mirror of
https://github.com/astral-sh/ruff.git
synced 2025-08-02 18:02:23 +00:00
c847cad389
Some checks are pending
CI / Determine changes (push) Waiting to run
CI / cargo fmt (push) Waiting to run
CI / cargo shear (push) Blocked by required conditions
CI / cargo clippy (push) Blocked by required conditions
CI / cargo test (linux) (push) Blocked by required conditions
CI / cargo test (windows) (push) Blocked by required conditions
CI / cargo test (wasm) (push) Blocked by required conditions
CI / cargo build (release) (push) Blocked by required conditions
CI / cargo build (msrv) (push) Blocked by required conditions
CI / cargo fuzz (push) Blocked by required conditions
CI / Fuzz the parser (push) Blocked by required conditions
CI / test scripts (push) Blocked by required conditions
CI / ecosystem (push) Blocked by required conditions
CI / python package (push) Waiting to run
CI / pre-commit (push) Waiting to run
CI / mkdocs (push) Waiting to run
CI / formatter instabilities and black similarity (push) Blocked by required conditions
CI / test ruff-lsp (push) Blocked by required conditions
CI / benchmarks (push) Blocked by required conditions
82 lines
2.7 KiB
Text
82 lines
2.7 KiB
Text
---
|
|
source: crates/ruff_server/tests/notebook.rs
|
|
expression: notebook_source(snapshot.query().as_notebook().unwrap())
|
|
snapshot_kind: text
|
|
---
|
|
# @title 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.
|
|
!pip install matplotlib tensorflow tensorflow-hub
|
|
import tensorflow as tf
|
|
import tensorflow_hub as hub
|
|
import matplotlib.pyplot as plt
|
|
|
|
print(tf.__version__)
|
|
model = hub.load("https://tfhub.dev/captain-pool/esrgan-tf2/1")
|
|
concrete_func = model.signatures[tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY]
|
|
|
|
|
|
@tf.function(input_signature=[tf.TensorSpec(shape=[1, 50, 50, 3], dtype=tf.float32)])
|
|
def f(input):
|
|
return concrete_func(input)
|
|
|
|
|
|
converter = tf.lite.TFLiteConverter.from_concrete_functions(
|
|
[f.get_concrete_function()], model
|
|
)
|
|
converter.optimizations = [tf.lite.Optimize.DEFAULT]
|
|
tflite_model = converter.convert()
|
|
|
|
# Save the TF Lite model.
|
|
with tf.io.gfile.GFile("ESRGAN.tflite", "wb") as f:
|
|
f.write(tflite_model)
|
|
|
|
esrgan_model_path = "./ESRGAN.tflite"
|
|
test_img_path = tf.keras.utils.get_file(
|
|
"lr.jpg",
|
|
"https://raw.githubusercontent.com/tensorflow/examples/master/lite/examples/super_resolution/android/app/src/main/assets/lr-1.jpg",
|
|
)
|
|
lr = tf.io.read_file(test_img_path)
|
|
lr = tf.image.decode_jpeg(lr)
|
|
lr = tf.expand_dims(lr, axis=0)
|
|
lr = tf.cast(lr, tf.float32)
|
|
|
|
# Load TFLite model and allocate tensors.
|
|
interpreter = tf.lite.Interpreter(model_path=esrgan_model_path)
|
|
interpreter.allocate_tensors()
|
|
|
|
# Get input and output tensors.
|
|
input_details = interpreter.get_input_details()
|
|
output_details = interpreter.get_output_details()
|
|
|
|
# Run the model
|
|
interpreter.set_tensor(input_details[0]["index"], lr)
|
|
interpreter.invoke()
|
|
|
|
# Extract the output and postprocess it
|
|
output_data = interpreter.get_tensor(output_details[0]["index"])
|
|
sr = tf.squeeze(output_data, axis=0)
|
|
sr = tf.clip_by_value(sr, 0, 255)
|
|
sr = tf.round(sr)
|
|
sr = tf.cast(sr, tf.uint8)
|
|
lr = tf.cast(tf.squeeze(lr, axis=0), tf.uint8)
|
|
plt.figure(figsize=(1, 1))
|
|
plt.title("LR")
|
|
plt.imshow(lr.numpy())
|
|
plt.figure(figsize=(10, 4))
|
|
plt.subplot(1, 2, 1)
|
|
plt.title(f"ESRGAN (x4)")
|
|
plt.imshow(sr.numpy())
|
|
bicubic = tf.image.resize(lr, [200, 200], tf.image.ResizeMethod.BICUBIC)
|
|
bicubic = tf.cast(bicubic, tf.uint8)
|
|
plt.subplot(1, 2, 2)
|
|
plt.title("Bicubic")
|
|
plt.imshow(bicubic.numpy());
|