TP Module 9 : Optimisation des modèles

But de l'optimisation des modèles :

• Réduire la taille du modèle.
• Accélérer le temps d'inférence.
• Réduire la consommation énergétique du modèle.

Quelles sont les différentes façon d'optimiser une modèle ?

• Réduire sa précision numérique.
• Les paramètres et fonctions d'activations d'un modèle sont le plus souvent représentés en float32. Quantification
• Toutes les opérations du graphe d'un modèle sont elles nécéssaires durant l'inférence ? Fusion des couches
• Tous les paramètres contribuent ils à la performance du modèle ? Pruning
• Améliorer les allers-retours entre GPU et CPU.

• Ref
• https://colab.research.google.com/github/sayakpaul/Adventures-in-TensorFlow-Lite/blob/master/A_tale_of_quantization.ipynb#scrollTo=HHZ-SsZU5VAW
• https://colab.research.google.com/github/sayakpaul/Adventures-in-TensorFlow-Lite/blob/master/Model_Pruning_in_Deep_Learning_with_tfmot.ipynb#scrollTo=cEDnyetVLTcM
• https://colab.research.google.com/github/sayakpaul/Adventures-in-TensorFlow-Lite/blob/master/Custom_Image_Classification_EdgeTPU.ipynb#scrollTo=57GUpImAP7KC

• TF Lite

• https://www.tensorflow.org/lite/performance/post_training_quantization

• https://www.tensorflow.org/lite/performance/post_training_quant

• https://www.tensorflow.org/lite/performance/post_training_quantization#dynamic_range_quantization

• https://www.tensorflow.org/lite/performance/best_practices#profile_your_application_with_platform_specific_tools

• https://www.tensorflow.org/lite/performance/post_training_integer_quant

• Model Optimization/Pruning

• https://www.tensorflow.org/model_optimization/guide/pruning/pruning_with_keras#overview

• https://www.tensorflow.org/model_optimization/api_docs/python/tfmot

• https://www.tensorflow.org/model_optimization/api_docs/python/tfmot/sparsity/keras/ConstantSparsity

-https://www.tensorflow.org/model_optimization/api_docs/python/tfmot/sparsity/keras/PruningSchedule

• https://www.tensorflow.org/model_optimization/guide/quantization/training_comprehensive_guide

-https://www.tensorflow.org/model_optimization/guide/pruning/comprehensive_guide#prune_whole_model_sequential_and_functional

• https://www.tensorflow.org/model_optimization/guide/quantization/training

• https://tensorflow.google.cn/model_optimization/guide/pruning/pruning_with_keras

• Save/Load

• https://www.tensorflow.org/tutorials/keras/save_and_load

Import libs

import tensorflow as tf
from tensorflow import keras

print(f'tf : {tf.__version__}')
print(f'keras : {keras.__version__}')

import pandas as pd
import numpy as np
import random
import os
import datetime

# freeze de l'aléatoire, pour avoir des expériences reproductibles.
RANDOM_SEED = 42

os.environ['PYTHONHASHSEED'] = str(RANDOM_SEED)
random.seed(RANDOM_SEED)
np.random.seed(RANDOM_SEED)
os.environ['TF_DETERMINISTIC_OPS'] = '1'
tf.random.set_seed(RANDOM_SEED)

tf : 2.1.0
keras : 2.2.4-tf

from tensorflow.keras.applications import MobileNetV2
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Activation
from tensorflow.keras.layers import GlobalAveragePooling2D
from tensorflow.keras.layers import Dropout
from tensorflow.keras.models import Model

from sklearn.model_selection import train_test_split

from imutils import paths
import matplotlib.pyplot as plt
import shutil
import time
import cv2

!nvidia-smi

Tue Sep  1 09:25:15 2020       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 450.66       Driver Version: 418.67       CUDA Version: 10.1     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |
| N/A   39C    P8     9W /  70W |      0MiB / 15079MiB |      0%      Default |
|                               |                      |                 ERR! |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+

comp = pd.DataFrame()
comp['model'] = []
comp['précision'] = []

Import du dataset

# Gather Flowers-17 dataset
data_root = tf.keras.utils.get_file(
  "/content/flower_photos", "https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz",
   untar=True)

# Untar the dataset
!tar -xvf flower_photos.tar.gz

flower_photos/
flower_photos/roses/
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flower_photos/roses/1446090416_f0cad5fde4.jpg
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flower_photos/dandelion/6888894675_524a6accab_n.jpg
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flower_photos/dandelion/5596093561_09b0301136_n.jpg
flower_photos/dandelion/1386449001_5d6da6bde6.jpg
flower_photos/dandelion/8740218495_23858355d8_n.jpg
flower_photos/dandelion/2478018280_1be353ca8c_m.jpg
flower_photos/dandelion/7141013005_d2f168c373.jpg
flower_photos/dandelion/14211880544_5d1f9d5aa8_n.jpg
flower_photos/dandelion/455728598_c5f3e7fc71_m.jpg
flower_photos/dandelion/6900157914_c3387c11d8.jpg

# All the paths of the flowers
ALL_IMG_PATHS = list(paths.list_images("flower_photos"))
ALL_IMG_PATHS[:5]

['flower_photos/tulips/13510057763_01b832d919.jpg',
 'flower_photos/tulips/486896118_bcc7b8e1d6.jpg',
 'flower_photos/tulips/7247182064_f8d6759446_n.jpg',
 'flower_photos/tulips/14093884601_c87b5cd663_n.jpg',
 'flower_photos/tulips/17224410762_402455ed8f.jpg']

Visualisation

# Visualize images
plt.figure(figsize=(15,15))
for i in range(25):
    image_path = np.random.choice(ALL_IMG_PATHS)
    image = plt.imread(image_path)
    image = cv2.resize(image, (128, 128))
    # you might want to verify the labels before 
    # you put this to use
    label = image_path.split("/")[1]
    plt.subplot(5,5,i+1)
    plt.xticks([])
    plt.yticks([])
    plt.grid(True)
    plt.imshow(image)
    plt.xlabel(label)
plt.show()

Train, val, test datasets

flower_train, flower_val = train_test_split(ALL_IMG_PATHS, test_size=0.20, random_state=RANDOM_SEED)
flower_test, flower_val = train_test_split(flower_val, test_size=0.5, random_state=RANDOM_SEED)
print(f'images in train : {len(flower_train)}, images in val : {len(flower_val)}, images in test {len(flower_test)}')

images in train : 2936, images in val : 367, images in test 367

dic = {b'daisy': 0, b'dandelion': 1, b'roses': 2, b'sunflowers': 3, b'tulips': 4}
label_train = [dic[tf.strings.split(filename, '/')[1].numpy()] for filename in flower_train]
label_val = [dic[tf.strings.split(filename, '/')[1].numpy()] for filename in flower_val]
label_test = [dic[tf.strings.split(filename, '/')[1].numpy()] for filename in flower_test]
print(f'labels in train : {len(label_train)}, labels in val : {len(label_val)}, labels in test {len(label_test)}')

labels in train : 2936, labels in val : 367, labels in test 367

NUM_CLASSES = 5
AUTOTUNE = tf.data.experimental.AUTOTUNE

def parse_image(filename,label):
  # convert the label to one-hot encoding
  label = tf.one_hot(label, NUM_CLASSES)

  #decode image
  image = tf.io.read_file(filename)
  #Don't use tf.image.decode_image, or the output shape will be undefined
  image = tf.image.decode_jpeg(image)
  #This will convert to float values in [0, 1]
  image = tf.image.convert_image_dtype(image, tf.float32)
  image = tf.image.resize(image, [224, 224])
  return image, label

def train_preprocess(image, label):
  image = tf.image.random_flip_left_right(image)
  image = tf.image.random_flip_up_down(image)

  return image, label

def create_train_dataset(features, labels, batch=32, repet=1, prefetch=1):
  dataset = tf.data.Dataset.from_tensor_slices((features,labels))
  dataset = dataset.shuffle(len(features), seed=RANDOM_SEED)
  dataset = dataset.repeat(repet)
  dataset = dataset.map(parse_image, num_parallel_calls=AUTOTUNE)
  dataset = dataset.map(train_preprocess, num_parallel_calls=AUTOTUNE)
  dataset = dataset.batch(batch)
  dataset = dataset.prefetch(prefetch)
  return dataset

def create_val_dataset(features, labels, batch=32, repet=1, prefetch=1):
  dataset = tf.data.Dataset.from_tensor_slices((features,labels))
  dataset = dataset.shuffle(len(features), seed=RANDOM_SEED)
  dataset = dataset.repeat(repet)
  dataset = dataset.map(parse_image, num_parallel_calls=AUTOTUNE)
  dataset = dataset.batch(batch)
  dataset = dataset.prefetch(prefetch)
  return dataset

ds_train = create_train_dataset(flower_train, label_train)
ds_val = create_val_dataset(flower_val, label_val)
ds_test = create_val_dataset(flower_test, label_test)

Construction du modèle & entraînement du modèle

Construction

# Load the MobileNetV2 model but exclude the classification layers
conv_base = MobileNetV2(weights="imagenet", 
                        include_top=False,
                        input_shape=(224, 224, 3))

def get_training_model():
    # We are fine-tuning the extractor model
    conv_base.trainable = True
    # Construct the head of the model that will be placed on top of the
    # the base model
    class_head = conv_base.output
    class_head = GlobalAveragePooling2D()(class_head)
    class_head = Dense(512, activation="relu")(class_head)
    class_head = Dropout(0.5)(class_head)
    class_head = Dense(5, activation="softmax")(class_head)

    # Create the new model
    classifier = Model(inputs=conv_base.input, outputs=class_head)

    # Compile and return the model
    classifier.compile(loss="categorical_crossentropy", 
                          optimizer="adam",
                          metrics=["accuracy"])

    return classifier

Entraînement

# LR schedule configuration
start_lr = 0.00001
min_lr = 0.00001
max_lr = 0.00005
rampup_epochs = 5
sustain_epochs = 0
exp_decay = .8

# LR schedule
def lrfn(epoch):
    def lr(epoch, start_lr, min_lr, max_lr, rampup_epochs, sustain_epochs, exp_decay):
        if epoch < rampup_epochs:
            lr = (max_lr - start_lr)/rampup_epochs * epoch + start_lr
        elif epoch < rampup_epochs + sustain_epochs:
            lr = max_lr
        else:
            lr = (max_lr - min_lr) * exp_decay**(epoch-rampup_epochs-sustain_epochs) + min_lr
        return lr
    return lr(epoch, start_lr, min_lr, max_lr, rampup_epochs, sustain_epochs, exp_decay)

lr_callback = tf.keras.callbacks.LearningRateScheduler(lambda epoch: lrfn(epoch), verbose=True)

# How does the LR schedule looks like?
rng = [i for i in range(50)]
y = [lrfn(x) for x in rng]
plt.plot(rng, [lrfn(x) for x in rng])
print(y[0], y[-1])

1e-05 1.0002177807148294e-05

# Train the model
flower_model = get_training_model()
start = time.time()
history = flower_model.fit(ds_train,
                           validation_data=ds_val,
                           epochs=10,
                           callbacks=[lr_callback])
print("Total training time: ",time.time()-start)

Train for 92 steps, validate for 12 steps

Epoch 00001: LearningRateScheduler reducing learning rate to 1e-05.
Epoch 1/10
92/92 [==============================] - 27s 291ms/step - loss: 1.6304 - accuracy: 0.3348 - val_loss: 1.2459 - val_accuracy: 0.5123

Epoch 00002: LearningRateScheduler reducing learning rate to 1.8000000000000004e-05.
Epoch 2/10
92/92 [==============================] - 19s 208ms/step - loss: 0.9983 - accuracy: 0.6117 - val_loss: 0.8482 - val_accuracy: 0.6621

Epoch 00003: LearningRateScheduler reducing learning rate to 2.6000000000000002e-05.
Epoch 3/10
92/92 [==============================] - 19s 210ms/step - loss: 0.6268 - accuracy: 0.7847 - val_loss: 0.5672 - val_accuracy: 0.7820

Epoch 00004: LearningRateScheduler reducing learning rate to 3.4000000000000007e-05.
Epoch 4/10
92/92 [==============================] - 20s 212ms/step - loss: 0.4487 - accuracy: 0.8341 - val_loss: 0.4797 - val_accuracy: 0.8202

Epoch 00005: LearningRateScheduler reducing learning rate to 4.2000000000000004e-05.
Epoch 5/10
92/92 [==============================] - 20s 215ms/step - loss: 0.3306 - accuracy: 0.8845 - val_loss: 0.4430 - val_accuracy: 0.8392

Epoch 00006: LearningRateScheduler reducing learning rate to 5e-05.
Epoch 6/10
92/92 [==============================] - 20s 218ms/step - loss: 0.2585 - accuracy: 0.9091 - val_loss: 0.4133 - val_accuracy: 0.8365

Epoch 00007: LearningRateScheduler reducing learning rate to 4.2000000000000004e-05.
Epoch 7/10
92/92 [==============================] - 20s 220ms/step - loss: 0.2015 - accuracy: 0.9275 - val_loss: 0.3557 - val_accuracy: 0.8638

Epoch 00008: LearningRateScheduler reducing learning rate to 3.5600000000000005e-05.
Epoch 8/10
92/92 [==============================] - 20s 217ms/step - loss: 0.1766 - accuracy: 0.9356 - val_loss: 0.3505 - val_accuracy: 0.8610

Epoch 00009: LearningRateScheduler reducing learning rate to 3.0480000000000006e-05.
Epoch 9/10
92/92 [==============================] - 20s 216ms/step - loss: 0.1449 - accuracy: 0.9499 - val_loss: 0.3420 - val_accuracy: 0.8719

Epoch 00010: LearningRateScheduler reducing learning rate to 2.6384000000000004e-05.
Epoch 10/10
92/92 [==============================] - 20s 217ms/step - loss: 0.1382 - accuracy: 0.9547 - val_loss: 0.2870 - val_accuracy: 0.8828
Total training time:  204.63329792022705

Evaluation

pd.DataFrame(history.history).plot(figsize=(12,8))
plt.grid(True)
plt.gca().set_ylim(0,1)
plt.show()

_, accuracy = flower_model.evaluate(ds_test)

comp = comp.append({'model': 'unoptim_model', 'précision' : accuracy}, ignore_index=True)
print(comp)

12/12 [==============================] - 1s 69ms/step - loss: 0.3031 - accuracy: 0.8965
           model  précision
0  unoptim_model   0.896458

Sauvegarde

Pour convertir un modèle, que ce soit avec TF Lite ou avec TensorRT, on doit le sauvegarder sous la format SavedModel qui est le format standard de TensorFlow. La méthode classique où l'on utilise le format .h5 est elle propre à tf.keras.

# Unoptimized (SavedModel format)
flower_model.save("base_model/flower_model_no_op")

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1786: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.
INFO:tensorflow:Assets written to: base_model/flower_model_no_op/assets

# Check size
!du --all -h base_model/flower_model_no_op

4.4M  base_model/flower_model_no_op/saved_model.pb
34M base_model/flower_model_no_op/variables/variables.data-00000-of-00001
140K    base_model/flower_model_no_op/variables/variables.data-00000-of-00002
44K base_model/flower_model_no_op/variables/variables.index
34M base_model/flower_model_no_op/variables/variables.data-00001-of-00002
67M base_model/flower_model_no_op/variables
4.0K    base_model/flower_model_no_op/assets
71M base_model/flower_model_no_op

Charger un modèle se fait de la même façon, que l'on utilise le format SavedModel ou .h5.

flower_model = tf.keras.models.load_model('base_model/flower_model_no_op')

# Check its architecture
flower_model.summary()

Model: "model"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_1 (InputLayer)            [(None, 224, 224, 3) 0                                            
__________________________________________________________________________________________________
Conv1_pad (ZeroPadding2D)       (None, 225, 225, 3)  0           input_1[0][0]                    
__________________________________________________________________________________________________
Conv1 (Conv2D)                  (None, 112, 112, 32) 864         Conv1_pad[0][0]                  
__________________________________________________________________________________________________
bn_Conv1 (BatchNormalization)   (None, 112, 112, 32) 128         Conv1[0][0]                      
__________________________________________________________________________________________________
Conv1_relu (ReLU)               (None, 112, 112, 32) 0           bn_Conv1[0][0]                   
__________________________________________________________________________________________________
expanded_conv_depthwise (Depthw (None, 112, 112, 32) 288         Conv1_relu[0][0]                 
__________________________________________________________________________________________________
expanded_conv_depthwise_BN (Bat (None, 112, 112, 32) 128         expanded_conv_depthwise[0][0]    
__________________________________________________________________________________________________
expanded_conv_depthwise_relu (R (None, 112, 112, 32) 0           expanded_conv_depthwise_BN[0][0] 
__________________________________________________________________________________________________
expanded_conv_project (Conv2D)  (None, 112, 112, 16) 512         expanded_conv_depthwise_relu[0][0
__________________________________________________________________________________________________
expanded_conv_project_BN (Batch (None, 112, 112, 16) 64          expanded_conv_project[0][0]      
__________________________________________________________________________________________________
block_1_expand (Conv2D)         (None, 112, 112, 96) 1536        expanded_conv_project_BN[0][0]   
__________________________________________________________________________________________________
block_1_expand_BN (BatchNormali (None, 112, 112, 96) 384         block_1_expand[0][0]             
__________________________________________________________________________________________________
block_1_expand_relu (ReLU)      (None, 112, 112, 96) 0           block_1_expand_BN[0][0]          
__________________________________________________________________________________________________
block_1_pad (ZeroPadding2D)     (None, 113, 113, 96) 0           block_1_expand_relu[0][0]        
__________________________________________________________________________________________________
block_1_depthwise (DepthwiseCon (None, 56, 56, 96)   864         block_1_pad[0][0]                
__________________________________________________________________________________________________
block_1_depthwise_BN (BatchNorm (None, 56, 56, 96)   384         block_1_depthwise[0][0]          
__________________________________________________________________________________________________
block_1_depthwise_relu (ReLU)   (None, 56, 56, 96)   0           block_1_depthwise_BN[0][0]       
__________________________________________________________________________________________________
block_1_project (Conv2D)        (None, 56, 56, 24)   2304        block_1_depthwise_relu[0][0]     
__________________________________________________________________________________________________
block_1_project_BN (BatchNormal (None, 56, 56, 24)   96          block_1_project[0][0]            
__________________________________________________________________________________________________
block_2_expand (Conv2D)         (None, 56, 56, 144)  3456        block_1_project_BN[0][0]         
__________________________________________________________________________________________________
block_2_expand_BN (BatchNormali (None, 56, 56, 144)  576         block_2_expand[0][0]             
__________________________________________________________________________________________________
block_2_expand_relu (ReLU)      (None, 56, 56, 144)  0           block_2_expand_BN[0][0]          
__________________________________________________________________________________________________
block_2_depthwise (DepthwiseCon (None, 56, 56, 144)  1296        block_2_expand_relu[0][0]        
__________________________________________________________________________________________________
block_2_depthwise_BN (BatchNorm (None, 56, 56, 144)  576         block_2_depthwise[0][0]          
__________________________________________________________________________________________________
block_2_depthwise_relu (ReLU)   (None, 56, 56, 144)  0           block_2_depthwise_BN[0][0]       
__________________________________________________________________________________________________
block_2_project (Conv2D)        (None, 56, 56, 24)   3456        block_2_depthwise_relu[0][0]     
__________________________________________________________________________________________________
block_2_project_BN (BatchNormal (None, 56, 56, 24)   96          block_2_project[0][0]            
__________________________________________________________________________________________________
block_2_add (Add)               (None, 56, 56, 24)   0           block_1_project_BN[0][0]         
                                                                 block_2_project_BN[0][0]         
__________________________________________________________________________________________________
block_3_expand (Conv2D)         (None, 56, 56, 144)  3456        block_2_add[0][0]                
__________________________________________________________________________________________________
block_3_expand_BN (BatchNormali (None, 56, 56, 144)  576         block_3_expand[0][0]             
__________________________________________________________________________________________________
block_3_expand_relu (ReLU)      (None, 56, 56, 144)  0           block_3_expand_BN[0][0]          
__________________________________________________________________________________________________
block_3_pad (ZeroPadding2D)     (None, 57, 57, 144)  0           block_3_expand_relu[0][0]        
__________________________________________________________________________________________________
block_3_depthwise (DepthwiseCon (None, 28, 28, 144)  1296        block_3_pad[0][0]                
__________________________________________________________________________________________________
block_3_depthwise_BN (BatchNorm (None, 28, 28, 144)  576         block_3_depthwise[0][0]          
__________________________________________________________________________________________________
block_3_depthwise_relu (ReLU)   (None, 28, 28, 144)  0           block_3_depthwise_BN[0][0]       
__________________________________________________________________________________________________
block_3_project (Conv2D)        (None, 28, 28, 32)   4608        block_3_depthwise_relu[0][0]     
__________________________________________________________________________________________________
block_3_project_BN (BatchNormal (None, 28, 28, 32)   128         block_3_project[0][0]            
__________________________________________________________________________________________________
block_4_expand (Conv2D)         (None, 28, 28, 192)  6144        block_3_project_BN[0][0]         
__________________________________________________________________________________________________
block_4_expand_BN (BatchNormali (None, 28, 28, 192)  768         block_4_expand[0][0]             
__________________________________________________________________________________________________
block_4_expand_relu (ReLU)      (None, 28, 28, 192)  0           block_4_expand_BN[0][0]          
__________________________________________________________________________________________________
block_4_depthwise (DepthwiseCon (None, 28, 28, 192)  1728        block_4_expand_relu[0][0]        
__________________________________________________________________________________________________
block_4_depthwise_BN (BatchNorm (None, 28, 28, 192)  768         block_4_depthwise[0][0]          
__________________________________________________________________________________________________
block_4_depthwise_relu (ReLU)   (None, 28, 28, 192)  0           block_4_depthwise_BN[0][0]       
__________________________________________________________________________________________________
block_4_project (Conv2D)        (None, 28, 28, 32)   6144        block_4_depthwise_relu[0][0]     
__________________________________________________________________________________________________
block_4_project_BN (BatchNormal (None, 28, 28, 32)   128         block_4_project[0][0]            
__________________________________________________________________________________________________
block_4_add (Add)               (None, 28, 28, 32)   0           block_3_project_BN[0][0]         
                                                                 block_4_project_BN[0][0]         
__________________________________________________________________________________________________
block_5_expand (Conv2D)         (None, 28, 28, 192)  6144        block_4_add[0][0]                
__________________________________________________________________________________________________
block_5_expand_BN (BatchNormali (None, 28, 28, 192)  768         block_5_expand[0][0]             
__________________________________________________________________________________________________
block_5_expand_relu (ReLU)      (None, 28, 28, 192)  0           block_5_expand_BN[0][0]          
__________________________________________________________________________________________________
block_5_depthwise (DepthwiseCon (None, 28, 28, 192)  1728        block_5_expand_relu[0][0]        
__________________________________________________________________________________________________
block_5_depthwise_BN (BatchNorm (None, 28, 28, 192)  768         block_5_depthwise[0][0]          
__________________________________________________________________________________________________
block_5_depthwise_relu (ReLU)   (None, 28, 28, 192)  0           block_5_depthwise_BN[0][0]       
__________________________________________________________________________________________________
block_5_project (Conv2D)        (None, 28, 28, 32)   6144        block_5_depthwise_relu[0][0]     
__________________________________________________________________________________________________
block_5_project_BN (BatchNormal (None, 28, 28, 32)   128         block_5_project[0][0]            
__________________________________________________________________________________________________
block_5_add (Add)               (None, 28, 28, 32)   0           block_4_add[0][0]                
                                                                 block_5_project_BN[0][0]         
__________________________________________________________________________________________________
block_6_expand (Conv2D)         (None, 28, 28, 192)  6144        block_5_add[0][0]                
__________________________________________________________________________________________________
block_6_expand_BN (BatchNormali (None, 28, 28, 192)  768         block_6_expand[0][0]             
__________________________________________________________________________________________________
block_6_expand_relu (ReLU)      (None, 28, 28, 192)  0           block_6_expand_BN[0][0]          
__________________________________________________________________________________________________
block_6_pad (ZeroPadding2D)     (None, 29, 29, 192)  0           block_6_expand_relu[0][0]        
__________________________________________________________________________________________________
block_6_depthwise (DepthwiseCon (None, 14, 14, 192)  1728        block_6_pad[0][0]                
__________________________________________________________________________________________________
block_6_depthwise_BN (BatchNorm (None, 14, 14, 192)  768         block_6_depthwise[0][0]          
__________________________________________________________________________________________________
block_6_depthwise_relu (ReLU)   (None, 14, 14, 192)  0           block_6_depthwise_BN[0][0]       
__________________________________________________________________________________________________
block_6_project (Conv2D)        (None, 14, 14, 64)   12288       block_6_depthwise_relu[0][0]     
__________________________________________________________________________________________________
block_6_project_BN (BatchNormal (None, 14, 14, 64)   256         block_6_project[0][0]            
__________________________________________________________________________________________________
block_7_expand (Conv2D)         (None, 14, 14, 384)  24576       block_6_project_BN[0][0]         
__________________________________________________________________________________________________
block_7_expand_BN (BatchNormali (None, 14, 14, 384)  1536        block_7_expand[0][0]             
__________________________________________________________________________________________________
block_7_expand_relu (ReLU)      (None, 14, 14, 384)  0           block_7_expand_BN[0][0]          
__________________________________________________________________________________________________
block_7_depthwise (DepthwiseCon (None, 14, 14, 384)  3456        block_7_expand_relu[0][0]        
__________________________________________________________________________________________________
block_7_depthwise_BN (BatchNorm (None, 14, 14, 384)  1536        block_7_depthwise[0][0]          
__________________________________________________________________________________________________
block_7_depthwise_relu (ReLU)   (None, 14, 14, 384)  0           block_7_depthwise_BN[0][0]       
__________________________________________________________________________________________________
block_7_project (Conv2D)        (None, 14, 14, 64)   24576       block_7_depthwise_relu[0][0]     
__________________________________________________________________________________________________
block_7_project_BN (BatchNormal (None, 14, 14, 64)   256         block_7_project[0][0]            
__________________________________________________________________________________________________
block_7_add (Add)               (None, 14, 14, 64)   0           block_6_project_BN[0][0]         
                                                                 block_7_project_BN[0][0]         
__________________________________________________________________________________________________
block_8_expand (Conv2D)         (None, 14, 14, 384)  24576       block_7_add[0][0]                
__________________________________________________________________________________________________
block_8_expand_BN (BatchNormali (None, 14, 14, 384)  1536        block_8_expand[0][0]             
__________________________________________________________________________________________________
block_8_expand_relu (ReLU)      (None, 14, 14, 384)  0           block_8_expand_BN[0][0]          
__________________________________________________________________________________________________
block_8_depthwise (DepthwiseCon (None, 14, 14, 384)  3456        block_8_expand_relu[0][0]        
__________________________________________________________________________________________________
block_8_depthwise_BN (BatchNorm (None, 14, 14, 384)  1536        block_8_depthwise[0][0]          
__________________________________________________________________________________________________
block_8_depthwise_relu (ReLU)   (None, 14, 14, 384)  0           block_8_depthwise_BN[0][0]       
__________________________________________________________________________________________________
block_8_project (Conv2D)        (None, 14, 14, 64)   24576       block_8_depthwise_relu[0][0]     
__________________________________________________________________________________________________
block_8_project_BN (BatchNormal (None, 14, 14, 64)   256         block_8_project[0][0]            
__________________________________________________________________________________________________
block_8_add (Add)               (None, 14, 14, 64)   0           block_7_add[0][0]                
                                                                 block_8_project_BN[0][0]         
__________________________________________________________________________________________________
block_9_expand (Conv2D)         (None, 14, 14, 384)  24576       block_8_add[0][0]                
__________________________________________________________________________________________________
block_9_expand_BN (BatchNormali (None, 14, 14, 384)  1536        block_9_expand[0][0]             
__________________________________________________________________________________________________
block_9_expand_relu (ReLU)      (None, 14, 14, 384)  0           block_9_expand_BN[0][0]          
__________________________________________________________________________________________________
block_9_depthwise (DepthwiseCon (None, 14, 14, 384)  3456        block_9_expand_relu[0][0]        
__________________________________________________________________________________________________
block_9_depthwise_BN (BatchNorm (None, 14, 14, 384)  1536        block_9_depthwise[0][0]          
__________________________________________________________________________________________________
block_9_depthwise_relu (ReLU)   (None, 14, 14, 384)  0           block_9_depthwise_BN[0][0]       
__________________________________________________________________________________________________
block_9_project (Conv2D)        (None, 14, 14, 64)   24576       block_9_depthwise_relu[0][0]     
__________________________________________________________________________________________________
block_9_project_BN (BatchNormal (None, 14, 14, 64)   256         block_9_project[0][0]            
__________________________________________________________________________________________________
block_9_add (Add)               (None, 14, 14, 64)   0           block_8_add[0][0]                
                                                                 block_9_project_BN[0][0]         
__________________________________________________________________________________________________
block_10_expand (Conv2D)        (None, 14, 14, 384)  24576       block_9_add[0][0]                
__________________________________________________________________________________________________
block_10_expand_BN (BatchNormal (None, 14, 14, 384)  1536        block_10_expand[0][0]            
__________________________________________________________________________________________________
block_10_expand_relu (ReLU)     (None, 14, 14, 384)  0           block_10_expand_BN[0][0]         
__________________________________________________________________________________________________
block_10_depthwise (DepthwiseCo (None, 14, 14, 384)  3456        block_10_expand_relu[0][0]       
__________________________________________________________________________________________________
block_10_depthwise_BN (BatchNor (None, 14, 14, 384)  1536        block_10_depthwise[0][0]         
__________________________________________________________________________________________________
block_10_depthwise_relu (ReLU)  (None, 14, 14, 384)  0           block_10_depthwise_BN[0][0]      
__________________________________________________________________________________________________
block_10_project (Conv2D)       (None, 14, 14, 96)   36864       block_10_depthwise_relu[0][0]    
__________________________________________________________________________________________________
block_10_project_BN (BatchNorma (None, 14, 14, 96)   384         block_10_project[0][0]           
__________________________________________________________________________________________________
block_11_expand (Conv2D)        (None, 14, 14, 576)  55296       block_10_project_BN[0][0]        
__________________________________________________________________________________________________
block_11_expand_BN (BatchNormal (None, 14, 14, 576)  2304        block_11_expand[0][0]            
__________________________________________________________________________________________________
block_11_expand_relu (ReLU)     (None, 14, 14, 576)  0           block_11_expand_BN[0][0]         
__________________________________________________________________________________________________
block_11_depthwise (DepthwiseCo (None, 14, 14, 576)  5184        block_11_expand_relu[0][0]       
__________________________________________________________________________________________________
block_11_depthwise_BN (BatchNor (None, 14, 14, 576)  2304        block_11_depthwise[0][0]         
__________________________________________________________________________________________________
block_11_depthwise_relu (ReLU)  (None, 14, 14, 576)  0           block_11_depthwise_BN[0][0]      
__________________________________________________________________________________________________
block_11_project (Conv2D)       (None, 14, 14, 96)   55296       block_11_depthwise_relu[0][0]    
__________________________________________________________________________________________________
block_11_project_BN (BatchNorma (None, 14, 14, 96)   384         block_11_project[0][0]           
__________________________________________________________________________________________________
block_11_add (Add)              (None, 14, 14, 96)   0           block_10_project_BN[0][0]        
                                                                 block_11_project_BN[0][0]        
__________________________________________________________________________________________________
block_12_expand (Conv2D)        (None, 14, 14, 576)  55296       block_11_add[0][0]               
__________________________________________________________________________________________________
block_12_expand_BN (BatchNormal (None, 14, 14, 576)  2304        block_12_expand[0][0]            
__________________________________________________________________________________________________
block_12_expand_relu (ReLU)     (None, 14, 14, 576)  0           block_12_expand_BN[0][0]         
__________________________________________________________________________________________________
block_12_depthwise (DepthwiseCo (None, 14, 14, 576)  5184        block_12_expand_relu[0][0]       
__________________________________________________________________________________________________
block_12_depthwise_BN (BatchNor (None, 14, 14, 576)  2304        block_12_depthwise[0][0]         
__________________________________________________________________________________________________
block_12_depthwise_relu (ReLU)  (None, 14, 14, 576)  0           block_12_depthwise_BN[0][0]      
__________________________________________________________________________________________________
block_12_project (Conv2D)       (None, 14, 14, 96)   55296       block_12_depthwise_relu[0][0]    
__________________________________________________________________________________________________
block_12_project_BN (BatchNorma (None, 14, 14, 96)   384         block_12_project[0][0]           
__________________________________________________________________________________________________
block_12_add (Add)              (None, 14, 14, 96)   0           block_11_add[0][0]               
                                                                 block_12_project_BN[0][0]        
__________________________________________________________________________________________________
block_13_expand (Conv2D)        (None, 14, 14, 576)  55296       block_12_add[0][0]               
__________________________________________________________________________________________________
block_13_expand_BN (BatchNormal (None, 14, 14, 576)  2304        block_13_expand[0][0]            
__________________________________________________________________________________________________
block_13_expand_relu (ReLU)     (None, 14, 14, 576)  0           block_13_expand_BN[0][0]         
__________________________________________________________________________________________________
block_13_pad (ZeroPadding2D)    (None, 15, 15, 576)  0           block_13_expand_relu[0][0]       
__________________________________________________________________________________________________
block_13_depthwise (DepthwiseCo (None, 7, 7, 576)    5184        block_13_pad[0][0]               
__________________________________________________________________________________________________
block_13_depthwise_BN (BatchNor (None, 7, 7, 576)    2304        block_13_depthwise[0][0]         
__________________________________________________________________________________________________
block_13_depthwise_relu (ReLU)  (None, 7, 7, 576)    0           block_13_depthwise_BN[0][0]      
__________________________________________________________________________________________________
block_13_project (Conv2D)       (None, 7, 7, 160)    92160       block_13_depthwise_relu[0][0]    
__________________________________________________________________________________________________
block_13_project_BN (BatchNorma (None, 7, 7, 160)    640         block_13_project[0][0]           
__________________________________________________________________________________________________
block_14_expand (Conv2D)        (None, 7, 7, 960)    153600      block_13_project_BN[0][0]        
__________________________________________________________________________________________________
block_14_expand_BN (BatchNormal (None, 7, 7, 960)    3840        block_14_expand[0][0]            
__________________________________________________________________________________________________
block_14_expand_relu (ReLU)     (None, 7, 7, 960)    0           block_14_expand_BN[0][0]         
__________________________________________________________________________________________________
block_14_depthwise (DepthwiseCo (None, 7, 7, 960)    8640        block_14_expand_relu[0][0]       
__________________________________________________________________________________________________
block_14_depthwise_BN (BatchNor (None, 7, 7, 960)    3840        block_14_depthwise[0][0]         
__________________________________________________________________________________________________
block_14_depthwise_relu (ReLU)  (None, 7, 7, 960)    0           block_14_depthwise_BN[0][0]      
__________________________________________________________________________________________________
block_14_project (Conv2D)       (None, 7, 7, 160)    153600      block_14_depthwise_relu[0][0]    
__________________________________________________________________________________________________
block_14_project_BN (BatchNorma (None, 7, 7, 160)    640         block_14_project[0][0]           
__________________________________________________________________________________________________
block_14_add (Add)              (None, 7, 7, 160)    0           block_13_project_BN[0][0]        
                                                                 block_14_project_BN[0][0]        
__________________________________________________________________________________________________
block_15_expand (Conv2D)        (None, 7, 7, 960)    153600      block_14_add[0][0]               
__________________________________________________________________________________________________
block_15_expand_BN (BatchNormal (None, 7, 7, 960)    3840        block_15_expand[0][0]            
__________________________________________________________________________________________________
block_15_expand_relu (ReLU)     (None, 7, 7, 960)    0           block_15_expand_BN[0][0]         
__________________________________________________________________________________________________
block_15_depthwise (DepthwiseCo (None, 7, 7, 960)    8640        block_15_expand_relu[0][0]       
__________________________________________________________________________________________________
block_15_depthwise_BN (BatchNor (None, 7, 7, 960)    3840        block_15_depthwise[0][0]         
__________________________________________________________________________________________________
block_15_depthwise_relu (ReLU)  (None, 7, 7, 960)    0           block_15_depthwise_BN[0][0]      
__________________________________________________________________________________________________
block_15_project (Conv2D)       (None, 7, 7, 160)    153600      block_15_depthwise_relu[0][0]    
__________________________________________________________________________________________________
block_15_project_BN (BatchNorma (None, 7, 7, 160)    640         block_15_project[0][0]           
__________________________________________________________________________________________________
block_15_add (Add)              (None, 7, 7, 160)    0           block_14_add[0][0]               
                                                                 block_15_project_BN[0][0]        
__________________________________________________________________________________________________
block_16_expand (Conv2D)        (None, 7, 7, 960)    153600      block_15_add[0][0]               
__________________________________________________________________________________________________
block_16_expand_BN (BatchNormal (None, 7, 7, 960)    3840        block_16_expand[0][0]            
__________________________________________________________________________________________________
block_16_expand_relu (ReLU)     (None, 7, 7, 960)    0           block_16_expand_BN[0][0]         
__________________________________________________________________________________________________
block_16_depthwise (DepthwiseCo (None, 7, 7, 960)    8640        block_16_expand_relu[0][0]       
__________________________________________________________________________________________________
block_16_depthwise_BN (BatchNor (None, 7, 7, 960)    3840        block_16_depthwise[0][0]         
__________________________________________________________________________________________________
block_16_depthwise_relu (ReLU)  (None, 7, 7, 960)    0           block_16_depthwise_BN[0][0]      
__________________________________________________________________________________________________
block_16_project (Conv2D)       (None, 7, 7, 320)    307200      block_16_depthwise_relu[0][0]    
__________________________________________________________________________________________________
block_16_project_BN (BatchNorma (None, 7, 7, 320)    1280        block_16_project[0][0]           
__________________________________________________________________________________________________
Conv_1 (Conv2D)                 (None, 7, 7, 1280)   409600      block_16_project_BN[0][0]        
__________________________________________________________________________________________________
Conv_1_bn (BatchNormalization)  (None, 7, 7, 1280)   5120        Conv_1[0][0]                     
__________________________________________________________________________________________________
out_relu (ReLU)                 (None, 7, 7, 1280)   0           Conv_1_bn[0][0]                  
__________________________________________________________________________________________________
global_average_pooling2d (Globa (None, 1280)         0           out_relu[0][0]                   
__________________________________________________________________________________________________
dense (Dense)                   (None, 512)          655872      global_average_pooling2d[0][0]   
__________________________________________________________________________________________________
dropout (Dropout)               (None, 512)          0           dense[0][0]                      
__________________________________________________________________________________________________
dense_1 (Dense)                 (None, 5)            2565        dropout[0][0]                    
==================================================================================================
Total params: 2,916,421
Trainable params: 2,882,309
Non-trainable params: 34,112
__________________________________________________________________________________________________

TensorRT

• https://github.com/tensorflow/tensorrt/blob/master/tftrt/examples/image-classification/Colab-TF-TRT-inference-from-Keras-saved-model.ipynb

• https://github.com/tensorflow/tensorrt/blob/master/tftrt/examples/image-classification/TF-TRT-inference-from-saved-model.ipynb

• https://github.com/tensorflow/tensorrt/tree/master/tftrt/examples/image-classification

• https://sayak.dev/tf.keras/tensorrt/tensorflow/2020/07/01/accelerated-inference-trt.html

Installation TensorRT Converter & Runtime

Actuellement, on a deux versions de Jetpack (le kit de développement de NVidia) disponibles : la 4.3 et la 4.4.

La version 4.4 fonctionne avec une version Tensorflow 2.2 optimisée pour Jetson Nano, et la 4.3 avec une version de Tensorflow 2.1 optimisée. Chacune de ces deux version a une versio différente de TensorRT :

• Jetpack 4.4 possède la version TensorRT 7.1.3,
• Jetpack 4.3 possède la version TensorRT 6.0.1.

Pour savoir quelle version l'on doit utiliser, il faut vérifier la version de cuDNN utilisée (le driver deep learning de Nvidia).

• Jetpack 4.4 supporte cuDNN 8.0, avec une version minimale de Cuda 10.2,
• Jetpack 4.3 supporte cuDNN 7.6.3, avec une version minimale de Cuda 10.0.

Pour voir les versions utilisées sur Colab (ou sur n'importe quel pc Linux), on lance les commandes suivantes.

!nvcc -V

nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2019 NVIDIA Corporation
Built on Sun_Jul_28_19:07:16_PDT_2019
Cuda compilation tools, release 10.1, V10.1.243

!cat /usr/include/cudnn.h | grep CUDNN_MAJOR -A 2

#define CUDNN_MAJOR 7
#define CUDNN_MINOR 6
#define CUDNN_PATCHLEVEL 5
--
#define CUDNN_VERSION (CUDNN_MAJOR * 1000 + CUDNN_MINOR * 100 + CUDNN_PATCHLEVEL)

#include "driver_types.h"

on est donc donc sous Cuda 10.1 avec cuDNN 7.6.5, e qui fait que l'on doit uiliser la version présente dans Jetpack 4.3.

Pour avoir des versions compatibles de TensorFlow & TensorRT, on va downgrader vers la version \(2.1\) de TensorFlow.

!pip install tensorflow-gpu==2.1.0

Collecting tensorflow-gpu==2.1.0
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  Downloading https://files.pythonhosted.org/packages/d9/41/bbf49b61370e4f4d245d4c6051dfb6db80cec672605c91b1652ac8cc3d38/tensorboard-2.1.1-py3-none-any.whl (3.8MB)
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  Downloading https://files.pythonhosted.org/packages/18/90/b77c328a1304437ab1310b463e533fa7689f4bfc41549593056d812fab8e/tensorflow_estimator-2.1.0-py2.py3-none-any.whl (448kB)
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Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.6/dist-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard<2.2.0,>=2.1.0->tensorflow-gpu==2.1.0) (0.4.8)
Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < "3.8"->markdown>=2.6.8->tensorboard<2.2.0,>=2.1.0->tensorflow-gpu==2.1.0) (3.1.0)
Building wheels for collected packages: gast
  Building wheel for gast (setup.py) ... done
  Created wheel for gast: filename=gast-0.2.2-cp36-none-any.whl size=7542 sha256=3bc75e9d32a7cb7e7f51cf622fb8940d62e0b24ed504ee3cbe5e28f7cf90b7bc
  Stored in directory: /root/.cache/pip/wheels/5c/2e/7e/a1d4d4fcebe6c381f378ce7743a3ced3699feb89bcfbdadadd
Successfully built gast
ERROR: tensorflow 2.3.0 has requirement gast==0.3.3, but you'll have gast 0.2.2 which is incompatible.
ERROR: tensorflow 2.3.0 has requirement tensorboard<3,>=2.3.0, but you'll have tensorboard 2.1.1 which is incompatible.
ERROR: tensorflow 2.3.0 has requirement tensorflow-estimator<2.4.0,>=2.3.0, but you'll have tensorflow-estimator 2.1.0 which is incompatible.
ERROR: tensorflow-probability 0.11.0 has requirement gast>=0.3.2, but you'll have gast 0.2.2 which is incompatible.
Installing collected packages: keras-applications, tensorboard, tensorflow-estimator, gast, tensorflow-gpu
  Found existing installation: tensorboard 2.3.0
    Uninstalling tensorboard-2.3.0:
      Successfully uninstalled tensorboard-2.3.0
  Found existing installation: tensorflow-estimator 2.3.0
    Uninstalling tensorflow-estimator-2.3.0:
      Successfully uninstalled tensorflow-estimator-2.3.0
  Found existing installation: gast 0.3.3
    Uninstalling gast-0.3.3:
      Successfully uninstalled gast-0.3.3
Successfully installed gast-0.2.2 keras-applications-1.0.8 tensorboard-2.1.1 tensorflow-estimator-2.1.0 tensorflow-gpu-2.1.0

%%bash
wget https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/libnvinfer6_6.0.1-1+cuda10.1_amd64.deb
dpkg -i libnvinfer6_6.0.1-1+cuda10.1_amd64.deb

Selecting previously unselected package libnvinfer6.
(Reading database ... 144579 files and directories currently installed.)
Preparing to unpack libnvinfer6_6.0.1-1+cuda10.1_amd64.deb ...
Unpacking libnvinfer6 (6.0.1-1+cuda10.1) ...
Setting up libnvinfer6 (6.0.1-1+cuda10.1) ...
Processing triggers for libc-bin (2.27-3ubuntu1) ...

--2020-09-01 08:47:40--  https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/libnvinfer6_6.0.1-1+cuda10.1_amd64.deb
Resolving developer.download.nvidia.com (developer.download.nvidia.com)... 152.195.19.142
Connecting to developer.download.nvidia.com (developer.download.nvidia.com)|152.195.19.142|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 71245796 (68M) [application/x-deb]
Saving to: ‘libnvinfer6_6.0.1-1+cuda10.1_amd64.deb’

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 11750K .......... .......... .......... .......... .......... 16% 4.58M 6s
 11800K .......... .......... .......... .......... .......... 17%  112M 6s
 11850K .......... .......... .......... .......... .......... 17% 74.4M 6s
 11900K .......... .......... .......... .......... .......... 17% 49.3M 6s
 11950K .......... .......... .......... .......... .......... 17% 2.78M 6s
 12000K .......... .......... .......... .......... .......... 17% 4.83M 6s
 12050K .......... .......... .......... .......... .......... 17% 46.8M 6s
 12100K .......... .......... .......... .......... .......... 17% 86.0M 6s
 12150K .......... .......... .......... .......... .......... 17% 54.1M 6s
 12200K .......... .......... .......... .......... .......... 17% 12.8M 6s
 12250K .......... .......... .......... .......... .......... 17% 3.37M 6s
 12300K .......... .......... .......... .......... .......... 17% 4.67M 6s
 12350K .......... .......... .......... .......... .......... 17% 58.7M 6s
 12400K .......... .......... .......... .......... .......... 17% 76.5M 6s
 12450K .......... .......... .......... .......... .......... 17% 33.5M 6s
 12500K .......... .......... .......... .......... .......... 18% 16.2M 6s
 12550K .......... .......... .......... .......... .......... 18% 3.28M 6s
 12600K .......... .......... .......... .......... .......... 18% 4.92M 6s
 12650K .......... .......... .......... .......... .......... 18% 51.0M 6s
 12700K .......... .......... .......... .......... .......... 18% 73.1M 6s
 12750K .......... .......... .......... .......... .......... 18% 43.0M 6s
 12800K .......... .......... .......... .......... .......... 18% 14.2M 6s
 12850K .......... .......... .......... .......... .......... 18% 3.28M 6s
 12900K .......... .......... .......... .......... .......... 18% 4.89M 6s
 12950K .......... .......... .......... .......... .......... 18% 55.4M 6s
 13000K .......... .......... .......... .......... .......... 18% 67.2M 6s
 13050K .......... .......... .......... .......... .......... 18% 52.1M 6s
 13100K .......... .......... .......... .......... .......... 18% 14.0M 6s
 13150K .......... .......... .......... .......... .......... 18% 3.28M 6s
 13200K .......... .......... .......... .......... .......... 19% 4.76M 6s
 13250K .......... .......... .......... .......... .......... 19% 59.4M 6s
 13300K .......... .......... .......... .......... .......... 19% 34.7M 6s
 13350K .......... .......... .......... .......... .......... 19%  136M 6s
 13400K .......... .......... .......... .......... .......... 19% 2.79M 6s
 13450K .......... .......... .......... .......... .......... 19% 71.0M 6s
 13500K .......... .......... .......... .......... .......... 19% 4.91M 6s
 13550K .......... .......... .......... .......... .......... 19% 50.9M 6s
 13600K .......... .......... .......... .......... .......... 19% 56.4M 6s
 13650K .......... .......... .......... .......... .......... 19% 12.4M 6s
 13700K .......... .......... .......... .......... .......... 19% 3.34M 6s
 13750K .......... .......... .......... .......... .......... 19% 68.6M 6s
 13800K .......... .......... .......... .......... .......... 19% 4.85M 6s
 13850K .......... .......... .......... .......... .......... 19% 64.1M 6s
 13900K .......... .......... .......... .......... .......... 20% 58.1M 6s
 13950K .......... .......... .......... .......... .......... 20% 12.3M 6s
 14000K .......... .......... .......... .......... .......... 20% 3.34M 6s
 14050K .......... .......... .......... .......... .......... 20% 4.77M 6s
 14100K .......... .......... .......... .......... .......... 20% 90.9M 6s
 14150K .......... .......... .......... .......... .......... 20% 57.5M 6s
 14200K .......... .......... .......... .......... .......... 20% 58.2M 6s
 14250K .......... .......... .......... .......... .......... 20% 13.0M 6s
 14300K .......... .......... .......... .......... .......... 20% 3.32M 6s
 14350K .......... .......... .......... .......... .......... 20% 4.82M 6s
 14400K .......... .......... .......... .......... .......... 20% 60.7M 6s
 14450K .......... .......... .......... .......... .......... 20% 70.1M 6s
 14500K .......... .......... .......... .......... .......... 20% 53.4M 6s
 14550K .......... .......... .......... .......... .......... 20% 12.3M 6s
 14600K .......... .......... .......... .......... .......... 21% 3.38M 6s
 14650K .......... .......... .......... .......... .......... 21% 4.83M 6s
 14700K .......... .......... .......... .......... .......... 21% 85.0M 6s
 14750K .......... .......... .......... .......... .......... 21% 49.1M 6s
 14800K .......... .......... .......... .......... .......... 21% 51.5M 6s
 14850K .......... .......... .......... .......... .......... 21% 13.9M 6s
 14900K .......... .......... .......... .......... .......... 21% 3.32M 6s
 14950K .......... .......... .......... .......... .......... 21% 4.86M 6s
 15000K .......... .......... .......... .......... .......... 21% 59.7M 6s
 15050K .......... .......... .......... .......... .......... 21% 60.5M 6s
 15100K .......... .......... .......... .......... .......... 21% 57.4M 6s
 15150K .......... .......... .......... .......... .......... 21% 12.8M 6s
 15200K .......... .......... .......... .......... .......... 21% 3.34M 6s
 15250K .......... .......... .......... .......... .......... 21% 4.80M 6s
 15300K .......... .......... .......... .......... .......... 22% 68.8M 6s
 15350K .......... .......... .......... .......... .......... 22% 70.7M 6s
 15400K .......... .......... .......... .......... .......... 22% 50.8M 6s
 15450K .......... .......... .......... .......... .......... 22% 13.6M 6s
 15500K .......... .......... .......... .......... .......... 22% 3.29M 6s
 15550K .......... .......... .......... .......... .......... 22% 4.75M 6s
 15600K .......... .......... .......... .......... .......... 22% 79.2M 6s
 15650K .......... .......... .......... .......... .......... 22% 59.9M 6s
 15700K .......... .......... .......... .......... .......... 22% 54.7M 6s
 15750K .......... .......... .......... .......... .......... 22% 13.9M 6s
 15800K .......... .......... .......... .......... .......... 22% 3.22M 6s
 15850K .......... .......... .......... .......... .......... 22% 5.08M 6s
 15900K .......... .......... .......... .......... .......... 22% 66.0M 6s
 15950K .......... .......... .......... .......... .......... 22% 42.9M 6s
 16000K .......... .......... .......... .......... .......... 23% 56.3M 6s
 16050K .......... .......... .......... .......... .......... 23% 14.5M 6s
 16100K .......... .......... .......... .......... .......... 23% 3.33M 6s
 16150K .......... .......... .......... .......... .......... 23% 4.75M 6s
 16200K .......... .......... .......... .......... .......... 23% 64.3M 6s
 16250K .......... .......... .......... .......... .......... 23% 76.7M 6s
 16300K .......... .......... .......... .......... .......... 23% 54.5M 6s
 16350K .......... .......... .......... .......... .......... 23% 12.7M 6s
 16400K .......... .......... .......... .......... .......... 23% 3.35M 6s
 16450K .......... .......... .......... .......... .......... 23% 4.78M 6s
 16500K .......... .......... .......... .......... .......... 23% 64.9M 6s
 16550K .......... .......... .......... .......... .......... 23% 82.4M 5s
 16600K .......... .......... .......... .......... .......... 23% 47.8M 5s
 16650K .......... .......... .......... .......... .......... 24% 13.9M 5s
 16700K .......... .......... .......... .......... .......... 24% 3.20M 5s
 16750K .......... .......... .......... .......... .......... 24% 5.02M 6s
 16800K .......... .......... .......... .......... .......... 24% 68.3M 5s
 16850K .......... .......... .......... .......... .......... 24% 62.1M 5s
 16900K .......... .......... .......... .......... .......... 24% 55.4M 5s
 16950K .......... .......... .......... .......... .......... 24% 13.8M 5s
 17000K .......... .......... .......... .......... .......... 24% 3.27M 5s
 17050K .......... .......... .......... .......... .......... 24% 4.96M 5s
 17100K .......... .......... .......... .......... .......... 24% 67.8M 5s
 17150K .......... .......... .......... .......... .......... 24% 21.2M 5s
 17200K .......... .......... .......... .......... .......... 24%  175M 5s
 17250K .......... .......... .......... .......... .......... 24% 17.7M 5s
 17300K .......... .......... .......... .......... .......... 24% 3.25M 5s
 17350K .......... .......... .......... .......... .......... 25% 4.99M 5s
 17400K .......... .......... .......... .......... .......... 25% 61.6M 5s
 17450K .......... .......... .......... .......... .......... 25% 24.5M 5s
 17500K .......... .......... .......... .......... .......... 25%  176M 5s
 17550K .......... .......... .......... .......... .......... 25% 15.4M 5s
 17600K .......... .......... .......... .......... .......... 25% 3.29M 5s
 17650K .......... .......... .......... .......... .......... 25% 4.94M 5s
 17700K .......... .......... .......... .......... .......... 25% 68.9M 5s
 17750K .......... .......... .......... .......... .......... 25% 25.4M 5s
 17800K .......... .......... .......... .......... .......... 25%  146M 5s
 17850K .......... .......... .......... .......... .......... 25% 16.6M 5s
 17900K .......... .......... .......... .......... .......... 25% 3.23M 5s
 17950K .......... .......... .......... .......... .......... 25% 4.94M 5s
 18000K .......... .......... .......... .......... .......... 25% 61.4M 5s
 18050K .......... .......... .......... .......... .......... 26% 26.4M 5s
 18100K .......... .......... .......... .......... .......... 26%  183M 5s
 18150K .......... .......... .......... .......... .......... 26% 15.9M 5s
 18200K .......... .......... .......... .......... .......... 26% 3.21M 5s
 18250K .......... .......... .......... .......... .......... 26% 5.11M 5s
 18300K .......... .......... .......... .......... .......... 26% 76.5M 5s
 18350K .......... .......... .......... .......... .......... 26% 21.0M 5s
 18400K .......... .......... .......... .......... .......... 26%  147M 5s
 18450K .......... .......... .......... .......... .......... 26% 17.7M 5s
 18500K .......... .......... .......... .......... .......... 26% 3.25M 5s
 18550K .......... .......... .......... .......... .......... 26% 4.97M 5s
 18600K .......... .......... .......... .......... .......... 26% 82.8M 5s
 18650K .......... .......... .......... .......... .......... 26% 59.4M 5s
 18700K .......... .......... .......... .......... .......... 26% 28.0M 5s
 18750K .......... .......... .......... .......... .......... 27% 16.7M 5s
 18800K .......... .......... .......... .......... .......... 27% 3.31M 5s
 18850K .......... .......... .......... .......... .......... 27% 4.82M 5s
 18900K .......... .......... .......... .......... .......... 27% 84.6M 5s
 18950K .......... .......... .......... .......... .......... 27% 67.2M 5s
 19000K .......... .......... .......... .......... .......... 27% 25.7M 5s
 19050K .......... .......... .......... .......... .......... 27% 18.4M 5s
 19100K .......... .......... .......... .......... .......... 27% 3.33M 5s
 19150K .......... .......... .......... .......... .......... 27% 4.72M 5s
 19200K .......... .......... .......... .......... .......... 27% 77.6M 5s
 19250K .......... .......... .......... .......... .......... 27% 65.9M 5s
 19300K .......... .......... .......... .......... .......... 27% 28.0M 5s
 19350K .......... .......... .......... .......... .......... 27% 18.0M 5s
 19400K .......... .......... .......... .......... .......... 27% 3.28M 5s
 19450K .......... .......... .......... .......... .......... 28% 75.6M 5s
 19500K .......... .......... .......... .......... .......... 28% 4.88M 5s
 19550K .......... .......... .......... .......... .......... 28% 68.8M 5s
 19600K .......... .......... .......... .......... .......... 28% 23.7M 5s
 19650K .......... .......... .......... .......... .......... 28% 19.2M 5s
 19700K .......... .......... .......... .......... .......... 28% 3.25M 5s
 19750K .......... .......... .......... .......... .......... 28%  101M 5s
 19800K .......... .......... .......... .......... .......... 28% 4.86M 5s
 19850K .......... .......... .......... .......... .......... 28% 80.3M 5s
 19900K .......... .......... .......... .......... .......... 28% 24.0M 5s
 19950K .......... .......... .......... .......... .......... 28%  102M 5s
 20000K .......... .......... .......... .......... .......... 28% 2.86M 5s
 20050K .......... .......... .......... .......... .......... 28% 70.4M 5s
 20100K .......... .......... .......... .......... .......... 28% 4.88M 5s
 20150K .......... .......... .......... .......... .......... 29% 76.7M 5s
 20200K .......... .......... .......... .......... .......... 29% 23.7M 5s
 20250K .......... .......... .......... .......... .......... 29%  146M 5s
 20300K .......... .......... .......... .......... .......... 29% 18.1M 5s
 20350K .......... .......... .......... .......... .......... 29% 3.23M 5s
 20400K .......... .......... .......... .......... .......... 29% 4.88M 5s
 20450K .......... .......... .......... .......... .......... 29% 57.8M 5s
 20500K .......... .......... .......... .......... .......... 29% 25.5M 5s
 20550K .......... .......... .......... .......... .......... 29%  124M 5s
 20600K .......... .......... .......... .......... .......... 29% 19.0M 5s
 20650K .......... .......... .......... .......... .......... 29% 3.28M 5s
 20700K .......... .......... .......... .......... .......... 29% 4.86M 5s
 20750K .......... .......... .......... .......... .......... 29% 53.6M 5s
 20800K .......... .......... .......... .......... .......... 29% 22.8M 5s
 20850K .......... .......... .......... .......... .......... 30% 88.6M 5s
 20900K .......... .......... .......... .......... .......... 30% 21.2M 5s
 20950K .......... .......... .......... .......... .......... 30% 3.29M 5s
 21000K .......... .......... .......... .......... .......... 30% 4.82M 5s
 21050K .......... .......... .......... .......... .......... 30% 42.8M 5s
 21100K .......... .......... .......... .......... .......... 30%  112M 5s
 21150K .......... .......... .......... .......... .......... 30% 27.0M 5s
 21200K .......... .......... .......... .......... .......... 30% 19.4M 5s
 21250K .......... .......... .......... .......... .......... 30% 3.25M 5s
 21300K .......... .......... .......... .......... .......... 30% 85.5M 5s
 21350K .......... .......... .......... .......... .......... 30% 4.88M 5s
 21400K .......... .......... .......... .......... .......... 30% 57.7M 5s
 21450K .......... .......... .......... .......... .......... 30% 24.7M 5s
 21500K .......... .......... .......... .......... .......... 30%  122M 5s
 21550K .......... .......... .......... .......... .......... 31% 2.87M 5s
 21600K .......... .......... .......... .......... .......... 31% 80.1M 5s
 21650K .......... .......... .......... .......... .......... 31% 4.88M 5s
 21700K .......... .......... .......... .......... .......... 31% 56.5M 5s
 21750K .......... .......... .......... .......... .......... 31% 27.7M 5s
 21800K .......... .......... .......... .......... .......... 31% 84.3M 5s
 21850K .......... .......... .......... .......... .......... 31% 18.6M 5s
 21900K .......... .......... .......... .......... .......... 31% 3.30M 5s
 21950K .......... .......... .......... .......... .......... 31% 4.80M 5s
 22000K .......... .......... .......... .......... .......... 31% 59.9M 5s
 22050K .......... .......... .......... .......... .......... 31% 25.8M 5s
 22100K .......... .......... .......... .......... .......... 31% 89.8M 5s
 22150K .......... .......... .......... .......... .......... 31% 20.4M 5s
 22200K .......... .......... .......... .......... .......... 31% 3.21M 5s
 22250K .......... .......... .......... .......... .......... 32% 4.93M 5s
 22300K .......... .......... .......... .......... .......... 32% 67.3M 5s
 22350K .......... .......... .......... .......... .......... 32% 21.4M 5s
 22400K .......... .......... .......... .......... .......... 32%  155M 5s
 22450K .......... .......... .......... .......... .......... 32% 20.0M 5s
 22500K .......... .......... .......... .......... .......... 32% 3.27M 5s
 22550K .......... .......... .......... .......... .......... 32% 4.90M 5s
 22600K .......... .......... .......... .......... .......... 32% 58.6M 5s
 22650K .......... .......... .......... .......... .......... 32% 67.5M 5s
 22700K .......... .......... .......... .......... .......... 32% 27.5M 5s
 22750K .......... .......... .......... .......... .......... 32% 18.3M 5s
 22800K .......... .......... .......... .......... .......... 32% 3.30M 5s
 22850K .......... .......... .......... .......... .......... 32% 4.81M 5s
 22900K .......... .......... .......... .......... .......... 32% 74.1M 5s
 22950K .......... .......... .......... .......... .......... 33% 65.8M 5s
 23000K .......... .......... .......... .......... .......... 33% 26.9M 5s
 23050K .......... .......... .......... .......... .......... 33% 20.5M 5s
 23100K .......... .......... .......... .......... .......... 33% 64.8M 5s
 23150K .......... .......... .......... .......... .......... 33% 3.22M 5s
 23200K .......... .......... .......... .......... .......... 33% 4.91M 5s
 23250K .......... .......... .......... .......... .......... 33% 66.3M 5s
 23300K .......... .......... .......... .......... .......... 33% 8.79M 5s
 23350K .......... .......... .......... .......... .......... 33%  186M 5s
 23400K .......... .......... .......... .......... .......... 33%  222M 5s
 23450K .......... .......... .......... .......... .......... 33% 3.48M 5s
 23500K .......... .......... .......... .......... .......... 33% 5.06M 5s
 23550K .......... .......... .......... .......... .......... 33% 44.0M 5s
 23600K .......... .......... .......... .......... .......... 33% 26.1M 5s
 23650K .......... .......... .......... .......... .......... 34%  104M 5s
 23700K .......... .......... .......... .......... .......... 34% 20.2M 5s
 23750K .......... .......... .......... .......... .......... 34% 3.24M 5s
 23800K .......... .......... .......... .......... .......... 34% 4.84M 5s
 23850K .......... .......... .......... .......... .......... 34% 83.0M 5s
 23900K .......... .......... .......... .......... .......... 34% 65.3M 5s
 23950K .......... .......... .......... .......... .......... 34% 24.2M 5s
 24000K .......... .......... .......... .......... .......... 34% 21.2M 5s
 24050K .......... .......... .......... .......... .......... 34% 3.23M 5s
 24100K .......... .......... .......... .......... .......... 34% 4.99M 5s
 24150K .......... .......... .......... .......... .......... 34% 57.1M 5s
 24200K .......... .......... .......... .......... .......... 34% 77.8M 5s
 24250K .......... .......... .......... .......... .......... 34% 24.7M 5s
 24300K .......... .......... .......... .......... .......... 34% 19.9M 5s
 24350K .......... .......... .......... .......... .......... 35% 3.25M 5s
 24400K .......... .......... .......... .......... .......... 35% 66.5M 5s
 24450K .......... .......... .......... .......... .......... 35% 4.99M 5s
 24500K .......... .......... .......... .......... .......... 35% 62.1M 5s
 24550K .......... .......... .......... .......... .......... 35% 23.8M 5s
 24600K .......... .......... .......... .......... .......... 35%  104M 5s
 24650K .......... .......... .......... .......... .......... 35% 19.9M 5s
 24700K .......... .......... .......... .......... .......... 35% 3.23M 5s
 24750K .......... .......... .......... .......... .......... 35% 4.89M 5s
 24800K .......... .......... .......... .......... .......... 35% 58.2M 5s
 24850K .......... .......... .......... .......... .......... 35% 25.6M 5s
 24900K .......... .......... .......... .......... .......... 35%  105M 5s
 24950K .......... .......... .......... .......... .......... 35% 19.9M 5s
 25000K .......... .......... .......... .......... .......... 36% 3.28M 5s
 25050K .......... .......... .......... .......... .......... 36% 4.89M 5s
 25100K .......... .......... .......... .......... .......... 36% 74.5M 5s
 25150K .......... .......... .......... .......... .......... 36% 20.3M 5s
 25200K .......... .......... .......... .......... .......... 36%  116M 5s
 25250K .......... .......... .......... .......... .......... 36% 20.3M 5s
 25300K .......... .......... .......... .......... .......... 36% 3.28M 5s
 25350K .......... .......... .......... .......... .......... 36% 53.9M 5s
 25400K .......... .......... .......... .......... .......... 36% 4.93M 5s
 25450K .......... .......... .......... .......... .......... 36% 84.6M 5s
 25500K .......... .......... .......... .......... .......... 36% 24.2M 5s
 25550K .......... .......... .......... .......... .......... 36% 19.7M 5s
 25600K .......... .......... .......... .......... .......... 36% 3.30M 5s
 25650K .......... .......... .......... .......... .......... 36% 58.2M 5s
 25700K .......... .......... .......... .......... .......... 37% 4.94M 5s
 25750K .......... .......... .......... .......... .......... 37% 64.0M 4s
 25800K .......... .......... .......... .......... .......... 37% 23.8M 4s
 25850K .......... .......... .......... .......... .......... 37%  106M 4s
 25900K .......... .......... .......... .......... .......... 37% 19.8M 4s
 25950K .......... .......... .......... .......... .......... 37% 3.31M 4s
 26000K .......... .......... .......... .......... .......... 37% 4.81M 4s
 26050K .......... .......... .......... .......... .......... 37% 54.2M 4s
 26100K .......... .......... .......... .......... .......... 37% 25.1M 4s
 26150K .......... .......... .......... .......... .......... 37% 77.5M 4s
 26200K .......... .......... .......... .......... .......... 37% 21.2M 4s
 26250K .......... .......... .......... .......... .......... 37% 3.30M 4s
 26300K .......... .......... .......... .......... .......... 37% 44.0M 4s
 26350K .......... .......... .......... .......... .......... 37% 5.03M 4s
 26400K .......... .......... .......... .......... .......... 38% 57.5M 4s
 26450K .......... .......... .......... .......... .......... 38% 25.0M 4s
 26500K .......... .......... .......... .......... .......... 38% 21.1M 4s
 26550K .......... .......... .......... .......... .......... 38% 99.3M 4s
 26600K .......... .......... .......... .......... .......... 38% 3.24M 4s
 26650K .......... .......... .......... .......... .......... 38% 4.97M 4s
 26700K .......... .......... .......... .......... .......... 38% 59.2M 4s
 26750K .......... .......... .......... .......... .......... 38% 22.7M 4s
 26800K .......... .......... .......... .......... .......... 38% 83.8M 4s
 26850K .......... .......... .......... .......... .......... 38% 21.5M 4s
 26900K .......... .......... .......... .......... .......... 38% 3.29M 4s
 26950K .......... .......... .......... .......... .......... 38% 4.91M 4s
 27000K .......... .......... .......... .......... .......... 38% 74.4M 4s
 27050K .......... .......... .......... .......... .......... 38% 55.7M 4s
 27100K .......... .......... .......... .......... .......... 39% 23.5M 4s
 27150K .......... .......... .......... .......... .......... 39% 19.7M 4s
 27200K .......... .......... .......... .......... .......... 39% 3.24M 4s
 27250K .......... .......... .......... .......... .......... 39%  125M 4s
 27300K .......... .......... .......... .......... .......... 39% 4.95M 4s
 27350K .......... .......... .......... .......... .......... 39% 65.0M 4s
 27400K .......... .......... .......... .......... .......... 39% 23.9M 4s
 27450K .......... .......... .......... .......... .......... 39% 82.7M 4s
 27500K .......... .......... .......... .......... .......... 39% 19.8M 4s
 27550K .......... .......... .......... .......... .......... 39% 3.27M 4s
 27600K .......... .......... .......... .......... .......... 39% 4.97M 4s
 27650K .......... .......... .......... .......... .......... 39% 55.7M 4s
 27700K .......... .......... .......... .......... .......... 39% 24.5M 4s
 27750K .......... .......... .......... .......... .......... 39% 93.7M 4s
 27800K .......... .......... .......... .......... .......... 40% 18.9M 4s
 27850K .......... .......... .......... .......... .......... 40% 3.28M 4s
 27900K .......... .......... .......... .......... .......... 40%  102M 4s
 27950K .......... .......... .......... .......... .......... 40% 4.89M 4s
 28000K .......... .......... .......... .......... .......... 40% 63.5M 4s
 28050K .......... .......... .......... .......... .......... 40% 23.3M 4s
 28100K .......... .......... .......... .......... .......... 40% 20.7M 4s
 28150K .......... .......... .......... .......... .......... 40% 87.2M 4s
 28200K .......... .......... .......... .......... .......... 40% 3.27M 4s
 28250K .......... .......... .......... .......... .......... 40% 4.99M 4s
 28300K .......... .......... .......... .......... .......... 40% 66.1M 4s
 28350K .......... .......... .......... .......... .......... 40% 21.7M 4s
 28400K .......... .......... .......... .......... .......... 40% 83.6M 4s
 28450K .......... .......... .......... .......... .......... 40% 20.2M 4s
 28500K .......... .......... .......... .......... .......... 41% 3.25M 4s
 28550K .......... .......... .......... .......... .......... 41% 4.97M 4s
 28600K .......... .......... .......... .......... .......... 41%  117M 4s
 28650K .......... .......... .......... .......... .......... 41% 58.2M 4s
 28700K .......... .......... .......... .......... .......... 41% 23.7M 4s
 28750K .......... .......... .......... .......... .......... 41% 20.7M 4s
 28800K .......... .......... .......... .......... .......... 41% 3.21M 4s
 28850K .......... .......... .......... .......... .......... 41% 69.6M 4s
 28900K .......... .......... .......... .......... .......... 41% 5.12M 4s
 28950K .......... .......... .......... .......... .......... 41% 51.6M 4s
 29000K .......... .......... .......... .......... .......... 41% 25.5M 4s
 29050K .......... .......... .......... .......... .......... 41% 98.8M 4s
 29100K .......... .......... .......... .......... .......... 41% 20.2M 4s
 29150K .......... .......... .......... .......... .......... 41% 3.21M 4s
 29200K .......... .......... .......... .......... .......... 42% 4.97M 4s
 29250K .......... .......... .......... .......... .......... 42%  111M 4s
 29300K .......... .......... .......... .......... .......... 42% 59.7M 4s
 29350K .......... .......... .......... .......... .......... 42% 22.5M 4s
 29400K .......... .......... .......... .......... .......... 42% 20.6M 4s
 29450K .......... .......... .......... .......... .......... 42%  116M 4s
 29500K .......... .......... .......... .......... .......... 42% 3.24M 4s
 29550K .......... .......... .......... .......... .......... 42% 5.00M 4s
 29600K .......... .......... .......... .......... .......... 42% 54.0M 4s
 29650K .......... .......... .......... .......... .......... 42% 24.7M 4s
 29700K .......... .......... .......... .......... .......... 42% 86.6M 4s
 29750K .......... .......... .......... .......... .......... 42% 19.6M 4s
 29800K .......... .......... .......... .......... .......... 42% 3.29M 4s
 29850K .......... .......... .......... .......... .......... 42% 4.96M 4s
 29900K .......... .......... .......... .......... .......... 43%  115M 4s
 29950K .......... .......... .......... .......... .......... 43% 21.4M 4s
 30000K .......... .......... .......... .......... .......... 43% 55.0M 4s
 30050K .......... .......... .......... .......... .......... 43% 22.7M 4s
 30100K .......... .......... .......... .......... .......... 43% 3.30M 4s
 30150K .......... .......... .......... .......... .......... 43% 52.2M 4s
 30200K .......... .......... .......... .......... .......... 43% 5.05M 4s
 30250K .......... .......... .......... .......... .......... 43% 76.9M 4s
 30300K .......... .......... .......... .......... .......... 43% 21.2M 4s
 30350K .......... .......... .......... .......... .......... 43% 64.8M 4s
 30400K .......... .......... .......... .......... .......... 43% 21.2M 4s
 30450K .......... .......... .......... .......... .......... 43% 3.29M 4s
 30500K .......... .......... .......... .......... .......... 43% 4.96M 4s
 30550K .......... .......... .......... .......... .......... 43% 97.4M 4s
 30600K .......... .......... .......... .......... .......... 44% 55.4M 4s
 30650K .......... .......... .......... .......... .......... 44% 24.3M 4s
 30700K .......... .......... .......... .......... .......... 44% 65.3M 4s
 30750K .......... .......... .......... .......... .......... 44% 2.91M 4s
 30800K .......... .......... .......... .......... .......... 44% 68.5M 4s
 30850K .......... .......... .......... .......... .......... 44% 5.14M 4s
 30900K .......... .......... .......... .......... .......... 44% 77.4M 4s
 30950K .......... .......... .......... .......... .......... 44% 21.5M 4s
 31000K .......... .......... .......... .......... .......... 44% 75.8M 4s
 31050K .......... .......... .......... .......... .......... 44% 21.9M 4s
 31100K .......... .......... .......... .......... .......... 44% 3.23M 4s
 31150K .......... .......... .......... .......... .......... 44% 4.96M 4s
 31200K .......... .......... .......... .......... .......... 44%  125M 4s
 31250K .......... .......... .......... .......... .......... 44% 60.0M 4s
 31300K .......... .......... .......... .......... .......... 45% 24.3M 4s
 31350K .......... .......... .......... .......... .......... 45% 62.3M 4s
 31400K .......... .......... .......... .......... .......... 45% 22.9M 4s
 31450K .......... .......... .......... .......... .......... 45% 3.28M 4s
 31500K .......... .......... .......... .......... .......... 45% 4.88M 4s
 31550K .......... .......... .......... .......... .......... 45% 76.0M 4s
 31600K .......... .......... .......... .......... .......... 45% 23.1M 4s
 31650K .......... .......... .......... .......... .......... 45% 58.8M 4s
 31700K .......... .......... .......... .......... .......... 45% 23.9M 4s
 31750K .......... .......... .......... .......... .......... 45% 3.22M 4s
 31800K .......... .......... .......... .......... .......... 45% 87.3M 4s
 31850K .......... .......... .......... .......... .......... 45% 5.10M 4s
 31900K .......... .......... .......... .......... .......... 45% 68.4M 4s
 31950K .......... .......... .......... .......... .......... 45% 20.7M 4s
 32000K .......... .......... .......... .......... .......... 46% 69.1M 4s
 32050K .......... .......... .......... .......... .......... 46% 22.9M 4s
 32100K .......... .......... .......... .......... .......... 46% 3.28M 4s
 32150K .......... .......... .......... .......... .......... 46% 4.84M 4s
 32200K .......... .......... .......... .......... .......... 46%  168M 4s
 32250K .......... .......... .......... .......... .......... 46% 52.7M 4s
 32300K .......... .......... .......... .......... .......... 46% 25.8M 4s
 32350K .......... .......... .......... .......... .......... 46% 58.7M 4s
 32400K .......... .......... .......... .......... .......... 46% 2.93M 4s
 32450K .......... .......... .......... .......... .......... 46% 67.5M 4s
 32500K .......... .......... .......... .......... .......... 46% 5.04M 4s
 32550K .......... .......... .......... .......... .......... 46% 84.7M 4s
 32600K .......... .......... .......... .......... .......... 46% 23.6M 4s
 32650K .......... .......... .......... .......... .......... 46% 58.5M 4s
 32700K .......... .......... .......... .......... .......... 47% 23.1M 4s
 32750K .......... .......... .......... .......... .......... 47% 3.27M 4s
 32800K .......... .......... .......... .......... .......... 47% 55.6M 4s
 32850K .......... .......... .......... .......... .......... 47% 5.00M 4s
 32900K .......... .......... .......... .......... .......... 47% 72.5M 4s
 32950K .......... .......... .......... .......... .......... 47% 24.5M 4s
 33000K .......... .......... .......... .......... .......... 47% 65.6M 4s
 33050K .......... .......... .......... .......... .......... 47% 22.4M 4s
 33100K .......... .......... .......... .......... .......... 47% 3.26M 4s
 33150K .......... .......... .......... .......... .......... 47% 5.01M 4s
 33200K .......... .......... .......... .......... .......... 47% 61.9M 4s
 33250K .......... .......... .......... .......... .......... 47% 57.2M 4s
 33300K .......... .......... .......... .......... .......... 47% 25.3M 4s
 33350K .......... .......... .......... .......... .......... 48% 23.3M 4s
 33400K .......... .......... .......... .......... .......... 48% 70.6M 4s
 33450K .......... .......... .......... .......... .......... 48% 3.27M 4s
 33500K .......... .......... .......... .......... .......... 48% 4.94M 4s
 33550K .......... .......... .......... .......... .......... 48% 57.0M 4s
 33600K .......... .......... .......... .......... .......... 48% 23.6M 4s
 33650K .......... .......... .......... .......... .......... 48% 80.1M 4s
 33700K .......... .......... .......... .......... .......... 48% 24.5M 4s
 33750K .......... .......... .......... .......... .......... 48% 3.19M 4s
 33800K .......... .......... .......... .......... .......... 48% 78.0M 4s
 33850K .......... .......... .......... .......... .......... 48% 5.04M 4s
 33900K .......... .......... .......... .......... .......... 48% 72.0M 4s
 33950K .......... .......... .......... .......... .......... 48% 23.7M 4s
 34000K .......... .......... .......... .......... .......... 48% 69.8M 4s
 34050K .......... .......... .......... .......... .......... 49% 22.0M 4s
 34100K .......... .......... .......... .......... .......... 49% 3.30M 4s
 34150K .......... .......... .......... .......... .......... 49% 4.90M 4s
 34200K .......... .......... .......... .......... .......... 49% 95.7M 4s
 34250K .......... .......... .......... .......... .......... 49% 54.3M 4s
 34300K .......... .......... .......... .......... .......... 49% 26.1M 4s
 34350K .......... .......... .......... .......... .......... 49% 20.5M 4s
 34400K .......... .......... .......... .......... .......... 49% 3.27M 4s
 34450K .......... .......... .......... .......... .......... 49% 79.1M 4s
 34500K .......... .......... .......... .......... .......... 49% 4.91M 4s
 34550K .......... .......... .......... .......... .......... 49%  102M 4s
 34600K .......... .......... .......... .......... .......... 49% 50.0M 4s
 34650K .......... .......... .......... .......... .......... 49% 25.7M 4s
 34700K .......... .......... .......... .......... .......... 49% 23.6M 3s
 34750K .......... .......... .......... .......... .......... 50% 3.19M 4s
 34800K .......... .......... .......... .......... .......... 50% 80.3M 3s
 34850K .......... .......... .......... .......... .......... 50% 5.09M 3s
 34900K .......... .......... .......... .......... .......... 50% 78.1M 3s
 34950K .......... .......... .......... .......... .......... 50% 18.5M 3s
 35000K .......... .......... .......... .......... .......... 50%  207M 3s
 35050K .......... .......... .......... .......... .......... 50% 24.5M 3s
 35100K .......... .......... .......... .......... .......... 50% 3.27M 3s
 35150K .......... .......... .......... .......... .......... 50% 42.2M 3s
 35200K .......... .......... .......... .......... .......... 50% 5.24M 3s
 35250K .......... .......... .......... .......... .......... 50% 46.5M 3s
 35300K .......... .......... .......... .......... .......... 50% 26.0M 3s
 35350K .......... .......... .......... .......... .......... 50% 64.0M 3s
 35400K .......... .......... .......... .......... .......... 50% 23.7M 3s
 35450K .......... .......... .......... .......... .......... 51% 3.29M 3s
 35500K .......... .......... .......... .......... .......... 51% 50.6M 3s
 35550K .......... .......... .......... .......... .......... 51% 4.95M 3s
 35600K .......... .......... .......... .......... .......... 51% 50.5M 3s
 35650K .......... .......... .......... .......... .......... 51% 27.7M 3s
 35700K .......... .......... .......... .......... .......... 51% 23.2M 3s
 35750K .......... .......... .......... .......... .......... 51% 73.8M 3s
 35800K .......... .......... .......... .......... .......... 51% 3.31M 3s
 35850K .......... .......... .......... .......... .......... 51% 4.03M 3s
 35900K .......... .......... .......... .......... .......... 51%  211M 3s
 35950K .......... .......... .......... .......... .......... 51%  116M 3s
 36000K .......... .......... .......... .......... .......... 51% 62.4M 3s
 36050K .......... .......... .......... .......... .......... 51% 24.1M 3s
 36100K .......... .......... .......... .......... .......... 51% 3.34M 3s
 36150K .......... .......... .......... .......... .......... 52% 58.6M 3s
 36200K .......... .......... .......... .......... .......... 52% 4.89M 3s
 36250K .......... .......... .......... .......... .......... 52%  100M 3s
 36300K .......... .......... .......... .......... .......... 52% 25.8M 3s
 36350K .......... .......... .......... .......... .......... 52% 27.8M 3s
 36400K .......... .......... .......... .......... .......... 52% 35.2M 3s
 36450K .......... .......... .......... .......... .......... 52% 3.29M 3s
 36500K .......... .......... .......... .......... .......... 52% 53.3M 3s
 36550K .......... .......... .......... .......... .......... 52% 5.01M 3s
 36600K .......... .......... .......... .......... .......... 52% 88.1M 3s
 36650K .......... .......... .......... .......... .......... 52% 24.2M 3s
 36700K .......... .......... .......... .......... .......... 52% 53.2M 3s
 36750K .......... .......... .......... .......... .......... 52% 24.2M 3s
 36800K .......... .......... .......... .......... .......... 52% 3.24M 3s
 36850K .......... .......... .......... .......... .......... 53% 57.4M 3s
 36900K .......... .......... .......... .......... .......... 53% 5.19M 3s
 36950K .......... .......... .......... .......... .......... 53% 58.3M 3s
 37000K .......... .......... .......... .......... .......... 53% 24.2M 3s
 37050K .......... .......... .......... .......... .......... 53% 52.1M 3s
 37100K .......... .......... .......... .......... .......... 53% 20.1M 3s
 37150K .......... .......... .......... .......... .......... 53% 3.39M 3s
 37200K .......... .......... .......... .......... .......... 53% 4.82M 3s
 37250K .......... .......... .......... .......... .......... 53%  196M 3s
 37300K .......... .......... .......... .......... .......... 53% 56.4M 3s
 37350K .......... .......... .......... .......... .......... 53% 24.5M 3s
 37400K .......... .......... .......... .......... .......... 53% 62.5M 3s
 37450K .......... .......... .......... .......... .......... 53% 22.6M 3s
 37500K .......... .......... .......... .......... .......... 53% 3.34M 3s
 37550K .......... .......... .......... .......... .......... 54% 4.71M 3s
 37600K .......... .......... .......... .......... .......... 54%  205M 3s
 37650K .......... .......... .......... .......... .......... 54% 51.3M 3s
 37700K .......... .......... .......... .......... .......... 54% 24.8M 3s
 37750K .......... .......... .......... .......... .......... 54% 23.1M 3s
 37800K .......... .......... .......... .......... .......... 54% 54.1M 3s
 37850K .......... .......... .......... .......... .......... 54% 3.39M 3s
 37900K .......... .......... .......... .......... .......... 54% 4.89M 3s
 37950K .......... .......... .......... .......... .......... 54% 66.0M 3s
 38000K .......... .......... .......... .......... .......... 54% 54.5M 3s
 38050K .......... .......... .......... .......... .......... 54% 22.2M 3s
 38100K .......... .......... .......... .......... .......... 54% 25.3M 3s
 38150K .......... .......... .......... .......... .......... 54% 47.2M 3s
 38200K .......... .......... .......... .......... .......... 54% 3.43M 3s
 38250K .......... .......... .......... .......... .......... 55% 4.85M 3s
 38300K .......... .......... .......... .......... .......... 55%  144M 3s
 38350K .......... .......... .......... .......... .......... 55% 21.8M 3s
 38400K .......... .......... .......... .......... .......... 55% 35.5M 3s
 38450K .......... .......... .......... .......... .......... 55% 28.7M 3s
 38500K .......... .......... .......... .......... .......... 55% 3.27M 3s
 38550K .......... .......... .......... .......... .......... 55%  137M 3s
 38600K .......... .......... .......... .......... .......... 55% 4.92M 3s
 38650K .......... .......... .......... .......... .......... 55%  101M 3s
 38700K .......... .......... .......... .......... .......... 55% 45.1M 3s
 38750K .......... .......... .......... .......... .......... 55% 23.0M 3s
 38800K .......... .......... .......... .......... .......... 55% 24.5M 3s
 38850K .......... .......... .......... .......... .......... 55% 3.32M 3s
 38900K .......... .......... .......... .......... .......... 55% 51.8M 3s
 38950K .......... .......... .......... .......... .......... 56% 5.03M 3s
 39000K .......... .......... .......... .......... .......... 56% 79.6M 3s
 39050K .......... .......... .......... .......... .......... 56% 26.7M 3s
 39100K .......... .......... .......... .......... .......... 56% 42.6M 3s
 39150K .......... .......... .......... .......... .......... 56% 23.8M 3s
 39200K .......... .......... .......... .......... .......... 56% 3.28M 3s
 39250K .......... .......... .......... .......... .......... 56% 75.6M 3s
 39300K .......... .......... .......... .......... .......... 56% 5.05M 3s
 39350K .......... .......... .......... .......... .......... 56% 74.8M 3s
 39400K .......... .......... .......... .......... .......... 56% 22.4M 3s
 39450K .......... .......... .......... .......... .......... 56% 39.3M 3s
 39500K .......... .......... .......... .......... .......... 56% 31.3M 3s
 39550K .......... .......... .......... .......... .......... 56% 3.31M 3s
 39600K .......... .......... .......... .......... .......... 56% 42.3M 3s
 39650K .......... .......... .......... .......... .......... 57% 5.19M 3s
 39700K .......... .......... .......... .......... .......... 57% 84.7M 3s
 39750K .......... .......... .......... .......... .......... 57% 23.5M 3s
 39800K .......... .......... .......... .......... .......... 57% 36.2M 3s
 39850K .......... .......... .......... .......... .......... 57% 29.6M 3s
 39900K .......... .......... .......... .......... .......... 57% 3.29M 3s
 39950K .......... .......... .......... .......... .......... 57% 52.4M 3s
 40000K .......... .......... .......... .......... .......... 57% 5.09M 3s
 40050K .......... .......... .......... .......... .......... 57% 87.7M 3s
 40100K .......... .......... .......... .......... .......... 57% 25.2M 3s
 40150K .......... .......... .......... .......... .......... 57% 33.5M 3s
 40200K .......... .......... .......... .......... .......... 57% 32.1M 3s
 40250K .......... .......... .......... .......... .......... 57% 3.34M 3s
 40300K .......... .......... .......... .......... .......... 57% 47.8M 3s
 40350K .......... .......... .......... .......... .......... 58% 4.95M 3s
 40400K .......... .......... .......... .......... .......... 58% 97.3M 3s
 40450K .......... .......... .......... .......... .......... 58% 25.8M 3s
 40500K .......... .......... .......... .......... .......... 58% 22.7M 3s
 40550K .......... .......... .......... .......... .......... 58% 61.6M 3s
 40600K .......... .......... .......... .......... .......... 58% 3.29M 3s
 40650K .......... .......... .......... .......... .......... 58% 60.2M 3s
 40700K .......... .......... .......... .......... .......... 58% 4.99M 3s
 40750K .......... .......... .......... .......... .......... 58% 57.7M 3s
 40800K .......... .......... .......... .......... .......... 58% 28.2M 3s
 40850K .......... .......... .......... .......... .......... 58% 21.7M 3s
 40900K .......... .......... .......... .......... .......... 58% 65.4M 3s
 40950K .......... .......... .......... .......... .......... 58% 3.31M 3s
 41000K .......... .......... .......... .......... .......... 59% 60.1M 3s
 41050K .......... .......... .......... .......... .......... 59% 4.93M 3s
 41100K .......... .......... .......... .......... .......... 59%  116M 3s
 41150K .......... .......... .......... .......... .......... 59% 22.6M 3s
 41200K .......... .......... .......... .......... .......... 59% 19.8M 3s
 41250K .......... .......... .......... .......... .......... 59% 36.0M 3s
 41300K .......... .......... .......... .......... .......... 59% 3.52M 3s
 41350K .......... .......... .......... .......... .......... 59% 60.0M 3s
 41400K .......... .......... .......... .......... .......... 59% 5.01M 3s
 41450K .......... .......... .......... .......... .......... 59% 86.6M 3s
 41500K .......... .......... .......... .......... .......... 59% 27.6M 3s
 41550K .......... .......... .......... .......... .......... 59% 19.8M 3s
 41600K .......... .......... .......... .......... .......... 59% 25.2M 3s
 41650K .......... .......... .......... .......... .......... 59% 3.62M 3s
 41700K .......... .......... .......... .......... .......... 60% 57.5M 3s
 41750K .......... .......... .......... .......... .......... 60% 5.03M 3s
 41800K .......... .......... .......... .......... .......... 60% 74.4M 3s
 41850K .......... .......... .......... .......... .......... 60% 26.8M 3s
 41900K .......... .......... .......... .......... .......... 60% 26.3M 3s
 41950K .......... .......... .......... .......... .......... 60% 22.3M 3s
 42000K .......... .......... .......... .......... .......... 60% 3.57M 3s
 42050K .......... .......... .......... .......... .......... 60% 50.3M 3s
 42100K .......... .......... .......... .......... .......... 60% 5.04M 3s
 42150K .......... .......... .......... .......... .......... 60% 75.4M 3s
 42200K .......... .......... .......... .......... .......... 60% 27.6M 3s
 42250K .......... .......... .......... .......... .......... 60% 29.7M 3s
 42300K .......... .......... .......... .......... .......... 60% 35.4M 3s
 42350K .......... .......... .......... .......... .......... 60% 3.28M 3s
 42400K .......... .......... .......... .......... .......... 61% 50.3M 3s
 42450K .......... .......... .......... .......... .......... 61% 5.00M 3s
 42500K .......... .......... .......... .......... .......... 61% 98.8M 3s
 42550K .......... .......... .......... .......... .......... 61% 60.8M 3s
 42600K .......... .......... .......... .......... .......... 61% 22.1M 3s
 42650K .......... .......... .......... .......... .......... 61% 29.5M 3s
 42700K .......... .......... .......... .......... .......... 61% 34.2M 3s
 42750K .......... .......... .......... .......... .......... 61% 3.37M 3s
 42800K .......... .......... .......... .......... .......... 61% 5.10M 3s
 42850K .......... .......... .......... .......... .......... 61% 58.1M 3s
 42900K .......... .......... .......... .......... .......... 61% 81.8M 3s
 42950K .......... .......... .......... .......... .......... 61% 23.1M 3s
 43000K .......... .......... .......... .......... .......... 61% 26.9M 3s
 43050K .......... .......... .......... .......... .......... 61% 33.1M 3s
 43100K .......... .......... .......... .......... .......... 62% 3.48M 3s
 43150K .......... .......... .......... .......... .......... 62% 4.91M 3s
 43200K .......... .......... .......... .......... .......... 62% 65.6M 3s
 43250K .......... .......... .......... .......... .......... 62% 58.3M 3s
 43300K .......... .......... .......... .......... .......... 62% 23.6M 3s
 43350K .......... .......... .......... .......... .......... 62% 27.3M 3s
 43400K .......... .......... .......... .......... .......... 62% 33.4M 3s
 43450K .......... .......... .......... .......... .......... 62% 3.46M 3s
 43500K .......... .......... .......... .......... .......... 62% 48.2M 3s
 43550K .......... .......... .......... .......... .......... 62% 5.15M 3s
 43600K .......... .......... .......... .......... .......... 62% 47.3M 3s
 43650K .......... .......... .......... .......... .......... 62% 25.7M 3s
 43700K .......... .......... .......... .......... .......... 62% 26.4M 3s
 43750K .......... .......... .......... .......... .......... 62% 35.1M 3s
 43800K .......... .......... .......... .......... .......... 63% 3.50M 3s
 43850K .......... .......... .......... .......... .......... 63% 33.1M 3s
 43900K .......... .......... .......... .......... .......... 63% 5.32M 3s
 43950K .......... .......... .......... .......... .......... 63% 57.6M 3s
 44000K .......... .......... .......... .......... .......... 63% 22.5M 3s
 44050K .......... .......... .......... .......... .......... 63% 26.3M 2s
 44100K .......... .......... .......... .......... .......... 63% 32.5M 2s
 44150K .......... .......... .......... .......... .......... 63% 3.54M 2s
 44200K .......... .......... .......... .......... .......... 63% 46.3M 2s
 44250K .......... .......... .......... .......... .......... 63% 4.89M 2s
 44300K .......... .......... .......... .......... .......... 63%  117M 2s
 44350K .......... .......... .......... .......... .......... 63% 25.4M 2s
 44400K .......... .......... .......... .......... .......... 63% 21.2M 2s
 44450K .......... .......... .......... .......... .......... 63% 38.1M 2s
 44500K .......... .......... .......... .......... .......... 64% 3.50M 2s
 44550K .......... .......... .......... .......... .......... 64% 59.0M 2s
 44600K .......... .......... .......... .......... .......... 64% 5.07M 2s
 44650K .......... .......... .......... .......... .......... 64% 54.4M 2s
 44700K .......... .......... .......... .......... .......... 64% 35.1M 2s
 44750K .......... .......... .......... .......... .......... 64% 19.0M 2s
 44800K .......... .......... .......... .......... .......... 64% 31.4M 2s
 44850K .......... .......... .......... .......... .......... 64% 3.48M 2s
 44900K .......... .......... .......... .......... .......... 64% 62.7M 2s
 44950K .......... .......... .......... .......... .......... 64% 5.10M 2s
 45000K .......... .......... .......... .......... .......... 64% 57.9M 2s
 45050K .......... .......... .......... .......... .......... 64% 61.0M 2s
 45100K .......... .......... .......... .......... .......... 64% 26.6M 2s
 45150K .......... .......... .......... .......... .......... 64% 25.6M 2s
 45200K .......... .......... .......... .......... .......... 65% 3.23M 2s
 45250K .......... .......... .......... .......... .......... 65%  110M 2s
 45300K .......... .......... .......... .......... .......... 65% 4.92M 2s
 45350K .......... .......... .......... .......... .......... 65% 66.2M 2s
 45400K .......... .......... .......... .......... .......... 65% 59.2M 2s
 45450K .......... .......... .......... .......... .......... 65% 27.8M 2s
 45500K .......... .......... .......... .......... .......... 65% 25.8M 2s
 45550K .......... .......... .......... .......... .......... 65% 3.24M 2s
 45600K .......... .......... .......... .......... .......... 65% 86.3M 2s
 45650K .......... .......... .......... .......... .......... 65% 4.75M 2s
 45700K .......... .......... .......... .......... .......... 65% 40.9M 2s
 45750K .......... .......... .......... .......... .......... 65%  162M 2s
 45800K .......... .......... .......... .......... .......... 65% 37.6M 2s
 45850K .......... .......... .......... .......... .......... 65% 19.1M 2s
 45900K .......... .......... .......... .......... .......... 66% 43.9M 2s
 45950K .......... .......... .......... .......... .......... 66% 3.50M 2s
 46000K .......... .......... .......... .......... .......... 66% 42.4M 2s
 46050K .......... .......... .......... .......... .......... 66% 5.03M 2s
 46100K .......... .......... .......... .......... .......... 66% 79.4M 2s
 46150K .......... .......... .......... .......... .......... 66% 28.4M 2s
 46200K .......... .......... .......... .......... .......... 66% 25.1M 2s
 46250K .......... .......... .......... .......... .......... 66% 32.3M 2s
 46300K .......... .......... .......... .......... .......... 66% 3.48M 2s
 46350K .......... .......... .......... .......... .......... 66% 45.9M 2s
 46400K .......... .......... .......... .......... .......... 66% 4.86M 2s
 46450K .......... .......... .......... .......... .......... 66%  147M 2s
 46500K .......... .......... .......... .......... .......... 66% 29.2M 2s
 46550K .......... .......... .......... .......... .......... 66% 32.1M 2s
 46600K .......... .......... .......... .......... .......... 67% 34.4M 2s
 46650K .......... .......... .......... .......... .......... 67% 3.34M 2s
 46700K .......... .......... .......... .......... .......... 67% 66.3M 2s
 46750K .......... .......... .......... .......... .......... 67% 4.87M 2s
 46800K .......... .......... .......... .......... .......... 67% 60.6M 2s
 46850K .......... .......... .......... .......... .......... 67% 51.8M 2s
 46900K .......... .......... .......... .......... .......... 67% 30.0M 2s
 46950K .......... .......... .......... .......... .......... 67% 26.1M 2s
 47000K .......... .......... .......... .......... .......... 67% 33.9M 2s
 47050K .......... .......... .......... .......... .......... 67% 3.48M 2s
 47100K .......... .......... .......... .......... .......... 67% 48.7M 2s
 47150K .......... .......... .......... .......... .......... 67% 4.43M 2s
 47200K .......... .......... .......... .......... .......... 67%  217M 2s
 47250K .......... .......... .......... .......... .......... 67% 26.7M 2s
 47300K .......... .......... .......... .......... .......... 68% 18.9M 2s
 47350K .......... .......... .......... .......... .......... 68%  120M 2s
 47400K .......... .......... .......... .......... .......... 68% 3.57M 2s
 47450K .......... .......... .......... .......... .......... 68% 64.5M 2s
 47500K .......... .......... .......... .......... .......... 68% 4.78M 2s
 47550K .......... .......... .......... .......... .......... 68% 37.3M 2s
 47600K .......... .......... .......... .......... .......... 68% 37.8M 2s
 47650K .......... .......... .......... .......... .......... 68% 30.6M 2s
 47700K .......... .......... .......... .......... .......... 68% 30.8M 2s
 47750K .......... .......... .......... .......... .......... 68% 3.48M 2s
 47800K .......... .......... .......... .......... .......... 68% 72.7M 2s
 47850K .......... .......... .......... .......... .......... 68% 5.06M 2s
 47900K .......... .......... .......... .......... .......... 68% 27.8M 2s
 47950K .......... .......... .......... .......... .......... 68% 30.0M 2s
 48000K .......... .......... .......... .......... .......... 69% 33.7M 2s
 48050K .......... .......... .......... .......... .......... 69% 50.0M 2s
 48100K .......... .......... .......... .......... .......... 69% 3.41M 2s
 48150K .......... .......... .......... .......... .......... 69% 67.3M 2s
 48200K .......... .......... .......... .......... .......... 69% 4.96M 2s
 48250K .......... .......... .......... .......... .......... 69% 49.1M 2s
 48300K .......... .......... .......... .......... .......... 69% 32.8M 2s
 48350K .......... .......... .......... .......... .......... 69% 38.0M 2s
 48400K .......... .......... .......... .......... .......... 69% 22.4M 2s
 48450K .......... .......... .......... .......... .......... 69% 39.5M 2s
 48500K .......... .......... .......... .......... .......... 69% 3.60M 2s
 48550K .......... .......... .......... .......... .......... 69% 38.5M 2s
 48600K .......... .......... .......... .......... .......... 69% 4.96M 2s
 48650K .......... .......... .......... .......... .......... 69% 38.8M 2s
 48700K .......... .......... .......... .......... .......... 70% 37.0M 2s
 48750K .......... .......... .......... .......... .......... 70% 27.4M 2s
 48800K .......... .......... .......... .......... .......... 70% 33.6M 2s
 48850K .......... .......... .......... .......... .......... 70% 3.55M 2s
 48900K .......... .......... .......... .......... .......... 70% 50.3M 2s
 48950K .......... .......... .......... .......... .......... 70% 5.14M 2s
 49000K .......... .......... .......... .......... .......... 70% 25.1M 2s
 49050K .......... .......... .......... .......... .......... 70% 70.1M 2s
 49100K .......... .......... .......... .......... .......... 70% 32.5M 2s
 49150K .......... .......... .......... .......... .......... 70% 26.6M 2s
 49200K .......... .......... .......... .......... .......... 70% 3.41M 2s
 49250K .......... .......... .......... .......... .......... 70% 65.5M 2s
 49300K .......... .......... .......... .......... .......... 70% 4.99M 2s
 49350K .......... .......... .......... .......... .......... 71% 46.8M 2s
 49400K .......... .......... .......... .......... .......... 71% 41.6M 2s
 49450K .......... .......... .......... .......... .......... 71% 34.6M 2s
 49500K .......... .......... .......... .......... .......... 71% 30.3M 2s
 49550K .......... .......... .......... .......... .......... 71% 25.9M 2s
 49600K .......... .......... .......... .......... .......... 71% 3.57M 2s
 49650K .......... .......... .......... .......... .......... 71% 46.6M 2s
 49700K .......... .......... .......... .......... .......... 71% 4.87M 2s
 49750K .......... .......... .......... .......... .......... 71% 41.0M 2s
 49800K .......... .......... .......... .......... .......... 71% 35.2M 2s
 49850K .......... .......... .......... .......... .......... 71% 34.1M 2s
 49900K .......... .......... .......... .......... .......... 71% 40.6M 2s
 49950K .......... .......... .......... .......... .......... 71% 3.38M 2s
 50000K .......... .......... .......... .......... .......... 71% 67.7M 2s
 50050K .......... .......... .......... .......... .......... 72% 5.09M 2s
 50100K .......... .......... .......... .......... .......... 72% 29.2M 2s
 50150K .......... .......... .......... .......... .......... 72% 85.1M 2s
 50200K .......... .......... .......... .......... .......... 72% 34.9M 2s
 50250K .......... .......... .......... .......... .......... 72% 21.6M 2s
 50300K .......... .......... .......... .......... .......... 72% 40.1M 2s
 50350K .......... .......... .......... .......... .......... 72% 3.53M 2s
 50400K .......... .......... .......... .......... .......... 72% 4.99M 2s
 50450K .......... .......... .......... .......... .......... 72% 44.5M 2s
 50500K .......... .......... .......... .......... .......... 72% 40.5M 2s
 50550K .......... .......... .......... .......... .......... 72% 35.3M 2s
 50600K .......... .......... .......... .......... .......... 72% 32.1M 2s
 50650K .......... .......... .......... .......... .......... 72% 30.9M 2s
 50700K .......... .......... .......... .......... .......... 72% 3.49M 2s
 50750K .......... .......... .......... .......... .......... 73% 62.8M 2s
 50800K .......... .......... .......... .......... .......... 73% 5.02M 2s
 50850K .......... .......... .......... .......... .......... 73% 28.4M 2s
 50900K .......... .......... .......... .......... .......... 73% 65.3M 2s
 50950K .......... .......... .......... .......... .......... 73% 30.8M 2s
 51000K .......... .......... .......... .......... .......... 73% 27.5M 2s
 51050K .......... .......... .......... .......... .......... 73% 43.7M 2s
 51100K .......... .......... .......... .......... .......... 73% 3.57M 2s
 51150K .......... .......... .......... .......... .......... 73% 4.82M 2s
 51200K .......... .......... .......... .......... .......... 73% 43.5M 2s
 51250K .......... .......... .......... .......... .......... 73% 47.8M 2s
 51300K .......... .......... .......... .......... .......... 73% 35.6M 2s
 51350K .......... .......... .......... .......... .......... 73% 30.7M 2s
 51400K .......... .......... .......... .......... .......... 73% 32.9M 2s
 51450K .......... .......... .......... .......... .......... 74% 3.53M 2s
 51500K .......... .......... .......... .......... .......... 74% 54.6M 2s
 51550K .......... .......... .......... .......... .......... 74% 5.07M 2s
 51600K .......... .......... .......... .......... .......... 74% 25.0M 2s
 51650K .......... .......... .......... .......... .......... 74% 38.1M 2s
 51700K .......... .......... .......... .......... .......... 74% 51.5M 2s
 51750K .......... .......... .......... .......... .......... 74% 27.6M 2s
 51800K .......... .......... .......... .......... .......... 74% 36.1M 2s
 51850K .......... .......... .......... .......... .......... 74% 3.63M 2s
 51900K .......... .......... .......... .......... .......... 74% 43.6M 2s
 51950K .......... .......... .......... .......... .......... 74% 4.96M 2s
 52000K .......... .......... .......... .......... .......... 74% 40.4M 2s
 52050K .......... .......... .......... .......... .......... 74% 34.1M 2s
 52100K .......... .......... .......... .......... .......... 74% 22.3M 2s
 52150K .......... .......... .......... .......... .......... 75% 38.8M 2s
 52200K .......... .......... .......... .......... .......... 75% 3.56M 2s
 52250K .......... .......... .......... .......... .......... 75% 70.7M 2s
 52300K .......... .......... .......... .......... .......... 75% 5.15M 2s
 52350K .......... .......... .......... .......... .......... 75% 21.8M 2s
 52400K .......... .......... .......... .......... .......... 75% 43.5M 2s
 52450K .......... .......... .......... .......... .......... 75% 27.3M 2s
 52500K .......... .......... .......... .......... .......... 75% 56.9M 2s
 52550K .......... .......... .......... .......... .......... 75% 33.8M 2s
 52600K .......... .......... .......... .......... .......... 75% 3.62M 2s
 52650K .......... .......... .......... .......... .......... 75% 4.91M 2s
 52700K .......... .......... .......... .......... .......... 75% 54.0M 2s
 52750K .......... .......... .......... .......... .......... 75% 39.0M 2s
 52800K .......... .......... .......... .......... .......... 75% 35.1M 2s
 52850K .......... .......... .......... .......... .......... 76% 21.8M 2s
 52900K .......... .......... .......... .......... .......... 76% 36.6M 2s
 52950K .......... .......... .......... .......... .......... 76% 3.60M 2s
 53000K .......... .......... .......... .......... .......... 76% 65.1M 2s
 53050K .......... .......... .......... .......... .......... 76% 5.14M 2s
 53100K .......... .......... .......... .......... .......... 76% 26.3M 2s
 53150K .......... .......... .......... .......... .......... 76% 35.3M 2s
 53200K .......... .......... .......... .......... .......... 76% 30.0M 2s
 53250K .......... .......... .......... .......... .......... 76% 42.8M 2s
 53300K .......... .......... .......... .......... .......... 76% 35.2M 2s
 53350K .......... .......... .......... .......... .......... 76% 3.63M 2s
 53400K .......... .......... .......... .......... .......... 76% 4.97M 2s
 53450K .......... .......... .......... .......... .......... 76% 43.4M 2s
 53500K .......... .......... .......... .......... .......... 76% 39.9M 2s
 53550K .......... .......... .......... .......... .......... 77% 32.5M 2s
 53600K .......... .......... .......... .......... .......... 77% 22.8M 2s
 53650K .......... .......... .......... .......... .......... 77% 38.3M 2s
 53700K .......... .......... .......... .......... .......... 77% 3.49M 2s
 53750K .......... .......... .......... .......... .......... 77%  109M 2s
 53800K .......... .......... .......... .......... .......... 77% 5.13M 2s
 53850K .......... .......... .......... .......... .......... 77% 26.9M 1s
 53900K .......... .......... .......... .......... .......... 77% 56.8M 1s
 53950K .......... .......... .......... .......... .......... 77% 23.1M 1s
 54000K .......... .......... .......... .......... .......... 77% 44.5M 1s
 54050K .......... .......... .......... .......... .......... 77% 36.5M 1s
 54100K .......... .......... .......... .......... .......... 77% 3.63M 1s
 54150K .......... .......... .......... .......... .......... 77% 44.4M 1s
 54200K .......... .......... .......... .......... .......... 77% 4.84M 1s
 54250K .......... .......... .......... .......... .......... 78% 44.9M 1s
 54300K .......... .......... .......... .......... .......... 78% 37.9M 1s
 54350K .......... .......... .......... .......... .......... 78% 17.3M 1s
 54400K .......... .......... .......... .......... .......... 78% 64.7M 1s
 54450K .......... .......... .......... .......... .......... 78% 3.39M 1s
 54500K .......... .......... .......... .......... .......... 78%  171M 1s
 54550K .......... .......... .......... .......... .......... 78% 5.21M 1s
 54600K .......... .......... .......... .......... .......... 78% 33.2M 1s
 54650K .......... .......... .......... .......... .......... 78% 43.4M 1s
 54700K .......... .......... .......... .......... .......... 78% 40.3M 1s
 54750K .......... .......... .......... .......... .......... 78% 22.2M 1s
 54800K .......... .......... .......... .......... .......... 78% 26.4M 1s
 54850K .......... .......... .......... .......... .......... 78% 3.70M 1s
 54900K .......... .......... .......... .......... .......... 78% 58.5M 1s
 54950K .......... .......... .......... .......... .......... 79% 5.00M 1s
 55000K .......... .......... .......... .......... .......... 79% 41.6M 1s
 55050K .......... .......... .......... .......... .......... 79% 38.1M 1s
 55100K .......... .......... .......... .......... .......... 79% 32.8M 1s
 55150K .......... .......... .......... .......... .......... 79% 19.7M 1s
 55200K .......... .......... .......... .......... .......... 79% 3.62M 1s
 55250K .......... .......... .......... .......... .......... 79%  175M 1s
 55300K .......... .......... .......... .......... .......... 79% 5.01M 1s
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 69550K .......... .......... .....                           100% 87.0M=6.4s

2020-09-01 08:47:46 (10.6 MB/s) - ‘libnvinfer6_6.0.1-1+cuda10.1_amd64.deb’ saved [71245796/71245796]

/sbin/ldconfig.real: /usr/local/lib/python3.6/dist-packages/ideep4py/lib/libmkldnn.so.0 is not a symbolic link

!apt install libnvinfer-plugin6=6.0.1-1+cuda10.1

Reading package lists... Done
Building dependency tree       
Reading state information... Done
The following package was automatically installed and is no longer required:
  libnvidia-common-440
Use 'apt autoremove' to remove it.
The following NEW packages will be installed:
  libnvinfer-plugin6
0 upgraded, 1 newly installed, 0 to remove and 40 not upgraded.
Need to get 1,751 kB of archives.
After this operation, 4,508 kB of additional disk space will be used.
Get:1 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  libnvinfer-plugin6 6.0.1-1+cuda10.1 [1,751 kB]
Fetched 1,751 kB in 0s (7,666 kB/s)
Selecting previously unselected package libnvinfer-plugin6.
(Reading database ... 144584 files and directories currently installed.)
Preparing to unpack .../libnvinfer-plugin6_6.0.1-1+cuda10.1_amd64.deb ...
Unpacking libnvinfer-plugin6 (6.0.1-1+cuda10.1) ...
Setting up libnvinfer-plugin6 (6.0.1-1+cuda10.1) ...
Processing triggers for libc-bin (2.27-3ubuntu1) ...
/sbin/ldconfig.real: /usr/local/lib/python3.6/dist-packages/ideep4py/lib/libmkldnn.so.0 is not a symbolic link

# check TensorRT version
print("TensorRT version: ")
!dpkg -l | grep nvinfer

TensorRT version: 
ii  libnvinfer-plugin6                     6.0.1-1+cuda10.1                                  amd64        TensorRT plugin libraries
ii  libnvinfer6                            6.0.1-1+cuda10.1                                  amd64        TensorRT runtime libraries

from tensorflow.compiler.tf2tensorrt.wrap_py_utils import get_linked_tensorrt_version
from tensorflow.compiler.tf2tensorrt.wrap_py_utils import get_loaded_tensorrt_version

print(f"Linked TensorRT version {get_linked_tensorrt_version()}")
print(f"Loaded TensorRT version {get_loaded_tensorrt_version()}")

Linked TensorRT version (6, 0, 1)
Loaded TensorRT version (6, 0, 1)

Les deux versions de Tensorflow et TensorRT installées sont maintenant compatibles. Lançons la conversion.

https://developer.ibm.com/components/ibm-power/tutorials/introducing-tensorflow-with-tensorrt/

https://github.com/tensorflow/tensorrt/tree/master/tftrt/examples/image-classification

from tensorflow.python.compiler.tensorrt import trt_convert as trt

• TensorFlow TensorRT (TF-TRT) prend en entrée pour optimiser le modèle un modèle entraîné via tf.keras sous la forme SavedModel, ie un .pb.

Définisoons un dataset de validation, comme pour Tensorflow Lite.

# Prepare validation sets
# Extract the image paths from the train set

# Empty labels for storing images and labels
val_images = []

# Iterate over the image paths
for image in flower_val:
    # Read the image from the current path, change the datatype, resize the image,
    # add batch dimension, normalize the pixel values
    image_pixels = plt.imread(image).astype("float32")
    image_pixels = cv2.resize(image_pixels, (224, 224))
    image_pixels = np.expand_dims(image_pixels, 0)
    image_pixels = image_pixels / 255.

    # Append to the list
    val_images.append(image_pixels)
    val_labels = label_val

# Create NumPy array
val_images = np.array(val_images)

TF-TRT FP32 modèle

On aura besoin de renseigner l'adresse input_saved_model_dir=dir, où dir/saved_model.pb existe. Passons là en variable.

dir = 'base_model/flower_model_no_op/'

!saved_model_cli show --all --dir base_model/flower_model_no_op/

2020-09-01 09:36:58.234629: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libnvinfer.so.6
2020-09-01 09:36:58.379139: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libnvinfer_plugin.so.6

MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs:

signature_def['__saved_model_init_op']:
  The given SavedModel SignatureDef contains the following input(s):
  The given SavedModel SignatureDef contains the following output(s):
    outputs['__saved_model_init_op'] tensor_info:
        dtype: DT_INVALID
        shape: unknown_rank
        name: NoOp
  Method name is: 

signature_def['serving_default']:
  The given SavedModel SignatureDef contains the following input(s):
    inputs['input_1'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 224, 224, 3)
        name: serving_default_input_1:0
  The given SavedModel SignatureDef contains the following output(s):
    outputs['dense_1'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 5)
        name: StatefulPartitionedCall:0
  Method name is: tensorflow/serving/predict
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1786: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.

Defined Functions:
  Function Name: '__call__'
    Option #1
      Callable with:
        Argument #1
          input_1: TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='input_1')
        Argument #2
          DType: bool
          Value: False
        Argument #3
          DType: NoneType
          Value: None
    Option #2
      Callable with:
        Argument #1
          inputs: TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='inputs')
        Argument #2
          DType: bool
          Value: False
        Argument #3
          DType: NoneType
          Value: None
    Option #3
      Callable with:
        Argument #1
          inputs: TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='inputs')
        Argument #2
          DType: bool
          Value: True
        Argument #3
          DType: NoneType
          Value: None
    Option #4
      Callable with:
        Argument #1
          input_1: TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='input_1')
        Argument #2
          DType: bool
          Value: True
        Argument #3
          DType: NoneType
          Value: None

  Function Name: '_default_save_signature'
    Option #1
      Callable with:
        Argument #1
          input_1: TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='input_1')

  Function Name: 'call_and_return_all_conditional_losses'
    Option #1
      Callable with:
        Argument #1
          inputs: TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='inputs')
        Argument #2
          DType: bool
          Value: True
        Argument #3
          DType: NoneType
          Value: None
    Option #2
      Callable with:
        Argument #1
          inputs: TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='inputs')
        Argument #2
          DType: bool
          Value: False
        Argument #3
          DType: NoneType
          Value: None
    Option #3
      Callable with:
        Argument #1
          input_1: TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='input_1')
        Argument #2
          DType: bool
          Value: True
        Argument #3
          DType: NoneType
          Value: None
    Option #4
      Callable with:
        Argument #1
          input_1: TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='input_1')
        Argument #2
          DType: bool
          Value: False
        Argument #3
          DType: NoneType
          Value: None

Conversion parameters

• There are additional parameters that can be passed to saved_model_cli and TrtGraphConverterV2:

  • precision_mode : The precision mode to use (FP32, FP16, or INT8)
  • minimum_segment_size : The minimum number of TensorFlow nodes required for a TensorRT subgraph to be valid.
  • is_dynamic_op : TensorRT engines are converted and built at model run time instead of during the converter.convert() call. This is required if there are tensors with unknown or dynamic shapes.
  • use_calibration : Only used if precision_mode='INT8'. If True, a calibration graph will be created, and converter.calibrate() should be called. This is the recommended option. If False, all tensors that will not be fused must have quantization nodes. See NVIDIA’s INT8 Quantization for details.
  • max_batch_size : Used when is_dynamic_op=False. This is the maximum batch size for TensorRT engines. At run time, smaller batch sizes can be used, but a larger batch size will result in an error.
  • maximum_cached_engines : Used when is_dynamic_op=True. This limits the number of TensorRT engines that are cached, per TRTEngineOp.

print('Converting to TF-TRT FP32...')
conversion_params = trt.DEFAULT_TRT_CONVERSION_PARAMS._replace(precision_mode=trt.TrtPrecisionMode.FP32)

converter = trt.TrtGraphConverterV2(input_saved_model_dir=dir,
                                    conversion_params=conversion_params)
converter.convert()
converter.save(output_saved_model_dir='dir_saved_model_TFTRT_FP32')
print('Done Converting to TF-TRT FP32')

Converting to TF-TRT FP32...
INFO:tensorflow:Linked TensorRT version: (6, 0, 1)
INFO:tensorflow:Loaded TensorRT version: (6, 0, 1)
INFO:tensorflow:Could not find TRTEngineOp_0 in TF-TRT cache. This can happen if build() is not called, which means TensorRT engines will be built and cached at runtime.
INFO:tensorflow:Assets written to: dir_saved_model_TFTRT_FP32/assets
Done Converting to TF-TRT FP32

!saved_model_cli show --all --dir dir_saved_model_TFTRT_FP32

2020-09-01 09:34:25.612510: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libnvinfer.so.6
2020-09-01 09:34:25.612670: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libnvinfer_plugin.so.6'; dlerror: libnvinfer_plugin.so.6: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib64-nvidia
2020-09-01 09:34:25.612690: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:30] Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.

MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs:

signature_def['__saved_model_init_op']:
  The given SavedModel SignatureDef contains the following input(s):
  The given SavedModel SignatureDef contains the following output(s):
    outputs['__saved_model_init_op'] tensor_info:
        dtype: DT_INVALID
        shape: unknown_rank
        name: NoOp
  Method name is: 

signature_def['serving_default']:
  The given SavedModel SignatureDef contains the following input(s):
    inputs['input_1'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 224, 224, 3)
        name: serving_default_input_1:0
  The given SavedModel SignatureDef contains the following output(s):
    outputs['dense_1'] tensor_info:
        dtype: DT_FLOAT
        shape: unknown_rank
        name: PartitionedCall:0
  Method name is: tensorflow/serving/predict
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1786: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.

Defined Functions:
  Function Name: '__call__'
    Option #1
      Callable with:
        Argument #1
          inputs: TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='inputs')
        Argument #2
          DType: bool
          Value: True
        Argument #3
          DType: NoneType
          Value: None
    Option #2
      Callable with:
        Argument #1
          input_1: TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='input_1')
        Argument #2
          DType: bool
          Value: True
        Argument #3
          DType: NoneType
          Value: None
    Option #3
      Callable with:
        Argument #1
          inputs: TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='inputs')
        Argument #2
          DType: bool
          Value: False
        Argument #3
          DType: NoneType
          Value: None
    Option #4
      Callable with:
        Argument #1
          input_1: TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='input_1')
        Argument #2
          DType: bool
          Value: False
        Argument #3
          DType: NoneType
          Value: None

  Function Name: '_default_save_signature'
    Option #1
      Callable with:
        Argument #1
          input_1: TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='input_1')

  Function Name: 'call_and_return_all_conditional_losses'
    Option #1
      Callable with:
        Argument #1
          inputs: TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='inputs')
        Argument #2
          DType: bool
          Value: False
        Argument #3
          DType: NoneType
          Value: None
    Option #2
      Callable with:
        Argument #1
          input_1: TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='input_1')
        Argument #2
          DType: bool
          Value: False
        Argument #3
          DType: NoneType
          Value: None
    Option #3
      Callable with:
        Argument #1
          input_1: TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='input_1')
        Argument #2
          DType: bool
          Value: True
        Argument #3
          DType: NoneType
          Value: None
    Option #4
      Callable with:
        Argument #1
          inputs: TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='inputs')
        Argument #2
          DType: bool
          Value: True
        Argument #3
          DType: NoneType
          Value: None

On charge le modèle converti est on lance une prédiction.

CLASSES = ['daisy', 'dandelion', 'roses', 'sunflowers', 'tulips']

from tensorflow.keras.preprocessing import image
from tensorflow.python.saved_model import tag_constants

def predict_tftrt(input_saved_model, img_path_i):
  """Runs prediction on a single image and shows the result.
  input_saved_model (string): Name of the input model stored in the current dir
  """
  img_path = ALL_IMG_PATHS[img_path_i]
  img = image.load_img(img_path, target_size=(224, 224))
  x = image.img_to_array(img)
  x = np.expand_dims(x, axis=0)
  #x = preprocess_input(x)
  x = tf.constant(x)

  saved_model_loaded = tf.saved_model.load(input_saved_model, tags=[tag_constants.SERVING])
  signature_keys = list(saved_model_loaded.signatures.keys())
  print(signature_keys)

  infer = saved_model_loaded.signatures['serving_default']
  print(infer.structured_outputs)

  labeling = infer(x)
  preds = labeling['dense_1'].numpy()
  print(f'True: {image_path.split("/")[1]} - Predicted: {CLASSES[np.argmax(preds)]}')
  plt.subplot(2,2,1)
  plt.imshow(img);
  plt.axis('off');
  plt.title('pred')

dataset = tf.data.Dataset.from_tensor_slices((val_images,val_labels))

def evaluate_tftrt_model(input_saved_model):
  acc = tf.keras.metrics.Accuracy()

  # Load TF TRT model
  saved_model_loaded = tf.saved_model.load(input_saved_model, tags=[tag_constants.SERVING])
  signature_keys = list(saved_model_loaded.signatures.keys())  
  infer = saved_model_loaded.signatures['serving_default']

  # Run predictions on every image in the "test" dataset.
  predictions = []

  for val_image, val_label in dataset:
    # Run inference.

    #print(infer.structured_outputs)

    labeling = infer(val_image)

    # Post-processing: remove batch dimension and find the digit with highest
    # probability.
    preds = labeling['dense_1'].numpy()
    flower_id = np.argmax(preds)
    predictions.append(flower_id)

    # Compare prediction results with ground truth labels to calculate accuracy.

  acc.update_state(val_labels, predictions)
  acc = acc.result().numpy()

  return accuracy

def benchmark_saved_model(input_saved_model, BATCH_SIZE=32):
  # Load TF TRT model
  saved_model_loaded = tf.saved_model.load(input_saved_model, tags=[tag_constants.SERVING])
  signature_keys = list(saved_model_loaded.signatures.keys())  
  print(signature_keys)

  infer = saved_model_loaded.signatures['serving_default']
  print(infer.structured_outputs)

  print('Warming up for 50 batches...')
  cnt = 0
  for x, y in dataset:
      labeling = infer(x)
      cnt += 1
      if cnt == 50:
          break

  print('Benchmarking inference engine...')
  num_hits = 0
  num_predict = 0
  start_time = time.time()
  for x, y in dataset:
      labeling = infer(x)
      preds = labeling['dense_1'].numpy()
      num_hits += np.sum(preds == y)
      num_predict += preds.shape[0]

  print(f'Accuracy : {100*num_hits/num_predict:.2f%%}')
  print(f'Inference speed: {num_predict/(time.time()-start_time):.2f} samples/s')

acc = evaluate_tftrt_model('dir_saved_model_TFTRT_FP32')

print(acc)

0.8964578

def get_gzipped_model_size(file):
  # Returns size of gzipped model, in bytes.
  import os
  import zipfile
  import tempfile

  _, zipped_file = tempfile.mkstemp('.zip')
  with zipfile.ZipFile(zipped_file, 'w', compression=zipfile.ZIP_DEFLATED) as f:
    f.write(file)

  return os.path.getsize(zipped_file)

print(f"Size of gzipped TF-TRT FP32 model: {get_gzipped_model_size('dir_saved_model_TFTRT_FP32/saved_model.pb')/1e6:.2f} Mb")

Size of gzipped pruned model trained from scratch: 22.02 Mb

predict_tftrt('dir_saved_model_TFTRT_FP32')

['serving_default']
{'dense_1': TensorSpec(shape=<unknown>, dtype=tf.float32, name='dense_1')}
flower_photos/tulips/7094415739_6b29e5215c_m.jpg - Predicted: dandelion

# Extract the image paths from the test set, shuffle them, and 
# choose 100 images
random.shuffle(flower_test)

batch_size = 32
batched_input = np.zeros((batch_size, 224, 224, 3), dtype=np.float32)

for i in range(batch_size):
  img_path = flower_test[i]
  img = image.load_img(img_path, target_size=(224, 224))
  x = image.img_to_array(img)
  x = np.expand_dims(x, axis=0)
  #x = preprocess_input(x)
  batched_input[i, :] = x
batched_input = tf.constant(batched_input)
print('batched_input shape: ', batched_input.shape)

batched_input shape:  (32, 224, 224, 3)

saved_model_loaded = tf.saved_model.load('dir_saved_model_TFTRT_FP32', tags=[tag_constants.SERVING])
infer = saved_model_loaded.signatures['serving_default']

labeling = infer(batched_input)

labeling

{'dense_1': <tf.Tensor: shape=(32, 5), dtype=float32, numpy=
 array([[0.0088747 , 0.9767918 , 0.00127777, 0.01128097, 0.00177473],
        [0.03964839, 0.47872713, 0.02123511, 0.37978247, 0.08060697],
        [0.6408535 , 0.23330621, 0.04096965, 0.02476202, 0.06010861],
        [0.03127632, 0.95195925, 0.00256521, 0.00962751, 0.0045717 ],
        [0.23046839, 0.33052164, 0.03368737, 0.07395303, 0.33136958],
        [0.07995716, 0.5434051 , 0.04632825, 0.15429737, 0.17601213],
        [0.05205392, 0.90882397, 0.00295803, 0.02629342, 0.0098706 ],
        [0.21822473, 0.67236066, 0.02545933, 0.03455472, 0.04940057],
        [0.02356102, 0.96842057, 0.0019952 , 0.0044627 , 0.00156046],
        [0.09176064, 0.86290795, 0.00751013, 0.01719821, 0.02062304],
        [0.23318937, 0.46196407, 0.02395418, 0.10345814, 0.17743419],
        [0.04559918, 0.8493675 , 0.00347995, 0.0747133 , 0.02684013],
        [0.02022551, 0.9180966 , 0.01522244, 0.02123133, 0.02522412],
        [0.02450896, 0.9172821 , 0.00481094, 0.02517896, 0.02821913],
        [0.43404263, 0.36382154, 0.04026186, 0.05822622, 0.10364769],
        [0.30016962, 0.5991007 , 0.06361873, 0.01602487, 0.0210861 ],
        [0.02560495, 0.8583455 , 0.006536  , 0.03781543, 0.07169813],
        [0.41791597, 0.38264656, 0.04154864, 0.06038012, 0.09750871],
        [0.31952348, 0.5942062 , 0.01773698, 0.03286676, 0.03566658],
        [0.05328519, 0.86068314, 0.00503189, 0.06404767, 0.01695201],
        [0.22895539, 0.5240973 , 0.03053663, 0.08612359, 0.13028705],
        [0.04869621, 0.82155716, 0.02925042, 0.05661807, 0.04387806],
        [0.10426427, 0.7885772 , 0.01618585, 0.02916272, 0.06181002],
        [0.30164632, 0.57752544, 0.03456168, 0.03043949, 0.05582702],
        [0.23405264, 0.53193617, 0.02175272, 0.09940863, 0.11284982],
        [0.17902733, 0.6940101 , 0.02832778, 0.05573856, 0.04289621],
        [0.22064471, 0.58679503, 0.06802008, 0.01932614, 0.10521407],
        [0.03830879, 0.83624995, 0.02486008, 0.04252513, 0.05805605],
        [0.18920578, 0.76590884, 0.00939537, 0.02154765, 0.01394244],
        [0.2575585 , 0.6262969 , 0.05659823, 0.02013459, 0.03941176],
        [0.33415112, 0.5626648 , 0.01746169, 0.03285992, 0.05286248],
        [0.14745806, 0.7814531 , 0.01624616, 0.02162713, 0.03321562]],
       dtype=float32)>}

def benchmark_tftrt(input_saved_model):
    saved_model_loaded = tf.saved_model.load(input_saved_model, tags=[tag_constants.SERVING])
    infer = saved_model_loaded.signatures['serving_default']

    N_warmup_run = 50
    N_run = 1000
    elapsed_time = []

    for i in range(N_warmup_run):
      labeling = infer(batched_input)

    for i in range(N_run):
      start_time = time.time()
      labeling = infer(batched_input)
      #prob = labeling['probs'].numpy()
      end_time = time.time()
      elapsed_time = np.append(elapsed_time, end_time - start_time)
      if i % 50 == 0:
        print(f'Step {i}: {(elapsed_time[-50:].mean() * 1000):4.1f}ms')

    print(f'Throughput: {N_run * batch_size / elapsed_time.sum():.0f} images/s')

benchmark_tftrt('dir_saved_model_TFTRT_FP32')

TF-TRT FP16 modèle

print('Converting to TF-TRT FP16...')
conversion_params = trt.DEFAULT_TRT_CONVERSION_PARAMS._replace(
    precision_mode=trt.TrtPrecisionMode.FP16)

converter = trt.TrtGraphConverterV2(input_saved_model_dir=dir,
                                    conversion_params=conversion_params)

converter.convert()

converter.save(output_saved_model_dir='dir_saved_model_TFTRT_FP16')
print('Done Converting to TF-TRT FP16')

Converting to TF-TRT FP16...
INFO:tensorflow:Linked TensorRT version: (6, 0, 1)
INFO:tensorflow:Loaded TensorRT version: (6, 0, 1)
INFO:tensorflow:Assets written to: dir_saved_model_TFTRT_FP16/assets
Done Converting to TF-TRT FP16

acc = evaluate_tftrt_model('dir_saved_model_TFTRT_FP16')

print(acc)

0.8964578

print(f"Size of gzipped TF-TRT FP16 model: {get_gzipped_model_size('dir_saved_model_TFTRT_FP16/saved_model.pb')/1e6:.2f} Mb")

Size of gzipped TF-TRT FP16 model: 22.02 Mb

TF-TRT INT8 modèle

batch_size = 32
batched_input = np.zeros((batch_size, 224, 224, 3), dtype=np.float32)

for i in range(batch_size):
  img_path = flower_test[i]
  img = image.load_img(img_path, target_size=(224, 224))
  x = image.img_to_array(img)
  x = np.expand_dims(x, axis=0)
  #x = preprocess_input(x)
  batched_input[i, :] = x
batched_input = tf.constant(batched_input)
print('batched_input shape: ', batched_input.shape)

batched_input shape:  (32, 224, 224, 3)

print('Converting to TF-TRT INT8...')
conversion_params = trt.DEFAULT_TRT_CONVERSION_PARAMS._replace(
    precision_mode=trt.TrtPrecisionMode.INT8, 
    use_calibration=True)
converter = trt.TrtGraphConverterV2(
    input_saved_model_dir=dir, 
    conversion_params=conversion_params)

def calibration_input_fn():
    yield (batched_input, )
converter.convert(calibration_input_fn=calibration_input_fn)

converter.save(output_saved_model_dir='dir_saved_model_TFTRT_INT8')
print('Done Converting to TF-TRT INT8')

Converting to TF-TRT INT8...

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-1-aca72c532aa7> in <module>()
      1 print('Converting to TF-TRT INT8...')
----> 2 conversion_params = trt.DEFAULT_TRT_CONVERSION_PARAMS._replace(
      3     precision_mode=trt.TrtPrecisionMode.INT8,
      4     use_calibration=True)
      5 converter = trt.TrtGraphConverterV2(

NameError: name 'trt' is not defined

print(f"Size of gzipped TF-TRT INT8 model: {get_gzipped_model_size('dir_saved_model_TFTRT_INT8/saved_model.pb')/1e6:.2f} Mb")

Size of gzipped pruned model trained from scratch: 22.02 Mb

acc = evaluate_tftrt_model('dir_saved_model_TFTRT_INT8')

print(acc)

0.8746594005449592

benchmark_tftrt('dir_saved_model_TFTRT_INT8')

Step 0: 81.2ms
Step 50: 81.2ms
Step 100: 81.2ms
Step 150: 80.9ms
Step 200: 80.9ms
Step 250: 80.8ms
Step 300: 80.8ms
Step 350: 80.6ms
Step 400: 80.5ms
Step 450: 80.7ms
Step 500: 80.9ms
Step 550: 80.6ms
Step 600: 80.8ms
Step 650: 80.8ms
Step 700: 80.8ms
Step 750: 80.9ms
Step 800: 80.8ms
Step 850: 80.7ms
Step 900: 80.6ms
Step 950: 80.7ms
Throughput: 396 images/s