SReC
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caoscott
what's the matter？
root@27e30a88143c:/SReC# python3 -um src.encode
Using torchac: True
['/SReC/src/encode.py']
Loaded model from models/openimages.pth.
Epoch: 49
ModuleList(
(0): ModuleList(
(0): Identity()
(1): Upsampler(
(0): Conv2d(64, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): PixelShuffle(upscale_factor=2)
(2): Identity()
)
(2): Upsampler(
(0): Conv2d(64, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): PixelShuffle(upscale_factor=2)
(2): Identity()
)
)
(1): ModuleList(
(0): StrongPixDecoder(
(loss_fn): DiscretizedMixLogisticLoss(DMLL: x=(0, 255), L=256, coeffs=True, P=4, bin_width=1.0)
(rgb_decs): ModuleList(
(0): EDSRDec(
(head): Conv2d(3, 64, kernel_size=(1, 1), stride=(1, 1))
(body): Sequential(
(0): ResBlock(Conv(64x3)/Act/Conv(64x3))
(1): ResBlock(Conv(64x3)/Act/Conv(64x3))
(2): ResBlock(Conv(64x3)/Act/Conv(64x3))
(3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(tail): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))
)
(1): EDSRDec(
(head): Conv2d(6, 64, kernel_size=(1, 1), stride=(1, 1))
(body): Sequential(
(0): ResBlock(Conv(64x3)/Act/Conv(64x3))
(1): ResBlock(Conv(64x3)/Act/Conv(64x3))
(2): ResBlock(Conv(64x3)/Act/Conv(64x3))
(3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(tail): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))
)
(2): EDSRDec(
(head): Conv2d(9, 64, kernel_size=(1, 1), stride=(1, 1))
(body): Sequential(
(0): ResBlock(Conv(64x3)/Act/Conv(64x3))
(1): ResBlock(Conv(64x3)/Act/Conv(64x3))
(2): ResBlock(Conv(64x3)/Act/Conv(64x3))
(3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(tail): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))
)
)
(mix_logits_prob_clf): ModuleList(
(0): AtrousProbabilityClassifier(C=3; K=10; Kp=120; rates=1,2,4)
(1): AtrousProbabilityClassifier(C=3; K=10; Kp=120; rates=1,2,4)
(2): AtrousProbabilityClassifier(C=3; K=10; Kp=120; rates=1,2,4)
)
(feat_convs): ModuleList(
(0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
)
(1): StrongPixDecoder(
(loss_fn): DiscretizedMixLogisticLoss(DMLL: x=(0, 255), L=256, coeffs=True, P=4, bin_width=1.0)
(rgb_decs): ModuleList(
(0): EDSRDec(
(head): Conv2d(3, 64, kernel_size=(1, 1), stride=(1, 1))
(body): Sequential(
(0): ResBlock(Conv(64x3)/Act/Conv(64x3))
(1): ResBlock(Conv(64x3)/Act/Conv(64x3))
(2): ResBlock(Conv(64x3)/Act/Conv(64x3))
(3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(tail): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))
)
(1): EDSRDec(
(head): Conv2d(6, 64, kernel_size=(1, 1), stride=(1, 1))
(body): Sequential(
(0): ResBlock(Conv(64x3)/Act/Conv(64x3))
(1): ResBlock(Conv(64x3)/Act/Conv(64x3))
(2): ResBlock(Conv(64x3)/Act/Conv(64x3))
(3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(tail): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))
)
(2): EDSRDec(
(head): Conv2d(9, 64, kernel_size=(1, 1), stride=(1, 1))
(body): Sequential(
(0): ResBlock(Conv(64x3)/Act/Conv(64x3))
(1): ResBlock(Conv(64x3)/Act/Conv(64x3))
(2): ResBlock(Conv(64x3)/Act/Conv(64x3))
(3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(tail): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))
)
)
(mix_logits_prob_clf): ModuleList(
(0): AtrousProbabilityClassifier(C=3; K=10; Kp=120; rates=1,2,4)
(1): AtrousProbabilityClassifier(C=3; K=10; Kp=120; rates=1,2,4)
(2): AtrousProbabilityClassifier(C=3; K=10; Kp=120; rates=1,2,4)
)
(feat_convs): ModuleList(
(0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
)
(2): StrongPixDecoder(
(loss_fn): DiscretizedMixLogisticLoss(DMLL: x=(0, 255), L=256, coeffs=True, P=4, bin_width=1.0)
(rgb_decs): ModuleList(
(0): EDSRDec(
(head): Conv2d(3, 64, kernel_size=(1, 1), stride=(1, 1))
(body): Sequential(
(0): ResBlock(Conv(64x3)/Act/Conv(64x3))
(1): ResBlock(Conv(64x3)/Act/Conv(64x3))
(2): ResBlock(Conv(64x3)/Act/Conv(64x3))
(3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(tail): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))
)
(1): EDSRDec(
(head): Conv2d(6, 64, kernel_size=(1, 1), stride=(1, 1))
(body): Sequential(
(0): ResBlock(Conv(64x3)/Act/Conv(64x3))
(1): ResBlock(Conv(64x3)/Act/Conv(64x3))
(2): ResBlock(Conv(64x3)/Act/Conv(64x3))
(3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(tail): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))
)
(2): EDSRDec(
(head): Conv2d(9, 64, kernel_size=(1, 1), stride=(1, 1))
(body): Sequential(
(0): ResBlock(Conv(64x3)/Act/Conv(64x3))
(1): ResBlock(Conv(64x3)/Act/Conv(64x3))
(2): ResBlock(Conv(64x3)/Act/Conv(64x3))
(3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(tail): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))
)
)
(mix_logits_prob_clf): ModuleList(
(0): AtrousProbabilityClassifier(C=3; K=10; Kp=120; rates=1,2,4)
(1): AtrousProbabilityClassifier(C=3; K=10; Kp=120; rates=1,2,4)
(2): AtrousProbabilityClassifier(C=3; K=10; Kp=120; rates=1,2,4)
)
(feat_convs): ModuleList(
(0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
)
)
)
Loaded directory with 12 images
Traceback (most recent call last):
File "/opt/conda/lib/python3.6/runpy.py", line 193, in _run_module_as_main
"main", mod_spec)
File "/opt/conda/lib/python3.6/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/SReC/src/encode.py", line 141, in
