[英]Keras Generator to tf.data.Dataset
I am working with the Mask RCNN keras implementation but the data generator hard locks on my systems when using use_multiprocessing=True
.我正在使用 Mask RCNN keras 实现,但使用use_multiprocessing=True
时数据生成器会在我的系统上硬锁定。 The data generator runs fine in single thread.数据生成器在单线程中运行良好。 I am trying to convert the data generator to a tf.data.Dataset
as recommended by tensorflow.我正在尝试按照tf.data.Dataset
的建议将数据生成器转换为 tf.data.Dataset。 I have no idea how to do this and have been unable to find any documentation on this.我不知道如何做到这一点,也无法找到任何有关此的文档。
Mask RCNN data generator: Mask RCNN 数据生成器:
class DataGenerator(KU.Sequence):
"""An iterable that returns images and corresponding target class ids,
bounding box deltas, and masks. It inherits from keras.utils.Sequence to avoid data redundancy
when multiprocessing=True.
dataset: The Dataset object to pick data from
config: The model config object
shuffle: If True, shuffles the samples before every epoch
augmentation: Optional. An imgaug (https://github.com/aleju/imgaug) augmentation.
For example, passing imgaug.augmenters.Fliplr(0.5) flips images
right/left 50% of the time.
random_rois: If > 0 then generate proposals to be used to train the
network classifier and mask heads. Useful if training
the Mask RCNN part without the RPN.
detection_targets: If True, generate detection targets (class IDs, bbox
deltas, and masks). Typically for debugging or visualizations because
in trainig detection targets are generated by DetectionTargetLayer.
Returns a Python iterable. Upon calling __getitem__() on it, the
iterable returns two lists, inputs and outputs. The contents
of the lists differ depending on the received arguments:
inputs list:
- images: [batch, H, W, C]
- image_meta: [batch, (meta data)] Image details. See compose_image_meta()
- rpn_match: [batch, N] Integer (1=positive anchor, -1=negative, 0=neutral)
- rpn_bbox: [batch, N, (dy, dx, log(dh), log(dw))] Anchor bbox deltas.
- gt_class_ids: [batch, MAX_GT_INSTANCES] Integer class IDs
- gt_boxes: [batch, MAX_GT_INSTANCES, (y1, x1, y2, x2)]
- gt_masks: [batch, height, width, MAX_GT_INSTANCES]. The height and width
are those of the image unless use_mini_mask is True, in which
case they are defined in MINI_MASK_SHAPE.
outputs list: Usually empty in regular training. But if detection_targets
is True then the outputs list contains target class_ids, bbox deltas,
and masks.
"""
def __init__(self, dataset, config, shuffle=True, augmentation=None,
random_rois=0, detection_targets=False):
self.image_ids = np.copy(dataset.image_ids)
self.dataset = dataset
self.config = config
# Anchors
# [anchor_count, (y1, x1, y2, x2)]
self.backbone_shapes = compute_backbone_shapes(config, config.IMAGE_SHAPE)
self.anchors = utils.generate_pyramid_anchors(config.RPN_ANCHOR_SCALES,
config.RPN_ANCHOR_RATIOS,
self.backbone_shapes,
config.BACKBONE_STRIDES,
config.RPN_ANCHOR_STRIDE)
self.shuffle = shuffle
self.augmentation = augmentation
self.random_rois = random_rois
self.batch_size = self.config.BATCH_SIZE
self.detection_targets = detection_targets
def __len__(self):
return int(np.ceil(len(self.image_ids) / float(self.batch_size)))
def __getitem__(self, idx):
b = 0
image_index = -1
while b < self.batch_size:
# Increment index to pick next image. Shuffle if at the start of an epoch.
image_index = (image_index + 1) % len(self.image_ids)
if self.shuffle and image_index == 0:
np.random.shuffle(self.image_ids)
# Get GT bounding boxes and masks for image.
image_id = self.image_ids[image_index]
image, image_meta, gt_class_ids, gt_boxes, gt_masks = \
load_image_gt(self.dataset, self.config, image_id,
augmentation=self.augmentation)
# Skip images that have no instances. This can happen in cases
# where we train on a subset of classes and the image doesn't
# have any of the classes we care about.
if not np.any(gt_class_ids > 0):
continue
# RPN Targets
rpn_match, rpn_bbox = build_rpn_targets(image.shape, self.anchors,
gt_class_ids, gt_boxes, self.config)
# Mask R-CNN Targets
if self.random_rois:
rpn_rois = generate_random_rois(
image.shape, self.random_rois, gt_class_ids, gt_boxes)
if self.detection_targets:
rois, mrcnn_class_ids, mrcnn_bbox, mrcnn_mask = \
build_detection_targets(
rpn_rois, gt_class_ids, gt_boxes, gt_masks, self.config)
# Init batch arrays
if b == 0:
batch_image_meta = np.zeros((self.batch_size,) + image_meta.shape, dtype=image_meta.dtype)
batch_rpn_match = np.zeros([self.batch_size, self.anchors.shape[0], 1], dtype=rpn_match.dtype)
batch_rpn_bbox = np.zeros([self.batch_size, self.config.RPN_TRAIN_ANCHORS_PER_IMAGE, 4], dtype=rpn_bbox.dtype)
batch_images = np.zeros((self.batch_size,) + image.shape, dtype=np.float32)
batch_gt_class_ids = np.zeros((self.batch_size, self.config.MAX_GT_INSTANCES), dtype=np.int32)
batch_gt_boxes = np.zeros((self.batch_size, self.config.MAX_GT_INSTANCES, 4), dtype=np.int32)
batch_gt_masks = np.zeros((self.batch_size, gt_masks.shape[0], gt_masks.shape[1],self.config.MAX_GT_INSTANCES), dtype=gt_masks.dtype)
if self.random_rois:
batch_rpn_rois = np.zeros((self.batch_size, rpn_rois.shape[0], 4), dtype=rpn_rois.dtype)
if self.detection_targets:
batch_rois = np.zeros((self.batch_size,) + rois.shape, dtype=rois.dtype)
batch_mrcnn_class_ids = np.zeros((self.batch_size,) + mrcnn_class_ids.shape, dtype=mrcnn_class_ids.dtype)
batch_mrcnn_bbox = np.zeros((self.batch_size,) + mrcnn_bbox.shape, dtype=mrcnn_bbox.dtype)
batch_mrcnn_mask = np.zeros((self.batch_size,) + mrcnn_mask.shape, dtype=mrcnn_mask.dtype)
# If more instances than fits in the array, sub-sample from them.
if gt_boxes.shape[0] > self.config.MAX_GT_INSTANCES:
ids = np.random.choice(
np.arange(gt_boxes.shape[0]), self.config.MAX_GT_INSTANCES, replace=False)
gt_class_ids = gt_class_ids[ids]
gt_boxes = gt_boxes[ids]
gt_masks = gt_masks[:, :, ids]
# Add to batch
batch_image_meta[b] = image_meta
batch_rpn_match[b] = rpn_match[:, np.newaxis]
batch_rpn_bbox[b] = rpn_bbox
batch_images[b] = mold_image(image.astype(np.float32), self.config)
batch_gt_class_ids[b, :gt_class_ids.shape[0]] = gt_class_ids
batch_gt_boxes[b, :gt_boxes.shape[0]] = gt_boxes
batch_gt_masks[b, :, :, :gt_masks.shape[-1]] = gt_masks
if self.random_rois:
batch_rpn_rois[b] = rpn_rois
if self.detection_targets:
batch_rois[b] = rois
batch_mrcnn_class_ids[b] = mrcnn_class_ids
batch_mrcnn_bbox[b] = mrcnn_bbox
batch_mrcnn_mask[b] = mrcnn_mask
b += 1
inputs = [batch_images, batch_image_meta, batch_rpn_match, batch_rpn_bbox,
batch_gt_class_ids, batch_gt_boxes, batch_gt_masks]
outputs = []
if self.random_rois:
inputs.extend([batch_rpn_rois])
if self.detection_targets:
inputs.extend([batch_rois])
# Keras requires that output and targets have the same number of dimensions
batch_mrcnn_class_ids = np.expand_dims(
batch_mrcnn_class_ids, -1)
outputs.extend(
[batch_mrcnn_class_ids, batch_mrcnn_bbox, batch_mrcnn_mask])
return inputs, outputs
I have tried to use the tf.data.Dataset.from_generator()
however it requires the output_types=
argument and the Mask RCNN outputs a number of lists, I can not figure out how to define output_types=
.我尝试使用tf.data.Dataset.from_generator()
但是它需要output_types=
参数并且 Mask RCNN 输出了许多列表,我不知道如何定义output_types=
。
I am using python3.7
, keras==2.2.5
, tensorflow==2.2.0
我正在使用python3.7
, keras==2.2.5
, tensorflow==2.2.0
In documentation ( https://www.tensorflow.org/api_docs/python/tf/data/Dataset#from_generator ), is stated that you can use output_types=None
as an argument.在文档( https://www.tensorflow.org/api_docs/python/tf/data/Dataset#from_generator )中,声明您可以使用output_types=None
作为参数。 Did you try this or something else?你试过这个还是别的?
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