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Merge pull request #21805 from rogday:pretty_fix

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Mat pretty printer: fix submatrix indexation

* fix submatrix indexation

* fix channels
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rogday 2022-04-20 07:55:04 +03:00 committed by GitHub
parent 5cc154147f
commit 438fe3f9db
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1 changed files with 17 additions and 7 deletions
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24
samples/gdb/mat_pretty_printer.py
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@ -122,28 +122,38 @@ class Mat:
(dtype, ctype) = flags.dtype() (dtype, ctype) = flags.dtype()
elsize = np.dtype(dtype).itemsize elsize = np.dtype(dtype).itemsize
ptr = m['data'] shape = size.to_numpy()
dataptr = int(ptr) steps = np.asarray([int(m['step']['p'][i]) for i in range(len(shape))], dtype=np.int64)
length = (int(m['dataend']) - dataptr) // elsize
start = (int(m['datastart']) - dataptr) // elsize
if length == 0: ptr = m['data']
# either we are default-constructed or sizes are zero
if int(ptr) == 0 or np.prod(shape * steps) == 0:
self.mat = np.array([]) self.mat = np.array([])
self.view = self.mat self.view = self.mat
return return
# we don't want to show excess brackets
if flags.channels() != 1:
shape = np.append(shape, flags.channels())
steps = np.append(steps, elsize)
# get the length of contiguous array from data to the last element of the matrix
length = 1 + np.sum((shape - 1) * steps) // elsize
if dtype != np.float16: if dtype != np.float16:
# read all elements into self.mat
ctype = gdb.lookup_type(ctype) ctype = gdb.lookup_type(ctype)
ptr = ptr.cast(ctype.array(length - 1).pointer()).dereference() ptr = ptr.cast(ctype.array(length - 1).pointer()).dereference()
self.mat = np.array([ptr[i] for i in range(length)], dtype=dtype) self.mat = np.array([ptr[i] for i in range(length)], dtype=dtype)
else: else:
# read as uint16_t and then reinterpret the bytes as float16
u16 = gdb.lookup_type('uint16_t') u16 = gdb.lookup_type('uint16_t')
ptr = ptr.cast(u16.array(length - 1).pointer()).dereference() ptr = ptr.cast(u16.array(length - 1).pointer()).dereference()
self.mat = np.array([ptr[i] for i in range(length)], dtype=np.uint16) self.mat = np.array([ptr[i] for i in range(length)], dtype=np.uint16)
self.mat = self.mat.view(np.float16) self.mat = self.mat.view(np.float16)
steps = np.asarray([int(m['step']['p'][i]) for i in range(size.dims())], dtype=np.int64) # numpy will do the heavy lifting of strided access
self.view = np.lib.stride_tricks.as_strided(self.mat[start:], shape=size.to_numpy(), strides=steps) self.view = np.lib.stride_tricks.as_strided(self.mat, shape=shape, strides=steps)
def __iter__(self): def __iter__(self):
return iter({'data': stri(self.view)}.items()) return iter({'data': stri(self.view)}.items())