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fix flasky unittest for deformable psroi pooling #12178

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Aug 17, 2018
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fix flasky unittest for deformable psroi pooling #12178

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db8e789
fix flasky unittest for deformable psroi pooling
HaozhiQi Aug 15, 2018
4ae0fc1
Fix incorrect gpu specification
HaozhiQi Aug 15, 2018
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79 changes: 66 additions & 13 deletions tests/python/unittest/test_operator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4985,23 +4985,80 @@ def test_deformable_convolution():
grad_nodes=grad_nodes, ctx=mx.gpu(0))


# Seed set because the test is not robust enough to operate on random data. Repro issue with:
# MXNET_TEST_SEED=1234 nosetests --verbose tests/python/gpu/test_operator_gpu.py:test_deformable_psroipooling
@with_seed(0)
def _validate_sample_location(input_rois, input_offset, spatial_scale, pooled_w, pooled_h, sample_per_part, part_size, output_dim, num_classes, trans_std, feat_h, feat_w):
num_rois = input_rois.shape[0]
output_offset = input_offset.copy()
# simulate deformable psroipooling forward function
for roi_idx in range(num_rois):
sub_rois = input_rois[roi_idx, :].astype(np.float32)
img_idx, x0, y0, x1, y1 = int(sub_rois[0]), sub_rois[1], sub_rois[2], sub_rois[3], sub_rois[4]
roi_start_w = round(x0) * spatial_scale - 0.5
roi_start_h = round(y0) * spatial_scale - 0.5
roi_end_w = round(x1 + 1) * spatial_scale - 0.5
roi_end_h = round(y1 + 1) * spatial_scale - 0.5
roi_w, roi_h = roi_end_w - roi_start_w, roi_end_h - roi_start_h
bin_size_w, bin_size_h = roi_w / pooled_w, roi_h / pooled_h
sub_bin_size_w, sub_bin_size_h = bin_size_w / sample_per_part, bin_size_h / sample_per_part
for c_top in range(output_dim):
channel_each_cls = output_dim / num_classes
class_id = int(c_top / channel_each_cls)
for ph in range(pooled_h):
for pw in range(pooled_w):
part_h = int(math.floor(float(ph) / pooled_h * part_size))
part_w = int(math.floor(float(pw) / pooled_w * part_size))
trans_x = input_offset[roi_idx, class_id * 2, part_h, part_w] * trans_std
trans_y = input_offset[roi_idx, class_id * 2 + 1, part_h, part_w] * trans_std
bin_h_start, bin_w_start = ph * bin_size_h + roi_start_h, pw * bin_size_w + roi_start_w

need_check = True
while need_check:
pass_check = True
for ih in range(sample_per_part):
for iw in range(sample_per_part):
h = bin_h_start + trans_y * roi_h + ih * sub_bin_size_h
w = bin_w_start + trans_x * roi_w + iw * sub_bin_size_w

if w < -0.5 or w > feat_w - 0.5 or h < -0.5 or h > feat_h - 0.5:
continue

w = min(max(w, 0.1), feat_w - 1.1)
h = min(max(h, 0.1), feat_h - 1.1)
# if the following condiiton holds, the sampling location is not differentiable
# therefore we need to re-do the sampling process
if h - math.floor(h) < 1e-3 or math.ceil(h) - h < 1e-3 or w - math.floor(w) < 1e-3 or math.ceil(w) - w < 1e-3:
trans_x, trans_y = random.random() * trans_std, random.random() * trans_std
pass_check = False
break
if not pass_check:
break
if pass_check:
output_offset[roi_idx, class_id * 2 + 1, part_h, part_w] = trans_y / trans_std
output_offset[roi_idx, class_id * 2, part_h, part_w] = trans_x / trans_std
need_check = False

return output_offset

@with_seed()
def test_deformable_psroipooling():
sample_per_part = 4
trans_std = 0.1
for num_rois in [1, 2]:
for num_classes, num_group in itertools.product([2, 3], [2, 3]):
for image_height, image_width in itertools.product([168, 224], [168, 224]):
for image_height, image_width in itertools.product([160, 224], [160, 224]):
for grad_nodes in [['im_data'], ['offset_data']]:
spatial_scale = 0.0625
stride = int(1 / spatial_scale)
feat_height = np.int(image_height * spatial_scale)
feat_width = np.int(image_width * spatial_scale)
im_data = np.random.rand(1, num_classes*num_group*num_group, feat_height, feat_width)
rois_data = np.zeros([num_rois, 5])
rois_data[:, [1,3]] = np.sort(np.random.rand(num_rois, 2)*(image_width-1))
rois_data[:, [2,4]] = np.sort(np.random.rand(num_rois, 2)*(image_height-1))
offset_data = np.random.rand(num_rois, 2*num_classes, num_group, num_group) * 0.1

rois_data[:, [1,3]] = np.sort(np.random.rand(num_rois, 2)*(image_width-1 - 2 * stride)) + stride
rois_data[:, [2,4]] = np.sort(np.random.rand(num_rois, 2)*(image_height-1 - 2 * stride)) + stride
offset_data = np.random.rand(num_rois, 2*num_classes, num_group, num_group)
# at certain points, the bilinear interpolation function may be non-differentiable
# to avoid this, we check whether the input locates on the valid points
offset_data = _validate_sample_location(rois_data, offset_data, spatial_scale, num_group, num_group,
sample_per_part, num_group, num_classes, num_classes, trans_std, feat_height, feat_width)
im_data_var = mx.symbol.Variable(name="im_data")
rois_data_var = mx.symbol.Variable(name="rois_data")
offset_data_var = mx.symbol.Variable(name="offset_data")
Expand All @@ -5010,11 +5067,7 @@ def test_deformable_psroipooling():
sample_per_part=4, group_size=num_group,
pooled_size=num_group, output_dim=num_classes,
trans_std=0.1, no_trans=False, name='test_op')
if grad_nodes[0] == 'offset_data':
# wider tolerance needed for coordinate differential
rtol, atol = 1.0, 1e-2
else:
rtol, atol = 1e-2, 1e-3
rtol, atol = 1e-2, 1e-3
# By now we only have gpu implementation
if default_context().device_type == 'gpu':
check_numeric_gradient(op, [im_data, rois_data, offset_data], rtol=rtol, atol=atol,
Expand Down