ProtoDiffusion/sample.py at main · ituvisionlab/ProtoDiffusion · GitHub

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
135
136
137
138
139
140
141
142
143
144
145
import os
import torch
import argparse
from math import ceil

from unet import Unet
from diffusion import GaussianDiffusion
from utils import get_named_beta_schedule
from embedding import ConditionalEmbedding

from torch.nn.parallel import DistributedDataParallel as DDP
from torch.distributed import get_rank, init_process_group, destroy_process_group, all_gather, get_world_size
from torchvision import transforms
from torchvision.utils import save_image

@torch.no_grad()
def sample(params:argparse.Namespace):
    """
    Function to generate samples using a trained model.
    """
    assert params.genbatch % (torch.cuda.device_count() * params.clsnum) == 0 , 'please re-set your genbatch!!!'
    # initialize settings
    init_process_group(backend="nccl")
    # get local rank for each process
    local_rank = get_rank()
    # set device
    device = torch.device("cuda", local_rank)
    # load models
    hyperparams_model = {}
    with open(os.path.join(params.path, 'params.txt'), 'r') as f:
        for line in f.readlines():
            key, val = line.split(':')
            hyperparams_model[key] = val[:-1]

    net = Unet(
                in_ch = int(hyperparams_model["inch"]),
                mod_ch = int(hyperparams_model["modch"]),
                out_ch = int(hyperparams_model["outch"]),
                ch_mul = [ int(i)  for i in hyperparams_model["chmul"][2:-1].split(",")],
                num_res_blocks = int(hyperparams_model["numres"]),
                cdim = int(hyperparams_model["cdim"]),
                use_conv= True if hyperparams_model["useconv"] == ' True' else False,
                droprate = float(hyperparams_model["droprate"]),
                dtype=torch.float32
            ).to(device)
    print("model is created")

    checkpoint = torch.load(os.path.join(params.path, f'checkpoints/ckpt_{params.epoch}_checkpoint.pt'), map_location='cpu')
    net.load_state_dict(checkpoint['net'])
    cemblayer = ConditionalEmbedding(params.clsnum, int(hyperparams_model["cdim"]), int(hyperparams_model["cdim"])).to(device)
    cemblayer.load_state_dict(checkpoint['cemblayer'])
    # settings for diffusion model
    betas = get_named_beta_schedule(num_diffusion_timesteps = int(hyperparams_model["T"]))
    diffusion = GaussianDiffusion(
                    dtype = torch.float32,
                    model = net,
                    betas = betas,
                    w = float(hyperparams_model["w"]),
                    v =  float(hyperparams_model["v"]),
                    device = device
                )
    # DDP settings
    diffusion.model = DDP(
                            diffusion.model,
                            device_ids = [local_rank],
                            output_device = local_rank
                        )
    cemblayer = DDP(
                    cemblayer,
                    device_ids = [local_rank],
                    output_device = local_rank
                )
    # eval mode
    print(hyperparams_model["dataset"].replace(' ','') == 'stl10')
    diffusion.model.eval()
    cemblayer.eval()
    cnt = torch.cuda.device_count()
    if params.fid:
        numloop = ceil(params.genum  / params.genbatch)
    else:
        numloop = 1
    each_device_batch = params.genbatch // cnt
    # label settings
    if params.label == 'range':
        lab = torch.ones(params.clsnum, each_device_batch // params.clsnum).type(torch.long) \
            * torch.arange(start = 0, end = params.clsnum).reshape(-1,1)
        lab = lab.reshape(-1, 1).squeeze()
        lab = lab.to(device)
    else:
        lab = torch.randint(low = 0, high = params.clsnum, size = (each_device_batch,), device=device)

    cemb = cemblayer(lab)
    genshape = (each_device_batch, 3, int(hyperparams_model["image_size"]), int(hyperparams_model["image_size"]))
    all_samples = []
    if local_rank == 0:
        print(numloop)
    for _ in range(numloop):
        if params.ddim:
            generated = diffusion.ddim_sample(genshape, params.num_steps, float(hyperparams_model["eta"]), hyperparams_model["select"][1:], cemb = cemb)
        else:
            generated = diffusion.sample(genshape, cemb = cemb)
        img = generated
        img = img.reshape(params.clsnum, each_device_batch // params.clsnum, 3, int(hyperparams_model["image_size"]), int(hyperparams_model["image_size"])).contiguous()
        gathered_samples = [torch.zeros_like(img) for _ in range(get_world_size())]
        all_gather(gathered_samples, img)
        all_samples.extend([img.cpu() for img in gathered_samples])
    samples = torch.concat(all_samples, dim = 1).reshape(params.genbatch * numloop, 3, int(hyperparams_model["image_size"]), int(hyperparams_model["image_size"]))

    invTrans = transforms.Compose([ transforms.Normalize(mean = [ 0., 0., 0. ],
                                                     std = [ 1/0.2471, 1/0.2435, 1/0.2616 ]),
                                transforms.Normalize(mean = [ -0.4914, -0.4822, -0.4465 ],
                                                     std = [ 1., 1., 1. ]),
                               ])
    if hyperparams_model["dataset"].replace(' ','') == 'stl10':
        samples = invTrans(samples)
        samples = samples.clamp(0, 1)

    if local_rank == 0:
        save_path = os.path.join(params.path, f'samples_{params.epoch}/')
        if not os.path.exists(save_path):
            os.makedirs(save_path)
        for i in range(params.genbatch * numloop):
            save_image(samples[i], os.path.join(save_path, f'{i}.png'),normalize = True)

    destroy_process_group()
def main():
    # several hyperparameters for models
    parser = argparse.ArgumentParser(description='test for diffusion model')

    parser.add_argument('--genbatch', type=int, default=80, help='batch size for sampling process')
    parser.add_argument('--path', type=str, default=None, help='path for loading models')
    parser.add_argument('--epoch', type=int, default=1000, help='epochs for loading models')
    parser.add_argument('--label', type=str, default='range', help='labels of generated images')
    parser.add_argument('--clsnum', type=int, default=10, help='num of label classes')
    parser.add_argument('--fid', type=lambda x:(str(x).lower() in ['true','1', 'yes']), default=False, help='generate samples used for quantative evaluation')
    parser.add_argument('--genum', type=int, default=5600, help='num of generated samples')
    parser.add_argument('--select', type=str, default='linear', help='selection stragies for DDIM')
    parser.add_argument('--ddim', type=lambda x:(str(x).lower() in ['true','1', 'yes']), default=False, help='whether to use ddim')
    parser.add_argument('--local_rank', default=-1, type=int, help='node rank for distributed training')
    parser.add_argument('--num_steps', type=int, default=50, help='sampling steps for DDIM')
    args = parser.parse_args()
    sample(args)

if __name__ == '__main__':
    main()