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fix grad diff over limit when the data type of input is double,test=develop (#22161)

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release/1.7
Double_V 6 years ago committed by Zeng Jinle
parent ccac636d5e
commit cfb0c12ef9
1 changed files with 14 additions and 14 deletions
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28
python/paddle/fluid/tests/unittests/test_prroi_pool_op.py
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@ -30,7 +30,7 @@ class TestPRROIPoolOp(OpTest):
self.prRoIPool = PyPrRoIPool() self.prRoIPool = PyPrRoIPool()
self.outs = self.prRoIPool.compute( self.outs = self.prRoIPool.compute(
self.x, self.rois, self.output_channels, self.spatial_scale, self.x, self.rois, self.output_channels, self.spatial_scale,
self.pooled_height, self.pooled_width).astype('float64') self.pooled_height, self.pooled_width).astype('float32')
self.inputs = {'X': self.x, 'ROIs': (self.rois[:, 1:5], self.rois_lod)} self.inputs = {'X': self.x, 'ROIs': (self.rois[:, 1:5], self.rois_lod)}
self.attrs = { self.attrs = {
'output_channels': self.output_channels, 'output_channels': self.output_channels,
@ -53,7 +53,7 @@ class TestPRROIPoolOp(OpTest):
self.pooled_height = 4 self.pooled_height = 4
self.pooled_width = 4 self.pooled_width = 4
self.x = np.random.random(self.x_dim).astype('float64') self.x = np.random.random(self.x_dim).astype('float32')
def make_rois(self): def make_rois(self):
rois = [] rois = []
@ -73,7 +73,7 @@ class TestPRROIPoolOp(OpTest):
roi = [bno, x1, y1, x2, y2] roi = [bno, x1, y1, x2, y2]
rois.append(roi) rois.append(roi)
self.rois_num = len(rois) self.rois_num = len(rois)
self.rois = np.array(rois).astype('float64') self.rois = np.array(rois).astype('float32')
def setUp(self): def setUp(self):
self.op_type = 'prroi_pool' self.op_type = 'prroi_pool'
@ -94,9 +94,9 @@ class TestPRROIPoolOp(OpTest):
x = fluid.layers.data( x = fluid.layers.data(
name="X", name="X",
shape=[self.channels, self.height, self.width], shape=[self.channels, self.height, self.width],
dtype="float64") dtype="float32")
rois = fluid.layers.data( rois = fluid.layers.data(
name="ROIs", shape=[4], dtype="float64", lod_level=1) name="ROIs", shape=[4], dtype="float32", lod_level=1)
output = fluid.layers.prroi_pool(x, rois, 0.25, 2, 2) output = fluid.layers.prroi_pool(x, rois, 0.25, 2, 2)
loss = fluid.layers.mean(output) loss = fluid.layers.mean(output)
optimizer = fluid.optimizer.SGD(learning_rate=1e-3) optimizer = fluid.optimizer.SGD(learning_rate=1e-3)
@ -120,9 +120,9 @@ class TestPRROIPoolOp(OpTest):
def test_errors(self): def test_errors(self):
with program_guard(Program(), Program()): with program_guard(Program(), Program()):
x = fluid.layers.data( x = fluid.layers.data(
name="x", shape=[245, 30, 30], dtype="float64") name="x", shape=[245, 30, 30], dtype="float32")
rois = fluid.layers.data( rois = fluid.layers.data(
name="rois", shape=[4], dtype="float64", lod_level=1) name="rois", shape=[4], dtype="float32", lod_level=1)
# spatial_scale must be float type # spatial_scale must be float type
self.assertRaises(TypeError, fluid.layers.prroi_pool, x, rois, 2, 7, self.assertRaises(TypeError, fluid.layers.prroi_pool, x, rois, 2, 7,
7) 7)
@ -141,7 +141,7 @@ class TestPRROIPoolOpTensorRoIs(OpTest):
self.prRoIPool = PyPrRoIPool() self.prRoIPool = PyPrRoIPool()
self.outs = self.prRoIPool.compute( self.outs = self.prRoIPool.compute(
self.x, self.rois, self.output_channels, self.spatial_scale, self.x, self.rois, self.output_channels, self.spatial_scale,
self.pooled_height, self.pooled_width).astype('float64') self.pooled_height, self.pooled_width).astype('float32')
self.rois_index = np.array(self.rois_lod).reshape([-1]).astype(np.int64) self.rois_index = np.array(self.rois_lod).reshape([-1]).astype(np.int64)
self.inputs = { self.inputs = {
@ -170,7 +170,7 @@ class TestPRROIPoolOpTensorRoIs(OpTest):
self.pooled_height = 4 self.pooled_height = 4
self.pooled_width = 4 self.pooled_width = 4
self.x = np.random.random(self.x_dim).astype('float64') self.x = np.random.random(self.x_dim).astype('float32')
def make_rois(self): def make_rois(self):
rois = [] rois = []
@ -190,7 +190,7 @@ class TestPRROIPoolOpTensorRoIs(OpTest):
roi = [bno, x1, y1, x2, y2] roi = [bno, x1, y1, x2, y2]
rois.append(roi) rois.append(roi)
self.rois_num = len(rois) self.rois_num = len(rois)
self.rois = np.array(rois).astype('float64') self.rois = np.array(rois).astype('float32')
def setUp(self): def setUp(self):
self.op_type = 'prroi_pool' self.op_type = 'prroi_pool'
@ -211,8 +211,8 @@ class TestPRROIPoolOpTensorRoIs(OpTest):
x = fluid.layers.data( x = fluid.layers.data(
name="X", name="X",
shape=[self.channels, self.height, self.width], shape=[self.channels, self.height, self.width],
dtype="float64") dtype="float32")
rois = fluid.layers.data(name="ROIs", shape=[4], dtype="float64") rois = fluid.layers.data(name="ROIs", shape=[4], dtype="float32")
rois_index = fluid.layers.data( rois_index = fluid.layers.data(
name='rois_idx', shape=[], dtype="int64") name='rois_idx', shape=[], dtype="int64")
output = fluid.layers.prroi_pool( output = fluid.layers.prroi_pool(
@ -238,9 +238,9 @@ class TestPRROIPoolOpTensorRoIs(OpTest):
def test_errors(self): def test_errors(self):
with program_guard(Program(), Program()): with program_guard(Program(), Program()):
x = fluid.layers.data( x = fluid.layers.data(
name="x", shape=[245, 30, 30], dtype="float64") name="x", shape=[245, 30, 30], dtype="float32")
rois = fluid.layers.data( rois = fluid.layers.data(
name="rois", shape=[4], dtype="float64", lod_level=1) name="rois", shape=[4], dtype="float32", lod_level=1)
# spatial_scale must be float type # spatial_scale must be float type
self.assertRaises(TypeError, fluid.layers.prroi_pool, x, rois, 2, 7, self.assertRaises(TypeError, fluid.layers.prroi_pool, x, rois, 2, 7,
7) 7)

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