wehub-resource-sync
823 lines
28 KiB
Python
823 lines
28 KiB
Python
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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from op_test import (
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OpTest,
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convert_float_to_uint16,
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get_device_place,
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is_custom_device,
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)
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import paddle
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from paddle.base import core
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def compute_segment_sum(x, segment_ids):
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length = segment_ids[-1] + 1
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target_shape = list(x.shape)
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target_shape[0] = length
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results = np.zeros(target_shape, dtype=x.dtype)
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for index, ids in enumerate(segment_ids):
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results[ids, :] += x[index, :]
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return results
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def compute_segment_mean(x, segment_ids):
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length = segment_ids[-1] + 1
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target_shape = list(x.shape)
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target_shape[0] = length
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results = np.zeros(target_shape, dtype=x.dtype)
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count = np.zeros(length, dtype=x.dtype) + 1e-8
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for index, ids in enumerate(segment_ids):
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results[ids, :] += x[index, :]
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count[ids] += 1
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results = results / count.reshape([-1, 1])
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return results
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def compute_segment_min_max(x, segment_ids, pooltype="MAX"):
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length = segment_ids[-1] + 1
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target_shape = list(x.shape)
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target_shape[0] = length
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gradient = np.zeros_like(x)
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results = np.zeros(target_shape, dtype=x.dtype)
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last_idx = 0
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current_id = segment_ids[0]
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for idx in range(1, len(segment_ids) + 1):
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if idx < len(segment_ids):
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if segment_ids[idx] == current_id:
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continue
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sub_x = x[last_idx:idx, :]
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if pooltype == "MAX":
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results[current_id] = np.amax(sub_x, axis=0)
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elif pooltype == "MIN":
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results[current_id] = np.amin(sub_x, axis=0)
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else:
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raise ValueError("Invalid pooltype, only MAX, MIN supported!")
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gradient[last_idx:idx, :][sub_x == results[current_id]] = 1
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last_idx = idx
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if idx < len(segment_ids):
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current_id = segment_ids[idx]
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return results, gradient / results.size
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def segment_pool_split(X, SegmentIds, pooltype):
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if pooltype == "SUM":
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return paddle.geometric.segment_sum(X, SegmentIds)
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elif pooltype == "MEAN":
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return paddle.geometric.segment_mean(X, SegmentIds)
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elif pooltype == "MIN":
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return paddle.geometric.segment_min(X, SegmentIds)
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elif pooltype == "MAX":
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return paddle.geometric.segment_max(X, SegmentIds)
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class TestSegmentOps(OpTest):
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def set_data(self):
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if self.dtype == np.uint16:
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x = np.random.uniform(-1, 1, self.shape).astype(self.np_dtype)
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else:
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x = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
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segment_ids = self.set_segment(len(x), len(x) // 5 + 1)
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return x, segment_ids
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def set_segment(self, origin_len, reduce_len):
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segment = np.zeros(reduce_len, dtype='int64')
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segment = np.random.randint(0, reduce_len, size=[origin_len])
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segment = np.sort(segment)
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return segment.astype('int64')
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def compute(self, x, segment_ids):
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return compute_segment_sum(x, segment_ids)
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def prepare(self):
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self.op_type = "segment_pool"
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self.python_api = segment_pool_split
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self.python_out_sig = ["Out"]
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self.dtype = np.float64
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self.shape = [30, 15]
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self.attrs = {"pooltype": "SUM"}
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def setUp(self):
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self.prepare()
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x, segment_ids = self.set_data()
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result = self.compute(x, segment_ids)
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self.inputs = {
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'X': x,
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'SegmentIds': segment_ids.astype(np.int64),
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}
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if self.dtype == np.uint16:
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self.outputs = {'Out': result.astype(self.np_dtype)}
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else:
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self.outputs = {'Out': result.astype(self.dtype)}
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self.convert_bf16()
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def test_check_output(self):
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self.check_output(check_pir=True, check_symbol_infer=False)
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def test_check_grad(self):
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self.check_grad(["X"], "Out", check_pir=True)
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def convert_bf16(self):
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if self.dtype == np.uint16:
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self.inputs['X'] = convert_float_to_uint16(self.inputs['X'])
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self.outputs['Out'] = convert_float_to_uint16(self.outputs['Out'])
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self.place = get_device_place()
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class TestSegmentSum2(TestSegmentOps):
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def prepare(self):
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super().prepare()
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self.shape = [40, 20]
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self.dtype = np.float32
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def setUp(self):
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self.prepare()
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x, segment_ids = self.set_data()
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result = self.compute(x, segment_ids)
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self.inputs = {
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'X': x.astype(self.dtype),
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'SegmentIds': segment_ids.astype(np.int32),
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}
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self.outputs = {'Out': result.astype(self.dtype)}
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class TestSegmentMax(TestSegmentOps):
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def compute(self, x, segment_ids):
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result, self.gradient = compute_segment_min_max(
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x, segment_ids, pooltype="MAX"
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)
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return result
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def prepare(self):
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super().prepare()
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self.shape = [40, 20]
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self.attrs = {'pooltype': "MAX"}
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def test_check_grad(self):
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self.check_grad(
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["X"], "Out", user_defined_grads=[self.gradient], check_pir=True
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)
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class TestSegmentMax2(TestSegmentMax):
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def prepare(self):
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super().prepare()
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self.dtype = np.float32
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class TestSegmentMin(TestSegmentMax):
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def compute(self, x, segment_ids):
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result, self.gradient = compute_segment_min_max(
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x, segment_ids, pooltype="MIN"
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)
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return result
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def prepare(self):
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super().prepare()
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self.attrs = {'pooltype': "MIN"}
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class TestSegmentMin2(TestSegmentMin):
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def prepare(self):
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super().prepare()
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self.dtype = np.float32
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class TestSegmentMean(TestSegmentOps):
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def compute(self, x, segment_ids):
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return compute_segment_mean(x, segment_ids)
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def prepare(self):
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super().prepare()
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self.shape = [40, 20]
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self.attrs = {'pooltype': "MEAN"}
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def setUp(self):
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self.prepare()
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x, segment_ids = self.set_data()
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result = self.compute(x, segment_ids)
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self.inputs = {'X': x, 'SegmentIds': segment_ids}
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if self.dtype == np.uint16:
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astype = self.np_dtype
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else:
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astype = self.dtype
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self.outputs = {
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'Out': result,
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'SummedIds': compute_segment_sum(
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np.ones([len(x), 1]).astype(astype), segment_ids
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),
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}
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self.convert_bf16()
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def test_check_output(self):
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if core.is_compiled_with_cuda() or is_custom_device():
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self.check_output_with_place(
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get_device_place(), check_pir=True, check_symbol_infer=False
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)
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# due to CPU kernel not implement calculate 'SummedIds'
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# so cannot check 'SummedIds'
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del self.outputs['SummedIds']
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self.check_output_with_place(
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core.CPUPlace(), check_pir=True, check_symbol_infer=False
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)
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class TestSegmentMean2(TestSegmentMean):
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def prepare(self):
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super().prepare()
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self.dtype = np.float32
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self.shape = [30, 20]
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self.attrs = {'pooltype': "MEAN"}
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class TestSegmentSumFP16Op(TestSegmentOps):
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def prepare(self):
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super().prepare()
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self.dtype = np.float16
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class TestSegmentMaxFP16Op(TestSegmentMax):
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def prepare(self):
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super().prepare()
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self.dtype = np.float16
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class TestSegmentMinFP16Op(TestSegmentMin):
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def prepare(self):
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super().prepare()
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self.dtype = np.float16
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class TestSegmentMeanFP16Op(TestSegmentMean):
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def prepare(self):
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super().prepare()
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self.dtype = np.float16
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class TestSegmentMaxFP16OddNumel(TestSegmentMax):
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"""Test MAX+float16 with odd output numel to exercise 4-byte alignment
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padding. Input [21,5], 7 segments -> output [7,5]=35 numel (odd),
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35*2=70 bytes, padded to 72."""
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def prepare(self):
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super().prepare()
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self.dtype = np.float16
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self.shape = [21, 5]
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def set_segment(self, origin_len, reduce_len):
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return np.sort(
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np.array(
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[0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6],
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dtype='int64',
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)
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)
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class TestSegmentMinFP16OddNumel(TestSegmentMin):
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"""Test MIN+float16 with odd output numel to exercise 4-byte alignment
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padding. Input [21,5], 7 segments -> output [7,5]=35 numel (odd),
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35*2=70 bytes, padded to 72."""
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def prepare(self):
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super().prepare()
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self.dtype = np.float16
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self.shape = [21, 5]
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def set_segment(self, origin_len, reduce_len):
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return np.sort(
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np.array(
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[0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6],
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dtype='int64',
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)
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)
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class TestSegmentMaxFP16OddNumelInt32(TestSegmentMax):
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"""Test MAX+float16+int32 segment_ids with odd output numel."""
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def prepare(self):
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super().prepare()
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self.dtype = np.float16
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self.shape = [21, 5]
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def set_segment(self, origin_len, reduce_len):
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return np.sort(
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np.array(
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[0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6],
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dtype='int32',
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)
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)
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def setUp(self):
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self.prepare()
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x, segment_ids = self.set_data()
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result = self.compute(x, segment_ids)
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self.inputs = {
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'X': x.astype(self.dtype),
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'SegmentIds': segment_ids.astype(np.int32),
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}
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self.outputs = {'Out': result.astype(self.dtype)}
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class TestSegmentMinFP16OddNumelInt32(TestSegmentMin):
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"""Test MIN+float16+int32 segment_ids with odd output numel."""
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def prepare(self):
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super().prepare()
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self.dtype = np.float16
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self.shape = [21, 5]
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def set_segment(self, origin_len, reduce_len):
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return np.sort(
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np.array(
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[0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6],
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dtype='int32',
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)
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)
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def setUp(self):
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self.prepare()
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x, segment_ids = self.set_data()
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result = self.compute(x, segment_ids)
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self.inputs = {
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'X': x.astype(self.dtype),
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'SegmentIds': segment_ids.astype(np.int32),
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}
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self.outputs = {'Out': result.astype(self.dtype)}
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device())
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or not core.is_bfloat16_supported(get_device_place()),
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"core is not compiled with CUDA or not support bfloat16",
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)
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class TestSegmentSumBF16Op(TestSegmentOps):
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def prepare(self):
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super().prepare()
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self.dtype = np.uint16
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self.np_dtype = np.float32
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def test_check_output(self):
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self.check_output_with_place(
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self.place, check_pir=True, check_symbol_infer=False
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)
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def test_check_grad(self):
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self.check_grad_with_place(self.place, ["X"], "Out", check_pir=True)
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device())
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or not core.is_bfloat16_supported(get_device_place()),
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"core is not compiled with CUDA or not support bfloat16",
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)
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class TestSegmentMaxBF16Op(TestSegmentMax):
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def prepare(self):
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super().prepare()
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self.dtype = np.uint16
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self.np_dtype = np.float32
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def test_check_output(self):
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self.check_output_with_place(
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self.place, check_pir=True, check_symbol_infer=False
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)
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def test_check_grad(self):
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self.check_grad_with_place(
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self.place,
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["X"],
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"Out",
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user_defined_grads=[self.gradient],
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check_pir=True,
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)
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device())
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or not core.is_bfloat16_supported(get_device_place()),
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"core is not compiled with CUDA or not support bfloat16",
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)
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class TestSegmentMinBF16Op(TestSegmentMin):
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def prepare(self):
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super().prepare()
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self.dtype = np.uint16
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self.np_dtype = np.float32
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def test_check_output(self):
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self.check_output_with_place(
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self.place, check_pir=True, check_symbol_infer=False
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)
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def test_check_grad(self):
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self.check_grad_with_place(
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self.place,
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["X"],
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"Out",
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user_defined_grads=[self.gradient],
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check_pir=True,
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)
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device())
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or not core.is_bfloat16_supported(get_device_place()),
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"core is not compiled with CUDA or not support bfloat16",
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)
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class TestSegmentMeanBF16Op(TestSegmentMean):
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def prepare(self):
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super().prepare()
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self.dtype = np.uint16
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self.np_dtype = np.float32
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def test_check_output(self):
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self.check_output_with_place(
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self.place, check_pir=True, check_symbol_infer=False
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)
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def test_check_grad(self):
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self.check_grad_with_place(self.place, ["X"], "Out", check_pir=True)
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|
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device())
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or not core.is_bfloat16_supported(get_device_place()),
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"core is not compiled with CUDA or not support bfloat16",
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)
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class TestSegmentMaxBF16OddNumel(TestSegmentMaxBF16Op):
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"""Test MAX+bfloat16 with odd output numel to exercise 4-byte alignment
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padding. Input [21,5], 7 segments -> output [7,5]=35 numel (odd),
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35*2=70 bytes, padded to 72."""
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def prepare(self):
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super().prepare()
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self.shape = [21, 5]
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def set_segment(self, origin_len, reduce_len):
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return np.sort(
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np.array(
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[0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6],
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dtype='int64',
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)
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)
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|
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device())
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or not core.is_bfloat16_supported(get_device_place()),
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"core is not compiled with CUDA or not support bfloat16",
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)
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class TestSegmentMinBF16OddNumel(TestSegmentMinBF16Op):
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"""Test MIN+bfloat16 with odd output numel to exercise 4-byte alignment
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padding. Input [21,5], 7 segments -> output [7,5]=35 numel (odd),
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35*2=70 bytes, padded to 72."""
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def prepare(self):
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super().prepare()
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self.shape = [21, 5]
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def set_segment(self, origin_len, reduce_len):
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return np.sort(
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np.array(
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[0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6],
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dtype='int64',
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)
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)
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# default SUM
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class TestSegmentOps_ZeroSize(OpTest):
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def set_data(self):
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x = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
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segment_ids = self.set_segment(self.shape[0])
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return x, segment_ids
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def set_segment(self, len):
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segment = np.random.randint(0, len, size=[len])
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segment = np.sort(segment)
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return segment.astype('int64')
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def compute(self, x, segment_ids):
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return compute_segment_sum(x, segment_ids)
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|
|
def prepare(self):
|
|
self.op_type = "segment_pool"
|
|
self.python_api = segment_pool_split
|
|
self.python_out_sig = ["Out"]
|
|
self.dtype = np.float64
|
|
self.shape = [30, 0]
|
|
self.attrs = {"pooltype": "SUM"}
|
|
|
|
def setUp(self):
|
|
self.prepare()
|
|
x, segment_ids = self.set_data()
|
|
result = self.compute(x, segment_ids)
|
|
self.inputs = {
|
|
'X': x,
|
|
'SegmentIds': segment_ids.astype(np.int64),
|
|
}
|
|
self.outputs = {'Out': result.astype(self.dtype)}
|
|
|
|
def test_check_output(self):
|
|
self.check_output(check_pir=True, check_symbol_infer=False)
|
|
|
|
def test_check_grad(self):
|
|
self.check_grad(["X"], "Out", check_pir=True)
|
|
|
|
|
|
class TestSegmentOps_Min_ZeroSize(TestSegmentOps_ZeroSize):
|
|
def compute(self, x, segment_ids):
|
|
result, self.gradient = compute_segment_min_max(
|
|
x, segment_ids, pooltype="MIN"
|
|
)
|
|
return result
|
|
|
|
def prepare(self):
|
|
super().prepare()
|
|
self.attrs = {'pooltype': "MIN"}
|
|
|
|
|
|
class TestSegmentOps_Max_ZeroSize(TestSegmentOps_ZeroSize):
|
|
def compute(self, x, segment_ids):
|
|
result, self.gradient = compute_segment_min_max(
|
|
x, segment_ids, pooltype="MAX"
|
|
)
|
|
return result
|
|
|
|
def prepare(self):
|
|
super().prepare()
|
|
self.attrs = {'pooltype': "MAX"}
|
|
|
|
|
|
class TestSegmentOps_Mean_ZeroSize(TestSegmentOps_ZeroSize):
|
|
def compute(self, x, segment_ids):
|
|
return compute_segment_mean(x, segment_ids)
|
|
|
|
def prepare(self):
|
|
super().prepare()
|
|
self.attrs = {'pooltype': "MEAN"}
|
|
|
|
|
|
class API_SegmentOpsTest(unittest.TestCase):
|
|
def test_static(self):
|
|
with paddle.static.program_guard(paddle.static.Program()):
|
|
x = paddle.static.data(name="x", shape=[3, 3], dtype="float32")
|
|
y = paddle.static.data(name='y', shape=[3], dtype='int32')
|
|
|
|
res_sum = paddle.incubate.segment_sum(x, y)
|
|
res_mean = paddle.incubate.segment_mean(x, y)
|
|
res_max = paddle.incubate.segment_max(x, y)
|
|
res_min = paddle.incubate.segment_min(x, y)
|
|
|
|
exe = paddle.static.Executor(paddle.CPUPlace())
|
|
data1 = np.array([[1, 2, 3], [3, 2, 1], [4, 5, 6]], dtype='float32')
|
|
data2 = np.array([0, 0, 1], dtype="int32")
|
|
|
|
np_sum = np.array([[4, 4, 4], [4, 5, 6]], dtype="float32")
|
|
np_mean = np.array([[2, 2, 2], [4, 5, 6]], dtype="float32")
|
|
np_max = np.array([[3, 2, 3], [4, 5, 6]], dtype="float32")
|
|
np_min = np.array([[1, 2, 1], [4, 5, 6]], dtype="float32")
|
|
|
|
ret = exe.run(
|
|
feed={'x': data1, 'y': data2},
|
|
fetch_list=[res_sum, res_mean, res_max, res_min],
|
|
)
|
|
|
|
for np_res, ret_res in zip([np_sum, np_mean, np_max, np_min], ret):
|
|
np.testing.assert_allclose(np_res, ret_res, rtol=1e-05, atol=1e-06)
|
|
|
|
def test_dygraph(self):
|
|
device = paddle.CPUPlace()
|
|
with paddle.base.dygraph.guard(device):
|
|
x = paddle.to_tensor(
|
|
[[1, 2, 3], [3, 2, 1], [4, 5, 6]], dtype='float32'
|
|
)
|
|
y = paddle.to_tensor([0, 0, 1], dtype="int32")
|
|
res_sum = paddle.incubate.segment_sum(x, y)
|
|
res_mean = paddle.incubate.segment_mean(x, y)
|
|
res_max = paddle.incubate.segment_max(x, y)
|
|
res_min = paddle.incubate.segment_min(x, y)
|
|
|
|
np_sum = np.array([[4, 4, 4], [4, 5, 6]], dtype="float32")
|
|
np_mean = np.array([[2, 2, 2], [4, 5, 6]], dtype="float32")
|
|
np_max = np.array([[3, 2, 3], [4, 5, 6]], dtype="float32")
|
|
np_min = np.array([[1, 2, 1], [4, 5, 6]], dtype="float32")
|
|
|
|
ret = [res_sum, res_mean, res_max, res_min]
|
|
|
|
for np_res, ret_res in zip([np_sum, np_mean, np_max, np_min], ret):
|
|
np.testing.assert_allclose(
|
|
np_res, ret_res.numpy(), rtol=1e-05, atol=1e-06
|
|
)
|
|
|
|
|
|
class API_GeometricSegmentOpsTest(unittest.TestCase):
|
|
def test_static(self):
|
|
with paddle.static.program_guard(paddle.static.Program()):
|
|
x = paddle.static.data(name="x", shape=[3, 3], dtype="float32")
|
|
y = paddle.static.data(name='y', shape=[3], dtype='int32')
|
|
|
|
res_sum = paddle.geometric.segment_sum(x, y)
|
|
res_mean = paddle.geometric.segment_mean(x, y)
|
|
res_max = paddle.geometric.segment_max(x, y)
|
|
res_min = paddle.geometric.segment_min(x, y)
|
|
|
|
exe = paddle.static.Executor(paddle.CPUPlace())
|
|
data1 = np.array([[1, 2, 3], [3, 2, 1], [4, 5, 6]], dtype='float32')
|
|
data2 = np.array([0, 0, 1], dtype="int32")
|
|
|
|
np_sum = np.array([[4, 4, 4], [4, 5, 6]], dtype="float32")
|
|
np_mean = np.array([[2, 2, 2], [4, 5, 6]], dtype="float32")
|
|
np_max = np.array([[3, 2, 3], [4, 5, 6]], dtype="float32")
|
|
np_min = np.array([[1, 2, 1], [4, 5, 6]], dtype="float32")
|
|
|
|
ret = exe.run(
|
|
feed={'x': data1, 'y': data2},
|
|
fetch_list=[res_sum, res_mean, res_max, res_min],
|
|
)
|
|
|
|
for np_res, ret_res in zip([np_sum, np_mean, np_max, np_min], ret):
|
|
np.testing.assert_allclose(np_res, ret_res, rtol=1e-05, atol=1e-06)
|
|
|
|
def test_dygraph(self):
|
|
device = paddle.CPUPlace()
|
|
with paddle.base.dygraph.guard(device):
|
|
x = paddle.to_tensor(
|
|
[[1, 2, 3], [3, 2, 1], [4, 5, 6]], dtype='float32'
|
|
)
|
|
y = paddle.to_tensor([0, 0, 1], dtype="int32")
|
|
res_sum = paddle.geometric.segment_sum(x, y)
|
|
res_mean = paddle.geometric.segment_mean(x, y)
|
|
res_max = paddle.geometric.segment_max(x, y)
|
|
res_min = paddle.geometric.segment_min(x, y)
|
|
|
|
np_sum = np.array([[4, 4, 4], [4, 5, 6]], dtype="float32")
|
|
np_mean = np.array([[2, 2, 2], [4, 5, 6]], dtype="float32")
|
|
np_max = np.array([[3, 2, 3], [4, 5, 6]], dtype="float32")
|
|
np_min = np.array([[1, 2, 1], [4, 5, 6]], dtype="float32")
|
|
|
|
ret = [res_sum, res_mean, res_max, res_min]
|
|
|
|
for np_res, ret_res in zip([np_sum, np_mean, np_max, np_min], ret):
|
|
np.testing.assert_allclose(
|
|
np_res, ret_res.numpy(), rtol=1e-05, atol=1e-06
|
|
)
|
|
|
|
def test_dygraph_cpu_float16(self):
|
|
device = paddle.CPUPlace()
|
|
with paddle.base.dygraph.guard(device):
|
|
x = paddle.to_tensor(
|
|
[[1, 2, 3], [3, 2, 1], [4, 5, 6]], dtype='float16'
|
|
)
|
|
y = paddle.to_tensor([0, 0, 1], dtype="int32")
|
|
res_sum = paddle.geometric.segment_sum(x, y)
|
|
res_mean = paddle.geometric.segment_mean(x, y)
|
|
res_max = paddle.geometric.segment_max(x, y)
|
|
res_min = paddle.geometric.segment_min(x, y)
|
|
|
|
np_sum = np.array([[4, 4, 4], [4, 5, 6]], dtype="float16")
|
|
np_mean = np.array([[2, 2, 2], [4, 5, 6]], dtype="float16")
|
|
np_max = np.array([[3, 2, 3], [4, 5, 6]], dtype="float16")
|
|
np_min = np.array([[1, 2, 1], [4, 5, 6]], dtype="float16")
|
|
|
|
ret = [res_sum, res_mean, res_max, res_min]
|
|
for np_res, ret_res in zip([np_sum, np_mean, np_max, np_min], ret):
|
|
np.testing.assert_allclose(
|
|
np_res, ret_res.numpy(), rtol=1e-05, atol=1e-06
|
|
)
|
|
|
|
def test_dygraph_cuda_float16(self):
|
|
if core.is_compiled_with_cuda() or is_custom_device():
|
|
device = get_device_place()
|
|
with paddle.base.dygraph.guard(device):
|
|
x = paddle.to_tensor(
|
|
[[1, 2, 3], [3, 2, 1], [4, 5, 6]], dtype='float16'
|
|
)
|
|
y = paddle.to_tensor([0, 0, 1], dtype="int32")
|
|
res_sum = paddle.geometric.segment_sum(x, y)
|
|
res_mean = paddle.geometric.segment_mean(x, y)
|
|
res_max = paddle.geometric.segment_max(x, y)
|
|
res_min = paddle.geometric.segment_min(x, y)
|
|
|
|
np_sum = np.array([[4, 4, 4], [4, 5, 6]], dtype="float16")
|
|
np_mean = np.array([[2, 2, 2], [4, 5, 6]], dtype="float16")
|
|
np_max = np.array([[3, 2, 3], [4, 5, 6]], dtype="float16")
|
|
np_min = np.array([[1, 2, 1], [4, 5, 6]], dtype="float16")
|
|
|
|
ret = [res_sum, res_mean, res_max, res_min]
|
|
|
|
for np_res, ret_res in zip([np_sum, np_mean, np_max, np_min], ret):
|
|
np.testing.assert_allclose(
|
|
np_res, ret_res.numpy(), rtol=1e-05, atol=1e-06
|
|
)
|
|
|
|
def test_infermeta_dimension_validation(self):
|
|
"""Test InferMeta dimension validation for both dynamic and static graph modes.
|
|
|
|
This test covers the dimension checking logic added to InferMeta:
|
|
- config.is_runtime || !contain_unknown_dim
|
|
- Should check in dynamic mode (is_runtime=true)
|
|
- Should check in static mode when dimensions are known (!contain_unknown_dim)
|
|
- Should skip in static mode when dimensions contain unknown (-1)
|
|
"""
|
|
|
|
device = paddle.CPUPlace()
|
|
|
|
# ========== Dynamic Graph Tests ==========
|
|
with paddle.base.dygraph.guard(device):
|
|
# Test 1: Normal case - dimensions match, should pass
|
|
x = paddle.to_tensor(
|
|
[[1, 2, 3], [3, 2, 1], [4, 5, 6]], dtype='float32'
|
|
) # [3, 3]
|
|
y = paddle.to_tensor([0, 0, 1], dtype='int32') # [3]
|
|
res_sum = paddle.geometric.segment_sum(x, y)
|
|
self.assertEqual(res_sum.shape[0], 2) # max segment id is 1
|
|
|
|
# Test 2: Error case - segment_ids is shorter
|
|
x = paddle.to_tensor(
|
|
[[1, 2, 3], [3, 2, 1], [4, 5, 6]], dtype='float32'
|
|
) # [3, 3]
|
|
y = paddle.to_tensor([0, 1], dtype='int32') # [2] - 2 vs 3
|
|
with self.assertRaises(ValueError) as cm:
|
|
paddle.geometric.segment_sum(x, y)
|
|
self.assertIn("same size as dimension 0", str(cm.exception))
|
|
|
|
# Test 3: Error case - segment_ids is longer
|
|
x = paddle.to_tensor(
|
|
[[1, 2, 3], [3, 2, 1], [4, 5, 6]], dtype='float32'
|
|
) # [3, 3]
|
|
y = paddle.to_tensor([0, 0, 1, 2], dtype='int32') # [4] - 4 vs 3
|
|
with self.assertRaises(ValueError) as cm:
|
|
paddle.geometric.segment_sum(x, y)
|
|
self.assertIn("same size as dimension 0", str(cm.exception))
|
|
|
|
# Test 4: Error case - segment_ids is multi-dimensional
|
|
x = paddle.to_tensor(
|
|
[[1, 2, 3], [3, 2, 1]], dtype='float32'
|
|
) # [3, 3]
|
|
y = paddle.to_tensor([[0, 0], [1, 1]], dtype='int32') # [2, 2]
|
|
with self.assertRaises(ValueError) as cm:
|
|
paddle.geometric.segment_sum(x, y)
|
|
self.assertIn("1-D tensor", str(cm.exception))
|
|
|
|
# Test 5: Error case - segment_ids shorter, verify error message
|
|
x = paddle.to_tensor(
|
|
[[1, 2, 3], [2, 1, 3], [3, 4, 5]], dtype='float32'
|
|
) # [3, 3]
|
|
y = paddle.to_tensor([0, 1], dtype='int32') # [2]
|
|
with self.assertRaises(ValueError) as cm:
|
|
paddle.geometric.segment_mean(x, y)
|
|
self.assertIn("same size as dimension 0", str(cm.exception))
|
|
|
|
# Test 6: Error case - segment_ids longer, verify error message
|
|
x = paddle.to_tensor(
|
|
[[1, 2, 3], [2, 1, 3], [3, 4, 5]], dtype='float32'
|
|
) # [3, 3]
|
|
y = paddle.to_tensor([0, 0, 1, 2], dtype='int32') # [4]
|
|
with self.assertRaises(ValueError) as cm:
|
|
paddle.geometric.segment_min(x, y)
|
|
self.assertIn("same size as dimension 0", str(cm.exception))
|
|
|
|
# ========== Static Graph Tests ==========
|
|
in_dygraph = paddle.in_dynamic_mode()
|
|
paddle.enable_static()
|
|
|
|
# Test 7: Static graph - known dimensions, should check at compile time
|
|
x = paddle.static.data(name='x', shape=[3, 3], dtype='float32')
|
|
y = paddle.static.data(name='y', shape=[3], dtype='int32')
|
|
# This should trigger InferMeta check during graph construction
|
|
# if dimensions are known at compile time
|
|
# Note: Actual check happens at compile time for static graph
|
|
# The error message might differ from dynamic graph
|
|
|
|
# Test 8: Static graph - unknown dimensions (-1), should NOT check at compile time
|
|
# Dimensions with -1 are deferred to runtime
|
|
x = paddle.static.data(name='x', shape=[-1, 3], dtype='float32')
|
|
y = paddle.static.data(name='y', shape=[-1], dtype='int32')
|
|
# Should NOT trigger InferMeta check during graph construction
|
|
# because contain_unknown_dim will be true
|
|
# Check happens at runtime when executor runs
|
|
|
|
if in_dygraph:
|
|
paddle.disable_static()
|
|
|
|
|
|
if __name__ == '__main__':
|
|
paddle.enable_static()
|
|
unittest.main()
|