mirror of
https://github.com/modelscope/modelscope.git
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245 lines
8.4 KiB
Python
245 lines
8.4 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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import glob
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import os
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import shutil
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import tempfile
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import unittest
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import json
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import requests
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import torch
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from modelscope.metainfo import Models, Pipelines, Trainers
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from modelscope.trainers import build_trainer
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from modelscope.utils.config import Config
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from modelscope.utils.constant import LogKeys, ModeKeys, Tasks
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from modelscope.utils.logger import get_logger
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from modelscope.utils.test_utils import DistributedTestCase, test_level
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from modelscope.utils.torch_utils import is_master
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def _download_data(url, save_dir):
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r = requests.get(url, verify=True)
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if not os.path.exists(save_dir):
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os.makedirs(save_dir)
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zip_name = os.path.split(url)[-1]
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save_path = os.path.join(save_dir, zip_name)
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with open(save_path, 'wb') as f:
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f.write(r.content)
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unpack_dir = os.path.join(save_dir, os.path.splitext(zip_name)[0])
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shutil.unpack_archive(save_path, unpack_dir)
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def train_func(work_dir, dist=False, log_config=3, imgs_per_gpu=4):
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import easycv
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config_path = os.path.join(
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os.path.dirname(easycv.__file__),
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'configs/detection/yolox/yolox_s_8xb16_300e_coco.py')
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data_dir = os.path.join(work_dir, 'small_coco_test')
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url = 'http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/EasyCV/datasets/small_coco.zip'
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if is_master():
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_download_data(url, data_dir)
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import time
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time.sleep(1)
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cfg = Config.from_file(config_path)
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cfg.work_dir = work_dir
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cfg.total_epochs = 2
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cfg.checkpoint_config.interval = 1
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cfg.eval_config.interval = 1
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cfg.log_config = dict(
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interval=log_config,
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hooks=[
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dict(type='TextLoggerHook'),
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dict(type='TensorboardLoggerHook')
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])
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cfg.data.train.data_source.ann_file = os.path.join(
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data_dir, 'small_coco/small_coco/instances_train2017_20.json')
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cfg.data.train.data_source.img_prefix = os.path.join(
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data_dir, 'small_coco/small_coco/train2017')
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cfg.data.val.data_source.ann_file = os.path.join(
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data_dir, 'small_coco/small_coco/instances_val2017_20.json')
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cfg.data.val.data_source.img_prefix = os.path.join(
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data_dir, 'small_coco/small_coco/val2017')
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cfg.data.imgs_per_gpu = imgs_per_gpu
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cfg.data.workers_per_gpu = 2
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cfg.data.val.imgs_per_gpu = 2
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ms_cfg_file = os.path.join(work_dir, 'ms_yolox_s_8xb16_300e_coco.json')
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from easycv.utils.ms_utils import to_ms_config
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if is_master():
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to_ms_config(
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cfg,
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dump=True,
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task=Tasks.image_object_detection,
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ms_model_name=Models.yolox,
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pipeline_name=Pipelines.easycv_detection,
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save_path=ms_cfg_file)
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trainer_name = Trainers.easycv
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kwargs = dict(
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task=Tasks.image_object_detection,
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cfg_file=ms_cfg_file,
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launcher='pytorch' if dist else None)
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trainer = build_trainer(trainer_name, kwargs)
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trainer.train()
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@unittest.skipIf(not torch.cuda.is_available(), 'cuda unittest')
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class EasyCVTrainerTestSingleGpu(unittest.TestCase):
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def setUp(self):
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self.logger = get_logger()
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self.logger.info(('Testing %s.%s' %
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(type(self).__name__, self._testMethodName)))
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self.tmp_dir = tempfile.TemporaryDirectory().name
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if not os.path.exists(self.tmp_dir):
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os.makedirs(self.tmp_dir)
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def tearDown(self):
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super().tearDown()
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shutil.rmtree(self.tmp_dir, ignore_errors=True)
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@unittest.skipIf(
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True, 'The test cases are all run in the master process, '
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'cause registry conflicts, and it should run in the subprocess.')
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def test_single_gpu(self):
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# TODO: run in subprocess
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train_func(self.tmp_dir)
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results_files = os.listdir(self.tmp_dir)
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json_files = glob.glob(os.path.join(self.tmp_dir, '*.log.json'))
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self.assertEqual(len(json_files), 1)
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with open(json_files[0], 'r') as f:
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lines = [i.strip() for i in f.readlines()]
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self.assertDictContainsSubset(
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{
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LogKeys.MODE: ModeKeys.TRAIN,
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LogKeys.EPOCH: 1,
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LogKeys.ITER: 3,
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LogKeys.LR: 0.00013
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}, json.loads(lines[0]))
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self.assertDictContainsSubset(
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{
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LogKeys.MODE: ModeKeys.EVAL,
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LogKeys.EPOCH: 1,
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LogKeys.ITER: 10
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}, json.loads(lines[1]))
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self.assertDictContainsSubset(
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{
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LogKeys.MODE: ModeKeys.TRAIN,
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LogKeys.EPOCH: 2,
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LogKeys.ITER: 3,
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LogKeys.LR: 0.00157
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}, json.loads(lines[2]))
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self.assertDictContainsSubset(
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{
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LogKeys.MODE: ModeKeys.EVAL,
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LogKeys.EPOCH: 2,
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LogKeys.ITER: 10
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}, json.loads(lines[3]))
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self.assertIn(f'{LogKeys.EPOCH}_1.pth', results_files)
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self.assertIn(f'{LogKeys.EPOCH}_2.pth', results_files)
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for i in [0, 2]:
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self.assertIn(LogKeys.DATA_LOAD_TIME, lines[i])
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self.assertIn(LogKeys.ITER_TIME, lines[i])
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self.assertIn(LogKeys.MEMORY, lines[i])
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self.assertIn('total_loss', lines[i])
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for i in [1, 3]:
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self.assertIn(
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'CocoDetectionEvaluator_DetectionBoxes_Precision/mAP',
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lines[i])
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self.assertIn('DetectionBoxes_Precision/mAP', lines[i])
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self.assertIn('DetectionBoxes_Precision/mAP@.50IOU', lines[i])
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self.assertIn('DetectionBoxes_Precision/mAP@.75IOU', lines[i])
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self.assertIn('DetectionBoxes_Precision/mAP (small)', lines[i])
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@unittest.skipIf(not torch.cuda.is_available()
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or torch.cuda.device_count() <= 1, 'distributed unittest')
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class EasyCVTrainerTestMultiGpus(DistributedTestCase):
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def setUp(self):
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self.logger = get_logger()
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self.logger.info(('Testing %s.%s' %
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(type(self).__name__, self._testMethodName)))
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self.tmp_dir = tempfile.TemporaryDirectory().name
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if not os.path.exists(self.tmp_dir):
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os.makedirs(self.tmp_dir)
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def tearDown(self):
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super().tearDown()
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shutil.rmtree(self.tmp_dir, ignore_errors=True)
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@unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
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def test_multi_gpus(self):
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self.start(
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train_func,
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num_gpus=2,
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work_dir=self.tmp_dir,
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dist=True,
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log_config=2,
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imgs_per_gpu=5)
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results_files = os.listdir(self.tmp_dir)
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json_files = glob.glob(os.path.join(self.tmp_dir, '*.log.json'))
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self.assertEqual(len(json_files), 1)
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with open(json_files[0], 'r') as f:
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lines = [i.strip() for i in f.readlines()]
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self.assertDictContainsSubset(
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{
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LogKeys.MODE: ModeKeys.TRAIN,
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LogKeys.EPOCH: 1,
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LogKeys.ITER: 2,
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LogKeys.LR: 0.0002
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}, json.loads(lines[0]))
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self.assertDictContainsSubset(
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{
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LogKeys.MODE: ModeKeys.EVAL,
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LogKeys.EPOCH: 1,
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LogKeys.ITER: 5
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}, json.loads(lines[1]))
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self.assertDictContainsSubset(
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{
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LogKeys.MODE: ModeKeys.TRAIN,
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LogKeys.EPOCH: 2,
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LogKeys.ITER: 2,
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LogKeys.LR: 0.0018
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}, json.loads(lines[2]))
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self.assertDictContainsSubset(
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{
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LogKeys.MODE: ModeKeys.EVAL,
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LogKeys.EPOCH: 2,
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LogKeys.ITER: 5
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}, json.loads(lines[3]))
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self.assertIn(f'{LogKeys.EPOCH}_1.pth', results_files)
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self.assertIn(f'{LogKeys.EPOCH}_2.pth', results_files)
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for i in [0, 2]:
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self.assertIn(LogKeys.DATA_LOAD_TIME, lines[i])
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self.assertIn(LogKeys.ITER_TIME, lines[i])
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self.assertIn(LogKeys.MEMORY, lines[i])
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self.assertIn('total_loss', lines[i])
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for i in [1, 3]:
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self.assertIn(
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'CocoDetectionEvaluator_DetectionBoxes_Precision/mAP',
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lines[i])
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self.assertIn('DetectionBoxes_Precision/mAP', lines[i])
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self.assertIn('DetectionBoxes_Precision/mAP@.50IOU', lines[i])
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self.assertIn('DetectionBoxes_Precision/mAP@.75IOU', lines[i])
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self.assertIn('DetectionBoxes_Precision/mAP (small)', lines[i])
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if __name__ == '__main__':
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unittest.main()
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