wehub-resource-sync
293 lines
9.5 KiB
Python
293 lines
9.5 KiB
Python
# Copyright (c) 2019 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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from itertools import product
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import numpy as np
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from get_test_cover_info import (
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XPUOpTestWrapper,
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create_test_class,
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get_xpu_op_support_types,
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)
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from op_test_xpu import OpTest, XPUOpTest
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import paddle
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from paddle.base import core
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def dmc_bilinear(data_im, height, width, h, w):
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h_low = int(np.floor(h))
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w_low = int(np.floor(w))
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h_high = h_low + 1
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w_high = w_low + 1
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lh = h - h_low
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lw = w - w_low
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hh = 1 - lh
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hw = 1 - lw
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v1 = 0
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if h_low >= 0 and w_low >= 0:
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v1 = data_im[h_low, w_low]
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v2 = 0
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if h_low >= 0 and w_high <= width - 1:
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v2 = data_im[h_low, w_high]
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v3 = 0
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if h_high <= height - 1 and w_low >= 0:
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v3 = data_im[h_high, w_low]
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v4 = 0
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if h_high <= height - 1 and w_high <= width - 1:
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v4 = data_im[h_high, w_high]
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w1, w2, w3, w4 = hh * hw, hh * lw, lh * hw, lh * lw
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val = w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4
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return val
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def dconv_im2col_gemm(input, offset, mask, filter, group, conv_param):
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in_n, in_c, in_h, in_w = input.shape
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out_c, f_c, f_h, f_w = filter.shape
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assert offset.shape == (in_n, 2 * f_h * f_w, in_h, in_w)
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assert mask.shape == (in_n, f_h * f_w, in_h, in_w)
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assert f_c * group == in_c
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assert np.mod(out_c, group) == 0
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stride, pad, dilation = (
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conv_param['stride'],
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conv_param['pad'],
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conv_param['dilation'],
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)
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out_h = 1 + (in_h + 2 * pad[0] - (dilation[0] * (f_h - 1) + 1)) // stride[0]
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out_w = 1 + (in_w + 2 * pad[1] - (dilation[1] * (f_w - 1) + 1)) // stride[1]
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assert out_h == in_h
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assert out_w == in_w
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col_buffer = np.zeros((in_n, in_c * f_h * f_w, in_h * in_w))
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for n, c, h, w, kh, kw in product(
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range(in_n),
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range(in_c),
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range(in_h),
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range(in_w),
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range(f_h),
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range(f_w),
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):
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offset_h_table = offset[n, ::2, h, w].reshape(f_h, f_w)
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offset_w_table = offset[n, 1::2, h, w].reshape(f_h, f_w)
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mask_table = mask[n, :, h, w].reshape(f_h, f_w)
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offset_h = offset_h_table[kh, kw]
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offset_w = offset_w_table[kh, kw]
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val = 0
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im_h = h * stride[0] + kh * dilation[0] + offset_h - pad[0]
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im_w = w * stride[0] + kw * dilation[0] + offset_w - pad[1]
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if im_h > -1 and im_w > -1 and im_h < in_h and im_w < in_h:
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val = dmc_bilinear(input[n, c], in_h, in_w, im_h, im_w)
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val_out = val * mask_table[kh, kw]
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col_buffer[n, c * f_h * f_w + kh * f_w + kw, h * in_w + w] = val_out
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out = np.zeros((in_n, group, int(out_c // group), out_h * out_w))
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weight = filter.reshape(group, int(out_c // group), f_c * f_h * f_w)
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col_buffer = col_buffer.reshape(
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(in_n, group, int(in_c // group * f_h * f_w), in_h * in_w)
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)
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for n in range(in_n):
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for g in range(group):
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out[n, g] = np.matmul(weight[g], col_buffer[n, g])
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out = out.reshape(in_n, out_c, out_h, out_w)
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return out
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class XPUTestModulatedDeformableConvOp(XPUOpTestWrapper):
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def __init__(self):
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self.op_name = 'deformable_conv'
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self.use_dynamic_create_class = False
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class TestModulatedDeformableConvOp(XPUOpTest):
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def setUp(self):
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self.op_type = "deformable_conv"
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# set to e-6 because of atomic add in XPU
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self.epsilon_xpu2xpu = 0.000001
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self.dtype = self.in_type
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self.place = paddle.XPUPlace(0)
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self.init_group()
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self.init_dilation()
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self.init_test_case()
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conv_param = {
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'stride': self.stride,
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'pad': self.pad,
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'dilation': self.dilations,
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}
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input = np.random.random(self.input_size).astype(self.dtype)
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offset = 10 * np.random.random(self.offset_size).astype(self.dtype)
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mask = 10 * np.random.random(self.mask_size).astype(self.dtype)
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filter = np.random.random(self.filter_size).astype(self.dtype)
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output = dconv_im2col_gemm(
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input, offset, mask, filter, self.groups, conv_param
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)
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output = output.astype(self.dtype)
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self.inputs = {
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'Input': OpTest.np_dtype_to_base_dtype(input),
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'Offset': OpTest.np_dtype_to_base_dtype(offset),
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'Mask': OpTest.np_dtype_to_base_dtype(mask),
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'Filter': OpTest.np_dtype_to_base_dtype(filter),
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}
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self.attrs = {
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'strides': self.stride,
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'paddings': self.pad,
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'groups': self.groups,
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'deformable_groups': self.deformable_groups,
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'im2col_step': self.im2col_step,
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'dilations': self.dilations,
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}
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self.outputs = {'Output': output}
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def test_check_output(self):
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if core.is_compiled_with_xpu():
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paddle.enable_static()
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self.check_output_with_place(self.place)
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def test_check_grad(self):
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if core.is_compiled_with_xpu():
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paddle.enable_static()
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self.check_grad_with_place(
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self.place,
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{'Input', 'Offset', 'Mask', 'Filter'},
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'Output',
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max_relative_error=0.06,
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)
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def init_test_case(self):
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self.pad = [1, 1]
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self.stride = [1, 1]
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self.dilations = [1, 1]
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self.input_size = [2, 8, 4, 4] # NCHW
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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 = [8, f_c, 3, 3]
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self.im2col_step = 1
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self.deformable_groups = 1
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offset_c = (
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2
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* self.deformable_groups
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* self.filter_size[2]
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* self.filter_size[3]
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)
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mask_c = (
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self.deformable_groups
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* self.filter_size[2]
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* self.filter_size[3]
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)
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self.offset_size = [
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self.input_size[0],
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offset_c,
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self.input_size[2],
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self.input_size[3],
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]
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self.mask_size = [
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self.input_size[0],
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mask_c,
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self.input_size[2],
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self.input_size[3],
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]
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def init_dilation(self):
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self.dilations = [1, 1]
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def init_group(self):
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self.groups = 1
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class TestWithDilation(TestModulatedDeformableConvOp):
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def init_test_case(self):
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self.pad = [2, 2]
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self.stride = [1, 1]
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self.input_size = [4, 3, 4, 4] # NCHW
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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 = [6, f_c, 3, 3]
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self.im2col_step = 1
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self.deformable_groups = 1
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offset_c = (
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2
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* self.deformable_groups
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* self.filter_size[2]
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* self.filter_size[3]
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)
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mask_c = (
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self.deformable_groups
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* self.filter_size[2]
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* self.filter_size[3]
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)
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self.offset_size = [
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self.input_size[0],
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offset_c,
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self.input_size[2],
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self.input_size[3],
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]
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self.mask_size = [
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self.input_size[0],
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mask_c,
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self.input_size[2],
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self.input_size[3],
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]
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def init_dilation(self):
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self.dilations = [2, 2]
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class TestWith3x3(TestModulatedDeformableConvOp):
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def init_test_case(self):
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self.pad = [1, 1]
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self.stride = [1, 1]
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self.input_size = [2, 3, 5, 5] # NCHW
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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 = [6, f_c, 3, 3]
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self.im2col_step = 1
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self.deformable_groups = 1
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offset_c = (
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2
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* self.deformable_groups
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* self.filter_size[2]
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* self.filter_size[3]
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)
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mask_c = (
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self.deformable_groups
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* self.filter_size[2]
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* self.filter_size[3]
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)
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self.offset_size = [
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self.input_size[0],
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offset_c,
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self.input_size[2],
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self.input_size[3],
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]
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self.mask_size = [
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self.input_size[0],
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mask_c,
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self.input_size[2],
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self.input_size[3],
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]
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support_types = get_xpu_op_support_types('deformable_conv')
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for stype in support_types:
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create_test_class(globals(), XPUTestModulatedDeformableConvOp, stype)
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if __name__ == '__main__':
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unittest.main()
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