])
starting_model_number = 280000
weights = torch.load(
f'{Paths.default.models()}/cardio_part_{str(starting_model_number).zfill(6)}.pt',
map_location="cpu"
)
enc_dec = StyleGanAutoEncoder().load_state_dict(weights, style=False).cuda()
discriminator_img = Discriminator(image_size)
discriminator_img.load_state_dict(weights['di'])
discriminator_img = discriminator_img.cuda()
heatmapper = ToGaussHeatMap(256, 4)
hg = HG_heatmap(heatmapper, num_classes=200)
# hg.load_state_dict(weights['gh'])
hg = hg.cuda()
hm_discriminator = Discriminator(image_size, input_nc=1, channel_multiplier=1)
hm_discriminator.load_state_dict(weights["dh"])
hm_discriminator = hm_discriminator.cuda()
gan_model_tuda = StyleGanModel[HeatmapToImage](enc_dec.generator, StyleGANLoss(discriminator_img), (0.001/4, 0.0015/4))
gan_model_obratno = StyleGanModel[HG_skeleton](hg, StyleGANLoss(hm_discriminator, r1=3), (2e-5, 0.0015/4))
style_opt = optim.Adam(enc_dec.style_encoder.parameters(), lr=1e-5)
print(f"board path: {Paths.default.board()}/cardio{int(time.time())}")
writer = SummaryWriter(f"{Paths.default.board()}/cardio{int(time.time())}")
WR.writer = writer
ToTensor(device),
])
starting_model_number = 280000
weights = torch.load(
f'{Paths.default.models()}/cardio_brule_unsup_{str(starting_model_number).zfill(6)}.pt',
map_location="cpu")
enc_dec = StyleGanAutoEncoder().load_state_dict(weights, style=False).cuda()
discriminator_img = Discriminator(image_size)
discriminator_img.load_state_dict(weights['di'])
discriminator_img = discriminator_img.cuda()
heatmapper = ToGaussHeatMap(256, 4)
hg = HG_heatmap(heatmapper, num_blocks=1, num_classes=200)
hg.load_state_dict(weights['gh'])
hg = hg.cuda()
cont_opt = optim.Adam(hg.parameters(), lr=2e-5, betas=(0, 0.8))
gan_model_tuda = StyleGanModel[HeatmapToImage](enc_dec.generator,
StyleGANLoss(discriminator_img),
(0.001 / 4, 0.0015 / 4))
style_opt = optim.Adam(enc_dec.style_encoder.parameters(), lr=1e-5)
writer = SummaryWriter(
f"{Paths.default.board()}/brule1_cardio_{int(time.time())}")
WR.writer = writer
batch = next(LazyLoader.cardio().loader_train_inf)
for k in vars(args):
print(f"{k}: {vars(args)[k]}")
device = torch.device(f"cuda:{args.cuda}" if torch.cuda.is_available() else "cpu")
torch.cuda.set_device(device)
Cardio.batch_size = batch_size
starting_model_number = 264000
weights = torch.load(
f'{Paths.default.models()}/cardio_brule_sup_{str(starting_model_number).zfill(6)}.pt',
map_location="cpu"
)
heatmapper = ToGaussHeatMap(256, 4)
hg = HG_heatmap(heatmapper, num_blocks=1, num_classes=200)
hg.load_state_dict(weights['gh'])
hg = hg.cuda()
cont_opt = optim.Adam(hg.parameters(), lr=2e-5, betas=(0, 0.8))
# gan_model_tuda = StyleGanModel[HeatmapToImage](enc_dec.generator, StyleGANLoss(discriminator_img), (0.001/4, 0.0015/4))
# style_opt = optim.Adam(enc_dec.style_encoder.parameters(), lr=1e-5)
writer = SummaryWriter(f"{Paths.default.board()}/brule1_cardio_{int(time.time())}")
WR.writer = writer
batch = next(iter(LazyLoader.cardio().test_loader))
test_img = batch["image"].cuda()
starting_model_number = 90000 + 210000
weights = torch.load(
f'{Paths.default.models()}/human_{str(starting_model_number).zfill(6)}.pt',
map_location="cpu")
enc_dec = StyleGanAutoEncoder(
hm_nc=measure_size, image_size=image_size).load_state_dict(weights).cuda()
discriminator_img = Discriminator(image_size)
discriminator_img.load_state_dict(weights['di'])
discriminator_img = discriminator_img.cuda()
heatmapper = ToGaussHeatMap(image_size, 2)
hg = HG_heatmap(heatmapper,
num_classes=measure_size,
image_size=image_size,
num_blocks=4)
hg.load_state_dict(weights['gh'])
hg = hg.cuda()
hm_discriminator = Discriminator(image_size,
input_nc=measure_size,
channel_multiplier=1)
hm_discriminator.load_state_dict(weights["dh"])
hm_discriminator = hm_discriminator.cuda()
gan_model_tuda = StyleGanModel[HeatmapToImage](enc_dec.generator,
StyleGANLoss(discriminator_img),
(0.001 / 4, 0.0015 / 4))
gan_model_obratno = StyleGanModel[HG_skeleton](hg,
StyleGANLoss(hm_discriminator),
(2e-5, 0.0015 / 4))
for k in vars(args):
print(f"{k}: {vars(args)[k]}")
device = torch.device(
f"cuda:{args.cuda}" if torch.cuda.is_available() else "cpu")
torch.cuda.set_device(device)
HumanLoader.batch_size = batch_size
starting_model_number = 90000 + 120000
weights = torch.load(
f'{Paths.default.models()}/human_{str(starting_model_number).zfill(6)}.pt',
map_location="cpu")
heatmapper = ToGaussHeatMap(image_size, 2)
hg = HG_heatmap(heatmapper,
num_classes=32,
image_size=image_size,
num_blocks=4)
hg.load_state_dict(weights['gh'])
hg = hg.cuda()
requires_grad(hg, False)
enc_dec = StyleGanAutoEncoder(
hm_nc=measure_size, image_size=image_size).load_state_dict(weights).cuda()
discriminator_img = Discriminator(image_size)
discriminator_img.load_state_dict(weights['di'])
discriminator_img = discriminator_img.cuda()
gan_model_tuda = StyleGanModel[HeatmapToImage](enc_dec.generator,
StyleGANLoss(discriminator_img),
(0.001, 0.0015))