def test_batch():
"""Test the batch-wise to submission."""
base_path = Path(__file__).parent / "data"
test_img_path = base_path / "rgb_mask_example.png"
mask = read_rgb_mask(test_img_path)
tensor_mask = from_numpy(mask)
batch = [
("alpha.jpg", "Pead", "Haricot", tensor_mask),
("bravo.jpg", "Bipbip", "Haricot", tensor_mask),
("charlie.jpg", "Roseau", "Mais", tensor_mask),
]
submission = {
filename: asdict(_sub)
for filename, _sub in
[sample_to_submission(a, b, c, d) for a, b, c, d in batch]
}
for _file, _team, _crop, _ in batch:
assert _file in submission
assert submission[_file]["shape"] == [1536, 2048]
assert submission[_file]["team"] == _team
assert submission[_file]["crop"] == _crop
assert submission[_file]["segmentation"]["crop"] is not None
assert submission[_file]["segmentation"]["weed"] is not None
def test_mask_to_json():
"""Test that the mask can be wrapped up as specified."""
base_path = Path(__file__).parent / "data"
test_img_path = base_path / "rgb_mask_example.png"
mask = read_rgb_mask(test_img_path)
tensor_mask = from_numpy(mask)
batch = ("arr_mask_example", "Bipbip", "Haricot", tensor_mask)
submission_dict = sample_to_submission(*batch)
assert submission_dict.filename == "arr_mask_example"
assert submission_dict.shape == [1536, 2048]
assert submission_dict.team == "Bipbip"
assert submission_dict.crop == "Haricot"
assert submission_dict.segmentation.crop is not None
assert submission_dict.segmentation.weed is not None
submission_dict = asdict(submission_dict)
submission_dict = {submission_dict.pop("filename"): submission_dict}
sample = base_path / "sample_submission.json"
with open(f"{sample}") as json_file:
sample_json = json.load(json_file)
assert sample_json == submission_dict
def test_step(self, batch: TestBatch, batch_idx: int) -> Dict[str, Dict[str, Any]]:
""""Predict the mask of a single test image and prepare it for submission."""
predicted_mask = self(batch.image).argmax(dim=1)
submission_i = sample_to_submission(
filename=batch.filename[0],
team_name=batch.team[0],
crop_name=batch.crop[0],
mask=predicted_mask.squeeze().cpu().detach().numpy(),
)
submission_dict_i = asdict(submission_i)
return {submission_dict_i.pop("filename"): submission_dict_i}
def test_mask_to_json():
"""Test that the mask can be wrapped up as specified."""
base_path = Path(__file__).parent / "data"
test_img_path = base_path / "rgb_mask_example.png"
mask = read_rgb_mask(test_img_path)
tensor_mask = from_numpy(mask)
batch = ("bob.jpg", "Bipbip", "Haricot", tensor_mask)
filename, submission_dict = sample_to_submission(*batch)
assert filename == "bob.jpg"
assert submission_dict.shape == [1536, 2048]
assert submission_dict.team == "Bipbip"
assert submission_dict.crop == "Haricot"
assert submission_dict.segmentation.crop is not None
assert submission_dict.segmentation.weed is not None