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import torch
from gfpgan.archs.gfpganv1_arch import FacialComponentDiscriminator, GFPGANv1, StyleGAN2GeneratorSFT
from gfpgan.archs.gfpganv1_clean_arch import GFPGANv1Clean, StyleGAN2GeneratorCSFT
def test_stylegan2generatorsft():
"""Test arch: StyleGAN2GeneratorSFT."""
# model init and forward (gpu)
if torch.cuda.is_available():
net = StyleGAN2GeneratorSFT(
out_size=32,
num_style_feat=512,
num_mlp=8,
channel_multiplier=1,
resample_kernel=(1, 3, 3, 1),
lr_mlp=0.01,
narrow=1,
sft_half=False).cuda().eval()
style = torch.rand((1, 512), dtype=torch.float32).cuda()
condition1 = torch.rand((1, 512, 8, 8), dtype=torch.float32).cuda()
condition2 = torch.rand((1, 512, 16, 16), dtype=torch.float32).cuda()
condition3 = torch.rand((1, 512, 32, 32), dtype=torch.float32).cuda()
conditions = [condition1, condition1, condition2, condition2, condition3, condition3]
output = net([style], conditions)
assert output[0].shape == (1, 3, 32, 32)
assert output[1] is None
# -------------------- with return_latents ----------------------- #
output = net([style], conditions, return_latents=True)
assert output[0].shape == (1, 3, 32, 32)
assert len(output[1]) == 1
# check latent
assert output[1][0].shape == (8, 512)
# -------------------- with randomize_noise = False ----------------------- #
output = net([style], conditions, randomize_noise=False)
assert output[0].shape == (1, 3, 32, 32)
assert output[1] is None
# -------------------- with truncation = 0.5 and mixing----------------------- #
output = net([style, style], conditions, truncation=0.5, truncation_latent=style)
assert output[0].shape == (1, 3, 32, 32)
assert output[1] is None
def test_gfpganv1():
"""Test arch: GFPGANv1."""
# model init and forward (gpu)
if torch.cuda.is_available():
net = GFPGANv1(
out_size=32,
num_style_feat=512,
channel_multiplier=1,
resample_kernel=(1, 3, 3, 1),
decoder_load_path=None,
fix_decoder=True,
# for stylegan decoder
num_mlp=8,
lr_mlp=0.01,
input_is_latent=False,
different_w=False,
narrow=1,
sft_half=True).cuda().eval()
img = torch.rand((1, 3, 32, 32), dtype=torch.float32).cuda()
output = net(img)
assert output[0].shape == (1, 3, 32, 32)
assert len(output[1]) == 3
# check out_rgbs for intermediate loss
assert output[1][0].shape == (1, 3, 8, 8)
assert output[1][1].shape == (1, 3, 16, 16)
assert output[1][2].shape == (1, 3, 32, 32)
# -------------------- with different_w = True ----------------------- #
net = GFPGANv1(
out_size=32,
num_style_feat=512,
channel_multiplier=1,
resample_kernel=(1, 3, 3, 1),
decoder_load_path=None,
fix_decoder=True,
# for stylegan decoder
num_mlp=8,
lr_mlp=0.01,
input_is_latent=False,
different_w=True,
narrow=1,
sft_half=True).cuda().eval()
img = torch.rand((1, 3, 32, 32), dtype=torch.float32).cuda()
output = net(img)
assert output[0].shape == (1, 3, 32, 32)
assert len(output[1]) == 3
# check out_rgbs for intermediate loss
assert output[1][0].shape == (1, 3, 8, 8)
assert output[1][1].shape == (1, 3, 16, 16)
assert output[1][2].shape == (1, 3, 32, 32)
def test_facialcomponentdiscriminator():
"""Test arch: FacialComponentDiscriminator."""
# model init and forward (gpu)
if torch.cuda.is_available():
net = FacialComponentDiscriminator().cuda().eval()
img = torch.rand((1, 3, 32, 32), dtype=torch.float32).cuda()
output = net(img)
assert len(output) == 2
assert output[0].shape == (1, 1, 8, 8)
assert output[1] is None
# -------------------- return intermediate features ----------------------- #
output = net(img, return_feats=True)
assert len(output) == 2
assert output[0].shape == (1, 1, 8, 8)
assert len(output[1]) == 2
assert output[1][0].shape == (1, 128, 16, 16)
assert output[1][1].shape == (1, 256, 8, 8)
def test_stylegan2generatorcsft():
"""Test arch: StyleGAN2GeneratorCSFT."""
# model init and forward (gpu)
if torch.cuda.is_available():
net = StyleGAN2GeneratorCSFT(
out_size=32, num_style_feat=512, num_mlp=8, channel_multiplier=1, narrow=1, sft_half=False).cuda().eval()
style = torch.rand((1, 512), dtype=torch.float32).cuda()
condition1 = torch.rand((1, 512, 8, 8), dtype=torch.float32).cuda()
condition2 = torch.rand((1, 512, 16, 16), dtype=torch.float32).cuda()
condition3 = torch.rand((1, 512, 32, 32), dtype=torch.float32).cuda()
conditions = [condition1, condition1, condition2, condition2, condition3, condition3]
output = net([style], conditions)
assert output[0].shape == (1, 3, 32, 32)
assert output[1] is None
# -------------------- with return_latents ----------------------- #
output = net([style], conditions, return_latents=True)
assert output[0].shape == (1, 3, 32, 32)
assert len(output[1]) == 1
# check latent
assert output[1][0].shape == (8, 512)
# -------------------- with randomize_noise = False ----------------------- #
output = net([style], conditions, randomize_noise=False)
assert output[0].shape == (1, 3, 32, 32)
assert output[1] is None
# -------------------- with truncation = 0.5 and mixing----------------------- #
output = net([style, style], conditions, truncation=0.5, truncation_latent=style)
assert output[0].shape == (1, 3, 32, 32)
assert output[1] is None
def test_gfpganv1clean():
"""Test arch: GFPGANv1Clean."""
# model init and forward (gpu)
if torch.cuda.is_available():
net = GFPGANv1Clean(
out_size=32,
num_style_feat=512,
channel_multiplier=1,
decoder_load_path=None,
fix_decoder=True,
# for stylegan decoder
num_mlp=8,
input_is_latent=False,
different_w=False,
narrow=1,
sft_half=True).cuda().eval()
img = torch.rand((1, 3, 32, 32), dtype=torch.float32).cuda()
output = net(img)
assert output[0].shape == (1, 3, 32, 32)
assert len(output[1]) == 3
# check out_rgbs for intermediate loss
assert output[1][0].shape == (1, 3, 8, 8)
assert output[1][1].shape == (1, 3, 16, 16)
assert output[1][2].shape == (1, 3, 32, 32)
# -------------------- with different_w = True ----------------------- #
net = GFPGANv1Clean(
out_size=32,
num_style_feat=512,
channel_multiplier=1,
decoder_load_path=None,
fix_decoder=True,
# for stylegan decoder
num_mlp=8,
input_is_latent=False,
different_w=True,
narrow=1,
sft_half=True).cuda().eval()
img = torch.rand((1, 3, 32, 32), dtype=torch.float32).cuda()
output = net(img)
assert output[0].shape == (1, 3, 32, 32)
assert len(output[1]) == 3
# check out_rgbs for intermediate loss
assert output[1][0].shape == (1, 3, 8, 8)
assert output[1][1].shape == (1, 3, 16, 16)
assert output[1][2].shape == (1, 3, 32, 32)
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