dkm
4 years ago
11 changed files with 192 additions and 9 deletions
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@ -1,3 +1,14 @@ |
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2021-4-2 |
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版本：v0.7.0 |
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更新内容: |
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1. 新功能: 新的量化算法支持(mmse), 添加支持tensorflow的预量化模型导入 |
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2. 添加了Caffe新OP支持：relu6/ConvolutionDepthwise/Transpose/reorg |
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3. 修复一些已知的bug: |
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1) 增加concat的非channel维度，非4维输入的支持 |
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2) 修复了第一层是scale的预处理bug |
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3）更新了onnxruntime==1.7.0的版本 |
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4. 更新了文档，更新了OP支持列表 |
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2021-3-1 |
2021-3-1 |
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版本：v0.6.0 |
版本：v0.6.0 |
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更新内容: |
更新内容: |
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@ -0,0 +1,3 @@ |
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This demo shows how to load a quantized model. |
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Download address of inception_v3_quant_frozen.pb: |
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https://storage.googleapis.com/download.tensorflow.org/models/tflite_11_05_08/inception_v3_quant.tgz |
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After Width: | Height: | Size: 85 KiB |
@ -0,0 +1,164 @@ |
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import numpy as np |
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import cv2 |
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import os |
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import urllib |
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import tarfile |
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import shutil |
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import traceback |
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import time |
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import sys |
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from rknn.api import RKNN |
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PB_FILE = './inception_v3_quant_frozen.pb' |
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RKNN_MODEL_PATH = './inception_v3_quant_frozen.rknn' |
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INPUTS = ['input'] |
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OUTPUTS = ['InceptionV3/Logits/SpatialSqueeze'] |
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IMG_PATH = './goldfish_299x299.jpg' |
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INPUT_SIZE = 299 |
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def show_outputs(outputs): |
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output = outputs[0][0] |
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output_sorted = sorted(output, reverse=True) |
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top5_str = 'inception_v3\n-----TOP 5-----\n' |
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for i in range(5): |
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value = output_sorted[i] |
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index = np.where(output == value) |
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for j in range(len(index)): |
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if (i + j) >= 5: |
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break |
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if value > 0: |
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topi = '{}: {}\n'.format(index[j], value) |
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else: |
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topi = '-1: 0.0\n' |
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top5_str += topi |
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print(top5_str) |
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def readable_speed(speed): |
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speed_bytes = float(speed) |
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speed_kbytes = speed_bytes / 1024 |
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if speed_kbytes > 1024: |
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speed_mbytes = speed_kbytes / 1024 |
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if speed_mbytes > 1024: |
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speed_gbytes = speed_mbytes / 1024 |
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return "{:.2f} GB/s".format(speed_gbytes) |
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else: |
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return "{:.2f} MB/s".format(speed_mbytes) |
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else: |
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return "{:.2f} KB/s".format(speed_kbytes) |
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def show_progress(blocknum, blocksize, totalsize): |
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speed = (blocknum * blocksize) / (time.time() - start_time) |
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speed_str = " Speed: {}".format(readable_speed(speed)) |
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recv_size = blocknum * blocksize |
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f = sys.stdout |
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progress = (recv_size / totalsize) |
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progress_str = "{:.2f}%".format(progress * 100) |
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n = round(progress * 50) |
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s = ('#' * n).ljust(50, '-') |
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f.write(progress_str.ljust(8, ' ') + '[' + s + ']' + speed_str) |
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f.flush() |
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f.write('\r\n') |
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if __name__ == '__main__': |
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# Create RKNN object |
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rknn = RKNN() |
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# If inception_v3_quant_frozen.pb does not exist, download it. |
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# Download address: |
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# https://storage.googleapis.com/download.tensorflow.org/models/tflite_11_05_08/inception_v3_quant.tgz |
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if not os.path.exists(PB_FILE): |
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print('--> Download {}'.format(PB_FILE)) |
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url = 'https://storage.googleapis.com/download.tensorflow.org/models/tflite_11_05_08/inception_v3_quant.tgz' |
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download_file = 'inception_v3_quant.tgz' |
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try: |
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start_time = time.time() |
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urllib.request.urlretrieve(url, download_file, show_progress) |
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except: |
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print('Download {} failed.'.format(download_file)) |
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print(traceback.format_exc()) |
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exit(-1) |
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try: |
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tar = tarfile.open(download_file) |
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target_dir = os.path.splitext(download_file)[0] |
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if os.path.isdir(target_dir): |
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pass |
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else: |
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os.mkdir(target_dir) |
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tar.extractall(target_dir) |
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tar.close() |
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except: |
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print('Extract {} failed.'.format(download_file)) |
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exit(-1) |
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pb_file = os.path.join(target_dir, PB_FILE) |
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if os.path.exists(pb_file): |
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shutil.copyfile(pb_file, './inception_v3_quant_frozen.pb') |
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shutil.rmtree(target_dir) |
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os.remove(download_file) |
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print('done') |
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# pre-process config |
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print('--> Config model') |
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rknn.config(reorder_channel=False) |
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print('done') |
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# Load tensorflow model |
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print('--> Loading model') |
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ret = rknn.load_tensorflow(tf_pb=PB_FILE, |
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inputs=INPUTS, |
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outputs=OUTPUTS, |
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input_size_list=[[1, INPUT_SIZE, INPUT_SIZE, 3]], |
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predef_file=None, |
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mean_values=[[128]], |
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std_values=[[128]]) |
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if ret != 0: |
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print('Load inception_v3_quant_frozen failed!') |
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exit(ret) |
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print('done') |
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# Build model |
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print('--> Building model') |
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ret = rknn.build(do_quantization=False) |
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if ret != 0: |
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print('Build inception_v3_quant_frozen.rknn failed!') |
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exit(ret) |
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print('done') |
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# Export rknn model |
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print('--> Export RKNN model') |
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ret = rknn.export_rknn(RKNN_MODEL_PATH) |
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if ret != 0: |
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print('Export inception_v3_quant_frozen.rknn failed!') |
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exit(ret) |
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print('done') |
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# Set inputs |
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img = cv2.imread(IMG_PATH) |
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) |
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# init runtime environment |
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print('--> Init runtime environment') |
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ret = rknn.init_runtime() |
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if ret != 0: |
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print('Init runtime environment failed') |
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exit(ret) |
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print('done') |
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# Inference |
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print('--> Running model') |
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outputs = rknn.inference(inputs=[img]) |
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x = outputs[0] |
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output = np.exp(x)/np.sum(np.exp(x)) |
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outputs = [output] |
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show_outputs(outputs) |
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print('done') |
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# perf |
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print('--> Begin evaluate model performance') |
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perf_results = rknn.eval_perf(inputs=[img]) |
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print('done') |
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rknn.release() |
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