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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 + "'>"; return "<Type: 'u" + self.c_type_string() + bitwidth + "'>";
}); });
type.def("__hash__", &ov::element::Type::hash);
type.def( type.def(
"__eq__", "__eq__",
[](const ov::element::Type& a, const ov::element::Type& b) { [](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 collections import defaultdict
from pathlib import Path 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 .constants import IMAGE_EXTENSIONS, BINARY_EXTENSIONS
from .logging import logger from .logging import logger
@ -138,7 +138,7 @@ def get_image_tensors(image_paths, info, batch_sizes):
niter = max(num_shapes, num_images) niter = max(num_shapes, num_images)
for i in range(niter): for i in range(niter):
shape = list(info.shapes[i % num_shapes]) if num_shapes else [] 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) images = np.ndarray(shape=shape, dtype=dtype)
image_index = processed_frames image_index = processed_frames
current_batch_size = 1 if process_with_original_shapes else batch_sizes[i % num_shapes] 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): def get_dtype(precision):
format_map = { format_map = {
'f32' : (np.float32, np.finfo(np.float32).min, np.finfo(np.float32).max), Type.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), Type.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), Type.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), Type.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), Type.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), Type.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), Type.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), Type.u8 : (np.uint8, np.iinfo(np.uint8).min, np.iinfo(np.uint8).max),
'boolean' : (np.uint8, 0, 1), Type.boolean : (np.uint8, 0, 1),
} }
if precision in format_map.keys(): if precision in format_map.keys():
return format_map[precision] return format_map[precision]
@ -224,7 +224,7 @@ def get_binary_tensors(binary_paths, info, batch_sizes):
tensors = [] tensors = []
for i in range(niter): for i in range(niter):
shape_id = i % num_shapes 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]) shape = list(info.shapes[shape_id])
binaries = np.ndarray(shape=shape, dtype=dtype) binaries = np.ndarray(shape=shape, dtype=dtype)
if info.layout.has_name('N'): 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): def get_image_info_tensors(image_sizes, layer):
im_infos = [] im_infos = []
for shape, image_size in zip(layer.shapes, image_sizes): 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 b in range(shape[0]):
for i in range(shape[1]): for i in range(shape[1]):
im_info[b][i] = image_size if i in [0, 1] else 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): 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 # np.random.uniform excludes high: add 1 to have it generated
if np.dtype(dtype).kind in ['i', 'u', 'b']: if np.dtype(dtype).kind in ['i', 'u', 'b']:
rand_max += 1 rand_max += 1