diff --git a/src/av2/evaluation/scene_flow/SUBMISSION_FORMAT.md b/src/av2/evaluation/scene_flow/SUBMISSION_FORMAT.md new file mode 100644 index 00000000..20fbe109 --- /dev/null +++ b/src/av2/evaluation/scene_flow/SUBMISSION_FORMAT.md @@ -0,0 +1,42 @@ +# 3D Scene Flow Submission Format + +The evaluation expects a zip archive of [Apache Feather](https://arrow.apache.org/docs/python/feather.html) files --- one for each example. The unzipped directory must have the format: + +```terminal +- / + - .feather + - .feather + - ... +- / +- ... +``` + +The evaluation is run on a subset of the test set. Use the utility function `get_eval_subset` to get the `SceneFlowDataloader` indices to submit. Each feather file should contain your flow predictions for the subset of points returned by `get_eval_mask` in the format: + +- `flow_tx_m` (float16): x-component of the flow (in meters) in the first sweeps's ego-vehicle reference frame. +- `flow_ty_m` (float16): y-component of the flow (in meters) in the first sweeps's ego-vehicle reference frame. +- `flow_tz_m` (float16): z-component of the flow (in meters) in the first sweeps's ego-vehicle reference frame. +- `is_dynamic` (bool): Predicted dynamic/static labels for each point. A point is considered dynamic if its ground truth flow has a $\ell^2$-norm greater then $0.05 \textit{ m}$ once ego-motion has been removed. + + +For example, the first log in the test set is `0c6e62d7-bdfa-3061-8d3d-03b13aa21f68` and the first timestamp is `315971435999927221`, so there should be a folder and file in the archive of the form: `0c6e62d7-bdfa-3061-8d3d-03b13aa21f68/315971435999927221.feather`. That fill should look like this: +```python + flow_tx_m flow_ty_m flow_tz_m +0 -0.699219 0.002869 0.020233 +1 -0.699219 0.002790 0.020493 +2 -0.699219 0.002357 0.020004 +3 -0.701172 0.001650 0.013390 +4 -0.699219 0.002552 0.020187 +... ... ... ... +68406 -0.703613 -0.001801 0.002373 +68407 -0.704102 -0.000905 0.002567 +68408 -0.704590 -0.001390 0.000397 +68409 -0.704102 -0.001608 0.002283 +68410 -0.704102 -0.001619 0.002207 +``` +The file `example_submission.py` contains a basic example of how to output the submission files. The script `make_submission_archive.py` will create the zip archive for you and validate the submission format. Then you can submit the outputted file to the competition leaderboard! + + +# Local Evaluation + +Before evaluating on the _test_ set, you will want to evaluate your model on the _validation_ set. To do this, first run `make_annotation_files.py` to create a set of files containing the minimum ground truth flow information needed to run the evaluation. Then, once you have your output saved in the feather files described above, run `eval.py` to compute all of the leaderboard metrics. diff --git a/src/av2/evaluation/scene_flow/constants.py b/src/av2/evaluation/scene_flow/constants.py new file mode 100644 index 00000000..7195b79d --- /dev/null +++ b/src/av2/evaluation/scene_flow/constants.py @@ -0,0 +1,110 @@ +"""Constants for scene flow evaluation.""" + +from __future__ import annotations + +from enum import Enum, unique +from typing import Final + +from av2.datasets.sensor.constants import AnnotationCategories + +SCENE_FLOW_DYNAMIC_THRESHOLD: Final = 0.05 +SWEEP_PAIR_TIME_DELTA: Final = 0.1 + +CATEGORY_TO_INDEX: Final = {**{"NONE": 0}, **{k.value: i + 1 for i, k in enumerate(AnnotationCategories)}} + + +@unique +class SceneFlowMetricType(str, Enum): + """Scene Flow metrics.""" + + ACCURACY_RELAX = "ACCURACY_RELAX" + ACCURACY_STRICT = "ACCURACY_STRICT" + ANGLE_ERROR = "ANGLE_ERROR" + EPE = "EPE" + + +@unique +class SegmentationMetricType(str, Enum): + """Segmentation metrics.""" + + TP = "TP" + TN = "TN" + FP = "FP" + FN = "FN" + + +@unique +class InanimateCategories(str, Enum): + """Annotation categories representing inanimate objects that aren't vehicles.""" + + BOLLARD = "BOLLARD" + CONSTRUCTION_BARREL = "CONSTRUCTION_BARREL" + CONSTRUCTION_CONE = "CONSTRUCTION_CONE" + MOBILE_PEDESTRIAN_CROSSING_SIGN = "MOBILE_PEDESTRIAN_CROSSING_SIGN" + SIGN = "SIGN" + STOP_SIGN = "STOP_SIGN" + + +@unique +class LeggedCategories(str, Enum): + """Annotation categories representing objects that move using legs.""" + + ANIMAL = "ANIMAL" + DOG = "DOG" + OFFICIAL_SIGNALER = "OFFICIAL_SIGNALER" + PEDESTRIAN = "PEDESTRIAN" + + +@unique +class SmallVehicleCategories(str, Enum): + """Annotation categories representing small vehicles.""" + + BICYCLE = "BICYCLE" + BICYCLIST = "BICYCLIST" + MOTORCYCLE = "MOTORCYCLE" + MOTORCYCLIST = "MOTORCYCLIST" + STROLLER = "STROLLER" + WHEELCHAIR = "WHEELCHAIR" + WHEELED_DEVICE = "WHEELED_DEVICE" + WHEELED_RIDER = "WHEELED_RIDER" + + +@unique +class VehicleCategories(str, Enum): + """Annotation categories representing regular vehicles.""" + + ARTICULATED_BUS = "ARTICULATED_BUS" + BOX_TRUCK = "BOX_TRUCK" + BUS = "BUS" + LARGE_VEHICLE = "LARGE_VEHICLE" + MESSAGE_BOARD_TRAILER = "MESSAGE_BOARD_TRAILER" + RAILED_VEHICLE = "RAILED_VEHICLE" + REGULAR_VEHICLE = "REGULAR_VEHICLE" + SCHOOL_BUS = "SCHOOL_BUS" + TRAFFIC_LIGHT_TRAILER = "TRAFFIC_LIGHT_TRAILER" + TRUCK = "TRUCK" + TRUCK_CAB = "TRUCK_CAB" + VEHICULAR_TRAILER = "VEHICULAR_TRAILER" + + +@unique +class MetricBreakdownCategories(str, Enum): + """Meta-categories for the scene flow task.""" + + ALL = "All" + BACKGROUND = "Background" + FOREGROUND = "Foreground" + + +NO_CLASS_BREAKDOWN: Final = {MetricBreakdownCategories.ALL: list(range(31))} +FOREGROUND_BACKGROUND_BREAKDOWN: Final = { + MetricBreakdownCategories.BACKGROUND: [0], + MetricBreakdownCategories.FOREGROUND: [ + CATEGORY_TO_INDEX[k.value] + for k in ( + list(InanimateCategories) + list(LeggedCategories) + list(SmallVehicleCategories) + list(VehicleCategories) + ) + ], +} + +FLOW_COLUMNS: Final = ("flow_tx_m", "flow_ty_m", "flow_tz_m") diff --git a/src/av2/evaluation/scene_flow/eval.py b/src/av2/evaluation/scene_flow/eval.py new file mode 100644 index 00000000..8824a711 --- /dev/null +++ b/src/av2/evaluation/scene_flow/eval.py @@ -0,0 +1,423 @@ +"""Argoverse 2 Scene Flow Evaluation.""" + +from __future__ import annotations + +from collections import defaultdict +from pathlib import Path +from typing import Any, DefaultDict, Dict, Final, List, Tuple, Union, cast + +import click +import numpy as np +import pandas as pd +from rich.progress import track + +import av2.evaluation.scene_flow.constants as constants +from av2.evaluation.scene_flow.constants import SceneFlowMetricType, SegmentationMetricType +from av2.utils.typing import NDArrayBool, NDArrayFloat, NDArrayInt + +ACCURACY_RELAX_DISTANCE_THRESHOLD: Final = 0.1 +ACCURACY_STRICT_DISTANCE_THRESHOLD: Final = 0.05 +NO_FMT_INDICES: Final = ("Background", "Dynamic") +EPS: Final = 1e-10 + + +def compute_end_point_error(dts: NDArrayFloat, gts: NDArrayFloat) -> NDArrayFloat: + """Compute the end-point error between predictions and ground truth. + + Args: + dts: (N,3) Array containing predicted flows. + gts: (N,3) Array containing ground truth flows. + + Returns: + The point-wise end-point error. + """ + end_point_error: NDArrayFloat = np.linalg.norm(dts - gts, axis=-1).astype(np.float64) + return end_point_error + + +def compute_accuracy(dts: NDArrayFloat, gts: NDArrayFloat, distance_threshold: float) -> NDArrayFloat: + """Compute the percent of inliers for a given threshold for a set of prediction and ground truth vectors. + + Args: + dts: (N,3) Array containing predicted flows. + gts: (N,3) Array containing ground truth flows. + distance_threshold: Distance threshold for classifying inliers. + + Returns: + The pointwise inlier assignments. + """ + l2_norm = np.linalg.norm(dts - gts, axis=-1) + gts_norm = np.linalg.norm(gts, axis=-1) + relative_error = np.divide(l2_norm, gts_norm + EPS) + abs_error_inlier = np.less(l2_norm, distance_threshold).astype(bool) + relative_error_inlier = np.less(relative_error, distance_threshold).astype(bool) + accuracy: NDArrayFloat = np.logical_or(abs_error_inlier, relative_error_inlier).astype(np.float64) + return accuracy + + +def compute_accuracy_strict(dts: NDArrayFloat, gts: NDArrayFloat) -> NDArrayFloat: + """Compute the acccuracy with a 0.05 threshold. + + Args: + dts: (N,3) Array containing predicted flows. + gts: (N,3) Array containing ground truth flows. + + Returns: + The pointwise inlier assignments at a 0.05 threshold + """ + return compute_accuracy(dts, gts, ACCURACY_STRICT_DISTANCE_THRESHOLD) + + +def compute_accuracy_relax(dts: NDArrayFloat, gts: NDArrayFloat) -> NDArrayFloat: + """Compute the acccuracy with a 0.1 threshold. + + Args: + dts: (N,3) Array containing predicted flows. + gts: (N,3) Array containing ground truth flows. + + Returns: + The pointwise inlier assignments at a 0.1 threshold. + """ + return compute_accuracy(dts, gts, ACCURACY_RELAX_DISTANCE_THRESHOLD) + + +def compute_angle_error(dts: NDArrayFloat, gts: NDArrayFloat) -> NDArrayFloat: + """Compute the angle error in space-time between the prediced and ground truth flow vectors. + + Args: + dts: (N,3) Array containing predicted flows. + gts: (N,3) Array containing ground truth flows. + + Returns: + The pointwise angle errors in space-time. + """ + # Convert the 3D flow vectors to 4D space-time vectors. + dts_space_time = np.pad(dts, ((0, 0), (0, 1)), constant_values=constants.SWEEP_PAIR_TIME_DELTA) + gts_space_time = np.pad(gts, ((0, 0), (0, 1)), constant_values=constants.SWEEP_PAIR_TIME_DELTA) + + dts_space_time_norm = np.linalg.norm(dts_space_time, axis=-1, keepdims=True) + gts_space_time_norm = np.linalg.norm(gts_space_time, axis=-1, keepdims=True) + unit_dts = dts_space_time / dts_space_time_norm + unit_gts = gts_space_time / gts_space_time_norm + + dot_product = np.einsum("bd,bd->b", unit_dts, unit_gts) + + # Floating point errors can cause `dot_product` to be slightly greater than 1 or less than -1. + clipped_dot_product = np.clip(dot_product, -1.0, 1.0) + angle_error: NDArrayFloat = np.arccos(clipped_dot_product).astype(np.float64) + return angle_error + + +def compute_true_positives(dts: NDArrayBool, gts: NDArrayBool) -> int: + """Compute true positive count. + + Args: + dts: (N,) Array containing predicted dynamic segmentation. + gts: (N,) Array containing ground truth dynamic segmentation. + + Returns: + The number of true positive classifications. + """ + return int(np.logical_and(dts, gts).sum()) + + +def compute_true_negatives(dts: NDArrayBool, gts: NDArrayBool) -> int: + """Compute true negative count. + + Args: + dts: (N,) Array containing predicted dynamic segmentation. + gts: (N,) Array containing ground truth dynamic segmentation. + + Returns: + The number of true negative classifications. + """ + return int(np.logical_and(~dts, ~gts).sum()) + + +def compute_false_positives(dts: NDArrayBool, gts: NDArrayBool) -> int: + """Compute false positive count. + + Args: + dts: (N,) Array containing predicted dynamic segmentation. + gts: (N,) Array containing ground truth dynamic segmentation. + + Returns: + The number of false positive classifications. + """ + return int(np.logical_and(dts, ~gts).sum()) + + +def compute_false_negatives(dts: NDArrayBool, gts: NDArrayBool) -> int: + """Compute false negative count. + + Args: + dts: (N,) Array containing predicted dynamic segmentation. + gts: (N,) Array containing ground truth dynamic segmentation. + + Returns: + The number of false negative classifications + """ + return int(np.logical_and(~dts, gts).sum()) + + +def compute_scene_flow_metrics( + dts: NDArrayFloat, gts: NDArrayFloat, scene_flow_metric_type: SceneFlowMetricType +) -> NDArrayFloat: + """Compute scene flow metrics. + + Args: + dts: (N,3) Array containing predicted flows. + gts: (N,3) Array containing ground truth flows. + scene_flow_metric_type: Scene flow metric type. + + Returns: + Scene flow metric corresponding to `scene_flow_metric_type`. + + Raises: + NotImplementedError: If the `scene_flow_metric_type` is not implemented. + """ + if scene_flow_metric_type == SceneFlowMetricType.ACCURACY_RELAX: + return compute_accuracy_relax(dts, gts) + elif scene_flow_metric_type == SceneFlowMetricType.ACCURACY_STRICT: + return compute_accuracy_strict(dts, gts) + elif scene_flow_metric_type == SceneFlowMetricType.ANGLE_ERROR: + return compute_angle_error(dts, gts) + elif scene_flow_metric_type == SceneFlowMetricType.EPE: + return compute_end_point_error(dts, gts) + else: + raise NotImplementedError(f"The scene flow metric type {scene_flow_metric_type} is not implemented!") + + +def compute_segmentation_metrics( + dts: NDArrayBool, gts: NDArrayBool, segmentation_metric_type: SegmentationMetricType +) -> int: + """Compute segmentation metrics. + + Args: + dts: (N,) Array containing predicted dynamic segmentation. + gts: (N,) Array containing ground truth dynamic segmentation. + segmentation_metric_type: Segmentation metric type. + + Returns: + Segmentation metric corresponding to `segmentation_metric_type`. + + Raises: + NotImplementedError: If the `segmentation_metric_type` is not implemented. + """ + if segmentation_metric_type == SegmentationMetricType.TP: + return compute_true_positives(dts, gts) + elif segmentation_metric_type == SegmentationMetricType.TN: + return compute_true_negatives(dts, gts) + elif segmentation_metric_type == SegmentationMetricType.FP: + return compute_false_positives(dts, gts) + elif segmentation_metric_type == SegmentationMetricType.FN: + return compute_false_negatives(dts, gts) + else: + raise NotImplementedError(f"The segmentation metric type {segmentation_metric_type} is not implemented!") + + +def compute_metrics( + pred_flow: NDArrayFloat, + pred_dynamic: NDArrayBool, + gts: NDArrayFloat, + category_indices: NDArrayInt, + is_dynamic: NDArrayBool, + is_close: NDArrayBool, + is_valid: NDArrayBool, + metric_categories: Dict[constants.MetricBreakdownCategories, List[int]], +) -> Dict[str, List[Any]]: + """Compute all the metrics for a given example and package them into a list to be put into a DataFrame. + + Args: + pred_flow: (N,3) Predicted flow vectors. + pred_dynamic: (N,) Predicted dynamic labels. + gts: (N,3) Ground truth flow vectors. + category_indices: (N,) Integer class labels for each point. + is_dynamic: (N,) Ground truth dynamic labels. + is_close: (N,) True for a point if it is within a 70m x 70m box around the AV. + is_valid: (N,) True for a point if its flow vector was succesfully computed. + metric_categories: A dictionary mapping segmentation labels to groups of category indices. + + Returns: + A dictionary of columns to create a long-form DataFrame of the results from. + One row for each subset in the breakdown. + """ + pred_flow = pred_flow[is_valid].astype(np.float64) + pred_dynamic = pred_dynamic[is_valid].astype(bool) + gts = gts[is_valid].astype(np.float64) + category_indices = category_indices[is_valid].astype(int) + is_dynamic = is_dynamic[is_valid].astype(bool) + is_close = is_close[is_valid].astype(bool) + + results: DefaultDict[str, List[Any]] = defaultdict(list) + + # Each metric is broken down by point labels on Object Class, Motion, and Distance from the AV. + # We iterate over all combinations of those three categories and compute average metrics on each subset. + for cls, category_idxs in metric_categories.items(): + # Compute the union of all masks within the meta-category. + category_mask = category_indices == category_idxs[0] + for i in category_idxs[1:]: + category_mask = np.logical_or(category_mask, (category_indices == i)) + + for motion, m_mask in [("Dynamic", is_dynamic), ("Static", ~is_dynamic)]: + for distance, d_mask in [("Close", is_close), ("Far", ~is_close)]: + mask = category_mask & m_mask & d_mask + subset_size = mask.sum().item() + gts_sub = gts[mask] + pred_sub = pred_flow[mask] + results["Class"] += [cls.value] + results["Motion"] += [motion] + results["Distance"] += [distance] + results["Count"] += [subset_size] + + # Check if there are any points in this subset and if so compute all the average metrics. + if subset_size > 0: + for flow_metric_type in SceneFlowMetricType: + results[flow_metric_type] += [ + compute_scene_flow_metrics(pred_sub, gts_sub, flow_metric_type).mean() + ] + for seg_metric_type in SegmentationMetricType: + results[seg_metric_type] += [ + compute_segmentation_metrics(pred_dynamic[mask], is_dynamic[mask], seg_metric_type) + ] + else: + for flow_metric_type in SceneFlowMetricType: + results[flow_metric_type] += [np.nan] + for seg_metric_type in SegmentationMetricType: + results[seg_metric_type] += [0.0] + return results + + +def evaluate_directories(annotations_dir: Path, predictions_dir: Path) -> pd.DataFrame: + """Run the evaluation on predictions and labels saved to disk. + + Args: + annotations_dir: Path to the directory containing the annotation files produced by `make_annotation_files.py`. + predictions_dir: Path to the prediction files in submission format. + + Returns: + DataFrame containing the average metrics on each subset of each example. + """ + results: DefaultDict[str, List[Any]] = defaultdict(list) + annotation_files = list(annotations_dir.rglob("*.feather")) + for anno_file in track(annotation_files, description="Evaluating..."): + gts = pd.read_feather(anno_file) + name: str = str(anno_file.relative_to(annotations_dir)) + pred_file = predictions_dir / name + if not pred_file.exists(): + print(f"Warning: File {name} is missing!") + continue + pred = pd.read_feather(pred_file) + current_example_results = compute_metrics( + pred[list(constants.FLOW_COLUMNS)].to_numpy().astype(float), + pred["is_dynamic"].to_numpy().astype(bool), + gts[list(constants.FLOW_COLUMNS)].to_numpy().astype(float), + gts["category_indices"].to_numpy().astype(np.uint8), + gts["is_dynamic"].to_numpy().astype(bool), + gts["is_close"].to_numpy().astype(bool), + gts["is_valid"].to_numpy().astype(bool), + constants.FOREGROUND_BACKGROUND_BREAKDOWN, + ) + num_subsets = len(list(current_example_results.values())[0]) + results["Example"] += [name for _ in range(num_subsets)] + for m in current_example_results: + results[m] += current_example_results[m] + df = pd.DataFrame( + results, + columns=["Example", "Class", "Motion", "Distance", "Count"] + + list(SceneFlowMetricType) + + list(SegmentationMetricType), + ) + return df + + +def results_to_dict(frame: pd.DataFrame) -> Dict[str, float]: + """Convert a results DataFrame to a dictionary of whole dataset metrics. + + Args: + frame: DataFrame returned by evaluate_directories. + + Returns: + Dictionary string keys "" mapped to average metrics on that subset. + """ + output = {} + grouped = frame.groupby(["Class", "Motion", "Distance"]) + + def weighted_average(x: pd.DataFrame, metric_type: Union[SceneFlowMetricType, SegmentationMetricType]) -> float: + """Weighted average of metric m using the Count column. + + Args: + x: Input data-frame. + metric_type: Metric type. + + Returns: + Weighted average over the metric_type; + """ + total = cast(int, x["Count"].sum()) + if total == 0: + return np.nan + averages: float = (x[metric_type.value] * x.Count).sum() / total + return averages + + for metric_type in SceneFlowMetricType: + avg: pd.Series[float] = grouped.apply(lambda x, m=metric_type: weighted_average(x, metric_type=m)) + segments: List[Tuple[str, str, str]] = avg.index.to_list() + for segment in segments: + if segment[:2] == NO_FMT_INDICES: + continue + + metric_type_str = ( + metric_type.title().replace("_", " ") if metric_type != SceneFlowMetricType.EPE else metric_type + ) + name = metric_type_str + "/" + "/".join([str(i) for i in segment]) + output[name] = avg.loc[segment] + + grouped = frame.groupby(["Class", "Motion"]) + for metric_type in SceneFlowMetricType: + avg_nodist: pd.Series[float] = grouped.apply(lambda x, m=metric_type: weighted_average(x, metric_type=m)) + segments_nodist: List[Tuple[str, str, str]] = avg_nodist.index.to_list() + for segment in segments_nodist: + if segment[:2] == NO_FMT_INDICES: + continue + + metric_type_str = ( + metric_type.title().replace("_", " ") if metric_type != SceneFlowMetricType.EPE else metric_type + ) + name = metric_type_str + "/" + "/".join([str(i) for i in segment]) + output[name] = avg_nodist.loc[segment] + output["Dynamic IoU"] = frame.TP.sum() / (frame.TP.sum() + frame.FP.sum() + frame.FN.sum()) + output["EPE 3-Way Average"] = ( + output["EPE/Foreground/Dynamic"] + output["EPE/Foreground/Static"] + output["EPE/Background/Static"] + ) / 3 + return output + + +def evaluate(annotations_dir: str, predictions_dir: str) -> Dict[str, float]: + """Evaluate a set of predictions and print the results. + + Args: + annotations_dir: Path to the directory containing the annotation files produced by `make_annotation_files.py`. + predictions_dir: Path to the prediction files in submission format. + + Returns: + The results as a dict of metric names and values. + """ + results_df = evaluate_directories(Path(annotations_dir), Path(predictions_dir)) + results_dict = results_to_dict(results_df) + + for metric in sorted(results_dict): + print(f"{metric}: {results_dict[metric]:.3f}") + + return results_dict + + +@click.command() +@click.argument("annotations_dir", type=str) +@click.argument("predictions_dir", type=str) +def _evaluate_entry(annotations_dir: str, predictions_dir: str) -> Dict[str, float]: + """Entry point for evaluate.""" + return evaluate(annotations_dir, predictions_dir) + + +if __name__ == "__main__": + _evaluate_entry() diff --git a/src/av2/evaluation/scene_flow/example_submission.py b/src/av2/evaluation/scene_flow/example_submission.py new file mode 100644 index 00000000..821fc412 --- /dev/null +++ b/src/av2/evaluation/scene_flow/example_submission.py @@ -0,0 +1,57 @@ +"""An example showing how to output flow predictions in the format required for submission.""" + +from pathlib import Path + +import click +import numpy as np +from kornia.geometry.linalg import transform_points +from rich.progress import track + +from av2.evaluation.scene_flow.utils import get_eval_point_mask, get_eval_subset, write_output_file +from av2.torch.data_loaders.scene_flow import SceneFlowDataloader + + +def example_submission(output_dir: str, mask_file: str, data_dir: str, name: str) -> None: + """Output example submission files for the leaderboard. Predicts the ego motion for every point. + + Args: + output_dir: Path to output directory. + mask_file: Archive of submission masks. + data_dir: Path to input data. + name: Name of the dataset (e.g. av2). + """ + data_loader = SceneFlowDataloader(Path(data_dir), name, "test") + + output_root = Path(output_dir) + output_root.mkdir(exist_ok=True) + + eval_inds = get_eval_subset(data_loader) + for i in track(eval_inds, description="Generating outputs..."): + sweep_0, sweep_1, ego_1_SE3_ego_0, flow = data_loader[i] + mask = get_eval_point_mask(sweep_0.sweep_uuid, Path(mask_file)) + + pc1 = sweep_0.lidar.as_tensor()[mask, :3] + pc1_rigid = transform_points(ego_1_SE3_ego_0.matrix(), pc1[None])[0] + rigid_flow = (pc1_rigid - pc1).detach().numpy() + is_dynamic = np.zeros(len(rigid_flow), dtype=bool) + + write_output_file(rigid_flow, is_dynamic, sweep_0.sweep_uuid, output_root) + + +@click.command() +@click.argument("output_dir", type=str) +@click.argument("data_dir", type=str) +@click.argument("mask_file", type=str) +@click.option( + "--name", + type=str, + help="the data should be located in //sensor/", + default="av2", +) +def _example_submission_entry(output_dir: str, mask_file: str, data_dir: str, name: str) -> None: + """Entry point for example_submission.""" + example_submission(output_dir, mask_file, data_dir, name) + + +if __name__ == "__main__": + _example_submission_entry() diff --git a/src/av2/evaluation/scene_flow/make_annotation_files.py b/src/av2/evaluation/scene_flow/make_annotation_files.py new file mode 100644 index 00000000..cba2570b --- /dev/null +++ b/src/av2/evaluation/scene_flow/make_annotation_files.py @@ -0,0 +1,125 @@ +"""Utility program for producing minimnal annotation files used for evaluation on the val and test splits.""" + +from pathlib import Path +from typing import Final, Tuple + +import click +import numpy as np +import pandas as pd +from rich.progress import track + +from av2.evaluation.scene_flow.utils import get_eval_point_mask, get_eval_subset +from av2.torch.data_loaders.scene_flow import SceneFlowDataloader +from av2.utils.typing import NDArrayBool, NDArrayFloat, NDArrayInt + +CLOSE_DISTANCE_THRESHOLD: Final = 35.0 + + +def write_annotation( + category_indices: NDArrayInt, + is_close: NDArrayBool, + is_dynamic: NDArrayBool, + is_valid: NDArrayBool, + flow: NDArrayFloat, + sweep_uuid: Tuple[str, int], + output_dir: Path, +) -> None: + """Write an annotation file. + + Args: + category_indices: Category label indices. + is_close: Close (inside 70 meter box) labels. + is_dynamic: Dynamic labels. + is_valid: Valid flow labels. + flow: Flow labels. + sweep_uuid: Log and timestamp of the sweep. + output_dir: Top level directory to store the output in. + """ + output = pd.DataFrame( + { + "category_indices": category_indices.astype(np.uint8), + "is_close": is_close.astype(bool), + "is_dynamic": is_dynamic.astype(bool), + "is_valid": is_valid.astype(bool), + "flow_tx_m": flow[:, 0].astype(np.float16), + "flow_ty_m": flow[:, 1].astype(np.float16), + "flow_tz_m": flow[:, 2].astype(np.float16), + } + ) + + log_id, timestamp_ns = sweep_uuid + + output_subdir = output_dir / log_id + output_subdir.mkdir(exist_ok=True) + output_file = output_subdir / f"{timestamp_ns}.feather" + output.to_feather(output_file) + + +def make_annotation_files(output_dir: str, mask_file: str, data_dir: str, name: str, split: str) -> None: + """Create annotation files for running the evaluation. + + Args: + output_dir: Path to output directory. + data_dir: Path to input data. + mask_file: Archive of submission masks. + name: Name of the dataset (e.g. av2). + split: Split to make annotations for. + + Raises: + ValueError: If the dataset does not have annotations. + """ + data_loader = SceneFlowDataloader(Path(data_dir), name, "val") + + output_root = Path(output_dir) + output_root.mkdir(exist_ok=True) + + eval_inds = get_eval_subset(data_loader) + for i in track(eval_inds): + sweep_0, _, _, flow_labels = data_loader[i] + if flow_labels is None: + raise ValueError("Missing flow annotations!") + + mask = get_eval_point_mask(sweep_0.sweep_uuid, Path(mask_file)) + + flow = flow_labels.flow[mask].numpy().astype(np.float16) + is_valid = flow_labels.is_valid[mask].numpy().astype(bool) + category_indices = flow_labels.category_indices[mask].numpy().astype(np.uint8) + is_dynamic = flow_labels.is_dynamic[mask].numpy().astype(bool) + + pc = sweep_0.lidar.as_tensor()[mask, :3].numpy() + is_close = np.logical_and.reduce(np.abs(pc[:, :2]) <= CLOSE_DISTANCE_THRESHOLD, axis=1).astype(bool) + + write_annotation( + category_indices, + is_close, + is_dynamic, + is_valid, + flow, + sweep_0.sweep_uuid, + output_root, + ) + + +@click.command() +@click.argument("output_dir", type=str) +@click.argument("data_dir", type=str) +@click.argument("mask_file", type=str) +@click.option( + "--name", + type=str, + help="the data should be located in //sensor/", + default="av2", +) +@click.option( + "--split", + help="the data should be located in //sensor/", + default="val", + type=click.Choice(["test", "val"]), +) +def _make_annotation_files_entry(output_dir: str, mask_file: str, data_dir: str, name: str, split: str) -> None: + """Entry point for make_annotation_files.""" + make_annotation_files(output_dir, mask_file, data_dir, name, split) + + +if __name__ == "__main__": + _make_annotation_files_entry() diff --git a/src/av2/evaluation/scene_flow/make_mask_files.py b/src/av2/evaluation/scene_flow/make_mask_files.py new file mode 100644 index 00000000..8c9895ec --- /dev/null +++ b/src/av2/evaluation/scene_flow/make_mask_files.py @@ -0,0 +1,79 @@ +"""Utility program for producing submission mask files.""" + +import zipfile +from pathlib import Path +from typing import BinaryIO + +import click +import pandas as pd +from kornia.geometry.liegroup import Se3 +from rich.progress import track + +from av2.evaluation.scene_flow.utils import compute_eval_point_mask, get_eval_subset +from av2.torch.data_loaders.scene_flow import SceneFlowDataloader +from av2.torch.structures.sweep import Sweep + + +def get_mask( + s0: Sweep, + s1: Sweep, + s1_SE3_s0: Se3, +) -> pd.DataFrame: + """Get a mask packaged up and ready for writing to disk. + + Args: + s0: The first sweep of the pair. + s1: The second sweep of the pair. + s1_SE3_s0: The relative ego-motion between the two sweeps. + + Returns: + DataFrame with a single column for the mask. + """ + mask = compute_eval_point_mask((s0, s1, s1_SE3_s0, None)) + output = pd.DataFrame({"mask": mask.numpy().astype(bool)}) + return output + + +def make_mask_files(output_file: str, data_dir: str, name: str, split: str) -> None: + """Create an archive file of pointwise masks for submission to the leaderboard. + + Args: + output_file: Path to output file archive. + data_dir: Path to input data. + name: Name of the dataset (e.g. av2). + split: Split to make masks for. + """ + data_loader = SceneFlowDataloader(Path(data_dir), name, split) + eval_inds = get_eval_subset(data_loader) + with zipfile.ZipFile(Path(output_file), "w") as maskzip: + for i in track(eval_inds): + sweep_0, sweep_1, ego, _ = data_loader[i] + mask_df = get_mask(sweep_0, sweep_1, ego) + log, timestamp_ns = sweep_0.sweep_uuid + output_path = f"{log}/{timestamp_ns}.feather" + with maskzip.open(output_path, "w") as zip_output_file: + mask_df.to_feather(zip_output_file) + + +@click.command() +@click.argument("output_dir", type=str) +@click.argument("data_dir", type=str) +@click.option( + "--name", + type=str, + help="the data should be located in //sensor/", + default="av2", +) +@click.option( + "--split", + help="the data should be located in //sensor/", + default="val", + type=click.Choice(["test", "val"]), +) +def _make_mask_files_entry(output_file: str, data_dir: str, name: str, split: str) -> None: + """Entry point for make_mask_files.""" + make_mask_files(output_file, data_dir, name, split) + + +if __name__ == "__main__": + _make_mask_files_entry() diff --git a/src/av2/evaluation/scene_flow/make_submission_archive.py b/src/av2/evaluation/scene_flow/make_submission_archive.py new file mode 100644 index 00000000..728003e4 --- /dev/null +++ b/src/av2/evaluation/scene_flow/make_submission_archive.py @@ -0,0 +1,101 @@ +"""Validate and package a set of prediction files for submission to the leaderboard.""" +import json +from pathlib import Path +from typing import Dict, Final +from zipfile import ZipFile + +import click +import numpy as np +import pandas as pd +from rich.progress import track + +import av2.evaluation.scene_flow.utils + +SUBMISSION_COLUMNS: Final = ("flow_tx_m", "flow_ty_m", "flow_tz_m", "is_dynamic") + + +def validate(submission_dir: Path, mask_file: Path) -> None: + """Validate the filenames and shapes of all predictions required for submission. + + Args: + submission_dir: Path to the top level submission file directory. + mask_file: Archive containing all the mask files required for submission. + + Raises: + FileNotFoundError: If any of the required files are missing + ValueError: If any supplied file is malformed + """ + with ZipFile(mask_file, "r") as masks: + mask_files = [f.filename for f in masks.filelist if f.filename.endswith(".feather")] + for filename in track(mask_files, description="Validating..."): + input_file = submission_dir / filename + if not input_file.exists(): + raise FileNotFoundError(f"{input_file} not found in submission directory") + pred = pd.read_feather(input_file) + expected_num_points = pd.read_feather(masks.open(filename)).sum() + + for c in SUBMISSION_COLUMNS: + if c not in pred.columns: + raise ValueError(f"{input_file} does not contain {c}") + if c == "is_dynamic": + if pred[c].dtype != bool: + raise ValueError(f"{input_file} column {c} should be bool but is {pred[c].dtype}") + else: + if pred[c].dtype != np.float16: + raise ValueError(f"{input_file} column {c} should be float16 but is {pred[c].dtype}") + + if len(pred.columns) > 4: + raise ValueError(f"{input_file} contains extra columns") + + if len(pred) != expected_num_points: + raise ValueError(f"{input_file} has {len(pred)} rows but it should have {expected_num_points}") + + +def zip(submission_dir: Path, mask_file: Path, output_file: Path) -> None: + """Package all validated submission files into a zip archive. + + Args: + submission_dir: Path to the top level submission file directory. + mask_file: Archive containing all the mask files required for submission. + output_file: File to store the zip archive in. + """ + with ZipFile(mask_file, "r") as masks: + mask_files = [f.filename for f in masks.filelist if f.filename.endswith(".feather")] + with ZipFile(output_file, "w") as myzip: + for filename in track(mask_files, description="Zipping..."): + input_file = submission_dir / filename + myzip.write(input_file, arcname=filename) + + +def make_submission_archive(submission_dir: str, mask_file: str, output_filename: str) -> bool: + """Package prediction files into a zip archive for submission. + + Args: + submission_dir: Directory containing the prediction files to submit. + mask_file: Archive containing all the mask files required for submission. + output_filename: Name of the submission archive. + + Returns: + True if validation and zipping succeeded, False otherwise. + """ + output_file = Path(output_filename) + try: + validate(Path(submission_dir), Path(mask_file)) + except (FileNotFoundError, ValueError) as e: + print(f"Input validation failed with: {e}") + return False + + zip(Path(submission_dir), Path(mask_file), output_file) + return True + + +@click.command() +@click.argument("submission_dir", type=str) +@click.argument("mask_file", type=str) +@click.option("--output_filename", type=str, help="name of the output archive file", default="submission.zip") +def _make_submission_archive_entry(submission_dir: str, mask_file: str, output_filename: str) -> bool: + return make_submission_archive(submission_dir, mask_file, output_filename) + + +if __name__ == "__main__": + _make_submission_archive_entry() diff --git a/src/av2/evaluation/scene_flow/utils.py b/src/av2/evaluation/scene_flow/utils.py new file mode 100644 index 00000000..59107998 --- /dev/null +++ b/src/av2/evaluation/scene_flow/utils.py @@ -0,0 +1,85 @@ +"""Utilities for generating output for the scene flow challenge.""" + +from pathlib import Path +from typing import Final, List, Optional, Tuple +from zipfile import ZipFile + +import numpy as np +import pandas as pd +import torch +from kornia.geometry.liegroup import Se3 +from torch import BoolTensor + +from av2.torch.data_loaders.scene_flow import SceneFlowDataloader +from av2.torch.structures.flow import Flow +from av2.torch.structures.sweep import Sweep +from av2.utils.typing import NDArrayBool, NDArrayFloat + +_EVAL_ROOT: Final = Path(__file__).resolve().parent + + +def get_eval_subset(dataloader: SceneFlowDataloader) -> List[int]: + """Return the indices of the test set used for evaluation on the leaderboard.""" + return list(range(len(dataloader)))[::5] + + +def get_eval_point_mask(sweep_uuid: Tuple[str, int], mask_file: Path) -> BoolTensor: + """Retrieve for a given sweep, a boolean mask indicating which points are evaluated on. + + Args: + sweep_uuid: The uuid of the first sweep in the pair to retrieve the mask for. + mask_file: Archive of submission masks. + + Returns: + The submission mask for that pair. + """ + with ZipFile(mask_file) as masks: + log_id, timestamp_ns = sweep_uuid + mask = pd.read_feather(masks.open(f"{log_id}/{timestamp_ns}.feather")).to_numpy().astype(bool) + + return BoolTensor(torch.from_numpy(mask).squeeze()) + + +def compute_eval_point_mask(datum: Tuple[Sweep, Sweep, Se3, Optional[Flow]]) -> BoolTensor: + """Compute for a given sweep, a boolean mask indicating which points are evaluated on. + + Note: This should NOT BE USED FOR CREATING SUBMISSIONS use get_eval_point_mask to ensure consistency. + + Args: + datum: Tuple returned from a `SceneFlowDataloader` to compute the mask for. + + Returns: + A mask indicating roughly which points will be evauated on. + + Raises: + ValueError: if datum does not have ground annotations. + """ + pcl = datum[0].lidar.as_tensor()[:, :3] + is_close = torch.logical_and((pcl[:, 0].abs() <= 50), (pcl[:, 1].abs() <= 50)).bool() + + if datum[0].is_ground is None: + raise ValueError("Must have ground annotations loaded to determine eval mask") + not_ground = torch.logical_not(datum[0].is_ground) + return BoolTensor(torch.logical_and(is_close, not_ground)) + + +def write_output_file( + flow: NDArrayFloat, is_dynamic: NDArrayBool, sweep_uuid: Tuple[str, int], output_dir: Path +) -> None: + """Write an output predictions file in the correct format for submission. + + Args: + flow: (N,3) Flow predictions. + is_dynamic: (N,) Dynamic segmentation prediction. + sweep_uuid: Identifier of the sweep being predicted (log_id, timestamp). + output_dir: Top level directory containing all predictions. + """ + output_log_dir = output_dir / sweep_uuid[0] + output_log_dir.mkdir(exist_ok=True, parents=True) + fx_m = flow[:, 0].astype(np.float16) + fy_m = flow[:, 1].astype(np.float16) + fz_m = flow[:, 2].astype(np.float16) + output = pd.DataFrame( + {"flow_tx_m": fx_m, "flow_ty_m": fy_m, "flow_tz_m": fz_m, "is_dynamic": is_dynamic.astype(bool)} + ) + output.to_feather(output_log_dir / f"{sweep_uuid[1]}.feather") diff --git a/src/av2/torch/data_loaders/scene_flow.py b/src/av2/torch/data_loaders/scene_flow.py index 54827206..cdb0f99a 100644 --- a/src/av2/torch/data_loaders/scene_flow.py +++ b/src/av2/torch/data_loaders/scene_flow.py @@ -5,22 +5,25 @@ import logging from dataclasses import dataclass, field from functools import cached_property -from typing import Optional, Tuple +from pathlib import Path +from typing import List, Optional, Tuple import pandas as pd +from kornia.geometry.liegroup import Se3 from torch.utils.data import Dataset import av2._r as rust +from av2.map.map_api import ArgoverseStaticMap +from av2.torch.structures.flow import Flow +from av2.torch.structures.sweep import Sweep from av2.utils.typing import PathType -from ..structures.sweep import Sweep - logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) @dataclass -class SceneFlowDataloader(Dataset[Tuple[Sweep, Optional[Sweep]]]): +class SceneFlowDataloader(Dataset[Tuple[Sweep, Sweep, Se3, Optional[Flow]]]): """PyTorch data-loader for the sensor dataset. Args: @@ -50,41 +53,60 @@ def __post_init__(self) -> None: self.num_accumulated_sweeps, self.memory_mapped, ) + self.data_dir = Path(self.root_dir) / self.dataset_name / "sensor" / self.split_name @cached_property def file_index(self) -> pd.DataFrame: """File index dataframe composed of (log_id, timestamp_ns).""" return self._backend.file_index.to_pandas() - def __getitem__(self, index: int) -> Tuple[Sweep, Optional[Sweep]]: - """Get a pair of sweeps for scene flow computation. - - Args: - index: Index in [0, num_sweeps - 1]. - - Returns: - Current sweep and the next sweep (if it exists). - """ - sweep = self._backend.get(index) - next_sweep = None - - next_index = index + 1 - if next_index < len(self): - candidate_log_id: str = self.file_index["log_id"][next_index] - current_log_id = sweep.sweep_uuid[0] - if candidate_log_id == current_log_id: - next_sweep = Sweep.from_rust(self._backend.get(next_index)) - return Sweep.from_rust(sweep), next_sweep + @cached_property + def index_map(self) -> List[int]: + """Create a mapping between indicies in this dataloader and the underlying one.""" + indices = [] + N = self._backend.__len__() + for i in range(N): + if i + 1 < N: + next_log_id = self.file_index.loc[i + 1, "log_id"] + current_log_id = self.file_index.loc[i, "log_id"] + if current_log_id == next_log_id: + indices.append(i) + return indices + + def get_log_id(self, index: int) -> str: + """Return the log name for a given sweep index.""" + return str(self.file_index.loc[index, "log_id"]) + + def __getitem__(self, index: int) -> Tuple[Sweep, Sweep, Se3, Optional[Flow]]: + """Get a pair of sweeps, ego motion, and flow if annotations are available.""" + backend_index = self.index_map[index] + log = self.file_index.loc[index, "log_id"] + log_dir_path = log_map_dirpath = self.data_dir / log + log_map_dirpath = self.data_dir / log / "map" + avm = ArgoverseStaticMap.from_map_dir(log_map_dirpath, build_raster=True) + + sweep = Sweep.from_rust(self._backend.get(backend_index), avm=avm) + next_sweep = Sweep.from_rust(self._backend.get(backend_index + 1), avm=avm) + + flow = None + if sweep.cuboids is not None: + flow = Flow.from_sweep_pair((sweep, next_sweep)) + + ego_1_SE3_ego_0 = next_sweep.city_SE3_ego.inverse() * sweep.city_SE3_ego + ego_1_SE3_ego_0.rotation._q.requires_grad_(False) + ego_1_SE3_ego_0.translation.requires_grad_(False) + + return sweep, next_sweep, ego_1_SE3_ego_0, flow def __len__(self) -> int: - """Length of the data-loader.""" - return self._backend.__len__() + """Length of the scene flow dataset (number of pairs of sweeps).""" + return len(self.index_map) def __iter__(self) -> SceneFlowDataloader: """Iterate method for the data-loader.""" return self - def __next__(self) -> Tuple[Sweep, Optional[Sweep]]: + def __next__(self) -> Tuple[Sweep, Sweep, Se3, Optional[Flow]]: """Return a tuple of sweeps for scene flow.""" if self._current_idx >= self.__len__(): raise StopIteration diff --git a/src/av2/torch/structures/flow.py b/src/av2/torch/structures/flow.py new file mode 100644 index 00000000..af3883be --- /dev/null +++ b/src/av2/torch/structures/flow.py @@ -0,0 +1,117 @@ +"""PyTorch flow sub-module.""" + +from __future__ import annotations + +from dataclasses import dataclass +from typing import Dict, Final, List, Tuple + +import torch +from kornia.geometry.linalg import transform_points +from torch import BoolTensor, ByteTensor, FloatTensor + +from av2.evaluation.scene_flow.constants import CATEGORY_TO_INDEX, SCENE_FLOW_DYNAMIC_THRESHOLD +from av2.structures.cuboid import Cuboid, CuboidList +from av2.torch.structures.cuboids import Cuboids +from av2.torch.structures.sweep import Sweep + +BOUNDING_BOX_EXPANSION: Final = 0.2 + + +@dataclass(frozen=True) +class Flow: + """Models scene flow pseudo-labels for a lidar sweep. + + Scene Flow pseudolabels come from the relative motion of all tracked objects + between two sweeps. + + + Args: + flow: (N,3) Motion vectors (x,y,z) in meters. + is_valid: (N,) 1 if the flow was succesfuly estimated for that point 0 otherwise + category_indices: (N,) the semantic object class of each point (0 is background) + is_dynamic: (N,) 1 if the point is considered dynamic 0 otherwise + """ + + flow: FloatTensor + is_valid: BoolTensor + category_indices: ByteTensor + is_dynamic: BoolTensor + + def __len__(self) -> int: + """Return the number of LiDAR returns in the aggregated sweep.""" + return int(self.flow.shape[0]) + + @classmethod + def from_sweep_pair(cls, sweeps: Tuple[Sweep, Sweep]) -> Flow: + """Create flow object from a pair of Sweeps. + + Args: + sweeps: Pair of sweeps to compute the flow between. + + Returns: + Flow object. + + Raises: + ValueError: If the sweeps do not have annotations loaded. + """ + current_sweep, next_sweep = (sweeps[0], sweeps[1]) + if current_sweep.cuboids is None or next_sweep.cuboids is None: + raise ValueError("Can only create flow from sweeps with annotations") + else: + current_cuboids, next_cuboids = current_sweep.cuboids, next_sweep.cuboids + city_SE3_ego0, city_SE3_ego1 = current_sweep.city_SE3_ego, next_sweep.city_SE3_ego + ego1_SE3_ego0 = city_SE3_ego1.inverse() * city_SE3_ego0 + + current_cuboid_map = cuboids_to_id_cuboid_map(current_cuboids) + next_cuboid_map = cuboids_to_id_cuboid_map(next_cuboids) + + current_pc = current_sweep.lidar.as_tensor()[:, :3] + next_pc = next_sweep.lidar.as_tensor()[:, :3] + + rigid_flow = (transform_points(ego1_SE3_ego0.matrix(), current_pc[None])[0] - current_pc).float().detach() + flow = rigid_flow.clone() + + is_valid = torch.ones(len(current_pc), dtype=torch.bool) + category_inds = torch.zeros(len(current_pc), dtype=torch.uint8) + + for id in current_cuboid_map: + c0 = current_cuboid_map[id] + c0.length_m += BOUNDING_BOX_EXPANSION # the bounding boxes are a little too tight sometimes + c0.width_m += BOUNDING_BOX_EXPANSION + obj_pts, obj_mask = [torch.from_numpy(arr) for arr in c0.compute_interior_points(current_pc.numpy())] + category_inds[obj_mask] = CATEGORY_TO_INDEX[str(c0.category)] + + if id in next_cuboid_map: + c1 = next_cuboid_map[id] + c1_SE3_c0 = c1.dst_SE3_object.compose(c0.dst_SE3_object.inverse()) + obj_flow = torch.from_numpy(c1_SE3_c0.transform_point_cloud(obj_pts.numpy())) - obj_pts + flow[obj_mask] = (obj_flow.float()).detach() + else: + is_valid[obj_mask] = 0 + + dynamic_norm = torch.linalg.vector_norm(flow - rigid_flow, dim=-1) + is_dynamic: BoolTensor = dynamic_norm >= SCENE_FLOW_DYNAMIC_THRESHOLD + + return cls( + flow=torch.FloatTensor(flow), + is_valid=torch.BoolTensor(is_valid), + category_indices=torch.ByteTensor(category_inds), + is_dynamic=torch.BoolTensor(is_dynamic), + ) + + +def cuboids_to_id_cuboid_map(cuboids: Cuboids) -> Dict[str, Cuboid]: + """Create a mapping between track UUIDs and cuboids. + + Args: + cuboids: Cuboids to transform into a mapping. + + Returns: + A dict with the UUIDs as keys and the coresponding cuboids as values. + """ + ids = cuboids.track_uuid + annotations_df_with_ts = cuboids._frame.assign(timestamp_ns=None) + cuboid_list = CuboidList.from_dataframe(annotations_df_with_ts) + + cuboids_and_ids = dict(zip(ids, cuboid_list.cuboids)) + return cuboids_and_ids diff --git a/src/av2/torch/structures/sweep.py b/src/av2/torch/structures/sweep.py index 1e09af2f..9c1fc596 100644 --- a/src/av2/torch/structures/sweep.py +++ b/src/av2/torch/structures/sweep.py @@ -5,9 +5,12 @@ from dataclasses import dataclass from typing import Optional, Tuple +import torch from kornia.geometry.liegroup import Se3 +from kornia.geometry.linalg import transform_points import av2._r as rust +from av2.map.map_api import ArgoverseStaticMap from av2.torch.structures.lidar import Lidar from .cuboids import Cuboids @@ -27,19 +30,22 @@ class Sweep: lidar: Lidar sensor data. sweep_uuid: Log id and nanosecond timestamp (unique identifier). cuboids: Cuboids representing objects in the scene. + is_ground: Tensor of boolean values indicating which points belong to the ground. """ city_SE3_ego: Se3 lidar: Lidar sweep_uuid: Tuple[str, int] cuboids: Optional[Cuboids] + is_ground: Optional[torch.Tensor] = None @classmethod - def from_rust(cls, sweep: rust.Sweep) -> Sweep: + def from_rust(cls, sweep: rust.Sweep, avm: Optional[ArgoverseStaticMap] = None) -> Sweep: """Build a sweep from the Rust backend. Args: sweep: Sweep object from the Rust backend data-loader. + avm: Map object for computing ground labels Returns: Sweep object. @@ -49,4 +55,17 @@ def from_rust(cls, sweep: rust.Sweep) -> Sweep: cuboids = Cuboids(_frame=sweep.cuboids.to_pandas()) city_SE3_ego = SE3_from_frame(frame=sweep.city_pose.to_pandas()) lidar = Lidar(sweep.lidar.to_pandas()) - return cls(city_SE3_ego=city_SE3_ego, lidar=lidar, sweep_uuid=sweep.sweep_uuid, cuboids=cuboids) + + is_ground = None + if avm is not None: + pcl_ego = lidar.as_tensor()[:, :3] + pcl_city_1 = transform_points(city_SE3_ego.matrix(), pcl_ego[None])[0] + is_ground = torch.from_numpy(avm.get_ground_points_boolean(pcl_city_1.numpy()).astype(bool)) + + return cls( + city_SE3_ego=city_SE3_ego, + lidar=lidar, + sweep_uuid=sweep.sweep_uuid, + is_ground=is_ground, + cuboids=cuboids, + ) diff --git a/src/av2/torch/structures/utils.py b/src/av2/torch/structures/utils.py index dd589443..3366a8c4 100644 --- a/src/av2/torch/structures/utils.py +++ b/src/av2/torch/structures/utils.py @@ -50,4 +50,6 @@ def SE3_from_frame(frame: pd.DataFrame) -> Se3: translation_npy = frame.loc[0, list(TRANSLATION_COLUMNS)].to_numpy().astype(np.float32) translation = torch.as_tensor(translation_npy, dtype=torch.float32) dst_SE3_src = Se3(rotation[None], translation[None]) + dst_SE3_src.rotation._q.requires_grad_(False) + dst_SE3_src.translation.requires_grad_(False) return dst_SE3_src diff --git a/tests/evaluation/scene_flow/test_sf_eval.py b/tests/evaluation/scene_flow/test_sf_eval.py new file mode 100644 index 00000000..62e7270f --- /dev/null +++ b/tests/evaluation/scene_flow/test_sf_eval.py @@ -0,0 +1,267 @@ +"""Scene Flow evaluation unit tests.""" + +import tempfile +from pathlib import Path + +import numpy as np + +import av2.evaluation.scene_flow.constants as constants +import av2.evaluation.scene_flow.eval as eval +from av2.evaluation.scene_flow.make_annotation_files import write_annotation +from av2.evaluation.scene_flow.utils import write_output_file +from av2.utils.typing import NDArrayBool, NDArrayFloat, NDArrayInt + +gts: NDArrayFloat = np.array( + [ + [0.0, 0.0, 0.0], + [1e-3, 0.0, 0.0], + [0.0, 1e-3, 0.0], + [0.0, 0.0, 1e-3], + [1e3, 0.0, 0.0], + [0.0, 1e3, 0.0], + [0.0, 0.0, 1e3], + [1.0, 0.0, 0.0], + [0.0, 1.0, 0.0], + [0.0, 0.0, 1.0], + ], + dtype=np.float64, +) + +dts_perfect: NDArrayFloat = gts.copy() +dts_very_close: NDArrayFloat = gts + 0.01 +dts_close: NDArrayFloat = gts + 0.05 +dts_zero: NDArrayFloat = np.zeros_like(gts).astype(np.float64) + +gts_dynamic: NDArrayBool = np.array([False, False, False, False, True, True, True, True, False, False]) +gts_classes: NDArrayInt = np.array([0, 0, 0, 0, 17, 17, 1, 11, 0, 23]) +gts_valid: NDArrayBool = np.array([False, True, True, True, True, True, True, True, True, True]) +gts_close: NDArrayBool = np.array([True, True, False, False, True, True, False, False, True, True]) + +dts_dynamic: NDArrayBool = np.array([False, True, False, True, False, True, False, True, False, True]) + + +def test_end_point_error() -> None: + """Initialize a detection and a ground truth label. + + Verify that calculated end-point error matches the expected value. + """ + epe_perfect = np.zeros(10) + epe_very_close = (np.sqrt(0.01**2 * 3, dtype=np.float64) * np.ones(10)).astype(np.float64) + epe_close = (np.sqrt(0.05**2 * 3, dtype=np.float64) * np.ones(10)).astype(np.float64) + epe_zero = np.array([0.0e00, 1.0e-3, 1.0e-3, 1.0e-3, 1.0e3, 1.0e3, 1.0e3, 1.0e00, 1.0e00, 1.0e00], dtype=np.float64) + + assert np.allclose(eval.compute_end_point_error(dts_perfect, gts), epe_perfect) + assert np.allclose(eval.compute_end_point_error(dts_very_close, gts), epe_very_close) + assert np.allclose(eval.compute_end_point_error(dts_close, gts), epe_close) + assert np.allclose(eval.compute_end_point_error(dts_zero, gts), epe_zero) + + +def test_accuracy_strict() -> None: + """Initialize a detection and a ground truth label. + + Verify that calculated strict accuracy matches the expected value. + """ + accS_perfect = np.ones(10) + accS_close = np.array([0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0]) + accS_very_close = np.ones(10) + accS_zero = np.array([1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]) + + assert np.allclose(eval.compute_accuracy_strict(dts_perfect, gts), accS_perfect) + assert np.allclose(eval.compute_accuracy_strict(dts_very_close, gts), accS_very_close) + assert np.allclose(eval.compute_accuracy_strict(dts_close, gts), accS_close) + assert np.allclose(eval.compute_accuracy_strict(dts_zero, gts), accS_zero) + + +def test_accuracy_relax() -> None: + """Initialize a detection and a ground truth label. + + Verify that calculated relax accuracy matches the expected value. + """ + accS_perfect = np.ones(10) + accS_close = np.ones(10) + accS_very_close = np.ones(10) + accS_zero = np.array([1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]) + + assert np.allclose(eval.compute_accuracy_relax(dts_perfect, gts), accS_perfect) + assert np.allclose(eval.compute_accuracy_relax(dts_very_close, gts), accS_very_close) + assert np.allclose(eval.compute_accuracy_relax(dts_close, gts), accS_close) + assert np.allclose(eval.compute_accuracy_relax(dts_zero, gts), accS_zero) + + +def test_angle_error() -> None: + """Initialize a detection and a ground truth label. + + Verify that calculated angle error matches the expected value. + """ + gts_norm = np.sqrt((gts**2).sum(-1) + 0.1**2) + dts_close_norm = np.sqrt((dts_close**2).sum(-1) + 0.1**2) + dts_very_close_norm = np.sqrt((dts_very_close**2).sum(-1) + 0.1**2) + dts_perfect_norm = np.sqrt((dts_perfect**2).sum(-1) + 0.1**2) + dts_zero_norm = np.sqrt((dts_zero**2).sum(-1) + 0.1**2) + + gts_nz_value = np.array([0.0, 1e-3, 1e-3, 1e-3, 1e3, 1e3, 1e3, 1.0, 1.0, 1.0], dtype=np.float64) + + ae_perfect = np.arccos( + np.clip( + 0.1 / dts_perfect_norm * 0.1 / gts_norm + + (gts_nz_value / gts_norm) * ((gts_nz_value + 0.00) / dts_perfect_norm), + -1.0, + 1.0, + ) + ) + ae_close = np.arccos( + 0.1 / dts_close_norm * 0.1 / gts_norm + (gts_nz_value / gts_norm) * ((gts_nz_value + 0.05) / dts_close_norm) + ) + ae_very_close = np.arccos( + 0.1 / dts_very_close_norm * 0.1 / gts_norm + + (gts_nz_value / gts_norm) * ((gts_nz_value + 0.01) / dts_very_close_norm) + ) + ae_zero = np.arccos(0.1 / dts_zero_norm * 0.1 / gts_norm) + + assert np.allclose(eval.compute_angle_error(dts_perfect, gts), ae_perfect) + assert np.allclose(eval.compute_angle_error(dts_very_close, gts), ae_very_close) + assert np.allclose(eval.compute_angle_error(dts_close, gts), ae_close) + assert np.allclose(eval.compute_angle_error(dts_zero, gts), ae_zero) + + +def test_true_positives() -> None: + """Initialize a detection and a ground truth label. + + Verify that calculated true positive count the expected value. + """ + assert eval.compute_true_positives(dts_dynamic, gts_dynamic) == 2 + + +def test_true_negatives() -> None: + """Initialize a detection and a ground truth label. + + Verify that calculated true negative count the expected value. + """ + assert eval.compute_true_negatives(dts_dynamic, gts_dynamic) == 3 + + +def test_false_positives() -> None: + """Initialize a detection and a ground truth label. + + Verify that calculated false positive count the expected value. + """ + assert eval.compute_false_positives(dts_dynamic, gts_dynamic) == 3 + + +def test_false_negatives() -> None: + """Initialize a detection and a ground truth label. + + Verify that calculated false negative count the expected value. + """ + assert eval.compute_false_negatives(dts_dynamic, gts_dynamic) == 2 + + +def test_metrics() -> None: + """Initialize dummy ground truth labels and predictions. + + Verify that the metric breakdown has the correct subset counts and values. + """ + metrics = eval.compute_metrics( + dts_perfect, + dts_dynamic, + gts, + gts_classes, + gts_dynamic, + gts_close, + gts_valid, + constants.FOREGROUND_BACKGROUND_BREAKDOWN, + ) + num_subsets = len(list(metrics.values())[0]) + assert num_subsets == 8 + + for i in range(num_subsets): + if metrics["Class"][i] == "Background" and metrics["Motion"][i] == "Dynamic": + assert metrics["Count"][i] == 0 + if metrics["Count"][i] == 0: + assert np.isnan(metrics["EPE"][i]) + assert np.isnan(metrics["ANGLE_ERROR"][i]) + assert np.isnan(metrics["ACCURACY_STRICT"][i]) + assert np.isnan(metrics["ACCURACY_RELAX"][i]) + else: + assert not np.isnan(metrics["EPE"][i]) + assert not np.isnan(metrics["ANGLE_ERROR"][i]) + assert not np.isnan(metrics["ACCURACY_STRICT"][i]) + assert not np.isnan(metrics["ACCURACY_RELAX"][i]) + + counted_points: int = sum(metrics["Count"]) + valid_points: int = sum(gts_valid.astype(int)) + assert counted_points == valid_points + + tp: int = sum(metrics["TP"]) + assert tp == 2 + tn: int = sum(metrics["TN"]) + assert tp == 2 # The first TN is marked invalid + fp: int = sum(metrics["FP"]) + assert fp == 3 + fn: int = sum(metrics["FN"]) + assert fn == 2 + + nz_subsets = sum([1 for count in metrics["Count"] if count > 0]) + assert nz_subsets == 5 + + assert np.allclose( + sum([accr for accr, count in zip(metrics["ACCURACY_RELAX"], metrics["Count"]) if count > 0]) / nz_subsets, 1.0 + ) + assert np.allclose( + sum([accs for accs, count in zip(metrics["ACCURACY_STRICT"], metrics["Count"]) if count > 0]) / nz_subsets, 1.0 + ) + assert np.allclose( + sum([ae for ae, count in zip(metrics["ANGLE_ERROR"], metrics["Count"]) if count > 0]) / nz_subsets, 0.0 + ) + assert np.allclose( + sum([epe for epe, count in zip(metrics["EPE"], metrics["Count"]) if count > 0]) / nz_subsets, 0.0 + ) + + +def test_average_metrics() -> None: + """Initialize dummy ground truth labels and predictions. + + Verify that the weighted average metric breakdown has the correct subset counts and values. + """ + with Path(tempfile.TemporaryDirectory().name) as test_dir: + test_dir.mkdir() + anno_dir = test_dir / "annotations" + anno_dir.mkdir() + + pred_dir = test_dir / "predictions" + pred_dir.mkdir() + + timestamp_ns_1 = 111111111111111111 + timestamp_ns_2 = 111111111111111112 + + write_annotation(gts_classes, gts_close, gts_dynamic, gts_valid, gts, ("log", timestamp_ns_1), anno_dir) + write_annotation(gts_classes, gts_close, gts_dynamic, gts_valid, gts, ("log", timestamp_ns_2), anno_dir) + + write_annotation(gts_classes, gts_close, gts_dynamic, gts_valid, gts, ("log_missing", timestamp_ns_1), anno_dir) + + write_output_file(dts_perfect, dts_dynamic, ("log", timestamp_ns_1), pred_dir) + write_output_file(dts_perfect, dts_dynamic, ("log", timestamp_ns_2), pred_dir) + + results_df = eval.evaluate_directories(anno_dir, pred_dir) + + assert len(results_df) == 16 + assert results_df.Count.sum() == 18 + + assert np.allclose(results_df.EPE.mean(), 0.0) + assert np.allclose(results_df["ACCURACY_STRICT"].mean(), 1.0) + assert np.allclose(results_df["ACCURACY_RELAX"].mean(), 1.0) + assert np.allclose(results_df["ANGLE_ERROR"].mean(), 0.0) + + assert results_df.TP.sum() == 2 * 2 + assert results_df.TN.sum() == 2 * 2 # First true negative marked invalid + assert results_df.FP.sum() == 3 * 2 + assert results_df.FN.sum() == 2 * 2 + + assert results_df.groupby(["Class", "Motion"]).Count.sum().Background.Dynamic == 0 + results_dict = eval.results_to_dict(results_df) + + assert len(results_dict) == 38 + assert np.isnan([results_dict[k] for k in results_dict if k.endswith("Foreground/Static/Far")]).all() + assert len([True for k in results_dict if "Background/Dyamic" in k]) == 0 + assert results_dict["Dynamic IoU"] == 2 / (3 + 2 + 2) + assert results_dict["EPE 3-Way Average"] == 0.0 diff --git a/tests/evaluation/scene_flow/test_sf_submission_pipeline.py b/tests/evaluation/scene_flow/test_sf_submission_pipeline.py new file mode 100644 index 00000000..4e5c9638 --- /dev/null +++ b/tests/evaluation/scene_flow/test_sf_submission_pipeline.py @@ -0,0 +1,49 @@ +"""Scene Flow evaluation unit tests.""" + +import tempfile +from pathlib import Path + +import numpy as np + +import av2.evaluation.scene_flow.constants +import av2.evaluation.scene_flow.eval as eval +from av2.evaluation.scene_flow.example_submission import example_submission +from av2.evaluation.scene_flow.make_annotation_files import make_annotation_files +from av2.evaluation.scene_flow.make_mask_files import make_mask_files +from av2.evaluation.scene_flow.make_submission_archive import make_submission_archive + +_TEST_DATA_ROOT = Path(__file__).resolve().parent.parent.parent + + +def test_submission() -> None: + """Test the whole pipeline for creating masks, annotations and submission files.""" + with Path(tempfile.TemporaryDirectory().name) as test_dir: + test_dir.mkdir() + mask_file = test_dir / "masks.zip" + make_mask_files(str(mask_file), str(_TEST_DATA_ROOT), "test_data", "val") + + annotations_dir = test_dir / "annotations" + make_annotation_files(str(annotations_dir), str(mask_file), str(_TEST_DATA_ROOT), "test_data", "val") + + predictions_dir = test_dir / "output" + example_submission(str(predictions_dir), str(mask_file), str(_TEST_DATA_ROOT), "test_data") + + results = eval.evaluate(str(annotations_dir), str(predictions_dir)) + for metric in results: + if metric.startswith("Accuracy"): + if "Static" in metric: + assert np.allclose(results[metric], 1.0) + elif "Dynamic" in metric and "Strict" in metric: + assert np.isnan(results[metric]) or np.allclose(results[metric], 0.0) + elif metric.startswith("EPE"): + if "Static" in metric: + if "Background" in metric: + assert np.allclose(results[metric], 0.0) + elif "Foreground" in metric: + assert results[metric] < 0.06 + elif metric.startswith("Angle"): + if "Static" in metric and "Background" in metric: + assert results[metric] < 1e-4 + + output_file = test_dir / "submission.zip" + make_submission_archive(str(predictions_dir), str(mask_file), str(output_file)) diff --git a/tests/test_data/sensor/test/7fab2350-7eaf-3b7e-a39d-6937a4c1bede/calibration/egovehicle_SE3_sensor.feather b/tests/test_data/sensor/test/7fab2350-7eaf-3b7e-a39d-6937a4c1bede/calibration/egovehicle_SE3_sensor.feather new file mode 100644 index 00000000..94096a15 Binary files /dev/null and b/tests/test_data/sensor/test/7fab2350-7eaf-3b7e-a39d-6937a4c1bede/calibration/egovehicle_SE3_sensor.feather differ diff --git a/tests/test_data/sensor/test/7fab2350-7eaf-3b7e-a39d-6937a4c1bede/calibration/intrinsics.feather b/tests/test_data/sensor/test/7fab2350-7eaf-3b7e-a39d-6937a4c1bede/calibration/intrinsics.feather new file mode 100644 index 00000000..298b7572 Binary files /dev/null and b/tests/test_data/sensor/test/7fab2350-7eaf-3b7e-a39d-6937a4c1bede/calibration/intrinsics.feather differ diff --git a/tests/test_data/sensor/test/7fab2350-7eaf-3b7e-a39d-6937a4c1bede/city_SE3_egovehicle.feather b/tests/test_data/sensor/test/7fab2350-7eaf-3b7e-a39d-6937a4c1bede/city_SE3_egovehicle.feather new file mode 100644 index 00000000..5695387e Binary files /dev/null and b/tests/test_data/sensor/test/7fab2350-7eaf-3b7e-a39d-6937a4c1bede/city_SE3_egovehicle.feather differ diff --git a/tests/test_data/sensor/test/7fab2350-7eaf-3b7e-a39d-6937a4c1bede/map/7fab2350-7eaf-3b7e-a39d-6937a4c1bede___img_Sim2_city.json b/tests/test_data/sensor/test/7fab2350-7eaf-3b7e-a39d-6937a4c1bede/map/7fab2350-7eaf-3b7e-a39d-6937a4c1bede___img_Sim2_city.json new file mode 100644 index 00000000..e29cc7e7 --- /dev/null +++ b/tests/test_data/sensor/test/7fab2350-7eaf-3b7e-a39d-6937a4c1bede/map/7fab2350-7eaf-3b7e-a39d-6937a4c1bede___img_Sim2_city.json @@ -0,0 +1 @@ +{"R": [1.0, 0.0, 0.0, 1.0], "t": [-5072.400390625, -2283.900390625], "s": 3.3333333333333335} \ No newline at end of file diff --git a/tests/test_data/sensor/test/7fab2350-7eaf-3b7e-a39d-6937a4c1bede/map/7fab2350-7eaf-3b7e-a39d-6937a4c1bede_ground_height_surface____PIT.npy b/tests/test_data/sensor/test/7fab2350-7eaf-3b7e-a39d-6937a4c1bede/map/7fab2350-7eaf-3b7e-a39d-6937a4c1bede_ground_height_surface____PIT.npy new 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end of file diff --git a/tests/test_data/sensor/val/7fab2350-7eaf-3b7e-a39d-6937a4c1bede/sensors/lidar/315966265259836000.feather b/tests/test_data/sensor/val/7fab2350-7eaf-3b7e-a39d-6937a4c1bede/sensors/lidar/315966265259836000.feather new file mode 100644 index 00000000..f5c2df94 Binary files /dev/null and b/tests/test_data/sensor/val/7fab2350-7eaf-3b7e-a39d-6937a4c1bede/sensors/lidar/315966265259836000.feather differ diff --git a/tests/test_data/sensor/val/7fab2350-7eaf-3b7e-a39d-6937a4c1bede/sensors/lidar/315966265360032000.feather b/tests/test_data/sensor/val/7fab2350-7eaf-3b7e-a39d-6937a4c1bede/sensors/lidar/315966265360032000.feather new file mode 100644 index 00000000..f950a7ba Binary files /dev/null and b/tests/test_data/sensor/val/7fab2350-7eaf-3b7e-a39d-6937a4c1bede/sensors/lidar/315966265360032000.feather differ diff --git a/tests/torch/data_loaders/test_scene_flow_dataloader.py b/tests/torch/data_loaders/test_scene_flow_dataloader.py new file mode 100644 index 00000000..247fcd4e --- /dev/null +++ b/tests/torch/data_loaders/test_scene_flow_dataloader.py @@ -0,0 +1,77 @@ +"""Unit tests on scene_flow data-loader.""" + +from pathlib import Path + +import numpy as np +import pandas as pd + +import av2.torch.data_loaders.scene_flow +from av2.evaluation.scene_flow.utils import compute_eval_point_mask, get_eval_subset +from av2.torch.structures.flow import Flow +from av2.torch.structures.sweep import Sweep +from av2.utils.typing import NDArrayBool + +_TEST_DATA_ROOT = Path(__file__).resolve().parent.parent.parent + + +def test_scene_flow_dataloader() -> None: + """Test loading a single pair with flow annotations. + + The computed flow should check the visually confirmed labels in flow_labels.feather. + """ + dl_test = av2.torch.data_loaders.scene_flow.SceneFlowDataloader(_TEST_DATA_ROOT, "test_data", "test") + sweep_0, sweep_1, ego, not_flow = dl_test[0] + assert not_flow is None + rust_sweep = dl_test._backend.get(0) + sweep_0 = Sweep.from_rust(rust_sweep) + assert sweep_0.cuboids is None + + failed = False + try: + compute_eval_point_mask((sweep_0, sweep_1, ego, not_flow)) + except ValueError: + failed = True + assert failed + + failed = False + try: + Flow.from_sweep_pair((sweep_0, sweep_1)) + except ValueError: + failed = True + assert failed + + data_loader = av2.torch.data_loaders.scene_flow.SceneFlowDataloader(_TEST_DATA_ROOT, "test_data", "val") + assert len(data_loader) == 1 + assert data_loader.get_log_id(0) == "7fab2350-7eaf-3b7e-a39d-6937a4c1bede" + + for datum in data_loader: + sweep_0, sweep_1, ego, maybe_flow = datum + + assert maybe_flow is not None + flow: Flow = maybe_flow + assert len(flow) == len(sweep_0.lidar.as_tensor()) + + log_dir = _TEST_DATA_ROOT / "test_data/sensor/val/7fab2350-7eaf-3b7e-a39d-6937a4c1bede" + flow_labels = pd.read_feather(log_dir / "flow_labels.feather") + + FLOW_COLS = ["flow_tx_m", "flow_ty_m", "flow_tz_m"] + err = np.abs(flow.flow.numpy() - flow_labels[FLOW_COLS].to_numpy()).sum(-1) + max_err_ind = np.argmax(err) + flow_err_val = flow.flow.numpy()[max_err_ind] + label_err_val = flow_labels[FLOW_COLS].to_numpy()[max_err_ind] + assert np.allclose( + flow.flow.numpy(), flow_labels[FLOW_COLS].to_numpy(), atol=1e-3 + ), f"max-diff {err[max_err_ind]} ind: {max_err_ind} flow: {flow_err_val} label: {label_err_val}" + assert np.allclose(flow.category_indices.numpy(), flow_labels.classes.to_numpy()) + assert np.allclose(flow.is_dynamic.numpy(), flow_labels.dynamic.to_numpy()) + assert sweep_0.is_ground is not None + ground_match: NDArrayBool = sweep_0.is_ground.numpy() == flow_labels.is_ground_0.to_numpy() + assert np.logical_not(ground_match).sum() < 10 + + gt_ego = np.load(log_dir / "ego_motion.npz") + + assert np.allclose(ego.matrix().numpy(), gt_ego["ego_motion"], atol=1e-3) + + eval_inds = get_eval_subset(data_loader) + assert len(eval_inds) == 1 + assert eval_inds[0] == 0