pytorch-CycleGAN-and-pix2pix
pytorch-CycleGAN-and-pix2pix copied to clipboard
Published
20 hours ago •
junyanz
junyanz
Trained a cyclegann model, Unexpected key error on testing it
I have trained a model with the following options:
----------------- Options ---------------
batch_size: 1
beta1: 0.5
checkpoints_dir: ./checkpoints
continue_train: False
crop_size: 256
dataroot: ./datasets/datasets/boundedwalls_50_0.1/ [default: None]
dataset_mode: aligned [default: unaligned]
direction: AtoB
display_env: main
display_freq: 400
display_id: 1
display_ncols: 4
display_port: 8097
display_server: http://localhost
display_winsize: 256
epoch: latest
epoch_count: 1
gan_mode: lsgan
gpu_ids: 0
init_gain: 0.02
init_type: normal
input_nc: 3
isTrain: True [default: None]
lambda_A: 10.0
lambda_B: 10.0
lambda_identity: 0.5
load_iter: 0 [default: 0]
load_size: 286
lr: 0.0002
lr_decay_iters: 50
lr_policy: linear
max_dataset_size: inf
model: cycle_gan
n_epochs: 100
n_epochs_decay: 100
n_layers_D: 3
name: cycbw50 [default: experiment_name]
ndf: 64
netD: basic
netG: resnet_9blocks
ngf: 64
no_dropout: True
no_flip: False
no_html: False
norm: batch [default: instance]
num_threads: 4
output_nc: 3
phase: train
pool_size: 50
preprocess: resize_and_crop
print_freq: 100
save_by_iter: False
save_epoch_freq: 5
save_latest_freq: 5000
serial_batches: False
suffix:
update_html_freq: 1000
verbose: False
----------------- End -------------------
And would like to test it, the test has the following options right now (correct me if you see what's wrong)
----------------- Options ---------------
aspect_ratio: 1.0
batch_size: 1
checkpoints_dir: ./checkpoints
crop_size: 256
dataroot: ./datasets/datasets/boundedwalls_50_0.1/ [default: None]
dataset_mode: unaligned
direction: AtoB
display_winsize: 256
epoch: latest
eval: False
gpu_ids: 0
init_gain: 0.02
init_type: normal
input_nc: 3
isTrain: False [default: None]
load_iter: 0 [default: 0]
load_size: 256
max_dataset_size: inf
model: cycle_gan [default: test]
n_layers_D: 3
name: cycbw50 [default: experiment_name]
ndf: 64
netD: basic
netG: resnet_9blocks
ngf: 64
no_dropout: True
no_flip: False
norm: instance
num_test: 50
num_threads: 4
output_nc: 3
phase: test
preprocess: resize_and_crop
results_dir: ./results/
serial_batches: False
suffix:
verbose: False
----------------- End -------------------
When I run the test however I get the error:
model [CycleGANModel] was created
loading the model from ./checkpoints/cycbw50/latest_net_G_A.pth
Traceback (most recent call last):
File "test.py", line 47, in <module>
model.setup(opt) # regular setup: load and print networks; create schedulers
File "/media/bitlockermount/SmartImageToDigitalTwin/SmartImageToDigitalTwin/bin/python/cyclegann/pytorch-CycleGAN-and-pix2pix/models/base_model.py", line 88, in setup
self.load_networks(load_suffix)
File "/media/bitlockermount/SmartImageToDigitalTwin/SmartImageToDigitalTwin/bin/python/cyclegann/pytorch-CycleGAN-and-pix2pix/models/base_model.py", line 199, in load_networks
net.load_state_dict(state_dict)
File "/home/bst/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 846, in load_state_dict
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for ResnetGenerator:
Missing key(s) in state_dict: "model.1.bias", "model.4.bias", "model.7.bias", "model.10.conv_block.1.bias", "model.10.conv_block.5.bias", "model.11.conv_block.1.bias", "model.11.conv_block.5.bias", "model.12.conv_block.1.bias", "model.12.conv_block.5.bias", "model.13.conv_block.1.bias", "model.13.conv_block.5.bias", "model.14.conv_block.1.bias", "model.14.conv_block.5.bias", "model.15.conv_block.1.bias", "model.15.conv_block.5.bias", "model.16.conv_block.1.bias", "model.16.conv_block.5.bias", "model.17.conv_block.1.bias", "model.17.conv_block.5.bias", "model.18.conv_block.1.bias", "model.18.conv_block.5.bias", "model.19.bias", "model.22.bias".
Unexpected key(s) in state_dict: "model.2.weight", "model.2.bias", "model.5.weight", "model.5.bias", "model.8.weight", "model.8.bias", "model.10.conv_block.2.weight", "model.10.conv_block.2.bias", "model.10.conv_block.6.weight", "model.10.conv_block.6.bias", "model.11.conv_block.2.weight", "model.11.conv_block.2.bias", "model.11.conv_block.6.weight", "model.11.conv_block.6.bias", "model.12.conv_block.2.weight", "model.12.conv_block.2.bias", "model.12.conv_block.6.weight", "model.12.conv_block.6.bias", "model.13.conv_block.2.weight", "model.13.conv_block.2.bias", "model.13.conv_block.6.weight", "model.13.conv_block.6.bias", "model.14.conv_block.2.weight", "model.14.conv_block.2.bias", "model.14.conv_block.6.weight", "model.14.conv_block.6.bias", "model.15.conv_block.2.weight", "model.15.conv_block.2.bias", "model.15.conv_block.6.weight", "model.15.conv_block.6.bias", "model.16.conv_block.2.weight", "model.16.conv_block.2.bias", "model.16.conv_block.6.weight", "model.16.conv_block.6.bias", "model.17.conv_block.2.weight", "model.17.conv_block.2.bias", "model.17.conv_block.6.weight", "model.17.conv_block.6.bias", "model.18.conv_block.2.weight", "model.18.conv_block.2.bias", "model.18.conv_block.6.weight", "model.18.conv_block.6.bias", "model.20.weight", "model.20.bias", "model.23.weight", "model.23.bias".
Do you see what I'm doing wrong here or is there some kind of bug in the code?
Hey,
I think this is due to you specified the norm=batch in your train_opt.txt. In the code, if you apply the batchNom instead of the instanceNorm, then the bias in each conv2d layer will be removed (check here and here for details). So your trained weights actually store no bias at all, that's why Missing key(s) errors were raised.
I think your test option should follow the same setting, i.e., specifying the norm=batch. Hope this helps.
