How to build graph once and run multi images/masks with different resolutions?

[c9o]

Really great works your deepfill!
Is it possible to build graph once then feed it with multi images/masks with different resolutions?

Similar to #12 (comment)
a loop was created to process multi images but he built graph for every input image.

In your code #12 (comment) graph only has to be built once but all image/mask should be resized to same size then fed to the graph.

Can we build graph once and feed images/masks with different resolutions?

Thanks.

[JiahuiYu]

Hey @jtetc . That is a very good question. And I appreciate that you have searched over issues before asking.

In principle our trained models support different image resolutions. You could have a try by setting input shapes as (1, None, None, 3).

However, as I tried last year, I failed. The major obstacle lies in the API of tf.extract_image_patches in tensorflow. I don't know if currently that API supports None shape input.

[c9o]

Thanks for your instant response @JiahuiYu
Your advice is great. It works after changing related resize ops with option dynamic=True

Thanks again.

[JiahuiYu]

@jtetc Great! Thanks for letting me know.

[ccc013]

Hi, @jtetc

Thanks for your instant response @JiahuiYu
Your advice is great. It works after changing related resize ops with option dynamic=True

Thanks again.

where are the resize ops you said? Did you mean the resize in resize_mask_like() function in inpaint_ops.py ?

And I want to set input shape as (1, None, None, 3), but I got an error like this:

Traceback (most recent call last): File "finetune_imageinfer_compare.py", line 220, in <module> input_image_ph, output, sess = deepfill_model(args.checkpoint_dir, args.image_height, args.image_width) File "finetune_imageinfer_compare.py", line 166, in deepfill_model output = model.build_server_graph(input_image_ph, dynamic=True) File "/home/luocai/generative_inpainting/inpaint_model.py", line 315, in build_server_graph config=None) File "/home/luocai/generative_inpainting/inpaint_model.py", line 61, in build_inpaint_net mask_s = resize_mask_like(mask, x, dynamic=dynamic) File "/home/luocai/generative_inpainting/inpaint_ops.py", line 226, in resize_mask_like func=tf.image.resize_nearest_neighbor, dynamic=dynamic) File "/home/luocai/generative_inpainting/neuralgym_c/ops/layers.py", line 128, in resize align_corners=align_corners) File "/root/anaconda3/envs/tf15/lib/python3.6/site-packages/tensorflow/python/ops/gen_image_ops.py", line 1643, in resize_nearest_neighbor align_corners=align_corners, name=name) File "/root/anaconda3/envs/tf15/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 519, in _apply_op_helper repr(values), type(values).__name__)) TypeError: Expected int32 passed to parameter 'size' of op 'ResizeNearestNeighbor', got [None, None] of type 'list' instead.

Here is my test code, I want to load model only once and test multiple images, but I failed,
`sess_config = tf.ConfigProto()
sess_config.gpu_options.allow_growth = True
sess = tf.Session(config=sess_config)

model = InpaintCAModel()
input_image_ph = tf.placeholder(
    tf.float32, shape=(1, None, None, 3))
output = model.build_server_graph(input_image_ph, dynamic=True)
output = (output + 1.) * 127.5
output = tf.reverse(output, [-1])
output = tf.saturate_cast(output, tf.uint8)
vars_list = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
assign_ops = []
for var in vars_list:
    vname = var.name
    from_name = vname
    var_value = tf.contrib.framework.load_variable(
        checkpoint_dir, from_name)
    assign_ops.append(tf.assign(var, var_value))
sess.run(assign_ops)
print('Model loaded.')
.....`

@JiahuiYu could we build graph once and feed images/masks with different resolutions?

Thanks

[JiahuiYu]

@jtetc If possible, please help @ccc013. Thanks!

[c9o]

@JiahuiYu With pleasure.
Hi @ccc013 , I tried to change related resize ops in inpaint_ops.py with option dynamic=True. You can check the function prototype in neuralgym.ops.layers and you will be clear about it.

[ccc013]

@JiahuiYu @jtetc Thanks for your fast response!

But, when I set the option dynamic=True in resize ops in inpaint_ops.py, I still get the same error:

Traceback (most recent call last):
  File "/root/anaconda3/envs/tf15/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 510, in _apply_op_helper
    preferred_dtype=default_dtype)
  File "/root/anaconda3/envs/tf15/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1022, in internal_convert_to_tensor
    ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
  File "/root/anaconda3/envs/tf15/lib/python3.6/site-packages/tensorflow/python/framework/constant_op.py", line 233, in _constant_tensor_conversion_function
    return constant(v, dtype=dtype, name=name)
  File "/root/anaconda3/envs/tf15/lib/python3.6/site-packages/tensorflow/python/framework/constant_op.py", line 212, in constant
    value, dtype=dtype, shape=shape, verify_shape=verify_shape))
  File "/root/anaconda3/envs/tf15/lib/python3.6/site-packages/tensorflow/python/framework/tensor_util.py", line 413, in make_tensor_proto
    _AssertCompatible(values, dtype)
  File "/root/anaconda3/envs/tf15/lib/python3.6/site-packages/tensorflow/python/framework/tensor_util.py", line 328, in _AssertCompatible
    (dtype.name, repr(mismatch), type(mismatch).__name__))
TypeError: Expected int32, got None of type '_Message' instead.

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "finetune_imageinfer_compare.py", line 220, in <module>
    input_image_ph, output, sess = deepfill_model(args.checkpoint_dir, args.image_height, args.image_width)
  File "finetune_imageinfer_compare.py", line 166, in deepfill_model
    output = model.build_server_graph(input_image_ph, dynamic=True)
  File "/home/luocai/generative_inpainting/inpaint_model.py", line 315, in build_server_graph
    config=None)
  File "/home/luocai/generative_inpainting/inpaint_model.py", line 61, in build_inpaint_net
    mask_s = resize_mask_like(mask, x, dynamic=dynamic)
  File "/home/luocai/generative_inpainting/inpaint_ops.py", line 227, in resize_mask_like
    func=tf.image.resize_nearest_neighbor, dynamic=dynamic)
  File "/home/luocai/generative_inpainting/neuralgym_c/ops/layers.py", line 128, in resize
    align_corners=align_corners)
  File "/root/anaconda3/envs/tf15/lib/python3.6/site-packages/tensorflow/python/ops/gen_image_ops.py", line 1643, in resize_nearest_neighbor
    align_corners=align_corners, name=name)
  File "/root/anaconda3/envs/tf15/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 519, in _apply_op_helper
    repr(values), type(values).__name__))
TypeError: Expected int32 passed to parameter 'size' of op 'ResizeNearestNeighbor', got [None, None] of type 'list' instead.

Here is my test code, and I use tensorflow 1.5 version, @jtetc I don't know if my code has some wrong place?

 def deepfill_model(checkpoint_dir):
    sess_config = tf.ConfigProto()
    sess_config.gpu_options.allow_growth = True
    sess = tf.Session(config=sess_config)
    model = InpaintCAModel()
    input_image_ph = tf.placeholder(
        tf.float32, shape=(1, None, None, 3))
    output = model.build_server_graph(input_image_ph, dynamic=True)
    output = (output + 1.) * 127.5
    output = tf.reverse(output, [-1])
    output = tf.saturate_cast(output, tf.uint8)
    vars_list = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
    assign_ops = []
    for var in vars_list:
        vname = var.name
        from_name = vname
        var_value = tf.contrib.framework.load_variable(
            checkpoint_dir, from_name)
        assign_ops.append(tf.assign(var, var_value))
    sess.run(assign_ops)
    print('Model loaded.')

    return input_image_ph, output, sess

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument('--imagedir', default='', type=str,
                        help='The filename of image to be completed.')
    parser.add_argument('--outdir', default='', type=str,
                        help='Where to write output.')
    parser.add_argument('--checkpoint_dir', default='', type=str,
                        help='The directory of tensorflow checkpoint.')

    args = parser.parse_args()

    check_folder(args.outdir)
    output_image_dir = os.path.join(args.outdir, "images")
    if not os.path.exists(output_image_dir):
        os.makedirs(output_image_dir)

    ng.get_gpus(1)
    counts = 0
    input_image_ph, output, sess = deepfill_model(args.checkpoint_dir, args.image_height, args.image_width)
    for img in os.listdir(args.imagedir):
        print("img: ", img)

        fre_img, ex = os.path.splitext(img)
        img_outname_deepfill = fre_img + "_output_deepfill.jpg"

        img_path = os.path.join(args.imagedir, img)
        img_in_path = os.path.join(output_image_dir, img)
        img_out_deepfill_path = os.path.join(output_image_dir, img_outname_deepfill)
        print("img_out_deepfill_path: ", img_out_deepfill_path)
        image = cv2.imread(img_path)
        modelflag, mask = getMask(image)

        counts += 1
        assert image.shape == mask.shape
        h, w, _ = image.shape
        grid = 8
        print('before shape of image: {}'.format(image.shape))
        image_deep = image[:h // grid * grid, :w // grid * grid, :]
        mask_deep = mask[:h // grid * grid, :w // grid * grid, :]
        print('Shape of image: {}'.format(image_deep.shape))

        image_deep = np.expand_dims(image_deep, 0)
        mask_deep = np.expand_dims(mask_deep, 0)
        input_image = np.concatenate([image_deep, mask_deep], axis=2)
        print("input_image shape: ", input_image.shape)
        # load pretrained model
        result = sess.run(output, feed_dict={input_image_ph: input_image})
        # logger.info('Processed: {}'.format(img_out_deepfill_path))
        cv2.imwrite(img_out_deepfill_path, result[0][:, :, ::-1])

[c9o]

@ccc013 I didn't use to_shape to resize but use scale instead. Here is sample code for your reference

@@ -174,10 +174,13 @@ def resize_mask_like(mask, x):
     Returns:
         tf.Tensor: resized mask
 
-    """
     mask_resize = resize(
         mask, to_shape=x.get_shape().as_list()[1:3],
         func=tf.image.resize_nearest_neighbor)
+    """
+    mask_resize =  resize(
+        mask, scale=1./4, dynamic=True
+    )
     return mask_resize

I am a green hand in tensorflow. I guess we cannot get the shape of x since input shape is (1, None, None, 3). And there are twice downsample before then I set scale=1./4. Such changes work in my tests. Hi @JiahuiYu , is my change available? Thanks!

[JiahuiYu]

You modification should be fine.

[ccc013]

@jtetc Thanks you very much!

I change the code like you do, and actually I also change other code which contains resize function, like gen_deconv and contextual_attention function.

particularly，I change the line 250 in contextual_attention function:

# downscaling foreground option: downscaling both foreground and
# background for matching and use original background for reconstruction.
f = resize(f, scale=1./rate, func=tf.image.resize_nearest_neighbor)
b = resize(b, to_shape=[int(raw_int_bs[1]/rate), int(raw_int_bs[2]/rate)], func=tf.image.resize_nearest_neighbor)  # https://github.com/tensorflow/tensorflow/issues/11651

to

# downscaling foreground option: downscaling both foreground and
# background for matching and use original background for reconstruction.
f = resize(f, scale=1./rate, func=tf.image.resize_nearest_neighbor, dynamic=True)
b = resize(b, scale=1. / rate,
                   func=tf.image.resize_nearest_neighbor, dynamic=True)

If I don't change this code, I stil got an error.

And my test code works after changing above function codes!

Hi, @JiahuiYu is my change above code in contextual_attention available? Thanks!

[c9o]

@ccc013 I have same modifications. They are same issues.

[c9o]

@ccc013 FYI

diff --git a/inpaint_ops.py b/inpaint_ops.py
index afdec3d..25624ea 100644
--- a/inpaint_ops.py
+++ b/inpaint_ops.py
@@ -63,7 +63,7 @@ def gen_deconv(x, cnum, name='upsample', padding='SAME', training=True):
 
     """
     with tf.variable_scope(name):
-        x = resize(x, func=tf.image.resize_nearest_neighbor)
+        x = resize(x, func=tf.image.resize_nearest_neighbor, dynamic=True)
         x = gen_conv(
             x, cnum, 3, 1, name=name+'_conv', padding=padding,
             training=training)
@@ -174,10 +174,13 @@ def resize_mask_like(mask, x):
     Returns:
         tf.Tensor: resized mask
 
-    """
     mask_resize = resize(
         mask, to_shape=x.get_shape().as_list()[1:3],
         func=tf.image.resize_nearest_neighbor)
+    """
+    mask_resize =  resize(
+        mask, scale=1./4, dynamic=True
+    )
     return mask_resize
 
 
@@ -246,10 +249,10 @@ def contextual_attention(f, b, mask=None, ksize=3, stride=1, rate=1,
     raw_w = tf.transpose(raw_w, [0, 2, 3, 4, 1])  # transpose to b*k*k*c*hw
     # downscaling foreground option: downscaling both foreground and
     # background for matching and use original background for reconstruction.
-    f = resize(f, scale=1./rate, func=tf.image.resize_nearest_neighbor)
-    b = resize(b, to_shape=[int(raw_int_bs[1]/rate), int(raw_int_bs[2]/rate)], func=tf.image.resize_nearest_neighbor)  # https://github.com/tensorflow/tensorflow/issues/11651
+    f = resize(f, scale=1./rate, func=tf.image.resize_nearest_neighbor, dynamic=True)
+    b = resize(b, scale=1./rate, func=tf.image.resize_nearest_neighbor, dynamic=True)
     if mask is not None:
-        mask = resize(mask, scale=1./rate, func=tf.image.resize_nearest_neighbor)
+        mask = resize(mask, scale=1./rate, func=tf.image.resize_nearest_neighbor, dynamic=True)
     fs = tf.shape(f)
     int_fs = f.get_shape().as_list()
     f_groups = tf.split(f, int_fs[0], axis=0)
@@ -320,7 +323,7 @@ def contextual_attention(f, b, mask=None, ksize=3, stride=1, rate=1,
     # # case2: visualize which pixels are attended
     # flow = highlight_flow_tf(offsets * tf.cast(mask, tf.int32))
     if rate != 1:
-        flow = resize(flow, scale=rate, func=tf.image.resize_nearest_neighbor)
+        flow = resize(flow, scale=rate, func=tf.image.resize_nearest_neighbor, dynamic=True)
     return y, flow
 

[JiahuiYu]

@jtetc Thanks for your contribution!

[ccc013]

@jtetc Thanks！

[awsssix]

@jtetc I have trained my model already. If I want build graph once when testing, do I need to retrain the model?

[c9o]

@jtetc I have trained my model already. If I want build graph once when testing, do I need to retrain the model?

@awsssix If you didn't modify the model, you don't need to retrain.

[awsssix]

@jtetc Thanks for your replay.
I saw above that you modified contextual_attention and gen_deconv, just test directly?

[c9o]

@jtetc Thanks for your replay.
I saw above that you modified contextual_attention and gen_deconv, just test directly?

You can take a try