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Morph mnasnet impl into a generic mobilenet that covers Mnasnet, MobileNetV1/V2, ChamNet, FBNet, and related

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* add an alternate RMSprop opt that applies eps like TF
* add bn params for passing through alternates and changing defaults to TF style
pull/1/head
Ross Wightman 5 years ago
parent e9c7961efc
commit bc264269c9
7 changed files with 1098 additions and 467 deletions
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models/genmobilenet.py
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""" Generic MobileNet
A generic MobileNet class with building blocks to support a variety of models:
* MNasNet B1, A1 (SE), Small
* MobileNetV2
* FBNet-C (TODO A & B)
* ChamNet (TODO still guessing at architecture definition)
* ShuffleNetV2 (TODO add IR shuffle block)
* And likely more...
TODO not all combinations and variations have been tested. Currently working on training hyper-params...
Hacked together by Ross Wightman
"""
import math
import re
from copy import deepcopy
import torch
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 data.transforms import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
__all__ = ['GenMobileNet', 'mnasnet0_50', 'mnasnet0_75', 'mnasnet1_00', 'mnasnet1_40',
'semnasnet0_50', 'semnasnet0_75', 'semnasnet1_00', 'semnasnet1_40',
'mnasnet_small']
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',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'conv_stem', 'classifier': 'classifier',
**kwargs
}
default_cfgs = {
'mnasnet0_50': _cfg(url=''),
'mnasnet0_75': _cfg(url=''),
'mnasnet1_00': _cfg(url=''),
'mnasnet1_40': _cfg(url=''),
'semnasnet0_50': _cfg(url=''),
'semnasnet0_75': _cfg(url=''),
'semnasnet1_00': _cfg(url=''),
'semnasnet1_40': _cfg(url=''),
'mnasnet_small': _cfg(url=''),
'mobilenetv1_1_00': _cfg(url=''),
'mobilenetv2_1_00': _cfg(url=''),
'chamnetv1_1_00': _cfg(url=''),
'chamnetv2_1_00': _cfg(url=''),
'fbnetc_1_00': _cfg(url=''),
}
_DEBUG = True
# default args for PyTorch BN impl
_BN_MOMENTUM_PT_DEFAULT = 0.1
_BN_EPS_PT_DEFAULT = 1e-5
# defaults used for Google/Tensorflow training of mobile networks /w RMSprop as per
# papers and TF reference implementations. PT momentum equiv for TF decay is (1 - TF decay)
_BN_MOMENTUM_TF_DEFAULT = 1 - 0.99 # NOTE this varies, .99 or .9997 depending on ref
_BN_EPS_TF_DEFAULT = 1e-3
def _resolve_bn_params(kwargs):
# NOTE kwargs passed as dict intentionally
bn_momentum_default = _BN_MOMENTUM_PT_DEFAULT
bn_eps_default = _BN_EPS_PT_DEFAULT
bn_tf = kwargs.pop('bn_tf', False)
if bn_tf:
bn_momentum_default = _BN_MOMENTUM_TF_DEFAULT
bn_eps_default = _BN_EPS_TF_DEFAULT
bn_momentum = kwargs.pop('bn_momentum', None)
bn_eps = kwargs.pop('bn_eps', None)
if bn_momentum is None:
bn_momentum = bn_momentum_default
if bn_eps is None:
bn_eps = bn_eps_default
return bn_momentum, bn_eps
def _round_channels(channels, depth_multiplier=1.0, depth_divisor=8, min_depth=None):
"""Round number of filters based on depth multiplier."""
if not depth_multiplier:
return channels
channels *= depth_multiplier
min_depth = min_depth or depth_divisor
new_channels = max(
int(channels + depth_divisor / 2) // depth_divisor * depth_divisor,
min_depth)
# Make sure that round down does not go down by more than 10%.
if new_channels < 0.9 * channels:
new_channels += depth_divisor
return new_channels
def _decode_block_str(block_str):
""" Decode block definition string
Gets a list of block arg (dicts) through a string notation of arguments.
E.g. ir_r2_k3_s2_e1_i32_o16_se0.25_noskip
All args can exist in any order with the exception of the leading string which
is assumed to indicate the block type.
leading string - block type (
ir = InvertedResidual, ds = DepthwiseSep, dsa = DeptwhiseSep with pw act,
ca = Cascade3x3, and possibly more)
r - number of repeat blocks,
k - kernel size,
s - strides (1-9),
e - expansion ratio,
c - output channels,
se - squeeze/excitation ratio
Args:
block_str: a string representation of block arguments.
Returns:
A list of block args (dicts)
Raises:
ValueError: if the string def not properly specified (TODO)
"""
assert isinstance(block_str, str)
ops = block_str.split('_')
block_type = ops[0] # take the block type off the front
ops = ops[1:]
options = {}
for op in ops:
splits = re.split(r'(\d.*)', op)
if len(splits) >= 2:
key, value = splits[:2]
options[key] = value
# FIXME validate args and throw
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']),
out_chs=int(options['c']),
exp_ratio=int(options['e']),
se_ratio=float(options['se']) if 'se' in options else None,
stride=int(options['s']),
noskip=('noskip' in block_str),
)
if 'g' in options:
block_args['pw_group'] = options['g']
if options['g'] > 1:
block_args['shuffle_type'] = 'mid'
elif block_type == 'ca':
block_args = dict(
block_type=block_type,
out_chs=int(options['c']),
stride=int(options['s']),
noskip=('noskip' in block_str),
)
elif block_type == 'ds' or block_type == 'dsa':
block_args = dict(
block_type=block_type,
kernel_size=int(options['k']),
out_chs=int(options['c']),
stride=int(options['s']),
noskip=block_type == 'dsa' or 'noskip' in block_str,
pw_act=block_type == 'dsa',
)
else:
assert False, 'Unknown block type (%s)' % block_type
# return a list of block args expanded by num_repeat
return [deepcopy(block_args) for _ in range(num_repeat)]
def _get_padding(kernel_size, stride, dilation):
padding = ((stride - 1) + dilation * (kernel_size - 1)) // 2
return padding
def _decode_arch_args(string_list):
block_args = []
for block_str in string_list:
block_args.append(_decode_block_str(block_str))
return block_args
def _decode_arch_def(arch_def):
arch_args = []
for stack_idx, block_strings in enumerate(arch_def):
assert isinstance(block_strings, list)
stack_args = []
for block_str in block_strings:
assert isinstance(block_str, str)
stack_args.extend(_decode_block_str(block_str))
arch_args.append(stack_args)
return arch_args
class _BlockBuilder:
""" Build Trunk Blocks
This ended up being somewhat of a cross between
https://github.com/tensorflow/tpu/blob/master/models/official/mnasnet/mnasnet_models.py
and
https://github.com/facebookresearch/maskrcnn-benchmark/blob/master/maskrcnn_benchmark/modeling/backbone/fbnet_builder.py
"""
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):
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.in_chs = None
def _round_channels(self, chs):
return _round_channels(chs, self.depth_multiplier, self.depth_divisor, self.min_depth)
def _make_block(self, ba):
bt = ba.pop('block_type')
ba['in_chs'] = self.in_chs
ba['out_chs'] = _round_channels(ba['out_chs'])
ba['bn_momentum'] = self.bn_momentum
ba['bn_eps'] = self.bn_eps
if _DEBUG:
print('args:', ba)
# could replace this with lambdas or functools binding if variety increases
if bt == 'ir':
block = InvertedResidual(**ba)
elif bt == 'ds' or bt == 'dsa':
block = DepthwiseSeparableConv(**ba)
elif bt == 'ca':
block = CascadeConv3x3(**ba)
else:
assert False, 'Uknkown block type (%s) while building model.' % bt
self.in_chs = ba['out_chs'] # update in_chs for arg of next block
return block
def _make_stack(self, stack_args):
blocks = []
# each stack (stage) contains a list of block arguments
for block_idx, ba in enumerate(stack_args):
if _DEBUG:
print('block', block_idx, end=', ')
if block_idx >= 1:
# only the first block in any stack/stage can have a stride > 1
ba['stride'] = 1
block = self._make_block(ba)
blocks.append(block)
return nn.Sequential(*blocks)
def __call__(self, in_chs, arch_def):
""" Build the blocks
Args:
in_chs: Number of input-channels passed to first block
arch_def: A list of lists, outer list defines stacks (or stages), inner
list contains strings defining block configuration(s)
Return:
List of block stacks (each stack wrapped in nn.Sequential)
"""
arch_args = _decode_arch_def(arch_def) # convert and expand string defs to arg dicts
if _DEBUG:
print('Building model trunk with %d stacks (stages)...' % len(arch_args))
self.in_chs = in_chs
blocks = []
# outer list of arch_args defines the stacks ('stages' by some conventions)
for stack_idx, stack in enumerate(arch_args):
if _DEBUG:
print('stack', stack_idx)
assert isinstance(stack, list)
stack = self._make_stack(stack)
blocks.append(stack)
if _DEBUG:
print()
return blocks
def _initialize_weight(m):
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2.0 / n))
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1.0)
m.bias.data.zero_()
elif isinstance(m, nn.Linear):
n = m.weight.size(1)
init_range = 1.0 / math.sqrt(n)
m.weight.data.uniform_(-init_range, init_range)
m.bias.data.zero_()
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):
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
self.conv_dw = nn.Conv2d(
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)
def forward(self, x):
residual = x
x = self.conv_dw(x)
x = self.bn1(x)
x = self.act_fn(x)
x = self.conv_pw(x)
x = self.bn2(x)
if self.has_pw_act:
x = self.act_fn(x)
if self.has_residual:
x += residual
return x
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):
super(CascadeConv3x3, self).__init__()
assert stride in [1, 2]
self.has_residual = not noskip and (stride == 1 and in_chs == out_chs)
self.act_fn = act_fn
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)
def forward(self, x):
residual = x
x = self.conv1(x)
x = self.bn1(x)
x = self.act_fn(x)
x = self.conv2(x)
x = self.bn2(x)
if self.has_residual:
x += residual
return x
class ChannelShuffle(nn.Module):
# FIXME lifted from maskrcnn_benchmark blocks, haven't used yet
def __init__(self, groups):
super(ChannelShuffle, self).__init__()
self.groups = groups
def forward(self, x):
"""Channel shuffle: [N,C,H,W] -> [N,g,C/g,H,W] -> [N,C/g,g,H,w] -> [N,C,H,W]"""
N, C, H, W = x.size()
g = self.groups
assert C % g == 0, "Incompatible group size {} for input channel {}".format(
g, C
)
return (
x.view(N, g, int(C / g), H, W)
.permute(0, 2, 1, 3, 4)
.contiguous()
.view(N, C, H, W)
)
class SqueezeExcite(nn.Module):
def __init__(self, in_chs, se_ratio=0.25, act_fn=F.relu):
super(SqueezeExcite, self).__init__()
self.act_fn = act_fn
reduced_chs = max(1, int(in_chs * se_ratio))
self.conv_reduce = nn.Conv2d(in_chs, reduced_chs, 1, bias=True)
self.conv_expand = nn.Conv2d(reduced_chs, in_chs, 1, bias=True)
def forward(self, x):
# NOTE adaptiveavgpool can be used here, but seems to cause issues with NVIDIA AMP performance
x_se = x.view(x.size(0), x.size(1), -1).mean(-1).view(x.size(0), x.size(1), 1, 1)
x_se = self.conv_reduce(x_se)
x_se = self.act_fn(x_se)
x_se = self.conv_expand(x_se)
x = torch.sigmoid(x_se) * x
return x
class InvertedResidual(nn.Module):
""" Inverted residual block w/ optional SE"""
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):
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
# 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.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)
# Squeeze-and-excitation
if self.has_se:
self.se = SqueezeExcite(mid_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)
def forward(self, x):
residual = x
# Point-wise expansion
x = self.conv_pw(x)
x = self.bn1(x)
x = self.act_fn(x)
# FIXME haven't tried this yet
# for channel shuffle when using groups with pointwise convs as per FBNet variants
if self.shuffle_type == "mid":
x = self.shuffle(x)
# Depth-wise convolution
x = self.conv_dw(x)
x = self.bn2(x)
x = self.act_fn(x)
# Squeeze-and-excitation
if self.has_se:
x = self.se(x)
# Point-wise linear projection
x = self.conv_pwl(x)
x = self.bn3(x)
if self.has_residual:
x += residual
# NOTE maskrcnn_benchmark building blocks have an SE module defined here for some variants
return x
class GenMobileNet(nn.Module):
""" Generic Mobile Net
An implementation of mobile optimized networks that covers:
* MobileNetV1
* MobileNetV2
* MNASNet A1, B1, and small
* FBNet A, B, and C
* ChamNet (arch details are murky)
"""
def __init__(self, block_args, num_classes=1000, in_chans=3, stem_size=32, num_features=1280,
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):
super(GenMobileNet, self).__init__()
self.num_classes = num_classes
self.depth_multiplier = depth_multiplier
self.drop_rate = drop_rate
self.act_fn = act_fn
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)
in_chs = stem_size
builder = _BlockBuilder(
depth_multiplier, depth_divisor, min_depth,
bn_momentum, bn_eps)
self.blocks = nn.Sequential(*builder(in_chs, block_args))
in_chs = builder.in_chs
if skip_head_conv:
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.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
self.classifier = nn.Linear(self.num_features, self.num_classes)
for m in self.modules():
_initialize_weight(m)
def get_classifier(self):
return self.classifier
def reset_classifier(self, num_classes, global_pool='avg'):
self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
self.num_classes = num_classes
del self.classifier
if num_classes:
self.classifier = nn.Linear(
self.num_features * self.global_pool.feat_mult(), num_classes)
else:
self.classifier = None
def forward_features(self, x, pool=True):
x = self.conv_stem(x)
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)
x = self.bn2(x)
x = self.act_fn(x)
if pool:
x = self.global_pool(x)
x = x.view(x.size(0), -1)
return x
def forward(self, x):
x = self.forward_features(x)
if self.drop_rate > 0.:
x = F.dropout(x, p=self.drop_rate, training=self.training)
return self.classifier(x)
def _gen_mnasnet_a1(depth_multiplier, num_classes=1000, **kwargs):
"""Creates a mnasnet-a1 model.
Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet
Paper: https://arxiv.org/pdf/1807.11626.pdf.
Args:
depth_multiplier: multiplier to number of channels per layer.
"""
arch_def = [
# stage 0, 112x112 in
['ds_r1_k3_s1_e1_c16_noskip'],
# stage 1, 112x112 in
['ir_r2_k3_s2_e6_c24'],
# stage 2, 56x56 in
['ir_r3_k5_s2_e3_c40_se0.25'],
# stage 3, 28x28 in
['ir_r4_k3_s2_e6_c80'],
# stage 4, 14x14in
['ir_r2_k3_s1_e6_c112_se0.25'],
# stage 5, 14x14in
['ir_r3_k5_s2_e6_c160_se0.25'],
# stage 6, 7x7 in
['ir_r1_k3_s1_e6_c320'],
]
bn_momentum, bn_eps = _resolve_bn_params(kwargs)
model = GenMobileNet(
arch_def,
num_classes=num_classes,
stem_size=32,
depth_multiplier=depth_multiplier,
depth_divisor=8,
min_depth=None,
bn_momentum=bn_momentum,
bn_eps=bn_eps,
**kwargs
)
return model
def _gen_mnasnet_b1(depth_multiplier, num_classes=1000, **kwargs):
"""Creates a mnasnet-b1 model.
Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet
Paper: https://arxiv.org/pdf/1807.11626.pdf.
Args:
depth_multiplier: multiplier to number of channels per layer.
"""
arch_def = [
# stage 0, 112x112 in
['ds_r1_k3_s1_c16_noskip'],
# stage 1, 112x112 in
['ir_r3_k3_s2_e3_c24'],
# stage 2, 56x56 in
['ir_r3_k5_s2_e3_c40'],
# stage 3, 28x28 in
['ir_r3_k5_s2_e6_c80'],
# stage 4, 14x14in
['ir_r2_k3_s1_e6_c96'],
# stage 5, 14x14in
['ir_r4_k5_s2_e6_c192'],
# stage 6, 7x7 in
['ir_r1_k3_s1_e6_c320_noskip']
]
bn_momentum, bn_eps = _resolve_bn_params(kwargs)
model = GenMobileNet(
arch_def,
num_classes=num_classes,
stem_size=32,
depth_multiplier=depth_multiplier,
depth_divisor=8,
min_depth=None,
bn_momentum=bn_momentum,
bn_eps=bn_eps,
**kwargs
)
return model
def _gen_mnasnet_small(depth_multiplier, num_classes=1000, **kwargs):
"""Creates a mnasnet-b1 model.
Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet
Paper: https://arxiv.org/pdf/1807.11626.pdf.
Args:
depth_multiplier: multiplier to number of channels per layer.
"""
arch_def = [
['ds_r1_k3_s1_c8'],
['ir_r1_k3_s2_e3_c16'],
['ir_r2_k3_s2_e6_c16'],
['ir_r4_k5_s2_e6_c32_se0.25'],
['ir_r3_k3_s1_e6_c32_se0.25'],
['ir_r3_k5_s2_e6_c88_se0.25'],
['ir_r1_k3_s1_e6_c144']
]
bn_momentum, bn_eps = _resolve_bn_params(kwargs)
model = GenMobileNet(
arch_def,
num_classes=num_classes,
stem_size=8,
depth_multiplier=depth_multiplier,
depth_divisor=8,
min_depth=None,
bn_momentum=bn_momentum,
bn_eps=bn_eps,
**kwargs
)
return model
def _gen_mobilenet_v1(depth_multiplier, num_classes=1000, **kwargs):
"""
Ref impl: https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet_v2.py
Paper: https://arxiv.org/abs/1801.04381
"""
arch_def = [
['dsa_r1_k3_s1_c64'],
['dsa_r2_k3_s2_c128'],
['dsa_r2_k3_s2_c256'],
['dsa_r6_k3_s2_c512'],
['dsa_r2_k3_s2_c1024'],
]
bn_momentum, bn_eps = _resolve_bn_params(kwargs)
model = GenMobileNet(
arch_def,
num_classes=num_classes,
stem_size=32,
num_features=1024,
depth_multiplier=depth_multiplier,
depth_divisor=8,
min_depth=None,
bn_momentum=bn_momentum,
bn_eps=bn_eps,
act_fn=F.relu6,
skip_head_conv=True,
**kwargs
)
return model
def _gen_mobilenet_v2(depth_multiplier, num_classes=1000, **kwargs):
""" Generate MobileNet-V2 network
Ref impl: https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet_v2.py
Paper: https://arxiv.org/abs/1801.04381
"""
arch_def = [
['ds_r1_k3_s1_c16'],
['ir_r2_k3_s2_e6_c24'],
['ir_r3_k3_s2_e6_c32'],
['ir_r4_k3_s2_e6_c64'],
['ir_r3_k3_s1_e6_c96'],
['ir_r3_k3_s2_e6_c160'],
['ir_r1_k3_s1_e6_c320'],
]
bn_momentum, bn_eps = _resolve_bn_params(kwargs)
model = GenMobileNet(
arch_def,
num_classes=num_classes,
stem_size=32,
depth_multiplier=depth_multiplier,
depth_divisor=8,
min_depth=None,
bn_momentum=bn_momentum,
bn_eps=bn_eps,
act_fn=F.relu6,
**kwargs
)
return model
def _gen_chamnet_v1(depth_multiplier, num_classes=1000, **kwargs):
""" Generate Chameleon Network (ChamNet)
Paper: https://arxiv.org/abs/1812.08934
Ref Impl: https://github.com/facebookresearch/maskrcnn-benchmark/blob/master/maskrcnn_benchmark/modeling/backbone/fbnet_modeldef.py
FIXME: this a bit of an educated guess based on trunkd def in maskrcnn_benchmark
"""
arch_def = [
['ir_r1_k3_s1_e1_c24'],
['ir_r2_k7_s2_e4_c48'],
['ir_r5_k3_s2_e7_c64'],
['ir_r7_k5_s2_e12_c56'],
['ir_r5_k3_s1_e8_c88'],
['ir_r4_k3_s2_e7_c152'],
['ir_r1_k3_s1_e10_c104'],
]
bn_momentum, bn_eps = _resolve_bn_params(kwargs)
model = GenMobileNet(
arch_def,
num_classes=num_classes,
stem_size=32,
num_features=1280, # no idea what this is? try mobile/mnasnet default?
depth_multiplier=depth_multiplier,
depth_divisor=8,
min_depth=None,
bn_momentum=bn_momentum,
bn_eps=bn_eps,
**kwargs
)
return model
def _gen_chamnet_v2(depth_multiplier, num_classes=1000, **kwargs):
""" Generate Chameleon Network (ChamNet)
Paper: https://arxiv.org/abs/1812.08934
Ref Impl: https://github.com/facebookresearch/maskrcnn-benchmark/blob/master/maskrcnn_benchmark/modeling/backbone/fbnet_modeldef.py
FIXME: this a bit of an educated guess based on trunk def in maskrcnn_benchmark
"""
arch_def = [
['ir_r1_k3_s1_e1_c24'],
['ir_r4_k5_s2_e8_c32'],
['ir_r6_k7_s2_e5_c48'],
['ir_r3_k5_s2_e9_c56'],
['ir_r6_k3_s1_e6_c56'],
['ir_r6_k3_s2_e2_c152'],
['ir_r1_k3_s1_e6_c112'],
]
bn_momentum, bn_eps = _resolve_bn_params(kwargs)
model = GenMobileNet(
arch_def,
num_classes=num_classes,
stem_size=32,
num_features=1280, # no idea what this is? try mobile/mnasnet default?
depth_multiplier=depth_multiplier,
depth_divisor=8,
min_depth=None,
bn_momentum=bn_momentum,
bn_eps=bn_eps,
**kwargs
)
return model
def _gen_fbnetc(depth_multiplier, num_classes=1000, **kwargs):
""" FBNet-C
Paper: https://arxiv.org/abs/1812.03443
Ref Impl: https://github.com/facebookresearch/maskrcnn-benchmark/blob/master/maskrcnn_benchmark/modeling/backbone/fbnet_modeldef.py
NOTE: the impl above does not relate to the 'C' variant here, that was derived from paper,
it was used to confirm some building block details
"""
arch_def = [
['ir_r1_k3_s1_e1_c16'],
['ir_r1_k3_s2_e6_c24', 'ir_r2_k3_s1_e1_c24'],
['ir_r1_k5_s2_e6_c32', 'ir_r1_k5_s1_e3_c32', 'ir_r1_k5_s1_e6_c32', 'ir_r1_k3_s1_e6_c32'],
['ir_r1_k5_s2_e6_c64', 'ir_r1_k5_s1_e3_c64', 'ir_r2_k5_s1_e6_c64'],
['ir_r3_k5_s1_e6_c112', 'ir_r1_k5_s1_e3_c112'],
['ir_r4_k5_s2_e6_c184'],
['ir_r1_k3_s1_e6_c352'],
]
bn_momentum, bn_eps = _resolve_bn_params(kwargs)
model = GenMobileNet(
arch_def,
num_classes=num_classes,
stem_size=16,
num_features=1984, # paper suggests this, but is not 100% clear
depth_multiplier=depth_multiplier,
depth_divisor=8,
min_depth=None,
bn_momentum=bn_momentum,
bn_eps=bn_eps,
**kwargs
)
return model
def mnasnet0_50(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
""" MNASNet B1, depth multiplier of 0.5. """
default_cfg = default_cfgs['mnasnet0_50']
model = _gen_mnasnet_b1(0.5, 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 mnasnet0_75(num_classes, in_chans=3, pretrained=False, **kwargs):
""" MNASNet B1, depth multiplier of 0.75. """
default_cfg = default_cfgs['mnasnet0_75']
model = _gen_mnasnet_b1(0.75, 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_00(num_classes, in_chans=3, pretrained=False, **kwargs):
""" MNASNet B1, depth multiplier of 1.0. """
default_cfg = default_cfgs['mnasnet1_00']
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']
model = _gen_mnasnet_b1(1.4, 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 semnasnet0_50(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
""" MNASNet A1 (w/ SE), depth multiplier of 0.5 """
default_cfg = default_cfgs['semnasnet0_50']
model = _gen_mnasnet_a1(0.5, 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 semnasnet0_75(num_classes, in_chans=3, pretrained=False, **kwargs):
""" MNASNet A1 (w/ SE), depth multiplier of 0.75. """
default_cfg = default_cfgs['semnasnet0_75']
model = _gen_mnasnet_a1(0.75, 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_00(num_classes, in_chans=3, pretrained=False, **kwargs):
""" MNASNet A1 (w/ SE), depth multiplier of 1.0. """
default_cfg = default_cfgs['semnasnet1_00']
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']
model = _gen_mnasnet_a1(1.4, 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 mnasnet_small(num_classes, in_chans=3, pretrained=False, **kwargs):
""" MNASNet Small, depth multiplier of 1.0. """
default_cfg = default_cfgs['mnasnet_small']
model = _gen_mnasnet_small(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 mobilenetv1_1_00(num_classes, in_chans=3, pretrained=False, **kwargs):
""" MobileNet V1 """
default_cfg = default_cfgs['mobilenetv1_1_00']
model = _gen_mobilenet_v1(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 mobilenetv2_1_00(num_classes, in_chans=3, pretrained=False, **kwargs):
""" MobileNet V2 """
default_cfg = default_cfgs['mobilenetv2_1_00']
model = _gen_mobilenet_v2(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 fbnetc_1_00(num_classes, in_chans=3, pretrained=False, **kwargs):
""" FBNet-C """
default_cfg = default_cfgs['fbnetc_1_00']
model = _gen_fbnetc(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 chamnetv1_1_00(num_classes, in_chans=3, pretrained=False, **kwargs):
""" ChamNet """
default_cfg = default_cfgs['chamnetv1_1_00']
model = _gen_chamnet_v1(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 chamnetv2_1_00(num_classes, in_chans=3, pretrained=False, **kwargs):
""" ChamNet """
default_cfg = default_cfgs['chamnetv2_1_00']
model = _gen_chamnet_v2(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

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models/mnasnet.py
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""" MNASNet (a1, b1, and small)
Based on offical TF implementation w/ round_channels,
decode_block_str, and model block args directly transferred
https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet
Original paper: https://arxiv.org/pdf/1807.11626.pdf.
Hacked together by Ross Wightman
"""
import math
import re
import torch
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 data.transforms import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
__all__ = ['MnasNet', 'mnasnet0_50', 'mnasnet0_75', 'mnasnet1_00', 'mnasnet1_40',
'semnasnet0_50', 'semnasnet0_75', 'semnasnet1_00', 'semnasnet1_40',
'mnasnet_small']
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',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'conv_stem', 'classifier': 'classifier',
**kwargs
}
default_cfgs = {
'mnasnet0_50': _cfg(url=''),
'mnasnet0_75': _cfg(url=''),
'mnasnet1_00': _cfg(url=''),
'mnasnet1_40': _cfg(url=''),
'semnasnet0_50': _cfg(url=''),
'semnasnet0_75': _cfg(url=''),
'semnasnet1_00': _cfg(url=''),
'semnasnet1_40': _cfg(url=''),
'mnasnet_small': _cfg(url=''),
}
_BN_MOMENTUM_DEFAULT = 1 - 0.99
_BN_EPS_DEFAULT = 1e-3
def _round_channels(channels, depth_multiplier=1.0, depth_divisor=8, min_depth=None):
"""Round number of filters based on depth multiplier."""
multiplier = depth_multiplier
divisor = depth_divisor
min_depth = min_depth
if not multiplier:
return channels
channels *= multiplier
min_depth = min_depth or divisor
new_channels = max(min_depth, int(channels + divisor / 2) // divisor * divisor)
# Make sure that round down does not go down by more than 10%.
if new_channels < 0.9 * channels:
new_channels += divisor
return new_channels
def _decode_block_str(block_str):
"""Gets a MNasNet block through a string notation of arguments.
E.g. r2_k3_s2_e1_i32_o16_se0.25_noskip:
r - number of repeat blocks,
k - kernel size,
s - strides (1-9),
e - expansion ratio,
i - input filters,
o - output filters,
se - squeeze/excitation ratio
Args:
block_string: a string, a string representation of block arguments.
Returns:
A BlockArgs instance.
Raises:
ValueError: if the strides option is not correctly specified.
"""
assert isinstance(block_str, str)
ops = block_str.split('_')
options = {}
for op in ops:
splits = re.split(r'(\d.*)', op)
if len(splits) >= 2:
key, value = splits[:2]
options[key] = value
if 's' not in options or len(options['s']) != 2:
raise ValueError('Strides options should be a pair of integers.')
return dict(
kernel_size=int(options['k']),
num_repeat=int(options['r']),
in_chs=int(options['i']),
out_chs=int(options['o']),
exp_ratio=int(options['e']),
id_skip=('noskip' not in block_str),
se_ratio=float(options['se']) if 'se' in options else None,
stride=int(options['s'][0]) # TF impl passes a list of two strides
)
def _decode_block_args(string_list):
block_args = []
for block_str in string_list:
block_args.append(_decode_block_str(block_str))
return block_args
def _initialize_weight(m):
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2.0 / n))
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1.0)
m.bias.data.zero_()
elif isinstance(m, nn.Linear):
n = m.weight.size(1)
init_range = 1.0 / math.sqrt(n)
m.weight.data.uniform_(-init_range, init_range)
m.bias.data.zero_()
class MnasBlock(nn.Module):
""" MNASNet Inverted residual block w/ optional SE"""
def __init__(self, in_chs, out_chs, kernel_size, stride,
exp_ratio=1.0, id_skip=True, se_ratio=0.,
bn_momentum=0.1, bn_eps=1e-3, act_fn=F.relu):
super(MnasBlock, self).__init__()
exp_chs = int(in_chs * exp_ratio)
self.has_expansion = exp_ratio != 1
self.has_se = se_ratio is not None and se_ratio > 0.
self.has_residual = id_skip and (in_chs == out_chs and stride == 1)
self.act_fn = act_fn
# Pointwise expansion
if self.has_expansion:
self.conv_expand = nn.Conv2d(in_chs, exp_chs, 1, bias=False)
self.bn0 = nn.BatchNorm2d(exp_chs, momentum=bn_momentum, eps=bn_eps)
# Depthwise convolution
self.conv_depthwise = nn.Conv2d(
exp_chs, exp_chs, kernel_size, padding=kernel_size // 2,
stride=stride, groups=exp_chs, bias=False)
self.bn1 = nn.BatchNorm2d(exp_chs, momentum=bn_momentum, eps=bn_eps)
# Squeeze-and-excitation
if self.has_se:
num_reduced_ch = max(1, int(in_chs * se_ratio))
self.conv_se_reduce = nn.Conv2d(exp_chs, num_reduced_ch, 1, bias=True)
self.conv_se_expand = nn.Conv2d(num_reduced_ch, exp_chs, 1, bias=True)
# Pointwise projection
self.conv_project = nn.Conv2d(exp_chs, out_chs, 1, bias=False)
self.bn2 = nn.BatchNorm2d(out_chs, momentum=bn_momentum, eps=bn_eps)
def forward(self, x):
residual = x
# Pointwise expansion
if self.has_expansion:
x = self.conv_expand(x)
x = self.bn0(x)
x = self.act_fn(x)
# Depthwise convolution
x = self.conv_depthwise(x)
x = self.bn1(x)
x = self.act_fn(x)
# Squeeze-and-excitation
if self.has_se:
x_se = x.view(x.size(0), x.size(1), -1).mean(-1).view(x.size(0), x.size(1), 1, 1)
x_se = self.conv_se_reduce(x_se)
x_se = F.relu(x_se)
x_se = self.conv_se_expand(x_se)
x = F.sigmoid(x_se) * x
# Pointwise projection
x = self.conv_project(x)
x = self.bn2(x)
if self.has_residual:
return x + residual
else:
return x
class MnasNet(nn.Module):
""" MNASNet
Based on offical TF implementation
https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet
Original paper: https://arxiv.org/pdf/1807.11626.pdf.
"""
def __init__(self, block_args, num_classes=1000, in_chans=3, stem_size=32,
depth_multiplier=1.0, depth_divisor=8, min_depth=None,
bn_momentum=_BN_MOMENTUM_DEFAULT, bn_eps=_BN_EPS_DEFAULT, drop_rate=0.,
global_pool='avg', act_fn=F.relu):
super(MnasNet, self).__init__()
self.num_classes = num_classes
self.depth_multiplier = depth_multiplier
self.bn_momentum = bn_momentum
self.bn_eps = bn_eps
self.drop_rate = drop_rate
self.act_fn = act_fn
self.num_features = 1280
self.conv_stem = nn.Conv2d(in_chans, stem_size, 3, padding=1, stride=2, bias=False)
self.bn0 = nn.BatchNorm2d(stem_size, momentum=self.bn_momentum, eps=self.bn_eps)
blocks = []
for i, a in enumerate(block_args):
print(a) #FIXME debug
a['in_chs'] = _round_channels(a['in_chs'], depth_multiplier, depth_divisor, min_depth)
a['out_chs'] = _round_channels(a['out_chs'], depth_multiplier, depth_divisor, min_depth)
blocks.append(self._make_stack(**a))
out_chs = a['out_chs']
self.blocks = nn.Sequential(*blocks)
self.conv_head = nn.Conv2d(out_chs, self.num_features, 1, padding=0, stride=1, bias=False)
self.bn1 = nn.BatchNorm2d(self.num_features, momentum=self.bn_momentum, eps=self.bn_eps)
self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
self.classifier = nn.Linear(self.num_features, self.num_classes)
for m in self.modules():
_initialize_weight(m)
def _make_stack(self, in_chs, out_chs, kernel_size, stride,
exp_ratio, id_skip, se_ratio, num_repeat):
blocks = [MnasBlock(
in_chs, out_chs, kernel_size, stride, exp_ratio, id_skip, se_ratio,
bn_momentum=self.bn_momentum, bn_eps=self.bn_eps)]
for _ in range(1, num_repeat):
blocks += [MnasBlock(
out_chs, out_chs, kernel_size, 1, exp_ratio, id_skip, se_ratio,
bn_momentum=self.bn_momentum, bn_eps=self.bn_eps)]
return nn.Sequential(*blocks)
def get_classifier(self):
return self.classifier
def reset_classifier(self, num_classes, global_pool='avg'):
self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
self.num_classes = num_classes
del self.classifier
if num_classes:
self.classifier = nn.Linear(
self.num_features * self.global_pool.feat_mult(), num_classes)
else:
self.classifier = None
def forward_features(self, x, pool=True):
x = self.conv_stem(x)
x = self.bn0(x)
x = self.act_fn(x)
x = self.blocks(x)
x = self.conv_head(x)
x = self.bn1(x)
x = self.act_fn(x)
if pool:
x = self.global_pool(x)
x = x.view(x.size(0), -1)
return x
def forward(self, x):
x = self.forward_features(x)
if self.drop_rate > 0.:
x = F.dropout(x, p=self.drop_rate, training=self.training)
return self.classifier(x)
def mnasnet_a1(depth_multiplier, num_classes=1000, **kwargs):
"""Creates a mnasnet-a1 model.
Args:
depth_multiplier: multiplier to number of channels per layer.
"""
# defs from https://github.com/tensorflow/tpu/blob/master/models/official/mnasnet/mnasnet_models.py
block_defs = [
'r1_k3_s11_e1_i32_o16_noskip',
'r2_k3_s22_e6_i16_o24',
'r3_k5_s22_e3_i24_o40_se0.25',
'r4_k3_s22_e6_i40_o80',
'r2_k3_s11_e6_i80_o112_se0.25',
'r3_k5_s22_e6_i112_o160_se0.25',
'r1_k3_s11_e6_i160_o320'
]
block_args = _decode_block_args(block_defs)
model = MnasNet(
block_args,
num_classes=num_classes,
depth_multiplier=depth_multiplier,
depth_divisor=8,
min_depth=None,
stem_size=32,
bn_momentum=_BN_MOMENTUM_DEFAULT,
bn_eps=_BN_EPS_DEFAULT,
#drop_rate=0.2,
**kwargs
)
return model
def mnasnet_b1(depth_multiplier, num_classes=1000, **kwargs):
"""Creates a mnasnet-b1 model.
Args:
depth_multiplier: multiplier to number of channels per layer.
"""
# from https://github.com/tensorflow/tpu/blob/master/models/official/mnasnet/mnasnet_models.py
blocks_defs = [
'r1_k3_s11_e1_i32_o16_noskip',
'r3_k3_s22_e3_i16_o24',
'r3_k5_s22_e3_i24_o40',
'r3_k5_s22_e6_i40_o80',
'r2_k3_s11_e6_i80_o96',
'r4_k5_s22_e6_i96_o192',
'r1_k3_s11_e6_i192_o320_noskip'
]
block_args = _decode_block_args(blocks_defs)
model = MnasNet(
block_args,
num_classes=num_classes,
depth_multiplier=depth_multiplier,
depth_divisor=8,
min_depth=None,
stem_size=32,
bn_momentum=_BN_MOMENTUM_DEFAULT,
bn_eps=_BN_EPS_DEFAULT,
#drop_rate=0.2,
**kwargs
)
return model
def mnasnet_small(depth_multiplier, num_classes=1000, **kwargs):
"""Creates a mnasnet-b1 model.
Args:
depth_multiplier: multiplier to number of channels per layer.
"""
# from https://github.com/tensorflow/tpu/blob/master/models/official/mnasnet/mnasnet_models.py
blocks_defs = [
'r1_k3_s11_e1_i16_o8',
'r1_k3_s22_e3_i8_o16',
'r2_k3_s22_e6_i16_o16',
'r4_k5_s22_e6_i16_o32_se0.25',
'r3_k3_s11_e6_i32_o32_se0.25',
'r3_k5_s22_e6_i32_o88_se0.25',
'r1_k3_s11_e6_i88_o144'
]
block_args = _decode_block_args(blocks_defs)
model = MnasNet(
block_args,
num_classes=num_classes,
depth_multiplier=depth_multiplier,
depth_divisor=8,
min_depth=None,
stem_size=8,
bn_momentum=_BN_MOMENTUM_DEFAULT,
bn_eps=_BN_EPS_DEFAULT,
#drop_rate=0.2,
**kwargs
)
return model
def mnasnet0_50(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
""" MNASNet B1, depth multiplier of 0.5. """
default_cfg = default_cfgs['mnasnet0_50']
model = mnasnet_b1(0.5, 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 mnasnet0_75(num_classes, in_chans=3, pretrained=False, **kwargs):
""" MNASNet B1, depth multiplier of 0.75. """
default_cfg = default_cfgs['mnasnet0_50']
model = mnasnet_b1(0.75, 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_00(num_classes, in_chans=3, pretrained=False, **kwargs):
""" MNASNet B1, depth multiplier of 1.0. """
default_cfg = default_cfgs['mnasnet0_50']
model = 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['mnasnet0_50']
model = mnasnet_b1(1.4, 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 semnasnet0_50(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
""" MNASNet A1 (w/ SE), depth multiplier of 0.5 """
default_cfg = default_cfgs['mnasnet0_50']
model = mnasnet_a1(0.5, 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 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
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model
def semnasnet1_00(num_classes, in_chans=3, pretrained=False, **kwargs):
""" MNASNet A1 (w/ SE), 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
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['mnasnet0_50']
model = mnasnet_a1(1.4, 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 mnasnet_small(num_classes, in_chans=3, pretrained=False, **kwargs):
""" MNASNet Small, depth multiplier of 1.0. """
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
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model

21
models/model_factory.py
Unescape View File

@ -8,12 +8,21 @@ from models.senet import seresnet18, seresnet34, seresnet50, seresnet101, seresn
seresnext26_32x4d, seresnext50_32x4d, seresnext101_32x4d
from models.xception import xception
from models.pnasnet import pnasnet5large
from models.mnasnet import mnasnet0_50, mnasnet0_75, mnasnet1_00, mnasnet1_40,\
semnasnet0_50, semnasnet0_75, semnasnet1_00, semnasnet1_40, mnasnet_small
from models.genmobilenet import \
mnasnet0_50, mnasnet0_75, mnasnet1_00, mnasnet1_40,\
semnasnet0_50, semnasnet0_75, semnasnet1_00, semnasnet1_40, mnasnet_small,\
mobilenetv1_1_00, mobilenetv2_1_00, fbnetc_1_00, chamnetv1_1_00, chamnetv2_1_00
from models.helpers import load_checkpoint
def _is_genmobilenet(name):
genmobilenets = ['mnasnet', 'semnasnet', 'fbnet', 'chamnet', 'mobilenet']
if any([name.startswith(x) for x in genmobilenets]):
return True
return False
def create_model(
model_name='resnet50',
pretrained=None,
@ -24,6 +33,14 @@ def create_model(
margs = dict(num_classes=num_classes, in_chans=in_chans, pretrained=pretrained)
# Not all models have support for batchnorm params passed as args, only genmobilenet variants
# FIXME better way to do this without pushing support into every other model fn?
supports_bn_params = _is_genmobilenet(model_name)
if not supports_bn_params and any([x in kwargs for x in ['bn_tf', 'bn_momentum', 'bn_eps']]):
kwargs.pop('bn_tf', None)
kwargs.pop('bn_momentum', None)
kwargs.pop('bn_eps', None)
if model_name in globals():
create_fn = globals()[model_name]
model = create_fn(**margs, **kwargs)

1
optim/__init__.py
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@ -1,3 +1,4 @@
from optim.adabound import AdaBound
from optim.nadam import Nadam
from optim.rmsprop_tf import RMSpropTF
from optim.optim_factory import create_optimizer

6
optim/optim_factory.py
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@ -1,5 +1,5 @@
from torch import optim as optim
from optim import Nadam, AdaBound
from optim import Nadam, AdaBound, RMSpropTF
def create_optimizer(args, parameters):
@ -24,6 +24,10 @@ def create_optimizer(args, parameters):
optimizer = optim.RMSprop(
parameters, lr=args.lr, alpha=0.9, eps=args.opt_eps,
momentum=args.momentum, weight_decay=args.weight_decay)
elif args.opt.lower() == 'rmsproptf':
optimizer = RMSpropTF(
parameters, lr=args.lr, alpha=0.9, eps=args.opt_eps,
momentum=args.momentum, weight_decay=args.weight_decay)
else:
assert False and "Invalid optimizer"
raise ValueError

105
optim/rmsprop_tf.py
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@ -0,0 +1,105 @@
import torch
from torch.optim import Optimizer
class RMSpropTF(Optimizer):
"""Implements RMSprop algorithm (TensorFlow style epsilon)
NOTE: This is a direct cut-and-paste of PyTorch RMSprop with eps applied before sqrt
to closer match Tensorflow for matching hyper-params.
Proposed by G. Hinton in his
`course <http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf>`_.
The centered version first appears in `Generating Sequences
With Recurrent Neural Networks <https://arxiv.org/pdf/1308.0850v5.pdf>`_.
Arguments:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
lr (float, optional): learning rate (default: 1e-2)
momentum (float, optional): momentum factor (default: 0)
alpha (float, optional): smoothing constant (default: 0.99)
eps (float, optional): term added to the denominator to improve
numerical stability (default: 1e-8)
centered (bool, optional) : if ``True``, compute the centered RMSProp,
the gradient is normalized by an estimation of its variance
weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
"""
def __init__(self, params, lr=1e-2, alpha=0.99, eps=1e-8, weight_decay=0, momentum=0, centered=False):
if not 0.0 <= lr:
raise ValueError("Invalid learning rate: {}".format(lr))
if not 0.0 <= eps:
raise ValueError("Invalid epsilon value: {}".format(eps))
if not 0.0 <= momentum:
raise ValueError("Invalid momentum value: {}".format(momentum))
if not 0.0 <= weight_decay:
raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
if not 0.0 <= alpha:
raise ValueError("Invalid alpha value: {}".format(alpha))
defaults = dict(lr=lr, momentum=momentum, alpha=alpha, eps=eps, centered=centered, weight_decay=weight_decay)
super(RMSpropTF, self).__init__(params, defaults)
def __setstate__(self, state):
super(RMSpropTF, self).__setstate__(state)
for group in self.param_groups:
group.setdefault('momentum', 0)
group.setdefault('centered', False)
def step(self, closure=None):
"""Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
loss = None
if closure is not None:
loss = closure()
for group in self.param_groups:
for p in group['params']:
if p.grad is None:
continue
grad = p.grad.data
if grad.is_sparse:
raise RuntimeError('RMSprop does not support sparse gradients')
state = self.state[p]
# State initialization
if len(state) == 0:
state['step'] = 0
state['square_avg'] = torch.zeros_like(p.data)
if group['momentum'] > 0:
state['momentum_buffer'] = torch.zeros_like(p.data)
if group['centered']:
state['grad_avg'] = torch.zeros_like(p.data)
square_avg = state['square_avg']
alpha = group['alpha']
state['step'] += 1
if group['weight_decay'] != 0:
grad = grad.add(group['weight_decay'], p.data)
square_avg.mul_(alpha).addcmul_(1 - alpha, grad, grad)
if group['centered']:
grad_avg = state['grad_avg']
grad_avg.mul_(alpha).add_(1 - alpha, grad)
avg = square_avg.addcmul(-1, grad_avg, grad_avg).add(group['eps']).sqrt_()
else:
avg = square_avg.add(group['eps']).sqrt_()
if group['momentum'] > 0:
buf = state['momentum_buffer']
buf.mul_(group['momentum']).addcdiv_(grad, avg)
p.data.add_(-group['lr'], buf)
else:
p.data.addcdiv_(-group['lr'], grad, avg)
return loss

13
train.py
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@ -77,10 +77,16 @@ parser.add_argument('--warmup-lr', type=float, default=0.0001, metavar='LR',
help='warmup learning rate (default: 0.0001)')
parser.add_argument('--momentum', type=float, default=0.9, metavar='M',
help='SGD momentum (default: 0.9)')
parser.add_argument('--weight-decay', type=float, default=0.0001, metavar='M',
parser.add_argument('--weight-decay', type=float, default=0.0001,
help='weight decay (default: 0.0001)')
parser.add_argument('--smoothing', type=float, default=0.1, metavar='M',
parser.add_argument('--smoothing', type=float, default=0.1,
help='label smoothing (default: 0.1)')
parser.add_argument('--bn-tf', action='store_true', default=False,
help='Use Tensorflow BatchNorm defaults for models that support it (default: False)')
parser.add_argument('--bn-momentum', type=float, default=None,
help='BatchNorm momentum override (if not None)')
parser.add_argument('--bn-eps', type=float, default=None,
help='BatchNorm epsilon override (if not None)')
parser.add_argument('--seed', type=int, default=42, metavar='S',
help='random seed (default: 42)')
parser.add_argument('--log-interval', type=int, default=50, metavar='N',
@ -154,6 +160,9 @@ def main():
num_classes=args.num_classes,
drop_rate=args.drop,
global_pool=args.gp,
bn_tf=args.bn_tf,
bn_momentum=args.bn_momentum,
bn_eps=args.bn_eps,
checkpoint_path=args.initial_checkpoint)
print('Model %s created, param count: %d' %

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