def image_summary_eager_fallback(tag, tensor, max_images, bad_color, name,
ctx):
if max_images is None:
max_images = 3
max_images = _execute.make_int(max_images, "max_images")
if bad_color is None:
bad_color = _execute.make_tensor(
"""dtype: DT_UINT8 tensor_shape { dim { size: 4 } } int_val: 255 int_val: 0 int_val: 0 int_val: 255""",
"bad_color")
bad_color = _execute.make_tensor(bad_color, "bad_color")
_attr_T, (tensor, ) = _execute.args_to_matching_eager([tensor], ctx, [
_dtypes.uint8,
_dtypes.float32,
_dtypes.half,
_dtypes.float64,
], _dtypes.float32)
tag = _ops.convert_to_tensor(tag, _dtypes.string)
_inputs_flat = [tag, tensor]
_attrs = ("max_images", max_images, "T", _attr_T, "bad_color", bad_color)
_result = _execute.execute(b"ImageSummary",
1,
inputs=_inputs_flat,
attrs=_attrs,
ctx=ctx,
name=name)
if _execute.must_record_gradient():
_execute.record_gradient("ImageSummary", _inputs_flat, _attrs, _result)
_result, = _result
return _result
def image_summary_eager_fallback(tag, tensor, max_images=3, bad_color=_execute.make_tensor("""dtype: DT_UINT8 tensor_shape { dim { size: 4 } } int_val: 255 int_val: 0 int_val: 0 int_val: 255""", "bad_color"), name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function image_summary
"""
_ctx = ctx if ctx else _context.context()
if max_images is None:
max_images = 3
max_images = _execute.make_int(max_images, "max_images")
if bad_color is None:
bad_color = _execute.make_tensor("""dtype: DT_UINT8 tensor_shape { dim { size: 4 } } int_val: 255 int_val: 0 int_val: 0 int_val: 255""", "bad_color")
bad_color = _execute.make_tensor(bad_color, "bad_color")
_attr_T, (tensor,) = _execute.args_to_matching_eager([tensor], _ctx, _dtypes.float32)
tag = _ops.convert_to_tensor(tag, _dtypes.string)
_inputs_flat = [tag, tensor]
_attrs = ("max_images", max_images, "T", _attr_T, "bad_color", bad_color)
_result = _execute.execute(b"ImageSummary", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"ImageSummary", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def image_summary(tag, tensor, max_images=3, bad_color=_execute.make_tensor("""dtype: DT_UINT8 tensor_shape { dim { size: 4 } } int_val: 255 int_val: 0 int_val: 0 int_val: 255""", "bad_color"), name=None):
r"""Outputs a `Summary` protocol buffer with images.
The summary has up to `max_images` summary values containing images. The
images are built from `tensor` which must be 4-D with shape `[batch_size,
height, width, channels]` and where `channels` can be:
* 1: `tensor` is interpreted as Grayscale.
* 3: `tensor` is interpreted as RGB.
* 4: `tensor` is interpreted as RGBA.
The images have the same number of channels as the input tensor. For float
input, the values are normalized one image at a time to fit in the range
`[0, 255]`. `uint8` values are unchanged. The op uses two different
normalization algorithms:
* If the input values are all positive, they are rescaled so the largest one
is 255.
* If any input value is negative, the values are shifted so input value 0.0
is at 127. They are then rescaled so that either the smallest value is 0,
or the largest one is 255.
The `tag` argument is a scalar `Tensor` of type `string`. It is used to
build the `tag` of the summary values:
* If `max_images` is 1, the summary value tag is '*tag*/image'.
* If `max_images` is greater than 1, the summary value tags are
generated sequentially as '*tag*/image/0', '*tag*/image/1', etc.
The `bad_color` argument is the color to use in the generated images for
non-finite input values. It is a `uint8` 1-D tensor of length `channels`.
Each element must be in the range `[0, 255]` (It represents the value of a
pixel in the output image). Non-finite values in the input tensor are
replaced by this tensor in the output image. The default value is the color
red.
Args:
tag: A `Tensor` of type `string`.
Scalar. Used to build the `tag` attribute of the summary values.
tensor: A `Tensor`. Must be one of the following types: `uint8`, `float32`, `half`, `float64`.
4-D of shape `[batch_size, height, width, channels]` where
`channels` is 1, 3, or 4.
max_images: An optional `int` that is `>= 1`. Defaults to `3`.
Max number of batch elements to generate images for.
bad_color: An optional `tf.TensorProto`. Defaults to `dtype: DT_UINT8 tensor_shape { dim { size: 4 } } int_val: 255 int_val: 0 int_val: 0 int_val: 255`.
Color to use for pixels with non-finite values.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `string`.
"""
_ctx = _context._context or _context.context()
tld = _ctx._thread_local_data
if tld.is_eager:
try:
_result = pywrap_tfe.TFE_Py_FastPathExecute(
_ctx._context_handle, tld.device_name, "ImageSummary", name,
tld.op_callbacks, tag, tensor, "max_images", max_images, "bad_color",
bad_color)
return _result
except _core._NotOkStatusException as e:
_ops.raise_from_not_ok_status(e, name)
except _core._FallbackException:
pass
try:
return image_summary_eager_fallback(
tag, tensor, max_images=max_images, bad_color=bad_color, name=name,
ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
# Add nodes to the TensorFlow graph.
if max_images is None:
max_images = 3
max_images = _execute.make_int(max_images, "max_images")
if bad_color is None:
bad_color = _execute.make_tensor("""dtype: DT_UINT8 tensor_shape { dim { size: 4 } } int_val: 255 int_val: 0 int_val: 0 int_val: 255""", "bad_color")
bad_color = _execute.make_tensor(bad_color, "bad_color")
_, _, _op, _outputs = _op_def_library._apply_op_helper(
"ImageSummary", tag=tag, tensor=tensor, max_images=max_images,
bad_color=bad_color, name=name)
_result = _outputs[:]
if _execute.must_record_gradient():
_attrs = ("max_images", _op._get_attr_int("max_images"), "T",
_op._get_attr_type("T"), "bad_color", _op.get_attr("bad_color"))
_inputs_flat = _op.inputs
_execute.record_gradient(
"ImageSummary", _inputs_flat, _attrs, _result)
_result, = _result
return _result
def _image_summary(
tag,
tensor,
max_images=3,
bad_color=_execute.
make_tensor(
"""dtype: DT_UINT8 tensor_shape { dim { size: 4 } } int_val: 255 int_val: 0 int_val: 0 int_val: 255""",
"bad_color"),
name=None):
r"""Outputs a `Summary` protocol buffer with images.
The summary has up to `max_images` summary values containing images. The
images are built from `tensor` which must be 4-D with shape `[batch_size,
height, width, channels]` and where `channels` can be:
* 1: `tensor` is interpreted as Grayscale.
* 3: `tensor` is interpreted as RGB.
* 4: `tensor` is interpreted as RGBA.
The images have the same number of channels as the input tensor. For float
input, the values are normalized one image at a time to fit in the range
`[0, 255]`. `uint8` values are unchanged. The op uses two different
normalization algorithms:
* If the input values are all positive, they are rescaled so the largest one
is 255.
* If any input value is negative, the values are shifted so input value 0.0
is at 127. They are then rescaled so that either the smallest value is 0,
or the largest one is 255.
The `tag` argument is a scalar `Tensor` of type `string`. It is used to
build the `tag` of the summary values:
* If `max_images` is 1, the summary value tag is '*tag*/image'.
* If `max_images` is greater than 1, the summary value tags are
generated sequentially as '*tag*/image/0', '*tag*/image/1', etc.
The `bad_color` argument is the color to use in the generated images for
non-finite input values. It is a `unit8` 1-D tensor of length `channels`.
Each element must be in the range `[0, 255]` (It represents the value of a
pixel in the output image). Non-finite values in the input tensor are
replaced by this tensor in the output image. The default value is the color
red.
Args:
tag: A `Tensor` of type `string`.
Scalar. Used to build the `tag` attribute of the summary values.
tensor: A `Tensor`. Must be one of the following types: `uint8`, `float32`, `half`, `float64`.
4-D of shape `[batch_size, height, width, channels]` where
`channels` is 1, 3, or 4.
max_images: An optional `int` that is `>= 1`. Defaults to `3`.
Max number of batch elements to generate images for.
bad_color: An optional `tf.TensorProto`. Defaults to `dtype: DT_UINT8 tensor_shape { dim { size: 4 } } int_val: 255 int_val: 0 int_val: 0 int_val: 255`.
Color to use for pixels with non-finite values.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `string`. Scalar. Serialized `Summary` protocol buffer.
"""
if max_images is None:
max_images = 3
max_images = _execute.make_int(max_images, "max_images")
if bad_color is None:
bad_color = _execute.make_tensor(
"""dtype: DT_UINT8 tensor_shape { dim { size: 4 } } int_val: 255 int_val: 0 int_val: 0 int_val: 255""",
"bad_color")
bad_color = _execute.make_tensor(bad_color, "bad_color")
_ctx = _context.context()
if _ctx.in_graph_mode():
_, _, _op = _op_def_lib._apply_op_helper("ImageSummary",
tag=tag,
tensor=tensor,
max_images=max_images,
bad_color=bad_color,
name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("max_images", _op.get_attr("max_images"), "T",
_op.get_attr("T"), "bad_color", _op.get_attr("bad_color"))
else:
_attr_T, (tensor, ) = _execute.args_to_matching_eager([tensor], _ctx,
_dtypes.float32)
_attr_T = _attr_T.as_datatype_enum
tag = _ops.convert_to_tensor(tag, _dtypes.string)
_inputs_flat = [tag, tensor]
_attrs = ("max_images", max_images, "T", _attr_T, "bad_color",
bad_color)
_result = _execute.execute(b"ImageSummary",
1,
inputs=_inputs_flat,
attrs=_attrs,
ctx=_ctx,
name=name)
_execute.record_gradient("ImageSummary", _inputs_flat, _attrs, _result,
name)
_result, = _result
return _result
