def eval():
g1 = tf.Graph()
with g1.as_default():
config = tf.ConfigProto(
allow_soft_placement=True,
log_device_placement=False) # 如果有gpu，则优先用gpu，否则走cpu资源
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
data_object = DataProcessing(args)
valid_batch_example = data_object.input_frame_data(
frame_path=args.valid_path, batch_size=160, num_epoch=1)
model = creat_model(sess, args, isTraining=False) # 构建模型计算图
# app_tag_model函数： 模型计算图的具体实现
ckpt = tf.train.get_checkpoint_state(args.model_dir)
# 判断是否存在模型
if ckpt and tf.train.checkpoint_exists(ckpt.model_checkpoint_path):
# print("Reloading model parameters..")
print("valid step: Reading model parameters from {}".format(
ckpt.model_checkpoint_path))
saver = tf.train.Saver(tf.global_variables())
saver.restore(sess=sess, save_path=ckpt.model_checkpoint_path
) # 调用saver接口，将各个tensor变量的值赋给对应的tensor
print(sess.run(model.global_step))
else:
if not os.path.exists(args.model_dir):
os.makedirs(args.model_dir)
print("valid step: Created new model parameters..")
sess.run(tf.global_variables_initializer())
valid_cate2_perr_list = []
valid_cate2_gap_list = []
valid_total_loss_list = []
try:
while True:
context_parsed, sequence_parsed = sess.run(
valid_batch_example)
batch_origin_labels = [
np.nonzero(row)[0].tolist()
for row in context_parsed['labels']
]
cate1_multilabel, cate2_multilabel, batch_origin_cate1, batch_origin_cate2 = data_object.get_cate1_cate2_label(
batch_origin_labels)
batch_vid_name = np.asarray(context_parsed['id'])
batch_cate1_label_multiHot = np.asarray(
cate1_multilabel) # batch,cate1_nums
batch_cate2_label_multiHot = np.asarray(
cate2_multilabel) # batch,cate2_nums
batch_rgb_fea_float_list = np.asarray(
sequence_parsed['rgb']) # batch,max_frame,1024
batch_audio_fea_float_list = np.asarray(
sequence_parsed['audio']) # batch,max_frame,128
batch_num_audio_rgb_true_frame = np.asarray(
context_parsed['num_audio_rgb_true_frame'])
feed = dict(
zip([
model.input_video_vidName,
model.input_cate1_multilabel,
model.input_cate2_multilabel,
model.input_video_RGB_feature,
model.input_video_Audio_feature,
model.input_rgb_audio_true_frame,
model.dropout_keep_prob
], [
batch_vid_name, batch_cate1_label_multiHot,
batch_cate2_label_multiHot,
batch_rgb_fea_float_list,
batch_audio_fea_float_list,
batch_num_audio_rgb_true_frame, 1.0
]))
cate2_probs, total_loss = sess.run(
[model.cate2_probs, model.total_loss], feed)
cate2_perr = eval_util.calculate_precision_at_equal_recall_rate(
cate2_probs, batch_cate2_label_multiHot)
cate2_gap = eval_util.calculate_gap(
cate2_probs, batch_cate2_label_multiHot)
valid_cate2_perr_list.append(cate2_perr)
valid_cate2_gap_list.append(cate2_gap)
valid_total_loss_list.append(total_loss)
except tf.errors.OutOfRangeError:
print("end!")
valid_cate2_perr_aver_loss = 1.0 * np.sum(
valid_cate2_perr_list) / len(valid_cate2_perr_list)
valid_cate2_gap_aver_loss = 1.0 * np.sum(
valid_cate2_gap_list) / len(valid_cate2_gap_list)
valid_total_valid_aver_loss = 1.0 * np.sum(
valid_total_loss_list) / len(valid_total_loss_list)
print('total valid cate2_perr_aver_loss: %0.4f' %
valid_cate2_perr_aver_loss)
print('total valid cate2_gap_aver_loss: %0.4f' %
valid_cate2_gap_aver_loss)
print('***********************')
print('total valid total_valid_aver_loss: %0.4f' %
valid_total_valid_aver_loss)
return valid_cate2_gap_aver_loss
def predict_test():
if os.path.exists(args.predict_out_path):
remove_file(args.predict_out_path)
with open(args.predict_out_path, 'a+') as fout1:
fout1.write("VideoId,LabelConfidencePairs" + '\n')
data_object = DataProcessing(args)
test_batch_example = data_object.input_frame_data(
frame_path=args.test_path, batch_size=200, num_epoch=1)
config = tf.ConfigProto(
allow_soft_placement=True,
log_device_placement=False) # 如果有gpu，则优先用gpu，否则走cpu资源
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
model = creat_model(sess, args, isTraining=False) # 构建模型计算图
num = 0
try:
while True:
batch_vid, batch_index, batch_value = [], [], []
context_parsed, sequence_parsed = sess.run(test_batch_example)
batch_origin_labels = [
np.nonzero(row)[0].tolist()
for row in context_parsed['labels']
]
cate1_multilabel, cate2_multilabel, batch_origin_cate1, batch_origin_cate2 = data_object.get_cate1_cate2_label(
batch_origin_labels)
batch_vid_name = np.asarray(context_parsed['id'])
batch_cate1_label_multiHot = np.asarray(
cate1_multilabel) # batch,cate1_nums
batch_cate2_label_multiHot = np.asarray(
cate2_multilabel) # batch,cate2_nums
batch_rgb_fea_float_list = np.asarray(
sequence_parsed['rgb']) # batch,max_frame,1024
batch_audio_fea_float_list = np.asarray(
sequence_parsed['audio']) # batch,max_frame,128
batch_num_audio_rgb_true_frame = np.asarray(
context_parsed['num_audio_rgb_true_frame'])
feed = dict(
zip([
model.input_video_vidName,
model.input_cate1_multilabel,
model.input_cate2_multilabel,
model.input_video_RGB_feature,
model.input_video_Audio_feature,
model.input_rgb_audio_true_frame,
model.dropout_keep_prob
], [
batch_vid_name, batch_cate1_label_multiHot,
batch_cate2_label_multiHot, batch_rgb_fea_float_list,
batch_audio_fea_float_list,
batch_num_audio_rgb_true_frame, 1.0
]))
cate2_top20_probs_index, cate2_top20_probs_value = sess.run([
model.cate2_top20_probs_index,
model.cate2_top20_probs_value
], feed)
num += 1
print("validSet step_num: ", num)
for one_batch_index in cate2_top20_probs_index:
batch_index.append(list(one_batch_index))
for one_batch_value in cate2_top20_probs_value:
batch_value.append(list(one_batch_value))
for one_batch_vid in batch_vid_name:
batch_vid.append(list(one_batch_vid))
with open(args.predict_out_path, 'a+') as fout2:
for vid, index, value in zip(
*[batch_vid, batch_index, batch_value]):
one_result = []
tmp_index_vale_list = []
vid = vid[0].decode(encoding='utf-8')
one_result.append(vid)
one_result.append(',')
for k, v in zip(*[index, value]):
tmp_index_vale_list.append(str(k))
tmp_index_vale_list.append(str("%.6f" % v))
one_result.append(' '.join(tmp_index_vale_list))
# print("one_result: ",''.join(one_result))
fout2.write(''.join(one_result) + '\n')
batch_vid, batch_index, batch_value = [], [], []
except tf.errors.OutOfRangeError:
print("train processing is finished!")
def train():
'''
模型，训练任务
:return:
'''
valid_max_accuracy = -9999
config = tf.ConfigProto(
allow_soft_placement=True,
log_device_placement=False) # 如果有gpu，则优先用gpu，否则走cpu资源
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
data_object = DataProcessing(args)
train_batch_example = data_object.input_frame_data(
frame_path=args.train_path,
batch_size=args.batch_size,
num_epoch=args.epoch)
model = creat_model(sess, args, isTraining=True) # 构建模型计算图
saver = tf.train.Saver(tf.global_variables(),
max_to_keep=3) # max_to_keep 表征只保留最好的3个模型
print("Begin training..")
train_cate1_perr_list = []
train_cate1_gap_list = []
train_cate2_perr_list = []
train_cate2_gap_list = []
train_total_loss_list = []
try:
while True:
context_parsed, sequence_parsed = sess.run(train_batch_example)
batch_origin_labels = [
np.nonzero(row)[0].tolist()
for row in context_parsed['labels']
]
cate1_multilabel, cate2_multilabel, batch_origin_cate1, batch_origin_cate2 = data_object.get_cate1_cate2_label(
batch_origin_labels)
batch_vid_name = np.asarray(context_parsed['id'])
batch_num_audio_rgb_true_frame = np.asarray(
context_parsed['num_audio_rgb_true_frame'])
batch_cate1_label_multiHot = np.asarray(
cate1_multilabel) # batch,cate1_nums
batch_cate2_label_multiHot = np.asarray(
cate2_multilabel) # batch,cate2_nums
batch_rgb_fea_float_list = np.asarray(
sequence_parsed['rgb']) # batch,max_frame,1024
batch_audio_fea_float_list = np.asarray(
sequence_parsed['audio']) # batch,max_frame,1
# print("batch_vid_name.shape:",batch_vid_name.shape)
# print("batch_cate1_label_multiHot.shape: ",batch_cate1_label_multiHot.shape)
# print("batch_cate2_label_multiHot.shape: ",batch_cate2_label_multiHot.shape)
# print("batch_rgb_fea_float_list.shape: ",batch_rgb_fea_float_list.shape)
# print("batch_audio_fea_float_list.shape: ",batch_audio_fea_float_list.shape)
# print("batch_num_audio_rgb_true_frame: ",batch_num_audio_rgb_true_frame)
# print("batch_num_audio_rgb_true_frame: ", np.asarray(batch_num_audio_rgb_true_frame).shape)
# assert 1==2
feed = dict(
zip([
model.input_video_vidName,
model.input_cate1_multilabel,
model.input_cate2_multilabel,
model.input_video_RGB_feature,
model.input_video_Audio_feature,
model.input_rgb_audio_true_frame,
model.dropout_keep_prob
], [
batch_vid_name, batch_cate1_label_multiHot,
batch_cate2_label_multiHot, batch_rgb_fea_float_list,
batch_audio_fea_float_list,
batch_num_audio_rgb_true_frame, 0.5
]))
cate1_probs, cate2_probs, total_loss, _ = sess.run([
model.cate1_probs, model.cate2_probs, model.total_loss,
model.optimizer
], feed)
train_cate1_perr = eval_util.calculate_precision_at_equal_recall_rate(
cate1_probs, batch_cate1_label_multiHot)
train_cate1_gap = eval_util.calculate_gap(
cate1_probs, batch_cate1_label_multiHot)
train_cate2_perr = eval_util.calculate_precision_at_equal_recall_rate(
cate2_probs, batch_cate2_label_multiHot)
train_cate2_gap = eval_util.calculate_gap(
cate2_probs, batch_cate2_label_multiHot)
train_cate1_perr_list.append(train_cate1_perr)
train_cate1_gap_list.append(train_cate1_gap)
train_cate2_perr_list.append(train_cate2_perr)
train_cate2_gap_list.append(train_cate2_gap)
train_total_loss_list.append(total_loss)
if model.global_step.eval() % args.report_freq == 0:
print("report_freq: ", args.report_freq)
print(
'cate1_train: Step:{} ; aver_train_cate1_perr:{} ; aver_train_cate1_gap_list:{} ; aver_total_loss:{}'
.format(
model.global_step.eval(),
1.0 * np.sum(train_cate1_perr_list) /
len(train_cate1_perr_list),
1.0 * np.sum(train_cate1_gap_list) /
len(train_cate1_gap_list),
1.0 * np.sum(train_total_loss_list) /
len(train_total_loss_list)))
print(
'cate2_train: Step:{} ; aver_train_cate2_perr:{} ; aver_train_cate2_gap_list:{} ; aver_total_loss:{}'
.format(
model.global_step.eval(),
1.0 * np.sum(train_cate2_perr_list) /
len(train_cate2_perr_list),
1.0 * np.sum(train_cate2_gap_list) /
len(train_cate2_gap_list),
1.0 * np.sum(train_total_loss_list) /
len(train_total_loss_list)))
train_cate1_perr_list = []
train_cate1_gap_list = []
train_cate2_perr_list = []
train_cate2_gap_list = []
train_total_loss_list = []
if model.global_step.eval(
) > 1 and model.global_step.eval() % args.valid_freq == 0:
# 统计验证集的准确率
print("valid infer is process 111!")
print('model.global_step.eval(): ',
model.global_step.eval())
valid_cate2_gap_aver_loss = eval()
# 保存当前验证集合准确率最高的模型
if valid_cate2_gap_aver_loss > valid_max_accuracy:
print("save the model, step= : ",
model.global_step.eval())
valid_max_accuracy = valid_cate2_gap_aver_loss
checkpoint_path = os.path.join(args.model_dir,
'model.ckpt')
saver.save(sess=sess,
save_path=checkpoint_path,
global_step=model.global_step.eval())
except tf.errors.OutOfRangeError:
print("train processing is finished!")