| |
|
|
| import torch as th |
|
|
| from torch import nn |
|
|
|
|
| class GlobalAvgPool(nn.Module): |
| def __init__(self): |
| super(GlobalAvgPool, self).__init__() |
|
|
| def forward(self, x): |
| return th.mean(x, dim=[-2, -1]) |
|
|
|
|
| def get_model(args): |
| assert args.type in ['2d', '3d', 'vmz', 's3d', 'vae'] |
| if args.type == '2d': |
| print('Loading 2D-ResNet-152 ...') |
| import torchvision.models as models |
| model = models.resnet152(pretrained=True) |
| model = nn.Sequential(*list(model.children())[:-2], GlobalAvgPool()) |
| model = model.cuda() |
| elif args.type == 'vmz': |
| print('Loading VMZ ...') |
| from vmz34 import r2plus1d_34 |
| model = r2plus1d_34(pretrained_path=args.vmz_model_path, pretrained_num_classes=487) |
| model = model.cuda() |
| elif args.type == 's3d': |
| |
| from mmpt.processors.models.s3dg import S3D |
| model = S3D('pretrained_models/s3d_dict.npy', 512) |
| model.load_state_dict(th.load('pretrained_models/s3d_howto100m.pth')) |
| model = model.cuda() |
|
|
| elif args.type == '3d': |
| print('Loading 3D-ResneXt-101 ...') |
| from videocnn.models import resnext |
| model = resnext.resnet101( |
| num_classes=400, |
| shortcut_type='B', |
| cardinality=32, |
| sample_size=112, |
| sample_duration=16, |
| last_fc=False) |
| model = model.cuda() |
| model_data = th.load(args.resnext101_model_path) |
| model.load_state_dict(model_data) |
| elif args.type == 'vae': |
| from openaivae import OpenAIParallelDiscreteVAE |
| model = OpenAIParallelDiscreteVAE() |
| model = model.cuda() |
| else: |
| raise ValueError("model not supported yet.") |
|
|
| model.eval() |
| print('loaded') |
| return model |
|
|
