如何解决ValueError:Python输入与input_signature不兼容:
系统信息
- OS平台和发行版:CentOS Linux版本7.7.1908 -TensorFlow版本:2.3.0
我正在关注以下示例:https://www.tensorflow.org/tutorials/text/image_captioning?hl=en
它可以正常工作并保存检查点,我现在想将其转换为TF Lite模型。
以下是完整转换代码的链接:https://colab.research.google.com/drive/1GJkGcwWvDAWMooTsECzuSRUSPbirADhb?usp=sharing
这里是完整火车代码的链接: https://colab.research.google.com/drive/1X2d9WW1EMEzN8Rgva3rtjevP0T_jFccj?usp=sharing
我也关注isssue#32999
这是我要保存的内容,它们将转换推理图:
@tf.function
def evaluate(image):
hidden = decoder.reset_states(batch_size=1)
temp_input = tf.expand_dims(load_image(image)[0],0)
img_tensor_val = image_features_extract_model(temp_input)
img_tensor_val = tf.reshape(img_tensor_val,(img_tensor_val.shape[0],-1,img_tensor_val.shape[3]))
features = encoder(img_tensor_val)
dec_input = tf.expand_dims([tokenizer.word_index['<start>']],0)
result = []
for i in range(max_length):
predictions,hidden,attention_weights = decoder(dec_input,features,hidden)
predicted_id = tf.random.categorical(predictions,1)[0][0]
# print(tokenizer.index_word)
print(predicted_id,predicted_id.dtype)
# for key,value in tokenizer.index_word.items():
# key = tf.convert_to_tensor(key)
# tf.dtypes.cast(key,tf.int64)
# print(key)
# print(tokenizer.index_word)
result.append(predicted_id)
# if tokenizer.index_word[predicted_id] == '<end>':
# return result
dec_input = tf.expand_dims([predicted_id],0)
return result
export_dir = "./"
tflite_enc_input = ''
ckpt.f = evaluate
to_save = evaluate.get_concrete_function('')
converter = tf.lite.TFLiteConverter.from_concrete_functions([to_save])
tflite_model = converter.convert()
但是我得到这个错误
ValueError: in user code:
convert2savedmodel.py:310 evaluate *
predictions,hidden)
/share/nishome/19930072_0/miniconda3/envs/tf2.3/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer.py:985 __call__ **
outputs = call_fn(inputs,*args,**kwargs)
/share/nishome/19930072_0/miniconda3/envs/tf2.3/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py:780 __call__
result = self._call(*args,**kwds)
/share/nishome/19930072_0/miniconda3/envs/tf2.3/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py:840 _call
return self._stateless_fn(*args,**kwds)
/share/nishome/19930072_0/miniconda3/envs/tf2.3/lib/python3.7/site-packages/tensorflow/python/eager/function.py:2828 __call__
graph_function,args,kwargs = self._maybe_define_function(args,kwargs)
/share/nishome/19930072_0/miniconda3/envs/tf2.3/lib/python3.7/site-packages/tensorflow/python/eager/function.py:3171 _maybe_define_function
*args,**kwargs)
/share/nishome/19930072_0/miniconda3/envs/tf2.3/lib/python3.7/site-packages/tensorflow/python/eager/function.py:2622 canonicalize_function_inputs
self._flat_input_signature)
/share/nishome/19930072_0/miniconda3/envs/tf2.3/lib/python3.7/site-packages/tensorflow/python/eager/function.py:2713 _convert_inputs_to_signature
format_error_message(inputs,input_signature))
ValueError: Python inputs incompatible with input_signature:
inputs: (
Tensor("ExpandDims_1:0",shape=(1,1),dtype=int32),Tensor("cnn__encoder/StatefulPartitionedCall:0",64,256),dtype=float32),Tensor("zeros:0",512),dtype=float32))
input_signature: (
TensorSpec(shape=(1,dtype=tf.int64,name=None),TensorSpec(shape=(1,dtype=tf.float32,name=None))
编码器型号:
class CNN_Encoder(tf.keras.Model):
def __init__(self,embedding):
super(CNN_Encoder,self).__init__()
# shape after fc == (batch_size,embedding_dim)
self.fc = tf.keras.layers.Dense(embedding_dim)
@tf.function(input_signature=[tf.TensorSpec(shape=(1,features_shape),dtype=tf.dtypes.float32)])
def call(self,x):
x = self.fc(x)
x = tf.nn.relu(x)
return x
解码器型号:
class RNN_Decoder(tf.keras.Model):
def __init__(self,embedding_dim,units,vocab_size):
super(RNN_Decoder,self).__init__()
self.units = units
self.embedding = tf.keras.layers.Embedding(vocab_size,embedding_dim)
self.gru = tf.keras.layers.GRU(self.units,return_sequences=True,return_state=True,recurrent_initializer='glorot_uniform',unroll = True)
self.fc1 = tf.keras.layers.Dense(self.units)
self.fc2 = tf.keras.layers.Dense(vocab_size)
self.attention = BahdanauAttention(self.units)
@tf.function(input_signature=[tf.TensorSpec(shape=[1,1],dtype=tf.int64),tf.TensorSpec(shape=[1,256],dtype=tf.float32),512],dtype=tf.float32)])
def call(self,x,hidden):
context_vector,attention_weights = self.attention(features,hidden)
#x shape after passing through embedding == (batch_size,1,embedding_dim)
x = self.embedding(x)
#x shape after concatenation == (batch_size,embedding_dim + hidden_size)
x = tf.concat([tf.expand_dims(context_vector,x],axis=-1)
output,state = self.gru(x)
#shape == (batch_size,max_length,hidden_size)
x = self.fc1(output)
#x shape == (batch_size,hidden_size)
x = tf.reshape(x,(-1,x.shape[2]))
# output shape == (batch_size * max_length,vocab)
x = self.fc2(x)
return x,state,attention_weights
def reset_states(self,batch_size):
return tf.zeros((batch_size,self.units))
我只是将tf.function更改为int32,如下所示:
@tf.function(input_signature=[tf.TensorSpec(shape=[1,dtype=tf.int32),dtype=tf.float32)])
但是出现另一个错误:
ValueError:Python输入与input_signature不兼容:
Tensor("ExpandDims_2:0",dtype=int64),Tensor("rnn__decoder/StatefulPartitionedCall:1",dtype=float32))
input_signature: (
TensorSpec(shape=(1,dtype=tf.int32,name=None))```
Why the dtypes of inputs change from int64 to int32?
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