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miguelCalado
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‎official/legacy/image_classification/classifier_trainer.py

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@@ -36,13 +36,15 @@
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from official.modeling import performance
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from official.utils import hyperparams_flags
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from official.utils.misc import keras_utils
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from official.vision.image_classification.vgg16 import vgg_model
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def get_models() -> Mapping[str, tf.keras.Model]:
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"""Returns the mapping from model type name to Keras model."""
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return {
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'efficientnet': efficientnet_model.EfficientNet.from_name,
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'resnet': resnet_model.resnet50,
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'vgg': vgg_model.vgg16,
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}
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‎official/legacy/image_classification/classifier_trainer_test.py

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@@ -53,6 +53,7 @@ def distribution_strategy_combinations() -> Iterable[Tuple[Any, ...]]:
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model=[
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'efficientnet',
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'resnet',
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'vgg',
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],
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dataset=[
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'imagenet',
@@ -149,6 +150,7 @@ def test_end_to_end_train_and_eval(self, distribution, model, dataset):
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model=[
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'efficientnet',
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'resnet',
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'vgg',
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],
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dataset='imagenet',
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dtype='float16',
@@ -193,6 +195,7 @@ def test_gpu_train(self, distribution, model, dataset, dtype):
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model=[
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'efficientnet',
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'resnet',
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'vgg',
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],
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dataset='imagenet',
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dtype='bfloat16',

‎official/legacy/image_classification/configs/configs.py

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@@ -24,6 +24,7 @@
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from official.legacy.image_classification.configs import base_configs
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from official.legacy.image_classification.efficientnet import efficientnet_config
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from official.legacy.image_classification.resnet import resnet_config
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from official.vision.image_classification.vgg16 import vgg_config
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@dataclasses.dataclass
@@ -92,12 +93,44 @@ class ResNetImagenetConfig(base_configs.ExperimentConfig):
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model: base_configs.ModelConfig = resnet_config.ResNetModelConfig()
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@dataclasses.dataclass
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class VGGImagenetConfig(base_configs.ExperimentConfig):
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"""Base configuration to train vgg-16 on ImageNet."""
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export: base_configs.ExportConfig = base_configs.ExportConfig()
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runtime: base_configs.RuntimeConfig = base_configs.RuntimeConfig()
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train_dataset: dataset_factory.DatasetConfig = \
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dataset_factory.ImageNetConfig(split='train',
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one_hot=False,
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mean_subtract=True,
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standardize=True)
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validation_dataset: dataset_factory.DatasetConfig = \
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dataset_factory.ImageNetConfig(split='validation',
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one_hot=False,
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mean_subtract=True,
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standardize=True)
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train: base_configs.TrainConfig = base_configs.TrainConfig(
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resume_checkpoint=True,
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epochs=90,
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steps=None,
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callbacks=base_configs.CallbacksConfig(
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enable_checkpoint_and_export=True, enable_tensorboard=True),
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metrics=['accuracy', 'top_5'],
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time_history=base_configs.TimeHistoryConfig(log_steps=100),
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tensorboard=base_configs.TensorBoardConfig(
120+
track_lr=True, write_model_weights=False),
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set_epoch_loop=False)
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evaluation: base_configs.EvalConfig = base_configs.EvalConfig(
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epochs_between_evals=1, steps=None)
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model: base_configs.ModelConfig = vgg_config.VGGModelConfig()
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def get_config(model: str, dataset: str) -> base_configs.ExperimentConfig:
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"""Given model and dataset names, return the ExperimentConfig."""
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dataset_model_config_map = {
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'imagenet': {
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'efficientnet': EfficientNetImageNetConfig(),
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'resnet': ResNetImagenetConfig(),
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'vgg': VGGImagenetConfig(),
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}
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}
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try:

‎official/vision/image_classification/configs/examples/vgg16/gpu.yaml renamed to ‎official/legacy/image_classification/configs/examples/vgg16/gpu.yaml

File renamed without changes.

‎official/vision/image_classification/vgg16/__init__.py renamed to ‎official/legacy/image_classification/vgg16/__init__.py

File renamed without changes.

‎official/vision/image_classification/vgg16/vgg_config.py renamed to ‎official/legacy/image_classification/vgg16/vgg_config.py

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@@ -49,5 +49,5 @@ class VGGModelConfig(base_configs.ModelConfig):
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examples_per_epoch=1281167,
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boundaries=[30, 60],
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warmup_epochs=0,
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scale_by_batch_size=1. / 128.,
52+
scale_by_batch_size=1. / 256.,
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multipliers=[0.01 / 256, 0.001 / 256, 0.0001 / 256]))

‎official/vision/image_classification/vgg16/vgg_model.py renamed to ‎official/legacy/image_classification/vgg16/vgg_model.py

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@@ -174,17 +174,23 @@ def vgg16(num_classes,
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x = layers.Flatten(name='flatten')(x)
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x = layers.Dense(4096,
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#kernel_initializer=tf.initializers.random_normal(stddev=0.01),
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kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
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#bias_regularizer=_gen_l2_regularizer(use_l2_regularizer),
178180
name='fc1')(x)
179181
x = layers.Activation('relu')(x)
180182
x = layers.Dropout(0.5)(x)
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x = layers.Dense(4096,
184+
#kernel_initializer=tf.initializers.random_normal(stddev=0.01),
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kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
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#bias_regularizer=_gen_l2_regularizer(use_l2_regularizer),
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name='fc2')(x)
184188
x = layers.Activation('relu')(x)
185189
x = layers.Dropout(0.5)(x)
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x = layers.Dense(num_classes,
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#kernel_initializer=tf.initializers.random_normal(stddev=0.01),
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kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
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#bias_regularizer=_gen_l2_regularizer(use_l2_regularizer),
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name='fc1000')(x)
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# A softmax that is followed by the model loss must be done cannot be done

‎official/vision/image_classification/classifier_trainer.py

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@@ -36,15 +36,13 @@
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from official.vision.image_classification.efficientnet import efficientnet_model
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from official.vision.image_classification.resnet import common
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from official.vision.image_classification.resnet import resnet_model
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from official.vision.image_classification.vgg16 import vgg_model
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def get_models() -> Mapping[str, tf.keras.Model]:
4342
"""Returns the mapping from model type name to Keras model."""
4443
return {
4544
'efficientnet': efficientnet_model.EfficientNet.from_name,
4645
'resnet': resnet_model.resnet50,
47-
'vgg': vgg_model.vgg16,
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}
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‎official/vision/image_classification/classifier_trainer_test.py

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@@ -53,7 +53,6 @@ def distribution_strategy_combinations() -> Iterable[Tuple[Any, ...]]:
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model=[
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'efficientnet',
5555
'resnet',
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'vgg',
5756
],
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dataset=[
5958
'imagenet',
@@ -150,7 +149,6 @@ def test_end_to_end_train_and_eval(self, distribution, model, dataset):
150149
model=[
151150
'efficientnet',
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'resnet',
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'vgg',
154152
],
155153
dataset='imagenet',
156154
dtype='float16',
@@ -195,7 +193,6 @@ def test_gpu_train(self, distribution, model, dataset, dtype):
195193
model=[
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'efficientnet',
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'resnet',
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'vgg',
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],
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dataset='imagenet',
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dtype='bfloat16',

‎official/vision/image_classification/configs/configs.py

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@@ -91,44 +91,13 @@ class ResNetImagenetConfig(base_configs.ExperimentConfig):
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epochs_between_evals=1, steps=None)
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model: base_configs.ModelConfig = resnet_config.ResNetModelConfig()
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94-
@dataclasses.dataclass
95-
class VGGImagenetConfig(base_configs.ExperimentConfig):
96-
"""Base configuration to train vgg-16 on ImageNet."""
97-
export: base_configs.ExportConfig = base_configs.ExportConfig()
98-
runtime: base_configs.RuntimeConfig = base_configs.RuntimeConfig()
99-
train_dataset: dataset_factory.DatasetConfig = \
100-
dataset_factory.ImageNetConfig(split='train',
101-
one_hot=False,
102-
mean_subtract=True,
103-
standardize=True)
104-
validation_dataset: dataset_factory.DatasetConfig = \
105-
dataset_factory.ImageNetConfig(split='validation',
106-
one_hot=False,
107-
mean_subtract=True,
108-
standardize=True)
109-
train: base_configs.TrainConfig = base_configs.TrainConfig(
110-
resume_checkpoint=True,
111-
epochs=90,
112-
steps=None,
113-
callbacks=base_configs.CallbacksConfig(
114-
enable_checkpoint_and_export=True, enable_tensorboard=True),
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metrics=['accuracy', 'top_5'],
116-
time_history=base_configs.TimeHistoryConfig(log_steps=100),
117-
tensorboard=base_configs.TensorBoardConfig(
118-
track_lr=True, write_model_weights=False),
119-
set_epoch_loop=False)
120-
evaluation: base_configs.EvalConfig = base_configs.EvalConfig(
121-
epochs_between_evals=1, steps=None)
122-
model: base_configs.ModelConfig = vgg_config.VGGModelConfig()
123-
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12595
def get_config(model: str, dataset: str) -> base_configs.ExperimentConfig:
12696
"""Given model and dataset names, return the ExperimentConfig."""
12797
dataset_model_config_map = {
12898
'imagenet': {
12999
'efficientnet': EfficientNetImageNetConfig(),
130100
'resnet': ResNetImagenetConfig(),
131-
'vgg': VGGImagenetConfig()
132101
}
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}
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try:

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