-
+
View source on GitHub
@@ -61,7 +61,7 @@
## Class `CorrelationCost`
-
+Correlation Cost Layer.
@@ -72,10 +72,29 @@
+This layer implements the correlation operation from FlowNet Learning
+Optical Flow with Convolutional Networks (Fischer et al.):
+https://arxiv.org/abs/1504.06
+
+#### Args:
+
+
+* `kernel_size`: An integer specifying the height and width of the
+ patch used to compute the per-patch costs.
+* `max_displacement`: An integer specifying the maximum search radius
+ for each position.
+* `stride_1`: An integer specifying the stride length in the input.
+* `stride_2`: An integer specifying the stride length in the patch.
+* `pad`: An integer specifying the paddings in height and width.
+* `data_format`: Specifies the data format.
+ Possible values are:
+ "channels_last" float [batch, height, width, channels]
+ "channels_first" float [batch, channels, height, width]
+ Defaults to `"channels_last"`.
__init__
-View source
+View source
``` python
__init__(
@@ -390,7 +409,7 @@ Output tensor(s).
build
-View source
+View source
``` python
build(input_shape)
@@ -426,7 +445,7 @@ None or a tensor (or list of tensors,
compute_output_shape
-View source
+View source
``` python
compute_output_shape(input_shape)
@@ -486,7 +505,7 @@ A layer instance.
get_config
-View source
+View source
``` python
get_config()
diff --git a/docs/api_docs/python/tfa/layers/optical_flow.md b/docs/api_docs/python/tfa/layers/optical_flow.md
index 2ea1538ea3..b7b44741a0 100644
--- a/docs/api_docs/python/tfa/layers/optical_flow.md
+++ b/docs/api_docs/python/tfa/layers/optical_flow.md
@@ -24,9 +24,5 @@ Tensorflow op performing correlation cost operation.
## Classes
-[`class CorrelationCost`](../../tfa/layers/CorrelationCost.md)
-
-## Functions
-
-[`correlation_cost(...)`](../../tfa/layers/optical_flow/correlation_cost.md): Correlation Cost Volume computation.
+[`class CorrelationCost`](../../tfa/layers/CorrelationCost.md): Correlation Cost Layer.
diff --git a/docs/api_docs/python/tfa/layers/optical_flow/correlation_cost.md b/docs/api_docs/python/tfa/layers/optical_flow/correlation_cost.md
deleted file mode 100644
index 2ce49dbd53..0000000000
--- a/docs/api_docs/python/tfa/layers/optical_flow/correlation_cost.md
+++ /dev/null
@@ -1,85 +0,0 @@
-
-
-
-
-
-# tfa.layers.optical_flow.correlation_cost
-
-
-
-
-
-
-Correlation Cost Volume computation.
-
-``` python
-tfa.layers.optical_flow.correlation_cost(
- input_a,
- input_b,
- kernel_size,
- max_displacement,
- stride_1,
- stride_2,
- pad,
- data_format='channels_last',
- name=None
-)
-```
-
-
-
-
-
-"FlowNet: Learning Optical Flow with Convolutional Networks"
-Philipp Fischer, Alexey Dosovitskiy, Eddy Ilg, Philip Hausser,
-Caner Hazirbas, Vladimir Golkov, Patrick van der Smagt,
-Daniel Cremers, Thomas Brox. https://arxiv.org/abs/1504.06852
-
-Computes a cost volume using correlation for two inputs. For feature
-maps A, B with spatial dimensions w, h, c it computes
-
- output(a, b) = sum_{l in [-k,k]**2} < I(a+l), J(b+l) >
-
-where the patches of size K=2d + 1 are centered in position a resp. b.
-
-The output shape is [B, C', H', W'], where
-
- r = max_displacement / stride_2;
- bd = max_displacement + (kernel_size - 1) / 2
- C' = (2 * r + 1) ** 2
- H' = H + 2 * (pad - bd) / stride_1
- W' = W + 2 * (pad - bd) / stride_1
-
-Note: When the data_format requests "channels_last", an additional explicit
- transpose operation is executed.
-
-#### Args:
-
-
-* `input_a`: A `Tensor` of the format specified by `data_format`.
-* `input_b`: A `Tensor` of the format specified by `data_format`.
-* `kernel_size`: An integer specifying the height and width of the
- patch used to compute the per-patch costs.
-* `max_displacement`: An integer specifying the maximum search radius
- for each position.
-* `stride_1`: An integer specifying the stride length in the input.
-* `stride_2`: An integer specifying the stride length in the patch.
-* `pad`: An integer specifying the paddings in height and width.
-* `data_format`: Specifies the data format.
- Possible values are:
- "channels_last" float [batch, height, width, channels]
- "channels_first" float [batch, channels, height, width]
- Defaults to `"channels_last"`.
-* `name`: A name for the operation (optional).
-
-
-#### Returns:
-
-A `Tensor` of the format specified by `data_format`.
diff --git a/setup.py b/setup.py
index 5d6d3cba40..ee55966130 100644
--- a/setup.py
+++ b/setup.py
@@ -69,9 +69,9 @@
if project_name == TFA_RELEASE:
# TODO: remove if-else condition when tf supports package consolidation.
if platform.system() == 'Linux':
- REQUIRED_PACKAGES.append('tensorflow-gpu == 2.0.0-rc0')
+ REQUIRED_PACKAGES.append('tensorflow-gpu == 2.0.0-rc1')
else:
- REQUIRED_PACKAGES.append('tensorflow == 2.0.0-rc0')
+ REQUIRED_PACKAGES.append('tensorflow == 2.0.0-rc1')
elif project_name == TFA_NIGHTLY:
# TODO: remove if-else condition when tf-nightly supports package consolidation.
if platform.system() == 'Linux':
diff --git a/tensorflow_addons/layers/optical_flow.py b/tensorflow_addons/layers/optical_flow.py
index c2c843548a..b3c58d28aa 100644
--- a/tensorflow_addons/layers/optical_flow.py
+++ b/tensorflow_addons/layers/optical_flow.py
@@ -27,15 +27,15 @@
@tf.function
-def correlation_cost(input_a,
- input_b,
- kernel_size,
- max_displacement,
- stride_1,
- stride_2,
- pad,
- data_format='channels_last',
- name=None):
+def _correlation_cost(input_a,
+ input_b,
+ kernel_size,
+ max_displacement,
+ stride_1,
+ stride_2,
+ pad,
+ data_format='channels_last',
+ name=None):
"""Correlation Cost Volume computation.
"FlowNet: Learning Optical Flow with Convolutional Networks"
@@ -141,6 +141,27 @@ def _correlation_cost_grad(op, grad_output):
@keras_utils.register_keras_custom_object
class CorrelationCost(tf.keras.layers.Layer):
+ """Correlation Cost Layer.
+
+ This layer implements the correlation operation from FlowNet Learning
+ Optical Flow with Convolutional Networks (Fischer et al.):
+ https://arxiv.org/abs/1504.06
+
+ Args:
+ kernel_size: An integer specifying the height and width of the
+ patch used to compute the per-patch costs.
+ max_displacement: An integer specifying the maximum search radius
+ for each position.
+ stride_1: An integer specifying the stride length in the input.
+ stride_2: An integer specifying the stride length in the patch.
+ pad: An integer specifying the paddings in height and width.
+ data_format: Specifies the data format.
+ Possible values are:
+ "channels_last" float [batch, height, width, channels]
+ "channels_first" float [batch, channels, height, width]
+ Defaults to `"channels_last"`.
+ """
+
def __init__(self, kernel_size, max_displacement, stride_1, stride_2, pad,
data_format, **kwargs):
self.kernel_size = kernel_size
@@ -169,7 +190,7 @@ def call(self, inputs):
input_a = tf.convert_to_tensor(inputs[0])
input_b = tf.convert_to_tensor(inputs[1])
- return correlation_cost(
+ return _correlation_cost(
input_a,
input_b,
kernel_size=self.kernel_size,
diff --git a/tensorflow_addons/layers/optical_flow_test.py b/tensorflow_addons/layers/optical_flow_test.py
index 060f572c5f..13e4845f7a 100644
--- a/tensorflow_addons/layers/optical_flow_test.py
+++ b/tensorflow_addons/layers/optical_flow_test.py
@@ -19,7 +19,7 @@
import numpy as np
import tensorflow as tf
-from tensorflow_addons.layers.optical_flow import correlation_cost, CorrelationCost
+from tensorflow_addons.layers.optical_flow import CorrelationCost
from tensorflow_addons.utils import test_utils
@@ -31,15 +31,13 @@ def _forward(self, input_a, input_b, kernel_size, max_displacement,
input_a_op = tf.convert_to_tensor(input_a, dtype=tf.float32)
input_b_op = tf.convert_to_tensor(input_b, dtype=tf.float32)
- output = correlation_cost(
- input_a_op,
- input_b_op,
+ output = CorrelationCost(
kernel_size=kernel_size,
max_displacement=max_displacement,
stride_1=stride_1,
stride_2=stride_2,
pad=pad,
- data_format=data_format)
+ data_format=data_format)([input_a_op, input_b_op])
return output
@@ -117,15 +115,13 @@ def _gradients(self, data_format):
input_b_op = tf.convert_to_tensor(input_b)
def correlation_fn(input_a, input_b):
- return correlation_cost(
- input_a,
- input_b,
+ return CorrelationCost(
kernel_size=kernel_size,
max_displacement=max_displacement,
stride_1=stride_1,
stride_2=stride_2,
pad=pad,
- data_format=data_format)
+ data_format=data_format)([input_a, input_b])
theoretical, numerical = tf.test.compute_gradient(
correlation_fn, [input_a_op, input_b_op])
@@ -159,7 +155,7 @@ def _keras(self, data_format):
x = [input_a, input_b]
y = layer(x)
- model = tf.python.keras.models.Model(x, y)
+ model = tf.keras.models.Model(x, y)
actual_output = model.predict([val_a, val_b])
expected_output_type = 'float32'
diff --git a/tensorflow_addons/seq2seq/beam_search_decoder.py b/tensorflow_addons/seq2seq/beam_search_decoder.py
index 2ed00b514f..e2e67e8c21 100644
--- a/tensorflow_addons/seq2seq/beam_search_decoder.py
+++ b/tensorflow_addons/seq2seq/beam_search_decoder.py
@@ -764,7 +764,7 @@ def output_dtype(self):
parent_ids=tf.int32)
def call(self,
- embeddning,
+ embedding,
start_tokens,
end_token,
initial_state,
@@ -782,7 +782,7 @@ def call(self,
parallel_iterations=self.parallel_iterations,
swap_memory=self.swap_memory,
training=training,
- decoder_init_input=embeddning,
+ decoder_init_input=embedding,
decoder_init_kwargs=init_kwargs)
|