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121 lines
5.0 KiB
121 lines
5.0 KiB
from torchvision.models import Inception3
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from .registry import register_model
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from .helpers import load_pretrained
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from timm.data import IMAGENET_DEFAULT_STD, IMAGENET_DEFAULT_MEAN, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
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__all__ = []
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default_cfgs = {
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# original PyTorch weights, ported from Tensorflow but modified
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'inception_v3': {
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'url': 'https://download.pytorch.org/models/inception_v3_google-1a9a5a14.pth',
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'input_size': (3, 299, 299),
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'crop_pct': 0.875,
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'interpolation': 'bicubic',
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'mean': IMAGENET_INCEPTION_MEAN, # also works well enough with resnet defaults
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'std': IMAGENET_INCEPTION_STD, # also works well enough with resnet defaults
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'num_classes': 1000,
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'first_conv': 'conv0',
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'classifier': 'fc'
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},
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# my port of Tensorflow SLIM weights (http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz)
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'tf_inception_v3': {
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'url': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_inception_v3-e0069de4.pth',
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'input_size': (3, 299, 299),
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'crop_pct': 0.875,
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'interpolation': 'bicubic',
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'mean': IMAGENET_INCEPTION_MEAN,
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'std': IMAGENET_INCEPTION_STD,
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'num_classes': 1001,
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'first_conv': 'conv0',
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'classifier': 'fc'
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},
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# my port of Tensorflow adversarially trained Inception V3 from
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# http://download.tensorflow.org/models/adv_inception_v3_2017_08_18.tar.gz
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'adv_inception_v3': {
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'url': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/adv_inception_v3-9e27bd63.pth',
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'input_size': (3, 299, 299),
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'crop_pct': 0.875,
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'interpolation': 'bicubic',
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'mean': IMAGENET_INCEPTION_MEAN,
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'std': IMAGENET_INCEPTION_STD,
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'num_classes': 1001,
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'first_conv': 'conv0',
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'classifier': 'fc'
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},
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# from gluon pretrained models, best performing in terms of accuracy/loss metrics
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# https://gluon-cv.mxnet.io/model_zoo/classification.html
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'gluon_inception_v3': {
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'url': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/gluon_inception_v3-9f746940.pth',
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'input_size': (3, 299, 299),
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'crop_pct': 0.875,
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'interpolation': 'bicubic',
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'mean': IMAGENET_DEFAULT_MEAN, # also works well with inception defaults
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'std': IMAGENET_DEFAULT_STD, # also works well with inception defaults
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'num_classes': 1000,
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'first_conv': 'conv0',
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'classifier': 'fc'
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}
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}
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def _assert_default_kwargs(kwargs):
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# for imported models (ie torchvision) without capability to change these params,
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# make sure they aren't being set to non-defaults
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assert kwargs.pop('global_pool', 'avg') == 'avg'
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assert kwargs.pop('drop_rate', 0.) == 0.
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@register_model
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def inception_v3(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
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# original PyTorch weights, ported from Tensorflow but modified
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default_cfg = default_cfgs['inception_v3']
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assert in_chans == 3
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_assert_default_kwargs(kwargs)
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model = Inception3(num_classes=num_classes, aux_logits=True, transform_input=False)
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if pretrained:
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load_pretrained(model, default_cfg, num_classes, in_chans)
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model.default_cfg = default_cfg
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return model
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@register_model
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def tf_inception_v3(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
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# my port of Tensorflow SLIM weights (http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz)
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default_cfg = default_cfgs['tf_inception_v3']
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assert in_chans == 3
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_assert_default_kwargs(kwargs)
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model = Inception3(num_classes=num_classes, aux_logits=False, transform_input=False)
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if pretrained:
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load_pretrained(model, default_cfg, num_classes, in_chans)
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model.default_cfg = default_cfg
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return model
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@register_model
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def adv_inception_v3(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
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# my port of Tensorflow adversarially trained Inception V3 from
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# http://download.tensorflow.org/models/adv_inception_v3_2017_08_18.tar.gz
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default_cfg = default_cfgs['adv_inception_v3']
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assert in_chans == 3
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_assert_default_kwargs(kwargs)
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model = Inception3(num_classes=num_classes, aux_logits=False, transform_input=False)
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if pretrained:
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load_pretrained(model, default_cfg, num_classes, in_chans)
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model.default_cfg = default_cfg
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return model
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@register_model
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def gluon_inception_v3(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
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# from gluon pretrained models, best performing in terms of accuracy/loss metrics
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# https://gluon-cv.mxnet.io/model_zoo/classification.html
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default_cfg = default_cfgs['gluon_inception_v3']
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assert in_chans == 3
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_assert_default_kwargs(kwargs)
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model = Inception3(num_classes=num_classes, aux_logits=False, transform_input=False)
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if pretrained:
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load_pretrained(model, default_cfg, num_classes, in_chans)
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model.default_cfg = default_cfg
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return model
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