本文整理汇总了Python中tensorflow.compat.v1.uint8方法的典型用法代码示例。如果您正苦于以下问题:Python v1.uint8方法的具体用法?Python v1.uint8怎么用?Python v1.uint8使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tensorflow.compat.v1
的用法示例。
在下文中一共展示了v1.uint8方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: _process_image
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import uint8 [as 别名]
def _process_image(coder, name):
"""Process a single image file.
If name is "train", a black image is returned. Otherwise, a white image is
returned.
Args:
coder: instance of ImageCoder to provide TensorFlow image coding utils.
name: string, unique identifier specifying the data set.
Returns:
image_buffer: bytes, JPEG encoding of RGB image.
height: integer, image height in pixels.
width: integer, image width in pixels.
"""
# Read the image file.
value = 0 if name == 'train' else 255
height = random.randint(30, 299)
width = random.randint(30, 299)
image = np.full((height, width, 3), value, np.uint8)
jpeg_data = coder.encode_jpeg(image)
return jpeg_data, height, width
示例2: preprocess
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import uint8 [as 别名]
def preprocess(self, image_buffer, bbox, batch_position):
"""Preprocessing image_buffer as a function of its batch position."""
if self.train:
image = train_image(image_buffer, self.height, self.width, bbox,
batch_position, self.resize_method, self.distortions,
None, summary_verbosity=self.summary_verbosity,
distort_color_in_yiq=self.distort_color_in_yiq,
fuse_decode_and_crop=self.fuse_decode_and_crop)
else:
image = tf.image.decode_jpeg(
image_buffer, channels=3, dct_method='INTEGER_FAST')
image = eval_image(image, self.height, self.width, batch_position,
self.resize_method,
summary_verbosity=self.summary_verbosity)
# Note: image is now float32 [height,width,3] with range [0, 255]
# image = tf.cast(image, tf.uint8) # HACK TESTING
if self.match_mlperf:
mlperf.logger.log(key=mlperf.tags.INPUT_MEAN_SUBTRACTION,
value=_CHANNEL_MEANS)
normalized = image - _CHANNEL_MEANS
else:
normalized = normalized_image(image)
return tf.cast(normalized, self.dtype)
示例3: image_summary
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import uint8 [as 别名]
def image_summary(predictions, targets, hparams):
"""Reshapes predictions and passes it to tensorboard.
Args:
predictions : The predicted image (logits).
targets : The ground truth.
hparams: model hparams.
Returns:
summary_proto: containing the summary images.
weights: A Tensor of zeros of the same shape as predictions.
"""
del hparams
results = tf.cast(tf.argmax(predictions, axis=-1), tf.uint8)
gold = tf.cast(targets, tf.uint8)
summary1 = tf.summary.image("prediction", results, max_outputs=2)
summary2 = tf.summary.image("data", gold, max_outputs=2)
summary = tf.summary.merge([summary1, summary2])
return summary, tf.zeros_like(predictions)
示例4: tpu_safe_image_summary
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import uint8 [as 别名]
def tpu_safe_image_summary(image):
if is_xla_compiled():
# We only support float32 images at the moment due to casting complications.
if image.dtype != tf.float32:
image = to_float(image)
else:
image = tf.cast(image, tf.uint8)
return image
# This has been (shamefully) copied from
# GitHub tensorflow/models/blob/master/research/slim/nets/cyclegan.py
#
# tensorflow/models cannot be pip installed, and even if it were we don't want
# to depend on all the models in it.
#
# Therefore copying and forgoing any more bugfixes into it is the most
# expedient way to use this function.
示例5: _encode_gif
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import uint8 [as 别名]
def _encode_gif(images, fps):
"""Encodes numpy images into gif string.
Args:
images: A 4-D `uint8` `np.array` (or a list of 3-D images) of shape
`[time, height, width, channels]` where `channels` is 1 or 3.
fps: frames per second of the animation
Returns:
The encoded gif string.
Raises:
IOError: If the ffmpeg command returns an error.
"""
writer = WholeVideoWriter(fps)
writer.write_multi(images)
return writer.finish()
示例6: __init__
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import uint8 [as 别名]
def __init__(self, batch_size, *args, **kwargs):
self._store_rollouts = kwargs.pop("store_rollouts", True)
super(T2TEnv, self).__init__(*args, **kwargs)
self.batch_size = batch_size
self._rollouts_by_epoch_and_split = collections.OrderedDict()
self.current_epoch = None
self._should_preprocess_on_reset = True
with tf.Graph().as_default() as tf_graph:
self._tf_graph = _Noncopyable(tf_graph)
self._decoded_image_p = _Noncopyable(
tf.placeholder(dtype=tf.uint8, shape=(None, None, None))
)
self._encoded_image_t = _Noncopyable(
tf.image.encode_png(self._decoded_image_p.obj)
)
self._encoded_image_p = _Noncopyable(tf.placeholder(tf.string))
self._decoded_image_t = _Noncopyable(
tf.image.decode_png(self._encoded_image_p.obj)
)
self._session = _Noncopyable(tf.Session())
示例7: image_to_tf_summary_value
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import uint8 [as 别名]
def image_to_tf_summary_value(image, tag):
"""Converts a NumPy image to a tf.Summary.Value object.
Args:
image: 3-D NumPy array.
tag: name for tf.Summary.Value for display in tensorboard.
Returns:
image_summary: A tf.Summary.Value object.
"""
curr_image = np.asarray(image, dtype=np.uint8)
height, width, n_channels = curr_image.shape
# If monochrome image, then reshape to [height, width]
if n_channels == 1:
curr_image = np.reshape(curr_image, [height, width])
s = io.BytesIO()
matplotlib_pyplot().imsave(s, curr_image, format="png")
img_sum = tf.Summary.Image(encoded_image_string=s.getvalue(),
height=height, width=width,
colorspace=n_channels)
return tf.Summary.Value(tag=tag, image=img_sum)
示例8: _extract_images
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import uint8 [as 别名]
def _extract_images(filename, num_images):
"""Extract the images into a numpy array.
Args:
filename: The path to an MNIST images file.
num_images: The number of images in the file.
Returns:
A numpy array of shape [number_of_images, height, width, channels].
"""
print('Extracting images from: ', filename)
with gzip.open(filename) as bytestream:
bytestream.read(16)
buf = bytestream.read(
_IMAGE_SIZE * _IMAGE_SIZE * num_images * _NUM_CHANNELS)
data = np.frombuffer(buf, dtype=np.uint8)
data = data.reshape(num_images, _IMAGE_SIZE, _IMAGE_SIZE, _NUM_CHANNELS)
return data
示例9: _extract_labels
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import uint8 [as 别名]
def _extract_labels(filename, num_labels):
"""Extract the labels into a vector of int64 label IDs.
Args:
filename: The path to an MNIST labels file.
num_labels: The number of labels in the file.
Returns:
A numpy array of shape [number_of_labels]
"""
print('Extracting labels from: ', filename)
with gzip.open(filename) as bytestream:
bytestream.read(8)
buf = bytestream.read(1 * num_labels)
labels = np.frombuffer(buf, dtype=np.uint8).astype(np.int64)
return labels
示例10: _decode_masks
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import uint8 [as 别名]
def _decode_masks(self, parsed_tensors):
"""Decode a set of PNG masks to the tf.float32 tensors."""
def _decode_png_mask(png_bytes):
mask = tf.squeeze(
tf.io.decode_png(png_bytes, channels=1, dtype=tf.uint8), axis=-1)
mask = tf.cast(mask, dtype=tf.float32)
mask.set_shape([None, None])
return mask
height = parsed_tensors['image/height']
width = parsed_tensors['image/width']
masks = parsed_tensors['image/object/mask']
return tf.cond(
tf.greater(tf.size(masks), 0),
lambda: tf.map_fn(_decode_png_mask, masks, dtype=tf.float32),
lambda: tf.zeros([0, height, width], dtype=tf.float32))
示例11: parse
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import uint8 [as 别名]
def parse(data_dict):
"""Parse dataset from _data_gen into the same format as sst_binary."""
sentiment = data_dict['label']
sentence = data_dict['sentence']
dense_chars = tf.decode_raw(sentence, tf.uint8)
dense_chars.set_shape((None,))
chars = tfp.math.dense_to_sparse(dense_chars)
if six.PY3:
safe_chr = lambda c: '?' if c >= 128 else chr(c)
else:
safe_chr = chr
to_char = np.vectorize(safe_chr)
chars = tf.SparseTensor(indices=chars.indices,
values=tf.py_func(to_char, [chars.values], tf.string),
dense_shape=chars.dense_shape)
return {'sentiment': sentiment,
'sentence': chars}
示例12: _transform_in_feature_specification
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import uint8 [as 别名]
def _transform_in_feature_specification(
self, tensor_spec_struct
):
"""The specification for the input features for the preprocess_fn.
Here we will transform the feature spec to represent the requirements
for preprocessing.
Args:
tensor_spec_struct: A flat spec structure {str: TensorSpec}.
Returns:
tensor_spec_struct: The transformed flat spec structure {str:
TensorSpec}.
"""
self.update_spec(
tensor_spec_struct,
'state/image',
shape=(512, 640, 3),
dtype=tf.uint8,
data_format='jpeg')
return tensor_spec_struct
示例13: test_pad_image_tensor_to_spec_shape
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import uint8 [as 别名]
def test_pad_image_tensor_to_spec_shape(self):
varlen_spec = utils.ExtendedTensorSpec(
shape=(3, 2, 2, 1),
dtype=tf.uint8,
name='varlen',
data_format='png',
varlen_default_value=3.0)
test_data = [[
[[[1]] * 2] * 2,
[[[2]] * 2] * 2,
]]
prepadded_tensor = tf.convert_to_tensor(test_data, dtype=varlen_spec.dtype)
tensor = utils.pad_or_clip_tensor_to_spec_shape(prepadded_tensor,
varlen_spec)
with self.session() as sess:
np_tensor = sess.run(tensor)
self.assertAllEqual(
np_tensor,
np.array([[
[[[1]] * 2] * 2,
[[[2]] * 2] * 2,
[[[3]] * 2] * 2,
]]))
示例14: neglogp
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import uint8 [as 别名]
def neglogp(self, x):
"""
return tf.nn.sparse_softmax_cross_entropy_with_logits(logits=self.logits, labels=x)
Note: we can't use sparse_softmax_cross_entropy_with_logits because
the implementation does not allow second-order derivatives...
"""
if x.dtype in {tf.uint8, tf.int32, tf.int64}:
# one-hot encoding
x_shape_list = x.shape.as_list()
logits_shape_list = self.logits.get_shape().as_list()[:-1]
for xs, ls in zip(x_shape_list, logits_shape_list):
if xs is not None and ls is not None:
assert xs == ls, 'shape mismatch: {} in x vs {} in logits'.format(xs, ls)
x = tf.one_hot(x, self.logits.get_shape().as_list()[-1])
else:
# already encoded
assert x.shape.as_list() == self.logits.shape.as_list()
return tf.nn.softmax_cross_entropy_with_logits_v2(
logits=self.logits,
labels=x)
示例15: serve_images
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import uint8 [as 别名]
def serve_images(self, image_arrays):
"""Serve a list of image arrays.
Args:
image_arrays: A list of image content with each image has shape [height,
width, 3] and uint8 type.
Returns:
A list of detections.
"""
if not self.sess:
self.build()
predictions = self.sess.run(
self.signitures['prediction'],
feed_dict={self.signitures['image_arrays']: image_arrays})
return predictions