-
Notifications
You must be signed in to change notification settings - Fork 8
/
wandb_logging.py
134 lines (108 loc) · 7.07 KB
/
wandb_logging.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
import os
import wandb
from matplotlib import cm
from torchvision.utils import make_grid
import torch.nn.functional as F
import numpy as np
import torch
import flow_vis
import flow_vis_torch
import matplotlib.pyplot as plt; imsave = lambda x,y=0: plt.imsave("/nobackup/users/camsmith/img/tmp%s.png"%y,x.cpu().numpy());
from einops import rearrange, repeat
import piqa
import imageio
def write_video(save_dir,frames,vid_name,step,write_frames=False):
frames = [(255*x).astype(np.uint8) for x in frames]
if "time" in vid_name: frames = frames + frames[::-1]
f = os.path.join(save_dir, f'{vid_name}_{step}.mp4')
imageio.mimwrite(f, frames, fps=8, quality=7)
wandb.log({f'vid/{vid_name}':wandb.Video(f, format='mp4', fps=8)})
print("writing video at",f)
if write_frames:
for i,img in enumerate(frames):
try: os.mkdir(os.path.join(save_dir, f'{vid_name}_{step}'))
except:pass
f=os.path.join(save_dir, f'{vid_name}_{step}/{i}.png');plt.imsave(f,img);print(f)
def normalize(a):
return (a - a.min()) / (a.max() - a.min())
def cvt(a):
a = a.permute(1, 2, 0).detach().cpu()
a = (a - a.min()) / (a.max() - a.min())
a = a.numpy()
return a
ch_fst = lambda src,x=None:rearrange(src,"... (x y) c -> ... c x y",x=int(src.size(-2)**(.5)) if x is None else x)
def _wandb_summary(loss, model_output, model_input, ground_truth, resolution,prefix=""):
resolution = list(model_input["ctxt_rgb"].flatten(0,1).shape)
nrow=model_input["trgt_rgb"].size(1)
imsly,imslx=model_input["ctxt_rgb"].shape[-2:]
resolution = list(model_input["ctxt_rgb"].flatten(0,1).permute(0,2,3,1).shape)
rgb_gt= ground_truth["trgt_rgb"]
rgb_pred,depth,=[model_output[x] for x in ["rgb","depth"]]
inv = lambda x : 1/(x+1e-3)
depth = make_grid(model_output["depth"].flatten(0,1).permute(0,2,1).unflatten(-1,(imsly,imslx)).cpu(),normalize=True,nrow=nrow)
rgb_pred = make_grid(model_output["rgb"].flatten(0,1).permute(0,2,1).unflatten(-1,(imsly,imslx)),normalize=True,nrow=nrow)
rgb_gt = make_grid(ground_truth["trgt_rgb"].flatten(0,1).permute(0,2,1).unflatten(-1,(imsly,imslx)),normalize=True,nrow=nrow)
ctxt_img = make_grid(model_input["ctxt_rgb"].cpu().flatten(0,1),normalize=True,nrow=nrow)
print("add psnr metric here")
wandb_out = {
"est/rgb_pred": rgb_pred,
"ref/rgb_gt": rgb_gt,
"ref/ctxt_img": ctxt_img,
"est/depth": depth,
}
if "trgt_depth" in ground_truth:
wandb_out["depthgt"]=make_grid(ground_truth["trgt_depth"].cpu().flatten(0,1).permute(0,2,1).unflatten(-1,(imsly,imslx)).cpu(),normalize=True,nrow=nrow)
for k,v in wandb_out.items(): print(k,v.max(),v.min(),v.shape)
#for k,v in wandb_out.items():plt.imsave("/nobackup/users/camsmith/img/%s.png"%k,v.permute(1,2,0).detach().cpu().numpy().clip(0,1));
wandb.log({"sanity/"+k+"_min":v.min() for k,v in wandb_out.items()})
wandb.log({"sanity/"+k+"_max":v.max() for k,v in wandb_out.items()})
wandb_out = {prefix+k:wandb.Image(v.permute(1, 2, 0).detach().clip(0,1).cpu().numpy()) for k,v in wandb_out.items()}
wandb.log(wandb_out)
#def dyn_wandb_summary(loss, model_output, model_input, ground_truth, resolution,prefix=""):
def wandb_summary(loss, model_output, model_input, ground_truth, resolution,prefix=""):
resolution = list(model_input["ctxt_rgb"].flatten(0,1).permute(0,2,3,1).shape)
nrow=model_input["trgt_rgb"].size(1)
imsl=model_input["ctxt_rgb"].shape[-2:]
inv = lambda x : 1/(x+1e-8)
depth = make_grid(model_output["depth"].cpu().flatten(0,1).permute(0,2,1).unflatten(-1,imsl).detach(),nrow=nrow)
wandb_out = {
"est/rgb_pred": make_grid(model_output["rgb"].cpu().flatten(0,1).permute(0,2,1).unflatten(-1,imsl).detach(),nrow=nrow),
"ref/rgb_gt": make_grid(ground_truth["trgt_rgb"].cpu().view(*resolution).detach().permute(0, -1, 1, 2),nrow=nrow),
"ref/ctxt_img": make_grid(model_input["ctxt_rgb"][:,0].cpu().detach(),nrow=1)*.5+.5,
"est/depth": depth,
"est/depth_1ch":make_grid(model_output["depth_raw"].flatten(0,1).permute(0,2,1).unflatten(-1,imsl).cpu(),normalize=True,nrow=nrow),
}
depthgt = (ground_truth["trgt_depth"] if "trgt_depth" in ground_truth else model_output["trgt_depth_inp"] if "trgt_depth_inp" in model_output
else model_input["trgt_depth"] if "trgt_depth" in model_input else None)
if depthgt is not None:
depthgt = make_grid(inv(depthgt).cpu().view(*resolution[:3]).detach().unsqueeze(1),normalize=True,nrow=nrow)
wandb_out["ref/depthgt"]= depthgt
if "fine_rgb" in model_output:
wandb_out["est/fine_rgb_pred"] = make_grid(model_output["fine_rgb"].cpu().flatten(0,1).permute(0,2,1).unflatten(-1,imsl).detach(),nrow=nrow)
wandb_out["est/fine_depth_pred"] = make_grid(model_output["fine_depth"].cpu().flatten(0,1).permute(0,2,1).unflatten(-1,imsl).detach(),nrow=nrow,normalize=True)
if "ctxt_rgb" in model_output:
wandb_out["est/ctxt_depth"] =make_grid(model_output["ctxt_depth"].cpu().flatten(0,1).permute(0,2,1).unflatten(-1,imsl).detach(),nrow=nrow)
wandb_out["est/ctxt_rgb_pred"] = ctxt_rgb_pred = make_grid(model_output["ctxt_rgb"].cpu().view(*resolution).detach().permute(0, -1, 1, 2),nrow=nrow)
if "corr_weights" in model_output:
#corr_weights = make_grid(model_output["corr_weights"].flatten(0,1)[:,:1].cpu().detach(),normalize=False,nrow=nrow)
corr_weights = make_grid(ch_fst(model_output["corr_weights"],resolution[1]).flatten(0,1)[:,:1].cpu().detach(),normalize=False,nrow=nrow)
wandb_out["est/corr_weights"] = corr_weights
if "flow" in model_output and not torch.isnan(model_output["flow"]).any() and not torch.isnan(model_output["flow_from_pose"]).any():
psnr = piqa.PSNR()(ch_fst(model_output["rgb"],imsl[0]).flatten(0,1).contiguous(),ch_fst(ground_truth["trgt_rgb"],imsl[0]).flatten(0,1).contiguous())
wandb.log({prefix+"metrics/psnr": psnr})
gt_flow_bwd = flow_vis_torch.flow_to_color(make_grid(model_output["flow_inp"].flatten(0,1),nrow=nrow))/255
est_flow = flow_vis_torch.flow_to_color(make_grid(model_output["flow"].flatten(0,1).permute(0,2,1).unflatten(-1,imsl),nrow=nrow))/255
wandb_out["est/flow_est"]= est_flow
wandb_out["ref/flow_gt_bwd"]=gt_flow_bwd
if "flow_from_pose" in model_output:
wandb_out["est/flow_est_pose"] = flow_vis_torch.flow_to_color(make_grid(model_output["flow_from_pose"].flatten(0,1).permute(0,2,1).unflatten(-1,imsl),nrow=nrow))/255
elif "flow" in model_output:
print("skipping nan flow")
for k,v in wandb_out.items(): print(k,v.max(),v.min())
#for k,v in wandb_out.items():plt.imsave("/nobackup/users/camsmith/img/%s.png"%k,v.permute(1,2,0).detach().cpu().numpy().clip(0,1));
#zz
#wandb.log({"sanity/"+k+"_min":v.min() for k,v in wandb_out.items()})
#wandb.log({"sanity/"+k+"_max":v.max() for k,v in wandb_out.items()})
#for k,v in wandb_out.items(): print(v.shape)
wandb_out = {prefix+k:wandb.Image(v.permute(1, 2, 0).float().detach().clip(0,1).cpu().numpy()) for k,v in wandb_out.items()}
wandb.log(wandb_out)