Yuliang commited on
Commit
c0b5d8f
1 Parent(s): 8a246ed

OOM solved

Browse files
.gitignore CHANGED
@@ -13,3 +13,4 @@ kaolin/
13
  neural_voxelization_layer/
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  pytorch3d/
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  force_push.sh
 
 
13
  neural_voxelization_layer/
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  pytorch3d/
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  force_push.sh
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+ results/
app.py CHANGED
@@ -117,7 +117,7 @@ with gr.Blocks() as demo:
117
 
118
  gr.Examples(examples=examples,
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  inputs=[inp, radio_choice],
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- cache_examples=False,
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  fn=generate_model,
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  outputs=out_lst)
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117
 
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  gr.Examples(examples=examples,
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  inputs=[inp, radio_choice],
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+ cache_examples=True,
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  fn=generate_model,
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  outputs=out_lst)
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apps/infer.py CHANGED
@@ -14,10 +14,9 @@
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  #
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  # Contact: [email protected]
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- import os
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  import logging
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- from lib.common.render import query_color
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  from lib.common.config import cfg
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  from lib.dataset.mesh_util import (
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  load_checkpoint,
@@ -403,7 +402,7 @@ def generate_model(in_path, model_type):
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  loop_cloth.set_description(pbar_desc)
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405
  # update params
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- cloth_loss.backward(retain_graph=True)
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  optimizer_cloth.step()
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  scheduler_cloth.step(cloth_loss)
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@@ -414,13 +413,6 @@ def generate_model(in_path, model_type):
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  process=False, maintains_order=True
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  )
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- # with front texture
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- # final_colors = query_color(
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- # mesh_pr.verts_packed().detach().squeeze(0).cpu(),
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- # mesh_pr.faces_packed().detach().squeeze(0).cpu(),
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- # in_tensor["image"],
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- # device=device,
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- # )
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  # without front texture
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  final_colors = (mesh_pr.verts_normals_padded().squeeze(0).detach().cpu() + 1.0) * 0.5 * 255.0
@@ -458,7 +450,7 @@ def generate_model(in_path, model_type):
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  for element in dir():
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  if 'path' not in element:
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  del locals()[element]
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-
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  torch.cuda.empty_cache()
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464
  return [smpl_path, smpl_path, smpl_npy_path, recon_path, recon_path, refine_path, refine_path, video_path, overlap_path]
 
14
  #
15
  # Contact: [email protected]
16
 
17
+ import os, gc
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  import logging
 
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  from lib.common.config import cfg
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  from lib.dataset.mesh_util import (
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  load_checkpoint,
 
402
  loop_cloth.set_description(pbar_desc)
403
 
404
  # update params
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+ cloth_loss.backward()
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  optimizer_cloth.step()
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  scheduler_cloth.step(cloth_loss)
408
 
 
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  process=False, maintains_order=True
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  )
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  # without front texture
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  final_colors = (mesh_pr.verts_normals_padded().squeeze(0).detach().cpu() + 1.0) * 0.5 * 255.0
 
450
  for element in dir():
451
  if 'path' not in element:
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  del locals()[element]
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+ gc.collect()
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  torch.cuda.empty_cache()
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456
  return [smpl_path, smpl_path, smpl_npy_path, recon_path, recon_path, refine_path, refine_path, video_path, overlap_path]
gradio_cached_examples/13/log.csv ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
+ 'flag','username','timestamp'
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+ '','','2022-08-01 22:47:24.908073'
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+ '','','2022-08-01 22:48:20.753663'
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+ '','','2022-08-01 22:49:15.504871'
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+ '','','2022-08-01 22:50:16.762017'
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+ '','','2022-08-01 22:51:09.444531'
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+ '','','2022-08-01 22:52:09.357219'
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+ '','','2022-08-01 22:53:02.217347'
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+ '','','2022-08-01 22:54:11.545178'
gradio_queue.db ADDED
Binary file (610 kB). View file
 
lib/net/FBNet.py CHANGED
@@ -81,7 +81,8 @@ def define_G(input_nc,
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  # print(netG)
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  if len(gpu_ids) > 0:
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  assert (torch.cuda.is_available())
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- netG.cuda(gpu_ids[0])
 
85
  netG.apply(weights_init)
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  return netG
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  # print(netG)
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  if len(gpu_ids) > 0:
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  assert (torch.cuda.is_available())
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+ device=torch.device(f"cuda:{gpu_ids[0]}")
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+ netG = netG.to(device)
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  netG.apply(weights_init)
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  return netG
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