def eval_imagenet_q(net_name, param_pickle_path):
netparams = load.load_netparams_tf_q(param_pickle_path)
data_spec = networks.get_data_spec(net_name)
input_node = tf.placeholder(tf.float32,
shape=(None, data_spec.crop_size,
data_spec.crop_size,
data_spec.channels))
label_node = tf.placeholder(tf.int32)
if net_name == 'alexnet':
logits_ = networks.alexnet(input_node, netparams)
elif net_name == 'googlenet':
logits_ = networks.googlenet(input_node, netparams)
elif net_name == 'nin':
logits_ = networks.nin(input_node, netparams)
elif net_name == 'resnet18':
logits_ = networks.resnet18(input_node, netparams)
elif net_name == 'resnet50':
logits_ = networks.resnet50(input_node, netparams)
elif net_name == 'squeezenet':
logits_ = networks.squeezenet(input_node, netparams)
elif net_name == 'vgg16net':
logits_ = networks.vgg16net_noisy(input_node, netparams)
loss_op = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(logits=logits_,
labels=label_node)) #
probs = softmax(logits_)
top_k_op = tf.nn.in_top_k(probs, label_node, 5)
optimizer = tf.train.AdamOptimizer(learning_rate=0.001, epsilon=0.1)
correct_pred = tf.equal(tf.argmax(probs, 1), tf.argmax(label_node, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
count = 0
correct = 0
cur_accuracy = 0
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
image_producer = dataset.ImageNetProducer(val_path=IMAGE_LABLE,
data_path=IMAGE_PATH,
data_spec=data_spec)
total = len(image_producer)
coordinator = tf.train.Coordinator()
threads = image_producer.start(session=sess, coordinator=coordinator)
for (labels, images) in image_producer.batches(sess):
correct += np.sum(
sess.run(top_k_op,
feed_dict={
input_node: images,
label_node: labels
}))
count += len(labels)
cur_accuracy = float(correct) * 100 / count
#print('{:>6}/{:<6} {:>6.2f}%'.format(count, total, cur_accuracy))
print(cur_accuracy)
coordinator.request_stop()
coordinator.join(threads, stop_grace_period_secs=2)
return cur_accuracy
def eval_imagenet(net_name,
param_path,
shift_back,
trainable=False,
err_mean=None,
err_stddev=None,
train_vars=None,
cost_factor=200.,
n_epoch=1):
if '.ckpt' in param_path:
netparams = load.load_netparams_tf(param_path, trainable=False)
else:
netparams = load.load_netparams_tf_q(param_path, trainable=False)
#print((len(netparams['biases'])))
data_spec = networks.get_data_spec(net_name)
input_node = tf.placeholder(tf.float32,
shape=(None, data_spec.crop_size,
data_spec.crop_size,
data_spec.channels))
label_node = tf.placeholder(tf.int32)
if net_name == 'alexnet_noisy':
logits_, err_w, err_b, err_lyr = networks.alexnet_noisy(
input_node, netparams, err_mean, err_stddev, train_vars)
elif net_name == 'alexnet':
logits_ = networks.alexnet(input_node, netparams)
elif net_name == 'alexnet_shift':
logits_ = networks.alexnet_shift(input_node, netparams)
elif net_name == 'googlenet':
logits_, err_w, err_b, err_lyr = networks.googlenet_noisy(
input_node, netparams, err_mean, err_stddev, train_vars)
elif net_name == 'nin':
logits_, err_w, err_b, err_lyr = networks.nin_noisy(
input_node, netparams, err_mean, err_stddev, train_vars)
elif net_name == 'resnet18':
logits_ = networks.resnet18(input_node, netparams)
#logits_, err_w, err_b, err_lyr = networks.resnet18_noisy(input_node, netparams, err_mean, err_stddev, train_vars)
elif net_name == 'resnet18_shift':
logits_ = networks.resnet18_shift(input_node, netparams, shift_back)
elif net_name == 'resnet50':
logits_, err_w, err_b, err_lyr = networks.resnet50_noisy(
input_node, netparams, err_mean, err_stddev, train_vars)
elif net_name == 'squeezenet':
logits_, err_w, err_b, err_lyr = networks.squeezenet_noisy(
input_node, netparams, err_mean, err_stddev, train_vars)
elif net_name == 'vgg16net':
logits_, err_w, err_b, err_lyr = networks.vgg16net_noisy(
input_node, netparams, err_mean, err_stddev, train_vars)
#square = [tf.nn.l2_loss(err_w[layer]) for layer in err_w]
#square_sum = tf.reduce_sum(square)
#loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits_, labels=label_node)) + cost_factor / (1. + square_sum)
# ======== calculating the quantization error of a certain layer
# here needs PARAM
w_q_pickle = '/home/behnam/results/quantized/resnet18/May12_resnet18_10_res2a_branch1_5_bits.pickle'
with open(w_q_pickle, 'r') as f:
params_quantized = pickle.load(f)
# here needs PARAM
layer = 'res2a_branch1'
params_quantized_layer = tf.get_variable(name='params_quantized_layer',
initializer=tf.constant(
params_quantized[0][layer]),
trainable=False)
q_diff = tf.subtract(params_quantized_layer, netparams['weights'][layer])
q_diff_cost = tf.nn.l2_loss(q_diff)
loss_op = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(
logits=logits_, labels=label_node)) + cost_factor * q_diff_cost
#loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits_, labels=label_node))
probs = softmax(logits_)
top_k_op = tf.nn.in_top_k(probs, label_node, 5)
optimizer = tf.train.AdamOptimizer(learning_rate=0.001, epsilon=0.1)
if trainable:
train_op = optimizer.minimize(loss_op)
correct_pred = tf.equal(tf.argmax(probs, 1), tf.argmax(label_node, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
if trainable:
count = 0
correct = 0
cur_accuracy = 0
for i in range(0, n_epoch):
#if cur_accuracy >= NET_ACC[net_name]:
#break
#image_producer = dataset.ImageNetProducer(val_path='/home/behnam/ILSVRC2012_img_val_40K/val_40.txt', data_path='/home/behnam/ILSVRC2012_img_val_40K', data_spec=data_spec)
path_train = '/home/behnam/ILSVRC2012_img_train'
image_producer = dataset.ImageNetProducer(
val_path=path_train + '/train_shuf_200k.txt',
data_path=path_train,
data_spec=data_spec)
total = len(image_producer) * n_epoch
coordinator = tf.train.Coordinator()
threads = image_producer.start(session=sess,
coordinator=coordinator)
for (labels, images) in image_producer.batches(sess):
one_hot_labels = np.zeros((len(labels), 1000))
for k in range(len(labels)):
one_hot_labels[k][labels[k]] = 1
sess.run(train_op,
feed_dict={
input_node: images,
label_node: one_hot_labels
})
correct += np.sum(
sess.run(top_k_op,
feed_dict={
input_node: images,
label_node: labels
}))
count += len(labels)
cur_accuracy = float(correct) * 100 / count
print('{:>6}/{:<6} {:>6.2f}%'.format(
count, total, cur_accuracy))
coordinator.request_stop()
coordinator.join(threads, stop_grace_period_secs=2)
#return sess.run(err_w), cur_accuracy
# "sess.run" returns the netparams as normal value (converts it from tf to normal python variable)
return cur_accuracy, sess.run(netparams)
else:
count = 0
correct = 0
cur_accuracy = 0
path_val = '/home/behnam/ILSVRC2012_img_val_40K'
image_producer = dataset.ImageNetProducer(val_path=path_val +
'/val_40.txt',
data_path=path_val,
data_spec=data_spec)
#image_producer = dataset.ImageNetProducer(val_path='/home/behnam/ILSVRC2012_img_val_40K/val_40.txt', data_path='/home/behnam/ILSVRC2012_img_val_40K', data_spec=data_spec)
total = len(image_producer)
coordinator = tf.train.Coordinator()
threads = image_producer.start(session=sess,
coordinator=coordinator)
for (labels, images) in image_producer.batches(sess):
one_hot_labels = np.zeros((len(labels), 1000))
for k in range(len(labels)):
one_hot_labels[k][labels[k]] = 1
correct += np.sum(
sess.run(top_k_op,
feed_dict={
input_node: images,
label_node: labels
}))
count += len(labels)
cur_accuracy = float(correct) * 100 / count
print('{:>6}/{:<6} {:>6.2f}%'.format(count, total,
cur_accuracy))
coordinator.request_stop()
coordinator.join(threads, stop_grace_period_secs=2)
return cur_accuracy, 0
print("Model Summary:")
print(model.summary())
if args.verbose:
print("\n{} Compiling Model...".format(current_time))
optimizer = Adam(lr=0.01)
model.compile(optimizer=optimizer,
loss='binary_crossentropy',
metrics=[MeanIoU(num_classes=2)])
if args.model == 'alexnet':
#Add code on data generators!
train_generator = None
validation_data = None
model = alexnet(batchnorm=model_config['batchnorm'],
dropout=model_config['dropout'])
model.compile(optimizer=Adam(),
loss='mean_square_error',
metrics=['accuracy'])
if not args.model:
print('Model must be specified.')
sys.exit(1)
#Create model
#if args.model == 'unet':
# model = unet(batchnorm=model_config['batchnorm'], dropout=model_config['dropout'])
#if args.model == 'alexnet':
# model = alexnet(batchnorm=model_config['batchnorm'], dropout=model_config['dropout'])
#else:
# print('No model selected!')