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Commit 42f76467 authored by Yeqing Li's avatar Yeqing Li Committed by A. Unique TensorFlower
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Removes unneeded content of the beta folder. 

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official/vision/beta/__init__.py deleted 100644 → 0
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# 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.
"""Vision package definition."""
# Lint as: python3
# pylint: disable=unused-import
from official.vision.beta import configs
from official.vision.beta import tasks
official/vision/beta/configs/__init__.py deleted 100644 → 0
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# 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.
"""Configs package definition."""
from official.vision.beta.configs import image_classification
from official.vision.beta.configs import maskrcnn
from official.vision.beta.configs import retinanet
from official.vision.beta.configs import semantic_segmentation
from official.vision.beta.configs import video_classification
official/vision/beta/configs/backbones.py deleted 100644 → 0
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# 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.
"""Backbones configurations."""
import dataclasses
from typing import Optional, List
# Import libraries
from official.modeling import hyperparams
@dataclasses.dataclass
class ResNet(hyperparams.Config):
"""ResNet config."""
model_id: int = 50
depth_multiplier: float = 1.0
stem_type: str = 'v0'
se_ratio: float = 0.0
stochastic_depth_drop_rate: float = 0.0
scale_stem: bool = True
resnetd_shortcut: bool = False
replace_stem_max_pool: bool = False
bn_trainable: bool = True
@dataclasses.dataclass
class DilatedResNet(hyperparams.Config):
"""DilatedResNet config."""
model_id: int = 50
output_stride: int = 16
multigrid: Optional[List[int]] = None
stem_type: str = 'v0'
last_stage_repeats: int = 1
se_ratio: float = 0.0
stochastic_depth_drop_rate: float = 0.0
@dataclasses.dataclass
class EfficientNet(hyperparams.Config):
"""EfficientNet config."""
model_id: str = 'b0'
se_ratio: float = 0.0
stochastic_depth_drop_rate: float = 0.0
@dataclasses.dataclass
class MobileNet(hyperparams.Config):
"""Mobilenet config."""
model_id: str = 'MobileNetV2'
filter_size_scale: float = 1.0
stochastic_depth_drop_rate: float = 0.0
output_stride: Optional[int] = None
output_intermediate_endpoints: bool = False
@dataclasses.dataclass
class SpineNet(hyperparams.Config):
"""SpineNet config."""
model_id: str = '49'
stochastic_depth_drop_rate: float = 0.0
min_level: int = 3
max_level: int = 7
@dataclasses.dataclass
class SpineNetMobile(hyperparams.Config):
"""SpineNet config."""
model_id: str = '49'
stochastic_depth_drop_rate: float = 0.0
se_ratio: float = 0.2
expand_ratio: int = 6
min_level: int = 3
max_level: int = 7
# If use_keras_upsampling_2d is True, model uses UpSampling2D keras layer
# instead of optimized custom TF op. It makes model be more keras style. We
# set this flag to True when we apply QAT from model optimization toolkit
# that requires the model should use keras layers.
use_keras_upsampling_2d: bool = False
@dataclasses.dataclass
class RevNet(hyperparams.Config):
"""RevNet config."""
# Specifies the depth of RevNet.
model_id: int = 56
@dataclasses.dataclass
class MobileDet(hyperparams.Config):
"""Mobiledet config."""
model_id: str = 'MobileDetCPU'
filter_size_scale: float = 1.0
@dataclasses.dataclass
class Backbone(hyperparams.OneOfConfig):
"""Configuration for backbones.
Attributes:
type: 'str', type of backbone be used, one of the fields below.
resnet: resnet backbone config.
dilated_resnet: dilated resnet backbone for semantic segmentation config.
revnet: revnet backbone config.
efficientnet: efficientnet backbone config.
spinenet: spinenet backbone config.
spinenet_mobile: mobile spinenet backbone config.
mobilenet: mobilenet backbone config.
mobiledet: mobiledet backbone config.
"""
type: Optional[str] = None
resnet: ResNet = ResNet()
dilated_resnet: DilatedResNet = DilatedResNet()
revnet: RevNet = RevNet()
efficientnet: EfficientNet = EfficientNet()
spinenet: SpineNet = SpineNet()
spinenet_mobile: SpineNetMobile = SpineNetMobile()
mobilenet: MobileNet = MobileNet()
mobiledet: MobileDet = MobileDet()
official/vision/beta/configs/backbones_3d.py deleted 100644 → 0
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# 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.
"""3D Backbones configurations."""
from typing import Optional, Tuple
# Import libraries
import dataclasses
from official.modeling import hyperparams
@dataclasses.dataclass
class ResNet3DBlock(hyperparams.Config):
"""Configuration of a ResNet 3D block."""
temporal_strides: int = 1
temporal_kernel_sizes: Tuple[int, ...] = ()
use_self_gating: bool = False
@dataclasses.dataclass
class ResNet3D(hyperparams.Config):
"""ResNet config."""
model_id: int = 50
stem_type: str = 'v0'
stem_conv_temporal_kernel_size: int = 5
stem_conv_temporal_stride: int = 2
stem_pool_temporal_stride: int = 2
block_specs: Tuple[ResNet3DBlock, ...] = ()
stochastic_depth_drop_rate: float = 0.0
se_ratio: float = 0.0
@dataclasses.dataclass
class ResNet3D50(ResNet3D):
"""Block specifications of the Resnet50 (3D) model."""
model_id: int = 50
block_specs: Tuple[
ResNet3DBlock, ResNet3DBlock, ResNet3DBlock, ResNet3DBlock] = (
ResNet3DBlock(temporal_strides=1,
temporal_kernel_sizes=(3, 3, 3),
use_self_gating=True),
ResNet3DBlock(temporal_strides=1,
temporal_kernel_sizes=(3, 1, 3, 1),
use_self_gating=True),
ResNet3DBlock(temporal_strides=1,
temporal_kernel_sizes=(3, 1, 3, 1, 3, 1),
use_self_gating=True),
ResNet3DBlock(temporal_strides=1,
temporal_kernel_sizes=(1, 3, 1),
use_self_gating=True))
@dataclasses.dataclass
class ResNet3DRS(ResNet3D):
"""Block specifications of the ResNet-RS (3D) model."""
model_id: int = 50
stem_type: str = 'v1'
stem_conv_temporal_kernel_size: int = 5
stem_conv_temporal_stride: int = 2
stem_pool_temporal_stride: int = 2
stochastic_depth_drop_rate: float = 0.1
se_ratio: float = 0.2
block_specs: Tuple[
ResNet3DBlock, ResNet3DBlock, ResNet3DBlock, ResNet3DBlock] = (
ResNet3DBlock(temporal_strides=1,
temporal_kernel_sizes=(1,),
use_self_gating=True),
ResNet3DBlock(temporal_strides=1,
temporal_kernel_sizes=(1,),
use_self_gating=True),
ResNet3DBlock(temporal_strides=1,
temporal_kernel_sizes=(3,),
use_self_gating=True),
ResNet3DBlock(temporal_strides=1,
temporal_kernel_sizes=(3,),
use_self_gating=True))
_RESNET3D50_DEFAULT_CFG = ResNet3D50()
_RESNET3DRS_DEFAULT_CFG = ResNet3DRS()
@dataclasses.dataclass
class Backbone3D(hyperparams.OneOfConfig):
"""Configuration for backbones.
Attributes:
type: 'str', type of backbone be used, one of the fields below.
resnet_3d: resnet3d backbone config.
resnet_3d_rs: resnet3d-rs backbone config.
"""
type: Optional[str] = None
resnet_3d: ResNet3D = _RESNET3D50_DEFAULT_CFG
resnet_3d_rs: ResNet3D = _RESNET3DRS_DEFAULT_CFG
official/vision/beta/configs/common.py deleted 100644 → 0
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# 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.
"""Common configurations."""
import dataclasses
from typing import List, Optional
# Import libraries
from official.core import config_definitions as cfg
from official.modeling import hyperparams
@dataclasses.dataclass
class TfExampleDecoder(hyperparams.Config):
"""A simple TF Example decoder config."""
regenerate_source_id: bool = False
mask_binarize_threshold: Optional[float] = None
@dataclasses.dataclass
class TfExampleDecoderLabelMap(hyperparams.Config):
"""TF Example decoder with label map config."""
regenerate_source_id: bool = False
mask_binarize_threshold: Optional[float] = None
label_map: str = ''
@dataclasses.dataclass
class DataDecoder(hyperparams.OneOfConfig):
"""Data decoder config.
Attributes:
type: 'str', type of data decoder be used, one of the fields below.
simple_decoder: simple TF Example decoder config.
label_map_decoder: TF Example decoder with label map config.
"""
type: Optional[str] = 'simple_decoder'
simple_decoder: TfExampleDecoder = TfExampleDecoder()
label_map_decoder: TfExampleDecoderLabelMap = TfExampleDecoderLabelMap()
@dataclasses.dataclass
class RandAugment(hyperparams.Config):
"""Configuration for RandAugment."""
num_layers: int = 2
magnitude: float = 10
cutout_const: float = 40
translate_const: float = 10
magnitude_std: float = 0.0
prob_to_apply: Optional[float] = None
exclude_ops: List[str] = dataclasses.field(default_factory=list)
@dataclasses.dataclass
class AutoAugment(hyperparams.Config):
"""Configuration for AutoAugment."""
augmentation_name: str = 'v0'
cutout_const: float = 100
translate_const: float = 250
@dataclasses.dataclass
class RandomErasing(hyperparams.Config):
"""Configuration for RandomErasing."""
probability: float = 0.25
min_area: float = 0.02
max_area: float = 1 / 3
min_aspect: float = 0.3
max_aspect = None
min_count = 1
max_count = 1
trials = 10
@dataclasses.dataclass
class MixupAndCutmix(hyperparams.Config):
"""Configuration for MixupAndCutmix."""
mixup_alpha: float = .8
cutmix_alpha: float = 1.
prob: float = 1.0
switch_prob: float = 0.5
label_smoothing: float = 0.1
@dataclasses.dataclass
class Augmentation(hyperparams.OneOfConfig):
"""Configuration for input data augmentation.
Attributes:
type: 'str', type of augmentation be used, one of the fields below.
randaug: RandAugment config.
autoaug: AutoAugment config.
"""
type: Optional[str] = None
randaug: RandAugment = RandAugment()
autoaug: AutoAugment = AutoAugment()
@dataclasses.dataclass
class NormActivation(hyperparams.Config):
activation: str = 'relu'
use_sync_bn: bool = True
norm_momentum: float = 0.99
norm_epsilon: float = 0.001
@dataclasses.dataclass
class PseudoLabelDataConfig(cfg.DataConfig):
"""Psuedo Label input config for training."""
input_path: str = ''
data_ratio: float = 1.0 # Per-batch ratio of pseudo-labeled to labeled data.
is_training: bool = True
dtype: str = 'float32'
shuffle_buffer_size: int = 10000
cycle_length: int = 10
aug_rand_hflip: bool = True
aug_type: Optional[
Augmentation] = None # Choose from AutoAugment and RandAugment.
file_type: str = 'tfrecord'
# Keep for backward compatibility.
aug_policy: Optional[str] = None # None, 'autoaug', or 'randaug'.
randaug_magnitude: Optional[int] = 10
official/vision/beta/configs/decoders.py deleted 100644 → 0
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# 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.
"""Decoders configurations."""
import dataclasses
from typing import List, Optional
# Import libraries
from official.modeling import hyperparams
@dataclasses.dataclass
class Identity(hyperparams.Config):
"""Identity config."""
pass
@dataclasses.dataclass
class FPN(hyperparams.Config):
"""FPN config."""
num_filters: int = 256
fusion_type: str = 'sum'
use_separable_conv: bool = False
@dataclasses.dataclass
class NASFPN(hyperparams.Config):
"""NASFPN config."""
num_filters: int = 256
num_repeats: int = 5
use_separable_conv: bool = False
@dataclasses.dataclass
class ASPP(hyperparams.Config):
"""ASPP config."""
level: int = 4
dilation_rates: List[int] = dataclasses.field(default_factory=list)
dropout_rate: float = 0.0
num_filters: int = 256
use_depthwise_convolution: bool = False
pool_kernel_size: Optional[List[int]] = None # Use global average pooling.
spp_layer_version: str = 'v1'
output_tensor: bool = False
@dataclasses.dataclass
class Decoder(hyperparams.OneOfConfig):
"""Configuration for decoders.
Attributes:
type: 'str', type of decoder be used, one of the fields below.
fpn: fpn config.
"""
type: Optional[str] = None
fpn: FPN = FPN()
nasfpn: NASFPN = NASFPN()
identity: Identity = Identity()
aspp: ASPP = ASPP()
official/vision/beta/configs/experiments/image_classification/imagenet_mobilenetv2_gpu.yaml deleted 100644 → 0
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# MobileNetV2_1.0 ImageNet classification. 71.0% top-1 and 90.0% top-5 accuracy.
runtime:
distribution_strategy: 'mirrored'
mixed_precision_dtype: 'float16'
loss_scale: 'dynamic'
task:
model:
num_classes: 1001
input_size: [224, 224, 3]
backbone:
type: 'mobilenet'
mobilenet:
model_id: 'MobileNetV2'
filter_size_scale: 1.0
dropout_rate: 0.2
losses:
l2_weight_decay: 0.00001
one_hot: true
label_smoothing: 0.1
train_data:
input_path: 'imagenet-2012-tfrecord/train*'
is_training: true
global_batch_size: 1024 # 128 * 8
dtype: 'float16'
validation_data:
input_path: 'imagenet-2012-tfrecord/valid*'
is_training: false
global_batch_size: 1024 # 128 * 8
dtype: 'float16'
drop_remainder: false
trainer:
train_steps: 625500 # 500 epochs
validation_steps: 49
validation_interval: 1251
steps_per_loop: 1251 # NUM_EXAMPLES (1281167) // global_batch_size
summary_interval: 1251
checkpoint_interval: 1251
optimizer_config:
learning_rate:
type: 'exponential'
exponential:
initial_learning_rate: 0.064 # 0.008 * batch_size / 128
decay_steps: 3127 # 2.5 * steps_per_epoch
decay_rate: 0.96
staircase: true
warmup:
type: 'linear'
linear:
warmup_steps: 6255
official/vision/beta/configs/experiments/image_classification/imagenet_mobilenetv2_tpu.yaml deleted 100644 → 0
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# MobileNetV2_1.0 ImageNet classification. 72.72% top-1 and 91.05% top-5 accuracy.
runtime:
distribution_strategy: 'tpu'
mixed_precision_dtype: 'bfloat16'
task:
model:
num_classes: 1001
input_size: [224, 224, 3]
backbone:
type: 'mobilenet'
mobilenet:
model_id: 'MobileNetV2'
filter_size_scale: 1.0
dropout_rate: 0.2
losses:
l2_weight_decay: 0.00001
one_hot: true
label_smoothing: 0.1
train_data:
input_path: 'imagenet-2012-tfrecord/train*'
is_training: true
global_batch_size: 4096
dtype: 'bfloat16'
validation_data:
input_path: 'imagenet-2012-tfrecord/valid*'
is_training: false
global_batch_size: 4096
dtype: 'bfloat16'
drop_remainder: false
trainer:
train_steps: 156000 # 500 epochs
validation_steps: 13
validation_interval: 312
steps_per_loop: 312 # NUM_EXAMPLES (1281167) // global_batch_size
summary_interval: 312
checkpoint_interval: 312
optimizer_config:
learning_rate:
type: 'exponential'
exponential:
initial_learning_rate: 0.256 # 0.008 * batch_size / 128
decay_steps: 780 # 2.5 * steps_per_epoch
decay_rate: 0.96
staircase: true
warmup:
type: 'linear'
linear:
warmup_steps: 1560
official/vision/beta/configs/experiments/image_classification/imagenet_mobilenetv3large_tpu.yaml deleted 100644 → 0
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# MobileNetV3-large_1.0 ImageNet classification: 74.96% top-1.
runtime:
distribution_strategy: 'tpu'
mixed_precision_dtype: 'bfloat16'
task:
model:
num_classes: 1001
input_size: [224, 224, 3]
backbone:
type: 'mobilenet'
mobilenet:
model_id: 'MobileNetV3Large'
filter_size_scale: 1.0
dropout_rate: 0.2
losses:
l2_weight_decay: 0.00001
one_hot: true
label_smoothing: 0.1
train_data:
input_path: 'imagenet-2012-tfrecord/train*'
is_training: true
global_batch_size: 4096
dtype: 'bfloat16'
# Enables Inception-style pre-processing.
decode_jpeg_only: false
validation_data:
input_path: 'imagenet-2012-tfrecord/valid*'
is_training: false
global_batch_size: 4096
dtype: 'bfloat16'
drop_remainder: false
# Enables Inception-style pre-processing.
decode_jpeg_only: false
trainer:
train_steps: 156000 # 500 epochs
validation_steps: 13
validation_interval: 312
steps_per_loop: 312 # NUM_EXAMPLES (1281167) // global_batch_size
summary_interval: 312
checkpoint_interval: 312
optimizer_config:
learning_rate:
type: 'cosine'
cosine:
alpha: 0.0
decay_steps: 156000
initial_learning_rate: 0.5
name: CosineDecay
offset: 0
warmup:
type: 'linear'
linear:
warmup_steps: 5000
official/vision/beta/configs/experiments/image_classification/imagenet_mobilenetv3small_tpu.yaml deleted 100644 → 0
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# MobileNetV3Small ImageNet classification. 67.5% top-1 and 87.6% top-5 accuracy.
runtime:
distribution_strategy: 'tpu'
mixed_precision_dtype: 'bfloat16'
task:
model:
num_classes: 1001
input_size: [224, 224, 3]
backbone:
type: 'mobilenet'
mobilenet:
model_id: 'MobileNetV3Small'
filter_size_scale: 1.0
norm_activation:
activation: 'relu'
norm_momentum: 0.997
norm_epsilon: 0.001
use_sync_bn: false
dropout_rate: 0.2
losses:
l2_weight_decay: 0.00001
one_hot: true
label_smoothing: 0.1
train_data:
input_path: 'imagenet-2012-tfrecord/train*'
is_training: true
global_batch_size: 4096
dtype: 'bfloat16'
validation_data:
input_path: 'imagenet-2012-tfrecord/valid*'
is_training: false
global_batch_size: 4096
dtype: 'bfloat16'
drop_remainder: false
trainer:
train_steps: 312000 # 1000 epochs
validation_steps: 12
validation_interval: 312
steps_per_loop: 312 # NUM_EXAMPLES (1281167) // global_batch_size
summary_interval: 312
checkpoint_interval: 312
optimizer_config:
optimizer:
type: 'rmsprop'
rmsprop:
rho: 0.9
momentum: 0.9
epsilon: 0.002
learning_rate:
type: 'exponential'
exponential:
initial_learning_rate: 0.01
decay_steps: 936 # 3 * steps_per_epoch
decay_rate: 0.99
staircase: true
ema:
average_decay: 0.9999
trainable_weights_only: false
warmup:
type: 'linear'
linear:
warmup_steps: 1560
warmup_learning_rate: 0.001
official/vision/beta/configs/experiments/image_classification/imagenet_resnet101_deeplab_tpu.yaml deleted 100644 → 0
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# Top-1 accuracy 81.6% on ImageNet
runtime:
distribution_strategy: 'tpu'
mixed_precision_dtype: 'bfloat16'
task:
model:
num_classes: 1001
input_size: [224, 224, 3]
backbone:
type: 'dilated_resnet'
dilated_resnet:
model_id: 101
output_stride: 16
stem_type: 'v1'
se_ratio: 0.25
stochastic_depth_drop_rate: 0.2
multigrid: [1, 2, 4]
last_stage_repeats: 1
norm_activation:
activation: 'swish'
losses:
l2_weight_decay: 0.00004
one_hot: true
label_smoothing: 0.1
train_data:
input_path: 'imagenet-2012-tfrecord/train*'
is_training: true
global_batch_size: 4096
dtype: 'bfloat16'
aug_policy: 'randaug'
validation_data:
input_path: 'imagenet-2012-tfrecord/valid*'
is_training: false
global_batch_size: 4096
dtype: 'bfloat16'
drop_remainder: false
trainer:
train_steps: 109200
validation_steps: 13
validation_interval: 312
steps_per_loop: 312
summary_interval: 312
checkpoint_interval: 312
optimizer_config:
optimizer:
type: 'sgd'
sgd:
momentum: 0.9
learning_rate:
type: 'cosine'
cosine:
initial_learning_rate: 1.6
decay_steps: 109200
warmup:
type: 'linear'
linear:
warmup_steps: 1560
official/vision/beta/configs/experiments/image_classification/imagenet_resnet101_tpu.yaml deleted 100644 → 0
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# ResNet-101 ImageNet classification. 79.1% top-1 and 94.5% top-5 accuracy.
runtime:
distribution_strategy: 'tpu'
mixed_precision_dtype: 'bfloat16'
task:
model:
num_classes: 1001
input_size: [224, 224, 3]
backbone:
type: 'resnet'
resnet:
model_id: 101
norm_activation:
activation: 'swish'
losses:
l2_weight_decay: 0.0001
one_hot: true
label_smoothing: 0.1
train_data:
input_path: 'imagenet-2012-tfrecord/train*'
is_training: true
global_batch_size: 4096
dtype: 'bfloat16'
validation_data:
input_path: 'imagenet-2012-tfrecord/valid*'
is_training: false
global_batch_size: 4096
dtype: 'bfloat16'
drop_remainder: false
trainer:
train_steps: 62400
validation_steps: 13
validation_interval: 312
steps_per_loop: 312
summary_interval: 312
checkpoint_interval: 312
optimizer_config:
optimizer:
type: 'sgd'
sgd:
momentum: 0.9
learning_rate:
type: 'cosine'
cosine:
initial_learning_rate: 1.6
decay_steps: 62400
warmup:
type: 'linear'
linear:
warmup_steps: 1560
official/vision/beta/configs/experiments/image_classification/imagenet_resnet152_tpu.yaml deleted 100644 → 0
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# ResNet-152 ImageNet classification. 79.4% top-1 and 94.7% top-5 accuracy.
runtime:
distribution_strategy: 'tpu'
mixed_precision_dtype: 'bfloat16'
task:
model:
num_classes: 1001
input_size: [224, 224, 3]
backbone:
type: 'resnet'
resnet:
model_id: 152
norm_activation:
activation: 'swish'
losses:
l2_weight_decay: 0.0001
one_hot: true
label_smoothing: 0.1
train_data:
input_path: 'imagenet-2012-tfrecord/train*'
is_training: true
global_batch_size: 4096
dtype: 'bfloat16'
validation_data:
input_path: 'imagenet-2012-tfrecord/valid*'
is_training: false
global_batch_size: 4096
dtype: 'bfloat16'
drop_remainder: false
trainer:
train_steps: 62400
validation_steps: 13
validation_interval: 312
steps_per_loop: 312
summary_interval: 312
checkpoint_interval: 312
optimizer_config:
optimizer:
type: 'sgd'
sgd:
momentum: 0.9
learning_rate:
type: 'cosine'
cosine:
initial_learning_rate: 1.6
decay_steps: 62400
warmup:
type: 'linear'
linear:
warmup_steps: 1560
official/vision/beta/configs/experiments/image_classification/imagenet_resnet50_deeplab_tpu.yaml deleted 100644 → 0
View file @ 45bd66a6
runtime:
distribution_strategy: 'tpu'
mixed_precision_dtype: 'bfloat16'
task:
model:
num_classes: 1001
input_size: [224, 224, 3]
backbone:
type: 'dilated_resnet'
dilated_resnet:
model_id: 50
output_stride: 16
norm_activation:
activation: 'swish'
losses:
l2_weight_decay: 0.0001
one_hot: true
label_smoothing: 0.1
train_data:
input_path: 'imagenet-2012-tfrecord/train*'
is_training: true
global_batch_size: 4096
dtype: 'bfloat16'
validation_data:
input_path: 'imagenet-2012-tfrecord/valid*'
is_training: false
global_batch_size: 4096
dtype: 'bfloat16'
drop_remainder: false
trainer:
train_steps: 62400
validation_steps: 13
validation_interval: 312
steps_per_loop: 312
summary_interval: 312
checkpoint_interval: 312
optimizer_config:
optimizer:
type: 'sgd'
sgd:
momentum: 0.9
learning_rate:
type: 'cosine'
cosine:
initial_learning_rate: 1.6
decay_steps: 62400
warmup:
type: 'linear'
linear:
warmup_steps: 1560
official/vision/beta/configs/experiments/image_classification/imagenet_resnet50_gpu.yaml deleted 100644 → 0
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runtime:
distribution_strategy: 'mirrored'
mixed_precision_dtype: 'float16'
loss_scale: 'dynamic'
task:
model:
num_classes: 1001
input_size: [224, 224, 3]
backbone:
type: 'resnet'
resnet:
model_id: 50
losses:
l2_weight_decay: 0.0001
one_hot: true
label_smoothing: 0.1
train_data:
input_path: 'imagenet-2012-tfrecord/train*'
is_training: true
global_batch_size: 2048
dtype: 'float16'
validation_data:
input_path: 'imagenet-2012-tfrecord/valid*'
is_training: false
global_batch_size: 2048
dtype: 'float16'
drop_remainder: false
trainer:
train_steps: 56160
validation_steps: 25
validation_interval: 625
steps_per_loop: 625
summary_interval: 625
checkpoint_interval: 625
optimizer_config:
optimizer:
type: 'sgd'
sgd:
momentum: 0.9
learning_rate:
type: 'stepwise'
stepwise:
boundaries: [18750, 37500, 50000]
values: [0.8, 0.08, 0.008, 0.0008]
warmup:
type: 'linear'
linear:
warmup_steps: 3125
official/vision/beta/configs/experiments/image_classification/imagenet_resnet50_tfds_tpu.yaml deleted 100644 → 0
View file @ 45bd66a6
# ResNet-50 ImageNet classification. 78.1% top-1 and 93.9% top-5 accuracy.
runtime:
distribution_strategy: 'tpu'
mixed_precision_dtype: 'bfloat16'
task:
model:
num_classes: 1001
input_size: [224, 224, 3]
backbone:
type: 'resnet'
resnet:
model_id: 50
norm_activation:
activation: 'swish'
losses:
l2_weight_decay: 0.0001
one_hot: true
label_smoothing: 0.1
train_data:
input_path: ''
tfds_name: 'imagenet2012'
tfds_split: 'train'
sharding: true
is_training: true
global_batch_size: 4096
dtype: 'bfloat16'
validation_data:
input_path: ''
tfds_name: 'imagenet2012'
tfds_split: 'validation'
sharding: true
is_training: false
global_batch_size: 4096
dtype: 'bfloat16'
drop_remainder: false
trainer:
train_steps: 62400
validation_steps: 13
validation_interval: 312
steps_per_loop: 312
summary_interval: 312
checkpoint_interval: 312
optimizer_config:
optimizer:
type: 'sgd'
sgd:
momentum: 0.9
learning_rate:
type: 'cosine'
cosine:
initial_learning_rate: 1.6
decay_steps: 62400
warmup:
type: 'linear'
linear:
warmup_steps: 1560
official/vision/beta/configs/experiments/image_classification/imagenet_resnet50_tpu.yaml deleted 100644 → 0
View file @ 45bd66a6
runtime:
distribution_strategy: 'tpu'
mixed_precision_dtype: 'bfloat16'
task:
model:
num_classes: 1001
input_size: [224, 224, 3]
backbone:
type: 'resnet'
resnet:
model_id: 50
losses:
l2_weight_decay: 0.0001
one_hot: true
label_smoothing: 0.1
train_data:
input_path: 'imagenet-2012-tfrecord/train*'
is_training: true
global_batch_size: 4096
dtype: 'bfloat16'
validation_data:
input_path: 'imagenet-2012-tfrecord/valid*'
is_training: false
global_batch_size: 4096
dtype: 'bfloat16'
drop_remainder: false
trainer:
train_steps: 28080
validation_steps: 13
validation_interval: 312
steps_per_loop: 312
summary_interval: 312
checkpoint_interval: 312
optimizer_config:
optimizer:
type: 'sgd'
sgd:
momentum: 0.9
learning_rate:
type: 'stepwise'
stepwise:
boundaries: [9360, 18720, 24960]
values: [1.6, 0.16, 0.016, 0.0016]
warmup:
type: 'linear'
linear:
warmup_steps: 1560
official/vision/beta/configs/experiments/image_classification/imagenet_resnetrs101_i160.yaml deleted 100644 → 0
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# ResNet-RS-101 ImageNet classification. 80.2% top-1 accuracy.
runtime:
distribution_strategy: 'tpu'
mixed_precision_dtype: 'bfloat16'
task:
model:
num_classes: 1001
input_size: [160, 160, 3]
backbone:
type: 'resnet'
resnet:
model_id: 101
replace_stem_max_pool: true
resnetd_shortcut: true
se_ratio: 0.25
stem_type: 'v1'
stochastic_depth_drop_rate: 0.0
norm_activation:
activation: 'swish'
norm_momentum: 0.0
use_sync_bn: false
dropout_rate: 0.25
losses:
l2_weight_decay: 0.00004
one_hot: true
label_smoothing: 0.1
train_data:
input_path: 'imagenet-2012-tfrecord/train*'
is_training: true
global_batch_size: 4096
dtype: 'bfloat16'
aug_type:
type: 'randaug'
randaug:
magnitude: 15
validation_data:
input_path: 'imagenet-2012-tfrecord/valid*'
is_training: false
global_batch_size: 4096
dtype: 'bfloat16'
drop_remainder: false
trainer:
train_steps: 109200
validation_steps: 13
validation_interval: 312
steps_per_loop: 312
summary_interval: 312
checkpoint_interval: 312
optimizer_config:
ema:
average_decay: 0.9999
optimizer:
type: 'sgd'
sgd:
momentum: 0.9
learning_rate:
type: 'cosine'
cosine:
initial_learning_rate: 1.6
decay_steps: 109200
warmup:
type: 'linear'
linear:
warmup_steps: 1560
official/vision/beta/configs/experiments/image_classification/imagenet_resnetrs101_i192.yaml deleted 100644 → 0
View file @ 45bd66a6
# ResNet-RS-101 ImageNet classification. 81.3% top-1 accuracy.
runtime:
distribution_strategy: 'tpu'
mixed_precision_dtype: 'bfloat16'
task:
model:
num_classes: 1001
input_size: [192, 192, 3]
backbone:
type: 'resnet'
resnet:
model_id: 101
replace_stem_max_pool: true
resnetd_shortcut: true
se_ratio: 0.25
stem_type: 'v1'
stochastic_depth_drop_rate: 0.0
norm_activation:
activation: 'swish'
norm_momentum: 0.0
use_sync_bn: false
dropout_rate: 0.25
losses:
l2_weight_decay: 0.00004
one_hot: true
label_smoothing: 0.1
train_data:
input_path: 'imagenet-2012-tfrecord/train*'
is_training: true
global_batch_size: 4096
dtype: 'bfloat16'
aug_type:
type: 'randaug'
randaug:
magnitude: 15
validation_data:
input_path: 'imagenet-2012-tfrecord/valid*'
is_training: false
global_batch_size: 4096
dtype: 'bfloat16'
drop_remainder: false
trainer:
train_steps: 109200
validation_steps: 13
validation_interval: 312
steps_per_loop: 312
summary_interval: 312
checkpoint_interval: 312
optimizer_config:
ema:
average_decay: 0.9999
optimizer:
type: 'sgd'
sgd:
momentum: 0.9
learning_rate:
type: 'cosine'
cosine:
initial_learning_rate: 1.6
decay_steps: 109200
warmup:
type: 'linear'
linear:
warmup_steps: 1560
official/vision/beta/configs/experiments/image_classification/imagenet_resnetrs152_i192.yaml deleted 100644 → 0
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# ResNet-RS-152 ImageNet classification. 81.9% top-1 accuracy.
runtime:
distribution_strategy: 'tpu'
mixed_precision_dtype: 'bfloat16'
task:
model:
num_classes: 1001
input_size: [192, 192, 3]
backbone:
type: 'resnet'
resnet:
model_id: 152
replace_stem_max_pool: true
resnetd_shortcut: true
se_ratio: 0.25
stem_type: 'v1'
stochastic_depth_drop_rate: 0.0
norm_activation:
activation: 'swish'
norm_momentum: 0.0
use_sync_bn: false
dropout_rate: 0.25
losses:
l2_weight_decay: 0.00004
one_hot: true
label_smoothing: 0.1
train_data:
input_path: 'imagenet-2012-tfrecord/train*'
is_training: true
global_batch_size: 4096
dtype: 'bfloat16'
aug_type:
type: 'randaug'
randaug:
magnitude: 15
validation_data:
input_path: 'imagenet-2012-tfrecord/valid*'
is_training: false
global_batch_size: 4096
dtype: 'bfloat16'
drop_remainder: false
trainer:
train_steps: 109200
validation_steps: 13
validation_interval: 312
steps_per_loop: 312
summary_interval: 312
checkpoint_interval: 312
optimizer_config:
ema:
average_decay: 0.9999
optimizer:
type: 'sgd'
sgd:
momentum: 0.9
learning_rate:
type: 'cosine'
cosine:
initial_learning_rate: 1.6
decay_steps: 109200
warmup:
type: 'linear'
linear:
warmup_steps: 1560
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