def test_cpu_gpu(n, s):
#n = 10
#s = 512
with cpu():
x_cpu = _randn(s, s)
y_cpu = _randn(s, s)
for i in range(10):
z_cpu = np.dot(x_cpu, y_cpu)
z_cpu.asnumpy()
t0 = time.time()
for i in range(n):
z_cpu = np.dot(x_cpu, y_cpu)
z_cpu.asnumpy()
t1 = time.time()
all_cpu_time = t1 - t0
with gpu(0):
x_gpu0 = _randn(s, s)
y_gpu0 = _randn(s, s)
for i in range(10):
z_gpu0 = np.dot(x_gpu0, y_gpu0)
z_gpu0.asnumpy()
t2 = time.time()
for i in range(n):
z_gpu0 = np.dot(x_gpu0, y_gpu0)
z_gpu0.asnumpy()
t3 = time.time()
all_gpu_time = t3 - t2
print("run on cpu:%.6f s/iter" % (all_cpu_time / n))
print("run on gpu:%.6f s/iter" % (all_gpu_time / n))
print("%s cpu_time/gpu_time:%.6f " % (s, all_cpu_time / all_gpu_time))
def test_context():
set_context(gpu(1)) # set the global context as gpu(1)
def sigmoid(x):
return 0.5 * (np.tanh(x / 2) + 1)
def predict(weights, inputs):
return sigmoid(np.dot(inputs, weights))
def training_loss(weights, inputs):
preds = predict(weights, inputs)
label_probabilities = preds * targets + (1 - preds) * (1 - targets)
l = -np.sum(np.log(label_probabilities))
return l
def training_accuracy(weights, inputs):
preds = predict(weights, inputs)
error = np.count_nonzero(np.argmax(preds, axis=1) - np.argmax(targets, axis=1))
return (256 - error) * 100 / 256.0
with gpu(0):
xshape = (256, 500)
wshape = (500, 250)
tshape = (256, 250)
inputs = random.rand(*xshape) - 0.5
targets = np.zeros(tshape)
truth = random.randint(0, 250, 256)
targets[np.arange(256), truth] = 1
weights = random.rand(*wshape) - 0.5
training_gradient_fun = grad(training_loss)
for i in range(20):
print('Trained loss accuracy #{}: {}%'.format(i, training_accuracy(weights, inputs)))
gr = training_gradient_fun(weights, inputs)
weights -= gr * 0.01
print("\nff and bp on {0}".format(weights.context))
print("\nexecute on cpu")
with cpu():
x_cpu = random.rand(32, 64) - 0.5
y_cpu = random.rand(64, 32) - 0.5
z_cpu = np.dot(x_cpu, y_cpu)
print('z_cpu.context = {0}'.format(z_cpu.context))
print("\nexecute on gpu(0)")
with gpu(0):
x_gpu0 = random.rand(32, 64) - 0.5
y_gpu0 = random.rand(64, 32) - 0.5
z_gpu0 = np.dot(x_gpu0, y_gpu0)
z_gpu0.asnumpy()
print('z_gpu0.context = {0}'.format(z_gpu0.context))
print("\n[use global context] execute on gpu(1)")
x_gpu1 = random.rand(32, 64) - 0.5
y_gpu1 = random.rand(64, 32) - 0.5
z_gpu1 = np.dot(x_gpu1, y_gpu1)
z_gpu1.asnumpy()
print('z_gpu1.context = {0}'.format(z_gpu1.context))
def test_policy_2():
with minpy.OnlyNumPyPolicy():
print(np.policy)
print(np.random.policy)
np.set_policy(minpy.PreferMXNetPolicy())
set_context(cpu())
print(np.policy)
print(np.random.policy)
def test_policy_2():
with minpy.OnlyNumPyPolicy():
print(np.policy)
print(np.random.policy)
np.set_policy(minpy.PreferMXNetPolicy())
set_context(cpu())
print(np.policy)
print(np.random.policy)
def test_context():
#set_context(gpu(1)) # set the global context as gpu(1)
def sigmoid(x):
return 0.5 * (np.tanh(x / 2) + 1)
def predict(weights, inputs):
return sigmoid(np.dot(inputs, weights))
def training_loss(weights, inputs):
preds = predict(weights, inputs)
label_probabilities = preds * targets + (1 - preds) * (1 - targets)
l = -np.sum(np.log(label_probabilities))
return l
def training_accuracy(weights, inputs):
preds = predict(weights, inputs)
error = np.count_nonzero(
np.argmax(preds, axis=1) - np.argmax(targets, axis=1))
return (256 - error) * 100 / 256.0
"""
with gpu(0):
xshape = (256, 500)
wshape = (500, 250)
tshape = (256, 250)
inputs = random.rand(*xshape) - 0.5
targets = np.zeros(tshape)
truth = random.randint(0, 250, 256)
targets[np.arange(256), truth] = 1
weights = random.rand(*wshape) - 0.5
training_gradient_fun = grad(training_loss)
for i in range(20):
print('Trained loss accuracy #{}: {}%'.format(i, training_accuracy(weights, inputs)))
gr = training_gradient_fun(weights, inputs)
weights -= gr * 0.01
print("\nff and bp on {0}".format(weights.context))
"""
print("\nexecute on cpu")
with cpu():
x_cpu = random.rand(32, 64) - 0.5
y_cpu = random.rand(64, 32) - 0.5
z_cpu = np.dot(x_cpu, y_cpu)
print('z_cpu.context = {0}'.format(z_cpu.context))
"""
import minpy.numpy as np
import minpy.nn.model_builder as builder
from minpy.context import set_context, cpu, gpu
# set_context(gpu(0))
set_context(cpu())
import sys
sys.path.append('../../nn')
from custom_layers import *
from facility import *
from solver_primitives import *
sys.path.append('../')
from utilities.data_utility import load_cifar10
data = load_cifar10(path='../utilities/cifar/', center=True, rescale=True)
X = data[0][:16]
hidden_layers = 4
shapes = (1024, ) * hidden_layers + (10, )
activation = builder.ReLU
storage = {}
mlp = builder.Sequential()
for i, shape in enumerate(shapes[:-1]):
mlp.append(builder.Affine(shape))
mlp.append(builder.Export('affine%d' % i, storage))
mlp.append(activation())
mlp.append(builder.Affine(shapes[-1]))
model = builder.Model(mlp, 'softmax', (3072, ))
import minpy.numpy as np
import minpy.numpy.random as random
from minpy.context import cpu, gpu
import time
n = 1000
with cpu():
x_cpu = random.rand(1024, 1024) - 0.5
y_cpu = random.rand(1024, 1024) - 0.5
# dry run
for i in range(10):
z_cpu = np.dot(x_cpu, y_cpu)
z_cpu.asnumpy()
# real run
t0 = time.time()
for i in range(n):
z_cpu = np.dot(x_cpu, y_cpu)
z_cpu.asnumpy()
t1 = time.time()
with gpu(0):
x_gpu0 = random.rand(1024, 1024) - 0.5
y_gpu0 = random.rand(1024, 1024) - 0.5
# dry run
for i in range(10):
z_gpu0 = np.dot(x_gpu0, y_gpu0)
z_gpu0.asnumpy()
def garchSim(ret2, p):
h = np.zeros(ret2.shape[0], dtype='float32')
h[0] = np.mean(ret2)
for i in range(1, ret2.shape[0]):
h[i] = p[0] + p[1] * ret2[i - 1] + p[2] * h[i - 1]
return h
def garchLLH(y, par):
h = garchSim(np.square(y), par)
T = y.shape[0]
llh = -0.5 * (T - 1) * math.log(
2 * math.pi) - 0.5 * np.sum(np.log(h) + (y / np.sqrt(h))**2)
return llh[0]
def llh_time():
ret, x0, val_llh = garch_data()
ret = np.array(ret)
x0 = np.array(x0)
t = benchmark(lambda: garchLLH(ret, x0), args.n, val_llh)
return t
with cpu() if args.mode == 'cpu' else gpu(0):
out['minpy-' + args.mode] = llh_time()
print(json.dumps(out))
'''
from examples.utils.data_utils import get_CIFAR10_data
data = get_CIFAR10_data(args.data_dir)
from minpy.nn.io import NDArrayIter
batch_size = 128
train_data_iter = NDArrayIter(data=data['X_train'], label=data['y_train'], batch_size=batch_size, shuffle=True)
val_data_iter = NDArrayIter(data=data['X_test'], label=data['y_test'], batch_size=batch_size, shuffle=False)
'''
from load_cifar10_data_iter import *
train_data_iter, val_data_iter = load_cifar10_data_iter(batch_size=128,
path=args.data_dir)
from minpy.context import set_context, cpu, gpu
if args.gpu_index < 0: set_context(cpu())
else: set_context(gpu(args.gpu_index))
model = ResNet(3, (16, 32, 64))
updater = Updater(model,
update_rule='sgd',
learning_rate=0.1,
momentem=0.9)
epoch_number = 0
iteration_number = 0
terminated = False
while not terminated:
# training
epoch_number += 1
'''
from examples.utils.data_utils import get_CIFAR10_data
data = get_CIFAR10_data(args.data_dir)
from minpy.nn.io import NDArrayIter
batch_size = 128
train_data_iter = NDArrayIter(data=data['X_train'], label=data['y_train'], batch_size=batch_size, shuffle=True)
val_data_iter = NDArrayIter(data=data['X_test'], label=data['y_test'], batch_size=batch_size, shuffle=False)
'''
from load_cifar10_data_iter import *
train_data_iter, val_data_iter = load_cifar10_data_iter(batch_size=128, path=args.data_dir)
from minpy.context import set_context, cpu, gpu
if args.gpu_index < 0: set_context(cpu())
else: set_context(gpu(args.gpu_index))
model = ResNet(3, (16, 32, 64))
updater = Updater(model, update_rule='sgd', learning_rate=0.1, momentem=0.9)
epoch_number = 0
iteration_number = 0
terminated = False
while not terminated:
# training
epoch_number += 1
train_data_iter.reset()
for iteration, batch in enumerate(train_data_iter):
