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Commit 598486d6 authored by Yanghan Wang's avatar Yanghan Wang Committed by Facebook GitHub Bot
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fix import prof test 

Reviewed By: newstzpz

Differential Revision: D32301322

fbshipit-source-id: a9e951b9de600012125b8b94c0c1ace929b491b8
parent 9c1c356c
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d2go/data/dataset_mappers/d2go_dataset_mapper.py
View file @ 598486d6
#!/usr/bin/env python3 #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
from .build import D2GO_DATA_MAPPER_REGISTRY
import copy from .d2go_dataset_mapper_impl import (
import logging D2GoDatasetMapper,
PREFETCHED_FILE_NAME,
import numpy as np PREFETCHED_SEM_SEG_FILE_NAME,
import torch
from d2go.data.dataset_mappers.data_reading import (
read_image_with_prefetch, read_image_with_prefetch,
read_sem_seg_file_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
return utils.convert_PIL_to_numpy(image, format) __all__ = [
"D2GoDatasetMapper",
"PREFETCHED_FILE_NAME",
"PREFETCHED_SEM_SEG_FILE_NAME",
"read_image_with_prefetch",
"read_sem_seg_file_with_prefetch",
]
def _custom_transform(self, image, dataset_dict):
"""
Override this method to inject custom transform.
"""
return image, dataset_dict
def __repr__(self): # NOTE: D2GoDatasetMapper might have different versions between internal and oss code,
return ( # which causes double-registration if the class is registered at declaration.
self.__class__.__name__ D2GO_DATA_MAPPER_REGISTRY.register(D2GoDatasetMapper)
+ ":\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 0 → 100644
View file @ 598486d6
#!/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])
),
]
)
)
d2go/runner/debug_runner.py
View file @ 598486d6
...@@ -8,10 +8,8 @@ import random ...@@ -8,10 +8,8 @@ import random
import torch import torch
import torch.nn as nn import torch.nn as nn
from d2go.modeling.quantization import QATCheckpointer from d2go.modeling.quantization import QATCheckpointer
from d2go.runner.default_runner import ( from d2go.runner.default_runner import BaseRunner
BaseRunner, from d2go.utils.get_default_cfg import add_tensorboard_default_configs
add_tensorboard_default_configs,
)
from detectron2.utils.file_io import PathManager from detectron2.utils.file_io import PathManager
......
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