SADN/test.py at master · hanlinwu/SADN · 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
import argparse
import os

import numpy as np
import torch
from litsr.data import DownsampledDataset
from litsr.models import load_model
from litsr.utils import mkdir, read_yaml
from matplotlib import pyplot as plt
from pytorch_lightning import seed_everything
from tqdm import tqdm

from archs import *

seed_everything(123)


def test_pipeline(args):
    # setup scales and datasets
    test_datasets = (
        [_ for _ in args.datasets.split(",")] if args.datasets else ["sr-geo-15"]
    )

    # config ckpt path
    exp_path = os.path.dirname(os.path.dirname(args.checkpoint))
    ckpt_path = args.checkpoint

    # read config
    config = read_yaml(os.path.join(exp_path, "hparams.yaml"))

    # create model
    model = load_model(config, ckpt_path, strict=False)
    model.eval()

    # set gpu
    if args.gpus:
        model.cuda()

    scales = args.scales.split(",") if args.scales else [2, 3, 4]
    scales = [float(s) for s in scales]

    for dataset_name in test_datasets:
        for scale in scales:
            # config result path
            if args.self_ensemble:
                rslt_folder_name = "results_plus"
            else:
                rslt_folder_name = "results"
            rslt_path = os.path.join(
                exp_path, rslt_folder_name, dataset_name, "x" + str(scale)
            )

            mkdir(rslt_path)

            print(
                "==== Dataset {}, Scale Factor x{:.2f} ====".format(dataset_name, scale)
            )

            dataset = DownsampledDataset(
                datapath="load/benchmark/{0}/HR".format(dataset_name),
                scale=scale,
                is_train=False,
                cache="bin",
                rgb_range=config.data_module.args.rgb_range,
                mean=config.data_module.args.get("mean"),
                std=config.data_module.args.get("std"),
                return_img_name=True,
            )
            dataloader = torch.utils.data.DataLoader(
                dataset, batch_size=1, shuffle=False
            )

            psnrs, ssims, run_times, losses = [], [], [], []
            for batch_idx, batch in tqdm(enumerate(dataloader), total=len(dataset)):
                if args.gpus:
                    lr, hr, name = batch
                    batch = (lr.cuda(), hr.cuda(), name)
                with torch.no_grad():
                    rslt = model.test_step(
                        batch,
                        batch_idx,
                        test_Y=False,
                        no_crop_border=False,
                        self_ensemble=args.self_ensemble,
                    )

                file_path = os.path.join(rslt_path, rslt["name"])

                if "log_img" in rslt.keys():
                    plt.imsave(file_path, rslt["log_img"])
                if "lr" in rslt.keys():
                    plt.imsave(file_path.replace(".png", "_lr.png"), rslt["lr"])
                if "val_loss" in rslt.keys():
                    losses.append(rslt["val_loss"])
                if "val_psnr" in rslt.keys():
                    psnrs.append(rslt["val_psnr"])
                if "val_ssim" in rslt.keys():
                    ssims.append(rslt["val_ssim"])
                if "time" in rslt.keys():
                    run_times.append(rslt["time"])

            if losses:
                mean_loss = np.array(losses).mean()
                print("- Loss: {:.4f}".format(mean_loss))
            if psnrs:
                mean_psnr = np.array(psnrs).mean()
                print("- PSNR: {:.4f}".format(mean_psnr))
            if ssims:
                mean_ssim = np.array(ssims).mean()
                print("- SSIM: {:.4f}".format(mean_ssim))
            if run_times:
                mean_runtime = np.array(run_times[1:]).mean()
                print("- Runtime : {:.4f}".format(mean_runtime))
            print("=" * 42)


def getTestParser():
    parser = argparse.ArgumentParser()
    parser.add_argument("-c", "--checkpoint", type=str, help="checkpoint index")
    parser.add_argument(
        "-g",
        "--gpus",
        default="0",
        type=str,
        help="indices of GPUs to enable (default: all)",
    )
    parser.add_argument("--datasets", default="", type=str, help="dataset names")
    parser.add_argument("--scales", default="", type=str, help="scale factors")
    parser.add_argument("--self_ensemble", action="store_true", help="self_ensemble")

    return parser


test_parser = getTestParser()

if __name__ == "__main__":
    args = test_parser.parse_args()
    test_pipeline(args)