mirror of
https://github.com/THU-MIG/yolov10.git
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ultralytics 8.0.176
update Dockerfile-arm64
to 22.04 (#4857)
Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Kalen Michael <kalenmike@gmail.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
This commit is contained in:
parent
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commit
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5
.github/dependabot.yml
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5
.github/dependabot.yml
vendored
@ -1,6 +1,5 @@
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# To get started with Dependabot version updates, you'll need to specify which
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# package ecosystems to update and where the package manifests are located.
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# Please see the documentation for all configuration options:
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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# Dependabot for package version updates
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# https://docs.github.com/github/administering-a-repository/configuration-options-for-dependency-updates
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version: 2
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8
.github/workflows/ci.yaml
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.github/workflows/ci.yaml
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@ -39,7 +39,7 @@ jobs:
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os: [ubuntu-latest]
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python-version: ['3.11']
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steps:
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- uses: actions/checkout@v3
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- uses: actions/checkout@v4
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- uses: actions/setup-python@v4
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with:
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python-version: ${{ matrix.python-version }}
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@ -102,7 +102,7 @@ jobs:
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python-version: ['3.10']
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model: [yolov8n]
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steps:
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- uses: actions/checkout@v3
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- uses: actions/checkout@v4
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- uses: actions/setup-python@v4
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with:
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python-version: ${{ matrix.python-version }}
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@ -170,7 +170,7 @@ jobs:
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python-version: '3.8' # torch 1.8.0 requires python >=3.6, <=3.8
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torch: '1.8.0' # min torch version CI https://pypi.org/project/torchvision/
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steps:
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- uses: actions/checkout@v3
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- uses: actions/checkout@v4
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- uses: actions/setup-python@v4
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with:
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python-version: ${{ matrix.python-version }}
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@ -212,7 +212,7 @@ jobs:
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timeout-minutes: 60
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runs-on: gpu-latest
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steps:
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- uses: actions/checkout@v3
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- uses: actions/checkout@v4
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- name: Install requirements
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run: pip install -e .
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- name: Check environment
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2
.github/workflows/codeql.yaml
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2
.github/workflows/codeql.yaml
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@ -24,7 +24,7 @@ jobs:
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steps:
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- name: Checkout repository
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uses: actions/checkout@v3
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uses: actions/checkout@v4
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# Initializes the CodeQL tools for scanning.
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- name: Initialize CodeQL
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2
.github/workflows/docker.yaml
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2
.github/workflows/docker.yaml
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@ -62,7 +62,7 @@ jobs:
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# platforms: "linux/amd64"
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steps:
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- name: Checkout repo
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uses: actions/checkout@v3
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uses: actions/checkout@v4
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with:
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fetch-depth: 0 # copy full .git directory to access full git history in Docker images
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2
.github/workflows/links.yml
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2
.github/workflows/links.yml
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@ -13,7 +13,7 @@ jobs:
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Links:
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v3
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- uses: actions/checkout@v4
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- name: Download and install lychee
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run: |
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2
.github/workflows/publish.yml
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2
.github/workflows/publish.yml
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@ -22,7 +22,7 @@ jobs:
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runs-on: ubuntu-latest
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steps:
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- name: Checkout code
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uses: actions/checkout@v3
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uses: actions/checkout@v4
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with:
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fetch-depth: "0" # pulls all commits (needed correct last updated dates in Docs)
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- name: Set up Python environment
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@ -3,7 +3,7 @@
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# Image is aarch64-compatible for Apple M1 and other ARM architectures i.e. Jetson Nano and Raspberry Pi
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# Start FROM Ubuntu image https://hub.docker.com/_/ubuntu
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FROM arm64v8/ubuntu:22.10
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FROM arm64v8/ubuntu:22.04
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# Downloads to user config dir
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ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/
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@ -26,14 +26,14 @@
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"<img width=\"1024\", src=\"https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png\"></a>\n",
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"\n",
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"<div align=\"center\">\n",
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" <a href=\"https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml\">\n",
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" <img src=\"https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml/badge.svg\" alt=\"CI CPU\"></a>\n",
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" <a href=\"https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/hub.ipynb\">\n",
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" <a href=\"https://github.com/ultralytics/hub/actions/workflows/ci.yaml\">\n",
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" <img src=\"https://github.com/ultralytics/hub/actions/workflows/ci.yaml/badge.svg\" alt=\"CI CPU\"></a>\n",
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" <a href=\"https://colab.research.google.com/github/ultralytics/hub/blob/master/hub.ipynb\">\n",
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" <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
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"\n",
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"Welcome to the [Ultralytics](https://ultralytics.com/) HUB notebook! \n",
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"Welcome to the [Ultralytics](https://ultralytics.com/) HUB notebook!\n",
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"\n",
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"This notebook allows you to train [YOLOv5](https://github.com/ultralytics/yolov5) and [YOLOv8](https://github.com/ultralytics/ultralytics) 🚀 models using [HUB](https://hub.ultralytics.com/). Please browse the YOLOv8 <a href=\"https://docs.ultralytics.com\">Docs</a> for details, raise an issue on <a href=\"https://github.com/ultralytics/ultralytics/issues/new/choose\">GitHub</a> for support, and join our <a href=\"https://ultralytics.com/discord\">Discord</a> community for questions and discussions!\n",
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"This notebook allows you to train [YOLOv5](https://github.com/ultralytics/yolov5) and [YOLOv8](https://github.com/ultralytics/ultralytics) 🚀 models using [HUB](https://hub.ultralytics.com/). Please browse the HUB <a href=\"https://docs.ultralytics.com/hub/\">Docs</a> for details, raise an issue on <a href=\"https://github.com/ultralytics/hub/issues/new/choose\">GitHub</a> for support, and join our <a href=\"https://ultralytics.com/discord\">Discord</a> community for questions and discussions!\n",
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"</div>"
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]
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},
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@ -55,21 +55,21 @@
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"outputId": "22dcbc27-9c6f-44fb-9745-620431f93793"
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"outputId": "f8dcca2f-d6d9-4e8d-90a7-85aa357d39fb"
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},
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"source": [
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"%pip install ultralytics # install\n",
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"from ultralytics import YOLO, checks, hub\n",
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"checks() # checks"
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],
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"execution_count": null,
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"execution_count": 1,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stderr",
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"text": [
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"Ultralytics YOLOv8.0.64 🚀 Python-3.9.16 torch-2.0.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\n",
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"Setup complete ✅ (2 CPUs, 12.7 GB RAM, 28.3/166.8 GB disk)\n"
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"Ultralytics YOLOv8.0.175 🚀 Python-3.10.12 torch-2.0.1+cu118 CUDA:0 (Tesla T4, 15102MiB)\n",
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"Setup complete ✅ (2 CPUs, 12.7 GB RAM, 26.2/78.2 GB disk)\n"
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]
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}
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]
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@ -94,7 +94,7 @@
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"hub.login('API_KEY') # use your API key\n",
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"\n",
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"model = YOLO('https://hub.ultralytics.com/MODEL_ID') # use your model URL\n",
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"model.train() # train model"
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"results = model.train() # train model"
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],
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"execution_count": null,
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"outputs": []
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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__version__ = '8.0.175'
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__version__ = '8.0.176'
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from ultralytics.models import RTDETR, SAM, YOLO
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from ultralytics.models.fastsam import FastSAM
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@ -117,8 +117,7 @@ class HUBTrainingSession:
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if data['status'] == 'new': # new model to start training
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self.train_args = {
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# TODO deprecate 'batch_size' argument in favor of 'batch'
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'batch': data['batch' if 'batch' in data else 'batch_size'],
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'batch': data['batch_size'], # note HUB argument is slightly different
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'epochs': data['epochs'],
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'imgsz': data['imgsz'],
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'patience': data['patience'],
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@ -159,6 +158,7 @@ class HUBTrainingSession:
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data = {'epoch': epoch}
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if final:
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data.update({'type': 'final', 'map': map})
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filesize = Path(weights).stat().st_size
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smart_request('post',
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url,
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data=data,
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@ -167,7 +167,7 @@ class HUBTrainingSession:
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retry=10,
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timeout=3600,
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thread=False,
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progress=True,
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progress=filesize,
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code=4)
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else:
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data.update({'type': 'epoch', 'isBest': bool(is_best)})
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(requests.Response): The response object from the HTTP request.
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Note:
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If 'progress' is set to True, the progress bar will display the download progress
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for responses with a known content length.
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- If 'progress' is set to True, the progress bar will display the download progress for responses with a known
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content length.
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- If 'progress' is a number then progress bar will display assuming content length = progress.
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"""
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progress = kwargs.pop('progress', False)
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if not progress:
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return requests.request(method, url, **kwargs)
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response = requests.request(method, url, stream=True, **kwargs)
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total = int(response.headers.get('content-length', 0)) # total size
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total = int(response.headers.get('content-length', 0) if isinstance(progress, bool) else progress) # total size
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try:
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pbar = TQDM(total=total, unit='B', unit_scale=True, unit_divisor=1024)
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for data in response.iter_content(chunk_size=1024):
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