_val_2017"))
assert os.path.exists(os.path.join(tmpdir, "coco_val_2017", "val2017.zip"))
assert os.path.exists(
os.path.join(tmpdir, "coco_val_2017", "annotations_trainval2017.zip"))
assert os.path.exists(os.path.join(tmpdir, "coco_val_2017", "val2017"))
assert os.path.exists(os.path.join(tmpdir, "coco_val_2017", "annotations"))
assert len(os.listdir(os.path.join(tmpdir, "coco_val_2017",
"val2017"))) == 5000
assert len(os.listdir(os.path.join(tmpdir, "coco_val_2017",
"annotations"))) == 6
json_file = os.path.join(tmpdir, "coco_val_2017", "annotations",
"instances_val2017.json")
if json_file is not None:
with open(json_file, 'r') as COCO:
js = json.loads(COCO.read())
assert {item['id']: item['name']
for item in js['categories']} == coco_val_2017_names