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def efficientnet_cc_b0_4e(pretrained=False, **kwargs):
' EfficientNet-CondConv-B0 w/ 8 Experts '
model = _gen_efficientnet_condconv('efficientnet_cc_b0_4e', channel_multiplier=1.0, depth_multiplier=1.0, pretrained=pretrained, **kwargs)
return model
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def efficientnet_cc_b0_8e(pretrained=False, **kwargs):
' EfficientNet-CondConv-B0 w/ 8 Experts '
model = _gen_efficientnet_condconv('efficientnet_cc_b0_8e', channel_multiplier=1.0, depth_multiplier=1.0, experts_multiplier=2, pretrained=pretrained, **kwargs)
return model
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def efficientnet_cc_b1_8e(pretrained=False, **kwargs):
' EfficientNet-CondConv-B1 w/ 8 Experts '
model = _gen_efficientnet_condconv('efficientnet_cc_b1_8e', channel_multiplier=1.0, depth_multiplier=1.1, experts_multiplier=2, pretrained=pretrained, **kwargs)
return model
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def efficientnet_lite0(pretrained=False, **kwargs):
' EfficientNet-Lite0 '
model = _gen_efficientnet_lite('efficientnet_lite0', channel_multiplier=1.0, depth_multiplier=1.0, pretrained=pretrained, **kwargs)
return model
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def efficientnet_lite1(pretrained=False, **kwargs):
' EfficientNet-Lite1 '
model = _gen_efficientnet_lite('efficientnet_lite1', channel_multiplier=1.0, depth_multiplier=1.1, pretrained=pretrained, **kwargs)
return model
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def efficientnet_lite2(pretrained=False, **kwargs):
' EfficientNet-Lite2 '
model = _gen_efficientnet_lite('efficientnet_lite2', channel_multiplier=1.1, depth_multiplier=1.2, pretrained=pretrained, **kwargs)
return model
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def efficientnet_lite3(pretrained=False, **kwargs):
' EfficientNet-Lite3 '
model = _gen_efficientnet_lite('efficientnet_lite3', channel_multiplier=1.2, depth_multiplier=1.4, pretrained=pretrained, **kwargs)
return model
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def efficientnet_lite4(pretrained=False, **kwargs):
' EfficientNet-Lite4 '
model = _gen_efficientnet_lite('efficientnet_lite4', channel_multiplier=1.4, depth_multiplier=1.8, pretrained=pretrained, **kwargs)
return model
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def tf_efficientnet_b0(pretrained=False, **kwargs):
' EfficientNet-B0 AutoAug. Tensorflow compatible variant '
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_efficientnet('tf_efficientnet_b0', channel_multiplier=1.0, depth_multiplier=1.0, pretrained=pretrained, **kwargs... |
def tf_efficientnet_b1(pretrained=False, **kwargs):
' EfficientNet-B1 AutoAug. Tensorflow compatible variant '
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_efficientnet('tf_efficientnet_b1', channel_multiplier=1.0, depth_multiplier=1.1, pretrained=pretrained, **kwargs... |
def tf_efficientnet_b2(pretrained=False, **kwargs):
' EfficientNet-B2 AutoAug. Tensorflow compatible variant '
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_efficientnet('tf_efficientnet_b2', channel_multiplier=1.1, depth_multiplier=1.2, pretrained=pretrained, **kwargs... |
def tf_efficientnet_b3(pretrained=False, **kwargs):
' EfficientNet-B3 AutoAug. Tensorflow compatible variant '
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_efficientnet('tf_efficientnet_b3', channel_multiplier=1.2, depth_multiplier=1.4, pretrained=pretrained, **kwargs)... |
def tf_efficientnet_b4(pretrained=False, **kwargs):
' EfficientNet-B4 AutoAug. Tensorflow compatible variant '
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_efficientnet('tf_efficientnet_b4', channel_multiplier=1.4, depth_multiplier=1.8, pretrained=pretrained, **kwargs)... |
def tf_efficientnet_b5(pretrained=False, **kwargs):
' EfficientNet-B5 RandAug. Tensorflow compatible variant '
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_efficientnet('tf_efficientnet_b5', channel_multiplier=1.6, depth_multiplier=2.2, pretrained=pretrained, **kwargs)... |
def tf_efficientnet_b6(pretrained=False, **kwargs):
' EfficientNet-B6 AutoAug. Tensorflow compatible variant '
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_efficientnet('tf_efficientnet_b6', channel_multiplier=1.8, depth_multiplier=2.6, pretrained=pretrained, **kwargs)... |
def tf_efficientnet_b7(pretrained=False, **kwargs):
' EfficientNet-B7 RandAug. Tensorflow compatible variant '
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_efficientnet('tf_efficientnet_b7', channel_multiplier=2.0, depth_multiplier=3.1, pretrained=pretrained, **kwargs)... |
def tf_efficientnet_b8(pretrained=False, **kwargs):
' EfficientNet-B8 RandAug. Tensorflow compatible variant '
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_efficientnet('tf_efficientnet_b8', channel_multiplier=2.2, depth_multiplier=3.6, pretrained=pretrained, **kwargs)... |
def tf_efficientnet_b0_ap(pretrained=False, **kwargs):
' EfficientNet-B0 AdvProp. Tensorflow compatible variant\n Paper: Adversarial Examples Improve Image Recognition (https://arxiv.org/abs/1911.09665)\n '
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_efficientne... |
def tf_efficientnet_b1_ap(pretrained=False, **kwargs):
' EfficientNet-B1 AdvProp. Tensorflow compatible variant\n Paper: Adversarial Examples Improve Image Recognition (https://arxiv.org/abs/1911.09665)\n '
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_efficientne... |
def tf_efficientnet_b2_ap(pretrained=False, **kwargs):
' EfficientNet-B2 AdvProp. Tensorflow compatible variant\n Paper: Adversarial Examples Improve Image Recognition (https://arxiv.org/abs/1911.09665)\n '
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_efficientne... |
def tf_efficientnet_b3_ap(pretrained=False, **kwargs):
' EfficientNet-B3 AdvProp. Tensorflow compatible variant\n Paper: Adversarial Examples Improve Image Recognition (https://arxiv.org/abs/1911.09665)\n '
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_efficientne... |
def tf_efficientnet_b4_ap(pretrained=False, **kwargs):
' EfficientNet-B4 AdvProp. Tensorflow compatible variant\n Paper: Adversarial Examples Improve Image Recognition (https://arxiv.org/abs/1911.09665)\n '
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_efficientne... |
def tf_efficientnet_b5_ap(pretrained=False, **kwargs):
' EfficientNet-B5 AdvProp. Tensorflow compatible variant\n Paper: Adversarial Examples Improve Image Recognition (https://arxiv.org/abs/1911.09665)\n '
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_efficientne... |
def tf_efficientnet_b6_ap(pretrained=False, **kwargs):
' EfficientNet-B6 AdvProp. Tensorflow compatible variant\n Paper: Adversarial Examples Improve Image Recognition (https://arxiv.org/abs/1911.09665)\n '
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_efficientne... |
def tf_efficientnet_b7_ap(pretrained=False, **kwargs):
' EfficientNet-B7 AdvProp. Tensorflow compatible variant\n Paper: Adversarial Examples Improve Image Recognition (https://arxiv.org/abs/1911.09665)\n '
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_efficientne... |
def tf_efficientnet_b8_ap(pretrained=False, **kwargs):
' EfficientNet-B8 AdvProp. Tensorflow compatible variant\n Paper: Adversarial Examples Improve Image Recognition (https://arxiv.org/abs/1911.09665)\n '
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_efficientne... |
def tf_efficientnet_b0_ns(pretrained=False, **kwargs):
' EfficientNet-B0 NoisyStudent. Tensorflow compatible variant\n Paper: Self-training with Noisy Student improves ImageNet classification (https://arxiv.org/abs/1911.04252)\n '
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
... |
def tf_efficientnet_b1_ns(pretrained=False, **kwargs):
' EfficientNet-B1 NoisyStudent. Tensorflow compatible variant\n Paper: Self-training with Noisy Student improves ImageNet classification (https://arxiv.org/abs/1911.04252)\n '
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
... |
def tf_efficientnet_b2_ns(pretrained=False, **kwargs):
' EfficientNet-B2 NoisyStudent. Tensorflow compatible variant\n Paper: Self-training with Noisy Student improves ImageNet classification (https://arxiv.org/abs/1911.04252)\n '
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
... |
def tf_efficientnet_b3_ns(pretrained=False, **kwargs):
' EfficientNet-B3 NoisyStudent. Tensorflow compatible variant\n Paper: Self-training with Noisy Student improves ImageNet classification (https://arxiv.org/abs/1911.04252)\n '
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
... |
def tf_efficientnet_b4_ns(pretrained=False, **kwargs):
' EfficientNet-B4 NoisyStudent. Tensorflow compatible variant\n Paper: Self-training with Noisy Student improves ImageNet classification (https://arxiv.org/abs/1911.04252)\n '
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
... |
def tf_efficientnet_b5_ns(pretrained=False, **kwargs):
' EfficientNet-B5 NoisyStudent. Tensorflow compatible variant\n Paper: Self-training with Noisy Student improves ImageNet classification (https://arxiv.org/abs/1911.04252)\n '
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
... |
def tf_efficientnet_b6_ns(pretrained=False, **kwargs):
' EfficientNet-B6 NoisyStudent. Tensorflow compatible variant\n Paper: Self-training with Noisy Student improves ImageNet classification (https://arxiv.org/abs/1911.04252)\n '
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
... |
def tf_efficientnet_b7_ns(pretrained=False, **kwargs):
' EfficientNet-B7 NoisyStudent. Tensorflow compatible variant\n Paper: Self-training with Noisy Student improves ImageNet classification (https://arxiv.org/abs/1911.04252)\n '
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
... |
def tf_efficientnet_l2_ns_475(pretrained=False, **kwargs):
' EfficientNet-L2 NoisyStudent @ 475x475. Tensorflow compatible variant\n Paper: Self-training with Noisy Student improves ImageNet classification (https://arxiv.org/abs/1911.04252)\n '
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type']... |
def tf_efficientnet_l2_ns(pretrained=False, **kwargs):
' EfficientNet-L2 NoisyStudent. Tensorflow compatible variant\n Paper: Self-training with Noisy Student improves ImageNet classification (https://arxiv.org/abs/1911.04252)\n '
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
... |
def tf_efficientnet_es(pretrained=False, **kwargs):
' EfficientNet-Edge Small. Tensorflow compatible variant '
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_efficientnet_edge('tf_efficientnet_es', channel_multiplier=1.0, depth_multiplier=1.0, pretrained=pretrained, **k... |
def tf_efficientnet_em(pretrained=False, **kwargs):
' EfficientNet-Edge-Medium. Tensorflow compatible variant '
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_efficientnet_edge('tf_efficientnet_em', channel_multiplier=1.0, depth_multiplier=1.1, pretrained=pretrained, **... |
def tf_efficientnet_el(pretrained=False, **kwargs):
' EfficientNet-Edge-Large. Tensorflow compatible variant '
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_efficientnet_edge('tf_efficientnet_el', channel_multiplier=1.2, depth_multiplier=1.4, pretrained=pretrained, **k... |
def tf_efficientnet_cc_b0_4e(pretrained=False, **kwargs):
' EfficientNet-CondConv-B0 w/ 4 Experts '
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_efficientnet_condconv('tf_efficientnet_cc_b0_4e', channel_multiplier=1.0, depth_multiplier=1.0, pretrained=pretrained, **kwa... |
def tf_efficientnet_cc_b0_8e(pretrained=False, **kwargs):
' EfficientNet-CondConv-B0 w/ 8 Experts '
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_efficientnet_condconv('tf_efficientnet_cc_b0_8e', channel_multiplier=1.0, depth_multiplier=1.0, experts_multiplier=2, pretra... |
def tf_efficientnet_cc_b1_8e(pretrained=False, **kwargs):
' EfficientNet-CondConv-B1 w/ 8 Experts '
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_efficientnet_condconv('tf_efficientnet_cc_b1_8e', channel_multiplier=1.0, depth_multiplier=1.1, experts_multiplier=2, pretra... |
def tf_efficientnet_lite0(pretrained=False, **kwargs):
' EfficientNet-Lite0. Tensorflow compatible variant '
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_efficientnet_lite('tf_efficientnet_lite0', channel_multiplier=1.0, depth_multiplier=1.0, pretrained=pretrained, **... |
def tf_efficientnet_lite1(pretrained=False, **kwargs):
' EfficientNet-Lite1. Tensorflow compatible variant '
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_efficientnet_lite('tf_efficientnet_lite1', channel_multiplier=1.0, depth_multiplier=1.1, pretrained=pretrained, **... |
def tf_efficientnet_lite2(pretrained=False, **kwargs):
' EfficientNet-Lite2. Tensorflow compatible variant '
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_efficientnet_lite('tf_efficientnet_lite2', channel_multiplier=1.1, depth_multiplier=1.2, pretrained=pretrained, **... |
def tf_efficientnet_lite3(pretrained=False, **kwargs):
' EfficientNet-Lite3. Tensorflow compatible variant '
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_efficientnet_lite('tf_efficientnet_lite3', channel_multiplier=1.2, depth_multiplier=1.4, pretrained=pretrained, **k... |
def tf_efficientnet_lite4(pretrained=False, **kwargs):
' EfficientNet-Lite4. Tensorflow compatible variant '
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_efficientnet_lite('tf_efficientnet_lite4', channel_multiplier=1.4, depth_multiplier=1.8, pretrained=pretrained, **k... |
def mixnet_s(pretrained=False, **kwargs):
'Creates a MixNet Small model.\n '
model = _gen_mixnet_s('mixnet_s', channel_multiplier=1.0, pretrained=pretrained, **kwargs)
return model
|
def mixnet_m(pretrained=False, **kwargs):
'Creates a MixNet Medium model.\n '
model = _gen_mixnet_m('mixnet_m', channel_multiplier=1.0, pretrained=pretrained, **kwargs)
return model
|
def mixnet_l(pretrained=False, **kwargs):
'Creates a MixNet Large model.\n '
model = _gen_mixnet_m('mixnet_l', channel_multiplier=1.3, pretrained=pretrained, **kwargs)
return model
|
def mixnet_xl(pretrained=False, **kwargs):
'Creates a MixNet Extra-Large model.\n Not a paper spec, experimental def by RW w/ depth scaling.\n '
model = _gen_mixnet_m('mixnet_xl', channel_multiplier=1.6, depth_multiplier=1.2, pretrained=pretrained, **kwargs)
return model
|
def mixnet_xxl(pretrained=False, **kwargs):
'Creates a MixNet Double Extra Large model.\n Not a paper spec, experimental def by RW w/ depth scaling.\n '
model = _gen_mixnet_m('mixnet_xxl', channel_multiplier=2.4, depth_multiplier=1.3, pretrained=pretrained, **kwargs)
return model
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def tf_mixnet_s(pretrained=False, **kwargs):
'Creates a MixNet Small model. Tensorflow compatible variant\n '
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_mixnet_s('tf_mixnet_s', channel_multiplier=1.0, pretrained=pretrained, **kwargs)
return model
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def tf_mixnet_m(pretrained=False, **kwargs):
'Creates a MixNet Medium model. Tensorflow compatible variant\n '
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_mixnet_m('tf_mixnet_m', channel_multiplier=1.0, pretrained=pretrained, **kwargs)
return model
|
def tf_mixnet_l(pretrained=False, **kwargs):
'Creates a MixNet Large model. Tensorflow compatible variant\n '
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_mixnet_m('tf_mixnet_l', channel_multiplier=1.3, pretrained=pretrained, **kwargs)
return model
|
def load_checkpoint(model, checkpoint_path):
if (checkpoint_path and os.path.isfile(checkpoint_path)):
print("=> Loading checkpoint '{}'".format(checkpoint_path))
checkpoint = torch.load(checkpoint_path)
if (isinstance(checkpoint, dict) and ('state_dict' in checkpoint)):
new_st... |
def load_pretrained(model, url, filter_fn=None, strict=True):
if (not url):
print('=> Warning: Pretrained model URL is empty, using random initialization.')
return
state_dict = load_state_dict_from_url(url, progress=False, map_location='cpu')
input_conv = 'conv_stem'
classifier = 'clas... |
class MobileNetV3(nn.Module):
" MobileNet-V3\n\n A this model utilizes the MobileNet-v3 specific 'efficient head', where global pooling is done before the\n head convolution without a final batch-norm layer before the classifier.\n\n Paper: https://arxiv.org/abs/1905.02244\n "
def __init__(self, ... |
def _create_model(model_kwargs, variant, pretrained=False):
as_sequential = model_kwargs.pop('as_sequential', False)
model = MobileNetV3(**model_kwargs)
if (pretrained and model_urls[variant]):
load_pretrained(model, model_urls[variant])
if as_sequential:
model = model.as_sequential()
... |
def _gen_mobilenet_v3_rw(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
'Creates a MobileNet-V3 model (RW variant).\n\n Paper: https://arxiv.org/abs/1905.02244\n\n This was my first attempt at reproducing the MobileNet-V3 from paper alone. It came close to the\n eventual Tensorflow referen... |
def _gen_mobilenet_v3(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
'Creates a MobileNet-V3 large/small/minimal models.\n\n Ref impl: https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet_v3.py\n Paper: https://arxiv.org/abs/1905.02244\n\n Args:\n ch... |
def mobilenetv3_rw(pretrained=False, **kwargs):
' MobileNet-V3 RW\n Attn: See note in gen function for this variant.\n '
if pretrained:
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
model = _gen_mobilenet_v3_rw('mobilenetv3_rw', 1.0, pretrained=pretrained, **kwargs)
return model
|
def mobilenetv3_large_075(pretrained=False, **kwargs):
' MobileNet V3 Large 0.75'
model = _gen_mobilenet_v3('mobilenetv3_large_075', 0.75, pretrained=pretrained, **kwargs)
return model
|
def mobilenetv3_large_100(pretrained=False, **kwargs):
' MobileNet V3 Large 1.0 '
model = _gen_mobilenet_v3('mobilenetv3_large_100', 1.0, pretrained=pretrained, **kwargs)
return model
|
def mobilenetv3_large_minimal_100(pretrained=False, **kwargs):
' MobileNet V3 Large (Minimalistic) 1.0 '
model = _gen_mobilenet_v3('mobilenetv3_large_minimal_100', 1.0, pretrained=pretrained, **kwargs)
return model
|
def mobilenetv3_small_075(pretrained=False, **kwargs):
' MobileNet V3 Small 0.75 '
model = _gen_mobilenet_v3('mobilenetv3_small_075', 0.75, pretrained=pretrained, **kwargs)
return model
|
def mobilenetv3_small_100(pretrained=False, **kwargs):
' MobileNet V3 Small 1.0 '
model = _gen_mobilenet_v3('mobilenetv3_small_100', 1.0, pretrained=pretrained, **kwargs)
return model
|
def mobilenetv3_small_minimal_100(pretrained=False, **kwargs):
' MobileNet V3 Small (Minimalistic) 1.0 '
model = _gen_mobilenet_v3('mobilenetv3_small_minimal_100', 1.0, pretrained=pretrained, **kwargs)
return model
|
def tf_mobilenetv3_large_075(pretrained=False, **kwargs):
' MobileNet V3 Large 0.75. Tensorflow compat variant. '
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_mobilenet_v3('tf_mobilenetv3_large_075', 0.75, pretrained=pretrained, **kwargs)
return model
|
def tf_mobilenetv3_large_100(pretrained=False, **kwargs):
' MobileNet V3 Large 1.0. Tensorflow compat variant. '
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_mobilenet_v3('tf_mobilenetv3_large_100', 1.0, pretrained=pretrained, **kwargs)
return model
|
def tf_mobilenetv3_large_minimal_100(pretrained=False, **kwargs):
' MobileNet V3 Large Minimalistic 1.0. Tensorflow compat variant. '
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_mobilenet_v3('tf_mobilenetv3_large_minimal_100', 1.0, pretrained=pretrained, **kwargs)
... |
def tf_mobilenetv3_small_075(pretrained=False, **kwargs):
' MobileNet V3 Small 0.75. Tensorflow compat variant. '
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_mobilenet_v3('tf_mobilenetv3_small_075', 0.75, pretrained=pretrained, **kwargs)
return model
|
def tf_mobilenetv3_small_100(pretrained=False, **kwargs):
' MobileNet V3 Small 1.0. Tensorflow compat variant.'
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_mobilenet_v3('tf_mobilenetv3_small_100', 1.0, pretrained=pretrained, **kwargs)
return model
|
def tf_mobilenetv3_small_minimal_100(pretrained=False, **kwargs):
' MobileNet V3 Small Minimalistic 1.0. Tensorflow compat variant. '
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_mobilenet_v3('tf_mobilenetv3_small_minimal_100', 1.0, pretrained=pretrained, **kwargs)
... |
def create_model(model_name='mnasnet_100', pretrained=None, num_classes=1000, in_chans=3, checkpoint_path='', **kwargs):
model_kwargs = dict(num_classes=num_classes, in_chans=in_chans, pretrained=pretrained, **kwargs)
if (model_name in globals()):
create_fn = globals()[model_name]
model = crea... |
def main():
args = parser.parse_args()
args.pretrained = True
if args.checkpoint:
args.pretrained = False
print('==> Creating PyTorch {} model'.format(args.model))
model = geffnet.create_model(args.model, num_classes=args.num_classes, in_chans=3, pretrained=args.pretrained, checkpoint_path... |
def traverse_graph(graph, prefix=''):
content = []
indent = (prefix + ' ')
graphs = []
num_nodes = 0
for node in graph.node:
(pn, gs) = onnx.helper.printable_node(node, indent, subgraphs=True)
assert isinstance(gs, list)
content.append(pn)
graphs.extend(gs)
... |
def main():
args = parser.parse_args()
onnx_model = onnx.load(args.model)
(num_original_nodes, original_graph_str) = traverse_graph(onnx_model.graph)
passes = ['eliminate_identity', 'eliminate_nop_dropout', 'eliminate_nop_pad', 'eliminate_nop_transpose', 'eliminate_unused_initializer', 'extract_consta... |
def main():
args = parser.parse_args()
onnx_model = onnx.load(args.model)
(caffe2_init, caffe2_predict) = Caffe2Backend.onnx_graph_to_caffe2_net(onnx_model)
caffe2_init_str = caffe2_init.SerializeToString()
with open((args.c2_prefix + '.init.pb'), 'wb') as f:
f.write(caffe2_init_str)
c... |
class AverageMeter():
'Computes and stores the average and current value'
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += (val * n)
... |
def accuracy(output, target, topk=(1,)):
'Computes the precision@k for the specified values of k'
maxk = max(topk)
batch_size = target.size(0)
(_, pred) = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, (- 1)).expand_as(pred))
res = []
for k in topk:
... |
def get_outdir(path, *paths, inc=False):
outdir = os.path.join(path, *paths)
if (not os.path.exists(outdir)):
os.makedirs(outdir)
elif inc:
count = 1
outdir_inc = ((outdir + '-') + str(count))
while os.path.exists(outdir_inc):
count = (count + 1)
out... |
def main():
args = parser.parse_args()
if ((not args.checkpoint) and (not args.pretrained)):
args.pretrained = True
amp_autocast = suppress
if args.amp:
if (not has_native_amp):
print('Native Torch AMP is not available (requires torch >= 1.6), using FP32.')
else:
... |
class Predictions_Initial_DiffusionNet():
def Predict(verts, faces, normals):
verts = torch.tensor(np.ascontiguousarray(verts)).float()
faces = torch.tensor(np.ascontiguousarray(faces)).long()
normals = torch.tensor(np.ascontiguousarray(normals)).float()
weights_initial_DiffusionN... |
class Predictions_Final_DiffusionNet():
def Predict(verts, faces, normals):
verts = torch.tensor(np.ascontiguousarray(verts)).float()
faces = torch.tensor(np.ascontiguousarray(faces)).long()
normals = torch.tensor(np.ascontiguousarray(normals)).float()
weights_final_DiffusionNet =... |
class Facial_segmentation():
def segment(folder_path, scan_file, translation, rot_m1, rot_m2, save_segmented_mesh):
ms = pymeshlab.MeshSet()
ms.load_new_mesh(os.path.join(folder_path, 'output', (scan_file[:(- 4)] + '_original_mm.obj')))
mesh = ms.current_mesh()
vertices_mm = torch... |
class DataLoader(object):
'\n Only load data file and information file.\n '
@staticmethod
def parse_data_args(parser):
'\n data loader related command line arguments parser\n :param parser:\n :return:\n '
parser.add_argument('--path', type=str, default='.... |
class DataProcessor(object):
data_columns = ['X']
@staticmethod
def parse_dp_args(parser):
'\n\t\tparse data processor related command line arguments\n\t\t'
parser.add_argument('--test_neg_n', type=int, default=10, help='Negative sample num for each instance in test/validation set.')
... |
def main():
parser = argparse.ArgumentParser(description='Model')
parser.add_argument('--gpu', type=str, default='0', help='Set CUDA_VISIBLE_DEVICES')
parser.add_argument('--verbose', type=int, default=logging.INFO, help='Logging Level, 0, 10, ..., 50')
parser.add_argument('--log_file', type=str, defa... |
class LossLayer(nn.Module):
'\n\thttps://github.com/melodyguan/enas/blob/master/src/cifar10/general_child.py#L245\n\t'
def __init__(self, layer_id, in_planes, out_planes, epsilon=1e-06):
super(LossLayer, self).__init__()
self.layer_id = layer_id
self.in_planes = in_planes
self... |
class LossFormula(nn.Module):
@staticmethod
def parse_Formula_args(parser):
parser.add_argument('--child_keep_prob', type=float, default=0.9)
parser.add_argument('--child_lr_max', type=float, default=0.05)
parser.add_argument('--child_lr_min', type=float, default=0.0005)
parse... |
class BaseRunner(object):
@staticmethod
def parse_runner_args(parser):
parser.add_argument('--load', type=int, default=0, help='Whether load model and continue to train')
parser.add_argument('--epoch', type=int, default=100, help='Number of epochs.')
parser.add_argument('--check_epoch... |
def subs(ss):
i = 0
return_s = ''
prev = 0
while (i < len(ss)):
if (ss[i] == '/'):
j = (i + 1)
count = 0
while (j < len(ss)):
if (ss[j] == '('):
count += 1
elif (ss[j] == ')'):
count -= ... |
def pos_f(input_x):
x = input_x
return eval(pos_s)
|
def neg_f(input_x):
x = input_x
return eval(neg_s)
|
def group_user_interactions_csv(in_csv, out_csv, label='label', sep='\t'):
print('group_user_interactions_csv', out_csv)
all_data = pd.read_csv(in_csv, sep=sep)
group_inters = group_user_interactions_df(in_df=all_data, label=label)
group_inters.to_csv(out_csv, sep=sep, index=False)
return group_in... |
def group_user_interactions_df(in_df, label='label', seq_sep=','):
all_data = in_df
if (label in all_data.columns):
all_data = all_data[(all_data[label] > 0)]
(uids, inters) = ([], [])
for (name, group) in all_data.groupby('uid'):
uids.append(name)
inters.append(seq_sep.join(gr... |
def parse_global_args(parser):
'\n 全局命令行参数\n :param parser:\n :return:\n \n Global command-line parameters\n :param parser:\n :return:\n '
parser.add_argument('--gpu', type=str, default='0', help='Set CUDA_VISIBLE_DEVICES')
parser.add_argument('--verbose', type=int, default=logging... |
def balance_data(data):
'\n 让正负样本数接近，正负样本数差距太大时使用\n :param data:\n :return:\n \n Make the number of positive and negative examples close, use when the difference between the number of positive/negative examples is too large\n :param data:\n :return:\n '
pos_indexes = np.where((data['Y'... |
def input_data_is_list(data):
'\n 如果data是一个dict的list，则合并这些dict，在测试多个数据集比如验证测试同时计算时\n :param data: dict or list\n :return:\n \n If data is a list of dict, then merge these dict, when testing multiple datasets, e.g., when validation and testing are done concurrently\n :param data: dict or list\n ... |
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