wehub-resource-sync
535 lines
16 KiB
Python
535 lines
16 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 op_test import OpTest
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import paddle
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paddle.enable_static()
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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(out_h),
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range(out_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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def deform_conv2d_wrapper(
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x,
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offset,
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weight,
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mask=None,
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stride=1,
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padding=0,
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dilation=1,
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deformable_groups=1,
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groups=1,
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im2col_step=1,
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):
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return paddle.vision.ops.deform_conv2d(
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x,
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offset,
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weight,
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None,
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stride,
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padding,
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dilation,
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deformable_groups,
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groups,
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mask,
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)
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class TestModulatedDeformableConvOp(OpTest):
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def setUp(self):
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self.python_api = deform_conv2d_wrapper
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self.op_type = "deformable_conv"
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self.init_type()
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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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self.check_output(check_pir=True)
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def test_check_grad(self):
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self.check_grad(
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{'Input', 'Offset', 'Mask', 'Filter'},
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'Output',
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max_relative_error=0.05,
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check_pir=True,
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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 = [4, 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 * self.filter_size[2] * 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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def init_type(self):
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self.dtype = np.float32
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class TestWithStride(TestModulatedDeformableConvOp):
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def init_test_case(self):
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self.pad = [3, 3]
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self.stride = [2, 2]
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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 * self.filter_size[2] * 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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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 * self.filter_size[2] * 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 * self.filter_size[2] * 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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class TestWithGroup(TestModulatedDeformableConvOp):
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def init_group(self):
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self.groups = 2
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class TestWithDouble(TestModulatedDeformableConvOp):
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def init_type(self):
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self.dtype = np.float64
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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, 6, 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 = [4, 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 * self.filter_size[2] * 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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class TestModulatedDeformableConvInvalidInput(unittest.TestCase):
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def test_error_api(self):
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def test_invalid_input():
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paddle.enable_static()
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input = [1, 3, 32, 32]
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offset = paddle.static.data(
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name='offset', shape=[None, 3, 32, 32], dtype='float32'
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)
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mask = paddle.static.data(
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name='mask', shape=[None, 3, 32, 32], dtype='float32'
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)
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loss = paddle.vision.ops.DeformConv2D(
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in_channels=input[1], out_channels=4, kernel_size=1
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)(input, offset, mask)
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self.assertRaises(TypeError, test_invalid_input)
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def test_invalid_offset():
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paddle.enable_static()
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input = paddle.static.data(
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name='input', shape=[None, 3, 32, 32], dtype='int32'
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)
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offset = paddle.static.data(
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name='offset', shape=[None, 3, 32, 32], dtype='float32'
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)
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mask = paddle.static.data(
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name='mask', shape=[None, 3, 32, 32], dtype='float32'
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)
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loss = paddle.vision.ops.DeformConv2D(
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in_channels=input.shape[1], out_channels=4, kernel_size=1
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)(input, offset, mask)
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self.assertRaises(TypeError, test_invalid_offset)
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def test_invalid_groups():
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paddle.enable_static()
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input = paddle.static.data(
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name='input_groups', shape=[1, 1, 1, 1], dtype='float32'
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)
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offset = paddle.static.data(
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name='offset_groups', shape=[1, 1], dtype='float32'
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)
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mask = paddle.static.data(
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name='mask_groups', shape=[1], dtype='float32'
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)
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loss = paddle.vision.ops.DeformConv2D(
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in_channels=input.shape[1],
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out_channels=1,
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kernel_size=1,
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padding=1,
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groups=0,
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)(input, offset, mask)
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self.assertRaises(ZeroDivisionError, test_invalid_groups)
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class TestDeformConv2DAPI(unittest.TestCase):
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def test_api(self):
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def test_deform_conv2d_v1():
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paddle.enable_static()
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input = paddle.static.data(
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name='input_v1', shape=[None, 3, 32, 32], dtype='float32'
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)
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offset = paddle.static.data(
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name='offset_v1', shape=[None, 4, 32, 32], dtype='float32'
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)
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out = paddle.vision.ops.DeformConv2D(
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in_channels=input.shape[1], out_channels=4, kernel_size=1
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)(input, offset, None)
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assert tuple(out.shape) == (-1, 4, 32, 32)
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test_deform_conv2d_v1()
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def test_deform_conv2d_v2():
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paddle.enable_static()
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input = paddle.static.data(
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name='input_v2', shape=[None, 3, 32, 32], dtype='float32'
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)
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offset = paddle.static.data(
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name='offset_v2', shape=[None, 4, 32, 32], dtype='float32'
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)
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mask = paddle.static.data(
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name='mask_v2', shape=[None, 2, 32, 32], dtype='float32'
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)
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out = paddle.vision.ops.DeformConv2D(
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in_channels=input.shape[1], out_channels=4, kernel_size=1
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)(input, offset, mask)
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assert tuple(out.shape) == (-1, 4, 32, 32)
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test_deform_conv2d_v2()
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class TestModulatedDeformableConvOp_ZeroSize(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.dilations = [1, 1]
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# 0-size
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self.input_size = [0, 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 = [4, 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 * self.filter_size[2] * 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 = [
|
|
self.input_size[0],
|
|
mask_c,
|
|
self.input_size[2],
|
|
self.input_size[3],
|
|
]
|
|
|
|
|
|
class TestDeformConv2DAPI_CPU_FP16(unittest.TestCase):
|
|
def setUp(self):
|
|
self.padding = [1, 1]
|
|
self.stride = [1, 1]
|
|
self.dilation = [1, 1]
|
|
self.groups = 1
|
|
self.data_format = "NCL"
|
|
|
|
def test_cpu_fp16(self):
|
|
with paddle.base.dygraph.guard(paddle.CPUPlace()):
|
|
x = paddle.ones([4, 5, 5, 5])
|
|
offset = paddle.ones([4, 90, 5, 5]).astype(paddle.float16)
|
|
weight = paddle.ones([5, 5, 3, 3]).astype(paddle.float16)
|
|
bias = paddle.ones([5]).astype(paddle.float16)
|
|
mask = paddle.ones([4, 45, 5, 5]).astype(paddle.float16)
|
|
|
|
# If there is an error, an error will be thrown.
|
|
out = paddle.vision.ops.deform_conv2d(
|
|
x,
|
|
offset,
|
|
weight,
|
|
bias,
|
|
stride=self.stride,
|
|
padding=self.padding,
|
|
dilation=self.dilation,
|
|
groups=self.groups,
|
|
deformable_groups=5,
|
|
mask=mask,
|
|
)
|
|
np.testing.assert_allclose(out.shape, [4, 5, 5, 5])
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|