增加keras-cv模型及训练代码

Keras/keras-cv/keras_cv/utils/preprocessing.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 backend
+
+from keras_cv import core
+
+
+def transform_value_range(images, original_range, target_range, dtype=tf.float32):
+    """transforms values in input tensor from original_range to target_range.
+    This function is intended to be used in preprocessing layers that
+    rely upon color values.  This allows us to assume internally that
+    the input tensor is always in the range [0, 255].
+
+    Args:
+        images: the set of images to transform to the target range range.
+        original_range: the value range to transform from.
+        target_range: the value range to transform to.
+        dtype: the dtype to compute the conversion with.  Defaults to tf.float32.
+
+    Returns:
+        a new Tensor with values in the target range.
+
+    Usage:
+    ```python
+    original_range = [0, 1]
+    target_range = [0, 255]
+    images = keras_cv.utils.preprocessing.transform_value_range(
+        images,
+        original_range,
+        target_range
+    )
+    images = tf.math.minimum(images + 10, 255)
+    images = keras_cv.utils.preprocessing.transform_value_range(
+        images,
+        target_range,
+        original_range
+    )
+    ```
+    """
+    if original_range[0] == target_range[0] and original_range[1] == target_range[1]:
+        return images
+
+    images = tf.cast(images, dtype=dtype)
+    original_min_value, original_max_value = _unwrap_value_range(
+        original_range, dtype=dtype
+    )
+    target_min_value, target_max_value = _unwrap_value_range(target_range, dtype=dtype)
+
+    # images in the [0, 1] scale
+    images = (images - original_min_value) / (original_max_value - original_min_value)
+
+    scale_factor = target_max_value - target_min_value
+    return (images * scale_factor) + target_min_value
+
+
+def _unwrap_value_range(value_range, dtype=tf.float32):
+    min_value, max_value = value_range
+    min_value = tf.cast(min_value, dtype=dtype)
+    max_value = tf.cast(max_value, dtype=dtype)
+    return min_value, max_value
+
+
+def blend(image1: tf.Tensor, image2: tf.Tensor, factor: float) -> tf.Tensor:
+    """Blend image1 and image2 using 'factor'.
+
+    FactorSampler should be in the range [0, 1].  A value of 0.0 means only image1
+    is used. A value of 1.0 means only image2 is used.  A value between 0.0
+    and 1.0 means we linearly interpolate the pixel values between the two
+    images.  A value greater than 1.0 "extrapolates" the difference
+    between the two pixel values, and we clip the results to values
+    between 0 and 255.
+    Args:
+      image1: An image Tensor of type tf.float32 with value range [0, 255].
+      image2: An image Tensor of type tf.float32 with value range [0, 255].
+      factor: A floating point value above 0.0.
+    Returns:
+      A blended image Tensor.
+    """
+    difference = image2 - image1
+    scaled = factor * difference
+    temp = image1 + scaled
+    return tf.clip_by_value(temp, 0.0, 255.0)
+
+
+def parse_factor(param, min_value=0.0, max_value=1.0, param_name="factor", seed=None):
+    if isinstance(param, core.FactorSampler):
+        return param
+
+    if isinstance(param, float) or isinstance(param, int):
+        param = (min_value, param)
+
+    if param[0] > param[1]:
+        raise ValueError(
+            f"`{param_name}[0] > {param_name}[1]`, `{param_name}[0]` must be <= "
+            f"`{param_name}[1]`.  Got `{param_name}={param}`"
+        )
+    if (min_value is not None and param[0] < min_value) or (
+        max_value is not None and param[1] > max_value
+    ):
+        raise ValueError(
+            f"`{param_name}` should be inside of range [{min_value}, {max_value}]. "
+            f"Got {param_name}={param}"
+        )
+
+    if param[0] == param[1]:
+        return core.ConstantFactorSampler(param[0])
+
+    return core.UniformFactorSampler(param[0], param[1], seed=seed)
+
+
+def random_inversion(random_generator):
+    """Randomly returns a -1 or a 1 based on the provided random_generator.
+
+    This can be used by KPLs to randomly invert sampled values.
+
+    Args:
+        random_generator: a Keras random number generator.  An instance can be passed
+        from the `self._random_generator` attribute of a `BaseImageAugmentationLayer`.
+
+    Returns:
+        either -1, or -1.
+    """
+    negate = random_generator.random_uniform((), 0, 1, dtype=tf.float32) > 0.5
+    negate = tf.cond(negate, lambda: -1.0, lambda: 1.0)
+    return negate
+
+
+def get_rotation_matrix(angles, image_height, image_width, name=None):
+    """Returns projective transform(s) for the given angle(s).
+    Args:
+      angles: A scalar angle to rotate all images by, or (for batches of images) a
+        vector with an angle to rotate each image in the batch. The rank must be
+        statically known (the shape is not `TensorShape(None)`).
+      image_height: Height of the image(s) to be transformed.
+      image_width: Width of the image(s) to be transformed.
+      name: The name of the op.
+    Returns:
+      A tensor of shape (num_images, 8). Projective transforms which can be given
+        to operation `image_projective_transform_v2`. If one row of transforms is
+         [a0, a1, a2, b0, b1, b2, c0, c1], then it maps the *output* point
+         `(x, y)` to a transformed *input* point
+         `(x', y') = ((a0 x + a1 y + a2) / k, (b0 x + b1 y + b2) / k)`,
+         where `k = c0 x + c1 y + 1`.
+    """
+    with backend.name_scope(name or "rotation_matrix"):
+        x_offset = (
+            (image_width - 1)
+            - (tf.cos(angles) * (image_width - 1) - tf.sin(angles) * (image_height - 1))
+        ) / 2.0
+        y_offset = (
+            (image_height - 1)
+            - (tf.sin(angles) * (image_width - 1) + tf.cos(angles) * (image_height - 1))
+        ) / 2.0
+        num_angles = tf.shape(angles)[0]
+        return tf.concat(
+            values=[
+                tf.cos(angles)[:, None],
+                -tf.sin(angles)[:, None],
+                x_offset[:, None],
+                tf.sin(angles)[:, None],
+                tf.cos(angles)[:, None],
+                y_offset[:, None],
+                tf.zeros((num_angles, 2), tf.float32),
+            ],
+            axis=1,
+        )
+
+
+def get_translation_matrix(translations, name=None):
+    """Returns projective transform(s) for the given translation(s).
+    Args:
+      translations: A matrix of 2-element lists representing `[dx, dy]`
+        to translate for each image (for a batch of images).
+      name: The name of the op.
+    Returns:
+      A tensor of shape `(num_images, 8)` projective transforms which can be given
+        to `transform`.
+    """
+    with backend.name_scope(name or "translation_matrix"):
+        num_translations = tf.shape(translations)[0]
+        # The translation matrix looks like:
+        #     [[1 0 -dx]
+        #      [0 1 -dy]
+        #      [0 0 1]]
+        # where the last entry is implicit.
+        # Translation matrices are always float32.
+        return tf.concat(
+            values=[
+                tf.ones((num_translations, 1), tf.float32),
+                tf.zeros((num_translations, 1), tf.float32),
+                -translations[:, 0, None],
+                tf.zeros((num_translations, 1), tf.float32),
+                tf.ones((num_translations, 1), tf.float32),
+                -translations[:, 1, None],
+                tf.zeros((num_translations, 2), tf.float32),
+            ],
+            axis=1,
+        )
+
+
+def transform(
+    images,
+    transforms,
+    fill_mode="reflect",
+    fill_value=0.0,
+    interpolation="bilinear",
+    output_shape=None,
+    name=None,
+):
+    """Applies the given transform(s) to the image(s).
+
+    Args:
+      images: A tensor of shape
+        `(num_images, num_rows, num_columns, num_channels)` (NHWC). The rank must
+        be statically known (the shape is not `TensorShape(None)`).
+      transforms: Projective transform matrix/matrices. A vector of length 8 or
+        tensor of size N x 8. If one row of transforms is [a0, a1, a2, b0, b1, b2,
+        c0, c1], then it maps the *output* point `(x, y)` to a transformed *input*
+        point `(x', y') = ((a0 x + a1 y + a2) / k, (b0 x + b1 y + b2) / k)`, where
+        `k = c0 x + c1 y + 1`. The transforms are *inverted* compared to the
+        transform mapping input points to output points. Note that gradients are
+        not backpropagated into transformation parameters.
+      fill_mode: Points outside the boundaries of the input are filled according
+        to the given mode (one of `{"constant", "reflect", "wrap", "nearest"}`).
+      fill_value: a float represents the value to be filled outside the boundaries
+        when `fill_mode="constant"`.
+      interpolation: Interpolation mode. Supported values: `"nearest"`,
+        `"bilinear"`.
+      output_shape: Output dimension after the transform, `[height, width]`.
+        If `None`, output is the same size as input image.
+      name: The name of the op.
+
+    Fill mode behavior for each valid value is as follows:
+
+    - reflect (d c b a | a b c d | d c b a)
+    The input is extended by reflecting about the edge of the last pixel.
+
+    - constant (k k k k | a b c d | k k k k)
+    The input is extended by filling all
+    values beyond the edge with the same constant value k = 0.
+
+    - wrap (a b c d | a b c d | a b c d)
+    The input is extended by wrapping around to the opposite edge.
+
+    - nearest (a a a a | a b c d | d d d d)
+    The input is extended by the nearest pixel.
+
+    Input shape:
+      4D tensor with shape: `(samples, height, width, channels)`,
+        in `"channels_last"` format.
+
+    Output shape:
+      4D tensor with shape: `(samples, height, width, channels)`,
+        in `"channels_last"` format.
+
+    Returns:
+      Image(s) with the same type and shape as `images`, with the given
+      transform(s) applied. Transformed coordinates outside of the input image
+      will be filled with zeros.
+
+    Raises:
+      TypeError: If `image` is an invalid type.
+      ValueError: If output shape is not 1-D int32 Tensor.
+    """
+    with backend.name_scope(name or "transform"):
+        if output_shape is None:
+            output_shape = tf.shape(images)[1:3]
+            if not tf.executing_eagerly():
+                output_shape_value = tf.get_static_value(output_shape)
+                if output_shape_value is not None:
+                    output_shape = output_shape_value
+
+        output_shape = tf.convert_to_tensor(output_shape, tf.int32, name="output_shape")
+
+        if not output_shape.get_shape().is_compatible_with([2]):
+            raise ValueError(
+                "output_shape must be a 1-D Tensor of 2 elements: "
+                "new_height, new_width, instead got "
+                "{}".format(output_shape)
+            )
+
+        fill_value = tf.convert_to_tensor(fill_value, tf.float32, name="fill_value")
+
+        return tf.raw_ops.ImageProjectiveTransformV3(
+            images=images,
+            output_shape=output_shape,
+            fill_value=fill_value,
+            transforms=transforms,
+            fill_mode=fill_mode.upper(),
+            interpolation=interpolation.upper(),
+        )
+
+
+def ensure_tensor(inputs, dtype=None):
+    """Ensures the input is a Tensor, SparseTensor or RaggedTensor."""
+    if not isinstance(inputs, (tf.Tensor, tf.RaggedTensor, tf.SparseTensor)):
+        inputs = tf.convert_to_tensor(inputs, dtype)
+    if dtype is not None and inputs.dtype != dtype:
+        inputs = tf.cast(inputs, dtype)
+    return inputs
+
+
+def check_fill_mode_and_interpolation(fill_mode, interpolation):
+    if fill_mode not in {"reflect", "wrap", "constant", "nearest"}:
+        raise NotImplementedError(
+            " Want fillmode  to be one of `reflect`, `wrap`, "
+            "`constant` or `nearest`. Got `fill_mode` {}. ".format(fill_mode)
+        )
+    if interpolation not in {"nearest", "bilinear"}:
+        raise NotImplementedError(
+            "Unknown `interpolation` {}. Only `nearest` and "
+            "`bilinear` are supported.".format(interpolation)
+        )

Keras/keras-cv/keras_cv/utils/preprocessing_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.utils import preprocessing
+
+
+class MockRandomGenerator:
+    def __init__(self, value):
+        self.value = value
+
+    def random_uniform(self, shape, minval, maxval, dtype=None):
+        del minval, maxval
+        return tf.constant(self.value, dtype=dtype)
+
+
+class PreprocessingTestCase(tf.test.TestCase):
+    def setUp(self):
+        super().setUp()
+
+    def test_transform_to_standard_range_neg_one_range(self):
+        x = tf.constant([-1, 0, 1])
+        x = preprocessing.transform_value_range(
+            x, original_range=[-1, 1], target_range=[0, 255]
+        )
+        self.assertAllClose(x, [0.0, 127.5, 255.0])
+
+    def test_transform_to_same_range(self):
+        x = tf.constant([-1, 0, 1])
+        x = preprocessing.transform_value_range(
+            x, original_range=[0, 255], target_range=[0, 255]
+        )
+        self.assertAllClose(x, [-1, 0, 1])
+
+    def test_transform_to_standard_range(self):
+        x = tf.constant([8 / 255, 9 / 255, 255 / 255])
+        x = preprocessing.transform_value_range(
+            x, original_range=[0, 1], target_range=[0, 255]
+        )
+        self.assertAllClose(x, [8.0, 9.0, 255.0])
+
+    def test_transform_to_value_range(self):
+        x = tf.constant([128.0, 255.0, 0.0])
+        x = preprocessing.transform_value_range(
+            x, original_range=[0, 255], target_range=[0, 1]
+        )
+        self.assertAllClose(x, [128 / 255, 1, 0])
+
+    def test_random_inversion(self):
+        generator = MockRandomGenerator(0.75)
+        self.assertEqual(preprocessing.random_inversion(generator), -1.0)
+        generator = MockRandomGenerator(0.25)
+        self.assertEqual(preprocessing.random_inversion(generator), 1.0)

Keras/keras-cv/keras_cv/utils/train.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 scale_loss_for_distribution(loss_value):
+    """Scales and returns the given loss value by the number of replicas."""
+    num_replicas = tf.distribute.get_strategy().num_replicas_in_sync
+    if num_replicas > 1:
+        loss_value *= 1.0 / num_replicas
+    return loss_value
+
+
+def convert_inputs_to_tf_dataset(x=None, y=None, sample_weight=None, batch_size=None):
+    if sample_weight is not None:
+        raise ValueError("Contrastive trainers do not yet support `sample_weight`.")
+
+    if isinstance(x, tf.data.Dataset):
+        if y is not None or batch_size is not None:
+            raise ValueError(
+                "When `x` is a `tf.data.Dataset`, please do not provide a value for "
+                f"`y` or `batch_size`.  Got `y={y}`, `batch_size={batch_size}`."
+            )
+        return x
+
+    # batch_size defaults to 32, as it does in fit().
+    batch_size = batch_size or 32
+    # Parse inputs
+    inputs = x
+    if y is not None:
+        inputs = (x, y)
+
+    # Construct tf.data.Dataset
+    dataset = tf.data.Dataset.from_tensor_slices(inputs)
+    if batch_size is not None:
+        dataset = dataset.batch(batch_size)
+    return dataset

Keras/keras-cv/keras_cv/version_check.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.
+
+"""KerasCV Version check."""
+
+
+import tensorflow as tf
+from packaging.version import parse
+
+MIN_VERSION = "2.9.0"
+
+
+def check_tf_version():
+    if parse(tf.__version__) < parse(MIN_VERSION):
+        raise RuntimeError(
+            f"The Tensorflow package version needs to be at least {MIN_VERSION} "
+            "for KerasCV to run. Currently, your TensorFlow version is "
+            f"{tf.__version__}. Please upgrade with `$ pip install --upgrade tensorflow`. "
+            "You can use `pip freeze` to check afterwards that everything is ok."
+        )

Keras/keras-cv/keras_cv/version_check_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 pytest
+
+from keras_cv import version_check
+
+
+def test_check_tf_version_error():
+    version_check.tf.__version__ = "2.8.0"
+
+    with pytest.raises(
+        RuntimeError, match="Tensorflow package version needs to be at least 2.9.0"
+    ):
+        version_check.check_tf_version()
+
+
+def test_check_tf_version_passes_rc2():
+    # should pass
+    version_check.tf.__version__ = "2.9.1rc2"
+    version_check.check_tf_version()
+
+
+def test_check_tf_version_passes_nightly():
+    # should pass
+    version_check.tf.__version__ = "2.10.0-dev20220419"
+    version_check.check_tf_version()

Keras/keras-cv/setup.cfg

+[metadata]
+license_file = LICENSE
+description-file = README.md
+version = attr: keras_cv.__version__
+
+[tool:pytest]
+filterwarnings =
+    error
+    ignore::DeprecationWarning
+    ignore::ImportWarning
+    ignore::RuntimeWarning
+    ignore::PendingDeprecationWarning
+    ignore::FutureWarning
+[flake8]
+max-line-length = 88
+max-doc-length = 200
+per-file-ignores =
+    ./keras_cv/__init__.py:E402, F401
+    ./examples/**/*:E402
+    **/__init__.py:F401
+ignore =
+    # Conflicts with black
+    E203
+    # defaults flake8 ignores
+    E121,E123,E126,E226,E24,E704,W503,W504
+    # Function name should be lowercase
+    N802
+    # lowercase ... imported as non lowercase
+    # Useful to ignore for "import keras.backend as K"
+    N812
+    # do not use bare 'except'
+    E722
+    # Escape characters check.
+    # Conflict with pytest error message regex.
+    W605
+    # Ignore for tf.cond lambda
+    E731
+
+[isort]
+profile = black
+force_single_line = True
+known_first_party = keras_cv,tests
+default_section = THIRDPARTY
+line_length = 88

Keras/keras-cv/setup.py

+# Copyright 2019 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.
+
+"""Setup script."""
+
+import pathlib
+
+from setuptools import find_packages
+from setuptools import setup
+from setuptools.dist import Distribution
+
+HERE = pathlib.Path(__file__).parent
+README = (HERE / "README.md").read_text()
+
+
+class BinaryDistribution(Distribution):
+    """This class is needed in order to create OS specific wheels."""
+
+    def has_ext_modules(self):
+        return True
+
+    def is_pure(self):
+        return False
+
+
+setup(
+    name="keras-cv",
+    description="Industry-strength computer Vision extensions for Keras.",
+    long_description=README,
+    long_description_content_type="text/markdown",
+    url="https://github.com/keras-team/keras-cv",
+    author="Keras team",
+    author_email="keras-cv@google.com",
+    license="Apache License 2.0",
+    install_requires=["packaging", "absl-py", "regex"],
+    python_requires=">=3.7",
+    extras_require={
+        "tests": ["flake8", "isort", "black", "pytest", "tensorflow-datasets"],
+        "examples": ["tensorflow_datasets", "matplotlib"],
+    },
+    distclass=BinaryDistribution,
+    classifiers=[
+        "Programming Language :: Python",
+        "Programming Language :: Python :: 3.7",
+        "Operating System :: Unix",
+        "Operating System :: Microsoft :: Windows",
+        "Operating System :: MacOS",
+        "Intended Audience :: Science/Research",
+        "Topic :: Scientific/Engineering",
+        "Topic :: Software Development",
+    ],
+    packages=find_packages(exclude=("*_test.py",)),
+    include_package_data=True,
+)

Keras/keras-cv/shell/clean.sh

+#!/bin/bash
+rm -rf keras_cv.egg-info/
+rm -rf keras_cv/**/__pycache__
+rm -rf keras_cv/__pycache__
+rm -rf build/

Keras/keras-cv/shell/format.sh

+#!/bin/bash
+isort --sl --profile=black .
+black .

Keras/keras-cv/shell/lint.sh

+#!/bin/bash
+# Usage: # lint.sh can be used without arguments to lint the entire project:
+#
+# ./lint.sh
+#
+# or with arguments to lint a subset of files
+#
+# ./lint.sh examples/*
+
+files="."
+if [ $# -ne 0  ]
+  then
+    files=$@
+fi
+
+isort -c $files
+if ! [ $? -eq 0 ]
+then
+  echo "Please run \"sh shell/format.sh\" to format the code."
+  isort --version
+  black --version
+  exit 1
+fi
+[ $# -eq 0  ] && echo "no issues with isort"
+
+# Allow --max-line-length=200 to support long links in docstrings
+flake8 --max-line-length=200 $files
+if ! [ $? -eq 0 ]
+then
+  echo "Please fix the code style issue."
+  exit 1
+fi
+[ $# -eq 0 ] && echo "no issues with flake8"
+black --check $files
+if ! [ $? -eq 0 ]
+then
+  echo "Please run \"sh shell/format.sh\" to format the code."
+    exit 1
+fi
+[ $# -eq 0  ] && echo "no issues with black"
+for i in $(find keras_cv -name '*.py') # or whatever other pattern...
+do
+  if ! grep -q Copyright $i
+  then
+    echo "Copyright not found in $i"
+    exit 1
+  fi
+done
+echo "linting success!"

Keras/keras-cv/shell/weights/process_weights.sh

+if [ "$#" -ne 4 ]; then
+  echo USAGE: ./process_weights.sh WEIGHTS_PATH OUTPUT_WEIGHTS_PATH MODEL_NAME GCS_PATH
+  exit 1
+fi
+
+WEIGHTS=$1
+OUTPUT_WEIGHTS=$2
+MODEL=$3
+GCS_PATH=$4
+
+python3 remove_top.py --weights_path=$WEIGHTS --output_weights_path=$OUTPUT_WEIGHTS --model_name=$MODEL
+
+echo With top: $GCS_PATH/$WEIGHTS
+echo With top checksum: $(shasum -a 256 $WEIGHTS)
+echo Without top: $GCS_PATH/$OUTPUT_WEIGHTS
+echo Without top checksum: $(shasum -a 256 $OUTPUT_WEIGHTS)
+
+gsutil cp $WEIGHTS $GCS_PATH/
+gsutil cp $OUTPUT_WEIGHTS $GCS_PATH/
+
+gsutil acl ch -u AllUsers:R $GCS_PATH/$WEIGHTS
+gsutil acl ch -u AllUsers:R $GCS_PATH/$OUTPUT_WEIGHTS

Keras/keras-cv/shell/weights/remove_top.py

+import sys
+
+import keras
+from absl import flags
+
+import keras_cv
+
+flags.DEFINE_string("weights_path", None, "Path of weights to load")
+flags.DEFINE_string("output_weights_path", None, "Path of notop weights to store")
+flags.DEFINE_string("model_name", None, "Name of the KerasCV.model")
+
+FLAGS = flags.FLAGS
+FLAGS(sys.argv)
+
+if not FLAGS.weights_path.endswith(".h5"):
+    raise ValueError("Weights path must end in .h5")
+
+model = eval(
+    f"keras_cv.models.{FLAGS.model_name}(include_rescaling=True, include_top=True, classes=1000, weights=FLAGS.weights_path)"
+)
+
+without_top = keras.models.Model(model.input, model.layers[-3].output)
+without_top.save_weights(FLAGS.output_weights_path)
+
+# Because the usage of keras_cv is in an eval() call, the linter is angry.
+# We include this to avoid an unused import warning
+keras_cv.models

Keras/keras-cv/shell/weights/update_training_history.py

+import json
+import os
+import sys
+
+import tensorboard as tb
+from absl import flags
+
+flags.DEFINE_string(
+    "model_name", None, "The name of the KerasCV.model that was trained"
+)
+flags.DEFINE_string("tensorboard_logs_path", None, "Path to tensorboard logs to load")
+flags.DEFINE_string("training_script_path", None, "Path to the training script")
+flags.DEFINE_string(
+    "script_version",
+    None,
+    "commit hash of the latest commit in KerasCV/master for the training script",
+)
+flags.DEFINE_string(
+    "weights_version",
+    None,
+    "The version of the training script used to produce the latest weights. For example, v0",
+)
+flags.DEFINE_string(
+    "contributor", None, "The GitHub username of the contributor of these results"
+)
+flags.DEFINE_string(
+    "accelerators", None, "The number of accelerators used for training."
+)
+
+FLAGS = flags.FLAGS
+FLAGS(sys.argv)
+
+model_name = FLAGS.model_name or input(
+    "Input the name of the KerasCV.model that was trained\n"
+)
+weights_version = FLAGS.weights_version or input(
+    "Input the weights version for your script\n"
+)
+
+training_script_path = FLAGS.training_script_path or input(
+    "Input the path to your training script\n"
+)
+full_training_script_path = os.path.abspath(training_script_path)
+
+# Build an experiment name structured as task/training_script_name/model_name-version
+training_script_rooted_at_training = full_training_script_path[
+    full_training_script_path.index("keras-cv/examples/training/") + 27 :
+]
+training_script_dirs = training_script_rooted_at_training.split("/")
+tensorboard_experiment_name = f"{training_script_dirs[0]}/{'/'.join(training_script_dirs[1:])[:-3]}/{model_name}-{weights_version}"
+
+training_script_json_path = full_training_script_path[
+    : full_training_script_path.index("keras-cv/examples/training/") + 27
+] + "/".join(training_script_dirs[:2] + ["training_history.json"])
+
+script_version = FLAGS.script_version or input(
+    "Input the commit hash of the latest commit in KerasCV/master for the training script used for training."
+)
+
+tensorboard_logs_path = FLAGS.tensorboard_logs_path or input(
+    "Input the path to the TensorBoard logs\n"
+)
+tensorboard_experiment_id = (
+    os.popen(
+        f"python3 -m tensorboard.main dev upload --logdir {tensorboard_logs_path} --name {tensorboard_experiment_name} --one_shot --verbose 0"
+    )
+    .read()
+    .split("/")[-2]
+)
+
+tensorboard_experiment = tb.data.experimental.ExperimentFromDev(
+    tensorboard_experiment_id
+)
+
+tensorboard_results = tensorboard_experiment.get_scalars()
+
+training_epochs = max(tensorboard_results[tensorboard_results.run == "train"].step)
+max_validation_accuracy = max(
+    tensorboard_results[
+        (tensorboard_results.run == "validation")
+        & (tensorboard_results.tag == "epoch_categorical_accuracy")
+    ].value
+)
+max_validation_accuracy = f"{max_validation_accuracy:.4f}"
+
+contributor = FLAGS.contributor or input(
+    "Input your GitHub username (or the username of the contributor, if it's not you)\n"
+)
+
+accelerators = FLAGS.accelerators or input(
+    "Input the number of accelerators used during training.\n"
+)
+
+args = input(
+    "Input any training arguments used for the training script.\n"
+    "Use comma-separate, colon-split key-value pairs. For example:\n"
+    "arg1:value, arg2:value\n"
+)
+
+args_dict = {}
+for arg in args.split(","):
+    if len(arg.strip()) == 0:
+        continue
+    key_value_pair = [s.strip() for s in arg.split(":")]
+    args_dict[key_value_pair[0]] = key_value_pair[1]
+
+new_results = {
+    "script": {"name": "/".join(training_script_dirs[2:]), "version": script_version},
+    "validation_accuracy": max_validation_accuracy,
+    "epochs_trained": training_epochs,
+    "tensorboard_logs": f"https://tensorboard.dev/experiment/{tensorboard_experiment_id}/",
+    "contributor": contributor,
+    "args": args_dict,
+    "accelerators": int(accelerators),
+}
+
+# Check if the JSON file already exists
+results_file = open(training_script_json_path, "r")
+results_string = results_file.read()
+results = json.loads(results_string) if results_string != "" else {}
+results_file.close()
+
+# If we've never run this script on this model, insert a record for it
+if model_name not in results:
+    results[model_name] = {}
+
+# Add this run's results to the model's record
+model_results = results[model_name]
+model_results[weights_version] = new_results
+
+# Save the updated results
+results_file = open(training_script_json_path, "w")
+json.dump(results, results_file, indent=4, sort_keys=True)
+results_file.close()