API/OP error message enhancement (#23684)
* `l1_norm` error message enhancement test=develop * `norm` error message enhancement test=develop * `squared_l2_norm` error message enhancement test=develop * `squared_l2_distance` error message enhancement test=develop * `conv_shift_op` error message enhancement test=develop * `sample_logits_op` error message enhancement test=develop * Fix format test=develop * use `PADDLE_ENFORCE_EQ` for '||' conditional test=develop * Fix string format test=develop * Add test for `sample_logits` test=develop * Add error message test=develop * Use ordered dict for outputs test=develop * Hard code fetch list so that order is deterministic test=develop * Fix license header year test=developrevert-22778-infer_var_type
Yang Zhang
5 years ago
committed by
GitHub
parent
14e7041c6d
commit
648f1d5b3f
@ -0,0 +1,107 @@
|
||||
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import unittest
|
||||
import collections
|
||||
import numpy as np
|
||||
from op_test import OpTest
|
||||
|
||||
|
||||
class TestSampleLogitsOp(OpTest):
|
||||
def setUp(self):
|
||||
self.op_type = "sample_logits"
|
||||
self.dtype = np.float64
|
||||
self.use_mkldnn = False
|
||||
bs = 2
|
||||
K = 20
|
||||
NT = 10
|
||||
S = 5
|
||||
|
||||
Samples = np.random.random([bs, NT + S]).astype('int64')
|
||||
Probabilities = np.random.random([bs, NT + S]).astype('float64')
|
||||
LogitsDim = np.array([bs, K], dtype=np.int64)
|
||||
LabelsDim = np.array([bs, NT], dtype=np.int64)
|
||||
SampledLogits = np.random.random([bs, NT + S]).astype('float64')
|
||||
SampledLabels = np.random.random([bs, NT]).astype('int64')
|
||||
|
||||
self.bs = bs
|
||||
self.K = K
|
||||
self.NT = NT
|
||||
self.S = S
|
||||
Labels = np.array(list(range(self.NT)) * self.bs).astype('int64')
|
||||
self.Labels = Labels.reshape(self.bs, -1)
|
||||
self.Logits = np.random.random([self.bs, self.K]).astype('float64')
|
||||
|
||||
self.inputs = {"Logits": self.Logits, "Labels": self.Labels}
|
||||
self.fetch_list = [
|
||||
'Samples', 'Probabilities', 'SampledLogits', 'SampledLabels'
|
||||
]
|
||||
self.outputs = collections.OrderedDict(
|
||||
(('Samples', Samples), ('Probabilities', Probabilities),
|
||||
('LogitsDim', LogitsDim), ('LabelsDim', LabelsDim),
|
||||
('SampledLogits', SampledLogits), ('SampledLabels',
|
||||
SampledLabels)))
|
||||
|
||||
self.attrs = {'num_samples': self.S}
|
||||
|
||||
def test_check_output(self):
|
||||
places = self._get_places()
|
||||
for p in places:
|
||||
(Samples, Probabilities, SampledLogits,
|
||||
SampledLabels) = [np.array(o) for o in self.calc_output(p)]
|
||||
|
||||
assert Samples.dtype == np.int64, \
|
||||
"Samples dtype is {}, not int64".format(Samples.dtype)
|
||||
assert Probabilities.dtype == np.float64, \
|
||||
"Probabilities dtype is {}, not float64".format(
|
||||
Probabilities.dtype)
|
||||
assert SampledLogits.dtype == np.float64, \
|
||||
"SampledLogits dtype is {}, not float64".format(
|
||||
SampledLogits.dtype)
|
||||
assert SampledLabels.dtype == np.int64, \
|
||||
"SampledLabels dtype is {}, not int64".format(
|
||||
SampledLabels.dtype)
|
||||
|
||||
assert Samples.shape == (self.bs, self.NT + self.S)
|
||||
assert Probabilities.shape == (self.bs, self.NT + self.S)
|
||||
assert SampledLogits.shape == (self.bs, self.NT + self.S)
|
||||
assert SampledLabels.shape == (self.bs, self.NT)
|
||||
|
||||
assert (SampledLabels == self.Labels).all()
|
||||
sampled_logits = self.Logits[:, Samples[0][:self.NT]]
|
||||
sampled_logits -= np.log(Probabilities[:, :self.NT])
|
||||
np.testing.assert_almost_equal(sampled_logits,
|
||||
SampledLogits[:, :self.NT])
|
||||
|
||||
def test_check_grad(self):
|
||||
self._check_grad_helper()
|
||||
for p in self._get_places():
|
||||
grads = self._get_gradient(['Logits'], p, ['SampledLogits'], [])
|
||||
np.testing.assert_almost_equal(grads[0].sum(), np.array([1.]))
|
||||
|
||||
|
||||
class TestSampleLogitsOpNoUniq(TestSampleLogitsOp):
|
||||
def setUp(self):
|
||||
super(TestSampleLogitsOpNoUniq, self).setUp()
|
||||
self.attrs = {'num_samples': self.S, 'uniq': False}
|
||||
|
||||
|
||||
class TestSampleLogitsOpWithAccidentalHits(TestSampleLogitsOp):
|
||||
def setUp(self):
|
||||
super(TestSampleLogitsOpWithAccidentalHits, self).setUp()
|
||||
self.attrs = {'num_samples': self.S, 'remove_accidental_hits': False}
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
Loading…
Reference in new issue