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from enum import Enum |
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import math |
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import numpy as np |
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import torch |
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from torch import nn |
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from torch.nn import Conv2d, BatchNorm2d, PReLU, Sequential, Module |
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from e4e.models.encoders.helpers import get_blocks, bottleneck_IR, bottleneck_IR_SE, _upsample_add |
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from e4e.models.stylegan2.model import EqualLinear |
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class ProgressiveStage(Enum): |
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WTraining = 0 |
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Delta1Training = 1 |
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Delta2Training = 2 |
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Delta3Training = 3 |
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Delta4Training = 4 |
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Delta5Training = 5 |
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Delta6Training = 6 |
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Delta7Training = 7 |
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Delta8Training = 8 |
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Delta9Training = 9 |
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Delta10Training = 10 |
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Delta11Training = 11 |
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Delta12Training = 12 |
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Delta13Training = 13 |
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Delta14Training = 14 |
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Delta15Training = 15 |
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Delta16Training = 16 |
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Delta17Training = 17 |
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Inference = 18 |
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class GradualStyleBlock(Module): |
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def __init__(self, in_c, out_c, spatial): |
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super(GradualStyleBlock, self).__init__() |
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self.out_c = out_c |
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self.spatial = spatial |
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num_pools = int(np.log2(spatial)) |
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modules = [] |
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modules += [Conv2d(in_c, out_c, kernel_size=3, stride=2, padding=1), |
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nn.LeakyReLU()] |
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for i in range(num_pools - 1): |
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modules += [ |
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Conv2d(out_c, out_c, kernel_size=3, stride=2, padding=1), |
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nn.LeakyReLU() |
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] |
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self.convs = nn.Sequential(*modules) |
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self.linear = EqualLinear(out_c, out_c, lr_mul=1) |
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def forward(self, x): |
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x = self.convs(x) |
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x = x.view(-1, self.out_c) |
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x = self.linear(x) |
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return x |
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class GradualStyleEncoder(Module): |
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def __init__(self, num_layers, mode='ir', opts=None): |
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super(GradualStyleEncoder, self).__init__() |
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assert num_layers in [50, 100, 152], 'num_layers should be 50,100, or 152' |
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assert mode in ['ir', 'ir_se'], 'mode should be ir or ir_se' |
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blocks = get_blocks(num_layers) |
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if mode == 'ir': |
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unit_module = bottleneck_IR |
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elif mode == 'ir_se': |
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unit_module = bottleneck_IR_SE |
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self.input_layer = Sequential(Conv2d(3, 64, (3, 3), 1, 1, bias=False), |
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BatchNorm2d(64), |
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PReLU(64)) |
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modules = [] |
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for block in blocks: |
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for bottleneck in block: |
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modules.append(unit_module(bottleneck.in_channel, |
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bottleneck.depth, |
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bottleneck.stride)) |
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self.body = Sequential(*modules) |
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self.styles = nn.ModuleList() |
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log_size = int(math.log(opts.stylegan_size, 2)) |
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self.style_count = 2 * log_size - 2 |
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self.coarse_ind = 3 |
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self.middle_ind = 7 |
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for i in range(self.style_count): |
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if i < self.coarse_ind: |
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style = GradualStyleBlock(512, 512, 16) |
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elif i < self.middle_ind: |
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style = GradualStyleBlock(512, 512, 32) |
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else: |
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style = GradualStyleBlock(512, 512, 64) |
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self.styles.append(style) |
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self.latlayer1 = nn.Conv2d(256, 512, kernel_size=1, stride=1, padding=0) |
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self.latlayer2 = nn.Conv2d(128, 512, kernel_size=1, stride=1, padding=0) |
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def forward(self, x): |
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x = self.input_layer(x) |
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latents = [] |
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modulelist = list(self.body._modules.values()) |
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for i, l in enumerate(modulelist): |
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x = l(x) |
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if i == 6: |
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c1 = x |
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elif i == 20: |
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c2 = x |
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elif i == 23: |
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c3 = x |
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for j in range(self.coarse_ind): |
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latents.append(self.styles[j](c3)) |
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p2 = _upsample_add(c3, self.latlayer1(c2)) |
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for j in range(self.coarse_ind, self.middle_ind): |
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latents.append(self.styles[j](p2)) |
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p1 = _upsample_add(p2, self.latlayer2(c1)) |
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for j in range(self.middle_ind, self.style_count): |
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latents.append(self.styles[j](p1)) |
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out = torch.stack(latents, dim=1) |
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return out |
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class Encoder4Editing(Module): |
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def __init__(self, num_layers, mode='ir', opts=None): |
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super(Encoder4Editing, self).__init__() |
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assert num_layers in [50, 100, 152], 'num_layers should be 50,100, or 152' |
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assert mode in ['ir', 'ir_se'], 'mode should be ir or ir_se' |
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blocks = get_blocks(num_layers) |
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if mode == 'ir': |
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unit_module = bottleneck_IR |
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elif mode == 'ir_se': |
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unit_module = bottleneck_IR_SE |
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self.input_layer = Sequential(Conv2d(3, 64, (3, 3), 1, 1, bias=False), |
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BatchNorm2d(64), |
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PReLU(64)) |
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modules = [] |
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for block in blocks: |
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for bottleneck in block: |
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modules.append(unit_module(bottleneck.in_channel, |
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bottleneck.depth, |
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bottleneck.stride)) |
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self.body = Sequential(*modules) |
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self.styles = nn.ModuleList() |
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log_size = int(math.log(opts.stylegan_size, 2)) |
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self.style_count = 2 * log_size - 2 |
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self.coarse_ind = 3 |
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self.middle_ind = 7 |
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for i in range(self.style_count): |
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if i < self.coarse_ind: |
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style = GradualStyleBlock(512, 512, 16) |
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elif i < self.middle_ind: |
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style = GradualStyleBlock(512, 512, 32) |
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else: |
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style = GradualStyleBlock(512, 512, 64) |
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self.styles.append(style) |
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self.latlayer1 = nn.Conv2d(256, 512, kernel_size=1, stride=1, padding=0) |
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self.latlayer2 = nn.Conv2d(128, 512, kernel_size=1, stride=1, padding=0) |
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self.progressive_stage = ProgressiveStage.Inference |
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def get_deltas_starting_dimensions(self): |
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''' Get a list of the initial dimension of every delta from which it is applied ''' |
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return list(range(self.style_count)) |
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def set_progressive_stage(self, new_stage: ProgressiveStage): |
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self.progressive_stage = new_stage |
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print('Changed progressive stage to: ', new_stage) |
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def forward(self, x): |
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x = self.input_layer(x) |
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modulelist = list(self.body._modules.values()) |
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for i, l in enumerate(modulelist): |
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x = l(x) |
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if i == 6: |
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c1 = x |
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elif i == 20: |
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c2 = x |
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elif i == 23: |
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c3 = x |
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w0 = self.styles[0](c3) |
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w = w0.repeat(self.style_count, 1, 1).permute(1, 0, 2) |
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stage = self.progressive_stage.value |
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features = c3 |
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for i in range(1, min(stage + 1, self.style_count)): |
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if i == self.coarse_ind: |
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p2 = _upsample_add(c3, self.latlayer1(c2)) |
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features = p2 |
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elif i == self.middle_ind: |
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p1 = _upsample_add(p2, self.latlayer2(c1)) |
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features = p1 |
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delta_i = self.styles[i](features) |
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w[:, i] += delta_i |
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return w |
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