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 :------:|:----------:|:--------: ![](samples/stage1_log_plots.png)| ![](samples/stage2_log_plots.png) | ![](samples/stage3_log_plots.png) #### 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. ![](samples/demo_verif.png) --- ## 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 :------:|:----------: ![](samples/lfw_roc_devTest.png)| ![](samples/lfw_roc_10fold.png) ================================================ 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 .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 # 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 Nestor_Kirchner 12 21 Nestor_Kirchner 17 25 Nicolas_Lapentti 1 2 Noah_Wyle 2 3 Nora_Bendijo 1 2 Nursultan_Nazarbayev 1 2 Pamela_Anderson 1 2 Pamela_Anderson 1 5 Patricia_Heaton 1 2 Patty_Schnyder 1 2 Patty_Schnyder 2 3 Patty_Schnyder 2 4 Patty_Schnyder 3 4 Paul_Bremer 6 17 Paul_Bremer 7 10 Paul_Bremer 9 15 Paul_Bremer 14 16 Paul_Sarbanes 1 3 Paul_Sarbanes 2 3 Paul_William_Hurley 1 2 Pervez_Musharraf 1 6 Pervez_Musharraf 2 16 Pervez_Musharraf 3 10 Pervez_Musharraf 4 14 Pervez_Musharraf 5 7 Pervez_Musharraf 5 15 Phil_Gramm 1 2 Prince_Edward 1 2 Ricardo_Lagos 3 12 Ricardo_Lagos 23 27 Ricardo_Monasterio 1 2 Rich_Gannon 1 2 Rick_Perry 1 4 Rick_Perry 1 5 Rick_Perry 2 4 Rick_Perry 3 4 Robert_Bonner 2 3 Robert_Evans 2 3 Robert_Fico 1 2 Romano_Prodi 3 5 Roy_Moore 2 5 Roy_Moore 3 6 Sachiko_Yamada 1 2 Sachiko_Yamada 1 4 Sachiko_Yamada 2 3 Sachiko_Yamada 2 4 Saeb_Erekat 1 2 Sam_Bith 1 2 Sam_Bith 1 3 Sam_Bith 2 3 Sean_Hayes 1 2 Sergio_Garcia 1 2 Silvia_Farina_Elia 1 2 Steve_Ballmer 1 2 Steve_Ballmer 1 3 Stockard_Channing 1 3 Stockard_Channing 2 3 Terry_McAuliffe 1 2 Terry_McAuliffe 1 3 Thomas_Rupprath 1 3 Thomas_Rupprath 2 3 Tom_Ridge 6 16 Tom_Ridge 8 25 Tom_Ridge 9 26 Tom_Ridge 18 22 Tommy_Haas 4 5 Tommy_Thompson 1 2 Tommy_Thompson 1 7 Tommy_Thompson 2 8 Tommy_Thompson 4 8 Tommy_Thompson 6 9 Tomoko_Hagiwara 1 2 Trent_Lott 1 2 Trent_Lott 2 3 Trent_Lott 2 8 Trent_Lott 6 11 Trent_Lott 7 10 Trent_Lott 8 16 Tsutomu_Takebe 1 2 Tyler_Hamilton 1 2 Tyra_Banks 1 2 Vaclav_Havel 2 4 Vaclav_Havel 2 5 Vaclav_Havel 2 6 Vaclav_Havel 4 9 Vaclav_Havel 5 7 Valerie_Harper 1 2 Vince_Carter 3 4 Vincent_Brooks 2 3 Vincent_Brooks 2 7 Vincent_Brooks 4 5 Vincent_Brooks 4 7 Vincent_Gallo 1 2 Vitali_Klitschko 1 2 Vitali_Klitschko 3 4 William_Macy 1 4 William_Macy 2 4 William_Macy 3 4 Woody_Allen 2 4 Woody_Allen 3 5 Yukiko_Okudo 1 2 Zico 1 2 Zico 2 3 Abdel_Madi_Shabneh 1 Dean_Barker 1 Abdel_Madi_Shabneh 1 Giancarlo_Fisichella 1 Abdel_Madi_Shabneh 1 Mikhail_Gorbachev 1 Abdul_Rahman 1 Portia_de_Rossi 1 Abel_Pacheco 1 Jong_Thae_Hwa 2 Abel_Pacheco 2 Jean-Francois_Lemounier 1 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Ruth_Harlow 2 Kim_Yong-il 2 Linda_Ham 1 Kwon_Yang-sook 3 Tommy_Robredo 1 Lane_Bryant 1 Yannos_Papantoniou 1 Larry_Hagman 1 Teddy_Kollek 1 Larry_Hagman 1 Val_Ackerman 1 Laura_Gobai 1 Robin_Wagner 1 Laura_Pausini 1 Thad_Matta 1 Laura_Romero 1 Paula_Zahn 2 Lee_Jun 1 Ruth_Pearce 1 Lee_Jun 2 Stephen_Friedman 1 Lela_Rochon 1 Michael_Haneke 1 Lela_Rochon 1 Yannos_Papantoniou 1 Lesley_Flood 1 Peter_Goldmark 1 Li_Changchun 1 Pieter_Bouw 1 Lina_Krasnoroutskaya 1 Troy_Polamalu 1 Linda_Lingle 1 Victor_Hanescu 1 Lisa_Stone 1 Rod_Paige 1 Lisa_Stone 1 Scott_Sullivan 2 Lou_Lang 1 Nova_Esther_Guthrie 1 Luc_Montagnier 1 Paul_Krueger 1 Luc_Montagnier 1 Pedro_Alvarez 1 Luc_Montagnier 1 Pierre_Pettigrew 2 Luis_Guzman 1 Patsy_Kensit 1 Marcelo_Ebrard 2 Mike_Scioscia 2 Marcelo_Rios 4 Maria_Shriver 3 Marcelo_Rios 4 Pierre_Pettigrew 1 Marcelo_Rios 4 Saeed_Mortazavi 1 Margaret_Thatcher 1 Pedro_Alvarez 1 Margaret_Thatcher 1 Salman_Rushdie 2 Maria_Simon 1 Mona_Rishmawi 1 Maria_Simon 1 Robert_Durst 1 Marlon_Devonish 1 Patrick_Clawsen 1 Max_von_Sydow 1 Zhu_Rongji 1 Mel_Karmazin 1 Pierre_Pettigrew 1 Melana_Scantlin 1 William_Ragland 1 Michael_Bloomberg 15 Ozzy_Osbourne 2 Michael_J_Fox 1 Ricky_Barnes 1 Michael_J_Fox 1 Thomas_Daily 1 Micky_Ward 1 Takenori_Kanzaki 1 Mike_Babcock 1 Tony_Parker 2 Mike_Martz 5 William_Burns 1 Mira_Sorvino 1 Simon_Cowell 2 Mira_Sorvino 1 Tom_Tunney 1 Miroljub 2 Theo_Epstein 1 Mitzi_Gaynor 1 Ruth_Harlow 1 Mohammed_Dahlan 1 Ronald_White 1 Natanaela_Barnova 1 Nuon_Chea 1 Noel_Forgeard 1 Zach_Pillar 1 Normand_Legault 1 Omar_Vizquel 1 Normand_Legault 1 Rupert_Grint 3 Nova_Esther_Guthrie 1 Stephen_Joseph 1 Ontario_Lett 1 Wallace_Capel 1 Orlando_Bloom 1 Ray_Liotta 1 Patrick_Clawsen 1 Sandra_Banning 1 Paul_Coppin 2 Rick_Husband 1 Paul_Murphy 1 Qazi_Hussain_Ahmed 1 Paul_Newman 1 Robert_Blake 3 Paula_Zahn 1 Tamara_Mowry 1 Peter_Ahearn 1 Romain_Duris 1 Peter_Gabriel 1 Peter_OToole 1 Peter_Lundgren 1 William_Rosenberg 1 Peter_OToole 1 Qazi_Afzal 1 Qais_al-Kazali 1 Ringo_Starr 1 Randy_Brown 1 Val_Ackerman 1 Rani_Mukherjee 1 Timothy_McVeigh 1 Ringo_Starr 1 Zach_Pillar 1 Roger_Corbett 1 Tocker_Pudwill 1 Ruth_Harlow 1 Virgina_Ruano_Pascal 1 Sandra_Banning 1 Wolfgang_Schneiderhan 1 Scott_Wolf 2 Troy_Polamalu 1 Sergei_Alexandrovitch_Ordzhonikidze 1 Yolanda_King 1 Shane_Loux 1 Val_Ackerman 1 Shawn_Marion 1 Shirley_Jones 1 Slobodan_Milosevic 2 Sok_An 1 ================================================ FILE: lfw/data/pairsDevTest.txt ================================================ 500 Abdullah_Gul 13 14 Abdullah_Gul 13 16 Abdullatif_Sener 1 2 Adel_Al-Jubeir 1 3 Al_Pacino 1 2 Alan_Greenspan 1 5 Albert_Costa 2 6 Albert_Costa 4 6 Albert_Costa 5 6 Alejandro_Atchugarry 1 2 Alex_Penelas 1 2 Ali_Naimi 1 3 Ali_Naimi 3 4 Allison_Janney 1 2 Allyson_Felix 2 5 Alvaro_Uribe 7 10 Alvaro_Uribe 17 26 Alvaro_Uribe 28 29 Amanda_Bynes 1 2 Amanda_Bynes 1 3 Amanda_Bynes 1 4 Amanda_Bynes 3 4 Amelie_Mauresmo 1 21 Amelie_Mauresmo 4 11 Amelie_Mauresmo 16 21 Ana_Palacio 1 6 Andrei_Mikhnevich 1 2 Andy_Hebb 1 2 Angelo_Reyes 1 2 Angelo_Reyes 2 3 Ann_Veneman 1 2 Ann_Veneman 5 11 Ann_Veneman 8 9 Anna_Nicole_Smith 1 2 Anne_Krueger 1 2 Anne_Krueger 2 3 Annette_Lu 1 2 Annette_Lu 1 3 Annette_Lu 2 3 Antonio_Palocci 3 8 Antonio_Trillanes 1 2 Antonio_Trillanes 2 3 Arlen_Specter 1 3 Augustin_Calleri 1 3 Augustin_Calleri 1 4 Barbara_Brezigar 1 2 Begum_Khaleda_Zia 1 2 Ben_Affleck 1 3 Ben_Affleck 2 6 Ben_Affleck 3 6 Bertie_Ahern 2 3 Bill_Frist 1 3 Bill_Frist 2 7 Bill_Frist 3 8 Bill_McBride 2 6 Bill_McBride 2 10 Bill_McBride 3 9 Bill_Sizemore 1 2 Bono 1 2 Bono 1 3 Bono 2 3 Brian_Cowen 1 2 Bridget_Fonda 1 3 Bridgette_Wilson-Sampras 1 2 Butch_Davis 1 2 Calista_Flockhart 2 6 Candice_Bergen 1 3 Carla_Del_Ponte 2 4 Carla_Del_Ponte 4 5 Carlos_Manuel_Pruneda 1 2 Carlos_Manuel_Pruneda 1 3 Carlos_Manuel_Pruneda 2 3 Carlos_Moya 7 11 Carlos_Ortega 1 3 Carolyn_Dawn_Johnson 2 3 Carrie-Anne_Moss 2 5 Carson_Daly 1 2 Cathy_Freeman 1 2 Chanda_Rubin 2 4 Chang_Dae-whan 1 2 Charles_Taylor 3 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Rowan_Williams 1 Laurel_Clark 1 Rahul_Dravid 1 Lawrence_Di_Rita 1 Tim_Allen 2 Lawrence_Roberts 1 Malik_Mahmud 1 Lea_Fastow 1 Todd_Parrott 1 Lee_Baca 1 Pat_Summerall 1 Leisel_Jones 1 Steven_Hatfill 1 Lena_Katina 1 Queen_Noor 1 Leonard_Glick 1 Terrence_Trammell 1 Leonard_Schrank 1 Ozzy_Osbourne 1 Leslie_Wiser_Jr 1 Mireya_Moscoso 1 Lisa_Murkowski 1 Svetlana_Belousova 1 Lisa_Raymond 2 Robert_Redford 7 Ludwig_Ovalle 1 Mauricio_Macri 1 Luis_Pujols 1 Rohman_al-Ghozi 1 Makhdoom_Amin_Fahim 1 Tang_Jiaxuan 5 Malcolm_Wild 1 Owen_Wilson 2 Mamdouh_Habib 1 Polona_Bas 1 Manijeh_Hekmat 1 Richard_Carl 1 Manijeh_Hekmat 1 Se_Hyuk_Joo 1 Marc_Anthony 1 Vince_Vaughan 1 Margie_Puente 1 Rafiq_Hariri 1 Maria_Callas 1 Melchor_Cob_Castro 1 Mario_Lemieux 1 Pedro_Solbes 4 Marissa_Jaret_Winokur 1 Vincent_Sombrotto 1 Mark_Dacey 2 Russ_Ortiz 1 Mark_Martin 1 Vladimir_Spidla 2 Mark_Mishkin 1 Nick_Reilly 1 Mark_Mishkin 1 Shawn_Marion 1 Marricia_Tate 1 Rita_Moreno 2 Mary_Landrieu 1 Robert_Gordon_Card 1 Maryn_McKenna 1 Shawn_Marion 1 Maryn_McKenna 1 Tim_Pawlenty 1 Masum_Turker 1 Norman_Mineta 1 Matt_Dillon 1 Regina_Ip 1 Matthias_Sammer 1 Robbie_Naish 1 Melissa_Etheridge 2 Teri_Files 1 Michael_J_Sheehan 1 Michael_Smith_Foster 1 Michael_Linscott 1 Robin_Tunney 1 Michael_Phelps 5 Pat_Burns 2 Michael_Sheehan 1 Vincent_Cianci_Jr 1 Michael_Smith_Foster 1 Rupert_Murdoch 1 Michelle_Yeoh 5 Shoshannah_Stern 1 Miguel_Aldana_Ibarra 1 Mikhail_Gorbachev 1 Miguel_Jimenez 1 Paris_Hilton 1 Mike_Helton 2 Reggie_Lewis 1 Mike_Leach 1 Thomas_Haeggstroem 1 Mike_Maroth 1 Stefan_Holm 1 Mike_Slive 1 Paul_Sarbanes 3 Mikhail_Youzhny 2 Vince_Dooley 1 Mikulas_Dzurinda 2 Shirley_Jones 1 Miles_Stewart 1 Yasushi_Akashi 1 Mirela_Manjani 1 Robert_Woody_Johnson 1 Mitchell_McLaughlin 1 Timothy_Goebel 1 Mohammed_Baqir_al-Hakim 3 Neil_Goldman 1 Mona_Ayoub 1 Todd_Parrott 1 Mufti_Mohammad_Syed 1 Ralf_Schumacher 4 Nathalie_Gagnon 1 Rupert_Grint 2 Neil_Goldman 1 Niall_Connolly 1 Nick_Reilly 1 Tassos_Papadopoulos 1 Nicolas_Sarkozy 1 Shanna_Zolman 1 Norm_Coleman 2 Osmond_Smith 1 Norman_Mailer 1 Steven_Curtis_Chapman 1 Oliver_Neuville 1 Shawn_Marion 1 Park_Jie-won 1 Steve_Patterson 1 Patty_Sheehan 1 William_Nessen 1 Paul_Hogan 1 Thomas_Watjen 1 Paul_Hogan 1 William_Martin 1 Paul_Kariya 1 Shannon_OBrien 2 Paul_Martin 1 Stephanie_Cohen_Aloro 1 Paul_Michael_Daniels 1 Simon_Chalk 1 Paul_Schrader 1 Teri_Files 1 Paula_Prentiss 1 Thomas_Mesereau_Jr 1 Pedro_Martinez 1 Stephen_Glassroth 1 Peter_Fitzgerald 1 Zelma_Novelo 1 Phil_Jackson 1 Richard_Penniman 1 Phillipe_Comtois 1 Richard_Carl 1 Pierre_Boulanger 1 Shania_Twain 1 Princess_Diana 1 Steffi_Graf 2 Priscilla_Presley 1 Sergey_Lavrov 5 Priscilla_Presley 1 Victoria_Beckham 3 Richard_Butler 1 Victoria_Beckham 1 Richard_Pennington 1 Stacy_Nelson 1 Rick_Caruso 1 Steve_Wariner 1 Rick_Perry 6 Svetlana_Koroleva 1 Ricky_Cottrill 1 Sananda_Maitreya 1 Robert_Flodquist 1 Winona_Ryder 11 Rod_Stewart 1 Tom_Scully 1 Rod_Stewart 3 Se_Hyuk_Joo 1 Rod_Thorn 1 Yves_Brodeur 1 Ron_Howard 1 Tim_Pawlenty 1 Ronaldo_Luis_Nazario_de_Lima 1 Samuel_Waksal 2 Sami_Al-Arian 1 Tommy_Lasorda 1 Sarah_Canale 1 Steven_Curtis_Chapman 1 Sasha_Cohen 1 Valery_Giscard_dEstaing 5 Scott_Dalton 1 Tamara_Mowry 1 Sergio_Castellitto 1 Steve_Lavin 5 Seth_Gorney 1 Wilton_Gregory 1 Shane_Mosley 1 Stacey_Dales-Schuman 1 Sheila_Taormina 1 Stephan_Eberharter 1 Stefano_Gabbana 1 Tang_Jiaxuan 3 Steve_Wariner 1 Toshi_Izawa 1 Steve_Zahn 1 Tab_Baldwin 1 Susan_Whelan 1 Wolfgang_Schneiderhan 1 Takeo_Fukui 1 Will_Ofenheusle 1 Tamara_Mowry 1 Zach_Parise 1 Tatiana_Kennedy_Schlossberg 1 Thomas_Watjen 1 Todd_Petit 1 Vicente_Fernandez 3 ================================================ FILE: lfw/eval_lfw.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 tqdm import numpy as np import sklearn.metrics from sklearn import metrics from scipy.optimize import brentq from scipy import interpolate 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 LFW verification task =============================================== Example usage: # Resnet 101 on 10 folds of LFW python lfw/eval_lfw.py -e lfw_eval_resnet101 --model_type resnet101 --fold 10 -m BEST_MODEL_PATH ''' def main(): parser = argparse.ArgumentParser() parser.add_argument('-e', '--exp_name', default='lfw_eval') parser.add_argument('-g', '--gpu', type=int, default=0) parser.add_argument('-d', '--dataset_path', default='/srv/data1/arunirc/datasets/lfw-deepfunneled') parser.add_argument('--fold', type=int, default=0, choices=[0,10]) parser.add_argument('--batch_size', type=int, default=100) parser.add_argument('-m', '--model_path', default=None, required=True, help='Path to pre-trained model') parser.add_argument('--model_type', default='resnet50', choices=['resnet50', 'resnet101', 'resnet101-512d']) 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 if args.fold == 0: pairs_path = './lfw/data/pairsDevTest.txt' else: pairs_path = './lfw/data/pairs.txt' # ----------------------------------------------------------------------------- # 1. Dataset # ----------------------------------------------------------------------------- file_ext = 'jpg' # observe, no '.' before jpg num_class = 8631 pairs = utils.read_pairs(pairs_path) path_list, issame_list = utils.get_paths(args.dataset_path, pairs, file_ext) # Define data transforms RGB_MEAN = [ 0.485, 0.456, 0.406 ] RGB_STD = [ 0.229, 0.224, 0.225 ] test_transform = transforms.Compose([ transforms.Scale((250,250)), # make 250x250 transforms.CenterCrop(150), # then take 150x150 center crop transforms.Scale((224,224)), # resized to the network's required input size transforms.ToTensor(), transforms.Normalize(mean = RGB_MEAN, std = RGB_STD), ]) # Create data loader test_loader = torch.utils.data.DataLoader( data_loader.LFWDataset( path_list, issame_list, test_transform), batch_size=args.batch_size, shuffle=False ) # ----------------------------------------------------------------------------- # 2. Model # ----------------------------------------------------------------------------- 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) else: raise NotImplementedError checkpoint = torch.load(args.model_path) if checkpoint['arch'] == 'DataParallel': # if we trained and saved our model using 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': model.eval() extractor = model extractor.fc = nn.Sequential(extractor.fc[0]) else: feature_map.pop() extractor = nn.Sequential(*feature_map) extractor.eval() # set to evaluation mode (fixes BatchNorm, dropout, etc.) # ----------------------------------------------------------------------------- # 3. Feature extraction # ----------------------------------------------------------------------------- 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() else: feat = feat.data features.append(feat) features = torch.stack(features) sz = features.size() features = features.view(sz[0]*sz[1], sz[2]) features = F.normalize(features, p=2, dim=1) # L2-normalize # TODO - cache features # ----------------------------------------------------------------------------- # 4. Verification # ----------------------------------------------------------------------------- num_feat = features.size()[0] feat_pair1 = features[np.arange(0,num_feat,2),:] feat_pair2 = features[np.arange(1,num_feat,2),:] feat_dist = (feat_pair1 - feat_pair2).norm(p=2, dim=1) feat_dist = feat_dist.numpy() # Eval metrics scores = -feat_dist gt = np.asarray(issame_list) if args.fold == 0: fig_path = osp.join(here, args.exp_name + '_' + args.model_type + '_lfw_roc_devTest.png') roc_auc = sklearn.metrics.roc_auc_score(gt, scores) fpr, tpr, thresholds = sklearn.metrics.roc_curve(gt, scores) print 'ROC-AUC: %.04f' % roc_auc # Plot and save ROC curve fig = plt.figure() plt.title('ROC - lfw dev-test') plt.plot(fpr, tpr, lw=2, label='ROC (auc = %0.4f)' % roc_auc) plt.xlim([0.0, 1.0]) plt.ylim([0.0, 1.05]) plt.grid() plt.xlabel('False Positive Rate') plt.ylabel('True Positive Rate') plt.legend(loc='lower right') plt.tight_layout() else: # 10 fold fold_size = 600 # 600 pairs in each fold roc_auc = np.zeros(10) roc_eer = np.zeros(10) fig = plt.figure() plt.xlim([0.0, 1.0]) plt.ylim([0.0, 1.05]) plt.grid() plt.xlabel('False Positive Rate') plt.ylabel('True Positive Rate') for i in tqdm.tqdm(range(10)): start = i * fold_size end = (i+1) * fold_size scores_fold = scores[start:end] gt_fold = gt[start:end] roc_auc[i] = sklearn.metrics.roc_auc_score(gt_fold, scores_fold) fpr, tpr, _ = sklearn.metrics.roc_curve(gt_fold, scores_fold) # EER calc: https://yangcha.github.io/EER-ROC/ roc_eer[i] = brentq( lambda x: 1. - x - interpolate.interp1d(fpr, tpr)(x), 0., 1.) plt.plot(fpr, tpr, alpha=0.4, lw=2, color='darkgreen', label='ROC(auc=%0.4f, eer=%0.4f)' % (roc_auc[i], roc_eer[i]) ) plt.title( 'AUC: %0.4f +/- %0.4f, EER: %0.4f +/- %0.4f' % (np.mean(roc_auc), np.std(roc_auc), np.mean(roc_eer), np.std(roc_eer)) ) plt.tight_layout() fig_path = osp.join(here, args.exp_name + '_' + args.model_type + '_lfw_roc_10fold.png') plt.savefig(fig_path, bbox_inches='tight') print 'ROC curve saved at: ' + fig_path if __name__ == '__main__': main() ================================================ FILE: models.py ================================================ # import fcn import os.path as osp import numpy as np import torch import torch.nn as nn import torch.nn.functional as F class NormFeat(nn.Module): ''' L2 normalization of features ''' def __init__(self, scale_factor=1.0): super(NormFeat, self).__init__() self.scale_factor = scale_factor def forward(self, input): return self.scale_factor * F.normalize(input, p=2, dim=1) class ScaleFeat(nn.Module): # https://discuss.pytorch.org/t/is-scale-layer-available-in-pytorch/7954/6?u=arunirc def __init__(self, scale_factor=50.0): super().__init__() self.scale = scale_factor def forward(self, input): return input * self.scale # https://github.com/shelhamer/fcn.berkeleyvision.org/blob/master/surgery.py def get_upsampling_weight(in_channels, out_channels, kernel_size): """Make a 2D bilinear kernel suitable for upsampling""" factor = (kernel_size + 1) // 2 if kernel_size % 2 == 1: center = factor - 1 else: center = factor - 0.5 og = np.ogrid[:kernel_size, :kernel_size] filt = (1 - abs(og[0] - center) / factor) * \ (1 - abs(og[1] - center) / factor) weight = np.zeros((in_channels, out_channels, kernel_size, kernel_size), dtype=np.float64) weight[range(in_channels), range(out_channels), :, :] = filt return torch.from_numpy(weight).float() class FCN32sColor(nn.Module): def __init__(self, n_class=32, bin_type='one-hot', batch_norm=True): super(FCN32sColor, self).__init__() self.n_class = n_class self.bin_type = bin_type self.batch_norm = batch_norm # conv1 self.conv1_1 = nn.Conv2d(1, 64, 3, padding=100) self.relu1_1 = nn.ReLU(inplace=True) if batch_norm: self.conv1_1_bn = nn.BatchNorm2d(64) self.conv1_2 = nn.Conv2d(64, 64, 3, padding=1) self.relu1_2 = nn.ReLU(inplace=True) if batch_norm: self.conv1_2_bn = nn.BatchNorm2d(64) self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True) # 1/2 # conv2 self.conv2_1 = nn.Conv2d(64, 128, 3, padding=1) self.relu2_1 = nn.ReLU(inplace=True) if batch_norm: self.conv2_1_bn = nn.BatchNorm2d(128) self.conv2_2 = nn.Conv2d(128, 128, 3, padding=1) self.relu2_2 = nn.ReLU(inplace=True) if batch_norm: self.conv2_2_bn = nn.BatchNorm2d(128) self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True) # 1/4 # conv3 self.conv3_1 = nn.Conv2d(128, 256, 3, padding=1) self.relu3_1 = nn.ReLU(inplace=True) if batch_norm: self.conv3_1_bn = nn.BatchNorm2d(256) self.conv3_2 = nn.Conv2d(256, 256, 3, padding=1) self.relu3_2 = nn.ReLU(inplace=True) if batch_norm: self.conv3_2_bn = nn.BatchNorm2d(256) self.conv3_3 = nn.Conv2d(256, 256, 3, padding=1) self.relu3_3 = nn.ReLU(inplace=True) if batch_norm: self.conv3_3_bn = nn.BatchNorm2d(256) self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True) # 1/8 # conv4 self.conv4_1 = nn.Conv2d(256, 512, 3, padding=1) self.relu4_1 = nn.ReLU(inplace=True) if batch_norm: self.conv4_1_bn = nn.BatchNorm2d(512) self.conv4_2 = nn.Conv2d(512, 512, 3, padding=1) self.relu4_2 = nn.ReLU(inplace=True) if batch_norm: self.conv4_2_bn = nn.BatchNorm2d(512) self.conv4_3 = nn.Conv2d(512, 512, 3, padding=1) self.relu4_3 = nn.ReLU(inplace=True) if batch_norm: self.conv4_3_bn = nn.BatchNorm2d(512) self.pool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True) # 1/16 # conv5 self.conv5_1 = nn.Conv2d(512, 512, 3, padding=1) self.relu5_1 = nn.ReLU(inplace=True) if batch_norm: self.conv5_1_bn = nn.BatchNorm2d(512) self.conv5_2 = nn.Conv2d(512, 512, 3, padding=1) self.relu5_2 = nn.ReLU(inplace=True) if batch_norm: self.conv5_2_bn = nn.BatchNorm2d(512) self.conv5_3 = nn.Conv2d(512, 512, 3, padding=1) self.relu5_3 = nn.ReLU(inplace=True) if batch_norm: self.conv5_3_bn = nn.BatchNorm2d(512) self.pool5 = nn.MaxPool2d(2, stride=2, ceil_mode=True) # 1/32 # fc6 self.fc6 = nn.Conv2d(512, 4096, 7) self.relu6 = nn.ReLU(inplace=True) if batch_norm: self.fc6_bn = nn.BatchNorm2d(4096) self.drop6 = nn.Dropout2d() # fc7 self.fc7 = nn.Conv2d(4096, 4096, 1) self.relu7 = nn.ReLU(inplace=True) self.fc7_bn = nn.BatchNorm2d(4096) self.drop7 = nn.Dropout2d() if bin_type == 'one-hot': # NOTE: *two* output prediction maps for hue and chroma # TODO - not implemented error should be raised for this! self.score_fr_hue = nn.Conv2d(4096, n_class, 1) self.upscore_hue = nn.ConvTranspose2d(n_class, n_class, 64, stride=32, bias=False) self.score_fr_chroma = nn.Conv2d(4096, n_class, 1) self.upscore_chroma = nn.ConvTranspose2d(n_class, n_class, 64, stride=32, bias=False) self.upscore_hue.weight.requires_grad = False self.upscore_chroma.weight.requires_grad = False elif bin_type == 'soft': self.score_fr = nn.Conv2d(4096, n_class, 1) self.upscore = nn.ConvTranspose2d(n_class, n_class, 64, stride=32, bias=False) self.upscore.weight.requires_grad = False # fix bilinear upsampler self._initialize_weights() # TODO - init from pre-trained network def _initialize_weights(self): for m in self.modules(): if isinstance(m, nn.Conv2d): pass # leave the default PyTorch init if isinstance(m, nn.ConvTranspose2d): assert m.kernel_size[0] == m.kernel_size[1] initial_weight = get_upsampling_weight( m.in_channels, m.out_channels, m.kernel_size[0]) m.weight.data.copy_(initial_weight) def forward(self, x): h = x h = self.conv1_1(h) if self.batch_norm: h = self.conv1_1_bn(h) h = self.relu1_1(h) h = self.conv1_2(h) if self.batch_norm: h = self.conv1_2_bn(h) h = self.relu1_2(h) h = self.pool1(h) if self.batch_norm: h = self.relu2_1(self.conv2_1_bn(self.conv2_1(h))) else: h = self.relu2_1(self.conv2_1(h)) if self.batch_norm: h = self.relu2_2(self.conv2_2_bn(self.conv2_2(h))) else: h = self.relu2_2(self.conv2_2_bn(self.conv2_2(h))) h = self.pool2(h) if self.batch_norm: h = self.relu3_1(self.conv3_1_bn(self.conv3_1(h))) else: h = self.relu3_1(self.conv3_1(h)) if self.batch_norm: h = self.relu3_2(self.conv3_2_bn(self.conv3_2(h))) else: h = self.relu3_2(self.conv3_2(h)) if self.batch_norm: h = self.relu3_3(self.conv3_3_bn(self.conv3_3(h))) else: h = self.relu3_3(self.conv3_3(h)) h = self.pool3(h) if self.batch_norm: h = self.relu4_1(self.conv4_1_bn(self.conv4_1(h))) else: h = self.relu4_1(self.conv4_1(h)) if self.batch_norm: h = self.relu4_2(self.conv4_2_bn(self.conv4_2(h))) else: h = self.relu4_2(self.conv4_2(h)) if self.batch_norm: h = self.relu4_3(self.conv4_3_bn(self.conv4_3(h))) else: h = self.relu4_3(self.conv4_3(h)) h = self.pool4(h) if self.batch_norm: h = self.relu5_1(self.conv5_1_bn(self.conv5_1(h))) else: h = self.relu5_1(self.conv5_1(h)) if self.batch_norm: h = self.relu5_2(self.conv5_2_bn(self.conv5_2(h))) else: h = self.relu5_2(self.conv5_2(h)) if self.batch_norm: h = self.relu5_3(self.conv5_3_bn(self.conv5_3(h))) else: h = self.relu5_3(self.conv5_3(h)) h = self.pool5(h) if self.batch_norm: h = self.relu6(self.fc6_bn(self.fc6(h))) else: h = self.relu6(self.fc6(h)) h = self.drop6(h) if self.batch_norm: h = self.relu7(self.fc7_bn(self.fc7(h))) else: h = self.relu7(self.fc7(h)) h = self.drop7(h) if self.bin_type == 'one-hot': # hue prediction map h_hue = self.score_fr_hue(h) h_hue = self.upscore_hue(h_hue) h_hue = h_hue[:, :, 19:19 + x.size()[2], 19:19 + x.size()[3]].contiguous() # chroma prediction map h_chroma = self.score_fr_chroma(h) h_chroma = self.upscore_chroma(h_chroma) h_chroma = h_chroma[:, :, 19:19 + x.size()[2], 19:19 + x.size()[3]].contiguous() h = (h_hue, h_chroma) elif self.bin_type == 'soft': h = self.score_fr(h) h = self.upscore(h) h = h[:, :, 19:19 + x.size()[2], 19:19 + x.size()[3]].contiguous() return h class FCN16sColor(nn.Module): def __init__(self, n_class=32, bin_type='one-hot', batch_norm=True): super(FCN16sColor, self).__init__() self.n_class = n_class self.bin_type = bin_type self.batch_norm = batch_norm # conv1 self.conv1_1 = nn.Conv2d(1, 64, 3, padding=100) self.relu1_1 = nn.ReLU(inplace=True) if batch_norm: self.conv1_1_bn = nn.BatchNorm2d(64) self.conv1_2 = nn.Conv2d(64, 64, 3, padding=1) self.relu1_2 = nn.ReLU(inplace=True) if batch_norm: self.conv1_2_bn = nn.BatchNorm2d(64) self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True) # 1/2 # conv2 self.conv2_1 = nn.Conv2d(64, 128, 3, padding=1) self.relu2_1 = nn.ReLU(inplace=True) if batch_norm: self.conv2_1_bn = nn.BatchNorm2d(128) self.conv2_2 = nn.Conv2d(128, 128, 3, padding=1) self.relu2_2 = nn.ReLU(inplace=True) if batch_norm: self.conv2_2_bn = nn.BatchNorm2d(128) self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True) # 1/4 # conv3 self.conv3_1 = nn.Conv2d(128, 256, 3, padding=1) self.relu3_1 = nn.ReLU(inplace=True) if batch_norm: self.conv3_1_bn = nn.BatchNorm2d(256) self.conv3_2 = nn.Conv2d(256, 256, 3, padding=1) self.relu3_2 = nn.ReLU(inplace=True) if batch_norm: self.conv3_2_bn = nn.BatchNorm2d(256) self.conv3_3 = nn.Conv2d(256, 256, 3, padding=1) self.relu3_3 = nn.ReLU(inplace=True) if batch_norm: self.conv3_3_bn = nn.BatchNorm2d(256) self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True) # 1/8 # conv4 self.conv4_1 = nn.Conv2d(256, 512, 3, padding=1) self.relu4_1 = nn.ReLU(inplace=True) if batch_norm: self.conv4_1_bn = nn.BatchNorm2d(512) self.conv4_2 = nn.Conv2d(512, 512, 3, padding=1) self.relu4_2 = nn.ReLU(inplace=True) if batch_norm: self.conv4_2_bn = nn.BatchNorm2d(512) self.conv4_3 = nn.Conv2d(512, 512, 3, padding=1) self.relu4_3 = nn.ReLU(inplace=True) if batch_norm: self.conv4_3_bn = nn.BatchNorm2d(512) self.pool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True) # 1/16 # conv5 self.conv5_1 = nn.Conv2d(512, 512, 3, padding=1) self.relu5_1 = nn.ReLU(inplace=True) if batch_norm: self.conv5_1_bn = nn.BatchNorm2d(512) self.conv5_2 = nn.Conv2d(512, 512, 3, padding=1) self.relu5_2 = nn.ReLU(inplace=True) if batch_norm: self.conv5_2_bn = nn.BatchNorm2d(512) self.conv5_3 = nn.Conv2d(512, 512, 3, padding=1) self.relu5_3 = nn.ReLU(inplace=True) if batch_norm: self.conv5_3_bn = nn.BatchNorm2d(512) self.pool5 = nn.MaxPool2d(2, stride=2, ceil_mode=True) # 1/32 # fc6 self.fc6 = nn.Conv2d(512, 4096, 7) self.relu6 = nn.ReLU(inplace=True) if batch_norm: self.fc6_bn = nn.BatchNorm2d(4096) self.drop6 = nn.Dropout2d() # fc7 self.fc7 = nn.Conv2d(4096, 4096, 1) self.relu7 = nn.ReLU(inplace=True) self.fc7_bn = nn.BatchNorm2d(4096) self.drop7 = nn.Dropout2d() if bin_type == 'one-hot': # NOTE: *two* output prediction maps for hue and chroma raise NotImplementedError('TODO - FCN 16s for separate hue-chroma') elif bin_type == 'soft': self.score_fr = nn.Conv2d(4096, n_class, 1) self.score_pool4 = nn.Conv2d(512, n_class, 1) self.upscore2 = nn.ConvTranspose2d(n_class, n_class, 4, stride=2, bias=False) self.upscore16 = nn.ConvTranspose2d(n_class, n_class, 32, stride=16, bias=False) self.upscore2.weight.requires_grad = False # fix bilinear upsamplers self.upscore16.weight.requires_grad = False self._initialize_weights() def _initialize_weights(self): for m in self.modules(): if isinstance(m, nn.Conv2d): pass # leave the default PyTorch init if isinstance(m, nn.ConvTranspose2d): assert m.kernel_size[0] == m.kernel_size[1] initial_weight = get_upsampling_weight( m.in_channels, m.out_channels, m.kernel_size[0]) m.weight.data.copy_(initial_weight) def forward(self, x): h = x h = self.conv1_1(h) if self.batch_norm: h = self.conv1_1_bn(h) h = self.relu1_1(h) h = self.conv1_2(h) if self.batch_norm: h = self.conv1_2_bn(h) h = self.relu1_2(h) h = self.pool1(h) if self.batch_norm: h = self.relu2_1(self.conv2_1_bn(self.conv2_1(h))) else: h = self.relu2_1(self.conv2_1(h)) if self.batch_norm: h = self.relu2_2(self.conv2_2_bn(self.conv2_2(h))) else: h = self.relu2_2(self.conv2_2_bn(self.conv2_2(h))) h = self.pool2(h) if self.batch_norm: h = self.relu3_1(self.conv3_1_bn(self.conv3_1(h))) else: h = self.relu3_1(self.conv3_1(h)) if self.batch_norm: h = self.relu3_2(self.conv3_2_bn(self.conv3_2(h))) else: h = self.relu3_2(self.conv3_2(h)) if self.batch_norm: h = self.relu3_3(self.conv3_3_bn(self.conv3_3(h))) else: h = self.relu3_3(self.conv3_3(h)) h = self.pool3(h) if self.batch_norm: h = self.relu4_1(self.conv4_1_bn(self.conv4_1(h))) else: h = self.relu4_1(self.conv4_1(h)) if self.batch_norm: h = self.relu4_2(self.conv4_2_bn(self.conv4_2(h))) else: h = self.relu4_2(self.conv4_2(h)) if self.batch_norm: h = self.relu4_3(self.conv4_3_bn(self.conv4_3(h))) else: h = self.relu4_3(self.conv4_3(h)) h = self.pool4(h) pool4 = h # 1/16 if self.batch_norm: h = self.relu5_1(self.conv5_1_bn(self.conv5_1(h))) else: h = self.relu5_1(self.conv5_1(h)) if self.batch_norm: h = self.relu5_2(self.conv5_2_bn(self.conv5_2(h))) else: h = self.relu5_2(self.conv5_2(h)) if self.batch_norm: h = self.relu5_3(self.conv5_3_bn(self.conv5_3(h))) else: h = self.relu5_3(self.conv5_3(h)) h = self.pool5(h) if self.batch_norm: h = self.relu6(self.fc6_bn(self.fc6(h))) else: h = self.relu6(self.fc6(h)) h = self.drop6(h) if self.batch_norm: h = self.relu7(self.fc7_bn(self.fc7(h))) else: h = self.relu7(self.fc7(h)) h = self.drop7(h) if self.bin_type == 'one-hot': raise NotImplementedError('TODO - FCN 16s for separate hue-chroma') elif self.bin_type == 'soft': h = self.score_fr(h) h = self.upscore2(h) upscore2 = h # 1/16 h = self.score_pool4(pool4) h = h[:, :, 5:5 + upscore2.size()[2], 5:5 + upscore2.size()[3]] score_pool4c = h # 1/16 h = upscore2 + score_pool4c h = self.upscore16(h) h = h[:, :, 27:27 + x.size()[2], 27:27 + x.size()[3]].contiguous() return h def copy_params_from_fcn32s(self, fcn32s): for name, l1 in fcn32s.named_children(): try: l2 = getattr(self, name) l2.weight # skip ReLU / Dropout except Exception: continue assert l1.weight.size() == l2.weight.size() assert l1.bias.size() == l2.bias.size() l2.weight.data.copy_(l1.weight.data) l2.bias.data.copy_(l1.bias.data) class FCN8sColor(nn.Module): def __init__(self, n_class=32, bin_type='one-hot', batch_norm=True): super(FCN8sColor, self).__init__() self.n_class = n_class self.bin_type = bin_type self.batch_norm = batch_norm # conv1 self.conv1_1 = nn.Conv2d(1, 64, 3, padding=100) self.relu1_1 = nn.ReLU(inplace=True) if batch_norm: self.conv1_1_bn = nn.BatchNorm2d(64) self.conv1_2 = nn.Conv2d(64, 64, 3, padding=1) self.relu1_2 = nn.ReLU(inplace=True) if batch_norm: self.conv1_2_bn = nn.BatchNorm2d(64) self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True) # 1/2 # conv2 self.conv2_1 = nn.Conv2d(64, 128, 3, padding=1) self.relu2_1 = nn.ReLU(inplace=True) if batch_norm: self.conv2_1_bn = nn.BatchNorm2d(128) self.conv2_2 = nn.Conv2d(128, 128, 3, padding=1) self.relu2_2 = nn.ReLU(inplace=True) if batch_norm: self.conv2_2_bn = nn.BatchNorm2d(128) self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True) # 1/4 # conv3 self.conv3_1 = nn.Conv2d(128, 256, 3, padding=1) self.relu3_1 = nn.ReLU(inplace=True) if batch_norm: self.conv3_1_bn = nn.BatchNorm2d(256) self.conv3_2 = nn.Conv2d(256, 256, 3, padding=1) self.relu3_2 = nn.ReLU(inplace=True) if batch_norm: self.conv3_2_bn = nn.BatchNorm2d(256) self.conv3_3 = nn.Conv2d(256, 256, 3, padding=1) self.relu3_3 = nn.ReLU(inplace=True) if batch_norm: self.conv3_3_bn = nn.BatchNorm2d(256) self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True) # 1/8 # conv4 self.conv4_1 = nn.Conv2d(256, 512, 3, padding=1) self.relu4_1 = nn.ReLU(inplace=True) if batch_norm: self.conv4_1_bn = nn.BatchNorm2d(512) self.conv4_2 = nn.Conv2d(512, 512, 3, padding=1) self.relu4_2 = nn.ReLU(inplace=True) if batch_norm: self.conv4_2_bn = nn.BatchNorm2d(512) self.conv4_3 = nn.Conv2d(512, 512, 3, padding=1) self.relu4_3 = nn.ReLU(inplace=True) if batch_norm: self.conv4_3_bn = nn.BatchNorm2d(512) self.pool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True) # 1/16 # conv5 self.conv5_1 = nn.Conv2d(512, 512, 3, padding=1) self.relu5_1 = nn.ReLU(inplace=True) if batch_norm: self.conv5_1_bn = nn.BatchNorm2d(512) self.conv5_2 = nn.Conv2d(512, 512, 3, padding=1) self.relu5_2 = nn.ReLU(inplace=True) if batch_norm: self.conv5_2_bn = nn.BatchNorm2d(512) self.conv5_3 = nn.Conv2d(512, 512, 3, padding=1) self.relu5_3 = nn.ReLU(inplace=True) if batch_norm: self.conv5_3_bn = nn.BatchNorm2d(512) self.pool5 = nn.MaxPool2d(2, stride=2, ceil_mode=True) # 1/32 # fc6 self.fc6 = nn.Conv2d(512, 4096, 7) self.relu6 = nn.ReLU(inplace=True) if batch_norm: self.fc6_bn = nn.BatchNorm2d(4096) self.drop6 = nn.Dropout2d() # fc7 self.fc7 = nn.Conv2d(4096, 4096, 1) self.relu7 = nn.ReLU(inplace=True) self.fc7_bn = nn.BatchNorm2d(4096) self.drop7 = nn.Dropout2d() if bin_type == 'one-hot': # NOTE: *two* output prediction maps for hue and chroma raise NotImplementedError('TODO - FCN 16s for separate hue-chroma') elif bin_type == 'soft': self.score_fr = nn.Conv2d(4096, n_class, 1) self.score_pool3 = nn.Conv2d(256, n_class, 1) self.score_pool4 = nn.Conv2d(512, n_class, 1) self.upscore2 = nn.ConvTranspose2d(n_class, n_class, 4, stride=2, bias=False) self.upscore8 = nn.ConvTranspose2d(n_class, n_class, 16, stride=8, bias=False) self.upscore_pool4 = nn.ConvTranspose2d(n_class, n_class, 4, stride=2, bias=False) self.upscore2.weight.requires_grad = False # fix bilinear upsamplers self.upscore8.weight.requires_grad = False self.upscore_pool4.weight.requires_grad = False self._initialize_weights() def _initialize_weights(self): for m in self.modules(): if isinstance(m, nn.Conv2d): pass # leave the default PyTorch init if isinstance(m, nn.ConvTranspose2d): assert m.kernel_size[0] == m.kernel_size[1] initial_weight = get_upsampling_weight( m.in_channels, m.out_channels, m.kernel_size[0]) m.weight.data.copy_(initial_weight) def forward(self, x): h = x h = self.conv1_1(h) if self.batch_norm: h = self.conv1_1_bn(h) h = self.relu1_1(h) h = self.conv1_2(h) if self.batch_norm: h = self.conv1_2_bn(h) h = self.relu1_2(h) h = self.pool1(h) if self.batch_norm: h = self.relu2_1(self.conv2_1_bn(self.conv2_1(h))) else: h = self.relu2_1(self.conv2_1(h)) if self.batch_norm: h = self.relu2_2(self.conv2_2_bn(self.conv2_2(h))) else: h = self.relu2_2(self.conv2_2_bn(self.conv2_2(h))) h = self.pool2(h) if self.batch_norm: h = self.relu3_1(self.conv3_1_bn(self.conv3_1(h))) else: h = self.relu3_1(self.conv3_1(h)) if self.batch_norm: h = self.relu3_2(self.conv3_2_bn(self.conv3_2(h))) else: h = self.relu3_2(self.conv3_2(h)) if self.batch_norm: h = self.relu3_3(self.conv3_3_bn(self.conv3_3(h))) else: h = self.relu3_3(self.conv3_3(h)) h = self.pool3(h) pool3 = h # 1/8 if self.batch_norm: h = self.relu4_1(self.conv4_1_bn(self.conv4_1(h))) else: h = self.relu4_1(self.conv4_1(h)) if self.batch_norm: h = self.relu4_2(self.conv4_2_bn(self.conv4_2(h))) else: h = self.relu4_2(self.conv4_2(h)) if self.batch_norm: h = self.relu4_3(self.conv4_3_bn(self.conv4_3(h))) else: h = self.relu4_3(self.conv4_3(h)) h = self.pool4(h) pool4 = h # 1/16 if self.batch_norm: h = self.relu5_1(self.conv5_1_bn(self.conv5_1(h))) else: h = self.relu5_1(self.conv5_1(h)) if self.batch_norm: h = self.relu5_2(self.conv5_2_bn(self.conv5_2(h))) else: h = self.relu5_2(self.conv5_2(h)) if self.batch_norm: h = self.relu5_3(self.conv5_3_bn(self.conv5_3(h))) else: h = self.relu5_3(self.conv5_3(h)) h = self.pool5(h) if self.batch_norm: h = self.relu6(self.fc6_bn(self.fc6(h))) else: h = self.relu6(self.fc6(h)) h = self.drop6(h) if self.batch_norm: h = self.relu7(self.fc7_bn(self.fc7(h))) else: h = self.relu7(self.fc7(h)) h = self.drop7(h) if self.bin_type == 'one-hot': raise NotImplementedError('TODO - FCN 16s for separate hue-chroma') elif self.bin_type == 'soft': h = self.score_fr(h) h = self.upscore2(h) upscore2 = h # 1/16 h = self.score_pool4(pool4) h = h[:, :, 5:5 + upscore2.size()[2], 5:5 + upscore2.size()[3]] score_pool4c = h # 1/16 h = upscore2 + score_pool4c # 1/16 h = self.upscore_pool4(h) upscore_pool4 = h # 1/8 h = self.score_pool3(pool3) h = h[:, :, 9:9 + upscore_pool4.size()[2], 9:9 + upscore_pool4.size()[3]] score_pool3c = h # 1/8 h = upscore_pool4 + score_pool3c # 1/8 h = self.upscore8(h) h = h[:, :, 31:31 + x.size()[2], 31:31 + x.size()[3]].contiguous() return h def copy_params_from_fcn16s(self, fcn16s): for name, l1 in fcn16s.named_children(): try: l2 = getattr(self, name) l2.weight # skip ReLU / Dropout except Exception: continue assert l1.weight.size() == l2.weight.size() l2.weight.data.copy_(l1.weight.data) if l1.bias is not None: assert l1.bias.size() == l2.bias.size() l2.bias.data.copy_(l1.bias.data) ================================================ FILE: run_resnet_demo.py ================================================ import torch import torchvision from torchvision import models import torch.nn as nn import torch.utils.data import torchvision.transforms as transforms from torch.autograd import Variable import torch.nn as nn import torch.nn.functional as F import os import os.path as osp import yaml import numpy as np import PIL.Image import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt import utils here = osp.dirname(osp.abspath(__file__)) # output folder is located here # ----------------------------------------------------------------------------- # 0. User-defined settings # ----------------------------------------------------------------------------- gpu = 0 # use gpu:0 by default # specify model path of trained ResNet-50 network: model_path = './umd-face/logs/MODEL-resnet_umdfaces_CFG-006_TIME-20180114-141943/model_best.pth.tar' num_class = 8277 # UMD-Faces had this many classes # ----------------------------------------------------------------------------- # 1. GPU setup # ----------------------------------------------------------------------------- os.environ['CUDA_VISIBLE_DEVICES'] = str(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 # ----------------------------------------------------------------------------- # 2. Data preparation # ----------------------------------------------------------------------------- # Samples images taken for demo purpose from LFW: # http://vis-www.cs.umass.edu/lfw/ data_root = './samples/verif' file_path = [osp.join(data_root, 'Recep_Tayyip_Erdogan_0012.jpg'), osp.join(data_root, 'Recep_Tayyip_Erdogan_0015.jpg'), osp.join(data_root, 'Quincy_Jones_0001.jpg')] image = [PIL.Image.open(f).convert('RGB') for f in file_path] # Data transforms # http://pytorch.org/docs/master/torchvision/transforms.html # NOTE: these should be consistent with the training script val_loader # Since LFW images (250x250) are not close-crops, we modify the cropping a bit. RGB_MEAN = [ 0.485, 0.456, 0.406 ] RGB_STD = [ 0.229, 0.224, 0.225 ] test_transform = transforms.Compose([ transforms.CenterCrop(150), # 150x150 center crop transforms.Scale((224,224)), # resized to the network's required input size transforms.ToTensor(), transforms.Normalize(mean = RGB_MEAN, std = RGB_STD), ]) # apply the transform inputs = [test_transform(im) for im in image] # ----------------------------------------------------------------------------- # 3. Model # ----------------------------------------------------------------------------- # PyTorch ResNet model definition: # https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py # ResNet docs: # http://pytorch.org/docs/master/torchvision/models.html#id3 model = torchvision.models.resnet50(pretrained=True) # Replace last layer (by default, resnet has 1000 output categories) model.fc = torch.nn.Linear(2048, num_class) # change to current dataset's classes # Pre-trained PyTorch model loaded from a file checkpoint = torch.load(model_path) if checkpoint['arch'] == 'DataParallel': # if we trained and saved our model using 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" # From https://github.com/meliketoy/fine-tuning.pytorch/blob/master/extract_features.py#L85 feature_map = list(model.children()) feature_map.pop() # remove the final "class prediction" layer extractor = nn.Sequential(*feature_map) # create feature extractor # Inspect the structure - it is a nested list of various modules print extractor[-1] # last layer of the model - avg-pool print extractor[-2][-1] # second-last layer's last module - output is 2048-dim # ----------------------------------------------------------------------------- # 4. Feature extraction # ----------------------------------------------------------------------------- # - simple, one input sample at a time features = [] for x in inputs: x = Variable(x, volatile=True) if cuda: x = x.cuda() x = x.view(1, x.size(0), x.size(1), x.size(2)) # add batch_dim=1 in the front feat = extractor(x).view(-1) # extract features of input `x`, reshape to 1-D vector features.append(feat) features = torch.stack(features) # N x 2048 for N inputs # get Tensors on CPU from autograd.Variables on GPU if cuda: features = features.data.cpu() else: features = features.data features = F.normalize(features, p=2, dim=1) # L2-normalize # ----------------------------------------------------------------------------- # 5. Face verification # ----------------------------------------------------------------------------- # L2-distance between features (Tensors) of same and different pairs d1 = (features[0] - features[1]).norm(p=2) # same pair d2 = (features[0] - features[2]).norm(p=2) # different pair print 'matched pair: %.2f' % d1 print 'mismatched pair: %.2f' % d2 assert d1 < d2 # visualizations fig, ax = plt.subplots(nrows=2, ncols=2) plt.subplot(2, 2, 1) plt.title('matched pair') plt.imshow(image[0]) plt.tight_layout() plt.subplot(2, 2, 2) plt.imshow(image[1]) plt.title('d = %.3f' % d1) plt.tight_layout() plt.subplot(2, 2, 3) plt.imshow(image[0]) plt.title('mismatched pair') plt.tight_layout() plt.subplot(2, 2, 4) plt.imshow(image[2]) plt.title('d = %.3f' % d2) plt.tight_layout() plt.savefig(osp.join(here, 'demo_verif.png'), bbox_inches='tight') print 'Visualization saved in ' + osp.join(here, 'demo_verif.png') ================================================ FILE: tests/test_colorizer.py ================================================ import argparse import os import os.path as osp import numpy as np import PIL.Image import skimage.io import skimage.color as color from skimage import img_as_ubyte import torch from torch.autograd import Variable import sys sys.path.append('/vis/home/arunirc/data1/Research/colorize-fcn/colorizer-fcn') # import matplotlib # matplotlib.use('agg') # import matplotlib.pyplot as plt # import models # import utils import data_loader root = '/srv/data1/arunirc/datasets/ImageNet/images/' cuda = torch.cuda.is_available() GMM_PATH = '/srv/data1/arunirc/Research/colorize-fcn/colorizer-fcn/logs/MODEL-fcn32s_color_CFG-014_VCS-db517d6_TIME-20171230-212406/gmm.pkl' MEAN_L_PATH = '/srv/data1/arunirc/Research/colorize-fcn/colorizer-fcn/logs/MODEL-fcn32s_color_CFG-014_VCS-db517d6_TIME-20171230-212406/mean_l.npy' def test_color_gmm(): print 'Entering: test_color' dataset = data_loader.ColorizeImageNet( root, split='val', set='tiny', bins='soft', num_hc_bins=16, gmm_path=GMM_PATH, mean_l_path=MEAN_L_PATH) img, labels = dataset.__getitem__(0) gmm = dataset.gmm labels = labels.numpy() img = img.squeeze().numpy() labels = labels.astype(gmm.means_.dtype) img = img.astype(gmm.means_.dtype) # expectation over GMM centroids hc_means = gmm.means_.astype(labels.dtype) im_hc = np.tensordot(labels, hc_means, (2,0)) im_l = img + dataset.mean_l.astype(img.dtype) im_rgb = dataset.hue_chroma_to_rgb(im_hc, im_l) low, high = np.min(im_rgb), np.max(im_rgb) im_rgb = (im_rgb - low) / (high - low) im_out = img_as_ubyte(im_rgb) skimage.io.imsave("tests/output.png", im_out) img_file = dataset.files['val'][0] im_orig = skimage.io.imread(img_file) skimage.io.imsave("tests/orig.png", im_orig) def main(): test_color_gmm() if __name__ == '__main__': main() ================================================ FILE: tests/test_data_reader.py ================================================ import argparse import os import os.path as osp import numpy as np import PIL.Image import skimage.io import skimage.color as color import torch from torch.autograd import Variable import sys sys.path.append('/vis/home/arunirc/data1/Research/colorize-fcn/colorizer-fcn') import models import train import utils import data_loader root = '/vis/home/arunirc/data1/datasets/ImageNet/images/' def test_single_read(): print 'Entering: test_single_read' dataset = data_loader.ColorizeImageNet(root, split='train', set='small') img, lbl = dataset.__getitem__(0) assert len(lbl)==2 assert np.min(lbl[0].numpy())==0 assert np.max(lbl[0].numpy())==30 print 'Test passed: test_single_read' def test_single_read_dimcheck(): print 'Entering: test_single_read_dimcheck' dataset = data_loader.ColorizeImageNet(root, split='train', set='small') img, lbl = dataset.__getitem__(0) assert len(lbl)==2 im_hue = lbl[0].numpy() im_chroma = lbl[1].numpy() assert im_chroma.shape==im_hue.shape, \ 'Labels (Hue and Chroma maps) should have same dimensions.' print 'Test passed: test_single_read_dimcheck' def test_train_loader(): print 'Entering: test_train_loader' train_loader = torch.utils.data.DataLoader( data_loader.ColorizeImageNet(root, split='train', set='small'), batch_size=1, shuffle=False) dataiter = iter(train_loader) img, label = dataiter.next() assert len(label)==2, \ 'Network should predict a 2-tuple: hue-map and chroma-map.' im_hue = label[0].numpy() im_chroma = label[1].numpy() assert im_chroma.shape==im_hue.shape, \ 'Labels (Hue and Chroma maps) should have same dimensions.' print 'Test passed: test_train_loader' def test_dataset_read(): ''' Read through the entire dataset. ''' dataset = data_loader.ColorizeImageNet(\ root, split='train', set='small') for i in xrange(len(dataset)): # if i > 44890: # HACK: skipping over some stuff img_file = dataset.files['train'][i] img, lbl = dataset.__getitem__(i) assert type(lbl) == torch.FloatTensor assert type(img) == torch.FloatTensor print 'iter: %d,\t file: %s,\t imsize: %s' % (i, img_file, img.size()) def test_cmyk_read(): ''' Handle CMYK images -- skip to previous image. ''' print 'Entering: test_cmyk_read' dataset = data_loader.ColorizeImageNet(\ root, split='train', set='small') idx = 44896 img_file = dataset.files['train'][idx] im1 = PIL.Image.open(img_file) im1 = np.asarray(im1, dtype=np.uint8) assert im1.shape[2]==4, 'Check that selected image is indeed CMYK.' img, lbl = dataset.__getitem__(idx) print 'Test passed: test_cmyk_read' def test_grayscale_read(): ''' Handle single-channel images -- skip to previous image. ''' print 'Entering: test_grayscale_read' dataset = data_loader.ColorizeImageNet(root, split='train', set='small') idx = 4606 img_file = dataset.files['train'][idx] im1 = PIL.Image.open(img_file) im1 = np.asarray(im1, dtype=np.uint8) assert len(im1.shape)==2, 'Check that selected image is indeed grayscale.' img, lbl = dataset.__getitem__(idx) print 'Test passed: test_grayscale_read' def test_rgb_hsv(): # DEFER dataset = data_loader.ColorizeImageNet(\ root, split='train', set='small') img_file = dataset.files['train'][100] img = PIL.Image.open(img_file) img = np.array(img, dtype=np.uint8) assert np.max(img.shape) == 400 def test_soft_bins(): dataset = \ data_loader.ColorizeImageNet(root, split='train', set='small', bins='soft') img, lbl = dataset.__getitem__(0) assert type(lbl) == torch.FloatTensor assert type(img) == torch.FloatTensor print 'Test passed: test_soft_bins' def test_lowpass_image(): dataset = \ data_loader.ColorizeImageNet(root, split='train', set='small', bins='soft', img_lowpass=8) img, lbl = dataset.__getitem__(0) assert type(lbl) == torch.FloatTensor assert type(img) == torch.FloatTensor print 'Test passed: test_soft_bins' def test_init_gmm(): # Pass paths to cached GMM and mean Lightness GMM_PATH = '/srv/data1/arunirc/Research/colorize-fcn/colorizer-fcn/logs/MODEL-fcn32s_color_CFG-014_VCS-db517d6_TIME-20171230-212406/gmm.pkl' MEAN_L_PATH = '/srv/data1/arunirc/Research/colorize-fcn/colorizer-fcn/logs/MODEL-fcn32s_color_CFG-014_VCS-db517d6_TIME-20171230-212406/mean_l.npy' dataset = \ data_loader.ColorizeImageNet( root, split='train', set='tiny', bins='soft', gmm_path=GMM_PATH, mean_l_path=MEAN_L_PATH) print 'Test passed: test_init_gmm' def main(): test_single_read() test_single_read_dimcheck() test_train_loader() test_cmyk_read() test_grayscale_read() test_soft_bins() test_lowpass_image() test_init_gmm() # # dataset.get_color_samples() # test_dataset_read() # TODO - test_labels # TODO - test colorspace conversions if __name__ == '__main__': main() ================================================ FILE: tests/test_fcn32s.py ================================================ # FIXME: Import order causes error: # ImportError: dlopen: cannot load any more object with static TL # https://github.com/pytorch/pytorch/issues/2083 import torch import matplotlib.pyplot as plt import numpy as np import skimage.data from torchfcn.models.fcn32s import get_upsampling_weight def test_get_upsampling_weight(): src = skimage.data.coffee() x = src.transpose(2, 0, 1) x = x[np.newaxis, :, :, :] x = torch.from_numpy(x).float() x = torch.autograd.Variable(x) in_channels = 3 out_channels = 3 kernel_size = 4 m = torch.nn.ConvTranspose2d( in_channels, out_channels, kernel_size, stride=2, bias=False) m.weight.data = get_upsampling_weight( in_channels, out_channels, kernel_size) y = m(x) y = y.data.numpy() y = y[0] y = y.transpose(1, 2, 0) dst = y.astype(np.uint8) assert abs(src.shape[0] * 2 - dst.shape[0]) <= 2 assert abs(src.shape[1] * 2 - dst.shape[1]) <= 2 return src, dst if __name__ == '__main__': src, dst = test_get_upsampling_weight() plt.subplot(121) plt.imshow(src) plt.title('x1: {}'.format(src.shape)) plt.subplot(122) plt.imshow(dst) plt.title('x2: {}'.format(dst.shape)) plt.show() ================================================ FILE: tests/vis_prediction.py ================================================ import argparse import os import os.path as osp import numpy as np import PIL.Image import skimage.io import skimage.color as color import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt import torch from torch.autograd import Variable import sys sys.path.append('/vis/home/arunirc/data1/Research/colorize-fcn/colorizer-fcn') import utils import data_loader root = '/vis/home/arunirc/data1/datasets/ImageNet/images/' out_path = '/data2/arunirc/Research/colorize-fcn/pytorch-fcn/tests/data_tests/' GMM_PATH = '/srv/data1/arunirc/Research/colorize-fcn/colorizer-fcn/logs/MODEL-fcn32s_color_CFG-014_VCS-db517d6_TIME-20171230-212406/gmm.pkl' MEAN_L_PATH = '/srv/data1/arunirc/Research/colorize-fcn/colorizer-fcn/logs/MODEL-fcn32s_color_CFG-014_VCS-db517d6_TIME-20171230-212406/mean_l.npy' cuda = torch.cuda.is_available() def main(): dataset = data_loader.ColorizeImageNet( root, split='val', set='small', bins='soft', num_hc_bins=16, gmm_path=GMM_PATH, mean_l_path=MEAN_L_PATH) img, labels = dataset.__getitem__(0) gmm = dataset.gmm mean_l = dataset.mean_l img_file = dataset.files['val'][1] im_orig = skimage.io.imread(img_file) # ... predicted labels and input image (mean subtracted) labels = labels.numpy() img = img.squeeze().numpy() im_rgb = utils.colorize_image_hc(labels, img, gmm, mean_l) plt.imshow(im_rgb) plt.show() # inputs = Variable(img) if cuda: inputs = inputs.cuda() outputs = model(inputs) # TODO: assertions # del inputs, outputs if __name__ == '__main__': main() ================================================ FILE: train.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.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.train() # ----------------------------------------------------------------------------- def train_epoch(self): # ----------------------------------------------------------------------------- self.model.train() 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): if batch_idx == len(self.train_loader)-1: break # discard last batch in epoch (unequal batch-sizes mess up BatchNorm) 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.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: umd-face/run_crop_face.py ================================================ import argparse import os import os.path as osp # import torch # import torchvision # import torch.utils.data # import torchvision.datasets as datasets import yaml import numpy as np import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt import PIL from tqdm import tqdm # from multiprocessing.pool import ThreadPool as Pool # pool_size = 5 # your "parallelness" # pool = Pool(pool_size) ''' Crops out the faces from UMDFace images using the annotations in umdfaces_batch*_ultraface.csv. Automatically creates "train" and "val" folders. ''' def main(): parser = argparse.ArgumentParser() parser.add_argument('-d', '--dataset_path', default='/srv/data1/arunirc/datasets/UMDFaces/', help='Location of the folders containing 3 batches of UMDFaces stills.') parser.add_argument('-o', '--output_path', default='/srv/data1/arunirc/datasets/UMDFaces/face_crops') parser.add_argument('-n', '--num_val', type=int, default=2) parser.add_argument('-b', '--batch', type=int, default=-1, help='crop faces of specified UMDFaces batch') args = parser.parse_args() # torch.manual_seed(1337) if not osp.exists(args.output_path): os.makedirs(args.output_path) if not osp.exists(osp.join(args.output_path, 'train')): os.makedirs(osp.join(args.output_path, 'train')) if not osp.exists(osp.join(args.output_path, 'val')): os.makedirs(osp.join(args.output_path, 'val')) # ----------------------------------------------------------------------------- # 1. Dataset # ----------------------------------------------------------------------------- dir_batch = ( osp.join(args.dataset_path, 'umdfaces_batch1'), osp.join(args.dataset_path, 'umdfaces_batch2'), osp.join(args.dataset_path, 'umdfaces_batch3')) # dataset_batch = [datasets.ImageFolder(b) for b in dir_batch] annot_files = ( osp.join(dir_batch[0], 'umdfaces_batch1_ultraface.csv'), osp.join(dir_batch[1], 'umdfaces_batch2_ultraface.csv'), osp.join(dir_batch[2], 'umdfaces_batch3_ultraface.csv')) for fn in annot_files: assert osp.exists(fn) if args.batch < 0: # by default loop over batches in order for i in range(len(dir_batch)): crop_batch(dir_batch[i], annot_files[i], args.output_path, args.num_val) else: i = args.batch crop_batch(dir_batch[i], annot_files[i], args.output_path, args.num_val) # dataset_all = torch.utils.data.ConcatDataset( # (dataset_batch1, dataset_batch2, dataset_batch3)) # for i in range(100): # pool.apply_async(f, (item,)) def crop_batch(data_dir, annot_fn, out_dir, nval): dat = np.genfromtxt(annot_fn, names=True, delimiter=',', autostrip=True, dtype=None) im_fn = dat['FILE'] (face_x, face_y, face_w, face_h) = ( dat['FACE_X'], dat['FACE_Y'], dat['FACE_WIDTH'], dat['FACE_HEIGHT']) class_ids = dat['SUBJECT_ID'] for c in tqdm(range(len(class_ids))): sel = (class_ids==class_ids[c]) class_image_fn = im_fn[sel] for i in xrange(len(class_image_fn)): # print class_image_fn[i] im = PIL.Image.open(osp.join(data_dir, class_image_fn[i])) rect = (face_x[sel][i], face_y[sel][i], face_x[sel][i]+ face_w[sel][i], face_y[sel][i]+face_h[sel][i]) imc = im.crop(rect) class_name, _ = osp.split(class_image_fn[i]) if not osp.exists(osp.join(out_dir, 'train', class_name)): os.makedirs(osp.join(out_dir, 'train', class_name)) if i < len(class_image_fn)-nval: imc.save(osp.join(out_dir, 'train', class_image_fn[i])) else: if not osp.exists(osp.join(out_dir, 'val', class_name)): os.makedirs(osp.join(out_dir, 'val', class_name)) imc.save(osp.join(out_dir, 'val', class_image_fn[i])) if __name__ == '__main__': main() ================================================ FILE: umd-face/train_resnet_umdface.py ================================================ import argparse import datetime import os import os.path as osp import pytz import torch import torchvision from torchvision import models import torch.nn as nn import torch.optim import torch.utils.data import torchvision.transforms as transforms import torchvision.datasets as datasets from torch.autograd import Variable import yaml import numpy as np 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 train from config import configurations import utils def main(): parser = argparse.ArgumentParser() parser.add_argument('-e', '--exp_name', default='resnet_umdfaces') parser.add_argument('-c', '--config', type=int, default=1, choices=configurations.keys()) parser.add_argument('-d', '--dataset_path', default='/srv/data1/arunirc/datasets/UMDFaces/face_crops') parser.add_argument('-m', '--model_path', default=None, help='Initialize from pre-trained model') parser.add_argument('--resume', help='Checkpoint path') args = parser.parse_args() # gpu = args.gpu cfg = configurations[args.config] out = get_log_dir(args.exp_name, args.config, cfg, verbose=False) resume = args.resume # os.environ['CUDA_VISIBLE_DEVICES'] = str(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. Dataset # ----------------------------------------------------------------------------- # Images should be arranged like this: # data_root/ # class_1/....jpg.. # class_2/....jpg.. # ......./....jpg.. data_root = args.dataset_path kwargs = {'num_workers': 4, 'pin_memory': True} if cuda else {} RGB_MEAN = [ 0.485, 0.456, 0.406 ] RGB_STD = [ 0.229, 0.224, 0.225 ] # Data transforms # http://pytorch.org/docs/master/torchvision/transforms.html train_transform = transforms.Compose([ transforms.Scale(256), # smaller side resized transforms.RandomCrop(224), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize(mean = RGB_MEAN, std = RGB_STD), ]) val_transform = transforms.Compose([ transforms.Scale((224,224)), # transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize(mean = RGB_MEAN, std = RGB_STD), ]) # Data loaders - using PyTorch built-in objects # loader = DataLoaderClass(DatasetClass) # * `DataLoaderClass` is PyTorch provided torch.utils.data.DataLoader # * `DatasetClass` loads samples from a dataset; can be a standard class # provided by PyTorch (datasets.ImageFolder) or a custom-made class. # - More info: http://pytorch.org/docs/master/torchvision/datasets.html#imagefolder # * Balanced class sampling: https://discuss.pytorch.org/t/balanced-sampling-between-classes-with-torchvision-dataloader/2703/3 traindir = osp.join(data_root, 'train') dataset_train = datasets.ImageFolder(traindir, train_transform) # For unbalanced dataset we create a weighted sampler weights = utils.make_weights_for_balanced_classes( dataset_train.imgs, len(dataset_train.classes)) weights = torch.DoubleTensor(weights) sampler = torch.utils.data.sampler.WeightedRandomSampler(weights, len(weights)) train_loader = torch.utils.data.DataLoader( dataset_train, batch_size=cfg['batch_size'], sampler = sampler, **kwargs) valdir = osp.join(data_root, 'val') val_loader = torch.utils.data.DataLoader( datasets.ImageFolder(valdir, val_transform), batch_size=cfg['batch_size'], shuffle=False, **kwargs) # print 'dataset classes:' + str(train_loader.dataset.classes) num_class = len(train_loader.dataset.classes) print 'Number of classes: %d' % num_class # ----------------------------------------------------------------------------- # 2. Model # ----------------------------------------------------------------------------- model = torchvision.models.resnet50(pretrained=True) # ImageNet pre-trained for quicker convergence # Check if final fc layer sizes match num_class if not model.fc.weight.size()[0] == num_class: # Replace last layer print model.fc model.fc = torch.nn.Linear(2048, num_class) print model.fc else: pass # TODO - config options for DataParallel and device_ids model = torch.nn.DataParallel(model, device_ids=[0, 1, 2, 3, 4]) if cuda: model.cuda() if args.model_path: # If existing model is to be loaded from a file checkpoint = torch.load(args.model_path) model.load_state_dict(checkpoint['model_state_dict']) start_epoch = 0 start_iteration = 0 # Loss - cross entropy between predicted scores (unnormalized) and class labels (integers) criterion = nn.CrossEntropyLoss() if cuda: criterion = criterion.cuda() if resume: # Resume training from last saved checkpoint checkpoint = torch.load(resume) model.load_state_dict(checkpoint['model_state_dict']) start_epoch = checkpoint['epoch'] start_iteration = checkpoint['iteration'] else: pass # ----------------------------------------------------------------------------- # 3. Optimizer # ----------------------------------------------------------------------------- params = filter(lambda p: p.requires_grad, model.parameters()) # Parameters with p.requires_grad=False are not updated during training. # This can be specified when defining the nn.Modules during model creation if 'optim' in cfg.keys(): if cfg['optim'].lower()=='sgd': optim = torch.optim.SGD(params, lr=cfg['lr'], momentum=cfg['momentum'], weight_decay=cfg['weight_decay']) elif cfg['optim'].lower()=='adam': optim = torch.optim.Adam(params, lr=cfg['lr'], weight_decay=cfg['weight_decay']) else: raise NotImplementedError('Optimizers: SGD or Adam') else: optim = torch.optim.SGD(params, lr=cfg['lr'], momentum=cfg['momentum'], weight_decay=cfg['weight_decay']) if resume: optim.load_state_dict(checkpoint['optim_state_dict']) # ----------------------------------------------------------------------------- # [optional] Sanity-check: forward pass with a single batch # ----------------------------------------------------------------------------- DEBUG = False if DEBUG: # model = model.cpu() dataiter = iter(val_loader) img, label = dataiter.next() print 'Labels: ' + str(label.size()) # batchSize x num_class print 'Input: ' + str(img.size()) # batchSize x 3 x 224 x 224 im = img.squeeze().numpy() im = im[0,:,:,:] # get first image in the batch im = im.transpose((1,2,0)) # permute to 224x224x3 im = im * [ 0.229, 0.224, 0.225 ] # unnormalize im = im + [ 0.485, 0.456, 0.406 ] im[im<0] = 0 f = plt.figure() plt.imshow(im) plt.savefig('sanity-check-im.jpg') # save transformed image in current folder inputs = Variable(img) if cuda: inputs = inputs.cuda() model.eval() outputs = model(inputs) print 'Network output: ' + str(outputs.size()) model.train() import pdb; pdb.set_trace() # breakpoint c5e7c878 // else: pass # ----------------------------------------------------------------------------- # 4. Training # ----------------------------------------------------------------------------- trainer = train.Trainer( cuda=cuda, model=model, criterion=criterion, optimizer=optim, init_lr=cfg['lr'], lr_decay_epoch = cfg['lr_decay_epoch'], train_loader=train_loader, val_loader=val_loader, out=out, max_iter=cfg['max_iteration'], interval_validate=cfg.get('interval_validate', len(train_loader)), ) trainer.epoch = start_epoch trainer.iteration = start_iteration trainer.train() def get_log_dir(model_name, config_id, cfg, verbose=True): # Creates an output directory for each experiment, timestamped name = 'MODEL-%s_CFG-%03d' % (model_name, config_id) if verbose: for k, v in cfg.items(): v = str(v) if '/' in v: continue name += '_%s-%s' % (k.upper(), v) now = datetime.datetime.now(pytz.timezone('US/Eastern')) name += '_TIME-%s' % now.strftime('%Y%m%d-%H%M%S') log_dir = osp.join(here, 'logs', name) if not osp.exists(log_dir): os.makedirs(log_dir) with open(osp.join(log_dir, 'config.yaml'), 'w') as f: yaml.safe_dump(cfg, f, default_flow_style=False) return log_dir if __name__ == '__main__': main() ================================================ FILE: utils.py ================================================ from __future__ import division import math import warnings try: import cv2 except ImportError: cv2 = None import numpy as np import scipy.ndimage import six import skimage import skimage.color from skimage import img_as_ubyte import os import os.path as osp import matplotlib matplotlib.use('agg') import matplotlib.pyplot as plt import csv import scipy.signal def make_weights_for_balanced_classes(images, nclasses): ''' Make a vector of weights for each image in the dataset, based on class frequency. The returned vector of weights can be used to create a WeightedRandomSampler for a DataLoader to have class balancing when sampling for a training batch. images - torchvisionDataset.imgs nclasses - len(torchvisionDataset.classes) https://discuss.pytorch.org/t/balanced-sampling-between-classes-with-torchvision-dataloader/2703/3 ''' count = [0] * nclasses for item in images: count[item[1]] += 1 # item is (img-data, label-id) weight_per_class = [0.] * nclasses N = float(sum(count)) # total number of images for i in range(nclasses): weight_per_class[i] = N/float(count[i]) weight = [0] * len(images) for idx, val in enumerate(images): weight[idx] = weight_per_class[val[1]] return weight def get_vgg_class_counts(log_path): ''' Dict of class frequencies from pre-computed text file ''' data_1 = np.genfromtxt(log_path, dtype=None) class_names = [x[0] for x in data_1] class_counts = [x[1] for x in data_1] class_count_dict = dict(zip(class_names, class_counts)) return class_count_dict def plot_log_csv(log_path): log_dir, _ = osp.split(log_path) dat = np.genfromtxt(log_path, names=True, delimiter=',', autostrip=True) train_loss = dat['trainloss'] train_loss_sel = ~np.isnan(train_loss) train_loss = train_loss[train_loss_sel] iter_train_loss = dat['iteration'][train_loss_sel] train_acc = dat['trainacc'] train_acc_sel = ~np.isnan(train_acc) train_acc = train_acc[train_acc_sel] iter_train_acc = dat['iteration'][train_acc_sel] val_loss = dat['validloss'] val_loss_sel = ~np.isnan(val_loss) val_loss = val_loss[val_loss_sel] iter_val_loss = dat['iteration'][val_loss_sel] mean_iu = dat['validacc'] mean_iu_sel = ~np.isnan(mean_iu) mean_iu = mean_iu[mean_iu_sel] iter_mean_iu = dat['iteration'][mean_iu_sel] fig, ax = plt.subplots(nrows=2, ncols=2) plt.subplot(2, 2, 1) plt.plot(iter_train_acc, train_acc, label='train') plt.ylabel('accuracy') plt.grid() plt.legend() plt.tight_layout() plt.subplot(2, 2, 2) plt.plot(iter_mean_iu, mean_iu, label='val') plt.grid() plt.legend() plt.tight_layout() plt.subplot(2, 2, 3) plt.plot(iter_train_loss, train_loss, label='train') plt.xlabel('iteration') plt.ylabel('loss') plt.grid() plt.legend() plt.tight_layout() plt.subplot(2, 2, 4) plt.plot(iter_val_loss, val_loss, label='val') plt.xlabel('iteration') plt.grid() plt.legend() plt.tight_layout() plt.savefig(osp.join(log_dir, 'log_plots.png'), bbox_inches='tight') def plot_log(log_path): log_dir, _ = osp.split(log_path) epoch = [] iteration = [] train_loss = [] train_acc = [] val_loss = [] val_acc = [] g = lambda x: x if x!='' else float('nan') reader = csv.reader( open(log_path, 'rb')) next(reader) # Skip header row. for line in reader: line_fields = [g(x) for x in line] epoch.append(float(line_fields[0])) iteration.append(float(line_fields[1])) train_loss.append(float(line_fields[2])) train_acc.append(float(line_fields[3])) val_loss.append(float(line_fields[4])) val_acc.append(float(line_fields[5])) epoch = np.array(epoch) iteration = np.array(iteration) train_loss = np.array(train_loss) train_acc = np.array(train_acc) val_loss = np.array(val_loss) val_acc = np.array(val_acc) train_loss_sel = ~np.isnan(train_loss) train_loss = train_loss[train_loss_sel] iter_train_loss = iteration[train_loss_sel] train_acc_sel = ~np.isnan(train_acc) train_acc = train_acc[train_acc_sel] iter_train_acc = iteration[train_acc_sel] val_loss_sel = ~np.isnan(val_loss) val_loss = val_loss[val_loss_sel] iter_val_loss = iteration[val_loss_sel] val_acc_sel = ~np.isnan(val_acc) val_acc = val_acc[val_acc_sel] iter_val_acc = iteration[val_acc_sel] fig, ax = plt.subplots(nrows=2, ncols=2) plt.subplot(2, 2, 1) plt.plot(iter_train_acc, train_acc, label='train', alpha=0.5, color='C0') box_pts = np.rint(np.sqrt(len(train_acc))).astype(np.int) plt.plot(iter_train_acc, savgol_smooth(train_acc, box_pts), color='C0') plt.ylabel('accuracy') plt.grid() plt.legend() plt.title('Training') plt.tight_layout() plt.subplot(2, 2, 2) plt.plot(iter_val_acc, val_acc, label='val', alpha=0.5, color='C1') box_pts = np.rint(np.sqrt(len(val_acc))).astype(np.int) plt.plot(iter_val_acc, savgol_smooth(val_acc, box_pts), color='C1') plt.grid() plt.legend() plt.title('Validation') plt.tight_layout() plt.subplot(2, 2, 3) plt.plot(iter_train_loss, train_loss, label='train', alpha=0.5, color='C0') box_pts = np.rint(np.sqrt(len(train_loss))).astype(np.int) plt.plot(iter_train_loss, savgol_smooth(train_loss, box_pts), color='C0') plt.xlabel('iteration') plt.ylabel('loss') plt.grid() plt.legend() plt.tight_layout() plt.subplot(2, 2, 4) plt.plot(iter_val_loss, val_loss, label='val', alpha=0.5, color='C1') box_pts = np.rint(np.sqrt(len(val_loss))).astype(np.int) plt.plot(iter_val_loss, savgol_smooth(val_loss, box_pts), color='C1') plt.xlabel('iteration') plt.grid() plt.legend() plt.tight_layout() plt.savefig(osp.join(log_dir, 'log_plots.png'), bbox_inches='tight') def savgol_smooth(y, box_pts): # use the Savitzky-Golay filter for 1-D smoothing if box_pts % 2 == 0: box_pts += 1 y_smooth = scipy.signal.savgol_filter(y, box_pts, 2) return y_smooth # ----------------------------------------------------------------------------- # LFW helper code from FaceNet: https://github.com/davidsandberg/facenet # ----------------------------------------------------------------------------- # MIT License # # Copyright (c) 2016 David Sandberg # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. def get_paths(lfw_dir, pairs, file_ext): nrof_skipped_pairs = 0 path_list = [] issame_list = [] for pair in pairs: if len(pair) == 3: path0 = os.path.join(lfw_dir, pair[0], pair[0] + '_' + '%04d' % int(pair[1])+'.'+file_ext) path1 = os.path.join(lfw_dir, pair[0], pair[0] + '_' + '%04d' % int(pair[2])+'.'+file_ext) issame = True elif len(pair) == 4: path0 = os.path.join(lfw_dir, pair[0], pair[0] + '_' + '%04d' % int(pair[1])+'.'+file_ext) path1 = os.path.join(lfw_dir, pair[2], pair[2] + '_' + '%04d' % int(pair[3])+'.'+file_ext) issame = False if os.path.exists(path0) and os.path.exists(path1): # Only add the pair if both paths exist path_list += (path0,path1) issame_list.append(issame) else: nrof_skipped_pairs += 1 if nrof_skipped_pairs>0: print('Skipped %d image pairs' % nrof_skipped_pairs) return path_list, issame_list def read_pairs(pairs_filename, lfw_flag=True): pairs = [] with open(pairs_filename, 'r') as f: if lfw_flag: for line in f.readlines()[1:]: pair = line.strip().split() pairs.append(pair) else: for line in f.readlines(): pair = line.strip().split() pairs.append(pair) return np.array(pairs) # ----------------------------------------------------------------------------- # IJB-A helper code # ----------------------------------------------------------------------------- def get_ijba_1_1_metadata(protocol_file): metadata = {} template_id = [] subject_id = [] img_filename = [] media_id = [] sighting_id = [] with open(protocol_file, 'r') as f: for line in f.readlines()[1:]: line_fields = line.strip().split(',') template_id.append(int(line_fields[0])) subject_id.append(int(line_fields[1])) img_filename.append(line_fields[2]) media_id.append(int(line_fields[3])) sighting_id.append(int(line_fields[4])) metadata['template_id'] = np.array(template_id) metadata['subject_id'] = np.array(subject_id) metadata['img_filename'] = np.array(img_filename) metadata['media_id'] = np.array(media_id) metadata['sighting_id'] = np.array(sighting_id) return metadata def read_ijba_pairs(pairs_filename): pairs = [] with open(pairs_filename, 'r') as f: for line in f.readlines(): pair = line.strip().split(',') pairs.append(pair) return np.array(pairs).astype(np.int) ================================================ FILE: vgg-face-2/calculate_image_mean.py ================================================ import argparse import os import os.path as osp import torch import torchvision import torch.utils.data import torchvision.transforms as transforms import torchvision.datasets as datasets import yaml import tqdm import numpy as np 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) ''' Calculate mean R, G, B values for VGGFace2 dataset -------------------------------------------------- Following implementation: [VGGFace2](https://arxiv.org/pdf/1710.08092.pdf) ''' def main(): parser = argparse.ArgumentParser() parser.add_argument('-d', '--dataset_path', default='/srv/data1/arunirc/datasets/vggface2') args = parser.parse_args() # os.environ['CUDA_VISIBLE_DEVICES'] = str(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. Dataset # ----------------------------------------------------------------------------- data_root = args.dataset_path kwargs = {'num_workers': 4, 'pin_memory': True} if cuda else {} # Data transforms train_transform = transforms.Compose([ transforms.Scale(256), # smaller side resized transforms.RandomCrop(224), transforms.RandomHorizontalFlip(), # transforms.RandomGrayscale(p=0.2), transforms.ToTensor() ]) # Data loaders traindir = osp.join(data_root, 'train') dataset = datasets.ImageFolder(traindir, train_transform) train_loader = torch.utils.data.DataLoader( dataset, shuffle=True, batch_size=128, **kwargs) rgb_mean = [] for batch_idx, (images, lbl) in tqdm.tqdm( enumerate(train_loader), total=len(train_loader), desc='Sampling images' ): rgb_mean.append( ((images.mean(dim=0)).mean(dim=-1)).mean(dim=-1) ) if batch_idx == 100: break print len(rgb_mean) rgb_mean = torch.mean( torch.stack(rgb_mean, dim=1), dim=1) print rgb_mean res = {} res['R'] = rgb_mean[0] res['G'] = rgb_mean[1] res['B'] = rgb_mean[2] with open(osp.join(here, 'mean_rgb.yaml'), 'w') as f: yaml.dump(res, f, default_flow_style=False) if __name__ == '__main__': main() ================================================ FILE: vgg-face-2/class_counts.sh ================================================ # Utility script to get class frequencies of VGGFace2 based on #images in class-folder # >>>>>> Set paths here <<<<<< DATA_PATH=/home/renyi/arunirc/data1/datasets/vggface2/train DATA_PATH=/home/renyi/arunirc/data1/datasets/vggface2/train-crop touch vgg-face-2/vggface_class_counts.txt for folder in `ls ${DATA_PATH}`; do img_count=`ls ${DATA_PATH}/${folder} | wc -l` echo ${folder}' '${img_count} echo ${folder}' '${img_count} >> vgg-face-2/vggface_class_counts.txt done ================================================ FILE: vgg-face-2/crop_face.sh ================================================ # Utility script to crop large image datasets from the console # INFO: kill all spawned sub-processes: kill $(ps -s $$ -o pid=) crop_image_multi () { DATA_PATH=$1 OUT_PATH=$2 ANNOT_FILE=$3 SUBJECT_FOLDER=$4 OLDIFS=$IFS IFS=, [ ! -f $ANNOT_FILE ] && { echo "$ANNOT_FILE file not found"; exit 99; } COUNT=0 while read flname sid xmin ymin width height do IFS='/' read -r -a array <<< "$flname" SUBJECT_DIR="${array[-2]}" if [ "${SUBJECT_DIR}" == "${SUBJECT_FOLDER}" ]; then mkdir -p ${OUT_PATH}/${SUBJECT_DIR} IMG_PATH=${DATA_PATH}/${array[-2]}/${array[-1]} IMG_OUT=${OUT_PATH}/${array[-2]}/${array[-1]} convert -crop ${width}x${height}+${xmin}+${ymin} ${IMG_PATH} ${IMG_OUT} fi done < $ANNOT_FILE IFS=$OLDIFS } create_val_dir () { # For each subject's folder in train_data, move 2 images into val_folder TRAIN_DATA=$1 VAL_DATA=$2 mkdir -p ${VAL_DATA} for folder in `ls ${TRAIN_DATA}`; do echo $folder mkdir -p ${VAL_DATA}/${folder} for filename in `ls ${OUT_PATH}/${folder} | tail -n 2`; do mv ${OUT_PATH}/${folder}/${filename} ${VAL_DATA}/${folder} done done } crop_image () { DATA_PATH=$1 OUT_PATH=$2 ANNOT_FILE=$3 OLDIFS=$IFS IFS=, [ ! -f $ANNOT_FILE ] && { echo "$ANNOT_FILE file not found"; exit 99; } COUNT=0 while read flname sid xmin ymin width height do IFS='/' read -r -a array <<< "$flname" SUBJECT_DIR="${array[-2]}" mkdir -p ${OUT_PATH}/${SUBJECT_DIR} IMG_PATH=${DATA_PATH}/${array[-2]}/${array[-1]} IMG_OUT=${OUT_PATH}/${array[-2]}/${array[-1]} convert -crop ${width}x${height}+${xmin}+${ymin} ${IMG_PATH} ${IMG_OUT} done < $ANNOT_FILE IFS=$OLDIFS } # >>>>>> Set paths here <<<<<< DATA_PATH=/home/renyi/arunirc/data1/datasets/vggface2/train OUT_PATH=/home/renyi/arunirc/data1/datasets/vggface2/train-crop ANNOT_FILE=/home/renyi/arunirc/data1/datasets/vggface2/vggface2_disk1.csv # Annotations format: filename subject_id xmin ymin width height VAL_PATH=/home/renyi/arunirc/data1/datasets/vggface2/val-crop # crop faces out of images and save into "train-crop" output folder mkdir -p ${OUT_PATH} date # crop_image ${DATA_PATH} ${OUT_PATH} ${ANNOT_FILE} date echo "Done cropping" # take 2 face images per subject and save into "val-crop" folder create_val_dir ${OUT_PATH} ${VAL_PATH} echo "Done creating validation set" # COUNT=0 # WAIT_COUNT=0 # MAXPROG=`ls ${DATA_PATH} | wc -l` # for folder in `ls ${DATA_PATH}`; do # # show progress # ((WAIT_COUNT += 1)) # ((COUNT += 1)) # echo ${COUNT} # PROGRESS=`echo ${COUNT}*100/${MAXPROG}|bc -l` # echo -n "${PROGRESS} % " # # echo -n "$((${COUNT}*100/${MAXPROG})) % " # echo -n R | tr 'R' '\r' # # if [ ${WAIT_COUNT} -eq 20 ]; then # # # don't spawn more than 20 processes at a time # # echo 'waiting for 20 processes to finish' # # echo "Progress: $((${COUNT}*100/${MAXPROG})) % " # # wait # # ((WAIT_COUNT=0)) # # fi # # if [ ${COUNT} -ge 20 ]; then # # break # # fi # done # wait # echo "Done cropping" # mkdir -p ${OUT_PATH} # date # crop_image ${DATA_PATH} ${OUT_PATH} ${ANNOT_FILE} # date ================================================ FILE: vgg-face-2/mean_rgb.yaml ================================================ B: 0.36426129937171936 G: 0.406549334526062 R: 0.49698397517204285 ================================================ FILE: vgg-face-2/train_resnet101_vggface.py ================================================ import argparse import datetime import os import os.path as osp import pytz import torch import torchvision from torchvision import models import torch.nn as nn import torch.optim import torch.utils.data import torchvision.transforms as transforms import torchvision.datasets as datasets from torch.autograd import Variable import yaml import numpy as np 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 train import models import utils from config import configurations def main(): parser = argparse.ArgumentParser() parser.add_argument('-e', '--exp_name', default='resnet101_vggface_scratch') parser.add_argument('-c', '--config', type=int, default=1, choices=configurations.keys()) parser.add_argument('-d', '--dataset_path', default='/srv/data1/arunirc/datasets/vggface2') parser.add_argument('-m', '--model_path', default=None, help='Initialize from pre-trained model') parser.add_argument('--resume', help='Checkpoint path') parser.add_argument('--bottleneck', action='store_true', default=False, help='Add a 512-dim bottleneck layer with L2 normalization') args = parser.parse_args() # gpu = args.gpu cfg = configurations[args.config] out = get_log_dir(args.exp_name, args.config, cfg, verbose=False) resume = args.resume # os.environ['CUDA_VISIBLE_DEVICES'] = str(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. Dataset # ----------------------------------------------------------------------------- # Images should be arranged like this: # data_root/ # class_1/....jpg.. # class_2/....jpg.. # ......./....jpg.. data_root = args.dataset_path kwargs = {'num_workers': 4, 'pin_memory': True} if cuda else {} RGB_MEAN = [ 0.485, 0.456, 0.406 ] RGB_STD = [ 0.229, 0.224, 0.225 ] # Data transforms # http://pytorch.org/docs/master/torchvision/transforms.html train_transform = transforms.Compose([ transforms.Scale(256), # smaller side resized transforms.RandomCrop(224), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize(mean = RGB_MEAN, std = RGB_STD), ]) val_transform = transforms.Compose([ transforms.Scale(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize(mean = RGB_MEAN, std = RGB_STD), ]) # Data loaders - using PyTorch built-in objects # loader = DataLoaderClass(DatasetClass) # * `DataLoaderClass` is PyTorch provided torch.utils.data.DataLoader # * `DatasetClass` loads samples from a dataset; can be a standard class # provided by PyTorch (datasets.ImageFolder) or a custom-made class. # - More info: http://pytorch.org/docs/master/torchvision/datasets.html#imagefolder traindir = osp.join(data_root, 'train') dataset_train = datasets.ImageFolder(traindir, train_transform) # For unbalanced dataset we create a weighted sampler # * Balanced class sampling: https://discuss.pytorch.org/t/balanced-sampling-between-classes-with-torchvision-dataloader/2703/3 weights = utils.make_weights_for_balanced_classes( dataset_train.imgs, len(dataset_train.classes)) weights = torch.DoubleTensor(weights) sampler = torch.utils.data.sampler.WeightedRandomSampler(weights, len(weights)) train_loader = torch.utils.data.DataLoader( dataset_train, batch_size=cfg['batch_size'], sampler = sampler, **kwargs) valdir = osp.join(data_root, 'val-crop') val_loader = torch.utils.data.DataLoader( datasets.ImageFolder(valdir, val_transform), batch_size=cfg['batch_size'], shuffle=False, **kwargs) # print 'dataset classes:' + str(train_loader.dataset.classes) num_class = len(train_loader.dataset.classes) print 'Number of classes: %d' % num_class # ----------------------------------------------------------------------------- # 2. Model # ----------------------------------------------------------------------------- model = torchvision.models.resnet101(pretrained=False) if type(model.fc) == torch.nn.modules.linear.Linear: # Check if final fc layer sizes match num_class if not model.fc.weight.size()[0] == num_class: # Replace last layer print model.fc model.fc = torch.nn.Linear(2048, num_class) print model.fc else: pass else: pass if args.model_path: # If existing model is to be loaded from a file checkpoint = torch.load(args.model_path) if checkpoint['arch'] == 'DataParallel': # if we trained and saved our model using DataParallel model = torch.nn.DataParallel(model, device_ids=[0, 1, 2, 3, 4, 5, 6, 7]) 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']) # Optionally add a "bottleneck + L2-norm" layer after GAP-layer # TODO -- loading a bottleneck model might be a problem .... do some unit-tests if args.bottleneck: layers = [] layers.append(torch.nn.Linear(2048, 512)) layers.append(nn.BatchNorm2d(512)) layers.append(torch.nn.ReLU(inplace=True)) layers.append(models.NormFeat()) # L2-normalization layer layers.append(torch.nn.Linear(512, num_class)) model.fc = torch.nn.Sequential(*layers) # TODO - config options for DataParallel and device_ids model = torch.nn.DataParallel(model, device_ids=[0, 1, 2, 3, 4, 5, 6, 7]) if cuda: model.cuda() start_epoch = 0 start_iteration = 0 # Loss - cross entropy between predicted scores (unnormalized) and class labels (integers) criterion = nn.CrossEntropyLoss() if cuda: criterion = criterion.cuda() if resume: # Resume training from last saved checkpoint checkpoint = torch.load(resume) model.load_state_dict(checkpoint['model_state_dict']) start_epoch = checkpoint['epoch'] start_iteration = checkpoint['iteration'] else: pass # ----------------------------------------------------------------------------- # 3. Optimizer # ----------------------------------------------------------------------------- params = filter(lambda p: p.requires_grad, model.parameters()) # Parameters with p.requires_grad=False are not updated during training. # This can be specified when defining the nn.Modules during model creation if 'optim' in cfg.keys(): if cfg['optim'].lower()=='sgd': optim = torch.optim.SGD(params, lr=cfg['lr'], momentum=cfg['momentum'], weight_decay=cfg['weight_decay']) elif cfg['optim'].lower()=='adam': optim = torch.optim.Adam(params, lr=cfg['lr'], weight_decay=cfg['weight_decay']) else: raise NotImplementedError('Optimizers: SGD or Adam') else: optim = torch.optim.SGD(params, lr=cfg['lr'], momentum=cfg['momentum'], weight_decay=cfg['weight_decay']) if resume: optim.load_state_dict(checkpoint['optim_state_dict']) # ----------------------------------------------------------------------------- # [optional] Sanity-check: forward pass with a single batch # ----------------------------------------------------------------------------- DEBUG = False if DEBUG: # model = model.cpu() dataiter = iter(val_loader) img, label = dataiter.next() print 'Labels: ' + str(label.size()) # batchSize x num_class print 'Input: ' + str(img.size()) # batchSize x 3 x 224 x 224 im = img.squeeze().numpy() im = im[0,:,:,:] # get first image in the batch im = im.transpose((1,2,0)) # permute to 224x224x3 im = im * [ 0.229, 0.224, 0.225 ] # unnormalize im = im + [ 0.485, 0.456, 0.406 ] im[im<0] = 0 f = plt.figure() plt.imshow(im) plt.savefig('sanity-check-im.jpg') # save transformed image in current folder inputs = Variable(img) if cuda: inputs = inputs.cuda() model.eval() outputs = model(inputs) print 'Network output: ' + str(outputs.size()) model.train() else: pass # ----------------------------------------------------------------------------- # 4. Training # ----------------------------------------------------------------------------- trainer = train.Trainer( cuda=cuda, model=model, criterion=criterion, optimizer=optim, init_lr=cfg['lr'], lr_decay_epoch = cfg['lr_decay_epoch'], train_loader=train_loader, val_loader=val_loader, out=out, max_iter=cfg['max_iteration'], interval_validate=cfg.get('interval_validate', len(train_loader)), ) trainer.epoch = start_epoch trainer.iteration = start_iteration trainer.train() def get_log_dir(model_name, config_id, cfg, verbose=True): # Creates an output directory for each experiment, timestamped name = 'MODEL-%s_CFG-%03d' % (model_name, config_id) if verbose: for k, v in cfg.items(): v = str(v) if '/' in v: continue name += '_%s-%s' % (k.upper(), v) now = datetime.datetime.now(pytz.timezone('US/Eastern')) name += '_TIME-%s' % now.strftime('%Y%m%d-%H%M%S') log_dir = osp.join(here, 'logs', name) if not osp.exists(log_dir): os.makedirs(log_dir) with open(osp.join(log_dir, 'config.yaml'), 'w') as f: yaml.safe_dump(cfg, f, default_flow_style=False) return log_dir if __name__ == '__main__': main() ================================================ FILE: vgg-face-2/train_resnet50_vggface_scratch.py ================================================ import argparse import datetime import os import os.path as osp import pytz import torch import torchvision from torchvision import models import torch.nn as nn import torch.optim import torch.utils.data import torchvision.transforms as transforms import torchvision.datasets as datasets from torch.autograd import Variable import yaml import numpy as np 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 train import models import utils from config import configurations def main(): parser = argparse.ArgumentParser() parser.add_argument('-e', '--exp_name', default='resnet50_vggface') parser.add_argument('-c', '--config', type=int, default=1, choices=configurations.keys()) parser.add_argument('-d', '--dataset_path', default='/srv/data1/arunirc/datasets/vggface2') parser.add_argument('-m', '--model_path', default=None, help='Initialize from pre-trained model') parser.add_argument('--resume', help='Checkpoint path') parser.add_argument('--bottleneck', action='store_true', default=False, help='Add a 512-dim bottleneck layer with L2 normalization') args = parser.parse_args() # gpu = args.gpu cfg = configurations[args.config] out = get_log_dir(args.exp_name, args.config, cfg, verbose=False) resume = args.resume # os.environ['CUDA_VISIBLE_DEVICES'] = str(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. Dataset # ----------------------------------------------------------------------------- # Images should be arranged like this: # data_root/ # class_1/....jpg.. # class_2/....jpg.. # ......./....jpg.. data_root = args.dataset_path kwargs = {'num_workers': 4, 'pin_memory': True} if cuda else {} RGB_MEAN = [ 0.485, 0.456, 0.406 ] RGB_STD = [ 0.229, 0.224, 0.225 ] # Data transforms # http://pytorch.org/docs/master/torchvision/transforms.html train_transform = transforms.Compose([ transforms.Scale(256), # smaller side resized transforms.RandomCrop(224), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize(mean = RGB_MEAN, std = RGB_STD), ]) val_transform = transforms.Compose([ transforms.Scale(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize(mean = RGB_MEAN, std = RGB_STD), ]) # Data loaders - using PyTorch built-in objects # loader = DataLoaderClass(DatasetClass) # * `DataLoaderClass` is PyTorch provided torch.utils.data.DataLoader # * `DatasetClass` loads samples from a dataset; can be a standard class # provided by PyTorch (datasets.ImageFolder) or a custom-made class. # - More info: http://pytorch.org/docs/master/torchvision/datasets.html#imagefolder traindir = osp.join(data_root, 'train') dataset_train = datasets.ImageFolder(traindir, train_transform) # For unbalanced dataset we create a weighted sampler # * Balanced class sampling: https://discuss.pytorch.org/t/balanced-sampling-between-classes-with-torchvision-dataloader/2703/3 weights = utils.make_weights_for_balanced_classes( dataset_train.imgs, len(dataset_train.classes)) weights = torch.DoubleTensor(weights) sampler = torch.utils.data.sampler.WeightedRandomSampler(weights, len(weights)) train_loader = torch.utils.data.DataLoader( dataset_train, batch_size=cfg['batch_size'], sampler = sampler, **kwargs) valdir = osp.join(data_root, 'val-crop') val_loader = torch.utils.data.DataLoader( datasets.ImageFolder(valdir, val_transform), batch_size=cfg['batch_size'], shuffle=False, **kwargs) # print 'dataset classes:' + str(train_loader.dataset.classes) num_class = len(train_loader.dataset.classes) print 'Number of classes: %d' % num_class # ----------------------------------------------------------------------------- # 2. Model # ----------------------------------------------------------------------------- model = torchvision.models.resnet50(pretrained=False) if type(model.fc) == torch.nn.modules.linear.Linear: # Check if final fc layer sizes match num_class if not model.fc.weight.size()[0] == num_class: # Replace last layer print model.fc model.fc = torch.nn.Linear(2048, num_class) print model.fc else: pass else: pass if args.model_path: # If existing model is to be loaded from a file checkpoint = torch.load(args.model_path) if checkpoint['arch'] == 'DataParallel': # if we trained and saved our model using DataParallel model = torch.nn.DataParallel(model, device_ids=[0, 1, 2, 3, 4, 5, 6, 7]) 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']) # Optionally add a "bottleneck + L2-norm" layer after GAP-layer # TODO -- loading a bottleneck model might be a problem .... do some unit-tests if args.bottleneck: layers = [] layers.append(torch.nn.Linear(2048, 512)) layers.append(nn.BatchNorm2d(512)) layers.append(torch.nn.ReLU(inplace=True)) layers.append(models.NormFeat()) # L2-normalization layer layers.append(torch.nn.Linear(512, num_class)) model.fc = torch.nn.Sequential(*layers) # TODO - config options for DataParallel and device_ids model = torch.nn.DataParallel(model, device_ids=[0, 1, 2, 3, 4, 5, 6, 7]) if cuda: model.cuda() start_epoch = 0 start_iteration = 0 # Loss - cross entropy between predicted scores (unnormalized) and class labels (integers) criterion = nn.CrossEntropyLoss() if cuda: criterion = criterion.cuda() if resume: # Resume training from last saved checkpoint checkpoint = torch.load(resume) model.load_state_dict(checkpoint['model_state_dict']) start_epoch = checkpoint['epoch'] start_iteration = checkpoint['iteration'] else: pass # ----------------------------------------------------------------------------- # 3. Optimizer # ----------------------------------------------------------------------------- params = filter(lambda p: p.requires_grad, model.parameters()) # Parameters with p.requires_grad=False are not updated during training. # This can be specified when defining the nn.Modules during model creation if 'optim' in cfg.keys(): if cfg['optim'].lower()=='sgd': optim = torch.optim.SGD(params, lr=cfg['lr'], momentum=cfg['momentum'], weight_decay=cfg['weight_decay']) elif cfg['optim'].lower()=='adam': optim = torch.optim.Adam(params, lr=cfg['lr'], weight_decay=cfg['weight_decay']) else: raise NotImplementedError('Optimizers: SGD or Adam') else: optim = torch.optim.SGD(params, lr=cfg['lr'], momentum=cfg['momentum'], weight_decay=cfg['weight_decay']) if resume: optim.load_state_dict(checkpoint['optim_state_dict']) # ----------------------------------------------------------------------------- # [optional] Sanity-check: forward pass with a single batch # ----------------------------------------------------------------------------- DEBUG = False if DEBUG: # model = model.cpu() dataiter = iter(val_loader) img, label = dataiter.next() print 'Labels: ' + str(label.size()) # batchSize x num_class print 'Input: ' + str(img.size()) # batchSize x 3 x 224 x 224 im = img.squeeze().numpy() im = im[0,:,:,:] # get first image in the batch im = im.transpose((1,2,0)) # permute to 224x224x3 im = im * [ 0.229, 0.224, 0.225 ] # unnormalize im = im + [ 0.485, 0.456, 0.406 ] im[im<0] = 0 f = plt.figure() plt.imshow(im) plt.savefig('sanity-check-im.jpg') # save transformed image in current folder inputs = Variable(img) if cuda: inputs = inputs.cuda() model.eval() outputs = model(inputs) print 'Network output: ' + str(outputs.size()) model.train() else: pass # ----------------------------------------------------------------------------- # 4. Training # ----------------------------------------------------------------------------- trainer = train.Trainer( cuda=cuda, model=model, criterion=criterion, optimizer=optim, init_lr=cfg['lr'], lr_decay_epoch = cfg['lr_decay_epoch'], train_loader=train_loader, val_loader=val_loader, out=out, max_iter=cfg['max_iteration'], interval_validate=cfg.get('interval_validate', len(train_loader)), ) trainer.epoch = start_epoch trainer.iteration = start_iteration trainer.train() def get_log_dir(model_name, config_id, cfg, verbose=True): # Creates an output directory for each experiment, timestamped name = 'MODEL-%s_CFG-%03d' % (model_name, config_id) if verbose: for k, v in cfg.items(): v = str(v) if '/' in v: continue name += '_%s-%s' % (k.upper(), v) now = datetime.datetime.now(pytz.timezone('US/Eastern')) name += '_TIME-%s' % now.strftime('%Y%m%d-%H%M%S') log_dir = osp.join(here, 'logs', name) if not osp.exists(log_dir): os.makedirs(log_dir) with open(osp.join(log_dir, 'config.yaml'), 'w') as f: yaml.safe_dump(cfg, f, default_flow_style=False) return log_dir if __name__ == '__main__': main() ================================================ FILE: vgg-face-2/vggface_class_counts.txt ================================================ n000002 313 n000003 203 n000004 385 n000005 227 n000006 483 n000007 257 n000008 271 n000010 154 n000011 378 n000012 380 n000013 312 n000014 286 n000015 246 n000016 447 n000017 317 n000018 281 n000019 372 n000020 448 n000021 284 n000022 394 n000023 396 n000024 449 n000025 307 n000026 369 n000027 380 n000028 262 n000030 265 n000031 303 n000032 517 n000033 355 n000034 303 n000035 235 n000036 372 n000037 204 n000038 415 n000039 374 n000041 389 n000042 261 n000043 491 n000044 362 n000045 244 n000046 409 n000047 328 n000048 214 n000049 437 n000050 394 n000051 325 n000052 210 n000053 379 n000054 436 n000055 448 n000056 302 n000057 362 n000058 442 n000059 344 n000060 493 n000061 374 n000062 319 n000063 434 n000064 376 n000065 391 n000066 337 n000067 509 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n001920 463 n001921 238 n001922 407 n001924 343 n001925 158 n001926 404 n001927 192 n001928 262 n001929 158 n001930 502 n001931 407 n001932 461 n001933 378 n001936 453 n001937 319 n001938 477 n001939 429 n001940 390 n001941 221 n001942 330 n001943 250 n001944 347 n001945 284 n001946 388 n001947 262 n001948 350 n001949 497 n001950 371 n001951 414 n001952 215 n001953 349 n001954 358 n001955 468 n001957 293 n001958 340 n001959 475 n001960 441 n001961 595 n001962 222 n001963 421 n001964 253 n001965 404 n001966 491 n001967 85 n001968 318 n001970 368 n001971 316 n001972 384 n001973 347 n001974 485 n001975 303 n001978 304 n001979 451 n001980 474 n001981 321 n001982 307 n001983 301 n001984 364 n001985 471 n001986 357 n001987 333 n001988 376 n001989 278 n001990 223 n001991 446 n001992 395 n001993 345 n001994 296 n001995 368 n001996 414 n001998 382 n001999 305 n002000 283 n002001 298 n002002 382 n002003 246 n002004 399 n002005 224 n002006 269 n002007 420 n002008 442 n002010 240 n002011 365 n002012 388 n002013 336 n002014 269 n002015 345 n002016 646 n002017 468 n002018 410 n002019 504 n002020 360 n002021 387 n002022 252 n002023 247 n002025 500 n002026 492 n002027 327 n002028 481 n002029 404 n002031 350 n002032 476 n002033 431 n002034 252 n002035 506 n002036 417 n002037 344 n002038 539 n002039 411 n002040 346 n002041 404 n002042 477 n002043 344 n002044 211 n002045 282 n002046 472 n002047 539 n002048 361 n002049 289 n002050 284 n002051 355 n002052 437 n002053 341 n002054 350 n002055 329 n002056 482 n002057 332 n002058 458 n002059 292 n002060 519 n002061 606 n002062 406 n002063 298 n002064 309 n002065 251 n002066 532 n002067 271 n002068 306 n002069 239 n002070 344 n002071 353 n002072 341 n002073 296 n002074 445 n002075 254 n002076 443 n002077 441 n002078 310 n002079 197 n002083 363 n002084 329 n002085 446 n002086 424 n002087 261 n002088 408 n002089 304 n002090 270 n002091 316 n002092 360 n002094 532 n002095 282 n002096 450 n002097 424 n002098 370 n002099 289 n002100 401 n002101 258 n002102 424 n002103 212 n002104 136 n002105 414 n002107 216 n002108 194 n002110 345 n002111 377 n002112 318 n002113 372 n002114 332 n002115 600 n002116 388 n002117 428 n002118 484 n002119 360 n002120 502 n002121 423 n002122 403 n002123 283 n002124 166 n002125 356 n002126 255 n002127 330 n002128 291 n002129 249 n002130 344 n002131 306 n002132 368 n002133 536 n002134 347 n002135 359 n002136 520 n002137 472 n002138 441 n002139 225 n002140 298 n002141 543 n002142 553 n002143 374 n002144 399 n002145 398 n002146 361 n002147 489 n002148 122 n002149 372 n002150 498 n002151 394 n002152 341 n002154 423 n002155 503 n002156 428 n002157 216 n002159 213 n002160 537 n002161 351 n002162 378 n002163 446 n002164 227 n002165 646 n002168 304 n002169 222 n002170 351 n002171 307 n002172 382 n002173 324 n002174 436 n002175 442 n002176 345 n002177 307 n002178 491 n002179 344 n002180 461 n002182 252 n002183 323 n002184 261 n002185 324 n002186 293 n002187 518 n002188 442 n002189 457 n002190 351 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n002285 407 n002286 332 n002287 394 n002288 374 n002289 310 n002290 396 n002291 266 n002292 482 n002293 433 n002294 364 n002295 373 n002296 261 n002297 562 n002298 544 n002299 436 n002300 310 n002301 318 n002302 439 n002303 285 n002304 156 n002305 456 n002306 241 n002307 276 n002308 355 n002310 454 n002311 328 n002312 246 n002313 374 n002314 444 n002315 350 n002316 445 n002317 353 n002318 400 n002319 454 n002320 310 n002321 342 n002322 324 n002323 336 n002324 390 n002325 371 n002326 365 n002327 446 n002328 398 n002330 312 n002331 372 n002332 406 n002333 378 n002334 444 n002335 331 n002336 409 n002337 277 n002338 428 n002339 602 n002340 436 n002341 238 n002342 387 n002343 295 n002344 270 n002345 400 n002346 258 n002347 367 n002348 348 n002349 237 n002350 585 n002352 372 n002353 256 n002354 312 n002355 392 n002356 245 n002357 335 n002358 344 n002359 519 n002360 521 n002361 443 n002362 477 n002363 295 n002364 444 n002365 177 n002366 558 n002367 517 n002368 499 n002369 104 n002370 374 n002371 235 n002373 389 n002374 296 n002375 184 n002376 274 n002377 269 n002378 370 n002379 391 n002380 306 n002382 414 n002383 528 n002386 284 n002387 465 n002388 419 n002390 269 n002391 475 n002392 302 n002393 204 n002394 291 n002395 231 n002396 395 n002397 581 n002398 289 n002399 213 n002400 517 n002401 308 n002402 306 n002403 439 n002404 354 n002405 307 n002406 419 n002407 392 n002408 460 n002409 455 n002410 264 n002411 326 n002412 376 n002413 322 n002415 265 n002416 322 n002417 198 n002418 316 n002419 456 n002420 397 n002422 427 n002423 335 n002424 182 n002425 474 n002426 523 n002427 426 n002428 325 n002430 276 n002431 434 n002432 338 n002433 153 n002434 227 n002435 287 n002436 308 n002437 349 n002438 494 n002439 249 n002440 411 n002441 202 n002442 339 n002443 360 n002444 460 n002445 134 n002446 290 n002447 549 n002448 464 n002449 402 n002450 428 n002451 331 n002452 497 n002453 316 n002454 541 n002455 227 n002456 302 n002457 339 n002458 318 n002459 357 n002460 398 n002461 353 n002462 557 n002463 482 n002464 423 n002465 347 n002466 280 n002467 413 n002468 395 n002469 340 n002470 223 n002471 480 n002472 265 n002473 363 n002476 580 n002477 192 n002478 501 n002479 453 n002480 337 n002481 326 n002482 176 n002483 437 n002484 478 n002485 265 n002486 360 n002487 388 n002488 335 n002489 496 n002490 298 n002491 242 n002492 364 n002493 246 n002494 378 n002495 282 n002496 213 n002497 417 n002498 503 n002499 398 n002500 390 n002501 452 n002502 593 n002504 374 n002505 445 n002506 390 n002507 520 n002508 348 n002509 347 n002510 352 n002512 424 n002513 191 n002514 284 n002515 302 n002516 461 n002518 339 n002519 320 n002520 496 n002521 331 n002522 579 n002523 258 n002524 248 n002525 520 n002526 424 n002527 274 n002528 424 n002529 221 n002530 343 n002531 326 n002532 437 n002533 469 n002534 252 n002535 246 n002536 335 n002537 604 n002538 597 n002539 428 n002541 370 n002542 285 n002543 233 n002544 304 n002545 550 n002546 417 n002547 638 n002548 357 n002549 490 n002550 366 n002551 199 n002552 533 n002553 401 n002554 529 n002555 213 n002557 529 n002558 343 n002559 171 n002560 520 n002562 327 n002563 300 n002564 231 n002565 310 n002566 319 n002567 331 n002568 497 n002569 435 n002570 353 n002571 446 n002572 556 n002573 440 n002575 364 n002576 359 n002577 496 n002578 443 n002579 352 n002580 157 n002582 357 n002583 315 n002584 466 n002585 369 n002586 428 n002588 442 n002589 258 n002590 244 n002591 404 n002592 450 n002593 273 n002594 403 n002595 468 n002597 376 n002598 296 n002599 362 n002600 216 n002601 307 n002602 316 n002603 496 n002605 344 n002606 388 n002607 299 n002608 515 n002609 323 n002610 199 n002611 411 n002612 464 n002613 230 n002614 280 n002615 250 n002616 256 n002617 431 n002618 521 n002619 335 n002620 279 n002621 518 n002622 464 n002623 325 n002624 381 n002625 236 n002626 367 n002627 410 n002628 418 n002629 278 n002630 471 n002631 345 n002632 511 n002633 410 n002634 221 n002635 335 n002636 351 n002637 326 n002638 384 n002639 387 n002640 529 n002641 424 n002642 251 n002643 305 n002644 690 n002645 324 n002646 352 n002648 519 n002649 361 n002650 291 n002651 310 n002652 293 n002653 305 n002654 593 n002655 289 n002656 259 n002657 330 n002660 278 n002661 443 n002662 218 n002663 311 n002665 334 n002666 260 n002667 417 n002668 521 n002670 406 n002671 437 n002672 381 n002673 429 n002674 250 n002675 480 n002676 165 n002677 379 n002678 569 n002679 471 n002680 249 n002682 276 n002683 268 n002685 402 n002686 290 n002687 246 n002688 272 n002689 435 n002690 125 n002691 336 n002692 495 n002693 251 n002694 359 n002695 481 n002696 345 n002697 292 n002698 172 n002699 358 n002700 336 n002701 269 n002702 351 n002704 327 n002705 390 n002706 383 n002707 263 n002708 324 n002709 318 n002710 400 n002711 406 n002712 304 n002713 309 n002714 351 n002716 318 n002717 309 n002718 315 n002719 446 n002720 440 n002721 208 n002722 312 n002723 503 n002724 520 n002725 404 n002726 233 n002727 388 n002728 277 n002729 371 n002730 465 n002731 217 n002732 530 n002733 309 n002734 249 n002735 244 n002736 407 n002737 377 n002738 457 n002739 350 n002740 249 n002741 564 n002742 374 n002744 487 n002745 385 n002746 709 n002747 494 n002748 491 n002750 304 n002751 338 n002752 335 n002753 301 n002754 208 n002755 213 n002756 229 n002757 188 n002758 404 n002759 510 n002760 333 n002762 201 n002764 179 n002765 283 n002766 418 n002767 362 n002769 218 n002771 403 n002772 387 n002774 304 n002776 259 n002777 404 n002778 436 n002779 479 n002780 459 n002781 461 n002782 512 n002783 395 n002784 479 n002785 464 n002786 424 n002787 198 n002788 277 n002789 549 n002790 243 n002791 185 n002792 426 n002793 100 n002794 324 n002795 235 n002796 200 n002797 205 n002798 391 n002799 345 n002800 343 n002801 277 n002802 428 n002804 384 n002805 374 n002806 369 n002807 299 n002808 235 n002809 350 n002811 306 n002812 318 n002813 317 n002814 385 n002815 522 n002816 246 n002817 272 n002818 195 n002819 176 n002820 260 n002821 243 n002822 374 n002823 310 n002824 188 n002825 341 n002826 208 n002827 324 n002828 437 n002829 354 n002830 361 n002831 487 n002832 193 n002833 236 n002834 380 n002835 278 n002836 302 n002837 404 n002839 326 n002841 234 n002842 587 n002843 312 n002844 434 n002845 202 n002846 179 n002847 274 n002848 391 n002849 289 n002850 355 n002851 266 n002852 360 n002853 407 n002854 435 n002856 282 n002858 337 n002859 161 n002860 280 n002861 135 n002862 479 n002863 261 n002864 402 n002865 448 n002866 177 n002867 262 n002868 328 n002870 350 n002871 370 n002872 443 n002874 371 n002875 212 n002876 259 n002877 253 n002879 385 n002881 492 n002882 442 n002883 448 n002885 564 n002886 594 n002887 333 n002888 623 n002890 265 n002892 453 n002893 341 n002895 334 n002896 321 n002897 410 n002898 310 n002899 292 n002900 372 n002901 285 n002902 373 n002903 300 n002904 139 n002905 284 n002906 235 n002907 249 n002908 228 n002909 534 n002910 479 n002911 353 n002912 170 n002913 244 n002914 336 n002915 411 n002916 277 n002917 414 n002918 128 n002919 456 n002920 395 n002921 398 n002922 315 n002923 476 n002924 254 n002925 366 n002926 347 n002927 393 n002928 333 n002929 468 n002930 387 n002931 139 n002932 273 n002933 432 n002934 229 n002935 440 n002936 227 n002937 293 n002938 286 n002939 336 n002940 386 n002941 367 n002942 502 n002943 307 n002944 368 n002945 390 n002946 365 n002947 570 n002948 274 n002949 305 n002950 455 n002951 275 n002952 350 n002953 246 n002954 416 n002955 436 n002956 275 n002957 370 n002958 265 n002959 332 n002960 397 n002961 198 n002962 422 n002963 487 n002964 238 n002965 459 n002966 395 n002967 378 n002968 297 n002969 339 n002970 469 n002971 426 n002972 297 n002973 350 n002974 475 n002975 382 n002976 320 n002977 329 n002978 294 n002979 259 n002980 364 n002981 204 n002982 478 n002983 225 n002984 415 n002985 390 n002986 367 n002987 488 n002988 462 n002989 416 n002990 346 n002991 255 n002992 438 n002993 382 n002994 457 n002995 331 n002997 403 n002998 461 n002999 467 n003000 431 n003002 366 n003003 405 n003004 429 n003005 345 n003006 383 n003007 392 n003008 318 n003010 209 n003011 258 n003012 269 n003013 495 n003014 385 n003015 447 n003016 354 n003017 219 n003018 235 n003019 336 n003020 297 n003021 565 n003022 338 n003023 234 n003024 265 n003025 433 n003026 325 n003027 474 n003028 366 n003029 420 n003030 683 n003031 279 n003032 357 n003033 366 n003034 213 n003035 523 n003036 320 n003037 297 n003038 225 n003039 339 n003040 478 n003041 168 n003042 403 n003043 341 n003044 317 n003045 315 n003046 340 n003047 505 n003048 518 n003049 242 n003050 293 n003051 276 n003052 108 n003053 278 n003054 360 n003055 432 n003056 273 n003057 318 n003058 399 n003059 308 n003060 310 n003061 284 n003062 187 n003063 474 n003064 309 n003065 484 n003066 410 n003067 395 n003068 310 n003069 334 n003070 438 n003071 338 n003072 451 n003073 505 n003074 383 n003075 221 n003076 539 n003077 318 n003078 330 n003080 349 n003081 255 n003082 312 n003083 345 n003084 120 n003085 364 n003086 399 n003087 327 n003088 322 n003089 268 n003090 318 n003091 205 n003094 260 n003095 285 n003096 376 n003097 286 n003098 451 n003099 267 n003100 329 n003101 426 n003102 467 n003103 287 n003105 115 n003106 313 n003107 560 n003108 326 n003109 457 n003110 393 n003111 369 n003112 259 n003113 213 n003114 364 n003116 247 n003117 506 n003118 162 n003119 295 n003120 413 n003121 513 n003122 277 n003123 431 n003124 461 n003125 198 n003126 583 n003127 201 n003128 378 n003129 396 n003130 342 n003131 316 n003132 453 n003133 306 n003135 401 n003136 487 n003137 392 n003138 442 n003139 444 n003142 411 n003143 270 n003144 292 n003145 310 n003146 488 n003147 299 n003148 495 n003149 283 n003150 299 n003151 514 n003152 295 n003153 507 n003154 406 n003155 432 n003156 190 n003157 354 n003158 291 n003159 407 n003160 258 n003161 410 n003162 468 n003163 462 n003164 240 n003165 121 n003166 193 n003167 536 n003168 246 n003169 359 n003170 512 n003171 315 n003172 338 n003173 389 n003174 395 n003175 305 n003176 391 n003177 493 n003178 466 n003179 472 n003180 301 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n003269 319 n003270 290 n003271 399 n003272 420 n003273 386 n003274 406 n003275 246 n003276 390 n003278 377 n003279 401 n003280 387 n003281 427 n003282 321 n003283 412 n003284 487 n003285 408 n003286 435 n003287 499 n003289 278 n003290 425 n003291 416 n003292 395 n003293 438 n003294 232 n003295 412 n003296 240 n003297 330 n003299 360 n003300 275 n003301 243 n003302 371 n003303 573 n003304 306 n003305 524 n003306 430 n003307 373 n003308 439 n003310 347 n003311 401 n003312 319 n003313 212 n003314 252 n003315 488 n003316 581 n003317 243 n003318 242 n003319 524 n003320 250 n003321 250 n003322 483 n003323 455 n003324 580 n003325 499 n003326 212 n003327 464 n003328 297 n003329 204 n003330 340 n003331 315 n003332 164 n003333 252 n003334 547 n003335 509 n003336 389 n003337 336 n003338 430 n003339 298 n003340 392 n003341 332 n003342 437 n003343 484 n003344 531 n003345 289 n003346 452 n003347 327 n003348 458 n003349 505 n003350 510 n003351 383 n003352 403 n003353 418 n003354 262 n003355 467 n003357 270 n003358 469 n003359 321 n003360 475 n003361 239 n003362 356 n003363 440 n003364 345 n003365 538 n003366 456 n003367 217 n003368 335 n003369 300 n003370 469 n003371 366 n003372 276 n003373 465 n003374 601 n003375 213 n003376 315 n003377 295 n003378 518 n003380 429 n003381 302 n003382 336 n003383 546 n003384 434 n003385 449 n003386 309 n003387 243 n003388 298 n003389 486 n003390 208 n003391 334 n003392 601 n003393 261 n003394 294 n003395 492 n003396 469 n003397 279 n003398 266 n003399 318 n003400 302 n003401 439 n003402 286 n003403 323 n003404 332 n003405 250 n003406 285 n003407 347 n003408 344 n003409 324 n003410 253 n003411 458 n003412 290 n003413 368 n003414 300 n003416 416 n003417 195 n003418 226 n003419 339 n003420 301 n003421 392 n003422 288 n003423 340 n003424 341 n003425 446 n003426 289 n003427 151 n003428 366 n003429 507 n003431 301 n003432 246 n003433 329 n003434 428 n003435 311 n003437 210 n003438 432 n003439 420 n003440 365 n003441 317 n003442 212 n003443 383 n003444 391 n003445 374 n003446 275 n003447 300 n003448 386 n003449 319 n003450 375 n003451 236 n003452 286 n003453 613 n003454 259 n003455 274 n003456 223 n003457 197 n003458 160 n003459 443 n003460 413 n003462 309 n003463 335 n003464 234 n003465 385 n003466 418 n003467 159 n003469 321 n003470 494 n003471 227 n003472 124 n003473 194 n003474 413 n003475 556 n003476 569 n003477 219 n003478 627 n003479 270 n003481 424 n003482 423 n003483 264 n003484 523 n003485 551 n003486 437 n003487 434 n003488 521 n003489 286 n003491 198 n003492 447 n003493 265 n003494 349 n003495 478 n003496 349 n003497 336 n003498 471 n003499 367 n003500 480 n003501 417 n003502 390 n003503 261 n003504 436 n003505 262 n003506 437 n003507 343 n003508 397 n003509 349 n003510 355 n003511 251 n003512 324 n003514 303 n003515 259 n003516 453 n003517 362 n003518 308 n003519 248 n003520 501 n003521 406 n003522 239 n003523 508 n003524 403 n003525 453 n003526 296 n003527 498 n003528 469 n003529 260 n003530 200 n003531 443 n003532 441 n003533 305 n003534 212 n003536 207 n003537 411 n003538 282 n003539 198 n003541 454 n003542 444 n003543 252 n003544 249 n003545 301 n003546 301 n003547 394 n003548 239 n003549 343 n003550 262 n003551 336 n003552 272 n003553 456 n003555 356 n003556 290 n003557 368 n003558 202 n003559 378 n003560 284 n003561 242 n003562 208 n003563 258 n003564 467 n003565 511 n003566 114 n003567 345 n003568 363 n003569 368 n003571 286 n003572 324 n003573 217 n003574 266 n003575 185 n003576 286 n003577 300 n003578 294 n003579 268 n003580 427 n003581 496 n003582 241 n003583 533 n003584 338 n003585 321 n003586 491 n003587 508 n003588 338 n003590 225 n003591 335 n003593 255 n003594 403 n003595 292 n003596 288 n003597 315 n003598 455 n003599 312 n003600 212 n003601 401 n003602 428 n003603 338 n003604 447 n003605 505 n003607 140 n003608 270 n003609 161 n003610 350 n003612 203 n003613 413 n003614 379 n003615 578 n003616 529 n003617 518 n003618 369 n003619 450 n003620 470 n003621 226 n003622 373 n003623 417 n003624 463 n003625 292 n003626 415 n003627 183 n003628 395 n003629 268 n003630 462 n003631 299 n003632 459 n003633 216 n003634 380 n003636 341 n003637 438 n003638 316 n003639 360 n003640 174 n003641 458 n003642 183 n003643 199 n003644 212 n003645 336 n003646 460 n003647 443 n003648 294 n003649 406 n003650 449 n003651 541 n003652 237 n003654 309 n003655 216 n003656 371 n003657 313 n003658 591 n003659 267 n003660 469 n003661 216 n003663 381 n003664 259 n003666 201 n003667 404 n003668 333 n003669 357 n003670 351 n003671 266 n003672 453 n003673 413 n003674 358 n003676 389 n003677 376 n003678 325 n003679 337 n003680 558 n003681 287 n003682 380 n003683 344 n003684 237 n003685 479 n003686 277 n003687 333 n003688 340 n003689 327 n003690 342 n003691 394 n003692 385 n003693 516 n003694 478 n003695 449 n003696 185 n003697 206 n003698 205 n003699 162 n003700 386 n003701 501 n003702 304 n003703 341 n003704 406 n003705 210 n003706 495 n003707 199 n003708 335 n003709 442 n003710 264 n003711 175 n003712 660 n003713 289 n003714 462 n003715 482 n003716 536 n003717 279 n003718 257 n003719 415 n003720 376 n003721 213 n003722 412 n003723 320 n003724 514 n003726 533 n003727 426 n003729 443 n003730 268 n003731 324 n003732 206 n003733 285 n003734 328 n003735 543 n003736 406 n003737 348 n003738 409 n003739 323 n003740 314 n003741 361 n003742 409 n003743 243 n003744 331 n003745 325 n003746 589 n003747 416 n003748 407 n003749 343 n003750 380 n003751 358 n003752 233 n003753 350 n003754 402 n003755 235 n003756 374 n003757 412 n003758 429 n003759 262 n003760 390 n003761 349 n003762 247 n003763 429 n003764 460 n003766 353 n003767 329 n003768 422 n003769 525 n003770 225 n003771 237 n003772 422 n003773 492 n003774 334 n003776 535 n003777 448 n003778 379 n003779 368 n003780 404 n003781 271 n003782 214 n003783 282 n003784 551 n003785 450 n003787 429 n003788 484 n003789 466 n003790 326 n003791 244 n003792 383 n003793 343 n003794 309 n003795 404 n003796 414 n003797 241 n003798 205 n003799 542 n003800 435 n003801 250 n003802 138 n003803 494 n003805 280 n003806 327 n003807 267 n003808 433 n003809 200 n003810 262 n003811 323 n003812 359 n003813 292 n003814 358 n003815 515 n003816 424 n003817 581 n003818 350 n003819 619 n003820 394 n003821 426 n003822 501 n003823 298 n003824 344 n003825 326 n003826 330 n003827 214 n003828 341 n003829 404 n003830 156 n003831 595 n003832 488 n003833 291 n003834 424 n003835 333 n003837 333 n003838 531 n003839 324 n003840 344 n003841 316 n003842 276 n003843 402 n003844 316 n003845 180 n003846 481 n003847 271 n003848 324 n003849 447 n003850 347 n003851 201 n003852 278 n003853 339 n003854 416 n003855 403 n003856 487 n003857 389 n003858 380 n003859 208 n003860 491 n003861 219 n003862 505 n003863 470 n003864 409 n003865 499 n003866 447 n003867 234 n003868 351 n003869 251 n003870 475 n003871 354 n003872 355 n003874 362 n003875 396 n003876 371 n003877 468 n003878 255 n003879 398 n003880 356 n003882 299 n003883 421 n003884 351 n003885 440 n003886 562 n003887 357 n003888 279 n003889 270 n003890 235 n003891 290 n003892 139 n003893 304 n003895 553 n003897 539 n003898 221 n003899 442 n003900 329 n003901 247 n003902 257 n003903 330 n003904 467 n003905 483 n003906 402 n003907 198 n003908 566 n003909 264 n003910 347 n003911 403 n003912 551 n003913 499 n003914 261 n003915 224 n003916 356 n003918 384 n003919 524 n003920 217 n003921 409 n003922 378 n003923 581 n003924 518 n003925 270 n003926 260 n003927 275 n003928 355 n003929 325 n003930 323 n003931 440 n003932 366 n003933 298 n003934 242 n003935 304 n003936 328 n003937 455 n003938 504 n003939 396 n003940 394 n003941 422 n003942 296 n003943 374 n003944 412 n003945 304 n003946 458 n003947 309 n003948 317 n003949 334 n003950 362 n003951 332 n003952 327 n003953 312 n003954 319 n003955 359 n003956 419 n003957 381 n003959 411 n003960 490 n003961 206 n003962 504 n003963 244 n003964 221 n003965 519 n003966 314 n003967 442 n003968 542 n003969 412 n003970 243 n003971 155 n003972 455 n003973 525 n003974 327 n003975 407 n003976 216 n003977 613 n003978 210 n003979 533 n003980 471 n003981 378 n003982 466 n003983 443 n003984 334 n003985 355 n003986 474 n003987 280 n003988 615 n003989 351 n003990 356 n003991 405 n003992 242 n003993 523 n003994 205 n003995 570 n003996 203 n003997 230 n003998 502 n003999 295 n004000 433 n004001 510 n004002 356 n004003 241 n004004 334 n004005 458 n004006 161 n004008 326 n004009 320 n004011 120 n004012 305 n004013 390 n004014 369 n004015 214 n004016 450 n004017 335 n004018 284 n004019 361 n004020 270 n004021 390 n004022 158 n004023 477 n004024 188 n004025 348 n004026 486 n004027 358 n004028 254 n004029 372 n004030 370 n004031 179 n004032 372 n004033 323 n004034 382 n004035 270 n004036 386 n004037 394 n004038 588 n004039 220 n004040 490 n004041 257 n004042 654 n004043 477 n004044 348 n004045 580 n004046 495 n004047 224 n004048 488 n004049 454 n004051 252 n004052 523 n004053 483 n004054 400 n004055 185 n004056 420 n004057 306 n004058 365 n004059 240 n004060 386 n004061 484 n004062 373 n004063 362 n004065 333 n004066 398 n004067 210 n004069 390 n004071 317 n004072 464 n004073 435 n004074 267 n004075 369 n004076 374 n004077 276 n004079 420 n004080 319 n004081 354 n004082 233 n004083 352 n004084 314 n004087 318 n004088 357 n004089 279 n004090 372 n004091 206 n004092 390 n004093 633 n004094 326 n004095 322 n004096 268 n004097 642 n004098 323 n004099 305 n004100 345 n004101 407 n004102 384 n004103 254 n004104 356 n004105 439 n004106 413 n004107 360 n004108 418 n004109 395 n004110 445 n004111 504 n004112 135 n004113 352 n004114 376 n004115 373 n004116 224 n004117 295 n004119 480 n004120 498 n004121 394 n004122 474 n004124 539 n004125 431 n004126 378 n004127 275 n004128 389 n004129 422 n004130 338 n004131 381 n004132 373 n004133 337 n004134 400 n004135 328 n004136 340 n004137 158 n004138 285 n004139 341 n004140 222 n004141 447 n004142 637 n004143 439 n004144 555 n004145 457 n004146 312 n004147 332 n004148 217 n004149 450 n004150 507 n004151 535 n004152 613 n004153 409 n004154 478 n004155 286 n004156 375 n004158 460 n004159 455 n004160 364 n004161 397 n004162 358 n004163 363 n004164 282 n004165 348 n004166 329 n004167 399 n004168 557 n004169 358 n004170 389 n004171 364 n004172 360 n004173 460 n004174 403 n004175 293 n004176 367 n004177 328 n004178 565 n004179 400 n004181 357 n004182 454 n004183 331 n004184 273 n004185 421 n004186 144 n004187 524 n004188 374 n004189 380 n004190 336 n004191 481 n004192 383 n004193 490 n004194 412 n004195 331 n004196 236 n004197 363 n004198 515 n004201 401 n004202 322 n004203 488 n004204 345 n004205 308 n004206 402 n004209 502 n004210 437 n004211 257 n004212 340 n004213 303 n004214 360 n004215 493 n004216 594 n004217 195 n004218 426 n004220 458 n004221 314 n004222 433 n004223 309 n004224 210 n004225 228 n004226 390 n004227 485 n004228 219 n004229 332 n004230 378 n004231 534 n004232 296 n004234 360 n004235 352 n004236 349 n004237 452 n004238 317 n004241 241 n004242 221 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n004330 262 n004331 427 n004332 397 n004334 293 n004335 200 n004336 267 n004337 539 n004339 263 n004340 211 n004341 303 n004342 354 n004343 323 n004344 387 n004345 279 n004347 313 n004348 462 n004349 442 n004350 404 n004351 442 n004352 365 n004354 302 n004355 313 n004356 531 n004358 360 n004359 424 n004360 296 n004361 386 n004362 293 n004363 318 n004364 367 n004365 501 n004367 364 n004368 359 n004369 398 n004370 376 n004371 448 n004373 663 n004374 389 n004375 513 n004376 284 n004377 243 n004378 303 n004379 309 n004381 436 n004382 350 n004383 360 n004384 285 n004385 387 n004386 456 n004388 209 n004389 213 n004390 375 n004391 512 n004392 532 n004393 328 n004395 327 n004396 346 n004397 383 n004398 426 n004399 476 n004401 442 n004402 302 n004403 245 n004404 198 n004405 327 n004406 373 n004407 617 n004408 547 n004409 335 n004410 454 n004412 475 n004413 493 n004414 491 n004415 448 n004416 404 n004417 259 n004418 589 n004419 312 n004420 365 n004421 317 n004422 580 n004423 104 n004425 370 n004426 492 n004427 282 n004428 413 n004429 411 n004430 269 n004431 389 n004432 348 n004433 339 n004434 118 n004435 211 n004436 277 n004437 245 n004438 209 n004439 598 n004441 304 n004442 462 n004443 575 n004444 368 n004445 344 n004446 465 n004447 432 n004448 353 n004450 323 n004451 261 n004452 419 n004453 146 n004454 290 n004455 320 n004456 393 n004457 564 n004458 134 n004459 357 n004460 355 n004462 312 n004463 122 n004464 410 n004465 365 n004466 274 n004467 485 n004468 184 n004470 446 n004471 475 n004472 362 n004473 478 n004474 259 n004475 409 n004476 447 n004477 345 n004478 396 n004479 401 n004480 381 n004481 420 n004483 410 n004484 291 n004485 481 n004487 496 n004488 490 n004489 512 n004490 551 n004491 328 n004492 349 n004493 512 n004494 349 n004495 150 n004496 235 n004497 400 n004498 314 n004499 333 n004500 423 n004501 235 n004502 377 n004503 331 n004504 373 n004505 462 n004506 369 n004507 506 n004508 607 n004509 367 n004510 573 n004511 478 n004512 403 n004513 246 n004514 278 n004515 424 n004516 322 n004517 431 n004518 423 n004519 250 n004520 227 n004521 414 n004522 307 n004523 392 n004524 209 n004525 439 n004526 298 n004527 310 n004528 556 n004529 319 n004530 332 n004531 206 n004532 202 n004533 605 n004534 276 n004535 530 n004536 393 n004537 412 n004538 458 n004539 284 n004540 216 n004541 388 n004542 496 n004543 674 n004544 325 n004545 133 n004546 239 n004547 361 n004548 471 n004549 511 n004550 526 n004551 360 n004552 235 n004553 436 n004554 381 n004556 168 n004557 224 n004558 314 n004559 544 n004560 431 n004561 564 n004562 461 n004564 342 n004565 588 n004566 300 n004567 361 n004568 424 n004569 463 n004570 234 n004571 304 n004572 415 n004573 197 n004574 163 n004575 416 n004576 467 n004577 177 n004578 322 n004579 216 n004581 227 n004582 397 n004583 251 n004584 149 n004585 300 n004587 376 n004589 687 n004590 336 n004591 562 n004592 341 n004593 356 n004594 375 n004595 298 n004596 290 n004597 379 n004598 535 n004599 358 n004600 293 n004601 342 n004602 422 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n004790 370 n004791 403 n004792 397 n004794 478 n004796 245 n004797 403 n004799 356 n004800 602 n004802 419 n004804 395 n004805 284 n004806 812 n004807 372 n004808 399 n004809 403 n004810 423 n004814 265 n004816 303 n004817 207 n004818 282 n004819 363 n004820 607 n004821 375 n004822 385 n004823 424 n004824 290 n004825 483 n004827 353 n004828 206 n004829 410 n004830 480 n004831 652 n004832 302 n004833 245 n004834 417 n004835 453 n004836 202 n004837 269 n004838 358 n004839 300 n004840 313 n004841 327 n004842 336 n004843 604 n004844 652 n004845 282 n004846 456 n004847 373 n004848 296 n004849 397 n004851 451 n004852 290 n004853 218 n004854 350 n004855 338 n004856 298 n004857 353 n004858 336 n004859 466 n004860 257 n004861 221 n004862 382 n004863 322 n004864 391 n004865 267 n004866 351 n004867 312 n004868 486 n004869 339 n004870 233 n004871 474 n004872 366 n004873 315 n004874 391 n004875 320 n004876 358 n004877 224 n004878 479 n004879 404 n004880 412 n004881 490 n004882 362 n004883 213 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n004976 410 n004977 416 n004978 444 n004979 501 n004980 397 n004981 339 n004982 309 n004983 233 n004984 531 n004985 276 n004986 361 n004987 423 n004988 493 n004989 302 n004990 485 n004991 344 n004992 502 n004993 384 n004994 465 n004995 277 n004996 358 n004997 343 n004998 475 n005000 276 n005001 419 n005002 222 n005003 490 n005004 318 n005005 439 n005006 235 n005007 451 n005008 215 n005009 438 n005010 503 n005011 485 n005012 377 n005013 418 n005014 503 n005015 645 n005016 374 n005017 512 n005018 674 n005019 467 n005020 346 n005021 371 n005022 575 n005023 262 n005024 390 n005025 303 n005026 437 n005027 349 n005028 300 n005029 352 n005030 422 n005031 447 n005032 297 n005033 267 n005034 402 n005035 302 n005036 424 n005037 330 n005038 308 n005039 346 n005040 205 n005041 187 n005042 320 n005043 365 n005044 195 n005045 361 n005046 372 n005047 368 n005048 292 n005050 365 n005051 225 n005052 312 n005053 537 n005054 406 n005055 380 n005056 406 n005057 480 n005058 397 n005059 390 n005060 390 n005061 306 n005062 352 n005063 246 n005064 333 n005065 395 n005066 368 n005067 383 n005069 416 n005070 324 n005071 322 n005072 355 n005074 363 n005075 381 n005076 346 n005077 297 n005078 444 n005079 368 n005080 576 n005081 455 n005082 533 n005083 300 n005084 439 n005085 462 n005086 528 n005087 416 n005089 693 n005090 374 n005091 230 n005092 431 n005093 298 n005094 471 n005095 448 n005096 333 n005097 302 n005098 382 n005099 304 n005100 267 n005102 347 n005103 486 n005105 321 n005106 361 n005107 279 n005108 229 n005109 261 n005110 413 n005111 263 n005113 284 n005115 515 n005116 405 n005117 317 n005118 243 n005119 401 n005121 359 n005124 522 n005125 647 n005126 365 n005127 322 n005128 326 n005129 517 n005130 393 n005131 291 n005132 362 n005133 350 n005134 371 n005138 460 n005139 373 n005140 333 n005141 417 n005142 517 n005143 106 n005144 212 n005146 271 n005147 436 n005149 388 n005150 287 n005151 369 n005152 337 n005153 473 n005158 369 n005160 227 n005161 283 n005162 461 n005163 271 n005164 578 n005165 195 n005166 207 n005167 534 n005168 450 n005169 344 n005170 364 n005171 213 n005172 328 n005173 331 n005174 204 n005175 647 n005176 618 n005177 213 n005178 344 n005180 587 n005182 291 n005183 257 n005184 248 n005185 253 n005186 325 n005187 318 n005189 288 n005190 423 n005191 604 n005192 160 n005193 113 n005194 323 n005195 309 n005196 415 n005197 397 n005198 422 n005199 269 n005200 406 n005201 272 n005202 441 n005203 340 n005204 425 n005205 386 n005206 514 n005207 362 n005208 371 n005209 216 n005210 367 n005211 316 n005212 393 n005213 404 n005214 172 n005215 303 n005216 321 n005217 378 n005218 341 n005219 589 n005220 213 n005221 391 n005222 363 n005223 428 n005224 285 n005227 524 n005228 436 n005229 330 n005230 357 n005231 330 n005232 284 n005234 379 n005235 347 n005236 340 n005237 233 n005238 348 n005239 383 n005240 463 n005241 230 n005242 372 n005243 259 n005244 319 n005245 327 n005246 318 n005247 347 n005248 316 n005249 617 n005250 337 n005251 422 n005252 326 n005253 389 n005254 450 n005255 278 n005256 228 n005257 291 n005258 283 n005259 339 n005260 303 n005261 218 n005262 489 n005263 403 n005264 418 n005265 340 n005266 513 n005267 274 n005268 320 n005269 527 n005270 474 n005271 297 n005272 301 n005273 359 n005274 281 n005275 384 n005276 278 n005277 515 n005278 304 n005279 520 n005281 262 n005283 532 n005284 300 n005285 461 n005286 237 n005287 414 n005288 323 n005289 368 n005290 487 n005291 335 n005292 591 n005293 275 n005295 423 n005296 317 n005297 396 n005298 352 n005299 397 n005300 388 n005302 369 n005304 272 n005305 569 n005307 440 n005308 144 n005309 331 n005310 357 n005311 422 n005313 401 n005314 405 n005315 233 n005317 282 n005318 586 n005320 417 n005321 237 n005322 357 n005323 490 n005324 267 n005325 298 n005327 298 n005329 214 n005330 410 n005331 339 n005332 273 n005333 372 n005335 501 n005336 330 n005337 390 n005338 583 n005339 333 n005341 468 n005342 482 n005343 508 n005344 144 n005345 473 n005346 392 n005348 370 n005349 395 n005350 370 n005351 193 n005352 226 n005353 311 n005354 171 n005355 315 n005356 391 n005357 331 n005358 339 n005360 320 n005361 340 n005362 448 n005363 327 n005364 517 n005365 247 n005366 384 n005367 397 n005368 279 n005369 417 n005370 246 n005371 412 n005372 245 n005374 273 n005375 386 n005376 429 n005378 441 n005379 292 n005381 309 n005382 338 n005383 445 n005384 518 n005385 307 n005386 350 n005387 355 n005388 248 n005389 282 n005390 482 n005391 334 n005392 415 n005393 216 n005394 382 n005395 500 n005396 597 n005397 390 n005398 347 n005399 290 n005400 435 n005401 444 n005402 434 n005403 276 n005404 227 n005405 222 n005406 388 n005407 226 n005408 522 n005409 381 n005410 431 n005411 365 n005412 478 n005413 603 n005414 555 n005415 618 n005416 534 n005418 519 n005419 570 n005420 548 n005421 304 n005422 220 n005423 483 n005424 379 n005426 464 n005428 269 n005429 308 n005430 381 n005431 412 n005432 489 n005433 456 n005434 165 n005435 271 n005436 414 n005437 441 n005438 191 n005439 120 n005440 362 n005441 418 n005442 583 n005443 466 n005444 257 n005445 452 n005446 413 n005447 421 n005449 655 n005450 241 n005451 342 n005452 479 n005453 404 n005454 345 n005455 391 n005456 489 n005457 390 n005458 266 n005459 375 n005460 470 n005461 465 n005462 282 n005463 383 n005464 587 n005465 287 n005466 326 n005467 404 n005468 391 n005469 443 n005470 311 n005471 396 n005472 178 n005475 533 n005476 468 n005477 404 n005478 357 n005479 437 n005480 347 n005481 391 n005482 334 n005483 595 n005484 347 n005485 445 n005486 244 n005487 391 n005488 255 n005489 496 n005491 534 n005492 215 n005493 575 n005494 201 n005495 478 n005496 518 n005497 425 n005498 264 n005499 388 n005500 216 n005501 475 n005502 355 n005503 358 n005504 310 n005505 261 n005506 412 n005507 425 n005508 449 n005509 382 n005510 262 n005511 281 n005512 331 n005514 448 n005515 202 n005516 365 n005517 317 n005518 453 n005519 404 n005520 288 n005521 131 n005522 436 n005523 365 n005524 265 n005525 389 n005526 368 n005527 380 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