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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m507.1/507.1 kB\u001b[0m \u001b[31m3.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.1/84.1 kB\u001b[0m \u001b[31m8.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m15.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"# Standard library imports\n",
"import warnings\n",
"\n",
"# Third-party imports\n",
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"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"import torch\n",
"import torch.nn as nn\n",
"from datasets import load_dataset\n",
"from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix, f1_score\n",
"from torchvision.transforms import Compose, Normalize, RandomResizedCrop, ToTensor\n",
"from transformers import (\n",
" AutoImageProcessor,\n",
" AutoModelForImageClassification,\n",
" DefaultDataCollator,\n",
" Trainer,\n",
" TrainingArguments,\n",
" logging,\n",
" pipeline,\n",
")\n",
"\n",
"# Disabling warnings\n",
"warnings.filterwarnings(\"ignore\")\n",
"logging.set_verbosity_error()"
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{
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],
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{
"cell_type": "code",
"source": [
"# Splitting the data into training and validation sets\n",
"mri_data_split = mri_data.train_test_split(\n",
" test_size=0.2,\n",
" stratify_by_column=\"label\",\n",
" seed=12345,\n",
")\n",
"mri_data_split"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Dyd7pfAcGLIb",
"outputId": "b7a247f2-0357-43da-a54b-4560387887ef"
},
"execution_count": 4,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"DatasetDict({\n",
" train: Dataset({\n",
" features: ['image', 'label'],\n",
" num_rows: 4096\n",
" })\n",
" test: Dataset({\n",
" features: ['image', 'label'],\n",
" num_rows: 1024\n",
" })\n",
"})"
]
},
"metadata": {},
"execution_count": 4
}
]
},
{
"cell_type": "markdown",
"source": [
"# Data analysis"
],
"metadata": {
"id": "1FPS6VONnKHT"
}
},
{
"cell_type": "code",
"source": [
"labels = mri_data_split[\"train\"].features[\"label\"].names\n",
"labels"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "yugFU2J7nKYs",
"outputId": "06f945f1-df5e-450b-cdea-75c4558f879d"
},
"execution_count": 5,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['Mild_Demented', 'Moderate_Demented', 'Non_Demented', 'Very_Mild_Demented']"
]
},
"metadata": {},
"execution_count": 5
}
]
},
{
"cell_type": "code",
"source": [
"# Computing the numbers of examples for each class (train/validation)\n",
"class_counts_train = np.bincount(mri_data_split[\"train\"][\"label\"])\n",
"class_counts_valid = np.bincount(mri_data_split[\"test\"][\"label\"])\n",
"\n",
"# Creating a summary DataFrame\n",
"dataset_info = {\n",
" \"train\": class_counts_train,\n",
" \"test\": class_counts_valid\n",
"}\n",
"class_counts_info = pd.DataFrame(data=dataset_info, index=labels)\n",
"class_counts_info"
],
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"height": 174
},
"id": "bEouAIqVnKz3",
"outputId": "e8b79143-de3a-4949-fb86-b01a9b8393a3"
},
"execution_count": 6,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" train test\n",
"Mild_Demented 579 145\n",
"Moderate_Demented 39 10\n",
"Non_Demented 2053 513\n",
"Very_Mild_Demented 1425 356"
],
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" async function convertToInteractive(key) {\n",
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" 90% {\n",
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"</style>\n",
"\n",
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" async function quickchart(key) {\n",
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"</div>\n",
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]
},
"metadata": {},
"execution_count": 6
}
]
},
{
"cell_type": "code",
"source": [
"last_example = mri_data_split[\"train\"][-1]\n",
"last_example"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "b14OvZSNplje",
"outputId": "21d9cee2-813d-4f81-d081-6d40b0695da9"
},
"execution_count": 7,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=L size=128x128>,\n",
" 'label': 3}"
]
},
"metadata": {},
"execution_count": 7
}
]
},
{
"cell_type": "code",
"source": [
"last_image = mri_data_split[\"train\"][\"image\"][-1]\n",
"last_image"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 145
},
"id": "uNCBGwDdlaiy",
"outputId": "230bfb26-98e5-4187-8214-1c057bbff5e2"
},
"execution_count": 8,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<PIL.JpegImagePlugin.JpegImageFile image mode=L size=128x128>"
],
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\n"
},
"metadata": {},
"execution_count": 8
}
]
},
{
"cell_type": "code",
"source": [
"last_label = labels[last_example[\"label\"]]\n",
"last_label"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"id": "eu7rKgnlk5Uw",
"outputId": "d13f6c0e-4f3d-4abb-ef3a-7df80e544073"
},
"execution_count": 9,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'Very_Mild_Demented'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 9
}
]
},
{
"cell_type": "markdown",
"source": [
"# Preprocessing images"
],
"metadata": {
"id": "QBbKfFFarcrP"
}
},
{
"cell_type": "code",
"source": [
"checkpoint = \"google/vit-base-patch16-224-in21k\"\n",
"image_processor = AutoImageProcessor.from_pretrained(checkpoint)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 81,
"referenced_widgets": [
"6d22d9200ff947499904a3c0e70c57ee",
"9a0bf4eb10dd496e8fdebf479dd213a8",
"6899fb2ffc484270a91590b62b74d30d",
"7e072bbbefd2401a8fbae2c4434b6d6e",
"9e76c465fa6a471c919bee5ff87dff0e",
"e7882c4d46b7403bb6e708256aa5c677",
"2fb0820949364c449a8e4f6bf600e96d",
"608afd4b4a5f4e6fb6a06aca188893e2",
"e894dfd8391a40a29810a4a5ddc2e48e",
"5f371a6c38974a37b108b3d7727ec5c7",
"921ac74643b94015a0e7bfc871218b3e",
"75e4ed05caeb4175abe96a636af587a6",
"6b8003426293483780e1c53a98b0c8c2",
"1c91090ab8db4ed58f08227b48054aec",
"564e2b2c8da141809b5b4ec46e10b22c",
"7ef2ee6b175b41829e916d1843bcee5c",
"bcaf871a618c41cab1fd5ea9d90b1477",
"92b225ca08d24cf3984f2e20c05ae983",
"1ddfb1c3da0b47a4a0ad6171304b9c6f",
"8bf3c972de1e44a8bdbd27325900111b",
"d00650f016da488cbdc49611394182ce",
"6fbf6bb2ae55458785d38bfa185e90d9"
]
},
"id": "zA8xoFzlGN8Y",
"outputId": "ffeab19c-035e-4b61-f416-7aa3e7017116"
},
"execution_count": 10,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"preprocessor_config.json: 0%| | 0.00/160 [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "6d22d9200ff947499904a3c0e70c57ee"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"config.json: 0%| | 0.00/502 [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "75e4ed05caeb4175abe96a636af587a6"
}
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"normalize = Normalize(mean=image_processor.image_mean, std=image_processor.image_std)\n",
"size = (\n",
" image_processor.size[\"shortest_edge\"]\n",
" if \"shortest_edge\" in image_processor.size\n",
" else (image_processor.size[\"height\"], image_processor.size[\"width\"])\n",
")\n",
"_transforms = Compose([RandomResizedCrop(size), ToTensor(), normalize])"
],
"metadata": {
"id": "Lpwi82xtrhi5"
},
"execution_count": 11,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def transforms(examples):\n",
" examples[\"pixel_values\"] = [_transforms(img.convert(\"RGB\")) for img in examples[\"image\"]]\n",
" del examples[\"image\"]\n",
" return examples"
],
"metadata": {
"id": "SH8Qbd2krhtQ"
},
"execution_count": 12,
"outputs": []
},
{
"cell_type": "code",
"source": [
"mri_data_split = mri_data_split.with_transform(transforms)\n",
"mri_data_split"
],
"metadata": {
"id": "CNkZRQwfrhvr",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "401168f9-fdef-4607-b24b-84399924091f"
},
"execution_count": 13,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"DatasetDict({\n",
" train: Dataset({\n",
" features: ['image', 'label'],\n",
" num_rows: 4096\n",
" })\n",
" test: Dataset({\n",
" features: ['image', 'label'],\n",
" num_rows: 1024\n",
" })\n",
"})"
]
},
"metadata": {},
"execution_count": 13
}
]
},
{
"cell_type": "code",
"source": [
"data_collator = DefaultDataCollator()"
],
"metadata": {
"id": "HcrLbfFIrsq4"
},
"execution_count": 14,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# Loading the model"
],
"metadata": {
"id": "cgswZ1nmsQ8p"
}
},
{
"cell_type": "code",
"source": [
"label2id, id2label = dict(), dict()\n",
"for i, label in enumerate(labels):\n",
" label2id[label] = str(i)\n",
" id2label[str(i)] = label"
],
"metadata": {
"id": "VZCwEcF4rstv"
},
"execution_count": 15,
"outputs": []
},
{
"cell_type": "code",
"source": [
"label2id"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "MatyMYrdrsxL",
"outputId": "57ef0be1-0559-4e42-b895-bffd6316098b"
},
"execution_count": 16,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'Mild_Demented': '0',\n",
" 'Moderate_Demented': '1',\n",
" 'Non_Demented': '2',\n",
" 'Very_Mild_Demented': '3'}"
]
},
"metadata": {},
"execution_count": 16
}
]
},
{
"cell_type": "code",
"source": [
"id2label"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "xM6wS3lvrhy_",
"outputId": "d973ef6b-4691-4f28-8071-f02d3539a1c1"
},
"execution_count": 17,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'0': 'Mild_Demented',\n",
" '1': 'Moderate_Demented',\n",
" '2': 'Non_Demented',\n",
" '3': 'Very_Mild_Demented'}"
]
},
"metadata": {},
"execution_count": 17
}
]
},
{
"cell_type": "code",
"source": [
"model = AutoModelForImageClassification.from_pretrained(\n",
" checkpoint,\n",
" num_labels=len(labels),\n",
" id2label=id2label,\n",
" label2id=label2id,\n",
")"
],
"metadata": {
"id": "5b-7f3Gmsgie",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 49,
"referenced_widgets": [
"03788ab3c9894f94bec4a6c9df056822",
"dfd1174facfb4ed1a00dabe5c2f496b7",
"29af8d274a674cbaa240d6fc630b57e1",
"c228595c34264be8a14183db167dec73",
"214f3fea09d8466886a12ab1c99f186c",
"bae73eda6d564cdbb6786538c2cebe22",
"560f7de1c38e4cc4821b27771fa13713",
"4216938aa47c4a988ff298d12422d458",
"fa7d5c7e171b44ac8b706f639e425485",
"b05fb39433ce4e498d8315a56bc6823f",
"0fe4ae4e16ea49928dfc9fed67e9c055"
]
},
"outputId": "a6467e6b-b4da-4d8d-c61d-aecb23ee6943"
},
"execution_count": 18,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"pytorch_model.bin: 0%| | 0.00/346M [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "03788ab3c9894f94bec4a6c9df056822"
}
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"# Configuring the Trainer"
],
"metadata": {
"id": "MLVwY-c0s2bD"
}
},
{
"cell_type": "code",
"source": [
"class_weights = 1 - (class_counts_train / len(mri_data_split[\"train\"]))\n",
"class_weights"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "dfyO5TqVsgku",
"outputId": "aac980e0-c029-40b1-a082-d334a4e88dd4"
},
"execution_count": 19,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([0.85864258, 0.99047852, 0.4987793 , 0.65209961])"
]
},
"metadata": {},
"execution_count": 19
}
]
},
{
"cell_type": "code",
"source": [
"class_weights = torch.from_numpy(class_weights).float().to(\"cuda\")\n",
"class_weights"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Fi4RZl86_jw6",
"outputId": "a3ef7eee-7367-48d0-f1dc-a1d9b3ce8809"
},
"execution_count": 20,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"tensor([0.8586, 0.9905, 0.4988, 0.6521], device='cuda:0')"
]
},
"metadata": {},
"execution_count": 20
}
]
},
{
"cell_type": "code",
"source": [
"mri_data_split = mri_data_split.rename_column(\"label\", \"labels\")\n",
"mri_data_split"
],
"metadata": {
"id": "BTTy0HRNw3Wz",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "7226ce0a-b99f-4756-d87a-baa1bd9b9e1b"
},
"execution_count": 21,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"DatasetDict({\n",
" train: Dataset({\n",
" features: ['image', 'labels'],\n",
" num_rows: 4096\n",
" })\n",
" test: Dataset({\n",
" features: ['image', 'labels'],\n",
" num_rows: 1024\n",
" })\n",
"})"
]
},
"metadata": {},
"execution_count": 21
}
]
},
{
"cell_type": "code",
"source": [
"class WeightedLossTrainer(Trainer):\n",
" def compute_loss(self, model, inputs, return_outputs=False):\n",
" labels = inputs.pop(\"labels\")\n",
" outputs = model(**inputs)\n",
" logits = outputs.get(\"logits\")\n",
" loss_func = nn.CrossEntropyLoss(weight=class_weights)\n",
" loss = loss_func(logits.view(-1, self.model.config.num_labels), labels.view(-1))\n",
"\n",
" return (loss, outputs) if return_outputs else loss"
],
"metadata": {
"id": "vxg9IQeSALHn"
},
"execution_count": 22,
"outputs": []
},
{
"cell_type": "code",
"source": [
"f1_metric = evaluate.load(\"f1\")\n",
"\n",
"def compute_metrics(eval_pred):\n",
" predictions, labels = eval_pred\n",
" predictions = np.argmax(predictions, axis=1)\n",
"\n",
" return f1_metric.compute(predictions=predictions, references=labels, average=\"weighted\")"
],
"metadata": {
"id": "WnbykNfAHy2b",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 49,
"referenced_widgets": [
"6c00af10020f4606b9c1c5ae05b89daa",
"cfb1093bf8574be2934784edb5c2f59d",
"280930dfda9a469e8ddf64e7615a427c",
"d18a69abf9ea4ec0bac83e577f3ec7f4",
"e887809c2c444b019071cacca6da0b9a",
"2e73de9b44c54f299c386ff3aec5e746",
"4ace42b181ed468193c5c70b374c781b",
"e4bc3a7a50924f53a4993ab079c3dc59",
"f78b36ee8b654c07a84c1c84d2dadccd",
"b1a4ed9b309149cdaf3d02c8e01e5798",
"e29cb7cd42c84362bdc650afc16cee2f"
]
},
"outputId": "5eb224ba-b002-42a3-d81d-4a500daabbe8"
},
"execution_count": 23,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading builder script: 0%| | 0.00/6.77k [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "6c00af10020f4606b9c1c5ae05b89daa"
}
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"batch_size = 64\n",
"logging_steps = len(mri_data_split[\"train\"]) // batch_size\n",
"logging_steps"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "-yAvy44SBG-L",
"outputId": "aaca8dda-1547-42db-cd06-62b1fa4d552c"
},
"execution_count": 24,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"64"
]
},
"metadata": {},
"execution_count": 24
}
]
},
{
"cell_type": "code",
"source": [
"training_args = TrainingArguments(\n",
" output_dir=\"alz-mri-vit\",\n",
" remove_unused_columns=False,\n",
" evaluation_strategy=\"epoch\",\n",
" save_strategy=\"epoch\",\n",
" learning_rate=2e-4,\n",
" per_device_train_batch_size=16,\n",
" gradient_accumulation_steps=4,\n",
" per_device_eval_batch_size=16,\n",
" gradient_checkpointing=True,\n",
" num_train_epochs=30,\n",
" warmup_ratio=0.1,\n",
" logging_steps=logging_steps,\n",
" fp16=True,\n",
" load_best_model_at_end=True,\n",
" metric_for_best_model=\"f1\",\n",
" weight_decay=0.01,\n",
" disable_tqdm=False,\n",
" push_to_hub=True,\n",
")\n",
"\n",
"trainer = WeightedLossTrainer(\n",
" model=model,\n",
" args=training_args,\n",
" data_collator=data_collator,\n",
" train_dataset=mri_data_split[\"train\"],\n",
" eval_dataset=mri_data_split[\"test\"],\n",
" tokenizer=image_processor,\n",
" compute_metrics=compute_metrics,\n",
")\n",
"\n",
"trainer.train()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "wBGFsJkjH5_E",
"outputId": "5969b884-0d2d-4b5b-dfac-6b9a127a754d"
},
"execution_count": 25,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='1920' max='1920' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [1920/1920 39:20, Epoch 30/30]\n",
" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Epoch</th>\n",
" <th>Training Loss</th>\n",
" <th>Validation Loss</th>\n",
" <th>F1</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>1.121800</td>\n",
" <td>0.941877</td>\n",
" <td>0.574249</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>0.940000</td>\n",
" <td>0.905402</td>\n",
" <td>0.602930</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>0.912300</td>\n",
" <td>0.901887</td>\n",
" <td>0.526183</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>0.862500</td>\n",
" <td>0.846535</td>\n",
" <td>0.602914</td>\n",
" </tr>\n",
" <tr>\n",
" <td>5</td>\n",
" <td>0.810400</td>\n",
" <td>0.781023</td>\n",
" <td>0.631924</td>\n",
" </tr>\n",
" <tr>\n",
" <td>6</td>\n",
" <td>0.724400</td>\n",
" <td>0.727819</td>\n",
" <td>0.703721</td>\n",
" </tr>\n",
" <tr>\n",
" <td>7</td>\n",
" <td>0.697000</td>\n",
" <td>0.629985</td>\n",
" <td>0.748010</td>\n",
" </tr>\n",
" <tr>\n",
" <td>8</td>\n",
" <td>0.586500</td>\n",
" <td>0.565929</td>\n",
" <td>0.766220</td>\n",
" </tr>\n",
" <tr>\n",
" <td>9</td>\n",
" <td>0.519900</td>\n",
" <td>0.544534</td>\n",
" <td>0.772069</td>\n",
" </tr>\n",
" <tr>\n",
" <td>10</td>\n",
" <td>0.473400</td>\n",
" <td>0.674992</td>\n",
" <td>0.718480</td>\n",
" </tr>\n",
" <tr>\n",
" <td>11</td>\n",
" <td>0.439900</td>\n",
" <td>0.489264</td>\n",
" <td>0.827378</td>\n",
" </tr>\n",
" <tr>\n",
" <td>12</td>\n",
" <td>0.381700</td>\n",
" <td>0.557753</td>\n",
" <td>0.784368</td>\n",
" </tr>\n",
" <tr>\n",
" <td>13</td>\n",
" <td>0.331800</td>\n",
" <td>0.469930</td>\n",
" <td>0.822824</td>\n",
" </tr>\n",
" <tr>\n",
" <td>14</td>\n",
" <td>0.309600</td>\n",
" <td>0.445963</td>\n",
" <td>0.839947</td>\n",
" </tr>\n",
" <tr>\n",
" <td>15</td>\n",
" <td>0.278700</td>\n",
" <td>0.410541</td>\n",
" <td>0.839936</td>\n",
" </tr>\n",
" <tr>\n",
" <td>16</td>\n",
" <td>0.251700</td>\n",
" <td>0.348793</td>\n",
" <td>0.857752</td>\n",
" </tr>\n",
" <tr>\n",
" <td>17</td>\n",
" <td>0.234600</td>\n",
" <td>0.387677</td>\n",
" <td>0.877273</td>\n",
" </tr>\n",
" <tr>\n",
" <td>18</td>\n",
" <td>0.228600</td>\n",
" <td>0.341952</td>\n",
" <td>0.857452</td>\n",
" </tr>\n",
" <tr>\n",
" <td>19</td>\n",
" <td>0.191400</td>\n",
" <td>0.412256</td>\n",
" <td>0.868153</td>\n",
" </tr>\n",
" <tr>\n",
" <td>20</td>\n",
" <td>0.184400</td>\n",
" <td>0.289442</td>\n",
" <td>0.891273</td>\n",
" </tr>\n",
" <tr>\n",
" <td>21</td>\n",
" <td>0.173000</td>\n",
" <td>0.319741</td>\n",
" <td>0.888702</td>\n",
" </tr>\n",
" <tr>\n",
" <td>22</td>\n",
" <td>0.168700</td>\n",
" <td>0.262622</td>\n",
" <td>0.907471</td>\n",
" </tr>\n",
" <tr>\n",
" <td>23</td>\n",
" <td>0.160100</td>\n",
" <td>0.295084</td>\n",
" <td>0.906839</td>\n",
" </tr>\n",
" <tr>\n",
" <td>24</td>\n",
" <td>0.146600</td>\n",
" <td>0.266578</td>\n",
" <td>0.904947</td>\n",
" </tr>\n",
" <tr>\n",
" <td>25</td>\n",
" <td>0.146800</td>\n",
" <td>0.213564</td>\n",
" <td>0.910279</td>\n",
" </tr>\n",
" <tr>\n",
" <td>26</td>\n",
" <td>0.122600</td>\n",
" <td>0.238661</td>\n",
" <td>0.912666</td>\n",
" </tr>\n",
" <tr>\n",
" <td>27</td>\n",
" <td>0.118600</td>\n",
" <td>0.213083</td>\n",
" <td>0.927114</td>\n",
" </tr>\n",
" <tr>\n",
" <td>28</td>\n",
" <td>0.095100</td>\n",
" <td>0.252022</td>\n",
" <td>0.912958</td>\n",
" </tr>\n",
" <tr>\n",
" <td>29</td>\n",
" <td>0.104900</td>\n",
" <td>0.209648</td>\n",
" <td>0.925930</td>\n",
" </tr>\n",
" <tr>\n",
" <td>30</td>\n",
" <td>0.093600</td>\n",
" <td>0.187472</td>\n",
" <td>0.930865</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
]
},
"metadata": {}
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"TrainOutput(global_step=1920, training_loss=0.3936845714847247, metrics={'train_runtime': 2362.4807, 'train_samples_per_second': 52.013, 'train_steps_per_second': 0.813, 'total_flos': 9.52238637305561e+18, 'train_loss': 0.3936845714847247, 'epoch': 30.0})"
]
},
"metadata": {},
"execution_count": 25
}
]
},
{
"cell_type": "code",
"source": [
"trainer.push_to_hub()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 52
},
"id": "vbfO9xPPdOm1",
"outputId": "b9a26771-d432-472e-cc13-8fd8836efebd"
},
"execution_count": 26,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"CommitInfo(commit_url='https://huggingface.co/spolivin/alz-mri-vit/commit/b2b0ecb84e796d5b50af69bcf0e3ebddfce7672b', commit_message='End of training', commit_description='', oid='b2b0ecb84e796d5b50af69bcf0e3ebddfce7672b', pr_url=None, pr_revision=None, pr_num=None)"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 26
}
]
},
{
"cell_type": "markdown",
"source": [
"# Inference"
],
"metadata": {
"id": "1d903JNhuOZW"
}
},
{
"cell_type": "code",
"source": [
"mri_data_test = load_dataset(\"Falah/Alzheimer_MRI\", split=\"test\")"
],
"metadata": {
"id": "C5QWE6aaJQxQ"
},
"execution_count": 27,
"outputs": []
},
{
"cell_type": "code",
"source": [
"id = -9\n",
"image = mri_data_test[\"image\"][id]\n",
"print(id2label[str(mri_data_test[\"label\"][id])])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "BTK1oVf3TbNx",
"outputId": "ba039f1a-49d4-45d5-951b-3c1fa3c666b6"
},
"execution_count": 28,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Mild_Demented\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"image"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 145
},
"id": "8uZk7V9pT1tk",
"outputId": "ef256bc3-c809-4e17-dc2e-e776d989d56b"
},
"execution_count": 29,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<PIL.JpegImagePlugin.JpegImageFile image mode=L size=128x128>"
],
"image/png": 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\n"
},
"metadata": {},
"execution_count": 29
}
]
},
{
"cell_type": "code",
"source": [
"classifier = pipeline(\"image-classification\", model=\"alz-mri-vit/checkpoint-1920\")\n",
"classifier(image)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "i6b1mob-S5a1",
"outputId": "3ba9cb17-c849-4247-cfdb-3c166503f8bc"
},
"execution_count": 30,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[{'score': 0.9944379329681396, 'label': 'Mild_Demented'},\n",
" {'score': 0.0023822528310120106, 'label': 'Moderate_Demented'},\n",
" {'score': 0.0016200037207454443, 'label': 'Non_Demented'},\n",
" {'score': 0.0015596540179103613, 'label': 'Very_Mild_Demented'}]"
]
},
"metadata": {},
"execution_count": 30
}
]
},
{
"cell_type": "code",
"source": [
"mri_data_test = mri_data_test.with_transform(transforms)\n",
"mri_data_test"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "FrZalBB0vu_Y",
"outputId": "a7326e9e-c7cd-4c29-c1ee-c802a8bd4637"
},
"execution_count": 31,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Dataset({\n",
" features: ['image', 'label'],\n",
" num_rows: 1280\n",
"})"
]
},
"metadata": {},
"execution_count": 31
}
]
},
{
"cell_type": "code",
"source": [
"predictions = trainer.predict(mri_data_test)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"id": "L1ml-lUxS9a9",
"outputId": "f1acebac-8f9a-4a71-d38c-9419570a2ee0"
},
"execution_count": 32,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": []
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"predictions"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "-0mnSSP9WWd7",
"outputId": "a92bb2d8-5000-47a7-b43f-7b82b50e5daa"
},
"execution_count": 33,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"PredictionOutput(predictions=array([[-2.2871094 , -3.9707031 , 1.1318359 , 3.2558594 ],\n",
" [ 3.8261719 , -2.0742188 , -2.3417969 , 0.33911133],\n",
" [-2.4882812 , -2.7617188 , 4.8125 , -0.9682617 ],\n",
" ...,\n",
" [-1.0458984 , -2.5234375 , -1.9599609 , 4.28125 ],\n",
" [-1.7138672 , -2.0761719 , 4.7070312 , -1.9384766 ],\n",
" [-1.2529297 , -2.1699219 , -2.0234375 , 4.3710938 ]],\n",
" dtype=float32), label_ids=array([3, 0, 2, ..., 3, 2, 3]), metrics={'test_loss': 0.21619494259357452, 'test_f1': 0.9302622806094544, 'test_runtime': 10.0534, 'test_samples_per_second': 127.321, 'test_steps_per_second': 7.958})"
]
},
"metadata": {},
"execution_count": 33
}
]
},
{
"cell_type": "code",
"source": [
"preds = np.argmax(predictions.predictions, axis=-1)\n",
"test_f1 = f1_score(preds, predictions.label_ids, average=\"weighted\")\n",
"\n",
"print(f\"Test f1-score: {test_f1:.4f}\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Zb7BytVtWp2-",
"outputId": "117bd469-d202-4032-c4f8-da681f1f536e"
},
"execution_count": 34,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Test f1-score: 0.9307\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"cm = confusion_matrix(y_true=predictions.label_ids, y_pred=preds)\n",
"disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)\n",
"disp.plot(cmap=plt.cm.Blues)\n",
"plt.xticks(rotation=45)\n",
"plt.show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 550
},
"id": "CkQWLVniY8X4",
"outputId": "3e7a004f-c7dd-48f1-efc8-59c7913d1a32"
},
"execution_count": 35,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
}
]
} |