Traceback (most recent call last):


 "train.py", line 43, in <module>

main()


 "train.py", line 39, in main

Dense2pModel.train_net(args=args)


 "/content/dense2p/dense2p.py", line 687, in train_net

launch_train_with_config(traincfg, trainer)


 "/usr/local/lib/python3.6/dist-packages/tensorpack/train/interface.py", line 87, in launch_train_with_config

model._build_graph_get_cost, model.get_optimizer)


 "/usr/local/lib/python3.6/dist-packages/tensorpack/utils/argtools.py", line 176, in wrapper

return func(*args, **kwargs)


 "/usr/local/lib/python3.6/dist-packages/tensorpack/train/tower.py", line 204, in setup_graph

train_callbacks = self._setup_graph(input, get_cost_fn, get_opt_fn)


 "/usr/local/lib/python3.6/dist-packages/tensorpack/train/trainers.py", line 186, in _setup_graph

self._make_get_grad_fn(input, get_cost_fn, get_opt_fn), get_opt_fn)


 "/usr/local/lib/python3.6/dist-packages/tensorpack/graph_builder/training.py", line 223, in build

use_vs=[False] + [True] * (len(self.towers) - 1))


 "/usr/local/lib/python3.6/dist-packages/tensorpack/graph_builder/training.py", line 119, in build_on_towers

ret.append(func())


 "/usr/local/lib/python3.6/dist-packages/tensorpack/train/tower.py", line 242, in get_grad_fn

opt = get_opt_fn()


 "/usr/local/lib/python3.6/dist-packages/tensorpack/utils/argtools.py", line 200, in wrapper

value = func(*args, **kwargs)


 "/usr/local/lib/python3.6/dist-packages/tensorpack/graph_builder/model_desc.py", line 230, in get_optimizer

return self.optimizer()


 "/usr/local/lib/python3.6/dist-packages/tensorpack/tfutils/scope_utils.py", line 52, in wrapper

return func(*args, **kwargs)


 "/content/dense2p/dense2p.py", line 66, in optimizer

lr = tf.get_variable('learning_rate', initializer=0.003, trainable=False)


 "/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/variable_scope.py", line 1487, in get_variable

aggregation=aggregation)


 "/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/variable_scope.py", line 1237, in get_variable

aggregation=aggregation)


 "/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/variable_scope.py", line 540, in get_variable

aggregation=aggregation)


 "/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/variable_scope.py", line 492, in _true_getter

aggregation=aggregation)


 "/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/variable_scope.py", line 879, in _get_single_variable

"reuse=tf.AUTO_REUSE in VarScope?" % name)
ValueError: Variable learning_rate does not exist, or was not created with tf.get_variable(). Did you mean to set reuse=tf.AUTO_REUSE in VarScope?

Traceback (most recent call last):


 "tools/train.py", line 142, in <module>

main()


 "tools/train.py", line 138, in main

meta=meta)


 "/home/mmdetection/mmdet/apis/train.py", line 111, in train_detector

meta=meta)


 "/home/mmdetection/mmdet/apis/train.py", line 235, in _non_dist_train

runner.run(data_loaders, cfg.workflow, cfg.total_epochs)


 "/usr/local/lib/python3.5/dist-packages/mmcv-0.4.0-py3.5-linux-x86_64.egg/mmcv/runner/runner.py", line 359, in run

epoch_runner(data_loaders[i], **kwargs)


 "/usr/local/lib/python3.5/dist-packages/mmcv-0.4.0-py3.5-linux-x86_64.egg/mmcv/runner/runner.py", line 263, in train

self.model, data_batch, train_mode=True, **kwargs)


 "/home/mmdetection/mmdet/apis/train.py", line 75, in batch_processor

losses = model(**data)


 "/usr/local/lib/python3.5/dist-packages/torch/nn/modules/module.py", line 493, in __call__

result = self.forward(*input, **kwargs)


 "/usr/local/lib/python3.5/dist-packages/torch/nn/parallel/data_parallel.py", line 153, in forward

return self.gather(outputs, self.output_device)


 "/usr/local/lib/python3.5/dist-packages/torch/nn/parallel/data_parallel.py", line 165, in gather

return gather(outputs, output_device, dim=self.dim)


 "/usr/local/lib/python3.5/dist-packages/torch/nn/parallel/scatter_gather.py", line 67, in gather

return gather_map(outputs)


 "/usr/local/lib/python3.5/dist-packages/torch/nn/parallel/scatter_gather.py", line 59, in gather_map

raise ValueError('All dicts must have the same number of keys')
ValueError: All dicts must have the same number of keys
