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# Copyright 2020 Erik Härkönen. All rights reserved.
# This file is licensed to you under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License. You may obtain a copy
# of the License at http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software distributed under
# the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR REPRESENTATIONS
# OF ANY KIND, either express or implied. See the License for the specific language
# governing permissions and limitations under the License.
import torch, numpy as np
from types import SimpleNamespace
import itertools
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent.parent))
from models import get_model
from config import Config
torch.backends.cudnn.benchmark = True
has_gpu = torch.cuda.is_available()
device = torch.device('cuda' if has_gpu else 'cpu')
B = 2 # test batch support
models = [
('BigGAN-128', 'husky'),
('BigGAN-256', 'husky'),
('BigGAN-512', 'husky'),
('StyleGAN', 'ffhq'),
('StyleGAN2', 'ffhq'),
]
for model_name, classname in models:
with torch.no_grad():
model = get_model(model_name, classname, device).to(device)
print(f'Testing {model_name}-{classname}', end='')
n_latents = model.get_max_latents()
assert n_latents > 1, 'Model reports max_latents=1'
#if hasattr(model, 'use_w'):
# model.use_w()
seed = 1234
torch.manual_seed(seed)
np.random.seed(seed)
latents = [model.sample_latent(B, seed=seed) for _ in range(10)]
# Test that partial-forward supports layerwise latent inputs
try:
layer_name, _ = list(model.named_modules())[-1]
_ = model.partial_forward(n_latents*[latents[0]], layer_name)
except Exception as e:
print('Error:', e)
raise RuntimeError(f"{model_name} partial forward doesn't support layerwise latent!")
# Test that layerwise and single give same result
for z in latents:
torch.manual_seed(0)
np.random.seed(0)
out1 = model.forward(z)
torch.manual_seed(0)
np.random.seed(0)
out2 = model.forward(n_latents*[z])
dist_rel = (torch.abs(out1 - out2).sum() / out1.sum()).item()
assert dist_rel < 1e-3, f'Layerwise latent mode working incorrectly for model {model_name}-{classname}: difference = {dist_rel*100}%'
print('.', end='')
print('OK!')
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