from __future__ import absolute_import
import torch
from .resnet import *
from .resnetmid import *
from .senet import *
from .densenet import *
from .inceptionresnetv2 import *
from .inceptionv4 import *
from .xception import *
from .nasnet import *
from .mobilenetv2 import *
from .shufflenet import *
from .squeezenet import *
from .shufflenetv2 import *
from .mudeep import *
from .hacnn import *
from .pcb import *
from .mlfn import *
__model_factory = {
# image classification models
'resnet18': resnet18,
'resnet34': resnet34,
'resnet50': resnet50,
'resnet101': resnet101,
'resnet152': resnet152,
'resnext50_32x4d': resnext50_32x4d,
'resnext101_32x8d': resnext101_32x8d,
'resnet50_fc512': resnet50_fc512,
'se_resnet50': se_resnet50,
'se_resnet50_fc512': se_resnet50_fc512,
'se_resnet101': se_resnet101,
'se_resnext50_32x4d': se_resnext50_32x4d,
'se_resnext101_32x4d': se_resnext101_32x4d,
'densenet121': densenet121,
'densenet169': densenet169,
'densenet201': densenet201,
'densenet161': densenet161,
'densenet121_fc512': densenet121_fc512,
'inceptionresnetv2': inceptionresnetv2,
'inceptionv4': inceptionv4,
'xception': xception,
# lightweight models
'nasnsetmobile': nasnetamobile,
'mobilenetv2_1dot0': mobilenetv2_1dot0,
'mobilenetv2_1dot4': mobilenetv2_1dot4,
'shufflenet': shufflenet,
'squeezenet1_0': squeezenet1_0,
'squeezenet1_0_fc512': squeezenet1_0_fc512,
'squeezenet1_1': squeezenet1_1,
'shufflenet_v2_x0_5': shufflenet_v2_x0_5,
'shufflenet_v2_x1_0': shufflenet_v2_x1_0,
'shufflenet_v2_x1_5': shufflenet_v2_x1_5,
'shufflenet_v2_x2_0': shufflenet_v2_x2_0,
# reid-specific models
'mudeep': MuDeep,
'resnet50mid': resnet50mid,
'hacnn': HACNN,
'pcb_p6': pcb_p6,
'pcb_p4': pcb_p4,
'mlfn': mlfn,
}
[docs]def show_avai_models():
"""Displays available models.
Examples::
>>> from torchreid import models
>>> models.show_avai_models()
"""
print(list(__model_factory.keys()))
[docs]def build_model(name, num_classes, loss='softmax', pretrained=True, use_gpu=True):
"""A function wrapper for building a model.
Args:
name (str): model name.
num_classes (int): number of training identities.
loss (str, optional): loss function to optimize the model. Currently
supports "softmax" and "triplet". Default is "softmax".
pretrained (bool, optional): whether to load ImageNet-pretrained weights.
Default is True.
use_gpu (bool, optional): whether to use gpu. Default is True.
Returns:
nn.Module
Examples::
>>> from torchreid import models
>>> model = models.build_model('resnet50', 751, loss='softmax')
"""
avai_models = list(__model_factory.keys())
if name not in avai_models:
raise KeyError('Unknown model: {}. Must be one of {}'.format(name, avai_models))
return __model_factory[name](
num_classes=num_classes,
loss=loss,
pretrained=pretrained,
use_gpu=use_gpu
)