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Timm Base

model class for loading the DFNE classifier.

TIMM_BaseClassifierInference

Bases: BaseClassifierInference

Base detector class for dinov2 classifier. This class provides utility methods for loading the model, performing single and batch image classifications, and formatting results. Make sure the appropriate file for the model weights has been downloaded to the "models" folder before running DFNE.

Source code in PytorchWildlife/models/classification/timm_base/base_classifier.py 
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class TIMM_BaseClassifierInference(BaseClassifierInference):
    """
    Base detector class for dinov2 classifier. This class provides utility methods
    for loading the model, performing single and batch image classifications, and 
    formatting results. Make sure the appropriate file for the model weights has been 
    downloaded to the "models" folder before running DFNE.
    """

    BACKBONE = None
    MODEL_NAME = None
    IMAGE_SIZE = None

    def __init__(self, weights=None, device="cpu", url=None, transform=None,
                 weights_key='model_state_dict', weights_prefix=''):
        """
        Initialize the model.

        Args:
            weights (str, optional): 
                Path to the model weights. Defaults to None.
            device (str, optional): 
                Device for model inference. Defaults to "cpu".
            url (str, optional): 
                URL to fetch the model weights. Defaults to None.
            weights_key (str, optional): 
                Key to fetch the model weights. Defaults to None.
            weights_prefix (str, optional): 
                prefix of model weight keys. Defaults to None.
        """
        super(TIMM_BaseClassifierInference, self).__init__()
        self.device = device

        if transform:
            self.transform = transform
        else:
            self.transform = pw_trans.Classification_Inference_Transform(target_size=self.IMAGE_SIZE)

        self._load_model(weights, url, weights_key, weights_prefix)

    def _load_model(self, weights=None, url=None, weights_key='model_state_dict', weights_prefix=''):
        """
        Load TIMM based model weights

        Args:
        weights (str, optional): 
            Path to the model weights. (defaults to None)
        url (str, optional): 
            url to the model weights. (defaults to None)
        """

        self.predictor = timm.create_model(
            self.BACKBONE, 
            pretrained = False, 
            num_classes = len(self.CLASS_NAMES),
            dynamic_img_size = True
        )

        if url:
            if not os.path.exists(os.path.join(torch.hub.get_dir(), "checkpoints", self.MODEL_NAME)):
                os.makedirs(os.path.join(torch.hub.get_dir(), "checkpoints"), exist_ok=True)
                weights = wget.download(url, out=os.path.join(torch.hub.get_dir(), "checkpoints"))
            else:
                weights = os.path.join(torch.hub.get_dir(), "checkpoints", self.MODEL_NAME)
        elif weights is None:
            raise Exception("Need weights for inference.")

        checkpoint = torch.load(
            f = weights,
            map_location = self.device,
            weights_only = False
        )[weights_key]

        checkpoint = OrderedDict({k.replace("{}".format(weights_prefix), ""): checkpoint[k]
                                    for k in checkpoint})

        self.predictor.load_state_dict(checkpoint)
        print("Model loaded from {}".format(os.path.join(torch.hub.get_dir(), "checkpoints", self.MODEL_NAME)))

        self.predictor.to(self.device)
        self.eval()

    def results_generation(self, logits: torch.Tensor, img_ids: list[str], id_strip: str = None) -> list[dict]:
        """
        Generate results for classification.

        Args:
            logits (torch.Tensor): Output tensor from the model.
            img_ids (list[str]): List of image identifiers.
            id_strip (str): Stripping string for better image ID saving.

        Returns:
            list[dict]: List of dictionaries containing image ID, prediction, and confidence score.
        """

        probs = torch.softmax(logits, dim=1)
        preds = probs.argmax(dim=1)
        confs = probs.max(dim=1)[0]
        confidences = probs[0].tolist()
        result = [[self.CLASS_NAMES[i], confidence] for i, confidence in enumerate(confidences)]

        results = []
        for pred, img_id, conf in zip(preds, img_ids, confs):
            r = {"img_id": str(img_id).strip(id_strip)}
            r["prediction"] = self.CLASS_NAMES[pred.item()]
            r["class_id"] = pred.item()
            r["confidence"] = conf.item()
            r["all_confidences"] = result
            results.append(r)

        return results

    def single_image_classification(self, img, img_id=None, id_strip=None):
        """
        Perform classification on a single image.

        Args:
            img (str or ndarray): 
                Image path or ndarray of images.
            img_id (str, optional): 
                Image path or identifier.
            id_strip (str, optional):
                Whether to strip stings in id. Defaults to None.

        Returns:
            (dict): Classification results.
        """
        if type(img) == str:
            img = Image.open(img).convert("RGB")
        else:
            img = Image.fromarray(img)
        img = self.transform(img)

        logits = self.predictor(img.unsqueeze(0).to(self.device))
        return self.results_generation(logits.cpu(), [img_id], id_strip=id_strip)[0]

    def batch_image_classification(self, data_path=None, det_results=None, id_strip=None,
                                   batch_size=32, num_workers=0, **kwargs):
        """
        Perform classification on a batch of images.

        Args:
            data_path (str): 
                Path containing all images for inference. Defaults to None. 
            det_results (dict):
                Dirct outputs from detectors. Defaults to None.
            id_strip (str, optional):
                Whether to strip stings in id. Defaults to None.
            batch_size (int, optional):
                Batch size for inference. Defaults to 32.
            num_workers (int, optional):
                Number of workers for dataloader. Defaults to 0.

        Returns:
            (dict): Classification results.
        """

        if data_path:
            dataset = pw_data.ImageFolder(
                data_path,
                transform=self.transform,
                path_head='.'
            )
        elif det_results:
            dataset = pw_data.DetectionCrops(
                det_results,
                transform=self.transform,
                path_head='.'
            )
        else:
            raise Exception("Need data for inference.")

        dataloader = DataLoader(dataset=dataset, batch_size=batch_size, num_workers=num_workers,
                                shuffle=False, pin_memory=True, drop_last=False, **kwargs)

        total_logits = []
        total_paths = []

        with tqdm(total=len(dataloader)) as pbar: 
            for batch in dataloader:
                imgs, paths = batch
                imgs = imgs.to(self.device)
                total_logits.append(self.predictor(imgs))
                total_paths.append(paths)
                pbar.update(1)

        total_logits = torch.cat(total_logits, dim=0).cpu()
        total_paths = np.concatenate(total_paths, axis=0)

        return self.results_generation(total_logits, total_paths, id_strip=id_strip)

__init__(weights=None, device='cpu', url=None, transform=None, weights_key='model_state_dict', weights_prefix='')

Initialize the model.

Parameters:

Name Type Description Default
weights str

Path to the model weights. Defaults to None.

 None
device str

Device for model inference. Defaults to "cpu".

 'cpu'
url str

URL to fetch the model weights. Defaults to None.

 None
weights_key str

Key to fetch the model weights. Defaults to None.

 'model_state_dict'
weights_prefix str

prefix of model weight keys. Defaults to None.

 ''
Source code in PytorchWildlife/models/classification/timm_base/base_classifier.py 
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def __init__(self, weights=None, device="cpu", url=None, transform=None,
             weights_key='model_state_dict', weights_prefix=''):
    """
    Initialize the model.

    Args:
        weights (str, optional): 
            Path to the model weights. Defaults to None.
        device (str, optional): 
            Device for model inference. Defaults to "cpu".
        url (str, optional): 
            URL to fetch the model weights. Defaults to None.
        weights_key (str, optional): 
            Key to fetch the model weights. Defaults to None.
        weights_prefix (str, optional): 
            prefix of model weight keys. Defaults to None.
    """
    super(TIMM_BaseClassifierInference, self).__init__()
    self.device = device

    if transform:
        self.transform = transform
    else:
        self.transform = pw_trans.Classification_Inference_Transform(target_size=self.IMAGE_SIZE)

    self._load_model(weights, url, weights_key, weights_prefix)

batch_image_classification(data_path=None, det_results=None, id_strip=None, batch_size=32, num_workers=0, **kwargs)

Perform classification on a batch of images.

Parameters:

Name Type Description Default
data_path str

Path containing all images for inference. Defaults to None.

None
det_results dict

Dirct outputs from detectors. Defaults to None.

 None
id_strip str

Whether to strip stings in id. Defaults to None.

 None
batch_size int

Batch size for inference. Defaults to 32.

 32
num_workers int

Number of workers for dataloader. Defaults to 0.

 0

Returns:

Type Description
dict

Classification results.
Source code in PytorchWildlife/models/classification/timm_base/base_classifier.py 
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def batch_image_classification(self, data_path=None, det_results=None, id_strip=None,
                               batch_size=32, num_workers=0, **kwargs):
    """
    Perform classification on a batch of images.

    Args:
        data_path (str): 
            Path containing all images for inference. Defaults to None. 
        det_results (dict):
            Dirct outputs from detectors. Defaults to None.
        id_strip (str, optional):
            Whether to strip stings in id. Defaults to None.
        batch_size (int, optional):
            Batch size for inference. Defaults to 32.
        num_workers (int, optional):
            Number of workers for dataloader. Defaults to 0.

    Returns:
        (dict): Classification results.
    """

    if data_path:
        dataset = pw_data.ImageFolder(
            data_path,
            transform=self.transform,
            path_head='.'
        )
    elif det_results:
        dataset = pw_data.DetectionCrops(
            det_results,
            transform=self.transform,
            path_head='.'
        )
    else:
        raise Exception("Need data for inference.")

    dataloader = DataLoader(dataset=dataset, batch_size=batch_size, num_workers=num_workers,
                            shuffle=False, pin_memory=True, drop_last=False, **kwargs)

    total_logits = []
    total_paths = []

    with tqdm(total=len(dataloader)) as pbar: 
        for batch in dataloader:
            imgs, paths = batch
            imgs = imgs.to(self.device)
            total_logits.append(self.predictor(imgs))
            total_paths.append(paths)
            pbar.update(1)

    total_logits = torch.cat(total_logits, dim=0).cpu()
    total_paths = np.concatenate(total_paths, axis=0)

    return self.results_generation(total_logits, total_paths, id_strip=id_strip)

results_generation(logits, img_ids, id_strip=None)

Generate results for classification.

Parameters:

Name Type Description Default
logits Tensor

Output tensor from the model.

 required
img_ids list[str]

List of image identifiers.

 required
id_strip str

Stripping string for better image ID saving.

 None

Returns:

Type Description
list[dict]

list[dict]: List of dictionaries containing image ID, prediction, and confidence score.
Source code in PytorchWildlife/models/classification/timm_base/base_classifier.py 
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def results_generation(self, logits: torch.Tensor, img_ids: list[str], id_strip: str = None) -> list[dict]:
    """
    Generate results for classification.

    Args:
        logits (torch.Tensor): Output tensor from the model.
        img_ids (list[str]): List of image identifiers.
        id_strip (str): Stripping string for better image ID saving.

    Returns:
        list[dict]: List of dictionaries containing image ID, prediction, and confidence score.
    """

    probs = torch.softmax(logits, dim=1)
    preds = probs.argmax(dim=1)
    confs = probs.max(dim=1)[0]
    confidences = probs[0].tolist()
    result = [[self.CLASS_NAMES[i], confidence] for i, confidence in enumerate(confidences)]

    results = []
    for pred, img_id, conf in zip(preds, img_ids, confs):
        r = {"img_id": str(img_id).strip(id_strip)}
        r["prediction"] = self.CLASS_NAMES[pred.item()]
        r["class_id"] = pred.item()
        r["confidence"] = conf.item()
        r["all_confidences"] = result
        results.append(r)

    return results

single_image_classification(img, img_id=None, id_strip=None)

Perform classification on a single image.

Parameters:

Name Type Description Default
img str or ndarray

Image path or ndarray of images.

 required
img_id str

Image path or identifier.

 None
id_strip str

Whether to strip stings in id. Defaults to None.

 None

Returns:

Type Description
dict

Classification results.
Source code in PytorchWildlife/models/classification/timm_base/base_classifier.py 
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def single_image_classification(self, img, img_id=None, id_strip=None):
    """
    Perform classification on a single image.

    Args:
        img (str or ndarray): 
            Image path or ndarray of images.
        img_id (str, optional): 
            Image path or identifier.
        id_strip (str, optional):
            Whether to strip stings in id. Defaults to None.

    Returns:
        (dict): Classification results.
    """
    if type(img) == str:
        img = Image.open(img).convert("RGB")
    else:
        img = Image.fromarray(img)
    img = self.transform(img)

    logits = self.predictor(img.unsqueeze(0).to(self.device))
    return self.results_generation(logits.cpu(), [img_id], id_strip=id_strip)[0]