wehub-resource-sync
615 lines
18 KiB
Python
Executable File
615 lines
18 KiB
Python
Executable File
# 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 platform
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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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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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from paddle.base import core
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from paddle.nn.functional import interpolate
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def create_test_case0(self):
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self.interp_method = 'linear'
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self.input_shape = [1, 3, 100]
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self.out_w = 50
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self.scale = 0.5
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self.out_size = np.array(
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[
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50,
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]
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).astype("int32")
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self.align_corners = False
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self.align_mode = 1
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def linear_interp_test(
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x,
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OutSize=None,
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SizeTensor=None,
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Scale=None,
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data_layout='NCHW',
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out_d=-1,
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out_h=-1,
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out_w=-1,
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scale=[],
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interp_method='linear',
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align_corners=True,
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align_mode=0,
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):
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if isinstance(scale, (float, int)):
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scale_list = []
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for _ in range(len(x.shape) - 2):
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scale_list.append(scale)
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scale = list(map(float, scale_list))
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elif isinstance(scale, (list, tuple)):
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scale = list(map(float, scale))
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if SizeTensor is not None:
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if not isinstance(SizeTensor, list) and not isinstance(
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SizeTensor, tuple
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):
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SizeTensor = [SizeTensor]
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return paddle._C_ops.linear_interp(
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x,
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OutSize,
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SizeTensor,
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Scale,
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data_layout,
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out_d,
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out_h,
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out_w,
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scale,
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interp_method,
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align_corners,
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align_mode,
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)
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def linear_interp_np(
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input,
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out_w,
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scale_w=0,
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out_size=None,
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actual_shape=None,
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align_corners=True,
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align_mode=0,
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data_layout='NCHW',
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):
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if data_layout == "NHWC":
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input = np.transpose(input, (0, 2, 1)) # NHWC => NCHW
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if out_size is not None:
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out_w = out_size[0]
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if actual_shape is not None:
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out_w = actual_shape[0]
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batch_size, channel, in_w = input.shape
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def compute_ratio(in_size, out_size, scale, align_corners):
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if align_corners:
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if out_size <= 1:
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return 0.0
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return (in_size - 1.0) / (out_size - 1.0)
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return 1.0 / scale if scale > 0 else in_size / out_size
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ratio_w = compute_ratio(in_w, out_w, scale_w, align_corners)
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out = np.zeros((batch_size, channel, out_w))
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for j in range(out_w):
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if align_mode == 0 and not align_corners:
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src_w = ratio_w * (j + 0.5) - 0.5
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else:
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src_w = ratio_w * j
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in_img_idx = int(min(max(0, src_w), in_w - 1))
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wid = 1 if in_img_idx < in_w - 1 else 0
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w1lambda = src_w - in_img_idx
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w2lambda = 1.0 - w1lambda
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out[:, :, j] = (
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w2lambda * input[:, :, in_img_idx]
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+ w1lambda * input[:, :, in_img_idx + wid]
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)
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if data_layout == "NHWC":
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out = np.transpose(out, (0, 2, 1)) # NCHW => NHWC
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return out.astype(input.dtype)
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class TestLinearInterpOp(OpTest):
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def setUp(self):
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self.python_api = linear_interp_test
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self.out_size = None
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self.actual_shape = None
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self.data_layout = 'NCHW'
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self.dtype = np.float64
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self.init_test_case()
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self.op_type = "linear_interp_v2"
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input_np = np.random.random(self.input_shape).astype(self.dtype)
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scale_w = 0
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if self.data_layout == "NCHW":
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in_w = self.input_shape[2]
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else:
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in_w = self.input_shape[1]
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if self.scale > 0:
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if isinstance(self.scale, (float, int)):
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self.scale = float(self.scale)
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if isinstance(self.scale, list):
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self.scale = float(self.scale[0])
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out_w = int(in_w * self.scale)
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else:
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out_w = self.out_w
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output_np = linear_interp_np(
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input_np,
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out_w,
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self.scale,
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self.out_size,
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self.actual_shape,
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self.align_corners,
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self.align_mode,
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self.data_layout,
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)
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self.inputs = {'X': input_np}
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if self.out_size is not None:
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self.inputs['OutSize'] = self.out_size
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if self.actual_shape is not None:
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self.inputs['OutSize'] = self.actual_shape
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self.attrs = {
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'out_w': self.out_w,
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'interp_method': self.interp_method,
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'align_corners': self.align_corners,
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'align_mode': self.align_mode,
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'data_layout': self.data_layout,
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}
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if self.scale > 0:
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if isinstance(self.scale, (float, int)):
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self.scale = [float(self.scale)]
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self.attrs['scale'] = self.scale
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self.outputs = {'Out': output_np}
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def test_check_output(self):
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if platform.system() == "Linux":
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self.check_output(atol=1e-7, check_pir=True)
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else:
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self.check_output(atol=1e-5, check_pir=True)
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def test_check_grad(self):
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self.check_grad(['X'], 'Out', in_place=True, check_pir=True)
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def init_test_case(self):
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create_test_case0(self)
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class TestLinearInterpOpDataLayout(TestLinearInterpOp):
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def init_test_case(self):
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self.interp_method = 'linear'
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self.input_shape = [1, 100, 3]
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self.out_w = 50
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self.scale = 0.5
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self.out_size = np.array(
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[
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50,
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]
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).astype("int32")
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self.align_corners = False
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self.align_mode = 1
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self.data_layout = 'NHWC'
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class TestLinearInterpOpAlignMode(TestLinearInterpOp):
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def init_test_case(self):
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self.interp_method = 'linear'
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self.input_shape = [1, 3, 100]
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self.out_w = 50
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self.scale = 0.5
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self.out_size = np.array(
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[
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50,
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]
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).astype("int32")
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self.align_corners = False
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self.align_mode = 0
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class TestLinearInterpOpScale(TestLinearInterpOp):
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def init_test_case(self):
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self.interp_method = 'linear'
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self.input_shape = [1, 3, 100]
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self.out_w = 50
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self.scale = 0.8
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self.out_size = np.array(
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[
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50,
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]
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).astype("int32")
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self.align_corners = False
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self.align_mode = 0
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class TestLinearInterpOpSizeTensor(TestLinearInterpOp):
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def setUp(self):
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self.python_api = linear_interp_test
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self.out_size = None
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self.actual_shape = None
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self.data_layout = 'NCHW'
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self.init_test_case()
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self.op_type = "linear_interp_v2"
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input_np = np.random.random(self.input_shape).astype("float64")
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self.shape_by_1Dtensor = False
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self.scale_by_1Dtensor = False
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if self.data_layout == "NCHW":
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in_w = self.input_shape[2]
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else:
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in_w = self.input_shape[1]
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if self.scale > 0:
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if isinstance(self.scale, (float, int)):
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self.scale = float(self.scale)
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if isinstance(self.scale, list):
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self.scale = float(self.scale[0])
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out_w = int(in_w * self.scale)
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else:
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out_w = self.out_w
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output_np = linear_interp_np(
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input_np,
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out_w,
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0,
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self.out_size,
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self.actual_shape,
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self.align_corners,
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self.align_mode,
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self.data_layout,
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)
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self.inputs = {'X': input_np}
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if self.out_size is not None and self.shape_by_1Dtensor:
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self.inputs['OutSize'] = self.out_size
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elif self.actual_shape is not None and self.shape_by_1Dtensor:
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self.inputs['OutSize'] = self.actual_shape
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else:
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size_tensor = []
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for index, ele in enumerate(self.out_size):
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size_tensor.append(
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("x" + str(index), np.ones(1).astype('int32') * ele)
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)
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self.inputs['SizeTensor'] = size_tensor
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self.attrs = {
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'out_w': self.out_w,
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'interp_method': self.interp_method,
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'align_corners': self.align_corners,
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'align_mode': self.align_mode,
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'data_layout': self.data_layout,
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}
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if self.scale > 0:
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if isinstance(self.scale, (float, int)):
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self.scale = [self.scale]
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if isinstance(self.scale, list) and len(self.scale) == 1:
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self.scale = [self.scale[0], self.scale[0]]
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self.attrs['scale'] = self.scale
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self.outputs = {'Out': output_np}
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def test_check_output(self):
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if platform.system() == "Linux":
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self.check_output(atol=1e-7, check_pir=False)
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else:
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self.check_output(atol=1e-5, check_pir=False)
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def test_check_grad(self):
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self.check_grad(['X'], 'Out', in_place=True, check_pir=True)
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class TestLinearInterpOpAPI2_0(unittest.TestCase):
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def test_case(self):
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# dygraph
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x_data = np.random.random((1, 3, 128)).astype("float32")
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us_1 = paddle.nn.Upsample(
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size=[64],
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mode='linear',
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align_mode=1,
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align_corners=False,
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)
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with base.dygraph.guard():
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x = paddle.to_tensor(x_data)
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interp = us_1(x)
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expect = linear_interp_np(
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x_data, out_w=64, align_mode=1, align_corners=False
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)
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np.testing.assert_allclose(interp.numpy(), expect, rtol=1e-05)
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class TestLinearInterpOpAPI2_0_case2(unittest.TestCase):
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def test_case(self):
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# dygraph
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x_data = np.random.random((1, 3, 128)).astype("float32")
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with base.dygraph.guard():
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x = paddle.to_tensor(x_data)
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interp = interpolate(
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x,
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size=[64],
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mode='linear',
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align_mode=1,
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align_corners=False,
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)
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expect = linear_interp_np(
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x_data, out_w=64, align_mode=1, align_corners=False
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)
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np.testing.assert_allclose(interp.numpy(), expect, rtol=1e-05)
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class TestLinearInterpOpFP16(TestLinearInterpOp):
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def test_check_output(self):
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self.check_output(atol=1e-3, check_pir=True)
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def test_check_grad(self):
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self.check_grad(
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['X'], 'Out', in_place=True, max_relative_error=1e-2, check_pir=True
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)
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def init_test_case(self):
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create_test_case0(self)
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self.dtype = np.float16
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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 the bfloat16",
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)
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class TestLinearInterpOpBF16(OpTest):
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def setUp(self):
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self.python_api = linear_interp_test
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self.out_size = None
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self.actual_shape = None
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self.data_layout = 'NCHW'
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self.init_test_case()
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self.op_type = "linear_interp_v2"
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self.dtype = np.uint16
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input_np = np.random.random(self.input_shape).astype("float32")
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scale_w = 0
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if self.data_layout == "NCHW":
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in_w = self.input_shape[2]
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else:
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in_w = self.input_shape[1]
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if self.scale > 0:
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if isinstance(self.scale, (float, int)):
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self.scale = float(self.scale)
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if isinstance(self.scale, list):
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self.scale = float(self.scale[0])
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out_w = int(in_w * self.scale)
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else:
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out_w = self.out_w
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output_np = linear_interp_np(
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input_np,
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out_w,
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self.scale,
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self.out_size,
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self.actual_shape,
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self.align_corners,
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self.align_mode,
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self.data_layout,
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)
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self.inputs = {'X': convert_float_to_uint16(input_np)}
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if self.out_size is not None:
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self.inputs['OutSize'] = self.out_size
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if self.actual_shape is not None:
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self.inputs['OutSize'] = self.actual_shape
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self.attrs = {
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'out_w': self.out_w,
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'interp_method': self.interp_method,
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'align_corners': self.align_corners,
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'align_mode': self.align_mode,
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'data_layout': self.data_layout,
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}
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if self.scale > 0:
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if isinstance(self.scale, (float, int)):
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self.scale = [float(self.scale)]
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self.attrs['scale'] = self.scale
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self.outputs = {'Out': convert_float_to_uint16(output_np)}
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def test_check_output(self):
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place = get_device_place()
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self.check_output_with_place(place, atol=1e-2, check_pir=True)
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def test_check_grad(self):
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place = get_device_place()
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self.check_grad_with_place(
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place,
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['X'],
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'Out',
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in_place=True,
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max_relative_error=1e-2,
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check_pir=True,
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)
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def init_test_case(self):
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create_test_case0(self)
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class TestResizeLinearOpUint8(OpTest):
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def setUp(self):
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self.out_size = None
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self.actual_shape = None
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self.init_test_case()
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self.op_type = "linear_interp_v2"
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self.python_api = linear_interp_test
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input_np = np.random.random(self.input_shape).astype("uint8")
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if self.scale > 0:
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if isinstance(self.scale, (float, int)):
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self.scale = float(self.scale)
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if isinstance(self.scale, list):
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self.scale = float(self.scale[0])
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out_w = int(self.input_shape[2] * self.scale)
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else:
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out_w = self.out_w
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output_np = linear_interp_np(
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input_np,
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out_w,
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0,
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self.out_size,
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self.actual_shape,
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self.align_corners,
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self.align_mode,
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)
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self.inputs = {'X': input_np}
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if self.out_size is not None:
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self.inputs['OutSize'] = self.out_size
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self.attrs = {
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'out_w': self.out_w,
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'interp_method': self.interp_method,
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'align_corners': self.align_corners,
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'align_mode': self.align_mode,
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}
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if self.scale > 0:
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if isinstance(self.scale, (float, int)):
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self.scale = [self.scale]
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if isinstance(self.scale, list) and len(self.scale) == 1:
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self.scale = [self.scale[0], self.scale[0]]
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self.attrs['scale'] = self.scale
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self.outputs = {'Out': output_np}
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def test_check_output(self):
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if platform.system() == "Linux":
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self.check_output_with_place(
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place=core.CPUPlace(), atol=1e-7, check_pir=True
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)
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else:
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self.check_output_with_place(
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place=core.CPUPlace(), atol=1e-5, check_pir=True
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)
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def init_test_case(self):
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self.interp_method = 'linear'
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self.input_shape = [2, 3, 100]
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self.out_w = 50
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self.scale = 0.0
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self.out_size = np.array(
|
|
[
|
|
50,
|
|
]
|
|
).astype("int32")
|
|
self.align_corners = True
|
|
self.align_mode = 1
|
|
|
|
|
|
class TestLinearInterpOpError(unittest.TestCase):
|
|
def test_error(self):
|
|
with (
|
|
paddle_static_guard(),
|
|
paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
),
|
|
):
|
|
|
|
def input_shape_error():
|
|
x1 = paddle.static.data(name="x1", shape=[1], dtype="float32")
|
|
out1 = paddle.nn.Upsample(
|
|
size=[256], data_format='NCW', mode='linear'
|
|
)
|
|
out1_res = out1(x1)
|
|
|
|
def data_format_error():
|
|
x2 = paddle.static.data(
|
|
name="x2", shape=[1, 3, 128], dtype="float32"
|
|
)
|
|
out2 = paddle.nn.Upsample(
|
|
size=[256], data_format='NHWCD', mode='linear'
|
|
)
|
|
out2_res = out2(x2)
|
|
|
|
def out_shape_error():
|
|
x3 = paddle.static.data(
|
|
name="x3", shape=[1, 3, 128], dtype="float32"
|
|
)
|
|
out3 = paddle.nn.Upsample(
|
|
size=[256, 256], data_format='NHWC', mode='linear'
|
|
)
|
|
out3_res = out3(x3)
|
|
|
|
self.assertRaises(ValueError, input_shape_error)
|
|
self.assertRaises(ValueError, data_format_error)
|
|
self.assertRaises(ValueError, out_shape_error)
|
|
|
|
|
|
@unittest.skipIf(
|
|
not (base.core.is_compiled_with_cuda() or is_custom_device()),
|
|
"core is not compiled with CUDA",
|
|
)
|
|
class TestLinearInterpOpForFloat16(unittest.TestCase):
|
|
def init_test_case(self):
|
|
self.interp_method = 'linear'
|
|
self.input_shape = [1, 3, 64]
|
|
self.scale = 2
|
|
self.align_corners = False
|
|
self.align_mode = 1
|
|
self.data_layout = 'NCW'
|
|
|
|
def check_main(self, x_np, dtype):
|
|
paddle.disable_static()
|
|
x_np = x_np.astype(dtype)
|
|
x = paddle.to_tensor(x_np)
|
|
x.stop_gradient = False
|
|
y = interpolate(
|
|
x,
|
|
scale_factor=self.scale,
|
|
mode=self.interp_method,
|
|
align_mode=self.align_mode,
|
|
align_corners=self.align_corners,
|
|
data_format=self.data_layout,
|
|
)
|
|
x_g = paddle.grad(y, x)
|
|
y_np = y[0].numpy().astype('float32')
|
|
x_g_np = x_g[0].numpy().astype('float32')
|
|
paddle.enable_static()
|
|
return y_np, x_g_np
|
|
|
|
def test_main(self):
|
|
self.init_test_case()
|
|
x_np = np.random.random(self.input_shape).astype("float16")
|
|
|
|
y_np_1, x_g_np_1 = self.check_main(x_np, 'float16')
|
|
y_np_2, x_g_np_2 = self.check_main(x_np, 'float32')
|
|
# forward
|
|
np.testing.assert_allclose(y_np_1, y_np_2, rtol=1e-03)
|
|
# backward
|
|
np.testing.assert_allclose(x_g_np_1, x_g_np_2)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|