Add MixNet (https://arxiv.org/abs/1907.09595) with pretrained weights converted from Tensorflow impl

* refactor 'same' convolution and add helper to use MixedConv2d when needed
* improve performance of 'same' padding for cases that can be handled statically
* add support for extra exp, pw, and dw kernel specs with grouping support to decoder/string defs for MixNet
* shuffle some args for a bit more consistency, a little less clutter overall in gen_efficientnet.py

README.md

@@ -31,8 +31,9 @@ I've included a few of my favourite models, but this is not an exhaustive collec
 * 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-68, DPN-68b, DPN-92, DPN-98, DPN-131, DPN-107
-* 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
+* Generic 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 (B0-B5) (https://arxiv.org/abs/1905.11946) -- validated, compat with TF weights
+    * MixNet (https://arxiv.org/abs/1907.09595) -- 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)
@@ -118,11 +119,17 @@ I've leveraged the training scripts in this repository to train a few of the mod
 | gluon_resnext50_32x4d    | 79.356 (20.644) | 94.424 (5.576) | 25.03  | bicubic | |
 | gluon_resnet101_v1b      | 79.304 (20.696) | 94.524 (5.476) | 44.55  | bicubic | |
 | gluon_resnet50_v1d       | 79.074 (20.926) | 94.476 (5.524) | 25.58  | bicubic | |
+| tf_mixnet_l *tfp         | 78.846 (21.154) | 94.212 (5.788) | 7.33  | bilinear | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet/mixnet) |
+| tf_mixnet_l              | 78.770 (21.230) | 94.004 (5.996) | 7.33  | bicubic | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet/mixnet) |
 | 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_mixnet_m *tfp         | 77.072 (22.928) | 93.368 (6.632) | 5.01  | bilinear | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet/mixnet) |
+| tf_mixnet_m              | 76.950 (23.050) | 93.156 (6.844) | 5.01  | bicubic | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet/mixnet) |
 | 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) |
+| tf_mixnet_s *tfp         | 75.800 (24.200) | 92.788 (7.212) | 4.13  | bilinear | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet/mixnet) |
+| tf_mixnet_s              | 75.648 (24.352) | 92.636 (7.364) | 4.13  | bicubic | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet/mixnet) |
 | gluon_resnet34_v1b       | 74.580 (25.420) | 91.988 (8.012) | 21.80 | bicubic | |
 | gluon_resnet18_v1b       | 70.830 (29.170) | 89.756 (10.244) | 11.69 | bicubic | |
 

timm/data/loader.py

@@ -112,7 +112,8 @@ def create_loader(
 
     if tf_preprocessing and use_prefetcher:
         from timm.data.tf_preprocessing import TfPreprocessTransform
-        transform = TfPreprocessTransform(is_training=is_training, size=img_size)
+        transform = TfPreprocessTransform(
+            is_training=is_training, size=img_size, interpolation=interpolation)
     else:
         if is_training:
             transform = transforms_imagenet_train(

timm/data/tf_preprocessing.py

@@ -83,7 +83,7 @@ def _at_least_x_are_equal(a, b, x):
     return tf.greater_equal(tf.reduce_sum(match), x)
 
 
-def _decode_and_random_crop(image_bytes, image_size):
+def _decode_and_random_crop(image_bytes, image_size, resize_method):
     """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(
@@ -100,13 +100,12 @@ def _decode_and_random_crop(image_bytes, image_size):
     image = tf.cond(
         bad,
         lambda: _decode_and_center_crop(image_bytes, image_size),
-        lambda: tf.image.resize_bicubic([image],  # pylint: disable=g-long-lambda
+        lambda: tf.image.resize([image], [image_size, image_size], resize_method)[0])
-                                        [image_size, image_size])[0])
 
     return image
 
 
-def _decode_and_center_crop(image_bytes, image_size):
+def _decode_and_center_crop(image_bytes, image_size, resize_method):
     """Crops to center of image with padding then scales image_size."""
     shape = tf.image.extract_jpeg_shape(image_bytes)
     image_height = shape[0]
@@ -122,7 +121,7 @@ def _decode_and_center_crop(image_bytes, image_size):
     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]
+    image = tf.image.resize([image], [image_size, image_size], resize_method)[0]
 
     return image
 
@@ -133,18 +132,20 @@ def _flip(image):
     return image
 
 
-def preprocess_for_train(image_bytes, use_bfloat16, image_size=IMAGE_SIZE):
+def preprocess_for_train(image_bytes, use_bfloat16, image_size=IMAGE_SIZE, interpolation='bicubic'):
     """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.
+      interpolation: image interpolation method
 
     Returns:
       A preprocessed image `Tensor`.
     """
-    image = _decode_and_random_crop(image_bytes, image_size)
+    resize_method = tf.image.ResizeMethod.BICUBIC if interpolation == 'bicubic' else tf.image.ResizeMethod.BILINEAR
+    image = _decode_and_random_crop(image_bytes, image_size, resize_method)
     image = _flip(image)
     image = tf.reshape(image, [image_size, image_size, 3])
     image = tf.image.convert_image_dtype(
@@ -152,18 +153,20 @@ def preprocess_for_train(image_bytes, use_bfloat16, image_size=IMAGE_SIZE):
     return image
 
 
-def preprocess_for_eval(image_bytes, use_bfloat16, image_size=IMAGE_SIZE):
+def preprocess_for_eval(image_bytes, use_bfloat16, image_size=IMAGE_SIZE, interpolation='bicubic'):
     """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.
+      interpolation: image interpolation method
 
     Returns:
       A preprocessed image `Tensor`.
     """
-    image = _decode_and_center_crop(image_bytes, image_size)
+    resize_method = tf.image.ResizeMethod.BICUBIC if interpolation == 'bicubic' else tf.image.ResizeMethod.BILINEAR
+    image = _decode_and_center_crop(image_bytes, image_size, resize_method)
     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)
@@ -173,7 +176,8 @@ def preprocess_for_eval(image_bytes, use_bfloat16, image_size=IMAGE_SIZE):
 def preprocess_image(image_bytes,
                      is_training=False,
                      use_bfloat16=False,
-                     image_size=IMAGE_SIZE):
+                     image_size=IMAGE_SIZE,
+                     interpolation='bicubic'):
     """Preprocesses the given image.
 
     Args:
@@ -181,21 +185,23 @@ def preprocess_image(image_bytes,
       is_training: `bool` for whether the preprocessing is for training.
       use_bfloat16: `bool` for whether to use bfloat16.
       image_size: image size.
+      interpolation: image interpolation method
 
     Returns:
       A preprocessed image `Tensor` with value range of [0, 255].
     """
     if is_training:
-        return preprocess_for_train(image_bytes, use_bfloat16, image_size)
+        return preprocess_for_train(image_bytes, use_bfloat16, image_size, interpolation)
     else:
-        return preprocess_for_eval(image_bytes, use_bfloat16, image_size)
+        return preprocess_for_eval(image_bytes, use_bfloat16, image_size, interpolation)
 
 
 class TfPreprocessTransform:
 
-    def __init__(self, is_training=False, size=224):
+    def __init__(self, is_training=False, size=224, interpolation='bicubic'):
         self.is_training = is_training
         self.size = size[0] if isinstance(size, tuple) else size
+        self.interpolation = interpolation
         self._image_bytes = None
         self.process_image = self._build_tf_graph()
         self.sess = None
@@ -206,7 +212,8 @@ class TfPreprocessTransform:
                 shape=[],
                 dtype=tf.string,
             )
-            img = preprocess_image(self._image_bytes, self.is_training, False, self.size)
+            img = preprocess_image(
+                self._image_bytes, self.is_training, False, self.size, self.interpolation)
         return img
 
     def __call__(self, image_bytes):

timm/models/conv2d_helpers.py

@@ -0,0 +1,120 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import math
+
+
+def _is_static_pad(kernel_size, stride=1, dilation=1, **_):
+    return stride == 1 and (dilation * (kernel_size - 1)) % 2 == 0
+
+
+def _get_padding(kernel_size, stride=1, dilation=1, **_):
+    padding = ((stride - 1) + dilation * (kernel_size - 1)) // 2
+    return padding
+
+
+def _calc_same_pad(i, k, s, d):
+    return max((math.ceil(i / s) - 1) * s + (k - 1) * d + 1 - i, 0)
+
+
+def _split_channels(num_chan, num_groups):
+    split = [num_chan // num_groups for _ in range(num_groups)]
+    split[0] += num_chan - sum(split)
+    return split
+
+
+class Conv2dSame(nn.Conv2d):
+    """ Tensorflow like 'SAME' convolution wrapper for 2D convolutions
+    """
+    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
+                 padding=0, dilation=1, groups=1, bias=True):
+        super(Conv2dSame, self).__init__(
+            in_channels, out_channels, kernel_size, stride, 0, dilation,
+            groups, bias)
+
+    def forward(self, x):
+        ih, iw = x.size()[-2:]
+        kh, kw = self.weight.size()[-2:]
+        pad_h = _calc_same_pad(ih, kh, self.stride[0], self.dilation[0])
+        pad_w = _calc_same_pad(iw, kw, self.stride[1], self.dilation[1])
+        if pad_h > 0 or pad_w > 0:
+            x = F.pad(x, [pad_w//2, pad_w - pad_w//2, pad_h//2, pad_h - pad_h//2])
+        return F.conv2d(x, self.weight, self.bias, self.stride,
+                        self.padding, self.dilation, self.groups)
+
+
+def conv2d_pad(in_chs, out_chs, kernel_size, **kwargs):
+    padding = kwargs.pop('padding', '')
+    kwargs.setdefault('bias', False)
+    if isinstance(padding, str):
+        # for any string padding, the padding will be calculated for you, one of three ways
+        padding = padding.lower()
+        if padding == 'same':
+            # TF compatible 'SAME' padding, has a performance and GPU memory allocation impact
+            if _is_static_pad(kernel_size, **kwargs):
+                # static case, no extra overhead
+                padding = _get_padding(kernel_size, **kwargs)
+                return nn.Conv2d(in_chs, out_chs, kernel_size, padding=padding, **kwargs)
+            else:
+                # dynamic padding
+                return Conv2dSame(in_chs, out_chs, kernel_size, **kwargs)
+        elif padding == 'valid':
+            # 'VALID' padding, same as padding=0
+            return nn.Conv2d(in_chs, out_chs, kernel_size, padding=0, **kwargs)
+        else:
+            # Default to PyTorch style 'same'-ish symmetric padding
+            padding = _get_padding(kernel_size, **kwargs)
+            return nn.Conv2d(in_chs, out_chs, kernel_size, padding=padding, **kwargs)
+    else:
+        # padding was specified as a number or pair
+        return nn.Conv2d(in_chs, out_chs, kernel_size, padding=padding, **kwargs)
+
+
+class MixedConv2d(nn.Module):
+    """ Mixed Grouped Convolution
+    Based on MDConv and GroupedConv in MixNet impl:
+      https://github.com/tensorflow/tpu/blob/master/models/official/mnasnet/mixnet/custom_layers.py
+    """
+
+    def __init__(self, in_channels, out_channels, kernel_size=3,
+                 stride=1, padding='', dilated=False, depthwise=False, **kwargs):
+        super(MixedConv2d, self).__init__()
+
+        kernel_size = kernel_size if isinstance(kernel_size, list) else [kernel_size]
+        num_groups = len(kernel_size)
+        in_splits = _split_channels(in_channels, num_groups)
+        out_splits = _split_channels(out_channels, num_groups)
+        for idx, (k, in_ch, out_ch) in enumerate(zip(kernel_size, in_splits, out_splits)):
+            d = 1
+            # FIXME make compat with non-square kernel/dilations/strides
+            if stride == 1 and dilated:
+                d, k = (k - 1) // 2, 3
+            conv_groups = out_ch if depthwise else 1
+            # use add_module to keep key space clean
+            self.add_module(
+                str(idx),
+                conv2d_pad(
+                    in_ch, out_ch, k, stride=stride,
+                    padding=padding, dilation=d, groups=conv_groups, **kwargs)
+            )
+        self.splits = in_splits
+
+    def forward(self, x):
+        x_split = torch.split(x, self.splits, 1)
+        x_out = [c(x) for x, c in zip(x_split, self._modules.values())]
+        x = torch.cat(x_out, 1)
+        return x
+
+
+# helper method
+def select_conv2d(in_chs, out_chs, kernel_size, **kwargs):
+    assert 'groups' not in kwargs  # only use 'depthwise' bool arg
+    if isinstance(kernel_size, list):
+        # We're going to use only lists for defining the MixedConv2d kernel groups,
+        # ints, tuples, other iterables will continue to pass to normal conv and specify h, w.
+        return MixedConv2d(in_chs, out_chs, kernel_size, **kwargs)
+    else:
+        depthwise = kwargs.pop('depthwise', False)
+        groups = out_chs if depthwise else 1
+        return conv2d_pad(in_chs, out_chs, kernel_size, groups=groups, **kwargs)
+

timm/models/conv2d_same.py

@@ -1,39 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import math
-
-
-class Conv2dSame(nn.Conv2d):
-    """ Tensorflow like 'SAME' convolution wrapper for 2D convolutions
-    """
-    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
-                 padding=0, dilation=1, groups=1, bias=True):
-        super(Conv2dSame, self).__init__(
-            in_channels, out_channels, kernel_size, stride, 0, dilation,
-            groups, bias)
-
-    def forward(self, x):
-        ih, iw = x.size()[-2:]
-        kh, kw = self.weight.size()[-2:]
-        oh = math.ceil(ih / self.stride[0])
-        ow = math.ceil(iw / self.stride[1])
-        pad_h = max((oh - 1) * self.stride[0] + (kh - 1) * self.dilation[0] + 1 - ih, 0)
-        pad_w = max((ow - 1) * self.stride[1] + (kw - 1) * self.dilation[1] + 1 - iw, 0)
-        if pad_h > 0 or pad_w > 0:
-            x = F.pad(x, [pad_w//2, pad_w - pad_w//2, pad_h//2, pad_h - pad_h//2])
-        return F.conv2d(x, self.weight, self.bias, self.stride,
-                        self.padding, self.dilation, self.groups)
-
-
-# helper method
-def sconv2d(in_chs, out_chs, kernel_size, **kwargs):
-    padding = kwargs.pop('padding', 0)
-    if isinstance(padding, str):
-        if padding.lower() == 'same':
-            return Conv2dSame(in_chs, out_chs, kernel_size, **kwargs)
-        else:
-            # 'valid'
-            return nn.Conv2d(in_chs, out_chs, kernel_size, padding=0, **kwargs)
-    else:
-        return nn.Conv2d(in_chs, out_chs, kernel_size, padding=padding, **kwargs)

timm/models/gen_efficientnet.py

@@ -1,13 +1,13 @@
 """ Generic EfficientNets
 
 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)
+* EfficientNet (B0-B5)
-* MNasNet B1, A1 (SE), Small
+* MixNet (Small, Medium, and Large)
-* MobileNet V1, V2, and V3 (work in progress)
+* MnasNet B1, A1 (SE), Small
+* MobileNet V1, V2, and V3
 * FBNet-C (TODO A & B)
 * ChamNet (TODO still guessing at architecture definition)
 * Single-Path NAS Pixel1
-* ShuffleNetV2 (TODO add IR shuffle block)
 * And likely more...
 
 TODO not all combinations and variations have been tested. Currently working on training hyper-params...
@@ -27,7 +27,7 @@ import torch.nn.functional as F
 from .registry import register_model
 from .helpers import load_pretrained
 from .adaptive_avgmax_pool import SelectAdaptivePool2d
-from .conv2d_same import sconv2d
+from .conv2d_helpers import select_conv2d
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
 
 
@@ -37,7 +37,7 @@ __all__ = ['GenEfficientNet']
 def _cfg(url='', **kwargs):
     return {
         'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
-        'crop_pct': 0.875, 'interpolation': 'bilinear',
+        'crop_pct': 0.875, 'interpolation': 'bicubic',
         'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
         'first_conv': 'conv_stem', 'classifier': 'classifier',
         **kwargs
@@ -48,14 +48,12 @@ default_cfgs = {
     'mnasnet_050': _cfg(url=''),
     'mnasnet_075': _cfg(url=''),
     'mnasnet_100': _cfg(
-        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mnasnet_b1-74cb7081.pth',
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mnasnet_b1-74cb7081.pth'),
-        interpolation='bicubic'),
     'mnasnet_140': _cfg(url=''),
     'semnasnet_050': _cfg(url=''),
     'semnasnet_075': _cfg(url=''),
     'semnasnet_100': _cfg(
-        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mnasnet_a1-d9418771.pth',
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mnasnet_a1-d9418771.pth'),
-        interpolation='bicubic'),
     'semnasnet_140': _cfg(url=''),
     'mnasnet_small': _cfg(url=''),
     'mobilenetv1_100': _cfg(url=''),
@@ -63,23 +61,23 @@ default_cfgs = {
     'mobilenetv3_050': _cfg(url=''),
     'mobilenetv3_075': _cfg(url=''),
     'mobilenetv3_100': _cfg(
-        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv3_100-35495452.pth',
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv3_100-35495452.pth'),
-        interpolation='bicubic'),
     'chamnetv1_100': _cfg(url=''),
     'chamnetv2_100': _cfg(url=''),
     'fbnetc_100': _cfg(
-        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/fbnetc_100-c345b898.pth'),
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/fbnetc_100-c345b898.pth',
+        interpolation='bilinear'),
     'spnasnet_100': _cfg(
-        url='https://www.dropbox.com/s/iieopt18rytkgaa/spnasnet_100-048bc3f4.pth?dl=1'),
+        url='https://www.dropbox.com/s/iieopt18rytkgaa/spnasnet_100-048bc3f4.pth?dl=1',
+        interpolation='bilinear'),
     'efficientnet_b0': _cfg(
-        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b0-d6904d92.pth',
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b0-d6904d92.pth'),
-        interpolation='bicubic'),
     'efficientnet_b1': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b1-533bc792.pth',
-        input_size=(3, 240, 240), pool_size=(8, 8), interpolation='bicubic', crop_pct=0.882),
+        input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882),
     'efficientnet_b2': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b2-cf78dc4d.pth',
-        input_size=(3, 260, 260), pool_size=(9, 9), interpolation='bicubic', crop_pct=0.890),
+        input_size=(3, 260, 260), pool_size=(9, 9), crop_pct=0.890),
     'efficientnet_b3': _cfg(
         url='', input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904),
     'efficientnet_b4': _cfg(
@@ -88,22 +86,31 @@ default_cfgs = {
         url='', input_size=(3, 456, 456), pool_size=(15, 15), crop_pct=0.934),
     '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'),
+        input_size=(3, 224, 224)),
     '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), pool_size=(8, 8), interpolation='bicubic', crop_pct=0.882),
+        input_size=(3, 240, 240), pool_size=(8, 8), 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), pool_size=(9, 9), interpolation='bicubic', crop_pct=0.890),
+        input_size=(3, 260, 260), pool_size=(9, 9), 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), pool_size=(10, 10), interpolation='bicubic', crop_pct=0.904),
+        input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904),
     'tf_efficientnet_b4': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b4-74ee3bed.pth',
-        input_size=(3, 380, 380), pool_size=(12, 12), interpolation='bicubic', crop_pct=0.922),
+        input_size=(3, 380, 380), pool_size=(12, 12), crop_pct=0.922),
     'tf_efficientnet_b5': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b5-c6949ce9.pth',
-        input_size=(3, 456, 456), pool_size=(15, 15), interpolation='bicubic', crop_pct=0.934)
+        input_size=(3, 456, 456), pool_size=(15, 15), crop_pct=0.934),
+    'mixnet_s': _cfg(url=''),
+    'mixnet_m': _cfg(url=''),
+    'mixnet_l': _cfg(url=''),
+    'tf_mixnet_s': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_s-89d3354b.pth'),
+    'tf_mixnet_m': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_m-0f4d8805.pth'),
+    'tf_mixnet_l': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_l-6c92e0c8.pth'),
 }
 
 
@@ -151,6 +158,13 @@ def _round_channels(channels, multiplier=1.0, divisor=8, channel_min=None):
     return new_channels
 
 
+def _parse_ksize(ss):
+    if ss.isdigit():
+        return int(ss)
+    else:
+        return [int(k) for k in ss.split('.')]
+
+
 def _decode_block_str(block_str, depth_multiplier=1.0):
     """ Decode block definition string
 
@@ -168,7 +182,7 @@ def _decode_block_str(block_str, depth_multiplier=1.0):
     e - expansion ratio,
     c - output channels,
     se - squeeze/excitation ratio
-    a - activation fn ('re', 'r6', or 'hs')
+    n - activation fn ('re', 'r6', 'hs', or 'sw')
     Args:
         block_str: a string representation of block arguments.
     Returns:
@@ -184,7 +198,9 @@ def _decode_block_str(block_str, depth_multiplier=1.0):
     noskip = False
     for op in ops:
         # string options being checked on individual basis, combine if they grow
-        if op.startswith('a'):
+        if op == 'noskip':
+            noskip = True
+        elif op.startswith('n'):
             # activation fn
             key = op[0]
             v = op[1:]
@@ -194,11 +210,11 @@ def _decode_block_str(block_str, depth_multiplier=1.0):
                 value = F.relu6
             elif v == 'hs':
                 value = hard_swish
+            elif v == 'sw':
+                value = swish
             else:
                 continue
             options[key] = value
-        elif op == 'noskip':
-            noskip = True
         else:
             # all numeric options
             splits = re.split(r'(\d.*)', op)
@@ -207,14 +223,18 @@ def _decode_block_str(block_str, depth_multiplier=1.0):
                 options[key] = value
 
     # if act_fn is None, the model default (passed to model init) will be used
-    act_fn = options['a'] if 'a' in options else None
+    act_fn = options['n'] if 'n' in options else None
+    exp_kernel_size = _parse_ksize(options['a']) if 'a' in options else 1
+    pw_kernel_size = _parse_ksize(options['p']) if 'p' in options else 1
 
     num_repeat = int(options['r'])
     # each type of block has different valid arguments, fill accordingly
     if block_type == 'ir':
         block_args = dict(
             block_type=block_type,
-            kernel_size=int(options['k']),
+            dw_kernel_size=_parse_ksize(options['k']),
+            exp_kernel_size=exp_kernel_size,
+            pw_kernel_size=pw_kernel_size,
             out_chs=int(options['c']),
             exp_ratio=float(options['e']),
             se_ratio=float(options['se']) if 'se' in options else None,
@@ -222,20 +242,17 @@ def _decode_block_str(block_str, depth_multiplier=1.0):
             act_fn=act_fn,
             noskip=noskip,
         )
-        if 'g' in options:
-            block_args['pw_group'] = options['g']
-            if options['g'] > 1:
-                block_args['shuffle_type'] = 'mid'
     elif block_type == 'ds' or block_type == 'dsa':
         block_args = dict(
             block_type=block_type,
-            kernel_size=int(options['k']),
+            dw_kernel_size=_parse_ksize(options['k']),
+            pw_kernel_size=pw_kernel_size,
             out_chs=int(options['c']),
             se_ratio=float(options['se']) if 'se' in options else None,
             stride=int(options['s']),
             act_fn=act_fn,
-            noskip=block_type == 'dsa' or noskip,
             pw_act=block_type == 'dsa',
+            noskip=block_type == 'dsa' or noskip,
         )
     elif block_type == 'cn':
         block_args = dict(
@@ -254,15 +271,6 @@ def _decode_block_str(block_str, depth_multiplier=1.0):
     return [deepcopy(block_args) for _ in range(num_repeat)]
 
 
-def _get_padding(kernel_size, stride, dilation=1):
-    padding = ((stride - 1) + dilation * (kernel_size - 1)) // 2
-    return padding
-
-
-def _padding_arg(default, padding_same=False):
-    return 'SAME' if padding_same else default
-
-
 def _decode_arch_args(string_list):
     block_args = []
     for block_str in string_list:
@@ -316,20 +324,18 @@ class _BlockBuilder:
     https://github.com/facebookresearch/maskrcnn-benchmark/blob/master/maskrcnn_benchmark/modeling/backbone/fbnet_builder.py
 
     """
-
     def __init__(self, channel_multiplier=1.0, channel_divisor=8, channel_min=None,
-                 drop_connect_rate=0., act_fn=None, se_gate_fn=sigmoid, se_reduce_mid=False,
+                 pad_type='', act_fn=None, se_gate_fn=sigmoid, se_reduce_mid=False,
-                 bn_args=_BN_ARGS_PT, padding_same=False,
+                 bn_args=_BN_ARGS_PT, drop_connect_rate=0., verbose=False):
-                 verbose=False):
         self.channel_multiplier = channel_multiplier
         self.channel_divisor = channel_divisor
         self.channel_min = channel_min
-        self.drop_connect_rate = drop_connect_rate
+        self.pad_type = pad_type
         self.act_fn = act_fn
         self.se_gate_fn = se_gate_fn
         self.se_reduce_mid = se_reduce_mid
         self.bn_args = bn_args
-        self.padding_same = padding_same
+        self.drop_connect_rate = drop_connect_rate
         self.verbose = verbose
 
         # updated during build
@@ -345,7 +351,7 @@ class _BlockBuilder:
         ba['in_chs'] = self.in_chs
         ba['out_chs'] = self._round_channels(ba['out_chs'])
         ba['bn_args'] = self.bn_args
-        ba['padding_same'] = self.padding_same
+        ba['pad_type'] = self.pad_type
         # block act fn overrides the model default
         ba['act_fn'] = ba['act_fn'] if ba['act_fn'] is not None else self.act_fn
         assert ba['act_fn'] is not None
@@ -493,16 +499,11 @@ class SqueezeExcite(nn.Module):
 
 class ConvBnAct(nn.Module):
     def __init__(self, in_chs, out_chs, kernel_size,
-                 stride=1, act_fn=F.relu,
+                 stride=1, pad_type='', act_fn=F.relu, bn_args=_BN_ARGS_PT):
-                 bn_args=_BN_ARGS_PT, padding_same=False):
         super(ConvBnAct, self).__init__()
         assert stride in [1, 2]
         self.act_fn = act_fn
-        padding = _padding_arg(_get_padding(kernel_size, stride), padding_same)
+        self.conv = select_conv2d(in_chs, out_chs, kernel_size, stride=stride, padding=pad_type)
-
-        self.conv = sconv2d(
-            in_chs, out_chs, kernel_size,
-            stride=stride, padding=padding, bias=False)
         self.bn1 = nn.BatchNorm2d(out_chs, **bn_args)
 
     def forward(self, x):
@@ -517,10 +518,11 @@ class DepthwiseSeparableConv(nn.Module):
     Used for DS convs in MobileNet-V1 and in the place of IR blocks with an expansion
     factor of 1.0. This is an alternative to having a IR with optional first pw conv.
     """
-    def __init__(self, in_chs, out_chs, kernel_size,
+    def __init__(self, in_chs, out_chs, dw_kernel_size=3,
-                 stride=1, act_fn=F.relu, noskip=False, pw_act=False,
+                 stride=1, pad_type='', act_fn=F.relu, noskip=False,
+                 pw_kernel_size=1, pw_act=False,
                  se_ratio=0., se_gate_fn=sigmoid,
-                 bn_args=_BN_ARGS_PT, padding_same=False, drop_connect_rate=0.):
+                 bn_args=_BN_ARGS_PT, drop_connect_rate=0.):
         super(DepthwiseSeparableConv, self).__init__()
         assert stride in [1, 2]
         self.has_se = se_ratio is not None and se_ratio > 0.
@@ -528,12 +530,9 @@ class DepthwiseSeparableConv(nn.Module):
         self.has_pw_act = pw_act  # activation after point-wise conv
         self.act_fn = act_fn
         self.drop_connect_rate = drop_connect_rate
-        dw_padding = _padding_arg(kernel_size // 2, padding_same)
-        pw_padding = _padding_arg(0, padding_same)
 
-        self.conv_dw = sconv2d(
+        self.conv_dw = select_conv2d(
-            in_chs, in_chs, kernel_size,
+            in_chs, in_chs, dw_kernel_size, stride=stride, padding=pad_type, depthwise=True)
-            stride=stride, padding=dw_padding, groups=in_chs, bias=False)
         self.bn1 = nn.BatchNorm2d(in_chs, **bn_args)
 
         # Squeeze-and-excitation
@@ -541,7 +540,7 @@ class DepthwiseSeparableConv(nn.Module):
             self.se = SqueezeExcite(
                 in_chs, reduce_chs=max(1, int(in_chs * se_ratio)), act_fn=act_fn, gate_fn=se_gate_fn)
 
-        self.conv_pw = sconv2d(in_chs, out_chs, 1, padding=pw_padding, bias=False)
+        self.conv_pw = select_conv2d(in_chs, out_chs, pw_kernel_size, padding=pad_type)
         self.bn2 = nn.BatchNorm2d(out_chs, **bn_args)
 
     def forward(self, x):
@@ -569,31 +568,29 @@ class DepthwiseSeparableConv(nn.Module):
 class InvertedResidual(nn.Module):
     """ Inverted residual block w/ optional SE"""
 
-    def __init__(self, in_chs, out_chs, kernel_size,
+    def __init__(self, in_chs, out_chs, dw_kernel_size=3,
-                 stride=1, act_fn=F.relu, exp_ratio=1.0, noskip=False,
+                 stride=1, pad_type='', act_fn=F.relu, noskip=False,
+                 exp_ratio=1.0, exp_kernel_size=1, pw_kernel_size=1,
                  se_ratio=0., se_reduce_mid=False, se_gate_fn=sigmoid,
-                 shuffle_type=None, pw_group=1,
+                 shuffle_type=None, bn_args=_BN_ARGS_PT, drop_connect_rate=0.):
-                 bn_args=_BN_ARGS_PT, padding_same=False, drop_connect_rate=0.):
         super(InvertedResidual, self).__init__()
         mid_chs = int(in_chs * exp_ratio)
         self.has_se = se_ratio is not None and se_ratio > 0.
         self.has_residual = (in_chs == out_chs and stride == 1) and not noskip
         self.act_fn = act_fn
         self.drop_connect_rate = drop_connect_rate
-        dw_padding = _padding_arg(kernel_size // 2, padding_same)
-        pw_padding = _padding_arg(0, padding_same)
 
         # Point-wise expansion
-        self.conv_pw = sconv2d(in_chs, mid_chs, 1, padding=pw_padding, groups=pw_group, bias=False)
+        self.conv_pw = select_conv2d(in_chs, mid_chs, exp_kernel_size, padding=pad_type)
         self.bn1 = nn.BatchNorm2d(mid_chs, **bn_args)
 
         self.shuffle_type = shuffle_type
-        if shuffle_type is not None:
+        if shuffle_type is not None and isinstance(exp_kernel_size, list):
-            self.shuffle = ChannelShuffle(pw_group)
+            self.shuffle = ChannelShuffle(len(exp_kernel_size))
 
         # Depth-wise convolution
-        self.conv_dw = sconv2d(
+        self.conv_dw = select_conv2d(
-            mid_chs, mid_chs, kernel_size, padding=dw_padding, stride=stride, groups=mid_chs, bias=False)
+            mid_chs, mid_chs, dw_kernel_size, stride=stride, padding=pad_type, depthwise=True)
         self.bn2 = nn.BatchNorm2d(mid_chs, **bn_args)
 
         # Squeeze-and-excitation
@@ -603,7 +600,7 @@ class InvertedResidual(nn.Module):
                 mid_chs, reduce_chs=max(1, int(se_base_chs * se_ratio)), act_fn=act_fn, gate_fn=se_gate_fn)
 
         # Point-wise linear projection
-        self.conv_pwl = sconv2d(mid_chs, out_chs, 1, padding=pw_padding, groups=pw_group, bias=False)
+        self.conv_pwl = select_conv2d(mid_chs, out_chs, pw_kernel_size, padding=pad_type)
         self.bn3 = nn.BatchNorm2d(out_chs, **bn_args)
 
     def forward(self, x):
@@ -649,18 +646,19 @@ class GenEfficientNet(nn.Module):
       * MobileNet-V1
       * MobileNet-V2
       * MobileNet-V3
-      * MNASNet A1, B1, and small
+      * MnasNet A1, B1, and small
       * FBNet A, B, and C
       * ChamNet (arch details are murky)
       * Single-Path NAS Pixel1
-      * EfficientNetB0-B4 (rest easy to add)
+      * EfficientNet B0-B5
+      * MixNet S, M, L
     """
 
     def __init__(self, block_args, num_classes=1000, in_chans=3, stem_size=32, num_features=1280,
                  channel_multiplier=1.0, channel_divisor=8, channel_min=None,
-                 drop_rate=0., drop_connect_rate=0., act_fn=F.relu,
+                 pad_type='', act_fn=F.relu, drop_rate=0., drop_connect_rate=0.,
                  se_gate_fn=sigmoid, se_reduce_mid=False, bn_args=_BN_ARGS_PT,
-                 global_pool='avg', head_conv='default', weight_init='goog', padding_same=False):
+                 global_pool='avg', head_conv='default', weight_init='goog'):
         super(GenEfficientNet, self).__init__()
         self.num_classes = num_classes
         self.drop_rate = drop_rate
@@ -668,16 +666,14 @@ class GenEfficientNet(nn.Module):
         self.num_features = num_features
 
         stem_size = _round_channels(stem_size, channel_multiplier, channel_divisor, channel_min)
-        self.conv_stem = sconv2d(
+        self.conv_stem = select_conv2d(in_chans, stem_size, 3, stride=2, padding=pad_type)
-            in_chans, stem_size, 3,
-            padding=_padding_arg(1, padding_same), stride=2, bias=False)
         self.bn1 = nn.BatchNorm2d(stem_size, **bn_args)
         in_chs = stem_size
 
         builder = _BlockBuilder(
             channel_multiplier, channel_divisor, channel_min,
-            drop_connect_rate, act_fn, se_gate_fn, se_reduce_mid,
+            pad_type, act_fn, se_gate_fn, se_reduce_mid,
-            bn_args, padding_same, verbose=_DEBUG)
+            bn_args, drop_connect_rate, verbose=_DEBUG)
         self.blocks = nn.Sequential(*builder(in_chs, block_args))
         in_chs = builder.in_chs
 
@@ -687,9 +683,7 @@ class GenEfficientNet(nn.Module):
             assert in_chs == self.num_features
         else:
             self.efficient_head = head_conv == 'efficient'
-            self.conv_head = sconv2d(
+            self.conv_head = select_conv2d(in_chs, self.num_features, 1, padding=pad_type)
-                in_chs, self.num_features, 1,
-                padding=_padding_arg(0, padding_same), bias=False)
             self.bn2 = None if self.efficient_head else nn.BatchNorm2d(self.num_features, **bn_args)
 
         self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
@@ -919,11 +913,11 @@ def _gen_mobilenet_v3(channel_multiplier, num_classes=1000, **kwargs):
     """
     arch_def = [
         # stage 0, 112x112 in
-        ['ds_r1_k3_s1_e1_c16_are_noskip'],  # relu
+        ['ds_r1_k3_s1_e1_c16_nre_noskip'],  # relu
         # stage 1, 112x112 in
-        ['ir_r1_k3_s2_e4_c24_are', 'ir_r1_k3_s1_e3_c24_are'],  # relu
+        ['ir_r1_k3_s2_e4_c24_nre', 'ir_r1_k3_s1_e3_c24_nre'],  # relu
         # stage 2, 56x56 in
-        ['ir_r3_k5_s2_e3_c40_se0.25_are'],  # relu
+        ['ir_r3_k5_s2_e3_c40_se0.25_nre'],  # relu
         # stage 3, 28x28 in
         ['ir_r1_k3_s2_e6_c80', 'ir_r1_k3_s1_e2.5_c80', 'ir_r2_k3_s1_e2.3_c80'],  # hard-swish
         # stage 4, 14x14in
@@ -1129,6 +1123,78 @@ def _gen_efficientnet(channel_multiplier=1.0, depth_multiplier=1.0, num_classes=
     return model
 
 
+def _gen_mixnet_s(channel_multiplier=1.0, num_classes=1000, **kwargs):
+    """Creates a MixNet Small model.
+
+    Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet/mixnet
+    Paper: https://arxiv.org/abs/1907.09595
+    """
+    arch_def = [
+        # stage 0, 112x112 in
+        ['ds_r1_k3_s1_e1_c16'],  # relu
+        # stage 1, 112x112 in
+        ['ir_r1_k3_a1.1_p1.1_s2_e6_c24', 'ir_r1_k3_a1.1_p1.1_s1_e3_c24'],  # relu
+        # stage 2, 56x56 in
+        ['ir_r1_k3.5.7_s2_e6_c40_se0.5_nsw', 'ir_r3_k3.5_a1.1_p1.1_s1_e6_c40_se0.5_nsw'],  # swish
+        # stage 3, 28x28 in
+        ['ir_r1_k3.5.7_p1.1_s2_e6_c80_se0.25_nsw', 'ir_r2_k3.5_p1.1_s1_e6_c80_se0.25_nsw'],  # swish
+        # stage 4, 14x14in
+        ['ir_r1_k3.5.7_a1.1_p1.1_s1_e6_c120_se0.5_nsw', 'ir_r2_k3.5.7.9_a1.1_p1.1_s1_e3_c120_se0.5_nsw'],  # swish
+        # stage 5, 14x14in
+        ['ir_r1_k3.5.7.9.11_s2_e6_c200_se0.5_nsw', 'ir_r2_k3.5.7.9_p1.1_s1_e6_c200_se0.5_nsw'],  # swish
+        # 7x7
+    ]
+    model = GenEfficientNet(
+        _decode_arch_def(arch_def),
+        num_classes=num_classes,
+        stem_size=16,
+        num_features=1536,
+        channel_multiplier=channel_multiplier,
+        channel_divisor=8,
+        channel_min=None,
+        bn_args=_resolve_bn_args(kwargs),
+        act_fn=F.relu,
+        **kwargs
+    )
+    return model
+
+
+def _gen_mixnet_m(channel_multiplier=1.0, num_classes=1000, **kwargs):
+    """Creates a MixNet Medium-Large model.
+
+    Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet/mixnet
+    Paper: https://arxiv.org/abs/1907.09595
+    """
+    arch_def = [
+        # stage 0, 112x112 in
+        ['ds_r1_k3_s1_e1_c24'],  # relu
+        # stage 1, 112x112 in
+        ['ir_r1_k3.5.7_a1.1_p1.1_s2_e6_c32', 'ir_r1_k3_a1.1_p1.1_s1_e3_c32'],  # relu
+        # stage 2, 56x56 in
+        ['ir_r1_k3.5.7.9_s2_e6_c40_se0.5_nsw', 'ir_r3_k3.5_a1.1_p1.1_s1_e6_c40_se0.5_nsw'],  # swish
+        # stage 3, 28x28 in
+        ['ir_r1_k3.5.7_s2_e6_c80_se0.25_nsw', 'ir_r3_k3.5.7.9_a1.1_p1.1_s1_e6_c80_se0.25_nsw'],  # swish
+        # stage 4, 14x14in
+        ['ir_r1_k3_s1_e6_c120_se0.5_nsw', 'ir_r3_k3.5.7.9_a1.1_p1.1_s1_e3_c120_se0.5_nsw'],  # swish
+        # stage 5, 14x14in
+        ['ir_r1_k3.5.7.9_s2_e6_c200_se0.5_nsw', 'ir_r3_k3.5.7.9_p1.1_s1_e6_c200_se0.5_nsw'],  # swish
+        # 7x7
+    ]
+    model = GenEfficientNet(
+        _decode_arch_def(arch_def),
+        num_classes=num_classes,
+        stem_size=24,
+        num_features=1536,
+        channel_multiplier=channel_multiplier,
+        channel_divisor=8,
+        channel_min=None,
+        bn_args=_resolve_bn_args(kwargs),
+        act_fn=F.relu,
+        **kwargs
+    )
+    return model
+
+
 @register_model
 def mnasnet_050(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
     """ MNASNet B1, depth multiplier of 0.5. """
@@ -1440,7 +1506,7 @@ def tf_efficientnet_b0(pretrained=False, num_classes=1000, in_chans=3, **kwargs)
     """ EfficientNet-B0. Tensorflow compatible variant  """
     default_cfg = default_cfgs['tf_efficientnet_b0']
     kwargs['bn_eps'] = _BN_EPS_TF_DEFAULT
-    kwargs['padding_same'] = True
+    kwargs['pad_type'] = 'same'
     model = _gen_efficientnet(
         channel_multiplier=1.0, depth_multiplier=1.0,
         num_classes=num_classes, in_chans=in_chans, **kwargs)
@@ -1455,7 +1521,7 @@ def tf_efficientnet_b1(pretrained=False, num_classes=1000, in_chans=3, **kwargs)
     """ EfficientNet-B1. Tensorflow compatible variant  """
     default_cfg = default_cfgs['tf_efficientnet_b1']
     kwargs['bn_eps'] = _BN_EPS_TF_DEFAULT
-    kwargs['padding_same'] = True
+    kwargs['pad_type'] = 'same'
     model = _gen_efficientnet(
         channel_multiplier=1.0, depth_multiplier=1.1,
         num_classes=num_classes, in_chans=in_chans, **kwargs)
@@ -1470,7 +1536,7 @@ def tf_efficientnet_b2(pretrained=False, num_classes=1000, in_chans=3, **kwargs)
     """ EfficientNet-B2. Tensorflow compatible variant  """
     default_cfg = default_cfgs['tf_efficientnet_b2']
     kwargs['bn_eps'] = _BN_EPS_TF_DEFAULT
-    kwargs['padding_same'] = True
+    kwargs['pad_type'] = 'same'
     model = _gen_efficientnet(
         channel_multiplier=1.1, depth_multiplier=1.2,
         num_classes=num_classes, in_chans=in_chans, **kwargs)
@@ -1485,7 +1551,7 @@ def tf_efficientnet_b3(pretrained=False, num_classes=1000, in_chans=3, **kwargs)
     """ EfficientNet-B3. Tensorflow compatible variant """
     default_cfg = default_cfgs['tf_efficientnet_b3']
     kwargs['bn_eps'] = _BN_EPS_TF_DEFAULT
-    kwargs['padding_same'] = True
+    kwargs['pad_type'] = 'same'
     model = _gen_efficientnet(
         channel_multiplier=1.2, depth_multiplier=1.4,
         num_classes=num_classes, in_chans=in_chans, **kwargs)
@@ -1500,7 +1566,7 @@ def tf_efficientnet_b4(pretrained=False, num_classes=1000, in_chans=3, **kwargs)
     """ EfficientNet-B4. Tensorflow compatible variant """
     default_cfg = default_cfgs['tf_efficientnet_b4']
     kwargs['bn_eps'] = _BN_EPS_TF_DEFAULT
-    kwargs['padding_same'] = True
+    kwargs['pad_type'] = 'same'
     model = _gen_efficientnet(
         channel_multiplier=1.4, depth_multiplier=1.8,
         num_classes=num_classes, in_chans=in_chans, **kwargs)
@@ -1515,7 +1581,7 @@ def tf_efficientnet_b5(pretrained=False, num_classes=1000, in_chans=3, **kwargs)
     """ EfficientNet-B5. Tensorflow compatible variant """
     default_cfg = default_cfgs['tf_efficientnet_b5']
     kwargs['bn_eps'] = _BN_EPS_TF_DEFAULT
-    kwargs['padding_same'] = True
+    kwargs['pad_type'] = 'same'
     model = _gen_efficientnet(
         channel_multiplier=1.6, depth_multiplier=2.2,
         num_classes=num_classes, in_chans=in_chans, **kwargs)
@@ -1525,5 +1591,89 @@ def tf_efficientnet_b5(pretrained=False, num_classes=1000, in_chans=3, **kwargs)
     return model
 
 
+@register_model
+def mixnet_s(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
+    """Creates a MixNet Small model.
+    """
+    default_cfg = default_cfgs['mixnet_m']
+    model = _gen_mixnet_s(
+        channel_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
+
+
+@register_model
+def mixnet_m(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
+    """Creates a MixNet Medium model.
+    """
+    default_cfg = default_cfgs['mixnet_m']
+    model = _gen_mixnet_m(
+        channel_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
+
+
+@register_model
+def mixnet_l(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
+    """Creates a MixNet Large model.
+    """
+    default_cfg = default_cfgs['mixnet_l']
+    model = _gen_mixnet_m(
+        channel_multiplier=1.3, 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
+
+
+@register_model
+def tf_mixnet_s(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
+    """Creates a MixNet Small model. Tensorflow compatible variant
+    """
+    default_cfg = default_cfgs['tf_mixnet_s']
+    kwargs['bn_eps'] = _BN_EPS_TF_DEFAULT
+    kwargs['pad_type'] = 'same'
+    model = _gen_mixnet_s(
+        channel_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
+
+
+@register_model
+def tf_mixnet_m(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
+    """Creates a MixNet Medium model. Tensorflow compatible variant
+    """
+    default_cfg = default_cfgs['tf_mixnet_m']
+    kwargs['bn_eps'] = _BN_EPS_TF_DEFAULT
+    kwargs['pad_type'] = 'same'
+    model = _gen_mixnet_m(
+        channel_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
+
+
+@register_model
+def tf_mixnet_l(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
+    """Creates a MixNet Large model. Tensorflow compatible variant
+    """
+    default_cfg = default_cfgs['tf_mixnet_l']
+    kwargs['bn_eps'] = _BN_EPS_TF_DEFAULT
+    kwargs['pad_type'] = 'same'
+    model = _gen_mixnet_m(
+        channel_multiplier=1.3, 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)