def test_table_specials():
table = wandb.Table(
columns=["image", "table"],
optional=False,
dtype=[data_types.Image, data_types.Table],
)
with pytest.raises(TypeError):
table.add_data(None, None)
# Infers specific types from first valid row
table.add_data(
data_types.Image(
np.random.rand(10, 10),
boxes=box_annotation,
masks=mask_annotation,
),
data_types.Table(data=[[1, True, None]]),
)
# Denies conflict
with pytest.raises(TypeError):
table.add_data(
"hello",
data_types.Table(data=[[1, True, None]]),
)
# Denies conflict
with pytest.raises(TypeError):
table.add_data(
data_types.Image(
np.random.rand(10, 10),
boxes=box_annotation,
masks=mask_annotation,
),
data_types.Table(data=[[1, "True", None]]),
)
# allows further refinement
table.add_data(
data_types.Image(
np.random.rand(10, 10),
boxes=box_annotation,
masks=mask_annotation,
),
data_types.Table(data=[[1, True, 1]]),
)
# allows addition
table.add_data(
data_types.Image(
np.random.rand(10, 10),
boxes=box_annotation,
masks=mask_annotation,
),
data_types.Table(data=[[1, True, 1]]),
)
def test_image_accepts_bounding_boxes_optional_args(mocked_run):
img = data_types.Image(image, boxes={"predictions": {"box_data":
boxes_with_removed_optional_args}})
img.bind_to_run(mocked_run, "images", 0)
img_json = img.to_json(mocked_run)
path = img_json["boxes"]["predictions"]["path"]
assert os.path.exists(os.path.join(mocked_run.dir, path))
def test_single_image(history):
image = np.random.randint(255, size=(28, 28))
history.add({"images": data_types.Image(image)})
h = disk_history()
assert h[0]["images"] == {'_type': 'images',
'count': 1, 'height': 28, 'width': 28}
assert os.path.exists("media/images/images_0.jpg")
def test_image_accepts_bounding_boxes_optional_args():
with CliRunner().isolated_filesystem():
run = wandb.wandb_run.Run()
img = data_types.Image(image, boxes=boxes_with_removed_optional_args)
img.bind_to_run(run, "images", 0)
img_json = img.to_json(run)
path = img_json["boxes"]["path"]
assert os.path.exists(os.path.join(run.dir, path))
def test_list_of_images(history):
image = np.random.randint(255, size=(28, 28))
history.add({"images": [data_types.Image(image)]})
h = disk_history(history)
expected = {
'_type': 'images',
'count': 1,
'height': 28,
'width': 28,
}
def test_single_image(history):
image = np.random.randint(255, size=(28, 28))
history.add({"images": data_types.Image(image)})
h = disk_history(history)
assert os.path.exists(
os.path.join(history._run.dir, h[0]['images']['path']))
assert set(h[0]["images"].items()) >= set({
'_type': 'image-file',
'height': 28,
'width': 28,
}.items())
def test_image_type():
wb_type = data_types._ImageFileType()
image_simple = data_types.Image(np.random.rand(10, 10))
wb_type_simple = data_types._ImageFileType.from_obj(image_simple)
image_annotated = data_types.Image(
np.random.rand(10, 10),
boxes={
"box_predictions": {
"box_data": [
{
"position": {
"minX": 0.1,
"maxX": 0.2,
"minY": 0.3,
"maxY": 0.4,
},
"class_id": 1,
"box_caption": "minMax(pixel)",
"scores": {
"acc": 0.1,
"loss": 1.2
},
},
],
"class_labels":
class_labels,
},
"box_ground_truth": {
"box_data": [
{
"position": {
"minX": 0.1,
"maxX": 0.2,
"minY": 0.3,
"maxY": 0.4,
},
"class_id": 1,
"box_caption": "minMax(pixel)",
"scores": {
"acc": 0.1,
"loss": 1.2
},
},
],
"class_labels":
class_labels,
},
},
masks={
"mask_predictions": {
"mask_data": np.random.randint(0, 4, size=(30, 30)),
"class_labels": class_labels,
},
"mask_ground_truth": {
"path": im_path,
"class_labels": class_labels
},
},
)
wb_type_annotated = data_types._ImageFileType.from_obj(image_annotated)
image_annotated_differently = data_types.Image(
np.random.rand(10, 10),
boxes={
"box_predictions": {
"box_data": [
{
"position": {
"minX": 0.1,
"maxX": 0.2,
"minY": 0.3,
"maxY": 0.4,
},
"class_id": 1,
"box_caption": "minMax(pixel)",
"scores": {
"acc": 0.1,
"loss": 1.2
},
},
],
"class_labels":
class_labels,
},
},
masks={
"mask_predictions": {
"mask_data": np.random.randint(0, 4, size=(30, 30)),
"class_labels": class_labels,
},
"mask_ground_truth_2": {
"path": im_path,
"class_labels": class_labels
},
},
)
assert wb_type.assign(image_simple) == wb_type_simple
assert wb_type.assign(image_annotated) == wb_type_annotated
# OK to assign Images with disjoint class set
assert wb_type_annotated.assign(image_simple) == wb_type_annotated
# Merge when disjoint
assert wb_type_annotated.assign(
image_annotated_differently) == data_types._ImageFileType(
box_layers={
"box_predictions": {1, 2, 3},
"box_ground_truth": {1, 2, 3}
},
box_score_keys={"loss", "acc"},
mask_layers={
"mask_ground_truth_2": set(),
"mask_ground_truth": set(),
"mask_predictions": {1, 2, 3},
},
class_map={
"1": "tree",
"2": "car",
"3": "road"
},
)
def test_image_type():
wb_type = data_types._ImageFileType()
image_simple = data_types.Image(np.random.rand(10, 10))
wb_type_simple = data_types._ImageFileType.from_obj(image_simple)
image_annotated = data_types.Image(
np.random.rand(10, 10),
boxes={
"box_predictions": {
"box_data": [
{
"position": {
"minX": 0.1,
"maxX": 0.2,
"minY": 0.3,
"maxY": 0.4,
},
"class_id": 1,
"box_caption": "minMax(pixel)",
"scores": {
"acc": 0.1,
"loss": 1.2
},
},
],
"class_labels":
class_labels,
},
"box_ground_truth": {
"box_data": [
{
"position": {
"minX": 0.1,
"maxX": 0.2,
"minY": 0.3,
"maxY": 0.4,
},
"class_id": 1,
"box_caption": "minMax(pixel)",
"scores": {
"acc": 0.1,
"loss": 1.2
},
},
],
"class_labels":
class_labels,
},
},
masks={
"mask_predictions": {
"mask_data": np.random.randint(0, 4, size=(30, 30)),
"class_labels": class_labels,
},
"mask_ground_truth": {
"path": im_path,
"class_labels": class_labels
},
},
)
wb_type_annotated = data_types._ImageFileType.from_obj(image_annotated)
image_annotated_differently = data_types.Image(
np.random.rand(10, 10),
boxes={
"box_predictions": {
"box_data": [
{
"position": {
"minX": 0.1,
"maxX": 0.2,
"minY": 0.3,
"maxY": 0.4,
},
"class_id": 1,
"box_caption": "minMax(pixel)",
"scores": {
"acc": 0.1,
"loss": 1.2
},
},
],
"class_labels":
class_labels,
},
},
masks={
"mask_predictions": {
"mask_data": np.random.randint(0, 4, size=(30, 30)),
"class_labels": class_labels,
},
"mask_ground_truth_2": {
"path": im_path,
"class_labels": class_labels
},
},
)
assert wb_type.assign(image_simple) == wb_type_simple
assert wb_type.assign(image_annotated) == wb_type_annotated
assert wb_type_annotated.assign(image_simple) == InvalidType()
assert wb_type_annotated.assign(
image_annotated_differently) == InvalidType()
