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Rnnpool facedetection (#215) 

* add m4 model

* add rnnpool sparsity

* revert to previous+remove basenet

* scut training and testing

* augmentations and data file changes

* update readme and eval files

* evaluation code

* fix bugs

* remove lists

* data prep script

* merge face detection and m4

* eval arch options

* finetune

* readme changes

* readme update

* readme update

* newlines and slashes

* add dataset directory as environment variable

* rpool face c detect bug

* support for multigpu

* multigpu fixes

* remove subset option

* readme edit

* mkdir

* readme changes

* fix warning

* rnnpool device

* add arch

* trace generation

* Update eval.py

* Update prior_box.py

* Update multibox_loss.py

* Update train.py

* Update scut_test.py

* eval bug + newlines

* Remove stray newline

Co-authored-by: Harsha Vardhan Simhadri <harsha-simhadri@users.noreply.github.com>
Co-authored-by: ShikharJ <jaiswalshikhar87@gmail.com>
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Oindrila Saha 2021-01-05 01:03:37 +05:30 коммит произвёл GitHub
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50
examples/pytorch/vision/Face_Detection/README.md
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@ -1,9 +1,10 @@
# Code for Face Detection experiments with RNNPool
# Code for Face Detection Experiments with RNNPool
Refer to README_M4.md for instructions related to the M4 model
## Requirements
1. Follow instructions to install requirements for EdgeML operators and the EdgeML operators [here](https://github.com/microsoft/EdgeML/blob/master/pytorch/README.md).
1. Follow instructions to install EdgeML operators and their pre-requisites [here](https://github.com/microsoft/EdgeML/blob/master/pytorch/README.md).
2. Install requirements for face detection model using
``` pip install -r requirements.txt ```
We have tested the installation and the code on Ubuntu 18.04 with Cuda 10.2 and CuDNN 7.6
We have tested the installation and the code on Ubuntu 18.04 with Python 3.6, Cuda 10.2 and CuDNN 7.6
## Dataset
1. Download WIDER face dataset images and annotations from http://shuoyang1213.me/WIDERFACE/ and place them all in a folder with name 'WIDER_FACE'. That is, download WIDER_train.zip, WIDER_test.zip, WIDER_val.zip, wider_face_split.zip and place it in WIDER_FACE folder, and unzip files using:
@ -18,12 +19,17 @@ cd ..
```
2. In `data/config.py` , set _C.HOME to the parent directory of the above folder, and set the _C.FACE.WIDER_DIR to the folder path.
That is, if the WIDER_FACE folder is created in /mnt folder, then _C.HOME='/mnt'
_C.FACE.WIDER_DIR='/mnt/WIDER_FACE'.
Similarly, change `data/config_qvga.py` to set _C.HOME and _C.FACE.WIDER_DIR.
2. Set environment variable DATA_HOME to the parent directory of the above folder
That is, if the WIDER_FACE folder is created in /mnt folder
``` export DATA_HOME='/mnt' ```
Note that for Windows '/' should be replaced by '\'.
For all following commands the environment variable IS_QVGA_MONO has to be set as 0 for using config.py (to use RGB 640x480 images) and as 1 for using config_qvga.py (to use monochrome 320x240 images) as the configuration file.
3. Run
``` python prepare_wider_data.py ```
``` IS_QVGA_MONO=1 python prepare_wider_data.py ```
# Usage
@ -64,15 +70,15 @@ There are two modes of testing the trained model -- the evaluation mode to gener
#### Evaluation Mode
Given a set of images in <your_image_folder>, `eval/py` generates bounding boxes around faces (where the confidence is higher than certain threshold) and write the images in <your_save_folder>. To evaluate the `rpool_face_best_state.pth` model (stored in ./weights), execute the following command:
Given a set of images in <your_image_folder>, `eval/py` generates bounding boxes around faces (where the confidence is higher than certain threshold) and write the images in <your_save_folder>. Specify if the model was trained in multigpu setting in --multigpu. To evaluate the `rpool_face_best_state.pth` model (stored in ./weights), execute the following command:
```shell
IS_QVGA_MONO=0 python eval.py --model_arch RPool_Face_Quant --model ./weights/RPool_Face_Quant_best_state.pth --image_folder <your_image_folder> --save_dir <your_save_folder>
IS_QVGA_MONO=0 python eval.py --model_arch RPool_Face_Quant --model ./weights/RPool_Face_Quant_best_state.pth --image_folder <your_image_folder> --save_dir <your_save_folder> --multigpu True
```
For QVGA:
```shell
IS_QVGA_MONO=1 python eval.py --model_arch RPool_Face_QVGA_monochrome --model ./weights/RPool_Face_QVGA_monochrome_best_state.pth --image_folder <your_image_folder> --save_dir <your_save_folder>
IS_QVGA_MONO=1 python eval.py --model_arch RPool_Face_QVGA_monochrome --model ./weights/RPool_Face_QVGA_monochrome_best_state.pth --image_folder <your_image_folder> --save_dir <your_save_folder> --multigpu True
```
This will save images in <your_save_folder> with bounding boxes around faces, where the confidence is high. Here is an example image with a single bounding box.
@ -86,15 +92,15 @@ If IS_QVGA_MONO=1 the evaluation code accepts an image of any size and resizes a
#### WIDER Set Test
In this mode, we test the generated model against the provided WIDER_FACE validation and test dataset.
For this, first run the following to generate predictions of the model and store output in the '--save_folder' folder.
For this, first run the following to generate predictions of the model and store output in the '--save_folder' folder. Specify if the model was trained in multigpu setting in --multigpu.
```shell
IS_QVGA_MONO=0 python wider_test.py --model_arch RPool_Face_Quant --model ./weights/RPool_Face_Quant_best_state.pth --save_folder rpool_face_quant_val --subset val
IS_QVGA_MONO=0 python wider_test.py --model_arch RPool_Face_Quant --model ./weights/RPool_Face_Quant_best_state.pth --save_folder rpool_face_quant_val --subset val --multigpu True
```
For QVGA:
```shell
IS_QVGA_MONO=1 python wider_test.py --model_arch RPool_Face_QVGA_monochrome --model ./weights/RPool_Face_QVGA_monochrome_best_state.pth --save_folder rpool_face_qvgamono_val --subset val
IS_QVGA_MONO=1 python wider_test.py --model_arch RPool_Face_QVGA_monochrome --model ./weights/RPool_Face_QVGA_monochrome_best_state.pth --save_folder rpool_face_qvgamono_val --subset val --multigpu True
```
The above command generates predictions for each image in the "validation" dataset. For each image, a separate prediction file is provided (image_name.txt file in appropriate folder). The first line of the prediction file contains the total number of boxes identified.
@ -104,8 +110,8 @@ If IS_QVGA_MONO=1 then testing is done by converting images to monochrome and QV
The architecture RPool_Face_QVGA_monochrome is for QVGA monochrome format while RPool_Face_C and RPool_Face_Quant are for VGA RGB format.
###### For calculating MAP scores:
Now using these boxes, we can compute the standard MAP score that is widely used in this literature (see [here](https://medium.com/@jonathan_hui/map-mean-average-precision-for-object-detection-45c121a31173) for more details) as follows:
###### For calculating mAP scores:
Now using these boxes, we can compute the standard mAP score that is widely used in this literature (see [here](https://medium.com/@jonathan_hui/map-mean-average-precision-for-object-detection-45c121a31173) for more details) as follows:
1. Download eval_tools.zip from http://shuoyang1213.me/WIDERFACE/support/eval_script/eval_tools.zip and unzip in a folder of same name in this directory.
@ -116,7 +122,7 @@ wget http://shuoyang1213.me/WIDERFACE/support/eval_script/eval_tools.zip
unzip eval_tools.zip
```
2. Set up scripts to use the Matlab '.mat' data files in eval_tools/ground_truth folder for MAP calculation: The following installs python files that provide the same functionality as the '.m' matlab scripts in eval_tools folder.
2. Set up scripts to use the Matlab '.mat' data files in eval_tools/ground_truth folder for mAP calculation: The following installs python files that provide the same functionality as the '.m' matlab scripts in eval_tools folder.
```
cd eval_tools
git clone https://github.com/wondervictor/WiderFace-Evaluation.git
@ -126,20 +132,20 @@ python3 setup.py build_ext --inplace
3. Run ```python3 evaluation.py -p <your_save_folder> -g <groud truth dir>``` in WiderFace-Evaluation folder
where `prediction_dir` is the '--save_folder' used for `wider_test.py` above and <groud truth dir> is the subfolder `eval_tools/ground_truth`. That is in, WiderFace-Evaluation directory, run:
where `-p` is the '--save_folder' used for `wider_test.py` above and <groud truth dir> is the subfolder `eval_tools/ground_truth`. That is in, WiderFace-Evaluation directory, run:
```shell
python3 evaluation.py -p <your_save_folder> -g ../ground_truth
python3 evaluation.py -p ../../rpool_face_qvgamono_val -g ../ground_truth
```
This script should output the MAP for the WIDER-easy, WIDER-medium, and WIDER-hard subsets of the dataset. Our best performance using RPool_Face_Quant model is: 0.80 (WIDER-easy), 0.78 (WIDER-medium), 0.53 (WIDER-hard).
This script should output the mAP for the WIDER-easy, WIDER-medium, and WIDER-hard subsets of the dataset. Our best performance using RPool_Face_Quant model is: 0.80 (WIDER-easy), 0.78 (WIDER-medium), 0.53 (WIDER-hard).
##### Dump RNNPool Input Output Traces and Weights
To save model weights and/or input output pairs for each patch through RNNPool in numpy format use the command below. Put images which you want to save traces for in <your_image_folder> . Specify output folder for saving model weights in numpy format in <your_save_model_numpy_folder>. Specify output folder for saving input output traces of RNNPool in numpy format in <your_save_traces_numpy_folder>. Note that input traces will be saved in a folder named 'inputs' and output traces in a folder named 'outputs' inside <your_save_traces_numpy_folder>.
For saving model weights and/or input output pairs for each patch through RNNPool in numpy format use the command below. Put images which you want to save traces for in <your_image_folder> . Specify output folder for saving model weights in numpy format in <your_save_model_numpy_folder>. Specify output folder for saving input output traces of RNNPool in numpy format in <your_save_traces_numpy_folder>. Note that input traces will be saved in a folder named 'inputs' and output traces in a folder named 'outputs' inside <your_save_traces_numpy_folder>.
```shell
python3 dump_model.py --model ./weights/RPool_Face_QVGA_monochrome_best_state.pth --model_arch RPool_Face_Quant --image_folder <your_image_folder> --save_model_npy_dir <your_save_model_numpy_folder> --save_traces_npy_dir <your_save_traces_numpy_folder>
python3 dump_model.py --model ./weights/RPool_Face_QVGA_monochrome_best_state.pth --model_arch RPool_Face_QVGA_monochrome --image_folder <your_image_folder> --save_model_npy_dir <your_save_model_numpy_folder> --save_traces_npy_dir <your_save_traces_numpy_folder>
```
If you wish to save only model weights, do not specify --save_traces_npy_dir. If you wish to save only traces do not specify --save_model_npy_dir.

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examples/pytorch/vision/Face_Detection/README_M4.md Normal file
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@ -0,0 +1,141 @@
# Code for Face Detection Experiments with RNNPool
## Requirements
1. Follow instructions to install EdgeML operators and their pre-requisites [here](https://github.com/microsoft/EdgeML/blob/master/pytorch/README.md).
2. Install requirements for face detection model using
``` pip install -r requirements.txt ```
We have tested the installation and the code on Ubuntu 18.04 with Python 3.6, Cuda 10.2 and CuDNN 7.6
## Dataset - WIDER Face
1. Download WIDER face dataset images and annotations from http://shuoyang1213.me/WIDERFACE/ and place them all in a folder with name 'WIDER_FACE'. That is, download WIDER_train.zip, WIDER_test.zip, WIDER_val.zip, wider_face_split.zip and place it in WIDER_FACE folder, and unzip files using:
```shell
cd WIDER_FACE
unzip WIDER_train.zip
unzip WIDER_test.zip
unzip WIDER_val.zip
unzip wider_face_split.zip
cd ..
```
2. Set environment variable DATA_HOME to the parent directory of the above folder
That is, if the WIDER_FACE folder is created in /mnt folder
``` export DATA_HOME='/mnt' ```
Note that for Windows '/' should be replaced by '\'.
3. Run
``` IS_QVGA_MONO=1 python prepare_wider_data.py ```
## Dataset - SCUT Head B
Download SCUT Head Part B dataset images and annotations from https://github.com/HCIILAB/SCUT-HEAD-Dataset-Release. Unzipping will create a folder by the name 'SCUT_HEAD_Part_B'. Place this folder in the same parent directory as the WIDER_FACE folder.
# Usage
## Training
```shell
IS_QVGA_MONO=1 python train.py --batch_size 128 --model_arch RPool_Face_M4 --cuda True --multigpu True --save_folder weights/ --epochs 300 --save_frequency 5000
```
This will save checkpoints after every '--save_frequency' number of iterations in a weight file with 'checkpoint.pth' at the end and weights for the best state in a file with 'best_state.pth' at the end. These will be saved in '--save_folder'. For resuming training from a checkpoint, use '--resume <checkpoint_name>.pth' with the above command. For example,
```shell
IS_QVGA_MONO=1 python train.py --batch_size 128 --model_arch RPool_Face_M4 --cuda True --multigpu True --save_folder weights/ --epochs 300 --save_frequency 5000 --resume <checkpoint_name>.pth
```
If IS_QVGA_MONO is 0 then training input images will be 640x640 and RGB.
If IS_QVGA_MONO is 1 then training input images will be 320x320 and converted to monochrome.
Input images for training models are cropped and reshaped to square to maintain consistency with [S3FD](https://arxiv.org/abs/1708.05237). However testing can be done on any size of images, thus we resize testing input image size to have area equal to VGA (640x480)/QVGA (320x240), so that aspect ratio is not changed.
The architecture RPool_Face_QVGA_monochrome and RPool_Face_M4 is for QVGA monochrome format while RPool_Face_C and RPool_Face_Quant are for VGA RGB format.
## Finetuning
To obtain a model better suited for conference room scenarios we finetune our model on the SCUT Head B dataset. Set --finetune as True and pass the model pretrained on WIDER_FACE in --resume as follows:
```shell
IS_QVGA_MONO=1 python train.py --batch_size 64 --model_arch RPool_Face_M4 --cuda True --multigpu True --save_folder weights/ --epochs 300 --save_frequency 5000 --resume ./weights/RPool_Face_M4_best_state.pth --finetune True
```
## Test
There are two modes of testing the trained model -- the evaluation mode to generate bounding boxes for a set of sample images, and the test mode to compute statistics like mAP scores.
#### Evaluation Mode
Given a set of images in <your_image_folder>, `eval/py` generates bounding boxes around faces (where the confidence is higher than certain threshold - 0.5 in this case) and write the images in <your_save_folder>. Specify if the model was trained in multigpu setting in --multigpu. To evaluate the `rpool_face_best_state.pth` model (stored in ./weights), execute the following command:
```shell
IS_QVGA_MONO=1 python eval.py --model_arch RPool_Face_M4 --model ./weights/RPool_Face_M4_best_state.pth --image_folder <your_image_folder> --save_dir <your_save_folder> --thresh 0.5 --multigpu True
```
This will save images in <your_save_folder> with bounding boxes around faces, where the confidence is high. It is recommended to use the model finetuned on SCUT Head for evaluation.
If IS_QVGA_MONO=0 the evaluation code accepts an image of any size and resizes it to 640x480x3 while preserving original image aspect ratio.
If IS_QVGA_MONO=1 the evaluation code accepts an image of any size and resizes and converts it to monochrome to make it 320x240x1 while preserving original image aspect ratio.
#### Saving Full Model Traces
Setting the flag --save_traces as True will save input output traces in two separate .npy files for each image in <your_image_folder>, given the architecture and trained model. Run:
```shell
IS_QVGA_MONO=1 python eval.py --model_arch RPool_Face_M4 --model ./weights/RPool_Face_M4_best_state.pth --image_folder <your_image_folder> --save_dir <your_save_folder> --thresh 0.5 --multigpu True --save_traces True
```
For generating traces on SCUT Head images, set <your_image_folder> as $DATA_HOME/SCUT_HEAD_Part_B/JPEGImages/
#### SCUT Head Validation Set Test
In this mode, we test the generated model against the provided SCUT Head Part B validation dataset. Use the SCUT Head finetuned model for this step.
For this, first run the following to generate predictions of the model and store output in the '--save_folder' folder. Specify if the model was trained in multigpu setting in --multigpu.
```shell
IS_QVGA_MONO=1 python scut_test.py --model_arch RPool_Face_M4 --model ./weights/RPool_Face_M4_best_state.pth --save_folder rpool_face_m4_val --multigpu True
```
The above command generates predictions for each image in the "validation" dataset. For each image, a separate prediction file is provided (image_name.txt file in appropriate folder). The first line of the prediction file contains the total number of boxes identified.
Then each line in the file corresponds to an identified box. For each box, five numbers are generated: length of the box, height of the box, x-axis offset, y-axis offset, confidence value for presence of a face in the box.
If IS_QVGA_MONO=1 then testing is done by converting images to monochrome and QVGA, else if IS_QVGA_MONO=0 then testing is done on VGA RGB images.
###### For calculating mAP scores:
Now using these boxes, we can compute the standard mAP score that is widely used in this literature (see [here](https://medium.com/@jonathan_hui/map-mean-average-precision-for-object-detection-45c121a31173) for more details).
In the current Face_Detection directory run:
```
git clone https://github.com/wondervictor/WiderFace-Evaluation.git
cd WiderFace-Evaluation
python3 setup.py build_ext --inplace
mv ../scut_evaluation.py ./
```
Run ```IS_QVGA_MONO=1 python3 scut_evaluation.py -p ../rpool_face_m4_val ``` in WiderFace-Evaluation folder.
where `-p` is the '--save_folder' used for `scut_test.py` above.
This script should output the mAP on SCUT Head Part B Validation set. Our best performance using RPool_Face_M4 model is: 0.61.
##### Dump RNNPool Input Output Traces and Weights
For saving model weights and/or input output pairs for each patch through RNNPool in numpy format use the command below. Put images which you want to save traces for in <your_image_folder> . Specify output folder for saving model weights in numpy format in <your_save_model_numpy_folder>. Specify output folder for saving input output traces of RNNPool in numpy format in <your_save_traces_numpy_folder>. Note that input traces will be saved in a folder named 'inputs' and output traces in a folder named 'outputs' inside <your_save_traces_numpy_folder>.
```shell
python3 dump_model.py --model ./weights/RPool_Face_M4_best_state.pth --model_arch RPool_Face_M4 --image_folder <your_image_folder> --save_model_npy_dir <your_save_model_numpy_folder> --save_traces_npy_dir <your_save_traces_numpy_folder>
```
If you wish to save only model weights, do not specify --save_traces_npy_dir. If you wish to save only traces do not specify --save_model_npy_dir.
Code has been built upon https://github.com/yxlijun/S3FD.pytorch

4
examples/pytorch/vision/Face_Detection/data/config.py
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@ -54,12 +54,8 @@ _C.NEG_POS_RATIOS = 3
_C.NUM_CLASSES = 2
_C.USE_NMS = True
# dataset config
_C.HOME = '/mnt/' ## change here ----------
# face config
_C.FACE = EasyDict()
_C.FACE.TRAIN_FILE = './data/face_train.txt'
_C.FACE.VAL_FILE = './data/face_val.txt'
_C.FACE.WIDER_DIR = '/mnt/WIDER_FACE' ## change here ---------
_C.FACE.OVERLAP_THRESH = [0.1, 0.35, 0.5]

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examples/pytorch/vision/Face_Detection/data/config_qvga.py
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@ -53,12 +53,8 @@ _C.NEG_POS_RATIOS = 3
_C.NUM_CLASSES = 2
_C.USE_NMS = True
# dataset config
_C.HOME = '/mnt/'
# face config
_C.FACE = EasyDict()
_C.FACE.TRAIN_FILE = './data/face_train.txt'
_C.FACE.VAL_FILE = './data/face_val.txt'
_C.FACE.WIDER_DIR = '/mnt/WIDER_FACE'
_C.FACE.OVERLAP_THRESH = [0.1, 0.35, 0.5]

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JPEGImages/PartB_00723.jpg 10 97 102 108 138 1 38 135 59 60 1 15 102 46 63 1 3 87 24 31 1 92 74 28 34 1 151 90 29 26 1 381 72 14 21 1 394 60 14 14 1 439 27 27 27 1 514 75 18 25 1
JPEGImages/PartB_01013.jpg 57 70 585 148 81 1 73 459 92 120 1 43 442 68 71 1 68 394 64 60 1 40 381 39 49 1 54 353 31 32 1 52 338 24 24 1 110 333 29 33 1 139 335 31 36 1 137 365 34 43 1 176 383 47 52 1 196 417 47 57 1 242 424 56 76 1 332 468 92 117 1 487 522 126 145 1 301 298 39 52 1 212 340 32 34 1 228 330 24 29 1 163 332 26 29 1 266 338 15 19 1 358 357 31 49 1 389 361 26 32 1 398 383 40 47 1 323 359 28 35 1 361 330 22 31 1 399 323 25 31 1 413 343 24 29 1 441 355 34 45 1 460 335 23 26 1 472 392 45 62 1 507 387 37 52 1 515 362 28 29 1 518 343 25 24 1 557 343 21 25 1 574 389 43 57 1 613 422 53 65 1 578 365 29 31 1 581 350 19 23 1 614 338 19 24 1 669 376 34 41 1 644 365 27 30 1 665 351 24 27 1 713 352 25 31 1 709 418 55 69 1 772 433 74 87 1 753 400 35 43 1 733 374 29 35 1 742 352 26 29 1 785 361 26 33 1 839 371 24 29 1 861 377 30 36 1 820 410 35 36 1 886 427 55 73 1 867 411 50 52 1 940 409 55 56 1 950 378 36 40 1 908 365 29 35 1
JPEGImages/PartB_02018.jpg 7 1 69 14 37 1 21 65 34 43 1 49 87 26 34 1 134 89 22 29 1 147 81 30 36 1 181 84 43 43 1 246 80 55 66 1
JPEGImages/PartB_02215.jpg 87 257 412 80 79 1 202 366 41 45 1 108 382 43 47 1 52 345 34 34 1 12 329 31 41 1 45 324 40 30 1 60 310 22 21 1 91 332 25 25 1 112 329 17 19 1 109 307 18 20 1 140 329 28 29 1 144 314 17 19 1 151 273 21 20 1 171 340 27 32 1 191 330 20 25 1 218 325 17 25 1 248 336 28 35 1 260 330 27 26 1 262 307 18 20 1 303 327 33 33 1 313 316 19 18 1 301 301 17 17 1 336 309 18 19 1 343 305 15 14 1 365 310 18 21 1 384 344 37 35 1 410 341 49 52 1 413 387 75 88 1 478 338 39 42 1 444 317 25 28 1 424 314 21 22 1 416 295 18 21 1 403 301 18 23 1 384 311 23 22 1 386 302 18 20 1 463 305 13 12 1 475 308 17 20 1 504 324 24 23 1 552 315 29 34 1 551 307 19 20 1 540 302 16 20 1 535 271 15 18 1 496 305 8 13 1 588 329 30 36 1 554 377 68 70 1 618 378 57 57 1 564 293 12 16 1 585 305 12 19 1 600 310 23 23 1 610 293 15 17 1 633 298 14 16 1 647 306 16 18 1 642 318 20 22 1 634 336 24 28 1 676 327 25 28 1 676 308 15 20 1 680 300 12 14 1 704 304 12 15 1 716 316 15 21 1 704 337 33 33 1 763 301 21 20 1 789 304 20 26 1 782 333 28 34 1 773 327 20 23 1 818 297 15 16 1 820 311 14 20 1 830 314 19 26 1 862 308 21 27 1 877 292 18 17 1 870 284 14 16 1 893 287 12 14 1 893 303 17 22 1 883 322 30 31 1 894 349 43 45 1 916 318 27 31 1 925 301 20 22 1 918 281 16 19 1 943 298 16 23 1 961 314 21 22 1 967 295 18 25 1 987 308 17 27 1 988 328 36 38 1 961 343 39 46 1 1003 307 21 25 1 1028 308 18 21 1 1051 300 20 25 1 1057 325 24 36 1
JPEGImages/PartB_01865.jpg 9 9 82 68 74 1 89 74 40 44 1 183 54 52 59 1 147 116 104 107 1 257 67 43 51 1 364 63 40 48 1 398 49 36 40 1 453 75 52 72 1 491 48 50 59 1
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24
examples/pytorch/vision/Face_Detection/data/widerface.py
Просмотреть файл

@ -7,13 +7,19 @@ import torch.utils.data as data
import numpy as np
import random
import sys; sys.path.append('../')
from utils.augmentations import preprocess
from utils.augmentations import preprocess, preprocess_qvga
from data.choose_config import cfg
cfg = cfg.cfg
import os
HOME = os.environ['DATA_HOME']
SCUT_ROOT = os.path.join(HOME, 'SCUT_HEAD_Part_B')
class WIDERDetection(data.Dataset):
"""docstring for WIDERDetection"""
def __init__(self, list_file, mode='train', mono_mode=False):
def __init__(self, list_file, mode='train', mono_mode=False, is_scut=False):
super(WIDERDetection, self).__init__()
self.mode = mode
self.mono_mode = mono_mode
@ -40,7 +46,10 @@ class WIDERDetection(data.Dataset):
box.append([x, y, x + w, y + h])
label.append(c)
if len(box) > 0:
self.fnames.append(line[0])
if is_scut==True:
self.fnames.append(SCUT_ROOT + '/' + line[0])
else:
self.fnames.append(line[0])
self.boxes.append(box)
self.labels.append(label)
@ -65,8 +74,13 @@ class WIDERDetection(data.Dataset):
np.array(self.boxes[index]), im_width, im_height)
label = np.array(self.labels[index])
bbox_labels = np.hstack((label[:, np.newaxis], boxes)).tolist()
img, sample_labels = preprocess(
img, bbox_labels, self.mode, image_path)
if self.mono_mode==False:
img, sample_labels = preprocess(
img, bbox_labels, self.mode, image_path)
else:
img, sample_labels = preprocess_qvga(
img, bbox_labels, self.mode, image_path)
sample_labels = np.array(sample_labels)
if len(sample_labels) > 0:
target = np.hstack(

48
examples/pytorch/vision/Face_Detection/eval.py
Просмотреть файл

@ -21,6 +21,8 @@ from utils.augmentations import to_chw_bgr
from importlib import import_module
def str2bool(v):
return v.lower() in ("yes", "true", "t", "1")
parser = argparse.ArgumentParser(description='face detection demo')
parser.add_argument('--save_dir', type=str, default='results/',
@ -29,11 +31,17 @@ parser.add_argument('--model', type=str,
default='weights/rpool_face_c.pth', help='trained model')
parser.add_argument('--thresh', default=0.17, type=float,
help='Final confidence threshold')
parser.add_argument('--multigpu',
default=False, type=str2bool,
help='Specify whether model was trained with multigpu')
parser.add_argument('--model_arch',
default='RPool_Face_C', type=str,
choices=['RPool_Face_C', 'RPool_Face_Quant', 'RPool_Face_QVGA_monochrome'],
choices=['RPool_Face_C', 'RPool_Face_Quant', 'RPool_Face_QVGA_monochrome', 'RPool_Face_M4'],
help='choose architecture among rpool variants')
parser.add_argument('--image_folder', default=None, type=str, help='folder containing images')
parser.add_argument('--save_traces',
default=False, type=str2bool,
help='Specify whether to save input output traces')
args = parser.parse_args()
@ -49,7 +57,7 @@ else:
torch.set_default_tensor_type('torch.FloatTensor')
def detect(net, img_path, thresh):
def detect(net, img_path, thresh, save_traces):
img = Image.open(img_path)
img = img.convert('RGB')
img = np.array(img)
@ -62,9 +70,14 @@ def detect(net, img_path, thresh):
max_im_shrink = np.sqrt(
640 * 480 / (img.shape[0] * img.shape[1]))
image = cv2.resize(img, None, None, fx=max_im_shrink,
if save_traces==True and os.environ['IS_QVGA_MONO'] == '1':
image = cv2.resize(img, (320, 240))
elif save_traces==True:
image = cv2.resize(img, (640, 480))
else:
image = cv2.resize(img, None, None, fx=max_im_shrink,
fy=max_im_shrink, interpolation=cv2.INTER_LINEAR)
# img = cv2.resize(img, (640, 640))
x = to_chw_bgr(image)
x = x.astype('float32')
x -= cfg.img_mean
@ -79,7 +92,7 @@ def detect(net, img_path, thresh):
if use_cuda:
x = x.cuda()
t1 = time.time()
y = net(x)
y, loc, conf = net(x)
detections = y.data
scale = torch.Tensor([img.shape[1], img.shape[0],
img.shape[1], img.shape[0]])
@ -94,9 +107,9 @@ def detect(net, img_path, thresh):
left_up, right_bottom = (pt[0], pt[1]), (pt[2], pt[3])
j += 1
cv2.rectangle(img, left_up, right_bottom, (0, 0, 255), 2)
conf = "{:.3f}".format(score)
conf_score = "{:.3f}".format(score)
point = (int(left_up[0]), int(left_up[1] - 5))
cv2.putText(img, conf, point, cv2.FONT_HERSHEY_COMPLEX,
cv2.putText(img, conf_score, point, cv2.FONT_HERSHEY_COMPLEX,
0.6, (0, 255, 0), 1)
t2 = time.time()
@ -104,13 +117,17 @@ def detect(net, img_path, thresh):
cv2.imwrite(os.path.join(args.save_dir, os.path.basename(img_path)), img)
if save_traces == True:
return x, loc, conf
if __name__ == '__main__':
module = import_module('models.' + args.model_arch)
net = module.build_s3fd('test', cfg.NUM_CLASSES)
net = torch.nn.DataParallel(net)
if args.multigpu == True:
net = torch.nn.DataParallel(net)
checkpoint_dict = torch.load(args.model)
@ -129,5 +146,18 @@ if __name__ == '__main__':
img_path = args.image_folder
img_list = [os.path.join(img_path, x)
for x in os.listdir(img_path)]
x = []
loc = []
conf = []
for path in img_list:
detect(net, path, args.thresh)
if args.save_traces == True:
x_temp, loc_temp, conf_temp = detect(net, path, args.thresh, args.save_traces)
x.append(x_temp)
loc.append(loc_temp)
conf.append(conf_temp)
else:
detect(net, path, args.thresh, args.save_traces)
if args.save_traces == True:
np.save('trace_inputs.npy', torch.cat(x).cpu().detach().numpy())
np.save('trace_outputs.npy', torch.cat([torch.cat(conf), torch.cat(loc)], dim=1).cpu().detach().numpy())

3
examples/pytorch/vision/Face_Detection/layers/modules/l2norm.py
Просмотреть файл

@ -24,6 +24,3 @@ class L2Norm(nn.Module):
x = torch.div(x,norm)
out = self.weight.unsqueeze(0).unsqueeze(2).unsqueeze(3).expand_as(x) * x
return out

4
examples/pytorch/vision/Face_Detection/layers/modules/multibox_loss.py
Просмотреть файл

@ -86,7 +86,7 @@ class MultiBoxLoss(nn.Module):
pos_idx = pos.unsqueeze(pos.dim()).expand_as(loc_data)
loc_p = loc_data[pos_idx].view(-1, 4)
loc_t = loc_t[pos_idx].view(-1, 4)
loss_l = F.smooth_l1_loss(loc_p, loc_t, size_average=False)
loss_l = F.smooth_l1_loss(loc_p, loc_t, reduction='sum')
# print(loc_p)
# Compute max conf across batch for hard negative mining
batch_conf = conf_data.view(-1, self.num_classes)
@ -109,7 +109,7 @@ class MultiBoxLoss(nn.Module):
conf_p = conf_data[(pos_idx + neg_idx).gt(0)
].view(-1, self.num_classes)
targets_weighted = conf_t[(pos + neg).gt(0)]
loss_c = F.cross_entropy(conf_p, targets_weighted, size_average=False)
loss_c = F.cross_entropy(conf_p, targets_weighted, reduction='sum')
# Sum of losses: L(x,c,l,g) = (Lconf(x, c) + αLloc(x,l,g)) / N
N = num_pos.data.sum() if num_pos.data.sum() > 0 else num

6
examples/pytorch/vision/Face_Detection/models/RPool_Face_C.py
Просмотреть файл

@ -171,7 +171,7 @@ class S3FD(nn.Module):
if self.phase == 'test':
output = detect_function(
output = detect_function(cfg,
loc.view(loc.size(0), -1, 4), # loc preds
self.softmax(conf.view(conf.size(0), -1,
self.num_classes)), # conf preds
@ -184,7 +184,7 @@ class S3FD(nn.Module):
conf.view(conf.size(0), -1, self.num_classes),
self.priors
)
return output
return output, loc, conf
def load_weights(self, base_file):
other, ext = os.path.splitext(base_file)
@ -304,7 +304,6 @@ class MobileNetV2(nn.Module):
# building first layer
input_channel = _make_divisible(input_channel * width_mult, round_nearest)
self.last_channel = _make_divisible(last_channel * max(1.0, width_mult), round_nearest)
self.layers = []
# building inverted residual blocks
for t, c, n, s in inverted_residual_setting:
@ -379,4 +378,3 @@ if __name__ == '__main__':
net = build_s3fd('train', num_classes=2)
inputs = Variable(torch.randn(4, 3, 640, 640))
output = net(inputs)

349
examples/pytorch/vision/Face_Detection/models/RPool_Face_M4.py Normal file
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@ -0,0 +1,349 @@
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT license.
from __future__ import division
from __future__ import absolute_import
from __future__ import print_function
import os
import torch
import torch.nn as nn
import torch.nn.init as init
import torch.nn.functional as F
from layers import *
from data.config_qvga import cfg
import numpy as np
from edgeml_pytorch.graph.rnnpool import *
class S3FD(nn.Module):
"""Single Shot Multibox Architecture
The network is composed of a base VGG network followed by the
added multibox conv layers. Each multibox layer branches into
1) conv2d for class conf scores
2) conv2d for localization predictions
3) associated priorbox layer to produce default bounding
boxes specific to the layer's feature map size.
See: https://arxiv.org/pdf/1512.02325.pdf for more details.
Args:
phase: (string) Can be "test" or "train"
size: input image size
base: VGG16 layers for input, size of either 300 or 500
extras: extra layers that feed to multibox loc and conf layers
head: "multibox head" consists of loc and conf conv layers
"""
def __init__(self, phase, base, head, num_classes):
super(S3FD, self).__init__()
self.phase = phase
self.num_classes = num_classes
'''
self.priorbox = PriorBox(size,cfg)
self.priors = Variable(self.priorbox.forward(), volatile=True)
'''
# SSD network
self.conv = ConvBNReLU(1, 4, stride=2)
self.unfold = nn.Unfold(kernel_size=(8,8),stride=(4,4))
self.rnn_model = RNNPool(8, 8, 16, 16, 4,
w1Sparsity=0.5, u1Sparsity=0.3, w2Sparsity=0.3, u2Sparsity=0.3)
self.mob = nn.ModuleList(base)
# Layer learns to scale the l2 normalized features from conv4_3
self.L2Norm3_3 = L2Norm(32, 10)
self.L2Norm4_3 = L2Norm(32, 8)
self.L2Norm5_3 = L2Norm(64, 5)
self.loc = nn.ModuleList(head[0])
self.conf = nn.ModuleList(head[1])
if self.phase == 'test':
self.softmax = nn.Softmax(dim=-1)
def forward(self, x):
"""Applies network layers and ops on input image(s) x.
Args:
x: input image or batch of images. Shape: [batch,3,300,300].
Return:
Depending on phase:
test:
Variable(tensor) of output class label predictions,
confidence score, and corresponding location predictions for
each object detected. Shape: [batch,topk,7]
train:
list of concat outputs from:
1: confidence layers, Shape: [batch*num_priors,num_classes]
2: localization layers, Shape: [batch,num_priors*4]
3: priorbox layers, Shape: [2,num_priors*4]
"""
size = x.size()[2:]
batch_size = x.shape[0]
sources = list()
loc = list()
conf = list()
x = self.conv(x)
patches = self.unfold(x)
patches = torch.cat(torch.unbind(patches,dim=2),dim=0)
patches = torch.reshape(patches,(-1,4,8,8))
output_x = int((x.shape[2]-8)/4 + 1)
output_y = int((x.shape[3]-8)/4 + 1)
rnnX = self.rnn_model(patches, int(batch_size)*output_x*output_y)
x = torch.stack(torch.split(rnnX, split_size_or_sections=int(batch_size), dim=0),dim=2)
x = F.fold(x, kernel_size=(1,1), output_size=(output_x,output_y))
x = F.pad(x, (0,1,0,1), mode='replicate')
for k in range(1):
x = self.mob[k](x)
s = self.L2Norm3_3(x)
sources.append(s)
# apply vgg up to fc7
for k in range(1, 2):
x = self.mob[k](x)
s = self.L2Norm4_3(x)
sources.append(s)
for k in range(2, 3):
x = self.mob[k](x)
s = self.L2Norm5_3(x)
sources.append(s)
for k in range(3, 4):
x = self.mob[k](x)
sources.append(x)
# apply multibox head to source layers
loc_x = self.loc[0](sources[0])
conf_x = self.conf[0](sources[0])
max_conf, _ = torch.max(conf_x[:, 0:3, :, :], dim=1, keepdim=True)
conf_x = torch.cat((max_conf, conf_x[:, 3:, :, :]), dim=1)
loc.append(loc_x.permute(0, 2, 3, 1).contiguous())
conf.append(conf_x.permute(0, 2, 3, 1).contiguous())
for i in range(1, len(sources)):
x = sources[i]
conf.append(self.conf[i](x).permute(0, 2, 3, 1).contiguous())
loc.append(self.loc[i](x).permute(0, 2, 3, 1).contiguous())
features_maps = []
for i in range(len(loc)):
feat = []
feat += [loc[i].size(1), loc[i].size(2)]
features_maps += [feat]
self.priorbox = PriorBox(size, features_maps, cfg)
self.priors = self.priorbox.forward()
loc = torch.cat([o.view(o.size(0), -1) for o in loc], 1)
conf = torch.cat([o.view(o.size(0), -1) for o in conf], 1)
if self.phase == 'test':
output = detect_function(cfg,
loc.view(loc.size(0), -1, 4), # loc preds
self.softmax(conf.view(conf.size(0), -1,
self.num_classes)), # conf preds
self.priors.type(type(x.data)) # default boxes
)
else:
output = (
loc.view(loc.size(0), -1, 4),
conf.view(conf.size(0), -1, self.num_classes),
self.priors
)
return output, loc, conf
def load_weights(self, base_file):
other, ext = os.path.splitext(base_file)
if ext == '.pkl' or '.pth':
print('Loading weights into state dict...')
mdata = torch.load(base_file,
map_location=lambda storage, loc: storage)
weights = mdata['weight']
epoch = mdata['epoch']
self.load_state_dict(weights)
print('Finished!')
else:
print('Sorry only .pth and .pkl files supported.')
return epoch
def xavier(self, param):
init.xavier_uniform(param)
def weights_init(self, m):
if isinstance(m, nn.Conv2d):
self.xavier(m.weight.data)
m.bias.data.zero_()
def _make_divisible(v, divisor, min_value=None):
"""
This function is taken from the original tf repo.
It ensures that all layers have a channel number that is divisible by 8
It can be seen here:
https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
:param v:
:param divisor:
:param min_value:
:return:
"""
if min_value is None:
min_value = divisor
new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
# Make sure that round down does not go down by more than 10%.
if new_v < 0.9 * v:
new_v += divisor
return new_v
class ConvBNReLU(nn.Sequential):
def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, groups=1):
padding = (kernel_size - 1) // 2
super(ConvBNReLU, self).__init__(
nn.Conv2d(in_planes, out_planes, kernel_size, stride, padding, groups=groups, bias=False),
nn.BatchNorm2d(out_planes),
nn.ReLU6(inplace=True)
)
class InvertedResidual(nn.Module):
def __init__(self, inp, oup, stride, expand_ratio):
super(InvertedResidual, self).__init__()
self.stride = stride
assert stride in [1, 2]
hidden_dim = int(round(inp * expand_ratio))
self.use_res_connect = self.stride == 1 and inp == oup
layers = []
if expand_ratio != 1:
# pw
layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1))
layers.extend([
# dw
ConvBNReLU(hidden_dim, hidden_dim, stride=stride, groups=hidden_dim),
# pw-linear
nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
nn.BatchNorm2d(oup),
])
self.conv = nn.Sequential(*layers)
def forward(self, x):
if self.use_res_connect:
return x + self.conv(x)
else:
return self.conv(x)
class MobileNetV2(nn.Module):
def __init__(self, num_classes=1000, width_mult=1.0, inverted_residual_setting=None, round_nearest=8):
"""
MobileNet V2 main class
Args:
num_classes (int): Number of classes
width_mult (float): Width multiplier - adjusts number of channels in each layer by this amount
inverted_residual_setting: Network structure
round_nearest (int): Round the number of channels in each layer to be a multiple of this number
Set to 1 to turn off rounding
"""
super(MobileNetV2, self).__init__()
block = InvertedResidual
input_channel = 64
if inverted_residual_setting is None:
inverted_residual_setting = [
# t, c, n, s
[2, 32, 1, 1],
[2, 32, 1, 1],
[2, 64, 1, 2],
[2, 64, 1, 1],
]
# only check the first element, assuming user knows t,c,n,s are required
if len(inverted_residual_setting) == 0 or len(inverted_residual_setting[0]) != 4:
raise ValueError("inverted_residual_setting should be non-empty "
"or a 4-element list, got {}".format(inverted_residual_setting))
# building first layer
input_channel = _make_divisible(input_channel * width_mult, round_nearest)
self.layers = []
# building inverted residual blocks
for t, c, n, s in inverted_residual_setting:
output_channel = _make_divisible(c * width_mult, round_nearest)
for i in range(n):
stride = s if i == 0 else 1
self.layers.append(block(input_channel, output_channel, stride, expand_ratio=t))
input_channel = output_channel
# weight initialization
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out')
if m.bias is not None:
nn.init.zeros_(m.bias)
elif isinstance(m, nn.BatchNorm2d):
nn.init.ones_(m.weight)
nn.init.zeros_(m.bias)
elif isinstance(m, nn.Linear):
nn.init.normal_(m.weight, 0, 0.01)
nn.init.zeros_(m.bias)
def multibox(mobilenet, num_classes):
loc_layers = []
conf_layers = []
loc_layers += [nn.Conv2d(32, 4, kernel_size=3, padding=1)]
conf_layers += [nn.Conv2d(32, 3 + (num_classes-1), kernel_size=3, padding=1)]
loc_layers += [nn.Conv2d(32, 4, kernel_size=3, padding=1)]
conf_layers += [nn.Conv2d(32, num_classes, kernel_size=3, padding=1)]
loc_layers += [nn.Conv2d(64, 4, kernel_size=3, padding=1)]
conf_layers += [nn.Conv2d(64, num_classes, kernel_size=3, padding=1)]
loc_layers += [nn.Conv2d(64, 4, kernel_size=3, padding=1)]
conf_layers += [nn.Conv2d(64, num_classes, kernel_size=3, padding=1)]
return mobilenet, (loc_layers, conf_layers)
def build_s3fd(phase, num_classes=2):
base_, head_ = multibox(
MobileNetV2().layers, num_classes)
return S3FD(phase, base_, head_, num_classes)
if __name__ == '__main__':
net = build_s3fd('train', num_classes=2)
inputs = Variable(torch.randn(4, 1, 320, 320))
output = net(inputs)

3
examples/pytorch/vision/Face_Detection/models/RPool_Face_QVGA_monochrome.py
Просмотреть файл

@ -172,7 +172,7 @@ class S3FD(nn.Module):
conf.view(conf.size(0), -1, self.num_classes),
self.priors
)
return output
return output, loc, conf
def load_weights(self, base_file):
other, ext = os.path.splitext(base_file)
@ -289,7 +289,6 @@ class MobileNetV2(nn.Module):
# building first layer
input_channel = _make_divisible(input_channel * width_mult, round_nearest)
self.last_channel = _make_divisible(last_channel * max(1.0, width_mult), round_nearest)
self.layers = []
# building inverted residual blocks
for t, c, n, s in inverted_residual_setting:

3
examples/pytorch/vision/Face_Detection/models/RPool_Face_Quant.py
Просмотреть файл

@ -180,7 +180,7 @@ class S3FD(nn.Module):
conf.view(conf.size(0), -1, self.num_classes),
self.priors
)
return output
return output, loc, conf
def load_weights(self, base_file):
other, ext = os.path.splitext(base_file)
@ -297,7 +297,6 @@ class MobileNetV2(nn.Module):
# building first layer
input_channel = _make_divisible(input_channel * width_mult, round_nearest)
self.last_channel = _make_divisible(last_channel * max(1.0, width_mult), round_nearest)
self.layers = []
# building inverted residual blocks
for t, c, n, s in inverted_residual_setting:

2
examples/pytorch/vision/Face_Detection/models/__init__.py
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@ -4,3 +4,5 @@
from __future__ import absolute_import
from .RPool_Face_C import *
from .RPool_Face_Quant import *
from .RPool_Face_QVGA_monochrome import *
from .RPool_Face_M4 import *

5
examples/pytorch/vision/Face_Detection/prepare_wider_data.py
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@ -10,7 +10,10 @@ import os
from data.config import cfg
import cv2
WIDER_ROOT = os.path.join(cfg.HOME, 'WIDER_FACE')
HOME = os.environ['DATA_HOME']
WIDER_ROOT = os.path.join(HOME, 'WIDER_FACE')
train_list_file = os.path.join(WIDER_ROOT, 'wider_face_split',
'wider_face_train_bbx_gt.txt')
val_list_file = os.path.join(WIDER_ROOT, 'wider_face_split',

219
examples/pytorch/vision/Face_Detection/scut_evaluation.py Normal file
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@ -0,0 +1,219 @@
"""
WiderFace evaluation code
author: wondervictor
mail: tianhengcheng@gmail.com
copyright@wondervictor
Code buit upon https://github.com/wondervictor/WiderFace-Evaluation having MIT License
"""
import os
import tqdm
import pickle
import argparse
import numpy as np
from scipy.io import loadmat
from bbox import bbox_overlaps
from IPython import embed
import cv2
import sys
sys.path.append('../')
from data.choose_config import cfg
cfg = cfg.cfg
HOME = os.environ['DATA_HOME']
SCUT_ROOT = os.path.join(HOME, 'SCUT_HEAD_Part_B')
def read_pred_file(filepath):
with open(filepath, 'r') as f:
lines = f.readlines()
img_file = lines[0].rstrip('\n\r')
lines = lines[2:]
boxes = np.array(list(map(lambda x: [float(a) for a in x.rstrip('\r\n').split(' ')], lines))).astype('float')
return img_file.split('/')[-1], boxes
def get_preds(pred_dir):
events = os.listdir(pred_dir)
boxes = dict()
pbar = tqdm.tqdm(events)
for event in pbar:
pbar.set_description('Reading Predictions ')
event_dir = os.path.join(pred_dir, event)
event_images = os.listdir(event_dir)
current_event = dict()
for imgtxt in event_images:
imgname, _boxes = read_pred_file(os.path.join(event_dir, imgtxt))
current_event[imgname.rstrip('.jpg')] = _boxes
boxes[event] = current_event
return boxes
def norm_score(pred):
""" norm score
pred {key: [[x1,y1,x2,y2,s]]}
"""
max_score = 0
min_score = 1
for _, k in pred.items():
for _, v in k.items():
if len(v) == 0:
continue
_min = np.min(v[:, -1])
_max = np.max(v[:, -1])
max_score = max(_max, max_score)
min_score = min(_min, min_score)
diff = max_score - min_score
for _, k in pred.items():
for _, v in k.items():
if len(v) == 0:
continue
v[:, -1] = (v[:, -1] - min_score)/diff
def image_eval(pred, gt, iou_thresh):
""" single image evaluation
pred: Nx5
gt: Nx4
ignore:
"""
_pred = pred.copy()
_gt = gt.copy()
pred_recall = np.zeros(_pred.shape[0])
recall_list = np.zeros(_gt.shape[0])
proposal_list = np.ones(_pred.shape[0])
_pred[:, 2] = _pred[:, 2] + _pred[:, 0]
_pred[:, 3] = _pred[:, 3] + _pred[:, 1]
_gt[:, 2] = _gt[:, 2] + _gt[:, 0]
_gt[:, 3] = _gt[:, 3] + _gt[:, 1]
overlaps = bbox_overlaps(_pred[:, :4], _gt)
for h in range(_pred.shape[0]):
gt_overlap = overlaps[h]
max_overlap, max_idx = gt_overlap.max(), gt_overlap.argmax()
if max_overlap >= iou_thresh:
if recall_list[max_idx] == 0:
recall_list[max_idx] = 1
r_keep_index = np.where(recall_list == 1)[0]
pred_recall[h] = len(r_keep_index)
return pred_recall, proposal_list
def img_pr_info(thresh_num, pred_info, proposal_list, pred_recall):
pr_info = np.zeros((thresh_num, 2)).astype('float')
for t in range(thresh_num):
thresh = 1 - (t+1)/thresh_num
r_index = np.where(pred_info[:, 4] >= thresh)[0]
if len(r_index) == 0:
pr_info[t, 0] = 0
pr_info[t, 1] = 0
else:
r_index = r_index[-1]
p_index = np.where(proposal_list[:r_index+1] == 1)[0]
pr_info[t, 0] = len(p_index)
pr_info[t, 1] = pred_recall[r_index]
return pr_info
def dataset_pr_info(thresh_num, pr_curve, count_face):
_pr_curve = np.zeros((thresh_num, 2))
for i in range(thresh_num):
_pr_curve[i, 0] = pr_curve[i, 1] / pr_curve[i, 0]
_pr_curve[i, 1] = pr_curve[i, 1] / count_face
return _pr_curve
def voc_ap(rec, prec):
# correct AP calculation
# first append sentinel values at the end
mrec = np.concatenate(([0.], rec, [1.]))
mpre = np.concatenate(([0.], prec, [0.]))
# compute the precision envelope
for i in range(mpre.size - 1, 0, -1):
mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])
# to calculate area under PR curve, look for points
# where X axis (recall) changes value
i = np.where(mrec[1:] != mrec[:-1])[0]
# and sum (\Delta recall) * prec
ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])
return ap
def evaluation(pred, iou_thresh=0.5):
f = open('../data/face_val_scutB.txt')
lines = f.readlines()
names = []
gt_list = []
for line in lines:
line = line.strip().split()
names.append(os.path.split(line[0])[1][0:-4])
th, tw, tc = cv2.imread(SCUT_ROOT + '/' + line[0]).shape
num_faces = int(line[1])
faces = []
for i in range(num_faces):
x = float(line[2 + 5 * i])
y = float(line[3 + 5 * i])
w = float(line[4 + 5 * i])
h = float(line[5 + 5 * i])
faces.append([x, y, w, h])
gt_list.append(faces)
thresh_num = 1000
aps = []
count_face = 0
pr_curve = np.zeros((thresh_num, 2)).astype('float')
for j in range(len(names)):
gt_boxes = np.asarray(gt_list[j]).astype('float')
pred_file = open(os.path.join(pred, names[j] + '.txt'))
pred_lines = pred_file.readlines()
pred_info = np.array(list(map(lambda x: [float(a) for a in x.rstrip('\r\n').split(' ')], pred_lines[2:]))).astype('float')
count_face += len(gt_boxes)
if len(gt_boxes) == 0 or len(pred_info) == 0:
continue
pred_recall, proposal_list = image_eval(pred_info, gt_boxes, iou_thresh)
_img_pr_info = img_pr_info(thresh_num, pred_info, proposal_list, pred_recall)
pr_curve += _img_pr_info
pr_curve = dataset_pr_info(thresh_num, pr_curve, count_face)
propose = pr_curve[:, 0]
recall = pr_curve[:, 1]
ap = voc_ap(recall, propose)
aps.append(ap)
print(ap)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-p', '--pred')
args = parser.parse_args()
evaluation(args.pred)

244
examples/pytorch/vision/Face_Detection/scut_test.py Normal file
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@ -0,0 +1,244 @@
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT license.
import os
import argparse
import torch
import torch.nn as nn
import torch.utils.data as data
import torch.backends.cudnn as cudnn
import torchvision.transforms as transforms
import os.path as osp
import cv2
import time
import numpy as np
from PIL import Image
import scipy.io as sio
from data.choose_config import cfg
cfg = cfg.cfg
from torch.autograd import Variable
from utils.augmentations import to_chw_bgr
from importlib import import_module
import warnings
warnings.filterwarnings("ignore")
HOME = os.environ['DATA_HOME']
SCUT_ROOT = os.path.join(HOME, 'SCUT_HEAD_Part_B')
def str2bool(v):
return v.lower() in ("yes", "true", "t", "1")
parser = argparse.ArgumentParser(description='s3fd evaluatuon wider')
parser.add_argument('--model', type=str,
default='./weights/rpool_face_m4.pth', help='trained model')
parser.add_argument('--thresh', default=0.05, type=float,
help='Final confidence threshold')
parser.add_argument('--multigpu',
default=False, type=str2bool,
help='Specify whether model was trained with multigpu')
parser.add_argument('--model_arch',
default='RPool_Face_M4', type=str,
choices=['RPool_Face_M4', 'RPool_Face_QVGA_monochrome'],
help='choose architecture among rpool variants')
parser.add_argument('--save_folder', type=str,
default='rpool_face_predictions', help='folder for saving predictions')
args = parser.parse_args()
use_cuda = torch.cuda.is_available()
if use_cuda:
torch.set_default_tensor_type('torch.cuda.FloatTensor')
else:
torch.set_default_tensor_type('torch.FloatTensor')
def detect_face(net, img, shrink):
if shrink != 1:
img = cv2.resize(img, None, None, fx=shrink, fy=shrink,
interpolation=cv2.INTER_LINEAR)
x = to_chw_bgr(img)
x = x.astype('float32')
x -= cfg.img_mean
x = x[[2, 1, 0], :, :]
if cfg.IS_MONOCHROME == True:
x = 0.299 * x[0] + 0.587 * x[1] + 0.114 * x[2]
x = torch.from_numpy(x).unsqueeze(0).unsqueeze(0)
else:
x = torch.from_numpy(x).unsqueeze(0)
if use_cuda:
x = x.cuda()
y = net(x)
detections = y.data
detections = detections.cpu().numpy()
det_conf = detections[0, 1, :, 0]
det_xmin = img.shape[1] * detections[0, 1, :, 1] / shrink
det_ymin = img.shape[0] * detections[0, 1, :, 2] / shrink
det_xmax = img.shape[1] * detections[0, 1, :, 3] / shrink
det_ymax = img.shape[0] * detections[0, 1, :, 4] / shrink
det = np.column_stack((det_xmin, det_ymin, det_xmax, det_ymax, det_conf))
keep_index = np.where(det[:, 4] >= args.thresh)[0]
det = det[keep_index, :]
return det
def multi_scale_test(net, image, max_im_shrink):
# shrink detecting and shrink only detect big face
st = 0.5 if max_im_shrink >= 0.75 else 0.5 * max_im_shrink
det_s = detect_face(net, image, st)
index = np.where(np.maximum(
det_s[:, 2] - det_s[:, 0] + 1, det_s[:, 3] - det_s[:, 1] + 1) > 30)[0]
det_s = det_s[index, :]
# enlarge one times
bt = min(2, max_im_shrink) if max_im_shrink > 1 else (
st + max_im_shrink) / 2
det_b = detect_face(net, image, bt)
# enlarge small image x times for small face
if max_im_shrink > 2:
bt *= 2
while bt < max_im_shrink:
det_b = np.row_stack((det_b, detect_face(net, image, bt)))
bt *= 2
det_b = np.row_stack((det_b, detect_face(net, image, max_im_shrink)))
# enlarge only detect small face
if bt > 1:
index = np.where(np.minimum(
det_b[:, 2] - det_b[:, 0] + 1, det_b[:, 3] - det_b[:, 1] + 1) < 100)[0]
det_b = det_b[index, :]
else:
index = np.where(np.maximum(
det_b[:, 2] - det_b[:, 0] + 1, det_b[:, 3] - det_b[:, 1] + 1) > 30)[0]
det_b = det_b[index, :]
return det_s, det_b
def flip_test(net, image, shrink):
image_f = cv2.flip(image, 1)
det_f = detect_face(net, image_f, shrink)
det_t = np.zeros(det_f.shape)
det_t[:, 0] = image.shape[1] - det_f[:, 2]
det_t[:, 1] = det_f[:, 1]
det_t[:, 2] = image.shape[1] - det_f[:, 0]
det_t[:, 3] = det_f[:, 3]
det_t[:, 4] = det_f[:, 4]
return det_t
def bbox_vote(det):
order = det[:, 4].ravel().argsort()[::-1]
det = det[order, :]
while det.shape[0] > 0:
# IOU
area = (det[:, 2] - det[:, 0] + 1) * (det[:, 3] - det[:, 1] + 1)
xx1 = np.maximum(det[0, 0], det[:, 0])
yy1 = np.maximum(det[0, 1], det[:, 1])
xx2 = np.minimum(det[0, 2], det[:, 2])
yy2 = np.minimum(det[0, 3], det[:, 3])
w = np.maximum(0.0, xx2 - xx1 + 1)
h = np.maximum(0.0, yy2 - yy1 + 1)
inter = w * h
o = inter / (area[0] + area[:] - inter)
# get needed merge det and delete these det
merge_index = np.where(o >= 0.3)[0]
det_accu = det[merge_index, :]
det = np.delete(det, merge_index, 0)
if merge_index.shape[0] <= 1:
continue
det_accu[:, 0:4] = det_accu[:, 0:4] * np.tile(det_accu[:, -1:], (1, 4))
max_score = np.max(det_accu[:, 4])
det_accu_sum = np.zeros((1, 5))
det_accu_sum[:, 0:4] = np.sum(
det_accu[:, 0:4], axis=0) / np.sum(det_accu[:, -1:])
det_accu_sum[:, 4] = max_score
try:
dets = np.row_stack((dets, det_accu_sum))
except:
dets = det_accu_sum
dets = dets[0:750, :]
return dets
if __name__ == '__main__':
cfg.USE_NMS = False
module = import_module('models.' + args.model_arch)
net = module.build_s3fd('test', cfg.NUM_CLASSES)
if args.multigpu == True:
net = torch.nn.DataParallel(net)
checkpoint_dict = torch.load(args.model)
model_dict = net.state_dict()
model_dict.update(checkpoint_dict)
net.load_state_dict(model_dict)
net.eval()
if use_cuda:
net.cuda()
cudnn.benckmark = True
counter = 0
f = open('./data/face_val_scutB.txt')
lines = f.readlines()
if os.path.exists('./{}'.format(args.save_folder)) is False:
os.mkdir('./{}'.format(args.save_folder))
for line in lines:
line = line.strip().split()
im_name = SCUT_ROOT + '/' + line[0]
img = Image.open(im_name)
img = img.convert('RGB')
img = np.array(img)
max_im_shrink = np.sqrt(
320 * 240 / (img.shape[0] * img.shape[1]))
shrink = max_im_shrink if max_im_shrink < 1 else 1
counter += 1
t1 = time.time()
det0 = detect_face(net, img, shrink)
dets = det0
t2 = time.time()
print('Detect %04d th image costs %.4f' % (counter, t2 - t1))
imgname = osp.split(line[0])[1]
save_path = './{}'.format(args.save_folder)
fout = open(osp.join(save_path, imgname[0:-4] + '.txt'), 'w')
fout.write('{:s}\n'.format(imgname[0:-4] + '.txt'))
fout.write('{:d}\n'.format(dets.shape[0]))
for i in range(dets.shape[0]):
xmin = dets[i][0]
ymin = dets[i][1]
xmax = dets[i][2]
ymax = dets[i][3]
score = dets[i][4]
fout.write('{:.1f} {:.1f} {:.1f} {:.1f} {:.3f}\n'.
format(xmin, ymin, (xmax - xmin + 1), (ymax - ymin + 1), score))

34
examples/pytorch/vision/Face_Detection/train.py
Просмотреть файл

@ -29,9 +29,6 @@ parser.add_argument('--dataset',
default='face',
choices=['hand', 'face', 'head'],
help='Train target')
parser.add_argument('--basenet',
default='vgg16_reducedfc.pth',
help='Pretrained base model')
parser.add_argument('--batch_size',
default=16, type=int,
help='Batch size for training')
@ -40,8 +37,11 @@ parser.add_argument('--resume',
help='Checkpoint state_dict file to resume training from')
parser.add_argument('--model_arch',
default='RPool_Face_C', type=str,
choices=['RPool_Face_C', 'RPool_Face_Quant', 'RPool_Face_QVGA_monochrome'],
choices=['RPool_Face_C', 'RPool_Face_Quant', 'RPool_Face_QVGA_monochrome', 'RPool_Face_M4'],
help='choose architecture among rpool variants')
parser.add_argument('--finetune',
default=False, type=str2bool,
help='Use CUDA to train model')
parser.add_argument('--num_workers',
default=128, type=int,
help='Number of workers used in dataloading')
@ -88,9 +88,13 @@ else:
if not os.path.exists(args.save_folder):
os.makedirs(args.save_folder)
if args.finetune==True:
train_dataset = WIDERDetection('./data/face_train_scutB.txt', mode='train', mono_mode=cfg.IS_MONOCHROME, is_scut=True)
val_dataset = WIDERDetection('./data/face_val_scutB.txt', mode='val', mono_mode=cfg.IS_MONOCHROME, is_scut=True)
else:
train_dataset = WIDERDetection(cfg.FACE.TRAIN_FILE, mode='train', mono_mode=cfg.IS_MONOCHROME)
val_dataset = WIDERDetection(cfg.FACE.VAL_FILE, mode='val', mono_mode=cfg.IS_MONOCHROME)
train_dataset = WIDERDetection(cfg.FACE.TRAIN_FILE, mode='train', mono_mode=cfg.IS_MONOCHROME)
val_dataset = WIDERDetection(cfg.FACE.VAL_FILE, mode='val', mono_mode=cfg.IS_MONOCHROME)
train_loader = data.DataLoader(train_dataset, args.batch_size,
num_workers=args.num_workers,
@ -112,7 +116,6 @@ module = import_module('models.' + args.model_arch)
net = module.build_s3fd('train', cfg.NUM_CLASSES)
if args.cuda:
if args.multigpu:
net = torch.nn.DataParallel(net)
@ -127,7 +130,7 @@ optimizer = optim.SGD(net.parameters(), lr=args.lr, momentum=args.momentum,
weight_decay=args.weight_decay)
criterion = MultiBoxLoss(cfg, args.dataset, args.cuda)
print('Loading wider dataset...')
print('Loading dataset...')
print('Using the specified args:')
print(args)
@ -153,7 +156,7 @@ def train():
t0 = time.time()
out = net(images)
out,_,_ = net(images)
# backprop
optimizer.zero_grad()
loss_l, loss_c = criterion(out, targets)
@ -179,9 +182,16 @@ def train():
os.path.join(args.save_folder, file))
iteration += 1
if args.model_arch == 'RPool_Face_M4' and args.finetune == True:
if args.multigpu == True:
net.module.rnn_model.cell_rnn.cell.sparsify()
net.module.rnn_model.cell_bidirrnn.cell.sparsify()
else:
net.rnn_model.cell_rnn.cell.sparsify()
net.rnn_model.cell_bidirrnn.cell.sparsify()
net.to('cuda')
val(epoch)
if iteration == cfg.MAX_STEPS:
break
def val(epoch):
@ -200,7 +210,7 @@ def val(epoch):
images = images
targets = [ann for ann in targets]
out = net(images)
out,_,_ = net(images)
loss_l, loss_c = criterion(out, targets)
loss = loss_l + loss_c
loc_loss += loss_l.item()

129
examples/pytorch/vision/Face_Detection/utils/augmentations.py
Просмотреть файл

@ -775,6 +775,87 @@ def anchor_crop_image_sampling(img,
return image, sampled_labels
def reduce_image(img, bbox_labels, img_width, img_height):
bbox_labels = np.array(bbox_labels)
scale = np.array([img_width, img_height, img_width, img_height])
boxes = bbox_labels[:, 1:5] * scale
boxArea = (boxes[:, 2] - boxes[:, 0] + 1) * (boxes[:, 3] - boxes[:, 1] + 1)
boxArea_max = np.amax(boxArea)
if boxArea_max > 48*48:
reduce_ratio = (boxArea_max/(48*48))**(0.5)
height = int(img_height / reduce_ratio)
width = int(img_width / reduce_ratio)
img = img.resize((width, height),
resample=Image.LANCZOS)
boxes = boxes//reduce_ratio
new_scale = np.array([width, height, width, height])
boxes = (boxes[:, 0:4]/new_scale)
bbox_labels[:,1:5] = boxes
bbox_labels = bbox_labels.tolist()
img_height = height
img_width = width
ratio_h = img_height/320
ratio_w = img_width/320
if ratio_h > 1 and ratio_w > 1:
expand_ratio = 1
else:
expand_ratio = 1/(min(ratio_h,ratio_w))
height = int(img_height * expand_ratio)
width = int(img_width * expand_ratio)
h_off = math.floor(np.random.uniform(0, height - img_height))
w_off = math.floor(np.random.uniform(0, width - img_width))
expand_bbox = bbox(-w_off / img_width, -h_off / img_height,
(width - w_off) / img_width,
(height - h_off) / img_height)
expand_img = np.ones((height, width, 3))
expand_img = np.uint8(expand_img * np.squeeze(cfg.img_mean))
expand_img = Image.fromarray(expand_img)
expand_img.paste(img, (int(w_off), int(h_off)))
bbox_labels = transform_labels(bbox_labels, expand_bbox)
return expand_img, bbox_labels, width, height
return img, bbox_labels.tolist(), img_width, img_height
def precrop(img, bbox_labels):
img_height, img_width = img.size[1], img.size[0]
scale = np.array([img_width, img_height, img_width, img_height])
bbox_labels = np.array(bbox_labels)
bbox_labels[:, 1:5] = bbox_labels[:, 1:5] * scale
if img_width-20 > img_height:
height_max, height_min = img_width + 20, img_width - 20
new_height = np.random.randint(height_min, height_max)
expand_img = np.ones((new_height, img_width, 3))
expand_img = np.uint8(expand_img * np.squeeze(cfg.img_mean))
expand_img = Image.fromarray(expand_img)
expand_img.paste(img, (int(0), int(0)))
img_height, img_width = new_height, img_width
img = expand_img
elif img_height-20 > img_width:
width_max, width_min = img_height + 20, img_height - 20
new_width = np.random.randint(width_min, width_max)
expand_img = np.ones((img_height, new_width, 3))
expand_img = np.uint8(expand_img * np.squeeze(cfg.img_mean))
expand_img = Image.fromarray(expand_img)
expand_img.paste(img, (int(0), int(0)))
img_height, img_width = img_height, new_width
img = expand_img
scale = np.array([img_width, img_height, img_width, img_height])
bbox_labels[:, 1:5] = bbox_labels[:, 1:5] / scale
return img, bbox_labels.tolist(), img_width, img_height
def preprocess(img, bbox_labels, mode, image_path):
img_width, img_height = img.size
@ -860,3 +941,51 @@ def preprocess(img, bbox_labels, mode, image_path):
#img = img * cfg.scale
return img, sampled_labels
def preprocess_qvga(img, bbox_labels, mode, image_path):
img_width, img_height = img.size
sampled_labels = bbox_labels
if mode == 'train':
img, bbox_labels, img_width, img_height = precrop(img, bbox_labels)
if cfg.apply_distort:
img = distort_image(img)
scale_array = np.array([8, 16, 32, 48])
img, bbox_labels, img_width, img_height = reduce_image(
img, bbox_labels, img_width, img_height)
img = np.array(img)
sampled_labels = bbox_labels
img = img.astype('uint8')
img = Image.fromarray(img)
interp_mode = [
Image.BILINEAR, Image.HAMMING, Image.NEAREST, Image.BICUBIC,
Image.LANCZOS
]
interp_indx = np.random.randint(0, 5)
img = img.resize((cfg.resize_width, cfg.resize_height),
resample=interp_mode[interp_indx])
img = np.array(img)
if mode == 'train':
mirror = int(np.random.uniform(0, 2))
if mirror == 1:
img = img[:, ::-1, :]
for i in six.moves.xrange(len(sampled_labels)):
tmp = sampled_labels[i][1]
sampled_labels[i][1] = 1 - sampled_labels[i][3]
sampled_labels[i][3] = 1 - tmp
img = to_chw_bgr(img)
img = img.astype('float32')
img -= cfg.img_mean
img = img[[2, 1, 0], :, :] # to RGB
return img, sampled_labels

17
examples/pytorch/vision/Face_Detection/wider_test.py
Просмотреть файл

@ -27,11 +27,19 @@ from importlib import import_module
import warnings
warnings.filterwarnings("ignore")
HOME = os.environ['DATA_HOME']
def str2bool(v):
return v.lower() in ("yes", "true", "t", "1")
parser = argparse.ArgumentParser(description='s3fd evaluatuon wider')
parser.add_argument('--model', type=str,
default='./weights/rpool_face_c.pth', help='trained model')
parser.add_argument('--thresh', default=0.05, type=float,
help='Final confidence threshold')
parser.add_argument('--multigpu',
default=False, type=str2bool,
help='Specify whether model was trained with multigpu')
parser.add_argument('--model_arch',
default='RPool_Face_C', type=str,
choices=['RPool_Face_C', 'RPool_Face_Quant', 'RPool_Face_QVGA_monochrome'],
@ -73,7 +81,7 @@ def detect_face(net, img, shrink):
if use_cuda:
x = x.cuda()
y = net(x)
y,_,_ = net(x)
detections = y.data
detections = detections.cpu().numpy()
@ -177,7 +185,7 @@ def bbox_vote(det):
def get_data():
subset = args.subset
WIDER_ROOT = os.path.join(cfg.HOME, 'WIDER_FACE')
WIDER_ROOT = os.path.join(HOME, 'WIDER_FACE')
if subset == 'val':
wider_face = sio.loadmat(
os.path.join(WIDER_ROOT, 'wider_face_split',
@ -191,7 +199,7 @@ def get_data():
del wider_face
imgs_path = os.path.join(
cfg.FACE.WIDER_DIR, 'WIDER_{}'.format(subset), 'images')
WIDER_ROOT, 'WIDER_{}'.format(subset), 'images')
save_path = './{}'.format(args.save_folder)
return event_list, file_list, imgs_path, save_path
@ -203,7 +211,8 @@ if __name__ == '__main__':
module = import_module('models.' + args.model_arch)
net = module.build_s3fd('test', cfg.NUM_CLASSES)
net = torch.nn.DataParallel(net)
if args.multigpu == True:
net = torch.nn.DataParallel(net)
checkpoint_dict = torch.load(args.model)

4
pytorch/edgeml_pytorch/graph/rnn.py
Просмотреть файл

@ -186,7 +186,9 @@ class RNNCell(nn.Module):
mats[i] = utils.hardThreshold(mats[i], self._wSparsity)
for i in range(endW, endU):
mats[i] = utils.hardThreshold(mats[i], self._uSparsity)
self.copy_previous_UW()
self.W.data.copy_(mats[0])
self.U.data.copy_(mats[1])
# self.copy_previous_UW()
def sparsifyWithSupport(self):
mats = self.getVars()

29
pytorch/edgeml_pytorch/graph/rnnpool.py
Просмотреть файл

@ -8,13 +8,18 @@ from edgeml_pytorch.graph.rnn import *
class RNNPool(nn.Module):
def __init__(self, nRows, nCols, nHiddenDims,
nHiddenDimsBiDir, inputDims):
nHiddenDimsBiDir, inputDims,
w1Sparsity=1.0, u1Sparsity=1.0, w2Sparsity=1.0, u2Sparsity=1.0):
super(RNNPool, self).__init__()
self.nRows = nRows
self.nCols = nCols
self.inputDims = inputDims
self.nHiddenDims = nHiddenDims
self.nHiddenDimsBiDir = nHiddenDimsBiDir
self.w1Sparsity = w1Sparsity
self.u1Sparsity = u1Sparsity
self.w2Sparsity = w2Sparsity
self.u2Sparsity = u2Sparsity
self._build()
@ -22,11 +27,13 @@ class RNNPool(nn.Module):
self.cell_rnn = FastGRNN(self.inputDims, self.nHiddenDims, gate_nonlinearity="sigmoid",
update_nonlinearity="tanh", zetaInit=100.0, nuInit=-100.0,
batch_first=False, bidirectional=False)
batch_first=False, bidirectional=False,
wSparsity=self.w1Sparsity, uSparsity=self.u1Sparsity)
self.cell_bidirrnn = FastGRNN(self.nHiddenDims, self.nHiddenDimsBiDir, gate_nonlinearity="sigmoid",
update_nonlinearity="tanh", zetaInit=100.0, nuInit=-100.0,
batch_first=False, bidirectional=True, is_shared_bidirectional=True)
batch_first=False, bidirectional=True, is_shared_bidirectional=True,
wSparsity=self.w2Sparsity, uSparsity=self.u2Sparsity)
def static_single(self,inputs, hidden, batch_size):
@ -40,24 +47,24 @@ class RNNPool(nn.Module):
row_timestack = torch.cat(torch.unbind(inputs, dim=3),dim=0)
stateList = self.static_single(torch.stack(torch.unbind(row_timestack,dim=2)),
(torch.zeros(1, batch_size * self.nRows, self.nHiddenDims).to(torch.device("cuda")),
torch.zeros(1, batch_size * self.nRows, self.nHiddenDims).to(torch.device("cuda"))),batch_size)
(torch.zeros(1, batch_size * self.nRows, self.nHiddenDims).to(inputs.device),
torch.zeros(1, batch_size * self.nRows, self.nHiddenDims).to(inputs.device)),batch_size)
outputs_cols = self.cell_bidirrnn(torch.stack(stateList),
torch.zeros(2, batch_size, self.nHiddenDimsBiDir).to(torch.device("cuda")),
torch.zeros(2, batch_size, self.nHiddenDimsBiDir).to(torch.device("cuda")))
torch.zeros(2, batch_size, self.nHiddenDimsBiDir).to(inputs.device),
torch.zeros(2, batch_size, self.nHiddenDimsBiDir).to(inputs.device))
## across columns
col_timestack = torch.cat(torch.unbind(inputs, dim=2),dim=0)
stateList = self.static_single(torch.stack(torch.unbind(col_timestack,dim=2)),
(torch.zeros(1, batch_size * self.nRows, self.nHiddenDims).to(torch.device("cuda")),
torch.zeros(1, batch_size * self.nRows, self.nHiddenDims).to(torch.device("cuda"))),batch_size)
(torch.zeros(1, batch_size * self.nRows, self.nHiddenDims).to(inputs.device),
torch.zeros(1, batch_size * self.nRows, self.nHiddenDims).to(inputs.device)),batch_size)
outputs_rows = self.cell_bidirrnn(torch.stack(stateList),
torch.zeros(2, batch_size, self.nHiddenDimsBiDir).to(torch.device("cuda")),
torch.zeros(2, batch_size, self.nHiddenDimsBiDir).to(torch.device("cuda")))
torch.zeros(2, batch_size, self.nHiddenDimsBiDir).to(inputs.device),
torch.zeros(2, batch_size, self.nHiddenDimsBiDir).to(inputs.device))