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Commit 9a264c9f authored by Yeqing Li's avatar Yeqing Li Committed by A. Unique TensorFlower
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Deprecating official/vision/detection folder. 

The folder is archived in the official/legacy/detection

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official/vision/detection/README.md
View file @ 9a264c9f
# Object Detection Models on TensorFlow 2
**WARNING**: This repository will be deprecated and replaced by the solid
implementations inside vision/beta/.
This repository is deprecated and replaced by the solid
implementations inside vision/beta/. All the content has been moved to
[official/legacy/detection](https://github.com/tensorflow/models/tree/master/official/legacy/detection).
## Prerequsite
To get started, download the code from TensorFlow models GitHub repository or
use the pre-installed Google Cloud VM.
```bash
git clone https://github.com/tensorflow/models.git
```
Next, make sure to use TensorFlow 2.1+ on Google Cloud. Also here are
a few package you need to install to get started:
```bash
sudo apt-get install -y python-tk && \
pip3 install -r ~/models/official/requirements.txt
```
## Train RetinaNet on TPU
### Train a vanilla ResNet-50 based RetinaNet.
```bash
TPU_NAME="<your GCP TPU name>"
MODEL_DIR="<path to the directory to store model files>"
RESNET_CHECKPOINT="<path to the pre-trained Resnet-50 checkpoint>"
TRAIN_FILE_PATTERN="<path to the TFRecord training data>"
EVAL_FILE_PATTERN="<path to the TFRecord validation data>"
VAL_JSON_FILE="<path to the validation annotation JSON file>"
python3 ~/models/official/vision/detection/main.py \
--strategy_type=tpu \
--tpu="${TPU_NAME?}" \
--model_dir="${MODEL_DIR?}" \
--mode=train \
--params_override="{ type: retinanet, train: { checkpoint: { path: ${RESNET_CHECKPOINT?}, prefix: resnet50/ }, train_file_pattern: ${TRAIN_FILE_PATTERN?} }, eval: { val_json_file: ${VAL_JSON_FILE?}, eval_file_pattern: ${EVAL_FILE_PATTERN?} } }"
```
The pre-trained ResNet-50 checkpoint can be downloaded [here](https://storage.cloud.google.com/cloud-tpu-checkpoints/model-garden-vision/detection/resnet50-2018-02-07.tar.gz).
Note: The ResNet implementation under
[detection/](https://github.com/tensorflow/models/tree/master/official/vision/detection)
is currently different from the one under
[classification/](https://github.com/tensorflow/models/tree/master/official/vision/image_classification),
so the checkpoints are not compatible.
We will unify the implementation soon.
### Train a SpineNet-49 based RetinaNet.
```bash
TPU_NAME="<your GCP TPU name>"
MODEL_DIR="<path to the directory to store model files>"
TRAIN_FILE_PATTERN="<path to the TFRecord training data>"
EVAL_FILE_PATTERN="<path to the TFRecord validation data>"
VAL_JSON_FILE="<path to the validation annotation JSON file>"
python3 ~/models/official/vision/detection/main.py \
--strategy_type=tpu \
--tpu="${TPU_NAME?}" \
--model_dir="${MODEL_DIR?}" \
--mode=train \
--params_override="{ type: retinanet, architecture: {backbone: spinenet, multilevel_features: identity}, spinenet: {model_id: 49}, train_file_pattern: ${TRAIN_FILE_PATTERN?} }, eval: { val_json_file: ${VAL_JSON_FILE?}, eval_file_pattern: ${EVAL_FILE_PATTERN?} } }"
```
### Train a custom RetinaNet using the config file.
First, create a YAML config file, e.g. *my_retinanet.yaml*. This file specifies
the parameters to be overridden, which should at least include the following
fields.
```YAML
# my_retinanet.yaml
type: 'retinanet'
train:
train_file_pattern: <path to the TFRecord training data>
eval:
eval_file_pattern: <path to the TFRecord validation data>
val_json_file: <path to the validation annotation JSON file>
```
Once the YAML config file is created, you can launch the training using the
following command.
```bash
TPU_NAME="<your GCP TPU name>"
MODEL_DIR="<path to the directory to store model files>"
python3 ~/models/official/vision/detection/main.py \
--strategy_type=tpu \
--tpu="${TPU_NAME?}" \
--model_dir="${MODEL_DIR?}" \
--mode=train \
--config_file="my_retinanet.yaml"
```
## Train RetinaNet on GPU
Training on GPU is similar to that on TPU. The major change is the strategy
type (use "[mirrored](https://www.tensorflow.org/api_docs/python/tf/distribute/MirroredStrategy)" for multiple GPU and
"[one_device](https://www.tensorflow.org/api_docs/python/tf/distribute/OneDeviceStrategy)" for single GPU).
Multi-GPUs example (assuming there are 8GPU connected to the host):
```bash
MODEL_DIR="<path to the directory to store model files>"
python3 ~/models/official/vision/detection/main.py \
--strategy_type=mirrored \
--num_gpus=8 \
--model_dir="${MODEL_DIR?}" \
--mode=train \
--config_file="my_retinanet.yaml"
```
```bash
MODEL_DIR="<path to the directory to store model files>"
python3 ~/models/official/vision/detection/main.py \
--strategy_type=one_device \
--num_gpus=1 \
--model_dir="${MODEL_DIR?}" \
--mode=train \
--config_file="my_retinanet.yaml"
```
An example with inline configuration (YAML or JSON format):
```
python3 ~/models/official/vision/detection/main.py \
--model_dir=<model folder> \
--strategy_type=one_device \
--num_gpus=1 \
--mode=train \
--params_override="eval:
eval_file_pattern: <Eval TFRecord file pattern>
batch_size: 8
val_json_file: <COCO format groundtruth JSON file>
predict:
predict_batch_size: 8
architecture:
use_bfloat16: False
train:
total_steps: 1
batch_size: 8
train_file_pattern: <Eval TFRecord file pattern>
use_tpu: False
"
```
---
## Train Mask R-CNN on TPU
### Train a vanilla ResNet-50 based Mask R-CNN.
```bash
TPU_NAME="<your GCP TPU name>"
MODEL_DIR="<path to the directory to store model files>"
RESNET_CHECKPOINT="<path to the pre-trained Resnet-50 checkpoint>"
TRAIN_FILE_PATTERN="<path to the TFRecord training data>"
EVAL_FILE_PATTERN="<path to the TFRecord validation data>"
VAL_JSON_FILE="<path to the validation annotation JSON file>"
python3 ~/models/official/vision/detection/main.py \
--strategy_type=tpu \
--tpu=${TPU_NAME} \
--model_dir=${MODEL_DIR} \
--mode=train \
--model=mask_rcnn \
--params_override="{train: { checkpoint: { path: ${RESNET_CHECKPOINT}, prefix: resnet50/ }, train_file_pattern: ${TRAIN_FILE_PATTERN} }, eval: { val_json_file: ${VAL_JSON_FILE}, eval_file_pattern: ${EVAL_FILE_PATTERN} } }"
```
The pre-trained ResNet-50 checkpoint can be downloaded [here](https://storage.cloud.google.com/cloud-tpu-checkpoints/model-garden-vision/detection/resnet50-2018-02-07.tar.gz).
Note: The ResNet implementation under
[detection/](https://github.com/tensorflow/models/tree/master/official/vision/detection)
is currently different from the one under
[classification/](https://github.com/tensorflow/models/tree/master/official/vision/image_classification),
so the checkpoints are not compatible.
We will unify the implementation soon.
### Train a SpineNet-49 based Mask R-CNN.
```bash
TPU_NAME="<your GCP TPU name>"
MODEL_DIR="<path to the directory to store model files>"
TRAIN_FILE_PATTERN="<path to the TFRecord training data>"
EVAL_FILE_PATTERN="<path to the TFRecord validation data>"
VAL_JSON_FILE="<path to the validation annotation JSON file>"
python3 ~/models/official/vision/detection/main.py \
--strategy_type=tpu \
--tpu="${TPU_NAME?}" \
--model_dir="${MODEL_DIR?}" \
--mode=train \
--model=mask_rcnn \
--params_override="{architecture: {backbone: spinenet, multilevel_features: identity}, spinenet: {model_id: 49}, train_file_pattern: ${TRAIN_FILE_PATTERN?} }, eval: { val_json_file: ${VAL_JSON_FILE?}, eval_file_pattern: ${EVAL_FILE_PATTERN?} } }"
```
### Train a custom Mask R-CNN using the config file.
First, create a YAML config file, e.g. *my_maskrcnn.yaml*.
This file specifies the parameters to be overridden,
which should at least include the following fields.
```YAML
# my_maskrcnn.yaml
train:
train_file_pattern: <path to the TFRecord training data>
eval:
eval_file_pattern: <path to the TFRecord validation data>
val_json_file: <path to the validation annotation JSON file>
```
Once the YAML config file is created, you can launch the training using the
following command.
```bash
TPU_NAME="<your GCP TPU name>"
MODEL_DIR="<path to the directory to store model files>"
python3 ~/models/official/vision/detection/main.py \
--strategy_type=tpu \
--tpu=${TPU_NAME} \
--model_dir=${MODEL_DIR} \
--mode=train \
--model=mask_rcnn \
--config_file="my_maskrcnn.yaml"
```
## Train Mask R-CNN on GPU
Training on GPU is similar to that on TPU. The major change is the strategy type
(use
"[mirrored](https://www.tensorflow.org/api_docs/python/tf/distribute/MirroredStrategy)"
for multiple GPU and
"[one_device](https://www.tensorflow.org/api_docs/python/tf/distribute/OneDeviceStrategy)"
for single GPU).
Multi-GPUs example (assuming there are 8GPU connected to the host):
```bash
MODEL_DIR="<path to the directory to store model files>"
python3 ~/models/official/vision/detection/main.py \
--strategy_type=mirrored \
--num_gpus=8 \
--model_dir=${MODEL_DIR} \
--mode=train \
--model=mask_rcnn \
--config_file="my_maskrcnn.yaml"
```
```bash
MODEL_DIR="<path to the directory to store model files>"
python3 ~/models/official/vision/detection/main.py \
--strategy_type=one_device \
--num_gpus=1 \
--model_dir=${MODEL_DIR} \
--mode=train \
--model=mask_rcnn \
--config_file="my_maskrcnn.yaml"
```
An example with inline configuration (YAML or JSON format):
```
python3 ~/models/official/vision/detection/main.py \
--model_dir=<model folder> \
--strategy_type=one_device \
--num_gpus=1 \
--mode=train \
--model=mask_rcnn \
--params_override="eval:
eval_file_pattern: <Eval TFRecord file pattern>
batch_size: 8
val_json_file: <COCO format groundtruth JSON file>
predict:
predict_batch_size: 8
architecture:
use_bfloat16: False
train:
total_steps: 1000
batch_size: 8
train_file_pattern: <Eval TFRecord file pattern>
use_tpu: False
"
```
## Train ShapeMask on TPU
### Train a ResNet-50 based ShapeMask.
```bash
TPU_NAME="<your GCP TPU name>"
MODEL_DIR="<path to the directory to store model files>"
RESNET_CHECKPOINT="<path to the pre-trained Resnet-50 checkpoint>"
TRAIN_FILE_PATTERN="<path to the TFRecord training data>"
EVAL_FILE_PATTERN="<path to the TFRecord validation data>"
VAL_JSON_FILE="<path to the validation annotation JSON file>"
SHAPE_PRIOR_PATH="<path to shape priors>"
python3 ~/models/official/vision/detection/main.py \
--strategy_type=tpu \
--tpu=${TPU_NAME} \
--model_dir=${MODEL_DIR} \
--mode=train \
--model=shapemask \
--params_override="{train: { checkpoint: { path: ${RESNET_CHECKPOINT}, prefix: resnet50/ }, train_file_pattern: ${TRAIN_FILE_PATTERN} }, eval: { val_json_file: ${VAL_JSON_FILE}, eval_file_pattern: ${EVAL_FILE_PATTERN} } shapemask_head: {use_category_for_mask: true, shape_prior_path: ${SHAPE_PRIOR_PATH}} }"
```
The pre-trained ResNet-50 checkpoint can be downloaded [here](https://storage.cloud.google.com/cloud-tpu-checkpoints/model-garden-vision/detection/resnet50-2018-02-07.tar.gz).
The shape priors can be downloaded [here]
(https://storage.googleapis.com/cloud-tpu-checkpoints/shapemask/kmeans_class_priors_91x20x32x32.npy)
### Train a custom ShapeMask using the config file.
First, create a YAML config file, e.g. *my_shapemask.yaml*.
This file specifies the parameters to be overridden:
```YAML
# my_shapemask.yaml
train:
train_file_pattern: <path to the TFRecord training data>
total_steps: <total steps to train>
batch_size: <training batch size>
eval:
eval_file_pattern: <path to the TFRecord validation data>
val_json_file: <path to the validation annotation JSON file>
batch_size: <evaluation batch size>
shapemask_head:
shape_prior_path: <path to shape priors>
```
Once the YAML config file is created, you can launch the training using the
following command.
```bash
TPU_NAME="<your GCP TPU name>"
MODEL_DIR="<path to the directory to store model files>"
python3 ~/models/official/vision/detection/main.py \
--strategy_type=tpu \
--tpu=${TPU_NAME} \
--model_dir=${MODEL_DIR} \
--mode=train \
--model=shapemask \
--config_file="my_shapemask.yaml"
```
## Train ShapeMask on GPU
Training on GPU is similar to that on TPU. The major change is the strategy type
(use
"[mirrored](https://www.tensorflow.org/api_docs/python/tf/distribute/MirroredStrategy)"
for multiple GPU and
"[one_device](https://www.tensorflow.org/api_docs/python/tf/distribute/OneDeviceStrategy)"
for single GPU).
Multi-GPUs example (assuming there are 8GPU connected to the host):
```bash
MODEL_DIR="<path to the directory to store model files>"
python3 ~/models/official/vision/detection/main.py \
--strategy_type=mirrored \
--num_gpus=8 \
--model_dir=${MODEL_DIR} \
--mode=train \
--model=shapemask \
--config_file="my_shapemask.yaml"
```
A single GPU example
```bash
MODEL_DIR="<path to the directory to store model files>"
python3 ~/models/official/vision/detection/main.py \
--strategy_type=one_device \
--num_gpus=1 \
--model_dir=${MODEL_DIR} \
--mode=train \
--model=shapemask \
--config_file="my_shapemask.yaml"
```
An example with inline configuration (YAML or JSON format):
```
python3 ~/models/official/vision/detection/main.py \
--model_dir=<model folder> \
--strategy_type=one_device \
--num_gpus=1 \
--mode=train \
--model=shapemask \
--params_override="eval:
eval_file_pattern: <Eval TFRecord file pattern>
batch_size: 8
val_json_file: <COCO format groundtruth JSON file>
train:
total_steps: 1000
batch_size: 8
train_file_pattern: <Eval TFRecord file pattern>
use_tpu: False
"
```
### Run the evaluation (after training)
```
python3 /usr/share/models/official/vision/detection/main.py \
--strategy_type=tpu \
--tpu=${TPU_NAME} \
--model_dir=${MODEL_DIR} \
--mode=eval \
--model=shapemask \
--params_override="{eval: { val_json_file: ${VAL_JSON_FILE}, eval_file_pattern: ${EVAL_FILE_PATTERN}, eval_samples: 5000 } }"
```
`MODEL_DIR` needs to point to the trained path of ShapeMask model.
Change `strategy_type=mirrored` and `num_gpus=1` to run on a GPU.
Note: The JSON groundtruth file is useful for [COCO dataset](http://cocodataset.org/#home) and can be
downloaded from the [COCO website](http://cocodataset.org/#download). For custom dataset, it is unncessary because the groundtruth can be included in the TFRecord files.
## References
1. [Focal Loss for Dense Object Detection](https://arxiv.org/abs/1708.02002).
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár. IEEE
International Conference on Computer Vision (ICCV), 2017.
official/vision/detection/__init__.py
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......@@ -12,3 +12,5 @@
# See the License for the specific language governing permissions and
# limitations under the License.
"""Deprecating the vision/detection folder."""
raise ImportError('This module has been moved to official/legacy/detection')
official/vision/detection/configs/__init__.py deleted 100644 → 0
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# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
official/vision/detection/configs/base_config.py deleted 100644 → 0
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# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Base config template."""
BACKBONES = [
'resnet',
'spinenet',
]
MULTILEVEL_FEATURES = [
'fpn',
'identity',
]
# pylint: disable=line-too-long
# For ResNet, this freezes the variables of the first conv1 and conv2_x
# layers [1], which leads to higher training speed and slightly better testing
# accuracy. The intuition is that the low-level architecture (e.g., ResNet-50)
# is able to capture low-level features such as edges; therefore, it does not
# need to be fine-tuned for the detection task.
# Note that we need to trailing `/` to avoid the incorrect match.
# [1]: https://github.com/facebookresearch/Detectron/blob/master/detectron/core/config.py#L198
RESNET_FROZEN_VAR_PREFIX = r'(resnet\d+)\/(conv2d(|_([1-9]|10))|batch_normalization(|_([1-9]|10)))\/'
REGULARIZATION_VAR_REGEX = r'.*(kernel|weight):0$'
BASE_CFG = {
'model_dir': '',
'use_tpu': True,
'strategy_type': 'tpu',
'isolate_session_state': False,
'train': {
'iterations_per_loop': 100,
'batch_size': 64,
'total_steps': 22500,
'num_cores_per_replica': None,
'input_partition_dims': None,
'optimizer': {
'type': 'momentum',
'momentum': 0.9,
'nesterov': True, # `False` is better for TPU v3-128.
},
'learning_rate': {
'type': 'step',
'warmup_learning_rate': 0.0067,
'warmup_steps': 500,
'init_learning_rate': 0.08,
'learning_rate_levels': [0.008, 0.0008],
'learning_rate_steps': [15000, 20000],
},
'checkpoint': {
'path': '',
'prefix': '',
},
# One can use 'RESNET_FROZEN_VAR_PREFIX' to speed up ResNet training
# when loading from the checkpoint.
'frozen_variable_prefix': '',
'train_file_pattern': '',
'train_dataset_type': 'tfrecord',
# TODO(b/142174042): Support transpose_input option.
'transpose_input': False,
'regularization_variable_regex': REGULARIZATION_VAR_REGEX,
'l2_weight_decay': 0.0001,
'gradient_clip_norm': 0.0,
'input_sharding': False,
},
'eval': {
'input_sharding': True,
'batch_size': 8,
'eval_samples': 5000,
'min_eval_interval': 180,
'eval_timeout': None,
'num_steps_per_eval': 1000,
'type': 'box',
'use_json_file': True,
'val_json_file': '',
'eval_file_pattern': '',
'eval_dataset_type': 'tfrecord',
# When visualizing images, set evaluation batch size to 40 to avoid
# potential OOM.
'num_images_to_visualize': 0,
},
'predict': {
'batch_size': 8,
},
'architecture': {
'backbone': 'resnet',
'min_level': 3,
'max_level': 7,
'multilevel_features': 'fpn',
'use_bfloat16': True,
# Note that `num_classes` is the total number of classes including
# one background classes whose index is 0.
'num_classes': 91,
},
'anchor': {
'num_scales': 3,
'aspect_ratios': [1.0, 2.0, 0.5],
'anchor_size': 4.0,
},
'norm_activation': {
'activation': 'relu',
'batch_norm_momentum': 0.997,
'batch_norm_epsilon': 1e-4,
'batch_norm_trainable': True,
'use_sync_bn': False,
},
'resnet': {
'resnet_depth': 50,
},
'spinenet': {
'model_id': '49',
},
'fpn': {
'fpn_feat_dims': 256,
'use_separable_conv': False,
'use_batch_norm': True,
},
'postprocess': {
'use_batched_nms': False,
'max_total_size': 100,
'nms_iou_threshold': 0.5,
'score_threshold': 0.05,
'pre_nms_num_boxes': 5000,
},
'enable_summary': False,
}
# pylint: enable=line-too-long
official/vision/detection/configs/factory.py deleted 100644 → 0
View file @ 8fb4e6a6
# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Factory to provide model configs."""
from official.modeling.hyperparams import params_dict
from official.vision.detection.configs import maskrcnn_config
from official.vision.detection.configs import olnmask_config
from official.vision.detection.configs import retinanet_config
from official.vision.detection.configs import shapemask_config
def config_generator(model):
"""Model function generator."""
if model == 'retinanet':
default_config = retinanet_config.RETINANET_CFG
restrictions = retinanet_config.RETINANET_RESTRICTIONS
elif model == 'mask_rcnn':
default_config = maskrcnn_config.MASKRCNN_CFG
restrictions = maskrcnn_config.MASKRCNN_RESTRICTIONS
elif model == 'olnmask':
default_config = olnmask_config.OLNMASK_CFG
restrictions = olnmask_config.OLNMASK_RESTRICTIONS
elif model == 'shapemask':
default_config = shapemask_config.SHAPEMASK_CFG
restrictions = shapemask_config.SHAPEMASK_RESTRICTIONS
else:
raise ValueError('Model %s is not supported.' % model)
return params_dict.ParamsDict(default_config, restrictions)
official/vision/detection/configs/maskrcnn_config.py deleted 100644 → 0
View file @ 8fb4e6a6
# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Config template to train Mask R-CNN."""
from official.modeling.hyperparams import params_dict
from official.vision.detection.configs import base_config
# pylint: disable=line-too-long
MASKRCNN_CFG = params_dict.ParamsDict(base_config.BASE_CFG)
MASKRCNN_CFG.override({
'type': 'mask_rcnn',
'eval': {
'type': 'box_and_mask',
'num_images_to_visualize': 0,
},
'architecture': {
'parser': 'maskrcnn_parser',
'min_level': 2,
'max_level': 6,
'include_mask': True,
'mask_target_size': 28,
},
'maskrcnn_parser': {
'output_size': [1024, 1024],
'num_channels': 3,
'rpn_match_threshold': 0.7,
'rpn_unmatched_threshold': 0.3,
'rpn_batch_size_per_im': 256,
'rpn_fg_fraction': 0.5,
'aug_rand_hflip': True,
'aug_scale_min': 1.0,
'aug_scale_max': 1.0,
'skip_crowd_during_training': True,
'max_num_instances': 100,
'mask_crop_size': 112,
},
'anchor': {
'num_scales': 1,
'anchor_size': 8,
},
'rpn_head': {
'num_convs': 2,
'num_filters': 256,
'use_separable_conv': False,
'use_batch_norm': False,
},
'frcnn_head': {
'num_convs': 0,
'num_filters': 256,
'use_separable_conv': False,
'num_fcs': 2,
'fc_dims': 1024,
'use_batch_norm': False,
},
'mrcnn_head': {
'num_convs': 4,
'num_filters': 256,
'use_separable_conv': False,
'use_batch_norm': False,
},
'rpn_score_loss': {
'rpn_batch_size_per_im': 256,
},
'rpn_box_loss': {
'huber_loss_delta': 1.0 / 9.0,
},
'frcnn_box_loss': {
'huber_loss_delta': 1.0,
},
'roi_proposal': {
'rpn_pre_nms_top_k': 2000,
'rpn_post_nms_top_k': 1000,
'rpn_nms_threshold': 0.7,
'rpn_score_threshold': 0.0,
'rpn_min_size_threshold': 0.0,
'test_rpn_pre_nms_top_k': 1000,
'test_rpn_post_nms_top_k': 1000,
'test_rpn_nms_threshold': 0.7,
'test_rpn_score_threshold': 0.0,
'test_rpn_min_size_threshold': 0.0,
'use_batched_nms': False,
},
'roi_sampling': {
'num_samples_per_image': 512,
'fg_fraction': 0.25,
'fg_iou_thresh': 0.5,
'bg_iou_thresh_hi': 0.5,
'bg_iou_thresh_lo': 0.0,
'mix_gt_boxes': True,
},
'mask_sampling': {
'num_mask_samples_per_image': 128, # Typically = `num_samples_per_image` * `fg_fraction`.
},
'postprocess': {
'pre_nms_num_boxes': 1000,
},
}, is_strict=False)
MASKRCNN_RESTRICTIONS = [
]
# pylint: enable=line-too-long
official/vision/detection/configs/olnmask_config.py deleted 100644 → 0
View file @ 8fb4e6a6
# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Config template to train Object Localization Network (OLN)."""
from official.modeling.hyperparams import params_dict
from official.vision.detection.configs import base_config
# pylint: disable=line-too-long
OLNMASK_CFG = params_dict.ParamsDict(base_config.BASE_CFG)
OLNMASK_CFG.override({
'type': 'olnmask',
'eval': {
'type': 'oln_xclass_box',
'use_category': False,
'seen_class': 'voc',
'num_images_to_visualize': 0,
},
'architecture': {
'parser': 'olnmask_parser',
'min_level': 2,
'max_level': 6,
'include_rpn_class': False,
'include_frcnn_class': False,
'include_frcnn_box': True,
'include_mask': False,
'mask_target_size': 28,
'num_classes': 2,
},
'olnmask_parser': {
'output_size': [640, 640],
'num_channels': 3,
'rpn_match_threshold': 0.7,
'rpn_unmatched_threshold': 0.3,
'rpn_batch_size_per_im': 256,
'rpn_fg_fraction': 0.5,
'aug_rand_hflip': True,
'aug_scale_min': 0.5,
'aug_scale_max': 2.0,
'skip_crowd_during_training': True,
'max_num_instances': 100,
'mask_crop_size': 112,
# centerness targets.
'has_centerness': True,
'rpn_center_match_iou_threshold': 0.3,
'rpn_center_unmatched_iou_threshold': 0.1,
'rpn_num_center_samples_per_im': 256,
# class manipulation.
'class_agnostic': True,
'train_class': 'voc',
},
'anchor': {
'num_scales': 1,
'aspect_ratios': [1.0],
'anchor_size': 8,
},
'rpn_head': {
'num_convs': 2,
'num_filters': 256,
'use_separable_conv': False,
'use_batch_norm': False,
# RPN-Centerness learning {
'has_centerness': True, # }
},
'frcnn_head': {
'num_convs': 0,
'num_filters': 256,
'use_separable_conv': False,
'num_fcs': 2,
'fc_dims': 1024,
'use_batch_norm': False,
'has_scoring': True,
},
'mrcnn_head': {
'num_convs': 4,
'num_filters': 256,
'use_separable_conv': False,
'use_batch_norm': False,
'has_scoring': False,
},
'rpn_score_loss': {
'rpn_batch_size_per_im': 256,
},
'rpn_box_loss': {
'huber_loss_delta': 1.0 / 9.0,
},
'frcnn_box_loss': {
'huber_loss_delta': 1.0,
},
'frcnn_box_score_loss': {
'ignore_threshold': 0.3,
},
'roi_proposal': {
'rpn_pre_nms_top_k': 2000,
'rpn_post_nms_top_k': 2000,
'rpn_nms_threshold': 0.7,
'rpn_score_threshold': 0.0,
'rpn_min_size_threshold': 0.0,
'test_rpn_pre_nms_top_k': 2000,
'test_rpn_post_nms_top_k': 2000,
'test_rpn_nms_threshold': 0.7,
'test_rpn_score_threshold': 0.0,
'test_rpn_min_size_threshold': 0.0,
'use_batched_nms': False,
},
'roi_sampling': {
'num_samples_per_image': 512,
'fg_fraction': 0.25,
'fg_iou_thresh': 0.5,
'bg_iou_thresh_hi': 0.5,
'bg_iou_thresh_lo': 0.0,
'mix_gt_boxes': True,
},
'mask_sampling': {
'num_mask_samples_per_image': 128, # Typically = `num_samples_per_image` * `fg_fraction`.
},
'postprocess': {
'use_batched_nms': False,
'max_total_size': 100,
'nms_iou_threshold': 0.5,
'score_threshold': 0.00,
'pre_nms_num_boxes': 2000,
},
}, is_strict=False)
OLNMASK_RESTRICTIONS = [
# 'anchor.aspect_ratios == [1.0]',
# 'anchor.scales == 1',
]
# pylint: enable=line-too-long
official/vision/detection/configs/retinanet_config.py deleted 100644 → 0
View file @ 8fb4e6a6
# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Config template to train Retinanet."""
from official.modeling.hyperparams import params_dict
from official.vision.detection.configs import base_config
# pylint: disable=line-too-long
RETINANET_CFG = params_dict.ParamsDict(base_config.BASE_CFG)
RETINANET_CFG.override({
'type': 'retinanet',
'architecture': {
'parser': 'retinanet_parser',
},
'retinanet_parser': {
'output_size': [640, 640],
'num_channels': 3,
'match_threshold': 0.5,
'unmatched_threshold': 0.5,
'aug_rand_hflip': True,
'aug_scale_min': 1.0,
'aug_scale_max': 1.0,
'use_autoaugment': False,
'autoaugment_policy_name': 'v0',
'skip_crowd_during_training': True,
'max_num_instances': 100,
},
'retinanet_head': {
'num_convs': 4,
'num_filters': 256,
'use_separable_conv': False,
},
'retinanet_loss': {
'focal_loss_alpha': 0.25,
'focal_loss_gamma': 1.5,
'huber_loss_delta': 0.1,
'box_loss_weight': 50,
},
'enable_summary': True,
}, is_strict=False)
RETINANET_RESTRICTIONS = [
]
# pylint: enable=line-too-long
official/vision/detection/configs/shapemask_config.py deleted 100644 → 0
View file @ 8fb4e6a6
# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Config to train shapemask on COCO."""
from official.modeling.hyperparams import params_dict
from official.vision.detection.configs import base_config
SHAPEMASK_RESNET_FROZEN_VAR_PREFIX = r'(conv2d(|_([1-9]|10))|batch_normalization(|_([1-9]|10)))\/'
SHAPEMASK_CFG = params_dict.ParamsDict(base_config.BASE_CFG)
SHAPEMASK_CFG.override({
'type': 'shapemask',
'architecture': {
'parser': 'shapemask_parser',
'backbone': 'resnet',
'multilevel_features': 'fpn',
'outer_box_scale': 1.25,
},
'train': {
'total_steps': 45000,
'learning_rate': {
'learning_rate_steps': [30000, 40000],
},
'frozen_variable_prefix': SHAPEMASK_RESNET_FROZEN_VAR_PREFIX,
'regularization_variable_regex': None,
},
'eval': {
'type': 'shapemask_box_and_mask',
'mask_eval_class': 'all', # 'all', 'voc', or 'nonvoc'.
},
'shapemask_parser': {
'output_size': [640, 640],
'num_channels': 3,
'match_threshold': 0.5,
'unmatched_threshold': 0.5,
'aug_rand_hflip': True,
'aug_scale_min': 0.8,
'aug_scale_max': 1.2,
'skip_crowd_during_training': True,
'max_num_instances': 100,
# Shapemask specific parameters
'mask_train_class': 'all', # 'all', 'voc', or 'nonvoc'.
'use_category': True,
'outer_box_scale': 1.25,
'num_sampled_masks': 8,
'mask_crop_size': 32,
'mask_min_level': 3,
'mask_max_level': 5,
'box_jitter_scale': 0.025,
'upsample_factor': 4,
},
'retinanet_head': {
'num_convs': 4,
'num_filters': 256,
'use_separable_conv': False,
'use_batch_norm': True,
},
'shapemask_head': {
'num_downsample_channels': 128,
'mask_crop_size': 32,
'use_category_for_mask': True,
'num_convs': 4,
'upsample_factor': 4,
'shape_prior_path': '',
},
'retinanet_loss': {
'focal_loss_alpha': 0.4,
'focal_loss_gamma': 1.5,
'huber_loss_delta': 0.15,
'box_loss_weight': 50,
},
'shapemask_loss': {
'shape_prior_loss_weight': 0.1,
'coarse_mask_loss_weight': 1.0,
'fine_mask_loss_weight': 1.0,
},
}, is_strict=False)
SHAPEMASK_RESTRICTIONS = [
'shapemask_head.mask_crop_size == shapemask_parser.mask_crop_size',
'shapemask_head.upsample_factor == shapemask_parser.upsample_factor',
'shapemask_parser.outer_box_scale == architecture.outer_box_scale',
]
# pylint: enable=line-too-long
official/vision/detection/dataloader/__init__.py deleted 100644 → 0
View file @ 8fb4e6a6
# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
official/vision/detection/dataloader/anchor.py deleted 100644 → 0
View file @ 8fb4e6a6
This diff is collapsed.
official/vision/detection/dataloader/factory.py deleted 100644 → 0
View file @ 8fb4e6a6
# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Model architecture factory."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from official.vision.detection.dataloader import maskrcnn_parser
from official.vision.detection.dataloader import olnmask_parser
from official.vision.detection.dataloader import retinanet_parser
from official.vision.detection.dataloader import shapemask_parser
def parser_generator(params, mode):
"""Generator function for various dataset parser."""
if params.architecture.parser == 'retinanet_parser':
anchor_params = params.anchor
parser_params = params.retinanet_parser
parser_fn = retinanet_parser.Parser(
output_size=parser_params.output_size,
min_level=params.architecture.min_level,
max_level=params.architecture.max_level,
num_scales=anchor_params.num_scales,
aspect_ratios=anchor_params.aspect_ratios,
anchor_size=anchor_params.anchor_size,
match_threshold=parser_params.match_threshold,
unmatched_threshold=parser_params.unmatched_threshold,
aug_rand_hflip=parser_params.aug_rand_hflip,
aug_scale_min=parser_params.aug_scale_min,
aug_scale_max=parser_params.aug_scale_max,
use_autoaugment=parser_params.use_autoaugment,
autoaugment_policy_name=parser_params.autoaugment_policy_name,
skip_crowd_during_training=parser_params.skip_crowd_during_training,
max_num_instances=parser_params.max_num_instances,
use_bfloat16=params.architecture.use_bfloat16,
mode=mode)
elif params.architecture.parser == 'maskrcnn_parser':
anchor_params = params.anchor
parser_params = params.maskrcnn_parser
parser_fn = maskrcnn_parser.Parser(
output_size=parser_params.output_size,
min_level=params.architecture.min_level,
max_level=params.architecture.max_level,
num_scales=anchor_params.num_scales,
aspect_ratios=anchor_params.aspect_ratios,
anchor_size=anchor_params.anchor_size,
rpn_match_threshold=parser_params.rpn_match_threshold,
rpn_unmatched_threshold=parser_params.rpn_unmatched_threshold,
rpn_batch_size_per_im=parser_params.rpn_batch_size_per_im,
rpn_fg_fraction=parser_params.rpn_fg_fraction,
aug_rand_hflip=parser_params.aug_rand_hflip,
aug_scale_min=parser_params.aug_scale_min,
aug_scale_max=parser_params.aug_scale_max,
skip_crowd_during_training=parser_params.skip_crowd_during_training,
max_num_instances=parser_params.max_num_instances,
include_mask=params.architecture.include_mask,
mask_crop_size=parser_params.mask_crop_size,
use_bfloat16=params.architecture.use_bfloat16,
mode=mode)
elif params.architecture.parser == 'olnmask_parser':
anchor_params = params.anchor
parser_params = params.olnmask_parser
parser_fn = olnmask_parser.Parser(
output_size=parser_params.output_size,
min_level=params.architecture.min_level,
max_level=params.architecture.max_level,
num_scales=anchor_params.num_scales,
aspect_ratios=anchor_params.aspect_ratios,
anchor_size=anchor_params.anchor_size,
rpn_match_threshold=parser_params.rpn_match_threshold,
rpn_unmatched_threshold=parser_params.rpn_unmatched_threshold,
rpn_batch_size_per_im=parser_params.rpn_batch_size_per_im,
rpn_fg_fraction=parser_params.rpn_fg_fraction,
aug_rand_hflip=parser_params.aug_rand_hflip,
aug_scale_min=parser_params.aug_scale_min,
aug_scale_max=parser_params.aug_scale_max,
skip_crowd_during_training=parser_params.skip_crowd_during_training,
max_num_instances=parser_params.max_num_instances,
include_mask=params.architecture.include_mask,
mask_crop_size=parser_params.mask_crop_size,
use_bfloat16=params.architecture.use_bfloat16,
mode=mode,
has_centerness=parser_params.has_centerness,
rpn_center_match_iou_threshold=(
parser_params.rpn_center_match_iou_threshold),
rpn_center_unmatched_iou_threshold=(
parser_params.rpn_center_unmatched_iou_threshold),
rpn_num_center_samples_per_im=(
parser_params.rpn_num_center_samples_per_im),
class_agnostic=parser_params.class_agnostic,
train_class=parser_params.train_class,)
elif params.architecture.parser == 'shapemask_parser':
anchor_params = params.anchor
parser_params = params.shapemask_parser
parser_fn = shapemask_parser.Parser(
output_size=parser_params.output_size,
min_level=params.architecture.min_level,
max_level=params.architecture.max_level,
num_scales=anchor_params.num_scales,
aspect_ratios=anchor_params.aspect_ratios,
anchor_size=anchor_params.anchor_size,
use_category=parser_params.use_category,
outer_box_scale=parser_params.outer_box_scale,
box_jitter_scale=parser_params.box_jitter_scale,
num_sampled_masks=parser_params.num_sampled_masks,
mask_crop_size=parser_params.mask_crop_size,
mask_min_level=parser_params.mask_min_level,
mask_max_level=parser_params.mask_max_level,
upsample_factor=parser_params.upsample_factor,
match_threshold=parser_params.match_threshold,
unmatched_threshold=parser_params.unmatched_threshold,
aug_rand_hflip=parser_params.aug_rand_hflip,
aug_scale_min=parser_params.aug_scale_min,
aug_scale_max=parser_params.aug_scale_max,
skip_crowd_during_training=parser_params.skip_crowd_during_training,
max_num_instances=parser_params.max_num_instances,
use_bfloat16=params.architecture.use_bfloat16,
mask_train_class=parser_params.mask_train_class,
mode=mode)
else:
raise ValueError('Parser %s is not supported.' % params.architecture.parser)
return parser_fn
official/vision/detection/dataloader/input_reader.py deleted 100644 → 0
View file @ 8fb4e6a6
# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Data loader and input processing."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from typing import Text, Optional
from official.modeling.hyperparams import params_dict
from official.vision.detection.dataloader import factory
from official.vision.detection.dataloader import mode_keys as ModeKeys
class InputFn(object):
"""Input function that creates dataset from files."""
def __init__(self,
file_pattern: Text,
params: params_dict.ParamsDict,
mode: Text,
batch_size: int,
num_examples: Optional[int] = -1):
"""Initialize.
Args:
file_pattern: the file pattern for the data example (TFRecords).
params: the parameter object for constructing example parser and model.
mode: ModeKeys.TRAIN or ModeKeys.Eval
batch_size: the data batch size.
num_examples: If positive, only takes this number of examples and raise
tf.errors.OutOfRangeError after that. If non-positive, it will be
ignored.
"""
assert file_pattern is not None
assert mode is not None
assert batch_size is not None
self._file_pattern = file_pattern
self._mode = mode
self._is_training = (mode == ModeKeys.TRAIN)
self._batch_size = batch_size
self._num_examples = num_examples
self._parser_fn = factory.parser_generator(params, mode)
self._dataset_fn = tf.data.TFRecordDataset
self._input_sharding = (not self._is_training)
try:
if self._is_training:
self._input_sharding = params.train.input_sharding
else:
self._input_sharding = params.eval.input_sharding
except AttributeError:
pass
def __call__(self, ctx=None, batch_size: int = None):
"""Provides tf.data.Dataset object.
Args:
ctx: context object.
batch_size: expected batch size input data.
Returns:
tf.data.Dataset object.
"""
if not batch_size:
batch_size = self._batch_size
assert batch_size is not None
dataset = tf.data.Dataset.list_files(
self._file_pattern, shuffle=self._is_training)
if self._input_sharding and ctx and ctx.num_input_pipelines > 1:
dataset = dataset.shard(ctx.num_input_pipelines, ctx.input_pipeline_id)
dataset = dataset.cache()
if self._is_training:
dataset = dataset.repeat()
dataset = dataset.interleave(
map_func=self._dataset_fn,
cycle_length=32,
num_parallel_calls=tf.data.experimental.AUTOTUNE)
if self._is_training:
dataset = dataset.shuffle(1000)
if self._num_examples > 0:
dataset = dataset.take(self._num_examples)
# Parses the fetched records to input tensors for model function.
dataset = dataset.map(
self._parser_fn, num_parallel_calls=tf.data.experimental.AUTOTUNE)
dataset = dataset.batch(batch_size, drop_remainder=True)
dataset = dataset.prefetch(tf.data.experimental.AUTOTUNE)
return dataset
official/vision/detection/dataloader/maskrcnn_parser.py deleted 100644 → 0
View file @ 8fb4e6a6
# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Data parser and processing for Mask R-CNN."""
import tensorflow as tf
from official.vision.detection.dataloader import anchor
from official.vision.detection.dataloader import mode_keys as ModeKeys
from official.vision.detection.dataloader import tf_example_decoder
from official.vision.detection.utils import box_utils
from official.vision.detection.utils import dataloader_utils
from official.vision.detection.utils import input_utils
class Parser(object):
"""Parser to parse an image and its annotations into a dictionary of tensors."""
def __init__(self,
output_size,
min_level,
max_level,
num_scales,
aspect_ratios,
anchor_size,
rpn_match_threshold=0.7,
rpn_unmatched_threshold=0.3,
rpn_batch_size_per_im=256,
rpn_fg_fraction=0.5,
aug_rand_hflip=False,
aug_scale_min=1.0,
aug_scale_max=1.0,
skip_crowd_during_training=True,
max_num_instances=100,
include_mask=False,
mask_crop_size=112,
use_bfloat16=True,
mode=None):
"""Initializes parameters for parsing annotations in the dataset.
Args:
output_size: `Tensor` or `list` for [height, width] of output image. The
output_size should be divided by the largest feature stride 2^max_level.
min_level: `int` number of minimum level of the output feature pyramid.
max_level: `int` number of maximum level of the output feature pyramid.
num_scales: `int` number representing intermediate scales added
on each level. For instances, num_scales=2 adds one additional
intermediate anchor scales [2^0, 2^0.5] on each level.
aspect_ratios: `list` of float numbers representing the aspect raito
anchors added on each level. The number indicates the ratio of width to
height. For instances, aspect_ratios=[1.0, 2.0, 0.5] adds three anchors
on each scale level.
anchor_size: `float` number representing the scale of size of the base
anchor to the feature stride 2^level.
rpn_match_threshold:
rpn_unmatched_threshold:
rpn_batch_size_per_im:
rpn_fg_fraction:
aug_rand_hflip: `bool`, if True, augment training with random
horizontal flip.
aug_scale_min: `float`, the minimum scale applied to `output_size` for
data augmentation during training.
aug_scale_max: `float`, the maximum scale applied to `output_size` for
data augmentation during training.
skip_crowd_during_training: `bool`, if True, skip annotations labeled with
`is_crowd` equals to 1.
max_num_instances: `int` number of maximum number of instances in an
image. The groundtruth data will be padded to `max_num_instances`.
include_mask: a bool to indicate whether parse mask groundtruth.
mask_crop_size: the size which groundtruth mask is cropped to.
use_bfloat16: `bool`, if True, cast output image to tf.bfloat16.
mode: a ModeKeys. Specifies if this is training, evaluation, prediction
or prediction with groundtruths in the outputs.
"""
self._mode = mode
self._max_num_instances = max_num_instances
self._skip_crowd_during_training = skip_crowd_during_training
self._is_training = (mode == ModeKeys.TRAIN)
self._example_decoder = tf_example_decoder.TfExampleDecoder(
include_mask=include_mask)
# Anchor.
self._output_size = output_size
self._min_level = min_level
self._max_level = max_level
self._num_scales = num_scales
self._aspect_ratios = aspect_ratios
self._anchor_size = anchor_size
# Target assigning.
self._rpn_match_threshold = rpn_match_threshold
self._rpn_unmatched_threshold = rpn_unmatched_threshold
self._rpn_batch_size_per_im = rpn_batch_size_per_im
self._rpn_fg_fraction = rpn_fg_fraction
# Data augmentation.
self._aug_rand_hflip = aug_rand_hflip
self._aug_scale_min = aug_scale_min
self._aug_scale_max = aug_scale_max
# Mask.
self._include_mask = include_mask
self._mask_crop_size = mask_crop_size
# Device.
self._use_bfloat16 = use_bfloat16
# Data is parsed depending on the model Modekey.
if mode == ModeKeys.TRAIN:
self._parse_fn = self._parse_train_data
elif mode == ModeKeys.EVAL:
self._parse_fn = self._parse_eval_data
elif mode == ModeKeys.PREDICT or mode == ModeKeys.PREDICT_WITH_GT:
self._parse_fn = self._parse_predict_data
else:
raise ValueError('mode is not defined.')
def __call__(self, value):
"""Parses data to an image and associated training labels.
Args:
value: a string tensor holding a serialized tf.Example proto.
Returns:
image, labels: if mode == ModeKeys.TRAIN. see _parse_train_data.
{'images': image, 'labels': labels}: if mode == ModeKeys.PREDICT
or ModeKeys.PREDICT_WITH_GT.
"""
with tf.name_scope('parser'):
data = self._example_decoder.decode(value)
return self._parse_fn(data)
def _parse_train_data(self, data):
"""Parses data for training.
Args:
data: the decoded tensor dictionary from TfExampleDecoder.
Returns:
image: image tensor that is preproessed to have normalized value and
dimension [output_size[0], output_size[1], 3]
labels: a dictionary of tensors used for training. The following describes
{key: value} pairs in the dictionary.
image_info: a 2D `Tensor` that encodes the information of the image and
the applied preprocessing. It is in the format of
[[original_height, original_width], [scaled_height, scaled_width],
anchor_boxes: ordered dictionary with keys
[min_level, min_level+1, ..., max_level]. The values are tensor with
shape [height_l, width_l, 4] representing anchor boxes at each level.
rpn_score_targets: ordered dictionary with keys
[min_level, min_level+1, ..., max_level]. The values are tensor with
shape [height_l, width_l, anchors_per_location]. The height_l and
width_l represent the dimension of class logits at l-th level.
rpn_box_targets: ordered dictionary with keys
[min_level, min_level+1, ..., max_level]. The values are tensor with
shape [height_l, width_l, anchors_per_location * 4]. The height_l and
width_l represent the dimension of bounding box regression output at
l-th level.
gt_boxes: Groundtruth bounding box annotations. The box is represented
in [y1, x1, y2, x2] format. The coordinates are w.r.t the scaled
image that is fed to the network. The tennsor is padded with -1 to
the fixed dimension [self._max_num_instances, 4].
gt_classes: Groundtruth classes annotations. The tennsor is padded
with -1 to the fixed dimension [self._max_num_instances].
gt_masks: groundtrugh masks cropped by the bounding box and
resized to a fixed size determined by mask_crop_size.
"""
classes = data['groundtruth_classes']
boxes = data['groundtruth_boxes']
if self._include_mask:
masks = data['groundtruth_instance_masks']
is_crowds = data['groundtruth_is_crowd']
# Skips annotations with `is_crowd` = True.
if self._skip_crowd_during_training and self._is_training:
num_groundtruths = tf.shape(classes)[0]
with tf.control_dependencies([num_groundtruths, is_crowds]):
indices = tf.cond(
tf.greater(tf.size(is_crowds), 0),
lambda: tf.where(tf.logical_not(is_crowds))[:, 0],
lambda: tf.cast(tf.range(num_groundtruths), tf.int64))
classes = tf.gather(classes, indices)
boxes = tf.gather(boxes, indices)
if self._include_mask:
masks = tf.gather(masks, indices)
# Gets original image and its size.
image = data['image']
image_shape = tf.shape(image)[0:2]
# Normalizes image with mean and std pixel values.
image = input_utils.normalize_image(image)
# Flips image randomly during training.
if self._aug_rand_hflip:
if self._include_mask:
image, boxes, masks = input_utils.random_horizontal_flip(
image, boxes, masks)
else:
image, boxes = input_utils.random_horizontal_flip(
image, boxes)
# Converts boxes from normalized coordinates to pixel coordinates.
# Now the coordinates of boxes are w.r.t. the original image.
boxes = box_utils.denormalize_boxes(boxes, image_shape)
# Resizes and crops image.
image, image_info = input_utils.resize_and_crop_image(
image,
self._output_size,
padded_size=input_utils.compute_padded_size(
self._output_size, 2 ** self._max_level),
aug_scale_min=self._aug_scale_min,
aug_scale_max=self._aug_scale_max)
image_height, image_width, _ = image.get_shape().as_list()
# Resizes and crops boxes.
# Now the coordinates of boxes are w.r.t the scaled image.
image_scale = image_info[2, :]
offset = image_info[3, :]
boxes = input_utils.resize_and_crop_boxes(
boxes, image_scale, image_info[1, :], offset)
# Filters out ground truth boxes that are all zeros.
indices = box_utils.get_non_empty_box_indices(boxes)
boxes = tf.gather(boxes, indices)
classes = tf.gather(classes, indices)
if self._include_mask:
masks = tf.gather(masks, indices)
# Transfer boxes to the original image space and do normalization.
cropped_boxes = boxes + tf.tile(tf.expand_dims(offset, axis=0), [1, 2])
cropped_boxes /= tf.tile(tf.expand_dims(image_scale, axis=0), [1, 2])
cropped_boxes = box_utils.normalize_boxes(cropped_boxes, image_shape)
num_masks = tf.shape(masks)[0]
masks = tf.image.crop_and_resize(
tf.expand_dims(masks, axis=-1),
cropped_boxes,
box_indices=tf.range(num_masks, dtype=tf.int32),
crop_size=[self._mask_crop_size, self._mask_crop_size],
method='bilinear')
masks = tf.squeeze(masks, axis=-1)
# Assigns anchor targets.
# Note that after the target assignment, box targets are absolute pixel
# offsets w.r.t. the scaled image.
input_anchor = anchor.Anchor(
self._min_level,
self._max_level,
self._num_scales,
self._aspect_ratios,
self._anchor_size,
(image_height, image_width))
anchor_labeler = anchor.RpnAnchorLabeler(
input_anchor,
self._rpn_match_threshold,
self._rpn_unmatched_threshold,
self._rpn_batch_size_per_im,
self._rpn_fg_fraction)
rpn_score_targets, rpn_box_targets = anchor_labeler.label_anchors(
boxes, tf.cast(tf.expand_dims(classes, axis=-1), dtype=tf.float32))
# If bfloat16 is used, casts input image to tf.bfloat16.
if self._use_bfloat16:
image = tf.cast(image, dtype=tf.bfloat16)
inputs = {
'image': image,
'image_info': image_info,
}
# Packs labels for model_fn outputs.
labels = {
'anchor_boxes': input_anchor.multilevel_boxes,
'image_info': image_info,
'rpn_score_targets': rpn_score_targets,
'rpn_box_targets': rpn_box_targets,
}
inputs['gt_boxes'] = input_utils.pad_to_fixed_size(boxes,
self._max_num_instances,
-1)
inputs['gt_classes'] = input_utils.pad_to_fixed_size(
classes, self._max_num_instances, -1)
if self._include_mask:
inputs['gt_masks'] = input_utils.pad_to_fixed_size(
masks, self._max_num_instances, -1)
return inputs, labels
def _parse_eval_data(self, data):
"""Parses data for evaluation."""
raise NotImplementedError('Not implemented!')
def _parse_predict_data(self, data):
"""Parses data for prediction.
Args:
data: the decoded tensor dictionary from TfExampleDecoder.
Returns:
A dictionary of {'images': image, 'labels': labels} where
image: image tensor that is preproessed to have normalized value and
dimension [output_size[0], output_size[1], 3]
labels: a dictionary of tensors used for training. The following
describes {key: value} pairs in the dictionary.
source_ids: Source image id. Default value -1 if the source id is
empty in the groundtruth annotation.
image_info: a 2D `Tensor` that encodes the information of the image
and the applied preprocessing. It is in the format of
[[original_height, original_width], [scaled_height, scaled_width],
anchor_boxes: ordered dictionary with keys
[min_level, min_level+1, ..., max_level]. The values are tensor with
shape [height_l, width_l, 4] representing anchor boxes at each
level.
"""
# Gets original image and its size.
image = data['image']
image_shape = tf.shape(image)[0:2]
# Normalizes image with mean and std pixel values.
image = input_utils.normalize_image(image)
# Resizes and crops image.
image, image_info = input_utils.resize_and_crop_image(
image,
self._output_size,
padded_size=input_utils.compute_padded_size(
self._output_size, 2 ** self._max_level),
aug_scale_min=1.0,
aug_scale_max=1.0)
image_height, image_width, _ = image.get_shape().as_list()
# If bfloat16 is used, casts input image to tf.bfloat16.
if self._use_bfloat16:
image = tf.cast(image, dtype=tf.bfloat16)
# Compute Anchor boxes.
input_anchor = anchor.Anchor(
self._min_level,
self._max_level,
self._num_scales,
self._aspect_ratios,
self._anchor_size,
(image_height, image_width))
labels = {
'image_info': image_info,
}
if self._mode == ModeKeys.PREDICT_WITH_GT:
# Converts boxes from normalized coordinates to pixel coordinates.
boxes = box_utils.denormalize_boxes(
data['groundtruth_boxes'], image_shape)
groundtruths = {
'source_id': data['source_id'],
'height': data['height'],
'width': data['width'],
'num_detections': tf.shape(data['groundtruth_classes']),
'boxes': boxes,
'classes': data['groundtruth_classes'],
'areas': data['groundtruth_area'],
'is_crowds': tf.cast(data['groundtruth_is_crowd'], tf.int32),
}
groundtruths['source_id'] = dataloader_utils.process_source_id(
groundtruths['source_id'])
groundtruths = dataloader_utils.pad_groundtruths_to_fixed_size(
groundtruths, self._max_num_instances)
# TODO(yeqing): Remove the `groundtrtuh` layer key (no longer needed).
labels['groundtruths'] = groundtruths
inputs = {
'image': image,
'image_info': image_info,
}
return inputs, labels
official/vision/detection/dataloader/mode_keys.py deleted 100644 → 0
View file @ 8fb4e6a6
# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Standard names for input dataloader modes.
The following standard keys are defined:
* `TRAIN`: training mode.
* `EVAL`: evaluation mode.
* `PREDICT`: prediction mode.
* `PREDICT_WITH_GT`: prediction mode with groundtruths in returned variables.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
TRAIN = 'train'
EVAL = 'eval'
PREDICT = 'predict'
PREDICT_WITH_GT = 'predict_with_gt'
official/vision/detection/dataloader/olnmask_parser.py deleted 100644 → 0
View file @ 8fb4e6a6
# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Data parser and processing for Mask R-CNN."""
import tensorflow as tf
from official.vision.detection.dataloader import anchor
from official.vision.detection.dataloader.maskrcnn_parser import Parser as MaskrcnnParser
from official.vision.detection.utils import box_utils
from official.vision.detection.utils import class_utils
from official.vision.detection.utils import input_utils
class Parser(MaskrcnnParser):
"""Parser to parse an image and its annotations into a dictionary of tensors."""
def __init__(self,
output_size,
min_level,
max_level,
num_scales,
aspect_ratios,
anchor_size,
rpn_match_threshold=0.7,
rpn_unmatched_threshold=0.3,
rpn_batch_size_per_im=256,
rpn_fg_fraction=0.5,
aug_rand_hflip=False,
aug_scale_min=1.0,
aug_scale_max=1.0,
skip_crowd_during_training=True,
max_num_instances=100,
include_mask=False,
mask_crop_size=112,
use_bfloat16=True,
mode=None,
# for centerness learning.
has_centerness=False,
rpn_center_match_iou_threshold=0.3,
rpn_center_unmatched_iou_threshold=0.1,
rpn_num_center_samples_per_im=256,
# for class manipulation.
class_agnostic=False,
train_class='all',
):
"""Initializes parameters for parsing annotations in the dataset.
Args:
output_size: `Tensor` or `list` for [height, width] of output image. The
output_size should be divided by the largest feature stride 2^max_level.
min_level: `int` number of minimum level of the output feature pyramid.
max_level: `int` number of maximum level of the output feature pyramid.
num_scales: `int` number representing intermediate scales added
on each level. For instances, num_scales=2 adds one additional
intermediate anchor scales [2^0, 2^0.5] on each level.
aspect_ratios: `list` of float numbers representing the aspect raito
anchors added on each level. The number indicates the ratio of width to
height. For instances, aspect_ratios=[1.0, 2.0, 0.5] adds three anchors
on each scale level.
anchor_size: `float` number representing the scale of size of the base
anchor to the feature stride 2^level.
rpn_match_threshold:
rpn_unmatched_threshold:
rpn_batch_size_per_im:
rpn_fg_fraction:
aug_rand_hflip: `bool`, if True, augment training with random
horizontal flip.
aug_scale_min: `float`, the minimum scale applied to `output_size` for
data augmentation during training.
aug_scale_max: `float`, the maximum scale applied to `output_size` for
data augmentation during training.
skip_crowd_during_training: `bool`, if True, skip annotations labeled with
`is_crowd` equals to 1.
max_num_instances: `int` number of maximum number of instances in an
image. The groundtruth data will be padded to `max_num_instances`.
include_mask: a bool to indicate whether parse mask groundtruth.
mask_crop_size: the size which groundtruth mask is cropped to.
use_bfloat16: `bool`, if True, cast output image to tf.bfloat16.
mode: a ModeKeys. Specifies if this is training, evaluation, prediction
or prediction with groundtruths in the outputs.
has_centerness: whether to create centerness targets
rpn_center_match_iou_threshold: iou threshold for valid centerness samples
,set to 0.3 by default.
rpn_center_unmatched_iou_threshold: iou threshold for invalid centerness
samples, set to 0.1 by default.
rpn_num_center_samples_per_im: number of centerness samples per image,
256 by default.
class_agnostic: whether to merge class ids into one foreground(=1) class,
False by default.
train_class: 'all' or 'voc' or 'nonvoc', 'all' by default.
"""
super(Parser, self).__init__(
output_size=output_size,
min_level=min_level,
max_level=max_level,
num_scales=num_scales,
aspect_ratios=aspect_ratios,
anchor_size=anchor_size,
rpn_match_threshold=rpn_match_threshold,
rpn_unmatched_threshold=rpn_unmatched_threshold,
rpn_batch_size_per_im=rpn_batch_size_per_im,
rpn_fg_fraction=rpn_fg_fraction,
aug_rand_hflip=aug_rand_hflip,
aug_scale_min=aug_scale_min,
aug_scale_max=aug_scale_max,
skip_crowd_during_training=skip_crowd_during_training,
max_num_instances=max_num_instances,
include_mask=include_mask,
mask_crop_size=mask_crop_size,
use_bfloat16=use_bfloat16,
mode=mode,)
# Centerness target assigning.
self._has_centerness = has_centerness
self._rpn_center_match_iou_threshold = rpn_center_match_iou_threshold
self._rpn_center_unmatched_iou_threshold = (
rpn_center_unmatched_iou_threshold)
self._rpn_num_center_samples_per_im = rpn_num_center_samples_per_im
# Class manipulation.
self._class_agnostic = class_agnostic
self._train_class = train_class
def _parse_train_data(self, data):
"""Parses data for training.
Args:
data: the decoded tensor dictionary from TfExampleDecoder.
Returns:
image: image tensor that is preproessed to have normalized value and
dimension [output_size[0], output_size[1], 3]
labels: a dictionary of tensors used for training. The following describes
{key: value} pairs in the dictionary.
image_info: a 2D `Tensor` that encodes the information of the image and
the applied preprocessing. It is in the format of
[[original_height, original_width], [scaled_height, scaled_width],
anchor_boxes: ordered dictionary with keys
[min_level, min_level+1, ..., max_level]. The values are tensor with
shape [height_l, width_l, 4] representing anchor boxes at each level.
rpn_score_targets: ordered dictionary with keys
[min_level, min_level+1, ..., max_level]. The values are tensor with
shape [height_l, width_l, anchors_per_location]. The height_l and
width_l represent the dimension of class logits at l-th level.
rpn_box_targets: ordered dictionary with keys
[min_level, min_level+1, ..., max_level]. The values are tensor with
shape [height_l, width_l, anchors_per_location * 4]. The height_l and
width_l represent the dimension of bounding box regression output at
l-th level.
gt_boxes: Groundtruth bounding box annotations. The box is represented
in [y1, x1, y2, x2] format. The coordinates are w.r.t the scaled
image that is fed to the network. The tennsor is padded with -1 to
the fixed dimension [self._max_num_instances, 4].
gt_classes: Groundtruth classes annotations. The tennsor is padded
with -1 to the fixed dimension [self._max_num_instances].
gt_masks: groundtrugh masks cropped by the bounding box and
resized to a fixed size determined by mask_crop_size.
"""
classes = data['groundtruth_classes']
boxes = data['groundtruth_boxes']
if self._include_mask:
masks = data['groundtruth_instance_masks']
is_crowds = data['groundtruth_is_crowd']
# Skips annotations with `is_crowd` = True.
if self._skip_crowd_during_training and self._is_training:
num_groundtruths = tf.shape(classes)[0]
with tf.control_dependencies([num_groundtruths, is_crowds]):
indices = tf.cond(
tf.greater(tf.size(is_crowds), 0),
lambda: tf.where(tf.logical_not(is_crowds))[:, 0],
lambda: tf.cast(tf.range(num_groundtruths), tf.int64))
classes = tf.gather(classes, indices)
boxes = tf.gather(boxes, indices)
if self._include_mask:
masks = tf.gather(masks, indices)
# Gets original image and its size.
image = data['image']
image_shape = tf.shape(image)[0:2]
# Normalizes image with mean and std pixel values.
image = input_utils.normalize_image(image)
# Flips image randomly during training.
if self._aug_rand_hflip:
if self._include_mask:
image, boxes, masks = input_utils.random_horizontal_flip(
image, boxes, masks)
else:
image, boxes = input_utils.random_horizontal_flip(
image, boxes)
# Converts boxes from normalized coordinates to pixel coordinates.
# Now the coordinates of boxes are w.r.t. the original image.
boxes = box_utils.denormalize_boxes(boxes, image_shape)
# Resizes and crops image.
image, image_info = input_utils.resize_and_crop_image(
image,
self._output_size,
padded_size=input_utils.compute_padded_size(
self._output_size, 2 ** self._max_level),
aug_scale_min=self._aug_scale_min,
aug_scale_max=self._aug_scale_max)
image_height, image_width, _ = image.get_shape().as_list()
# Resizes and crops boxes.
# Now the coordinates of boxes are w.r.t the scaled image.
image_scale = image_info[2, :]
offset = image_info[3, :]
boxes = input_utils.resize_and_crop_boxes(
boxes, image_scale, image_info[1, :], offset)
# Filters out ground truth boxes that are all zeros.
indices = box_utils.get_non_empty_box_indices(boxes)
boxes = tf.gather(boxes, indices)
classes = tf.gather(classes, indices)
if self._include_mask:
masks = tf.gather(masks, indices)
# Transfer boxes to the original image space and do normalization.
cropped_boxes = boxes + tf.tile(tf.expand_dims(offset, axis=0), [1, 2])
cropped_boxes /= tf.tile(tf.expand_dims(image_scale, axis=0), [1, 2])
cropped_boxes = box_utils.normalize_boxes(cropped_boxes, image_shape)
num_masks = tf.shape(masks)[0]
masks = tf.image.crop_and_resize(
tf.expand_dims(masks, axis=-1),
cropped_boxes,
box_indices=tf.range(num_masks, dtype=tf.int32),
crop_size=[self._mask_crop_size, self._mask_crop_size],
method='bilinear')
masks = tf.squeeze(masks, axis=-1)
# Class manipulation.
# Filter out novel split classes from training.
if self._train_class != 'all':
valid_classes = tf.cast(
class_utils.coco_split_class_ids(self._train_class),
dtype=classes.dtype)
match = tf.reduce_any(tf.equal(
tf.expand_dims(valid_classes, 1),
tf.expand_dims(classes, 0)), 0)
# kill novel split classes and boxes.
boxes = tf.gather(boxes, tf.where(match)[:, 0])
classes = tf.gather(classes, tf.where(match)[:, 0])
if self._include_mask:
masks = tf.gather(masks, tf.where(match)[:, 0])
# Assigns anchor targets.
# Note that after the target assignment, box targets are absolute pixel
# offsets w.r.t. the scaled image.
input_anchor = anchor.Anchor(
self._min_level,
self._max_level,
self._num_scales,
self._aspect_ratios,
self._anchor_size,
(image_height, image_width))
anchor_labeler = anchor.OlnAnchorLabeler(
input_anchor,
self._rpn_match_threshold,
self._rpn_unmatched_threshold,
self._rpn_batch_size_per_im,
self._rpn_fg_fraction,
# for centerness target.
self._has_centerness,
self._rpn_center_match_iou_threshold,
self._rpn_center_unmatched_iou_threshold,
self._rpn_num_center_samples_per_im,)
if self._has_centerness:
rpn_score_targets, _, rpn_lrtb_targets, rpn_center_targets = (
anchor_labeler.label_anchors_lrtb(
gt_boxes=boxes,
gt_labels=tf.cast(
tf.expand_dims(classes, axis=-1), dtype=tf.float32)))
else:
rpn_score_targets, rpn_box_targets = anchor_labeler.label_anchors(
boxes, tf.cast(tf.expand_dims(classes, axis=-1), dtype=tf.float32))
# For base rpn, dummy placeholder for centerness target.
rpn_center_targets = rpn_score_targets.copy()
# If bfloat16 is used, casts input image to tf.bfloat16.
if self._use_bfloat16:
image = tf.cast(image, dtype=tf.bfloat16)
inputs = {
'image': image,
'image_info': image_info,
}
# Packs labels for model_fn outputs.
labels = {
'anchor_boxes': input_anchor.multilevel_boxes,
'image_info': image_info,
'rpn_score_targets': rpn_score_targets,
'rpn_box_targets': (rpn_lrtb_targets if self._has_centerness
else rpn_box_targets),
'rpn_center_targets': rpn_center_targets,
}
# If class_agnostic, convert to binary classes.
if self._class_agnostic:
classes = tf.where(tf.greater(classes, 0),
tf.ones_like(classes),
tf.zeros_like(classes))
inputs['gt_boxes'] = input_utils.pad_to_fixed_size(boxes,
self._max_num_instances,
-1)
inputs['gt_classes'] = input_utils.pad_to_fixed_size(
classes, self._max_num_instances, -1)
if self._include_mask:
inputs['gt_masks'] = input_utils.pad_to_fixed_size(
masks, self._max_num_instances, -1)
return inputs, labels
official/vision/detection/dataloader/retinanet_parser.py deleted 100644 → 0
View file @ 8fb4e6a6
# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Data parser and processing.
Parse image and ground truths in a dataset to training targets and package them
into (image, labels) tuple for RetinaNet.
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollar
Focal Loss for Dense Object Detection. arXiv:1708.02002
"""
import tensorflow as tf
from official.vision.detection.dataloader import anchor
from official.vision.detection.dataloader import mode_keys as ModeKeys
from official.vision.detection.dataloader import tf_example_decoder
from official.vision.detection.utils import box_utils
from official.vision.detection.utils import input_utils
def process_source_id(source_id):
"""Processes source_id to the right format."""
if source_id.dtype == tf.string:
source_id = tf.cast(tf.strings.to_number(source_id), tf.int32)
with tf.control_dependencies([source_id]):
source_id = tf.cond(
pred=tf.equal(tf.size(input=source_id), 0),
true_fn=lambda: tf.cast(tf.constant(-1), tf.int32),
false_fn=lambda: tf.identity(source_id))
return source_id
def pad_groundtruths_to_fixed_size(gt, n):
"""Pads the first dimension of groundtruths labels to the fixed size."""
gt['boxes'] = input_utils.pad_to_fixed_size(gt['boxes'], n, -1)
gt['is_crowds'] = input_utils.pad_to_fixed_size(gt['is_crowds'], n, 0)
gt['areas'] = input_utils.pad_to_fixed_size(gt['areas'], n, -1)
gt['classes'] = input_utils.pad_to_fixed_size(gt['classes'], n, -1)
return gt
class Parser(object):
"""Parser to parse an image and its annotations into a dictionary of tensors."""
def __init__(self,
output_size,
min_level,
max_level,
num_scales,
aspect_ratios,
anchor_size,
match_threshold=0.5,
unmatched_threshold=0.5,
aug_rand_hflip=False,
aug_scale_min=1.0,
aug_scale_max=1.0,
use_autoaugment=False,
autoaugment_policy_name='v0',
skip_crowd_during_training=True,
max_num_instances=100,
use_bfloat16=True,
mode=None):
"""Initializes parameters for parsing annotations in the dataset.
Args:
output_size: `Tensor` or `list` for [height, width] of output image. The
output_size should be divided by the largest feature stride 2^max_level.
min_level: `int` number of minimum level of the output feature pyramid.
max_level: `int` number of maximum level of the output feature pyramid.
num_scales: `int` number representing intermediate scales added on each
level. For instances, num_scales=2 adds one additional intermediate
anchor scales [2^0, 2^0.5] on each level.
aspect_ratios: `list` of float numbers representing the aspect raito
anchors added on each level. The number indicates the ratio of width to
height. For instances, aspect_ratios=[1.0, 2.0, 0.5] adds three anchors
on each scale level.
anchor_size: `float` number representing the scale of size of the base
anchor to the feature stride 2^level.
match_threshold: `float` number between 0 and 1 representing the
lower-bound threshold to assign positive labels for anchors. An anchor
with a score over the threshold is labeled positive.
unmatched_threshold: `float` number between 0 and 1 representing the
upper-bound threshold to assign negative labels for anchors. An anchor
with a score below the threshold is labeled negative.
aug_rand_hflip: `bool`, if True, augment training with random horizontal
flip.
aug_scale_min: `float`, the minimum scale applied to `output_size` for
data augmentation during training.
aug_scale_max: `float`, the maximum scale applied to `output_size` for
data augmentation during training.
use_autoaugment: `bool`, if True, use the AutoAugment augmentation policy
during training.
autoaugment_policy_name: `string` that specifies the name of the
AutoAugment policy that will be used during training.
skip_crowd_during_training: `bool`, if True, skip annotations labeled with
`is_crowd` equals to 1.
max_num_instances: `int` number of maximum number of instances in an
image. The groundtruth data will be padded to `max_num_instances`.
use_bfloat16: `bool`, if True, cast output image to tf.bfloat16.
mode: a ModeKeys. Specifies if this is training, evaluation, prediction or
prediction with groundtruths in the outputs.
"""
self._mode = mode
self._max_num_instances = max_num_instances
self._skip_crowd_during_training = skip_crowd_during_training
self._is_training = (mode == ModeKeys.TRAIN)
self._example_decoder = tf_example_decoder.TfExampleDecoder(
include_mask=False)
# Anchor.
self._output_size = output_size
self._min_level = min_level
self._max_level = max_level
self._num_scales = num_scales
self._aspect_ratios = aspect_ratios
self._anchor_size = anchor_size
self._match_threshold = match_threshold
self._unmatched_threshold = unmatched_threshold
# Data augmentation.
self._aug_rand_hflip = aug_rand_hflip
self._aug_scale_min = aug_scale_min
self._aug_scale_max = aug_scale_max
# Data Augmentation with AutoAugment.
self._use_autoaugment = use_autoaugment
self._autoaugment_policy_name = autoaugment_policy_name
# Device.
self._use_bfloat16 = use_bfloat16
# Data is parsed depending on the model Modekey.
if mode == ModeKeys.TRAIN:
self._parse_fn = self._parse_train_data
elif mode == ModeKeys.EVAL:
self._parse_fn = self._parse_eval_data
elif mode == ModeKeys.PREDICT or mode == ModeKeys.PREDICT_WITH_GT:
self._parse_fn = self._parse_predict_data
else:
raise ValueError('mode is not defined.')
def __call__(self, value):
"""Parses data to an image and associated training labels.
Args:
value: a string tensor holding a serialized tf.Example proto.
Returns:
image: image tensor that is preproessed to have normalized value and
dimension [output_size[0], output_size[1], 3]
labels:
cls_targets: ordered dictionary with keys
[min_level, min_level+1, ..., max_level]. The values are tensor with
shape [height_l, width_l, anchors_per_location]. The height_l and
width_l represent the dimension of class logits at l-th level.
box_targets: ordered dictionary with keys
[min_level, min_level+1, ..., max_level]. The values are tensor with
shape [height_l, width_l, anchors_per_location * 4]. The height_l and
width_l represent the dimension of bounding box regression output at
l-th level.
num_positives: number of positive anchors in the image.
anchor_boxes: ordered dictionary with keys
[min_level, min_level+1, ..., max_level]. The values are tensor with
shape [height_l, width_l, 4] representing anchor boxes at each level.
image_info: a 2D `Tensor` that encodes the information of the image and
the applied preprocessing. It is in the format of
[[original_height, original_width], [scaled_height, scaled_width],
[y_scale, x_scale], [y_offset, x_offset]].
groundtruths:
source_id: source image id. Default value -1 if the source id is empty
in the groundtruth annotation.
boxes: groundtruth bounding box annotations. The box is represented in
[y1, x1, y2, x2] format. The tennsor is padded with -1 to the fixed
dimension [self._max_num_instances, 4].
classes: groundtruth classes annotations. The tennsor is padded with
-1 to the fixed dimension [self._max_num_instances].
areas: groundtruth areas annotations. The tennsor is padded with -1
to the fixed dimension [self._max_num_instances].
is_crowds: groundtruth annotations to indicate if an annotation
represents a group of instances by value {0, 1}. The tennsor is
padded with 0 to the fixed dimension [self._max_num_instances].
"""
with tf.name_scope('parser'):
data = self._example_decoder.decode(value)
return self._parse_fn(data)
def _parse_train_data(self, data):
"""Parses data for training and evaluation."""
classes = data['groundtruth_classes']
boxes = data['groundtruth_boxes']
is_crowds = data['groundtruth_is_crowd']
# Skips annotations with `is_crowd` = True.
if self._skip_crowd_during_training and self._is_training:
num_groundtrtuhs = tf.shape(input=classes)[0]
with tf.control_dependencies([num_groundtrtuhs, is_crowds]):
indices = tf.cond(
pred=tf.greater(tf.size(input=is_crowds), 0),
true_fn=lambda: tf.where(tf.logical_not(is_crowds))[:, 0],
false_fn=lambda: tf.cast(tf.range(num_groundtrtuhs), tf.int64))
classes = tf.gather(classes, indices)
boxes = tf.gather(boxes, indices)
# Gets original image and its size.
image = data['image']
image_shape = tf.shape(input=image)[0:2]
# Normalizes image with mean and std pixel values.
image = input_utils.normalize_image(image)
# Flips image randomly during training.
if self._aug_rand_hflip:
image, boxes = input_utils.random_horizontal_flip(image, boxes)
# Converts boxes from normalized coordinates to pixel coordinates.
boxes = box_utils.denormalize_boxes(boxes, image_shape)
# Resizes and crops image.
image, image_info = input_utils.resize_and_crop_image(
image,
self._output_size,
padded_size=input_utils.compute_padded_size(self._output_size,
2**self._max_level),
aug_scale_min=self._aug_scale_min,
aug_scale_max=self._aug_scale_max)
image_height, image_width, _ = image.get_shape().as_list()
# Resizes and crops boxes.
image_scale = image_info[2, :]
offset = image_info[3, :]
boxes = input_utils.resize_and_crop_boxes(boxes, image_scale,
image_info[1, :], offset)
# Filters out ground truth boxes that are all zeros.
indices = box_utils.get_non_empty_box_indices(boxes)
boxes = tf.gather(boxes, indices)
classes = tf.gather(classes, indices)
# Assigns anchors.
input_anchor = anchor.Anchor(self._min_level, self._max_level,
self._num_scales, self._aspect_ratios,
self._anchor_size, (image_height, image_width))
anchor_labeler = anchor.AnchorLabeler(input_anchor, self._match_threshold,
self._unmatched_threshold)
(cls_targets, box_targets, num_positives) = anchor_labeler.label_anchors(
boxes, tf.cast(tf.expand_dims(classes, axis=1), tf.float32))
# If bfloat16 is used, casts input image to tf.bfloat16.
if self._use_bfloat16:
image = tf.cast(image, dtype=tf.bfloat16)
# Packs labels for model_fn outputs.
labels = {
'cls_targets': cls_targets,
'box_targets': box_targets,
'anchor_boxes': input_anchor.multilevel_boxes,
'num_positives': num_positives,
'image_info': image_info,
}
return image, labels
def _parse_eval_data(self, data):
"""Parses data for training and evaluation."""
groundtruths = {}
classes = data['groundtruth_classes']
boxes = data['groundtruth_boxes']
# Gets original image and its size.
image = data['image']
image_shape = tf.shape(input=image)[0:2]
# Normalizes image with mean and std pixel values.
image = input_utils.normalize_image(image)
# Converts boxes from normalized coordinates to pixel coordinates.
boxes = box_utils.denormalize_boxes(boxes, image_shape)
# Resizes and crops image.
image, image_info = input_utils.resize_and_crop_image(
image,
self._output_size,
padded_size=input_utils.compute_padded_size(self._output_size,
2**self._max_level),
aug_scale_min=1.0,
aug_scale_max=1.0)
image_height, image_width, _ = image.get_shape().as_list()
# Resizes and crops boxes.
image_scale = image_info[2, :]
offset = image_info[3, :]
boxes = input_utils.resize_and_crop_boxes(boxes, image_scale,
image_info[1, :], offset)
# Filters out ground truth boxes that are all zeros.
indices = box_utils.get_non_empty_box_indices(boxes)
boxes = tf.gather(boxes, indices)
classes = tf.gather(classes, indices)
# Assigns anchors.
input_anchor = anchor.Anchor(self._min_level, self._max_level,
self._num_scales, self._aspect_ratios,
self._anchor_size, (image_height, image_width))
anchor_labeler = anchor.AnchorLabeler(input_anchor, self._match_threshold,
self._unmatched_threshold)
(cls_targets, box_targets, num_positives) = anchor_labeler.label_anchors(
boxes, tf.cast(tf.expand_dims(classes, axis=1), tf.float32))
# If bfloat16 is used, casts input image to tf.bfloat16.
if self._use_bfloat16:
image = tf.cast(image, dtype=tf.bfloat16)
# Sets up groundtruth data for evaluation.
groundtruths = {
'source_id':
data['source_id'],
'num_groundtrtuhs':
tf.shape(data['groundtruth_classes']),
'image_info':
image_info,
'boxes':
box_utils.denormalize_boxes(data['groundtruth_boxes'], image_shape),
'classes':
data['groundtruth_classes'],
'areas':
data['groundtruth_area'],
'is_crowds':
tf.cast(data['groundtruth_is_crowd'], tf.int32),
}
groundtruths['source_id'] = process_source_id(groundtruths['source_id'])
groundtruths = pad_groundtruths_to_fixed_size(groundtruths,
self._max_num_instances)
# Packs labels for model_fn outputs.
labels = {
'cls_targets': cls_targets,
'box_targets': box_targets,
'anchor_boxes': input_anchor.multilevel_boxes,
'num_positives': num_positives,
'image_info': image_info,
'groundtruths': groundtruths,
}
return image, labels
def _parse_predict_data(self, data):
"""Parses data for prediction."""
# Gets original image and its size.
image = data['image']
image_shape = tf.shape(input=image)[0:2]
# Normalizes image with mean and std pixel values.
image = input_utils.normalize_image(image)
# Resizes and crops image.
image, image_info = input_utils.resize_and_crop_image(
image,
self._output_size,
padded_size=input_utils.compute_padded_size(self._output_size,
2**self._max_level),
aug_scale_min=1.0,
aug_scale_max=1.0)
image_height, image_width, _ = image.get_shape().as_list()
# If bfloat16 is used, casts input image to tf.bfloat16.
if self._use_bfloat16:
image = tf.cast(image, dtype=tf.bfloat16)
# Compute Anchor boxes.
input_anchor = anchor.Anchor(self._min_level, self._max_level,
self._num_scales, self._aspect_ratios,
self._anchor_size, (image_height, image_width))
labels = {
'anchor_boxes': input_anchor.multilevel_boxes,
'image_info': image_info,
}
# If mode is PREDICT_WITH_GT, returns groundtruths and training targets
# in labels.
if self._mode == ModeKeys.PREDICT_WITH_GT:
# Converts boxes from normalized coordinates to pixel coordinates.
boxes = box_utils.denormalize_boxes(data['groundtruth_boxes'],
image_shape)
groundtruths = {
'source_id': data['source_id'],
'num_detections': tf.shape(data['groundtruth_classes']),
'boxes': boxes,
'classes': data['groundtruth_classes'],
'areas': data['groundtruth_area'],
'is_crowds': tf.cast(data['groundtruth_is_crowd'], tf.int32),
}
groundtruths['source_id'] = process_source_id(groundtruths['source_id'])
groundtruths = pad_groundtruths_to_fixed_size(groundtruths,
self._max_num_instances)
labels['groundtruths'] = groundtruths
# Computes training objective for evaluation loss.
classes = data['groundtruth_classes']
image_scale = image_info[2, :]
offset = image_info[3, :]
boxes = input_utils.resize_and_crop_boxes(boxes, image_scale,
image_info[1, :], offset)
# Filters out ground truth boxes that are all zeros.
indices = box_utils.get_non_empty_box_indices(boxes)
boxes = tf.gather(boxes, indices)
# Assigns anchors.
anchor_labeler = anchor.AnchorLabeler(input_anchor, self._match_threshold,
self._unmatched_threshold)
(cls_targets, box_targets, num_positives) = anchor_labeler.label_anchors(
boxes, tf.cast(tf.expand_dims(classes, axis=1), tf.float32))
labels['cls_targets'] = cls_targets
labels['box_targets'] = box_targets
labels['num_positives'] = num_positives
return image, labels
official/vision/detection/dataloader/tf_example_decoder.py deleted 100644 → 0
View file @ 8fb4e6a6
# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tensorflow Example proto decoder for object detection.
A decoder to decode string tensors containing serialized tensorflow.Example
protos for object detection.
"""
import tensorflow as tf
class TfExampleDecoder(object):
"""Tensorflow Example proto decoder."""
def __init__(self, include_mask=False):
self._include_mask = include_mask
self._keys_to_features = {
'image/encoded':
tf.io.FixedLenFeature((), tf.string),
'image/source_id':
tf.io.FixedLenFeature((), tf.string),
'image/height':
tf.io.FixedLenFeature((), tf.int64),
'image/width':
tf.io.FixedLenFeature((), tf.int64),
'image/object/bbox/xmin':
tf.io.VarLenFeature(tf.float32),
'image/object/bbox/xmax':
tf.io.VarLenFeature(tf.float32),
'image/object/bbox/ymin':
tf.io.VarLenFeature(tf.float32),
'image/object/bbox/ymax':
tf.io.VarLenFeature(tf.float32),
'image/object/class/label':
tf.io.VarLenFeature(tf.int64),
'image/object/area':
tf.io.VarLenFeature(tf.float32),
'image/object/is_crowd':
tf.io.VarLenFeature(tf.int64),
}
if include_mask:
self._keys_to_features.update({
'image/object/mask':
tf.io.VarLenFeature(tf.string),
})
def _decode_image(self, parsed_tensors):
"""Decodes the image and set its static shape."""
image = tf.io.decode_image(parsed_tensors['image/encoded'], channels=3)
image.set_shape([None, None, 3])
return image
def _decode_boxes(self, parsed_tensors):
"""Concat box coordinates in the format of [ymin, xmin, ymax, xmax]."""
xmin = parsed_tensors['image/object/bbox/xmin']
xmax = parsed_tensors['image/object/bbox/xmax']
ymin = parsed_tensors['image/object/bbox/ymin']
ymax = parsed_tensors['image/object/bbox/ymax']
return tf.stack([ymin, xmin, ymax, xmax], axis=-1)
def _decode_masks(self, parsed_tensors):
"""Decode a set of PNG masks to the tf.float32 tensors."""
def _decode_png_mask(png_bytes):
mask = tf.squeeze(
tf.io.decode_png(png_bytes, channels=1, dtype=tf.uint8), axis=-1)
mask = tf.cast(mask, dtype=tf.float32)
mask.set_shape([None, None])
return mask
height = parsed_tensors['image/height']
width = parsed_tensors['image/width']
masks = parsed_tensors['image/object/mask']
return tf.cond(
pred=tf.greater(tf.size(input=masks), 0),
true_fn=lambda: tf.map_fn(_decode_png_mask, masks, dtype=tf.float32),
false_fn=lambda: tf.zeros([0, height, width], dtype=tf.float32))
def _decode_areas(self, parsed_tensors):
xmin = parsed_tensors['image/object/bbox/xmin']
xmax = parsed_tensors['image/object/bbox/xmax']
ymin = parsed_tensors['image/object/bbox/ymin']
ymax = parsed_tensors['image/object/bbox/ymax']
return tf.cond(
tf.greater(tf.shape(parsed_tensors['image/object/area'])[0], 0),
lambda: parsed_tensors['image/object/area'],
lambda: (xmax - xmin) * (ymax - ymin))
def decode(self, serialized_example):
"""Decode the serialized example.
Args:
serialized_example: a single serialized tf.Example string.
Returns:
decoded_tensors: a dictionary of tensors with the following fields:
- image: a uint8 tensor of shape [None, None, 3].
- source_id: a string scalar tensor.
- height: an integer scalar tensor.
- width: an integer scalar tensor.
- groundtruth_classes: a int64 tensor of shape [None].
- groundtruth_is_crowd: a bool tensor of shape [None].
- groundtruth_area: a float32 tensor of shape [None].
- groundtruth_boxes: a float32 tensor of shape [None, 4].
- groundtruth_instance_masks: a float32 tensor of shape
[None, None, None].
- groundtruth_instance_masks_png: a string tensor of shape [None].
"""
parsed_tensors = tf.io.parse_single_example(
serialized=serialized_example, features=self._keys_to_features)
for k in parsed_tensors:
if isinstance(parsed_tensors[k], tf.SparseTensor):
if parsed_tensors[k].dtype == tf.string:
parsed_tensors[k] = tf.sparse.to_dense(
parsed_tensors[k], default_value='')
else:
parsed_tensors[k] = tf.sparse.to_dense(
parsed_tensors[k], default_value=0)
image = self._decode_image(parsed_tensors)
boxes = self._decode_boxes(parsed_tensors)
areas = self._decode_areas(parsed_tensors)
is_crowds = tf.cond(
tf.greater(tf.shape(parsed_tensors['image/object/is_crowd'])[0], 0),
lambda: tf.cast(parsed_tensors['image/object/is_crowd'], dtype=tf.bool),
lambda: tf.zeros_like(parsed_tensors['image/object/class/label'], dtype=tf.bool)) # pylint: disable=line-too-long
if self._include_mask:
masks = self._decode_masks(parsed_tensors)
decoded_tensors = {
'image': image,
'source_id': parsed_tensors['image/source_id'],
'height': parsed_tensors['image/height'],
'width': parsed_tensors['image/width'],
'groundtruth_classes': parsed_tensors['image/object/class/label'],
'groundtruth_is_crowd': is_crowds,
'groundtruth_area': areas,
'groundtruth_boxes': boxes,
}
if self._include_mask:
decoded_tensors.update({
'groundtruth_instance_masks': masks,
'groundtruth_instance_masks_png': parsed_tensors['image/object/mask'],
})
return decoded_tensors
official/vision/detection/evaluation/__init__.py deleted 100644 → 0
View file @ 8fb4e6a6
# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
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