pytorch cyclegann 测试时出现 Missing key 错误
pytorch cyclegann gives a Missing key error when testing
我已经使用 pix2pix pytorch implementation 训练了一个模型并想对其进行测试。
但是当我测试它时我得到了错误
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".
培训的选择文件：
----------------- 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 -------------------
测试时我使用了以下测试设置
python3 test.py --dataroot ./datasets/datasets/boundedwalls_50_0.1 --name cycbw50 --model pix2pix --netG resnet_9blocks --direction BtoA --dataset_mode aligned --norm batch --load_size 286
----------------- 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 -------------------
有人看到我在这里做错了什么吗？我想 运行 这个网络，这样我就可以得到单个图像的结果，到目前为止，测试函数似乎是最有希望的，但它只是在这个神经网络上崩溃了。
我认为这里的问题是 bias=None 的某个层，但是在测试模型时需要这个，您应该检查代码以了解详细信息。
我在训练和测试中检查了你的配置后，norm 是不同的。对于GitHub中的代码，范数差可以设置偏置项为True或False。
if type(norm_layer) == functools.partial:
use_bias = norm_layer.func == nn.InstanceNorm2d
else:
use_bias = norm_layer == nn.InstanceNorm2d
model = [nn.ReflectionPad2d(3),
nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0, bias=use_bias),
norm_layer(ngf),
nn.ReLU(True)]
你可以查看一下here。
我已经使用 pix2pix pytorch implementation 训练了一个模型并想对其进行测试。
但是当我测试它时我得到了错误
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".
培训的选择文件：
----------------- 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 -------------------
测试时我使用了以下测试设置
python3 test.py --dataroot ./datasets/datasets/boundedwalls_50_0.1 --name cycbw50 --model pix2pix --netG resnet_9blocks --direction BtoA --dataset_mode aligned --norm batch --load_size 286
----------------- 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 -------------------
有人看到我在这里做错了什么吗？我想 运行 这个网络，这样我就可以得到单个图像的结果，到目前为止，测试函数似乎是最有希望的，但它只是在这个神经网络上崩溃了。
我认为这里的问题是 bias=None 的某个层，但是在测试模型时需要这个，您应该检查代码以了解详细信息。
我在训练和测试中检查了你的配置后，norm 是不同的。对于GitHub中的代码，范数差可以设置偏置项为True或False。
if type(norm_layer) == functools.partial:
use_bias = norm_layer.func == nn.InstanceNorm2d
else:
use_bias = norm_layer == nn.InstanceNorm2d
model = [nn.ReflectionPad2d(3),
nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0, bias=use_bias),
norm_layer(ngf),
nn.ReLU(True)]
你可以查看一下here。