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{
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"# Copyright 2020 Erik Härkönen. All rights reserved.\n",
"# This file is licensed to you under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License. You may obtain a copy\n",
"# of the License at http://www.apache.org/licenses/LICENSE-2.0\n",
"\n",
"# Unless required by applicable law or agreed to in writing, software distributed under\n",
"# the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR REPRESENTATIONS\n",
"# OF ANY KIND, either express or implied. See the License for the specific language\n",
"# governing permissions and limitations under the License.\n",
"\n",
"%matplotlib inline\n",
"from notebook_init import *\n",
"import scipy\n",
"\n",
"outdir = Path('out/figures/random_baseline')\n",
"makedirs(outdir, exist_ok=True)\n",
"\n",
"# Project tensor 'X' onto orthonormal basis 'comp', return coordinates\n",
"def project_ortho(X, comp):\n",
" N = comp.shape[0]\n",
" coords = (comp.reshape(N, -1) * X.reshape(-1)).sum(dim=1)\n",
" return coords.reshape([N]+[1]*X.ndim)\n",
"\n",
"def show_img(img_np, W=6, H=6):\n",
" #plt.figure(figsize=(W,H))\n",
" plt.axis('off')\n",
" plt.tight_layout()\n",
" plt.imshow(img_np, interpolation='bilinear')\n",
" \n",
"inst = None # reused when possible"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": false,
"tags": []
},
"outputs": [],
"source": [
"from torchvision.utils import make_grid\n",
"\n",
"def generate(model_name, class_name, seed=None, trunc=0.6, N=5, use_random_basis=True):\n",
" global inst\n",
" \n",
" config = Config(n=1_000_000, batch_size=500, model=model_name,\n",
" output_class=class_name, use_w=('StyleGAN' in model_name))\n",
" \n",
" if model_name == 'StyleGAN2':\n",
" config.layer = 'style'\n",
" elif model_name == 'StyleGAN':\n",
" config.layer = 'g_mapping'\n",
" else:\n",
" config.layer = 'generator.gen_z'\n",
" config.n = 1_000_000\n",
" config.output_class = 'husky'\n",
" \n",
" inst = get_instrumented_model(config, torch.device('cuda'), inst=inst)\n",
" model = inst.model\n",
"\n",
" K = model.get_latent_dims()\n",
" config.components = K\n",
" \n",
" dump_name = get_or_compute(config, inst)\n",
"\n",
" with np.load(dump_name) as data:\n",
" lat_comp = torch.from_numpy(data['lat_comp']).cuda()\n",
" lat_mean = torch.from_numpy(data['lat_mean']).cuda()\n",
" lat_std = torch.from_numpy(data['lat_stdev']).cuda()\n",
" \n",
" B = 6\n",
" if seed is None:\n",
" seed = np.random.randint(np.iinfo(np.int32).max - B)\n",
" model.truncation = trunc\n",
" \n",
" if 'BigGAN' in model_name:\n",
" model.set_output_class(class_name)\n",
"\n",
" print(f'Seeds: {seed} - {seed+B}')\n",
"\n",
" # Resampling test\n",
" w_base = model.sample_latent(1, seed=seed + B)\n",
" plt.imshow(model.sample_np(w_base))\n",
" plt.axis('off')\n",
" plt.show()\n",
"\n",
" # Resample some components\n",
" def get_batch(indices, basis):\n",
" w_batch = torch.zeros(B, K).cuda()\n",
" coord_base = project_ortho(w_base - lat_mean, basis)\n",
"\n",
" for i in range(B):\n",
" w = model.sample_latent(1, seed=seed + i)\n",
" coords = coord_base.clone()\n",
" coords_resampled = project_ortho(w - lat_mean, basis)\n",
" coords[indices, :, :] = coords_resampled[indices, :, :]\n",
" w_batch[i, :] = lat_mean + torch.sum(coords * basis, dim=0)\n",
"\n",
" return w_batch\n",
"\n",
" def show_grid(w, title):\n",
" out = model.forward(w)\n",
" if class_name == 'car':\n",
" out = out[:, :, 64:-64, :]\n",
" elif class_name == 'cat':\n",
" out = out[:, :, 18:-8, :]\n",
" grid = make_grid(out, nrow=3)\n",
" grid_np = grid.clamp(0, 1).permute(1, 2, 0).cpu().numpy()\n",
" show_img(grid_np)\n",
" plt.title(title)\n",
"\n",
" def save_imgs(w, prefix):\n",
" for i, img in enumerate(model.sample_np(w)):\n",
" if class_name == 'car':\n",
" img = img[64:-64, :, :]\n",
" elif class_name == 'cat':\n",
" img = img[18:-8, :, :]\n",
" outpath = outdir / f'{model_name}_{class_name}' / f'{prefix}_{i}.png'\n",
" makedirs(outpath.parent, exist_ok=True)\n",
" Image.fromarray(np.uint8(img * 255)).save(outpath)\n",
" #print('Saving', outpath)\n",
"\n",
" def orthogonalize_rows(V):\n",
" Q, R = np.linalg.qr(V.T)\n",
" return Q.T\n",
" \n",
" # V = [n_comp, n_dim]\n",
" def assert_orthonormal(V):\n",
" M = np.dot(V, V.T) # [n_comp, n_comp]\n",
" det = np.linalg.det(M)\n",
" assert np.allclose(M, np.identity(M.shape[0]), atol=1e-5), f'Basis is not orthonormal (det={det})'\n",
"\n",
" plt.figure(figsize=((12,6.5) if class_name in ['car', 'cat'] else (12,8)))\n",
" \n",
" # First N fixed\n",
" ind_rand = np.array(range(N, K)) # N -> K rerandomized\n",
" b1 = get_batch(ind_rand, lat_comp)\n",
" plt.subplot(2, 2, 1)\n",
" show_grid(b1, f'Keep {N} first pca -> Consistent pose')\n",
" save_imgs(b1, f'keep_{N}_first_{seed}')\n",
"\n",
" # First N randomized\n",
" ind_rand = np.array(range(0, N)) # 0 -> N rerandomized\n",
" b2 = get_batch(ind_rand, lat_comp)\n",
" plt.subplot(2, 2, 2)\n",
" show_grid(b2, f'Randomize {N} first pca -> Consistent style')\n",
" save_imgs(b2, f'randomize_{N}_first_{seed}')\n",
"\n",
" if use_random_basis:\n",
" # Random orthonormal basis drawn from p(w)\n",
" # Highly shaped by W, sort of a noisy pseudo-PCA\n",
" #V = (model.sample_latent(K, seed=seed + B + 1) - lat_mean).cpu().numpy()\n",
" #V = V / np.sqrt(np.sum(V*V, axis=-1, keepdims=True)) # normalize rows\n",
" #V = orthogonalize_rows(V)\n",
" \n",
" # Isotropic random basis\n",
" V = scipy.stats.special_ortho_group.rvs(K)\n",
" assert_orthonormal(V)\n",
"\n",
" rand_basis = torch.from_numpy(V).float().view(lat_comp.shape).to(device)\n",
" assert rand_basis.shape == lat_comp.shape, f'Shape mismatch: {rand_basis.shape} != {lat_comp.shape}'\n",
"\n",
" ind_perm = range(K)\n",
" else:\n",
" # Just use shuffled PCA basis\n",
" rng = np.random.RandomState(seed=seed)\n",
" perm = rng.permutation(range(K))\n",
" rand_basis = lat_comp[perm, :]\n",
"\n",
" basis_type_str = 'random' if use_random_basis else 'pca_shfl'\n",
"\n",
" # First N random fixed\n",
" ind_rand = np.array(range(N, K)) # N -> K rerandomized\n",
" b3 = get_batch(ind_rand, rand_basis)\n",
" plt.subplot(2, 2, 3)\n",
" show_grid(b3, f'Keep {N} first {basis_type_str} -> Little consistency')\n",
" save_imgs(b3, f'keep_{N}_first_{basis_type_str}_{seed}')\n",
" \n",
" # First N random rerandomized\n",
" ind_rand = np.array(range(0, N)) # 0 -> N rerandomized\n",
" b4 = get_batch(ind_rand, rand_basis)\n",
" plt.subplot(2, 2, 4)\n",
" show_grid(b4, f'Randomize {N} first {basis_type_str} -> Little variation')\n",
" save_imgs(b4, f'randomize_{N}_first_{basis_type_str}_{seed}')\n",
" \n",
" plt.show()\n",
"\n",
"\n",
"# In paper\n",
"generate('StyleGAN2', 'cat', seed=1866827965, trunc=0.55, N=8)\n",
" \n",
"# In supplemental\n",
"generate('StyleGAN', 'bedrooms', seed=1382244162, trunc=1.0, N=10)\n",
"generate('StyleGAN', 'ffhq', seed=598174413, trunc=1.0, N=10)\n",
"generate('BigGAN-256', 'duck', seed=1134462557, trunc=1.0, N=10)\n",
"generate('StyleGAN2', 'car', seed=1257084100, trunc=0.7, N=5)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
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