wehub-resource-sync
413 lines
14 KiB
Python
413 lines
14 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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get_device_place,
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get_places,
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is_custom_device,
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paddle_static_guard,
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)
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import paddle
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from paddle import base
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class TestLRNOp(OpTest):
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def get_input(self):
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r''' TODO(gongweibao): why it's grad diff is so large?
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x = np.ndarray(
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shape=(self.N, self.C, self.H, self.W), dtype=float, order='C')
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for m in range(0, self.N):
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for i in range(0, self.C):
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for h in range(0, self.H):
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for w in range(0, self.W):
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x[m][i][h][w] = m * self.C * self.H * self.W + \
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i * self.H * self.W + \
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h * self.W + w + 1
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'''
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x = np.random.rand(self.N, self.C, self.H, self.W).astype("float32")
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return x + 1
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def get_out(self):
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start = -(self.n - 1) // 2
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end = start + self.n
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mid = np.empty((self.N, self.C, self.H, self.W)).astype("float32")
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mid.fill(self.k)
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for m in range(0, self.N):
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for i in range(0, self.C):
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for c in range(start, end):
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ch = i + c
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if ch < 0 or ch >= self.C:
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continue
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s = mid[m][i][:][:]
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r = self.x[m][ch][:][:]
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s += np.square(r) * self.alpha
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mid2 = np.power(mid, -self.beta)
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return np.multiply(self.x, mid2), mid
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def get_attrs(self):
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attrs = {
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'n': self.n,
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'k': self.k,
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'alpha': self.alpha,
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'beta': self.beta,
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'data_format': self.data_format,
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}
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return attrs
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def setUp(self):
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self.op_type = "lrn"
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self.init_test_case()
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self.N = 2
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self.C = 3
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self.H = 5
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self.W = 5
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self.n = 5
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self.k = 2.0
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self.alpha = 0.0001
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self.beta = 0.75
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self.x = self.get_input()
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self.out, self.mid_out = self.get_out()
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if self.data_format == 'NHWC':
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self.x = np.transpose(self.x, [0, 2, 3, 1])
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self.out = np.transpose(self.out, [0, 2, 3, 1])
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self.mid_out = np.transpose(self.mid_out, [0, 2, 3, 1])
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self.inputs = {'X': self.x}
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self.outputs = {'Out': self.out, 'MidOut': self.mid_out}
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self.attrs = self.get_attrs()
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def init_test_case(self):
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self.data_format = 'NCHW'
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def test_check_output(self):
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self.check_output(check_dygraph=False)
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def test_check_grad_normal(self):
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self.check_grad(['X'], 'Out', check_dygraph=False)
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class TestLRNOpAttrDataFormat(TestLRNOp):
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def init_test_case(self):
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self.data_format = 'NHWC'
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class TestLocalResponseNormFAPI(unittest.TestCase):
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def setUp(self):
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np.random.seed(123)
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self.places = get_places()
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def check_static_3d_input(self, place):
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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in_np1 = np.random.random([3, 40, 40]).astype("float32")
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in_np2 = np.transpose(in_np1, (0, 2, 1))
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input1 = paddle.static.data(
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name="input1", shape=[3, 40, 40], dtype="float32"
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)
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input2 = paddle.static.data(
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name="input2", shape=[3, 40, 40], dtype="float32"
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)
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res1 = paddle.nn.functional.local_response_norm(
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x=input1, size=5, data_format='NCL'
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)
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res2 = paddle.nn.functional.local_response_norm(
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x=input2, size=5, data_format='NLC'
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)
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exe = base.Executor(place)
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fetches = exe.run(
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paddle.static.default_main_program(),
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feed={"input1": in_np1, "input2": in_np2},
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fetch_list=[res1, res2],
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)
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fetches1_tran = np.transpose(fetches[1], (0, 2, 1))
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np.testing.assert_allclose(fetches[0], fetches1_tran, rtol=1e-05)
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def check_static_4d_input(self, place):
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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input1 = paddle.static.data(
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name="input1", shape=[3, 3, 40, 40], dtype="float32"
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)
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input2 = paddle.static.data(
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name="input2", shape=[3, 40, 40, 3], dtype="float32"
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)
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res1 = paddle.nn.functional.local_response_norm(
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x=input1, size=5, data_format='NCHW'
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)
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res2 = paddle.nn.functional.local_response_norm(
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x=input2, size=5, data_format='NHWC'
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)
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in_np1 = np.random.random([3, 3, 40, 40]).astype("float32")
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in_np2 = np.transpose(in_np1, (0, 2, 3, 1))
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exe = base.Executor(place)
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fetches = exe.run(
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paddle.static.default_main_program(),
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feed={"input1": in_np1, "input2": in_np2},
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fetch_list=[res1, res2],
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)
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fetches1_tran = np.transpose(fetches[1], (0, 3, 1, 2))
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np.testing.assert_allclose(fetches[0], fetches1_tran, rtol=1e-05)
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def check_static_5d_input(self, place):
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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input1 = paddle.static.data(
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name="input1", shape=[3, 3, 3, 40, 40], dtype="float32"
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)
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input2 = paddle.static.data(
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name="input2", shape=[3, 3, 40, 40, 3], dtype="float32"
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)
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res1 = paddle.nn.functional.local_response_norm(
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x=input1, size=5, data_format='NCDHW'
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)
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res2 = paddle.nn.functional.local_response_norm(
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x=input2, size=5, data_format='NDHWC'
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)
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in_np1 = np.random.random([3, 3, 3, 40, 40]).astype("float32")
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in_np2 = np.transpose(in_np1, (0, 2, 3, 4, 1))
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exe = base.Executor(place)
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fetches = exe.run(
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paddle.static.default_main_program(),
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feed={"input1": in_np1, "input2": in_np2},
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fetch_list=[res1, res2],
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)
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fetches1_tran = np.transpose(fetches[1], (0, 4, 1, 2, 3))
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np.testing.assert_allclose(fetches[0], fetches1_tran, rtol=1e-05)
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def test_static(self):
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with paddle_static_guard():
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for place in self.places:
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self.check_static_3d_input(place=place)
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self.check_static_4d_input(place=place)
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self.check_static_5d_input(place=place)
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def check_dygraph_3d_input(self, place):
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with base.dygraph.guard(place):
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in_np1 = np.random.random([3, 40, 40]).astype("float32")
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in_np2 = np.transpose(in_np1, (0, 2, 1))
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in1 = paddle.to_tensor(in_np1)
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in2 = paddle.to_tensor(in_np2)
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res1 = paddle.nn.functional.local_response_norm(
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x=in1, size=5, data_format='NCL'
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)
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res2 = paddle.nn.functional.local_response_norm(
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x=in2, size=5, data_format='NLC'
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)
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res2_tran = np.transpose(res2.numpy(), (0, 2, 1))
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np.testing.assert_allclose(res1.numpy(), res2_tran, rtol=1e-05)
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def check_dygraph_4d_input(self, place):
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with base.dygraph.guard(place):
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in_np1 = np.random.random([3, 3, 40, 40]).astype("float32")
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in_np2 = np.transpose(in_np1, (0, 2, 3, 1))
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in1 = paddle.to_tensor(in_np1)
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in2 = paddle.to_tensor(in_np2)
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res1 = paddle.nn.functional.local_response_norm(
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x=in1, size=5, data_format='NCHW'
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)
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res2 = paddle.nn.functional.local_response_norm(
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x=in2, size=5, data_format='NHWC'
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)
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res2_tran = np.transpose(res2.numpy(), (0, 3, 1, 2))
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np.testing.assert_allclose(res1.numpy(), res2_tran, rtol=1e-05)
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def check_dygraph_5d_input(self, place):
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with base.dygraph.guard(place):
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in_np1 = np.random.random([3, 3, 3, 40, 40]).astype("float32")
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in_np2 = np.transpose(in_np1, (0, 2, 3, 4, 1))
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in1 = paddle.to_tensor(in_np1)
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in2 = paddle.to_tensor(in_np2)
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res1 = paddle.nn.functional.local_response_norm(
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x=in1, size=5, data_format='NCDHW'
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)
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res2 = paddle.nn.functional.local_response_norm(
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x=in2, size=5, data_format='NDHWC'
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)
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res2_tran = np.transpose(res2.numpy(), (0, 4, 1, 2, 3))
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np.testing.assert_allclose(res1.numpy(), res2_tran, rtol=1e-05)
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def test_dygraph(self):
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for place in self.places:
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self.check_dygraph_3d_input(place)
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self.check_dygraph_4d_input(place)
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self.check_dygraph_5d_input(place)
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class TestLocalResponseNormFAPIError(unittest.TestCase):
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def test_errors(self):
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with (
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paddle_static_guard(),
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paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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),
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):
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def test_Variable():
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# the input of lrn must be Variable.
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x1 = base.create_lod_tensor(
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np.array([-1, 3, 5, 5]),
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[[1, 1, 1, 1]],
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base.CPUPlace(),
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)
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paddle.nn.functional.local_response_norm(x1, size=5)
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self.assertRaises(TypeError, test_Variable)
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def test_datatype():
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x = paddle.static.data(
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name='x', shape=[3, 4, 5, 6], dtype="int32"
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)
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paddle.nn.functional.local_response_norm(x, size=5)
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self.assertRaises(TypeError, test_datatype)
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def test_dataformat():
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x = paddle.static.data(
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name='x', shape=[3, 4, 5, 6], dtype="float32"
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)
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paddle.nn.functional.local_response_norm(
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x, size=5, data_format="NCTHW"
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)
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self.assertRaises(ValueError, test_dataformat)
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def test_dim():
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x = paddle.static.data(name='x', shape=[3, 4], dtype="float32")
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paddle.nn.functional.local_response_norm(x, size=5)
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self.assertRaises(ValueError, test_dim)
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def test_shape():
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x = paddle.rand(shape=[0, 0, 2, 3], dtype="float32")
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paddle.nn.functional.local_response_norm(x, size=5)
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self.assertRaises(ValueError, test_shape)
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class TestLocalResponseNormCAPI(unittest.TestCase):
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def setUp(self):
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np.random.seed(123)
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self.places = get_places()
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def test_dygraph(self):
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for place in self.places:
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with base.dygraph.guard(place):
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in1 = paddle.rand(shape=(3, 3, 40, 40), dtype="float32")
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in2 = paddle.transpose(in1, [0, 2, 3, 1])
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m1 = paddle.nn.LocalResponseNorm(size=5, data_format='NCHW')
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m2 = paddle.nn.LocalResponseNorm(size=5, data_format='NHWC')
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res1 = m1(in1)
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res2 = m2(in2)
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res2_tran = np.transpose(res2.numpy(), (0, 3, 1, 2))
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np.testing.assert_allclose(res1.numpy(), res2_tran, rtol=1e-05)
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def test_static_fp16_gpu(self):
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if paddle.base.core.is_compiled_with_cuda() or is_custom_device():
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place = get_device_place()
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with (
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paddle_static_guard(),
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paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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),
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):
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input = np.random.random([3, 3, 112, 112]).astype("float16")
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x = paddle.static.data(
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name="x", shape=[3, 3, 112, 112], dtype="float16"
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)
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m = paddle.nn.LocalResponseNorm(size=5)
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y = m(x)
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exe = paddle.static.Executor(place)
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res = exe.run(
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paddle.static.default_main_program(),
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feed={
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"x": input,
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},
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fetch_list=[y],
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)
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np.testing.assert_array_equal(res[0].shape, input.shape)
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class TestLocalResponseNormAPI_ZeroSize(unittest.TestCase):
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def setUp(self):
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np.random.seed(123)
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self.places = get_places()
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def check_dygraph(self, place):
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with base.dygraph.guard(place):
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in_np1 = np.random.random([0, 40, 40]).astype("float32")
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in_np2 = np.transpose(in_np1, (0, 2, 1))
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in1 = paddle.to_tensor(in_np1)
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in1.stop_gradient = False
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in2 = paddle.to_tensor(in_np2)
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res1 = paddle.nn.functional.local_response_norm(
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x=in1, size=5, data_format='NCL'
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)
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res2 = paddle.nn.functional.local_response_norm(
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x=in2, size=5, data_format='NLC'
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)
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res2_tran = np.transpose(res2.numpy(), (0, 2, 1))
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np.testing.assert_allclose(res1.numpy(), res2_tran, rtol=1e-05)
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res1.sum().backward()
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np.testing.assert_allclose(in1.grad.shape, in1.shape)
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def test_dygraph(self):
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for place in self.places:
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self.check_dygraph(place)
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if __name__ == "__main__":
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unittest.main()
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