How can i use the model to predict for bigger image like 720p?

need i to train using the same size image,like 720p?

I have resolved it.
By now I think you should fine-tuning the models to make it fit for big size image,but the m_iou decrease.

Hi George ... Could you pls elaborate on how to resolved it? I am stuck here!

我已经解决了。
到目前为止，我认为你应该对模型进行微调以使其适合大尺寸图像，但是m_iou会减少。
hi.did u like to share me how to solve this problem?

嗨乔治......你能详细说明如何解决它吗？我被困在这里！

Did u solve it? I also has the problem

No, i couldn't

I met this problem after train deeplab model.
This is caused by, deferent image size of train and test.
For example, train with (513,513), in input_preprocess.py preprocess_image_and_label function.
if is_training and label is not None

will crop the image.
but in test phase, is_training is False.
So the image size will not be changed.
So my temple solution is that, add else branch:

else:
  rr = tf.minimum(tf.cast(crop_height,tf.float32)/tf.cast(image_height,tf.float32),\
           tf.cast(crop_width,tf.float32)/tf.cast(image_width,tf.float32))
   newh = tf.cast(tf.cast(image_height, tf.float32)*rr, tf.float32)
   neww = tf.cast((tf.cast(image_width, tf.float32)*rr), tf.float32)
   processed_image = tf.image.resize_images(
       processed_image, (newh, neww), method=tf.image.ResizeMethod.BILINEAR, align_corners=True)
   processed_image = preprocess_utils.pad_to_bounding_box(
                       processed_image, 0, 0, crop_height, crop_width, mean_pixel)

*In detail, if you choose xception, in xception.py:
inputs = fixed_padding(inputs, kernel_size, rate)

I met this problem after train deeplab model.
This is caused by, deferent image size of train and test.
For example, train with (513,513), in input_preprocess.py preprocess_image_and_label function.
if is_training and label is not None

will crop the image.
but in test phase, is_training is False.
So the image size will not be changed.
So my temple solution is that, add else branch:

else:
  rr = tf.minimum(tf.cast(crop_height,tf.float32)/tf.cast(image_height,tf.float32),\
           tf.cast(crop_width,tf.float32)/tf.cast(image_width,tf.float32))
   newh = tf.cast(tf.cast(image_height, tf.float32)*rr, tf.float32)
   neww = tf.cast((tf.cast(image_width, tf.float32)*rr), tf.float32)
   processed_image = tf.image.resize_images(
       processed_image, (newh, neww), method=tf.image.ResizeMethod.BILINEAR, align_corners=True)
   processed_image = preprocess_utils.pad_to_bounding_box(
                       processed_image, 0, 0, crop_height, crop_width, mean_pixel)

*In detail, if you choose xception, in xception.py:
inputs = fixed_padding(inputs, kernel_size, rate)

it works for the eval.py and vis.py of deeplab!
but one mistake: the cast of neww and newh should be tf.int32
wrong:

newh = tf.cast(tf.cast(image_height, tf.float32)*rr, tf.float32) 
neww = tf.cast((tf.cast(image_width, tf.float32)*rr), tf.float32)

right:

newh = tf.cast(tf.cast(image_height, tf.float32)*rr, tf.int32) 
neww = tf.cast((tf.cast(image_width, tf.float32)*rr), tf.int32)

and the visualize result is crop region of original image which may not be the purpose of the auther. here is a reference about the explaination of the auther:
https://github.com/tensorflow/models/issues/3939

@northeastsquare , @muxizju
Big thanks guys, now it works. After hours of struggling your else branch did the job :)

I met this problem after train deeplab model.
This is caused by, deferent image size of train and test.
For example, train with (513,513), in input_preprocess.py preprocess_image_and_label function.
if is_training and label is not None

will crop the image.
but in test phase, is_training is False.
So the image size will not be changed.
So my temple solution is that, add else branch:

else:
  rr = tf.minimum(tf.cast(crop_height,tf.float32)/tf.cast(image_height,tf.float32),\
           tf.cast(crop_width,tf.float32)/tf.cast(image_width,tf.float32))
   newh = tf.cast(tf.cast(image_height, tf.float32)*rr, tf.float32)
   neww = tf.cast((tf.cast(image_width, tf.float32)*rr), tf.float32)
   processed_image = tf.image.resize_images(
       processed_image, (newh, neww), method=tf.image.ResizeMethod.BILINEAR, align_corners=True)
   processed_image = preprocess_utils.pad_to_bounding_box(
                       processed_image, 0, 0, crop_height, crop_width, mean_pixel)

*In detail, if you choose xception, in xception.py:
inputs = fixed_padding(inputs, kernel_size, rate)

what does In detail mean? need to change something ?

@mohhao Did you figure out what to do with the xception.py file?

@northeastsquare Can you explain the changes to be maid to xception.py file?