TFUG KANSAI 20190928

Transcript

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6. conv2d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in

   a future version. Instructions for updating: Use `tf.keras.layers.Conv2D` instead. flatten (from tensorflow.python.layers.core) is deprecated and will be removed in a future version. Instructions for updating: Use keras.layers.flatten instead. dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version. Instructions for updating: Use keras.layers.dense instead. dropout (from tensorflow.python.layers.core) is deprecated and will be removed in a future version. Instructions for updating: Use keras.layers.dropout instead.  JT
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7. def layers(tag_name, image, training=False, reuse=None): scope = '%s/%s' % (tag_name,

   MODEL_NAME) with tf.variable_scope(scope, reuse=reuse): conv = tf.layers.conv2D(image, BASE_CHANNEL, [3, 3], [1, 1], padding='SAME', activation=tf.nn.relu, use_bias=True, name='conv')  UGMBZFST

8. def layers(tag_name, image, training=False, reuse=None): scope = '%s/%s' % (tag_name,

   MODEL_NAME) with tf.variable_scope(scope, reuse=reuse): conv = tf.keras.layers.Conv2D(BASE_CHANNEL, [3, 3], [1, 1], padding='SAME', activation=tf.nn.relu, use_bias=True, name='conv')(image)  UGLFSBTMBZFST

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12. ౖΓͷ(JU)VC
    *TTVF  IUUQTHJUIVCDPNUFOTPSqPXUFOTPSqPXJTTVFT tf.keras.layers (in TF 1.13.1): variable_scope does not

    work

13. def layers(tag_name, image, training=False, reuse=None): scope = '%s/%s' % (tag_name,

    MODEL_NAME) with tf.variable_scope(scope, reuse=reuse): conv = tf.keras.layers.Conv2D(BASE_CHANNEL, [3, 3], [1, 1], padding='SAME', activation=tf.nn.relu, use_bias=True, name='conv')(image)  UGWBSJBCMF@TDPQF

14. def _build_train_graph(train_path_list): image_batch, label_batch = _load_dataset(train_path_list, is_train=True) logits = model.layers('megane',

    image_batch, training=True) train_op = _optimize(logits, label_batch) return train_op def _build_eval_graph(eval_path_list): image_batch, label_batch = _load_dataset(eval_path_list, is_train=False) logits = model.layers('megane', image_batch, reuse=True) accuracy = _calc_accuracy(logits, label_batch) return accuracy  Ϟσϧͷ࠶ར༻

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16. LABEL_BYTE = 1 IMAGE_BYTES = 32 * 32 * 3

    RECORD_BYTES = LABEL_BYTE + IMAGE_BYTES def _load_dataset(data_path_list): def _process_record(record): value = tf.io.decode_raw(record, tf.uint8) label = value[0] image = value[1:] image = tf.reshape(image, (3, 32, 32)) image = tf.transpose(image, (1, 2, 0)) image = tf.cast(image, tf.float32) image = image / 255 return image, label dataset = tf.data.FixedLengthRecordDataset( data_path_list, RECORD_BYTES) return dataset.map(_process_record)  %BUBTFU
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17. class MyModel(Model): def __init__(self): super(MyModel, self).__init__() self.conv1 = Conv2D(32, 3,

    activation='relu') self.flatten = Flatten() self.d1 = Dense(128, activation='relu') self.d2 = Dense(10, activation='softmax') def call(self, x): x = self.conv1(x) x = self.flatten(x) x = self.d1(x) return self.d2(x) # Create an instance of the model model = MyModel()  Ϟσϧͷఆٛ

18. loss_object = tf.keras.losses.SparseCategoricalCrossentropy() optimizer = tf.keras.optimizers.Adam()  ଛࣦؔ਺ɾ࠷దԽΞϧΰϦζϜ

19. train_loss = tf.keras.metrics.Mean(name='train_loss') train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy') @tf.function def train_step(images, labels):

    with tf.GradientTape() as tape: predictions = model(images) loss = loss_object(labels, predictions) gradients = tape.gradient(loss, model.trainable_variables) optimizer.apply_gradients(zip(gradients, model.trainable_variables)) train_loss(loss) train_accuracy(labels, predictions)  ֶशͱධՁ

20. test_loss = tf.keras.metrics.Mean(name='test_loss') test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy') @tf.function def test_step(images, labels):

    predictions = model(images) t_loss = loss_object(labels, predictions) test_loss(t_loss) test_accuracy(labels, predictions)  ֶशͱධՁ

21. EPOCHS = 5 SUMMARY_DIR = './summary' TRAIN_BATCH_SIZE = 32 TEST_BATCH_SIZE

    = 32 import time train_dataset = _load_dataset(train_path_list).batch(TRAIN_BATCH_SIZE) test_dataset = _load_dataset(test_path_list).batch(TEST_BATCH_SIZE)  ֶशͱධՁ

22. for epoch in range(EPOCHS): for images, labels in train_dataset: train_step(images,

    labels) for test_images, test_labels in test_dataset: test_step(test_images, test_labels) template = 'Epoch {}, Train Loss: {}, Train Accuracy: {}, Test Loss: {}, Test Accuracy: {}' print(template.format(epoch+1, train_loss.result(), train_accuracy.result()*100, test_loss.result(), test_accuracy.result()*100)) # Reset the metrics for the next epoch train_loss.reset_states() train_accuracy.reset_states() test_loss.reset_states() test_accuracy.reset_states() 

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25. g = tf.get_default_graph() tf.contrib.quantize.create_training_graph(input_graph=g, quant_delay=100000)  2VBOUJ[BUJPOBXBSF
    5SBJOJOH •ϑΝΠϯνϡʔχϯάͰ࢖͏ •quant_delayΛ஫ҙਂ͘ઃఆ͢Δ •άϥϑͷॻ͖׵͑Λߦ͏ͨΊɺ


    ύϥϝʔλʔϑΝΠϧͷޓ׵ੑ͸ࣦΘΕΔ

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 ύϥϝʔλʔϑΝΠϧͷޓ׵ੑ͸ࣦΘΕΔ Quantization-aware ༗Γ
 quant-delay MAX_VALUE

    Quantization-aware ༗Γ
 quant-delay 0 Quantization-aware ແ͠

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28. quantization-aware training Λαϙʔτ͍ͯͨ͠ܭࢉάϥϑͷॻ͖׵͑ؔ਺͸ɺTensorFlow 2.0ʹΑΔϞσϧΛ αϙʔτ͠·ͤΜɻ ·ͨɺTensorFlow Lite ͷ quantization API

    ͸ɺKeras API Λ௨ͯ͡ quantization-aware training Λαϙʔτ͢Δํ޲Ͱ࡞Γ௚͠ͱ߹ཧԽΛקΊ͍ͯΔ࠷தͰ͢ɻ ৽͍͠ quantization API ͕ϩʔϯν ͞ΕΔ·Ͱ͸ɺ͜ΕΒͷଐੑ͸ 2.0 API ͔Β࡟আ͞Ε·͢ɻ ॻ͖׵͑ؔ਺ʹΑͬͯϞσϧΛม׵͍ͨ͠৔߹ ͸ tf.compat.v1.TFLiteConverter Λ࢖͍ͬͯͩ͘͞ɻ  IUUQTXXXUFOTPSqPXPSHMJUFDPOWFSUQZUIPO@BQJRVBOUJ[BUJPOBXBSF@USBJOJOH

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32. ֓ཁ  ػցֶशج൫ ετϨʔδ
 αʔόʔ σʔλऩूݩ αʔϏε Android
 ΫϥΠΞϯτ Web

    ΠϯλʔϑΣʔε σʔληοτ؅ཧαʔόʔ σʔλϕʔε σʔλͷऩू MeganeCo Playground ֶशσʔλͷੜ੒ ֶशࡁϞσϧ
 ʹΑΔਪ࿦ Quad GPUs Radeon RX Vega 64 x 2 6TB HDD 256GB SSD ֶश༻σʔλʢTFRecordʣ ֶशࡁϞσϧ

33. ػցֶशج൫  Quad GPUs Radeon RX Vega 64 x 2

    ͘͞ΒͷVPSʢ2Gʣ Master Kubernetes Argo workflow

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36. 

37.  Label input_size Positive sample Negative sample Train step (100

    step) Sensitivity Specificity IoU megane 256x256 19,601 46,950 3,920 0.9242 0.9578 0.8726 favorite 256x256 16,921 29,960 1,050 0.8545 0.7926 0.7377 illust 256x256 24,421 38,587 1,825 0.9601 0.9707 0.9434 photo 256x256 16,296 30,606 1,725 0.9651 0.9339 0.9141 comic 256x256 18,646 4,067 900 0.9950 0.9727 0.9068 face 256x256 19,573 25,033 2,000 0.9629 0.9168 0.9413 nsfw 256x256 18,002 41,258 3,730 0.8399 0.8748 0.7179 nsfw(mobile) 128x128 18,002 41,258 2,230 0.8313 0.8139 0.6637 kemono 256x256 5,863 21,174 1,225 0.8460 0.8383 0.5908 sailor_uniform 256x256 3,060 10,242 1,980 0.8501 0.8954 0.6904 blazer_uniform 256x256 958 8,681 550 0.7126 0.8555 0.3229 cat 256x256 5,167 21,417 725 0.8992 0.9325 0.7472 food 256x256 5,873 20,165 995 0.9198 0.9487 0.8173 food(mobile) 128x128 5,873 20,165 1,570 0.9355 0.9366 0.8099

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42. def distort(image, model_input_size, channels, use_random_crop, use_random_colorized): with tf.name_scope('distort'): if use_random_crop:

    with tf.name_scope('random_crop'): image_size_ratio = tf.random.uniform([], 100, 120, tf.int32) image_size = tf.cast(model_input_size * image_size_ratio / 100, tf.int32) image = tf.image.resize(image, (image_size, image_size)) image = tf.image.random_crop( image, (model_input_size, model_input_size, channels) ) else: image = tf.image.resize(image, (model_input_size, model_input_size))  %BUB
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43. python ./create_dataset.py \ --base_dir /dataset/source/ \ --output_tfrecords_dir $TFRECORDS_DIR \ --output_catalogs_dir

    $CATALOGS_DIR \ --image_size 256  σʔληοτੜ੒ 256x256 TFRecord labels 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... masks 1, 0, 1, 0, 0, 1, 0, ...

44. σʔληοτੜ੒  Resampling

45. python ./create_dataset.py \ --base_dir /dataset/source/ \ --output_tfrecords_dir $TFRECORDS_DIR \ --output_catalogs_dir

    $CATALOGS_DIR \ --image_size 512  σʔληοτੜ੒ 512x512 TFRecord labels 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... masks 1, 0, 1, 0, 0, 1, 0, ...

46. def distort(image, request_image_size, channels, use_random_crop, use_random_colorized): with tf.name_scope('distort'): if use_random_crop:

    with tf.name_scope('random_crop'): image_size_ratio = tf.random.uniform([], 100, 120, tf.int32) image_size = tf.cast(request_image_size * image_size_ratio / 100, tf.int32) image = tf.image.resize(image, (image_size, image_size)) image = tf.image.random_crop( image, (request_image_size, request_image_size, channels) ) else: image = tf.image.resize(image, (request_image_size, request_image_size))  %BUB
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