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feat: rename single-letter vars in resnet.py #3868

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Merged
merged 2 commits into from
Jun 28, 2023
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feat: rename single-letter vars in resnet.py #3868

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feat: rename single-letter vars
SauravMaheshkar Jun 25, 2023
df3f115
Merge branch 'main' into saurav/rename-single-letter-var
SauravMaheshkar Jun 27, 2023
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52 changes: 26 additions & 26 deletions src/diffusers/models/resnet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -95,9 +95,9 @@ def __init__(self, channels, use_conv=False, out_channels=None, padding=1, name=
assert self.channels == self.out_channels
self.conv = nn.AvgPool1d(kernel_size=stride, stride=stride)

def forward(self, x):
assert x.shape[1] == self.channels
return self.conv(x)
def forward(self, inputs):
assert inputs.shape[1] == self.channels
return self.conv(inputs)


class Upsample2D(nn.Module):
Expand Down Expand Up @@ -431,13 +431,13 @@ def __init__(self, pad_mode="reflect"):
self.pad = kernel_1d.shape[1] // 2 - 1
self.register_buffer("kernel", kernel_1d.T @ kernel_1d, persistent=False)

def forward(self, x):
x = F.pad(x, (self.pad,) * 4, self.pad_mode)
weight = x.new_zeros([x.shape[1], x.shape[1], self.kernel.shape[0], self.kernel.shape[1]])
indices = torch.arange(x.shape[1], device=x.device)
kernel = self.kernel.to(weight)[None, :].expand(x.shape[1], -1, -1)
def forward(self, inputs):
inputs = F.pad(inputs, (self.pad,) * 4, self.pad_mode)
weight = inputs.new_zeros([inputs.shape[1], inputs.shape[1], self.kernel.shape[0], self.kernel.shape[1]])
indices = torch.arange(inputs.shape[1], device=inputs.device)
kernel = self.kernel.to(weight)[None, :].expand(inputs.shape[1], -1, -1)
weight[indices, indices] = kernel
return F.conv2d(x, weight, stride=2)
return F.conv2d(inputs, weight, stride=2)


class KUpsample2D(nn.Module):
Expand All @@ -448,13 +448,13 @@ def __init__(self, pad_mode="reflect"):
self.pad = kernel_1d.shape[1] // 2 - 1
self.register_buffer("kernel", kernel_1d.T @ kernel_1d, persistent=False)

def forward(self, x):
x = F.pad(x, ((self.pad + 1) // 2,) * 4, self.pad_mode)
weight = x.new_zeros([x.shape[1], x.shape[1], self.kernel.shape[0], self.kernel.shape[1]])
indices = torch.arange(x.shape[1], device=x.device)
kernel = self.kernel.to(weight)[None, :].expand(x.shape[1], -1, -1)
def forward(self, inputs):
inputs = F.pad(inputs, ((self.pad + 1) // 2,) * 4, self.pad_mode)
weight = inputs.new_zeros([inputs.shape[1], inputs.shape[1], self.kernel.shape[0], self.kernel.shape[1]])
indices = torch.arange(inputs.shape[1], device=inputs.device)
kernel = self.kernel.to(weight)[None, :].expand(inputs.shape[1], -1, -1)
weight[indices, indices] = kernel
return F.conv_transpose2d(x, weight, stride=2, padding=self.pad * 2 + 1)
return F.conv_transpose2d(inputs, weight, stride=2, padding=self.pad * 2 + 1)


class ResnetBlock2D(nn.Module):
Expand Down Expand Up @@ -664,13 +664,13 @@ def __init__(self, inp_channels, out_channels, kernel_size, n_groups=8):
self.group_norm = nn.GroupNorm(n_groups, out_channels)
self.mish = nn.Mish()

def forward(self, x):
x = self.conv1d(x)
x = rearrange_dims(x)
x = self.group_norm(x)
x = rearrange_dims(x)
x = self.mish(x)
return x
def forward(self, inputs):
intermediate_repr = self.conv1d(inputs)
intermediate_repr = rearrange_dims(intermediate_repr)
intermediate_repr = self.group_norm(intermediate_repr)
intermediate_repr = rearrange_dims(intermediate_repr)
output = self.mish(intermediate_repr)
return output


# unet_rl.py
Expand All @@ -687,20 +687,20 @@ def __init__(self, inp_channels, out_channels, embed_dim, kernel_size=5):
nn.Conv1d(inp_channels, out_channels, 1) if inp_channels != out_channels else nn.Identity()
)

def forward(self, x, t):
def forward(self, inputs, t):
"""
Args:
x : [ batch_size x inp_channels x horizon ]
inputs : [ batch_size x inp_channels x horizon ]
t : [ batch_size x embed_dim ]

returns:
out : [ batch_size x out_channels x horizon ]
"""
t = self.time_emb_act(t)
t = self.time_emb(t)
out = self.conv_in(x) + rearrange_dims(t)
out = self.conv_in(inputs) + rearrange_dims(t)
out = self.conv_out(out)
return out + self.residual_conv(x)
return out + self.residual_conv(inputs)


def upsample_2d(hidden_states, kernel=None, factor=2, gain=1):
Expand Down