eval_svm_voc.py

from __future__ import print_function

import os
import sys
import time
import torch
import torch.optim as optim
import torch.backends.cudnn as cudnn
import torch.nn.functional as F
import argparse
import random
import numpy as np

from torchvision import transforms, datasets
import torchvision.models as models

from voc import Voc2007Classification

from sklearn.svm import LinearSVC


def parse_option():
    model_names = sorted(name for name in models.__dict__
        if name.islower() and not name.startswith("__")
        and callable(models.__dict__[name]))

    parser = argparse.ArgumentParser('argument for training')
    
    parser.add_argument('data', metavar='DIR',
                        help='path to dataset')
    parser.add_argument('--batch-size', type=int, default=128, help='batch size')
    parser.add_argument('--num-workers', type=int, default=8, help='num of workers to use')
    parser.add_argument('--cost', type=str, default='0.5')
    parser.add_argument('--seed', default=0, type=int)
    
    # model definition
    parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet50',
                        choices=model_names,
                        help='model architecture: ' +
                            ' | '.join(model_names) +
                            ' (default: resnet50)')    
    parser.add_argument('--pretrained', default='', type=str,
                        help='path to pretrained checkpoint')
    # dataset
    parser.add_argument('--low-shot', default=False, action='store_true', help='whether to perform low-shot training.')    
    
    opt = parser.parse_args()

    opt.num_class = 20
    
    # if low shot experiment, do 5 random runs
    if opt.low_shot:
        opt.n_run = 5
    else:
        opt.n_run = 1
    return opt


def calculate_ap(rec, prec):
    """
    Computes the AP under the precision recall curve.
    """
    rec, prec = rec.reshape(rec.size, 1), prec.reshape(prec.size, 1)
    z, o = np.zeros((1, 1)), np.ones((1, 1))
    mrec, mpre = np.vstack((z, rec, o)), np.vstack((z, prec, z))
    for i in range(len(mpre) - 2, -1, -1):
        mpre[i] = max(mpre[i], mpre[i + 1])

    indices = np.where(mrec[1:] != mrec[0:-1])[0] + 1
    ap = 0
    for i in indices:
        ap = ap + (mrec[i] - mrec[i - 1]) * mpre[i]
    return ap

def get_precision_recall(targets, preds):
    """
    [P, R, score, ap] = get_precision_recall(targets, preds)
    Input    :
        targets  : number of occurrences of this class in the ith image
        preds    : score for this image
    Output   :
        P, R   : precision and recall
        score  : score which corresponds to the particular precision and recall
        ap     : average precision
    """
    # binarize targets
    targets = np.array(targets > 0, dtype=np.float32)
    tog = np.hstack((
        targets[:, np.newaxis].astype(np.float64),
        preds[:, np.newaxis].astype(np.float64)
    ))
    ind = np.argsort(preds)
    ind = ind[::-1]
    score = np.array([tog[i, 1] for i in ind])
    sortcounts = np.array([tog[i, 0] for i in ind])

    tp = sortcounts
    fp = sortcounts.copy()
    for i in range(sortcounts.shape[0]):
        if sortcounts[i] >= 1:
            fp[i] = 0.
        elif sortcounts[i] < 1:
            fp[i] = 1.
    P = np.cumsum(tp) / (np.cumsum(tp) + np.cumsum(fp))
    numinst = np.sum(targets)
    R = np.cumsum(tp) / numinst
    ap = calculate_ap(R, P)
    return P, R, score, ap


def main():
    args = parse_option()
    
    random.seed(args.seed)
    np.random.seed(args.seed)
       
    mean = [0.485, 0.456, 0.406]
    std = [0.229, 0.224, 0.225]
    normalize = transforms.Normalize(mean=mean, std=std)        
    transform = transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),        
        transforms.ToTensor(),
        normalize,
    ])

    train_dataset = Voc2007Classification(args.data,set='trainval',transform = transform)
    val_dataset = Voc2007Classification(args.data,set='test',transform = transform)

    val_loader = torch.utils.data.DataLoader(
        val_dataset, batch_size=args.batch_size, shuffle=False,
        num_workers=args.num_workers, pin_memory=True)

    # create model
    print("=> creating model '{}'".format(args.arch))
    model = models.__dict__[args.arch](num_classes=128)

    # load from pre-trained
    if args.pretrained:
        if os.path.isfile(args.pretrained):
            print("=> loading checkpoint '{}'".format(args.pretrained))
            checkpoint = torch.load(args.pretrained, map_location="cpu")            
            state_dict = checkpoint['state_dict']
            # rename pre-trained keys
            for k in list(state_dict.keys()):
                if k.startswith('module.encoder_q') and not k.startswith('module.encoder_q.fc'):
                    # remove prefix
                    state_dict[k[len("module.encoder_q."):]] = state_dict[k]
                # delete renamed or unused k
                del state_dict[k]  
            model.load_state_dict(state_dict, strict=False)
            model.fc = torch.nn.Identity()
            print("=> loaded pre-trained model '{}'".format(args.pretrained))
        else:
            print("=> no checkpoint found at '{}'".format(args.pretrained))
            
    model.cuda()   
    model.eval()   

    test_feats = []
    test_labels = []    
    print('==> calculate test features')
    for idx, (images, target) in enumerate(val_loader):
        images = images.cuda(non_blocking=True)
        feat = model(images)
        feat = feat.detach().cpu()
        test_feats.append(feat)
        test_labels.append(target)

    test_feats = torch.cat(test_feats,0).numpy()
    test_labels = torch.cat(test_labels,0).numpy()
               
    test_feats_norm = np.linalg.norm(test_feats, axis=1)
    test_feats = test_feats / (test_feats_norm + 1e-5)[:, np.newaxis]
    
    result={}
    
    if args.low_shot:
        k_list = [1,2,4,8,16] #number of samples per-class for low-shot classifcation
    else:
        k_list = ['full']
        
    for k in k_list:
        cost_list = args.cost.split(',')
        result_k = np.zeros(len(cost_list))
        for i,cost in enumerate(cost_list):
            cost = float(cost)
            avg_map = []
            for run in range(args.n_run):
                if args.low_shot: # sample k-shot training data
                    print('==> re-sampling training data')
                    train_dataset.convert_low_shot(k)    
                print(len(train_dataset))

                train_loader = torch.utils.data.DataLoader(
                    train_dataset, batch_size=args.batch_size, shuffle=False,
                    num_workers=args.num_workers, pin_memory=True)

                train_feats = []
                train_labels = []
                print('==> calculate train features')
                for idx, (images, target) in enumerate(train_loader):
                    images = images.cuda(non_blocking=True)
                    feat = model(images)
                    feat = feat.detach()  

                    train_feats.append(feat)                
                    train_labels.append(target)

                train_feats = torch.cat(train_feats,0).cpu().numpy()
                train_labels = torch.cat(train_labels,0).cpu().numpy()

                train_feats_norm = np.linalg.norm(train_feats, axis=1)
                train_feats = train_feats / (train_feats_norm + 1e-5)[:, np.newaxis]

                print('==> training SVM Classifier')
                cls_ap = np.zeros((args.num_class, 1))
                test_labels[test_labels==0] = -1 
                train_labels[train_labels==0] = -1 
                for cls in range(args.num_class):
                    clf = LinearSVC(
                        C=cost, class_weight={1: 2, -1: 1}, intercept_scaling=1.0,
                        penalty='l2', loss='squared_hinge', tol=1e-4,
                        dual=True, max_iter=2000, random_state=0)
                    clf.fit(train_feats, train_labels[:,cls])

                    prediction = clf.decision_function(test_feats)                                      
                    P, R, score, ap = get_precision_recall(test_labels[:,cls], prediction)
                    cls_ap[cls][0] = ap*100
                mean_ap = np.mean(cls_ap, axis=0)

                print('==> Run%d mAP is %.2f: '%(run,mean_ap))
                avg_map.append(mean_ap)

            avg_map = np.asarray(avg_map)     
            print('Cost:%.2f - Average ap is: %.2f' %(cost,avg_map.mean()))
            print('Cost:%.2f - Std is: %.2f' %(cost,avg_map.std()))
            result_k[i]=avg_map.mean()
        result[k] = result_k.max()    
    print(result)    
    
if __name__ == '__main__':
    main()