This is an automated email from the ASF dual-hosted git repository.
anirudh2290 pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git
The following commit(s) were added to refs/heads/master by this push:
new 4e19a32 remove flaky test and add consistency test for stable testing
(#12427)
4e19a32 is described below
commit 4e19a328ae94c893ed11591b798aaebf33f39052
Author: Hao Jin <haoj...@users.noreply.github.com>
AuthorDate: Wed Sep 5 11:34:54 2018 -0700
remove flaky test and add consistency test for stable testing (#12427)
---
src/operator/bilinear_sampler-inl.h | 3 +
src/operator/bilinear_sampler.cu | 6 +-
tests/python/gpu/test_operator_gpu.py | 59 ++++++++++++
tests/python/unittest/test_operator.py | 160 ---------------------------------
4 files changed, 67 insertions(+), 161 deletions(-)
diff --git a/src/operator/bilinear_sampler-inl.h
b/src/operator/bilinear_sampler-inl.h
index 657aeba..e0b4db7 100644
--- a/src/operator/bilinear_sampler-inl.h
+++ b/src/operator/bilinear_sampler-inl.h
@@ -44,7 +44,10 @@ enum BilinearSamplerOpOutputs {kOut, kTmp};
}
struct BilinearSamplerParam : public dmlc::Parameter<BilinearSamplerParam> {
+ dmlc::optional<bool> cudnn_off;
DMLC_DECLARE_PARAMETER(BilinearSamplerParam) {
+ DMLC_DECLARE_FIELD(cudnn_off).set_default(dmlc::optional<bool>())
+ .describe("whether to turn cudnn off");
}
};
diff --git a/src/operator/bilinear_sampler.cu b/src/operator/bilinear_sampler.cu
index 0ab628d..e1f2052 100644
--- a/src/operator/bilinear_sampler.cu
+++ b/src/operator/bilinear_sampler.cu
@@ -212,7 +212,11 @@ Operator* CreateOp<gpu>(BilinearSamplerParam param, int
dtype) {
Operator *op = NULL;
#if MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 5
MSHADOW_REAL_TYPE_SWITCH(dtype, DType, {
- op = new CuDNNBilinearSamplerOp<DType>(param);
+ if (param.cudnn_off.has_value() && param.cudnn_off.value()) {
+ op = new BilinearSamplerOp<gpu, DType>(param);
+ } else {
+ op = new CuDNNBilinearSamplerOp<DType>(param);
+ }
})
#else
MSHADOW_REAL_TYPE_SWITCH(dtype, DType, {
diff --git a/tests/python/gpu/test_operator_gpu.py
b/tests/python/gpu/test_operator_gpu.py
index 0ff33e1..7b75275 100644
--- a/tests/python/gpu/test_operator_gpu.py
+++ b/tests/python/gpu/test_operator_gpu.py
@@ -1918,6 +1918,65 @@ def test_softmax_activation():
assert_almost_equal(cpu_a.grad.asnumpy(), gpu_a.grad.asnumpy(),
atol = 1e-3, rtol = 1e-3)
+
+@with_seed()
+def test_bilinear_sampler_versions():
+ data = mx.sym.Variable('data')
+ grid = mx.sym.Variable('grid')
+ sym1 = mx.sym.BilinearSampler(data=data, grid=grid)
+ sym2 = mx.sym.BilinearSampler(data=data, grid=grid, cudnn_off=True)
+ sym3 = mx.sym.BilinearSampler(data=data, grid=grid)
+
+ test_cases = [[(1,3,15,16),(1,2,10,10)],
+ [(1,6,7,16),(1,2,10,4)],
+ [(1,7,3,16),(1,2,8,11)],
+ [(1,9,50,50),(1,2,50,50)]]
+
+ for item in test_cases:
+ data_shape, grid_shape = item
+ # kWriteTo
+ exe_cpu = sym1.simple_bind(data=data_shape, grid=grid_shape,
ctx=mx.cpu(), grad_req='write')
+ exe_gpu = sym2.simple_bind(data=data_shape, grid=grid_shape,
ctx=default_context(), grad_req='write')
+ exe_cudnn = sym3.simple_bind(data=data_shape, grid=grid_shape,
ctx=default_context(), grad_req='write')
+ exe_list = [exe_cpu, exe_gpu, exe_cudnn]
+ ref_idx = 0
+ test_data = np.random.uniform(low=-0.1,
high=0.1,size=data_shape).astype(np.float32)
+ test_grid = np.random.uniform(low=-2, high=2,
size=grid_shape).astype(np.float32)
+ for exe in exe_list:
+ exe.arg_dict['data'][:] = test_data
+ exe.arg_dict['grid'][:] = test_grid
+ exe.forward(is_train=True)
+ assert_almost_equal(exe_list[0].outputs[0].asnumpy(),
exe.outputs[0].asnumpy(), rtol=1e-3, atol=1e-5)
+
+ out_grad = np.random.uniform(low=-0.01, high=0.01,size=data_shape[:2]
+ grid_shape[2:]).astype(np.float32)
+ for exe in exe_list:
+ exe.backward(mx.nd.array(out_grad))
+ assert_almost_equal(exe.grad_dict['data'].asnumpy(),
exe_list[ref_idx].grad_dict['data'].asnumpy(), rtol=1e-3, atol=1e-5)
+ assert_almost_equal(exe.grad_dict['grid'].asnumpy(),
exe_list[ref_idx].grad_dict['grid'].asnumpy(), rtol=1e-3, atol=1e-5)
+
+ data_grad = exe_list[ref_idx].grad_dict['data'].asnumpy()
+ grid_grad = exe_list[ref_idx].grad_dict['grid'].asnumpy()
+
+ # kAddTo
+ exe_cpu_addto = sym1.simple_bind(data=data_shape, grid=grid_shape,
ctx=mx.cpu(), grad_req='add')
+ exe_gpu_addto = sym2.simple_bind(data=data_shape, grid=grid_shape,
ctx=default_context(), grad_req='add')
+ exe_cudnn_addto = sym3.simple_bind(data=data_shape, grid=grid_shape,
ctx=default_context(), grad_req='add')
+ exe_list = [exe_cpu_addto, exe_gpu_addto, exe_cudnn_addto]
+ data_initial_grad =
np.random.normal(size=exe_list[ref_idx].grad_dict['data'].shape).astype(np.float32)
+ grid_initial_grad =
np.random.normal(size=exe_list[ref_idx].grad_dict['grid'].shape).astype(np.float32)
+ for exe in exe_list:
+ exe.arg_dict['data'][:] = test_data
+ exe.arg_dict['grid'][:] = test_grid
+ exe.grad_dict['data'][:] = data_initial_grad
+ exe.grad_dict['grid'][:] = grid_initial_grad
+ exe.forward(is_train=True)
+ exe.backward(mx.nd.array(out_grad))
+ assert_almost_equal(exe.grad_dict['data'].asnumpy(),
exe_list[ref_idx].grad_dict['data'].asnumpy(), rtol=1e-3, atol=1e-5)
+ assert_almost_equal(exe.grad_dict['grid'].asnumpy(),
exe_list[ref_idx].grad_dict['grid'].asnumpy(), rtol=1e-3, atol=1e-5)
+ assert_almost_equal(exe_list[ref_idx].grad_dict['data'].asnumpy(),
data_grad + data_initial_grad, rtol=1e-3, atol=1e-5)
+ assert_almost_equal(exe_list[ref_idx].grad_dict['grid'].asnumpy(),
grid_grad + grid_initial_grad, rtol=1e-3, atol=1e-5)
+
+
def test_context_num_gpus():
# Test that num_gpus reports at least one GPU, as the test is run on a GPU
host.
assert mx.context.num_gpus() > 0
diff --git a/tests/python/unittest/test_operator.py
b/tests/python/unittest/test_operator.py
index ca358ef..9842a69 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -3945,166 +3945,6 @@ def test_grid_generator():
assert_almost_equal(exe_add.grad_dict['flow'].asnumpy(), grad_est +
flow_grad_npy, rtol=1e-3, atol=1e-5)
-@unittest.skip("Flaky test
https://github.com/apache/incubator-mxnet/issues/12248";)
-def test_bilinear_sampler():
- from math import floor
-
- def between(x, lowerbound, upperbound):
- return x>=lowerbound and x<=upperbound
-
- def bilinear_forward_numpy(data, grid):
-
- batchsize = data.shape[0]
- input_height = data.shape[2]
- input_width = data.shape[3]
- num_channel = data.shape[1]
-
- output_height = grid.shape[2]
- output_width = grid.shape[3]
- out = np.zeros(data.shape[:2] + grid.shape[2:], dtype=np.float32)
-
- for i in range(batchsize):
- for yout in range(output_height):
- for xout in range(output_width):
-
- xcoord = np.float32((grid[i, 0, yout, xout] + 1) *
(input_width-1) / 2.0)
- ycoord = np.float32((grid[i, 1, yout, xout] + 1) *
(input_height-1) / 2.0)
-
- xInTopLeft = int(floor(xcoord))
- xWeightTopLeft = np.float32(1-(xcoord - xInTopLeft))
-
- yInTopLeft = int(floor(ycoord))
- yWeightTopLeft = np.float32(1-(ycoord - yInTopLeft))
-
- # interpolation
- for channel in range(num_channel):
-
- inTopLeft = data[i,channel,yInTopLeft, xInTopLeft] \
- if between(xInTopLeft,0,input_width-1) and
between(yInTopLeft,0,input_height-1) else 0.0
- inTopRight = data[i,channel,yInTopLeft, xInTopLeft+1] \
- if between(xInTopLeft+1,0,input_width-1) and
between(yInTopLeft,0,input_height-1) else 0.0
- inBottomLeft = data[i,channel,yInTopLeft+1,
xInTopLeft] \
- if between(xInTopLeft,0,input_width-1) and
between(yInTopLeft+1,0,input_height-1) else 0.0
- inBottomRight = data[i,channel,yInTopLeft+1,
xInTopLeft+1] \
- if between(xInTopLeft+1,0,input_width-1) and
between(yInTopLeft+1,0,input_height-1) else 0.0
-
- out[i,channel,yout,xout] = xWeightTopLeft *
yWeightTopLeft * inTopLeft\
- + (1-xWeightTopLeft)*yWeightTopLeft *
inTopRight\
- + xWeightTopLeft * (1-yWeightTopLeft) *
inBottomLeft\
- +(1-xWeightTopLeft) * (1-yWeightTopLeft) *
inBottomRight
- return out
-
- def bilinear_backward_numpy(out_grad, data, grid):
-
- data_grad = np.zeros(data.shape, dtype=np.float32)
- grid_grad = np.zeros(grid.shape, dtype=np.float32)
-
- batchsize = data.shape[0]
- input_height = data.shape[2]
- input_width = data.shape[3]
- num_channel = data.shape[1]
- output_height = grid.shape[2]
- output_width = grid.shape[3]
-
- for i in range(batchsize):
- for yout in range(output_height):
- for xout in range(output_width):
-
- top_left_y_gw = np.float32(0.0);
- top_left_x_gw = np.float32(0.0);
-
- xcoord = np.float32((grid[i, 0, yout, xout] + 1) *
(input_width-1) / 2.0)
- ycoord = np.float32((grid[i, 1, yout, xout] + 1) *
(input_height-1) / 2.0)
-
- xInTopLeft = int(floor(xcoord))
- xWeightTopLeft = np.float32(1-(xcoord - xInTopLeft))
-
- yInTopLeft = int(floor(ycoord))
- yWeightTopLeft = np.float32(1-(ycoord - yInTopLeft))
-
- topLeftDotProduct = np.float32(0)
- topRightDotProduct = np.float32(0)
- bottomLeftDotProduct = np.float32(0)
- bottomRightDotProduct = np.float32(0)
-
- for channel in range(num_channel):
- # left top
- if between(xInTopLeft,0,input_width-1) and
between(yInTopLeft,0,input_height-1):
- topLeftDotProduct += data[i,channel,yInTopLeft,
xInTopLeft] * \
- out_grad[i,channel,yout,xout]
- data_grad[i, channel, yInTopLeft, xInTopLeft] +=
xWeightTopLeft * \
- yWeightTopLeft * out_grad[i,channel,yout,xout]
- # right top
- if between(xInTopLeft+1,0,input_width-1) and
between(yInTopLeft,0,input_height-1):
- topRightDotProduct += data[i, channel,
yInTopLeft,xInTopLeft+1] * \
- out_grad[i, channel, yout,xout]
- data_grad[i, channel,yInTopLeft, xInTopLeft+1] +=
(1-xWeightTopLeft) * \
- yWeightTopLeft * out_grad[i,channel,yout,xout]
- # left bottom
- if between(xInTopLeft,0,input_width-1) and
between(yInTopLeft+1,0,input_height-1):
- bottomLeftDotProduct += data[i,
channel,yInTopLeft+1, xInTopLeft] * \
- out_grad[i,channel,yout,xout]
-
data_grad[i,channel,yInTopLeft+1,xInTopLeft]+=xWeightTopLeft * \
- (1-yWeightTopLeft)*
out_grad[i,channel,yout,xout]
- # right bottom
- if between(xInTopLeft+1,0,input_width-1) and
between(yInTopLeft+1,0,input_height-1):
- bottomRightDotProduct +=
data[i,channel,yInTopLeft+1, xInTopLeft+1] * \
- out_grad[i,channel,yout,xout]
- data_grad[i,channel,yInTopLeft+1,xInTopLeft+1]+=
(1-xWeightTopLeft) * \
-
(1-yWeightTopLeft)*out_grad[i,channel,yout,xout]
-
- yf = np.float32(-xWeightTopLeft * topLeftDotProduct +
xWeightTopLeft*bottomLeftDotProduct - \
- (1-xWeightTopLeft)* topRightDotProduct +
(1-xWeightTopLeft)*bottomRightDotProduct)
- xf = np.float32(-yWeightTopLeft * topLeftDotProduct +
yWeightTopLeft*topRightDotProduct - \
- (1-yWeightTopLeft)*bottomLeftDotProduct +
(1-yWeightTopLeft)*bottomRightDotProduct)
-
- grid_grad[i,0,yout,xout] = xf * (input_width-1) / 2.0
- grid_grad[i,1,yout,xout] = yf * (input_height-1) / 2.0
-
- return data_grad, grid_grad
-
- data = mx.sym.Variable('data')
- grid = mx.sym.Variable('grid')
- net = mx.sym.BilinearSampler(data=data,grid=grid)
-
- test_case = [[(1,3,15,16),(1,2,10,10)],
- [(1,6,7,16),(1,2,10,4)],
- [(1,7,3,16),(1,2,8,11)],
- [(1,9,50,50),(1,2,50,50)]]
-
- for ctx in [default_context()]:
- for item in test_case:
- data_shape, grid_shape = item
- exe =
net.simple_bind(data=data_shape,grid=grid_shape,ctx=ctx,grad_req='write')
- # check forward
- exe.arg_dict['data'][:] = np.random.uniform(low=-0.1,
high=0.1,size=data_shape).astype(np.float32)
- exe.arg_dict['grid'][:] = np.random.uniform(low=-2, high=2,
size=grid_shape).astype(np.float32)
- exe.forward(is_train=True)
- out = bilinear_forward_numpy(exe.arg_dict['data'].asnumpy(),
exe.arg_dict['grid'].asnumpy())
- assert_almost_equal(exe.outputs[0].asnumpy(), out,
rtol=1e-3,atol=1e-5)
-
- # check backward
- out_grad = np.random.uniform(low=-0.01,
high=0.01,size=data_shape[:2] + grid_shape[2:]).astype(np.float32)
- exe.backward(mx.nd.array(out_grad))
- data_grad, grid_grad =
bilinear_backward_numpy(out_grad,exe.arg_dict['data'].asnumpy(),
-
exe.arg_dict['grid'].asnumpy())
- assert_almost_equal(exe.grad_dict['data'].asnumpy(), data_grad,
rtol=1e-3, atol=1e-5)
- assert_almost_equal(exe.grad_dict['grid'].asnumpy(), grid_grad,
rtol=1e-3, atol=1e-5)
-
- # check kAddTo
- exe_addto = net.simple_bind(data=data_shape, grid=grid_shape,
ctx=ctx, grad_req='add')
- data_initial_grid =
np.random.normal(size=exe_addto.grad_dict['data'].shape).astype(np.float32)
- grid_initial_grid =
np.random.normal(size=exe_addto.grad_dict['grid'].shape).astype(np.float32)
- exe_addto.arg_dict['data'][:] = exe.arg_dict['data'][:]
- exe_addto.arg_dict['grid'][:] = exe.arg_dict['grid'][:]
- exe_addto.grad_dict['data'][:] = data_initial_grid
- exe_addto.grad_dict['grid'][:] = grid_initial_grid
- exe_addto.forward(is_train=True)
- exe_addto.backward(mx.nd.array(out_grad))
- assert_almost_equal(exe_addto.grad_dict['data'].asnumpy(),
data_grad + data_initial_grid, rtol=1e-3,atol=1e-5)
- assert_almost_equal(exe_addto.grad_dict['grid'].asnumpy(),
grid_grad + grid_initial_grid, rtol=1e-3,atol=1e-5)
-
-
@with_seed()
def test_index2d():
for _ in range(30):
