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Fix infer_wild.py error when running on windows #69

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Fix infer_wild.py error when running on windows #69

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ebea9bf
Fix infer_wild.py error running in windows
upbit Jul 18, 2023
eeec91b
Update infer_wild.py
upbit Jul 18, 2023
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147 changes: 80 additions & 67 deletions infer_wild.py
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Original file line number Diff line number Diff line change
Expand Up @@ -25,73 +25,86 @@ def parse_args():
opts = parser.parse_args()
return opts

opts = parse_args()
args = get_config(opts.config)
if __name__ == '__main__':
# due to RuntimeError: freeze_support() on Windows: https://github.com/pytorch/pytorch/issues/5858
torch.multiprocessing.freeze_support()

model_backbone = load_backbone(args)
if torch.cuda.is_available():
model_backbone = nn.DataParallel(model_backbone)
model_backbone = model_backbone.cuda()
opts = parse_args()
args = get_config(opts.config)

model_backbone = load_backbone(args)
if torch.cuda.is_available():
model_backbone = nn.DataParallel(model_backbone)
model_backbone = model_backbone.cuda()

print('Loading checkpoint', opts.evaluate)
checkpoint = torch.load(opts.evaluate, map_location=lambda storage, loc: storage)

print('Loading checkpoint', opts.evaluate)
checkpoint = torch.load(opts.evaluate, map_location=lambda storage, loc: storage)
model_backbone.load_state_dict(checkpoint['model_pos'], strict=True)
model_pos = model_backbone
model_pos.eval()
testloader_params = {
'batch_size': 1,
'shuffle': False,
'num_workers': 8,
'pin_memory': True,
'prefetch_factor': 4,
'persistent_workers': True,
'drop_last': False
}
# fix KeyError: ‘unexpected key “module.*
# https://discuss.pytorch.org/t/solved-keyerror-unexpected-key-module-encoder-embedding-weight-in-state-dict/1686/3
from collections import OrderedDict
new_state_dict = OrderedDict()
for k, v in checkpoint['model_pos'].items():
name = k[7:] # remove `module.`
new_state_dict[name] = v

vid = imageio.get_reader(opts.vid_path, 'ffmpeg')
fps_in = vid.get_meta_data()['fps']
vid_size = vid.get_meta_data()['size']
os.makedirs(opts.out_path, exist_ok=True)

if opts.pixel:
# Keep relative scale with pixel coornidates
wild_dataset = WildDetDataset(opts.json_path, clip_len=opts.clip_len, vid_size=vid_size, scale_range=None, focus=opts.focus)
else:
# Scale to [-1,1]
wild_dataset = WildDetDataset(opts.json_path, clip_len=opts.clip_len, scale_range=[1,1], focus=opts.focus)

test_loader = DataLoader(wild_dataset, **testloader_params)

results_all = []
with torch.no_grad():
for batch_input in tqdm(test_loader):
N, T = batch_input.shape[:2]
if torch.cuda.is_available():
batch_input = batch_input.cuda()
if args.no_conf:
batch_input = batch_input[:, :, :, :2]
if args.flip:
batch_input_flip = flip_data(batch_input)
predicted_3d_pos_1 = model_pos(batch_input)
predicted_3d_pos_flip = model_pos(batch_input_flip)
predicted_3d_pos_2 = flip_data(predicted_3d_pos_flip) # Flip back
predicted_3d_pos = (predicted_3d_pos_1 + predicted_3d_pos_2) / 2.0
else:
predicted_3d_pos = model_pos(batch_input)
if args.rootrel:
predicted_3d_pos[:,:,0,:]=0 # [N,T,17,3]
else:
predicted_3d_pos[:,0,0,2]=0
pass
if args.gt_2d:
predicted_3d_pos[...,:2] = batch_input[...,:2]
results_all.append(predicted_3d_pos.cpu().numpy())

results_all = np.hstack(results_all)
results_all = np.concatenate(results_all)
render_and_save(results_all, '%s/X3D.mp4' % (opts.out_path), keep_imgs=False, fps=fps_in)
if opts.pixel:
# Convert to pixel coordinates
results_all = results_all * (min(vid_size) / 2.0)
results_all[:,:,:2] = results_all[:,:,:2] + np.array(vid_size) / 2.0
np.save('%s/X3D.npy' % (opts.out_path), results_all)
model_backbone.load_state_dict(new_state_dict, strict=True)
model_pos = model_backbone
model_pos.eval()
testloader_params = {
'batch_size': 1,
'shuffle': False,
'num_workers': 8,
'pin_memory': True,
'prefetch_factor': 4,
'persistent_workers': True,
'drop_last': False
}

vid = imageio.get_reader(opts.vid_path, 'ffmpeg')
fps_in = vid.get_meta_data()['fps']
vid_size = vid.get_meta_data()['size']
os.makedirs(opts.out_path, exist_ok=True)

if opts.pixel:
# Keep relative scale with pixel coornidates
wild_dataset = WildDetDataset(opts.json_path, clip_len=opts.clip_len, vid_size=vid_size, scale_range=None, focus=opts.focus)
else:
# Scale to [-1,1]
wild_dataset = WildDetDataset(opts.json_path, clip_len=opts.clip_len, scale_range=[1,1], focus=opts.focus)

test_loader = DataLoader(wild_dataset, **testloader_params)

results_all = []
with torch.no_grad():
for batch_input in tqdm(test_loader):
N, T = batch_input.shape[:2]
if torch.cuda.is_available():
batch_input = batch_input.cuda()
if args.no_conf:
batch_input = batch_input[:, :, :, :2]
if args.flip:
batch_input_flip = flip_data(batch_input)
predicted_3d_pos_1 = model_pos(batch_input)
predicted_3d_pos_flip = model_pos(batch_input_flip)
predicted_3d_pos_2 = flip_data(predicted_3d_pos_flip) # Flip back
predicted_3d_pos = (predicted_3d_pos_1 + predicted_3d_pos_2) / 2.0
else:
predicted_3d_pos = model_pos(batch_input)
if args.rootrel:
predicted_3d_pos[:,:,0,:]=0 # [N,T,17,3]
else:
predicted_3d_pos[:,0,0,2]=0
pass
if args.gt_2d:
predicted_3d_pos[...,:2] = batch_input[...,:2]
results_all.append(predicted_3d_pos.cpu().numpy())

results_all = np.hstack(results_all)
results_all = np.concatenate(results_all)
render_and_save(results_all, '%s/X3D.mp4' % (opts.out_path), keep_imgs=False, fps=fps_in)
if opts.pixel:
# Convert to pixel coordinates
results_all = results_all * (min(vid_size) / 2.0)
results_all[:,:,:2] = results_all[:,:,:2] + np.array(vid_size) / 2.0
np.save('%s/X3D.npy' % (opts.out_path), results_all)