pull/1/head
Ross Wightman
5 years ago
parent
c88e80081d
commit
6b4f9ba223
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""" MNASNet (a1, b1, and small)
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Based on offical TF implementation w/ round_channels,
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decode_block_str, and model block args directly transferred
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https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet
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Original paper: https://arxiv.org/pdf/1807.11626.pdf.
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Hacked together by Ross Wightman
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"""
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import math
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import re
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from models.helpers import load_pretrained
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from models.adaptive_avgmax_pool import SelectAdaptivePool2d
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from data.transforms import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
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__all__ = ['MnasNet', 'mnasnet0_50', 'mnasnet0_75', 'mnasnet1_00', 'mnasnet1_40',
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'semnasnet0_50', 'semnasnet0_75', 'semnasnet1_00', 'semnasnet1_40',
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'mnasnet_small']
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def _cfg(url='', **kwargs):
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return {
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'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
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'crop_pct': 0.875, 'interpolation': 'bilinear',
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'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
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'first_conv': 'layer0.conv1', 'classifier': 'last_linear',
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**kwargs
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}
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default_cfgs = {
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'mnasnet0_50': _cfg(url=''),
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'mnasnet0_75': _cfg(url=''),
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'mnasnet1_00': _cfg(url=''),
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'mnasnet1_40': _cfg(url=''),
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'semnasnet0_50': _cfg(url=''),
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'semnasnet0_75': _cfg(url=''),
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'semnasnet1_00': _cfg(url=''),
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'semnasnet1_40': _cfg(url=''),
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'mnasnet_small': _cfg(url=''),
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}
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_BN_MOMENTUM_DEFAULT = 1 - 0.99
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_BN_EPS_DEFAULT = 1e-3
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def _round_channels(channels, depth_multiplier=1.0, depth_divisor=8, min_depth=None):
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"""Round number of filters based on depth multiplier."""
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multiplier = depth_multiplier
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divisor = depth_divisor
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min_depth = min_depth
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if not multiplier:
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return channels
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channels *= multiplier
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min_depth = min_depth or divisor
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new_channels = max(min_depth, int(channels + divisor / 2) // divisor * divisor)
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# Make sure that round down does not go down by more than 10%.
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if new_channels < 0.9 * channels:
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new_channels += divisor
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return new_channels
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def _decode_block_str(block_str):
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"""Gets a MNasNet block through a string notation of arguments.
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E.g. r2_k3_s2_e1_i32_o16_se0.25_noskip:
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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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e - expansion ratio,
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i - input filters,
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o - output filters,
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se - squeeze/excitation ratio
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Args:
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block_string: a string, a string representation of block arguments.
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Returns:
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A BlockArgs instance.
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Raises:
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ValueError: if the strides option is not correctly specified.
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"""
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assert isinstance(block_str, str)
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ops = block_str.split('_')
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options = {}
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for op in ops:
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splits = re.split(r'(\d.*)', op)
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if len(splits) >= 2:
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key, value = splits[:2]
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options[key] = value
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if 's' not in options or len(options['s']) != 2:
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raise ValueError('Strides options should be a pair of integers.')
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return dict(
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kernel_size=int(options['k']),
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num_repeat=int(options['r']),
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in_chs=int(options['i']),
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out_chs=int(options['o']),
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exp_ratio=int(options['e']),
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id_skip=('noskip' not in block_str),
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se_ratio=float(options['se']) if 'se' in options else None,
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stride=int(options['s'][0]) # TF impl passes a list of two strides
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)
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def _decode_block_args(string_list):
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block_args = []
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for block_str in string_list:
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block_args.append(_decode_block_str(block_str))
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return block_args
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def _initialize_weight(m):
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if isinstance(m, nn.Conv2d):
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n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
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m.weight.data.normal_(0, math.sqrt(2.0 / n))
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if m.bias is not None:
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m.bias.data.zero_()
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elif isinstance(m, nn.BatchNorm2d):
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m.weight.data.fill_(1.0)
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m.bias.data.zero_()
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elif isinstance(m, nn.Linear):
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n = m.weight.size(1)
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init_range = 1.0 / math.sqrt(n)
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m.weight.data.uniform_(-init_range, init_range)
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m.bias.data.zero_()
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class MnasBlock(nn.Module):
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""" MNASNet Inverted residual block w/ optional SE"""
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def __init__(self, in_chs, out_chs, kernel_size, stride,
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exp_ratio=1.0, id_skip=True, se_ratio=0.,
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bn_momentum=0.1, bn_eps=1e-3, act_fn=F.relu):
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super(MnasBlock, self).__init__()
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exp_chs = int(in_chs * exp_ratio)
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self.has_expansion = exp_ratio != 1
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self.has_se = se_ratio is not None and se_ratio > 0.
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self.has_residual = id_skip and (in_chs == out_chs and stride == 1)
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self.act_fn = act_fn
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# Pointwise expansion
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if self.has_expansion:
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self.conv_expand = nn.Conv2d(in_chs, exp_chs, 1, bias=False)
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self.bn0 = nn.BatchNorm2d(exp_chs, momentum=bn_momentum, eps=bn_eps)
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# Depthwise convolution
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self.conv_depthwise = nn.Conv2d(
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exp_chs, exp_chs, kernel_size, padding=kernel_size // 2,
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stride=stride, groups=exp_chs, bias=False)
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self.bn1 = nn.BatchNorm2d(exp_chs, momentum=bn_momentum, eps=bn_eps)
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# Squeeze-and-excitation
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if self.has_se:
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num_reduced_ch = max(1, int(in_chs * se_ratio))
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self.conv_se_reduce = nn.Conv2d(exp_chs, num_reduced_ch, 1, bias=True)
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self.conv_se_expand = nn.Conv2d(num_reduced_ch, exp_chs, 1, bias=True)
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# Pointwise projection
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self.conv_project = nn.Conv2d(exp_chs, out_chs, 1, bias=False)
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self.bn2 = 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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# Pointwise expansion
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if self.has_expansion:
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x = self.conv_expand(x)
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x = self.bn0(x)
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x = self.act_fn(x)
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# Depthwise convolution
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x = self.conv_depthwise(x)
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x = self.bn1(x)
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x = self.act_fn(x)
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# Squeeze-and-excitation
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if self.has_se:
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x_se = x.view(x.size(0), x.size(1), -1).mean(-1).view(x.size(0), x.size(1), 1, 1)
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x_se = self.conv_se_reduce(x_se)
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x_se = F.relu(x_se)
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x_se = self.conv_se_expand(x_se)
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x = F.sigmoid(x_se) * x
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# Pointwise projection
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x = self.conv_project(x)
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x = self.bn2(x)
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# Residual
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if self.has_residual:
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return x + residual
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else:
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return x
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class MnasNet(nn.Module):
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""" MNASNet
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Based on offical TF implementation
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https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet
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Original paper: https://arxiv.org/pdf/1807.11626.pdf.
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"""
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def __init__(self, block_args, num_classes=1000, in_chans=3, stem_size=32,
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depth_multiplier=1.0, depth_divisor=8, min_depth=None,
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bn_momentum=_BN_MOMENTUM_DEFAULT, bn_eps=_BN_EPS_DEFAULT, drop_rate=0.,
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global_pool='avg', act_fn=F.relu):
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super(MnasNet, 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.global_pool = global_pool
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self.act_fn = act_fn
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self.depth_multiplier = depth_multiplier
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self.bn_momentum = bn_momentum
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self.bn_eps = bn_eps
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self.num_features = 1280
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self.conv_stem = nn.Conv2d(in_chans, stem_size, 3, padding=1, stride=2, bias=False)
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self.bn0 = nn.BatchNorm2d(stem_size, momentum=self.bn_momentum, eps=self.bn_eps)
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blocks = []
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for i, a in enumerate(block_args):
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print(a) #FIXME debug
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a['in_chs'] = _round_channels(a['in_chs'], depth_multiplier, depth_divisor, min_depth)
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a['out_chs'] = _round_channels(a['out_chs'], depth_multiplier, depth_divisor, min_depth)
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blocks.append(self._make_stack(**a))
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out_chs = a['out_chs']
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self.blocks = nn.Sequential(*blocks)
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self.conv_head = nn.Conv2d(out_chs, self.num_features, 1, padding=0, stride=1, bias=False)
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self.bn1 = nn.BatchNorm2d(self.num_features, momentum=self.bn_momentum, eps=self.bn_eps)
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self.avg_pool = nn.AdaptiveAvgPool2d(1)
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self.classifier = nn.Linear(self.num_features, self.num_classes)
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for m in self.modules():
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_initialize_weight(m)
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def _make_stack(self, in_chs, out_chs, kernel_size, stride,
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exp_ratio, id_skip, se_ratio, num_repeat):
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blocks = [MnasBlock(
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in_chs, out_chs, kernel_size, stride, exp_ratio, id_skip, se_ratio,
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bn_momentum=self.bn_momentum, bn_eps=self.bn_eps)]
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for _ in range(1, num_repeat):
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blocks += [MnasBlock(
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out_chs, out_chs, kernel_size, 1, exp_ratio, id_skip, se_ratio,
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bn_momentum=self.bn_momentum, bn_eps=self.bn_eps)]
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return nn.Sequential(*blocks)
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def get_classifier(self):
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return self.classifier
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def reset_classifier(self, num_classes, global_pool='avg'):
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#self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
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self.num_classes = num_classes
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del self.classifier
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if num_classes:
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self.classifier = nn.Linear(self.num_features, num_classes)
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else:
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self.classifier = None
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def forward_features(self, x, pool=True):
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x = self.conv_stem(x)
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x = self.bn0(x)
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x = self.act_fn(x)
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x = self.blocks(x)
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x = self.conv_head(x)
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x = self.bn1(x)
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x = self.act_fn(x)
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if pool:
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x = self.avg_pool(x)
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x = x.view(x.size(0), -1)
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return x
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def forward(self, x):
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x = self.forward_features(x)
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if self.drop_rate > 0.:
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x = F.dropout(x, p=self.drop_rate, training=self.training)
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return self.classifier(x)
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def mnasnet_a1(depth_multiplier, num_classes=1000, **kwargs):
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"""Creates a mnasnet-a1 model.
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Args:
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depth_multiplier: multiplier to number of filters per layer.
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"""
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# defs from https://github.com/tensorflow/tpu/blob/master/models/official/mnasnet/mnasnet_models.py
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block_defs = [
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'r1_k3_s11_e1_i32_o16_noskip',
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'r2_k3_s22_e6_i16_o24',
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'r3_k5_s22_e3_i24_o40_se0.25',
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'r4_k3_s22_e6_i40_o80',
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'r2_k3_s11_e6_i80_o112_se0.25',
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'r3_k5_s22_e6_i112_o160_se0.25',
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'r1_k3_s11_e6_i160_o320'
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]
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block_args = _decode_block_args(block_defs)
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model = MnasNet(
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block_args,
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num_classes=num_classes,
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depth_multiplier=depth_multiplier,
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depth_divisor=8,
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min_depth=None,
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stem_size=32,
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bn_momentum=_BN_MOMENTUM_DEFAULT,
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bn_eps=_BN_EPS_DEFAULT,
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#drop_rate=0.2,
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**kwargs
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)
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return model
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def mnasnet_b1(depth_multiplier, num_classes=1000, **kwargs):
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"""Creates a mnasnet-b1 model.
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Args:
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depth_multiplier: multiplier to number of filters per layer.
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"""
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# from https://github.com/tensorflow/tpu/blob/master/models/official/mnasnet/mnasnet_models.py
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blocks_defs = [
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'r1_k3_s11_e1_i32_o16_noskip',
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'r3_k3_s22_e3_i16_o24',
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'r3_k5_s22_e3_i24_o40',
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'r3_k5_s22_e6_i40_o80',
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'r2_k3_s11_e6_i80_o96',
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'r4_k5_s22_e6_i96_o192',
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'r1_k3_s11_e6_i192_o320_noskip'
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]
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block_args = _decode_block_args(blocks_defs)
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model = MnasNet(
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block_args,
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num_classes=num_classes,
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depth_multiplier=depth_multiplier,
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depth_divisor=8,
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min_depth=None,
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stem_size=32,
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bn_momentum=_BN_MOMENTUM_DEFAULT,
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bn_eps=_BN_EPS_DEFAULT,
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#drop_rate=0.2,
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**kwargs
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)
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return model
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def mnasnet_small(depth_multiplier, num_classes=1000, **kwargs):
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"""Creates a mnasnet-b1 model.
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Args:
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depth_multiplier: multiplier to number of filters per layer.
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"""
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# from https://github.com/tensorflow/tpu/blob/master/models/official/mnasnet/mnasnet_models.py
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blocks_defs = [
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'r1_k3_s11_e1_i16_o8',
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'r1_k3_s22_e3_i8_o16',
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'r2_k3_s22_e6_i16_o16',
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'r4_k5_s22_e6_i16_o32_se0.25',
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'r3_k3_s11_e6_i32_o32_se0.25',
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'r3_k5_s22_e6_i32_o88_se0.25',
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'r1_k3_s11_e6_i88_o144'
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]
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block_args = _decode_block_args(blocks_defs)
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model = MnasNet(
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block_args,
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num_classes=num_classes,
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depth_multiplier=depth_multiplier,
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depth_divisor=8,
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min_depth=None,
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stem_size=8,
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bn_momentum=_BN_MOMENTUM_DEFAULT,
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bn_eps=_BN_EPS_DEFAULT,
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#drop_rate=0.2,
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**kwargs
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)
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return model
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def mnasnet0_50(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
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""" MNASNet B1, depth multiplier of 0.5. """
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default_cfg = default_cfgs['mnasnet0_50']
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model = mnasnet_b1(0.5, num_classes=num_classes, in_chans=in_chans, **kwargs)
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model.default_cfg = default_cfg
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return model
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def mnasnet0_75(num_classes, in_chans=3, pretrained=False, **kwargs):
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""" MNASNet B1, depth multiplier of 0.75. """
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default_cfg = default_cfgs['mnasnet0_50']
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model = mnasnet_b1(0.75, num_classes=num_classes, in_chans=in_chans, **kwargs)
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model.default_cfg = default_cfg
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return model
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def mnasnet1_00(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['mnasnet0_50']
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model = mnasnet_b1(1.0, num_classes=num_classes, in_chans=in_chans, **kwargs)
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model.default_cfg = default_cfg
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return model
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def mnasnet1_40(num_classes, in_chans=3, pretrained=False, **kwargs):
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""" MNASNet B1, depth multiplier of 1.4 """
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default_cfg = default_cfgs['mnasnet0_50']
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model = mnasnet_b1(1.4, num_classes=num_classes, in_chans=in_chans, **kwargs)
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model.default_cfg = default_cfg
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return model
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def semnasnet0_50(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
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""" MNASNet A1 (w/ SE), depth multiplier of 0.5 """
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default_cfg = default_cfgs['mnasnet0_50']
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model = mnasnet_a1(0.5, num_classes=num_classes, in_chans=in_chans, **kwargs)
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model.default_cfg = default_cfg
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return model
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def semnasnet0_75(num_classes, in_chans=3, pretrained=False, **kwargs):
|
||||
""" MNASNet A1 (w/ SE), depth multiplier of 0.75. """
|
||||
default_cfg = default_cfgs['mnasnet0_50']
|
||||
model = mnasnet_a1(0.75, num_classes=num_classes, in_chans=in_chans, **kwargs)
|
||||
model.default_cfg = default_cfg
|
||||
return model
|
||||
|
||||
|
||||
def semnasnet1_00(num_classes, in_chans=3, pretrained=False, **kwargs):
|
||||
""" MNASNet Small, depth multiplier of 1.0. """
|
||||
default_cfg = default_cfgs['mnasnet0_50']
|
||||
model = mnasnet_a1(1.0, num_classes=num_classes, in_chans=in_chans, **kwargs)
|
||||
model.default_cfg = default_cfg
|
||||
return model
|
||||
|
||||
|
||||
def semnasnet1_40(num_classes, in_chans=3, pretrained=False, **kwargs):
|
||||
""" MNASNet with depth multiplier of 1.3. """
|
||||
default_cfg = default_cfgs['mnasnet0_50']
|
||||
model = mnasnet_a1(1.4, num_classes=num_classes, in_chans=in_chans, **kwargs)
|
||||
model.default_cfg = default_cfg
|
||||
return model
|
||||
|
||||
|
||||
def mnasnet_small(num_classes, in_chans=3, pretrained=False, **kwargs):
|
||||
default_cfg = default_cfgs['mnasnet_small']
|
||||
model = mnasnet_small(1.0, num_classes=num_classes, in_chans=in_chans, **kwargs)
|
||||
model.default_cfg = default_cfg
|
||||
return model
|
||||
Loading…
Reference in new issue