I am not a jupyter user although I assume I have the repository setup correctly as it seems to be doing work. I have been working with other code bases for a while using the same modules so a dependency issue is likely not an issue.

When I step through the code for dlib_video_face_detection.ipynb I get to the code block where moviepy does some manipulation of an input video. According to the timestamp on the output I am looking at a very long time before it is complete.

0%| | 9/15887 [11:09<329:34:28, 74.72s/it]

The target video is 1280x720, 00:08:50 long with a data rate of 1413kbps. My hardware consists of a 3.5ghz i5, gtx1080, and 16gb of ram. Training large data sets have not been a problem for me so I am unsure of why processing a video frame by frame would take this long.

What is the purpose of the

output = '_.mp4'
clip1 = VideoFileClip("x-cropped.mp4")
clip = clip1.fl_image(process_video)#.subclip(0,10) #NOTE: this function expects color images!!
%time clip.write_videofile(output, audio=False)

block besides to run the process_video method on each frame? I have used the dlib module as a stand alone script and it processes the video in a handful of minutes pulling many faces as a result.

Would it be beneficial to pre-process the video file in ffmpeg before handing the work to the notebook in any way? Perhaps rip the frames beforehand so that moviepy would not need to step frame by frame?

try:
	encoder.load_weights("models/encoder.h5")
	decoder_A.load_weights("models/decoder_A.h5")
	decoder_B.load_weights("models/decoder_B.h5")
	# netDA.load_weights("models/netDA.h5") 
	# netDB.load_weights("models/netDB.h5") 
	print("model loaded.")
except:
	print("weights file not found.")
	pass

At this point in the code, it always fails, saying it couldn't find the weight files. They are located at ./faceswap-GAN-master/models/. Is this incorrect? I should note that the model is the Trump to Cage model from the deepfakes/faceswap project, and that I commented out netDA and netDB because they do not exist.

Any help? Thank you.

https://gfycat.com/RingedGranularBluefintuna I'm using FaceSwap_GAN_v2_train for the traing and creating video. Every setting is by default. am I supposed to use larger img size? New to this, please help. thx

I've been training the new version for 10000 + 6000 iterations and the output (from the show_g function) doesn't even start to change... I saw some outputs where the network tried to mimic PersonB but at the end of the day the three columns [test_A, path_A(test_A), path_B(test_A)] all look the same as test_A and vice versa...

Not only that, when I tried turning use_mixup to False I had this error about different number of channels here:

number of input channels does not match corresponding dimension of filter, 3 != 6 `output_real = netD(real) # positive

It seems we have to manually change nc_D_inp to 3 instead of 6.

Keep up the good work...

I am running the script in gtx 1060 with 6G ram laptop, it halts on 56 niters and said out of memory, is there any way I could lower the memory requirement? Thanks.

When I am running this line in FaceSwap_GAN_v2.2_train_test.ipynb result_img, result_rgb, result_mask = ftrans.transform( aligned_det_face_im, direction="AtoB", roi_coverage=0.93, color_correction="adain_xyz", IMAGE_SHAPE=(RESOLUTION, RESOLUTION, 3) )
I got this error: Invalid argument: transpose expects a vector of size 3. But input(1) is a vector of size 4 [[{{node model_22/conv2d_157/convolution-0-TransposeNHWCToNCHW-LayoutOptimizer}}]] [[concatenate_24/concat/_2775]] Has anybody encountered this?

I have access to a machine that has two K60 Tesla cards attached to it. Running FaceSwap_GAN_v2_train.ipynb I can see in nvidia-smi that one GPU was at 100% usage and one was at 0% usage.

Is there any configuration I need to do to allow the libraries that your script calls to use more than one GPU?

I am wondering if the train screen changes every 1875 seconds and this speed is correct. Can not use this program with CUDA?

[2/150][50] Loss_DA: 0.205057 Loss_DB: 0.193327 Loss_GA: 0.415360 Loss_GB: 0.413623 time: 1875.640280 [4/150][100] Loss_DA: 0.200707 Loss_DB: 0.211183 Loss_GA: 0.295002 Loss_GB: 0.341839 time: 3606.393274

try: encoder.load_weights("models/encoder.h5") decoder_A.load_weights("models/decoder_A.h5") decoder_B.load_weights("models/decoder_B.h5") # netDA.load_weights("models/netDA.h5") # netDB.load_weights("models/netDB.h5") print("model loaded.") except: print("weights file not found.") pass

Failed loading. Something goes wrong with weights files.

I ran the FaceSwap_GAN_v2.1_train at the beginning, and threw this exception. So where can I find the all weight files, or I need to run which file first?

Hi, I see a WGAN in temp folder. Could you help change the input and output to 128, 128 for WGAN model? I tried to change it but not succeed, Thanks!!

I have a GTX 1080 and have trained successfully the original NN, but when I try to train these, they run for like an hour and then stop training at a given iteration. If I rerun the function, it stops at the same one.

when I running the FaceSwap_GAN_v2.2_train_test, there are something wrong was happened Exception: URL fetch failure on https://github.com/rcmalli/keras-vggface/releases/download/v2.0/rcmalli_vggface_tf_notop_resnet50.h5 : None -- retrieval incomplete: got only 43057152 out of 94694792 bytes

how can i resolve it?

[Asian-celeb dataset]

• Training data(Asian-celeb)

The dataset consists of the crawled images of celebrities on the he web.The ima images are covered under a Creative Commons Attribution-NonCommercial 4.0 International license (Please read the license terms here. e. http://creativecommons.org/licenses/by-nc/4.0/).

[train_msra.tar.gz]

MD5:c5b668f2204c400099b14f367069aef5

Content: Train dataset called MS-Celeb-1M-v1c with 86,876 ids/3,923,399 aligned images cleaned from MS-Celeb-1M dataset.

This dataset has been excluded from both LFW and Asian-Celeb.

Format: *.jpg

Google: https://drive.google.com/file/d/1aaPdI0PkmQzRbWErazOgYtbLA1mwJIfK/view?usp=sharing

[msra_lmk.tar.gz]

MD5:7c053dd0462b4af243bb95b7b31da6e6

Content: A list of five-point landmarks for the 3,923,399 images in MS-Celeb-1M-v1c.

Format: .....

while is the path of images in tar file train_msceleb.tar.gz.

Label is an integer ranging from 0 to 86,875.

(x,y) is the coordinate of a key point on the aligned images.

left eye right eye nose tip mouth left mouth right

Google: https://drive.google.com/file/d/1FQ7P4ItyKCneNEvYfJhW2Kff7cOAFpgk/view?usp=sharing

[train_celebrity.tar.gz]

MD5:9f2e9858afb6c1032c4f9d7332a92064

Content: Train dataset called Asian-Celeb with 93,979 ids/2,830,146 aligned images.

This dataset has been excluded from both LFW and MS-Celeb-1M-v1c.

Format: *.jpg

Google: https://drive.google.com/file/d/1-p2UKlcX06MhRDJxJukSZKTz986Brk8N/view?usp=sharing

[celebrity_lmk.tar.gz]

MD5:9c0260c77c13fbb32692fc06a5dbfaf0

Content: A list of five-point landmarks for the 2,830,146 images in Asian-Celeb.

Format: .....

while is the path of images in tar file train_celebrity.tar.gz.

Label is an integer ranging from 86,876 to 196,319.

(x,y) is the coordinate of a key point on the aligned images.

left eye right eye nose tip mouth left mouth right

Google: https://drive.google.com/file/d/1sQVV9epoF_8jS3ge6DqbilpWk3UNE8U7/view?usp=sharing

[testdata.tar.gz]

MD5:f17c4712f7562ea6d45f0a158e59b792

Content: Test dataset with 1,862,120 aligned images.

Format: *.jpg

Google: https://drive.google.com/file/d/1ghzuEQqmUFN3nVujfrZfBx_CeGUpWzuw/view?usp=sharing

[testdata_lmk.tar]

MD5:7e4995eb9976a2cfd2b23db05d76572c

Content: A list of five-point landmarks for the 1,862,120 images in testdata.tar.gz.

Features should be extracted in the same sequence and with the same amount with this list.

Format: .....

while is the path of images in tar file testdata.tar.gz.

(x,y) is the coordinate of a key point on the aligned images.

left eye right eye nose tip mouth left mouth right

Google: https://drive.google.com/file/d/1lYzqnPyHXRVgXJYbEVh6zTXn3Wq4JO-I/view?usp=sharing

[feature_tools.tar.gz]

MD5:227b069d7a83aa43b0cb738c2252dbc4

Content: Feature format transform tool and a sample feature file.

Format: We use the same format as Megaface(http://megaface.cs.washington.edu/) except that we merge all files into a single binary file.

Google: https://drive.google.com/file/d/1bjZwOonyZ9KnxecuuTPVdY95mTIXMeuP/view?usp=sharing

#from keras_vggface.vggface import VGGFace

VGGFace ResNet50

#vggface = VGGFace(include_top=False, model='resnet50', input_shape=(224, 224, 3))'

from colab_demo.vggface_models import RESNET50 vggface = RESNET50(include_top=False, weights=None, input_shape=(224, 224, 3)) vggface.load_weights("rcmalli_vggface_tf_notop_resnet50.h5")

#from keras.applications.resnet50 import ResNet50 #vggface = ResNet50(include_top=False, input_shape=(224, 224, 3))

#vggface.summary()

model.build_pl_model(vggface_model=vggface, before_activ=loss_config["PL_before_activ"]) model.build_train_functions(loss_weights=loss_weights, **loss_config)

Error:

AttributeError Traceback (most recent call last) in () 6 from colab_demo.vggface_models import RESNET50 7 vggface = RESNET50(include_top=False, weights=None, input_shape=(224, 224, 3)) ----> 8 vggface.load_weights("rcmalli_vggface_tf_notop_resnet50.h5") 9 10 #from keras.applications.resnet50 import ResNet50

1 frames /usr/local/lib/python3.7/dist-packages/keras/engine/topology.py in load_weights_from_hdf5_group(f, layers, reshape) 3326 """ 3327 if 'keras_version' in f.attrs: -> 3328 original_keras_version = f.attrs['keras_version'].decode('utf8') 3329 else: 3330 original_keras_version = '1'

AttributeError: 'str' object has no attribute 'decode'

'''' AssertionError Traceback (most recent call last)

in () 14 15 model.build_pl_model(vggface_model=vggface, before_activ=loss_config["PL_before_activ"]) ---> 16 model.build_train_functions(loss_weights=loss_weights, **loss_config)

2 frames

/usr/local/lib/python3.7/dist-packages/keras/legacy/interfaces.py in get_updates_arg_preprocessing(args, kwargs) 652 kwargs['params'] = params 653 return [opt], kwargs, [] --> 654 elif len(args) == 3: 655 if isinstance(args[1], (list, tuple)): 656 assert isinstance(args[2], dict)

AssertionError: '''

AttributeError Traceback (most recent call last) in 112 data_A = train_batchA.get_next_batch() 113 data_B = train_batchB.get_next_batch() --> 114 errDA, errDB = model.train_one_batch_D(data_A=data_A, data_B=data_B) 115 errDA_sum +=errDA[0] 116 errDB_sum +=errDB[0]

F:\faceswap-GAN-master\networks\faceswap_gan_model.py in train_one_batch_D(self, data_A, data_B) 326 else: 327 raise ValueError("Something's wrong with the input data generator.") --> 328 errDA = self.netDA_train([warped_A, target_A]) 329 errDB = self.netDB_train([warped_B, target_B]) 330 return errDA, errDB

AttributeError: 'FaceswapGANModel' object has no attribute 'netDA_train'

How can I solve this problem