Fixup a few comments, add PyTorch version aware Flatten and finish as_sequential for GenEfficientNet

timm/models/conv2d_layers.py

@@ -8,6 +8,7 @@ import numpy as np
 import math
 
 
+# Tuple helpers ripped from PyTorch
 def _ntuple(n):
     def parse(x):
         if isinstance(x, container_abcs.Iterable):
@@ -77,7 +78,7 @@ def get_padding_value(padding, kernel_size, **kwargs):
                 # static case, no extra overhead
                 padding = _get_padding(kernel_size, **kwargs)
             else:
-                # dynamic padding
+                # dynamic 'SAME' padding, has runtime/GPU memory overhead
                 padding = 0
                 dynamic = True
         elif padding == 'valid':
@@ -101,6 +102,7 @@ def create_conv2d_pad(in_chs, out_chs, kernel_size, **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
     """
@@ -152,7 +154,11 @@ def get_condconv_initializer(initializer, num_experts, expert_shape):
 
 class CondConv2d(nn.Module):
     """ Conditional Convolution
+
     Inspired by: https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/condconv/condconv_layers.py
+
+    Grouped convolution hackery for parallel execution of the per-sample kernel filters inspired by this discussion:
+    https://github.com/pytorch/pytorch/issues/17983
     """
 
     def __init__(self, in_channels, out_channels, kernel_size=3,
@@ -211,6 +217,7 @@ class CondConv2d(nn.Module):
         if self._use_groups:
             new_weight_shape = (B * self.out_channels, self.in_channels // self.groups) + self.kernel_size
             weight = weight.view(new_weight_shape)
+            # move batch elements with channels so each batch element can be efficiently convolved with separate kernel
             x = x.view(1, B * C, H, W)
             out = self.conv_fn(
                 x, weight, bias, stride=self.stride, padding=self.padding,

timm/models/gen_efficientnet.py

@@ -2,6 +2,8 @@
 
 A generic class with building blocks to support a variety of models with efficient architectures:
 * EfficientNet (B0-B7)
+* EfficientNet-EdgeTPU
+* EfficientNet-CondConv
 * MixNet (Small, Medium, and Large)
 * MnasNet B1, A1 (SE), Small
 * MobileNet V1, V2, and V3
@@ -31,6 +33,7 @@ from .registry import register_model, model_entrypoint
 from .helpers import load_pretrained
 from .adaptive_avgmax_pool import SelectAdaptivePool2d
 from .conv2d_layers import select_conv2d
+from .layers import Flatten
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
 
 
@@ -1050,16 +1053,14 @@ class GenEfficientNet(_GenEfficientNet):
         layers = [self.conv_stem, self.bn1, self.act1]
         layers.extend(self.blocks)
         if self.head_conv == 'efficient':
-            layers.extend([self.global_pool, self.bn2, self.act2])
+            layers.extend([self.global_pool, self.conv_head, self.act2])
         else:
             layers.extend([self.conv_head, self.bn2, self.act2])
             if self.global_pool is not None:
                 layers.append(self.global_pool)
-        #append flatten layer
-        layers.append(self.classifier)
+        layers.extend([Flatten(), nn.Dropout(self.drop_rate), self.classifier])
         return nn.Sequential(*layers)
 
-
     def get_classifier(self):
         return self.classifier
 
@@ -1106,7 +1107,8 @@ class GenEfficientNetFeatures(_GenEfficientNet):
         #assert len(block_args) >= num_stages - 1
         #block_args = block_args[:num_stages - 1]
 
-        super(GenEfficientNetFeatures, self).__init__(  # FIXME it would be nice if Python made this nicer
+        # FIXME it would be nice if Python made this nicer without using kwargs and erasing IDE hints, etc
+        super(GenEfficientNetFeatures, self).__init__(
             block_args, in_chans=in_chans, stem_size=stem_size,
             output_stride=output_stride, pad_type=pad_type, act_layer=act_layer,
             drop_rate=drop_rate, drop_connect_rate=drop_connect_rate, feature_location=feature_location,
@@ -1548,6 +1550,11 @@ def _gen_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, pre
 
 
 def _gen_efficientnet_edge(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs):
+    """ Creates an EfficientNet-EdgeTPU model
+
+    Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/edgetpu
+    """
+
     arch_def = [
         # NOTE `fc` is present to override a mismatch between stem channels and in chs not
         # present in other models
@@ -1573,8 +1580,10 @@ def _gen_efficientnet_edge(variant, channel_multiplier=1.0, depth_multiplier=1.0
 
 def _gen_efficientnet_condconv(
         variant, channel_multiplier=1.0, depth_multiplier=1.0, experts_multiplier=1, pretrained=False, **kwargs):
+    """Creates an EfficientNet-CondConv model.
 
-    """Creates an efficientnet-condconv model."""
+    Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/condconv
+    """
     arch_def = [
       ['ds_r1_k3_s1_e1_c16_se0.25'],
       ['ir_r2_k3_s2_e6_c24_se0.25'],
@@ -1584,6 +1593,8 @@ def _gen_efficientnet_condconv(
       ['ir_r4_k5_s2_e6_c192_se0.25_cc4'],
       ['ir_r1_k3_s1_e6_c320_se0.25_cc4'],
     ]
+    # NOTE unlike official impl, this one uses `cc<x>` option where x is the base number of experts for each stage and
+    # the expert_multiplier increases that on a per-model basis as with depth/channel multipliers
     model_kwargs = dict(
         block_args=_decode_arch_def(arch_def, depth_multiplier, experts_multiplier=experts_multiplier),
         num_features=_round_channels(1280, channel_multiplier, 8, None),
@@ -2056,7 +2067,7 @@ def tf_efficientnet_el(pretrained=False, **kwargs):
 
 @register_model
 def tf_efficientnet_cc_b0_4e(pretrained=False, **kwargs):
-    """ EfficientNet-B0 """
+    """ EfficientNet-CondConv-B0 w/ 4 Experts. Tensorflow compatible variant """
     # NOTE for train, drop_rate should be 0.2
     #kwargs['drop_connect_rate'] = 0.2  # set when training, TODO add as cmd arg
     kwargs['bn_eps'] = _BN_EPS_TF_DEFAULT
@@ -2068,7 +2079,7 @@ def tf_efficientnet_cc_b0_4e(pretrained=False, **kwargs):
 
 @register_model
 def tf_efficientnet_cc_b0_8e(pretrained=False, **kwargs):
-    """ EfficientNet-B0 """
+    """ EfficientNet-CondConv-B0 w/ 8 Experts. Tensorflow compatible variant """
     # NOTE for train, drop_rate should be 0.2
     #kwargs['drop_connect_rate'] = 0.2  # set when training, TODO add as cmd arg
     kwargs['bn_eps'] = _BN_EPS_TF_DEFAULT
@@ -2080,7 +2091,7 @@ def tf_efficientnet_cc_b0_8e(pretrained=False, **kwargs):
 
 @register_model
 def tf_efficientnet_cc_b1_8e(pretrained=False, **kwargs):
-    """ EfficientNet-B0 """
+    """ EfficientNet-CondConv-B1 w/ 8 Experts. Tensorflow compatible variant """
     # NOTE for train, drop_rate should be 0.2
     #kwargs['drop_connect_rate'] = 0.2  # set when training, TODO add as cmd arg
     kwargs['bn_eps'] = _BN_EPS_TF_DEFAULT

timm/models/layers.py

@@ -0,0 +1,31 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+def versiontuple(v):
+    return tuple(map(int, (v.split("."))))[:3]
+
+
+if versiontuple(torch.__version__) >= versiontuple('1.2.0'):
+    Flatten = nn.Flatten
+else:
+    class Flatten(nn.Module):
+        r"""
+        Flattens a contiguous range of dims into a tensor. For use with :class:`~nn.Sequential`.
+        Args:
+            start_dim: first dim to flatten (default = 1).
+            end_dim: last dim to flatten (default = -1).
+        Shape:
+            - Input: :math:`(N, *dims)`
+            - Output: :math:`(N, \prod *dims)` (for the default case).
+        """
+        __constants__ = ['start_dim', 'end_dim']
+
+        def __init__(self, start_dim=1, end_dim=-1):
+            super(Flatten, self).__init__()
+            self.start_dim = start_dim
+            self.end_dim = end_dim
+
+        def forward(self, input):
+            return input.flatten(self.start_dim, self.end_dim)