wehub-resource-sync
677 lines
21 KiB
Python
677 lines
21 KiB
Python
#!/usr/bin/env python3
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# Copyright (c) 2021 CINN 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 math
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import unittest
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import conv2d_utils
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import numpy as np
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import pool_utils
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from test_utils import SingleOpTester
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from paddle.cinn import framework
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class OpTest_relu(SingleOpTester):
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def create_target_data(self, inputs_data, attrs):
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[X] = inputs_data
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return np.maximum(X, np.zeros(X.shape).astype("float32"))
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def test_op(self):
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attrs = framework.NodeAttr()
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self.to_test_op([[32]], [[32]], "relu", attrs)
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class OpTest_relu6(SingleOpTester):
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def create_target_data(self, inputs_data, attrs):
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[X] = inputs_data
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return np.minimum(
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np.maximum(X, np.zeros(np.array(X).shape).astype("float32")), 6
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)
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def test_op(self):
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attrs = framework.NodeAttr()
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self.to_test_op([[32, 32]], [[32, 32]], "relu6", attrs)
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class OpTest_conv2d_nchw(SingleOpTester):
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def init_testcase(self):
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self.input_size = [1, 3, 10, 10]
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self.groups = 1
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assert np.mod(self.input_size[1], self.groups) == 0
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f_c = self.input_size[1] // self.groups
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self.filter_size = [2, f_c, 2, 2]
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assert np.mod(self.filter_size[0], self.groups) == 0
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self.data_format = "NCHW"
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self.attrs = framework.NodeAttr()
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self.padding = [1, 1]
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self.stride = [2, 2]
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self.dilation = [2, 2]
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self.attrs.set_attr("stride", self.stride)
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self.attrs.set_attr("padding", self.padding)
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self.attrs.set_attr("dilation", self.dilation)
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self.attrs.set_attr("groups", self.groups)
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self.attrs.set_attr("data_format", self.data_format)
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def create_target_data(self, inputs_data, attrs):
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return conv2d_utils.conv2d_native(
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inputs_data, self.input_size, self.filter_size, self.attrs, False
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)
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def test_op(self):
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self.init_testcase()
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self.to_test_op(
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[self.input_size, self.filter_size],
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None,
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"conv2d",
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self.attrs,
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0,
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True,
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)
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class OpTest_conv2d_nchw_1(SingleOpTester):
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def init_testcase(self):
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self.input_size = [1, 3, 224, 224]
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self.groups = 1
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assert np.mod(self.input_size[1], self.groups) == 0
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f_c = self.input_size[1] // self.groups
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self.filter_size = [64, f_c, 7, 7]
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self.data_format = "NCHW"
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self.attrs = framework.NodeAttr()
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self.padding = [3, 3]
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self.stride = [2, 2]
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self.dilation = [1, 1]
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self.attrs.set_attr("stride", self.stride)
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self.attrs.set_attr("padding", self.padding)
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self.attrs.set_attr("dilation", self.dilation)
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self.attrs.set_attr("groups", self.groups)
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self.attrs.set_attr("data_format", self.data_format)
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def create_target_data(self, inputs_data, attrs):
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return conv2d_utils.conv2d_native(
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inputs_data, self.input_size, self.filter_size, self.attrs, False
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)
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def test_op(self):
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self.init_testcase()
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self.to_test_op(
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[self.input_size, self.filter_size],
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None,
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"conv2d",
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self.attrs,
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0,
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True,
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)
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class OpTest_conv2d_nchw_group(SingleOpTester):
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def init_testcase(self):
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self.input_size = [2, 8, 10, 10]
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self.groups = 4
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assert np.mod(self.input_size[1], self.groups) == 0
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f_c = self.input_size[1] // self.groups
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self.filter_size = [16, f_c, 7, 7]
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self.data_format = "NCHW"
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self.attrs = framework.NodeAttr()
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self.padding = [1, 1]
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self.stride = [2, 2]
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self.dilation = [1, 1]
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self.attrs.set_attr("stride", self.stride)
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self.attrs.set_attr("padding", self.padding)
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self.attrs.set_attr("dilation", self.dilation)
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self.attrs.set_attr("groups", self.groups)
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self.attrs.set_attr("data_format", self.data_format)
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def create_target_data(self, inputs_data, attrs):
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return conv2d_utils.conv2d_native(
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inputs_data, self.input_size, self.filter_size, self.attrs, False
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)
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def test_op(self):
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self.init_testcase()
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self.to_test_op(
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[self.input_size, self.filter_size],
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None,
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"conv2d",
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self.attrs,
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0,
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True,
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)
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class OpTest_conv2d_nchw_depthwise(SingleOpTester):
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def init_testcase(self):
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self.input_size = [2, 8, 10, 10]
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self.groups = 8
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assert np.mod(self.input_size[1], self.groups) == 0
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f_c = self.input_size[1] // self.groups
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self.filter_size = [16, f_c, 7, 7]
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self.data_format = "NCHW"
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self.attrs = framework.NodeAttr()
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self.padding = [1, 1]
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self.stride = [2, 2]
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self.dilation = [1, 1]
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self.attrs.set_attr("stride", self.stride)
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self.attrs.set_attr("padding", self.padding)
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self.attrs.set_attr("dilation", self.dilation)
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self.attrs.set_attr("groups", self.groups)
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self.attrs.set_attr("data_format", self.data_format)
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def create_target_data(self, inputs_data, attrs):
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return conv2d_utils.conv2d_native(
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inputs_data, self.input_size, self.filter_size, self.attrs, False
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)
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def test_op(self):
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self.init_testcase()
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self.to_test_op(
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[self.input_size, self.filter_size],
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None,
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"conv2d",
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self.attrs,
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0,
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True,
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)
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class OpTest_conv2d_nhwc_group(SingleOpTester):
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def init_testcase(self):
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self.input_size = [2, 10, 10, 8]
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self.groups = 4
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assert np.mod(self.input_size[3], self.groups) == 0
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f_c = self.input_size[3] // self.groups
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self.filter_size = [16, f_c, 7, 7]
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self.data_format = "NHWC"
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self.attrs = framework.NodeAttr()
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self.padding = [2, 2]
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self.stride = [2, 2]
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self.dilation = [2, 2]
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self.attrs.set_attr("stride", self.stride)
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self.attrs.set_attr("padding", self.padding)
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self.attrs.set_attr("dilation", self.dilation)
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self.attrs.set_attr("groups", self.groups)
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self.attrs.set_attr("data_format", self.data_format)
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def create_target_data(self, inputs_data, attrs):
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return conv2d_utils.conv2d_native(
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inputs_data, self.input_size, self.filter_size, self.attrs, False
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)
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def test_op(self):
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self.init_testcase()
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self.to_test_op(
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[self.input_size, self.filter_size],
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None,
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"conv2d",
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self.attrs,
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0,
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True,
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)
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class OpTest_conv2d_nhwc_depthwise(SingleOpTester):
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def init_testcase(self):
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self.input_size = [2, 10, 10, 8]
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self.groups = 8
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assert np.mod(self.input_size[3], self.groups) == 0
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f_c = self.input_size[3] // self.groups
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self.filter_size = [16, f_c, 7, 7]
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self.data_format = "NHWC"
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self.attrs = framework.NodeAttr()
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self.padding = [1, 1]
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self.stride = [2, 2]
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self.dilation = [1, 1]
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self.attrs.set_attr("stride", self.stride)
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self.attrs.set_attr("padding", self.padding)
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self.attrs.set_attr("dilation", self.dilation)
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self.attrs.set_attr("groups", self.groups)
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self.attrs.set_attr("data_format", self.data_format)
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def create_target_data(self, inputs_data, attrs):
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return conv2d_utils.conv2d_native(
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inputs_data, self.input_size, self.filter_size, self.attrs, False
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)
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def test_op(self):
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self.init_testcase()
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self.to_test_op(
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[self.input_size, self.filter_size],
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None,
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"conv2d",
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self.attrs,
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0,
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True,
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)
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# test channel multiplier format
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class OpTest_depthwise_conv2d_nchw(SingleOpTester):
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def init_testcase(self):
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self.input_size = [2, 8, 10, 10]
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self.groups = self.input_size[1]
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assert np.mod(self.input_size[1], self.groups) == 0
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channel_multiplier = 1
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self.filter_size = [self.input_size[1], channel_multiplier, 7, 7]
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self.data_format = "NCHW"
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self.attrs = framework.NodeAttr()
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self.padding = [1, 1]
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self.stride = [2, 2]
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self.dilation = [1, 1]
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self.attrs.set_attr("stride", self.stride)
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self.attrs.set_attr("padding", self.padding)
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self.attrs.set_attr("dilation", self.dilation)
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self.attrs.set_attr("groups", self.groups)
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self.attrs.set_attr("data_format", self.data_format)
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def create_target_data(self, inputs_data, attrs):
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return conv2d_utils.conv2d_native(
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inputs_data, self.input_size, self.filter_size, self.attrs, True
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)
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def test_op(self):
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self.init_testcase()
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self.to_test_op(
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[self.input_size, self.filter_size],
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None,
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"depthwise_conv2d",
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self.attrs,
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0,
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True,
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)
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# test channel multiplier format
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class OpTest_depthwise_conv2d_nhwc(SingleOpTester):
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def init_testcase(self):
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self.input_size = [2, 10, 10, 8]
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self.groups = self.input_size[3]
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assert np.mod(self.input_size[3], self.groups) == 0
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channel_multiplier = 4
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self.filter_size = [self.input_size[3], channel_multiplier, 7, 7]
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self.data_format = "NHWC"
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self.attrs = framework.NodeAttr()
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self.padding = [1, 1]
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self.stride = [2, 2]
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self.dilation = [1, 1]
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self.attrs.set_attr("stride", self.stride)
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self.attrs.set_attr("padding", self.padding)
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self.attrs.set_attr("dilation", self.dilation)
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self.attrs.set_attr("groups", self.groups)
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self.attrs.set_attr("data_format", self.data_format)
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def create_target_data(self, inputs_data, attrs):
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return conv2d_utils.conv2d_native(
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inputs_data, self.input_size, self.filter_size, self.attrs, True
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)
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def test_op(self):
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self.init_testcase()
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self.to_test_op(
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[self.input_size, self.filter_size],
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None,
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"depthwise_conv2d",
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self.attrs,
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0,
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True,
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)
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class OpTest_pool1d(SingleOpTester):
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attrs = framework.NodeAttr()
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attrs.set_attr("kernel_size", [2])
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attrs.set_attr("stride_size", [2])
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attrs.set_attr("padding_size", [1, 1])
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attrs.set_attr("pool_type", "max")
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attrs.set_attr("ceil_mode", False)
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attrs.set_attr("exclusive", True)
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attrs.set_attr("data_format", "NCW")
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def create_target_data(self, inputs_data, attrs):
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return pool_utils.pool1d(inputs_data[0], self.attrs)
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def test_op(self):
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input_shape = [1, 3, 8]
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self.to_test_op([input_shape], None, "pool1d", self.attrs)
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class OpTest_pool1d_1(SingleOpTester):
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attrs = framework.NodeAttr()
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attrs.set_attr("kernel_size", [2])
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attrs.set_attr("stride_size", [2])
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attrs.set_attr("padding_size", [2, 3])
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attrs.set_attr("pool_type", "avg")
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attrs.set_attr("ceil_mode", False)
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attrs.set_attr("exclusive", True)
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attrs.set_attr("data_format", "NCW")
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def create_target_data(self, inputs_data, attrs):
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return pool_utils.pool1d(inputs_data[0], self.attrs)
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def test_op(self):
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input_shape = [1, 3, 8]
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self.to_test_op([input_shape], None, "pool1d", self.attrs)
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class OpTest_pool1d_2(SingleOpTester):
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attrs = framework.NodeAttr()
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attrs.set_attr("kernel_size", [2])
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attrs.set_attr("stride_size", [3])
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attrs.set_attr("padding_size", [4, 5])
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attrs.set_attr("pool_type", "avg")
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attrs.set_attr("ceil_mode", True)
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attrs.set_attr("exclusive", False)
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attrs.set_attr("data_format", "NWC")
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def create_target_data(self, inputs_data, attrs):
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return pool_utils.pool1d(inputs_data[0], self.attrs)
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def test_op(self):
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input_shape = [1, 8, 3]
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self.to_test_op([input_shape], None, "pool1d", self.attrs)
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class OpTest_pool2d(SingleOpTester):
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attrs = framework.NodeAttr()
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attrs.set_attr("kernel_size", [2, 2])
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attrs.set_attr("stride_size", [2, 2])
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attrs.set_attr("padding_size", [1, 1, 1, 1])
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attrs.set_attr("pool_type", "max")
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attrs.set_attr("ceil_mode", False)
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attrs.set_attr("exclusive", True)
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attrs.set_attr("data_format", "NCHW")
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def create_target_data(self, inputs_data, attrs):
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return pool_utils.pool2d(inputs_data[0], self.attrs)
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def test_op(self):
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input_shape = [1, 3, 8, 8]
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self.to_test_op([input_shape], None, "pool2d", self.attrs)
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class OpTest_pool2d_1(SingleOpTester):
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attrs = framework.NodeAttr()
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attrs.set_attr("kernel_size", [2, 2])
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attrs.set_attr("stride_size", [2, 2])
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attrs.set_attr("padding_size", [2, 3, 4, 5])
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attrs.set_attr("pool_type", "avg")
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attrs.set_attr("ceil_mode", False)
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attrs.set_attr("exclusive", True)
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attrs.set_attr("data_format", "NCHW")
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def create_target_data(self, inputs_data, attrs):
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return pool_utils.pool2d(inputs_data[0], self.attrs)
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def test_op(self):
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input_shape = [1, 3, 8, 8]
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self.to_test_op([input_shape], None, "pool2d", self.attrs)
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class OpTest_pool2d_2(SingleOpTester):
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attrs = framework.NodeAttr()
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attrs.set_attr("kernel_size", [2, 2])
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attrs.set_attr("stride_size", [3, 3])
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attrs.set_attr("padding_size", [2, 3, 4, 5])
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attrs.set_attr("pool_type", "avg")
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attrs.set_attr("ceil_mode", True)
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attrs.set_attr("exclusive", False)
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attrs.set_attr("data_format", "NHWC")
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def create_target_data(self, inputs_data, attrs):
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return pool_utils.pool2d(inputs_data[0], self.attrs)
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def test_op(self):
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input_shape = [1, 8, 8, 3]
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self.to_test_op([input_shape], None, "pool2d", self.attrs)
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# The following test is temporarily broken
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# class OpTest_pool3d(SingleOpTester):
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# attrs = framework.NodeAttr()
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# attrs.attr_store = {
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# "kernel_size": [2, 2, 2],
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# "stride_size": [2, 2, 2],
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# "padding_size": [1, 2, 3, 4, 5, 6],
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# "pool_type": "max",
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# "ceil_mode": False,
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# "exclusive": True,
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# "data_format": "NCDHW"
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# }
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# def create_target_data(self, inputs_data, attrs):
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# return pool_utils.pool3d(inputs_data[0], self.attrs)
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# def test_op(self):
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# input_shape = [2, 3, 8, 8, 8]
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# self.to_test_op([input_shape], None, "pool3d", self.attrs)
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class OpTest_pool3d_1(SingleOpTester):
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attrs = framework.NodeAttr()
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attrs.set_attr("kernel_size", [2, 2, 2])
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attrs.set_attr("stride_size", [2, 2, 2])
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attrs.set_attr("padding_size", [1, 1, 1, 1, 1, 1])
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attrs.set_attr("pool_type", "avg")
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attrs.set_attr("ceil_mode", False)
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attrs.set_attr("exclusive", True)
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attrs.set_attr("data_format", "NCDHW")
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def create_target_data(self, inputs_data, attrs):
|
|
return pool_utils.pool3d(inputs_data[0], self.attrs)
|
|
|
|
def test_op(self):
|
|
input_shape = [1, 3, 8, 8, 8]
|
|
self.to_test_op([input_shape], None, "pool3d", self.attrs)
|
|
|
|
|
|
class OpTest_pool3d_2(SingleOpTester):
|
|
attrs = framework.NodeAttr()
|
|
attrs.set_attr("kernel_size", [2, 2, 2])
|
|
attrs.set_attr("stride_size", [2, 2, 2])
|
|
attrs.set_attr("padding_size", [1, 2, 3, 4, 5, 6])
|
|
attrs.set_attr("pool_type", "avg")
|
|
attrs.set_attr("ceil_mode", True)
|
|
attrs.set_attr("exclusive", False)
|
|
attrs.set_attr("data_format", "NDHWC")
|
|
|
|
def create_target_data(self, inputs_data, attrs):
|
|
return pool_utils.pool3d(inputs_data[0], self.attrs)
|
|
|
|
def test_op(self):
|
|
input_shape = [1, 8, 8, 8, 3]
|
|
self.to_test_op([input_shape], None, "pool3d", self.attrs)
|
|
|
|
|
|
class OpTest_batchnorm(SingleOpTester):
|
|
def create_target_data(self, inputs_data, attrs):
|
|
[X, Scale, Bias, Mean, Variance] = inputs_data
|
|
c = X.shape[1]
|
|
for i in range(0, c):
|
|
X[:, i, :, :] = (X[:, i, :, :] - Mean[i]) / math.sqrt(
|
|
Variance[i] + 0.00001
|
|
) * Scale[i] + Bias[i]
|
|
return X
|
|
|
|
def test_op(self):
|
|
attrs = framework.NodeAttr()
|
|
self.to_test_op(
|
|
[[1, 64, 112, 112], [64], [64], [64], [64]],
|
|
[[1, 64, 112, 112]],
|
|
"batch_norm",
|
|
attrs,
|
|
)
|
|
|
|
|
|
class OpTest_softmax_0(SingleOpTester):
|
|
def create_target_data(self, inputs_data, attrs):
|
|
[X] = inputs_data
|
|
Y = np.zeros(X.shape).astype("float32")
|
|
for i in range(0, Y.shape[1]):
|
|
Y[:, i, :] = (
|
|
np.exp(X[:, i, :])
|
|
/ np.sum(np.exp(X), axis=1, keepdims=True)[:, 0, :]
|
|
)
|
|
return Y
|
|
|
|
def test_op(self):
|
|
attrs = framework.NodeAttr()
|
|
attrs.set_attr("axis", 1)
|
|
self.to_test_op(
|
|
[[12, 224, 224]],
|
|
[[12, 224, 224], [12, 224, 224]],
|
|
"softmax",
|
|
attrs,
|
|
0,
|
|
)
|
|
|
|
|
|
class OpTest_softmax_1(SingleOpTester):
|
|
def create_target_data(self, inputs_data, attrs):
|
|
[X] = inputs_data
|
|
Y = np.zeros(X.shape).astype("float32")
|
|
for i in range(0, Y.shape[2]):
|
|
Y[:, :, i] = (
|
|
np.exp(X[:, :, i])
|
|
/ np.sum(np.exp(X), axis=2, keepdims=True)[:, :, 0]
|
|
)
|
|
return Y
|
|
|
|
def test_op(self):
|
|
attrs = framework.NodeAttr()
|
|
attrs.set_attr("axis", -1)
|
|
self.to_test_op(
|
|
[[12, 224, 224]],
|
|
[[12, 224, 224], [12, 224, 224]],
|
|
"softmax",
|
|
attrs,
|
|
0,
|
|
)
|
|
|
|
|
|
class OpTest_softmax_2(SingleOpTester):
|
|
def create_target_data(self, inputs_data, attrs):
|
|
[X] = inputs_data
|
|
Y = np.zeros(X.shape).astype("float32")
|
|
for i in range(0, Y.shape[0]):
|
|
Y[i, :, :] = (
|
|
np.exp(X[i, :, :])
|
|
/ np.sum(np.exp(X), axis=0, keepdims=True)[0, :, :]
|
|
)
|
|
return Y
|
|
|
|
def test_op(self):
|
|
attrs = framework.NodeAttr()
|
|
attrs.set_attr("axis", 0)
|
|
self.to_test_op(
|
|
[[12, 224, 224]],
|
|
[[12, 224, 224], [12, 224, 224]],
|
|
"softmax",
|
|
attrs,
|
|
0,
|
|
)
|
|
|
|
|
|
class OpTest_sigmoid(SingleOpTester):
|
|
def create_target_data(self, inputs_data, attrs):
|
|
x = np.array(inputs_data[0])
|
|
y = 1 / (1 + np.exp(-x))
|
|
return y
|
|
|
|
def test_op(self):
|
|
attrs = framework.NodeAttr()
|
|
self.to_test_op([[3, 224, 224]], [[3, 224, 224]], "sigmoid", attrs)
|
|
|
|
|
|
class OpTest_slice_0(SingleOpTester):
|
|
def create_target_data(self, inputs_data, attrs):
|
|
[X] = inputs_data
|
|
Y = X[:, 0:2, 2:4, :]
|
|
return Y
|
|
|
|
def test_op(self):
|
|
attrs = framework.NodeAttr()
|
|
attrs.set_attr("axes", [0, 1, 2])
|
|
attrs.set_attr("starts", [-3, 0, 2])
|
|
attrs.set_attr("ends", [3, 2, 4])
|
|
self.to_test_op([[3, 4, 5, 6]], [[3, 2, 2, 6]], "slice", attrs)
|
|
|
|
|
|
class OpTest_slice_1(SingleOpTester):
|
|
def create_target_data(self, inputs_data, attrs):
|
|
[X] = inputs_data
|
|
Y = X[:, 0:3, 1:2, 2:4]
|
|
return Y
|
|
|
|
def test_op(self):
|
|
attrs = framework.NodeAttr()
|
|
attrs.set_attr("axes", [1, 2, 3])
|
|
attrs.set_attr("starts", [0, 1, 2])
|
|
attrs.set_attr("ends", [3, 2, 4])
|
|
self.to_test_op([[3, 4, 5, 6]], [[3, 3, 1, 2]], "slice", attrs)
|
|
|
|
|
|
class OpTest_dropout_infer_0(SingleOpTester):
|
|
def init_testcase(self):
|
|
self.attrs = framework.NodeAttr()
|
|
self.attrs.set_attr("dropout_prob", 0.2)
|
|
self.attrs.set_attr("dropout_implementation", "downgrade_in_infer")
|
|
|
|
def create_target_data(self, inputs_data, attrs):
|
|
[X] = inputs_data
|
|
assert "dropout_implementation" in self.attrs.attr_store
|
|
if (
|
|
self.attrs.attr_store["dropout_implementation"]
|
|
== "downgrade_in_infer"
|
|
):
|
|
return X * (1 - self.attrs.attr_store["dropout_prob"])
|
|
else:
|
|
return X
|
|
|
|
def test_op(self):
|
|
self.init_testcase()
|
|
self.to_test_op(
|
|
[[2, 1280, 2, 2]], [[2, 1280, 2, 2]], "dropout_infer", self.attrs
|
|
)
|
|
|
|
|
|
class OpTest_dropout_infer_1(SingleOpTester):
|
|
def init_testcase(self):
|
|
self.attrs = framework.NodeAttr()
|
|
self.attrs.set_attr("dropout_prob", 0.2)
|
|
self.attrs.set_attr("dropout_implementation", "upscale_in_train")
|
|
|
|
def create_target_data(self, inputs_data, attrs):
|
|
[X] = inputs_data
|
|
assert "dropout_implementation" in self.attrs.attr_store
|
|
if (
|
|
self.attrs.attr_store["dropout_implementation"]
|
|
== "downgrade_in_infer"
|
|
):
|
|
return X * (1 - self.attrs.attr_store["dropout_prob"])
|
|
else:
|
|
return X
|
|
|
|
def test_op(self):
|
|
self.init_testcase()
|
|
self.to_test_op(
|
|
[[2, 1280, 2, 2]], [[2, 1280, 2, 2]], "dropout_infer", self.attrs
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|