# 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 #---------------------------------------------------------------------------- @misc.profiled_function def normalize_2nd_moment(x, dim=1, eps=1e-8): return x * (x.square().mean(dim=dim, keepdim=True) + eps).rsqrt() #---------------------------------------------------------------------------- @misc.profiled_function 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 #---------------------------------------------------------------------------- @persistence.persistent_class 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) + ')' #---------------------------------------------------------------------------- @persistence.persistent_class 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 @persistence.persistent_class 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 @persistence.persistent_class 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 @persistence.persistent_class 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 #---------------------------------------------------------------------------- @persistence.persistent_class 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 #---------------------------------------------------------------------------- @persistence.persistent_class 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 #---------------------------------------------------------------------------- @persistence.persistent_class 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 #---------------------------------------------------------------------------- @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_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 #---------------------------------------------------------------------------- @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 = 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 #---------------------------------------------------------------------------- @persistence.persistent_class 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 #---------------------------------------------------------------------------- @persistence.persistent_class 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 #---------------------------------------------------------------------------- @persistence.persistent_class 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 #---------------------------------------------------------------------------- @persistence.persistent_class 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 #----------------------------------------------------------------------------