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How to train gray images by one channel?
See original GitHub issueI’m sorry for asking this stupid question, I have tried for so many days, but I can’t figure it out. I can ask no one in my laboratory, I wish someone could help me, thank you!
model.py
from __future__ import print_function
import os
import time
import random
import datetime
import scipy.misc
import numpy as np
import tensorflow as tf
import tensorflow.contrib.slim as slim
from datetime import datetime
from util.util import *
from util.BasicConvLSTMCell import *
import sys
reload(sys)
sys.setdefaultencoding('utf8')
class DEBLUR(object):
def __init__(self, args):
self.args = args
self.n_levels = 3
self.scale = 0.5
self.chns = 1
# if args.phase == 'train':
self.crop_size = 128
self.data_list = open(args.datalist, 'rt').read().splitlines()
self.data_list = list(map(lambda x: x.split(' '), self.data_list))
random.shuffle(self.data_list)
self.train_dir = os.path.join('./checkpoints', args.model)
if not os.path.exists(self.train_dir):
os.makedirs(self.train_dir)
self.batch_size = args.batch_size
self.epoch = args.epoch
self.data_size = (len(self.data_list)) // self.batch_size
self.max_steps = int(self.epoch * self.data_size)
self.learning_rate = args.learning_rate
def input_producer(self, batch_size=10):
def read_data():
img_a = tf.image.decode_image(tf.read_file(tf.string_join(['./training_set/', self.data_queue[0]])),
channels=0)
img_b = tf.image.decode_image(tf.read_file(tf.string_join(['./training_set/', self.data_queue[1]])),
channels=0)
img_a, img_b = preprocessing([img_a, img_b])
return img_a, img_b
def preprocessing(imgs):
imgs = [tf.cast(img, tf.float32) / 255.0 for img in imgs]
if self.args.model != 'color':
imgs = [tf.image.rgb_to_grayscale(img) for img in imgs]
img_crop = tf.unstack(tf.random_crop(tf.stack(imgs, axis=0), [2, self.crop_size, self.crop_size, self.chns]),
axis=0)
return img_crop
with tf.variable_scope('input'):
List_all = tf.convert_to_tensor(self.data_list, dtype=tf.string)
gt_list = List_all[:, 0]
in_list = List_all[:, 1]
self.data_queue = tf.train.slice_input_producer([in_list, gt_list], capacity=20)
image_in, image_gt = read_data()
batch_in, batch_gt = tf.train.batch([image_in, image_gt], batch_size=batch_size, num_threads=8, capacity=20)
return batch_in, batch_gt
def generator(self, inputs, reuse=False, scope='g_net'):
n, h, w, c = inputs.get_shape().as_list()
if self.args.model == 'lstm':
with tf.variable_scope('LSTM'):
cell = BasicConvLSTMCell([h / 4, w / 4], [3, 3], 128)
rnn_state = cell.zero_state(batch_size=self.batch_size, dtype=tf.float32)
x_unwrap = []
with tf.variable_scope(scope, reuse=reuse):
with slim.arg_scope([slim.conv2d, slim.conv2d_transpose],
activation_fn=tf.nn.relu, padding='SAME', normalizer_fn=None,
weights_initializer=tf.contrib.layers.xavier_initializer(uniform=True),
biases_initializer=tf.constant_initializer(0.0)):
inp_pred = inputs
for i in xrange(self.n_levels):
scale = self.scale ** (self.n_levels - i - 1)
hi = int(round(h * scale))
wi = int(round(w * scale))
inp_blur = tf.image.resize_images(inputs, [hi, wi], method=0)
inp_pred = tf.stop_gradient(tf.image.resize_images(inp_pred, [hi, wi], method=0))
inp_all = tf.concat([inp_blur, inp_pred], axis=3, name='inp')
if self.args.model == 'lstm':
rnn_state = tf.image.resize_images(rnn_state, [hi // 4, wi // 4], method=0)
# encoder
conv1_1 = slim.conv2d(inp_all, 32, [5, 5], scope='enc1_1')
conv1_2 = ResnetBlock(conv1_1, 32, 5, scope='enc1_2')
conv1_3 = ResnetBlock(conv1_2, 32, 5, scope='enc1_3')
conv1_4 = ResnetBlock(conv1_3, 32, 5, scope='enc1_4')
conv2_1 = slim.conv2d(conv1_4, 64, [5, 5], stride=2, scope='enc2_1')
conv2_2 = ResnetBlock(conv2_1, 64, 5, scope='enc2_2')
conv2_3 = ResnetBlock(conv2_2, 64, 5, scope='enc2_3')
conv2_4 = ResnetBlock(conv2_3, 64, 5, scope='enc2_4')
conv3_1 = slim.conv2d(conv2_4, 128, [5, 5], stride=2, scope='enc3_1')
conv3_2 = ResnetBlock(conv3_1, 128, 5, scope='enc3_2')
conv3_3 = ResnetBlock(conv3_2, 128, 5, scope='enc3_3')
conv3_4 = ResnetBlock(conv3_3, 128, 5, scope='enc3_4')
if self.args.model == 'lstm':
deconv3_4, rnn_state = cell(conv3_4, rnn_state)
else:
deconv3_4 = conv3_4
# decoder
deconv3_3 = ResnetBlock(deconv3_4, 128, 5, scope='dec3_3')
deconv3_2 = ResnetBlock(deconv3_3, 128, 5, scope='dec3_2')
deconv3_1 = ResnetBlock(deconv3_2, 128, 5, scope='dec3_1')
deconv2_4 = slim.conv2d_transpose(deconv3_1, 64, [4, 4], stride=2, scope='dec2_4')
cat2 = deconv2_4 + conv2_4
deconv2_3 = ResnetBlock(cat2, 64, 5, scope='dec2_3')
deconv2_2 = ResnetBlock(deconv2_3, 64, 5, scope='dec2_2')
deconv2_1 = ResnetBlock(deconv2_2, 64, 5, scope='dec2_1')
deconv1_4 = slim.conv2d_transpose(deconv2_1, 32, [4, 4], stride=2, scope='dec1_4')
cat1 = deconv1_4 + conv1_4
deconv1_3 = ResnetBlock(cat1, 32, 5, scope='dec1_3')
deconv1_2 = ResnetBlock(deconv1_3, 32, 5, scope='dec1_2')
deconv1_1 = ResnetBlock(deconv1_2, 32, 5, scope='dec1_1')
inp_pred = slim.conv2d(deconv1_1, self.chns, [5, 5], activation_fn=None, scope='dec1_0')
if i >= 0:
x_unwrap.append(inp_pred)
if i == 0:
tf.get_variable_scope().reuse_variables()
return x_unwrap
def build_model(self):
img_in, img_gt = self.input_producer(self.batch_size)
tf.summary.image('img_in', im2uint8(img_in))
tf.summary.image('img_gt', im2uint8(img_gt))
print('img_in, img_gt', img_in.get_shape(), img_gt.get_shape())
# generator
x_unwrap = self.generator(img_in, reuse=False, scope='g_net')
# calculate multi-scale loss
self.loss_total = 0
for i in xrange(self.n_levels):
_, hi, wi, _ = x_unwrap[i].get_shape().as_list()
gt_i = tf.image.resize_images(img_gt, [hi, wi], method=0)
loss = tf.reduce_mean((gt_i - x_unwrap[i]) ** 2)
self.loss_total += loss
tf.summary.image('out_' + str(i), im2uint8(x_unwrap[i]))
tf.summary.scalar('loss_' + str(i), loss)
# losses
tf.summary.scalar('loss_total', self.loss_total)
# training vars
all_vars = tf.trainable_variables()
self.all_vars = all_vars
self.g_vars = [var for var in all_vars if 'g_net' in var.name]
self.lstm_vars = [var for var in all_vars if 'LSTM' in var.name]
for var in all_vars:
print(var.name)
def train(self):
def get_optimizer(loss, global_step=None, var_list=None, is_gradient_clip=False):
train_op = tf.train.AdamOptimizer(self.lr)
if is_gradient_clip:
grads_and_vars = train_op.compute_gradients(loss, var_list=var_list)
unchanged_gvs = [(grad, var) for grad, var in grads_and_vars if not 'LSTM' in var.name]
rnn_grad = [grad for grad, var in grads_and_vars if 'LSTM' in var.name]
rnn_var = [var for grad, var in grads_and_vars if 'LSTM' in var.name]
capped_grad, _ = tf.clip_by_global_norm(rnn_grad, clip_norm=3)
capped_gvs = list(zip(capped_grad, rnn_var))
train_op = train_op.apply_gradients(grads_and_vars=capped_gvs + unchanged_gvs, global_step=global_step)
else:
train_op = train_op.minimize(loss, global_step, var_list)
return train_op
global_step = tf.Variable(initial_value=0, dtype=tf.int32, trainable=False)
self.global_step = global_step
# build model
self.build_model() # TODO
# learning rate decay
self.lr = tf.train.polynomial_decay(self.learning_rate, global_step, self.max_steps, end_learning_rate=0.0,
power=0.3)
tf.summary.scalar('learning_rate', self.lr)
# training operators
train_gnet = get_optimizer(self.loss_total, global_step, self.all_vars)
# session and thread
gpu_options = tf.GPUOptions(allow_growth=True)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
self.sess = sess
sess.run(tf.global_variables_initializer())
self.saver = tf.train.Saver(max_to_keep=50, keep_checkpoint_every_n_hours=1)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
# training summary
summary_op = tf.summary.merge_all()
summary_writer = tf.summary.FileWriter(self.train_dir, sess.graph, flush_secs=30)
for step in xrange(sess.run(global_step), self.max_steps + 1):
start_time = time.time()
# update G network
_, loss_total_val = sess.run([train_gnet, self.loss_total])
duration = time.time() - start_time
# print loss_value
assert not np.isnan(loss_total_val), 'Model diverged with loss = NaN'
if step % 5 == 0:
num_examples_per_step = self.batch_size
examples_per_sec = num_examples_per_step / duration
sec_per_batch = float(duration)
format_str = ('%s: step %d, loss = (%.5f; %.5f, %.5f)(%.1f data/s; %.3f s/bch)')
print(format_str % (datetime.now().strftime('%Y-%m-%d %H:%M:%S'), step, loss_total_val, 0.0,
0.0, examples_per_sec, sec_per_batch))
if step % 20 == 0:
# summary_str = sess.run(summary_op, feed_dict={inputs:batch_input, gt:batch_gt})
summary_str = sess.run(summary_op)
summary_writer.add_summary(summary_str, global_step=step)
# Save the model checkpoint periodically.
if step % 1000 == 0 or step == self.max_steps:
checkpoint_path = os.path.join(self.train_dir, 'checkpoints')
self.save(sess, checkpoint_path, step)
def save(self, sess, checkpoint_dir, step):
model_name = "deblur.model"
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
self.saver.save(sess, os.path.join(checkpoint_dir, model_name), global_step=step)
def load(self, sess, checkpoint_dir, step=None):
print(" [*] Reading checkpoints...")
model_name = "deblur.model"
ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
if step is not None:
ckpt_name = model_name + '-' + str(step)
self.saver.restore(sess, os.path.join(checkpoint_dir, ckpt_name))
print(" [*] Reading intermediate checkpoints... Success")
return str(step)
elif ckpt and ckpt.model_checkpoint_path:
ckpt_name = os.path.basename(ckpt.model_checkpoint_path)
ckpt_iter = ckpt_name.split('-')[1]
self.saver.restore(sess, os.path.join(checkpoint_dir, ckpt_name))
print(" [*] Reading updated checkpoints... Success")
return ckpt_iter
else:
print(" [*] Reading checkpoints... ERROR")
return False
def test(self, height, width, input_path, output_path):
if not os.path.exists(output_path):
os.makedirs(output_path)
imgsName = sorted(os.listdir(input_path))
H, W = height, width
inp_chns = 3 if self.args.model == 'color' else 1
#self.batch_size = 1 if self.args.model == 'color' else 3
self.batch_size = 1
inputs = tf.placeholder(shape=[self.batch_size, H, W, inp_chns], dtype=tf.float32)
outputs = self.generator(inputs, reuse=False)
sess = tf.Session(config=tf.ConfigProto(gpu_options=tf.GPUOptions(allow_growth=True)))
self.saver = tf.train.Saver()
self.load(sess, self.train_dir, step=523000)
for imgName in imgsName:
blur = scipy.misc.imread(os.path.join(input_path, imgName))
if self.args.model == 'gray': # gai
blur = blur.reshape(blur.shape[0], blur.shape[1], 1)
h, w, c = blur.shape
# make sure the width is larger than the height
rot = False
if h > w:
blur = np.transpose(blur, [1, 0, 2])
rot = True
h = int(blur.shape[0])
w = int(blur.shape[1])
resize = False
if h > H or w > W:
scale = min(1.0 * H / h, 1.0 * W / w)
new_h = int(h * scale)
new_w = int(w * scale)
blur = scipy.misc.imresize(blur, [new_h, new_w], 'bicubic')
resize = True
blurPad = np.pad(blur, ((0, H - new_h), (0, W - new_w), (0, 0)), 'edge')
else:
blurPad = np.pad(blur, ((0, H - h), (0, W - w), (0, 0)), 'edge')
blurPad = np.expand_dims(blurPad, 0)
if self.args.model != 'color':
blurPad = np.transpose(blurPad, (3, 1, 2, 0))
start = time.time()
deblur = sess.run(outputs, feed_dict={inputs: blurPad / 255.0})
duration = time.time() - start
print('Saving results: %s ... %4.3fs' % (os.path.join(output_path, imgName), duration))
res = deblur[-1]
if self.args.model != 'color':
res = np.transpose(res, (3, 1, 2, 0))
res = im2uint8(res[0, :, :, :])
# crop the image into original size
if resize:
res = res[:new_h, :new_w, :]
res = scipy.misc.imresize(res, [h, w], 'bicubic')
else:
res = res[:h, :w, :]
if rot:
res = np.transpose(res, [1, 0, 2])
if self.args.model == 'gray': # gai
res = res.reshape(res.shape[0], res.shape[1])
imgName = imgName.replace('blur', 'deblur')
scipy.misc.imsave(os.path.join(output_path, imgName), res)
error infomation
/home/faelan/anaconda3/envs/py27/bin/python /media/faelan/WestData/code/Deblur/SRN-Deblur/run_model.py
img_in, img_gt (1, 128, 128, 1) (1, 128, 128, 1)
g_net/enc1_1/weights:0
g_net/enc1_1/biases:0
g_net/enc1_2/conv1/weights:0
g_net/enc1_2/conv1/biases:0
g_net/enc1_2/conv2/weights:0
g_net/enc1_2/conv2/biases:0
g_net/enc1_3/conv1/weights:0
g_net/enc1_3/conv1/biases:0
g_net/enc1_3/conv2/weights:0
g_net/enc1_3/conv2/biases:0
g_net/enc1_4/conv1/weights:0
g_net/enc1_4/conv1/biases:0
g_net/enc1_4/conv2/weights:0
g_net/enc1_4/conv2/biases:0
g_net/enc2_1/weights:0
g_net/enc2_1/biases:0
g_net/enc2_2/conv1/weights:0
g_net/enc2_2/conv1/biases:0
g_net/enc2_2/conv2/weights:0
g_net/enc2_2/conv2/biases:0
g_net/enc2_3/conv1/weights:0
g_net/enc2_3/conv1/biases:0
g_net/enc2_3/conv2/weights:0
g_net/enc2_3/conv2/biases:0
g_net/enc2_4/conv1/weights:0
g_net/enc2_4/conv1/biases:0
g_net/enc2_4/conv2/weights:0
g_net/enc2_4/conv2/biases:0
g_net/enc3_1/weights:0
g_net/enc3_1/biases:0
g_net/enc3_2/conv1/weights:0
g_net/enc3_2/conv1/biases:0
g_net/enc3_2/conv2/weights:0
g_net/enc3_2/conv2/biases:0
g_net/enc3_3/conv1/weights:0
g_net/enc3_3/conv1/biases:0
g_net/enc3_3/conv2/weights:0
g_net/enc3_3/conv2/biases:0
g_net/enc3_4/conv1/weights:0
g_net/enc3_4/conv1/biases:0
g_net/enc3_4/conv2/weights:0
g_net/enc3_4/conv2/biases:0
g_net/convLSTM/LSTM_conv/weights:0
g_net/convLSTM/LSTM_conv/biases:0
g_net/dec3_3/conv1/weights:0
g_net/dec3_3/conv1/biases:0
g_net/dec3_3/conv2/weights:0
g_net/dec3_3/conv2/biases:0
g_net/dec3_2/conv1/weights:0
g_net/dec3_2/conv1/biases:0
g_net/dec3_2/conv2/weights:0
g_net/dec3_2/conv2/biases:0
g_net/dec3_1/conv1/weights:0
g_net/dec3_1/conv1/biases:0
g_net/dec3_1/conv2/weights:0
g_net/dec3_1/conv2/biases:0
g_net/dec2_4/weights:0
g_net/dec2_4/biases:0
g_net/dec2_3/conv1/weights:0
g_net/dec2_3/conv1/biases:0
g_net/dec2_3/conv2/weights:0
g_net/dec2_3/conv2/biases:0
g_net/dec2_2/conv1/weights:0
g_net/dec2_2/conv1/biases:0
g_net/dec2_2/conv2/weights:0
g_net/dec2_2/conv2/biases:0
g_net/dec2_1/conv1/weights:0
g_net/dec2_1/conv1/biases:0
g_net/dec2_1/conv2/weights:0
g_net/dec2_1/conv2/biases:0
g_net/dec1_4/weights:0
g_net/dec1_4/biases:0
g_net/dec1_3/conv1/weights:0
g_net/dec1_3/conv1/biases:0
g_net/dec1_3/conv2/weights:0
g_net/dec1_3/conv2/biases:0
g_net/dec1_2/conv1/weights:0
g_net/dec1_2/conv1/biases:0
g_net/dec1_2/conv2/weights:0
g_net/dec1_2/conv2/biases:0
g_net/dec1_1/conv1/weights:0
g_net/dec1_1/conv1/biases:0
g_net/dec1_1/conv2/weights:0
g_net/dec1_1/conv2/biases:0
g_net/dec1_0/weights:0
g_net/dec1_0/biases:0
2019-11-15 18:50:58.905733: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
2019-11-15 18:50:58.985701: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:892] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-11-15 18:50:58.986010: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1030] Found device 0 with properties:
name: GeForce GTX 1060 6GB major: 6 minor: 1 memoryClockRate(GHz): 1.8095
pciBusID: 0000:0c:00.0
totalMemory: 5.93GiB freeMemory: 5.49GiB
2019-11-15 18:50:58.986029: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1120] Creating TensorFlow device (/device:GPU:0) -> (device: 0, name: GeForce GTX 1060 6GB, pci bus id: 0000:0c:00.0, compute capability: 6.1)
2019-11-15 18:51:00.790961: W tensorflow/core/framework/op_kernel.cc:1192] Invalid argument: Number of channels must be 3 or 4, was 1
[[Node: input/decode_image/cond_jpeg/cond_png/cond_gif/DecodeBmp = DecodeBmp[channels=0, _device="/job:localhost/replica:0/task:0/device:CPU:0"](input/decode_image/cond_jpeg/cond_png/cond_gif/Substr/Switch, ^input/decode_image/cond_jpeg/cond_png/cond_gif/Assert_1/Assert, ^input/decode_image/cond_jpeg/cond_png/cond_gif/Assert_2/Assert)]]
2019-11-15 18:51:00.834833: W tensorflow/core/framework/op_kernel.cc:1192] Invalid argument: Number of channels must be 3 or 4, was 1
[[Node: input/decode_image_1/cond_jpeg/cond_png/cond_gif/DecodeBmp = DecodeBmp[channels=0, _device="/job:localhost/replica:0/task:0/device:CPU:0"](input/decode_image_1/cond_jpeg/cond_png/cond_gif/Substr/Switch, ^input/decode_image_1/cond_jpeg/cond_png/cond_gif/Assert_1/Assert, ^input/decode_image_1/cond_jpeg/cond_png/cond_gif/Assert_2/Assert)]]
2019-11-15 18:51:00.835682: W tensorflow/core/framework/op_kernel.cc:1192] Invalid argument: Number of channels must be 3 or 4, was 1
[[Node: input/decode_image/cond_jpeg/cond_png/cond_gif/DecodeBmp = DecodeBmp[channels=0, _device="/job:localhost/replica:0/task:0/device:CPU:0"](input/decode_image/cond_jpeg/cond_png/cond_gif/Substr/Switch, ^input/decode_image/cond_jpeg/cond_png/cond_gif/Assert_1/Assert, ^input/decode_image/cond_jpeg/cond_png/cond_gif/Assert_2/Assert)]]
2019-11-15 18:51:00.837560: W tensorflow/core/framework/op_kernel.cc:1192] Invalid argument: Number of channels must be 3 or 4, was 1
[[Node: input/decode_image/cond_jpeg/cond_png/cond_gif/DecodeBmp = DecodeBmp[channels=0, _device="/job:localhost/replica:0/task:0/device:CPU:0"](input/decode_image/cond_jpeg/cond_png/cond_gif/Substr/Switch, ^input/decode_image/cond_jpeg/cond_png/cond_gif/Assert_1/Assert, ^input/decode_image/cond_jpeg/cond_png/cond_gif/Assert_2/Assert)]]
2019-11-15 18:51:00.837569: W tensorflow/core/framework/op_kernel.cc:1192] Invalid argument: Number of channels must be 3 or 4, was 1
[[Node: input/decode_image/cond_jpeg/cond_png/cond_gif/DecodeBmp = DecodeBmp[channels=0, _device="/job:localhost/replica:0/task:0/device:CPU:0"](input/decode_image/cond_jpeg/cond_png/cond_gif/Substr/Switch, ^input/decode_image/cond_jpeg/cond_png/cond_gif/Assert_1/Assert, ^input/decode_image/cond_jpeg/cond_png/cond_gif/Assert_2/Assert)]]
2019-11-15 18:51:00.837582: W tensorflow/core/framework/op_kernel.cc:1192] Invalid argument: Number of channels must be 3 or 4, was 1
[[Node: input/decode_image/cond_jpeg/cond_png/cond_gif/DecodeBmp = DecodeBmp[channels=0, _device="/job:localhost/replica:0/task:0/device:CPU:0"](input/decode_image/cond_jpeg/cond_png/cond_gif/Substr/Switch, ^input/decode_image/cond_jpeg/cond_png/cond_gif/Assert_1/Assert, ^input/decode_image/cond_jpeg/cond_png/cond_gif/Assert_2/Assert)]]
2019-11-15 18:51:00.837729: W tensorflow/core/framework/op_kernel.cc:1192] Invalid argument: Number of channels must be 3 or 4, was 1
[[Node: input/decode_image_1/cond_jpeg/cond_png/cond_gif/DecodeBmp = DecodeBmp[channels=0, _device="/job:localhost/replica:0/task:0/device:CPU:0"](input/decode_image_1/cond_jpeg/cond_png/cond_gif/Substr/Switch, ^input/decode_image_1/cond_jpeg/cond_png/cond_gif/Assert_1/Assert, ^input/decode_image_1/cond_jpeg/cond_png/cond_gif/Assert_2/Assert)]]
2019-11-15 18:51:00.837744: W tensorflow/core/framework/op_kernel.cc:1192] Invalid argument: Number of channels must be 3 or 4, was 1
[[Node: input/decode_image_1/cond_jpeg/cond_png/cond_gif/DecodeBmp = DecodeBmp[channels=0, _device="/job:localhost/replica:0/task:0/device:CPU:0"](input/decode_image_1/cond_jpeg/cond_png/cond_gif/Substr/Switch, ^input/decode_image_1/cond_jpeg/cond_png/cond_gif/Assert_1/Assert, ^input/decode_image_1/cond_jpeg/cond_png/cond_gif/Assert_2/Assert)]]
2019-11-15 18:51:00.837721: W tensorflow/core/framework/op_kernel.cc:1192] Invalid argument: Number of channels must be 3 or 4, was 1
[[Node: input/decode_image_1/cond_jpeg/cond_png/cond_gif/DecodeBmp = DecodeBmp[channels=0, _device="/job:localhost/replica:0/task:0/device:CPU:0"](input/decode_image_1/cond_jpeg/cond_png/cond_gif/Substr/Switch, ^input/decode_image_1/cond_jpeg/cond_png/cond_gif/Assert_1/Assert, ^input/decode_image_1/cond_jpeg/cond_png/cond_gif/Assert_2/Assert)]]
2019-11-15 18:51:00.838160: W tensorflow/core/framework/op_kernel.cc:1192] Invalid argument: Number of channels must be 3 or 4, was 1
[[Node: input/decode_image_1/cond_jpeg/cond_png/cond_gif/DecodeBmp = DecodeBmp[channels=0, _device="/job:localhost/replica:0/task:0/device:CPU:0"](input/decode_image_1/cond_jpeg/cond_png/cond_gif/Substr/Switch, ^input/decode_image_1/cond_jpeg/cond_png/cond_gif/Assert_1/Assert, ^input/decode_image_1/cond_jpeg/cond_png/cond_gif/Assert_2/Assert)]]
2019-11-15 18:51:00.838148: W tensorflow/core/framework/op_kernel.cc:1192] Invalid argument: Number of channels must be 3 or 4, was 1
[[Node: input/decode_image/cond_jpeg/cond_png/cond_gif/DecodeBmp = DecodeBmp[channels=0, _device="/job:localhost/replica:0/task:0/device:CPU:0"](input/decode_image/cond_jpeg/cond_png/cond_gif/Substr/Switch, ^input/decode_image/cond_jpeg/cond_png/cond_gif/Assert_1/Assert, ^input/decode_image/cond_jpeg/cond_png/cond_gif/Assert_2/Assert)]]
2019-11-15 18:51:00.838247: W tensorflow/core/framework/op_kernel.cc:1192] Invalid argument: Number of channels must be 3 or 4, was 1
[[Node: input/decode_image_1/cond_jpeg/cond_png/cond_gif/DecodeBmp = DecodeBmp[channels=0, _device="/job:localhost/replica:0/task:0/device:CPU:0"](input/decode_image_1/cond_jpeg/cond_png/cond_gif/Substr/Switch, ^input/decode_image_1/cond_jpeg/cond_png/cond_gif/Assert_1/Assert, ^input/decode_image_1/cond_jpeg/cond_png/cond_gif/Assert_2/Assert)]]
2019-11-15 18:51:00.838156: W tensorflow/core/framework/op_kernel.cc:1192] Invalid argument: Number of channels must be 3 or 4, was 1
[[Node: input/decode_image/cond_jpeg/cond_png/cond_gif/DecodeBmp = DecodeBmp[channels=0, _device="/job:localhost/replica:0/task:0/device:CPU:0"](input/decode_image/cond_jpeg/cond_png/cond_gif/Substr/Switch, ^input/decode_image/cond_jpeg/cond_png/cond_gif/Assert_1/Assert, ^input/decode_image/cond_jpeg/cond_png/cond_gif/Assert_2/Assert)]]
2019-11-15 18:51:00.838156: W tensorflow/core/framework/op_kernel.cc:1192] Invalid argument: Number of channels must be 3 or 4, was 1
[[Node: input/decode_image/cond_jpeg/cond_png/cond_gif/DecodeBmp = DecodeBmp[channels=0, _device="/job:localhost/replica:0/task:0/device:CPU:0"](input/decode_image/cond_jpeg/cond_png/cond_gif/Substr/Switch, ^input/decode_image/cond_jpeg/cond_png/cond_gif/Assert_1/Assert, ^input/decode_image/cond_jpeg/cond_png/cond_gif/Assert_2/Assert)]]
2019-11-15 18:51:00.838317: W tensorflow/core/framework/op_kernel.cc:1192] Invalid argument: Number of channels must be 3 or 4, was 1
[[Node: input/decode_image_1/cond_jpeg/cond_png/cond_gif/DecodeBmp = DecodeBmp[channels=0, _device="/job:localhost/replica:0/task:0/device:CPU:0"](input/decode_image_1/cond_jpeg/cond_png/cond_gif/Substr/Switch, ^input/decode_image_1/cond_jpeg/cond_png/cond_gif/Assert_1/Assert, ^input/decode_image_1/cond_jpeg/cond_png/cond_gif/Assert_2/Assert)]]
2019-11-15 18:51:00.838385: W tensorflow/core/framework/op_kernel.cc:1192] Invalid argument: Number of channels must be 3 or 4, was 1
[[Node: input/decode_image_1/cond_jpeg/cond_png/cond_gif/DecodeBmp = DecodeBmp[channels=0, _device="/job:localhost/replica:0/task:0/device:CPU:0"](input/decode_image_1/cond_jpeg/cond_png/cond_gif/Substr/Switch, ^input/decode_image_1/cond_jpeg/cond_png/cond_gif/Assert_1/Assert, ^input/decode_image_1/cond_jpeg/cond_png/cond_gif/Assert_2/Assert)]]
2019-11-15 18:51:00.838392: W tensorflow/core/framework/op_kernel.cc:1192] Invalid argument: Number of channels must be 3 or 4, was 1
[[Node: input/decode_image_1/cond_jpeg/cond_png/cond_gif/DecodeBmp = DecodeBmp[channels=0, _device="/job:localhost/replica:0/task:0/device:CPU:0"](input/decode_image_1/cond_jpeg/cond_png/cond_gif/Substr/Switch, ^input/decode_image_1/cond_jpeg/cond_png/cond_gif/Assert_1/Assert, ^input/decode_image_1/cond_jpeg/cond_png/cond_gif/Assert_2/Assert)]]
2019-11-15 18:51:00.838418: W tensorflow/core/framework/op_kernel.cc:1192] Invalid argument: Number of channels must be 3 or 4, was 1
[[Node: input/decode_image/cond_jpeg/cond_png/cond_gif/DecodeBmp = DecodeBmp[channels=0, _device="/job:localhost/replica:0/task:0/device:CPU:0"](input/decode_image/cond_jpeg/cond_png/cond_gif/Substr/Switch, ^input/decode_image/cond_jpeg/cond_png/cond_gif/Assert_1/Assert, ^input/decode_image/cond_jpeg/cond_png/cond_gif/Assert_2/Assert)]]
2019-11-15 18:51:00.838435: W tensorflow/core/framework/op_kernel.cc:1192] Invalid argument: Number of channels must be 3 or 4, was 1
[[Node: input/decode_image_1/cond_jpeg/cond_png/cond_gif/DecodeBmp = DecodeBmp[channels=0, _device="/job:localhost/replica:0/task:0/device:CPU:0"](input/decode_image_1/cond_jpeg/cond_png/cond_gif/Substr/Switch, ^input/decode_image_1/cond_jpeg/cond_png/cond_gif/Assert_1/Assert, ^input/decode_image_1/cond_jpeg/cond_png/cond_gif/Assert_2/Assert)]]
2019-11-15 18:51:00.846275: W tensorflow/core/framework/op_kernel.cc:1192] Invalid argument: Number of channels must be 3 or 4, was 1
[[Node: input/decode_image_1/cond_jpeg/cond_png/cond_gif/DecodeBmp = DecodeBmp[channels=0, _device="/job:localhost/replica:0/task:0/device:CPU:0"](input/decode_image_1/cond_jpeg/cond_png/cond_gif/Substr/Switch, ^input/decode_image_1/cond_jpeg/cond_png/cond_gif/Assert_1/Assert, ^input/decode_image_1/cond_jpeg/cond_png/cond_gif/Assert_2/Assert)]]
2019-11-15 18:51:00.846288: W tensorflow/core/framework/op_kernel.cc:1192] Invalid argument: Number of channels must be 3 or 4, was 1
[[Node: input/decode_image_1/cond_jpeg/cond_png/cond_gif/DecodeBmp = DecodeBmp[channels=0, _device="/job:localhost/replica:0/task:0/device:CPU:0"](input/decode_image_1/cond_jpeg/cond_png/cond_gif/Substr/Switch, ^input/decode_image_1/cond_jpeg/cond_png/cond_gif/Assert_1/Assert, ^input/decode_image_1/cond_jpeg/cond_png/cond_gif/Assert_2/Assert)]]
2019-11-15 18:51:00.893219: W tensorflow/core/framework/op_kernel.cc:1192] Invalid argument: Number of channels must be 3 or 4, was 1
[[Node: input/decode_image/cond_jpeg/cond_png/cond_gif/DecodeBmp = DecodeBmp[channels=0, _device="/job:localhost/replica:0/task:0/device:CPU:0"](input/decode_image/cond_jpeg/cond_png/cond_gif/Substr/Switch, ^input/decode_image/cond_jpeg/cond_png/cond_gif/Assert_1/Assert, ^input/decode_image/cond_jpeg/cond_png/cond_gif/Assert_2/Assert)]]
2019-11-15 18:51:00.893239: W tensorflow/core/framework/op_kernel.cc:1192] Invalid argument: Number of channels must be 3 or 4, was 1
[[Node: input/decode_image/cond_jpeg/cond_png/cond_gif/DecodeBmp = DecodeBmp[channels=0, _device="/job:localhost/replica:0/task:0/device:CPU:0"](input/decode_image/cond_jpeg/cond_png/cond_gif/Substr/Switch, ^input/decode_image/cond_jpeg/cond_png/cond_gif/Assert_1/Assert, ^input/decode_image/cond_jpeg/cond_png/cond_gif/Assert_2/Assert)]]
Traceback (most recent call last):
File "/media/faelan/WestData/code/Deblur/SRN-Deblur/run_model.py", line 54, in <module>
tf.app.run()
File "/home/faelan/anaconda3/envs/py27/lib/python2.7/site-packages/tensorflow/python/platform/app.py", line 48, in run
_sys.exit(main(_sys.argv[:1] + flags_passthrough))
File "/media/faelan/WestData/code/Deblur/SRN-Deblur/run_model.py", line 48, in main
deblur.train()
File "/media/faelan/WestData/code/Deblur/SRN-Deblur/models/model.py", line 214, in train
_, loss_total_val = sess.run([train_gnet, self.loss_total])
File "/home/faelan/anaconda3/envs/py27/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 889, in run
run_metadata_ptr)
File "/home/faelan/anaconda3/envs/py27/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1120, in _run
feed_dict_tensor, options, run_metadata)
File "/home/faelan/anaconda3/envs/py27/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1317, in _do_run
options, run_metadata)
File "/home/faelan/anaconda3/envs/py27/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1336, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.OutOfRangeError: FIFOQueue '_1_input/batch/fifo_queue' is closed and has insufficient elements (requested 1, current size 0)
[[Node: input/batch = QueueDequeueManyV2[component_types=[DT_FLOAT, DT_FLOAT], timeout_ms=-1, _device="/job:localhost/replica:0/task:0/device:CPU:0"](input/batch/fifo_queue, input/batch/n)]]
Caused by op u'input/batch', defined at:
File "/media/faelan/WestData/code/Deblur/SRN-Deblur/run_model.py", line 54, in <module>
tf.app.run()
File "/home/faelan/anaconda3/envs/py27/lib/python2.7/site-packages/tensorflow/python/platform/app.py", line 48, in run
_sys.exit(main(_sys.argv[:1] + flags_passthrough))
File "/media/faelan/WestData/code/Deblur/SRN-Deblur/run_model.py", line 48, in main
deblur.train()
File "/media/faelan/WestData/code/Deblur/SRN-Deblur/models/model.py", line 186, in train
self.build_model() # TODO
File "/media/faelan/WestData/code/Deblur/SRN-Deblur/models/model.py", line 137, in build_model
img_in, img_gt = self.input_producer(self.batch_size)
File "/media/faelan/WestData/code/Deblur/SRN-Deblur/models/model.py", line 64, in input_producer
batch_in, batch_gt = tf.train.batch([image_in, image_gt], batch_size=batch_size, num_threads=8, capacity=20)
File "/home/faelan/anaconda3/envs/py27/lib/python2.7/site-packages/tensorflow/python/training/input.py", line 927, in batch
name=name)
File "/home/faelan/anaconda3/envs/py27/lib/python2.7/site-packages/tensorflow/python/training/input.py", line 722, in _batch
dequeued = queue.dequeue_many(batch_size, name=name)
File "/home/faelan/anaconda3/envs/py27/lib/python2.7/site-packages/tensorflow/python/ops/data_flow_ops.py", line 464, in dequeue_many
self._queue_ref, n=n, component_types=self._dtypes, name=name)
File "/home/faelan/anaconda3/envs/py27/lib/python2.7/site-packages/tensorflow/python/ops/gen_data_flow_ops.py", line 2418, in _queue_dequeue_many_v2
component_types=component_types, timeout_ms=timeout_ms, name=name)
File "/home/faelan/anaconda3/envs/py27/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "/home/faelan/anaconda3/envs/py27/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2956, in create_op
op_def=op_def)
File "/home/faelan/anaconda3/envs/py27/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1470, in __init__
self._traceback = self._graph._extract_stack() # pylint: disable=protected-access
OutOfRangeError (see above for traceback): FIFOQueue '_1_input/batch/fifo_queue' is closed and has insufficient elements (requested 1, current size 0)
[[Node: input/batch = QueueDequeueManyV2[component_types=[DT_FLOAT, DT_FLOAT], timeout_ms=-1, _device="/job:localhost/replica:0/task:0/device:CPU:0"](input/batch/fifo_queue, input/batch/n)]]
Issue Analytics
- State:
- Created 4 years ago
- Comments:6 (1 by maintainers)
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1.Option"model" should be “gray” 2.Training set should be grayscale images. 3. "Channels"should be 3 not 0.
I have trained grayscale images successfully.
The code is no error, my dataset has some problems. The code runs well with another dataset. Thank you!