File size: 13,938 Bytes
3894c45 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
" # CoMoGan\n",
"Notebook to test our model after training."
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2024-08-24T11:36:37.046598Z",
"start_time": "2024-08-24T11:36:35.526168Z"
}
},
"source": [
"import ipywidgets as widgets\n",
"import pytorch_lightning as pl\n",
"import pathlib\n",
"import torch\n",
"import yaml\n",
"import os\n",
"\n",
"from math import pi\n",
"from PIL import Image\n",
"from munch import Munch\n",
"from threading import Timer\n",
"from IPython.display import clear_output\n",
"from torchvision.transforms import ToPILImage\n",
"\n",
"from data import create_dataset\n",
"from torchvision.transforms import ToTensor\n",
"from data.base_dataset import get_transform\n",
"from networks import find_model_using_name, create_model"
],
"outputs": [],
"execution_count": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load the model with a checkpoint\n",
"Choose the directory that contains the checkpoint that you want."
]
},
{
"cell_type": "code",
"metadata": {},
"source": [
"import pathlib\n",
"\n",
"# Load names of directories inside /logs\n",
"p = pathlib.Path('./logs')\n",
"\n",
"# Use x.name to get the directory name instead of splitting the path\n",
"list_run_id = [x.name for x in p.iterdir() if x.is_dir()]\n",
"\n",
"import ipywidgets as widgets\n",
"from IPython.display import display, clear_output\n",
"import os\n",
"\n",
"w_run = widgets.Dropdown(options=list_run_id,\n",
" description='Select RUN_ID',\n",
" disabled=False,\n",
" style=dict(description_width='initial'))\n",
"\n",
"\n",
"w_check = None\n",
"root_dir = None\n",
"\n",
"def on_value_change_check(change):\n",
" global w_check, w_run, root_dir\n",
" \n",
" clear_output(wait=True)\n",
" \n",
" root_dir = os.path.join('logs', w_run.value, 'tensorboard', 'default', 'version_0')\n",
" p = pathlib.Path(root_dir + '/checkpoints')\n",
" \n",
" # Load a list of checkpoints, use the last one by default\n",
" list_checkpoint = [x.name for x in p.iterdir() if 'iter' in x.name]\n",
" list_checkpoint.sort(reverse=True, key=lambda x: int(x.split('_')[1].split('.pth')[0]))\n",
" \n",
" w_check = widgets.Dropdown(options=list_checkpoint,\n",
" description='Select checkpoint',\n",
" disabled=False,\n",
" style=dict(description_width='initial'))\n",
" display(widgets.HBox([w_run, w_check]))\n",
"\n",
"on_value_change_check({'new': w_run.value})\n",
"w_run.observe(on_value_change_check, names='value')\n"
],
"execution_count": 2,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2024-08-24T11:36:39.368141Z",
"start_time": "2024-08-24T11:36:37.080571Z"
}
},
"source": [
"RUN_ID = w_run.value\n",
"CHECKPOINT = w_check.value\n",
"\n",
"# Load parameters\n",
"with open(os.path.join(root_dir, 'hparams.yaml')) as cfg_file:\n",
" opt = Munch(yaml.safe_load(cfg_file))\n",
"\n",
"opt.no_flip = True\n",
"# Load parameters to the model, load the checkpoint\n",
"model = create_model(opt)\n",
"model = model.load_from_checkpoint((os.path.join(root_dir, 'checkpoints/', CHECKPOINT)))\n",
"# Transfer the model to the GPU\n",
"model.to('cpu');"
],
"outputs": [],
"execution_count": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load the validation dataset"
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2024-08-24T11:36:39.383167Z",
"start_time": "2024-08-24T11:36:39.370142Z"
}
},
"source": [
"import pathlib\n",
"from PIL import Image\n",
"\n",
"# Set opt.dataroot to the imgs_test directory\n",
"opt.dataroot = 'imgs_test/'\n",
"\n",
"# Load paths of all files contained in /imgs_test\n",
"p = pathlib.Path(opt.dataroot)\n",
"dataset_paths = [str(x.relative_to(opt.dataroot)) for x in p.iterdir()]\n",
"dataset_paths.sort()\n",
"\n",
"sequence_name = {}\n",
"# Make a dict with each sequence name as a key and\n",
"# a list of paths to the images of the sequence as a value\n",
"for file in dataset_paths:\n",
" # Keep only the sequence part contained in the name of the image\n",
" strip_name = file.split('_')[0]\n",
" \n",
" if strip_name not in sequence_name:\n",
" sequence_name[strip_name] = [file]\n",
" else:\n",
" sequence_name[strip_name].append(file)\n"
],
"outputs": [],
"execution_count": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Select a sequence, an image and the moment of the day\n",
"Select the sequence on which you want to work before choosing which image should be used.<br>\n",
"Select the moment of the day, by choosing the angle of the sun $\\phi$ between [0,2$\\pi$],\n",
"which maps to a sun elevation ∈ [+30◦,−40◦]\n",
"<ul>\n",
"<li>0 means day</li>\n",
"<li>$\\pi$/2 means dusk</li>\n",
"<li>$\\pi$ means night</li>\n",
"<li>$\\pi$ + $\\pi$/2 means dawn</li>\n",
"<li>2$\\pi$ means day (again)</li>\n",
"</ul>"
]
},
{
"cell_type": "code",
"metadata": {
"scrolled": true,
"ExecuteTime": {
"end_time": "2024-08-24T11:40:52.233134Z",
"start_time": "2024-08-24T11:40:45.511403Z"
}
},
"source": [
"def drop_list():\n",
" # Select the sequence on which you want to make your test\n",
" return widgets.Dropdown(options=sequence_name.keys(),\n",
" description='Select sequence',\n",
" disabled=False,\n",
" style=dict(description_width='initial'))\n",
"def slider_img():\n",
" # Select an image from the sequence\n",
" return widgets.IntSlider(min=0, max=len(sequence_name[w_seq.value]) - 1,\n",
" description='Select image')\n",
"def slider_time():\n",
" # Select time\n",
" return widgets.FloatSlider(value=0, min=0, max=pi*2, step=0.01,\n",
" description='Select time',\n",
" readout_format='.2f')\n",
"\n",
"def debounce(wait):\n",
" # Decorator that will debounce the call to a function\n",
" def decorator(fn):\n",
" timer = None\n",
" def debounced(*args, **kwargs):\n",
" nonlocal timer\n",
" def call_it():\n",
" fn(*args, **kwargs)\n",
" if timer is not None:\n",
" timer.cancel()\n",
" timer = Timer(wait, call_it)\n",
" timer.start()\n",
" return debounced\n",
" return decorator\n",
" \n",
"def inference(seq, index_img, phi, output = True):\n",
" global sequence_name, w_img_time, w_seq, opt, out\n",
" # Load the image\n",
" A_path = os.path.join(opt.dataroot, sequence_name[seq.value][index_img])\n",
" A_img = Image.open(A_path).convert('RGB')\n",
" # Apply image transformation\n",
" A = get_transform(opt, convert=False)(A_img)\n",
" # Normalization between -1 and 1\n",
" img_real = (((ToTensor()(A)) * 2) - 1).unsqueeze(0)\n",
" # Forward our image into the model with the specified ɸ\n",
" img_fake = model.forward(img_real.cpu(), phi.cpu()) \n",
" # Encapsulate the initial image beside our result\n",
" new_im = Image.new('RGB', (A_img.width * 2, A_img.height))\n",
" new_im.paste(A_img, (0, 0))\n",
" new_im.paste(ToPILImage()((img_fake[0].cpu() + 1) / 2), (A_img.width, 0))\n",
" # Clear the output and display the widgets and the images\n",
" if output:\n",
" out.clear_output(wait=True)\n",
" with out:\n",
" # Resize the output\n",
" O_img = new_im.resize((new_im.width // 2, new_im.height // 2))\n",
" display(w_img_time, O_img)\n",
" display(out)\n",
" \n",
" return new_im\n",
"\n",
"@debounce(0.2)\n",
"def on_value_change_img(change):\n",
" global w_seq, w_time\n",
" inference(w_seq, change['new'], torch.tensor(w_time.value))\n",
" \n",
"@debounce(0.2)\n",
"def on_value_change_time(change):\n",
" global w_seq, w_img\n",
" inference(w_seq, w_img.value, torch.tensor(change['new']))\n",
" \n",
"def on_value_change_seq(change):\n",
" global w_seq, w_img, w_time\n",
" w_img = slider_img()\n",
" w_time = slider_time()\n",
" inference(w_seq, w_img.value, torch.tensor(w_time.value))\n",
" \n",
"w_seq = drop_list()\n",
"w_img = slider_img()\n",
"w_time = slider_time()\n",
"w_img_time = widgets.VBox([w_seq, widgets.HBox([w_img, w_time])])\n",
"# Set the size of the output cell\n",
"out = widgets.Output(layout=widgets.Layout(width='auto', height='240px'))\n",
"\n",
"inference(w_seq, w_img.value, torch.tensor(w_time.value))\n",
"w_img.observe(on_value_change_img, names='value')\n",
"w_time.observe(on_value_change_time, names='value')\n",
"w_seq.observe(on_value_change_seq, names='value')"
],
"outputs": [
{
"data": {
"text/plain": [
"Output(layout=Layout(height='240px', width='auto'))"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "665fdbd928f148d19f3e341ab0993ab7"
}
},
"metadata": {},
"output_type": "display_data"
}
],
"execution_count": 7
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Sequence to video\n",
"The code below translates a sequence of images into a video.<br>\n",
"By default, the 'Select time' slider is on 'Variable phi' ($\\phi$), in this case the time parameter will progressively increase from 0 to 2$\\pi$.<br>\n",
"2$\\pi$ is reached at the end of the video. If you move the slider, you can select a fixed $\\phi$.<br>\n",
"Require to install two more packages <code>pip install imageio imageio-ffmpeg<code>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from IPython.display import Video\n",
"import numpy as np\n",
"import imageio\n",
"\n",
"w_button = widgets.Button(description='Start', tooltip='Start the inference of a sequence',\n",
" icon='play')\n",
"\n",
"phi_opt = ['Variable phi'] + [str(round(f, 2)) for f in np.arange(0, pi*2, 0.01)]\n",
"w_vid_time = widgets.SelectionSlider(options=phi_opt, value=phi_opt[0], description='Select time : ')\n",
"\n",
"w_vid_seq = drop_list()\n",
"\n",
"w_button_seq = widgets.VBox([widgets.HBox([w_vid_seq, w_vid_time]), w_button])\n",
"display(w_button_seq)\n",
"\n",
"def get_video(bt):\n",
" global sequence_name, w_vid_seq, w_vid_time, w_button_seq\n",
" \n",
" clear_output(wait=True)\n",
" display(w_button_seq)\n",
" seq_size = len(sequence_name[w_vid_seq.value])\n",
" # Display progress bar\n",
" w_prog = widgets.IntProgress(value=0, min=0, max=seq_size, description='Loading:')\n",
" display(w_prog)\n",
" # Create a videos directory to save our video\n",
" save_name = str(pathlib.Path('.').absolute()) + '/videos/'\n",
" os.makedirs(save_name, exist_ok=True)\n",
" # If variable phi\n",
" if w_vid_time.value == 'Variable phi':\n",
" # Write our video in the project folder\n",
" save_name += 'comogan_{}_phi_{}.mp4'.format(w_vid_seq.value.replace('segment-', 'seg_'),\n",
" 'variable')\n",
" else:\n",
" save_name += 'comogan_{}_phi_{}.mp4'.format(w_vid_seq.value.replace('segment-', 'seg_'),\n",
" w_vid_time.value.replace('.', '_'))\n",
" writer = imageio.get_writer(save_name, fps=10)\n",
" # Loop on the images contained in the sequence\n",
" for i in range(seq_size):\n",
" if w_vid_time.value == 'Variable phi':\n",
" # Inference of the i image in sequence_name[key]\n",
" phi_var = 2*pi/seq_size * i\n",
" my_img = inference(w_vid_seq, i, torch.tensor(phi_var), False)\n",
" else:\n",
" my_img = inference(w_vid_seq, i, torch.tensor(float(w_vid_time.value)), False)\n",
" writer.append_data(np.array(my_img))\n",
" # Progress bar\n",
" w_prog.value += 1\n",
" \n",
" writer.close()\n",
" display(Video(save_name, embed=True))\n",
"\n",
"w_button.on_click(get_video)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|