516 lines
26 KiB
Python
516 lines
26 KiB
Python
# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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#
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# NVIDIA CORPORATION and its licensors retain all intellectual property
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# and proprietary rights in and to this software, related documentation
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# and any modifications thereto. Any use, reproduction, disclosure or
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# distribution of this software and related documentation without an express
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# license agreement from NVIDIA CORPORATION is strictly prohibited.
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"""Generator architecture from the paper
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"Alias-Free Generative Adversarial Networks"."""
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import numpy as np
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import scipy.signal
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import scipy.optimize
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import torch
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from torch_utils import misc
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from torch_utils import persistence
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from torch_utils.ops import conv2d_gradfix
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from torch_utils.ops import filtered_lrelu
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from torch_utils.ops import bias_act
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#----------------------------------------------------------------------------
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@misc.profiled_function
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def modulated_conv2d(
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x, # Input tensor: [batch_size, in_channels, in_height, in_width]
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w, # Weight tensor: [out_channels, in_channels, kernel_height, kernel_width]
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s, # Style tensor: [batch_size, in_channels]
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demodulate = True, # Apply weight demodulation?
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padding = 0, # Padding: int or [padH, padW]
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input_gain = None, # Optional scale factors for the input channels: [], [in_channels], or [batch_size, in_channels]
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):
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with misc.suppress_tracer_warnings(): # this value will be treated as a constant
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batch_size = int(x.shape[0])
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out_channels, in_channels, kh, kw = w.shape
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misc.assert_shape(w, [out_channels, in_channels, kh, kw]) # [OIkk]
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misc.assert_shape(x, [batch_size, in_channels, None, None]) # [NIHW]
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misc.assert_shape(s, [batch_size, in_channels]) # [NI]
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# Pre-normalize inputs.
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if demodulate:
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w = w * w.square().mean([1,2,3], keepdim=True).rsqrt()
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s = s * s.square().mean().rsqrt()
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# Modulate weights.
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w = w.unsqueeze(0) # [NOIkk]
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w = w * s.unsqueeze(1).unsqueeze(3).unsqueeze(4) # [NOIkk]
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# Demodulate weights.
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if demodulate:
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dcoefs = (w.square().sum(dim=[2,3,4]) + 1e-8).rsqrt() # [NO]
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w = w * dcoefs.unsqueeze(2).unsqueeze(3).unsqueeze(4) # [NOIkk]
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# Apply input scaling.
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if input_gain is not None:
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input_gain = input_gain.expand(batch_size, in_channels) # [NI]
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w = w * input_gain.unsqueeze(1).unsqueeze(3).unsqueeze(4) # [NOIkk]
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# Execute as one fused op using grouped convolution.
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x = x.reshape(1, -1, *x.shape[2:])
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w = w.reshape(-1, in_channels, kh, kw)
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x = conv2d_gradfix.conv2d(input=x, weight=w.to(x.dtype), padding=padding, groups=batch_size)
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x = x.reshape(batch_size, -1, *x.shape[2:])
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return x
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#----------------------------------------------------------------------------
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@persistence.persistent_class
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class FullyConnectedLayer(torch.nn.Module):
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def __init__(self,
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in_features, # Number of input features.
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out_features, # Number of output features.
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activation = 'linear', # Activation function: 'relu', 'lrelu', etc.
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bias = True, # Apply additive bias before the activation function?
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lr_multiplier = 1, # Learning rate multiplier.
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weight_init = 1, # Initial standard deviation of the weight tensor.
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bias_init = 0, # Initial value of the additive bias.
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):
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super().__init__()
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self.in_features = in_features
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self.out_features = out_features
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self.activation = activation
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self.weight = torch.nn.Parameter(torch.randn([out_features, in_features]) * (weight_init / lr_multiplier))
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bias_init = np.broadcast_to(np.asarray(bias_init, dtype=np.float32), [out_features])
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self.bias = torch.nn.Parameter(torch.from_numpy(bias_init / lr_multiplier)) if bias else None
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self.weight_gain = lr_multiplier / np.sqrt(in_features)
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self.bias_gain = lr_multiplier
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def forward(self, x):
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w = self.weight.to(x.dtype) * self.weight_gain
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b = self.bias
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if b is not None:
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b = b.to(x.dtype)
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if self.bias_gain != 1:
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b = b * self.bias_gain
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if self.activation == 'linear' and b is not None:
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x = torch.addmm(b.unsqueeze(0), x, w.t())
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else:
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x = x.matmul(w.t())
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x = bias_act.bias_act(x, b, act=self.activation)
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return x
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def extra_repr(self):
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return f'in_features={self.in_features:d}, out_features={self.out_features:d}, activation={self.activation:s}'
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#----------------------------------------------------------------------------
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@persistence.persistent_class
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class MappingNetwork(torch.nn.Module):
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def __init__(self,
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z_dim, # Input latent (Z) dimensionality.
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c_dim, # Conditioning label (C) dimensionality, 0 = no labels.
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w_dim, # Intermediate latent (W) dimensionality.
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num_ws, # Number of intermediate latents to output.
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num_layers = 2, # Number of mapping layers.
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lr_multiplier = 0.01, # Learning rate multiplier for the mapping layers.
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w_avg_beta = 0.998, # Decay for tracking the moving average of W during training.
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):
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super().__init__()
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self.z_dim = z_dim
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self.c_dim = c_dim
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self.w_dim = w_dim
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self.num_ws = num_ws
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self.num_layers = num_layers
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self.w_avg_beta = w_avg_beta
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# Construct layers.
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self.embed = FullyConnectedLayer(self.c_dim, self.w_dim) if self.c_dim > 0 else None
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features = [self.z_dim + (self.w_dim if self.c_dim > 0 else 0)] + [self.w_dim] * self.num_layers
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for idx, in_features, out_features in zip(range(num_layers), features[:-1], features[1:]):
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layer = FullyConnectedLayer(in_features, out_features, activation='lrelu', lr_multiplier=lr_multiplier)
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setattr(self, f'fc{idx}', layer)
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self.register_buffer('w_avg', torch.zeros([w_dim]))
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def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, update_emas=False):
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misc.assert_shape(z, [None, self.z_dim])
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if truncation_cutoff is None:
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truncation_cutoff = self.num_ws
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# Embed, normalize, and concatenate inputs.
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x = z.to(torch.float32)
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x = x * (x.square().mean(1, keepdim=True) + 1e-8).rsqrt()
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if self.c_dim > 0:
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misc.assert_shape(c, [None, self.c_dim])
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y = self.embed(c.to(torch.float32))
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y = y * (y.square().mean(1, keepdim=True) + 1e-8).rsqrt()
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x = torch.cat([x, y], dim=1) if x is not None else y
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# Execute layers.
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for idx in range(self.num_layers):
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x = getattr(self, f'fc{idx}')(x)
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# Update moving average of W.
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if update_emas:
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self.w_avg.copy_(x.detach().mean(dim=0).lerp(self.w_avg, self.w_avg_beta))
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# Broadcast and apply truncation.
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x = x.unsqueeze(1).repeat([1, self.num_ws, 1])
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if truncation_psi != 1:
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x[:, :truncation_cutoff] = self.w_avg.lerp(x[:, :truncation_cutoff], truncation_psi)
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return x
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def extra_repr(self):
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return f'z_dim={self.z_dim:d}, c_dim={self.c_dim:d}, w_dim={self.w_dim:d}, num_ws={self.num_ws:d}'
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#----------------------------------------------------------------------------
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@persistence.persistent_class
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class SynthesisInput(torch.nn.Module):
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def __init__(self,
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w_dim, # Intermediate latent (W) dimensionality.
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channels, # Number of output channels.
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size, # Output spatial size: int or [width, height].
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sampling_rate, # Output sampling rate.
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bandwidth, # Output bandwidth.
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):
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super().__init__()
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self.w_dim = w_dim
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self.channels = channels
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self.size = np.broadcast_to(np.asarray(size), [2])
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self.sampling_rate = sampling_rate
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self.bandwidth = bandwidth
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# Draw random frequencies from uniform 2D disc.
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freqs = torch.randn([self.channels, 2])
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radii = freqs.square().sum(dim=1, keepdim=True).sqrt()
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freqs /= radii * radii.square().exp().pow(0.25)
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freqs *= bandwidth
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phases = torch.rand([self.channels]) - 0.5
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# Setup parameters and buffers.
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self.weight = torch.nn.Parameter(torch.randn([self.channels, self.channels]))
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self.affine = FullyConnectedLayer(w_dim, 4, weight_init=0, bias_init=[1,0,0,0])
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self.register_buffer('transform', torch.eye(3, 3)) # User-specified inverse transform wrt. resulting image.
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self.register_buffer('freqs', freqs)
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self.register_buffer('phases', phases)
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def forward(self, w):
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# Introduce batch dimension.
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transforms = self.transform.unsqueeze(0) # [batch, row, col]
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freqs = self.freqs.unsqueeze(0) # [batch, channel, xy]
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phases = self.phases.unsqueeze(0) # [batch, channel]
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# Apply learned transformation.
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t = self.affine(w) # t = (r_c, r_s, t_x, t_y)
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t = t / t[:, :2].norm(dim=1, keepdim=True) # t' = (r'_c, r'_s, t'_x, t'_y)
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m_r = torch.eye(3, device=w.device).unsqueeze(0).repeat([w.shape[0], 1, 1]) # Inverse rotation wrt. resulting image.
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m_r[:, 0, 0] = t[:, 0] # r'_c
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m_r[:, 0, 1] = -t[:, 1] # r'_s
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m_r[:, 1, 0] = t[:, 1] # r'_s
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m_r[:, 1, 1] = t[:, 0] # r'_c
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m_t = torch.eye(3, device=w.device).unsqueeze(0).repeat([w.shape[0], 1, 1]) # Inverse translation wrt. resulting image.
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m_t[:, 0, 2] = -t[:, 2] # t'_x
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m_t[:, 1, 2] = -t[:, 3] # t'_y
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transforms = m_r @ m_t @ transforms # First rotate resulting image, then translate, and finally apply user-specified transform.
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# Transform frequencies.
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phases = phases + (freqs @ transforms[:, :2, 2:]).squeeze(2)
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freqs = freqs @ transforms[:, :2, :2]
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# Dampen out-of-band frequencies that may occur due to the user-specified transform.
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amplitudes = (1 - (freqs.norm(dim=2) - self.bandwidth) / (self.sampling_rate / 2 - self.bandwidth)).clamp(0, 1)
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# Construct sampling grid.
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theta = torch.eye(2, 3, device=w.device)
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theta[0, 0] = 0.5 * self.size[0] / self.sampling_rate
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theta[1, 1] = 0.5 * self.size[1] / self.sampling_rate
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grids = torch.nn.functional.affine_grid(theta.unsqueeze(0), [1, 1, self.size[1], self.size[0]], align_corners=False)
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# Compute Fourier features.
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x = (grids.unsqueeze(3) @ freqs.permute(0, 2, 1).unsqueeze(1).unsqueeze(2)).squeeze(3) # [batch, height, width, channel]
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x = x + phases.unsqueeze(1).unsqueeze(2)
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x = torch.sin(x * (np.pi * 2))
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x = x * amplitudes.unsqueeze(1).unsqueeze(2)
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# Apply trainable mapping.
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weight = self.weight / np.sqrt(self.channels)
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x = x @ weight.t()
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# Ensure correct shape.
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x = x.permute(0, 3, 1, 2) # [batch, channel, height, width]
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misc.assert_shape(x, [w.shape[0], self.channels, int(self.size[1]), int(self.size[0])])
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return x
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def extra_repr(self):
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return '\n'.join([
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f'w_dim={self.w_dim:d}, channels={self.channels:d}, size={list(self.size)},',
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f'sampling_rate={self.sampling_rate:g}, bandwidth={self.bandwidth:g}'])
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#----------------------------------------------------------------------------
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@persistence.persistent_class
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class SynthesisLayer(torch.nn.Module):
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def __init__(self,
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w_dim, # Intermediate latent (W) dimensionality.
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is_torgb, # Is this the final ToRGB layer?
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is_critically_sampled, # Does this layer use critical sampling?
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use_fp16, # Does this layer use FP16?
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# Input & output specifications.
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in_channels, # Number of input channels.
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out_channels, # Number of output channels.
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in_size, # Input spatial size: int or [width, height].
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out_size, # Output spatial size: int or [width, height].
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in_sampling_rate, # Input sampling rate (s).
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out_sampling_rate, # Output sampling rate (s).
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in_cutoff, # Input cutoff frequency (f_c).
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out_cutoff, # Output cutoff frequency (f_c).
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in_half_width, # Input transition band half-width (f_h).
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out_half_width, # Output Transition band half-width (f_h).
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# Hyperparameters.
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conv_kernel = 3, # Convolution kernel size. Ignored for final the ToRGB layer.
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filter_size = 6, # Low-pass filter size relative to the lower resolution when up/downsampling.
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lrelu_upsampling = 2, # Relative sampling rate for leaky ReLU. Ignored for final the ToRGB layer.
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use_radial_filters = False, # Use radially symmetric downsampling filter? Ignored for critically sampled layers.
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conv_clamp = 256, # Clamp the output to [-X, +X], None = disable clamping.
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magnitude_ema_beta = 0.999, # Decay rate for the moving average of input magnitudes.
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):
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super().__init__()
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self.w_dim = w_dim
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self.is_torgb = is_torgb
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self.is_critically_sampled = is_critically_sampled
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self.use_fp16 = use_fp16
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self.in_channels = in_channels
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self.out_channels = out_channels
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self.in_size = np.broadcast_to(np.asarray(in_size), [2])
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self.out_size = np.broadcast_to(np.asarray(out_size), [2])
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self.in_sampling_rate = in_sampling_rate
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self.out_sampling_rate = out_sampling_rate
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self.tmp_sampling_rate = max(in_sampling_rate, out_sampling_rate) * (1 if is_torgb else lrelu_upsampling)
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self.in_cutoff = in_cutoff
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self.out_cutoff = out_cutoff
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self.in_half_width = in_half_width
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self.out_half_width = out_half_width
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self.conv_kernel = 1 if is_torgb else conv_kernel
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self.conv_clamp = conv_clamp
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self.magnitude_ema_beta = magnitude_ema_beta
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# Setup parameters and buffers.
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self.affine = FullyConnectedLayer(self.w_dim, self.in_channels, bias_init=1)
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self.weight = torch.nn.Parameter(torch.randn([self.out_channels, self.in_channels, self.conv_kernel, self.conv_kernel]))
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self.bias = torch.nn.Parameter(torch.zeros([self.out_channels]))
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self.register_buffer('magnitude_ema', torch.ones([]))
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# Design upsampling filter.
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self.up_factor = int(np.rint(self.tmp_sampling_rate / self.in_sampling_rate))
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assert self.in_sampling_rate * self.up_factor == self.tmp_sampling_rate
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self.up_taps = filter_size * self.up_factor if self.up_factor > 1 and not self.is_torgb else 1
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self.register_buffer('up_filter', self.design_lowpass_filter(
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numtaps=self.up_taps, cutoff=self.in_cutoff, width=self.in_half_width*2, fs=self.tmp_sampling_rate))
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# Design downsampling filter.
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self.down_factor = int(np.rint(self.tmp_sampling_rate / self.out_sampling_rate))
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assert self.out_sampling_rate * self.down_factor == self.tmp_sampling_rate
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self.down_taps = filter_size * self.down_factor if self.down_factor > 1 and not self.is_torgb else 1
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self.down_radial = use_radial_filters and not self.is_critically_sampled
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self.register_buffer('down_filter', self.design_lowpass_filter(
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numtaps=self.down_taps, cutoff=self.out_cutoff, width=self.out_half_width*2, fs=self.tmp_sampling_rate, radial=self.down_radial))
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# Compute padding.
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pad_total = (self.out_size - 1) * self.down_factor + 1 # Desired output size before downsampling.
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pad_total -= (self.in_size + self.conv_kernel - 1) * self.up_factor # Input size after upsampling.
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pad_total += self.up_taps + self.down_taps - 2 # Size reduction caused by the filters.
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pad_lo = (pad_total + self.up_factor) // 2 # Shift sample locations according to the symmetric interpretation (Appendix C.3).
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pad_hi = pad_total - pad_lo
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self.padding = [int(pad_lo[0]), int(pad_hi[0]), int(pad_lo[1]), int(pad_hi[1])]
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def forward(self, x, w, noise_mode='random', force_fp32=False, update_emas=False):
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assert noise_mode in ['random', 'const', 'none'] # unused
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misc.assert_shape(x, [None, self.in_channels, int(self.in_size[1]), int(self.in_size[0])])
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misc.assert_shape(w, [x.shape[0], self.w_dim])
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# Track input magnitude.
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if update_emas:
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with torch.autograd.profiler.record_function('update_magnitude_ema'):
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magnitude_cur = x.detach().to(torch.float32).square().mean()
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self.magnitude_ema.copy_(magnitude_cur.lerp(self.magnitude_ema, self.magnitude_ema_beta))
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input_gain = self.magnitude_ema.rsqrt()
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# Execute affine layer.
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styles = self.affine(w)
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if self.is_torgb:
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weight_gain = 1 / np.sqrt(self.in_channels * (self.conv_kernel ** 2))
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styles = styles * weight_gain
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# Execute modulated conv2d.
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dtype = torch.float16 if (self.use_fp16 and not force_fp32 and x.device.type == 'cuda') else torch.float32
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x = modulated_conv2d(x=x.to(dtype), w=self.weight, s=styles,
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padding=self.conv_kernel-1, demodulate=(not self.is_torgb), input_gain=input_gain)
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# Execute bias, filtered leaky ReLU, and clamping.
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gain = 1 if self.is_torgb else np.sqrt(2)
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slope = 1 if self.is_torgb else 0.2
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x = filtered_lrelu.filtered_lrelu(x=x, fu=self.up_filter, fd=self.down_filter, b=self.bias.to(x.dtype),
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up=self.up_factor, down=self.down_factor, padding=self.padding, gain=gain, slope=slope, clamp=self.conv_clamp)
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# Ensure correct shape and dtype.
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misc.assert_shape(x, [None, self.out_channels, int(self.out_size[1]), int(self.out_size[0])])
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assert x.dtype == dtype
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return x
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@staticmethod
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def design_lowpass_filter(numtaps, cutoff, width, fs, radial=False):
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assert numtaps >= 1
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# Identity filter.
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if numtaps == 1:
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return None
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# Separable Kaiser low-pass filter.
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if not radial:
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f = scipy.signal.firwin(numtaps=numtaps, cutoff=cutoff, width=width, fs=fs)
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return torch.as_tensor(f, dtype=torch.float32)
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# Radially symmetric jinc-based filter.
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x = (np.arange(numtaps) - (numtaps - 1) / 2) / fs
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r = np.hypot(*np.meshgrid(x, x))
|
|
f = scipy.special.j1(2 * cutoff * (np.pi * r)) / (np.pi * r)
|
|
beta = scipy.signal.kaiser_beta(scipy.signal.kaiser_atten(numtaps, width / (fs / 2)))
|
|
w = np.kaiser(numtaps, beta)
|
|
f *= np.outer(w, w)
|
|
f /= np.sum(f)
|
|
return torch.as_tensor(f, dtype=torch.float32)
|
|
|
|
def extra_repr(self):
|
|
return '\n'.join([
|
|
f'w_dim={self.w_dim:d}, is_torgb={self.is_torgb},',
|
|
f'is_critically_sampled={self.is_critically_sampled}, use_fp16={self.use_fp16},',
|
|
f'in_sampling_rate={self.in_sampling_rate:g}, out_sampling_rate={self.out_sampling_rate:g},',
|
|
f'in_cutoff={self.in_cutoff:g}, out_cutoff={self.out_cutoff:g},',
|
|
f'in_half_width={self.in_half_width:g}, out_half_width={self.out_half_width:g},',
|
|
f'in_size={list(self.in_size)}, out_size={list(self.out_size)},',
|
|
f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}'])
|
|
|
|
#----------------------------------------------------------------------------
|
|
|
|
@persistence.persistent_class
|
|
class SynthesisNetwork(torch.nn.Module):
|
|
def __init__(self,
|
|
w_dim, # Intermediate latent (W) dimensionality.
|
|
img_resolution, # Output image resolution.
|
|
img_channels, # Number of color channels.
|
|
channel_base = 32768, # Overall multiplier for the number of channels.
|
|
channel_max = 512, # Maximum number of channels in any layer.
|
|
num_layers = 14, # Total number of layers, excluding Fourier features and ToRGB.
|
|
num_critical = 2, # Number of critically sampled layers at the end.
|
|
first_cutoff = 2, # Cutoff frequency of the first layer (f_{c,0}).
|
|
first_stopband = 2**2.1, # Minimum stopband of the first layer (f_{t,0}).
|
|
last_stopband_rel = 2**0.3, # Minimum stopband of the last layer, expressed relative to the cutoff.
|
|
margin_size = 10, # Number of additional pixels outside the image.
|
|
output_scale = 0.25, # Scale factor for the output image.
|
|
num_fp16_res = 4, # Use FP16 for the N highest resolutions.
|
|
**layer_kwargs, # Arguments for SynthesisLayer.
|
|
):
|
|
super().__init__()
|
|
self.w_dim = w_dim
|
|
self.num_ws = num_layers + 2
|
|
self.img_resolution = img_resolution
|
|
self.img_channels = img_channels
|
|
self.num_layers = num_layers
|
|
self.num_critical = num_critical
|
|
self.margin_size = margin_size
|
|
self.output_scale = output_scale
|
|
self.num_fp16_res = num_fp16_res
|
|
|
|
# Geometric progression of layer cutoffs and min. stopbands.
|
|
last_cutoff = self.img_resolution / 2 # f_{c,N}
|
|
last_stopband = last_cutoff * last_stopband_rel # f_{t,N}
|
|
exponents = np.minimum(np.arange(self.num_layers + 1) / (self.num_layers - self.num_critical), 1)
|
|
cutoffs = first_cutoff * (last_cutoff / first_cutoff) ** exponents # f_c[i]
|
|
stopbands = first_stopband * (last_stopband / first_stopband) ** exponents # f_t[i]
|
|
|
|
# Compute remaining layer parameters.
|
|
sampling_rates = np.exp2(np.ceil(np.log2(np.minimum(stopbands * 2, self.img_resolution)))) # s[i]
|
|
half_widths = np.maximum(stopbands, sampling_rates / 2) - cutoffs # f_h[i]
|
|
sizes = sampling_rates + self.margin_size * 2
|
|
sizes[-2:] = self.img_resolution
|
|
channels = np.rint(np.minimum((channel_base / 2) / cutoffs, channel_max))
|
|
channels[-1] = self.img_channels
|
|
|
|
# Construct layers.
|
|
self.input = SynthesisInput(
|
|
w_dim=self.w_dim, channels=int(channels[0]), size=int(sizes[0]),
|
|
sampling_rate=sampling_rates[0], bandwidth=cutoffs[0])
|
|
self.layer_names = []
|
|
for idx in range(self.num_layers + 1):
|
|
prev = max(idx - 1, 0)
|
|
is_torgb = (idx == self.num_layers)
|
|
is_critically_sampled = (idx >= self.num_layers - self.num_critical)
|
|
use_fp16 = (sampling_rates[idx] * (2 ** self.num_fp16_res) > self.img_resolution)
|
|
layer = SynthesisLayer(
|
|
w_dim=self.w_dim, is_torgb=is_torgb, is_critically_sampled=is_critically_sampled, use_fp16=use_fp16,
|
|
in_channels=int(channels[prev]), out_channels= int(channels[idx]),
|
|
in_size=int(sizes[prev]), out_size=int(sizes[idx]),
|
|
in_sampling_rate=int(sampling_rates[prev]), out_sampling_rate=int(sampling_rates[idx]),
|
|
in_cutoff=cutoffs[prev], out_cutoff=cutoffs[idx],
|
|
in_half_width=half_widths[prev], out_half_width=half_widths[idx],
|
|
**layer_kwargs)
|
|
name = f'L{idx}_{layer.out_size[0]}_{layer.out_channels}'
|
|
setattr(self, name, layer)
|
|
self.layer_names.append(name)
|
|
|
|
def forward(self, ws, **layer_kwargs):
|
|
misc.assert_shape(ws, [None, self.num_ws, self.w_dim])
|
|
ws = ws.to(torch.float32).unbind(dim=1)
|
|
|
|
# Execute layers.
|
|
x = self.input(ws[0])
|
|
for name, w in zip(self.layer_names, ws[1:]):
|
|
x = getattr(self, name)(x, w, **layer_kwargs)
|
|
if self.output_scale != 1:
|
|
x = x * self.output_scale
|
|
|
|
# Ensure correct shape and dtype.
|
|
misc.assert_shape(x, [None, self.img_channels, self.img_resolution, self.img_resolution])
|
|
x = x.to(torch.float32)
|
|
return x
|
|
|
|
def extra_repr(self):
|
|
return '\n'.join([
|
|
f'w_dim={self.w_dim:d}, num_ws={self.num_ws:d},',
|
|
f'img_resolution={self.img_resolution:d}, img_channels={self.img_channels:d},',
|
|
f'num_layers={self.num_layers:d}, num_critical={self.num_critical:d},',
|
|
f'margin_size={self.margin_size:d}, num_fp16_res={self.num_fp16_res:d}'])
|
|
|
|
#----------------------------------------------------------------------------
|
|
|
|
@persistence.persistent_class
|
|
class Generator(torch.nn.Module):
|
|
def __init__(self,
|
|
z_dim, # Input latent (Z) dimensionality.
|
|
c_dim, # Conditioning label (C) dimensionality.
|
|
w_dim, # Intermediate latent (W) dimensionality.
|
|
img_resolution, # Output resolution.
|
|
img_channels, # Number of output color channels.
|
|
mapping_kwargs = {}, # Arguments for MappingNetwork.
|
|
**synthesis_kwargs, # Arguments for SynthesisNetwork.
|
|
):
|
|
super().__init__()
|
|
self.z_dim = z_dim
|
|
self.c_dim = c_dim
|
|
self.w_dim = w_dim
|
|
self.img_resolution = img_resolution
|
|
self.img_channels = img_channels
|
|
self.synthesis = SynthesisNetwork(w_dim=w_dim, img_resolution=img_resolution, img_channels=img_channels, **synthesis_kwargs)
|
|
self.num_ws = self.synthesis.num_ws
|
|
self.mapping = MappingNetwork(z_dim=z_dim, c_dim=c_dim, w_dim=w_dim, num_ws=self.num_ws, **mapping_kwargs)
|
|
|
|
def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, update_emas=False, **synthesis_kwargs):
|
|
ws = self.mapping(z, c, truncation_psi=truncation_psi, truncation_cutoff=truncation_cutoff, update_emas=update_emas)
|
|
img = self.synthesis(ws, update_emas=update_emas, **synthesis_kwargs)
|
|
return img
|
|
|
|
#----------------------------------------------------------------------------
|