Tensorflow 2.2.0 implementation of DeepLabV3-Plus architecture as proposed by the paper Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation.
Project Link: https://github.com/deepwrex/DeepLabV3-Plus/projects/.
Experiments: https://app.wandb.ai/19soumik-rakshit96/deeplabv3-plus.
Model Architectures can be found here.
-
CamVid
cd dataset bash camvid.sh -
Multi-Person Human Parsing
Register on https://www.kaggle.com/.
Generate Kaggle API Token
bash download_human_parsing_dataset.sh <kaggle-username> <kaggle-key>
from deeplabv3plus.model.deeplabv3_plus import DeeplabV3Plus
model = DeepLabV3Plus(backbone='resnet50', num_classes=20)
input_shape = (1, 512, 512, 3)
input_tensor = tf.random.normal(input_shape)
result = model(input_tensor) # build model by one forward pass
model.summary()Use the trainer.py script as documented with the help description below:
usage: trainer.py [-h] [--wandb_api_key WANDB_API_KEY] config_key
Runs DeeplabV3+ trainer with the given config setting.
Registered config_key values:
camvid_resnet50
human_parsing_resnet50
positional arguments:
config_key Key to use while looking up configuration from the CONFIG_MAP dictionary.
optional arguments:
-h, --help show this help message and exit
--wandb_api_key WANDB_API_KEY
Wandb API Key for logging run on Wandb.
If provided, checkpoint_dir is set to wandb://
(Model checkpoints are saved to wandb.)
config/camvid_resnet50.py
#!/usr/bin/env python
"""Module for training deeplabv3plus on camvid dataset."""
from glob import glob
import tensorflow as tf
# Sample Configuration
CONFIG = {
# We mandate specifying project_name and experiment_name in every config
# file. They are used for wandb runs if wandb api key is specified.
'project_name': 'deeplabv3-plus',
'experiment_name': 'camvid-segmentation-resnet-50-backbone',
'train_dataset_config': {
'images': sorted(glob('./dataset/camvid/train/*')),
'labels': sorted(glob('./dataset/camvid/trainannot/*')),
'height': 512, 'width': 512, 'batch_size': 8
},
'val_dataset_config': {
'images': sorted(glob('./dataset/camvid/val/*')),
'labels': sorted(glob('./dataset/camvid/valannot/*')),
'height': 512, 'width': 512, 'batch_size': 8
},
'strategy': tf.distribute.OneDeviceStrategy(device="/gpu:0"),
'num_classes': 20, 'backbone': 'resnet50', 'learning_rate': 0.0001,
'checkpoint_dir': "./checkpoints/",
'checkpoint_file_prefix': "deeplabv3plus_with_resnet50_",
'epochs': 100
}config/__init__.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""__init__ module for configs. Register your config file here by adding it's
entry in the CONFIG_MAP as shown.
"""
import config.camvid_resnet50
import config.human_parsing_resnet50
CONFIG_MAP = {
'camvid_resnet50': config.camvid_resnet50.CONFIG, # the config file we defined above
'human_parsing_resnet50': config.human_parsing_resnet50.CONFIG # another config
}./trainer.py camvid_resnet50 --wandb_api_key <YOUR_WANDB_API_KEY>or, if you don't need wandb logging:
./trainer.py camvid_resnet50Sample Inference Code:
model_file = './dataset/deeplabv3-plus-human-parsing-resnet-50-backbone.h5'
train_images = glob('./dataset/instance-level_human_parsing/Training/Images/*')
val_images = glob('./dataset/instance-level_human_parsing/Validation/Images/*')
test_images = glob('./dataset/instance-level_human_parsing/Testing/Images/*')
def plot_predictions(images_list, size):
for image_file in images_list:
image_tensor = read_image(image_file, size)
prediction = infer(
image_tensor=image_tensor,
model_file=model_file
)
plot_samples_matplotlib(
[image_tensor, prediction], figsize=(10, 6)
)
plot_predictions(train_images[:4], (512, 512))
@misc{1802.02611,
Author = {Liang-Chieh Chen and Yukun Zhu and George Papandreou and Florian Schroff and Hartwig Adam},
Title = {Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation},
Year = {2018},
Eprint = {arXiv:1802.02611},
}
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