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- #define PyObject_Format(obj, fmt) PyObject_CallMethod(obj, "__format__", "O", fmt)
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-#define __Pyx_PyString_FormatSafe(a, b) ((unlikely((a) == Py_None || (PyString_Check(b) && !PyString_CheckExact(b)))) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b))
-#define __Pyx_PyUnicode_FormatSafe(a, b) ((unlikely((a) == Py_None || (PyUnicode_Check(b) && !PyUnicode_CheckExact(b)))) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b))
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- #define __Pyx_PyString_Format(a, b) PyUnicode_Format(a, b)
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- #define __Pyx_SET_REFCNT(obj, refcnt) Py_REFCNT(obj) = (refcnt)
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- #define __Pyx_PySequence_SIZE(seq) Py_SIZE(seq)
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- #define __Pyx_PySequence_SIZE(seq) PySequence_Size(seq)
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- #define PyIntObject PyLongObject
- #define PyInt_Type PyLong_Type
- #define PyInt_Check(op) PyLong_Check(op)
- #define PyInt_CheckExact(op) PyLong_CheckExact(op)
- #define PyInt_FromString PyLong_FromString
- #define PyInt_FromUnicode PyLong_FromUnicode
- #define PyInt_FromLong PyLong_FromLong
- #define PyInt_FromSize_t PyLong_FromSize_t
- #define PyInt_FromSsize_t PyLong_FromSsize_t
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- #ifndef PyUnicode_InternFromString
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- typedef long Py_hash_t;
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- #define __Pyx_PyInt_AsHash_t PyInt_AsSsize_t
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- #define __Pyx_PyMethod_New(func, self, klass) ((self) ? ((void)(klass), PyMethod_New(func, self)) : __Pyx_NewRef(func))
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- #define __Pyx_PyMethod_New(func, self, klass) PyMethod_New(func, self, klass)
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- #if PY_VERSION_HEX >= 0x030500B1
- #define __Pyx_PyAsyncMethodsStruct PyAsyncMethods
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- #define __Pyx_PyType_AsAsync(obj) NULL
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-#ifndef __Pyx_PyAsyncMethodsStruct
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- } __Pyx_PyAsyncMethodsStruct;
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-
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- #define _USE_MATH_DEFINES
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-#define __PYX_NAN() ((float) NAN)
-#else
-static CYTHON_INLINE float __PYX_NAN() {
- float value;
- memset(&value, 0xFF, sizeof(value));
- return value;
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-#if defined(__CYGWIN__) && defined(_LDBL_EQ_DBL)
-#define __Pyx_truncl trunc
-#else
-#define __Pyx_truncl truncl
-#endif
-
-#define __PYX_MARK_ERR_POS(f_index, lineno) \
- { __pyx_filename = __pyx_f[f_index]; (void)__pyx_filename; __pyx_lineno = lineno; (void)__pyx_lineno; __pyx_clineno = __LINE__; (void)__pyx_clineno; }
-#define __PYX_ERR(f_index, lineno, Ln_error) \
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-
-#ifndef __PYX_EXTERN_C
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- #define __PYX_EXTERN_C extern "C"
- #else
- #define __PYX_EXTERN_C extern
- #endif
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-
-#define __PYX_HAVE__bbox
-#define __PYX_HAVE_API__bbox
-/* Early includes */
-#include <string.h>
-#include <stdio.h>
-#include "numpy/arrayobject.h"
-#include "numpy/ufuncobject.h"
-
- /* NumPy API declarations from "numpy/__init__.pxd" */
-
-#ifdef _OPENMP
-#include <omp.h>
-#endif /* _OPENMP */
-
-#if defined(PYREX_WITHOUT_ASSERTIONS) && !defined(CYTHON_WITHOUT_ASSERTIONS)
-#define CYTHON_WITHOUT_ASSERTIONS
-#endif
-
-typedef struct {PyObject **p; const char *s; const Py_ssize_t n; const char* encoding;
- const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry;
-
-#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0
-#define __PYX_DEFAULT_STRING_ENCODING_IS_UTF8 0
-#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT (PY_MAJOR_VERSION >= 3 && __PYX_DEFAULT_STRING_ENCODING_IS_UTF8)
-#define __PYX_DEFAULT_STRING_ENCODING ""
-#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString
-#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize
-#define __Pyx_uchar_cast(c) ((unsigned char)c)
-#define __Pyx_long_cast(x) ((long)x)
-#define __Pyx_fits_Py_ssize_t(v, type, is_signed) (\
- (sizeof(type) < sizeof(Py_ssize_t)) ||\
- (sizeof(type) > sizeof(Py_ssize_t) &&\
- likely(v < (type)PY_SSIZE_T_MAX ||\
- v == (type)PY_SSIZE_T_MAX) &&\
- (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||\
- v == (type)PY_SSIZE_T_MIN))) ||\
- (sizeof(type) == sizeof(Py_ssize_t) &&\
- (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\
- v == (type)PY_SSIZE_T_MAX))) )
-static CYTHON_INLINE int __Pyx_is_valid_index(Py_ssize_t i, Py_ssize_t limit) {
- return (size_t) i < (size_t) limit;
-}
-#if defined (__cplusplus) && __cplusplus >= 201103L
- #include <cstdlib>
- #define __Pyx_sst_abs(value) std::abs(value)
-#elif SIZEOF_INT >= SIZEOF_SIZE_T
- #define __Pyx_sst_abs(value) abs(value)
-#elif SIZEOF_LONG >= SIZEOF_SIZE_T
- #define __Pyx_sst_abs(value) labs(value)
-#elif defined (_MSC_VER)
- #define __Pyx_sst_abs(value) ((Py_ssize_t)_abs64(value))
-#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
- #define __Pyx_sst_abs(value) llabs(value)
-#elif defined (__GNUC__)
- #define __Pyx_sst_abs(value) __builtin_llabs(value)
-#else
- #define __Pyx_sst_abs(value) ((value<0) ? -value : value)
-#endif
-static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject*);
-static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length);
-#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s))
-#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l)
-#define __Pyx_PyBytes_FromString PyBytes_FromString
-#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize
-static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*);
-#if PY_MAJOR_VERSION < 3
- #define __Pyx_PyStr_FromString __Pyx_PyBytes_FromString
- #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize
-#else
- #define __Pyx_PyStr_FromString __Pyx_PyUnicode_FromString
- #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize
-#endif
-#define __Pyx_PyBytes_AsWritableString(s) ((char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsWritableSString(s) ((signed char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsWritableUString(s) ((unsigned char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsString(s) ((const char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsSString(s) ((const signed char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsUString(s) ((const unsigned char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyObject_AsWritableString(s) ((char*) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_AsWritableSString(s) ((signed char*) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_AsWritableUString(s) ((unsigned char*) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_AsSString(s) ((const signed char*) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_AsUString(s) ((const unsigned char*) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_FromCString(s) __Pyx_PyObject_FromString((const char*)s)
-#define __Pyx_PyBytes_FromCString(s) __Pyx_PyBytes_FromString((const char*)s)
-#define __Pyx_PyByteArray_FromCString(s) __Pyx_PyByteArray_FromString((const char*)s)
-#define __Pyx_PyStr_FromCString(s) __Pyx_PyStr_FromString((const char*)s)
-#define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s)
-static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) {
- const Py_UNICODE *u_end = u;
- while (*u_end++) ;
- return (size_t)(u_end - u - 1);
-}
-#define __Pyx_PyUnicode_FromUnicode(u) PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u))
-#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode
-#define __Pyx_PyUnicode_AsUnicode PyUnicode_AsUnicode
-#define __Pyx_NewRef(obj) (Py_INCREF(obj), obj)
-#define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None)
-static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b);
-static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*);
-static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject*);
-static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x);
-#define __Pyx_PySequence_Tuple(obj)\
- (likely(PyTuple_CheckExact(obj)) ? __Pyx_NewRef(obj) : PySequence_Tuple(obj))
-static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*);
-static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t);
-#if CYTHON_ASSUME_SAFE_MACROS
-#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x))
-#else
-#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x)
-#endif
-#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x))
-#if PY_MAJOR_VERSION >= 3
-#define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x))
-#else
-#define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x))
-#endif
-#define __Pyx_PyNumber_Float(x) (PyFloat_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Float(x))
-#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
-static int __Pyx_sys_getdefaultencoding_not_ascii;
-static int __Pyx_init_sys_getdefaultencoding_params(void) {
- PyObject* sys;
- PyObject* default_encoding = NULL;
- PyObject* ascii_chars_u = NULL;
- PyObject* ascii_chars_b = NULL;
- const char* default_encoding_c;
- sys = PyImport_ImportModule("sys");
- if (!sys) goto bad;
- default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL);
- Py_DECREF(sys);
- if (!default_encoding) goto bad;
- default_encoding_c = PyBytes_AsString(default_encoding);
- if (!default_encoding_c) goto bad;
- if (strcmp(default_encoding_c, "ascii") == 0) {
- __Pyx_sys_getdefaultencoding_not_ascii = 0;
- } else {
- char ascii_chars[128];
- int c;
- for (c = 0; c < 128; c++) {
- ascii_chars[c] = c;
- }
- __Pyx_sys_getdefaultencoding_not_ascii = 1;
- ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL);
- if (!ascii_chars_u) goto bad;
- ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL);
- if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) {
- PyErr_Format(
- PyExc_ValueError,
- "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.",
- default_encoding_c);
- goto bad;
- }
- Py_DECREF(ascii_chars_u);
- Py_DECREF(ascii_chars_b);
- }
- Py_DECREF(default_encoding);
- return 0;
-bad:
- Py_XDECREF(default_encoding);
- Py_XDECREF(ascii_chars_u);
- Py_XDECREF(ascii_chars_b);
- return -1;
-}
-#endif
-#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3
-#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL)
-#else
-#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL)
-#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
-static char* __PYX_DEFAULT_STRING_ENCODING;
-static int __Pyx_init_sys_getdefaultencoding_params(void) {
- PyObject* sys;
- PyObject* default_encoding = NULL;
- char* default_encoding_c;
- sys = PyImport_ImportModule("sys");
- if (!sys) goto bad;
- default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL);
- Py_DECREF(sys);
- if (!default_encoding) goto bad;
- default_encoding_c = PyBytes_AsString(default_encoding);
- if (!default_encoding_c) goto bad;
- __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c) + 1);
- if (!__PYX_DEFAULT_STRING_ENCODING) goto bad;
- strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c);
- Py_DECREF(default_encoding);
- return 0;
-bad:
- Py_XDECREF(default_encoding);
- return -1;
-}
-#endif
-#endif
-
-
-/* Test for GCC > 2.95 */
-#if defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)))
- #define likely(x) __builtin_expect(!!(x), 1)
- #define unlikely(x) __builtin_expect(!!(x), 0)
-#else /* !__GNUC__ or GCC < 2.95 */
- #define likely(x) (x)
- #define unlikely(x) (x)
-#endif /* __GNUC__ */
-static CYTHON_INLINE void __Pyx_pretend_to_initialize(void* ptr) { (void)ptr; }
-
-static PyObject *__pyx_m = NULL;
-static PyObject *__pyx_d;
-static PyObject *__pyx_b;
-static PyObject *__pyx_cython_runtime = NULL;
-static PyObject *__pyx_empty_tuple;
-static PyObject *__pyx_empty_bytes;
-static PyObject *__pyx_empty_unicode;
-static int __pyx_lineno;
-static int __pyx_clineno = 0;
-static const char * __pyx_cfilenm= __FILE__;
-static const char *__pyx_filename;
-
-/* Header.proto */
-#if !defined(CYTHON_CCOMPLEX)
- #if defined(__cplusplus)
- #define CYTHON_CCOMPLEX 1
- #elif defined(_Complex_I)
- #define CYTHON_CCOMPLEX 1
- #else
- #define CYTHON_CCOMPLEX 0
- #endif
-#endif
-#if CYTHON_CCOMPLEX
- #ifdef __cplusplus
- #include <complex>
- #else
- #include <complex.h>
- #endif
-#endif
-#if CYTHON_CCOMPLEX && !defined(__cplusplus) && defined(__sun__) && defined(__GNUC__)
- #undef _Complex_I
- #define _Complex_I 1.0fj
-#endif
-
-
-static const char *__pyx_f[] = {
- "box_overlaps.pyx",
- "__init__.pxd",
- "type.pxd",
-};
-/* BufferFormatStructs.proto */
-#define IS_UNSIGNED(type) (((type) -1) > 0)
-struct __Pyx_StructField_;
-#define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0)
-typedef struct {
- const char* name;
- struct __Pyx_StructField_* fields;
- size_t size;
- size_t arraysize[8];
- int ndim;
- char typegroup;
- char is_unsigned;
- int flags;
-} __Pyx_TypeInfo;
-typedef struct __Pyx_StructField_ {
- __Pyx_TypeInfo* type;
- const char* name;
- size_t offset;
-} __Pyx_StructField;
-typedef struct {
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-} __Pyx_BufFmt_StackElem;
-typedef struct {
- __Pyx_StructField root;
- __Pyx_BufFmt_StackElem* head;
- size_t fmt_offset;
- size_t new_count, enc_count;
- size_t struct_alignment;
- int is_complex;
- char enc_type;
- char new_packmode;
- char enc_packmode;
- char is_valid_array;
-} __Pyx_BufFmt_Context;
-
-
-/* "../../../../root/anaconda3/envs/DSFD/lib/python3.7/site-packages/numpy/__init__.pxd":689
- * # in Cython to enable them only on the right systems.
- *
- * ctypedef npy_int8 int8_t # <<<<<<<<<<<<<<
- * ctypedef npy_int16 int16_t
- * ctypedef npy_int32 int32_t
- */
-typedef npy_int8 __pyx_t_5numpy_int8_t;
-
-/* "../../../../root/anaconda3/envs/DSFD/lib/python3.7/site-packages/numpy/__init__.pxd":690
- *
- * ctypedef npy_int8 int8_t
- * ctypedef npy_int16 int16_t # <<<<<<<<<<<<<<
- * ctypedef npy_int32 int32_t
- * ctypedef npy_int64 int64_t
- */
-typedef npy_int16 __pyx_t_5numpy_int16_t;
-
-/* "../../../../root/anaconda3/envs/DSFD/lib/python3.7/site-packages/numpy/__init__.pxd":691
- * ctypedef npy_int8 int8_t
- * ctypedef npy_int16 int16_t
- * ctypedef npy_int32 int32_t # <<<<<<<<<<<<<<
- * ctypedef npy_int64 int64_t
- * #ctypedef npy_int96 int96_t
- */
-typedef npy_int32 __pyx_t_5numpy_int32_t;
-
-/* "../../../../root/anaconda3/envs/DSFD/lib/python3.7/site-packages/numpy/__init__.pxd":692
- * ctypedef npy_int16 int16_t
- * ctypedef npy_int32 int32_t
- * ctypedef npy_int64 int64_t # <<<<<<<<<<<<<<
- * #ctypedef npy_int96 int96_t
- * #ctypedef npy_int128 int128_t
- */
-typedef npy_int64 __pyx_t_5numpy_int64_t;
-
-/* "../../../../root/anaconda3/envs/DSFD/lib/python3.7/site-packages/numpy/__init__.pxd":696
- * #ctypedef npy_int128 int128_t
- *
- * ctypedef npy_uint8 uint8_t # <<<<<<<<<<<<<<
- * ctypedef npy_uint16 uint16_t
- * ctypedef npy_uint32 uint32_t
- */
-typedef npy_uint8 __pyx_t_5numpy_uint8_t;
-
-/* "../../../../root/anaconda3/envs/DSFD/lib/python3.7/site-packages/numpy/__init__.pxd":697
- *
- * ctypedef npy_uint8 uint8_t
- * ctypedef npy_uint16 uint16_t # <<<<<<<<<<<<<<
- * ctypedef npy_uint32 uint32_t
- * ctypedef npy_uint64 uint64_t
- */
-typedef npy_uint16 __pyx_t_5numpy_uint16_t;
-
-/* "../../../../root/anaconda3/envs/DSFD/lib/python3.7/site-packages/numpy/__init__.pxd":698
- * ctypedef npy_uint8 uint8_t
- * ctypedef npy_uint16 uint16_t
- * ctypedef npy_uint32 uint32_t # <<<<<<<<<<<<<<
- * ctypedef npy_uint64 uint64_t
- * #ctypedef npy_uint96 uint96_t
- */
-typedef npy_uint32 __pyx_t_5numpy_uint32_t;
-
-/* "../../../../root/anaconda3/envs/DSFD/lib/python3.7/site-packages/numpy/__init__.pxd":699
- * ctypedef npy_uint16 uint16_t
- * ctypedef npy_uint32 uint32_t
- * ctypedef npy_uint64 uint64_t # <<<<<<<<<<<<<<
- * #ctypedef npy_uint96 uint96_t
- * #ctypedef npy_uint128 uint128_t
- */
-typedef npy_uint64 __pyx_t_5numpy_uint64_t;
-
-/* "../../../../root/anaconda3/envs/DSFD/lib/python3.7/site-packages/numpy/__init__.pxd":703
- * #ctypedef npy_uint128 uint128_t
- *
- * ctypedef npy_float32 float32_t # <<<<<<<<<<<<<<
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-typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong;
-#endif
-static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, CYTHON_UNUSED int is_complex) {
- switch (ch) {
- case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
- case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short);
- case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int);
- case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long);
-#ifdef HAVE_LONG_LONG
- case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG);
-#endif
- case 'f': return sizeof(__Pyx_st_float) - sizeof(float);
- case 'd': return sizeof(__Pyx_st_double) - sizeof(double);
- case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double);
- case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*);
- default:
- __Pyx_BufFmt_RaiseUnexpectedChar(ch);
- return 0;
- }
-}
-/* These are for computing the padding at the end of the struct to align
- on the first member of the struct. This will probably the same as above,
- but we don't have any guarantees.
- */
-typedef struct { short x; char c; } __Pyx_pad_short;
-typedef struct { int x; char c; } __Pyx_pad_int;
-typedef struct { long x; char c; } __Pyx_pad_long;
-typedef struct { float x; char c; } __Pyx_pad_float;
-typedef struct { double x; char c; } __Pyx_pad_double;
-typedef struct { long double x; char c; } __Pyx_pad_longdouble;
-typedef struct { void *x; char c; } __Pyx_pad_void_p;
-#ifdef HAVE_LONG_LONG
-typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong;
-#endif
-static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) {
- switch (ch) {
- case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
- case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short);
- case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int);
- case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long);
-#ifdef HAVE_LONG_LONG
- case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG);
-#endif
- case 'f': return sizeof(__Pyx_pad_float) - sizeof(float);
- case 'd': return sizeof(__Pyx_pad_double) - sizeof(double);
- case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double);
- case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*);
- default:
- __Pyx_BufFmt_RaiseUnexpectedChar(ch);
- return 0;
- }
-}
-static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) {
- switch (ch) {
- case 'c':
- return 'H';
- case 'b': case 'h': case 'i':
- case 'l': case 'q': case 's': case 'p':
- return 'I';
- case '?': case 'B': case 'H': case 'I': case 'L': case 'Q':
- return 'U';
- case 'f': case 'd': case 'g':
- return (is_complex ? 'C' : 'R');
- case 'O':
- return 'O';
- case 'P':
- return 'P';
- default: {
- __Pyx_BufFmt_RaiseUnexpectedChar(ch);
- return 0;
- }
- }
-}
-static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) {
- if (ctx->head == NULL || ctx->head->field == &ctx->root) {
- const char* expected;
- const char* quote;
- if (ctx->head == NULL) {
- expected = "end";
- quote = "";
- } else {
- expected = ctx->head->field->type->name;
- quote = "'";
- }
- PyErr_Format(PyExc_ValueError,
- "Buffer dtype mismatch, expected %s%s%s but got %s",
- quote, expected, quote,
- __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex));
- } else {
- __Pyx_StructField* field = ctx->head->field;
- __Pyx_StructField* parent = (ctx->head - 1)->field;
- PyErr_Format(PyExc_ValueError,
- "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'",
- field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex),
- parent->type->name, field->name);
- }
-}
-static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) {
- char group;
- size_t size, offset, arraysize = 1;
- if (ctx->enc_type == 0) return 0;
- if (ctx->head->field->type->arraysize[0]) {
- int i, ndim = 0;
- if (ctx->enc_type == 's' || ctx->enc_type == 'p') {
- ctx->is_valid_array = ctx->head->field->type->ndim == 1;
- ndim = 1;
- if (ctx->enc_count != ctx->head->field->type->arraysize[0]) {
- PyErr_Format(PyExc_ValueError,
- "Expected a dimension of size %zu, got %zu",
- ctx->head->field->type->arraysize[0], ctx->enc_count);
- return -1;
- }
- }
- if (!ctx->is_valid_array) {
- PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d",
- ctx->head->field->type->ndim, ndim);
- return -1;
- }
- for (i = 0; i < ctx->head->field->type->ndim; i++) {
- arraysize *= ctx->head->field->type->arraysize[i];
- }
- ctx->is_valid_array = 0;
- ctx->enc_count = 1;
- }
- group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex);
- do {
- __Pyx_StructField* field = ctx->head->field;
- __Pyx_TypeInfo* type = field->type;
- if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') {
- size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex);
- } else {
- size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex);
- }
- if (ctx->enc_packmode == '@') {
- size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex);
- size_t align_mod_offset;
- if (align_at == 0) return -1;
- align_mod_offset = ctx->fmt_offset % align_at;
- if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset;
- if (ctx->struct_alignment == 0)
- ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type,
- ctx->is_complex);
- }
- if (type->size != size || type->typegroup != group) {
- if (type->typegroup == 'C' && type->fields != NULL) {
- size_t parent_offset = ctx->head->parent_offset + field->offset;
- ++ctx->head;
- ctx->head->field = type->fields;
- ctx->head->parent_offset = parent_offset;
- continue;
- }
- if ((type->typegroup == 'H' || group == 'H') && type->size == size) {
- } else {
- __Pyx_BufFmt_RaiseExpected(ctx);
- return -1;
- }
- }
- offset = ctx->head->parent_offset + field->offset;
- if (ctx->fmt_offset != offset) {
- PyErr_Format(PyExc_ValueError,
- "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected",
- (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset);
- return -1;
- }
- ctx->fmt_offset += size;
- if (arraysize)
- ctx->fmt_offset += (arraysize - 1) * size;
- --ctx->enc_count;
- while (1) {
- if (field == &ctx->root) {
- ctx->head = NULL;
- if (ctx->enc_count != 0) {
- __Pyx_BufFmt_RaiseExpected(ctx);
- return -1;
- }
- break;
- }
- ctx->head->field = ++field;
- if (field->type == NULL) {
- --ctx->head;
- field = ctx->head->field;
- continue;
- } else if (field->type->typegroup == 'S') {
- size_t parent_offset = ctx->head->parent_offset + field->offset;
- if (field->type->fields->type == NULL) continue;
- field = field->type->fields;
- ++ctx->head;
- ctx->head->field = field;
- ctx->head->parent_offset = parent_offset;
- break;
- } else {
- break;
- }
- }
- } while (ctx->enc_count);
- ctx->enc_type = 0;
- ctx->is_complex = 0;
- return 0;
-}
-static PyObject *
-__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp)
-{
- const char *ts = *tsp;
- int i = 0, number, ndim;
- ++ts;
- if (ctx->new_count != 1) {
- PyErr_SetString(PyExc_ValueError,
- "Cannot handle repeated arrays in format string");
- return NULL;
- }
- if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
- ndim = ctx->head->field->type->ndim;
- while (*ts && *ts != ')') {
- switch (*ts) {
- case ' ': case '\f': case '\r': case '\n': case '\t': case '\v': continue;
- default: break;
- }
- number = __Pyx_BufFmt_ExpectNumber(&ts);
- if (number == -1) return NULL;
- if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i])
- return PyErr_Format(PyExc_ValueError,
- "Expected a dimension of size %zu, got %d",
- ctx->head->field->type->arraysize[i], number);
- if (*ts != ',' && *ts != ')')
- return PyErr_Format(PyExc_ValueError,
- "Expected a comma in format string, got '%c'", *ts);
- if (*ts == ',') ts++;
- i++;
- }
- if (i != ndim)
- return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d",
- ctx->head->field->type->ndim, i);
- if (!*ts) {
- PyErr_SetString(PyExc_ValueError,
- "Unexpected end of format string, expected ')'");
- return NULL;
- }
- ctx->is_valid_array = 1;
- ctx->new_count = 1;
- *tsp = ++ts;
- return Py_None;
-}
-static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) {
- int got_Z = 0;
- while (1) {
- switch(*ts) {
- case 0:
- if (ctx->enc_type != 0 && ctx->head == NULL) {
- __Pyx_BufFmt_RaiseExpected(ctx);
- return NULL;
- }
- if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
- if (ctx->head != NULL) {
- __Pyx_BufFmt_RaiseExpected(ctx);
- return NULL;
- }
- return ts;
- case ' ':
- case '\r':
- case '\n':
- ++ts;
- break;
- case '<':
- if (!__Pyx_Is_Little_Endian()) {
- PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler");
- return NULL;
- }
- ctx->new_packmode = '=';
- ++ts;
- break;
- case '>':
- case '!':
- if (__Pyx_Is_Little_Endian()) {
- PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler");
- return NULL;
- }
- ctx->new_packmode = '=';
- ++ts;
- break;
- case '=':
- case '@':
- case '^':
- ctx->new_packmode = *ts++;
- break;
- case 'T':
- {
- const char* ts_after_sub;
- size_t i, struct_count = ctx->new_count;
- size_t struct_alignment = ctx->struct_alignment;
- ctx->new_count = 1;
- ++ts;
- if (*ts != '{') {
- PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'");
- return NULL;
- }
- if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
- ctx->enc_type = 0;
- ctx->enc_count = 0;
- ctx->struct_alignment = 0;
- ++ts;
- ts_after_sub = ts;
- for (i = 0; i != struct_count; ++i) {
- ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts);
- if (!ts_after_sub) return NULL;
- }
- ts = ts_after_sub;
- if (struct_alignment) ctx->struct_alignment = struct_alignment;
- }
- break;
- case '}':
- {
- size_t alignment = ctx->struct_alignment;
- ++ts;
- if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
- ctx->enc_type = 0;
- if (alignment && ctx->fmt_offset % alignment) {
- ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment);
- }
- }
- return ts;
- case 'x':
- if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
- ctx->fmt_offset += ctx->new_count;
- ctx->new_count = 1;
- ctx->enc_count = 0;
- ctx->enc_type = 0;
- ctx->enc_packmode = ctx->new_packmode;
- ++ts;
- break;
- case 'Z':
- got_Z = 1;
- ++ts;
- if (*ts != 'f' && *ts != 'd' && *ts != 'g') {
- __Pyx_BufFmt_RaiseUnexpectedChar('Z');
- return NULL;
- }
- CYTHON_FALLTHROUGH;
- case '?': case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I':
- case 'l': case 'L': case 'q': case 'Q':
- case 'f': case 'd': case 'g':
- case 'O': case 'p':
- if ((ctx->enc_type == *ts) && (got_Z == ctx->is_complex) &&
- (ctx->enc_packmode == ctx->new_packmode) && (!ctx->is_valid_array)) {
- ctx->enc_count += ctx->new_count;
- ctx->new_count = 1;
- got_Z = 0;
- ++ts;
- break;
- }
- CYTHON_FALLTHROUGH;
- case 's':
- if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
- ctx->enc_count = ctx->new_count;
- ctx->enc_packmode = ctx->new_packmode;
- ctx->enc_type = *ts;
- ctx->is_complex = got_Z;
- ++ts;
- ctx->new_count = 1;
- got_Z = 0;
- break;
- case ':':
- ++ts;
- while(*ts != ':') ++ts;
- ++ts;
- break;
- case '(':
- if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL;
- break;
- default:
- {
- int number = __Pyx_BufFmt_ExpectNumber(&ts);
- if (number == -1) return NULL;
- ctx->new_count = (size_t)number;
- }
- }
- }
-}
-
-/* BufferGetAndValidate */
- static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info) {
- if (unlikely(info->buf == NULL)) return;
- if (info->suboffsets == __Pyx_minusones) info->suboffsets = NULL;
- __Pyx_ReleaseBuffer(info);
-}
-static void __Pyx_ZeroBuffer(Py_buffer* buf) {
- buf->buf = NULL;
- buf->obj = NULL;
- buf->strides = __Pyx_zeros;
- buf->shape = __Pyx_zeros;
- buf->suboffsets = __Pyx_minusones;
-}
-static int __Pyx__GetBufferAndValidate(
- Py_buffer* buf, PyObject* obj, __Pyx_TypeInfo* dtype, int flags,
- int nd, int cast, __Pyx_BufFmt_StackElem* stack)
-{
- buf->buf = NULL;
- if (unlikely(__Pyx_GetBuffer(obj, buf, flags) == -1)) {
- __Pyx_ZeroBuffer(buf);
- return -1;
- }
- if (unlikely(buf->ndim != nd)) {
- PyErr_Format(PyExc_ValueError,
- "Buffer has wrong number of dimensions (expected %d, got %d)",
- nd, buf->ndim);
- goto fail;
- }
- if (!cast) {
- __Pyx_BufFmt_Context ctx;
- __Pyx_BufFmt_Init(&ctx, stack, dtype);
- if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail;
- }
- if (unlikely((size_t)buf->itemsize != dtype->size)) {
- PyErr_Format(PyExc_ValueError,
- "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "d byte%s) does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "d byte%s)",
- buf->itemsize, (buf->itemsize > 1) ? "s" : "",
- dtype->name, (Py_ssize_t)dtype->size, (dtype->size > 1) ? "s" : "");
- goto fail;
- }
- if (buf->suboffsets == NULL) buf->suboffsets = __Pyx_minusones;
- return 0;
-fail:;
- __Pyx_SafeReleaseBuffer(buf);
- return -1;
-}
-
-/* PyDictVersioning */
- #if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS
-static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj) {
- PyObject *dict = Py_TYPE(obj)->tp_dict;
- return likely(dict) ? __PYX_GET_DICT_VERSION(dict) : 0;
-}
-static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj) {
- PyObject **dictptr = NULL;
- Py_ssize_t offset = Py_TYPE(obj)->tp_dictoffset;
- if (offset) {
-#if CYTHON_COMPILING_IN_CPYTHON
- dictptr = (likely(offset > 0)) ? (PyObject **) ((char *)obj + offset) : _PyObject_GetDictPtr(obj);
-#else
- dictptr = _PyObject_GetDictPtr(obj);
-#endif
- }
- return (dictptr && *dictptr) ? __PYX_GET_DICT_VERSION(*dictptr) : 0;
-}
-static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version) {
- PyObject *dict = Py_TYPE(obj)->tp_dict;
- if (unlikely(!dict) || unlikely(tp_dict_version != __PYX_GET_DICT_VERSION(dict)))
- return 0;
- return obj_dict_version == __Pyx_get_object_dict_version(obj);
-}
-#endif
-
-/* GetModuleGlobalName */
- #if CYTHON_USE_DICT_VERSIONS
-static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value)
-#else
-static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name)
-#endif
-{
- PyObject *result;
-#if !CYTHON_AVOID_BORROWED_REFS
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1
- result = _PyDict_GetItem_KnownHash(__pyx_d, name, ((PyASCIIObject *) name)->hash);
- __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version)
- if (likely(result)) {
- return __Pyx_NewRef(result);
- } else if (unlikely(PyErr_Occurred())) {
- return NULL;
- }
-#else
- result = PyDict_GetItem(__pyx_d, name);
- __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version)
- if (likely(result)) {
- return __Pyx_NewRef(result);
- }
-#endif
-#else
- result = PyObject_GetItem(__pyx_d, name);
- __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version)
- if (likely(result)) {
- return __Pyx_NewRef(result);
- }
- PyErr_Clear();
-#endif
- return __Pyx_GetBuiltinName(name);
-}
-
-/* PyObjectCall */
- #if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) {
- PyObject *result;
- ternaryfunc call = Py_TYPE(func)->tp_call;
- if (unlikely(!call))
- return PyObject_Call(func, arg, kw);
- if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object")))
- return NULL;
- result = (*call)(func, arg, kw);
- Py_LeaveRecursiveCall();
- if (unlikely(!result) && unlikely(!PyErr_Occurred())) {
- PyErr_SetString(
- PyExc_SystemError,
- "NULL result without error in PyObject_Call");
- }
- return result;
-}
-#endif
-
-/* ExtTypeTest */
- static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) {
- if (unlikely(!type)) {
- PyErr_SetString(PyExc_SystemError, "Missing type object");
- return 0;
- }
- if (likely(__Pyx_TypeCheck(obj, type)))
- return 1;
- PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s",
- Py_TYPE(obj)->tp_name, type->tp_name);
- return 0;
-}
-
-/* BufferIndexError */
- static void __Pyx_RaiseBufferIndexError(int axis) {
- PyErr_Format(PyExc_IndexError,
- "Out of bounds on buffer access (axis %d)", axis);
-}
-
-/* PyErrFetchRestore */
- #if CYTHON_FAST_THREAD_STATE
-static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) {
- PyObject *tmp_type, *tmp_value, *tmp_tb;
- tmp_type = tstate->curexc_type;
- tmp_value = tstate->curexc_value;
- tmp_tb = tstate->curexc_traceback;
- tstate->curexc_type = type;
- tstate->curexc_value = value;
- tstate->curexc_traceback = tb;
- Py_XDECREF(tmp_type);
- Py_XDECREF(tmp_value);
- Py_XDECREF(tmp_tb);
-}
-static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
- *type = tstate->curexc_type;
- *value = tstate->curexc_value;
- *tb = tstate->curexc_traceback;
- tstate->curexc_type = 0;
- tstate->curexc_value = 0;
- tstate->curexc_traceback = 0;
-}
-#endif
-
-/* GetTopmostException */
- #if CYTHON_USE_EXC_INFO_STACK
-static _PyErr_StackItem *
-__Pyx_PyErr_GetTopmostException(PyThreadState *tstate)
-{
- _PyErr_StackItem *exc_info = tstate->exc_info;
- while ((exc_info->exc_type == NULL || exc_info->exc_type == Py_None) &&
- exc_info->previous_item != NULL)
- {
- exc_info = exc_info->previous_item;
- }
- return exc_info;
-}
-#endif
-
-/* SaveResetException */
- #if CYTHON_FAST_THREAD_STATE
-static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
- #if CYTHON_USE_EXC_INFO_STACK
- _PyErr_StackItem *exc_info = __Pyx_PyErr_GetTopmostException(tstate);
- *type = exc_info->exc_type;
- *value = exc_info->exc_value;
- *tb = exc_info->exc_traceback;
- #else
- *type = tstate->exc_type;
- *value = tstate->exc_value;
- *tb = tstate->exc_traceback;
- #endif
- Py_XINCREF(*type);
- Py_XINCREF(*value);
- Py_XINCREF(*tb);
-}
-static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) {
- PyObject *tmp_type, *tmp_value, *tmp_tb;
- #if CYTHON_USE_EXC_INFO_STACK
- _PyErr_StackItem *exc_info = tstate->exc_info;
- tmp_type = exc_info->exc_type;
- tmp_value = exc_info->exc_value;
- tmp_tb = exc_info->exc_traceback;
- exc_info->exc_type = type;
- exc_info->exc_value = value;
- exc_info->exc_traceback = tb;
- #else
- tmp_type = tstate->exc_type;
- tmp_value = tstate->exc_value;
- tmp_tb = tstate->exc_traceback;
- tstate->exc_type = type;
- tstate->exc_value = value;
- tstate->exc_traceback = tb;
- #endif
- Py_XDECREF(tmp_type);
- Py_XDECREF(tmp_value);
- Py_XDECREF(tmp_tb);
-}
-#endif
-
-/* PyErrExceptionMatches */
- #if CYTHON_FAST_THREAD_STATE
-static int __Pyx_PyErr_ExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) {
- Py_ssize_t i, n;
- n = PyTuple_GET_SIZE(tuple);
-#if PY_MAJOR_VERSION >= 3
- for (i=0; i<n; i++) {
- if (exc_type == PyTuple_GET_ITEM(tuple, i)) return 1;
- }
-#endif
- for (i=0; i<n; i++) {
- if (__Pyx_PyErr_GivenExceptionMatches(exc_type, PyTuple_GET_ITEM(tuple, i))) return 1;
- }
- return 0;
-}
-static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err) {
- PyObject *exc_type = tstate->curexc_type;
- if (exc_type == err) return 1;
- if (unlikely(!exc_type)) return 0;
- if (unlikely(PyTuple_Check(err)))
- return __Pyx_PyErr_ExceptionMatchesTuple(exc_type, err);
- return __Pyx_PyErr_GivenExceptionMatches(exc_type, err);
-}
-#endif
-
-/* GetException */
- #if CYTHON_FAST_THREAD_STATE
-static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb)
-#else
-static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb)
-#endif
-{
- PyObject *local_type, *local_value, *local_tb;
-#if CYTHON_FAST_THREAD_STATE
- PyObject *tmp_type, *tmp_value, *tmp_tb;
- local_type = tstate->curexc_type;
- local_value = tstate->curexc_value;
- local_tb = tstate->curexc_traceback;
- tstate->curexc_type = 0;
- tstate->curexc_value = 0;
- tstate->curexc_traceback = 0;
-#else
- PyErr_Fetch(&local_type, &local_value, &local_tb);
-#endif
- PyErr_NormalizeException(&local_type, &local_value, &local_tb);
-#if CYTHON_FAST_THREAD_STATE
- if (unlikely(tstate->curexc_type))
-#else
- if (unlikely(PyErr_Occurred()))
-#endif
- goto bad;
- #if PY_MAJOR_VERSION >= 3
- if (local_tb) {
- if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0))
- goto bad;
- }
- #endif
- Py_XINCREF(local_tb);
- Py_XINCREF(local_type);
- Py_XINCREF(local_value);
- *type = local_type;
- *value = local_value;
- *tb = local_tb;
-#if CYTHON_FAST_THREAD_STATE
- #if CYTHON_USE_EXC_INFO_STACK
- {
- _PyErr_StackItem *exc_info = tstate->exc_info;
- tmp_type = exc_info->exc_type;
- tmp_value = exc_info->exc_value;
- tmp_tb = exc_info->exc_traceback;
- exc_info->exc_type = local_type;
- exc_info->exc_value = local_value;
- exc_info->exc_traceback = local_tb;
- }
- #else
- tmp_type = tstate->exc_type;
- tmp_value = tstate->exc_value;
- tmp_tb = tstate->exc_traceback;
- tstate->exc_type = local_type;
- tstate->exc_value = local_value;
- tstate->exc_traceback = local_tb;
- #endif
- Py_XDECREF(tmp_type);
- Py_XDECREF(tmp_value);
- Py_XDECREF(tmp_tb);
-#else
- PyErr_SetExcInfo(local_type, local_value, local_tb);
-#endif
- return 0;
-bad:
- *type = 0;
- *value = 0;
- *tb = 0;
- Py_XDECREF(local_type);
- Py_XDECREF(local_value);
- Py_XDECREF(local_tb);
- return -1;
-}
-
-/* RaiseException */
- #if PY_MAJOR_VERSION < 3
-static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb,
- CYTHON_UNUSED PyObject *cause) {
- __Pyx_PyThreadState_declare
- Py_XINCREF(type);
- if (!value || value == Py_None)
- value = NULL;
- else
- Py_INCREF(value);
- if (!tb || tb == Py_None)
- tb = NULL;
- else {
- Py_INCREF(tb);
- if (!PyTraceBack_Check(tb)) {
- PyErr_SetString(PyExc_TypeError,
- "raise: arg 3 must be a traceback or None");
- goto raise_error;
- }
- }
- if (PyType_Check(type)) {
-#if CYTHON_COMPILING_IN_PYPY
- if (!value) {
- Py_INCREF(Py_None);
- value = Py_None;
- }
-#endif
- PyErr_NormalizeException(&type, &value, &tb);
- } else {
- if (value) {
- PyErr_SetString(PyExc_TypeError,
- "instance exception may not have a separate value");
- goto raise_error;
- }
- value = type;
- type = (PyObject*) Py_TYPE(type);
- Py_INCREF(type);
- if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) {
- PyErr_SetString(PyExc_TypeError,
- "raise: exception class must be a subclass of BaseException");
- goto raise_error;
- }
- }
- __Pyx_PyThreadState_assign
- __Pyx_ErrRestore(type, value, tb);
- return;
-raise_error:
- Py_XDECREF(value);
- Py_XDECREF(type);
- Py_XDECREF(tb);
- return;
-}
-#else
-static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) {
- PyObject* owned_instance = NULL;
- if (tb == Py_None) {
- tb = 0;
- } else if (tb && !PyTraceBack_Check(tb)) {
- PyErr_SetString(PyExc_TypeError,
- "raise: arg 3 must be a traceback or None");
- goto bad;
- }
- if (value == Py_None)
- value = 0;
- if (PyExceptionInstance_Check(type)) {
- if (value) {
- PyErr_SetString(PyExc_TypeError,
- "instance exception may not have a separate value");
- goto bad;
- }
- value = type;
- type = (PyObject*) Py_TYPE(value);
- } else if (PyExceptionClass_Check(type)) {
- PyObject *instance_class = NULL;
- if (value && PyExceptionInstance_Check(value)) {
- instance_class = (PyObject*) Py_TYPE(value);
- if (instance_class != type) {
- int is_subclass = PyObject_IsSubclass(instance_class, type);
- if (!is_subclass) {
- instance_class = NULL;
- } else if (unlikely(is_subclass == -1)) {
- goto bad;
- } else {
- type = instance_class;
- }
- }
- }
- if (!instance_class) {
- PyObject *args;
- if (!value)
- args = PyTuple_New(0);
- else if (PyTuple_Check(value)) {
- Py_INCREF(value);
- args = value;
- } else
- args = PyTuple_Pack(1, value);
- if (!args)
- goto bad;
- owned_instance = PyObject_Call(type, args, NULL);
- Py_DECREF(args);
- if (!owned_instance)
- goto bad;
- value = owned_instance;
- if (!PyExceptionInstance_Check(value)) {
- PyErr_Format(PyExc_TypeError,
- "calling %R should have returned an instance of "
- "BaseException, not %R",
- type, Py_TYPE(value));
- goto bad;
- }
- }
- } else {
- PyErr_SetString(PyExc_TypeError,
- "raise: exception class must be a subclass of BaseException");
- goto bad;
- }
- if (cause) {
- PyObject *fixed_cause;
- if (cause == Py_None) {
- fixed_cause = NULL;
- } else if (PyExceptionClass_Check(cause)) {
- fixed_cause = PyObject_CallObject(cause, NULL);
- if (fixed_cause == NULL)
- goto bad;
- } else if (PyExceptionInstance_Check(cause)) {
- fixed_cause = cause;
- Py_INCREF(fixed_cause);
- } else {
- PyErr_SetString(PyExc_TypeError,
- "exception causes must derive from "
- "BaseException");
- goto bad;
- }
- PyException_SetCause(value, fixed_cause);
- }
- PyErr_SetObject(type, value);
- if (tb) {
-#if CYTHON_COMPILING_IN_PYPY
- PyObject *tmp_type, *tmp_value, *tmp_tb;
- PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb);
- Py_INCREF(tb);
- PyErr_Restore(tmp_type, tmp_value, tb);
- Py_XDECREF(tmp_tb);
-#else
- PyThreadState *tstate = __Pyx_PyThreadState_Current;
- PyObject* tmp_tb = tstate->curexc_traceback;
- if (tb != tmp_tb) {
- Py_INCREF(tb);
- tstate->curexc_traceback = tb;
- Py_XDECREF(tmp_tb);
- }
-#endif
- }
-bad:
- Py_XDECREF(owned_instance);
- return;
-}
-#endif
-
-/* TypeImport */
- #ifndef __PYX_HAVE_RT_ImportType
-#define __PYX_HAVE_RT_ImportType
-static PyTypeObject *__Pyx_ImportType(PyObject *module, const char *module_name, const char *class_name,
- size_t size, enum __Pyx_ImportType_CheckSize check_size)
-{
- PyObject *result = 0;
- char warning[200];
- Py_ssize_t basicsize;
-#ifdef Py_LIMITED_API
- PyObject *py_basicsize;
-#endif
- result = PyObject_GetAttrString(module, class_name);
- if (!result)
- goto bad;
- if (!PyType_Check(result)) {
- PyErr_Format(PyExc_TypeError,
- "%.200s.%.200s is not a type object",
- module_name, class_name);
- goto bad;
- }
-#ifndef Py_LIMITED_API
- basicsize = ((PyTypeObject *)result)->tp_basicsize;
-#else
- py_basicsize = PyObject_GetAttrString(result, "__basicsize__");
- if (!py_basicsize)
- goto bad;
- basicsize = PyLong_AsSsize_t(py_basicsize);
- Py_DECREF(py_basicsize);
- py_basicsize = 0;
- if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred())
- goto bad;
-#endif
- if ((size_t)basicsize < size) {
- PyErr_Format(PyExc_ValueError,
- "%.200s.%.200s size changed, may indicate binary incompatibility. "
- "Expected %zd from C header, got %zd from PyObject",
- module_name, class_name, size, basicsize);
- goto bad;
- }
- if (check_size == __Pyx_ImportType_CheckSize_Error && (size_t)basicsize != size) {
- PyErr_Format(PyExc_ValueError,
- "%.200s.%.200s size changed, may indicate binary incompatibility. "
- "Expected %zd from C header, got %zd from PyObject",
- module_name, class_name, size, basicsize);
- goto bad;
- }
- else if (check_size == __Pyx_ImportType_CheckSize_Warn && (size_t)basicsize > size) {
- PyOS_snprintf(warning, sizeof(warning),
- "%s.%s size changed, may indicate binary incompatibility. "
- "Expected %zd from C header, got %zd from PyObject",
- module_name, class_name, size, basicsize);
- if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad;
- }
- return (PyTypeObject *)result;
-bad:
- Py_XDECREF(result);
- return NULL;
-}
-#endif
-
-/* Import */
- static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) {
- PyObject *empty_list = 0;
- PyObject *module = 0;
- PyObject *global_dict = 0;
- PyObject *empty_dict = 0;
- PyObject *list;
- #if PY_MAJOR_VERSION < 3
- PyObject *py_import;
- py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import);
- if (!py_import)
- goto bad;
- #endif
- if (from_list)
- list = from_list;
- else {
- empty_list = PyList_New(0);
- if (!empty_list)
- goto bad;
- list = empty_list;
- }
- global_dict = PyModule_GetDict(__pyx_m);
- if (!global_dict)
- goto bad;
- empty_dict = PyDict_New();
- if (!empty_dict)
- goto bad;
- {
- #if PY_MAJOR_VERSION >= 3
- if (level == -1) {
- if ((1) && (strchr(__Pyx_MODULE_NAME, '.'))) {
- module = PyImport_ImportModuleLevelObject(
- name, global_dict, empty_dict, list, 1);
- if (!module) {
- if (!PyErr_ExceptionMatches(PyExc_ImportError))
- goto bad;
- PyErr_Clear();
- }
- }
- level = 0;
- }
- #endif
- if (!module) {
- #if PY_MAJOR_VERSION < 3
- PyObject *py_level = PyInt_FromLong(level);
- if (!py_level)
- goto bad;
- module = PyObject_CallFunctionObjArgs(py_import,
- name, global_dict, empty_dict, list, py_level, (PyObject *)NULL);
- Py_DECREF(py_level);
- #else
- module = PyImport_ImportModuleLevelObject(
- name, global_dict, empty_dict, list, level);
- #endif
- }
- }
-bad:
- #if PY_MAJOR_VERSION < 3
- Py_XDECREF(py_import);
- #endif
- Py_XDECREF(empty_list);
- Py_XDECREF(empty_dict);
- return module;
-}
-
-/* CLineInTraceback */
- #ifndef CYTHON_CLINE_IN_TRACEBACK
-static int __Pyx_CLineForTraceback(CYTHON_NCP_UNUSED PyThreadState *tstate, int c_line) {
- PyObject *use_cline;
- PyObject *ptype, *pvalue, *ptraceback;
-#if CYTHON_COMPILING_IN_CPYTHON
- PyObject **cython_runtime_dict;
-#endif
- if (unlikely(!__pyx_cython_runtime)) {
- return c_line;
- }
- __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback);
-#if CYTHON_COMPILING_IN_CPYTHON
- cython_runtime_dict = _PyObject_GetDictPtr(__pyx_cython_runtime);
- if (likely(cython_runtime_dict)) {
- __PYX_PY_DICT_LOOKUP_IF_MODIFIED(
- use_cline, *cython_runtime_dict,
- __Pyx_PyDict_GetItemStr(*cython_runtime_dict, __pyx_n_s_cline_in_traceback))
- } else
-#endif
- {
- PyObject *use_cline_obj = __Pyx_PyObject_GetAttrStr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback);
- if (use_cline_obj) {
- use_cline = PyObject_Not(use_cline_obj) ? Py_False : Py_True;
- Py_DECREF(use_cline_obj);
- } else {
- PyErr_Clear();
- use_cline = NULL;
- }
- }
- if (!use_cline) {
- c_line = 0;
- PyObject_SetAttr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback, Py_False);
- }
- else if (use_cline == Py_False || (use_cline != Py_True && PyObject_Not(use_cline) != 0)) {
- c_line = 0;
- }
- __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback);
- return c_line;
-}
-#endif
-
-/* CodeObjectCache */
- static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) {
- int start = 0, mid = 0, end = count - 1;
- if (end >= 0 && code_line > entries[end].code_line) {
- return count;
- }
- while (start < end) {
- mid = start + (end - start) / 2;
- if (code_line < entries[mid].code_line) {
- end = mid;
- } else if (code_line > entries[mid].code_line) {
- start = mid + 1;
- } else {
- return mid;
- }
- }
- if (code_line <= entries[mid].code_line) {
- return mid;
- } else {
- return mid + 1;
- }
-}
-static PyCodeObject *__pyx_find_code_object(int code_line) {
- PyCodeObject* code_object;
- int pos;
- if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) {
- return NULL;
- }
- pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
- if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) {
- return NULL;
- }
- code_object = __pyx_code_cache.entries[pos].code_object;
- Py_INCREF(code_object);
- return code_object;
-}
-static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) {
- int pos, i;
- __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries;
- if (unlikely(!code_line)) {
- return;
- }
- if (unlikely(!entries)) {
- entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry));
- if (likely(entries)) {
- __pyx_code_cache.entries = entries;
- __pyx_code_cache.max_count = 64;
- __pyx_code_cache.count = 1;
- entries[0].code_line = code_line;
- entries[0].code_object = code_object;
- Py_INCREF(code_object);
- }
- return;
- }
- pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
- if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) {
- PyCodeObject* tmp = entries[pos].code_object;
- entries[pos].code_object = code_object;
- Py_DECREF(tmp);
- return;
- }
- if (__pyx_code_cache.count == __pyx_code_cache.max_count) {
- int new_max = __pyx_code_cache.max_count + 64;
- entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc(
- __pyx_code_cache.entries, ((size_t)new_max) * sizeof(__Pyx_CodeObjectCacheEntry));
- if (unlikely(!entries)) {
- return;
- }
- __pyx_code_cache.entries = entries;
- __pyx_code_cache.max_count = new_max;
- }
- for (i=__pyx_code_cache.count; i>pos; i--) {
- entries[i] = entries[i-1];
- }
- entries[pos].code_line = code_line;
- entries[pos].code_object = code_object;
- __pyx_code_cache.count++;
- Py_INCREF(code_object);
-}
-
-/* AddTraceback */
- #include "compile.h"
-#include "frameobject.h"
-#include "traceback.h"
-static PyCodeObject* __Pyx_CreateCodeObjectForTraceback(
- const char *funcname, int c_line,
- int py_line, const char *filename) {
- PyCodeObject *py_code = 0;
- PyObject *py_srcfile = 0;
- PyObject *py_funcname = 0;
- #if PY_MAJOR_VERSION < 3
- py_srcfile = PyString_FromString(filename);
- #else
- py_srcfile = PyUnicode_FromString(filename);
- #endif
- if (!py_srcfile) goto bad;
- if (c_line) {
- #if PY_MAJOR_VERSION < 3
- py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
- #else
- py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
- #endif
- }
- else {
- #if PY_MAJOR_VERSION < 3
- py_funcname = PyString_FromString(funcname);
- #else
- py_funcname = PyUnicode_FromString(funcname);
- #endif
- }
- if (!py_funcname) goto bad;
- py_code = __Pyx_PyCode_New(
- 0,
- 0,
- 0,
- 0,
- 0,
- __pyx_empty_bytes, /*PyObject *code,*/
- __pyx_empty_tuple, /*PyObject *consts,*/
- __pyx_empty_tuple, /*PyObject *names,*/
- __pyx_empty_tuple, /*PyObject *varnames,*/
- __pyx_empty_tuple, /*PyObject *freevars,*/
- __pyx_empty_tuple, /*PyObject *cellvars,*/
- py_srcfile, /*PyObject *filename,*/
- py_funcname, /*PyObject *name,*/
- py_line,
- __pyx_empty_bytes /*PyObject *lnotab*/
- );
- Py_DECREF(py_srcfile);
- Py_DECREF(py_funcname);
- return py_code;
-bad:
- Py_XDECREF(py_srcfile);
- Py_XDECREF(py_funcname);
- return NULL;
-}
-static void __Pyx_AddTraceback(const char *funcname, int c_line,
- int py_line, const char *filename) {
- PyCodeObject *py_code = 0;
- PyFrameObject *py_frame = 0;
- PyThreadState *tstate = __Pyx_PyThreadState_Current;
- if (c_line) {
- c_line = __Pyx_CLineForTraceback(tstate, c_line);
- }
- py_code = __pyx_find_code_object(c_line ? -c_line : py_line);
- if (!py_code) {
- py_code = __Pyx_CreateCodeObjectForTraceback(
- funcname, c_line, py_line, filename);
- if (!py_code) goto bad;
- __pyx_insert_code_object(c_line ? -c_line : py_line, py_code);
- }
- py_frame = PyFrame_New(
- tstate, /*PyThreadState *tstate,*/
- py_code, /*PyCodeObject *code,*/
- __pyx_d, /*PyObject *globals,*/
- 0 /*PyObject *locals*/
- );
- if (!py_frame) goto bad;
- __Pyx_PyFrame_SetLineNumber(py_frame, py_line);
- PyTraceBack_Here(py_frame);
-bad:
- Py_XDECREF(py_code);
- Py_XDECREF(py_frame);
-}
-
-#if PY_MAJOR_VERSION < 3
-static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) {
- if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags);
- PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name);
- return -1;
-}
-static void __Pyx_ReleaseBuffer(Py_buffer *view) {
- PyObject *obj = view->obj;
- if (!obj) return;
- if (PyObject_CheckBuffer(obj)) {
- PyBuffer_Release(view);
- return;
- }
- if ((0)) {}
- view->obj = NULL;
- Py_DECREF(obj);
-}
-#endif
-
-
- /* CIntFromPyVerify */
- #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\
- __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0)
-#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\
- __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1)
-#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\
- {\
- func_type value = func_value;\
- if (sizeof(target_type) < sizeof(func_type)) {\
- if (unlikely(value != (func_type) (target_type) value)) {\
- func_type zero = 0;\
- if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\
- return (target_type) -1;\
- if (is_unsigned && unlikely(value < zero))\
- goto raise_neg_overflow;\
- else\
- goto raise_overflow;\
- }\
- }\
- return (target_type) value;\
- }
-
-/* Declarations */
- #if CYTHON_CCOMPLEX
- #ifdef __cplusplus
- static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {
- return ::std::complex< float >(x, y);
- }
- #else
- static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {
- return x + y*(__pyx_t_float_complex)_Complex_I;
- }
- #endif
-#else
- static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {
- __pyx_t_float_complex z;
- z.real = x;
- z.imag = y;
- return z;
- }
-#endif
-
-/* Arithmetic */
- #if CYTHON_CCOMPLEX
-#else
- static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
- return (a.real == b.real) && (a.imag == b.imag);
- }
- static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
- __pyx_t_float_complex z;
- z.real = a.real + b.real;
- z.imag = a.imag + b.imag;
- return z;
- }
- static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
- __pyx_t_float_complex z;
- z.real = a.real - b.real;
- z.imag = a.imag - b.imag;
- return z;
- }
- static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
- __pyx_t_float_complex z;
- z.real = a.real * b.real - a.imag * b.imag;
- z.imag = a.real * b.imag + a.imag * b.real;
- return z;
- }
- #if 1
- static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
- if (b.imag == 0) {
- return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real);
- } else if (fabsf(b.real) >= fabsf(b.imag)) {
- if (b.real == 0 && b.imag == 0) {
- return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.imag);
- } else {
- float r = b.imag / b.real;
- float s = (float)(1.0) / (b.real + b.imag * r);
- return __pyx_t_float_complex_from_parts(
- (a.real + a.imag * r) * s, (a.imag - a.real * r) * s);
- }
- } else {
- float r = b.real / b.imag;
- float s = (float)(1.0) / (b.imag + b.real * r);
- return __pyx_t_float_complex_from_parts(
- (a.real * r + a.imag) * s, (a.imag * r - a.real) * s);
- }
- }
- #else
- static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
- if (b.imag == 0) {
- return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real);
- } else {
- float denom = b.real * b.real + b.imag * b.imag;
- return __pyx_t_float_complex_from_parts(
- (a.real * b.real + a.imag * b.imag) / denom,
- (a.imag * b.real - a.real * b.imag) / denom);
- }
- }
- #endif
- static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex a) {
- __pyx_t_float_complex z;
- z.real = -a.real;
- z.imag = -a.imag;
- return z;
- }
- static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex a) {
- return (a.real == 0) && (a.imag == 0);
- }
- static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex a) {
- __pyx_t_float_complex z;
- z.real = a.real;
- z.imag = -a.imag;
- return z;
- }
- #if 1
- static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex z) {
- #if !defined(HAVE_HYPOT) || defined(_MSC_VER)
- return sqrtf(z.real*z.real + z.imag*z.imag);
- #else
- return hypotf(z.real, z.imag);
- #endif
- }
- static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
- __pyx_t_float_complex z;
- float r, lnr, theta, z_r, z_theta;
- if (b.imag == 0 && b.real == (int)b.real) {
- if (b.real < 0) {
- float denom = a.real * a.real + a.imag * a.imag;
- a.real = a.real / denom;
- a.imag = -a.imag / denom;
- b.real = -b.real;
- }
- switch ((int)b.real) {
- case 0:
- z.real = 1;
- z.imag = 0;
- return z;
- case 1:
- return a;
- case 2:
- return __Pyx_c_prod_float(a, a);
- case 3:
- z = __Pyx_c_prod_float(a, a);
- return __Pyx_c_prod_float(z, a);
- case 4:
- z = __Pyx_c_prod_float(a, a);
- return __Pyx_c_prod_float(z, z);
- }
- }
- if (a.imag == 0) {
- if (a.real == 0) {
- return a;
- } else if (b.imag == 0) {
- z.real = powf(a.real, b.real);
- z.imag = 0;
- return z;
- } else if (a.real > 0) {
- r = a.real;
- theta = 0;
- } else {
- r = -a.real;
- theta = atan2f(0.0, -1.0);
- }
- } else {
- r = __Pyx_c_abs_float(a);
- theta = atan2f(a.imag, a.real);
- }
- lnr = logf(r);
- z_r = expf(lnr * b.real - theta * b.imag);
- z_theta = theta * b.real + lnr * b.imag;
- z.real = z_r * cosf(z_theta);
- z.imag = z_r * sinf(z_theta);
- return z;
- }
- #endif
-#endif
-
-/* Declarations */
- #if CYTHON_CCOMPLEX
- #ifdef __cplusplus
- static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
- return ::std::complex< double >(x, y);
- }
- #else
- static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
- return x + y*(__pyx_t_double_complex)_Complex_I;
- }
- #endif
-#else
- static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
- __pyx_t_double_complex z;
- z.real = x;
- z.imag = y;
- return z;
- }
-#endif
-
-/* Arithmetic */
- #if CYTHON_CCOMPLEX
-#else
- static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
- return (a.real == b.real) && (a.imag == b.imag);
- }
- static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
- __pyx_t_double_complex z;
- z.real = a.real + b.real;
- z.imag = a.imag + b.imag;
- return z;
- }
- static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
- __pyx_t_double_complex z;
- z.real = a.real - b.real;
- z.imag = a.imag - b.imag;
- return z;
- }
- static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
- __pyx_t_double_complex z;
- z.real = a.real * b.real - a.imag * b.imag;
- z.imag = a.real * b.imag + a.imag * b.real;
- return z;
- }
- #if 1
- static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
- if (b.imag == 0) {
- return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real);
- } else if (fabs(b.real) >= fabs(b.imag)) {
- if (b.real == 0 && b.imag == 0) {
- return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.imag);
- } else {
- double r = b.imag / b.real;
- double s = (double)(1.0) / (b.real + b.imag * r);
- return __pyx_t_double_complex_from_parts(
- (a.real + a.imag * r) * s, (a.imag - a.real * r) * s);
- }
- } else {
- double r = b.real / b.imag;
- double s = (double)(1.0) / (b.imag + b.real * r);
- return __pyx_t_double_complex_from_parts(
- (a.real * r + a.imag) * s, (a.imag * r - a.real) * s);
- }
- }
- #else
- static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
- if (b.imag == 0) {
- return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real);
- } else {
- double denom = b.real * b.real + b.imag * b.imag;
- return __pyx_t_double_complex_from_parts(
- (a.real * b.real + a.imag * b.imag) / denom,
- (a.imag * b.real - a.real * b.imag) / denom);
- }
- }
- #endif
- static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex a) {
- __pyx_t_double_complex z;
- z.real = -a.real;
- z.imag = -a.imag;
- return z;
- }
- static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex a) {
- return (a.real == 0) && (a.imag == 0);
- }
- static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex a) {
- __pyx_t_double_complex z;
- z.real = a.real;
- z.imag = -a.imag;
- return z;
- }
- #if 1
- static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex z) {
- #if !defined(HAVE_HYPOT) || defined(_MSC_VER)
- return sqrt(z.real*z.real + z.imag*z.imag);
- #else
- return hypot(z.real, z.imag);
- #endif
- }
- static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
- __pyx_t_double_complex z;
- double r, lnr, theta, z_r, z_theta;
- if (b.imag == 0 && b.real == (int)b.real) {
- if (b.real < 0) {
- double denom = a.real * a.real + a.imag * a.imag;
- a.real = a.real / denom;
- a.imag = -a.imag / denom;
- b.real = -b.real;
- }
- switch ((int)b.real) {
- case 0:
- z.real = 1;
- z.imag = 0;
- return z;
- case 1:
- return a;
- case 2:
- return __Pyx_c_prod_double(a, a);
- case 3:
- z = __Pyx_c_prod_double(a, a);
- return __Pyx_c_prod_double(z, a);
- case 4:
- z = __Pyx_c_prod_double(a, a);
- return __Pyx_c_prod_double(z, z);
- }
- }
- if (a.imag == 0) {
- if (a.real == 0) {
- return a;
- } else if (b.imag == 0) {
- z.real = pow(a.real, b.real);
- z.imag = 0;
- return z;
- } else if (a.real > 0) {
- r = a.real;
- theta = 0;
- } else {
- r = -a.real;
- theta = atan2(0.0, -1.0);
- }
- } else {
- r = __Pyx_c_abs_double(a);
- theta = atan2(a.imag, a.real);
- }
- lnr = log(r);
- z_r = exp(lnr * b.real - theta * b.imag);
- z_theta = theta * b.real + lnr * b.imag;
- z.real = z_r * cos(z_theta);
- z.imag = z_r * sin(z_theta);
- return z;
- }
- #endif
-#endif
-
-/* CIntToPy */
- static CYTHON_INLINE PyObject* __Pyx_PyInt_From_unsigned_int(unsigned int value) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
- const unsigned int neg_one = (unsigned int) -1, const_zero = (unsigned int) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
- const int is_unsigned = neg_one > const_zero;
- if (is_unsigned) {
- if (sizeof(unsigned int) < sizeof(long)) {
- return PyInt_FromLong((long) value);
- } else if (sizeof(unsigned int) <= sizeof(unsigned long)) {
- return PyLong_FromUnsignedLong((unsigned long) value);
-#ifdef HAVE_LONG_LONG
- } else if (sizeof(unsigned int) <= sizeof(unsigned PY_LONG_LONG)) {
- return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
-#endif
- }
- } else {
- if (sizeof(unsigned int) <= sizeof(long)) {
- return PyInt_FromLong((long) value);
-#ifdef HAVE_LONG_LONG
- } else if (sizeof(unsigned int) <= sizeof(PY_LONG_LONG)) {
- return PyLong_FromLongLong((PY_LONG_LONG) value);
-#endif
- }
- }
- {
- int one = 1; int little = (int)*(unsigned char *)&one;
- unsigned char *bytes = (unsigned char *)&value;
- return _PyLong_FromByteArray(bytes, sizeof(unsigned int),
- little, !is_unsigned);
- }
-}
-
-/* CIntFromPy */
- static CYTHON_INLINE unsigned int __Pyx_PyInt_As_unsigned_int(PyObject *x) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
- const unsigned int neg_one = (unsigned int) -1, const_zero = (unsigned int) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
- const int is_unsigned = neg_one > const_zero;
-#if PY_MAJOR_VERSION < 3
- if (likely(PyInt_Check(x))) {
- if (sizeof(unsigned int) < sizeof(long)) {
- __PYX_VERIFY_RETURN_INT(unsigned int, long, PyInt_AS_LONG(x))
- } else {
- long val = PyInt_AS_LONG(x);
- if (is_unsigned && unlikely(val < 0)) {
- goto raise_neg_overflow;
- }
- return (unsigned int) val;
- }
- } else
-#endif
- if (likely(PyLong_Check(x))) {
- if (is_unsigned) {
-#if CYTHON_USE_PYLONG_INTERNALS
- const digit* digits = ((PyLongObject*)x)->ob_digit;
- switch (Py_SIZE(x)) {
- case 0: return (unsigned int) 0;
- case 1: __PYX_VERIFY_RETURN_INT(unsigned int, digit, digits[0])
- case 2:
- if (8 * sizeof(unsigned int) > 1 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(unsigned int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(unsigned int) >= 2 * PyLong_SHIFT) {
- return (unsigned int) (((((unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0]));
- }
- }
- break;
- case 3:
- if (8 * sizeof(unsigned int) > 2 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(unsigned int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(unsigned int) >= 3 * PyLong_SHIFT) {
- return (unsigned int) (((((((unsigned int)digits[2]) << PyLong_SHIFT) | (unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0]));
- }
- }
- break;
- case 4:
- if (8 * sizeof(unsigned int) > 3 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(unsigned int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(unsigned int) >= 4 * PyLong_SHIFT) {
- return (unsigned int) (((((((((unsigned int)digits[3]) << PyLong_SHIFT) | (unsigned int)digits[2]) << PyLong_SHIFT) | (unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0]));
- }
- }
- break;
- }
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON
- if (unlikely(Py_SIZE(x) < 0)) {
- goto raise_neg_overflow;
- }
-#else
- {
- int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
- if (unlikely(result < 0))
- return (unsigned int) -1;
- if (unlikely(result == 1))
- goto raise_neg_overflow;
- }
-#endif
- if (sizeof(unsigned int) <= sizeof(unsigned long)) {
- __PYX_VERIFY_RETURN_INT_EXC(unsigned int, unsigned long, PyLong_AsUnsignedLong(x))
-#ifdef HAVE_LONG_LONG
- } else if (sizeof(unsigned int) <= sizeof(unsigned PY_LONG_LONG)) {
- __PYX_VERIFY_RETURN_INT_EXC(unsigned int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
-#endif
- }
- } else {
-#if CYTHON_USE_PYLONG_INTERNALS
- const digit* digits = ((PyLongObject*)x)->ob_digit;
- switch (Py_SIZE(x)) {
- case 0: return (unsigned int) 0;
- case -1: __PYX_VERIFY_RETURN_INT(unsigned int, sdigit, (sdigit) (-(sdigit)digits[0]))
- case 1: __PYX_VERIFY_RETURN_INT(unsigned int, digit, +digits[0])
- case -2:
- if (8 * sizeof(unsigned int) - 1 > 1 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(unsigned int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(unsigned int) - 1 > 2 * PyLong_SHIFT) {
- return (unsigned int) (((unsigned int)-1)*(((((unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0])));
- }
- }
- break;
- case 2:
- if (8 * sizeof(unsigned int) > 1 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(unsigned int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(unsigned int) - 1 > 2 * PyLong_SHIFT) {
- return (unsigned int) ((((((unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0])));
- }
- }
- break;
- case -3:
- if (8 * sizeof(unsigned int) - 1 > 2 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(unsigned int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(unsigned int) - 1 > 3 * PyLong_SHIFT) {
- return (unsigned int) (((unsigned int)-1)*(((((((unsigned int)digits[2]) << PyLong_SHIFT) | (unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0])));
- }
- }
- break;
- case 3:
- if (8 * sizeof(unsigned int) > 2 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(unsigned int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(unsigned int) - 1 > 3 * PyLong_SHIFT) {
- return (unsigned int) ((((((((unsigned int)digits[2]) << PyLong_SHIFT) | (unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0])));
- }
- }
- break;
- case -4:
- if (8 * sizeof(unsigned int) - 1 > 3 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(unsigned int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(unsigned int) - 1 > 4 * PyLong_SHIFT) {
- return (unsigned int) (((unsigned int)-1)*(((((((((unsigned int)digits[3]) << PyLong_SHIFT) | (unsigned int)digits[2]) << PyLong_SHIFT) | (unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0])));
- }
- }
- break;
- case 4:
- if (8 * sizeof(unsigned int) > 3 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(unsigned int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(unsigned int) - 1 > 4 * PyLong_SHIFT) {
- return (unsigned int) ((((((((((unsigned int)digits[3]) << PyLong_SHIFT) | (unsigned int)digits[2]) << PyLong_SHIFT) | (unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0])));
- }
- }
- break;
- }
-#endif
- if (sizeof(unsigned int) <= sizeof(long)) {
- __PYX_VERIFY_RETURN_INT_EXC(unsigned int, long, PyLong_AsLong(x))
-#ifdef HAVE_LONG_LONG
- } else if (sizeof(unsigned int) <= sizeof(PY_LONG_LONG)) {
- __PYX_VERIFY_RETURN_INT_EXC(unsigned int, PY_LONG_LONG, PyLong_AsLongLong(x))
-#endif
- }
- }
- {
-#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
- PyErr_SetString(PyExc_RuntimeError,
- "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
-#else
- unsigned int val;
- PyObject *v = __Pyx_PyNumber_IntOrLong(x);
- #if PY_MAJOR_VERSION < 3
- if (likely(v) && !PyLong_Check(v)) {
- PyObject *tmp = v;
- v = PyNumber_Long(tmp);
- Py_DECREF(tmp);
- }
- #endif
- if (likely(v)) {
- int one = 1; int is_little = (int)*(unsigned char *)&one;
- unsigned char *bytes = (unsigned char *)&val;
- int ret = _PyLong_AsByteArray((PyLongObject *)v,
- bytes, sizeof(val),
- is_little, !is_unsigned);
- Py_DECREF(v);
- if (likely(!ret))
- return val;
- }
-#endif
- return (unsigned int) -1;
- }
- } else {
- unsigned int val;
- PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
- if (!tmp) return (unsigned int) -1;
- val = __Pyx_PyInt_As_unsigned_int(tmp);
- Py_DECREF(tmp);
- return val;
- }
-raise_overflow:
- PyErr_SetString(PyExc_OverflowError,
- "value too large to convert to unsigned int");
- return (unsigned int) -1;
-raise_neg_overflow:
- PyErr_SetString(PyExc_OverflowError,
- "can't convert negative value to unsigned int");
- return (unsigned int) -1;
-}
-
-/* CIntToPy */
- static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
- const long neg_one = (long) -1, const_zero = (long) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
- const int is_unsigned = neg_one > const_zero;
- if (is_unsigned) {
- if (sizeof(long) < sizeof(long)) {
- return PyInt_FromLong((long) value);
- } else if (sizeof(long) <= sizeof(unsigned long)) {
- return PyLong_FromUnsignedLong((unsigned long) value);
-#ifdef HAVE_LONG_LONG
- } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) {
- return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
-#endif
- }
- } else {
- if (sizeof(long) <= sizeof(long)) {
- return PyInt_FromLong((long) value);
-#ifdef HAVE_LONG_LONG
- } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) {
- return PyLong_FromLongLong((PY_LONG_LONG) value);
-#endif
- }
- }
- {
- int one = 1; int little = (int)*(unsigned char *)&one;
- unsigned char *bytes = (unsigned char *)&value;
- return _PyLong_FromByteArray(bytes, sizeof(long),
- little, !is_unsigned);
- }
-}
-
-/* CIntFromPy */
- static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
- const long neg_one = (long) -1, const_zero = (long) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
- const int is_unsigned = neg_one > const_zero;
-#if PY_MAJOR_VERSION < 3
- if (likely(PyInt_Check(x))) {
- if (sizeof(long) < sizeof(long)) {
- __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x))
- } else {
- long val = PyInt_AS_LONG(x);
- if (is_unsigned && unlikely(val < 0)) {
- goto raise_neg_overflow;
- }
- return (long) val;
- }
- } else
-#endif
- if (likely(PyLong_Check(x))) {
- if (is_unsigned) {
-#if CYTHON_USE_PYLONG_INTERNALS
- const digit* digits = ((PyLongObject*)x)->ob_digit;
- switch (Py_SIZE(x)) {
- case 0: return (long) 0;
- case 1: __PYX_VERIFY_RETURN_INT(long, digit, digits[0])
- case 2:
- if (8 * sizeof(long) > 1 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(long) >= 2 * PyLong_SHIFT) {
- return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
- }
- }
- break;
- case 3:
- if (8 * sizeof(long) > 2 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(long) >= 3 * PyLong_SHIFT) {
- return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
- }
- }
- break;
- case 4:
- if (8 * sizeof(long) > 3 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(long) >= 4 * PyLong_SHIFT) {
- return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
- }
- }
- break;
- }
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON
- if (unlikely(Py_SIZE(x) < 0)) {
- goto raise_neg_overflow;
- }
-#else
- {
- int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
- if (unlikely(result < 0))
- return (long) -1;
- if (unlikely(result == 1))
- goto raise_neg_overflow;
- }
-#endif
- if (sizeof(long) <= sizeof(unsigned long)) {
- __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x))
-#ifdef HAVE_LONG_LONG
- } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) {
- __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
-#endif
- }
- } else {
-#if CYTHON_USE_PYLONG_INTERNALS
- const digit* digits = ((PyLongObject*)x)->ob_digit;
- switch (Py_SIZE(x)) {
- case 0: return (long) 0;
- case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, (sdigit) (-(sdigit)digits[0]))
- case 1: __PYX_VERIFY_RETURN_INT(long, digit, +digits[0])
- case -2:
- if (8 * sizeof(long) - 1 > 1 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
- return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
- }
- }
- break;
- case 2:
- if (8 * sizeof(long) > 1 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
- return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
- }
- }
- break;
- case -3:
- if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
- return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
- }
- }
- break;
- case 3:
- if (8 * sizeof(long) > 2 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
- return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
- }
- }
- break;
- case -4:
- if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
- return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
- }
- }
- break;
- case 4:
- if (8 * sizeof(long) > 3 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
- return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
- }
- }
- break;
- }
-#endif
- if (sizeof(long) <= sizeof(long)) {
- __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x))
-#ifdef HAVE_LONG_LONG
- } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) {
- __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x))
-#endif
- }
- }
- {
-#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
- PyErr_SetString(PyExc_RuntimeError,
- "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
-#else
- long val;
- PyObject *v = __Pyx_PyNumber_IntOrLong(x);
- #if PY_MAJOR_VERSION < 3
- if (likely(v) && !PyLong_Check(v)) {
- PyObject *tmp = v;
- v = PyNumber_Long(tmp);
- Py_DECREF(tmp);
- }
- #endif
- if (likely(v)) {
- int one = 1; int is_little = (int)*(unsigned char *)&one;
- unsigned char *bytes = (unsigned char *)&val;
- int ret = _PyLong_AsByteArray((PyLongObject *)v,
- bytes, sizeof(val),
- is_little, !is_unsigned);
- Py_DECREF(v);
- if (likely(!ret))
- return val;
- }
-#endif
- return (long) -1;
- }
- } else {
- long val;
- PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
- if (!tmp) return (long) -1;
- val = __Pyx_PyInt_As_long(tmp);
- Py_DECREF(tmp);
- return val;
- }
-raise_overflow:
- PyErr_SetString(PyExc_OverflowError,
- "value too large to convert to long");
- return (long) -1;
-raise_neg_overflow:
- PyErr_SetString(PyExc_OverflowError,
- "can't convert negative value to long");
- return (long) -1;
-}
-
-/* CIntFromPy */
- static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
- const int neg_one = (int) -1, const_zero = (int) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
- const int is_unsigned = neg_one > const_zero;
-#if PY_MAJOR_VERSION < 3
- if (likely(PyInt_Check(x))) {
- if (sizeof(int) < sizeof(long)) {
- __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x))
- } else {
- long val = PyInt_AS_LONG(x);
- if (is_unsigned && unlikely(val < 0)) {
- goto raise_neg_overflow;
- }
- return (int) val;
- }
- } else
-#endif
- if (likely(PyLong_Check(x))) {
- if (is_unsigned) {
-#if CYTHON_USE_PYLONG_INTERNALS
- const digit* digits = ((PyLongObject*)x)->ob_digit;
- switch (Py_SIZE(x)) {
- case 0: return (int) 0;
- case 1: __PYX_VERIFY_RETURN_INT(int, digit, digits[0])
- case 2:
- if (8 * sizeof(int) > 1 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(int) >= 2 * PyLong_SHIFT) {
- return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
- }
- }
- break;
- case 3:
- if (8 * sizeof(int) > 2 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(int) >= 3 * PyLong_SHIFT) {
- return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
- }
- }
- break;
- case 4:
- if (8 * sizeof(int) > 3 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(int) >= 4 * PyLong_SHIFT) {
- return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
- }
- }
- break;
- }
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON
- if (unlikely(Py_SIZE(x) < 0)) {
- goto raise_neg_overflow;
- }
-#else
- {
- int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
- if (unlikely(result < 0))
- return (int) -1;
- if (unlikely(result == 1))
- goto raise_neg_overflow;
- }
-#endif
- if (sizeof(int) <= sizeof(unsigned long)) {
- __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x))
-#ifdef HAVE_LONG_LONG
- } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) {
- __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
-#endif
- }
- } else {
-#if CYTHON_USE_PYLONG_INTERNALS
- const digit* digits = ((PyLongObject*)x)->ob_digit;
- switch (Py_SIZE(x)) {
- case 0: return (int) 0;
- case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, (sdigit) (-(sdigit)digits[0]))
- case 1: __PYX_VERIFY_RETURN_INT(int, digit, +digits[0])
- case -2:
- if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {
- return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
- }
- }
- break;
- case 2:
- if (8 * sizeof(int) > 1 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {
- return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
- }
- }
- break;
- case -3:
- if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {
- return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
- }
- }
- break;
- case 3:
- if (8 * sizeof(int) > 2 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {
- return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
- }
- }
- break;
- case -4:
- if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) {
- return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
- }
- }
- break;
- case 4:
- if (8 * sizeof(int) > 3 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) {
- return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
- }
- }
- break;
- }
-#endif
- if (sizeof(int) <= sizeof(long)) {
- __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x))
-#ifdef HAVE_LONG_LONG
- } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) {
- __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x))
-#endif
- }
- }
- {
-#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
- PyErr_SetString(PyExc_RuntimeError,
- "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
-#else
- int val;
- PyObject *v = __Pyx_PyNumber_IntOrLong(x);
- #if PY_MAJOR_VERSION < 3
- if (likely(v) && !PyLong_Check(v)) {
- PyObject *tmp = v;
- v = PyNumber_Long(tmp);
- Py_DECREF(tmp);
- }
- #endif
- if (likely(v)) {
- int one = 1; int is_little = (int)*(unsigned char *)&one;
- unsigned char *bytes = (unsigned char *)&val;
- int ret = _PyLong_AsByteArray((PyLongObject *)v,
- bytes, sizeof(val),
- is_little, !is_unsigned);
- Py_DECREF(v);
- if (likely(!ret))
- return val;
- }
-#endif
- return (int) -1;
- }
- } else {
- int val;
- PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
- if (!tmp) return (int) -1;
- val = __Pyx_PyInt_As_int(tmp);
- Py_DECREF(tmp);
- return val;
- }
-raise_overflow:
- PyErr_SetString(PyExc_OverflowError,
- "value too large to convert to int");
- return (int) -1;
-raise_neg_overflow:
- PyErr_SetString(PyExc_OverflowError,
- "can't convert negative value to int");
- return (int) -1;
-}
-
-/* FastTypeChecks */
- #if CYTHON_COMPILING_IN_CPYTHON
-static int __Pyx_InBases(PyTypeObject *a, PyTypeObject *b) {
- while (a) {
- a = a->tp_base;
- if (a == b)
- return 1;
- }
- return b == &PyBaseObject_Type;
-}
-static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b) {
- PyObject *mro;
- if (a == b) return 1;
- mro = a->tp_mro;
- if (likely(mro)) {
- Py_ssize_t i, n;
- n = PyTuple_GET_SIZE(mro);
- for (i = 0; i < n; i++) {
- if (PyTuple_GET_ITEM(mro, i) == (PyObject *)b)
- return 1;
- }
- return 0;
- }
- return __Pyx_InBases(a, b);
-}
-#if PY_MAJOR_VERSION == 2
-static int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject* exc_type2) {
- PyObject *exception, *value, *tb;
- int res;
- __Pyx_PyThreadState_declare
- __Pyx_PyThreadState_assign
- __Pyx_ErrFetch(&exception, &value, &tb);
- res = exc_type1 ? PyObject_IsSubclass(err, exc_type1) : 0;
- if (unlikely(res == -1)) {
- PyErr_WriteUnraisable(err);
- res = 0;
- }
- if (!res) {
- res = PyObject_IsSubclass(err, exc_type2);
- if (unlikely(res == -1)) {
- PyErr_WriteUnraisable(err);
- res = 0;
- }
- }
- __Pyx_ErrRestore(exception, value, tb);
- return res;
-}
-#else
-static CYTHON_INLINE int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject *exc_type2) {
- int res = exc_type1 ? __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type1) : 0;
- if (!res) {
- res = __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type2);
- }
- return res;
-}
-#endif
-static int __Pyx_PyErr_GivenExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) {
- Py_ssize_t i, n;
- assert(PyExceptionClass_Check(exc_type));
- n = PyTuple_GET_SIZE(tuple);
-#if PY_MAJOR_VERSION >= 3
- for (i=0; i<n; i++) {
- if (exc_type == PyTuple_GET_ITEM(tuple, i)) return 1;
- }
-#endif
- for (i=0; i<n; i++) {
- PyObject *t = PyTuple_GET_ITEM(tuple, i);
- #if PY_MAJOR_VERSION < 3
- if (likely(exc_type == t)) return 1;
- #endif
- if (likely(PyExceptionClass_Check(t))) {
- if (__Pyx_inner_PyErr_GivenExceptionMatches2(exc_type, NULL, t)) return 1;
- } else {
- }
- }
- return 0;
-}
-static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject* exc_type) {
- if (likely(err == exc_type)) return 1;
- if (likely(PyExceptionClass_Check(err))) {
- if (likely(PyExceptionClass_Check(exc_type))) {
- return __Pyx_inner_PyErr_GivenExceptionMatches2(err, NULL, exc_type);
- } else if (likely(PyTuple_Check(exc_type))) {
- return __Pyx_PyErr_GivenExceptionMatchesTuple(err, exc_type);
- } else {
- }
- }
- return PyErr_GivenExceptionMatches(err, exc_type);
-}
-static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *exc_type1, PyObject *exc_type2) {
- assert(PyExceptionClass_Check(exc_type1));
- assert(PyExceptionClass_Check(exc_type2));
- if (likely(err == exc_type1 || err == exc_type2)) return 1;
- if (likely(PyExceptionClass_Check(err))) {
- return __Pyx_inner_PyErr_GivenExceptionMatches2(err, exc_type1, exc_type2);
- }
- return (PyErr_GivenExceptionMatches(err, exc_type1) || PyErr_GivenExceptionMatches(err, exc_type2));
-}
-#endif
-
-/* CheckBinaryVersion */
- static int __Pyx_check_binary_version(void) {
- char ctversion[4], rtversion[4];
- PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION);
- PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion());
- if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) {
- char message[200];
- PyOS_snprintf(message, sizeof(message),
- "compiletime version %s of module '%.100s' "
- "does not match runtime version %s",
- ctversion, __Pyx_MODULE_NAME, rtversion);
- return PyErr_WarnEx(NULL, message, 1);
- }
- return 0;
-}
-
-/* InitStrings */
- static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) {
- while (t->p) {
- #if PY_MAJOR_VERSION < 3
- if (t->is_unicode) {
- *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL);
- } else if (t->intern) {
- *t->p = PyString_InternFromString(t->s);
- } else {
- *t->p = PyString_FromStringAndSize(t->s, t->n - 1);
- }
- #else
- if (t->is_unicode | t->is_str) {
- if (t->intern) {
- *t->p = PyUnicode_InternFromString(t->s);
- } else if (t->encoding) {
- *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL);
- } else {
- *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1);
- }
- } else {
- *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1);
- }
- #endif
- if (!*t->p)
- return -1;
- if (PyObject_Hash(*t->p) == -1)
- return -1;
- ++t;
- }
- return 0;
-}
-
-static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) {
- return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str));
-}
-static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject* o) {
- Py_ssize_t ignore;
- return __Pyx_PyObject_AsStringAndSize(o, &ignore);
-}
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
-#if !CYTHON_PEP393_ENABLED
-static const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
- char* defenc_c;
- PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL);
- if (!defenc) return NULL;
- defenc_c = PyBytes_AS_STRING(defenc);
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
- {
- char* end = defenc_c + PyBytes_GET_SIZE(defenc);
- char* c;
- for (c = defenc_c; c < end; c++) {
- if ((unsigned char) (*c) >= 128) {
- PyUnicode_AsASCIIString(o);
- return NULL;
- }
- }
- }
-#endif
- *length = PyBytes_GET_SIZE(defenc);
- return defenc_c;
-}
-#else
-static CYTHON_INLINE const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
- if (unlikely(__Pyx_PyUnicode_READY(o) == -1)) return NULL;
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
- if (likely(PyUnicode_IS_ASCII(o))) {
- *length = PyUnicode_GET_LENGTH(o);
- return PyUnicode_AsUTF8(o);
- } else {
- PyUnicode_AsASCIIString(o);
- return NULL;
- }
-#else
- return PyUnicode_AsUTF8AndSize(o, length);
-#endif
-}
-#endif
-#endif
-static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
- if (
-#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
- __Pyx_sys_getdefaultencoding_not_ascii &&
-#endif
- PyUnicode_Check(o)) {
- return __Pyx_PyUnicode_AsStringAndSize(o, length);
- } else
-#endif
-#if (!CYTHON_COMPILING_IN_PYPY) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE))
- if (PyByteArray_Check(o)) {
- *length = PyByteArray_GET_SIZE(o);
- return PyByteArray_AS_STRING(o);
- } else
-#endif
- {
- char* result;
- int r = PyBytes_AsStringAndSize(o, &result, length);
- if (unlikely(r < 0)) {
- return NULL;
- } else {
- return result;
- }
- }
-}
-static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) {
- int is_true = x == Py_True;
- if (is_true | (x == Py_False) | (x == Py_None)) return is_true;
- else return PyObject_IsTrue(x);
-}
-static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject* x) {
- int retval;
- if (unlikely(!x)) return -1;
- retval = __Pyx_PyObject_IsTrue(x);
- Py_DECREF(x);
- return retval;
-}
-static PyObject* __Pyx_PyNumber_IntOrLongWrongResultType(PyObject* result, const char* type_name) {
-#if PY_MAJOR_VERSION >= 3
- if (PyLong_Check(result)) {
- if (PyErr_WarnFormat(PyExc_DeprecationWarning, 1,
- "__int__ returned non-int (type %.200s). "
- "The ability to return an instance of a strict subclass of int "
- "is deprecated, and may be removed in a future version of Python.",
- Py_TYPE(result)->tp_name)) {
- Py_DECREF(result);
- return NULL;
- }
- return result;
- }
-#endif
- PyErr_Format(PyExc_TypeError,
- "__%.4s__ returned non-%.4s (type %.200s)",
- type_name, type_name, Py_TYPE(result)->tp_name);
- Py_DECREF(result);
- return NULL;
-}
-static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) {
-#if CYTHON_USE_TYPE_SLOTS
- PyNumberMethods *m;
-#endif
- const char *name = NULL;
- PyObject *res = NULL;
-#if PY_MAJOR_VERSION < 3
- if (likely(PyInt_Check(x) || PyLong_Check(x)))
-#else
- if (likely(PyLong_Check(x)))
-#endif
- return __Pyx_NewRef(x);
-#if CYTHON_USE_TYPE_SLOTS
- m = Py_TYPE(x)->tp_as_number;
- #if PY_MAJOR_VERSION < 3
- if (m && m->nb_int) {
- name = "int";
- res = m->nb_int(x);
- }
- else if (m && m->nb_long) {
- name = "long";
- res = m->nb_long(x);
- }
- #else
- if (likely(m && m->nb_int)) {
- name = "int";
- res = m->nb_int(x);
- }
- #endif
-#else
- if (!PyBytes_CheckExact(x) && !PyUnicode_CheckExact(x)) {
- res = PyNumber_Int(x);
- }
-#endif
- if (likely(res)) {
-#if PY_MAJOR_VERSION < 3
- if (unlikely(!PyInt_Check(res) && !PyLong_Check(res))) {
-#else
- if (unlikely(!PyLong_CheckExact(res))) {
-#endif
- return __Pyx_PyNumber_IntOrLongWrongResultType(res, name);
- }
- }
- else if (!PyErr_Occurred()) {
- PyErr_SetString(PyExc_TypeError,
- "an integer is required");
- }
- return res;
-}
-static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) {
- Py_ssize_t ival;
- PyObject *x;
-#if PY_MAJOR_VERSION < 3
- if (likely(PyInt_CheckExact(b))) {
- if (sizeof(Py_ssize_t) >= sizeof(long))
- return PyInt_AS_LONG(b);
- else
- return PyInt_AsSsize_t(b);
- }
-#endif
- if (likely(PyLong_CheckExact(b))) {
- #if CYTHON_USE_PYLONG_INTERNALS
- const digit* digits = ((PyLongObject*)b)->ob_digit;
- const Py_ssize_t size = Py_SIZE(b);
- if (likely(__Pyx_sst_abs(size) <= 1)) {
- ival = likely(size) ? digits[0] : 0;
- if (size == -1) ival = -ival;
- return ival;
- } else {
- switch (size) {
- case 2:
- if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) {
- return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
- }
- break;
- case -2:
- if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) {
- return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
- }
- break;
- case 3:
- if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) {
- return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
- }
- break;
- case -3:
- if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) {
- return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
- }
- break;
- case 4:
- if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) {
- return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
- }
- break;
- case -4:
- if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) {
- return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
- }
- break;
- }
- }
- #endif
- return PyLong_AsSsize_t(b);
- }
- x = PyNumber_Index(b);
- if (!x) return -1;
- ival = PyInt_AsSsize_t(x);
- Py_DECREF(x);
- return ival;
-}
-static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b) {
- return b ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False);
-}
-static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) {
- return PyInt_FromSize_t(ival);
-}
-
-
-#endif /* Py_PYTHON_H */
deleted file mode 100644
@@ -1,55 +0,0 @@
-# --------------------------------------------------------
-# Fast R-CNN
-# Copyright (c) 2015 Microsoft
-# Licensed under The MIT License [see LICENSE for details]
-# Written by Sergey Karayev
-# --------------------------------------------------------
-
-cimport cython
-import numpy as np
-cimport numpy as np
-
-DTYPE = np.float
-ctypedef np.float_t DTYPE_t
-
-def bbox_overlaps(
- np.ndarray[DTYPE_t, ndim=2] boxes,
- np.ndarray[DTYPE_t, ndim=2] query_boxes):
- """
- Parameters
- ----------
- boxes: (N, 4) ndarray of float
- query_boxes: (K, 4) ndarray of float
- Returns
- -------
- overlaps: (N, K) ndarray of overlap between boxes and query_boxes
- """
- cdef unsigned int N = boxes.shape[0]
- cdef unsigned int K = query_boxes.shape[0]
- cdef np.ndarray[DTYPE_t, ndim=2] overlaps = np.zeros((N, K), dtype=DTYPE)
- cdef DTYPE_t iw, ih, box_area
- cdef DTYPE_t ua
- cdef unsigned int k, n
- for k in range(K):
- box_area = (
- (query_boxes[k, 2] - query_boxes[k, 0] + 1) *
- (query_boxes[k, 3] - query_boxes[k, 1] + 1)
- )
- for n in range(N):
- iw = (
- min(boxes[n, 2], query_boxes[k, 2]) -
- max(boxes[n, 0], query_boxes[k, 0]) + 1
- )
- if iw > 0:
- ih = (
- min(boxes[n, 3], query_boxes[k, 3]) -
- max(boxes[n, 1], query_boxes[k, 1]) + 1
- )
- if ih > 0:
- ua = float(
- (boxes[n, 2] - boxes[n, 0] + 1) *
- (boxes[n, 3] - boxes[n, 1] + 1) +
- box_area - iw * ih
- )
- overlaps[n, k] = iw * ih / ua
- return overlaps
\ No newline at end of file
deleted file mode 100644
@@ -1,22 +0,0 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-#coding=utf-8
-
-from distutils.core import setup, Extension
-from Cython.Build import cythonize
-import numpy
-
-package = Extension('bbox', ['box_overlaps.pyx'], include_dirs=[numpy.get_include()])
-setup(ext_modules=cythonize([package]))
deleted file mode 100644
@@ -1,23 +0,0 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-#-*- coding:utf-8 -*-
-
-from __future__ import division
-from __future__ import absolute_import
-from __future__ import print_function
-
-
-from .functions import *
-from .modules import *
deleted file mode 100644
@@ -1,324 +0,0 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-#coding=utf-8
-
-from __future__ import division
-from __future__ import absolute_import
-from __future__ import print_function
-
-
-import torch
-
-
-def point_form(boxes):
- """ Convert prior_boxes to (xmin, ymin, xmax, ymax)
- representation for comparison to point form ground truth data.
- Args:
- boxes: (tensor) center-size default boxes from priorbox layers.
- Return:
- boxes: (tensor) Converted xmin, ymin, xmax, ymax form of boxes.
- """
- return torch.cat((boxes[:, :2] - boxes[:, 2:] / 2, # xmin, ymin
- boxes[:, :2] + boxes[:, 2:] / 2), 1) # xmax, ymax
-
-
-def center_size(boxes):
- """ Convert prior_boxes to (cx, cy, w, h)
- representation for comparison to center-size form ground truth data.
- Args:
- boxes: (tensor) point_form boxes
- Return:
- boxes: (tensor) Converted xmin, ymin, xmax, ymax form of boxes.
- """
- return torch.cat([(boxes[:, 2:] + boxes[:, :2]) / 2, # cx, cy
- boxes[:, 2:] - boxes[:, :2]], 1) # w, h
-
-
-def intersect(box_a, box_b):
- """ We resize both tensors to [A,B,2] without new malloc:
- [A,2] -> [A,1,2] -> [A,B,2]
- [B,2] -> [1,B,2] -> [A,B,2]
- Then we compute the area of intersect between box_a and box_b.
- Args:
- box_a: (tensor) bounding boxes, Shape: [A,4].
- box_b: (tensor) bounding boxes, Shape: [B,4].
- Return:
- (tensor) intersection area, Shape: [A,B].
- """
- A = box_a.size(0)
- B = box_b.size(0)
- max_xy = torch.min(box_a[:, 2:].unsqueeze(1).expand(A, B, 2),
- box_b[:, 2:].unsqueeze(0).expand(A, B, 2))
- min_xy = torch.max(box_a[:, :2].unsqueeze(1).expand(A, B, 2),
- box_b[:, :2].unsqueeze(0).expand(A, B, 2))
- inter = torch.clamp((max_xy - min_xy), min=0)
- return inter[:, :, 0] * inter[:, :, 1]
-
-
-def jaccard(box_a, box_b):
- """Compute the jaccard overlap of two sets of boxes. The jaccard overlap
- is simply the intersection over union of two boxes. Here we operate on
- ground truth boxes and default boxes.
- E.g.:
- A ∩ B / A ∪ B = A ∩ B / (area(A) + area(B) - A ∩ B)
- Args:
- box_a: (tensor) Ground truth bounding boxes, Shape: [num_objects,4]
- box_b: (tensor) Prior boxes from priorbox layers, Shape: [num_priors,4]
- Return:
- jaccard overlap: (tensor) Shape: [box_a.size(0), box_b.size(0)]
- """
- inter = intersect(box_a, box_b)
- area_a = ((box_a[:, 2] - box_a[:, 0]) *
- (box_a[:, 3] - box_a[:, 1])).unsqueeze(1).expand_as(inter) # [A,B]
- area_b = ((box_b[:, 2] - box_b[:, 0]) *
- (box_b[:, 3] - box_b[:, 1])).unsqueeze(0).expand_as(inter) # [A,B]
- union = area_a + area_b - inter
- return inter / union # [A,B]
-
-
-def match(threshold, truths, priors, variances, labels, loc_t, conf_t, idx):
- """Match each prior box with the ground truth box of the highest jaccard
- overlap, encode the bounding boxes, then return the matched indices
- corresponding to both confidence and location preds.
- Args:
- threshold: (float) The overlap threshold used when mathing boxes.
- truths: (tensor) Ground truth boxes, Shape: [num_obj, num_priors].
- priors: (tensor) Prior boxes from priorbox layers, Shape: [n_priors,4].
- variances: (tensor) Variances corresponding to each prior coord,
- Shape: [num_priors, 4].
- labels: (tensor) All the class labels for the image, Shape: [num_obj].
- loc_t: (tensor) Tensor to be filled w/ endcoded location targets.
- conf_t: (tensor) Tensor to be filled w/ matched indices for conf preds.
- idx: (int) current batch index
- Return:
- The matched indices corresponding to 1)location and 2)confidence preds.
- """
- # jaccard index
- overlaps = jaccard(
- truths,
- point_form(priors)
- )
- # (Bipartite Matching)
- # [1,num_objects] best prior for each ground truth
- best_prior_overlap, best_prior_idx = overlaps.max(1, keepdim=True)
- # [1,num_priors] best ground truth for each prior
- best_truth_overlap, best_truth_idx = overlaps.max(
- 0, keepdim=True) # 0-2000
- best_truth_idx.squeeze_(0)
- best_truth_overlap.squeeze_(0)
- best_prior_idx.squeeze_(1)
- best_prior_overlap.squeeze_(1)
- best_truth_overlap.index_fill_(0, best_prior_idx, 2) # ensure best prior
- # TODO refactor: index best_prior_idx with long tensor
- # ensure every gt matches with its prior of max overlap
- for j in range(best_prior_idx.size(0)):
- best_truth_idx[best_prior_idx[j]] = j
- _th1, _th2, _th3 = threshold # _th1 = 0.1 ,_th2 = 0.35,_th3 = 0.5
-
- N = (torch.sum(best_prior_overlap >= _th2) +
- torch.sum(best_prior_overlap >= _th3)) // 2
- matches = truths[best_truth_idx] # Shape: [num_priors,4]
- conf = labels[best_truth_idx] # Shape: [num_priors]
- conf[best_truth_overlap < _th2] = 0 # label as background
-
- best_truth_overlap_clone = best_truth_overlap.clone()
- add_idx = best_truth_overlap_clone.gt(
- _th1).eq(best_truth_overlap_clone.lt(_th2))
- best_truth_overlap_clone[1 - add_idx] = 0
- stage2_overlap, stage2_idx = best_truth_overlap_clone.sort(descending=True)
-
- stage2_overlap = stage2_overlap.gt(_th1)
-
- if N > 0:
- N = torch.sum(stage2_overlap[:N]) if torch.sum(
- stage2_overlap[:N]) < N else N
- conf[stage2_idx[:N]] += 1
-
- loc = encode(matches, priors, variances)
- loc_t[idx] = loc # [num_priors,4] encoded offsets to learn
- conf_t[idx] = conf # [num_priors] top class label for each prior
-
-
-def match_ssd(threshold, truths, priors, variances, labels, loc_t, conf_t, idx):
- """Match each prior box with the ground truth box of the highest jaccard
- overlap, encode the bounding boxes, then return the matched indices
- corresponding to both confidence and location preds.
- Args:
- threshold: (float) The overlap threshold used when mathing boxes.
- truths: (tensor) Ground truth boxes, Shape: [num_obj, num_priors].
- priors: (tensor) Prior boxes from priorbox layers, Shape: [n_priors,4].
- variances: (tensor) Variances corresponding to each prior coord,
- Shape: [num_priors, 4].
- labels: (tensor) All the class labels for the image, Shape: [num_obj].
- loc_t: (tensor) Tensor to be filled w/ endcoded location targets.
- conf_t: (tensor) Tensor to be filled w/ matched indices for conf preds.
- idx: (int) current batch index
- Return:
- The matched indices corresponding to 1)location and 2)confidence preds.
- """
- # jaccard index
- overlaps = jaccard(
- truths,
- point_form(priors)
- )
- # (Bipartite Matching)
- # [1,num_objects] best prior for each ground truth
- best_prior_overlap, best_prior_idx = overlaps.max(1, keepdim=True)
- # [1,num_priors] best ground truth for each prior
- best_truth_overlap, best_truth_idx = overlaps.max(
- 0, keepdim=True) # 0-2000
- best_truth_idx.squeeze_(0)
- best_truth_overlap.squeeze_(0)
- best_prior_idx.squeeze_(1)
- best_prior_overlap.squeeze_(1)
- best_truth_overlap.index_fill_(0, best_prior_idx, 2) # ensure best prior
- # TODO refactor: index best_prior_idx with long tensor
- # ensure every gt matches with its prior of max overlap
- for j in range(best_prior_idx.size(0)):
- best_truth_idx[best_prior_idx[j]] = j
- matches = truths[best_truth_idx] # Shape: [num_priors,4]
- conf = labels[best_truth_idx] # Shape: [num_priors]
- conf[best_truth_overlap < threshold] = 0 # label as background
- loc = encode(matches, priors, variances)
- loc_t[idx] = loc # [num_priors,4] encoded offsets to learn
- conf_t[idx] = conf # [num_priors] top class label for each prior
-
-
-def encode(matched, priors, variances):
- """Encode the variances from the priorbox layers into the ground truth boxes
- we have matched (based on jaccard overlap) with the prior boxes.
- Args:
- matched: (tensor) Coords of ground truth for each prior in point-form
- Shape: [num_priors, 4].
- priors: (tensor) Prior boxes in center-offset form
- Shape: [num_priors,4].
- variances: (list[float]) Variances of priorboxes
- Return:
- encoded boxes (tensor), Shape: [num_priors, 4]
- """
-
- # dist b/t match center and prior's center
- g_cxcy = (matched[:, :2] + matched[:, 2:]) / 2 - priors[:, :2]
- # encode variance
- g_cxcy /= (variances[0] * priors[:, 2:])
- # match wh / prior wh
- g_wh = (matched[:, 2:] - matched[:, :2]) / priors[:, 2:]
- #g_wh = torch.log(g_wh) / variances[1]
- g_wh = torch.log(g_wh) / variances[1]
- # return target for smooth_l1_loss
- return torch.cat([g_cxcy, g_wh], 1) # [num_priors,4]
-
-
-# Adapted from https://github.com/Hakuyume/chainer-ssd
-def decode(loc, priors, variances):
- """Decode locations from predictions using priors to undo
- the encoding we did for offset regression at train time.
- Args:
- loc (tensor): location predictions for loc layers,
- Shape: [num_priors,4]
- priors (tensor): Prior boxes in center-offset form.
- Shape: [num_priors,4].
- variances: (list[float]) Variances of priorboxes
- Return:
- decoded bounding box predictions
- """
-
- boxes = torch.cat((
- priors[:, :2] + loc[:, :2] * variances[0] * priors[:, 2:],
- priors[:, 2:] * torch.exp(loc[:, 2:] * variances[1])), 1)
- boxes[:, :2] -= boxes[:, 2:] / 2
- boxes[:, 2:] += boxes[:, :2]
- return boxes
-
-
-def log_sum_exp(x):
- """Utility function for computing log_sum_exp while determining
- This will be used to determine unaveraged confidence loss across
- all examples in a batch.
- Args:
- x (Variable(tensor)): conf_preds from conf layers
- """
- x_max = x.data.max()
- return torch.log(torch.sum(torch.exp(x - x_max), 1, keepdim=True)) + x_max
-
-
-# Original author: Francisco Massa:
-# https://github.com/fmassa/object-detection.torch
-# Ported to PyTorch by Max deGroot (02/01/2017)
-def nms(boxes, scores, overlap=0.5, top_k=200):
- """Apply non-maximum suppression at test time to avoid detecting too many
- overlapping bounding boxes for a given object.
- Args:
- boxes: (tensor) The location preds for the img, Shape: [num_priors,4].
- scores: (tensor) The class predscores for the img, Shape:[num_priors].
- overlap: (float) The overlap thresh for suppressing unnecessary boxes.
- top_k: (int) The Maximum number of box preds to consider.
- Return:
- The indices of the kept boxes with respect to num_priors.
- """
-
- keep = scores.new(scores.size(0)).zero_().long()
- if boxes.numel() == 0:
- return keep
- x1 = boxes[:, 0]
- y1 = boxes[:, 1]
- x2 = boxes[:, 2]
- y2 = boxes[:, 3]
- area = torch.mul(x2 - x1, y2 - y1)
- v, idx = scores.sort(0) # sort in ascending order
- # I = I[v >= 0.01]
- idx = idx[-top_k:] # indices of the top-k largest vals
- xx1 = boxes.new()
- yy1 = boxes.new()
- xx2 = boxes.new()
- yy2 = boxes.new()
- w = boxes.new()
- h = boxes.new()
-
- # keep = torch.Tensor()
- count = 0
- while idx.numel() > 0:
- i = idx[-1] # index of current largest val
- # keep.append(i)
- keep[count] = i
- count += 1
- if idx.size(0) == 1:
- break
- idx = idx[:-1] # remove kept element from view
- # load bboxes of next highest vals
- torch.index_select(x1, 0, idx, out=xx1)
- torch.index_select(y1, 0, idx, out=yy1)
- torch.index_select(x2, 0, idx, out=xx2)
- torch.index_select(y2, 0, idx, out=yy2)
- # store element-wise max with next highest score
- xx1 = torch.clamp(xx1, min=x1[i])
- yy1 = torch.clamp(yy1, min=y1[i])
- xx2 = torch.clamp(xx2, max=x2[i])
- yy2 = torch.clamp(yy2, max=y2[i])
- w.resize_as_(xx2)
- h.resize_as_(yy2)
- w = xx2 - xx1
- h = yy2 - yy1
- # check sizes of xx1 and xx2.. after each iteration
- w = torch.clamp(w, min=0.0)
- h = torch.clamp(h, min=0.0)
- inter = w * h
- # IoU = i / (area(a) + area(b) - i)
- rem_areas = torch.index_select(area, 0, idx) # load remaining areas)
- union = (rem_areas - inter) + area[i]
- IoU = inter / union # store result in iou
- # keep only elements with an IoU <= overlap
- idx = idx[IoU.le(overlap)]
- return keep, count
deleted file mode 100644
@@ -1,19 +0,0 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from .prior_box import PriorBox
-from .detection import Detect
-
-__all__=['Detect','PriorBox']
-
deleted file mode 100644
@@ -1,87 +0,0 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# coding=utf-8
-
-from __future__ import division
-from __future__ import absolute_import
-from __future__ import print_function
-
-import torch
-
-from ..bbox_utils import decode, nms
-from torch.autograd import Function
-
-
-class Detect(Function):
- """At test time, Detect is the final layer of SSD. Decode location preds,
- apply non-maximum suppression to location predictions based on conf
- scores and threshold to a top_k number of output predictions for both
- confidence score and locations.
- """
-
- def __init__(self, cfg):
- self.num_classes = cfg.NUM_CLASSES
- self.top_k = cfg.TOP_K
- self.nms_thresh = cfg.NMS_THRESH
- self.conf_thresh = cfg.CONF_THRESH
- self.variance = cfg.VARIANCE
- self.nms_top_k = cfg.NMS_TOP_K
-
- def forward(self, loc_data, conf_data, prior_data):
- """
- Args:
- loc_data: (tensor) Loc preds from loc layers
- Shape: [batch,num_priors*4]
- conf_data: (tensor) Shape: Conf preds from conf layers
- Shape: [batch*num_priors,num_classes]
- prior_data: (tensor) Prior boxes and variances from priorbox layers
- Shape: [1,num_priors,4]
- """
- num = loc_data.size(0)
- num_priors = prior_data.size(0)
-
- conf_preds = conf_data.view(
- num, num_priors, self.num_classes).transpose(2, 1)
- batch_priors = prior_data.view(-1, num_priors,
- 4).expand(num, num_priors, 4)
- batch_priors = batch_priors.contiguous().view(-1, 4)
-
- decoded_boxes = decode(loc_data.view(-1, 4),
- batch_priors, self.variance)
- decoded_boxes = decoded_boxes.view(num, num_priors, 4)
-
- output = torch.zeros(num, self.num_classes, self.top_k, 5)
-
- for i in range(num):
- boxes = decoded_boxes[i].clone()
- conf_scores = conf_preds[i].clone()
-
- for cl in range(1, self.num_classes):
- c_mask = conf_scores[cl].gt(self.conf_thresh)
- scores = conf_scores[cl][c_mask]
-
- #VIVID
- if scores.size(0) == 0:
- continue
- l_mask = c_mask.unsqueeze(1).expand_as(boxes)
- boxes_ = boxes[l_mask].view(-1, 4)
- ids, count = nms(boxes_, scores, self.nms_thresh, self.nms_top_k)
- count = count if count < self.top_k else self.top_k
-
- output[i, cl, :count] = torch.cat((scores[ids[:count]].unsqueeze(1),
- boxes_[ids[:count]]), 1)
- print("get test detect res:", output)
-
- return output
deleted file mode 100644
@@ -1,78 +0,0 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-#-*- coding:utf-8 -*-
-
-from __future__ import division
-from __future__ import absolute_import
-from __future__ import print_function
-
-import torch
-from itertools import product as product
-import math
-
-
-class PriorBox(object):
- """Compute priorbox coordinates in center-offset form for each source
- feature map.
- """
-
- def __init__(self, input_size, feature_maps,cfg,pal=2):
- super(PriorBox, self).__init__()
- self.imh = input_size[0]
- self.imw = input_size[1]
- # number of priors for feature map location (either 4 or 6)
- self.variance = cfg.VARIANCE or [0.1]
- #self.feature_maps = cfg.FEATURE_MAPS
- if pal==1:
- self.min_sizes = cfg.ANCHOR_SIZES1
- elif pal==2:
- self.min_sizes = cfg.ANCHOR_SIZES2
- self.aspect_ratio = cfg.ASPECT_RATIO
- self.steps = cfg.STEPS
- self.clip = cfg.CLIP
- for v in self.variance:
- if v <= 0:
- raise ValueError('Variances must be greater than 0')
- self.feature_maps = feature_maps
-
-
- def forward(self):
- mean = []
- for k in range(len(self.feature_maps)):
- feath = self.feature_maps[k][0]
- featw = self.feature_maps[k][1]
- for i, j in product(range(feath), range(featw)):
- f_kw = self.imw / self.steps[k]
- f_kh = self.imh / self.steps[k]
-
- cx = (j + 0.5) / f_kw
- cy = (i + 0.5) / f_kh
-
- s_kw = self.min_sizes[k] / self.imw
- s_kh = self.min_sizes[k] / self.imh
- for ar in self.aspect_ratio:
- mean += [cx, cy, s_kw/math.sqrt(ar), s_kh*math.sqrt(ar)]
-
- output = torch.Tensor(mean).view(-1, 4)
- if self.clip:
- output.clamp_(max=1, min=0)
- return output
-
-
-if __name__ == '__main__':
- from data.config import cfg
- p = PriorBox([640, 640], cfg)
- out = p.forward()
- print(out.size())
deleted file mode 100644
@@ -1,26 +0,0 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-#-*- coding:utf-8 -*-
-
-from __future__ import division
-from __future__ import absolute_import
-from __future__ import print_function
-
-
-from .l2norm import L2Norm
-from .multibox_loss import MultiBoxLoss
-
-__all__ = ['L2Norm', 'MultiBoxLoss']
-
deleted file mode 100644
@@ -1,50 +0,0 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-#-*- coding:utf-8 -*-
-
-from __future__ import division
-from __future__ import absolute_import
-from __future__ import print_function
-
-
-import torch
-import torch.nn as nn
-import torch.nn.init as init
-from torch.autograd import Function
-from torch.autograd import Variable
-
-
-
-class L2Norm(nn.Module):
- def __init__(self,n_channels, scale):
- super(L2Norm,self).__init__()
- self.n_channels = n_channels
- self.gamma = scale or None
- self.eps = 1e-10
- self.weight = nn.Parameter(torch.Tensor(self.n_channels))
- self.reset_parameters()
-
- def reset_parameters(self):
- init.constant(self.weight,self.gamma)
-
- def forward(self, x):
- norm = x.pow(2).sum(dim=1, keepdim=True).sqrt()+self.eps
- #x /= norm
- x = torch.div(x,norm)
- out = self.weight.unsqueeze(0).unsqueeze(2).unsqueeze(3).expand_as(x) * x
- return out
-
-
-
\ No newline at end of file
deleted file mode 100644
@@ -1,134 +0,0 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-#-*- coding:utf-8 -*-
-
-from __future__ import division
-from __future__ import absolute_import
-from __future__ import print_function
-
-import math
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.autograd import Variable
-
-
-from ..bbox_utils import match, log_sum_exp, match_ssd
-
-
-class MultiBoxLoss(nn.Module):
- """SSD Weighted Loss Function
- Compute Targets:
- 1) Produce Confidence Target Indices by matching ground truth boxes
- with (default) 'priorboxes' that have jaccard index > threshold parameter
- (default threshold: 0.5).
- 2) Produce localization target by 'encoding' variance into offsets of ground
- truth boxes and their matched 'priorboxes'.
- 3) Hard negative mining to filter the excessive number of negative examples
- that comes with using a large number of default bounding boxes.
- (default negative:positive ratio 3:1)
- Objective Loss:
- L(x,c,l,g) = (Lconf(x, c) + αLloc(x,l,g)) / N
- Where, Lconf is the CrossEntropy Loss and Lloc is the SmoothL1 Loss
- weighted by α which is set to 1 by cross val.
- Args:
- c: class confidences,
- l: predicted boxes,
- g: ground truth boxes
- N: number of matched default boxes
- See: https://arxiv.org/pdf/1512.02325.pdf for more details.
- """
-
- def __init__(self, cfg, use_gpu=True):
- super(MultiBoxLoss, self).__init__()
- self.use_gpu = use_gpu
- self.num_classes = cfg.NUM_CLASSES
- self.negpos_ratio = cfg.NEG_POS_RATIOS
- self.variance = cfg.VARIANCE
-
- self.threshold = cfg.FACE.OVERLAP_THRESH
- self.match = match_ssd
-
- def forward(self, predictions, targets):
- """Multibox Loss
- Args:
- predictions (tuple): A tuple containing loc preds, conf preds,
- and prior boxes from SSD net.
- conf shape: torch.size(batch_size,num_priors,num_classes)
- loc shape: torch.size(batch_size,num_priors,4)
- priors shape: torch.size(num_priors,4)
-
- targets (tensor): Ground truth boxes and labels for a batch,
- shape: [batch_size,num_objs,5] (last idx is the label).
- """
- loc_data, conf_data, priors = predictions
- num = loc_data.size(0)
- priors = priors[:loc_data.size(1), :]
- num_priors = (priors.size(0))
- num_classes = self.num_classes
-
- # match priors (default boxes) and ground truth boxes
- loc_t = torch.Tensor(num, num_priors, 4)
- conf_t = torch.LongTensor(num, num_priors)
- for idx in range(num):
- truths = targets[idx][:, :-1].data
- labels = targets[idx][:, -1].data
- defaults = priors.data
- self.match(self.threshold, truths, defaults, self.variance, labels,
- loc_t, conf_t, idx)
- if self.use_gpu:
- loc_t = loc_t.cuda()
- conf_t = conf_t.cuda()
- # wrap targets
- loc_t = Variable(loc_t, requires_grad=False)
- conf_t = Variable(conf_t, requires_grad=False)
-
- pos = conf_t > 0
- num_pos = pos.sum(dim=1, keepdim=True)
- # Localization Loss (Smooth L1)
- # Shape: [batch,num_priors,4]
- pos_idx = pos.unsqueeze(pos.dim()).expand_as(loc_data)
- loc_p = loc_data[pos_idx].view(-1, 4)
- loc_t = loc_t[pos_idx].view(-1, 4)
- loss_l = F.smooth_l1_loss(loc_p, loc_t, size_average=False)
- # print(loc_p)
- # Compute max conf across batch for hard negative mining
- batch_conf = conf_data.view(-1, self.num_classes)
- loss_c = log_sum_exp(batch_conf) - \
- batch_conf.gather(1, conf_t.view(-1, 1))
-
- # Hard Negative Mining
- loss_c[pos.view(-1, 1)] = 0 # filter out pos boxes for now
- loss_c = loss_c.view(num, -1)
- _, loss_idx = loss_c.sort(1, descending=True)
- _, idx_rank = loss_idx.sort(1)
- num_pos = pos.long().sum(1, keepdim=True)
- num_neg = torch.clamp(self.negpos_ratio *
- num_pos, max=pos.size(1) - 1)
- neg = idx_rank < num_neg.expand_as(idx_rank)
-
- # Confidence Loss Including Positive and Negative Examples
- pos_idx = pos.unsqueeze(2).expand_as(conf_data)
- neg_idx = neg.unsqueeze(2).expand_as(conf_data)
- conf_p = conf_data[(pos_idx + neg_idx).gt(0)
- ].view(-1, self.num_classes)
- targets_weighted = conf_t[(pos + neg).gt(0)]
- loss_c = F.cross_entropy(conf_p, targets_weighted, size_average=False)
-
- # Sum of losses: L(x,c,l,g) = (Lconf(x, c) + αLloc(x,l,g)) / N
- N = num_pos.data.sum() if num_pos.data.sum() > 0 else num
- loss_l /= N
- loss_c /= N
- return loss_l, loss_c
deleted file mode 100644
@@ -1,438 +0,0 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-#coding=utf-8
-from __future__ import division
-from __future__ import print_function
-from __future__ import absolute_import
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.nn.init as init
-
-from torch.autograd import Variable
-
-from layers import *
-from data.config import cur_config as cfg
-
-def conv3x3(in_planes, out_planes, stride=1, bias=False):
- """3x3 convolution with padding"""
- return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
- padding=1, bias=bias)
-
-
-def conv1x1(in_planes, out_planes, stride=1):
- """1x1 convolution"""
- return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
-
-
-class BasicBlock(nn.Module):
- expansion = 1
-
- def __init__(self, inplanes, planes, stride=1, downsample=None):
- super(BasicBlock, self).__init__()
- self.conv1 = conv3x3(inplanes, planes, stride)
- self.bn1 = nn.BatchNorm2d(planes)
- self.relu = nn.ReLU(inplace=True)
- self.conv2 = conv3x3(planes, planes)
- self.bn2 = nn.BatchNorm2d(planes)
- self.downsample = downsample
- self.stride = stride
-
- def forward(self, x):
- residual = x
-
- out = self.conv1(x)
- out = self.bn1(out)
- out = self.relu(out)
-
- out = self.conv2(out)
- out = self.bn2(out)
-
- if self.downsample is not None:
- residual = self.downsample(x)
-
- out += residual
- out = self.relu(out)
-
- return out
-
-
-class Bottleneck(nn.Module):
- expansion = 4
-
- def __init__(self, inplanes, planes, stride=1, downsample=None):
- super(Bottleneck, self).__init__()
- self.conv1 = conv1x1(inplanes, planes)
- self.bn1 = nn.BatchNorm2d(planes)
- self.conv2 = conv3x3(planes, planes, stride)
- self.bn2 = nn.BatchNorm2d(planes)
- self.conv3 = conv1x1(planes, planes * self.expansion)
- self.bn3 = nn.BatchNorm2d(planes * self.expansion)
- self.relu = nn.ReLU(inplace=True)
- self.downsample = downsample
- self.stride = stride
-
- def forward(self, x):
- residual = x
-
- out = self.conv1(x)
- out = self.bn1(out)
- out = self.relu(out)
-
- out = self.conv2(out)
- out = self.bn2(out)
- out = self.relu(out)
-
- out = self.conv3(out)
- out = self.bn3(out)
-
- if self.downsample is not None:
- residual = self.downsample(x)
-
- out += residual
- out = self.relu(out)
-
- return out
-
-
-class ResNet(nn.Module):
-
- def __init__(self, block, layers):
- super(ResNet, self).__init__()
- self.inplanes = 64
- self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
- bias=False)
- self.bn1 = nn.BatchNorm2d(64)
- self.relu = nn.ReLU(inplace=True)
- self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
- self.layer1 = self._make_layer(block, 64, layers[0])
- self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
- self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
- self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
-
- def _make_layer(self, block, planes, blocks, stride=1):
- downsample = None
- if stride != 1 or self.inplanes != planes * block.expansion:
- downsample = nn.Sequential(
- conv1x1(self.inplanes, planes * block.expansion, stride),
- nn.BatchNorm2d(planes * block.expansion),
- )
-
- layers = []
- layers.append(block(self.inplanes, planes, stride, downsample))
- self.inplanes = planes * block.expansion
- for _ in range(1, blocks):
- layers.append(block(self.inplanes, planes))
-
- return nn.Sequential(*layers)
-
- def forward(self, x):
- sources = []
-
- x = self.conv1(x)
- x = self.bn1(x)
- x = self.relu(x)
- x = self.maxpool(x)
-
- x = self.layer1(x)
- sources += [x]
-
- x = self.layer2(x)
- sources += [x]
-
- x = self.layer3(x)
- sources += [x]
-
- x = self.layer4(x)
- sources += [x]
-
- return sources
-
-
-class FEM(nn.Module):
- """docstring for FEM"""
-
- def __init__(self, in_planes):
- super(FEM, self).__init__()
- inter_planes = in_planes // 3
- inter_planes1 = in_planes - 2 * inter_planes
- self.branch1 = nn.Conv2d(
- in_planes, inter_planes, kernel_size=3, stride=1, padding=3, dilation=3)
-
- self.branch2 = nn.Sequential(
- nn.Conv2d(in_planes, inter_planes, kernel_size=3,
- stride=1, padding=3, dilation=3),
- nn.ReLU(inplace=True),
- nn.Conv2d(inter_planes, inter_planes, kernel_size=3,
- stride=1, padding=3, dilation=3)
- )
- self.branch3 = nn.Sequential(
- nn.Conv2d(in_planes, inter_planes1, kernel_size=3,
- stride=1, padding=3, dilation=3),
- nn.ReLU(inplace=True),
- nn.Conv2d(inter_planes1, inter_planes1, kernel_size=3,
- stride=1, padding=3, dilation=3),
- nn.ReLU(inplace=True),
- nn.Conv2d(inter_planes1, inter_planes1, kernel_size=3,
- stride=1, padding=3, dilation=3)
- )
-
- def forward(self, x):
- x1 = self.branch1(x)
- x2 = self.branch2(x)
- x3 = self.branch3(x)
- out = torch.cat((x1, x2, x3), dim=1)
- out = F.relu(out, inplace=True)
- return out
-
-
-class DSFD(nn.Module):
- """docstring for SRN"""
-
- def __init__(self, phase, base, extras, fem_modules, head1, head2, num_classes=2):
- super(DSFD, self).__init__()
- self.resnet = base
- self.phase = phase
- self.num_classes = num_classes
- self.extras = nn.ModuleList(extras)
-
- self.fpn_topdown = nn.ModuleList(fem_modules[0])
- self.fpn_latlayer = nn.ModuleList(fem_modules[1])
- self.fpn_fem = nn.ModuleList(fem_modules[2])
-
- self.loc_pal1 = nn.ModuleList(head1[0])
- self.conf_pal1 = nn.ModuleList(head1[1])
- self.loc_pal2 = nn.ModuleList(head2[0])
- self.conf_pal2 = nn.ModuleList(head2[1])
-
- if self.phase == 'test':
- self.softmax = nn.Softmax(dim=-1)
- self.detect = Detect(cfg)
-
- def _upsample_prod(self, x, y):
- _, _, H, W = y.size()
- return F.upsample(x, size=(H, W), mode='bilinear') * y
-
- def forward(self, x):
- size = x.size()[2:]
- of1, of2, of3, of4 = self.resnet(x)
-
- x = of4
- for i in range(2):
- x = F.relu(self.extras[i](x), inplace=True)
- of5 = x
-
- for i in range(2, len(self.extras)):
- x = F.relu(self.extras[i](x), inplace=True)
- of6 = x
-
- conv7 = F.relu(self.fpn_topdown[0](of6), inplace=True)
-
- x = F.relu(self.fpn_topdown[1](conv7), inplace=True)
- conv6 = F.relu(self._upsample_prod(
- x, self.fpn_latlayer[0](of5)), inplace=True)
-
- x = F.relu(self.fpn_topdown[2](conv6), inplace=True)
- conv5 = F.relu(self._upsample_prod(
- x, self.fpn_latlayer[1](of4)), inplace=True)
-
- x = F.relu(self.fpn_topdown[3](conv5), inplace=True)
- conv4 = F.relu(self._upsample_prod(
- x, self.fpn_latlayer[2](of3)), inplace=True)
-
- x = F.relu(self.fpn_topdown[4](conv4), inplace=True)
- conv3 = F.relu(self._upsample_prod(
- x, self.fpn_latlayer[3](of2)), inplace=True)
-
- x = F.relu(self.fpn_topdown[5](conv3), inplace=True)
- conv2 = F.relu(self._upsample_prod(
- x, self.fpn_latlayer[4](of1)), inplace=True)
-
- ef1 = self.fpn_fem[0](conv2)
- ef2 = self.fpn_fem[1](conv3)
- ef3 = self.fpn_fem[2](conv4)
- ef4 = self.fpn_fem[3](conv5)
- ef5 = self.fpn_fem[4](conv6)
- ef6 = self.fpn_fem[5](conv7)
-
- sources_pal1 = [of1, of2, of3, of4, of5, of6]
- sources_pal2 = [ef1, ef2, ef3, ef4, ef5, ef6]
- loc_pal1, conf_pal1 = list(), list()
- loc_pal2, conf_pal2 = list(), list()
-
- for (x, l, c) in zip(sources_pal1, self.loc_pal1, self.conf_pal1):
- loc_pal1.append(l(x).permute(0, 2, 3, 1).contiguous())
- conf_pal1.append(c(x).permute(0, 2, 3, 1).contiguous())
-
- for (x, l, c) in zip(sources_pal2, self.loc_pal2, self.conf_pal2):
- loc_pal2.append(l(x).permute(0, 2, 3, 1).contiguous())
- conf_pal2.append(c(x).permute(0, 2, 3, 1).contiguous())
-
- features_maps = []
- for i in range(len(loc_pal1)):
- feat = []
- feat += [loc_pal1[i].size(1), loc_pal1[i].size(2)]
- features_maps += [feat]
-
- loc_pal1 = torch.cat([o.view(o.size(0), -1) for o in loc_pal1], 1)
- conf_pal1 = torch.cat([o.view(o.size(0), -1) for o in conf_pal1], 1)
-
- loc_pal2 = torch.cat([o.view(o.size(0), -1) for o in loc_pal2], 1)
- conf_pal2 = torch.cat([o.view(o.size(0), -1) for o in conf_pal2], 1)
-
- priorbox = PriorBox(size, features_maps, cfg, pal=1)
- self.priors_pal1 = Variable(priorbox.forward(), volatile=True)
-
- priorbox = PriorBox(size, features_maps, cfg, pal=2)
- self.priors_pal2 = Variable(priorbox.forward(), volatile=True)
-
- if self.phase == 'test':
- output = self.detect(
- loc_pal2.view(loc_pal2.size(0), -1, 4),
- self.softmax(conf_pal2.view(conf_pal2.size(0), -1,
- self.num_classes)), # conf preds
- self.priors_pal2.type(type(x.data))
- )
-
- else:
- output = (
- loc_pal1.view(loc_pal1.size(0), -1, 4),
- conf_pal1.view(conf_pal1.size(0), -1, self.num_classes),
- self.priors_pal1,
- loc_pal2.view(loc_pal2.size(0), -1, 4),
- conf_pal2.view(conf_pal2.size(0), -1, self.num_classes),
- self.priors_pal2)
- return output
-
- def load_weights(self, base_file):
- other, ext = os.path.splitext(base_file)
- if ext == '.pkl' or '.pth':
- print('Loading weights into state dict...')
- mdata = torch.load(base_file,
- map_location=lambda storage, loc: storage)
- weights = mdata['weight']
- epoch = mdata['epoch']
- self.load_state_dict(weights)
- print('Finished!')
- else:
- print('Sorry only .pth and .pkl files supported.')
- return epoch
-
- def xavier(self, param):
- init.xavier_uniform(param)
-
- def weights_init(self, m):
- if isinstance(m, nn.Conv2d):
- self.xavier(m.weight.data)
- m.bias.data.zero_()
-
- if isinstance(m, nn.ConvTranspose2d):
- self.xavier(m.weight.data)
- if 'bias' in m.state_dict().keys():
- m.bias.data.zero_()
-
- if isinstance(m, nn.BatchNorm2d):
- m.weight.data[...] = 1
- m.bias.data.zero_()
-
-extras_cfg = [256, 'S', 512, 128, 'S', 256]
-
-net_cfg = [256, 512, 1024, 2048, 512, 256]
-
-
-def add_extras(cfg, i):
- layers = []
- in_channels = i
- flag = False
- for k, v in enumerate(cfg):
- if in_channels != 'S':
- if v == 'S':
- layers += [nn.Conv2d(in_channels, cfg[k + 1],
- kernel_size=(1, 3)[flag], stride=2, padding=1)]
- else:
- layers += [nn.Conv2d(in_channels, v, kernel_size=(1, 3)[flag])]
- flag = not flag
- in_channels = v
- return layers
-
-
-def multibox(cfg, num_classes=2):
- conf_layers = []
- loc_layers = []
- for k, v in enumerate(cfg):
- loc_layers += [nn.Conv2d(v, 4, kernel_size=3, padding=1)]
- conf_layers += [nn.Conv2d(v, num_classes, kernel_size=3, padding=1)]
- return (loc_layers, conf_layers)
-
-
-def fem_module(cfg):
- topdown_layers = []
- lat_layers = []
- fem_layers = []
-
- topdown_layers += [nn.Conv2d(cfg[-1], cfg[-1],
- kernel_size=1, stride=1, padding=0)]
- for k, v in enumerate(cfg):
- fem_layers += [FEM(v)]
- cur_channel = cfg[len(cfg) - 1 - k]
- if len(cfg) - 1 - k > 0:
- last_channel = cfg[len(cfg) - 2 - k]
- topdown_layers += [nn.Conv2d(cur_channel, last_channel,
- kernel_size=1, stride=1, padding=0)]
- lat_layers += [nn.Conv2d(last_channel, last_channel,
- kernel_size=1, stride=1, padding=0)]
- return (topdown_layers, lat_layers, fem_layers)
-
-
-def resnet50():
- """Constructs a ResNet-50 model.
- Args:
- pretrained (bool): If True, returns a model pre-trained on ImageNet
- """
- model = ResNet(Bottleneck, [3, 4, 6, 3])
- return model
-
-
-def resnet101():
- model = ResNet(Bottleneck, [3, 4, 23, 3])
- return model
-
-
-def resnet152():
- model = ResNet(Bottleneck, [3, 8, 36, 3])
- return model
-
-
-def model_map(net_name='resnet50'):
- _dicts = {'resnet50': resnet50,
- 'resnet101': resnet101, 'resnet152': resnet152}
- return _dicts[net_name]()
-
-
-def build_net_resnet(phase, num_classes=2, net_name='resnet50'):
- resnet = model_map(net_name)
- extras = add_extras(extras_cfg, 2048)
- head_pal1 = multibox(net_cfg, num_classes)
- head_pal2 = multibox(net_cfg, num_classes)
- fem_modules = fem_module(net_cfg)
- model = DSFD(phase, resnet, extras, fem_modules,
- head_pal1, head_pal2, num_classes)
- return model
-
-if __name__ == '__main__':
- inputs = Variable(torch.randn(1, 3, 640, 640))
- net = build_net('train', 2, 101)
- out = net(inputs)
deleted file mode 100644
@@ -1,374 +0,0 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-#coding=utf-8
-
-from __future__ import division
-from __future__ import absolute_import
-from __future__ import print_function
-
-import os
-import numpy as np
-
-import torch
-import torch.nn as nn
-import torch.nn.init as init
-import torch.nn.functional as F
-from torch.autograd import Variable
-
-from layers import *
-from data.config import cur_config as cfg
-
-
-class FEM(nn.Module):
- """docstring for FEM"""
-
- def __init__(self, in_planes):
- super(FEM, self).__init__()
- inter_planes = in_planes // 3
- inter_planes1 = in_planes - 2 * inter_planes
- self.branch1 = nn.Conv2d(
- in_planes, inter_planes, kernel_size=3, stride=1, padding=3, dilation=3)
-
- self.branch2 = nn.Sequential(
- nn.Conv2d(in_planes, inter_planes, kernel_size=3,
- stride=1, padding=3, dilation=3),
- nn.ReLU(inplace=True),
- nn.Conv2d(inter_planes, inter_planes, kernel_size=3,
- stride=1, padding=3, dilation=3)
- )
- self.branch3 = nn.Sequential(
- nn.Conv2d(in_planes, inter_planes1, kernel_size=3,
- stride=1, padding=3, dilation=3),
- nn.ReLU(inplace=True),
- nn.Conv2d(inter_planes1, inter_planes1, kernel_size=3,
- stride=1, padding=3, dilation=3),
- nn.ReLU(inplace=True),
- nn.Conv2d(inter_planes1, inter_planes1, kernel_size=3,
- stride=1, padding=3, dilation=3)
- )
-
- def forward(self, x):
- x1 = self.branch1(x)
- x2 = self.branch2(x)
- x3 = self.branch3(x)
- out = torch.cat((x1, x2, x3), dim=1)
- out = F.relu(out, inplace=True)
- return out
-
-
-class DSFD(nn.Module):
- """Single Shot Multibox Architecture
- The network is composed of a base VGG network followed by the
- added multibox conv layers. Each multibox layer branches into
- 1) conv2d for class conf scores
- 2) conv2d for localization predictions
- 3) associated priorbox layer to produce default bounding
- boxes specific to the layer's feature map size.
- See: https://arxiv.org/pdf/1512.02325.pdf for more details.
-
- Args:
- phase: (string) Can be "test" or "train"
- size: input image size
- base: VGG16 layers for input, size of either 300 or 500
- extras: extra layers that feed to multibox loc and conf layers
- head: "multibox head" consists of loc and conf conv layers
- """
-
- def __init__(self, phase, base, extras, fem, head1, head2, num_classes):
- super(DSFD, self).__init__()
- self.phase = phase
- self.num_classes = num_classes
- self.vgg = nn.ModuleList(base)
-
- self.L2Normof1 = L2Norm(256, 10)
- self.L2Normof2 = L2Norm(512, 8)
- self.L2Normof3 = L2Norm(512, 5)
-
- self.extras = nn.ModuleList(extras)
- self.fpn_topdown = nn.ModuleList(fem[0])
- self.fpn_latlayer = nn.ModuleList(fem[1])
-
- self.fpn_fem = nn.ModuleList(fem[2])
-
- self.L2Normef1 = L2Norm(256, 10)
- self.L2Normef2 = L2Norm(512, 8)
- self.L2Normef3 = L2Norm(512, 5)
-
- self.loc_pal1 = nn.ModuleList(head1[0])
- self.conf_pal1 = nn.ModuleList(head1[1])
-
- self.loc_pal2 = nn.ModuleList(head2[0])
- self.conf_pal2 = nn.ModuleList(head2[1])
-
- if self.phase=='test':
- self.softmax = nn.Softmax(dim=-1)
- self.detect = Detect(cfg)
-
- def _upsample_prod(self, x, y):
- _, _, H, W = y.size()
- return F.upsample(x, size=(H, W), mode='bilinear') * y
-
- def forward(self, x):
- size = x.size()[2:]
- pal1_sources = list()
- pal2_sources = list()
- loc_pal1 = list()
- conf_pal1 = list()
- loc_pal2 = list()
- conf_pal2 = list()
-
- # apply vgg up to conv4_3 relu
- for k in range(16):
- x = self.vgg[k](x)
- of1 = x
- s = self.L2Normof1(of1)
- pal1_sources.append(s)
- # apply vgg up to fc7
- for k in range(16, 23):
- x = self.vgg[k](x)
- of2 = x
- s = self.L2Normof2(of2)
- pal1_sources.append(s)
-
- for k in range(23, 30):
- x = self.vgg[k](x)
- of3 = x
- s = self.L2Normof3(of3)
- pal1_sources.append(s)
-
- for k in range(30, len(self.vgg)):
- x = self.vgg[k](x)
- of4 = x
- pal1_sources.append(of4)
- # apply extra layers and cache source layer outputs
-
- for k in range(2):
- x = F.relu(self.extras[k](x), inplace=True)
- of5 = x
- pal1_sources.append(of5)
- for k in range(2, 4):
- x = F.relu(self.extras[k](x), inplace=True)
- of6 = x
- pal1_sources.append(of6)
-
- conv7 = F.relu(self.fpn_topdown[0](of6), inplace=True)
-
- x = F.relu(self.fpn_topdown[1](conv7), inplace=True)
- conv6 = F.relu(self._upsample_prod(
- x, self.fpn_latlayer[0](of5)), inplace=True)
-
- x = F.relu(self.fpn_topdown[2](conv6), inplace=True)
- convfc7_2 = F.relu(self._upsample_prod(
- x, self.fpn_latlayer[1](of4)), inplace=True)
-
- x = F.relu(self.fpn_topdown[3](convfc7_2), inplace=True)
- conv5 = F.relu(self._upsample_prod(
- x, self.fpn_latlayer[2](of3)), inplace=True)
-
- x = F.relu(self.fpn_topdown[4](conv5), inplace=True)
- conv4 = F.relu(self._upsample_prod(
- x, self.fpn_latlayer[3](of2)), inplace=True)
-
- x = F.relu(self.fpn_topdown[5](conv4), inplace=True)
- conv3 = F.relu(self._upsample_prod(
- x, self.fpn_latlayer[4](of1)), inplace=True)
-
- ef1 = self.fpn_fem[0](conv3)
- ef1 = self.L2Normef1(ef1)
- ef2 = self.fpn_fem[1](conv4)
- ef2 = self.L2Normef2(ef2)
- ef3 = self.fpn_fem[2](conv5)
- ef3 = self.L2Normef3(ef3)
- ef4 = self.fpn_fem[3](convfc7_2)
- ef5 = self.fpn_fem[4](conv6)
- ef6 = self.fpn_fem[5](conv7)
-
- pal2_sources = (ef1, ef2, ef3, ef4, ef5, ef6)
- for (x, l, c) in zip(pal1_sources, self.loc_pal1, self.conf_pal1):
- loc_pal1.append(l(x).permute(0, 2, 3, 1).contiguous())
- conf_pal1.append(c(x).permute(0, 2, 3, 1).contiguous())
-
- for (x, l, c) in zip(pal2_sources, self.loc_pal2, self.conf_pal2):
- loc_pal2.append(l(x).permute(0, 2, 3, 1).contiguous())
- conf_pal2.append(c(x).permute(0, 2, 3, 1).contiguous())
-
- features_maps = []
- for i in range(len(loc_pal1)):
- feat = []
- feat += [loc_pal1[i].size(1), loc_pal1[i].size(2)]
- features_maps += [feat]
-
- loc_pal1 = torch.cat([o.view(o.size(0), -1)
- for o in loc_pal1], 1)
- conf_pal1 = torch.cat([o.view(o.size(0), -1)
- for o in conf_pal1], 1)
-
- loc_pal2 = torch.cat([o.view(o.size(0), -1)
- for o in loc_pal2], 1)
- conf_pal2 = torch.cat([o.view(o.size(0), -1)
- for o in conf_pal2], 1)
-
- priorbox = PriorBox(size, features_maps, cfg, pal=1)
- self.priors_pal1 = Variable(priorbox.forward(), volatile=True)
-
- priorbox = PriorBox(size, features_maps, cfg, pal=2)
- self.priors_pal2 = Variable(priorbox.forward(), volatile=True)
-
- if self.phase == 'test':
- output = self.detect(
- loc_pal2.view(loc_pal2.size(0), -1, 4),
- self.softmax(conf_pal2.view(conf_pal2.size(0), -1,
- self.num_classes)), # conf preds
- self.priors_pal2.type(type(x.data))
- )
-
- else:
- output = (
- loc_pal1.view(loc_pal1.size(0), -1, 4),
- conf_pal1.view(conf_pal1.size(0), -1, self.num_classes),
- self.priors_pal1,
- loc_pal2.view(loc_pal2.size(0), -1, 4),
- conf_pal2.view(conf_pal2.size(0), -1, self.num_classes),
- self.priors_pal2)
- return output
-
- def load_weights(self, base_file):
- other, ext = os.path.splitext(base_file)
- if ext == '.pkl' or '.pth':
- print('Loading weights into state dict...')
- mdata = torch.load(base_file,
- map_location=lambda storage, loc: storage)
- weights = mdata['weight']
- epoch = mdata['epoch']
- self.load_state_dict(weights)
- print('Finished!')
- else:
- print('Sorry only .pth and .pkl files supported.')
- return epoch
-
- def xavier(self, param):
- init.xavier_uniform(param)
-
- def weights_init(self, m):
- if isinstance(m, nn.Conv2d):
- self.xavier(m.weight.data)
- m.bias.data.zero_()
-
- if isinstance(m,nn.ConvTranspose2d):
- self.xavier(m.weight.data)
- if 'bias' in m.state_dict().keys():
- m.bias.data.zero_()
-
- if isinstance(m,nn.BatchNorm2d):
- m.weight.data[...] = 1
- m.bias.data.zero_()
-
-
-vgg_cfg = [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'C', 512, 512, 512, 'M',
- 512, 512, 512, 'M']
-
-extras_cfg = [256, 'S', 512, 128, 'S', 256]
-
-fem_cfg = [256, 512, 512, 1024, 512, 256]
-
-
-def fem_module(cfg):
- topdown_layers = []
- lat_layers = []
- fem_layers = []
-
- topdown_layers += [nn.Conv2d(cfg[-1], cfg[-1],
- kernel_size=1, stride=1, padding=0)]
- for k, v in enumerate(cfg):
- fem_layers += [FEM(v)]
- cur_channel = cfg[len(cfg) - 1 - k]
- if len(cfg) - 1 - k > 0:
- last_channel = cfg[len(cfg) - 2 - k]
- topdown_layers += [nn.Conv2d(cur_channel, last_channel,
- kernel_size=1, stride=1, padding=0)]
- lat_layers += [nn.Conv2d(last_channel, last_channel,
- kernel_size=1, stride=1, padding=0)]
- return (topdown_layers, lat_layers, fem_layers)
-
-
-def vgg(cfg, i, batch_norm=False):
- layers = []
- in_channels = i
- for v in cfg:
- if v == 'M':
- layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
- elif v == 'C':
- layers += [nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)]
- else:
- conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
- if batch_norm:
- layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]
- else:
- layers += [conv2d, nn.ReLU(inplace=True)]
- in_channels = v
- conv6 = nn.Conv2d(512, 1024, kernel_size=3, padding=3, dilation=3)
- conv7 = nn.Conv2d(1024, 1024, kernel_size=1)
- layers += [conv6,
- nn.ReLU(inplace=True), conv7, nn.ReLU(inplace=True)]
- return layers
-
-
-def add_extras(cfg, i, batch_norm=False):
- # Extra layers added to VGG for feature scaling
- layers = []
- in_channels = i
- flag = False
- for k, v in enumerate(cfg):
- if in_channels != 'S':
- if v == 'S':
- layers += [nn.Conv2d(in_channels, cfg[k + 1],
- kernel_size=(1, 3)[flag], stride=2, padding=1)]
- else:
- layers += [nn.Conv2d(in_channels, v, kernel_size=(1, 3)[flag])]
- flag = not flag
- in_channels = v
- return layers
-
-
-def multibox(vgg, extra_layers, num_classes):
- loc_layers = []
- conf_layers = []
- vgg_source = [14, 21, 28, -2]
-
- for k, v in enumerate(vgg_source):
- loc_layers += [nn.Conv2d(vgg[v].out_channels,
- 4, kernel_size=3, padding=1)]
- conf_layers += [nn.Conv2d(vgg[v].out_channels,
- num_classes, kernel_size=3, padding=1)]
- for k, v in enumerate(extra_layers[1::2], 2):
- loc_layers += [nn.Conv2d(v.out_channels,
- 4, kernel_size=3, padding=1)]
- conf_layers += [nn.Conv2d(v.out_channels,
- num_classes, kernel_size=3, padding=1)]
- return (loc_layers, conf_layers)
-
-
-def build_net_vgg(phase, num_classes=2):
- base = vgg(vgg_cfg, 3)
- extras = add_extras(extras_cfg, 1024)
- head1 = multibox(base, extras, num_classes)
- head2 = multibox(base, extras, num_classes)
- fem = fem_module(fem_cfg)
- return DSFD(phase, base, extras, fem, head1, head2, num_classes)
-
-if __name__ == '__main__':
- inputs = Variable(torch.randn(1, 3, 640, 640))
- net = build_net('train', 2)
- out = net(inputs)
deleted file mode 100644
@@ -1,13 +0,0 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
\ No newline at end of file
deleted file mode 100644
@@ -1,50 +0,0 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# -*- coding: utf-8 -*-
-from __future__ import division
-from __future__ import print_function
-from __future__ import absolute_import
-
-import torch
-import torch.backends.cudnn as cudnn
-
-#from .DSFD_vgg import build_net_vgg
-from .DSFD_resnet import build_net_resnet
-
-
-def build_net(phase, num_classes=2, model='vgg'):
- if phase != "test" and phase != "train":
- print("ERROR: Phase: " + phase + " not recognized")
- return
-
- if model != 'vgg' and 'resnet' not in model:
- print("ERROR: model:" + model + " not recognized")
- return
-
- if model == 'vgg':
- return build_net_vgg(phase, num_classes)
- else:
- return build_net_resnet(phase, num_classes, model)
-
-
-
-def basenet_factory(model='vgg'):
- if model=='vgg':
- basenet = 'vgg16_reducedfc.pth'
-
- elif 'resnet' in model:
- basenet = '{}.pth'.format(model)
- return basenet
-