123 KiB
123 KiB
NOTE: For the most up to date version of this notebook, please be sure to copy from this link:
Training YOLOv3 object detection on a custom dataset
💡 Recommendation: Open this blog post to continue.
Overview
This notebook walks through how to train a YOLOv3 object detection model on your own dataset using Roboflow and Colab.
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.
Our Data
Our dataset of 289 chess images (and 2894 annotations!) is hosted publicly on Roboflow here.
Our Model
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.
The GitHub repo containing the majority of the code we'll use is available here.
Training
Google Colab provides free GPU resources. Click "Runtime" → "Change runtime type" → Hardware Accelerator dropdown to "GPU."
Colab does have memory limitations, and notebooks must be open in your browser to run. Sessions automatically clear themselves after 24 hours.
Inference
We'll leverage the python_video.py script to produce predictions. Arguments are specified below.
It's recommended that you expand the left-hand panel to view this notebook's Table of contents, Code Snippets, and Files.
Then, click "Files." You'll see files appear here as we work through the notebook.
About
Roboflow makes managing, preprocessing, augmenting, and versioning datasets for computer vision seamless.
Developers reduce 50% of their boilerplate code when using Roboflow's workflow, save training time, and increase model reproducibility.
Setup our environment
First, we'll install the version of Keras our YOLOv3 implementation calls for and verify it installs corrects.
# Get our kernel running
print("Hello, Roboflow")Hello, Roboflow
# Our YOLOv3 implementation calls for this Keras version
!pip install keras==2.2.4Collecting keras==2.2.4 [?25l Downloading https://files.pythonhosted.org/packages/5e/10/aa32dad071ce52b5502266b5c659451cfd6ffcbf14e6c8c4f16c0ff5aaab/Keras-2.2.4-py2.py3-none-any.whl (312kB) [K |█ | 10kB 22.0MB/s eta 0:00:01 [K |██ | 20kB 27.1MB/s eta 0:00:01 [K |███▏ | 30kB 30.9MB/s eta 0:00:01 [K |████▏ | 40kB 31.0MB/s eta 0:00:01 [K |█████▎ | 51kB 18.5MB/s eta 0:00:01 [K |██████▎ | 61kB 17.0MB/s eta 0:00:01 [K |███████▍ | 71kB 16.1MB/s eta 0:00:01 [K |████████▍ | 81kB 17.6MB/s eta 0:00:01 [K |█████████▍ | 92kB 15.1MB/s eta 0:00:01 [K |██████████▌ | 102kB 15.5MB/s eta 0:00:01 [K |███████████▌ | 112kB 15.5MB/s eta 0:00:01 [K |████████████▋ | 122kB 15.5MB/s eta 0:00:01 [K |█████████████▋ | 133kB 15.5MB/s eta 0:00:01 [K |██████████████▊ | 143kB 15.5MB/s eta 0:00:01 [K |███████████████▊ | 153kB 15.5MB/s eta 0:00:01 [K |████████████████▊ | 163kB 15.5MB/s eta 0:00:01 [K |█████████████████▉ | 174kB 15.5MB/s eta 0:00:01 [K |██████████████████▉ | 184kB 15.5MB/s eta 0:00:01 [K |████████████████████ | 194kB 15.5MB/s eta 0:00:01 [K |█████████████████████ | 204kB 15.5MB/s eta 0:00:01 [K |██████████████████████ | 215kB 15.5MB/s eta 0:00:01 [K |███████████████████████ | 225kB 15.5MB/s eta 0:00:01 [K |████████████████████████▏ | 235kB 15.5MB/s eta 0:00:01 [K |█████████████████████████▏ | 245kB 15.5MB/s eta 0:00:01 [K |██████████████████████████▏ | 256kB 15.5MB/s eta 0:00:01 [K |███████████████████████████▎ | 266kB 15.5MB/s eta 0:00:01 [K |████████████████████████████▎ | 276kB 15.5MB/s eta 0:00:01 [K |█████████████████████████████▍ | 286kB 15.5MB/s eta 0:00:01 [K |██████████████████████████████▍ | 296kB 15.5MB/s eta 0:00:01 [K |███████████████████████████████▌| 307kB 15.5MB/s eta 0:00:01 [K |████████████████████████████████| 317kB 15.5MB/s [?25hRequirement already satisfied: pyyaml in /usr/local/lib/python3.6/dist-packages (from keras==2.2.4) (3.13) Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.6/dist-packages (from keras==2.2.4) (1.12.0) Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras==2.2.4) (2.10.0) Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/dist-packages (from keras==2.2.4) (1.0.8) Requirement already satisfied: scipy>=0.14 in /usr/local/lib/python3.6/dist-packages (from keras==2.2.4) (1.4.1) Requirement already satisfied: numpy>=1.9.1 in /usr/local/lib/python3.6/dist-packages (from keras==2.2.4) (1.18.2) Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from keras==2.2.4) (1.1.0) Installing collected packages: keras Found existing installation: Keras 2.3.1 Uninstalling Keras-2.3.1: Successfully uninstalled Keras-2.3.1 Successfully installed keras-2.2.4
# use TF 1.x
%tensorflow_version 1.xTensorFlow 1.x selected.
# Verify our version is correct
!python -c 'import keras; print(keras.__version__)'Using TensorFlow backend. 2.2.4
# Next, we'll grab all the code from our repository of interest
!git clone https://github.com/roboflow-ai/keras-yolo3.gitCloning into 'keras-yolo3'...
remote: Enumerating objects: 165, done.[K
Receiving objects: 100% (165/165), 156.01 KiB | 300.00 KiB/s, done.
remote: Total 165 (delta 0), reused 0 (delta 0), pack-reused 165
Resolving deltas: 100% (79/79), done.
# here's what we cloned (also, see "Files" in the left-hand Colab pane)
%ls[0m[01;34mkeras-yolo3[0m/ [01;34msample_data[0m/
# change directory to the repo we cloned
%cd keras-yolo3//content/keras-yolo3
# show the contents of our repo
%lscoco_annotation.py kmeans.py train_bottleneck.py yolo.py convert.py LICENSE train.py yolov3.cfg darknet53.cfg [0m[01;34mmodel_data[0m/ voc_annotation.py yolov3-tiny.cfg [01;34mfont[0m/ README.md [01;34myolo3[0m/ yolo_video.py
Get our training data from Roboflow
Next, we need to add our data from Roboflow into our environment.
Our dataset, with annotations, is here.
Here's how to bring those images from Roboflow to Colab:
- Visit this link.
- Click the "416x416auto-orient" under Downloads.
- On the dataset detail page, select "Download" in the upper right-hand corner.
- 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.
- On the download popup, select the YOLOv3 Keras option and the "Show download
code". - Copy the code snippet Roboflow generates for you, and paste it in the next cell.
This is the download menu you want (from step 5):
The top code snippet is the one you want to copy (from step 6) and paste in the next notebook cell:
This cell below is only one you need to change to have YOLOv3 train on your own Roboflow dataset.
# Paste Roboflow code from snippet here from above to here! eg !curl -L https://app.roboflow.ai/ds/eOSXbt7KWu?key=YOURKEY | jar -x
!curl -L https://app.roboflow.ai/ds/REPLACE-THIS-LINk > roboflow.zip; unzip roboflow.zip; rm roboflow.zip
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0
100 27 100 27 0 0 59 0 --:--:-- --:--:-- --:--:-- 59
Archive: roboflow.zip
End-of-central-directory signature not found. Either this file is not
a zipfile, or it constitutes one disk of a multi-part archive. In the
latter case the central directory and zipfile comment will be found on
the last disk(s) of this archive.
unzip: cannot find zipfile directory in one of roboflow.zip or
roboflow.zip.zip, and cannot find roboflow.zip.ZIP, period.
%lscoco_annotation.py kmeans.py train_bottleneck.py yolo.py convert.py LICENSE train.py yolov3.cfg darknet53.cfg [0m[01;34mmodel_data[0m/ voc_annotation.py yolov3-tiny.cfg [01;34mfont[0m/ README.md [01;34myolo3[0m/ yolo_video.py
# change directory into our export folder from Roboflow
%cd train[Errno 2] No such file or directory: 'train' /content/keras-yolo3
# show what came with the Roboflow export
%lscoco_annotation.py kmeans.py train_bottleneck.py yolo.py convert.py LICENSE train.py yolov3.cfg darknet53.cfg [0m[01;34mmodel_data[0m/ voc_annotation.py yolov3-tiny.cfg [01;34mfont[0m/ README.md [01;34myolo3[0m/ yolo_video.py
# move everything from the Roboflow export to the root of our keras-yolo3 folder
%mv * ../# change directory back to our
%cd ../content
# show that all our images, _annotations.txt, and _classes.txt made it to our root directory
%lscoco_annotation.py kmeans.py train_bottleneck.py yolov3.cfg convert.py LICENSE train.py yolov3-tiny.cfg darknet53.cfg [0m[01;34mmodel_data[0m/ voc_annotation.py yolo_video.py [01;34mfont[0m/ README.md [01;34myolo3[0m/ [01;34mkeras-yolo3[0m/ [01;34msample_data[0m/ yolo.py
Set up and train our model
Next, we'll download pre-trained weighs weights from DarkNet, set up our YOLOv3 architecture with those pre-trained weights, and initiate training.
# download our DarkNet weights
!wget https://pjreddie.com/media/files/yolov3.weights--2020-04-17 20:07:32-- https://pjreddie.com/media/files/yolov3.weights Resolving pjreddie.com (pjreddie.com)... 128.208.4.108 Connecting to pjreddie.com (pjreddie.com)|128.208.4.108|:443... connected. HTTP request sent, awaiting response... 200 OK Length: 248007048 (237M) [application/octet-stream] Saving to: ‘yolov3.weights’ yolov3.weights 100%[===================>] 236.52M 254KB/s in 12m 13s 2020-04-17 20:19:47 (330 KB/s) - ‘yolov3.weights’ saved [248007048/248007048]
# call a Python script to set up our architecture with downloaded pre-trained weights
!python convert.py yolov3.cfg yolov3.weights model_data/yolo.h5Using TensorFlow backend.
Loading weights.
Weights Header: 0 2 0 [32013312]
Parsing Darknet config.
Creating Keras model.
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.
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.
Parsing section net_0
Parsing section convolutional_0
conv2d bn leaky (3, 3, 3, 32)
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.
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.
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.
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.
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
2020-04-17 20:19:52.147911: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2000120000 Hz
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:
2020-04-17 20:19:52.148429: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version
2020-04-17 20:19:52.153494: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
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
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:
2020-04-17 20:19:52.379790: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Tesla T4, Compute Capability 7.5
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
2020-04-17 20:19:52.381958: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Found device 0 with properties:
name: Tesla T4 major: 7 minor: 5 memoryClockRate(GHz): 1.59
pciBusID: 0000:00:04.0
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
2020-04-17 20:19:52.383893: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2020-04-17 20:19:52.385764: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2020-04-17 20:19:52.386109: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2020-04-17 20:19:52.387660: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2020-04-17 20:19:52.412685: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2020-04-17 20:19:52.415845: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
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
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
2020-04-17 20:19:52.417026: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1767] Adding visible gpu devices: 0
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
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:
2020-04-17 20:19:52.424093: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1186] 0
2020-04-17 20:19:52.424104: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1199] 0: 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
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
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.
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)
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.
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.
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.
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.
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.
Parsing section convolutional_1
conv2d bn leaky (3, 3, 32, 64)
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conv2d bn leaky (1, 1, 64, 32)
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conv2d bn leaky (3, 3, 32, 64)
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conv2d bn leaky (3, 3, 64, 128)
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conv2d bn leaky (1, 1, 128, 64)
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conv2d bn leaky (3, 3, 64, 128)
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conv2d bn leaky (1, 1, 128, 64)
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conv2d bn leaky (3, 3, 64, 128)
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conv2d bn leaky (3, 3, 128, 256)
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conv2d bn leaky (1, 1, 256, 128)
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conv2d bn leaky (3, 3, 128, 256)
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conv2d bn leaky (1, 1, 256, 128)
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conv2d bn leaky (3, 3, 128, 256)
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conv2d bn leaky (1, 1, 256, 128)
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conv2d bn leaky (3, 3, 128, 256)
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conv2d bn leaky (1, 1, 256, 128)
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conv2d bn leaky (3, 3, 128, 256)
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conv2d bn leaky (1, 1, 256, 128)
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conv2d bn leaky (3, 3, 128, 256)
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conv2d bn leaky (1, 1, 256, 128)
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conv2d bn leaky (3, 3, 128, 256)
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conv2d bn leaky (1, 1, 256, 128)
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conv2d bn leaky (3, 3, 128, 256)
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conv2d bn leaky (1, 1, 256, 128)
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conv2d bn leaky (3, 3, 128, 256)
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conv2d bn leaky (3, 3, 256, 512)
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conv2d bn leaky (1, 1, 512, 256)
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conv2d bn leaky (3, 3, 256, 512)
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conv2d bn leaky (1, 1, 512, 256)
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conv2d bn leaky (3, 3, 256, 512)
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conv2d bn leaky (1, 1, 512, 256)
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conv2d bn leaky (3, 3, 256, 512)
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conv2d bn leaky (1, 1, 512, 256)
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conv2d bn leaky (3, 3, 256, 512)
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conv2d bn leaky (1, 1, 512, 256)
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conv2d bn leaky (3, 3, 256, 512)
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conv2d bn leaky (1, 1, 512, 256)
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conv2d bn leaky (3, 3, 256, 512)
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conv2d bn leaky (1, 1, 512, 256)
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conv2d bn leaky (3, 3, 256, 512)
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conv2d bn leaky (1, 1, 512, 256)
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conv2d bn leaky (3, 3, 256, 512)
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Parsing section convolutional_43
conv2d bn leaky (3, 3, 512, 1024)
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conv2d bn leaky (1, 1, 1024, 512)
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conv2d bn leaky (3, 3, 512, 1024)
Parsing section shortcut_19
Parsing section convolutional_46
conv2d bn leaky (1, 1, 1024, 512)
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conv2d bn leaky (3, 3, 512, 1024)
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Parsing section convolutional_48
conv2d bn leaky (1, 1, 1024, 512)
Parsing section convolutional_49
conv2d bn leaky (3, 3, 512, 1024)
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Parsing section convolutional_50
conv2d bn leaky (1, 1, 1024, 512)
Parsing section convolutional_51
conv2d bn leaky (3, 3, 512, 1024)
Parsing section shortcut_22
Parsing section convolutional_52
conv2d bn leaky (1, 1, 1024, 512)
Parsing section convolutional_53
conv2d bn leaky (3, 3, 512, 1024)
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conv2d bn leaky (1, 1, 1024, 512)
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conv2d bn leaky (3, 3, 512, 1024)
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conv2d bn leaky (1, 1, 1024, 512)
Parsing section convolutional_57
conv2d bn leaky (3, 3, 512, 1024)
Parsing section convolutional_58
conv2d linear (1, 1, 1024, 255)
Parsing section yolo_0
Parsing section route_0
Parsing section convolutional_59
conv2d bn leaky (1, 1, 512, 256)
Parsing section upsample_0
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.
Parsing section route_1
Concatenating route layers: [<tf.Tensor 'up_sampling2d_1/ResizeNearestNeighbor:0' shape=(?, ?, ?, 256) dtype=float32>, <tf.Tensor 'add_19/add:0' shape=(?, ?, ?, 512) dtype=float32>]
Parsing section convolutional_60
conv2d bn leaky (1, 1, 768, 256)
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conv2d bn leaky (3, 3, 256, 512)
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conv2d bn leaky (1, 1, 512, 256)
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conv2d bn leaky (3, 3, 256, 512)
Parsing section convolutional_64
conv2d bn leaky (1, 1, 512, 256)
Parsing section convolutional_65
conv2d bn leaky (3, 3, 256, 512)
Parsing section convolutional_66
conv2d linear (1, 1, 512, 255)
Parsing section yolo_1
Parsing section route_2
Parsing section convolutional_67
conv2d bn leaky (1, 1, 256, 128)
Parsing section upsample_1
Parsing section route_3
Concatenating route layers: [<tf.Tensor 'up_sampling2d_2/ResizeNearestNeighbor:0' shape=(?, ?, ?, 128) dtype=float32>, <tf.Tensor 'add_11/add:0' shape=(?, ?, ?, 256) dtype=float32>]
Parsing section convolutional_68
conv2d bn leaky (1, 1, 384, 128)
Parsing section convolutional_69
conv2d bn leaky (3, 3, 128, 256)
Parsing section convolutional_70
conv2d bn leaky (1, 1, 256, 128)
Parsing section convolutional_71
conv2d bn leaky (3, 3, 128, 256)
Parsing section convolutional_72
conv2d bn leaky (1, 1, 256, 128)
Parsing section convolutional_73
conv2d bn leaky (3, 3, 128, 256)
Parsing section convolutional_74
conv2d linear (1, 1, 256, 255)
Parsing section yolo_2
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) (None, None, None, 3 0
__________________________________________________________________________________________________
conv2d_1 (Conv2D) (None, None, None, 3 864 input_1[0][0]
__________________________________________________________________________________________________
batch_normalization_1 (BatchNor (None, None, None, 3 128 conv2d_1[0][0]
__________________________________________________________________________________________________
leaky_re_lu_1 (LeakyReLU) (None, None, None, 3 0 batch_normalization_1[0][0]
__________________________________________________________________________________________________
zero_padding2d_1 (ZeroPadding2D (None, None, None, 3 0 leaky_re_lu_1[0][0]
__________________________________________________________________________________________________
conv2d_2 (Conv2D) (None, None, None, 6 18432 zero_padding2d_1[0][0]
__________________________________________________________________________________________________
batch_normalization_2 (BatchNor (None, None, None, 6 256 conv2d_2[0][0]
__________________________________________________________________________________________________
leaky_re_lu_2 (LeakyReLU) (None, None, None, 6 0 batch_normalization_2[0][0]
__________________________________________________________________________________________________
conv2d_3 (Conv2D) (None, None, None, 3 2048 leaky_re_lu_2[0][0]
__________________________________________________________________________________________________
batch_normalization_3 (BatchNor (None, None, None, 3 128 conv2d_3[0][0]
__________________________________________________________________________________________________
leaky_re_lu_3 (LeakyReLU) (None, None, None, 3 0 batch_normalization_3[0][0]
__________________________________________________________________________________________________
conv2d_4 (Conv2D) (None, None, None, 6 18432 leaky_re_lu_3[0][0]
__________________________________________________________________________________________________
batch_normalization_4 (BatchNor (None, None, None, 6 256 conv2d_4[0][0]
__________________________________________________________________________________________________
leaky_re_lu_4 (LeakyReLU) (None, None, None, 6 0 batch_normalization_4[0][0]
__________________________________________________________________________________________________
add_1 (Add) (None, None, None, 6 0 leaky_re_lu_2[0][0]
leaky_re_lu_4[0][0]
__________________________________________________________________________________________________
zero_padding2d_2 (ZeroPadding2D (None, None, None, 6 0 add_1[0][0]
__________________________________________________________________________________________________
conv2d_5 (Conv2D) (None, None, None, 1 73728 zero_padding2d_2[0][0]
__________________________________________________________________________________________________
batch_normalization_5 (BatchNor (None, None, None, 1 512 conv2d_5[0][0]
__________________________________________________________________________________________________
leaky_re_lu_5 (LeakyReLU) (None, None, None, 1 0 batch_normalization_5[0][0]
__________________________________________________________________________________________________
conv2d_6 (Conv2D) (None, None, None, 6 8192 leaky_re_lu_5[0][0]
__________________________________________________________________________________________________
batch_normalization_6 (BatchNor (None, None, None, 6 256 conv2d_6[0][0]
__________________________________________________________________________________________________
leaky_re_lu_6 (LeakyReLU) (None, None, None, 6 0 batch_normalization_6[0][0]
__________________________________________________________________________________________________
conv2d_7 (Conv2D) (None, None, None, 1 73728 leaky_re_lu_6[0][0]
__________________________________________________________________________________________________
batch_normalization_7 (BatchNor (None, None, None, 1 512 conv2d_7[0][0]
__________________________________________________________________________________________________
leaky_re_lu_7 (LeakyReLU) (None, None, None, 1 0 batch_normalization_7[0][0]
__________________________________________________________________________________________________
add_2 (Add) (None, None, None, 1 0 leaky_re_lu_5[0][0]
leaky_re_lu_7[0][0]
__________________________________________________________________________________________________
conv2d_8 (Conv2D) (None, None, None, 6 8192 add_2[0][0]
__________________________________________________________________________________________________
batch_normalization_8 (BatchNor (None, None, None, 6 256 conv2d_8[0][0]
__________________________________________________________________________________________________
leaky_re_lu_8 (LeakyReLU) (None, None, None, 6 0 batch_normalization_8[0][0]
__________________________________________________________________________________________________
conv2d_9 (Conv2D) (None, None, None, 1 73728 leaky_re_lu_8[0][0]
__________________________________________________________________________________________________
batch_normalization_9 (BatchNor (None, None, None, 1 512 conv2d_9[0][0]
__________________________________________________________________________________________________
leaky_re_lu_9 (LeakyReLU) (None, None, None, 1 0 batch_normalization_9[0][0]
__________________________________________________________________________________________________
add_3 (Add) (None, None, None, 1 0 add_2[0][0]
leaky_re_lu_9[0][0]
__________________________________________________________________________________________________
zero_padding2d_3 (ZeroPadding2D (None, None, None, 1 0 add_3[0][0]
__________________________________________________________________________________________________
conv2d_10 (Conv2D) (None, None, None, 2 294912 zero_padding2d_3[0][0]
__________________________________________________________________________________________________
batch_normalization_10 (BatchNo (None, None, None, 2 1024 conv2d_10[0][0]
__________________________________________________________________________________________________
leaky_re_lu_10 (LeakyReLU) (None, None, None, 2 0 batch_normalization_10[0][0]
__________________________________________________________________________________________________
conv2d_11 (Conv2D) (None, None, None, 1 32768 leaky_re_lu_10[0][0]
__________________________________________________________________________________________________
batch_normalization_11 (BatchNo (None, None, None, 1 512 conv2d_11[0][0]
__________________________________________________________________________________________________
leaky_re_lu_11 (LeakyReLU) (None, None, None, 1 0 batch_normalization_11[0][0]
__________________________________________________________________________________________________
conv2d_12 (Conv2D) (None, None, None, 2 294912 leaky_re_lu_11[0][0]
__________________________________________________________________________________________________
batch_normalization_12 (BatchNo (None, None, None, 2 1024 conv2d_12[0][0]
__________________________________________________________________________________________________
leaky_re_lu_12 (LeakyReLU) (None, None, None, 2 0 batch_normalization_12[0][0]
__________________________________________________________________________________________________
add_4 (Add) (None, None, None, 2 0 leaky_re_lu_10[0][0]
leaky_re_lu_12[0][0]
__________________________________________________________________________________________________
conv2d_13 (Conv2D) (None, None, None, 1 32768 add_4[0][0]
__________________________________________________________________________________________________
batch_normalization_13 (BatchNo (None, None, None, 1 512 conv2d_13[0][0]
__________________________________________________________________________________________________
leaky_re_lu_13 (LeakyReLU) (None, None, None, 1 0 batch_normalization_13[0][0]
__________________________________________________________________________________________________
conv2d_14 (Conv2D) (None, None, None, 2 294912 leaky_re_lu_13[0][0]
__________________________________________________________________________________________________
batch_normalization_14 (BatchNo (None, None, None, 2 1024 conv2d_14[0][0]
__________________________________________________________________________________________________
leaky_re_lu_14 (LeakyReLU) (None, None, None, 2 0 batch_normalization_14[0][0]
__________________________________________________________________________________________________
add_5 (Add) (None, None, None, 2 0 add_4[0][0]
leaky_re_lu_14[0][0]
__________________________________________________________________________________________________
conv2d_15 (Conv2D) (None, None, None, 1 32768 add_5[0][0]
__________________________________________________________________________________________________
batch_normalization_15 (BatchNo (None, None, None, 1 512 conv2d_15[0][0]
__________________________________________________________________________________________________
leaky_re_lu_15 (LeakyReLU) (None, None, None, 1 0 batch_normalization_15[0][0]
__________________________________________________________________________________________________
conv2d_16 (Conv2D) (None, None, None, 2 294912 leaky_re_lu_15[0][0]
__________________________________________________________________________________________________
batch_normalization_16 (BatchNo (None, None, None, 2 1024 conv2d_16[0][0]
__________________________________________________________________________________________________
leaky_re_lu_16 (LeakyReLU) (None, None, None, 2 0 batch_normalization_16[0][0]
__________________________________________________________________________________________________
add_6 (Add) (None, None, None, 2 0 add_5[0][0]
leaky_re_lu_16[0][0]
__________________________________________________________________________________________________
conv2d_17 (Conv2D) (None, None, None, 1 32768 add_6[0][0]
__________________________________________________________________________________________________
batch_normalization_17 (BatchNo (None, None, None, 1 512 conv2d_17[0][0]
__________________________________________________________________________________________________
leaky_re_lu_17 (LeakyReLU) (None, None, None, 1 0 batch_normalization_17[0][0]
__________________________________________________________________________________________________
conv2d_18 (Conv2D) (None, None, None, 2 294912 leaky_re_lu_17[0][0]
__________________________________________________________________________________________________
batch_normalization_18 (BatchNo (None, None, None, 2 1024 conv2d_18[0][0]
__________________________________________________________________________________________________
leaky_re_lu_18 (LeakyReLU) (None, None, None, 2 0 batch_normalization_18[0][0]
__________________________________________________________________________________________________
add_7 (Add) (None, None, None, 2 0 add_6[0][0]
leaky_re_lu_18[0][0]
__________________________________________________________________________________________________
conv2d_19 (Conv2D) (None, None, None, 1 32768 add_7[0][0]
__________________________________________________________________________________________________
batch_normalization_19 (BatchNo (None, None, None, 1 512 conv2d_19[0][0]
__________________________________________________________________________________________________
leaky_re_lu_19 (LeakyReLU) (None, None, None, 1 0 batch_normalization_19[0][0]
__________________________________________________________________________________________________
conv2d_20 (Conv2D) (None, None, None, 2 294912 leaky_re_lu_19[0][0]
__________________________________________________________________________________________________
batch_normalization_20 (BatchNo (None, None, None, 2 1024 conv2d_20[0][0]
__________________________________________________________________________________________________
leaky_re_lu_20 (LeakyReLU) (None, None, None, 2 0 batch_normalization_20[0][0]
__________________________________________________________________________________________________
add_8 (Add) (None, None, None, 2 0 add_7[0][0]
leaky_re_lu_20[0][0]
__________________________________________________________________________________________________
conv2d_21 (Conv2D) (None, None, None, 1 32768 add_8[0][0]
__________________________________________________________________________________________________
batch_normalization_21 (BatchNo (None, None, None, 1 512 conv2d_21[0][0]
__________________________________________________________________________________________________
leaky_re_lu_21 (LeakyReLU) (None, None, None, 1 0 batch_normalization_21[0][0]
__________________________________________________________________________________________________
conv2d_22 (Conv2D) (None, None, None, 2 294912 leaky_re_lu_21[0][0]
__________________________________________________________________________________________________
batch_normalization_22 (BatchNo (None, None, None, 2 1024 conv2d_22[0][0]
__________________________________________________________________________________________________
leaky_re_lu_22 (LeakyReLU) (None, None, None, 2 0 batch_normalization_22[0][0]
__________________________________________________________________________________________________
add_9 (Add) (None, None, None, 2 0 add_8[0][0]
leaky_re_lu_22[0][0]
__________________________________________________________________________________________________
conv2d_23 (Conv2D) (None, None, None, 1 32768 add_9[0][0]
__________________________________________________________________________________________________
batch_normalization_23 (BatchNo (None, None, None, 1 512 conv2d_23[0][0]
__________________________________________________________________________________________________
leaky_re_lu_23 (LeakyReLU) (None, None, None, 1 0 batch_normalization_23[0][0]
__________________________________________________________________________________________________
conv2d_24 (Conv2D) (None, None, None, 2 294912 leaky_re_lu_23[0][0]
__________________________________________________________________________________________________
batch_normalization_24 (BatchNo (None, None, None, 2 1024 conv2d_24[0][0]
__________________________________________________________________________________________________
leaky_re_lu_24 (LeakyReLU) (None, None, None, 2 0 batch_normalization_24[0][0]
__________________________________________________________________________________________________
add_10 (Add) (None, None, None, 2 0 add_9[0][0]
leaky_re_lu_24[0][0]
__________________________________________________________________________________________________
conv2d_25 (Conv2D) (None, None, None, 1 32768 add_10[0][0]
__________________________________________________________________________________________________
batch_normalization_25 (BatchNo (None, None, None, 1 512 conv2d_25[0][0]
__________________________________________________________________________________________________
leaky_re_lu_25 (LeakyReLU) (None, None, None, 1 0 batch_normalization_25[0][0]
__________________________________________________________________________________________________
conv2d_26 (Conv2D) (None, None, None, 2 294912 leaky_re_lu_25[0][0]
__________________________________________________________________________________________________
batch_normalization_26 (BatchNo (None, None, None, 2 1024 conv2d_26[0][0]
__________________________________________________________________________________________________
leaky_re_lu_26 (LeakyReLU) (None, None, None, 2 0 batch_normalization_26[0][0]
__________________________________________________________________________________________________
add_11 (Add) (None, None, None, 2 0 add_10[0][0]
leaky_re_lu_26[0][0]
__________________________________________________________________________________________________
zero_padding2d_4 (ZeroPadding2D (None, None, None, 2 0 add_11[0][0]
__________________________________________________________________________________________________
conv2d_27 (Conv2D) (None, None, None, 5 1179648 zero_padding2d_4[0][0]
__________________________________________________________________________________________________
batch_normalization_27 (BatchNo (None, None, None, 5 2048 conv2d_27[0][0]
__________________________________________________________________________________________________
leaky_re_lu_27 (LeakyReLU) (None, None, None, 5 0 batch_normalization_27[0][0]
__________________________________________________________________________________________________
conv2d_28 (Conv2D) (None, None, None, 2 131072 leaky_re_lu_27[0][0]
__________________________________________________________________________________________________
batch_normalization_28 (BatchNo (None, None, None, 2 1024 conv2d_28[0][0]
__________________________________________________________________________________________________
leaky_re_lu_28 (LeakyReLU) (None, None, None, 2 0 batch_normalization_28[0][0]
__________________________________________________________________________________________________
conv2d_29 (Conv2D) (None, None, None, 5 1179648 leaky_re_lu_28[0][0]
__________________________________________________________________________________________________
batch_normalization_29 (BatchNo (None, None, None, 5 2048 conv2d_29[0][0]
__________________________________________________________________________________________________
leaky_re_lu_29 (LeakyReLU) (None, None, None, 5 0 batch_normalization_29[0][0]
__________________________________________________________________________________________________
add_12 (Add) (None, None, None, 5 0 leaky_re_lu_27[0][0]
leaky_re_lu_29[0][0]
__________________________________________________________________________________________________
conv2d_30 (Conv2D) (None, None, None, 2 131072 add_12[0][0]
__________________________________________________________________________________________________
batch_normalization_30 (BatchNo (None, None, None, 2 1024 conv2d_30[0][0]
__________________________________________________________________________________________________
leaky_re_lu_30 (LeakyReLU) (None, None, None, 2 0 batch_normalization_30[0][0]
__________________________________________________________________________________________________
conv2d_31 (Conv2D) (None, None, None, 5 1179648 leaky_re_lu_30[0][0]
__________________________________________________________________________________________________
batch_normalization_31 (BatchNo (None, None, None, 5 2048 conv2d_31[0][0]
__________________________________________________________________________________________________
leaky_re_lu_31 (LeakyReLU) (None, None, None, 5 0 batch_normalization_31[0][0]
__________________________________________________________________________________________________
add_13 (Add) (None, None, None, 5 0 add_12[0][0]
leaky_re_lu_31[0][0]
__________________________________________________________________________________________________
conv2d_32 (Conv2D) (None, None, None, 2 131072 add_13[0][0]
__________________________________________________________________________________________________
batch_normalization_32 (BatchNo (None, None, None, 2 1024 conv2d_32[0][0]
__________________________________________________________________________________________________
leaky_re_lu_32 (LeakyReLU) (None, None, None, 2 0 batch_normalization_32[0][0]
__________________________________________________________________________________________________
conv2d_33 (Conv2D) (None, None, None, 5 1179648 leaky_re_lu_32[0][0]
__________________________________________________________________________________________________
batch_normalization_33 (BatchNo (None, None, None, 5 2048 conv2d_33[0][0]
__________________________________________________________________________________________________
leaky_re_lu_33 (LeakyReLU) (None, None, None, 5 0 batch_normalization_33[0][0]
__________________________________________________________________________________________________
add_14 (Add) (None, None, None, 5 0 add_13[0][0]
leaky_re_lu_33[0][0]
__________________________________________________________________________________________________
conv2d_34 (Conv2D) (None, None, None, 2 131072 add_14[0][0]
__________________________________________________________________________________________________
batch_normalization_34 (BatchNo (None, None, None, 2 1024 conv2d_34[0][0]
__________________________________________________________________________________________________
leaky_re_lu_34 (LeakyReLU) (None, None, None, 2 0 batch_normalization_34[0][0]
__________________________________________________________________________________________________
conv2d_35 (Conv2D) (None, None, None, 5 1179648 leaky_re_lu_34[0][0]
__________________________________________________________________________________________________
batch_normalization_35 (BatchNo (None, None, None, 5 2048 conv2d_35[0][0]
__________________________________________________________________________________________________
leaky_re_lu_35 (LeakyReLU) (None, None, None, 5 0 batch_normalization_35[0][0]
__________________________________________________________________________________________________
add_15 (Add) (None, None, None, 5 0 add_14[0][0]
leaky_re_lu_35[0][0]
__________________________________________________________________________________________________
conv2d_36 (Conv2D) (None, None, None, 2 131072 add_15[0][0]
__________________________________________________________________________________________________
batch_normalization_36 (BatchNo (None, None, None, 2 1024 conv2d_36[0][0]
__________________________________________________________________________________________________
leaky_re_lu_36 (LeakyReLU) (None, None, None, 2 0 batch_normalization_36[0][0]
__________________________________________________________________________________________________
conv2d_37 (Conv2D) (None, None, None, 5 1179648 leaky_re_lu_36[0][0]
__________________________________________________________________________________________________
batch_normalization_37 (BatchNo (None, None, None, 5 2048 conv2d_37[0][0]
__________________________________________________________________________________________________
leaky_re_lu_37 (LeakyReLU) (None, None, None, 5 0 batch_normalization_37[0][0]
__________________________________________________________________________________________________
add_16 (Add) (None, None, None, 5 0 add_15[0][0]
leaky_re_lu_37[0][0]
__________________________________________________________________________________________________
conv2d_38 (Conv2D) (None, None, None, 2 131072 add_16[0][0]
__________________________________________________________________________________________________
batch_normalization_38 (BatchNo (None, None, None, 2 1024 conv2d_38[0][0]
__________________________________________________________________________________________________
leaky_re_lu_38 (LeakyReLU) (None, None, None, 2 0 batch_normalization_38[0][0]
__________________________________________________________________________________________________
conv2d_39 (Conv2D) (None, None, None, 5 1179648 leaky_re_lu_38[0][0]
__________________________________________________________________________________________________
batch_normalization_39 (BatchNo (None, None, None, 5 2048 conv2d_39[0][0]
__________________________________________________________________________________________________
leaky_re_lu_39 (LeakyReLU) (None, None, None, 5 0 batch_normalization_39[0][0]
__________________________________________________________________________________________________
add_17 (Add) (None, None, None, 5 0 add_16[0][0]
leaky_re_lu_39[0][0]
__________________________________________________________________________________________________
conv2d_40 (Conv2D) (None, None, None, 2 131072 add_17[0][0]
__________________________________________________________________________________________________
batch_normalization_40 (BatchNo (None, None, None, 2 1024 conv2d_40[0][0]
__________________________________________________________________________________________________
leaky_re_lu_40 (LeakyReLU) (None, None, None, 2 0 batch_normalization_40[0][0]
__________________________________________________________________________________________________
conv2d_41 (Conv2D) (None, None, None, 5 1179648 leaky_re_lu_40[0][0]
__________________________________________________________________________________________________
batch_normalization_41 (BatchNo (None, None, None, 5 2048 conv2d_41[0][0]
__________________________________________________________________________________________________
leaky_re_lu_41 (LeakyReLU) (None, None, None, 5 0 batch_normalization_41[0][0]
__________________________________________________________________________________________________
add_18 (Add) (None, None, None, 5 0 add_17[0][0]
leaky_re_lu_41[0][0]
__________________________________________________________________________________________________
conv2d_42 (Conv2D) (None, None, None, 2 131072 add_18[0][0]
__________________________________________________________________________________________________
batch_normalization_42 (BatchNo (None, None, None, 2 1024 conv2d_42[0][0]
__________________________________________________________________________________________________
leaky_re_lu_42 (LeakyReLU) (None, None, None, 2 0 batch_normalization_42[0][0]
__________________________________________________________________________________________________
conv2d_43 (Conv2D) (None, None, None, 5 1179648 leaky_re_lu_42[0][0]
__________________________________________________________________________________________________
batch_normalization_43 (BatchNo (None, None, None, 5 2048 conv2d_43[0][0]
__________________________________________________________________________________________________
leaky_re_lu_43 (LeakyReLU) (None, None, None, 5 0 batch_normalization_43[0][0]
__________________________________________________________________________________________________
add_19 (Add) (None, None, None, 5 0 add_18[0][0]
leaky_re_lu_43[0][0]
__________________________________________________________________________________________________
zero_padding2d_5 (ZeroPadding2D (None, None, None, 5 0 add_19[0][0]
__________________________________________________________________________________________________
conv2d_44 (Conv2D) (None, None, None, 1 4718592 zero_padding2d_5[0][0]
__________________________________________________________________________________________________
batch_normalization_44 (BatchNo (None, None, None, 1 4096 conv2d_44[0][0]
__________________________________________________________________________________________________
leaky_re_lu_44 (LeakyReLU) (None, None, None, 1 0 batch_normalization_44[0][0]
__________________________________________________________________________________________________
conv2d_45 (Conv2D) (None, None, None, 5 524288 leaky_re_lu_44[0][0]
__________________________________________________________________________________________________
batch_normalization_45 (BatchNo (None, None, None, 5 2048 conv2d_45[0][0]
__________________________________________________________________________________________________
leaky_re_lu_45 (LeakyReLU) (None, None, None, 5 0 batch_normalization_45[0][0]
__________________________________________________________________________________________________
conv2d_46 (Conv2D) (None, None, None, 1 4718592 leaky_re_lu_45[0][0]
__________________________________________________________________________________________________
batch_normalization_46 (BatchNo (None, None, None, 1 4096 conv2d_46[0][0]
__________________________________________________________________________________________________
leaky_re_lu_46 (LeakyReLU) (None, None, None, 1 0 batch_normalization_46[0][0]
__________________________________________________________________________________________________
add_20 (Add) (None, None, None, 1 0 leaky_re_lu_44[0][0]
leaky_re_lu_46[0][0]
__________________________________________________________________________________________________
conv2d_47 (Conv2D) (None, None, None, 5 524288 add_20[0][0]
__________________________________________________________________________________________________
batch_normalization_47 (BatchNo (None, None, None, 5 2048 conv2d_47[0][0]
__________________________________________________________________________________________________
leaky_re_lu_47 (LeakyReLU) (None, None, None, 5 0 batch_normalization_47[0][0]
__________________________________________________________________________________________________
conv2d_48 (Conv2D) (None, None, None, 1 4718592 leaky_re_lu_47[0][0]
__________________________________________________________________________________________________
batch_normalization_48 (BatchNo (None, None, None, 1 4096 conv2d_48[0][0]
__________________________________________________________________________________________________
leaky_re_lu_48 (LeakyReLU) (None, None, None, 1 0 batch_normalization_48[0][0]
__________________________________________________________________________________________________
add_21 (Add) (None, None, None, 1 0 add_20[0][0]
leaky_re_lu_48[0][0]
__________________________________________________________________________________________________
conv2d_49 (Conv2D) (None, None, None, 5 524288 add_21[0][0]
__________________________________________________________________________________________________
batch_normalization_49 (BatchNo (None, None, None, 5 2048 conv2d_49[0][0]
__________________________________________________________________________________________________
leaky_re_lu_49 (LeakyReLU) (None, None, None, 5 0 batch_normalization_49[0][0]
__________________________________________________________________________________________________
conv2d_50 (Conv2D) (None, None, None, 1 4718592 leaky_re_lu_49[0][0]
__________________________________________________________________________________________________
batch_normalization_50 (BatchNo (None, None, None, 1 4096 conv2d_50[0][0]
__________________________________________________________________________________________________
leaky_re_lu_50 (LeakyReLU) (None, None, None, 1 0 batch_normalization_50[0][0]
__________________________________________________________________________________________________
add_22 (Add) (None, None, None, 1 0 add_21[0][0]
leaky_re_lu_50[0][0]
__________________________________________________________________________________________________
conv2d_51 (Conv2D) (None, None, None, 5 524288 add_22[0][0]
__________________________________________________________________________________________________
batch_normalization_51 (BatchNo (None, None, None, 5 2048 conv2d_51[0][0]
__________________________________________________________________________________________________
leaky_re_lu_51 (LeakyReLU) (None, None, None, 5 0 batch_normalization_51[0][0]
__________________________________________________________________________________________________
conv2d_52 (Conv2D) (None, None, None, 1 4718592 leaky_re_lu_51[0][0]
__________________________________________________________________________________________________
batch_normalization_52 (BatchNo (None, None, None, 1 4096 conv2d_52[0][0]
__________________________________________________________________________________________________
leaky_re_lu_52 (LeakyReLU) (None, None, None, 1 0 batch_normalization_52[0][0]
__________________________________________________________________________________________________
add_23 (Add) (None, None, None, 1 0 add_22[0][0]
leaky_re_lu_52[0][0]
__________________________________________________________________________________________________
conv2d_53 (Conv2D) (None, None, None, 5 524288 add_23[0][0]
__________________________________________________________________________________________________
batch_normalization_53 (BatchNo (None, None, None, 5 2048 conv2d_53[0][0]
__________________________________________________________________________________________________
leaky_re_lu_53 (LeakyReLU) (None, None, None, 5 0 batch_normalization_53[0][0]
__________________________________________________________________________________________________
conv2d_54 (Conv2D) (None, None, None, 1 4718592 leaky_re_lu_53[0][0]
__________________________________________________________________________________________________
batch_normalization_54 (BatchNo (None, None, None, 1 4096 conv2d_54[0][0]
__________________________________________________________________________________________________
leaky_re_lu_54 (LeakyReLU) (None, None, None, 1 0 batch_normalization_54[0][0]
__________________________________________________________________________________________________
conv2d_55 (Conv2D) (None, None, None, 5 524288 leaky_re_lu_54[0][0]
__________________________________________________________________________________________________
batch_normalization_55 (BatchNo (None, None, None, 5 2048 conv2d_55[0][0]
__________________________________________________________________________________________________
leaky_re_lu_55 (LeakyReLU) (None, None, None, 5 0 batch_normalization_55[0][0]
__________________________________________________________________________________________________
conv2d_56 (Conv2D) (None, None, None, 1 4718592 leaky_re_lu_55[0][0]
__________________________________________________________________________________________________
batch_normalization_56 (BatchNo (None, None, None, 1 4096 conv2d_56[0][0]
__________________________________________________________________________________________________
leaky_re_lu_56 (LeakyReLU) (None, None, None, 1 0 batch_normalization_56[0][0]
__________________________________________________________________________________________________
conv2d_57 (Conv2D) (None, None, None, 5 524288 leaky_re_lu_56[0][0]
__________________________________________________________________________________________________
batch_normalization_57 (BatchNo (None, None, None, 5 2048 conv2d_57[0][0]
__________________________________________________________________________________________________
leaky_re_lu_57 (LeakyReLU) (None, None, None, 5 0 batch_normalization_57[0][0]
__________________________________________________________________________________________________
conv2d_60 (Conv2D) (None, None, None, 2 131072 leaky_re_lu_57[0][0]
__________________________________________________________________________________________________
batch_normalization_59 (BatchNo (None, None, None, 2 1024 conv2d_60[0][0]
__________________________________________________________________________________________________
leaky_re_lu_59 (LeakyReLU) (None, None, None, 2 0 batch_normalization_59[0][0]
__________________________________________________________________________________________________
up_sampling2d_1 (UpSampling2D) (None, None, None, 2 0 leaky_re_lu_59[0][0]
__________________________________________________________________________________________________
concatenate_1 (Concatenate) (None, None, None, 7 0 up_sampling2d_1[0][0]
add_19[0][0]
__________________________________________________________________________________________________
conv2d_61 (Conv2D) (None, None, None, 2 196608 concatenate_1[0][0]
__________________________________________________________________________________________________
batch_normalization_60 (BatchNo (None, None, None, 2 1024 conv2d_61[0][0]
__________________________________________________________________________________________________
leaky_re_lu_60 (LeakyReLU) (None, None, None, 2 0 batch_normalization_60[0][0]
__________________________________________________________________________________________________
conv2d_62 (Conv2D) (None, None, None, 5 1179648 leaky_re_lu_60[0][0]
__________________________________________________________________________________________________
batch_normalization_61 (BatchNo (None, None, None, 5 2048 conv2d_62[0][0]
__________________________________________________________________________________________________
leaky_re_lu_61 (LeakyReLU) (None, None, None, 5 0 batch_normalization_61[0][0]
__________________________________________________________________________________________________
conv2d_63 (Conv2D) (None, None, None, 2 131072 leaky_re_lu_61[0][0]
__________________________________________________________________________________________________
batch_normalization_62 (BatchNo (None, None, None, 2 1024 conv2d_63[0][0]
__________________________________________________________________________________________________
leaky_re_lu_62 (LeakyReLU) (None, None, None, 2 0 batch_normalization_62[0][0]
__________________________________________________________________________________________________
conv2d_64 (Conv2D) (None, None, None, 5 1179648 leaky_re_lu_62[0][0]
__________________________________________________________________________________________________
batch_normalization_63 (BatchNo (None, None, None, 5 2048 conv2d_64[0][0]
__________________________________________________________________________________________________
leaky_re_lu_63 (LeakyReLU) (None, None, None, 5 0 batch_normalization_63[0][0]
__________________________________________________________________________________________________
conv2d_65 (Conv2D) (None, None, None, 2 131072 leaky_re_lu_63[0][0]
__________________________________________________________________________________________________
batch_normalization_64 (BatchNo (None, None, None, 2 1024 conv2d_65[0][0]
__________________________________________________________________________________________________
leaky_re_lu_64 (LeakyReLU) (None, None, None, 2 0 batch_normalization_64[0][0]
__________________________________________________________________________________________________
conv2d_68 (Conv2D) (None, None, None, 1 32768 leaky_re_lu_64[0][0]
__________________________________________________________________________________________________
batch_normalization_66 (BatchNo (None, None, None, 1 512 conv2d_68[0][0]
__________________________________________________________________________________________________
leaky_re_lu_66 (LeakyReLU) (None, None, None, 1 0 batch_normalization_66[0][0]
__________________________________________________________________________________________________
up_sampling2d_2 (UpSampling2D) (None, None, None, 1 0 leaky_re_lu_66[0][0]
__________________________________________________________________________________________________
concatenate_2 (Concatenate) (None, None, None, 3 0 up_sampling2d_2[0][0]
add_11[0][0]
__________________________________________________________________________________________________
conv2d_69 (Conv2D) (None, None, None, 1 49152 concatenate_2[0][0]
__________________________________________________________________________________________________
batch_normalization_67 (BatchNo (None, None, None, 1 512 conv2d_69[0][0]
__________________________________________________________________________________________________
leaky_re_lu_67 (LeakyReLU) (None, None, None, 1 0 batch_normalization_67[0][0]
__________________________________________________________________________________________________
conv2d_70 (Conv2D) (None, None, None, 2 294912 leaky_re_lu_67[0][0]
__________________________________________________________________________________________________
batch_normalization_68 (BatchNo (None, None, None, 2 1024 conv2d_70[0][0]
__________________________________________________________________________________________________
leaky_re_lu_68 (LeakyReLU) (None, None, None, 2 0 batch_normalization_68[0][0]
__________________________________________________________________________________________________
conv2d_71 (Conv2D) (None, None, None, 1 32768 leaky_re_lu_68[0][0]
__________________________________________________________________________________________________
batch_normalization_69 (BatchNo (None, None, None, 1 512 conv2d_71[0][0]
__________________________________________________________________________________________________
leaky_re_lu_69 (LeakyReLU) (None, None, None, 1 0 batch_normalization_69[0][0]
__________________________________________________________________________________________________
conv2d_72 (Conv2D) (None, None, None, 2 294912 leaky_re_lu_69[0][0]
__________________________________________________________________________________________________
batch_normalization_70 (BatchNo (None, None, None, 2 1024 conv2d_72[0][0]
__________________________________________________________________________________________________
leaky_re_lu_70 (LeakyReLU) (None, None, None, 2 0 batch_normalization_70[0][0]
__________________________________________________________________________________________________
conv2d_73 (Conv2D) (None, None, None, 1 32768 leaky_re_lu_70[0][0]
__________________________________________________________________________________________________
batch_normalization_71 (BatchNo (None, None, None, 1 512 conv2d_73[0][0]
__________________________________________________________________________________________________
leaky_re_lu_71 (LeakyReLU) (None, None, None, 1 0 batch_normalization_71[0][0]
__________________________________________________________________________________________________
conv2d_58 (Conv2D) (None, None, None, 1 4718592 leaky_re_lu_57[0][0]
__________________________________________________________________________________________________
conv2d_66 (Conv2D) (None, None, None, 5 1179648 leaky_re_lu_64[0][0]
__________________________________________________________________________________________________
conv2d_74 (Conv2D) (None, None, None, 2 294912 leaky_re_lu_71[0][0]
__________________________________________________________________________________________________
batch_normalization_58 (BatchNo (None, None, None, 1 4096 conv2d_58[0][0]
__________________________________________________________________________________________________
batch_normalization_65 (BatchNo (None, None, None, 5 2048 conv2d_66[0][0]
__________________________________________________________________________________________________
batch_normalization_72 (BatchNo (None, None, None, 2 1024 conv2d_74[0][0]
__________________________________________________________________________________________________
leaky_re_lu_58 (LeakyReLU) (None, None, None, 1 0 batch_normalization_58[0][0]
__________________________________________________________________________________________________
leaky_re_lu_65 (LeakyReLU) (None, None, None, 5 0 batch_normalization_65[0][0]
__________________________________________________________________________________________________
leaky_re_lu_72 (LeakyReLU) (None, None, None, 2 0 batch_normalization_72[0][0]
__________________________________________________________________________________________________
conv2d_59 (Conv2D) (None, None, None, 2 261375 leaky_re_lu_58[0][0]
__________________________________________________________________________________________________
conv2d_67 (Conv2D) (None, None, None, 2 130815 leaky_re_lu_65[0][0]
__________________________________________________________________________________________________
conv2d_75 (Conv2D) (None, None, None, 2 65535 leaky_re_lu_72[0][0]
==================================================================================================
Total params: 62,001,757
Trainable params: 61,949,149
Non-trainable params: 52,608
__________________________________________________________________________________________________
None
Saved Keras model to model_data/yolo.h5
Read 62001757 of 62001757.0 from Darknet weights.
Below, we'll call a "self-contained" Python script that initiates training our model on our custom dataset.
Pay notable attention to:
- setting the paths for our
annotation_path,classes_path,class_names. If you move the Roboflow data location, you'll need to update these. val_splitdictates the size of our training data relative to our taining datalr=1e-3to set the learning rate of the model. Smaller optimizes more slowly but potentially more precisely.batch_sizefor the number of images trained per batch-
epochinsidemodel.fit_generator()sets the number training epochs to increase/decrease training examples (and time)
Consider reading the YOLOv3 paper here.
"""
Self-contained Python script to train YOLOv3 on your own dataset
"""
import numpy as np
import keras.backend as K
from keras.layers import Input, Lambda
from keras.models import Model
from keras.optimizers import Adam
from keras.callbacks import TensorBoard, ModelCheckpoint, ReduceLROnPlateau, EarlyStopping
from yolo3.model import preprocess_true_boxes, yolo_body, tiny_yolo_body, yolo_loss
from yolo3.utils import get_random_data
def _main():
annotation_path = '_annotations.txt' # path to Roboflow data annotations
log_dir = 'logs/000/' # where we're storing our logs
classes_path = '_classes.txt' # path to Roboflow class names
anchors_path = 'model_data/yolo_anchors.txt'
class_names = get_classes(classes_path)
print("-------------------CLASS NAMES-------------------")
print(class_names)
print("-------------------CLASS NAMES-------------------")
num_classes = len(class_names)
anchors = get_anchors(anchors_path)
input_shape = (416,416) # multiple of 32, hw
is_tiny_version = len(anchors)==6 # default setting
if is_tiny_version:
model = create_tiny_model(input_shape, anchors, num_classes,
freeze_body=2, weights_path='model_data/tiny_yolo_weights.h5')
else:
model = create_model(input_shape, anchors, num_classes,
freeze_body=2, weights_path='model_data/yolo.h5') # make sure you know what you freeze
logging = TensorBoard(log_dir=log_dir)
checkpoint = ModelCheckpoint(log_dir + 'ep{epoch:03d}-loss{loss:.3f}-val_loss{val_loss:.3f}.h5',
monitor='val_loss', save_weights_only=True, save_best_only=True, period=3)
reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=3, verbose=1)
early_stopping = EarlyStopping(monitor='val_loss', min_delta=0, patience=10, verbose=1)
val_split = 0.2 # set the size of the validation set
with open(annotation_path) as f:
lines = f.readlines()
np.random.seed(10101)
np.random.shuffle(lines)
np.random.seed(None)
num_val = int(len(lines)*val_split)
num_train = len(lines) - num_val
# Train with frozen layers first, to get a stable loss.
# Adjust num epochs to your dataset. This step is enough to obtain a not bad model.
if True:
model.compile(optimizer=Adam(lr=1e-3), loss={
# use custom yolo_loss Lambda layer.
'yolo_loss': lambda y_true, y_pred: y_pred})
batch_size = 32
print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size))
model.fit_generator(data_generator_wrapper(lines[:num_train], batch_size, input_shape, anchors, num_classes),
steps_per_epoch=max(1, num_train//batch_size),
validation_data=data_generator_wrapper(lines[num_train:], batch_size, input_shape, anchors, num_classes),
validation_steps=max(1, num_val//batch_size),
epochs=500,
initial_epoch=0,
callbacks=[logging, checkpoint])
model.save_weights(log_dir + 'trained_weights_stage_1.h5')
# Unfreeze and continue training, to fine-tune.
# Train longer if the result is not good.
if True:
for i in range(len(model.layers)):
model.layers[i].trainable = True
model.compile(optimizer=Adam(lr=1e-4), loss={'yolo_loss': lambda y_true, y_pred: y_pred}) # recompile to apply the change
print('Unfreeze all of the layers.')
batch_size = 32 # note that more GPU memory is required after unfreezing the body
print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size))
model.fit_generator(data_generator_wrapper(lines[:num_train], batch_size, input_shape, anchors, num_classes),
steps_per_epoch=max(1, num_train//batch_size),
validation_data=data_generator_wrapper(lines[num_train:], batch_size, input_shape, anchors, num_classes),
validation_steps=max(1, num_val//batch_size),
epochs=100,
initial_epoch=50,
callbacks=[logging, checkpoint, reduce_lr, early_stopping])
model.save_weights(log_dir + 'trained_weights_final.h5')
# Further training if needed.
def get_classes(classes_path):
'''loads the classes'''
with open(classes_path) as f:
class_names = f.readlines()
class_names = [c.strip() for c in class_names]
return class_names
def get_anchors(anchors_path):
'''loads the anchors from a file'''
with open(anchors_path) as f:
anchors = f.readline()
anchors = [float(x) for x in anchors.split(',')]
return np.array(anchors).reshape(-1, 2)
def create_model(input_shape, anchors, num_classes, load_pretrained=True, freeze_body=2,
weights_path='model_data/yolo.h5'):
'''create the training model'''
K.clear_session() # get a new session
image_input = Input(shape=(None, None, 3))
h, w = input_shape
num_anchors = len(anchors)
y_true = [Input(shape=(h//{0:32, 1:16, 2:8}[l], w//{0:32, 1:16, 2:8}[l], \
num_anchors//3, num_classes+5)) for l in range(3)]
model_body = yolo_body(image_input, num_anchors//3, num_classes)
print('Create YOLOv3 model with {} anchors and {} classes.'.format(num_anchors, num_classes))
if load_pretrained:
model_body.load_weights(weights_path, by_name=True, skip_mismatch=True)
print('Load weights {}.'.format(weights_path))
if freeze_body in [1, 2]:
# Freeze darknet53 body or freeze all but 3 output layers.
num = (185, len(model_body.layers)-3)[freeze_body-1]
for i in range(num): model_body.layers[i].trainable = False
print('Freeze the first {} layers of total {} layers.'.format(num, len(model_body.layers)))
model_loss = Lambda(yolo_loss, output_shape=(1,), name='yolo_loss',
arguments={'anchors': anchors, 'num_classes': num_classes, 'ignore_thresh': 0.5})(
[*model_body.output, *y_true])
model = Model([model_body.input, *y_true], model_loss)
return model
def create_tiny_model(input_shape, anchors, num_classes, load_pretrained=True, freeze_body=2,
weights_path='model_data/tiny_yolo_weights.h5'):
'''create the training model, for Tiny YOLOv3'''
K.clear_session() # get a new session
image_input = Input(shape=(None, None, 3))
h, w = input_shape
num_anchors = len(anchors)
y_true = [Input(shape=(h//{0:32, 1:16}[l], w//{0:32, 1:16}[l], \
num_anchors//2, num_classes+5)) for l in range(2)]
model_body = tiny_yolo_body(image_input, num_anchors//2, num_classes)
print('Create Tiny YOLOv3 model with {} anchors and {} classes.'.format(num_anchors, num_classes))
if load_pretrained:
model_body.load_weights(weights_path, by_name=True, skip_mismatch=True)
print('Load weights {}.'.format(weights_path))
if freeze_body in [1, 2]:
# Freeze the darknet body or freeze all but 2 output layers.
num = (20, len(model_body.layers)-2)[freeze_body-1]
for i in range(num): model_body.layers[i].trainable = False
print('Freeze the first {} layers of total {} layers.'.format(num, len(model_body.layers)))
model_loss = Lambda(yolo_loss, output_shape=(1,), name='yolo_loss',
arguments={'anchors': anchors, 'num_classes': num_classes, 'ignore_thresh': 0.7})(
[*model_body.output, *y_true])
model = Model([model_body.input, *y_true], model_loss)
return model
def data_generator(annotation_lines, batch_size, input_shape, anchors, num_classes):
'''data generator for fit_generator'''
n = len(annotation_lines)
i = 0
while True:
image_data = []
box_data = []
for b in range(batch_size):
if i==0:
np.random.shuffle(annotation_lines)
image, box = get_random_data(annotation_lines[i], input_shape, random=True)
image_data.append(image)
box_data.append(box)
i = (i+1) % n
image_data = np.array(image_data)
box_data = np.array(box_data)
y_true = preprocess_true_boxes(box_data, input_shape, anchors, num_classes)
yield [image_data, *y_true], np.zeros(batch_size)
def data_generator_wrapper(annotation_lines, batch_size, input_shape, anchors, num_classes):
n = len(annotation_lines)
if n==0 or batch_size<=0: return None
return data_generator(annotation_lines, batch_size, input_shape, anchors, num_classes)
if __name__ == '__main__':
_main()Using TensorFlow backend.
[0;31m---------------------------------------------------------------------------[0m [0;31mFileNotFoundError[0m Traceback (most recent call last) [0;32m<ipython-input-18-fd737c45ef07>[0m in [0;36m<module>[0;34m()[0m [1;32m 189[0m [0;34m[0m[0m [1;32m 190[0m [0;32mif[0m [0m__name__[0m [0;34m==[0m [0;34m'__main__'[0m[0;34m:[0m[0;34m[0m[0;34m[0m[0m [0;32m--> 191[0;31m [0m_main[0m[0;34m([0m[0;34m)[0m[0;34m[0m[0;34m[0m[0m [0m [0;32m<ipython-input-18-fd737c45ef07>[0m in [0;36m_main[0;34m()[0m [1;32m 19[0m [0mclasses_path[0m [0;34m=[0m [0;34m'_classes.txt'[0m [0;31m# path to Roboflow class names[0m[0;34m[0m[0;34m[0m[0m [1;32m 20[0m [0manchors_path[0m [0;34m=[0m [0;34m'model_data/yolo_anchors.txt'[0m[0;34m[0m[0;34m[0m[0m [0;32m---> 21[0;31m [0mclass_names[0m [0;34m=[0m [0mget_classes[0m[0;34m([0m[0mclasses_path[0m[0;34m)[0m[0;34m[0m[0;34m[0m[0m [0m[1;32m 22[0m [0mprint[0m[0;34m([0m[0;34m"-------------------CLASS NAMES-------------------"[0m[0;34m)[0m[0;34m[0m[0;34m[0m[0m [1;32m 23[0m [0mprint[0m[0;34m([0m[0mclass_names[0m[0;34m)[0m[0;34m[0m[0;34m[0m[0m [0;32m<ipython-input-18-fd737c45ef07>[0m in [0;36mget_classes[0;34m(classes_path)[0m [1;32m 93[0m [0;32mdef[0m [0mget_classes[0m[0;34m([0m[0mclasses_path[0m[0;34m)[0m[0;34m:[0m[0;34m[0m[0;34m[0m[0m [1;32m 94[0m [0;34m'''loads the classes'''[0m[0;34m[0m[0;34m[0m[0m [0;32m---> 95[0;31m [0;32mwith[0m [0mopen[0m[0;34m([0m[0mclasses_path[0m[0;34m)[0m [0;32mas[0m [0mf[0m[0;34m:[0m[0;34m[0m[0;34m[0m[0m [0m[1;32m 96[0m [0mclass_names[0m [0;34m=[0m [0mf[0m[0;34m.[0m[0mreadlines[0m[0;34m([0m[0;34m)[0m[0;34m[0m[0;34m[0m[0m [1;32m 97[0m [0mclass_names[0m [0;34m=[0m [0;34m[[0m[0mc[0m[0;34m.[0m[0mstrip[0m[0;34m([0m[0;34m)[0m [0;32mfor[0m [0mc[0m [0;32min[0m [0mclass_names[0m[0;34m][0m[0;34m[0m[0;34m[0m[0m [0;31mFileNotFoundError[0m: [Errno 2] No such file or directory: '_classes.txt'
## can call this cell instead of the above
# !python train.pyUse our model for inference
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.
Additional arguments for yolo_video.py are as follows:
usage: yolo_video.py [-h] [--model MODEL] [--anchors ANCHORS]
[--classes CLASSES] [--gpu_num GPU_NUM] [--image]
[--input] [--output]
positional arguments:
--input Video input path
--output Video output path
optional arguments:
-h, --help show this help message and exit
--model MODEL path to model weight file, default model_data/yolo.h5
--anchors ANCHORS path to anchor definitions, default
model_data/yolo_anchors.txt
--classes CLASSES path to class definitions, default
model_data/coco_classes.txt
--gpu_num GPU_NUM Number of GPU to use, default 1
--image Image detection mode, will ignore all positional arguments
!python yolo_video.py --model="./logs/000/trained_weights_stage_1.h5" --classes="_classes.txt" --imageFor input image names into the above, consider trying the following:
00a7a49c47d51fd16a4cbb17e2d2cf86.jpg# white-king works! + knight015d0d7ff365f0b7492ff079c8c7d56c.jpg# black-queen mixes up176b28b5c417f39a9e5d37545fca5b4c.jpg# finds only five4673f994f60a2ea7afdddc1b752947c0.jpg# white-rook (thinks king)5ca7f0cb1c500554e65ad031190f8e9f.jpg# white-pawn (missed white-king)fbf15139f38a46e02b5f4061c0c9b08f.jpg# black-king success!
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 ).
Move currently trained model to GDrive
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.
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.
# mount Google Drive
from google.colab import drive
drive.mount('/content/drive')# create a copy of the weights file with a datetime
# and move that file to your own Drive
%cp ./logs/000/trained_weights_stage_1.h5 ./logs/000/trained_weights_stage_1_$(date +%F-%H:%M).h5
%mv ./logs/000/trained_weights_stage_1_$(date +%F-%H:%M).h5 /content/drive/My\ Drive/