File size: 8,061 Bytes
82d966b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
import argparse
import os
import sys
import json
from multiprocessing import cpu_count

import torch

try:
    import intel_extension_for_pytorch as ipex  # pylint: disable=import-error, unused-import

    if torch.xpu.is_available():
        from infer.modules.ipex import ipex_init

        ipex_init()
except Exception:  # pylint: disable=broad-exception-caught
    pass
import logging

logger = logging.getLogger(__name__)


version_config_list = [
    "v1/32k.json",
    "v1/40k.json",
    "v1/48k.json",
    "v2/48k.json",
    "v2/32k.json",
]


def singleton_variable(func):
    def wrapper(*args, **kwargs):
        if not wrapper.instance:
            wrapper.instance = func(*args, **kwargs)
        return wrapper.instance

    wrapper.instance = None
    return wrapper


@singleton_variable
class Config:
    def __init__(self):
        self.device = "cuda:0"
        self.is_half = True
        self.use_jit = False
        self.n_cpu = 0
        self.gpu_name = None
        self.json_config = self.load_config_json()
        self.gpu_mem = None
        (
            self.python_cmd,
            self.listen_port,
            self.iscolab,
            self.noparallel,
            self.noautoopen,
            self.dml,
        ) = self.arg_parse()
        self.instead = ""
        self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config()

    @staticmethod
    def load_config_json() -> dict:
        d = {}
        for config_file in version_config_list:
            with open(f"configs/{config_file}", "r") as f:
                d[config_file] = json.load(f)
        return d

    @staticmethod
    def arg_parse() -> tuple:
        exe = sys.executable or "python"
        parser = argparse.ArgumentParser()
        parser.add_argument("--port", type=int, default=7865, help="Listen port")
        parser.add_argument("--pycmd", type=str, default=exe, help="Python command")
        parser.add_argument("--colab", action="store_true", help="Launch in colab")
        parser.add_argument(
            "--noparallel", action="store_true", help="Disable parallel processing"
        )
        parser.add_argument(
            "--noautoopen",
            action="store_true",
            help="Do not open in browser automatically",
        )
        parser.add_argument(
            "--dml",
            action="store_true",
            help="torch_dml",
        )
        cmd_opts = parser.parse_args()

        cmd_opts.port = cmd_opts.port if 0 <= cmd_opts.port <= 65535 else 7865

        return (
            cmd_opts.pycmd,
            cmd_opts.port,
            cmd_opts.colab,
            cmd_opts.noparallel,
            cmd_opts.noautoopen,
            cmd_opts.dml,
        )

    # has_mps is only available in nightly pytorch (for now) and MasOS 12.3+.
    # check `getattr` and try it for compatibility
    @staticmethod
    def has_mps() -> bool:
        if not torch.backends.mps.is_available():
            return False
        try:
            torch.zeros(1).to(torch.device("mps"))
            return True
        except Exception:
            return False

    @staticmethod
    def has_xpu() -> bool:
        if hasattr(torch, "xpu") and torch.xpu.is_available():
            return True
        else:
            return False

    def use_fp32_config(self):
        for config_file in version_config_list:
            self.json_config[config_file]["train"]["fp16_run"] = False
            with open(f"configs/{config_file}", "r") as f:
                strr = f.read().replace("true", "false")
            with open(f"configs/{config_file}", "w") as f:
                f.write(strr)
        with open("infer/modules/train/preprocess.py", "r") as f:
            strr = f.read().replace("3.7", "3.0")
        with open("infer/modules/train/preprocess.py", "w") as f:
            f.write(strr)
        print("overwrite preprocess and configs.json")

    def device_config(self) -> tuple:
        if torch.cuda.is_available():
            if self.has_xpu():
                self.device = self.instead = "xpu:0"
                self.is_half = True
            i_device = int(self.device.split(":")[-1])
            self.gpu_name = torch.cuda.get_device_name(i_device)
            if (
                ("16" in self.gpu_name and "V100" not in self.gpu_name.upper())
                or "P40" in self.gpu_name.upper()
                or "P10" in self.gpu_name.upper()
                or "1060" in self.gpu_name
                or "1070" in self.gpu_name
                or "1080" in self.gpu_name
            ):
                logger.info("Found GPU %s, force to fp32", self.gpu_name)
                self.is_half = False
                self.use_fp32_config()
            else:
                logger.info("Found GPU %s", self.gpu_name)
            self.gpu_mem = int(
                torch.cuda.get_device_properties(i_device).total_memory
                / 1024
                / 1024
                / 1024
                + 0.4
            )
            if self.gpu_mem <= 4:
                with open("infer/modules/train/preprocess.py", "r") as f:
                    strr = f.read().replace("3.7", "3.0")
                with open("infer/modules/train/preprocess.py", "w") as f:
                    f.write(strr)
        elif self.has_mps():
            logger.info("No supported Nvidia GPU found")
            self.device = self.instead = "mps"
            self.is_half = False
            self.use_fp32_config()
        else:
            logger.info("No supported Nvidia GPU found")
            self.device = self.instead = "cpu"
            self.is_half = False
            self.use_fp32_config()

        if self.n_cpu == 0:
            self.n_cpu = cpu_count()

        if self.is_half:
            # 6G显存配置
            x_pad = 3
            x_query = 10
            x_center = 60
            x_max = 65
        else:
            # 5G显存配置
            x_pad = 1
            x_query = 6
            x_center = 38
            x_max = 41

        if self.gpu_mem is not None and self.gpu_mem <= 4:
            x_pad = 1
            x_query = 5
            x_center = 30
            x_max = 32
        if self.dml:
            logger.info("Use DirectML instead")
            if (
                os.path.exists(
                    "runtime\Lib\site-packages\onnxruntime\capi\DirectML.dll"
                )
                == False
            ):
                try:
                    os.rename(
                        "runtime\Lib\site-packages\onnxruntime",
                        "runtime\Lib\site-packages\onnxruntime-cuda",
                    )
                except:
                    pass
                try:
                    os.rename(
                        "runtime\Lib\site-packages\onnxruntime-dml",
                        "runtime\Lib\site-packages\onnxruntime",
                    )
                except:
                    pass
            # if self.device != "cpu":
            import torch_directml

            self.device = torch_directml.device(torch_directml.default_device())
            self.is_half = False
        else:
            if self.instead:
                logger.info(f"Use {self.instead} instead")
            if (
                os.path.exists(
                    "runtime\Lib\site-packages\onnxruntime\capi\onnxruntime_providers_cuda.dll"
                )
                == False
            ):
                try:
                    os.rename(
                        "runtime\Lib\site-packages\onnxruntime",
                        "runtime\Lib\site-packages\onnxruntime-dml",
                    )
                except:
                    pass
                try:
                    os.rename(
                        "runtime\Lib\site-packages\onnxruntime-cuda",
                        "runtime\Lib\site-packages\onnxruntime",
                    )
                except:
                    pass
        print("is_half:%s, device:%s" % (self.is_half, self.device))
        return x_pad, x_query, x_center, x_max