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--- /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 --------------------------------------------------------------------
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