我想从tf.estimator.Estimator模型构建预测变量。因此，我需要指定一个input_receiver_fn，以指定从接收方张量到将由预测变量传递给model_fn的特征的预处理图。

以下是估算器的eval_input_fn的示例：

def eval_input_fn(params):
    ds = tf.data.Dataset.from_generator(
        generator=Eval_Generator(params),output_types=(tf.uint16,tf.uint16),output_shapes = ([3]+params['crop_size'],[2]+params['crop_size']))
    augmentations = [Convert,Downsample,Clip]
    ds = ds.repeat()
    for augmentation in augmentations:
        ds = ds.map(augmentation,num_parallel_calls=params['threads'])
    ds = ds.batch(1).prefetch(None)
    return ds

我将增强函数从接受两个参数（功能：tf.Tensor，标签：tf.Tensor）更改为仅接受一个参数（功能：tf.Tensor），并编写了如下所示的input_receiver_fn：>

def serving_input_receiver_fn():
    rec_raw = tf.placeholder(tf.float32,[3,256,256],name='raw')
    raw = Convert(rec_raw)
    raw = Downsample(raw)
    raw = Clip(raw)
    raw = tf.expand_dims(raw,0)
    return tf.estimator.export.TensorServingInputReceiver(features=raw,receiver_tensors=rec_raw)

该函数返回以下对象：

TensorServingInputReceiver(features=<tf.Tensor 'ExpandDims_1:0' shape=(1,3,128,128) dtype=float32>,receiver_tensors={'input': <tf.Tensor 'raw:0' shape=(3,256) dtype=float32>},receiver_tensors_alternatives=None)

这似乎很正确。但是当它尝试通过以下方式实例化预测变量时：

config = tf.estimator.Runconfig(model_dir = params['model_dir'])
estimator = tf.estimator.Estimator(model_fn=model_fn,params=params,config=config)
predict_fn = tf.contrib.predictor.from_estimator(estimator,serving_input_receiver_fn)

我将收到以下错误消息：

INFO:tensorflow:Calling model_fn.
Traceback (most recent call last):
  File "/home/jrumber/anaconda3/envs/tf1.12_gpuenv/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py",line 510,in _apply_op_helper
    preferred_dtype=default_dtype)
  File "/home/jrumber/anaconda3/envs/tf1.12_gpuenv/lib/python3.6/site-packages/tensorflow/python/framework/ops.py",line 1146,in internal_convert_to_tensor
    ret = conversion_func(value,dtype=dtype,name=name,as_ref=as_ref)
  File "/home/jrumber/anaconda3/envs/tf1.12_gpuenv/lib/python3.6/site-packages/tensorflow/python/framework/constant_op.py",line 229,in _constant_tensor_conversion_function
    return constant(v,name=name)
  File "/home/jrumber/anaconda3/envs/tf1.12_gpuenv/lib/python3.6/site-packages/tensorflow/python/framework/constant_op.py",line 208,in constant
    value,shape=shape,verify_shape=verify_shape))
  File "/home/jrumber/anaconda3/envs/tf1.12_gpuenv/lib/python3.6/site-packages/tensorflow/python/framework/tensor_util.py",line 430,in make_tensor_proto
    raise ValueError("None values not supported.")
ValueError: None values not supported.

  File "/home/jrumber/anaconda3/envs/tf1.12_gpuenv/lib/python3.6/site-packages/tensorflow/contrib/predictor/predictor_factories.py",line 105,in from_estimator
    config=config)
  File "/home/jrumber/anaconda3/envs/tf1.12_gpuenv/lib/python3.6/site-packages/tensorflow/contrib/predictor/core_estimator_predictor.py",line 72,in __init__
    serving_input_receiver,estimator,output_key)
  File "/home/jrumber/anaconda3/envs/tf1.12_gpuenv/lib/python3.6/site-packages/tensorflow/contrib/predictor/core_estimator_predictor.py",line 37,in _get_signature_def
    estimator.config)
  File "/home/jrumber/anaconda3/envs/tf1.12_gpuenv/lib/python3.6/site-packages/tensorflow/python/estimator/estimator.py",line 235,in public_model_fn
    return self._call_model_fn(features,labels,mode,config)
  File "/home/jrumber/anaconda3/envs/tf1.12_gpuenv/lib/python3.6/site-packages/tensorflow/python/estimator/estimator.py",line 1195,in _call_model_fn
    model_fn_results = self._model_fn(features=features,**kwargs)
  File "/fast/AG_Kainmueller/jrumber/flylight_01/train_tf.py",line 227,in model_fn
    gt,fg = tf.unstack(labels,num=2,axis=1)
  File "/home/jrumber/anaconda3/envs/tf1.12_gpuenv/lib/python3.6/site-packages/tensorflow/python/ops/array_ops.py",line 1027,in unstack
    return gen_array_ops.unpack(value,num=num,axis=axis,name=name)
  File "/home/jrumber/anaconda3/envs/tf1.12_gpuenv/lib/python3.6/site-packages/tensorflow/python/ops/gen_array_ops.py",line 9429,in unpack
    "Unpack",value=value,name=name)
  File "/home/jrumber/anaconda3/envs/tf1.12_gpuenv/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py",line 528,in _apply_op_helper
    (input_name,err))
ValueError: Tried to convert 'value' to a tensor and failed. Error: None values not supported.

由于我的model_fn可能有问题，我也将其发布：

def model_fn(features,params):
    gt,axis=1)
    gt.set_shape([1]+params['input_size'])
    fg.set_shape([1]+params['input_size'])
    features.set_shape([1,3]+params['input_size'])
    # first layer to set input_shape
    features = tf.keras.layers.Conv3D(
        input_shape = tuple([3]+params['input_size']),data_format = 'channels_first',filters  = params['chan'],kernel_size = [3,3],strides=(1,1,1),padding='same',activation='relu',kernel_regularizer=tf.keras.regularizers.l2(l=0.01))(features)
    # U-Net 
    out = unet(features,params['unet_initial_filters'],params['width_factor'],params['architecture'])
    # Embedding conv pass
    output_batched = conv_pass(
        out,kernel_size=1,num_fmaps=params['chan'],num_repetitions=1,activation=None,name='conv_embedding')
    output = tf.squeeze(output_batched)
    # Fg/Bg segmentation conv pass
    mask_batched = conv_pass(
        out,num_fmaps=1,activation='sigmoid',name='conv_mask')
    prob_mask = tf.squeeze(mask_batched)
    logits_mask = logit(prob_mask)
    # store predictions in dict
    predictions = {
        'prob_mask': tf.expand_dims(prob_mask,0),'embedding': output,'gt': tf.squeeze(gt,0)}
    #  RAIN mode
    if mode == tf.contrib.learn.ModeKeys.TRAIN:
        loss,l_var,l_dist,l_reg = discriminative_loss_single(prediction=output,correct_label=tf.squeeze(gt),feature_dim=params['chan'],delta_v= params['delta_v'],delta_d= params['delta_d'],param_var= params['param_var'],param_dist= params['param_dist'],param_reg= params['param_reg']
                                                                )
        mask_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
                    labels=tf.squeeze(fg),logits=logits_mask))
        reg_loss =  tf.losses.get_regularization_loss() * 1e-6
        loss += mask_loss + reg_loss
        opt = tf.train.AdamOptimizer(
                learning_rate=0.5e-4,beta1=0.95,beta2=0.999,epsilon=1e-8)
        optimizer = opt.minimize(loss,global_step=tf.train.get_global_step())
        global_step = tf.Variable(1,name='global_step',trainable=False,dtype=tf.int32)
        increment_global_step_op = tf.assign(global_step,global_step+1)
        logging_hook = tf.train.LoggingTensorHook({"loss" : loss,'global_step':increment_global_step_op},every_n_iter=1)
        return tf.estimator.EstimatorSpec(mode=mode,predictions=predictions,loss=loss,train_op=optimizer,training_hooks=[logging_hook])
    # PREDICT mode
    if mode == tf.estimator.ModeKeys.PREDICT:
        export_outputs = {
            'predict_output': tf.estimator.export.PredictOutput(predictions)
        }
        return tf.estimator.EstimatorSpec(mode=mode,export_outputs=export_outputs)
    # EVAL mode
    if mode == tf.estimator.ModeKeys.EVAL:
        export_outputs = {
            'eval_output': tf.estimator.export.EvalOutput(predictions)
        }
        return tf.estimator.EstimatorSpec(mode=mode,export_outputs=export_outputs)

有人在这里发现我的错误吗？ 最好：）