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Commit 96ed89d1 authored by A. Unique TensorFlower's avatar A. Unique TensorFlower
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official/vision/beta/projects/panoptic_maskrcnn/configs/panoptic_deeplab.py
View file @ 96ed89d1
...@@ -180,7 +180,7 @@ class PanopticDeeplabTask(cfg.TaskConfig): ...@@ -180,7 +180,7 @@ class PanopticDeeplabTask(cfg.TaskConfig):
@exp_factory.register_config_factory('panoptic_deeplab_resnet_coco') @exp_factory.register_config_factory('panoptic_deeplab_resnet_coco')
def panoptic_deeplab_coco() -> cfg.ExperimentConfig: def panoptic_deeplab_resnet_coco() -> cfg.ExperimentConfig:
"""COCO panoptic segmentation with Panoptic Deeplab.""" """COCO panoptic segmentation with Panoptic Deeplab."""
train_steps = 200000 train_steps = 200000
train_batch_size = 64 train_batch_size = 64
...@@ -344,3 +344,327 @@ def panoptic_deeplab_coco() -> cfg.ExperimentConfig: ...@@ -344,3 +344,327 @@ def panoptic_deeplab_coco() -> cfg.ExperimentConfig:
'task.validation_data.is_training != None' 'task.validation_data.is_training != None'
]) ])
return config return config
@exp_factory.register_config_factory('panoptic_deeplab_mobilenetv3_large_coco')
def panoptic_deeplab_mobilenetv3_large_coco() -> cfg.ExperimentConfig:
"""COCO panoptic segmentation with Panoptic Deeplab."""
train_steps = 200000
train_batch_size = 64
eval_batch_size = 1
steps_per_epoch = _COCO_TRAIN_EXAMPLES // train_batch_size
validation_steps = _COCO_VAL_EXAMPLES // eval_batch_size
num_panoptic_categories = 201
num_thing_categories = 91
ignore_label = 0
is_thing = [False]
for idx in range(1, num_panoptic_categories):
is_thing.append(True if idx <= num_thing_categories else False)
input_size = [640, 640, 3]
output_stride = 16
aspp_dilation_rates = [6, 12, 18]
level = int(np.math.log2(output_stride))
config = cfg.ExperimentConfig(
runtime=cfg.RuntimeConfig(
mixed_precision_dtype='float32', enable_xla=True),
task=PanopticDeeplabTask(
init_checkpoint='gs://tf_model_garden/vision/panoptic/panoptic_deeplab/imagenet/mobilenetv3_large/ckpt-156000',
init_checkpoint_modules=['backbone'],
model=PanopticDeeplab(
num_classes=num_panoptic_categories,
input_size=input_size,
backbone=backbones.Backbone(
type='mobilenet', mobilenet=backbones.MobileNet(
model_id='MobileNetV3Large',
filter_size_scale=1.0,
stochastic_depth_drop_rate=0.0,
output_stride=output_stride)),
decoder=decoders.Decoder(
type='aspp',
aspp=decoders.ASPP(
level=level,
num_filters=256,
pool_kernel_size=input_size[:2],
dilation_rates=aspp_dilation_rates,
use_depthwise_convolution=True,
dropout_rate=0.1)),
semantic_head=SemanticHead(
level=level,
num_convs=1,
num_filters=256,
kernel_size=5,
use_depthwise_convolution=True,
upsample_factor=1,
low_level=[3, 2],
low_level_num_filters=[64, 32],
fusion_num_output_filters=256,
prediction_kernel_size=1),
instance_head=InstanceHead(
level=level,
num_convs=1,
num_filters=32,
kernel_size=5,
use_depthwise_convolution=True,
upsample_factor=1,
low_level=[3, 2],
low_level_num_filters=[32, 16],
fusion_num_output_filters=128,
prediction_kernel_size=1),
shared_decoder=False,
generate_panoptic_masks=True,
post_processor=PanopticDeeplabPostProcessor(
output_size=input_size[:2],
center_score_threshold=0.1,
thing_class_ids=list(range(1, num_thing_categories)),
label_divisor=256,
stuff_area_limit=4096,
ignore_label=ignore_label,
nms_kernel=41,
keep_k_centers=200,
rescale_predictions=True)),
losses=Losses(
label_smoothing=0.0,
ignore_label=ignore_label,
l2_weight_decay=0.0,
top_k_percent_pixels=0.2,
segmentation_loss_weight=1.0,
center_heatmap_loss_weight=200,
center_offset_loss_weight=0.01),
train_data=DataConfig(
input_path=os.path.join(_COCO_INPUT_PATH_BASE, 'train*'),
is_training=True,
global_batch_size=train_batch_size,
parser=Parser(
aug_scale_min=0.5,
aug_scale_max=2.0,
aug_rand_hflip=True,
aug_type=common.Augmentation(
type='autoaug',
autoaug=common.AutoAugment(
augmentation_name='panoptic_deeplab_policy')),
sigma=8.0,
small_instance_area_threshold=4096,
small_instance_weight=3.0)),
validation_data=DataConfig(
input_path=os.path.join(_COCO_INPUT_PATH_BASE, 'val*'),
is_training=False,
global_batch_size=eval_batch_size,
parser=Parser(
resize_eval_groundtruth=False,
groundtruth_padded_size=[640, 640],
aug_scale_min=1.0,
aug_scale_max=1.0,
aug_rand_hflip=False,
aug_type=None,
sigma=8.0,
small_instance_area_threshold=4096,
small_instance_weight=3.0),
drop_remainder=False),
evaluation=Evaluation(
ignored_label=ignore_label,
max_instances_per_category=256,
offset=256*256*256,
is_thing=is_thing,
rescale_predictions=True,
report_per_class_pq=False,
report_per_class_iou=False,
report_train_mean_iou=False)),
trainer=cfg.TrainerConfig(
train_steps=train_steps,
validation_steps=validation_steps,
validation_interval=steps_per_epoch,
steps_per_loop=steps_per_epoch,
summary_interval=steps_per_epoch,
checkpoint_interval=steps_per_epoch,
optimizer_config=optimization.OptimizationConfig({
'optimizer': {
'type': 'adam',
},
'learning_rate': {
'type': 'polynomial',
'polynomial': {
'initial_learning_rate': 0.001,
'decay_steps': train_steps,
'end_learning_rate': 0.0,
'power': 0.9
}
},
'warmup': {
'type': 'linear',
'linear': {
'warmup_steps': 2000,
'warmup_learning_rate': 0
}
}
})),
restrictions=[
'task.train_data.is_training != None',
'task.validation_data.is_training != None'
])
return config
@exp_factory.register_config_factory('panoptic_deeplab_mobilenetv3_small_coco')
def panoptic_deeplab_mobilenetv3_small_coco() -> cfg.ExperimentConfig:
"""COCO panoptic segmentation with Panoptic Deeplab."""
train_steps = 200000
train_batch_size = 64
eval_batch_size = 1
steps_per_epoch = _COCO_TRAIN_EXAMPLES // train_batch_size
validation_steps = _COCO_VAL_EXAMPLES // eval_batch_size
num_panoptic_categories = 201
num_thing_categories = 91
ignore_label = 0
is_thing = [False]
for idx in range(1, num_panoptic_categories):
is_thing.append(True if idx <= num_thing_categories else False)
input_size = [640, 640, 3]
output_stride = 16
aspp_dilation_rates = [6, 12, 18]
level = int(np.math.log2(output_stride))
config = cfg.ExperimentConfig(
runtime=cfg.RuntimeConfig(
mixed_precision_dtype='float32', enable_xla=True),
task=PanopticDeeplabTask(
init_checkpoint='gs://tf_model_garden/vision/panoptic/panoptic_deeplab/imagenet/mobilenetv3_small/ckpt-312000',
init_checkpoint_modules=['backbone'],
model=PanopticDeeplab(
num_classes=num_panoptic_categories,
input_size=input_size,
backbone=backbones.Backbone(
type='mobilenet', mobilenet=backbones.MobileNet(
model_id='MobileNetV3Small',
filter_size_scale=1.0,
stochastic_depth_drop_rate=0.0,
output_stride=output_stride)),
decoder=decoders.Decoder(
type='aspp',
aspp=decoders.ASPP(
level=level,
num_filters=256,
pool_kernel_size=input_size[:2],
dilation_rates=aspp_dilation_rates,
use_depthwise_convolution=True,
dropout_rate=0.1)),
semantic_head=SemanticHead(
level=level,
num_convs=1,
num_filters=256,
kernel_size=5,
use_depthwise_convolution=True,
upsample_factor=1,
low_level=[3, 2],
low_level_num_filters=[64, 32],
fusion_num_output_filters=256,
prediction_kernel_size=1),
instance_head=InstanceHead(
level=level,
num_convs=1,
num_filters=32,
kernel_size=5,
use_depthwise_convolution=True,
upsample_factor=1,
low_level=[3, 2],
low_level_num_filters=[32, 16],
fusion_num_output_filters=128,
prediction_kernel_size=1),
shared_decoder=False,
generate_panoptic_masks=True,
post_processor=PanopticDeeplabPostProcessor(
output_size=input_size[:2],
center_score_threshold=0.1,
thing_class_ids=list(range(1, num_thing_categories)),
label_divisor=256,
stuff_area_limit=4096,
ignore_label=ignore_label,
nms_kernel=41,
keep_k_centers=200,
rescale_predictions=True)),
losses=Losses(
label_smoothing=0.0,
ignore_label=ignore_label,
l2_weight_decay=0.0,
top_k_percent_pixels=0.2,
segmentation_loss_weight=1.0,
center_heatmap_loss_weight=200,
center_offset_loss_weight=0.01),
train_data=DataConfig(
input_path=os.path.join(_COCO_INPUT_PATH_BASE, 'train*'),
is_training=True,
global_batch_size=train_batch_size,
parser=Parser(
aug_scale_min=0.5,
aug_scale_max=2.0,
aug_rand_hflip=True,
aug_type=common.Augmentation(
type='autoaug',
autoaug=common.AutoAugment(
augmentation_name='panoptic_deeplab_policy')),
sigma=8.0,
small_instance_area_threshold=4096,
small_instance_weight=3.0)),
validation_data=DataConfig(
input_path=os.path.join(_COCO_INPUT_PATH_BASE, 'val*'),
is_training=False,
global_batch_size=eval_batch_size,
parser=Parser(
resize_eval_groundtruth=False,
groundtruth_padded_size=[640, 640],
aug_scale_min=1.0,
aug_scale_max=1.0,
aug_rand_hflip=False,
aug_type=None,
sigma=8.0,
small_instance_area_threshold=4096,
small_instance_weight=3.0),
drop_remainder=False),
evaluation=Evaluation(
ignored_label=ignore_label,
max_instances_per_category=256,
offset=256*256*256,
is_thing=is_thing,
rescale_predictions=True,
report_per_class_pq=False,
report_per_class_iou=False,
report_train_mean_iou=False)),
trainer=cfg.TrainerConfig(
train_steps=train_steps,
validation_steps=validation_steps,
validation_interval=steps_per_epoch,
steps_per_loop=steps_per_epoch,
summary_interval=steps_per_epoch,
checkpoint_interval=steps_per_epoch,
optimizer_config=optimization.OptimizationConfig({
'optimizer': {
'type': 'adam',
},
'learning_rate': {
'type': 'polynomial',
'polynomial': {
'initial_learning_rate': 0.001,
'decay_steps': train_steps,
'end_learning_rate': 0.0,
'power': 0.9
}
},
'warmup': {
'type': 'linear',
'linear': {
'warmup_steps': 2000,
'warmup_learning_rate': 0
}
}
})),
restrictions=[
'task.train_data.is_training != None',
'task.validation_data.is_training != None'
])
return config
official/vision/beta/projects/panoptic_maskrcnn/configs/panoptic_deeplab_test.py 0 → 100644
View file @ 96ed89d1
# Copyright 2022 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.
"""Tests for panoptic deeplab config."""
# pylint: disable=unused-import
from absl.testing import parameterized
import tensorflow as tf
from official.core import config_definitions as cfg
from official.core import exp_factory
from official.vision.beta.projects.panoptic_maskrcnn.configs import panoptic_deeplab as exp_cfg
class PanopticMaskRCNNConfigTest(tf.test.TestCase, parameterized.TestCase):
@parameterized.parameters(
('panoptic_deeplab_resnet_coco', 'dilated_resnet'),
('panoptic_deeplab_mobilenetv3_large_coco', 'mobilenet'),
)
def test_panoptic_deeplab_configs(self, config_name, backbone_type):
config = exp_factory.get_exp_config(config_name)
self.assertIsInstance(config, cfg.ExperimentConfig)
self.assertIsInstance(config.task, exp_cfg.PanopticDeeplabTask)
self.assertIsInstance(config.task.model, exp_cfg.PanopticDeeplab)
self.assertIsInstance(config.task.train_data, exp_cfg.DataConfig)
self.assertEqual(config.task.model.backbone.type, backbone_type)
config.validate()
config.task.train_data.is_training = None
with self.assertRaisesRegex(KeyError, 'Found inconsistncy between key'):
config.validate()
if __name__ == '__main__':
tf.test.main()
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