quick fix to include non-fir kernels for sde-vp
This commit is contained in:
Patrick von Platen
parent
11667d08d3
commit
dcb9070bc2
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@ -237,24 +237,23 @@ class ResnetBlock(nn.Module):
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elif non_linearity == "silu":
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self.nonlinearity = nn.SiLU()
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# if up:
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# self.h_upd = Upsample(in_channels, use_conv=False, dims=2)
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# self.x_upd = Upsample(in_channels, use_conv=False, dims=2)
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# elif down:
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# self.h_upd = Downsample(in_channels, use_conv=False, dims=2, padding=1, name="op")
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# self.x_upd = Downsample(in_channels, use_conv=False, dims=2, padding=1, name="op")
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self.upsample = self.downsample = None
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if self.up and kernel == "fir":
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fir_kernel = (1, 3, 3, 1)
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self.upsample = lambda x: upsample_2d(x, k=fir_kernel)
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elif self.up and kernel is None:
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self.upsample = Upsample(in_channels, use_conv=False, dims=2)
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elif self.down and kernel == "fir":
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fir_kernel = (1, 3, 3, 1)
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self.downsample = lambda x: downsample_2d(x, k=fir_kernel)
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elif self.down and kernel is None:
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self.downsample = Downsample(in_channels, use_conv=False, dims=2, padding=1, name="op")
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if self.up:
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if kernel == "fir":
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fir_kernel = (1, 3, 3, 1)
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self.upsample = lambda x: upsample_2d(x, k=fir_kernel)
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elif kernel == "sde_vp":
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self.upsample = partial(F.interpolate, scale_factor=2.0, mode="nearest")
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else:
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self.upsample = Upsample(in_channels, use_conv=False, dims=2)
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elif self.down:
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if kernel == "fir":
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fir_kernel = (1, 3, 3, 1)
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self.downsample = lambda x: downsample_2d(x, k=fir_kernel)
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elif kernel == "sde_vp":
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self.downsample = partial(F.avg_pool2d, kernel_size=2, stride=2)
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else:
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self.downsample = Downsample(in_channels, use_conv=False, dims=2, padding=1, name="op")
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self.use_nin_shortcut = self.in_channels != self.out_channels if use_nin_shortcut is None else use_nin_shortcut
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@ -473,87 +472,6 @@ class Block(torch.nn.Module):
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)
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# unet_score_estimation.py
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class ResnetBlockBigGANpp(nn.Module):
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def __init__(
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self,
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act,
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in_ch,
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out_ch=None,
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temb_dim=None,
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up=False,
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down=False,
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dropout=0.1,
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fir=False,
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fir_kernel=(1, 3, 3, 1),
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skip_rescale=True,
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init_scale=0.0,
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):
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super().__init__()
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out_ch = out_ch if out_ch else in_ch
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self.GroupNorm_0 = nn.GroupNorm(num_groups=min(in_ch // 4, 32), num_channels=in_ch, eps=1e-6)
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self.up = up
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self.down = down
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self.fir = fir
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self.fir_kernel = fir_kernel
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if self.up:
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if self.fir:
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self.upsample = partial(upsample_2d, k=self.fir_kernel, factor=2)
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else:
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self.upsample = partial(F.interpolate, scale_factor=2.0, mode="nearest")
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elif self.down:
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if self.fir:
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self.downsample = partial(downsample_2d, k=self.fir_kernel, factor=2)
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else:
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self.downsample = partial(F.avg_pool2d, kernel_size=2, stride=2)
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self.Conv_0 = conv2d(in_ch, out_ch, kernel_size=3, padding=1)
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if temb_dim is not None:
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self.Dense_0 = nn.Linear(temb_dim, out_ch)
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self.Dense_0.weight.data = variance_scaling()(self.Dense_0.weight.shape)
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nn.init.zeros_(self.Dense_0.bias)
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self.GroupNorm_1 = nn.GroupNorm(num_groups=min(out_ch // 4, 32), num_channels=out_ch, eps=1e-6)
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self.Dropout_0 = nn.Dropout(dropout)
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self.Conv_1 = conv2d(out_ch, out_ch, init_scale=init_scale, kernel_size=3, padding=1)
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if in_ch != out_ch or up or down:
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# 1x1 convolution with DDPM initialization.
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self.Conv_2 = conv2d(in_ch, out_ch, kernel_size=1, padding=0)
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self.skip_rescale = skip_rescale
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self.act = act
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self.in_ch = in_ch
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self.out_ch = out_ch
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def forward(self, x, temb=None):
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h = self.act(self.GroupNorm_0(x))
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if self.up:
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h = self.upsample(h)
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x = self.upsample(x)
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elif self.down:
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h = self.downsample(h)
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x = self.downsample(x)
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h = self.Conv_0(h)
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# Add bias to each feature map conditioned on the time embedding
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if temb is not None:
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h += self.Dense_0(self.act(temb))[:, :, None, None]
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h = self.act(self.GroupNorm_1(h))
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h = self.Dropout_0(h)
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h = self.Conv_1(h)
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if self.in_ch != self.out_ch or self.up or self.down:
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x = self.Conv_2(x)
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if not self.skip_rescale:
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return x + h
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else:
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return (x + h) / np.sqrt(2.0)
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# unet_rl.py
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class ResidualTemporalBlock(nn.Module):
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def __init__(self, inp_channels, out_channels, embed_dim, horizon, kernel_size=5):
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@ -373,7 +373,7 @@ class NCSNpp(ModelMixin, ConfigMixin):
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groups_out=min(out_ch // 4, 32),
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overwrite_for_score_vde=True,
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down=True,
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kernel="fir", # TODO(Patrick) - it seems like both fir and non-fir kernels are fine
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kernel="fir" if self.fir else "sde_vp",
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use_nin_shortcut=True,
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)
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)
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@ -473,7 +473,7 @@ class NCSNpp(ModelMixin, ConfigMixin):
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groups_out=min(out_ch // 4, 32),
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overwrite_for_score_vde=True,
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up=True,
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kernel="fir", # TODO(Patrick) - it seems like both fir and non-fir kernels are fine
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kernel="fir" if self.fir else "sde_vp",
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use_nin_shortcut=True,
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)
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)
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