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Revert D22330340: [C2] Fixed a bug in normalization operator

Browse Source 
Test Plan: revert-hammer

Differential Revision:
D22330340 (ce63f70981)

Original commit changeset: 0bccf925bb76

fbshipit-source-id: e27d70dee0fbe9e708b0cf3be81dbd33c4015026
This commit is contained in:
Vitaly Fedyunin 2020-07-02 16:02:55 -07:00 committed by Facebook GitHub Bot
parent 9cc73966b3
commit a1c234e372
3 changed files with 3 additions and 29 deletions
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2
caffe2/operators/normalize_l1_op.h
View File

@ -23,7 +23,7 @@ class NormalizeL1Op final : public Operator<Context> {
const auto canonical_axis = x.canonical_axis_index(
this->template GetSingleArgument<int>("axis", -1));
const int m = x.dim32(canonical_axis);
const int n = x.numel() == 0 ? 0 : x.numel() / m;
const int n = x.numel() / m;
const int sf = x.size_from_dim(canonical_axis + 1);
DoNormalize(xData, yData, m, n, sf);
return true;

4
caffe2/operators/normalize_op.h
View File

@ -28,7 +28,7 @@ class NormalizeOp final : public Operator<Context> {
const auto canonical_axis = x.canonical_axis_index(
this->template GetSingleArgument<int>("axis", -1));
const int64_t m = x.dim(canonical_axis);
const size_t n = x.numel() == 0 ? 0 : x.numel() / m;
const size_t n = x.numel() / m;
const size_t sf = x.size_from_dim(canonical_axis + 1);
DoNormalize(xData, yData, m, n, sf);
return true;
@ -80,7 +80,7 @@ class NormalizeGradientOp final : public Operator<Context> {
const auto canonical_axis = x.canonical_axis_index(
this->template GetSingleArgument<int>("axis", -1));
const int m = x.dim32(canonical_axis);
const int n = x.numel() == 0 ? 0 : x.numel() / m;
const int n = x.numel() / m;
const int sf = x.size_from_dim(canonical_axis + 1);
DoNormalize(xData, gOutData, gInData, m, n, sf);
return true;

26
caffe2/python/operator_test/normalize_op_test.py
View File

@ -35,20 +35,6 @@ class TestNormalizeOp(hu.HypothesisTestCase):
self.assertDeviceChecks(dc, op, [x], [0])
self.assertGradientChecks(gc, op, [x], 0, [0])
@given(**hu.gcs)
def test_normalize_empty(self, gc, dc):
def ref_normalize(_):
return (np.array([]).astype(np.float32),)
x = np.array([]).astype(np.float32)
op = core.CreateOperator("Normalize", "X", "Y")
self.assertReferenceChecks(
gc, op, [x], ref_normalize
)
self.assertDeviceChecks(dc, op, [x], [0])
self.assertGradientChecks(gc, op, [x], 0, [0])
@given(
X=hu.tensor(
min_dim=1, max_dim=5, elements=hu.floats(min_value=0.5, max_value=1.0)
@ -65,15 +51,3 @@ class TestNormalizeOp(hu.HypothesisTestCase):
op = core.CreateOperator("NormalizeL1", "X", "Y", axis=axis)
self.assertReferenceChecks(gc, op, [X], functools.partial(ref, axis=axis))
self.assertDeviceChecks(dc, op, [X], [0])
@given(**hu.gcs)
def test_normalize_L1_empty(self, gc, dc):
def ref_normalize(_):
return (np.array([]).astype(np.float32),)
x = np.array([]).astype(np.float32)
op = core.CreateOperator("NormalizeL1", "X", "Y")
self.assertReferenceChecks(
gc, op, [x], ref_normalize
)
self.assertDeviceChecks(dc, op, [x], [0])