Ported Tensorflow pretrained EfficientNet weights and some model cleanup

* B0-B3 weights ported from TF with close to paper accuracy
* Renamed gen_mobilenet to gen_efficientnet since scaling params go well beyond 'mobile' specific
* Add Tensorflow preprocessing option for closer images to source repo

README.md

@@ -29,8 +29,8 @@ I've included a few of my favourite models, but this is not an exhaustive collec
 * PNasNet (from [Cadene](https://github.com/Cadene/pretrained-models.pytorch))
 * DPN (from [me](https://github.com/rwightman/pytorch-dpn-pretrained), weights hosted by Cadene)
     * DPN-68, DPN-68b, DPN-92, DPN-98, DPN-131, DPN-107
-* Generic MobileNet (from my standalone [GenMobileNet](https://github.com/rwightman/genmobilenet-pytorch)) - A generic model that implements many of the mobile optimized architecture search derived models that utilize similar DepthwiseSeparable and InvertedResidual blocks
+* Generic EfficientNet (from my standalone [GenMobileNet](https://github.com/rwightman/genmobilenet-pytorch)) - A generic model that implements many of the mobile optimized architecture search derived models that utilize similar DepthwiseSeparable and InvertedResidual blocks
-    * EfficientNet (B0-B4) (https://arxiv.org/abs/1905.11946) -- work in progress, validating
+    * EfficientNet (B0-B4) (https://arxiv.org/abs/1905.11946) -- validated, compat with TF weights
     * MNASNet B1, A1 (Squeeze-Excite), and Small (https://arxiv.org/abs/1807.11626)
     * MobileNet-V1 (https://arxiv.org/abs/1704.04861)
     * MobileNet-V2 (https://arxiv.org/abs/1801.04381)
@@ -39,60 +39,8 @@ I've included a few of my favourite models, but this is not an exhaustive collec
     * FBNet-C (https://arxiv.org/abs/1812.03443) -- TODO A/B variants
     * Single-Path NAS (https://arxiv.org/abs/1904.02877) -- pixel1 variant
     
-The full list of model strings that can be passed to model factory via `--model` arg for train, validation, inference scripts:
+Use the  `--model` arg to specify model for train, validation, inference scripts. Match the all lowercase
-```
+creation fn for the model you'd like.
-chamnetv1_100
-chamnetv2_100
-densenet121
-densenet161
-densenet169
-densenet201
-dpn107
-dpn131
-dpn68
-dpn68b
-dpn92
-dpn98
-fbnetc_100
-inception_resnet_v2
-inception_v4
-mnasnet_050
-mnasnet_075
-mnasnet_100
-mnasnet_140
-mnasnet_small
-mobilenetv1_100
-mobilenetv2_100
-mobilenetv3_050
-mobilenetv3_075
-mobilenetv3_100
-pnasnet5large
-resnet101
-resnet152
-resnet18
-resnet34
-resnet50
-resnext101_32x4d
-resnext101_64x4d
-resnext152_32x4d
-resnext50_32x4d
-semnasnet_050
-semnasnet_075
-semnasnet_100
-semnasnet_140
-seresnet101
-seresnet152
-seresnet18
-seresnet34
-seresnet50
-seresnext101_32x4d
-seresnext26_32x4d
-seresnext50_32x4d
-spnasnet_100
-tflite_mnasnet_100
-tflite_semnasnet_100
-xception
-```
 
 ## Features
 Several (less common) features that I often utilize in my projects are included. Many of their additions are the reason why I maintain my own set of models, instead of using others' via PIP:
@@ -147,20 +95,40 @@ I've leveraged the training scripts in this repository to train a few of the mod
 | gluon_resnet50_v1s       | 78.712 (21.288) | 94.242 (5.758) | 25.68  | bicubic | |
 | gluon_resnet50_v1c       | 78.010 (21.990) | 93.988 (6.012) | 25.58  | bicubic | |
 | gluon_resnet50_v1b       | 77.578 (22.422) | 93.718 (6.282) | 25.56  | bicubic | |
+| tf_efficientnet_b0 *tfp  | 76.828 (23.172) | 93.226 (6.774) | 5.29  | bicubic | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) |
+| tf_efficientnet_b0       | 76.528 (23.472) | 93.010 (6.990) | 5.29  | bicubic | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) |
 | gluon_resnet34_v1b       | 74.580 (25.420) | 91.988 (8.012) | 21.80 | bicubic | |
-| SE-MNASNet 1.00 (A1) | 73.086 (26.914) | 91.336 (8.664) | 3.87  | bicubic | [Google TFLite](https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet) |
+| tflite_semnasnet_100     | 73.086 (26.914) | 91.336 (8.664) | 3.87  | 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) |  4.36 | bicubic | [Google TFLite](https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet) 
+| tflite_mnasnet_100       | 72.398 (27.602) | 90.930 (9.070) |  4.36 | bicubic | [Google TFLite](https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet) 
 | gluon_resnet18_v1b       | 70.830 (29.170) | 89.756 (10.244) | 11.69 | bicubic | |
 
-#### @ 299x299
+#### @ 240x240
 | Model | Prec@1 (Err) | Prec@5 (Err) | Param # | Image Scaling | Source |
 |---|---|---|---|---|---|
-| 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) |
+| tf_efficientnet_b1 *tfp  | 78.796 (21.204) | 94.232 (5.768)  | 7.79  | bicubic | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) |
-| 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) |
+| tf_efficientnet_b1       | 78.554 (21.446) | 94.098 (5.902)  | 7.79  | bicubic | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) |
-| 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) |
+
+#### @ 260x260
+| Model | Prec@1 (Err) | Prec@5 (Err) | Param # | Image Scaling | Source |
+|---|---|---|---|---|---|
+| tf_efficientnet_b2 *tfp  | 79.782 (20.218) | 94.800 (5.200)  | 9.11  | bicubic | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) |
+| tf_efficientnet_b2       | 79.606 (20.394) | 94.712 (5.288)  | 9.11  | bicubic | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) |
+
+#### @ 299x299 and 300x300
+| Model | Prec@1 (Err) | Prec@5 (Err) | Param # | Image Scaling | Source |
+|---|---|---|---|---|---|
+| tf_efficientnet_b3 *tfp  | 80.982 (19.018) | 95.332 (4.668)  | 12.23  | bicubic | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) |
+| tf_efficientnet_b3       | 80.874 (19.126) | 95.302 (4.698)  | 12.23  | bicubic | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) |
+| 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) |
+| tf_inception_v3 | 77.856 (22.144) | 93.644 (6.356) | 27.16M | bicubic | [Tensorflow Slim](https://github.com/tensorflow/models/tree/master/research/slim) |
+| adv_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) |
 
 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.
 
+Models with `*tfp` next to them were scored with `--tf-preprocessing` flag. 
+
+The `tf_efficientnet` and `tflite_(se)mnasnet` models require an equivalent for 'SAME' padding as their arch results in asymmetric padding. I've added this in the model creation wrapper, but it does come with a performance penalty. 
+
 ## Script Usage
 
 ### Training

convert/convert_from_mxnet.py

@@ -7,7 +7,7 @@ import gluoncv
 import torch
 from models.model_factory import create_model
 
-parser = argparse.ArgumentParser(description='Training')
+parser = argparse.ArgumentParser(description='Convert from MXNet')
 parser.add_argument('--model', default='all', type=str, metavar='MODEL',
                     help='Name of model to train (default: "all"')
 

data/dataset.py

@@ -54,6 +54,7 @@ class Dataset(data.Dataset):
     def __init__(
             self,
             root,
+            load_bytes=False,
             transform=None):
 
         imgs, _, _ = find_images_and_targets(root)
@@ -62,11 +63,12 @@ class Dataset(data.Dataset):
                                "Supported image extensions are: " + ",".join(IMG_EXTENSIONS)))
         self.root = root
         self.imgs = imgs
+        self.load_bytes = load_bytes
         self.transform = transform
 
     def __getitem__(self, index):
         path, target = self.imgs[index]
-        img = Image.open(path).convert('RGB')
+        img = open(path, 'rb').read() if self.load_bytes else Image.open(path).convert('RGB')
         if self.transform is not None:
             img = self.transform(img)
         if target is None:

data/loader.py

@@ -89,12 +89,17 @@ def create_loader(
         distributed=False,
         crop_pct=None,
         collate_fn=None,
+        tf_preprocessing=False,
 ):
     if isinstance(input_size, tuple):
         img_size = input_size[-2:]
     else:
         img_size = input_size
 
+    if tf_preprocessing and use_prefetcher:
+        from data.tf_preprocessing import TfPreprocessTransform
+        transform = TfPreprocessTransform(is_training=is_training, size=img_size)
+    else:
         if is_training:
             transform = transforms_imagenet_train(
                 img_size,

data/tf_preprocessing.py

@@ -0,0 +1,220 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""ImageNet preprocessing for MnasNet."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+import numpy as np
+
+IMAGE_SIZE = 224
+CROP_PADDING = 32
+
+
+def distorted_bounding_box_crop(image_bytes,
+                                bbox,
+                                min_object_covered=0.1,
+                                aspect_ratio_range=(0.75, 1.33),
+                                area_range=(0.05, 1.0),
+                                max_attempts=100,
+                                scope=None):
+    """Generates cropped_image using one of the bboxes randomly distorted.
+
+    See `tf.image.sample_distorted_bounding_box` for more documentation.
+
+    Args:
+      image_bytes: `Tensor` of binary image data.
+      bbox: `Tensor` of bounding boxes arranged `[1, num_boxes, coords]`
+          where each coordinate is [0, 1) and the coordinates are arranged
+          as `[ymin, xmin, ymax, xmax]`. If num_boxes is 0 then use the whole
+          image.
+      min_object_covered: An optional `float`. Defaults to `0.1`. The cropped
+          area of the image must contain at least this fraction of any bounding
+          box supplied.
+      aspect_ratio_range: An optional list of `float`s. The cropped area of the
+          image must have an aspect ratio = width / height within this range.
+      area_range: An optional list of `float`s. The cropped area of the image
+          must contain a fraction of the supplied image within in this range.
+      max_attempts: An optional `int`. Number of attempts at generating a cropped
+          region of the image of the specified constraints. After `max_attempts`
+          failures, return the entire image.
+      scope: Optional `str` for name scope.
+    Returns:
+      cropped image `Tensor`
+    """
+    with tf.name_scope(scope, 'distorted_bounding_box_crop', [image_bytes, bbox]):
+        shape = tf.image.extract_jpeg_shape(image_bytes)
+        sample_distorted_bounding_box = tf.image.sample_distorted_bounding_box(
+            shape,
+            bounding_boxes=bbox,
+            min_object_covered=min_object_covered,
+            aspect_ratio_range=aspect_ratio_range,
+            area_range=area_range,
+            max_attempts=max_attempts,
+            use_image_if_no_bounding_boxes=True)
+        bbox_begin, bbox_size, _ = sample_distorted_bounding_box
+
+        # Crop the image to the specified bounding box.
+        offset_y, offset_x, _ = tf.unstack(bbox_begin)
+        target_height, target_width, _ = tf.unstack(bbox_size)
+        crop_window = tf.stack([offset_y, offset_x, target_height, target_width])
+        image = tf.image.decode_and_crop_jpeg(image_bytes, crop_window, channels=3)
+
+        return image
+
+
+def _at_least_x_are_equal(a, b, x):
+    """At least `x` of `a` and `b` `Tensors` are equal."""
+    match = tf.equal(a, b)
+    match = tf.cast(match, tf.int32)
+    return tf.greater_equal(tf.reduce_sum(match), x)
+
+
+def _decode_and_random_crop(image_bytes, image_size):
+    """Make a random crop of image_size."""
+    bbox = tf.constant([0.0, 0.0, 1.0, 1.0], dtype=tf.float32, shape=[1, 1, 4])
+    image = distorted_bounding_box_crop(
+        image_bytes,
+        bbox,
+        min_object_covered=0.1,
+        aspect_ratio_range=(3. / 4, 4. / 3.),
+        area_range=(0.08, 1.0),
+        max_attempts=10,
+        scope=None)
+    original_shape = tf.image.extract_jpeg_shape(image_bytes)
+    bad = _at_least_x_are_equal(original_shape, tf.shape(image), 3)
+
+    image = tf.cond(
+        bad,
+        lambda: _decode_and_center_crop(image_bytes, image_size),
+        lambda: tf.image.resize_bicubic([image],  # pylint: disable=g-long-lambda
+                                        [image_size, image_size])[0])
+
+    return image
+
+
+def _decode_and_center_crop(image_bytes, image_size):
+    """Crops to center of image with padding then scales image_size."""
+    shape = tf.image.extract_jpeg_shape(image_bytes)
+    image_height = shape[0]
+    image_width = shape[1]
+
+    padded_center_crop_size = tf.cast(
+        ((image_size / (image_size + CROP_PADDING)) *
+         tf.cast(tf.minimum(image_height, image_width), tf.float32)),
+        tf.int32)
+
+    offset_height = ((image_height - padded_center_crop_size) + 1) // 2
+    offset_width = ((image_width - padded_center_crop_size) + 1) // 2
+    crop_window = tf.stack([offset_height, offset_width,
+                            padded_center_crop_size, padded_center_crop_size])
+    image = tf.image.decode_and_crop_jpeg(image_bytes, crop_window, channels=3)
+    image = tf.image.resize_bicubic([image], [image_size, image_size])[0]
+
+    return image
+
+
+def _flip(image):
+    """Random horizontal image flip."""
+    image = tf.image.random_flip_left_right(image)
+    return image
+
+
+def preprocess_for_train(image_bytes, use_bfloat16, image_size=IMAGE_SIZE):
+    """Preprocesses the given image for evaluation.
+
+    Args:
+      image_bytes: `Tensor` representing an image binary of arbitrary size.
+      use_bfloat16: `bool` for whether to use bfloat16.
+      image_size: image size.
+
+    Returns:
+      A preprocessed image `Tensor`.
+    """
+    image = _decode_and_random_crop(image_bytes, image_size)
+    image = _flip(image)
+    image = tf.reshape(image, [image_size, image_size, 3])
+    image = tf.image.convert_image_dtype(
+        image, dtype=tf.bfloat16 if use_bfloat16 else tf.float32)
+    return image
+
+
+def preprocess_for_eval(image_bytes, use_bfloat16, image_size=IMAGE_SIZE):
+    """Preprocesses the given image for evaluation.
+
+    Args:
+      image_bytes: `Tensor` representing an image binary of arbitrary size.
+      use_bfloat16: `bool` for whether to use bfloat16.
+      image_size: image size.
+
+    Returns:
+      A preprocessed image `Tensor`.
+    """
+    image = _decode_and_center_crop(image_bytes, image_size)
+    image = tf.reshape(image, [image_size, image_size, 3])
+    image = tf.image.convert_image_dtype(
+        image, dtype=tf.bfloat16 if use_bfloat16 else tf.float32)
+    return image
+
+
+def preprocess_image(image_bytes,
+                     is_training=False,
+                     use_bfloat16=False,
+                     image_size=IMAGE_SIZE):
+    """Preprocesses the given image.
+
+    Args:
+      image_bytes: `Tensor` representing an image binary of arbitrary size.
+      is_training: `bool` for whether the preprocessing is for training.
+      use_bfloat16: `bool` for whether to use bfloat16.
+      image_size: image size.
+
+    Returns:
+      A preprocessed image `Tensor` with value range of [0, 255].
+    """
+    if is_training:
+        return preprocess_for_train(image_bytes, use_bfloat16, image_size)
+    else:
+        return preprocess_for_eval(image_bytes, use_bfloat16, image_size)
+
+
+class TfPreprocessTransform:
+
+    def __init__(self, is_training=False, size=224):
+        self.is_training = is_training
+        self.size = size[0] if isinstance(size, tuple) else size
+        self._image_bytes = None
+        self.process_image = self._build_tf_graph()
+        self.sess = None
+
+    def _build_tf_graph(self):
+        with tf.device('/cpu:0'):
+            self._image_bytes = tf.placeholder(
+                shape=[],
+                dtype=tf.string,
+            )
+            img = preprocess_image(self._image_bytes, self.is_training, False, self.size)
+        return img
+
+    def __call__(self, image_bytes):
+        if self.sess is None:
+            self.sess = tf.Session()
+        img = self.sess.run(self.process_image, feed_dict={self._image_bytes: image_bytes})
+        img = img.round().clip(0, 255).astype(np.uint8)
+        if img.ndim < 3:
+            img = np.expand_dims(img, axis=-1)
+        img = np.rollaxis(img, 2)  # HWC to CHW
+        return img

models/gen_efficientnet.py

@@ -1,6 +1,6 @@
-""" Generic MobileNet
+""" Generic EfficientNets
 
-A generic MobileNet class with building blocks to support a variety of models:
+A generic class with building blocks to support a variety of models with efficient architectures:
 * EfficientNet (B0-B4 in code right now, work in progress, still verifying)
 * MNasNet B1, A1 (SE), Small
 * MobileNet V1, V2, and V3 (work in progress)
@@ -32,8 +32,9 @@ _models = [
     'semnasnet_100', 'semnasnet_140', 'mnasnet_small', 'mobilenetv1_100', 'mobilenetv2_100',
     'mobilenetv3_050', 'mobilenetv3_075', 'mobilenetv3_100', 'chamnetv1_100', 'chamnetv2_100',
     'fbnetc_100', 'spnasnet_100', 'tflite_mnasnet_100', 'tflite_semnasnet_100', 'efficientnet_b0',
-    'efficientnet_b1', 'efficientnet_b2', 'efficientnet_b3', 'efficientnet_b4']
+    'efficientnet_b1', 'efficientnet_b2', 'efficientnet_b3', 'efficientnet_b4', 'tf_efficientnet_b0',
-__all__ = ['GenMobileNet', 'genmobilenet_model_names'] + _models
+    'tf_efficientnet_b1', 'tf_efficientnet_b2', 'tf_efficientnet_b3']
+__all__ = ['GenEfficientNet', 'gen_efficientnet_model_names'] + _models
 
 
 def _cfg(url='', **kwargs):
@@ -74,6 +75,18 @@ default_cfgs = {
     'efficientnet_b2': _cfg(url='', input_size=(3, 260, 260)),
     'efficientnet_b3': _cfg(url='', input_size=(3, 300, 300)),
     'efficientnet_b4': _cfg(url='', input_size=(3, 380, 380)),
+    'tf_efficientnet_b0': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b0-0af12548.pth',
+        input_size=(3, 224, 224), interpolation='bicubic'),
+    'tf_efficientnet_b1': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b1-5c1377c4.pth',
+        input_size=(3, 240, 240), interpolation='bicubic', crop_pct=0.882),
+    'tf_efficientnet_b2': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b2-e393ef04.pth',
+        input_size=(3, 260, 260), interpolation='bicubic', crop_pct=0.890),
+    'tf_efficientnet_b3': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b3-e3bd6955.pth',
+        input_size=(3, 300, 300), interpolation='bicubic', crop_pct=0.904),
 }
 
 _DEBUG = False
@@ -648,10 +661,10 @@ class InvertedResidual(nn.Module):
         return x
 
 
-class GenMobileNet(nn.Module):
+class GenEfficientNet(nn.Module):
-    """ Generic Mobile Net
+    """ Generic EfficientNet
 
-    An implementation of mobile optimized networks that covers:
+    An implementation of efficient network architectures, in many cases mobile optimized networks:
       * MobileNet-V1
       * MobileNet-V2
       * MobileNet-V3
@@ -659,7 +672,7 @@ class GenMobileNet(nn.Module):
       * FBNet A, B, and C
       * ChamNet (arch details are murky)
       * Single-Path NAS Pixel1
-      * EfficientNet
+      * EfficientNetB0-B4 (rest easy to add)
     """
 
     def __init__(self, block_args, num_classes=1000, in_chans=3, stem_size=32, num_features=1280,
@@ -669,7 +682,7 @@ class GenMobileNet(nn.Module):
                  se_gate_fn=torch.sigmoid, se_reduce_mid=False,
                  global_pool='avg', head_conv='default', weight_init='goog',
                  folded_bn=False, padding_same=False,):
-        super(GenMobileNet, self).__init__()
+        super(GenEfficientNet, self).__init__()
         self.num_classes = num_classes
         self.drop_rate = drop_rate
         self.drop_connect_rate = drop_connect_rate
@@ -783,7 +796,7 @@ def _gen_mnasnet_a1(channel_multiplier, num_classes=1000, **kwargs):
         ['ir_r1_k3_s1_e6_c320'],
     ]
     bn_momentum, bn_eps = _resolve_bn_params(kwargs)
-    model = GenMobileNet(
+    model = GenEfficientNet(
         _decode_arch_def(arch_def),
         num_classes=num_classes,
         stem_size=32,
@@ -823,7 +836,7 @@ def _gen_mnasnet_b1(channel_multiplier, num_classes=1000, **kwargs):
         ['ir_r1_k3_s1_e6_c320_noskip']
     ]
     bn_momentum, bn_eps = _resolve_bn_params(kwargs)
-    model = GenMobileNet(
+    model = GenEfficientNet(
         _decode_arch_def(arch_def),
         num_classes=num_classes,
         stem_size=32,
@@ -856,7 +869,7 @@ def _gen_mnasnet_small(channel_multiplier, num_classes=1000, **kwargs):
         ['ir_r1_k3_s1_e6_c144']
     ]
     bn_momentum, bn_eps = _resolve_bn_params(kwargs)
-    model = GenMobileNet(
+    model = GenEfficientNet(
         _decode_arch_def(arch_def),
         num_classes=num_classes,
         stem_size=8,
@@ -883,7 +896,7 @@ def _gen_mobilenet_v1(channel_multiplier, num_classes=1000, **kwargs):
         ['dsa_r2_k3_s2_c1024'],
     ]
     bn_momentum, bn_eps = _resolve_bn_params(kwargs)
-    model = GenMobileNet(
+    model = GenEfficientNet(
         _decode_arch_def(arch_def),
         num_classes=num_classes,
         stem_size=32,
@@ -915,7 +928,7 @@ def _gen_mobilenet_v2(channel_multiplier, num_classes=1000, **kwargs):
         ['ir_r1_k3_s1_e6_c320'],
     ]
     bn_momentum, bn_eps = _resolve_bn_params(kwargs)
-    model = GenMobileNet(
+    model = GenEfficientNet(
         _decode_arch_def(arch_def),
         num_classes=num_classes,
         stem_size=32,
@@ -956,7 +969,7 @@ def _gen_mobilenet_v3(channel_multiplier, num_classes=1000, **kwargs):
         ['cn_r1_k1_s1_c960'],  # hard-swish
     ]
     bn_momentum, bn_eps = _resolve_bn_params(kwargs)
-    model = GenMobileNet(
+    model = GenEfficientNet(
         _decode_arch_def(arch_def),
         num_classes=num_classes,
         stem_size=16,
@@ -992,7 +1005,7 @@ def _gen_chamnet_v1(channel_multiplier, num_classes=1000, **kwargs):
         ['ir_r1_k3_s1_e10_c104'],
     ]
     bn_momentum, bn_eps = _resolve_bn_params(kwargs)
-    model = GenMobileNet(
+    model = GenEfficientNet(
         _decode_arch_def(arch_def),
         num_classes=num_classes,
         stem_size=32,
@@ -1025,7 +1038,7 @@ def _gen_chamnet_v2(channel_multiplier, num_classes=1000, **kwargs):
         ['ir_r1_k3_s1_e6_c112'],
     ]
     bn_momentum, bn_eps = _resolve_bn_params(kwargs)
-    model = GenMobileNet(
+    model = GenEfficientNet(
         _decode_arch_def(arch_def),
         num_classes=num_classes,
         stem_size=32,
@@ -1059,7 +1072,7 @@ def _gen_fbnetc(channel_multiplier, num_classes=1000, **kwargs):
         ['ir_r1_k3_s1_e6_c352'],
     ]
     bn_momentum, bn_eps = _resolve_bn_params(kwargs)
-    model = GenMobileNet(
+    model = GenEfficientNet(
         _decode_arch_def(arch_def),
         num_classes=num_classes,
         stem_size=16,
@@ -1099,7 +1112,7 @@ def _gen_spnasnet(channel_multiplier, num_classes=1000, **kwargs):
         ['ir_r1_k3_s1_e6_c320_noskip']
     ]
     bn_momentum, bn_eps = _resolve_bn_params(kwargs)
-    model = GenMobileNet(
+    model = GenEfficientNet(
         _decode_arch_def(arch_def),
         num_classes=num_classes,
         stem_size=32,
@@ -1119,9 +1132,21 @@ def _gen_efficientnet(channel_multiplier=1.0, depth_multiplier=1.0, num_classes=
     Ref impl: https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/efficientnet_model.py
     Paper: https://arxiv.org/abs/1905.11946
 
+    EfficientNet params
+    name: (channel_multiplier, depth_multiplier, resolution, dropout_rate)
+    'efficientnet-b0': (1.0, 1.0, 224, 0.2),
+    'efficientnet-b1': (1.0, 1.1, 240, 0.2),
+    'efficientnet-b2': (1.1, 1.2, 260, 0.3),
+    'efficientnet-b3': (1.2, 1.4, 300, 0.3),
+    'efficientnet-b4': (1.4, 1.8, 380, 0.4),
+    'efficientnet-b5': (1.6, 2.2, 456, 0.4),
+    'efficientnet-b6': (1.8, 2.6, 528, 0.5),
+    'efficientnet-b7': (2.0, 3.1, 600, 0.5),
+
     Args:
       channel_multiplier: multiplier to number of channels per layer
       depth_multiplier: multiplier to number of repeats per stage
+
     """
     arch_def = [
         ['ds_r1_k3_s1_e1_c16_se0.25'],
@@ -1133,13 +1158,16 @@ def _gen_efficientnet(channel_multiplier=1.0, depth_multiplier=1.0, num_classes=
         ['ir_r1_k3_s1_e6_c320_se0.25'],
     ]
     bn_momentum, bn_eps = _resolve_bn_params(kwargs)
-    model = GenMobileNet(
+    # NOTE: other models in the family didn't scale the feature count
+    num_features = _round_channels(1280, channel_multiplier, 8, None)
+    model = GenEfficientNet(
         _decode_arch_def(arch_def, depth_multiplier),
         num_classes=num_classes,
         stem_size=32,
         channel_multiplier=channel_multiplier,
         channel_divisor=8,
         channel_min=None,
+        num_features=num_features,
         bn_momentum=bn_momentum,
         bn_eps=bn_eps,
         act_fn=swish,
@@ -1357,19 +1385,8 @@ def spnasnet_100(num_classes, in_chans=3, pretrained=False, **kwargs):
     return model
 
 
-# EfficientNet params
-# (width_coefficient, depth_coefficient, resolution, dropout_rate)
-# 'efficientnet-b0': (1.0, 1.0, 224, 0.2),
-# 'efficientnet-b1': (1.0, 1.1, 240, 0.2),
-# 'efficientnet-b2': (1.1, 1.2, 260, 0.3),
-# 'efficientnet-b3': (1.2, 1.4, 300, 0.3),
-# 'efficientnet-b4': (1.4, 1.8, 380, 0.4),
-# 'efficientnet-b5': (1.6, 2.2, 456, 0.4),
-# 'efficientnet-b6': (1.8, 2.6, 528, 0.5),
-# 'efficientnet-b7': (2.0, 3.1, 600, 0.5),
-
 def efficientnet_b0(num_classes, in_chans=3, pretrained=False, **kwargs):
-    """ EfficientNet """
+    """ EfficientNet-B0 """
     default_cfg = default_cfgs['efficientnet_b0']
     # NOTE for train, drop_rate should be 0.2
     model = _gen_efficientnet(
@@ -1382,7 +1399,7 @@ def efficientnet_b0(num_classes, in_chans=3, pretrained=False, **kwargs):
 
 
 def efficientnet_b1(num_classes, in_chans=3, pretrained=False, **kwargs):
-    """ EfficientNet """
+    """ EfficientNet-B1 """
     default_cfg = default_cfgs['efficientnet_b1']
     # NOTE for train, drop_rate should be 0.2
     model = _gen_efficientnet(
@@ -1395,7 +1412,7 @@ def efficientnet_b1(num_classes, in_chans=3, pretrained=False, **kwargs):
 
 
 def efficientnet_b2(num_classes, in_chans=3, pretrained=False, **kwargs):
-    """ EfficientNet """
+    """ EfficientNet-B2 """
     default_cfg = default_cfgs['efficientnet_b2']
     # NOTE for train, drop_rate should be 0.3
     model = _gen_efficientnet(
@@ -1408,7 +1425,7 @@ def efficientnet_b2(num_classes, in_chans=3, pretrained=False, **kwargs):
 
 
 def efficientnet_b3(num_classes, in_chans=3, pretrained=False, **kwargs):
-    """ EfficientNet """
+    """ EfficientNet-B3 """
     default_cfg = default_cfgs['efficientnet_b3']
     # NOTE for train, drop_rate should be 0.3
     model = _gen_efficientnet(
@@ -1421,7 +1438,7 @@ def efficientnet_b3(num_classes, in_chans=3, pretrained=False, **kwargs):
 
 
 def efficientnet_b4(num_classes, in_chans=3, pretrained=False, **kwargs):
-    """ EfficientNet """
+    """ EfficientNet-B4 """
     default_cfg = default_cfgs['efficientnet_b4']
     # NOTE for train, drop_rate should be 0.4
     model = _gen_efficientnet(
@@ -1433,5 +1450,61 @@ def efficientnet_b4(num_classes, in_chans=3, pretrained=False, **kwargs):
     return model
 
 
-def genmobilenet_model_names():
+def tf_efficientnet_b0(num_classes, in_chans=3, pretrained=False, **kwargs):
+    """ EfficientNet-B0. Tensorflow compatible variant  """
+    default_cfg = default_cfgs['tf_efficientnet_b0']
+    kwargs['bn_eps'] = _BN_EPS_TF_DEFAULT
+    kwargs['padding_same'] = True
+    model = _gen_efficientnet(
+        channel_multiplier=1.0, depth_multiplier=1.0,
+        num_classes=num_classes, in_chans=in_chans, **kwargs)
+    model.default_cfg = default_cfg
+    if pretrained:
+        load_pretrained(model, default_cfg, num_classes, in_chans)
+    return model
+
+
+def tf_efficientnet_b1(num_classes, in_chans=3, pretrained=False, **kwargs):
+    """ EfficientNet-B1. Tensorflow compatible variant  """
+    default_cfg = default_cfgs['tf_efficientnet_b1']
+    kwargs['bn_eps'] = _BN_EPS_TF_DEFAULT
+    kwargs['padding_same'] = True
+    model = _gen_efficientnet(
+        channel_multiplier=1.0, depth_multiplier=1.1,
+        num_classes=num_classes, in_chans=in_chans, **kwargs)
+    model.default_cfg = default_cfg
+    if pretrained:
+        load_pretrained(model, default_cfg, num_classes, in_chans)
+    return model
+
+
+def tf_efficientnet_b2(num_classes, in_chans=3, pretrained=False, **kwargs):
+    """ EfficientNet-B2. Tensorflow compatible variant  """
+    default_cfg = default_cfgs['tf_efficientnet_b2']
+    kwargs['bn_eps'] = _BN_EPS_TF_DEFAULT
+    kwargs['padding_same'] = True
+    model = _gen_efficientnet(
+        channel_multiplier=1.1, depth_multiplier=1.2,
+        num_classes=num_classes, in_chans=in_chans, **kwargs)
+    model.default_cfg = default_cfg
+    if pretrained:
+        load_pretrained(model, default_cfg, num_classes, in_chans)
+    return model
+
+
+def tf_efficientnet_b3(num_classes, in_chans=3, pretrained=False, **kwargs):
+    """ EfficientNet-B3. Tensorflow compatible variant """
+    default_cfg = default_cfgs['tf_efficientnet_b3']
+    kwargs['bn_eps'] = _BN_EPS_TF_DEFAULT
+    kwargs['padding_same'] = True
+    model = _gen_efficientnet(
+        channel_multiplier=1.2, depth_multiplier=1.4,
+        num_classes=num_classes, in_chans=in_chans, **kwargs)
+    model.default_cfg = default_cfg
+    if pretrained:
+        load_pretrained(model, default_cfg, num_classes, in_chans)
+    return model
+
+
+def gen_efficientnet_model_names():
     return set(_models)

models/model_factory.py

@@ -6,7 +6,7 @@ from models.dpn import *
 from models.senet import *
 from models.xception import *
 from models.pnasnet import *
-from models.genmobilenet import *
+from models.gen_efficientnet import *
 from models.inception_v3 import *
 from models.gluon_resnet import *
 
@@ -23,8 +23,8 @@ def create_model(
 
     margs = dict(num_classes=num_classes, in_chans=in_chans, pretrained=pretrained)
 
-    # Not all models have support for batchnorm params passed as args, only genmobilenet variants
+    # Not all models have support for batchnorm params passed as args, only gen_efficientnet variants
-    supports_bn_params = model_name in genmobilenet_model_names()
+    supports_bn_params = model_name in gen_efficientnet_model_names()
     if not supports_bn_params and any([x in kwargs for x in ['bn_tf', 'bn_momentum', 'bn_eps']]):
         kwargs.pop('bn_tf', None)
         kwargs.pop('bn_momentum', None)

validate.py

@@ -44,6 +44,8 @@ parser.add_argument('--num-gpu', type=int, default=1,
                     help='Number of GPUS to use')
 parser.add_argument('--no-test-pool', dest='no_test_pool', action='store_true',
                     help='disable test time pool')
+parser.add_argument('--tf-preprocessing', dest='tf_preprocessing', action='store_true',
+                    help='Use Tensorflow preprocessing pipeline (require CPU TF installed')
 
 
 def main():
@@ -71,7 +73,7 @@ def main():
     criterion = nn.CrossEntropyLoss().cuda()
 
     loader = create_loader(
-        Dataset(args.data),
+        Dataset(args.data, load_bytes=args.tf_preprocessing),
         input_size=data_config['input_size'],
         batch_size=args.batch_size,
         use_prefetcher=True,
@@ -79,7 +81,8 @@ def main():
         mean=data_config['mean'],
         std=data_config['std'],
         num_workers=args.workers,
-        crop_pct=1.0 if test_time_pool else data_config['crop_pct'])
+        crop_pct=1.0 if test_time_pool else data_config['crop_pct'],
+        tf_preprocessing=args.tf_preprocessing)
 
     batch_time = AverageMeter()
     losses = AverageMeter()