Merge remote-tracking branch 'upstream/master'

README.md

@@ -2,6 +2,11 @@
 
 ## What's New
 
+### Feb 1/2, 2020
+* Port new EfficientNet-B8 (RandAugment) weights, these are different than the B8 AdvProp, different input normalization.
+* Update results csv files on all models for ImageNet validation and three other test sets
+* Push PyPi package update
+
 ### Jan 31, 2020
 * Update ResNet50 weights with a new 79.038 result from further JSD / AugMix experiments. Full command line for reproduction in training section below.
 
@@ -87,9 +92,9 @@ Included models:
     * Original variant from [Cadene](https://github.com/Cadene/pretrained-models.pytorch)
     * MXNet Gluon 'modified aligned' Xception-65 and 71 models from [Gluon ModelZoo](https://github.com/dmlc/gluon-cv/tree/master/gluoncv/model_zoo)
 * PNasNet & NASNet-A (from [Cadene](https://github.com/Cadene/pretrained-models.pytorch))
-* DPN (from [me](https://github.com/rwightman/pytorch-dpn-pretrained), weights hosted by Cadene)
+* DPN (from [myself](https://github.com/rwightman/pytorch-dpn-pretrained))
     * DPN-68, DPN-68b, DPN-92, DPN-98, DPN-131, DPN-107
-* EfficientNet (from my standalone [GenMobileNet](https://github.com/rwightman/genmobilenet-pytorch)) - A generic model that implements many of the efficient models that utilize similar DepthwiseSeparable and InvertedResidual blocks
+* EfficientNet (from my standalone [GenEfficientNet](https://github.com/rwightman/gen-efficientnet-pytorch)) - A generic model that implements many of the efficient models that utilize similar DepthwiseSeparable and InvertedResidual blocks
     * EfficientNet AdvProp (B0-B8) (https://arxiv.org/abs/1911.09665) -- TF weights ported
     * EfficientNet (B0-B7) (https://arxiv.org/abs/1905.11946) -- TF weights ported, B0-B2 finetuned PyTorch
     * EfficientNet-EdgeTPU (S, M, L) (https://ai.googleblog.com/2019/08/efficientnet-edgetpu-creating.html) --TF weights ported
@@ -136,8 +141,8 @@ I've leveraged the training scripts in this repository to train a few of the mod
 
 |Model | Prec@1 (Err) | Prec@5 (Err) | Param # | Image Scaling  | Image Size |
 |---|---|---|---|---|---|
-| efficientnet_b3a | 81.874 (18.126) | 95.840 (4.160) | 9.11M | bicubic | 320 (1.0 crop) |
+| efficientnet_b3a | 81.874 (18.126) | 95.840 (4.160) | 12.23M | bicubic | 320 (1.0 crop) |
-| efficientnet_b3 | 81.498 (18.502) | 95.718 (4.282) | 9.11M | bicubic | 300 |
+| efficientnet_b3 | 81.498 (18.502) | 95.718 (4.282) | 12.23M | bicubic | 300 |
 | efficientnet_b2a | 80.608 (19.392) | 95.310 (4.690) | 9.11M | bicubic | 288 (1.0 crop) |
 | mixnet_xl | 80.478 (19.522) | 94.932 (5.068) | 11.90M | bicubic | 224 |
 | efficientnet_b2 | 80.402 (19.598) | 95.076 (4.924) | 9.11M | bicubic | 260 |
@@ -170,6 +175,8 @@ For the models below, the model code and weight porting from Tensorflow or MXNet
 | Model | Prec@1 (Err) | Prec@5 (Err) | Param # | Image Scaling | Image Size |
 |---|---|---|---|---|---|
 | tf_efficientnet_b8_ap *tfp | 85.436 (14.564) | 97.272 (2.728) | 87.4 | bicubic | 672 |
+| tf_efficientnet_b8 *tfp | 85.384 (14.616) | 97.394 (2.606) | 87.4 | bicubic | 672 |
+| tf_efficientnet_b8      | 85.37 (14.63) | 97.39 (2.61) | 87.4 | bicubic | 672 |
 | tf_efficientnet_b8_ap      | 85.368 (14.632) | 97.294 (2.706) | 87.4 | bicubic | 672 |
 | tf_efficientnet_b7_ap *tfp | 85.154 (14.846) | 97.244 (2.756) | 66.35 | bicubic | 600 |
 | tf_efficientnet_b7_ap      | 85.118 (14.882) | 97.252 (2.748) | 66.35 | bicubic | 600 |
@@ -309,13 +316,13 @@ Trained on two older 1080Ti cards, this took a while. Only slightly, non statist
 
 All development and testing has been done in Conda Python 3 environments on Linux x86-64 systems, specifically Python 3.6.x and 3.7.x. Little to no care has been taken to be Python 2.x friendly and I don't plan to support it. If you run into any challenges running on Windows, or other OS, I'm definitely open to looking into those issues so long as it's in a reproducible (read Conda) environment.
 
-PyTorch versions 1.2 and 1.3.1 have been tested with this code. 
+PyTorch versions 1.2, 1.3.1, and 1.4 have been tested with this code.
 
 I've tried to keep the dependencies minimal, the setup is as per the PyTorch default install instructions for Conda:
 ```
 conda create -n torch-env
 conda activate torch-env
-conda install -c pytorch pytorch torchvision cudatoolkit=10
+conda install -c pytorch pytorch torchvision cudatoolkit=10.1
 conda install pyyaml
 ```
 

timm/version.py

@@ -1 +1 @@
-__version__ = '0.1.14'
+__version__ = '0.1.16'

train.py

@@ -79,6 +79,8 @@ parser.add_argument('--interpolation', default='', type=str, metavar='NAME',
                     help='Image resize interpolation type (overrides model)')
 parser.add_argument('-b', '--batch-size', type=int, default=32, metavar='N',
                     help='input batch size for training (default: 32)')
+parser.add_argument('-vb', '--validation-batch-size-multiplier', type=int, default=1, metavar='N',
+                    help='ratio of validation batch size to training batch size (default: 1)')
 parser.add_argument('--drop', type=float, default=0.0, metavar='DROP',
                     help='Dropout rate (default: 0.)')
 parser.add_argument('--drop-connect', type=float, default=0.0, metavar='DROP',
@@ -388,7 +390,7 @@ def main():
     loader_eval = create_loader(
         dataset_eval,
         input_size=data_config['input_size'],
-        batch_size=4 * args.batch_size,
+        batch_size=args.validation_batch_size_multiplier * args.batch_size,
         is_training=False,
         use_prefetcher=args.prefetcher,
         interpolation=data_config['interpolation'],