def test_max_pool3d_grad_grad():
shps = [(1, 1, 12),
(1, 1, 1, 1, 1),
(1, 1, 1, 1, 1025),
(1, 1, 2, 2, 2),
(1, 1, 7, 7, 7),
(1, 1, 9, 10, 11),
(1, 6, 18, 18, 18),
(1, 1, 6, 24, 24),
(1, 10, 1, 24, 24),
(1, 10, 6, 24, 24),
(1, 30, 6, 12, 12),
(1, 30, 2, 24, 24),
(1, 30, 6, 24, 24),
(1, 10, 10, 10, 11),
(1, 1, 10, 10, 1025),
(1, 1, 10, 10, 1023),
(1, 1, 10, 1025, 10),
(1, 1, 10, 1023, 10), ]
numpy.random.RandomState(utt.fetch_seed()).shuffle(shps)
test_ds = (2, 2, 2), (3, 2, 3), (1, 1, 1)
test_st = (2, 2, 2), (2, 3, 2), (1, 1, 1)
for shp in shps:
for ds, st in itertools.product(test_ds, test_st):
if ds[0] > shp[-3] or ds[1] > shp[-2] or ds[2] > shp[-1]:
continue
for ignore_border, pad in zip((True, False), [(1, 1, 1), (0, 0, 0)]):
if pad[0] >= ds[0] or pad[1] >= ds[1] or pad[2] >= ds[2]:
continue
# print('test_downsample', shp, ds, st, pad, ignore_border)
ds_op = Pool(ndim=len(ds), ignore_border=ignore_border)
a = theano.shared(rand(*shp), 'a')
ggf = gradient.Lop(tensor.grad((ds_op(
tensor.as_tensor_variable(a), ds, st, pad)**2).sum(), a), a, a)
ref_mode = copy.copy(mode_without_gpu)
ref_mode.check_py_code = False
gpu_mode = copy.copy(mode_with_gpu)
gpu_mode.check_py_code = False
gg = theano.function([], ggf, mode=gpu_mode)
gg2 = theano.function([], ggf, mode=ref_mode)
assert any([
isinstance(node.op, GpuDownsampleFactorMaxGradGrad)
for node in gg.maker.fgraph.toposort()
])
assert any([
isinstance(node.op, DownsampleFactorMaxGradGrad)
for node in gg2.maker.fgraph.toposort()
])
assert numpy.allclose(gg(), gg2()), (shp, ds, st,
ignore_border)
def test_pool2d():
shps = [
(1, 12),
(1, 1, 12),
(1, 1, 1, 12),
(1, 1, 2, 2),
(1, 1, 1, 1),
(1, 1, 4, 4),
(1, 1, 10, 11),
(1, 2, 2, 2),
(3, 5, 4, 4),
(25, 1, 7, 7),
(1, 1, 12, 12),
(1, 1, 2, 14),
(1, 1, 12, 14),
(1, 1, 14, 14),
(1, 1, 16, 16),
(1, 1, 18, 18),
(1, 1, 24, 24),
(1, 6, 24, 24),
(10, 1, 24, 24),
(10, 6, 24, 24),
(30, 6, 12, 12),
(30, 2, 24, 24),
(30, 6, 24, 24),
(10, 10, 10, 11),
(1, 1, 10, 1025),
(1, 1, 10, 1023),
(1, 1, 1025, 10),
(1, 1, 1023, 10),
(3, 2, 16, 16, 16),
(3, 2, 6, 6, 6, 5),
(3, 2, 6, 6, 6, 5, 7),
]
numpy.random.RandomState(utt.fetch_seed()).shuffle(shps)
test_ws = (2, 2), (3, 2), (1, 1)
test_st = (2, 2), (3, 2), (1, 1)
test_mode = ['max', 'sum', 'average_inc_pad', 'average_exc_pad']
ref_mode = copy.copy(mode_without_gpu)
ref_mode.check_py_code = False
gpu_mode = copy.copy(mode_with_gpu).excluding("cudnn")
gpu_mode.check_py_code = False
for shp in shps:
for mode, ws, st in itertools.product(test_mode, test_ws, test_st):
if ws[0] > shp[-2] or ws[1] > shp[-1]:
continue
for ignore_border, pad in zip((True, False), [(1, 1), (0, 0)]):
if pad[0] >= ws[0] or pad[1] >= ws[1]:
continue
if mode == 'average_exc_pad' and (pad[0] > 0 or pad[1] > 0):
continue
# print('test_pool2d', shp, ws, st, pad, mode, ignore_border)
ds_op = Pool(ndim=len(ws),
mode=mode,
ignore_border=ignore_border)
a = theano.shared(rand(*shp), 'a')
a_pooled = ds_op(tensor.as_tensor_variable(a), ws, st, pad)
f = theano.function([], a_pooled, mode=gpu_mode)
f2 = theano.function([], a_pooled, mode=ref_mode)
assert any([
isinstance(node.op, GpuPool)
for node in f.maker.fgraph.toposort()
])
assert any([
isinstance(node.op, Pool)
for node in f2.maker.fgraph.toposort()
])
assert numpy.allclose(f(), f2()), (shp, ws, st, pad, mode,
ignore_border)
a_pooled_grad = tensor.grad(a_pooled.sum(), a)
g = theano.function([], a_pooled_grad, mode=gpu_mode)
g2 = theano.function([], a_pooled_grad, mode=ref_mode)
if mode == 'max':
gop = GpuMaxPoolGrad
gop2 = MaxPoolGrad
else:
gop = GpuAveragePoolGrad
gop2 = AveragePoolGrad
assert any([
isinstance(node.op, gop)
for node in g.maker.fgraph.toposort()
])
assert any([
isinstance(node.op, gop2)
for node in g2.maker.fgraph.toposort()
])
assert numpy.allclose(g(), g2()), (shp, ws, st, pad, mode,
ignore_border)
# test grad grad for max pooling
# for average pooling grad grad is just average pooling grad
if mode != 'max':
continue
ggf = gradient.Lop(tensor.grad((a_pooled**2).sum(), a), a, a)
gg = theano.function([], ggf, mode=gpu_mode)
gg2 = theano.function([], ggf, mode=ref_mode)
assert any([
isinstance(node.op, GpuDownsampleFactorMaxGradGrad)
for node in gg.maker.fgraph.toposort()
])
assert any([
isinstance(node.op, DownsampleFactorMaxGradGrad)
for node in gg2.maker.fgraph.toposort()
])
assert numpy.allclose(gg(), gg2()), (shp, ws, st, pad, mode,
ignore_border)
def test_downsample():
shps = [(1, 12),
(1, 1, 12),
(1, 1, 1, 12),
(1, 1, 2, 2),
(1, 1, 1, 1),
(1, 1, 4, 4),
(1, 1, 10, 11),
(1, 2, 2, 2),
(3, 5, 4, 4),
(25, 1, 7, 7),
(1, 1, 12, 12),
(1, 1, 2, 14),
(1, 1, 12, 14),
(1, 1, 14, 14),
(1, 1, 16, 16),
(1, 1, 18, 18),
(1, 1, 24, 24),
(1, 6, 24, 24),
(10, 1, 24, 24),
(10, 6, 24, 24),
(30, 6, 12, 12),
(30, 2, 24, 24),
(30, 6, 24, 24),
(10, 10, 10, 11),
(1, 1, 10, 1025),
(1, 1, 10, 1023),
(1, 1, 1025, 10),
(1, 1, 1023, 10),
(65536, 1, 10, 10),
(1, 65536, 10, 10), ]
numpy.random.RandomState(unittest_tools.fetch_seed()).shuffle(shps)
for shp in shps:
for ds in (2, 2), (3, 2), (1, 1):
if ds[0] > shp[-2]:
continue
if ds[1] > shp[-1]:
continue
# GpuDownsampleFactorMax doesn't like having more than 512 columns
# in the output tensor.
if float(shp[-1]) / ds[1] > 512:
continue
for ignore_border in (True, False):
# print 'test_downsample', shp, ds, ignore_border
ds_op = Pool(ndim=len(ds), ignore_border=ignore_border)
a = tcn.shared_constructor(my_rand(*shp), 'a')
f = pfunc([], ds_op(tensor.as_tensor_variable(a), ds),
mode=mode_with_gpu.excluding('cudnn'))
f2 = pfunc([], ds_op(tensor.as_tensor_variable(a), ds),
mode=mode_without_gpu)
assert any([isinstance(node.op,
tcn.blas.GpuDownsampleFactorMax)
for node in f.maker.fgraph.toposort()])
assert any([isinstance(node.op, Pool)
for node in f2.maker.fgraph.toposort()])
assert numpy.allclose(f(), f2())
# The grad is too slow on GT220 GPU
# This cause the computer to freeze...
# Remove this when it gets optimized enough
# This only bypass the last 2 checks
# Those tests where passing in all Mode on a GTX470
if shp[0] > 30000 or shp[1] > 30000:
continue
g = pfunc(
[],
tensor.grad(ds_op(tensor.as_tensor_variable(a), ds).sum(),
a),
mode=mode_with_gpu.excluding('cudnn'))
g2 = pfunc(
[],
tensor.grad(ds_op(tensor.as_tensor_variable(a), ds).sum(),
a),
mode=mode_without_gpu)
assert any([isinstance(node.op,
tcn.blas.GpuDownsampleFactorMaxGrad)
for node in g.maker.fgraph.toposort()])
assert any([isinstance(node.op, PoolGrad)
for node in g2.maker.fgraph.toposort()])
assert numpy.allclose(g(), g2()), shp
ggf = gradient.Lop(tensor.grad((ds_op(
tensor.as_tensor_variable(a), ds)**2).sum(), a), a, a)
ref_mode = copy.copy(mode_without_gpu)
ref_mode.check_py_code = False
gpu_mode = copy.copy(mode_with_gpu)
gpu_mode.check_py_code = False
gg = pfunc([], ggf, mode=gpu_mode)
gg2 = pfunc([], ggf, mode=ref_mode)
assert any([isinstance(
node.op, tcn.blas.GpuDownsampleFactorMaxGradGrad)
for node in gg.maker.fgraph.toposort()])
assert any([isinstance(
node.op, DownsampleFactorMaxGradGrad)
for node in gg2.maker.fgraph.toposort()])
assert numpy.allclose(gg(), gg2()), shp
