diff options
Diffstat (limited to 'python/openvino/runtime/python_demos/OpenVINO_classification_sample_async/classification_sample_async.patch')
| -rw-r--r-- | python/openvino/runtime/python_demos/OpenVINO_classification_sample_async/classification_sample_async.patch | 116 |
1 files changed, 116 insertions, 0 deletions
diff --git a/python/openvino/runtime/python_demos/OpenVINO_classification_sample_async/classification_sample_async.patch b/python/openvino/runtime/python_demos/OpenVINO_classification_sample_async/classification_sample_async.patch new file mode 100644 index 0000000..28ae75c --- /dev/null +++ b/python/openvino/runtime/python_demos/OpenVINO_classification_sample_async/classification_sample_async.patch @@ -0,0 +1,116 @@ +--- /nfs/site/disks/swip_dla_1/resources/inference_engine/2023.3.0_with_dev_tools/1/linux64/suse12/samples/python/classification_sample_async/classification_sample_async.py 2024-03-01 14:01:24.460131000 -0500 ++++ ./runtime/python_demos/OpenVINO_classification_sample_async/classification_sample_async.py 2024-04-16 10:33:28.810439000 -0400 +@@ -1,15 +1,18 @@ + #!/usr/bin/env python3 + # -*- coding: utf-8 -*- +-# Copyright (C) 2018-2023 Intel Corporation ++# Copyright (C) 2018-2022 Intel Corporation + # SPDX-License-Identifier: Apache-2.0 + + import argparse + import logging as log ++import os + import sys ++import warnings + + import cv2 + import numpy as np +-import openvino as ov ++from openvino.preprocess import PrePostProcessor ++from openvino.runtime import AsyncInferQueue, Core, InferRequest, Layout, Type + + + def parse_args() -> argparse.Namespace: +@@ -24,14 +27,14 @@ + args.add_argument('-i', '--input', type=str, required=True, nargs='+', + help='Required. Path to an image file(s).') + args.add_argument('-d', '--device', type=str, default='CPU', +- help='Optional. Specify the target device to infer on; CPU, GPU, GNA or HETERO: ' ++ help='Optional. Specify the target device to infer on; CPU, GPU, MYRIAD, HDDL or HETERO: ' + 'is acceptable. The sample will look for a suitable plugin for device specified. ' + 'Default value is CPU.') + # fmt: on + return parser.parse_args() + + +-def completion_callback(infer_request: ov.InferRequest, image_path: str) -> None: ++def completion_callback(infer_request: InferRequest, image_path: str) -> None: + predictions = next(iter(infer_request.results.values())) + + # Change a shape of a numpy.ndarray with results to get another one with one dimension +@@ -60,7 +63,17 @@ + + # --------------------------- Step 1. Initialize OpenVINO Runtime Core ------------------------------------------------ + log.info('Creating OpenVINO Runtime Core') +- core = ov.Core() ++ dla_plugins = os.environ.get('DLA_PLUGINS', default='') ++ if dla_plugins == '': ++ # Backwards compatability for old DLA_PLUGINS_XML_FILE ++ warnings.warn("DLA_PLUGINS_XML_FILE option is deprecated as of 2024.1, Please use DLA_PLUGINS") ++ dla_plugins = os.environ.get('DLA_PLUGINS_XML_FILE', default='') ++ core = Core(dla_plugins) ++ if "FPGA" in args.device: ++ dla_arch_file = os.environ.get('DLA_ARCH_FILE') ++ if dla_arch_file is None: ++ raise Exception(f"To use FPGA, you need to specify the path to an arch_file!") ++ core.set_property(device_name="FPGA", properties={"ARCH_PATH": dla_arch_file}) + + # --------------------------- Step 2. Read a model -------------------------------------------------------------------- + log.info(f'Reading the model: {args.model}') +@@ -80,29 +93,38 @@ + images = [cv2.imread(image_path) for image_path in args.input] + + # Resize images to model input dims +- _, _, h, w = model.input().shape ++ # Assuming we always have w=h, we will ++ # figure out the layout from the dimensions ++ # start with the assumption of NHWC (TF) ++ _, h, w, c = model.input().shape ++ ++ if h != w: ++ c = h ++ h = w ++ + resized_images = [cv2.resize(image, (w, h)) for image in images] + + # Add N dimension + input_tensors = [np.expand_dims(image, 0) for image in resized_images] + ++ # Transpose from NHWC to NCHW ++ input_tensors = [np.transpose(tensor, (0, 3, 1, 2)) for tensor in input_tensors] ++ + # --------------------------- Step 4. Apply preprocessing ------------------------------------------------------------- +- ppp = ov.preprocess.PrePostProcessor(model) ++ ppp = PrePostProcessor(model) + + # 1) Set input tensor information: + # - input() provides information about a single model input +- # - precision of tensor is supposed to be 'u8' +- # - layout of data is 'NHWC' +- ppp.input().tensor() \ +- .set_element_type(ov.Type.u8) \ +- .set_layout(ov.Layout('NHWC')) # noqa: N400 ++ # - layout of data is 'NCHW' ++ ppp.input().tensor().set_layout(Layout('NCHW')) # noqa: N400 + + # 2) Here we suppose model has 'NCHW' layout for input +- ppp.input().model().set_layout(ov.Layout('NCHW')) ++ # DLA --> We let the demo select the layout based on the model ++ # ppp.input().model().set_layout(Layout('NCHW')) + + # 3) Set output tensor information: + # - precision of tensor is supposed to be 'f32' +- ppp.output().tensor().set_element_type(ov.Type.f32) ++ ppp.output().tensor().set_element_type(Type.f32) + + # 4) Apply preprocessing modifing the original 'model' + model = ppp.build() +@@ -114,7 +136,7 @@ + # --------------------------- Step 6. Create infer request queue ------------------------------------------------------ + log.info('Starting inference in asynchronous mode') + # create async queue with optimal number of infer requests +- infer_queue = ov.AsyncInferQueue(compiled_model) ++ infer_queue = AsyncInferQueue(compiled_model) + infer_queue.set_callback(completion_callback) + + # --------------------------- Step 7. Do inference -------------------------------------------------------------------- |