问题描述
我正在使用Tensorflow 2.2，并尝试将模型转换为TensorRT。我以一个示例为例，该示例成功地适用于接受图像作为输入的模型。不幸的是，我冻结了一个接受TF Example作为输入而不是图像的模型。现在，尝试创建tf数据集管道已成为噩梦。
我的代码是：
def get_dataset(images_dir,annotation_path,batch_size,input_size,dtype=tf.float32):
image_ids = None
coco = COCO(annotation_file=annotation_path)
image_ids = coco.getImgIds()
image_paths = []
for image_id in image_ids:
coco_img = coco.imgs[image_id]
image_paths.append(os.path.join(images_dir,coco_img['file_name']))
dataset = tf.data.Dataset.from_tensor_slices(image_paths)
def conv_jpeg_to_tfexample_tensor(input_img_):
feature_dict = {
'image/encoded': dataset_util.bytes_feature(input_img_)
}
temp_var = tf.train.Features(feature=feature_dict)
file_ex = tf.train.Example(features=temp_var).SerializetoString()
return tf.convert_to_tensor(file_ex)
def preprocess_fn(path):
image = tf.io.read_file(path)
if input_size is not None:
image = tf.image.decode_jpeg(image,channels=3)
image = tf.image.convert_image_dtype(image,tf.float32)
image = tf.image.resize(image,size=(input_size,input_size))
image = tf.cast(image,tf.uint8)
image = tf.image.encode_jpeg(image) #.numpy()
return image
dataset = dataset.map(map_func=preprocess_fn,num_parallel_calls=3)
dataset = dataset.map(map_func=conv_jpeg_to_tfexample_tensor,num_parallel_calls=3)
dataset = dataset.batch(batch_size)
dataset = dataset.repeat(count=1)
return dataset,image_ids
dataset,image_ids = get_dataset(
images_dir=args.data_dir,annotation_path=args.annotation_path,batch_size=args.batch_size,input_size=args.input_size)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-152-193739a79a8a> in <module>
5 batch_size=args.batch_size,----> 6 input_size=args.input_size)
<ipython-input-151-1d1f15019758> in get_dataset(images_dir,dtype)
76 dataset = dataset.map(map_func=preprocess_fn,num_parallel_calls=3)
---> 77 dataset = dataset.map(map_func=conv_jpeg_to_tfexample_tensor,num_parallel_calls=3)
78 dataset = dataset.batch(batch_size)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/data/ops/dataset_ops.py in map(self,map_func,num_parallel_calls,deterministic)
1626 num_parallel_calls,1627 deterministic,-> 1628 preserve_cardinality=True)
1629
1630 def flat_map(self,map_func):
/opt/conda/lib/python3.7/site-packages/tensorflow/python/data/ops/dataset_ops.py in __init__(self,input_dataset,deterministic,use_inter_op_parallelism,preserve_cardinality,use_legacy_function)
4018 self._transformation_name(),4019 dataset=input_dataset,-> 4020 use_legacy_function=use_legacy_function)
4021 if deterministic is None:
4022 self._deterministic = "default"
/opt/conda/lib/python3.7/site-packages/tensorflow/python/data/ops/dataset_ops.py in __init__(self,func,transformation_name,dataset,input_classes,input_shapes,input_types,input_structure,add_to_graph,use_legacy_function,defun_kwargs)
3219 with tracking.resource_tracker_scope(resource_tracker):
3220 # Todo(b/141462134): Switch to using garbage collection.
-> 3221 self._function = wrapper_fn.get_concrete_function()
3222
3223 if add_to_graph:
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in get_concrete_function(self,*args,**kwargs)
2530 """
2531 graph_function = self._get_concrete_function_garbage_collected(
-> 2532 *args,**kwargs)
2533 graph_function._garbage_collector.release() # pylint: disable=protected-access
2534 return graph_function
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_garbage_collected(self,**kwargs)
2494 args,kwargs = None,None
2495 with self._lock:
-> 2496 graph_function,args,kwargs = self._maybe_define_function(args,kwargs)
2497 if self.input_signature:
2498 args = self.input_signature
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self,kwargs)
2775
2776 self._function_cache.missed.add(call_context_key)
-> 2777 graph_function = self._create_graph_function(args,kwargs)
2778 self._function_cache.primary[cache_key] = graph_function
2779 return graph_function,kwargs
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self,kwargs,override_flat_arg_shapes)
2665 arg_names=arg_names,2666 override_flat_arg_shapes=override_flat_arg_shapes,-> 2667 capture_by_value=self._capture_by_value),2668 self._function_attributes,2669 # Tell the ConcreteFunction to clean up its graph once it goes out of
/opt/conda/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name,python_func,signature,func_graph,autograph,autograph_options,add_control_dependencies,arg_names,op_return_value,collections,capture_by_value,override_flat_arg_shapes)
979 _,original_func = tf_decorator.unwrap(python_func)
980
--> 981 func_outputs = python_func(*func_args,**func_kwargs)
982
983 # invariant: `func_outputs` contains only Tensors,CompositeTensors,/opt/conda/lib/python3.7/site-packages/tensorflow/python/data/ops/dataset_ops.py in wrapper_fn(*args)
3212 attributes=defun_kwargs)
3213 def wrapper_fn(*args): # pylint: disable=missing-docstring
-> 3214 ret = _wrapper_helper(*args)
3215 ret = structure.to_tensor_list(self._output_structure,ret)
3216 return [ops.convert_to_tensor(t) for t in ret]
/opt/conda/lib/python3.7/site-packages/tensorflow/python/data/ops/dataset_ops.py in _wrapper_helper(*args)
3154 nested_args = (nested_args,)
3155
-> 3156 ret = autograph.tf_convert(func,ag_ctx)(*nested_args)
3157 # If `func` returns a list of tensors,`nest.flatten()` and
3158 # `ops.convert_to_tensor()` would conspire to attempt to stack
/opt/conda/lib/python3.7/site-packages/tensorflow/python/autograph/impl/api.py in wrapper(*args,**kwargs)
263 except Exception as e: # pylint:disable=broad-except
264 if hasattr(e,'ag_error_Metadata'):
--> 265 raise e.ag_error_Metadata.to_exception(e)
266 else:
267 raise
TypeError: in user code:
<ipython-input-143-1d1f15019758>:53 conv_jpeg_to_tfexample_tensor *
feature_dict = {
/opt/conda/lib/python3.7/site-packages/object_detection/utils/dataset_util.py:34 bytes_feature *
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
TypeError: <tf.Tensor 'args_0:0' shape=() dtype=string> has type Tensor,but expected one of: bytes
解决方法
我本可以使用简单的鸟图像来重现您面临的错误。
重新创建错误的代码-
%tensorflow_version 2.x
import tensorflow as tf
from keras.preprocessing.image import load_img
from keras.preprocessing.image import img_to_array,array_to_img
from matplotlib import pyplot as plt
import numpy as np
from object_detection.utils import dataset_util
def load_file_and_process(path):
image = tf.io.read_file(path)
image = tf.image.decode_jpeg(image,channels=3)
image = tf.image.central_crop(image,np.random.uniform(0.50,1.00))
image = tf.cast(image,tf.uint8)
image = tf.image.encode_jpeg(image)
return image
train_dataset = tf.data.Dataset.list_files('/content/bird.jpg')
train_dataset = train_dataset.map(load_file_and_process)
def conv_jpeg_to_tfexample_tensor(input_img_):
feature_dict = {
'image/encoded': dataset_util.bytes_feature(input_img_)
}
temp_var = tf.train.Features(feature=feature_dict)
file_ex = tf.train.Example(features=temp_var).SerializeToString()
return tf.convert_to_tensor(file_ex)
train_dataset = train_dataset.map(map_func=conv_jpeg_to_tfexample_tensor,num_parallel_calls=3)
输出-
<MapDataset shapes: (),types: tf.string>
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-44-89ae6292ad21> in <module>()
28 return tf.convert_to_tensor(file_ex)
29
---> 30 train_dataset = train_dataset.map(map_func=conv_jpeg_to_tfexample_tensor,num_parallel_calls=3)
10 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/autograph/impl/api.py in wrapper(*args,**kwargs)
256 except Exception as e: # pylint:disable=broad-except
257 if hasattr(e,'ag_error_metadata'):
--> 258 raise e.ag_error_metadata.to_exception(e)
259 else:
260 raise
TypeError: in user code:
<ipython-input-44-89ae6292ad21>:23 conv_jpeg_to_tfexample_tensor *
feature_dict = {
/usr/local/lib/python3.6/dist-packages/object_detection/utils/dataset_util.py:30 bytes_feature *
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
TypeError: <tf.Tensor 'args_0:0' shape=() dtype=string> has type Tensor,but expected one of: bytes
建议您参考此tutorial，它解释了如何使用TFRecords读取和写入图像数据的端到端示例。
参考本教程，我为单个图像编写了TFRecord。
代码-
# This is an example,just using the bird image.
image_string = open('/content/bird.jpg','rb').read()
def _bytes_feature(value):
"""Returns a bytes_list from a string / byte."""
if isinstance(value,type(tf.constant(0))):
value = value.numpy() # BytesList won't unpack a string from an EagerTensor.
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
def _int64_feature(value):
"""Returns an int64_list from a bool / enum / int / uint."""
return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))
# Create a dictionary with features that may be relevant.
def image_example(image_string):
image_shape = tf.image.decode_jpeg(image_string).shape
feature = {
'height': _int64_feature(image_shape[0]),'width': _int64_feature(image_shape[1]),'depth': _int64_feature(image_shape[2]),'image_raw': _bytes_feature(image_string),}
return tf.train.Example(features=tf.train.Features(feature=feature))
for line in str(image_example(image_string)).split('\n')[:15]:
print(line)
record_file = 'images.tfrecords'
with tf.io.TFRecordWriter(record_file) as writer:
image_string = open('/content/bird.jpg','rb').read()
tf_example = image_example(image_string)
writer.write(tf_example.SerializeToString())
输出-
features {
feature {
key: "depth"
value {
int64_list {
value: 3
}
}
}
feature {
key: "height"
value {
int64_list {
value: 426
}
