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cb3fe0e197
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fb7d854abe
7 changed files with 339 additions and 335 deletions
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@ -1,2 +0,0 @@
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training-runs
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datasets
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@ -1,22 +1,8 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "c08a9c8e-bcce-4b45-94ad-b422ad60bea9",
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"metadata": {},
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"source": [
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"# This Place Does Exists - Utilities for Stylegan3\n",
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"\n",
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"This notebook contains utility functions for working with the models created by StyleGAN3. \n",
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"\n",
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"## Usage\n",
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"\n",
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"Include it in any notebook using `%run ThisPlaceDoesExist.ipynb`. After which everything from this notebook becomes available. Including a `runs` variable which is a list containing all the `Run` objects."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": 35,
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"id": "f4ee99c4-9c28-4fe4-9408-e130a0d446d3",
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"metadata": {},
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"outputs": [],
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@ -66,7 +52,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 12,
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"id": "4c428611-8d75-4f9a-ae5a-7960e7b01470",
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"metadata": {},
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"outputs": [],
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@ -82,6 +68,16 @@
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"See [Snapshot_images.ipynb](Snapshot_images.ipynb) for examples of each run/snapshot."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "9a5c4a18-0389-4d36-989e-7a120db590ab",
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"metadata": {},
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"outputs": [],
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"source": [
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"# snapshot = runs[3].snapshots[70]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c3aa8404-aeb0-4f63-b4b4-c278f0cf3766",
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@ -94,7 +90,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 10,
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"id": "c180db45-9bf1-4f0a-ab55-62b476a5897c",
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"metadata": {},
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"outputs": [],
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@ -119,10 +115,22 @@
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 9,
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"id": "22c74f41-65e7-461a-9094-f0b3d8738c82",
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"ename": "NameError",
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"evalue": "name 'runs' is not defined",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m/tmp/ipykernel_1/862876608.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# def is_main():\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mplot\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplot_runs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mruns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbbox_to_anchor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"lower left\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;31mNameError\u001b[0m: name 'runs' is not defined"
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]
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}
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],
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"source": [
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"def is_main():\n",
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" plot = plot_runs(runs)\n",
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@ -441,11 +449,11 @@
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" runnr = snapshot.run.as_nr\n",
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" # !!python pbaylies_projector.py --network $snapshot_pkl --outdir out/projections/$runnr-$imagenr --target-image $image_filename --use-clip=False\n",
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" \n",
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" if replace_if_exists or not os.path.exists(f\"out/projections/{snapshot.id}/{image_name}/proj.png\"):\n",
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" if replace_if_exists or not os.path.exists(f\"out/projections/{runnr}/{image_name}/proj.png\"):\n",
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" process = subprocess.Popen([\n",
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" \"python\", \"pbaylies_projector.py\",\n",
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" \"--network\" , snapshot.pkl_path,\n",
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" \"--outdir\", f\"out/projections/{snapshot.id}/{image_name}\",\n",
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" \"--outdir\", f\"out/projections/{runnr}/{image_name}\",\n",
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" \"--target-image\", image_filename,\n",
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" \"--use-clip\", \"False\",\n",
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" \"--num-steps\", str(steps),\n",
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@ -461,10 +469,10 @@
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" loss, dist = (None, None)\n",
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"\n",
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" return {\n",
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" \"img\": f\"out/projections/{snapshot.id}/{image_name}/proj.png\",\n",
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" \"src_img\": f\"out/projections/{snapshot.id}/{image_name}/target.png\",\n",
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" \"img\": f\"out/projections/{runnr}-{imagenr}/proj.png\",\n",
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" \"src_img\": f\"out/projections/{runnr}-{imagenr}/target.png\",\n",
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" \"src\": image_filename,\n",
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" \"npz\": f\"out/projections/{snapshot.id}/{image_name}/projected_w.npz\",\n",
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" \"npz\": f\"out/projections/{runnr}-{imagenr}/projected_w.npz\",\n",
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" \"loss\": loss,\n",
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" \"dist\": dist\n",
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" }\n",
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@ -560,6 +568,43 @@
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" # output.release()\n",
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" writer.close()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 46,
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"id": "6ff4c6db-b48f-4ff4-b08c-e85138f0f307",
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"metadata": {},
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"outputs": [],
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"source": [
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 33,
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"id": "d0661104-72a7-4320-980a-1a702388659f",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"True"
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]
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},
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"execution_count": 33,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e2aa2c98-cf1e-465a-9455-fe4a02f145ad",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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@ -90,7 +90,7 @@ def open_image_folder(source_dir, *, max_images: Optional[int]):
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arch_fname = os.path.relpath(fname, source_dir)
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arch_fname = arch_fname.replace('\\', '/')
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img = np.array(PIL.Image.open(fname))
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yield dict(img=img, label=labels.get(arch_fname), filename=fname)
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yield dict(img=img, label=labels.get(arch_fname))
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if idx >= max_idx-1:
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break
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return max_idx, iterate_images()
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@ -119,7 +119,7 @@ def open_image_zip(source, *, max_images: Optional[int]):
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with z.open(fname, 'r') as file:
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img = PIL.Image.open(file) # type: ignore
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img = np.array(img)
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yield dict(img=img, label=labels.get(fname), filename=fname)
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yield dict(img=img, label=labels.get(fname))
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if idx >= max_idx-1:
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break
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return max_idx, iterate_images()
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10
runs.py
10
runs.py
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import os
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import datetime
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import json
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from typing import List, Optional
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from typing import List
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from PIL import Image
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from enum import Enum
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import logging
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import numpy as np
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import dnnlib
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import legacy
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from dataset_tool import open_dataset
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logger = logging.getLogger('runs')
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@ -132,13 +131,6 @@ class Run():
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def dataset_is_conditional(self):
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return bool(self.training_options["training_set_kwargs"]["use_labels"])
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def dataset_iterator(self, max_images: Optional[int] = None):
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max_images, iterator = open_dataset(
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self.training_options["training_set_kwargs"]["path"],
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max_images=max_images
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)
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return iterator
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@property
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def resolution(self):
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return self.training_options["training_set_kwargs"]["resolution"]
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<section id='cover'>
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<h1 class="title">This Place Does Exist</h1>
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<h2>Stylegan 3 Snapshots</h2>
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<div class='authors'>Ward Goes & Ruben van de Ven</div>
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</section>
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<section id="introduction">
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{{introduction}}
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</section>
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<section id="toc">
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