563 lines
22 KiB
Python
563 lines
22 KiB
Python
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from typing import Dict, List, Optional, Tuple, Union
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import librosa
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import numpy as np
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import torch
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from coqpit import Coqpit
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from torch import nn
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from torch.nn import Conv1d, Conv2d, ConvTranspose1d
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from torch.nn import functional as F
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from torch.nn.utils import spectral_norm
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from torch.nn.utils.parametrizations import weight_norm
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from torch.nn.utils.parametrize import remove_parametrizations
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import TTS.vc.modules.freevc.commons as commons
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import TTS.vc.modules.freevc.modules as modules
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from TTS.tts.utils.speakers import SpeakerManager
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from TTS.utils.io import load_fsspec
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from TTS.vc.configs.freevc_config import FreeVCConfig
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from TTS.vc.models.base_vc import BaseVC
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from TTS.vc.modules.freevc.commons import get_padding, init_weights
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from TTS.vc.modules.freevc.mel_processing import mel_spectrogram_torch
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from TTS.vc.modules.freevc.speaker_encoder.speaker_encoder import SpeakerEncoder as SpeakerEncoderEx
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from TTS.vc.modules.freevc.wavlm import get_wavlm
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class ResidualCouplingBlock(nn.Module):
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def __init__(self, channels, hidden_channels, kernel_size, dilation_rate, n_layers, n_flows=4, gin_channels=0):
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super().__init__()
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self.channels = channels
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self.hidden_channels = hidden_channels
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self.kernel_size = kernel_size
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self.dilation_rate = dilation_rate
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self.n_layers = n_layers
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self.n_flows = n_flows
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self.gin_channels = gin_channels
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self.flows = nn.ModuleList()
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for i in range(n_flows):
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self.flows.append(
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modules.ResidualCouplingLayer(
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channels,
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hidden_channels,
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kernel_size,
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dilation_rate,
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n_layers,
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gin_channels=gin_channels,
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mean_only=True,
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)
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)
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self.flows.append(modules.Flip())
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def forward(self, x, x_mask, g=None, reverse=False):
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if not reverse:
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for flow in self.flows:
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x, _ = flow(x, x_mask, g=g, reverse=reverse)
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else:
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for flow in reversed(self.flows):
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x = flow(x, x_mask, g=g, reverse=reverse)
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return x
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class Encoder(nn.Module):
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def __init__(
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self, in_channels, out_channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0
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):
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super().__init__()
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self.in_channels = in_channels
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self.out_channels = out_channels
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self.hidden_channels = hidden_channels
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self.kernel_size = kernel_size
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self.dilation_rate = dilation_rate
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self.n_layers = n_layers
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self.gin_channels = gin_channels
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self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
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self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
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self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
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def forward(self, x, x_lengths, g=None):
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x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
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x = self.pre(x) * x_mask
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x = self.enc(x, x_mask, g=g)
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stats = self.proj(x) * x_mask
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m, logs = torch.split(stats, self.out_channels, dim=1)
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z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
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return z, m, logs, x_mask
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class Generator(torch.nn.Module):
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def __init__(
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self,
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initial_channel,
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resblock,
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resblock_kernel_sizes,
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resblock_dilation_sizes,
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upsample_rates,
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upsample_initial_channel,
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upsample_kernel_sizes,
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gin_channels=0,
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):
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super(Generator, self).__init__()
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self.num_kernels = len(resblock_kernel_sizes)
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self.num_upsamples = len(upsample_rates)
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self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
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resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
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self.ups = nn.ModuleList()
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for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
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self.ups.append(
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weight_norm(
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ConvTranspose1d(
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upsample_initial_channel // (2**i),
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upsample_initial_channel // (2 ** (i + 1)),
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k,
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u,
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padding=(k - u) // 2,
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)
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)
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)
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self.resblocks = nn.ModuleList()
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for i in range(len(self.ups)):
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ch = upsample_initial_channel // (2 ** (i + 1))
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for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
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self.resblocks.append(resblock(ch, k, d))
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self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
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self.ups.apply(init_weights)
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if gin_channels != 0:
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self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
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def forward(self, x, g=None):
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x = self.conv_pre(x)
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if g is not None:
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x = x + self.cond(g)
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for i in range(self.num_upsamples):
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x = F.leaky_relu(x, modules.LRELU_SLOPE)
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x = self.ups[i](x)
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xs = None
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for j in range(self.num_kernels):
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if xs is None:
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xs = self.resblocks[i * self.num_kernels + j](x)
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else:
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xs += self.resblocks[i * self.num_kernels + j](x)
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x = xs / self.num_kernels
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x = F.leaky_relu(x)
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x = self.conv_post(x)
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x = torch.tanh(x)
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return x
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def remove_weight_norm(self):
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print("Removing weight norm...")
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for l in self.ups:
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remove_parametrizations(l, "weight")
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for l in self.resblocks:
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remove_parametrizations(l, "weight")
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class DiscriminatorP(torch.nn.Module):
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def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
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super(DiscriminatorP, self).__init__()
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self.period = period
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self.use_spectral_norm = use_spectral_norm
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norm_f = weight_norm if use_spectral_norm == False else spectral_norm
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self.convs = nn.ModuleList(
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[
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norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
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norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
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norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
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norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
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norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
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]
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)
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self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
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def forward(self, x):
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fmap = []
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# 1d to 2d
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b, c, t = x.shape
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if t % self.period != 0: # pad first
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n_pad = self.period - (t % self.period)
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x = F.pad(x, (0, n_pad), "reflect")
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t = t + n_pad
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x = x.view(b, c, t // self.period, self.period)
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for l in self.convs:
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x = l(x)
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x = F.leaky_relu(x, modules.LRELU_SLOPE)
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fmap.append(x)
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x = self.conv_post(x)
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fmap.append(x)
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x = torch.flatten(x, 1, -1)
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return x, fmap
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class DiscriminatorS(torch.nn.Module):
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def __init__(self, use_spectral_norm=False):
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super(DiscriminatorS, self).__init__()
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norm_f = weight_norm if use_spectral_norm == False else spectral_norm
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self.convs = nn.ModuleList(
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[
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norm_f(Conv1d(1, 16, 15, 1, padding=7)),
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norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
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norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
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norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
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norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
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norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
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]
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)
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self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
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def forward(self, x):
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fmap = []
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for l in self.convs:
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x = l(x)
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x = F.leaky_relu(x, modules.LRELU_SLOPE)
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fmap.append(x)
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x = self.conv_post(x)
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fmap.append(x)
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x = torch.flatten(x, 1, -1)
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return x, fmap
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class MultiPeriodDiscriminator(torch.nn.Module):
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def __init__(self, use_spectral_norm=False):
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super(MultiPeriodDiscriminator, self).__init__()
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periods = [2, 3, 5, 7, 11]
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discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
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discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
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self.discriminators = nn.ModuleList(discs)
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def forward(self, y, y_hat):
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y_d_rs = []
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y_d_gs = []
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fmap_rs = []
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fmap_gs = []
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for i, d in enumerate(self.discriminators):
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y_d_r, fmap_r = d(y)
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y_d_g, fmap_g = d(y_hat)
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y_d_rs.append(y_d_r)
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y_d_gs.append(y_d_g)
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fmap_rs.append(fmap_r)
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fmap_gs.append(fmap_g)
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return y_d_rs, y_d_gs, fmap_rs, fmap_gs
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class SpeakerEncoder(torch.nn.Module):
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def __init__(self, mel_n_channels=80, model_num_layers=3, model_hidden_size=256, model_embedding_size=256):
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super(SpeakerEncoder, self).__init__()
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self.lstm = nn.LSTM(mel_n_channels, model_hidden_size, model_num_layers, batch_first=True)
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self.linear = nn.Linear(model_hidden_size, model_embedding_size)
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self.relu = nn.ReLU()
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def forward(self, mels):
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self.lstm.flatten_parameters()
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_, (hidden, _) = self.lstm(mels)
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embeds_raw = self.relu(self.linear(hidden[-1]))
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return embeds_raw / torch.norm(embeds_raw, dim=1, keepdim=True)
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def compute_partial_slices(self, total_frames, partial_frames, partial_hop):
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mel_slices = []
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for i in range(0, total_frames - partial_frames, partial_hop):
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mel_range = torch.arange(i, i + partial_frames)
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mel_slices.append(mel_range)
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return mel_slices
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def embed_utterance(self, mel, partial_frames=128, partial_hop=64):
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mel_len = mel.size(1)
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last_mel = mel[:, -partial_frames:]
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if mel_len > partial_frames:
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mel_slices = self.compute_partial_slices(mel_len, partial_frames, partial_hop)
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mels = list(mel[:, s] for s in mel_slices)
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mels.append(last_mel)
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mels = torch.stack(tuple(mels), 0).squeeze(1)
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with torch.no_grad():
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partial_embeds = self(mels)
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embed = torch.mean(partial_embeds, axis=0).unsqueeze(0)
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# embed = embed / torch.linalg.norm(embed, 2)
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else:
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with torch.no_grad():
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embed = self(last_mel)
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return embed
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class FreeVC(BaseVC):
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"""
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Papaer::
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https://arxiv.org/abs/2210.15418#
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Paper Abstract::
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Voice conversion (VC) can be achieved by first extracting source content information and target speaker
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information, and then reconstructing waveform with these information. However, current approaches normally
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either extract dirty content information with speaker information leaked in, or demand a large amount of
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annotated data for training. Besides, the quality of reconstructed waveform can be degraded by the
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mismatch between conversion model and vocoder. In this paper, we adopt the end-to-end framework of VITS for
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high-quality waveform reconstruction, and propose strategies for clean content information extraction without
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text annotation. We disentangle content information by imposing an information bottleneck to WavLM features,
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and propose the spectrogram-resize based data augmentation to improve the purity of extracted content
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information. Experimental results show that the proposed method outperforms the latest VC models trained with
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annotated data and has greater robustness.
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Original Code::
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https://github.com/OlaWod/FreeVC
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Examples:
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>>> from TTS.vc.configs.freevc_config import FreeVCConfig
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>>> from TTS.vc.models.freevc import FreeVC
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>>> config = FreeVCConfig()
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>>> model = FreeVC(config)
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"""
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def __init__(self, config: Coqpit, speaker_manager: SpeakerManager = None):
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super().__init__(config, None, speaker_manager, None)
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self.init_multispeaker(config)
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self.spec_channels = self.args.spec_channels
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self.inter_channels = self.args.inter_channels
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self.hidden_channels = self.args.hidden_channels
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self.filter_channels = self.args.filter_channels
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self.n_heads = self.args.n_heads
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self.n_layers = self.args.n_layers
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self.kernel_size = self.args.kernel_size
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|
self.p_dropout = self.args.p_dropout
|
||
|
|
self.resblock = self.args.resblock
|
||
|
|
self.resblock_kernel_sizes = self.args.resblock_kernel_sizes
|
||
|
|
self.resblock_dilation_sizes = self.args.resblock_dilation_sizes
|
||
|
|
self.upsample_rates = self.args.upsample_rates
|
||
|
|
self.upsample_initial_channel = self.args.upsample_initial_channel
|
||
|
|
self.upsample_kernel_sizes = self.args.upsample_kernel_sizes
|
||
|
|
self.segment_size = self.args.segment_size
|
||
|
|
self.gin_channels = self.args.gin_channels
|
||
|
|
self.ssl_dim = self.args.ssl_dim
|
||
|
|
self.use_spk = self.args.use_spk
|
||
|
|
|
||
|
|
self.enc_p = Encoder(self.args.ssl_dim, self.inter_channels, self.hidden_channels, 5, 1, 16)
|
||
|
|
self.dec = Generator(
|
||
|
|
self.inter_channels,
|
||
|
|
self.resblock,
|
||
|
|
self.resblock_kernel_sizes,
|
||
|
|
self.resblock_dilation_sizes,
|
||
|
|
self.upsample_rates,
|
||
|
|
self.upsample_initial_channel,
|
||
|
|
self.upsample_kernel_sizes,
|
||
|
|
gin_channels=self.gin_channels,
|
||
|
|
)
|
||
|
|
self.enc_q = Encoder(
|
||
|
|
self.spec_channels, self.inter_channels, self.hidden_channels, 5, 1, 16, gin_channels=self.gin_channels
|
||
|
|
)
|
||
|
|
self.flow = ResidualCouplingBlock(
|
||
|
|
self.inter_channels, self.hidden_channels, 5, 1, 4, gin_channels=self.gin_channels
|
||
|
|
)
|
||
|
|
if not self.use_spk:
|
||
|
|
self.enc_spk = SpeakerEncoder(model_hidden_size=self.gin_channels, model_embedding_size=self.gin_channels)
|
||
|
|
else:
|
||
|
|
self.load_pretrained_speaker_encoder()
|
||
|
|
|
||
|
|
self.wavlm = get_wavlm()
|
||
|
|
|
||
|
|
@property
|
||
|
|
def device(self):
|
||
|
|
return next(self.parameters()).device
|
||
|
|
|
||
|
|
def load_pretrained_speaker_encoder(self):
|
||
|
|
"""Load pretrained speaker encoder model as mentioned in the paper."""
|
||
|
|
print(" > Loading pretrained speaker encoder model ...")
|
||
|
|
self.enc_spk_ex = SpeakerEncoderEx(
|
||
|
|
"https://github.com/coqui-ai/TTS/releases/download/v0.13.0_models/speaker_encoder.pt"
|
||
|
|
)
|
||
|
|
|
||
|
|
def init_multispeaker(self, config: Coqpit):
|
||
|
|
"""Initialize multi-speaker modules of a model. A model can be trained either with a speaker embedding layer
|
||
|
|
or with external `d_vectors` computed from a speaker encoder model.
|
||
|
|
|
||
|
|
You must provide a `speaker_manager` at initialization to set up the multi-speaker modules.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
config (Coqpit): Model configuration.
|
||
|
|
data (List, optional): Dataset items to infer number of speakers. Defaults to None.
|
||
|
|
"""
|
||
|
|
self.num_spks = self.args.num_spks
|
||
|
|
if self.speaker_manager:
|
||
|
|
self.num_spks = self.speaker_manager.num_spks
|
||
|
|
|
||
|
|
def forward(
|
||
|
|
self,
|
||
|
|
c: torch.Tensor,
|
||
|
|
spec: torch.Tensor,
|
||
|
|
g: Optional[torch.Tensor] = None,
|
||
|
|
mel: Optional[torch.Tensor] = None,
|
||
|
|
c_lengths: Optional[torch.Tensor] = None,
|
||
|
|
spec_lengths: Optional[torch.Tensor] = None,
|
||
|
|
) -> Tuple[
|
||
|
|
torch.Tensor,
|
||
|
|
torch.Tensor,
|
||
|
|
torch.Tensor,
|
||
|
|
Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor],
|
||
|
|
]:
|
||
|
|
"""
|
||
|
|
Forward pass of the model.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
c: WavLM features. Shape: (batch_size, c_seq_len).
|
||
|
|
spec: The input spectrogram. Shape: (batch_size, spec_seq_len, spec_dim).
|
||
|
|
g: The speaker embedding. Shape: (batch_size, spk_emb_dim).
|
||
|
|
mel: The input mel-spectrogram for the speaker encoder. Shape: (batch_size, mel_seq_len, mel_dim).
|
||
|
|
c_lengths: The lengths of the WavLM features. Shape: (batch_size,).
|
||
|
|
spec_lengths: The lengths of the spectrogram. Shape: (batch_size,).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
o: The output spectrogram. Shape: (batch_size, spec_seq_len, spec_dim).
|
||
|
|
ids_slice: The slice indices. Shape: (batch_size, num_slices).
|
||
|
|
spec_mask: The spectrogram mask. Shape: (batch_size, spec_seq_len).
|
||
|
|
(z, z_p, m_p, logs_p, m_q, logs_q): A tuple of latent variables.
|
||
|
|
"""
|
||
|
|
|
||
|
|
# If c_lengths is None, set it to the length of the last dimension of c
|
||
|
|
if c_lengths is None:
|
||
|
|
c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
|
||
|
|
|
||
|
|
# If spec_lengths is None, set it to the length of the last dimension of spec
|
||
|
|
if spec_lengths is None:
|
||
|
|
spec_lengths = (torch.ones(spec.size(0)) * spec.size(-1)).to(spec.device)
|
||
|
|
|
||
|
|
# If use_spk is False, compute g from mel using enc_spk
|
||
|
|
g = None
|
||
|
|
if not self.use_spk:
|
||
|
|
g = self.enc_spk(mel).unsqueeze(-1)
|
||
|
|
|
||
|
|
# Compute m_p, logs_p, z, m_q, logs_q, and spec_mask using enc_p and enc_q
|
||
|
|
_, m_p, logs_p, _ = self.enc_p(c, c_lengths)
|
||
|
|
z, m_q, logs_q, spec_mask = self.enc_q(spec.transpose(1, 2), spec_lengths, g=g)
|
||
|
|
|
||
|
|
# Compute z_p using flow
|
||
|
|
z_p = self.flow(z, spec_mask, g=g)
|
||
|
|
|
||
|
|
# Randomly slice z and compute o using dec
|
||
|
|
z_slice, ids_slice = commons.rand_slice_segments(z, spec_lengths, self.segment_size)
|
||
|
|
o = self.dec(z_slice, g=g)
|
||
|
|
|
||
|
|
return o, ids_slice, spec_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
|
||
|
|
|
||
|
|
@torch.no_grad()
|
||
|
|
def inference(self, c, g=None, mel=None, c_lengths=None):
|
||
|
|
"""
|
||
|
|
Inference pass of the model
|
||
|
|
|
||
|
|
Args:
|
||
|
|
c (torch.Tensor): Input tensor. Shape: (batch_size, c_seq_len).
|
||
|
|
g (torch.Tensor): Speaker embedding tensor. Shape: (batch_size, spk_emb_dim).
|
||
|
|
mel (torch.Tensor): Mel-spectrogram tensor. Shape: (batch_size, mel_seq_len, mel_dim).
|
||
|
|
c_lengths (torch.Tensor): Lengths of the input tensor. Shape: (batch_size,).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
torch.Tensor: Output tensor.
|
||
|
|
"""
|
||
|
|
if c_lengths == None:
|
||
|
|
c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
|
||
|
|
if not self.use_spk:
|
||
|
|
g = self.enc_spk.embed_utterance(mel)
|
||
|
|
g = g.unsqueeze(-1)
|
||
|
|
z_p, m_p, logs_p, c_mask = self.enc_p(c, c_lengths)
|
||
|
|
z = self.flow(z_p, c_mask, g=g, reverse=True)
|
||
|
|
o = self.dec(z * c_mask, g=g)
|
||
|
|
return o
|
||
|
|
|
||
|
|
def extract_wavlm_features(self, y):
|
||
|
|
"""Extract WavLM features from an audio tensor.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
y (torch.Tensor): Audio tensor. Shape: (batch_size, audio_seq_len).
|
||
|
|
"""
|
||
|
|
|
||
|
|
with torch.no_grad():
|
||
|
|
c = self.wavlm.extract_features(y)[0]
|
||
|
|
c = c.transpose(1, 2)
|
||
|
|
return c
|
||
|
|
|
||
|
|
def load_audio(self, wav):
|
||
|
|
"""Read and format the input audio."""
|
||
|
|
if isinstance(wav, str):
|
||
|
|
wav, _ = librosa.load(wav, sr=self.config.audio.input_sample_rate)
|
||
|
|
if isinstance(wav, np.ndarray):
|
||
|
|
wav = torch.from_numpy(wav).to(self.device)
|
||
|
|
if isinstance(wav, torch.Tensor):
|
||
|
|
wav = wav.to(self.device)
|
||
|
|
if isinstance(wav, list):
|
||
|
|
wav = torch.from_numpy(np.array(wav)).to(self.device)
|
||
|
|
return wav.float()
|
||
|
|
|
||
|
|
@torch.inference_mode()
|
||
|
|
def voice_conversion(self, src, tgt):
|
||
|
|
"""
|
||
|
|
Voice conversion pass of the model.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
src (str or torch.Tensor): Source utterance.
|
||
|
|
tgt (str or torch.Tensor): Target utterance.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
torch.Tensor: Output tensor.
|
||
|
|
"""
|
||
|
|
|
||
|
|
wav_tgt = self.load_audio(tgt).cpu().numpy()
|
||
|
|
wav_tgt, _ = librosa.effects.trim(wav_tgt, top_db=20)
|
||
|
|
|
||
|
|
if self.config.model_args.use_spk:
|
||
|
|
g_tgt = self.enc_spk_ex.embed_utterance(wav_tgt)
|
||
|
|
g_tgt = torch.from_numpy(g_tgt)[None, :, None].to(self.device)
|
||
|
|
else:
|
||
|
|
wav_tgt = torch.from_numpy(wav_tgt).unsqueeze(0).to(self.device)
|
||
|
|
mel_tgt = mel_spectrogram_torch(
|
||
|
|
wav_tgt,
|
||
|
|
self.config.audio.filter_length,
|
||
|
|
self.config.audio.n_mel_channels,
|
||
|
|
self.config.audio.input_sample_rate,
|
||
|
|
self.config.audio.hop_length,
|
||
|
|
self.config.audio.win_length,
|
||
|
|
self.config.audio.mel_fmin,
|
||
|
|
self.config.audio.mel_fmax,
|
||
|
|
)
|
||
|
|
# src
|
||
|
|
wav_src = self.load_audio(src)
|
||
|
|
c = self.extract_wavlm_features(wav_src[None, :])
|
||
|
|
|
||
|
|
if self.config.model_args.use_spk:
|
||
|
|
audio = self.inference(c, g=g_tgt)
|
||
|
|
else:
|
||
|
|
audio = self.inference(c, mel=mel_tgt.transpose(1, 2))
|
||
|
|
audio = audio[0][0].data.cpu().float().numpy()
|
||
|
|
return audio
|
||
|
|
|
||
|
|
def eval_step():
|
||
|
|
...
|
||
|
|
|
||
|
|
@staticmethod
|
||
|
|
def init_from_config(config: FreeVCConfig, samples: Union[List[List], List[Dict]] = None, verbose=True):
|
||
|
|
model = FreeVC(config)
|
||
|
|
return model
|
||
|
|
|
||
|
|
def load_checkpoint(self, config, checkpoint_path, eval=False, strict=True, cache=False):
|
||
|
|
state = load_fsspec(checkpoint_path, map_location=torch.device("cpu"), cache=cache)
|
||
|
|
self.load_state_dict(state["model"], strict=strict)
|
||
|
|
if eval:
|
||
|
|
self.eval()
|
||
|
|
|
||
|
|
def train_step():
|
||
|
|
...
|