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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 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",
"# Show top 10 PCs for StyleGAN2 ffhq\n",
"# Center along component before manipulation\n",
"# Also show cleaned up PCs based on top10, a couple of cleaned up later style PCs\n",
"%matplotlib inline\n",
"from notebook_init import *\n",
"\n",
"out_root = Path('out/figures/pca_cleanup')\n",
"makedirs(out_root / 'tuned', exist_ok=True)\n",
"makedirs(out_root / 'global', exist_ok=True)\n",
"rand = lambda : np.random.randint(np.iinfo(np.int32).max)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"use_w = True\n",
"inst = get_instrumented_model('StyleGAN2', 'ffhq', 'style', device, inst=inst, use_w=use_w)\n",
"model = inst.model\n",
"model.truncation = 1.0\n",
"\n",
"pc_config = Config(components=80, n=1_000_000, use_w=use_w,\n",
" layer='style', model='StyleGAN2', output_class='ffhq')\n",
"dump_name = get_or_compute(pc_config, inst)\n",
"\n",
"with np.load(dump_name) as data:\n",
" lat_comp = torch.from_numpy(data['lat_comp']).to(device)\n",
" lat_mean = torch.from_numpy(data['lat_mean']).to(device)\n",
" lat_std = data['lat_stdev']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"seeds_ffhq = [366745668] #, 1502970553, 1235907362, 1302626592]\n",
"#seeds_ffhq = [rand() for _ in range(50)]\n",
"\n",
"n_pcs = 14\n",
"\n",
"# Case 1: Normal centered PCs\n",
"for seed in seeds_ffhq:\n",
" print(seed)\n",
" \n",
" strips = []\n",
" \n",
" for i in range(n_pcs):\n",
" z = model.sample_latent(1, seed=seed)\n",
" batch_frames = create_strip_centered(inst, 'latent', 'style', [z],\n",
" 0, lat_comp[i], 0, lat_std[i], 0, lat_mean, 2.0, 0, 18, num_frames=7)[0]\n",
" strips.append(np.hstack(pad_frames(batch_frames)))\n",
" for j, frame in enumerate(batch_frames):\n",
" Image.fromarray(np.uint8(frame*255)).save(out_root / 'global' / f'{seed}_pc{i}_{j}.png')\n",
" \n",
" #col_left = np.vstack(pad_frames(strips[:n_pcs//2], 0, 64))\n",
" #col_right = np.vstack(pad_frames(strips[n_pcs//2:], 0, 64))\n",
" grid = np.vstack(strips)\n",
" \n",
" Image.fromarray(np.uint8(grid*255)).save(out_root / f'grid_{seed}.jpg')\n",
" \n",
" plt.figure(figsize=(20,40))\n",
" plt.imshow(grid)\n",
" plt.axis('off')\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"# Case 2: hand-tuned layer ranges for some directions\n",
"hand_tuned = [\n",
" ( 0, (1, 7), 2.0), # gender, keep age\n",
" ( 1, (0, 3), 2.0), # rotate, keep gender\n",
" ( 2, (3, 8), 2.0), # gender, keep geometry\n",
" ( 3, (2, 8), 2.0), # age, keep lighting, no hat\n",
" ( 4, (5, 18), 2.0), # background, keep geometry\n",
" ( 5, (0, 4), 2.0), # hat, keep lighting and age\n",
" ( 6, (7, 18), 2.0), # just lighting\n",
" ( 7, (5, 9), 2.0), # just lighting\n",
" ( 8, (1, 7), 2.0), # age, keep lighting\n",
" ( 9, (0, 5), 2.0), # keep lighting\n",
" (10, (7, 9), 2.0), # hair color, keep geom\n",
" (11, (0, 5), 2.0), # hair length, keep color\n",
" (12, (8, 9), 2.0), # light dir lr\n",
"# (12, (4, 10), 2.0), # light position LR\n",
" (13, (0, 6), 2.0), # about the same\n",
"]\n",
"\n",
"for seed in seeds_ffhq:\n",
" print(seed)\n",
" \n",
" strips = []\n",
" \n",
" for i, (s, e), sigma in hand_tuned:\n",
" z = model.sample_latent(1, seed=seed)\n",
" \n",
" batch_frames = create_strip_centered(inst, 'latent', 'style', [z],\n",
" 0, lat_comp[i], 0, lat_std[i], 0, lat_mean, sigma, s, e, num_frames=7)[0]\n",
" strips.append(np.hstack(pad_frames(batch_frames)))\n",
" for j, frame in enumerate(batch_frames):\n",
" Image.fromarray(np.uint8(frame*255)).save(out_root / 'tuned' / f'{seed}_pc{i}_s{s}_e{e}_{j}.png')\n",
" \n",
" #col_left = np.vstack(pad_frames(strips[:len(strips)//2], 0, 64))\n",
" #col_right = np.vstack(pad_frames(strips[len(strips)//2:], 0, 64))\n",
" #grid = np.hstack(pad_frames(strips, 16))\n",
" grid = np.vstack(strips)\n",
" \n",
" Image.fromarray(np.uint8(grid*255)).save(out_root / f'grid_{seed}_tuned.jpg')\n",
" \n",
" plt.figure(figsize=(20,40))\n",
" plt.imshow(grid)\n",
" plt.axis('off')\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
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