Thx author again!I have train x4 nettwork and then train x8 network.
source myenv/bin/activate
cd code
python train.py -opt ./confs/SRFlow_DF2K_4X.yml
python train.py -opt ./confs/SRFlow_DF2K_8X.yml
The wrong is that:
(myenv) (base) ubuntu@ubuntu:~/Desktop/data1024/SRFlow-master/code$ python train.py -opt ./confs/SRFlow_DF2K_8X.yml
OrderedDict([('manual_seed', 10), ('lr_G', 0.0005), ('weight_decay_G', 0), ('beta1', 0.9), ('beta2', 0.99), ('lr_scheme', 'MultiStepLR'), ('warmup_iter', -1), ('lr_steps_rel', [0.5, 0.75, 0.9, 0.95]), ('lr_gamma', 0.5), ('niter', 200000), ('val_freq', 40000), ('lr_steps', [100000, 150000, 180000, 190000])])
Disabled distributed training.
pretrain_model path will be ignored when resuming training.
21-01-27 01:59:25.817 - INFO: name: train
use_tb_logger: True
model: SRFlow
distortion: sr
scale: 8
gpu_ids: [0]
datasets:[
train:[
name: CelebA_160_tr
mode: LRHR_PKL
dataroot_GT: ../datasets/DF2K-tr.pklv4
dataroot_LQ: ../datasets/DF2K-tr_X8.pklv4
quant: 32
use_shuffle: True
n_workers: 3
batch_size: 16
GT_size: 160
use_flip: True
color: RGB
phase: train
scale: 8
data_type: img
]
val:[
name: CelebA_160_va
mode: LRHR_PKL
dataroot_GT: ../datasets/DIV2K-va.pklv4
dataroot_LQ: ../datasets/DIV2K-va_X8.pklv4
quant: 32
n_max: 20
phase: val
scale: 8
data_type: img
]
]
dataroot_GT: ../datasets/div2k-validation-modcrop8-gt
dataroot_LR: ../datasets/div2k-validation-modcrop8-x8
model_path: ../pretrained_models/SRFlow_DF2K_8X.pth
heat: 0.9
network_G:[
which_model_G: SRFlowNet
in_nc: 3
out_nc: 3
nf: 64
nb: 23
upscale: 8
train_RRDB: False
train_RRDB_delay: 0.5
flow:[
K: 16
L: 4
noInitialInj: True
coupling: CondAffineSeparatedAndCond
additionalFlowNoAffine: 2
split:[
enable: True
]
fea_up0: True
stackRRDB:[
blocks: [1, 3, 5, 7]
concat: True
]
]
scale: 8
]
path:[
pretrain_model_G: /home/ubuntu/Desktop/data1024/SRFlow-master/experiments/train/models/200000_G.pth
strict_load: True
resume_state: auto
root: /home/ubuntu/Desktop/data1024/SRFlow-master
experiments_root: /home/ubuntu/Desktop/data1024/SRFlow-master/experiments/train
models: /home/ubuntu/Desktop/data1024/SRFlow-master/experiments/train/models
training_state: /home/ubuntu/Desktop/data1024/SRFlow-master/experiments/train/training_state
log: /home/ubuntu/Desktop/data1024/SRFlow-master/experiments/train
val_images: /home/ubuntu/Desktop/data1024/SRFlow-master/experiments/train/val_images
]
train:[
manual_seed: 10
lr_G: 0.0005
weight_decay_G: 0
beta1: 0.9
beta2: 0.99
lr_scheme: MultiStepLR
warmup_iter: -1
lr_steps_rel: [0.5, 0.75, 0.9, 0.95]
lr_gamma: 0.5
niter: 200000
val_freq: 40000
lr_steps: [100000, 150000, 180000, 190000]
]
val:[
heats: [0.0, 0.5, 0.75, 1.0]
n_sample: 3
]
test:[
heats: [0.0, 0.7, 0.8, 0.9]
]
logger:[
print_freq: 100
save_checkpoint_freq: 1000.0
]
is_train: True
dist: False
21-01-27 01:59:25.867 - INFO: Random seed: 10
{'name': 'CelebA_160_tr', 'mode': 'LRHR_PKL', 'dataroot_GT': '../datasets/DF2K-tr.pklv4', 'dataroot_LQ': '../datasets/DF2K-tr_X8.pklv4', 'quant': 32, 'use_shuffle': True, 'n_workers': 3, 'batch_size': 16, 'GT_size': 160, 'use_flip': True, 'color': 'RGB', 'phase': 'train', 'scale': 8, 'data_type': 'img'}
Loaded 162150 HR images with [0.00, 255.00] in 15.97s from ../datasets/DF2K-tr.pklv4
Loaded 162150 LR images with [0.00, 255.00] in 15.97s from ../datasets/DF2K-tr_X8.pklv4
21-01-27 01:59:41.838 - INFO: Dataset [LRHR_PKLDataset - CelebA_160_tr] is created.
Dataset created
21-01-27 01:59:41.845 - INFO: Number of train images: 162,150, iters: 10,135
21-01-27 01:59:41.845 - INFO: Total epochs needed: 20 for iters 200,000
{'name': 'CelebA_160_va', 'mode': 'LRHR_PKL', 'dataroot_GT': '../datasets/DIV2K-va.pklv4', 'dataroot_LQ': '../datasets/DIV2K-va_X8.pklv4', 'quant': 32, 'n_max': 20, 'phase': 'val', 'scale': 8, 'data_type': 'img'}
Loaded 20 HR images with [0.00, 255.00] in 0.87s from ../datasets/DIV2K-va.pklv4
Loaded 20 LR images with [0.00, 255.00] in 0.87s from ../datasets/DIV2K-va_X8.pklv4
21-01-27 01:59:42.711 - INFO: Dataset [LRHR_PKLDataset - CelebA_160_va] is created.
21-01-27 01:59:42.711 - INFO: Number of val images in [CelebA_160_va]: 20
21-01-27 01:59:45.179 - INFO: Network G structure: DataParallel - SRFlowNet, with parameters: 50,821,891
21-01-27 01:59:45.180 - INFO: SRFlowNet(
(RRDB): RRDBNet(
(conv_first): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(RRDB_trunk): Sequential(
(0): RRDB(
(RDB1): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
(RDB2): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
(RDB3): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
)
(1): RRDB(
(RDB1): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
(RDB2): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
(RDB3): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
)
(2): RRDB(
(RDB1): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
(RDB2): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
(RDB3): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
)
(3): RRDB(
(RDB1): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
(RDB2): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
(RDB3): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
)
(4): RRDB(
(RDB1): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
(RDB2): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
(RDB3): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
)
(5): RRDB(
(RDB1): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
(RDB2): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
(RDB3): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
)
(6): RRDB(
(RDB1): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
(RDB2): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
(RDB3): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
)
(7): RRDB(
(RDB1): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
(RDB2): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
(RDB3): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
)
(8): RRDB(
(RDB1): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
(RDB2): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
(RDB3): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
)
(9): RRDB(
(RDB1): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
(RDB2): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
(RDB3): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
)
(10): RRDB(
(RDB1): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
(RDB2): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
(RDB3): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
)
(11): RRDB(
(RDB1): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
(RDB2): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
(RDB3): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
)
(12): RRDB(
(RDB1): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
(RDB2): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
(RDB3): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
)
(13): RRDB(
(RDB1): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
(RDB2): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
(RDB3): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
)
(14): RRDB(
(RDB1): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
(RDB2): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
(RDB3): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
)
(15): RRDB(
(RDB1): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
(RDB2): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
(RDB3): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
)
(16): RRDB(
(RDB1): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
(RDB2): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
(RDB3): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
)
(17): RRDB(
(RDB1): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
(RDB2): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
(RDB3): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
)
(18): RRDB(
(RDB1): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
(RDB2): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
(RDB3): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
)
(19): RRDB(
(RDB1): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
(RDB2): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
(RDB3): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
)
(20): RRDB(
(RDB1): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
(RDB2): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
(RDB3): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
)
(21): RRDB(
(RDB1): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
(RDB2): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
(RDB3): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
)
(22): RRDB(
(RDB1): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
(RDB2): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
(RDB3): ResidualDenseBlock_5C(
(conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
)
)
(trunk_conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(upconv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(upconv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(upconv3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(HRconv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv_last): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
(flowUpsamplerNet): FlowUpsamplerNet(
(layers): ModuleList(
(0): SqueezeLayer()
(1): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
)
(2): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
)
(3): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
326, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 12, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 24, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(4): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
326, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 12, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 24, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(5): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
326, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 12, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 24, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(6): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
326, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 12, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 24, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(7): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
326, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 12, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 24, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(8): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
326, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 12, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 24, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(9): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
326, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 12, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 24, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(10): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
326, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 12, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 24, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(11): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
326, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 12, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 24, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(12): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
326, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 12, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 24, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(13): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
326, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 12, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 24, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(14): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
326, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 12, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 24, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(15): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
326, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 12, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 24, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(16): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
326, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 12, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 24, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(17): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
326, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 12, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 24, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(18): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
326, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 12, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 24, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(19): Split2d(
(conv): Conv2dZeros(6, 12, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(20): SqueezeLayer()
(21): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
)
(22): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
)
(23): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
332, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 24, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 48, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(24): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
332, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 24, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 48, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(25): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
332, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 24, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 48, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(26): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
332, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 24, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 48, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(27): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
332, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 24, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 48, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(28): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
332, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 24, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 48, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(29): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
332, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 24, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 48, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(30): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
332, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 24, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 48, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(31): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
332, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 24, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 48, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(32): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
332, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 24, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 48, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(33): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
332, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 24, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 48, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(34): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
332, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 24, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 48, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(35): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
332, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 24, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 48, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(36): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
332, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 24, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 48, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(37): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
332, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 24, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 48, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(38): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
332, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 24, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 48, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(39): Split2d(
(conv): Conv2dZeros(12, 24, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(40): SqueezeLayer()
(41): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
)
(42): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
)
(43): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
344, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 48, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 96, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(44): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
344, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 48, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 96, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(45): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
344, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 48, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 96, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(46): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
344, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 48, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 96, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(47): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
344, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 48, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 96, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(48): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
344, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 48, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 96, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(49): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
344, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 48, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 96, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(50): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
344, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 48, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 96, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(51): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
344, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 48, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 96, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(52): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
344, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 48, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 96, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(53): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
344, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 48, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 96, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(54): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
344, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 48, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 96, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(55): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
344, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 48, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 96, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(56): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
344, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 48, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 96, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(57): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
344, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 48, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 96, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(58): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
344, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 48, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 96, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(59): SqueezeLayer()
(60): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
)
(61): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
)
(62): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
416, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 192, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 384, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(63): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
416, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 192, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 384, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(64): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
416, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 192, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 384, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(65): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
416, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 192, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 384, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(66): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
416, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 192, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 384, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(67): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
416, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 192, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 384, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(68): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
416, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 192, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 384, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(69): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
416, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 192, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 384, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(70): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
416, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 192, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 384, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(71): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
416, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 192, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 384, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(72): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
416, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 192, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 384, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(73): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
416, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 192, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 384, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(74): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
416, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 192, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 384, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(75): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
416, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 192, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 384, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(76): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
416, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 192, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 384, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
(77): FlowStep(
(actnorm): ActNorm2d()
(invconv): InvertibleConv1x1()
(affine): CondAffineSeparatedAndCond(
(fAffine): Sequential(
(0): Conv2d(
416, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 192, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
(fFeatures): Sequential(
(0): Conv2d(
320, 64, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], bias=False
(actnorm): ActNorm2d()
)
(1): ReLU()
(2): Conv2d(
64, 64, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], bias=False
(actnorm): ActNorm2d()
)
(3): ReLU()
(4): Conv2dZeros(64, 384, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1])
)
)
)
)
(f): Sequential(
(0): Conv2d(320, 96, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
)
)
)
Traceback (most recent call last):
File "train.py", line 324, in
main()
File "train.py", line 158, in main
model = create_model(opt, current_step)
File "/home/ubuntu/Desktop/data1024/SRFlow-master/code/models/init.py", line 50, in create_model
m = M(opt, step)
File "/home/ubuntu/Desktop/data1024/SRFlow-master/code/models/SRFlow_model.py", line 58, in init
self.load()
File "/home/ubuntu/Desktop/data1024/SRFlow-master/code/models/SRFlow_model.py", line 267, in load
self.load_network(get_resume_model_path, self.netG, strict=True, submodule=None)
File "/home/ubuntu/Desktop/data1024/SRFlow-master/code/models/base_model.py", line 124, in load_network
network.load_state_dict(load_net_clean, strict=strict)
File "/home/ubuntu/Desktop/data1024/SRFlow-master/myenv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1051, in load_state_dict
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for SRFlowNet:
Missing key(s) in state_dict: "RRDB.upconv3.weight", "RRDB.upconv3.bias", "flowUpsamplerNet.layers.39.conv.weight", "flowUpsamplerNet.layers.39.conv.bias", "flowUpsamplerNet.layers.39.conv.logs", "flowUpsamplerNet.layers.58.actnorm.bias", "flowUpsamplerNet.layers.58.actnorm.logs", "flowUpsamplerNet.layers.58.invconv.weight", "flowUpsamplerNet.layers.58.affine.fAffine.0.weight", "flowUpsamplerNet.layers.58.affine.fAffine.0.actnorm.bias", "flowUpsamplerNet.layers.58.affine.fAffine.0.actnorm.logs", "flowUpsamplerNet.layers.58.affine.fAffine.2.weight", "flowUpsamplerNet.layers.58.affine.fAffine.2.actnorm.bias", "flowUpsamplerNet.layers.58.affine.fAffine.2.actnorm.logs", "flowUpsamplerNet.layers.58.affine.fAffine.4.weight", "flowUpsamplerNet.layers.58.affine.fAffine.4.bias", "flowUpsamplerNet.layers.58.affine.fAffine.4.logs", "flowUpsamplerNet.layers.58.affine.fFeatures.0.weight", "flowUpsamplerNet.layers.58.affine.fFeatures.0.actnorm.bias", "flowUpsamplerNet.layers.58.affine.fFeatures.0.actnorm.logs", "flowUpsamplerNet.layers.58.affine.fFeatures.2.weight", "flowUpsamplerNet.layers.58.affine.fFeatures.2.actnorm.bias", "flowUpsamplerNet.layers.58.affine.fFeatures.2.actnorm.logs", "flowUpsamplerNet.layers.58.affine.fFeatures.4.weight", "flowUpsamplerNet.layers.58.affine.fFeatures.4.bias", "flowUpsamplerNet.layers.58.affine.fFeatures.4.logs", "flowUpsamplerNet.layers.60.actnorm.bias", "flowUpsamplerNet.layers.60.actnorm.logs", "flowUpsamplerNet.layers.60.invconv.weight", "flowUpsamplerNet.layers.61.actnorm.bias", "flowUpsamplerNet.layers.61.actnorm.logs", "flowUpsamplerNet.layers.61.invconv.weight", "flowUpsamplerNet.layers.62.actnorm.bias", "flowUpsamplerNet.layers.62.actnorm.logs", "flowUpsamplerNet.layers.62.invconv.weight", "flowUpsamplerNet.layers.62.affine.fAffine.0.weight", "flowUpsamplerNet.layers.62.affine.fAffine.0.actnorm.bias", "flowUpsamplerNet.layers.62.affine.fAffine.0.actnorm.logs", "flowUpsamplerNet.layers.62.affine.fAffine.2.weight", "flowUpsamplerNet.layers.62.affine.fAffine.2.actnorm.bias", "flowUpsamplerNet.layers.62.affine.fAffine.2.actnorm.logs", "flowUpsamplerNet.layers.62.affine.fAffine.4.weight", "flowUpsamplerNet.layers.62.affine.fAffine.4.bias", "flowUpsamplerNet.layers.62.affine.fAffine.4.logs", "flowUpsamplerNet.layers.62.affine.fFeatures.0.weight", "flowUpsamplerNet.layers.62.affine.fFeatures.0.actnorm.bias", "flowUpsamplerNet.layers.62.affine.fFeatures.0.actnorm.logs", "flowUpsamplerNet.layers.62.affine.fFeatures.2.weight", "flowUpsamplerNet.layers.62.affine.fFeatures.2.actnorm.bias", "flowUpsamplerNet.layers.62.affine.fFeatures.2.actnorm.logs", "flowUpsamplerNet.layers.62.affine.fFeatures.4.weight", "flowUpsamplerNet.layers.62.affine.fFeatures.4.bias", "flowUpsamplerNet.layers.62.affine.fFeatures.4.logs", "flowUpsamplerNet.layers.63.actnorm.bias", "flowUpsamplerNet.layers.63.actnorm.logs", "flowUpsamplerNet.layers.63.invconv.weight", "flowUpsamplerNet.layers.63.affine.fAffine.0.weight", "flowUpsamplerNet.layers.63.affine.fAffine.0.actnorm.bias", "flowUpsamplerNet.layers.63.affine.fAffine.0.actnorm.logs", "flowUpsamplerNet.layers.63.affine.fAffine.2.weight", "flowUpsamplerNet.layers.63.affine.fAffine.2.actnorm.bias", "flowUpsamplerNet.layers.63.affine.fAffine.2.actnorm.logs", "flowUpsamplerNet.layers.63.affine.fAffine.4.weight", "flowUpsamplerNet.layers.63.affine.fAffine.4.bias", "flowUpsamplerNet.layers.63.affine.fAffine.4.logs", "flowUpsamplerNet.layers.63.affine.fFeatures.0.weight", "flowUpsamplerNet.layers.63.affine.fFeatures.0.actnorm.bias", "flowUpsamplerNet.layers.63.affine.fFeatures.0.actnorm.logs", "flowUpsamplerNet.layers.63.affine.fFeatures.2.weight", "flowUpsamplerNet.layers.63.affine.fFeatures.2.actnorm.bias", "flowUpsamplerNet.layers.63.affine.fFeatures.2.actnorm.logs", "flowUpsamplerNet.layers.63.affine.fFeatures.4.weight", "flowUpsamplerNet.layers.63.affine.fFeatures.4.bias", "flowUpsamplerNet.layers.63.affine.fFeatures.4.logs", "flowUpsamplerNet.layers.64.actnorm.bias", "flowUpsamplerNet.layers.64.actnorm.logs", "flowUpsamplerNet.layers.64.invconv.weight", "flowUpsamplerNet.layers.64.affine.fAffine.0.weight", "flowUpsamplerNet.layers.64.affine.fAffine.0.actnorm.bias", "flowUpsamplerNet.layers.64.affine.fAffine.0.actnorm.logs", "flowUpsamplerNet.layers.64.affine.fAffine.2.weight", "flowUpsamplerNet.layers.64.affine.fAffine.2.actnorm.bias", "flowUpsamplerNet.layers.64.affine.fAffine.2.actnorm.logs", "flowUpsamplerNet.layers.64.affine.fAffine.4.weight", "flowUpsamplerNet.layers.64.affine.fAffine.4.bias", "flowUpsamplerNet.layers.64.affine.fAffine.4.logs", "flowUpsamplerNet.layers.64.affine.fFeatures.0.weight", "flowUpsamplerNet.layers.64.affine.fFeatures.0.actnorm.bias", "flowUpsamplerNet.layers.64.affine.fFeatures.0.actnorm.logs", "flowUpsamplerNet.layers.64.affine.fFeatures.2.weight", "flowUpsamplerNet.layers.64.affine.fFeatures.2.actnorm.bias", "flowUpsamplerNet.layers.64.affine.fFeatures.2.actnorm.logs", "flowUpsamplerNet.layers.64.affine.fFeatures.4.weight", "flowUpsamplerNet.layers.64.affine.fFeatures.4.bias", "flowUpsamplerNet.layers.64.affine.fFeatures.4.logs", "flowUpsamplerNet.layers.65.actnorm.bias", "flowUpsamplerNet.layers.65.actnorm.logs", "flowUpsamplerNet.layers.65.invconv.weight", "flowUpsamplerNet.layers.65.affine.fAffine.0.weight", "flowUpsamplerNet.layers.65.affine.fAffine.0.actnorm.bias", "flowUpsamplerNet.layers.65.affine.fAffine.0.actnorm.logs", "flowUpsamplerNet.layers.65.affine.fAffine.2.weight", "flowUpsamplerNet.layers.65.affine.fAffine.2.actnorm.bias", "flowUpsamplerNet.layers.65.affine.fAffine.2.actnorm.logs", "flowUpsamplerNet.layers.65.affine.fAffine.4.weight", "flowUpsamplerNet.layers.65.affine.fAffine.4.bias", "flowUpsamplerNet.layers.65.affine.fAffine.4.logs", "flowUpsamplerNet.layers.65.affine.fFeatures.0.weight", "flowUpsamplerNet.layers.65.affine.fFeatures.0.actnorm.bias", 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"flowUpsamplerNet.layers.70.affine.fFeatures.2.weight", "flowUpsamplerNet.layers.70.affine.fFeatures.2.actnorm.bias", "flowUpsamplerNet.layers.70.affine.fFeatures.2.actnorm.logs", "flowUpsamplerNet.layers.70.affine.fFeatures.4.weight", "flowUpsamplerNet.layers.70.affine.fFeatures.4.bias", "flowUpsamplerNet.layers.70.affine.fFeatures.4.logs", "flowUpsamplerNet.layers.71.actnorm.bias", "flowUpsamplerNet.layers.71.actnorm.logs", "flowUpsamplerNet.layers.71.invconv.weight", "flowUpsamplerNet.layers.71.affine.fAffine.0.weight", "flowUpsamplerNet.layers.71.affine.fAffine.0.actnorm.bias", "flowUpsamplerNet.layers.71.affine.fAffine.0.actnorm.logs", "flowUpsamplerNet.layers.71.affine.fAffine.2.weight", "flowUpsamplerNet.layers.71.affine.fAffine.2.actnorm.bias", "flowUpsamplerNet.layers.71.affine.fAffine.2.actnorm.logs", "flowUpsamplerNet.layers.71.affine.fAffine.4.weight", "flowUpsamplerNet.layers.71.affine.fAffine.4.bias", "flowUpsamplerNet.layers.71.affine.fAffine.4.logs", "flowUpsamplerNet.layers.71.affine.fFeatures.0.weight", "flowUpsamplerNet.layers.71.affine.fFeatures.0.actnorm.bias", "flowUpsamplerNet.layers.71.affine.fFeatures.0.actnorm.logs", "flowUpsamplerNet.layers.71.affine.fFeatures.2.weight", "flowUpsamplerNet.layers.71.affine.fFeatures.2.actnorm.bias", "flowUpsamplerNet.layers.71.affine.fFeatures.2.actnorm.logs", "flowUpsamplerNet.layers.71.affine.fFeatures.4.weight", "flowUpsamplerNet.layers.71.affine.fFeatures.4.bias", "flowUpsamplerNet.layers.71.affine.fFeatures.4.logs", "flowUpsamplerNet.layers.72.actnorm.bias", "flowUpsamplerNet.layers.72.actnorm.logs", "flowUpsamplerNet.layers.72.invconv.weight", "flowUpsamplerNet.layers.72.affine.fAffine.0.weight", "flowUpsamplerNet.layers.72.affine.fAffine.0.actnorm.bias", "flowUpsamplerNet.layers.72.affine.fAffine.0.actnorm.logs", "flowUpsamplerNet.layers.72.affine.fAffine.2.weight", "flowUpsamplerNet.layers.72.affine.fAffine.2.actnorm.bias", "flowUpsamplerNet.layers.72.affine.fAffine.2.actnorm.logs", "flowUpsamplerNet.layers.72.affine.fAffine.4.weight", "flowUpsamplerNet.layers.72.affine.fAffine.4.bias", "flowUpsamplerNet.layers.72.affine.fAffine.4.logs", "flowUpsamplerNet.layers.72.affine.fFeatures.0.weight", "flowUpsamplerNet.layers.72.affine.fFeatures.0.actnorm.bias", "flowUpsamplerNet.layers.72.affine.fFeatures.0.actnorm.logs", "flowUpsamplerNet.layers.72.affine.fFeatures.2.weight", "flowUpsamplerNet.layers.72.affine.fFeatures.2.actnorm.bias", "flowUpsamplerNet.layers.72.affine.fFeatures.2.actnorm.logs", "flowUpsamplerNet.layers.72.affine.fFeatures.4.weight", "flowUpsamplerNet.layers.72.affine.fFeatures.4.bias", "flowUpsamplerNet.layers.72.affine.fFeatures.4.logs", "flowUpsamplerNet.layers.73.actnorm.bias", "flowUpsamplerNet.layers.73.actnorm.logs", "flowUpsamplerNet.layers.73.invconv.weight", "flowUpsamplerNet.layers.73.affine.fAffine.0.weight", "flowUpsamplerNet.layers.73.affine.fAffine.0.actnorm.bias", "flowUpsamplerNet.layers.73.affine.fAffine.0.actnorm.logs", "flowUpsamplerNet.layers.73.affine.fAffine.2.weight", "flowUpsamplerNet.layers.73.affine.fAffine.2.actnorm.bias", "flowUpsamplerNet.layers.73.affine.fAffine.2.actnorm.logs", "flowUpsamplerNet.layers.73.affine.fAffine.4.weight", "flowUpsamplerNet.layers.73.affine.fAffine.4.bias", "flowUpsamplerNet.layers.73.affine.fAffine.4.logs", "flowUpsamplerNet.layers.73.affine.fFeatures.0.weight", "flowUpsamplerNet.layers.73.affine.fFeatures.0.actnorm.bias", "flowUpsamplerNet.layers.73.affine.fFeatures.0.actnorm.logs", "flowUpsamplerNet.layers.73.affine.fFeatures.2.weight", "flowUpsamplerNet.layers.73.affine.fFeatures.2.actnorm.bias", "flowUpsamplerNet.layers.73.affine.fFeatures.2.actnorm.logs", "flowUpsamplerNet.layers.73.affine.fFeatures.4.weight", "flowUpsamplerNet.layers.73.affine.fFeatures.4.bias", "flowUpsamplerNet.layers.73.affine.fFeatures.4.logs", "flowUpsamplerNet.layers.74.actnorm.bias", "flowUpsamplerNet.layers.74.actnorm.logs", "flowUpsamplerNet.layers.74.invconv.weight", "flowUpsamplerNet.layers.74.affine.fAffine.0.weight", "flowUpsamplerNet.layers.74.affine.fAffine.0.actnorm.bias", "flowUpsamplerNet.layers.74.affine.fAffine.0.actnorm.logs", "flowUpsamplerNet.layers.74.affine.fAffine.2.weight", "flowUpsamplerNet.layers.74.affine.fAffine.2.actnorm.bias", "flowUpsamplerNet.layers.74.affine.fAffine.2.actnorm.logs", "flowUpsamplerNet.layers.74.affine.fAffine.4.weight", "flowUpsamplerNet.layers.74.affine.fAffine.4.bias", "flowUpsamplerNet.layers.74.affine.fAffine.4.logs", "flowUpsamplerNet.layers.74.affine.fFeatures.0.weight", "flowUpsamplerNet.layers.74.affine.fFeatures.0.actnorm.bias", "flowUpsamplerNet.layers.74.affine.fFeatures.0.actnorm.logs", "flowUpsamplerNet.layers.74.affine.fFeatures.2.weight", "flowUpsamplerNet.layers.74.affine.fFeatures.2.actnorm.bias", "flowUpsamplerNet.layers.74.affine.fFeatures.2.actnorm.logs", "flowUpsamplerNet.layers.74.affine.fFeatures.4.weight", "flowUpsamplerNet.layers.74.affine.fFeatures.4.bias", "flowUpsamplerNet.layers.74.affine.fFeatures.4.logs", "flowUpsamplerNet.layers.75.actnorm.bias", "flowUpsamplerNet.layers.75.actnorm.logs", "flowUpsamplerNet.layers.75.invconv.weight", "flowUpsamplerNet.layers.75.affine.fAffine.0.weight", "flowUpsamplerNet.layers.75.affine.fAffine.0.actnorm.bias", "flowUpsamplerNet.layers.75.affine.fAffine.0.actnorm.logs", "flowUpsamplerNet.layers.75.affine.fAffine.2.weight", "flowUpsamplerNet.layers.75.affine.fAffine.2.actnorm.bias", "flowUpsamplerNet.layers.75.affine.fAffine.2.actnorm.logs", "flowUpsamplerNet.layers.75.affine.fAffine.4.weight", "flowUpsamplerNet.layers.75.affine.fAffine.4.bias", "flowUpsamplerNet.layers.75.affine.fAffine.4.logs", "flowUpsamplerNet.layers.75.affine.fFeatures.0.weight", "flowUpsamplerNet.layers.75.affine.fFeatures.0.actnorm.bias", "flowUpsamplerNet.layers.75.affine.fFeatures.0.actnorm.logs", "flowUpsamplerNet.layers.75.affine.fFeatures.2.weight", "flowUpsamplerNet.layers.75.affine.fFeatures.2.actnorm.bias", "flowUpsamplerNet.layers.75.affine.fFeatures.2.actnorm.logs", "flowUpsamplerNet.layers.75.affine.fFeatures.4.weight", "flowUpsamplerNet.layers.75.affine.fFeatures.4.bias", "flowUpsamplerNet.layers.75.affine.fFeatures.4.logs", "flowUpsamplerNet.layers.76.actnorm.bias", "flowUpsamplerNet.layers.76.actnorm.logs", "flowUpsamplerNet.layers.76.invconv.weight", "flowUpsamplerNet.layers.76.affine.fAffine.0.weight", "flowUpsamplerNet.layers.76.affine.fAffine.0.actnorm.bias", "flowUpsamplerNet.layers.76.affine.fAffine.0.actnorm.logs", "flowUpsamplerNet.layers.76.affine.fAffine.2.weight", "flowUpsamplerNet.layers.76.affine.fAffine.2.actnorm.bias", "flowUpsamplerNet.layers.76.affine.fAffine.2.actnorm.logs", "flowUpsamplerNet.layers.76.affine.fAffine.4.weight", "flowUpsamplerNet.layers.76.affine.fAffine.4.bias", "flowUpsamplerNet.layers.76.affine.fAffine.4.logs", "flowUpsamplerNet.layers.76.affine.fFeatures.0.weight", "flowUpsamplerNet.layers.76.affine.fFeatures.0.actnorm.bias", "flowUpsamplerNet.layers.76.affine.fFeatures.0.actnorm.logs", "flowUpsamplerNet.layers.76.affine.fFeatures.2.weight", "flowUpsamplerNet.layers.76.affine.fFeatures.2.actnorm.bias", "flowUpsamplerNet.layers.76.affine.fFeatures.2.actnorm.logs", "flowUpsamplerNet.layers.76.affine.fFeatures.4.weight", "flowUpsamplerNet.layers.76.affine.fFeatures.4.bias", "flowUpsamplerNet.layers.76.affine.fFeatures.4.logs", "flowUpsamplerNet.layers.77.actnorm.bias", "flowUpsamplerNet.layers.77.actnorm.logs", "flowUpsamplerNet.layers.77.invconv.weight", "flowUpsamplerNet.layers.77.affine.fAffine.0.weight", "flowUpsamplerNet.layers.77.affine.fAffine.0.actnorm.bias", "flowUpsamplerNet.layers.77.affine.fAffine.0.actnorm.logs", "flowUpsamplerNet.layers.77.affine.fAffine.2.weight", "flowUpsamplerNet.layers.77.affine.fAffine.2.actnorm.bias", "flowUpsamplerNet.layers.77.affine.fAffine.2.actnorm.logs", "flowUpsamplerNet.layers.77.affine.fAffine.4.weight", "flowUpsamplerNet.layers.77.affine.fAffine.4.bias", "flowUpsamplerNet.layers.77.affine.fAffine.4.logs", "flowUpsamplerNet.layers.77.affine.fFeatures.0.weight", "flowUpsamplerNet.layers.77.affine.fFeatures.0.actnorm.bias", "flowUpsamplerNet.layers.77.affine.fFeatures.0.actnorm.logs", "flowUpsamplerNet.layers.77.affine.fFeatures.2.weight", "flowUpsamplerNet.layers.77.affine.fFeatures.2.actnorm.bias", "flowUpsamplerNet.layers.77.affine.fFeatures.2.actnorm.logs", "flowUpsamplerNet.layers.77.affine.fFeatures.4.weight", "flowUpsamplerNet.layers.77.affine.fFeatures.4.bias", "flowUpsamplerNet.layers.77.affine.fFeatures.4.logs".
Unexpected key(s) in state_dict: "flowUpsamplerNet.layers.40.actnorm.bias", "flowUpsamplerNet.layers.40.actnorm.logs", "flowUpsamplerNet.layers.40.invconv.weight", "flowUpsamplerNet.layers.42.affine.fAffine.0.weight", "flowUpsamplerNet.layers.42.affine.fAffine.0.actnorm.bias", "flowUpsamplerNet.layers.42.affine.fAffine.0.actnorm.logs", "flowUpsamplerNet.layers.42.affine.fAffine.2.weight", "flowUpsamplerNet.layers.42.affine.fAffine.2.actnorm.bias", "flowUpsamplerNet.layers.42.affine.fAffine.2.actnorm.logs", "flowUpsamplerNet.layers.42.affine.fAffine.4.weight", "flowUpsamplerNet.layers.42.affine.fAffine.4.bias", "flowUpsamplerNet.layers.42.affine.fAffine.4.logs", "flowUpsamplerNet.layers.42.affine.fFeatures.0.weight", "flowUpsamplerNet.layers.42.affine.fFeatures.0.actnorm.bias", "flowUpsamplerNet.layers.42.affine.fFeatures.0.actnorm.logs", "flowUpsamplerNet.layers.42.affine.fFeatures.2.weight", "flowUpsamplerNet.layers.42.affine.fFeatures.2.actnorm.bias", "flowUpsamplerNet.layers.42.affine.fFeatures.2.actnorm.logs", "flowUpsamplerNet.layers.42.affine.fFeatures.4.weight", "flowUpsamplerNet.layers.42.affine.fFeatures.4.bias", "flowUpsamplerNet.layers.42.affine.fFeatures.4.logs".
size mismatch for flowUpsamplerNet.layers.41.actnorm.bias: copying a param with shape torch.Size([1, 96, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 48, 1, 1]).
size mismatch for flowUpsamplerNet.layers.41.actnorm.logs: copying a param with shape torch.Size([1, 96, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 48, 1, 1]).
size mismatch for flowUpsamplerNet.layers.41.invconv.weight: copying a param with shape torch.Size([96, 96]) from checkpoint, the shape in current model is torch.Size([48, 48]).
size mismatch for flowUpsamplerNet.layers.42.actnorm.bias: copying a param with shape torch.Size([1, 96, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 48, 1, 1]).
size mismatch for flowUpsamplerNet.layers.42.actnorm.logs: copying a param with shape torch.Size([1, 96, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 48, 1, 1]).
size mismatch for flowUpsamplerNet.layers.42.invconv.weight: copying a param with shape torch.Size([96, 96]) from checkpoint, the shape in current model is torch.Size([48, 48]).
size mismatch for flowUpsamplerNet.layers.43.actnorm.bias: copying a param with shape torch.Size([1, 96, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 48, 1, 1]).
size mismatch for flowUpsamplerNet.layers.43.actnorm.logs: copying a param with shape torch.Size([1, 96, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 48, 1, 1]).
size mismatch for flowUpsamplerNet.layers.43.invconv.weight: copying a param with shape torch.Size([96, 96]) from checkpoint, the shape in current model is torch.Size([48, 48]).
size mismatch for flowUpsamplerNet.layers.43.affine.fAffine.0.weight: copying a param with shape torch.Size([64, 368, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 344, 3, 3]).
size mismatch for flowUpsamplerNet.layers.43.affine.fAffine.4.weight: copying a param with shape torch.Size([96, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([48, 64, 3, 3]).
size mismatch for flowUpsamplerNet.layers.43.affine.fAffine.4.bias: copying a param with shape torch.Size([96]) from checkpoint, the shape in current model is torch.Size([48]).
size mismatch for flowUpsamplerNet.layers.43.affine.fAffine.4.logs: copying a param with shape torch.Size([96, 1, 1]) from checkpoint, the shape in current model is torch.Size([48, 1, 1]).
size mismatch for flowUpsamplerNet.layers.43.affine.fFeatures.4.weight: copying a param with shape torch.Size([192, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([96, 64, 3, 3]).
size mismatch for flowUpsamplerNet.layers.43.affine.fFeatures.4.bias: copying a param with shape torch.Size([192]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for flowUpsamplerNet.layers.43.affine.fFeatures.4.logs: copying a param with shape torch.Size([192, 1, 1]) from checkpoint, the shape in current model is torch.Size([96, 1, 1]).
size mismatch for flowUpsamplerNet.layers.44.actnorm.bias: copying a param with shape torch.Size([1, 96, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 48, 1, 1]).
size mismatch for flowUpsamplerNet.layers.44.actnorm.logs: copying a param with shape torch.Size([1, 96, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 48, 1, 1]).
size mismatch for flowUpsamplerNet.layers.44.invconv.weight: copying a param with shape torch.Size([96, 96]) from checkpoint, the shape in current model is torch.Size([48, 48]).
size mismatch for flowUpsamplerNet.layers.44.affine.fAffine.0.weight: copying a param with shape torch.Size([64, 368, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 344, 3, 3]).
size mismatch for flowUpsamplerNet.layers.44.affine.fAffine.4.weight: copying a param with shape torch.Size([96, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([48, 64, 3, 3]).
size mismatch for flowUpsamplerNet.layers.44.affine.fAffine.4.bias: copying a param with shape torch.Size([96]) from checkpoint, the shape in current model is torch.Size([48]).
size mismatch for flowUpsamplerNet.layers.44.affine.fAffine.4.logs: copying a param with shape torch.Size([96, 1, 1]) from checkpoint, the shape in current model is torch.Size([48, 1, 1]).
size mismatch for flowUpsamplerNet.layers.44.affine.fFeatures.4.weight: copying a param with shape torch.Size([192, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([96, 64, 3, 3]).
size mismatch for flowUpsamplerNet.layers.44.affine.fFeatures.4.bias: copying a param with shape torch.Size([192]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for flowUpsamplerNet.layers.44.affine.fFeatures.4.logs: copying a param with shape torch.Size([192, 1, 1]) from checkpoint, the shape in current model is torch.Size([96, 1, 1]).
size mismatch for flowUpsamplerNet.layers.45.actnorm.bias: copying a param with shape torch.Size([1, 96, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 48, 1, 1]).
size mismatch for flowUpsamplerNet.layers.45.actnorm.logs: copying a param with shape torch.Size([1, 96, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 48, 1, 1]).
size mismatch for flowUpsamplerNet.layers.45.invconv.weight: copying a param with shape torch.Size([96, 96]) from checkpoint, the shape in current model is torch.Size([48, 48]).
size mismatch for flowUpsamplerNet.layers.45.affine.fAffine.0.weight: copying a param with shape torch.Size([64, 368, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 344, 3, 3]).
size mismatch for flowUpsamplerNet.layers.45.affine.fAffine.4.weight: copying a param with shape torch.Size([96, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([48, 64, 3, 3]).
size mismatch for flowUpsamplerNet.layers.45.affine.fAffine.4.bias: copying a param with shape torch.Size([96]) from checkpoint, the shape in current model is torch.Size([48]).
size mismatch for flowUpsamplerNet.layers.45.affine.fAffine.4.logs: copying a param with shape torch.Size([96, 1, 1]) from checkpoint, the shape in current model is torch.Size([48, 1, 1]).
size mismatch for flowUpsamplerNet.layers.45.affine.fFeatures.4.weight: copying a param with shape torch.Size([192, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([96, 64, 3, 3]).
size mismatch for flowUpsamplerNet.layers.45.affine.fFeatures.4.bias: copying a param with shape torch.Size([192]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for flowUpsamplerNet.layers.45.affine.fFeatures.4.logs: copying a param with shape torch.Size([192, 1, 1]) from checkpoint, the shape in current model is torch.Size([96, 1, 1]).
size mismatch for flowUpsamplerNet.layers.46.actnorm.bias: copying a param with shape torch.Size([1, 96, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 48, 1, 1]).
size mismatch for flowUpsamplerNet.layers.46.actnorm.logs: copying a param with shape torch.Size([1, 96, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 48, 1, 1]).
size mismatch for flowUpsamplerNet.layers.46.invconv.weight: copying a param with shape torch.Size([96, 96]) from checkpoint, the shape in current model is torch.Size([48, 48]).
size mismatch for flowUpsamplerNet.layers.46.affine.fAffine.0.weight: copying a param with shape torch.Size([64, 368, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 344, 3, 3]).
size mismatch for flowUpsamplerNet.layers.46.affine.fAffine.4.weight: copying a param with shape torch.Size([96, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([48, 64, 3, 3]).
size mismatch for flowUpsamplerNet.layers.46.affine.fAffine.4.bias: copying a param with shape torch.Size([96]) from checkpoint, the shape in current model is torch.Size([48]).
size mismatch for flowUpsamplerNet.layers.46.affine.fAffine.4.logs: copying a param with shape torch.Size([96, 1, 1]) from checkpoint, the shape in current model is torch.Size([48, 1, 1]).
size mismatch for flowUpsamplerNet.layers.46.affine.fFeatures.4.weight: copying a param with shape torch.Size([192, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([96, 64, 3, 3]).
size mismatch for flowUpsamplerNet.layers.46.affine.fFeatures.4.bias: copying a param with shape torch.Size([192]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for flowUpsamplerNet.layers.46.affine.fFeatures.4.logs: copying a param with shape torch.Size([192, 1, 1]) from checkpoint, the shape in current model is torch.Size([96, 1, 1]).
size mismatch for flowUpsamplerNet.layers.47.actnorm.bias: copying a param with shape torch.Size([1, 96, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 48, 1, 1]).
size mismatch for flowUpsamplerNet.layers.47.actnorm.logs: copying a param with shape torch.Size([1, 96, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 48, 1, 1]).
size mismatch for flowUpsamplerNet.layers.47.invconv.weight: copying a param with shape torch.Size([96, 96]) from checkpoint, the shape in current model is torch.Size([48, 48]).
size mismatch for flowUpsamplerNet.layers.47.affine.fAffine.0.weight: copying a param with shape torch.Size([64, 368, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 344, 3, 3]).
size mismatch for flowUpsamplerNet.layers.47.affine.fAffine.4.weight: copying a param with shape torch.Size([96, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([48, 64, 3, 3]).
size mismatch for flowUpsamplerNet.layers.47.affine.fAffine.4.bias: copying a param with shape torch.Size([96]) from checkpoint, the shape in current model is torch.Size([48]).
size mismatch for flowUpsamplerNet.layers.47.affine.fAffine.4.logs: copying a param with shape torch.Size([96, 1, 1]) from checkpoint, the shape in current model is torch.Size([48, 1, 1]).
size mismatch for flowUpsamplerNet.layers.47.affine.fFeatures.4.weight: copying a param with shape torch.Size([192, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([96, 64, 3, 3]).
size mismatch for flowUpsamplerNet.layers.47.affine.fFeatures.4.bias: copying a param with shape torch.Size([192]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for flowUpsamplerNet.layers.47.affine.fFeatures.4.logs: copying a param with shape torch.Size([192, 1, 1]) from checkpoint, the shape in current model is torch.Size([96, 1, 1]).
size mismatch for flowUpsamplerNet.layers.48.actnorm.bias: copying a param with shape torch.Size([1, 96, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 48, 1, 1]).
size mismatch for flowUpsamplerNet.layers.48.actnorm.logs: copying a param with shape torch.Size([1, 96, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 48, 1, 1]).
size mismatch for flowUpsamplerNet.layers.48.invconv.weight: copying a param with shape torch.Size([96, 96]) from checkpoint, the shape in current model is torch.Size([48, 48]).
size mismatch for flowUpsamplerNet.layers.48.affine.fAffine.0.weight: copying a param with shape torch.Size([64, 368, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 344, 3, 3]).
size mismatch for flowUpsamplerNet.layers.48.affine.fAffine.4.weight: copying a param with shape torch.Size([96, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([48, 64, 3, 3]).
size mismatch for flowUpsamplerNet.layers.48.affine.fAffine.4.bias: copying a param with shape torch.Size([96]) from checkpoint, the shape in current model is torch.Size([48]).
size mismatch for flowUpsamplerNet.layers.48.affine.fAffine.4.logs: copying a param with shape torch.Size([96, 1, 1]) from checkpoint, the shape in current model is torch.Size([48, 1, 1]).
size mismatch for flowUpsamplerNet.layers.48.affine.fFeatures.4.weight: copying a param with shape torch.Size([192, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([96, 64, 3, 3]).
size mismatch for flowUpsamplerNet.layers.48.affine.fFeatures.4.bias: copying a param with shape torch.Size([192]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for flowUpsamplerNet.layers.48.affine.fFeatures.4.logs: copying a param with shape torch.Size([192, 1, 1]) from checkpoint, the shape in current model is torch.Size([96, 1, 1]).
size mismatch for flowUpsamplerNet.layers.49.actnorm.bias: copying a param with shape torch.Size([1, 96, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 48, 1, 1]).
size mismatch for flowUpsamplerNet.layers.49.actnorm.logs: copying a param with shape torch.Size([1, 96, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 48, 1, 1]).
size mismatch for flowUpsamplerNet.layers.49.invconv.weight: copying a param with shape torch.Size([96, 96]) from checkpoint, the shape in current model is torch.Size([48, 48]).
size mismatch for flowUpsamplerNet.layers.49.affine.fAffine.0.weight: copying a param with shape torch.Size([64, 368, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 344, 3, 3]).
size mismatch for flowUpsamplerNet.layers.49.affine.fAffine.4.weight: copying a param with shape torch.Size([96, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([48, 64, 3, 3]).
size mismatch for flowUpsamplerNet.layers.49.affine.fAffine.4.bias: copying a param with shape torch.Size([96]) from checkpoint, the shape in current model is torch.Size([48]).
size mismatch for flowUpsamplerNet.layers.49.affine.fAffine.4.logs: copying a param with shape torch.Size([96, 1, 1]) from checkpoint, the shape in current model is torch.Size([48, 1, 1]).
size mismatch for flowUpsamplerNet.layers.49.affine.fFeatures.4.weight: copying a param with shape torch.Size([192, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([96, 64, 3, 3]).
size mismatch for flowUpsamplerNet.layers.49.affine.fFeatures.4.bias: copying a param with shape torch.Size([192]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for flowUpsamplerNet.layers.49.affine.fFeatures.4.logs: copying a param with shape torch.Size([192, 1, 1]) from checkpoint, the shape in current model is torch.Size([96, 1, 1]).
size mismatch for flowUpsamplerNet.layers.50.actnorm.bias: copying a param with shape torch.Size([1, 96, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 48, 1, 1]).
size mismatch for flowUpsamplerNet.layers.50.actnorm.logs: copying a param with shape torch.Size([1, 96, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 48, 1, 1]).
size mismatch for flowUpsamplerNet.layers.50.invconv.weight: copying a param with shape torch.Size([96, 96]) from checkpoint, the shape in current model is torch.Size([48, 48]).
size mismatch for flowUpsamplerNet.layers.50.affine.fAffine.0.weight: copying a param with shape torch.Size([64, 368, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 344, 3, 3]).
size mismatch for flowUpsamplerNet.layers.50.affine.fAffine.4.weight: copying a param with shape torch.Size([96, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([48, 64, 3, 3]).
size mismatch for flowUpsamplerNet.layers.50.affine.fAffine.4.bias: copying a param with shape torch.Size([96]) from checkpoint, the shape in current model is torch.Size([48]).
size mismatch for flowUpsamplerNet.layers.50.affine.fAffine.4.logs: copying a param with shape torch.Size([96, 1, 1]) from checkpoint, the shape in current model is torch.Size([48, 1, 1]).
size mismatch for flowUpsamplerNet.layers.50.affine.fFeatures.4.weight: copying a param with shape torch.Size([192, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([96, 64, 3, 3]).
size mismatch for flowUpsamplerNet.layers.50.affine.fFeatures.4.bias: copying a param with shape torch.Size([192]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for flowUpsamplerNet.layers.50.affine.fFeatures.4.logs: copying a param with shape torch.Size([192, 1, 1]) from checkpoint, the shape in current model is torch.Size([96, 1, 1]).
size mismatch for flowUpsamplerNet.layers.51.actnorm.bias: copying a param with shape torch.Size([1, 96, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 48, 1, 1]).
size mismatch for flowUpsamplerNet.layers.51.actnorm.logs: copying a param with shape torch.Size([1, 96, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 48, 1, 1]).
size mismatch for flowUpsamplerNet.layers.51.invconv.weight: copying a param with shape torch.Size([96, 96]) from checkpoint, the shape in current model is torch.Size([48, 48]).
size mismatch for flowUpsamplerNet.layers.51.affine.fAffine.0.weight: copying a param with shape torch.Size([64, 368, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 344, 3, 3]).
size mismatch for flowUpsamplerNet.layers.51.affine.fAffine.4.weight: copying a param with shape torch.Size([96, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([48, 64, 3, 3]).
size mismatch for flowUpsamplerNet.layers.51.affine.fAffine.4.bias: copying a param with shape torch.Size([96]) from checkpoint, the shape in current model is torch.Size([48]).
size mismatch for flowUpsamplerNet.layers.51.affine.fAffine.4.logs: copying a param with shape torch.Size([96, 1, 1]) from checkpoint, the shape in current model is torch.Size([48, 1, 1]).
size mismatch for flowUpsamplerNet.layers.51.affine.fFeatures.4.weight: copying a param with shape torch.Size([192, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([96, 64, 3, 3]).
size mismatch for flowUpsamplerNet.layers.51.affine.fFeatures.4.bias: copying a param with shape torch.Size([192]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for flowUpsamplerNet.layers.51.affine.fFeatures.4.logs: copying a param with shape torch.Size([192, 1, 1]) from checkpoint, the shape in current model is torch.Size([96, 1, 1]).
size mismatch for flowUpsamplerNet.layers.52.actnorm.bias: copying a param with shape torch.Size([1, 96, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 48, 1, 1]).
size mismatch for flowUpsamplerNet.layers.52.actnorm.logs: copying a param with shape torch.Size([1, 96, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 48, 1, 1]).
size mismatch for flowUpsamplerNet.layers.52.invconv.weight: copying a param with shape torch.Size([96, 96]) from checkpoint, the shape in current model is torch.Size([48, 48]).
size mismatch for flowUpsamplerNet.layers.52.affine.fAffine.0.weight: copying a param with shape torch.Size([64, 368, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 344, 3, 3]).
size mismatch for flowUpsamplerNet.layers.52.affine.fAffine.4.weight: copying a param with shape torch.Size([96, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([48, 64, 3, 3]).
size mismatch for flowUpsamplerNet.layers.52.affine.fAffine.4.bias: copying a param with shape torch.Size([96]) from checkpoint, the shape in current model is torch.Size([48]).
size mismatch for flowUpsamplerNet.layers.52.affine.fAffine.4.logs: copying a param with shape torch.Size([96, 1, 1]) from checkpoint, the shape in current model is torch.Size([48, 1, 1]).
size mismatch for flowUpsamplerNet.layers.52.affine.fFeatures.4.weight: copying a param with shape torch.Size([192, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([96, 64, 3, 3]).
size mismatch for flowUpsamplerNet.layers.52.affine.fFeatures.4.bias: copying a param with shape torch.Size([192]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for flowUpsamplerNet.layers.52.affine.fFeatures.4.logs: copying a param with shape torch.Size([192, 1, 1]) from checkpoint, the shape in current model is torch.Size([96, 1, 1]).
size mismatch for flowUpsamplerNet.layers.53.actnorm.bias: copying a param with shape torch.Size([1, 96, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 48, 1, 1]).
size mismatch for flowUpsamplerNet.layers.53.actnorm.logs: copying a param with shape torch.Size([1, 96, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 48, 1, 1]).
size mismatch for flowUpsamplerNet.layers.53.invconv.weight: copying a param with shape torch.Size([96, 96]) from checkpoint, the shape in current model is torch.Size([48, 48]).
size mismatch for flowUpsamplerNet.layers.53.affine.fAffine.0.weight: copying a param with shape torch.Size([64, 368, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 344, 3, 3]).
size mismatch for flowUpsamplerNet.layers.53.affine.fAffine.4.weight: copying a param with shape torch.Size([96, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([48, 64, 3, 3]).
size mismatch for flowUpsamplerNet.layers.53.affine.fAffine.4.bias: copying a param with shape torch.Size([96]) from checkpoint, the shape in current model is torch.Size([48]).
size mismatch for flowUpsamplerNet.layers.53.affine.fAffine.4.logs: copying a param with shape torch.Size([96, 1, 1]) from checkpoint, the shape in current model is torch.Size([48, 1, 1]).
size mismatch for flowUpsamplerNet.layers.53.affine.fFeatures.4.weight: copying a param with shape torch.Size([192, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([96, 64, 3, 3]).
size mismatch for flowUpsamplerNet.layers.53.affine.fFeatures.4.bias: copying a param with shape torch.Size([192]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for flowUpsamplerNet.layers.53.affine.fFeatures.4.logs: copying a param with shape torch.Size([192, 1, 1]) from checkpoint, the shape in current model is torch.Size([96, 1, 1]).
size mismatch for flowUpsamplerNet.layers.54.actnorm.bias: copying a param with shape torch.Size([1, 96, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 48, 1, 1]).
size mismatch for flowUpsamplerNet.layers.54.actnorm.logs: copying a param with shape torch.Size([1, 96, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 48, 1, 1]).
size mismatch for flowUpsamplerNet.layers.54.invconv.weight: copying a param with shape torch.Size([96, 96]) from checkpoint, the shape in current model is torch.Size([48, 48]).
size mismatch for flowUpsamplerNet.layers.54.affine.fAffine.0.weight: copying a param with shape torch.Size([64, 368, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 344, 3, 3]).
size mismatch for flowUpsamplerNet.layers.54.affine.fAffine.4.weight: copying a param with shape torch.Size([96, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([48, 64, 3, 3]).
size mismatch for flowUpsamplerNet.layers.54.affine.fAffine.4.bias: copying a param with shape torch.Size([96]) from checkpoint, the shape in current model is torch.Size([48]).
size mismatch for flowUpsamplerNet.layers.54.affine.fAffine.4.logs: copying a param with shape torch.Size([96, 1, 1]) from checkpoint, the shape in current model is torch.Size([48, 1, 1]).
size mismatch for flowUpsamplerNet.layers.54.affine.fFeatures.4.weight: copying a param with shape torch.Size([192, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([96, 64, 3, 3]).
size mismatch for flowUpsamplerNet.layers.54.affine.fFeatures.4.bias: copying a param with shape torch.Size([192]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for flowUpsamplerNet.layers.54.affine.fFeatures.4.logs: copying a param with shape torch.Size([192, 1, 1]) from checkpoint, the shape in current model is torch.Size([96, 1, 1]).
size mismatch for flowUpsamplerNet.layers.55.actnorm.bias: copying a param with shape torch.Size([1, 96, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 48, 1, 1]).
size mismatch for flowUpsamplerNet.layers.55.actnorm.logs: copying a param with shape torch.Size([1, 96, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 48, 1, 1]).
size mismatch for flowUpsamplerNet.layers.55.invconv.weight: copying a param with shape torch.Size([96, 96]) from checkpoint, the shape in current model is torch.Size([48, 48]).
size mismatch for flowUpsamplerNet.layers.55.affine.fAffine.0.weight: copying a param with shape torch.Size([64, 368, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 344, 3, 3]).
size mismatch for flowUpsamplerNet.layers.55.affine.fAffine.4.weight: copying a param with shape torch.Size([96, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([48, 64, 3, 3]).
size mismatch for flowUpsamplerNet.layers.55.affine.fAffine.4.bias: copying a param with shape torch.Size([96]) from checkpoint, the shape in current model is torch.Size([48]).
size mismatch for flowUpsamplerNet.layers.55.affine.fAffine.4.logs: copying a param with shape torch.Size([96, 1, 1]) from checkpoint, the shape in current model is torch.Size([48, 1, 1]).
size mismatch for flowUpsamplerNet.layers.55.affine.fFeatures.4.weight: copying a param with shape torch.Size([192, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([96, 64, 3, 3]).
size mismatch for flowUpsamplerNet.layers.55.affine.fFeatures.4.bias: copying a param with shape torch.Size([192]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for flowUpsamplerNet.layers.55.affine.fFeatures.4.logs: copying a param with shape torch.Size([192, 1, 1]) from checkpoint, the shape in current model is torch.Size([96, 1, 1]).
size mismatch for flowUpsamplerNet.layers.56.actnorm.bias: copying a param with shape torch.Size([1, 96, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 48, 1, 1]).
size mismatch for flowUpsamplerNet.layers.56.actnorm.logs: copying a param with shape torch.Size([1, 96, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 48, 1, 1]).
size mismatch for flowUpsamplerNet.layers.56.invconv.weight: copying a param with shape torch.Size([96, 96]) from checkpoint, the shape in current model is torch.Size([48, 48]).
size mismatch for flowUpsamplerNet.layers.56.affine.fAffine.0.weight: copying a param with shape torch.Size([64, 368, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 344, 3, 3]).
size mismatch for flowUpsamplerNet.layers.56.affine.fAffine.4.weight: copying a param with shape torch.Size([96, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([48, 64, 3, 3]).
size mismatch for flowUpsamplerNet.layers.56.affine.fAffine.4.bias: copying a param with shape torch.Size([96]) from checkpoint, the shape in current model is torch.Size([48]).
size mismatch for flowUpsamplerNet.layers.56.affine.fAffine.4.logs: copying a param with shape torch.Size([96, 1, 1]) from checkpoint, the shape in current model is torch.Size([48, 1, 1]).
size mismatch for flowUpsamplerNet.layers.56.affine.fFeatures.4.weight: copying a param with shape torch.Size([192, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([96, 64, 3, 3]).
size mismatch for flowUpsamplerNet.layers.56.affine.fFeatures.4.bias: copying a param with shape torch.Size([192]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for flowUpsamplerNet.layers.56.affine.fFeatures.4.logs: copying a param with shape torch.Size([192, 1, 1]) from checkpoint, the shape in current model is torch.Size([96, 1, 1]).
size mismatch for flowUpsamplerNet.layers.57.actnorm.bias: copying a param with shape torch.Size([1, 96, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 48, 1, 1]).
size mismatch for flowUpsamplerNet.layers.57.actnorm.logs: copying a param with shape torch.Size([1, 96, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 48, 1, 1]).
size mismatch for flowUpsamplerNet.layers.57.invconv.weight: copying a param with shape torch.Size([96, 96]) from checkpoint, the shape in current model is torch.Size([48, 48]).
size mismatch for flowUpsamplerNet.layers.57.affine.fAffine.0.weight: copying a param with shape torch.Size([64, 368, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 344, 3, 3]).
size mismatch for flowUpsamplerNet.layers.57.affine.fAffine.4.weight: copying a param with shape torch.Size([96, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([48, 64, 3, 3]).
size mismatch for flowUpsamplerNet.layers.57.affine.fAffine.4.bias: copying a param with shape torch.Size([96]) from checkpoint, the shape in current model is torch.Size([48]).
size mismatch for flowUpsamplerNet.layers.57.affine.fAffine.4.logs: copying a param with shape torch.Size([96, 1, 1]) from checkpoint, the shape in current model is torch.Size([48, 1, 1]).
size mismatch for flowUpsamplerNet.layers.57.affine.fFeatures.4.weight: copying a param with shape torch.Size([192, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([96, 64, 3, 3]).
size mismatch for flowUpsamplerNet.layers.57.affine.fFeatures.4.bias: copying a param with shape torch.Size([192]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for flowUpsamplerNet.layers.57.affine.fFeatures.4.logs: copying a param with shape torch.Size([192, 1, 1]) from checkpoint, the shape in current model is torch.Size([96, 1, 1]).