File size: 12,610 Bytes
97b6013 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 |
# Object Detection Models on TensorFlow 2
**Note**: This repository is still under construction.
More features and instructions will be added soon.
## Prerequsite
To get started, download the code from TensorFlow models GitHub repository or
use the pre-installed Google Cloud VM.
```bash
git clone https://github.com/tensorflow/models.git
```
Next, make sure to use TensorFlow 2.1+ on Google Cloud. Also here are
a few package you need to install to get started:
```bash
sudo apt-get install -y python-tk && \
pip3 install -r ~/models/official/requirements.txt
```
## Train RetinaNet on TPU
### Train a vanilla ResNet-50 based RetinaNet.
```bash
TPU_NAME="<your GCP TPU name>"
MODEL_DIR="<path to the directory to store model files>"
RESNET_CHECKPOINT="<path to the pre-trained Resnet-50 checkpoint>"
TRAIN_FILE_PATTERN="<path to the TFRecord training data>"
EVAL_FILE_PATTERN="<path to the TFRecord validation data>"
VAL_JSON_FILE="<path to the validation annotation JSON file>"
python3 ~/models/official/vision/detection/main.py \
--strategy_type=tpu \
--tpu="${TPU_NAME?}" \
--model_dir="${MODEL_DIR?}" \
--mode=train \
--params_override="{ type: retinanet, train: { checkpoint: { path: ${RESNET_CHECKPOINT?}, prefix: resnet50/ }, train_file_pattern: ${TRAIN_FILE_PATTERN?} }, eval: { val_json_file: ${VAL_JSON_FILE?}, eval_file_pattern: ${EVAL_FILE_PATTERN?} } }"
```
The pre-trained ResNet-50 checkpoint can be downloaded [here](https://storage.cloud.google.com/cloud-tpu-checkpoints/model-garden-vision/detection/resnet50-2018-02-07.tar.gz).
Note: The ResNet implementation under
[detection/](https://github.com/tensorflow/models/tree/master/official/vision/detection)
is currently different from the one under
[classification/](https://github.com/tensorflow/models/tree/master/official/vision/image_classification),
so the checkpoints are not compatible.
We will unify the implementation soon.
### Train a custom RetinaNet using the config file.
First, create a YAML config file, e.g. *my_retinanet.yaml*. This file specifies
the parameters to be overridden, which should at least include the following
fields.
```YAML
# my_retinanet.yaml
type: 'retinanet'
train:
train_file_pattern: <path to the TFRecord training data>
eval:
eval_file_pattern: <path to the TFRecord validation data>
val_json_file: <path to the validation annotation JSON file>
```
Once the YAML config file is created, you can launch the training using the
following command.
```bash
TPU_NAME="<your GCP TPU name>"
MODEL_DIR="<path to the directory to store model files>"
python3 ~/models/official/vision/detection/main.py \
--strategy_type=tpu \
--tpu="${TPU_NAME?}" \
--model_dir="${MODEL_DIR?}" \
--mode=train \
--config_file="my_retinanet.yaml"
```
## Train RetinaNet on GPU
Training on GPU is similar to that on TPU. The major change is the strategy
type (use "[mirrored](https://www.tensorflow.org/api_docs/python/tf/distribute/MirroredStrategy)" for multiple GPU and
"[one_device](https://www.tensorflow.org/api_docs/python/tf/distribute/OneDeviceStrategy)" for single GPU).
Multi-GPUs example (assuming there are 8GPU connected to the host):
```bash
MODEL_DIR="<path to the directory to store model files>"
python3 ~/models/official/vision/detection/main.py \
--strategy_type=mirrored \
--num_gpus=8 \
--model_dir="${MODEL_DIR?}" \
--mode=train \
--config_file="my_retinanet.yaml"
```
```bash
MODEL_DIR="<path to the directory to store model files>"
python3 ~/models/official/vision/detection/main.py \
--strategy_type=one_device \
--num_gpus=1 \
--model_dir="${MODEL_DIR?}" \
--mode=train \
--config_file="my_retinanet.yaml"
```
An example with inline configuration (YAML or JSON format):
```
python3 ~/models/official/vision/detection/main.py \
--model_dir=<model folder> \
--strategy_type=one_device \
--num_gpus=1 \
--mode=train \
--params_override="eval:
eval_file_pattern: <Eval TFRecord file pattern>
batch_size: 8
val_json_file: <COCO format groundtruth JSON file>
predict:
predict_batch_size: 8
architecture:
use_bfloat16: False
train:
total_steps: 1
batch_size: 8
train_file_pattern: <Eval TFRecord file pattern>
use_tpu: False
"
```
---
## Train Mask R-CNN on TPU
### Train a vanilla ResNet-50 based Mask R-CNN.
```bash
TPU_NAME="<your GCP TPU name>"
MODEL_DIR="<path to the directory to store model files>"
RESNET_CHECKPOINT="<path to the pre-trained Resnet-50 checkpoint>"
TRAIN_FILE_PATTERN="<path to the TFRecord training data>"
EVAL_FILE_PATTERN="<path to the TFRecord validation data>"
VAL_JSON_FILE="<path to the validation annotation JSON file>"
python3 ~/models/official/vision/detection/main.py \
--strategy_type=tpu \
--tpu=${TPU_NAME} \
--model_dir=${MODEL_DIR} \
--mode=train \
--model=mask_rcnn \
--params_override="{train: { checkpoint: { path: ${RESNET_CHECKPOINT}, prefix: resnet50/ }, train_file_pattern: ${TRAIN_FILE_PATTERN} }, eval: { val_json_file: ${VAL_JSON_FILE}, eval_file_pattern: ${EVAL_FILE_PATTERN} } }"
```
The pre-trained ResNet-50 checkpoint can be downloaded [here](https://storage.cloud.google.com/cloud-tpu-checkpoints/model-garden-vision/detection/resnet50-2018-02-07.tar.gz).
Note: The ResNet implementation under
[detection/](https://github.com/tensorflow/models/tree/master/official/vision/detection)
is currently different from the one under
[classification/](https://github.com/tensorflow/models/tree/master/official/vision/image_classification),
so the checkpoints are not compatible.
We will unify the implementation soon.
### Train a custom Mask R-CNN using the config file.
First, create a YAML config file, e.g. *my_maskrcnn.yaml*.
This file specifies the parameters to be overridden,
which should at least include the following fields.
```YAML
# my_maskrcnn.yaml
train:
train_file_pattern: <path to the TFRecord training data>
eval:
eval_file_pattern: <path to the TFRecord validation data>
val_json_file: <path to the validation annotation JSON file>
```
Once the YAML config file is created, you can launch the training using the
following command.
```bash
TPU_NAME="<your GCP TPU name>"
MODEL_DIR="<path to the directory to store model files>"
python3 ~/models/official/vision/detection/main.py \
--strategy_type=tpu \
--tpu=${TPU_NAME} \
--model_dir=${MODEL_DIR} \
--mode=train \
--model=mask_rcnn \
--config_file="my_maskrcnn.yaml"
```
## Train Mask R-CNN on GPU
Training on GPU is similar to that on TPU. The major change is the strategy type
(use
"[mirrored](https://www.tensorflow.org/api_docs/python/tf/distribute/MirroredStrategy)"
for multiple GPU and
"[one_device](https://www.tensorflow.org/api_docs/python/tf/distribute/OneDeviceStrategy)"
for single GPU).
Multi-GPUs example (assuming there are 8GPU connected to the host):
```bash
MODEL_DIR="<path to the directory to store model files>"
python3 ~/models/official/vision/detection/main.py \
--strategy_type=mirrored \
--num_gpus=8 \
--model_dir=${MODEL_DIR} \
--mode=train \
--model=mask_rcnn \
--config_file="my_maskrcnn.yaml"
```
```bash
MODEL_DIR="<path to the directory to store model files>"
python3 ~/models/official/vision/detection/main.py \
--strategy_type=one_device \
--num_gpus=1 \
--model_dir=${MODEL_DIR} \
--mode=train \
--model=mask_rcnn \
--config_file="my_maskrcnn.yaml"
```
An example with inline configuration (YAML or JSON format):
```
python3 ~/models/official/vision/detection/main.py \
--model_dir=<model folder> \
--strategy_type=one_device \
--num_gpus=1 \
--mode=train \
--model=mask_rcnn \
--params_override="eval:
eval_file_pattern: <Eval TFRecord file pattern>
batch_size: 8
val_json_file: <COCO format groundtruth JSON file>
predict:
predict_batch_size: 8
architecture:
use_bfloat16: False
train:
total_steps: 1000
batch_size: 8
train_file_pattern: <Eval TFRecord file pattern>
use_tpu: False
"
```
## Train ShapeMask on TPU
### Train a ResNet-50 based ShapeMask.
```bash
TPU_NAME="<your GCP TPU name>"
MODEL_DIR="<path to the directory to store model files>"
RESNET_CHECKPOINT="<path to the pre-trained Resnet-50 checkpoint>"
TRAIN_FILE_PATTERN="<path to the TFRecord training data>"
EVAL_FILE_PATTERN="<path to the TFRecord validation data>"
VAL_JSON_FILE="<path to the validation annotation JSON file>"
SHAPE_PRIOR_PATH="<path to shape priors>"
python3 ~/models/official/vision/detection/main.py \
--strategy_type=tpu \
--tpu=${TPU_NAME} \
--model_dir=${MODEL_DIR} \
--mode=train \
--model=shapemask \
--params_override="{train: { checkpoint: { path: ${RESNET_CHECKPOINT}, prefix: resnet50/ }, train_file_pattern: ${TRAIN_FILE_PATTERN} }, eval: { val_json_file: ${VAL_JSON_FILE}, eval_file_pattern: ${EVAL_FILE_PATTERN} } shapemask_head: {use_category_for_mask: true, shape_prior_path: ${SHAPE_PRIOR_PATH}} }"
```
The pre-trained ResNet-50 checkpoint can be downloaded [here](https://storage.cloud.google.com/cloud-tpu-checkpoints/model-garden-vision/detection/resnet50-2018-02-07.tar.gz).
The shape priors can be downloaded [here]
(https://storage.googleapis.com/cloud-tpu-checkpoints/shapemask/kmeans_class_priors_91x20x32x32.npy)
### Train a custom ShapeMask using the config file.
First, create a YAML config file, e.g. *my_shapemask.yaml*.
This file specifies the parameters to be overridden:
```YAML
# my_shapemask.yaml
train:
train_file_pattern: <path to the TFRecord training data>
total_steps: <total steps to train>
batch_size: <training batch size>
eval:
eval_file_pattern: <path to the TFRecord validation data>
val_json_file: <path to the validation annotation JSON file>
batch_size: <evaluation batch size>
shapemask_head:
shape_prior_path: <path to shape priors>
```
Once the YAML config file is created, you can launch the training using the
following command.
```bash
TPU_NAME="<your GCP TPU name>"
MODEL_DIR="<path to the directory to store model files>"
python3 ~/models/official/vision/detection/main.py \
--strategy_type=tpu \
--tpu=${TPU_NAME} \
--model_dir=${MODEL_DIR} \
--mode=train \
--model=shapemask \
--config_file="my_shapemask.yaml"
```
## Train ShapeMask on GPU
Training on GPU is similar to that on TPU. The major change is the strategy type
(use
"[mirrored](https://www.tensorflow.org/api_docs/python/tf/distribute/MirroredStrategy)"
for multiple GPU and
"[one_device](https://www.tensorflow.org/api_docs/python/tf/distribute/OneDeviceStrategy)"
for single GPU).
Multi-GPUs example (assuming there are 8GPU connected to the host):
```bash
MODEL_DIR="<path to the directory to store model files>"
python3 ~/models/official/vision/detection/main.py \
--strategy_type=mirrored \
--num_gpus=8 \
--model_dir=${MODEL_DIR} \
--mode=train \
--model=shapemask \
--config_file="my_shapemask.yaml"
```
A single GPU example
```bash
MODEL_DIR="<path to the directory to store model files>"
python3 ~/models/official/vision/detection/main.py \
--strategy_type=one_device \
--num_gpus=1 \
--model_dir=${MODEL_DIR} \
--mode=train \
--model=shapemask \
--config_file="my_shapemask.yaml"
```
An example with inline configuration (YAML or JSON format):
```
python3 ~/models/official/vision/detection/main.py \
--model_dir=<model folder> \
--strategy_type=one_device \
--num_gpus=1 \
--mode=train \
--model=shapemask \
--params_override="eval:
eval_file_pattern: <Eval TFRecord file pattern>
batch_size: 8
val_json_file: <COCO format groundtruth JSON file>
train:
total_steps: 1000
batch_size: 8
train_file_pattern: <Eval TFRecord file pattern>
use_tpu: False
"
```
### Run the evaluation (after training)
```
python3 /usr/share/models/official/vision/detection/main.py \
--strategy_type=tpu \
--tpu=${TPU_NAME} \
--model_dir=${MODEL_DIR} \
--mode=eval \
--model=shapemask \
--params_override="{eval: { val_json_file: ${VAL_JSON_FILE}, eval_file_pattern: ${EVAL_FILE_PATTERN}, eval_samples: 5000 } }"
```
`MODEL_DIR` needs to point to the trained path of ShapeMask model.
Change `strategy_type=mirrored` and `num_gpus=1` to run on a GPU.
Note: The JSON groundtruth file is useful for [COCO dataset](http://cocodataset.org/#home) and can be
downloaded from the [COCO website](http://cocodataset.org/#download). For custom dataset, it is unncessary because the groundtruth can be included in the TFRecord files.
## References
1. [Focal Loss for Dense Object Detection](https://arxiv.org/abs/1708.02002).
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár. IEEE
International Conference on Computer Vision (ICCV), 2017.
|