roi/ccv_nnc_roi_align_cpu_ref.c

Bug Summary

File:	nnc/cmd/roi/ccv_nnc_roi_align_cpu_ref.c
Warning:	line 158, column 10 Assigned value is garbage or undefined

Annotated Source Code

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clang -cc1 -cc1 -triple x86_64-unknown-linux-gnu -analyze -disable-free -clear-ast-before-backend -disable-llvm-verifier -discard-value-names -main-file-name ccv_nnc_roi_align_cpu_ref.c -analyzer-checker=core -analyzer-checker=apiModeling -analyzer-checker=unix -analyzer-checker=deadcode -analyzer-checker=security.insecureAPI.UncheckedReturn -analyzer-checker=security.insecureAPI.getpw -analyzer-checker=security.insecureAPI.gets -analyzer-checker=security.insecureAPI.mktemp -analyzer-checker=security.insecureAPI.mkstemp -analyzer-checker=security.insecureAPI.vfork -analyzer-checker=nullability.NullPassedToNonnull -analyzer-checker=nullability.NullReturnedFromNonnull -analyzer-output plist -w -setup-static-analyzer -mrelocation-model pic -pic-level 2 -pic-is-pie -mframe-pointer=none -fmath-errno -ffp-contract=on -fno-rounding-math -mconstructor-aliases -funwind-tables=2 -target-cpu x86-64 -target-feature +sse2 -tune-cpu generic -debugger-tuning=gdb -fdebug-compilation-dir=/home/liu/actions-runner/_work/ccv/ccv/lib/nnc/cmd -fcoverage-compilation-dir=/home/liu/actions-runner/_work/ccv/ccv/lib/nnc/cmd -resource-dir /usr/local/lib/clang/18 -I ../../ -I .. -I /usr/local/cuda/include -D HAVE_CBLAS -D HAVE_LIBPNG -D HAVE_LIBJPEG -D HAVE_FFTW3 -D HAVE_PTHREAD -D HAVE_LIBLINEAR -D HAVE_TESSERACT -D HAVE_AVCODEC -D HAVE_AVFORMAT -D HAVE_AVUTIL -D HAVE_SWSCALE -D HAVE_SSE2 -D HAVE_GSL -D HAVE_CUDA -D HAVE_CUDNN -D HAVE_NCCL -D USE_SYSTEM_CUB -D HAVE_CUDA_SM80 -I /usr/local/include -internal-isystem /usr/local/lib/clang/18/include -internal-isystem /usr/local/include -internal-isystem /usr/lib/gcc/x86_64-linux-gnu/12/../../../../x86_64-linux-gnu/include -internal-externc-isystem /usr/include/x86_64-linux-gnu -internal-externc-isystem /include -internal-externc-isystem /usr/include -O3 -ferror-limit 19 -fgnuc-version=4.2.1 -fskip-odr-check-in-gmf -vectorize-loops -vectorize-slp -analyzer-output=html -faddrsig -D__GCC_HAVE_DWARF2_CFI_ASM=1 -o /home/liu/actions-runner/_work/ccv/ccv/_analyze/2024-06-11-102135-38375-1 -x c roi/ccv_nnc_roi_align_cpu_ref.c

1#include "ccv.h"
2#include "ccv_internal.h"
3#include "nnc/ccv_nnc.h"
4#include "nnc/ccv_nnc_easy.h"
5#include "nnc/ccv_nnc_internal.h"
6#ifdef USE_OPENMP
7#include <omp.h>
8#endif
9#ifdef USE_DISPATCH
10#include <dispatch/dispatch.h>
11#endif
12 
13typedef struct {
14	int i0, i1, mute;
15	float r;
16} roi_align_coeffs_t;
17 
18static void _ccv_nnc_bilinear_coeffs(ccv_nnc_stream_context_t* const stream_context, const int h, const int w, const float roi_y, const float roi_x, const float roi_h, const float roi_w, const int pool_h, const int pool_w, int* const bin_h_ref, int* const bin_w_ref, roi_align_coeffs_t** const y_coeffs_ref, roi_align_coeffs_t** const x_coeffs_ref, int** const bin_h_at_y_ref, int** const bin_w_at_x_ref, int* const start_h_ref, int* const start_w_ref, int* const end_h_ref, int* const end_w_ref)
19{
20	const int bin_h = (int)ceilf(roi_h / pool_h); // How many bins in each point of the pool. We slightly sampling at higher resolution (due to ceiling) with bilinear interpolation.
21	const int bin_w = (int)ceilf(roi_w / pool_w);
22	const int bin_pool_h = bin_h * pool_h; // Before averaging, what's the size of the region in integral term.
23	const int bin_pool_w = bin_w * pool_w;
24	const float scale_y = roi_h / bin_pool_h; // The scale to multiply back to get original coordinate.
25	const float scale_x = roi_w / bin_pool_w;
26	int x, y, i, j;
27	roi_align_coeffs_t* const y_coeffs = (roi_align_coeffs_t*)ccv_nnc_stream_context_get_workspace(stream_context, sizeof(roi_align_coeffs_t) * (bin_pool_h + bin_pool_w) + sizeof(int) * (pool_h + pool_w), CCV_TENSOR_CPU_MEMORY);
28	roi_align_coeffs_t* const x_coeffs = y_coeffs + bin_pool_h;
29	int* const bin_h_at_y = (int*)(x_coeffs + bin_pool_w);
30	int* const bin_w_at_x = bin_h_at_y + pool_h;
31	for (i = 0; i < pool_h; i++)
32	{
33		const int pi = i * bin_h;
34		int count = 0;
35		for (y = 0; y < bin_h; y++)
36		{
37			const float ay = roi_y + (y + pi + 0.5) * scale_y - 0.5;
38			const int iy = (int)floorf(ay);
39			const float ry = ay - iy;
40			const int iy0 = ccv_clamp(iy, 0, h - 1)({ typeof (0) _a = (0); typeof (h - 1) _b = (h - 1); typeof (
iy) _x = (iy); (_x < _a) ? _a : ((_x > _b) ? _b : _x); }
);
41			const int iy1 = ccv_clamp(iy + 1, 0, h - 1)({ typeof (0) _a = (0); typeof (h - 1) _b = (h - 1); typeof (
iy + 1) _x = (iy + 1); (_x < _a) ? _a : ((_x > _b) ? _b
 : _x); });
42			y_coeffs[pi + y].i0 = iy0;
43			y_coeffs[pi + y].i1 = iy1;
44			y_coeffs[pi + y].r = ry;
45			const int mute = (iy + 1 < 0 || iy > h - 1);
46			y_coeffs[pi + y].mute = mute;
47			if (!mute)
48				++count;
49		}
50		bin_h_at_y[i] = count;
51	}
52	int start_h = pool_h;
53	for (i = 0; start_h == pool_h && i < pool_h; i++)
54		if (bin_h_at_y[i] > 0)
55			start_h = i;
56	int end_h = 0;
57	for (i = pool_h - 1; end_h == 0 && i >= 0; i--)
58		if (bin_h_at_y[i] > 0)
59			end_h = i + 1;
60	for (j = 0; j < pool_w; j++)
61	{
62		const int pj = j * bin_w;
63		int count = 0;
64		for (x = 0; x < bin_w; x++)
65		{
66			const float ax = roi_x + (x + pj + 0.5) * scale_x - 0.5;
67			const int ix = (int)floorf(ax);
68			const float rx = ax - ix;
69			const int ix0 = ccv_clamp(ix, 0, w - 1)({ typeof (0) _a = (0); typeof (w - 1) _b = (w - 1); typeof (
ix) _x = (ix); (_x < _a) ? _a : ((_x > _b) ? _b : _x); }
);
70			const int ix1 = ccv_clamp(ix + 1, 0, w - 1)({ typeof (0) _a = (0); typeof (w - 1) _b = (w - 1); typeof (
ix + 1) _x = (ix + 1); (_x < _a) ? _a : ((_x > _b) ? _b
 : _x); });
71			x_coeffs[pj + x].i0 = ix0;
72			x_coeffs[pj + x].i1 = ix1;
73			x_coeffs[pj + x].r = rx;
74			const int mute = (ix + 1 < 0 || ix > w - 1);
75			x_coeffs[pj + x].mute = mute;
76			if (!mute)
77				++count;
78		}
79		bin_w_at_x[j] = count;
80	}
81	int start_w = pool_w;
82	for (j = 0; start_w == pool_w && j < pool_w; j++)
83		if (bin_w_at_x[j] > 0)
84			start_w = j;
85	int end_w = 0;
86	for (j = pool_w - 1; end_w == 0 && j >= 0; j--)
87		if (bin_w_at_x[j] > 0)
88			end_w = j + 1;
89	*bin_h_ref = bin_h;
90	*bin_w_ref = bin_w;
91	*y_coeffs_ref = y_coeffs;
92	*x_coeffs_ref = x_coeffs;
93	*bin_h_at_y_ref = bin_h_at_y;
94	*bin_w_at_x_ref = bin_w_at_x;
95	*start_h_ref = start_h;
96	*start_w_ref = start_w;
97	*end_h_ref = end_h;
98	*end_w_ref = end_w;
99}
100 
101static int _ccv_nnc_roi_align_forw(const ccv_nnc_cmd_t cmd, const ccv_nnc_hint_t hint, const int flags, ccv_nnc_tensor_t* const* const inputs, const int input_size, ccv_nnc_tensor_t* const* const outputs, const int output_size, ccv_nnc_stream_context_t* const stream_context)
102{
103	assert(input_size == 2)((void) sizeof ((input_size == 2) ? 1 : 0), __extension__ ({ if
 (input_size == 2) ; else __assert_fail ("input_size == 2", "roi/ccv_nnc_roi_align_cpu_ref.c"
, 103, __extension__ __PRETTY_FUNCTION__); }));
1
Assuming 'input_size' is equal to 2→
2
←
Taking true branch→
104	const ccv_nnc_tensor_view_t* a = (ccv_nnc_tensor_view_t*)inputs[0];
105	assert(output_size == 1)((void) sizeof ((output_size == 1) ? 1 : 0), __extension__ ({
 if (output_size == 1) ; else __assert_fail ("output_size == 1"
, "roi/ccv_nnc_roi_align_cpu_ref.c", 105, __extension__ __PRETTY_FUNCTION__
); }));
3
←
Assuming 'output_size' is equal to 1→
4
←
Taking true branch→
106	const ccv_nnc_tensor_view_t* b = (ccv_nnc_tensor_view_t*)inputs[1];
107	ccv_nnc_tensor_view_t* c = (ccv_nnc_tensor_view_t*)outputs[0];
108	const int a_nd = ccv_nnc_tensor_nd(a->info.dim);
109	assert(a_nd == CCV_NNC_MAX_DIM + 1 || a_nd == CCV_NNC_MAX_DIM + 2)((void) sizeof ((a_nd == (2) + 1 || a_nd == (2) + 2) ? 1 : 0)
, __extension__ ({ if (a_nd == (2) + 1 || a_nd == (2) + 2) ; else
 __assert_fail ("a_nd == CCV_NNC_MAX_DIM + 1 || a_nd == CCV_NNC_MAX_DIM + 2"
, "roi/ccv_nnc_roi_align_cpu_ref.c", 109, __extension__ __PRETTY_FUNCTION__
); }));
5
←
Assuming the condition is true→
110	const int* adim = (a_nd == CCV_NNC_MAX_DIM(2) + 1) ? a->info.dim : a->info.dim + 1;
6
←
'?' condition is true→
111	const int h = adim[0];
112	const int w = adim[1];
113	const int c_nd = ccv_nnc_tensor_nd(c->info.dim);
114	assert(c_nd == CCV_NNC_MAX_DIM + 1 || c_nd == CCV_NNC_MAX_DIM + 2)((void) sizeof ((c_nd == (2) + 1 || c_nd == (2) + 2) ? 1 : 0)
, __extension__ ({ if (c_nd == (2) + 1 || c_nd == (2) + 2) ; else
 __assert_fail ("c_nd == CCV_NNC_MAX_DIM + 1 || c_nd == CCV_NNC_MAX_DIM + 2"
, "roi/ccv_nnc_roi_align_cpu_ref.c", 114, __extension__ __PRETTY_FUNCTION__
); }));
7
←
Assuming the condition is true→
115	const int* cdim = (c_nd == CCV_NNC_MAX_DIM(2) + 1) ? c->info.dim : c->info.dim + 1;
8
←
'?' condition is true→
116	const int pool_h = cdim[0];
117	const int pool_w = cdim[1];
118	assert(cdim[2] == adim[2])((void) sizeof ((cdim[2] == adim[2]) ? 1 : 0), __extension__ (
{ if (cdim[2] == adim[2]) ; else __assert_fail ("cdim[2] == adim[2]"
, "roi/ccv_nnc_roi_align_cpu_ref.c", 118, __extension__ __PRETTY_FUNCTION__
); }));
9
←
Assuming the condition is true→
10
←
Taking true branch→
119	const int ch = cdim[2];
120	const float* const ap = a->data.f32;
121	int astride[CCV_NNC_MAX_DIM_ALLOC(12)];
122	ccv_nnc_tensor_view_get_stride(a, astride);
123	const float* const bp = b->data.f32;
124	float* cp = c->data.f32;
125	int cstride[CCV_NNC_MAX_DIM_ALLOC(12)];
126	ccv_nnc_tensor_view_get_stride(c, cstride);
127	const int a_n = ccv_nnc_tensor_get_n(a->info);
128	const int b_nd = ccv_nnc_tensor_nd(b->info.dim);
129	assert(b_nd == 1 || b_nd == 2)((void) sizeof ((b_nd == 1 || b_nd == 2) ? 1 : 0), __extension__
 ({ if (b_nd == 1 || b_nd == 2) ; else __assert_fail ("b_nd == 1 || b_nd == 2"
, "roi/ccv_nnc_roi_align_cpu_ref.c", 129, __extension__ __PRETTY_FUNCTION__
); }));
11
←
Assuming 'b_nd' is not equal to 1→
12
←
Assuming 'b_nd' is equal to 2→
13
←
Taking true branch→
130	const int b_n = b_nd13.1
'b_nd' is not equal to 1
 == 1 ? 1 : b->info.dim[0];
14
←
'?' condition is false→
131	const int c_n = ccv_nnc_tensor_get_n(c->info);
132	assert(c_n == ccv_max(a_n, b_n))((void) sizeof ((c_n == ({ typeof (a_n) _a = (a_n); typeof (b_n
) _b = (b_n); (_a > _b) ? _a : _b; })) ? 1 : 0), __extension__
 ({ if (c_n == ({ typeof (a_n) _a = (a_n); typeof (b_n) _b = (
b_n); (_a > _b) ? _a : _b; })) ; else __assert_fail ("c_n == ccv_max(a_n, b_n)"
, "roi/ccv_nnc_roi_align_cpu_ref.c", 132, __extension__ __PRETTY_FUNCTION__
); }));
15
←
Assuming '_a' is > '_b'→
16
←
'?' condition is true→
17
←
Assuming the condition is true→
18
←
Taking true branch→
133	const int aninc = a_nd == CCV_NNC_MAX_DIM(2) + 1 ? 0 : astride[0];
19
←
'?' condition is true→
134	int bstride[CCV_NNC_MAX_DIM_ALLOC(12)];
135	ccv_nnc_tensor_view_get_stride(b, bstride);
136	const int bninc = b_nd19.1
'b_nd' is not equal to 1
 == 1 ? 0 : bstride[CCV_NNC_MAX_DIM(2) + 2 - b_nd];
20
←
'?' condition is false→
137	const int cninc = c_nd == CCV_NNC_MAX_DIM(2) + 1 ? 0 : cstride[0];
21
←
'?' condition is true→
138	ccv_nnc_tensor_zero(c);
139	int bin_h, bin_w;
140	roi_align_coeffs_t* y_coeffs;
141	roi_align_coeffs_t* x_coeffs;
142	int* bin_h_at_y;
143	int* bin_w_at_x;
144	int start_h, start_w, end_h, end_w;
22
←
'start_h' declared without an initial value→
145	int n;
146	for (n = 0; n < c_n; n++)
23
←
Assuming 'n' is < 'c_n'→
24
←
Loop condition is true.  Entering loop body→
147	{
148		const float* const apn = ap + (n % a_n) * aninc;
149		float* cpn = cp + n * cninc;
150		const float roi_x = bp[(n % b_n) * bninc] * w; // These assumed it is real-coordinate, with range between 0 to w - 1.
151		const float roi_y = bp[(n % b_n) * bninc + 1] * h;
152		const float roi_w = bp[(n % b_n) * bninc + 2] * w;
153		const float roi_h = bp[(n % b_n) * bninc + 3] * h;
154		// Re-compute the offsets if b changes or it is the first time.
155		if ((b_n == 1 && n == 0) || b_n > 1)
25
←
Assuming 'b_n' is not equal to 1→
26
←
Assuming 'b_n' is <= 1→
27
←
Taking false branch→
156			_ccv_nnc_bilinear_coeffs(stream_context, h, w, roi_y, roi_x, roi_h, roi_w, pool_h, pool_w, &bin_h, &bin_w, &y_coeffs, &x_coeffs, &bin_h_at_y, &bin_w_at_x, &start_h, &start_w, &end_h, &end_w);
157		int i, j, x, y, k;
158		for (i = start_h; i < end_h; i++)
28
←
Assigned value is garbage or undefined
159		{
160			const int pi = i * bin_h;
161			const int bin_hz = bin_h_at_y[i];
162			for (j = start_w; j < end_w; j++)
163			{
164				const int pj = j * bin_w;
165				const int bin_wz = bin_w_at_x[j];
166				const float inv = 1.0 / (bin_hz * bin_wz);
167				float* const cpz = cpn + j * cstride[CCV_NNC_MAX_DIM(2)];
168				for (y = 0; y < bin_h; y++)
169				{
170					if (y_coeffs[pi + y].mute)
171						continue;
172					const float ry = y_coeffs[pi + y].r;
173					const int iy0 = y_coeffs[pi + y].i0;
174					const int iy1 = y_coeffs[pi + y].i1;
175					for (x = 0; x < bin_w; x++)
176					{
177						if (x_coeffs[pj + x].mute)
178							continue;
179						const float rx = x_coeffs[pj + x].r;
180						const int ix0 = x_coeffs[pj + x].i0;
181						const int ix1 = x_coeffs[pj + x].i1;
182						const float c00 = (1 - ry) * (1 - rx);
183						const float c01 = (1 - ry) * rx;
184						const float c10 = ry * (1 - rx);
185						const float c11 = ry * rx;
186						const float* const ap00 = apn + iy0 * astride[CCV_NNC_MAX_DIM(2) - 1] + ix0 * astride[CCV_NNC_MAX_DIM(2)];
187						const float* const ap01 = apn + iy0 * astride[CCV_NNC_MAX_DIM(2) - 1] + ix1 * astride[CCV_NNC_MAX_DIM(2)];
188						const float* const ap10 = apn + iy1 * astride[CCV_NNC_MAX_DIM(2) - 1] + ix0 * astride[CCV_NNC_MAX_DIM(2)];
189						const float* const ap11 = apn + iy1 * astride[CCV_NNC_MAX_DIM(2) - 1] + ix1 * astride[CCV_NNC_MAX_DIM(2)];
190						for (k = 0; k < ch; k++)
191							cpz[k] += ap00[k] * c00 + ap01[k] * c01 + ap10[k] * c10 + ap11[k] * c11;
192					}
193				}
194				for (k = 0; k < ch; k++)
195					cpz[k] *= inv;
196			}
197			cpn += cstride[CCV_NNC_MAX_DIM(2) - 1];
198		}
199	}
200	return CCV_NNC_EXEC_SUCCESS;
201}
202 
203static int _ccv_nnc_roi_align_back(const ccv_nnc_cmd_t cmd, const ccv_nnc_hint_t hint, const int flags, ccv_nnc_tensor_t* const* const inputs, const int input_size, ccv_nnc_tensor_t* const* const outputs, const int output_size, ccv_nnc_stream_context_t* const stream_context)
204{
205	assert(input_size >= 3)((void) sizeof ((input_size >= 3) ? 1 : 0), __extension__ (
{ if (input_size >= 3) ; else __assert_fail ("input_size >= 3"
, "roi/ccv_nnc_roi_align_cpu_ref.c", 205, __extension__ __PRETTY_FUNCTION__
); }));
206	const ccv_nnc_tensor_view_t* g = (ccv_nnc_tensor_view_t*)inputs[0];
207	assert(output_size == 1)((void) sizeof ((output_size == 1) ? 1 : 0), __extension__ ({
 if (output_size == 1) ; else __assert_fail ("output_size == 1"
, "roi/ccv_nnc_roi_align_cpu_ref.c", 207, __extension__ __PRETTY_FUNCTION__
); }));
208	ccv_nnc_tensor_view_t* o = (ccv_nnc_tensor_view_t*)outputs[0];
209	const int g_nd = ccv_nnc_tensor_nd(g->info.dim);
210	assert(g_nd == CCV_NNC_MAX_DIM + 1 || g_nd == CCV_NNC_MAX_DIM + 2)((void) sizeof ((g_nd == (2) + 1 || g_nd == (2) + 2) ? 1 : 0)
, __extension__ ({ if (g_nd == (2) + 1 || g_nd == (2) + 2) ; else
 __assert_fail ("g_nd == CCV_NNC_MAX_DIM + 1 || g_nd == CCV_NNC_MAX_DIM + 2"
, "roi/ccv_nnc_roi_align_cpu_ref.c", 210, __extension__ __PRETTY_FUNCTION__
); }));
211	const int* gdim = (g_nd == CCV_NNC_MAX_DIM(2) + 1) ? g->info.dim : g->info.dim + 1;
212	const int pool_h = gdim[0];
213	const int pool_w = gdim[1];
214	const int o_nd = ccv_nnc_tensor_nd(o->info.dim);
215	assert(o_nd == CCV_NNC_MAX_DIM + 1 || o_nd == CCV_NNC_MAX_DIM + 2)((void) sizeof ((o_nd == (2) + 1 || o_nd == (2) + 2) ? 1 : 0)
, __extension__ ({ if (o_nd == (2) + 1 || o_nd == (2) + 2) ; else
 __assert_fail ("o_nd == CCV_NNC_MAX_DIM + 1 || o_nd == CCV_NNC_MAX_DIM + 2"
, "roi/ccv_nnc_roi_align_cpu_ref.c", 215, __extension__ __PRETTY_FUNCTION__
); }));
216	const int* odim = (o_nd == CCV_NNC_MAX_DIM(2) + 1) ? o->info.dim : o->info.dim + 1;
217	const int h = odim[0];
218	const int w = odim[1];
219	assert(gdim[2] == odim[2])((void) sizeof ((gdim[2] == odim[2]) ? 1 : 0), __extension__ (
{ if (gdim[2] == odim[2]) ; else __assert_fail ("gdim[2] == odim[2]"
, "roi/ccv_nnc_roi_align_cpu_ref.c", 219, __extension__ __PRETTY_FUNCTION__
); }));
220	const int ch = gdim[2];
221	float* gp = g->data.f32;
222	int gstride[CCV_NNC_MAX_DIM_ALLOC(12)];
223	ccv_nnc_tensor_view_get_stride(g, gstride);
224	float* op = o->data.f32;
225	int ostride[CCV_NNC_MAX_DIM_ALLOC(12)];
226	ccv_nnc_tensor_view_get_stride(o, ostride);
227	const ccv_nnc_tensor_view_t* b = (ccv_nnc_tensor_view_t*)inputs[2];
228	const float* const bp = b->data.f32;
229	const int o_n = ccv_nnc_tensor_get_n(o->info);
230	const int b_nd = ccv_nnc_tensor_nd(b->info.dim);
231	assert(b_nd == 1 || b_nd == 2)((void) sizeof ((b_nd == 1 || b_nd == 2) ? 1 : 0), __extension__
 ({ if (b_nd == 1 || b_nd == 2) ; else __assert_fail ("b_nd == 1 || b_nd == 2"
, "roi/ccv_nnc_roi_align_cpu_ref.c", 231, __extension__ __PRETTY_FUNCTION__
); }));
232	const int b_n = b_nd == 1 ? 1 : b->info.dim[0];
233	const int g_n = ccv_nnc_tensor_get_n(g->info);
234	assert(g_n == ccv_max(o_n, b_n))((void) sizeof ((g_n == ({ typeof (o_n) _a = (o_n); typeof (b_n
) _b = (b_n); (_a > _b) ? _a : _b; })) ? 1 : 0), __extension__
 ({ if (g_n == ({ typeof (o_n) _a = (o_n); typeof (b_n) _b = (
b_n); (_a > _b) ? _a : _b; })) ; else __assert_fail ("g_n == ccv_max(o_n, b_n)"
, "roi/ccv_nnc_roi_align_cpu_ref.c", 234, __extension__ __PRETTY_FUNCTION__
); }));
235	const int oninc = o_nd == CCV_NNC_MAX_DIM(2) + 1 ? 0 : ostride[0];
236	int bstride[CCV_NNC_MAX_DIM_ALLOC(12)];
237	ccv_nnc_tensor_view_get_stride(b, bstride);
238	const int bninc = b_nd == 1 ? 0 : bstride[CCV_NNC_MAX_DIM(2) + 2 - b_nd];
239	const int gninc = g_nd == CCV_NNC_MAX_DIM(2) + 1 ? 0 : gstride[0];
240	int bin_h, bin_w;
241	roi_align_coeffs_t* y_coeffs;
242	roi_align_coeffs_t* x_coeffs;
243	int* bin_h_at_y;
244	int* bin_w_at_x;
245	int start_h, start_w, end_h, end_w;
246	int n;
247	ccv_nnc_tensor_zero(o);
248	for (n = 0; n < g_n; n++)
249	{
250		const float roi_x = bp[(n % b_n) * bninc] * w; // These assumed it is real-coordinate, with range between 0 to w - 1.
251		const float roi_y = bp[(n % b_n) * bninc + 1] * h;
252		const float roi_w = bp[(n % b_n) * bninc + 2] * w;
253		const float roi_h = bp[(n % b_n) * bninc + 3] * h;
254		// Re-compute the offsets if b changes or it is the first time.
255		if ((b_n == 1 && n == 0) || b_n > 1)
256			_ccv_nnc_bilinear_coeffs(stream_context, h, w, roi_y, roi_x, roi_h, roi_w, pool_h, pool_w, &bin_h, &bin_w, &y_coeffs, &x_coeffs, &bin_h_at_y, &bin_w_at_x, &start_h, &start_w, &end_h, &end_w);
257		const float* gpn = gp + n * gninc;
258		float* const opn = op + (n % o_n) * oninc;
259		int x, y, i, j, k;
260		for (i = 0; i < pool_h; i++)
261		{
262			const int pi = i * bin_h;
263			const int bin_hz = bin_h_at_y[i];
264			for (j = 0; j < pool_w; j++)
265			{
266				const int pj = j * bin_w;
267				const int bin_wz = bin_w_at_x[j];
268				const float inv = 1.0 / (bin_hz * bin_wz);
269				const float* const gpz = gpn + j * gstride[CCV_NNC_MAX_DIM(2)];
270				for (y = 0; y < bin_h; y++)
271				{
272					if (y_coeffs[pi + y].mute)
273						continue;
274					const float ry = y_coeffs[pi + y].r;
275					const int iy0 = y_coeffs[pi + y].i0;
276					const int iy1 = y_coeffs[pi + y].i1;
277					for (x = 0; x < bin_w; x++)
278					{
279						if (x_coeffs[pj + x].mute)
280							continue;
281						const float rx = x_coeffs[pj + x].r;
282						const int ix0 = x_coeffs[pj + x].i0;
283						const int ix1 = x_coeffs[pj + x].i1;
284						const float c00 = (1 - ry) * (1 - rx);
285						const float c01 = (1 - ry) * rx;
286						const float c10 = ry * (1 - rx);
287						const float c11 = ry * rx;
288						float* const op00 = opn + iy0 * ostride[CCV_NNC_MAX_DIM(2) - 1] + ix0 * ostride[CCV_NNC_MAX_DIM(2)];
289						float* const op01 = opn + iy0 * ostride[CCV_NNC_MAX_DIM(2) - 1] + ix1 * ostride[CCV_NNC_MAX_DIM(2)];
290						float* const op10 = opn + iy1 * ostride[CCV_NNC_MAX_DIM(2) - 1] + ix0 * ostride[CCV_NNC_MAX_DIM(2)];
291						float* const op11 = opn + iy1 * ostride[CCV_NNC_MAX_DIM(2) - 1] + ix1 * ostride[CCV_NNC_MAX_DIM(2)];
292						for (k = 0; k < ch; k++)
293						{
294							op00[k] += gpz[k] * c00 * inv;
295							op01[k] += gpz[k] * c01 * inv;
296							op10[k] += gpz[k] * c10 * inv;
297							op11[k] += gpz[k] * c11 * inv;
298						}
299					}
300				}
301			}
302			gpn += gstride[CCV_NNC_MAX_DIM(2) - 1];
303		}
304	}
305	return CCV_NNC_EXEC_SUCCESS;
306}
307 
308REGISTER_COMMAND_BACKEND(CCV_NNC_ROI_ALIGN_FORWARD, CCV_NNC_BACKEND_CPU_REF)void _register_command_CCV_NNC_ROI_ALIGN_FORWARD_backend_CCV_NNC_BACKEND_CPU_REF(ccv_nnc_cmd_backend_registry_t* const registry)
309{
310	registry->tensor_formats = CCV_TENSOR_FORMAT_NHWC;
311	registry->tensor_datatypes = CCV_32F;
312	registry->tensor_memory = CCV_TENSOR_CPU_MEMORY;
313	registry->algorithms = 1;
314	registry->exec = _ccv_nnc_roi_align_forw;
315}
316 
317REGISTER_COMMAND_BACKEND(CCV_NNC_ROI_ALIGN_BACKWARD, CCV_NNC_BACKEND_CPU_REF)void _register_command_CCV_NNC_ROI_ALIGN_BACKWARD_backend_CCV_NNC_BACKEND_CPU_REF(ccv_nnc_cmd_backend_registry_t* const registry)
318{
319	registry->tensor_formats = CCV_TENSOR_FORMAT_NHWC;
320	registry->tensor_datatypes = CCV_32F;
321	registry->tensor_memory = CCV_TENSOR_CPU_MEMORY;
322	registry->algorithms = 1;
323	registry->exec = _ccv_nnc_roi_align_back;
324}