Spaces:
Sleeping
Sleeping
| # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. | |
| # | |
| # NVIDIA CORPORATION and its licensors retain all intellectual property | |
| # and proprietary rights in and to this software, related documentation | |
| # and any modifications thereto. Any use, reproduction, disclosure or | |
| # distribution of this software and related documentation without an express | |
| # license agreement from NVIDIA CORPORATION is strictly prohibited. | |
| import numpy as np | |
| import torch | |
| from torch_utils import misc | |
| from torch_utils import persistence | |
| from torch_utils.ops import conv2d_resample | |
| from torch_utils.ops import upfirdn2d | |
| from torch_utils.ops import bias_act | |
| from torch_utils.ops import fma | |
| from training.flow import DDSF | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from collections import Counter | |
| #---------------------------------------------------------------------------- | |
| def normalize_2nd_moment(x, dim=1, eps=1e-8): | |
| return x * (x.square().mean(dim=dim, keepdim=True) + eps).rsqrt() | |
| #---------------------------------------------------------------------------- | |
| def modulated_conv2d( | |
| x, # Input tensor of shape [batch_size, in_channels, in_height, in_width]. | |
| weight, # Weight tensor of shape [out_channels, in_channels, kernel_height, kernel_width]. | |
| styles, # Modulation coefficients of shape [batch_size, in_channels]. | |
| noise = None, # Optional noise tensor to add to the output activations. | |
| up = 1, # Integer upsampling factor. | |
| down = 1, # Integer downsampling factor. | |
| padding = 0, # Padding with respect to the upsampled image. | |
| resample_filter = None, # Low-pass filter to apply when resampling activations. Must be prepared beforehand by calling upfirdn2d.setup_filter(). | |
| demodulate = True, # Apply weight demodulation? | |
| flip_weight = True, # False = convolution, True = correlation (matches torch.nn.functional.conv2d). | |
| fused_modconv = True, # Perform modulation, convolution, and demodulation as a single fused operation? | |
| ): | |
| batch_size = x.shape[0] | |
| out_channels, in_channels, kh, kw = weight.shape | |
| misc.assert_shape(weight, [out_channels, in_channels, kh, kw]) # [OIkk] | |
| misc.assert_shape(x, [batch_size, in_channels, None, None]) # [NIHW] | |
| misc.assert_shape(styles, [batch_size, in_channels]) # [NI] | |
| # Pre-normalize inputs to avoid FP16 overflow. | |
| if x.dtype == torch.float16 and demodulate: | |
| weight = weight * (1 / np.sqrt(in_channels * kh * kw) / weight.norm(float('inf'), dim=[1,2,3], keepdim=True)) # max_Ikk | |
| styles = styles / styles.norm(float('inf'), dim=1, keepdim=True) # max_I | |
| # Calculate per-sample weights and demodulation coefficients. | |
| w = None | |
| dcoefs = None | |
| if demodulate or fused_modconv: | |
| w = weight.unsqueeze(0) # [NOIkk] | |
| w = w * styles.reshape(batch_size, 1, -1, 1, 1) # [NOIkk] | |
| if demodulate: | |
| dcoefs = (w.square().sum(dim=[2,3,4]) + 1e-8).rsqrt() # [NO] | |
| if demodulate and fused_modconv: | |
| w = w * dcoefs.reshape(batch_size, -1, 1, 1, 1) # [NOIkk] | |
| # Execute by scaling the activations before and after the convolution. | |
| if not fused_modconv: | |
| x = x * styles.to(x.dtype).reshape(batch_size, -1, 1, 1) | |
| x = conv2d_resample.conv2d_resample(x=x, w=weight.to(x.dtype), f=resample_filter, up=up, down=down, padding=padding, flip_weight=flip_weight) | |
| if demodulate and noise is not None: | |
| x = fma.fma(x, dcoefs.to(x.dtype).reshape(batch_size, -1, 1, 1), noise.to(x.dtype)) | |
| elif demodulate: | |
| x = x * dcoefs.to(x.dtype).reshape(batch_size, -1, 1, 1) | |
| elif noise is not None: | |
| x = x.add_(noise.to(x.dtype)) | |
| return x | |
| # Execute as one fused op using grouped convolution. | |
| with misc.suppress_tracer_warnings(): # this value will be treated as a constant | |
| batch_size = int(batch_size) | |
| misc.assert_shape(x, [batch_size, in_channels, None, None]) | |
| x = x.reshape(1, -1, *x.shape[2:]) | |
| w = w.reshape(-1, in_channels, kh, kw) | |
| x = conv2d_resample.conv2d_resample(x=x, w=w.to(x.dtype), f=resample_filter, up=up, down=down, padding=padding, groups=batch_size, flip_weight=flip_weight) | |
| x = x.reshape(batch_size, -1, *x.shape[2:]) | |
| if noise is not None: | |
| x = x.add_(noise) | |
| return x | |
| #---------------------------------------------------------------------------- | |
| class FullyConnectedLayer(torch.nn.Module): | |
| def __init__(self, | |
| in_features, # Number of input features. | |
| out_features, # Number of output features. | |
| bias = True, # Apply additive bias before the activation function? | |
| activation = 'linear', # Activation function: 'relu', 'lrelu', etc. | |
| lr_multiplier = 1, # Learning rate multiplier. | |
| bias_init = 0, # Initial value for the additive bias. | |
| init = 'randn', | |
| ): | |
| super().__init__() | |
| self.activation = activation | |
| self.in_features = in_features | |
| self.out_features = out_features | |
| self.lr_multiplier = lr_multiplier | |
| if init == 'randn': | |
| self.weight = torch.nn.Parameter(torch.randn([out_features, in_features]) / lr_multiplier) | |
| else: | |
| self.weight = torch.nn.Parameter(torch.full([out_features, in_features],0.) / lr_multiplier) | |
| self.bias = torch.nn.Parameter(torch.full([out_features], np.float32(bias_init))) if bias else None | |
| self.weight_gain = lr_multiplier / np.sqrt(in_features) | |
| self.bias_gain = lr_multiplier | |
| def forward(self, x): | |
| w = self.weight.to(x.dtype) * self.weight_gain | |
| b = self.bias | |
| if b is not None: | |
| b = b.to(x.dtype) | |
| if self.bias_gain != 1: | |
| b = b * self.bias_gain | |
| if self.activation == 'linear' and b is not None: | |
| x = torch.addmm(b.unsqueeze(0), x, w.t()) | |
| else: | |
| x = x.matmul(w.t()) | |
| x = bias_act.bias_act(x, b, act=self.activation) | |
| return x | |
| def __repr__(self): | |
| return self.__class__.__name__ + '(' + 'in:%s, out:%s, lr:%s, act:%s' % \ | |
| (self.in_features, self.out_features, self.lr_multiplier, self.activation) + ')' | |
| #---------------------------------------------------------------------------- | |
| class Conv2dLayer(torch.nn.Module): | |
| def __init__(self, | |
| in_channels, # Number of input channels. | |
| out_channels, # Number of output channels. | |
| kernel_size, # Width and height of the convolution kernel. | |
| bias = True, # Apply additive bias before the activation function? | |
| activation = 'linear', # Activation function: 'relu', 'lrelu', etc. | |
| up = 1, # Integer upsampling factor. | |
| down = 1, # Integer downsampling factor. | |
| resample_filter = [1,3,3,1], # Low-pass filter to apply when resampling activations. | |
| conv_clamp = None, # Clamp the output to +-X, None = disable clamping. | |
| channels_last = False, # Expect the input to have memory_format=channels_last? | |
| trainable = True, # Update the weights of this layer during training? | |
| ): | |
| super().__init__() | |
| self.activation = activation | |
| self.up = up | |
| self.down = down | |
| self.conv_clamp = conv_clamp | |
| self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter)) | |
| self.padding = kernel_size // 2 | |
| self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size ** 2)) | |
| self.act_gain = bias_act.activation_funcs[activation].def_gain | |
| memory_format = torch.channels_last if channels_last else torch.contiguous_format | |
| weight = torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format) | |
| bias = torch.zeros([out_channels]) if bias else None | |
| if trainable: | |
| self.weight = torch.nn.Parameter(weight) | |
| self.bias = torch.nn.Parameter(bias) if bias is not None else None | |
| else: | |
| self.register_buffer('weight', weight) | |
| if bias is not None: | |
| self.register_buffer('bias', bias) | |
| else: | |
| self.bias = None | |
| def forward(self, x, gain=1): | |
| w = self.weight * self.weight_gain | |
| b = self.bias.to(x.dtype) if self.bias is not None else None | |
| flip_weight = (self.up == 1) # slightly faster | |
| x = conv2d_resample.conv2d_resample(x=x, w=w.to(x.dtype), f=self.resample_filter, up=self.up, down=self.down, padding=self.padding, flip_weight=flip_weight) | |
| act_gain = self.act_gain * gain | |
| act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None | |
| x = bias_act.bias_act(x, b, act=self.activation, gain=act_gain, clamp=act_clamp) | |
| return x | |
| #---------------------------------------------------------------------------- | |
| def gumbel_sigmoid(logits, tau: float = 1, hard: bool = False, threshold: float = 0.5, eval=False): | |
| gumbels = ( | |
| -torch.empty_like(logits, memory_format=torch.legacy_contiguous_format).exponential_().log() | |
| ) # ~Gumbel(0, 1) | |
| gumbels = (logits + gumbels) / tau # ~Gumbel(logits, tau) | |
| y_soft = gumbels.sigmoid() | |
| indices = (y_soft > threshold).nonzero(as_tuple=True) | |
| y_hard = torch.zeros_like(logits, memory_format=torch.legacy_contiguous_format) | |
| y_hard[indices[0], indices[1]] = 1.0 | |
| ret = y_hard - y_soft.detach() + y_soft | |
| return ret, y_soft | |
| def topk_gumbel_sigmoid(logits, tau: float = 1, hard: bool = False, threshold: float = 0.5, eval=False, topk=2): | |
| soft_mask = torch.sigmoid(logits) | |
| gumbels = ( | |
| -torch.empty_like(logits, memory_format=torch.legacy_contiguous_format).exponential_().log() | |
| ) # ~Gumbel(0, 1) | |
| gumbels = (logits + gumbels) / tau # ~Gumbel(logits, tau) | |
| y_soft = gumbels.sigmoid() | |
| indices = (y_soft > threshold).nonzero(as_tuple=True) | |
| y_hard = torch.zeros_like(logits, memory_format=torch.legacy_contiguous_format) | |
| y_hard[indices[0], indices[1]] = 1.0 | |
| topk_values, topk_indices = soft_mask.topk(topk, dim=-1) | |
| mask = torch.zeros_like(logits) | |
| mask.scatter_(1, topk_indices, 1) | |
| ret = y_hard * mask + y_soft - y_soft.detach() | |
| return ret, y_soft | |
| def sample_gumbel(shape, eps=1e-20): | |
| U = torch.rand(shape) | |
| return -torch.autograd.Variable(torch.log(-torch.log(U + eps) + eps)) | |
| def gumbel_softmax_sample(logits, temperature, eval): | |
| gumbels = sample_gumbel(logits.size()).to(logits.device) | |
| if eval: | |
| y = logits | |
| else: | |
| y = logits + gumbels | |
| return F.softmax(y/temperature, dim=-1) | |
| def hard_softmax(logits, temperature=1): | |
| y = F.softmax(logits / temperature, dim=1) | |
| shape = y.size() | |
| _, ind = y.max(dim=-1) | |
| y_hard = torch.zeros_like(y).view(-1, shape[-1]) | |
| y_hard.scatter_(1, ind.view(-1, 1), 1) | |
| y_hard = y_hard.view(*shape) | |
| return (y_hard - y).detach() + y | |
| def gumbel_softmax(logits, temperature, eval): | |
| """ | |
| input: [*, n_class] | |
| return: [*, n_class] an one-hot vector | |
| """ | |
| y = gumbel_softmax_sample(logits, temperature, eval) | |
| y_soft = y | |
| shape = y.size() | |
| _, ind = y.max(dim=-1) | |
| y_hard = torch.zeros_like(y).view(-1, shape[-1]) | |
| y_hard.scatter_(1, ind.view(-1, 1), 1) | |
| y_hard = y_hard.view(*shape) | |
| return (y_hard - y).detach() + y, y_soft | |
| def get_onehot(y): | |
| shape = y.size() | |
| _, ind = y.max(dim=-1) | |
| y_hard = torch.zeros_like(y).view(-1, shape[-1]) | |
| y_hard.scatter_(1, ind.view(-1, 1), 1) | |
| y_hard = y_hard.view(*shape) | |
| return y_hard | |
| def orthogonal_loss(x): | |
| hard = (x>0.5) | |
| hard_expanded = hard.unsqueeze(2).float() | |
| hard_expanded_t = hard.unsqueeze(1).float() | |
| filter = (hard_expanded * hard_expanded_t).float() | |
| x_expanded = x.unsqueeze(2) | |
| x_expanded_t = x.unsqueeze(1) | |
| l1_distance = torch.sum(torch.abs(x_expanded - x_expanded_t)*filter, dim=0) | |
| l1_distance = torch.triu(l1_distance, diagonal=1) | |
| loss = -torch.mean(l1_distance) | |
| return loss | |
| def get_topk(logit, topk=2): | |
| y_sigmoid = torch.sigmoid(logit) | |
| topk_values, topk_indices = y_sigmoid.topk(topk, dim=-1) | |
| mask = torch.zeros_like(logit) | |
| mask.scatter_(1, topk_indices, 1) | |
| return mask*y_sigmoid | |
| list = [[1,0,0,1,0,0,0,0,0,0,0,0,0], | |
| [1,0,0,0,1,0,0,0,0,0,0,0,0], | |
| [0,1,0,1,0,0,0,0,0,0,0,0,0], | |
| [0,1,0,0,1,0,0,0,0,0,0,0,0], | |
| [0,1,0,0,0,1,0,0,0,0,0,0,0], | |
| [0,1,0,0,0,0,1,0,0,0,0,0,0], | |
| [0,1,0,0,0,0,0,1,0,0,0,0,0], | |
| [0,1,0,0,0,0,0,0,1,0,0,0,0], | |
| [0,1,0,0,0,0,0,0,0,1,0,0,0], | |
| [0,1,0,0,0,0,0,0,0,0,1,0,0], | |
| [0,1,0,0,0,0,0,0,0,0,0,1,0], | |
| [0,1,0,0,0,0,0,0,0,0,0,0,1], | |
| [1,0,0,0,0,1,0,0,0,0,0,0,0], | |
| [0,0,1,1,0,0,0,0,0,0,0,0,0], | |
| [0,0,1,0,1,0,0,0,0,0,0,0,0], | |
| [0,0,1,0,0,1,0,0,0,0,0,0,0], | |
| [0,0,1,0,0,0,1,0,0,0,0,0,0], | |
| [0,0,1,0,0,0,0,1,0,0,0,0,0], | |
| [0,0,1,0,0,0,0,0,1,0,0,0,0], | |
| [0,0,1,0,0,0,0,0,0,1,0,0,0], | |
| [0,0,1,0,0,0,0,0,0,0,1,0,0], | |
| [0,0,1,0,0,0,0,0,0,0,0,1,0], | |
| [0,0,1,0,0,0,0,0,0,0,0,0,1], | |
| [1,0,0,0,0,0,1,0,0,0,0,0,0], | |
| [1,0,0,0,0,0,0,1,0,0,0,0,0], | |
| [1,0,0,0,0,0,0,0,1,0,0,0,0], | |
| [1,0,0,0,0,0,0,0,0,1,0,0,0], | |
| [1,0,0,0,0,0,0,0,0,0,1,0,0], | |
| [1,0,0,0,0,0,0,0,0,0,0,1,0], | |
| [1,0,0,0,0,0,0,0,0,0,0,0,1], | |
| ] | |
| ground_truth = torch.tensor(list).float() | |
| ORTHO = torch.tensor([[-0.1580, -0.0408, -0.1414, 0.1170, 0.1882, -0.1885, -0.0104, 0.1914, | |
| 0.0509, -0.1441, -0.2948, 0.1251, -0.1669], | |
| [ 0.1422, -0.0457, 0.0277, 0.1680, 0.0185, -0.2122, -0.1040, 0.0041, | |
| 0.2174, -0.0141, 0.3782, 0.0765, -0.0174], | |
| [ 0.2878, 0.0051, -0.2083, -0.2044, -0.1188, 0.0932, 0.2063, 0.0671, | |
| 0.2095, -0.1755, -0.1170, 0.2188, 0.1370], | |
| [ 0.2111, -0.2580, 0.2177, -0.0026, -0.1872, -0.2515, -0.2492, -0.0404, | |
| 0.0025, -0.0382, 0.0111, -0.0352, 0.0146], | |
| [-0.1945, 0.0164, 0.0754, 0.0189, -0.1675, 0.2053, 0.0155, 0.2645, | |
| -0.2101, -0.0701, -0.1351, 0.1118, 0.1543], | |
| [ 0.1182, -0.2252, 0.2003, -0.0680, 0.1287, -0.1387, -0.2203, -0.0670, | |
| -0.1644, 0.1111, 0.0084, 0.0150, -0.1737], | |
| [ 0.1151, 0.1054, 0.2348, 0.0781, -0.0773, 0.1233, -0.1349, -0.2370, | |
| -0.1207, -0.0505, -0.1951, 0.0498, 0.0416], | |
| [-0.3048, -0.0374, -0.2396, 0.0777, -0.3567, -0.2694, -0.2050, -0.0189, | |
| 0.0231, 0.0996, -0.1684, 0.1197, -0.1330], | |
| [-0.1611, 0.1017, 0.2342, -0.0282, 0.0928, -0.0018, 0.1993, -0.1433, | |
| -0.0727, -0.1053, -0.2802, -0.2779, -0.0839], | |
| [ 0.1368, -0.0055, -0.0157, 0.1525, 0.2246, 0.1000, -0.2871, -0.1524, | |
| 0.0486, -0.1028, 0.0836, -0.2623, 0.0859], | |
| [-0.0752, -0.2077, -0.2013, 0.0231, 0.1458, -0.1430, 0.1958, -0.3751, | |
| -0.2262, 0.0225, -0.0760, 0.0732, 0.0299], | |
| [-0.1740, -0.2048, 0.0612, -0.2909, 0.1386, 0.0709, 0.1275, 0.0899, | |
| -0.1081, 0.2981, -0.0162, -0.1816, -0.1441], | |
| [-0.0404, -0.1640, 0.1119, 0.2111, 0.0357, 0.0474, 0.1237, -0.0681, | |
| 0.0655, -0.0334, 0.0118, 0.0699, -0.1380], | |
| [ 0.1107, -0.3759, 0.1586, -0.0606, -0.1565, 0.0428, 0.1039, 0.0608, | |
| 0.1760, 0.0363, -0.0802, 0.0947, -0.0543], | |
| [-0.0961, 0.1985, -0.0256, 0.0054, 0.0669, 0.0624, -0.1788, 0.1346, | |
| 0.1483, -0.0602, 0.0913, -0.1270, 0.2608], | |
| [ 0.0254, 0.2653, 0.0828, 0.1078, 0.2526, -0.1783, -0.0357, 0.1296, | |
| 0.2522, 0.2490, 0.0941, 0.0162, 0.0863], | |
| [-0.0473, -0.0018, -0.0941, 0.0532, -0.1086, 0.0423, 0.0508, -0.0304, | |
| -0.2711, -0.1112, 0.0677, -0.1355, 0.3170], | |
| [ 0.0134, -0.2489, -0.1870, 0.0748, -0.0764, -0.2126, -0.0964, -0.0404, | |
| 0.1241, -0.3033, -0.1322, -0.2577, -0.0973], | |
| [-0.2806, -0.1622, 0.0704, -0.1336, 0.0622, 0.1741, 0.0632, 0.1448, | |
| -0.0587, -0.1539, 0.1617, -0.0279, 0.0363], | |
| [ 0.1749, 0.2154, 0.0963, -0.1549, -0.3061, -0.1309, -0.1245, -0.1402, | |
| -0.1692, -0.1410, 0.0752, -0.2097, -0.2414], | |
| [ 0.1401, 0.1368, -0.0531, -0.0407, 0.0151, 0.0888, 0.1823, 0.0020, | |
| 0.0277, -0.1578, 0.2602, 0.1338, -0.3879], | |
| [ 0.2555, 0.1114, 0.4299, 0.0905, 0.0623, -0.0975, 0.1664, 0.3538, | |
| -0.2299, -0.0775, -0.0997, 0.0390, -0.1250], | |
| [ 0.0677, -0.0907, 0.1597, -0.1814, 0.1798, 0.0861, -0.0755, -0.1682, | |
| 0.1742, -0.2388, -0.2412, -0.0729, 0.2385], | |
| [ 0.0193, 0.0062, 0.0031, 0.2542, -0.0530, 0.0144, 0.0780, -0.1098, | |
| -0.3371, -0.3164, 0.1891, 0.3701, 0.1644], | |
| [ 0.0799, 0.0810, -0.2274, 0.0251, 0.2413, -0.2354, 0.2490, 0.0038, | |
| -0.0775, 0.1271, 0.0097, -0.0019, 0.0399], | |
| [ 0.1140, -0.0978, -0.0916, -0.0106, -0.1800, 0.2802, 0.1105, -0.2525, | |
| -0.1132, 0.1886, 0.2471, -0.1485, 0.0221], | |
| [-0.1721, -0.1800, 0.1773, -0.2792, 0.0896, -0.1571, -0.1935, 0.0797, | |
| -0.1068, -0.1013, 0.2210, 0.0663, 0.2321], | |
| [-0.1225, -0.2221, 0.0689, -0.1025, 0.0532, 0.0361, -0.0843, -0.0799, | |
| 0.0211, 0.0660, 0.0502, 0.4224, -0.0926], | |
| [ 0.0038, -0.0429, -0.1468, 0.2407, 0.0102, 0.3251, -0.1180, 0.0744, | |
| -0.1236, 0.0411, -0.0278, -0.1991, -0.2766], | |
| [-0.0863, -0.0660, 0.0396, -0.1107, -0.2354, -0.2599, 0.2507, 0.0745, | |
| 0.0313, 0.0755, 0.2473, -0.2315, 0.2333], | |
| [-0.0166, 0.1887, -0.1992, -0.3009, -0.1392, -0.1847, -0.0457, 0.1943, | |
| -0.1482, -0.2822, -0.0030, -0.0758, -0.1392], | |
| [ 0.0020, -0.0024, 0.0625, 0.1569, 0.1537, -0.1713, -0.0473, 0.0963, | |
| -0.2901, 0.0191, 0.0918, 0.0159, -0.0347], | |
| [ 0.3627, -0.0454, -0.0322, -0.0956, -0.1250, -0.0341, 0.0803, 0.1461, | |
| -0.0883, 0.2248, -0.2998, 0.0395, 0.2176], | |
| [-0.2538, 0.3111, 0.2308, 0.0210, -0.1303, -0.2091, 0.0714, -0.3307, | |
| -0.0379, 0.2251, -0.1021, 0.0730, 0.0378], | |
| [ 0.0450, 0.0436, 0.0105, -0.1059, 0.1118, -0.1238, 0.1615, -0.0552, | |
| -0.0455, -0.0910, 0.1500, -0.0011, -0.0275], | |
| [-0.1807, -0.0692, 0.0591, 0.0076, 0.0127, 0.0891, -0.0765, 0.2371, | |
| -0.0692, -0.0207, 0.1000, -0.1159, -0.0882], | |
| [ 0.0912, -0.2033, -0.1026, 0.1210, 0.2329, -0.1923, 0.2109, -0.0092, | |
| -0.1319, -0.1324, -0.0528, -0.2045, 0.0713], | |
| [ 0.0842, 0.0389, -0.2070, 0.0645, 0.1097, 0.0028, -0.3349, 0.0986, | |
| -0.2645, 0.1306, -0.1034, 0.0816, 0.1803], | |
| [-0.2133, 0.0506, 0.1856, 0.1944, -0.0223, -0.0026, 0.1085, -0.0809, | |
| 0.2229, -0.3000, -0.0402, 0.0148, 0.0605], | |
| [-0.0125, -0.1673, 0.0749, 0.4695, -0.2778, -0.0443, 0.1531, 0.2020, | |
| 0.0622, 0.1171, 0.0037, -0.1444, 0.1088]]) | |
| class ResidualLinearBlock(torch.nn.Module): | |
| def __init__(self, w_dim=512): | |
| super().__init__() | |
| self.fc1 = FullyConnectedLayer(w_dim, w_dim, activation='lrelu', lr_multiplier=1) | |
| self.norm1 = nn.LayerNorm(w_dim) | |
| self.fc2 = FullyConnectedLayer(w_dim, w_dim, activation='lrelu', lr_multiplier=1) | |
| self.norm2 = nn.LayerNorm(w_dim) | |
| def forward(self, x): | |
| h = self.norm1(x) | |
| h = self.fc1(h) | |
| h = self.norm2(h) | |
| h = self.fc2(h) | |
| return h + x | |
| class ConceptMaskNetwork(nn.Module): | |
| def __init__(self, c_dim, i_dim, w_dim=512, activation='lrelu'): | |
| super().__init__() | |
| self.mask_net = nn.Sequential( | |
| FullyConnectedLayer(c_dim, w_dim, activation=activation, lr_multiplier=1), | |
| nn.LayerNorm(w_dim), | |
| FullyConnectedLayer(w_dim, w_dim, activation=activation, lr_multiplier=1), | |
| nn.LayerNorm(w_dim), | |
| FullyConnectedLayer(w_dim, i_dim, activation='linear', lr_multiplier=1, init='zeros'), | |
| ) | |
| #self.param_net = nn.Parameter(-1e8*torch.ones([c_dim, i_dim])) | |
| """ | |
| self.register_buffer('use_param', torch.zeros([c_dim, i_dim])) | |
| self.register_buffer('target_value', torch.zeros([c_dim, i_dim])) | |
| print(self) | |
| """ | |
| self.register_buffer('use_param', torch.ones([c_dim, i_dim])) | |
| target_value = torch.tensor([ | |
| [1,0,0,0,1,1,0], | |
| [1,0,0,1,0,1,0], | |
| [0,1,0,0,1,0,1], | |
| [0,1,0,0,1,1,0], | |
| [0,1,0,1,0,0,1], | |
| [0,1,0,1,0,1,0], | |
| [0,0,1,0,1,0,1], | |
| [0,0,1,1,0,0,1], | |
| ]) | |
| self.register_buffer('target_value', target_value.float()) | |
| def forward(self, c=None): | |
| mlp_out = (torch.tanh(self.mask_net(c))+1)/2 | |
| buffer_out = self.target_value[c.argmax(dim=1)] | |
| use_param = self.use_param[c.argmax(dim=1)] | |
| return mlp_out * (1-use_param) + (use_param)*buffer_out | |
| import pickle | |
| class ConceptMappingNetwork(torch.nn.Module): | |
| def __init__(self, | |
| z_dim, # Input latent (Z) dimensionality, 0 = no latent. | |
| c_dim, # Conditioning label (C) dimensionality, 0 = no label. | |
| w_dim, # Intermediate latent (W) dimensionality. | |
| num_ws, # Number of intermediate latents to output, None = do not broadcast. | |
| num_layers = 8, # Number of mapping layers. | |
| embed_features = None, # Label embedding dimensionality, None = same as w_dim. | |
| layer_features = None, # Number of intermediate features in the mapping layers, None = same as w_dim. | |
| activation = 'lrelu', # Activation function: 'relu', 'lrelu', etc. | |
| lr_multiplier = 0.01, # Learning rate multiplier for the mapping layers. | |
| w_avg_beta = 0.995, # Decay for tracking the moving average of W during training, None = do not track. | |
| cond_mode = 'concat', # mode of coonditioning, stylegan3 uses concatenation | |
| i_dim = 4, | |
| p_dim = 64, | |
| flow_blocks = 2, | |
| flow_dim = 10, | |
| flow_norm = 1, | |
| use_label = 0, | |
| temperature = 0.07 | |
| ): | |
| super().__init__() | |
| self.z_dim = z_dim | |
| self.c_dim = c_dim | |
| self.w_dim = w_dim | |
| self.i_dim = i_dim | |
| self.p_dim = p_dim | |
| self.temperature = temperature | |
| self.temperature = 1 | |
| self.num_ws = num_ws | |
| self.num_layers = num_layers | |
| self.w_avg_beta = w_avg_beta | |
| self.cond_mode = cond_mode | |
| self.flow_norm = flow_norm | |
| self.use_label = use_label | |
| if embed_features is None: | |
| embed_features = w_dim | |
| if c_dim == 0: | |
| embed_features = 0 | |
| if layer_features is None: | |
| layer_features = w_dim | |
| #embedding_path = 'rgbmnist_pretrained_embedding.pkl' | |
| #with open(embedding_path, 'rb') as f: | |
| # self.pretrained_embedding = pickle.load(f) | |
| #print('pretrained embedding loaded >>>>>>>>>> ', self.pretrained_embedding.shape) | |
| self.p_dim = p_dim | |
| for i in range(i_dim): | |
| mlp_net = nn.Sequential(FullyConnectedLayer(p_dim, w_dim, activation=activation, lr_multiplier=lr_multiplier), | |
| FullyConnectedLayer(w_dim, p_dim, activation=activation, lr_multiplier=lr_multiplier),) | |
| setattr(self, f'map_net{i}', mlp_net) | |
| self.deactivate_map_net = nn.Sequential(FullyConnectedLayer(p_dim, w_dim, activation=activation, lr_multiplier=lr_multiplier), | |
| FullyConnectedLayer(w_dim, p_dim, activation=activation, lr_multiplier=lr_multiplier),) | |
| self.main_map_net = nn.Sequential(FullyConnectedLayer((z_dim-i_dim*p_dim), w_dim, activation=activation, lr_multiplier=lr_multiplier), | |
| FullyConnectedLayer(w_dim, z_dim-i_dim*p_dim, activation=activation, lr_multiplier=lr_multiplier),) | |
| print(self) | |
| if num_ws is not None and w_avg_beta is not None: | |
| self.register_buffer('w_avg', torch.zeros([w_dim])) | |
| def forward(self, z, soft_mask, mask_mode='gumbel_hard',truncation_psi=1, truncation_cutoff=None, skip_w_avg_update=False, | |
| sparse_loss=False, label=None, entropy_thr=0.5, temperature=1.0): | |
| # Embed, normalize, and concat inputs. | |
| self.temperature = temperature | |
| x = None | |
| progress = 0 | |
| outs = [] | |
| """ | |
| print(c.size(), ' >>>>>>>>>>.c size ssss') | |
| # Get unique elements and their counts | |
| unique_elements, counts = torch.unique(c.argmax(dim=1), return_counts=True) | |
| # Sort the counts in descending order, and sort unique elements according to this order | |
| sorted_counts, sorted_indices = torch.sort(counts, descending=True) | |
| sorted_elements = unique_elements[sorted_indices] | |
| print() | |
| # Print the sorted elements and their counts as pairs | |
| for element, count in zip(sorted_elements, sorted_counts): | |
| print(f"Element: {element.item()}, Count: {count.item()}") | |
| print() | |
| """ | |
| with (torch.autograd.profiler.record_function('input')): | |
| if self.z_dim > 0: | |
| assert soft_mask.size() == (len(z), self.i_dim) | |
| #soft_mask = (torch.tanh(mask_logit)+1)/2 | |
| hard_version = (soft_mask > 0.5).float() | |
| hard_mask = hard_version - soft_mask.detach() + soft_mask | |
| for i in range(self.i_dim): | |
| cur_z = normalize_2nd_moment(z[:, i*self.p_dim:(i+1)*self.p_dim]) | |
| cur_map_net = getattr(self, f'map_net{i}') | |
| cur_act_out = cur_map_net(cur_z) | |
| cur_deact_out = self.deactivate_map_net(cur_z) | |
| cur_out = cur_act_out*hard_mask[:, i].view(-1,1) + cur_deact_out*(1-hard_mask[:, i].view(-1,1)) | |
| outs.append(cur_out) | |
| rest_z = normalize_2nd_moment(z[:, self.i_dim*self.p_dim:]) | |
| x = self.main_map_net(rest_z) | |
| outs.append(x) | |
| x = torch.cat(outs, dim=1) | |
| old_ws = x | |
| # Update moving average of W. | |
| if self.w_avg_beta is not None and self.training and not skip_w_avg_update: | |
| with torch.autograd.profiler.record_function('update_w_avg'): | |
| self.w_avg.copy_(x.detach().mean(dim=0).lerp(self.w_avg, self.w_avg_beta)) | |
| # Broadcast. | |
| if self.num_ws is not None: | |
| with torch.autograd.profiler.record_function('broadcast'): | |
| x = x.unsqueeze(1).repeat([1, self.num_ws, 1]) | |
| # Apply truncation. | |
| if truncation_psi != 1: | |
| with torch.autograd.profiler.record_function('truncate'): | |
| assert self.w_avg_beta is not None | |
| if self.num_ws is None or truncation_cutoff is None: | |
| x = self.w_avg.lerp(x, truncation_psi) | |
| else: | |
| x[:, :truncation_cutoff] = self.w_avg.lerp(x[:, :truncation_cutoff], truncation_psi) | |
| if sparse_loss: | |
| if self.cond_mode in ['flow', 'mlp']: | |
| loss_sparse = 0 | |
| loss_entropy = 0 | |
| loss_ortho = 0 | |
| loss_path = 0 | |
| loss_epsilon = 0 | |
| loss_var = 0 | |
| loss_colvar = 0 | |
| loss_rowvar = 0 | |
| loss_equal = 0 | |
| entropy = -(soft_mask*torch.log(soft_mask+1e-20) + (1-soft_mask)*torch.log(1-soft_mask+1e-20)) | |
| ent_cri = ((soft_mask>=entropy_thr)) | |
| loss_entropy = torch.mean(entropy*ent_cri) | |
| filter_soft_mask = soft_mask*(soft_mask>0.5).float() | |
| crit = torch.sum(filter_soft_mask, dim=1).detach().view(-1,1) | |
| loss_sparse = torch.mean(hard_mask*crit*(soft_mask>0.5).float()) | |
| loss_sparse = torch.mean(crit*hard_mask*(soft_mask>0.5).float()) | |
| loss_sparse = torch.mean(crit*hard_mask*(soft_mask>0.5).float()) | |
| loss_sparse = torch.mean(hard_mask*(soft_mask>0.5).float()) | |
| #loss_sparse = torch.mean(crit*hard_mask*(soft_mask>0.5).float()) | |
| crit = torch.sum(hard_mask, dim=1).view(-1,1).detach() | |
| loss_sparse = torch.mean(soft_mask*(soft_mask>0.5).float()*(soft_mask<0.9)) | |
| sum_vec = torch.sum(soft_mask*(soft_mask>0.5).float(), dim=1) | |
| act_sum = torch.var(sum_vec) | |
| loss_rowvar = act_sum | |
| filter_hard_mask = hard_mask.detach()*(soft_mask>0.9)+hard_mask*(soft_mask<=0.9) | |
| sum_vec = torch.sum(filter_hard_mask, dim=1) | |
| act_sum = torch.var(sum_vec) | |
| loss_colvar = act_sum | |
| """ | |
| cin = torch.arange(self.c_dim) | |
| cin = F.one_hot(cin, num_classes=self.c_dim).float().to(z.device) | |
| whole_soft_mask = self.mask_net(cin) | |
| whole_soft_mask = torch.sigmoid(whole_soft_mask) | |
| whole_soft_mask = whole_soft_mask*(whole_soft_mask>0.5).float() | |
| ortho_mat = torch.matmul(whole_soft_mask.t(), whole_soft_mask) | |
| ortho_mat = ortho_mat * (1-torch.eye(self.i_dim).to(z.device)) | |
| loss_ortho = torch.mean(ortho_mat) | |
| """ | |
| loss_dict = { | |
| } | |
| loss_dict['loss_sparse'] = loss_sparse | |
| loss_dict['loss_entropy'] = loss_entropy | |
| loss_dict['loss_ortho'] = loss_ortho | |
| loss_dict['loss_path'] = loss_path | |
| loss_dict['loss_epsilon'] = loss_epsilon | |
| loss_dict['loss_cls'] = 0 | |
| loss_dict['loss_colvar'] = loss_colvar | |
| loss_dict['loss_rowvar'] = loss_rowvar | |
| loss_dict['loss_equal'] = loss_equal | |
| return x, loss_dict | |
| else: | |
| return x, torch.tensor(0.) | |
| else: | |
| return x | |
| class MappingNetwork(torch.nn.Module): | |
| def __init__(self, | |
| z_dim, # Input latent (Z) dimensionality, 0 = no latent. | |
| c_dim, # Conditioning label (C) dimensionality, 0 = no label. | |
| w_dim, # Intermediate latent (W) dimensionality. | |
| num_ws, # Number of intermediate latents to output, None = do not broadcast. | |
| num_layers = 8, # Number of mapping layers. | |
| embed_features = None, # Label embedding dimensionality, None = same as w_dim. | |
| layer_features = None, # Number of intermediate features in the mapping layers, None = same as w_dim. | |
| activation = 'lrelu', # Activation function: 'relu', 'lrelu', etc. | |
| lr_multiplier = 0.01, # Learning rate multiplier for the mapping layers. | |
| w_avg_beta = 0.995, # Decay for tracking the moving average of W during training, None = do not track. | |
| ): | |
| super().__init__() | |
| self.z_dim = z_dim | |
| self.c_dim = c_dim | |
| self.w_dim = w_dim | |
| self.num_ws = num_ws | |
| self.num_layers = num_layers | |
| self.w_avg_beta = w_avg_beta | |
| if embed_features is None: | |
| embed_features = w_dim | |
| if c_dim == 0: | |
| embed_features = 0 | |
| if layer_features is None: | |
| layer_features = w_dim | |
| features_list = [z_dim + embed_features] + [layer_features] * (num_layers - 1) + [w_dim] | |
| if c_dim > 0: | |
| self.embed = FullyConnectedLayer(c_dim, embed_features) | |
| for idx in range(num_layers): | |
| in_features = features_list[idx] | |
| out_features = features_list[idx + 1] | |
| layer = FullyConnectedLayer(in_features, out_features, activation=activation, lr_multiplier=lr_multiplier) | |
| setattr(self, f'fc{idx}', layer) | |
| if num_ws is not None and w_avg_beta is not None: | |
| self.register_buffer('w_avg', torch.zeros([w_dim])) | |
| def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, skip_w_avg_update=False): | |
| # Embed, normalize, and concat inputs. | |
| x = None | |
| with torch.autograd.profiler.record_function('input'): | |
| if self.z_dim > 0: | |
| misc.assert_shape(z, [None, self.z_dim]) | |
| x = normalize_2nd_moment(z.to(torch.float32)) | |
| if self.c_dim > 0: | |
| misc.assert_shape(c, [None, self.c_dim]) | |
| y = normalize_2nd_moment(self.embed(c.to(torch.float32))) | |
| x = torch.cat([x, y], dim=1) if x is not None else y | |
| # Main layers. | |
| for idx in range(self.num_layers): | |
| layer = getattr(self, f'fc{idx}') | |
| x = layer(x) | |
| # Update moving average of W. | |
| if self.w_avg_beta is not None and self.training and not skip_w_avg_update: | |
| with torch.autograd.profiler.record_function('update_w_avg'): | |
| self.w_avg.copy_(x.detach().mean(dim=0).lerp(self.w_avg, self.w_avg_beta)) | |
| # Broadcast. | |
| if self.num_ws is not None: | |
| with torch.autograd.profiler.record_function('broadcast'): | |
| x = x.unsqueeze(1).repeat([1, self.num_ws, 1]) | |
| # Apply truncation. | |
| if truncation_psi != 1: | |
| with torch.autograd.profiler.record_function('truncate'): | |
| assert self.w_avg_beta is not None | |
| if self.num_ws is None or truncation_cutoff is None: | |
| x = self.w_avg.lerp(x, truncation_psi) | |
| else: | |
| x[:, :truncation_cutoff] = self.w_avg.lerp(x[:, :truncation_cutoff], truncation_psi) | |
| return x | |
| #---------------------------------------------------------------------------- | |
| class SynthesisLayer(torch.nn.Module): | |
| def __init__(self, | |
| in_channels, # Number of input channels. | |
| out_channels, # Number of output channels. | |
| w_dim, # Intermediate latent (W) dimensionality. | |
| resolution, # Resolution of this layer. | |
| kernel_size = 3, # Convolution kernel size. | |
| up = 1, # Integer upsampling factor. | |
| use_noise = True, # Enable noise input? | |
| activation = 'lrelu', # Activation function: 'relu', 'lrelu', etc. | |
| resample_filter = [1,3,3,1], # Low-pass filter to apply when resampling activations. | |
| conv_clamp = None, # Clamp the output of convolution layers to +-X, None = disable clamping. | |
| channels_last = False, # Use channels_last format for the weights? | |
| ): | |
| super().__init__() | |
| self.resolution = resolution | |
| self.up = up | |
| self.use_noise = use_noise | |
| self.activation = activation | |
| self.conv_clamp = conv_clamp | |
| self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter)) | |
| self.padding = kernel_size // 2 | |
| self.act_gain = bias_act.activation_funcs[activation].def_gain | |
| self.affine = FullyConnectedLayer(w_dim, in_channels, bias_init=1) | |
| memory_format = torch.channels_last if channels_last else torch.contiguous_format | |
| self.weight = torch.nn.Parameter(torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format)) | |
| if use_noise: | |
| self.register_buffer('noise_const', torch.randn([resolution, resolution])) | |
| self.noise_strength = torch.nn.Parameter(torch.zeros([])) | |
| self.bias = torch.nn.Parameter(torch.zeros([out_channels])) | |
| def forward(self, x, w, noise_mode='random', fused_modconv=True, gain=1): | |
| assert noise_mode in ['random', 'const', 'none'] | |
| in_resolution = self.resolution // self.up | |
| misc.assert_shape(x, [None, self.weight.shape[1], in_resolution, in_resolution]) | |
| styles = self.affine(w) | |
| noise = None | |
| if self.use_noise and noise_mode == 'random': | |
| noise = torch.randn([x.shape[0], 1, self.resolution, self.resolution], device=x.device) * self.noise_strength | |
| if self.use_noise and noise_mode == 'const': | |
| noise = self.noise_const * self.noise_strength | |
| flip_weight = (self.up == 1) # slightly faster | |
| x = modulated_conv2d(x=x, weight=self.weight, styles=styles, noise=noise, up=self.up, | |
| padding=self.padding, resample_filter=self.resample_filter, flip_weight=flip_weight, fused_modconv=fused_modconv) | |
| act_gain = self.act_gain * gain | |
| act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None | |
| x = bias_act.bias_act(x, self.bias.to(x.dtype), act=self.activation, gain=act_gain, clamp=act_clamp) | |
| return x | |
| #---------------------------------------------------------------------------- | |
| class ToRGBLayer(torch.nn.Module): | |
| def __init__(self, in_channels, out_channels, w_dim, kernel_size=1, conv_clamp=None, channels_last=False): | |
| super().__init__() | |
| self.conv_clamp = conv_clamp | |
| self.affine = FullyConnectedLayer(w_dim, in_channels, bias_init=1) | |
| memory_format = torch.channels_last if channels_last else torch.contiguous_format | |
| self.weight = torch.nn.Parameter(torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format)) | |
| self.bias = torch.nn.Parameter(torch.zeros([out_channels])) | |
| self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size ** 2)) | |
| def forward(self, x, w, fused_modconv=True): | |
| styles = self.affine(w) * self.weight_gain | |
| x = modulated_conv2d(x=x, weight=self.weight, styles=styles, demodulate=False, fused_modconv=fused_modconv) | |
| x = bias_act.bias_act(x, self.bias.to(x.dtype), clamp=self.conv_clamp) | |
| return x | |
| #---------------------------------------------------------------------------- | |
| class SynthesisBlock(torch.nn.Module): | |
| def __init__(self, | |
| in_channels, # Number of input channels, 0 = first block. | |
| out_channels, # Number of output channels. | |
| w_dim, # Intermediate latent (W) dimensionality. | |
| resolution, # Resolution of this block. | |
| img_channels, # Number of output color channels. | |
| is_last, # Is this the last block? | |
| architecture = 'skip', # Architecture: 'orig', 'skip', 'resnet'. | |
| resample_filter = [1,3,3,1], # Low-pass filter to apply when resampling activations. | |
| conv_clamp = None, # Clamp the output of convolution layers to +-X, None = disable clamping. | |
| use_fp16 = False, # Use FP16 for this block? | |
| fp16_channels_last = False, # Use channels-last memory format with FP16? | |
| **layer_kwargs, # Arguments for SynthesisLayer. | |
| ): | |
| assert architecture in ['orig', 'skip', 'resnet'] | |
| super().__init__() | |
| self.in_channels = in_channels | |
| self.w_dim = w_dim | |
| self.resolution = resolution | |
| self.img_channels = img_channels | |
| self.is_last = is_last | |
| self.architecture = architecture | |
| self.use_fp16 = use_fp16 | |
| self.channels_last = (use_fp16 and fp16_channels_last) | |
| self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter)) | |
| self.num_conv = 0 | |
| self.num_torgb = 0 | |
| if in_channels == 0: | |
| self.const = torch.nn.Parameter(torch.randn([out_channels, resolution, resolution])) | |
| if in_channels != 0: | |
| self.conv0 = SynthesisLayer(in_channels, out_channels, w_dim=w_dim, resolution=resolution, up=2, | |
| resample_filter=resample_filter, conv_clamp=conv_clamp, channels_last=self.channels_last, **layer_kwargs) | |
| self.num_conv += 1 | |
| self.conv1 = SynthesisLayer(out_channels, out_channels, w_dim=w_dim, resolution=resolution, | |
| conv_clamp=conv_clamp, channels_last=self.channels_last, **layer_kwargs) | |
| self.num_conv += 1 | |
| if is_last or architecture == 'skip': | |
| self.torgb = ToRGBLayer(out_channels, img_channels, w_dim=w_dim, | |
| conv_clamp=conv_clamp, channels_last=self.channels_last) | |
| self.num_torgb += 1 | |
| if in_channels != 0 and architecture == 'resnet': | |
| self.skip = Conv2dLayer(in_channels, out_channels, kernel_size=1, bias=False, up=2, | |
| resample_filter=resample_filter, channels_last=self.channels_last) | |
| def forward(self, x, img, ws, force_fp32=False, fused_modconv=None, **layer_kwargs): | |
| misc.assert_shape(ws, [None, self.num_conv + self.num_torgb, self.w_dim]) | |
| w_iter = iter(ws.unbind(dim=1)) | |
| dtype = torch.float16 if self.use_fp16 and not force_fp32 else torch.float32 | |
| memory_format = torch.channels_last if self.channels_last and not force_fp32 else torch.contiguous_format | |
| if fused_modconv is None: | |
| with misc.suppress_tracer_warnings(): # this value will be treated as a constant | |
| fused_modconv = (not self.training) and (dtype == torch.float32 or int(x.shape[0]) == 1) | |
| # Input. | |
| if self.in_channels == 0: | |
| x = self.const.to(dtype=dtype, memory_format=memory_format) | |
| x = x.unsqueeze(0).repeat([ws.shape[0], 1, 1, 1]) | |
| else: | |
| misc.assert_shape(x, [None, self.in_channels, self.resolution // 2, self.resolution // 2]) | |
| x = x.to(dtype=dtype, memory_format=memory_format) | |
| # Main layers. | |
| if self.in_channels == 0: | |
| x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs) | |
| elif self.architecture == 'resnet': | |
| y = self.skip(x, gain=np.sqrt(0.5)) | |
| x = self.conv0(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs) | |
| x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv, gain=np.sqrt(0.5), **layer_kwargs) | |
| x = y.add_(x) | |
| else: | |
| x = self.conv0(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs) | |
| x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs) | |
| # ToRGB. | |
| if img is not None: | |
| misc.assert_shape(img, [None, self.img_channels, self.resolution // 2, self.resolution // 2]) | |
| img = upfirdn2d.upsample2d(img, self.resample_filter) | |
| if self.is_last or self.architecture == 'skip': | |
| y = self.torgb(x, next(w_iter), fused_modconv=fused_modconv) | |
| y = y.to(dtype=torch.float32, memory_format=torch.contiguous_format) | |
| img = img.add_(y) if img is not None else y | |
| assert x.dtype == dtype | |
| assert img is None or img.dtype == torch.float32 | |
| return x, img | |
| #---------------------------------------------------------------------------- | |
| 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_fp16_res = 0, # Use FP16 for the N highest resolutions. | |
| **block_kwargs, # Arguments for SynthesisBlock. | |
| ): | |
| assert img_resolution >= 4 and img_resolution & (img_resolution - 1) == 0 | |
| super().__init__() | |
| self.w_dim = w_dim | |
| self.img_resolution = img_resolution | |
| self.img_resolution_log2 = int(np.log2(img_resolution)) | |
| self.img_channels = img_channels | |
| self.block_resolutions = [2 ** i for i in range(2, self.img_resolution_log2 + 1)] | |
| channels_dict = {res: min(channel_base // res, channel_max) for res in self.block_resolutions} | |
| fp16_resolution = max(2 ** (self.img_resolution_log2 + 1 - num_fp16_res), 8) | |
| self.num_ws = 0 | |
| for res in self.block_resolutions: | |
| in_channels = channels_dict[res // 2] if res > 4 else 0 | |
| out_channels = channels_dict[res] | |
| use_fp16 = (res >= fp16_resolution) | |
| is_last = (res == self.img_resolution) | |
| block = SynthesisBlock(in_channels, out_channels, w_dim=w_dim, resolution=res, | |
| img_channels=img_channels, is_last=is_last, use_fp16=use_fp16, **block_kwargs) | |
| self.num_ws += block.num_conv | |
| if is_last: | |
| self.num_ws += block.num_torgb | |
| setattr(self, f'b{res}', block) | |
| def forward(self, ws, **block_kwargs): | |
| block_ws = [] | |
| with torch.autograd.profiler.record_function('split_ws'): | |
| misc.assert_shape(ws, [None, self.num_ws, self.w_dim]) | |
| ws = ws.to(torch.float32) | |
| w_idx = 0 | |
| for res in self.block_resolutions: | |
| block = getattr(self, f'b{res}') | |
| block_ws.append(ws.narrow(1, w_idx, block.num_conv + block.num_torgb)) | |
| w_idx += block.num_conv | |
| x = img = None | |
| for res, cur_ws in zip(self.block_resolutions, block_ws): | |
| block = getattr(self, f'b{res}') | |
| x, img = block(x, img, cur_ws, **block_kwargs) | |
| return img | |
| #---------------------------------------------------------------------------- | |
| 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 = ConceptMappingNetwork(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, label=None, **synthesis_kwargs): | |
| ws = self.mapping(z, c, truncation_psi=truncation_psi, truncation_cutoff=truncation_cutoff, label=label) | |
| img = self.synthesis(ws, **synthesis_kwargs) | |
| return img | |
| #---------------------------------------------------------------------------- | |
| class DiscriminatorBlock(torch.nn.Module): | |
| def __init__(self, | |
| in_channels, # Number of input channels, 0 = first block. | |
| tmp_channels, # Number of intermediate channels. | |
| out_channels, # Number of output channels. | |
| resolution, # Resolution of this block. | |
| img_channels, # Number of input color channels. | |
| first_layer_idx, # Index of the first layer. | |
| architecture = 'resnet', # Architecture: 'orig', 'skip', 'resnet'. | |
| activation = 'lrelu', # Activation function: 'relu', 'lrelu', etc. | |
| resample_filter = [1,3,3,1], # Low-pass filter to apply when resampling activations. | |
| conv_clamp = None, # Clamp the output of convolution layers to +-X, None = disable clamping. | |
| use_fp16 = False, # Use FP16 for this block? | |
| fp16_channels_last = False, # Use channels-last memory format with FP16? | |
| freeze_layers = 0, # Freeze-D: Number of layers to freeze. | |
| ): | |
| assert in_channels in [0, tmp_channels] | |
| assert architecture in ['orig', 'skip', 'resnet'] | |
| super().__init__() | |
| self.in_channels = in_channels | |
| self.resolution = resolution | |
| self.img_channels = img_channels | |
| self.first_layer_idx = first_layer_idx | |
| self.architecture = architecture | |
| self.use_fp16 = use_fp16 | |
| self.channels_last = (use_fp16 and fp16_channels_last) | |
| self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter)) | |
| self.num_layers = 0 | |
| def trainable_gen(): | |
| while True: | |
| layer_idx = self.first_layer_idx + self.num_layers | |
| trainable = (layer_idx >= freeze_layers) | |
| self.num_layers += 1 | |
| yield trainable | |
| trainable_iter = trainable_gen() | |
| if in_channels == 0 or architecture == 'skip': | |
| self.fromrgb = Conv2dLayer(img_channels, tmp_channels, kernel_size=1, activation=activation, | |
| trainable=next(trainable_iter), conv_clamp=conv_clamp, channels_last=self.channels_last) | |
| self.conv0 = Conv2dLayer(tmp_channels, tmp_channels, kernel_size=3, activation=activation, | |
| trainable=next(trainable_iter), conv_clamp=conv_clamp, channels_last=self.channels_last) | |
| self.conv1 = Conv2dLayer(tmp_channels, out_channels, kernel_size=3, activation=activation, down=2, | |
| trainable=next(trainable_iter), resample_filter=resample_filter, conv_clamp=conv_clamp, channels_last=self.channels_last) | |
| if architecture == 'resnet': | |
| self.skip = Conv2dLayer(tmp_channels, out_channels, kernel_size=1, bias=False, down=2, | |
| trainable=next(trainable_iter), resample_filter=resample_filter, channels_last=self.channels_last) | |
| def forward(self, x, img, force_fp32=False): | |
| dtype = torch.float16 if self.use_fp16 and not force_fp32 else torch.float32 | |
| memory_format = torch.channels_last if self.channels_last and not force_fp32 else torch.contiguous_format | |
| # Input. | |
| if x is not None: | |
| misc.assert_shape(x, [None, self.in_channels, self.resolution, self.resolution]) | |
| x = x.to(dtype=dtype, memory_format=memory_format) | |
| # FromRGB. | |
| if self.in_channels == 0 or self.architecture == 'skip': | |
| misc.assert_shape(img, [None, self.img_channels, self.resolution, self.resolution]) | |
| img = img.to(dtype=dtype, memory_format=memory_format) | |
| y = self.fromrgb(img) | |
| x = x + y if x is not None else y | |
| img = upfirdn2d.downsample2d(img, self.resample_filter) if self.architecture == 'skip' else None | |
| # Main layers. | |
| if self.architecture == 'resnet': | |
| y = self.skip(x, gain=np.sqrt(0.5)) | |
| x = self.conv0(x) | |
| x = self.conv1(x, gain=np.sqrt(0.5)) | |
| x = y.add_(x) | |
| else: | |
| x = self.conv0(x) | |
| x = self.conv1(x) | |
| assert x.dtype == dtype | |
| return x, img | |
| #---------------------------------------------------------------------------- | |
| class MinibatchStdLayer(torch.nn.Module): | |
| def __init__(self, group_size, num_channels=1): | |
| super().__init__() | |
| self.group_size = group_size | |
| self.num_channels = num_channels | |
| def forward(self, x): | |
| N, C, H, W = x.shape | |
| with misc.suppress_tracer_warnings(): # as_tensor results are registered as constants | |
| G = torch.min(torch.as_tensor(self.group_size), torch.as_tensor(N)) if self.group_size is not None else N | |
| F = self.num_channels | |
| c = C // F | |
| y = x.reshape(G, -1, F, c, H, W) # [GnFcHW] Split minibatch N into n groups of size G, and channels C into F groups of size c. | |
| y = y - y.mean(dim=0) # [GnFcHW] Subtract mean over group. | |
| y = y.square().mean(dim=0) # [nFcHW] Calc variance over group. | |
| y = (y + 1e-8).sqrt() # [nFcHW] Calc stddev over group. | |
| y = y.mean(dim=[2,3,4]) # [nF] Take average over channels and pixels. | |
| y = y.reshape(-1, F, 1, 1) # [nF11] Add missing dimensions. | |
| y = y.repeat(G, 1, H, W) # [NFHW] Replicate over group and pixels. | |
| x = torch.cat([x, y], dim=1) # [NCHW] Append to input as new channels. | |
| return x | |
| #---------------------------------------------------------------------------- | |
| class DiscriminatorEpilogue(torch.nn.Module): | |
| def __init__(self, | |
| in_channels, # Number of input channels. | |
| cmap_dim, # Dimensionality of mapped conditioning label, 0 = no label. | |
| resolution, # Resolution of this block. | |
| img_channels, # Number of input color channels. | |
| architecture = 'resnet', # Architecture: 'orig', 'skip', 'resnet'. | |
| mbstd_group_size = 4, # Group size for the minibatch standard deviation layer, None = entire minibatch. | |
| mbstd_num_channels = 1, # Number of features for the minibatch standard deviation layer, 0 = disable. | |
| activation = 'lrelu', # Activation function: 'relu', 'lrelu', etc. | |
| conv_clamp = None, # Clamp the output of convolution layers to +-X, None = disable clamping. | |
| ): | |
| assert architecture in ['orig', 'skip', 'resnet'] | |
| super().__init__() | |
| self.in_channels = in_channels | |
| self.cmap_dim = cmap_dim | |
| self.resolution = resolution | |
| self.img_channels = img_channels | |
| self.architecture = architecture | |
| if architecture == 'skip': | |
| self.fromrgb = Conv2dLayer(img_channels, in_channels, kernel_size=1, activation=activation) | |
| self.mbstd = MinibatchStdLayer(group_size=mbstd_group_size, num_channels=mbstd_num_channels) if mbstd_num_channels > 0 else None | |
| self.conv = Conv2dLayer(in_channels + mbstd_num_channels, in_channels, kernel_size=3, activation=activation, conv_clamp=conv_clamp) | |
| self.fc = FullyConnectedLayer(in_channels * (resolution ** 2), in_channels, activation=activation) | |
| self.out = FullyConnectedLayer(in_channels, 1 if cmap_dim == 0 else cmap_dim) | |
| #self.out = FullyConnectedLayer(in_channels, 1) | |
| def forward(self, x, img, cmap, force_fp32=False): | |
| misc.assert_shape(x, [None, self.in_channels, self.resolution, self.resolution]) # [NCHW] | |
| _ = force_fp32 # unused | |
| dtype = torch.float32 | |
| memory_format = torch.contiguous_format | |
| # FromRGB. | |
| x = x.to(dtype=dtype, memory_format=memory_format) | |
| if self.architecture == 'skip': | |
| misc.assert_shape(img, [None, self.img_channels, self.resolution, self.resolution]) | |
| img = img.to(dtype=dtype, memory_format=memory_format) | |
| x = x + self.fromrgb(img) | |
| # Main layers. | |
| if self.mbstd is not None: | |
| x = self.mbstd(x) | |
| x = self.conv(x) | |
| x = self.fc(x.flatten(1)) | |
| x = self.out(x) | |
| # Conditioning. | |
| if self.cmap_dim > 0: | |
| misc.assert_shape(cmap, [None, self.cmap_dim]) | |
| x = (x * cmap).sum(dim=1, keepdim=True) * (1 / np.sqrt(self.cmap_dim)) | |
| #x = x + 0 * cmap.sum() | |
| assert x.dtype == dtype | |
| return x | |
| #---------------------------------------------------------------------------- | |
| class Discriminator(torch.nn.Module): | |
| def __init__(self, | |
| c_dim, # Conditioning label (C) dimensionality. | |
| img_resolution, # Input resolution. | |
| img_channels, # Number of input color channels. | |
| architecture = 'resnet', # Architecture: 'orig', 'skip', 'resnet'. | |
| channel_base = 32768, # Overall multiplier for the number of channels. | |
| channel_max = 512, # Maximum number of channels in any layer. | |
| num_fp16_res = 0, # Use FP16 for the N highest resolutions. | |
| conv_clamp = None, # Clamp the output of convolution layers to +-X, None = disable clamping. | |
| cmap_dim = None, # Dimensionality of mapped conditioning label, None = default. | |
| block_kwargs = {}, # Arguments for DiscriminatorBlock. | |
| mapping_kwargs = {}, # Arguments for MappingNetwork. | |
| epilogue_kwargs = {}, # Arguments for DiscriminatorEpilogue. | |
| ): | |
| super().__init__() | |
| self.c_dim = c_dim | |
| self.img_resolution = img_resolution | |
| self.img_resolution_log2 = int(np.log2(img_resolution)) | |
| self.img_channels = img_channels | |
| self.block_resolutions = [2 ** i for i in range(self.img_resolution_log2, 2, -1)] | |
| channels_dict = {res: min(channel_base // res, channel_max) for res in self.block_resolutions + [4]} | |
| fp16_resolution = max(2 ** (self.img_resolution_log2 + 1 - num_fp16_res), 8) | |
| if cmap_dim is None: | |
| cmap_dim = channels_dict[4] | |
| if c_dim == 0: | |
| cmap_dim = 0 | |
| common_kwargs = dict(img_channels=img_channels, architecture=architecture, conv_clamp=conv_clamp) | |
| cur_layer_idx = 0 | |
| for res in self.block_resolutions: | |
| in_channels = channels_dict[res] if res < img_resolution else 0 | |
| tmp_channels = channels_dict[res] | |
| out_channels = channels_dict[res // 2] | |
| use_fp16 = (res >= fp16_resolution) | |
| block = DiscriminatorBlock(in_channels, tmp_channels, out_channels, resolution=res, | |
| first_layer_idx=cur_layer_idx, use_fp16=use_fp16, **block_kwargs, **common_kwargs) | |
| setattr(self, f'b{res}', block) | |
| cur_layer_idx += block.num_layers | |
| if c_dim > 0: | |
| self.mapping = MappingNetwork(z_dim=0, c_dim=c_dim, w_dim=cmap_dim, num_ws=None, w_avg_beta=None, **mapping_kwargs) | |
| self.b4 = DiscriminatorEpilogue(channels_dict[4], cmap_dim=cmap_dim, resolution=4, **epilogue_kwargs, **common_kwargs) | |
| def forward(self, img, c, **block_kwargs): | |
| x = None | |
| for res in self.block_resolutions: | |
| block = getattr(self, f'b{res}') | |
| x, img = block(x, img, **block_kwargs) | |
| #print(img.size(), ' >>>>>>>> img sizeesssssssssss ', c.size(), ' ', res) | |
| cmap = None | |
| if self.c_dim > 0: | |
| cmap = self.mapping(None, c) | |
| x = self.b4(x, img, cmap) | |
| return x | |
| #---------------------------------------------------------------------------- | |