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added segmentation_models for further adjustments
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‎orthophoto-segmentation-benchmark-toolkit/model_backends/segmentation_models/LICENSE

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The MIT License
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Copyright (c) 2018, Pavel Yakubovskiy
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in
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all copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
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THE SOFTWARE.

‎orthophoto-segmentation-benchmark-toolkit/model_backends/segmentation_models/__init__.py

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import os
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import functools
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from .__version__ import __version__
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from . import base
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_KERAS_FRAMEWORK_NAME = 'keras'
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_TF_KERAS_FRAMEWORK_NAME = 'tf.keras'
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_DEFAULT_KERAS_FRAMEWORK = _KERAS_FRAMEWORK_NAME
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_KERAS_FRAMEWORK = None
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_KERAS_BACKEND = None
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_KERAS_LAYERS = None
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_KERAS_MODELS = None
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_KERAS_UTILS = None
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_KERAS_LOSSES = None
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def inject_global_losses(func):
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@functools.wraps(func)
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def wrapper(*args, **kwargs):
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kwargs['losses'] = _KERAS_LOSSES
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return func(*args, **kwargs)
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return wrapper
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def inject_global_submodules(func):
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@functools.wraps(func)
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def wrapper(*args, **kwargs):
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kwargs['backend'] = _KERAS_BACKEND
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kwargs['layers'] = _KERAS_LAYERS
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kwargs['models'] = _KERAS_MODELS
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kwargs['utils'] = _KERAS_UTILS
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return func(*args, **kwargs)
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return wrapper
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def filter_kwargs(func):
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@functools.wraps(func)
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def wrapper(*args, **kwargs):
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new_kwargs = {k: v for k, v in kwargs.items() if k in ['backend', 'layers', 'models', 'utils']}
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return func(*args, **new_kwargs)
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return wrapper
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def framework():
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"""Return name of Segmentation Models framework"""
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return _KERAS_FRAMEWORK
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def set_framework(name):
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"""Set framework for Segmentation Models
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Args:
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name (str): one of ``keras``, ``tf.keras``, case insensitive.
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Raises:
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ValueError: in case of incorrect framework name.
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ImportError: in case framework is not installed.
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"""
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name = name.lower()
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if name == _KERAS_FRAMEWORK_NAME:
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import keras
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import efficientnet.keras # init custom objects
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elif name == _TF_KERAS_FRAMEWORK_NAME:
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from tensorflow import keras
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import efficientnet.tfkeras # init custom objects
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else:
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raise ValueError('Not correct module name `{}`, use `{}` or `{}`'.format(
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name, _KERAS_FRAMEWORK_NAME, _TF_KERAS_FRAMEWORK_NAME))
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global _KERAS_BACKEND, _KERAS_LAYERS, _KERAS_MODELS
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global _KERAS_UTILS, _KERAS_LOSSES, _KERAS_FRAMEWORK
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_KERAS_FRAMEWORK = name
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_KERAS_BACKEND = keras.backend
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_KERAS_LAYERS = keras.layers
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_KERAS_MODELS = keras.models
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_KERAS_UTILS = keras.utils
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_KERAS_LOSSES = keras.losses
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# allow losses/metrics get keras submodules
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base.KerasObject.set_submodules(
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backend=keras.backend,
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layers=keras.layers,
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models=keras.models,
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utils=keras.utils,
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)
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# set default framework
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_framework = os.environ.get('SM_FRAMEWORK', _DEFAULT_KERAS_FRAMEWORK)
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try:
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set_framework(_framework)
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except ImportError:
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other = _TF_KERAS_FRAMEWORK_NAME if _framework == _KERAS_FRAMEWORK_NAME else _KERAS_FRAMEWORK_NAME
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set_framework(other)
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print('Segmentation Models: using `{}` framework.'.format(_KERAS_FRAMEWORK))
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# import helper modules
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from . import losses
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from . import metrics
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from . import utils
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# wrap segmentation models with framework modules
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from .backbones.backbones_factory import Backbones
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from .models.unet import Unet as _Unet
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from .models.pspnet import PSPNet as _PSPNet
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from .models.linknet import Linknet as _Linknet
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from .models.fpn import FPN as _FPN
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Unet = inject_global_submodules(_Unet)
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PSPNet = inject_global_submodules(_PSPNet)
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Linknet = inject_global_submodules(_Linknet)
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FPN = inject_global_submodules(_FPN)
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get_available_backbone_names = Backbones.models_names
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def get_preprocessing(name):
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preprocess_input = Backbones.get_preprocessing(name)
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# add bakcend, models, layers, utils submodules in kwargs
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preprocess_input = inject_global_submodules(preprocess_input)
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# delete other kwargs
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# keras-applications preprocessing raise an error if something
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# except `backend`, `layers`, `models`, `utils` passed in kwargs
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preprocess_input = filter_kwargs(preprocess_input)
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return preprocess_input
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__all__ = [
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'Unet', 'PSPNet', 'FPN', 'Linknet',
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'set_framework', 'framework',
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'get_preprocessing', 'get_available_backbone_names',
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'losses', 'metrics', 'utils',
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'__version__',
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]

‎orthophoto-segmentation-benchmark-toolkit/model_backends/segmentation_models/__version__.py

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VERSION = (1, 0, 1)
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__version__ = '.'.join(map(str, VERSION))

‎orthophoto-segmentation-benchmark-toolkit/model_backends/segmentation_models/backbones/__init__.py

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‎orthophoto-segmentation-benchmark-toolkit/model_backends/segmentation_models/backbones/backbones_factory.py

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import copy
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import efficientnet.model as eff
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from classification_models.models_factory import ModelsFactory
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from . import inception_resnet_v2 as irv2
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from . import inception_v3 as iv3
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class BackbonesFactory(ModelsFactory):
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_default_feature_layers = {
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# List of layers to take features from backbone in the following order:
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# (x16, x8, x4, x2, x1) - `x4` mean that features has 4 times less spatial
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# resolution (Height x Width) than input image.
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# VGG
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'vgg16': ('block5_conv3', 'block4_conv3', 'block3_conv3', 'block2_conv2', 'block1_conv2'),
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'vgg19': ('block5_conv4', 'block4_conv4', 'block3_conv4', 'block2_conv2', 'block1_conv2'),
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# ResNets
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'resnet18': ('stage4_unit1_relu1', 'stage3_unit1_relu1', 'stage2_unit1_relu1', 'relu0'),
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'resnet34': ('stage4_unit1_relu1', 'stage3_unit1_relu1', 'stage2_unit1_relu1', 'relu0'),
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'resnet50': ('stage4_unit1_relu1', 'stage3_unit1_relu1', 'stage2_unit1_relu1', 'relu0'),
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'resnet101': ('stage4_unit1_relu1', 'stage3_unit1_relu1', 'stage2_unit1_relu1', 'relu0'),
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'resnet152': ('stage4_unit1_relu1', 'stage3_unit1_relu1', 'stage2_unit1_relu1', 'relu0'),
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# ResNeXt
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'resnext50': ('stage4_unit1_relu1', 'stage3_unit1_relu1', 'stage2_unit1_relu1', 'relu0'),
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'resnext101': ('stage4_unit1_relu1', 'stage3_unit1_relu1', 'stage2_unit1_relu1', 'relu0'),
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# Inception
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'inceptionv3': (228, 86, 16, 9),
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'inceptionresnetv2': (594, 260, 16, 9),
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# DenseNet
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'densenet121': (311, 139, 51, 4),
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'densenet169': (367, 139, 51, 4),
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'densenet201': (479, 139, 51, 4),
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# SE models
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'seresnet18': ('stage4_unit1_relu1', 'stage3_unit1_relu1', 'stage2_unit1_relu1', 'relu0'),
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'seresnet34': ('stage4_unit1_relu1', 'stage3_unit1_relu1', 'stage2_unit1_relu1', 'relu0'),
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'seresnet50': (246, 136, 62, 4),
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'seresnet101': (552, 136, 62, 4),
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'seresnet152': (858, 208, 62, 4),
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'seresnext50': (1078, 584, 254, 4),
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'seresnext101': (2472, 584, 254, 4),
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'senet154': (6884, 1625, 454, 12),
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# Mobile Nets
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'mobilenet': ('conv_pw_11_relu', 'conv_pw_5_relu', 'conv_pw_3_relu', 'conv_pw_1_relu'),
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'mobilenetv2': ('block_13_expand_relu', 'block_6_expand_relu', 'block_3_expand_relu',
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'block_1_expand_relu'),
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# EfficientNets
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'efficientnetb0': ('block6a_expand_activation', 'block4a_expand_activation',
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'block3a_expand_activation', 'block2a_expand_activation'),
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'efficientnetb1': ('block6a_expand_activation', 'block4a_expand_activation',
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'block3a_expand_activation', 'block2a_expand_activation'),
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'efficientnetb2': ('block6a_expand_activation', 'block4a_expand_activation',
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'block3a_expand_activation', 'block2a_expand_activation'),
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'efficientnetb3': ('block6a_expand_activation', 'block4a_expand_activation',
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'block3a_expand_activation', 'block2a_expand_activation'),
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'efficientnetb4': ('block6a_expand_activation', 'block4a_expand_activation',
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'block3a_expand_activation', 'block2a_expand_activation'),
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'efficientnetb5': ('block6a_expand_activation', 'block4a_expand_activation',
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'block3a_expand_activation', 'block2a_expand_activation'),
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'efficientnetb6': ('block6a_expand_activation', 'block4a_expand_activation',
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'block3a_expand_activation', 'block2a_expand_activation'),
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'efficientnetb7': ('block6a_expand_activation', 'block4a_expand_activation',
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'block3a_expand_activation', 'block2a_expand_activation'),
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}
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_models_update = {
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'inceptionresnetv2': [irv2.InceptionResNetV2, irv2.preprocess_input],
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'inceptionv3': [iv3.InceptionV3, iv3.preprocess_input],
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'efficientnetb0': [eff.EfficientNetB0, eff.preprocess_input],
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'efficientnetb1': [eff.EfficientNetB1, eff.preprocess_input],
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'efficientnetb2': [eff.EfficientNetB2, eff.preprocess_input],
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'efficientnetb3': [eff.EfficientNetB3, eff.preprocess_input],
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'efficientnetb4': [eff.EfficientNetB4, eff.preprocess_input],
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'efficientnetb5': [eff.EfficientNetB5, eff.preprocess_input],
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'efficientnetb6': [eff.EfficientNetB6, eff.preprocess_input],
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'efficientnetb7': [eff.EfficientNetB7, eff.preprocess_input],
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}
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# currently not supported
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_models_delete = ['resnet50v2', 'resnet101v2', 'resnet152v2',
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'nasnetlarge', 'nasnetmobile', 'xception']
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@property
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def models(self):
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all_models = copy.copy(self._models)
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all_models.update(self._models_update)
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for k in self._models_delete:
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del all_models[k]
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return all_models
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def get_backbone(self, name, *args, **kwargs):
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model_fn, _ = self.get(name)
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model = model_fn(*args, **kwargs)
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return model
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def get_feature_layers(self, name, n=5):
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return self._default_feature_layers[name][:n]
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def get_preprocessing(self, name):
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return self.get(name)[1]
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Backbones = BackbonesFactory()

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