NVIDIA-GPU-Bootcamp-Training/ai/Distributed_Deep_Learning/English/python/source_code/N4/cifar_base.py

#!/usr/bin/env python
# coding: utf-8
# %%
import argparse
import tensorflow as tf
from tensorflow.keras.datasets import cifar10
from tensorflow.keras.layers import Input, Add, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D, AveragePooling2D, MaxPooling2D, GlobalMaxPooling2D
from tensorflow.keras.models import Model, load_model
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.imagenet_utils import preprocess_input
from tensorflow.keras import backend as K
from tensorflow.keras.initializers import glorot_uniform
import horovod.tensorflow.keras as hvd
import sys
import time
tf.random.set_seed(1330)


def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument("--batch-size", type=int, default=256, help="Batch size")
    args = parser.parse_args()

    return args

args = parse_args()
global g_args
g_args = args
batch_size = args.batch_size

# Horovod: initialize Horovod.
hvd.init()

# Horovod: pin GPU to be used to process local rank (one GPU per process)
gpus = tf.config.experimental.list_physical_devices('GPU')
for gpu in gpus:
    tf.config.experimental.set_memory_growth(gpu, True)
if gpus:
    tf.config.experimental.set_visible_devices(gpus[hvd.local_rank()], 'GPU')

(images, labels), _ =     tf.keras.datasets.cifar10.load_data()

dataset = tf.data.Dataset.from_tensor_slices(
    (tf.cast(images[...] / 255.0, tf.float32),
             tf.cast(labels, tf.int64))
)
dataset = dataset.repeat().shuffle(10000).batch(batch_size)

def convolutional_block(X, f, filters, stage, block, s=2):

    # Defining name basis
    conv_name_base = 'res' + str(stage) + block + '_branch'
    bn_name_base = 'bn' + str(stage) + block + '_branch'

    # Retrieve Filters
    F1, F2, F3 = filters

    # Save the input value
    X_shortcut = X

    ##### MAIN PATH #####
    # First component of main path 
    X = Conv2D(filters=F1, kernel_size=(1, 1), strides=(s, s), padding='valid', name=conv_name_base + '2a', kernel_initializer=glorot_uniform(seed=0))(X)
    #X = BatchNormalization(axis=3, name=bn_name_base + '2a')(X)
    X = Activation('relu')(X)

    # Second component of main path
    X = Conv2D(filters=F2, kernel_size=(f, f), strides=(1, 1), padding='same', name=conv_name_base + '2b', kernel_initializer=glorot_uniform(seed=0))(X)
    #X = BatchNormalization(axis=3, name=bn_name_base + '2b')(X)
    X = Activation('relu')(X)

    # Third component of main path
    X = Conv2D(filters=F3, kernel_size=(1, 1), strides=(1, 1), padding='valid', name=conv_name_base + '2c', kernel_initializer=glorot_uniform(seed=0))(X)
    #X = BatchNormalization(axis=3, name=bn_name_base + '2c')(X)

    ##### SHORTCUT PATH #### 
    X_shortcut = Conv2D(filters=F3, kernel_size=(1, 1), strides=(s, s), padding='valid', name=conv_name_base + '1', kernel_initializer=glorot_uniform(seed=0))(X_shortcut)
    #X_shortcut = BatchNormalization(axis=3, name=bn_name_base + '1')(X_shortcut)

    # Final step: Add shortcut value to main path, and pass it through a RELU activation
    X = Add()([X, X_shortcut])
    X = Activation('relu')(X)

    return X

def ResNet(input_shape = (28, 28, 1), classes = 10):
    
    # Define the input as a tensor with shape input_shape
    X_input = Input(shape=input_shape)

    
    # Zero-Padding
    X = ZeroPadding2D((3, 3))(X_input)
    
    # Stage 1
    X = Conv2D(64, (7, 7), strides = (2, 2), name = 'conv1', kernel_initializer = glorot_uniform(seed=0))(X)
    #X = BatchNormalization(axis = 3, name = 'bn_conv1')(X)
    X = Activation('relu')(X)
    X = MaxPooling2D((3, 3), strides=(2, 2))(X)

    # Stage 2
    X = convolutional_block(X, f = 3, filters = [64, 64, 256], stage = 2, block='a', s = 1)

    # Stage 3
    X = convolutional_block(X, f=3, filters=[128, 128, 512], stage=3, block='a', s=2)

    # AVGPOOL
    X = AveragePooling2D(pool_size=(2,2), padding='same')(X)

    # Output layer
    X = Flatten()(X)
    X = Dense(classes, activation='softmax', name='fc' + str(classes), kernel_initializer = glorot_uniform(seed=0))(X)
    
    
    # Create model
    model = Model(inputs = X_input, outputs = X, name='ResNet')

    return model

model = ResNet(input_shape = (32, 32, 3), classes = 10)

# %%
# Horovod: adjust learning rate based on number of GPUs.
opt = tf.optimizers.Adam()



# Horovod: add Horovod DistributedOptimizer.
opt = hvd.DistributedOptimizer(
    opt, backward_passes_per_step=1, average_aggregated_gradients=True)

# Horovod: Specify `experimental_run_tf_function=False` to ensure TensorFlow
# uses hvd.DistributedOptimizer() to compute gradients.
model.compile(loss=tf.losses.SparseCategoricalCrossentropy(),
                    optimizer=opt,
                    metrics=['accuracy'],
                    experimental_run_tf_function=False)

class PrintLR(tf.keras.callbacks.Callback):
    def __init__(self, total_images=0):
        self.total_images = total_images
    def on_epoch_begin(self, epoch, logs=None):
        self.epoch_start_time = time.time()
    def on_epoch_end(self, epoch, logs=None):
        if hvd.rank() == 0 :
            epoch_time = time.time() - self.epoch_start_time
            print('Epoch time : {}'.format(epoch_time))
            images_per_sec = round(self.total_images / epoch_time, 2)
            print('Images/sec: {}'.format(images_per_sec))
            

callbacks = [
    # Horovod: broadcast initial variable states from rank 0 to all other processes.
    # This is necessary to ensure consistent initialization of all workers when
    # training is started with random weights or restored from a checkpoint.
    hvd.callbacks.BroadcastGlobalVariablesCallback(0),

    # Horovod: average metrics among workers at the end of every epoch.
    #
    # Note: This callback must be in the list before the ReduceLROnPlateau,
    # TensorBoard or other metrics-based callbacks.
    hvd.callbacks.MetricAverageCallback(),
    PrintLR(total_images=len(labels))

]

# model.summary()

# Horovod: write logs on worker 0.
verbose = 1 if hvd.rank() == 0 else 0

# Train the model.
# Horovod: adjust number of steps based on number of GPUs.
model.fit(dataset, steps_per_epoch=len(labels) // (batch_size*hvd.size()), callbacks=callbacks, epochs=12, verbose=verbose)