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add yolov3 and ocr to detect license plate numbers
2023-01-22 21:00:32 +01:00
{
"nbformat": 4,
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"metadata": {
"colab": {
"name": "Roboflow-Yolov3.ipynb",
"provenance": [],
"collapsed_sections": [],
"toc_visible": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "jFhMDyD-vXLs",
"colab_type": "text"
},
"source": [
"NOTE: For the most up to date version of this notebook, please be sure to copy from this link:\n",
" \n",
"[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1ByRi9d6_Yzu0nrEKArmLMLuMaZjYfygO#scrollTo=WgHANbxqWJPa)\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "WgHANbxqWJPa",
"colab_type": "text"
},
"source": [
"## **Training YOLOv3 object detection on a custom dataset**\n",
"\n",
"💡 Recommendation: [Open this blog post](https://blog.roboflow.ai/training-a-yolov3-object-detection-model-with-a-custom-dataset/) to continue.\n",
"\n",
"### **Overview**\n",
"\n",
"This notebook walks through how to train a YOLOv3 object detection model on your own dataset using Roboflow and Colab.\n",
"\n",
"In this specific example, we'll training an object detection model to recognize chess pieces in images. **To adapt this example to your own dataset, you only need to change one line of code in this notebook.**\n",
"\n",
"![Chess Example](https://i.imgur.com/nkjobw1.png)\n",
"\n",
"### **Our Data**\n",
"\n",
"Our dataset of 289 chess images (and 2894 annotations!) is hosted publicly on Roboflow [here](https://public.roboflow.ai/object-detection/chess-full).\n",
"\n",
"### **Our Model**\n",
"\n",
"We'll be training a YOLOv3 (You Only Look Once) model. This specific model is a one-shot learner, meaning each image only passes through the network once to make a prediction, which allows the architecture to be very performant, viewing up to 60 frames per second in predicting against video feeds.\n",
"\n",
"The GitHub repo containing the majority of the code we'll use is available [here](https://github.com/roboflow-ai/keras-yolo3.git).\n",
"\n",
"### **Training**\n",
"\n",
"Google Colab provides free GPU resources. Click \"Runtime\" → \"Change runtime type\" → Hardware Accelerator dropdown to \"GPU.\"\n",
"\n",
"Colab does have memory limitations, and notebooks must be open in your browser to run. Sessions automatically clear themselves after 24 hours.\n",
"\n",
"### **Inference**\n",
"\n",
"We'll leverage the `python_video.py` script to produce predictions. Arguments are specified below.\n",
"\n",
"It's recommended that you expand the left-hand panel to view this notebook's Table of contents, Code Snippets, and Files. \n",
"\n",
"![Expand Colab](https://i.imgur.com/r8kWzIv.png \"Click here\")\n",
"\n",
"Then, click \"Files.\" You'll see files appear here as we work through the notebook.\n",
"\n",
"\n",
"### **About**\n",
"\n",
"[Roboflow](https://roboflow.ai) makes managing, preprocessing, augmenting, and versioning datasets for computer vision seamless.\n",
"\n",
"Developers reduce 50% of their boilerplate code when using Roboflow's workflow, save training time, and increase model reproducibility.\n",
"\n",
"#### ![Roboflow Workmark](https://i.imgur.com/WHFqYSJ.png)\n",
"\n",
"\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "aHNPC6kwbKAL",
"colab_type": "text"
},
"source": [
"## Setup our environment\n",
"\n",
"First, we'll install the version of Keras our YOLOv3 implementation calls for and verify it installs corrects. "
]
},
{
"cell_type": "code",
"metadata": {
"id": "-pyrwfpiiEkH",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "59d901f9-0c5b-4607-87d6-b5668eb50662"
},
"source": [
"# Get our kernel running\n",
"print(\"Hello, Roboflow\")"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": [
"Hello, Roboflow\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "uRIj10jNhqH1",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 292
},
"outputId": "7549d3a2-cf5d-4d51-b07d-6f1d1cf84f75"
},
"source": [
"# Our YOLOv3 implementation calls for this Keras version\n",
"!pip install keras==2.2.4"
],
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"text": [
"Collecting keras==2.2.4\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/5e/10/aa32dad071ce52b5502266b5c659451cfd6ffcbf14e6c8c4f16c0ff5aaab/Keras-2.2.4-py2.py3-none-any.whl (312kB)\n",
"\r\u001b[K |█ | 10kB 22.0MB/s eta 0:00:01\r\u001b[K |██ | 20kB 27.1MB/s eta 0:00:01\r\u001b[K |███▏ | 30kB 30.9MB/s eta 0:00:01\r\u001b[K |████▏ | 40kB 31.0MB/s eta 0:00:01\r\u001b[K |█████▎ | 51kB 18.5MB/s eta 0:00:01\r\u001b[K |██████▎ | 61kB 17.0MB/s eta 0:00:01\r\u001b[K |███████▍ | 71kB 16.1MB/s eta 0:00:01\r\u001b[K |████████▍ | 81kB 17.6MB/s eta 0:00:01\r\u001b[K |█████████▍ | 92kB 15.1MB/s eta 0:00:01\r\u001b[K |██████████▌ | 102kB 15.5MB/s eta 0:00:01\r\u001b[K |███████████▌ | 112kB 15.5MB/s eta 0:00:01\r\u001b[K |████████████▋ | 122kB 15.5MB/s eta 0:00:01\r\u001b[K |█████████████▋ | 133kB 15.5MB/s eta 0:00:01\r\u001b[K |██████████████▊ | 143kB 15.5MB/s eta 0:00:01\r\u001b[K |███████████████▊ | 153kB 15.5MB/s eta 0:00:01\r\u001b[K |████████████████▊ | 163kB 15.5MB/s eta 0:00:01\r\u001b[K |█████████████████▉ | 174kB 15.5MB/s eta 0:00:01\r\u001b[K |██████████████████▉ | 184kB 15.5MB/s eta 0:00:01\r\u001b[K |████████████████████ | 194kB 15.5MB/s eta 0:00:01\r\u001b[K |█████████████████████ | 204kB 15.5MB/s eta 0:00:01\r\u001b[K |██████████████████████ | 215kB 15.5MB/s eta 0:00:01\r\u001b[K |███████████████████████ | 225kB 15.5MB/s eta 0:00:01\r\u001b[K |████████████████████████▏ | 235kB 15.5MB/s eta 0:00:01\r\u001b[K |█████████████████████████▏ | 245kB 15.5MB/s eta 0:00:01\r\u001b[K |██████████████████████████▏ | 256kB 15.5MB/s eta 0:00:01\r\u001b[K |███████████████████████████▎ | 266kB 15.5MB/s eta 0:00:01\r\u001b[K |████████████████████████████▎ | 276kB 15.5MB/s eta 0:00:01\r\u001b[K |█████████████████████████████▍ | 286kB 15.5MB/s eta 0:00:01\r\u001b[K |██████████████████████████████▍ | 296kB 15.5MB/s eta 0:00:01\r\u001b[K |███████████████████████████████▌| 307kB 15.5MB/s eta 0:00:01\r\u001b[K |████████████████████████████████| 317kB 15.5MB/s \n",
"\u001b[?25hRequirement already satisfied: pyyaml in /usr/local/lib/python3.6/dist-packages (from keras==2.2.4) (3.13)\n",
"Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.6/dist-packages (from keras==2.2.4) (1.12.0)\n",
"Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras==2.2.4) (2.10.0)\n",
"Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/dist-packages (from keras==2.2.4) (1.0.8)\n",
"Requirement already satisfied: scipy>=0.14 in /usr/local/lib/python3.6/dist-packages (from keras==2.2.4) (1.4.1)\n",
"Requirement already satisfied: numpy>=1.9.1 in /usr/local/lib/python3.6/dist-packages (from keras==2.2.4) (1.18.2)\n",
"Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from keras==2.2.4) (1.1.0)\n",
"Installing collected packages: keras\n",
" Found existing installation: Keras 2.3.1\n",
" Uninstalling Keras-2.3.1:\n",
" Successfully uninstalled Keras-2.3.1\n",
"Successfully installed keras-2.2.4\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "r788kvmKuD5O",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "29863815-b9fc-461a-81ba-2eb4603866dc"
},
"source": [
"# use TF 1.x\n",
"%tensorflow_version 1.x"
],
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"text": [
"TensorFlow 1.x selected.\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "UMI-zNrrhmuG",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 51
},
"outputId": "8e00c871-89db-44a1-f19b-46389e0fe0e8"
},
"source": [
"# Verify our version is correct\n",
"!python -c 'import keras; print(keras.__version__)'"
],
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n",
"2.2.4\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "lweWDcTyVeLs",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 102
},
"outputId": "a62151f4-0fef-4b74-d74e-ca5b03e7a5cc"
},
"source": [
"# Next, we'll grab all the code from our repository of interest \n",
"!git clone https://github.com/roboflow-ai/keras-yolo3.git"
],
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"text": [
"Cloning into 'keras-yolo3'...\n",
"remote: Enumerating objects: 165, done.\u001b[K\n",
"Receiving objects: 100% (165/165), 156.01 KiB | 300.00 KiB/s, done.\n",
"remote: Total 165 (delta 0), reused 0 (delta 0), pack-reused 165\n",
"Resolving deltas: 100% (79/79), done.\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "CyPfLjFBbOAw",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "3fe57c5b-604b-4144-a62e-c9305f65dd68"
},
"source": [
"# here's what we cloned (also, see \"Files\" in the left-hand Colab pane)\n",
"%ls"
],
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"text": [
"\u001b[0m\u001b[01;34mkeras-yolo3\u001b[0m/ \u001b[01;34msample_data\u001b[0m/\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "adwdKfxBVlom",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "73cefd94-6b68-43a2-a809-0a558edad4bf"
},
"source": [
"# change directory to the repo we cloned\n",
"%cd keras-yolo3/"
],
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"text": [
"/content/keras-yolo3\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "R6DNWhOEbGB6",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 85
},
"outputId": "b7d62815-a456-43b7-8786-879641e610b3"
},
"source": [
"# show the contents of our repo\n",
"%ls"
],
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"text": [
"coco_annotation.py kmeans.py train_bottleneck.py yolo.py\n",
"convert.py LICENSE train.py yolov3.cfg\n",
"darknet53.cfg \u001b[0m\u001b[01;34mmodel_data\u001b[0m/ voc_annotation.py yolov3-tiny.cfg\n",
"\u001b[01;34mfont\u001b[0m/ README.md \u001b[01;34myolo3\u001b[0m/ yolo_video.py\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "I--RqDmpwqmv",
"colab_type": "text"
},
"source": [
"## Get our training data from Roboflow\n",
"\n",
"Next, we need to add our data from Roboflow into our environment.\n",
"\n",
"Our dataset, with annotations, is [here](https://public.roboflow.ai/object-detection/chess-full).\n",
"\n",
"Here's how to bring those images from Roboflow to Colab:\n",
"\n",
"1. Visit this [link](https://public.roboflow.ai/object-detection/chess-full).\n",
"2. Click the \"416x416auto-orient\" under Downloads.\n",
"3. On the dataset detail page, select \"Download\" in the upper right-hand corner.\n",
"4. If you are not signed in, you will be prompted to create a free account (sign in with GitHub or email), and redirected to the dataset page to Download.\n",
"5. On the download popup, select the YOLOv3 Keras option **and** the \"Show download `code`\". \n",
"6. Copy the code snippet Roboflow generates for you, and paste it in the next cell.\n",
"\n",
"This is the download menu you want (from step 5):\n",
"#### ![Download Menu](https://i.imgur.com/KW2PyQO.png)\n",
"\n",
"The top code snippet is the one you want to copy (from step 6) and paste in the next notebook cell:\n",
"### ![Code Snippet](https://i.imgur.com/qzJckWR.png)\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6AmSSTFFWud7",
"colab_type": "text"
},
"source": [
"**This cell below is only one you need to change to have YOLOv3 train on your own Roboflow dataset.**"
]
},
{
"cell_type": "code",
"metadata": {
"id": "0nclkjonbT25",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 187
},
"outputId": "a913b741-3f7f-4c4d-e0fc-77599b9a628c"
},
"source": [
"# Paste Roboflow code from snippet here from above to here! eg !curl -L https://app.roboflow.ai/ds/eOSXbt7KWu?key=YOURKEY | jar -x\n",
"!curl -L https://app.roboflow.ai/ds/REPLACE-THIS-LINk > roboflow.zip; unzip roboflow.zip; rm roboflow.zip\n",
"\n"
],
"execution_count": 9,
"outputs": [
{
"output_type": "stream",
"text": [
" % Total % Received % Xferd Average Speed Time Time Time Current\n",
" Dload Upload Total Spent Left Speed\n",
"\r 0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0\r100 27 100 27 0 0 59 0 --:--:-- --:--:-- --:--:-- 59\n",
"Archive: roboflow.zip\n",
" End-of-central-directory signature not found. Either this file is not\n",
" a zipfile, or it constitutes one disk of a multi-part archive. In the\n",
" latter case the central directory and zipfile comment will be found on\n",
" the last disk(s) of this archive.\n",
"unzip: cannot find zipfile directory in one of roboflow.zip or\n",
" roboflow.zip.zip, and cannot find roboflow.zip.ZIP, period.\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "izGzSaeJzqAl",
"colab_type": "code",
"outputId": "2e639055-227e-4aab-dbd4-ad690fb5b430",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 85
}
},
"source": [
"%ls"
],
"execution_count": 10,
"outputs": [
{
"output_type": "stream",
"text": [
"coco_annotation.py kmeans.py train_bottleneck.py yolo.py\n",
"convert.py LICENSE train.py yolov3.cfg\n",
"darknet53.cfg \u001b[0m\u001b[01;34mmodel_data\u001b[0m/ voc_annotation.py yolov3-tiny.cfg\n",
"\u001b[01;34mfont\u001b[0m/ README.md \u001b[01;34myolo3\u001b[0m/ yolo_video.py\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "v1PagPopmUIG",
"colab_type": "code",
"outputId": "4652fa36-a7ac-44d6-d08d-6868fbbaace9",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 51
}
},
"source": [
"# change directory into our export folder from Roboflow\n",
"%cd train"
],
"execution_count": 11,
"outputs": [
{
"output_type": "stream",
"text": [
"[Errno 2] No such file or directory: 'train'\n",
"/content/keras-yolo3\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "YJ372c7gWN_p",
"colab_type": "code",
"outputId": "72c7deba-133f-4d9e-ed97-68ba79276c28",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 85
}
},
"source": [
"# show what came with the Roboflow export\n",
"%ls"
],
"execution_count": 12,
"outputs": [
{
"output_type": "stream",
"text": [
"coco_annotation.py kmeans.py train_bottleneck.py yolo.py\n",
"convert.py LICENSE train.py yolov3.cfg\n",
"darknet53.cfg \u001b[0m\u001b[01;34mmodel_data\u001b[0m/ voc_annotation.py yolov3-tiny.cfg\n",
"\u001b[01;34mfont\u001b[0m/ README.md \u001b[01;34myolo3\u001b[0m/ yolo_video.py\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "EUWFxHW_mjlT",
"colab_type": "code",
"colab": {}
},
"source": [
"# move everything from the Roboflow export to the root of our keras-yolo3 folder\n",
"%mv * ../"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "200_8-VImWmK",
"colab_type": "code",
"outputId": "0e45e9db-767d-45d0-858c-12d170caecd1",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"# change directory back to our \n",
"%cd .."
],
"execution_count": 14,
"outputs": [
{
"output_type": "stream",
"text": [
"/content\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "CQASf1hzmxE7",
"colab_type": "code",
"outputId": "71b77f85-b57b-4db7-c540-84a25ebe3d42",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 102
}
},
"source": [
"# show that all our images, _annotations.txt, and _classes.txt made it to our root directory\n",
"%ls"
],
"execution_count": 15,
"outputs": [
{
"output_type": "stream",
"text": [
"coco_annotation.py kmeans.py train_bottleneck.py yolov3.cfg\n",
"convert.py LICENSE train.py yolov3-tiny.cfg\n",
"darknet53.cfg \u001b[0m\u001b[01;34mmodel_data\u001b[0m/ voc_annotation.py yolo_video.py\n",
"\u001b[01;34mfont\u001b[0m/ README.md \u001b[01;34myolo3\u001b[0m/\n",
"\u001b[01;34mkeras-yolo3\u001b[0m/ \u001b[01;34msample_data\u001b[0m/ yolo.py\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "WvzqgP92W7bt",
"colab_type": "text"
},
"source": [
"## Set up and train our model\n",
"\n",
"Next, we'll download pre-trained weighs weights from DarkNet, set up our YOLOv3 architecture with those pre-trained weights, and initiate training.\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "fJzW08g2VlwD",
"colab_type": "code",
"outputId": "ad469e73-2dbe-4e8b-bbbf-19c80fdb99a8",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
}
},
"source": [
"# download our DarkNet weights \n",
"!wget https://pjreddie.com/media/files/yolov3.weights"
],
"execution_count": 16,
"outputs": [
{
"output_type": "stream",
"text": [
"--2020-04-17 20:07:32-- https://pjreddie.com/media/files/yolov3.weights\n",
"Resolving pjreddie.com (pjreddie.com)... 128.208.4.108\n",
"Connecting to pjreddie.com (pjreddie.com)|128.208.4.108|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 248007048 (237M) [application/octet-stream]\n",
"Saving to: ‘yolov3.weights’\n",
"\n",
"yolov3.weights 100%[===================>] 236.52M 254KB/s in 12m 13s \n",
"\n",
"2020-04-17 20:19:47 (330 KB/s) - ‘yolov3.weights’ saved [248007048/248007048]\n",
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "mub8GJMBVluA",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"outputId": "266ebd55-0150-45da-d813-0f8b46f5d5c9"
},
"source": [
"# call a Python script to set up our architecture with downloaded pre-trained weights\n",
"!python convert.py yolov3.cfg yolov3.weights model_data/yolo.h5"
],
"execution_count": 17,
"outputs": [
{
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n",
"Loading weights.\n",
"Weights Header: 0 2 0 [32013312]\n",
"Parsing Darknet config.\n",
"Creating Keras model.\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
"\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
"\n",
"Parsing section net_0\n",
"Parsing section convolutional_0\n",
"conv2d bn leaky (3, 3, 3, 32)\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
"\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:174: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
"\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:181: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
"\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:186: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
"\n",
"2020-04-17 20:19:52.070888: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX512F\n",
"2020-04-17 20:19:52.147911: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2000120000 Hz\n",
"2020-04-17 20:19:52.148388: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x19b8a00 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n",
"2020-04-17 20:19:52.148429: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version\n",
"2020-04-17 20:19:52.153494: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1\n",
"2020-04-17 20:19:52.378955: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] 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",
"2020-04-17 20:19:52.379755: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x19b8bc0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
"2020-04-17 20:19:52.379790: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Tesla T4, Compute Capability 7.5\n",
"2020-04-17 20:19:52.381224: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] 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",
"2020-04-17 20:19:52.381958: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Found device 0 with properties: \n",
"name: Tesla T4 major: 7 minor: 5 memoryClockRate(GHz): 1.59\n",
"pciBusID: 0000:00:04.0\n",
"2020-04-17 20:19:52.382253: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
"2020-04-17 20:19:52.383893: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
"2020-04-17 20:19:52.385764: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
"2020-04-17 20:19:52.386109: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
"2020-04-17 20:19:52.387660: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
"2020-04-17 20:19:52.412685: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
"2020-04-17 20:19:52.415845: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
"2020-04-17 20:19:52.415946: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] 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",
"2020-04-17 20:19:52.416522: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] 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",
"2020-04-17 20:19:52.417026: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1767] Adding visible gpu devices: 0\n",
"2020-04-17 20:19:52.422944: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
"2020-04-17 20:19:52.424063: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1180] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
"2020-04-17 20:19:52.424093: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1186] 0 \n",
"2020-04-17 20:19:52.424104: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1199] 0: N \n",
"2020-04-17 20:19:52.426496: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] 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",
"2020-04-17 20:19:52.427106: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] 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",
"2020-04-17 20:19:52.427711: 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",
"2020-04-17 20:19:52.427755: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1325] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 14221 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5)\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
"\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:199: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n",
"\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:206: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
"\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1834: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead.\n",
"\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
"\n",
"Parsing section convolutional_1\n",
"conv2d bn leaky (3, 3, 32, 64)\n",
"Parsing section convolutional_2\n",
"conv2d bn leaky (1, 1, 64, 32)\n",
"Parsing section convolutional_3\n",
"conv2d bn leaky (3, 3, 32, 64)\n",
"Parsing section shortcut_0\n",
"Parsing section convolutional_4\n",
"conv2d bn leaky (3, 3, 64, 128)\n",
"Parsing section convolutional_5\n",
"conv2d bn leaky (1, 1, 128, 64)\n",
"Parsing section convolutional_6\n",
"conv2d bn leaky (3, 3, 64, 128)\n",
"Parsing section shortcut_1\n",
"Parsing section convolutional_7\n",
"conv2d bn leaky (1, 1, 128, 64)\n",
"Parsing section convolutional_8\n",
"conv2d bn leaky (3, 3, 64, 128)\n",
"Parsing section shortcut_2\n",
"Parsing section convolutional_9\n",
"conv2d bn leaky (3, 3, 128, 256)\n",
"Parsing section convolutional_10\n",
"conv2d bn leaky (1, 1, 256, 128)\n",
"Parsing section convolutional_11\n",
"conv2d bn leaky (3, 3, 128, 256)\n",
"Parsing section shortcut_3\n",
"Parsing section convolutional_12\n",
"conv2d bn leaky (1, 1, 256, 128)\n",
"Parsing section convolutional_13\n",
"conv2d bn leaky (3, 3, 128, 256)\n",
"Parsing section shortcut_4\n",
"Parsing section convolutional_14\n",
"conv2d bn leaky (1, 1, 256, 128)\n",
"Parsing section convolutional_15\n",
"conv2d bn leaky (3, 3, 128, 256)\n",
"Parsing section shortcut_5\n",
"Parsing section convolutional_16\n",
"conv2d bn leaky (1, 1, 256, 128)\n",
"Parsing section convolutional_17\n",
"conv2d bn leaky (3, 3, 128, 256)\n",
"Parsing section shortcut_6\n",
"Parsing section convolutional_18\n",
"conv2d bn leaky (1, 1, 256, 128)\n",
"Parsing section convolutional_19\n",
"conv2d bn leaky (3, 3, 128, 256)\n",
"Parsing section shortcut_7\n",
"Parsing section convolutional_20\n",
"conv2d bn leaky (1, 1, 256, 128)\n",
"Parsing section convolutional_21\n",
"conv2d bn leaky (3, 3, 128, 256)\n",
"Parsing section shortcut_8\n",
"Parsing section convolutional_22\n",
"conv2d bn leaky (1, 1, 256, 128)\n",
"Parsing section convolutional_23\n",
"conv2d bn leaky (3, 3, 128, 256)\n",
"Parsing section shortcut_9\n",
"Parsing section convolutional_24\n",
"conv2d bn leaky (1, 1, 256, 128)\n",
"Parsing section convolutional_25\n",
"conv2d bn leaky (3, 3, 128, 256)\n",
"Parsing section shortcut_10\n",
"Parsing section convolutional_26\n",
"conv2d bn leaky (3, 3, 256, 512)\n",
"Parsing section convolutional_27\n",
"conv2d bn leaky (1, 1, 512, 256)\n",
"Parsing section convolutional_28\n",
"conv2d bn leaky (3, 3, 256, 512)\n",
"Parsing section shortcut_11\n",
"Parsing section convolutional_29\n",
"conv2d bn leaky (1, 1, 512, 256)\n",
"Parsing section convolutional_30\n",
"conv2d bn leaky (3, 3, 256, 512)\n",
"Parsing section shortcut_12\n",
"Parsing section convolutional_31\n",
"conv2d bn leaky (1, 1, 512, 256)\n",
"Parsing section convolutional_32\n",
"conv2d bn leaky (3, 3, 256, 512)\n",
"Parsing section shortcut_13\n",
"Parsing section convolutional_33\n",
"conv2d bn leaky (1, 1, 512, 256)\n",
"Parsing section convolutional_34\n",
"conv2d bn leaky (3, 3, 256, 512)\n",
"Parsing section shortcut_14\n",
"Parsing section convolutional_35\n",
"conv2d bn leaky (1, 1, 512, 256)\n",
"Parsing section convolutional_36\n",
"conv2d bn leaky (3, 3, 256, 512)\n",
"Parsing section shortcut_15\n",
"Parsing section convolutional_37\n",
"conv2d bn leaky (1, 1, 512, 256)\n",
"Parsing section convolutional_38\n",
"conv2d bn leaky (3, 3, 256, 512)\n",
"Parsing section shortcut_16\n",
"Parsing section convolutional_39\n",
"conv2d bn leaky (1, 1, 512, 256)\n",
"Parsing section convolutional_40\n",
"conv2d bn leaky (3, 3, 256, 512)\n",
"Parsing section shortcut_17\n",
"Parsing section convolutional_41\n",
"conv2d bn leaky (1, 1, 512, 256)\n",
"Parsing section convolutional_42\n",
"conv2d bn leaky (3, 3, 256, 512)\n",
"Parsing section shortcut_18\n",
"Parsing section convolutional_43\n",
"conv2d bn leaky (3, 3, 512, 1024)\n",
"Parsing section convolutional_44\n",
"conv2d bn leaky (1, 1, 1024, 512)\n",
"Parsing section convolutional_45\n",
"conv2d bn leaky (3, 3, 512, 1024)\n",
"Parsing section shortcut_19\n",
"Parsing section convolutional_46\n",
"conv2d bn leaky (1, 1, 1024, 512)\n",
"Parsing section convolutional_47\n",
"conv2d bn leaky (3, 3, 512, 1024)\n",
"Parsing section shortcut_20\n",
"Parsing section convolutional_48\n",
"conv2d bn leaky (1, 1, 1024, 512)\n",
"Parsing section convolutional_49\n",
"conv2d bn leaky (3, 3, 512, 1024)\n",
"Parsing section shortcut_21\n",
"Parsing section convolutional_50\n",
"conv2d bn leaky (1, 1, 1024, 512)\n",
"Parsing section convolutional_51\n",
"conv2d bn leaky (3, 3, 512, 1024)\n",
"Parsing section shortcut_22\n",
"Parsing section convolutional_52\n",
"conv2d bn leaky (1, 1, 1024, 512)\n",
"Parsing section convolutional_53\n",
"conv2d bn leaky (3, 3, 512, 1024)\n",
"Parsing section convolutional_54\n",
"conv2d bn leaky (1, 1, 1024, 512)\n",
"Parsing section convolutional_55\n",
"conv2d bn leaky (3, 3, 512, 1024)\n",
"Parsing section convolutional_56\n",
"conv2d bn leaky (1, 1, 1024, 512)\n",
"Parsing section convolutional_57\n",
"conv2d bn leaky (3, 3, 512, 1024)\n",
"Parsing section convolutional_58\n",
"conv2d linear (1, 1, 1024, 255)\n",
"Parsing section yolo_0\n",
"Parsing section route_0\n",
"Parsing section convolutional_59\n",
"conv2d bn leaky (1, 1, 512, 256)\n",
"Parsing section upsample_0\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:2018: The name tf.image.resize_nearest_neighbor is deprecated. Please use tf.compat.v1.image.resize_nearest_neighbor instead.\n",
"\n",
"Parsing section route_1\n",
"Concatenating route layers: [<tf.Tensor 'up_sampling2d_1/ResizeNearestNeighbor:0' shape=(?, ?, ?, 256) dtype=float32>, <tf.Tensor 'add_19/add:0' shape=(?, ?, ?, 512) dtype=float32>]\n",
"Parsing section convolutional_60\n",
"conv2d bn leaky (1, 1, 768, 256)\n",
"Parsing section convolutional_61\n",
"conv2d bn leaky (3, 3, 256, 512)\n",
"Parsing section convolutional_62\n",
"conv2d bn leaky (1, 1, 512, 256)\n",
"Parsing section convolutional_63\n",
"conv2d bn leaky (3, 3, 256, 512)\n",
"Parsing section convolutional_64\n",
"conv2d bn leaky (1, 1, 512, 256)\n",
"Parsing section convolutional_65\n",
"conv2d bn leaky (3, 3, 256, 512)\n",
"Parsing section convolutional_66\n",
"conv2d linear (1, 1, 512, 255)\n",
"Parsing section yolo_1\n",
"Parsing section route_2\n",
"Parsing section convolutional_67\n",
"conv2d bn leaky (1, 1, 256, 128)\n",
"Parsing section upsample_1\n",
"Parsing section route_3\n",
"Concatenating route layers: [<tf.Tensor 'up_sampling2d_2/ResizeNearestNeighbor:0' shape=(?, ?, ?, 128) dtype=float32>, <tf.Tensor 'add_11/add:0' shape=(?, ?, ?, 256) dtype=float32>]\n",
"Parsing section convolutional_68\n",
"conv2d bn leaky (1, 1, 384, 128)\n",
"Parsing section convolutional_69\n",
"conv2d bn leaky (3, 3, 128, 256)\n",
"Parsing section convolutional_70\n",
"conv2d bn leaky (1, 1, 256, 128)\n",
"Parsing section convolutional_71\n",
"conv2d bn leaky (3, 3, 128, 256)\n",
"Parsing section convolutional_72\n",
"conv2d bn leaky (1, 1, 256, 128)\n",
"Parsing section convolutional_73\n",
"conv2d bn leaky (3, 3, 128, 256)\n",
"Parsing section convolutional_74\n",
"conv2d linear (1, 1, 256, 255)\n",
"Parsing section yolo_2\n",
"__________________________________________________________________________________________________\n",
"Layer (type) Output Shape Param # Connected to \n",
"==================================================================================================\n",
"input_1 (InputLayer) (None, None, None, 3 0 \n",
"__________________________________________________________________________________________________\n",
"conv2d_1 (Conv2D) (None, None, None, 3 864 input_1[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_1 (BatchNor (None, None, None, 3 128 conv2d_1[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_1 (LeakyReLU) (None, None, None, 3 0 batch_normalization_1[0][0] \n",
"__________________________________________________________________________________________________\n",
"zero_padding2d_1 (ZeroPadding2D (None, None, None, 3 0 leaky_re_lu_1[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_2 (Conv2D) (None, None, None, 6 18432 zero_padding2d_1[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_2 (BatchNor (None, None, None, 6 256 conv2d_2[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_2 (LeakyReLU) (None, None, None, 6 0 batch_normalization_2[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_3 (Conv2D) (None, None, None, 3 2048 leaky_re_lu_2[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_3 (BatchNor (None, None, None, 3 128 conv2d_3[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_3 (LeakyReLU) (None, None, None, 3 0 batch_normalization_3[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_4 (Conv2D) (None, None, None, 6 18432 leaky_re_lu_3[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_4 (BatchNor (None, None, None, 6 256 conv2d_4[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_4 (LeakyReLU) (None, None, None, 6 0 batch_normalization_4[0][0] \n",
"__________________________________________________________________________________________________\n",
"add_1 (Add) (None, None, None, 6 0 leaky_re_lu_2[0][0] \n",
" leaky_re_lu_4[0][0] \n",
"__________________________________________________________________________________________________\n",
"zero_padding2d_2 (ZeroPadding2D (None, None, None, 6 0 add_1[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_5 (Conv2D) (None, None, None, 1 73728 zero_padding2d_2[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_5 (BatchNor (None, None, None, 1 512 conv2d_5[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_5 (LeakyReLU) (None, None, None, 1 0 batch_normalization_5[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_6 (Conv2D) (None, None, None, 6 8192 leaky_re_lu_5[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_6 (BatchNor (None, None, None, 6 256 conv2d_6[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_6 (LeakyReLU) (None, None, None, 6 0 batch_normalization_6[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_7 (Conv2D) (None, None, None, 1 73728 leaky_re_lu_6[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_7 (BatchNor (None, None, None, 1 512 conv2d_7[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_7 (LeakyReLU) (None, None, None, 1 0 batch_normalization_7[0][0] \n",
"__________________________________________________________________________________________________\n",
"add_2 (Add) (None, None, None, 1 0 leaky_re_lu_5[0][0] \n",
" leaky_re_lu_7[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_8 (Conv2D) (None, None, None, 6 8192 add_2[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_8 (BatchNor (None, None, None, 6 256 conv2d_8[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_8 (LeakyReLU) (None, None, None, 6 0 batch_normalization_8[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_9 (Conv2D) (None, None, None, 1 73728 leaky_re_lu_8[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_9 (BatchNor (None, None, None, 1 512 conv2d_9[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_9 (LeakyReLU) (None, None, None, 1 0 batch_normalization_9[0][0] \n",
"__________________________________________________________________________________________________\n",
"add_3 (Add) (None, None, None, 1 0 add_2[0][0] \n",
" leaky_re_lu_9[0][0] \n",
"__________________________________________________________________________________________________\n",
"zero_padding2d_3 (ZeroPadding2D (None, None, None, 1 0 add_3[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_10 (Conv2D) (None, None, None, 2 294912 zero_padding2d_3[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_10 (BatchNo (None, None, None, 2 1024 conv2d_10[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_10 (LeakyReLU) (None, None, None, 2 0 batch_normalization_10[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_11 (Conv2D) (None, None, None, 1 32768 leaky_re_lu_10[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_11 (BatchNo (None, None, None, 1 512 conv2d_11[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_11 (LeakyReLU) (None, None, None, 1 0 batch_normalization_11[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_12 (Conv2D) (None, None, None, 2 294912 leaky_re_lu_11[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_12 (BatchNo (None, None, None, 2 1024 conv2d_12[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_12 (LeakyReLU) (None, None, None, 2 0 batch_normalization_12[0][0] \n",
"__________________________________________________________________________________________________\n",
"add_4 (Add) (None, None, None, 2 0 leaky_re_lu_10[0][0] \n",
" leaky_re_lu_12[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_13 (Conv2D) (None, None, None, 1 32768 add_4[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_13 (BatchNo (None, None, None, 1 512 conv2d_13[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_13 (LeakyReLU) (None, None, None, 1 0 batch_normalization_13[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_14 (Conv2D) (None, None, None, 2 294912 leaky_re_lu_13[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_14 (BatchNo (None, None, None, 2 1024 conv2d_14[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_14 (LeakyReLU) (None, None, None, 2 0 batch_normalization_14[0][0] \n",
"__________________________________________________________________________________________________\n",
"add_5 (Add) (None, None, None, 2 0 add_4[0][0] \n",
" leaky_re_lu_14[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_15 (Conv2D) (None, None, None, 1 32768 add_5[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_15 (BatchNo (None, None, None, 1 512 conv2d_15[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_15 (LeakyReLU) (None, None, None, 1 0 batch_normalization_15[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_16 (Conv2D) (None, None, None, 2 294912 leaky_re_lu_15[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_16 (BatchNo (None, None, None, 2 1024 conv2d_16[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_16 (LeakyReLU) (None, None, None, 2 0 batch_normalization_16[0][0] \n",
"__________________________________________________________________________________________________\n",
"add_6 (Add) (None, None, None, 2 0 add_5[0][0] \n",
" leaky_re_lu_16[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_17 (Conv2D) (None, None, None, 1 32768 add_6[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_17 (BatchNo (None, None, None, 1 512 conv2d_17[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_17 (LeakyReLU) (None, None, None, 1 0 batch_normalization_17[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_18 (Conv2D) (None, None, None, 2 294912 leaky_re_lu_17[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_18 (BatchNo (None, None, None, 2 1024 conv2d_18[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_18 (LeakyReLU) (None, None, None, 2 0 batch_normalization_18[0][0] \n",
"__________________________________________________________________________________________________\n",
"add_7 (Add) (None, None, None, 2 0 add_6[0][0] \n",
" leaky_re_lu_18[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_19 (Conv2D) (None, None, None, 1 32768 add_7[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_19 (BatchNo (None, None, None, 1 512 conv2d_19[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_19 (LeakyReLU) (None, None, None, 1 0 batch_normalization_19[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_20 (Conv2D) (None, None, None, 2 294912 leaky_re_lu_19[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_20 (BatchNo (None, None, None, 2 1024 conv2d_20[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_20 (LeakyReLU) (None, None, None, 2 0 batch_normalization_20[0][0] \n",
"__________________________________________________________________________________________________\n",
"add_8 (Add) (None, None, None, 2 0 add_7[0][0] \n",
" leaky_re_lu_20[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_21 (Conv2D) (None, None, None, 1 32768 add_8[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_21 (BatchNo (None, None, None, 1 512 conv2d_21[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_21 (LeakyReLU) (None, None, None, 1 0 batch_normalization_21[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_22 (Conv2D) (None, None, None, 2 294912 leaky_re_lu_21[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_22 (BatchNo (None, None, None, 2 1024 conv2d_22[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_22 (LeakyReLU) (None, None, None, 2 0 batch_normalization_22[0][0] \n",
"__________________________________________________________________________________________________\n",
"add_9 (Add) (None, None, None, 2 0 add_8[0][0] \n",
" leaky_re_lu_22[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_23 (Conv2D) (None, None, None, 1 32768 add_9[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_23 (BatchNo (None, None, None, 1 512 conv2d_23[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_23 (LeakyReLU) (None, None, None, 1 0 batch_normalization_23[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_24 (Conv2D) (None, None, None, 2 294912 leaky_re_lu_23[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_24 (BatchNo (None, None, None, 2 1024 conv2d_24[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_24 (LeakyReLU) (None, None, None, 2 0 batch_normalization_24[0][0] \n",
"__________________________________________________________________________________________________\n",
"add_10 (Add) (None, None, None, 2 0 add_9[0][0] \n",
" leaky_re_lu_24[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_25 (Conv2D) (None, None, None, 1 32768 add_10[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_25 (BatchNo (None, None, None, 1 512 conv2d_25[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_25 (LeakyReLU) (None, None, None, 1 0 batch_normalization_25[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_26 (Conv2D) (None, None, None, 2 294912 leaky_re_lu_25[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_26 (BatchNo (None, None, None, 2 1024 conv2d_26[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_26 (LeakyReLU) (None, None, None, 2 0 batch_normalization_26[0][0] \n",
"__________________________________________________________________________________________________\n",
"add_11 (Add) (None, None, None, 2 0 add_10[0][0] \n",
" leaky_re_lu_26[0][0] \n",
"__________________________________________________________________________________________________\n",
"zero_padding2d_4 (ZeroPadding2D (None, None, None, 2 0 add_11[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_27 (Conv2D) (None, None, None, 5 1179648 zero_padding2d_4[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_27 (BatchNo (None, None, None, 5 2048 conv2d_27[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_27 (LeakyReLU) (None, None, None, 5 0 batch_normalization_27[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_28 (Conv2D) (None, None, None, 2 131072 leaky_re_lu_27[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_28 (BatchNo (None, None, None, 2 1024 conv2d_28[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_28 (LeakyReLU) (None, None, None, 2 0 batch_normalization_28[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_29 (Conv2D) (None, None, None, 5 1179648 leaky_re_lu_28[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_29 (BatchNo (None, None, None, 5 2048 conv2d_29[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_29 (LeakyReLU) (None, None, None, 5 0 batch_normalization_29[0][0] \n",
"__________________________________________________________________________________________________\n",
"add_12 (Add) (None, None, None, 5 0 leaky_re_lu_27[0][0] \n",
" leaky_re_lu_29[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_30 (Conv2D) (None, None, None, 2 131072 add_12[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_30 (BatchNo (None, None, None, 2 1024 conv2d_30[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_30 (LeakyReLU) (None, None, None, 2 0 batch_normalization_30[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_31 (Conv2D) (None, None, None, 5 1179648 leaky_re_lu_30[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_31 (BatchNo (None, None, None, 5 2048 conv2d_31[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_31 (LeakyReLU) (None, None, None, 5 0 batch_normalization_31[0][0] \n",
"__________________________________________________________________________________________________\n",
"add_13 (Add) (None, None, None, 5 0 add_12[0][0] \n",
" leaky_re_lu_31[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_32 (Conv2D) (None, None, None, 2 131072 add_13[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_32 (BatchNo (None, None, None, 2 1024 conv2d_32[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_32 (LeakyReLU) (None, None, None, 2 0 batch_normalization_32[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_33 (Conv2D) (None, None, None, 5 1179648 leaky_re_lu_32[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_33 (BatchNo (None, None, None, 5 2048 conv2d_33[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_33 (LeakyReLU) (None, None, None, 5 0 batch_normalization_33[0][0] \n",
"__________________________________________________________________________________________________\n",
"add_14 (Add) (None, None, None, 5 0 add_13[0][0] \n",
" leaky_re_lu_33[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_34 (Conv2D) (None, None, None, 2 131072 add_14[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_34 (BatchNo (None, None, None, 2 1024 conv2d_34[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_34 (LeakyReLU) (None, None, None, 2 0 batch_normalization_34[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_35 (Conv2D) (None, None, None, 5 1179648 leaky_re_lu_34[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_35 (BatchNo (None, None, None, 5 2048 conv2d_35[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_35 (LeakyReLU) (None, None, None, 5 0 batch_normalization_35[0][0] \n",
"__________________________________________________________________________________________________\n",
"add_15 (Add) (None, None, None, 5 0 add_14[0][0] \n",
" leaky_re_lu_35[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_36 (Conv2D) (None, None, None, 2 131072 add_15[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_36 (BatchNo (None, None, None, 2 1024 conv2d_36[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_36 (LeakyReLU) (None, None, None, 2 0 batch_normalization_36[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_37 (Conv2D) (None, None, None, 5 1179648 leaky_re_lu_36[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_37 (BatchNo (None, None, None, 5 2048 conv2d_37[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_37 (LeakyReLU) (None, None, None, 5 0 batch_normalization_37[0][0] \n",
"__________________________________________________________________________________________________\n",
"add_16 (Add) (None, None, None, 5 0 add_15[0][0] \n",
" leaky_re_lu_37[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_38 (Conv2D) (None, None, None, 2 131072 add_16[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_38 (BatchNo (None, None, None, 2 1024 conv2d_38[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_38 (LeakyReLU) (None, None, None, 2 0 batch_normalization_38[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_39 (Conv2D) (None, None, None, 5 1179648 leaky_re_lu_38[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_39 (BatchNo (None, None, None, 5 2048 conv2d_39[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_39 (LeakyReLU) (None, None, None, 5 0 batch_normalization_39[0][0] \n",
"__________________________________________________________________________________________________\n",
"add_17 (Add) (None, None, None, 5 0 add_16[0][0] \n",
" leaky_re_lu_39[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_40 (Conv2D) (None, None, None, 2 131072 add_17[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_40 (BatchNo (None, None, None, 2 1024 conv2d_40[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_40 (LeakyReLU) (None, None, None, 2 0 batch_normalization_40[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_41 (Conv2D) (None, None, None, 5 1179648 leaky_re_lu_40[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_41 (BatchNo (None, None, None, 5 2048 conv2d_41[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_41 (LeakyReLU) (None, None, None, 5 0 batch_normalization_41[0][0] \n",
"__________________________________________________________________________________________________\n",
"add_18 (Add) (None, None, None, 5 0 add_17[0][0] \n",
" leaky_re_lu_41[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_42 (Conv2D) (None, None, None, 2 131072 add_18[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_42 (BatchNo (None, None, None, 2 1024 conv2d_42[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_42 (LeakyReLU) (None, None, None, 2 0 batch_normalization_42[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_43 (Conv2D) (None, None, None, 5 1179648 leaky_re_lu_42[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_43 (BatchNo (None, None, None, 5 2048 conv2d_43[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_43 (LeakyReLU) (None, None, None, 5 0 batch_normalization_43[0][0] \n",
"__________________________________________________________________________________________________\n",
"add_19 (Add) (None, None, None, 5 0 add_18[0][0] \n",
" leaky_re_lu_43[0][0] \n",
"__________________________________________________________________________________________________\n",
"zero_padding2d_5 (ZeroPadding2D (None, None, None, 5 0 add_19[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_44 (Conv2D) (None, None, None, 1 4718592 zero_padding2d_5[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_44 (BatchNo (None, None, None, 1 4096 conv2d_44[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_44 (LeakyReLU) (None, None, None, 1 0 batch_normalization_44[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_45 (Conv2D) (None, None, None, 5 524288 leaky_re_lu_44[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_45 (BatchNo (None, None, None, 5 2048 conv2d_45[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_45 (LeakyReLU) (None, None, None, 5 0 batch_normalization_45[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_46 (Conv2D) (None, None, None, 1 4718592 leaky_re_lu_45[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_46 (BatchNo (None, None, None, 1 4096 conv2d_46[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_46 (LeakyReLU) (None, None, None, 1 0 batch_normalization_46[0][0] \n",
"__________________________________________________________________________________________________\n",
"add_20 (Add) (None, None, None, 1 0 leaky_re_lu_44[0][0] \n",
" leaky_re_lu_46[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_47 (Conv2D) (None, None, None, 5 524288 add_20[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_47 (BatchNo (None, None, None, 5 2048 conv2d_47[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_47 (LeakyReLU) (None, None, None, 5 0 batch_normalization_47[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_48 (Conv2D) (None, None, None, 1 4718592 leaky_re_lu_47[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_48 (BatchNo (None, None, None, 1 4096 conv2d_48[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_48 (LeakyReLU) (None, None, None, 1 0 batch_normalization_48[0][0] \n",
"__________________________________________________________________________________________________\n",
"add_21 (Add) (None, None, None, 1 0 add_20[0][0] \n",
" leaky_re_lu_48[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_49 (Conv2D) (None, None, None, 5 524288 add_21[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_49 (BatchNo (None, None, None, 5 2048 conv2d_49[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_49 (LeakyReLU) (None, None, None, 5 0 batch_normalization_49[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_50 (Conv2D) (None, None, None, 1 4718592 leaky_re_lu_49[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_50 (BatchNo (None, None, None, 1 4096 conv2d_50[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_50 (LeakyReLU) (None, None, None, 1 0 batch_normalization_50[0][0] \n",
"__________________________________________________________________________________________________\n",
"add_22 (Add) (None, None, None, 1 0 add_21[0][0] \n",
" leaky_re_lu_50[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_51 (Conv2D) (None, None, None, 5 524288 add_22[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_51 (BatchNo (None, None, None, 5 2048 conv2d_51[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_51 (LeakyReLU) (None, None, None, 5 0 batch_normalization_51[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_52 (Conv2D) (None, None, None, 1 4718592 leaky_re_lu_51[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_52 (BatchNo (None, None, None, 1 4096 conv2d_52[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_52 (LeakyReLU) (None, None, None, 1 0 batch_normalization_52[0][0] \n",
"__________________________________________________________________________________________________\n",
"add_23 (Add) (None, None, None, 1 0 add_22[0][0] \n",
" leaky_re_lu_52[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_53 (Conv2D) (None, None, None, 5 524288 add_23[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_53 (BatchNo (None, None, None, 5 2048 conv2d_53[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_53 (LeakyReLU) (None, None, None, 5 0 batch_normalization_53[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_54 (Conv2D) (None, None, None, 1 4718592 leaky_re_lu_53[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_54 (BatchNo (None, None, None, 1 4096 conv2d_54[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_54 (LeakyReLU) (None, None, None, 1 0 batch_normalization_54[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_55 (Conv2D) (None, None, None, 5 524288 leaky_re_lu_54[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_55 (BatchNo (None, None, None, 5 2048 conv2d_55[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_55 (LeakyReLU) (None, None, None, 5 0 batch_normalization_55[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_56 (Conv2D) (None, None, None, 1 4718592 leaky_re_lu_55[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_56 (BatchNo (None, None, None, 1 4096 conv2d_56[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_56 (LeakyReLU) (None, None, None, 1 0 batch_normalization_56[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_57 (Conv2D) (None, None, None, 5 524288 leaky_re_lu_56[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_57 (BatchNo (None, None, None, 5 2048 conv2d_57[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_57 (LeakyReLU) (None, None, None, 5 0 batch_normalization_57[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_60 (Conv2D) (None, None, None, 2 131072 leaky_re_lu_57[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_59 (BatchNo (None, None, None, 2 1024 conv2d_60[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_59 (LeakyReLU) (None, None, None, 2 0 batch_normalization_59[0][0] \n",
"__________________________________________________________________________________________________\n",
"up_sampling2d_1 (UpSampling2D) (None, None, None, 2 0 leaky_re_lu_59[0][0] \n",
"__________________________________________________________________________________________________\n",
"concatenate_1 (Concatenate) (None, None, None, 7 0 up_sampling2d_1[0][0] \n",
" add_19[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_61 (Conv2D) (None, None, None, 2 196608 concatenate_1[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_60 (BatchNo (None, None, None, 2 1024 conv2d_61[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_60 (LeakyReLU) (None, None, None, 2 0 batch_normalization_60[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_62 (Conv2D) (None, None, None, 5 1179648 leaky_re_lu_60[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_61 (BatchNo (None, None, None, 5 2048 conv2d_62[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_61 (LeakyReLU) (None, None, None, 5 0 batch_normalization_61[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_63 (Conv2D) (None, None, None, 2 131072 leaky_re_lu_61[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_62 (BatchNo (None, None, None, 2 1024 conv2d_63[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_62 (LeakyReLU) (None, None, None, 2 0 batch_normalization_62[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_64 (Conv2D) (None, None, None, 5 1179648 leaky_re_lu_62[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_63 (BatchNo (None, None, None, 5 2048 conv2d_64[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_63 (LeakyReLU) (None, None, None, 5 0 batch_normalization_63[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_65 (Conv2D) (None, None, None, 2 131072 leaky_re_lu_63[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_64 (BatchNo (None, None, None, 2 1024 conv2d_65[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_64 (LeakyReLU) (None, None, None, 2 0 batch_normalization_64[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_68 (Conv2D) (None, None, None, 1 32768 leaky_re_lu_64[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_66 (BatchNo (None, None, None, 1 512 conv2d_68[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_66 (LeakyReLU) (None, None, None, 1 0 batch_normalization_66[0][0] \n",
"__________________________________________________________________________________________________\n",
"up_sampling2d_2 (UpSampling2D) (None, None, None, 1 0 leaky_re_lu_66[0][0] \n",
"__________________________________________________________________________________________________\n",
"concatenate_2 (Concatenate) (None, None, None, 3 0 up_sampling2d_2[0][0] \n",
" add_11[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_69 (Conv2D) (None, None, None, 1 49152 concatenate_2[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_67 (BatchNo (None, None, None, 1 512 conv2d_69[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_67 (LeakyReLU) (None, None, None, 1 0 batch_normalization_67[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_70 (Conv2D) (None, None, None, 2 294912 leaky_re_lu_67[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_68 (BatchNo (None, None, None, 2 1024 conv2d_70[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_68 (LeakyReLU) (None, None, None, 2 0 batch_normalization_68[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_71 (Conv2D) (None, None, None, 1 32768 leaky_re_lu_68[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_69 (BatchNo (None, None, None, 1 512 conv2d_71[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_69 (LeakyReLU) (None, None, None, 1 0 batch_normalization_69[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_72 (Conv2D) (None, None, None, 2 294912 leaky_re_lu_69[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_70 (BatchNo (None, None, None, 2 1024 conv2d_72[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_70 (LeakyReLU) (None, None, None, 2 0 batch_normalization_70[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_73 (Conv2D) (None, None, None, 1 32768 leaky_re_lu_70[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_71 (BatchNo (None, None, None, 1 512 conv2d_73[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_71 (LeakyReLU) (None, None, None, 1 0 batch_normalization_71[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_58 (Conv2D) (None, None, None, 1 4718592 leaky_re_lu_57[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_66 (Conv2D) (None, None, None, 5 1179648 leaky_re_lu_64[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_74 (Conv2D) (None, None, None, 2 294912 leaky_re_lu_71[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_58 (BatchNo (None, None, None, 1 4096 conv2d_58[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_65 (BatchNo (None, None, None, 5 2048 conv2d_66[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_72 (BatchNo (None, None, None, 2 1024 conv2d_74[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_58 (LeakyReLU) (None, None, None, 1 0 batch_normalization_58[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_65 (LeakyReLU) (None, None, None, 5 0 batch_normalization_65[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_72 (LeakyReLU) (None, None, None, 2 0 batch_normalization_72[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_59 (Conv2D) (None, None, None, 2 261375 leaky_re_lu_58[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_67 (Conv2D) (None, None, None, 2 130815 leaky_re_lu_65[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_75 (Conv2D) (None, None, None, 2 65535 leaky_re_lu_72[0][0] \n",
"==================================================================================================\n",
"Total params: 62,001,757\n",
"Trainable params: 61,949,149\n",
"Non-trainable params: 52,608\n",
"__________________________________________________________________________________________________\n",
"None\n",
"Saved Keras model to model_data/yolo.h5\n",
"Read 62001757 of 62001757.0 from Darknet weights.\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "BEDHwJ36YyXA",
"colab_type": "text"
},
"source": [
"Below, we'll call a \"self-contained\" Python script that initiates training our model on our custom dataset.\n",
"\n",
"Pay notable attention to:\n",
"- setting the paths for our `annotation_path`, `classes_path`, `class_names`. If you move the Roboflow data location, you'll need to update these. \n",
"- `val_split` dictates the size of our training data relative to our taining data\n",
"- `lr=1e-3` to set the learning rate of the model. Smaller optimizes more slowly but potentially more precisely.\n",
"- `batch_size` for the number of images trained per batch\n",
"- `epoch` inside `model.fit_generator()` sets the number training epochs to increase/decrease training examples (and time)\n",
"\n",
"Consider reading the YOLOv3 paper [here](https://pjreddie.com/media/files/papers/YOLOv3.pdf)."
]
},
{
"cell_type": "code",
"metadata": {
"id": "4hBFndz8VeI6",
"colab_type": "code",
"outputId": "286072a9-b6ae-4b57-fc58-069e85287580",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 358
}
},
"source": [
"\"\"\"\n",
"Self-contained Python script to train YOLOv3 on your own dataset\n",
"\"\"\"\n",
"\n",
"import numpy as np\n",
"import keras.backend as K\n",
"from keras.layers import Input, Lambda\n",
"from keras.models import Model\n",
"from keras.optimizers import Adam\n",
"from keras.callbacks import TensorBoard, ModelCheckpoint, ReduceLROnPlateau, EarlyStopping\n",
"\n",
"from yolo3.model import preprocess_true_boxes, yolo_body, tiny_yolo_body, yolo_loss\n",
"from yolo3.utils import get_random_data\n",
"\n",
"\n",
"def _main():\n",
" annotation_path = '_annotations.txt' # path to Roboflow data annotations\n",
" log_dir = 'logs/000/' # where we're storing our logs\n",
" classes_path = '_classes.txt' # path to Roboflow class names\n",
" anchors_path = 'model_data/yolo_anchors.txt'\n",
" class_names = get_classes(classes_path)\n",
" print(\"-------------------CLASS NAMES-------------------\")\n",
" print(class_names)\n",
" print(\"-------------------CLASS NAMES-------------------\")\n",
" num_classes = len(class_names)\n",
" anchors = get_anchors(anchors_path)\n",
"\n",
" input_shape = (416,416) # multiple of 32, hw\n",
"\n",
" is_tiny_version = len(anchors)==6 # default setting\n",
" if is_tiny_version:\n",
" model = create_tiny_model(input_shape, anchors, num_classes,\n",
" freeze_body=2, weights_path='model_data/tiny_yolo_weights.h5')\n",
" else:\n",
" model = create_model(input_shape, anchors, num_classes,\n",
" freeze_body=2, weights_path='model_data/yolo.h5') # make sure you know what you freeze\n",
"\n",
" logging = TensorBoard(log_dir=log_dir)\n",
" checkpoint = ModelCheckpoint(log_dir + 'ep{epoch:03d}-loss{loss:.3f}-val_loss{val_loss:.3f}.h5',\n",
" monitor='val_loss', save_weights_only=True, save_best_only=True, period=3)\n",
" reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=3, verbose=1)\n",
" early_stopping = EarlyStopping(monitor='val_loss', min_delta=0, patience=10, verbose=1)\n",
"\n",
" val_split = 0.2 # set the size of the validation set\n",
" with open(annotation_path) as f:\n",
" lines = f.readlines()\n",
" np.random.seed(10101)\n",
" np.random.shuffle(lines)\n",
" np.random.seed(None)\n",
" num_val = int(len(lines)*val_split)\n",
" num_train = len(lines) - num_val\n",
"\n",
" # Train with frozen layers first, to get a stable loss.\n",
" # Adjust num epochs to your dataset. This step is enough to obtain a not bad model.\n",
" if True:\n",
" model.compile(optimizer=Adam(lr=1e-3), loss={\n",
" # use custom yolo_loss Lambda layer.\n",
" 'yolo_loss': lambda y_true, y_pred: y_pred})\n",
"\n",
" batch_size = 32\n",
" print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size))\n",
" model.fit_generator(data_generator_wrapper(lines[:num_train], batch_size, input_shape, anchors, num_classes),\n",
" steps_per_epoch=max(1, num_train//batch_size),\n",
" validation_data=data_generator_wrapper(lines[num_train:], batch_size, input_shape, anchors, num_classes),\n",
" validation_steps=max(1, num_val//batch_size),\n",
" epochs=500,\n",
" initial_epoch=0,\n",
" callbacks=[logging, checkpoint])\n",
" model.save_weights(log_dir + 'trained_weights_stage_1.h5')\n",
"\n",
" # Unfreeze and continue training, to fine-tune.\n",
" # Train longer if the result is not good.\n",
" if True:\n",
" for i in range(len(model.layers)):\n",
" model.layers[i].trainable = True\n",
" model.compile(optimizer=Adam(lr=1e-4), loss={'yolo_loss': lambda y_true, y_pred: y_pred}) # recompile to apply the change\n",
" print('Unfreeze all of the layers.')\n",
"\n",
" batch_size = 32 # note that more GPU memory is required after unfreezing the body\n",
" print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size))\n",
" model.fit_generator(data_generator_wrapper(lines[:num_train], batch_size, input_shape, anchors, num_classes),\n",
" steps_per_epoch=max(1, num_train//batch_size),\n",
" validation_data=data_generator_wrapper(lines[num_train:], batch_size, input_shape, anchors, num_classes),\n",
" validation_steps=max(1, num_val//batch_size),\n",
" epochs=100,\n",
" initial_epoch=50,\n",
" callbacks=[logging, checkpoint, reduce_lr, early_stopping])\n",
" model.save_weights(log_dir + 'trained_weights_final.h5')\n",
"\n",
" # Further training if needed.\n",
"\n",
"\n",
"def get_classes(classes_path):\n",
" '''loads the classes'''\n",
" with open(classes_path) as f:\n",
" class_names = f.readlines()\n",
" class_names = [c.strip() for c in class_names]\n",
" return class_names\n",
"\n",
"def get_anchors(anchors_path):\n",
" '''loads the anchors from a file'''\n",
" with open(anchors_path) as f:\n",
" anchors = f.readline()\n",
" anchors = [float(x) for x in anchors.split(',')]\n",
" return np.array(anchors).reshape(-1, 2)\n",
"\n",
"\n",
"def create_model(input_shape, anchors, num_classes, load_pretrained=True, freeze_body=2,\n",
" weights_path='model_data/yolo.h5'):\n",
" '''create the training model'''\n",
" K.clear_session() # get a new session\n",
" image_input = Input(shape=(None, None, 3))\n",
" h, w = input_shape\n",
" num_anchors = len(anchors)\n",
"\n",
" y_true = [Input(shape=(h//{0:32, 1:16, 2:8}[l], w//{0:32, 1:16, 2:8}[l], \\\n",
" num_anchors//3, num_classes+5)) for l in range(3)]\n",
"\n",
" model_body = yolo_body(image_input, num_anchors//3, num_classes)\n",
" print('Create YOLOv3 model with {} anchors and {} classes.'.format(num_anchors, num_classes))\n",
"\n",
" if load_pretrained:\n",
" model_body.load_weights(weights_path, by_name=True, skip_mismatch=True)\n",
" print('Load weights {}.'.format(weights_path))\n",
" if freeze_body in [1, 2]:\n",
" # Freeze darknet53 body or freeze all but 3 output layers.\n",
" num = (185, len(model_body.layers)-3)[freeze_body-1]\n",
" for i in range(num): model_body.layers[i].trainable = False\n",
" print('Freeze the first {} layers of total {} layers.'.format(num, len(model_body.layers)))\n",
"\n",
" model_loss = Lambda(yolo_loss, output_shape=(1,), name='yolo_loss',\n",
" arguments={'anchors': anchors, 'num_classes': num_classes, 'ignore_thresh': 0.5})(\n",
" [*model_body.output, *y_true])\n",
" model = Model([model_body.input, *y_true], model_loss)\n",
"\n",
" return model\n",
"\n",
"def create_tiny_model(input_shape, anchors, num_classes, load_pretrained=True, freeze_body=2,\n",
" weights_path='model_data/tiny_yolo_weights.h5'):\n",
" '''create the training model, for Tiny YOLOv3'''\n",
" K.clear_session() # get a new session\n",
" image_input = Input(shape=(None, None, 3))\n",
" h, w = input_shape\n",
" num_anchors = len(anchors)\n",
"\n",
" y_true = [Input(shape=(h//{0:32, 1:16}[l], w//{0:32, 1:16}[l], \\\n",
" num_anchors//2, num_classes+5)) for l in range(2)]\n",
"\n",
" model_body = tiny_yolo_body(image_input, num_anchors//2, num_classes)\n",
" print('Create Tiny YOLOv3 model with {} anchors and {} classes.'.format(num_anchors, num_classes))\n",
"\n",
" if load_pretrained:\n",
" model_body.load_weights(weights_path, by_name=True, skip_mismatch=True)\n",
" print('Load weights {}.'.format(weights_path))\n",
" if freeze_body in [1, 2]:\n",
" # Freeze the darknet body or freeze all but 2 output layers.\n",
" num = (20, len(model_body.layers)-2)[freeze_body-1]\n",
" for i in range(num): model_body.layers[i].trainable = False\n",
" print('Freeze the first {} layers of total {} layers.'.format(num, len(model_body.layers)))\n",
"\n",
" model_loss = Lambda(yolo_loss, output_shape=(1,), name='yolo_loss',\n",
" arguments={'anchors': anchors, 'num_classes': num_classes, 'ignore_thresh': 0.7})(\n",
" [*model_body.output, *y_true])\n",
" model = Model([model_body.input, *y_true], model_loss)\n",
"\n",
" return model\n",
"\n",
"def data_generator(annotation_lines, batch_size, input_shape, anchors, num_classes):\n",
" '''data generator for fit_generator'''\n",
" n = len(annotation_lines)\n",
" i = 0\n",
" while True:\n",
" image_data = []\n",
" box_data = []\n",
" for b in range(batch_size):\n",
" if i==0:\n",
" np.random.shuffle(annotation_lines)\n",
" image, box = get_random_data(annotation_lines[i], input_shape, random=True)\n",
" image_data.append(image)\n",
" box_data.append(box)\n",
" i = (i+1) % n\n",
" image_data = np.array(image_data)\n",
" box_data = np.array(box_data)\n",
" y_true = preprocess_true_boxes(box_data, input_shape, anchors, num_classes)\n",
" yield [image_data, *y_true], np.zeros(batch_size)\n",
"\n",
"def data_generator_wrapper(annotation_lines, batch_size, input_shape, anchors, num_classes):\n",
" n = len(annotation_lines)\n",
" if n==0 or batch_size<=0: return None\n",
" return data_generator(annotation_lines, batch_size, input_shape, anchors, num_classes)\n",
"\n",
"if __name__ == '__main__':\n",
" _main()"
],
"execution_count": 18,
"outputs": [
{
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
],
"name": "stderr"
},
{
"output_type": "error",
"ename": "FileNotFoundError",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-18-fd737c45ef07>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 189\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 190\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m__name__\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'__main__'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 191\u001b[0;31m \u001b[0m_main\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m<ipython-input-18-fd737c45ef07>\u001b[0m in \u001b[0;36m_main\u001b[0;34m()\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[0mclasses_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'_classes.txt'\u001b[0m \u001b[0;31m# path to Roboflow class names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0manchors_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'model_data/yolo_anchors.txt'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m \u001b[0mclass_names\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_classes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclasses_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 22\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"-------------------CLASS NAMES-------------------\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclass_names\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-18-fd737c45ef07>\u001b[0m in \u001b[0;36mget_classes\u001b[0;34m(classes_path)\u001b[0m\n\u001b[1;32m 93\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_classes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclasses_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 94\u001b[0m \u001b[0;34m'''loads the classes'''\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 95\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclasses_path\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 96\u001b[0m \u001b[0mclass_names\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadlines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[0mclass_names\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mclass_names\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '_classes.txt'"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "48yw4UaOYgQS",
"colab_type": "code",
"colab": {}
},
"source": [
"## can call this cell instead of the above\n",
"# !python train.py"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "dFX-2_M8bMQ3",
"colab_type": "text"
},
"source": [
"## Use our model for inference\n",
"\n",
"For predictions, we'll call a a Python script called `yolo_video.py` with required arguments for our use case: a path to our specific first stage trained weights (see our blog for why we're using only stage one), a path to our custom class names, and a flag to specify we're using images."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "AlVyevd8b8gG",
"colab_type": "text"
},
"source": [
"Additional arguments for `yolo_video.py` are as follows:\n",
"\n",
"```\n",
"usage: yolo_video.py [-h] [--model MODEL] [--anchors ANCHORS]\n",
" [--classes CLASSES] [--gpu_num GPU_NUM] [--image]\n",
" [--input] [--output]\n",
"\n",
"positional arguments:\n",
" --input Video input path\n",
" --output Video output path\n",
"\n",
"optional arguments:\n",
" -h, --help show this help message and exit\n",
" --model MODEL path to model weight file, default model_data/yolo.h5\n",
" --anchors ANCHORS path to anchor definitions, default\n",
" model_data/yolo_anchors.txt\n",
" --classes CLASSES path to class definitions, default\n",
" model_data/coco_classes.txt\n",
" --gpu_num GPU_NUM Number of GPU to use, default 1\n",
" --image Image detection mode, will ignore all positional arguments\n",
"```"
]
},
{
"cell_type": "code",
"metadata": {
"id": "zcJbmgNEO1bE",
"colab_type": "code",
"colab": {}
},
"source": [
"!python yolo_video.py --model=\"./logs/000/trained_weights_stage_1.h5\" --classes=\"_classes.txt\" --image"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "EbACT_RJdGVg",
"colab_type": "text"
},
"source": [
"For input image names into the above, consider trying the following:\n",
"\n",
"- `00a7a49c47d51fd16a4cbb17e2d2cf86.jpg` # white-king works! + knight\n",
"- `015d0d7ff365f0b7492ff079c8c7d56c.jpg` # black-queen mixes up\n",
"- `176b28b5c417f39a9e5d37545fca5b4c.jpg` # finds only five\n",
"- `4673f994f60a2ea7afdddc1b752947c0.jpg` # white-rook (thinks king)\n",
"- `5ca7f0cb1c500554e65ad031190f8e9f.jpg` # white-pawn (missed white-king)\n",
"- `fbf15139f38a46e02b5f4061c0c9b08f.jpg` # black-king success!\n",
"\n",
"You can view these images in your Colab notebook by clicking on the image name in the expanded left-hand panel (Files → keras-yolo3 → IMG_NAME )."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "88oJlBl4dumo",
"colab_type": "text"
},
"source": [
"## Move currently trained model to GDrive\n",
"\n",
"Optionally, you may want to save the new weights that your model trained so that the next time you run this notebook, you can either skip training and use these weights for inference or begin training where you left off with this weights file.\n",
"\n",
"Following the below will link your Colab notebook to your Google Drive, and save the weights (named as the current time you saved them to enforce a unique file name) in your Drive folder."
]
},
{
"cell_type": "code",
"metadata": {
"id": "B4t94dBNdsxz",
"colab_type": "code",
"colab": {}
},
"source": [
"# mount Google Drive\n",
"from google.colab import drive\n",
"drive.mount('/content/drive')"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "DLe0Y4Z8BOVF",
"colab_type": "code",
"colab": {}
},
"source": [
"# create a copy of the weights file with a datetime \n",
"# and move that file to your own Drive\n",
"%cp ./logs/000/trained_weights_stage_1.h5 ./logs/000/trained_weights_stage_1_$(date +%F-%H:%M).h5\n",
"%mv ./logs/000/trained_weights_stage_1_$(date +%F-%H:%M).h5 /content/drive/My\\ Drive/"
],
"execution_count": 0,
"outputs": []
}
]
}
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