Spaces:
Runtime error
Runtime error
File size: 6,480 Bytes
92ec8d3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 |
from enum import Enum
import numpy as np
from torch import nn
from torch.nn import Conv2d, BatchNorm2d, PReLU, Sequential, Module
from torchvision.models import resnet34
from models.stylegan2.model import EqualLinear
from models.hyperstyle.encoders.helpers import get_blocks, bottleneck_IR, bottleneck_IR_SE
class ProgressiveStage(Enum):
WTraining = 0
Delta1Training = 1
Delta2Training = 2
Delta3Training = 3
Delta4Training = 4
Delta5Training = 5
Delta6Training = 6
Delta7Training = 7
Delta8Training = 8
Delta9Training = 9
Delta10Training = 10
Delta11Training = 11
Delta12Training = 12
Delta13Training = 13
Delta14Training = 14
Delta15Training = 15
Delta16Training = 16
Delta17Training = 17
Inference = 18
class GradualStyleBlock(Module):
def __init__(self, in_c, out_c, spatial):
super(GradualStyleBlock, self).__init__()
self.out_c = out_c
self.spatial = spatial
num_pools = int(np.log2(spatial))
modules = []
modules += [Conv2d(in_c, out_c, kernel_size=3, stride=2, padding=1),
nn.LeakyReLU()]
for i in range(num_pools - 1):
modules += [
Conv2d(out_c, out_c, kernel_size=3, stride=2, padding=1),
nn.LeakyReLU()
]
self.convs = nn.Sequential(*modules)
self.linear = EqualLinear(out_c, out_c, lr_mul=1)
def forward(self, x):
x = self.convs(x)
x = x.view(-1, self.out_c)
x = self.linear(x)
return x
class ProgressiveBackboneEncoder(Module):
"""
The simpler backbone architecture used by ReStyle where all style vectors are extracted from the final 16x16 feature
map of the encoder. This classes uses the simplified architecture applied over an ResNet IRSE50 backbone with the
progressive training scheme from e4e_modules.
Note this class is designed to be used for the human facial domain.
"""
def __init__(self, num_layers, mode='ir', n_styles=18, opts=None):
super(ProgressiveBackboneEncoder, self).__init__()
assert num_layers in [50, 100, 152], 'num_layers should be 50,100, or 152'
assert mode in ['ir', 'ir_se'], 'mode should be ir or ir_se'
blocks = get_blocks(num_layers)
if mode == 'ir':
unit_module = bottleneck_IR
elif mode == 'ir_se':
unit_module = bottleneck_IR_SE
self.input_layer = Sequential(Conv2d(opts.input_nc, 64, (3, 3), 1, 1, bias=False),
BatchNorm2d(64),
PReLU(64))
modules = []
for block in blocks:
for bottleneck in block:
modules.append(unit_module(bottleneck.in_channel,
bottleneck.depth,
bottleneck.stride))
self.body = Sequential(*modules)
self.styles = nn.ModuleList()
self.style_count = n_styles
for i in range(self.style_count):
style = GradualStyleBlock(512, 512, 16)
self.styles.append(style)
self.progressive_stage = ProgressiveStage.Inference
def get_deltas_starting_dimensions(self):
''' Get a list of the initial dimension of every delta from which it is applied '''
return list(range(self.style_count)) # Each dimension has a delta applied to
def set_progressive_stage(self, new_stage: ProgressiveStage):
# In this encoder we train all the pyramid (At least as a first stage experiment
self.progressive_stage = new_stage
print('Changed progressive stage to: ', new_stage)
def forward(self, x):
x = self.input_layer(x)
x = self.body(x)
# get initial w0 from first map2style layer
w0 = self.styles[0](x)
w = w0.repeat(self.style_count, 1, 1).permute(1, 0, 2)
# learn the deltas up to the current stage
stage = self.progressive_stage.value
for i in range(1, min(stage + 1, self.style_count)):
delta_i = self.styles[i](x)
w[:, i] += delta_i
return w
class ResNetProgressiveBackboneEncoder(Module):
"""
The simpler backbone architecture used by ReStyle where all style vectors are extracted from the final 16x16 feature
map of the encoder. This classes uses the simplified architecture applied over an ResNet34 backbone with the
progressive training scheme from e4e_modules.
"""
def __init__(self, n_styles=18, opts=None):
super(ResNetProgressiveBackboneEncoder, self).__init__()
self.conv1 = nn.Conv2d(opts.input_nc, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = BatchNorm2d(64)
self.relu = PReLU(64)
resnet_basenet = resnet34(pretrained=True)
blocks = [
resnet_basenet.layer1,
resnet_basenet.layer2,
resnet_basenet.layer3,
resnet_basenet.layer4
]
modules = []
for block in blocks:
for bottleneck in block:
modules.append(bottleneck)
self.body = Sequential(*modules)
self.styles = nn.ModuleList()
self.style_count = n_styles
for i in range(self.style_count):
style = GradualStyleBlock(512, 512, 16)
self.styles.append(style)
self.progressive_stage = ProgressiveStage.Inference
def get_deltas_starting_dimensions(self):
''' Get a list of the initial dimension of every delta from which it is applied '''
return list(range(self.style_count)) # Each dimension has a delta applied to
def set_progressive_stage(self, new_stage: ProgressiveStage):
# In this encoder we train all the pyramid (At least as a first stage experiment
self.progressive_stage = new_stage
print('Changed progressive stage to: ', new_stage)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.body(x)
# get initial w0 from first map2style layer
w0 = self.styles[0](x)
w = w0.repeat(self.style_count, 1, 1).permute(1, 0, 2)
# learn the deltas up to the current stage
stage = self.progressive_stage.value
for i in range(1, min(stage + 1, self.style_count)):
delta_i = self.styles[i](x)
w[:, i] += delta_i
return w
|