Skip to content

P
pytensor
提交 19c1624f authored 作者: Gijs van Tulder's avatar Gijs van Tulder
浏览文件
操作

Update gpuarray/cuda pooling tests.

Now use the CPU implementation as the reference (and other changes).
上级 8150bbd3
隐藏空白字符变更
内嵌 并排
正在显示 包含 234 行增加 和 350 行删除
theano/gpuarray/tests/test_dnn.py
浏览文件 @ 19c1624f
......@@ -10,7 +10,6 @@ import theano
from six import StringIO
import theano.tensor as T
import theano.tests.unittest_tools as utt
from theano.sandbox.neighbours import images2neibs
from theano.tensor.signal.pool import pool_2d, pool_3d
from theano.tensor.signal.pool import Pool, MaxPoolGrad, AveragePoolGrad
......@@ -132,63 +131,6 @@ def test_dnn_conv_inplace():
assert len([n for n in topo if isinstance(n.op, GpuAllocEmpty)]) == 2
def pool_2d_i2n(input, ds=(2, 2), strides=None,
pad=(0, 0),
pool_function=T.max, mode='ignore_borders'):
if strides is None:
strides = ds
if strides[0] > ds[0] or strides[1] > ds[1]:
raise RuntimeError(
"strides should be smaller than or equal to ds,"
" strides=(%d, %d) and ds=(%d, %d)" %
(strides + ds))
shape = input.shape
if pad != (0, 0):
assert pool_function is T.max
pad_x = pad[0]
pad_y = pad[1]
a = T.alloc(-numpy.inf, shape[0], shape[1], shape[2] + pad_x * 2,
shape[3] + pad_y * 2)
input = T.set_subtensor(a[:, :,
pad_x:pad_x + shape[2],
pad_y:pad_y + shape[3]],
input)
shape = input.shape
neibs = images2neibs(input, ds, strides, mode=mode)
pooled_neibs = pool_function(neibs, axis=1)
output_width = (shape[2] - ds[0]) // strides[0] + 1
output_height = (shape[3] - ds[1]) // strides[1] + 1
pooled_output = pooled_neibs.reshape((shape[0], shape[1],
output_width, output_height))
return pooled_output
def pool3d2d(input, ds=(2, 2, 2), strides=None, pad=(0, 0, 0),
pool_function=T.max, mode='ignore_borders'):
if strides is None:
strides = ds
assert input.ndim == 5
shape = input.shape
# reshape to B, C*0, 1, 2 and do the pooling on 1, 2
first = input.reshape((shape[0], shape[1] * shape[2], shape[3], shape[4]))
pooled1 = pool_2d_i2n(first, ds=ds[1:], strides=strides[1:], pad=pad[1:],
pool_function=pool_function, mode=mode)
shp1 = pooled1.shape
# reshape to B, C, 0, 1*2 and do the pooling on 0
second = pooled1.reshape((shape[0], shape[1], shape[2], shp1[2] * shp1[3]))
pooled2 = pool_2d_i2n(second, ds=(ds[0], 1), strides=(strides[0], 1),
pad=(pad[0], 0), pool_function=pool_function, mode=mode)
shp2 = pooled2.shape
return pooled2.reshape((shape[0], shape[1], shp2[2], shp1[2], shp1[3]))
def test_pooling():
if not dnn.dnn_available(test_ctx_name):
raise SkipTest(dnn.dnn_available.msg)
......@@ -202,12 +144,8 @@ def test_pooling():
x = T.ftensor4()
for mode, pad in product(modes,
((0, 0), (1, 0), (0, 1), (2, 3), (3, 2))):
if mode == 'max':
func = T.max
else:
func = T.mean
if pad != (0, 0) and func is T.mean:
if pad != (0, 0) and mode == 'average_exc_pad':
# Not implemented
continue
for ws in (4, 2, 5):
......@@ -218,29 +156,32 @@ def test_pooling():
# Not implemented
continue
# We will check that the opt introduced it.
out1 = pool_2d(x, (ws, ws),
st=(stride, stride),
ignore_border=True,
padding=pad, mode=mode)
out2 = pool_2d_i2n(x, ds=(ws, ws), strides=(stride, stride),
pad=pad,
pool_function=func)
out = pool_2d(x, (ws, ws),
st=(stride, stride),
ignore_border=True,
padding=pad, mode=mode)
mode_without_gpu2 = mode_without_gpu.including()
mode_without_gpu2.check_isfinite = False
f1 = theano.function([x], out1, mode=mode_with_gpu)
# GPU implementation
f_gpu = theano.function([x], out, mode=mode_with_gpu)
assert any([isinstance(node.op, dnn.GpuDnnPool)
for node in f1.maker.fgraph.apply_nodes])
f2 = theano.function([x], out2, mode=mode_without_gpu2)
for node in f_gpu.maker.fgraph.apply_nodes])
# CPU implementation
f_cpu = theano.function([x], out, mode=mode_without_gpu2)
assert not any([isinstance(node.op, dnn.GpuDnnPool)
for node in f2.maker.fgraph.apply_nodes])
for node in f_cpu.maker.fgraph.apply_nodes])
assert any([isinstance(node.op, Pool)
for node in f_cpu.maker.fgraph.apply_nodes])
for shp in [(1, 10, 100, 100),
(1, 3, 99, 99),
(32, 1, 147, 197),
]:
data = numpy.random.normal(0, 1, shp).astype("float32")
a = f1(data)
b = f2(data)
a = f_cpu(data).__array__()
b = f_gpu(data).__array__()
utt.assert_allclose(a, b)
# Test the grad
......@@ -254,7 +195,7 @@ def test_pooling():
# Not implemented
continue
# This test the CPU grad + opt + GPU implemtentation
# This tests the CPU grad + opt + GPU implementation
def fn(x):
return pool_2d(x, (ws, ws), ignore_border=True,
padding=pad, mode=mode)
......@@ -279,27 +220,69 @@ def test_pooling():
mode=mode_with_gpu)
assert any([isinstance(node.op, dnn.GpuDnnPoolGrad)
for node in fg.maker.fgraph.toposort()])
g_out = fg(data)
# Compare against the CPU result
out = pool_2d(x, (ws, ws),
padding=pad,
ignore_border=True, mode=mode)
fc = theano.function([x], theano.grad(out.sum(), x),
mode=mode_without_gpu)
if mode == 'max':
assert any([isinstance(node.op, MaxPoolGrad)
for node in fc.maker.fgraph.toposort()])
else:
assert any([isinstance(node.op, AveragePoolGrad)
for node in fc.maker.fgraph.toposort()])
c_out = fc(data)
utt.assert_allclose(c_out, g_out)
def test_pooling_3d():
def test_pooling_with_tensor_vars():
if not dnn.dnn_available(test_ctx_name):
raise SkipTest(dnn.dnn_available.msg)
x = T.ftensor4()
ws = theano.shared(numpy.array([2, 2], dtype='int32'))
st = theano.shared(numpy.array([1, 1], dtype='int32'))
pad = theano.shared(numpy.array([0, 0], dtype='int32'))
mode = 'max'
def fn(x):
dnn_op = dnn.dnn_pool(
x, ws=ws,
stride=st,
pad=pad,
mode=mode)
return dnn_op
for shp in [(1, 1, 2, 2),
(1, 1, 3, 3)]:
data = numpy.random.normal(0, 1, shp).astype("float32") * 10
theano.tests.unittest_tools.verify_grad(
fn, [data], mode=mode_with_gpu)
mode_without_gpu2 = mode_without_gpu.including()
mode_without_gpu2.check_isfinite = False
# GPU implementation
f_gpu = theano.function([x], fn(x), mode=mode_with_gpu)
assert any([isinstance(node.op, dnn.GpuDnnPool)
for node in f_gpu.maker.fgraph.apply_nodes])
# CPU implementation
out_cpu = pool_2d(x, ws, ignore_border=True, st=st, padding=pad, mode=mode)
f_cpu = theano.function([x], out_cpu, mode=mode_without_gpu2)
assert not any([isinstance(node.op, dnn.GpuDnnPool)
for node in f_cpu.maker.fgraph.apply_nodes])
assert any([isinstance(node.op, Pool)
for node in f_cpu.maker.fgraph.apply_nodes])
i = 1
for shp in [(1, 10, 100, 100),
(1, 3, 99, 99),
(32, 1, 147, 197)]:
data = numpy.random.normal(0, 1, shp).astype("float32")
# Change the window size dynamically
ws.set_value(numpy.array([i, i]).astype('int32'))
a = f_gpu(data).__array__()
b = f_cpu(data).__array__()
utt.assert_allclose(a, b)
i += 1
def test_pooling3d():
# 3d pooling requires version 3 or newer.
if not dnn.dnn_available(test_ctx_name) or dnn.version(raises=False) < 3000:
raise SkipTest(dnn.dnn_available.msg)
# We force the FAST_RUN as we don't want the reference to run in DebugMode.
mode_without_gpu_ref = theano.compile.mode.get_mode(
'FAST_RUN').excluding('gpuarray')
# 'average_exc_pad' is disabled for versions < 4004
if dnn.version(raises=False) < 4004:
......@@ -310,13 +293,9 @@ def test_pooling_3d():
x = T.ftensor5()
for mode, pad in product(modes,
((0, 0, 0), (1, 0, 0), (0, 1, 0), (0, 0, 1),
(3, 2, 2), (2, 3, 2), (2, 2, 3))):
if mode == 'max':
func = T.max
else:
func = T.mean
if pad != (0, 0, 0) and func is T.mean:
(2, 3, 2), (3, 2, 2), (2, 2, 3))):
if pad != (0, 0, 0) and mode == 'average_exc_pad':
# Not implemented
continue
for ws in (4, 2, 5):
......@@ -326,35 +305,41 @@ def test_pooling_3d():
if pad[0] > stride or pad[1] > stride or pad[2] > stride:
# Not implemented
continue
# We will check that the opt introduced it.
out1 = pool_3d(x, (ws, ws, ws),
st=(stride, stride, stride),
ignore_border=True,
padding=pad, mode=mode)
out2 = pool3d2d(x, ds=(ws, ws, ws), strides=(stride, stride, stride),
pad=pad,
pool_function=func)
mode_without_gpu2 = mode_without_gpu.including()
mode_without_gpu2.check_isfinite = False
f1 = theano.function([x], out1, mode=mode_with_gpu)
out = pool_3d(x, (ws, ws, ws),
st=(stride, stride, stride),
ignore_border=True,
padding=pad, mode=mode)
# GPU implementation
f_gpu = theano.function([x], out, mode=mode_with_gpu)
assert any([isinstance(node.op, dnn.GpuDnnPool)
for node in f1.maker.fgraph.apply_nodes])
f2 = theano.function([x], out2, mode=mode_without_gpu2)
for node in f_gpu.maker.fgraph.apply_nodes])
# CPU implementation
f_cpu = theano.function([x], out, mode=mode_without_gpu_ref)
assert not any([isinstance(node.op, dnn.GpuDnnPool)
for node in f2.maker.fgraph.apply_nodes])
for shp in [(1, 10, 30, 30, 30),
(1, 3, 29, 29, 29),
(3, 1, 47, 97, 47),
for node in f_cpu.maker.fgraph.apply_nodes])
assert any([isinstance(node.op, Pool)
for node in f_cpu.maker.fgraph.apply_nodes])
for shp in [(1, 5, 50, 20, 50),
(1, 3, 99, 99, 29),
(2, 1, 147, 97, 37),
]:
data = numpy.random.normal(0, 1, shp).astype("float32")
a = f1(data)
b = f2(data)
utt.assert_allclose(a, b)
a = f_cpu(data).__array__()
b = f_gpu(data).__array__()
utt.assert_allclose(a, b,
atol=numpy.finfo(numpy.float32).eps)
# Test the grad
for shp in [(1, 1, 2, 2, 2),
(1, 1, 3, 3, 3)]:
(1, 1, 3, 3, 3),
(1, 1, 3, 3, 4),
(1, 1, 3, 4, 3),
(1, 1, 4, 3, 3),
(1, 1, 4, 4, 4),
(1, 1, 5, 5, 5)]:
data = numpy.random.normal(0, 1, shp).astype("float32") * 10
ws = 2
......@@ -363,17 +348,6 @@ def test_pooling_3d():
# Not implemented
continue
# This test the CPU grad + opt + GPU implemtentation
def fn(x):
return pool_3d(x, (ws, ws, ws), ignore_border=True,
padding=pad, mode=mode)
utt.verify_grad(fn, [data], mode=mode_with_gpu)
# Confirm that the opt would have inserted it.
fg = theano.function([x], theano.grad(fn(x).sum(), x),
mode=mode_with_gpu)
assert any([isinstance(node.op, dnn.GpuDnnPoolGrad)
for node in fg.maker.fgraph.toposort()])
# Test the GPU grad + GPU implementation
def fn(x):
dnn_op = dnn.dnn_pool(
......@@ -388,69 +362,6 @@ def test_pooling_3d():
mode=mode_with_gpu)
assert any([isinstance(node.op, dnn.GpuDnnPoolGrad)
for node in fg.maker.fgraph.toposort()])
g_out = fg(data)
# Compare against the CPU result
out = pool_3d(x, (ws, ws, ws),
padding=pad,
ignore_border=True, mode=mode)
fc = theano.function([x], theano.grad(out.sum(), x),
mode=mode_without_gpu)
if mode == 'max':
assert any([isinstance(node.op, MaxPoolGrad)
for node in fc.maker.fgraph.toposort()])
else:
assert any([isinstance(node.op, AveragePoolGrad)
for node in fc.maker.fgraph.toposort()])
c_out = fc(data)
utt.assert_allclose(c_out, g_out)
def test_pooling_with_tensor_vars():
if not dnn.dnn_available(test_ctx_name):
raise SkipTest(dnn.dnn_available.msg)
x = T.ftensor4()
ws = theano.shared(numpy.array([2, 2], dtype='int32'))
st = theano.shared(numpy.array([1, 1], dtype='int32'))
pad = theano.shared(numpy.array([0, 0], dtype='int32'))
mode = 'max'
def fn(x):
dnn_op = dnn.dnn_pool(x,
ws=ws,
stride=st,
pad=pad,
mode=mode)
return dnn_op
for shp in [(1, 1, 2, 2),
(1, 1, 3, 3)]:
data = numpy.random.normal(0, 1, shp).astype("float32") * 10
theano.tests.unittest_tools.verify_grad(
fn, [data], mode=mode_with_gpu)
out2 = pool_2d_i2n(x, ds=(2, 2), strides=(1, 1),
pad=(0, 0),
pool_function=T.max)
mode_without_gpu2 = mode_without_gpu.including()
mode_without_gpu2.check_isfinite = False
f1 = theano.function([x], fn(x), mode=mode_with_gpu)
assert any([isinstance(node.op, dnn.GpuDnnPool)
for node in f1.maker.fgraph.apply_nodes])
f2 = theano.function([x], out2, mode=mode_without_gpu2)
assert not any([isinstance(node.op, dnn.GpuDnnPool)
for node in f2.maker.fgraph.apply_nodes])
for shp in [(1, 10, 100, 100),
(1, 3, 99, 99),
(32, 1, 147, 197),
]:
data = numpy.random.normal(0, 1, shp).astype("float32")
a = f1(data).__array__()
b = f2(data).__array__()
utt.assert_allclose(a, b)
def test_pooling_opt():
......
theano/sandbox/cuda/tests/test_dnn.py
浏览文件 @ 19c1624f
......@@ -14,7 +14,6 @@ import numpy
import theano
import theano.tensor as T
import theano.tests.unittest_tools as utt
from theano.sandbox.neighbours import images2neibs
from theano.tensor.signal.pool import pool_2d, pool_3d
from theano.tensor.signal.pool import Pool, MaxPoolGrad, AveragePoolGrad
import theano.sandbox.cuda.dnn as dnn
......@@ -169,62 +168,6 @@ def test_dnn_conv_inplace():
assert len([n for n in topo if isinstance(n.op, GpuAllocEmpty)]) == 2
def pool3d2d(input, ds=(2, 2, 2), strides=None, pad=(0, 0, 0),
pool_function=T.max, mode='ignore_borders'):
if strides is None:
strides = ds
assert input.ndim == 5
shape = input.shape
# resahpe to B, C*0, 1, 2 and do the pooling on 1, 2
first = input.reshape((shape[0], shape[1] * shape[2], shape[3], shape[4]))
pooled1 = pool_2d_i2n(first, ds=ds[1:], strides=strides[1:], pad=pad[1:],
pool_function=pool_function, mode=mode)
shp1 = pooled1.shape
# reshape to B, C, 0, 1*2 and do the pooling on 0
second = pooled1.reshape((shape[0], shape[1], shape[2], shp1[2] * shp1[3]))
pooled2 = pool_2d_i2n(second, ds=(ds[0], 1), strides=(strides[0], 1),
pad=(pad[0], 0), pool_function=pool_function, mode=mode)
shp2 = pooled2.shape
return pooled2.reshape((shape[0], shape[1], shp2[2], shp1[2], shp1[3]))
def pool_2d_i2n(input, ds=(2, 2), strides=None, pad=(0, 0),
pool_function=T.max, mode='ignore_borders'):
if strides is None:
strides = ds
if strides[0] > ds[0] or strides[1] > ds[1]:
raise RuntimeError(
"strides should be smaller than or equal to ds,"
" strides=(%d, %d) and ds=(%d, %d)" %
(strides + ds))
shape = input.shape
if pad != (0, 0):
assert pool_function is T.max
pad_x = pad[0]
pad_y = pad[1]
a = T.alloc(-numpy.inf, shape[0], shape[1], shape[2] + pad_x * 2,
shape[3] + pad_y * 2)
input = T.set_subtensor(a[:, :,
pad_x:pad_x + shape[2],
pad_y:pad_y + shape[3]],
input)
shape = input.shape
neibs = images2neibs(input, ds, strides, mode=mode)
pooled_neibs = pool_function(neibs, axis=1)
output_width = (shape[2] - ds[0]) // strides[0] + 1
output_height = (shape[3] - ds[1]) // strides[1] + 1
pooled_output = pooled_neibs.reshape((shape[0], shape[1],
output_width, output_height))
return pooled_output
def test_pooling():
if not cuda.dnn.dnn_available():
raise SkipTest(cuda.dnn.dnn_available.msg)
......@@ -237,13 +180,9 @@ def test_pooling():
x = T.ftensor4()
for mode, pad in product(modes,
((0, 0), (1, 0), (1, 0), (2, 3), (3, 2))):
if mode == 'max':
func = T.max
else:
func = T.mean
if pad != (0, 0) and func is T.mean:
((0, 0), (1, 0), (0, 1), (2, 3), (3, 2))):
if pad != (0, 0) and mode == 'average_exc_pad':
# Not implemented
continue
for ws in (4, 2, 5):
......@@ -254,29 +193,32 @@ def test_pooling():
# Not implemented
continue
# We will check that the opt introduced it.
out1 = pool_2d(x, (ws, ws),
st=(stride, stride),
ignore_border=True,
padding=pad, mode=mode)
out2 = pool_2d_i2n(x, ds=(ws, ws), strides=(stride, stride),
pad=pad,
pool_function=func)
out = pool_2d(x, (ws, ws),
st=(stride, stride),
ignore_border=True,
padding=pad, mode=mode)
mode_without_gpu2 = mode_without_gpu.including()
mode_without_gpu2.check_isfinite = False
f1 = theano.function([x], out1, mode=mode_with_gpu)
# GPU implementation
f_gpu = theano.function([x], out, mode=mode_with_gpu)
assert any([isinstance(node.op, cuda.dnn.GpuDnnPool)
for node in f1.maker.fgraph.apply_nodes])
f2 = theano.function([x], out2, mode=mode_without_gpu2)
for node in f_gpu.maker.fgraph.apply_nodes])
# CPU implementation
f_cpu = theano.function([x], out, mode=mode_without_gpu2)
assert not any([isinstance(node.op, cuda.dnn.GpuDnnPool)
for node in f2.maker.fgraph.apply_nodes])
for node in f_cpu.maker.fgraph.apply_nodes])
assert any([isinstance(node.op, Pool)
for node in f_cpu.maker.fgraph.apply_nodes])
for shp in [(1, 10, 100, 100),
(1, 3, 99, 99),
(32, 1, 147, 197),
]:
data = numpy.random.normal(0, 1, shp).astype("float32")
a = f1(data).__array__()
b = f2(data).__array__()
a = f_cpu(data).__array__()
b = f_gpu(data).__array__()
utt.assert_allclose(a, b)
# Test the grad
......@@ -290,12 +232,11 @@ def test_pooling():
# Not implemented
continue
# This test the CPU grad + opt + GPU implemtentation
# This tests the CPU grad + opt + GPU implementation
def fn(x):
return pool_2d(x, (ws, ws), ignore_border=True,
padding=pad, mode=mode)
theano.tests.unittest_tools.verify_grad(fn, [data],
mode=mode_with_gpu)
utt.verify_grad(fn, [data], mode=mode_with_gpu)
# Confirm that the opt would have inserted it.
fg = theano.function([x], theano.grad(fn(x).sum(), x),
mode=mode_with_gpu)
......@@ -310,29 +251,12 @@ def test_pooling():
pad=pad,
mode=mode)
return dnn_op
theano.tests.unittest_tools.verify_grad(
fn, [data], mode=mode_with_gpu)
utt.verify_grad(fn, [data], mode=mode_with_gpu)
# Confirm that we get the good op.
fg = theano.function([x], theano.grad(fn(x).sum(), x),
mode=mode_with_gpu)
assert any([isinstance(node.op, cuda.dnn.GpuDnnPoolGrad)
for node in fg.maker.fgraph.toposort()])
g_out = fg(data)
# Compare again the CPU result
out = pool_2d(x, (ws, ws),
padding=pad,
ignore_border=True, mode=mode)
fc = theano.function([x], theano.grad(out.sum(), x),
mode=mode_without_gpu)
if mode == 'max':
assert any([isinstance(node.op, MaxPoolGrad)
for node in fc.maker.fgraph.toposort()])
else:
assert any([isinstance(node.op, AveragePoolGrad)
for node in fc.maker.fgraph.toposort()])
c_out = fc(data)
utt.assert_allclose(c_out, g_out)
def test_pooling_with_tensor_vars():
......@@ -361,24 +285,26 @@ def test_pooling_with_tensor_vars():
mode_without_gpu2 = mode_without_gpu.including()
mode_without_gpu2.check_isfinite = False
# GPU implementation
f_gpu = theano.function([x], fn(x), mode=mode_with_gpu)
assert any([isinstance(node.op, cuda.dnn.GpuDnnPool)
for node in f_gpu.maker.fgraph.apply_nodes])
# CPU implementation
out_cpu = pool_2d(x, ws, ignore_border=True, st=st, padding=pad, mode=mode)
f_cpu = theano.function([x], out_cpu, mode=mode_without_gpu2)
assert not any([isinstance(node.op, cuda.dnn.GpuDnnPool)
for node in f_cpu.maker.fgraph.apply_nodes])
assert any([isinstance(node.op, Pool)
for node in f_cpu.maker.fgraph.apply_nodes])
i = 1
for shp in [(1, 10, 100, 100),
(1, 3, 99, 99),
(32, 1, 147, 197)]:
data = numpy.random.normal(0, 1, shp).astype("float32")
out = pool_2d_i2n(x, ds=(i, i), strides=(1, 1),
pad=(0, 0),
pool_function=T.max)
f_cpu = theano.function([x], out, mode=mode_without_gpu2)
assert not any([isinstance(node.op, cuda.dnn.GpuDnnPool)
for node in f_cpu.maker.fgraph.apply_nodes])
# Change the window size dynamically for gpu op
# Change the window size dynamically
ws.set_value(numpy.array([i, i]).astype('int32'))
a = f_gpu(data).__array__()
b = f_cpu(data).__array__()
......@@ -426,12 +352,8 @@ def test_pooling3d():
for mode, pad in product(modes,
((0, 0, 0), (1, 0, 0), (0, 1, 0), (0, 0, 1),
(2, 3, 2), (3, 2, 2), (2, 2, 3))):
if mode == 'max':
func = T.max
else:
func = T.mean
if pad != (0, 0, 0) and func is T.mean:
if pad != (0, 0, 0) and mode == 'average_exc_pad':
# Not implemented
continue
for ws in (4, 2, 5):
......@@ -441,28 +363,30 @@ def test_pooling3d():
if pad[0] > stride or pad[1] > stride or pad[2] > stride:
# Not implemented
continue
out1 = pool_3d(x, (ws, ws, ws),
st=(stride, stride, stride),
ignore_border=True,
padding=pad, mode=mode)
out2 = pool3d2d(x, ds=(ws, ws, ws), strides=(stride, stride, stride),
pad=pad,
pool_function=func)
f1 = theano.function([x], out1, mode=mode_with_gpu)
out = pool_3d(x, (ws, ws, ws),
st=(stride, stride, stride),
ignore_border=True,
padding=pad, mode=mode)
# GPU implementation
f_gpu = theano.function([x], out, mode=mode_with_gpu)
assert any([isinstance(node.op, cuda.dnn.GpuDnnPool)
for node in f1.maker.fgraph.apply_nodes])
f2 = theano.function([x], out2, mode=mode_without_gpu_ref)
for node in f_gpu.maker.fgraph.apply_nodes])
# CPU implementation
f_cpu = theano.function([x], out, mode=mode_without_gpu_ref)
assert not any([isinstance(node.op, cuda.dnn.GpuDnnPool)
for node in f2.maker.fgraph.apply_nodes])
for shp in [(1, 10, 100, 100, 100),
(1, 3, 99, 99, 99),
(32, 1, 147, 197, 37),
for node in f_cpu.maker.fgraph.apply_nodes])
assert any([isinstance(node.op, Pool)
for node in f_cpu.maker.fgraph.apply_nodes])
for shp in [(1, 5, 50, 20, 50),
(1, 3, 99, 99, 29),
(2, 1, 147, 97, 37),
]:
data = numpy.random.normal(0, 1, shp).astype("float32")
a = f1(data).__array__()
b = f2(data).__array__()
a = f_cpu(data).__array__()
b = f_gpu(data).__array__()
utt.assert_allclose(a, b,
atol=numpy.finfo(numpy.float32).eps)
......@@ -490,29 +414,12 @@ def test_pooling3d():
pad=pad,
mode=mode)
return dnn_op
theano.tests.unittest_tools.verify_grad(
fn, [data], mode=mode_with_gpu)
utt.verify_grad(fn, [data], mode=mode_with_gpu)
# Confirm that we get the good op.
fg = theano.function([x], theano.grad(fn(x).sum(), x),
mode=mode_with_gpu)
assert any([isinstance(node.op, cuda.dnn.GpuDnnPoolGrad)
for node in fg.maker.fgraph.toposort()])
g_out = fg(data)
# Compare again the CPU result
out = pool_3d(x, (ws, ws, ws),
padding=pad,
ignore_border=True, mode=mode)
fc = theano.function([x], theano.grad(out.sum(), x),
mode=mode_without_gpu_ref)
if mode == 'max':
assert any([isinstance(node.op, MaxPoolGrad)
for node in fc.maker.fgraph.toposort()])
else:
assert any([isinstance(node.op, AveragePoolGrad)
for node in fc.maker.fgraph.toposort()])
c_out = fc(data)
utt.assert_allclose(c_out, g_out)
def test_pooling_opt():
......@@ -1269,6 +1176,33 @@ class TestDnnInferShapes(utt.InferShapeTester):
dnn.GpuDnnPool
)
def test_pool_3d(self):
if not dnn.dnn_available():
raise SkipTest(dnn.dnn_available.msg)
img = T.ftensor5('img')
img_val = numpy.asarray(
numpy.random.rand(2, 3, 4, 5, 6),
dtype='float32'
)
# 'average_exc_pad' is disabled for versions < 4004
if cuda.dnn.version() < (4004, 4004):
modes = ['max', 'average_inc_pad']
else:
modes = ['max', 'average_inc_pad', 'average_exc_pad']
for params in product(
[(1, 1, 1), (2, 2, 2), (3, 3, 3)],
[(1, 1, 1), (2, 2, 2), (3, 3, 3)],
modes
):
self._compile_and_check(
[img],
[dnn.GpuDnnPool(mode=params[2])(img, params[0], params[1], (0, 0, 0))],
[img_val],
dnn.GpuDnnPool
)
def test_pool_grad(self):
if not dnn.dnn_available():
raise SkipTest(dnn.dnn_available.msg)
......@@ -1308,6 +1242,45 @@ class TestDnnInferShapes(utt.InferShapeTester):
dnn.GpuDnnPoolGrad
)
def test_pool_3d_grad(self):
if not dnn.dnn_available():
raise SkipTest(dnn.dnn_available.msg)
img = T.ftensor5('img')
img_grad = T.ftensor5('img_grad')
out = T.ftensor5('out')
img_val = numpy.asarray(
numpy.random.rand(2, 3, 4, 5, 6),
dtype='float32'
)
img_grad_val = numpy.asarray(
numpy.random.rand(2, 3, 4, 5, 6),
dtype='float32'
)
out_val = numpy.asarray(
numpy.random.rand(2, 3, 4, 5, 6),
dtype='float32'
)
for params in product(
[(1, 1, 1), (2, 2, 2), (3, 3, 3)],
[(1, 1, 1), (2, 2, 2), (3, 3, 3)],
['max', 'average_inc_pad']
):
pool_grad = dnn.GpuDnnPoolGrad(mode=params[2])(
img,
out,
img_grad,
params[0],
params[1],
(0, 0, 0)
)
self._compile_and_check(
[img, img_grad, out],
[pool_grad],
[img_val, img_grad_val, out_val],
dnn.GpuDnnPoolGrad
)
# this has been a problem in the past
def test_dnn_conv_border_mode():
......
Markdown 格式
0% 或
您添加了 0 人 到此讨论。请谨慎行事。
请先完成此评论的编辑！
请 注册 或者 后发表评论