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import torch |
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import torch.nn as nn |
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import torch.nn.functional as F |
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from audiotools import AudioSignal |
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from audiotools import ml |
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from audiotools import STFTParams |
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from einops import rearrange |
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from torch.nn.utils import weight_norm |
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def WNConv1d(*args, **kwargs): |
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act = kwargs.pop("act", True) |
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conv = weight_norm(nn.Conv1d(*args, **kwargs)) |
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if not act: |
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return conv |
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return nn.Sequential(conv, nn.LeakyReLU(0.1)) |
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def WNConv2d(*args, **kwargs): |
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act = kwargs.pop("act", True) |
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conv = weight_norm(nn.Conv2d(*args, **kwargs)) |
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if not act: |
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return conv |
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return nn.Sequential(conv, nn.LeakyReLU(0.1)) |
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class MPD(nn.Module): |
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def __init__(self, period): |
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super().__init__() |
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self.period = period |
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self.convs = nn.ModuleList( |
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[ |
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WNConv2d(1, 32, (5, 1), (3, 1), padding=(2, 0)), |
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WNConv2d(32, 128, (5, 1), (3, 1), padding=(2, 0)), |
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WNConv2d(128, 512, (5, 1), (3, 1), padding=(2, 0)), |
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WNConv2d(512, 1024, (5, 1), (3, 1), padding=(2, 0)), |
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WNConv2d(1024, 1024, (5, 1), 1, padding=(2, 0)), |
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] |
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) |
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self.conv_post = WNConv2d( |
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1024, 1, kernel_size=(3, 1), padding=(1, 0), act=False |
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) |
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def pad_to_period(self, x): |
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t = x.shape[-1] |
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x = F.pad(x, (0, self.period - t % self.period), mode="reflect") |
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return x |
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def forward(self, x): |
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fmap = [] |
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x = self.pad_to_period(x) |
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x = rearrange(x, "b c (l p) -> b c l p", p=self.period) |
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for layer in self.convs: |
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x = layer(x) |
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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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return fmap |
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class MSD(nn.Module): |
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def __init__(self, rate: int = 1, sample_rate: int = 44100): |
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super().__init__() |
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self.convs = nn.ModuleList( |
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[ |
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WNConv1d(1, 16, 15, 1, padding=7), |
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WNConv1d(16, 64, 41, 4, groups=4, padding=20), |
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WNConv1d(64, 256, 41, 4, groups=16, padding=20), |
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WNConv1d(256, 1024, 41, 4, groups=64, padding=20), |
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WNConv1d(1024, 1024, 41, 4, groups=256, padding=20), |
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WNConv1d(1024, 1024, 5, 1, padding=2), |
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] |
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) |
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self.conv_post = WNConv1d(1024, 1, 3, 1, padding=1, act=False) |
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self.sample_rate = sample_rate |
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self.rate = rate |
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def forward(self, x): |
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x = AudioSignal(x, self.sample_rate) |
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x.resample(self.sample_rate // self.rate) |
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x = x.audio_data |
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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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fmap.append(x) |
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x = self.conv_post(x) |
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fmap.append(x) |
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return fmap |
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BANDS = [(0.0, 0.1), (0.1, 0.25), (0.25, 0.5), (0.5, 0.75), (0.75, 1.0)] |
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class MRD(nn.Module): |
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def __init__( |
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self, |
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window_length: int, |
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hop_factor: float = 0.25, |
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sample_rate: int = 44100, |
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bands: list = BANDS, |
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): |
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"""Complex multi-band spectrogram discriminator. |
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Parameters |
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---------- |
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window_length : int |
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Window length of STFT. |
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hop_factor : float, optional |
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Hop factor of the STFT, defaults to ``0.25 * window_length``. |
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sample_rate : int, optional |
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Sampling rate of audio in Hz, by default 44100 |
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bands : list, optional |
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Bands to run discriminator over. |
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""" |
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super().__init__() |
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self.window_length = window_length |
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self.hop_factor = hop_factor |
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self.sample_rate = sample_rate |
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self.stft_params = STFTParams( |
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window_length=window_length, |
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hop_length=int(window_length * hop_factor), |
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match_stride=True, |
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) |
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n_fft = window_length // 2 + 1 |
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bands = [(int(b[0] * n_fft), int(b[1] * n_fft)) for b in bands] |
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self.bands = bands |
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ch = 32 |
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convs = lambda: nn.ModuleList( |
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[ |
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WNConv2d(2, ch, (3, 9), (1, 1), padding=(1, 4)), |
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WNConv2d(ch, ch, (3, 9), (1, 2), padding=(1, 4)), |
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WNConv2d(ch, ch, (3, 9), (1, 2), padding=(1, 4)), |
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WNConv2d(ch, ch, (3, 9), (1, 2), padding=(1, 4)), |
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WNConv2d(ch, ch, (3, 3), (1, 1), padding=(1, 1)), |
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] |
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) |
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self.band_convs = nn.ModuleList([convs() for _ in range(len(self.bands))]) |
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self.conv_post = WNConv2d(ch, 1, (3, 3), (1, 1), padding=(1, 1), act=False) |
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def spectrogram(self, x): |
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x = AudioSignal(x, self.sample_rate, stft_params=self.stft_params) |
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x = torch.view_as_real(x.stft()) |
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x = rearrange(x, "b 1 f t c -> (b 1) c t f") |
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x_bands = [x[..., b[0] : b[1]] for b in self.bands] |
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return x_bands |
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def forward(self, x): |
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x_bands = self.spectrogram(x) |
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fmap = [] |
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x = [] |
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for band, stack in zip(x_bands, self.band_convs): |
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for layer in stack: |
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band = layer(band) |
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fmap.append(band) |
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x.append(band) |
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x = torch.cat(x, dim=-1) |
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x = self.conv_post(x) |
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fmap.append(x) |
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return fmap |
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class Discriminator(ml.BaseModel): |
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def __init__( |
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self, |
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rates: list = [], |
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periods: list = [2, 3, 5, 7, 11], |
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fft_sizes: list = [2048, 1024, 512], |
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sample_rate: int = 44100, |
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bands: list = BANDS, |
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): |
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"""Discriminator that combines multiple discriminators. |
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Parameters |
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---------- |
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rates : list, optional |
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sampling rates (in Hz) to run MSD at, by default [] |
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If empty, MSD is not used. |
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periods : list, optional |
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periods (of samples) to run MPD at, by default [2, 3, 5, 7, 11] |
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fft_sizes : list, optional |
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Window sizes of the FFT to run MRD at, by default [2048, 1024, 512] |
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sample_rate : int, optional |
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Sampling rate of audio in Hz, by default 44100 |
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bands : list, optional |
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Bands to run MRD at, by default `BANDS` |
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""" |
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super().__init__() |
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discs = [] |
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discs += [MPD(p) for p in periods] |
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discs += [MSD(r, sample_rate=sample_rate) for r in rates] |
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discs += [MRD(f, sample_rate=sample_rate, bands=bands) for f in fft_sizes] |
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self.discriminators = nn.ModuleList(discs) |
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def preprocess(self, y): |
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y = y - y.mean(dim=-1, keepdims=True) |
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y = 0.8 * y / (y.abs().max(dim=-1, keepdim=True)[0] + 1e-9) |
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return y |
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def forward(self, x): |
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x = self.preprocess(x) |
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fmaps = [d(x) for d in self.discriminators] |
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return fmaps |
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if __name__ == "__main__": |
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disc = Discriminator() |
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x = torch.zeros(1, 1, 44100) |
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results = disc(x) |
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for i, result in enumerate(results): |
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print(f"disc{i}") |
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for i, r in enumerate(result): |
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print(r.shape, r.mean(), r.min(), r.max()) |
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print() |
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