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Merge pull request #1349 from leondgarse/master

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Reformat and speed up IJB evaluation
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AnXiang
2020-12-08 20:54:34 +08:00
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parent 23111ce969 aebd8376e3
commit 5b40f4bfce
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evaluation/IJB/IJB_evals.py Executable file
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#!/usr/bin/env python3
import os
import numpy as np
from tqdm import tqdm
from skimage import transform
from sklearn.preprocessing import normalize
from sklearn.metrics import roc_curve, auc
import pandas as pd
import cv2
class Mxnet_model_interf:
import mxnet as mx
def __init__(self, model_file, layer="fc1", image_size=(112, 112)):
cvd = os.environ.get("CUDA_VISIBLE_DEVICES", "").strip()
if len(cvd) > 0 and int(cvd) != -1:
ctx = [self.mx.gpu(ii) for ii in range(len(cvd.split(",")))]
else:
ctx = [self.mx.cpu()]
prefix, epoch = model_file.split(",")
print(">>>> loading mxnet model:", prefix, epoch, ctx)
sym, arg_params, aux_params = self.mx.model.load_checkpoint(prefix, int(epoch))
all_layers = sym.get_internals()
sym = all_layers[layer + "_output"]
model = self.mx.mod.Module(symbol=sym, context=ctx, label_names=None)
model.bind(data_shapes=[("data", (1, 3, image_size[0], image_size[1]))])
model.set_params(arg_params, aux_params)
self.model = model
def __call__(self, imgs):
# print(imgs.shape, imgs[0])
imgs = imgs.transpose(0, 3, 1, 2)
data = self.mx.nd.array(imgs)
db = self.mx.io.DataBatch(data=(data,))
self.model.forward(db, is_train=False)
emb = self.model.get_outputs()[0].asnumpy()
return emb
def keras_model_interf(model_file):
import tensorflow as tf
mm = tf.keras.models.load_model(model_file, compile=False)
return lambda imgs: mm((tf.cast(imgs, "float32") - 127.5) * 0.0078125).numpy()
def face_align_landmark(img, landmark, image_size=(112, 112), method="similar"):
tform = transform.AffineTransform() if method == "affine" else transform.SimilarityTransform()
src = np.array(
[[38.2946, 51.6963], [73.5318, 51.5014], [56.0252, 71.7366], [41.5493, 92.3655], [70.729904, 92.2041]], dtype=np.float32
)
tform.estimate(landmark, src)
# ndimage = transform.warp(img, tform.inverse, output_shape=image_size)
# ndimage = (ndimage * 255).astype(np.uint8)
M = tform.params[0:2, :]
ndimage = cv2.warpAffine(img, M, image_size, borderValue=0.0)
if len(ndimage.shape) == 2:
ndimage = np.stack([ndimage, ndimage, ndimage], -1)
else:
ndimage = cv2.cvtColor(ndimage, cv2.COLOR_BGR2RGB)
return ndimage
def read_IJB_meta_columns_to_int(file_path, columns, dtype=str, skiprows=0, delimiter=None):
meta = np.loadtxt(file_path, dtype=dtype, skiprows=skiprows, delimiter=delimiter)
return (meta[:, ii].astype("int") for ii in columns)
def extract_IJB_data_11(data_path, subset, save_path=None, force_reload=False):
if save_path == None:
save_path = os.path.join(data_path, subset + "_backup.npz")
if not force_reload and os.path.exists(save_path):
print(">>>> Reloading from backup: %s ..." % save_path)
aa = np.load(save_path)
return (
aa["templates"],
aa["medias"],
aa["p1"],
aa["p2"],
aa["label"],
aa["img_names"],
aa["landmarks"],
aa["face_scores"],
)
if subset == "IJBB":
media_list_path = os.path.join(data_path, "IJBB/meta/ijbb_face_tid_mid.txt")
pair_list_path = os.path.join(data_path, "IJBB/meta/ijbb_template_pair_label.txt")
img_path = os.path.join(data_path, "IJBB/loose_crop")
img_list_path = os.path.join(data_path, "IJBB/meta/ijbb_name_5pts_score.txt")
else:
media_list_path = os.path.join(data_path, "IJBC/meta/ijbc_face_tid_mid.txt")
pair_list_path = os.path.join(data_path, "IJBC/meta/ijbc_template_pair_label.txt")
img_path = os.path.join(data_path, "IJBC/loose_crop")
img_list_path = os.path.join(data_path, "IJBC/meta/ijbc_name_5pts_score.txt")
print(">>>> Loading templates and medias...")
templates, medias = read_IJB_meta_columns_to_int(media_list_path, columns=[1, 2]) # ['1.jpg', '1', '69544']
print("templates: %s, medias: %s, unique templates: %s" % (templates.shape, medias.shape, np.unique(templates).shape))
# (227630,) (227630,) (12115,)
print(">>>> Loading pairs...")
p1, p2, label = read_IJB_meta_columns_to_int(pair_list_path, columns=[0, 1, 2]) # ['1', '11065', '1']
print("p1: %s, unique p1: %s" % (p1.shape, np.unique(p1).shape))
print("p2: %s, unique p2: %s" % (p2.shape, np.unique(p2).shape))
print("label: %s, label value counts: %s" % (label.shape, dict(zip(*np.unique(label, return_counts=True)))))
# (8010270,) (8010270,) (8010270,) (1845,) (10270,) # 10270 + 1845 = 12115
# {0: 8000000, 1: 10270}
print(">>>> Loading images...")
with open(img_list_path, "r") as ff:
# 1.jpg 46.060 62.026 87.785 60.323 68.851 77.656 52.162 99.875 86.450 98.648 0.999
img_records = np.array([ii.strip().split(" ") for ii in ff.readlines()])
img_names = np.array([os.path.join(img_path, ii) for ii in img_records[:, 0]])
landmarks = img_records[:, 1:-1].astype("float32").reshape(-1, 5, 2)
face_scores = img_records[:, -1].astype("float32")
print("img_names: %s, landmarks: %s, face_scores: %s" % (img_names.shape, landmarks.shape, face_scores.shape))
# (227630,) (227630, 5, 2) (227630,)
print("face_scores value counts:", dict(zip(*np.histogram(face_scores, bins=9)[::-1])))
# {0.1: 2515, 0.2: 0, 0.3: 62, 0.4: 94, 0.5: 136, 0.6: 197, 0.7: 291, 0.8: 538, 0.9: 223797}
print(">>>> Saving backup to: %s ..." % save_path)
np.savez(
save_path,
templates=templates,
medias=medias,
p1=p1,
p2=p2,
label=label,
img_names=img_names,
landmarks=landmarks,
face_scores=face_scores,
)
print()
return templates, medias, p1, p2, label, img_names, landmarks, face_scores
def extract_gallery_prob_data(data_path, subset, save_path=None, force_reload=False):
if save_path == None:
save_path = os.path.join(data_path, subset + "_gallery_prob_backup.npz")
if not force_reload and os.path.exists(save_path):
print(">>>> Reloading from backup: %s ..." % save_path)
aa = np.load(save_path)
return (
aa["gallery_templates"],
aa["gallery_subject_ids"],
aa["probe_mixed_templates"],
aa["probe_mixed_subject_ids"],
)
if subset == "IJBC":
meta_dir = os.path.join(data_path, "IJBC/meta")
gallery_s1_record = os.path.join(meta_dir, "ijbc_1N_gallery_G1.csv")
gallery_s2_record = os.path.join(meta_dir, "ijbc_1N_gallery_G2.csv")
probe_mixed_record = os.path.join(meta_dir, "ijbc_1N_probe_mixed.csv")
else:
meta_dir = os.path.join(data_path, "IJBB/meta")
gallery_s1_record = os.path.join(meta_dir, "ijbb_1N_gallery_S1.csv")
gallery_s2_record = os.path.join(meta_dir, "ijbb_1N_gallery_S2.csv")
probe_mixed_record = os.path.join(meta_dir, "ijbb_1N_probe_mixed.csv")
print(">>>> Loading gallery feature...")
s1_templates, s1_subject_ids = read_IJB_meta_columns_to_int(gallery_s1_record, columns=[0, 1], skiprows=1, delimiter=",")
s2_templates, s2_subject_ids = read_IJB_meta_columns_to_int(gallery_s2_record, columns=[0, 1], skiprows=1, delimiter=",")
gallery_templates = np.concatenate([s1_templates, s2_templates])
gallery_subject_ids = np.concatenate([s1_subject_ids, s2_subject_ids])
print("s1 gallery: %s, ids: %s, unique: %s" % (s1_templates.shape, s1_subject_ids.shape, np.unique(s1_templates).shape))
print("s2 gallery: %s, ids: %s, unique: %s" % (s2_templates.shape, s2_subject_ids.shape, np.unique(s2_templates).shape))
print(
"total gallery: %s, ids: %s, unique: %s"
% (gallery_templates.shape, gallery_subject_ids.shape, np.unique(gallery_templates).shape)
)
print(">>>> Loading prope feature...")
probe_mixed_templates, probe_mixed_subject_ids = read_IJB_meta_columns_to_int(
probe_mixed_record, columns=[0, 1], skiprows=1, delimiter=","
)
print("probe_mixed_templates: %s, unique: %s" % (probe_mixed_templates.shape, np.unique(probe_mixed_templates).shape))
print("probe_mixed_subject_ids: %s, unique: %s" % (probe_mixed_subject_ids.shape, np.unique(probe_mixed_subject_ids).shape))
print(">>>> Saving backup to: %s ..." % save_path)
np.savez(
save_path,
gallery_templates=gallery_templates,
gallery_subject_ids=gallery_subject_ids,
probe_mixed_templates=probe_mixed_templates,
probe_mixed_subject_ids=probe_mixed_subject_ids,
)
print()
return gallery_templates, gallery_subject_ids, probe_mixed_templates, probe_mixed_subject_ids
def get_embeddings(model_interf, img_names, landmarks, batch_size=64, flip=True):
steps = int(np.ceil(len(img_names) / batch_size))
embs, embs_f = [], []
for batch_id in tqdm(range(0, len(img_names), batch_size), "Embedding", total=steps):
batch_imgs, batch_landmarks = img_names[batch_id : batch_id + batch_size], landmarks[batch_id : batch_id + batch_size]
ndimages = [face_align_landmark(cv2.imread(img), landmark) for img, landmark in zip(batch_imgs, batch_landmarks)]
ndimages = np.stack(ndimages)
embs.extend(model_interf(ndimages))
if flip:
embs_f.extend(model_interf(ndimages[:, :, ::-1, :]))
return np.array(embs), np.array(embs_f)
def process_embeddings(embs, embs_f=[], use_flip_test=True, use_norm_score=False, use_detector_score=True, face_scores=None):
if use_flip_test and len(embs_f) != 0:
embs = embs + embs_f
if use_norm_score:
embs = normalize(embs)
if use_detector_score and face_scores is not None:
embs = embs * np.expand_dims(face_scores, -1)
return embs
def image2template_feature(img_feats=None, templates=None, medias=None, choose_templates=None, choose_ids=None):
if choose_templates is not None: # 1N
unique_templates, indices = np.unique(choose_templates, return_index=True)
unique_subjectids = choose_ids[indices]
else: # 11
unique_templates = np.unique(templates)
unique_subjectids = None
template_feats = np.zeros((len(unique_templates), img_feats.shape[1]))
for count_template, uqt in tqdm(enumerate(unique_templates), "Extract template feature", total=len(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], 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, 0)
template_norm_feats = normalize(template_feats)
return template_norm_feats, unique_templates, unique_subjectids
def verification_11(template_norm_feats=None, unique_templates=None, p1=None, p2=None, batch_size=100000):
template2id = np.zeros((max(unique_templates) + 1, 1), dtype=int)
for count_template, uqt in enumerate(unique_templates):
template2id[uqt] = count_template
steps = int(np.ceil(len(p1) / batch_size))
score = []
for id in tqdm(range(steps), "Verification"):
feat1 = template_norm_feats[template2id[p1[id * batch_size : (id + 1) * batch_size]].flatten()]
feat2 = template_norm_feats[template2id[p2[id * batch_size : (id + 1) * batch_size]].flatten()]
score.extend(np.sum(feat1 * feat2, -1))
return np.array(score)
def evaluation_1N(query_feats, gallery_feats, query_ids, reg_ids):
import heapq
Fars = [0.01, 0.1]
print("query_feats: %s, gallery_feats: %s" % (query_feats.shape, gallery_feats.shape))
query_num = query_feats.shape[0]
gallery_num = gallery_feats.shape[0]
similarity = np.dot(query_feats, gallery_feats.T)
print("similarity shape:", similarity.shape)
top_inds = np.argsort(-similarity)
print("top_inds shape:", top_inds.shape)
# gen_mask
mask = []
for query_id in query_ids:
pos = [i for i, x in enumerate(reg_ids) if query_id == x]
if len(pos) != 1:
raise RuntimeError("RegIdsError with id = {}， duplicate = {} ".format(query_id, len(pos)))
mask.append(pos[0])
# calculate top_n
correct_num_1, correct_num_5, correct_num_10 = 0, 0, 0
for i in range(query_num):
top_1, top_5, top_10 = top_inds[i, 0], top_inds[i, 0:5], top_inds[i, 0:10]
if mask[i] == top_1:
correct_num_1 += 1
if mask[i] in top_5:
correct_num_5 += 1
if mask[i] in top_10:
correct_num_10 += 1
print("top1: %f, top5: %f, top10: %f" % (correct_num_1 / query_num, correct_num_5 / query_num, correct_num_10 / query_num))
# neg_pair_num = query_num * gallery_num - query_num
# print("neg_pair_num:", neg_pair_num)
required_topk = [int(np.ceil(query_num * x)) for x in Fars]
top_sims = similarity
# calculate fars and tprs
pos_sims = []
for i in range(query_num):
gt = mask[i]
pos_sims.append(top_sims[i, gt])
top_sims[i, gt] = -2.0
pos_sims = np.array(pos_sims)
neg_sims = top_sims[np.where(top_sims > -2.0)]
neg_sims_sorted = heapq.nlargest(max(required_topk), neg_sims) # heap sort
print("pos_sims: %s, neg_sims: %s, neg_sims_sorted: %d" % (pos_sims.shape, neg_sims.shape, len(neg_sims_sorted)))
for far, pos in zip(Fars, required_topk):
th = neg_sims[pos - 1]
recall = np.sum(pos_sims > th) / query_num
print("far = {:.10f} pr = {:.10f} th = {:.10f}".format(far, recall, th))
class IJB_test:
def __init__(self, model_file, data_path, subset, batch_size=64, force_reload=False, restore_embs=None):
templates, medias, p1, p2, label, img_names, landmarks, face_scores = extract_IJB_data_11(
data_path, subset, force_reload=force_reload
)
if model_file != None:
interf_func = keras_model_interf(model_file) if model_file.endswith(".h5") else Mxnet_model_interf(model_file)
self.embs, self.embs_f = get_embeddings(interf_func, img_names, landmarks, batch_size=batch_size)
elif restore_embs != None:
print(">>>> Reload embeddings from:", restore_embs)
aa = np.load(restore_embs)
if "embs" in aa and "embs_f" in aa:
self.embs, self.embs_f = aa["embs"], aa["embs_f"]
else:
print("ERROR: %s NOT containing embs / embs_f" % restore_embs)
exit(1)
self.data_path, self.subset, self.force_reload = data_path, subset, force_reload
self.templates, self.medias, self.p1, self.p2, self.face_scores = templates, medias, p1, p2, face_scores
self.label = label
def run_model_test_single(self, use_flip_test=True, use_norm_score=False, use_detector_score=True):
img_input_feats = process_embeddings(
self.embs,
self.embs_f,
use_flip_test=use_flip_test,
use_norm_score=use_norm_score,
use_detector_score=use_detector_score,
face_scores=self.face_scores,
)
template_norm_feats, unique_templates, _ = image2template_feature(img_input_feats, self.templates, self.medias)
score = verification_11(template_norm_feats, unique_templates, self.p1, self.p2)
return score
def run_model_test_bunch(self):
scores, names = [], []
for use_norm_score in [True, False]:
for use_detector_score in [True, False]:
for use_flip_test in [True, False]:
name = "N{:d}D{:d}F{:d}".format(use_norm_score, use_detector_score, use_flip_test)
print(">>>>", name, use_norm_score, use_detector_score, use_flip_test)
names.append(name)
scores.append(self.run_model_test_single(use_flip_test, use_norm_score, use_detector_score))
return scores, names
def run_model_test_1N(self):
gallery_templates, gallery_subject_ids, probe_mixed_templates, probe_mixed_subject_ids = extract_gallery_prob_data(
self.data_path, self.subset, force_reload=self.force_reload
)
img_input_feats = process_embeddings(
self.embs,
self.embs_f,
use_flip_test=True,
use_norm_score=False,
use_detector_score=True,
face_scores=self.face_scores,
)
gallery_templates_feature, gallery_unique_templates, gallery_unique_subject_ids = image2template_feature(
img_input_feats, self.templates, self.medias, gallery_templates, gallery_subject_ids
)
print("gallery_templates_feature:", gallery_templates_feature.shape)
print("gallery_unique_subject_ids:", gallery_unique_subject_ids.shape)
probe_mixed_templates_feature, probe_mixed_unique_templates, probe_mixed_unique_subject_ids = image2template_feature(
img_input_feats, self.templates, self.medias, probe_mixed_templates, probe_mixed_subject_ids
)
print("probe_mixed_templates_feature:", probe_mixed_templates_feature.shape)
print("probe_mixed_unique_subject_ids:", probe_mixed_unique_subject_ids.shape)
evaluation_1N(
probe_mixed_templates_feature, gallery_templates_feature, probe_mixed_unique_subject_ids, gallery_unique_subject_ids
)
def plot_roc_and_calculate_tpr(scores, names=None, label=None):
score_dict = {}
for id, score in enumerate(scores):
name = None if names is None else names[id]
if isinstance(score, str) and score.endswith(".npz"):
aa = np.load(score)
score = aa.get("scores", [])
label = aa["label"] if label is None and "label" in aa else label
score_name = aa.get("names", [])
for ss, nn in zip(score, score_name):
score_dict[nn] = ss
elif isinstance(score, str) and score.endswith(".npy"):
name = name if name is not None else os.path.splitext(os.path.basename(score))[0]
score_dict[name] = np.load(score)
elif isinstance(score, str) and score.endswith(".txt"):
# IJB meta data like ijbb_template_pair_label.txt
pairs = np.loadtxt(score, dtype=str)
label = pairs[:, 2].astype(np.int)
else:
name = name if name is not None else str(id)
score_dict[name] = score
if label is None:
print("Error: Label data is not provided")
return None, None
x_labels = [10 ** (-ii) for ii in range(1, 7)[::-1]]
fpr_dict, tpr_dict, roc_auc_dict, tpr_result = {}, {}, {}, {}
for name, score in score_dict.items():
fpr, tpr, _ = roc_curve(label, score)
roc_auc = auc(fpr, tpr)
fpr, tpr = np.flipud(fpr), np.flipud(tpr) # select largest tpr at same fpr
tpr_result[name] = [tpr[np.argmin(abs(fpr - ii))] for ii in x_labels]
fpr_dict[name], tpr_dict[name], roc_auc_dict[name] = fpr, tpr, roc_auc
tpr_result_df = pd.DataFrame(tpr_result, index=x_labels).T
tpr_result_df.columns.name = "Methods"
print(tpr_result_df.to_markdown())
# print(tpr_result_df)
try:
import matplotlib.pyplot as plt
fig = plt.figure()
for name in score_dict:
plt.plot(fpr_dict[name], tpr_dict[name], lw=1, label="[%s (AUC = %0.4f%%)]" % (name, roc_auc_dict[name] * 100))
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.tight_layout()
plt.show()
except:
print("Missing matplotlib")
fig = None
return tpr_result_df, fig
def parse_arguments(argv):
import argparse
default_save_result_name = "IJB_result/{model_name}_{subset}.npz"
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("-m", "--model_file", type=str, default=None, help="Saved model file path, could be keras / mxnet one")
parser.add_argument("-d", "--data_path", type=str, default="./", help="Dataset path")
parser.add_argument("-s", "--subset", type=str, default="IJBB", help="Subset test target, could be IJBB / IJBC")
parser.add_argument("-b", "--batch_size", type=int, default=64, help="Batch size for get_embeddings")
parser.add_argument(
"-R", "--save_result", type=str, default=default_save_result_name, help="Filename for saving / restore result"
)
parser.add_argument("-L", "--save_label", action="store_true", help="Save label data, useful for plot only")
parser.add_argument("-E", "--save_embeddings", action="store_true", help="Save embeddings data")
parser.add_argument("-B", "--is_bunch", action="store_true", help="Run all 8 tests N{0,1}D{0,1}F{0,1}")
parser.add_argument("-N", "--is_one_2_N", action="store_true", help="Run 1:N test instead of 1:1")
parser.add_argument("-F", "--force_reload", action="store_true", help="Force reload, instead of using cache")
parser.add_argument("-p", "--plot_only", nargs="*", type=str, help="Plot saved results, Format 1 2 3 or 1, 2, 3 or *.npy")
args = parser.parse_known_args(argv)[0]
if args.plot_only != None and len(args.plot_only) != 0:
# Plot only
from glob2 import glob
score_files = []
for ss in args.plot_only:
score_files.extend(glob(ss.replace(",", "").strip()))
args.plot_only = score_files
elif args.model_file == None and args.save_result == default_save_result_name:
print("Please provide -m MODEL_FILE, see `--help` for usage.")
exit(1)
elif args.model_file != None:
if args.model_file.endswith(".h5"):
# Keras model file "model.h5"
model_name = os.path.splitext(os.path.basename(args.model_file))[0]
else:
# MXNet model file "models/r50-arcface-emore/model,1"
model_name = os.path.basename(os.path.dirname(args.model_file))
if args.save_result == default_save_result_name:
args.save_result = default_save_result_name.format(model_name=model_name, subset=args.subset)
return args
if __name__ == "__main__":
import sys
args = parse_arguments(sys.argv[1:])
if args.plot_only != None and len(args.plot_only) != 0:
plot_roc_and_calculate_tpr(args.plot_only)
else:
save_name = os.path.splitext(args.save_result)[0]
save_items = {}
tt = IJB_test(args.model_file, args.data_path, args.subset, args.batch_size, args.force_reload, args.save_result)
if args.is_one_2_N: # 1:N test
tt.run_model_test_1N()
elif args.is_bunch: # All 8 tests N{0,1}D{0,1}F{0,1}
scores, names = tt.run_model_test_bunch()
names = [save_name + "_" + ii for ii in names]
save_items.update({"scores": scores, "names": names})
else: # Basic 1:1 N0D1F1 test
score = tt.run_model_test_single()
scores, names = [score], [save_name]
save_items.update({"scores": scores, "names": names})
if args.save_embeddings:
save_items.update({"embs": tt.embs, "embs_f": tt.embs_f})
if args.save_label:
save_items.update({"label": tt.label})
if args.model_file != None or args.save_embeddings: # embeddings not restored from file or should save_embeddings again
save_path = os.path.dirname(args.save_result)
if not os.path.exists(save_path):
os.makedirs(save_path)
np.savez(args.save_result, **save_items)
if not args.is_one_2_N:
plot_roc_and_calculate_tpr(scores, names=names, label=tt.label)