PatrickHaopc
commented
3 years ago checkpoints_dir: ./checkpoints
crop_size: 256
dataroot: ./datasets/Task12_BBR [default: None]
dataset_mode: single
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: 1920 [default: 256]
max_dataset_size: inf
model: test
model_suffix:
n_layers_D: 3
name: spot [default: experiment_name]
ndf: 64
netD: basic
netG: resnet_9blocks
ngf: 64
no_dropout: True [default: False]
no_flip: False
norm: instance
num_test: 50
num_threads: 4
output_nc: 3
phase: test
preprocess: scale_width [default: resize_and_crop]
results_dir: ./results/
serial_batches: False
suffix:
verbose: False ----------------- End ------------------- initialize network with normal model [TestModel] was created ---------- Networks initialized ------------- DataParallel( (module): ResnetGenerator( (model): Sequential( (0): ReflectionPad2d((3, 3, 3, 3)) (1): Conv2d(3, 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=True) (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=True) (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=True) (10): ResnetBlock( (conv_block): Sequential( (0): ReflectionPad2d((1, 1, 1, 1)) (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1)) (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) (3): ReLU(inplace=True) (4): ReflectionPad2d((1, 1, 1, 1)) (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1)) (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) ) ) (11): ResnetBlock( (conv_block): Sequential( (0): ReflectionPad2d((1, 1, 1, 1)) (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1)) (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) (3): ReLU(inplace=True) (4): ReflectionPad2d((1, 1, 1, 1)) (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1)) (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) ) ) (12): ResnetBlock( (conv_block): Sequential( (0): ReflectionPad2d((1, 1, 1, 1)) (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1)) (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) (3): ReLU(inplace=True) (4): ReflectionPad2d((1, 1, 1, 1)) (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1)) (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) ) ) (13): ResnetBlock( (conv_block): Sequential( (0): ReflectionPad2d((1, 1, 1, 1)) (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1)) (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) (3): ReLU(inplace=True) (4): ReflectionPad2d((1, 1, 1, 1)) (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1)) (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) ) ) (14): ResnetBlock( (conv_block): Sequential( (0): ReflectionPad2d((1, 1, 1, 1)) (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1)) (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) (3): ReLU(inplace=True) (4): ReflectionPad2d((1, 1, 1, 1)) (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1)) (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) ) ) (15): ResnetBlock( (conv_block): Sequential( (0): ReflectionPad2d((1, 1, 1, 1)) (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1)) (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) (3): ReLU(inplace=True) (4): ReflectionPad2d((1, 1, 1, 1)) (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1)) (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) ) ) (16): ResnetBlock( (conv_block): Sequential( (0): ReflectionPad2d((1, 1, 1, 1)) (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1)) (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) (3): ReLU(inplace=True) (4): ReflectionPad2d((1, 1, 1, 1)) (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1)) (6): InstanceNorm2d(256, 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(256, 256, kernel_size=(3, 3), stride=(1, 1)) (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) (3): ReLU(inplace=True) (4): ReflectionPad2d((1, 1, 1, 1)) (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1)) (6): InstanceNorm2d(256, 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(256, 256, kernel_size=(3, 3), stride=(1, 1)) (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) (3): ReLU(inplace=True) (4): ReflectionPad2d((1, 1, 1, 1)) (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1)) (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) ) ) (19): ConvTranspose2d(256, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1)) (20): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) (21): ReLU(inplace=True) (22): ConvTranspose2d(128, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1)) (23): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) (24): ReLU(inplace=True) (25): ReflectionPad2d((3, 3, 3, 3)) (26): Conv2d(64, 3, kernel_size=(7, 7), stride=(1, 1)) (27): Tanh() ) ) ) [Network G] Total number of parameters : 11.378 M
Thanks for your geart work! I have traind my cycle gan model with the official CycleGAN codes, but when I run gs.py the error showed : RuntimeError: Error(s) in loading state_dict for Generator: Missing key(s) in state_dict: "model.2.weight", "model.2.bias", "model.5.weight", "model.5.bias", "model.10.conv_block.2.weight", "model.10.conv_block.2.bias", "model.11.conv_block.2.weight", "model.11.conv_block.2.bias", "model.12.conv_block.2.weight", "model.12.conv_block.2.bias", "model.13.conv_block.2.weight", "model.13.conv_block.2.bias", "model.14.conv_block.2.weight", "model.14.conv_block.2.bias", "model.15.conv_block.2.weight", "model.15.conv_block.2.bias", "model.16.conv_block.2.weight", "model.16.conv_block.2.bias", "model.17.conv_block.2.weight", "model.17.conv_block.2.bias", "model.18.conv_block.2.weight", "model.18.conv_block.2.bias", "model.20.weight", "model.20.bias", "model.23.weight", "model.23.bias".
I didn't change the official cycle gan codes, and the options I trained my model is "python train.py --dataroot ./datasets/Task12_BBR2color --name BBR2color --model cycle_gan --pool_size 50 --no_dropout --gpu_ids 1 --preprocess scale_width_and_crop --load_size 1920 --crop_size 360 --batch_size 1 --display_port 2020" The pytorch version is 1.7