12dmodel
commented
3 years ago Sorry for the delay in response. There are downsampling/upsampling steps and we haven't been too careful with making sure the shapes are exactly the same. A quick fix would be to make sure your image dimensions are power of 2s.
On Wed, Apr 21, 2021 at 6:04 PM Jonathan Alis Lima @.***> wrote:
I have faced an error (below) while running run_on_test_videos.sh on my sequence of images Wondering what may be, the only clue I got was tensorflow.python.framework.errors_impl.InvalidArgumentError: ConcatOp : Dimensions of inputs should match: shape[0] = [1,322,364,32] vs. shape[1] = [1,321,364,32]. Seems somewhere the shapes do not match, but I do not know from what, or what should I change in the code. However, observing the images I put in the data/vids/'videoname', their sizes are 727 x 642. Then I realize that the error showed numbers close to the half of these image dimensions. However, the problem do not seem to be with 727 (as ceil(727/2)=364 match in both shape[0] and shape[1] in the error description), so, the problem might be with the dimension 642, as 332 do not match with 321 (respectively 642/2 +1 and 642/2)
So, my question is: there is any known restriction on the image sizes? They must be odd? Which image sizes could make the approximation errors that break the program? Knowing it Would help me to crop the images in a size the can work.
Thank you in advance.
The full error:
Traceback (most recent call last): File "/home/jonathan/Dropbox/UnB/motionMag/momagenv/lib/python3.7/site-packages/tensorflow_core/python/client/session.py", line 1365, in _do_call return fn(*args) File "/home/jonathan/Dropbox/UnB/motionMag/momagenv/lib/python3.7/site-packages/tensorflow_core/python/client/session.py", line 1350, in _run_fn target_list, run_metadata) File "/home/jonathan/Dropbox/UnB/motionMag/momagenv/lib/python3.7/site-packages/tensorflow_core/python/client/session.py", line 1443, in _call_tf_sessionrun run_metadata) tensorflow.python.framework.errors_impl.InvalidArgumentError: ConcatOp : Dimensions of inputs should match: shape[0] = [1,322,364,32] vs. shape[1] = [1,321,364,32] [[{{node ynet_3frames/decoder/concat}}]]
During handling of the above exception, another exception occurred:
Traceback (most recent call last): File "main.py", line 102, in
main(arguments) File "main.py", line 83, in main args.velocity_mag) File "/home/jonathan/Dropbox/UnB/motionMag/deep_motion_mag-master/magnet.py", line 279, in run out_amp = self.inference(prev_frame, frame, amplification_factor) File "/home/jonathan/Dropbox/UnB/motionMag/deep_motion_mag-master/magnet.py", line 235, in inference [amplification_factor]}) File "/home/jonathan/Dropbox/UnB/motionMag/momagenv/lib/python3.7/site-packages/tensorflow_core/python/client/session.py", line 956, in run run_metadata_ptr) File "/home/jonathan/Dropbox/UnB/motionMag/momagenv/lib/python3.7/site-packages/tensorflow_core/python/client/session.py", line 1180, in _run feed_dict_tensor, options, run_metadata) File "/home/jonathan/Dropbox/UnB/motionMag/momagenv/lib/python3.7/site-packages/tensorflow_core/python/client/session.py", line 1359, in _do_run run_metadata) File "/home/jonathan/Dropbox/UnB/motionMag/momagenv/lib/python3.7/site-packages/tensorflow_core/python/client/session.py", line 1384, in _do_call raise type(e)(node_def, op, message) tensorflow.python.framework.errors_impl.InvalidArgumentError: ConcatOp : Dimensions of inputs should match: shape[0] = [1,322,364,32] vs. shape[1] = [1,321,364,32] [[node ynet_3frames/decoder/concat (defined at /home/jonathan/Dropbox/UnB/motionMag/momagenv/lib/python3.7/site-packages/tensorflow_core/python/framework/ops.py:1748) ]] Original stack trace for 'ynet_3frames/decoder/concat': File "main.py", line 102, in
main(arguments) File "main.py", line 83, in main args.velocity_mag) File "/home/jonathan/Dropbox/UnB/motionMag/deep_motion_mag-master/magnet.py", line 265, in run self.setup_for_inference(checkpoint_dir, image_width, image_height) File "/home/jonathan/Dropbox/UnB/motionMag/deep_motion_mag-master/magnet.py", line 209, in setup_for_inference self._build_feed_model() File "/home/jonathan/Dropbox/UnB/motionMag/deep_motion_mag-master/magnet.py", line 196, in _build_feed_model False) File "/home/jonathan/Dropbox/UnB/motionMag/deep_motion_mag-master/magnet.py", line 145, in image_transformer return self._decoder(self.texture_b, self.out_shape_enc) File "/home/jonathan/Dropbox/UnB/motionMag/deep_motion_mag-master/magnet.py", line 119, in _decoder enc = tf.concat([texture_enc, shape_enc], axis=3) File "/home/jonathan/Dropbox/UnB/motionMag/momagenv/lib/python3.7/site-packages/tensorflow_core/python/util/dispatch.py", line 180, in wrapper return target(*args, *kwargs) File "/home/jonathan/Dropbox/UnB/motionMag/momagenv/lib/python3.7/site-packages/tensorflow_core/python/ops/array_ops.py", line 1420, in concat return gen_array_ops.concat_v2(values=values, axis=axis, name=name) File "/home/jonathan/Dropbox/UnB/motionMag/momagenv/lib/python3.7/site-packages/tensorflow_core/python/ops/gen_array_ops.py", line 1257, in concat_v2 "ConcatV2", values=values, axis=axis, name=name) File "/home/jonathan/Dropbox/UnB/motionMag/momagenv/lib/python3.7/site-packages/tensorflow_core/python/framework/op_def_library.py", line 794, in _apply_op_helper op_def=op_def) File "/home/jonathan/Dropbox/UnB/motionMag/momagenv/lib/python3.7/site-packages/tensorflow_core/python/util/deprecation.py", line 507, in new_func return func(args, **kwargs) File "/home/jonathan/Dropbox/UnB/motionMag/momagenv/lib/python3.7/site-packages/tensorflow_core/python/framework/ops.py", line 3357, in create_op attrs, op_def, compute_device) File "/home/jonathan/Dropbox/UnB/motionMag/momagenv/lib/python3.7/site-packages/tensorflow_core/python/framework/ops.py", line 3426, in _create_op_internal op_def=op_def) File "/home/jonathan/Dropbox/UnB/motionMag/momagenv/lib/python3.7/site-packages/tensorflow_core/python/framework/ops.py", line 1748, in init self._traceback = tf_stack.extract_stack() — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub https://github.com/12dmodel/deep_motion_mag/issues/20, or unsubscribe https://github.com/notifications/unsubscribe-auth/AANUQV3KYTRBTE4YRS3MAMTTJ5YYHANCNFSM43LNML2A .
pratyushganguly
xiao316980913
I have faced an error (below) while running run_on_test_videos.sh on my sequence of images Wondering what may be, the only clue I got was
tensorflow.python.framework.errors_impl.InvalidArgumentError: ConcatOp : Dimensions of inputs should match: shape[0] = [1,322,364,32] vs. shape[1] = [1,321,364,32]. Seems somewhere the shapes do not match, but I do not know from what, or what should I change in the code. However, observing the images I put in the data/vids/'videoname', their sizes are 727 x 642. Then I realize that the error showed numbers close to the half of these image dimensions. However, the problem do not seem to be with 727 (as ceil(727/2)=364 match in both shape[0] and shape[1] in the error description), so, the problem might be with the dimension 642, as 332 do not match with 321 (respectively 642/2 +1 and 642/2)So, my question is: there is any known restriction on the image sizes? They must be odd? Which image sizes could make the approximation errors that break the program? Knowing it Would help me to crop the images in a size the can work.
Thank you in advance.
The full error: