tf.dataset，多路径输入，每批映射加载图片
tf.dataset, multiple path inputs, and mapping per batch to load images
我正在加载包含多个输入图像的数据集。输入图像路径应该只在批处理时解码，以便处理大型数据集。
数据集是N个图片路径输入，M个浮点输出。每个输入的图像具有不同的分辨率。
Data is ([img_input_1.png, img_input_2.png, ...], [0.65, 0.7, 0.8])
该模型在符号模式下使用 Keras 函数 api。
这是最近编辑的代码
from itertools import zip_longest
def read_image(path, shape):
try:
image = tf.io.read_file(path)
image = tf.image.decode_png(image)
image = tf.image.resize(image, [shape[1],shape[2]])
image /= 255.0
return image
except:
print('ERROR: preprocess_image: bad path', path)
def load_image(x, y, shp):
pout = [(k, x[k]) for k in x.keys()]
l1 = tf.convert_to_tensor(list(x))
l2 = tf.convert_to_tensor(list(x.values()))
pl = tf.map_fn(
lambda args: (read_image(args[0], shp), args[1]), [l1, l2], dtype=(tf.float32, tf.float32)
)
pl = {path: (pl[0][i], pl[1][i]) for i, path in enumerate(x)}
return (pl,y)
def dataset_prep(json_data, seq, batch_size):
# LOAD DATA FROM JSON
x,y = json_parse_x_y(json_data[seq])
xx = [*zip_longest(*x)] # NOTE: goes from variable sized input to {'input_N':...}
yy = [*zip_longest(*y)]
# GET SHAPES (hard coded atm)
lns = [[len(xxx)] for xxx in xx]
rzs = [[24,512,1],[96,512,1]] # TEMP TODO! grab grom [(v['h'],v['w'],v['c']) for v in xx]
shp = [*zip_longest(*[lns,rzs])]
shp = [list(s) for s in shp]
shp = [[*itertools.chain.from_iterable(s)] for s in shp]
xd = dict([[ "input_{}".format(i+1),np.array(y)] for i,y in [*enumerate(xx)]])
yd = dict([["output_{}".format(i+1),np.array(y)] for i,y in [*enumerate(yy)]])
ds = tf.data.Dataset.from_tensor_slices((xd, yd))
ds = ds.shuffle(10000)
ds = ds.repeat()
ds = ds.map(map_func=lambda x,y: load_image(x, y, shp), num_parallel_calls=AUTOTUNE)
ds = ds.batch(batch_size) if batch_size else ds
ds = ds.prefetch(AUTOTUNE)
return ds
这是我遇到的错误：
Traceback (most recent call last):
File "/home/me/.local/bin/wavfeat", line 11, in <module>
load_entry_point('wavfeat==0.1.0', 'console_scripts', 'wavfeat')()
File "/home/me/.local/lib/python3.6/site-packages/wavfeat/__main__.py", line 91, in main
analysis_batch_sql(obj)
File "/home/me/.local/lib/python3.6/site-packages/wavfeat/analysis_run_csv.py", line 50, in analysis_batch_sql
qy = [*map(lambda c: run_elm(c[0], c[1]), ch)]
File "/home/me/.local/lib/python3.6/site-packages/wavfeat/analysis_run_csv.py", line 50, in <lambda>
qy = [*map(lambda c: run_elm(c[0], c[1]), ch)]
File "/home/me/.local/lib/python3.6/site-packages/wavfeat/analysis_run_csv.py", line 23, in run_elm
out = fn(input, elm)
File "/home/me/.local/lib/python3.6/site-packages/wavfeat/one_sec_onset.py", line 196, in one_sec_onset_train
return train(input, elm)
File "/home/me/.local/lib/python3.6/site-packages/wavfeat/one_sec_onset.py", line 182, in train
ts = dataset_prep(jd, 'train', bc)
File "/home/me/.local/lib/python3.6/site-packages/wavfeat/one_sec_onset.py", line 123, in dataset_prep
ds = ds.map(map_func=lambda x,y: load_image(x, y, shp), num_parallel_calls=AUTOTUNE)
File "/home/me/.local/lib/python3.6/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 1146, in map
self, map_func, num_parallel_calls, preserve_cardinality=True)
File "/home/me/.local/lib/python3.6/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 3264, in __init__
use_legacy_function=use_legacy_function)
File "/home/me/.local/lib/python3.6/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 2591, in __init__
self._function = wrapper_fn._get_concrete_function_internal()
File "/home/me/.local/lib/python3.6/site-packages/tensorflow/python/eager/function.py", line 1366, in _get_concrete_function_internal
*args, **kwargs)
File "/home/me/.local/lib/python3.6/site-packages/tensorflow/python/eager/function.py", line 1360, in _get_concrete_function_internal_garbage_collected
graph_function, _, _ = self._maybe_define_function(args, kwargs)
File "/home/me/.local/lib/python3.6/site-packages/tensorflow/python/eager/function.py", line 1648, in _maybe_define_function
graph_function = self._create_graph_function(args, kwargs)
File "/home/me/.local/lib/python3.6/site-packages/tensorflow/python/eager/function.py", line 1541, in _create_graph_function
capture_by_value=self._capture_by_value),
File "/home/me/.local/lib/python3.6/site-packages/tensorflow/python/framework/func_graph.py", line 716, in func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
File "/home/me/.local/lib/python3.6/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 2585, in wrapper_fn
ret = _wrapper_helper(*args)
File "/home/me/.local/lib/python3.6/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 2530, in _wrapper_helper
ret = func(*nested_args)
File "/home/me/.local/lib/python3.6/site-packages/wavfeat/one_sec_onset.py", line 123, in <lambda>
ds = ds.map(map_func=lambda x,y: load_image(x, y, shp), num_parallel_calls=AUTOTUNE)
File "/home/me/.local/lib/python3.6/site-packages/wavfeat/one_sec_data_loader.py", line 91, in load_image
print("x['input_1'].values(): ", x['input_1'].values())
AttributeError: 'Tensor' object has no attribute 'values'
我在做什么阻止加载路径？
编辑：
在尝试 pandrey 的修复时，我遇到了输入错误。这是 from_tensor_slices 和 ds.map 之前以及之后的数据：
pre_from_tensor_slices x: {'input_1': array(['/media/me/sp_data/sp_data/datasets/chr_01/one_sec_onset_11_oac-leg/7388_39216_30--id=7388__sql_table=oac_1__sql_idx=405167__pitch=30__onset=39216.png',
'/media/me/sp_data/sp_data/datasets/chr_01/one_sec_onset_11_oac-leg/2447_864_27--id=2447__sql_table=oac_1__sql_idx=415458__pitch=27__onset=864.png',
'/media/me/sp_data/sp_data/datasets/chr_01/one_sec_onset_11_oac-leg/2386_20208_38--id=2386__sql_table=oac_1__sql_idx=433248__pitch=38__onset=20208.png',
...,
'/media/me/sp_data/sp_data/datasets/chr_01/one_sec_onset_11_oac-leg/6261_24528_57--id=6261__sql_table=oac_1__sql_idx=449753__pitch=57__onset=24528.png',
'/media/me/sp_data/sp_data/datasets/chr_01/one_sec_onset_11_oac-leg/3727_22944_31--id=3727__sql_table=oac_1__sql_idx=407620__pitch=31__onset=22944.png',
'/media/me/sp_data/sp_data/datasets/chr_01/one_sec_onset_11_oac-leg/1668_7056_60--id=1668__sql_table=oac_1__sql_idx=381152__pitch=60__onset=7056.png'],
dtype='<U162'), 'input_2': array(['/media/me/sp_data/sp_data/datasets/mel_01/one_sec_onset_11_oac-leg/7388_39216_30--id=7388__sql_table=oac_1__sql_idx=405167__pitch=30__onset=39216.png',
'/media/me/sp_data/sp_data/datasets/mel_01/one_sec_onset_11_oac-leg/2447_864_27--id=2447__sql_table=oac_1__sql_idx=415458__pitch=27__onset=864.png',
'/media/me/sp_data/sp_data/datasets/mel_01/one_sec_onset_11_oac-leg/2386_20208_38--id=2386__sql_table=oac_1__sql_idx=433248__pitch=38__onset=20208.png',
...,
'/media/me/sp_data/sp_data/datasets/mel_01/one_sec_onset_11_oac-leg/6261_24528_57--id=6261__sql_table=oac_1__sql_idx=449753__pitch=57__onset=24528.png',
'/media/me/sp_data/sp_data/datasets/mel_01/one_sec_onset_11_oac-leg/3727_22944_31--id=3727__sql_table=oac_1__sql_idx=407620__pitch=31__onset=22944.png',
'/media/me/sp_data/sp_data/datasets/mel_01/one_sec_onset_11_oac-leg/1668_7056_60--id=1668__sql_table=oac_1__sql_idx=381152__pitch=60__onset=7056.png'],
dtype='<U162')}
pre_from_tensor_slices y: {'output_1': array([0.817, 0.018, 0.421, ..., 0.511, 0.478, 0.147])}
_________________________
y: {'output_1': <tf.Tensor 'args_2:0' shape=() dtype=float64>}
x: {'input_1': <tf.Tensor 'args_0:0' shape=() dtype=string>, 'input_2': <tf.Tensor 'args_1:0' shape=() dtype=string>}
x.values(): dict_values([<tf.Tensor 'args_0:0' shape=() dtype=string>, <tf.Tensor 'args_1:0' shape=() dtype=string>])
x['input_1']: Tensor("args_0:0", shape=(), dtype=string)
运行 x['input_1'].values() 抛出错误：'Tensor' object has no attribute 'values'
我在 map_fn
附近收到一个错误
File "/home/me/.local/lib/python3.6/site-packages/tensorflow/python/framework/constant_op.py", line 284, in _constant_impl
allow_broadcast=allow_broadcast))
File "/home/me/.local/lib/python3.6/site-packages/tensorflow/python/framework/tensor_util.py", line 455, in make_tensor_proto
raise ValueError("None values not supported.")
ValueError: None values not supported.
编辑 2
尝试最新的我得到以下错误
Traceback (most recent call last):
File "/home/me/.local/bin/wavfeat", line 11, in <module>
load_entry_point('wavfeat==0.1.0', 'console_scripts', 'wavfeat')()
File "/home/me/.local/lib/python3.6/site-packages/wavfeat/__main__.py", line 91, in main
analysis_batch_sql(obj)
File "/home/me/.local/lib/python3.6/site-packages/wavfeat/analysis_run_csv.py", line 50, in analysis_batch_sql
qy = [*map(lambda c: run_elm(c[0], c[1]), ch)]
File "/home/me/.local/lib/python3.6/site-packages/wavfeat/analysis_run_csv.py", line 50, in <lambda>
qy = [*map(lambda c: run_elm(c[0], c[1]), ch)]
File "/home/me/.local/lib/python3.6/site-packages/wavfeat/analysis_run_csv.py", line 23, in run_elm
out = fn(input, elm)
File "/home/me/.local/lib/python3.6/site-packages/wavfeat/one_sec_onset.py", line 216, in one_sec_onset_train
return train(input, elm)
File "/home/me/.local/lib/python3.6/site-packages/wavfeat/one_sec_onset.py", line 203, in train
vs = validation_prep(jd, 'validation', bc)
File "/home/me/.local/lib/python3.6/site-packages/wavfeat/one_sec_onset.py", line 176, in validation_prep
ds = ds.map(map_func=load_and_preprocess_from_path_label, num_parallel_calls=AUTOTUNE)
File "/home/me/.local/lib/python3.6/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 1146, in map
self, map_func, num_parallel_calls, preserve_cardinality=True)
File "/home/me/.local/lib/python3.6/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 3264, in __init__
use_legacy_function=use_legacy_function)
File "/home/me/.local/lib/python3.6/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 2591, in __init__
self._function = wrapper_fn._get_concrete_function_internal()
File "/home/me/.local/lib/python3.6/site-packages/tensorflow/python/eager/function.py", line 1366, in _get_concrete_function_internal
*args, **kwargs)
File "/home/me/.local/lib/python3.6/site-packages/tensorflow/python/eager/function.py", line 1360, in _get_concrete_function_internal_garbage_collected
graph_function, _, _ = self._maybe_define_function(args, kwargs)
File "/home/me/.local/lib/python3.6/site-packages/tensorflow/python/eager/function.py", line 1648, in _maybe_define_function
graph_function = self._create_graph_function(args, kwargs)
File "/home/me/.local/lib/python3.6/site-packages/tensorflow/python/eager/function.py", line 1541, in _create_graph_function
capture_by_value=self._capture_by_value),
File "/home/me/.local/lib/python3.6/site-packages/tensorflow/python/framework/func_graph.py", line 716, in func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
File "/home/me/.local/lib/python3.6/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 2585, in wrapper_fn
ret = _wrapper_helper(*args)
File "/home/me/.local/lib/python3.6/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 2530, in _wrapper_helper
ret = func(*nested_args)
File "/home/me/.local/lib/python3.6/site-packages/wavfeat/one_sec_data_loader.py", line 47, in load_and_preprocess_from_path_label
pl = dict([(pk, tf.map_fn(load_and_preprocess_image, po, dtype=tf.float32)) for pk,po in pout])
File "/home/me/.local/lib/python3.6/site-packages/wavfeat/one_sec_data_loader.py", line 47, in <listcomp>
pl = dict([(pk, tf.map_fn(load_and_preprocess_image, po, dtype=tf.float32)) for pk,po in pout])
File "/home/me/.local/lib/python3.6/site-packages/tensorflow/python/ops/map_fn.py", line 214, in map_fn
raise ValueError("elems must be a 1+ dimensional Tensor, not a scalar")
ValueError: elems must be a 1+ dimensional Tensor, not a scalar
附加组件：不使用字典结构
这是一个完整的代码（除了定义 json_parse_x_y 和声明 AUTOTUNE），无需使用字典结构即可实现您正在尝试的目标。
我测试过 make_dataset 有效（见下面的示例），所以如果您遇到问题，应该是由于 load_tensors.
的规范错误
from itertools import zip_longest
import tensorflow as tf
# additionnally, `json_parse_x_y` must be defined
# and `AUTOTUNE` must be declared (in my example, I set it to 2)
def read_image(path, shape):
"""Read an image of givent filepath and tensor shape.
Return a float tensor of given shape.
"""
try:
image = tf.io.read_file(path)
image = tf.image.decode_png(image)
image = tf.image.resize(image, [shape[1], shape[2]])
image /= 255.0
return image
except:
raise FileNotFoundError("preprocess_image: bad path '%s'" % path)
def load_images(paths, shapes):
"""Load an ensemble of images (associated with a single sample).
paths : rank-1 string Tensor
shapes : list of images' shapes (same length as `paths`)
Return a tuple of float tensors containing the loaded images.
"""
return tuple((
read_image(paths[i], shapes[i])
for i in range(len(shapes))
))
def load_tensors(json_data, seq):
"""Load images descriptors from a json dump.
Return a tuple containing:
* a rank-2 tensor containing lists of image paths (str)
* a rank-2 tensor containing resolution values (float)
* a list of image shapes, of same length as the rank-2
tensor's second axis
"""
x,y = json_parse_x_y(json_data[seq])
xx = [*zip_longest(*x)] # NOTE: goes from variable sized input to {'input_N':...}
yy = [*zip_longest(*y)]
# GET SHAPES (hard coded atm)
lns = [[len(xxx)] for xxx in xx]
rzs = [[24,512,1],[96,512,1]] # TEMP TODO! grab grom [(v['h'],v['w'],v['c']) for v in xx]
shp = [*zip_longest(*[lns,rzs])]
shp = [list(s) for s in shp]
shp = [[*itertools.chain.from_iterable(s)] for s in shp]
return (xx, yy, shp)
def make_dataset(xx, yy, shp, batch_size):
"""Build a Dataset instance containing loaded images.
xx, yy, shp : see the specification of `load_tensors`'s outputs
batch_size : batch size to set on the Dataset
Return a Dataset instance where each batched sample is a tuple
containing two elements: first, a tuple containing N loaded images'
rank-3 tensors; second, a rank-1 tensor containing M float values.
(to be clear: batching adds a dimension to all those tensors)
"""
data = tf.data.Dataset.from_tensor_slices((xx, yy))
data = data.shuffle(10000)
data = data.map(lambda x, y: (load_images(x, shapes), y))
data = data.repeat()
data = data.batch(batch_size) if batch_size else data
data = data.prefetch(AUTOTUNE)
return data
def dataset_prep(json_data, seq, batch_size):
"""Full pipeline to making a Dataset from json."""
xx, yy, shapes = load_tensors(json_data, seq)
return make_dataset(xx, yy, shapes)
例如，使用“手工制作”值；所有图像实际上都是
this classic image，形状为 [512, 512, 3]。
import numpy as np
import tensorflow as tf
# import previous code
# Here, N = 2, and I make 2 samples.
x = tf.convert_to_tensor(np.array([
['image_1a.png', 'image_1b.png'],
['image_2a.png', 'image_2b.png']
]))
shapes = [[1, 512, 512], [1, 512, 512]] # images are initially [512, 512, 3]
# Here, M = 3, and I make 2 samples. Values are purely random.
y = tf.convert_to_tensor(np.array([
[.087, .92, .276],
[.242, .37, .205]
]))
# This should work.
data = make_dataset(x, y, shapes, batch_size=1)
# Output signature is <PrefetchDataset shapes:
# (((None, 512, 512, None), (None, 512, 512, None)), (None, 3)),
# types: ((tf.float32, tf.float32), tf.float64)
# >
# Where the first None is actually `batch_size`
# and the second is, in this case, 3.
当前问题的答案：
好的，您现在遇到的问题是修改后的load_image函数不符合数据集的规范，因此引发了异常。请在下面找到一个似乎有效的完整编辑代码（我 运行 在我的计算机上使用自定义图像进行测试，xd / yd dict 初始化为看起来像你报告的 x 和 y -数据集张量）。它不漂亮，我个人建议放弃 dict 结构，但它有效：
from itertools import zip_longest
def read_image(path, shape):
try:
image = tf.io.read_file(path)
image = tf.image.decode_png(image)
image = tf.image.resize(image, [shape[1],shape[2]])
image /= 255.0
return image
except:
raise FileNotFoundError("preprocess_image: bad path '%s'" % path)
# CHANGED: load_image is actually useless
def dataset_prep(json_data, seq, batch_size):
# LOAD DATA FROM JSON
x,y = json_parse_x_y(json_data[seq])
xx = [*zip_longest(*x)] # NOTE: goes from variable sized input to {'input_N':...}
yy = [*zip_longest(*y)]
# GET SHAPES (hard coded atm)
lns = [[len(xxx)] for xxx in xx]
rzs = [[24,512,1],[96,512,1]] # TEMP TODO! grab grom [(v['h'],v['w'],v['c']) for v in xx]
shp = [*zip_longest(*[lns,rzs])]
shp = [list(s) for s in shp]
shp = [[*itertools.chain.from_iterable(s)] for s in shp]
xd = dict([[ "input_{}".format(i+1),np.array(y)] for i,y in [*enumerate(xx)]])
yd = dict([["output_{}".format(i+1),np.array(y)] for i,y in [*enumerate(yy)]])
ds = tf.data.Dataset.from_tensor_slices((xd, yd))
ds = ds.shuffle(10000)
# CHANGED: the following line, to run images import (also moved epeat instruction later)
ds = ds.map(
lambda x, y: (
{key: read_image(path, shp[i]) for i, (key, path) in enumerate(x.items())},
y
),
num_parallel_calls=AUTOTUNE
)
ds = ds.repeat()
ds = ds.batch(batch_size) if batch_size else ds
ds = ds.prefetch(AUTOTUNE)
return ds
初始答案（问题编辑前）：
我只会处理此答案中 load_image 提出的异常，但可能还有其他工作要做 - 我没有对此进行测试，因为手头没有方便的数据集。
异常消息实际上非常明确：您正在传递标量元素（例如 n in [(k, tf.map_fn(lambda x: read_image(x, shp), n, dtype=tf.float32)) for k,n in pout]）作为elems参数tf.map_fn，当它需要张量（或（可能嵌套的）张量列表或元组）时，如其文档中明确指定的那样。
您还在引用的代码行中以错误的方式使用了 tf.map_fn，因为基本上您将它与 python 意图列表混在一起，而您应该使用其中一个或另一个.
带有意图列表（也替换了 load_image 函数之前无用的行）：
pl = {path: (load_image(path, shp), res) for path, res in x.items()}
与tf.map_fn:
# Read all images, return two tensors, one with images, the other with resolutions.
# (so, resolutions inclusion in this is actually useless and should be redesigned)
pl = tf.map_fn(
lambda args: (read_image(args[0], shp), args[1]),
[tf.convert_to_tensor(list(x)), tf.convert_to_tensor(list(x.values()))],
dtype=(tf.float32, tf.float32)
)
# If you really, really want to return a dict, but is it an optimal design?
pl = {path: (pl[0][i], pl[1][i]) for i, path in enumerate(x)}
我不知道返回以这种方式指定的 dict 是否与 Dataset 实例化是最佳的（甚至兼容），但是如果您的其余代码正常工作，这应该可以解决问题。
无论如何，如果您想遍历一个字典，请继续使用第一个版本或第二个版本的修改版本（这可能具有并行图像读取的优势）。
希望对您有所帮助:-)
我正在加载包含多个输入图像的数据集。输入图像路径应该只在批处理时解码，以便处理大型数据集。
数据集是N个图片路径输入，M个浮点输出。每个输入的图像具有不同的分辨率。
Data is ([img_input_1.png, img_input_2.png, ...], [0.65, 0.7, 0.8])
该模型在符号模式下使用 Keras 函数 api。
这是最近编辑的代码
from itertools import zip_longest
def read_image(path, shape):
try:
image = tf.io.read_file(path)
image = tf.image.decode_png(image)
image = tf.image.resize(image, [shape[1],shape[2]])
image /= 255.0
return image
except:
print('ERROR: preprocess_image: bad path', path)
def load_image(x, y, shp):
pout = [(k, x[k]) for k in x.keys()]
l1 = tf.convert_to_tensor(list(x))
l2 = tf.convert_to_tensor(list(x.values()))
pl = tf.map_fn(
lambda args: (read_image(args[0], shp), args[1]), [l1, l2], dtype=(tf.float32, tf.float32)
)
pl = {path: (pl[0][i], pl[1][i]) for i, path in enumerate(x)}
return (pl,y)
def dataset_prep(json_data, seq, batch_size):
# LOAD DATA FROM JSON
x,y = json_parse_x_y(json_data[seq])
xx = [*zip_longest(*x)] # NOTE: goes from variable sized input to {'input_N':...}
yy = [*zip_longest(*y)]
# GET SHAPES (hard coded atm)
lns = [[len(xxx)] for xxx in xx]
rzs = [[24,512,1],[96,512,1]] # TEMP TODO! grab grom [(v['h'],v['w'],v['c']) for v in xx]
shp = [*zip_longest(*[lns,rzs])]
shp = [list(s) for s in shp]
shp = [[*itertools.chain.from_iterable(s)] for s in shp]
xd = dict([[ "input_{}".format(i+1),np.array(y)] for i,y in [*enumerate(xx)]])
yd = dict([["output_{}".format(i+1),np.array(y)] for i,y in [*enumerate(yy)]])
ds = tf.data.Dataset.from_tensor_slices((xd, yd))
ds = ds.shuffle(10000)
ds = ds.repeat()
ds = ds.map(map_func=lambda x,y: load_image(x, y, shp), num_parallel_calls=AUTOTUNE)
ds = ds.batch(batch_size) if batch_size else ds
ds = ds.prefetch(AUTOTUNE)
return ds
这是我遇到的错误：
Traceback (most recent call last):
File "/home/me/.local/bin/wavfeat", line 11, in <module>
load_entry_point('wavfeat==0.1.0', 'console_scripts', 'wavfeat')()
File "/home/me/.local/lib/python3.6/site-packages/wavfeat/__main__.py", line 91, in main
analysis_batch_sql(obj)
File "/home/me/.local/lib/python3.6/site-packages/wavfeat/analysis_run_csv.py", line 50, in analysis_batch_sql
qy = [*map(lambda c: run_elm(c[0], c[1]), ch)]
File "/home/me/.local/lib/python3.6/site-packages/wavfeat/analysis_run_csv.py", line 50, in <lambda>
qy = [*map(lambda c: run_elm(c[0], c[1]), ch)]
File "/home/me/.local/lib/python3.6/site-packages/wavfeat/analysis_run_csv.py", line 23, in run_elm
out = fn(input, elm)
File "/home/me/.local/lib/python3.6/site-packages/wavfeat/one_sec_onset.py", line 196, in one_sec_onset_train
return train(input, elm)
File "/home/me/.local/lib/python3.6/site-packages/wavfeat/one_sec_onset.py", line 182, in train
ts = dataset_prep(jd, 'train', bc)
File "/home/me/.local/lib/python3.6/site-packages/wavfeat/one_sec_onset.py", line 123, in dataset_prep
ds = ds.map(map_func=lambda x,y: load_image(x, y, shp), num_parallel_calls=AUTOTUNE)
File "/home/me/.local/lib/python3.6/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 1146, in map
self, map_func, num_parallel_calls, preserve_cardinality=True)
File "/home/me/.local/lib/python3.6/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 3264, in __init__
use_legacy_function=use_legacy_function)
File "/home/me/.local/lib/python3.6/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 2591, in __init__
self._function = wrapper_fn._get_concrete_function_internal()
File "/home/me/.local/lib/python3.6/site-packages/tensorflow/python/eager/function.py", line 1366, in _get_concrete_function_internal
*args, **kwargs)
File "/home/me/.local/lib/python3.6/site-packages/tensorflow/python/eager/function.py", line 1360, in _get_concrete_function_internal_garbage_collected
graph_function, _, _ = self._maybe_define_function(args, kwargs)
File "/home/me/.local/lib/python3.6/site-packages/tensorflow/python/eager/function.py", line 1648, in _maybe_define_function
graph_function = self._create_graph_function(args, kwargs)
File "/home/me/.local/lib/python3.6/site-packages/tensorflow/python/eager/function.py", line 1541, in _create_graph_function
capture_by_value=self._capture_by_value),
File "/home/me/.local/lib/python3.6/site-packages/tensorflow/python/framework/func_graph.py", line 716, in func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
File "/home/me/.local/lib/python3.6/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 2585, in wrapper_fn
ret = _wrapper_helper(*args)
File "/home/me/.local/lib/python3.6/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 2530, in _wrapper_helper
ret = func(*nested_args)
File "/home/me/.local/lib/python3.6/site-packages/wavfeat/one_sec_onset.py", line 123, in <lambda>
ds = ds.map(map_func=lambda x,y: load_image(x, y, shp), num_parallel_calls=AUTOTUNE)
File "/home/me/.local/lib/python3.6/site-packages/wavfeat/one_sec_data_loader.py", line 91, in load_image
print("x['input_1'].values(): ", x['input_1'].values())
AttributeError: 'Tensor' object has no attribute 'values'
我在做什么阻止加载路径？
编辑：
在尝试 pandrey 的修复时，我遇到了输入错误。这是 from_tensor_slices 和 ds.map 之前以及之后的数据：
pre_from_tensor_slices x: {'input_1': array(['/media/me/sp_data/sp_data/datasets/chr_01/one_sec_onset_11_oac-leg/7388_39216_30--id=7388__sql_table=oac_1__sql_idx=405167__pitch=30__onset=39216.png',
'/media/me/sp_data/sp_data/datasets/chr_01/one_sec_onset_11_oac-leg/2447_864_27--id=2447__sql_table=oac_1__sql_idx=415458__pitch=27__onset=864.png',
'/media/me/sp_data/sp_data/datasets/chr_01/one_sec_onset_11_oac-leg/2386_20208_38--id=2386__sql_table=oac_1__sql_idx=433248__pitch=38__onset=20208.png',
...,
'/media/me/sp_data/sp_data/datasets/chr_01/one_sec_onset_11_oac-leg/6261_24528_57--id=6261__sql_table=oac_1__sql_idx=449753__pitch=57__onset=24528.png',
'/media/me/sp_data/sp_data/datasets/chr_01/one_sec_onset_11_oac-leg/3727_22944_31--id=3727__sql_table=oac_1__sql_idx=407620__pitch=31__onset=22944.png',
'/media/me/sp_data/sp_data/datasets/chr_01/one_sec_onset_11_oac-leg/1668_7056_60--id=1668__sql_table=oac_1__sql_idx=381152__pitch=60__onset=7056.png'],
dtype='<U162'), 'input_2': array(['/media/me/sp_data/sp_data/datasets/mel_01/one_sec_onset_11_oac-leg/7388_39216_30--id=7388__sql_table=oac_1__sql_idx=405167__pitch=30__onset=39216.png',
'/media/me/sp_data/sp_data/datasets/mel_01/one_sec_onset_11_oac-leg/2447_864_27--id=2447__sql_table=oac_1__sql_idx=415458__pitch=27__onset=864.png',
'/media/me/sp_data/sp_data/datasets/mel_01/one_sec_onset_11_oac-leg/2386_20208_38--id=2386__sql_table=oac_1__sql_idx=433248__pitch=38__onset=20208.png',
...,
'/media/me/sp_data/sp_data/datasets/mel_01/one_sec_onset_11_oac-leg/6261_24528_57--id=6261__sql_table=oac_1__sql_idx=449753__pitch=57__onset=24528.png',
'/media/me/sp_data/sp_data/datasets/mel_01/one_sec_onset_11_oac-leg/3727_22944_31--id=3727__sql_table=oac_1__sql_idx=407620__pitch=31__onset=22944.png',
'/media/me/sp_data/sp_data/datasets/mel_01/one_sec_onset_11_oac-leg/1668_7056_60--id=1668__sql_table=oac_1__sql_idx=381152__pitch=60__onset=7056.png'],
dtype='<U162')}
pre_from_tensor_slices y: {'output_1': array([0.817, 0.018, 0.421, ..., 0.511, 0.478, 0.147])}
_________________________
y: {'output_1': <tf.Tensor 'args_2:0' shape=() dtype=float64>}
x: {'input_1': <tf.Tensor 'args_0:0' shape=() dtype=string>, 'input_2': <tf.Tensor 'args_1:0' shape=() dtype=string>}
x.values(): dict_values([<tf.Tensor 'args_0:0' shape=() dtype=string>, <tf.Tensor 'args_1:0' shape=() dtype=string>])
x['input_1']: Tensor("args_0:0", shape=(), dtype=string)
运行 x['input_1'].values() 抛出错误：'Tensor' object has no attribute 'values'
我在 map_fn
附近收到一个错误 File "/home/me/.local/lib/python3.6/site-packages/tensorflow/python/framework/constant_op.py", line 284, in _constant_impl
allow_broadcast=allow_broadcast))
File "/home/me/.local/lib/python3.6/site-packages/tensorflow/python/framework/tensor_util.py", line 455, in make_tensor_proto
raise ValueError("None values not supported.")
ValueError: None values not supported.
编辑 2
尝试最新的我得到以下错误
Traceback (most recent call last):
File "/home/me/.local/bin/wavfeat", line 11, in <module>
load_entry_point('wavfeat==0.1.0', 'console_scripts', 'wavfeat')()
File "/home/me/.local/lib/python3.6/site-packages/wavfeat/__main__.py", line 91, in main
analysis_batch_sql(obj)
File "/home/me/.local/lib/python3.6/site-packages/wavfeat/analysis_run_csv.py", line 50, in analysis_batch_sql
qy = [*map(lambda c: run_elm(c[0], c[1]), ch)]
File "/home/me/.local/lib/python3.6/site-packages/wavfeat/analysis_run_csv.py", line 50, in <lambda>
qy = [*map(lambda c: run_elm(c[0], c[1]), ch)]
File "/home/me/.local/lib/python3.6/site-packages/wavfeat/analysis_run_csv.py", line 23, in run_elm
out = fn(input, elm)
File "/home/me/.local/lib/python3.6/site-packages/wavfeat/one_sec_onset.py", line 216, in one_sec_onset_train
return train(input, elm)
File "/home/me/.local/lib/python3.6/site-packages/wavfeat/one_sec_onset.py", line 203, in train
vs = validation_prep(jd, 'validation', bc)
File "/home/me/.local/lib/python3.6/site-packages/wavfeat/one_sec_onset.py", line 176, in validation_prep
ds = ds.map(map_func=load_and_preprocess_from_path_label, num_parallel_calls=AUTOTUNE)
File "/home/me/.local/lib/python3.6/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 1146, in map
self, map_func, num_parallel_calls, preserve_cardinality=True)
File "/home/me/.local/lib/python3.6/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 3264, in __init__
use_legacy_function=use_legacy_function)
File "/home/me/.local/lib/python3.6/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 2591, in __init__
self._function = wrapper_fn._get_concrete_function_internal()
File "/home/me/.local/lib/python3.6/site-packages/tensorflow/python/eager/function.py", line 1366, in _get_concrete_function_internal
*args, **kwargs)
File "/home/me/.local/lib/python3.6/site-packages/tensorflow/python/eager/function.py", line 1360, in _get_concrete_function_internal_garbage_collected
graph_function, _, _ = self._maybe_define_function(args, kwargs)
File "/home/me/.local/lib/python3.6/site-packages/tensorflow/python/eager/function.py", line 1648, in _maybe_define_function
graph_function = self._create_graph_function(args, kwargs)
File "/home/me/.local/lib/python3.6/site-packages/tensorflow/python/eager/function.py", line 1541, in _create_graph_function
capture_by_value=self._capture_by_value),
File "/home/me/.local/lib/python3.6/site-packages/tensorflow/python/framework/func_graph.py", line 716, in func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
File "/home/me/.local/lib/python3.6/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 2585, in wrapper_fn
ret = _wrapper_helper(*args)
File "/home/me/.local/lib/python3.6/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 2530, in _wrapper_helper
ret = func(*nested_args)
File "/home/me/.local/lib/python3.6/site-packages/wavfeat/one_sec_data_loader.py", line 47, in load_and_preprocess_from_path_label
pl = dict([(pk, tf.map_fn(load_and_preprocess_image, po, dtype=tf.float32)) for pk,po in pout])
File "/home/me/.local/lib/python3.6/site-packages/wavfeat/one_sec_data_loader.py", line 47, in <listcomp>
pl = dict([(pk, tf.map_fn(load_and_preprocess_image, po, dtype=tf.float32)) for pk,po in pout])
File "/home/me/.local/lib/python3.6/site-packages/tensorflow/python/ops/map_fn.py", line 214, in map_fn
raise ValueError("elems must be a 1+ dimensional Tensor, not a scalar")
ValueError: elems must be a 1+ dimensional Tensor, not a scalar
附加组件：不使用字典结构
这是一个完整的代码（除了定义 json_parse_x_y 和声明 AUTOTUNE），无需使用字典结构即可实现您正在尝试的目标。
我测试过 make_dataset 有效（见下面的示例），所以如果您遇到问题，应该是由于 load_tensors.
from itertools import zip_longest
import tensorflow as tf
# additionnally, `json_parse_x_y` must be defined
# and `AUTOTUNE` must be declared (in my example, I set it to 2)
def read_image(path, shape):
"""Read an image of givent filepath and tensor shape.
Return a float tensor of given shape.
"""
try:
image = tf.io.read_file(path)
image = tf.image.decode_png(image)
image = tf.image.resize(image, [shape[1], shape[2]])
image /= 255.0
return image
except:
raise FileNotFoundError("preprocess_image: bad path '%s'" % path)
def load_images(paths, shapes):
"""Load an ensemble of images (associated with a single sample).
paths : rank-1 string Tensor
shapes : list of images' shapes (same length as `paths`)
Return a tuple of float tensors containing the loaded images.
"""
return tuple((
read_image(paths[i], shapes[i])
for i in range(len(shapes))
))
def load_tensors(json_data, seq):
"""Load images descriptors from a json dump.
Return a tuple containing:
* a rank-2 tensor containing lists of image paths (str)
* a rank-2 tensor containing resolution values (float)
* a list of image shapes, of same length as the rank-2
tensor's second axis
"""
x,y = json_parse_x_y(json_data[seq])
xx = [*zip_longest(*x)] # NOTE: goes from variable sized input to {'input_N':...}
yy = [*zip_longest(*y)]
# GET SHAPES (hard coded atm)
lns = [[len(xxx)] for xxx in xx]
rzs = [[24,512,1],[96,512,1]] # TEMP TODO! grab grom [(v['h'],v['w'],v['c']) for v in xx]
shp = [*zip_longest(*[lns,rzs])]
shp = [list(s) for s in shp]
shp = [[*itertools.chain.from_iterable(s)] for s in shp]
return (xx, yy, shp)
def make_dataset(xx, yy, shp, batch_size):
"""Build a Dataset instance containing loaded images.
xx, yy, shp : see the specification of `load_tensors`'s outputs
batch_size : batch size to set on the Dataset
Return a Dataset instance where each batched sample is a tuple
containing two elements: first, a tuple containing N loaded images'
rank-3 tensors; second, a rank-1 tensor containing M float values.
(to be clear: batching adds a dimension to all those tensors)
"""
data = tf.data.Dataset.from_tensor_slices((xx, yy))
data = data.shuffle(10000)
data = data.map(lambda x, y: (load_images(x, shapes), y))
data = data.repeat()
data = data.batch(batch_size) if batch_size else data
data = data.prefetch(AUTOTUNE)
return data
def dataset_prep(json_data, seq, batch_size):
"""Full pipeline to making a Dataset from json."""
xx, yy, shapes = load_tensors(json_data, seq)
return make_dataset(xx, yy, shapes)
例如，使用“手工制作”值；所有图像实际上都是 this classic image，形状为 [512, 512, 3]。
import numpy as np
import tensorflow as tf
# import previous code
# Here, N = 2, and I make 2 samples.
x = tf.convert_to_tensor(np.array([
['image_1a.png', 'image_1b.png'],
['image_2a.png', 'image_2b.png']
]))
shapes = [[1, 512, 512], [1, 512, 512]] # images are initially [512, 512, 3]
# Here, M = 3, and I make 2 samples. Values are purely random.
y = tf.convert_to_tensor(np.array([
[.087, .92, .276],
[.242, .37, .205]
]))
# This should work.
data = make_dataset(x, y, shapes, batch_size=1)
# Output signature is <PrefetchDataset shapes:
# (((None, 512, 512, None), (None, 512, 512, None)), (None, 3)),
# types: ((tf.float32, tf.float32), tf.float64)
# >
# Where the first None is actually `batch_size`
# and the second is, in this case, 3.
当前问题的答案：
好的，您现在遇到的问题是修改后的load_image函数不符合数据集的规范，因此引发了异常。请在下面找到一个似乎有效的完整编辑代码（我 运行 在我的计算机上使用自定义图像进行测试，xd / yd dict 初始化为看起来像你报告的 x 和 y -数据集张量）。它不漂亮，我个人建议放弃 dict 结构，但它有效：
from itertools import zip_longest
def read_image(path, shape):
try:
image = tf.io.read_file(path)
image = tf.image.decode_png(image)
image = tf.image.resize(image, [shape[1],shape[2]])
image /= 255.0
return image
except:
raise FileNotFoundError("preprocess_image: bad path '%s'" % path)
# CHANGED: load_image is actually useless
def dataset_prep(json_data, seq, batch_size):
# LOAD DATA FROM JSON
x,y = json_parse_x_y(json_data[seq])
xx = [*zip_longest(*x)] # NOTE: goes from variable sized input to {'input_N':...}
yy = [*zip_longest(*y)]
# GET SHAPES (hard coded atm)
lns = [[len(xxx)] for xxx in xx]
rzs = [[24,512,1],[96,512,1]] # TEMP TODO! grab grom [(v['h'],v['w'],v['c']) for v in xx]
shp = [*zip_longest(*[lns,rzs])]
shp = [list(s) for s in shp]
shp = [[*itertools.chain.from_iterable(s)] for s in shp]
xd = dict([[ "input_{}".format(i+1),np.array(y)] for i,y in [*enumerate(xx)]])
yd = dict([["output_{}".format(i+1),np.array(y)] for i,y in [*enumerate(yy)]])
ds = tf.data.Dataset.from_tensor_slices((xd, yd))
ds = ds.shuffle(10000)
# CHANGED: the following line, to run images import (also moved epeat instruction later)
ds = ds.map(
lambda x, y: (
{key: read_image(path, shp[i]) for i, (key, path) in enumerate(x.items())},
y
),
num_parallel_calls=AUTOTUNE
)
ds = ds.repeat()
ds = ds.batch(batch_size) if batch_size else ds
ds = ds.prefetch(AUTOTUNE)
return ds
初始答案（问题编辑前）：
我只会处理此答案中 load_image 提出的异常，但可能还有其他工作要做 - 我没有对此进行测试，因为手头没有方便的数据集。
异常消息实际上非常明确：您正在传递标量元素（例如 n in [(k, tf.map_fn(lambda x: read_image(x, shp), n, dtype=tf.float32)) for k,n in pout]）作为elems参数tf.map_fn，当它需要张量（或（可能嵌套的）张量列表或元组）时，如其文档中明确指定的那样。
您还在引用的代码行中以错误的方式使用了 tf.map_fn，因为基本上您将它与 python 意图列表混在一起，而您应该使用其中一个或另一个.
带有意图列表（也替换了 load_image 函数之前无用的行）：
pl = {path: (load_image(path, shp), res) for path, res in x.items()}
与tf.map_fn:
# Read all images, return two tensors, one with images, the other with resolutions.
# (so, resolutions inclusion in this is actually useless and should be redesigned)
pl = tf.map_fn(
lambda args: (read_image(args[0], shp), args[1]),
[tf.convert_to_tensor(list(x)), tf.convert_to_tensor(list(x.values()))],
dtype=(tf.float32, tf.float32)
)
# If you really, really want to return a dict, but is it an optimal design?
pl = {path: (pl[0][i], pl[1][i]) for i, path in enumerate(x)}
我不知道返回以这种方式指定的 dict 是否与 Dataset 实例化是最佳的（甚至兼容），但是如果您的其余代码正常工作，这应该可以解决问题。
无论如何，如果您想遍历一个字典，请继续使用第一个版本或第二个版本的修改版本（这可能具有并行图像读取的优势）。
希望对您有所帮助:-)