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run_catsdogs.py
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run_catsdogs.py
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from __future__ import print_function
import os
import time
import json
import argparse
import densenet
import numpy as np
import keras.backend as K
from keras.optimizers import Adam, SGD
from keras.utils import np_utils
from keras.preprocessing.image import ImageDataGenerator
DATASET_DIR = '/home/zixuan/neurocompann/Datasets/cats_vs_dogs'
def sample_latency_ANN(ann, batch_shape, repeat):
samples = []
# drop first run
ann.predict(np.random.random(batch_shape), batch_size=batch_shape[0])
for i in range(repeat):
data_in = np.random.random(batch_shape)
start_time = time.time()
ann.predict(data_in, batch_size=batch_shape[0])
samples.append(time.time() - start_time)
per_frame_latency = np.array(samples) / batch_shape[0]
avg_latency_per_frame = np.average(per_frame_latency)
std_dev_per_frame = np.std(per_frame_latency)
return(avg_latency_per_frame, std_dev_per_frame)
def run_gtsrb(batch_size,
nb_epoch,
depth,
nb_dense_block,
nb_filter,
growth_rate,
dropout_rate,
learning_rate,
weight_decay,
logfile,
plot_architecture):
""" Run GTSRB experiments
:param batch_size: int -- batch size
:param nb_epoch: int -- number of training epochs
:param depth: int -- network depth
:param nb_dense_block: int -- number of dense blocks
:param nb_filter: int -- initial number of conv filter
:param growth_rate: int -- number of new filters added by conv layers
:param dropout_rate: float -- dropout rate
:param learning_rate: float -- learning rate
:param weight_decay: float -- weight decay
:param plot_architecture: bool -- whether to plot network architecture
"""
###################
# Data processing #
###################
tr_x = np.load(os.path.join(DATASET_DIR, 'rgb_train_in.npy'))
tr_y = np.load(os.path.join(DATASET_DIR, 'rgb_train_out.npy'))
te_x = np.load(os.path.join(DATASET_DIR, 'rgb_test_in.npy'))
te_y = np.load(os.path.join(DATASET_DIR, 'rgb_test_out.npy'))
va_x = np.load(os.path.join(DATASET_DIR, 'rgb_valid_in.npy'))
va_y = np.load(os.path.join(DATASET_DIR, 'rgb_valid_out.npy'))
X_train = tr_x
Y_train = tr_y
X_test = np.vstack((te_x, va_x))
Y_test = np.vstack((te_y, va_y))
nb_classes = Y_train.shape[1]
img_dim = X_train.shape[1:]
if K.image_data_format() == "channels_first":
n_channels = X_train.shape[1]
else:
n_channels = X_train.shape[-1]
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
# Normalisation
X = np.vstack((X_train, X_test))
# 2 cases depending on the image ordering
if K.image_data_format() == "channels_first":
for i in range(n_channels):
mean = np.mean(X[:, i, :, :])
std = np.std(X[:, i, :, :])
X_train[:, i, :, :] = (X_train[:, i, :, :] - mean) / std
X_test[:, i, :, :] = (X_test[:, i, :, :] - mean) / std
elif K.image_data_format() == "channels_last":
for i in range(n_channels):
mean = np.mean(X[:, :, :, i])
std = np.std(X[:, :, :, i])
X_train[:, :, :, i] = (X_train[:, :, :, i] - mean) / std
X_test[:, :, :, i] = (X_test[:, :, :, i] - mean) / std
###################
# Construct model #
###################
model = densenet.DenseNet(nb_classes,
img_dim,
depth,
nb_dense_block,
growth_rate,
nb_filter,
dropout_rate=dropout_rate,
weight_decay=weight_decay)
# Model output
model.summary()
# Build optimizer
# opt = Adam(lr=learning_rate, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
opt = SGD(lr=learning_rate, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy',
optimizer=opt,
metrics=["accuracy"])
if plot_architecture:
from keras.utils.visualize_util import plot
plot(model, to_file='./figures/densenet_archi.png', show_shapes=True)
####################
# Network training #
####################
print("Training")
list_train_loss = []
list_test_loss = []
list_learning_rate = []
datagen = ImageDataGenerator()
for e in range(nb_epoch):
if e == int(0.5 * nb_epoch):
K.set_value(model.optimizer.lr, np.float32(learning_rate / 10.))
if e == int(0.75 * nb_epoch):
K.set_value(model.optimizer.lr, np.float32(learning_rate / 100.))
l_train_loss = []
start = time.time()
model.fit_generator(datagen.flow(X_train, Y_train, batch_size), epochs=1)
test_logloss, test_acc = model.evaluate(X_test,
Y_test,
verbose=1,
batch_size=64)
list_test_loss.append([test_logloss, test_acc])
list_learning_rate.append(float(K.get_value(model.optimizer.lr)))
# to convert numpy array to json serializable
print('Epoch %s/%s, Time: %s' % (e + 1, nb_epoch, time.time() - start))
d_log = {}
d_log["batch_size"] = batch_size
d_log["nb_epoch"] = nb_epoch
d_log["optimizer"] = opt.get_config()
# d_log["train_loss"] = list_train_loss
d_log["test_loss"] = list_test_loss
d_log["learning_rate"] = list_learning_rate
json_file = os.path.join('./log', logfile)
with open(json_file, 'w') as fp:
json.dump(d_log, fp, indent=4, sort_keys=True)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Run GTSRB experiment')
parser.add_argument('--batch_size', default=64, type=int,
help='Batch size')
parser.add_argument('--nb_epoch', default=30, type=int,
help='Number of epochs')
parser.add_argument('--depth', type=int, default=7,
help='Network depth')
parser.add_argument('--nb_dense_block', type=int, default=1,
help='Number of dense blocks')
parser.add_argument('--nb_filter', type=int, default=16,
help='Initial number of conv filters')
parser.add_argument('--growth_rate', type=int, default=12,
help='Number of new filters added by conv layers')
parser.add_argument('--dropout_rate', type=float, default=0.2,
help='Dropout rate')
parser.add_argument('--learning_rate', type=float, default=1E-3,
help='Learning rate')
parser.add_argument('--weight_decay', type=float, default=1E-4,
help='L2 regularization on weights')
parser.add_argument('--logfile', type=str, default='experiment_log_cifar10.json',
help='logfile name')
parser.add_argument('--plot_architecture', type=bool, default=False,
help='Save a plot of the network architecture')
args = parser.parse_args()
print("Network configuration:")
for name, value in parser.parse_args()._get_kwargs():
print(name, value)
list_dir = ["./log", "./figures"]
for d in list_dir:
if not os.path.exists(d):
os.makedirs(d)
run_gtsrb(args.batch_size,
args.nb_epoch,
args.depth,
args.nb_dense_block,
args.nb_filter,
args.growth_rate,
args.dropout_rate,
args.learning_rate,
args.weight_decay,
args.logfile,
args.plot_architecture)