import argparse import os import pickle import timeit import warnings from pathlib import Path import cv2 import matplotlib import matplotlib.pyplot as plt import mxnet as mx import numpy as np import pandas as pd import sklearn from menpo.visualize.viewmatplotlib import sample_colours_from_colourmap from mxnet.gluon.data import Dataset, DataLoader from prettytable import PrettyTable from skimage import transform as trans from sklearn import preprocessing from sklearn.metrics import roc_curve, auc from tqdm import tqdm matplotlib.use('Agg') warnings.filterwarnings("ignore") parser = argparse.ArgumentParser(description='do ijb test') # general parser.add_argument('--model-prefix', default='', help='path to load model.') parser.add_argument('--model-epoch', default=1, type=int, help='') parser.add_argument('--image-path', default='', type=str, help='') parser.add_argument('--result-dir', default='.', type=str, help='') parser.add_argument('--gpu', default='0', type=str, help='gpu id') parser.add_argument('--batch-size', default=128, type=int, help='') parser.add_argument('--job', default='insightface', type=str, help='job name') parser.add_argument('-es', '--emb-size', type=int, help='embedding size') parser.add_argument('--target', default='IJBC', type=str, help='target, set to IJBC or IJBB') args = parser.parse_args() os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu target = args.target model_path = args.model_prefix image_path = args.image_path result_dir = args.result_dir epoch = args.model_epoch use_norm_score = True # if Ture, TestMode(N1) use_detector_score = True # if Ture, TestMode(D1) use_flip_test = True # if Ture, TestMode(F1) job = args.job batch_size = args.batch_size class DatasetIJB(Dataset): def __init__(self, root, lines, align=True): self.src = np.array( [[30.2946, 51.6963], [65.5318, 51.5014], [48.0252, 71.7366], [33.5493, 92.3655], [62.7299, 92.2041]], dtype=np.float32) self.src[:, 0] += 8.0 self.lines = lines self.img_root = root self.align = align def __len__(self): return len(self.lines) def __getitem__(self, idx): each_line = self.lines[idx] name_lmk_score = each_line.strip().split(' ') # "name lmk score" img_name = os.path.join(self.img_root, name_lmk_score[0]) img = cv2.imread(img_name) if self.align: landmark = np.array([float(x) for x in name_lmk_score[1:-1]], dtype=np.float32) landmark = landmark.reshape((5, 2)) # assert landmark.shape[0] == 68 or landmark.shape[0] == 5 assert landmark.shape[1] == 2 if landmark.shape[0] == 68: landmark5 = np.zeros((5, 2), dtype=np.float32) landmark5[0] = (landmark[36] + landmark[39]) / 2 landmark5[1] = (landmark[42] + landmark[45]) / 2 landmark5[2] = landmark[30] landmark5[3] = landmark[48] landmark5[4] = landmark[54] else: landmark5 = landmark # tform = trans.SimilarityTransform() tform.estimate(landmark5, self.src) # M = tform.params[0:2, :] img = cv2.warpAffine(img, M, (112, 112), borderValue=0.0) img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) img_flip = np.fliplr(img) img = np.transpose(img, (2, 0, 1)) # 3*112*112, RGB img_flip = np.transpose(img_flip, (2, 0, 1)) input_blob = np.zeros((2, 3, 112, 112), dtype=np.uint8) input_blob[0] = img input_blob[1] = img_flip return mx.nd.array(input_blob) def extract_parallel(prefix, epoch, dataset, batch_size, size): # init model_list = list() num_ctx = len(os.environ['CUDA_VISIBLE_DEVICES'].split(",")) num_iter = 0 feat_mat = mx.nd.zeros(shape=(len(dataset), 2 * size)) def batchify_fn(data): return mx.nd.concat(*data, dim=0) data_loader = DataLoader(dataset, batch_size, last_batch='keep', num_workers=8, thread_pool=True, prefetch=16, batchify_fn=batchify_fn) symbol, arg_params, aux_params = mx.module.module.load_checkpoint( prefix, epoch) all_layers = symbol.get_internals() symbol = all_layers['fc1_output'] # init model list for i in range(num_ctx): model = mx.mod.Module(symbol, context=mx.gpu(i), label_names=None) model.bind(for_training=False, data_shapes=[('data', (2 * batch_size, 3, 112, 112))]) model.set_params(arg_params, aux_params) model_list.append(model) # extract parallel and async num_model = len(model_list) for image in tqdm(data_loader): data_batch = mx.io.DataBatch(data=(image, )) model_list[num_iter % num_model].forward(data_batch, is_train=False) feat = model_list[num_iter % num_model].get_outputs(merge_multi_context=True)[0] feat = mx.nd.L2Normalization(feat) feat = mx.nd.reshape(feat, (-1, size * 2)) feat_mat[batch_size * num_iter:batch_size * num_iter + feat.shape[0], :] = feat.as_in_context(mx.cpu()) num_iter += 1 #if num_iter % 20 == 0: # mx.nd.waitall() return feat_mat.asnumpy() # 将一个list尽量均分成n份，限制len(list)==n，份数大于原list内元素个数则分配空list[] def divideIntoNstrand(listTemp, n): twoList = [[] for i in range(n)] for i, e in enumerate(listTemp): twoList[i % n].append(e) return twoList def read_template_media_list(path): ijb_meta = pd.read_csv(path, sep=' ', header=None).values templates = ijb_meta[:, 1].astype(np.int) medias = ijb_meta[:, 2].astype(np.int) return templates, medias def read_template_pair_list(path): pairs = pd.read_csv(path, sep=' ', header=None).values t1 = pairs[:, 0].astype(np.int) t2 = pairs[:, 1].astype(np.int) label = pairs[:, 2].astype(np.int) return t1, t2, label def read_image_feature(path): with open(path, 'rb') as fid: img_feats = pickle.load(fid) return img_feats def image2template_feature(img_feats=None, templates=None, medias=None): # ========================================================== # 1. face image feature l2 normalization. img_feats:[number_image x feats_dim] # 2. compute media feature. # 3. compute template feature. # ========================================================== unique_templates = np.unique(templates) template_feats = np.zeros((len(unique_templates), img_feats.shape[1])) for count_template, uqt in enumerate(unique_templates): (ind_t, ) = np.where(templates == uqt) face_norm_feats = img_feats[ind_t] face_medias = medias[ind_t] unique_medias, unique_media_counts = np.unique(face_medias, return_counts=True) media_norm_feats = [] for u, ct in zip(unique_medias, unique_media_counts): (ind_m, ) = np.where(face_medias == u) if ct == 1: media_norm_feats += [face_norm_feats[ind_m]] else: # image features from the same video will be aggregated into one feature media_norm_feats += [ np.mean(face_norm_feats[ind_m], axis=0, keepdims=True) ] media_norm_feats = np.array(media_norm_feats) # media_norm_feats = media_norm_feats / np.sqrt(np.sum(media_norm_feats ** 2, -1, keepdims=True)) template_feats[count_template] = np.sum(media_norm_feats, axis=0) if count_template % 2000 == 0: print('Finish Calculating {} template features.'.format( count_template)) # template_norm_feats = template_feats / np.sqrt(np.sum(template_feats ** 2, -1, keepdims=True)) template_norm_feats = sklearn.preprocessing.normalize(template_feats) # print(template_norm_feats.shape) return template_norm_feats, unique_templates # In[ ]: def verification(template_norm_feats=None, unique_templates=None, p1=None, p2=None): # ========================================================== # Compute set-to-set Similarity Score. # ========================================================== template2id = np.zeros((max(unique_templates) + 1, 1), dtype=int) for count_template, uqt in enumerate(unique_templates): template2id[uqt] = count_template score = np.zeros((len(p1), )) # save cosine distance between pairs total_pairs = np.array(range(len(p1))) batchsize = 100000 # small batchsize instead of all pairs in one batch due to the memory limiation sublists = [ total_pairs[i:i + batchsize] for i in range(0, len(p1), batchsize) ] total_sublists = len(sublists) for c, s in enumerate(sublists): feat1 = template_norm_feats[template2id[p1[s]]] feat2 = template_norm_feats[template2id[p2[s]]] similarity_score = np.sum(feat1 * feat2, -1) score[s] = similarity_score.flatten() if c % 10 == 0: print('Finish {}/{} pairs.'.format(c, total_sublists)) return score # In[ ]: def verification2(template_norm_feats=None, unique_templates=None, p1=None, p2=None): template2id = np.zeros((max(unique_templates) + 1, 1), dtype=int) for count_template, uqt in enumerate(unique_templates): template2id[uqt] = count_template score = np.zeros((len(p1), )) # save cosine distance between pairs total_pairs = np.array(range(len(p1))) batchsize = 100000 # small batchsize instead of all pairs in one batch due to the memory limiation sublists = [ total_pairs[i:i + batchsize] for i in range(0, len(p1), batchsize) ] total_sublists = len(sublists) for c, s in enumerate(sublists): feat1 = template_norm_feats[template2id[p1[s]]] feat2 = template_norm_feats[template2id[p2[s]]] similarity_score = np.sum(feat1 * feat2, -1) score[s] = similarity_score.flatten() if c % 10 == 0: print('Finish {}/{} pairs.'.format(c, total_sublists)) return score def read_score(path): with open(path, 'rb') as fid: img_feats = pickle.load(fid) return img_feats # # Step1: Load Meta Data assert target == 'IJBC' or target == 'IJBB' # ============================================================= # load image and template relationships for template feature embedding # tid --> template id, mid --> media id # format: # image_name tid mid # ============================================================= start = timeit.default_timer() templates, medias = read_template_media_list( os.path.join('%s/meta' % image_path, '%s_face_tid_mid.txt' % target.lower())) stop = timeit.default_timer() print('Time: %.2f s. ' % (stop - start)) # ============================================================= # load template pairs for template-to-template verification # tid : template id, label : 1/0 # format: # tid_1 tid_2 label # ============================================================= start = timeit.default_timer() p1, p2, label = read_template_pair_list( os.path.join('%s/meta' % image_path, '%s_template_pair_label.txt' % target.lower())) stop = timeit.default_timer() print('Time: %.2f s. ' % (stop - start)) # # Step 2: Get Image Features # ============================================================= # load image features # format: # img_feats: [image_num x feats_dim] (227630, 512) # ============================================================= start = timeit.default_timer() img_path = '%s/loose_crop' % image_path img_list_path = '%s/meta/%s_name_5pts_score.txt' % (image_path, target.lower()) img_list = open(img_list_path) files = img_list.readlines() dataset = DatasetIJB(root=img_path, lines=files, align=True) img_feats = extract_parallel(args.model_prefix, args.model_epoch, dataset, args.batch_size, size=args.emb_size) faceness_scores = [] for each_line in files: name_lmk_score = each_line.split() faceness_scores.append(name_lmk_score[-1]) faceness_scores = np.array(faceness_scores).astype(np.float32) stop = timeit.default_timer() print('Time: %.2f s. ' % (stop - start)) print('Feature Shape: ({} , {}) .'.format(img_feats.shape[0], img_feats.shape[1])) # # Step3: Get Template Features # In[ ]: # ============================================================= # compute template features from image features. # ============================================================= start = timeit.default_timer() # ========================================================== # Norm feature before aggregation into template feature? # Feature norm from embedding network and faceness score are able to decrease weights for noise samples (not face). # ========================================================== # 1. FaceScore （Feature Norm） # 2. FaceScore （Detector） if use_flip_test: # concat --- F1 # img_input_feats = img_feats # add --- F2 img_input_feats = img_feats[:, 0:img_feats.shape[1] // 2] + img_feats[:, img_feats.shape[1] // 2:] else: img_input_feats = img_feats[:, 0:img_feats.shape[1] // 2] if use_norm_score: img_input_feats = img_input_feats else: # normalise features to remove norm information img_input_feats = img_input_feats / np.sqrt( np.sum(img_input_feats**2, -1, keepdims=True)) if use_detector_score: print(img_input_feats.shape, faceness_scores.shape) # img_input_feats = img_input_feats * np.matlib.repmat(faceness_scores[:,np.newaxis], 1, img_input_feats.shape[1]) img_input_feats = img_input_feats * faceness_scores[:, np.newaxis] else: img_input_feats = img_input_feats template_norm_feats, unique_templates = image2template_feature( img_input_feats, templates, medias) stop = timeit.default_timer() print('Time: %.2f s. ' % (stop - start)) # # Step 4: Get Template Similarity Scores # In[ ]: # ============================================================= # compute verification scores between template pairs. # ============================================================= start = timeit.default_timer() score = verification(template_norm_feats, unique_templates, p1, p2) stop = timeit.default_timer() print('Time: %.2f s. ' % (stop - start)) # In[ ]: save_path = result_dir + '/%s_result' % target if not os.path.exists(save_path): os.makedirs(save_path) score_save_file = os.path.join(save_path, "%s.npy" % job) np.save(score_save_file, score) # # Step 5: Get ROC Curves and TPR@FPR Table # In[ ]: files = [score_save_file] methods = [] scores = [] for file in files: methods.append(Path(file).stem) scores.append(np.load(file)) methods = np.array(methods) scores = dict(zip(methods, scores)) colours = dict( zip(methods, sample_colours_from_colourmap(methods.shape[0], 'Set2'))) # x_labels = [1/(10**x) for x in np.linspace(6, 0, 6)] x_labels = [10**-6, 10**-5, 10**-4, 10**-3, 10**-2, 10**-1] tpr_fpr_table = PrettyTable(['Methods'] + [str(x) for x in x_labels]) fig = plt.figure() for method in methods: fpr, tpr, _ = roc_curve(label, scores[method]) roc_auc = auc(fpr, tpr) fpr = np.flipud(fpr) tpr = np.flipud(tpr) # select largest tpr at same fpr plt.plot(fpr, tpr, color=colours[method], lw=1, label=('[%s (AUC = %0.4f %%)]' % (method.split('-')[-1], roc_auc * 100))) tpr_fpr_row = [] tpr_fpr_row.append("%s-%s" % (method, target)) for fpr_iter in np.arange(len(x_labels)): _, min_index = min( list(zip(abs(fpr - x_labels[fpr_iter]), range(len(fpr))))) # tpr_fpr_row.append('%.4f' % tpr[min_index]) tpr_fpr_row.append('%.2f' % (tpr[min_index] * 100)) tpr_fpr_table.add_row(tpr_fpr_row) plt.xlim([10**-6, 0.1]) plt.ylim([0.3, 1.0]) plt.grid(linestyle='--', linewidth=1) plt.xticks(x_labels) plt.yticks(np.linspace(0.3, 1.0, 8, endpoint=True)) plt.xscale('log') plt.xlabel('False Positive Rate') plt.ylabel('True Positive Rate') plt.title('ROC on IJB') plt.legend(loc="lower right") # plt.show() fig.savefig(os.path.join(save_path, '%s.pdf' % job)) print(tpr_fpr_table)