print( 'Invalid pool type %s specified. Defaulting to average pooling.' % pool_type) x = F.avg_pool3d(x, kernel_size=(x.size(2), x.size(3), x.size(4)), padding=padding, count_include_pad=count_include_pad) return x if __name__ == '__main__': import torchvision import numpy as np import torch from torch.autograd import Variable from dpn import dpn107 dpn107_2d = dpn107() dpn107_i3d = i3d_dpn107() data = np.ones((1, 3, 224, 224), dtype=np.float32) tensor = torch.from_numpy(data) inputs = Variable(tensor) out1 = dpn107_2d(inputs) print(out1) data2 = np.ones((1, 3, 32, 224, 224), dtype=np.float32) tensor2 = torch.from_numpy(data2) inputs2 = Variable(tensor2) out2 = dpn107_i3d(inputs2) print(out2)
def create_model(model_name, num_classes=1000, pretrained=False, **kwargs): if 'test_time_pool' in kwargs: test_time_pool = kwargs.pop('test_time_pool') else: test_time_pool = True if 'extra' in kwargs: extra = kwargs.pop('extra') else: extra = True if model_name == 'dpn68': model = dpn68(num_classes=num_classes, pretrained=pretrained, test_time_pool=test_time_pool) elif model_name == 'dpn68b': model = dpn68b(num_classes=num_classes, pretrained=pretrained, test_time_pool=test_time_pool) elif model_name == 'dpn92': model = dpn92(num_classes=num_classes, pretrained=pretrained, test_time_pool=test_time_pool, extra=extra) elif model_name == 'dpn98': model = dpn98(num_classes=num_classes, pretrained=pretrained, test_time_pool=test_time_pool) elif model_name == 'dpn131': model = dpn131(num_classes=num_classes, pretrained=pretrained, test_time_pool=test_time_pool) elif model_name == 'dpn107': model = dpn107(num_classes=num_classes, pretrained=pretrained, test_time_pool=test_time_pool) elif model_name == 'resnet18': model = resnet18(num_classes=num_classes, pretrained=pretrained, **kwargs) elif model_name == 'resnet34': model = resnet34(num_classes=num_classes, pretrained=pretrained, **kwargs) elif model_name == 'resnet50': model = resnet50(num_classes=num_classes, pretrained=pretrained, **kwargs) elif model_name == 'resnet101': model = resnet101(num_classes=num_classes, pretrained=pretrained, **kwargs) elif model_name == 'resnet152': model = resnet152(num_classes=num_classes, pretrained=pretrained, **kwargs) elif model_name == 'densenet121': model = densenet121(num_classes=num_classes, pretrained=pretrained, **kwargs) elif model_name == 'densenet161': model = densenet161(num_classes=num_classes, pretrained=pretrained, **kwargs) elif model_name == 'densenet169': model = densenet169(num_classes=num_classes, pretrained=pretrained, **kwargs) elif model_name == 'densenet201': model = densenet201(num_classes=num_classes, pretrained=pretrained, **kwargs) elif model_name == 'inception_v3': model = inception_v3(num_classes=num_classes, pretrained=pretrained, transform_input=False, **kwargs) else: assert False, "Unknown model architecture (%s)" % model_name return model