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train_model_v4_dynamic_weights_debug.py
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train_model_v4_dynamic_weights_debug.py
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# -*- coding: UTF-8 -*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from pympler import muppy
# print('pid: {} GPU: {}'.format(os.getpid(), os.environ['CUDA_VISIBLE_DEVICES']))
import tensorflow as tf
import numpy as np
import cv2
import argparse
import sys
# import matplotlib
# matplotlib.use('Agg')
import math
import time
from generate_data import DateSet
from train.model2 import create_model as create_model_v2
from train.model2_v1 import create_model as create_model_v1
from train.model2_v4 import create_model as create_model_v4
from utils import train_model
from config import config
config = config.config
from lk.lk_config import lk_config
from gen_data import load_data
def main(args):
os.environ['CUDA_VISIBLE_DEVICES'] = args.cuda_visibale_device
list_ops = {}
debug = (args.debug == 'True')
print(args)
np.random.seed(args.seed)
with tf.Graph().as_default():
with tf.variable_scope('multi_gpu'):
print('reading train dataset')
# train_dataset, num_train_file = DateSet(args.file_list, args, debug, args.img_dir)
assert len(args.train_file) == len(args.batch_size)
assert len(args.train_file) == len(args.train_data_type)
list_ops['next_batch_group'] = None
gloabl_batch_size = 0
global_num_data = 0
# for i m (test_file, )
# test_lmk_file = args.test_lmk_file
# test_attr_file = args.test_attr_file
# test_batch_size = args.test_batch_size
# test_lmk_data_set, test_lmk_num = load_data(data_set_file=test_lmk_file, datatype='jd', args=args,
# imgdir=args.img_dir)
# test_attribute_data_set, test_attribute_num = load_data(data_set_file=test_attr_file, datatype='celeba',
# args=args, imgdir=args.img_dir)
# test_lmk_dataset_batch = test_lmk_data_set.batch(test_batch_size).repeat()
# test_lmk_data_set_batch_it = test_lmk_dataset_batch.make_one_shot_iterator()
# test_attribute_data_set_batch = test_attribute_data_set.batch(test_batch_size).repeat()
# test_attribute_data_set_batch_it = test_attribute_data_set_batch.make_one_shot_iterator()
# test_lmk_data_set_batch_next = test_lmk_data_set_batch_it.get_next()
# test_attribute_data_set_batch_next = test_attribute_data_set_batch_it.get_next()
# list_ops['test_lmk_next'] = test_lmk_data_set_batch_next
# list_ops['test_attr_next'] = test_attribute_data_set_batch_next
for i, (train_file, batch_size, data_type) in enumerate(
zip(args.train_file, args.batch_size, args.train_data_type)):
data_set, num_data = load_data(data_set_file=train_file, datatype=data_type, args=args, imgdir=args.img_dir, isTrain=False)
if 'jd' == data_type:
gloabl_batch_size = batch_size
global_num_data = num_data
data_set_batch = data_set.batch(batch_size).repeat()
data_set_batch_itor = data_set_batch.make_one_shot_iterator()
data_set_next_batch = data_set_batch_itor.get_next()
list_ops['train_data_set_%d' % i] = data_set_next_batch
list_ops['train_num_data_%d' % i] = num_data
for i, (test_file, data_type) in enumerate(
zip(args.test_file, args.test_data_type)):
data_set, num_data = load_data(data_set_file=test_file, datatype=data_type, args=args,
imgdir=args.img_dir,isTrain=False)
# print('init test data set ... %d' % i)
data_set_batch = data_set.batch(np.mean(args.batch_size).astype(int)).repeat()
data_set_batch_itor = data_set_batch.make_one_shot_iterator()
data_set_next_batch = data_set_batch_itor.get_next()
list_ops['test_data_set_%d' % i] = data_set_next_batch
list_ops['test_num_data_%d' % i] = num_data
if 'jd' == data_type:
gloabl_test_batch_size = gloabl_batch_size
global_test_num_data = num_data
# if list_ops['next_batch_group'] is None:
# list_ops['next_batch_group'] = data_set_next_batch
# else:
# train_dataset, num_train_file = load_data(args.train_file[i])
# print('reading test dataser')
# test_dataset, num_test_file = DateSet(args.test_list, args, debug, args.test_img_dir)
# train_video_fataset, num_video_images = VideoDataSet(args.video, args)
# print('reading video dataset')
# batch_train_dataset = train_dataset.batch(args.batch_size).repeat()
# train_iterator = batch_train_dataset.make_one_shot_iterator()
# train_next_element = train_iterator.get_next()
# batch_test_dataset = test_dataset.batch(args.batch_size).repeat()
# test_iterator = batch_test_dataset.make_one_shot_iterator()
# test_next_element = test_iterator.get_next()
# batch_train_video_dataset = train_video_fataset.batch(
# lk_config['video_count'] * lk_config['video_seq']).repeat()
# train_video_iterator = batch_train_video_dataset.make_one_shot_iterator()
# train_video_next_element = train_video_iterator.get_next()
#
# list_ops['num_train_file'] = num_train_file
# list_ops['num_test_file'] = num_test_file
model_dir = args.model_dir
# model_dir = args.model
log_dir = args.log_dir
# if 'test' in model_dir and debug and os.path.exists(model_dir):
# import shutil
# shutil.rmtree(model_dir)
# assert not os.path.exists(model_dir)
# os.mkdir(model_dir)
# print('Total number of examples: {}'.format(num_train_file))
# print('Test number of examples: {}'.format(num_test_file))
print('Model dir: {}'.format(model_dir))
tf.set_random_seed(args.seed)
global_step = tf.Variable(0, trainable=False)
list_ops['global_step'] = global_step
# list_ops['train_dataset'] = train_dataset
# list_ops['test_dataset'] = test_dataset
# list_ops['train_next_element'] = train_next_element
# list_ops['test_next_element'] = test_next_element
# list_ops['train_video_next_elements'] = train_video_next_element
epoch_size = global_num_data // gloabl_batch_size
test_epoch_size = global_test_num_data // gloabl_test_batch_size
print('Number of batches per epoch: {}'.format(epoch_size))
image_batch = tf.placeholder(tf.float32, shape=(None, args.image_size, args.image_size, 3), \
name='image_batch')
landmark_batch = tf.placeholder(tf.float32, shape=(None, 106 * 2), name='landmark_batch')
euler_angles_gt_batch = tf.placeholder(tf.float32, shape=(None, 3), name='euler_angles_gt_batch')
# attribute4_batch = tf.placeholder(tf.float32, (None, config.anno_attr_count), name='attr4_batch')
race_gt = tf.placeholder(tf.float32, (None, 4), name='race_gt')
race_st = tf.placeholder(tf.float32, (None, 3), name='race_st')
# angle_by_gt = tf.placeholder(tf.float32,(None,3),name='')
angles_by_st = tf.placeholder(tf.float32, (None, 3), name='angles_by_st')
expressions = tf.placeholder(tf.float32, (None, 10), name='expressions')
attr6_gt = tf.placeholder(tf.float32, (None, 5), name='attr6_gt')
attr6_st = tf.placeholder(tf.float32, (None, 5), name='attr6_st')
age_gt = tf.placeholder(tf.float32, (None, 1), name='age_gt')
age_st = tf.placeholder(tf.float32, (None, 1), name='age_st')
gender_gt = tf.placeholder(tf.float32, (None, 1), name='gender_gt')
gender_st = tf.placeholder(tf.float32, (None, 1), name='gender_st')
young = tf.placeholder(tf.float32, (None, 1), name='young')
mask = tf.placeholder(tf.float32, (None, 1), name='mask')
def __produceAttr(attr):
n, m = attr.shape
attr_new = np.zeros([n, m], dtype=np.int32)
attr_new[:, :] = np.where(attr >= 0.5, 1, 0).astype(np.int32)
# attr_new[:, 1::2] = np.where(attr >= 0.5, 0, 1).astype(np.int32)
# attr_new[:,:] = 1
return attr_new
# attribute_batch = tf.placeholder(tf.int32, shape=(None, 5), name='attribute_batch')
attribute_batch = tf.py_func(__produceAttr, [attr6_st], tf.int32,
name='attribute_batch')
print(attribute_batch.get_shape())
'''open_eye_gt 0 1
open_eye_st 1 1
mouth_open_slightly 0 1
mouth_open_widely 0 1
sunglasses_gt 0 1
sunglasses_st 1 1
forty_attr 0 33'''
open_eye_gt = tf.placeholder(tf.float32, (None, 1), name='open_eye_gt')
open_eye_st = tf.placeholder(tf.float32, (None, 1), name='open_eye_st')
mouth_open_slightly = tf.placeholder(tf.float32, (None, 1), name='mouth_open_slightly')
mouth_open_widely = tf.placeholder(tf.float32, (None, 1), name='mouth_open_widely')
sunglasses_gt = tf.placeholder(tf.float32, (None, 1), name='sunglasses_gt')
sunglasses_st = tf.placeholder(tf.float32, (None, 1), name='sunglasses_st')
forty_attr = tf.placeholder(tf.float32, (None, 33), name='forty_attr')
indicates = tf.placeholder(tf.float32, (None, 21), name='indicates')
loss_weights = tf.placeholder(tf.float32,[14],name='loss_weights')
list_ops['loss_weights'] = loss_weights
list_ops['image_batch'] = image_batch
list_ops['landmark_batch'] = landmark_batch
list_ops['attribute_batch'] = attribute_batch
list_ops['euler_angles_gt_batch'] = euler_angles_gt_batch
list_ops['race_gt_batch'] = race_gt
list_ops['race_st_batch'] = race_st
list_ops['angles_by_st_batch'] = angles_by_st
list_ops['expressions_baatch'] = expressions
list_ops['attr6_gt_batch'] = attr6_gt
list_ops['attr6_st_batch'] = attr6_st
list_ops['age_gt_batch'] = age_gt
list_ops['age_st_batch'] = age_st
list_ops['gender_gt_batch'] = gender_gt
list_ops['gender_st_batch'] = gender_st
list_ops['young_batch'] = young
list_ops['mask_batch'] = mask
list_ops['open_eye_gt_batch'] = open_eye_gt
list_ops['open_eye_st_batch'] = open_eye_st
list_ops['mouth_open_slightly_batch'] = mouth_open_slightly
list_ops['mouth_open_widely_batch'] = mouth_open_widely
list_ops['sunglasses_gt_batch'] = sunglasses_gt
list_ops['sunglasses_st_batch'] = sunglasses_st
list_ops['forty_attr_batch'] = forty_attr
list_ops['indicates_batch'] = indicates
phase_train_placeholder = tf.placeholder(tf.bool, name='phase_train')
list_ops['phase_train_placeholder'] = phase_train_placeholder
print('Building training graph.')
# total_loss, landmarks, heatmaps_loss, heatmaps= create_model(image_batch, landmark_batch,\
# phase_train_placeholder, args)
create_model = None
if args.model_version == 1:
create_model = create_model_v1
elif args.model_version == 2:
create_model = create_model_v2
elif args.model_version == 4:
create_model = create_model_v4
landmarks_pre, race3_pre, race4_pre, angles_gt_pre, angles_st_pre, expressions_pre, attr6_pre, age_pre, \
gender_pre, young_pre, mask_pre, open_eye_pre, open_mouth_degrees_pre, sunglasses_pre, forty_attrs_pre, \
euler_angles_pre = create_model(image_batch, phase_train_placeholder, args)
list_ops['landmarks_pre'] = landmarks_pre
list_ops['race3_pre'] = race3_pre
list_ops['race4_pre'] = race4_pre
list_ops['angles_gt_pre'] = angles_gt_pre
list_ops['angles_st_pre'] = angles_st_pre
list_ops['expressions_pre'] = expressions_pre
list_ops['attr6_pre'] = attr6_pre
list_ops['age_pre'] = age_pre
list_ops['gender_pre'] = gender_pre
list_ops['young_pre'] = young_pre
list_ops['mask_pre'] = mask_pre
list_ops['open_eye_pre'] = open_eye_pre
list_ops['open_mouth_degrees_pre'] = open_mouth_degrees_pre
list_ops['sunglasses_pre'] = sunglasses_pre
list_ops['forty_attrs_pre'] = forty_attrs_pre
list_ops['euler_angles_pre'] = euler_angles_pre
landmarks_pre_lmk_loss = tf.reduce_sum(tf.square(landmarks_pre - landmark_batch), axis=1) * indicates[:,
0] # [0,1)
race_gt_loss = tf.nn.softmax_cross_entropy_with_logits(labels=race_gt, logits=race4_pre) * indicates[:, 1]
race_st_loss = tf.nn.softmax_cross_entropy_with_logits(labels=race_st, logits=race3_pre) * indicates[:, 2]
angles_gt_loss = tf.reduce_sum((1 - tf.cos(euler_angles_gt_batch - angles_gt_pre)), axis=1) * indicates[:,
3] # rad real
angles_st_loss = tf.reduce_sum((1 - tf.cos(angles_by_st - angles_st_pre)), axis=1) * indicates[:, 4] # rad real
# before Nov 22
# xxx : sum = 100?
expressions_loss = tf.nn.softmax_cross_entropy_with_logits(labels=expressions,
logits=expressions_pre) * indicates[:,
5] # [0,1], probability
# after Nov 22
# expressions_loss = tf.square(expressions_pre - expressions) * indicates[:,5:6] # [0, 1], probability sum=1
attr6_gt_loss = tf.reduce_sum(tf.nn.sigmoid_cross_entropy_with_logits(labels=attr6_gt, logits=attr6_pre),
axis=1) * indicates[:, 6]
attr6_st_loss = tf.reduce_sum(tf.nn.sigmoid_cross_entropy_with_logits(labels=attr6_st, logits=attr6_pre),
axis=1) * indicates[:, 7] # [0,1]
# way 1
attr6_combined_loss = (attr6_gt_loss + attr6_st_loss) / (indicates[:, 6] + indicates[:, 7])
# # way 2
# attr6_combined_loss = (attr6_gt_loss + attr6_st_loss ) * indicates[:,6:7] * indicates[:,7:8] \
# / (indicates[:,6:7] + indicates[:,7:8] + 1e-10)
age_gt_loss = tf.square(age_gt - age_pre)[:,0] * indicates[:, 8] # x/100.0
age_st_loss = tf.square(age_st - age_pre)[:,0] * indicates[:, 9] # x/100.0
age_combined_loss = (age_gt_loss + age_st_loss) / (indicates[:, 8] + indicates[:, 9])
# sum_indicates_age = indicates[:,8:9] + indicates[]
# sigmoid
gender_gt_loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=gender_gt, logits=gender_pre)[:,0] * indicates[:,
10]
gender_st_loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=gender_st, logits=gender_pre)[:,0] * indicates[:,
11]
# gender_gt_loss = tf.square(gender_gt - gender_pre) * indicates[:, 10:11] #0 or 1
# gender_st_loss = tf.square(gender_st - gender_pre) * indicates[:, 11:12] # [0,1] , probability
gender_combined_loss = (gender_gt_loss + gender_st_loss) / (indicates[:, 10] + indicates[:, 11])
list_ops['gender_com_loss_array'] = gender_combined_loss
list_ops['raw_gender_st_loss'] = tf.nn.sigmoid_cross_entropy_with_logits(labels=gender_st, logits=gender_pre)
list_ops['raw_gender_gt_loss'] = tf.nn.sigmoid_cross_entropy_with_logits(labels=gender_gt, logits=gender_pre)
list_ops['gender_gt_indicate'] = indicates[:,10]
list_ops['gender_st_indicate'] = indicates[:,11]
# sigmoid
# young_loss = tf.square(young - young_pre) * indicates[:, 12:13] #0 or 1
young_loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=young, logits=young_pre)[:,0] * indicates[:, 12]
# sigmoid
# mask_loss = tf.square(mask - mask_pre) * indicates[:, 13:14] #[0,1]
mask_loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=mask, logits=mask_pre)[:,0] * indicates[:, 13]
# sigmoid
# open_eye_gt_loss = tf.square(open_eye_gt - open_eye_pre) * indicates[:, 14:15] # 0 or 1 no
# open_eye_st_loss = tf.square(open_eye_st - open_eye_pre) * indicates[:, 15:16] # [0,1], probability
open_eye_gt_loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=open_eye_gt, logits=open_eye_pre)[:,0] * indicates[
:, 14]
open_eye_st_loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=open_eye_st, logits=open_eye_pre)[:,0] * indicates[
:, 15]
open_eye_combined_loss = (open_eye_gt_loss + open_eye_st_loss) / (indicates[:, 14] + indicates[:, 15])
# sigmoid
# sunglasses_st_loss = tf.square(sunglasses_st - sunglasses_pre) * indicates[:, 18:19] # 0 or 1
# sunglasses_gt_loss = tf.square(sunglasses_gt - sunglasses_pre) * indicates[:, 19:20] # [0,1],probability
sunglasses_gt_loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=sunglasses_gt,
logits=sunglasses_pre)[:,0] * indicates[:, 18]
sunglasses_st_loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=sunglasses_st,
logits=sunglasses_pre)[:,0] * indicates[:, 19]
sunglasses_combined_loss = (sunglasses_gt_loss + sunglasses_st_loss) / (
indicates[:, 18] + indicates[:, 19])
# forty_attr_loss = tf.nn.sigmoid(label forty_attr, forty_attrs_pre) * indicates[:, 20:21]
forty_attr_loss = tf.reduce_sum(tf.nn.sigmoid_cross_entropy_with_logits(labels=forty_attr,
logits=forty_attrs_pre),
axis=1) * indicates[:,
20] # 0 or 1
list_ops['raw_losses'] = []
list_ops['raw_losses'].append(landmarks_pre_lmk_loss)
list_ops['raw_losses'].append(race_gt_loss)
list_ops['raw_losses'].append(race_st_loss)
list_ops['raw_losses'].append(angles_gt_loss)
list_ops['raw_losses'].append(angles_st_loss)
list_ops['raw_losses'].append(expressions_loss)
def __reduce_sum_indicates(indicate):
def __indicate_reduce_sum(_indicate):
sum_indicate = np.sum(_indicate)
sum_indicate = sum_indicate if sum_indicate != 0 else 1e-20
sum_indicate = np.float32(sum_indicate)
return sum_indicate
return tf.py_func(__indicate_reduce_sum, [indicate], tf.float32)
list_ops['landmarks_pre_lmk_loss'] = tf.reduce_sum(
landmarks_pre_lmk_loss)/ __reduce_sum_indicates(indicates[:,0]) # each valid = mean * (sum(batch)) / batch_jd / 212
list_ops['race_gt_loss'] = tf.reduce_sum(race_gt_loss) / __reduce_sum_indicates(indicates[:,1]) # each valid = mean * (sum(batch)) / batch_lfw / 4
list_ops['race_st_loss'] = tf.reduce_sum(race_st_loss) / __reduce_sum_indicates(indicates[:,2]) # each valid = mean / 3
list_ops['angles_gt_loss'] = tf.reduce_sum(angles_gt_loss) / __reduce_sum_indicates(indicates[:, 3])
list_ops['angles_st_loss'] = tf.reduce_sum(angles_st_loss) / __reduce_sum_indicates(indicates[:, 4]) # each valid = mean / 3
# each valid = mean * sum(batch) / batch_jd / 3
# list_ops['angles_gt_loss'] = angles_gt_loss
list_ops['expressions_loss'] = tf.reduce_sum(expressions_loss) / __reduce_sum_indicates(indicates[:,5]) # each valid = mean / 10
list_ops['attr6_combined_loss'] = tf.reduce_mean(attr6_combined_loss) # each valid = mean / 5
list_ops['age_gt_loss'] = tf.reduce_sum(age_gt_loss) / __reduce_sum_indicates(indicates[:,8]) # each valid = mean * sum(batch) / batch_imdb
list_ops['age_st_loss'] = tf.reduce_sum(age_st_loss) / __reduce_sum_indicates(indicates[:,9]) # each valid = mean
list_ops['age_combined_loss'] = tf.reduce_mean(age_combined_loss)
list_ops['gender_gt_loss'] = tf.reduce_sum(
gender_gt_loss) / __reduce_sum_indicates(indicates[:,10]) # each valid = mean * sum(batch) / (batch_imdb + batch_lfw + batch_celeba)
list_ops['gender_st_loss'] = tf.reduce_sum(gender_st_loss) / __reduce_sum_indicates(indicates[:,11])
list_ops['gender_st_loss_sum'] = tf.reduce_sum(gender_st_loss)
list_ops['gender_st_batch_size'] = __reduce_sum_indicates(indicates[:,11])
list_ops['gender_combined_loss'] = tf.reduce_mean(gender_combined_loss)
list_ops['young_loss'] = tf.reduce_sum(young_loss) / __reduce_sum_indicates(indicates[:,12]) # each valid = mean * sum(batch) / batch_celeba
list_ops['mask_loss'] = tf.reduce_sum(mask_loss) / __reduce_sum_indicates(indicates[:,13]) # each valid = mean
list_ops['open_eye_gt_loss'] = tf.reduce_sum(open_eye_gt_loss) / __reduce_sum_indicates(indicates[:,14]) # each valid = mean * sum(batch) / batch_lfw
list_ops['open_eye_st_loss'] = tf.reduce_sum(open_eye_st_loss) / __reduce_sum_indicates(indicates[:,15]) # each valid = mean
list_ops['open_eye_combined_loss'] = tf.reduce_mean(open_eye_combined_loss) # each valid = mean
# each valid = mean
list_ops['sunglasses_gt_loss'] = tf.reduce_sum(
sunglasses_gt_loss) / __reduce_sum_indicates(indicates[:,18]) # each valid = mean * sum(batch) / (batch_lfw)
list_ops['sunglasses_st_loss'] = tf.reduce_sum(sunglasses_st_loss) / __reduce_sum_indicates(indicates[:, 19])
list_ops['sunglasses_combined_loss'] = tf.reduce_mean(sunglasses_combined_loss) # mean
list_ops['forty_attr_loss'] = tf.reduce_sum(
forty_attr_loss) / __reduce_sum_indicates(indicates[:,20]) # each valid = mean * sum(batch) / (batch_lfw + batch_celeba)
attributes_w_n = tf.to_float(attribute_batch[:])
# _num = attributes_w_n.shape[0]
mat_ratio = tf.reduce_mean(attributes_w_n, axis=0)
list_ops['mat_ratio'] = mat_ratio
mat_ratio = tf.map_fn(lambda x: (tf.cond(x > 0, lambda: 1 / x, lambda: float(sum(args.batch_size)))), mat_ratio)
list_ops['mat_ratio2'] = mat_ratio
list_ops['attr_w_n0'] = attributes_w_n
attributes_w_n = tf.convert_to_tensor(attributes_w_n * mat_ratio)
list_ops['attr_w_n1'] = attributes_w_n
attributes_w_n = tf.reduce_sum(attributes_w_n, axis=1)
list_ops['attributes_w_n_batch'] = attributes_w_n
L2_loss = tf.add_n(tf.losses.get_regularization_losses())
_sum_k = tf.reduce_sum(tf.map_fn(lambda x: 1 - tf.cos(abs(x)), euler_angles_gt_batch - euler_angles_pre),
axis=1) * indicates[:, 3]
list_ops['sum_k_loss'] = tf.reduce_sum(_sum_k) / __reduce_sum_indicates(indicates[:,3])
loss_sum = tf.reduce_sum(tf.square(landmark_batch - landmarks_pre), axis=1) * indicates[:, 0]
list_ops['landmark_loss'] = tf.reduce_sum(loss_sum) / __reduce_sum_indicates(indicates[:,0])
# if args.multi_task:
# attr_loss = tf.reduce_sum(config.multi_task_weight * attr_loss, axis=1)
# loss_sum += attr_loss
# attr_loss = tf.reduce_mean(attr_loss)
if args.loss_type == 0: # 不加类别加L2Loss
loss_sum = tf.reduce_mean(loss_sum * _sum_k)
loss_sum += L2_loss
elif args.loss_type == 1: # 不加类别不加L2_loss
loss_sum = tf.reduce_mean(loss_sum * _sum_k)
# loss_sum += L2_loss
#
elif args.loss_type == 2: # 加类别加L2-loss
list_ops['splited_loss_sum'] = loss_sum
list_ops['splited_sum_k'] = _sum_k
list_ops['splited_attr_w_n'] = attributes_w_n
list_ops['splited_indicates'] = indicates[:, 0]
loss_sum = tf.reduce_sum(loss_sum * _sum_k * attributes_w_n) / __reduce_sum_indicates(indicates[:,0])
elif args.loss_type == 3: # 加类别，不加L2Loss
loss_sum = tf.reduce_mean(loss_sum * _sum_k * attributes_w_n)
# loss_sum += L2_loss
list_ops['pfld_sum_loss'] = loss_sum
if not args.no_addition:
loss_sum = loss_weights[0] * loss_sum + \
loss_weights[1] * list_ops['race_gt_loss'] + \
loss_weights[2] * list_ops['race_st_loss'] + \
loss_weights[3] * list_ops['angles_gt_loss'] + \
loss_weights[4] * list_ops['angles_st_loss'] + \
loss_weights[5] * list_ops['expressions_loss'] + \
loss_weights[6] * list_ops['attr6_combined_loss'] + \
loss_weights[7] * list_ops['age_combined_loss'] + \
loss_weights[8] * list_ops['gender_combined_loss'] + \
loss_weights[9] * list_ops['young_loss'] + \
loss_weights[10] * list_ops['mask_loss'] + \
loss_weights[11] * list_ops['open_eye_combined_loss'] + \
loss_weights[12] * list_ops['sunglasses_combined_loss'] + \
loss_weights[13] * list_ops['forty_attr_loss'] + \
L2_loss
# loss_sum = np.array([10],dtype=np.float32)
# loss_sum = list_ops['landmarks_pre_lmk_loss']
# loss_sum +=indicates[0] * list_ops['landmarks_pre_lmk_loss'] + \
# indicates[1] * list_ops['race_gt_loss'] + \
# indicates[2] *
train_op, lr_op = train_model(loss_sum, global_step, global_num_data, gloabl_batch_size, args)
list_ops['landmarks'] = landmarks_pre
list_ops['L2_loss'] = L2_loss
list_ops['loss'] = loss_sum
list_ops['train_op'] = train_op
list_ops['lr_op'] = lr_op
#
# list_ops['attr_pre'] = attr_pre
# list_ops['attr_pre_sigmoid'] = tf.nn.sigmoid(attr_pre)
# list_ops['attr_loss'] = attr_loss
# test_mean_error = tf.Variable(tf.constant(0.0), dtype=tf.float32, name='ME', trainable=False)
# test_failure_rate = tf.Variable(tf.constant(0.0), dtype=tf.float32, name='FR', trainable=False)
# test_10_loss = tf.Variable(tf.constant(0.0), dtype=tf.float32, name='TestLoss', trainable=False)
# train_loss = tf.Variable(tf.constant(0.0), dtype=tf.float32, name='TrainLoss', trainable=False)
# train_loss_l2 = tf.Variable(tf.constant(0.0), dtype=tf.float32, name='TrainLoss2', trainable=False)
# train_attr_loss = tf.Variable(tf.constant(0.0), dtype=tf.float32, name='Train_attr_loss', trainable=False)
# test_attr_loss = tf.Variable(tf.constant(0.0), dtype=tf.float32, name='Test_attr_loss', trainable=False)
# train_landmark_loss = tf.Variable(tf.constant(0.0), dtype=tf.float32, name='train_landmark_loss',
# trainable=False)
# loss_list[]
loss_name_list = '''
loss
pfld_sum_loss
landmark_loss
sum_k_loss
landmarks_pre_lmk_loss
race_gt_loss
race_st_loss
angles_gt_loss
angles_st_loss
expressions_loss
attr6_combined_loss
age_gt_loss
age_st_loss
age_combined_loss
gender_gt_loss
gender_st_loss
gender_combined_loss
young_loss
mask_loss
open_eye_gt_loss
open_eye_st_loss
open_eye_combined_loss
sunglasses_gt_loss
sunglasses_st_loss
sunglasses_combined_loss
forty_attr_loss'''.split()
loss_list = {}
for key in loss_name_list:
loss_true_placeholder = tf.placeholder(shape=(), dtype=tf.float32, name='%s_ph' % key)
loss_true_test_place_holder = tf.placeholder(shape=(), dtype=tf.float32, name='%s_test_ph' % key)
loss_place_holder = tf.Variable(tf.constant(0.0), dtype=tf.float32, name='%s_var' % key, trainable=False)
loss_test_place_holder = tf.Variable(tf.constant(0.0), dtype=tf.float32, name='%s_test_var' % key,
trainable=False)
tf.summary.scalar(key, loss_place_holder)
tf.summary.scalar(key + '_test', loss_test_place_holder)
list_ops[key+'_loss_place_holder'] = loss_true_placeholder
list_ops[key+'_test_loss_place_holder'] = loss_true_test_place_holder
loss_list[key+'_assgin'] = loss_place_holder.assign(loss_true_placeholder)
loss_list[key+'_test_assign'] = loss_test_place_holder.assign(loss_true_test_place_holder)
merged = tf.summary.merge_all()
# loss_lmk = __test(sess, next_batch_lmk, [list_ops['landmarks_pre_lmk_loss'], list_ops['angles_gt_loss']])
# loss_attr = __test(sess, next_batch_attr,
# [list_ops['attr6_combined_loss'], list_ops['open_eye_gt_loss'],
# list_ops['sunglasses_st_loss'],
# list_ops['expressions_loss']])
# test_loss_attr_list = ['attr6_combined_loss', 'open_eye_gt_loss', 'sunglasses_st_loss', 'expressions_loss']
# test_loss_lmk_list = ['landmarks_pre_lmk_loss', 'angles_gt_loss']
# test_loss_list = {}
# for loss_name in test_loss_attr_list+test_loss_lmk_list:
# loss_place_holder = tf.Variable(tf.constant(0.0), dtype=tf.float32, name = '%s_test_var' % loss_name, trainable=False)
# tf.summary.scalar('Test_%s' % loss_name, loss_place_holder)
# loss_list[key + '_test_place_holder'] = loss_place_holder
# eulerangle_loss = tf.Variable(tf.constant(0.0), dtype=tf.float32, name='eulerAngleLoss')
# tf.summary.scalar('test_mean_error', test_mean_error)
# tf.summary.scalar('test_failure_rate', test_failure_rate)
# tf.summary.scalar('test_10_loss', test_10_loss)
# tf.summary.scalar('train_loss', train_loss)
# tf.summary.scalar('train_loss_l2', train_loss_l2)
# tf.summary.scalar('train_attr_loss', train_attr_loss)
# tf.summary.scalar('test_attr_loss', test_attr_loss)
# tf.summary.scalar('train_landmark_loss', train_landmark_loss)
# tf.summary.scalar('euler_angle_loss', eulerangle_loss)
save_params = tf.trainable_variables()
saver = tf.train.Saver(save_params, max_to_keep=None)
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=1.0)
tfConfig = tf.ConfigProto(gpu_options=gpu_options, allow_soft_placement=False, log_device_placement=False)
tfConfig.gpu_options.allow_growth = True
sess = tf.Session(config=tfConfig)
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
with sess.as_default():
epoch_start = 0
try:
if args.pretrained_model:
pretrained_model = args.pretrained_model
if (not os.path.isdir(pretrained_model)):
print('Restoring pretrained model: {}'.format(pretrained_model))
saver.restore(sess, args.pretrained_model)
else:
print('Model directory: {}'.format(pretrained_model))
ckpt = tf.train.get_checkpoint_state(pretrained_model)
model_path = ckpt.model_checkpoint_path
assert (ckpt and model_path)
epoch_start = int(model_path[model_path.find('model.ckpt-') + 11:]) + 1
print('Checkpoint file: {}'.format(model_path))
saver.restore(sess, model_path)
except:
epoch_start = 0
pass
# if args.save_image_example:
# save_image_example(sess, list_ops, args)
sess.graph.finalize()
print('Running train.')
# for j, key in enumerate(loss_name_list):
# loss_list[key+'_assgin'] = loss_place_holder.assign(loss_true_placeholder)
# loss_list[key+'_test_assign']
# loss_list[key + '_place_holder'].assign(training_loss[i])
# loss_var_list.append(loss_list[key+'_assgin'])
# loss_var_list.append(loss_list[key+'_test_assign'])
train_write = tf.summary.FileWriter(log_dir, sess.graph)
# loss_weights = np.ones([14],dtype=np.float32)
testing_loss = None
smooth_losses = None
loss_weights = None
lastNodesSet = None
all_objects = None
for epoch in range(epoch_start, args.max_epoch):
# gen_loss_weights(sess,)
# if loss_weights is None and os.path.exists(args.model_dir+'/.dyn_temp_testing_loss.npy'):
# testing_loss = np.load(args.model_dir+'/.dyn_temp_testing_loss.npy')
# smooth_losses = np.load(args.model_dir+'/.dyn_temp_smooth_loss.npy')
# loss_weights = np.load(args.model_dir+'/.dyn_temp_loss_weights.npy')
loss_weights, smooth_losses, weights_changed = gen_loss_weights(testing_loss,smooth_losses,loss_weights,epoch)
# np.save(args.model_dir+'/.dyn_temp_smooth_loss.npy', smooth_losses)
# if weights_changed:
# np.save(args.model_dir+'/.dyn_temp_loss_weights.npy', loss_weights)
print(loss_weights)
print(smooth_losses)
start = time.time()
# loss_weights[1:] = np.where(loss_weights[1:] > 0.001, 0.001, loss_weights[1:])
training_loss = train(sess, epoch_size, epoch, list_ops, args,loss_weights)
print("train time: {}".format(time.time() - start))
checkpoint_path = os.path.join(model_dir, 'model.ckpt')
metagraph_path = os.path.join(model_dir, 'model.meta')
saver.save(sess, checkpoint_path, global_step=epoch, write_meta_graph=False)
if not os.path.exists(metagraph_path):
saver.export_meta_graph(metagraph_path)
start = time.time()
testing_loss = test_v41(sess, test_epoch_size, epoch, list_ops, args,loss_weights)
# np.save(args.model_dir+'/.dyn_temp_testing_loss.npy', testing_loss)
print("test time: {}".format(time.time() - start))
# print(training_loss)
# loss_var_list = [merged]
# print('%s %s' % (key + '_place_holder', training_loss[j]))
# for j, key in enumerate(test_loss_attr_list):
# loss_list = [merged]
# for key in list_ops.keys():
# if key[-5:] == '_loss':
# loss_list.append()
# summary, _ = sess.run(
# [merged
# loss_list['young_loss_place_holder'].assign(training_loss[12])
# train_loss.assign(training_loss[0]),
# train_loss_l2.assign(train_L2),
# train_attr_loss.assign(trainAttrLoss),
# train_landmark_loss.assign(training_loss[1])
# ])
feed_dict={}
assign_list = []
for key, train_loss, test_loss in zip(loss_name_list, training_loss, testing_loss):
# list_ops[key+'_loss_place_holder'] = loss_true_placeholder
# list_ops[key+'_test_loss_place_holder'] = loss_true_test_place_holder
feed_dict[list_ops[key+'_loss_place_holder']] = train_loss
feed_dict[list_ops[key+'_test_loss_place_holder']] = test_loss
# loss_list[key+'_assgin'] = loss_place_holder.assign(loss_true_placeholder)
# loss_list[key+'_test_assign']
assign_list.append(loss_list[key+'_assgin'])
assign_list.append(loss_list[key+'_test_assign'])
sess.run(assign_list,feed_dict=feed_dict)
summary = sess.run(merged)
train_write.add_summary(summary, epoch)
tensors = [n.name for n in tf.get_default_graph().as_graph_def().node]
print('node size ', len(tensors))
if lastNodesSet is None:
lastNodesSet = set(tensors)
else:
for t in tensors:
if t not in lastNodesSet:
print (t)
# all_objs = muppy.get_objects()
# if all_objects is None:
# all_objects = all_objs
# else:
# diff = muppy.get_diff(all_objects, all_objs)
# print (diff['+'])
# print(diff['-'])
# print(type(all_objs))
# print (len(all_objs))
# input()
def train(sess, epoch_size, epoch, list_ops, args, loss_weights):
# input()
loss_name_list = '''
loss
pfld_sum_loss
landmark_loss
sum_k_loss
landmarks_pre_lmk_loss
race_gt_loss
race_st_loss
angles_gt_loss
angles_st_loss
expressions_loss
attr6_combined_loss
age_gt_loss
age_st_loss
age_combined_loss
gender_gt_loss
gender_st_loss
gender_combined_loss
young_loss
mask_loss
open_eye_gt_loss
open_eye_st_loss
open_eye_combined_loss
sunglasses_gt_loss
sunglasses_st_loss
sunglasses_combined_loss
forty_attr_loss'''.split()
# image_batch, landmarks_batch, attribute_batch, euler_batch, attr4_batch = list_ops['train_next_element']
next_batches = []
for i in range(len(args.batch_size)):
next_batches.append(list_ops['train_data_set_%d' % i])
# for key in list_ops.keys():
# if 'train_data_set_' == key[:15]:
# video_batch = None
# if 'train_video_next_element' in list_ops:
# video_batch = list_ops['train_video_next_element']
# video_batch = []
# print(epoch_size)
losses = []
losses.append(list_ops['lr_op'])
losses.append(list_ops['train_op'])
for name in loss_name_list:
losses.append(list_ops[name])
# losses = \
# [list_ops['loss']] + \
# [list_ops['landmarks_pre_lmk_loss']] + \
# [list_ops['race_gt_loss']] + \
# [list_ops['race_st_loss']] + \
# [list_ops['angles_gt_loss']] + \
# [list_ops['angles_st_loss']] + \
# [list_ops['expressions_loss']] + \
# [list_ops['attr6_combined_loss']] + \
# [list_ops['age_gt_loss']] + \
# [list_ops['age_st_loss']] + \
# [list_ops['gender_gt_loss']] + \
# [list_ops['gender_st_loss']] + \
# [list_ops['young_loss']] + \
# [list_ops['mask_loss']] + \
# [list_ops['open_eye_gt_loss']] + \
# [list_ops['open_eye_st_loss']] + \
# [list_ops['sunglasses_gt_loss']] + \
# [list_ops['sunglasses_st_loss']] + \
# [list_ops['forty_attr_loss']] + \
# [list_ops['lr_op']]
total_loss = np.array([0.0] * (len(losses) - 2), dtype=np.float32)
total_batch = 0.0
landmark_diffs = []
training = True
for i in range(epoch_size):
# print('training btach %d ...' % i)
# TODO : get the w_n and euler_angles_gt_batch
# for bi in range(2889):
filename = []
feeding_data = []
# print('len next batches : %d' % len(next_batches))
for k, next_batch in enumerate(next_batches):
# if k == 0:
# filename = next_batch
# continue
data_batch = sess.run(next_batch)
# print ('len %d' % len(data_batch[0]))
while args.batch_size[k] != len(data_batch[0]):
data_batch = sess.run(next_batch)
# print(data_batch.shape)
# print('batch size : %d len next_natch : %d' % (args.batch_size[k], len(data_batch[0])))
# training = False
# break
if len(feeding_data) == 0:
for j, data_item in enumerate(data_batch):
if j == 0:
data_item = data_item.astype(np.str).reshape([-1, 1])
else:
data_item = data_item.astype(np.float32)
feeding_data.append(data_item)
else:
for j, data_item in enumerate(data_batch):
if j == 0:
data_item = data_item.astype(np.str).reshape([-1, 1])
else:
data_item = data_item.astype(np.float32)
# print (feeding_data[j].shape, data_item.shape)
# input()
feeding_data[j] = np.vstack((feeding_data[j], data_item))
# index = list(range(0, len(feeding_data[0])))
# np.random.shuffle(index)
# for j in range(len(feeding_data)):
# feeding_data[j] = feeding_data[j][index]
# print(feeding_data)
# for fd in feeding_data:
# print(fd.shape)
# input()
# continue
# if not training:
# break
'''
calculate the w_n: return the batch [-1,1]
c :
#201: 表情(expression) 0->正常表情(normal expression) 1->夸张的表情(exaggerate expression)
#202: 照度(illumination) 0->正常照明(normal illumination) 1->极端照明(extreme illumination)
#203: 化妆(make-up) 0->无化妆(no make-up) 1->化妆(make-up)
#204: 遮挡(occlusion) 0->无遮挡(no occlusion) 1->遮挡(occlusion)
#205: 模糊(blur) 0->清晰(clear) 1->模糊(blur)
'''
# attributes_w_n = sess.run(list_ops['attributes_w_n_batch'],
# feed_dict={list_ops['attr6_st_batch']: feeding_data[8],list_ops['indicates_batch']:feeding_data[-1]})
# for i in range(len(feeding_data)):
# feeding_data[i][:] = 0.0
# np.save('/lp2/img.npy',feeding_data[0])
# np.save('/lp2/lmk.npy',feeding_data[1])
# exit()
# for j in range(len(feeding_data)):
# feeding_data[j] = feeding_data[j].astype(np.float32)
# print(feeding_data[-1][:,0:1])
feed_dict = {
list_ops['image_batch']: feeding_data[1],
list_ops['landmark_batch']: feeding_data[2],
# list_ops['attribute_batch']: feeding_data[9],
list_ops['phase_train_placeholder']: True,
list_ops['euler_angles_gt_batch']: feeding_data[5],
# list_ops['attributes_w_n_batch']: attributes_w_n,
list_ops['race_gt_batch']: feeding_data[3],
list_ops['race_st_batch']: feeding_data[4],
list_ops['angles_by_st_batch']: feeding_data[6],
list_ops['expressions_baatch']: feeding_data[7],
list_ops['attr6_gt_batch']: feeding_data[8],
list_ops['attr6_st_batch']: feeding_data[9],
list_ops['age_gt_batch']: feeding_data[10],
list_ops['age_st_batch']: feeding_data[11],
list_ops['gender_gt_batch']: feeding_data[12],
list_ops['gender_st_batch']: feeding_data[13],
list_ops['young_batch']: feeding_data[14],
list_ops['mask_batch']: feeding_data[15],
list_ops['open_eye_gt_batch']: feeding_data[16],
list_ops['open_eye_st_batch']: feeding_data[17],
list_ops['mouth_open_slightly_batch']: feeding_data[18],
list_ops['mouth_open_widely_batch']: feeding_data[19],
list_ops['sunglasses_gt_batch']: feeding_data[20],
list_ops['sunglasses_st_batch']: feeding_data[21],
list_ops['forty_attr_batch']: feeding_data[-2],
list_ops['indicates_batch']: feeding_data[-1],
list_ops['loss_weights']:loss_weights
}
# age_diff = np.abs(feeding_data[10] - feeding_data[11])
# print(age_diff).reshape([-1,1])
# print (feeding_data[9])
# datas = []
# datas.append(list_ops['splited_loss_sum'])
# datas.append(list_ops['splited_sum_k'])
# datas.append(list_ops['splited_attr_w_n'])
# datas.append(list_ops['splited_indicates'])
# datas = sess.run(datas,feed_dict = feed_dict)
# datas = np.array(datas).transpose()
# # print (datas)
# aw0, aw1, mat1, mat2, a, aw,l, s, L = sess.run([list_ops['attr_w_n0'],list_ops['attr_w_n1'], list_ops['mat_ratio'], list_ops['mat_ratio2'], list_ops['attribute_batch'], list_ops['attributes_w_n_batch'], list_ops['landmark_loss'], list_ops['sum_k_loss'], list_ops['loss']],feed_dict=feed_dict)
# # print ('aw0 ', aw0.shape, 'aw1 ',aw1.shape,'mat1', mat1.shape,'mat2', mat2.shape,'aw', aw.shape)
# # print(a.mean(axis=0), l, s, L)
# shapes_to_com = [list_ops['splited_loss_sum']]+ \
# [list_ops['splited_sum_k']] + \
# [list_ops['splited_attr_w_n'] ] + \
# [list_ops['splited_indicates']]
# shapes = sess.run(shapes_to_com,feed_dict = feed_dict)
# shapes = sess.run(list_ops['raw_losses'],feed_dict = feed_dict)
# for shape in shapes:
# print (shape.shape)
# exit()
# exit()
'''
list_ops['image_batch'] = image_batch
list_ops['landmark_batch'] = landmark_batch
list_ops['attribute_batch'] = attribute_batch
list_ops['euler_angles_gt_batch'] = euler_angles_gt_batch
list_ops['race_gt_batch'] = race_gt
list_ops['race_st_batch'] = race_st
list_ops['angles_by_st_batch'] = angles_by_st
list_ops['expressions_baatch'] = expressions
list_ops['attr6_gt_batch'] = attr6_gt
list_ops['attr6_st_batch'] = attr6_st
list_ops['age_gt_batch'] = age_gt
list_ops['age_st_batch'] = age_st
list_ops['gender_gt_batch'] = gender_gt
list_ops['gender_st_batch'] = gender_st
list_ops['young_batch'] = young
list_ops['mask_batch'] = mask
list_ops['open_eye_gt_batch'] = open_eye_gt
list_ops['open_eye_st_batch'] = open_eye_st
list_ops['mouth_open_slightly_batch'] = mouth_open_slightly
list_ops['mouth_open_widely_batch'] = mouth_open_widely
list_ops['sunglasses_gt_batch'] = sunglasses_gt
list_ops['sunglasses_st_batch'] = sunglasses_st
list_ops['forty_attr_batch'] = forty_attr
list_ops['indicates_batch'] = indicates'''
# losses = [list_ops['train_op']] + \
# [list_ops['']]
# sess.run(list_ops['train_op'], feed_dict=feed_dict)
# fortyattr_pre = sess.run(list_ops['forty_attrs_pre'],feed_dict=feed_dict)
# print(fortyattr_pre.shape, feeding_data[21].shape, feeding_data[22].shape)
# # the_open_eye_gt = np.hstack((filename.reshape([-1,1]),fortyattr_pre, feeding_data[21], feeding_data[22]))
# print(np.hstack((filename.reshape([-1,1]), feeding_data[22][:,20:21])))
# temp_loss = np.square(fortyattr_pre - feeding_data[21]) * feeding_data[22][:,20:21]
# print(np.mean(temp_loss))
# print(the_open_eye_gt)
# loss_weights[0] * loss_sum + \
# loss_weights[1] * list_ops['race_gt_loss'] + \
# loss_weights[2] * list_ops['race_st_loss'] + \
# loss_weights[3] * list_ops['angles_gt_loss'] + \
# loss_weights[4] * list_ops['angles_st_loss'] + \
# loss_weights[5] * list_ops['expressions_loss'] + \
# loss_weights[6] * list_ops['attr6_combined_loss'] + \
# loss_weights[7] * list_ops['age_combined_loss'] + \
# loss_weights[8] * list_ops['gender_combined_loss'] + \
# loss_weights[9] * list_ops['young_loss'] + \
# loss_weights[10] * list_ops['mask_loss'] + \