-
Notifications
You must be signed in to change notification settings - Fork 0
/
main_diff_bfs.py
132 lines (119 loc) · 4.08 KB
/
main_diff_bfs.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
import sys
import argparse
import pdb
from datetime import datetime
import os
from torch.utils.data import DataLoader
import torch
import numpy as np
import json
"""
Internal pacakage
"""
from main_seq_bfs import Args as SEQ_ARGS
from mimagen_pytorch import Unet3D, ElucidatedImagen, ImagenTrainer
sys.path.insert(0, './util')
from utils import save_args, read_args_txt
sys.path.insert(0, './data')
from data_bfs_preprocess import bfs_dataset
sys.path.insert(0, './train_test_spatial')
from train_diff import train_diff
class Args:
def __init__(self):
self.parser = argparse.ArgumentParser()
"""
for finding the dynamics dir
"""
self.parser.add_argument("--bfs_dynamic_folder",
default='output/bfs_les_2023_10_31_17_49_35',
help='all the information of ks training')
"""
for diffusion model
"""
self.parser.add_argument("--Nt",
default = 10,
help = 'Time steps we use as a single seq')
self.parser.add_argument("--unet_dim",
default=32,
help='The unet dimension')
self.parser.add_argument("--num_sample_steps",
default=20,
help='The noise forward/reverse step')
"""
for training
"""
self.parser.add_argument("--batch_size", default = 1)
self.parser.add_argument("--epoch_num", default = 20)
self.parser.add_argument("--device", type=str, default = "cuda:1")
self.parser.add_argument("--shuffle",default=True)
def update_args(self):
args = self.parser.parse_args()
# output dataset
args.experiment_path = os.path.join(args.bfs_dynamic_folder,'diffusion_folder')
if not os.path.isdir(args.experiment_path):
os.makedirs(args.experiment_path)
args.model_save_path = os.path.join(args.experiment_path,'model_save')
if not os.path.isdir(args.model_save_path):
os.makedirs(args.model_save_path)
args.logging_path = os.path.join( args.experiment_path,'logging')
if not os.path.isdir(args.logging_path):
os.makedirs(args.logging_path)
args.seq_args_txt = os.path.join(args.bfs_dynamic_folder,
'logging','args.txt' )
return args
if __name__ == '__main__':
"""
Diff args
"""
diff_args = Args()
diff_args = diff_args.update_args()
save_args(diff_args)
"""
Sequence args
"""
seq_args = read_args_txt(SEQ_ARGS(),diff_args.seq_args_txt)
"""
Fetch dataset
"""
data_set = bfs_dataset(data_location = seq_args.data_location,
trajec_max_len = diff_args.Nt,#seq_args.trajec_max_len,
start_n = seq_args.start_n,
n_span = seq_args.n_span)
data_loader = DataLoader(dataset=data_set,
shuffle=diff_args.shuffle,
batch_size=diff_args.batch_size)
"""
Create diffusion model
"""
unet1 = Unet3D(dim=diff_args.unet_dim,
cond_images_channels=2,
memory_efficient=True,
dim_mults=(1, 2, 4, 8)).to(torch.device(diff_args.device)) #mid: mid channel
image_sizes = (512)
image_width = (512)
imagen = ElucidatedImagen(
unets = (unet1),
image_sizes = image_sizes,
image_width = image_width,
channels = 2, # Han Gao add the input to this args explicity
random_crop_sizes = None,
num_sample_steps = diff_args.num_sample_steps, # original is 10
cond_drop_prob = 0.1,
sigma_min = 0.002,
sigma_max = (80), # max noise level, double the max noise level for upsampler （80，160）
sigma_data = 0.5, # standard deviation of data distribution
rho = 7, # controls the sampling schedule
P_mean = -1.2, # mean of log-normal distribution from which noise is drawn for training
P_std = 1.2, # standard deviation of log-normal distribution from which noise is drawn for training
S_churn = 80, # parameters for stochastic sampling - depends on dataset, Table 5 in apper
S_tmin = 0.05,
S_tmax = 50,
S_noise = 1.003,
condition_on_text = False,
auto_normalize_img = False # Han Gao make it false
).to(torch.device(diff_args.device))
trainer = ImagenTrainer(imagen, device =torch.device(diff_args.device))
train_diff(diff_args=diff_args,
seq_args=seq_args,
trainer=trainer,
data_loader=data_loader)