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wip mat float16

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This commit is contained in:
Qing
2023-03-25 22:46:28 +08:00
parent 7e028c3908
commit eb304ba696
3 changed files with 63 additions and 181 deletions
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22
lama_cleaner/model/utils.py
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@@ -27,7 +27,7 @@ def make_beta_schedule(
if schedule == "linear":
betas = (
torch.linspace(
linear_start**0.5, linear_end**0.5, n_timestep, dtype=torch.float64
linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64
)
** 2
)
@@ -134,8 +134,10 @@ def timestep_embedding(device, timesteps, dim, max_period=10000, repeat_only=Fal
###### MAT and FcF #######
def normalize_2nd_moment(x, dim=1, eps=1e-8):
return x * (x.square().mean(dim=dim, keepdim=True) + eps).rsqrt()
def normalize_2nd_moment(x, dim=1):
return (
x * (x.square().mean(dim=dim, keepdim=True) + torch.finfo(x.dtype).eps).rsqrt()
)
class EasyDict(dict):
@@ -460,7 +462,7 @@ def _upfirdn2d_ref(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1):
if f is None:
f = torch.ones([1, 1], dtype=torch.float32, device=x.device)
assert isinstance(f, torch.Tensor) and f.ndim in [1, 2]
assert f.dtype == torch.float32 and not f.requires_grad
assert not f.requires_grad
batch_size, num_channels, in_height, in_width = x.shape
# upx, upy = _parse_scaling(up)
# downx, downy = _parse_scaling(down)
@@ -733,9 +735,7 @@ def conv2d_resample(
# Validate arguments.
assert isinstance(x, torch.Tensor) and (x.ndim == 4)
assert isinstance(w, torch.Tensor) and (w.ndim == 4) and (w.dtype == x.dtype)
assert f is None or (
isinstance(f, torch.Tensor) and f.ndim in [1, 2] and f.dtype == torch.float32
)
assert f is None or (isinstance(f, torch.Tensor) and f.ndim in [1, 2])
assert isinstance(up, int) and (up >= 1)
assert isinstance(down, int) and (down >= 1)
# assert isinstance(groups, int) and (groups >= 1), f"!!!!!! groups: {groups} isinstance(groups, int) {isinstance(groups, int)} {type(groups)}"
@@ -772,7 +772,7 @@ def conv2d_resample(
f=f,
up=up,
padding=[px0, px1, py0, py1],
gain=up**2,
gain=up ** 2,
flip_filter=flip_filter,
)
return x
@@ -814,7 +814,7 @@ def conv2d_resample(
x=x,
f=f,
padding=[px0 + pxt, px1 + pxt, py0 + pyt, py1 + pyt],
gain=up**2,
gain=up ** 2,
flip_filter=flip_filter,
)
if down > 1:
@@ -834,7 +834,7 @@ def conv2d_resample(
f=(f if up > 1 else None),
up=up,
padding=[px0, px1, py0, py1],
gain=up**2,
gain=up ** 2,
flip_filter=flip_filter,
)
x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight)
@@ -870,7 +870,7 @@ class Conv2dLayer(torch.nn.Module):
self.register_buffer("resample_filter", setup_filter(resample_filter))
self.conv_clamp = conv_clamp
self.padding = kernel_size // 2
self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size**2))
self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size ** 2))
self.act_gain = activation_funcs[activation].def_gain
memory_format = (