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Published 20 hours ago • verified publisher icon NVIDIA

How to generate different "styles"?

Open wottpal opened this issue 5 years ago • 12 comments

In the readme there are examples of a single input image where the network is able to generate multiple versions (styles) during inference. I have successfully trained my model but I don't know how to achieve that with the given scripts (esp. test.py).

Thanks a lot in advance!

wottpal avatar Oct 29 '18 18:10 wottpal

This can be done by using the feat.sh scripts, where you concatenate some feature vector as input.

tcwang0509 avatar Oct 30 '18 01:10 tcwang0509

Oh I see. Thanks for answering so quickly!

I have some questions regarding the scripts and it would be so awesome if you could provide some guidance :)

  • I don't need to precompute feature maps when initially training in 512p, right? (like in train_512p_feat.sh)
  • I can't reuse already trained 512p models without the --instance_feat flag, right?
  • Do I need to provide instance-maps in this case or can I use --instance_feat in conjunction with --no_instance?
  • This might sound silly but how actually does the model decide which "style" to use in test_512p_feat.sh? Or does this generate multiple outcomes and if yes how many?

wottpal avatar Oct 30 '18 09:10 wottpal

  1. No you don't
  2. I think you can finetune using that, which should be faster than retraining from scratch
  3. If you don't have instance maps, please use label_feat instead
  4. It will precompute some clusters on the training images, then randomly select a cluster at inference.

tcwang0509 avatar Oct 30 '18 22:10 tcwang0509

Thanks, once again :) So I need to input additional "feature vectors" for training with --label_feat? How would I generate them and in which format?

Currently, when I'm just trying to train like usual (with train_A and train_B folders) and add the --label_feat option I'm getting an exception:

------------ Options -------------
batchSize: 1
beta1: 0.5
checkpoints_dir: ./pix2pixHD/checkpoints/
continue_train: False
data_type: 32
dataroot: ./pix2pixHD/dataset/
debug: False
display_freq: 50
display_winsize: 512
feat_num: 3
fineSize: 512
gpu_ids: [0]
input_nc: 3
instance_feat: False
isTrain: True
label_feat: True
label_nc: 0
lambda_feat: 10.0
loadSize: 512
load_features: False
load_pretrain:
lr: 0.0002
max_dataset_size: inf
model: pix2pixHD
nThreads: 2
n_blocks_global: 9
n_blocks_local: 3
n_clusters: 10
n_downsample_E: 4
n_downsample_global: 4
n_layers_D: 3
n_local_enhancers: 1
name: label2city_512p_feat
ndf: 64
nef: 16
netG: local
ngf: 32
niter: 100
niter_decay: 100
niter_fix_global: 0
no_flip: False
no_ganFeat_loss: False
no_html: False
no_instance: True
no_lsgan: False
no_vgg_loss: False
norm: instance
num_D: 2
output_nc: 3
phase: train
pool_size: 0
print_freq: 50
resize_or_crop: none
save_epoch_freq: 10
save_latest_freq: 500
serial_batches: False
tf_log: False
use_dropout: False
verbose: True
which_epoch: latest
-------------- End ----------------
CustomDatasetDataLoader
dataset [AlignedDataset] was created
#training images = 12353
LocalEnhancer(
  (model): Sequential(
    (0): ReflectionPad2d((3, 3, 3, 3))
    (1): Conv2d(6, 64, kernel_size=(7, 7), stride=(1, 1))
    (2): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (3): ReLU(inplace)
    (4): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
    (5): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (6): ReLU(inplace)
    (7): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
    (8): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (9): ReLU(inplace)
    (10): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
    (11): InstanceNorm2d(512, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (12): ReLU(inplace)
    (13): Conv2d(512, 1024, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
    (14): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (15): ReLU(inplace)
    (16): ResnetBlock(
      (conv_block): Sequential(
        (0): ReflectionPad2d((1, 1, 1, 1))
        (1): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        (3): ReLU(inplace)
        (4): ReflectionPad2d((1, 1, 1, 1))
        (5): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      )
    )
    (17): ResnetBlock(
      (conv_block): Sequential(
        (0): ReflectionPad2d((1, 1, 1, 1))
        (1): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        (3): ReLU(inplace)
        (4): ReflectionPad2d((1, 1, 1, 1))
        (5): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      )
    )
    (18): ResnetBlock(
      (conv_block): Sequential(
        (0): ReflectionPad2d((1, 1, 1, 1))
        (1): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        (3): ReLU(inplace)
        (4): ReflectionPad2d((1, 1, 1, 1))
        (5): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      )
    )
    (19): ResnetBlock(
      (conv_block): Sequential(
        (0): ReflectionPad2d((1, 1, 1, 1))
        (1): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        (3): ReLU(inplace)
        (4): ReflectionPad2d((1, 1, 1, 1))
        (5): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      )
    )
    (20): ResnetBlock(
      (conv_block): Sequential(
        (0): ReflectionPad2d((1, 1, 1, 1))
        (1): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        (3): ReLU(inplace)
        (4): ReflectionPad2d((1, 1, 1, 1))
        (5): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      )
    )
    (21): ResnetBlock(
      (conv_block): Sequential(
        (0): ReflectionPad2d((1, 1, 1, 1))
        (1): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        (3): ReLU(inplace)
        (4): ReflectionPad2d((1, 1, 1, 1))
        (5): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      )
    )
    (22): ResnetBlock(
      (conv_block): Sequential(
        (0): ReflectionPad2d((1, 1, 1, 1))
        (1): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        (3): ReLU(inplace)
        (4): ReflectionPad2d((1, 1, 1, 1))
        (5): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      )
    )
    (23): ResnetBlock(
      (conv_block): Sequential(
        (0): ReflectionPad2d((1, 1, 1, 1))
        (1): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        (3): ReLU(inplace)
        (4): ReflectionPad2d((1, 1, 1, 1))
        (5): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      )
    )
    (24): ResnetBlock(
      (conv_block): Sequential(
        (0): ReflectionPad2d((1, 1, 1, 1))
        (1): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        (3): ReLU(inplace)
        (4): ReflectionPad2d((1, 1, 1, 1))
        (5): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      )
    )
    (25): ConvTranspose2d(1024, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
    (26): InstanceNorm2d(512, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (27): ReLU(inplace)
    (28): ConvTranspose2d(512, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
    (29): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (30): ReLU(inplace)
    (31): ConvTranspose2d(256, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
    (32): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (33): ReLU(inplace)
    (34): ConvTranspose2d(128, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
    (35): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (36): ReLU(inplace)
  )
  (model1_1): Sequential(
    (0): ReflectionPad2d((3, 3, 3, 3))
    (1): Conv2d(6, 32, kernel_size=(7, 7), stride=(1, 1))
    (2): InstanceNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (3): ReLU(inplace)
    (4): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
    (5): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (6): ReLU(inplace)
  )
  (model1_2): Sequential(
    (0): ResnetBlock(
      (conv_block): Sequential(
        (0): ReflectionPad2d((1, 1, 1, 1))
        (1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        (3): ReLU(inplace)
        (4): ReflectionPad2d((1, 1, 1, 1))
        (5): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      )
    )
    (1): ResnetBlock(
      (conv_block): Sequential(
        (0): ReflectionPad2d((1, 1, 1, 1))
        (1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        (3): ReLU(inplace)
        (4): ReflectionPad2d((1, 1, 1, 1))
        (5): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      )
    )
    (2): ResnetBlock(
      (conv_block): Sequential(
        (0): ReflectionPad2d((1, 1, 1, 1))
        (1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        (3): ReLU(inplace)
        (4): ReflectionPad2d((1, 1, 1, 1))
        (5): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      )
    )
    (3): ConvTranspose2d(64, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
    (4): InstanceNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (5): ReLU(inplace)
    (6): ReflectionPad2d((3, 3, 3, 3))
    (7): Conv2d(32, 3, kernel_size=(7, 7), stride=(1, 1))
    (8): Tanh()
  )
  (downsample): AvgPool2d(kernel_size=3, stride=2, padding=[1, 1])
)
MultiscaleDiscriminator(
  (scale0_layer0): Sequential(
    (0): Conv2d(6, 64, kernel_size=(4, 4), stride=(2, 2), padding=(2, 2))
    (1): LeakyReLU(negative_slope=0.2, inplace)
  )
  (scale0_layer1): Sequential(
    (0): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(2, 2))
    (1): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (2): LeakyReLU(negative_slope=0.2, inplace)
  )
  (scale0_layer2): Sequential(
    (0): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(2, 2))
    (1): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (2): LeakyReLU(negative_slope=0.2, inplace)
  )
  (scale0_layer3): Sequential(
    (0): Conv2d(256, 512, kernel_size=(4, 4), stride=(1, 1), padding=(2, 2))
    (1): InstanceNorm2d(512, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (2): LeakyReLU(negative_slope=0.2, inplace)
  )
  (scale0_layer4): Sequential(
    (0): Conv2d(512, 1, kernel_size=(4, 4), stride=(1, 1), padding=(2, 2))
  )
  (scale1_layer0): Sequential(
    (0): Conv2d(6, 64, kernel_size=(4, 4), stride=(2, 2), padding=(2, 2))
    (1): LeakyReLU(negative_slope=0.2, inplace)
  )
  (scale1_layer1): Sequential(
    (0): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(2, 2))
    (1): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (2): LeakyReLU(negative_slope=0.2, inplace)
  )
  (scale1_layer2): Sequential(
    (0): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(2, 2))
    (1): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (2): LeakyReLU(negative_slope=0.2, inplace)
  )
  (scale1_layer3): Sequential(
    (0): Conv2d(256, 512, kernel_size=(4, 4), stride=(1, 1), padding=(2, 2))
    (1): InstanceNorm2d(512, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (2): LeakyReLU(negative_slope=0.2, inplace)
  )
  (scale1_layer4): Sequential(
    (0): Conv2d(512, 1, kernel_size=(4, 4), stride=(1, 1), padding=(2, 2))
  )
  (downsample): AvgPool2d(kernel_size=3, stride=2, padding=[1, 1])
)
Encoder(
  (model): Sequential(
    (0): ReflectionPad2d((3, 3, 3, 3))
    (1): Conv2d(3, 16, kernel_size=(7, 7), stride=(1, 1))
    (2): InstanceNorm2d(16, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (3): ReLU(inplace)
    (4): Conv2d(16, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
    (5): InstanceNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (6): ReLU(inplace)
    (7): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
    (8): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (9): ReLU(inplace)
    (10): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
    (11): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (12): ReLU(inplace)
    (13): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
    (14): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (15): ReLU(inplace)
    (16): ConvTranspose2d(256, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
    (17): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (18): ReLU(inplace)
    (19): ConvTranspose2d(128, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
    (20): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (21): ReLU(inplace)
    (22): ConvTranspose2d(64, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
    (23): InstanceNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (24): ReLU(inplace)
    (25): ConvTranspose2d(32, 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
    (26): InstanceNorm2d(16, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (27): ReLU(inplace)
    (28): ReflectionPad2d((3, 3, 3, 3))
    (29): Conv2d(16, 3, kernel_size=(7, 7), stride=(1, 1))
    (30): Tanh()
  )
)
---------- Networks initialized -------------
model [Pix2PixHDModel] was created
create web directory ./pix2pixHD/checkpoints/label2city_512p_feat/web...
Exception ignored in: <bound method _DataLoaderIter.__del__ of <torch.utils.data.dataloader._DataLoaderIter object at 0x7f378df9f128>>
Traceback (most recent call last):
  File "/home/ubuntu/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 399, in __del__
    self._shutdown_workers()
  File "/home/ubuntu/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 378, in _shutdown_workers
    self.worker_result_queue.get()
  File "/home/ubuntu/anaconda3/envs/pytorch_p36/lib/python3.6/multiprocessing/queues.py", line 337, in get
    return _ForkingPickler.loads(res)
  File "/home/ubuntu/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/multiprocessing/reductions.py", line 151, in rebuild_storage_fd
    fd = df.detach()
  File "/home/ubuntu/anaconda3/envs/pytorch_p36/lib/python3.6/multiprocessing/resource_sharer.py", line 58, in detach
    return reduction.recv_handle(conn)
  File "/home/ubuntu/anaconda3/envs/pytorch_p36/lib/python3.6/multiprocessing/reduction.py", line 182, in recv_handle
    return recvfds(s, 1)[0]
  File "/home/ubuntu/anaconda3/envs/pytorch_p36/lib/python3.6/multiprocessing/reduction.py", line 153, in recvfds
    msg, ancdata, flags, addr = sock.recvmsg(1, socket.CMSG_LEN(bytes_size))
ConnectionResetError: [Errno 104] Connection reset by peer
Traceback (most recent call last):
  File "./pix2pixHD/train.py", line 61, in <module>
    Variable(data['image']), Variable(data['feat']), infer=save_fake)
  File "/home/ubuntu/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/nn/modules/module.py", line 477, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/ubuntu/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/nn/parallel/data_parallel.py", line 121, in forward
    return self.module(*inputs[0], **kwargs[0])
  File "/home/ubuntu/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/nn/modules/module.py", line 477, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/ubuntu/project/pix2pixHD/models/pix2pixHD_model.py", line 162, in forward
    feat_map = self.netE.forward(real_image, inst_map)
  File "/home/ubuntu/project/pix2pixHD/models/networks.py", line 289, in forward
    output_ins = outputs[indices[:,0] + b, indices[:,1] + j, indices[:,2], indices[:,3]]
IndexError: index 1 is out of bounds for dimension 1 with size 1

Command:

python ./pix2pixHD/train.py   --verbose \
                              --loadSize 512 --fineSize 512 \
                              --resize_or_crop none \
                              --label_nc 0 \
                              --label_feat \
                              --no_instance \
                              --name label2city_512p_feat \
                              --checkpoints_dir "./pix2pixHD/checkpoints/"\
                              --dataroot "./pix2pixHD/dataset/"\
                              --save_latest_freq 500 \
                              --display_freq 50 --print_freq 50 \
                              --netG local --ngf 32

wottpal avatar Nov 01 '18 08:11 wottpal

Would be 🔥 if you could help me with some more guidance :)

Regards from Germany

wottpal avatar Nov 08 '18 20:11 wottpal

@wottpal did you manage to generate good images with different styles?

ShaniGam avatar Nov 05 '19 14:11 ShaniGam

@wottpal HI, I have the same problem. If you solve it, can you please teach me?

shen113 avatar Apr 07 '20 16:04 shen113

same problem, not enough docs on this

Kitty-sunray avatar Nov 04 '21 23:11 Kitty-sunray

@wottpal same here. did you solve this problem?

daeunni avatar Aug 02 '22 08:08 daeunni

Sorry @ShaniGam @shen113 @daeunni, but never solved it.

wottpal avatar Aug 02 '22 08:08 wottpal

@tcwang0509 how can i solve this same problem?

daeunni avatar Aug 02 '22 09:08 daeunni

I changed output_ins = outputs[indices[:,0] + b, indices[:,1] + j, indices[:,2], indices[:,3]] to output_ins = outputs[indices[:,0] + b, indices[:,1], indices[:,2], indices[:,3]] outputs_mean[indices[:,0] + b, indices[:,1] + j, indices[:,2], indices[:,3]] = mean_feat to outputs_mean[indices[:,0] + b, indices[:,1], indices[:,2], indices[:,3]] = mean_feat in networks.py(maybe 287 line) and it works fine.

coco1578 avatar Oct 19 '22 05:10 coco1578