loss/ccv_nnc_mse_cpu_ref.c

Bug Summary

File:	nnc/cmd/loss/ccv_nnc_mse_cpu_ref.c
Warning:	line 99, column 2 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_mse_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/2024-09-15-184933-339151-1 -x c loss/ccv_nnc_mse_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 
13static int _ccv_nnc_mse_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)
14{
15	assert(input_size == 2)((void) sizeof ((input_size == 2) ? 1 : 0), __extension__ ({ if
 (input_size == 2) ; else __assert_fail ("input_size == 2", "loss/ccv_nnc_mse_cpu_ref.c"
, 15, __extension__ __PRETTY_FUNCTION__); }));
16	const ccv_nnc_tensor_view_t* a = (ccv_nnc_tensor_view_t*)inputs[0];
17	assert(ccv_nnc_tensor_nd(a->info.dim) <= 2)((void) sizeof ((ccv_nnc_tensor_nd(a->info.dim) <= 2) ?
 1 : 0), __extension__ ({ if (ccv_nnc_tensor_nd(a->info.dim
) <= 2) ; else __assert_fail ("ccv_nnc_tensor_nd(a->info.dim) <= 2"
, "loss/ccv_nnc_mse_cpu_ref.c", 17, __extension__ __PRETTY_FUNCTION__
); }));
18	const ccv_nnc_tensor_view_t* b = (ccv_nnc_tensor_view_t*)inputs[1];
19	assert(output_size == 1)((void) sizeof ((output_size == 1) ? 1 : 0), __extension__ ({
 if (output_size == 1) ; else __assert_fail ("output_size == 1"
, "loss/ccv_nnc_mse_cpu_ref.c", 19, __extension__ __PRETTY_FUNCTION__
); }));
20	ccv_nnc_tensor_view_t* c = (ccv_nnc_tensor_view_t*)outputs[0];
21	int dim[CCV_NNC_MAX_DIM_ALLOC(12)];
22	int astride[CCV_NNC_MAX_DIM_ALLOC(12)];
23	int bstride[CCV_NNC_MAX_DIM_ALLOC(12)];
24	int cstride[CCV_NNC_MAX_DIM_ALLOC(12)];
25	ccv_nnc_tensor_view_get_dim(a, dim);
26	assert(ccv_nnc_tensor_view_check_dim(b, dim))((void) sizeof ((ccv_nnc_tensor_view_check_dim(b, dim)) ? 1 :
 0), __extension__ ({ if (ccv_nnc_tensor_view_check_dim(b, dim
)) ; else __assert_fail ("ccv_nnc_tensor_view_check_dim(b, dim)"
, "loss/ccv_nnc_mse_cpu_ref.c", 26, __extension__ __PRETTY_FUNCTION__
); }));
27	ccv_nnc_tensor_view_get_stride(a, astride);
28	ccv_nnc_tensor_view_get_stride(b, bstride);
29	ccv_nnc_tensor_view_get_stride(c, cstride);
30	assert(ccv_nnc_tensor_nd(a->info.dim) <= 2)((void) sizeof ((ccv_nnc_tensor_nd(a->info.dim) <= 2) ?
 1 : 0), __extension__ ({ if (ccv_nnc_tensor_nd(a->info.dim
) <= 2) ; else __assert_fail ("ccv_nnc_tensor_nd(a->info.dim) <= 2"
, "loss/ccv_nnc_mse_cpu_ref.c", 30, __extension__ __PRETTY_FUNCTION__
); }));
31	const int batch_size = dim[CCV_NNC_MAX_DIM(2)];
32	assert(ccv_nnc_tensor_count(c->info) == batch_size)((void) sizeof ((ccv_nnc_tensor_count(c->info) == batch_size
) ? 1 : 0), __extension__ ({ if (ccv_nnc_tensor_count(c->info
) == batch_size) ; else __assert_fail ("ccv_nnc_tensor_count(c->info) == batch_size"
, "loss/ccv_nnc_mse_cpu_ref.c", 32, __extension__ __PRETTY_FUNCTION__
); }));
33	const int count = dim[CCV_NNC_MAX_DIM(2) + 1];
34	const int astep = astride[CCV_NNC_MAX_DIM(2)];
35	const int bstep = bstride[CCV_NNC_MAX_DIM(2)];
36	const int cstep = ccv_nnc_tensor_nd(c->info.dim) == 1 ? 1 : cstride[CCV_NNC_MAX_DIM(2)];
37	if (cmd.info.mse.reduce_op == CCV_NNC_MSE_REDUCE_MEAN)
38	{
39		const float inv_mean = 1.0 / (float)count;
40		parallel_for(i, batch_size){ int i; for ((i) = 0; (i) < (batch_size); (i)++) { {
41			int j;
42			const float* const ap = a->data.f32 + i * astep;
43			const float* const bp = b->data.f32 + i * bstep;
44			float cp = 0;
45			for (j = 0; j < count; j++)
46				cp += (bp[j] - ap[j]) * (bp[j] - ap[j]);
47			c->data.f32[i * cstep] = cp * inv_mean;
48		} parallel_endfor} }
49	} else {
50		assert(cmd.info.mse.reduce_op == CCV_NNC_MSE_REDUCE_SUM)((void) sizeof ((cmd.info.mse.reduce_op == CCV_NNC_MSE_REDUCE_SUM
) ? 1 : 0), __extension__ ({ if (cmd.info.mse.reduce_op == CCV_NNC_MSE_REDUCE_SUM
) ; else __assert_fail ("cmd.info.mse.reduce_op == CCV_NNC_MSE_REDUCE_SUM"
, "loss/ccv_nnc_mse_cpu_ref.c", 50, __extension__ __PRETTY_FUNCTION__
); }));
51		parallel_for(i, batch_size){ int i; for ((i) = 0; (i) < (batch_size); (i)++) { {
52			int j;
53			const float* const ap = a->data.f32 + i * astep;
54			const float* const bp = b->data.f32 + i * bstep;
55			float cp = 0;
56			for (j = 0; j < count; j++)
57				cp += (bp[j] - ap[j]) * (bp[j] - ap[j]);
58			c->data.f32[i * cstep] = cp;
59		} parallel_endfor} }
60	}
61	return CCV_NNC_EXEC_SUCCESS;
62}
63 
64static int _ccv_nnc_mse_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)
65{
66	assert(input_size >= 3)((void) sizeof ((input_size >= 3) ? 1 : 0), __extension__ (
{ if (input_size >= 3) ; else __assert_fail ("input_size >= 3"
, "loss/ccv_nnc_mse_cpu_ref.c", 66, __extension__ __PRETTY_FUNCTION__
); }));
1
Assuming 'input_size' is >= 3→
2
←
Taking true branch→
67	assert(output_size >= 1)((void) sizeof ((output_size >= 1) ? 1 : 0), __extension__
 ({ if (output_size >= 1) ; else __assert_fail ("output_size >= 1"
, "loss/ccv_nnc_mse_cpu_ref.c", 67, __extension__ __PRETTY_FUNCTION__
); }));
3
←
Assuming 'output_size' is >= 1→
4
←
Taking true branch→
68	const ccv_nnc_tensor_view_t* const g = (ccv_nnc_tensor_view_t*)inputs[0];
69	assert(!g || !CCV_IS_TENSOR_VIEW(g))((void) sizeof ((!g || !((*(int*)(g)) & CCV_TENSOR_VIEW))
 ? 1 : 0), __extension__ ({ if (!g || !((*(int*)(g)) & CCV_TENSOR_VIEW
)) ; else __assert_fail ("!g || !CCV_IS_TENSOR_VIEW(g)", "loss/ccv_nnc_mse_cpu_ref.c"
, 69, __extension__ __PRETTY_FUNCTION__); }));
5
←
Assuming 'g' is null→
70	const ccv_nnc_tensor_view_t* const a = (ccv_nnc_tensor_view_t*)inputs[1];
71	const ccv_nnc_tensor_view_t* const b = (ccv_nnc_tensor_view_t*)inputs[2];
72	ccv_nnc_tensor_view_t* const ha = (ccv_nnc_tensor_view_t*)outputs[0];
73	ccv_nnc_tensor_view_t* const hb = output_size >= 2 ? (ccv_nnc_tensor_view_t*)outputs[1] : 0;
6
←
Assuming 'output_size' is < 2→
7
←
'?' condition is false→
74	int dim[CCV_NNC_MAX_DIM_ALLOC(12)];
75	int astride[CCV_NNC_MAX_DIM_ALLOC(12)];
76	int bstride[CCV_NNC_MAX_DIM_ALLOC(12)];
77	int hastride[CCV_NNC_MAX_DIM_ALLOC(12)];
78	int hbstride[CCV_NNC_MAX_DIM_ALLOC(12)];
79	ccv_nnc_tensor_view_get_dim(a, dim);
80	assert(ccv_nnc_tensor_view_check_dim(b, dim))((void) sizeof ((ccv_nnc_tensor_view_check_dim(b, dim)) ? 1 :
 0), __extension__ ({ if (ccv_nnc_tensor_view_check_dim(b, dim
)) ; else __assert_fail ("ccv_nnc_tensor_view_check_dim(b, dim)"
, "loss/ccv_nnc_mse_cpu_ref.c", 80, __extension__ __PRETTY_FUNCTION__
); }));
8
←
Assuming the condition is true→
9
←
Taking true branch→
81	if (ha)
10
←
Assuming 'ha' is non-null→
11
←
Taking true branch→
82		{ assert(ccv_nnc_tensor_view_check_dim(ha, dim))((void) sizeof ((ccv_nnc_tensor_view_check_dim(ha, dim)) ? 1 :
 0), __extension__ ({ if (ccv_nnc_tensor_view_check_dim(ha, dim
)) ; else __assert_fail ("ccv_nnc_tensor_view_check_dim(ha, dim)"
, "loss/ccv_nnc_mse_cpu_ref.c", 82, __extension__ __PRETTY_FUNCTION__
); })); }
12
←
Assuming the condition is true→
13
←
Taking true branch→
83	if (hb13.1
'hb' is null
)
14
←
Taking false branch→
84		{ assert(ccv_nnc_tensor_view_check_dim(hb, dim))((void) sizeof ((ccv_nnc_tensor_view_check_dim(hb, dim)) ? 1 :
 0), __extension__ ({ if (ccv_nnc_tensor_view_check_dim(hb, dim
)) ; else __assert_fail ("ccv_nnc_tensor_view_check_dim(hb, dim)"
, "loss/ccv_nnc_mse_cpu_ref.c", 84, __extension__ __PRETTY_FUNCTION__
); })); }
85	ccv_nnc_tensor_view_get_stride(a, astride);
86	ccv_nnc_tensor_view_get_stride(b, bstride);
87	if (ha14.1
'ha' is non-null
)
15
←
Taking true branch→
88		ccv_nnc_tensor_view_get_stride(ha, hastride);
89	if (hb15.1
'hb' is null
)
16
←
Taking false branch→
90		ccv_nnc_tensor_view_get_stride(hb, hbstride);
91	assert(ccv_nnc_tensor_nd(a->info.dim) <= 2)((void) sizeof ((ccv_nnc_tensor_nd(a->info.dim) <= 2) ?
 1 : 0), __extension__ ({ if (ccv_nnc_tensor_nd(a->info.dim
) <= 2) ; else __assert_fail ("ccv_nnc_tensor_nd(a->info.dim) <= 2"
, "loss/ccv_nnc_mse_cpu_ref.c", 91, __extension__ __PRETTY_FUNCTION__
); }));
17
←
Assuming the condition is true→
18
←
Taking true branch→
92	const int batch_size = dim[CCV_NNC_MAX_DIM(2)];
93	const int count = dim[CCV_NNC_MAX_DIM(2) + 1];
94	const float inv_mean_2 = cmd.info.mse.reduce_op == CCV_NNC_MSE_REDUCE_MEAN ? 2.0 / (float)count : 2.0;
19
←
Assuming field 'reduce_op' is equal to CCV_NNC_MSE_REDUCE_MEAN→
20
←
'?' condition is true→
95	assert(cmd.info.mse.reduce_op == CCV_NNC_MSE_REDUCE_MEAN || cmd.info.mse.reduce_op == CCV_NNC_MSE_REDUCE_SUM)((void) sizeof ((cmd.info.mse.reduce_op == CCV_NNC_MSE_REDUCE_MEAN
 || cmd.info.mse.reduce_op == CCV_NNC_MSE_REDUCE_SUM) ? 1 : 0
), __extension__ ({ if (cmd.info.mse.reduce_op == CCV_NNC_MSE_REDUCE_MEAN
 || cmd.info.mse.reduce_op == CCV_NNC_MSE_REDUCE_SUM) ; else __assert_fail
 ("cmd.info.mse.reduce_op == CCV_NNC_MSE_REDUCE_MEAN || cmd.info.mse.reduce_op == CCV_NNC_MSE_REDUCE_SUM"
, "loss/ccv_nnc_mse_cpu_ref.c", 95, __extension__ __PRETTY_FUNCTION__
); }));
96	const int astep = astride[CCV_NNC_MAX_DIM(2)];
97	const int bstep = bstride[CCV_NNC_MAX_DIM(2)];
98	const int hastep = hastride[CCV_NNC_MAX_DIM(2)];
99	const int hbstep = hbstride[CCV_NNC_MAX_DIM(2)];
21
←
Assigned value is garbage or undefined
100	if (g)
101	{
102		int gstride[CCV_NNC_MAX_DIM_ALLOC(12)];
103		ccv_nnc_tensor_view_get_stride(g, gstride);
104		assert(ccv_nnc_tensor_count(g->info) == batch_size)((void) sizeof ((ccv_nnc_tensor_count(g->info) == batch_size
) ? 1 : 0), __extension__ ({ if (ccv_nnc_tensor_count(g->info
) == batch_size) ; else __assert_fail ("ccv_nnc_tensor_count(g->info) == batch_size"
, "loss/ccv_nnc_mse_cpu_ref.c", 104, __extension__ __PRETTY_FUNCTION__
); }));
105		const int gstep = ccv_nnc_tensor_nd(g->info.dim) == 1 ? 1 : gstride[CCV_NNC_MAX_DIM(2)];
106		if (ha)
107		{
108			parallel_for(i, batch_size){ int i; for ((i) = 0; (i) < (batch_size); (i)++) { {
109				int j;
110				const float* const ap = a->data.f32 + i * astep;
111				const float* const bp = b->data.f32 + i * bstep;
112				float* const hp = ha->data.f32 + i * hastep;
113				const float gp = inv_mean_2 * g->data.f32[i * gstep];
114				for (j = 0; j < count; j++)
115					hp[j] = gp * (ap[j] - bp[j]);
116			} parallel_endfor} }
117		}
118		if (hb)
119		{
120			parallel_for(i, batch_size){ int i; for ((i) = 0; (i) < (batch_size); (i)++) { {
121				int j;
122				const float* const ap = a->data.f32 + i * astep;
123				const float* const bp = b->data.f32 + i * bstep;
124				float* const hp = hb->data.f32 + i * hbstep;
125				const float gp = inv_mean_2 * g->data.f32[i * gstep];
126				for (j = 0; j < count; j++)
127					hp[j] = gp * (bp[j] - ap[j]);
128			} parallel_endfor} }
129		}
130	} else {
131		if (ha)
132		{
133			parallel_for(i, batch_size){ int i; for ((i) = 0; (i) < (batch_size); (i)++) { {
134				int j;
135				const float* const ap = a->data.f32 + i * astep;
136				const float* const bp = b->data.f32 + i * bstep;
137				float* const hp = ha->data.f32 + i * hastep;
138				for (j = 0; j < count; j++)
139					hp[j] = inv_mean_2 * (ap[j] - bp[j]);
140			} parallel_endfor} }
141		}
142		if (hb)
143		{
144			parallel_for(i, batch_size){ int i; for ((i) = 0; (i) < (batch_size); (i)++) { {
145				int j;
146				const float* const ap = a->data.f32 + i * astep;
147				const float* const bp = b->data.f32 + i * bstep;
148				float* const hp = hb->data.f32 + i * hbstep;
149				for (j = 0; j < count; j++)
150					hp[j] = inv_mean_2 * (bp[j] - ap[j]);
151			} parallel_endfor} }
152		}
153	}
154	return CCV_NNC_EXEC_SUCCESS;
155}
156 
157REGISTER_COMMAND_BACKEND(CCV_NNC_MSE_FORWARD, CCV_NNC_BACKEND_CPU_REF)void _register_command_CCV_NNC_MSE_FORWARD_backend_CCV_NNC_BACKEND_CPU_REF(ccv_nnc_cmd_backend_registry_t* const registry)
158{
159	registry->tensor_formats = CCV_TENSOR_FORMAT_NHWC | CCV_TENSOR_FORMAT_NCHW;
160	registry->tensor_datatypes = CCV_32F;
161	registry->tensor_memory = CCV_TENSOR_CPU_MEMORY;
162	registry->algorithms = 1;
163	registry->exec = _ccv_nnc_mse_forw;
164}
165 
166REGISTER_COMMAND_BACKEND(CCV_NNC_MSE_BACKWARD, CCV_NNC_BACKEND_CPU_REF)void _register_command_CCV_NNC_MSE_BACKWARD_backend_CCV_NNC_BACKEND_CPU_REF(ccv_nnc_cmd_backend_registry_t* const registry)
167{
168	registry->tensor_formats = CCV_TENSOR_FORMAT_NHWC | CCV_TENSOR_FORMAT_NCHW;
169	registry->tensor_datatypes = CCV_32F;
170	registry->tensor_memory = CCV_TENSOR_CPU_MEMORY;
171	registry->algorithms = 1;
172	registry->exec = _ccv_nnc_mse_back;
173}