Can't get style.py to do anything!

[glennamarshall]

I'm a total noob - so apologies in advance, I've hopefully installed everything correctly - but when I run style.py there is no output - and the process takes a very suspiciously short time.

Here's the call python style.py --checkpoint-dir C:\Users\ga_ma\Desktop\DeepArt\fast-style-transfer-master --style C:\Users\ga_ma\Desktop\DeepArt\fast-style-transfer-master\examples\style\wave.jpg --test C:\Users\ga_ma\Desktop\DeepArt\fast-style-transfer-master\examples\content\stata.jpg --test-dir C:\Users\ga_ma\Desktop\DeepArt\fast-style-transfer-master --content-weight 1.5e1 --checkpoint-iterations 1000 --batch-size 20 --vgg-path C:\Users\ga_ma\Desktop\DeepArt\fast-style-transfer-master\data\imagenet-vgg-verydeep-19.mat

and here's the output from the terminal (base) C:\Users\ga_ma\Desktop\DeepArt\fast-style-transfer-master>python style.py --checkpoint-dir C:\Users\ga_ma\Desktop\DeepArt\fast-style-transfer-master --style C:\Users\ga_ma\Desktop\DeepArt\fast-style-transfer-master\examples\style\wave.jpg --test C:\Users\ga_ma\Desktop\DeepArt\fast-style-transfer-master\examples\content\stata.jpg --test-dir C:\Users\ga_ma\Desktop\DeepArt\fast-style-transfer-master --content-weight 1.5e1 --checkpoint-iterations 1000 --batch-size 20 --vgg-path C:\Users\ga_ma\Desktop\DeepArt\fast-style-transfer-master\data\imagenet-vgg-verydeep-19.mat (1, 514, 928, 3) 2018-07-20 16:59:43.855680: I T:\src\github\tensorflow\tensorflow\core\platform\cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 2018-07-20 16:59:44.862621: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1392] Found device 0 with properties: name: GeForce GTX 1050 major: 6 minor: 1 memoryClockRate(GHz): 1.493 pciBusID: 0000:01:00.0 totalMemory: 4.00GiB freeMemory: 3.30GiB 2018-07-20 16:59:44.870256: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1471] Adding visible gpu devices: 0 2018-07-20 16:59:45.518940: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:952] Device interconnect StreamExecutor with strength 1 edge matrix: 2018-07-20 16:59:45.522176: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:958] 0 2018-07-20 16:59:45.524477: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:971] 0: N 2018-07-20 16:59:45.526639: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1084] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3025 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1050, pci bus id: 0000:01:00.0, compute capability: 6.1) 2018-07-20 16:59:55.793411: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1471] Adding visible gpu devices: 0 2018-07-20 16:59:55.795700: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:952] Device interconnect StreamExecutor with strength 1 edge matrix: 2018-07-20 16:59:55.798504: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:958] 0 2018-07-20 16:59:55.800505: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:971] 0: N 2018-07-20 16:59:55.803158: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1084] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3025 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1050, pci bus id: 0000:01:00.0, compute capability: 6.1) UID: 33 Training complete. For evaluation: python evaluate.py --checkpoint C:\Users\ga_ma\Desktop\DeepArt\fast-style-transfer-master ...

[ddilanchi]

I have this issue also.

[ZexueHe]

I have this issue too. And I want to know what does that mean. Thanks.

[JangminSon]

I dont know how long you've trained model ,but it looks like success to train your model.

Checkout your ./checkpoints dir and you can find what you want

[ivanjanev]

I have experienced the same thing today, anyone found a solution?

[JEF1056]

It doesn't look like you've downloaded the COCO train2014 folder, which contains the image files needed to train the model. Use "setup.sh" to download and unzip that.

Use the argument --train-path to change the path to wherever your unzipped train2014 folder is.

I would also recommend changing your --batch-size from 20 to the default 4, since having such a high bach size will result in the extreme trimming of the dataset, and may also cause this "extremely fast training" issue.

I do know that python generally dislikes backslashes, try and use / instead

I am working on a L-BFGS optimizer update for this repo, that may fix your issue since this is using the ADAM optimizer.

[whud99]

just wanted to comment that I was having the same issue even though I had the coco 2014 data. My issue was I was pointing to the train2014.zip when I needed to point to the unzipped file. hope this is helpful to someone ^-^