Merge branch 'dtk22.04' into 'main'

Dtk22.04

See merge request dcutoolkit/deeplearing/dlexamples_new!49

Keras/keras-cv/keras_cv/models/weights.py

+# Copyright 2022 The KerasCV Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+import tensorflow as tf
+from keras.utils import data_utils
+
+
+def parse_weights(weights, include_top, model_type):
+    if not weights or tf.io.gfile.exists(weights):
+        return weights
+    if weights in ALIASES[model_type]:
+        weights = ALIASES[model_type][weights]
+    if weights in WEIGHTS_CONFIG[model_type]:
+        if not include_top:
+            weights = weights + "-notop"
+        return data_utils.get_file(
+            origin=f"{BASE_PATH}/{model_type}/{weights}.h5",
+            cache_subdir="models",
+            file_hash=WEIGHTS_CONFIG[model_type][weights],
+        )
+
+    raise ValueError(
+        "The `weights` argument should be either `None`, a the path to the "
+        "weights file to be loaded, or the name of pre-trained weights from "
+        "https://github.com/keras-team/keras-cv/blob/master/keras_cv/models/weights.py. "
+        f"Invalid `weights` argument: {weights}"
+    )
+
+
+BASE_PATH = "https://storage.googleapis.com/keras-cv/models"
+
+ALIASES = {
+    "densenet121": {
+        "imagenet": "imagenet/classification-v0",
+        "imagenet/classification": "imagenet/classification-v0",
+    },
+    "densenet169": {
+        "imagenet": "imagenet/classification-v0",
+        "imagenet/classification": "imagenet/classification-v0",
+    },
+    "densenet201": {
+        "imagenet": "imagenet/classification-v0",
+        "imagenet/classification": "imagenet/classification-v0",
+    },
+    "efficientnetv2b0": {
+        "imagenet": "imagenet/classification-v0",
+        "imagenet/classification": "imagenet/classification-v0",
+    },
+    "efficientnetv2b1": {
+        "imagenet": "imagenet/classification-v0",
+        "imagenet/classification": "imagenet/classification-v0",
+    },
+    "efficientnetv2b2": {
+        "imagenet": "imagenet/classification-v0",
+        "imagenet/classification": "imagenet/classification-v0",
+    },
+    "resnet50v2": {
+        "imagenet": "imagenet/classification-v2",
+        "imagenet/classification": "imagenet/classification-v2",
+    },
+}
+
+WEIGHTS_CONFIG = {
+    "densenet121": {
+        "imagenet/classification-v0": "13de3d077ad9d9816b9a0acc78215201d9b6e216c7ed8e71d69cc914f8f0775b",
+        "imagenet/classification-v0-notop": "709afe0321d9f2b2562e562ff9d0dc44cca10ed09e0e2cfba08d783ff4dab6bf",
+    },
+    "densenet169": {
+        "imagenet/classification-v0": "4cd2a661d0cb2378574073b23129ee4d06ea53c895c62a8863c44ee039e236a1",
+        "imagenet/classification-v0-notop": "a99d1bb2cbe1a59a1cdd1f435fb265453a97c2a7b723d26f4ebee96e5fb49d62",
+    },
+    "densenet201": {
+        "imagenet/classification-v0": "3b6032e744e5e5babf7457abceaaba11fcd449fe2d07016ae5076ac3c3c6cf0c",
+        "imagenet/classification-v0-notop": "c1189a934f12c1a676a9cf52238e5994401af925e2adfc0365bad8133c052060",
+    },
+    "efficientnetv2b0": {
+        "imagenet/classification-v0": "da7975b6d4200dfdc3f859b0d028774e5e5dd4031d3e998a27dadc492dec4f3e",
+        "imagenet/classification-v0-notop": "defe635bfa3cc3f2b9e89bfd53bbc3de28a1dc67026b4437a14f44476e7d0549",
+    },
+    "efficientnetv2b1": {
+        "imagenet/classification-v0": "3f92fc9d7b141ec9e85ffe60d301fb49103ba17b148bdd638971a77f1b8db010",
+        "imagenet/classification-v0-notop": "359aaa5c1e863c8438d94052791e72ef29345d07703d06284e1069829f85932f",
+    },
+    "efficientnetv2b2": {
+        "imagenet/classification-v0": "1667d21b50e6c5b851a69c98503fa5ae707b82dbae8c900fe59ab1a93d60d694",
+        "imagenet/classification-v0-notop": "e118aadfab7e93ff939fb81c88c189cbd7fb2b7ddd7314fbf2badb7c551aa119",
+    },
+    "resnet50v2": {
+        "imagenet/classification-v0": "11bde945b54d1dca65101be2648048abca8a96a51a42820d87403486389790db",
+        "imagenet/classification-v0-notop": "5b4aca4932c433d84f6aef58135472a4312ed2fa565d53fedcd6b0c24b54ab4a",
+        "imagenet/classification-v1": "a32e5d9998e061527f6f947f36d8e794ad54dad71edcd8921cda7804912f3ee7",
+        "imagenet/classification-v1-notop": "ac46b82c11070ab2f69673c41fbe5039c9eb686cca4f34cd1d79412fd136f1ae",
+        "imagenet/classification-v2": "5ee5a8ac650aaa59342bc48ffe770e6797a5550bcc35961e1d06685292c15921",
+        "imagenet/classification-v2-notop": "e711c83d6db7034871f6d345a476c8184eab99dbf3ffcec0c1d8445684890ad9",
+    },
+}

Keras/keras-cv/keras_cv/ops/__init__.py

+# Copyright 2022 The KerasCV Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from keras_cv.ops.iou_3d import IoU3D
+from keras_cv.ops.point_cloud import _box_area
+from keras_cv.ops.point_cloud import _center_xyzWHD_to_corner_xyz
+from keras_cv.ops.point_cloud import _is_on_lefthand_side
+from keras_cv.ops.point_cloud import coordinate_transform
+from keras_cv.ops.point_cloud import is_within_box2d
+from keras_cv.ops.point_cloud import is_within_box3d
+from keras_cv.ops.point_cloud import spherical_coordinate_transform

Keras/keras-cv/keras_cv/ops/box_matcher.py

+# Copyright 2022 The KerasCV Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"Argmax-based box matching"
+
+from typing import List
+from typing import Tuple
+
+import tensorflow as tf
+
+
+class ArgmaxBoxMatcher:
+    """Box matching logic based on argmax of highest value (e.g., IOU).
+
+    This class computes matches from a similarity matrix. Each row will be
+    matched to at least one column, the matched result can either be positive
+     / negative, or simply ignored depending on the setting.
+
+    The settings include `thresholds` and `match_values`, for example if:
+    1) thresholds=[negative_threshold, positive_threshold], and
+       match_values=[negative_value=0, ignore_value=-1, positive_value=1]: the rows will
+       be assigned to positive_value if its argmax result >=
+       positive_threshold; the rows will be assigned to negative_value if its
+       argmax result < negative_threshold, and the rows will be assigned
+       to ignore_value if its argmax result is between [negative_threshold, positive_threshold).
+    2) thresholds=[negative_threshold, positive_threshold], and
+       match_values=[ignore_value=-1, negative_value=0, positive_value=1]: the rows will
+       be assigned to positive_value if its argmax result >=
+       positive_threshold; the rows will be assigned to ignore_value if its
+       argmax result < negative_threshold, and the rows will be assigned
+       to negative_value if its argmax result is between [negative_threshold ,positive_threshold).
+       This is different from case 1) by swapping first two
+       values.
+    3) thresholds=[positive_threshold], and
+       match_values=[negative_values, positive_value]: the rows will be assigned to
+       positive value if its argmax result >= positive_threshold; the rows
+       will be assigned to negative_value if its argmax result < negative_threshold.
+
+    Args:
+        thresholds: A sorted list of floats to classify the matches into
+          different results (e.g. positive or negative or ignored match). The
+          list will be prepended with -Inf and and appended with +Inf.
+        match_values: A list of integers representing matched results (e.g.
+          positive or negative or ignored match). len(`match_values`) must
+          equal to len(`thresholds`) + 1.
+        force_match_for_each_col: each row will be argmax matched to at
+          least one column. This means some columns will be matched to
+          multiple rows while some columns will not be matched to any rows.
+          Filtering by `thresholds` will make less columns match to positive
+          result. Setting this to True guarantees that each column will be
+          matched to positive result to at least one row.
+
+    Raises:
+        ValueError: if `thresholds` not sorted or
+        len(`match_values`) != len(`thresholds`) + 1
+
+    Usage:
+
+    ```python
+    box_matcher = keras_cv.ops.ArgmaxBoxMatcher([0.3, 0.7], [-1, 0, 1])
+    iou_metric = keras_cv.bounding_box.compute_iou(anchors, gt_boxes)
+    matched_columns, matched_match_values = box_matcher(iou_metric)
+    cls_mask = tf.less_equal(matched_match_values, 0)
+    ```
+
+    TODO(tanzhenyu): document when to use which mode.
+
+    """
+
+    def __init__(
+        self,
+        thresholds: List[float],
+        match_values: List[int],
+        force_match_for_each_col: bool = False,
+    ):
+        if sorted(thresholds) != thresholds:
+            raise ValueError(f"`threshold` must be sorted, got {thresholds}")
+        self.match_values = match_values
+        if len(match_values) != len(thresholds) + 1:
+            raise ValueError(
+                f"len(`match_values`) must be len(`thresholds`) + 1, got "
+                f"match_values {match_values}, thresholds {thresholds}"
+            )
+        thresholds.insert(0, -float("inf"))
+        thresholds.append(float("inf"))
+        self.thresholds = thresholds
+        self.force_match_for_each_col = force_match_for_each_col
+
+    def __call__(self, similarity_matrix: tf.Tensor) -> Tuple[tf.Tensor, tf.Tensor]:
+        """Matches each row to a column based on argmax
+
+        TODO(tanzhenyu): consider swapping rows and cols.
+        Args:
+          similarity_matrix: A float Tensor of shape [num_rows, num_cols] or
+            [batch_size, num_rows, num_cols] representing any similarity metric.
+
+        Returns:
+          matched_columns: An integer tensor of shape [num_rows] or [batch_size,
+            num_rows] storing the index of the matched colum for each row.
+          matched_values: An integer tensor of shape [num_rows] or [batch_size,
+            num_rows] storing the match result (positive match, negative match,
+            ignored match).
+        """
+        squeeze_result = False
+        if len(similarity_matrix.shape) == 2:
+            squeeze_result = True
+            similarity_matrix = tf.expand_dims(similarity_matrix, axis=0)
+        static_shape = similarity_matrix.shape.as_list()
+        num_rows = static_shape[1] or tf.shape(similarity_matrix)[1]
+        batch_size = static_shape[0] or tf.shape(similarity_matrix)[0]
+
+        def _match_when_cols_are_empty():
+            """Performs matching when the rows of similarity matrix are empty.
+            When the rows are empty, all detections are false positives. So we return
+            a tensor of -1's to indicate that the rows do not match to any columns.
+            Returns:
+                matched_columns: An integer tensor of shape [batch_size, num_rows]
+                  storing the index of the matched column for each row.
+                matched_values: An integer tensor of shape [batch_size, num_rows]
+                  storing the match type indicator (e.g. positive or negative
+                  or ignored match).
+            """
+            with tf.name_scope("empty_gt_boxes"):
+                matched_columns = tf.zeros([batch_size, num_rows], dtype=tf.int32)
+                matched_values = -tf.ones([batch_size, num_rows], dtype=tf.int32)
+                return matched_columns, matched_values
+
+        def _match_when_cols_are_non_empty():
+            """Performs matching when the rows of similarity matrix are non empty.
+            Returns:
+                matched_columns: An integer tensor of shape [batch_size, num_rows]
+                  storing the index of the matched column for each row.
+                matched_values: An integer tensor of shape [batch_size, num_rows]
+                  storing the match type indicator (e.g. positive or negative
+                  or ignored match).
+            """
+            with tf.name_scope("non_empty_gt_boxes"):
+                matched_columns = tf.argmax(
+                    similarity_matrix, axis=-1, output_type=tf.int32
+                )
+
+                # Get logical indices of ignored and unmatched columns as tf.int64
+                matched_vals = tf.reduce_max(similarity_matrix, axis=-1)
+                matched_values = tf.zeros([batch_size, num_rows], tf.int32)
+
+                match_dtype = matched_vals.dtype
+                for (ind, low, high) in zip(
+                    self.match_values, self.thresholds[:-1], self.thresholds[1:]
+                ):
+                    low_threshold = tf.cast(low, match_dtype)
+                    high_threshold = tf.cast(high, match_dtype)
+                    mask = tf.logical_and(
+                        tf.greater_equal(matched_vals, low_threshold),
+                        tf.less(matched_vals, high_threshold),
+                    )
+                    matched_values = self._set_values_using_indicator(
+                        matched_values, mask, ind
+                    )
+
+                if self.force_match_for_each_col:
+                    # [batch_size, num_cols], for each column (groundtruth_box), find the
+                    # best matching row (anchor).
+                    matching_rows = tf.argmax(
+                        input=similarity_matrix, axis=1, output_type=tf.int32
+                    )
+                    # [batch_size, num_cols, num_rows], a transposed 0-1 mapping matrix M,
+                    # where M[j, i] = 1 means column j is matched to row i.
+                    column_to_row_match_mapping = tf.one_hot(
+                        matching_rows, depth=num_rows
+                    )
+                    # [batch_size, num_rows], for each row (anchor), find the matched
+                    # column (groundtruth_box).
+                    force_matched_columns = tf.argmax(
+                        input=column_to_row_match_mapping, axis=1, output_type=tf.int32
+                    )
+                    # [batch_size, num_rows]
+                    force_matched_column_mask = tf.cast(
+                        tf.reduce_max(column_to_row_match_mapping, axis=1), tf.bool
+                    )
+                    # [batch_size, num_rows]
+                    matched_columns = tf.where(
+                        force_matched_column_mask,
+                        force_matched_columns,
+                        matched_columns,
+                    )
+                    matched_values = tf.where(
+                        force_matched_column_mask,
+                        self.match_values[-1]
+                        * tf.ones([batch_size, num_rows], dtype=tf.int32),
+                        matched_values,
+                    )
+
+                return matched_columns, matched_values
+
+        num_gt_boxes = (
+            similarity_matrix.shape.as_list()[-1] or tf.shape(similarity_matrix)[-1]
+        )
+        matched_columns, matched_values = tf.cond(
+            pred=tf.greater(num_gt_boxes, 0),
+            true_fn=_match_when_cols_are_non_empty,
+            false_fn=_match_when_cols_are_empty,
+        )
+
+        if squeeze_result:
+            matched_columns = tf.squeeze(matched_columns, axis=0)
+            matched_values = tf.squeeze(matched_values, axis=0)
+
+        return matched_columns, matched_values
+
+    def _set_values_using_indicator(self, x, indicator, val):
+        """Set the indicated fields of x to val.
+
+        Args:
+          x: tensor.
+          indicator: boolean with same shape as x.
+          val: scalar with value to set.
+        Returns:
+          modified tensor.
+        """
+        indicator = tf.cast(indicator, x.dtype)
+        return tf.add(tf.multiply(x, 1 - indicator), val * indicator)
+
+    def get_config(self):
+        config = {
+            "thresholds": self.thresholds[1:-1],
+            "match_values": self.match_values,
+            "force_match_for_each_col": self.force_match_for_each_col,
+        }
+        return config

Keras/keras-cv/keras_cv/ops/box_matcher_test.py

+# Copyright 2022 The KerasCV Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import tensorflow as tf
+
+from keras_cv.ops.box_matcher import ArgmaxBoxMatcher
+
+
+class ArgmaxBoxMatcherTest(tf.test.TestCase):
+    def test_box_matcher_invalid_length(self):
+        fg_threshold = 0.5
+        bg_thresh_hi = 0.2
+        bg_thresh_lo = 0.0
+
+        with self.assertRaisesRegex(ValueError, "must be len"):
+            _ = ArgmaxBoxMatcher(
+                thresholds=[bg_thresh_lo, bg_thresh_hi, fg_threshold],
+                match_values=[-3, -2, -1],
+            )
+
+    def test_box_matcher_unsorted_thresholds(self):
+        fg_threshold = 0.5
+        bg_thresh_hi = 0.2
+        bg_thresh_lo = 0.0
+
+        with self.assertRaisesRegex(ValueError, "must be sorted"):
+            _ = ArgmaxBoxMatcher(
+                thresholds=[bg_thresh_hi, bg_thresh_lo, fg_threshold],
+                match_values=[-3, -2, -1, 1],
+            )
+
+    def test_box_matcher_unbatched(self):
+        sim_matrix = tf.constant([[0.04, 0, 0, 0], [0, 0, 1.0, 0]], dtype=tf.float32)
+
+        fg_threshold = 0.5
+        bg_thresh_hi = 0.2
+        bg_thresh_lo = 0.0
+
+        matcher = ArgmaxBoxMatcher(
+            thresholds=[bg_thresh_lo, bg_thresh_hi, fg_threshold],
+            match_values=[-3, -2, -1, 1],
+        )
+        match_indices, matched_values = matcher(sim_matrix)
+        positive_matches = tf.greater_equal(matched_values, 0)
+        negative_matches = tf.equal(matched_values, -2)
+
+        self.assertAllEqual(positive_matches.numpy(), [False, True])
+        self.assertAllEqual(negative_matches.numpy(), [True, False])
+        self.assertAllEqual(match_indices.numpy(), [0, 2])
+        self.assertAllEqual(matched_values.numpy(), [-2, 1])
+
+    def test_box_matcher_batched(self):
+        sim_matrix = tf.constant([[[0.04, 0, 0, 0], [0, 0, 1.0, 0]]], dtype=tf.float32)
+
+        fg_threshold = 0.5
+        bg_thresh_hi = 0.2
+        bg_thresh_lo = 0.0
+
+        matcher = ArgmaxBoxMatcher(
+            thresholds=[bg_thresh_lo, bg_thresh_hi, fg_threshold],
+            match_values=[-3, -2, -1, 1],
+        )
+        match_indices, matched_values = matcher(sim_matrix)
+        positive_matches = tf.greater_equal(matched_values, 0)
+        negative_matches = tf.equal(matched_values, -2)
+
+        self.assertAllEqual(positive_matches.numpy(), [[False, True]])
+        self.assertAllEqual(negative_matches.numpy(), [[True, False]])
+        self.assertAllEqual(match_indices.numpy(), [[0, 2]])
+        self.assertAllEqual(matched_values.numpy(), [[-2, 1]])
+
+    def test_box_matcher_force_match(self):
+        sim_matrix = tf.constant(
+            [[0, 0.04, 0, 0.1], [0, 0, 1.0, 0], [0.1, 0, 0, 0], [0, 0, 0, 0.6]],
+            dtype=tf.float32,
+        )
+
+        fg_threshold = 0.5
+        bg_thresh_hi = 0.2
+        bg_thresh_lo = 0.0
+
+        matcher = ArgmaxBoxMatcher(
+            thresholds=[bg_thresh_lo, bg_thresh_hi, fg_threshold],
+            match_values=[-3, -2, -1, 1],
+            force_match_for_each_col=True,
+        )
+        match_indices, matched_values = matcher(sim_matrix)
+        positive_matches = tf.greater_equal(matched_values, 0)
+        negative_matches = tf.equal(matched_values, -2)
+
+        self.assertAllEqual(positive_matches.numpy(), [True, True, True, True])
+        self.assertAllEqual(negative_matches.numpy(), [False, False, False, False])
+        # the first anchor cannot be matched to 4th gt box given that is matched to
+        # the last anchor.
+        self.assertAllEqual(match_indices.numpy(), [1, 2, 0, 3])
+        self.assertAllEqual(matched_values.numpy(), [1, 1, 1, 1])
+
+    def test_box_matcher_empty_gt_boxes(self):
+        sim_matrix = tf.constant([[], []], dtype=tf.float32)
+
+        fg_threshold = 0.5
+        bg_thresh_hi = 0.2
+        bg_thresh_lo = 0.0
+
+        matcher = ArgmaxBoxMatcher(
+            thresholds=[bg_thresh_lo, bg_thresh_hi, fg_threshold],
+            match_values=[-3, -2, -1, 1],
+        )
+        match_indices, matched_values = matcher(sim_matrix)
+        positive_matches = tf.greater_equal(matched_values, 0)
+        ignore_matches = tf.equal(matched_values, -1)
+
+        self.assertAllEqual(positive_matches.numpy(), [False, False])
+        self.assertAllEqual(ignore_matches.numpy(), [True, True])
+        self.assertAllEqual(match_indices.numpy(), [0, 0])
+        self.assertAllEqual(matched_values.numpy(), [-1, -1])

Keras/keras-cv/keras_cv/ops/iou_3d.py

+# Copyright 2022 The KerasCV Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""IoU3D using a custom TF op."""
+
+from tensorflow.python.framework import load_library
+from tensorflow.python.platform import resource_loader
+
+
+class IoU3D:
+    """Implements IoU computation for 3D upright rotated bounding boxes.
+
+    Note that this is implemented using a custom TensorFlow op. Initializing an
+    IoU3D object will attempt to load the binary for that op.
+
+    Boxes should have the format [center_x, center_y, center_z, dimension_x,
+    dimension_y, dimension_z, heading (in radians)].
+
+    Sample Usage:
+    ```python
+    y_true = [[0, 0, 0, 2, 2, 2, 0], [1, 1, 1, 2, 2, 2, 3 * math.pi / 4]]
+    y_pred = [[1, 1, 1, 2, 2, 2, math.pi / 4], [1, 1, 1, 2, 2, 2, 0]]
+    iou = IoU3D()
+    iou(y_true, y_pred)
+    ```
+    """
+
+    def __init__(self):
+        pairwise_iou_op = load_library.load_op_library(
+            resource_loader.get_path_to_datafile(
+                "../custom_ops/_keras_cv_custom_ops.so"
+            )
+        )
+        self.iou_3d = pairwise_iou_op.pairwise_iou3d
+
+    def __call__(self, y_true, y_pred):
+        return self.iou_3d(y_true, y_pred)

Keras/keras-cv/keras_cv/ops/iou_3d_test.py

+# Copyright 2022 The KerasCV Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""Tests for IoU3D using custom op."""
+import math
+import os
+
+import pytest
+import tensorflow as tf
+
+from keras_cv.ops import IoU3D
+
+
+class IoU3DTest(tf.test.TestCase):
+    @pytest.mark.skipif(
+        "TEST_CUSTOM_OPS" not in os.environ or os.environ["TEST_CUSTOM_OPS"] != "true",
+        reason="Requires binaries compiled from source",
+    )
+    def testOpCall(self):
+        # Predicted boxes:
+        # 0: a 2x2x2 box centered at 0,0,0, rotated 0 degrees
+        # 1: a 2x2x2 box centered at 1,1,1, rotated 135 degrees
+        # Ground Truth boxes:
+        # 0: a 2x2x2 box centered at 1,1,1, rotated 45 degrees (idential to predicted box 1)
+        # 1: a 2x2x2 box centered at 1,1,1, rotated 0 degrees
+        box_preds = [[0, 0, 0, 2, 2, 2, 0], [1, 1, 1, 2, 2, 2, 3 * math.pi / 4]]
+        box_gt = [[1, 1, 1, 2, 2, 2, math.pi / 4], [1, 1, 1, 2, 2, 2, 0]]
+
+        # Predicted box 0 and both ground truth boxes overlap by 1/8th of the box.
+        # Therefore, IiU is 1/15
+        # Predicted box 1 is the same as ground truth box 0, therefore IoU is 1
+        # Predicted box 1 shares an origin with ground truth box 1, but is rotated by 135 degrees.
+        # Their IoU can be reduced to that of two overlapping squares that share a center with
+        # the same offset of 135 degrees, which reduces to the square root of 0.5.
+        expected_ious = [[1 / 15, 1 / 15], [1, 0.5**0.5]]
+
+        iou_3d = IoU3D()
+
+        self.assertAllClose(iou_3d(box_preds, box_gt), expected_ious)
+
+
+if __name__ == "__main__":
+    tf.test.main()

Keras/keras-cv/keras_cv/ops/point_cloud.py

+# Copyright 2022 The KerasCV Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import tensorflow as tf
+
+
+def get_rank(tensor):
+    return tensor.shape.ndims or tf.rank(tensor)
+
+
+def _get_3d_rotation_matrix(yaw, roll, pitch):
+    """Creates 3x3 rotation matrix from yaw, roll, pitch (angles in radians).
+
+    Note: Yaw -> Z, Roll -> X, Pitch -> Y
+
+    Args:
+      yaw: float tensor representing a yaw angle in radians.
+      roll: float tensor representing a roll angle in radians.
+      pitch: float tensor representing a pitch angle in radians.
+
+    Returns:
+      A [3, 3] tensor corresponding to a rotation matrix.
+
+    """
+
+    def _UnitX(angle):
+        return tf.reshape(
+            [
+                1.0,
+                0.0,
+                0.0,
+                0.0,
+                tf.cos(angle),
+                -tf.sin(angle),
+                0.0,
+                tf.sin(angle),
+                tf.cos(angle),
+            ],
+            shape=[3, 3],
+        )
+
+    def _UnitY(angle):
+        return tf.reshape(
+            [
+                tf.cos(angle),
+                0.0,
+                tf.sin(angle),
+                0.0,
+                1.0,
+                0.0,
+                -tf.sin(angle),
+                0.0,
+                tf.cos(angle),
+            ],
+            shape=[3, 3],
+        )
+
+    def _UnitZ(angle):
+        return tf.reshape(
+            [
+                tf.cos(angle),
+                -tf.sin(angle),
+                0.0,
+                tf.sin(angle),
+                tf.cos(angle),
+                0.0,
+                0.0,
+                0.0,
+                1.0,
+            ],
+            shape=[3, 3],
+        )
+
+    return tf.matmul(tf.matmul(_UnitZ(yaw), _UnitX(roll)), _UnitY(pitch))
+
+
+def _center_xyzWHD_to_corner_xyz(boxes):
+    """convert from center format to corner format.
+    Args:
+      boxes: [..., num_boxes, 7] float32 Tensor for 3d boxes in [x, y, z, dx,
+        dy, dz, phi].
+    Returns:
+      corners: [..., num_boxes, 8, 3] float32 Tensor for 3d corners in [x, y, z].
+    """
+    # relative corners w.r.t to origin point
+    # this will return all corners in top-down counter clockwise instead of
+    # only left top and bottom right.
+    rel_corners = tf.constant(
+        [
+            [0.5, 0.5, 0.5],  # top
+            [-0.5, 0.5, 0.5],  # top
+            [-0.5, -0.5, 0.5],  # top
+            [0.5, -0.5, 0.5],  # top
+            [0.5, 0.5, -0.5],  # bottom
+            [-0.5, 0.5, -0.5],  # bottom
+            [-0.5, -0.5, -0.5],  # bottom
+            [0.5, -0.5, -0.5],  # bottom
+        ]
+    )
+
+    centers = boxes[..., :3]
+    dimensions = boxes[..., 3:6]
+    phi_world = boxes[..., 6]
+    leading_shapes = boxes.shape.as_list()[:-1]
+    cos = tf.cos(phi_world)
+    sin = tf.sin(phi_world)
+    zero = tf.zeros_like(cos)
+    one = tf.ones_like(cos)
+    rotations = tf.reshape(
+        tf.stack([cos, -sin, zero, sin, cos, zero, zero, zero, one], axis=-1),
+        leading_shapes + [3, 3],
+    )
+    # apply the delta to convert from centers to relative corners format
+    rel_corners = tf.einsum("...ni,ji->...nji", dimensions, rel_corners)
+    # apply rotation matrix on relative corners
+    rel_corners = tf.einsum("...nij,...nkj->...nki", rotations, rel_corners)
+    # translate back to absolute corners format
+    corners = rel_corners + tf.reshape(centers, leading_shapes + [1, 3])
+    return corners
+
+
+def _is_on_lefthand_side(points, v1, v2):
+    """Checks if points lay on a vector direction or to its left.
+
+    Args:
+      point: float Tensor of [num_points, 2] of points to check
+      v1: float Tensor of [num_points, 2] of starting point of the vector
+      v2: float Tensor of [num_points, 2] of ending point of the vector
+
+    Returns:
+      a boolean Tensor of [num_points] indicate whether each point is on
+      the left of the vector or on the vector direction.
+    """
+    # Prepare for broadcast: All point operations are on the right,
+    # and all v1/v2 operations are on the left. This is faster than left/right
+    # under the assumption that we have more points than vertices.
+    points_x = points[..., tf.newaxis, :, 0]
+    points_y = points[..., tf.newaxis, :, 1]
+    v1_x = v1[..., 0, tf.newaxis]
+    v2_x = v2[..., 0, tf.newaxis]
+    v1_y = v1[..., 1, tf.newaxis]
+    v2_y = v2[..., 1, tf.newaxis]
+    d1 = (points_y - v1_y) * (v2_x - v1_x)
+    d2 = (points_x - v1_x) * (v2_y - v1_y)
+    return d1 >= d2
+
+
+def _box_area(boxes):
+    """Compute the area of 2-d boxes.
+
+    Vertices must be ordered counter-clockwise. This function can
+    technically handle any kind of convex polygons.
+
+    Args:
+      boxes: a float Tensor of [..., 4, 2] of boxes. The last coordinates
+        are the four corners of the box and (x, y). The corners must be given in
+        counter-clockwise order.
+    """
+    boxes_roll = tf.roll(boxes, shift=1, axis=-2)
+    det = (
+        tf.reduce_sum(
+            boxes[..., 0] * boxes_roll[..., 1] - boxes[..., 1] * boxes_roll[..., 0],
+            axis=-1,
+            keepdims=True,
+        )
+        / 2.0
+    )
+    return tf.abs(det)
+
+
+def is_within_box2d(points, boxes):
+    """Checks if 3d points are within 2d bounding boxes.
+    Currently only xy format is supported.
+    This function returns true if points are strictly inside the box or on edge.
+
+    Args:
+      points: [num_points, 2] float32 Tensor for 2d points in xy format.
+      boxes: [num_boxes, 4, 2] float32 Tensor for 2d boxes in xy format,
+        counter clockwise.
+
+    Returns:
+      boolean Tensor of shape [num_points, num_boxes]
+    """
+    v1, v2, v3, v4 = (
+        boxes[..., 0, :],
+        boxes[..., 1, :],
+        boxes[..., 2, :],
+        boxes[..., 3, :],
+    )
+    is_inside = tf.math.logical_and(
+        tf.math.logical_and(
+            _is_on_lefthand_side(points, v1, v2), _is_on_lefthand_side(points, v2, v3)
+        ),
+        tf.math.logical_and(
+            _is_on_lefthand_side(points, v3, v4), _is_on_lefthand_side(points, v4, v1)
+        ),
+    )
+    valid_area = tf.greater(_box_area(boxes), 0)
+    is_inside = tf.math.logical_and(is_inside, valid_area)
+    # swap the last two dimensions
+    is_inside = tf.einsum("...ij->...ji", tf.cast(is_inside, tf.int32))
+    return tf.cast(is_inside, tf.bool)
+
+
+def is_within_box3d(points, boxes):
+    """Checks if 3d points are within 3d bounding boxes.
+    Currently only xyz format is supported.
+
+    Args:
+      points: [..., num_points, 3] float32 Tensor for 3d points in xyz format.
+      boxes: [..., num_boxes, 7] float32 Tensor for 3d boxes in [x, y, z, dx,
+        dy, dz, phi].
+
+    Returns:
+      boolean Tensor of shape [..., num_points, num_boxes] indicating whether
+      the point belongs to the box.
+
+    """
+    # step 1 -- determine if points are within xy range
+
+    # convert from center format to corner format
+    boxes_corner = _center_xyzWHD_to_corner_xyz(boxes)
+    # project to 2d boxes by only taking x, y on top plane
+    boxes_2d = boxes_corner[..., 0:4, 0:2]
+    # project to 2d points by only taking x, y
+    points_2d = points[..., :2]
+    # check whether points are within 2d boxes, [..., num_points, num_boxes]
+    is_inside_2d = is_within_box2d(points_2d, boxes_2d)
+
+    # step 2 -- determine if points are within z range
+
+    [_, _, z, _, _, dz, _] = tf.split(boxes, 7, axis=-1)
+    z = z[..., 0]
+    dz = dz[..., 0]
+    bottom = z - dz / 2.0
+    # [..., 1, num_boxes]
+    bottom = bottom[..., tf.newaxis, :]
+    top = z + dz / 2.0
+    top = top[..., tf.newaxis, :]
+    # [..., num_points, 1]
+    points_z = points[..., 2:]
+    # [..., num_points, num_boxes]
+    is_inside_z = tf.math.logical_and(
+        tf.less_equal(points_z, top), tf.greater_equal(points_z, bottom)
+    )
+    return tf.math.logical_and(is_inside_z, is_inside_2d)
+
+
+def coordinate_transform(points, pose):
+    """
+    Translate 'points' to coordinates according to 'pose' vector.
+    pose should contain 6 floating point values:
+      translate_x, translate_y, translate_z: The translation to apply.
+      yaw, roll, pitch: The rotation angles in radians.
+
+    Args:
+      points: Float shape [..., 3]: Points to transform to new coordinates.
+      pose: Float shape [6]: [translate_x, translate_y, translate_z, yaw, roll,
+        pitch]. The pose in the frame that 'points' comes from, and the definition
+        of the rotation and translation angles to apply to points.
+    Returns:
+    'points' transformed to the coordinates defined by 'pose'.
+    """
+    translate_x = pose[0]
+    translate_y = pose[1]
+    translate_z = pose[2]
+
+    # Translate the points so the origin is the pose's center.
+    translation = tf.reshape([translate_x, translate_y, translate_z], shape=[3])
+    translated_points = points + translation
+
+    # Compose the rotations along the three axes.
+    #
+    # Note: Yaw->Z, Roll->X, Pitch->Y.
+    yaw, roll, pitch = pose[3], pose[4], pose[5]
+    rotation_matrix = _get_3d_rotation_matrix(yaw, roll, pitch)
+    # Finally, rotate the points about the pose's origin according to the
+    # rotation matrix.
+    rotated_points = tf.einsum("...i,...ij->...j", translated_points, rotation_matrix)
+    return rotated_points
+
+
+def spherical_coordinate_transform(points):
+    """Converts points from xyz coordinates to spherical coordinates.
+    https://en.wikipedia.org/wiki/Spherical_coordinate_system#Coordinate_system_conversions
+    for definitions of the transformations.
+    Args:
+      points_xyz: A floating point tensor with shape [..., 3], where the inner 3
+        dimensions correspond to xyz coordinates.
+    Returns:
+      A floating point tensor with the same shape [..., 3], where the inner
+      dimensions correspond to (dist, theta, phi), where phi corresponds to
+      azimuth/yaw (rotation around z), and theta corresponds to pitch/inclination
+      (rotation around y).
+    """
+    dist = tf.sqrt(tf.reduce_sum(tf.square(points), axis=-1))
+    theta = tf.acos(points[..., 2] / tf.maximum(dist, 1e-7))
+    # Note: tf.atan2 takes in (y, x).
+    phi = tf.atan2(points[..., 1], points[..., 0])
+    return tf.stack([dist, theta, phi], axis=-1)

Keras/keras-cv/keras_cv/ops/point_cloud_test.py

+# Copyright 2022 The KerasCV Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import numpy as np
+import tensorflow as tf
+from absl.testing import parameterized
+
+from keras_cv import ops
+
+
+class Boxes3DTestCase(tf.test.TestCase, parameterized.TestCase):
+    def test_convert_center_to_corners(self):
+        boxes = tf.constant(
+            [
+                [[1, 2, 3, 4, 3, 6, 0], [1, 2, 3, 4, 3, 6, 0]],
+                [[1, 2, 3, 4, 3, 6, np.pi / 2.0], [1, 2, 3, 4, 3, 6, np.pi / 2.0]],
+            ]
+        )
+        corners = ops._center_xyzWHD_to_corner_xyz(boxes)
+        self.assertEqual((2, 2, 8, 3), corners.shape)
+        for i in [0, 1]:
+            self.assertAllClose(-1, np.min(corners[0, i, :, 0]))
+            self.assertAllClose(3, np.max(corners[0, i, :, 0]))
+            self.assertAllClose(0.5, np.min(corners[0, i, :, 1]))
+            self.assertAllClose(3.5, np.max(corners[0, i, :, 1]))
+            self.assertAllClose(0, np.min(corners[0, i, :, 2]))
+            self.assertAllClose(6, np.max(corners[0, i, :, 2]))
+
+        for i in [0, 1]:
+            self.assertAllClose(-0.5, np.min(corners[1, i, :, 0]))
+            self.assertAllClose(2.5, np.max(corners[1, i, :, 0]))
+            self.assertAllClose(0.0, np.min(corners[1, i, :, 1]))
+            self.assertAllClose(4.0, np.max(corners[1, i, :, 1]))
+            self.assertAllClose(0, np.min(corners[1, i, :, 2]))
+            self.assertAllClose(6, np.max(corners[1, i, :, 2]))
+
+    def test_within_box2d(self):
+        boxes = tf.constant(
+            [[[0.0, 0.0], [1.0, 0.0], [1.0, 1.0], [0.0, 1.0]]], dtype=tf.float32
+        )
+        points = tf.constant(
+            [
+                [-0.5, -0.5],
+                [0.5, -0.5],
+                [1.5, -0.5],
+                [1.5, 0.5],
+                [1.5, 1.5],
+                [0.5, 1.5],
+                [-0.5, 1.5],
+                [-0.5, 0.5],
+                [1.0, 1.0],
+                [0.5, 0.5],
+            ],
+            dtype=tf.float32,
+        )
+        is_inside = ops.is_within_box2d(points, boxes)
+        expected = [[False]] * 8 + [[True]] * 2
+        self.assertAllEqual(expected, is_inside)
+
+    def test_within_zero_box2d(self):
+        bbox = tf.constant(
+            [[[0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0]]], dtype=tf.float32
+        )
+        points = tf.constant(
+            [
+                [-0.5, -0.5],
+                [0.5, -0.5],
+                [1.5, -0.5],
+                [1.5, 0.5],
+                [1.5, 1.5],
+                [0.5, 1.5],
+                [-0.5, 1.5],
+                [-0.5, 0.5],
+                [1.0, 1.0],
+                [0.5, 0.5],
+            ],
+            dtype=tf.float32,
+        )
+        is_inside = ops.is_within_box2d(points, bbox)
+        expected = [[False]] * 10
+        self.assertAllEqual(expected, is_inside)
+
+    def test_is_on_lefthand_side(self):
+        v1 = tf.constant([[0.0, 0.0]], dtype=tf.float32)
+        v2 = tf.constant([[1.0, 0.0]], dtype=tf.float32)
+        p = tf.constant([[0.5, 0.5], [-1.0, -3], [-1.0, 1.0]], dtype=tf.float32)
+        res = ops._is_on_lefthand_side(p, v1, v2)
+        self.assertAllEqual([[True, False, True]], res)
+        res = ops._is_on_lefthand_side(v1, v1, v2)
+        self.assertAllEqual([[True]], res)
+        res = ops._is_on_lefthand_side(v2, v1, v2)
+        self.assertAllEqual([[True]], res)
+
+    @parameterized.named_parameters(
+        ("without_rotation", 0.0),
+        ("with_rotation_1_rad", 1.0),
+        ("with_rotation_2_rad", 2.0),
+        ("with_rotation_3_rad", 3.0),
+    )
+    def test_box_area(self, angle):
+        boxes = tf.constant(
+            [
+                [[0.0, 0.0], [1.0, 0.0], [1.0, 1.0], [0.0, 1.0]],
+                [[0.0, 0.0], [2.0, 0.0], [2.0, 1.0], [0.0, 1.0]],
+                [[0.0, 0.0], [2.0, 0.0], [2.0, 2.0], [0.0, 2.0]],
+            ],
+            dtype=tf.float32,
+        )
+        expected = [[1.0], [2.0], [4.0]]
+
+        def _rotate(bbox, theta):
+            rotation_matrix = tf.reshape(
+                [tf.cos(theta), -tf.sin(theta), tf.sin(theta), tf.cos(theta)],
+                shape=(2, 2),
+            )
+            return tf.matmul(bbox, rotation_matrix)
+
+        rotated_bboxes = _rotate(boxes, angle)
+        res = ops._box_area(rotated_bboxes)
+        self.assertAllClose(expected, res)
+
+    def test_within_box3d(self):
+        num_points, num_boxes = 19, 4
+        # rotate the first box by pi / 2 so dim_x and dim_y are swapped.
+        # The last box is a cube rotated by 45 degrees.
+        bboxes = tf.constant(
+            [
+                [1.0, 2.0, 3.0, 6.0, 0.4, 6.0, np.pi / 2],
+                [4.0, 5.0, 6.0, 7.0, 0.8, 7.0, 0.0],
+                [0.4, 0.3, 0.2, 0.1, 0.1, 0.2, 0.0],
+                [-10.0, -10.0, -10.0, 3.0, 3.0, 3.0, np.pi / 4],
+            ],
+            dtype=tf.float32,
+        )
+        points = tf.constant(
+            [
+                [1.0, 2.0, 3.0],  # box 0 (centroid)
+                [0.8, 2.0, 3.0],  # box 0 (below x)
+                [1.1, 2.0, 3.0],  # box 0 (above x)
+                [1.3, 2.0, 3.0],  # box 0 (too far x)
+                [0.7, 2.0, 3.0],  # box 0 (too far x)
+                [4.0, 5.0, 6.0],  # box 1 (centroid)
+                [4.0, 4.6, 6.0],  # box 1 (below y)
+                [4.0, 5.4, 6.0],  # box 1 (above y)
+                [4.0, 4.5, 6.0],  # box 1 (too far y)
+                [4.0, 5.5, 6.0],  # box 1 (too far y)
+                [0.4, 0.3, 0.2],  # box 2 (centroid)
+                [0.4, 0.3, 0.1],  # box 2 (below z)
+                [0.4, 0.3, 0.3],  # box 2 (above z)
+                [0.4, 0.3, 0.0],  # box 2 (too far z)
+                [0.4, 0.3, 0.4],  # box 2 (too far z)
+                [5.0, 7.0, 8.0],  # none
+                [1.0, 5.0, 3.6],  # box0, box1
+                [-11.6, -10.0, -10.0],  # box3 (rotated corner point).
+                [-11.4, -11.4, -10.0],  # not in box3, would be if not rotated.
+            ],
+            dtype=tf.float32,
+        )
+        expected_is_inside = np.array(
+            [
+                [True, False, False, False],
+                [True, False, False, False],
+                [True, False, False, False],
+                [False, False, False, False],
+                [False, False, False, False],
+                [False, True, False, False],
+                [False, True, False, False],
+                [False, True, False, False],
+                [False, False, False, False],
+                [False, False, False, False],
+                [False, False, True, False],
+                [False, False, True, False],
+                [False, False, True, False],
+                [False, False, False, False],
+                [False, False, False, False],
+                [False, False, False, False],
+                [True, True, False, False],
+                [False, False, False, True],
+                [False, False, False, False],
+            ]
+        )
+        assert points.shape[0] == num_points
+        assert bboxes.shape[0] == num_boxes
+        assert expected_is_inside.shape[0] == num_points
+        assert expected_is_inside.shape[1] == num_boxes
+        is_inside = ops.is_within_box3d(points, bboxes)
+        self.assertAllEqual([num_points, num_boxes], is_inside.shape)
+        self.assertAllEqual(expected_is_inside, is_inside)
+        # Add a batch dimension to the data and see that it still works
+        # as expected.
+        batch_size = 3
+        points = tf.tile(points[tf.newaxis, ...], [batch_size, 1, 1])
+        bboxes = tf.tile(bboxes[tf.newaxis, ...], [batch_size, 1, 1])
+        is_inside = ops.is_within_box3d(points, bboxes)
+        self.assertAllEqual([batch_size, num_points, num_boxes], is_inside.shape)
+        for batch_idx in range(batch_size):
+            self.assertAllEqual(expected_is_inside, is_inside[batch_idx])
+
+    def testCoordinateTransform(self):
+        # This is a validated test case from a real scene.
+        #
+        # A single point [1, 1, 3].
+        point = tf.constant(
+            [[[5736.94580078, 1264.85168457, 45.0271225]]], dtype=tf.float32
+        )
+        # Replicate the point to test broadcasting behavior.
+        replicated_points = tf.tile(point, [2, 4, 1])
+
+        # Pose of the car (x, y, z, yaw, roll, pitch).
+        #
+        # We negate the translations so that the coordinates are translated
+        # such that the car is at the origin.
+        pose = tf.constant(
+            [
+                -5728.77148438,
+                -1264.42236328,
+                -45.06399918,
+                -3.10496902,
+                0.03288471,
+                0.00115049,
+            ],
+            dtype=tf.float32,
+        )
+
+        result = ops.coordinate_transform(replicated_points, pose)
+
+        # We expect the point to be translated close to the car, and then rotated
+        # mostly around the x-axis.
+        # the result is device dependent, skip or ignore this test locally if it fails.
+        expected = np.tile([[[-8.184512, -0.13086952, -0.04200769]]], [2, 4, 1])
+
+        self.assertAllClose(expected, result)
+
+    def testSphericalCoordinatesTransform(self):
+        np_xyz = np.random.randn(5, 6, 3)
+        points = tf.constant(np_xyz, dtype=tf.float32)
+        spherical_coordinates = ops.spherical_coordinate_transform(points)
+
+        # Convert coordinates back to xyz to verify.
+        dist = spherical_coordinates[..., 0]
+        theta = spherical_coordinates[..., 1]
+        phi = spherical_coordinates[..., 2]
+
+        x = dist * np.sin(theta) * np.cos(phi)
+        y = dist * np.sin(theta) * np.sin(phi)
+        z = dist * np.cos(theta)
+
+        self.assertAllClose(x, np_xyz[..., 0])
+        self.assertAllClose(y, np_xyz[..., 1])
+        self.assertAllClose(z, np_xyz[..., 2])

Keras/keras-cv/keras_cv/ops/sampling.py

+# Copyright 2022 The KerasCV Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import tensorflow as tf
+
+
+def balanced_sample(
+    positive_matches: tf.Tensor,
+    negative_matches: tf.Tensor,
+    num_samples: int,
+    positive_fraction: float,
+):
+    """
+    Sampling ops to balance positive and negative samples, deals with both
+    batched and unbatched inputs.
+
+    Args:
+      positive_matches: [N] or [batch_size, N] boolean Tensor, True for
+        indicating the index is a positive sample
+      negative_matches: [N] or [batch_size, N] boolean Tensor, True for
+        indicating the index is a negative sample
+      num_samples: int, representing the number of samples to collect
+      positive_fraction: float. 0.5 means positive samples should be half
+        of all collected samples.
+
+    Returns:
+      selected_indicators: [N] or [batch_size, N]
+        integer Tensor, 1 for indicating the index is sampled, 0 for
+        indicating the index is not sampled.
+    """
+
+    N = positive_matches.get_shape().as_list()[-1]
+    if N < num_samples:
+        raise ValueError(
+            f"passed in {positive_matches.shape} has less element than {num_samples}"
+        )
+    # random_val = tf.random.uniform(tf.shape(positive_matches), minval=0., maxval=1.)
+    zeros = tf.zeros_like(positive_matches, dtype=tf.float32)
+    ones = tf.ones_like(positive_matches, dtype=tf.float32)
+    ones_rand = ones + tf.random.uniform(ones.shape, minval=-0.2, maxval=0.2)
+    halfs = 0.5 * tf.ones_like(positive_matches, dtype=tf.float32)
+    halfs_rand = halfs + tf.random.uniform(halfs.shape, minval=-0.2, maxval=0.2)
+    values = zeros
+    values = tf.where(positive_matches, ones_rand, values)
+    values = tf.where(negative_matches, halfs_rand, values)
+    num_pos_samples = int(num_samples * positive_fraction)
+    valid_matches = tf.logical_or(positive_matches, negative_matches)
+    # this might contain negative samples as well
+    _, positive_indices = tf.math.top_k(values, k=num_pos_samples)
+    selected_indicators = tf.cast(
+        tf.reduce_sum(tf.one_hot(positive_indices, depth=N), axis=-2), tf.bool
+    )
+    # setting all selected samples to zeros
+    values = tf.where(selected_indicators, zeros, values)
+    # setting all excessive positive matches to zeros as well
+    values = tf.where(positive_matches, zeros, values)
+    num_neg_samples = num_samples - num_pos_samples
+    _, negative_indices = tf.math.top_k(values, k=num_neg_samples)
+    selected_indices = tf.concat([positive_indices, negative_indices], axis=-1)
+    selected_indicators = tf.reduce_sum(tf.one_hot(selected_indices, depth=N), axis=-2)
+    selected_indicators = tf.minimum(
+        selected_indicators, tf.ones_like(selected_indicators)
+    )
+    selected_indicators = tf.where(
+        valid_matches, selected_indicators, tf.zeros_like(selected_indicators)
+    )
+    return selected_indicators

Keras/keras-cv/keras_cv/ops/sampling_test.py

+# Copyright 2022 The KerasCV Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import tensorflow as tf
+
+from keras_cv.ops.sampling import balanced_sample
+
+
+class BalancedSamplingTest(tf.test.TestCase):
+    def test_balanced_sampling(self):
+        positive_matches = tf.constant(
+            [True, False, False, False, False, False, False, False, False, False]
+        )
+        negative_matches = tf.constant(
+            [False, True, True, True, True, True, True, True, True, True]
+        )
+        num_samples = 5
+        positive_fraction = 0.2
+        res = balanced_sample(
+            positive_matches, negative_matches, num_samples, positive_fraction
+        )
+        # The 1st element must be selected, given it's the only one.
+        self.assertAllClose(res[0], 1)
+
+    def test_balanced_batched_sampling(self):
+        positive_matches = tf.constant(
+            [
+                [True, False, False, False, False, False, False, False, False, False],
+                [False, False, False, False, False, False, True, False, False, False],
+            ]
+        )
+        negative_matches = tf.constant(
+            [
+                [False, True, True, True, True, True, True, True, True, True],
+                [True, True, True, True, True, True, False, True, True, True],
+            ]
+        )
+        num_samples = 5
+        positive_fraction = 0.2
+        res = balanced_sample(
+            positive_matches, negative_matches, num_samples, positive_fraction
+        )
+        # the 1st element from the 1st batch must be selected, given it's the only one
+        self.assertAllClose(res[0][0], 1)
+        # the 7th element from the 2nd batch must be selected, given it's the only one
+        self.assertAllClose(res[1][6], 1)
+
+    def test_balanced_sampling_over_positive_fraction(self):
+        positive_matches = tf.constant(
+            [True, False, False, False, False, False, False, False, False, False]
+        )
+        negative_matches = tf.constant(
+            [False, True, True, True, True, True, True, True, True, True]
+        )
+        num_samples = 5
+        positive_fraction = 0.4
+        res = balanced_sample(
+            positive_matches, negative_matches, num_samples, positive_fraction
+        )
+        # only 1 positive sample exists, thus it is chosen
+        self.assertAllClose(res[0], 1)
+
+    def test_balanced_sampling_under_positive_fraction(self):
+        positive_matches = tf.constant(
+            [True, False, False, False, False, False, False, False, False, False]
+        )
+        negative_matches = tf.constant(
+            [False, True, True, True, True, True, True, True, True, True]
+        )
+        num_samples = 5
+        positive_fraction = 0.1
+        res = balanced_sample(
+            positive_matches, negative_matches, num_samples, positive_fraction
+        )
+        # no positive is chosen
+        self.assertAllClose(res[0], 0)
+        self.assertAllClose(tf.reduce_sum(res), 5)
+
+    def test_balanced_sampling_over_num_samples(self):
+        positive_matches = tf.constant(
+            [True, False, False, False, False, False, False, False, False, False]
+        )
+        negative_matches = tf.constant(
+            [False, True, True, True, True, True, True, True, True, True]
+        )
+        # users want to get 20 samples, but only 10 are available
+        num_samples = 20
+        positive_fraction = 0.1
+        with self.assertRaisesRegex(ValueError, "has less element"):
+            _ = balanced_sample(
+                positive_matches, negative_matches, num_samples, positive_fraction
+            )
+
+    def test_balanced_sampling_no_positive(self):
+        positive_matches = tf.constant(
+            [False, False, False, False, False, False, False, False, False, False]
+        )
+        # the rest are neither positive nor negative, but ignord matches
+        negative_matches = tf.constant(
+            [False, False, True, False, False, True, False, False, True, False]
+        )
+        num_samples = 5
+        positive_fraction = 0.5
+        res = balanced_sample(
+            positive_matches, negative_matches, num_samples, positive_fraction
+        )
+        # given only 3 negative and 0 positive, select all of them
+        self.assertAllClose(res, [0, 0, 1, 0, 0, 1, 0, 0, 1, 0])
+
+    def test_balanced_sampling_no_negative(self):
+        positive_matches = tf.constant(
+            [True, True, False, False, False, False, False, False, False, False]
+        )
+        # 2-9 indices are neither positive nor negative, they're ignored matches
+        negative_matches = tf.constant([False] * 10)
+        num_samples = 5
+        positive_fraction = 0.5
+        res = balanced_sample(
+            positive_matches, negative_matches, num_samples, positive_fraction
+        )
+        # given only 2 positive and 0 negative, select all of them.
+        self.assertAllClose(res, [1, 1, 0, 0, 0, 0, 0, 0, 0, 0])
+
+    def test_balanced_sampling_many_samples(self):
+        positive_matches = tf.random.uniform(
+            [2, 1000], minval=0, maxval=1, dtype=tf.float32
+        )
+        positive_matches = positive_matches > 0.98
+        negative_matches = tf.logical_not(positive_matches)
+        num_samples = 256
+        positive_fraction = 0.25
+        _ = balanced_sample(
+            positive_matches, negative_matches, num_samples, positive_fraction
+        )

Keras/keras-cv/keras_cv/ops/target_gather.py

+# Copyright 2022 The KerasCV Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Optional
+
+import tensorflow as tf
+
+
+def _target_gather(
+    targets: tf.Tensor,
+    indices: tf.Tensor,
+    mask: Optional[tf.Tensor] = None,
+    mask_val: Optional[float] = 0.0,
+):
+    """A utility function wrapping tf.gather, which deals with:
+     1) both batched and unbatched `targets`
+     2) when unbatched `targets` have empty rows, the result will be filled
+        with `mask_val`
+     3) target masking.
+
+    Args:
+     targets: [N, ...] or [batch_size, N, ...] Tensor representing targets such
+       as boxes, keypoints, etc.
+     indices: [M] or [batch_size, M] int32 Tensor representing indices within
+       `targets` to gather.
+     mask: optional [M, ...] or [batch_size, M, ...] boolean
+       Tensor representing the masking for each target. `True` means the corresponding
+       entity should be masked to `mask_val`, `False` means the corresponding entity
+       should be the target value.
+     mask_val: optinal float representing the masking value if `mask` is True on
+       the entity.
+
+     Returns:
+       targets: [M, ...] or [batch_size, M, ...] Tensor representing selected targets.
+
+     Raise:
+       ValueError: If `targets` is higher than rank 3.
+    """
+    targets_shape = targets.get_shape().as_list()
+    if len(targets_shape) > 3:
+        raise ValueError(
+            "`target_gather` does not support `targets` with rank "
+            "larger than 3, got {}".format(len(targets.shape))
+        )
+
+    def _gather_unbatched(labels, match_indices, mask, mask_val):
+        """Gather based on unbatched labels and boxes."""
+        num_gt_boxes = tf.shape(labels)[0]
+
+        def _assign_when_rows_empty():
+            if len(labels.shape) > 1:
+                mask_shape = [match_indices.shape[0], labels.shape[-1]]
+            else:
+                mask_shape = [match_indices.shape[0]]
+            return tf.cast(mask_val, labels.dtype) * tf.ones(
+                mask_shape, dtype=labels.dtype
+            )
+
+        def _assign_when_rows_not_empty():
+            targets = tf.gather(labels, match_indices)
+            if mask is None:
+                return targets
+            else:
+                masked_targets = tf.cast(mask_val, labels.dtype) * tf.ones_like(
+                    mask, dtype=labels.dtype
+                )
+                return tf.where(mask, masked_targets, targets)
+
+        return tf.cond(
+            tf.greater(num_gt_boxes, 0),
+            _assign_when_rows_not_empty,
+            _assign_when_rows_empty,
+        )
+
+    def _gather_batched(labels, match_indices, mask, mask_val):
+        """Gather based on batched labels."""
+        batch_size = labels.shape[0]
+        if batch_size == 1:
+            if mask is not None:
+                result = _gather_unbatched(
+                    tf.squeeze(labels, axis=0),
+                    tf.squeeze(match_indices, axis=0),
+                    tf.squeeze(mask, axis=0),
+                    mask_val,
+                )
+            else:
+                result = _gather_unbatched(
+                    tf.squeeze(labels, axis=0),
+                    tf.squeeze(match_indices, axis=0),
+                    None,
+                    mask_val,
+                )
+            return tf.expand_dims(result, axis=0)
+        else:
+            indices_shape = tf.shape(match_indices)
+            indices_dtype = match_indices.dtype
+            batch_indices = tf.expand_dims(
+                tf.range(indices_shape[0], dtype=indices_dtype), axis=-1
+            ) * tf.ones([1, indices_shape[-1]], dtype=indices_dtype)
+            gather_nd_indices = tf.stack([batch_indices, match_indices], axis=-1)
+            targets = tf.gather_nd(labels, gather_nd_indices)
+            if mask is None:
+                return targets
+            else:
+                masked_targets = tf.cast(mask_val, labels.dtype) * tf.ones_like(
+                    mask, dtype=labels.dtype
+                )
+                return tf.where(mask, masked_targets, targets)
+
+    if len(targets_shape) <= 2:
+        return _gather_unbatched(targets, indices, mask, mask_val)
+    elif len(targets_shape) == 3:
+        return _gather_batched(targets, indices, mask, mask_val)

Keras/keras-cv/keras_cv/ops/target_gather_test.py

+# Copyright 2022 The KerasCV Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import tensorflow as tf
+
+from keras_cv.ops.target_gather import _target_gather
+
+
+class TargetGatherTest(tf.test.TestCase):
+    def test_target_gather_boxes_batched(self):
+        target_boxes = tf.constant(
+            [[0, 0, 5, 5], [0, 5, 5, 10], [5, 0, 10, 5], [5, 5, 10, 10]]
+        )
+        target_boxes = target_boxes[tf.newaxis, ...]
+        indices = tf.constant([[0, 2]], dtype=tf.int32)
+        expected_boxes = tf.constant([[0, 0, 5, 5], [5, 0, 10, 5]])
+        expected_boxes = expected_boxes[tf.newaxis, ...]
+        res = _target_gather(target_boxes, indices)
+        self.assertAllClose(expected_boxes, res)
+
+    def test_target_gather_boxes_unbatched(self):
+        target_boxes = tf.constant(
+            [[0, 0, 5, 5], [0, 5, 5, 10], [5, 0, 10, 5], [5, 5, 10, 10]]
+        )
+        indices = tf.constant([0, 2], dtype=tf.int32)
+        expected_boxes = tf.constant([[0, 0, 5, 5], [5, 0, 10, 5]])
+        res = _target_gather(target_boxes, indices)
+        self.assertAllClose(expected_boxes, res)
+
+    def test_target_gather_classes_batched(self):
+        target_classes = tf.constant([[1, 2, 3, 4]])
+        target_classes = target_classes[..., tf.newaxis]
+        indices = tf.constant([[0, 2]], dtype=tf.int32)
+        expected_classes = tf.constant([[1, 3]])
+        expected_classes = expected_classes[..., tf.newaxis]
+        res = _target_gather(target_classes, indices)
+        self.assertAllClose(expected_classes, res)
+
+    def test_target_gather_classes_unbatched(self):
+        target_classes = tf.constant([1, 2, 3, 4])
+        target_classes = target_classes[..., tf.newaxis]
+        indices = tf.constant([0, 2], dtype=tf.int32)
+        expected_classes = tf.constant([1, 3])
+        expected_classes = expected_classes[..., tf.newaxis]
+        res = _target_gather(target_classes, indices)
+        self.assertAllClose(expected_classes, res)
+
+    def test_target_gather_classes_batched_with_mask(self):
+        target_classes = tf.constant([[1, 2, 3, 4]])
+        target_classes = target_classes[..., tf.newaxis]
+        indices = tf.constant([[0, 2]], dtype=tf.int32)
+        masks = tf.constant(([[False, True]]))
+        masks = masks[..., tf.newaxis]
+        # the second element is masked
+        expected_classes = tf.constant([[1, 0]])
+        expected_classes = expected_classes[..., tf.newaxis]
+        res = _target_gather(target_classes, indices, masks)
+        self.assertAllClose(expected_classes, res)
+
+    def test_target_gather_classes_batched_with_mask_val(self):
+        target_classes = tf.constant([[1, 2, 3, 4]])
+        target_classes = target_classes[..., tf.newaxis]
+        indices = tf.constant([[0, 2]], dtype=tf.int32)
+        masks = tf.constant(([[False, True]]))
+        masks = masks[..., tf.newaxis]
+        # the second element is masked
+        expected_classes = tf.constant([[1, -1]])
+        expected_classes = expected_classes[..., tf.newaxis]
+        res = _target_gather(target_classes, indices, masks, -1)
+        self.assertAllClose(expected_classes, res)
+
+    def test_target_gather_classes_unbatched_with_mask(self):
+        target_classes = tf.constant([1, 2, 3, 4])
+        target_classes = target_classes[..., tf.newaxis]
+        indices = tf.constant([0, 2], dtype=tf.int32)
+        masks = tf.constant([False, True])
+        masks = masks[..., tf.newaxis]
+        expected_classes = tf.constant([1, 0])
+        expected_classes = expected_classes[..., tf.newaxis]
+        res = _target_gather(target_classes, indices, masks)
+        self.assertAllClose(expected_classes, res)
+
+    def test_target_gather_with_empty_targets(self):
+        target_classes = tf.constant([])
+        target_classes = target_classes[..., tf.newaxis]
+        indices = tf.constant([0, 2], dtype=tf.int32)
+        # return all 0s since input is empty
+        expected_classes = tf.constant([0, 0])
+        expected_classes = expected_classes[..., tf.newaxis]
+        res = _target_gather(target_classes, indices)
+        self.assertAllClose(expected_classes, res)
+
+    def test_target_gather_classes_multi_batch(self):
+        target_classes = tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]])
+        target_classes = target_classes[..., tf.newaxis]
+        indices = tf.constant([[0, 2], [1, 3]], dtype=tf.int32)
+        expected_classes = tf.constant([[1, 3], [6, 8]])
+        expected_classes = expected_classes[..., tf.newaxis]
+        res = _target_gather(target_classes, indices)
+        self.assertAllClose(expected_classes, res)
+
+    def test_target_gather_invalid_rank(self):
+        targets = tf.random.normal([32, 2, 2, 2])
+        indices = tf.constant([0, 1], dtype=tf.int32)
+        with self.assertRaisesRegex(ValueError, "larger than 3"):
+            _ = _target_gather(targets, indices)

Keras/keras-cv/keras_cv/training/__init__.py

+# Copyright 2022 The KerasCV Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from keras_cv.training.contrastive.contrastive_trainer import ContrastiveTrainer
+from keras_cv.training.contrastive.simclr_trainer import SimCLRAugmenter
+from keras_cv.training.contrastive.simclr_trainer import SimCLRTrainer

Keras/keras-cv/keras_cv/training/contrastive/__init__.py

+# Copyright 2022 The KerasCV Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.

Keras/keras-cv/keras_cv/training/contrastive/contrastive_trainer.py

+# Copyright 2022 The KerasCV Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import tensorflow as tf
+from tensorflow import keras
+
+from keras_cv.utils.train import convert_inputs_to_tf_dataset
+
+
+class ContrastiveTrainer(keras.Model):
+    """Creates a self-supervised contrastive trainer for a model.
+
+    Args:
+        encoder: a `keras.Model` to be pre-trained. In most cases, this encoder
+            should not include a top dense layer.
+        augmenter: a preprocessing layer to randomly augment input images for contrastive learning,
+            or a tuple of two separate augmenters for the two sides of the contrastive pipeline.
+        projector: a projection model for contrastive training, or a tuple of two separate
+            projectors for the two sides of the contrastive pipeline. This shrinks
+            the feature map produced by the encoder, and is usually a 1 or
+            2-layer dense MLP.
+        probe: An optional Keras layer or model which will be trained against
+            class labels at train-time using the encoder output as input.
+            Note that this should be specified iff training with labeled images.
+            This predicts class labels based on the feature map produced by the
+            encoder and is usually a 1 or 2-layer dense MLP.
+
+    Returns:
+      A `keras.Model` instance.
+
+
+    Usage:
+    ```python
+    encoder = keras_cv.models.DenseNet121(include_rescaling=True, include_top=False, pooling="avg")
+    augmenter = keras_cv.layers.preprocessing.RandomFlip()
+    projector = keras.layers.Dense(64)
+    probe = keras_cv.training.ContrastiveTrainer.linear_probe(classes=10)
+
+    trainer = keras_cv.training.ContrastiveTrainer(
+        encoder=encoder,
+        augmenter=augmenter,
+        projector=projector,
+        probe=probe
+    )
+
+    trainer.compile(
+        encoder_optimizer=keras.optimizers.Adam(),
+        encoder_loss=keras_cv.losses.SimCLRLoss(temperature=0.5),
+        probe_optimizer=keras.optimizers.Adam(),
+        probe_loss=keras.losses.CategoricalCrossentropy(from_logits=True),
+        probe_metrics=[keras.metrics.CategoricalAccuracy(name="probe_accuracy")]
+    )
+
+    (x_train, y_train), (x_test, y_test) = keras.datasets.cifar10.load_data()
+    y_train = keras.utils.to_categorical(y_train, 10)
+
+    trainer.fit(x_train, y_train)
+    ```
+
+    """
+
+    def __init__(
+        self,
+        encoder,
+        augmenter,
+        projector,
+        probe=None,
+    ):
+        super().__init__()
+
+        if encoder.output.shape.rank != 2:
+            raise ValueError(
+                f"`encoder` must have a flattened output.  Expected rank(encoder.output.shape)=2, got encoder.output.shape={encoder.output.shape}"
+            )
+
+        if type(augmenter) is tuple and len(augmenter) != 2:
+            raise ValueError(
+                "`augmenter` must be either a single augmenter or a tuple of exactly 2 augmenters."
+            )
+
+        if type(projector) is tuple and len(projector) != 2:
+            raise ValueError(
+                "`projector` must be either a single augmenter or a tuple of exactly 2 augmenters."
+            )
+
+        self.augmenters = (
+            augmenter if type(augmenter) is tuple else (augmenter, augmenter)
+        )
+        self.encoder = encoder
+        self.projectors = (
+            projector if type(projector) is tuple else (projector, projector)
+        )
+        self.probe = probe
+
+        self.loss_metric = keras.metrics.Mean(name="loss")
+
+        if probe is not None:
+            self.probe_loss_metric = keras.metrics.Mean(name="probe_loss")
+            self.probe_metrics = []
+
+    def compile(
+        self,
+        encoder_loss,
+        encoder_optimizer,
+        encoder_metrics=None,
+        probe_optimizer=None,
+        probe_loss=None,
+        probe_metrics=None,
+        **kwargs,
+    ):
+        super().compile(
+            loss=encoder_loss,
+            optimizer=encoder_optimizer,
+            metrics=encoder_metrics,
+            **kwargs,
+        )
+
+        if self.probe and not probe_optimizer:
+            raise ValueError(
+                "`probe_optimizer` must be specified when a probe is included."
+            )
+
+        if self.probe and not probe_loss:
+            raise ValueError("`probe_loss` must be specified when a probe is included.")
+
+        if "loss" in kwargs:
+            raise ValueError(
+                "`loss` parameter in ContrastiveTrainer.compile is ambiguous. Please specify `encoder_loss` or `probe_loss`."
+            )
+
+        if "optimizer" in kwargs:
+            raise ValueError(
+                "`optimizer` parameter in ContrastiveTrainer.compile is ambiguous. Please specify `encoder_optimizer` or `probe_optimizer`."
+            )
+
+        if "metrics" in kwargs:
+            raise ValueError(
+                "`metrics` parameter in ContrastiveTrainer.compile is ambiguous. Please specify `encoder_metrics` or `probe_metrics`."
+            )
+
+        if self.probe:
+            self.probe_loss = probe_loss
+            self.probe_optimizer = probe_optimizer
+            self.probe_metrics = probe_metrics or []
+
+    @property
+    def metrics(self):
+        metrics = [
+            self.loss_metric,
+        ]
+        if self.probe:
+            metrics += [self.probe_loss_metric]
+            metrics += self.probe_metrics
+        return super().metrics + metrics
+
+    def fit(
+        self,
+        x=None,
+        y=None,
+        sample_weight=None,
+        batch_size=None,
+        **kwargs,
+    ):
+        dataset = convert_inputs_to_tf_dataset(
+            x=x, y=y, sample_weight=sample_weight, batch_size=batch_size
+        )
+
+        dataset = dataset.map(self.run_augmenters, num_parallel_calls=tf.data.AUTOTUNE)
+        dataset = dataset.prefetch(tf.data.AUTOTUNE)
+
+        return super().fit(x=dataset, **kwargs)
+
+    def run_augmenters(self, x, y=None):
+        inputs = {"images": x}
+        if y is not None:
+            inputs["labels"] = y
+
+        inputs["augmented_images_0"] = self.augmenters[0](x, training=True)
+        inputs["augmented_images_1"] = self.augmenters[1](x, training=True)
+
+        return inputs
+
+    def train_step(self, data):
+        images = data["images"]
+        labels = data["labels"] if "labels" in data else None
+        augmented_images_0 = data["augmented_images_0"]
+        augmented_images_1 = data["augmented_images_1"]
+
+        with tf.GradientTape() as tape:
+            features_0 = self.encoder(augmented_images_0, training=True)
+            features_1 = self.encoder(augmented_images_1, training=True)
+
+            projections_0 = self.projectors[0](features_0, training=True)
+            projections_1 = self.projectors[1](features_1, training=True)
+
+            loss = self.compiled_loss(
+                projections_0, projections_1, regularization_losses=self.encoder.losses
+            )
+
+        gradients = tape.gradient(
+            loss,
+            self.encoder.trainable_weights
+            + self.projectors[0].trainable_weights
+            + self.projectors[1].trainable_weights,
+        )
+
+        self.optimizer.apply_gradients(
+            zip(
+                gradients,
+                self.encoder.trainable_weights
+                + self.projectors[0].trainable_weights
+                + self.projectors[1].trainable_weights,
+            )
+        )
+        self.loss_metric.update_state(loss)
+
+        if self.probe:
+            if labels is None:
+                raise ValueError("Targets must be provided when a probe is specified")
+            with tf.GradientTape() as tape:
+                features = tf.stop_gradient(self.encoder(images, training=False))
+                class_logits = self.probe(features, training=True)
+                probe_loss = self.probe_loss(labels, class_logits)
+            gradients = tape.gradient(probe_loss, self.probe.trainable_weights)
+            self.probe_optimizer.apply_gradients(
+                zip(gradients, self.probe.trainable_weights)
+            )
+            self.probe_loss_metric.update_state(probe_loss)
+            for metric in self.probe_metrics:
+                metric.update_state(labels, class_logits)
+
+        return {metric.name: metric.result() for metric in self.metrics}
+
+    def call(self, inputs):
+        raise NotImplementedError(
+            "ContrastiveTrainer.call() is not implemented - please call your model directly."
+        )
+
+    @staticmethod
+    def linear_probe(classes, **kwargs):
+        return keras.Sequential(keras.layers.Dense(classes), **kwargs)

Keras/keras-cv/keras_cv/training/contrastive/contrastive_trainer_test.py

+# Copyright 2022 The KerasCV Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras import layers
+from tensorflow.keras import metrics
+from tensorflow.keras import optimizers
+
+from keras_cv.layers import preprocessing
+from keras_cv.losses import SimCLRLoss
+from keras_cv.models import DenseNet121
+from keras_cv.training import ContrastiveTrainer
+
+
+class ContrastiveTrainerTest(tf.test.TestCase):
+    def test_probe_requires_probe_optimizer(self):
+        trainer = ContrastiveTrainer(
+            encoder=self.build_encoder(),
+            augmenter=self.build_augmenter(),
+            projector=self.build_projector(),
+            probe=self.build_probe(),
+        )
+        with self.assertRaises(ValueError):
+            trainer.compile(
+                encoder_optimizer=optimizers.Adam(),
+                encoder_loss=SimCLRLoss(temperature=0.5),
+            )
+
+    def test_targets_required_if_probing(self):
+        trainer_with_probing = ContrastiveTrainer(
+            encoder=self.build_encoder(),
+            augmenter=self.build_augmenter(),
+            projector=self.build_projector(),
+            probe=self.build_probe(),
+        )
+        trainer_without_probing = ContrastiveTrainer(
+            encoder=self.build_encoder(),
+            augmenter=self.build_augmenter(),
+            projector=self.build_projector(),
+            probe=None,
+        )
+
+        images = tf.random.uniform((1, 50, 50, 3))
+
+        trainer_with_probing.compile(
+            encoder_optimizer=optimizers.Adam(),
+            encoder_loss=SimCLRLoss(temperature=0.5),
+            probe_optimizer=optimizers.Adam(),
+            probe_loss=keras.losses.CategoricalCrossentropy(from_logits=True),
+        )
+        trainer_without_probing.compile(
+            encoder_optimizer=optimizers.Adam(),
+            encoder_loss=SimCLRLoss(temperature=0.5),
+        )
+
+        with self.assertRaises(ValueError):
+            trainer_with_probing.fit(images)
+
+    def test_train_with_probing(self):
+        trainer_with_probing = ContrastiveTrainer(
+            encoder=self.build_encoder(),
+            augmenter=self.build_augmenter(),
+            projector=self.build_projector(),
+            probe=self.build_probe(classes=20),
+        )
+
+        images = tf.random.uniform((1, 50, 50, 3))
+        targets = tf.ones((1, 20))
+
+        trainer_with_probing.compile(
+            encoder_optimizer=optimizers.Adam(),
+            encoder_loss=SimCLRLoss(temperature=0.5),
+            probe_metrics=[metrics.TopKCategoricalAccuracy(3, "top3_probe_accuracy")],
+            probe_optimizer=optimizers.Adam(),
+            probe_loss=keras.losses.CategoricalCrossentropy(from_logits=True),
+        )
+
+        trainer_with_probing.fit(images, targets)
+
+    def test_train_without_probing(self):
+        trainer_without_probing = ContrastiveTrainer(
+            encoder=self.build_encoder(),
+            augmenter=self.build_augmenter(),
+            projector=self.build_projector(),
+            probe=None,
+        )
+
+        images = tf.random.uniform((1, 50, 50, 3))
+        targets = tf.ones((1, 20))
+
+        trainer_without_probing.compile(
+            encoder_optimizer=optimizers.Adam(),
+            encoder_loss=SimCLRLoss(temperature=0.5),
+        )
+
+        trainer_without_probing.fit(images)
+        trainer_without_probing.fit(images, targets)
+
+    def test_inference_not_supported(self):
+        trainer = ContrastiveTrainer(
+            encoder=self.build_encoder(),
+            augmenter=self.build_augmenter(),
+            projector=self.build_projector(),
+            probe=None,
+        )
+        trainer.compile(
+            encoder_optimizer=optimizers.Adam(),
+            encoder_loss=SimCLRLoss(temperature=0.5),
+        )
+
+        with self.assertRaises(NotImplementedError):
+            trainer(tf.ones((1, 50, 50, 3)))
+
+    def test_encoder_must_have_flat_output(self):
+        with self.assertRaises(ValueError):
+            _ = ContrastiveTrainer(
+                # A DenseNet without pooling does not have a flat output
+                encoder=DenseNet121(include_rescaling=False, include_top=False),
+                augmenter=self.build_augmenter(),
+                projector=self.build_projector(),
+                probe=None,
+            )
+
+    def test_with_multiple_augmenters_and_projectors(self):
+        augmenter0 = preprocessing.RandomFlip("horizontal")
+        augmenter1 = preprocessing.RandomFlip("vertical")
+
+        projector0 = layers.Dense(64, name="projector0")
+        projector1 = keras.Sequential(
+            [projector0, layers.ReLU(), layers.Dense(64, name="projector1")]
+        )
+
+        trainer_without_probing = ContrastiveTrainer(
+            encoder=self.build_encoder(),
+            augmenter=(augmenter0, augmenter1),
+            projector=(projector0, projector1),
+            probe=None,
+        )
+
+        images = tf.random.uniform((1, 50, 50, 3))
+
+        trainer_without_probing.compile(
+            encoder_optimizer=optimizers.Adam(),
+            encoder_loss=SimCLRLoss(temperature=0.5),
+        )
+
+        trainer_without_probing.fit(images)
+
+    def build_augmenter(self):
+        return preprocessing.RandomFlip("horizontal")
+
+    def build_encoder(self):
+        return DenseNet121(include_rescaling=False, include_top=False, pooling="avg")
+
+    def build_projector(self):
+        return layers.Dense(128)
+
+    def build_probe(self, classes=20):
+        return layers.Dense(classes)

Keras/keras-cv/keras_cv/training/contrastive/simclr_trainer.py

+# Copyright 2022 The KerasCV Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from tensorflow import keras
+from tensorflow.keras import layers
+
+from keras_cv.layers import preprocessing
+from keras_cv.training import ContrastiveTrainer
+
+
+class SimCLRTrainer(ContrastiveTrainer):
+    """Creates a SimCLRTrainer.
+
+    References:
+        - [SimCLR paper](https://arxiv.org/pdf/2002.05709)
+
+    Args:
+        encoder: a `keras.Model` to be pre-trained. In most cases, this encoder
+            should not include a top dense layer.
+        augmenter: a SimCLRAugmenter layer to randomly augment input
+            images for contrastive learning
+        projection_width: the width of the two-layer dense model used for
+            projection in the SimCLR paper
+    """
+
+    def __init__(self, encoder, augmenter, projection_width=128, **kwargs):
+        super().__init__(
+            encoder=encoder,
+            augmenter=augmenter,
+            projector=keras.Sequential(
+                [
+                    layers.Dense(projection_width, activation="relu"),
+                    layers.Dense(projection_width),
+                    layers.BatchNormalization(),
+                ],
+                name="projector",
+            ),
+            **kwargs,
+        )
+
+
+class SimCLRAugmenter(preprocessing.Augmenter):
+    def __init__(
+        self,
+        value_range,
+        height=128,
+        width=128,
+        crop_area_factor=(0.08, 1.0),
+        aspect_ratio_factor=(3 / 4, 4 / 3),
+        grayscale_rate=0.2,
+        color_jitter_rate=0.8,
+        brightness_factor=0.2,
+        contrast_factor=0.8,
+        saturation_factor=(0.3, 0.7),
+        hue_factor=0.2,
+        **kwargs,
+    ):
+        return super().__init__(
+            [
+                preprocessing.RandomFlip("horizontal"),
+                preprocessing.RandomCropAndResize(
+                    target_size=(height, width),
+                    crop_area_factor=crop_area_factor,
+                    aspect_ratio_factor=aspect_ratio_factor,
+                ),
+                preprocessing.MaybeApply(
+                    preprocessing.Grayscale(output_channels=3), rate=grayscale_rate
+                ),
+                preprocessing.MaybeApply(
+                    preprocessing.RandomColorJitter(
+                        value_range=value_range,
+                        brightness_factor=brightness_factor,
+                        contrast_factor=contrast_factor,
+                        saturation_factor=saturation_factor,
+                        hue_factor=hue_factor,
+                    ),
+                    rate=color_jitter_rate,
+                ),
+            ],
+            **kwargs,
+        )

Keras/keras-cv/keras_cv/training/contrastive/simclr_trainer_test.py

+# Copyright 2022 The KerasCV Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import tensorflow as tf
+from tensorflow.keras import optimizers
+
+from keras_cv.losses import SimCLRLoss
+from keras_cv.models import ResNet50V2
+from keras_cv.training import SimCLRAugmenter
+from keras_cv.training import SimCLRTrainer
+
+
+class SimCLRTrainerTest(tf.test.TestCase):
+    def test_train_without_probing(self):
+        simclr_without_probing = SimCLRTrainer(
+            self.build_encoder(),
+            augmenter=SimCLRAugmenter(value_range=(0, 255)),
+        )
+
+        images = tf.random.uniform((10, 512, 512, 3))
+
+        simclr_without_probing.compile(
+            encoder_optimizer=optimizers.Adam(),
+            encoder_loss=SimCLRLoss(temperature=0.5),
+        )
+        simclr_without_probing.fit(images)
+
+    def build_encoder(self):
+        return ResNet50V2(include_rescaling=False, include_top=False, pooling="avg")

Keras/keras-cv/keras_cv/utils/__init__.py

+# Copyright 2022 The KerasCV Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from keras_cv.utils.fill_utils import fill_rectangle
+from keras_cv.utils.preprocessing import blend
+from keras_cv.utils.preprocessing import parse_factor
+from keras_cv.utils.preprocessing import transform
+from keras_cv.utils.preprocessing import transform_value_range
+from keras_cv.utils.train import convert_inputs_to_tf_dataset
+from keras_cv.utils.train import scale_loss_for_distribution

Keras/keras-cv/keras_cv/utils/conv_utils.py

+# Copyright 2022 The KerasCV Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+def normalize_tuple(value, n, name, allow_zero=False):
+    """Transforms non-negative/positive integer/integers into an integer tuple.
+    Args:
+      value: The value to validate and convert. Could an int, or any iterable of
+        ints.
+      n: The size of the tuple to be returned.
+      name: The name of the argument being validated, e.g. "strides" or
+        "kernel_size". This is only used to format error messages.
+      allow_zero: Default to False. A ValueError will raised if zero is received
+        and this param is False.
+    Returns:
+      A tuple of n integers.
+    Raises:
+      ValueError: If something else than an int/long or iterable thereof or a
+      negative value is
+        passed.
+    """
+    error_msg = (
+        f"The `{name}` argument must be a tuple of {n} " f"integers. Received: {value}"
+    )
+
+    if isinstance(value, int):
+        value_tuple = (value,) * n
+    else:
+        try:
+            value_tuple = tuple(value)
+        except TypeError:
+            raise ValueError(error_msg)
+        if len(value_tuple) != n:
+            raise ValueError(error_msg)
+        for single_value in value_tuple:
+            try:
+                int(single_value)
+            except (ValueError, TypeError):
+                error_msg += (
+                    f"including element {single_value} of " f"type {type(single_value)}"
+                )
+                raise ValueError(error_msg)
+
+    if allow_zero:
+        unqualified_values = {v for v in value_tuple if v < 0}
+        req_msg = ">= 0"
+    else:
+        unqualified_values = {v for v in value_tuple if v <= 0}
+        req_msg = "> 0"
+
+    if unqualified_values:
+        error_msg += (
+            f" including {unqualified_values}"
+            f" that does not satisfy the requirement `{req_msg}`."
+        )
+        raise ValueError(error_msg)
+
+    return value_tuple