Here are the examples of the python api numpy.int8 taken from open source projects. By voting up you can indicate which examples are most useful and appropriate.
1125 Examples
3
Source : function_blocks.py
with MIT License
from adamsolomou
with MIT License
from adamsolomou
def invert(x):
# Change of sign
return np.logical_not(x).astype(np.int8)
def add(x,y,s_in,upscale=False,s_out=None):
3
Source : function_blocks.py
with MIT License
from adamsolomou
with MIT License
from adamsolomou
def non_lin_sat(x,d):
"""
Approximates a non-linear block
"""
no_samples = x.size
z = np.empty(no_samples,dtype=np.int8)
for sample in range(no_samples):
start_idx = sample
end_idx = (sample+d-1)%no_samples
if(popcount(x[start_idx:end_idx]) >= d/2):
z[sample] = 1
else:
z[sample] = 0
return z
def Stanh(x,N):
3
Source : function_blocks.py
with MIT License
from adamsolomou
with MIT License
from adamsolomou
def vec_Srelu(x):
"""
Vectorised form of Srelu
"""
(x_rows, x_cols, no_samples) = x.shape
# Initialise the output 3D-array
z = np.empty((x_rows,x_cols,no_samples),dtype=np.int8)
for row_idx in range(x_rows):
for col_idx in range(x_cols):
z[row_idx,col_idx,:] = Srelu(x[row_idx,col_idx,:])
return z
3
Source : test_dtype.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_fields_by_index(self):
dt = np.dtype([('a', np.int8), ('b', np.float32, 3)])
assert_dtype_equal(dt[0], np.dtype(np.int8))
assert_dtype_equal(dt[1], np.dtype((np.float32, 3)))
assert_dtype_equal(dt[-1], dt[1])
assert_dtype_equal(dt[-2], dt[0])
assert_raises(IndexError, lambda: dt[-3])
assert_raises(TypeError, operator.getitem, dt, 3.0)
assert_raises(TypeError, operator.getitem, dt, [])
assert_equal(dt[1], dt[np.int8(1)])
class TestSubarray(object):
3
Source : test_getlimits.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_basic(self):
dts = list(zip(['i1', 'i2', 'i4', 'i8',
'u1', 'u2', 'u4', 'u8'],
[np.int8, np.int16, np.int32, np.int64,
np.uint8, np.uint16, np.uint32, np.uint64]))
for dt1, dt2 in dts:
for attr in ('bits', 'min', 'max'):
assert_equal(getattr(iinfo(dt1), attr),
getattr(iinfo(dt2), attr), attr)
assert_raises(ValueError, iinfo, 'f4')
def test_unsigned_max(self):
3
Source : test_mem_overlap.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_shares_memory_api():
x = np.zeros([4, 5, 6], dtype=np.int8)
assert_equal(np.shares_memory(x, x), True)
assert_equal(np.shares_memory(x, x.copy()), False)
a = x[:,::2,::3]
b = x[:,::3,::2]
assert_equal(np.shares_memory(a, b), True)
assert_equal(np.shares_memory(a, b, max_work=None), True)
assert_raises(np.TooHardError, np.shares_memory, a, b, max_work=1)
assert_raises(np.TooHardError, np.shares_memory, a, b, max_work=long(1))
def test_may_share_memory_bad_max_work():
3
Source : test_regression.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_recarray_tolist(self):
# Ticket #793, changeset r5215
# Comparisons fail for NaN, so we can't use random memory
# for the test.
buf = np.zeros(40, dtype=np.int8)
a = np.recarray(2, formats="i4,f8,f8", names="id,x,y", buf=buf)
b = a.tolist()
assert_( a[0].tolist() == b[0])
assert_( a[1].tolist() == b[1])
def test_nonscalar_item_method(self):
3
Source : test_regression.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_lexsort_buffer_length(self):
# Ticket #1217, don't segfault.
a = np.ones(100, dtype=np.int8)
b = np.ones(100, dtype=np.int32)
i = np.lexsort((a[::-1], b))
assert_equal(i, np.arange(100, dtype=int))
def test_object_array_to_fixed_string(self):
3
Source : test_scalarmath.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_lower_align(self):
# check data that is not aligned to element size
# i.e doubles are aligned to 4 bytes on i386
d = np.zeros(23 * 8, dtype=np.int8)[4:-4].view(np.float64)
o = np.zeros(23 * 8, dtype=np.int8)[4:-4].view(np.float64)
assert_almost_equal(d + d, d * 2)
np.add(d, d, out=o)
np.add(np.ones_like(d), d, out=o)
np.add(d, np.ones_like(d), out=o)
np.add(np.ones_like(d), d)
np.add(d, np.ones_like(d))
class TestPower(object):
3
Source : test_scalarmath.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_small_types(self):
for t in [np.int8, np.int16, np.float16]:
a = t(3)
b = a ** 4
assert_(b == 81, "error with %r: got %r" % (t, b))
def test_large_types(self):
3
Source : test_scalarmath.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_mixed_types(self):
typelist = [np.int8, np.int16, np.float16,
np.float32, np.float64, np.int8,
np.int16, np.int32, np.int64]
for t1 in typelist:
for t2 in typelist:
a = t1(3)
b = t2(2)
result = a**b
msg = ("error with %r and %r:"
"got %r, expected %r") % (t1, t2, result, 9)
if np.issubdtype(np.dtype(result), np.integer):
assert_(result == 9, msg)
else:
assert_almost_equal(result, 9, err_msg=msg)
def test_modular_power(self):
3
Source : test_umath.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_ldexp(self):
# The default Python int type should work
assert_almost_equal(ncu.ldexp(2., 3), 16.)
# The following int types should all be accepted
self._check_ldexp(np.int8)
self._check_ldexp(np.int16)
self._check_ldexp(np.int32)
self._check_ldexp('i')
self._check_ldexp('l')
def test_ldexp_overflow(self):
3
Source : test_umath.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_lower_align(self):
# check data that is not aligned to element size
# i.e doubles are aligned to 4 bytes on i386
d = np.zeros(23 * 8, dtype=np.int8)[4:-4].view(np.float64)
assert_equal(d.max(), d[0])
assert_equal(d.min(), d[0])
def test_reduce_warns(self):
3
Source : test_umath.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_lower_align(self):
# check data that is not aligned to element size
# i.e doubles are aligned to 4 bytes on i386
d = np.zeros(23 * 8, dtype=np.int8)[4:-4].view(np.float64)
assert_equal(np.abs(d), d)
assert_equal(np.negative(d), -d)
np.negative(d, out=d)
np.negative(np.ones_like(d), out=d)
np.abs(d, out=d)
np.abs(np.ones_like(d), out=d)
class TestPositive(object):
3
Source : test_twodim_base.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_dtypes(self):
c = array([11, -12, 13], dtype=np.int8)
v = vander(c)
expected = np.array([[121, 11, 1],
[144, -12, 1],
[169, 13, 1]])
yield (assert_array_equal, v, expected)
c = array([1.0+1j, 1.0-1j])
v = vander(c, N=3)
expected = np.array([[2j, 1+1j, 1],
[-2j, 1-1j, 1]])
# The data is floating point, but the values are small integers,
# so assert_array_equal *should* be safe here (rather than, say,
# assert_array_almost_equal).
yield (assert_array_equal, v, expected)
if __name__ == "__main__":
3
Source : test_core.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_extremum_fill_value_subdtype(self):
a = array(([2, 3, 4],), dtype=[('value', np.int8, 3)])
test = minimum_fill_value(a)
assert_equal(test.dtype, a.dtype)
assert_equal(test[0], np.full(3, minimum_fill_value(a['value'])))
test = maximum_fill_value(a)
assert_equal(test.dtype, a.dtype)
assert_equal(test[0], np.full(3, maximum_fill_value(a['value'])))
def test_fillvalue_individual_fields(self):
3
Source : test_defmatrix.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_scalar_type_pow(self):
m = matrix([[1, 2], [3, 4]])
for scalar_t in [np.int8, np.uint8]:
two = scalar_t(2)
assert_array_almost_equal(m ** 2, m ** two)
def test_notimplemented(self):
3
Source : test_multiarray.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_keywords(self):
x = np.array([(1, 2)], dtype=[('a', np.int8), ('b', np.int8)])
# We must be specific about the endianness here:
y = x.view(dtype=' < i2', type=np.matrix)
assert_array_equal(y, [[513]])
assert_(isinstance(y, np.matrix))
assert_equal(y.dtype, np.dtype(' < i2'))
if __name__ == "__main__":
3
Source : stata.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def get_base_missing_value(cls, dtype):
if dtype == np.int8:
value = cls.BASE_MISSING_VALUES['int8']
elif dtype == np.int16:
value = cls.BASE_MISSING_VALUES['int16']
elif dtype == np.int32:
value = cls.BASE_MISSING_VALUES['int32']
elif dtype == np.float32:
value = cls.BASE_MISSING_VALUES['float32']
elif dtype == np.float64:
value = cls.BASE_MISSING_VALUES['float64']
else:
raise ValueError('Unsupported dtype')
return value
class StataParser(object):
3
Source : test_api.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_recode_to_categories(self, codes, old, new, expected):
codes = np.asanyarray(codes, dtype=np.int8)
expected = np.asanyarray(expected, dtype=np.int8)
old = Index(old)
new = Index(new)
result = _recode_for_categories(codes, old, new)
tm.assert_numpy_array_equal(result, expected)
def test_recode_to_categories_large(self):
3
Source : test_indexing.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_getitem(self):
assert self.factor[0] == 'a'
assert self.factor[-1] == 'c'
subf = self.factor[[0, 1, 2]]
tm.assert_numpy_array_equal(subf._codes,
np.array([0, 1, 1], dtype=np.int8))
subf = self.factor[np.asarray(self.factor) == 'c']
tm.assert_numpy_array_equal(subf._codes,
np.array([2, 2, 2], dtype=np.int8))
def test_setitem(self):
3
Source : test_function.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_lower_int_prec_count():
df = DataFrame({'a': np.array(
[0, 1, 2, 100], np.int8),
'b': np.array(
[1, 2, 3, 6], np.uint32),
'c': np.array(
[4, 5, 6, 8], np.int16),
'grp': list('ab' * 2)})
result = df.groupby('grp').count()
expected = DataFrame({'a': [2, 2],
'b': [2, 2],
'c': [2, 2]}, index=pd.Index(list('ab'),
name='grp'))
tm.assert_frame_equal(result, expected)
def test_count_uses_size_on_exception():
3
Source : test_coercion.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_setitem_series_int8(self, val, exp_dtype):
obj = pd.Series([1, 2, 3, 4], dtype=np.int8)
assert obj.dtype == np.int8
if exp_dtype is np.int16:
exp = pd.Series([1, 0, 3, 4], dtype=np.int8)
self._assert_setitem_series_conversion(obj, val, exp, np.int8)
pytest.xfail("BUG: it must be Series([1, 1, 3, 4], dtype=np.int16")
exp = pd.Series([1, val, 3, 4], dtype=np.int8)
self._assert_setitem_series_conversion(obj, val, exp, exp_dtype)
@pytest.mark.parametrize("val,exp_dtype", [
3
Source : test_stata.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_read_write_reread_dta15(self, file):
expected = self.read_csv(self.csv15)
expected['byte_'] = expected['byte_'].astype(np.int8)
expected['int_'] = expected['int_'].astype(np.int16)
expected['long_'] = expected['long_'].astype(np.int32)
expected['float_'] = expected['float_'].astype(np.float32)
expected['double_'] = expected['double_'].astype(np.float64)
expected['date_td'] = expected['date_td'].apply(
datetime.strptime, args=('%Y-%m-%d',))
file = getattr(self, file)
parsed = self.read_dta(file)
tm.assert_frame_equal(expected, parsed)
@pytest.mark.parametrize('version', [114, 117])
3
Source : test_interpolate.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_overflow_nearest(self):
# Test that the x range doesn't overflow when given integers as input
for kind in ('nearest', 'previous', 'next'):
x = np.array([0, 50, 127], dtype=np.int8)
ii = interp1d(x, x, kind=kind)
assert_array_almost_equal(ii(x), x)
def test_local_nans(self):
3
Source : test_measurements.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_sum11():
labels = np.array([1, 2], np.int8)
for type in types:
input = np.array([[1, 2], [3, 4]], type)
output = ndimage.sum(input, labels=labels,
index=2)
assert_almost_equal(output, 6.0)
def test_sum12():
3
Source : test_measurements.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_sum12():
labels = np.array([[1, 2], [2, 4]], np.int8)
for type in types:
input = np.array([[1, 2], [3, 4]], type)
output = ndimage.sum(input, labels=labels,
index=[4, 8, 2])
assert_array_almost_equal(output, [4.0, 0.0, 5.0])
def test_mean01():
3
Source : test_measurements.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_mean04():
labels = np.array([[1, 2], [2, 4]], np.int8)
olderr = np.seterr(all='ignore')
try:
for type in types:
input = np.array([[1, 2], [3, 4]], type)
output = ndimage.mean(input, labels=labels,
index=[4, 8, 2])
assert_array_almost_equal(output[[0,2]], [4.0, 2.5])
assert_(np.isnan(output[1]))
finally:
np.seterr(**olderr)
def test_minimum01():
3
Source : test_ndimage.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def setup_method(self):
# list of numarray data types
self.integer_types = [
numpy.int8, numpy.uint8, numpy.int16, numpy.uint16,
numpy.int32, numpy.uint32, numpy.int64, numpy.uint64]
self.float_types = [numpy.float32, numpy.float64]
self.types = self.integer_types + self.float_types
# list of boundary modes:
self.modes = ['nearest', 'wrap', 'reflect', 'mirror', 'constant']
def test_correlate01(self):
3
Source : test_base.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_constructor4(self):
# regression test for gh-6292: bsr_matrix((data, indices, indptr)) was
# trying to compare an int to a None
n = 8
data = np.ones((n, n, 1), dtype=np.int8)
indptr = np.array([0, n], dtype=np.int32)
indices = np.arange(n, dtype=np.int32)
bsr_matrix((data, indices, indptr), blocksize=(n, 1), copy=False)
def test_eliminate_zeros(self):
3
Source : test_sparsetools.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_dia_matvec(self):
# Check: huge dia_matrix _matvec
n = self.n
data = np.ones((n, n), dtype=np.int8)
offsets = np.arange(n)
m = dia_matrix((data, offsets), shape=(n, n))
v = np.ones(m.shape[1], dtype=np.int8)
r = m.dot(v)
assert_equal(r[0], np.int8(n))
del data, offsets, m, v, r
gc.collect()
_bsr_ops = [pytest.param("matmat", marks=pytest.mark.xslow),
3
Source : test_sparsetools.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def _check_bsr_matvecs(self, m):
m = m()
n = self.n
# _matvecs
r = m.dot(np.ones((n, 2), dtype=np.int8))
assert_equal(r[0,0], np.int8(n))
def _check_bsr_matvec(self, m):
3
Source : test_sparsetools.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def _check_bsr_matvec(self, m):
m = m()
n = self.n
# _matvec
r = m.dot(np.ones((n,), dtype=np.int8))
assert_equal(r[0], np.int8(n))
def _check_bsr_diagonal(self, m):
3
Source : test_sparsetools.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def _check_bsr_matmat(self, m):
m = m()
n = self.n
# _bsr_matmat
m2 = bsr_matrix(np.ones((n, 2), dtype=np.int8), blocksize=(m.blocksize[1], 2))
m.dot(m2) # shouldn't SIGSEGV
del m2
# _bsr_matmat
m2 = bsr_matrix(np.ones((2, n), dtype=np.int8), blocksize=(2, m.blocksize[0]))
m2.dot(m) # shouldn't SIGSEGV
@pytest.mark.skip(reason="64-bit indices in sparse matrices not available")
3
Source : quantized.py
with MIT License
from alibaba
with MIT License
from alibaba
def parse(self, node, attrs, args, graph_converter):
super().parse(node, attrs, args, graph_converter)
self.run(node)
# Only int8 kernel is supported
if self.q_type == np.int8:
self.elementwise_unary(tfl.EluOperator, graph_converter)
else:
ops = []
inputs = [self.find_or_create_input(0, graph_converter)]
outputs = self.to_tfl_tensors(self.output_names, self.output_tensors)
ops.append(tfl.EluOperator(inputs, outputs))
ops = self.wrap_ops_with_dequant_quants(ops)
for op in ops:
graph_converter.add_operator(op)
3
Source : vta.py
with MIT License
from alipay
with MIT License
from alipay
def zero_runs(a):
# Create an array that is 1 where a is 0, and pad each end with an extra 0.
iszero = np.concatenate(([0], np.equal(a, 0).view(np.int8), [0]))
absdiff = np.abs(np.diff(iszero))
# Runs start and end where absdiff is 1.
ranges = np.where(absdiff == 1)[0].reshape(-1, 2)
return ranges
def cut_path(path: np.ndarray, diagonal_thres):
3
Source : test_twodim_base.py
with MIT License
from alvarobartt
with MIT License
from alvarobartt
def test_dtypes(self):
c = array([11, -12, 13], dtype=np.int8)
v = vander(c)
expected = np.array([[121, 11, 1],
[144, -12, 1],
[169, 13, 1]])
assert_array_equal(v, expected)
c = array([1.0+1j, 1.0-1j])
v = vander(c, N=3)
expected = np.array([[2j, 1+1j, 1],
[-2j, 1-1j, 1]])
# The data is floating point, but the values are small integers,
# so assert_array_equal *should* be safe here (rather than, say,
# assert_array_almost_equal).
assert_array_equal(v, expected)
3
Source : test_multiarray.py
with MIT License
from alvarobartt
with MIT License
from alvarobartt
def test_keywords(self):
x = np.array([(1, 2)], dtype=[('a', np.int8), ('b', np.int8)])
# We must be specific about the endianness here:
y = x.view(dtype=' < i2', type=np.matrix)
assert_array_equal(y, [[513]])
assert_(isinstance(y, np.matrix))
assert_equal(y.dtype, np.dtype(' < i2'))
3
Source : gotenna_sink.py
with GNU General Public License v3.0
from argilo
with GNU General Public License v3.0
from argilo
def __init__(self):
gr.sync_block.__init__(
self,
name="Gotenna decoder",
in_sig=[np.int8],
out_sig=None
)
# if an attribute with the same name as a parameter is found,
# a callback is registered (properties work, too).
self.prefix = "10"*16 + "0010110111010100"
self.bits = ""
def work(self, input_items, output_items):
3
Source : common.py
with Apache License 2.0
from artemis-analytics
with Apache License 2.0
from artemis-analytics
def get_random_ascii(n, seed=42):
"""
Get a random ASCII-only unicode string of size *n*.
"""
arr = np.frombuffer(get_random_bytes(n, seed=seed), dtype=np.int8) & 0x7F
result, _ = codecs.ascii_decode(arr)
assert isinstance(result, str)
assert len(result) == n
return result
def _random_unicode_letters(n, seed=42):
3
Source : imagenet_torch_preprocess.py
with Apache License 2.0
from Ascend
with Apache License 2.0
from Ascend
def gen_input_bin(mode_type, file_batches, batch):
i = 0
for file in file_batches[batch]:
i = i + 1
print("batch", batch, file, "===", i)
# RGBA to RGB
image = Image.open(os.path.join(src_path, file)).convert('RGB')
image = resize(image, model_config[mode_type]['resize']) # Resize
image = center_crop(image, model_config[mode_type]['centercrop']) # CenterCrop
img = np.array(image, dtype=np.int8)
img.tofile(os.path.join(save_path, file.split('.')[0] + ".bin"))
def preprocess(mode_type, src_path, save_path):
3
Source : imagenet_torch_preprocess.py
with Apache License 2.0
from Ascend
with Apache License 2.0
from Ascend
def gen_input_bin(mode_type, file_batches, batch):
i = 0
for file in file_batches[batch]:
i = i + 1
print("batch", batch, file, "===", i)
# RGBA to RGB
image = Image.open(os.path.join(src_path, file)).convert('RGB')
image = resize(image, model_config[mode_type]['resize']) # Resize
image = center_crop(image, model_config[mode_type]['centercrop']) # CenterCrop
img = np.array(image, dtype=np.int8)
img.tofile(os.path.join(save_path, file.split('.')[0] + ".bin"))
def preprocess(mode_type, src_path, save_path):
3
Source : pytorch_transfer.py
with Apache License 2.0
from Ascend
with Apache License 2.0
from Ascend
def preprocess(mode_type, src_path, save_path):
files = os.listdir(src_path)
i = 0
for file in files:
if not file.lower().endswith(".jpeg"):
continue
print("start to process image {}....".format(file))
i = i + 1
print("file", file, "===", i)
path_image = os.path.join(src_path, file)
# RGBA to RGB
image = Image.open(path_image).convert('RGB')
image = resize(image, model_config[mode_type]['resize']) # Resize
image = center_crop(image, model_config[mode_type]['centercrop']) # CenterCrop
img = np.array(image, dtype=np.int8)
img.tofile(os.path.join(save_path, file.split('.')[0] + ".bin"))
if __name__ == '__main__':
3
Source : imagenet_torch_preprocess.py
with Apache License 2.0
from Ascend
with Apache License 2.0
from Ascend
def gen_input_bin(mode_type, file_batches, batch):
i = 0
for file in file_batches[batch]:
i = i + 1
print("batch", batch, file, "===", i)
# RGBA to RGB
image = Image.open(os.path.join(src_path, file)).convert('RGB')
image = resize(image, model_config[mode_type]['resize']) # Resize
image = center_crop(image, model_config[mode_type]['centercrop']) # CenterCrop
img = np.array(image, dtype=np.int8)
img.tofile(os.path.join(save_path, file.split('.')[0] + ".bin"))
def preprocess_s(mode_type, src_path, save_path):
3
Source : imagenet_torch_preprocess.py
with Apache License 2.0
from Ascend
with Apache License 2.0
from Ascend
def preprocess_s(mode_type, src_path, save_path):
files = os.listdir(src_path)
i = 0
for file in files:
if not file.endswith(".jpeg"):
continue
print("start to process image {}....".format(file))
i = i + 1
print("file", file, "===", i)
path_image = os.path.join(src_path, file)
# RGBA to RGB
image = Image.open(path_image).convert('RGB')
image = resize(image, model_config[mode_type]['resize']) # Resize
image = center_crop(image, model_config[mode_type]['centercrop']) # CenterCrop
img = np.array(image, dtype=np.int8)
img.tofile(os.path.join(save_path, file.split('.')[0] + ".bin"))
def preprocess(mode_type, src_path, save_path):
3
Source : deeplabv3.py
with Apache License 2.0
from Ascend
with Apache License 2.0
from Ascend
def preprocess(picPath):
"""preprocess"""
#read img
bgr_img = cv.imread(picPath)
#get img shape
orig_shape = bgr_img.shape[:2]
#resize img
img = cv.resize(bgr_img, (MODEL_WIDTH, MODEL_HEIGHT)).astype(np.int8)
# save memory C_CONTIGUOUS mode
if not img.flags['C_CONTIGUOUS']:
img = np.ascontiguousarray(img)
return orig_shape, img
def postprocess(result_list, pic, orig_shape, pic_path):
3
Source : utils.py
with Apache License 2.0
from AstraZeneca
with Apache License 2.0
from AstraZeneca
def get_features(smiles: str):
"""Get a morgan fingerprint vector for the given molecule."""
molecule = rdkit.Chem.MolFromSmiles(smiles)
features = AllChem.GetHashedMorganFingerprint(molecule, 2, nBits=256)
array = np.zeros((0,), dtype=np.int8)
DataStructs.ConvertToNumpyArray(features, array)
return array.tolist()
def write_drugs_json(drugs_raw: Mapping[str, str], output_directory: Path) -> Path:
3
Source : gym_wrapper_test.py
with MIT License
from awilliea
with MIT License
from awilliea
def test_spec_from_gym_space_multi_binary(self):
multi_binary_space = gym.spaces.MultiBinary(4)
spec = gym_wrapper.spec_from_gym_space(multi_binary_space)
self.assertEqual((4,), spec.shape)
self.assertEqual(np.int8, spec.dtype)
np.testing.assert_array_equal(np.array([0], dtype=np.int), spec.minimum)
np.testing.assert_array_equal(np.array([1], dtype=np.int), spec.maximum)
def test_spec_from_gym_space_box_scalars(self):
3
Source : test_umath.py
with Apache License 2.0
from aws-samples
with Apache License 2.0
from aws-samples
def test_lower_align(self):
# check data that is not aligned to element size
# i.e doubles are aligned to 4 bytes on i386
d = np.zeros(23 * 8, dtype=np.int8)[4:-4].view(np.float64)
assert_equal(d.max(), d[0])
assert_equal(d.min(), d[0])
def test_reduce_reorder(self):
3
Source : category.py
with Apache License 2.0
from aws-samples
with Apache License 2.0
from aws-samples
def _engine_type(self):
# self.codes can have dtype int8, int16, int32 or int64, so we need
# to return the corresponding engine type (libindex.Int8Engine, etc.).
return {np.int8: libindex.Int8Engine,
np.int16: libindex.Int16Engine,
np.int32: libindex.Int32Engine,
np.int64: libindex.Int64Engine,
}[self.codes.dtype.type]
_attributes = ['name']
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