def check_correctness(f):
rng = np.random.RandomState([2012, 7, 19])
batch_size = 5
rows = 32
cols = 30
channels = 3
pool_rows = 2
pool_cols = 3
zv = rng.randn(batch_size, rows, cols, channels).astype(
config.floatX) * 2. - 3.
p_np, h_np = max_pool_python(zv, (pool_rows, pool_cols))
z_th = T.TensorType(broadcastable=(False, False, False, False),
dtype=config.floatX)()
z_th.name = 'z_th'
p_th, h_th = f(z_th, (pool_rows, pool_cols))
func = function([z_th], [p_th, h_th])
pv, hv = func(zv)
assert p_np.shape == pv.shape
assert h_np.shape == hv.shape
if not np.allclose(h_np, hv):
print((h_np.min(), h_np.max()))
print((hv.min(), hv.max()))
assert False
assert np.allclose(p_np, pv)
def check_correctness_c01b(f):
"""
Tests that the theano expression emitted by f computes the same values
as the ground truth python function
Note: to keep the python version as dead simple as possible (i.e., to make
sure there are not bugs in the ground truth) it uses the numerically
unstable version of softmax. So this test does not work with too big of
numbers.
"""
rng = np.random.RandomState([2013, 5, 6])
batch_size = 5
rows = 32
cols = 30
channels = 3
pool_rows = 2
pool_cols = 3
# Do the python ground truth in b01c format
zv = rng.randn(batch_size, rows, cols,
channels).astype(config.floatX) * 1. - 1.5
top_down_v = rng.randn(batch_size, rows / pool_rows, cols / pool_cols,
channels).astype(config.floatX)
p_np, h_np = max_pool_python(zv, (pool_rows, pool_cols), top_down_v)
# Dimshuffle the inputs into c01b for the theano implementation
z_th = T.TensorType(broadcastable=(False, False, False, False),
dtype = config.floatX)()
z_th.tag.test_value = zv
z_th.name = 'z_th'
zr = z_th.dimshuffle(3, 1, 2, 0)
top_down_th = T.TensorType(broadcastable=(False, False, False, False),
dtype = config.floatX)()
top_down_th.name = 'top_down_th'
top_down_th.tag.test_value = top_down_v
top_down_r = top_down_th.dimshuffle(3, 1, 2, 0)
p_th, h_th = f(zr, (pool_rows, pool_cols), top_down_r)
func = function([z_th, top_down_th], [p_th.dimshuffle(3, 1, 2, 0),
h_th.dimshuffle(3, 1, 2, 0)])
pv, hv = func(zv, top_down_v)
if not p_np.shape == pv.shape:
raise AssertionError(str((p_np.shape, pv.shape)))
assert h_np.shape == hv.shape
if not np.allclose(h_np, hv):
print (h_np.min(), h_np.max())
print (hv.min(), hv.max())
assert False
if not np.allclose(p_np, pv):
diff = abs(p_np - pv)
print 'max diff ', diff.max()
print 'min diff ', diff.min()
print 'ave diff ', diff.mean()
assert False
warnings.warn("TODO: make sampling tests run on c01b format of pooling.")
def check_correctness(f):
rng = np.random.RandomState([2012, 7, 19])
batch_size = 5
rows = 32
cols = 30
channels = 3
pool_rows = 2
pool_cols = 3
zv = rng.randn(batch_size, rows, cols,
channels).astype(config.floatX) * 2. - 3.
p_np, h_np = max_pool_python(zv, (pool_rows, pool_cols))
z_th = T.TensorType(broadcastable=(False, False, False, False),
dtype=config.floatX)()
z_th.name = 'z_th'
p_th, h_th = f(z_th, (pool_rows, pool_cols))
func = function([z_th], [p_th, h_th])
pv, hv = func(zv)
assert p_np.shape == pv.shape
assert h_np.shape == hv.shape
if not np.allclose(h_np, hv):
print (h_np.min(), h_np.max())
print (hv.min(), hv.max())
assert False
assert np.allclose(p_np, pv)
def check_correctness_c01b(f):
# Tests that the theano expression emitted by f computes the same values
# as the ground truth python function
# Note: to keep the python version as dead simple as possible (i.e., to make
# sure there are not bugs in the ground truth) it uses the numerically
# unstable version of softmax. So this test does not work with too big of
# numbers.
rng = np.random.RandomState([2013, 5, 6])
batch_size = 5
rows = 32
cols = 30
channels = 3
pool_rows = 2
pool_cols = 3
# Do the python ground truth in b01c format
zv = rng.randn(batch_size, rows, cols, channels).astype(
config.floatX) * 1. - 1.5
top_down_v = rng.randn(batch_size, rows / pool_rows, cols / pool_cols,
channels).astype(config.floatX)
p_np, h_np = max_pool_python(zv, (pool_rows, pool_cols), top_down_v)
# Dimshuffle the inputs into c01b for the theano implementation
z_th = T.TensorType(broadcastable=(False, False, False, False),
dtype=config.floatX)()
z_th.tag.test_value = zv
z_th.name = 'z_th'
zr = z_th.dimshuffle(3, 1, 2, 0)
top_down_th = T.TensorType(broadcastable=(False, False, False, False),
dtype=config.floatX)()
top_down_th.name = 'top_down_th'
top_down_th.tag.test_value = top_down_v
top_down_r = top_down_th.dimshuffle(3, 1, 2, 0)
p_th, h_th = f(zr, (pool_rows, pool_cols), top_down_r)
func = function([z_th, top_down_th],
[p_th.dimshuffle(3, 1, 2, 0),
h_th.dimshuffle(3, 1, 2, 0)])
pv, hv = func(zv, top_down_v)
if not p_np.shape == pv.shape:
raise AssertionError(str((p_np.shape, pv.shape)))
assert h_np.shape == hv.shape
if not np.allclose(h_np, hv):
print(h_np.min(), h_np.max())
print(hv.min(), hv.max())
assert False
if not np.allclose(p_np, pv):
diff = abs(p_np - pv)
print 'max diff ', diff.max()
print 'min diff ', diff.min()
print 'ave diff ', diff.mean()
assert False
warnings.warn("TODO: make sampling tests run on c01b format of pooling.")
def check_correctness_bc01(f):
"""
Tests that the theano expression emitted by f computes the same values
as the ground truth python function
Note: to keep the python version as dead simple as possible (i.e., to make
sure there are not bugs in the ground truth) it uses the numerically
unstable verison of softmax. So this test does not work with too big of
numbers.
"""
rng = np.random.RandomState([2012, 7, 19])
batch_size = 5
rows = 32
cols = 30
channels = 3
pool_rows = 2
pool_cols = 3
zv = rng.randn(batch_size, rows, cols, channels).astype(
config.floatX) * 1. - 1.5
top_down_v = rng.randn(batch_size, rows / pool_rows, cols / pool_cols,
channels).astype(config.floatX)
p_np, h_np = max_pool_python(zv, (pool_rows, pool_cols), top_down_v)
z_th = T.TensorType(broadcastable=(False, False, False, False),
dtype=config.floatX)()
z_th.name = 'z_th'
zr = z_th.dimshuffle(0, 3, 1, 2)
top_down_th = T.TensorType(broadcastable=(False, False, False, False),
dtype=config.floatX)()
top_down_th.name = 'top_down_th'
top_down_r = top_down_th.dimshuffle(0, 3, 1, 2)
p_th, h_th = f(zr, (pool_rows, pool_cols), top_down_r)
func = function([z_th, top_down_th],
[p_th.dimshuffle(0, 2, 3, 1),
h_th.dimshuffle(0, 2, 3, 1)])
pv, hv = func(zv, top_down_v)
assert p_np.shape == pv.shape
assert h_np.shape == hv.shape
if not np.allclose(h_np, hv):
print((h_np.min(), h_np.max()))
print((hv.min(), hv.max()))
assert False
if not np.allclose(p_np, pv):
diff = abs(p_np - pv)
print('max diff ', diff.max())
print('min diff ', diff.min())
print('ave diff ', diff.mean())
assert False
def check_correctness_bc01(f):
"""
Tests that the theano expression emitted by f computes the same values
as the ground truth python function
Note: to keep the python version as dead simple as possible (i.e., to make
sure there are not bugs in the ground truth) it uses the numerically
unstable verison of softmax. So this test does not work with too big of
numbers.
"""
rng = np.random.RandomState([2012, 7, 19])
batch_size = 5
rows = 32
cols = 30
channels = 3
pool_rows = 2
pool_cols = 3
zv = rng.randn(batch_size, rows, cols,
channels).astype(config.floatX) * 1. - 1.5
top_down_v = rng.randn(batch_size, rows / pool_rows, cols / pool_cols,
channels).astype(config.floatX)
p_np, h_np = max_pool_python(zv, (pool_rows, pool_cols), top_down_v)
z_th = T.TensorType(broadcastable=(False, False, False, False),
dtype = config.floatX)()
z_th.name = 'z_th'
zr = z_th.dimshuffle(0, 3, 1, 2)
top_down_th = T.TensorType(broadcastable=(False, False, False, False),
dtype = config.floatX)()
top_down_th.name = 'top_down_th'
top_down_r = top_down_th.dimshuffle(0, 3, 1, 2)
p_th, h_th = f(zr, (pool_rows, pool_cols), top_down_r)
func = function([z_th, top_down_th], [p_th.dimshuffle(0, 2, 3, 1),
h_th.dimshuffle(0, 2, 3, 1)])
pv, hv = func(zv, top_down_v)
assert p_np.shape == pv.shape
assert h_np.shape == hv.shape
if not np.allclose(h_np, hv):
print (h_np.min(), h_np.max())
print (hv.min(), hv.max())
assert False
if not np.allclose(p_np, pv):
diff = abs(p_np - pv)
print 'max diff ', diff.max()
print 'min diff ', diff.min()
print 'ave diff ', diff.mean()
assert False
