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Runtime error
ethanNeuralImage
commited on
Commit
•
9d408b4
1
Parent(s):
34e88c0
changing around sg2/op
Browse files- app.py +1 -1
- models/stylegan2/model.py +2 -6
- models/stylegan2/op/conv2d_gradfix.py +0 -227
- models/stylegan2/op/fused_act.py +18 -64
- models/stylegan2/op/upfirdn2d.py +10 -137
app.py
CHANGED
@@ -27,7 +27,7 @@ from PIL import Image
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opts_args = ['--no_fine_mapper']
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opts = GradioTestOptions().parse(opts_args)
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device = 'cuda' if torch.cuda.is_available() else 'cpu'
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opts.device=device
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mapper_dict = {
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'afro':'./pretrained_models/styleCLIP_mappers/afro_hairstyle.pt',
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opts_args = ['--no_fine_mapper']
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opts = GradioTestOptions().parse(opts_args)
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device = 'cuda' if torch.cuda.is_available() else 'cpu'
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opts.device= device if opts.device is None else opts.device
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mapper_dict = {
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'afro':'./pretrained_models/styleCLIP_mappers/afro_hairstyle.pt',
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models/stylegan2/model.py
CHANGED
@@ -5,12 +5,8 @@ import torch
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from torch import nn
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from torch.nn import functional as F
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from op.upfirdn2d import upfirdn2d
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else:
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from models.stylegan2.op.fused_act_cpu import FusedLeakyReLU, fused_leaky_relu
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from models.stylegan2.op.upfirdn2d_cpu import upfirdn2d
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class PixelNorm(nn.Module):
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def __init__(self):
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from torch import nn
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from torch.nn import functional as F
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from models.stylegan2.op.fused_act import FusedLeakyReLU, fused_leaky_relu
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from models.stylegan2.op.upfirdn2d import upfirdn2d
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class PixelNorm(nn.Module):
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def __init__(self):
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models/stylegan2/op/conv2d_gradfix.py
DELETED
@@ -1,227 +0,0 @@
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import contextlib
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import warnings
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import torch
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from torch import autograd
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from torch.nn import functional as F
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enabled = True
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weight_gradients_disabled = False
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@contextlib.contextmanager
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def no_weight_gradients():
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global weight_gradients_disabled
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old = weight_gradients_disabled
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weight_gradients_disabled = True
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yield
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weight_gradients_disabled = old
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def conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1):
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if could_use_op(input):
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return conv2d_gradfix(
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transpose=False,
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weight_shape=weight.shape,
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stride=stride,
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padding=padding,
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output_padding=0,
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dilation=dilation,
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groups=groups,
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).apply(input, weight, bias)
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return F.conv2d(
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input=input,
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weight=weight,
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bias=bias,
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stride=stride,
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padding=padding,
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dilation=dilation,
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groups=groups,
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)
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def conv_transpose2d(
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input,
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weight,
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bias=None,
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stride=1,
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padding=0,
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output_padding=0,
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groups=1,
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dilation=1,
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):
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if could_use_op(input):
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return conv2d_gradfix(
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transpose=True,
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weight_shape=weight.shape,
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stride=stride,
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padding=padding,
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output_padding=output_padding,
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groups=groups,
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dilation=dilation,
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).apply(input, weight, bias)
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return F.conv_transpose2d(
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input=input,
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weight=weight,
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bias=bias,
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stride=stride,
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padding=padding,
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output_padding=output_padding,
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dilation=dilation,
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groups=groups,
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)
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def could_use_op(input):
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if (not enabled) or (not torch.backends.cudnn.enabled):
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return False
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if input.device.type != "cuda":
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return False
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if any(torch.__version__.startswith(x) for x in ["1.7.", "1.8."]):
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return True
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warnings.warn(
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f"conv2d_gradfix not supported on PyTorch {torch.__version__}. Falling back to torch.nn.functional.conv2d()."
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)
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return False
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def ensure_tuple(xs, ndim):
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xs = tuple(xs) if isinstance(xs, (tuple, list)) else (xs,) * ndim
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return xs
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conv2d_gradfix_cache = dict()
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def conv2d_gradfix(
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transpose, weight_shape, stride, padding, output_padding, dilation, groups
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):
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ndim = 2
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weight_shape = tuple(weight_shape)
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stride = ensure_tuple(stride, ndim)
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padding = ensure_tuple(padding, ndim)
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output_padding = ensure_tuple(output_padding, ndim)
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dilation = ensure_tuple(dilation, ndim)
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key = (transpose, weight_shape, stride, padding, output_padding, dilation, groups)
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if key in conv2d_gradfix_cache:
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return conv2d_gradfix_cache[key]
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common_kwargs = dict(
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stride=stride, padding=padding, dilation=dilation, groups=groups
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)
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def calc_output_padding(input_shape, output_shape):
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if transpose:
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return [0, 0]
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return [
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input_shape[i + 2]
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- (output_shape[i + 2] - 1) * stride[i]
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- (1 - 2 * padding[i])
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- dilation[i] * (weight_shape[i + 2] - 1)
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for i in range(ndim)
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]
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class Conv2d(autograd.Function):
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@staticmethod
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def forward(ctx, input, weight, bias):
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if not transpose:
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out = F.conv2d(input=input, weight=weight, bias=bias, **common_kwargs)
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else:
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out = F.conv_transpose2d(
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input=input,
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weight=weight,
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bias=bias,
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output_padding=output_padding,
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**common_kwargs,
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)
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ctx.save_for_backward(input, weight)
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return out
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@staticmethod
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def backward(ctx, grad_output):
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input, weight = ctx.saved_tensors
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grad_input, grad_weight, grad_bias = None, None, None
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if ctx.needs_input_grad[0]:
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p = calc_output_padding(
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input_shape=input.shape, output_shape=grad_output.shape
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)
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grad_input = conv2d_gradfix(
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transpose=(not transpose),
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weight_shape=weight_shape,
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output_padding=p,
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**common_kwargs,
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).apply(grad_output, weight, None)
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if ctx.needs_input_grad[1] and not weight_gradients_disabled:
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grad_weight = Conv2dGradWeight.apply(grad_output, input)
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if ctx.needs_input_grad[2]:
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grad_bias = grad_output.sum((0, 2, 3))
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return grad_input, grad_weight, grad_bias
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class Conv2dGradWeight(autograd.Function):
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@staticmethod
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def forward(ctx, grad_output, input):
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op = torch._C._jit_get_operation(
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"aten::cudnn_convolution_backward_weight"
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if not transpose
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else "aten::cudnn_convolution_transpose_backward_weight"
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)
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flags = [
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torch.backends.cudnn.benchmark,
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torch.backends.cudnn.deterministic,
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torch.backends.cudnn.allow_tf32,
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]
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grad_weight = op(
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weight_shape,
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grad_output,
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input,
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padding,
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stride,
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dilation,
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groups,
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*flags,
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)
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ctx.save_for_backward(grad_output, input)
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return grad_weight
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@staticmethod
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def backward(ctx, grad_grad_weight):
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grad_output, input = ctx.saved_tensors
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grad_grad_output, grad_grad_input = None, None
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if ctx.needs_input_grad[0]:
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grad_grad_output = Conv2d.apply(input, grad_grad_weight, None)
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if ctx.needs_input_grad[1]:
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p = calc_output_padding(
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input_shape=input.shape, output_shape=grad_output.shape
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)
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grad_grad_input = conv2d_gradfix(
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transpose=(not transpose),
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weight_shape=weight_shape,
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output_padding=p,
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**common_kwargs,
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).apply(grad_output, grad_grad_weight, None)
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return grad_grad_output, grad_grad_input
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conv2d_gradfix_cache[key] = Conv2d
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return Conv2d
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models/stylegan2/op/fused_act.py
CHANGED
@@ -2,72 +2,10 @@ import os
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import torch
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from torch import nn
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from torch.
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from torch.utils.cpp_extension import load
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module_path = os.path.dirname(__file__)
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fused = load(
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'fused',
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sources=[
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os.path.join(module_path, 'fused_bias_act.cpp'),
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os.path.join(module_path, 'fused_bias_act_kernel.cu'),
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],
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)
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class FusedLeakyReLUFunctionBackward(Function):
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@staticmethod
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def forward(ctx, grad_output, out, negative_slope, scale):
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ctx.save_for_backward(out)
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ctx.negative_slope = negative_slope
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ctx.scale = scale
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empty = grad_output.new_empty(0)
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grad_input = fused.fused_bias_act(
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grad_output, empty, out, 3, 1, negative_slope, scale
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)
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dim = [0]
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if grad_input.ndim > 2:
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dim += list(range(2, grad_input.ndim))
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grad_bias = grad_input.sum(dim).detach()
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return grad_input, grad_bias
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@staticmethod
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def backward(ctx, gradgrad_input, gradgrad_bias):
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out, = ctx.saved_tensors
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gradgrad_out = fused.fused_bias_act(
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gradgrad_input, gradgrad_bias, out, 3, 1, ctx.negative_slope, ctx.scale
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)
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return gradgrad_out, None, None, None
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class FusedLeakyReLUFunction(Function):
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@staticmethod
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def forward(ctx, input, bias, negative_slope, scale):
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empty = input.new_empty(0)
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out = fused.fused_bias_act(input, bias, empty, 3, 0, negative_slope, scale)
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ctx.save_for_backward(out)
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ctx.negative_slope = negative_slope
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ctx.scale = scale
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return out
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@staticmethod
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def backward(ctx, grad_output):
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out, = ctx.saved_tensors
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grad_input, grad_bias = FusedLeakyReLUFunctionBackward.apply(
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grad_output, out, ctx.negative_slope, ctx.scale
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)
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return grad_input, grad_bias, None, None
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class FusedLeakyReLU(nn.Module):
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def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
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import torch
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from torch import nn
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from torch.nn import functional as F
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module_path = os.path.dirname(__file__)
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|
8 |
|
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|
|
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|
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|
9 |
|
10 |
|
11 |
class FusedLeakyReLU(nn.Module):
|
|
|
21 |
|
22 |
|
23 |
def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
|
24 |
+
rest_dim = [1] * (input.ndim - bias.ndim - 1)
|
25 |
+
input = input.cuda()
|
26 |
+
if input.ndim == 3:
|
27 |
+
return (
|
28 |
+
F.leaky_relu(
|
29 |
+
input + bias.view(1, *rest_dim, bias.shape[0]), negative_slope=negative_slope
|
30 |
+
)
|
31 |
+
* scale
|
32 |
+
)
|
33 |
+
else:
|
34 |
+
return (
|
35 |
+
F.leaky_relu(
|
36 |
+
input + bias.view(1, bias.shape[0], *rest_dim), negative_slope=negative_slope
|
37 |
+
)
|
38 |
+
* scale
|
39 |
+
)
|
40 |
+
|
models/stylegan2/op/upfirdn2d.py
CHANGED
@@ -1,149 +1,16 @@
|
|
1 |
import os
|
2 |
|
3 |
import torch
|
4 |
-
from torch.
|
5 |
-
from torch.utils.cpp_extension import load
|
6 |
|
7 |
|
8 |
module_path = os.path.dirname(__file__)
|
9 |
-
upfirdn2d_op = load(
|
10 |
-
'upfirdn2d',
|
11 |
-
sources=[
|
12 |
-
os.path.join(module_path, 'upfirdn2d.cpp'),
|
13 |
-
os.path.join(module_path, 'upfirdn2d_kernel.cu'),
|
14 |
-
],
|
15 |
-
)
|
16 |
|
17 |
|
18 |
-
class UpFirDn2dBackward(Function):
|
19 |
-
@staticmethod
|
20 |
-
def forward(
|
21 |
-
ctx, grad_output, kernel, grad_kernel, up, down, pad, g_pad, in_size, out_size
|
22 |
-
):
|
23 |
-
|
24 |
-
up_x, up_y = up
|
25 |
-
down_x, down_y = down
|
26 |
-
g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1 = g_pad
|
27 |
-
|
28 |
-
grad_output = grad_output.reshape(-1, out_size[0], out_size[1], 1)
|
29 |
-
|
30 |
-
grad_input = upfirdn2d_op.upfirdn2d(
|
31 |
-
grad_output,
|
32 |
-
grad_kernel,
|
33 |
-
down_x,
|
34 |
-
down_y,
|
35 |
-
up_x,
|
36 |
-
up_y,
|
37 |
-
g_pad_x0,
|
38 |
-
g_pad_x1,
|
39 |
-
g_pad_y0,
|
40 |
-
g_pad_y1,
|
41 |
-
)
|
42 |
-
grad_input = grad_input.view(in_size[0], in_size[1], in_size[2], in_size[3])
|
43 |
-
|
44 |
-
ctx.save_for_backward(kernel)
|
45 |
-
|
46 |
-
pad_x0, pad_x1, pad_y0, pad_y1 = pad
|
47 |
-
|
48 |
-
ctx.up_x = up_x
|
49 |
-
ctx.up_y = up_y
|
50 |
-
ctx.down_x = down_x
|
51 |
-
ctx.down_y = down_y
|
52 |
-
ctx.pad_x0 = pad_x0
|
53 |
-
ctx.pad_x1 = pad_x1
|
54 |
-
ctx.pad_y0 = pad_y0
|
55 |
-
ctx.pad_y1 = pad_y1
|
56 |
-
ctx.in_size = in_size
|
57 |
-
ctx.out_size = out_size
|
58 |
-
|
59 |
-
return grad_input
|
60 |
-
|
61 |
-
@staticmethod
|
62 |
-
def backward(ctx, gradgrad_input):
|
63 |
-
kernel, = ctx.saved_tensors
|
64 |
-
|
65 |
-
gradgrad_input = gradgrad_input.reshape(-1, ctx.in_size[2], ctx.in_size[3], 1)
|
66 |
-
|
67 |
-
gradgrad_out = upfirdn2d_op.upfirdn2d(
|
68 |
-
gradgrad_input,
|
69 |
-
kernel,
|
70 |
-
ctx.up_x,
|
71 |
-
ctx.up_y,
|
72 |
-
ctx.down_x,
|
73 |
-
ctx.down_y,
|
74 |
-
ctx.pad_x0,
|
75 |
-
ctx.pad_x1,
|
76 |
-
ctx.pad_y0,
|
77 |
-
ctx.pad_y1,
|
78 |
-
)
|
79 |
-
# gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.out_size[0], ctx.out_size[1], ctx.in_size[3])
|
80 |
-
gradgrad_out = gradgrad_out.view(
|
81 |
-
ctx.in_size[0], ctx.in_size[1], ctx.out_size[0], ctx.out_size[1]
|
82 |
-
)
|
83 |
-
|
84 |
-
return gradgrad_out, None, None, None, None, None, None, None, None
|
85 |
-
|
86 |
-
|
87 |
-
class UpFirDn2d(Function):
|
88 |
-
@staticmethod
|
89 |
-
def forward(ctx, input, kernel, up, down, pad):
|
90 |
-
up_x, up_y = up
|
91 |
-
down_x, down_y = down
|
92 |
-
pad_x0, pad_x1, pad_y0, pad_y1 = pad
|
93 |
-
|
94 |
-
kernel_h, kernel_w = kernel.shape
|
95 |
-
batch, channel, in_h, in_w = input.shape
|
96 |
-
ctx.in_size = input.shape
|
97 |
-
|
98 |
-
input = input.reshape(-1, in_h, in_w, 1)
|
99 |
-
|
100 |
-
ctx.save_for_backward(kernel, torch.flip(kernel, [0, 1]))
|
101 |
-
|
102 |
-
out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1
|
103 |
-
out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1
|
104 |
-
ctx.out_size = (out_h, out_w)
|
105 |
-
|
106 |
-
ctx.up = (up_x, up_y)
|
107 |
-
ctx.down = (down_x, down_y)
|
108 |
-
ctx.pad = (pad_x0, pad_x1, pad_y0, pad_y1)
|
109 |
-
|
110 |
-
g_pad_x0 = kernel_w - pad_x0 - 1
|
111 |
-
g_pad_y0 = kernel_h - pad_y0 - 1
|
112 |
-
g_pad_x1 = in_w * up_x - out_w * down_x + pad_x0 - up_x + 1
|
113 |
-
g_pad_y1 = in_h * up_y - out_h * down_y + pad_y0 - up_y + 1
|
114 |
-
|
115 |
-
ctx.g_pad = (g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1)
|
116 |
-
|
117 |
-
out = upfirdn2d_op.upfirdn2d(
|
118 |
-
input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1
|
119 |
-
)
|
120 |
-
# out = out.view(major, out_h, out_w, minor)
|
121 |
-
out = out.view(-1, channel, out_h, out_w)
|
122 |
-
|
123 |
-
return out
|
124 |
-
|
125 |
-
@staticmethod
|
126 |
-
def backward(ctx, grad_output):
|
127 |
-
kernel, grad_kernel = ctx.saved_tensors
|
128 |
-
|
129 |
-
grad_input = UpFirDn2dBackward.apply(
|
130 |
-
grad_output,
|
131 |
-
kernel,
|
132 |
-
grad_kernel,
|
133 |
-
ctx.up,
|
134 |
-
ctx.down,
|
135 |
-
ctx.pad,
|
136 |
-
ctx.g_pad,
|
137 |
-
ctx.in_size,
|
138 |
-
ctx.out_size,
|
139 |
-
)
|
140 |
-
|
141 |
-
return grad_input, None, None, None, None
|
142 |
-
|
143 |
|
144 |
def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
|
145 |
-
out =
|
146 |
-
input, kernel,
|
147 |
)
|
148 |
|
149 |
return out
|
@@ -152,6 +19,9 @@ def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
|
|
152 |
def upfirdn2d_native(
|
153 |
input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1
|
154 |
):
|
|
|
|
|
|
|
155 |
_, in_h, in_w, minor = input.shape
|
156 |
kernel_h, kernel_w = kernel.shape
|
157 |
|
@@ -182,6 +52,9 @@ def upfirdn2d_native(
|
|
182 |
in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1,
|
183 |
)
|
184 |
out = out.permute(0, 2, 3, 1)
|
|
|
185 |
|
186 |
-
|
|
|
187 |
|
|
|
|
1 |
import os
|
2 |
|
3 |
import torch
|
4 |
+
from torch.nn import functional as F
|
|
|
5 |
|
6 |
|
7 |
module_path = os.path.dirname(__file__)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
8 |
|
9 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
10 |
|
11 |
def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
|
12 |
+
out = upfirdn2d_native(
|
13 |
+
input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1]
|
14 |
)
|
15 |
|
16 |
return out
|
|
|
19 |
def upfirdn2d_native(
|
20 |
input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1
|
21 |
):
|
22 |
+
_, channel, in_h, in_w = input.shape
|
23 |
+
input = input.reshape(-1, in_h, in_w, 1)
|
24 |
+
|
25 |
_, in_h, in_w, minor = input.shape
|
26 |
kernel_h, kernel_w = kernel.shape
|
27 |
|
|
|
52 |
in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1,
|
53 |
)
|
54 |
out = out.permute(0, 2, 3, 1)
|
55 |
+
out = out[:, ::down_y, ::down_x, :]
|
56 |
|
57 |
+
out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1
|
58 |
+
out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1
|
59 |
|
60 |
+
return out.view(-1, channel, out_h, out_w)
|