Yardım Kaggle üzerinde TensorFlow ile Büyük Bariyer Resifi korumak Meydan Üyelik

XNNPACK ile cihaz içi çıkarım için budama

TensorFlow.org'da görüntüleyin Google Colab'da çalıştırın Kaynağı GitHub'da görüntüleyin Not defterini indir

İle Cihazdaki çıkarsama gecikme geliştirmek için budama Keras ağırlıkları üzerindeki kılavuzuna hoş geldiniz XNNPACK .

Bu kılavuz sunuyor yeni tanıtılan kullanım tfmot.sparsity.keras.PruningPolicy API ve kullanan çağdaş CPU'lar çoğunlukla evrışimlı modelleri hızlandırılması için nasıl kullanılabileceğini gösteriyor XNNPACK Seyrek çıkarım .

Kılavuz, model oluşturma sürecinin aşağıdaki adımlarını kapsar:

  • Yoğun taban çizgisini oluşturun ve eğitin
  • Budama ile ince ayar modeli
  • TFLite'a Dönüştür
  • Cihaz içi kıyaslama

Kılavuz, budama ile ince ayar için en iyi uygulamaları kapsamamaktadır. Bu konuyla ilgili daha ayrıntılı bilgi için lütfen kontrol edin kapsamlı bir rehber .

Kurmak

 pip install -q tensorflow
 pip install -q tensorflow-model-optimization
import tempfile

import tensorflow as tf
import numpy as np

from tensorflow import keras
import tensorflow_datasets as tfds
import tensorflow_model_optimization as tfmot

%load_ext tensorboard

Yoğun modeli oluşturun ve eğitin

Biz inşa etmek ve üzerinde sınıflandırma görev için basit bir temel CNN tren CIFAR10 veri kümesi.

# Load CIFAR10 dataset.
(ds_train, ds_val, ds_test), ds_info = tfds.load(
    'cifar10',
    split=['train[:90%]', 'train[90%:]', 'test'],
    as_supervised=True,
    with_info=True,
)

# Normalize the input image so that each pixel value is between 0 and 1.
def normalize_img(image, label):
  """Normalizes images: `uint8` -> `float32`."""
  return tf.image.convert_image_dtype(image, tf.float32), label

# Load the data in batches of 128 images.
batch_size = 128
def prepare_dataset(ds, buffer_size=None):
  ds = ds.map(normalize_img, num_parallel_calls=tf.data.experimental.AUTOTUNE)
  ds = ds.cache()
  if buffer_size:
    ds = ds.shuffle(buffer_size)
  ds = ds.batch(batch_size)
  ds = ds.prefetch(tf.data.experimental.AUTOTUNE)
  return ds

ds_train = prepare_dataset(ds_train,
                           buffer_size=ds_info.splits['train'].num_examples)
ds_val = prepare_dataset(ds_val)
ds_test = prepare_dataset(ds_test)

# Build the dense baseline model.
dense_model = keras.Sequential([
    keras.layers.InputLayer(input_shape=(32, 32, 3)),
    keras.layers.ZeroPadding2D(padding=1),
    keras.layers.Conv2D(
        filters=8,
        kernel_size=(3, 3),
        strides=(2, 2),
        padding='valid'),
    keras.layers.BatchNormalization(),
    keras.layers.ReLU(),
    keras.layers.DepthwiseConv2D(kernel_size=(3, 3), padding='same'),
    keras.layers.BatchNormalization(),
    keras.layers.ReLU(),
    keras.layers.Conv2D(filters=16, kernel_size=(1, 1)),
    keras.layers.BatchNormalization(),
    keras.layers.ReLU(),
    keras.layers.ZeroPadding2D(padding=1),
    keras.layers.DepthwiseConv2D(
        kernel_size=(3, 3), strides=(2, 2), padding='valid'),
    keras.layers.BatchNormalization(),
    keras.layers.ReLU(),
    keras.layers.Conv2D(filters=32, kernel_size=(1, 1)),
    keras.layers.BatchNormalization(),
    keras.layers.ReLU(),
    keras.layers.GlobalAveragePooling2D(),
    keras.layers.Flatten(),
    keras.layers.Dense(10)
])

# Compile and train the dense model for 10 epochs.
dense_model.compile(
    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    optimizer='adam',
    metrics=['accuracy'])

dense_model.fit(
  ds_train,
  epochs=10,
  validation_data=ds_val)

# Evaluate the dense model.
_, dense_model_accuracy = dense_model.evaluate(ds_test, verbose=0)
2021-08-13 11:13:35.517009: E tensorflow/stream_executor/cuda/cuda_driver.cc:271] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected
2021-08-13 11:13:35.517068: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (kokoro-gcp-ubuntu-prod-1682665100): /proc/driver/nvidia/version does not exist
2021-08-13 11:13:35.517823: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
Epoch 1/10
2021-08-13 11:13:36.392179: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)
352/352 [==============================] - 12s 21ms/step - loss: 1.9929 - accuracy: 0.2651 - val_loss: 2.5594 - val_accuracy: 0.1466
Epoch 2/10
352/352 [==============================] - 7s 19ms/step - loss: 1.7293 - accuracy: 0.3582 - val_loss: 1.7533 - val_accuracy: 0.3414
Epoch 3/10
352/352 [==============================] - 7s 19ms/step - loss: 1.6531 - accuracy: 0.3849 - val_loss: 1.6463 - val_accuracy: 0.3886
Epoch 4/10
352/352 [==============================] - 7s 19ms/step - loss: 1.6073 - accuracy: 0.4024 - val_loss: 1.6127 - val_accuracy: 0.3980
Epoch 5/10
352/352 [==============================] - 7s 19ms/step - loss: 1.5692 - accuracy: 0.4200 - val_loss: 1.5552 - val_accuracy: 0.4228
Epoch 6/10
352/352 [==============================] - 7s 19ms/step - loss: 1.5358 - accuracy: 0.4344 - val_loss: 1.6375 - val_accuracy: 0.4030
Epoch 7/10
352/352 [==============================] - 7s 19ms/step - loss: 1.5074 - accuracy: 0.4475 - val_loss: 1.5514 - val_accuracy: 0.4258
Epoch 8/10
352/352 [==============================] - 7s 19ms/step - loss: 1.4810 - accuracy: 0.4598 - val_loss: 1.7087 - val_accuracy: 0.3866
Epoch 9/10
352/352 [==============================] - 7s 19ms/step - loss: 1.4610 - accuracy: 0.4669 - val_loss: 1.5219 - val_accuracy: 0.4492
Epoch 10/10
352/352 [==============================] - 7s 19ms/step - loss: 1.4445 - accuracy: 0.4748 - val_loss: 1.5329 - val_accuracy: 0.4302

Seyrek modeli oluşturun

Talimatına göre, kapsamlı bir rehber , biz uygulamak tfmot.sparsity.keras.prune_low_magnitude parametreler hedef Cihazdaki ivme yoluyla budama yani birlikte işlevini tfmot.sparsity.keras.PruneForLatencyOnXNNPack politikası.

prune_low_magnitude = tfmot.sparsity.keras.prune_low_magnitude

# Compute end step to finish pruning after after 5 epochs.
end_epoch = 5

num_iterations_per_epoch = len(ds_train)
end_step =  num_iterations_per_epoch * end_epoch

# Define parameters for pruning.
pruning_params = {
      'pruning_schedule': tfmot.sparsity.keras.PolynomialDecay(initial_sparsity=0.25,
                                                               final_sparsity=0.75,
                                                               begin_step=0,
                                                               end_step=end_step),
      'pruning_policy': tfmot.sparsity.keras.PruneForLatencyOnXNNPack()
}

# Try to apply pruning wrapper with pruning policy parameter.
try:
  model_for_pruning = prune_low_magnitude(dense_model, **pruning_params)
except ValueError as e:
  print(e)
Could not find a `GlobalAveragePooling2D` layer with `keepdims = True` in all output branches

Çağrı prune_low_magnitude sonuçları ValueError mesajla Could not find a GlobalAveragePooling2D layer with keepdims = True in all output branches . Mesaj modeli politikası ile budama için desteklenmez belirtir tfmot.sparsity.keras.PruneForLatencyOnXNNPack katman ve özellikle GlobalAveragePooling2D parametre gerektirir keepdims = True . Hadi düzeltme olduğunu ve tekrar başvuruda prune_low_magnitude fonksiyonu.

fixed_dense_model = keras.Sequential([
    keras.layers.InputLayer(input_shape=(32, 32, 3)),
    keras.layers.ZeroPadding2D(padding=1),
    keras.layers.Conv2D(
        filters=8,
        kernel_size=(3, 3),
        strides=(2, 2),
        padding='valid'),
    keras.layers.BatchNormalization(),
    keras.layers.ReLU(),
    keras.layers.DepthwiseConv2D(kernel_size=(3, 3), padding='same'),
    keras.layers.BatchNormalization(),
    keras.layers.ReLU(),
    keras.layers.Conv2D(filters=16, kernel_size=(1, 1)),
    keras.layers.BatchNormalization(),
    keras.layers.ReLU(),
    keras.layers.ZeroPadding2D(padding=1),
    keras.layers.DepthwiseConv2D(
        kernel_size=(3, 3), strides=(2, 2), padding='valid'),
    keras.layers.BatchNormalization(),
    keras.layers.ReLU(),
    keras.layers.Conv2D(filters=32, kernel_size=(1, 1)),
    keras.layers.BatchNormalization(),
    keras.layers.ReLU(),
    keras.layers.GlobalAveragePooling2D(keepdims=True),
    keras.layers.Flatten(),
    keras.layers.Dense(10)
])

# Use the pretrained model for pruning instead of training from scratch.
fixed_dense_model.set_weights(dense_model.get_weights())

# Try to reapply pruning wrapper.
model_for_pruning = prune_low_magnitude(fixed_dense_model, **pruning_params)
/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/keras/engine/base_layer.py:2223: UserWarning: `layer.add_variable` is deprecated and will be removed in a future version. Please use `layer.add_weight` method instead.
  warnings.warn('`layer.add_variable` is deprecated and '

Çağırma prune_low_magnitude modeli tam olarak desteklenir; yani hatasız tamamladı tfmot.sparsity.keras.PruneForLatencyOnXNNPack politikası ve kullanılarak hızlandırılabilir XNNPACK Seyrek çıkarım .

Seyrek modelde ince ayar yapın

Aşağıdaki budama örneği yoğun modelinin ağırlıkları kullanılarak, biz ince ayarlar seyrek modeli. Modelin ince ayarına %25 seyreklik ile başlıyoruz (ağırlıkların %25'i sıfıra ayarlanmış) ve %75 seyreklik ile bitiriyoruz.

logdir = tempfile.mkdtemp()

callbacks = [
  tfmot.sparsity.keras.UpdatePruningStep(),
  tfmot.sparsity.keras.PruningSummaries(log_dir=logdir),
]

model_for_pruning.compile(
    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    optimizer='adam',
    metrics=['accuracy'])

model_for_pruning.fit(
  ds_train,
  epochs=15,
  validation_data=ds_val,
  callbacks=callbacks)

# Evaluate the dense model.
_, pruned_model_accuracy = model_for_pruning.evaluate(ds_test, verbose=0)

print('Dense model test accuracy:', dense_model_accuracy)
print('Pruned model test accuracy:', pruned_model_accuracy)
2021-08-13 11:14:50.266658: I tensorflow/core/profiler/lib/profiler_session.cc:131] Profiler session initializing.
2021-08-13 11:14:50.266694: I tensorflow/core/profiler/lib/profiler_session.cc:146] Profiler session started.
2021-08-13 11:14:50.833248: I tensorflow/core/profiler/lib/profiler_session.cc:164] Profiler session tear down.
2021-08-13 11:14:50.851018: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.
Epoch 1/15
 10/352 [..............................] - ETA: 8s - loss: 1.4245 - accuracy: 0.5016
2021-08-13 11:14:52.593103: I tensorflow/core/profiler/lib/profiler_session.cc:131] Profiler session initializing.
2021-08-13 11:14:52.593147: I tensorflow/core/profiler/lib/profiler_session.cc:146] Profiler session started.
2021-08-13 11:14:52.617240: I tensorflow/core/profiler/lib/profiler_session.cc:66] Profiler session collecting data.
2021-08-13 11:14:52.619415: I tensorflow/core/profiler/lib/profiler_session.cc:164] Profiler session tear down.
2021-08-13 11:14:52.623098: I tensorflow/core/profiler/rpc/client/save_profile.cc:136] Creating directory: /tmp/tmpkwu32h8j/train/plugins/profile/2021_08_13_11_14_52

2021-08-13 11:14:52.625016: I tensorflow/core/profiler/rpc/client/save_profile.cc:142] Dumped gzipped tool data for trace.json.gz to /tmp/tmpkwu32h8j/train/plugins/profile/2021_08_13_11_14_52/kokoro-gcp-ubuntu-prod-1682665100.trace.json.gz
2021-08-13 11:14:52.628674: I tensorflow/core/profiler/rpc/client/save_profile.cc:136] Creating directory: /tmp/tmpkwu32h8j/train/plugins/profile/2021_08_13_11_14_52

2021-08-13 11:14:52.628785: I tensorflow/core/profiler/rpc/client/save_profile.cc:142] Dumped gzipped tool data for memory_profile.json.gz to /tmp/tmpkwu32h8j/train/plugins/profile/2021_08_13_11_14_52/kokoro-gcp-ubuntu-prod-1682665100.memory_profile.json.gz
2021-08-13 11:14:52.629073: I tensorflow/core/profiler/rpc/client/capture_profile.cc:251] Creating directory: /tmp/tmpkwu32h8j/train/plugins/profile/2021_08_13_11_14_52
Dumped tool data for xplane.pb to /tmp/tmpkwu32h8j/train/plugins/profile/2021_08_13_11_14_52/kokoro-gcp-ubuntu-prod-1682665100.xplane.pb
Dumped tool data for overview_page.pb to /tmp/tmpkwu32h8j/train/plugins/profile/2021_08_13_11_14_52/kokoro-gcp-ubuntu-prod-1682665100.overview_page.pb
Dumped tool data for input_pipeline.pb to /tmp/tmpkwu32h8j/train/plugins/profile/2021_08_13_11_14_52/kokoro-gcp-ubuntu-prod-1682665100.input_pipeline.pb
Dumped tool data for tensorflow_stats.pb to /tmp/tmpkwu32h8j/train/plugins/profile/2021_08_13_11_14_52/kokoro-gcp-ubuntu-prod-1682665100.tensorflow_stats.pb
Dumped tool data for kernel_stats.pb to /tmp/tmpkwu32h8j/train/plugins/profile/2021_08_13_11_14_52/kokoro-gcp-ubuntu-prod-1682665100.kernel_stats.pb
352/352 [==============================] - 9s 20ms/step - loss: 1.4474 - accuracy: 0.4732 - val_loss: 1.5224 - val_accuracy: 0.4368
Epoch 2/15
352/352 [==============================] - 7s 19ms/step - loss: 1.4763 - accuracy: 0.4601 - val_loss: 1.9179 - val_accuracy: 0.3514
Epoch 3/15
352/352 [==============================] - 7s 19ms/step - loss: 1.4861 - accuracy: 0.4602 - val_loss: 1.5849 - val_accuracy: 0.4100
Epoch 4/15
352/352 [==============================] - 7s 19ms/step - loss: 1.4838 - accuracy: 0.4614 - val_loss: 1.5123 - val_accuracy: 0.4412
Epoch 5/15
352/352 [==============================] - 7s 19ms/step - loss: 1.4669 - accuracy: 0.4696 - val_loss: 1.7005 - val_accuracy: 0.3620
Epoch 6/15
352/352 [==============================] - 7s 19ms/step - loss: 1.4497 - accuracy: 0.4772 - val_loss: 1.4644 - val_accuracy: 0.4576
Epoch 7/15
352/352 [==============================] - 7s 19ms/step - loss: 1.4397 - accuracy: 0.4799 - val_loss: 1.4532 - val_accuracy: 0.4710
Epoch 8/15
352/352 [==============================] - 7s 19ms/step - loss: 1.4307 - accuracy: 0.4844 - val_loss: 2.0308 - val_accuracy: 0.3674
Epoch 9/15
352/352 [==============================] - 7s 19ms/step - loss: 1.4254 - accuracy: 0.4849 - val_loss: 1.6031 - val_accuracy: 0.4180
Epoch 10/15
352/352 [==============================] - 7s 19ms/step - loss: 1.4200 - accuracy: 0.4834 - val_loss: 1.8140 - val_accuracy: 0.3768
Epoch 11/15
352/352 [==============================] - 7s 19ms/step - loss: 1.4132 - accuracy: 0.4892 - val_loss: 1.4289 - val_accuracy: 0.4810
Epoch 12/15
352/352 [==============================] - 7s 19ms/step - loss: 1.4075 - accuracy: 0.4915 - val_loss: 1.4257 - val_accuracy: 0.4734
Epoch 13/15
352/352 [==============================] - 7s 19ms/step - loss: 1.4032 - accuracy: 0.4922 - val_loss: 1.4693 - val_accuracy: 0.4620
Epoch 14/15
352/352 [==============================] - 7s 19ms/step - loss: 1.3992 - accuracy: 0.4950 - val_loss: 1.3901 - val_accuracy: 0.4860
Epoch 15/15
352/352 [==============================] - 7s 19ms/step - loss: 1.3957 - accuracy: 0.4952 - val_loss: 1.4754 - val_accuracy: 0.4620
Dense model test accuracy: 0.43209999799728394
Pruned model test accuracy: 0.4596000015735626

Günlükler, seyrekliğin ilerlemesini katman bazında gösterir.

#docs_infra: no_execute
%tensorboard --logdir={logdir}

Budama ile ince ayardan sonra, test doğruluğu yoğun modele kıyasla mütevazi bir gelişme (%43 ila %44) göstermektedir. Gecikmesinin cihaz ile ilgili karşılaştıralım TFLite kriter .

Model dönüştürme ve kıyaslama

TFLite içine budanmış modeli dönüştürmek için, biz yerine gerek PruneLowMagnitude aracılığıyla orijinal katmanlarla sarmalayıcılarını strip_pruning fonksiyonu. (Budanmış modelin ağırlıkları Aynı zamanda, model_for_pruning ) sıfır, çoğunlukla, biz bir optimizasyon geçerli olabilir tf.lite.Optimize.EXPERIMENTAL_SPARSITY verimli TFLite modeli sonucu depolamak için. Bu optimizasyon bayrağı, yoğun model için gerekli değildir.

converter = tf.lite.TFLiteConverter.from_keras_model(dense_model)
dense_tflite_model = converter.convert()

_, dense_tflite_file = tempfile.mkstemp('.tflite')
with open(dense_tflite_file, 'wb') as f:
  f.write(dense_tflite_model)

model_for_export = tfmot.sparsity.keras.strip_pruning(model_for_pruning)

converter = tf.lite.TFLiteConverter.from_keras_model(model_for_export)
converter.optimizations = [tf.lite.Optimize.EXPERIMENTAL_SPARSITY]
pruned_tflite_model = converter.convert()

_, pruned_tflite_file = tempfile.mkstemp('.tflite')
with open(pruned_tflite_file, 'wb') as f:
  f.write(pruned_tflite_model)
INFO:tensorflow:Assets written to: /tmp/tmp0yx5e3fy/assets
INFO:tensorflow:Assets written to: /tmp/tmp0yx5e3fy/assets
2021-08-13 11:16:36.564681: I tensorflow/core/grappler/devices.cc:66] Number of eligible GPUs (core count >= 8, compute capability >= 0.0): 0
2021-08-13 11:16:36.564926: I tensorflow/core/grappler/clusters/single_machine.cc:357] Starting new session
2021-08-13 11:16:36.568512: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:1137] Optimization results for grappler item: graph_to_optimize
  function_optimizer: function_optimizer did nothing. time = 0.008ms.
  function_optimizer: function_optimizer did nothing. time = 0.001ms.
WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
2021-08-13 11:16:36.664551: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:351] Ignored output_format.
2021-08-13 11:16:36.664597: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:354] Ignored drop_control_dependency.
2021-08-13 11:16:36.668981: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:210] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
INFO:tensorflow:Assets written to: /tmp/tmpenn8hns6/assets
INFO:tensorflow:Assets written to: /tmp/tmpenn8hns6/assets
2021-08-13 11:16:39.184787: I tensorflow/core/grappler/devices.cc:66] Number of eligible GPUs (core count >= 8, compute capability >= 0.0): 0
2021-08-13 11:16:39.185019: I tensorflow/core/grappler/clusters/single_machine.cc:357] Starting new session
2021-08-13 11:16:39.188948: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:1137] Optimization results for grappler item: graph_to_optimize
  function_optimizer: function_optimizer did nothing. time = 0.01ms.
  function_optimizer: function_optimizer did nothing. time = 0.002ms.

2021-08-13 11:16:39.294765: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:351] Ignored output_format.
2021-08-13 11:16:39.294816: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:354] Ignored drop_control_dependency.

Talimatları takip TFLite Modeli Kıyaslama Aracı , biz aracı inşa Yoğun ve budanmış TFLite modelleri ve kriter cihazda iki modelde ile birlikte Android cihazı yükleyin.

! adb shell /data/local/tmp/benchmark_model \
    --graph=/data/local/tmp/dense_model.tflite \
    --use_xnnpack=true \
    --num_runs=100 \
    --num_threads=1
/bin/bash: adb: command not found
! adb shell /data/local/tmp/benchmark_model \
    --graph=/data/local/tmp/pruned_model.tflite \
    --use_xnnpack=true \
    --num_runs=100 \
    --num_threads=1
/bin/bash: adb: command not found

Pixel 4 Deneyler budanmış modeli için yoğun model ve 12us için 17us ortalama çıkarım sürede sonuçlandı. Cihazdaki kriterler berrak 5US hatta bu gibi küçük modeller için gecikme% 30 iyileşme göstermiştir. Deneyimlerimize göre, daha büyük modeller dayalı MobileNetV3 veya EfficientNet-lite gösteri benzer performans iyileştirmeleri. Hızlanma, 1x1 evrişimlerin genel modele göreli katkısına göre değişir.

Çözüm

Bu öğreticide, TF MOT API ve XNNPack tarafından sunulan yeni işlevsellik kullanılarak daha hızlı cihaz performansı için seyrek modellerin nasıl oluşturulabileceğini gösteriyoruz. Bu seyrek modeller, kalitelerini korurken veya hatta aşarken, yoğun muadillerinden daha küçük ve daha hızlıdır.

Modellerinizi cihaza dağıtmak için özellikle önemli olabilecek bu yeni özelliği denemenizi öneririz.