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提交 bbd9ff3f authored 作者: Harm de Vries's avatar Harm de Vries
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flake8

上级 5c172018
隐藏空白字符变更
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正在显示 包含 38 行增加 和 43 行删除
theano/sandbox/cuda/dnn.py
浏览文件 @ bbd9ff3f
......@@ -1351,7 +1351,7 @@ class GpuDnnPoolDesc(GpuOp):
class GpuDnnPool(DnnBase):
"""
"""
Pooling.
Parameters
......@@ -1371,7 +1371,7 @@ class GpuDnnPool(DnnBase):
"""
__props__ = ("mode",)
def __init__(self, mode='max'):
super(GpuDnnPool, self).__init__()
if mode == 'average':
......@@ -1382,13 +1382,13 @@ class GpuDnnPool(DnnBase):
def make_node(self, img, ws, stride, pad):
img = as_cuda_ndarray_variable(img)
assert (img.ndim in [4, 5])
ws = tensor.as_tensor_variable(ws)
stride = tensor.as_tensor_variable(stride)
pad = tensor.as_tensor_variable(pad)
assert ws.type.ndim == stride.type.ndim and ws.type.ndim == pad.type.ndim
assert ws.type.ndim == 1
return Apply(self, [img, ws, stride, pad], [img.type()])
def infer_shape(self, node, shape):
......@@ -1457,7 +1457,7 @@ if (pool%(name)s != NULL) { cudnnDestroyPoolingDescriptor(pool%(name)s); }
raise Exception("cudnn v1 do not support average_exc_pad")
else:
raise NotImplementedError("Unsupported pooling model.")
return """
fprintf(stderr, "test_forward\\n");
cudnnStatus_t err;
......@@ -1530,8 +1530,8 @@ if (err != CUDNN_STATUS_SUCCESS) {
""" % dict(out=out, fail=sub['fail'],
name=name, input=inputs[0],
ws=ws, pad=pad, str=stride,
nd=node.inputs[0].ndim-2, input_desc="input"+name,
output_desc="output"+name,
nd=node.inputs[0].ndim - 2, input_desc="input" + name,
output_desc="output" + name,
mode_flag=mode_flag)
def grad(self, inp, grads):
......@@ -1550,8 +1550,8 @@ if (err != CUDNN_STATUS_SUCCESS) {
# not connected to desc
return [[1], [0], [0], [0]]
#def c_code_cache_version(self):
# return (8, version())
def c_code_cache_version(self):
return (8, version())
class GpuDnnPoolGrad(DnnBase):
......@@ -1579,7 +1579,7 @@ class GpuDnnPoolGrad(DnnBase):
"""
__props__ = ('mode',)
def __init__(self, mode='max'):
super(GpuDnnPoolGrad, self).__init__()
if mode == 'average':
......@@ -1594,13 +1594,13 @@ class GpuDnnPoolGrad(DnnBase):
assert (inp_grad.ndim in [4, 5])
out = as_cuda_ndarray_variable(out)
assert(out.ndim in [4, 5])
ws = tensor.as_tensor_variable(ws)
stride = tensor.as_tensor_variable(stride)
pad = tensor.as_tensor_variable(pad)
assert ws.type.ndim == stride.type.ndim and ws.type.ndim == pad.type.ndim
assert ws.type.ndim == 1
return Apply(self, [inp, out, inp_grad, ws, stride, pad],
[inp.type()])
......@@ -1661,19 +1661,14 @@ if (output%(name)s != NULL) { cudnnDestroyTensorDescriptor(output%(name)s); }
if (output_grad%(name)s != NULL) { cudnnDestroyTensorDescriptor(output_grad%(name)s); }
if (pool%(name)s != NULL) { cudnnDestroyPoolingDescriptor(pool%(name)s); }
""" % dict(name=name)
# def perform(self, node, inputs_storage, output_storage):
# output_storage[0][0] = inputs_storage[0].copy()
# return
def c_code(self, node, name, inputs, outputs, sub):
# raise NotImplementedError()
# Here the name out and inp are based on the cudnn definition.
# Not the definition of this class.
# This make it complicated.
out, inp, inp_grad, ws, stride, pad = inputs
out_grad, = outputs
if self.mode == 'max':
mode_flag = 'CUDNN_POOLING_MAX'
elif self.mode == "average_inc_pad":
......@@ -1770,16 +1765,15 @@ if (err%(name)s != CUDNN_STATUS_SUCCESS) {
""" % dict(output_grad=out_grad,
fail=sub['fail'], name=name,
input=inp, input_grad=inp_grad, output=out,
input_desc="input"+name,
input_grad_desc="input_grad"+name,
output_desc="output"+name,
output_grad_desc="output_grad"+name,
input_desc="input" + name,
input_grad_desc="input_grad" + name,
output_desc="output" + name,
output_grad_desc="output_grad" + name,
mode_flag=mode_flag, nd=node.inputs[0].ndim - 2,
ws=ws, pad=pad, str=stride)
def c_code_cache_version(self):
return
#return (7, version())
return (8, version())
def infer_shape(self, node, shape):
return [shape[0]]
......@@ -1801,7 +1795,7 @@ def dnn_pool(img, ws, stride=(1, 1), mode='max', pad=(0, 0)):
stride
Subsampling stride (default: (1, 1)).
mode : {'max', 'average_inc_pad', 'average_exc_pad}
pad :
pad :
(pad_h, pad_w) padding information.
pad_h is the number of zero-valued pixels added to each of the top and
bottom borders.
......@@ -2296,11 +2290,11 @@ if True:
return
inp, out, inp_grad = node.inputs
ds = node.op.ds
return [GpuDnnPoolGrad(mode='max')(gpu_contiguous(inp),
gpu_contiguous(out),
gpu_contiguous(inp_grad),
ds, ds, (0, 0))]
gpu_contiguous(out),
gpu_contiguous(inp_grad),
ds, ds, (0, 0))]
@register_opt('cudnn')
@local_optimizer([MaxPoolGrad])
......@@ -2322,9 +2316,9 @@ if True:
HostFromGpu))):
ret = GpuDnnPoolGrad(mode=mode)(gpu_contiguous(inp),
gpu_contiguous(out),
gpu_contiguous(inp_grad),
ds, st, pad)
gpu_contiguous(out),
gpu_contiguous(inp_grad),
ds, st, pad)
return [host_from_gpu(ret)]
@register_opt('cudnn')
......@@ -2346,12 +2340,11 @@ if True:
HostFromGpu))):
contiguous_inp_grad = gpu_contiguous(inp_grad)
ret = GpuDnnPoolGrad(mode=mode)(gpu_contiguous(inp),
contiguous_inp_grad,
contiguous_inp_grad,
ds, st, pad)
contiguous_inp_grad,
contiguous_inp_grad,
ds, st, pad)
return [host_from_gpu(ret)]
@register_opt('cudnn')
@local_optimizer([GpuSoftmax])
def local_softmax_dnn(node):
......
theano/sandbox/cuda/tests/test_dnn.py
浏览文件 @ bbd9ff3f
......@@ -240,7 +240,7 @@ def test_pooling():
modes = ('max', 'average_inc_pad')
else:
modes = ('max', 'average_inc_pad', 'average_exc_pad')
x = T.ftensor4()
for mode, pad in product(modes,
((0, 0), (1, 0), (1, 0), (2, 3), (3, 2))):
......@@ -300,7 +300,8 @@ def test_pooling():
# Not implemented
continue
pad_ = theano.shared(numpy.array(pad))
## This test the CPU grad + opt + GPU implemtentation
# This test the CPU grad + opt + GPU implemtentation
def fn(x):
return pool_2d(x, (2, 2), ignore_border=True,
padding=pad, mode=mode)
......@@ -312,7 +313,7 @@ def test_pooling():
mode=mode_with_gpu)
assert any([isinstance(node.op, cuda.dnn.GpuDnnPoolGrad)
for node in fg.maker.fgraph.toposort()])
# Test the GPU grad + GPU implementation
def fn(x):
dnn_op = cuda.dnn.dnn_pool(
......@@ -321,7 +322,7 @@ def test_pooling():
pad=pad_,
mode=mode)
return dnn_op
theano.tests.unittest_tools.verify_grad(
fn, [data],
cast_to_output_type=False,
......@@ -337,7 +338,7 @@ def test_pooling():
out = pool_2d(x, (2, 2), st=(1, 1),
padding=pad,
ignore_border=True, mode=mode)
fc = theano.function([x], theano.grad(out.sum(), x),
mode=mode_without_gpu)
if mode == 'max':
......@@ -1005,7 +1006,8 @@ class TestDnnInferShapes(utt.InferShapeTester):
):
self._compile_and_check(
[img],
[dnn.GpuDnnPool(mode=params[2])(img, params[0], params[1], (0,0))],
[dnn.GpuDnnPool(mode=params[2])
(img, params[0], params[1], (0, 0))],
[img_val],
dnn.GpuDnnPool
)
......
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