from ideep4py import convolution2D
from ideep4py import linear
try:
import testing
from testing import condition
from testing.conv import col2im_cpu
from testing.conv import im2col_cpu
except Exception as ex:
print('*** testing directory is missing: %s' % ex)
sys.exit(-1)
@testing.parameterize(*testing.product({
'dtype': [numpy.float32],
'bs': [256],
'ic': [256],
'oc': [256],
}))
class TestPooling2DPyF32(unittest.TestCase):
def setUp(self):
self.x = numpy.random.uniform(
-1, 1, (self.bs, self.ic, 13, 13)).astype(self.dtype)
self.W = numpy.random.uniform(-1, 1, (self.oc, self.ic, 3, 3)).astype(
self.dtype)
self.linear_gy = numpy.random.uniform(-1, 1, (self.bs, 4096)).astype(
self.dtype)
self.linear_W = \
numpy.random.uniform(-1, 1, (4096, 9216)).astype(self.dtype)
self.cp = convolution2DParam((self.bs, self.oc, 13, 13), 1, 1, 1, 1, 1,
import numpy
import ideep4py
import testing
import unittest
@testing.parameterize(*testing.product({
'dtype': [numpy.float32],
'shape': [(3, 16, 2, 4), (2, 7, 1, 1), (2, 7, 3, 2), (1, 32, 224, 224),
(2, 2, 2, 2), (3, 4), (1, 1)],
}))
class TestTanh(unittest.TestCase):
def setUp(self):
self.x = numpy.random.uniform(-1, 1, self.shape).astype(self.dtype)
self.y = numpy.tanh(self.x)
self.check_options = {'atol': 1e-5, 'rtol': 1e-4}
def test_tanh(self):
mx = ideep4py.mdarray(self.x)
x2 = numpy.array(mx)
numpy.testing.assert_allclose(self.x, x2)
def test_tanhForward(self):
mx = ideep4py.mdarray(self.x)
my = ideep4py._ideep4py.tanh.Forward(mx)
y2 = numpy.array(my)
numpy.testing.assert_allclose(self.y, y2, **self.check_options)
def test_tanhBackward(self):
x = numpy.random.uniform(-1, 1, self.shape).astype(self.dtype)
gy = numpy.random.uniform(-1, 1, self.shape).astype(self.dtype)
from ideep4py import dropout
try:
import testing
except Exception as ex:
print('*** testing directory is missing: %s' % ex)
sys.exit(-1)
def _dropout(x, creator):
return x * creator.mask
@testing.parameterize(*testing.product({
'dropout_ratio': [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
'dtype': [
numpy.float32,
],
}))
@testing.fix_random()
class TestDropoutF32(unittest.TestCase):
def setUp(self):
self.x = numpy.random.rand(128, 3, 224, 224).astype(self.dtype)
self.x_md = ideep4py.mdarray(self.x)
self.gy = numpy.random.rand(128, 3, 224, 224).astype(self.dtype)
def check_forward(self, x, x_md):
mask, y = dropout.Forward(x_md, self.dropout_ratio)
y = numpy.array(y, dtype=self.dtype)
y_expect = x * mask
numpy.testing.assert_allclose(y, y_expect)
import sys
import unittest
import numpy
import ideep4py
from ideep4py import linear
try:
import testing
from testing import condition
except Exception as ex:
print('*** testing directory is missing: %s' % ex)
sys.exit(-1)
@testing.parameterize(*testing.product({
'x_dtype': [numpy.float32],
'W_dtype': [numpy.float32],
}))
class TestLinearPyF32(unittest.TestCase):
def setUp(self):
self.W = numpy.random.uniform(-1, 1, (2, 3)).astype(self.W_dtype)
self.b = numpy.random.uniform(-1, 1, 2).astype(self.x_dtype)
self.x = numpy.random.uniform(-1, 1, (4, 3)).astype(self.x_dtype)
self.gy = numpy.random.uniform(-1, 1, (4, 2)).astype(self.x_dtype)
self.check_forward_options = {'atol': 5e-4, 'rtol': 5e-3}
self.check_backward_options = {'atol': 5e-4, 'rtol': 5e-3}
def check_forward(self, x, W, b, y_expect):
with_bias = True if b is not None else False
import unittest
import numpy
import ideep4py
from ideep4py import relu
try:
import testing
except Exception as ex:
print('*** testing directory is missing: %s' % ex)
sys.exit(-1)
@testing.parameterize(*testing.product({
'shape': [(3, 2), (224, 224)],
'dtype': [numpy.float32, ],
}))
@testing.fix_random()
class TestReluPyF32(unittest.TestCase):
def setUp(self):
self.x = numpy.random.uniform(-1, 1, self.shape).astype(self.dtype)
self.y = numpy.maximum(self.x, 0, dtype=(self.x).dtype)
self.gy = numpy.random.uniform(-1, 1, self.shape).astype(self.dtype)
self.gx = (self.x > 0) * self.gy
def check_forward(self, x, y):
mx = ideep4py.mdarray(x)
x2 = numpy.array(mx)
numpy.testing.assert_allclose(x, x2)
my = relu.Forward(mx)
from testing import condition
from testing.conv import col2im_cpu
except Exception as ex:
print('*** testing directory is missing: %s' % ex)
sys.exit(-1)
if bool(int(os.environ.get('ENALE_TRAVIS_TEST', '0'))):
bs_list = [1, 2, 4, 5, 6, 8, 10, 16, 24, 32, 64, ]
else:
bs_list = [1, 2, 4, 5, 6, 8, 10, 16, 24, 32, 64, 96, 128, 196, 256, ]
print('bs_list: ', bs_list)
@testing.parameterize(*testing.product({
'dtype': [numpy.float32],
'channel': [1, 2, 4, 8, 10, 16, 24, 32, 64, ],
'bs': bs_list,
'stride': [2, ],
}))
class TestPooling2DPyF32(unittest.TestCase):
def setUp(self):
self.x = numpy.random.uniform(
-1, 1, (self.bs, self.channel, 4, 3)).astype(self.dtype)
self.gy = numpy.random.uniform(
-1, 1, (self.bs, self.channel, 2, 2)).astype(self.dtype)
self.pp_fwd = pooling2DParam(
self.gy.shape, 3, 3, self.stride, self.stride, 1, 1,
1, 1, pooling2DParam.pooling_avg_include_padding)
self.pp_bwd = pooling2DParam(
(self.bs, self.channel, 4, 3), 3, 3, self.stride, self.stride,
def _x_hat(x, mean, inv_std):
x_mu = x - mean
x_mu *= inv_std
return x_mu
def _batch_normalization(expander, gamma, beta, x, mean, var):
mean = mean[expander]
std = numpy.sqrt(var)[expander]
y_expect = (gamma[expander] * (x - mean) / std + beta[expander])
return y_expect
@testing.parameterize(*(testing.product({
'param_shape': [(3, ), ],
'ndim': [2, ],
'dtype': [numpy.float32],
})))
class TestBatchNormalizationF32(unittest.TestCase):
def setUp(self):
self.eps = 2e-5
self.expander = (None, Ellipsis) + (None,) * self.ndim
self.gamma = numpy.random.uniform(.5, 1,
self.param_shape).astype(self.dtype)
self.beta = numpy.random.uniform(-1, 1,
self.param_shape).astype(self.dtype)
self.head_ndim = self.gamma.ndim + 1
shape = (5,) + self.param_shape + (2,) * self.ndim
self.x = numpy.random.uniform(-1, 1, shape).astype(self.dtype)
self.gy = numpy.random.uniform(-1, 1, shape).astype(self.dtype)
self.ph, d=self.dy) or
in_w != get_conv_outsize(self.outw, kw, self.sx,
self.pw, d=self.dx))
if bool(int(os.environ.get('ENALE_TRAVIS_TEST', '0'))):
bs_list = [1, 2, 4, 5, 8, 10, 16, 32, 64, ]
else:
bs_list = [1, 2, 4, 5, 8, 10, 16, 32, 64, 96, 128, 192, 256, 512, ]
print('bs_list: ', bs_list)
@testing.parameterize(*testing.product({
'dtype': [numpy.float32, ],
'cover_all': [False, True],
'channel': [1, 2, 4, 8, 10, ],
'bs': bs_list,
'with_bias': [True, ],
}))
@testing.fix_random()
class TestConvolution2DPyF32(unittest.TestCase):
def setUp(self):
self.x_shape = (self.bs, self.channel, 224, 224)
self.w_shape = (self.channel, self.channel, 3, 3)
self.b_shape = self.channel
self.x = numpy.random.uniform(-1, 1, self.x_shape).astype(self.dtype)
self.x = ideep4py.mdarray(self.x)
self.w = numpy.random.uniform(-1, 1, self.w_shape).astype(self.dtype)
self.w = ideep4py.mdarray(self.w)
import six
import ideep4py
from ideep4py import localResponseNormalizationParam
from ideep4py import localResponseNormalization
try:
import testing
except Exception as ex:
print('*** testing directory is missing: %s' % ex)
sys.exit(-1)
@testing.parameterize(*testing.product({
'dtype': [numpy.float32],
'shape': [
(2, 7, 1, 1),
(2, 7, 3, 2),
],
}))
class TestLocalResponseNormalizationPyF32(unittest.TestCase):
def setUp(self):
self.x = numpy.random.uniform(-1, 1, self.shape).astype(self.dtype)
self.gy = numpy.random.uniform(-1, 1, self.shape).astype(self.dtype)
self.pp = localResponseNormalizationParam(
5, 2, 1e-4, .75,
ideep4py.localResponseNormalizationParam.lrn_across_channels)
self.check_forward_options = {'atol': 1e-4, 'rtol': 1e-3}
self.check_backward_options = {'atol': 1e-4, 'rtol': 1e-3}
def check_forward(self, x, pp):
x_mdarray = ideep4py.mdarray(x)
