mirror of
https://github.com/deepinsight/insightface.git
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656 lines
31 KiB
Python
Executable File
656 lines
31 KiB
Python
Executable File
#!/usr/bin/env python3
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import os
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import cv2
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import numpy as np
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import pandas as pd
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from tqdm import tqdm
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from skimage import transform
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from sklearn.preprocessing import normalize
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from sklearn.metrics import roc_curve, auc
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class Mxnet_model_interf:
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def __init__(self, model_file, layer="fc1", image_size=(112, 112)):
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import mxnet as mx
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self.mx = mx
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cvd = os.environ.get("CUDA_VISIBLE_DEVICES", "").strip()
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if len(cvd) > 0 and int(cvd) != -1:
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ctx = [self.mx.gpu(ii) for ii in range(len(cvd.split(",")))]
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else:
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ctx = [self.mx.cpu()]
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prefix, epoch = model_file.split(",")
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print(">>>> loading mxnet model:", prefix, epoch, ctx)
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sym, arg_params, aux_params = self.mx.model.load_checkpoint(prefix, int(epoch))
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all_layers = sym.get_internals()
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sym = all_layers[layer + "_output"]
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model = self.mx.mod.Module(symbol=sym, context=ctx, label_names=None)
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model.bind(data_shapes=[("data", (1, 3, image_size[0], image_size[1]))])
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model.set_params(arg_params, aux_params)
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self.model = model
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def __call__(self, imgs):
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# print(imgs.shape, imgs[0])
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imgs = imgs.transpose(0, 3, 1, 2)
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data = self.mx.nd.array(imgs)
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db = self.mx.io.DataBatch(data=(data,))
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self.model.forward(db, is_train=False)
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emb = self.model.get_outputs()[0].asnumpy()
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return emb
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class Torch_model_interf:
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def __init__(self, model_file, image_size=(112, 112)):
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import torch
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self.torch = torch
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cvd = os.environ.get("CUDA_VISIBLE_DEVICES", "").strip()
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device_name = "cuda:0" if len(cvd) > 0 and int(cvd) != -1 else "cpu"
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self.device = self.torch.device(device_name)
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try:
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self.model = self.torch.jit.load(model_file, map_location=device_name)
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except:
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print("Error: %s is weights only, please load and save the entire model by `torch.jit.save`" % model_file)
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self.model = None
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def __call__(self, imgs):
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# print(imgs.shape, imgs[0])
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imgs = imgs.transpose(0, 3, 1, 2).copy().astype("float32")
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imgs = (imgs - 127.5) * 0.0078125
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output = self.model(self.torch.from_numpy(imgs).to(self.device).float())
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return output.cpu().detach().numpy()
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class ONNX_model_interf:
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def __init__(self, model_file, image_size=(112, 112)):
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import onnxruntime as ort
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ort.set_default_logger_severity(3)
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self.ort_session = ort.InferenceSession(model_file)
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self.output_names = [self.ort_session.get_outputs()[0].name]
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self.input_name = self.ort_session.get_inputs()[0].name
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def __call__(self, imgs):
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imgs = imgs.transpose(0, 3, 1, 2).astype("float32")
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imgs = (imgs - 127.5) * 0.0078125
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outputs = self.ort_session.run(self.output_names, {self.input_name: imgs})
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return outputs[0]
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def keras_model_interf(model_file):
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import tensorflow as tf
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from tensorflow_addons.layers import StochasticDepth
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for gpu in tf.config.experimental.list_physical_devices("GPU"):
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tf.config.experimental.set_memory_growth(gpu, True)
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mm = tf.keras.models.load_model(model_file, compile=False)
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return lambda imgs: mm((tf.cast(imgs, "float32") - 127.5) * 0.0078125).numpy()
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def face_align_landmark(img, landmark, image_size=(112, 112), method="similar"):
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tform = transform.AffineTransform() if method == "affine" else transform.SimilarityTransform()
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src = np.array(
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[[38.2946, 51.6963], [73.5318, 51.5014], [56.0252, 71.7366], [41.5493, 92.3655], [70.729904, 92.2041]], dtype=np.float32
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)
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tform.estimate(landmark, src)
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# ndimage = transform.warp(img, tform.inverse, output_shape=image_size)
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# ndimage = (ndimage * 255).astype(np.uint8)
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M = tform.params[0:2, :]
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ndimage = cv2.warpAffine(img, M, image_size, borderValue=0.0)
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if len(ndimage.shape) == 2:
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ndimage = np.stack([ndimage, ndimage, ndimage], -1)
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else:
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ndimage = cv2.cvtColor(ndimage, cv2.COLOR_BGR2RGB)
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return ndimage
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def read_IJB_meta_columns_to_int(file_path, columns, sep=" ", skiprows=0, header=None):
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# meta = np.loadtxt(file_path, skiprows=skiprows, delimiter=sep)
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meta = pd.read_csv(file_path, sep=sep, skiprows=skiprows, header=header).values
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return (meta[:, ii].astype("int") for ii in columns)
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def extract_IJB_data_11(data_path, subset, save_path=None, force_reload=False):
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if save_path == None:
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save_path = os.path.join(data_path, subset + "_backup.npz")
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if not force_reload and os.path.exists(save_path):
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print(">>>> Reload from backup: %s ..." % save_path)
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aa = np.load(save_path)
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return (
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aa["templates"],
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aa["medias"],
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aa["p1"],
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aa["p2"],
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aa["label"],
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aa["img_names"],
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aa["landmarks"],
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aa["face_scores"],
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)
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if subset == "IJBB":
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media_list_path = os.path.join(data_path, "IJBB/meta/ijbb_face_tid_mid.txt")
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pair_list_path = os.path.join(data_path, "IJBB/meta/ijbb_template_pair_label.txt")
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img_path = os.path.join(data_path, "IJBB/loose_crop")
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img_list_path = os.path.join(data_path, "IJBB/meta/ijbb_name_5pts_score.txt")
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else:
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media_list_path = os.path.join(data_path, "IJBC/meta/ijbc_face_tid_mid.txt")
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pair_list_path = os.path.join(data_path, "IJBC/meta/ijbc_template_pair_label.txt")
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img_path = os.path.join(data_path, "IJBC/loose_crop")
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img_list_path = os.path.join(data_path, "IJBC/meta/ijbc_name_5pts_score.txt")
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print(">>>> Loading templates and medias...")
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templates, medias = read_IJB_meta_columns_to_int(media_list_path, columns=[1, 2]) # ['1.jpg', '1', '69544']
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print("templates: %s, medias: %s, unique templates: %s" % (templates.shape, medias.shape, np.unique(templates).shape))
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# templates: (227630,), medias: (227630,), unique templates: (12115,)
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print(">>>> Loading pairs...")
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p1, p2, label = read_IJB_meta_columns_to_int(pair_list_path, columns=[0, 1, 2]) # ['1', '11065', '1']
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print("p1: %s, unique p1: %s" % (p1.shape, np.unique(p1).shape))
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print("p2: %s, unique p2: %s" % (p2.shape, np.unique(p2).shape))
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print("label: %s, label value counts: %s" % (label.shape, dict(zip(*np.unique(label, return_counts=True)))))
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# p1: (8010270,), unique p1: (1845,)
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# p2: (8010270,), unique p2: (10270,) # 10270 + 1845 = 12115 --> np.unique(templates).shape
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# label: (8010270,), label value counts: {0: 8000000, 1: 10270}
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print(">>>> Loading images...")
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with open(img_list_path, "r") as ff:
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# 1.jpg 46.060 62.026 87.785 60.323 68.851 77.656 52.162 99.875 86.450 98.648 0.999
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img_records = np.array([ii.strip().split(" ") for ii in ff.readlines()])
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img_names = np.array([os.path.join(img_path, ii) for ii in img_records[:, 0]])
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landmarks = img_records[:, 1:-1].astype("float32").reshape(-1, 5, 2)
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face_scores = img_records[:, -1].astype("float32")
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print("img_names: %s, landmarks: %s, face_scores: %s" % (img_names.shape, landmarks.shape, face_scores.shape))
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# img_names: (227630,), landmarks: (227630, 5, 2), face_scores: (227630,)
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print("face_scores value counts:", dict(zip(*np.histogram(face_scores, bins=9)[::-1])))
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# {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}
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print(">>>> Saving backup to: %s ..." % save_path)
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np.savez(
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save_path,
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templates=templates,
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medias=medias,
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p1=p1,
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p2=p2,
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label=label,
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img_names=img_names,
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landmarks=landmarks,
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face_scores=face_scores,
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)
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print()
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return templates, medias, p1, p2, label, img_names, landmarks, face_scores
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def extract_gallery_prob_data(data_path, subset, save_path=None, force_reload=False):
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if save_path == None:
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save_path = os.path.join(data_path, subset + "_gallery_prob_backup.npz")
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if not force_reload and os.path.exists(save_path):
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print(">>>> Reload from backup: %s ..." % save_path)
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aa = np.load(save_path)
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return (
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aa["s1_templates"],
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aa["s1_subject_ids"],
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aa["s2_templates"],
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aa["s2_subject_ids"],
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aa["probe_mixed_templates"],
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aa["probe_mixed_subject_ids"],
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)
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if subset == "IJBC":
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meta_dir = os.path.join(data_path, "IJBC/meta")
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gallery_s1_record = os.path.join(meta_dir, "ijbc_1N_gallery_G1.csv")
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gallery_s2_record = os.path.join(meta_dir, "ijbc_1N_gallery_G2.csv")
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probe_mixed_record = os.path.join(meta_dir, "ijbc_1N_probe_mixed.csv")
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else:
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meta_dir = os.path.join(data_path, "IJBB/meta")
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gallery_s1_record = os.path.join(meta_dir, "ijbb_1N_gallery_S1.csv")
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gallery_s2_record = os.path.join(meta_dir, "ijbb_1N_gallery_S2.csv")
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probe_mixed_record = os.path.join(meta_dir, "ijbb_1N_probe_mixed.csv")
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print(">>>> Loading gallery feature...")
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s1_templates, s1_subject_ids = read_IJB_meta_columns_to_int(gallery_s1_record, columns=[0, 1], skiprows=1, sep=",")
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s2_templates, s2_subject_ids = read_IJB_meta_columns_to_int(gallery_s2_record, columns=[0, 1], skiprows=1, sep=",")
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print("s1 gallery: %s, ids: %s, unique: %s" % (s1_templates.shape, s1_subject_ids.shape, np.unique(s1_templates).shape))
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print("s2 gallery: %s, ids: %s, unique: %s" % (s2_templates.shape, s2_subject_ids.shape, np.unique(s2_templates).shape))
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print(">>>> Loading prope feature...")
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probe_mixed_templates, probe_mixed_subject_ids = read_IJB_meta_columns_to_int(
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probe_mixed_record, columns=[0, 1], skiprows=1, sep=","
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)
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print("probe_mixed_templates: %s, unique: %s" % (probe_mixed_templates.shape, np.unique(probe_mixed_templates).shape))
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print("probe_mixed_subject_ids: %s, unique: %s" % (probe_mixed_subject_ids.shape, np.unique(probe_mixed_subject_ids).shape))
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print(">>>> Saving backup to: %s ..." % save_path)
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np.savez(
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save_path,
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s1_templates=s1_templates,
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s1_subject_ids=s1_subject_ids,
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s2_templates=s2_templates,
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s2_subject_ids=s2_subject_ids,
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probe_mixed_templates=probe_mixed_templates,
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probe_mixed_subject_ids=probe_mixed_subject_ids,
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)
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print()
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return s1_templates, s1_subject_ids, s2_templates, s2_subject_ids, probe_mixed_templates, probe_mixed_subject_ids
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def get_embeddings(model_interf, img_names, landmarks, batch_size=64, flip=True):
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steps = int(np.ceil(len(img_names) / batch_size))
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embs, embs_f = [], []
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for batch_id in tqdm(range(0, len(img_names), batch_size), "Embedding", total=steps):
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batch_imgs, batch_landmarks = img_names[batch_id : batch_id + batch_size], landmarks[batch_id : batch_id + batch_size]
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ndimages = [face_align_landmark(cv2.imread(img), landmark) for img, landmark in zip(batch_imgs, batch_landmarks)]
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ndimages = np.stack(ndimages)
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embs.extend(model_interf(ndimages))
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if flip:
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embs_f.extend(model_interf(ndimages[:, :, ::-1, :]))
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return np.array(embs), np.array(embs_f)
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def process_embeddings(embs, embs_f=[], use_flip_test=True, use_norm_score=False, use_detector_score=True, face_scores=None):
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print(">>>> process_embeddings: Norm {}, Detect_score {}, Flip {}".format(use_norm_score, use_detector_score, use_flip_test))
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if use_flip_test and len(embs_f) != 0:
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embs = embs + embs_f
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if use_norm_score:
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embs = normalize(embs)
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if use_detector_score and face_scores is not None:
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embs = embs * np.expand_dims(face_scores, -1)
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return embs
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def image2template_feature(img_feats=None, templates=None, medias=None, choose_templates=None, choose_ids=None):
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if choose_templates is not None: # 1:N
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unique_templates, indices = np.unique(choose_templates, return_index=True)
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unique_subjectids = choose_ids[indices]
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else: # 1:1
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unique_templates = np.unique(templates)
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unique_subjectids = None
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# template_feats = np.zeros((len(unique_templates), img_feats.shape[1]), dtype=img_feats.dtype)
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template_feats = np.zeros((len(unique_templates), img_feats.shape[1]))
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for count_template, uqt in tqdm(enumerate(unique_templates), "Extract template feature", total=len(unique_templates)):
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(ind_t,) = np.where(templates == uqt)
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face_norm_feats = img_feats[ind_t]
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face_medias = medias[ind_t]
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unique_medias, unique_media_counts = np.unique(face_medias, return_counts=True)
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media_norm_feats = []
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for u, ct in zip(unique_medias, unique_media_counts):
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(ind_m,) = np.where(face_medias == u)
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if ct == 1:
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media_norm_feats += [face_norm_feats[ind_m]]
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else: # image features from the same video will be aggregated into one feature
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media_norm_feats += [np.mean(face_norm_feats[ind_m], 0, keepdims=True)]
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media_norm_feats = np.array(media_norm_feats)
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# media_norm_feats = media_norm_feats / np.sqrt(np.sum(media_norm_feats ** 2, -1, keepdims=True))
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template_feats[count_template] = np.sum(media_norm_feats, 0)
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template_norm_feats = normalize(template_feats)
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return template_norm_feats, unique_templates, unique_subjectids
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def verification_11(template_norm_feats=None, unique_templates=None, p1=None, p2=None, batch_size=10000):
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try:
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print(">>>> Trying cupy.")
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import cupy as cp
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template_norm_feats = cp.array(template_norm_feats)
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score_func = lambda feat1, feat2: cp.sum(feat1 * feat2, axis=-1).get()
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test = score_func(template_norm_feats[:batch_size], template_norm_feats[:batch_size])
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except:
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score_func = lambda feat1, feat2: np.sum(feat1 * feat2, -1)
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template2id = np.zeros(max(unique_templates) + 1, dtype=int)
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template2id[unique_templates] = np.arange(len(unique_templates))
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steps = int(np.ceil(len(p1) / batch_size))
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score = []
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for id in tqdm(range(steps), "Verification"):
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feat1 = template_norm_feats[template2id[p1[id * batch_size : (id + 1) * batch_size]]]
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feat2 = template_norm_feats[template2id[p2[id * batch_size : (id + 1) * batch_size]]]
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score.extend(score_func(feat1, feat2))
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return np.array(score)
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def evaluation_1N(query_feats, gallery_feats, query_ids, reg_ids, fars=[0.01, 0.1]):
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print("query_feats: %s, gallery_feats: %s" % (query_feats.shape, gallery_feats.shape))
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similarity = np.dot(query_feats, gallery_feats.T) # (19593, 3531)
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top_1_count, top_5_count, top_10_count = 0, 0, 0
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pos_sims, neg_sims, non_gallery_sims = [], [], []
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for index, query_id in enumerate(query_ids):
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if query_id in reg_ids:
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gallery_label = np.argwhere(reg_ids == query_id)[0, 0]
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index_sorted = np.argsort(similarity[index])[::-1]
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top_1_count += gallery_label in index_sorted[:1]
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top_5_count += gallery_label in index_sorted[:5]
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top_10_count += gallery_label in index_sorted[:10]
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pos_sims.append(similarity[index][reg_ids == query_id][0])
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neg_sims.append(similarity[index][reg_ids != query_id])
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else:
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non_gallery_sims.append(similarity[index])
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total_pos = len(pos_sims)
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pos_sims, neg_sims, non_gallery_sims = np.array(pos_sims), np.array(neg_sims), np.array(non_gallery_sims)
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print("pos_sims: %s, neg_sims: %s, non_gallery_sims: %s" % (pos_sims.shape, neg_sims.shape, non_gallery_sims.shape))
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print("top1: %f, top5: %f, top10: %f" % (top_1_count / total_pos, top_5_count / total_pos, top_10_count / total_pos))
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correct_pos_cond = pos_sims > neg_sims.max(1)
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non_gallery_sims_sorted = np.sort(non_gallery_sims.max(1))[::-1]
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threshes, recalls = [], []
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for far in fars:
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# thresh = non_gallery_sims_sorted[int(np.ceil(non_gallery_sims_sorted.shape[0] * far)) - 1]
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thresh = non_gallery_sims_sorted[max(int((non_gallery_sims_sorted.shape[0]) * far) - 1, 0)]
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recall = np.logical_and(correct_pos_cond, pos_sims > thresh).sum() / pos_sims.shape[0]
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threshes.append(thresh)
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recalls.append(recall)
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# print("FAR = {:.10f} TPIR = {:.10f} th = {:.10f}".format(far, recall, thresh))
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cmc_scores = list(zip(neg_sims, pos_sims.reshape(-1, 1))) + list(zip(non_gallery_sims, [None] * non_gallery_sims.shape[0]))
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return top_1_count, top_5_count, top_10_count, threshes, recalls, cmc_scores
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class IJB_test:
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def __init__(self, model_file, data_path, subset, batch_size=64, force_reload=False, restore_embs=None):
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templates, medias, p1, p2, label, img_names, landmarks, face_scores = extract_IJB_data_11(
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data_path, subset, force_reload=force_reload
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)
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if model_file != None:
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if model_file.endswith(".h5"):
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interf_func = keras_model_interf(model_file)
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elif model_file.endswith(".pth") or model_file.endswith(".pt"):
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interf_func = Torch_model_interf(model_file)
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elif model_file.endswith(".onnx") or model_file.endswith(".ONNX"):
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interf_func = ONNX_model_interf(model_file)
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else:
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interf_func = Mxnet_model_interf(model_file)
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self.embs, self.embs_f = get_embeddings(interf_func, img_names, landmarks, batch_size=batch_size)
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elif restore_embs != None:
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print(">>>> Reload embeddings from:", restore_embs)
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aa = np.load(restore_embs)
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if "embs" in aa and "embs_f" in aa:
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self.embs, self.embs_f = aa["embs"], aa["embs_f"]
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else:
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print("ERROR: %s NOT containing embs / embs_f" % restore_embs)
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exit(1)
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print(">>>> Done.")
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self.data_path, self.subset, self.force_reload = data_path, subset, force_reload
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self.templates, self.medias, self.p1, self.p2, self.label = templates, medias, p1, p2, label
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self.face_scores = face_scores.astype(self.embs.dtype)
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def run_model_test_single(self, use_flip_test=True, use_norm_score=False, use_detector_score=True):
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img_input_feats = process_embeddings(
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self.embs,
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self.embs_f,
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use_flip_test=use_flip_test,
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use_norm_score=use_norm_score,
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use_detector_score=use_detector_score,
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face_scores=self.face_scores,
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)
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template_norm_feats, unique_templates, _ = image2template_feature(img_input_feats, self.templates, self.medias)
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score = verification_11(template_norm_feats, unique_templates, self.p1, self.p2)
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return score
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def run_model_test_bunch(self):
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from itertools import product
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scores, names = [], []
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for use_norm_score, use_detector_score, use_flip_test in product([True, False], [True, False], [True, False]):
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name = "N{:d}D{:d}F{:d}".format(use_norm_score, use_detector_score, use_flip_test)
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print(">>>>", name, use_norm_score, use_detector_score, use_flip_test)
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names.append(name)
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scores.append(self.run_model_test_single(use_flip_test, use_norm_score, use_detector_score))
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return scores, names
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def run_model_test_1N(self, npoints=100):
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fars_cal = [10 ** ii for ii in np.arange(-4, 0, 4 / npoints)] + [1] # plot in range [10-4, 1]
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fars_show_idx = np.arange(len(fars_cal))[:: npoints // 4] # npoints=100, fars_show=[0.0001, 0.001, 0.01, 0.1, 1.0]
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|
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g1_templates, g1_ids, g2_templates, g2_ids, probe_mixed_templates, probe_mixed_ids = extract_gallery_prob_data(
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self.data_path, self.subset, force_reload=self.force_reload
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)
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img_input_feats = process_embeddings(
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self.embs,
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self.embs_f,
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use_flip_test=True,
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use_norm_score=False,
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use_detector_score=True,
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face_scores=self.face_scores,
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)
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g1_templates_feature, g1_unique_templates, g1_unique_ids = image2template_feature(
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img_input_feats, self.templates, self.medias, g1_templates, g1_ids
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)
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g2_templates_feature, g2_unique_templates, g2_unique_ids = image2template_feature(
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img_input_feats, self.templates, self.medias, g2_templates, g2_ids
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)
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probe_mixed_templates_feature, probe_mixed_unique_templates, probe_mixed_unique_subject_ids = image2template_feature(
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img_input_feats, self.templates, self.medias, probe_mixed_templates, probe_mixed_ids
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)
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print("g1_templates_feature:", g1_templates_feature.shape) # (1772, 512)
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print("g2_templates_feature:", g2_templates_feature.shape) # (1759, 512)
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print("probe_mixed_templates_feature:", probe_mixed_templates_feature.shape) # (19593, 512)
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print("probe_mixed_unique_subject_ids:", probe_mixed_unique_subject_ids.shape) # (19593,)
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|
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print(">>>> Gallery 1")
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g1_top_1_count, g1_top_5_count, g1_top_10_count, g1_threshes, g1_recalls, g1_cmc_scores = evaluation_1N(
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probe_mixed_templates_feature, g1_templates_feature, probe_mixed_unique_subject_ids, g1_unique_ids, fars_cal
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)
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print(">>>> Gallery 2")
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g2_top_1_count, g2_top_5_count, g2_top_10_count, g2_threshes, g2_recalls, g2_cmc_scores = evaluation_1N(
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probe_mixed_templates_feature, g2_templates_feature, probe_mixed_unique_subject_ids, g2_unique_ids, fars_cal
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)
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print(">>>> Mean")
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query_num = probe_mixed_templates_feature.shape[0]
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top_1 = (g1_top_1_count + g2_top_1_count) / query_num
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top_5 = (g1_top_5_count + g2_top_5_count) / query_num
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top_10 = (g1_top_10_count + g2_top_10_count) / query_num
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print("[Mean] top1: %f, top5: %f, top10: %f" % (top_1, top_5, top_10))
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|
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mean_tpirs = (np.array(g1_recalls) + np.array(g2_recalls)) / 2
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show_result = {}
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for id, far in enumerate(fars_cal):
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if id in fars_show_idx:
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show_result.setdefault("far", []).append(far)
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show_result.setdefault("g1_tpir", []).append(g1_recalls[id])
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show_result.setdefault("g1_thresh", []).append(g1_threshes[id])
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show_result.setdefault("g2_tpir", []).append(g2_recalls[id])
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show_result.setdefault("g2_thresh", []).append(g2_threshes[id])
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show_result.setdefault("mean_tpir", []).append(mean_tpirs[id])
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print(pd.DataFrame(show_result).set_index("far").to_markdown())
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return fars_cal, mean_tpirs, g1_cmc_scores, g2_cmc_scores
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|
|
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def plot_roc_and_calculate_tpr(scores, names=None, label=None):
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print(">>>> plot roc and calculate tpr...")
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score_dict = {}
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for id, score in enumerate(scores):
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name = None if names is None else names[id]
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if isinstance(score, str) and score.endswith(".npz"):
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aa = np.load(score)
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score = aa.get("scores", [])
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label = aa["label"] if label is None and "label" in aa else label
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score_name = aa.get("names", [])
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for ss, nn in zip(score, score_name):
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score_dict[nn] = ss
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elif isinstance(score, str) and score.endswith(".npy"):
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name = name if name is not None else os.path.splitext(os.path.basename(score))[0]
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score_dict[name] = np.load(score)
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elif isinstance(score, str) and score.endswith(".txt"):
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# IJB meta data like ijbb_template_pair_label.txt
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label = pd.read_csv(score, sep=" ", header=None).values[:, 2]
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else:
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name = name if name is not None else str(id)
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score_dict[name] = score
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if label is None:
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print("Error: Label data is not provided")
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return None, None
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x_labels = [10 ** (-ii) for ii in range(1, 7)[::-1]]
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fpr_dict, tpr_dict, roc_auc_dict, tpr_result = {}, {}, {}, {}
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for name, score in score_dict.items():
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fpr, tpr, _ = roc_curve(label, score)
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roc_auc = auc(fpr, tpr)
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fpr, tpr = np.flipud(fpr), np.flipud(tpr) # select largest tpr at same fpr
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tpr_result[name] = [tpr[np.argmin(abs(fpr - ii))] for ii in x_labels]
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fpr_dict[name], tpr_dict[name], roc_auc_dict[name] = fpr, tpr, roc_auc
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tpr_result_df = pd.DataFrame(tpr_result, index=x_labels).T
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tpr_result_df['AUC'] = pd.Series(roc_auc_dict)
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tpr_result_df.columns.name = "Methods"
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print(tpr_result_df.to_markdown())
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# print(tpr_result_df)
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try:
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import matplotlib.pyplot as plt
|
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fig = plt.figure()
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for name in score_dict:
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plt.plot(fpr_dict[name], tpr_dict[name], lw=1, label="[%s (AUC = %0.4f%%)]" % (name, roc_auc_dict[name] * 100))
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title = "ROC on IJB" + name.split("IJB")[-1][0] if "IJB" in name else "ROC on IJB"
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|
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plt.xlim([10 ** -6, 0.1])
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plt.xscale("log")
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plt.xticks(x_labels)
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plt.xlabel("False Positive Rate")
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plt.ylim([0.3, 1.0])
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plt.yticks(np.linspace(0.3, 1.0, 8, endpoint=True))
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plt.ylabel("True Positive Rate")
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|
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plt.grid(linestyle="--", linewidth=1)
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plt.title(title)
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plt.legend(loc="lower right", fontsize='x-small')
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plt.tight_layout()
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plt.show()
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except:
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print("matplotlib plot failed")
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fig = None
|
|
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|
return tpr_result_df, fig
|
|
|
|
|
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def plot_dir_far_cmc_scores(scores, names=None):
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try:
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import matplotlib.pyplot as plt
|
|
|
|
fig = plt.figure()
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|
for id, score in enumerate(scores):
|
|
name = None if names is None else names[id]
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if isinstance(score, str) and score.endswith(".npz"):
|
|
aa = np.load(score)
|
|
score, name = aa.get("scores")[0], aa.get("names")[0]
|
|
fars, tpirs = score[0], score[1]
|
|
name = name if name is not None else str(id)
|
|
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|
auc_value = auc(fars, tpirs)
|
|
label = "[%s (AUC = %0.4f%%)]" % (name, auc_value * 100)
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plt.plot(fars, tpirs, lw=1, label=label)
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|
|
|
plt.xlabel("False Alarm Rate")
|
|
plt.xlim([0.0001, 1])
|
|
plt.xscale("log")
|
|
plt.ylabel("Detection & Identification Rate (%)")
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|
plt.ylim([0, 1])
|
|
|
|
plt.grid(linestyle="--", linewidth=1)
|
|
plt.legend(fontsize='x-small')
|
|
plt.tight_layout()
|
|
plt.show()
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|
except:
|
|
print("matplotlib plot failed")
|
|
fig = None
|
|
|
|
return fig
|
|
|
|
|
|
def parse_arguments(argv):
|
|
import argparse
|
|
|
|
default_save_result_name = "IJB_result/{model_name}_{subset}_{type}.npz"
|
|
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
|
|
parser.add_argument("-m", "--model_file", type=str, default=None, help="Saved model, keras h5 / pytorch jit pth / onnx / mxnet")
|
|
parser.add_argument("-d", "--data_path", type=str, default="./", help="Dataset path containing IJBB and IJBC sub folder")
|
|
parser.add_argument("-s", "--subset", type=str, default="IJBC", help="Subset test target, could be IJBB / IJBC")
|
|
parser.add_argument("-b", "--batch_size", type=int, default=128, 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") or args.model_file.endswith(".pth") or args.model_file.endswith(".pt") or args.model_file.endswith(".onnx"):
|
|
# Keras model file "model.h5", pytorch model ends with `.pth` or `.pt`, onnx model ends with `.onnx`
|
|
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:
|
|
type = "1N" if args.is_one_2_N else "11"
|
|
args.save_result = default_save_result_name.format(model_name=model_name, subset=args.subset, type=type)
|
|
return args
|
|
|
|
|
|
if __name__ == "__main__":
|
|
import sys
|
|
|
|
args = parse_arguments(sys.argv[1:])
|
|
if args.plot_only != None and len(args.plot_only) != 0:
|
|
if args.is_one_2_N:
|
|
plot_dir_far_cmc_scores(args.plot_only)
|
|
else:
|
|
plot_roc_and_calculate_tpr(args.plot_only)
|
|
else:
|
|
save_name = os.path.splitext(os.path.basename(args.save_result))[0]
|
|
save_items = {}
|
|
save_path = os.path.dirname(args.save_result)
|
|
if len(save_path) != 0 and not os.path.exists(save_path):
|
|
os.makedirs(save_path)
|
|
|
|
tt = IJB_test(args.model_file, args.data_path, args.subset, args.batch_size, args.force_reload, args.save_result)
|
|
if args.save_embeddings: # Save embeddings first, in case of any error happens later...
|
|
np.savez(args.save_result, embs=tt.embs, embs_f=tt.embs_f)
|
|
|
|
if args.is_one_2_N: # 1:N test
|
|
fars, tpirs, _, _ = tt.run_model_test_1N()
|
|
scores = [(fars, tpirs)]
|
|
names = [save_name]
|
|
save_items.update({"scores": scores, "names": names})
|
|
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]
|
|
label = tt.label
|
|
save_items.update({"scores": scores, "names": names})
|
|
else: # Basic 1:1 N0D1F1 test
|
|
score = tt.run_model_test_single()
|
|
scores, names, label = [score], [save_name], tt.label
|
|
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": label})
|
|
|
|
if args.model_file != None or args.save_embeddings: # embeddings not restored from file or should save_embeddings again
|
|
np.savez(args.save_result, **save_items)
|
|
|
|
if args.is_one_2_N:
|
|
plot_dir_far_cmc_scores(scores=scores, names=names)
|
|
else:
|
|
plot_roc_and_calculate_tpr(scores, names=names, label=label)
|