holywu / vs-femasr Goto Github PK

# Imports
import vapoursynth as vs
# getting Vapoursynth core
core = vs.core
import site
import os
import ctypes
# Adding torch dependencies to PATH
path = site.getsitepackages()[0]+'/torch_dependencies/'
ctypes.windll.kernel32.SetDllDirectoryW(path)
path = path.replace('\\', '/')
os.environ["PATH"] = path + os.pathsep + os.environ["PATH"]
# Loading Plugins
core.std.LoadPlugin(path="i:/Hybrid/64bit/vsfilters/Support/fmtconv.dll")
core.std.LoadPlugin(path="i:/Hybrid/64bit/vsfilters/SourceFilter/LSmashSource/vslsmashsource.dll")
# source: 'C:\Users\Selur\Desktop\howToSharpenThis.mp4'
# current color space: YUV420P10, bit depth: 10, resolution: 3840x2160, fps: 50, color matrix: 709, yuv luminance scale: limited, scanorder: progressive
# Loading C:\Users\Selur\Desktop\howToSharpenThis.mp4 using LibavSMASHSource
clip = core.lsmas.LibavSMASHSource(source="C:/Users/Selur/Desktop/howToSharpenThis.mp4")
# Setting color matrix to 709.
clip = core.std.SetFrameProps(clip, _Matrix=1)
clip = clip if not core.text.FrameProps(clip,'_Transfer') else core.std.SetFrameProps(clip, _Transfer=1)
clip = clip if not core.text.FrameProps(clip,'_Primaries') else core.std.SetFrameProps(clip, _Primaries=9)
# Setting color range to TV (limited) range.
clip = core.std.SetFrameProp(clip=clip, prop="_ColorRange", intval=1)
# making sure frame rate is set to 50
clip = core.std.AssumeFPS(clip=clip, fpsnum=50, fpsden=1)
clip = core.std.SetFrameProp(clip=clip, prop="_FieldBased", intval=0)
# cropping the video to 820x820
clip = core.std.CropRel(clip=clip, left=1020, right=2000, top=540, bottom=800)

clip = core.resize.Bicubic(clip=clip, format=vs.RGBS, matrix_in_s="470bg", range_s="limited")
org = core.resize.Bicubic(clip=clip, width=1640, height=1640)

from vsfemasr import femasr
clip = femasr(clip)

# adjusting output color from: RGBS to YUV420P10 for x265Model
clip = core.resize.Bicubic(clip=clip, format=vs.YUV420P10, matrix_s="470bg", range_s="limited", dither_type="error_diffusion")
org = core.resize.Bicubic(clip=org, format=vs.YUV420P10, matrix_s="470bg", range_s="limited", dither_type="error_diffusion")
clip = core.std.StackHorizontal([org.text.Text("Original"), clip.text.Text("Filtered")])

# Output
clip.set_output()

clip = core.resize.Bicubic(clip=clip, format=vs.RGBH, matrix_in_s="470bg", range_s="limited")




from vsfemasr import femasr


clip = femasr(clip, trt=True, trt_cache_path=r"G:\Temp")


clip = femasr(clip, trt=True, trt_cache_path="G:/Temp")

Python exception: 

Traceback (most recent call last):
File "src\cython\vapoursynth.pyx", line 2866, in vapoursynth._vpy_evaluate
File "src\cython\vapoursynth.pyx", line 2867, in vapoursynth._vpy_evaluate
File "C:\Users\Selur\Desktop\test_2.vpy", line 38, in 
clip = femasr(clip, trt=True, trt_cache_path="G:/Temp")
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\autograd\grad_mode.py", line 27, in decorate_context
return func(*args, **kwargs)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\vsfemasr\__init__.py", line 196, in femasr
module = lowerer(
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch_tensorrt\fx\lower.py", line 323, in __call__
return do_lower(module, inputs)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch_tensorrt\fx\passes\pass_utils.py", line 117, in pass_with_validation
processed_module = pass_(module, input, *args, **kwargs)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch_tensorrt\fx\lower.py", line 320, in do_lower
lower_result = pm(module)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\fx\passes\pass_manager.py", line 240, in __call__
out = _pass(out)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\fx\passes\pass_manager.py", line 240, in __call__
out = _pass(out)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch_tensorrt\fx\passes\lower_pass_manager_builder.py", line 167, in lower_func
lowered_module = self._lower_func(
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch_tensorrt\fx\lower.py", line 180, in lower_pass
interp_res: TRTInterpreterResult = interpreter(mod, input, module_name)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch_tensorrt\fx\lower.py", line 132, in __call__
interp_result: TRTInterpreterResult = interpreter.run(
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch_tensorrt\fx\fx2trt.py", line 252, in run
assert engine
AssertionError

Python exception: 

Traceback (most recent call last):
File "src\cython\vapoursynth.pyx", line 2866, in vapoursynth._vpy_evaluate
File "src\cython\vapoursynth.pyx", line 2867, in vapoursynth._vpy_evaluate
File "C:\Users\Selur\Desktop\test_2.vpy", line 38, in 
clip = femasr(clip, trt=True)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\autograd\grad_mode.py", line 27, in decorate_context
return func(*args, **kwargs)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\vsfemasr\__init__.py", line 196, in femasr
module = lowerer(
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch_tensorrt\fx\lower.py", line 323, in __call__
return do_lower(module, inputs)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch_tensorrt\fx\passes\pass_utils.py", line 117, in pass_with_validation
processed_module = pass_(module, input, *args, **kwargs)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch_tensorrt\fx\lower.py", line 320, in do_lower
lower_result = pm(module)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\fx\passes\pass_manager.py", line 240, in __call__
out = _pass(out)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\fx\passes\pass_manager.py", line 240, in __call__
out = _pass(out)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch_tensorrt\fx\passes\lower_pass_manager_builder.py", line 167, in lower_func
lowered_module = self._lower_func(
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch_tensorrt\fx\lower.py", line 180, in lower_pass
interp_res: TRTInterpreterResult = interpreter(mod, input, module_name)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch_tensorrt\fx\lower.py", line 132, in __call__
interp_result: TRTInterpreterResult = interpreter.run(
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch_tensorrt\fx\fx2trt.py", line 252, in run
assert engine
AssertionError

# Imports
import vapoursynth as vs
import os
import ctypes
# Loading Support Files
Dllref = ctypes.windll.LoadLibrary("i:/Hybrid/64bit/vsfilters/Support/libfftw3f-3.dll")
import sys
# getting Vapoursynth core
core = vs.core
# Import scripts folder
scriptPath = 'i:/Hybrid/64bit/vsscripts'
sys.path.insert(0, os.path.abspath(scriptPath))
import site
# Adding torch dependencies to PATH
path = site.getsitepackages()[0]+'/torch_dependencies/bin/'
ctypes.windll.kernel32.SetDllDirectoryW(path)
path = path.replace('\\', '/')
os.environ["PATH"] = path + os.pathsep + os.environ["PATH"]
os.environ["CUDA_MODULE_LOADING"] = "LAZY"
# Loading Plugins
core.std.LoadPlugin(path="i:/Hybrid/64bit/vsfilters/GrainFilter/RemoveGrain/RemoveGrainVS.dll")
core.std.LoadPlugin(path="i:/Hybrid/64bit/vsfilters/GrainFilter/AddGrain/AddGrain.dll")
core.std.LoadPlugin(path="i:/Hybrid/64bit/vsfilters/DenoiseFilter/DFTTest/DFTTest.dll")
core.std.LoadPlugin(path="i:/Hybrid/64bit/vsfilters/DenoiseFilter/FFT3DFilter/fft3dfilter.dll")
core.std.LoadPlugin(path="i:/Hybrid/64bit/vsfilters/Support/EEDI3m_opencl.dll")
core.std.LoadPlugin(path="i:/Hybrid/64bit/vsfilters/ResizeFilter/nnedi3/NNEDI3CL.dll")
core.std.LoadPlugin(path="i:/Hybrid/64bit/vsfilters/Support/libmvtools.dll")
core.std.LoadPlugin(path="i:/Hybrid/64bit/vsfilters/Support/scenechange.dll")
core.std.LoadPlugin(path="i:/Hybrid/64bit/vsfilters/Support/fmtconv.dll")
core.std.LoadPlugin(path="i:/Hybrid/64bit/vsfilters/MiscFilter/MiscFilters/MiscFilters.dll")
core.std.LoadPlugin(path="i:/Hybrid/64bit/vsfilters/DeinterlaceFilter/Bwdif/Bwdif.dll")
core.std.LoadPlugin(path="i:/Hybrid/64bit/vsfilters/SourceFilter/DGDecNV/DGDecodeNV.dll")
# Import scripts
import havsfunc
# source: 'G:\TestClips&Co\1983-i.mkv'
# current color space: YUV420P8, bit depth: 8, resolution: 720x576, fps: 25, color matrix: 470bg, yuv luminance scale: limited, scanorder: top field first
# Loading G:\TestClips&Co\1983-i.mkv using DGSource
clip = core.dgdecodenv.DGSource("J:/tmp/mkv_85eb1ac8734d03d3e24c3850e2e9287a_853323747.dgi",fieldop=0)# 25 fps, scanorder: top field first
# Setting detected color matrix (470bg).
clip = core.std.SetFrameProps(clip, _Matrix=5)
# Setting color transfer info (470bg), when it is not set
clip = clip if not core.text.FrameProps(clip,'_Transfer') else core.std.SetFrameProps(clip, _Transfer=5)
# Setting color primaries info (), when it is not set
clip = clip if not core.text.FrameProps(clip,'_Primaries') else core.std.SetFrameProps(clip, _Primaries=5)
# Setting color range to TV (limited) range.
clip = core.std.SetFrameProp(clip=clip, prop="_ColorRange", intval=1)
# making sure frame rate is set to 25
clip = core.std.AssumeFPS(clip=clip, fpsnum=25, fpsden=1)
clip = core.std.SetFrameProp(clip=clip, prop="_FieldBased", intval=2) # tff
# Deinterlacing using QTGMC
clip = havsfunc.QTGMC(Input=clip, Preset="Fast", TFF=True, opencl=True) # new fps: 50
# Making sure content is preceived as frame based
clip = core.std.SetFrameProp(clip=clip, prop="_FieldBased", intval=0) # progressive
from vsfemasr import femasr as FeMaSR
# adjusting color space from YUV420P8 to RGBH for VsFeMaSR
clip = core.resize.Bicubic(clip=clip, format=vs.RGBH, matrix_in_s="470bg", range_s="limited")
# resizing using FeMaSR
clip = FeMaSR(clip=clip, device_index=0, trt=True, trt_cache_path=r"J:\tmp") # 1440x1152
# resizing 1440x1152 to 1280x960
# adjusting resizing
clip = core.resize.Bicubic(clip=clip, format=vs.RGBS, range_s="limited")
clip = core.fmtc.resample(clip=clip, w=1280, h=960, kernel="lanczos", interlaced=False, interlacedd=False)
# adjusting output color from: RGBS to YUV420P10 for x265Model
clip = core.resize.Bicubic(clip=clip, format=vs.YUV420P10, matrix_s="470bg", range_s="limited", dither_type="error_diffusion")
# set output frame rate to 50fps (progressive)
clip = core.std.AssumeFPS(clip=clip, fpsnum=50, fpsden=1)
# Output
clip.set_output()

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2023-03-26 17:12:10.862 : Found bad pattern: y.reshape((x, ...)). Reshape node: reshape_568 : Found bad pattern: y.reshape((x, ...)). Reshape node: reshape_569 : Found bad pattern: y.reshape((x, ...)). Reshape node: reshape_569 : Found bad pattern: y.reshape((x, ...)). Reshape node: reshape_576 : Found bad pattern: y.reshape((x, ...)). Reshape node: reshape_576 : Found bad pattern: y.reshape((x, ...)). Reshape node: reshape_579 : Found bad pattern: y.reshape((x, ...)). Reshape node: reshape_579 : Found bad pattern: y.reshape((x, ...)). Reshape node: reshape_581 : Found bad pattern: y.reshape((x, ...)). Reshape node: reshape_581 : Found bad pattern: y.reshape((x, ...)). Reshape node: reshape_582 : Found bad pattern: y.reshape((x, ...)). Reshape node: reshape_582 : Found bad pattern: y.reshape((x, ...)). Reshape node: reshape_589 : Found bad pattern: y.reshape((x, ...)). Reshape node: reshape_589 : Found bad pattern: y.reshape((x, ...)). Reshape node: reshape_592 : Found bad pattern: y.reshape((x, ...)). Reshape node: reshape_592 : Found bad pattern: y.reshape((x, ...)). Reshape node: reshape_594 : Found bad pattern: y.reshape((x, ...)). Reshape node: reshape_594 : Found bad pattern: y.reshape((x, ...)). Reshape node: reshape_595 : Found bad pattern: y.reshape((x, ...)). Reshape node: reshape_595 : Found bad pattern: y.reshape((x, ...)). Reshape node: reshape_602 : Found bad pattern: y.reshape((x, ...)). Reshape node: reshape_602 : Found bad pattern: y.reshape((x, ...)). Reshape node: reshape_605 : Found bad pattern: y.reshape((x, ...)). Reshape node: reshape_605 : Found bad pattern: y.reshape((x, ...)). Reshape node: reshape_607 : Found bad pattern: y.reshape((x, ...)). Reshape node: reshape_607 : Found bad pattern: y.reshape((x, ...)). Reshape node: reshape_608 : Found bad pattern: y.reshape((x, ...)). Reshape node: reshape_608 : Found bad pattern: y.reshape((x, ...)). Reshape node: reshape_615 : Found bad pattern: y.reshape((x, ...)). Reshape node: reshape_615 : Found bad pattern: y.reshape((x, ...)). Reshape node: reshape_618 : Found bad pattern: y.reshape((x, ...)). Reshape node: reshape_618 : Found bad pattern: y.reshape((x, ...)). Reshape node: reshape_620 : Found bad pattern: y.reshape((x, ...)). Reshape node: reshape_620 : Found bad pattern: y.reshape((x, ...)). Reshape node: reshape_621 : Found bad pattern: y.reshape((x, ...)). Reshape node: reshape_621 : Found bad pattern: y.reshape((x, ...)). Reshape node: reshape_628 : Found bad pattern: y.reshape((x, ...)). Reshape node: reshape_628 : Found bad pattern: y.reshape((x, ...)). Reshape node: reshape_631 : Found bad pattern: y.reshape((x, ...)). Reshape node: reshape_631 : Found bad pattern: y.reshape((x, ...)). Reshape node: reshape_633 : Found bad pattern: y.reshape((x, ...)). Reshape node: reshape_633 : Found bad pattern: y.reshape((x, ...)). Reshape node: reshape_634 : Found bad pattern: y.reshape((x, ...)). Reshape node: reshape_634 : Found bad pattern: y.reshape((x, ...)). Reshape node: reshape_635 : Found bad pattern: y.reshape((x, ...)). Reshape node: reshape_635 Now lowering submodule _run_on_acc_0 Now lowering submodule _run_on_acc_0 split_name=_run_on_acc_0, input_specs=[InputTensorSpec(shape=torch.Size([1, 3, 576, 736]), dtype=torch.float16, device=device(type='cuda', index=0), shape_ranges=[], has_batch_dim=True)] split_name=_run_on_acc_0, input_specs=[InputTensorSpec(shape=torch.Size([1, 3, 576, 736]), dtype=torch.float16, device=device(type='cuda', index=0), shape_ranges=[], has_batch_dim=True)] Timing cache is used! Timing cache is used! TRT INetwork construction elapsed time: 0:00:00.001000 TRT INetwork construction elapsed time: 0:00:00.001000
2023-03-26 17:12:16.295 Build TRT engine elapsed time: 0:00:02.024681 Build TRT engine elapsed time: 0:00:02.024681 Lowering submodule _run_on_acc_0 elapsed time 0:00:03.686569 Lowering submodule _run_on_acc_0 elapsed time 0:00:03.686569 Now lowering submodule _run_on_acc_2 Now lowering submodule _run_on_acc_2 split_name=_run_on_acc_2, input_specs=[InputTensorSpec(shape=torch.Size([1, 256, 288, 368]), dtype=torch.float16, device=device(type='cuda', index=0), shape_ranges=[], has_batch_dim=True)] split_name=_run_on_acc_2, input_specs=[InputTensorSpec(shape=torch.Size([1, 256, 288, 368]), dtype=torch.float16, device=device(type='cuda', index=0), shape_ranges=[], has_batch_dim=True)] Timing cache is used! Timing cache is used! TRT INetwork construction elapsed time: 0:00:00.001505 TRT INetwork construction elapsed time: 0:00:00.001505
2023-03-26 17:12:37.284 Build TRT engine elapsed time: 0:00:20.702133 Build TRT engine elapsed time: 0:00:20.702133 Lowering submodule _run_on_acc_2 elapsed time 0:00:20.896665 Lowering submodule _run_on_acc_2 elapsed time 0:00:20.896665 Now lowering submodule _run_on_acc_4 Now lowering submodule _run_on_acc_4 split_name=_run_on_acc_4, input_specs=[InputTensorSpec(shape=torch.Size([1, 256, 288, 368]), dtype=torch.float16, device=device(type='cuda', index=0), shape_ranges=[], has_batch_dim=True), InputTensorSpec(shape=torch.Size([1, 256, 288, 368]), dtype=torch.float16, device=device(type='cuda', index=0), shape_ranges=[], has_batch_dim=True)] split_name=_run_on_acc_4, input_specs=[InputTensorSpec(shape=torch.Size([1, 256, 288, 368]), dtype=torch.float16, device=device(type='cuda', index=0), shape_ranges=[], has_batch_dim=True), InputTensorSpec(shape=torch.Size([1, 256, 288, 368]), dtype=torch.float16, device=device(type='cuda', index=0), shape_ranges=[], has_batch_dim=True)] Timing cache is used! Timing cache is used! TRT INetwork construction elapsed time: 0:00:00.001001 TRT INetwork construction elapsed time: 0:00:00.001001
2023-03-26 17:12:40.478 Build TRT engine elapsed time: 0:00:02.913266 Build TRT engine elapsed time: 0:00:02.913266 Lowering submodule _run_on_acc_4 elapsed time 0:00:03.107299 Lowering submodule _run_on_acc_4 elapsed time 0:00:03.107299 Now lowering submodule _run_on_acc_6 Now lowering submodule _run_on_acc_6 split_name=_run_on_acc_6, input_specs=[InputTensorSpec(shape=torch.Size([1, 256, 288, 368]), dtype=torch.float16, device=device(type='cuda', index=0), shape_ranges=[], has_batch_dim=True)] split_name=_run_on_acc_6, input_specs=[InputTensorSpec(shape=torch.Size([1, 256, 288, 368]), dtype=torch.float16, device=device(type='cuda', index=0), shape_ranges=[], has_batch_dim=True)] Timing cache is used! Timing cache is used! TRT INetwork construction elapsed time: 0:00:00.001001 TRT INetwork construction elapsed time: 0:00:00.001001
2023-03-26 17:12:43.662 Build TRT engine elapsed time: 0:00:02.897112 Build TRT engine elapsed time: 0:00:02.897112 Lowering submodule _run_on_acc_6 elapsed time 0:00:03.091014 Lowering submodule _run_on_acc_6 elapsed time 0:00:03.091014 Now lowering submodule _run_on_acc_8 Now lowering submodule _run_on_acc_8 split_name=_run_on_acc_8, input_specs=[InputTensorSpec(shape=torch.Size([1, 256, 288, 368]), dtype=torch.float16, device=device(type='cuda', index=0), shape_ranges=[], has_batch_dim=True), InputTensorSpec(shape=torch.Size([1, 256, 288, 368]), dtype=torch.float16, device=device(type='cuda', index=0), shape_ranges=[], has_batch_dim=True)] split_name=_run_on_acc_8, input_specs=[InputTensorSpec(shape=torch.Size([1, 256, 288, 368]), dtype=torch.float16, device=device(type='cuda', index=0), shape_ranges=[], has_batch_dim=True), InputTensorSpec(shape=torch.Size([1, 256, 288, 368]), dtype=torch.float16, device=device(type='cuda', index=0), shape_ranges=[], has_batch_dim=True)] Timing cache is used! Timing cache is used! TRT INetwork construction elapsed time: 0:00:00.002001 TRT INetwork construction elapsed time: 0:00:00.002001
2023-03-26 17:12:47.461 Build TRT engine elapsed time: 0:00:03.509264 Build TRT engine elapsed time: 0:00:03.509264 Lowering submodule _run_on_acc_8 elapsed time 0:00:03.711010 Lowering submodule _run_on_acc_8 elapsed time 0:00:03.711010 Now lowering submodule _run_on_acc_10 Now lowering submodule _run_on_acc_10 split_name=_run_on_acc_10, input_specs=[InputTensorSpec(shape=torch.Size([1, 256, 144, 184]), dtype=torch.float16, device=device(type='cuda', index=0), shape_ranges=[], has_batch_dim=True)] split_name=_run_on_acc_10, input_specs=[InputTensorSpec(shape=torch.Size([1, 256, 144, 184]), dtype=torch.float16, device=device(type='cuda', index=0), shape_ranges=[], has_batch_dim=True)] Timing cache is used! Timing cache is used! TRT INetwork construction elapsed time: 0:00:00.002001 TRT INetwork construction elapsed time: 0:00:00.002001 Build TRT engine elapsed time: 0:00:01.452047 Build TRT engine elapsed time: 0:00:01.452047 Lowering submodule _run_on_acc_10 elapsed time 0:00:01.644582 Lowering submodule _run_on_acc_10 elapsed time 0:00:01.644582 Now lowering submodule _run_on_acc_12 Now lowering submodule _run_on_acc_12 split_name=_run_on_acc_12, input_specs=[InputTensorSpec(shape=torch.Size([1, 256, 144, 184]), dtype=torch.float16, device=device(type='cuda', index=0), shape_ranges=[], has_batch_dim=True), InputTensorSpec(shape=torch.Size([1, 256, 144, 184]), dtype=torch.float16, device=device(type='cuda', index=0), shape_ranges=[], has_batch_dim=True)] split_name=_run_on_acc_12, input_specs=[InputTensorSpec(shape=torch.Size([1, 256, 144, 184]), dtype=torch.float16, device=device(type='cuda', index=0), shape_ranges=[], has_batch_dim=True), InputTensorSpec(shape=torch.Size([1, 256, 144, 184]), dtype=torch.float16, device=device(type='cuda', index=0), shape_ranges=[], has_batch_dim=True)] Timing cache is used! Timing cache is used! TRT INetwork construction elapsed time: 0:00:00.000999 TRT INetwork construction elapsed time: 0:00:00.000999 Build TRT engine elapsed time: 0:00:01.452022 Build TRT engine elapsed time: 0:00:01.452022 Lowering submodule _run_on_acc_12 elapsed time 0:00:01.650969 Lowering submodule _run_on_acc_12 elapsed time 0:00:01.650969 Now lowering submodule _run_on_acc_14 Now lowering submodule _run_on_acc_14 split_name=_run_on_acc_14, input_specs=[InputTensorSpec(shape=torch.Size([1, 256, 144, 184]), dtype=torch.float16, device=device(type='cuda', index=0), shape_ranges=[], has_batch_dim=True)] split_name=_run_on_acc_14, input_specs=[InputTensorSpec(shape=torch.Size([1, 256, 144, 184]), dtype=torch.float16, device=device(type='cuda', index=0), shape_ranges=[], has_batch_dim=True)] Timing cache is used! Timing cache is used! TRT INetwork construction elapsed time: 0:00:00.001000 TRT INetwork construction elapsed time: 0:00:00.001000 Build TRT engine elapsed time: 0:00:01.454924 Build TRT engine elapsed time: 0:00:01.454924 Lowering submodule _run_on_acc_14 elapsed time 0:00:01.652692 Lowering submodule _run_on_acc_14 elapsed time 0:00:01.652692 Now lowering submodule _run_on_acc_16 Now lowering submodule _run_on_acc_16 split_name=_run_on_acc_16, input_specs=[InputTensorSpec(shape=torch.Size([1, 256, 144, 184]), dtype=torch.float16, device=device(type='cuda', index=0), shape_ranges=[], has_batch_dim=True), InputTensorSpec(shape=torch.Size([1, 256, 144, 184]), dtype=torch.float16, device=device(type='cuda', index=0), shape_ranges=[], has_batch_dim=True)] split_name=_run_on_acc_16, input_specs=[InputTensorSpec(shape=torch.Size([1, 256, 144, 184]), dtype=torch.float16, device=device(type='cuda', index=0), shape_ranges=[], has_batch_dim=True), InputTensorSpec(shape=torch.Size([1, 256, 144, 184]), dtype=torch.float16, device=device(type='cuda', index=0), shape_ranges=[], has_batch_dim=True)] Timing cache is used! Timing cache is used! TRT INetwork construction elapsed time: 0:00:00.001000 TRT INetwork construction elapsed time: 0:00:00.001000 Build TRT engine elapsed time: 0:00:01.471268 Build TRT engine elapsed time: 0:00:01.471268 Lowering submodule _run_on_acc_16 elapsed time 0:00:01.675096 Lowering submodule _run_on_acc_16 elapsed time 0:00:01.675096 Now lowering submodule _run_on_acc_18 Now lowering submodule _run_on_acc_18 split_name=_run_on_acc_18, input_specs=[InputTensorSpec(shape=torch.Size([1, 256, 26496]), dtype=torch.float16, device=device(type='cuda', index=0), shape_ranges=[], has_batch_dim=True)] split_name=_run_on_acc_18, input_specs=[InputTensorSpec(shape=torch.Size([1, 256, 26496]), dtype=torch.float16, device=device(type='cuda', index=0), shape_ranges=[], has_batch_dim=True)] Timing cache is used! Timing cache is used!
2023-03-26 17:12:54.710 Unable to find layer norm plugin, fall back to TensorRT implementation. Unable to find layer norm plugin, fall back to TensorRT implementation.
2023-03-26 17:12:54.761 TRT INetwork construction elapsed time: 0:00:00.050510 TRT INetwork construction elapsed time: 0:00:00.050510
2023-03-26 17:13:16.078 Build TRT engine elapsed time: 0:00:21.268365 Build TRT engine elapsed time: 0:00:21.268365 Lowering submodule _run_on_acc_18 elapsed time 0:00:21.520086 Lowering submodule _run_on_acc_18 elapsed time 0:00:21.520086 Now lowering submodule _run_on_acc_20 Now lowering submodule _run_on_acc_20 split_name=_run_on_acc_20, input_specs=[InputTensorSpec(shape=torch.Size([1, 144, 184, 256]), dtype=torch.float16, device=device(type='cuda', index=0), shape_ranges=[], has_batch_dim=True), InputTensorSpec(shape=torch.Size([1, 144, 184, 1]), dtype=torch.float16, device=device(type='cuda', index=0), shape_ranges=[], has_batch_dim=True)] split_name=_run_on_acc_20, input_specs=[InputTensorSpec(shape=torch.Size([1, 144, 184, 256]), dtype=torch.float16, device=device(type='cuda', index=0), shape_ranges=[], has_batch_dim=True), InputTensorSpec(shape=torch.Size([1, 144, 184, 1]), dtype=torch.float16, device=device(type='cuda', index=0), shape_ranges=[], has_batch_dim=True)] Timing cache is used! Timing cache is used! I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch_tensorrt\fx\converters\converter_utils.py:457: UserWarning: Both operands of the binary elementwise op floordiv_144 are constant. In this case, please consider constant fold the model first.warnings.warn( I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch_tensorrt\fx\converters\converter_utils.py:457: UserWarning: Both operands of the binary elementwise op floordiv_144 are constant. In this case, please consider constant fold the model first.warnings.warn( I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch_tensorrt\fx\converters\converter_utils.py:457: UserWarning: Both operands of the binary elementwise op floordiv_145 are constant. In this case, please consider constant fold the model first.warnings.warn( I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch_tensorrt\fx\converters\converter_utils.py:457: UserWarning: Both operands of the binary elementwise op floordiv_145 are constant. In this case, please consider constant fold the model first.warnings.warn( I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch_tensorrt\fx\converters\converter_utils.py:457: UserWarning: Both operands of the binary elementwise op floordiv_146 are constant. In this case, please consider constant fold the model first.warnings.warn( I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch_tensorrt\fx\converters\converter_utils.py:457: UserWarning: Both operands of the binary elementwise op floordiv_146 are constant. In this case, please consider constant fold the model first.warnings.warn( I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch_tensorrt\fx\converters\converter_utils.py:457: UserWarning: Both operands of the binary elementwise op floordiv_147 are constant. In this case, please consider constant fold the model first.warnings.warn( I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch_tensorrt\fx\converters\converter_utils.py:457: UserWarning: Both operands of the binary elementwise op floordiv_147 are constant. In this case, please consider constant fold the model first.warnings.warn( TRT INetwork construction elapsed time: 0:00:00.226765 TRT INetwork construction elapsed time: 0:00:00.226765 Build TRT engine elapsed time: 0:00:00.463744 Build TRT engine elapsed time: 0:00:00.463744 Lowering submodule _run_on_acc_20 elapsed time 0:00:00.918417 Lowering submodule _run_on_acc_20 elapsed time 0:00:00.918417 Now lowering submodule _run_on_acc_22 Now lowering submodule _run_on_acc_22 split_name=_run_on_acc_22, input_specs=[InputTensorSpec(shape=torch.Size([414, 64, 256]), dtype=torch.float16, device=device(type='cuda', index=0), shape_ranges=[], has_batch_dim=True), InputTensorSpec(shape=torch.Size([414, 64, 64]), dtype=torch.float16, device=device(type='cuda', index=0), shape_ranges=[], has_batch_dim=True), InputTensorSpec(shape=torch.Size([1, 414, 1, 64, 64]), dtype=torch.float16, device=device(type='cuda', index=0), shape_ranges=[], has_batch_dim=True)] split_name=_run_on_acc_22, input_specs=[InputTensorSpec(shape=torch.Size([414, 64, 256]), dtype=torch.float16, device=device(type='cuda', index=0), shape_ranges=[], has_batch_dim=True), InputTensorSpec(shape=torch.Size([414, 64, 64]), dtype=torch.float16, device=device(type='cuda', index=0), shape_ranges=[], has_batch_dim=True), InputTensorSpec(shape=torch.Size([1, 414, 1, 64, 64]), dtype=torch.float16, device=device(type='cuda', index=0), shape_ranges=[], has_batch_dim=True)] Timing cache is used! Timing cache is used! I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch_tensorrt\fx\converters\converter_utils.py:457: UserWarning: Both operands of the binary elementwise op floordiv_148 are constant. In this case, please consider constant fold the model first.warnings.warn( I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch_tensorrt\fx\converters\converter_utils.py:457: UserWarning: Both operands of the binary elementwise op floordiv_148 are constant. In this case, please consider constant fold the model first.warnings.warn( I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch_tensorrt\fx\converters\converter_utils.py:457: UserWarning: Both operands of the binary elementwise op floordiv_149 are constant. In this case, please consider constant fold the model first.warnings.warn( I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch_tensorrt\fx\converters\converter_utils.py:457: UserWarning: Both operands of the binary elementwise op floordiv_149 are constant. In this case, please consider constant fold the model first.warnings.warn( TRT INetwork construction elapsed time: 0:00:00.126577 TRT INetwork construction elapsed time: 0:00:00.126577
2023-03-26 17:13:22.295 Failed to evaluate the script:Python exception: Traceback (most recent call last):File "src\cython\vapoursynth.pyx", line 2866, in vapoursynth._vpy_evaluateFile "src\cython\vapoursynth.pyx", line 2867, in vapoursynth._vpy_evaluateFile "J:\tmp\tempPreviewVapoursynthFile17_12_01_348.vpy", line 63, in clip = FeMaSR(clip=clip, device_index=0, trt=True, trt_cache_path=r"J:\tmp") # 1440x1152File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\autograd\grad_mode.py", line 27, in decorate_contextreturn func(*args, **kwargs)File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\vsfemasr\__init__.py", line 171, in femasrmodule = lowerer(File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch_tensorrt\fx\lower.py", line 323, in __call__return do_lower(module, inputs)File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch_tensorrt\fx\passes\pass_utils.py", line 117, in pass_with_validationprocessed_module = pass_(module, input, *args, **kwargs)File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch_tensorrt\fx\lower.py", line 320, in do_lowerlower_result = pm(module)File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\fx\passes\pass_manager.py", line 240, in __call__out = _pass(out)File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\fx\passes\pass_manager.py", line 240, in __call__out = _pass(out)File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch_tensorrt\fx\passes\lower_pass_manager_builder.py", line 167, in lower_funclowered_module = self._lower_func(File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch_tensorrt\fx\lower.py", line 180, in lower_passinterp_res: TRTInterpreterResult = interpreter(mod, input, module_name)File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch_tensorrt\fx\lower.py", line 132, in __call__interp_result: TRTInterpreterResult = interpreter.run(File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch_tensorrt\fx\fx2trt.py", line 252, in runassert engineAssertionError

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