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Repository: AruniRC/resnet-face-pytorch
Branch: master
Commit: 97aafd010a06
Files: 29
Total size: 431.5 KB
Directory structure:
gitextract_qt8sxhd_/
├── .gitignore
├── README.md
├── config.py
├── data_loader.py
├── environment.yml
├── finetune.py
├── ijba/
│ ├── README.md
│ └── eval_ijba_1_1.py
├── lfw/
│ ├── README.md
│ ├── data/
│ │ ├── pairs.txt
│ │ └── pairsDevTest.txt
│ └── eval_lfw.py
├── models.py
├── run_resnet_demo.py
├── tests/
│ ├── test_colorizer.py
│ ├── test_data_reader.py
│ ├── test_fcn32s.py
│ └── vis_prediction.py
├── train.py
├── umd-face/
│ ├── run_crop_face.py
│ └── train_resnet_umdface.py
├── utils.py
└── vgg-face-2/
├── calculate_image_mean.py
├── class_counts.sh
├── crop_face.sh
├── mean_rgb.yaml
├── train_resnet101_vggface.py
├── train_resnet50_vggface_scratch.py
└── vggface_class_counts.txt
================================================
FILE CONTENTS
================================================
================================================
FILE: .gitignore
================================================
logs
*.pyc
lfw/*.png
================================================
FILE: README.md
================================================
This repository shows how to train ResNet models in PyTorch on publicly available face recognition datasets.
### Setup
* Install [Anaconda](https://conda.io/docs/user-guide/install/linux.html) if not already installed in the system.
* Create an Anaconda environment: `conda create -n resnet-face python=2.7` and activate it: `source activate resnet-face`.
* Install PyTorch and TorchVision inside the Anaconda environment. First add a channel to conda: `conda config --add channels soumith`. Then install: `conda install pytorch torchvision cuda80 -c soumith`.
* Install the dependencies using conda: `conda install scipy Pillow tqdm scikit-learn scikit-image numpy matplotlib ipython pyyaml`.
* *Notes*:
* Multiple GPUs (we used 5 GeForce GTX 1080Ti in parallel) recommended for the training to finish in reasonable time.
* Tested on server running CentOS
* Using [PyTorch](http://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html)
* Optional - an explanatory [blogpost](https://blog.waya.ai/deep-residual-learning-9610bb62c355) on Deep Residual Networks (ResNets) structure.
### Contents
- [ResNet-50 on UMD-Faces](https://github.com/AruniRC/resnet-face-pytorch#pytorch-resnet-on-umd-face)
- [Dataset preparation](https://github.com/AruniRC/resnet-face-pytorch#dataset-preparation)
- [Training](https://github.com/AruniRC/resnet-face-pytorch#training)
- [Evaluation demo](https://github.com/AruniRC/resnet-face-pytorch#evaluation)
- [ResNet-50 on VGGFace2](https://github.com/AruniRC/resnet-face-pytorch#pytorch-resnet-on-vggface2)
- [Dataset preparation](https://github.com/AruniRC/resnet-face-pytorch#dataset-preparation-1)
- [Training](https://github.com/AruniRC/resnet-face-pytorch#training-1)
- [Evaluation LFW](https://github.com/AruniRC/resnet-face-pytorch#evaluation-1)
## PyTorch ResNet on UMD-Face
Demo to train a ResNet-50 model on the [UMDFaces](http://www.umdfaces.io/) dataset.
### Dataset preparation
* Download the [UMDFaces](http://www.umdfaces.io/) dataset (the 3 batches of _still_ images), which contains 367,888 face annotations for 8,277 subjects, split into 3 batches.
* The images need to be cropped into 'train' and 'val' folders. Since the UMDFaces dataset does not specify training and validation sets, by default we select two images from every subject for validation.
* The cropping code is in the Python script [umd-face/run_crop_face.py](./umd-face/run_crop_face.py). It takes the following command-line arguments:
* --dataset_path (-d)
* --output_path (-o)
* --batch (-b)
* The following shell command does the cropping for each batch in parallel, using default `dataset_path` and `output_path`.
`for i in {0..2}; python umd-face/run_crop_face -b $i &; done`.
:small_red_triangle: **TODO** - takes very long, convert into shell+ImageMagick script.
### Usage
#### Training:
* Training script is [umd-face/train_resnet_umdface.py](./umd-face/train_resnet_umdface.py)
* Multiple GPUs:
* Under section 3 ("Model") of the training script, we specify which GPUs to use in parallel: `model = torch.nn.DataParallel(model, device_ids=[0, 1, 2, 3, 4]).cuda()`. Change these numbers depending on the number of available GPUs.
* Use `watch -d nvidia-smi` to constantly monitor the multi-GPU usage from the terminal.
* :small_red_triangle: **TODO** - make this into command-line args.
* At the terminal, specify where the cropped face images are saved using an environment variable: `DATASET_PATH=local/path/to/cropped/umd/faces`
* [config.py](./config.py) lists all the training configurations (i.e. model hyper-parameters) in a numbered Python dict.
* The training of ResNet-50 was done in 3 stages (*configs 4, 5 and 6*), each of *30 epochs*. For the first stage, we started with the ImageNet-pre-trained model from PyTorch. After the first stage, we started from the saved model of the previous stage (using the `--model_path` or `-m` command-line argument) and divided the learning rate by a factor of 10.
* Stage 1 (config-4): train on the *full UMDFaces dataset for 30 epochs* (42180 iterations with batchsize 250) with a learning rate of 0.001, starting from an ImageNet pre-trained model. These settings are defined in *config-4* of [config.py](./config.py), which is selected using the `-c 4` flag in the command. Example to train a ResNet-50 on UMDFaces dataset using config-4: run `python umd-face/train_resnet_umdface.py -c 4 -d $DATASET_PATH`.
* Stage 2 (config-5): use the best model checkpointed from config-4 to initialize the network and train it using config-5 `python umd-face/train_resnet_umdface.py -c 5 -m ./umd-face/logs/MODEL-resnet_umdfaces_CFG-004_TIMESTAMP/model_best.pth.tar -d $DATASET_PATH` and so on for the subsequent stages.
* *Training logs:* Each time the training script is run, a new output folder with a timestamp is created by default under `./umd-face/logs` , i.e. `./umd-face/logs/MODEL-CFG-TIMESTAMP/`. Under an experiment's log folder the settings for each experiment can be viewed in `config.yaml`; metrics such as the training and validation losses are updated in `log.csv`.
Most of the usual settings (data augmentations, learning rates, number of epochs to train, etc.) can be customized by editing `config.py` and `umd-face/train_resnet_umdface.py`.
* *Plotting CSV logs:* The log-file plotting utility function can be called from the command line as shown in the snippet below. The figure is saved under the log folder in the output location of that experiment.
* `LOG_FILE=umd-face/logs/MODEL-resnet_umdfaces_CFG-004_TIMESTAMP/log.csv`
* `python -c "from utils import plot_log_csv; plot_log_csv('$LOG_FILE')"`
* If that gives parsing errors: `python -c "from utils import plot_log; plot_log('$LOG_FILE')"`
stage 1 | stage 2 | stage 3
:------:|:----------:|:--------:
|  | 
#### Pre-trained model:
:red_circle: TODO - release pre-trained ResNet-50 on UMD-Faces :construction:
#### Evaluation:
*Verification demo:* We have a short script, [run_resnet_demo.py](./run_resnet_demo.py) to demonstrate the usage of the model on a toy face verification example. The visualized output of the demo is saved in the the root directory of the project. The 3 sample images are taken from the [LFW](http://vis-www.cs.umass.edu/lfw/) dataset.
---
## PyTorch ResNet on VGGFace2
Training a *ResNet-50* model in PyTorch on the [VGGFace2](https://www.robots.ox.ac.uk/~vgg/data/vgg_face2/) dataset.
### Dataset preparation
* Register on the [VGGFace2](https://www.robots.ox.ac.uk/~vgg/data/vgg_face2/) website and download their dataset
* VGGFace2 provides loosely-cropped images. We use crops from the [Faster R-CNN face detector](https://github.com/playerkk/face-py-faster-rcnn), saved as a CSV in `[filename, subject_id, xmin, ymin, width, height]` format (the CSV with pre-computed face crops is not yet made available).
* The `vgg-face-2/crop_face.sh` script is used to crop the face images into a separate output folder. Please look at the settings section in the script to assign correct paths depending on where the VGGFace2 data was downloaded on your local machine. This takes about a day. **TODO** - multi-process.
* Training images are under `OUTPUT_PATH/train-crop`
* Validation images (2 images per subject) under `OUTPUT_PATH/val-crop`
### Training
* We used 7 GeForce GTX 1080Ti GPUs in parallel (PyTorch DataParallel) to train the network, using Batch Normalization, following the training procedure described in the [VGGFace2](https://www.robots.ox.ac.uk/~vgg/data/vgg_face2/) paper.
* Training settings are defined under *configs-20, 21, 22* in [config.py](./config.py).
* Briefly, training is done using SGD optimizer, starting with a learning rate of 0.1, which gets divided by 10 in subsequent stages. A new stage is begun whenever the validation curve flattens.
* First stage training command, starting with a ResNet-50 from scratch: `python vgg-face-2/train_resnet50_vggface_scratch.py -c 20`
* Subsequent training stages (with lowered learning rates) would be:
- `python vgg-face-2/train_resnet50_vggface_scratch.py -c 21 -m PATH_TO_BEST_MODEL_CFG-20`
- `python vgg-face-2/train_resnet50_vggface_scratch.py -c 22 -m PATH_TO_BEST_MODEL_CFG-21`
### Evaluation
Instructions on how to setup and run the LFW evaluation are at [lfw/README.md](./lfw/README.md).
DevTest | 10 fold
:------:|:----------:
| 
================================================
FILE: config.py
================================================
configurations = {
# same configuration as original work
# https://github.com/shelhamer/fcn.berkeleyvision.org
0: dict(
max_iteration=100000,
lr=1.0e-10,
momentum=0.99,
weight_decay=0.0005,
interval_validate=4000,
),
# python train_*.py -g 0 -c 1
1: dict(
max_iteration=100,
lr=0.01, # changed learning rate
lr_decay_epoch=None, # disable automatic lr decay
momentum=0.9,
weight_decay=0.0005,
interval_validate=10,
optim='Adam',
batch_size=100,
),
2: dict(
max_iteration=8670, # num_iter_per_epoch = ceil(num_images/batch_size)
lr=0.01, # high learning rate
lr_decay_epoch=None, # disable automatic lr decay
momentum=0.9,
weight_decay=0.0005,
interval_validate=10,
optim='Adam',
batch_size=250,
),
3: dict(
# num_iter_per_epoch = ceil(num_images/batch_size)
max_iteration=8670, # 10 epochs on subset of images
lr=0.001, # lowered learning rate
lr_decay_epoch=None, # disable automatic lr decay
momentum=0.9,
weight_decay=0.0005,
interval_validate=10,
optim='Adam',
batch_size=250,
),
# ---------------------------------------------------------------------------------
# ResNet-50 on UMDFaces: stage 1
4: dict(
# num_iter_per_epoch = ceil(num_images/batch_size)
max_iteration=42180, # 30 epochs on full dataset (about 10 hours)
lr=0.001, # learning rate
lr_decay_epoch=None, # disable automatic lr decay
momentum=0.9,
weight_decay=0.0005,
interval_validate=50,
optim='Adam',
batch_size=250, # DataParallel over 5 gpus
),
# ResNet-50 on UMDFaces: stage 2
5: dict(
# num_iter_per_epoch = ceil(num_images/batch_size)
max_iteration=42180, # 30 epochs on full dataset
lr=0.0001, # lowered learning rate
lr_decay_epoch=None, # disable automatic lr decay
momentum=0.9,
weight_decay=0.0005,
interval_validate=50,
optim='Adam',
batch_size=250,
),
# ResNet-50 on UMDFaces: stage 3
6: dict(
# num_iter_per_epoch = ceil(num_images/batch_size)
max_iteration=42180, # 30 epochs on full dataset
lr=0.00001, # lowered learning rate
lr_decay_epoch=None, # disable automatic lr decay
momentum=0.9,
weight_decay=0.0005,
interval_validate=50,
optim='Adam',
batch_size=250,
),
# ---------------------------------------------------------------------------------
# ResNet-101 on VGGFace2: stage 1
7: dict(
# num_iter_per_epoch = ceil(num_images/batch_size)
max_iteration=267630, # 30 epochs on full dataset
lr=0.001, # learning rate
lr_decay_epoch=None, # disable automatic lr decay
momentum=0.9,
weight_decay=0.0005,
interval_validate=500,
optim='Adam',
batch_size=350, # DataParallel over 7 gpus
),
# python vgg-face-2/train_resnet_vggface.py -c 8 -m PATH-TO-CFG-7-SAVED-MODEL
8: dict(
# num_iter_per_epoch = ceil(num_images/batch_size)
max_iteration=267630, # 30 epochs on full dataset
lr=0.0001, # lowered learning rate
lr_decay_epoch=None, # disable automatic lr decay
momentum=0.9,
weight_decay=0.0005,
interval_validate=500,
optim='Adam',
batch_size=350, # DataParallel over 7 gpus
),
####
# ResNet-101 on VGGFace2: stage 1
11: dict(
# num_iter_per_epoch = ceil(num_images/batch_size)
max_iteration=267630, # 30 epochs on full dataset
lr=0.1, # learning rate
lr_decay_epoch=None, # disable automatic lr decay
momentum=0.9,
weight_decay=0.0005,
interval_validate=200,
optim='Adam',
batch_size=350, # DataParallel over 7 gpus
),
# ResNet-101 on VGGFace2: stage 2
# python vgg-face-2/train_resnet_vggface_scratch.py -c 12 -m ./vgg-face-2/logs/MODEL..-CFG-11...
12: dict(
# num_iter_per_epoch = ceil(num_images/batch_size)
max_iteration=267630, # 30 epochs on full dataset
lr=0.01, # reduced learning rate by factor 10
lr_decay_epoch=None, # disable automatic lr decay
momentum=0.9,
weight_decay=0.0005,
interval_validate=200,
optim='Adam',
batch_size=350, # DataParallel over 7 gpus
),
13: dict(
# num_iter_per_epoch = ceil(num_images/batch_size)
max_iteration=267630, # 30 epochs on full dataset
lr=0.001, # reduced learning rate by factor 10
lr_decay_epoch=None, # disable automatic lr decay
momentum=0.9,
weight_decay=0.0005,
interval_validate=200,
optim='Adam',
batch_size=350, # DataParallel over 7 gpus
),
# ResNet from scratch with SGD
20: dict(
# num_iter_per_epoch = ceil(num_images/batch_size)
max_iteration=267630, # 22 epochs on full dataset
lr=0.1,
lr_decay_epoch=None, # disable automatic lr decay
momentum=0.9,
weight_decay=0.0005,
interval_validate=200,
optim='SGD',
batch_size=256, # DataParallel over 7 gpus
),
21: dict(
# num_iter_per_epoch = ceil(num_images/batch_size)
max_iteration=267630, # 22 epochs on full dataset
lr=0.01,
lr_decay_epoch=None, # disable automatic lr decay
momentum=0.9,
weight_decay=0.0005,
interval_validate=200,
optim='SGD',
batch_size=256, # DataParallel over 7 gpus
),
22: dict(
# num_iter_per_epoch = ceil(num_images/batch_size)
max_iteration=267630, # 22 epochs on full dataset
lr=0.001,
lr_decay_epoch=None, # disable automatic lr decay
momentum=0.9,
weight_decay=0.0005,
interval_validate=200,
optim='SGD',
batch_size=256, # DataParallel over 7 gpus
),
# Used to fine-tune Resnet101-512d in the second stage
# (after the new layers are converged, entire net is fine-tuned)
23: dict(
# num_iter_per_epoch = ceil(num_images/batch_size)
max_iteration=267630, # 22 epochs on full dataset
lr=0.0001,
lr_decay_epoch=None, # disable automatic lr decay
momentum=0.9,
weight_decay=0.0005,
interval_validate=200,
optim='SGD',
batch_size=256, # DataParallel over 7 gpus
),
# lower learning rates on fine-tuning bottleneck (Resnet101-512d)
24: dict(
# num_iter_per_epoch = ceil(num_images/batch_size)
max_iteration=267630, # 22 epochs on full dataset
lr=0.00001, # lowered learning rate
lr_decay_epoch=None, # disable automatic lr decay
momentum=0.9,
weight_decay=0.0005,
interval_validate=200,
optim='SGD',
batch_size=256, # DataParallel over 7 gpus
),
}
================================================
FILE: data_loader.py
================================================
import collections
import os.path as osp
# from __future__ import division
import numpy as np
import PIL.Image
import scipy.io
import skimage
import skimage.color as color
from skimage.transform import rescale
from skimage.transform import resize
import torch
from torch.utils import data
DEBUG = False
class DemoFaceDataset(data.Dataset):
'''
Dataset subclass for demonstrating how to load images in PyTorch.
'''
# -----------------------------------------------------------------------------
def __init__(self, root, split='train', set='tiny', im_size=250):
# -----------------------------------------------------------------------------
'''
Parameters
----------
root - Path to root of ImageNet dataset
split - Either 'train' or 'val'
set - Can be 'full', 'small' or 'tiny' (5 images)
'''
self.root = root # E.g. '.../ImageNet/images' or '.../vgg-face/images'
self.split = split
self.files = collections.defaultdict(list)
self.im_size = im_size # scale image to im_size x im_size
self.set = set
if set == 'small':
raise NotImplementedError()
elif set == 'tiny':
# DEBUG: 5 images
files_list = osp.join(root, 'tiny_face_' + self.split + '.txt')
elif set == 'full':
raise NotImplementedError()
else:
raise ValueError('Valid sets: `full`, `small`, `tiny`.')
assert osp.exists(files_list), 'File does not exist: %s' % files_list
imfn = []
with open(files_list, 'r') as ftrain:
for line in ftrain:
imfn.append(osp.join(root, line.strip()))
self.files[split] = imfn
# -----------------------------------------------------------------------------
def __len__(self):
# -----------------------------------------------------------------------------
return len(self.files[self.split])
# -----------------------------------------------------------------------------
def __getitem__(self, index):
# -----------------------------------------------------------------------------
img_file = self.files[self.split][index]
img = PIL.Image.open(img_file)
# HACK: for non-RGB images - 4-channel CMYK or 1-channel grayscale
if len(img.getbands()) != 3:
while len(img.getbands()) != 3:
index -= 1
img_file = self.files[self.split][index] # if -1, wrap-around
img = PIL.Image.open(img_file)
if self.im_size > 0:
# Scales image to a square of default size 250x250
scaled_dim = (self.im_size.astype(np.int32),
self.im_size.astype(np.int32))
img = img.resize(scaled_dim, PIL.Image.BILINEAR)
label = 1 # TODO: read in a class label for each image
img = np.array(img, dtype=np.uint8)
im_out = torch.from_numpy(im_out).float()
im_out = im_out.permute(2,0,1) # C x H x W
return im_out, label
class LFWDataset(data.Dataset):
'''
Dataset subclass for loading LFW images in PyTorch.
This returns multiple images in a batch.
'''
def __init__(self, path_list, issame_list, transforms, split = 'test'):
'''
Parameters
----------
path_list - List of full path-names to LFW images
'''
self.files = collections.defaultdict(list)
self.split = split
self.files[split] = path_list
self.pair_label = issame_list
self.transforms = transforms
def __len__(self):
return len(self.files[self.split])
def __getitem__(self, index):
img_file = self.files[self.split][index]
img = PIL.Image.open(img_file)
if DEBUG:
print img_file
im_out = self.transforms(img)
return im_out
class IJBADataset(data.Dataset):
'''
Dataset subclass for loading IJB-A images in PyTorch.
This returns multiple images in a batch.
Path_list -- full paths to cropped images saved as <sighting_id>.jpg
'''
def __init__(self, path_list, transforms, split=1):
'''
Parameters
----------
path_list - List of full path-names to IJB-A images of one split
'''
self.files = collections.defaultdict(list)
self.split = split
self.files[split] = path_list
self.transforms = transforms
def __len__(self):
return len(self.files[self.split])
def __getitem__(self, index):
img_file = self.files[self.split][index]
img = PIL.Image.open(img_file)
if not img.mode == 'RGB':
img = img.convert('RGB')
if DEBUG:
print img_file
im_out = self.transforms(img)
return im_out
================================================
FILE: environment.yml
================================================
name: resnet-demo
dependencies:
- python=2.7
- scipy
- Pillow
- tqdm
- scikit-learn
- scikit-image
- numpy
- matplotlib
- ipython
- pyyaml
================================================
FILE: finetune.py
================================================
import datetime
import math
import os
import os.path as osp
import shutil
import numpy as np
import PIL.Image
import pytz
import scipy.misc
import torch
from torch.autograd import Variable
import torch.nn.functional as F
import tqdm
import utils
import gc
def lr_scheduler(optimizer, epoch, init_lr=0.001, lr_decay_epoch=7):
"""Decay learning rate by a factor of 0.1 every lr_decay_epoch epochs."""
lr = init_lr * (0.1**(epoch // lr_decay_epoch))
if epoch % lr_decay_epoch == 0:
print('LR is set to {}'.format(lr))
for param_group in optimizer.param_groups:
param_group['lr'] = lr
return optimizer
class Trainer(object):
# -----------------------------------------------------------------------------
def __init__(self, cuda, model, criterion, optimizer, init_lr,
train_loader, val_loader, out, max_iter,
lr_decay_epoch=None, interval_validate=None):
# -----------------------------------------------------------------------------
self.cuda = cuda
self.model = model
self.criterion = criterion
self.optim = optimizer
self.train_loader = train_loader
self.val_loader = val_loader
self.best_acc = 0
self.init_lr = init_lr
self.lr_decay_epoch = lr_decay_epoch
self.epoch = 0
self.iteration = 0
self.max_iter = max_iter
self.best_loss = 0
self.timestamp_start = \
datetime.datetime.now(pytz.timezone('US/Eastern'))
if interval_validate is None:
self.interval_validate = len(self.train_loader)
else:
self.interval_validate = interval_validate
self.out = out
if not osp.exists(self.out):
os.makedirs(self.out)
self.log_headers = [
'epoch',
'iteration',
'train/loss',
'train/acc',
'valid/loss',
'valid/acc',
'elapsed_time',
]
if not osp.exists(osp.join(self.out, 'log.csv')):
with open(osp.join(self.out, 'log.csv'), 'w') as f:
f.write(','.join(self.log_headers) + '\n')
# -----------------------------------------------------------------------------
def validate(self, max_num=500):
# -----------------------------------------------------------------------------
training = self.model.module.fc.training
self.model.eval()
MAX_NUM = max_num # HACK: stop after 500 images
n_class = len(self.val_loader.dataset.classes)
val_loss = 0
label_trues, label_preds = [], []
for batch_idx, (data, (target)) in tqdm.tqdm(
enumerate(self.val_loader), total=len(self.val_loader),
desc='Val=%d' % self.iteration, ncols=80,
leave=False):
# Computing val losses
if self.cuda:
data, target = data.cuda(), target.cuda()
data, target = Variable(data), Variable(target)
score = self.model(data)
loss = self.criterion(score, target)
if np.isnan(float(loss.data[0])):
raise ValueError('loss is NaN while validating')
val_loss += float(loss.data[0]) / len(data)
lbl_pred = score.data.max(1)[1].cpu().numpy()
lbl_true = target.data.cpu()
lbl_pred = lbl_pred.squeeze()
lbl_true = np.squeeze(lbl_true.numpy())
del target, score
label_trues.append(lbl_true)
label_preds.append(lbl_pred)
del lbl_true, lbl_pred, data, loss
if batch_idx > MAX_NUM:
break
# Computing metrics
val_loss /= len(self.val_loader)
val_acc = self.eval_metric(label_trues, label_preds)
# Logging
with open(osp.join(self.out, 'log.csv'), 'a') as f:
elapsed_time = (
datetime.datetime.now(pytz.timezone('US/Eastern')) -
self.timestamp_start).total_seconds()
log = [self.epoch, self.iteration] + [''] * 2 + \
[val_loss, val_acc] + [elapsed_time]
log = map(str, log)
f.write(','.join(log) + '\n')
del label_trues, label_preds
# Saving the best performing model
is_best = val_acc > self.best_acc
if is_best:
self.best_acc = val_acc
torch.save({
'epoch': self.epoch,
'iteration': self.iteration,
'arch': self.model.__class__.__name__,
'optim_state_dict': self.optim.state_dict(),
'model_state_dict': self.model.state_dict(),
'best_acc': self.best_acc,
}, osp.join(self.out, 'checkpoint.pth.tar'))
if is_best:
shutil.copy(osp.join(self.out, 'checkpoint.pth.tar'),
osp.join(self.out, 'model_best.pth.tar'))
if training:
self.model.module.fc.train()
# -----------------------------------------------------------------------------
def train_epoch(self):
# -----------------------------------------------------------------------------
self.model.module.fc.train() # for dataParallel finetuning (TODO - make general)
n_class = len(self.train_loader.dataset.classes)
for batch_idx, (data, target) in tqdm.tqdm(
enumerate(self.train_loader), total=len(self.train_loader),
desc='Train epoch=%d' % self.epoch, ncols=80, leave=False):
iteration = batch_idx + self.epoch * len(self.train_loader)
if self.iteration != 0 and (iteration - 1) != self.iteration:
continue # for resuming
self.iteration = iteration
if self.iteration % self.interval_validate == 0:
self.validate()
assert self.model.module.fc.training
# Computing Losses
if self.cuda:
data, target = data.cuda(), target.cuda()
data, target = Variable(data), Variable(target)
score = self.model(data) # batch_size x num_class
loss = self.criterion(score, target)
if np.isnan(float(loss.data[0])):
raise ValueError('loss is NaN while training')
# print list(self.model.parameters())[0].grad
# Gradient descent
self.optim.zero_grad()
loss.backward()
self.optim.step()
# Computing metrics
lbl_pred = score.data.max(1)[1].cpu().numpy()
lbl_pred = lbl_pred.squeeze()
lbl_true = target.data.cpu()
lbl_true = np.squeeze(lbl_true.numpy())
train_accu = self.eval_metric([lbl_pred], [lbl_true])
# Logging
with open(osp.join(self.out, 'log.csv'), 'a') as f:
elapsed_time = (
datetime.datetime.now(pytz.timezone('US/Eastern')) -
self.timestamp_start).total_seconds()
log = [self.epoch, self.iteration] + [loss.data[0]] + \
[train_accu] + [''] * 2 + [elapsed_time]
log = map(str, log)
f.write(','.join(log) + '\n')
# print '\nEpoch: ' + str(self.epoch) + ' Iter: ' + str(self.iteration) + \
# ' Loss: ' + str(loss.data[0])
if self.iteration >= self.max_iter:
break
# -----------------------------------------------------------------------------
def eval_metric(self, lbl_pred, lbl_true):
# -----------------------------------------------------------------------------
# Over-all accuracy
# TODO: per-class accuracy
accu = []
for lt, lp in zip(lbl_true, lbl_pred):
accu.append(np.mean(lt == lp))
return np.mean(accu)
# -----------------------------------------------------------------------------
def train(self):
# -----------------------------------------------------------------------------
max_epoch = int(math.ceil(1. * self.max_iter / len(self.train_loader)))
print 'Number of iters in an epoch: %d' % len(self.train_loader)
print 'Total epochs: %d' % max_epoch
for epoch in tqdm.trange(self.epoch, max_epoch,
desc='Train epochs', ncols=80, leave=True):
self.epoch = epoch
if self.lr_decay_epoch is None:
pass
else:
assert self.lr_decay_epoch < max_epoch
lr_scheduler(self.optim, self.epoch,
init_lr=self.init_lr,
lr_decay_epoch=self.lr_decay_epoch)
self.train_epoch()
if self.iteration >= self.max_iter:
break
================================================
FILE: ijba/README.md
================================================
## Resnet-101, scale-224x224 [best scaling strategy]
TAR @ FAR=0.0001 : 0.5276
TAR @ FAR=0.0010 : 0.7609
TAR @ FAR=0.0100 : 0.9148
TAR @ FAR=0.1000 : 0.9845
## Resnet-101, scale-256, center-crop-224
TAR @ FAR=0.0001 : 0.3485
TAR @ FAR=0.0010 : 0.6033
TAR @ FAR=0.0100 : 0.8752
TAR @ FAR=0.1000 : 0.9767
## Resnet-101, scale-224, center-crop-224
TAR @ FAR=0.0001 : 0.4173
TAR @ FAR=0.0010 : 0.6968
TAR @ FAR=0.0100 : 0.9129
TAR @ FAR=0.1000 : 0.9850
---
## Resnet-101-512d-norm, scale-224x224 [cfg-23]
TAR @ FAR=0.0001 : 0.5790
TAR @ FAR=0.0010 : 0.7704
TAR @ FAR=0.0100 : 0.9240
TAR @ FAR=0.1000 : 0.9884
(epoch3)
TAR @ FAR=0.0001 : *0.6100*
TAR @ FAR=0.0010 : *0.7848*
TAR @ FAR=0.0100 : 0.9262
TAR @ FAR=0.1000 : 0.9878
( + sqrt)
TAR @ FAR=0.0001 : 0.6112
TAR @ FAR=0.0010 : 0.7984
TAR @ FAR=0.0100 : 0.9251
TAR @ FAR=0.1000 : 0.9889
( + cosine)
TAR @ FAR=0.0001 : 0.6100
TAR @ FAR=0.0010 : 0.7914
TAR @ FAR=0.0100 : 0.9262
TAR @ FAR=0.1000 : 0.9878
( + cosine + sqrt)
TAR @ FAR=0.0001 : 0.6067
TAR @ FAR=0.0010 : 0.7987
TAR @ FAR=0.0100 : 0.9248
TAR @ FAR=0.1000 : 0.9885
[cfg-24]
---
## Resnet-101-512d-norm, scale-224x224 [cfg-22, ft stage-2]
TAR @ FAR=0.0001 : 0.5814
TAR @ FAR=0.0010 : 0.7651
TAR @ FAR=0.0100 : 0.9242
TAR @ FAR=0.1000 : 0.9867
---
## Resnet-101-512d, scale-224x224 [cfg-23, ft stage-2]
TAR @ FAR=0.0001 : 0.5926
TAR @ FAR=0.0010 : **0.7919**
TAR @ FAR=0.0100 : 0.9262
TAR @ FAR=0.1000 : 0.9872
## Resnet-101-512d, scale-224x224 [cfg-21, ft stage-1]
TAR @ FAR=0.0001 : 0.5723
TAR @ FAR=0.0010 : 0.7834
TAR @ FAR=0.0100 : 0.9240
TAR @ FAR=0.1000 : 0.9845
================================================
FILE: ijba/eval_ijba_1_1.py
================================================
import argparse
import os
import os.path as osp
import torch
import torchvision
from torchvision import models
import torch.nn as nn
import torch.utils.data
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from torch.autograd import Variable
import torch.nn.functional as F
import yaml
import tqdm
import numpy as np
import sklearn.metrics
from sklearn import metrics
from scipy import interpolate
import scipy.io as sio
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
here = osp.dirname(osp.abspath(__file__)) # output folder is located here
root_dir,_ = osp.split(here)
import sys
sys.path.append(root_dir)
import models
import utils
import data_loader
'''
Evaluate a network on the IJB-A 1:1 verification task
=====================================================
Example usage: TODO ***
# Resnet 101 on 10 folds of IJB-A 1:1
'''
# MODEL_PATH = '/srv/data1/arunirc/Research/resnet-face-pytorch/vgg-face-2/logs/MODEL-resnet101_vggface_scratch_CFG-022_TIME-20180210-201442/model_best.pth.tar'
# Resnet101-512d-norm
# MODEL_PATH = '/home/renyi/arunirc/data1/Research/resnet-face-pytorch/vgg-face-2/logs/MODEL-resnet101_512d_L2norm_ft2_CFG-023_TIME-20180214-020054/model_best.pth.tar'
MODEL_TYPE = 'resnet101-512d-norm'
# MODEL_PATH = '/home/renyi/arunirc/data1/Research/resnet-face-pytorch/vgg-face-2/logs/MODEL-resnet101_512d_L2norm_ft2_CFG-022_TIME-20180214-015313/model_best.pth.tar'
MODEL_PATH = '/home/renyi/arunirc/data1/Research/resnet-face-pytorch/vgg-face-2/logs/MODEL-resnet101_512d_L2norm_ft2_CFG-024_TIME-20180214-160410/model_best.pth.tar'
# Resnet101-512d
# MODEL_PATH = '/home/renyi/arunirc/data1/Research/resnet-face-pytorch/vgg-face-2/logs/MODEL-resnet101_bottleneck_ft2_CFG-023_TIME-20180213-091016/model_best.pth.tar'
# MODEL_TYPE = 'resnet101-512d'
# MODEL_PATH = '/home/renyi/arunirc/data1/Research/resnet-face-pytorch/vgg-face-2/logs/MODEL-resnet101_bottleneck_ft1_CFG-021_TIME-20180212-192332/model_best.pth.tar'
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-e', '--exp_name', default='ijba_eval')
parser.add_argument('-g', '--gpu', type=int, default=0)
parser.add_argument('-d', '--data_dir',
default='/home/renyi/arunirc/data1/datasets/CS2')
parser.add_argument('-p', '--protocol_dir',
default='/home/renyi/arunirc/data1/datasets/IJB-A/IJB-A_11_sets/')
parser.add_argument('--fold', type=int, default=1, choices=[1,10])
parser.add_argument('--sqrt', action='store_true', default=False,
help='Add signed sqrt normalization')
parser.add_argument('--cosine', action='store_true', default=False,
help='Use cosine similarity instead of L2 distance')
parser.add_argument('--batch_size', type=int, default=100)
parser.add_argument('-m', '--model_path',
default=MODEL_PATH,
help='Path to pre-trained model')
parser.add_argument('--model_type', default=MODEL_TYPE,
choices=['resnet50', 'resnet101', 'resnet101-512d', 'resnet101-512d-norm'])
args = parser.parse_args()
# CUDA setup
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu)
cuda = torch.cuda.is_available()
torch.manual_seed(1337)
if cuda:
torch.cuda.manual_seed(1337)
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True # enable if all images are same size
# -----------------------------------------------------------------------------
# 1. Model
# -----------------------------------------------------------------------------
num_class = 8631
if args.model_type == 'resnet50':
model = torchvision.models.resnet50(pretrained=False)
model.fc = torch.nn.Linear(2048, num_class)
elif args.model_type == 'resnet101':
model = torchvision.models.resnet101(pretrained=False)
model.fc = torch.nn.Linear(2048, num_class)
elif args.model_type == 'resnet101-512d':
model = torchvision.models.resnet101(pretrained=False)
layers = []
layers.append(torch.nn.Linear(2048, 512))
layers.append(torch.nn.Linear(512, num_class))
model.fc = torch.nn.Sequential(*layers)
elif args.model_type == 'resnet101-512d-norm':
model = torchvision.models.resnet101(pretrained=False)
layers = []
layers.append(torch.nn.Linear(2048, 512))
layers.append(models.NormFeat(scale_factor=50.0))
layers.append(torch.nn.Linear(512, num_class))
model.fc = torch.nn.Sequential(*layers)
else:
raise NotImplementedError
checkpoint = torch.load(args.model_path)
if checkpoint['arch'] == 'DataParallel':
model = torch.nn.DataParallel(model, device_ids=[0, 1, 2, 3, 4])
model.load_state_dict(checkpoint['model_state_dict'])
model = model.module # get network module from inside its DataParallel wrapper
else:
model.load_state_dict(checkpoint['model_state_dict'])
if cuda:
model = model.cuda()
# Convert the trained network into a "feature extractor"
feature_map = list(model.children())
if args.model_type == 'resnet101-512d' or args.model_type == 'resnet101-512d-norm':
model.eval()
extractor = model
extractor.fc = nn.Sequential(extractor.fc[0])
else:
feature_map.pop()
extractor = nn.Sequential(*feature_map)
extractor.eval() # ALWAYS set to evaluation mode (fixes BatchNorm, dropout, etc.)
# -----------------------------------------------------------------------------
# 2. Dataset
# -----------------------------------------------------------------------------
fold_id = 1
file_ext = '.jpg'
RGB_MEAN = [ 0.485, 0.456, 0.406 ]
RGB_STD = [ 0.229, 0.224, 0.225 ]
test_transform = transforms.Compose([
# transforms.Scale(224),
# transforms.CenterCrop(224),
transforms.Scale((224,224)),
transforms.ToTensor(),
transforms.Normalize(mean = RGB_MEAN,
std = RGB_STD),
])
pairs_path = osp.join(args.protocol_dir, 'split%d' % fold_id,
'verify_comparisons_%d.csv' % fold_id)
pairs = utils.read_ijba_pairs(pairs_path)
protocol_file = osp.join(args.protocol_dir, 'split%d' % fold_id,
'verify_metadata_%d.csv' % fold_id)
metadata = utils.get_ijba_1_1_metadata(protocol_file) # dict
assert np.all(np.unique(pairs) == np.unique(metadata['template_id'])) # sanity-check
path_list = np.array([osp.join(args.data_dir, str(x)+file_ext)
for x in metadata['sighting_id'] ]) # face crops saved as <sighting_id.jpg>
# Create data loader
test_loader = torch.utils.data.DataLoader(
data_loader.IJBADataset(
path_list, test_transform, split=fold_id),
batch_size=args.batch_size, shuffle=False )
# testing
# for i in range(len(test_loader.dataset)):
# img = test_loader.dataset.__getitem__(i)
# sz = img.shape
# if sz[0] != 3:
# print sz
# -----------------------------------------------------------------------------
# 3. Feature extraction
# -----------------------------------------------------------------------------
print 'Feature extraction...'
cache_dir = osp.join(here, 'cache-' + args.model_type)
if not osp.exists(cache_dir):
os.makedirs(cache_dir)
feat_path = osp.join(cache_dir, 'feat-fold-%d.mat' % fold_id)
if not osp.exists(feat_path):
features = []
for batch_idx, images in tqdm.tqdm(enumerate(test_loader),
total=len(test_loader),
desc='Extracting features'):
x = Variable(images, volatile=True) # test-time memory conservation
if cuda:
x = x.cuda()
feat = extractor(x)
if cuda:
feat = feat.data.cpu() # free up GPU
else:
feat = feat.data
features.append(feat)
features = torch.cat(features, dim=0) # (n_batch*batch_sz) x 512
sio.savemat(feat_path, {'feat': features.cpu().numpy() })
else:
dat = sio.loadmat(feat_path)
features = torch.FloatTensor(dat['feat'])
del dat
print 'Loaded.'
# -----------------------------------------------------------------------------
# 4. Verification
# -----------------------------------------------------------------------------
scores = []
labels = []
# labels: is_same_subject
print 'Computing pair labels . . . '
for pair in tqdm.tqdm(pairs): # TODO - check tqdm
sel_t0 = np.where(metadata['template_id'] == pair[0])
sel_t1 = np.where(metadata['template_id'] == pair[1])
subject0 = np.unique(metadata['subject_id'][sel_t0])
subject1 = np.unique(metadata['subject_id'][sel_t1])
labels.append(int(subject0 == subject1))
labels = np.array(labels)
print 'done'
# templates: average pool, then L2-normalize
print 'Pooling templates . . . '
pooled_features = []
template_set = np.unique(metadata['template_id'])
for tid in tqdm.tqdm(template_set):
sel = np.where(metadata['template_id'] == tid)
# pool template: 1 x n x 512 -> 1 x 512
feat = features[sel,:].mean(1)
if args.sqrt: # signed-square-root normalization
feat = torch.mul(torch.sign(feat),torch.sqrt(torch.abs(feat)+1e-12))
pooled_features.append(F.normalize(feat, p=2, dim=1) )
pooled_features = torch.cat(pooled_features, dim=0) # (n_batch*batch_sz) x 512
print 'done'
print 'Computing pair distances . . . '
for pair in tqdm.tqdm(pairs):
sel_t0 = np.where(template_set == pair[0])
sel_t1 = np.where(template_set == pair[1])
if args.cosine:
feat_dist = torch.dot(torch.squeeze(pooled_features[sel_t0]),
torch.squeeze(pooled_features[sel_t1]))
else:
feat_dist = (pooled_features[sel_t0] - pooled_features[sel_t1]).norm(p=2, dim=1)
feat_dist = -torch.squeeze(feat_dist)
feat_dist = feat_dist.numpy()
scores.append(feat_dist) # score: negative of L2-distance
scores = np.array(scores)
# Metrics: TAR (tpr) at FAR (fpr)
fpr, tpr, thresholds = sklearn.metrics.roc_curve(labels, scores)
fpr_levels = [0.0001, 0.001, 0.01, 0.1]
f_interp = interpolate.interp1d(fpr, tpr)
tpr_at_fpr = [ f_interp(x) for x in fpr_levels ]
for (far, tar) in zip(fpr_levels, tpr_at_fpr):
print 'TAR @ FAR=%.4f : %.4f' % (far, tar)
res = {}
res['TAR'] = tpr_at_fpr
res['FAR'] = fpr_levels
with open( osp.join(cache_dir, 'result-1-1-fold-%d.yaml' % fold_id),
'w') as f:
yaml.dump(res, f, default_flow_style=False)
sio.savemat(osp.join(cache_dir, 'roc-1-1-fold-%d.mat' % fold_id),
{'fpr': fpr, 'tpr': tpr, 'thresholds': thresholds,
'tpr_at_fpr': tpr_at_fpr})
if __name__ == '__main__':
main()
================================================
FILE: lfw/README.md
================================================
## LFW setup
This README shows how to set up the downloaded face images from the LFW dataset for face verification evaluation.
Download the deep-funneled (roughly-aligned) images from LFW: `http://vis-www.cs.umass.edu/lfw/lfw-deepfunneled.tgz`. Extract these at some local folder, referred to henceforth as `LFW_DIR`.
NOTE: all scripts are to be executed from the project root directory, *not* the sub-folder where this README file is located.
### Validation results
The LFW database provides DevTrain and DevTest splits as validation sets for developing code without overfitting to the 10 folds used in the final evaluation. The DevTest pairs are saved at `./lfw/data/pairsDevTest.txt`
Run `python ./lfw/eval_lfw.py -d LFW_DIR -m MODEL_PATH --fold 0` to get AUC score for verification on the dev test split. The number should be around 0.9989. Setting the `--fold` flag to 10 will evaluate on the full 10 folds of LFW test set. Note, ROC curves and AUC metrics are reported (not EER).
================================================
FILE: lfw/data/pairs.txt
================================================
10 300
Abel_Pacheco 1 4
Akhmed_Zakayev 1 3
Akhmed_Zakayev 2 3
Amber_Tamblyn 1 2
Anders_Fogh_Rasmussen 1 3
Anders_Fogh_Rasmussen 1 4
Angela_Bassett 1 5
Angela_Bassett 2 5
Angela_Bassett 3 4
Ann_Veneman 3 5
Ann_Veneman 6 10
Ann_Veneman 10 11
Anthony_Fauci 1 2
Antony_Leung 1 2
Antony_Leung 2 3
Anwar_Ibrahim 1 2
Augusto_Pinochet 1 2
Barbara_Brezigar 1 2
Benjamin_Netanyahu 1 4
Benjamin_Netanyahu 4 5
Bernard_Law 2 3
Bernard_Law 3 4
Bertrand_Bonello 1 2
Bill_Frist 2 9
Bill_Frist 4 5
Bob_Graham 2 4
Bob_Graham 3 6
Bob_Graham 4 6
Bob_Graham 5 6
Boris_Becker 2 6
Brad_Johnson 1 3
Brad_Johnson 2 3
Brad_Johnson 2 4
Brian_Griese 1 2
Candice_Bergen 1 2
Candice_Bergen 2 3
Carla_Myers 1 2
Cathy_Freeman 1 2
Chang_Dae-whan 1 2
Charles_Grassley 1 2
Clare_Short 1 3
Clare_Short 1 4
Corinne_Coman 1 2
Dai_Bachtiar 1 2
Dale_Earnhardt_Jr 1 3
David_Caruso 1 2
Demi_Moore 1 3
Dick_Vermeil 1 2
Doris_Roberts 1 2
Doris_Roberts 1 3
Drew_Barrymore 1 2
Edmund_Stoiber 4 7
Edmund_Stoiber 5 7
Edmund_Stoiber 7 12
Edmund_Stoiber 9 11
Elinor_Caplan 1 2
Emmanuelle_Beart 1 2
Emmanuelle_Beart 1 3
Federico_Trillo 1 2
Federico_Trillo 1 3
Federico_Trillo 2 3
Francis_Ford_Coppola 1 2
Fred_Thompson 1 2
Fred_Thompson 1 3
Fred_Thompson 2 3
Garry_Trudeau 1 2
Gary_Williams 1 2
Gene_Robinson 1 2
Gene_Robinson 1 3
Gene_Robinson 2 3
George_Galloway 1 3
George_Galloway 2 3
George_Galloway 2 4
Geraldine_Chaplin 1 2
Geraldine_Chaplin 1 4
Geraldine_Chaplin 3 4
Gerardo_Gambala 1 2
Gian_Marco 1 2
Gian_Marco 2 3
Gillian_Anderson 1 2
Gordon_Brown 6 9
Gordon_Brown 10 13
Grant_Hackett 1 2
Grant_Hackett 1 3
Grant_Hackett 1 5
Gray_Davis 7 22
Gray_Davis 21 26
Gregg_Popovich 1 5
Gregg_Popovich 3 4
Guy_Ritchie 1 2
Hamzah_Haz 1 2
Harbhajan_Singh 1 2
Hootie_Johnson 1 2
Hugo_Chavez 2 27
Hugo_Chavez 27 53
Hugo_Chavez 36 51
Hugo_Chavez 41 50
Hugo_Chavez 45 56
Isaiah_Washington 1 2
Jack_Grubman 1 2
Jacques_Rogge 1 9
Jacques_Rogge 3 7
Jacques_Rogge 3 10
Jacques_Rogge 4 6
Jacques_Rogge 4 9
Jacques_Rogge 5 8
Jacques_Rogge 6 8
James_Caan 1 2
James_Caan 1 3
James_Caan 2 3
James_Jones 1 2
Jamling_Norgay 1 2
Janica_Kostelic 1 2
Jeanne_Moreau 1 2
Jeffrey_Archer 1 2
Jennifer_Garner 1 3
Jennifer_Garner 4 9
Jennifer_Garner 5 6
Jennifer_Garner 5 9
Jennifer_Garner 7 12
Jennifer_Garner 8 11
Jeremy_Greenstock 2 24
Jeremy_Greenstock 3 22
Jeremy_Greenstock 5 11
Jeremy_Greenstock 10 14
Jeremy_Greenstock 17 21
Jessica_Lange 1 2
Jimmy_Carter 1 7
Jimmy_Carter 4 6
Jimmy_Carter 5 8
Jimmy_Carter 6 7
Jodie_Foster 1 2
John_Manley 1 6
John_Manley 2 5
John_McCain 2 3
John_McCain 3 7
John_McCain 6 7
John_Snow 3 15
John_Snow 4 16
John_Snow 9 11
Johnson_Panjaitan 1 2
Jorge_Castaneda 1 2
Jose_Serra 1 9
Jose_Serra 2 8
Jose_Serra 3 5
Jose_Serra 3 7
Jose_Serra 6 9
Jose_Theodore 1 2
Joseph_Blatter 1 2
Joseph_Ralston 1 2
Juan_Pablo_Montoya 1 8
Juan_Pablo_Montoya 2 4
Juan_Pablo_Montoya 4 5
Julianna_Margulies 1 2
Justin_Guarini 1 2
Justin_Guarini 2 3
Justine_Henin 2 3
Kate_Winslet 1 2
Kate_Winslet 1 4
Kate_Winslet 2 3
Kate_Winslet 2 4
Kathy_Winters 1 2
Kelvin_Sampson 1 2
Kevin_Stallings 1 2
Kim_Jong-Il 2 3
Kristin_Davis 1 2
Kristin_Davis 2 3
Lance_Armstrong 1 17
Lance_Armstrong 5 10
Lance_Armstrong 7 11
Lance_Armstrong 10 18
Lance_Armstrong 14 17
Laurent_Jalabert 1 2
Lindsey_Graham 1 2
Lisa_Gottsegen 1 2
Lleyton_Hewitt 1 13
Lleyton_Hewitt 16 27
Lleyton_Hewitt 18 37
Lleyton_Hewitt 24 36
Luis_Ernesto_Derbez_Bautista 3 6
Mario_Dumont 1 2
Mark_Wahlberg 1 3
Mark_Wahlberg 2 3
Mark_Wahlberg 2 4
Marlene_Weingartner 1 2
Martha_Bowen 1 2
Martin_Cauchon 1 2
Martin_Hoellwarth 1 2
Martin_Sheen 1 2
Matthew_Broderick 1 4
Matthew_Broderick 2 3
Mike_Krzyzewski 1 6
Mikhail_Gorbachev 1 2
Monica_Bellucci 1 4
Monica_Bellucci 2 4
Naomi_Campbell 1 2
Nestor_Kirchner 3 30
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gitextract_qt8sxhd_/
├── .gitignore
├── README.md
├── config.py
├── data_loader.py
├── environment.yml
├── finetune.py
├── ijba/
│ ├── README.md
│ └── eval_ijba_1_1.py
├── lfw/
│ ├── README.md
│ ├── data/
│ │ ├── pairs.txt
│ │ └── pairsDevTest.txt
│ └── eval_lfw.py
├── models.py
├── run_resnet_demo.py
├── tests/
│ ├── test_colorizer.py
│ ├── test_data_reader.py
│ ├── test_fcn32s.py
│ └── vis_prediction.py
├── train.py
├── umd-face/
│ ├── run_crop_face.py
│ └── train_resnet_umdface.py
├── utils.py
└── vgg-face-2/
├── calculate_image_mean.py
├── class_counts.sh
├── crop_face.sh
├── mean_rgb.yaml
├── train_resnet101_vggface.py
├── train_resnet50_vggface_scratch.py
└── vggface_class_counts.txt
SYMBOL INDEX (82 symbols across 16 files)
FILE: data_loader.py
class DemoFaceDataset (line 21) | class DemoFaceDataset(data.Dataset):
method __init__ (line 28) | def __init__(self, root, split='train', set='tiny', im_size=250):
method __len__ (line 66) | def __len__(self):
method __getitem__ (line 72) | def __getitem__(self, index):
class LFWDataset (line 100) | class LFWDataset(data.Dataset):
method __init__ (line 106) | def __init__(self, path_list, issame_list, transforms, split = 'test'):
method __len__ (line 118) | def __len__(self):
method __getitem__ (line 121) | def __getitem__(self, index):
class IJBADataset (line 131) | class IJBADataset(data.Dataset):
method __init__ (line 137) | def __init__(self, path_list, transforms, split=1):
method __len__ (line 148) | def __len__(self):
method __getitem__ (line 151) | def __getitem__(self, index):
FILE: finetune.py
function lr_scheduler (line 20) | def lr_scheduler(optimizer, epoch, init_lr=0.001, lr_decay_epoch=7):
class Trainer (line 34) | class Trainer(object):
method __init__ (line 37) | def __init__(self, cuda, model, criterion, optimizer, init_lr,
method validate (line 85) | def validate(self, max_num=500):
method train_epoch (line 169) | def train_epoch(self):
method eval_metric (line 230) | def eval_metric(self, lbl_pred, lbl_true):
method train (line 241) | def train(self):
FILE: ijba/eval_ijba_1_1.py
function main (line 59) | def main():
FILE: lfw/eval_lfw.py
function main (line 46) | def main():
FILE: models.py
class NormFeat (line 11) | class NormFeat(nn.Module):
method __init__ (line 13) | def __init__(self, scale_factor=1.0):
method forward (line 17) | def forward(self, input):
class ScaleFeat (line 21) | class ScaleFeat(nn.Module):
method __init__ (line 23) | def __init__(self, scale_factor=50.0):
method forward (line 27) | def forward(self, input):
function get_upsampling_weight (line 32) | def get_upsampling_weight(in_channels, out_channels, kernel_size):
class FCN32sColor (line 49) | class FCN32sColor(nn.Module):
method __init__ (line 51) | def __init__(self, n_class=32, bin_type='one-hot', batch_norm=True):
method _initialize_weights (line 159) | def _initialize_weights(self):
method forward (line 170) | def forward(self, x):
class FCN16sColor (line 268) | class FCN16sColor(nn.Module):
method __init__ (line 270) | def __init__(self, n_class=32, bin_type='one-hot', batch_norm=True):
method _initialize_weights (line 373) | def _initialize_weights(self):
method forward (line 384) | def forward(self, x):
method copy_params_from_fcn32s (line 480) | def copy_params_from_fcn32s(self, fcn32s):
class FCN8sColor (line 494) | class FCN8sColor(nn.Module):
method __init__ (line 496) | def __init__(self, n_class=32, bin_type='one-hot', batch_norm=True):
method _initialize_weights (line 604) | def _initialize_weights(self):
method forward (line 615) | def forward(self, x):
method copy_params_from_fcn16s (line 722) | def copy_params_from_fcn16s(self, fcn16s):
FILE: tests/test_colorizer.py
function test_color_gmm (line 27) | def test_color_gmm():
function main (line 58) | def main():
FILE: tests/test_data_reader.py
function test_single_read (line 22) | def test_single_read():
function test_single_read_dimcheck (line 32) | def test_single_read_dimcheck():
function test_train_loader (line 44) | def test_train_loader():
function test_dataset_read (line 60) | def test_dataset_read():
function test_cmyk_read (line 76) | def test_cmyk_read():
function test_grayscale_read (line 92) | def test_grayscale_read():
function test_rgb_hsv (line 107) | def test_rgb_hsv():
function test_soft_bins (line 117) | def test_soft_bins():
function test_lowpass_image (line 127) | def test_lowpass_image():
function test_init_gmm (line 137) | def test_init_gmm():
function main (line 149) | def main():
FILE: tests/test_fcn32s.py
function test_get_upsampling_weight (line 13) | def test_get_upsampling_weight():
FILE: tests/vis_prediction.py
function main (line 29) | def main():
FILE: train.py
function lr_scheduler (line 20) | def lr_scheduler(optimizer, epoch, init_lr=0.001, lr_decay_epoch=7):
class Trainer (line 34) | class Trainer(object):
method __init__ (line 37) | def __init__(self, cuda, model, criterion, optimizer, init_lr,
method validate (line 85) | def validate(self, max_num=500):
method train_epoch (line 168) | def train_epoch(self):
method eval_metric (line 231) | def eval_metric(self, lbl_pred, lbl_true):
method train (line 242) | def train(self):
FILE: umd-face/run_crop_face.py
function main (line 28) | def main():
function crop_batch (line 87) | def crop_batch(data_dir, annot_fn, out_dir, nval):
FILE: umd-face/train_resnet_umdface.py
function main (line 36) | def main():
function get_log_dir (line 258) | def get_log_dir(model_name, config_id, cfg, verbose=True):
FILE: utils.py
function make_weights_for_balanced_classes (line 28) | def make_weights_for_balanced_classes(images, nclasses):
function get_vgg_class_counts (line 51) | def get_vgg_class_counts(log_path):
function plot_log_csv (line 61) | def plot_log_csv(log_path):
function plot_log (line 122) | def plot_log(log_path):
function savgol_smooth (line 208) | def savgol_smooth(y, box_pts):
function get_paths (line 240) | def get_paths(lfw_dir, pairs, file_ext):
function read_pairs (line 264) | def read_pairs(pairs_filename, lfw_flag=True):
function get_ijba_1_1_metadata (line 281) | def get_ijba_1_1_metadata(protocol_file):
function read_ijba_pairs (line 306) | def read_ijba_pairs(pairs_filename):
FILE: vgg-face-2/calculate_image_mean.py
function main (line 29) | def main():
FILE: vgg-face-2/train_resnet101_vggface.py
function main (line 36) | def main():
function get_log_dir (line 281) | def get_log_dir(model_name, config_id, cfg, verbose=True):
FILE: vgg-face-2/train_resnet50_vggface_scratch.py
function main (line 36) | def main():
function get_log_dir (line 281) | def get_log_dir(model_name, config_id, cfg, verbose=True):
Condensed preview — 29 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (481K chars).
[
{
"path": ".gitignore",
"chars": 21,
"preview": "logs\n*.pyc\nlfw/*.png\n"
},
{
"path": "README.md",
"chars": 8648,
"preview": "\nThis repository shows how to train ResNet models in PyTorch on publicly available face recognition datasets.\n\n### Setup"
},
{
"path": "config.py",
"chars": 7467,
"preview": "\n\n\nconfigurations = {\n # same configuration as original work\n # https://github.com/shelhamer/fcn.berkeleyvision.or"
},
{
"path": "data_loader.py",
"chars": 4974,
"preview": "import collections\nimport os.path as osp\n# from __future__ import division\n\nimport numpy as np\nimport PIL.Image\nimport s"
},
{
"path": "environment.yml",
"chars": 150,
"preview": "name: resnet-demo\ndependencies:\n - python=2.7\n - scipy\n - Pillow\n - tqdm\n - scikit-learn\n - scikit-image\n - numpy\n - mat"
},
{
"path": "finetune.py",
"chars": 8901,
"preview": "import datetime\nimport math\nimport os\nimport os.path as osp\nimport shutil\n\nimport numpy as np\nimport PIL.Image\nimport py"
},
{
"path": "ijba/README.md",
"chars": 1602,
"preview": "\n\n\n## Resnet-101, scale-224x224 [best scaling strategy]\nTAR @ FAR=0.0001 : 0.5276\nTAR @ FAR=0.0010 : 0.7609\nTAR @ FAR=0"
},
{
"path": "ijba/eval_ijba_1_1.py",
"chars": 11367,
"preview": "import argparse\nimport os\nimport os.path as osp\n\nimport torch\nimport torchvision\nfrom torchvision import models\nimport t"
},
{
"path": "lfw/README.md",
"chars": 994,
"preview": "## LFW setup\n\nThis README shows how to set up the downloaded face images from the LFW dataset for face verification eval"
},
{
"path": "lfw/data/pairs.txt",
"chars": 155335,
"preview": "10\t300\nAbel_Pacheco\t1\t4\nAkhmed_Zakayev\t1\t3\nAkhmed_Zakayev\t2\t3\nAmber_Tamblyn\t1\t2\nAnders_Fogh_Rasmussen\t1\t3\nAnders_Fogh_Ra"
},
{
"path": "lfw/data/pairsDevTest.txt",
"chars": 26002,
"preview": "500\nAbdullah_Gul\t13\t14\nAbdullah_Gul\t13\t16\nAbdullatif_Sener\t1\t2\nAdel_Al-Jubeir\t1\t3\nAl_Pacino\t1\t2\nAlan_Greenspan\t1\t5\nAlber"
},
{
"path": "lfw/eval_lfw.py",
"chars": 8626,
"preview": "import argparse\nimport os\nimport os.path as osp\n\nimport torch\nimport torchvision\nfrom torchvision import models\nimport t"
},
{
"path": "models.py",
"chars": 26276,
"preview": "\n# import fcn\nimport os.path as osp\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as "
},
{
"path": "run_resnet_demo.py",
"chars": 5926,
"preview": "\nimport torch\nimport torchvision\nfrom torchvision import models\nimport torch.nn as nn\nimport torch.utils.data\nimport tor"
},
{
"path": "tests/test_colorizer.py",
"chars": 1893,
"preview": "import argparse\nimport os\nimport os.path as osp\nimport numpy as np\nimport PIL.Image\nimport skimage.io\nimport skimage.col"
},
{
"path": "tests/test_data_reader.py",
"chars": 5267,
"preview": "import argparse\nimport os\nimport os.path as osp\n\nimport numpy as np\nimport PIL.Image\nimport skimage.io\nimport skimage.co"
},
{
"path": "tests/test_fcn32s.py",
"chars": 1244,
"preview": "# FIXME: Import order causes error:\n# ImportError: dlopen: cannot load any more object with static TL\n# https://github.c"
},
{
"path": "tests/vis_prediction.py",
"chars": 1647,
"preview": "import argparse\nimport os\nimport os.path as osp\nimport numpy as np\nimport PIL.Image\nimport skimage.io\nimport skimage.col"
},
{
"path": "train.py",
"chars": 8954,
"preview": "import datetime\nimport math\nimport os\nimport os.path as osp\nimport shutil\n\nimport numpy as np\nimport PIL.Image\nimport py"
},
{
"path": "umd-face/run_crop_face.py",
"chars": 4434,
"preview": "import argparse\nimport os\nimport os.path as osp\n\n# import torch\n# import torchvision\n# import torch.utils.data\n# import "
},
{
"path": "umd-face/train_resnet_umdface.py",
"chars": 9645,
"preview": "import argparse\nimport datetime\nimport os\nimport os.path as osp\nimport pytz\n\nimport torch\nimport torchvision\nfrom torchv"
},
{
"path": "utils.py",
"chars": 10731,
"preview": "from __future__ import division\n\nimport math\nimport warnings\n\ntry:\n import cv2\nexcept ImportError:\n cv2 = None\n\nim"
},
{
"path": "vgg-face-2/calculate_image_mean.py",
"chars": 2622,
"preview": "import argparse\nimport os\nimport os.path as osp\n\nimport torch\nimport torchvision\nimport torch.utils.data\nimport torchvis"
},
{
"path": "vgg-face-2/class_counts.sh",
"chars": 487,
"preview": "\n\n# Utility script to get class frequencies of VGGFace2 based on #images in class-folder\n\n# >>>>>> Set paths here <<<<<<"
},
{
"path": "vgg-face-2/crop_face.sh",
"chars": 3381,
"preview": "\n\n# Utility script to crop large image datasets from the console\n\n# INFO: kill all spawned sub-processes: kill $(ps -s $"
},
{
"path": "vgg-face-2/mean_rgb.yaml",
"chars": 67,
"preview": "B: 0.36426129937171936\nG: 0.406549334526062\nR: 0.49698397517204285\n"
},
{
"path": "vgg-face-2/train_resnet101_vggface.py",
"chars": 10812,
"preview": "import argparse\nimport datetime\nimport os\nimport os.path as osp\nimport pytz\n\nimport torch\nimport torchvision\nfrom torchv"
},
{
"path": "vgg-face-2/train_resnet50_vggface_scratch.py",
"chars": 10802,
"preview": "import argparse\nimport datetime\nimport os\nimport os.path as osp\nimport pytz\n\nimport torch\nimport torchvision\nfrom torchv"
},
{
"path": "vgg-face-2/vggface_class_counts.txt",
"chars": 103570,
"preview": "n000002 313\nn000003 203\nn000004 385\nn000005 227\nn000006 483\nn000007 257\nn000008 271\nn000010 154\nn000011 378\nn000012 380\n"
}
]
About this extraction
This page contains the full source code of the AruniRC/resnet-face-pytorch GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 29 files (431.5 KB), approximately 178.6k tokens, and a symbol index with 82 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.