m53297601
/
Paddle
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

fix pool3d bug, test=develop (#27718)

Browse Source 
* fix pool3d bug, test=develop

* fix unitest, test=develop

* fix test and fix pool2d bug, test=develop
my_2.0rc
Double_V 5 years ago committed by GitHub
parent 0d27591642
commit f6ad2375be
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
3 changed files with 278 additions and 153 deletions
Show Stats Download Patch File Download Diff File

348
paddle/fluid/operators/math/pooling.cc
View File

File diff suppressed because it is too large Load Diff

43
python/paddle/fluid/tests/unittests/test_pool2d_op.py
Unescape View File

@ -102,14 +102,21 @@ def avg_pool2D_forward_naive(x,
c_start = adaptive_start_index(j, W, ksize[1])
c_end = adaptive_end_index(j, W, ksize[1])
else:
r_start = np.max((i * strides[0] - paddings[0], 0))
r_end = np.min((i * strides[0] + ksize[0] - paddings[0], H))
c_start = np.max((j * strides[1] - paddings[1], 0))
c_end = np.min((j * strides[1] + ksize[1] - paddings[1], W))
r_start = i * strides[0] - paddings[0]
r_end = i * strides[0] + ksize[0] - paddings[0]
c_start = j * strides[1] - paddings[1]
c_end = j * strides[1] + ksize[1] - paddings[1]
field_size = (r_end - r_start) * (c_end - c_start)
r_start = np.max((r_start, 0))
r_end = np.min((r_end, H))
c_start = np.max((c_start, 0))
c_end = np.min((c_end, W))
x_masked = x[:, :, r_start:r_end, c_start:c_end]
field_size = ((r_end - r_start) * (c_end - c_start)) \
if (exclusive or adaptive) else (ksize[0] * ksize[1])
if (exclusive or adaptive):
field_size = (r_end - r_start) * (c_end - c_start)
if data_type == np.int8 or data_type == np.uint8:
out[:, :, i, j] = (np.rint(
np.sum(x_masked, axis=(2, 3)) /
@ -207,22 +214,34 @@ def pool2D_forward_naive(x,
in_w_start = adaptive_start_index(j, W, ksize[1])
in_w_end = adaptive_end_index(j, W, ksize[1])
else:
in_w_start = np.max((j * strides[1] - pad_w_left, 0))
in_w_end = np.min((j * strides[1] + ksize[1] - pad_w_left, W))
in_h_start = i * strides[0] - pad_h_up
in_w_start = j * strides[1] - pad_w_left
in_h_end = i * strides[0] + ksize[0] - pad_h_up
in_w_end = j * strides[1] + ksize[1] - pad_w_left
field_size = (in_h_end - in_h_start) * (in_w_end - in_w_start)
in_h_start = np.max((in_h_start, 0))
in_w_start = np.max((in_w_start, 0))
in_h_end = np.min((in_h_end, H))
in_w_end = np.min((in_w_end, W))
if data_format == 'NCHW':
x_masked = x[:, :, in_h_start:in_h_end, in_w_start:in_w_end]
if pool_type == 'avg':
field_size = ((in_h_end - in_h_start) * (in_w_end - in_w_start)) \
if (exclusive or adaptive) else (ksize[0] * ksize[1])
if (exclusive or adaptive):
field_size = (in_h_end - in_h_start) * (
in_w_end - in_w_start)
# if (exclusive or adaptive) else (ksize[0] * ksize[1])
out[:, :, i, j] = np.sum(x_masked, axis=(2, 3)) / field_size
elif pool_type == 'max':
out[:, :, i, j] = np.max(x_masked, axis=(2, 3))
elif data_format == 'NHWC':
x_masked = x[:, in_h_start:in_h_end, in_w_start:in_w_end, :]
if pool_type == 'avg':
field_size = ((in_h_end - in_h_start) * (in_w_end - in_w_start)) \
if (exclusive or adaptive) else (ksize[0] * ksize[1])
if (exclusive or adaptive):
field_size = (in_h_end - in_h_start) * (
in_w_end - in_w_start)
out[:, i, j, :] = np.sum(x_masked, axis=(1, 2)) / field_size
elif pool_type == 'max':
out[:, i, j, :] = np.max(x_masked, axis=(1, 2))

40
python/paddle/fluid/tests/unittests/test_pool3d_op.py
Unescape View File

@ -116,32 +116,44 @@ def pool3D_forward_naive(x,
if adaptive:
d_start = adaptive_start_index(k, D, ksize[0])
d_end = adaptive_end_index(k, D, ksize[0])
else:
d_start = np.max((k * strides[0] - pad_d_forth, 0))
d_end = np.min((k * strides[0] + ksize[0] - pad_d_forth, D))
for i in range(H_out):
if adaptive:
h_start = adaptive_start_index(i, H, ksize[1])
h_end = adaptive_end_index(i, H, ksize[1])
else:
h_start = np.max((i * strides[1] - pad_h_up, 0))
h_end = np.min((i * strides[1] + ksize[1] - pad_h_up, H))
for j in range(W_out):
if adaptive:
w_start = adaptive_start_index(j, W, ksize[2])
w_end = adaptive_end_index(j, W, ksize[2])
else:
w_start = np.max((j * strides[2] - pad_w_left, 0))
w_end = np.min((j * strides[2] + ksize[2] - pad_w_left, W))
d_start = k * strides[0] - pad_d_forth
d_end = np.min((k * strides[0] + ksize[0] - pad_d_forth,
D + pad_d_back))
h_start = i * strides[1] - pad_h_up
h_end = np.min(
(i * strides[1] + ksize[1] - pad_h_up, H + pad_h_down))
w_start = j * strides[2] - pad_w_left
w_end = np.min((j * strides[2] + ksize[2] - pad_w_left,
W + pad_w_right))
field_size = (d_end - d_start) * (h_end - h_start) * (
w_end - w_start)
w_start = np.max((w_start, 0))
d_start = np.max((d_start, 0))
h_start = np.max((h_start, 0))
w_end = np.min((w_end, W))
d_end = np.min((d_end, D))
h_end = np.min((h_end, H))
if data_format == 'NCDHW':
x_masked = x[:, :, d_start:d_end, h_start:h_end, w_start:
w_end]
if pool_type == 'avg':
field_size = (d_end - d_start) * (h_end - h_start) * (w_end - w_start) \
if (exclusive or adaptive) else ksize[0] * ksize[1] * ksize[2]
if (exclusive or adaptive):
field_size = (d_end - d_start) * (
h_end - h_start) * (w_end - w_start)
out[:, :, k, i, j] = np.sum(x_masked,
axis=(2, 3, 4)) / field_size
elif pool_type == 'max':
@ -151,8 +163,10 @@ def pool3D_forward_naive(x,
x_masked = x[:, d_start:d_end, h_start:h_end, w_start:
w_end, :]
if pool_type == 'avg':
field_size = (d_end - d_start) * (h_end - h_start) * (w_end - w_start) \
if (exclusive or adaptive) else ksize[0] * ksize[1] * ksize[2]
if (exclusive or adaptive):
field_size = (d_end - d_start) * (
h_end - h_start) * (w_end - w_start)
out[:, k, i, j, :] = np.sum(x_masked,
axis=(1, 2, 3)) / field_size
elif pool_type == 'max':
@ -564,7 +578,7 @@ class TestAvgInclude_AsyPadding(TestCase2):
self.exclusive = False
def init_paddings(self):
self.paddings = [1, 2, 1, 1, 1, 0]
self.paddings = [2, 2, 1, 1, 0, 0]
@unittest.skipIf(not core.is_compiled_with_cuda(),

Write Preview
Loading…
Cancel
Save