def inception_v3(pretrained=False, model_root=None, **kwargs):
if pretrained:
if 'transform_input' not in kwargs:
kwargs['transform_input'] = True
model = Inception3(**kwargs)
misc.load_state_dict(model, model_urls['inception_v3_google'],
model_root)
return model
return Inception3(**kwargs)
def resnet152(pretrained=False, model_root=None, **kwargs):
model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
if pretrained:
misc.load_state_dict(model, model_urls['resnet152'], model_root)
return model
def resnet34(pretrained=False, model_root=None, **kwargs):
model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
if pretrained:
misc.load_state_dict(model, model_urls['resnet34'], model_root)
return model
def sphere64a(pretrained=False, model_root=None, stage = 0):
model=SphereNet(BasicBlock, [3,8,16,3], stage)
if pretrained:
misc.load_state_dict(model, model_root)
return model
# compute the transformation between the current source and nearest destination points
T, R, t = best_fit_transform(vertices2, vertices1[indices])
# update the current source
vertices2 = (np.dot(R, vertices2.T)).T + t
# check error
mean_error = np.sqrt(np.mean(distances ** 2))
if np.abs(prev_error - mean_error) < 0.00001:
break
prev_error = mean_error
# print(mean_error)
return mean_error
if __name__ == "__main__":
print("loading model...")
dict_file = "/home/jdq/model/dict.cl_382500.cl"
model = net.sphere64a(pretrained=True, model_root=dict_file)
model = model.cuda()
print("Loading...\n")
state = torch.load(dict_file)
state_dict = state
misc.load_state_dict(model, dict_file)
print("evaluting...")
e = EvalToolBox()
e.get_micc_rmse(model)
# evalMICC()
