""" Generic EfficientNets
A generic class with building blocks to support a variety of models with efficient architectures:
* EfficientNet (B0-B4 in code right now, work in progress, still verifying)
* MNasNet B1, A1 (SE), Small
* MobileNet V1, V2, and V3 (work in progress)
* FBNet-C (TODO A & B)
* ChamNet (TODO still guessing at architecture definition)
* Single-Path NAS Pixel1
* 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
import logging
from copy import deepcopy
import torch
import torch.nn as nn
import torch.nn.functional as F
from .registry import register_model
from .helpers import load_pretrained
from .adaptive_avgmax_pool import SelectAdaptivePool2d
from .conv2d_same import sconv2d
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
__all__ = ['GenEfficientNet']
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 = {
'mnasnet_050': _cfg(url=''),
'mnasnet_075': _cfg(url=''),
'mnasnet_100': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mnasnet_b1-74cb7081.pth',
interpolation='bicubic'),
'tflite_mnasnet_100': _cfg(
url='https://www.dropbox.com/s/q55ir3tx8mpeyol/tflite_mnasnet_100-31639cdc.pth?dl=1',
interpolation='bicubic'),
'mnasnet_140': _cfg(url=''),
'semnasnet_050': _cfg(url=''),
'semnasnet_075': _cfg(url=''),
'semnasnet_100': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mnasnet_a1-d9418771.pth',
interpolation='bicubic'),
'tflite_semnasnet_100': _cfg(
url='https://www.dropbox.com/s/yiori47sr9dydev/tflite_semnasnet_100-7c780429.pth?dl=1',
interpolation='bicubic'),
'semnasnet_140': _cfg(url=''),
'mnasnet_small': _cfg(url=''),
'mobilenetv1_100': _cfg(url=''),
'mobilenetv2_100': _cfg(url=''),
'mobilenetv3_050': _cfg(url=''),
'mobilenetv3_075': _cfg(url=''),
'mobilenetv3_100': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv3_100-35495452.pth',
interpolation='bicubic'),
'chamnetv1_100': _cfg(url=''),
'chamnetv2_100': _cfg(url=''),
'fbnetc_100': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/fbnetc_100-c345b898.pth'),
'spnasnet_100': _cfg(
url='https://www.dropbox.com/s/iieopt18rytkgaa/spnasnet_100-048bc3f4.pth?dl=1'),
'efficientnet_b0': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b0-d6904d92.pth',
interpolation='bicubic'),
'efficientnet_b1': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b1-533bc792.pth',
input_size=(3, 240, 240), pool_size=(8, 8), interpolation='bicubic', crop_pct=0.882),
'efficientnet_b2': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b2-cf78dc4d.pth',
input_size=(3, 260, 260), pool_size=(9, 9), interpolation='bicubic', crop_pct=0.890),
'efficientnet_b3': _cfg(
url='', input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904),
'efficientnet_b4': _cfg(
url='', input_size=(3, 380, 380), pool_size=(12, 12), crop_pct=0.922),
'efficientnet_b5': _cfg(
url='', input_size=(3, 456, 456), pool_size=(15, 15), crop_pct=0.934),
'tf_efficientnet_b0': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b0-0af12548.pth',
input_size=(3, 224, 224), interpolation='bicubic'),
'tf_efficientnet_b1': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b1-5c1377c4.pth',
input_size=(3, 240, 240), pool_size=(8, 8), interpolation='bicubic', crop_pct=0.882),
'tf_efficientnet_b2': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b2-e393ef04.pth',
input_size=(3, 260, 260), pool_size=(9, 9), interpolation='bicubic', crop_pct=0.890),
'tf_efficientnet_b3': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b3-e3bd6955.pth',
input_size=(3, 300, 300), pool_size=(10, 10), interpolation='bicubic', crop_pct=0.904),
'tf_efficientnet_b4': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b4-74ee3bed.pth',
input_size=(3, 380, 380), pool_size=(12, 12), interpolation='bicubic', crop_pct=0.922),
'tf_efficientnet_b5': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b5-c6949ce9.pth',
input_size=(3, 456, 456), pool_size=(15, 15), interpolation='bicubic', crop_pct=0.934)
}
_DEBUG = False
# 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)
# NOTE: momentum varies btw .99 and .9997 depending on source
# .99 in official TF TPU impl
# .9997 (/w .999 in search space) for paper
_BN_MOMENTUM_TF_DEFAULT = 1 - 0.99
_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, multiplier=1.0, divisor=8, channel_min=None):
"""Round number of filters based on depth multiplier."""
if not multiplier:
return channels
channels *= multiplier
channel_min = channel_min or divisor
new_channels = max(
int(channels + divisor / 2) // divisor * divisor,
channel_min)
# 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, depth_multiplier=1.0):
""" 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, cn = ConvBnAct)
r - number of repeat blocks,
k - kernel size,
s - strides (1-9),
e - expansion ratio,
c - output channels,
se - squeeze/excitation ratio
a - activation fn ('re', 'r6', or 'hs')
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 = {}
noskip = False
for op in ops:
# string options being checked on individual basis, combine if they grow
if op.startswith('a'):
# activation fn
key = op[0]
v = op[1:]
if v == 're':
value = F.relu
elif v == 'r6':
value = F.relu6
elif v == 'hs':
value = hard_swish
else:
continue
options[key] = value
elif op == 'noskip':
noskip = True
else:
# all numeric options
splits = re.split(r'(\d.*)', op)
if len(splits) >= 2:
key, value = splits[:2]
options[key] = value
# if act_fn is None, the model default (passed to model init) will be used
act_fn = options['a'] if 'a' in options else None
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=float(options['e']),
se_ratio=float(options['se']) if 'se' in options else None,
stride=int(options['s']),
act_fn=act_fn,
noskip=noskip,
)
if 'g' in options:
block_args['pw_group'] = options['g']
if options['g'] > 1:
block_args['shuffle_type'] = 'mid'
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']),
se_ratio=float(options['se']) if 'se' in options else None,
stride=int(options['s']),
act_fn=act_fn,
noskip=block_type == 'dsa' or noskip,
pw_act=block_type == 'dsa',
)
elif block_type == 'cn':
block_args = dict(
block_type=block_type,
kernel_size=int(options['k']),
out_chs=int(options['c']),
stride=int(options['s']),
act_fn=act_fn,
)
else:
assert False, 'Unknown block type (%s)' % block_type
# return a list of block args expanded by num_repeat and
# scaled by depth_multiplier
num_repeat = int(math.ceil(num_repeat * depth_multiplier))
return [deepcopy(block_args) for _ in range(num_repeat)]
def _get_padding(kernel_size, stride, dilation=1):
padding = ((stride - 1) + dilation * (kernel_size - 1)) // 2
return padding
def _padding_arg(default, padding_same=False):
return 'SAME' if padding_same else default
def _decode_arch_args(string_list):
block_args = []
for block_str in string_list:
block_args.append(_decode_block_str(block_str))
return block_args
def _decode_arch_def(arch_def, depth_multiplier=1.0):
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, depth_multiplier))
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, channel_multiplier=1.0, channel_divisor=8, channel_min=None,
drop_connect_rate=0., act_fn=None, se_gate_fn=torch.sigmoid, se_reduce_mid=False,
bn_momentum=_BN_MOMENTUM_PT_DEFAULT, bn_eps=_BN_EPS_PT_DEFAULT,
folded_bn=False, padding_same=False, verbose=False):
self.channel_multiplier = channel_multiplier
self.channel_divisor = channel_divisor
self.channel_min = channel_min
self.drop_connect_rate = drop_connect_rate
self.act_fn = act_fn
self.se_gate_fn = se_gate_fn
self.se_reduce_mid = se_reduce_mid
self.bn_momentum = bn_momentum
self.bn_eps = bn_eps
self.folded_bn = folded_bn
self.padding_same = padding_same
self.verbose = verbose
# updated during build
self.in_chs = None
self.block_idx = 0
self.block_count = 0
def _round_channels(self, chs):
return _round_channels(chs, self.channel_multiplier, self.channel_divisor, self.channel_min)
def _make_block(self, ba):
bt = ba.pop('block_type')
ba['in_chs'] = self.in_chs
ba['out_chs'] = self._round_channels(ba['out_chs'])
ba['bn_momentum'] = self.bn_momentum
ba['bn_eps'] = self.bn_eps
ba['folded_bn'] = self.folded_bn
ba['padding_same'] = self.padding_same
# block act fn overrides the model default
ba['act_fn'] = ba['act_fn'] if ba['act_fn'] is not None else self.act_fn
assert ba['act_fn'] is not None
if bt == 'ir':
ba['drop_connect_rate'] = self.drop_connect_rate * self.block_idx / self.block_count
ba['se_gate_fn'] = self.se_gate_fn
ba['se_reduce_mid'] = self.se_reduce_mid
if self.verbose:
logging.info(' InvertedResidual {}, Args: {}'.format(self.block_idx, str(ba)))
block = InvertedResidual(**ba)
elif bt == 'ds' or bt == 'dsa':
ba['drop_connect_rate'] = self.drop_connect_rate * self.block_idx / self.block_count
if self.verbose:
logging.info(' DepthwiseSeparable {}, Args: {}'.format(self.block_idx, str(ba)))
block = DepthwiseSeparableConv(**ba)
elif bt == 'cn':
if self.verbose:
logging.info(' ConvBnAct {}, Args: {}'.format(self.block_idx, str(ba)))
block = ConvBnAct(**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 i, ba in enumerate(stack_args):
if self.verbose:
logging.info(' Block: {}'.format(i))
if i >= 1:
# only the first block in any stack can have a stride > 1
ba['stride'] = 1
block = self._make_block(ba)
blocks.append(block)
self.block_idx += 1 # incr global idx (across all stacks)
return nn.Sequential(*blocks)
def __call__(self, in_chs, block_args):
""" Build the blocks
Args:
in_chs: Number of input-channels passed to first block
block_args: A list of lists, outer list defines stages, inner
list contains strings defining block configuration(s)
Return:
List of block stacks (each stack wrapped in nn.Sequential)
"""
if self.verbose:
logging.info('Building model trunk with %d stages...' % len(block_args))
self.in_chs = in_chs
self.block_count = sum([len(x) for x in block_args])
self.block_idx = 0
blocks = []
# outer list of block_args defines the stacks ('stages' by some conventions)
for stack_idx, stack in enumerate(block_args):
if self.verbose:
logging.info('Stack: {}'.format(stack_idx))
assert isinstance(stack, list)
stack = self._make_stack(stack)
blocks.append(stack)
return blocks
def _initialize_weight_goog(m):
# weight init as per Tensorflow Official impl
# https://github.com/tensorflow/tpu/blob/master/models/official/mnasnet/mnasnet_model.py
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels # fan-out
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(0) # fan-out
init_range = 1.0 / math.sqrt(n)
m.weight.data.uniform_(-init_range, init_range)
m.bias.data.zero_()
def _initialize_weight_default(m):
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1.0)
m.bias.data.zero_()
elif isinstance(m, nn.Linear):
nn.init.kaiming_uniform_(m.weight, mode='fan_in', nonlinearity='linear')
def swish(x):
return x * torch.sigmoid(x)
def hard_swish(x):
return x * F.relu6(x + 3.) / 6.
def hard_sigmoid(x):
return F.relu6(x + 3.) / 6.
def drop_connect(inputs, training=False, drop_connect_rate=0.):
"""Apply drop connect."""
if not training:
return inputs
keep_prob = 1 - drop_connect_rate
random_tensor = keep_prob + torch.rand(
(inputs.size()[0], 1, 1, 1), dtype=inputs.dtype, device=inputs.device)
random_tensor.floor_() # binarize
output = inputs.div(keep_prob) * random_tensor
return output
class ChannelShuffle(nn.Module):
# FIXME 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, reduce_chs=None, act_fn=F.relu, gate_fn=torch.sigmoid):
super(SqueezeExcite, self).__init__()
self.act_fn = act_fn
self.gate_fn = gate_fn
reduced_chs = reduce_chs or in_chs
self.conv_reduce = nn.Conv2d(in_chs, reduced_chs, 1, bias=True)
self.conv_expand = nn.Conv2d(reduced_chs, in_chs, 1, bias=True)
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 = self.gate_fn(x_se) * x
return x
class ConvBnAct(nn.Module):
def __init__(self, in_chs, out_chs, kernel_size,
stride=1, act_fn=F.relu,
bn_momentum=_BN_MOMENTUM_PT_DEFAULT, bn_eps=_BN_EPS_PT_DEFAULT,
folded_bn=False, padding_same=False):
super(ConvBnAct, self).__init__()
assert stride in [1, 2]
self.act_fn = act_fn
padding = _padding_arg(_get_padding(kernel_size, stride), padding_same)
self.conv = sconv2d(
in_chs, out_chs, kernel_size,
stride=stride, padding=padding, bias=folded_bn)
self.bn1 = None if folded_bn else nn.BatchNorm2d(out_chs, momentum=bn_momentum, eps=bn_eps)
def forward(self, x):
x = self.conv(x)
if self.bn1 is not None:
x = self.bn1(x)
x = self.act_fn(x)
return x
class DepthwiseSeparableConv(nn.Module):
""" DepthwiseSeparable block
Used for DS convs in MobileNet-V1 and in the place of IR blocks with an expansion
factor of 1.0. This is an alternative to having a IR with optional first pw conv.
"""
def __init__(self, in_chs, out_chs, kernel_size,
stride=1, act_fn=F.relu, noskip=False, pw_act=False,
se_ratio=0., se_gate_fn=torch.sigmoid,
bn_momentum=_BN_MOMENTUM_PT_DEFAULT, bn_eps=_BN_EPS_PT_DEFAULT,
folded_bn=False, padding_same=False, drop_connect_rate=0.):
super(DepthwiseSeparableConv, self).__init__()
assert stride in [1, 2]
self.has_se = se_ratio is not None and se_ratio > 0.
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.drop_connect_rate = drop_connect_rate
dw_padding = _padding_arg(kernel_size // 2, padding_same)
pw_padding = _padding_arg(0, padding_same)
self.conv_dw = sconv2d(
in_chs, in_chs, kernel_size,
stride=stride, padding=dw_padding, groups=in_chs, bias=folded_bn)
self.bn1 = None if folded_bn else nn.BatchNorm2d(in_chs, momentum=bn_momentum, eps=bn_eps)
# Squeeze-and-excitation
if self.has_se:
self.se = SqueezeExcite(
in_chs, reduce_chs=max(1, int(in_chs * se_ratio)), act_fn=act_fn, gate_fn=se_gate_fn)
self.conv_pw = sconv2d(in_chs, out_chs, 1, padding=pw_padding, bias=folded_bn)
self.bn2 = None if folded_bn else nn.BatchNorm2d(out_chs, momentum=bn_momentum, eps=bn_eps)
def forward(self, x):
residual = x
x = self.conv_dw(x)
if self.bn1 is not None:
x = self.bn1(x)
x = self.act_fn(x)
if self.has_se:
x = self.se(x)
x = self.conv_pw(x)
if self.bn2 is not None:
x = self.bn2(x)
if self.has_pw_act:
x = self.act_fn(x)
if self.has_residual:
if self.drop_connect_rate > 0.:
x = drop_connect(x, self.training, self.drop_connect_rate)
x += residual
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., se_reduce_mid=False, se_gate_fn=torch.sigmoid,
shuffle_type=None, pw_group=1,
bn_momentum=_BN_MOMENTUM_PT_DEFAULT, bn_eps=_BN_EPS_PT_DEFAULT,
folded_bn=False, padding_same=False, drop_connect_rate=0.):
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
self.drop_connect_rate = drop_connect_rate
dw_padding = _padding_arg(kernel_size // 2, padding_same)
pw_padding = _padding_arg(0, padding_same)
# Point-wise expansion
self.conv_pw = sconv2d(in_chs, mid_chs, 1, padding=pw_padding, groups=pw_group, bias=folded_bn)
self.bn1 = None if folded_bn else nn.BatchNorm2d(mid_chs, momentum=bn_momentum, eps=bn_eps)
self.shuffle_type = shuffle_type
if shuffle_type is not None:
self.shuffle = ChannelShuffle(pw_group)
# Depth-wise convolution
self.conv_dw = sconv2d(
mid_chs, mid_chs, kernel_size, padding=dw_padding, stride=stride, groups=mid_chs, bias=folded_bn)
self.bn2 = None if folded_bn else nn.BatchNorm2d(mid_chs, momentum=bn_momentum, eps=bn_eps)
# Squeeze-and-excitation
if self.has_se:
se_base_chs = mid_chs if se_reduce_mid else in_chs
self.se = SqueezeExcite(
mid_chs, reduce_chs=max(1, int(se_base_chs * se_ratio)), act_fn=act_fn, gate_fn=se_gate_fn)
# Point-wise linear projection
self.conv_pwl = sconv2d(mid_chs, out_chs, 1, padding=pw_padding, groups=pw_group, bias=folded_bn)
self.bn3 = None if folded_bn else nn.BatchNorm2d(out_chs, momentum=bn_momentum, eps=bn_eps)
def forward(self, x):
residual = x
# Point-wise expansion
x = self.conv_pw(x)
if self.bn1 is not None:
x = self.bn1(x)
x = self.act_fn(x)
# 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)
if self.bn2 is not None:
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)
if self.bn3 is not None:
x = self.bn3(x)
if self.has_residual:
if self.drop_connect_rate > 0.:
x = drop_connect(x, self.training, self.drop_connect_rate)
x += residual
# NOTE maskrcnn_benchmark building blocks have an SE module defined here for some variants
return x
class GenEfficientNet(nn.Module):
""" Generic EfficientNet
An implementation of efficient network architectures, in many cases mobile optimized networks:
* MobileNet-V1
* MobileNet-V2
* MobileNet-V3
* MNASNet A1, B1, and small
* FBNet A, B, and C
* ChamNet (arch details are murky)
* Single-Path NAS Pixel1
* EfficientNetB0-B4 (rest easy to add)
"""
def __init__(self, block_args, num_classes=1000, in_chans=3, stem_size=32, num_features=1280,
channel_multiplier=1.0, channel_divisor=8, channel_min=None,
bn_momentum=_BN_MOMENTUM_PT_DEFAULT, bn_eps=_BN_EPS_PT_DEFAULT,
drop_rate=0., drop_connect_rate=0., act_fn=F.relu,
se_gate_fn=torch.sigmoid, se_reduce_mid=False,
global_pool='avg', head_conv='default', weight_init='goog',
folded_bn=False, padding_same=False,):
super(GenEfficientNet, self).__init__()
self.num_classes = num_classes
self.drop_rate = drop_rate
self.act_fn = act_fn
self.num_features = num_features
stem_size = _round_channels(stem_size, channel_multiplier, channel_divisor, channel_min)
self.conv_stem = sconv2d(
in_chans, stem_size, 3,
padding=_padding_arg(1, padding_same), stride=2, bias=folded_bn)
self.bn1 = None if folded_bn else nn.BatchNorm2d(stem_size, momentum=bn_momentum, eps=bn_eps)
in_chs = stem_size
builder = _BlockBuilder(
channel_multiplier, channel_divisor, channel_min,
drop_connect_rate, act_fn, se_gate_fn, se_reduce_mid,
bn_momentum, bn_eps, folded_bn, padding_same, verbose=_DEBUG)
self.blocks = nn.Sequential(*builder(in_chs, block_args))
in_chs = builder.in_chs
if not head_conv or head_conv == 'none':
self.efficient_head = False
self.conv_head = None
assert in_chs == self.num_features
else:
self.efficient_head = head_conv == 'efficient'
self.conv_head = sconv2d(
in_chs, self.num_features, 1,
padding=_padding_arg(0, padding_same), bias=folded_bn and not self.efficient_head)
self.bn2 = None if (folded_bn or self.efficient_head) else \
nn.BatchNorm2d(self.num_features, momentum=bn_momentum, eps=bn_eps)
self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
self.classifier = nn.Linear(self.num_features * self.global_pool.feat_mult(), self.num_classes)
for m in self.modules():
if weight_init == 'goog':
_initialize_weight_goog(m)
else:
_initialize_weight_default(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)
if self.bn1 is not None:
x = self.bn1(x)
x = self.act_fn(x)
x = self.blocks(x)
if self.efficient_head:
# efficient head, currently only mobilenet-v3 performs pool before last 1x1 conv
x = self.global_pool(x) # always need to pool here regardless of flag
x = self.conv_head(x)
# no BN
x = self.act_fn(x)
if pool:
# expect flattened output if pool is true, otherwise keep dim
x = x.view(x.size(0), -1)
else:
if self.conv_head is not None:
x = self.conv_head(x)
if self.bn2 is not None:
x = self.bn2(x)
x = self.act_fn(x)
if pool:
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(channel_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:
channel_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 = GenEfficientNet(
_decode_arch_def(arch_def),
num_classes=num_classes,
stem_size=32,
channel_multiplier=channel_multiplier,
channel_divisor=8,
channel_min=None,
bn_momentum=bn_momentum,
bn_eps=bn_eps,
**kwargs
)
return model
def _gen_mnasnet_b1(channel_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:
channel_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 = GenEfficientNet(
_decode_arch_def(arch_def),
num_classes=num_classes,
stem_size=32,
channel_multiplier=channel_multiplier,
channel_divisor=8,
channel_min=None,
bn_momentum=bn_momentum,
bn_eps=bn_eps,
**kwargs
)
return model
def _gen_mnasnet_small(channel_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:
channel_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 = GenEfficientNet(
_decode_arch_def(arch_def),
num_classes=num_classes,
stem_size=8,
channel_multiplier=channel_multiplier,
channel_divisor=8,
channel_min=None,
bn_momentum=bn_momentum,
bn_eps=bn_eps,
**kwargs
)
return model
def _gen_mobilenet_v1(channel_multiplier, num_classes=1000, **kwargs):
""" Generate MobileNet-V1 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 = [
['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 = GenEfficientNet(
_decode_arch_def(arch_def),
num_classes=num_classes,
stem_size=32,
num_features=1024,
channel_multiplier=channel_multiplier,
channel_divisor=8,
channel_min=None,
bn_momentum=bn_momentum,
bn_eps=bn_eps,
act_fn=F.relu6,
head_conv='none',
**kwargs
)
return model
def _gen_mobilenet_v2(channel_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 = GenEfficientNet(
_decode_arch_def(arch_def),
num_classes=num_classes,
stem_size=32,
channel_multiplier=channel_multiplier,
channel_divisor=8,
channel_min=None,
bn_momentum=bn_momentum,
bn_eps=bn_eps,
act_fn=F.relu6,
**kwargs
)
return model
def _gen_mobilenet_v3(channel_multiplier, num_classes=1000, **kwargs):
"""Creates a MobileNet-V3 model.
Ref impl: ?
Paper: https://arxiv.org/abs/1905.02244
Args:
channel_multiplier: multiplier to number of channels per layer.
"""
arch_def = [
# stage 0, 112x112 in
['ds_r1_k3_s1_e1_c16_are_noskip'], # relu
# stage 1, 112x112 in
['ir_r1_k3_s2_e4_c24_are', 'ir_r1_k3_s1_e3_c24_are'], # relu
# stage 2, 56x56 in
['ir_r3_k5_s2_e3_c40_se0.25_are'], # relu
# stage 3, 28x28 in
['ir_r1_k3_s2_e6_c80', 'ir_r1_k3_s1_e2.5_c80', 'ir_r2_k3_s1_e2.3_c80'], # hard-swish
# stage 4, 14x14in
['ir_r2_k3_s1_e6_c112_se0.25'], # hard-swish
# stage 5, 14x14in
['ir_r3_k5_s2_e6_c160_se0.25'], # hard-swish
# stage 6, 7x7 in
['cn_r1_k1_s1_c960'], # hard-swish
]
bn_momentum, bn_eps = _resolve_bn_params(kwargs)
model = GenEfficientNet(
_decode_arch_def(arch_def),
num_classes=num_classes,
stem_size=16,
channel_multiplier=channel_multiplier,
channel_divisor=8,
channel_min=None,
bn_momentum=bn_momentum,
bn_eps=bn_eps,
act_fn=hard_swish,
se_gate_fn=hard_sigmoid,
se_reduce_mid=True,
head_conv='efficient',
**kwargs
)
return model
def _gen_chamnet_v1(channel_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 = GenEfficientNet(
_decode_arch_def(arch_def),
num_classes=num_classes,
stem_size=32,
num_features=1280, # no idea what this is? try mobile/mnasnet default?
channel_multiplier=channel_multiplier,
channel_divisor=8,
channel_min=None,
bn_momentum=bn_momentum,
bn_eps=bn_eps,
**kwargs
)
return model
def _gen_chamnet_v2(channel_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 = GenEfficientNet(
_decode_arch_def(arch_def),
num_classes=num_classes,
stem_size=32,
num_features=1280, # no idea what this is? try mobile/mnasnet default?
channel_multiplier=channel_multiplier,
channel_divisor=8,
channel_min=None,
bn_momentum=bn_momentum,
bn_eps=bn_eps,
**kwargs
)
return model
def _gen_fbnetc(channel_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 = GenEfficientNet(
_decode_arch_def(arch_def),
num_classes=num_classes,
stem_size=16,
num_features=1984, # paper suggests this, but is not 100% clear
channel_multiplier=channel_multiplier,
channel_divisor=8,
channel_min=None,
bn_momentum=bn_momentum,
bn_eps=bn_eps,
**kwargs
)
return model
def _gen_spnasnet(channel_multiplier, num_classes=1000, **kwargs):
"""Creates the Single-Path NAS model from search targeted for Pixel1 phone.
Paper: https://arxiv.org/abs/1904.02877
Args:
channel_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_r1_k5_s2_e6_c40', 'ir_r3_k3_s1_e3_c40'],
# stage 3, 28x28 in
['ir_r1_k5_s2_e6_c80', 'ir_r3_k3_s1_e3_c80'],
# stage 4, 14x14in
['ir_r1_k5_s1_e6_c96', 'ir_r3_k5_s1_e3_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 = GenEfficientNet(
_decode_arch_def(arch_def),
num_classes=num_classes,
stem_size=32,
channel_multiplier=channel_multiplier,
channel_divisor=8,
channel_min=None,
bn_momentum=bn_momentum,
bn_eps=bn_eps,
**kwargs
)
return model
def _gen_efficientnet(channel_multiplier=1.0, depth_multiplier=1.0, num_classes=1000, **kwargs):
"""Creates an EfficientNet model.
Ref impl: https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/efficientnet_model.py
Paper: https://arxiv.org/abs/1905.11946
EfficientNet params
name: (channel_multiplier, depth_multiplier, resolution, dropout_rate)
'efficientnet-b0': (1.0, 1.0, 224, 0.2),
'efficientnet-b1': (1.0, 1.1, 240, 0.2),
'efficientnet-b2': (1.1, 1.2, 260, 0.3),
'efficientnet-b3': (1.2, 1.4, 300, 0.3),
'efficientnet-b4': (1.4, 1.8, 380, 0.4),
'efficientnet-b5': (1.6, 2.2, 456, 0.4),
'efficientnet-b6': (1.8, 2.6, 528, 0.5),
'efficientnet-b7': (2.0, 3.1, 600, 0.5),
Args:
channel_multiplier: multiplier to number of channels per layer
depth_multiplier: multiplier to number of repeats per stage
"""
arch_def = [
['ds_r1_k3_s1_e1_c16_se0.25'],
['ir_r2_k3_s2_e6_c24_se0.25'],
['ir_r2_k5_s2_e6_c40_se0.25'],
['ir_r3_k3_s2_e6_c80_se0.25'],
['ir_r3_k5_s1_e6_c112_se0.25'],
['ir_r4_k5_s2_e6_c192_se0.25'],
['ir_r1_k3_s1_e6_c320_se0.25'],
]
bn_momentum, bn_eps = _resolve_bn_params(kwargs)
# NOTE: other models in the family didn't scale the feature count
num_features = _round_channels(1280, channel_multiplier, 8, None)
model = GenEfficientNet(
_decode_arch_def(arch_def, depth_multiplier),
num_classes=num_classes,
stem_size=32,
channel_multiplier=channel_multiplier,
channel_divisor=8,
channel_min=None,
num_features=num_features,
bn_momentum=bn_momentum,
bn_eps=bn_eps,
act_fn=swish,
**kwargs
)
return model
@register_model
def mnasnet_050(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
""" MNASNet B1, depth multiplier of 0.5. """
default_cfg = default_cfgs['mnasnet_050']
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
@register_model
def mnasnet_075(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
""" MNASNet B1, depth multiplier of 0.75. """
default_cfg = default_cfgs['mnasnet_075']
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
@register_model
def mnasnet_100(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
""" MNASNet B1, depth multiplier of 1.0. """
default_cfg = default_cfgs['mnasnet_100']
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
@register_model
def mnasnet_b1(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
""" MNASNet B1, depth multiplier of 1.0. """
return mnasnet_100(pretrained, num_classes, in_chans, **kwargs)
@register_model
def tflite_mnasnet_100(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
""" MNASNet B1, depth multiplier of 1.0. """
default_cfg = default_cfgs['tflite_mnasnet_100']
# these two args are for compat with tflite pretrained weights
kwargs['folded_bn'] = True
kwargs['padding_same'] = True
model = _gen_mnasnet_b1(1.0, num_classes=num_classes, in_chans=in_chans, **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model
@register_model
def mnasnet_140(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
""" MNASNet B1, depth multiplier of 1.4 """
default_cfg = default_cfgs['mnasnet_140']
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
@register_model
def semnasnet_050(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
""" MNASNet A1 (w/ SE), depth multiplier of 0.5 """
default_cfg = default_cfgs['semnasnet_050']
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
@register_model
def semnasnet_075(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
""" MNASNet A1 (w/ SE), depth multiplier of 0.75. """
default_cfg = default_cfgs['semnasnet_075']
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
@register_model
def semnasnet_100(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
""" MNASNet A1 (w/ SE), depth multiplier of 1.0. """
default_cfg = default_cfgs['semnasnet_100']
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
@register_model
def mnasnet_a1(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
""" MNASNet A1 (w/ SE), depth multiplier of 1.0. """
return semnasnet_100(pretrained, num_classes, in_chans, **kwargs)
@register_model
def tflite_semnasnet_100(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
""" MNASNet A1, depth multiplier of 1.0. """
default_cfg = default_cfgs['tflite_semnasnet_100']
# these two args are for compat with tflite pretrained weights
kwargs['folded_bn'] = True
kwargs['padding_same'] = True
model = _gen_mnasnet_a1(1.0, num_classes=num_classes, in_chans=in_chans, **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model
@register_model
def semnasnet_140(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
""" MNASNet A1 (w/ SE), depth multiplier of 1.4. """
default_cfg = default_cfgs['semnasnet_140']
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
@register_model
def mnasnet_small(pretrained=False, num_classes=1000, in_chans=3, **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
@register_model
def mobilenetv1_100(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
""" MobileNet V1 """
default_cfg = default_cfgs['mobilenetv1_100']
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
@register_model
def mobilenetv2_100(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
""" MobileNet V2 """
default_cfg = default_cfgs['mobilenetv2_100']
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
@register_model
def mobilenetv3_050(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
""" MobileNet V3 """
default_cfg = default_cfgs['mobilenetv3_050']
model = _gen_mobilenet_v3(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
@register_model
def mobilenetv3_075(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
""" MobileNet V3 """
default_cfg = default_cfgs['mobilenetv3_075']
model = _gen_mobilenet_v3(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
@register_model
def mobilenetv3_100(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
""" MobileNet V3 """
default_cfg = default_cfgs['mobilenetv3_100']
if pretrained:
# pretrained model trained with non-default BN epsilon
kwargs['bn_eps'] = _BN_EPS_TF_DEFAULT
model = _gen_mobilenet_v3(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
@register_model
def fbnetc_100(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
""" FBNet-C """
default_cfg = default_cfgs['fbnetc_100']
if pretrained:
# pretrained model trained with non-default BN epsilon
kwargs['bn_eps'] = _BN_EPS_TF_DEFAULT
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
@register_model
def chamnetv1_100(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
""" ChamNet """
default_cfg = default_cfgs['chamnetv1_100']
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
@register_model
def chamnetv2_100(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
""" ChamNet """
default_cfg = default_cfgs['chamnetv2_100']
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
@register_model
def spnasnet_100(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
""" Single-Path NAS Pixel1"""
default_cfg = default_cfgs['spnasnet_100']
model = _gen_spnasnet(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
@register_model
def efficientnet_b0(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
""" EfficientNet-B0 """
default_cfg = default_cfgs['efficientnet_b0']
# NOTE for train, drop_rate should be 0.2
#kwargs['drop_connect_rate'] = 0.2 # set when training, TODO add as cmd arg
model = _gen_efficientnet(
channel_multiplier=1.0, depth_multiplier=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
@register_model
def efficientnet_b1(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
""" EfficientNet-B1 """
default_cfg = default_cfgs['efficientnet_b1']
# NOTE for train, drop_rate should be 0.2
kwargs['drop_connect_rate'] = 0.2 # set when training, TODO add as cmd arg
model = _gen_efficientnet(
channel_multiplier=1.0, depth_multiplier=1.1,
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
@register_model
def efficientnet_b2(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
""" EfficientNet-B2 """
default_cfg = default_cfgs['efficientnet_b2']
# NOTE for train, drop_rate should be 0.3
kwargs['drop_connect_rate'] = 0.2 # set when training, TODO add as cmd arg
model = _gen_efficientnet(
channel_multiplier=1.1, depth_multiplier=1.2,
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
@register_model
def efficientnet_b3(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
""" EfficientNet-B3 """
default_cfg = default_cfgs['efficientnet_b3']
# NOTE for train, drop_rate should be 0.3
#kwargs['drop_connect_rate'] = 0.2 # set when training, TODO add as cmd arg
model = _gen_efficientnet(
channel_multiplier=1.2, depth_multiplier=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
@register_model
def efficientnet_b4(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
""" EfficientNet-B4 """
default_cfg = default_cfgs['efficientnet_b4']
# NOTE for train, drop_rate should be 0.4
#kwargs['drop_connect_rate'] = 0.2 # set when training, TODO add as cmd arg
model = _gen_efficientnet(
channel_multiplier=1.4, depth_multiplier=1.8,
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
@register_model
def efficientnet_b5(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
""" EfficientNet-B5 """
# NOTE for train, drop_rate should be 0.4
#kwargs['drop_connect_rate'] = 0.2 # set when training, TODO add as cmd arg
default_cfg = default_cfgs['efficientnet_b5']
model = _gen_efficientnet(
channel_multiplier=1.6, depth_multiplier=2.2,
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
@register_model
def tf_efficientnet_b0(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
""" EfficientNet-B0. Tensorflow compatible variant """
default_cfg = default_cfgs['tf_efficientnet_b0']
kwargs['bn_eps'] = _BN_EPS_TF_DEFAULT
kwargs['padding_same'] = True
model = _gen_efficientnet(
channel_multiplier=1.0, depth_multiplier=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
@register_model
def tf_efficientnet_b1(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
""" EfficientNet-B1. Tensorflow compatible variant """
default_cfg = default_cfgs['tf_efficientnet_b1']
kwargs['bn_eps'] = _BN_EPS_TF_DEFAULT
kwargs['padding_same'] = True
model = _gen_efficientnet(
channel_multiplier=1.0, depth_multiplier=1.1,
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
@register_model
def tf_efficientnet_b2(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
""" EfficientNet-B2. Tensorflow compatible variant """
default_cfg = default_cfgs['tf_efficientnet_b2']
kwargs['bn_eps'] = _BN_EPS_TF_DEFAULT
kwargs['padding_same'] = True
model = _gen_efficientnet(
channel_multiplier=1.1, depth_multiplier=1.2,
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
@register_model
def tf_efficientnet_b3(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
""" EfficientNet-B3. Tensorflow compatible variant """
default_cfg = default_cfgs['tf_efficientnet_b3']
kwargs['bn_eps'] = _BN_EPS_TF_DEFAULT
kwargs['padding_same'] = True
model = _gen_efficientnet(
channel_multiplier=1.2, depth_multiplier=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
@register_model
def tf_efficientnet_b4(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
""" EfficientNet-B4. Tensorflow compatible variant """
default_cfg = default_cfgs['tf_efficientnet_b4']
kwargs['bn_eps'] = _BN_EPS_TF_DEFAULT
kwargs['padding_same'] = True
model = _gen_efficientnet(
channel_multiplier=1.4, depth_multiplier=1.8,
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
@register_model
def tf_efficientnet_b5(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
""" EfficientNet-B5. Tensorflow compatible variant """
default_cfg = default_cfgs['tf_efficientnet_b5']
kwargs['bn_eps'] = _BN_EPS_TF_DEFAULT
kwargs['padding_same'] = True
model = _gen_efficientnet(
channel_multiplier=1.6, depth_multiplier=2.2,
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 gen_efficientnet_model_names():
return set(_models)