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https://github.com/huggingface/pytorch-image-models.git
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Add the MnasNet-B1 variant weights, add A1/B1 model names as in the stand-alone repo, remove a bit of unused code
This commit is contained in:
Ross Wightman
parent
c1a84ecb22
commit
f3134973b5
@ -69,10 +69,11 @@ I've leveraged the training scripts in this repository to train a few of the mod
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| seresnext26_32x4d | 77.104 (22.896) | 93.316 (6.684) | 16.8M | bicubic |
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| efficientnet_b0 | 76.912 (23.088) | 93.210 (6.790) | 5.29M | bicubic |
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| mobilenetv3_100 | 75.634 (24.366) | 92.708 (7.292) | 5.5M | bicubic |
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| semnasnet_100 | 75.448 (24.552) | 92.604 (7.396) | 3.89M | bicubic |
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| mnasnet_a1 | 75.448 (24.552) | 92.604 (7.396) | 3.89M | bicubic |
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| fbnetc_100 | 75.124 (24.876) | 92.386 (7.614) | 5.6M | bilinear |
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| resnet34 | 75.110 (24.890) | 92.284 (7.716) | 22M | bilinear |
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| seresnet34 | 74.808 (25.192) | 92.124 (7.876) | 22M | bilinear |
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| mnasnet_b1 | 74.658 (25.342) | 92.114 (7.886) | 4.38M | bicubic |
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| spnasnet_100 | 74.084 (25.916) | 91.818 (8.182) | 4.42M | bilinear |
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| seresnet18 | 71.742 (28.258) | 90.334 (9.666) | 11.8M | bicubic |
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@ -28,8 +28,8 @@ from models.conv2d_same import sconv2d
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from data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
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_models = [
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'mnasnet_050', 'mnasnet_075', 'mnasnet_100', 'mnasnet_140', 'semnasnet_050', 'semnasnet_075',
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'semnasnet_100', 'semnasnet_140', 'mnasnet_small', 'mobilenetv1_100', 'mobilenetv2_100',
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'mnasnet_050', 'mnasnet_075', 'mnasnet_100', 'mnasnet_b1', 'mnasnet_140', 'semnasnet_050', 'semnasnet_075',
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'semnasnet_100', 'mnasnet_a1', 'semnasnet_140', 'mnasnet_small', 'mobilenetv1_100', 'mobilenetv2_100',
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'mobilenetv3_050', 'mobilenetv3_075', 'mobilenetv3_100', 'chamnetv1_100', 'chamnetv2_100',
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'fbnetc_100', 'spnasnet_100', 'tflite_mnasnet_100', 'tflite_semnasnet_100', 'efficientnet_b0',
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'efficientnet_b1', 'efficientnet_b2', 'efficientnet_b3', 'efficientnet_b4', 'tf_efficientnet_b0',
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@ -50,7 +50,9 @@ def _cfg(url='', **kwargs):
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default_cfgs = {
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'mnasnet_050': _cfg(url=''),
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'mnasnet_075': _cfg(url=''),
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'mnasnet_100': _cfg(url=''),
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'mnasnet_100': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mnasnet_b1-74cb7081.pth',
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interpolation='bicubic'),
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'tflite_mnasnet_100': _cfg(
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url='https://www.dropbox.com/s/q55ir3tx8mpeyol/tflite_mnasnet_100-31639cdc.pth?dl=1',
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interpolation='bicubic'),
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@ -161,8 +163,7 @@ def _decode_block_str(block_str, depth_multiplier=1.0):
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is assumed to indicate the block type.
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leading string - block type (
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ir = InvertedResidual, ds = DepthwiseSep, dsa = DeptwhiseSep with pw act,
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ca = Cascade3x3, and possibly more)
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ir = InvertedResidual, ds = DepthwiseSep, dsa = DeptwhiseSep with pw act, cn = ConvBnAct)
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r - number of repeat blocks,
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k - kernel size,
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s - strides (1-9),
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@ -227,15 +228,6 @@ def _decode_block_str(block_str, depth_multiplier=1.0):
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block_args['pw_group'] = options['g']
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if options['g'] > 1:
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block_args['shuffle_type'] = 'mid'
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elif block_type == 'ca':
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block_args = dict(
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block_type=block_type,
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kernel_size=int(options['k']),
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out_chs=int(options['c']),
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stride=int(options['s']),
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act_fn=act_fn,
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noskip=noskip,
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)
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elif block_type == 'ds' or block_type == 'dsa':
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block_args = dict(
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block_type=block_type,
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@ -345,8 +337,6 @@ class _BlockBuilder:
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elif bt == 'ds' or bt == 'dsa':
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ba['drop_connect_rate'] = self.drop_connect_rate
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block = DepthwiseSeparableConv(**ba)
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elif bt == 'ca':
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block = CascadeConv(**ba)
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elif bt == 'cn':
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block = ConvBnAct(**ba)
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else:
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@ -565,36 +555,6 @@ class DepthwiseSeparableConv(nn.Module):
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return x
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class CascadeConv(nn.Sequential):
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# FIXME haven't used yet
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def __init__(self, in_chs, out_chs, kernel_size=3, stride=2, act_fn=F.relu, noskip=False,
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bn_momentum=_BN_MOMENTUM_PT_DEFAULT, bn_eps=_BN_EPS_PT_DEFAULT,
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folded_bn=False, padding_same=False):
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super(CascadeConv, self).__init__()
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assert stride in [1, 2]
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self.has_residual = (stride == 1 and in_chs == out_chs) and not noskip
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self.act_fn = act_fn
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padding = _padding_arg(1, padding_same)
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self.conv1 = sconv2d(in_chs, in_chs, kernel_size, stride=stride, padding=padding, bias=folded_bn)
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self.bn1 = None if folded_bn else nn.BatchNorm2d(in_chs, momentum=bn_momentum, eps=bn_eps)
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self.conv2 = sconv2d(in_chs, out_chs, kernel_size, stride=1, padding=padding, bias=folded_bn)
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self.bn2 = None if folded_bn else nn.BatchNorm2d(out_chs, momentum=bn_momentum, eps=bn_eps)
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def forward(self, x):
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residual = x
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x = self.conv1(x)
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if self.bn1 is not None:
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x = self.bn1(x)
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x = self.act_fn(x)
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x = self.conv2(x)
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if self.bn2 is not None:
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x = self.bn2(x)
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if self.has_residual:
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x += residual
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return x
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class InvertedResidual(nn.Module):
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""" Inverted residual block w/ optional SE"""
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@ -699,7 +659,6 @@ class GenEfficientNet(nn.Module):
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super(GenEfficientNet, self).__init__()
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self.num_classes = num_classes
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self.drop_rate = drop_rate
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self.drop_connect_rate = drop_connect_rate
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self.act_fn = act_fn
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self.num_features = num_features
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@ -730,7 +689,7 @@ class GenEfficientNet(nn.Module):
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nn.BatchNorm2d(self.num_features, momentum=bn_momentum, eps=bn_eps)
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self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
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self.classifier = nn.Linear(self.num_features, self.num_classes)
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self.classifier = nn.Linear(self.num_features * self.global_pool.feat_mult(), self.num_classes)
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for m in self.modules():
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if weight_init == 'goog':
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@ -1220,6 +1179,11 @@ def mnasnet_100(num_classes, in_chans=3, pretrained=False, **kwargs):
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return model
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def mnasnet_b1(num_classes, in_chans=3, pretrained=False, **kwargs):
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""" MNASNet B1, depth multiplier of 1.0. """
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return mnasnet_100(num_classes, in_chans, pretrained, **kwargs)
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def tflite_mnasnet_100(num_classes, in_chans=3, pretrained=False, **kwargs):
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""" MNASNet B1, depth multiplier of 1.0. """
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default_cfg = default_cfgs['tflite_mnasnet_100']
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@ -1273,6 +1237,11 @@ def semnasnet_100(num_classes, in_chans=3, pretrained=False, **kwargs):
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return model
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def mnasnet_a1(num_classes, in_chans=3, pretrained=False, **kwargs):
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""" MNASNet A1 (w/ SE), depth multiplier of 1.0. """
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return semnasnet_100(num_classes, in_chans, pretrained, **kwargs)
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def tflite_semnasnet_100(num_classes, in_chans=3, pretrained=False, **kwargs):
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""" MNASNet A1, depth multiplier of 1.0. """
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default_cfg = default_cfgs['tflite_semnasnet_100']
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