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[PYTHON API] add __hash__ for Type (#10059)

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* define hash operator for type

* Fix code style
This commit is contained in:
Alexey Lebedev 2022-02-08 13:28:25 +03:00 committed by GitHub
parent 47b8c77a59
commit 24c4ccc621
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GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 15 additions and 14 deletions
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1
src/bindings/python/src/pyopenvino/graph/types/element_type.cpp
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@ -38,6 +38,7 @@ void regclass_graph_Type(py::module m) {
return "<Type: 'u" + self.c_type_string() + bitwidth + "'>";
});
type.def("__hash__", &ov::element::Type::hash);
type.def(
"__eq__",
[](const ov::element::Type& a, const ov::element::Type& b) {

28
tools/benchmark_tool/openvino/tools/benchmark/utils/inputs_filling.py
View File

@ -8,7 +8,7 @@ import numpy as np
from collections import defaultdict
from pathlib import Path
from openvino.runtime import Tensor, PartialShape
from openvino.runtime import Tensor, PartialShape, Type
from .constants import IMAGE_EXTENSIONS, BINARY_EXTENSIONS
from .logging import logger
@ -138,7 +138,7 @@ def get_image_tensors(image_paths, info, batch_sizes):
niter = max(num_shapes, num_images)
for i in range(niter):
shape = list(info.shapes[i % num_shapes]) if num_shapes else []
dtype = get_dtype(info.element_type.get_type_name())[0]
dtype = get_dtype(info.element_type)[0]
images = np.ndarray(shape=shape, dtype=dtype)
image_index = processed_frames
current_batch_size = 1 if process_with_original_shapes else batch_sizes[i % num_shapes]
@ -201,15 +201,15 @@ def get_image_tensors(image_paths, info, batch_sizes):
def get_dtype(precision):
format_map = {
'f32' : (np.float32, np.finfo(np.float32).min, np.finfo(np.float32).max),
'i32' : (np.int32, np.iinfo(np.int32).min, np.iinfo(np.int32).max),
'i64' : (np.int64, np.iinfo(np.int64).min, np.iinfo(np.int64).max),
'f16' : (np.float16, np.finfo(np.float16).min, np.finfo(np.float16).max),
'i16' : (np.int16, np.iinfo(np.int16).min, np.iinfo(np.int16).max),
'u16' : (np.uint16, np.iinfo(np.uint16).min, np.iinfo(np.uint16).max),
'i8' : (np.int8, np.iinfo(np.int8).min, np.iinfo(np.int8).max),
'u8' : (np.uint8, np.iinfo(np.uint8).min, np.iinfo(np.uint8).max),
'boolean' : (np.uint8, 0, 1),
Type.f32 : (np.float32, np.finfo(np.float32).min, np.finfo(np.float32).max),
Type.i32 : (np.int32, np.iinfo(np.int32).min, np.iinfo(np.int32).max),
Type.i64 : (np.int64, np.iinfo(np.int64).min, np.iinfo(np.int64).max),
Type.f16 : (np.float16, np.finfo(np.float16).min, np.finfo(np.float16).max),
Type.i16 : (np.int16, np.iinfo(np.int16).min, np.iinfo(np.int16).max),
Type.u16 : (np.uint16, np.iinfo(np.uint16).min, np.iinfo(np.uint16).max),
Type.i8 : (np.int8, np.iinfo(np.int8).min, np.iinfo(np.int8).max),
Type.u8 : (np.uint8, np.iinfo(np.uint8).min, np.iinfo(np.uint8).max),
Type.boolean : (np.uint8, 0, 1),
}
if precision in format_map.keys():
return format_map[precision]
@ -224,7 +224,7 @@ def get_binary_tensors(binary_paths, info, batch_sizes):
tensors = []
for i in range(niter):
shape_id = i % num_shapes
dtype = get_dtype(info.element_type.get_type_name())[0]
dtype = get_dtype(info.element_type)[0]
shape = list(info.shapes[shape_id])
binaries = np.ndarray(shape=shape, dtype=dtype)
if info.layout.has_name('N'):
@ -266,7 +266,7 @@ def get_image_sizes(app_input_info):
def get_image_info_tensors(image_sizes, layer):
im_infos = []
for shape, image_size in zip(layer.shapes, image_sizes):
im_info = np.ndarray(shape, dtype=get_dtype(layer.element_type.get_type_name())[0])
im_info = np.ndarray(shape, dtype=get_dtype(layer.element_type)[0])
for b in range(shape[0]):
for i in range(shape[1]):
im_info[b][i] = image_size if i in [0, 1] else 1
@ -275,7 +275,7 @@ def get_image_info_tensors(image_sizes, layer):
def fill_tensors_with_random(layer):
dtype, rand_min, rand_max = get_dtype(layer.element_type.get_type_name())
dtype, rand_min, rand_max = get_dtype(layer.element_type)
# np.random.uniform excludes high: add 1 to have it generated
if np.dtype(dtype).kind in ['i', 'u', 'b']:
rand_max += 1