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Unverified Commit 6028d58c authored by Yih-Dar's avatar Yih-Dar Committed by GitHub
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Remove dead code in `resnet.py` (#218) 



remove dead code in resnet.py
Co-authored-by: default avatarydshieh <ydshieh@users.noreply.github.com>
parent 60a14734
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src/diffusers/models/resnet.py
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...@@ -364,412 +364,12 @@ class ResnetBlock(nn.Module): ...@@ -364,412 +364,12 @@ class ResnetBlock(nn.Module):
return out return out
def set_weight(self, resnet):
self.norm1.weight.data = resnet.norm1.weight.data
self.norm1.bias.data = resnet.norm1.bias.data
self.conv1.weight.data = resnet.conv1.weight.data
self.conv1.bias.data = resnet.conv1.bias.data
if self.time_emb_proj is not None:
self.time_emb_proj.weight.data = resnet.temb_proj.weight.data
self.time_emb_proj.bias.data = resnet.temb_proj.bias.data
self.norm2.weight.data = resnet.norm2.weight.data
self.norm2.bias.data = resnet.norm2.bias.data
self.conv2.weight.data = resnet.conv2.weight.data
self.conv2.bias.data = resnet.conv2.bias.data
if self.use_nin_shortcut:
self.conv_shortcut.weight.data = resnet.nin_shortcut.weight.data
self.conv_shortcut.bias.data = resnet.nin_shortcut.bias.data
# THE FOLLOWING SHOULD BE DELETED ONCE ALL CHECKPOITNS ARE CONVERTED
# unet.py, unet_grad_tts.py, unet_ldm.py, unet_glide.py, unet_score_vde.py
# => All 2D-Resnets are included here now!
class ResnetBlock2D(nn.Module):
def __init__(
self,
*,
in_channels,
out_channels=None,
conv_shortcut=False,
dropout=0.0,
temb_channels=512,
groups=32,
groups_out=None,
pre_norm=True,
eps=1e-6,
non_linearity="swish",
time_embedding_norm="default",
kernel=None,
output_scale_factor=1.0,
use_nin_shortcut=None,
up=False,
down=False,
overwrite_for_grad_tts=False,
overwrite_for_ldm=False,
overwrite_for_glide=False,
overwrite_for_score_vde=False,
):
super().__init__()
self.pre_norm = pre_norm
self.in_channels = in_channels
out_channels = in_channels if out_channels is None else out_channels
self.out_channels = out_channels
self.use_conv_shortcut = conv_shortcut
self.time_embedding_norm = time_embedding_norm
self.up = up
self.down = down
self.output_scale_factor = output_scale_factor
if groups_out is None:
groups_out = groups
if self.pre_norm:
self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True)
else:
self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=out_channels, eps=eps, affine=True)
self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
if time_embedding_norm == "default" and temb_channels > 0:
self.temb_proj = torch.nn.Linear(temb_channels, out_channels)
elif time_embedding_norm == "scale_shift" and temb_channels > 0:
self.temb_proj = torch.nn.Linear(temb_channels, 2 * out_channels)
self.norm2 = torch.nn.GroupNorm(num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True)
self.dropout = torch.nn.Dropout(dropout)
self.conv2 = torch.nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
if non_linearity == "swish":
self.nonlinearity = lambda x: F.silu(x)
elif non_linearity == "mish":
self.nonlinearity = Mish()
elif non_linearity == "silu":
self.nonlinearity = nn.SiLU()
self.upsample = self.downsample = None
if self.up:
if kernel == "fir":
fir_kernel = (1, 3, 3, 1)
self.upsample = lambda x: upsample_2d(x, k=fir_kernel)
elif kernel == "sde_vp":
self.upsample = partial(F.interpolate, scale_factor=2.0, mode="nearest")
else:
self.upsample = Upsample2D(in_channels, use_conv=False)
elif self.down:
if kernel == "fir":
fir_kernel = (1, 3, 3, 1)
self.downsample = lambda x: downsample_2d(x, k=fir_kernel)
elif kernel == "sde_vp":
self.downsample = partial(F.avg_pool2d, kernel_size=2, stride=2)
else:
self.downsample = Downsample2D(in_channels, use_conv=False, padding=1, name="op")
self.use_nin_shortcut = self.in_channels != self.out_channels if use_nin_shortcut is None else use_nin_shortcut
self.nin_shortcut = None
if self.use_nin_shortcut:
self.nin_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
# TODO(SURAJ, PATRICK): ALL OF THE FOLLOWING OF THE INIT METHOD CAN BE DELETED ONCE WEIGHTS ARE CONVERTED
self.is_overwritten = False
self.overwrite_for_glide = overwrite_for_glide
self.overwrite_for_grad_tts = overwrite_for_grad_tts
self.overwrite_for_ldm = overwrite_for_ldm or overwrite_for_glide
self.overwrite_for_score_vde = overwrite_for_score_vde
if self.overwrite_for_grad_tts:
dim = in_channels
dim_out = out_channels
time_emb_dim = temb_channels
self.mlp = torch.nn.Sequential(Mish(), torch.nn.Linear(time_emb_dim, dim_out))
self.pre_norm = pre_norm
self.block1 = Block(dim, dim_out, groups=groups)
self.block2 = Block(dim_out, dim_out, groups=groups)
if dim != dim_out:
self.res_conv = torch.nn.Conv2d(dim, dim_out, 1)
else:
self.res_conv = torch.nn.Identity()
elif self.overwrite_for_ldm:
channels = in_channels
emb_channels = temb_channels
use_scale_shift_norm = False
non_linearity = "silu"
self.in_layers = nn.Sequential(
normalization(channels, swish=1.0),
nn.Identity(),
nn.Conv2d(channels, self.out_channels, 3, padding=1),
)
self.emb_layers = nn.Sequential(
nn.SiLU(),
linear(
emb_channels,
2 * self.out_channels if self.time_embedding_norm == "scale_shift" else self.out_channels,
),
)
self.out_layers = nn.Sequential(
normalization(self.out_channels, swish=0.0 if use_scale_shift_norm else 1.0),
nn.SiLU() if use_scale_shift_norm else nn.Identity(),
nn.Dropout(p=dropout),
zero_module(nn.Conv2d(self.out_channels, self.out_channels, 3, padding=1)),
)
if self.out_channels == in_channels:
self.skip_connection = nn.Identity()
else:
self.skip_connection = nn.Conv2d(channels, self.out_channels, 1)
self.set_weights_ldm()
elif self.overwrite_for_score_vde:
in_ch = in_channels
out_ch = out_channels
eps = 1e-6
num_groups = min(in_ch // 4, 32)
num_groups_out = min(out_ch // 4, 32)
temb_dim = temb_channels
self.GroupNorm_0 = nn.GroupNorm(num_groups=num_groups, num_channels=in_ch, eps=eps)
self.up = up
self.down = down
self.Conv_0 = nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1)
if temb_dim is not None:
self.Dense_0 = nn.Linear(temb_dim, out_ch)
nn.init.zeros_(self.Dense_0.bias)
self.GroupNorm_1 = nn.GroupNorm(num_groups=num_groups_out, num_channels=out_ch, eps=eps)
self.Dropout_0 = nn.Dropout(dropout)
self.Conv_1 = nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1)
if in_ch != out_ch or up or down:
# 1x1 convolution with DDPM initialization.
self.Conv_2 = nn.Conv2d(in_ch, out_ch, kernel_size=1, padding=0)
self.in_ch = in_ch
self.out_ch = out_ch
self.set_weights_score_vde()
def set_weights_grad_tts(self):
self.conv1.weight.data = self.block1.block[0].weight.data
self.conv1.bias.data = self.block1.block[0].bias.data
self.norm1.weight.data = self.block1.block[1].weight.data
self.norm1.bias.data = self.block1.block[1].bias.data
self.conv2.weight.data = self.block2.block[0].weight.data
self.conv2.bias.data = self.block2.block[0].bias.data
self.norm2.weight.data = self.block2.block[1].weight.data
self.norm2.bias.data = self.block2.block[1].bias.data
self.temb_proj.weight.data = self.mlp[1].weight.data
self.temb_proj.bias.data = self.mlp[1].bias.data
if self.in_channels != self.out_channels:
self.nin_shortcut.weight.data = self.res_conv.weight.data
self.nin_shortcut.bias.data = self.res_conv.bias.data
def set_weights_ldm(self):
self.norm1.weight.data = self.in_layers[0].weight.data
self.norm1.bias.data = self.in_layers[0].bias.data
self.conv1.weight.data = self.in_layers[-1].weight.data
self.conv1.bias.data = self.in_layers[-1].bias.data
self.temb_proj.weight.data = self.emb_layers[-1].weight.data
self.temb_proj.bias.data = self.emb_layers[-1].bias.data
self.norm2.weight.data = self.out_layers[0].weight.data
self.norm2.bias.data = self.out_layers[0].bias.data
self.conv2.weight.data = self.out_layers[-1].weight.data
self.conv2.bias.data = self.out_layers[-1].bias.data
if self.in_channels != self.out_channels:
self.nin_shortcut.weight.data = self.skip_connection.weight.data
self.nin_shortcut.bias.data = self.skip_connection.bias.data
def set_weights_score_vde(self):
self.conv1.weight.data = self.Conv_0.weight.data
self.conv1.bias.data = self.Conv_0.bias.data
self.norm1.weight.data = self.GroupNorm_0.weight.data
self.norm1.bias.data = self.GroupNorm_0.bias.data
self.conv2.weight.data = self.Conv_1.weight.data
self.conv2.bias.data = self.Conv_1.bias.data
self.norm2.weight.data = self.GroupNorm_1.weight.data
self.norm2.bias.data = self.GroupNorm_1.bias.data
self.temb_proj.weight.data = self.Dense_0.weight.data
self.temb_proj.bias.data = self.Dense_0.bias.data
if self.in_channels != self.out_channels or self.up or self.down:
self.nin_shortcut.weight.data = self.Conv_2.weight.data
self.nin_shortcut.bias.data = self.Conv_2.bias.data
def forward(self, x, temb, hey=False, mask=1.0):
# TODO(Patrick) eventually this class should be split into multiple classes
# too many if else statements
if self.overwrite_for_grad_tts and not self.is_overwritten:
self.set_weights_grad_tts()
self.is_overwritten = True
# elif self.overwrite_for_score_vde and not self.is_overwritten:
# self.set_weights_score_vde()
# self.is_overwritten = True
# h2 tensor(110029.2109)
# h3 tensor(49596.9492)
h = x
h = h * mask
if self.pre_norm:
h = self.norm1(h)
h = self.nonlinearity(h)
if self.upsample is not None:
x = self.upsample(x)
h = self.upsample(h)
elif self.downsample is not None:
x = self.downsample(x)
h = self.downsample(h)
h = self.conv1(h)
if not self.pre_norm:
h = self.norm1(h)
h = self.nonlinearity(h)
h = h * mask
if temb is not None:
temb = self.temb_proj(self.nonlinearity(temb))[:, :, None, None]
else:
temb = 0
if self.time_embedding_norm == "scale_shift":
scale, shift = torch.chunk(temb, 2, dim=1)
h = self.norm2(h)
h = h + h * scale + shift
h = self.nonlinearity(h)
elif self.time_embedding_norm == "default":
h = h + temb
h = h * mask
if self.pre_norm:
h = self.norm2(h)
h = self.nonlinearity(h)
h = self.dropout(h)
h = self.conv2(h)
if not self.pre_norm:
h = self.norm2(h)
h = self.nonlinearity(h)
h = h * mask
x = x * mask
if self.nin_shortcut is not None:
x = self.nin_shortcut(x)
out = (x + h) / self.output_scale_factor
return out
# TODO(Patrick) - just there to convert the weights; can delete afterward
class Block(torch.nn.Module):
def __init__(self, dim, dim_out, groups=8):
super(Block, self).__init__()
self.block = torch.nn.Sequential(
torch.nn.Conv2d(dim, dim_out, 3, padding=1), torch.nn.GroupNorm(groups, dim_out), Mish()
)
# HELPER Modules
def normalization(channels, swish=0.0):
"""
Make a standard normalization layer, with an optional swish activation.
:param channels: number of input channels. :return: an nn.Module for normalization.
"""
return GroupNorm32(num_channels=channels, num_groups=32, swish=swish)
class GroupNorm32(nn.GroupNorm):
def __init__(self, num_groups, num_channels, swish, eps=1e-5):
super().__init__(num_groups=num_groups, num_channels=num_channels, eps=eps)
self.swish = swish
def forward(self, x):
y = super().forward(x.float()).to(x.dtype)
if self.swish == 1.0:
y = F.silu(y)
elif self.swish:
y = y * F.sigmoid(y * float(self.swish))
return y
def linear(*args, **kwargs):
"""
Create a linear module.
"""
return nn.Linear(*args, **kwargs)
def zero_module(module):
"""
Zero out the parameters of a module and return it.
"""
for p in module.parameters():
p.detach().zero_()
return module
class Mish(torch.nn.Module): class Mish(torch.nn.Module):
def forward(self, x): def forward(self, x):
return x * torch.tanh(torch.nn.functional.softplus(x)) return x * torch.tanh(torch.nn.functional.softplus(x))
class Conv1dBlock(nn.Module):
"""
Conv1d --> GroupNorm --> Mish
"""
def __init__(self, inp_channels, out_channels, kernel_size, n_groups=8):
super().__init__()
self.block = nn.Sequential(
nn.Conv1d(inp_channels, out_channels, kernel_size, padding=kernel_size // 2),
RearrangeDim(),
# Rearrange("batch channels horizon -> batch channels 1 horizon"),
nn.GroupNorm(n_groups, out_channels),
RearrangeDim(),
# Rearrange("batch channels 1 horizon -> batch channels horizon"),
nn.Mish(),
)
def forward(self, x):
return self.block(x)
class RearrangeDim(nn.Module):
def __init__(self):
super().__init__()
def forward(self, tensor):
if len(tensor.shape) == 2:
return tensor[:, :, None]
if len(tensor.shape) == 3:
return tensor[:, :, None, :]
elif len(tensor.shape) == 4:
return tensor[:, :, 0, :]
else:
raise ValueError(f"`len(tensor)`: {len(tensor)} has to be 2, 3 or 4.")
def upsample_2d(x, k=None, factor=2, gain=1): def upsample_2d(x, k=None, factor=2, gain=1):
r"""Upsample2D a batch of 2D images with the given filter. r"""Upsample2D a batch of 2D images with the given filter.
......
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