def verify_concatenate(ishape, axis):
x = [sym.Variable("x%d" % i, shape=ishape[i]) for i in range(len(ishape))]
y = sym.concatenate(*x, axis=axis) + 1
def forward(**kwargs):
return np.concatenate(list(kwargs.values()), axis=axis) + 1
check_function(y, forward)
def verify_split(ishape, indices_or_sections, axis):
x = sym.Variable("x", shape=ishape)
y = sym.split(x, indices_or_sections=indices_or_sections, axis=axis)
def forward(x):
return np.split(x, indices_or_sections, axis=axis)
check_function(y, forward)
def verify_concatenate(ishape, axis):
x = [sym.Variable("x%d" % i, shape=ishape[i]) for i in range(len(ishape))]
y = sym.concatenate(*x, axis=axis) + 1
def forward(**kwargs):
return np.concatenate(list(kwargs.values()), axis=axis) + 1
check_function(y, forward)
def verify_split(ishape, indices_or_sections, axis):
x = sym.Variable("x", shape=ishape)
y = sym.split(x, indices_or_sections=indices_or_sections, axis=axis)
def forward(x):
return np.split(x, indices_or_sections, axis=axis)
check_function(y, forward)
def test_prelu_nhwc():
x = sym.Variable("x")
a = sym.Variable("a")
y = sym.prelu(data=x, alpha=a, axis=3)
def forward(x, a):
return (x < 0) * (x * a.reshape(1, 1, 3)) + (x>=0) * x
shape = {'x': (1, 32, 32, 3), 'a': (3,)}
check_function(y, forward, shape=shape)
def test_prelu_nhwc():
x = sym.Variable("x")
a = sym.Variable("a")
y = sym.prelu(data=x, alpha=a, axis=3)
def forward(x, a):
return (x < 0) * (x * a.reshape(1, 1, 3)) + (x >= 0) * x
shape = {'x': (1, 32, 32, 3), 'a': (3, )}
check_function(y, forward, shape=shape)
def _check_function_must_fail(*args, **kwargs):
error = AssertionError
if 'error' in kwargs:
error = kwargs['error']
del kwargs['error']
try:
check_function(*args, quiet=True, **kwargs)
except error:
pass
else:
raise AssertionError("check_function didn't raise an exception")
def test_pad():
x = sym.Variable("x")
y = sym.pad(x, pad_width=((0, 0), (0, 0), (0, 1), (2, 3)), pad_value=1.)
def forward(x):
return np.pad(x,
pad_width=((0, 0), (0, 0), (0, 1), (2, 3)),
mode='constant', constant_values=1.)
shape = {'x': (1, 3, 28, 28)}
check_function(y, forward, shape=shape)
def _check_function_must_fail(*args, **kwargs):
error = AssertionError
if 'error' in kwargs:
error = kwargs['error']
del kwargs['error']
try:
check_function(*args, quiet=True, **kwargs)
except error:
pass
else:
raise AssertionError("check_function didn't raise an exception")
def test_pad():
x = sym.Variable("x")
y = sym.pad(x, pad_width=((0, 0), (0, 0), (0, 1), (2, 3)), pad_value=1.)
def forward(x):
return np.pad(x,
pad_width=((0, 0), (0, 0), (0, 1), (2, 3)),
mode='constant', constant_values=1.)
shape = {'x': (1, 3, 28, 28)}
check_function(y, forward, shape=shape)
def test_log():
x = sym.Variable("x")
y = sym.log(x)
def forward(x):
return np.log(x)
def backward(head_grads, x):
return [1. / x * head_grads]
shape = {'x': (1, 3, 32, 32)}
check_function(y, forward, backward, in_range=(0.002, 2.0), shape=shape)
def test_exp():
x = sym.Variable("x")
y = sym.exp(x)
def forward(x):
return np.exp(x)
def backward(head_grads, x):
return [np.exp(x) * head_grads]
shape = {'x': (1, 3, 32, 32)}
check_function(y, forward, backward, shape=shape)
def test_log():
x = sym.Variable("x")
y = sym.log(x)
def forward(x):
return np.log(x)
def backward(head_grads, x):
return [1. / x * head_grads]
shape = {'x': (1, 3, 32, 32)}
check_function(y, forward, backward, in_range=(0.002, 2.0), shape=shape)
def test_exp():
x = sym.Variable("x")
y = sym.exp(x)
def forward(x):
return np.exp(x)
def backward(head_grads, x):
return [np.exp(x) * head_grads]
shape = {'x': (1, 3, 32, 32)}
check_function(y, forward, backward, shape=shape)
def verify_elemwise_sum(num_args):
s = [sym.Variable("input" + str(i)) for i in range(num_args)]
y = sym.elemwise_sum(*s, num_args=num_args)
def forward(**inputs):
return np.sum(np.array(list(inputs.values())), axis=0)
def backward(head_grads, **inputs):
return [head_grads] * num_args
shape = {s[i]: (3, 4, 5) for i in range(num_args)}
check_function(y, forward, backward, shape=shape)
def verify_elemwise_sum(num_args):
s = [sym.Variable("input" + str(i)) for i in range(num_args)]
y = sym.elemwise_sum(*s, num_args=num_args)
def forward(**inputs):
return np.sum(np.array(list(inputs.values())), axis=0)
def backward(head_grads, **inputs):
return [head_grads] * num_args
shape = {s[i]: (3, 4, 5) for i in range(num_args)}
check_function(y, forward, backward, shape=shape)
def test_sigmoid():
x = sym.Variable("x")
y = sym.sigmoid(x)
def forward(x):
return 1.0 / (1.0 + np.exp(-x))
def backward(head_grads, x):
y_np = forward(x)
return [y_np *(1 - y_np) * head_grads]
shape = {'x': (1, 3, 32, 32)}
check_function(y, forward, backward, shape=shape)
def test_tanh():
x = sym.Variable("x")
y = sym.tanh(x)
def forward(x):
return np.sinh(x) / np.cosh(x)
def backward(head_grads, x):
y_np = forward(x)
return [(1 - y_np**2) * head_grads]
shape = {'x': (1, 3, 32, 32)}
check_function(y, forward, backward, shape=shape)
def test_less():
l = sym.Variable("l")
r = sym.Variable("r")
y = sym.less(l, r)
def forward(l, r):
return np.less(l, r).astype("float32")
def backward(head_grads, l, r):
return {'l': np.zeros_like(l)}
shape = {'l': (3, 4, 5), 'r': (3, 4, 5)}
check_function(y, forward, backward, shape=shape)
def test_reshape_like():
x = sym.Variable("x")
y = sym.Variable("y")
z = sym.reshape_like(x, y)
def forward(x, y):
return np.reshape(x, y.shape)
def backward(head_grads, x, y):
return [np.reshape(head_grads, x.shape), np.zeros_like(y)]
shape = {'x': (3, 4, 5), 'y': (5, 4, 3)}
check_function(z, forward, backward, shape=shape)
def test_sym_scalar_pow():
scalar = 3
x = sym.Variable("x")
y = x**scalar
def forward(x):
return x**scalar
def backward(head_grads, x):
return [scalar * x**(scalar - 1) * head_grads]
shape = {'x': (1, 3, 32, 32)}
check_function(y, forward, backward, shape=shape)
def test_scalar_sym_pow():
scalar = 3
x = sym.Variable("x")
y = scalar**x
def forward(x):
return scalar**x
def backward(head_grads, x):
return [np.log(scalar) * scalar**x * head_grads]
shape = {'x': (1, 3, 32, 32)}
check_function(y, forward, backward, shape=shape)
def test_less():
l = sym.Variable("l")
r = sym.Variable("r")
y = sym.less(l, r)
def forward(l, r):
return np.less(l, r).astype("float32")
def backward(head_grads, l, r):
return {'l': np.zeros_like(l)}
shape = {'l': (3, 4, 5), 'r': (3, 4, 5)}
check_function(y, forward, backward, shape=shape)
def test_block_grad():
x = sym.Variable("x")
y = sym.block_grad(x)
def forward(x):
return x
def backward(head_grads, x):
return [np.zeros_like(head_grads)]
shape = {'x': (3, 4, 5)}
# Numerical grad checking would fail for this function
check_function(y, forward, backward, shape=shape, numerical_grads=False)
def test_sigmoid():
x = sym.Variable("x")
y = sym.sigmoid(x)
def forward(x):
return 1.0 / (1.0 + np.exp(-x))
def backward(head_grads, x):
y_np = forward(x)
return [y_np * (1 - y_np) * head_grads]
shape = {'x': (1, 3, 32, 32)}
check_function(y, forward, backward, shape=shape)
def test_scalar_sym_pow():
scalar = 3
x = sym.Variable("x")
y = scalar**x
def forward(x):
return scalar**x
def backward(head_grads, x):
return [np.log(scalar) * scalar**x * head_grads]
shape = {'x': (1, 3, 32, 32)}
check_function(y, forward, backward, shape=shape)
def test_sym_scalar_pow():
scalar = 3
x = sym.Variable("x")
y = x**scalar
def forward(x):
return x**scalar
def backward(head_grads, x):
return [scalar * x**(scalar - 1) * head_grads]
shape = {'x': (1, 3, 32, 32)}
check_function(y, forward, backward, shape=shape)
def test_tanh():
x = sym.Variable("x")
y = sym.tanh(x)
def forward(x):
return np.sinh(x) / np.cosh(x)
def backward(head_grads, x):
y_np = forward(x)
return [(1 - y_np**2) * head_grads]
shape = {'x': (1, 3, 32, 32)}
check_function(y, forward, backward, shape=shape)
def test_reshape_like():
x = sym.Variable("x")
y = sym.Variable("y")
z = sym.reshape_like(x, y)
def forward(x, y):
return np.reshape(x, y.shape)
def backward(head_grads, x, y):
return [np.reshape(head_grads, x.shape),
np.zeros_like(y)]
shape = {'x': (3, 4, 5), 'y': (5, 4, 3)}
check_function(z, forward, backward, shape=shape)
def test_block_grad():
x = sym.Variable("x")
y = sym.block_grad(x)
def forward(x):
return x
def backward(head_grads, x):
return [np.zeros_like(head_grads)]
shape = {'x': (3, 4, 5)}
# Numerical grad checking would fail for this function
check_function(y, forward, backward, shape=shape, numerical_grads=False)
def test_relu():
x = sym.Variable("x")
y = sym.relu(sym.leaky_relu(x, alpha=0.3) - 0.2)
def forward(x):
x = (x < 0) * x * 0.3 + (x > 0) * x - 0.2
return (x > 0) * x
def backward(head_grads, x):
sub = (x < 0) * x * 0.3 + (x > 0) * x - 0.2
return [(sub > 0).astype("float") * \
((x > 0).astype("float") + 0.3 * (x < 0).astype("float")) * head_grads]
shape = {'x': (1, 3, 32, 32)}
check_function(y, forward, backward, shape=shape)
def verify_squeeze(shape, axis):
x = sym.Variable("x")
if axis is not None:
y = sym.squeeze(x, axis=axis)
else:
y = sym.squeeze(x)
y = y + 1
def forward(x):
return np.squeeze(x, axis=axis) + 1
def backward(head_grads, x):
return [np.reshape(head_grads, x.shape)]
check_function(y, forward, backward, shape=shape)
def test_relu():
x = sym.Variable("x")
y = sym.relu(sym.leaky_relu(x, alpha=0.3) - 0.2)
def forward(x):
x = (x < 0) * x * 0.3 + (x > 0) * x - 0.2
return (x > 0) * x
def backward(head_grads, x):
sub = (x < 0) * x * 0.3 + (x > 0) * x - 0.2
return [(sub > 0).astype("float") * \
((x > 0).astype("float") + 0.3 * (x < 0).astype("float")) * head_grads]
shape = {'x': (1, 3, 32, 32)}
check_function(y, forward, backward, shape=shape)
def verify_squeeze(shape, axis):
x = sym.Variable("x")
if axis is not None:
y = sym.squeeze(x, axis=axis)
else:
y = sym.squeeze(x)
y = y + 1
def forward(x):
return np.squeeze(x, axis=axis) + 1
def backward(head_grads, x):
return [np.reshape(head_grads, x.shape)]
check_function(y, forward, backward, shape=shape)
def verify_lrn(ishape, size, axis, bias, alpha, beta):
x = sym.Variable("x", shape=ishape)
y = sym.lrn(x, size=size, axis=axis, bias=bias, alpha=alpha, beta=beta)
def forward1(x):
return topi.testing.lrn_python(x, size, axis, bias, alpha, beta)
check_function(y, forward1)
def forward2(x):
y = forward1(x)
return (y > 0) * y
#Checking LRN op followed by elementwise op relu
check_function(sym.relu(y), forward2, in_range={'x': (-10.0, 10.0)})
def verify_l2_normalize(ishape, eps, axis):
x = sym.Variable("x", shape=ishape)
y = sym.l2_normalize(x, eps=eps, axis=axis)
def forward1(x):
return topi.testing.l2_normalize_python(x, eps, axis)
check_function(y, forward1)
def forward2(x):
y = forward1(x)
return (y > 0)*y
#Checking L2 normalization op followed by elementwise op relu
check_function(sym.relu(y), forward2, in_range={'x': (-10.0, 10.0)})
def verify_lrn(ishape, size, axis, bias, alpha, beta):
x = sym.Variable("x", shape=ishape)
y = sym.lrn(x, size=size, axis=axis, bias=bias, alpha=alpha, beta=beta)
def forward1(x):
return topi.testing.lrn_python(x, size, axis, bias, alpha, beta)
check_function(y, forward1)
def forward2(x):
y = forward1(x)
return (y > 0)*y
#Checking LRN op followed by elementwise op relu
check_function(sym.relu(y), forward2, in_range={'x': (-10.0, 10.0)})
def verify_l2_normalize(ishape, eps, axis):
x = sym.Variable("x", shape=ishape)
y = sym.l2_normalize(x, eps=eps, axis=axis)
def forward1(x):
return topi.testing.l2_normalize_python(x, eps, axis)
check_function(y, forward1)
def forward2(x):
y = forward1(x)
return (y > 0) * y
#Checking L2 normalization op followed by elementwise op relu
check_function(sym.relu(y), forward2, in_range={'x': (-10.0, 10.0)})
def check_map(symfunc,
np_func,
np_backward=None,
dtype="float32",
rnd_min=-1,
rnd_max=1):
x = sym.Variable("x")
y = symfunc(x)
shape = {'x': (1, 3, 32, 32)}
check_function(y,
lambda x: np_func(x),
np_backward,
dtype=dtype,
shape=shape,
in_range=(rnd_min, rnd_max))
def verify_take(src_shape, indices_src, axis=None):
src_dtype = "float32"
indices_dtype = "int32"
indices_src = np.array(indices_src, dtype=indices_dtype)
a = sym.Variable("a", shape=src_shape)
indices = sym.Variable("indices", shape=indices_src.shape)
y = sym.take(a, indices, axis=axis)
def forward(a, indices):
return np.take(a, indices=indices, axis=axis)
a_src = np.arange(np.prod(src_shape), dtype=src_dtype).reshape(src_shape)
check_function(y, forward,
dtype={'a': src_dtype, 'indices': indices_dtype},
values={'a': a_src, 'indices': indices_src})
def verify_take(src_shape, indices_src, axis=None):
src_dtype = "float32"
indices_dtype = "int32"
indices_src = np.array(indices_src, dtype=indices_dtype)
a = sym.Variable("a", shape=src_shape)
indices = sym.Variable("indices", shape=indices_src.shape)
y = sym.take(a, indices, axis=axis)
def forward(a, indices):
return np.take(a, indices=indices, axis=axis)
a_src = np.arange(np.prod(src_shape), dtype=src_dtype).reshape(src_shape)
check_function(y, forward,
dtype={'a': src_dtype, 'indices': indices_dtype},
values={'a': a_src, 'indices': indices_src})
def test_log_softmax():
x = sym.Variable("x")
y = sym.log_softmax(x)
def forward(x):
return topi.testing.log_softmax_python(x)
def backward(head_grads, x):
y = topi.testing.log_softmax_python(x)
grad = head_grads - np.exp(y) * np.sum(head_grads, axis=1, keepdims=True)
return [grad]
check_function(y, forward, backward,
shape={'x': (10, 1000)}, numerical_grads=False)
check_function(y, forward, backward,
shape={'x': (2, 10)})
def test_clip():
x = sym.Variable("x")
a_min=0.2
a_max=0.75
y = sym.clip(x, a_min=a_min, a_max=a_max)
def forward(x):
return np.clip(x, a_min=a_min, a_max=a_max)
def backward(head_grads, x):
mask1 = np.greater_equal(x, a_min).astype("float")
mask2 = np.less_equal(x, a_max).astype("float")
return [head_grads * mask1 * mask2]
shape = {'x': (3, 4, 5)}
check_function(y, forward, backward, shape=shape)
def verify_gather_nd(src_shape, indices_src):
src_dtype = "float32"
indices_dtype = "int32"
indices_src = np.array(indices_src, dtype=indices_dtype)
a = sym.Variable("a", shape=src_shape)
indices = sym.Variable("indices", shape=indices_src.shape)
y = sym.gather_nd(a, indices)
def forward(a, indices):
return topi.testing.gather_nd_python(a, indices)
a_src = np.arange(np.prod(src_shape), dtype=src_dtype).reshape(src_shape)
check_function(y, forward,
dtype={'a': src_dtype, 'indices': indices_dtype},
values={'a': a_src, 'indices': indices_src})
def test_log_softmax():
x = sym.Variable("x")
y = sym.log_softmax(x)
def forward(x):
return topi.testing.log_softmax_python(x)
def backward(head_grads, x):
y = topi.testing.log_softmax_python(x)
grad = head_grads - np.exp(y) * np.sum(head_grads, axis=1, keepdims=True)
return [grad]
check_function(y, forward, backward,
shape={'x': (10, 1000)}, numerical_grads=False)
check_function(y, forward, backward,
shape={'x': (2, 10)})
def test_clip():
x = sym.Variable("x")
a_min = 0.2
a_max = 0.75
y = sym.clip(x, a_min=a_min, a_max=a_max)
def forward(x):
return np.clip(x, a_min=a_min, a_max=a_max)
def backward(head_grads, x):
mask1 = np.greater_equal(x, a_min).astype("float")
mask2 = np.less_equal(x, a_max).astype("float")
return [head_grads * mask1 * mask2]
shape = {'x': (3, 4, 5)}
check_function(y, forward, backward, shape=shape)
def verify_strided_slice(ishape, begin, end, strideinp=None):
stride = strideinp if strideinp else [1, 1, 1]
x = sym.Variable("x", shape=ishape)
if strideinp:
y = sym.strided_slice(x, begin = begin, end = end, stride = stride) + 1
else:
y = sym.strided_slice(x, begin = begin, end = end) + 1
for i in range(len(begin), 3):
begin.append(0)
for i in range(len(end), 3):
end.append(ishape[i])
def test_forward(x):
return x[begin[0]:end[0]:stride[0],
begin[1]:end[1]:stride[1], begin[2]:end[2]:stride[2]] + 1
check_function(y, test_forward)
def verify_strided_slice(ishape, begin, end, strideinp=None):
stride = strideinp if strideinp else [1, 1, 1]
x = sym.Variable("x", shape=ishape)
if strideinp:
y = sym.strided_slice(x, begin=begin, end=end, stride=stride) + 1
else:
y = sym.strided_slice(x, begin=begin, end=end) + 1
for i in range(len(begin), 3):
begin.append(0)
for i in range(len(end), 3):
end.append(ishape[i])
def test_forward(x):
return x[begin[0]:end[0]:stride[0], begin[1]:end[1]:stride[1],
begin[2]:end[2]:stride[2]] + 1
check_function(y, test_forward)
def verify_expand_like(in_shape, out_shape, axis, exclude):
x = sym.Variable("x")
y = sym.Variable("y")
z = sym.expand_like(x, y, axis=axis, exclude=exclude)
def forward(x, y):
odim = len(out_shape)
if len(x.shape) == len(y.shape):
return np.broadcast_to(x, y.shape)
if x.shape == (1, ) and len(y.shape) == odim:
x = np.reshape(x, ())
real_axis = [i if i >= 0 else i + odim for i in axis]
real_axis = sorted(real_axis)
if exclude:
real_axis = list(set(range(odim)) - set(real_axis))
for i in real_axis:
x = np.expand_dims(x, i).astype(x.dtype)
for i in real_axis:
x = np.concatenate([x] * out_shape[i], axis=i).astype(x.dtype)
return x
def backward(head_grads, x, y):
odim = len(out_shape)
keepdims = len(x.shape) == len(y.shape)
if x.shape == (1, ) and len(y.shape) == odim:
x = np.reshape(x, ())
real_axis = [i if i >= 0 else i + odim for i in axis]
real_axis = sorted(real_axis)
if exclude:
real_axis = list(set(range(odim)) - set(real_axis))
return [
np.sum(head_grads, axis=tuple(real_axis), keepdims=keepdims),
np.zeros_like(y)
]
shape = {'x': in_shape, 'y': out_shape}
check_function(z, forward, backward, shape=shape)
def verify_expand_like(in_shape, out_shape, axis, exclude):
x = sym.Variable("x")
y = sym.Variable("y")
z = sym.expand_like(x, y, axis=axis, exclude=exclude)
def forward(x, y):
odim = len(out_shape)
if len(x.shape) == len(y.shape):
return np.broadcast_to(x, y.shape)
if x.shape == (1,) and len(y.shape) == odim:
x = np.reshape(x, ())
real_axis = [i if i >= 0 else i + odim for i in axis]
real_axis = sorted(real_axis)
if exclude:
real_axis = list(set(range(odim)) - set(real_axis))
for i in real_axis:
x = np.expand_dims(x, i).astype(x.dtype)
for i in real_axis:
x = np.concatenate([x]*out_shape[i], axis=i).astype(x.dtype)
return x
def backward(head_grads, x, y):
odim = len(out_shape)
keepdims = len(x.shape) == len(y.shape)
if x.shape == (1,) and len(y.shape) == odim:
x = np.reshape(x, ())
real_axis = [i if i >= 0 else i + odim for i in axis]
real_axis = sorted(real_axis)
if exclude:
real_axis = list(set(range(odim)) - set(real_axis))
return [np.sum(head_grads, axis=tuple(real_axis), keepdims=keepdims),
np.zeros_like(y)]
shape = {'x': in_shape, 'y': out_shape}
check_function(z, forward, backward, shape=shape)
def test_dense():
x = sym.Variable("x", shape=(10, 100))
w = sym.Variable("dense_weight", shape=(3, 100))
b = sym.Variable("dense_bias", shape=(3,))
y = sym.dense(x, w, b, use_bias=True, units=3, name="dense")
y = sym.flatten(y)
def forward(x, dense_weight, dense_bias):
return np.dot(x, dense_weight.T) + dense_bias
shape = {
'x': (10, 100),
'w': (3, 100),
'b': (3,)
}
# Don't check gradients on cuda because is doesn't yet support ewise after reduce
check_function(y, forward, shape=shape,
exclude_targets={'cuda'}, numerical_grads=True)
check_function(y, forward, shape=shape,
only_targets={'cuda'}, numerical_grads=False)
def test_dense():
x = sym.Variable("x", shape=(10, 100))
w = sym.Variable("dense_weight", shape=(3, 100))
b = sym.Variable("dense_bias", shape=(3,))
y = sym.dense(x, w, b, use_bias=True, units=3, name="dense")
y = sym.flatten(y)
def forward(x, dense_weight, dense_bias):
return np.dot(x, dense_weight.T) + dense_bias
shape = {
'x': (10, 100),
'w': (3, 100),
'b': (3,)
}
# Don't check gradients on cuda because is doesn't yet support ewise after reduce
check_function(y, forward, shape=shape,
exclude_targets={'cuda'}, numerical_grads=True)
check_function(y, forward, shape=shape,
only_targets={'cuda'}, numerical_grads=False)
def test_batchnorm():
x = sym.Variable("x")
beta = sym.Variable("beta")
gamma = sym.Variable("gamma")
moving_var = sym.Variable("moving_var")
moving_mean = sym.Variable("moving_mean")
eps = 1e-5
y = sym.batch_norm(x, gamma, beta, moving_mean, moving_var, epsilon=eps)
def forward(x, gamma, beta, moving_mean, moving_var):
return (x - moving_mean) / np.sqrt(moving_var + eps) * gamma + beta
shape = {
'x': (10, 20),
'gamma': (20, ),
'beta': (20, ),
'moving_mean': (20, ),
'moving_var': (20, )
}
check_function(y, forward, in_range=(0.001, 1.0), shape=shape)
def test_batchnorm():
x = sym.Variable("x")
beta = sym.Variable("beta")
gamma = sym.Variable("gamma")
moving_var = sym.Variable("moving_var")
moving_mean = sym.Variable("moving_mean")
eps = 1e-5
y = sym.batch_norm(
x, gamma, beta, moving_mean, moving_var, epsilon=eps)
def forward(x, gamma, beta, moving_mean, moving_var):
return (x - moving_mean) / np.sqrt(moving_var + eps) * gamma + beta
shape = {
'x': (10, 20),
'gamma': (20,),
'beta': (20,),
'moving_mean': (20,),
'moving_var': (20,)
}
check_function(y, forward, in_range=(0.001, 1.0), shape=shape)
def verify_gather_nd(src_shape, indices_src):
src_dtype = "float32"
indices_dtype = "int32"
indices_src = np.array(indices_src, dtype=indices_dtype)
a = sym.Variable("a", shape=src_shape)
indices = sym.Variable("indices", shape=indices_src.shape)
y = sym.gather_nd(a, indices)
def forward(a, indices):
return topi.testing.gather_nd_python(a, indices)
a_src = np.arange(np.prod(src_shape), dtype=src_dtype).reshape(src_shape)
check_function(y,
forward,
dtype={
'a': src_dtype,
'indices': indices_dtype
},
values={
'a': a_src,
'indices': indices_src
})
def test_broadcast():
a = sym.Variable("a")
b = sym.Variable("b")
shape = {'a': (3, 4, 5), 'b': (1, 5)}
def _collapse(g):
return g.reshape(-1, shape['b'][-1]).sum(0, keepdims=True)
y = sym.broadcast_add(a, b)
def _backward_add(head_grads, a, b):
da = head_grads
db = _collapse(head_grads)
return da, db
check_function(y, lambda a, b: a + b, _backward_add, shape=shape)
y = sym.broadcast_sub(a, b)
def _backward_sub(head_grads, a, b):
da = head_grads
db = -_collapse(head_grads)
return da, db
check_function(y, lambda a, b: a - b, _backward_sub, shape=shape)
y = sym.broadcast_mul(a, b)
def _backward_mul(head_grads, a, b):
da = head_grads * b
db = _collapse(head_grads * a)
return da, db
check_function(y, lambda a, b: a * b, _backward_mul, shape=shape)
y = sym.broadcast_div(a, b)
def _backward_div(head_grads, a, b):
da = head_grads / b
db = _collapse(- head_grads * a / b**2)
return da, db
# We avoid computing numerical derivatives too close to zero here
check_function(y, lambda a, b: a / b, _backward_div, shape=shape, numerical_grads=False)
check_function(y, lambda a, b: a / b, _backward_div, shape=shape,
in_range={'b': (0.1, 20)})
y = sym.broadcast_mod(a, b)
check_function(y,
lambda a, b: np.mod(a, b),
in_range={'a': (0.001, 100), 'b': (1, 100)}, dtype='int32', shape=shape)
y = sym.broadcast_max(a, b)
check_function(y, lambda a, b: np.maximum(a, b), shape=shape)
y = sym.broadcast_min(a, b)
check_function(y, lambda a, b: np.minimum(a, b), shape=shape)
y = sym.broadcast_pow(a, b)
check_function(y,
lambda a, b: np.power(a, b),
in_range={'a': (0.001, 100), 'b': (0.001, 2)}, shape=shape)
y = sym.broadcast_left_shift(a, b)
check_function(y, lambda a, b: a << b, dtype='int32', shape=shape)
y = sym.broadcast_right_shift(a, b)
check_function(y, lambda a, b: a >> b, dtype='int32', shape=shape)
y = sym.broadcast_greater(a, b)
check_function(y, lambda a, b: np.greater(a, b), shape=shape)
y = sym.broadcast_less(a, b)
check_function(y, lambda a, b: np.less(a, b), shape=shape)
y = sym.broadcast_equal(a, b)
check_function(y, lambda a, b: np.equal(a, b),
in_range={'a': (-2, 2), 'b': (-2, 2)}, dtype='int32', shape=shape)
y = sym.broadcast_not_equal(a, b)
check_function(y, lambda a, b: np.not_equal(a, b),
in_range={'a': (-2, 2), 'b': (-2, 2)}, dtype='int32', shape=shape)
y = sym.broadcast_greater_equal(a, b)
check_function(y, lambda a, b: np.greater_equal(a, b),
in_range={'a': (-3, 3), 'b': (-3, 3)}, dtype='int32', shape=shape)
y = sym.broadcast_less_equal(a, b)
check_function(y, lambda a, b: np.less_equal(a, b),
in_range={'a': (-3, 3), 'b': (-3, 3)}, dtype='int32', shape=shape)
def test_broadcast():
a = sym.Variable("a")
b = sym.Variable("b")
shape = {'a': (3, 4, 5), 'b': (1, 5)}
def _collapse(g):
return g.reshape(-1, shape['b'][-1]).sum(0, keepdims=True)
y = sym.broadcast_add(a, b)
def _backward_add(head_grads, a, b):
da = head_grads
db = _collapse(head_grads)
return da, db
check_function(y, lambda a, b: a + b, _backward_add, shape=shape)
y = sym.broadcast_sub(a, b)
def _backward_sub(head_grads, a, b):
da = head_grads
db = -_collapse(head_grads)
return da, db
check_function(y, lambda a, b: a - b, _backward_sub, shape=shape)
y = sym.broadcast_mul(a, b)
def _backward_mul(head_grads, a, b):
da = head_grads * b
db = _collapse(head_grads * a)
return da, db
check_function(y, lambda a, b: a * b, _backward_mul, shape=shape)
y = sym.broadcast_div(a, b)
def _backward_div(head_grads, a, b):
da = head_grads / b
db = _collapse(-head_grads * a / b**2)
return da, db
# We avoid computing numerical derivatives too close to zero here
check_function(y,
lambda a, b: a / b,
_backward_div,
shape=shape,
numerical_grads=False)
check_function(y,
lambda a, b: a / b,
_backward_div,
shape=shape,
in_range={'b': (0.1, 20)})
y = sym.broadcast_mod(a, b)
check_function(y,
lambda a, b: np.mod(a, b),
in_range={
'a': (0.001, 100),
'b': (1, 100)
},
dtype='int32',
shape=shape)
y = sym.broadcast_max(a, b)
check_function(y, lambda a, b: np.maximum(a, b), shape=shape)
y = sym.broadcast_min(a, b)
check_function(y, lambda a, b: np.minimum(a, b), shape=shape)
y = sym.broadcast_pow(a, b)
check_function(y,
lambda a, b: np.power(a, b),
in_range={
'a': (0.001, 100),
'b': (0.001, 2)
},
shape=shape)
y = sym.broadcast_left_shift(a, b)
check_function(y, lambda a, b: a << b, dtype='int32', shape=shape)
y = sym.broadcast_right_shift(a, b)
check_function(y, lambda a, b: a >> b, dtype='int32', shape=shape)
y = sym.broadcast_greater(a, b)
check_function(y, lambda a, b: np.greater(a, b), shape=shape)
y = sym.broadcast_less(a, b)
check_function(y, lambda a, b: np.less(a, b), shape=shape)
y = sym.broadcast_equal(a, b)
check_function(y,
lambda a, b: np.equal(a, b),
in_range={
'a': (-2, 2),
'b': (-2, 2)
},
dtype='int32',
shape=shape)
y = sym.broadcast_not_equal(a, b)
check_function(y,
lambda a, b: np.not_equal(a, b),
in_range={
'a': (-2, 2),
'b': (-2, 2)
},
dtype='int32',
shape=shape)
y = sym.broadcast_greater_equal(a, b)
check_function(y,
lambda a, b: np.greater_equal(a, b),
in_range={
'a': (-3, 3),
'b': (-3, 3)
},
dtype='int32',
shape=shape)
y = sym.broadcast_less_equal(a, b)
check_function(y,
lambda a, b: np.less_equal(a, b),
in_range={
'a': (-3, 3),
'b': (-3, 3)
},
dtype='int32',
shape=shape)
def backward(head_grads, x, conv_kernel, sparse_kernel, kernel, pad, stride,
**args):
return
dtype = "float32"
shape = {'x': (1, 1, 3, 3)}
localtime = time.asctime(time.localtime(time.time()))
print("Start time:" + localtime)
for _ in range(1):
check_function(y,
forward=forward,
backward=backward,
numerical_grads=False,
values=np.ones(shape['x'], dtype),
dtype=dtype,
shape=shape,
additional_params={
'kernel': [3, 3],
'pad': [0, 0],
'stride': [1, 1]
})
localtime = time.asctime(time.localtime(time.time()))
print("End time:" + localtime)
'''
batch_size = 1
num_class = 1000
image_shape = (3, 224, 224)
data_shape = (batch_size,) + image_shape
out_shape = (batch_size, num_class)
opt_level = 3
def test_check_function():
# test the testing function
x = sym.Variable("x")
y = sym.Variable("y")
# different styles of returning gradients from the backward function
check_function(x + 2*y, lambda x, y: x + 2*y,
lambda x, y, head_grads: [head_grads, 2*head_grads],
shape={'x': (1, 2), y: (1, 2)}, dtype='float32')
check_function(x + 2*y, lambda x, y: x + 2*y,
lambda x, y, head_grads: (head_grads, 2*head_grads),
shape={'x': (1, 2), y: (1, 2)}, dtype='float32')
check_function(x + 2*y, lambda x, y: x + 2*y,
lambda x, y, head_grads: {'x': head_grads, 'y': 2*head_grads},
shape={'x': (1, 2), y: (1, 2)}, dtype='float32')
check_function(x + 2*y, lambda x, y: x + 2*y,
lambda x, y, head_grads: {'y': 2*head_grads},
shape={'x': (1, 2), y: (1, 2)}, dtype='float32')
check_function(x + 2*y, lambda x, y: x + 2*y,
lambda x, y, head_grads: [2*head_grads],
grad_input_vars=[y],
shape={'x': (1, 2), y: (1, 2)}, dtype='float32')
check_function(x + 2*y, lambda x, y: x + 2*y,
lambda x, y, head_grads: 2*head_grads,
grad_input_vars=[y],
shape={'x': (1, 2), y: (1, 2)}, dtype='float32')
check_function(x + 2*y, lambda x, y: x + 2*y,
lambda x, y, head_grads: 2*head_grads,
grad_input_vars=[y],
shape={'x': (1, 2), y: (1, 2)}, dtype='float64')
# test just numerical gradients
# different styles of shape and dtype passing
check_function(x + 2*y, shape={'x': (1, 2), y: (1, 2)},
numerical_grads=True)
check_function(x + 2*y, shape={'x': (1, 2), y: (1, 2)}, dtype='float32',
numerical_grads=True)
check_function(x + 2*y, shape={'x': (1, 2), y: (1, 2)}, dtype={x: 'float32', 'y': 'float32'},
numerical_grads=True)
check_function(x + 2*y, shape=(1, 2), dtype='float32',
numerical_grads=True)
# specifying variable attributes on variable creation
# (in this case type codes must be used)
x = sym.Variable("x", dtype=0, shape=(1, 2))
check_function(x + 2*y, shape={y: (1, 2)}, dtype={'y': 'float32'}, numerical_grads=True)
y = sym.Variable("y", dtype=0, shape=(1, 2))
# shape overriding
def _fwd1(x, y):
assert x.shape == (1, 1)
assert y.shape == (1, 2)
return x + 2*y
check_function(x + 2*y, _fwd1, shape={x: (1, 1)})
# in_range
def _fwd2(x, y):
assert x.shape == (100,)
assert (x <= 0.9).all()
assert (x >= 0.8).all()
return x + 2*y
check_function(x + 2*y, _fwd2, shape=(100,), in_range=(0.8, 0.9), numerical_grads=False)
check_function(x + 2*y, _fwd2, shape=(100,), in_range={'x': (0.8, 0.9)}, numerical_grads=False)
check_function(x + 2*y, backward=lambda x, y, head_grads: [1.0, 2.0],
in_range={'head_grads_0': (1.0, 1.0)})
# explicit passing of values
check_function(x + 2*y, backward=lambda x, y, head_grads: [1.0, 2.0],
values={'head_grads_0': np.full((1, 2), 1.0)})
# check that the function reports errors
def _check_function_must_fail(*args, **kwargs):
error = AssertionError
if 'error' in kwargs:
error = kwargs['error']
del kwargs['error']
try:
check_function(*args, quiet=True, **kwargs)
except error:
pass
else:
raise AssertionError("check_function didn't raise an exception")
_check_function_must_fail(x + 2*y, error=ValueError)
_check_function_must_fail(x + 2*y, lambda x, y: x + y)
_check_function_must_fail(x + 2*y, backward=lambda x, y, head_grads: [1.0, 2.0])
_check_function_must_fail(sym.block_grad(x + 2*y), numerical_grads=True)
_check_function_must_fail(x*x, numerical_grads=True,
numerical_grads_params={'atol': 0.0, 'rtol': 0.0})
_check_function_must_fail(sym.log(-x*x), numerical_grads=True, error=ValueError)
# different styles of returning results from the forward function
check_function(x + 2*y, lambda x, y: [x + 2*y], numerical_grads=False)
_check_function_must_fail(x + 2*y, lambda x, y: [x + 2*y, x], numerical_grads=False,
error=ValueError)
_check_function_must_fail(x + 2*y, lambda x, y: [], numerical_grads=False,
error=ValueError)
# multiple outputs
z = sym.Group([2*x + y, x + 2*y])
check_function(z, lambda x, y: [2*x + y, x + 2*y])
check_function(z, lambda x, y: (2*x + y, x + 2*y))
check_function(z, backward=lambda x, y, head_grads: [2*head_grads[0] + head_grads[1],
head_grads[0] + 2*head_grads[1]])
_check_function_must_fail(z, backward=lambda x, y, head_grads: [2*head_grads[0],
2*head_grads[1]])
check_function(z, backward=lambda x, y, head_grads: [head_grads[1], 2*head_grads[1]],
in_range={'head_grads_0': (0, 0)})
check_function(z, numerical_grads=True)
z = sym.Group([sym.block_grad(2*x + y), x + 2*y])
check_function(z, lambda x, y: [2*x + y, x + 2*y], numerical_grads=False)
_check_function_must_fail(z, lambda x, y: [2*x + y, x + 2*y])
_check_function_must_fail(z, numerical_grads=True)
z = sym.Group([2*x + y, sym.block_grad(x + 2*y)])
_check_function_must_fail(z, numerical_grads=True)
z = sym.Group([2*x + y, x + 2*y, x, y, sym.sum(x)])
check_function(z, lambda x, y: [2*x + y, x + 2*y, x, y, np.sum(x)])
# passing additional parameters to forward and backward
def _fwd3(x, p):
assert p == 'v'
return x + 1
def _bwd3(x, p, head_grads):
assert p == 'v'
return head_grads
check_function(x + 1, _fwd3, _bwd3, additional_params={'p': 'v'})
# implicitly created variables and shape/dtype inference for inputs
x = sym.Variable("x", shape=(2, 3), dtype=0)
b = sym.Variable("b")
y = sym.dense(data=x, bias=b, units=4)
# Don't check gradients on cuda because is doesn't yet support ewise after reduce
check_function(y, exclude_targets={'cuda'}, numerical_grads=True)
check_function(y, shape={'x': (3, 4)}, exclude_targets={'cuda'}, numerical_grads=True)
check_function(y, dtype={'x': 'float64'}, exclude_targets={'cuda'}, numerical_grads=True)
x = sym.Variable("x")
b = sym.Variable("b")
w = sym.Variable("w")
y = sym.dense(data=x, bias=b, weight=w, units=4)
def _fwd_dense(x, w, b):
return np.dot(x, w.T) + b
check_function(y, _fwd_dense, shape={'x': (1,2)}, dtype={'x': 'float32'}, numerical_grads=False)
check_function(y, _fwd_dense, shape={'x': (1,2)}, dtype={'w': 'float64'}, numerical_grads=False)
_check_function_must_fail(y, _fwd_dense, shape={'x': (1,2)},
dtype={'w': 'float64', 'b': 'float32'},
numerical_grads=False,
error=nnvm._base.NNVMError)
# fails because no shape
_check_function_must_fail(y, _fwd_dense, numerical_grads=False, error=ValueError)
# ok because type is float32 by default
check_function(y, _fwd_dense, shape={'x': (1,2)}, numerical_grads=False)
def check_map(symfunc, np_func, np_backward=None, dtype="float32", rnd_min=-1, rnd_max=1):
x = sym.Variable("x")
y = symfunc(x)
shape = {'x': (1, 3, 32, 32)}
check_function(y, lambda x: np_func(x), np_backward,
dtype=dtype, shape=shape, in_range=(rnd_min, rnd_max))
