Last active
August 15, 2021 10:31
-
-
Save manaspalaparthi/35a8463d6ae209d4642563961e579c11 to your computer and use it in GitHub Desktop.
object_detection_workshop (2).ipynb
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| { | |
| "nbformat": 4, | |
| "nbformat_minor": 0, | |
| "metadata": { | |
| "accelerator": "GPU", | |
| "colab": { | |
| "name": "object_detection_workshop (2).ipynb", | |
| "provenance": [], | |
| "collapsed_sections": [ | |
| "heyqGCAd05kU" | |
| ], | |
| "machine_shape": "hm", | |
| "include_colab_link": true | |
| }, | |
| "kernelspec": { | |
| "display_name": "Python 3", | |
| "name": "python3" | |
| }, | |
| "language_info": { | |
| "name": "python" | |
| } | |
| }, | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/manaspalaparthi/35a8463d6ae209d4642563961e579c11/object_detection_workshop-2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "A8Prw5P2UWy7" | |
| }, | |
| "source": [ | |
| "" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "Q5qig5hVUnrL" | |
| }, | |
| "source": [ | |
| "# **Computer Vision Workshop (object detection)**" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "88zpjg2eujpH", | |
| "outputId": "e1c3eb8e-521a-4898-c155-2c8b504ce5cf" | |
| }, | |
| "source": [ | |
| "from google.colab import drive\n", | |
| "drive.mount('/content/drive')" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "Mounted at /content/drive\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "p11zjF4our5H", | |
| "outputId": "9acc4de6-05cf-4035-ea9a-624b7bd4eccf" | |
| }, | |
| "source": [ | |
| "cd /content/drive/MyDrive/" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "/content/drive/MyDrive\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "7Y1_ZrLquVA1" | |
| }, | |
| "source": [ | |
| "## **Clone CV1 workshop**" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "XH3pJZs5u2ZS", | |
| "outputId": "e5b352d1-b482-4365-b140-5289afbd4c83" | |
| }, | |
| "source": [ | |
| "!git clone https://github.com/deakin-launchpad/CV1_workshop.git" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "Cloning into 'CV1_workshop'...\n", | |
| "remote: Enumerating objects: 136, done.\u001b[K\n", | |
| "remote: Counting objects: 100% (136/136), done.\u001b[K\n", | |
| "remote: Compressing objects: 100% (78/78), done.\u001b[K\n", | |
| "remote: Total 136 (delta 58), reused 134 (delta 56), pack-reused 0\u001b[K\n", | |
| "Receiving objects: 100% (136/136), 12.97 MiB | 22.46 MiB/s, done.\n", | |
| "Resolving deltas: 100% (58/58), done.\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "yGYh7ss6vGYk", | |
| "outputId": "b92f5632-8f21-4edb-e1bb-929a426be870" | |
| }, | |
| "source": [ | |
| "cd CV1_workshop/" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "/content/drive/MyDrive/CV1_workshop\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "Box6dhRqvMB8" | |
| }, | |
| "source": [ | |
| "## **Install tensorflow-gpu 2.3**" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "GnVG7OmvsAOR", | |
| "outputId": "b747e1b7-1229-40d4-aaeb-8cda80eb117c" | |
| }, | |
| "source": [ | |
| "!pip install tensorflow-gpu==2.3" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "Requirement already satisfied: tensorflow-gpu==2.3 in /usr/local/lib/python3.7/dist-packages (2.3.0)\n", | |
| "Requirement already satisfied: astunparse==1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorflow-gpu==2.3) (1.6.3)\n", | |
| "Requirement already satisfied: h5py<2.11.0,>=2.10.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow-gpu==2.3) (2.10.0)\n", | |
| "Requirement already satisfied: tensorflow-estimator<2.4.0,>=2.3.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow-gpu==2.3) (2.3.0)\n", | |
| "Requirement already satisfied: scipy==1.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow-gpu==2.3) (1.4.1)\n", | |
| "Requirement already satisfied: tensorboard<3,>=2.3.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow-gpu==2.3) (2.5.0)\n", | |
| "Requirement already satisfied: gast==0.3.3 in /usr/local/lib/python3.7/dist-packages (from tensorflow-gpu==2.3) (0.3.3)\n", | |
| "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow-gpu==2.3) (3.3.0)\n", | |
| "Requirement already satisfied: numpy<1.19.0,>=1.16.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow-gpu==2.3) (1.18.5)\n", | |
| "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow-gpu==2.3) (0.12.0)\n", | |
| "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow-gpu==2.3) (1.1.0)\n", | |
| "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.7/dist-packages (from tensorflow-gpu==2.3) (1.34.1)\n", | |
| "Requirement already satisfied: google-pasta>=0.1.8 in /usr/local/lib/python3.7/dist-packages (from tensorflow-gpu==2.3) (0.2.0)\n", | |
| "Requirement already satisfied: protobuf>=3.9.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow-gpu==2.3) (3.17.3)\n", | |
| "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow-gpu==2.3) (1.15.0)\n", | |
| "Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow-gpu==2.3) (1.12.1)\n", | |
| "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.7/dist-packages (from tensorflow-gpu==2.3) (0.37.0)\n", | |
| "Requirement already satisfied: keras-preprocessing<1.2,>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow-gpu==2.3) (1.1.2)\n", | |
| "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard<3,>=2.3.0->tensorflow-gpu==2.3) (3.3.4)\n", | |
| "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard<3,>=2.3.0->tensorflow-gpu==2.3) (2.23.0)\n", | |
| "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard<3,>=2.3.0->tensorflow-gpu==2.3) (0.4.5)\n", | |
| "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard<3,>=2.3.0->tensorflow-gpu==2.3) (1.8.0)\n", | |
| "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard<3,>=2.3.0->tensorflow-gpu==2.3) (57.2.0)\n", | |
| "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard<3,>=2.3.0->tensorflow-gpu==2.3) (1.0.1)\n", | |
| "Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard<3,>=2.3.0->tensorflow-gpu==2.3) (1.34.0)\n", | |
| "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard<3,>=2.3.0->tensorflow-gpu==2.3) (0.6.1)\n", | |
| "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard<3,>=2.3.0->tensorflow-gpu==2.3) (4.7.2)\n", | |
| "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard<3,>=2.3.0->tensorflow-gpu==2.3) (0.2.8)\n", | |
| "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard<3,>=2.3.0->tensorflow-gpu==2.3) (4.2.2)\n", | |
| "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<3,>=2.3.0->tensorflow-gpu==2.3) (1.3.0)\n", | |
| "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard<3,>=2.3.0->tensorflow-gpu==2.3) (4.6.3)\n", | |
| "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard<3,>=2.3.0->tensorflow-gpu==2.3) (0.4.8)\n", | |
| "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<3,>=2.3.0->tensorflow-gpu==2.3) (2021.5.30)\n", | |
| "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<3,>=2.3.0->tensorflow-gpu==2.3) (2.10)\n", | |
| "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<3,>=2.3.0->tensorflow-gpu==2.3) (1.24.3)\n", | |
| "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<3,>=2.3.0->tensorflow-gpu==2.3) (3.0.4)\n", | |
| "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<3,>=2.3.0->tensorflow-gpu==2.3) (3.1.1)\n", | |
| "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->markdown>=2.6.8->tensorboard<3,>=2.3.0->tensorflow-gpu==2.3) (3.5.0)\n", | |
| "Requirement already satisfied: typing-extensions>=3.6.4 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->markdown>=2.6.8->tensorboard<3,>=2.3.0->tensorflow-gpu==2.3) (3.7.4.3)\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "oeyP0aYes9Zs", | |
| "outputId": "1d69e398-912f-4dbb-da40-d00b0b62b9dc" | |
| }, | |
| "source": [ | |
| "import tensorflow as tf\n", | |
| "print(tf.__version__)" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "2.3.0\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "DEGpXLl3tQVX" | |
| }, | |
| "source": [ | |
| "## **Cloning TF object detection 2.0 Github**" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "e4v5LMFRvbV0", | |
| "outputId": "682416b2-9609-45f7-d5c0-9de2d8ae4436" | |
| }, | |
| "source": [ | |
| "cd /content/drive/My Drive/CV1_workshop" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "/content/drive/My Drive/CV1_workshop\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "kFkdXoEltLY9", | |
| "outputId": "2ad69444-73fe-4247-e85a-dcc14bf4a41b" | |
| }, | |
| "source": [ | |
| "!git clone https://github.com/tensorflow/models.git" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "Cloning into 'models'...\n", | |
| "remote: Enumerating objects: 60234, done.\u001b[K\n", | |
| "remote: Counting objects: 100% (242/242), done.\u001b[K\n", | |
| "remote: Compressing objects: 100% (131/131), done.\u001b[K\n", | |
| "remote: Total 60234 (delta 131), reused 206 (delta 109), pack-reused 59992\u001b[K\n", | |
| "Receiving objects: 100% (60234/60234), 573.78 MiB | 19.61 MiB/s, done.\n", | |
| "Resolving deltas: 100% (41850/41850), done.\n", | |
| "Checking out files: 100% (2596/2596), done.\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "-ozNemVBwbV6" | |
| }, | |
| "source": [ | |
| "navigate to research folder inside models folder" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "6ZwI0sTdtMsc", | |
| "outputId": "62b0cca5-6807-4200-d0ff-fd060cdeee2c" | |
| }, | |
| "source": [ | |
| "cd /content/drive/My Drive/CV1_workshop/models/research" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "/content/drive/My Drive/CV1_workshop/models/research\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "ihnuGmg7whI-" | |
| }, | |
| "source": [ | |
| "install all the proto files using protoc protoc installer" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "NozH3MfAtMyR" | |
| }, | |
| "source": [ | |
| "!protoc object_detection/protos/*.proto --python_out=." | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "HkPEiHzfyIIb" | |
| }, | |
| "source": [ | |
| "clone cocoapi to install necessary dependancy" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "alOqL7ortM1F", | |
| "outputId": "94ad531f-f7d4-4811-c448-b0a1cf35ce70" | |
| }, | |
| "source": [ | |
| "!git clone https://github.com/cocodataset/cocoapi.git\n" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "Cloning into 'cocoapi'...\n", | |
| "remote: Enumerating objects: 975, done.\u001b[K\n", | |
| "remote: Total 975 (delta 0), reused 0 (delta 0), pack-reused 975\u001b[K\n", | |
| "Receiving objects: 100% (975/975), 11.72 MiB | 17.76 MiB/s, done.\n", | |
| "Resolving deltas: 100% (576/576), done.\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "0XRlMiuEtM4R", | |
| "outputId": "c07f8115-7f8c-47cb-e3dc-4a79833958a1" | |
| }, | |
| "source": [ | |
| "cd cocoapi/PythonAPI" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "/content/drive/My Drive/CV1_workshop/models/research/cocoapi/PythonAPI\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "PZ-xj6MUtM7U", | |
| "outputId": "27eff65b-4039-436d-8f24-02782441e10b" | |
| }, | |
| "source": [ | |
| "!make" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "python setup.py build_ext --inplace\n", | |
| "running build_ext\n", | |
| "cythoning pycocotools/_mask.pyx to pycocotools/_mask.c\n", | |
| "/usr/local/lib/python3.7/dist-packages/Cython/Compiler/Main.py:369: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release! File: /content/drive/My Drive/CV1_workshop/models/research/cocoapi/PythonAPI/pycocotools/_mask.pyx\n", | |
| " tree = Parsing.p_module(s, pxd, full_module_name)\n", | |
| "building 'pycocotools._mask' extension\n", | |
| "creating build\n", | |
| "creating build/common\n", | |
| "creating build/temp.linux-x86_64-3.7\n", | |
| "creating build/temp.linux-x86_64-3.7/pycocotools\n", | |
| "x86_64-linux-gnu-gcc -pthread -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fdebug-prefix-map=/build/python3.7-LSlbJj/python3.7-3.7.11=. -fstack-protector-strong -Wformat -Werror=format-security -g -fdebug-prefix-map=/build/python3.7-LSlbJj/python3.7-3.7.11=. -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/local/lib/python3.7/dist-packages/numpy/core/include -I../common -I/usr/include/python3.7m -c ../common/maskApi.c -o build/temp.linux-x86_64-3.7/../common/maskApi.o -Wno-cpp -Wno-unused-function -std=c99\n", | |
| "\u001b[01m\u001b[K../common/maskApi.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KrleDecode\u001b[m\u001b[K’:\n", | |
| "\u001b[01m\u001b[K../common/maskApi.c:46:7:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kfor\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\n", | |
| " \u001b[01;35m\u001b[Kfor\u001b[m\u001b[K( k=0; k<R[i].cnts[j]; k++ ) *(M++)=v; v=!v; }}\n", | |
| " \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n", | |
| "\u001b[01m\u001b[K../common/maskApi.c:46:49:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K...this statement, but the latter is misleadingly indented as if it were guarded by the ‘\u001b[01m\u001b[Kfor\u001b[m\u001b[K’\n", | |
| " for( k=0; k<R[i].cnts[j]; k++ ) *(M++)=v; \u001b[01;36m\u001b[Kv\u001b[m\u001b[K=!v; }}\n", | |
| " \u001b[01;36m\u001b[K^\u001b[m\u001b[K\n", | |
| "\u001b[01m\u001b[K../common/maskApi.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KrleFrPoly\u001b[m\u001b[K’:\n", | |
| "\u001b[01m\u001b[K../common/maskApi.c:166:3:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kfor\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\n", | |
| " \u001b[01;35m\u001b[Kfor\u001b[m\u001b[K(j=0; j<k; j++) x[j]=(int)(scale*xy[j*2+0]+.5); x[k]=x[0];\n", | |
| " \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n", | |
| "\u001b[01m\u001b[K../common/maskApi.c:166:54:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K...this statement, but the latter is misleadingly indented as if it were guarded by the ‘\u001b[01m\u001b[Kfor\u001b[m\u001b[K’\n", | |
| " for(j=0; j<k; j++) x[j]=(int)(scale*xy[j*2+0]+.5); \u001b[01;36m\u001b[Kx\u001b[m\u001b[K[k]=x[0];\n", | |
| " \u001b[01;36m\u001b[K^\u001b[m\u001b[K\n", | |
| "\u001b[01m\u001b[K../common/maskApi.c:167:3:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kfor\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\n", | |
| " \u001b[01;35m\u001b[Kfor\u001b[m\u001b[K(j=0; j<k; j++) y[j]=(int)(scale*xy[j*2+1]+.5); y[k]=y[0];\n", | |
| " \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n", | |
| "\u001b[01m\u001b[K../common/maskApi.c:167:54:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K...this statement, but the latter is misleadingly indented as if it were guarded by the ‘\u001b[01m\u001b[Kfor\u001b[m\u001b[K’\n", | |
| " for(j=0; j<k; j++) y[j]=(int)(scale*xy[j*2+1]+.5); \u001b[01;36m\u001b[Ky\u001b[m\u001b[K[k]=y[0];\n", | |
| " \u001b[01;36m\u001b[K^\u001b[m\u001b[K\n", | |
| "\u001b[01m\u001b[K../common/maskApi.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KrleToString\u001b[m\u001b[K’:\n", | |
| "\u001b[01m\u001b[K../common/maskApi.c:212:7:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kif\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\n", | |
| " \u001b[01;35m\u001b[Kif\u001b[m\u001b[K(more) c |= 0x20; c+=48; s[p++]=c;\n", | |
| " \u001b[01;35m\u001b[K^~\u001b[m\u001b[K\n", | |
| "\u001b[01m\u001b[K../common/maskApi.c:212:27:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K...this statement, but the latter is misleadingly indented as if it were guarded by the ‘\u001b[01m\u001b[Kif\u001b[m\u001b[K’\n", | |
| " if(more) c |= 0x20; \u001b[01;36m\u001b[Kc\u001b[m\u001b[K+=48; s[p++]=c;\n", | |
| " \u001b[01;36m\u001b[K^\u001b[m\u001b[K\n", | |
| "\u001b[01m\u001b[K../common/maskApi.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KrleFrString\u001b[m\u001b[K’:\n", | |
| "\u001b[01m\u001b[K../common/maskApi.c:220:3:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kwhile\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\n", | |
| " \u001b[01;35m\u001b[Kwhile\u001b[m\u001b[K( s[m] ) m++; cnts=malloc(sizeof(uint)*m); m=0;\n", | |
| " \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n", | |
| "\u001b[01m\u001b[K../common/maskApi.c:220:22:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K...this statement, but the latter is misleadingly indented as if it were guarded by the ‘\u001b[01m\u001b[Kwhile\u001b[m\u001b[K’\n", | |
| " while( s[m] ) m++; \u001b[01;36m\u001b[Kcnts\u001b[m\u001b[K=malloc(sizeof(uint)*m); m=0;\n", | |
| " \u001b[01;36m\u001b[K^~~~\u001b[m\u001b[K\n", | |
| "\u001b[01m\u001b[K../common/maskApi.c:228:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kif\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\n", | |
| " \u001b[01;35m\u001b[Kif\u001b[m\u001b[K(m>2) x+=(long) cnts[m-2]; cnts[m++]=(uint) x;\n", | |
| " \u001b[01;35m\u001b[K^~\u001b[m\u001b[K\n", | |
| "\u001b[01m\u001b[K../common/maskApi.c:228:34:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K...this statement, but the latter is misleadingly indented as if it were guarded by the ‘\u001b[01m\u001b[Kif\u001b[m\u001b[K’\n", | |
| " if(m>2) x+=(long) cnts[m-2]; \u001b[01;36m\u001b[Kcnts\u001b[m\u001b[K[m++]=(uint) x;\n", | |
| " \u001b[01;36m\u001b[K^~~~\u001b[m\u001b[K\n", | |
| "\u001b[01m\u001b[K../common/maskApi.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KrleToBbox\u001b[m\u001b[K’:\n", | |
| "\u001b[01m\u001b[K../common/maskApi.c:141:31:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[Kxp\u001b[m\u001b[K’ may be used uninitialized in this function [\u001b[01;35m\u001b[K-Wmaybe-uninitialized\u001b[m\u001b[K]\n", | |
| " if(j%2==0) xp=x; else if\u001b[01;35m\u001b[K(\u001b[m\u001b[Kxp<x) { ys=0; ye=h-1; }\n", | |
| " \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n", | |
| "x86_64-linux-gnu-gcc -pthread -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fdebug-prefix-map=/build/python3.7-LSlbJj/python3.7-3.7.11=. -fstack-protector-strong -Wformat -Werror=format-security -g -fdebug-prefix-map=/build/python3.7-LSlbJj/python3.7-3.7.11=. -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/local/lib/python3.7/dist-packages/numpy/core/include -I../common -I/usr/include/python3.7m -c pycocotools/_mask.c -o build/temp.linux-x86_64-3.7/pycocotools/_mask.o -Wno-cpp -Wno-unused-function -std=c99\n", | |
| "creating build/lib.linux-x86_64-3.7\n", | |
| "creating build/lib.linux-x86_64-3.7/pycocotools\n", | |
| "x86_64-linux-gnu-gcc -pthread -shared -Wl,-O1 -Wl,-Bsymbolic-functions -Wl,-Bsymbolic-functions -Wl,-z,relro -Wl,-Bsymbolic-functions -Wl,-z,relro -g -fdebug-prefix-map=/build/python3.7-LSlbJj/python3.7-3.7.11=. -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 build/temp.linux-x86_64-3.7/../common/maskApi.o build/temp.linux-x86_64-3.7/pycocotools/_mask.o -o build/lib.linux-x86_64-3.7/pycocotools/_mask.cpython-37m-x86_64-linux-gnu.so\n", | |
| "copying build/lib.linux-x86_64-3.7/pycocotools/_mask.cpython-37m-x86_64-linux-gnu.so -> pycocotools\n", | |
| "rm -rf build\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 35 | |
| }, | |
| "id": "HKVyzHmhD4Id", | |
| "outputId": "5c308890-ad0c-4e40-c592-284ec5ee41fc" | |
| }, | |
| "source": [ | |
| "pwd" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "application/vnd.google.colaboratory.intrinsic+json": { | |
| "type": "string" | |
| }, | |
| "text/plain": [ | |
| "'/content/drive/My Drive/CV1_workshop/models/research/cocoapi/PythonAPI'" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 13 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "A5Qr7ACCtM_b", | |
| "outputId": "17b4308f-35f4-470d-fb4e-f42b9bb9a9ea" | |
| }, | |
| "source": [ | |
| "cp -r pycocotools /content/drive/My Drive/CV1_workshop/models/research" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "cp: target 'Drive/CV1_workshop/models/research' is not a directory\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "rLq79dR0uQFt" | |
| }, | |
| "source": [ | |
| "## **Install the Object Detection API**" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 35 | |
| }, | |
| "id": "DM2bgHvLtNFt", | |
| "outputId": "4236b90a-c629-483f-81e7-ceabe3509a4e" | |
| }, | |
| "source": [ | |
| "pwd" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "application/vnd.google.colaboratory.intrinsic+json": { | |
| "type": "string" | |
| }, | |
| "text/plain": [ | |
| "'/content/drive/My Drive/CV1_workshop/models/research/cocoapi/PythonAPI'" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 14 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "MZouxA5TuWgV", | |
| "outputId": "24ed5e50-7052-4239-847e-9e54627d14f5" | |
| }, | |
| "source": [ | |
| "cd /content/drive/My Drive/CV1_workshop/models/research" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "/content/drive/My Drive/CV1_workshop/models/research\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "Q635Jl58uWjI" | |
| }, | |
| "source": [ | |
| "cp object_detection/packages/tf2/setup.py ." | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "ZyrPaXSxuWmI", | |
| "outputId": "8e6aea37-f2c3-40d9-c24d-309fceb95035" | |
| }, | |
| "source": [ | |
| "!python -m pip install ." | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "Processing /content/drive/My Drive/CV1_workshop/models/research\n", | |
| "\u001b[33m DEPRECATION: A future pip version will change local packages to be built in-place without first copying to a temporary directory. We recommend you use --use-feature=in-tree-build to test your packages with this new behavior before it becomes the default.\n", | |
| " pip 21.3 will remove support for this functionality. You can find discussion regarding this at https://github.com/pypa/pip/issues/7555.\u001b[0m\n", | |
| "Collecting avro-python3\n", | |
| " Downloading avro-python3-1.10.2.tar.gz (38 kB)\n", | |
| "Collecting apache-beam\n", | |
| " Downloading apache_beam-2.31.0-cp37-cp37m-manylinux2010_x86_64.whl (9.7 MB)\n", | |
| "\u001b[K |████████████████████████████████| 9.7 MB 10.7 MB/s \n", | |
| "\u001b[?25hRequirement already satisfied: pillow in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (7.1.2)\n", | |
| "Requirement already satisfied: lxml in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (4.2.6)\n", | |
| "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (3.2.2)\n", | |
| "Requirement already satisfied: Cython in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (0.29.24)\n", | |
| "Requirement already satisfied: contextlib2 in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (0.5.5)\n", | |
| "Collecting tf-slim\n", | |
| " Downloading tf_slim-1.1.0-py2.py3-none-any.whl (352 kB)\n", | |
| "\u001b[K |████████████████████████████████| 352 kB 62.8 MB/s \n", | |
| "\u001b[?25hRequirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (1.15.0)\n", | |
| "Requirement already satisfied: pycocotools in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (2.0.2)\n", | |
| "Collecting lvis\n", | |
| " Downloading lvis-0.5.3-py3-none-any.whl (14 kB)\n", | |
| "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (1.4.1)\n", | |
| "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (1.1.5)\n", | |
| "Collecting tf-models-official>=2.5.1\n", | |
| " Downloading tf_models_official-2.5.1-py2.py3-none-any.whl (1.6 MB)\n", | |
| "\u001b[K |████████████████████████████████| 1.6 MB 62.6 MB/s \n", | |
| "\u001b[?25hCollecting pyyaml>=5.1\n", | |
| " Downloading PyYAML-5.4.1-cp37-cp37m-manylinux1_x86_64.whl (636 kB)\n", | |
| "\u001b[K |████████████████████████████████| 636 kB 57.2 MB/s \n", | |
| "\u001b[?25hRequirement already satisfied: gin-config in /usr/local/lib/python3.7/dist-packages (from tf-models-official>=2.5.1->object-detection==0.1) (0.4.0)\n", | |
| "Collecting py-cpuinfo>=3.3.0\n", | |
| " Downloading py-cpuinfo-8.0.0.tar.gz (99 kB)\n", | |
| "\u001b[K |████████████████████████████████| 99 kB 13.3 MB/s \n", | |
| "\u001b[?25hRequirement already satisfied: tensorflow-datasets in /usr/local/lib/python3.7/dist-packages (from tf-models-official>=2.5.1->object-detection==0.1) (4.0.1)\n", | |
| "Collecting sentencepiece\n", | |
| " Downloading sentencepiece-0.1.96-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2 MB)\n", | |
| "\u001b[K |████████████████████████████████| 1.2 MB 62.6 MB/s \n", | |
| "\u001b[?25hCollecting opencv-python-headless\n", | |
| " Downloading opencv_python_headless-4.5.3.56-cp37-cp37m-manylinux2014_x86_64.whl (37.1 MB)\n", | |
| "\u001b[K |████████████████████████████████| 37.1 MB 47 kB/s \n", | |
| "\u001b[?25hRequirement already satisfied: tensorflow-hub>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from tf-models-official>=2.5.1->object-detection==0.1) (0.12.0)\n", | |
| "Requirement already satisfied: google-api-python-client>=1.6.7 in /usr/local/lib/python3.7/dist-packages (from tf-models-official>=2.5.1->object-detection==0.1) (1.12.8)\n", | |
| "Requirement already satisfied: kaggle>=1.3.9 in /usr/local/lib/python3.7/dist-packages (from tf-models-official>=2.5.1->object-detection==0.1) (1.5.12)\n", | |
| "Collecting sacrebleu\n", | |
| " Downloading sacrebleu-2.0.0-py3-none-any.whl (90 kB)\n", | |
| "\u001b[K |████████████████████████████████| 90 kB 12.8 MB/s \n", | |
| "\u001b[?25hRequirement already satisfied: psutil>=5.4.3 in /usr/local/lib/python3.7/dist-packages (from tf-models-official>=2.5.1->object-detection==0.1) (5.4.8)\n", | |
| "Requirement already satisfied: tensorflow>=2.5.0 in /usr/local/lib/python3.7/dist-packages (from tf-models-official>=2.5.1->object-detection==0.1) (2.5.0)\n", | |
| "Requirement already satisfied: oauth2client in /usr/local/lib/python3.7/dist-packages (from tf-models-official>=2.5.1->object-detection==0.1) (4.1.3)\n", | |
| "Collecting tensorflow-addons\n", | |
| " Downloading tensorflow_addons-0.13.0-cp37-cp37m-manylinux2010_x86_64.whl (679 kB)\n", | |
| "\u001b[K |████████████████████████████████| 679 kB 69.8 MB/s \n", | |
| "\u001b[?25hCollecting tensorflow-model-optimization>=0.4.1\n", | |
| " Downloading tensorflow_model_optimization-0.6.0-py2.py3-none-any.whl (211 kB)\n", | |
| "\u001b[K |████████████████████████████████| 211 kB 71.8 MB/s \n", | |
| "\u001b[?25hRequirement already satisfied: numpy>=1.15.4 in /usr/local/lib/python3.7/dist-packages (from tf-models-official>=2.5.1->object-detection==0.1) (1.18.5)\n", | |
| "Collecting seqeval\n", | |
| " Downloading seqeval-1.2.2.tar.gz (43 kB)\n", | |
| "\u001b[K |████████████████████████████████| 43 kB 2.8 MB/s \n", | |
| "\u001b[?25hRequirement already satisfied: uritemplate<4dev,>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from google-api-python-client>=1.6.7->tf-models-official>=2.5.1->object-detection==0.1) (3.0.1)\n", | |
| "Requirement already satisfied: httplib2<1dev,>=0.15.0 in /usr/local/lib/python3.7/dist-packages (from google-api-python-client>=1.6.7->tf-models-official>=2.5.1->object-detection==0.1) (0.17.4)\n", | |
| "Requirement already satisfied: google-auth-httplib2>=0.0.3 in /usr/local/lib/python3.7/dist-packages (from google-api-python-client>=1.6.7->tf-models-official>=2.5.1->object-detection==0.1) (0.0.4)\n", | |
| "Requirement already satisfied: google-auth>=1.16.0 in /usr/local/lib/python3.7/dist-packages (from google-api-python-client>=1.6.7->tf-models-official>=2.5.1->object-detection==0.1) (1.34.0)\n", | |
| "Requirement already satisfied: google-api-core<2dev,>=1.21.0 in /usr/local/lib/python3.7/dist-packages (from google-api-python-client>=1.6.7->tf-models-official>=2.5.1->object-detection==0.1) (1.26.3)\n", | |
| "Requirement already satisfied: protobuf>=3.12.0 in /usr/local/lib/python3.7/dist-packages (from google-api-core<2dev,>=1.21.0->google-api-python-client>=1.6.7->tf-models-official>=2.5.1->object-detection==0.1) (3.17.3)\n", | |
| "Requirement already satisfied: requests<3.0.0dev,>=2.18.0 in /usr/local/lib/python3.7/dist-packages (from google-api-core<2dev,>=1.21.0->google-api-python-client>=1.6.7->tf-models-official>=2.5.1->object-detection==0.1) (2.23.0)\n", | |
| "Requirement already satisfied: setuptools>=40.3.0 in /usr/local/lib/python3.7/dist-packages (from google-api-core<2dev,>=1.21.0->google-api-python-client>=1.6.7->tf-models-official>=2.5.1->object-detection==0.1) (57.2.0)\n", | |
| "Requirement already satisfied: pytz in /usr/local/lib/python3.7/dist-packages (from google-api-core<2dev,>=1.21.0->google-api-python-client>=1.6.7->tf-models-official>=2.5.1->object-detection==0.1) (2018.9)\n", | |
| "Requirement already satisfied: packaging>=14.3 in /usr/local/lib/python3.7/dist-packages (from google-api-core<2dev,>=1.21.0->google-api-python-client>=1.6.7->tf-models-official>=2.5.1->object-detection==0.1) (21.0)\n", | |
| "Requirement already satisfied: googleapis-common-protos<2.0dev,>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from google-api-core<2dev,>=1.21.0->google-api-python-client>=1.6.7->tf-models-official>=2.5.1->object-detection==0.1) (1.53.0)\n", | |
| "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth>=1.16.0->google-api-python-client>=1.6.7->tf-models-official>=2.5.1->object-detection==0.1) (0.2.8)\n", | |
| "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth>=1.16.0->google-api-python-client>=1.6.7->tf-models-official>=2.5.1->object-detection==0.1) (4.2.2)\n", | |
| "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.7/dist-packages (from google-auth>=1.16.0->google-api-python-client>=1.6.7->tf-models-official>=2.5.1->object-detection==0.1) (4.7.2)\n", | |
| "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from kaggle>=1.3.9->tf-models-official>=2.5.1->object-detection==0.1) (4.62.0)\n", | |
| "Requirement already satisfied: python-slugify in /usr/local/lib/python3.7/dist-packages (from kaggle>=1.3.9->tf-models-official>=2.5.1->object-detection==0.1) (5.0.2)\n", | |
| "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.7/dist-packages (from kaggle>=1.3.9->tf-models-official>=2.5.1->object-detection==0.1) (2.8.2)\n", | |
| "Requirement already satisfied: certifi in /usr/local/lib/python3.7/dist-packages (from kaggle>=1.3.9->tf-models-official>=2.5.1->object-detection==0.1) (2021.5.30)\n", | |
| "Requirement already satisfied: urllib3 in /usr/local/lib/python3.7/dist-packages (from kaggle>=1.3.9->tf-models-official>=2.5.1->object-detection==0.1) (1.24.3)\n", | |
| "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=14.3->google-api-core<2dev,>=1.21.0->google-api-python-client>=1.6.7->tf-models-official>=2.5.1->object-detection==0.1) (2.4.7)\n", | |
| "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth>=1.16.0->google-api-python-client>=1.6.7->tf-models-official>=2.5.1->object-detection==0.1) (0.4.8)\n", | |
| "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0dev,>=2.18.0->google-api-core<2dev,>=1.21.0->google-api-python-client>=1.6.7->tf-models-official>=2.5.1->object-detection==0.1) (2.10)\n", | |
| "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0dev,>=2.18.0->google-api-core<2dev,>=1.21.0->google-api-python-client>=1.6.7->tf-models-official>=2.5.1->object-detection==0.1) (3.0.4)\n", | |
| "Requirement already satisfied: absl-py~=0.10 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.5.0->tf-models-official>=2.5.1->object-detection==0.1) (0.12.0)\n", | |
| "Requirement already satisfied: keras-preprocessing~=1.1.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.5.0->tf-models-official>=2.5.1->object-detection==0.1) (1.1.2)\n", | |
| "Requirement already satisfied: tensorboard~=2.5 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.5.0->tf-models-official>=2.5.1->object-detection==0.1) (2.5.0)\n", | |
| "Requirement already satisfied: opt-einsum~=3.3.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.5.0->tf-models-official>=2.5.1->object-detection==0.1) (3.3.0)\n", | |
| "Collecting gast==0.4.0\n", | |
| " Downloading gast-0.4.0-py3-none-any.whl (9.8 kB)\n", | |
| "Collecting numpy>=1.15.4\n", | |
| " Downloading numpy-1.19.5-cp37-cp37m-manylinux2010_x86_64.whl (14.8 MB)\n", | |
| "\u001b[K |████████████████████████████████| 14.8 MB 173 kB/s \n", | |
| "\u001b[?25hRequirement already satisfied: termcolor~=1.1.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.5.0->tf-models-official>=2.5.1->object-detection==0.1) (1.1.0)\n", | |
| "Collecting h5py~=3.1.0\n", | |
| " Downloading h5py-3.1.0-cp37-cp37m-manylinux1_x86_64.whl (4.0 MB)\n", | |
| "\u001b[K |████████████████████████████████| 4.0 MB 35.2 MB/s \n", | |
| "\u001b[?25hRequirement already satisfied: wrapt~=1.12.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.5.0->tf-models-official>=2.5.1->object-detection==0.1) (1.12.1)\n", | |
| "Requirement already satisfied: wheel~=0.35 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.5.0->tf-models-official>=2.5.1->object-detection==0.1) (0.37.0)\n", | |
| "Collecting tensorflow-estimator<2.6.0,>=2.5.0rc0\n", | |
| " Downloading tensorflow_estimator-2.5.0-py2.py3-none-any.whl (462 kB)\n", | |
| "\u001b[K |████████████████████████████████| 462 kB 60.7 MB/s \n", | |
| "\u001b[?25hRequirement already satisfied: google-pasta~=0.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.5.0->tf-models-official>=2.5.1->object-detection==0.1) (0.2.0)\n", | |
| "Requirement already satisfied: astunparse~=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.5.0->tf-models-official>=2.5.1->object-detection==0.1) (1.6.3)\n", | |
| "Requirement already satisfied: keras-nightly~=2.5.0.dev in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.5.0->tf-models-official>=2.5.1->object-detection==0.1) (2.5.0.dev2021032900)\n", | |
| "Requirement already satisfied: grpcio~=1.34.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.5.0->tf-models-official>=2.5.1->object-detection==0.1) (1.34.1)\n", | |
| "Requirement already satisfied: typing-extensions~=3.7.4 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.5.0->tf-models-official>=2.5.1->object-detection==0.1) (3.7.4.3)\n", | |
| "Requirement already satisfied: flatbuffers~=1.12.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.5.0->tf-models-official>=2.5.1->object-detection==0.1) (1.12)\n", | |
| "Requirement already satisfied: cached-property in /usr/local/lib/python3.7/dist-packages (from h5py~=3.1.0->tensorflow>=2.5.0->tf-models-official>=2.5.1->object-detection==0.1) (1.5.2)\n", | |
| "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.5->tensorflow>=2.5.0->tf-models-official>=2.5.1->object-detection==0.1) (1.8.0)\n", | |
| "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.5->tensorflow>=2.5.0->tf-models-official>=2.5.1->object-detection==0.1) (0.4.5)\n", | |
| "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.5->tensorflow>=2.5.0->tf-models-official>=2.5.1->object-detection==0.1) (3.3.4)\n", | |
| "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.5->tensorflow>=2.5.0->tf-models-official>=2.5.1->object-detection==0.1) (0.6.1)\n", | |
| "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.5->tensorflow>=2.5.0->tf-models-official>=2.5.1->object-detection==0.1) (1.0.1)\n", | |
| "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.5->tensorflow>=2.5.0->tf-models-official>=2.5.1->object-detection==0.1) (1.3.0)\n", | |
| "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard~=2.5->tensorflow>=2.5.0->tf-models-official>=2.5.1->object-detection==0.1) (4.6.3)\n", | |
| "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.5->tensorflow>=2.5.0->tf-models-official>=2.5.1->object-detection==0.1) (3.1.1)\n", | |
| "Requirement already satisfied: dm-tree~=0.1.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow-model-optimization>=0.4.1->tf-models-official>=2.5.1->object-detection==0.1) (0.1.6)\n", | |
| "Collecting requests<3.0.0dev,>=2.18.0\n", | |
| " Downloading requests-2.26.0-py2.py3-none-any.whl (62 kB)\n", | |
| "\u001b[K |████████████████████████████████| 62 kB 1.1 MB/s \n", | |
| "\u001b[?25hRequirement already satisfied: crcmod<2.0,>=1.7 in /usr/local/lib/python3.7/dist-packages (from apache-beam->object-detection==0.1) (1.7)\n", | |
| "Collecting avro-python3\n", | |
| " Downloading avro-python3-1.9.2.1.tar.gz (37 kB)\n", | |
| "Collecting hdfs<3.0.0,>=2.1.0\n", | |
| " Downloading hdfs-2.6.0-py3-none-any.whl (33 kB)\n", | |
| "Requirement already satisfied: pymongo<4.0.0,>=3.8.0 in /usr/local/lib/python3.7/dist-packages (from apache-beam->object-detection==0.1) (3.12.0)\n", | |
| "Collecting fastavro<2,>=0.21.4\n", | |
| " Downloading fastavro-1.4.4-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.3 MB)\n", | |
| "\u001b[K |████████████████████████████████| 2.3 MB 57.0 MB/s \n", | |
| "\u001b[?25hCollecting future<1.0.0,>=0.18.2\n", | |
| " Downloading future-0.18.2.tar.gz (829 kB)\n", | |
| "\u001b[K |████████████████████████████████| 829 kB 62.1 MB/s \n", | |
| "\u001b[?25hRequirement already satisfied: pyarrow<5.0.0,>=0.15.1 in /usr/local/lib/python3.7/dist-packages (from apache-beam->object-detection==0.1) (3.0.0)\n", | |
| "Requirement already satisfied: pydot<2,>=1.2.0 in /usr/local/lib/python3.7/dist-packages (from apache-beam->object-detection==0.1) (1.3.0)\n", | |
| "Collecting dill<0.3.2,>=0.3.1.1\n", | |
| " Downloading dill-0.3.1.1.tar.gz (151 kB)\n", | |
| "\u001b[K |████████████████████████████████| 151 kB 64.1 MB/s \n", | |
| "\u001b[?25hRequirement already satisfied: docopt in /usr/local/lib/python3.7/dist-packages (from hdfs<3.0.0,>=2.1.0->apache-beam->object-detection==0.1) (0.6.2)\n", | |
| "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0dev,>=2.18.0->google-api-core<2dev,>=1.21.0->google-api-python-client>=1.6.7->tf-models-official>=2.5.1->object-detection==0.1) (2.0.4)\n", | |
| "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->markdown>=2.6.8->tensorboard~=2.5->tensorflow>=2.5.0->tf-models-official>=2.5.1->object-detection==0.1) (3.5.0)\n", | |
| "Requirement already satisfied: opencv-python>=4.1.0.25 in /usr/local/lib/python3.7/dist-packages (from lvis->object-detection==0.1) (4.1.2.30)\n", | |
| "Requirement already satisfied: cycler>=0.10.0 in /usr/local/lib/python3.7/dist-packages (from lvis->object-detection==0.1) (0.10.0)\n", | |
| "Requirement already satisfied: kiwisolver>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from lvis->object-detection==0.1) (1.3.1)\n", | |
| "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.7/dist-packages (from python-slugify->kaggle>=1.3.9->tf-models-official>=2.5.1->object-detection==0.1) (1.3)\n", | |
| "Collecting portalocker\n", | |
| " Downloading portalocker-2.3.0-py2.py3-none-any.whl (15 kB)\n", | |
| "Collecting colorama\n", | |
| " Downloading colorama-0.4.4-py2.py3-none-any.whl (16 kB)\n", | |
| "Requirement already satisfied: regex in /usr/local/lib/python3.7/dist-packages (from sacrebleu->tf-models-official>=2.5.1->object-detection==0.1) (2019.12.20)\n", | |
| "Requirement already satisfied: tabulate>=0.8.9 in /usr/local/lib/python3.7/dist-packages (from sacrebleu->tf-models-official>=2.5.1->object-detection==0.1) (0.8.9)\n", | |
| "Requirement already satisfied: scikit-learn>=0.21.3 in /usr/local/lib/python3.7/dist-packages (from seqeval->tf-models-official>=2.5.1->object-detection==0.1) (0.22.2.post1)\n", | |
| "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.21.3->seqeval->tf-models-official>=2.5.1->object-detection==0.1) (1.0.1)\n", | |
| "Requirement already satisfied: typeguard>=2.7 in /usr/local/lib/python3.7/dist-packages (from tensorflow-addons->tf-models-official>=2.5.1->object-detection==0.1) (2.7.1)\n", | |
| "Requirement already satisfied: importlib-resources in /usr/local/lib/python3.7/dist-packages (from tensorflow-datasets->tf-models-official>=2.5.1->object-detection==0.1) (5.2.2)\n", | |
| "Requirement already satisfied: promise in /usr/local/lib/python3.7/dist-packages (from tensorflow-datasets->tf-models-official>=2.5.1->object-detection==0.1) (2.3)\n", | |
| "Requirement already satisfied: attrs>=18.1.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow-datasets->tf-models-official>=2.5.1->object-detection==0.1) (21.2.0)\n", | |
| "Requirement already satisfied: tensorflow-metadata in /usr/local/lib/python3.7/dist-packages (from tensorflow-datasets->tf-models-official>=2.5.1->object-detection==0.1) (1.2.0)\n", | |
| "Building wheels for collected packages: object-detection, py-cpuinfo, avro-python3, dill, future, seqeval\n", | |
| " Building wheel for object-detection (setup.py) ... \u001b[?25l\u001b[?25hdone\n", | |
| " Created wheel for object-detection: filename=object_detection-0.1-py3-none-any.whl size=1660486 sha256=a4f97afe19caa3318ea90748477acbadedb794672b762e96760b262dd07ab3a2\n", | |
| " Stored in directory: /tmp/pip-ephem-wheel-cache-b6znaer8/wheels/46/73/76/3b825ef94288d1d8abcaf7fe9dfd9a9ed427c3cb688ac8e17a\n", | |
| " Building wheel for py-cpuinfo (setup.py) ... \u001b[?25l\u001b[?25hdone\n", | |
| " Created wheel for py-cpuinfo: filename=py_cpuinfo-8.0.0-py3-none-any.whl size=22258 sha256=770736ac47358b0cdf47c378babc383d0a2e3864b725795c8f15373e9a5ae7ef\n", | |
| " Stored in directory: /root/.cache/pip/wheels/d2/f1/1f/041add21dc9c4220157f1bd2bd6afe1f1a49524c3396b94401\n", | |
| " Building wheel for avro-python3 (setup.py) ... \u001b[?25l\u001b[?25hdone\n", | |
| " Created wheel for avro-python3: filename=avro_python3-1.9.2.1-py3-none-any.whl size=43512 sha256=26554113697e7ffc83a9012ed12efc55720222f5729629e22757673642040be5\n", | |
| " Stored in directory: /root/.cache/pip/wheels/bc/49/5f/fdb5b9d85055c478213e0158ac122b596816149a02d82e0ab1\n", | |
| " Building wheel for dill (setup.py) ... \u001b[?25l\u001b[?25hdone\n", | |
| " Created wheel for dill: filename=dill-0.3.1.1-py3-none-any.whl size=78546 sha256=ff45582b77ca93214131b193d876b6c4dd49223415f675cb918722da6cc97726\n", | |
| " Stored in directory: /root/.cache/pip/wheels/a4/61/fd/c57e374e580aa78a45ed78d5859b3a44436af17e22ca53284f\n", | |
| " Building wheel for future (setup.py) ... \u001b[?25l\u001b[?25hdone\n", | |
| " Created wheel for future: filename=future-0.18.2-py3-none-any.whl size=491070 sha256=3f2cdcb2c0878baa3c580c921b59811e14c9d7b8d867841413c77dfd12d23604\n", | |
| " Stored in directory: /root/.cache/pip/wheels/56/b0/fe/4410d17b32f1f0c3cf54cdfb2bc04d7b4b8f4ae377e2229ba0\n", | |
| " Building wheel for seqeval (setup.py) ... \u001b[?25l\u001b[?25hdone\n", | |
| " Created wheel for seqeval: filename=seqeval-1.2.2-py3-none-any.whl size=16181 sha256=fca5739fb7fa36e8f190f12a5a6b0c3b6839db330fabf61e32fd7820101c1f73\n", | |
| " Stored in directory: /root/.cache/pip/wheels/05/96/ee/7cac4e74f3b19e3158dce26a20a1c86b3533c43ec72a549fd7\n", | |
| "Successfully built object-detection py-cpuinfo avro-python3 dill future seqeval\n", | |
| "Installing collected packages: requests, numpy, tensorflow-estimator, portalocker, h5py, gast, future, dill, colorama, tf-slim, tensorflow-model-optimization, tensorflow-addons, seqeval, sentencepiece, sacrebleu, pyyaml, py-cpuinfo, opencv-python-headless, hdfs, fastavro, avro-python3, tf-models-official, lvis, apache-beam, object-detection\n", | |
| " Attempting uninstall: requests\n", | |
| " Found existing installation: requests 2.23.0\n", | |
| " Uninstalling requests-2.23.0:\n", | |
| " Successfully uninstalled requests-2.23.0\n", | |
| " Attempting uninstall: numpy\n", | |
| " Found existing installation: numpy 1.18.5\n", | |
| " Uninstalling numpy-1.18.5:\n", | |
| " Successfully uninstalled numpy-1.18.5\n", | |
| " Attempting uninstall: tensorflow-estimator\n", | |
| " Found existing installation: tensorflow-estimator 2.3.0\n", | |
| " Uninstalling tensorflow-estimator-2.3.0:\n", | |
| " Successfully uninstalled tensorflow-estimator-2.3.0\n", | |
| " Attempting uninstall: h5py\n", | |
| " Found existing installation: h5py 2.10.0\n", | |
| " Uninstalling h5py-2.10.0:\n", | |
| " Successfully uninstalled h5py-2.10.0\n", | |
| " Attempting uninstall: gast\n", | |
| " Found existing installation: gast 0.3.3\n", | |
| " Uninstalling gast-0.3.3:\n", | |
| " Successfully uninstalled gast-0.3.3\n", | |
| " Attempting uninstall: future\n", | |
| " Found existing installation: future 0.16.0\n", | |
| " Uninstalling future-0.16.0:\n", | |
| " Successfully uninstalled future-0.16.0\n", | |
| " Attempting uninstall: dill\n", | |
| " Found existing installation: dill 0.3.4\n", | |
| " Uninstalling dill-0.3.4:\n", | |
| " Successfully uninstalled dill-0.3.4\n", | |
| " Attempting uninstall: pyyaml\n", | |
| " Found existing installation: PyYAML 3.13\n", | |
| " Uninstalling PyYAML-3.13:\n", | |
| " Successfully uninstalled PyYAML-3.13\n", | |
| "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", | |
| "tensorflow-gpu 2.3.0 requires gast==0.3.3, but you have gast 0.4.0 which is incompatible.\n", | |
| "tensorflow-gpu 2.3.0 requires h5py<2.11.0,>=2.10.0, but you have h5py 3.1.0 which is incompatible.\n", | |
| "tensorflow-gpu 2.3.0 requires numpy<1.19.0,>=1.16.0, but you have numpy 1.19.5 which is incompatible.\n", | |
| "tensorflow-gpu 2.3.0 requires tensorflow-estimator<2.4.0,>=2.3.0, but you have tensorflow-estimator 2.5.0 which is incompatible.\n", | |
| "multiprocess 0.70.12.2 requires dill>=0.3.4, but you have dill 0.3.1.1 which is incompatible.\n", | |
| "google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.26.0 which is incompatible.\n", | |
| "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\n", | |
| "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n", | |
| "Successfully installed apache-beam-2.31.0 avro-python3-1.9.2.1 colorama-0.4.4 dill-0.3.1.1 fastavro-1.4.4 future-0.18.2 gast-0.4.0 h5py-3.1.0 hdfs-2.6.0 lvis-0.5.3 numpy-1.19.5 object-detection-0.1 opencv-python-headless-4.5.3.56 portalocker-2.3.0 py-cpuinfo-8.0.0 pyyaml-5.4.1 requests-2.26.0 sacrebleu-2.0.0 sentencepiece-0.1.96 seqeval-1.2.2 tensorflow-addons-0.13.0 tensorflow-estimator-2.5.0 tensorflow-model-optimization-0.6.0 tf-models-official-2.5.1 tf-slim-1.1.0\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "cjlR4lsmuWpE", | |
| "outputId": "9fde1334-a2e6-4b6b-8afc-d0ab5c89fe99" | |
| }, | |
| "source": [ | |
| "# From within TensorFlow/models/research/\n", | |
| "!python object_detection/builders/model_builder_tf2_test.py" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "2021-08-15 07:02:50.616649: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n", | |
| "Running tests under Python 3.7.11: /usr/bin/python3\n", | |
| "[ RUN ] ModelBuilderTF2Test.test_create_center_net_deepmac\n", | |
| "2021-08-15 07:02:52.895916: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcuda.so.1\n", | |
| "2021-08-15 07:02:52.952721: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", | |
| "2021-08-15 07:02:52.953368: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties: \n", | |
| "pciBusID: 0000:00:04.0 name: Tesla T4 computeCapability: 7.5\n", | |
| "coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.75GiB deviceMemoryBandwidth: 298.08GiB/s\n", | |
| "2021-08-15 07:02:52.953414: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n", | |
| "2021-08-15 07:02:53.196308: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10\n", | |
| "2021-08-15 07:02:53.325123: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10\n", | |
| "2021-08-15 07:02:53.379832: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10\n", | |
| "2021-08-15 07:02:53.617167: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10\n", | |
| "2021-08-15 07:02:53.672446: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10\n", | |
| "2021-08-15 07:02:54.198155: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7\n", | |
| "2021-08-15 07:02:54.198406: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", | |
| "2021-08-15 07:02:54.199334: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", | |
| "2021-08-15 07:02:54.199897: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0\n", | |
| "2021-08-15 07:02:54.200266: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN)to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", | |
| "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", | |
| "2021-08-15 07:02:54.205663: I tensorflow/core/platform/profile_utils/cpu_utils.cc:104] CPU Frequency: 2199995000 Hz\n", | |
| "2021-08-15 07:02:54.205868: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55d97d36ebc0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n", | |
| "2021-08-15 07:02:54.205895: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version\n", | |
| "2021-08-15 07:02:54.425109: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", | |
| "2021-08-15 07:02:54.425922: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55d97d36ea00 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", | |
| "2021-08-15 07:02:54.425954: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Tesla T4, Compute Capability 7.5\n", | |
| "2021-08-15 07:02:54.426154: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", | |
| "2021-08-15 07:02:54.426745: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties: \n", | |
| "pciBusID: 0000:00:04.0 name: Tesla T4 computeCapability: 7.5\n", | |
| "coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.75GiB deviceMemoryBandwidth: 298.08GiB/s\n", | |
| "2021-08-15 07:02:54.426791: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n", | |
| "2021-08-15 07:02:54.426842: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10\n", | |
| "2021-08-15 07:02:54.426865: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10\n", | |
| "2021-08-15 07:02:54.426892: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10\n", | |
| "2021-08-15 07:02:54.426913: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10\n", | |
| "2021-08-15 07:02:54.426933: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10\n", | |
| "2021-08-15 07:02:54.426954: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7\n", | |
| "2021-08-15 07:02:54.427031: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", | |
| "2021-08-15 07:02:54.427653: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", | |
| "2021-08-15 07:02:54.428189: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0\n", | |
| "2021-08-15 07:02:54.428255: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n", | |
| "2021-08-15 07:02:54.931596: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1257] Device interconnect StreamExecutor with strength 1 edge matrix:\n", | |
| "2021-08-15 07:02:54.931659: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1263] 0 \n", | |
| "2021-08-15 07:02:54.931675: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1276] 0: N \n", | |
| "2021-08-15 07:02:54.931880: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", | |
| "2021-08-15 07:02:54.932545: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", | |
| "2021-08-15 07:02:54.933073: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n", | |
| "2021-08-15 07:02:54.933118: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1402] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 13970 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5)\n", | |
| "W0815 07:02:55.221746 140204072736640 model_builder.py:1088] Building experimental DeepMAC meta-arch. Some features may be omitted.\n", | |
| "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_deepmac): 2.79s\n", | |
| "I0815 07:02:55.682067 140204072736640 test_util.py:1973] time(__main__.ModelBuilderTF2Test.test_create_center_net_deepmac): 2.79s\n", | |
| "[ OK ] ModelBuilderTF2Test.test_create_center_net_deepmac\n", | |
| "[ RUN ] ModelBuilderTF2Test.test_create_center_net_model0 (customize_head_params=True)\n", | |
| "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_model0 (customize_head_params=True)): 0.62s\n", | |
| "I0815 07:02:56.298874 140204072736640 test_util.py:1973] time(__main__.ModelBuilderTF2Test.test_create_center_net_model0 (customize_head_params=True)): 0.62s\n", | |
| "[ OK ] ModelBuilderTF2Test.test_create_center_net_model0 (customize_head_params=True)\n", | |
| "[ RUN ] ModelBuilderTF2Test.test_create_center_net_model1 (customize_head_params=False)\n", | |
| "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_model1 (customize_head_params=False)): 0.38s\n", | |
| "I0815 07:02:56.676721 140204072736640 test_util.py:1973] time(__main__.ModelBuilderTF2Test.test_create_center_net_model1 (customize_head_params=False)): 0.38s\n", | |
| "[ OK ] ModelBuilderTF2Test.test_create_center_net_model1 (customize_head_params=False)\n", | |
| "[ RUN ] ModelBuilderTF2Test.test_create_center_net_model_from_keypoints\n", | |
| "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_model_from_keypoints): 0.34s\n", | |
| "I0815 07:02:57.013760 140204072736640 test_util.py:1973] time(__main__.ModelBuilderTF2Test.test_create_center_net_model_from_keypoints): 0.34s\n", | |
| "[ OK ] ModelBuilderTF2Test.test_create_center_net_model_from_keypoints\n", | |
| "[ RUN ] ModelBuilderTF2Test.test_create_center_net_model_mobilenet\n", | |
| "WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n", | |
| "W0815 07:02:57.015822 140204072736640 mobilenet_v2.py:285] `input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n", | |
| "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_224_no_top.h5\n", | |
| "9412608/9406464 [==============================] - 0s 0us/step\n", | |
| "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_model_mobilenet): 1.41s\n", | |
| "I0815 07:02:58.421128 140204072736640 test_util.py:1973] time(__main__.ModelBuilderTF2Test.test_create_center_net_model_mobilenet): 1.41s\n", | |
| "[ FAILED ] ModelBuilderTF2Test.test_create_center_net_model_mobilenet\n", | |
| "[ RUN ] ModelBuilderTF2Test.test_create_experimental_model\n", | |
| "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_experimental_model): 0.0s\n", | |
| "I0815 07:02:58.423875 140204072736640 test_util.py:1973] time(__main__.ModelBuilderTF2Test.test_create_experimental_model): 0.0s\n", | |
| "[ OK ] ModelBuilderTF2Test.test_create_experimental_model\n", | |
| "[ RUN ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True)\n", | |
| "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True)): 0.03s\n", | |
| "I0815 07:02:58.450020 140204072736640 test_util.py:1973] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True)): 0.03s\n", | |
| "[ OK ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True)\n", | |
| "[ RUN ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)\n", | |
| "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)): 0.02s\n", | |
| "I0815 07:02:58.471272 140204072736640 test_util.py:1973] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)): 0.02s\n", | |
| "[ OK ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)\n", | |
| "[ RUN ] ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner\n", | |
| "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner): 0.02s\n", | |
| "I0815 07:02:58.492288 140204072736640 test_util.py:1973] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner): 0.02s\n", | |
| "[ OK ] ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner\n", | |
| "[ RUN ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul\n", | |
| "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul): 0.14s\n", | |
| "I0815 07:02:58.628415 140204072736640 test_util.py:1973] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul): 0.14s\n", | |
| "[ OK ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul\n", | |
| "[ RUN ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul\n", | |
| "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul): 0.14s\n", | |
| "I0815 07:02:58.769121 140204072736640 test_util.py:1973] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul): 0.14s\n", | |
| "[ OK ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul\n", | |
| "[ RUN ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul\n", | |
| "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul): 0.14s\n", | |
| "I0815 07:02:58.909881 140204072736640 test_util.py:1973] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul): 0.14s\n", | |
| "[ OK ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul\n", | |
| "[ RUN ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul\n", | |
| "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul): 0.15s\n", | |
| "I0815 07:02:59.061209 140204072736640 test_util.py:1973] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul): 0.15s\n", | |
| "[ OK ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul\n", | |
| "[ RUN ] ModelBuilderTF2Test.test_create_rfcn_model_from_config\n", | |
| "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_rfcn_model_from_config): 0.15s\n", | |
| "I0815 07:02:59.213168 140204072736640 test_util.py:1973] time(__main__.ModelBuilderTF2Test.test_create_rfcn_model_from_config): 0.15s\n", | |
| "[ OK ] ModelBuilderTF2Test.test_create_rfcn_model_from_config\n", | |
| "[ RUN ] ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config\n", | |
| "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config): 0.04s\n", | |
| "I0815 07:02:59.252921 140204072736640 test_util.py:1973] time(__main__.ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config): 0.04s\n", | |
| "[ OK ] ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config\n", | |
| "[ RUN ] ModelBuilderTF2Test.test_create_ssd_models_from_config\n", | |
| "I0815 07:02:59.548091 140204072736640 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b0\n", | |
| "I0815 07:02:59.548271 140204072736640 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 64\n", | |
| "I0815 07:02:59.548347 140204072736640 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 3\n", | |
| "I0815 07:02:59.552970 140204072736640 efficientnet_model.py:147] round_filter input=32 output=32\n", | |
| "I0815 07:02:59.579520 140204072736640 efficientnet_model.py:147] round_filter input=32 output=32\n", | |
| "I0815 07:02:59.579647 140204072736640 efficientnet_model.py:147] round_filter input=16 output=16\n", | |
| "I0815 07:02:59.648380 140204072736640 efficientnet_model.py:147] round_filter input=16 output=16\n", | |
| "I0815 07:02:59.648524 140204072736640 efficientnet_model.py:147] round_filter input=24 output=24\n", | |
| "I0815 07:02:59.835134 140204072736640 efficientnet_model.py:147] round_filter input=24 output=24\n", | |
| "I0815 07:02:59.835297 140204072736640 efficientnet_model.py:147] round_filter input=40 output=40\n", | |
| "I0815 07:03:00.027659 140204072736640 efficientnet_model.py:147] round_filter input=40 output=40\n", | |
| "I0815 07:03:00.027828 140204072736640 efficientnet_model.py:147] round_filter input=80 output=80\n", | |
| "I0815 07:03:00.321174 140204072736640 efficientnet_model.py:147] round_filter input=80 output=80\n", | |
| "I0815 07:03:00.321356 140204072736640 efficientnet_model.py:147] round_filter input=112 output=112\n", | |
| "I0815 07:03:00.705639 140204072736640 efficientnet_model.py:147] round_filter input=112 output=112\n", | |
| "I0815 07:03:00.705806 140204072736640 efficientnet_model.py:147] round_filter input=192 output=192\n", | |
| "I0815 07:03:01.092546 140204072736640 efficientnet_model.py:147] round_filter input=192 output=192\n", | |
| "I0815 07:03:01.092718 140204072736640 efficientnet_model.py:147] round_filter input=320 output=320\n", | |
| "I0815 07:03:01.176787 140204072736640 efficientnet_model.py:147] round_filter input=1280 output=1280\n", | |
| "I0815 07:03:01.212541 140204072736640 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.0, depth_coefficient=1.0, resolution=224, dropout_rate=0.2, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n", | |
| "I0815 07:03:01.295174 140204072736640 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b1\n", | |
| "I0815 07:03:01.295319 140204072736640 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 88\n", | |
| "I0815 07:03:01.295391 140204072736640 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 4\n", | |
| "I0815 07:03:01.299946 140204072736640 efficientnet_model.py:147] round_filter input=32 output=32\n", | |
| "I0815 07:03:01.321336 140204072736640 efficientnet_model.py:147] round_filter input=32 output=32\n", | |
| "I0815 07:03:01.321449 140204072736640 efficientnet_model.py:147] round_filter input=16 output=16\n", | |
| "I0815 07:03:01.463688 140204072736640 efficientnet_model.py:147] round_filter input=16 output=16\n", | |
| "I0815 07:03:01.463827 140204072736640 efficientnet_model.py:147] round_filter input=24 output=24\n", | |
| "I0815 07:03:01.752176 140204072736640 efficientnet_model.py:147] round_filter input=24 output=24\n", | |
| "I0815 07:03:01.752351 140204072736640 efficientnet_model.py:147] round_filter input=40 output=40\n", | |
| "I0815 07:03:02.041553 140204072736640 efficientnet_model.py:147] round_filter input=40 output=40\n", | |
| "I0815 07:03:02.041728 140204072736640 efficientnet_model.py:147] round_filter input=80 output=80\n", | |
| "I0815 07:03:02.440425 140204072736640 efficientnet_model.py:147] round_filter input=80 output=80\n", | |
| "I0815 07:03:02.440592 140204072736640 efficientnet_model.py:147] round_filter input=112 output=112\n", | |
| "I0815 07:03:02.937495 140204072736640 efficientnet_model.py:147] round_filter input=112 output=112\n", | |
| "I0815 07:03:02.937719 140204072736640 efficientnet_model.py:147] round_filter input=192 output=192\n", | |
| "I0815 07:03:03.422956 140204072736640 efficientnet_model.py:147] round_filter input=192 output=192\n", | |
| "I0815 07:03:03.423132 140204072736640 efficientnet_model.py:147] round_filter input=320 output=320\n", | |
| "I0815 07:03:03.611779 140204072736640 efficientnet_model.py:147] round_filter input=1280 output=1280\n", | |
| "I0815 07:03:03.646566 140204072736640 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.0, depth_coefficient=1.1, resolution=240, dropout_rate=0.2, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n", | |
| "I0815 07:03:03.743449 140204072736640 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b2\n", | |
| "I0815 07:03:03.743596 140204072736640 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 112\n", | |
| "I0815 07:03:03.743666 140204072736640 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 5\n", | |
| "I0815 07:03:03.748201 140204072736640 efficientnet_model.py:147] round_filter input=32 output=32\n", | |
| "I0815 07:03:03.769742 140204072736640 efficientnet_model.py:147] round_filter input=32 output=32\n", | |
| "I0815 07:03:03.769859 140204072736640 efficientnet_model.py:147] round_filter input=16 output=16\n", | |
| "I0815 07:03:03.911207 140204072736640 efficientnet_model.py:147] round_filter input=16 output=16\n", | |
| "I0815 07:03:03.911359 140204072736640 efficientnet_model.py:147] round_filter input=24 output=24\n", | |
| "I0815 07:03:04.215220 140204072736640 efficientnet_model.py:147] round_filter input=24 output=24\n", | |
| "I0815 07:03:04.215402 140204072736640 efficientnet_model.py:147] round_filter input=40 output=48\n", | |
| "I0815 07:03:04.499072 140204072736640 efficientnet_model.py:147] round_filter input=40 output=48\n", | |
| "I0815 07:03:04.499286 140204072736640 efficientnet_model.py:147] round_filter input=80 output=88\n", | |
| "I0815 07:03:04.882962 140204072736640 efficientnet_model.py:147] round_filter input=80 output=88\n", | |
| "I0815 07:03:04.883137 140204072736640 efficientnet_model.py:147] round_filter input=112 output=120\n", | |
| "I0815 07:03:05.270792 140204072736640 efficientnet_model.py:147] round_filter input=112 output=120\n", | |
| "I0815 07:03:05.270965 140204072736640 efficientnet_model.py:147] round_filter input=192 output=208\n", | |
| "I0815 07:03:05.747875 140204072736640 efficientnet_model.py:147] round_filter input=192 output=208\n", | |
| "I0815 07:03:05.748046 140204072736640 efficientnet_model.py:147] round_filter input=320 output=352\n", | |
| "I0815 07:03:06.096881 140204072736640 efficientnet_model.py:147] round_filter input=1280 output=1408\n", | |
| "I0815 07:03:06.132167 140204072736640 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.1, depth_coefficient=1.2, resolution=260, dropout_rate=0.3, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n", | |
| "I0815 07:03:06.223334 140204072736640 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b3\n", | |
| "I0815 07:03:06.223469 140204072736640 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 160\n", | |
| "I0815 07:03:06.223536 140204072736640 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 6\n", | |
| "I0815 07:03:06.228106 140204072736640 efficientnet_model.py:147] round_filter input=32 output=40\n", | |
| "I0815 07:03:06.249448 140204072736640 efficientnet_model.py:147] round_filter input=32 output=40\n", | |
| "I0815 07:03:06.249561 140204072736640 efficientnet_model.py:147] round_filter input=16 output=24\n", | |
| "I0815 07:03:06.389986 140204072736640 efficientnet_model.py:147] round_filter input=16 output=24\n", | |
| "I0815 07:03:06.390129 140204072736640 efficientnet_model.py:147] round_filter input=24 output=32\n", | |
| "I0815 07:03:06.673733 140204072736640 efficientnet_model.py:147] round_filter input=24 output=32\n", | |
| "I0815 07:03:06.673890 140204072736640 efficientnet_model.py:147] round_filter input=40 output=48\n", | |
| "I0815 07:03:06.961445 140204072736640 efficientnet_model.py:147] round_filter input=40 output=48\n", | |
| "I0815 07:03:06.961614 140204072736640 efficientnet_model.py:147] round_filter input=80 output=96\n", | |
| "I0815 07:03:07.443664 140204072736640 efficientnet_model.py:147] round_filter input=80 output=96\n", | |
| "I0815 07:03:07.443834 140204072736640 efficientnet_model.py:147] round_filter input=112 output=136\n", | |
| "I0815 07:03:07.925651 140204072736640 efficientnet_model.py:147] round_filter input=112 output=136\n", | |
| "I0815 07:03:07.925843 140204072736640 efficientnet_model.py:147] round_filter input=192 output=232\n", | |
| "I0815 07:03:08.527515 140204072736640 efficientnet_model.py:147] round_filter input=192 output=232\n", | |
| "I0815 07:03:08.527703 140204072736640 efficientnet_model.py:147] round_filter input=320 output=384\n", | |
| "I0815 07:03:08.715101 140204072736640 efficientnet_model.py:147] round_filter input=1280 output=1536\n", | |
| "I0815 07:03:08.750598 140204072736640 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.2, depth_coefficient=1.4, resolution=300, dropout_rate=0.3, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n", | |
| "I0815 07:03:08.855907 140204072736640 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b4\n", | |
| "I0815 07:03:08.856086 140204072736640 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 224\n", | |
| "I0815 07:03:08.856153 140204072736640 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 7\n", | |
| "I0815 07:03:08.861208 140204072736640 efficientnet_model.py:147] round_filter input=32 output=48\n", | |
| "I0815 07:03:08.884893 140204072736640 efficientnet_model.py:147] round_filter input=32 output=48\n", | |
| "I0815 07:03:08.885003 140204072736640 efficientnet_model.py:147] round_filter input=16 output=24\n", | |
| "I0815 07:03:09.029518 140204072736640 efficientnet_model.py:147] round_filter input=16 output=24\n", | |
| "I0815 07:03:09.029664 140204072736640 efficientnet_model.py:147] round_filter input=24 output=32\n", | |
| "I0815 07:03:09.608934 140204072736640 efficientnet_model.py:147] round_filter input=24 output=32\n", | |
| "I0815 07:03:09.609110 140204072736640 efficientnet_model.py:147] round_filter input=40 output=56\n", | |
| "I0815 07:03:10.022582 140204072736640 efficientnet_model.py:147] round_filter input=40 output=56\n", | |
| "I0815 07:03:10.022752 140204072736640 efficientnet_model.py:147] round_filter input=80 output=112\n", | |
| "I0815 07:03:10.600517 140204072736640 efficientnet_model.py:147] round_filter input=80 output=112\n", | |
| "I0815 07:03:10.600687 140204072736640 efficientnet_model.py:147] round_filter input=112 output=160\n", | |
| "I0815 07:03:11.194673 140204072736640 efficientnet_model.py:147] round_filter input=112 output=160\n", | |
| "I0815 07:03:11.194846 140204072736640 efficientnet_model.py:147] round_filter input=192 output=272\n", | |
| "I0815 07:03:11.979151 140204072736640 efficientnet_model.py:147] round_filter input=192 output=272\n", | |
| "I0815 07:03:11.979336 140204072736640 efficientnet_model.py:147] round_filter input=320 output=448\n", | |
| "I0815 07:03:12.166356 140204072736640 efficientnet_model.py:147] round_filter input=1280 output=1792\n", | |
| "I0815 07:03:12.201721 140204072736640 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.4, depth_coefficient=1.8, resolution=380, dropout_rate=0.4, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n", | |
| "I0815 07:03:12.317284 140204072736640 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b5\n", | |
| "I0815 07:03:12.317439 140204072736640 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 288\n", | |
| "I0815 07:03:12.317518 140204072736640 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 7\n", | |
| "I0815 07:03:12.322260 140204072736640 efficientnet_model.py:147] round_filter input=32 output=48\n", | |
| "I0815 07:03:12.344724 140204072736640 efficientnet_model.py:147] round_filter input=32 output=48\n", | |
| "I0815 07:03:12.344836 140204072736640 efficientnet_model.py:147] round_filter input=16 output=24\n", | |
| "I0815 07:03:12.564036 140204072736640 efficientnet_model.py:147] round_filter input=16 output=24\n", | |
| "I0815 07:03:12.564194 140204072736640 efficientnet_model.py:147] round_filter input=24 output=40\n", | |
| "I0815 07:03:13.045732 140204072736640 efficientnet_model.py:147] round_filter input=24 output=40\n", | |
| "I0815 07:03:13.045907 140204072736640 efficientnet_model.py:147] round_filter input=40 output=64\n", | |
| "I0815 07:03:13.532472 140204072736640 efficientnet_model.py:147] round_filter input=40 output=64\n", | |
| "I0815 07:03:13.532662 140204072736640 efficientnet_model.py:147] round_filter input=80 output=128\n", | |
| "I0815 07:03:14.464758 140204072736640 efficientnet_model.py:147] round_filter input=80 output=128\n", | |
| "I0815 07:03:14.464941 140204072736640 efficientnet_model.py:147] round_filter input=112 output=176\n", | |
| "I0815 07:03:15.157216 140204072736640 efficientnet_model.py:147] round_filter input=112 output=176\n", | |
| "I0815 07:03:15.157427 140204072736640 efficientnet_model.py:147] round_filter input=192 output=304\n", | |
| "I0815 07:03:16.058582 140204072736640 efficientnet_model.py:147] round_filter input=192 output=304\n", | |
| "I0815 07:03:16.058786 140204072736640 efficientnet_model.py:147] round_filter input=320 output=512\n", | |
| "I0815 07:03:16.355264 140204072736640 efficientnet_model.py:147] round_filter input=1280 output=2048\n", | |
| "I0815 07:03:16.390288 140204072736640 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.6, depth_coefficient=2.2, resolution=456, dropout_rate=0.4, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n", | |
| "I0815 07:03:16.516403 140204072736640 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b6\n", | |
| "I0815 07:03:16.516544 140204072736640 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 384\n", | |
| "I0815 07:03:16.516614 140204072736640 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 8\n", | |
| "I0815 07:03:16.521185 140204072736640 efficientnet_model.py:147] round_filter input=32 output=56\n", | |
| "I0815 07:03:16.542450 140204072736640 efficientnet_model.py:147] round_filter input=32 output=56\n", | |
| "I0815 07:03:16.542557 140204072736640 efficientnet_model.py:147] round_filter input=16 output=32\n", | |
| "I0815 07:03:16.758009 140204072736640 efficientnet_model.py:147] round_filter input=16 output=32\n", | |
| "I0815 07:03:16.758165 140204072736640 efficientnet_model.py:147] round_filter input=24 output=40\n", | |
| "I0815 07:03:17.333450 140204072736640 efficientnet_model.py:147] round_filter input=24 output=40\n", | |
| "I0815 07:03:17.333657 140204072736640 efficientnet_model.py:147] round_filter input=40 output=72\n", | |
| "I0815 07:03:17.918712 140204072736640 efficientnet_model.py:147] round_filter input=40 output=72\n", | |
| "I0815 07:03:17.918913 140204072736640 efficientnet_model.py:147] round_filter input=80 output=144\n", | |
| "I0815 07:03:18.698948 140204072736640 efficientnet_model.py:147] round_filter input=80 output=144\n", | |
| "I0815 07:03:18.699128 140204072736640 efficientnet_model.py:147] round_filter input=112 output=200\n", | |
| "I0815 07:03:19.759788 140204072736640 efficientnet_model.py:147] round_filter input=112 output=200\n", | |
| "I0815 07:03:19.759960 140204072736640 efficientnet_model.py:147] round_filter input=192 output=344\n", | |
| "I0815 07:03:20.838617 140204072736640 efficientnet_model.py:147] round_filter input=192 output=344\n", | |
| "I0815 07:03:20.838799 140204072736640 efficientnet_model.py:147] round_filter input=320 output=576\n", | |
| "I0815 07:03:21.131700 140204072736640 efficientnet_model.py:147] round_filter input=1280 output=2304\n", | |
| "I0815 07:03:21.167100 140204072736640 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.8, depth_coefficient=2.6, resolution=528, dropout_rate=0.5, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n", | |
| "I0815 07:03:21.314660 140204072736640 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b7\n", | |
| "I0815 07:03:21.314818 140204072736640 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 384\n", | |
| "I0815 07:03:21.314892 140204072736640 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 8\n", | |
| "I0815 07:03:21.319524 140204072736640 efficientnet_model.py:147] round_filter input=32 output=64\n", | |
| "I0815 07:03:21.344161 140204072736640 efficientnet_model.py:147] round_filter input=32 output=64\n", | |
| "I0815 07:03:21.344283 140204072736640 efficientnet_model.py:147] round_filter input=16 output=32\n", | |
| "I0815 07:03:21.646214 140204072736640 efficientnet_model.py:147] round_filter input=16 output=32\n", | |
| "I0815 07:03:21.646394 140204072736640 efficientnet_model.py:147] round_filter input=24 output=48\n", | |
| "I0815 07:03:22.327858 140204072736640 efficientnet_model.py:147] round_filter input=24 output=48\n", | |
| "I0815 07:03:22.328031 140204072736640 efficientnet_model.py:147] round_filter input=40 output=80\n", | |
| "I0815 07:03:22.998819 140204072736640 efficientnet_model.py:147] round_filter input=40 output=80\n", | |
| "I0815 07:03:22.999004 140204072736640 efficientnet_model.py:147] round_filter input=80 output=160\n", | |
| "I0815 07:03:23.968167 140204072736640 efficientnet_model.py:147] round_filter input=80 output=160\n", | |
| "I0815 07:03:23.968369 140204072736640 efficientnet_model.py:147] round_filter input=112 output=224\n", | |
| "I0815 07:03:24.926559 140204072736640 efficientnet_model.py:147] round_filter input=112 output=224\n", | |
| "I0815 07:03:24.926767 140204072736640 efficientnet_model.py:147] round_filter input=192 output=384\n", | |
| "I0815 07:03:26.601955 140204072736640 efficientnet_model.py:147] round_filter input=192 output=384\n", | |
| "I0815 07:03:26.602134 140204072736640 efficientnet_model.py:147] round_filter input=320 output=640\n", | |
| "I0815 07:03:26.999985 140204072736640 efficientnet_model.py:147] round_filter input=1280 output=2560\n", | |
| "I0815 07:03:27.035832 140204072736640 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=2.0, depth_coefficient=3.1, resolution=600, dropout_rate=0.5, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n", | |
| "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_ssd_models_from_config): 27.95s\n", | |
| "I0815 07:03:27.200632 140204072736640 test_util.py:1973] time(__main__.ModelBuilderTF2Test.test_create_ssd_models_from_config): 27.95s\n", | |
| "[ OK ] ModelBuilderTF2Test.test_create_ssd_models_from_config\n", | |
| "[ RUN ] ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update\n", | |
| "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update): 0.0s\n", | |
| "I0815 07:03:27.208311 140204072736640 test_util.py:1973] time(__main__.ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update): 0.0s\n", | |
| "[ OK ] ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update\n", | |
| "[ RUN ] ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold\n", | |
| "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold): 0.0s\n", | |
| "I0815 07:03:27.210372 140204072736640 test_util.py:1973] time(__main__.ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold): 0.0s\n", | |
| "[ OK ] ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold\n", | |
| "[ RUN ] ModelBuilderTF2Test.test_invalid_model_config_proto\n", | |
| "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_model_config_proto): 0.0s\n", | |
| "I0815 07:03:27.210994 140204072736640 test_util.py:1973] time(__main__.ModelBuilderTF2Test.test_invalid_model_config_proto): 0.0s\n", | |
| "[ OK ] ModelBuilderTF2Test.test_invalid_model_config_proto\n", | |
| "[ RUN ] ModelBuilderTF2Test.test_invalid_second_stage_batch_size\n", | |
| "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_second_stage_batch_size): 0.0s\n", | |
| "I0815 07:03:27.212561 140204072736640 test_util.py:1973] time(__main__.ModelBuilderTF2Test.test_invalid_second_stage_batch_size): 0.0s\n", | |
| "[ OK ] ModelBuilderTF2Test.test_invalid_second_stage_batch_size\n", | |
| "[ RUN ] ModelBuilderTF2Test.test_session\n", | |
| "[ SKIPPED ] ModelBuilderTF2Test.test_session\n", | |
| "[ RUN ] ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor\n", | |
| "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor): 0.0s\n", | |
| "I0815 07:03:27.214142 140204072736640 test_util.py:1973] time(__main__.ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor): 0.0s\n", | |
| "[ OK ] ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor\n", | |
| "[ RUN ] ModelBuilderTF2Test.test_unknown_meta_architecture\n", | |
| "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_unknown_meta_architecture): 0.0s\n", | |
| "I0815 07:03:27.214718 140204072736640 test_util.py:1973] time(__main__.ModelBuilderTF2Test.test_unknown_meta_architecture): 0.0s\n", | |
| "[ OK ] ModelBuilderTF2Test.test_unknown_meta_architecture\n", | |
| "[ RUN ] ModelBuilderTF2Test.test_unknown_ssd_feature_extractor\n", | |
| "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_unknown_ssd_feature_extractor): 0.0s\n", | |
| "I0815 07:03:27.215811 140204072736640 test_util.py:1973] time(__main__.ModelBuilderTF2Test.test_unknown_ssd_feature_extractor): 0.0s\n", | |
| "[ OK ] ModelBuilderTF2Test.test_unknown_ssd_feature_extractor\n", | |
| "======================================================================\n", | |
| "ERROR: test_create_center_net_model_mobilenet (__main__.ModelBuilderTF2Test)\n", | |
| "ModelBuilderTF2Test.test_create_center_net_model_mobilenet\n", | |
| "Test building a CenterNet model using bilinear interpolation.\n", | |
| "----------------------------------------------------------------------\n", | |
| "Traceback (most recent call last):\n", | |
| " File \"object_detection/builders/model_builder_tf2_test.py\", line 497, in test_create_center_net_model_mobilenet\n", | |
| " model = model_builder.build(config, is_training=True)\n", | |
| " File \"/usr/local/lib/python3.7/dist-packages/object_detection/builders/model_builder.py\", line 1227, in build\n", | |
| " add_summaries)\n", | |
| " File \"/usr/local/lib/python3.7/dist-packages/object_detection/builders/model_builder.py\", line 1078, in _build_center_net_model\n", | |
| " center_net_config.feature_extractor, is_training)\n", | |
| " File \"/usr/local/lib/python3.7/dist-packages/object_detection/builders/model_builder.py\", line 1192, in _build_center_net_feature_extractor\n", | |
| " **kwargs)\n", | |
| " File \"/usr/local/lib/python3.7/dist-packages/object_detection/models/center_net_mobilenet_v2_fpn_feature_extractor.py\", line 156, in mobilenet_v2_fpn\n", | |
| " weights='imagenet' if depth_multiplier == 1.0 else None)\n", | |
| " File \"/usr/local/lib/python3.7/dist-packages/object_detection/models/keras_models/mobilenet_v2.py\", line 333, in mobilenet_v2\n", | |
| " **kwargs)\n", | |
| " File \"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/applications/mobilenet_v2.py\", line 410, in MobileNetV2\n", | |
| " model.load_weights(weights_path)\n", | |
| " File \"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py\", line 2211, in load_weights\n", | |
| " hdf5_format.load_weights_from_hdf5_group(f, self.layers)\n", | |
| " File \"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/saving/hdf5_format.py\", line 660, in load_weights_from_hdf5_group\n", | |
| " original_keras_version = f.attrs['keras_version'].decode('utf8')\n", | |
| "AttributeError: 'str' object has no attribute 'decode'\n", | |
| "\n", | |
| "----------------------------------------------------------------------\n", | |
| "Ran 24 tests in 34.328s\n", | |
| "\n", | |
| "FAILED (errors=1, skipped=1)\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "MOZvxdg2zr8s" | |
| }, | |
| "source": [ | |
| "## **Select and download a pretrained model from the tensorflow model zoo**\n", | |
| "[tensorflow model zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_detection_zoo.md)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "cP16Ht3d0GbC" | |
| }, | |
| "source": [ | |
| "Create a new folder per_trained_models inside CV1_workshop" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "gL4BBRoZuWr8", | |
| "outputId": "b0bb1d3c-8f15-40d2-eef6-8dce5fa4514c" | |
| }, | |
| "source": [ | |
| "cd /content/drive/My Drive/CV1_workshop/" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "/content/drive/My Drive/CV1_workshop\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "BljozMwT0rlN" | |
| }, | |
| "source": [ | |
| "mkdir per_trained_models" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "6W1MlMAR454r", | |
| "outputId": "6cb81713-e124-4ccc-aa33-36097e38b400" | |
| }, | |
| "source": [ | |
| "cd per_trained_models" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "/content/drive/My Drive/CV1_workshop/per_trained_models\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "oDX_3heq0u9L" | |
| }, | |
| "source": [ | |
| "download your pretrained model and extract the tar.gz file" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "MItGLVY3uWu8", | |
| "outputId": "8eeec3e2-a9f7-4fac-ae2a-0e2e42c66093" | |
| }, | |
| "source": [ | |
| "!wget http://download.tensorflow.org/models/object_detection/tf2/20200711/ssd_mobilenet_v1_fpn_640x640_coco17_tpu-8.tar.gz" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "--2021-08-15 07:04:39-- http://download.tensorflow.org/models/object_detection/tf2/20200711/ssd_mobilenet_v1_fpn_640x640_coco17_tpu-8.tar.gz\n", | |
| "Resolving download.tensorflow.org (download.tensorflow.org)... 74.125.142.128, 2607:f8b0:400e:c08::80\n", | |
| "Connecting to download.tensorflow.org (download.tensorflow.org)|74.125.142.128|:80... connected.\n", | |
| "HTTP request sent, awaiting response... 200 OK\n", | |
| "Length: 90453990 (86M) [application/x-tar]\n", | |
| "Saving to: ‘ssd_mobilenet_v1_fpn_640x640_coco17_tpu-8.tar.gz’\n", | |
| "\n", | |
| "ssd_mobilenet_v1_fp 100%[===================>] 86.26M 72.2MB/s in 1.2s \n", | |
| "\n", | |
| "2021-08-15 07:04:40 (72.2 MB/s) - ‘ssd_mobilenet_v1_fpn_640x640_coco17_tpu-8.tar.gz’ saved [90453990/90453990]\n", | |
| "\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "ZEJ9IYlrHtE0", | |
| "outputId": "01e69f76-f6a6-48da-a9ec-f01975e88846" | |
| }, | |
| "source": [ | |
| "ls" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "ssd_mobilenet_v1_fpn_640x640_coco17_tpu-8.tar.gz\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "tzlPcDPLuWye", | |
| "outputId": "df892fd2-0455-4f14-e89f-a4d35241fb01" | |
| }, | |
| "source": [ | |
| "!tar -xvf ssd_mobilenet_v1_fpn_640x640_coco17_tpu-8.tar.gz" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "ssd_mobilenet_v1_fpn_640x640_coco17_tpu-8/\n", | |
| "ssd_mobilenet_v1_fpn_640x640_coco17_tpu-8/checkpoint/\n", | |
| "ssd_mobilenet_v1_fpn_640x640_coco17_tpu-8/checkpoint/ckpt-0.data-00000-of-00001\n", | |
| "ssd_mobilenet_v1_fpn_640x640_coco17_tpu-8/checkpoint/checkpoint\n", | |
| "ssd_mobilenet_v1_fpn_640x640_coco17_tpu-8/checkpoint/ckpt-0.index\n", | |
| "ssd_mobilenet_v1_fpn_640x640_coco17_tpu-8/pipeline.config\n", | |
| "ssd_mobilenet_v1_fpn_640x640_coco17_tpu-8/saved_model/\n", | |
| "ssd_mobilenet_v1_fpn_640x640_coco17_tpu-8/saved_model/saved_model.pb\n", | |
| "ssd_mobilenet_v1_fpn_640x640_coco17_tpu-8/saved_model/variables/\n", | |
| "ssd_mobilenet_v1_fpn_640x640_coco17_tpu-8/saved_model/variables/variables.data-00000-of-00001\n", | |
| "ssd_mobilenet_v1_fpn_640x640_coco17_tpu-8/saved_model/variables/variables.index\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "heyqGCAd05kU" | |
| }, | |
| "source": [ | |
| "## **Data pre-processing** " | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "VRuysBN0H0NB", | |
| "outputId": "b8198180-a885-4953-8ab2-76291d45dc76" | |
| }, | |
| "source": [ | |
| "cd /content/drive/My Drive/CV1_workshop/" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "/content/drive/My Drive/CV1_workshop\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "MFA0L4rmyGae" | |
| }, | |
| "source": [ | |
| "mkdir tfrecords" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "hU2lVZfzyuar", | |
| "outputId": "dcc29624-78eb-4ba5-86c7-bba2aa87bade" | |
| }, | |
| "source": [ | |
| "# Create train data:\n", | |
| "!python xml_to_tfrecord.py\n", | |
| "\n", | |
| "# Create test data:\n", | |
| "#!python xml_to_tfrecord.py -x /content/trainingdemo/images/test -l /content/trainingdemo/annotations/label_map.pbtxt -o /content/trainingdemo/annotations/test.record" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "1\n", | |
| "2\n", | |
| "3\n", | |
| "4\n", | |
| "5\n", | |
| "6\n", | |
| "7\n", | |
| "8\n", | |
| "9\n", | |
| "10\n", | |
| "11\n", | |
| "12\n", | |
| "13\n", | |
| "14\n", | |
| "15\n", | |
| "16\n", | |
| "17\n", | |
| "18\n", | |
| "19\n", | |
| "20\n", | |
| "21\n", | |
| "22\n", | |
| "23\n", | |
| "24\n", | |
| "25\n", | |
| "26\n", | |
| "27\n", | |
| "28\n", | |
| "29\n", | |
| "30\n", | |
| "31\n", | |
| "32\n", | |
| "33\n", | |
| "34\n", | |
| "35\n", | |
| "36\n", | |
| "37\n", | |
| "38\n", | |
| "39\n", | |
| "40\n", | |
| "41\n", | |
| "42\n", | |
| "43\n", | |
| "44\n", | |
| "45\n", | |
| "46\n", | |
| "47\n", | |
| "48\n", | |
| "49\n", | |
| "50\n", | |
| "51\n", | |
| "Successfully created the TFRecord file: tfrecords/train.record\n", | |
| "Successfully created the CSV file: virtual_file.csv\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "Cf21qQ-l3_BI" | |
| }, | |
| "source": [ | |
| "## **Model training**" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "t4GcwT8x4U4L" | |
| }, | |
| "source": [ | |
| "pipeline_config_path : attach pipeline.config file\n", | |
| "\n", | |
| "num_train_steps: set training steps \n", | |
| "\n", | |
| "\n", | |
| "**EDIT pipline.config file**\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "JIQvAKy88F0H", | |
| "outputId": "a5631484-c357-40c8-db79-9ee426775deb" | |
| }, | |
| "source": [ | |
| "cd /content/drive/My Drive/CV1_workshop" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "/content/drive/My Drive/CV1_workshop\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "pjl-zU-Spdj5" | |
| }, | |
| "source": [ | |
| "mkdir training_checkpoints" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "bJICQfmX8z4a" | |
| }, | |
| "source": [ | |
| "### Enable tensorboard for monitoring loss and accuracy" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "_v3fsH-d9DgX", | |
| "outputId": "19fccd05-9cd8-4468-acfb-72bce03687ab" | |
| }, | |
| "source": [ | |
| "!wget https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip\n", | |
| "!unzip -o ngrok-stable-linux-amd64.zip" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "--2021-08-15 08:47:11-- https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip\n", | |
| "Resolving bin.equinox.io (bin.equinox.io)... 34.226.109.249, 3.228.156.171, 35.174.87.164, ...\n", | |
| "Connecting to bin.equinox.io (bin.equinox.io)|34.226.109.249|:443... connected.\n", | |
| "HTTP request sent, awaiting response... 200 OK\n", | |
| "Length: 13832437 (13M) [application/octet-stream]\n", | |
| "Saving to: ‘ngrok-stable-linux-amd64.zip.2’\n", | |
| "\n", | |
| "ngrok-stable-linux- 100%[===================>] 13.19M 15.3MB/s in 0.9s \n", | |
| "\n", | |
| "2021-08-15 08:47:12 (15.3 MB/s) - ‘ngrok-stable-linux-amd64.zip.2’ saved [13832437/13832437]\n", | |
| "\n", | |
| "Archive: ngrok-stable-linux-amd64.zip\n", | |
| " inflating: ngrok \n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "59vGuEtc9JM4" | |
| }, | |
| "source": [ | |
| "LOG_DIR = \"/content/drive/MyDrive/CV1_workshop/training_checkpoints\"\n", | |
| "get_ipython().system_raw(\n", | |
| " 'tensorboard --logdir {} --host 0.0.0.0 --port 6006 &'\n", | |
| " .format(LOG_DIR)\n", | |
| ")" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "va0rbDzv9LoK" | |
| }, | |
| "source": [ | |
| "get_ipython().system_raw('./ngrok http 6006 &')" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "T9FzqvEX9N4Y", | |
| "outputId": "fb55be45-b62f-4e17-a43d-22522186adf5" | |
| }, | |
| "source": [ | |
| "! curl -s http://localhost:4040/api/tunnels | python3 -c \\\n", | |
| " \"import sys, json; print(json.load(sys.stdin)['tunnels'][0]['public_url'])\"" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "https://0bca9104dd94.ngrok.io\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "resources": { | |
| "https://localhost:6007/?tensorboardColab=true": { | |
| "data": "<!doctype html><meta name="tb-relative-root" content="./"><!doctype html><!--
@license
Copyright 2019 The TensorFlow Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
--><html><head><meta charset="utf-8">
<title>TensorBoard</title>
<link rel="shortcut icon" href="data:image/png;base64,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">
<link rel="apple-touch-icon" href="data:image/png;base64,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">

<style>
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/uYECMKoHcO9x1wdmbyHIm3-_kf6ByYO6CLYdB4HQE-Y.woff2) format('woff2');
  unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/sTdaA6j0Psb920Vjv-mrzH-_kf6ByYO6CLYdB4HQE-Y.woff2) format('woff2');
  unicode-range: U+0460-052F, U+20B4, U+2DE0-2DFF, U+A640-A69F;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/_VYFx-s824kXq_Ul2BHqYH-_kf6ByYO6CLYdB4HQE-Y.woff2) format('woff2');
  unicode-range: U+0370-03FF;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/tnj4SB6DNbdaQnsM8CFqBX-_kf6ByYO6CLYdB4HQE-Y.woff2) format('woff2');
  unicode-range: U+1F00-1FFF;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/oMMgfZMQthOryQo9n22dcuvvDin1pK8aKteLpeZ5c0A.woff2) format('woff2');
  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2212, U+2215;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/Ks_cVxiCiwUWVsFWFA3Bjn-_kf6ByYO6CLYdB4HQE-Y.woff2) format('woff2');
  unicode-range: U+0100-024F, U+1E00-1EFF, U+20A0-20AB, U+20AD-20CF, U+2C60-2C7F, U+A720-A7FF;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/NJ4vxlgWwWbEsv18dAhqnn-_kf6ByYO6CLYdB4HQE-Y.woff2) format('woff2');
  unicode-range: U+0102-0103, U+1EA0-1EF9, U+20AB;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 700;
  src: local('Roboto Bold'), local('Roboto-Bold'), url(/font-roboto/isZ-wbCXNKAbnjo6_TwHToX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 700;
  src: local('Roboto Bold'), local('Roboto-Bold'), url(/font-roboto/77FXFjRbGzN4aCrSFhlh3oX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+0460-052F, U+20B4, U+2DE0-2DFF, U+A640-A69F;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 700;
  src: local('Roboto Bold'), local('Roboto-Bold'), url(/font-roboto/jSN2CGVDbcVyCnfJfjSdfIX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+0370-03FF;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 700;
  src: local('Roboto Bold'), local('Roboto-Bold'), url(/font-roboto/UX6i4JxQDm3fVTc1CPuwqoX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+1F00-1FFF;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 700;
  src: local('Roboto Bold'), local('Roboto-Bold'), url(/font-roboto/d-6IYplOFocCacKzxwXSOJBw1xU1rKptJj_0jans920.woff2) format('woff2');
  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2212, U+2215;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 700;
  src: local('Roboto Bold'), local('Roboto-Bold'), url(/font-roboto/97uahxiqZRoncBaCEI3aW4X0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+0100-024F, U+1E00-1EFF, U+20A0-20AB, U+20AD-20CF, U+2C60-2C7F, U+A720-A7FF;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 700;
  src: local('Roboto Bold'), local('Roboto-Bold'), url(/font-roboto/PwZc-YbIL414wB9rB1IAPYX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+0102-0103, U+1EA0-1EF9, U+20AB;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 700;
  src: local('Roboto Bold Italic'), local('Roboto-BoldItalic'), url(/font-roboto/t6Nd4cfPRhZP44Q5QAjcC14sYYdJg5dU2qzJEVSuta0.woff2) format('woff2');
  unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 700;
  src: local('Roboto Bold Italic'), local('Roboto-BoldItalic'), url(/font-roboto/t6Nd4cfPRhZP44Q5QAjcC_ZraR2Tg8w2lzm7kLNL0-w.woff2) format('woff2');
  unicode-range: U+0460-052F, U+20B4, U+2DE0-2DFF, U+A640-A69F;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 700;
  src: local('Roboto Bold Italic'), local('Roboto-BoldItalic'), url(/font-roboto/t6Nd4cfPRhZP44Q5QAjcCwt_Rm691LTebKfY2ZkKSmI.woff2) format('woff2');
  unicode-range: U+0370-03FF;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 700;
  src: local('Roboto Bold Italic'), local('Roboto-BoldItalic'), url(/font-roboto/t6Nd4cfPRhZP44Q5QAjcC1BW26QxpSj-_ZKm_xT4hWw.woff2) format('woff2');
  unicode-range: U+1F00-1FFF;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 700;
  src: local('Roboto Bold Italic'), local('Roboto-BoldItalic'), url(/font-roboto/t6Nd4cfPRhZP44Q5QAjcC4gp9Q8gbYrhqGlRav_IXfk.woff2) format('woff2');
  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2212, U+2215;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 700;
  src: local('Roboto Bold Italic'), local('Roboto-BoldItalic'), url(/font-roboto/t6Nd4cfPRhZP44Q5QAjcC6E8kM4xWR1_1bYURRojRGc.woff2) format('woff2');
  unicode-range: U+0100-024F, U+1E00-1EFF, U+20A0-20AB, U+20AD-20CF, U+2C60-2C7F, U+A720-A7FF;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 700;
  src: local('Roboto Bold Italic'), local('Roboto-BoldItalic'), url(/font-roboto/t6Nd4cfPRhZP44Q5QAjcC9DiNsR5a-9Oe_Ivpu8XWlY.woff2) format('woff2');
  unicode-range: U+0102-0103, U+1EA0-1EF9, U+20AB;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 400;
  src: local('Roboto Italic'), local('Roboto-Italic'), url(/font-roboto/OpXUqTo0UgQQhGj_SFdLWBkAz4rYn47Zy2rvigWQf6w.woff2) format('woff2');
  unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 400;
  src: local('Roboto Italic'), local('Roboto-Italic'), url(/font-roboto/WxrXJa0C3KdtC7lMafG4dRkAz4rYn47Zy2rvigWQf6w.woff2) format('woff2');
  unicode-range: U+0460-052F, U+20B4, U+2DE0-2DFF, U+A640-A69F;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 400;
  src: local('Roboto Italic'), local('Roboto-Italic'), url(/font-roboto/cDKhRaXnQTOVbaoxwdOr9xkAz4rYn47Zy2rvigWQf6w.woff2) format('woff2');
  unicode-range: U+0370-03FF;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 400;
  src: local('Roboto Italic'), local('Roboto-Italic'), url(/font-roboto/1hZf02POANh32k2VkgEoUBkAz4rYn47Zy2rvigWQf6w.woff2) format('woff2');
  unicode-range: U+1F00-1FFF;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 400;
  src: local('Roboto Italic'), local('Roboto-Italic'), url(/font-roboto/vPcynSL0qHq_6dX7lKVByXYhjbSpvc47ee6xR_80Hnw.woff2) format('woff2');
  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2212, U+2215;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 400;
  src: local('Roboto Italic'), local('Roboto-Italic'), url(/font-roboto/vSzulfKSK0LLjjfeaxcREhkAz4rYn47Zy2rvigWQf6w.woff2) format('woff2');
  unicode-range: U+0100-024F, U+1E00-1EFF, U+20A0-20AB, U+20AD-20CF, U+2C60-2C7F, U+A720-A7FF;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 400;
  src: local('Roboto Italic'), local('Roboto-Italic'), url(/font-roboto/K23cxWVTrIFD6DJsEVi07RkAz4rYn47Zy2rvigWQf6w.woff2) format('woff2');
  unicode-range: U+0102-0103, U+1EA0-1EF9, U+20AB;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 300;
  src: local('Roboto Light'), local('Roboto-Light'), url(/font-roboto/Fl4y0QdOxyyTHEGMXX8kcYX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 300;
  src: local('Roboto Light'), local('Roboto-Light'), url(/font-roboto/0eC6fl06luXEYWpBSJvXCIX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+0460-052F, U+20B4, U+2DE0-2DFF, U+A640-A69F;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 300;
  src: local('Roboto Light'), local('Roboto-Light'), url(/font-roboto/I3S1wsgSg9YCurV6PUkTOYX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+0370-03FF;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 300;
  src: local('Roboto Light'), local('Roboto-Light'), url(/font-roboto/-L14Jk06m6pUHB-5mXQQnYX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+1F00-1FFF;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 300;
  src: local('Roboto Light'), local('Roboto-Light'), url(/font-roboto/Hgo13k-tfSpn0qi1SFdUfZBw1xU1rKptJj_0jans920.woff2) format('woff2');
  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2212, U+2215;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 300;
  src: local('Roboto Light'), local('Roboto-Light'), url(/font-roboto/Pru33qjShpZSmG3z6VYwnYX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+0100-024F, U+1E00-1EFF, U+20A0-20AB, U+20AD-20CF, U+2C60-2C7F, U+A720-A7FF;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 300;
  src: local('Roboto Light'), local('Roboto-Light'), url(/font-roboto/NYDWBdD4gIq26G5XYbHsFIX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+0102-0103, U+1EA0-1EF9, U+20AB;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 300;
  src: local('Roboto Light Italic'), local('Roboto-LightItalic'), url(/font-roboto/7m8l7TlFO-S3VkhHuR0at14sYYdJg5dU2qzJEVSuta0.woff2) format('woff2');
  unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 300;
  src: local('Roboto Light Italic'), local('Roboto-LightItalic'), url(/font-roboto/7m8l7TlFO-S3VkhHuR0at_ZraR2Tg8w2lzm7kLNL0-w.woff2) format('woff2');
  unicode-range: U+0460-052F, U+20B4, U+2DE0-2DFF, U+A640-A69F;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 300;
  src: local('Roboto Light Italic'), local('Roboto-LightItalic'), url(/font-roboto/7m8l7TlFO-S3VkhHuR0atwt_Rm691LTebKfY2ZkKSmI.woff2) format('woff2');
  unicode-range: U+0370-03FF;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 300;
  src: local('Roboto Light Italic'), local('Roboto-LightItalic'), url(/font-roboto/7m8l7TlFO-S3VkhHuR0at1BW26QxpSj-_ZKm_xT4hWw.woff2) format('woff2');
  unicode-range: U+1F00-1FFF;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 300;
  src: local('Roboto Light Italic'), local('Roboto-LightItalic'), url(/font-roboto/7m8l7TlFO-S3VkhHuR0at4gp9Q8gbYrhqGlRav_IXfk.woff2) format('woff2');
  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2212, U+2215;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 300;
  src: local('Roboto Light Italic'), local('Roboto-LightItalic'), url(/font-roboto/7m8l7TlFO-S3VkhHuR0at6E8kM4xWR1_1bYURRojRGc.woff2) format('woff2');
  unicode-range: U+0100-024F, U+1E00-1EFF, U+20A0-20AB, U+20AD-20CF, U+2C60-2C7F, U+A720-A7FF;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 300;
  src: local('Roboto Light Italic'), local('Roboto-LightItalic'), url(/font-roboto/7m8l7TlFO-S3VkhHuR0at9DiNsR5a-9Oe_Ivpu8XWlY.woff2) format('woff2');
  unicode-range: U+0102-0103, U+1EA0-1EF9, U+20AB;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 500;
  src: local('Roboto Medium'), local('Roboto-Medium'), url(/font-roboto/oHi30kwQWvpCWqAhzHcCSIX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 500;
  src: local('Roboto Medium'), local('Roboto-Medium'), url(/font-roboto/ZLqKeelYbATG60EpZBSDy4X0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+0460-052F, U+20B4, U+2DE0-2DFF, U+A640-A69F;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 500;
  src: local('Roboto Medium'), local('Roboto-Medium'), url(/font-roboto/mx9Uck6uB63VIKFYnEMXrYX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+0370-03FF;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 500;
  src: local('Roboto Medium'), local('Roboto-Medium'), url(/font-roboto/rGvHdJnr2l75qb0YND9NyIX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+1F00-1FFF;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 500;
  src: local('Roboto Medium'), local('Roboto-Medium'), url(/font-roboto/RxZJdnzeo3R5zSexge8UUZBw1xU1rKptJj_0jans920.woff2) format('woff2');
  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2212, U+2215;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 500;
  src: local('Roboto Medium'), local('Roboto-Medium'), url(/font-roboto/oOeFwZNlrTefzLYmlVV1UIX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+0100-024F, U+1E00-1EFF, U+20A0-20AB, U+20AD-20CF, U+2C60-2C7F, U+A720-A7FF;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 500;
  src: local('Roboto Medium'), local('Roboto-Medium'), url(/font-roboto/mbmhprMH69Zi6eEPBYVFhYX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+0102-0103, U+1EA0-1EF9, U+20AB;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 500;
  src: local('Roboto Medium Italic'), local('Roboto-MediumItalic'), url(/font-roboto/OLffGBTaF0XFOW1gnuHF0V4sYYdJg5dU2qzJEVSuta0.woff2) format('woff2');
  unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 500;
  src: local('Roboto Medium Italic'), local('Roboto-MediumItalic'), url(/font-roboto/OLffGBTaF0XFOW1gnuHF0fZraR2Tg8w2lzm7kLNL0-w.woff2) format('woff2');
  unicode-range: U+0460-052F, U+20B4, U+2DE0-2DFF, U+A640-A69F;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 500;
  src: local('Roboto Medium Italic'), local('Roboto-MediumItalic'), url(/font-roboto/OLffGBTaF0XFOW1gnuHF0Qt_Rm691LTebKfY2ZkKSmI.woff2) format('woff2');
  unicode-range: U+0370-03FF;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 500;
  src: local('Roboto Medium Italic'), local('Roboto-MediumItalic'), url(/font-roboto/OLffGBTaF0XFOW1gnuHF0VBW26QxpSj-_ZKm_xT4hWw.woff2) format('woff2');
  unicode-range: U+1F00-1FFF;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 500;
  src: local('Roboto Medium Italic'), local('Roboto-MediumItalic'), url(/font-roboto/OLffGBTaF0XFOW1gnuHF0Ygp9Q8gbYrhqGlRav_IXfk.woff2) format('woff2');
  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2212, U+2215;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 500;
  src: local('Roboto Medium Italic'), local('Roboto-MediumItalic'), url(/font-roboto/OLffGBTaF0XFOW1gnuHF0aE8kM4xWR1_1bYURRojRGc.woff2) format('woff2');
  unicode-range: U+0100-024F, U+1E00-1EFF, U+20A0-20AB, U+20AD-20CF, U+2C60-2C7F, U+A720-A7FF;
}
@font-face {
  font-family: 'Roboto';
  font-style: italic;
  font-weight: 500;
  src: local('Roboto Medium Italic'), local('Roboto-MediumItalic'), url(/font-roboto/OLffGBTaF0XFOW1gnuHF0dDiNsR5a-9Oe_Ivpu8XWlY.woff2) format('woff2');
  unicode-range: U+0102-0103, U+1EA0-1EF9, U+20AB;
}
@font-face {
  font-family: 'Roboto Mono';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto Mono'), local('RobotoMono-Regular'), url(/font-roboto/hMqPNLsu_dywMa4C_DEpY14sYYdJg5dU2qzJEVSuta0.woff2) format('woff2');
  unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
}
@font-face {
  font-family: 'Roboto Mono';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto Mono'), local('RobotoMono-Regular'), url(/font-roboto/hMqPNLsu_dywMa4C_DEpY_ZraR2Tg8w2lzm7kLNL0-w.woff2) format('woff2');
  unicode-range: U+0460-052F, U+20B4, U+2DE0-2DFF, U+A640-A69F;
}
@font-face {
  font-family: 'Roboto Mono';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto Mono'), local('RobotoMono-Regular'), url(/font-roboto/hMqPNLsu_dywMa4C_DEpYwt_Rm691LTebKfY2ZkKSmI.woff2) format('woff2');
  unicode-range: U+0370-03FF;
}
@font-face {
  font-family: 'Roboto Mono';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto Mono'), local('RobotoMono-Regular'), url(/font-roboto/hMqPNLsu_dywMa4C_DEpY1BW26QxpSj-_ZKm_xT4hWw.woff2) format('woff2');
  unicode-range: U+1F00-1FFF;
}
@font-face {
  font-family: 'Roboto Mono';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto Mono'), local('RobotoMono-Regular'), url(/font-roboto/hMqPNLsu_dywMa4C_DEpY4gp9Q8gbYrhqGlRav_IXfk.woff2) format('woff2');
  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2212, U+2215;
}
@font-face {
  font-family: 'Roboto Mono';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto Mono'), local('RobotoMono-Regular'), url(/font-roboto/hMqPNLsu_dywMa4C_DEpY6E8kM4xWR1_1bYURRojRGc.woff2) format('woff2');
  unicode-range: U+0100-024F, U+1E00-1EFF, U+20A0-20AB, U+20AD-20CF, U+2C60-2C7F, U+A720-A7FF;
}
@font-face {
  font-family: 'Roboto Mono';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto Mono'), local('RobotoMono-Regular'), url(/font-roboto/hMqPNLsu_dywMa4C_DEpY9DiNsR5a-9Oe_Ivpu8XWlY.woff2) format('woff2');
  unicode-range: U+0102-0103, U+1EA0-1EF9, U+20AB;
}
@font-face {
  font-family: 'Roboto Mono';
  font-style: normal;
  font-weight: 700;
  src: local('Roboto Mono Bold'), local('RobotoMono-Bold'), url(/font-roboto/N4duVc9C58uwPiY8_59Fz1x-M1I1w5OMiqnVF8xBLhU.woff2) format('woff2');
  unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
}
@font-face {
  font-family: 'Roboto Mono';
  font-style: normal;
  font-weight: 700;
  src: local('Roboto Mono Bold'), local('RobotoMono-Bold'), url(/font-roboto/N4duVc9C58uwPiY8_59FzwXaAXup5mZlfK6xRLrhsco.woff2) format('woff2');
  unicode-range: U+0460-052F, U+20B4, U+2DE0-2DFF, U+A640-A69F;
}
@font-face {
  font-family: 'Roboto Mono';
  font-style: normal;
  font-weight: 700;
  src: local('Roboto Mono Bold'), local('RobotoMono-Bold'), url(/font-roboto/N4duVc9C58uwPiY8_59Fzwn6Wqxo-xwxilDXPU8chVU.woff2) format('woff2');
  unicode-range: U+0370-03FF;
}
@font-face {
  font-family: 'Roboto Mono';
  font-style: normal;
  font-weight: 700;
  src: local('Roboto Mono Bold'), local('RobotoMono-Bold'), url(/font-roboto/N4duVc9C58uwPiY8_59Fz1T7aJLK6nKpn36IMwTcMMc.woff2) format('woff2');
  unicode-range: U+1F00-1FFF;
}
@font-face {
  font-family: 'Roboto Mono';
  font-style: normal;
  font-weight: 700;
  src: local('Roboto Mono Bold'), local('RobotoMono-Bold'), url(/font-roboto/N4duVc9C58uwPiY8_59Fz_79_ZuUxCigM2DespTnFaw.woff2) format('woff2');
  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2212, U+2215;
}
@font-face {
  font-family: 'Roboto Mono';
  font-style: normal;
  font-weight: 700;
  src: local('Roboto Mono Bold'), local('RobotoMono-Bold'), url(/font-roboto/N4duVc9C58uwPiY8_59Fz4gd9OEPUCN3AdYW0e8tat4.woff2) format('woff2');
  unicode-range: U+0100-024F, U+1E00-1EFF, U+20A0-20AB, U+20AD-20CF, U+2C60-2C7F, U+A720-A7FF;
}
@font-face {
  font-family: 'Roboto Mono';
  font-style: normal;
  font-weight: 700;
  src: local('Roboto Mono Bold'), local('RobotoMono-Bold'), url(/font-roboto/N4duVc9C58uwPiY8_59Fz8bIQSYZnWLaWC9QNCpTK_U.woff2) format('woff2');
  unicode-range: U+0102-0103, U+1EA0-1EF9, U+20AB;
}
</style>



<style>.mat-badge-content{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small .mat-badge-content{font-size:9px}.mat-badge-large .mat-badge-content{font-size:24px}.mat-h1,.mat-headline,.mat-typography h1{font:400 24px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h2,.mat-title,.mat-typography h2{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h3,.mat-subheading-2,.mat-typography h3{font:400 16px/28px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h4,.mat-subheading-1,.mat-typography h4{font:400 15px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h5,.mat-typography h5{font:400 calc(14px * 0.83)/20px Roboto, "Helvetica Neue", sans-serif;margin:0 0 12px}.mat-h6,.mat-typography h6{font:400 calc(14px * 0.67)/20px Roboto, "Helvetica Neue", sans-serif;margin:0 0 12px}.mat-body-strong,.mat-body-2{font:500 14px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-body,.mat-body-1,.mat-typography{font:400 14px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-body p,.mat-body-1 p,.mat-typography p{margin:0 0 12px}.mat-small,.mat-caption{font:400 12px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-display-4,.mat-typography .mat-display-4{font:300 112px/112px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.05em;margin:0 0 56px}.mat-display-3,.mat-typography .mat-display-3{font:400 56px/56px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.02em;margin:0 0 64px}.mat-display-2,.mat-typography .mat-display-2{font:400 45px/48px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.005em;margin:0 0 64px}.mat-display-1,.mat-typography .mat-display-1{font:400 34px/40px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 64px}.mat-bottom-sheet-container{font:400 14px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-button,.mat-raised-button,.mat-icon-button,.mat-stroked-button,.mat-flat-button,.mat-fab,.mat-mini-fab{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-button-toggle{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-card{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-card-title{font-size:24px;font-weight:500}.mat-card-header .mat-card-title{font-size:20px}.mat-card-subtitle,.mat-card-content{font-size:14px}.mat-checkbox{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-checkbox-layout .mat-checkbox-label{line-height:24px}.mat-chip{font-size:14px;font-weight:500}.mat-chip .mat-chip-trailing-icon.mat-icon,.mat-chip .mat-chip-remove.mat-icon{font-size:18px}.mat-table{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-header-cell{font-size:12px;font-weight:500}.mat-cell,.mat-footer-cell{font-size:14px}.mat-calendar{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-calendar-body{font-size:13px}.mat-calendar-body-label,.mat-calendar-period-button{font-size:14px;font-weight:500}.mat-calendar-table-header th{font-size:11px;font-weight:400}.mat-dialog-title{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-expansion-panel-header{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:15px;font-weight:400}.mat-expansion-panel-content{font:400 14px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-form-field{font-size:inherit;font-weight:400;line-height:1.125;font-family:Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-form-field-wrapper{padding-bottom:1.34375em}.mat-form-field-prefix .mat-icon,.mat-form-field-suffix .mat-icon{font-size:150%;line-height:1.125}.mat-form-field-prefix .mat-icon-button,.mat-form-field-suffix .mat-icon-button{height:1.5em;width:1.5em}.mat-form-field-prefix .mat-icon-button .mat-icon,.mat-form-field-suffix .mat-icon-button .mat-icon{height:1.125em;line-height:1.125}.mat-form-field-infix{padding:.5em 0;border-top:.84375em solid transparent}.mat-form-field-can-float.mat-form-field-should-float .mat-form-field-label,.mat-form-field-can-float .mat-input-server:focus+.mat-form-field-label-wrapper .mat-form-field-label{transform:translateY(-1.34375em) scale(0.75);width:133.3333333333%}.mat-form-field-can-float .mat-input-server[label]:not(:label-shown)+.mat-form-field-label-wrapper .mat-form-field-label{transform:translateY(-1.34374em) scale(0.75);width:133.3333433333%}.mat-form-field-label-wrapper{top:-0.84375em;padding-top:.84375em}.mat-form-field-label{top:1.34375em}.mat-form-field-underline{bottom:1.34375em}.mat-form-field-subscript-wrapper{font-size:75%;margin-top:.6666666667em;top:calc(100% - 1.7916666667em)}.mat-form-field-appearance-legacy .mat-form-field-wrapper{padding-bottom:1.25em}.mat-form-field-appearance-legacy .mat-form-field-infix{padding:.4375em 0}.mat-form-field-appearance-legacy.mat-form-field-can-float.mat-form-field-should-float .mat-form-field-label,.mat-form-field-appearance-legacy.mat-form-field-can-float .mat-input-server:focus+.mat-form-field-label-wrapper .mat-form-field-label{transform:translateY(-1.28125em) scale(0.75) perspective(100px) translateZ(0.001px);-ms-transform:translateY(-1.28125em) scale(0.75);width:133.3333333333%}.mat-form-field-appearance-legacy.mat-form-field-can-float .mat-form-field-autofill-control:-webkit-autofill+.mat-form-field-label-wrapper .mat-form-field-label{transform:translateY(-1.28125em) scale(0.75) perspective(100px) translateZ(0.00101px);-ms-transform:translateY(-1.28124em) scale(0.75);width:133.3333433333%}.mat-form-field-appearance-legacy.mat-form-field-can-float .mat-input-server[label]:not(:label-shown)+.mat-form-field-label-wrapper .mat-form-field-label{transform:translateY(-1.28125em) scale(0.75) perspective(100px) translateZ(0.00102px);-ms-transform:translateY(-1.28123em) scale(0.75);width:133.3333533333%}.mat-form-field-appearance-legacy .mat-form-field-label{top:1.28125em}.mat-form-field-appearance-legacy .mat-form-field-underline{bottom:1.25em}.mat-form-field-appearance-legacy .mat-form-field-subscript-wrapper{margin-top:.5416666667em;top:calc(100% - 1.6666666667em)}@media print{.mat-form-field-appearance-legacy.mat-form-field-can-float.mat-form-field-should-float .mat-form-field-label,.mat-form-field-appearance-legacy.mat-form-field-can-float .mat-input-server:focus+.mat-form-field-label-wrapper .mat-form-field-label{transform:translateY(-1.28122em) scale(0.75)}.mat-form-field-appearance-legacy.mat-form-field-can-float .mat-form-field-autofill-control:-webkit-autofill+.mat-form-field-label-wrapper .mat-form-field-label{transform:translateY(-1.28121em) scale(0.75)}.mat-form-field-appearance-legacy.mat-form-field-can-float .mat-input-server[label]:not(:label-shown)+.mat-form-field-label-wrapper .mat-form-field-label{transform:translateY(-1.2812em) scale(0.75)}}.mat-form-field-appearance-fill .mat-form-field-infix{padding:.25em 0 .75em 0}.mat-form-field-appearance-fill .mat-form-field-label{top:1.09375em;margin-top:-0.5em}.mat-form-field-appearance-fill.mat-form-field-can-float.mat-form-field-should-float .mat-form-field-label,.mat-form-field-appearance-fill.mat-form-field-can-float .mat-input-server:focus+.mat-form-field-label-wrapper .mat-form-field-label{transform:translateY(-0.59375em) scale(0.75);width:133.3333333333%}.mat-form-field-appearance-fill.mat-form-field-can-float .mat-input-server[label]:not(:label-shown)+.mat-form-field-label-wrapper .mat-form-field-label{transform:translateY(-0.59374em) scale(0.75);width:133.3333433333%}.mat-form-field-appearance-outline .mat-form-field-infix{padding:1em 0 1em 0}.mat-form-field-appearance-outline .mat-form-field-label{top:1.84375em;margin-top:-0.25em}.mat-form-field-appearance-outline.mat-form-field-can-float.mat-form-field-should-float .mat-form-field-label,.mat-form-field-appearance-outline.mat-form-field-can-float .mat-input-server:focus+.mat-form-field-label-wrapper .mat-form-field-label{transform:translateY(-1.59375em) scale(0.75);width:133.3333333333%}.mat-form-field-appearance-outline.mat-form-field-can-float .mat-input-server[label]:not(:label-shown)+.mat-form-field-label-wrapper .mat-form-field-label{transform:translateY(-1.59374em) scale(0.75);width:133.3333433333%}.mat-grid-tile-header,.mat-grid-tile-footer{font-size:14px}.mat-grid-tile-header .mat-line,.mat-grid-tile-footer .mat-line{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-grid-tile-header .mat-line:nth-child(n+2),.mat-grid-tile-footer .mat-line:nth-child(n+2){font-size:12px}input.mat-input-element{margin-top:-0.0625em}.mat-menu-item{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:400}.mat-paginator,.mat-paginator-page-size .mat-select-trigger{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:12px}.mat-radio-button{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-select{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-select-trigger{height:1.125em}.mat-slide-toggle-content{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-slider-thumb-label-text{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:12px;font-weight:500}.mat-stepper-vertical,.mat-stepper-horizontal{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-step-label{font-size:14px;font-weight:400}.mat-step-sub-label-error{font-weight:normal}.mat-step-label-error{font-size:14px}.mat-step-label-selected{font-size:14px;font-weight:500}.mat-tab-group{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-tab-label,.mat-tab-link{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-toolbar,.mat-toolbar h1,.mat-toolbar h2,.mat-toolbar h3,.mat-toolbar h4,.mat-toolbar h5,.mat-toolbar h6{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0}.mat-tooltip{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:10px;padding-top:6px;padding-bottom:6px}.mat-tooltip-handset{font-size:14px;padding-top:8px;padding-bottom:8px}.mat-list-item{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-list-option{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-list-base .mat-list-item{font-size:16px}.mat-list-base .mat-list-item .mat-line{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base .mat-list-item .mat-line:nth-child(n+2){font-size:14px}.mat-list-base .mat-list-option{font-size:16px}.mat-list-base .mat-list-option .mat-line{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base .mat-list-option .mat-line:nth-child(n+2){font-size:14px}.mat-list-base .mat-subheader{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-list-base[dense] .mat-list-item{font-size:12px}.mat-list-base[dense] .mat-list-item .mat-line{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[dense] .mat-list-item .mat-line:nth-child(n+2){font-size:12px}.mat-list-base[dense] .mat-list-option{font-size:12px}.mat-list-base[dense] .mat-list-option .mat-line{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[dense] .mat-list-option .mat-line:nth-child(n+2){font-size:12px}.mat-list-base[dense] .mat-subheader{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:12px;font-weight:500}.mat-option{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:16px}.mat-optgroup-label{font:500 14px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-simple-snackbar{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px}.mat-simple-snackbar-action{line-height:1;font-family:inherit;font-size:inherit;font-weight:500}.mat-tree{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-tree-node,.mat-nested-tree-node{font-weight:400;font-size:14px}.mat-ripple{overflow:hidden;position:relative}.mat-ripple:not(:empty){transform:translateZ(0)}.mat-ripple.mat-ripple-unbounded{overflow:visible}.mat-ripple-element{position:absolute;border-radius:50%;pointer-events:none;transition:opacity,transform 0ms cubic-bezier(0, 0, 0.2, 1);transform:scale(0)}.cdk-high-contrast-active .mat-ripple-element{display:none}.cdk-visually-hidden{border:0;clip:rect(0 0 0 0);height:1px;margin:-1px;overflow:hidden;padding:0;position:absolute;width:1px;outline:0;-webkit-appearance:none;-moz-appearance:none}.cdk-overlay-container,.cdk-global-overlay-wrapper{pointer-events:none;top:0;left:0;height:100%;width:100%}.cdk-overlay-container{position:fixed;z-index:1000}.cdk-overlay-container:empty{display:none}.cdk-global-overlay-wrapper{display:flex;position:absolute;z-index:1000}.cdk-overlay-pane{position:absolute;pointer-events:auto;box-sizing:border-box;z-index:1000;display:flex;max-width:100%;max-height:100%}.cdk-overlay-backdrop{position:absolute;top:0;bottom:0;left:0;right:0;z-index:1000;pointer-events:auto;-webkit-tap-highlight-color:transparent;transition:opacity 400ms cubic-bezier(0.25, 0.8, 0.25, 1);opacity:0}.cdk-overlay-backdrop.cdk-overlay-backdrop-showing{opacity:1}@media screen and (-ms-high-contrast: active){.cdk-overlay-backdrop.cdk-overlay-backdrop-showing{opacity:.6}}.cdk-overlay-dark-backdrop{background:rgba(0,0,0,.32)}.cdk-overlay-transparent-backdrop,.cdk-overlay-transparent-backdrop.cdk-overlay-backdrop-showing{opacity:0}.cdk-overlay-connected-position-bounding-box{position:absolute;z-index:1000;display:flex;flex-direction:column;min-width:1px;min-height:1px}.cdk-global-scrollblock{position:fixed;width:100%;overflow-y:scroll}@keyframes cdk-text-field-autofill-start{/*!*/}@keyframes cdk-text-field-autofill-end{/*!*/}.cdk-text-field-autofill-monitored:-webkit-autofill{animation:cdk-text-field-autofill-start 0s 1ms}.cdk-text-field-autofill-monitored:not(:-webkit-autofill){animation:cdk-text-field-autofill-end 0s 1ms}textarea.cdk-textarea-autosize{resize:none}textarea.cdk-textarea-autosize-measuring{padding:2px 0 !important;box-sizing:content-box !important;height:auto !important;overflow:hidden !important}textarea.cdk-textarea-autosize-measuring-firefox{padding:2px 0 !important;box-sizing:content-box !important;height:0 !important}.mat-focus-indicator{position:relative}.mat-mdc-focus-indicator{position:relative}.mat-ripple-element{background-color:rgba(0,0,0,.1)}.mat-option{color:#212121}.mat-option:hover:not(.mat-option-disabled),.mat-option:focus:not(.mat-option-disabled){background:rgba(0,0,0,.04)}.mat-option.mat-selected:not(.mat-option-multiple):not(.mat-option-disabled){background:rgba(0,0,0,.04)}.mat-option.mat-active{background:rgba(0,0,0,.04);color:#212121}.mat-option.mat-option-disabled{color:rgba(0,0,0,.38)}.mat-primary .mat-option.mat-selected:not(.mat-option-disabled){color:#f57c00}.mat-accent .mat-option.mat-selected:not(.mat-option-disabled){color:#ff9800}.mat-warn .mat-option.mat-selected:not(.mat-option-disabled){color:#f44336}.mat-optgroup-label{color:#616161}.mat-optgroup-disabled .mat-optgroup-label{color:rgba(0,0,0,.38)}.mat-pseudo-checkbox{color:#616161}.mat-pseudo-checkbox::after{color:#fafafa}.mat-pseudo-checkbox-disabled{color:#b0b0b0}.mat-primary .mat-pseudo-checkbox-checked,.mat-primary .mat-pseudo-checkbox-indeterminate{background:#f57c00}.mat-pseudo-checkbox-checked,.mat-pseudo-checkbox-indeterminate,.mat-accent .mat-pseudo-checkbox-checked,.mat-accent .mat-pseudo-checkbox-indeterminate{background:#ff9800}.mat-warn .mat-pseudo-checkbox-checked,.mat-warn .mat-pseudo-checkbox-indeterminate{background:#f44336}.mat-pseudo-checkbox-checked.mat-pseudo-checkbox-disabled,.mat-pseudo-checkbox-indeterminate.mat-pseudo-checkbox-disabled{background:#b0b0b0}.mat-app-background{background-color:#fafafa;color:#212121}.mat-elevation-z0{box-shadow:0px 0px 0px 0px rgba(0, 0, 0, 0.2),0px 0px 0px 0px rgba(0, 0, 0, 0.14),0px 0px 0px 0px rgba(0, 0, 0, 0.12)}.mat-elevation-z1{box-shadow:0px 2px 1px -1px rgba(0, 0, 0, 0.2),0px 1px 1px 0px rgba(0, 0, 0, 0.14),0px 1px 3px 0px rgba(0, 0, 0, 0.12)}.mat-elevation-z2{box-shadow:0px 3px 1px -2px rgba(0, 0, 0, 0.2),0px 2px 2px 0px rgba(0, 0, 0, 0.14),0px 1px 5px 0px rgba(0, 0, 0, 0.12)}.mat-elevation-z3{box-shadow:0px 3px 3px -2px rgba(0, 0, 0, 0.2),0px 3px 4px 0px rgba(0, 0, 0, 0.14),0px 1px 8px 0px rgba(0, 0, 0, 0.12)}.mat-elevation-z4{box-shadow:0px 2px 4px -1px rgba(0, 0, 0, 0.2),0px 4px 5px 0px rgba(0, 0, 0, 0.14),0px 1px 10px 0px rgba(0, 0, 0, 0.12)}.mat-elevation-z5{box-shadow:0px 3px 5px -1px rgba(0, 0, 0, 0.2),0px 5px 8px 0px rgba(0, 0, 0, 0.14),0px 1px 14px 0px rgba(0, 0, 0, 0.12)}.mat-elevation-z6{box-shadow:0px 3px 5px -1px rgba(0, 0, 0, 0.2),0px 6px 10px 0px rgba(0, 0, 0, 0.14),0px 1px 18px 0px rgba(0, 0, 0, 0.12)}.mat-elevation-z7{box-shadow:0px 4px 5px -2px rgba(0, 0, 0, 0.2),0px 7px 10px 1px rgba(0, 0, 0, 0.14),0px 2px 16px 1px rgba(0, 0, 0, 0.12)}.mat-elevation-z8{box-shadow:0px 5px 5px -3px rgba(0, 0, 0, 0.2),0px 8px 10px 1px rgba(0, 0, 0, 0.14),0px 3px 14px 2px rgba(0, 0, 0, 0.12)}.mat-elevation-z9{box-shadow:0px 5px 6px -3px rgba(0, 0, 0, 0.2),0px 9px 12px 1px rgba(0, 0, 0, 0.14),0px 3px 16px 2px rgba(0, 0, 0, 0.12)}.mat-elevation-z10{box-shadow:0px 6px 6px -3px rgba(0, 0, 0, 0.2),0px 10px 14px 1px rgba(0, 0, 0, 0.14),0px 4px 18px 3px rgba(0, 0, 0, 0.12)}.mat-elevation-z11{box-shadow:0px 6px 7px -4px rgba(0, 0, 0, 0.2),0px 11px 15px 1px rgba(0, 0, 0, 0.14),0px 4px 20px 3px rgba(0, 0, 0, 0.12)}.mat-elevation-z12{box-shadow:0px 7px 8px -4px rgba(0, 0, 0, 0.2),0px 12px 17px 2px rgba(0, 0, 0, 0.14),0px 5px 22px 4px rgba(0, 0, 0, 0.12)}.mat-elevation-z13{box-shadow:0px 7px 8px -4px rgba(0, 0, 0, 0.2),0px 13px 19px 2px rgba(0, 0, 0, 0.14),0px 5px 24px 4px rgba(0, 0, 0, 0.12)}.mat-elevation-z14{box-shadow:0px 7px 9px -4px rgba(0, 0, 0, 0.2),0px 14px 21px 2px rgba(0, 0, 0, 0.14),0px 5px 26px 4px rgba(0, 0, 0, 0.12)}.mat-elevation-z15{box-shadow:0px 8px 9px -5px rgba(0, 0, 0, 0.2),0px 15px 22px 2px rgba(0, 0, 0, 0.14),0px 6px 28px 5px rgba(0, 0, 0, 0.12)}.mat-elevation-z16{box-shadow:0px 8px 10px -5px rgba(0, 0, 0, 0.2),0px 16px 24px 2px rgba(0, 0, 0, 0.14),0px 6px 30px 5px rgba(0, 0, 0, 0.12)}.mat-elevation-z17{box-shadow:0px 8px 11px -5px rgba(0, 0, 0, 0.2),0px 17px 26px 2px rgba(0, 0, 0, 0.14),0px 6px 32px 5px rgba(0, 0, 0, 0.12)}.mat-elevation-z18{box-shadow:0px 9px 11px -5px rgba(0, 0, 0, 0.2),0px 18px 28px 2px rgba(0, 0, 0, 0.14),0px 7px 34px 6px rgba(0, 0, 0, 0.12)}.mat-elevation-z19{box-shadow:0px 9px 12px -6px rgba(0, 0, 0, 0.2),0px 19px 29px 2px rgba(0, 0, 0, 0.14),0px 7px 36px 6px rgba(0, 0, 0, 0.12)}.mat-elevation-z20{box-shadow:0px 10px 13px -6px rgba(0, 0, 0, 0.2),0px 20px 31px 3px rgba(0, 0, 0, 0.14),0px 8px 38px 7px rgba(0, 0, 0, 0.12)}.mat-elevation-z21{box-shadow:0px 10px 13px -6px rgba(0, 0, 0, 0.2),0px 21px 33px 3px rgba(0, 0, 0, 0.14),0px 8px 40px 7px rgba(0, 0, 0, 0.12)}.mat-elevation-z22{box-shadow:0px 10px 14px -6px rgba(0, 0, 0, 0.2),0px 22px 35px 3px rgba(0, 0, 0, 0.14),0px 8px 42px 7px rgba(0, 0, 0, 0.12)}.mat-elevation-z23{box-shadow:0px 11px 14px -7px rgba(0, 0, 0, 0.2),0px 23px 36px 3px rgba(0, 0, 0, 0.14),0px 9px 44px 8px rgba(0, 0, 0, 0.12)}.mat-elevation-z24{box-shadow:0px 11px 15px -7px rgba(0, 0, 0, 0.2),0px 24px 38px 3px rgba(0, 0, 0, 0.14),0px 9px 46px 8px rgba(0, 0, 0, 0.12)}.mat-theme-loaded-marker{display:none}.mat-autocomplete-panel{background:#fff;color:#212121}.mat-autocomplete-panel:not([class*=mat-elevation-z]){box-shadow:0px 2px 4px -1px rgba(0, 0, 0, 0.2),0px 4px 5px 0px rgba(0, 0, 0, 0.14),0px 1px 10px 0px rgba(0, 0, 0, 0.12)}.mat-autocomplete-panel .mat-option.mat-selected:not(.mat-active):not(:hover){background:#fff}.mat-autocomplete-panel .mat-option.mat-selected:not(.mat-active):not(:hover):not(.mat-option-disabled){color:#212121}.mat-badge-content{color:#fff;background:#f57c00}.cdk-high-contrast-active .mat-badge-content{outline:solid 1px;border-radius:0}.mat-badge-accent .mat-badge-content{background:#ff9800;color:#fff}.mat-badge-warn .mat-badge-content{color:#fff;background:#f44336}.mat-badge{position:relative}.mat-badge-hidden .mat-badge-content{display:none}.mat-badge-disabled .mat-badge-content{background:#b9b9b9;color:#757575}.mat-badge-content{position:absolute;text-align:center;display:inline-block;border-radius:50%;transition:transform 200ms ease-in-out;transform:scale(0.6);overflow:hidden;white-space:nowrap;text-overflow:ellipsis;pointer-events:none}.ng-animate-disabled .mat-badge-content,.mat-badge-content._mat-animation-noopable{transition:none}.mat-badge-content.mat-badge-active{transform:none}.mat-badge-small .mat-badge-content{width:16px;height:16px;line-height:16px}.mat-badge-small.mat-badge-above .mat-badge-content{top:-8px}.mat-badge-small.mat-badge-below .mat-badge-content{bottom:-8px}.mat-badge-small.mat-badge-before .mat-badge-content{left:-16px}[dir=rtl] .mat-badge-small.mat-badge-before .mat-badge-content{left:auto;right:-16px}.mat-badge-small.mat-badge-after .mat-badge-content{right:-16px}[dir=rtl] .mat-badge-small.mat-badge-after .mat-badge-content{right:auto;left:-16px}.mat-badge-small.mat-badge-overlap.mat-badge-before .mat-badge-content{left:-8px}[dir=rtl] .mat-badge-small.mat-badge-overlap.mat-badge-before .mat-badge-content{left:auto;right:-8px}.mat-badge-small.mat-badge-overlap.mat-badge-after .mat-badge-content{right:-8px}[dir=rtl] .mat-badge-small.mat-badge-overlap.mat-badge-after .mat-badge-content{right:auto;left:-8px}.mat-badge-medium .mat-badge-content{width:22px;height:22px;line-height:22px}.mat-badge-medium.mat-badge-above .mat-badge-content{top:-11px}.mat-badge-medium.mat-badge-below .mat-badge-content{bottom:-11px}.mat-badge-medium.mat-badge-before .mat-badge-content{left:-22px}[dir=rtl] .mat-badge-medium.mat-badge-before .mat-badge-content{left:auto;right:-22px}.mat-badge-medium.mat-badge-after .mat-badge-content{right:-22px}[dir=rtl] .mat-badge-medium.mat-badge-after .mat-badge-content{right:auto;left:-22px}.mat-badge-medium.mat-badge-overlap.mat-badge-before .mat-badge-content{left:-11px}[dir=rtl] .mat-badge-medium.mat-badge-overlap.mat-badge-before .mat-badge-content{left:auto;right:-11px}.mat-badge-medium.mat-badge-overlap.mat-badge-after .mat-badge-content{right:-11px}[dir=rtl] .mat-badge-medium.mat-badge-overlap.mat-badge-after .mat-badge-content{right:auto;left:-11px}.mat-badge-large .mat-badge-content{width:28px;height:28px;line-height:28px}.mat-badge-large.mat-badge-above .mat-badge-content{top:-14px}.mat-badge-large.mat-badge-below .mat-badge-content{bottom:-14px}.mat-badge-large.mat-badge-before .mat-badge-content{left:-28px}[dir=rtl] .mat-badge-large.mat-badge-before .mat-badge-content{left:auto;right:-28px}.mat-badge-large.mat-badge-after .mat-badge-content{right:-28px}[dir=rtl] .mat-badge-large.mat-badge-after .mat-badge-content{right:auto;left:-28px}.mat-badge-large.mat-badge-overlap.mat-badge-before .mat-badge-content{left:-14px}[dir=rtl] .mat-badge-large.mat-badge-overlap.mat-badge-before .mat-badge-content{left:auto;right:-14px}.mat-badge-large.mat-badge-overlap.mat-badge-after .mat-badge-content{right:-14px}[dir=rtl] .mat-badge-large.mat-badge-overlap.mat-badge-after .mat-badge-content{right:auto;left:-14px}.mat-bottom-sheet-container{box-shadow:0px 8px 10px -5px rgba(0, 0, 0, 0.2),0px 16px 24px 2px rgba(0, 0, 0, 0.14),0px 6px 30px 5px rgba(0, 0, 0, 0.12);background:#fff;color:#212121}.mat-button,.mat-icon-button,.mat-stroked-button{color:inherit;background:transparent}.mat-button.mat-primary,.mat-icon-button.mat-primary,.mat-stroked-button.mat-primary{color:#f57c00}.mat-button.mat-accent,.mat-icon-button.mat-accent,.mat-stroked-button.mat-accent{color:#ff9800}.mat-button.mat-warn,.mat-icon-button.mat-warn,.mat-stroked-button.mat-warn{color:#f44336}.mat-button.mat-primary.mat-button-disabled,.mat-button.mat-accent.mat-button-disabled,.mat-button.mat-warn.mat-button-disabled,.mat-button.mat-button-disabled.mat-button-disabled,.mat-icon-button.mat-primary.mat-button-disabled,.mat-icon-button.mat-accent.mat-button-disabled,.mat-icon-button.mat-warn.mat-button-disabled,.mat-icon-button.mat-button-disabled.mat-button-disabled,.mat-stroked-button.mat-primary.mat-button-disabled,.mat-stroked-button.mat-accent.mat-button-disabled,.mat-stroked-button.mat-warn.mat-button-disabled,.mat-stroked-button.mat-button-disabled.mat-button-disabled{color:rgba(0,0,0,.26)}.mat-button.mat-primary .mat-button-focus-overlay,.mat-icon-button.mat-primary .mat-button-focus-overlay,.mat-stroked-button.mat-primary .mat-button-focus-overlay{background-color:#f57c00}.mat-button.mat-accent .mat-button-focus-overlay,.mat-icon-button.mat-accent .mat-button-focus-overlay,.mat-stroked-button.mat-accent .mat-button-focus-overlay{background-color:#ff9800}.mat-button.mat-warn .mat-button-focus-overlay,.mat-icon-button.mat-warn .mat-button-focus-overlay,.mat-stroked-button.mat-warn .mat-button-focus-overlay{background-color:#f44336}.mat-button.mat-button-disabled .mat-button-focus-overlay,.mat-icon-button.mat-button-disabled .mat-button-focus-overlay,.mat-stroked-button.mat-button-disabled .mat-button-focus-overlay{background-color:transparent}.mat-button .mat-ripple-element,.mat-icon-button .mat-ripple-element,.mat-stroked-button .mat-ripple-element{opacity:.1;background-color:currentColor}.mat-button-focus-overlay{background:#000}.mat-stroked-button:not(.mat-button-disabled){border-color:rgba(0,0,0,.12)}.mat-flat-button,.mat-raised-button,.mat-fab,.mat-mini-fab{color:#212121;background-color:#fff}.mat-flat-button.mat-primary,.mat-raised-button.mat-primary,.mat-fab.mat-primary,.mat-mini-fab.mat-primary{color:#fff}.mat-flat-button.mat-accent,.mat-raised-button.mat-accent,.mat-fab.mat-accent,.mat-mini-fab.mat-accent{color:#fff}.mat-flat-button.mat-warn,.mat-raised-button.mat-warn,.mat-fab.mat-warn,.mat-mini-fab.mat-warn{color:#fff}.mat-flat-button.mat-primary.mat-button-disabled,.mat-flat-button.mat-accent.mat-button-disabled,.mat-flat-button.mat-warn.mat-button-disabled,.mat-flat-button.mat-button-disabled.mat-button-disabled,.mat-raised-button.mat-primary.mat-button-disabled,.mat-raised-button.mat-accent.mat-button-disabled,.mat-raised-button.mat-warn.mat-button-disabled,.mat-raised-button.mat-button-disabled.mat-button-disabled,.mat-fab.mat-primary.mat-button-disabled,.mat-fab.mat-accent.mat-button-disabled,.mat-fab.mat-warn.mat-button-disabled,.mat-fab.mat-button-disabled.mat-button-disabled,.mat-mini-fab.mat-primary.mat-button-disabled,.mat-mini-fab.mat-accent.mat-button-disabled,.mat-mini-fab.mat-warn.mat-button-disabled,.mat-mini-fab.mat-button-disabled.mat-button-disabled{color:rgba(0,0,0,.26)}.mat-flat-button.mat-primary,.mat-raised-button.mat-primary,.mat-fab.mat-primary,.mat-mini-fab.mat-primary{background-color:#f57c00}.mat-flat-button.mat-accent,.mat-raised-button.mat-accent,.mat-fab.mat-accent,.mat-mini-fab.mat-accent{background-color:#ff9800}.mat-flat-button.mat-warn,.mat-raised-button.mat-warn,.mat-fab.mat-warn,.mat-mini-fab.mat-warn{background-color:#f44336}.mat-flat-button.mat-primary.mat-button-disabled,.mat-flat-button.mat-accent.mat-button-disabled,.mat-flat-button.mat-warn.mat-button-disabled,.mat-flat-button.mat-button-disabled.mat-button-disabled,.mat-raised-button.mat-primary.mat-button-disabled,.mat-raised-button.mat-accent.mat-button-disabled,.mat-raised-button.mat-warn.mat-button-disabled,.mat-raised-button.mat-button-disabled.mat-button-disabled,.mat-fab.mat-primary.mat-button-disabled,.mat-fab.mat-accent.mat-button-disabled,.mat-fab.mat-warn.mat-button-disabled,.mat-fab.mat-button-disabled.mat-button-disabled,.mat-mini-fab.mat-primary.mat-button-disabled,.mat-mini-fab.mat-accent.mat-button-disabled,.mat-mini-fab.mat-warn.mat-button-disabled,.mat-mini-fab.mat-button-disabled.mat-button-disabled{background-color:rgba(0,0,0,.12)}.mat-flat-button.mat-primary .mat-ripple-element,.mat-raised-button.mat-primary .mat-ripple-element,.mat-fab.mat-primary .mat-ripple-element,.mat-mini-fab.mat-primary .mat-ripple-element{background-color:rgba(255,255,255,.1)}.mat-flat-button.mat-accent .mat-ripple-element,.mat-raised-button.mat-accent .mat-ripple-element,.mat-fab.mat-accent .mat-ripple-element,.mat-mini-fab.mat-accent .mat-ripple-element{background-color:rgba(255,255,255,.1)}.mat-flat-button.mat-warn .mat-ripple-element,.mat-raised-button.mat-warn .mat-ripple-element,.mat-fab.mat-warn .mat-ripple-element,.mat-mini-fab.mat-warn .mat-ripple-element{background-color:rgba(255,255,255,.1)}.mat-stroked-button:not([class*=mat-elevation-z]),.mat-flat-button:not([class*=mat-elevation-z]){box-shadow:0px 0px 0px 0px rgba(0, 0, 0, 0.2),0px 0px 0px 0px rgba(0, 0, 0, 0.14),0px 0px 0px 0px rgba(0, 0, 0, 0.12)}.mat-raised-button:not([class*=mat-elevation-z]){box-shadow:0px 3px 1px -2px rgba(0, 0, 0, 0.2),0px 2px 2px 0px rgba(0, 0, 0, 0.14),0px 1px 5px 0px rgba(0, 0, 0, 0.12)}.mat-raised-button:not(.mat-button-disabled):active:not([class*=mat-elevation-z]){box-shadow:0px 5px 5px -3px rgba(0, 0, 0, 0.2),0px 8px 10px 1px rgba(0, 0, 0, 0.14),0px 3px 14px 2px rgba(0, 0, 0, 0.12)}.mat-raised-button.mat-button-disabled:not([class*=mat-elevation-z]){box-shadow:0px 0px 0px 0px rgba(0, 0, 0, 0.2),0px 0px 0px 0px rgba(0, 0, 0, 0.14),0px 0px 0px 0px rgba(0, 0, 0, 0.12)}.mat-fab:not([class*=mat-elevation-z]),.mat-mini-fab:not([class*=mat-elevation-z]){box-shadow:0px 3px 5px -1px rgba(0, 0, 0, 0.2),0px 6px 10px 0px rgba(0, 0, 0, 0.14),0px 1px 18px 0px rgba(0, 0, 0, 0.12)}.mat-fab:not(.mat-button-disabled):active:not([class*=mat-elevation-z]),.mat-mini-fab:not(.mat-button-disabled):active:not([class*=mat-elevation-z]){box-shadow:0px 7px 8px -4px rgba(0, 0, 0, 0.2),0px 12px 17px 2px rgba(0, 0, 0, 0.14),0px 5px 22px 4px rgba(0, 0, 0, 0.12)}.mat-fab.mat-button-disabled:not([class*=mat-elevation-z]),.mat-mini-fab.mat-button-disabled:not([class*=mat-elevation-z]){box-shadow:0px 0px 0px 0px rgba(0, 0, 0, 0.2),0px 0px 0px 0px rgba(0, 0, 0, 0.14),0px 0px 0px 0px rgba(0, 0, 0, 0.12)}.mat-button-toggle-standalone,.mat-button-toggle-group{box-shadow:0px 3px 1px -2px rgba(0, 0, 0, 0.2),0px 2px 2px 0px rgba(0, 0, 0, 0.14),0px 1px 5px 0px rgba(0, 0, 0, 0.12)}.mat-button-toggle-standalone.mat-button-toggle-appearance-standard,.mat-button-toggle-group-appearance-standard{box-shadow:none}.mat-button-toggle{color:rgba(0,0,0,.38)}.mat-button-toggle .mat-button-toggle-focus-overlay{background-color:rgba(0,0,0,.12)}.mat-button-toggle-appearance-standard{color:#212121;background:#fff}.mat-button-toggle-appearance-standard .mat-button-toggle-focus-overlay{background-color:#000}.mat-button-toggle-group-appearance-standard .mat-button-toggle+.mat-button-toggle{border-left:solid 1px rgba(0,0,0,.12)}[dir=rtl] .mat-button-toggle-group-appearance-standard .mat-button-toggle+.mat-button-toggle{border-left:none;border-right:solid 1px rgba(0,0,0,.12)}.mat-button-toggle-group-appearance-standard.mat-button-toggle-vertical .mat-button-toggle+.mat-button-toggle{border-left:none;border-right:none;border-top:solid 1px rgba(0,0,0,.12)}.mat-button-toggle-checked{background-color:#e0e0e0;color:#616161}.mat-button-toggle-checked.mat-button-toggle-appearance-standard{color:#212121}.mat-button-toggle-disabled{color:rgba(0,0,0,.26);background-color:#eee}.mat-button-toggle-disabled.mat-button-toggle-appearance-standard{background:#fff}.mat-button-toggle-disabled.mat-button-toggle-checked{background-color:#bdbdbd}.mat-button-toggle-standalone.mat-button-toggle-appearance-standard,.mat-button-toggle-group-appearance-standard{border:solid 1px rgba(0,0,0,.12)}.mat-button-toggle-appearance-standard .mat-button-toggle-label-content{line-height:48px}.mat-card{background:#fff;color:#212121}.mat-card:not([class*=mat-elevation-z]){box-shadow:0px 2px 1px -1px rgba(0, 0, 0, 0.2),0px 1px 1px 0px rgba(0, 0, 0, 0.14),0px 1px 3px 0px rgba(0, 0, 0, 0.12)}.mat-card.mat-card-flat:not([class*=mat-elevation-z]){box-shadow:0px 0px 0px 0px rgba(0, 0, 0, 0.2),0px 0px 0px 0px rgba(0, 0, 0, 0.14),0px 0px 0px 0px rgba(0, 0, 0, 0.12)}.mat-card-subtitle{color:#616161}.mat-checkbox-frame{border-color:#616161}.mat-checkbox-checkmark{fill:#fafafa}.mat-checkbox-checkmark-path{stroke:#fafafa !important}.mat-checkbox-mixedmark{background-color:#fafafa}.mat-checkbox-indeterminate.mat-primary .mat-checkbox-background,.mat-checkbox-checked.mat-primary .mat-checkbox-background{background-color:#f57c00}.mat-checkbox-indeterminate.mat-accent .mat-checkbox-background,.mat-checkbox-checked.mat-accent .mat-checkbox-background{background-color:#ff9800}.mat-checkbox-indeterminate.mat-warn .mat-checkbox-background,.mat-checkbox-checked.mat-warn .mat-checkbox-background{background-color:#f44336}.mat-checkbox-disabled.mat-checkbox-checked .mat-checkbox-background,.mat-checkbox-disabled.mat-checkbox-indeterminate .mat-checkbox-background{background-color:#b0b0b0}.mat-checkbox-disabled:not(.mat-checkbox-checked) .mat-checkbox-frame{border-color:#b0b0b0}.mat-checkbox-disabled .mat-checkbox-label{color:#616161}.mat-checkbox .mat-ripple-element{background-color:#000}.mat-checkbox-checked:not(.mat-checkbox-disabled).mat-primary .mat-ripple-element,.mat-checkbox:active:not(.mat-checkbox-disabled).mat-primary .mat-ripple-element{background:#f57c00}.mat-checkbox-checked:not(.mat-checkbox-disabled).mat-accent .mat-ripple-element,.mat-checkbox:active:not(.mat-checkbox-disabled).mat-accent .mat-ripple-element{background:#ff9800}.mat-checkbox-checked:not(.mat-checkbox-disabled).mat-warn .mat-ripple-element,.mat-checkbox:active:not(.mat-checkbox-disabled).mat-warn .mat-ripple-element{background:#f44336}.mat-chip.mat-standard-chip{background-color:#e0e0e0;color:#212121}.mat-chip.mat-standard-chip .mat-chip-remove{color:#212121;opacity:.4}.mat-chip.mat-standard-chip:not(.mat-chip-disabled):active{box-shadow:0px 3px 3px -2px rgba(0, 0, 0, 0.2),0px 3px 4px 0px rgba(0, 0, 0, 0.14),0px 1px 8px 0px rgba(0, 0, 0, 0.12)}.mat-chip.mat-standard-chip:not(.mat-chip-disabled) .mat-chip-remove:hover{opacity:.54}.mat-chip.mat-standard-chip.mat-chip-disabled{opacity:.4}.mat-chip.mat-standard-chip::after{background:#000}.mat-chip.mat-standard-chip.mat-chip-selected.mat-primary{background-color:#f57c00;color:#fff}.mat-chip.mat-standard-chip.mat-chip-selected.mat-primary .mat-chip-remove{color:#fff;opacity:.4}.mat-chip.mat-standard-chip.mat-chip-selected.mat-primary .mat-ripple-element{background-color:rgba(255,255,255,.1)}.mat-chip.mat-standard-chip.mat-chip-selected.mat-warn{background-color:#f44336;color:#fff}.mat-chip.mat-standard-chip.mat-chip-selected.mat-warn .mat-chip-remove{color:#fff;opacity:.4}.mat-chip.mat-standard-chip.mat-chip-selected.mat-warn .mat-ripple-element{background-color:rgba(255,255,255,.1)}.mat-chip.mat-standard-chip.mat-chip-selected.mat-accent{background-color:#ff9800;color:#fff}.mat-chip.mat-standard-chip.mat-chip-selected.mat-accent .mat-chip-remove{color:#fff;opacity:.4}.mat-chip.mat-standard-chip.mat-chip-selected.mat-accent .mat-ripple-element{background-color:rgba(255,255,255,.1)}.mat-table{background:#fff}.mat-table thead,.mat-table tbody,.mat-table tfoot,mat-header-row,mat-row,mat-footer-row,[mat-header-row],[mat-row],[mat-footer-row],.mat-table-sticky{background:inherit}mat-row,mat-header-row,mat-footer-row,th.mat-header-cell,td.mat-cell,td.mat-footer-cell{border-bottom-color:rgba(0,0,0,.12)}.mat-header-cell{color:#616161}.mat-cell,.mat-footer-cell{color:#212121}.mat-calendar-arrow{border-top-color:rgba(0,0,0,.54)}.mat-datepicker-toggle,.mat-datepicker-content .mat-calendar-next-button,.mat-datepicker-content .mat-calendar-previous-button{color:rgba(0,0,0,.54)}.mat-calendar-table-header{color:rgba(0,0,0,.38)}.mat-calendar-table-header-divider::after{background:rgba(0,0,0,.12)}.mat-calendar-body-label{color:#616161}.mat-calendar-body-cell-content,.mat-date-range-input-separator{color:#212121;border-color:transparent}.mat-calendar-body-disabled>.mat-calendar-body-cell-content:not(.mat-calendar-body-selected):not(.mat-calendar-body-comparison-identical){color:#757575}.mat-form-field-disabled .mat-date-range-input-separator{color:#757575}.mat-calendar-body-cell:not(.mat-calendar-body-disabled):hover>.mat-calendar-body-cell-content:not(.mat-calendar-body-selected):not(.mat-calendar-body-comparison-identical),.cdk-keyboard-focused .mat-calendar-body-active>.mat-calendar-body-cell-content:not(.mat-calendar-body-selected):not(.mat-calendar-body-comparison-identical),.cdk-program-focused .mat-calendar-body-active>.mat-calendar-body-cell-content:not(.mat-calendar-body-selected):not(.mat-calendar-body-comparison-identical){background-color:rgba(0,0,0,.04)}.mat-calendar-body-in-preview{color:rgba(0,0,0,.24)}.mat-calendar-body-today:not(.mat-calendar-body-selected):not(.mat-calendar-body-comparison-identical){border-color:rgba(0,0,0,.38)}.mat-calendar-body-disabled>.mat-calendar-body-today:not(.mat-calendar-body-selected):not(.mat-calendar-body-comparison-identical){border-color:rgba(0,0,0,.18)}.mat-calendar-body-in-range::before{background:rgba(245,124,0,.2)}.mat-calendar-body-comparison-identical,.mat-calendar-body-in-comparison-range::before{background:rgba(249,171,0,.2)}.mat-calendar-body-comparison-bridge-start::before,[dir=rtl] .mat-calendar-body-comparison-bridge-end::before{background:linear-gradient(to right, rgba(245, 124, 0, 0.2) 50%, rgba(249, 171, 0, 0.2) 50%)}.mat-calendar-body-comparison-bridge-end::before,[dir=rtl] .mat-calendar-body-comparison-bridge-start::before{background:linear-gradient(to left, rgba(245, 124, 0, 0.2) 50%, rgba(249, 171, 0, 0.2) 50%)}.mat-calendar-body-in-range>.mat-calendar-body-comparison-identical,.mat-calendar-body-in-comparison-range.mat-calendar-body-in-range::after{background:#a8dab5}.mat-calendar-body-comparison-identical.mat-calendar-body-selected,.mat-calendar-body-in-comparison-range>.mat-calendar-body-selected{background:#46a35e}.mat-calendar-body-selected{background-color:#f57c00;color:#fff}.mat-calendar-body-disabled>.mat-calendar-body-selected{background-color:rgba(245,124,0,.4)}.mat-calendar-body-today.mat-calendar-body-selected{box-shadow:inset 0 0 0 1px #fff}.mat-datepicker-content{box-shadow:0px 2px 4px -1px rgba(0, 0, 0, 0.2),0px 4px 5px 0px rgba(0, 0, 0, 0.14),0px 1px 10px 0px rgba(0, 0, 0, 0.12);background-color:#fff;color:#212121}.mat-datepicker-content.mat-accent .mat-calendar-body-in-range::before{background:rgba(255,152,0,.2)}.mat-datepicker-content.mat-accent .mat-calendar-body-comparison-identical,.mat-datepicker-content.mat-accent .mat-calendar-body-in-comparison-range::before{background:rgba(249,171,0,.2)}.mat-datepicker-content.mat-accent .mat-calendar-body-comparison-bridge-start::before,.mat-datepicker-content.mat-accent [dir=rtl] .mat-calendar-body-comparison-bridge-end::before{background:linear-gradient(to right, rgba(255, 152, 0, 0.2) 50%, rgba(249, 171, 0, 0.2) 50%)}.mat-datepicker-content.mat-accent .mat-calendar-body-comparison-bridge-end::before,.mat-datepicker-content.mat-accent [dir=rtl] .mat-calendar-body-comparison-bridge-start::before{background:linear-gradient(to left, rgba(255, 152, 0, 0.2) 50%, rgba(249, 171, 0, 0.2) 50%)}.mat-datepicker-content.mat-accent .mat-calendar-body-in-range>.mat-calendar-body-comparison-identical,.mat-datepicker-content.mat-accent .mat-calendar-body-in-comparison-range.mat-calendar-body-in-range::after{background:#a8dab5}.mat-datepicker-content.mat-accent .mat-calendar-body-comparison-identical.mat-calendar-body-selected,.mat-datepicker-content.mat-accent .mat-calendar-body-in-comparison-range>.mat-calendar-body-selected{background:#46a35e}.mat-datepicker-content.mat-accent .mat-calendar-body-selected{background-color:#ff9800;color:#fff}.mat-datepicker-content.mat-accent .mat-calendar-body-disabled>.mat-calendar-body-selected{background-color:rgba(255,152,0,.4)}.mat-datepicker-content.mat-accent .mat-calendar-body-today.mat-calendar-body-selected{box-shadow:inset 0 0 0 1px #fff}.mat-datepicker-content.mat-warn .mat-calendar-body-in-range::before{background:rgba(244,67,54,.2)}.mat-datepicker-content.mat-warn .mat-calendar-body-comparison-identical,.mat-datepicker-content.mat-warn .mat-calendar-body-in-comparison-range::before{background:rgba(249,171,0,.2)}.mat-datepicker-content.mat-warn .mat-calendar-body-comparison-bridge-start::before,.mat-datepicker-content.mat-warn [dir=rtl] .mat-calendar-body-comparison-bridge-end::before{background:linear-gradient(to right, rgba(244, 67, 54, 0.2) 50%, rgba(249, 171, 0, 0.2) 50%)}.mat-datepicker-content.mat-warn .mat-calendar-body-comparison-bridge-end::before,.mat-datepicker-content.mat-warn [dir=rtl] .mat-calendar-body-comparison-bridge-start::before{background:linear-gradient(to left, rgba(244, 67, 54, 0.2) 50%, rgba(249, 171, 0, 0.2) 50%)}.mat-datepicker-content.mat-warn .mat-calendar-body-in-range>.mat-calendar-body-comparison-identical,.mat-datepicker-content.mat-warn .mat-calendar-body-in-comparison-range.mat-calendar-body-in-range::after{background:#a8dab5}.mat-datepicker-content.mat-warn .mat-calendar-body-comparison-identical.mat-calendar-body-selected,.mat-datepicker-content.mat-warn .mat-calendar-body-in-comparison-range>.mat-calendar-body-selected{background:#46a35e}.mat-datepicker-content.mat-warn .mat-calendar-body-selected{background-color:#f44336;color:#fff}.mat-datepicker-content.mat-warn .mat-calendar-body-disabled>.mat-calendar-body-selected{background-color:rgba(244,67,54,.4)}.mat-datepicker-content.mat-warn .mat-calendar-body-today.mat-calendar-body-selected{box-shadow:inset 0 0 0 1px #fff}.mat-datepicker-content-touch{box-shadow:0px 0px 0px 0px rgba(0, 0, 0, 0.2),0px 0px 0px 0px rgba(0, 0, 0, 0.14),0px 0px 0px 0px rgba(0, 0, 0, 0.12)}.mat-datepicker-toggle-active{color:#f57c00}.mat-datepicker-toggle-active.mat-accent{color:#ff9800}.mat-datepicker-toggle-active.mat-warn{color:#f44336}.mat-date-range-input-inner[disabled]{color:#757575}.mat-dialog-container{box-shadow:0px 11px 15px -7px rgba(0, 0, 0, 0.2),0px 24px 38px 3px rgba(0, 0, 0, 0.14),0px 9px 46px 8px rgba(0, 0, 0, 0.12);background:#fff;color:#212121}.mat-divider{border-top-color:rgba(0,0,0,.12)}.mat-divider-vertical{border-right-color:rgba(0,0,0,.12)}.mat-expansion-panel{background:#fff;color:#212121}.mat-expansion-panel:not([class*=mat-elevation-z]){box-shadow:0px 3px 1px -2px rgba(0, 0, 0, 0.2),0px 2px 2px 0px rgba(0, 0, 0, 0.14),0px 1px 5px 0px rgba(0, 0, 0, 0.12)}.mat-action-row{border-top-color:rgba(0,0,0,.12)}.mat-expansion-panel .mat-expansion-panel-header.cdk-keyboard-focused:not([aria-disabled=true]),.mat-expansion-panel .mat-expansion-panel-header.cdk-program-focused:not([aria-disabled=true]),.mat-expansion-panel:not(.mat-expanded) .mat-expansion-panel-header:hover:not([aria-disabled=true]){background:rgba(0,0,0,.04)}@media(hover: none){.mat-expansion-panel:not(.mat-expanded):not([aria-disabled=true]) .mat-expansion-panel-header:hover{background:#fff}}.mat-expansion-panel-header-title{color:#212121}.mat-expansion-panel-header-description,.mat-expansion-indicator::after{color:#616161}.mat-expansion-panel-header[aria-disabled=true]{color:rgba(0,0,0,.26)}.mat-expansion-panel-header[aria-disabled=true] .mat-expansion-panel-header-title,.mat-expansion-panel-header[aria-disabled=true] .mat-expansion-panel-header-description{color:inherit}.mat-expansion-panel-header{height:48px}.mat-expansion-panel-header.mat-expanded{height:64px}.mat-form-field-label{color:rgba(97,97,97,.6)}.mat-hint{color:rgba(97,97,97,.6)}.mat-form-field.mat-focused .mat-form-field-label{color:#f57c00}.mat-form-field.mat-focused .mat-form-field-label.mat-accent{color:#ff9800}.mat-form-field.mat-focused .mat-form-field-label.mat-warn{color:#f44336}.mat-focused .mat-form-field-required-marker{color:#ff9800}.mat-form-field-ripple{background-color:rgba(0,0,0,.87)}.mat-form-field.mat-focused .mat-form-field-ripple{background-color:#f57c00}.mat-form-field.mat-focused .mat-form-field-ripple.mat-accent{background-color:#ff9800}.mat-form-field.mat-focused .mat-form-field-ripple.mat-warn{background-color:#f44336}.mat-form-field-type-mat-native-select.mat-focused:not(.mat-form-field-invalid) .mat-form-field-infix::after{color:#f57c00}.mat-form-field-type-mat-native-select.mat-focused:not(.mat-form-field-invalid).mat-accent .mat-form-field-infix::after{color:#ff9800}.mat-form-field-type-mat-native-select.mat-focused:not(.mat-form-field-invalid).mat-warn .mat-form-field-infix::after{color:#f44336}.mat-form-field.mat-form-field-invalid .mat-form-field-label{color:#f44336}.mat-form-field.mat-form-field-invalid .mat-form-field-label.mat-accent,.mat-form-field.mat-form-field-invalid .mat-form-field-label .mat-form-field-required-marker{color:#f44336}.mat-form-field.mat-form-field-invalid .mat-form-field-ripple,.mat-form-field.mat-form-field-invalid .mat-form-field-ripple.mat-accent{background-color:#f44336}.mat-error{color:#f44336}.mat-form-field-appearance-legacy .mat-form-field-label{color:#616161}.mat-form-field-appearance-legacy .mat-hint{color:#616161}.mat-form-field-appearance-legacy .mat-form-field-underline{background-color:rgba(0,0,0,.42)}.mat-form-field-appearance-legacy.mat-form-field-disabled .mat-form-field-underline{background-image:linear-gradient(to right, rgba(0, 0, 0, 0.42) 0%, rgba(0, 0, 0, 0.42) 33%, transparent 0%);background-size:4px 100%;background-repeat:repeat-x}.mat-form-field-appearance-standard .mat-form-field-underline{background-color:rgba(0,0,0,.42)}.mat-form-field-appearance-standard.mat-form-field-disabled .mat-form-field-underline{background-image:linear-gradient(to right, rgba(0, 0, 0, 0.42) 0%, rgba(0, 0, 0, 0.42) 33%, transparent 0%);background-size:4px 100%;background-repeat:repeat-x}.mat-form-field-appearance-fill .mat-form-field-flex{background-color:rgba(0,0,0,.04)}.mat-form-field-appearance-fill.mat-form-field-disabled .mat-form-field-flex{background-color:rgba(0,0,0,.02)}.mat-form-field-appearance-fill .mat-form-field-underline::before{background-color:rgba(0,0,0,.42)}.mat-form-field-appearance-fill.mat-form-field-disabled .mat-form-field-label{color:#757575}.mat-form-field-appearance-fill.mat-form-field-disabled .mat-form-field-underline::before{background-color:transparent}.mat-form-field-appearance-outline .mat-form-field-outline{color:rgba(0,0,0,.12)}.mat-form-field-appearance-outline .mat-form-field-outline-thick{color:rgba(0,0,0,.87)}.mat-form-field-appearance-outline.mat-focused .mat-form-field-outline-thick{color:#f57c00}.mat-form-field-appearance-outline.mat-focused.mat-accent .mat-form-field-outline-thick{color:#ff9800}.mat-form-field-appearance-outline.mat-focused.mat-warn .mat-form-field-outline-thick{color:#f44336}.mat-form-field-appearance-outline.mat-form-field-invalid.mat-form-field-invalid .mat-form-field-outline-thick{color:#f44336}.mat-form-field-appearance-outline.mat-form-field-disabled .mat-form-field-label{color:#757575}.mat-form-field-appearance-outline.mat-form-field-disabled .mat-form-field-outline{color:rgba(0,0,0,.06)}.mat-icon.mat-primary{color:#f57c00}.mat-icon.mat-accent{color:#ff9800}.mat-icon.mat-warn{color:#f44336}.mat-form-field-type-mat-native-select .mat-form-field-infix::after{color:#616161}.mat-input-element:disabled,.mat-form-field-type-mat-native-select.mat-form-field-disabled .mat-form-field-infix::after{color:#757575}.mat-input-element{caret-color:#f57c00}.mat-input-element::placeholder{color:rgba(97,97,97,.42)}.mat-input-element::-moz-placeholder{color:rgba(97,97,97,.42)}.mat-input-element::-webkit-input-placeholder{color:rgba(97,97,97,.42)}.mat-input-element:-ms-input-placeholder{color:rgba(97,97,97,.42)}.mat-form-field.mat-accent .mat-input-element{caret-color:#ff9800}.mat-form-field.mat-warn .mat-input-element,.mat-form-field-invalid .mat-input-element{caret-color:#f44336}.mat-form-field-type-mat-native-select.mat-form-field-invalid .mat-form-field-infix::after{color:#f44336}.mat-list-base .mat-list-item{color:#212121}.mat-list-base .mat-list-option{color:#212121}.mat-list-base .mat-subheader{color:#616161}.mat-list-item-disabled{background-color:#eee}.mat-list-option:hover,.mat-list-option:focus,.mat-nav-list .mat-list-item:hover,.mat-nav-list .mat-list-item:focus,.mat-action-list .mat-list-item:hover,.mat-action-list .mat-list-item:focus{background:rgba(0,0,0,.04)}.mat-list-single-selected-option,.mat-list-single-selected-option:hover,.mat-list-single-selected-option:focus{background:rgba(0,0,0,.12)}.mat-menu-panel{background:#fff}.mat-menu-panel:not([class*=mat-elevation-z]){box-shadow:0px 2px 4px -1px rgba(0, 0, 0, 0.2),0px 4px 5px 0px rgba(0, 0, 0, 0.14),0px 1px 10px 0px rgba(0, 0, 0, 0.12)}.mat-menu-item{background:transparent;color:#212121}.mat-menu-item[disabled],.mat-menu-item[disabled]::after{color:rgba(0,0,0,.38)}.mat-menu-item .mat-icon-no-color,.mat-menu-item-submenu-trigger::after{color:rgba(0,0,0,.54)}.mat-menu-item:hover:not([disabled]),.mat-menu-item.cdk-program-focused:not([disabled]),.mat-menu-item.cdk-keyboard-focused:not([disabled]),.mat-menu-item-highlighted:not([disabled]){background:rgba(0,0,0,.04)}.mat-paginator{background:#fff}.mat-paginator,.mat-paginator-page-size .mat-select-trigger{color:#616161}.mat-paginator-decrement,.mat-paginator-increment{border-top:2px solid rgba(0,0,0,.54);border-right:2px solid rgba(0,0,0,.54)}.mat-paginator-first,.mat-paginator-last{border-top:2px solid rgba(0,0,0,.54)}.mat-icon-button[disabled] .mat-paginator-decrement,.mat-icon-button[disabled] .mat-paginator-increment,.mat-icon-button[disabled] .mat-paginator-first,.mat-icon-button[disabled] .mat-paginator-last{border-color:rgba(0,0,0,.38)}.mat-paginator-container{min-height:56px}.mat-progress-bar-background{fill:#ff9800}.mat-progress-bar-buffer{background-color:#ff9800}.mat-progress-bar-fill::after{background-color:#f57c00}.mat-progress-bar.mat-accent .mat-progress-bar-background{fill:#ff9800}.mat-progress-bar.mat-accent .mat-progress-bar-buffer{background-color:#ff9800}.mat-progress-bar.mat-accent .mat-progress-bar-fill::after{background-color:#ff9800}.mat-progress-bar.mat-warn .mat-progress-bar-background{fill:#ffcdd2}.mat-progress-bar.mat-warn .mat-progress-bar-buffer{background-color:#ffcdd2}.mat-progress-bar.mat-warn .mat-progress-bar-fill::after{background-color:#f44336}.mat-progress-spinner circle,.mat-spinner circle{stroke:#f57c00}.mat-progress-spinner.mat-accent circle,.mat-spinner.mat-accent circle{stroke:#ff9800}.mat-progress-spinner.mat-warn circle,.mat-spinner.mat-warn circle{stroke:#f44336}.mat-radio-outer-circle{border-color:#616161}.mat-radio-button.mat-primary.mat-radio-checked .mat-radio-outer-circle{border-color:#f57c00}.mat-radio-button.mat-primary .mat-radio-inner-circle,.mat-radio-button.mat-primary .mat-radio-ripple .mat-ripple-element:not(.mat-radio-persistent-ripple),.mat-radio-button.mat-primary.mat-radio-checked .mat-radio-persistent-ripple,.mat-radio-button.mat-primary:active .mat-radio-persistent-ripple{background-color:#f57c00}.mat-radio-button.mat-accent.mat-radio-checked .mat-radio-outer-circle{border-color:#ff9800}.mat-radio-button.mat-accent .mat-radio-inner-circle,.mat-radio-button.mat-accent .mat-radio-ripple .mat-ripple-element:not(.mat-radio-persistent-ripple),.mat-radio-button.mat-accent.mat-radio-checked .mat-radio-persistent-ripple,.mat-radio-button.mat-accent:active .mat-radio-persistent-ripple{background-color:#ff9800}.mat-radio-button.mat-warn.mat-radio-checked .mat-radio-outer-circle{border-color:#f44336}.mat-radio-button.mat-warn .mat-radio-inner-circle,.mat-radio-button.mat-warn .mat-radio-ripple .mat-ripple-element:not(.mat-radio-persistent-ripple),.mat-radio-button.mat-warn.mat-radio-checked .mat-radio-persistent-ripple,.mat-radio-button.mat-warn:active .mat-radio-persistent-ripple{background-color:#f44336}.mat-radio-button.mat-radio-disabled.mat-radio-checked .mat-radio-outer-circle,.mat-radio-button.mat-radio-disabled .mat-radio-outer-circle{border-color:rgba(0,0,0,.38)}.mat-radio-button.mat-radio-disabled .mat-radio-ripple .mat-ripple-element,.mat-radio-button.mat-radio-disabled .mat-radio-inner-circle{background-color:rgba(0,0,0,.38)}.mat-radio-button.mat-radio-disabled .mat-radio-label-content{color:rgba(0,0,0,.38)}.mat-radio-button .mat-ripple-element{background-color:#000}.mat-select-value{color:#212121}.mat-select-placeholder{color:rgba(97,97,97,.42)}.mat-select-disabled .mat-select-value{color:#757575}.mat-select-arrow{color:#616161}.mat-select-panel{background:#fff}.mat-select-panel:not([class*=mat-elevation-z]){box-shadow:0px 2px 4px -1px rgba(0, 0, 0, 0.2),0px 4px 5px 0px rgba(0, 0, 0, 0.14),0px 1px 10px 0px rgba(0, 0, 0, 0.12)}.mat-select-panel .mat-option.mat-selected:not(.mat-option-multiple){background:rgba(0,0,0,.12)}.mat-form-field.mat-focused.mat-primary .mat-select-arrow{color:#f57c00}.mat-form-field.mat-focused.mat-accent .mat-select-arrow{color:#ff9800}.mat-form-field.mat-focused.mat-warn .mat-select-arrow{color:#f44336}.mat-form-field .mat-select.mat-select-invalid .mat-select-arrow{color:#f44336}.mat-form-field .mat-select.mat-select-disabled .mat-select-arrow{color:#757575}.mat-drawer-container{background-color:#fafafa;color:#212121}.mat-drawer{background-color:#fff;color:#212121}.mat-drawer.mat-drawer-push{background-color:#fff}.mat-drawer:not(.mat-drawer-side){box-shadow:0px 8px 10px -5px rgba(0, 0, 0, 0.2),0px 16px 24px 2px rgba(0, 0, 0, 0.14),0px 6px 30px 5px rgba(0, 0, 0, 0.12)}.mat-drawer-side{border-right:solid 1px rgba(0,0,0,.12)}.mat-drawer-side.mat-drawer-end{border-left:solid 1px rgba(0,0,0,.12);border-right:none}[dir=rtl] .mat-drawer-side{border-left:solid 1px rgba(0,0,0,.12);border-right:none}[dir=rtl] .mat-drawer-side.mat-drawer-end{border-left:none;border-right:solid 1px rgba(0,0,0,.12)}.mat-drawer-backdrop.mat-drawer-shown{background-color:rgba(0,0,0,.6)}.mat-slide-toggle.mat-checked .mat-slide-toggle-thumb{background-color:#ff9800}.mat-slide-toggle.mat-checked .mat-slide-toggle-bar{background-color:rgba(255,152,0,.54)}.mat-slide-toggle.mat-checked .mat-ripple-element{background-color:#ff9800}.mat-slide-toggle.mat-primary.mat-checked .mat-slide-toggle-thumb{background-color:#f57c00}.mat-slide-toggle.mat-primary.mat-checked .mat-slide-toggle-bar{background-color:rgba(245,124,0,.54)}.mat-slide-toggle.mat-primary.mat-checked .mat-ripple-element{background-color:#f57c00}.mat-slide-toggle.mat-warn.mat-checked .mat-slide-toggle-thumb{background-color:#f44336}.mat-slide-toggle.mat-warn.mat-checked .mat-slide-toggle-bar{background-color:rgba(244,67,54,.54)}.mat-slide-toggle.mat-warn.mat-checked .mat-ripple-element{background-color:#f44336}.mat-slide-toggle:not(.mat-checked) .mat-ripple-element{background-color:#000}.mat-slide-toggle-thumb{box-shadow:0px 2px 1px -1px rgba(0, 0, 0, 0.2),0px 1px 1px 0px rgba(0, 0, 0, 0.14),0px 1px 3px 0px rgba(0, 0, 0, 0.12);background-color:#fafafa}.mat-slide-toggle-bar{background-color:rgba(0,0,0,.38)}.mat-slider-track-background{background-color:rgba(0,0,0,.26)}.mat-primary .mat-slider-track-fill,.mat-primary .mat-slider-thumb,.mat-primary .mat-slider-thumb-label{background-color:#f57c00}.mat-primary .mat-slider-thumb-label-text{color:#fff}.mat-primary .mat-slider-focus-ring{background-color:rgba(245,124,0,.2)}.mat-accent .mat-slider-track-fill,.mat-accent .mat-slider-thumb,.mat-accent .mat-slider-thumb-label{background-color:#ff9800}.mat-accent .mat-slider-thumb-label-text{color:#fff}.mat-accent .mat-slider-focus-ring{background-color:rgba(255,152,0,.2)}.mat-warn .mat-slider-track-fill,.mat-warn .mat-slider-thumb,.mat-warn .mat-slider-thumb-label{background-color:#f44336}.mat-warn .mat-slider-thumb-label-text{color:#fff}.mat-warn .mat-slider-focus-ring{background-color:rgba(244,67,54,.2)}.mat-slider:hover .mat-slider-track-background,.cdk-focused .mat-slider-track-background{background-color:rgba(0,0,0,.38)}.mat-slider-disabled .mat-slider-track-background,.mat-slider-disabled .mat-slider-track-fill,.mat-slider-disabled .mat-slider-thumb{background-color:rgba(0,0,0,.26)}.mat-slider-disabled:hover .mat-slider-track-background{background-color:rgba(0,0,0,.26)}.mat-slider-min-value .mat-slider-focus-ring{background-color:rgba(0,0,0,.12)}.mat-slider-min-value.mat-slider-thumb-label-showing .mat-slider-thumb,.mat-slider-min-value.mat-slider-thumb-label-showing .mat-slider-thumb-label{background-color:rgba(0,0,0,.87)}.mat-slider-min-value.mat-slider-thumb-label-showing.cdk-focused .mat-slider-thumb,.mat-slider-min-value.mat-slider-thumb-label-showing.cdk-focused .mat-slider-thumb-label{background-color:rgba(0,0,0,.26)}.mat-slider-min-value:not(.mat-slider-thumb-label-showing) .mat-slider-thumb{border-color:rgba(0,0,0,.26);background-color:transparent}.mat-slider-min-value:not(.mat-slider-thumb-label-showing):hover .mat-slider-thumb,.mat-slider-min-value:not(.mat-slider-thumb-label-showing).cdk-focused .mat-slider-thumb{border-color:rgba(0,0,0,.38)}.mat-slider-min-value:not(.mat-slider-thumb-label-showing):hover.mat-slider-disabled .mat-slider-thumb,.mat-slider-min-value:not(.mat-slider-thumb-label-showing).cdk-focused.mat-slider-disabled .mat-slider-thumb{border-color:rgba(0,0,0,.26)}.mat-slider-has-ticks .mat-slider-wrapper::after{border-color:rgba(0,0,0,.7)}.mat-slider-horizontal .mat-slider-ticks{background-image:repeating-linear-gradient(to right, rgba(0, 0, 0, 0.7), rgba(0, 0, 0, 0.7) 2px, transparent 0, transparent);background-image:-moz-repeating-linear-gradient(0.0001deg, rgba(0, 0, 0, 0.7), rgba(0, 0, 0, 0.7) 2px, transparent 0, transparent)}.mat-slider-vertical .mat-slider-ticks{background-image:repeating-linear-gradient(to bottom, rgba(0, 0, 0, 0.7), rgba(0, 0, 0, 0.7) 2px, transparent 0, transparent)}.mat-step-header.cdk-keyboard-focused,.mat-step-header.cdk-program-focused,.mat-step-header:hover{background-color:rgba(0,0,0,.04)}@media(hover: none){.mat-step-header:hover{background:none}}.mat-step-header .mat-step-label,.mat-step-header .mat-step-optional{color:#616161}.mat-step-header .mat-step-icon{background-color:#616161;color:#fff}.mat-step-header .mat-step-icon-selected,.mat-step-header .mat-step-icon-state-done,.mat-step-header .mat-step-icon-state-edit{background-color:#f57c00;color:#fff}.mat-step-header .mat-step-icon-state-error{background-color:transparent;color:#f44336}.mat-step-header .mat-step-label.mat-step-label-active{color:#212121}.mat-step-header .mat-step-label.mat-step-label-error{color:#f44336}.mat-stepper-horizontal,.mat-stepper-vertical{background-color:#fff}.mat-stepper-vertical-line::before{border-left-color:rgba(0,0,0,.12)}.mat-horizontal-stepper-header::before,.mat-horizontal-stepper-header::after,.mat-stepper-horizontal-line{border-top-color:rgba(0,0,0,.12)}.mat-horizontal-stepper-header{height:72px}.mat-stepper-label-position-bottom .mat-horizontal-stepper-header,.mat-vertical-stepper-header{padding:24px 24px}.mat-stepper-vertical-line::before{top:-16px;bottom:-16px}.mat-stepper-label-position-bottom .mat-horizontal-stepper-header::after,.mat-stepper-label-position-bottom .mat-horizontal-stepper-header::before{top:36px}.mat-stepper-label-position-bottom .mat-stepper-horizontal-line{top:36px}.mat-sort-header-arrow{color:#616161}.mat-tab-nav-bar,.mat-tab-header{border-bottom:1px solid rgba(0,0,0,.12)}.mat-tab-group-inverted-header .mat-tab-nav-bar,.mat-tab-group-inverted-header .mat-tab-header{border-top:1px solid rgba(0,0,0,.12);border-bottom:none}.mat-tab-label,.mat-tab-link{color:#212121}.mat-tab-label.mat-tab-disabled,.mat-tab-link.mat-tab-disabled{color:#757575}.mat-tab-header-pagination-chevron{border-color:#212121}.mat-tab-header-pagination-disabled .mat-tab-header-pagination-chevron{border-color:#757575}.mat-tab-group[class*=mat-background-] .mat-tab-header,.mat-tab-nav-bar[class*=mat-background-]{border-bottom:none;border-top:none}.mat-tab-group.mat-primary .mat-tab-label.cdk-keyboard-focused:not(.mat-tab-disabled),.mat-tab-group.mat-primary .mat-tab-label.cdk-program-focused:not(.mat-tab-disabled),.mat-tab-group.mat-primary .mat-tab-link.cdk-keyboard-focused:not(.mat-tab-disabled),.mat-tab-group.mat-primary .mat-tab-link.cdk-program-focused:not(.mat-tab-disabled),.mat-tab-nav-bar.mat-primary .mat-tab-label.cdk-keyboard-focused:not(.mat-tab-disabled),.mat-tab-nav-bar.mat-primary .mat-tab-label.cdk-program-focused:not(.mat-tab-disabled),.mat-tab-nav-bar.mat-primary .mat-tab-link.cdk-keyboard-focused:not(.mat-tab-disabled),.mat-tab-nav-bar.mat-primary .mat-tab-link.cdk-program-focused:not(.mat-tab-disabled){background-color:rgba(255,152,0,.3)}.mat-tab-group.mat-primary .mat-ink-bar,.mat-tab-nav-bar.mat-primary .mat-ink-bar{background-color:#f57c00}.mat-tab-group.mat-primary.mat-background-primary .mat-ink-bar,.mat-tab-nav-bar.mat-primary.mat-background-primary .mat-ink-bar{background-color:#fff}.mat-tab-group.mat-accent .mat-tab-label.cdk-keyboard-focused:not(.mat-tab-disabled),.mat-tab-group.mat-accent .mat-tab-label.cdk-program-focused:not(.mat-tab-disabled),.mat-tab-group.mat-accent .mat-tab-link.cdk-keyboard-focused:not(.mat-tab-disabled),.mat-tab-group.mat-accent .mat-tab-link.cdk-program-focused:not(.mat-tab-disabled),.mat-tab-nav-bar.mat-accent .mat-tab-label.cdk-keyboard-focused:not(.mat-tab-disabled),.mat-tab-nav-bar.mat-accent .mat-tab-label.cdk-program-focused:not(.mat-tab-disabled),.mat-tab-nav-bar.mat-accent .mat-tab-link.cdk-keyboard-focused:not(.mat-tab-disabled),.mat-tab-nav-bar.mat-accent .mat-tab-link.cdk-program-focused:not(.mat-tab-disabled){background-color:rgba(255,152,0,.3)}.mat-tab-group.mat-accent .mat-ink-bar,.mat-tab-nav-bar.mat-accent .mat-ink-bar{background-color:#ff9800}.mat-tab-group.mat-accent.mat-background-accent .mat-ink-bar,.mat-tab-nav-bar.mat-accent.mat-background-accent .mat-ink-bar{background-color:#fff}.mat-tab-group.mat-warn .mat-tab-label.cdk-keyboard-focused:not(.mat-tab-disabled),.mat-tab-group.mat-warn .mat-tab-label.cdk-program-focused:not(.mat-tab-disabled),.mat-tab-group.mat-warn .mat-tab-link.cdk-keyboard-focused:not(.mat-tab-disabled),.mat-tab-group.mat-warn .mat-tab-link.cdk-program-focused:not(.mat-tab-disabled),.mat-tab-nav-bar.mat-warn .mat-tab-label.cdk-keyboard-focused:not(.mat-tab-disabled),.mat-tab-nav-bar.mat-warn .mat-tab-label.cdk-program-focused:not(.mat-tab-disabled),.mat-tab-nav-bar.mat-warn .mat-tab-link.cdk-keyboard-focused:not(.mat-tab-disabled),.mat-tab-nav-bar.mat-warn .mat-tab-link.cdk-program-focused:not(.mat-tab-disabled){background-color:rgba(255,205,210,.3)}.mat-tab-group.mat-warn .mat-ink-bar,.mat-tab-nav-bar.mat-warn .mat-ink-bar{background-color:#f44336}.mat-tab-group.mat-warn.mat-background-warn .mat-ink-bar,.mat-tab-nav-bar.mat-warn.mat-background-warn .mat-ink-bar{background-color:#fff}.mat-tab-group.mat-background-primary .mat-tab-label.cdk-keyboard-focused:not(.mat-tab-disabled),.mat-tab-group.mat-background-primary .mat-tab-label.cdk-program-focused:not(.mat-tab-disabled),.mat-tab-group.mat-background-primary .mat-tab-link.cdk-keyboard-focused:not(.mat-tab-disabled),.mat-tab-group.mat-background-primary .mat-tab-link.cdk-program-focused:not(.mat-tab-disabled),.mat-tab-nav-bar.mat-background-primary .mat-tab-label.cdk-keyboard-focused:not(.mat-tab-disabled),.mat-tab-nav-bar.mat-background-primary .mat-tab-label.cdk-program-focused:not(.mat-tab-disabled),.mat-tab-nav-bar.mat-background-primary .mat-tab-link.cdk-keyboard-focused:not(.mat-tab-disabled),.mat-tab-nav-bar.mat-background-primary .mat-tab-link.cdk-program-focused:not(.mat-tab-disabled){background-color:rgba(255,152,0,.3)}.mat-tab-group.mat-background-primary .mat-tab-header,.mat-tab-group.mat-background-primary .mat-tab-links,.mat-tab-group.mat-background-primary .mat-tab-header-pagination,.mat-tab-nav-bar.mat-background-primary .mat-tab-header,.mat-tab-nav-bar.mat-background-primary .mat-tab-links,.mat-tab-nav-bar.mat-background-primary .mat-tab-header-pagination{background-color:#f57c00}.mat-tab-group.mat-background-primary .mat-tab-label,.mat-tab-group.mat-background-primary .mat-tab-link,.mat-tab-nav-bar.mat-background-primary .mat-tab-label,.mat-tab-nav-bar.mat-background-primary .mat-tab-link{color:#fff}.mat-tab-group.mat-background-primary .mat-tab-label.mat-tab-disabled,.mat-tab-group.mat-background-primary .mat-tab-link.mat-tab-disabled,.mat-tab-nav-bar.mat-background-primary .mat-tab-label.mat-tab-disabled,.mat-tab-nav-bar.mat-background-primary .mat-tab-link.mat-tab-disabled{color:rgba(255,255,255,.4)}.mat-tab-group.mat-background-primary .mat-tab-header-pagination-chevron,.mat-tab-nav-bar.mat-background-primary .mat-tab-header-pagination-chevron{border-color:#fff}.mat-tab-group.mat-background-primary .mat-tab-header-pagination-disabled .mat-tab-header-pagination-chevron,.mat-tab-nav-bar.mat-background-primary .mat-tab-header-pagination-disabled .mat-tab-header-pagination-chevron{border-color:rgba(255,255,255,.4)}.mat-tab-group.mat-background-primary .mat-ripple-element,.mat-tab-nav-bar.mat-background-primary .mat-ripple-element{background-color:rgba(255,255,255,.12)}.mat-tab-group.mat-background-accent .mat-tab-label.cdk-keyboard-focused:not(.mat-tab-disabled),.mat-tab-group.mat-background-accent .mat-tab-label.cdk-program-focused:not(.mat-tab-disabled),.mat-tab-group.mat-background-accent .mat-tab-link.cdk-keyboard-focused:not(.mat-tab-disabled),.mat-tab-group.mat-background-accent .mat-tab-link.cdk-program-focused:not(.mat-tab-disabled),.mat-tab-nav-bar.mat-background-accent .mat-tab-label.cdk-keyboard-focused:not(.mat-tab-disabled),.mat-tab-nav-bar.mat-background-accent .mat-tab-label.cdk-program-focused:not(.mat-tab-disabled),.mat-tab-nav-bar.mat-background-accent .mat-tab-link.cdk-keyboard-focused:not(.mat-tab-disabled),.mat-tab-nav-bar.mat-background-accent .mat-tab-link.cdk-program-focused:not(.mat-tab-disabled){background-color:rgba(255,152,0,.3)}.mat-tab-group.mat-background-accent .mat-tab-header,.mat-tab-group.mat-background-accent .mat-tab-links,.mat-tab-group.mat-background-accent .mat-tab-header-pagination,.mat-tab-nav-bar.mat-background-accent .mat-tab-header,.mat-tab-nav-bar.mat-background-accent .mat-tab-links,.mat-tab-nav-bar.mat-background-accent .mat-tab-header-pagination{background-color:#ff9800}.mat-tab-group.mat-background-accent .mat-tab-label,.mat-tab-group.mat-background-accent .mat-tab-link,.mat-tab-nav-bar.mat-background-accent .mat-tab-label,.mat-tab-nav-bar.mat-background-accent .mat-tab-link{color:#fff}.mat-tab-group.mat-background-accent .mat-tab-label.mat-tab-disabled,.mat-tab-group.mat-background-accent .mat-tab-link.mat-tab-disabled,.mat-tab-nav-bar.mat-background-accent .mat-tab-label.mat-tab-disabled,.mat-tab-nav-bar.mat-background-accent .mat-tab-link.mat-tab-disabled{color:rgba(255,255,255,.4)}.mat-tab-group.mat-background-accent .mat-tab-header-pagination-chevron,.mat-tab-nav-bar.mat-background-accent .mat-tab-header-pagination-chevron{border-color:#fff}.mat-tab-group.mat-background-accent .mat-tab-header-pagination-disabled .mat-tab-header-pagination-chevron,.mat-tab-nav-bar.mat-background-accent .mat-tab-header-pagination-disabled .mat-tab-header-pagination-chevron{border-color:rgba(255,255,255,.4)}.mat-tab-group.mat-background-accent .mat-ripple-element,.mat-tab-nav-bar.mat-background-accent .mat-ripple-element{background-color:rgba(255,255,255,.12)}.mat-tab-group.mat-background-warn .mat-tab-label.cdk-keyboard-focused:not(.mat-tab-disabled),.mat-tab-group.mat-background-warn .mat-tab-label.cdk-program-focused:not(.mat-tab-disabled),.mat-tab-group.mat-background-warn .mat-tab-link.cdk-keyboard-focused:not(.mat-tab-disabled),.mat-tab-group.mat-background-warn .mat-tab-link.cdk-program-focused:not(.mat-tab-disabled),.mat-tab-nav-bar.mat-background-warn .mat-tab-label.cdk-keyboard-focused:not(.mat-tab-disabled),.mat-tab-nav-bar.mat-background-warn .mat-tab-label.cdk-program-focused:not(.mat-tab-disabled),.mat-tab-nav-bar.mat-background-warn .mat-tab-link.cdk-keyboard-focused:not(.mat-tab-disabled),.mat-tab-nav-bar.mat-background-warn .mat-tab-link.cdk-program-focused:not(.mat-tab-disabled){background-color:rgba(255,205,210,.3)}.mat-tab-group.mat-background-warn .mat-tab-header,.mat-tab-group.mat-background-warn .mat-tab-links,.mat-tab-group.mat-background-warn .mat-tab-header-pagination,.mat-tab-nav-bar.mat-background-warn .mat-tab-header,.mat-tab-nav-bar.mat-background-warn .mat-tab-links,.mat-tab-nav-bar.mat-background-warn .mat-tab-header-pagination{background-color:#f44336}.mat-tab-group.mat-background-warn .mat-tab-label,.mat-tab-group.mat-background-warn .mat-tab-link,.mat-tab-nav-bar.mat-background-warn .mat-tab-label,.mat-tab-nav-bar.mat-background-warn .mat-tab-link{color:#fff}.mat-tab-group.mat-background-warn .mat-tab-label.mat-tab-disabled,.mat-tab-group.mat-background-warn .mat-tab-link.mat-tab-disabled,.mat-tab-nav-bar.mat-background-warn .mat-tab-label.mat-tab-disabled,.mat-tab-nav-bar.mat-background-warn .mat-tab-link.mat-tab-disabled{color:rgba(255,255,255,.4)}.mat-tab-group.mat-background-warn .mat-tab-header-pagination-chevron,.mat-tab-nav-bar.mat-background-warn .mat-tab-header-pagination-chevron{border-color:#fff}.mat-tab-group.mat-background-warn .mat-tab-header-pagination-disabled .mat-tab-header-pagination-chevron,.mat-tab-nav-bar.mat-background-warn .mat-tab-header-pagination-disabled .mat-tab-header-pagination-chevron{border-color:rgba(255,255,255,.4)}.mat-tab-group.mat-background-warn .mat-ripple-element,.mat-tab-nav-bar.mat-background-warn .mat-ripple-element{background-color:rgba(255,255,255,.12)}.mat-toolbar{background:#f5f5f5;color:#212121}.mat-toolbar.mat-primary{background:#f57c00;color:#fff}.mat-toolbar.mat-accent{background:#ff9800;color:#fff}.mat-toolbar.mat-warn{background:#f44336;color:#fff}.mat-toolbar .mat-form-field-underline,.mat-toolbar .mat-form-field-ripple,.mat-toolbar .mat-focused .mat-form-field-ripple{background-color:currentColor}.mat-toolbar .mat-form-field-label,.mat-toolbar .mat-focused .mat-form-field-label,.mat-toolbar .mat-select-value,.mat-toolbar .mat-select-arrow,.mat-toolbar .mat-form-field.mat-focused .mat-select-arrow{color:inherit}.mat-toolbar .mat-input-element{caret-color:currentColor}.mat-toolbar-multiple-rows{min-height:64px}.mat-toolbar-row,.mat-toolbar-single-row{height:64px}@media(max-width: 599px){.mat-toolbar-multiple-rows{min-height:56px}.mat-toolbar-row,.mat-toolbar-single-row{height:56px}}.mat-tooltip{background:rgba(97,97,97,.9)}.mat-tree{background:#fff}.mat-tree-node,.mat-nested-tree-node{color:#212121}.mat-tree-node{min-height:48px}.mat-snack-bar-container{color:rgba(255,255,255,.7);background:#323232;box-shadow:0px 3px 5px -1px rgba(0, 0, 0, 0.2),0px 6px 10px 0px rgba(0, 0, 0, 0.14),0px 1px 18px 0px rgba(0, 0, 0, 0.12)}.mat-simple-snackbar-action{color:#ff9800}
</style>

<style>
  html,
  body {
    margin: 0;
    padding: 0;
    height: 100%;
    font-family: Roboto, sans-serif;
    color: var(--primary-text-color);

    /* Legacy mechanism to avoid issues with subpixel anti-aliasing on macOS.
     *
     * In the past [1], macOS subpixel AA caused excessive bolding for light-on-dark text; this rule
     * avoids that by requesting non-subpixel AA always, rather than the default behavior, which is
     * to use subpixel AA when available. The original issue was "fixed" by removing subpixel AA in
     * macOS 14 (Mojave), but for legacy reasons they preserved the bolding effect as an option.
     * Chrome then in turn updated its font rendering to apply that bolding effect [2], which means
     * that even though the `-webkit-font-smoothing` docs [3] suggest that setting `antialiased`
     * would have no effect for recent versions of macOS, it still is needed to avoid the bolding.
     *
     * [1]: http://www.lighterra.com/articles/macosxtextaabug/
     * [2]: https://bugs.chromium.org/p/chromium/issues/detail?id=858861
     * [3]: https://developer.mozilla.org/en-US/docs/Web/CSS/font-smooth
     *
     */

    -webkit-font-smoothing: antialiased;
  }
  noscript {
    display: block;
    margin: 0 auto;
    max-width: 600px;
    padding: 10px;
  }
</style>

</head><body><noscript>
    <h1>TensorBoard requires JavaScript</h1>
    <p>Please enable JavaScript and reload this page.</p>
  </noscript><tb-webapp></tb-webapp><script src="index.js?_file_hash=8bbeb739"></script></body></html>", | |
| "ok": true, | |
| "headers": [ | |
| [ | |
| "content-type", | |
| "text/html; charset=utf-8" | |
| ] | |
| ], | |
| "status": 200, | |
| "status_text": "" | |
| }, | |
| "https://localhost:6007/font-roboto/oMMgfZMQthOryQo9n22dcuvvDin1pK8aKteLpeZ5c0A.woff2": { | |
| "data": "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", | |
| "ok": true, | |
| "headers": [ | |
| [ | |
| "content-type", | |
| "font/woff2" | |
| ] | |
| ], | |
| "status": 200, | |
| "status_text": "" | |
| }, | |
| "https://localhost:6007/index.js?_file_hash=8bbeb739": { |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment