def setUp(self):
super(MultiplexerDataProviderTest, self).setUp()
self.logdir = self.get_temp_dir()
logdir = os.path.join(self.logdir, "polynomials")
with tf.summary.create_file_writer(logdir).as_default():
for i in xrange(10):
scalar_summary.scalar("square", i ** 2, step=2 * i, description="boxen")
scalar_summary.scalar("cube", i ** 3, step=3 * i)
logdir = os.path.join(self.logdir, "waves")
with tf.summary.create_file_writer(logdir).as_default():
for i in xrange(10):
scalar_summary.scalar("sine", tf.sin(float(i)), step=i)
scalar_summary.scalar("square", tf.sign(tf.sin(float(i))), step=i)
# Summary with rank-0 data but not owned by the scalars plugin.
metadata = summary_pb2.SummaryMetadata()
metadata.plugin_data.plugin_name = "marigraphs"
tf.summary.write("high_tide", tensor=i, step=i, metadata=metadata)
logdir = os.path.join(self.logdir, "pictures")
with tf.summary.create_file_writer(logdir).as_default():
purple = tf.constant([[[255, 0, 255]]], dtype=tf.uint8)
for i in xrange(1, 11):
image_summary.image("purple", [tf.tile(purple, [i, i, 1])], step=i)
def f():
def body(step, tokens):
next_token = random_ops.random_uniform([bsz])
tokens = tokens.write(step, next_token)
return (step + 1, tokens)
def cond(step, tokens):
del tokens
return math_ops.less(step, max_length)
tokens_var = tensor_array_ops.TensorArray(
dtype=dtypes.float32,
size=max_length,
dynamic_size=False,
clear_after_read=False,
element_shape=(bsz, ),
name="tokens_accumulator",
)
step = constant_op.constant(0)
step, tokens_var = control_flow_ops.while_loop(
cond, body, [step, tokens_var])
image_flat = array_ops.transpose(tokens_var.stack(),
[1, 0])
image = array_ops.tile(
array_ops.reshape(image_flat, [bsz, 32, 32, 1]),
[1, 1, 1, 3])
image_summary_v2.image(
"image_sample", image,
constant_op.constant(5, dtype=dtypes.int64))
def call(self, x):
# Add summary image using compat v2 implementation.
image_summary_v2.image('custom_image_summary_v2', x)
return x
def setUp(self):
super(MultiplexerDataProviderTest, self).setUp()
self.logdir = self.get_temp_dir()
self.ctx = context.RequestContext()
logdir = os.path.join(self.logdir, "polynomials")
with tf.summary.create_file_writer(logdir).as_default():
for i in range(10):
scalar_summary.scalar(
"square", i ** 2, step=2 * i, description="boxen"
)
scalar_summary.scalar("cube", i ** 3, step=3 * i)
logdir = os.path.join(self.logdir, "waves")
with tf.summary.create_file_writer(logdir).as_default():
for i in range(10):
scalar_summary.scalar("sine", tf.sin(float(i)), step=i)
scalar_summary.scalar(
"square", tf.sign(tf.sin(float(i))), step=i
)
# Summary with rank-0 data but not owned by the scalars plugin.
metadata = summary_pb2.SummaryMetadata()
metadata.plugin_data.plugin_name = "marigraphs"
metadata.data_class = summary_pb2.DATA_CLASS_SCALAR
tf.summary.write(
"high_tide", tensor=i, step=i, metadata=metadata
)
# Summary with rank-1 data of scalar data class (bad!).
metadata = summary_pb2.SummaryMetadata()
metadata.plugin_data.plugin_name = "greetings"
metadata.data_class = summary_pb2.DATA_CLASS_SCALAR
tf.summary.write(
"bad", tensor=[i, i], step=i, metadata=metadata
)
logdir = os.path.join(self.logdir, "lebesgue")
with tf.summary.create_file_writer(logdir).as_default():
data = [
("very smooth", (0.0, 0.25, 0.5, 0.75, 1.0), "uniform"),
("very smoothn't", (0.0, 0.01, 0.99, 1.0), "bimodal"),
]
for (description, distribution, name) in data:
tensor = tf.constant([distribution], dtype=tf.float64)
for i in range(1, 11):
histogram_summary.histogram(
name, tensor * i, step=i, description=description
)
logdir = os.path.join(self.logdir, "mondrian")
with tf.summary.create_file_writer(logdir).as_default():
data = [
("red", (221, 28, 38), "top-right"),
("blue", (1, 91, 158), "bottom-left"),
("yellow", (239, 220, 111), "bottom-right"),
]
for (name, color, description) in data:
image_1x1 = tf.constant([[[color]]], dtype=tf.uint8)
for i in range(1, 11):
# Use a non-monotonic sequence of sample sizes to
# test `max_length` calculation.
k = 6 - abs(6 - i) # 1, .., 6, .., 2
# a `k`-sample image summary of `i`-by-`i` images
image = tf.tile(image_1x1, [k, i, i, 1])
image_summary.image(
name,
image,
step=i,
description=description,
max_outputs=99,
)
