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gcn_train.py
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from gcn import GCN
from data_utils import DataSet
import cv2
import os
import tensorflow as tf
import pandas as pd
import numpy as np
from color_utils import color_predicts
from predicts_utils import total_image_predict
from metric_utils import iou
class args:
batch_size = 4
lr = 2e-4
test_display = 2000
weight_decay = 5e-4
model_name = 'gcn1'
batch_norm_decay = 0.95
test_image_path = 'dataset/val/images/00001.png'
test_label_path = 'dataset/val/labels/00001.png'
multi_scale = True # 是否多尺度预测
gpu_num = 0
pretraining = True
# 打印以下超参数
for key in args.__dict__:
if key.find('__') == -1:
offset = 20 - key.__len__()
print(key + ' ' * offset, args.__dict__[key])
# 使用那一块显卡
os.environ["CUDA_VISIBLE_DEVICES"] = "2"
data_path_df = pd.read_csv('dataset/path_list.csv')
data_path_df = data_path_df.sample(frac=1) # 第一次打乱
dataset = DataSet(image_path=data_path_df['image'].values, label_path=data_path_df['label'].values)
model = GCN(batch_norm_decay=args.batch_norm_decay)
image = tf.placeholder(tf.float32, [None, 1024, 1024, 3], name='input_x')
label = tf.placeholder(tf.int32, [None, 1024, 1024])
lr = tf.placeholder(tf.float32, )
logits = model.forward_pass(image)
logits_prob = tf.nn.softmax(logits=logits, name='logits_prob')
predicts = tf.argmax(logits, axis=-1, name='predicts')
variables_to_restore = tf.trainable_variables(scope='resnet_v2_50')
# finetune resnet_v2_50的参数(block1到block4)
restorer = tf.train.Saver(variables_to_restore)
# cross_entropy
cross_entropy = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=label))
# 只将weight加入到weight_decay中
# https://arxiv.org/pdf/1807.11205.pdf
# weight_for_weightdecay = []
# for var in tf.trainable_variables():
# if var.name.__contains__('weight'):
# weight_for_weightdecay.append(var)
# print(var.op.name)
# else:
# continue
# l2_norm l2正则化
with tf.name_scope('weight_decay'):
l2_loss = args.weight_decay * tf.add_n(
[tf.nn.l2_loss(tf.cast(v, tf.float32)) for v in tf.trainable_variables()])
optimizer = tf.train.AdamOptimizer(learning_rate=lr)
loss = cross_entropy + l2_loss
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
# 计算梯度
grads = optimizer.compute_gradients(loss=loss, var_list=tf.trainable_variables())
# for grad, var in grads:
# if grad is not None:
# tf.summary.histogram(name='%s_gradients' % var.op.name, values=grad)
# tf.summary.histogram(name='%s' % var.op.name, values=var)
# 梯度裁剪
# gradients, variables = zip(*grads)
# gradients, global_norm = tf.clip_by_global_norm(gradients, 5)
# 更新梯度
apply_gradient_op = optimizer.apply_gradients(grads_and_vars=grads, global_step=tf.train.get_or_create_global_step())
batch_norm_updates_op = tf.group(*tf.get_collection(tf.GraphKeys.UPDATE_OPS))
train_op = tf.group(apply_gradient_op, batch_norm_updates_op)
saver = tf.train.Saver(tf.all_variables())
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
# summary_op = tf.summary.merge_all()
with tf.Session(config=config) as sess:
sess.run(tf.local_variables_initializer())
sess.run(tf.global_variables_initializer())
graph = tf.get_default_graph()
if args.pretraining:
# finetune resnet_v2_50参数，需要下载权重文件
restorer.restore(sess, 'ckpts/resnet_v2_50/resnet_v2_50.ckpt')
log_path = 'logs/%s/' % args.model_name
model_path = 'ckpts/%s/deeplabV3' % args.model_name
if not os.path.exists(model_path): os.makedirs(model_path)
if not os.path.exists('./logs'): os.makedirs('./logs')
if not os.path.exists(log_path): os.makedirs(log_path)
summary_writer = tf.summary.FileWriter('%s/' % log_path, sess.graph)
learning_rate = args.lr
saver = tf.train.Saver()
global_step = 10000
for step in range(1, 100001):
if step == 30000 or step == 60000:
learning_rate = learning_rate / 10
x_tr, y_tr = dataset.next_batch(args.batch_size)
loss_tr, l2_loss_tr, predicts_tr, _ = sess.run(
fetches=[cross_entropy, l2_loss, predicts, train_op],
feed_dict={
image: x_tr,
label: y_tr,
model._is_training: True,
lr: learning_rate})
if step * args.batch_size % 5000 == 0:
train_text = 'step: {}, loss_tr: {}'.format(
step, loss_tr)
print(train_text)
if step == 50000 or step == 75000:
saver.save(sess, model_path, write_meta_graph=True, global_step=step)
# 前50, 100, 200 看一下是否搞错了
if (step in [50, 500]) or (step > 0 and step % args.test_display == 0):
test_predict = total_image_predict(
ori_image_path=args.test_image_path,
input_placeholder=image,
logits_prob_node=logits_prob,
is_training_placeholder=model._is_training,
sess=sess,
multi_scale=args.multi_scale
)
test_label = cv2.imread(args.test_label_path, cv2.IMREAD_GRAYSCALE)
# 保存图片
cv2.imwrite(filename='%spredict_color_%d.png' % (log_path, step),
img=color_predicts(img=test_predict))
result = iou(y_pre=np.reshape(test_predict, -1),
y_true=np.reshape(test_label, -1))
print("======================%d======================" % step)
for key in result.keys():
offset = 40 - key.__len__()
print(key + ' ' * offset + '%.4f' % result[key])
test_summary = tf.Summary(
value=[tf.Summary.Value(tag=key, simple_value=result[key]) for key in result.keys()]
)
# 记录summary
summary_writer.add_summary(test_summary, step)
summary_writer.flush()
saver.save(sess, model_path, write_meta_graph=True, global_step=100000)