def _eval_predictions(self, tasks, predictions):
"""
Evaluate predictions on the given tasks.
Fill self._results with the metrics of the tasks.
"""
self._logger.info("Preparing results for COCO format ...")
coco_results = list(
itertools.chain(*[x["instances"] for x in predictions]))
# unmap the category ids for COCO
if hasattr(self._metadata, "thing_dataset_id_to_contiguous_id"):
reverse_id_mapping = {
v: k
for k, v in
self._metadata.thing_dataset_id_to_contiguous_id.items()
}
for result in coco_results:
result["category_id"] = reverse_id_mapping[
result["category_id"]]
if self._output_dir:
file_path = os.path.join(self._output_dir,
"coco_instances_results.json")
self._logger.info("Saving results to {}".format(file_path))
with PathManager.open(file_path, "w") as f:
f.write(json.dumps(coco_results))
f.flush()
if not self._do_evaluation:
self._logger.info("Annotations are not available for evaluation.")
return
self._logger.info("Evaluating predictions ...")
for task in sorted(tasks):
assert task == "bbox", "Task {} is not supported".format(task)
coco_eval = (
self._evaluate_predictions_on_coco(
self._coco_api, coco_results) if len(coco_results) > 0 else
None # cocoapi does not handle empty results very well
)
res = self._derive_coco_results(
coco_eval,
task,
class_names=self._metadata.get("thing_classes"))
self._results[task] = res
def find_relative_file(original_file, relative_import_path, level):
cur_file = os.path.dirname(original_file)
for _ in range(level - 1):
cur_file = os.path.dirname(cur_file)
cur_name = relative_import_path.lstrip(".")
for part in cur_name.split("."):
cur_file = os.path.join(cur_file, part)
# NOTE: directory import is not handled. Because then it's unclear
# if such import should produce python module or DictConfig. This can
# be discussed further if needed.
if not cur_file.endswith(".py"):
cur_file += ".py"
if not PathManager.isfile(cur_file):
raise ImportError(
f"Cannot import name {relative_import_path} from "
f"{original_file}: {cur_file} has to exist.")
return cur_file
def execute_on_entry(cls: type, entry: Dict[str, Any], context: Dict[str,
Any]):
import cv2
import numpy as np
image_fpath = PathManager.get_local_path(entry["file_name"])
image = cv2.imread(image_fpath, cv2.IMREAD_GRAYSCALE)
image = np.tile(image[:, :, np.newaxis], [1, 1, 3])
datas = cls._extract_data_for_visualizers_from_entry(
context["vis_specs"], entry)
visualizer = context["visualizer"]
image_vis = visualizer.visualize(image, datas)
entry_idx = context["entry_idx"] + 1
out_fname = cls._get_out_fname(entry_idx, context["out_fname"])
cv2.imwrite(out_fname, image_vis)
logger.info(f"Output saved to {out_fname}")
context["entry_idx"] += 1
def inject_gan_datasets(cfg):
if cfg.D2GO_DATA.DATASETS.GAN_INJECTION.ENABLE:
name = cfg.D2GO_DATA.DATASETS.GAN_INJECTION.NAME
cfg.merge_from_list(
["DATASETS.TRAIN", [name], "DATASETS.TEST", [name]])
json_path = cfg.D2GO_DATA.DATASETS.GAN_INJECTION.JSON_PATH
assert PathManager.isfile(json_path), (
"{} is not valid!".format(json_path))
input_src_path = cfg.D2GO_DATA.DATASETS.GAN_INJECTION.INPUT_SRC_DIR
assert PathManager.isfile(input_src_path), (
"{} is not valid!".format(input_src_path))
input_folder = "/tmp/{}/input".format(name)
gt_src_path = cfg.D2GO_DATA.DATASETS.GAN_INJECTION.GT_SRC_DIR
if PathManager.isfile(gt_src_path):
gt_folder = "/tmp/{}/gt".format(name)
else:
gt_src_path = None
gt_folder = None
mask_src_path = cfg.D2GO_DATA.DATASETS.GAN_INJECTION.MASK_SRC_DIR
if PathManager.isfile(mask_src_path):
mask_folder = "/tmp/{}/mask".format(name)
else:
mask_src_path = None
mask_folder = None
real_src_path = cfg.D2GO_DATA.DATASETS.GAN_INJECTION.REAL_SRC_DIR
if PathManager.isfile(real_src_path):
real_folder = "/tmp/{}/mask".format(name)
real_json_path = cfg.D2GO_DATA.DATASETS.GAN_INJECTION.REAL_JSON_PATH
assert PathManager.isfile(real_json_path), (
"{} is not valid!".format(real_json_path))
else:
real_src_path = None
real_folder = None
real_json_path = None
register_folder_dataset(
name,
json_path,
input_folder,
gt_folder,
mask_folder,
input_src_path,
gt_src_path,
mask_src_path,
real_json_path,
real_folder,
real_src_path,
)
def evaluate(self):
"""
Returns:
dict: has a key "segm", whose value is a dict of "AP" and "AP50".
"""
comm.synchronize()
if comm.get_rank() > 0:
return
import cityscapesscripts.evaluation.evalInstanceLevelSemanticLabeling as cityscapes_eval
self._logger.info("Evaluating results under {} ...".format(
self._temp_dir))
# set some global states in cityscapes evaluation API, before evaluating
cityscapes_eval.args.predictionPath = os.path.abspath(self._temp_dir)
cityscapes_eval.args.predictionWalk = None
cityscapes_eval.args.JSONOutput = False
cityscapes_eval.args.colorized = False
cityscapes_eval.args.gtInstancesFile = os.path.join(
self._temp_dir, "gtInstances.json")
# These lines are adopted from
# https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/evalInstanceLevelSemanticLabeling.py # noqa
gt_dir = PathManager.get_local_path(self._metadata.gt_dir)
groundTruthImgList = glob.glob(
os.path.join(gt_dir, "*", "*_gtFine_instanceIds.png"))
assert len(
groundTruthImgList
), "Cannot find any ground truth images to use for evaluation. Searched for: {}".format(
cityscapes_eval.args.groundTruthSearch)
predictionImgList = []
for gt in groundTruthImgList:
predictionImgList.append(
cityscapes_eval.getPrediction(gt, cityscapes_eval.args))
results = cityscapes_eval.evaluateImgLists(
predictionImgList, groundTruthImgList,
cityscapes_eval.args)["averages"]
ret = OrderedDict()
ret["segm"] = {
"AP": results["allAp"] * 100,
"AP50": results["allAp50%"] * 100
}
self._working_dir.cleanup()
return ret
def load_lvis_json(annotations_json_file: str, image_root: str, dataset_name: str):
"""
Loads a JSON file with annotations in LVIS instances format.
Replaces `detectron2.data.datasets.coco.load_lvis_json` to handle metadata
in a more flexible way. Postpones category mapping to a later stage to be
able to combine several datasets with different (but coherent) sets of
categories.
Args:
annotations_json_file: str
Path to the JSON file with annotations in COCO instances format.
image_root: str
directory that contains all the images
dataset_name: str
the name that identifies a dataset, e.g. "densepose_coco_2014_train"
extra_annotation_keys: Optional[List[str]]
If provided, these keys are used to extract additional data from
the annotations.
"""
lvis_api = _load_lvis_annotations(PathManager.get_local_path(annotations_json_file))
_add_categories_metadata(dataset_name)
# sort indices for reproducible results
img_ids = sorted(lvis_api.imgs.keys())
# imgs is a list of dicts, each looks something like:
# {'license': 4,
# 'url': 'http://farm6.staticflickr.com/5454/9413846304_881d5e5c3b_z.jpg',
# 'file_name': 'COCO_val2014_000000001268.jpg',
# 'height': 427,
# 'width': 640,
# 'date_captured': '2013-11-17 05:57:24',
# 'id': 1268}
imgs = lvis_api.load_imgs(img_ids)
logger = logging.getLogger(__name__)
logger.info("Loaded {} images in LVIS format from {}".format(len(imgs), annotations_json_file))
# anns is a list[list[dict]], where each dict is an annotation
# record for an object. The inner list enumerates the objects in an image
# and the outer list enumerates over images.
anns = [lvis_api.img_ann_map[img_id] for img_id in img_ids]
_verify_annotations_have_unique_ids(annotations_json_file, anns)
dataset_records = _combine_images_with_annotations(dataset_name, image_root, imgs, anns)
return dataset_records
def _eval_box_proposals(self, predictions):
"""
Evaluate the box proposals in predictions.
Fill self._results with the metrics for "box_proposals" task.
"""
if self._output_dir:
# Saving generated box proposals to file.
# Predicted box_proposals are in XYXY_ABS mode.
bbox_mode = BoxMode.XYXY_ABS.value
ids, boxes, objectness_logits = [], [], []
for prediction in predictions:
ids.append(prediction["image_id"])
boxes.append(
prediction["proposals"].proposal_boxes.tensor.numpy())
objectness_logits.append(
prediction["proposals"].objectness_logits.numpy())
proposal_data = {
"boxes": boxes,
"objectness_logits": objectness_logits,
"ids": ids,
"bbox_mode": bbox_mode,
}
with PathManager.open(
os.path.join(self._output_dir, "box_proposals.pkl"),
"wb") as f:
pickle.dump(proposal_data, f)
if not self._do_evaluation:
self._logger.info("Annotations are not available for evaluation.")
return
self._logger.info("Evaluating bbox proposals ...")
res = {}
areas = {"all": "", "small": "s", "medium": "m", "large": "l"}
for limit in [100, 1000]:
for area, suffix in areas.items():
stats = _evaluate_box_proposals(predictions,
self._coco_api,
area=area,
limit=limit)
key = "AR{}@{:d}".format(suffix, limit)
res[key] = float(stats["ar"].item() * 100)
self._logger.info("Proposal metrics: \n" + create_small_table(res))
self._results["box_proposals"] = res
def read_sem_seg_file_with_prefetch(file_name: str, prefetched=None):
"""
Segmentation mask annotations can be stored as:
.PNG files
.npy uncompressed numpy files
"""
assert file_name.endswith(".png") or file_name.endswith(".npy")
sem_seg_type = file_name[-len(".---"):]
if sem_seg_type == ".png":
return read_image_with_prefetch(file_name,
format="L",
prefetched=prefetched)
elif sem_seg_type == ".npy":
if prefetched is None:
with PathManager.open(file_name, "rb") as f:
return np.load(f)
else:
return prefetched.numpy()
def export_caffe2_tracing(cfg, torch_model, inputs):
tracer = Caffe2Tracer(cfg, torch_model, inputs)
if args.format == "caffe2":
caffe2_model = tracer.export_caffe2()
caffe2_model.save_protobuf(args.output)
# draw the caffe2 graph
caffe2_model.save_graph(os.path.join(args.output, "model.svg"),
inputs=inputs)
return caffe2_model
elif args.format == "onnx":
onnx_model = tracer.export_onnx()
onnx.save(onnx_model, os.path.join(args.output, "model.onnx"))
elif args.format == "torchscript":
ts_model = tracer.export_torchscript()
with PathManager.open(os.path.join(args.output, "model.ts"),
"wb") as f:
torch.jit.save(ts_model, f)
dump_torchscript_IR(ts_model, args.output)
def get_path_pairs(img_folder, mask_folder):
img_paths = []
mask_paths = []
print("Before listing", img_folder)
filenames = PathManager.ls(img_folder)
for filename in filenames:
print("found: ", filename)
basename, _ = os.path.splitext(filename)
if filename.endswith(".jpg"):
imgpath = os.path.join(img_folder, filename)
maskname = basename + ".png"
maskpath = os.path.join(mask_folder, maskname)
img_paths.append(imgpath)
mask_paths.append(maskpath)
# if PathManager.isfile(maskpath):
# else:
# print('cannot find the mask:', maskpath)
return img_paths, mask_paths
def __init__(self,
dataset_name,
distributed,
output_dir=None,
embedder=None):
self._embedder = embedder
self._distributed = distributed
self._output_dir = output_dir
self._cpu_device = torch.device("cpu")
self._logger = logging.getLogger(__name__)
self._metadata = MetadataCatalog.get(dataset_name)
self._min_threshold = 0.5
json_file = PathManager.get_local_path(self._metadata.json_file)
with contextlib.redirect_stdout(io.StringIO()):
self._coco_api = COCO(json_file)
maybe_filter_and_map_categories_cocoapi(dataset_name, self._coco_api)
def __init__(self,
dataset_name,
distributed,
output_dir=None,
embedder=None):
self._embedder = embedder
self._distributed = distributed
self._output_dir = output_dir
self._cpu_device = torch.device("cpu")
self._logger = logging.getLogger(__name__)
self._metadata = MetadataCatalog.get(dataset_name)
self._min_threshold = 0.5
json_file = PathManager.get_local_path(self._metadata.json_file)
with contextlib.redirect_stdout(io.StringIO()):
self._coco_api = COCO(json_file)
self.mesh_name = self._coco_api.anns[list(
self._coco_api.anns.keys())[0]].get("ref_model", "smpl_27554")
def __init__(self, dataset_name):
"""
Args:
dataset_name (str): name of the dataset, e.g., "voc_2007_test"
"""
self._dataset_name = dataset_name
meta = MetadataCatalog.get(dataset_name)
# Too many tiny files, download all to local for speed.
annotation_dir_local = PathManager.get_local_path(
os.path.join(meta.dirname, "Annotations/")
)
self._anno_file_template = os.path.join(annotation_dir_local, "{}.xml")
self._image_set_path = os.path.join(meta.dirname, "ImageSets", "Main", meta.split + ".txt")
self._class_names = meta.thing_classes
assert meta.year in [2007, 2012], meta.year
self._is_2007 = meta.year == 2007
self._cpu_device = torch.device("cpu")
self._logger = logging.getLogger(__name__)
def read_image(file_name, format=None):
"""
Read an image into the given format.
Will apply rotation and flipping if the image has such exif information.
Args:
file_name (str): image file path
format (str): one of the supported image modes in PIL, or "BGR" or "YUV-BT.601".
Returns:
image (np.ndarray): an HWC image in the given format, which is 0-255, uint8 for
supported image modes in PIL or "BGR"; float (0-1 for Y) for YUV-BT.601.
"""
with PathManager.open(file_name, "rb") as f:
image = Image.open(f)
# work around this bug: https://github.com/python-pillow/Pillow/issues/3973
image = _apply_exif_orientation(image)
return convert_PIL_to_numpy(image, format)
def after_step(self):
if len(self.prefixes) == 0:
return
if (self.trainer.iter + 1) % self._period == 0 or (
self.trainer.iter >= self.trainer.max_iter - 1):
# Get latest ckpt file name
latest_ckpt_path = os.path.join(self._output_dir,
"last_checkpoint")
assert os.path.exists(latest_ckpt_path), (
f"File: {latest_ckpt_path} not found!")
with open(latest_ckpt_path) as fp:
latest_ckpt = fp.read().strip()
# Dump all log files
# Some log files may have been uploaded before, but we need to update them
for dir_path, dir_names, file_names in os.walk(self._output_dir):
for file_name in sorted(file_names):
local = os.path.join(dir_path, file_name)
if re.match(r"model_\d*\.pth",
file_name) and file_name != latest_ckpt:
self.logger.info(
"Remove local log file: {}.".format(local))
os.remove(local)
continue
for prefix, bucket in zip(self.prefixes, self.buckets):
remote = os.path.join(
prefix, "detectron2",
dir_path.split("/detectron2/")[1], file_name)
upload_result = PathManager.upload(local,
remote,
bucket=bucket)
if upload_result:
self.logger.info(
"Dump output file from {} to {}.".format(
local, remote))
else:
self.logger.warning(
"Failed to dump output file from {} to {}.".
format(local, remote))
def export_scripting(torch_model):
assert TORCH_VERSION >= (1, 8)
fields = {
"proposal_boxes": Boxes,
"objectness_logits": Tensor,
"pred_boxes": Boxes,
"scores": Tensor,
"pred_classes": Tensor,
"pred_masks": Tensor,
"pred_keypoints": torch.Tensor,
"pred_keypoint_heatmaps": torch.Tensor,
}
assert args.format == "torchscript", "Scripting only supports torchscript format."
ts_model = export_torchscript_with_instances(torch_model, fields)
with PathManager.open(os.path.join(args.output, "model.ts"), "wb") as f:
torch.jit.save(ts_model, f)
dump_torchscript_IR(ts_model, args.output)
# TODO inference in Python now missing postprocessing glue code
return None
def merge_from_file(self,
cfg_filename: str,
allow_unsafe: bool = True) -> None:
assert PathManager.isfile(
cfg_filename), f"Config file '{cfg_filename}' does not exist!"
loaded_cfg = self.load_yaml_with_base(cfg_filename,
allow_unsafe=allow_unsafe)
loaded_cfg = type(self)(loaded_cfg)
# defaults.py needs to import CfgNode
from .defaults import _C
latest_ver = _C.VERSION
assert (
latest_ver == self.VERSION
), "CfgNode.merge_from_file is only allowed on a config object of latest version!"
logger = logging.getLogger(__name__)
loaded_ver = loaded_cfg.get("VERSION", None)
if loaded_ver is None:
from .compat import guess_version
loaded_ver = guess_version(loaded_cfg, cfg_filename)
assert loaded_ver <= self.VERSION, "Cannot merge a v{} config into a v{} config.".format(
loaded_ver, self.VERSION)
if loaded_ver == self.VERSION:
self.merge_from_other_cfg(loaded_cfg)
else:
# compat.py needs to import CfgNode
from .compat import upgrade_config, downgrade_config
logger.warning(
"Loading an old v{} config file '{}' by automatically upgrading to v{}. "
"See docs/CHANGELOG.md for instructions to update your files.".
format(loaded_ver, cfg_filename, self.VERSION))
# To convert, first obtain a full config at an old version
old_self = downgrade_config(self, to_version=loaded_ver)
old_self.merge_from_other_cfg(loaded_cfg)
new_config = upgrade_config(old_self)
self.clear()
self.update(new_config)
def export_scripting(torch_model):
assert TORCH_VERSION >= (1, 8)
fields = {
"proposal_boxes": Boxes,
"objectness_logits": Tensor,
"pred_boxes": Boxes,
"scores": Tensor,
"pred_classes": Tensor,
"pred_masks": Tensor,
"pred_keypoints": torch.Tensor,
"pred_keypoint_heatmaps": torch.Tensor,
}
assert args.format == "torchscript", "Scripting only supports torchscript format."
class ScriptableAdapterBase(nn.Module):
# Use this adapter to workaround https://github.com/pytorch/pytorch/issues/46944
# by not retuning instances but dicts. Otherwise the exported model is not deployable
def __init__(self):
super().__init__()
self.model = torch_model
self.eval()
if isinstance(torch_model, GeneralizedRCNN):
class ScriptableAdapter(ScriptableAdapterBase):
def forward(self, inputs: Tuple[Dict[str, torch.Tensor]]) -> List[Dict[str, Tensor]]:
instances = self.model.inference(inputs, do_postprocess=False)
return [i.get_fields() for i in instances]
else:
class ScriptableAdapter(ScriptableAdapterBase):
def forward(self, inputs: Tuple[Dict[str, torch.Tensor]]) -> List[Dict[str, Tensor]]:
instances = self.model(inputs)
return [i.get_fields() for i in instances]
ts_model = scripting_with_instances(ScriptableAdapter(), fields)
with PathManager.open(os.path.join(args.output, "model.ts"), "wb") as f:
torch.jit.save(ts_model, f)
dump_torchscript_IR(ts_model, args.output)
# TODO inference in Python now missing postprocessing glue code
return None
def evaluate(self):
comm.synchronize()
if comm.get_rank() > 0:
return
# Load the Cityscapes eval script *after* setting the required env var,
# since the script reads CITYSCAPES_DATASET into global variables at load time.
import cityscapesscripts.evaluation.evalPixelLevelSemanticLabeling as cityscapes_eval
self._logger.info("Evaluating results under {} ...".format(
self._temp_dir))
# set some global states in cityscapes evaluation API, before evaluating
cityscapes_eval.args.predictionPath = os.path.abspath(self._temp_dir)
cityscapes_eval.args.predictionWalk = None
cityscapes_eval.args.JSONOutput = False
cityscapes_eval.args.colorized = False
# These lines are adopted from
# https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/evalPixelLevelSemanticLabeling.py # noqa
gt_dir = PathManager.get_local_path(self._metadata.gt_dir)
groundTruthImgList = glob.glob(
os.path.join(gt_dir, "*", "*_gtFine_labelIds.png"))
assert len(
groundTruthImgList
), "Cannot find any ground truth images to use for evaluation. Searched for: {}".format(
cityscapes_eval.args.groundTruthSearch)
predictionImgList = []
for gt in groundTruthImgList:
predictionImgList.append(
cityscapes_eval.getPrediction(cityscapes_eval.args, gt))
results = cityscapes_eval.evaluateImgLists(predictionImgList,
groundTruthImgList,
cityscapes_eval.args)
ret = OrderedDict()
ret["sem_seg"] = {
"IoU": 100.0 * results["averageScoreClasses"],
"iIoU": 100.0 * results["averageScoreInstClasses"],
"IoU_sup": 100.0 * results["averageScoreCategories"],
"iIoU_sup": 100.0 * results["averageScoreInstCategories"],
}
self._working_dir.cleanup()
return ret
def process_predictions(frame, predictions):
with PathManager.open("instances_predictions.pth", "a") as f:
torch.save(predictions, f)
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
if "panoptic_seg" in predictions:
panoptic_seg, segments_info = predictions["panoptic_seg"]
vis_frame = video_visualizer.draw_panoptic_seg_predictions(
frame, panoptic_seg.to(self.cpu_device), segments_info)
elif "instances" in predictions:
predictions = predictions["instances"].to(self.cpu_device)
vis_frame = video_visualizer.draw_instance_predictions(
frame, predictions)
elif "sem_seg" in predictions:
vis_frame = video_visualizer.draw_sem_seg(
frame,
predictions["sem_seg"].argmax(dim=0).to(self.cpu_device))
# Converts Matplotlib RGB format to OpenCV BGR format
vis_frame = cv2.cvtColor(vis_frame.get_image(), cv2.COLOR_RGB2BGR)
return vis_frame
def get_sample_coco_image(tensor=True):
"""
Args:
tensor (bool): if True, returns 3xHxW tensor.
else, returns a HxWx3 numpy array.
Returns:
an image, in BGR color.
"""
try:
file_name = DatasetCatalog.get("coco_2017_val_100")[0]["file_name"]
if not PathManager.exists(file_name):
raise FileNotFoundError()
except IOError:
# for public CI to run
file_name = "http://images.cocodataset.org/train2017/000000000009.jpg"
ret = read_image(file_name, format="BGR")
if tensor:
ret = torch.from_numpy(np.ascontiguousarray(ret.transpose(2, 0, 1)))
return ret
def _load_lvis_annotations(json_file: str):
"""
Load COCO annotations from a JSON file
Args:
json_file: str
Path to the file to load annotations from
Returns:
Instance of `pycocotools.coco.COCO` that provides access to annotations
data
"""
from lvis import LVIS
json_file = PathManager.get_local_path(json_file)
logger = logging.getLogger(__name__)
timer = Timer()
lvis_api = LVIS(json_file)
if timer.seconds() > 1:
logger.info("Loading {} takes {:.2f} seconds.".format(json_file, timer.seconds()))
return lvis_api
def _load_file(self, filename):
if filename.endswith(".pkl"):
with PathManager.open(filename, "rb") as f:
data = pickle.load(f, encoding="latin1")
if "model" in data and "__author__" in data:
# file is in Detectron2 model zoo format
self.logger.info("Reading a file from '{}'".format(data["__author__"]))
return data
else:
# assume file is from Caffe2 / Detectron1 model zoo
if "blobs" in data:
# Detection models have "blobs", but ImageNet models don't
data = data["blobs"]
data = {k: v for k, v in data.items() if not k.endswith("_momentum")}
return {"model": data, "__author__": "Caffe2", "matching_heuristics": True}
loaded = super()._load_file(filename) # load native pth checkpoint
if "model" not in loaded:
loaded = {"model": loaded}
return loaded
def main(
cfg: CfgNode,
output_dir: str,
task_cls: Type[GeneralizedRCNNTask] = GeneralizedRCNNTask,
eval_only: bool = False,
num_machines: int = 1,
num_processes: int = 1,
) -> TrainOutput:
"""Main function for launching a training with lightning trainer
Args:
cfg: D2go config node
num_machines: Number of nodes used for distributed training
num_processes: Number of processes on each node.
eval_only: True if run evaluation only.
"""
# FIXME: make comm.get_world_size() work properly.
setup_after_launch(cfg, output_dir, _scale_world_size=False)
auto_scale_world_size(cfg, new_world_size=num_machines * num_processes)
task = task_cls.from_config(cfg, eval_only)
trainer_params = get_trainer_params(cfg, num_machines, num_processes)
last_checkpoint = os.path.join(cfg.OUTPUT_DIR, "last.ckpt")
if PathManager.exists(last_checkpoint):
# resume training from checkpoint
trainer_params["resume_from_checkpoint"] = last_checkpoint
logger.info(f"Resuming training from checkpoint: {last_checkpoint}.")
trainer = pl.Trainer(**trainer_params)
model_configs = None
if eval_only:
do_test(trainer, task)
else:
model_configs = do_train(cfg, trainer, task)
return TrainOutput(
output_dir=cfg.OUTPUT_DIR,
tensorboard_log_dir=trainer_params["logger"].log_dir,
accuracy=task.eval_res,
model_configs=model_configs,
)
def parse_rec(filename):
"""Parse a PASCAL VOC xml file."""
with PathManager.open(filename) as f:
tree = ET.parse(f)
objects = []
for obj in tree.findall("object"):
obj_struct = {}
obj_struct["name"] = obj.find("name").text
obj_struct["pose"] = obj.find("pose").text
obj_struct["truncated"] = int(obj.find("truncated").text)
obj_struct["difficult"] = int(obj.find("difficult").text)
bbox = obj.find("bndbox")
obj_struct["bbox"] = [
int(bbox.find("xmin").text),
int(bbox.find("ymin").text),
int(bbox.find("xmax").text),
int(bbox.find("ymax").text),
]
objects.append(obj_struct)
return objects
def __init__(self,
dataset_name,
tasks=None,
distributed=True,
output_dir=None):
"""
Args:
dataset_name (str): name of the dataset to be evaluated.
It must have the following corresponding metadata:
"json_file": the path to the LVIS format annotation
tasks (tuple[str]): tasks that can be evaluated under the given
configuration. A task is one of "bbox", "segm".
By default, will infer this automatically from predictions.
distributed (True): if True, will collect results from all ranks for evaluation.
Otherwise, will evaluate the results in the current process.
output_dir (str): optional, an output directory to dump results.
"""
from lvis import LVIS
self._logger = logging.getLogger(__name__)
if tasks is not None and isinstance(tasks, CfgNode):
self._logger.warn(
"COCO Evaluator instantiated using config, this is deprecated behavior."
" Please pass in explicit arguments instead.")
self._tasks = None # Infering it from predictions should be better
else:
self._tasks = tasks
self._distributed = distributed
self._output_dir = output_dir
self._cpu_device = torch.device("cpu")
self._metadata = MetadataCatalog.get(dataset_name)
json_file = PathManager.get_local_path(self._metadata.json_file)
self._lvis_api = LVIS(json_file)
# Test set json files do not contain annotations (evaluation must be
# performed using the LVIS evaluation server).
self._do_evaluation = len(self._lvis_api.get_ann_ids()) > 0
def new_import(name, globals=None, locals=None, fromlist=(), level=0):
if (
# Only deal with relative imports inside config files
level != 0
and globals is not None
and (globals.get("__package__", "") or "").startswith(_CFG_PACKAGE_NAME)
):
cur_file = find_relative_file(globals["__file__"], name, level)
_validate_py_syntax(cur_file)
spec = importlib.machinery.ModuleSpec(
_random_package_name(cur_file), None, origin=cur_file
)
module = importlib.util.module_from_spec(spec)
module.__file__ = cur_file
with PathManager.open(cur_file) as f:
content = f.read()
exec(compile(content, cur_file, "exec"), module.__dict__)
for name in fromlist: # turn imported dict into DictConfig automatically
val = _cast_to_config(module.__dict__[name])
module.__dict__[name] = val
return module
return old_import(name, globals, locals, fromlist=fromlist, level=level)
def read_keyframe_helper_data(fpath: str):
"""
Read keyframe data from a file in CSV format: the header should contain
"video_id" and "keyframes" fields. Value specifications are:
video_id: int
keyframes: list(int)
Example of contents:
video_id,keyframes
2,"[1,11,21,31,41,51,61,71,81]"
Args:
fpath (str): File containing keyframe data
Return:
video_id_to_keyframes (dict: int -> list(int)): for a given video ID it
contains a list of keyframes for that video
"""
video_id_to_keyframes = {}
try:
with PathManager.open(fpath, "r") as io:
csv_reader = csv.reader(io) # pyre-ignore[6]
header = next(csv_reader)
video_id_idx = header.index("video_id")
keyframes_idx = header.index("keyframes")
for row in csv_reader:
video_id = int(row[video_id_idx])
assert (
video_id not in video_id_to_keyframes
), f"Duplicate keyframes entry for video {fpath}"
video_id_to_keyframes[video_id] = (
[int(v) for v in row[keyframes_idx][1:-1].split(",")]
if len(row[keyframes_idx]) > 2
else []
)
except Exception as e:
logger = logging.getLogger(__name__)
logger.warning(f"Error reading keyframe helper data from {fpath}: {e}")
return video_id_to_keyframes
def prepare_for_launch(args):
"""
Load config, figure out working directory, create runner.
- when args.config_file is empty, returned cfg will be the default one
- returned output_dir will always be non empty, args.output_dir has higher
priority than cfg.OUTPUT_DIR.
"""
print(args)
runner = create_runner(args.runner)
cfg = runner.get_default_cfg()
if args.config_file:
with PathManager.open(reroute_config_path(args.config_file), "r") as f:
print("Loaded config file {}:\n{}".format(args.config_file, f.read()))
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
else:
cfg = create_cfg_from_cli_args(args, default_cfg=cfg)
cfg.freeze()
assert args.output_dir or args.config_file
output_dir = args.output_dir or cfg.OUTPUT_DIR
return cfg, output_dir, runner
def __init__(self,
img_folder,
ann_folder,
ann_file,
transforms=None,
return_masks=True):
with PathManager.open(ann_file, "r") as f:
self.coco = json.load(f)
# sort 'images' field so that they are aligned with 'annotations'
# i.e., in alphabetical order
self.coco["images"] = sorted(self.coco["images"],
key=lambda x: x["id"])
# sanity check
if "annotations" in self.coco:
for img, ann in zip(self.coco["images"], self.coco["annotations"]):
assert img["file_name"][:-4] == ann["file_name"][:-4]
self.img_folder = img_folder
self.ann_folder = ann_folder
self.ann_file = ann_file
self.transforms = transforms
self.return_masks = return_masks
