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