Closed ducanh841988 closed 7 years ago
kwotsin
commented
7 years ago As the error implies, your FIFO queue is empty. Could you post the part where you enqueue the data? You should make sure the list you feed into the tf.convert_to_tensor function is not empty. The later error shows the shape mismatch, which has to do with your input data channels.
ducanh841988
commented
7 years ago this is the code for enqueue training data
images = tf.convert_to_tensor(image_files)
import pdb; pdb.set_trace()
annotations = tf.convert_to_tensor(annotation_files)
input_queue = tf.train.slice_input_producer([images, annotations]) #Slice_input producer shuffles the data by default.
print "Input Queue ", input_queue
#Decode the image and annotation raw content
image = tf.read_file(input_queue[0])
image = tf.image.decode_image(image, channels=3)
#import pdb; pdb.set_trace()
annotation = tf.read_file(input_queue[1])
annotation = tf.image.decode_image(annotation)
#preprocess and batch up the image and annotation
preprocessed_image, preprocessed_annotation = preprocess(image, annotation, image_height, image_width)
images, annotations = tf.train.batch([preprocessed_image, preprocessed_annotation], batch_size=batch_size, allow_smaller_final_batch=True)and this for validation data
#Load the files into one input queue
images_val = tf.convert_to_tensor(image_val_files)
annotations_val = tf.convert_to_tensor(annotation_val_files)
input_queue_val = tf.train.slice_input_producer([images_val, annotations_val], num_epochs=1)
#Decode the image and annotation raw content
image_val = tf.read_file(input_queue_val[0])
image_val = tf.image.decode_jpeg(image_val, channels=3)
annotation_val = tf.read_file(input_queue_val[1])
annotation_val = tf.image.decode_png(annotation_val)
#preprocess and batch up the image and annotation
preprocessed_image_val, preprocessed_annotation_val = preprocess(image_val, annotation_val, image_height, image_width)
images_val, annotations_val = tf.train.batch([preprocessed_image_val, preprocessed_annotation_val], batch_size=eval_batch_size, allow_smaller_final_batch=True)Note that I used this code to run camvid data. It worked well.
kwotsin
commented
7 years ago @ducanh841988 I edited your error code (it is easier to look this way). So if you look at the error:
Shape mismatch in tuple component 1. Expected [202,360,1], got [202,360,3] you might be feeding in an RGB image instead of a grayscale image annotation. The ground truth annotation must be a grayscale image where each pixel is labelled with its class and not the RGB values.
rocklinsuv
commented
5 years ago @kwotsin Hi kwotsin,
I recently have encountered a similar issue of shape mismatch. The annotation tool I used is Labelme, which produces a 3-channel png labelled image. Will a simple color2grayscale conversion (like the opencv rgb2gray function) enough to resolve this issue? Thanks!
SreenijaK
commented
4 years ago @kwotsin Hi kwotsin,
I recently have encountered a similar issue of shape mismatch. The annotation tool I used is Labelme, which produces a 3-channel png labelled image. Will a simple color2grayscale conversion (like the opencv rgb2gray function) enough to resolve this issue? Thanks!
@kwotsin were you able to solve the issue. did rgb2gray work for you?
I tried to train the Enet with own data but it caused a error. It is a problem with FIFOQueue. Our data have 715 images for training and 105 for validation and 100 for testing.
Can you help me to figure out this problem. Thanks a lot. Anh