Add support for tflite mnasnet pretrained weights and included spnasnet pretrained weights of my own.

* tensorflow 'SAME' padding support added to GenMobileNet models for tflite pretrained weights
* folded batch norm support (made batch norm optional and enable conv bias) for tflite pretrained weights
* add url for spnasnet1_00 weights that I recently trained
* fix SE reduction size for semnasnet models

models/conv2d_same.py

@@ -0,0 +1,39 @@
+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)

models/genmobilenet.py

@@ -23,6 +23,7 @@ import torch.nn as nn
 import torch.nn.functional as F
 from models.helpers import load_pretrained
 from models.adaptive_avgmax_pool import SelectAdaptivePool2d
+from models.conv2d_same import sconv2d
 from data.transforms import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
 
 __all__ = ['GenMobileNet', 'mnasnet0_50', 'mnasnet0_75', 'mnasnet1_00', 'mnasnet1_40',
@@ -45,10 +46,12 @@ default_cfgs = {
     'mnasnet0_50': _cfg(url=''),
     'mnasnet0_75': _cfg(url=''),
     'mnasnet1_00': _cfg(url=''),
+    'tflite_mnasnet1_00': _cfg(url='', interpolation='bicubic'),
     'mnasnet1_40': _cfg(url=''),
     'semnasnet0_50': _cfg(url=''),
     'semnasnet0_75': _cfg(url=''),
     'semnasnet1_00': _cfg(url=''),
+    'tflite_semnasnet1_00': _cfg(url='', interpolation='bicubic'),
     'semnasnet1_40': _cfg(url=''),
     'mnasnet_small': _cfg(url=''),
     'mobilenetv1_1_00': _cfg(url=''),
@@ -56,7 +59,7 @@ default_cfgs = {
     'chamnetv1_1_00': _cfg(url=''),
     'chamnetv2_1_00': _cfg(url=''),
     'fbnetc_1_00': _cfg(url=''),
-    'spnasnet1_00': _cfg(url=''),
+    'spnasnet1_00': _cfg(url='https://www.dropbox.com/s/iieopt18rytkgaa/spnasnet1_00-048bc3f4.pth?dl=1'),
 }
 
 _DEBUG = True
@@ -184,11 +187,15 @@ def _decode_block_str(block_str):
     return [deepcopy(block_args) for _ in range(num_repeat)]
 
 
-def _get_padding(kernel_size, stride, dilation):
+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:
@@ -219,12 +226,15 @@ class _BlockBuilder:
     """
 
     def __init__(self, depth_multiplier=1.0, depth_divisor=8, min_depth=None,
-                 bn_momentum=_BN_MOMENTUM_PT_DEFAULT, bn_eps=_BN_EPS_PT_DEFAULT):
+                 bn_momentum=_BN_MOMENTUM_PT_DEFAULT, bn_eps=_BN_EPS_PT_DEFAULT,
+                 folded_bn=False, padding_same=False):
         self.depth_multiplier = depth_multiplier
         self.depth_divisor = depth_divisor
         self.min_depth = min_depth
         self.bn_momentum = bn_momentum
         self.bn_eps = bn_eps
+        self.folded_bn = folded_bn
+        self.padding_same = padding_same
         self.in_chs = None
 
     def _round_channels(self, chs):
@@ -236,6 +246,8 @@ class _BlockBuilder:
         ba['out_chs'] = _round_channels(ba['out_chs'])
         ba['bn_momentum'] = self.bn_momentum
         ba['bn_eps'] = self.bn_eps
+        ba['folded_bn'] = self.folded_bn
+        ba['padding_same'] = self.padding_same
         if _DEBUG:
             print('args:', ba)
         #  could replace this with lambdas or functools binding if variety increases
@@ -320,29 +332,37 @@ def _initialize_weight_default(m):
 class DepthwiseSeparableConv(nn.Module):
     def __init__(self, in_chs, out_chs, kernel_size,
                  stride=1, act_fn=F.relu, noskip=False, pw_act=False,
-                 bn_momentum=_BN_MOMENTUM_PT_DEFAULT, bn_eps=_BN_EPS_PT_DEFAULT):
+                 bn_momentum=_BN_MOMENTUM_PT_DEFAULT, bn_eps=_BN_EPS_PT_DEFAULT,
+                 folded_bn=False, padding_same=False):
         super(DepthwiseSeparableConv, self).__init__()
         assert stride in [1, 2]
         self.has_residual = (stride == 1 and in_chs == out_chs) and not noskip
         self.has_pw_act = pw_act  # activation after point-wise conv
         self.act_fn = act_fn
+        dw_padding = _padding_arg(kernel_size // 2, padding_same)
+        pw_padding = _padding_arg(0, padding_same)
 
-        self.conv_dw = nn.Conv2d(
+        self.conv_dw = sconv2d(
             in_chs, in_chs, kernel_size,
-            stride=stride, padding=kernel_size // 2, groups=in_chs, bias=False)
-        self.bn1 = nn.BatchNorm2d(in_chs, momentum=bn_momentum, eps=bn_eps)
-        self.conv_pw = nn.Conv2d(in_chs, out_chs, 1, bias=False)
-        self.bn2 = nn.BatchNorm2d(out_chs, momentum=bn_momentum, eps=bn_eps)
+            stride=stride, padding=dw_padding, groups=in_chs, bias=folded_bn)
+        self.bn1 = None if folded_bn else nn.BatchNorm2d(in_chs, momentum=bn_momentum, eps=bn_eps)
+        self.conv_pw = sconv2d(in_chs, out_chs, 1, padding=pw_padding, bias=folded_bn)
+        self.bn2 = None if folded_bn else nn.BatchNorm2d(out_chs, momentum=bn_momentum, eps=bn_eps)
 
     def forward(self, x):
         residual = x
+
         x = self.conv_dw(x)
+        if self.bn1 is not None:
             x = self.bn1(x)
         x = self.act_fn(x)
+
         x = self.conv_pw(x)
+        if self.bn2 is not None:
             x = self.bn2(x)
         if self.has_pw_act:
             x = self.act_fn(x)
+
         if self.has_residual:
             x += residual
         return x
@@ -351,23 +371,27 @@ class DepthwiseSeparableConv(nn.Module):
 class CascadeConv3x3(nn.Sequential):
     # FIXME lifted from maskrcnn_benchmark blocks, haven't used yet
     def __init__(self, in_chs, out_chs, stride, act_fn=F.relu, noskip=False,
-                 bn_momentum=_BN_MOMENTUM_PT_DEFAULT, bn_eps=_BN_EPS_PT_DEFAULT):
+                 bn_momentum=_BN_MOMENTUM_PT_DEFAULT, bn_eps=_BN_EPS_PT_DEFAULT,
+                 folded_bn=False, padding_same=False):
         super(CascadeConv3x3, self).__init__()
         assert stride in [1, 2]
-        self.has_residual = not noskip and (stride == 1 and in_chs == out_chs)
+        self.has_residual = (stride == 1 and in_chs == out_chs) and not noskip
         self.act_fn = act_fn
+        padding = _padding_arg(1, padding_same)
 
-        self.conv1 = nn.Conv2d(in_chs, in_chs, 3, stride=stride, padding=1, bias=False)
-        self.bn1 = nn.BatchNorm2d(in_chs, momentum=bn_momentum, eps=bn_eps)
-        self.conv2 = nn.Conv2d(in_chs, out_chs, 3, stride=1, padding=1, bias=False)
-        self.bn2 = nn.BatchNorm2d(out_chs, momentum=bn_momentum, eps=bn_eps)
+        self.conv1 = sconv2d(in_chs, in_chs, 3, stride=stride, padding=padding, bias=folded_bn)
+        self.bn1 = None if folded_bn else nn.BatchNorm2d(in_chs, momentum=bn_momentum, eps=bn_eps)
+        self.conv2 = sconv2d(in_chs, out_chs, 3, stride=1, padding=padding, bias=folded_bn)
+        self.bn2 = None if folded_bn else nn.BatchNorm2d(out_chs, momentum=bn_momentum, eps=bn_eps)
 
     def forward(self, x):
         residual = x
         x = self.conv1(x)
+        if self.bn1 is not None:
             x = self.bn1(x)
         x = self.act_fn(x)
         x = self.conv2(x)
+        if self.bn2 is not None:
             x = self.bn2(x)
         if self.has_residual:
             x += residual
@@ -396,10 +420,10 @@ class ChannelShuffle(nn.Module):
 
 
 class SqueezeExcite(nn.Module):
-    def __init__(self, in_chs, se_ratio=0.25, act_fn=F.relu):
+    def __init__(self, in_chs, reduce_chs=None, act_fn=F.relu):
         super(SqueezeExcite, self).__init__()
         self.act_fn = act_fn
-        reduced_chs = max(1, int(in_chs * se_ratio))
+        reduced_chs = reduce_chs or in_chs
         self.conv_reduce = nn.Conv2d(in_chs, reduced_chs, 1, bias=True)
         self.conv_expand = nn.Conv2d(reduced_chs, in_chs, 1, bias=True)
 
@@ -419,40 +443,43 @@ class InvertedResidual(nn.Module):
     def __init__(self, in_chs, out_chs, kernel_size,
                  stride=1, act_fn=F.relu, exp_ratio=1.0, noskip=False,
                  se_ratio=0., shuffle_type=None, pw_group=1,
-                 bn_momentum=_BN_MOMENTUM_PT_DEFAULT, bn_eps=_BN_EPS_PT_DEFAULT):
+                 bn_momentum=_BN_MOMENTUM_PT_DEFAULT, bn_eps=_BN_EPS_PT_DEFAULT,
+                 folded_bn=False, padding_same=False):
         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
+        dw_padding = _padding_arg(kernel_size // 2, padding_same)
+        pw_padding = _padding_arg(0, padding_same)
 
         # Point-wise expansion
-        self.conv_pw = nn.Conv2d(in_chs, mid_chs, 1, groups=pw_group, bias=False)
-        self.bn1 = nn.BatchNorm2d(mid_chs, momentum=bn_momentum, eps=bn_eps)
+        self.conv_pw = sconv2d(in_chs, mid_chs, 1, padding=pw_padding, groups=pw_group, bias=folded_bn)
+        self.bn1 = None if folded_bn else nn.BatchNorm2d(mid_chs, momentum=bn_momentum, eps=bn_eps)
 
         self.shuffle_type = shuffle_type
         if shuffle_type is not None:
             self.shuffle = ChannelShuffle(pw_group)
 
         # Depth-wise convolution
-        self.conv_dw = nn.Conv2d(
-            mid_chs, mid_chs, kernel_size, padding=kernel_size // 2,
-            stride=stride, groups=mid_chs, bias=False)
-        self.bn2 = nn.BatchNorm2d(mid_chs, momentum=bn_momentum, eps=bn_eps)
+        self.conv_dw = sconv2d(
+            mid_chs, mid_chs, kernel_size, padding=dw_padding, stride=stride, groups=mid_chs, bias=folded_bn)
+        self.bn2 = None if folded_bn else nn.BatchNorm2d(mid_chs, momentum=bn_momentum, eps=bn_eps)
 
         # Squeeze-and-excitation
         if self.has_se:
-            self.se = SqueezeExcite(mid_chs, se_ratio)
+            self.se = SqueezeExcite(mid_chs, reduce_chs=max(1, int(in_chs * se_ratio)))
 
         # Point-wise linear projection
-        self.conv_pwl = nn.Conv2d(mid_chs, out_chs, 1, groups=pw_group, bias=False)
-        self.bn3 = nn.BatchNorm2d(out_chs, momentum=bn_momentum, eps=bn_eps)
+        self.conv_pwl = sconv2d(mid_chs, out_chs, 1, padding=pw_padding, groups=pw_group, bias=folded_bn)
+        self.bn3 = None if folded_bn else nn.BatchNorm2d(out_chs, momentum=bn_momentum, eps=bn_eps)
 
     def forward(self, x):
         residual = x
 
         # Point-wise expansion
         x = self.conv_pw(x)
+        if self.bn1 is not None:
             x = self.bn1(x)
         x = self.act_fn(x)
 
@@ -463,6 +490,7 @@ class InvertedResidual(nn.Module):
 
         # Depth-wise convolution
         x = self.conv_dw(x)
+        if self.bn2 is not None:
             x = self.bn2(x)
         x = self.act_fn(x)
 
@@ -472,6 +500,7 @@ class InvertedResidual(nn.Module):
 
         # Point-wise linear projection
         x = self.conv_pwl(x)
+        if self.bn3 is not None:
             x = self.bn3(x)
 
         if self.has_residual:
@@ -498,7 +527,7 @@ class GenMobileNet(nn.Module):
                  depth_multiplier=1.0, depth_divisor=8, min_depth=None,
                  bn_momentum=_BN_MOMENTUM_PT_DEFAULT, bn_eps=_BN_EPS_PT_DEFAULT,
                  drop_rate=0., act_fn=F.relu, global_pool='avg', skip_head_conv=False,
-                 weight_init='goog'):
+                 weight_init='goog', folded_bn=False, padding_same=False):
         super(GenMobileNet, self).__init__()
         self.num_classes = num_classes
         self.depth_multiplier = depth_multiplier
@@ -507,13 +536,15 @@ class GenMobileNet(nn.Module):
         self.num_features = num_features
 
         stem_size = _round_channels(stem_size, depth_multiplier, depth_divisor, min_depth)
-        self.conv_stem = nn.Conv2d(in_chans, stem_size, 3, padding=1, stride=2, bias=False)
-        self.bn1 = nn.BatchNorm2d(stem_size, momentum=bn_momentum, eps=bn_eps)
+        self.conv_stem = sconv2d(
+            in_chans, stem_size, 3,
+            padding=_padding_arg(1, padding_same), stride=2, bias=folded_bn)
+        self.bn1 = None if folded_bn else nn.BatchNorm2d(stem_size, momentum=bn_momentum, eps=bn_eps)
         in_chs = stem_size
 
         builder = _BlockBuilder(
             depth_multiplier, depth_divisor, min_depth,
-            bn_momentum, bn_eps)
+            bn_momentum, bn_eps, folded_bn, padding_same)
         self.blocks = nn.Sequential(*builder(in_chs, block_args))
         in_chs = builder.in_chs
 
@@ -521,8 +552,10 @@ class GenMobileNet(nn.Module):
             self.conv_head = None
             assert in_chs == self.num_features
         else:
-            self.conv_head = nn.Conv2d(in_chs, self.num_features, 1, padding=0, stride=1, bias=False)
-            self.bn2 = nn.BatchNorm2d(self.num_features, momentum=bn_momentum, eps=bn_eps)
+            self.conv_head = sconv2d(
+                in_chs, self.num_features, 1,
+                padding=_padding_arg(0, padding_same), bias=folded_bn)
+            self.bn2 = None if folded_bn else nn.BatchNorm2d(self.num_features, momentum=bn_momentum, eps=bn_eps)
 
         self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
         self.classifier = nn.Linear(self.num_features, self.num_classes)
@@ -548,11 +581,13 @@ class GenMobileNet(nn.Module):
 
     def forward_features(self, x, pool=True):
         x = self.conv_stem(x)
+        if self.bn1 is not None:
             x = self.bn1(x)
         x = self.act_fn(x)
         x = self.blocks(x)
         if self.conv_head is not None:
             x = self.conv_head(x)
+            if self.bn2 is not None:
                 x = self.bn2(x)
             x = self.act_fn(x)
         if pool:
@@ -909,6 +944,19 @@ def mnasnet1_00(num_classes, in_chans=3, pretrained=False, **kwargs):
     return model
 
 
+def tflite_mnasnet1_00(num_classes, in_chans=3, pretrained=False, **kwargs):
+    """ MNASNet B1, depth multiplier of 1.0. """
+    default_cfg = default_cfgs['tflite_mnasnet1_00']
+    # these two args are for compat with tflite pretrained weights
+    kwargs['folded_bn'] = True
+    kwargs['padding_same'] = True
+    model = _gen_mnasnet_b1(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 mnasnet1_40(num_classes, in_chans=3, pretrained=False, **kwargs):
     """ MNASNet B1,  depth multiplier of 1.4 """
     default_cfg = default_cfgs['mnasnet1_40']
@@ -949,6 +997,19 @@ def semnasnet1_00(num_classes, in_chans=3, pretrained=False, **kwargs):
     return model
 
 
+def tflite_semnasnet1_00(num_classes, in_chans=3, pretrained=False, **kwargs):
+    """ MNASNet A1, depth multiplier of 1.0. """
+    default_cfg = default_cfgs['tflite_semnasnet1_00']
+    # these two args are for compat with tflite pretrained weights
+    kwargs['folded_bn'] = True
+    kwargs['padding_same'] = True
+    model = _gen_mnasnet_a1(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 semnasnet1_40(num_classes, in_chans=3, pretrained=False, **kwargs):
     """ MNASNet A1 (w/ SE), depth multiplier of 1.4. """
     default_cfg = default_cfgs['semnasnet1_40']

models/model_factory.py

@@ -9,8 +9,8 @@ from models.senet import seresnet18, seresnet34, seresnet50, seresnet101, seresn
 from models.xception import xception
 from models.pnasnet import pnasnet5large
 from models.genmobilenet import \
-    mnasnet0_50, mnasnet0_75, mnasnet1_00, mnasnet1_40,\
-    semnasnet0_50, semnasnet0_75, semnasnet1_00, semnasnet1_40, mnasnet_small,\
+    mnasnet0_50, mnasnet0_75, mnasnet1_00, mnasnet1_40, tflite_mnasnet1_00,\
+    semnasnet0_50, semnasnet0_75, semnasnet1_00, semnasnet1_40, tflite_semnasnet1_00, mnasnet_small,\
     mobilenetv1_1_00, mobilenetv2_1_00, fbnetc_1_00, chamnetv1_1_00, chamnetv2_1_00,\
     spnasnet1_00