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[Python API] Support of FP16 blobs (#3893)

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* [Python API] Support of FP16 blobs

* test_Blob refactoring

* support fp16 for exec_net.infer method

* add precisions

Co-authored-by: anastasia.kuporosova <akuporos@akuporos.inn.intel.com>
This commit is contained in:
Anastasia Kuporosova 2021-02-01 09:12:32 +03:00 committed by GitHub
parent abaf155b42
commit 8cb25fdb98
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3 changed files with 24 additions and 58 deletions
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5
inference-engine/ie_bridges/python/src/openvino/inference_engine/constants.pyx
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@ -18,7 +18,7 @@ from .cimport ie_api_impl_defs as C
import numpy as np
from enum import Enum
supported_precisions = ["FP32", "FP64", "FP16", "I64", "U64", "I32", "U32", "I16", "I8", "U16", "U8"]
supported_precisions = ["FP32", "FP64", "FP16", "I64", "U64", "I32", "U32", "I16", "I8", "U16", "U8", "BOOL"]
known_plugins = ['CPU', 'GPU', 'FPGA', 'MYRIAD', 'HETERO', 'HDDL', 'MULTI']
@ -34,7 +34,8 @@ format_map = {
'U16' : np.uint16,
'I8' : np.int8,
'U8' : np.uint8,
'I64' : np.int64
'I64' : np.int64,
'BOOL' : np.uint8
}
layout_str_to_enum = {'ANY': C.Layout.ANY,

8
inference-engine/ie_bridges/python/src/openvino/inference_engine/ie_api.pyx
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@ -173,7 +173,8 @@ cdef class Blob:
fp64_array_memview = self._array_data
self._ptr = C.make_shared_blob[double](c_tensor_desc, &fp64_array_memview[0], fp64_array_memview.shape[0])
elif precision == "FP16":
raise RuntimeError("Currently, it's impossible to set_blob with FP16 precision")
I16_array_memview = self._array_data.view(dtype=np.int16)
self._ptr = C.make_shared_blob[int16_t](c_tensor_desc, &I16_array_memview[0], I16_array_memview.shape[0])
elif precision == "I16":
I16_array_memview = self._array_data
self._ptr = C.make_shared_blob[int16_t](c_tensor_desc, &I16_array_memview[0], I16_array_memview.shape[0])
@ -1222,7 +1223,10 @@ cdef class InferRequest:
def _fill_inputs(self, inputs):
for k, v in inputs.items():
assert k in self._inputs_list, f"No input with name {k} found in network"
self.input_blobs[k].buffer[:] = v
if self.input_blobs[k].tensor_desc.precision == "FP16":
self.input_blobs[k].buffer[:] = v.view(dtype=np.int16)
else:
self.input_blobs[k].buffer[:] = v
## This class contains the information about the network model read from IR and allows you to manipulate with

69
inference-engine/ie_bridges/python/tests/test_Blob.py
View File

@ -36,87 +36,48 @@ def test_get_buffer():
blob = Blob(tensor_desc, array)
assert np.array_equal(blob.buffer, array)
def test_write_to_buffer_fp32():
tensor_desc = TensorDesc("FP32", [1, 3, 127, 127], "NCHW")
array = np.zeros(shape=(1, 3, 127, 127), dtype=np.float32)
def write_to_buffer(precision, numpy_precision):
tensor_desc = TensorDesc(precision, [1, 3, 127, 127], "NCHW")
array = np.zeros(shape=(1, 3, 127, 127), dtype=numpy_precision)
blob = Blob(tensor_desc, array)
ones_arr = np.ones(shape=(1, 3, 127, 127), dtype=np.float32)
ones_arr = np.ones(shape=(1, 3, 127, 127), dtype=numpy_precision)
blob.buffer[:] = ones_arr
assert np.array_equal(blob.buffer, ones_arr)
def test_write_to_buffer_fp32():
write_to_buffer("FP32", np.float32)
def test_write_to_buffer_fp64():
tensor_desc = TensorDesc("FP64", [1, 3, 127, 127], "NCHW")
array = np.zeros(shape=(1, 3, 127, 127), dtype=np.float64)
blob = Blob(tensor_desc, array)
ones_arr = np.ones(shape=(1, 3, 127, 127), dtype=np.float64)
blob.buffer[:] = ones_arr
assert np.array_equal(blob.buffer, ones_arr)
write_to_buffer("FP64", np.float64)
@pytest.mark.skip(reason="Need to figure out how to implement right conversion")
def test_write_to_buffer_fp16():
tensor_desc = TensorDesc("FP16", [1, 3, 127, 127], "NCHW")
array = np.zeros(shape=(1, 3, 127, 127), dtype=np.float16)
blob = Blob(tensor_desc, array)
ones_arr = np.ones(shape=(1, 3, 127, 127), dtype=np.float16)
blob.buffer[:] = ones_arr
assert np.array_equal(blob.buffer, ones_arr)
write_to_buffer("FP16", np.float16)
def test_write_to_buffer_int8():
tensor_desc = TensorDesc("I8", [1, 3, 127, 127], "NCHW")
array = np.zeros(shape=(1, 3, 127, 127), dtype=np.int8)
blob = Blob(tensor_desc, array)
ones_arr = np.ones(shape=(1, 3, 127, 127), dtype=np.int8)
blob.buffer[:] = ones_arr
assert np.array_equal(blob.buffer, ones_arr)
write_to_buffer("I8", np.int8)
def test_write_to_buffer_uint8():
tensor_desc = TensorDesc("U8", [1, 3, 127, 127], "NCHW")
array = np.zeros(shape=(1, 3, 127, 127), dtype=np.uint8)
blob = Blob(tensor_desc, array)
ones_arr = np.ones(shape=(1, 3, 127, 127), dtype=np.uint8)
blob.buffer[:] = ones_arr
assert np.array_equal(blob.buffer, ones_arr)
write_to_buffer("U8", np.uint8)
def test_write_to_buffer_int32():
tensor_desc = TensorDesc("I32", [1, 3, 127, 127], "NCHW")
array = np.zeros(shape=(1, 3, 127, 127), dtype=np.int32)
blob = Blob(tensor_desc, array)
ones_arr = np.ones(shape=(1, 3, 127, 127), dtype=np.int32)
blob.buffer[:] = ones_arr
assert np.array_equal(blob.buffer, ones_arr)
write_to_buffer("I32", np.int32)
def test_write_to_buffer_int16():
tensor_desc = TensorDesc("I16", [1, 3, 127, 127], "NCHW")
array = np.zeros(shape=(1, 3, 127, 127), dtype=np.int16)
blob = Blob(tensor_desc, array)
ones_arr = np.ones(shape=(1, 3, 127, 127), dtype=np.int16)
blob.buffer[:] = ones_arr
assert np.array_equal(blob.buffer, ones_arr)
write_to_buffer("I16", np.int16)
def test_write_to_buffer_uint16():
tensor_desc = TensorDesc("U16", [1, 3, 127, 127], "NCHW")
array = np.zeros(shape=(1, 3, 127, 127), dtype=np.uint16)
blob = Blob(tensor_desc, array)
ones_arr = np.ones(shape=(1, 3, 127, 127), dtype=np.uint16)
blob.buffer[:] = ones_arr
assert np.array_equal(blob.buffer, ones_arr)
write_to_buffer("U16", np.uint16)
def test_write_to_buffer_int64():
tensor_desc = TensorDesc("I64", [1, 3, 127, 127], "NCHW")
array = np.zeros(shape=(1, 3, 127, 127), dtype=np.int64)
blob = Blob(tensor_desc, array)
ones_arr = np.ones(shape=(1, 3, 127, 127), dtype=np.int64)
blob.buffer[:] = ones_arr
assert np.array_equal(blob.buffer, ones_arr)
write_to_buffer("I64", np.int64)
def test_write_numpy_scalar_int64():