Upload dnmetis test tool for NPU inference
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alexcheng88
Gitee
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## 1.Install dnmetis_backend
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As README.md:
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backend_C++/dnmetis_backend/README.md
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## 2.Download dataset and model(.om)
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1.download Imagenet-val dataset URL: http://www.image-net.org/download-imageurls
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2.download efficientnet-b8 model(.om) URL: obs://hwwheel23/efficientnet-b8.om
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3.process the original Imagenet-val dataset as list:
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## 3.Start execute the inference:
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sh run_efficientnet-b8.sh
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or
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python3.7 main.py --model=/data/hwwheel123/model/efficientnet-b8.om --image_size='672,672,3' --inputs='images:0' --outputs='Softmax:0' --precision=fp16
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## 4.ATC offline model generate (optional):
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1.download efficientnet-b8 model(.pb) URL: obs://hwwheel23/efficientnet-b8.pb
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2.atc --model=$MODEL_DIR/efficientnet-b8.pb --framework=3 --input_shape='images:1,672,672,3' --output=$MODEL_DIR/efficientnet-b8 --mode=0 --out_nodes='Softmax:0' --soc_version=Ascend310 --input_fp16_nodes=images --output_type=FP16
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## 5.Imagenet2012-val Top1 Accuracy:
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+150
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import argparse
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import array
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import collections
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import json
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import os
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import sys
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import threading
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import time
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from queue import Queue
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#import env
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import cv2
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import numpy as np
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import re
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import pdb
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# import converter.converter as converter
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#from backend.backend_acl import AclBackend
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last_timing = []
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last_device_timing = []
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def get_args():
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"""Parse commandline."""
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parser = argparse.ArgumentParser()
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parser.add_argument("--dataset_path", default="./datasets/imagenet_10", help="path to the dataset")
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parser.add_argument("--backend", default="acl", help="runtime to use")
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parser.add_argument("--model", required=True, help="model file path")
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parser.add_argument("--image_size",default='224,224,3',help="model inputs imagesize")
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parser.add_argument("--inputs", default="input:0", help="model inputs nodes eg: data1:0 ")
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parser.add_argument("--outputs",default="softmax:0", help="model outputs nodes list eg:fc1:0,fc2:0,fc3:0 ")
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# below will override DNMetis rules compliant settings - don't use for official submission
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parser.add_argument("--count", default=1000, type=int, help="dataset items to infer")
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parser.add_argument("--precision", default="fp32", choices=["fp32", "fp16", "int8", "uint8"],
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help="precision mode, one of " + str(["fp32", "fp16", "int8", "uint8"]))
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parser.add_argument("--feed", default=[], help="feed")
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parser.add_argument("--image_list", default=[], help="image_list")
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parser.add_argument("--label_list", default=[], help="label_list")
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parser.add_argument("--cfg_path",default="./backend_cfg/built-in_config.txt")
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args = parser.parse_args()
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# don't use defaults in argparser. Instead we default to a dict, override that with a profile
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# and take this as default unless command line give
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#if args.image_size is None:
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# args.image_size = SUPPORTED_DATASETS[args.dataset][3]['image_size']
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if args.inputs:
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args.inputs = args.inputs.split(",")
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if args.outputs:
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args.outputs = args.outputs.split(",")
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if args.image_size:
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args.image_size = list(map(int, args.image_size.split(",")))
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return args
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def get_backend(backend):
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if backend == "acl":
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from backend.backend_acl import AclBackend
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backend = AclBackend()
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return backend
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def resize_with_aspectratio(img, out_height, out_width, scale=87.5, inter_pol=cv2.INTER_LINEAR):
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height, width = img.shape[:2]
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new_height = int(100. * out_height / scale)
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new_width = int(100. * out_width / scale)
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if height > width:
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w = new_width
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h = int(new_height * height / width)
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else:
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h = new_height
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w = int(new_width * width / height)
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img = cv2.resize(img, (w, h), interpolation=inter_pol)
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return img
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def center_crop(img, out_height, out_width):
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height, width = img.shape[:2]
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left = int((width - out_width) / 2)
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right = int((width + out_width) / 2)
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top = int((height - out_height) / 2)
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bottom = int((height + out_height) / 2)
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img = img[top:bottom, left:right]
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return img
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def pre_process_noisy(img, dims=None, precision="fp32"):
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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output_height, output_width, _ = dims
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cv2_interpol = cv2.INTER_CUBIC
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img = resize_with_aspectratio(img, output_height, output_width, inter_pol=cv2_interpol)
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img = center_crop(img, output_height, output_width)
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MEAN_RGB = [0.485 * 255, 0.456 * 255, 0.406 * 255]
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STDDEV_RGB = [0.229 * 255, 0.224 * 255, 0.225 * 255]
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if precision=="fp32":
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img = np.asarray(img, dtype='float32')
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if precision=="fp16":
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img = np.asarray(img, dtype='float16')
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means = np.array([0.485 * 255, 0.456 * 255, 0.406 * 255], dtype=np.float32)
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img -= means
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stddev = np.array([0.229 * 255, 0.224 * 255, 0.225 * 255], dtype=np.float32)
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img /= stddev
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return img
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def preprocess_dataset(args,offset=0):
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with open(args.dataset_path + '/val_map.txt', 'r') as f:
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for s in f:
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image_name, label = re.split(r"\s+", s.strip())
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src = os.path.join(args.dataset_path, image_name)
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img_org = cv2.imread(src)
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processed_img = pre_process_noisy(img_org, dims=args.image_size, precision = args.precision)
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args.feed.append(processed_img)
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args.image_list.append(image_name)
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args.label_list.append(int(label)+offset)
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def main():
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good = 0
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total = 0
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#args
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args = get_args()
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# find backend
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backend = get_backend(args.backend)
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# load model to backend
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model = backend.load(args)
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#
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# preprocess_dataset
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preprocess_dataset(args,offset=0)
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#start inference:
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for i in range(len(args.feed)):
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predictions = backend.predict(args.feed[i])
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#print(args.feed[i].shape)
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print('img_orig:',args.image_list[i],'label:',args.label_list[i],'predictions:',np.argmax(predictions),'\n')
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if args.label_list[i] == np.argmax(predictions):
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good +=1
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total +=1
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print('Predict total jpeg:',len(args.image_list),' Accuracy: ',good / total)
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backend.unload()
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if __name__ == "__main__":
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main()
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python3.7 main.py --model=./model/efficientnet-b8.om --image_size='672,672,3' --precision=fp16
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@@ -0,0 +1,2 @@
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version = '1.0'
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