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{
"cells": [
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true,
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"from operator import itemgetter\n",
"from mmaction.apis import init_recognizer, inference_recognizer"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"config_file = '../demo/demo_configs/tsn_r50_1x1x8_video_infer.py'\n",
"# download the checkpoint from model zoo and put it in `checkpoints/`\n",
"checkpoint_file = '../checkpoints/tsn_r50_8xb32-1x1x8-100e_kinetics400-rgb_20220818-2692d16c.pth'"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loads checkpoint by local backend from path: ../checkpoints/tsn_r50_8xb32-1x1x8-100e_kinetics400-rgb_20220818-2692d16c.pth\n"
]
}
],
"source": [
"# build the model from a config file and a checkpoint file\n",
"model = init_recognizer(config_file, checkpoint_file, device='cpu')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"# test a single video and show the result:\n",
"video = 'demo.mp4'\n",
"label = '../tools/data/kinetics/label_map_k400.txt'\n",
"results = inference_recognizer(model, video)\n",
"\n",
"pred_scores = results.pred_score.tolist()\n",
"score_tuples = tuple(zip(range(len(pred_scores)), pred_scores))\n",
"score_sorted = sorted(score_tuples, key=itemgetter(1), reverse=True)\n",
"top5_label = score_sorted[:5]\n",
"\n",
"labels = open(label).readlines()\n",
"labels = [x.strip() for x in labels]\n",
"results = [(labels[k[0]], k[1]) for k in top5_label]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"arm wrestling: 1.0\n",
"rock scissors paper: 1.698846019067312e-15\n",
"massaging feet: 5.157996544393221e-16\n",
"stretching leg: 1.018867278715779e-16\n",
"bench pressing: 7.110452486439706e-17\n"
]
}
],
"source": [
"# show the results\n",
"for result in results:\n",
" print(f'{result[0]}: ', result[1])"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "mmact_dev",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.13 (default, Mar 29 2022, 02:18:16) \n[GCC 7.5.0]"
},
"vscode": {
"interpreter": {
"hash": "189c342a4747645665e89db23000ac4d4edb7a87c4cd0b2f881610f468fb778d"
}
}
},
"nbformat": 4,
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}
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