深度余弦度量学习(cosine-metric-learning)在VeRi数据集调试

深度余弦度量学习cosine-metric-learning在VeRi数据集调试

训练部分
数据读取部分

训练部分

在VeRi数据集上调试深度余弦度量学习时，出现了很多bug，其中有一个是由于输入图像的维度和placehold的维度不一致报错，导致我花费了很多时间和精力去找问题，后来通过更改了源代码，才得以调通。
在train_app.py上
原来代码：

filename_var = tf.placeholder(tf.string, (None, ))
image_var = tf.map_fn(lambda x:tf.image.decode_jpeg(tf.read_file(x), channels=num_channels),filename_var, back_prop=False, dtype=tf.float32)
image_var = tf.image.resize_images(image_var, image_shape[:2])

更改后的代码：

filename_var = tf.placeholder(tf.string, (None, ))
image_var = tf.map_fn(lambda x: tf.image.resize_images(tf.image.decode_jpeg(tf.read_file(x), channels=num_channels), image_shape[:2]),filename_var, back_prop=False, dtype=tf.float32)

数据读取部分

**
VeRi.py

# vim: expandtab:ts=4:sw=4
import os
import numpy as np
import cv2
import scipy.io as sio# The maximum person ID in the dataset.
MAX_LABEL = 769   # VeRi max label IMAGE_SHAPE = 128, 64, 3  # 此处为了简单不改变模型结构，保持与Market1501一致def _parse_filename(filename):filename_base, ext = os.path.splitext(filename)if '.' in filename_base:# Some images have double filename extensions.filename_base, ext = os.path.splitext(filename_base)if ext != ".jpg":return Noneperson_id, cam_seq, frame_idx, detection_idx = filename_base.split('_')return int(person_id), int(cam_seq[1]), filename_base, extdef read_train_split_to_str(dataset_dir):filenames, ids, camera_indices = [], [], []image_dir = os.path.join(dataset_dir, "image_train")for filename in sorted(os.listdir(image_dir)):meta_data = _parse_filename(filename)if meta_data is None:# This is not a valid filename (e.g., Thumbs.db).continuefilenames.append(os.path.join(image_dir, filename))ids.append(meta_data[0])camera_indices.append(meta_data[1])return filenames, ids, camera_indicesdef read_train_split_to_image(dataset_dir):filenames, ids, camera_indices = read_train_split_to_str(dataset_dir)images = np.zeros((len(filenames), 128, 64, 3), np.uint8)for i, filename in enumerate(filenames):# 将图片resize为（128， 64， 3） 保持与之前结构一致images[i] = cv2.resize(cv2.imread(filename, cv2.IMREAD_COLOR), (64, 128))ids = np.asarray(ids, np.int64)camera_indices = np.asarray(camera_indices, np.int64)return images, ids, camera_indicesdef read_test_split_to_str(dataset_dir):# Read gallery.gallery_filenames, gallery_ids = [], []image_dir = os.path.join(dataset_dir, "bounding_box_test")for filename in sorted(os.listdir(image_dir)):meta_data = _parse_filename(filename)if meta_data is None:# This is not a valid filename (e.g., Thumbs.db).continuegallery_filenames.append(os.path.join(image_dir, filename))gallery_ids.append(meta_data[0])# Read queries.query_filenames, query_ids, query_junk_indices = [], [], []image_dir = os.path.join(dataset_dir, "query")for filename in sorted(os.listdir(image_dir)):meta_data = _parse_filename(filename)if meta_data is None:# This is not a valid filename (e.g., Thumbs.db).continuefilename_base = meta_data[2]junk_matfile = filename_base + "_junk.mat"mat = sio.loadmat(os.path.join(dataset_dir, "gt_query", junk_matfile))if np.any(mat["junk_index"] < 1):indices = []else:# MATLAB to Python index.indices = list(mat["junk_index"].astype(np.int64).ravel() - 1)query_junk_indices.append(indices)query_filenames.append(os.path.join(image_dir, filename))query_ids.append(meta_data[0])# The following matrix maps from query (row) to gallery image (column) such# that element (i, j) evaluates to 0 if query i and gallery image j should# be excluded from computation of the evaluation metrics and 1 otherwise.good_mask = np.ones((len(query_filenames), len(gallery_filenames)), np.float32)for i, junk_indices in enumerate(query_junk_indices):good_mask[i, junk_indices] = 0.return gallery_filenames, gallery_ids, query_filenames, query_ids, good_maskdef read_test_split_to_image(dataset_dir):gallery_filenames, gallery_ids, query_filenames, query_ids, good_mask = (read_test_split_to_str(dataset_dir))gallery_images = np.zeros((len(gallery_filenames), 128, 64, 3), np.uint8)for i, filename in enumerate(gallery_filenames):# 将图片resize为（128， 64， 3） 保持与之前结构一致gallery_images[i] = cv2.resize(cv2.imread(filename, cv2.IMREAD_COLOR), (64, 128))query_images = np.zeros((len(query_filenames), 128, 64, 3), np.uint8)for i, filename in enumerate(query_filenames):query_images[i] = cv2.imread(filename, cv2.IMREAD_COLOR)gallery_ids = np.asarray(gallery_ids, np.int64)query_ids = np.asarray(query_ids, np.int64)return gallery_images, gallery_ids, query_images, query_ids, good_mask

train_VeRi_dataset.py

# vim: expandtab:ts=4:sw=4
import functools
import os
import numpy as np
import scipy.io as sio
import train_app
from datasets import market1501
from datasets import util
import nets.deep_sort.network_definition as netclass VeRi_dataset(object):def __init__(self, dataset_dir, num_validation_y=0.1, seed=1):# 切分训练集的10%为验证集self._dataset_dir = dataset_dirself._num_validation_y = num_validation_yself._seed = seeddef read_train(self):filenames, ids, camera_indices = VeRi.read_train_split_to_str(self._dataset_dir)train_indices, _ = util.create_validation_split(np.asarray(ids, np.int64), self._num_validation_y, self._seed)filenames = [filenames[i] for i in train_indices]ids = [ids[i] for i in train_indices]camera_indices = [camera_indices[i] for i in train_indices]return filenames, ids, camera_indicesdef read_validation(self):filenames, ids, camera_indices = VeRi.read_train_split_to_str(self._dataset_dir)_, valid_indices = util.create_validation_split(np.asarray(ids, np.int64), self._num_validation_y, self._seed)filenames = [filenames[i] for i in valid_indices]ids = [ids[i] for i in valid_indices]camera_indices = [camera_indices[i] for i in valid_indices]return filenames, ids, camera_indicesdef read_test(self):return VeRi.read_test_split_to_str(self._dataset_dir)def main():arg_parser = train_app.create_default_argument_parser("VeRi")arg_parser.add_argument("--dataset_dir", help="Path to Market1501 dataset directory.",default="data/VeRi")args = arg_parser.parse_args()dataset = VeRi(args.dataset_dir, num_validation_y=0.1, seed=1234)if args.mode == "train":train_x, train_y, _ = dataset.read_train()print("Train set size: %d images, %d identities" % (len(train_x), len(np.unique(train_y))))network_factory = net.create_network_factory(is_training=True, num_classes=VeRi.MAX_LABEL + 1,add_logits=args.loss_mode == "cosine-softmax")train_kwargs = train_app.to_train_kwargs(args)train_app.train_loop(net.preprocess, network_factory, train_x, train_y,num_images_per_id=4, image_shape=VeRi.IMAGE_SHAPE,**train_kwargs)elif args.mode == "eval":valid_x, valid_y, camera_indices = dataset.read_validation()print("Validation set size: %d images, %d identities" % (len(valid_x), len(np.unique(valid_y))))network_factory = net.create_network_factory(is_training=False, num_classes=VeRi.MAX_LABEL + 1,add_logits=args.loss_mode == "cosine-softmax")eval_kwargs = train_app.to_eval_kwargs(args)train_app.eval_loop(net.preprocess, network_factory, valid_x, valid_y, camera_indices,image_shape=VeRi.IMAGE_SHAPE, **eval_kwargs)elif args.mode == "export":# Export one specific model.gallery_filenames, _, query_filenames, _, _ = dataset.read_test()network_factory = net.create_network_factory(is_training=False, num_classes=VeRi.MAX_LABEL + 1,add_logits=False, reuse=None)gallery_features = train_app.encode(net.preprocess, network_factory, args.restore_path,gallery_filenames, image_shape=VeRi.IMAGE_SHAPE)sio.savemat(os.path.join(args.sdk_dir, "feat_test.mat"),{"features": gallery_features})network_factory = net.create_network_factory(is_training=False, num_classes=VeRi.MAX_LABEL + 1,add_logits=False, reuse=True)query_features = train_app.encode(net.preprocess, network_factory, args.restore_path,query_filenames, image_shape=VeRi.IMAGE_SHAPE)sio.savemat(os.path.join(args.sdk_dir, "feat_query.mat"),{"features": query_features})elif args.mode == "finalize":network_factory = net.create_network_factory(is_training=False, num_classes=VeRi.MAX_LABEL + 1,add_logits=False, reuse=None)train_app.finalize(functools.partial(net.preprocess, input_is_bgr=True),network_factory, args.restore_path,image_shape=VeRi.IMAGE_SHAPE,output_filename="./VeRi.ckpt")elif args.mode == "freeze":network_factory = net.create_network_factory(is_training=False, num_classes=VeRi.MAX_LABEL + 1,add_logits=False, reuse=None)train_app.freeze(functools.partial(net.preprocess, input_is_bgr=True),network_factory, args.restore_path,image_shape=VeRi.IMAGE_SHAPE,output_filename="./VeRi.pb")else:raise ValueError("Invalid mode argument.")if __name__ == "__main__":main()

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深度余弦度量学习(cosine-metric-learning)在VeRi数据集调试

深度余弦度量学习cosine-metric-learning在VeRi数据集调试

训练部分

数据读取部分

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