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Commit ca094a0a authored by Miquel Jubert Hermoso's avatar Miquel Jubert Hermoso Committed by Facebook GitHub Bot
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Reformat d2go_dataset_mapper to break circular dependency 

Summary:
Pull Request resolved: https://github.com/facebookresearch/d2go/pull/225

*This diff is part of a stack which has the goal of "buckifying" D2 (https://github.com/facebookresearch/d2go/commit/87374efb134e539090e0b5c476809dc35bf6aedb)Go core and enabling autodeps and other tooling. The last diff in the stack introduces the TARGETS. The diffs earlier in the stack are resolving circular dependencies and other issues which prevent the buckification from occurring.*

The overriding pattern applied in d2go_dataset_mapper, with the d2go_dataset_mapper_impl and d2go_dataset_mapper_impl_fb makes it possible that internal users get the _fb behaviour and external users the regular one, with the same import. But, this makes it necessary to put both files in the same dependency.

This causes a circular dependency. In general, one reasonable assumption is that fb-only dependencies can import oss dependencies but not vice versa. In the current setup, grouping both _impl files in a buck target makes that buck target contain both fb and oss code, and depend on both. This is causing circular buck dependency issues.

To fix this, while keeping the transparent import behavior, the following changes are done:

1. The implementation file is moved to a directory under .fb. It will have it's own target.
2. The non-fb version is renamed to DualInputDatasetMapper, as per Yanghan's suggestion. This simplifies the change, since it seems the fb version's behavior is not used atm.
3. d2go_dataset_mapper is moved to have the implementation itself

Reviewed By: tglik

Differential Revision: D35930993

fbshipit-source-id: ac57337d221df24f53e360d5dcb38ffa4164fef5
parent 9f746159
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d2go/data/dataset_mappers/d2go_dataset_mapper.py
View file @ ca094a0a
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
from .build import D2GO_DATA_MAPPER_REGISTRY
from .d2go_dataset_mapper_impl import (
D2GoDatasetMapper,
PREFETCHED_FILE_NAME,
PREFETCHED_SEM_SEG_FILE_NAME,
import copy
import logging
import numpy as np
import torch
from d2go.data.dataset_mappers.data_reading import (
read_image_with_prefetch,
read_sem_seg_file_with_prefetch,
)
from d2go.utils.helper import retryable
from detectron2.data import detection_utils as utils, transforms as T
from detectron2.data.transforms.augmentation import AugInput, AugmentationList
from .build import D2GO_DATA_MAPPER_REGISTRY
logger = logging.getLogger(__name__)
PREFETCHED_FILE_NAME = "prefetch_image"
PREFETCHED_SEM_SEG_FILE_NAME = "prefetch_sem_seg"
@D2GO_DATA_MAPPER_REGISTRY.register()
class D2GoDatasetMapper(object):
def __init__(self, cfg, is_train=True, image_loader=None, tfm_gens=None):
self.tfm_gens = (
tfm_gens
if tfm_gens is not None
else utils.build_transform_gen(cfg, is_train)
)
if cfg.INPUT.CROP.ENABLED and is_train:
self.crop_gen = T.RandomCrop(cfg.INPUT.CROP.TYPE, cfg.INPUT.CROP.SIZE)
# D2GO NOTE: when INPUT.CROP.ENABLED, don't allow using RandomCropOp
assert all(not isinstance(gen, T.RandomCrop) for gen in self.tfm_gens)
else:
self.crop_gen = None
# fmt: off
self.img_format = cfg.INPUT.FORMAT # noqa
self.mask_on = cfg.MODEL.MASK_ON # noqa
self.mask_format = cfg.INPUT.MASK_FORMAT # noqa
self.keypoint_on = cfg.MODEL.KEYPOINT_ON # noqa
# fmt: on
if self.keypoint_on and is_train:
# Flip only makes sense in training
self.keypoint_hflip_indices = utils.create_keypoint_hflip_indices(
cfg.DATASETS.TRAIN
)
else:
self.keypoint_hflip_indices = None
self.load_proposals = cfg.MODEL.LOAD_PROPOSALS
if self.load_proposals:
self.proposal_min_box_size = cfg.MODEL.PROPOSAL_GENERATOR.MIN_SIZE
self.proposal_topk = (
cfg.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TRAIN
if is_train
else cfg.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TEST
)
self.is_train = is_train
# Setup image loader:
self.image_loader = image_loader
self.backfill_size = cfg.D2GO_DATA.MAPPER.BACKFILL_SIZE
self.retry = cfg.D2GO_DATA.MAPPER.RETRY
self.catch_exception = cfg.D2GO_DATA.MAPPER.CATCH_EXCEPTION
if self.backfill_size:
if cfg.DATALOADER.ASPECT_RATIO_GROUPING:
logger.warning(
"ASPECT_RATIO_GROUPING may not work if image's width & height"
" are not given in json dataset when calling extended_coco_load,"
" if you encounter issue, consider disable ASPECT_RATIO_GROUPING."
)
self._error_count = 0
self._total_counts = 0
self._error_types = {}
def _original_call(self, dataset_dict):
"""
Modified from detectron2's original __call__ in DatasetMapper
"""
dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below
image = self._read_image(dataset_dict, format=self.img_format)
if not self.backfill_size:
utils.check_image_size(dataset_dict, image)
image, dataset_dict = self._custom_transform(image, dataset_dict)
inputs = AugInput(image=image)
if "annotations" not in dataset_dict:
transforms = AugmentationList(
([self.crop_gen] if self.crop_gen else []) + self.tfm_gens
)(inputs)
image = inputs.image
else:
# pass additional arguments, will only be used when the Augmentation
# takes `annotations` as input
inputs.annotations = dataset_dict["annotations"]
# Crop around an instance if there are instances in the image.
if self.crop_gen:
crop_tfm = utils.gen_crop_transform_with_instance(
self.crop_gen.get_crop_size(image.shape[:2]),
image.shape[:2],
np.random.choice(dataset_dict["annotations"]),
)
inputs.image = crop_tfm.apply_image(image)
transforms = AugmentationList(self.tfm_gens)(inputs)
image = inputs.image
if self.crop_gen:
transforms = crop_tfm + transforms
image_shape = image.shape[:2] # h, w
if image.ndim == 2:
image = np.expand_dims(image, 2)
dataset_dict["image"] = torch.as_tensor(
image.transpose(2, 0, 1).astype("float32")
)
# Can use uint8 if it turns out to be slow some day
if self.load_proposals:
utils.transform_proposals(
dataset_dict,
image_shape,
transforms,
proposal_topk=self.proposal_topk,
min_box_size=self.proposal_min_box_size,
)
if not self.is_train:
dataset_dict.pop("annotations", None)
dataset_dict.pop("sem_seg_file_name", None)
return dataset_dict
if "annotations" in dataset_dict:
for anno in dataset_dict["annotations"]:
if not self.mask_on:
anno.pop("segmentation", None)
if not self.keypoint_on:
anno.pop("keypoints", None)
annos = [
utils.transform_instance_annotations(
obj,
transforms,
image_shape,
keypoint_hflip_indices=self.keypoint_hflip_indices,
)
for obj in dataset_dict.pop("annotations")
if obj.get("iscrowd", 0) == 0
]
instances = utils.annotations_to_instances(
annos, image_shape, mask_format=self.mask_format
)
# Create a tight bounding box from masks, useful when image is cropped
if self.crop_gen and instances.has("gt_masks"):
instances.gt_boxes = instances.gt_masks.get_bounding_boxes()
dataset_dict["instances"] = utils.filter_empty_instances(instances)
if "sem_seg_file_name" in dataset_dict:
sem_seg_gt = read_sem_seg_file_with_prefetch(
dataset_dict.pop("sem_seg_file_name"),
prefetched=dataset_dict.get(PREFETCHED_SEM_SEG_FILE_NAME, None),
)
if len(sem_seg_gt.shape) > 2:
sem_seg_gt = sem_seg_gt.squeeze(2)
sem_seg_gt = transforms.apply_segmentation(sem_seg_gt)
sem_seg_gt = torch.as_tensor(sem_seg_gt.astype("long"))
dataset_dict["sem_seg"] = sem_seg_gt
# extend standard D2 semantic segmentation to support multiple segmentation
# files, each file can represent a class
if "multi_sem_seg_file_names" in dataset_dict:
raise NotImplementedError()
if "_post_process_" in dataset_dict:
proc_func = dataset_dict.pop("_post_process_")
dataset_dict = proc_func(dataset_dict)
return dataset_dict
def __call__(self, dataset_dict):
self._total_counts += 1
@retryable(num_tries=self.retry, sleep_time=0.1)
def _f():
return self._original_call(dataset_dict)
if not self.catch_exception:
return _f()
try:
return _f()
except Exception as e:
self._error_count += 1
# if self._error_count % 10 == 1:
# # print the stacktrace for easier debugging
# traceback.print_exc()
error_type = type(e).__name__
self._error_types[error_type] = self._error_types.get(error_type, 0) + 1
if self._error_count % 100 == 0:
logger.warning(
"{}Error when applying transform for dataset_dict: {};"
" error rate {}/{} ({:.2f}%), msg: {}".format(
self._get_logging_prefix(),
dataset_dict,
self._error_count,
self._total_counts,
100.0 * self._error_count / self._total_counts,
repr(e),
)
)
self._log_error_type_stats()
# NOTE: the contract with MapDataset allows return `None` such that
# it'll randomly use other element in the dataset. We use this
# feature to handle error.
return None
def _get_logging_prefix(self):
worker_info = torch.utils.data.get_worker_info()
if not worker_info:
return ""
prefix = "[worker: {}/{}] ".format(worker_info.id, worker_info.num_workers)
return prefix
def _log_error_type_stats(self):
error_type_count_msgs = [
"{}: {}/{} ({}%)".format(
k, v, self._total_counts, 100.0 * v / self._total_counts
)
for k, v in self._error_types.items()
]
logger.warning(
"{}Error statistics:\n{}".format(
self._get_logging_prefix(), "\n".join(error_type_count_msgs)
)
)
def _read_image(self, dataset_dict, format=None):
if not (self.image_loader and self.image_loader.support(dataset_dict)):
# fallback to use D2's read_image
image = read_image_with_prefetch(
dataset_dict["file_name"],
format=format,
prefetched=dataset_dict.get(PREFETCHED_FILE_NAME),
)
if self.backfill_size:
h, w, _ = image.shape
dataset_dict["width"] = w
dataset_dict["height"] = h
return image
image = self.image_loader(dataset_dict)
if self.backfill_size:
dataset_dict["width"] = image.width
dataset_dict["height"] = image.height
__all__ = [
"D2GoDatasetMapper",
"PREFETCHED_FILE_NAME",
"PREFETCHED_SEM_SEG_FILE_NAME",
"read_image_with_prefetch",
"read_sem_seg_file_with_prefetch",
]
return utils.convert_PIL_to_numpy(image, format)
def _custom_transform(self, image, dataset_dict):
"""
Override this method to inject custom transform.
"""
return image, dataset_dict
# NOTE: D2GoDatasetMapper might have different versions between internal and oss code,
# which causes double-registration if the class is registered at declaration.
D2GO_DATA_MAPPER_REGISTRY.register(D2GoDatasetMapper)
def __repr__(self):
return (
self.__class__.__name__
+ ":\n"
+ "\n".join(
[
" is_train: {}".format(self.is_train),
" image_loader: {}".format(self.image_loader),
" tfm_gens: \n{}".format(
"\n".join([" - {}".format(x) for x in self.tfm_gens])
),
]
)
)
d2go/data/dataset_mappers/d2go_dataset_mapper_impl.py deleted 100644 → 0
View file @ 9f746159
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import copy
import logging
import numpy as np
import torch
from d2go.data.dataset_mappers.data_reading import (
read_image_with_prefetch,
read_sem_seg_file_with_prefetch,
)
from d2go.utils.helper import retryable
from detectron2.data import detection_utils as utils, transforms as T
from detectron2.data.transforms.augmentation import AugInput, AugmentationList
logger = logging.getLogger(__name__)
PREFETCHED_FILE_NAME = "prefetch_image"
PREFETCHED_SEM_SEG_FILE_NAME = "prefetch_sem_seg"
class D2GoDatasetMapper(object):
def __init__(self, cfg, is_train=True, image_loader=None, tfm_gens=None):
self.tfm_gens = (
tfm_gens
if tfm_gens is not None
else utils.build_transform_gen(cfg, is_train)
)
if cfg.INPUT.CROP.ENABLED and is_train:
self.crop_gen = T.RandomCrop(cfg.INPUT.CROP.TYPE, cfg.INPUT.CROP.SIZE)
# D2GO NOTE: when INPUT.CROP.ENABLED, don't allow using RandomCropOp
assert all(not isinstance(gen, T.RandomCrop) for gen in self.tfm_gens)
else:
self.crop_gen = None
# fmt: off
self.img_format = cfg.INPUT.FORMAT # noqa
self.mask_on = cfg.MODEL.MASK_ON # noqa
self.mask_format = cfg.INPUT.MASK_FORMAT # noqa
self.keypoint_on = cfg.MODEL.KEYPOINT_ON # noqa
# fmt: on
if self.keypoint_on and is_train:
# Flip only makes sense in training
self.keypoint_hflip_indices = utils.create_keypoint_hflip_indices(
cfg.DATASETS.TRAIN
)
else:
self.keypoint_hflip_indices = None
self.load_proposals = cfg.MODEL.LOAD_PROPOSALS
if self.load_proposals:
self.proposal_min_box_size = cfg.MODEL.PROPOSAL_GENERATOR.MIN_SIZE
self.proposal_topk = (
cfg.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TRAIN
if is_train
else cfg.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TEST
)
self.is_train = is_train
# Setup image loader:
self.image_loader = image_loader
self.backfill_size = cfg.D2GO_DATA.MAPPER.BACKFILL_SIZE
self.retry = cfg.D2GO_DATA.MAPPER.RETRY
self.catch_exception = cfg.D2GO_DATA.MAPPER.CATCH_EXCEPTION
if self.backfill_size:
if cfg.DATALOADER.ASPECT_RATIO_GROUPING:
logger.warning(
"ASPECT_RATIO_GROUPING may not work if image's width & height"
" are not given in json dataset when calling extended_coco_load,"
" if you encounter issue, consider disable ASPECT_RATIO_GROUPING."
)
self._error_count = 0
self._total_counts = 0
self._error_types = {}
def _original_call(self, dataset_dict):
"""
Modified from detectron2's original __call__ in DatasetMapper
"""
dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below
image = self._read_image(dataset_dict, format=self.img_format)
if not self.backfill_size:
utils.check_image_size(dataset_dict, image)
image, dataset_dict = self._custom_transform(image, dataset_dict)
inputs = AugInput(image=image)
if "annotations" not in dataset_dict:
transforms = AugmentationList(
([self.crop_gen] if self.crop_gen else []) + self.tfm_gens
)(inputs)
image = inputs.image
else:
# pass additional arguments, will only be used when the Augmentation
# takes `annotations` as input
inputs.annotations = dataset_dict["annotations"]
# Crop around an instance if there are instances in the image.
if self.crop_gen:
crop_tfm = utils.gen_crop_transform_with_instance(
self.crop_gen.get_crop_size(image.shape[:2]),
image.shape[:2],
np.random.choice(dataset_dict["annotations"]),
)
inputs.image = crop_tfm.apply_image(image)
transforms = AugmentationList(self.tfm_gens)(inputs)
image = inputs.image
if self.crop_gen:
transforms = crop_tfm + transforms
image_shape = image.shape[:2] # h, w
if image.ndim == 2:
image = np.expand_dims(image, 2)
dataset_dict["image"] = torch.as_tensor(
image.transpose(2, 0, 1).astype("float32")
)
# Can use uint8 if it turns out to be slow some day
if self.load_proposals:
utils.transform_proposals(
dataset_dict,
image_shape,
transforms,
proposal_topk=self.proposal_topk,
min_box_size=self.proposal_min_box_size,
)
if not self.is_train:
dataset_dict.pop("annotations", None)
dataset_dict.pop("sem_seg_file_name", None)
return dataset_dict
if "annotations" in dataset_dict:
for anno in dataset_dict["annotations"]:
if not self.mask_on:
anno.pop("segmentation", None)
if not self.keypoint_on:
anno.pop("keypoints", None)
annos = [
utils.transform_instance_annotations(
obj,
transforms,
image_shape,
keypoint_hflip_indices=self.keypoint_hflip_indices,
)
for obj in dataset_dict.pop("annotations")
if obj.get("iscrowd", 0) == 0
]
instances = utils.annotations_to_instances(
annos, image_shape, mask_format=self.mask_format
)
# Create a tight bounding box from masks, useful when image is cropped
if self.crop_gen and instances.has("gt_masks"):
instances.gt_boxes = instances.gt_masks.get_bounding_boxes()
dataset_dict["instances"] = utils.filter_empty_instances(instances)
if "sem_seg_file_name" in dataset_dict:
sem_seg_gt = read_sem_seg_file_with_prefetch(
dataset_dict.pop("sem_seg_file_name"),
prefetched=dataset_dict.get(PREFETCHED_SEM_SEG_FILE_NAME, None),
)
if len(sem_seg_gt.shape) > 2:
sem_seg_gt = sem_seg_gt.squeeze(2)
sem_seg_gt = transforms.apply_segmentation(sem_seg_gt)
sem_seg_gt = torch.as_tensor(sem_seg_gt.astype("long"))
dataset_dict["sem_seg"] = sem_seg_gt
# extend standard D2 semantic segmentation to support multiple segmentation
# files, each file can represent a class
if "multi_sem_seg_file_names" in dataset_dict:
raise NotImplementedError()
if "_post_process_" in dataset_dict:
proc_func = dataset_dict.pop("_post_process_")
dataset_dict = proc_func(dataset_dict)
return dataset_dict
def __call__(self, dataset_dict):
self._total_counts += 1
@retryable(num_tries=self.retry, sleep_time=0.1)
def _f():
return self._original_call(dataset_dict)
if not self.catch_exception:
return _f()
try:
return _f()
except Exception as e:
self._error_count += 1
# if self._error_count % 10 == 1:
# # print the stacktrace for easier debugging
# traceback.print_exc()
error_type = type(e).__name__
self._error_types[error_type] = self._error_types.get(error_type, 0) + 1
if self._error_count % 100 == 0:
logger.warning(
"{}Error when applying transform for dataset_dict: {};"
" error rate {}/{} ({:.2f}%), msg: {}".format(
self._get_logging_prefix(),
dataset_dict,
self._error_count,
self._total_counts,
100.0 * self._error_count / self._total_counts,
repr(e),
)
)
self._log_error_type_stats()
# NOTE: the contract with MapDataset allows return `None` such that
# it'll randomly use other element in the dataset. We use this
# feature to handle error.
return None
def _get_logging_prefix(self):
worker_info = torch.utils.data.get_worker_info()
if not worker_info:
return ""
prefix = "[worker: {}/{}] ".format(worker_info.id, worker_info.num_workers)
return prefix
def _log_error_type_stats(self):
error_type_count_msgs = [
"{}: {}/{} ({}%)".format(
k, v, self._total_counts, 100.0 * v / self._total_counts
)
for k, v in self._error_types.items()
]
logger.warning(
"{}Error statistics:\n{}".format(
self._get_logging_prefix(), "\n".join(error_type_count_msgs)
)
)
def _read_image(self, dataset_dict, format=None):
if not (self.image_loader and self.image_loader.support(dataset_dict)):
# fallback to use D2's read_image
image = read_image_with_prefetch(
dataset_dict["file_name"],
format=format,
prefetched=dataset_dict.get(PREFETCHED_FILE_NAME),
)
if self.backfill_size:
h, w, _ = image.shape
dataset_dict["width"] = w
dataset_dict["height"] = h
return image
image = self.image_loader(dataset_dict)
if self.backfill_size:
dataset_dict["width"] = image.width
dataset_dict["height"] = image.height
return utils.convert_PIL_to_numpy(image, format)
def _custom_transform(self, image, dataset_dict):
"""
Override this method to inject custom transform.
"""
return image, dataset_dict
def __repr__(self):
return (
self.__class__.__name__
+ ":\n"
+ "\n".join(
[
" is_train: {}".format(self.is_train),
" image_loader: {}".format(self.image_loader),
" tfm_gens: \n{}".format(
"\n".join([" - {}".format(x) for x in self.tfm_gens])
),
]
)
)
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