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