Very off result despite same packages version
kayabutterkun opened this issue · comments
Hello! I am using the same package version but my results are very off. What am I missing?
Python 3.6.13
PyTorch version: 1.1.0
Torchvision version: 0.3.0
CV2 version: 3.4.3
Logs:
------------ Options -------------
batchSize: 1
data_type: 32
dataroot: /datasets/john/tryOn/Tests
display_winsize: 512
fineSize: 512
gen_checkpoint: /datasets/john/tryOn/Flow-Style-VTON-Checkpoints/PFAFN_gen_epoch_101.pth
gpu_ids: [0]
input_nc: 3
isTrain: False
loadSize: 512
max_dataset_size: inf
nThreads: 1
name: demo
no_flip: False
norm: instance
output: /work/output/2024-01-26-1434
output_nc: 3
phase: test
resize_or_crop: None
serial_batches: False
test_pair: /datasets/john/tryOn/Tests/test_pairs.txt
tf_log: False
use_dropout: False
verbose: False
warp_checkpoint: /datasets/john/tryOn/Flow-Style-VTON-Checkpoints/PFAFN_warp_epoch_101.pth
-------------- End ----------------
CustomDatasetDataLoader
dataset [AlignedDataset] was created
6
AFWM(
(image_features): FeatureEncoder(
(encoders): ModuleList(
(0): Sequential(
(0): DownSample(
(block): Sequential(
(0): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(3, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
)
)
(1): ResBlock(
(block): Sequential(
(0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU(inplace)
(5): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(2): ResBlock(
(block): Sequential(
(0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU(inplace)
(5): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
)
(1): Sequential(
(0): DownSample(
(block): Sequential(
(0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
)
)
(1): ResBlock(
(block): Sequential(
(0): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(3): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU(inplace)
(5): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(2): ResBlock(
(block): Sequential(
(0): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(3): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU(inplace)
(5): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
)
(2): Sequential(
(0): DownSample(
(block): Sequential(
(0): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
)
)
(1): ResBlock(
(block): Sequential(
(0): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU(inplace)
(5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(2): ResBlock(
(block): Sequential(
(0): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU(inplace)
(5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
)
(3): Sequential(
(0): DownSample(
(block): Sequential(
(0): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
)
)
(1): ResBlock(
(block): Sequential(
(0): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU(inplace)
(5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(2): ResBlock(
(block): Sequential(
(0): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU(inplace)
(5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
)
(4): Sequential(
(0): DownSample(
(block): Sequential(
(0): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
)
)
(1): ResBlock(
(block): Sequential(
(0): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU(inplace)
(5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(2): ResBlock(
(block): Sequential(
(0): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU(inplace)
(5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
)
)
)
(cond_features): FeatureEncoder(
(encoders): ModuleList(
(0): Sequential(
(0): DownSample(
(block): Sequential(
(0): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(3, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
)
)
(1): ResBlock(
(block): Sequential(
(0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU(inplace)
(5): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(2): ResBlock(
(block): Sequential(
(0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU(inplace)
(5): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
)
(1): Sequential(
(0): DownSample(
(block): Sequential(
(0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
)
)
(1): ResBlock(
(block): Sequential(
(0): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(3): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU(inplace)
(5): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(2): ResBlock(
(block): Sequential(
(0): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(3): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU(inplace)
(5): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
)
(2): Sequential(
(0): DownSample(
(block): Sequential(
(0): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
)
)
(1): ResBlock(
(block): Sequential(
(0): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU(inplace)
(5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(2): ResBlock(
(block): Sequential(
(0): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU(inplace)
(5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
)
(3): Sequential(
(0): DownSample(
(block): Sequential(
(0): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
)
)
(1): ResBlock(
(block): Sequential(
(0): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU(inplace)
(5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(2): ResBlock(
(block): Sequential(
(0): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU(inplace)
(5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
)
(4): Sequential(
(0): DownSample(
(block): Sequential(
(0): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
)
)
(1): ResBlock(
(block): Sequential(
(0): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU(inplace)
(5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(2): ResBlock(
(block): Sequential(
(0): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU(inplace)
(5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
)
)
)
(image_FPN): RefinePyramid(
(adaptive): ModuleList(
(0): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
(1): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
(2): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
(3): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1))
(4): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1))
)
(smooth): ModuleList(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(4): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
)
(cond_FPN): RefinePyramid(
(adaptive): ModuleList(
(0): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
(1): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
(2): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
(3): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1))
(4): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1))
)
(smooth): ModuleList(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(4): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
)
(aflow_net): AFlowNet(
(netRefine): ModuleList(
(0): Sequential(
(0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
(2): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): LeakyReLU(negative_slope=0.1)
(4): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(5): LeakyReLU(negative_slope=0.1)
(6): Conv2d(32, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(1): Sequential(
(0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
(2): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): LeakyReLU(negative_slope=0.1)
(4): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(5): LeakyReLU(negative_slope=0.1)
(6): Conv2d(32, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(2): Sequential(
(0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
(2): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): LeakyReLU(negative_slope=0.1)
(4): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(5): LeakyReLU(negative_slope=0.1)
(6): Conv2d(32, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(3): Sequential(
(0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
(2): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): LeakyReLU(negative_slope=0.1)
(4): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(5): LeakyReLU(negative_slope=0.1)
(6): Conv2d(32, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(4): Sequential(
(0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
(2): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): LeakyReLU(negative_slope=0.1)
(4): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(5): LeakyReLU(negative_slope=0.1)
(6): Conv2d(32, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
)
(netStyle): ModuleList(
(0): StyledConvBlock(
(conv0): ModulatedConv2d(
(mlp_class_std): EqualLinear(
(linear): Linear(in_features=256, out_features=256, bias=True)
)
(padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
)
(actvn0): LeakyReLU(negative_slope=0.2, inplace)
(conv1): ModulatedConv2d(
(mlp_class_std): EqualLinear(
(linear): Linear(in_features=256, out_features=49, bias=True)
)
(padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
)
(actvn1): LeakyReLU(negative_slope=0.2, inplace)
)
(1): StyledConvBlock(
(conv0): ModulatedConv2d(
(mlp_class_std): EqualLinear(
(linear): Linear(in_features=256, out_features=256, bias=True)
)
(padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
)
(actvn0): LeakyReLU(negative_slope=0.2, inplace)
(conv1): ModulatedConv2d(
(mlp_class_std): EqualLinear(
(linear): Linear(in_features=256, out_features=49, bias=True)
)
(padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
)
(actvn1): LeakyReLU(negative_slope=0.2, inplace)
)
(2): StyledConvBlock(
(conv0): ModulatedConv2d(
(mlp_class_std): EqualLinear(
(linear): Linear(in_features=256, out_features=256, bias=True)
)
(padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
)
(actvn0): LeakyReLU(negative_slope=0.2, inplace)
(conv1): ModulatedConv2d(
(mlp_class_std): EqualLinear(
(linear): Linear(in_features=256, out_features=49, bias=True)
)
(padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
)
(actvn1): LeakyReLU(negative_slope=0.2, inplace)
)
(3): StyledConvBlock(
(conv0): ModulatedConv2d(
(mlp_class_std): EqualLinear(
(linear): Linear(in_features=256, out_features=256, bias=True)
)
(padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
)
(actvn0): LeakyReLU(negative_slope=0.2, inplace)
(conv1): ModulatedConv2d(
(mlp_class_std): EqualLinear(
(linear): Linear(in_features=256, out_features=49, bias=True)
)
(padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
)
(actvn1): LeakyReLU(negative_slope=0.2, inplace)
)
(4): StyledConvBlock(
(conv0): ModulatedConv2d(
(mlp_class_std): EqualLinear(
(linear): Linear(in_features=256, out_features=256, bias=True)
)
(padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
)
(actvn0): LeakyReLU(negative_slope=0.2, inplace)
(conv1): ModulatedConv2d(
(mlp_class_std): EqualLinear(
(linear): Linear(in_features=256, out_features=49, bias=True)
)
(padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
)
(actvn1): LeakyReLU(negative_slope=0.2, inplace)
)
)
(netF): ModuleList(
(0): Styled_F_ConvBlock(
(conv0): ModulatedConv2d(
(mlp_class_std): EqualLinear(
(linear): Linear(in_features=256, out_features=49, bias=True)
)
(padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
)
(actvn0): LeakyReLU(negative_slope=0.2, inplace)
(conv1): ModulatedConv2d(
(mlp_class_std): EqualLinear(
(linear): Linear(in_features=256, out_features=128, bias=True)
)
(padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
)
)
(1): Styled_F_ConvBlock(
(conv0): ModulatedConv2d(
(mlp_class_std): EqualLinear(
(linear): Linear(in_features=256, out_features=49, bias=True)
)
(padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
)
(actvn0): LeakyReLU(negative_slope=0.2, inplace)
(conv1): ModulatedConv2d(
(mlp_class_std): EqualLinear(
(linear): Linear(in_features=256, out_features=128, bias=True)
)
(padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
)
)
(2): Styled_F_ConvBlock(
(conv0): ModulatedConv2d(
(mlp_class_std): EqualLinear(
(linear): Linear(in_features=256, out_features=49, bias=True)
)
(padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
)
(actvn0): LeakyReLU(negative_slope=0.2, inplace)
(conv1): ModulatedConv2d(
(mlp_class_std): EqualLinear(
(linear): Linear(in_features=256, out_features=128, bias=True)
)
(padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
)
)
(3): Styled_F_ConvBlock(
(conv0): ModulatedConv2d(
(mlp_class_std): EqualLinear(
(linear): Linear(in_features=256, out_features=49, bias=True)
)
(padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
)
(actvn0): LeakyReLU(negative_slope=0.2, inplace)
(conv1): ModulatedConv2d(
(mlp_class_std): EqualLinear(
(linear): Linear(in_features=256, out_features=128, bias=True)
)
(padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
)
)
(4): Styled_F_ConvBlock(
(conv0): ModulatedConv2d(
(mlp_class_std): EqualLinear(
(linear): Linear(in_features=256, out_features=49, bias=True)
)
(padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
)
(actvn0): LeakyReLU(negative_slope=0.2, inplace)
(conv1): ModulatedConv2d(
(mlp_class_std): EqualLinear(
(linear): Linear(in_features=256, out_features=128, bias=True)
)
(padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
)
)
)
(cond_style): Sequential(
(0): Conv2d(256, 128, kernel_size=(8, 6), stride=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
)
(image_style): Sequential(
(0): Conv2d(256, 128, kernel_size=(8, 6), stride=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
)
)
)
###############################
/datasets/john/tryOn/Flow-Style-VTON-Checkpoints/PFAFN_warp_epoch_101.pth
/datasets/john/tryOn/Flow-Style-VTON-Checkpoints/PFAFN_warp_epoch_101.pth
No checkpoint!
/datasets/john/tryOn/Flow-Style-VTON-Checkpoints/PFAFN_gen_epoch_101.pth
No checkpoint!
/usr/local/lib/python3.6/site-packages/torch/nn/functional.py:2539: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.
"See the documentation of nn.Upsample for details.".format(mode))
['016962_0.jpg']
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