imgclsmob项目的PDF打印和预训练模型下载(pre_models+model_PDF)+tensorflow权重转pytorch权重
https://github.com/lukemelas/EfficientNet-PyTorch/tree/master/tf_to_pytorch/convert_tf_to_pt
tensorflow权重转pytorch权重
https://travis-ci.org/osmr/imgclsmob
https://github.com/osmr/imgclsmob
# -*-coding:utf-8-*-
from __future__ import print_function
import os
import torch
import torch.onnx
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.autograd import Variable
from torchvision import datasets, transforms, models
import argparse
from pytorchcv.model_provider import get_model as ptcv_get_model
def train(args, model, device, train_loader, optimizer, epoch):
model.train()
# for batch_idx, (data, target) in enumerate(train_loader):
for batch_idx, data_ynh in enumerate(train_loader):
# 获取图片和标签
data, target = data_ynh
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
output = model(data)
output1 = torch.nn.functional.log_softmax(output, dim=1)
loss = F.nll_loss(output1, target)
# loss = F.l1_loss(output, target)
loss.backward()
optimizer.step()
# new ynh
# 每10个batch画个点用于loss曲线
if batch_idx % 10 == 0:
niter = epoch * len(train_loader) + batch_idx
if batch_idx % args.log_interval == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * len(data), len(train_loader.dataset),
100. * batch_idx / len(train_loader), loss.item()))
def test(args, model, device, test_loader, epoch):
model.eval()
test_loss = 0
correct = 0
with torch.no_grad():
# for data, target in test_loader:
for data_ynh in test_loader:
# 获取图片和标签
data, target = data_ynh
data, target = data.to(device), target.to(device)
output = model(data)
output1 = torch.nn.functional.log_softmax(output, dim=1)
test_loss += F.nll_loss(output1, target, reduction='sum').item() # sum up batch loss
pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability
correct += pred.eq(target.view_as(pred)).sum().item()
test_loss /= len(test_loader.dataset)
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
test_loss, correct, len(test_loader.dataset),
100. * correct / len(test_loader.dataset)))
def main():
# Training settings
parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
parser.add_argument('--batch-size', type=int, default=10, metavar='N',
help='input batch size for training (default: 64)')
parser.add_argument('--test-batch-size', type=int, default=10, metavar='N',
help='input batch size for testing (default: 1000)')
parser.add_argument('--epochs', type=int, default=10, metavar='N',
help='number of epochs to train (default: 10)')
parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
help='learning rate (default: 0.01)')
parser.add_argument('--momentum', type=float, default=0.5, metavar='M',
help='SGD momentum (default: 0.5)')
parser.add_argument('--no-cuda', action='store_true', default=False,
help='disables CUDA training')
parser.add_argument('--seed', type=int, default=1, metavar='S',
help='random seed (default: 1)')
parser.add_argument('--log-interval', type=int, default=10, metavar='N',
help='how many batches to wait before logging training status')
parser.add_argument('--save-model', action='store_true', default=False,
help='For Saving the current Model')
args = parser.parse_args()
use_cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
device = torch.device("cuda" if use_cuda else "cpu")
kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
# -------------------------------------------- step 1/5 : 加载数据 -------------------------------------------
train_txt_path = './Data/train.txt'
valid_txt_path = './Data/valid.txt'
# 数据预处理设置
# normMean = [0.4948052, 0.48568845, 0.44682974]
# normStd = [0.24580306, 0.24236229, 0.2603115]
normMean = [104, 117, 123]
normStd = [1, 1, 1]
normTransform = transforms.Normalize(normMean, normStd)
trainTransform = transforms.Compose([
transforms.Resize(224),
# transforms.RandomCrop(224, padding=4),
transforms.ToTensor(),
# normTransform
])
validTransform = transforms.Compose([
transforms.Resize(224),
transforms.ToTensor(),
# normTransform
])
train_loader = torch.utils.data.DataLoader(
datasets.MNIST('./data/', train=True, download=True,
transform=transforms.Compose([
transforms.Resize((224), interpolation=2),
transforms.Grayscale(3),
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=args.batch_size, shuffle=True, **kwargs)
valid_loader = torch.utils.data.DataLoader(
datasets.MNIST('./data/', train=False, transform=transforms.Compose([
transforms.Resize((224), interpolation=2),
transforms.Grayscale(3),
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=args.test_batch_size, shuffle=True, **kwargs)
# blocks_args, global_params = utils.get_model_params('efficientnet-b0', override_params=None)
# model = EfficientNet(blocks_args, global_params)
#model = EfficientNet.from_pretrained('efficientnet-b0').to(device) # .cuda()
modelname = 'efficientnet_b0'
#efficientnet_b0
root = os.path.join("./", "pre_models")
model = ptcv_get_model(modelname, num_classes=10, pretrained=True, root=root) #True #False
#print(model)
model = model.cuda()
# dummy_input = torch.rand(1, 3, 224, 224).requires_grad_(True)
# writer.add_graph(model, (dummy_input,))
# no cuda()
# vis_graph = make_dot(model(dummy_input), params=dict(model.named_parameters()))
# vis_graph = make_dot(model(dummy_input), params=dict(list(model.named_parameters()) + [('x', dummy_input)]))
# vis_graph.view()
# no cuda()
#import hiddenlayer as hl
#temp_input = torch.zeros([1, 3, 224, 224]).cuda()
#hl_graph = hl.build_graph(model, temp_input)
#hl_graph.theme = hl.graph.THEMES["blue"].copy()
#hl_graph.save(os.path.join("./model_PDF", modelname+"_hl.pdf"))
# netron这个工具来可视化(读取ONNX文件)
# https://discuss.pytorch.org/t/onnx-export-failed-couldnt-export-operator-aten-adaptive-avg-pool1d/30204
# https://ptorch.com/news/95.html
# model.train(False)
# dummy_input = torch.randn(10, 3, 224, 224, device='cuda')
# torch_out = torch.onnx._export(model, dummy_input, "./efficientnet-b0.onnx", export_params=True, verbose=True)
# no .cuda()
import tensorwatch as tw
tw_graph = tw.draw_model(model, torch.zeros([1, 3, 224, 224]).cuda())
tw_graph.save(os.path.join("./model_PDF", modelname+"_tw.pdf"))
# print(model)
# model.to(device)
# summary(model, (3, 224, 224))
# model.cpu()
# stat(model, (3, 224, 224))
print("-------------------------------------------")
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
for epoch in range(1, args.epochs + 1):
train(args, model, device, train_loader, optimizer, epoch)
test(args, model, device, valid_loader, epoch)
if (args.save_model):
torch.save(model.state_dict(), "mnist_cnn.pt")
if __name__ == '__main__':
main()# -*-coding:utf-8-*-
from __future__ import print_function
import os
import torch
import torch.onnx
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.autograd import Variable
from torchvision import datasets, transforms, models
import argparse
from pytorchcv.model_provider import get_model as ptcv_get_model
from pytorchcv.models.model_store import *
_models = {
'alexnet': 'alexnet',
'alexnetb': 'alexnetb',
'zfnet': 'zfnet',
'vgg11': 'vgg11',
'vgg13': 'vgg13',
'vgg16': 'vgg16',
'vgg19': 'vgg19',
'bn_vgg11': 'bn_vgg11',
'bn_vgg13': 'bn_vgg13',
'bn_vgg16': 'bn_vgg16',
'bn_vgg19': 'bn_vgg19',
'bn_vgg11b': 'bn_vgg11b',
'bn_vgg13b': 'bn_vgg13b',
'bn_vgg16b': 'bn_vgg16b',
'bn_vgg19b': 'bn_vgg19b',
'bninception': 'bninception',
'resnet10': 'resnet10',
'resnet12': 'resnet12',
'resnet14': 'resnet14',
'resnetbc14b': 'resnetbc14b',
'resnet16': 'resnet16',
'resnet18_wd4': 'resnet18_wd4',
'resnet18_wd2': 'resnet18_wd2',
'resnet18_w3d4': 'resnet18_w3d4',
'resnet18': 'resnet18',
'resnet26': 'resnet26',
'resnetbc26b': 'resnetbc26b',
'resnet34': 'resnet34',
'resnetbc38b': 'resnetbc38b',
'resnet50': 'resnet50',
'resnet50b': 'resnet50b',
'resnet101': 'resnet101',
'resnet101b': 'resnet101b',
'resnet152': 'resnet152',
'resnet152b': 'resnet152b',
'resnet200': 'resnet200',
'resnet200b': 'resnet200b',
'preresnet10': 'preresnet10',
'preresnet12': 'preresnet12',
'preresnet14': 'preresnet14',
'preresnetbc14b': 'preresnetbc14b',
'preresnet16': 'preresnet16',
'preresnet18_wd4': 'preresnet18_wd4',
'preresnet18_wd2': 'preresnet18_wd2',
'preresnet18_w3d4': 'preresnet18_w3d4',
'preresnet18': 'preresnet18',
'preresnet26': 'preresnet26',
'preresnetbc26b': 'preresnetbc26b',
'preresnet34': 'preresnet34',
'preresnetbc38b': 'preresnetbc38b',
'preresnet50': 'preresnet50',
'preresnet50b': 'preresnet50b',
'preresnet101': 'preresnet101',
'preresnet101b': 'preresnet101b',
'preresnet152': 'preresnet152',
'preresnet152b': 'preresnet152b',
'preresnet200': 'preresnet200',
'preresnet200b': 'preresnet200b',
'preresnet269b': 'preresnet269b',
'resnext14_16x4d': 'resnext14_16x4d',
'resnext14_32x2d': 'resnext14_32x2d',
'resnext14_32x4d': 'resnext14_32x4d',
'resnext26_16x4d': 'resnext26_16x4d',
'resnext26_32x2d': 'resnext26_32x2d',
'resnext26_32x4d': 'resnext26_32x4d',
'resnext38_32x4d': 'resnext38_32x4d',
'resnext50_32x4d': 'resnext50_32x4d',
'resnext101_32x4d': 'resnext101_32x4d',
'resnext101_64x4d': 'resnext101_64x4d',
'seresnet10': 'seresnet10',
'seresnet12': 'seresnet12',
'seresnet14': 'seresnet14',
'seresnet16': 'seresnet16',
'seresnet18': 'seresnet18',
'seresnet26': 'seresnet26',
'seresnetbc26b': 'seresnetbc26b',
'seresnet34': 'seresnet34',
'seresnetbc38b': 'seresnetbc38b',
'seresnet50': 'seresnet50',
'seresnet50b': 'seresnet50b',
'seresnet101': 'seresnet101',
'seresnet101b': 'seresnet101b',
'seresnet152': 'seresnet152',
'seresnet152b': 'seresnet152b',
'seresnet200': 'seresnet200',
'seresnet200b': 'seresnet200b',
'sepreresnet10': 'sepreresnet10',
'sepreresnet12': 'sepreresnet12',
'sepreresnet14': 'sepreresnet14',
'sepreresnet16': 'sepreresnet16',
'sepreresnet18': 'sepreresnet18',
'sepreresnet26': 'sepreresnet26',
'sepreresnetbc26b': 'sepreresnetbc26b',
'sepreresnet34': 'sepreresnet34',
'sepreresnetbc38b': 'sepreresnetbc38b',
'sepreresnet50': 'sepreresnet50',
'sepreresnet50b': 'sepreresnet50b',
'sepreresnet101': 'sepreresnet101',
'sepreresnet101b': 'sepreresnet101b',
'sepreresnet152': 'sepreresnet152',
'sepreresnet152b': 'sepreresnet152b',
'sepreresnet200': 'sepreresnet200',
'sepreresnet200b': 'sepreresnet200b',
'seresnext50_32x4d': 'seresnext50_32x4d',
'seresnext101_32x4d': 'seresnext101_32x4d',
'seresnext101_64x4d': 'seresnext101_64x4d',
'senet16': 'senet16',
'senet28': 'senet28',
'senet40': 'senet40',
'senet52': 'senet52',
'senet103': 'senet103',
'senet154': 'senet154',
'ibn_resnet50': 'ibn_resnet50',
'ibn_resnet101': 'ibn_resnet101',
'ibn_resnet152': 'ibn_resnet152',
'ibnb_resnet50': 'ibnb_resnet50',
'ibnb_resnet101': 'ibnb_resnet101',
'ibnb_resnet152': 'ibnb_resnet152',
'ibn_resnext50_32x4d': 'ibn_resnext50_32x4d',
'ibn_resnext101_32x4d': 'ibn_resnext101_32x4d',
'ibn_resnext101_64x4d': 'ibn_resnext101_64x4d',
'ibn_densenet121': 'ibn_densenet121',
'ibn_densenet161': 'ibn_densenet161',
'ibn_densenet169': 'ibn_densenet169',
'ibn_densenet201': 'ibn_densenet201',
'airnet50_1x64d_r2': 'airnet50_1x64d_r2',
'airnet50_1x64d_r16': 'airnet50_1x64d_r16',
'airnet101_1x64d_r2': 'airnet101_1x64d_r2',
'airnext50_32x4d_r2': 'airnext50_32x4d_r2',
'airnext101_32x4d_r2': 'airnext101_32x4d_r2',
'airnext101_32x4d_r16': 'airnext101_32x4d_r16',
'bam_resnet18': 'bam_resnet18',
'bam_resnet34': 'bam_resnet34',
'bam_resnet50': 'bam_resnet50',
'bam_resnet101': 'bam_resnet101',
'bam_resnet152': 'bam_resnet152',
'cbam_resnet18': 'cbam_resnet18',
'cbam_resnet34': 'cbam_resnet34',
'cbam_resnet50': 'cbam_resnet50',
'cbam_resnet101': 'cbam_resnet101',
'cbam_resnet152': 'cbam_resnet152',
'resattnet56': 'resattnet56',
'resattnet92': 'resattnet92',
'resattnet128': 'resattnet128',
'resattnet164': 'resattnet164',
'resattnet200': 'resattnet200',
'resattnet236': 'resattnet236',
'resattnet452': 'resattnet452',
'sknet50': 'sknet50',
'sknet101': 'sknet101',
'sknet152': 'sknet152',
'diaresnet10': 'diaresnet10',
'diaresnet12': 'diaresnet12',
'diaresnet14': 'diaresnet14',
'diaresnetbc14b': 'diaresnetbc14b',
'diaresnet16': 'diaresnet16',
'diaresnet18': 'diaresnet18',
'diaresnet26': 'diaresnet26',
'diaresnetbc26b': 'diaresnetbc26b',
'diaresnet34': 'diaresnet34',
'diaresnetbc38b': 'diaresnetbc38b',
'diaresnet50': 'diaresnet50',
'diaresnet50b': 'diaresnet50b',
'diaresnet101': 'diaresnet101',
'diaresnet101b': 'diaresnet101b',
'diaresnet152': 'diaresnet152',
'diaresnet152b': 'diaresnet152b',
'diaresnet200': 'diaresnet200',
'diaresnet200b': 'diaresnet200b',
'diapreresnet10': 'diapreresnet10',
'diapreresnet12': 'diapreresnet12',
'diapreresnet14': 'diapreresnet14',
'diapreresnetbc14b': 'diapreresnetbc14b',
'diapreresnet16': 'diapreresnet16',
'diapreresnet18': 'diapreresnet18',
'diapreresnet26': 'diapreresnet26',
'diapreresnetbc26b': 'diapreresnetbc26b',
'diapreresnet34': 'diapreresnet34',
'diapreresnetbc38b': 'diapreresnetbc38b',
'diapreresnet50': 'diapreresnet50',
'diapreresnet50b': 'diapreresnet50b',
'diapreresnet101': 'diapreresnet101',
'diapreresnet101b': 'diapreresnet101b',
'diapreresnet152': 'diapreresnet152',
'diapreresnet152b': 'diapreresnet152b',
'diapreresnet200': 'diapreresnet200',
'diapreresnet200b': 'diapreresnet200b',
'diapreresnet269b': 'diapreresnet269b',
'pyramidnet101_a360': 'pyramidnet101_a360',
'diracnet18v2': 'diracnet18v2',
'diracnet34v2': 'diracnet34v2',
'sharesnet18': 'sharesnet18',
'sharesnet34': 'sharesnet34',
'sharesnet50': 'sharesnet50',
'sharesnet50b': 'sharesnet50b',
'sharesnet101': 'sharesnet101',
'sharesnet101b': 'sharesnet101b',
'sharesnet152': 'sharesnet152',
'sharesnet152b': 'sharesnet152b',
'densenet121': 'densenet121',
'densenet161': 'densenet161',
'densenet169': 'densenet169',
'densenet201': 'densenet201',
'condensenet74_c4_g4': 'condensenet74_c4_g4',
'condensenet74_c8_g8': 'condensenet74_c8_g8',
'sparsenet121': 'sparsenet121',
'sparsenet161': 'sparsenet161',
'sparsenet169': 'sparsenet169',
'sparsenet201': 'sparsenet201',
'sparsenet264': 'sparsenet264',
'peleenet': 'peleenet',
'wrn50_2': 'wrn50_2',
'drnc26': 'drnc26',
'drnc42': 'drnc42',
'drnc58': 'drnc58',
'drnd22': 'drnd22',
'drnd38': 'drnd38',
'drnd54': 'drnd54',
'drnd105': 'drnd105',
'dpn68': 'dpn68',
'dpn68b': 'dpn68b',
'dpn98': 'dpn98',
'dpn107': 'dpn107',
'dpn131': 'dpn131',
'darknet_ref': 'darknet_ref',
'darknet_tiny': 'darknet_tiny',
'darknet19': 'darknet19',
'darknet53': 'darknet53',
'channelnet': 'channelnet',
'revnet38': 'revnet38',
'revnet110': 'revnet110',
'revnet164': 'revnet164',
'irevnet301': 'irevnet301',
'bagnet9': 'bagnet9',
'bagnet17': 'bagnet17',
'bagnet33': 'bagnet33',
'dla34': 'dla34',
'dla46c': 'dla46c',
'dla46xc': 'dla46xc',
'dla60': 'dla60',
'dla60x': 'dla60x',
'dla60xc': 'dla60xc',
'dla102': 'dla102',
'dla102x': 'dla102x',
'dla102x2': 'dla102x2',
'dla169': 'dla169',
'msdnet22': 'msdnet22',
'fishnet99': 'fishnet99',
'fishnet150': 'fishnet150',
'espnetv2_wd2': 'espnetv2_wd2',
'espnetv2_w1': 'espnetv2_w1',
'espnetv2_w5d4': 'espnetv2_w5d4',
'espnetv2_w3d2': 'espnetv2_w3d2',
'espnetv2_w2': 'espnetv2_w2',
'xdensenet121_2': 'xdensenet121_2',
'xdensenet161_2': 'xdensenet161_2',
'xdensenet169_2': 'xdensenet169_2',
'xdensenet201_2': 'xdensenet201_2',
'squeezenet_v1_0': 'squeezenet_v1_0',
'squeezenet_v1_1': 'squeezenet_v1_1',
'squeezeresnet_v1_0': 'squeezeresnet_v1_0',
'squeezeresnet_v1_1': 'squeezeresnet_v1_1',
'sqnxt23_w1': 'sqnxt23_w1',
'sqnxt23_w3d2': 'sqnxt23_w3d2',
'sqnxt23_w2': 'sqnxt23_w2',
'sqnxt23v5_w1': 'sqnxt23v5_w1',
'sqnxt23v5_w3d2': 'sqnxt23v5_w3d2',
'sqnxt23v5_w2': 'sqnxt23v5_w2',
'shufflenet_g1_w1': 'shufflenet_g1_w1',
'shufflenet_g2_w1': 'shufflenet_g2_w1',
'shufflenet_g3_w1': 'shufflenet_g3_w1',
'shufflenet_g4_w1': 'shufflenet_g4_w1',
'shufflenet_g8_w1': 'shufflenet_g8_w1',
'shufflenet_g1_w3d4': 'shufflenet_g1_w3d4',
'shufflenet_g3_w3d4': 'shufflenet_g3_w3d4',
'shufflenet_g1_wd2': 'shufflenet_g1_wd2',
'shufflenet_g3_wd2': 'shufflenet_g3_wd2',
'shufflenet_g1_wd4': 'shufflenet_g1_wd4',
'shufflenet_g3_wd4': 'shufflenet_g3_wd4',
'shufflenetv2_wd2': 'shufflenetv2_wd2',
'shufflenetv2_w1': 'shufflenetv2_w1',
'shufflenetv2_w3d2': 'shufflenetv2_w3d2',
'shufflenetv2_w2': 'shufflenetv2_w2',
'shufflenetv2b_wd2': 'shufflenetv2b_wd2',
'shufflenetv2b_w1': 'shufflenetv2b_w1',
'shufflenetv2b_w3d2': 'shufflenetv2b_w3d2',
'shufflenetv2b_w2': 'shufflenetv2b_w2',
'menet108_8x1_g3': 'menet108_8x1_g3',
'menet128_8x1_g4': 'menet128_8x1_g4',
'menet160_8x1_g8': 'menet160_8x1_g8',
'menet228_12x1_g3': 'menet228_12x1_g3',
'menet256_12x1_g4': 'menet256_12x1_g4',
'menet348_12x1_g3': 'menet348_12x1_g3',
'menet352_12x1_g8': 'menet352_12x1_g8',
'menet456_24x1_g3': 'menet456_24x1_g3',
'mobilenet_w1': 'mobilenet_w1',
'mobilenet_w3d4': 'mobilenet_w3d4',
'mobilenet_wd2': 'mobilenet_wd2',
'mobilenet_wd4': 'mobilenet_wd4',
'fdmobilenet_w1': 'fdmobilenet_w1',
'fdmobilenet_w3d4': 'fdmobilenet_w3d4',
'fdmobilenet_wd2': 'fdmobilenet_wd2',
'fdmobilenet_wd4': 'fdmobilenet_wd4',
'mobilenetv2_w1': 'mobilenetv2_w1',
'mobilenetv2_w3d4': 'mobilenetv2_w3d4',
'mobilenetv2_wd2': 'mobilenetv2_wd2',
'mobilenetv2_wd4': 'mobilenetv2_wd4',
'mobilenetv3_small_w7d20': 'mobilenetv3_small_w7d20',
'mobilenetv3_small_wd2': 'mobilenetv3_small_wd2',
'mobilenetv3_small_w3d4': 'mobilenetv3_small_w3d4',
'mobilenetv3_small_w1': 'mobilenetv3_small_w1',
'mobilenetv3_small_w5d4': 'mobilenetv3_small_w5d4',
'mobilenetv3_large_w7d20': 'mobilenetv3_large_w7d20',
'mobilenetv3_large_wd2': 'mobilenetv3_large_wd2',
'mobilenetv3_large_w3d4': 'mobilenetv3_large_w3d4',
'mobilenetv3_large_w1': 'mobilenetv3_large_w1',
'mobilenetv3_large_w5d4': 'mobilenetv3_large_w5d4',
'igcv3_w1': 'igcv3_w1',
'igcv3_w3d4': 'igcv3_w3d4',
'igcv3_wd2': 'igcv3_wd2',
'igcv3_wd4': 'igcv3_wd4',
'mnasnet': 'mnasnet',
'darts': 'darts',
'proxylessnas_cpu': 'proxylessnas_cpu',
'proxylessnas_gpu': 'proxylessnas_gpu',
'proxylessnas_mobile': 'proxylessnas_mobile',
'proxylessnas_mobile14': 'proxylessnas_mobile14',
'xception': 'xception',
'inceptionv3': 'inceptionv3',
'inceptionv4': 'inceptionv4',
'inceptionresnetv2': 'inceptionresnetv2',
'polynet': 'polynet',
'nasnet_4a1056': 'nasnet_4a1056',
'nasnet_6a4032': 'nasnet_6a4032',
'pnasnet5large': 'pnasnet5large',
'efficientnet_b0': 'efficientnet_b0',
'efficientnet_b1': 'efficientnet_b1',
'efficientnet_b2': 'efficientnet_b2',
'efficientnet_b3': 'efficientnet_b3',
'efficientnet_b4': 'efficientnet_b4',
'efficientnet_b5': 'efficientnet_b5',
'efficientnet_b6': 'efficientnet_b6',
'efficientnet_b7': 'efficientnet_b7',
'efficientnet_b0b': 'efficientnet_b0b',
'efficientnet_b1b': 'efficientnet_b1b',
'efficientnet_b2b': 'efficientnet_b2b',
'efficientnet_b3b': 'efficientnet_b3b',
'nin_cifar10': 'nin_cifar10',
'nin_cifar100': 'nin_cifar100',
'nin_svhn': 'nin_svhn',
'resnet20_cifar10': 'resnet20_cifar10',
'resnet20_cifar100': 'resnet20_cifar100',
'resnet20_svhn': 'resnet20_svhn',
'resnet56_cifar10': 'resnet56_cifar10',
'resnet56_cifar100': 'resnet56_cifar100',
'resnet56_svhn': 'resnet56_svhn',
'resnet110_cifar10': 'resnet110_cifar10',
'resnet110_cifar100': 'resnet110_cifar100',
'resnet110_svhn': 'resnet110_svhn',
'resnet164bn_cifar10': 'resnet164bn_cifar10',
'resnet164bn_cifar100': 'resnet164bn_cifar100',
'resnet164bn_svhn': 'resnet164bn_svhn',
'resnet272bn_cifar10': 'resnet272bn_cifar10',
'resnet272bn_cifar100': 'resnet272bn_cifar100',
'resnet272bn_svhn': 'resnet272bn_svhn',
'resnet542bn_cifar10': 'resnet542bn_cifar10',
'resnet542bn_cifar100': 'resnet542bn_cifar100',
'resnet542bn_svhn': 'resnet542bn_svhn',
'resnet1001_cifar10': 'resnet1001_cifar10',
'resnet1001_cifar100': 'resnet1001_cifar100',
'resnet1001_svhn': 'resnet1001_svhn',
'resnet1202_cifar10': 'resnet1202_cifar10',
'resnet1202_cifar100': 'resnet1202_cifar100',
'resnet1202_svhn': 'resnet1202_svhn',
'preresnet20_cifar10': 'preresnet20_cifar10',
'preresnet20_cifar100': 'preresnet20_cifar100',
'preresnet20_svhn': 'preresnet20_svhn',
'preresnet56_cifar10': 'preresnet56_cifar10',
'preresnet56_cifar100': 'preresnet56_cifar100',
'preresnet56_svhn': 'preresnet56_svhn',
'preresnet110_cifar10': 'preresnet110_cifar10',
'preresnet110_cifar100': 'preresnet110_cifar100',
'preresnet110_svhn': 'preresnet110_svhn',
'preresnet164bn_cifar10': 'preresnet164bn_cifar10',
'preresnet164bn_cifar100': 'preresnet164bn_cifar100',
'preresnet164bn_svhn': 'preresnet164bn_svhn',
'preresnet272bn_cifar10': 'preresnet272bn_cifar10',
'preresnet272bn_cifar100': 'preresnet272bn_cifar100',
'preresnet272bn_svhn': 'preresnet272bn_svhn',
'preresnet542bn_cifar10': 'preresnet542bn_cifar10',
'preresnet542bn_cifar100': 'preresnet542bn_cifar100',
'preresnet542bn_svhn': 'preresnet542bn_svhn',
'preresnet1001_cifar10': 'preresnet1001_cifar10',
'preresnet1001_cifar100': 'preresnet1001_cifar100',
'preresnet1001_svhn': 'preresnet1001_svhn',
'preresnet1202_cifar10': 'preresnet1202_cifar10',
'preresnet1202_cifar100': 'preresnet1202_cifar100',
'preresnet1202_svhn': 'preresnet1202_svhn',
'resnext20_16x4d_cifar10': 'resnext20_16x4d_cifar10',
'resnext20_16x4d_cifar100': 'resnext20_16x4d_cifar100',
'resnext20_16x4d_svhn': 'resnext20_16x4d_svhn',
'resnext20_32x2d_cifar10': 'resnext20_32x2d_cifar10',
'resnext20_32x2d_cifar100': 'resnext20_32x2d_cifar100',
'resnext20_32x2d_svhn': 'resnext20_32x2d_svhn',
'resnext20_32x4d_cifar10': 'resnext20_32x4d_cifar10',
'resnext20_32x4d_cifar100': 'resnext20_32x4d_cifar100',
'resnext20_32x4d_svhn': 'resnext20_32x4d_svhn',
'resnext29_32x4d_cifar10': 'resnext29_32x4d_cifar10',
'resnext29_32x4d_cifar100': 'resnext29_32x4d_cifar100',
'resnext29_32x4d_svhn': 'resnext29_32x4d_svhn',
'resnext29_16x64d_cifar10': 'resnext29_16x64d_cifar10',
'resnext29_16x64d_cifar100': 'resnext29_16x64d_cifar100',
'resnext29_16x64d_svhn': 'resnext29_16x64d_svhn',
'resnext272_1x64d_cifar10': 'resnext272_1x64d_cifar10',
'resnext272_1x64d_cifar100': 'resnext272_1x64d_cifar100',
'resnext272_1x64d_svhn': 'resnext272_1x64d_svhn',
'resnext272_2x32d_cifar10': 'resnext272_2x32d_cifar10',
'resnext272_2x32d_cifar100': 'resnext272_2x32d_cifar100',
'resnext272_2x32d_svhn': 'resnext272_2x32d_svhn',
'seresnet20_cifar10': 'seresnet20_cifar10',
'seresnet20_cifar100': 'seresnet20_cifar100',
'seresnet20_svhn': 'seresnet20_svhn',
'seresnet56_cifar10': 'seresnet56_cifar10',
'seresnet56_cifar100': 'seresnet56_cifar100',
'seresnet56_svhn': 'seresnet56_svhn',
'seresnet110_cifar10': 'seresnet110_cifar10',
'seresnet110_cifar100': 'seresnet110_cifar100',
'seresnet110_svhn': 'seresnet110_svhn',
'seresnet164bn_cifar10': 'seresnet164bn_cifar10',
'seresnet164bn_cifar100': 'seresnet164bn_cifar100',
'seresnet164bn_svhn': 'seresnet164bn_svhn',
'seresnet272bn_cifar10': 'seresnet272bn_cifar10',
'seresnet272bn_cifar100': 'seresnet272bn_cifar100',
'seresnet272bn_svhn': 'seresnet272bn_svhn',
'seresnet542bn_cifar10': 'seresnet542bn_cifar10',
'seresnet542bn_cifar100': 'seresnet542bn_cifar100',
'seresnet542bn_svhn': 'seresnet542bn_svhn',
'seresnet1001_cifar10': 'seresnet1001_cifar10',
'seresnet1001_cifar100': 'seresnet1001_cifar100',
'seresnet1001_svhn': 'seresnet1001_svhn',
'seresnet1202_cifar10': 'seresnet1202_cifar10',
'seresnet1202_cifar100': 'seresnet1202_cifar100',
'seresnet1202_svhn': 'seresnet1202_svhn',
'sepreresnet20_cifar10': 'sepreresnet20_cifar10',
'sepreresnet20_cifar100': 'sepreresnet20_cifar100',
'sepreresnet20_svhn': 'sepreresnet20_svhn',
'sepreresnet56_cifar10': 'sepreresnet56_cifar10',
'sepreresnet56_cifar100': 'sepreresnet56_cifar100',
'sepreresnet56_svhn': 'sepreresnet56_svhn',
'sepreresnet110_cifar10': 'sepreresnet110_cifar10',
'sepreresnet110_cifar100': 'sepreresnet110_cifar100',
'sepreresnet110_svhn': 'sepreresnet110_svhn',
'sepreresnet164bn_cifar10': 'sepreresnet164bn_cifar10',
'sepreresnet164bn_cifar100': 'sepreresnet164bn_cifar100',
'sepreresnet164bn_svhn': 'sepreresnet164bn_svhn',
'sepreresnet272bn_cifar10': 'sepreresnet272bn_cifar10',
'sepreresnet272bn_cifar100': 'sepreresnet272bn_cifar100',
'sepreresnet272bn_svhn': 'sepreresnet272bn_svhn',
'sepreresnet542bn_cifar10': 'sepreresnet542bn_cifar10',
'sepreresnet542bn_cifar100': 'sepreresnet542bn_cifar100',
'sepreresnet542bn_svhn': 'sepreresnet542bn_svhn',
'sepreresnet1001_cifar10': 'sepreresnet1001_cifar10',
'sepreresnet1001_cifar100': 'sepreresnet1001_cifar100',
'sepreresnet1001_svhn': 'sepreresnet1001_svhn',
'sepreresnet1202_cifar10': 'sepreresnet1202_cifar10',
'sepreresnet1202_cifar100': 'sepreresnet1202_cifar100',
'sepreresnet1202_svhn': 'sepreresnet1202_svhn',
'pyramidnet110_a48_cifar10': 'pyramidnet110_a48_cifar10',
'pyramidnet110_a48_cifar100': 'pyramidnet110_a48_cifar100',
'pyramidnet110_a48_svhn': 'pyramidnet110_a48_svhn',
'pyramidnet110_a84_cifar10': 'pyramidnet110_a84_cifar10',
'pyramidnet110_a84_cifar100': 'pyramidnet110_a84_cifar100',
'pyramidnet110_a84_svhn': 'pyramidnet110_a84_svhn',
'pyramidnet110_a270_cifar10': 'pyramidnet110_a270_cifar10',
'pyramidnet110_a270_cifar100': 'pyramidnet110_a270_cifar100',
'pyramidnet110_a270_svhn': 'pyramidnet110_a270_svhn',
'pyramidnet164_a270_bn_cifar10': 'pyramidnet164_a270_bn_cifar10',
'pyramidnet164_a270_bn_cifar100': 'pyramidnet164_a270_bn_cifar100',
'pyramidnet164_a270_bn_svhn': 'pyramidnet164_a270_bn_svhn',
'pyramidnet200_a240_bn_cifar10': 'pyramidnet200_a240_bn_cifar10',
'pyramidnet200_a240_bn_cifar100': 'pyramidnet200_a240_bn_cifar100',
'pyramidnet200_a240_bn_svhn': 'pyramidnet200_a240_bn_svhn',
'pyramidnet236_a220_bn_cifar10': 'pyramidnet236_a220_bn_cifar10',
'pyramidnet236_a220_bn_cifar100': 'pyramidnet236_a220_bn_cifar100',
'pyramidnet236_a220_bn_svhn': 'pyramidnet236_a220_bn_svhn',
'pyramidnet272_a200_bn_cifar10': 'pyramidnet272_a200_bn_cifar10',
'pyramidnet272_a200_bn_cifar100': 'pyramidnet272_a200_bn_cifar100',
'pyramidnet272_a200_bn_svhn': 'pyramidnet272_a200_bn_svhn',
'densenet40_k12_cifar10': 'densenet40_k12_cifar10',
'densenet40_k12_cifar100': 'densenet40_k12_cifar100',
'densenet40_k12_svhn': 'densenet40_k12_svhn',
'densenet40_k12_bc_cifar10': 'densenet40_k12_bc_cifar10',
'densenet40_k12_bc_cifar100': 'densenet40_k12_bc_cifar100',
'densenet40_k12_bc_svhn': 'densenet40_k12_bc_svhn',
'densenet40_k24_bc_cifar10': 'densenet40_k24_bc_cifar10',
'densenet40_k24_bc_cifar100': 'densenet40_k24_bc_cifar100',
'densenet40_k24_bc_svhn': 'densenet40_k24_bc_svhn',
'densenet40_k36_bc_cifar10': 'densenet40_k36_bc_cifar10',
'densenet40_k36_bc_cifar100': 'densenet40_k36_bc_cifar100',
'densenet40_k36_bc_svhn': 'densenet40_k36_bc_svhn',
'densenet100_k12_cifar10': 'densenet100_k12_cifar10',
'densenet100_k12_cifar100': 'densenet100_k12_cifar100',
'densenet100_k12_svhn': 'densenet100_k12_svhn',
'densenet100_k24_cifar10': 'densenet100_k24_cifar10',
'densenet100_k24_cifar100': 'densenet100_k24_cifar100',
'densenet100_k24_svhn': 'densenet100_k24_svhn',
'densenet100_k12_bc_cifar10': 'densenet100_k12_bc_cifar10',
'densenet100_k12_bc_cifar100': 'densenet100_k12_bc_cifar100',
'densenet100_k12_bc_svhn': 'densenet100_k12_bc_svhn',
'densenet190_k40_bc_cifar10': 'densenet190_k40_bc_cifar10',
'densenet190_k40_bc_cifar100': 'densenet190_k40_bc_cifar100',
'densenet190_k40_bc_svhn': 'densenet190_k40_bc_svhn',
'densenet250_k24_bc_cifar10': 'densenet250_k24_bc_cifar10',
'densenet250_k24_bc_cifar100': 'densenet250_k24_bc_cifar100',
'densenet250_k24_bc_svhn': 'densenet250_k24_bc_svhn',
'xdensenet40_2_k24_bc_cifar10': 'xdensenet40_2_k24_bc_cifar10',
'xdensenet40_2_k24_bc_cifar100': 'xdensenet40_2_k24_bc_cifar100',
'xdensenet40_2_k24_bc_svhn': 'xdensenet40_2_k24_bc_svhn',
'xdensenet40_2_k36_bc_cifar10': 'xdensenet40_2_k36_bc_cifar10',
'xdensenet40_2_k36_bc_cifar100': 'xdensenet40_2_k36_bc_cifar100',
'xdensenet40_2_k36_bc_svhn': 'xdensenet40_2_k36_bc_svhn',
'wrn16_10_cifar10': 'wrn16_10_cifar10',
'wrn16_10_cifar100': 'wrn16_10_cifar100',
'wrn16_10_svhn': 'wrn16_10_svhn',
'wrn28_10_cifar10': 'wrn28_10_cifar10',
'wrn28_10_cifar100': 'wrn28_10_cifar100',
'wrn28_10_svhn': 'wrn28_10_svhn',
'wrn40_8_cifar10': 'wrn40_8_cifar10',
'wrn40_8_cifar100': 'wrn40_8_cifar100',
'wrn40_8_svhn': 'wrn40_8_svhn',
'wrn20_10_1bit_cifar10': 'wrn20_10_1bit_cifar10',
'wrn20_10_1bit_cifar100': 'wrn20_10_1bit_cifar100',
'wrn20_10_1bit_svhn': 'wrn20_10_1bit_svhn',
'wrn20_10_32bit_cifar10': 'wrn20_10_32bit_cifar10',
'wrn20_10_32bit_cifar100': 'wrn20_10_32bit_cifar100',
'wrn20_10_32bit_svhn': 'wrn20_10_32bit_svhn',
'ror3_56_cifar10': 'ror3_56_cifar10',
'ror3_56_cifar100': 'ror3_56_cifar100',
'ror3_56_svhn': 'ror3_56_svhn',
'ror3_110_cifar10': 'ror3_110_cifar10',
'ror3_110_cifar100': 'ror3_110_cifar100',
'ror3_110_svhn': 'ror3_110_svhn',
'ror3_164_cifar10': 'ror3_164_cifar10',
'ror3_164_cifar100': 'ror3_164_cifar100',
'ror3_164_svhn': 'ror3_164_svhn',
'rir_cifar10': 'rir_cifar10',
'rir_cifar100': 'rir_cifar100',
'rir_svhn': 'rir_svhn',
'msdnet22_cifar10': 'msdnet22_cifar10',
'resdropresnet20_cifar10': 'resdropresnet20_cifar10',
'resdropresnet20_cifar100': 'resdropresnet20_cifar100',
'resdropresnet20_svhn': 'resdropresnet20_svhn',
'shakeshakeresnet20_2x16d_cifar10': 'shakeshakeresnet20_2x16d_cifar10',
'shakeshakeresnet20_2x16d_cifar100': 'shakeshakeresnet20_2x16d_cifar100',
'shakeshakeresnet20_2x16d_svhn': 'shakeshakeresnet20_2x16d_svhn',
'shakeshakeresnet26_2x32d_cifar10': 'shakeshakeresnet26_2x32d_cifar10',
'shakeshakeresnet26_2x32d_cifar100': 'shakeshakeresnet26_2x32d_cifar100',
'shakeshakeresnet26_2x32d_svhn': 'shakeshakeresnet26_2x32d_svhn',
'shakedropresnet20_cifar10': 'shakedropresnet20_cifar10',
'shakedropresnet20_cifar100': 'shakedropresnet20_cifar100',
'shakedropresnet20_svhn': 'shakedropresnet20_svhn',
'fractalnet_cifar10': 'fractalnet_cifar10',
'fractalnet_cifar100': 'fractalnet_cifar100',
'diaresnet20_cifar10': 'diaresnet20_cifar10',
'diaresnet20_cifar100': 'diaresnet20_cifar100',
'diaresnet20_svhn': 'diaresnet20_svhn',
'diaresnet56_cifar10': 'diaresnet56_cifar10',
'diaresnet56_cifar100': 'diaresnet56_cifar100',
'diaresnet56_svhn': 'diaresnet56_svhn',
'diaresnet110_cifar10': 'diaresnet110_cifar10',
'diaresnet110_cifar100': 'diaresnet110_cifar100',
'diaresnet110_svhn': 'diaresnet110_svhn',
'diaresnet164bn_cifar10': 'diaresnet164bn_cifar10',
'diaresnet164bn_cifar100': 'diaresnet164bn_cifar100',
'diaresnet164bn_svhn': 'diaresnet164bn_svhn',
'diaresnet1001_cifar10': 'diaresnet1001_cifar10',
'diaresnet1001_cifar100': 'diaresnet1001_cifar100',
'diaresnet1001_svhn': 'diaresnet1001_svhn',
'diaresnet1202_cifar10': 'diaresnet1202_cifar10',
'diaresnet1202_cifar100': 'diaresnet1202_cifar100',
'diaresnet1202_svhn': 'diaresnet1202_svhn',
'diapreresnet20_cifar10': 'diapreresnet20_cifar10',
'diapreresnet20_cifar100': 'diapreresnet20_cifar100',
'diapreresnet20_svhn': 'diapreresnet20_svhn',
'diapreresnet56_cifar10': 'diapreresnet56_cifar10',
'diapreresnet56_cifar100': 'diapreresnet56_cifar100',
'diapreresnet56_svhn': 'diapreresnet56_svhn',
'diapreresnet110_cifar10': 'diapreresnet110_cifar10',
'diapreresnet110_cifar100': 'diapreresnet110_cifar100',
'diapreresnet110_svhn': 'diapreresnet110_svhn',
'diapreresnet164bn_cifar10': 'diapreresnet164bn_cifar10',
'diapreresnet164bn_cifar100': 'diapreresnet164bn_cifar100',
'diapreresnet164bn_svhn': 'diapreresnet164bn_svhn',
'diapreresnet1001_cifar10': 'diapreresnet1001_cifar10',
'diapreresnet1001_cifar100': 'diapreresnet1001_cifar100',
'diapreresnet1001_svhn': 'diapreresnet1001_svhn',
'diapreresnet1202_cifar10': 'diapreresnet1202_cifar10',
'diapreresnet1202_cifar100': 'diapreresnet1202_cifar100',
'diapreresnet1202_svhn': 'diapreresnet1202_svhn',
'isqrtcovresnet18': 'isqrtcovresnet18',
'isqrtcovresnet34': 'isqrtcovresnet34',
'isqrtcovresnet50': 'isqrtcovresnet50',
'isqrtcovresnet50b': 'isqrtcovresnet50b',
'isqrtcovresnet101': 'isqrtcovresnet101',
'isqrtcovresnet101b': 'isqrtcovresnet101b',
'resnetd50b': 'resnetd50b',
'resnetd101b': 'resnetd101b',
'resnetd152b': 'resnetd152b',
'octresnet10_ad2': 'octresnet10_ad2',
'octresnet50b_ad2': 'octresnet50b_ad2',
'resnet10_cub': 'resnet10_cub',
'resnet12_cub': 'resnet12_cub',
'resnet14_cub': 'resnet14_cub',
'resnetbc14b_cub': 'resnetbc14b_cub',
'resnet16_cub': 'resnet16_cub',
'resnet18_cub': 'resnet18_cub',
'resnet26_cub': 'resnet26_cub',
'resnetbc26b_cub': 'resnetbc26b_cub',
'resnet34_cub': 'resnet34_cub',
'resnetbc38b_cub': 'resnetbc38b_cub',
'resnet50_cub': 'resnet50_cub',
'resnet50b_cub': 'resnet50b_cub',
'resnet101_cub': 'resnet101_cub',
'resnet101b_cub': 'resnet101b_cub',
'resnet152_cub': 'resnet152_cub',
'resnet152b_cub': 'resnet152b_cub',
'resnet200_cub': 'resnet200_cub',
'resnet200b_cub': 'resnet200b_cub',
'seresnet10_cub': 'seresnet10_cub',
'seresnet12_cub': 'seresnet12_cub',
'seresnet14_cub': 'seresnet14_cub',
'seresnetbc14b_cub': 'seresnetbc14b_cub',
'seresnet16_cub': 'seresnet16_cub',
'seresnet18_cub': 'seresnet18_cub',
'seresnet26_cub': 'seresnet26_cub',
'seresnetbc26b_cub': 'seresnetbc26b_cub',
'seresnet34_cub': 'seresnet34_cub',
'seresnetbc38b_cub': 'seresnetbc38b_cub',
'seresnet50_cub': 'seresnet50_cub',
'seresnet50b_cub': 'seresnet50b_cub',
'seresnet101_cub': 'seresnet101_cub',
'seresnet101b_cub': 'seresnet101b_cub',
'seresnet152_cub': 'seresnet152_cub',
'seresnet152b_cub': 'seresnet152b_cub',
'seresnet200_cub': 'seresnet200_cub',
'seresnet200b_cub': 'seresnet200b_cub',
'mobilenet_w1_cub': 'mobilenet_w1_cub',
'mobilenet_w3d4_cub': 'mobilenet_w3d4_cub',
'mobilenet_wd2_cub': 'mobilenet_wd2_cub',
'mobilenet_wd4_cub': 'mobilenet_wd4_cub',
'fdmobilenet_w1_cub': 'fdmobilenet_w1_cub',
'fdmobilenet_w3d4_cub': 'fdmobilenet_w3d4_cub',
'fdmobilenet_wd2_cub': 'fdmobilenet_wd2_cub',
'fdmobilenet_wd4_cub': 'fdmobilenet_wd4_cub',
'proxylessnas_cpu_cub': 'proxylessnas_cpu_cub',
'proxylessnas_gpu_cub': 'proxylessnas_gpu_cub',
'proxylessnas_mobile_cub': 'proxylessnas_mobile_cub',
'proxylessnas_mobile14_cub': 'proxylessnas_mobile14_cub',
'ntsnet_cub': 'ntsnet_cub',
'fcn8sd_resnetd50b_voc': 'fcn8sd_resnetd50b_voc',
'fcn8sd_resnetd101b_voc': 'fcn8sd_resnetd101b_voc',
'fcn8sd_resnetd50b_coco': 'fcn8sd_resnetd50b_coco',
'fcn8sd_resnetd101b_coco': 'fcn8sd_resnetd101b_coco',
'fcn8sd_resnetd50b_ade20k': 'fcn8sd_resnetd50b_ade20k',
'fcn8sd_resnetd101b_ade20k': 'fcn8sd_resnetd101b_ade20k',
'fcn8sd_resnetd50b_cityscapes': 'fcn8sd_resnetd50b_cityscapes',
'fcn8sd_resnetd101b_cityscapes': 'fcn8sd_resnetd101b_cityscapes',
'pspnet_resnetd50b_voc': 'pspnet_resnetd50b_voc',
'pspnet_resnetd101b_voc': 'pspnet_resnetd101b_voc',
'pspnet_resnetd50b_coco': 'pspnet_resnetd50b_coco',
'pspnet_resnetd101b_coco': 'pspnet_resnetd101b_coco',
'pspnet_resnetd50b_ade20k': 'pspnet_resnetd50b_ade20k',
'pspnet_resnetd101b_ade20k': 'pspnet_resnetd101b_ade20k',
'pspnet_resnetd50b_cityscapes': 'pspnet_resnetd50b_cityscapes',
'pspnet_resnetd101b_cityscapes': 'pspnet_resnetd101b_cityscapes',
'deeplabv3_resnetd50b_voc': 'deeplabv3_resnetd50b_voc',
'deeplabv3_resnetd101b_voc': 'deeplabv3_resnetd101b_voc',
'deeplabv3_resnetd152b_voc': 'deeplabv3_resnetd152b_voc',
'deeplabv3_resnetd50b_coco': 'deeplabv3_resnetd50b_coco',
'deeplabv3_resnetd101b_coco': 'deeplabv3_resnetd101b_coco',
'deeplabv3_resnetd152b_coco': 'deeplabv3_resnetd152b_coco',
'deeplabv3_resnetd50b_ade20k': 'deeplabv3_resnetd50b_ade20k',
'deeplabv3_resnetd101b_ade20k': 'deeplabv3_resnetd101b_ade20k',
'deeplabv3_resnetd50b_cityscapes': 'deeplabv3_resnetd50b_cityscapes',
'deeplabv3_resnetd101b_cityscapes': 'deeplabv3_resnetd101b_cityscapes',
'superpointnet': 'superpointnet',
# 'oth_superpointnet': 'oth_superpointnet',
}
for key in _models:
#root = os.path.join("./", "pre_models")
print(key)
print("\n")
try:
file_path = get_model_file(model_name=_models[key], local_model_store_dir_path = os.path.join("./", "pre_models"))
except:
continue