Upload 2 files
Browse files- Untitled6.ipynb +1011 -0
- untitled6.py +234 -0
Untitled6.ipynb
ADDED
@@ -0,0 +1,1011 @@
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1 |
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"text": [
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"Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (2.31.0)\n",
|
384 |
+
"Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.41.0)\n",
|
385 |
+
"Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests) (3.3.2)\n",
|
386 |
+
"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests) (3.7)\n",
|
387 |
+
"Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests) (2.0.7)\n",
|
388 |
+
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests) (2024.2.2)\n",
|
389 |
+
"Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.14.0)\n",
|
390 |
+
"Requirement already satisfied: huggingface-hub<1.0,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.23.0)\n",
|
391 |
+
"Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.25.2)\n",
|
392 |
+
"Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (24.0)\n",
|
393 |
+
"Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.1)\n",
|
394 |
+
"Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2023.12.25)\n",
|
395 |
+
"Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.19.1)\n",
|
396 |
+
"Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.3)\n",
|
397 |
+
"Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.4)\n",
|
398 |
+
"Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.0->transformers) (2023.6.0)\n",
|
399 |
+
"Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.0->transformers) (4.11.0)\n"
|
400 |
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},
|
404 |
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{
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+
"cell_type": "code",
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"source": [
|
407 |
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"import requests\n",
|
408 |
+
"import zipfile\n",
|
409 |
+
"import os\n",
|
410 |
+
"\n",
|
411 |
+
"# URL du fichier zip sur Hugging Face\n",
|
412 |
+
"zip_url = 'https://huggingface.co/datasets/Dabococo/wheeloh_dataset/images'\n",
|
413 |
+
"\n",
|
414 |
+
"# Nom local du fichier zip\n",
|
415 |
+
"zip_file = 'images.zip'\n",
|
416 |
+
"\n",
|
417 |
+
"# Télécharger le fichier zip\n",
|
418 |
+
"response = requests.get(zip_url)\n",
|
419 |
+
"content_type = response.headers.get('Content-Type')\n",
|
420 |
+
"\n",
|
421 |
+
"# Vérifier que le fichier est un fichier zip\n",
|
422 |
+
"if 'zip' not in content_type:\n",
|
423 |
+
" raise ValueError(\"Le fichier téléchargé n'est pas un fichier zip. Content-Type: {}\".format(content_type))\n",
|
424 |
+
"\n",
|
425 |
+
"# Sauvegarder le contenu téléchargé dans un fichier\n",
|
426 |
+
"with open(zip_file, 'wb') as f:\n",
|
427 |
+
" f.write(response.content)\n",
|
428 |
+
"\n",
|
429 |
+
"# Vérifier la taille du fichier\n",
|
430 |
+
"file_size = os.path.getsize(zip_file)\n",
|
431 |
+
"print(\"Taille du fichier téléchargé:\", file_size, \"octets\")\n",
|
432 |
+
"\n",
|
433 |
+
"# Afficher le début du contenu du fichier pour vérifier\n",
|
434 |
+
"with open(zip_file, 'rb') as f:\n",
|
435 |
+
" print(f.read(100)) # Lire les 100 premiers octets\n",
|
436 |
+
"\n",
|
437 |
+
"# Créer un répertoire pour extraire les fichiers\n",
|
438 |
+
"extract_dir = 'extracted_files'\n",
|
439 |
+
"os.makedirs(extract_dir, exist_ok=True)\n",
|
440 |
+
"\n",
|
441 |
+
"# Extraire le contenu du fichier zip\n",
|
442 |
+
"try:\n",
|
443 |
+
" with zipfile.ZipFile(zip_file, 'r') as zip_ref:\n",
|
444 |
+
" zip_ref.extractall(extract_dir)\n",
|
445 |
+
" # Afficher les fichiers extraits\n",
|
446 |
+
" extracted_files = os.listdir(extract_dir)\n",
|
447 |
+
" print(\"Fichiers extraits :\", extracted_files)\n",
|
448 |
+
"except zipfile.BadZipFile:\n",
|
449 |
+
" print(\"Erreur : le fichier téléchargé n'est pas un fichier zip valide.\")"
|
450 |
+
],
|
451 |
+
"metadata": {
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452 |
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"colab": {
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+
"base_uri": "https://localhost:8080/",
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+
"height": 241
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},
|
456 |
+
"id": "WX_U7Z6GRpTR",
|
457 |
+
"outputId": "5efdb53c-196c-4692-91ff-e9c131fce1f4"
|
458 |
+
},
|
459 |
+
"execution_count": 22,
|
460 |
+
"outputs": [
|
461 |
+
{
|
462 |
+
"output_type": "error",
|
463 |
+
"ename": "ValueError",
|
464 |
+
"evalue": "Le fichier téléchargé n'est pas un fichier zip. Content-Type: text/html; charset=utf-8",
|
465 |
+
"traceback": [
|
466 |
+
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
467 |
+
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
|
468 |
+
"\u001b[0;32m<ipython-input-22-9e950cb9106e>\u001b[0m in \u001b[0;36m<cell line: 16>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0;31m# Vérifier que le fichier est un fichier zip\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m'zip'\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcontent_type\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Le fichier téléchargé n'est pas un fichier zip. Content-Type: {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcontent_type\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\u001b[0m\u001b[1;32m 18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[0;31m# Sauvegarder le contenu téléchargé dans un fichier\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
469 |
+
"\u001b[0;31mValueError\u001b[0m: Le fichier téléchargé n'est pas un fichier zip. Content-Type: text/html; charset=utf-8"
|
470 |
+
]
|
471 |
+
}
|
472 |
+
]
|
473 |
+
},
|
474 |
+
{
|
475 |
+
"cell_type": "code",
|
476 |
+
"source": [
|
477 |
+
"from torchvision.datasets import ImageFolder\n",
|
478 |
+
"import os\n",
|
479 |
+
"from torchvision.datasets.folder import has_file_allowed_extension, IMG_EXTENSIONS, default_loader\n",
|
480 |
+
"\n",
|
481 |
+
"class CustomImageFolder(ImageFolder):\n",
|
482 |
+
" def __init__(self, root, transform=None, loader=default_loader, is_valid_file=None):\n",
|
483 |
+
" super().__init__(root, transform=transform, loader=loader, is_valid_file=is_valid_file)\n",
|
484 |
+
"\n",
|
485 |
+
" def find_classes(self, directory):\n",
|
486 |
+
" # Ignorer les répertoires cachés\n",
|
487 |
+
" classes = [d.name for d in os.scandir(directory) if d.is_dir() and not d.name.startswith('.')]\n",
|
488 |
+
" classes.sort()\n",
|
489 |
+
" class_to_idx = {cls_name: i for i, cls_name in enumerate(classes)}\n",
|
490 |
+
" return classes, class_to_idx\n",
|
491 |
+
"\n",
|
492 |
+
" def make_dataset(self, directory, class_to_idx, extensions=None, is_valid_file=None, allow_empty=False):\n",
|
493 |
+
" instances = []\n",
|
494 |
+
" directory = os.path.expanduser(directory)\n",
|
495 |
+
" both_none = extensions is None and is_valid_file is None\n",
|
496 |
+
" if both_none:\n",
|
497 |
+
" raise ValueError(\"Both extensions and is_valid_file cannot be None\")\n",
|
498 |
+
" if extensions is not None:\n",
|
499 |
+
" def is_valid_file(x):\n",
|
500 |
+
" return has_file_allowed_extension(x, extensions)\n",
|
501 |
+
"\n",
|
502 |
+
" for target_class in sorted(class_to_idx.keys()):\n",
|
503 |
+
" class_index = class_to_idx[target_class]\n",
|
504 |
+
" target_dir = os.path.join(directory, target_class)\n",
|
505 |
+
" if not os.path.isdir(target_dir):\n",
|
506 |
+
" continue\n",
|
507 |
+
" for root, _, fnames in sorted(os.walk(target_dir)):\n",
|
508 |
+
" for fname in sorted(fnames):\n",
|
509 |
+
" path = os.path.join(root, fname)\n",
|
510 |
+
" if is_valid_file(path) and not fname.startswith('.'):\n",
|
511 |
+
" item = path, class_index\n",
|
512 |
+
" instances.append(item)\n",
|
513 |
+
"\n",
|
514 |
+
" if not allow_empty and len(instances) == 0:\n",
|
515 |
+
" raise RuntimeError(f\"Found 0 files in subfolders of: {directory}. Supported extensions are: {','.join(extensions)}\")\n",
|
516 |
+
"\n",
|
517 |
+
" return instances"
|
518 |
+
],
|
519 |
+
"metadata": {
|
520 |
+
"id": "5y-PWXOnoH-k"
|
521 |
+
},
|
522 |
+
"execution_count": 7,
|
523 |
+
"outputs": []
|
524 |
+
},
|
525 |
+
{
|
526 |
+
"cell_type": "code",
|
527 |
+
"execution_count": 9,
|
528 |
+
"metadata": {
|
529 |
+
"colab": {
|
530 |
+
"base_uri": "https://localhost:8080/"
|
531 |
+
},
|
532 |
+
"id": "CuyZBWiyP88V",
|
533 |
+
"outputId": "fdef3381-9464-4433-c96d-2343bc22a1f6"
|
534 |
+
},
|
535 |
+
"outputs": [
|
536 |
+
{
|
537 |
+
"output_type": "stream",
|
538 |
+
"name": "stderr",
|
539 |
+
"text": [
|
540 |
+
"100%|██████████| 16/16 [00:08<00:00, 1.86it/s]\n"
|
541 |
+
]
|
542 |
+
},
|
543 |
+
{
|
544 |
+
"output_type": "stream",
|
545 |
+
"name": "stdout",
|
546 |
+
"text": [
|
547 |
+
"Epoch [1/10], Loss: 0.2799\n"
|
548 |
+
]
|
549 |
+
},
|
550 |
+
{
|
551 |
+
"output_type": "stream",
|
552 |
+
"name": "stderr",
|
553 |
+
"text": [
|
554 |
+
"100%|██████████| 16/16 [00:08<00:00, 1.96it/s]\n"
|
555 |
+
]
|
556 |
+
},
|
557 |
+
{
|
558 |
+
"output_type": "stream",
|
559 |
+
"name": "stdout",
|
560 |
+
"text": [
|
561 |
+
"Epoch [2/10], Loss: 0.0916\n"
|
562 |
+
]
|
563 |
+
},
|
564 |
+
{
|
565 |
+
"output_type": "stream",
|
566 |
+
"name": "stderr",
|
567 |
+
"text": [
|
568 |
+
"100%|██████████| 16/16 [00:07<00:00, 2.08it/s]\n"
|
569 |
+
]
|
570 |
+
},
|
571 |
+
{
|
572 |
+
"output_type": "stream",
|
573 |
+
"name": "stdout",
|
574 |
+
"text": [
|
575 |
+
"Epoch [3/10], Loss: 0.0356\n"
|
576 |
+
]
|
577 |
+
},
|
578 |
+
{
|
579 |
+
"output_type": "stream",
|
580 |
+
"name": "stderr",
|
581 |
+
"text": [
|
582 |
+
"100%|██████████| 16/16 [00:08<00:00, 1.86it/s]\n"
|
583 |
+
]
|
584 |
+
},
|
585 |
+
{
|
586 |
+
"output_type": "stream",
|
587 |
+
"name": "stdout",
|
588 |
+
"text": [
|
589 |
+
"Epoch [4/10], Loss: 0.0253\n"
|
590 |
+
]
|
591 |
+
},
|
592 |
+
{
|
593 |
+
"output_type": "stream",
|
594 |
+
"name": "stderr",
|
595 |
+
"text": [
|
596 |
+
"100%|██████████| 16/16 [00:07<00:00, 2.25it/s]\n"
|
597 |
+
]
|
598 |
+
},
|
599 |
+
{
|
600 |
+
"output_type": "stream",
|
601 |
+
"name": "stdout",
|
602 |
+
"text": [
|
603 |
+
"Epoch [5/10], Loss: 0.0101\n"
|
604 |
+
]
|
605 |
+
},
|
606 |
+
{
|
607 |
+
"output_type": "stream",
|
608 |
+
"name": "stderr",
|
609 |
+
"text": [
|
610 |
+
"100%|██████████| 16/16 [00:09<00:00, 1.73it/s]\n"
|
611 |
+
]
|
612 |
+
},
|
613 |
+
{
|
614 |
+
"output_type": "stream",
|
615 |
+
"name": "stdout",
|
616 |
+
"text": [
|
617 |
+
"Epoch [6/10], Loss: 0.0089\n"
|
618 |
+
]
|
619 |
+
},
|
620 |
+
{
|
621 |
+
"output_type": "stream",
|
622 |
+
"name": "stderr",
|
623 |
+
"text": [
|
624 |
+
"100%|██████████| 16/16 [00:08<00:00, 1.81it/s]\n"
|
625 |
+
]
|
626 |
+
},
|
627 |
+
{
|
628 |
+
"output_type": "stream",
|
629 |
+
"name": "stdout",
|
630 |
+
"text": [
|
631 |
+
"Epoch [7/10], Loss: 0.0096\n"
|
632 |
+
]
|
633 |
+
},
|
634 |
+
{
|
635 |
+
"output_type": "stream",
|
636 |
+
"name": "stderr",
|
637 |
+
"text": [
|
638 |
+
"100%|██████████| 16/16 [00:07<00:00, 2.23it/s]\n"
|
639 |
+
]
|
640 |
+
},
|
641 |
+
{
|
642 |
+
"output_type": "stream",
|
643 |
+
"name": "stdout",
|
644 |
+
"text": [
|
645 |
+
"Epoch [8/10], Loss: 0.0101\n"
|
646 |
+
]
|
647 |
+
},
|
648 |
+
{
|
649 |
+
"output_type": "stream",
|
650 |
+
"name": "stderr",
|
651 |
+
"text": [
|
652 |
+
"100%|██████████| 16/16 [00:08<00:00, 1.85it/s]\n"
|
653 |
+
]
|
654 |
+
},
|
655 |
+
{
|
656 |
+
"output_type": "stream",
|
657 |
+
"name": "stdout",
|
658 |
+
"text": [
|
659 |
+
"Epoch [9/10], Loss: 0.0631\n"
|
660 |
+
]
|
661 |
+
},
|
662 |
+
{
|
663 |
+
"output_type": "stream",
|
664 |
+
"name": "stderr",
|
665 |
+
"text": [
|
666 |
+
"100%|██████████| 16/16 [00:07<00:00, 2.20it/s]\n"
|
667 |
+
]
|
668 |
+
},
|
669 |
+
{
|
670 |
+
"output_type": "stream",
|
671 |
+
"name": "stdout",
|
672 |
+
"text": [
|
673 |
+
"Epoch [10/10], Loss: 0.0186\n",
|
674 |
+
"Finished Training\n"
|
675 |
+
]
|
676 |
+
}
|
677 |
+
],
|
678 |
+
"source": [
|
679 |
+
"import torch\n",
|
680 |
+
"import torch.nn as nn\n",
|
681 |
+
"import torch.optim as optim\n",
|
682 |
+
"from torch.utils.data import DataLoader\n",
|
683 |
+
"from torchvision import transforms, models\n",
|
684 |
+
"from tqdm import tqdm\n",
|
685 |
+
"\n",
|
686 |
+
"# Configuration\n",
|
687 |
+
"batch_size = 32\n",
|
688 |
+
"num_epochs = 10\n",
|
689 |
+
"learning_rate = 0.001\n",
|
690 |
+
"num_classes = 2\n",
|
691 |
+
"\n",
|
692 |
+
"# Préparer les transformations\n",
|
693 |
+
"transform = transforms.Compose([\n",
|
694 |
+
" transforms.Resize((224, 224)),\n",
|
695 |
+
" transforms.ToTensor(),\n",
|
696 |
+
" transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
|
697 |
+
"])\n",
|
698 |
+
"\n",
|
699 |
+
"# Charger les données d'entraînement avec CustomImageFolder\n",
|
700 |
+
"train_dataset = CustomImageFolder(root='/content/dataset/train', transform=transform)\n",
|
701 |
+
"train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=4, pin_memory=True)\n",
|
702 |
+
"\n",
|
703 |
+
"# Définir le modèle\n",
|
704 |
+
"model = models.resnet18(pretrained=True)\n",
|
705 |
+
"model.fc = nn.Linear(model.fc.in_features, num_classes)\n",
|
706 |
+
"model = model.to('cuda')\n",
|
707 |
+
"\n",
|
708 |
+
"# Définir la perte et l'optimiseur\n",
|
709 |
+
"criterion = nn.CrossEntropyLoss()\n",
|
710 |
+
"optimizer = optim.Adam(model.parameters(), lr=learning_rate)\n",
|
711 |
+
"\n",
|
712 |
+
"# Utiliser le scaler pour l'AMP\n",
|
713 |
+
"scaler = torch.cuda.amp.GradScaler()\n",
|
714 |
+
"\n",
|
715 |
+
"# Entraînement\n",
|
716 |
+
"for epoch in range(num_epochs):\n",
|
717 |
+
" model.train()\n",
|
718 |
+
" running_loss = 0.0\n",
|
719 |
+
" for inputs, labels in tqdm(train_loader):\n",
|
720 |
+
" inputs, labels = inputs.to('cuda'), labels.to('cuda')\n",
|
721 |
+
"\n",
|
722 |
+
" # Zero the parameter gradients\n",
|
723 |
+
" optimizer.zero_grad()\n",
|
724 |
+
"\n",
|
725 |
+
" # Forward pass with autocast\n",
|
726 |
+
" with torch.cuda.amp.autocast():\n",
|
727 |
+
" outputs = model(inputs)\n",
|
728 |
+
" loss = criterion(outputs, labels)\n",
|
729 |
+
"\n",
|
730 |
+
" # Backward pass with scaler\n",
|
731 |
+
" scaler.scale(loss).backward()\n",
|
732 |
+
" scaler.step(optimizer)\n",
|
733 |
+
" scaler.update()\n",
|
734 |
+
"\n",
|
735 |
+
" running_loss += loss.item() * inputs.size(0)\n",
|
736 |
+
"\n",
|
737 |
+
" epoch_loss = running_loss / len(train_loader.dataset)\n",
|
738 |
+
" print(f'Epoch [{epoch + 1}/{num_epochs}], Loss: {epoch_loss:.4f}')\n",
|
739 |
+
"\n",
|
740 |
+
"print('Finished Training')\n",
|
741 |
+
"\n",
|
742 |
+
"# Sauvegarder le modèle\n",
|
743 |
+
"torch.save(model.state_dict(), 'model.pth')"
|
744 |
+
]
|
745 |
+
},
|
746 |
+
{
|
747 |
+
"cell_type": "code",
|
748 |
+
"source": [
|
749 |
+
"#Ici pour charger le modèle"
|
750 |
+
],
|
751 |
+
"metadata": {
|
752 |
+
"id": "aJBYukdNfx8P"
|
753 |
+
},
|
754 |
+
"execution_count": null,
|
755 |
+
"outputs": []
|
756 |
+
},
|
757 |
+
{
|
758 |
+
"cell_type": "code",
|
759 |
+
"source": [
|
760 |
+
"import torch\n",
|
761 |
+
"import torch.nn as nn\n",
|
762 |
+
"from torchvision import models, transforms\n",
|
763 |
+
"from PIL import Image\n",
|
764 |
+
"\n",
|
765 |
+
"# Définir le modèle\n",
|
766 |
+
"num_classes = 2 # Canard et Perroquet\n",
|
767 |
+
"model = models.resnet18(pretrained=False)\n",
|
768 |
+
"model.fc = nn.Linear(model.fc.in_features, num_classes)\n",
|
769 |
+
"model = model.to('cuda')\n",
|
770 |
+
"\n",
|
771 |
+
"# Charger les poids du modèle enregistré\n",
|
772 |
+
"model.load_state_dict(torch.load('model.pth'))\n",
|
773 |
+
"model.eval() # Mettre le modèle en mode évaluation\n",
|
774 |
+
"\n",
|
775 |
+
"# Définir les transformations\n",
|
776 |
+
"transform = transforms.Compose([\n",
|
777 |
+
" transforms.Resize((224, 224)),\n",
|
778 |
+
" transforms.ToTensor(),\n",
|
779 |
+
" transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
|
780 |
+
"])\n",
|
781 |
+
"\n",
|
782 |
+
"# Charger et transformer une nouvelle image\n",
|
783 |
+
"def load_image(image_path):\n",
|
784 |
+
" image = Image.open(image_path).convert('RGB')\n",
|
785 |
+
" image = transform(image)\n",
|
786 |
+
" image = image.unsqueeze(0) # Ajouter une dimension pour le batch\n",
|
787 |
+
" return image\n",
|
788 |
+
"\n",
|
789 |
+
"# Exemple de chargement d'une image\n",
|
790 |
+
"image_path = '/content/lg.jpeg'\n",
|
791 |
+
"image = load_image(image_path).to('cuda')\n",
|
792 |
+
"\n",
|
793 |
+
"# Passer l'image dans le modèle pour obtenir des prédictions\n",
|
794 |
+
"with torch.no_grad(): # Désactiver la grad pour l'inférence\n",
|
795 |
+
" outputs = model(image)\n",
|
796 |
+
" _, predicted = torch.max(outputs, 1)\n",
|
797 |
+
"\n",
|
798 |
+
" classes = ['Alpine', 'Bugatti']\n",
|
799 |
+
" predicted_class = classes[predicted.item()]\n",
|
800 |
+
" print(f'Predicted class: {predicted_class}')"
|
801 |
+
],
|
802 |
+
"metadata": {
|
803 |
+
"colab": {
|
804 |
+
"base_uri": "https://localhost:8080/"
|
805 |
+
},
|
806 |
+
"id": "oj0pgYdyQFXz",
|
807 |
+
"outputId": "13c95418-6dcd-440c-cb78-2edbfdb8b386"
|
808 |
+
},
|
809 |
+
"execution_count": 22,
|
810 |
+
"outputs": [
|
811 |
+
{
|
812 |
+
"output_type": "stream",
|
813 |
+
"name": "stdout",
|
814 |
+
"text": [
|
815 |
+
"Predicted class: Bugatti\n"
|
816 |
+
]
|
817 |
+
}
|
818 |
+
]
|
819 |
+
},
|
820 |
+
{
|
821 |
+
"cell_type": "code",
|
822 |
+
"source": [
|
823 |
+
"!pip install transformers huggingface_hub"
|
824 |
+
],
|
825 |
+
"metadata": {
|
826 |
+
"colab": {
|
827 |
+
"base_uri": "https://localhost:8080/"
|
828 |
+
},
|
829 |
+
"id": "cksp1PveZU22",
|
830 |
+
"outputId": "eb8bdf38-4cc2-42ce-a047-d02b22c004bb"
|
831 |
+
},
|
832 |
+
"execution_count": 23,
|
833 |
+
"outputs": [
|
834 |
+
{
|
835 |
+
"output_type": "stream",
|
836 |
+
"name": "stdout",
|
837 |
+
"text": [
|
838 |
+
"Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.41.0)\n",
|
839 |
+
"Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.10/dist-packages (0.23.0)\n",
|
840 |
+
"Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.14.0)\n",
|
841 |
+
"Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.25.2)\n",
|
842 |
+
"Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (24.0)\n",
|
843 |
+
"Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.1)\n",
|
844 |
+
"Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2023.12.25)\n",
|
845 |
+
"Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.31.0)\n",
|
846 |
+
"Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.19.1)\n",
|
847 |
+
"Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.3)\n",
|
848 |
+
"Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.4)\n",
|
849 |
+
"Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (2023.6.0)\n",
|
850 |
+
"Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (4.11.0)\n",
|
851 |
+
"Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.3.2)\n",
|
852 |
+
"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.7)\n",
|
853 |
+
"Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.7)\n",
|
854 |
+
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2024.2.2)\n"
|
855 |
+
]
|
856 |
+
}
|
857 |
+
]
|
858 |
+
},
|
859 |
+
{
|
860 |
+
"cell_type": "code",
|
861 |
+
"source": [
|
862 |
+
"!huggingface-cli login"
|
863 |
+
],
|
864 |
+
"metadata": {
|
865 |
+
"colab": {
|
866 |
+
"base_uri": "https://localhost:8080/"
|
867 |
+
},
|
868 |
+
"id": "c-3Qxby1ZWFV",
|
869 |
+
"outputId": "66148da3-bf28-4fa4-baf6-0034b8b5b35e"
|
870 |
+
},
|
871 |
+
"execution_count": 11,
|
872 |
+
"outputs": [
|
873 |
+
{
|
874 |
+
"output_type": "stream",
|
875 |
+
"name": "stdout",
|
876 |
+
"text": [
|
877 |
+
"\n",
|
878 |
+
" _| _| _| _| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _|_|_|_| _|_| _|_|_| _|_|_|_|\n",
|
879 |
+
" _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|\n",
|
880 |
+
" _|_|_|_| _| _| _| _|_| _| _|_| _| _| _| _| _| _|_| _|_|_| _|_|_|_| _| _|_|_|\n",
|
881 |
+
" _| _| _| _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|\n",
|
882 |
+
" _| _| _|_| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _| _| _| _|_|_| _|_|_|_|\n",
|
883 |
+
"\n",
|
884 |
+
" To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
|
885 |
+
"Enter your token (input will not be visible): \n",
|
886 |
+
"Add token as git credential? (Y/n) Y\n",
|
887 |
+
"Token is valid (permission: read).\n",
|
888 |
+
"\u001b[1m\u001b[31mCannot authenticate through git-credential as no helper is defined on your machine.\n",
|
889 |
+
"You might have to re-authenticate when pushing to the Hugging Face Hub.\n",
|
890 |
+
"Run the following command in your terminal in case you want to set the 'store' credential helper as default.\n",
|
891 |
+
"\n",
|
892 |
+
"git config --global credential.helper store\n",
|
893 |
+
"\n",
|
894 |
+
"Read https://git-scm.com/book/en/v2/Git-Tools-Credential-Storage for more details.\u001b[0m\n",
|
895 |
+
"Token has not been saved to git credential helper.\n",
|
896 |
+
"Your token has been saved to /root/.cache/huggingface/token\n",
|
897 |
+
"Login successful\n"
|
898 |
+
]
|
899 |
+
}
|
900 |
+
]
|
901 |
+
},
|
902 |
+
{
|
903 |
+
"cell_type": "code",
|
904 |
+
"source": [
|
905 |
+
"from huggingface_hub import HfApi, HfFolder, Repository\n",
|
906 |
+
"\n",
|
907 |
+
"# Variables\n",
|
908 |
+
"model_path = \"Wheeloh-model_1.pth\"\n",
|
909 |
+
"repo_name = \"Wheeloh-model_1\" # Remplacez par le nom de votre dépôt\n",
|
910 |
+
"commit_message = \"Initial commit\"\n",
|
911 |
+
"\n",
|
912 |
+
"# Se connecter à l'API\n",
|
913 |
+
"api = HfApi()\n",
|
914 |
+
"\n",
|
915 |
+
"# Obtenir le token d'authentification\n",
|
916 |
+
"token = HfFolder.get_token()\n",
|
917 |
+
"\n",
|
918 |
+
"# Cloner le dépôt Hugging Face\n",
|
919 |
+
"repo_url = api.create_repo(repo_name, token=token, exist_ok=True)\n",
|
920 |
+
"repo = Repository(local_dir=repo_name, clone_from=repo_url)\n",
|
921 |
+
"\n",
|
922 |
+
"# Copier le fichier du modèle dans le dépôt local\n",
|
923 |
+
"import shutil\n",
|
924 |
+
"shutil.copy(model_path, repo_name)\n",
|
925 |
+
"\n",
|
926 |
+
"# Pousser le modèle sur le hub\n",
|
927 |
+
"repo.push_to_hub(commit_message=commit_message)\n"
|
928 |
+
],
|
929 |
+
"metadata": {
|
930 |
+
"colab": {
|
931 |
+
"base_uri": "https://localhost:8080/",
|
932 |
+
"height": 421,
|
933 |
+
"referenced_widgets": [
|
934 |
+
"a710457b00a849da9c24e1e1afb9d616",
|
935 |
+
"7b58972d4c704aa3bcc59a7a3c18682f",
|
936 |
+
"624e284bf09843a7a0b10256fbc416d0",
|
937 |
+
"e5b7191907914d75983b7e4b717b86d3",
|
938 |
+
"6434432da36a4755981ad001472e0cff",
|
939 |
+
"40a3244cfaa4404bb7ce4247564df6ed",
|
940 |
+
"234f2286e51c4a53b36584b49cd5a01f",
|
941 |
+
"66ade188379547aaae0724b4e3d87051",
|
942 |
+
"4f8c9691cd1b4faab91f7fb34348b716",
|
943 |
+
"7e6ba9a0ee6046b8961da2bf09d287ac",
|
944 |
+
"8ac32f54acd140b483ae523e22a9652b"
|
945 |
+
]
|
946 |
+
},
|
947 |
+
"id": "LDyh9-MfZiMX",
|
948 |
+
"outputId": "e0e9ab72-d5df-4f21-eb3a-5226263071de"
|
949 |
+
},
|
950 |
+
"execution_count": 25,
|
951 |
+
"outputs": [
|
952 |
+
{
|
953 |
+
"output_type": "stream",
|
954 |
+
"name": "stderr",
|
955 |
+
"text": [
|
956 |
+
"/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
|
957 |
+
"The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
|
958 |
+
"To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
|
959 |
+
"You will be able to reuse this secret in all of your notebooks.\n",
|
960 |
+
"Please note that authentication is recommended but still optional to access public models or datasets.\n",
|
961 |
+
" warnings.warn(\n",
|
962 |
+
"/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_deprecation.py:131: FutureWarning: 'Repository' (from 'huggingface_hub.repository') is deprecated and will be removed from version '1.0'. Please prefer the http-based alternatives instead. Given its large adoption in legacy code, the complete removal is only planned on next major release.\n",
|
963 |
+
"For more details, please read https://huggingface.co/docs/huggingface_hub/concepts/git_vs_http.\n",
|
964 |
+
" warnings.warn(warning_message, FutureWarning)\n",
|
965 |
+
"Cloning https://huggingface.co/Dabococo/Wheeloh-model_1 into local empty directory.\n",
|
966 |
+
"WARNING:huggingface_hub.repository:Cloning https://huggingface.co/Dabococo/Wheeloh-model_1 into local empty directory.\n"
|
967 |
+
]
|
968 |
+
},
|
969 |
+
{
|
970 |
+
"output_type": "display_data",
|
971 |
+
"data": {
|
972 |
+
"text/plain": [
|
973 |
+
"Upload file Wheeloh-model_1.pth: 0%| | 1.00/42.7M [00:00<?, ?B/s]"
|
974 |
+
],
|
975 |
+
"application/vnd.jupyter.widget-view+json": {
|
976 |
+
"version_major": 2,
|
977 |
+
"version_minor": 0,
|
978 |
+
"model_id": "a710457b00a849da9c24e1e1afb9d616"
|
979 |
+
}
|
980 |
+
},
|
981 |
+
"metadata": {}
|
982 |
+
},
|
983 |
+
{
|
984 |
+
"output_type": "stream",
|
985 |
+
"name": "stderr",
|
986 |
+
"text": [
|
987 |
+
"To https://huggingface.co/Dabococo/Wheeloh-model_1\n",
|
988 |
+
" 09e573f..8606a55 main -> main\n",
|
989 |
+
"\n",
|
990 |
+
"WARNING:huggingface_hub.repository:To https://huggingface.co/Dabococo/Wheeloh-model_1\n",
|
991 |
+
" 09e573f..8606a55 main -> main\n",
|
992 |
+
"\n"
|
993 |
+
]
|
994 |
+
},
|
995 |
+
{
|
996 |
+
"output_type": "execute_result",
|
997 |
+
"data": {
|
998 |
+
"text/plain": [
|
999 |
+
"'https://huggingface.co/Dabococo/Wheeloh-model_1/commit/8606a554a318e75a54b8b841b31825c29f577de8'"
|
1000 |
+
],
|
1001 |
+
"application/vnd.google.colaboratory.intrinsic+json": {
|
1002 |
+
"type": "string"
|
1003 |
+
}
|
1004 |
+
},
|
1005 |
+
"metadata": {},
|
1006 |
+
"execution_count": 25
|
1007 |
+
}
|
1008 |
+
]
|
1009 |
+
}
|
1010 |
+
]
|
1011 |
+
}
|
untitled6.py
ADDED
@@ -0,0 +1,234 @@
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|
|
|
1 |
+
# -*- coding: utf-8 -*-
|
2 |
+
"""Untitled6.ipynb
|
3 |
+
|
4 |
+
Automatically generated by Colab.
|
5 |
+
|
6 |
+
Original file is located at
|
7 |
+
https://colab.research.google.com/drive/1b3-0ogrDvdw3WtHwOta0Ihlo46MAwDsk
|
8 |
+
"""
|
9 |
+
|
10 |
+
!pip install requests transformers
|
11 |
+
|
12 |
+
import requests
|
13 |
+
import zipfile
|
14 |
+
import os
|
15 |
+
|
16 |
+
# URL du fichier zip sur Hugging Face
|
17 |
+
zip_url = 'https://huggingface.co/datasets/Dabococo/wheeloh_dataset/images'
|
18 |
+
|
19 |
+
# Nom local du fichier zip
|
20 |
+
zip_file = 'images.zip'
|
21 |
+
|
22 |
+
# Télécharger le fichier zip
|
23 |
+
response = requests.get(zip_url)
|
24 |
+
content_type = response.headers.get('Content-Type')
|
25 |
+
|
26 |
+
# Vérifier que le fichier est un fichier zip
|
27 |
+
if 'zip' not in content_type:
|
28 |
+
raise ValueError("Le fichier téléchargé n'est pas un fichier zip. Content-Type: {}".format(content_type))
|
29 |
+
|
30 |
+
# Sauvegarder le contenu téléchargé dans un fichier
|
31 |
+
with open(zip_file, 'wb') as f:
|
32 |
+
f.write(response.content)
|
33 |
+
|
34 |
+
# Vérifier la taille du fichier
|
35 |
+
file_size = os.path.getsize(zip_file)
|
36 |
+
print("Taille du fichier téléchargé:", file_size, "octets")
|
37 |
+
|
38 |
+
# Afficher le début du contenu du fichier pour vérifier
|
39 |
+
with open(zip_file, 'rb') as f:
|
40 |
+
print(f.read(100)) # Lire les 100 premiers octets
|
41 |
+
|
42 |
+
# Créer un répertoire pour extraire les fichiers
|
43 |
+
extract_dir = 'extracted_files'
|
44 |
+
os.makedirs(extract_dir, exist_ok=True)
|
45 |
+
|
46 |
+
# Extraire le contenu du fichier zip
|
47 |
+
try:
|
48 |
+
with zipfile.ZipFile(zip_file, 'r') as zip_ref:
|
49 |
+
zip_ref.extractall(extract_dir)
|
50 |
+
# Afficher les fichiers extraits
|
51 |
+
extracted_files = os.listdir(extract_dir)
|
52 |
+
print("Fichiers extraits :", extracted_files)
|
53 |
+
except zipfile.BadZipFile:
|
54 |
+
print("Erreur : le fichier téléchargé n'est pas un fichier zip valide.")
|
55 |
+
|
56 |
+
from torchvision.datasets import ImageFolder
|
57 |
+
import os
|
58 |
+
from torchvision.datasets.folder import has_file_allowed_extension, IMG_EXTENSIONS, default_loader
|
59 |
+
|
60 |
+
class CustomImageFolder(ImageFolder):
|
61 |
+
def __init__(self, root, transform=None, loader=default_loader, is_valid_file=None):
|
62 |
+
super().__init__(root, transform=transform, loader=loader, is_valid_file=is_valid_file)
|
63 |
+
|
64 |
+
def find_classes(self, directory):
|
65 |
+
# Ignorer les répertoires cachés
|
66 |
+
classes = [d.name for d in os.scandir(directory) if d.is_dir() and not d.name.startswith('.')]
|
67 |
+
classes.sort()
|
68 |
+
class_to_idx = {cls_name: i for i, cls_name in enumerate(classes)}
|
69 |
+
return classes, class_to_idx
|
70 |
+
|
71 |
+
def make_dataset(self, directory, class_to_idx, extensions=None, is_valid_file=None, allow_empty=False):
|
72 |
+
instances = []
|
73 |
+
directory = os.path.expanduser(directory)
|
74 |
+
both_none = extensions is None and is_valid_file is None
|
75 |
+
if both_none:
|
76 |
+
raise ValueError("Both extensions and is_valid_file cannot be None")
|
77 |
+
if extensions is not None:
|
78 |
+
def is_valid_file(x):
|
79 |
+
return has_file_allowed_extension(x, extensions)
|
80 |
+
|
81 |
+
for target_class in sorted(class_to_idx.keys()):
|
82 |
+
class_index = class_to_idx[target_class]
|
83 |
+
target_dir = os.path.join(directory, target_class)
|
84 |
+
if not os.path.isdir(target_dir):
|
85 |
+
continue
|
86 |
+
for root, _, fnames in sorted(os.walk(target_dir)):
|
87 |
+
for fname in sorted(fnames):
|
88 |
+
path = os.path.join(root, fname)
|
89 |
+
if is_valid_file(path) and not fname.startswith('.'):
|
90 |
+
item = path, class_index
|
91 |
+
instances.append(item)
|
92 |
+
|
93 |
+
if not allow_empty and len(instances) == 0:
|
94 |
+
raise RuntimeError(f"Found 0 files in subfolders of: {directory}. Supported extensions are: {','.join(extensions)}")
|
95 |
+
|
96 |
+
return instances
|
97 |
+
|
98 |
+
import torch
|
99 |
+
import torch.nn as nn
|
100 |
+
import torch.optim as optim
|
101 |
+
from torch.utils.data import DataLoader
|
102 |
+
from torchvision import transforms, models
|
103 |
+
from tqdm import tqdm
|
104 |
+
|
105 |
+
# Configuration
|
106 |
+
batch_size = 32
|
107 |
+
num_epochs = 10
|
108 |
+
learning_rate = 0.001
|
109 |
+
num_classes = 2
|
110 |
+
|
111 |
+
# Préparer les transformations
|
112 |
+
transform = transforms.Compose([
|
113 |
+
transforms.Resize((224, 224)),
|
114 |
+
transforms.ToTensor(),
|
115 |
+
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
|
116 |
+
])
|
117 |
+
|
118 |
+
# Charger les données d'entraînement avec CustomImageFolder
|
119 |
+
train_dataset = CustomImageFolder(root='/content/dataset/train', transform=transform)
|
120 |
+
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=4, pin_memory=True)
|
121 |
+
|
122 |
+
# Définir le modèle
|
123 |
+
model = models.resnet18(pretrained=True)
|
124 |
+
model.fc = nn.Linear(model.fc.in_features, num_classes)
|
125 |
+
model = model.to('cuda')
|
126 |
+
|
127 |
+
# Définir la perte et l'optimiseur
|
128 |
+
criterion = nn.CrossEntropyLoss()
|
129 |
+
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
|
130 |
+
|
131 |
+
# Utiliser le scaler pour l'AMP
|
132 |
+
scaler = torch.cuda.amp.GradScaler()
|
133 |
+
|
134 |
+
# Entraînement
|
135 |
+
for epoch in range(num_epochs):
|
136 |
+
model.train()
|
137 |
+
running_loss = 0.0
|
138 |
+
for inputs, labels in tqdm(train_loader):
|
139 |
+
inputs, labels = inputs.to('cuda'), labels.to('cuda')
|
140 |
+
|
141 |
+
# Zero the parameter gradients
|
142 |
+
optimizer.zero_grad()
|
143 |
+
|
144 |
+
# Forward pass with autocast
|
145 |
+
with torch.cuda.amp.autocast():
|
146 |
+
outputs = model(inputs)
|
147 |
+
loss = criterion(outputs, labels)
|
148 |
+
|
149 |
+
# Backward pass with scaler
|
150 |
+
scaler.scale(loss).backward()
|
151 |
+
scaler.step(optimizer)
|
152 |
+
scaler.update()
|
153 |
+
|
154 |
+
running_loss += loss.item() * inputs.size(0)
|
155 |
+
|
156 |
+
epoch_loss = running_loss / len(train_loader.dataset)
|
157 |
+
print(f'Epoch [{epoch + 1}/{num_epochs}], Loss: {epoch_loss:.4f}')
|
158 |
+
|
159 |
+
print('Finished Training')
|
160 |
+
|
161 |
+
# Sauvegarder le modèle
|
162 |
+
torch.save(model.state_dict(), 'model.pth')
|
163 |
+
|
164 |
+
#Ici pour charger le modèle
|
165 |
+
|
166 |
+
import torch
|
167 |
+
import torch.nn as nn
|
168 |
+
from torchvision import models, transforms
|
169 |
+
from PIL import Image
|
170 |
+
|
171 |
+
# Définir le modèle
|
172 |
+
num_classes = 2 # Canard et Perroquet
|
173 |
+
model = models.resnet18(pretrained=False)
|
174 |
+
model.fc = nn.Linear(model.fc.in_features, num_classes)
|
175 |
+
model = model.to('cuda')
|
176 |
+
|
177 |
+
# Charger les poids du modèle enregistré
|
178 |
+
model.load_state_dict(torch.load('model.pth'))
|
179 |
+
model.eval() # Mettre le modèle en mode évaluation
|
180 |
+
|
181 |
+
# Définir les transformations
|
182 |
+
transform = transforms.Compose([
|
183 |
+
transforms.Resize((224, 224)),
|
184 |
+
transforms.ToTensor(),
|
185 |
+
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
|
186 |
+
])
|
187 |
+
|
188 |
+
# Charger et transformer une nouvelle image
|
189 |
+
def load_image(image_path):
|
190 |
+
image = Image.open(image_path).convert('RGB')
|
191 |
+
image = transform(image)
|
192 |
+
image = image.unsqueeze(0) # Ajouter une dimension pour le batch
|
193 |
+
return image
|
194 |
+
|
195 |
+
# Exemple de chargement d'une image
|
196 |
+
image_path = '/content/lg.jpeg'
|
197 |
+
image = load_image(image_path).to('cuda')
|
198 |
+
|
199 |
+
# Passer l'image dans le modèle pour obtenir des prédictions
|
200 |
+
with torch.no_grad(): # Désactiver la grad pour l'inférence
|
201 |
+
outputs = model(image)
|
202 |
+
_, predicted = torch.max(outputs, 1)
|
203 |
+
|
204 |
+
classes = ['Alpine', 'Bugatti']
|
205 |
+
predicted_class = classes[predicted.item()]
|
206 |
+
print(f'Predicted class: {predicted_class}')
|
207 |
+
|
208 |
+
!pip install transformers huggingface_hub
|
209 |
+
|
210 |
+
!huggingface-cli login
|
211 |
+
|
212 |
+
from huggingface_hub import HfApi, HfFolder, Repository
|
213 |
+
|
214 |
+
# Variables
|
215 |
+
model_path = "Wheeloh-model_1.pth"
|
216 |
+
repo_name = "Wheeloh-model_1" # Remplacez par le nom de votre dépôt
|
217 |
+
commit_message = "Initial commit"
|
218 |
+
|
219 |
+
# Se connecter à l'API
|
220 |
+
api = HfApi()
|
221 |
+
|
222 |
+
# Obtenir le token d'authentification
|
223 |
+
token = HfFolder.get_token()
|
224 |
+
|
225 |
+
# Cloner le dépôt Hugging Face
|
226 |
+
repo_url = api.create_repo(repo_name, token=token, exist_ok=True)
|
227 |
+
repo = Repository(local_dir=repo_name, clone_from=repo_url)
|
228 |
+
|
229 |
+
# Copier le fichier du modèle dans le dépôt local
|
230 |
+
import shutil
|
231 |
+
shutil.copy(model_path, repo_name)
|
232 |
+
|
233 |
+
# Pousser le modèle sur le hub
|
234 |
+
repo.push_to_hub(commit_message=commit_message)
|