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Add MxNet Gluon ResNet variants w/ converted pretrained weights. Very well trained set of models.

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pull/2/head
Ross Wightman 5 years ago
parent 2da0b4dbc1
commit 7419e9835f
3 changed files with 795 additions and 0 deletions
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107
convert/convert_from_mxnet.py
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import argparse
import hashlib
import os
import mxnet as mx
import gluoncv
import torch
from models.model_factory import create_model
parser = argparse.ArgumentParser(description='Training')
parser.add_argument('--model', default='all', type=str, metavar='MODEL',
help='Name of model to train (default: "all"')
def convert(mxnet_name, torch_name):
# download and load the pre-trained model
net = gluoncv.model_zoo.get_model(mxnet_name, pretrained=True)
# create corresponding torch model
torch_net = create_model(torch_name)
mxp = [(k, v) for k, v in net.collect_params().items() if 'running' not in k]
torchp = list(torch_net.named_parameters())
torch_params = {}
# convert parameters
# NOTE: we are relying on the fact that the order of parameters
# are usually exactly the same between these models, thus no key name mapping
# is necessary. Asserts will trip if this is not the case.
for (tn, tv), (mn, mv) in zip(torchp, mxp):
m_split = mn.split('_')
t_split = tn.split('.')
print(t_split, m_split)
print(tv.shape, mv.shape)
# ensure ordering of BN params match since their sizes are not specific
if m_split[-1] == 'gamma':
assert t_split[-1] == 'weight'
if m_split[-1] == 'beta':
assert t_split[-1] == 'bias'
# ensure shapes match
assert all(t == m for t, m in zip(tv.shape, mv.shape))
torch_tensor = torch.from_numpy(mv.data().asnumpy())
torch_params[tn] = torch_tensor
# convert buffers (batch norm running stats)
mxb = [(k, v) for k, v in net.collect_params().items() if any(x in k for x in ['running_mean', 'running_var'])]
torchb = [(k, v) for k, v in torch_net.named_buffers() if 'num_batches' not in k]
for (tn, tv), (mn, mv) in zip(torchb, mxb):
print(tn, mn)
print(tv.shape, mv.shape)
# ensure ordering of BN params match since their sizes are not specific
if 'running_var' in tn:
assert 'running_var' in mn
if 'running_mean' in tn:
assert 'running_mean' in mn
torch_tensor = torch.from_numpy(mv.data().asnumpy())
torch_params[tn] = torch_tensor
torch_net.load_state_dict(torch_params)
torch_filename = './%s.pth' % torch_name
torch.save(torch_net.state_dict(), torch_filename)
with open(torch_filename, 'rb') as f:
sha_hash = hashlib.sha256(f.read()).hexdigest()
final_filename = os.path.splitext(torch_filename)[0] + '-' + sha_hash[:8] + '.pth'
os.rename(torch_filename, final_filename)
print("=> Saved converted model to '{}, SHA256: {}'".format(final_filename, sha_hash))
def map_mx_to_torch_model(mx_name):
torch_name = mx_name.lower()
if torch_name.startswith('se_'):
torch_name = torch_name.replace('se_', 'se')
elif torch_name.startswith('senet_'):
torch_name = torch_name.replace('senet_', 'senet')
elif torch_name.startswith('inceptionv3'):
torch_name = torch_name.replace('inceptionv3', 'inception_v3')
torch_name = 'gluon_' + torch_name
return torch_name
ALL = ['resnet18_v1b', 'resnet34_v1b', 'resnet50_v1b', 'resnet101_v1b', 'resnet152_v1b',
'resnet50_v1c', 'resnet101_v1c', 'resnet152_v1c', 'resnet50_v1d', 'resnet101_v1d', 'resnet152_v1d',
#'resnet50_v1e', 'resnet101_v1e', 'resnet152_v1e',
'resnet50_v1s', 'resnet101_v1s', 'resnet152_v1s', 'resnext50_32x4d', 'resnext101_32x4d', 'resnext101_64x4d',
'se_resnext50_32x4d', 'se_resnext101_32x4d', 'se_resnext101_64x4d', 'senet_154', 'inceptionv3']
def main():
args = parser.parse_args()
if not args.model or args.model == 'all':
for mx_model in ALL:
torch_model = map_mx_to_torch_model(mx_model)
convert(mx_model, torch_model)
else:
mx_model = args.model
torch_model = map_mx_to_torch_model(mx_model)
convert(mx_model, torch_model)
if __name__ == '__main__':
main()

680
models/gluon_resnet.py
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"""Pytorch ResNet implementation w/ tweaks
This file is a copy of https://github.com/pytorch/vision 'resnet.py' (BSD-3-Clause) with
additional dropout and dynamic global avg/max pool.
ResNext additions added by Ross Wightman
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from models.helpers import load_pretrained
from models.adaptive_avgmax_pool import SelectAdaptivePool2d
from data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
__all__ = ['GluonResNet', 'gluon_resnet18_v1b', 'gluon_resnet34_v1b', 'gluon_resnet50_v1b', 'gluon_resnet101_v1b',
'gluon_resnet152_v1b', 'gluon_resnet50_v1c', 'gluon_resnet101_v1c', 'gluon_resnet152_v1c', 'gluon_resnet50_v1d',
'gluon_resnet101_v1d', 'gluon_resnet152_v1d', 'gluon_resnet50_v1e', 'gluon_resnet101_v1e', 'gluon_resnet152_v1e',
'gluon_resnet50_v1s', 'gluon_resnet101_v1s', 'gluon_resnet152_v1s', 'gluon_resnext50_32x4d',
'gluon_resnext101_32x4d', 'gluon_resnext101_64x4d', 'gluon_resnext152_32x4d', 'gluon_seresnext50_32x4d',
'gluon_seresnext101_32x4d', 'gluon_seresnext101_64x4d', 'gluon_seresnext152_32x4d', 'gluon_senet154'
]
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
'crop_pct': 0.875, 'interpolation': 'bicubic',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'conv1', 'classifier': 'fc',
**kwargs
}
default_cfgs = {
'gluon_resnet18_v1b': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet18_v1b-0757602b.pth'),
'gluon_resnet34_v1b': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet34_v1b-c6d82d59.pth'),
'gluon_resnet50_v1b': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1b-0ebe02e2.pth'),
'gluon_resnet101_v1b': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1b-3b017079.pth'),
'gluon_resnet152_v1b': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1b-c1edb0dd.pth'),
'gluon_resnet50_v1c': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1c-48092f55.pth'),
'gluon_resnet101_v1c': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1c-1f26822a.pth'),
'gluon_resnet152_v1c': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1c-a3bb0b98.pth'),
'gluon_resnet50_v1d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1d-818a1b1b.pth'),
'gluon_resnet101_v1d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1d-0f9c8644.pth'),
'gluon_resnet152_v1d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1d-bd354e12.pth'),
'gluon_resnet50_v1e': _cfg(url=''),
'gluon_resnet101_v1e': _cfg(url=''),
'gluon_resnet152_v1e': _cfg(url=''),
'gluon_resnet50_v1s': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1s-1762acc0.pth'),
'gluon_resnet101_v1s': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1s-60fe0cc1.pth'),
'gluon_resnet152_v1s': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1s-dcc41b81.pth'),
'gluon_resnext50_32x4d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnext50_32x4d-e6a097c1.pth'),
'gluon_resnext101_32x4d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnext101_32x4d-b253c8c4.pth'),
'gluon_resnext101_64x4d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnext101_64x4d-f9a8e184.pth'),
'gluon_resnext152_32x4d': _cfg(url=''),
'gluon_seresnext50_32x4d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_seresnext50_32x4d-90cf2d6e.pth'),
'gluon_seresnext101_32x4d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_seresnext101_32x4d-cf52900d.pth'),
'gluon_seresnext101_64x4d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_seresnext101_64x4d-f9926f93.pth'),
'gluon_seresnext152_32x4d': _cfg(url=''),
'gluon_senet154': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_senet154-70a1a3c0.pth'),
}
def _get_padding(kernel_size, stride, dilation=1):
padding = ((stride - 1) + dilation * (kernel_size - 1)) // 2
return padding
class SEModule(nn.Module):
def __init__(self, channels, reduction_channels):
super(SEModule, self).__init__()
#self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc1 = nn.Conv2d(
channels, reduction_channels, kernel_size=1, padding=0, bias=True)
self.relu = nn.ReLU()
self.fc2 = nn.Conv2d(
reduction_channels, channels, kernel_size=1, padding=0, bias=True)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
module_input = x
#x = self.avg_pool(x)
x = x.view(x.size(0), x.size(1), -1).mean(-1).view(x.size(0), x.size(1), 1, 1)
x = self.fc1(x)
x = self.relu(x)
x = self.fc2(x)
x = self.sigmoid(x)
return module_input * x
class BasicBlockGl(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None,
cardinality=1, base_width=64, use_se=False,
reduce_first=1, dilation=1, previous_dilation=1, norm_layer=nn.BatchNorm2d):
super(BasicBlockGl, self).__init__()
assert cardinality == 1, 'BasicBlock only supports cardinality of 1'
assert base_width == 64, 'BasicBlock doest not support changing base width'
first_planes = planes // reduce_first
outplanes = planes * self.expansion
self.conv1 = nn.Conv2d(
inplanes, first_planes, kernel_size=3, stride=stride, padding=dilation,
dilation=dilation, bias=False)
self.bn1 = norm_layer(first_planes)
self.relu = nn.ReLU()
self.conv2 = nn.Conv2d(
first_planes, outplanes, kernel_size=3, padding=previous_dilation,
dilation=previous_dilation, bias=False)
self.bn2 = norm_layer(outplanes)
self.se = SEModule(outplanes, planes // 4) if use_se else None
self.downsample = downsample
self.stride = stride
self.dilation = dilation
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.se is not None:
out = self.se(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class BottleneckGl(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None,
cardinality=1, base_width=64, use_se=False,
reduce_first=1, dilation=1, previous_dilation=1, norm_layer=nn.BatchNorm2d):
super(BottleneckGl, self).__init__()
width = int(math.floor(planes * (base_width / 64)) * cardinality)
first_planes = width // reduce_first
outplanes = planes * self.expansion
self.conv1 = nn.Conv2d(inplanes, first_planes, kernel_size=1, bias=False)
self.bn1 = norm_layer(first_planes)
self.conv2 = nn.Conv2d(
first_planes, width, kernel_size=3, stride=stride,
padding=dilation, dilation=dilation, groups=cardinality, bias=False)
self.bn2 = norm_layer(width)
self.conv3 = nn.Conv2d(width, outplanes, kernel_size=1, bias=False)
self.bn3 = norm_layer(outplanes)
self.se = SEModule(outplanes, planes // 4) if use_se else None
self.relu = nn.ReLU()
self.downsample = downsample
self.stride = stride
self.dilation = dilation
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.se is not None:
out = self.se(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class GluonResNet(nn.Module):
""" Gluon ResNet (https://gluon-cv.mxnet.io/model_zoo/classification.html)
This class implements all variants of ResNet, ResNeXt, SE-ResNeXt, and SENet found in the gluon model zoo that
* have stride in 3x3 conv layer of bottleneck
* have conv-bn-act ordering
Included ResNet variants are:
* v1b - 7x7 stem, stem_width=64, same as torchvision ResNet (checkpoint compatible), or NVIDIA ResNet 'v1.5'
* v1c - 3 layer deep 3x3 stem, stem_width = 32
* v1d - 3 layer deep 3x3 stem, stem_width = 32, average pool in downsample
* v1e - 3 layer deep 3x3 stem, stem_width = 64, average pool in downsample *no pretrained weights available
* v1s - 3 layer deep 3x3 stem, stem_width = 64
ResNeXt is standard and checkpoint compatible with torchvision pretrained models. 7x7 stem,
stem_width = 64, standard cardinality and base width calcs
SE-ResNeXt is standard. 7x7 stem, stem_width = 64,
checkpoints are not compatible with Cadene pretrained, but could be with key mapping
SENet-154 is standard. 3 layer deep 3x3 stem (same as v1c-v1s), stem_width = 64, cardinality=64,
reduction by 2 on width of first bottleneck convolution, 3x3 downsample convs after first block
Original ResNet-V1, ResNet-V2 (bn-act-conv), and SE-ResNet (stride in first bottleneck conv) are NOT supported.
They do have Gluon pretrained weights but are, at best, comparable (or inferior) to the supported models.
Parameters
----------
block : Block
Class for the residual block. Options are BasicBlockGl, BottleneckGl.
layers : list of int
Numbers of layers in each block
num_classes : int, default 1000
Number of classification classes.
deep_stem : bool, default False
Whether to replace the 7x7 conv1 with 3 3x3 convolution layers.
block_reduce_first: int, default 1
Reduction factor for first convolution output width of residual blocks,
1 for all archs except senets, where 2
down_kernel_size: int, default 1
Kernel size of residual block downsampling path, 1x1 for most archs, 3x3 for senets
avg_down : bool, default False
Whether to use average pooling for projection skip connection between stages/downsample.
dilated : bool, default False
Applying dilation strategy to pretrained ResNet yielding a stride-8 model,
typically used in Semantic Segmentation.
"""
def __init__(self, block, layers, num_classes=1000, in_chans=3, use_se=False,
cardinality=1, base_width=64, stem_width=64, deep_stem=False,
block_reduce_first=1, down_kernel_size=1, avg_down=False, dilated=False,
norm_layer=nn.BatchNorm2d, drop_rate=0.0, global_pool='avg'):
self.num_classes = num_classes
self.inplanes = stem_width * 2 if deep_stem else 64
self.cardinality = cardinality
self.base_width = base_width
self.drop_rate = drop_rate
self.expansion = block.expansion
self.dilated = dilated
super(GluonResNet, self).__init__()
if not deep_stem:
self.conv1 = nn.Conv2d(in_chans, stem_width, kernel_size=7, stride=2, padding=3, bias=False)
else:
conv1_modules = [
nn.Conv2d(in_chans, stem_width, 3, stride=2, padding=1, bias=False),
norm_layer(stem_width),
nn.ReLU(),
nn.Conv2d(stem_width, stem_width, 3, stride=1, padding=1, bias=False),
norm_layer(stem_width),
nn.ReLU(),
nn.Conv2d(stem_width, self.inplanes, 3, stride=1, padding=1, bias=False),
]
self.conv1 = nn.Sequential(*conv1_modules)
self.bn1 = norm_layer(self.inplanes)
self.relu = nn.ReLU()
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
stride_3_4 = 1 if self.dilated else 2
dilation_3 = 2 if self.dilated else 1
dilation_4 = 4 if self.dilated else 1
self.layer1 = self._make_layer(
block, 64, layers[0], stride=1, reduce_first=block_reduce_first,
use_se=use_se, avg_down=avg_down, down_kernel_size=1, norm_layer=norm_layer)
self.layer2 = self._make_layer(
block, 128, layers[1], stride=2, reduce_first=block_reduce_first,
use_se=use_se, avg_down=avg_down, down_kernel_size=down_kernel_size, norm_layer=norm_layer)
self.layer3 = self._make_layer(
block, 256, layers[2], stride=stride_3_4, dilation=dilation_3, reduce_first=block_reduce_first,
use_se=use_se, avg_down=avg_down, down_kernel_size=down_kernel_size, norm_layer=norm_layer)
self.layer4 = self._make_layer(
block, 512, layers[3], stride=stride_3_4, dilation=dilation_4, reduce_first=block_reduce_first,
use_se=use_se, avg_down=avg_down, down_kernel_size=down_kernel_size, norm_layer=norm_layer)
self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
self.num_features = 512 * block.expansion
self.fc = nn.Linear(self.num_features * self.global_pool.feat_mult(), num_classes)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1.)
nn.init.constant_(m.bias, 0.)
def _make_layer(self, block, planes, blocks, stride=1, dilation=1, reduce_first=1,
use_se=False, avg_down=False, down_kernel_size=1, norm_layer=nn.BatchNorm2d):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample_padding = _get_padding(down_kernel_size, stride)
if avg_down:
avg_stride = stride if dilation == 1 else 1
downsample_layers = [
nn.AvgPool2d(avg_stride, avg_stride, ceil_mode=True, count_include_pad=False),
nn.Conv2d(self.inplanes, planes * block.expansion, down_kernel_size,
stride=1, padding=downsample_padding, bias=False),
norm_layer(planes * block.expansion),
]
else:
downsample_layers = [
nn.Conv2d(self.inplanes, planes * block.expansion, down_kernel_size,
stride=stride, padding=downsample_padding, bias=False),
norm_layer(planes * block.expansion),
]
downsample = nn.Sequential(*downsample_layers)
first_dilation = 1 if dilation in (1, 2) else 2
layers = [block(
self.inplanes, planes, stride, downsample,
cardinality=self.cardinality, base_width=self.base_width, reduce_first=reduce_first,
use_se=use_se, dilation=first_dilation, previous_dilation=dilation, norm_layer=norm_layer)]
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(
self.inplanes, planes,
cardinality=self.cardinality, base_width=self.base_width, reduce_first=reduce_first,
use_se=use_se, dilation=dilation, previous_dilation=dilation, norm_layer=norm_layer))
return nn.Sequential(*layers)
def get_classifier(self):
return self.fc
def reset_classifier(self, num_classes, global_pool='avg'):
self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
self.num_classes = num_classes
del self.fc
if num_classes:
self.fc = nn.Linear(self.num_features * self.global_pool.feat_mult(), num_classes)
else:
self.fc = None
def forward_features(self, x, pool=True):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(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)
x = self.fc(x)
return x
def gluon_resnet18_v1b(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
"""Constructs a ResNet-18 model.
"""
default_cfg = default_cfgs['gluon_resnet18_v1b']
model = GluonResNet(BasicBlockGl, [2, 2, 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 gluon_resnet34_v1b(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
"""Constructs a ResNet-34 model.
"""
default_cfg = default_cfgs['gluon_resnet34_v1b']
model = GluonResNet(BasicBlockGl, [3, 4, 6, 3], 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 gluon_resnet50_v1b(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
"""Constructs a ResNet-50 model.
"""
default_cfg = default_cfgs['gluon_resnet50_v1b']
model = GluonResNet(BottleneckGl, [3, 4, 6, 3], 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 gluon_resnet101_v1b(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
"""Constructs a ResNet-101 model.
"""
default_cfg = default_cfgs['gluon_resnet101_v1b']
model = GluonResNet(BottleneckGl, [3, 4, 23, 3], 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 gluon_resnet152_v1b(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
"""Constructs a ResNet-152 model.
"""
default_cfg = default_cfgs['gluon_resnet152_v1b']
model = GluonResNet(BottleneckGl, [3, 8, 36, 3], 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 gluon_resnet50_v1c(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
"""Constructs a ResNet-50 model.
"""
default_cfg = default_cfgs['gluon_resnet50_v1c']
model = GluonResNet(BottleneckGl, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans,
stem_width=32, deep_stem=True, **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model
def gluon_resnet101_v1c(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
"""Constructs a ResNet-101 model.
"""
default_cfg = default_cfgs['gluon_resnet101_v1c']
model = GluonResNet(BottleneckGl, [3, 4, 23, 3], num_classes=num_classes, in_chans=in_chans,
stem_width=32, deep_stem=True, **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model
def gluon_resnet152_v1c(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
"""Constructs a ResNet-152 model.
"""
default_cfg = default_cfgs['gluon_resnet152_v1c']
model = GluonResNet(BottleneckGl, [3, 8, 36, 3], num_classes=num_classes, in_chans=in_chans,
stem_width=32, deep_stem=True, **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model
def gluon_resnet50_v1d(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
"""Constructs a ResNet-50 model.
"""
default_cfg = default_cfgs['gluon_resnet50_v1d']
model = GluonResNet(BottleneckGl, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans,
stem_width=32, deep_stem=True, avg_down=True, **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model
def gluon_resnet101_v1d(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
"""Constructs a ResNet-101 model.
"""
default_cfg = default_cfgs['gluon_resnet101_v1d']
model = GluonResNet(BottleneckGl, [3, 4, 23, 3], num_classes=num_classes, in_chans=in_chans,
stem_width=32, deep_stem=True, avg_down=True, **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model
def gluon_resnet152_v1d(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
"""Constructs a ResNet-152 model.
"""
default_cfg = default_cfgs['gluon_resnet152_v1d']
model = GluonResNet(BottleneckGl, [3, 8, 36, 3], num_classes=num_classes, in_chans=in_chans,
stem_width=32, deep_stem=True, avg_down=True, **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model
def gluon_resnet50_v1e(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
"""Constructs a ResNet-50-V1e model. No pretrained weights for any 'e' variants
"""
default_cfg = default_cfgs['gluon_resnet50_v1e']
model = GluonResNet(BottleneckGl, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans,
stem_width=64, deep_stem=True, avg_down=True, **kwargs)
model.default_cfg = default_cfg
#if pretrained:
# load_pretrained(model, default_cfg, num_classes, in_chans)
return model
def gluon_resnet101_v1e(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
"""Constructs a ResNet-101 model.
"""
default_cfg = default_cfgs['gluon_resnet101_v1e']
model = GluonResNet(BottleneckGl, [3, 4, 23, 3], num_classes=num_classes, in_chans=in_chans,
stem_width=64, deep_stem=True, avg_down=True, **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model
def gluon_resnet152_v1e(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
"""Constructs a ResNet-152 model.
"""
default_cfg = default_cfgs['gluon_resnet152_v1e']
model = GluonResNet(BottleneckGl, [3, 8, 36, 3], num_classes=num_classes, in_chans=in_chans,
stem_width=64, deep_stem=True, avg_down=True, **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model
def gluon_resnet50_v1s(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
"""Constructs a ResNet-50 model.
"""
default_cfg = default_cfgs['gluon_resnet50_v1s']
model = GluonResNet(BottleneckGl, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans,
stem_width=64, deep_stem=True, **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model
def gluon_resnet101_v1s(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
"""Constructs a ResNet-101 model.
"""
default_cfg = default_cfgs['gluon_resnet101_v1s']
model = GluonResNet(BottleneckGl, [3, 4, 23, 3], num_classes=num_classes, in_chans=in_chans,
stem_width=64, deep_stem=True, **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model
def gluon_resnet152_v1s(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
"""Constructs a ResNet-152 model.
"""
default_cfg = default_cfgs['gluon_resnet152_v1s']
model = GluonResNet(BottleneckGl, [3, 8, 36, 3], num_classes=num_classes, in_chans=in_chans,
stem_width=64, deep_stem=True, **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model
def gluon_resnext50_32x4d(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
"""Constructs a ResNeXt50-32x4d model.
"""
default_cfg = default_cfgs['gluon_resnext50_32x4d']
model = GluonResNet(
BottleneckGl, [3, 4, 6, 3], cardinality=32, base_width=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 gluon_resnext101_32x4d(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
"""Constructs a ResNeXt-101 model.
"""
default_cfg = default_cfgs['gluon_resnext101_32x4d']
model = GluonResNet(
BottleneckGl, [3, 4, 23, 3], cardinality=32, base_width=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 gluon_resnext101_64x4d(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
"""Constructs a ResNeXt-101 model.
"""
default_cfg = default_cfgs['gluon_resnext101_64x4d']
model = GluonResNet(
BottleneckGl, [3, 4, 23, 3], cardinality=64, base_width=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 gluon_resnext152_32x4d(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
"""Constructs a ResNeXt152-32x4d model.
"""
default_cfg = default_cfgs['gluon_resnext152_32x4d']
model = GluonResNet(
BottleneckGl, [3, 8, 36, 3], cardinality=32, base_width=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 gluon_seresnext50_32x4d(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
"""Constructs a SEResNeXt50-32x4d model.
"""
default_cfg = default_cfgs['gluon_seresnext50_32x4d']
model = GluonResNet(
BottleneckGl, [3, 4, 6, 3], cardinality=32, base_width=4, use_se=True,
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 gluon_seresnext101_32x4d(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
"""Constructs a SEResNeXt-101-32x4d model.
"""
default_cfg = default_cfgs['gluon_seresnext101_32x4d']
model = GluonResNet(
BottleneckGl, [3, 4, 23, 3], cardinality=32, base_width=4, use_se=True,
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 gluon_seresnext101_64x4d(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
"""Constructs a SEResNeXt-101-64x4d model.
"""
default_cfg = default_cfgs['gluon_seresnext101_64x4d']
model = GluonResNet(
BottleneckGl, [3, 4, 23, 3], cardinality=64, base_width=4, use_se=True,
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 gluon_seresnext152_32x4d(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
"""Constructs a SEResNeXt152-32x4d model.
"""
default_cfg = default_cfgs['gluon_seresnext152_32x4d']
model = GluonResNet(
BottleneckGl, [3, 8, 36, 3], cardinality=32, base_width=4, use_se=True,
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 gluon_senet154(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
"""Constructs an SENet-154 model.
"""
default_cfg = default_cfgs['gluon_senet154']
model = GluonResNet(
BottleneckGl, [3, 8, 36, 3], cardinality=64, base_width=4, use_se=True,
deep_stem=True, down_kernel_size=3, block_reduce_first=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

8
models/model_factory.py
Unescape View File

@ -14,6 +14,14 @@ from models.genmobilenet import \
mobilenetv1_100, mobilenetv2_100, mobilenetv3_050, mobilenetv3_075, mobilenetv3_100,\
fbnetc_100, chamnetv1_100, chamnetv2_100, spnasnet_100
from models.inception_v3 import inception_v3, gluon_inception_v3, tf_inception_v3, adv_inception_v3
from models.gluon_resnet import gluon_resnet18_v1b, gluon_resnet34_v1b, gluon_resnet50_v1b, gluon_resnet101_v1b, \
gluon_resnet152_v1b, gluon_resnet50_v1c, gluon_resnet101_v1c, gluon_resnet152_v1c, \
gluon_resnet50_v1d, gluon_resnet101_v1d, gluon_resnet152_v1d, \
gluon_resnet50_v1e, gluon_resnet101_v1e, gluon_resnet152_v1e, \
gluon_resnet50_v1s, gluon_resnet101_v1s, gluon_resnet152_v1s, \
gluon_resnext50_32x4d, gluon_resnext101_32x4d , gluon_resnext101_64x4d, gluon_resnext152_32x4d, \
gluon_seresnext50_32x4d, gluon_seresnext101_32x4d, gluon_seresnext101_64x4d, gluon_seresnext152_32x4d, \
gluon_senet154
from models.helpers import load_checkpoint

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