From 2da0b4dbc1096e00f0c388a6f77e5aa92179a5d8 Mon Sep 17 00:00:00 2001 From: Ross Wightman Date: Thu, 23 May 2019 10:59:31 -0700 Subject: [PATCH] Add inception_v3 models via torchvision, 4 different pretrained weight choices --- README.md | 8 ++- models/inception_v3.py | 113 ++++++++++++++++++++++++++++++++++++++++ models/model_factory.py | 1 + 3 files changed, 120 insertions(+), 2 deletions(-) create mode 100644 models/inception_v3.py diff --git a/README.md b/README.md index 601fa38b..4913a33e 100644 --- a/README.md +++ b/README.md @@ -110,7 +110,7 @@ Several (less common) features that I often utilize in my projects are included. * Mixup (as in https://arxiv.org/abs/1710.09412) - currently implementing/testing * An inference script that dumps output to CSV is provided as an example -### Custom Weights +### Self-trained Weights I've leveraged the training scripts in this repository to train a few of the models with missing weights to good levels of performance. These numbers are all for 224x224 training and validation image sizing with the usual 87.5% validation crop. |Model | Prec@1 (Err) | Prec@5 (Err) | Param # | Image Scaling | @@ -125,8 +125,12 @@ I've leveraged the training scripts in this repository to train a few of the mod ### Ported Weights |Model | Prec@1 (Err) | Prec@5 (Err) | Param # | Image Scaling | Source | |---|---|---|---|---|---| -| MNASNet 1.00 (B1) | 72.398 (27.602) | 90.930 (9.070) | 4.36M | bicubic | [Google TFLite](https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet) | +| Gluon Inception-V3 | 78.804 (21.196) | 94.380 (5.620) | 27.16M | bicubic | [MxNet Gluon](https://gluon-cv.mxnet.io/model_zoo/classification.html) | +| Tensorflow Inception-V3 | 77.856 (22.144) | 93.644 (6.356) | 27.16M | bicubic | [Tensorflow Slim](https://github.com/tensorflow/models/tree/master/research/slim) | +| Adversarially trained Inception-V3 | 77.576 (22.424) | 93.724 (6.276) | 27.16M | bicubic | [Tensorflow Adv models](https://github.com/tensorflow/models/tree/master/research/adv_imagenet_models) | | SE-MNASNet 1.00 (A1) | 73.086 (26.914) | 91.336 (8.664) | 3.87M | bicubic | [Google TFLite](https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet) | +| MNASNet 1.00 (B1) | 72.398 (27.602) | 90.930 (9.070) | 4.36M | bicubic | [Google TFLite](https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet) | + NOTE: For some reason I can't hit the stated accuracy with my impl of MNASNet and Google's tflite weights. Using a TF equivalent to 'SAME' padding was important to get > 70%, but something small is still missing. Trying to train my own weights from scratch with these models has so far to leveled off in the same 72-73% range. diff --git a/models/inception_v3.py b/models/inception_v3.py new file mode 100644 index 00000000..0e57ec38 --- /dev/null +++ b/models/inception_v3.py @@ -0,0 +1,113 @@ +from torchvision.models import Inception3 +from models.helpers import load_pretrained +from data import IMAGENET_DEFAULT_STD, IMAGENET_DEFAULT_MEAN, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD + +default_cfgs = { + # original PyTorch weights, ported from Tensorflow but modified + 'inception_v3': { + 'url': 'https://download.pytorch.org/models/inception_v3_google-1a9a5a14.pth', + 'input_size': (3, 299, 299), + 'crop_pct': 0.875, + 'interpolation': 'bicubic', + 'mean': IMAGENET_INCEPTION_MEAN, # also works well enough with resnet defaults + 'std': IMAGENET_INCEPTION_STD, # also works well enough with resnet defaults + 'num_classes': 1000, + 'first_conv': 'conv0', + 'classifier': 'fc' + }, + # my port of Tensorflow SLIM weights (http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz) + 'tf_inception_v3': { + 'url': 'https://www.dropbox.com/s/xdh32bpdgqzpx8t/tf_inception_v3-e0069de4.pth?dl=1', + 'input_size': (3, 299, 299), + 'crop_pct': 0.875, + 'interpolation': 'bicubic', + 'mean': IMAGENET_INCEPTION_MEAN, + 'std': IMAGENET_INCEPTION_STD, + 'num_classes': 1001, + 'first_conv': 'conv0', + 'classifier': 'fc' + }, + # my port of Tensorflow adversarially trained Inception V3 from + # http://download.tensorflow.org/models/adv_inception_v3_2017_08_18.tar.gz + 'adv_inception_v3': { + 'url': 'https://www.dropbox.com/s/b5pudqh84gtl7i8/adv_inception_v3-9e27bd63.pth?dl=1', + 'input_size': (3, 299, 299), + 'crop_pct': 0.875, + 'interpolation': 'bicubic', + 'mean': IMAGENET_INCEPTION_MEAN, + 'std': IMAGENET_INCEPTION_STD, + 'num_classes': 1001, + 'first_conv': 'conv0', + 'classifier': 'fc' + }, + # from gluon pretrained models, best performing in terms of accuracy/loss metrics + # https://gluon-cv.mxnet.io/model_zoo/classification.html + 'gluon_inception_v3': { + 'url': 'https://www.dropbox.com/s/8uv6wrl6it6394u/gluon_inception_v3-9f746940.pth?dl=1', + 'input_size': (3, 299, 299), + 'crop_pct': 0.875, + 'interpolation': 'bicubic', + 'mean': IMAGENET_DEFAULT_MEAN, # also works well with inception defaults + 'std': IMAGENET_DEFAULT_STD, # also works well with inception defaults + 'num_classes': 1000, + 'first_conv': 'conv0', + 'classifier': 'fc' + } +} + + +def _assert_default_kwargs(kwargs): + # for imported models (ie torchvision) without capability to change these params, + # make sure they aren't being set to non-defaults + assert kwargs.pop('global_pool', 'avg') == 'avg' + assert kwargs.pop('drop_rate', 0.) == 0. + + +def inception_v3(num_classes=1000, in_chans=3, pretrained=False, **kwargs): + # original PyTorch weights, ported from Tensorflow but modified + default_cfg = default_cfgs['inception_v3'] + assert in_chans == 3 + _assert_default_kwargs(kwargs) + model = Inception3(num_classes=num_classes, aux_logits=True, transform_input=False) + if pretrained: + load_pretrained(model, default_cfg, num_classes, in_chans) + model.default_cfg = default_cfg + return model + + +def tf_inception_v3(num_classes=1000, in_chans=3, pretrained=False, **kwargs): + # my port of Tensorflow SLIM weights (http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz) + default_cfg = default_cfgs['tf_inception_v3'] + assert in_chans == 3 + _assert_default_kwargs(kwargs) + model = Inception3(num_classes=num_classes, aux_logits=False, transform_input=False) + if pretrained: + load_pretrained(model, default_cfg, num_classes, in_chans) + model.default_cfg = default_cfg + return model + + +def adv_inception_v3(num_classes=1000, in_chans=3, pretrained=False, **kwargs): + # my port of Tensorflow adversarially trained Inception V3 from + # http://download.tensorflow.org/models/adv_inception_v3_2017_08_18.tar.gz + default_cfg = default_cfgs['adv_inception_v3'] + assert in_chans == 3 + _assert_default_kwargs(kwargs) + model = Inception3(num_classes=num_classes, aux_logits=False, transform_input=False) + if pretrained: + load_pretrained(model, default_cfg, num_classes, in_chans) + model.default_cfg = default_cfg + return model + + +def gluon_inception_v3(num_classes=1000, in_chans=3, pretrained=False, **kwargs): + # from gluon pretrained models, best performing in terms of accuracy/loss metrics + # https://gluon-cv.mxnet.io/model_zoo/classification.html + default_cfg = default_cfgs['gluon_inception_v3'] + assert in_chans == 3 + _assert_default_kwargs(kwargs) + model = Inception3(num_classes=num_classes, aux_logits=False, transform_input=False) + if pretrained: + load_pretrained(model, default_cfg, num_classes, in_chans) + model.default_cfg = default_cfg + return model diff --git a/models/model_factory.py b/models/model_factory.py index 39a6f2d2..ca108274 100644 --- a/models/model_factory.py +++ b/models/model_factory.py @@ -13,6 +13,7 @@ from models.genmobilenet import \ semnasnet_050, semnasnet_075, semnasnet_100, semnasnet_140, tflite_semnasnet_100, mnasnet_small,\ mobilenetv1_100, mobilenetv2_100, mobilenetv3_050, mobilenetv3_075, mobilenetv3_100,\ fbnetc_100, chamnetv1_100, chamnetv2_100, spnasnet_100 +from models.inception_v3 import inception_v3, gluon_inception_v3, tf_inception_v3, adv_inception_v3 from models.helpers import load_checkpoint