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7 +++++- utils/dataloaders/mnist_0_3.py | 27 ++++++++++++++------- 3 files changed, 61 insertions(+), 16 deletions(-) diff --git a/main.py b/main.py index fe380df..1620858 100644 --- a/main.py +++ b/main.py @@ -43,16 +43,27 @@ def main(): augmentations = transforms.Compose([transforms.ToTensor()]) # Dataset + assert args.validation_split_percentage < 1.0 and args.validation_split_percentage > 0, "Validation split should be in interval (0,1)" traindata = load_data( args.dataset, - train=True, + split="train", + split_percentage=args.validation_split_percentage, data_path=args.datafolder, download=args.download_data, transform=augmentations, ) validata = load_data( args.dataset, - train=False, + split="validation", + split_percentage=args.validation_split_percentage, + data_path=args.datafolder, + download=args.download_data, + transform=augmentations, + ) + testdata = load_data( + args.dataset, + split="test", + split_percentage=args.validation_split_percentage, data_path=args.datafolder, download=args.download_data, transform=augmentations, @@ -83,6 +94,9 @@ def main(): valiloader = DataLoader( validata, batch_size=args.batchsize, shuffle=False, pin_memory=True ) + testloader = DataLoader( + testdata, batch_size=args.batchsize, shuffle=False, pin_memory=True + ) criterion = nn.CrossEntropyLoss() optimizer = th.optim.Adam(model.parameters(), lr=args.learning_rate) @@ -140,30 +154,45 @@ def main(): wandb.log(metrics.accumulate(str_prefix="Train ")) metrics.reset() - evalloss = [] - # Eval loop start + valloss = [] + # Validation loop start model.eval() with th.no_grad(): for x, y in tqdm(valiloader, desc="Validation"): x, y = x.to(device), y.to(device) logits = model.forward(x) loss = criterion(logits, y) - evalloss.append(loss.item()) + valloss.append(loss.item()) preds = th.argmax(logits, dim=1) metrics(y, preds) - wandb.log(metrics.accumulate(str_prefix="Evaluation ")) + wandb.log(metrics.accumulate(str_prefix="Validation ")) metrics.reset() wandb.log( { "Epoch": epoch, "Train loss": np.mean(trainingloss), - "Evaluation Loss": np.mean(evalloss), + "Validation loss": np.mean(valloss), } ) + + testloss = [] + model.eval() + with th.no_grad(): + for x, y in tqdm(testloader, desc="Testing"): + x, y = x.to(device), y.to(device) + logits = model.forward(x) + loss = criterion(logits, y) + testloss.append(loss.item()) + + preds = th.argmax(logits, dim=1) + metrics(y, preds) + wandb.log(metrics.accumulate(str_prefix="Test ")) + metrics.reset() + wandb.log({"Test loss": np.mean(testloss)}) if __name__ == "__main__": main() diff --git a/utils/arg_parser.py b/utils/arg_parser.py index 2620b98..5ced4d0 100644 --- a/utils/arg_parser.py +++ b/utils/arg_parser.py @@ -54,7 +54,12 @@ def get_args(): choices=["svhn", "usps_0-6", "uspsh5_7_9", "mnist_0-3"], help="Which dataset to train the model on.", ) - + parser.add_argument( + "--validation_split_percentage", + type=float, + default=0.2, + help="Percentage of training dataset to be used as validation dataset - must be within (0,1).", + ) parser.add_argument( "--metric", type=str, diff --git a/utils/dataloaders/mnist_0_3.py b/utils/dataloaders/mnist_0_3.py index bad3bd9..bbf3f14 100644 --- a/utils/dataloaders/mnist_0_3.py +++ b/utils/dataloaders/mnist_0_3.py @@ -2,9 +2,10 @@ import os import urllib.request from pathlib import Path +import torch import numpy as np -from torch.utils.data import Dataset +from torch.utils.data import Dataset, random_split class MNISTDataset0_3(Dataset): @@ -59,20 +60,25 @@ class MNISTDataset0_3(Dataset): def __init__( self, + split: str, + split_percentage: float, data_path: Path, - train: bool = False, - transform=None, download: bool = False, + transform=None, ): super().__init__() self.data_path = data_path self.mnist_path = self.data_path / "MNIST" - self.train = train + self.split = split + self.split_percentage = split_percentage self.transform = transform self.download = download self.num_classes = 4 + if self.split == "train" or self.split == "validation": + train = True # used to decide whether to load training or test dataset + if not self.download and not self._chech_is_downloaded(): raise ValueError( "Data not found. Set --download-data=True to download the data." @@ -87,13 +93,18 @@ def __init__( "train-labels-idx1-ubyte" if train else "t10k-labels-idx1-ubyte" ) - labels = self._parse_labels(train=self.train) - + labels = self._parse_labels() + self.idx = np.where(labels < 4)[0] - + + if self.split != "test": + generator1 = torch.Generator().manual_seed(42) + tr, val = random_split(self.idx, [1-self.split_percentage, self.split_percentage], generator=generator1) + self.idx = tr if self.split == "train" else val + self.length = len(self.idx) - def _parse_labels(self, train): + def _parse_labels(self): with open(self.labels_path, "rb") as f: data = np.frombuffer(f.read(), dtype=np.uint8, offset=8) return data From f840af4d59d8fa9396e1a3cf32404ca597a877f8 Mon Sep 17 00:00:00 2001 From: Jan Zavadil Date: Fri, 7 Feb 2025 12:42:12 +0100 Subject: [PATCH 03/47] ruffed and isorted --- main.py | 12 +++++---- utils/dataloaders/mnist_0_3.py | 18 ++++++++----- utils/load_metric.py | 3 --- utils/metrics/EntropyPred.py | 2 +- utils/models/magnus_model.py | 49 +++++++++++++++------------------- 5 files changed, 41 insertions(+), 43 deletions(-) diff --git a/main.py b/main.py index 1620858..9582e9d 100644 --- a/main.py +++ b/main.py @@ -3,11 +3,11 @@ import numpy as np import torch as th import torch.nn as nn -import wandb from torch.utils.data import DataLoader from torchvision import transforms from tqdm import tqdm +import wandb from utils import MetricWrapper, createfolders, get_args, load_data, load_model @@ -27,7 +27,6 @@ def main(): args = get_args() - createfolders(args.datafolder, args.resultfolder, args.modelfolder) device = args.device @@ -43,7 +42,9 @@ def main(): augmentations = transforms.Compose([transforms.ToTensor()]) # Dataset - assert args.validation_split_percentage < 1.0 and args.validation_split_percentage > 0, "Validation split should be in interval (0,1)" + assert ( + args.validation_split_percentage < 1.0 and args.validation_split_percentage > 0 + ), "Validation split should be in interval (0,1)" traindata = load_data( args.dataset, split="train", @@ -177,7 +178,7 @@ def main(): "Validation loss": np.mean(valloss), } ) - + testloss = [] model.eval() with th.no_grad(): @@ -186,7 +187,7 @@ def main(): logits = model.forward(x) loss = criterion(logits, y) testloss.append(loss.item()) - + preds = th.argmax(logits, dim=1) metrics(y, preds) @@ -194,5 +195,6 @@ def main(): metrics.reset() wandb.log({"Test loss": np.mean(testloss)}) + if __name__ == "__main__": main() diff --git a/utils/dataloaders/mnist_0_3.py b/utils/dataloaders/mnist_0_3.py index bbf3f14..709a67e 100644 --- a/utils/dataloaders/mnist_0_3.py +++ b/utils/dataloaders/mnist_0_3.py @@ -2,9 +2,9 @@ import os import urllib.request from pathlib import Path -import torch import numpy as np +import torch from torch.utils.data import Dataset, random_split @@ -77,8 +77,8 @@ def __init__( self.num_classes = 4 if self.split == "train" or self.split == "validation": - train = True # used to decide whether to load training or test dataset - + train = True # used to decide whether to load training or test dataset + if not self.download and not self._chech_is_downloaded(): raise ValueError( "Data not found. Set --download-data=True to download the data." @@ -94,14 +94,18 @@ def __init__( ) labels = self._parse_labels() - + self.idx = np.where(labels < 4)[0] - + if self.split != "test": generator1 = torch.Generator().manual_seed(42) - tr, val = random_split(self.idx, [1-self.split_percentage, self.split_percentage], generator=generator1) + tr, val = random_split( + self.idx, + [1 - self.split_percentage, self.split_percentage], + generator=generator1, + ) self.idx = tr if self.split == "train" else val - + self.length = len(self.idx) def _parse_labels(self): diff --git a/utils/load_metric.py b/utils/load_metric.py index 7a7ba90..a321845 100644 --- a/utils/load_metric.py +++ b/utils/load_metric.py @@ -7,7 +7,6 @@ class MetricWrapper(nn.Module): - """ Wrapper class for metrics, that runs multiple metrics on the same data. @@ -46,9 +45,7 @@ class MetricWrapper(nn.Module): {'entropy': [], 'f1': [], 'precision': []} """ - def __init__(self, *metrics, num_classes): - super().__init__() self.metrics = {} self.num_classes = num_classes diff --git a/utils/metrics/EntropyPred.py b/utils/metrics/EntropyPred.py index d6da55a..4175f2b 100644 --- a/utils/metrics/EntropyPred.py +++ b/utils/metrics/EntropyPred.py @@ -9,4 +9,4 @@ def __call__(self, y_true, y_false_logits): return def __reset__(self): - pass \ No newline at end of file + pass diff --git a/utils/models/magnus_model.py b/utils/models/magnus_model.py index db4c8ba..08faf8a 100644 --- a/utils/models/magnus_model.py +++ b/utils/models/magnus_model.py @@ -2,13 +2,9 @@ class MagnusModel(nn.Module): - def __init__(self, - imagesize: int, - imagechannels: int, - n_classes:int=10): - + def __init__(self, imagesize: int, imagechannels: int, n_classes: int = 10): """ - Magnus model contains the model for Magnus' part of the homeexam. + Magnus model contains the model for Magnus' part of the homeexam. This class contains a neural network consisting of three linear layers of 133 neurons each, with ReLU activation between each layer. @@ -16,30 +12,29 @@ def __init__(self, ---- imagesize (int): Expected size of input image. This is needed to scale first layer input imagechannels (int): Expected number of image channels. This is needed to scale first layer input - n_classes (int): Number of classes we are to provide. + n_classes (int): Number of classes we are to provide. Returns ------- MagnusModel (nn.Module): Neural network as described above in this docstring. """ - - + super().__init__() - self.imagesize = imagesize + self.imagesize = imagesize self.imagechannels = imagechannels - - self.layer1 = nn.Sequential(*([ - nn.Linear(self.imagechannels*self.imagesize*self.imagesize, 133), - nn.ReLU() - ])) - self.layer2 = nn.Sequential(*([ - nn.Linear(133, 133), - nn.ReLU() - ])) - self.layer3 = nn.Sequential(*([ - nn.Linear(133, n_classes), - nn.ReLU() - ])) + + self.layer1 = nn.Sequential( + *( + [ + nn.Linear( + self.imagechannels * self.imagesize * self.imagesize, 133 + ), + nn.ReLU(), + ] + ) + ) + self.layer2 = nn.Sequential(*([nn.Linear(133, 133), nn.ReLU()])) + self.layer3 = nn.Sequential(*([nn.Linear(133, n_classes), nn.ReLU()])) def forward(self, x): """ @@ -48,17 +43,17 @@ def forward(self, x): Args ---- x (th.Tensor): Four-dimensional tensor in the form (Batch Size x Channels x Image Height x Image Width) - + Returns ------- out (th.Tensor): Class-logits of network given input x """ assert len(x.size) == 4 - + x = x.view(x.size(0), -1) - + x = self.layer1(x) x = self.layer2(x) out = self.layer3(x) - + return out From efc78f3a48bd4c8b8760a76f3d490a999cab3197 Mon Sep 17 00:00:00 2001 From: Jan Zavadil Date: Fri, 7 Feb 2025 12:48:29 +0100 Subject: [PATCH 04/47] fix in mnist0-3 --- utils/dataloaders/mnist_0_3.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/utils/dataloaders/mnist_0_3.py b/utils/dataloaders/mnist_0_3.py index 709a67e..1f8124d 100644 --- a/utils/dataloaders/mnist_0_3.py +++ b/utils/dataloaders/mnist_0_3.py @@ -76,8 +76,10 @@ def __init__( self.download = download self.num_classes = 4 - if self.split == "train" or self.split == "validation": - train = True # used to decide whether to load training or test dataset + if self.split == "test": + train = False # used to decide whether to load training or test dataset + else: + train = True if not self.download and not self._chech_is_downloaded(): raise ValueError( From 54f38830ef10861f82c681f95998170baacd2f0c Mon Sep 17 00:00:00 2001 From: Jan Zavadil Date: Fri, 7 Feb 2025 13:59:00 +0100 Subject: [PATCH 05/47] Added micro/macro averaging option to MetricsWrapper and as commandline argument --- main.py | 2 +- utils/arg_parser.py | 8 ++++- utils/load_metric.py | 11 ++++--- utils/metrics/accuracy.py | 68 ++++++++++++++++++++++++++++++++++++--- 4 files changed, 78 insertions(+), 11 deletions(-) diff --git a/main.py b/main.py index fe380df..00ec327 100644 --- a/main.py +++ b/main.py @@ -58,7 +58,7 @@ def main(): transform=augmentations, ) - metrics = MetricWrapper(*args.metric, num_classes=traindata.num_classes) + metrics = MetricWrapper(*args.metric, num_classes=traindata.num_classes, macro_averaging=args.macro_averaging) # Find the shape of the data, if is 2D, add a channel dimension data_shape = traindata[0][0].shape diff --git a/utils/arg_parser.py b/utils/arg_parser.py index 2620b98..d021116 100644 --- a/utils/arg_parser.py +++ b/utils/arg_parser.py @@ -63,6 +63,12 @@ def get_args(): nargs="+", help="Which metric to use for evaluation", ) + parser.add_argument( + "--macro_averaging", + action="store_true", + help="If the flag is included, the metrics will be calculated using macro averaging.", + ) + # Training specific values parser.add_argument( @@ -93,6 +99,6 @@ def get_args(): parser.add_argument( "--dry_run", action="store_true", - help="If true, the code will not run the training loop.", + help="If the flag is included, the code will not run the training loop.", ) return parser.parse_args() diff --git a/utils/load_metric.py b/utils/load_metric.py index 7a7ba90..20d2bba 100644 --- a/utils/load_metric.py +++ b/utils/load_metric.py @@ -47,11 +47,12 @@ class MetricWrapper(nn.Module): """ - def __init__(self, *metrics, num_classes): + def __init__(self, *metrics, num_classes, macro_averaging=False): super().__init__() self.metrics = {} self.num_classes = num_classes + self.macro_averaging = macro_averaging for metric in metrics: self.metrics[metric] = self._get_metric(metric) @@ -77,13 +78,13 @@ def _get_metric(self, key): case "entropy": return EntropyPrediction(num_classes=self.num_classes) case "f1": - return F1Score(num_classes=self.num_classes) + return F1Score(num_classes=self.num_classes, macro_averaging=self.macro_averaging) case "recall": - return Recall(num_classes=self.num_classes) + return Recall(num_classes=self.num_classes, macro_averaging=self.macro_averaging) case "precision": - return Precision(num_classes=self.num_classes) + return Precision(num_classes=self.num_classes, macro_averaging=self.macro_averaging) case "accuracy": - return Accuracy(num_classes=self.num_classes) + return Accuracy(num_classes=self.num_classes, macro_averaging=self.macro_averaging) case _: raise ValueError(f"Metric {key} not supported") diff --git a/utils/metrics/accuracy.py b/utils/metrics/accuracy.py index 4d1cdd1..22a1283 100644 --- a/utils/metrics/accuracy.py +++ b/utils/metrics/accuracy.py @@ -3,10 +3,11 @@ class Accuracy(nn.Module): - def __init__(self, num_classes): + def __init__(self, num_classes, macro_averaging=False): super().__init__() self.num_classes = num_classes - + self.macro_averaging = macro_averaging + def forward(self, y_true, y_pred): """ Compute the accuracy of the model. @@ -23,12 +24,71 @@ def forward(self, y_true, y_pred): float Accuracy score. """ + if y_pred.dim() > 1: + y_pred = y_pred.argmax(dim=1) + if self.macro_averaging: + return self._macro_acc(y_true, y_pred) + else: + return self._micro_acc(y_true, y_pred) + + def _macro_acc(self, y_true, y_pred): + """ + Compute the macro-average accuracy. + + Parameters + ---------- + y_true : torch.Tensor + True labels. + y_pred : torch.Tensor + Predicted labels. + + Returns + ------- + float + Macro-average accuracy score. + """ + y_true, y_pred = y_true.flatten(), y_pred.flatten() # Ensure 1D shape + + classes = torch.unique(y_true) # Find unique class labels + acc_per_class = [] + + for c in classes: + mask = (y_true == c) # Mask for class c + acc = (y_pred[mask] == y_true[mask]).float().mean() # Accuracy for class c + acc_per_class.append(acc) + + macro_acc = torch.stack(acc_per_class).mean().item() # Average across classes + return macro_acc + + def _micro_acc(self, y_true, y_pred): + """ + Compute the micro-average accuracy. + + Parameters + ---------- + y_true : torch.Tensor + True labels. + y_pred : torch.Tensor + Predicted labels. + + Returns + ------- + float + Micro-average accuracy score. + """ return (y_true == y_pred).float().mean().item() if __name__ == "__main__": + accuracy = Accuracy(5) + macro_accuracy = Accuracy(5, macro_averaging=True) + y_true = torch.tensor([0, 3, 2, 3, 4]) y_pred = torch.tensor([0, 1, 2, 3, 4]) - - accuracy = Accuracy() print(accuracy(y_true, y_pred)) + print(macro_accuracy(y_true, y_pred)) + + y_true = torch.tensor([0, 3, 2, 3, 4]) + y_onehot_pred = torch.tensor([[1, 0, 0, 0, 0], [0, 1, 0, 0, 0], [0, 0, 1, 0, 0], [0, 0, 0, 1, 0], [0, 0, 0, 0, 1]]) + print(accuracy(y_true, y_onehot_pred)) + print(macro_accuracy(y_true, y_onehot_pred)) From a2606e129da49c3cdae329c0854d95e8b7282133 Mon Sep 17 00:00:00 2001 From: salomaestro Date: Sat, 8 Feb 2025 19:45:53 +0100 Subject: [PATCH 06/47] Make separate downloader class that handles everything related to downloading --- utils/dataloaders/__init__.py | 8 +- utils/dataloaders/download.py | 141 ++++++++++++++++++++++++++++++++++ 2 files changed, 148 insertions(+), 1 deletion(-) create mode 100644 utils/dataloaders/download.py diff --git a/utils/dataloaders/__init__.py b/utils/dataloaders/__init__.py index 1f506e6..0d0bcb6 100644 --- a/utils/dataloaders/__init__.py +++ b/utils/dataloaders/__init__.py @@ -1,5 +1,11 @@ -__all__ = ["USPSDataset0_6", "USPSH5_Digit_7_9_Dataset", "MNISTDataset0_3"] +__all__ = [ + "USPSDataset0_6", + "USPSH5_Digit_7_9_Dataset", + "MNISTDataset0_3", + "Downloader", +] +from .download import Downloader from .mnist_0_3 import MNISTDataset0_3 from .usps_0_6 import USPSDataset0_6 from .uspsh5_7_9 import USPSH5_Digit_7_9_Dataset diff --git a/utils/dataloaders/download.py b/utils/dataloaders/download.py new file mode 100644 index 0000000..7a7fa13 --- /dev/null +++ b/utils/dataloaders/download.py @@ -0,0 +1,141 @@ +import bz2 +import hashlib +from pathlib import Path +from tempfile import TemporaryDirectory +from urllib.request import urlretrieve + +import h5py as h5 +import numpy as np +from PIL import Image + +from .datasources import USPS_SOURCE + + +class Downloader: + """ + Class to download and load the USPS dataset. + + Methods + ------- + mnist(data_dir: Path) -> tuple[np.ndarray, np.ndarray] + Download the MNIST dataset and save it as an HDF5 file to `data_dir`. + svhn(data_dir: Path) -> tuple[np.ndarray, np.ndarray] + Download the SVHN dataset and save it as an HDF5 file to `data_dir`. + usps(data_dir: Path) -> tuple[np.ndarray, np.ndarray] + Download the USPS dataset and save it as an HDF5 file to `data_dir`. + + Raises + ------ + NotImplementedError + If the download method is not implemented for the dataset. + + Examples + -------- + >>> from pathlib import Path + >>> from utils import Downloader + >>> dir = Path('tmp') + >>> dir.mkdir(exist_ok=True) + >>> train, test = Downloader().usps(dir) + """ + + def mnist(self, data_dir: Path) -> tuple[np.ndarray, np.ndarray]: + raise NotImplementedError("MNIST download not implemented yet") + + def svhn(self, data_dir: Path) -> tuple[np.ndarray, np.ndarray]: + raise NotImplementedError("SVHN download not implemented yet") + + def usps(self, data_dir: Path) -> tuple[np.ndarray, np.ndarray]: + """ + Download the USPS dataset and save it as an HDF5 file to `data_dir/usps.h5`. + """ + + def already_downloaded(path): + if not path.exists() or not path.is_file(): + return False + + with h5.File(path, "r") as f: + return "train" in f and "test" in f + + filename = data_dir / "usps.h5" + + if already_downloaded(filename): + with h5.File(filename, "r") as f: + return f["train"]["target"][:], f["test"]["target"][:] + + url_train, _, train_md5 = USPS_SOURCE["train"] + url_test, _, test_md5 = USPS_SOURCE["test"] + + # Using temporary directory ensures temporary files are deleted after use + with TemporaryDirectory() as tmp_dir: + train_path = Path(tmp_dir) / "train" + test_path = Path(tmp_dir) / "test" + + # Download the dataset and report the progress + urlretrieve(url_train, train_path, reporthook=self.__reporthook) + self.__check_integrity(train_path, train_md5) + train_targets = self.__extract_usps(train_path, filename, "train") + + urlretrieve(url_test, test_path, reporthook=self.__reporthook) + self.__check_integrity(test_path, test_md5) + test_targets = self.__extract_usps(test_path, filename, "test") + + return train_targets, test_targets + + def __extract_usps(self, src: Path, dest: Path, mode: str): + # Load the dataset and save it as an HDF5 file + with bz2.open(src) as fp: + raw = [line.decode().split() for line in fp.readlines()] + + tmp = [[x.split(":")[-1] for x in data[1:]] for data in raw] + + imgs = np.asarray(tmp, dtype=np.float32) + imgs = ((imgs + 1) / 2 * 255).astype(dtype=np.uint8) + + targets = [int(d[0]) - 1 for d in raw] + + with h5.File(dest, "a") as f: + f.create_dataset(f"{mode}/data", data=imgs, dtype=np.float32) + f.create_dataset(f"{mode}/target", data=targets, dtype=np.int32) + + return targets + + @staticmethod + def __reporthook(blocknum, blocksize, totalsize): + """ + Use this function to report download progress + for the urllib.request.urlretrieve function. + """ + + denom = 1024 * 1024 + readsofar = blocknum * blocksize + + if totalsize > 0: + percent = readsofar * 1e2 / totalsize + s = f"\r{int(percent):^3}% {readsofar / denom:.2f} of {totalsize / denom:.2f} MB" + print(s, end="", flush=True) + if readsofar >= totalsize: + print() + + @staticmethod + def __check_integrity(filepath, checksum): + """Check the integrity of the USPS dataset file. + + Args + ---- + filepath : pathlib.Path + Path to the USPS dataset file. + checksum : str + MD5 checksum of the dataset file. + + Returns + ------- + bool + True if the checksum of the file matches the expected checksum, False otherwise + """ + + file_hash = hashlib.md5(filepath.read_bytes()).hexdigest() + + if not checksum == file_hash: + raise ValueError( + f"File integrity check failed. Expected {checksum}, got {file_hash}" + ) From a58e495119de7e32778a61cbcb029804d0929411 Mon Sep 17 00:00:00 2001 From: salomaestro Date: Sat, 8 Feb 2025 19:51:49 +0100 Subject: [PATCH 07/47] downloader handles wheter to download data or not, so remove option --- utils/arg_parser.py | 8 +------- 1 file changed, 1 insertion(+), 7 deletions(-) diff --git a/utils/arg_parser.py b/utils/arg_parser.py index 5ced4d0..4d001d3 100644 --- a/utils/arg_parser.py +++ b/utils/arg_parser.py @@ -33,12 +33,6 @@ def get_args(): help="Whether model should be saved or not.", ) - parser.add_argument( - "--download-data", - action="store_true", - help="Whether the data should be downloaded or not. Might cause code to start a bit slowly.", - ) - # Data/Model specific values parser.add_argument( "--modelname", @@ -55,7 +49,7 @@ def get_args(): help="Which dataset to train the model on.", ) parser.add_argument( - "--validation_split_percentage", + "--val_size", type=float, default=0.2, help="Percentage of training dataset to be used as validation dataset - must be within (0,1).", From a9e2cade27ee7d378e3cc31310829bc9e90b820b Mon Sep 17 00:00:00 2001 From: salomaestro Date: Sat, 8 Feb 2025 19:53:01 +0100 Subject: [PATCH 08/47] Remove downloading logic from USPS dataset --- utils/dataloaders/usps_0_6.py | 171 ++-------------------------------- 1 file changed, 9 insertions(+), 162 deletions(-) diff --git a/utils/dataloaders/usps_0_6.py b/utils/dataloaders/usps_0_6.py index 3673fa9..85b3114 100644 --- a/utils/dataloaders/usps_0_6.py +++ b/utils/dataloaders/usps_0_6.py @@ -4,11 +4,7 @@ This module contains the Dataset class for the USPS dataset with labels 0-6. """ -import bz2 -import hashlib from pathlib import Path -from tempfile import TemporaryDirectory -from urllib.request import urlretrieve import h5py as h5 import numpy as np @@ -16,8 +12,6 @@ from torch.utils.data import Dataset from torchvision import transforms -from .datasources import USPS_SOURCE - class USPSDataset0_6(Dataset): """ @@ -87,9 +81,9 @@ class USPSDataset0_6(Dataset): def __init__( self, data_path: Path, + sample_ids: list, train: bool = False, transform=None, - download: bool = False, ): super().__init__() @@ -97,168 +91,21 @@ def __init__( self.filepath = path / self.filename self.transform = transform self.mode = "train" if train else "test" + self.sample_ids = sample_ids - # Download the dataset if it does not exist in a temporary directory - # to automatically clean up the downloaded file - if download and not self._dataset_ok(): - url, _, checksum = USPS_SOURCE[self.mode] - - print(f"Downloading USPS dataset ({self.mode})...") - self.download(url, self.filepath, checksum, self.mode) - - self.idx = self._index() - - def _dataset_ok(self): - """Check if the dataset file exists and contains the required datasets.""" - - if not self.filepath.exists(): - print(f"Dataset file {self.filepath} does not exist.") - return False - - with h5.File(self.filepath, "r") as f: - for mode in ["train", "test"]: - if mode not in f: - print( - f"Dataset file {self.filepath} is missing the {mode} dataset." - ) - return False - - return True - - def download(self, url, filepath, checksum, mode): - """Download the USPS dataset, and save it as an HDF5 file. - - Args - ---- - url : str - URL to download the dataset from. - filepath : pathlib.Path - Path to save the downloaded dataset. - checksum : str - MD5 checksum of the downloaded file. - mode : str - Mode of the dataset, either train or test. - - Raises - ------ - ValueError - If the checksum of the downloaded file does not match the expected checksum. - """ - - def reporthook(blocknum, blocksize, totalsize): - """Report download progress.""" - denom = 1024 * 1024 - readsofar = blocknum * blocksize - if totalsize > 0: - percent = readsofar * 1e2 / totalsize - s = f"\r{int(percent):^3}% {readsofar / denom:.2f} of {totalsize / denom:.2f} MB" - print(s, end="", flush=True) - if readsofar >= totalsize: - print() - - # Download the dataset to a temporary file - with TemporaryDirectory() as tmpdir: - tmpdir = Path(tmpdir) - tmpfile = tmpdir / "usps.bz2" - urlretrieve( - url, - tmpfile, - reporthook=reporthook, - ) - - # For fun we can check the integrity of the downloaded file - if not self.check_integrity(tmpfile, checksum): - errmsg = ( - "The checksum of the downloaded file does " - "not match the expected checksum." - ) - raise ValueError(errmsg) - - # Load the dataset and save it as an HDF5 file - with bz2.open(tmpfile) as fp: - raw = [line.decode().split() for line in fp.readlines()] - - tmp = [[x.split(":")[-1] for x in data[1:]] for data in raw] - - imgs = np.asarray(tmp, dtype=np.float32) - imgs = ((imgs + 1) / 2 * 255).astype(dtype=np.uint8) - - targets = [int(d[0]) - 1 for d in raw] - - with h5.File(self.filepath, "a") as f: - f.create_dataset(f"{mode}/data", data=imgs, dtype=np.float32) - f.create_dataset(f"{mode}/target", data=targets, dtype=np.int32) - - @staticmethod - def check_integrity(filepath, checksum): - """Check the integrity of the USPS dataset file. - - Args - ---- - filepath : pathlib.Path - Path to the USPS dataset file. - checksum : str - MD5 checksum of the dataset file. - - Returns - ------- - bool - True if the checksum of the file matches the expected checksum, False otherwise - """ - - file_hash = hashlib.md5(filepath.read_bytes()).hexdigest() - - return checksum == file_hash - - def _index(self): - with h5.File(self.filepath, "r") as f: - labels = f[self.mode]["target"][:] - - # Get indices of samples with labels 0-6 - mask = labels <= 6 - idx = np.where(mask)[0] + def __len__(self): + return len(self.sample_ids) - return idx + def __getitem__(self, id): + index = self.sample_ids[id] - def _load_data(self, idx): with h5.File(self.filepath, "r") as f: - data = f[self.mode]["data"][idx].astype(np.uint8) - label = f[self.mode]["target"][idx] + data = f[self.mode]["data"][index].astype(np.uint8) + label = f[self.mode]["target"][index] - return data, label - - def __len__(self): - return len(self.idx) - - def __getitem__(self, idx): - data, target = self._load_data(self.idx[idx]) data = Image.fromarray(data, mode="L") - # one hot encode the target - target = np.eye(self.num_classes, dtype=np.float32)[target] - if self.transform: data = self.transform(data) - return data, target - - -if __name__ == "__main__": - # Example usage: - transform = transforms.Compose( - [ - transforms.Resize((16, 16)), - transforms.ToTensor(), - ] - ) - - dataset = USPSDataset0_6( - data_path="data", - train=True, - download=False, - transform=transform, - ) - print(len(dataset)) - data, target = dataset[0] - print(data.shape) - print(target) + return data, label From 34539b313466ded471d3a55c2a010ed9441cba42 Mon Sep 17 00:00:00 2001 From: salomaestro Date: Sat, 8 Feb 2025 19:53:38 +0100 Subject: [PATCH 09/47] `load_data` now splits the data, downloads data and returns all splits --- main.py | 32 ++++---------------- utils/load_data.py | 74 +++++++++++++++++++++++++++++++++++++--------- 2 files changed, 65 insertions(+), 41 deletions(-) diff --git a/main.py b/main.py index 9582e9d..05483bf 100644 --- a/main.py +++ b/main.py @@ -3,11 +3,11 @@ import numpy as np import torch as th import torch.nn as nn +import wandb from torch.utils.data import DataLoader from torchvision import transforms from tqdm import tqdm -import wandb from utils import MetricWrapper, createfolders, get_args, load_data, load_model @@ -32,42 +32,20 @@ def main(): device = args.device if args.dataset.lower() in ["usps_0-6", "uspsh5_7_9"]: - augmentations = transforms.Compose( + transform = transforms.Compose( [ transforms.Resize((16, 16)), transforms.ToTensor(), ] ) else: - augmentations = transforms.Compose([transforms.ToTensor()]) + transform = transforms.Compose([transforms.ToTensor()]) - # Dataset - assert ( - args.validation_split_percentage < 1.0 and args.validation_split_percentage > 0 - ), "Validation split should be in interval (0,1)" - traindata = load_data( - args.dataset, - split="train", - split_percentage=args.validation_split_percentage, - data_path=args.datafolder, - download=args.download_data, - transform=augmentations, - ) - validata = load_data( - args.dataset, - split="validation", - split_percentage=args.validation_split_percentage, - data_path=args.datafolder, - download=args.download_data, - transform=augmentations, - ) - testdata = load_data( + traindata, validata, testdata = load_data( args.dataset, - split="test", - split_percentage=args.validation_split_percentage, data_path=args.datafolder, + transform=transform, download=args.download_data, - transform=augmentations, ) metrics = MetricWrapper(*args.metric, num_classes=traindata.num_classes) diff --git a/utils/load_data.py b/utils/load_data.py index 9060013..bf49ad6 100644 --- a/utils/load_data.py +++ b/utils/load_data.py @@ -1,11 +1,20 @@ -from torch.utils.data import Dataset +from torch.utils.data import Dataset, random_split -from .dataloaders import MNISTDataset0_3, USPSDataset0_6, USPSH5_Digit_7_9_Dataset +from .dataloaders import ( + Downloader, + MNISTDataset0_3, + USPSDataset0_6, + USPSH5_Digit_7_9_Dataset, +) -def load_data(dataset: str, *args, **kwargs) -> Dataset: +def filter_labels(samples: list, wanted_labels: list) -> list: + return list(filter(lambda x: x in wanted_labels, samples)) + + +def load_data(dataset: str, *args, **kwargs) -> tuple: """ - Load the dataset based on the dataset name. + load the dataset based on the dataset name. Args ---- @@ -18,8 +27,8 @@ def load_data(dataset: str, *args, **kwargs) -> Dataset: Returns ------- - dataset : torch.utils.data.Dataset - Dataset object. + tuple + Tuple of train, validation and test datasets. Raises ------ @@ -28,17 +37,54 @@ def load_data(dataset: str, *args, **kwargs) -> Dataset: Examples -------- - >>> from utils import load_data - >>> dataset = load_data("usps_0-6", data_path="data", train=True, download=True) - >>> len(dataset) - 5460 + >>> from utils import setup_data + >>> train, val, test = setup_data("usps_0-6", data_path="data", train=True, download=True) + >>> len(train), len(val), len(test) + (4914, 546, 1782) """ + match dataset.lower(): case "usps_0-6": - return USPSDataset0_6(*args, **kwargs) - case "mnist_0-3": - return MNISTDataset0_3(*args, **kwargs) + dataset = USPSDataset0_6 + train_samples, test_samples = Downloader.usps(*args, **kwargs) + labels = range(7) case "usps_7-9": - return USPSH5_Digit_7_9_Dataset(*args, **kwargs) + dataset = USPSH5_Digit_7_9_Dataset + train_samples, test_samples = Downloader.usps(*args, **kwargs) + labels = range(7, 10) + case "mnist_0-3": + dataset = MNISTDataset0_3 + train_samples, test_samples = Downloader.mnist(*args, **kwargs) + labels = range(4) case _: raise NotImplementedError(f"Dataset: {dataset} not implemented.") + + val_size = kwargs.get("val_size", 0.1) + + train_samples = filter_labels(train_samples, labels) + test_samples = filter_labels(test_samples, labels) + + train_samples, val_samples = random_split(train_samples, [1 - val_size, val_size]) + + train = dataset( + *args, + sample_ids=train_samples, + train=True, + **kwargs, + ) + + val = dataset( + *args, + sample_ids=val_samples, + train=True, + **kwargs, + ) + + test = dataset( + *args, + sample_ids=test_samples, + train=False, + **kwargs, + ) + + return train, val, test From 6c6f7b57bf0f56ee8f75222dea11c1f10ccaf81a Mon Sep 17 00:00:00 2001 From: salomaestro Date: Sat, 8 Feb 2025 20:18:45 +0100 Subject: [PATCH 10/47] Made a whoopsie --- utils/dataloaders/download.py | 1 - 1 file changed, 1 deletion(-) diff --git a/utils/dataloaders/download.py b/utils/dataloaders/download.py index 7a7fa13..c99f657 100644 --- a/utils/dataloaders/download.py +++ b/utils/dataloaders/download.py @@ -6,7 +6,6 @@ import h5py as h5 import numpy as np -from PIL import Image from .datasources import USPS_SOURCE From 20faa24e148d4dd76208f17337e3aa550e1eb3ff Mon Sep 17 00:00:00 2001 From: salomaestro Date: Sat, 8 Feb 2025 20:18:54 +0100 Subject: [PATCH 11/47] Add the size thing --- main.py | 1 + 1 file changed, 1 insertion(+) diff --git a/main.py b/main.py index 05483bf..ab78830 100644 --- a/main.py +++ b/main.py @@ -46,6 +46,7 @@ def main(): data_path=args.datafolder, transform=transform, download=args.download_data, + val_size=args.val_size, ) metrics = MetricWrapper(*args.metric, num_classes=traindata.num_classes) From d7526bf66243dda5f0325c3650c4c6504366c6a1 Mon Sep 17 00:00:00 2001 From: salomaestro Date: Sat, 8 Feb 2025 20:19:27 +0100 Subject: [PATCH 12/47] Actually send the indices, not labels to datasets --- utils/load_data.py | 24 +++++++++++++++--------- 1 file changed, 15 insertions(+), 9 deletions(-) diff --git a/utils/load_data.py b/utils/load_data.py index bf49ad6..aa968f6 100644 --- a/utils/load_data.py +++ b/utils/load_data.py @@ -1,3 +1,4 @@ +import numpy as np from torch.utils.data import Dataset, random_split from .dataloaders import ( @@ -46,23 +47,28 @@ def load_data(dataset: str, *args, **kwargs) -> tuple: match dataset.lower(): case "usps_0-6": dataset = USPSDataset0_6 - train_samples, test_samples = Downloader.usps(*args, **kwargs) - labels = range(7) + train_labels, test_labels = Downloader.usps(*args, **kwargs) + labels = np.arange(7) case "usps_7-9": dataset = USPSH5_Digit_7_9_Dataset - train_samples, test_samples = Downloader.usps(*args, **kwargs) - labels = range(7, 10) + train_labels, test_labels = Downloader.usps(*args, **kwargs) + labels = np.arange(7, 10) case "mnist_0-3": dataset = MNISTDataset0_3 - train_samples, test_samples = Downloader.mnist(*args, **kwargs) - labels = range(4) + train_labels, test_labels = Downloader.mnist(*args, **kwargs) + labels = np.arange(4) case _: raise NotImplementedError(f"Dataset: {dataset} not implemented.") - val_size = kwargs.get("val_size", 0.1) + val_size = kwargs.get("val_size", 0.2) - train_samples = filter_labels(train_samples, labels) - test_samples = filter_labels(test_samples, labels) + # Get the indices of the samples + train_indices = np.arange(len(train_labels)) + test_indices = np.arange(len(test_labels)) + + # Filter the labels to only get indices of the wanted labels + train_samples = train_indices[np.isin(train_labels, labels)] + test_samples = test_indices[np.isin(test_labels, labels)] train_samples, val_samples = random_split(train_samples, [1 - val_size, val_size]) From bd35ae639283e6f77eeb86d5dfc73b76a5c03f65 Mon Sep 17 00:00:00 2001 From: salomaestro Date: Sat, 8 Feb 2025 20:24:57 +0100 Subject: [PATCH 13/47] Format --- tests/test_models.py | 1 - 1 file changed, 1 deletion(-) diff --git a/tests/test_models.py b/tests/test_models.py index efc5412..4dd3fa8 100644 --- a/tests/test_models.py +++ b/tests/test_models.py @@ -32,4 +32,3 @@ def test_jan_model(image_shape, num_classes): y = model(x) assert y.shape == (n, num_classes), f"Shape: {y.shape}" - From 0f3206454123a33ed2ac25cb3935d06b7985014f Mon Sep 17 00:00:00 2001 From: salomaestro Date: Sat, 8 Feb 2025 20:27:08 +0100 Subject: [PATCH 14/47] More formatting --- main.py | 2 -- utils/dataloaders/usps_0_6.py | 1 - utils/load_data.py | 2 +- 3 files changed, 1 insertion(+), 4 deletions(-) diff --git a/main.py b/main.py index ab78830..69383db 100644 --- a/main.py +++ b/main.py @@ -1,5 +1,3 @@ -from pathlib import Path - import numpy as np import torch as th import torch.nn as nn diff --git a/utils/dataloaders/usps_0_6.py b/utils/dataloaders/usps_0_6.py index 85b3114..70286dc 100644 --- a/utils/dataloaders/usps_0_6.py +++ b/utils/dataloaders/usps_0_6.py @@ -10,7 +10,6 @@ import numpy as np from PIL import Image from torch.utils.data import Dataset -from torchvision import transforms class USPSDataset0_6(Dataset): diff --git a/utils/load_data.py b/utils/load_data.py index aa968f6..d2c4621 100644 --- a/utils/load_data.py +++ b/utils/load_data.py @@ -1,5 +1,5 @@ import numpy as np -from torch.utils.data import Dataset, random_split +from torch.utils.data import random_split from .dataloaders import ( Downloader, From ad159404c94cf52ef8ff2e3108bf54e6f1dc90b9 Mon Sep 17 00:00:00 2001 From: salomaestro Date: Sat, 8 Feb 2025 20:34:40 +0100 Subject: [PATCH 15/47] Adjust test to comply with new functionality --- tests/test_dataloaders.py | 15 +++++++++++---- 1 file changed, 11 insertions(+), 4 deletions(-) diff --git a/tests/test_dataloaders.py b/tests/test_dataloaders.py index 9f58ae4..32634d6 100644 --- a/tests/test_dataloaders.py +++ b/tests/test_dataloaders.py @@ -17,18 +17,25 @@ def test_uspsdataset0_6(): # Create a h5 file with h5py.File(tf, "w") as f: + targets = np.array([6, 5, 4, 3, 2, 1, 0, 0, 0, 0]) + indices = np.arange(len(targets)) # Populate the file with data f["train/data"] = np.random.rand(10, 16 * 16) - f["train/target"] = np.array([6, 5, 4, 3, 2, 1, 0, 0, 0, 0]) + f["train/target"] = targets trans = transforms.Compose( [ - transforms.Resize((16, 16)), # At least for USPS + transforms.Resize((16, 16)), transforms.ToTensor(), ] ) - dataset = USPSDataset0_6(data_path=tempdir, train=True, transform=trans) + dataset = USPSDataset0_6( + data_path=tempdir, + sample_ids=indices, + train=True, + transform=trans, + ) assert len(dataset) == 10 data, target = dataset[0] assert data.shape == (1, 16, 16) - assert all(target == np.array([0, 0, 0, 0, 0, 0, 1])) + assert target == 6 From 177258b2640378024aebf516a6a73090dc46e7e1 Mon Sep 17 00:00:00 2001 From: Solveig Date: Mon, 10 Feb 2025 11:07:02 +0100 Subject: [PATCH 16/47] added micro/macro to F1 --- utils/metrics/F1.py | 113 ++++++++++++++++++++++++++++++++------------ 1 file changed, 83 insertions(+), 30 deletions(-) diff --git a/utils/metrics/F1.py b/utils/metrics/F1.py index 1e0e795..0c7a5e2 100644 --- a/utils/metrics/F1.py +++ b/utils/metrics/F1.py @@ -4,29 +4,39 @@ class F1Score(nn.Module): """ - F1 Score implementation with direct averaging inside the compute method. + F1 Score implementation with support for both macro and micro averaging. + + This class computes the F1 score during training using either macro or micro averaging. + The F1 score is calculated based on the true positives (TP), false positives (FP), + and false negatives (FN) for each class. Parameters ---------- num_classes : int - Number of classes. + The number of classes in the classification task. + + macro_averaging : bool, optional, default=False + If True, computes the macro-averaged F1 score. If False, computes the micro-averaged F1 score. Attributes ---------- num_classes : int - The number of classes. + The number of classes in the classification task. tp : torch.Tensor - Tensor for True Positives (TP) for each class. + Tensor storing the count of True Positives (TP) for each class. fp : torch.Tensor - Tensor for False Positives (FP) for each class. + Tensor storing the count of False Positives (FP) for each class. fn : torch.Tensor - Tensor for False Negatives (FN) for each class. + Tensor storing the count of False Negatives (FN) for each class. + + macro_averaging : bool + A flag indicating whether to compute the macro-averaged F1 score or not. """ - def __init__(self, num_classes): + def __init__(self, num_classes, macro_averaging=False): """ Initializes the F1Score object, setting up the necessary state variables. @@ -35,28 +45,81 @@ def __init__(self, num_classes): num_classes : int The number of classes in the classification task. + macro_averaging : bool, optional, default=False + If True, computes the macro-averaged F1 score. If False, computes the micro-averaged F1 score. """ - super().__init__() self.num_classes = num_classes + self.macro_averaging = macro_averaging - # Initialize variables for True Positives (TP), False Positives (FP), and False Negatives (FN) + # Initialize variables for True Positives (TP), False Positives (FP), and False Negatives (FN) self.tp = torch.zeros(num_classes) self.fp = torch.zeros(num_classes) self.fn = torch.zeros(num_classes) - def update(self, preds, target): + def _micro_F1(self): + """ + Compute the Micro F1 score by aggregating TP, FP, and FN across all classes. + + Micro F1 score is calculated globally by considering all predictions together, regardless of class. + + Returns + ------- + torch.Tensor + The micro-averaged F1 score. """ - Update the variables with predictions and true labels. + tp = torch.sum(self.tp) + fp = torch.sum(self.fp) + fn = torch.sum(self.fn) + + precision = tp / (tp + fp + 1e-8) # Avoid division by zero + recall = tp / (tp + fn + 1e-8) # Avoid division by zero + + f1 = 2 * precision * recall / (precision + recall + 1e-8) # Avoid division by zero + return f1 + + def _macro_F1(self): + """ + Compute the Macro F1 score by calculating the F1 score per class and averaging. + + Macro F1 score is calculated as the average of per-class F1 scores. This approach treats all classes equally, + regardless of their frequency. + + Returns + ------- + torch.Tensor + The macro-averaged F1 score. + """ + precision_per_class = self.tp / (self.tp + self.fp + 1e-8) # Avoid division by zero + recall_per_class = self.tp / (self.tp + self.fn + 1e-8) # Avoid division by zero + f1_per_class = 2 * precision_per_class * recall_per_class / ( + precision_per_class + recall_per_class + 1e-8) # Avoid division by zero + + # Take the average of F1 scores across all classes + f1_score = torch.mean(f1_per_class) + return f1_score + + def forward(self, preds, target): + """ + Update the True Positives, False Positives, and False Negatives, and compute the F1 score. + + This method computes the F1 score based on the predictions and true labels. It can compute either the + macro-averaged or micro-averaged F1 score, depending on the `macro_averaging` flag. Parameters ---------- preds : torch.Tensor - Predicted logits (shape: [batch_size, num_classes]). + Predicted logits or class indices (shape: [batch_size, num_classes]). + These logits are typically the output of a softmax or sigmoid activation. target : torch.Tensor - True labels (shape: [batch_size]). + True labels (shape: [batch_size]), where each element is an integer representing the true class. + + Returns + ------- + torch.Tensor + The computed F1 score (either micro or macro, based on `macro_averaging`). """ preds = torch.argmax(preds, dim=1) @@ -66,21 +129,11 @@ def update(self, preds, target): self.fp[i] += torch.sum((preds == i) & (target != i)).float() self.fn[i] += torch.sum((preds != i) & (target == i)).float() - def compute(self): - """ - Compute the F1 score. + if self.macro_averaging: + # Calculate Macro F1 score + f1_score = self._macro_F1() + else: + # Calculate Micro F1 score + f1_score = self._micro_F1() - Returns - ------- - torch.Tensor - The computed F1 score. - """ - - # Compute F1 score based on the specified averaging method - f1_score = ( - 2 - * torch.sum(self.tp) - / (2 * torch.sum(self.tp) + torch.sum(self.fp) + torch.sum(self.fn)) - ) - - return f1_score + return f1_score \ No newline at end of file From 15c99ea46fb2622b1b4b9c9ef480b956fac280a3 Mon Sep 17 00:00:00 2001 From: Jan Zavadil Date: Mon, 10 Feb 2025 13:44:05 +0100 Subject: [PATCH 17/47] added MNIST downloader, adjusted minor thinks for the code to run --- main.py | 3 +- utils/dataloaders/datasources.py | 19 +++++ utils/dataloaders/download.py | 46 ++++++++++- utils/dataloaders/mnist_0_3.py | 132 ++++++------------------------- utils/load_data.py | 22 +++--- 5 files changed, 101 insertions(+), 121 deletions(-) diff --git a/main.py b/main.py index 69383db..8b4fd93 100644 --- a/main.py +++ b/main.py @@ -41,9 +41,8 @@ def main(): traindata, validata, testdata = load_data( args.dataset, - data_path=args.datafolder, + data_dir=args.datafolder, transform=transform, - download=args.download_data, val_size=args.val_size, ) diff --git a/utils/dataloaders/datasources.py b/utils/dataloaders/datasources.py index f0d2e01..9fb8276 100644 --- a/utils/dataloaders/datasources.py +++ b/utils/dataloaders/datasources.py @@ -17,3 +17,22 @@ "8ea070ee2aca1ac39742fdd1ef5ed118", ], } + +MNIST_SOURCE = { + "train_images": ["https://storage.googleapis.com/cvdf-datasets/mnist/train-images-idx3-ubyte.gz", + "train-images-idx3-ubyte", + None + ], + "train_labels": ["https://storage.googleapis.com/cvdf-datasets/mnist/train-labels-idx1-ubyte.gz", + "train-labels-idx1-ubyte", + None + ], + "test_images": ["https://storage.googleapis.com/cvdf-datasets/mnist/t10k-images-idx3-ubyte.gz", + "t10k-images-idx3-ubyte", + None + ], + "test_labels": ["https://storage.googleapis.com/cvdf-datasets/mnist/t10k-labels-idx1-ubyte.gz", + "t10k-labels-idx1-ubyte", + None + ], +} diff --git a/utils/dataloaders/download.py b/utils/dataloaders/download.py index c99f657..7cbd5db 100644 --- a/utils/dataloaders/download.py +++ b/utils/dataloaders/download.py @@ -1,5 +1,7 @@ import bz2 import hashlib +import os +import gzip from pathlib import Path from tempfile import TemporaryDirectory from urllib.request import urlretrieve @@ -7,7 +9,7 @@ import h5py as h5 import numpy as np -from .datasources import USPS_SOURCE +from .datasources import USPS_SOURCE, MNIST_SOURCE class Downloader: @@ -38,7 +40,47 @@ class Downloader: """ def mnist(self, data_dir: Path) -> tuple[np.ndarray, np.ndarray]: - raise NotImplementedError("MNIST download not implemented yet") + def _chech_is_downloaded(path: Path) -> bool: + path = path / "MNIST" + if path.exists(): + required_files = [MNIST_SOURCE[key][1] for key in MNIST_SOURCE.keys()] + if all([(path / file).exists() for file in required_files]): + print("MNIST Dataset already downloaded.") + return True + else: + return False + else: + path.mkdir(parents=True, exist_ok=True) + return False + + def _download_data(path: Path) -> None: + urls = {key: MNIST_SOURCE[key][0] for key in MNIST_SOURCE.keys()} + + for name, url in urls.items(): + file_path = os.path.join(path, url.split("/")[-1]) + if not os.path.exists(file_path.replace(".gz", "")): # Avoid re-downloading + urlretrieve(url, file_path) + with gzip.open(file_path, "rb") as f_in: + with open(file_path.replace(".gz", ""), "wb") as f_out: + f_out.write(f_in.read()) + os.remove(file_path) # Remove compressed file + + def _get_labels(path: Path) -> np.ndarray: + with open(path, "rb") as f: + data = np.frombuffer(f.read(), dtype=np.uint8, offset=8) + return data + + if not _chech_is_downloaded(data_dir): + _download_data(data_dir) + + train_labels_path = data_dir / "MNIST" / MNIST_SOURCE["train_labels"][1] + test_labels_path = data_dir / "MNIST" / MNIST_SOURCE["test_labels"][1] + + train_labels = _get_labels(train_labels_path) + test_labels = _get_labels(test_labels_path) + + return train_labels, test_labels + def svhn(self, data_dir: Path) -> tuple[np.ndarray, np.ndarray]: raise NotImplementedError("SVHN download not implemented yet") diff --git a/utils/dataloaders/mnist_0_3.py b/utils/dataloaders/mnist_0_3.py index 1f8124d..fa96960 100644 --- a/utils/dataloaders/mnist_0_3.py +++ b/utils/dataloaders/mnist_0_3.py @@ -1,154 +1,72 @@ -import gzip -import os -import urllib.request from pathlib import Path import numpy as np -import torch -from torch.utils.data import Dataset, random_split +from torch.utils.data import Dataset +from .datasources import MNIST_SOURCE class MNISTDataset0_3(Dataset): """ - A custom dataset class for loading MNIST data, specifically for digits 0 through 3. - + A custom Dataset class for loading a subset of the MNIST dataset containing digits 0 to 3. Parameters ---------- data_path : Path - The root directory where the MNIST data is stored or will be downloaded. + The root directory where the MNIST data is stored. + sample_ids : list + A list of indices specifying which samples to load. train : bool, optional - If True, loads the training data, otherwise loads the test data. Default is False. + If True, load training data, otherwise load test data. Default is False. transform : callable, optional - A function/transform that takes in an image and returns a transformed version. Default is None. - download : bool, optional - If True, downloads the dataset if it is not already present in the specified data_path. Default is False. - + A function/transform to apply to the images. Default is None. Attributes ---------- data_path : Path The root directory where the MNIST data is stored. mnist_path : Path - The directory where the MNIST data files are stored. + The directory where the MNIST dataset is located within the root directory. + idx : list + A list of indices specifying which samples to load. train : bool - Indicates whether the training data or test data is being used. + Indicates whether to load training data or test data. transform : callable - A function/transform that takes in an image and returns a transformed version. - download : bool - Indicates whether the dataset should be downloaded if not present. + A function/transform to apply to the images. + num_classes : int + The number of classes in the dataset (0 to 3). images_path : Path - The path to the image file (training or test) based on the `train` flag. + The path to the image file (train or test) based on the `train` flag. labels_path : Path - The path to the label file (training or test) based on the `train` flag. - idx : numpy.ndarray - Indices of the labels that are less than 4. + The path to the label file (train or test) based on the `train` flag. length : int The number of samples in the dataset. - Methods ------- - _parse_labels(train) - Parses the labels from the label file. - _chech_is_downloaded() - Checks if the dataset is already downloaded. - _download_data() - Downloads and extracts the MNIST dataset. __len__() Returns the number of samples in the dataset. __getitem__(index) - Returns the image and label at the specified index. + Retrieves the image and label at the specified index. """ def __init__( self, - split: str, - split_percentage: float, data_path: Path, - download: bool = False, + sample_ids: list, + train: bool = False, transform=None, ): super().__init__() self.data_path = data_path self.mnist_path = self.data_path / "MNIST" - self.split = split - self.split_percentage = split_percentage + self.idx = sample_ids + self.train = train self.transform = transform - self.download = download self.num_classes = 4 - if self.split == "test": - train = False # used to decide whether to load training or test dataset - else: - train = True - - if not self.download and not self._chech_is_downloaded(): - raise ValueError( - "Data not found. Set --download-data=True to download the data." - ) - if self.download and not self._chech_is_downloaded(): - self._download_data() - - self.images_path = self.mnist_path / ( - "train-images-idx3-ubyte" if train else "t10k-images-idx3-ubyte" - ) - self.labels_path = self.mnist_path / ( - "train-labels-idx1-ubyte" if train else "t10k-labels-idx1-ubyte" - ) - - labels = self._parse_labels() - - self.idx = np.where(labels < 4)[0] - - if self.split != "test": - generator1 = torch.Generator().manual_seed(42) - tr, val = random_split( - self.idx, - [1 - self.split_percentage, self.split_percentage], - generator=generator1, - ) - self.idx = tr if self.split == "train" else val + self.images_path = self.mnist_path / (MNIST_SOURCE["train_images"][1] if train else MNIST_SOURCE["test_images"][1]) + self.labels_path = self.mnist_path / (MNIST_SOURCE["train_labels"][1] if train else MNIST_SOURCE["test_labels"][1]) self.length = len(self.idx) - - def _parse_labels(self): - with open(self.labels_path, "rb") as f: - data = np.frombuffer(f.read(), dtype=np.uint8, offset=8) - return data - - def _chech_is_downloaded(self): - if self.mnist_path.exists(): - required_files = [ - "train-images-idx3-ubyte", - "train-labels-idx1-ubyte", - "t10k-images-idx3-ubyte", - "t10k-labels-idx1-ubyte", - ] - if all([(self.mnist_path / file).exists() for file in required_files]): - print("MNIST Dataset already downloaded.") - return True - else: - return False - else: - self.mnist_path.mkdir(parents=True, exist_ok=True) - return False - - def _download_data(self): - urls = { - "train_images": "https://storage.googleapis.com/cvdf-datasets/mnist/train-images-idx3-ubyte.gz", - "train_labels": "https://storage.googleapis.com/cvdf-datasets/mnist/train-labels-idx1-ubyte.gz", - "test_images": "https://storage.googleapis.com/cvdf-datasets/mnist/t10k-images-idx3-ubyte.gz", - "test_labels": "https://storage.googleapis.com/cvdf-datasets/mnist/t10k-labels-idx1-ubyte.gz", - } - - for name, url in urls.items(): - file_path = os.path.join(self.mnist_path, url.split("/")[-1]) - if not os.path.exists(file_path.replace(".gz", "")): # Avoid re-downloading - urllib.request.urlretrieve(url, file_path) - with gzip.open(file_path, "rb") as f_in: - with open(file_path.replace(".gz", ""), "wb") as f_out: - f_out.write(f_in.read()) - os.remove(file_path) # Remove compressed file - + def __len__(self): return self.length diff --git a/utils/load_data.py b/utils/load_data.py index d2c4621..1c3923d 100644 --- a/utils/load_data.py +++ b/utils/load_data.py @@ -43,19 +43,21 @@ def load_data(dataset: str, *args, **kwargs) -> tuple: >>> len(train), len(val), len(test) (4914, 546, 1782) """ - + downloader = Downloader() + data_dir = kwargs.get("data_dir") + transform = kwargs.get("transform") match dataset.lower(): case "usps_0-6": dataset = USPSDataset0_6 - train_labels, test_labels = Downloader.usps(*args, **kwargs) + train_labels, test_labels = downloader.usps(data_dir=data_dir) labels = np.arange(7) case "usps_7-9": dataset = USPSH5_Digit_7_9_Dataset - train_labels, test_labels = Downloader.usps(*args, **kwargs) + train_labels, test_labels = downloader.usps(data_dir=data_dir) labels = np.arange(7, 10) case "mnist_0-3": dataset = MNISTDataset0_3 - train_labels, test_labels = Downloader.mnist(*args, **kwargs) + train_labels, test_labels = downloader.mnist(data_dir=data_dir) labels = np.arange(4) case _: raise NotImplementedError(f"Dataset: {dataset} not implemented.") @@ -73,24 +75,24 @@ def load_data(dataset: str, *args, **kwargs) -> tuple: train_samples, val_samples = random_split(train_samples, [1 - val_size, val_size]) train = dataset( - *args, + data_path=data_dir, sample_ids=train_samples, train=True, - **kwargs, + transform=transform, ) val = dataset( - *args, + data_path=data_dir, sample_ids=val_samples, train=True, - **kwargs, + transform=transform, ) test = dataset( - *args, + data_path=data_dir, sample_ids=test_samples, train=False, - **kwargs, + transform=transform, ) return train, val, test From 601caca8e6fb1962fae31829c0509cadbfd91606 Mon Sep 17 00:00:00 2001 From: Jan Zavadil Date: Mon, 10 Feb 2025 13:46:24 +0100 Subject: [PATCH 18/47] ruffed, isorted --- utils/dataloaders/datasources.py | 28 ++++++++++++++++------------ utils/dataloaders/download.py | 21 +++++++++++---------- utils/dataloaders/mnist_0_3.py | 11 ++++++++--- 3 files changed, 35 insertions(+), 25 deletions(-) diff --git a/utils/dataloaders/datasources.py b/utils/dataloaders/datasources.py index 9fb8276..936d32e 100644 --- a/utils/dataloaders/datasources.py +++ b/utils/dataloaders/datasources.py @@ -19,20 +19,24 @@ } MNIST_SOURCE = { - "train_images": ["https://storage.googleapis.com/cvdf-datasets/mnist/train-images-idx3-ubyte.gz", - "train-images-idx3-ubyte", - None + "train_images": [ + "https://storage.googleapis.com/cvdf-datasets/mnist/train-images-idx3-ubyte.gz", + "train-images-idx3-ubyte", + None, ], - "train_labels": ["https://storage.googleapis.com/cvdf-datasets/mnist/train-labels-idx1-ubyte.gz", - "train-labels-idx1-ubyte", - None + "train_labels": [ + "https://storage.googleapis.com/cvdf-datasets/mnist/train-labels-idx1-ubyte.gz", + "train-labels-idx1-ubyte", + None, ], - "test_images": ["https://storage.googleapis.com/cvdf-datasets/mnist/t10k-images-idx3-ubyte.gz", - "t10k-images-idx3-ubyte", - None + "test_images": [ + "https://storage.googleapis.com/cvdf-datasets/mnist/t10k-images-idx3-ubyte.gz", + "t10k-images-idx3-ubyte", + None, ], - "test_labels": ["https://storage.googleapis.com/cvdf-datasets/mnist/t10k-labels-idx1-ubyte.gz", - "t10k-labels-idx1-ubyte", - None + "test_labels": [ + "https://storage.googleapis.com/cvdf-datasets/mnist/t10k-labels-idx1-ubyte.gz", + "t10k-labels-idx1-ubyte", + None, ], } diff --git a/utils/dataloaders/download.py b/utils/dataloaders/download.py index 7cbd5db..9f667a3 100644 --- a/utils/dataloaders/download.py +++ b/utils/dataloaders/download.py @@ -1,7 +1,7 @@ import bz2 +import gzip import hashlib import os -import gzip from pathlib import Path from tempfile import TemporaryDirectory from urllib.request import urlretrieve @@ -9,7 +9,7 @@ import h5py as h5 import numpy as np -from .datasources import USPS_SOURCE, MNIST_SOURCE +from .datasources import MNIST_SOURCE, USPS_SOURCE class Downloader: @@ -52,35 +52,36 @@ def _chech_is_downloaded(path: Path) -> bool: else: path.mkdir(parents=True, exist_ok=True) return False - + def _download_data(path: Path) -> None: urls = {key: MNIST_SOURCE[key][0] for key in MNIST_SOURCE.keys()} for name, url in urls.items(): file_path = os.path.join(path, url.split("/")[-1]) - if not os.path.exists(file_path.replace(".gz", "")): # Avoid re-downloading + if not os.path.exists( + file_path.replace(".gz", "") + ): # Avoid re-downloading urlretrieve(url, file_path) with gzip.open(file_path, "rb") as f_in: with open(file_path.replace(".gz", ""), "wb") as f_out: f_out.write(f_in.read()) os.remove(file_path) # Remove compressed file - + def _get_labels(path: Path) -> np.ndarray: with open(path, "rb") as f: data = np.frombuffer(f.read(), dtype=np.uint8, offset=8) return data - + if not _chech_is_downloaded(data_dir): _download_data(data_dir) - + train_labels_path = data_dir / "MNIST" / MNIST_SOURCE["train_labels"][1] test_labels_path = data_dir / "MNIST" / MNIST_SOURCE["test_labels"][1] - + train_labels = _get_labels(train_labels_path) test_labels = _get_labels(test_labels_path) - + return train_labels, test_labels - def svhn(self, data_dir: Path) -> tuple[np.ndarray, np.ndarray]: raise NotImplementedError("SVHN download not implemented yet") diff --git a/utils/dataloaders/mnist_0_3.py b/utils/dataloaders/mnist_0_3.py index fa96960..52a5a28 100644 --- a/utils/dataloaders/mnist_0_3.py +++ b/utils/dataloaders/mnist_0_3.py @@ -2,6 +2,7 @@ import numpy as np from torch.utils.data import Dataset + from .datasources import MNIST_SOURCE @@ -62,11 +63,15 @@ def __init__( self.transform = transform self.num_classes = 4 - self.images_path = self.mnist_path / (MNIST_SOURCE["train_images"][1] if train else MNIST_SOURCE["test_images"][1]) - self.labels_path = self.mnist_path / (MNIST_SOURCE["train_labels"][1] if train else MNIST_SOURCE["test_labels"][1]) + self.images_path = self.mnist_path / ( + MNIST_SOURCE["train_images"][1] if train else MNIST_SOURCE["test_images"][1] + ) + self.labels_path = self.mnist_path / ( + MNIST_SOURCE["train_labels"][1] if train else MNIST_SOURCE["test_labels"][1] + ) self.length = len(self.idx) - + def __len__(self): return self.length From b7bffa31cfcc3337201721072742921b96410855 Mon Sep 17 00:00:00 2001 From: Jan Zavadil Date: Mon, 10 Feb 2025 15:19:16 +0100 Subject: [PATCH 19/47] ruffisorted :'( --- utils/arg_parser.py | 17 ++++++----------- utils/dataloaders/__init__.py | 2 +- utils/dataloaders/svhn.py | 23 ++++++++++++----------- utils/load_data.py | 2 +- utils/models/magnus_model.py | 24 ++++++++++++------------ 5 files changed, 32 insertions(+), 36 deletions(-) diff --git a/utils/arg_parser.py b/utils/arg_parser.py index 402df3f..655515b 100644 --- a/utils/arg_parser.py +++ b/utils/arg_parser.py @@ -68,20 +68,15 @@ def get_args(): nargs="+", help="Which metric to use for evaluation", ) - - parser.add_argument( - '--imagesize', - type=int, - default=28, - help='Imagesize' - ) - + + parser.add_argument("--imagesize", type=int, default=28, help="Imagesize") + parser.add_argument( - '--nr_channels', + "--nr_channels", type=int, default=1, - choices=[1,3], - help='Number of image channels' + choices=[1, 3], + help="Number of image channels", ) # Training specific values diff --git a/utils/dataloaders/__init__.py b/utils/dataloaders/__init__.py index 0c5047c..5f14335 100644 --- a/utils/dataloaders/__init__.py +++ b/utils/dataloaders/__init__.py @@ -8,6 +8,6 @@ from .download import Downloader from .mnist_0_3 import MNISTDataset0_3 +from .svhn import SVHNDataset from .usps_0_6 import USPSDataset0_6 from .uspsh5_7_9 import USPSH5_Digit_7_9_Dataset -from .svhn import SVHNDataset diff --git a/utils/dataloaders/svhn.py b/utils/dataloaders/svhn.py index e71de73..f0bd18c 100644 --- a/utils/dataloaders/svhn.py +++ b/utils/dataloaders/svhn.py @@ -1,4 +1,5 @@ import os + import numpy as np from scipy.io import loadmat from torch.utils.data import Dataset @@ -7,13 +8,13 @@ class SVHNDataset(Dataset): def __init__( - self, - data_path: str, + self, + data_path: str, train: bool, - transform=None, - download:bool=True, - nr_channels=3 - ): + transform=None, + download: bool = True, + nr_channels=3, + ): """ Initializes the SVHNDataset object. Args: @@ -26,8 +27,8 @@ def __init__( """ super().__init__() # assert split == "train" or split == "test" - self.split = 'train' if train else 'test' - + self.split = "train" if train else "test" + if download: self._download_data(data_path) @@ -37,7 +38,7 @@ def __init__( self.images = data["X"].transpose(3, 1, 0, 2) self.labels = data["y"].flatten() self.labels[self.labels == 10] = 0 - + self.nr_channels = nr_channels self.transforms = transform @@ -49,7 +50,7 @@ def _download_data(self, path: str): split (str): The dataset split to download, either 'train' or 'test'. """ print(f"Downloading SVHN data into {path}") - + SVHN(path, split=self.split, download=True) def __len__(self): @@ -72,7 +73,7 @@ def __getitem__(self, index): if self.nr_channels == 1: img = np.mean(img, axis=2, keepdims=True) - + if self.transforms is not None: img = self.transforms(img) diff --git a/utils/load_data.py b/utils/load_data.py index b03462a..a3bfe42 100644 --- a/utils/load_data.py +++ b/utils/load_data.py @@ -4,9 +4,9 @@ from .dataloaders import ( Downloader, MNISTDataset0_3, + SVHNDataset, USPSDataset0_6, USPSH5_Digit_7_9_Dataset, - SVHNDataset, ) diff --git a/utils/models/magnus_model.py b/utils/models/magnus_model.py index c80fae0..48386ce 100644 --- a/utils/models/magnus_model.py +++ b/utils/models/magnus_model.py @@ -22,18 +22,18 @@ def __init__(self, image_shape: int, num_classes: int, imagechannels: int): self.image_shape = image_shape self.imagechannels = imagechannels - self.layer1 = nn.Sequential(*([ - nn.Linear(self.imagechannels * self.imagesize * self.imagesize, 133), - nn.ReLU(), - ])) - self.layer2 = nn.Sequential(*([ - nn.Linear(133, 133), - nn.ReLU() - ])) - self.layer3 = nn.Sequential(*([ - nn.Linear(133, num_classes), - nn.ReLU() - ])) + self.layer1 = nn.Sequential( + *( + [ + nn.Linear( + self.imagechannels * self.imagesize * self.imagesize, 133 + ), + nn.ReLU(), + ] + ) + ) + self.layer2 = nn.Sequential(*([nn.Linear(133, 133), nn.ReLU()])) + self.layer3 = nn.Sequential(*([nn.Linear(133, num_classes), nn.ReLU()])) def forward(self, x): """ From 5d8309b91cee95b15bd36dacec3739831edced66 Mon Sep 17 00:00:00 2001 From: Jan Zavadil Date: Mon, 10 Feb 2025 16:22:43 +0100 Subject: [PATCH 20/47] preparing for overall test --- environment.yml | 1 + main.py | 10 +++++----- 2 files changed, 6 insertions(+), 5 deletions(-) diff --git a/environment.yml b/environment.yml index c003c73..8871cfd 100644 --- a/environment.yml +++ b/environment.yml @@ -20,6 +20,7 @@ dependencies: - scalene - tqdm - scipy + - wandb - pip: - torch - torchvision diff --git a/main.py b/main.py index d6c3c03..6ac8127 100644 --- a/main.py +++ b/main.py @@ -7,7 +7,7 @@ import wandb from utils import MetricWrapper, createfolders, get_args, load_data, load_model - +from wandb_api import WANDB_API def main(): """ @@ -29,7 +29,7 @@ def main(): device = args.device - if args.dataset.lower() in ["usps_0-6", "uspsh5_7_9"]: + if args.dataset.lower() in ["usps_0-6", "usps_7-9"]: transform = transforms.Compose( [ transforms.Resize((16, 16)), @@ -107,13 +107,13 @@ def main(): # wandb.login(key=WANDB_API) wandb.init( - entity="ColabCode-org", + entity="ColabCode", # entity="FYS-8805 Exam", - project="Test", + project="Jan", tags=[args.modelname, args.dataset] ) wandb.watch(model) - exit() + for epoch in range(args.epoch): # Training loop start trainingloss = [] From 19a6ea1df1088aaacf677e23974e3819bb3d15cb Mon Sep 17 00:00:00 2001 From: Jan Zavadil Date: Mon, 10 Feb 2025 17:02:46 +0100 Subject: [PATCH 21/47] hopefully fixed f1 test --- main.py | 57 +++++++++++++++++++++++++++---------------- tests/test_metrics.py | 2 +- utils/arg_parser.py | 11 --------- 3 files changed, 37 insertions(+), 33 deletions(-) diff --git a/main.py b/main.py index a6e828b..65ecc86 100644 --- a/main.py +++ b/main.py @@ -9,6 +9,7 @@ from utils import MetricWrapper, createfolders, get_args, load_data, load_model from wandb_api import WANDB_API + def main(): """ @@ -46,7 +47,21 @@ def main(): val_size=args.val_size, ) - metrics = MetricWrapper(*args.metric, num_classes=traindata.num_classes, macro_averaging=args.macro_averaging) + train_metrics = MetricWrapper( + *args.metric, + num_classes=traindata.num_classes, + macro_averaging=args.macro_averaging, + ) + val_metrics = MetricWrapper( + *args.metric, + num_classes=traindata.num_classes, + macro_averaging=args.macro_averaging, + ) + test_metrics = MetricWrapper( + *args.metric, + num_classes=traindata.num_classes, + macro_averaging=args.macro_averaging, + ) # Find the shape of the data, if is 2D, add a channel dimension data_shape = traindata[0][0].shape @@ -98,22 +113,22 @@ def main(): optimizer.step() optimizer.zero_grad(set_to_none=True) - metrics(y, logits) + train_metrics(y, logits) break - print(metrics.accumulate()) + print(train_metrics.accumulate()) print("Dry run completed successfully.") exit() # wandb.login(key=WANDB_API) wandb.init( - entity="ColabCode", - # entity="FYS-8805 Exam", - project="Jan", - tags=[args.modelname, args.dataset] - ) + entity="ColabCode", + # entity="FYS-8805 Exam", + project="Jan", + tags=[args.modelname, args.dataset], + ) wandb.watch(model) - + for epoch in range(args.epoch): # Training loop start trainingloss = [] @@ -129,10 +144,7 @@ def main(): optimizer.zero_grad(set_to_none=True) trainingloss.append(loss.item()) - metrics(y, logits) - - wandb.log(metrics.accumulate(str_prefix="Train ")) - metrics.reset() + train_metrics(y, logits) valloss = [] # Validation loop start @@ -144,10 +156,7 @@ def main(): loss = criterion(logits, y) valloss.append(loss.item()) - metrics(y, logits) - - wandb.log(metrics.accumulate(str_prefix="Validation ")) - metrics.reset() + val_metrics(y, logits) wandb.log( { @@ -155,7 +164,11 @@ def main(): "Train loss": np.mean(trainingloss), "Validation loss": np.mean(valloss), } + | train_metrics.accumulate(str_prefix="Train ") + | val_metrics.accumulate(str_prefix="Validation ") ) + train_metrics.reset() + val_metrics.reset() testloss = [] model.eval() @@ -167,11 +180,13 @@ def main(): testloss.append(loss.item()) preds = th.argmax(logits, dim=1) - metrics(y, preds) + test_metrics(y, preds) - wandb.log(metrics.accumulate(str_prefix="Test ")) - metrics.reset() - wandb.log({"Test loss": np.mean(testloss)}) + wandb.log( + {"Epoch": 1, "Test loss": np.mean(testloss)} + | test_metrics.accumulate(str_prefix="Test ") + ) + test_metrics.reset() if __name__ == "__main__": diff --git a/tests/test_metrics.py b/tests/test_metrics.py index d6da0ab..97d651a 100644 --- a/tests/test_metrics.py +++ b/tests/test_metrics.py @@ -26,7 +26,7 @@ def test_f1score(): target = torch.tensor([0, 1, 0, 2]) - f1_metric.update(preds, target) + f1_metric(preds, target) assert f1_metric.tp.sum().item() > 0, "Expected some true positives." assert f1_metric.fp.sum().item() > 0, "Expected some false positives." assert f1_metric.fn.sum().item() > 0, "Expected some false negatives." diff --git a/utils/arg_parser.py b/utils/arg_parser.py index 239754b..618e8d2 100644 --- a/utils/arg_parser.py +++ b/utils/arg_parser.py @@ -73,17 +73,6 @@ def get_args(): action="store_true", help="If the flag is included, the metrics will be calculated using macro averaging.", ) - - - parser.add_argument("--imagesize", type=int, default=28, help="Imagesize") - - parser.add_argument( - "--nr_channels", - type=int, - default=1, - choices=[1, 3], - help="Number of image channels", - ) # Training specific values parser.add_argument( From 68f47368cf4ff18c930ae39631ed83d634025791 Mon Sep 17 00:00:00 2001 From: salomaestro Date: Mon, 10 Feb 2025 21:19:28 +0100 Subject: [PATCH 22/47] Try to fix conda error: "undefined symbol: H5Pset_fapl_ros3" by setting version Set to h5py==3.12.1 --- environment.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/environment.yml b/environment.yml index 8871cfd..47a9cd6 100644 --- a/environment.yml +++ b/environment.yml @@ -9,7 +9,7 @@ dependencies: - sphinx-autobuild - sphinx-rtd-theme - pip - - h5py + - h5py==3.12.1 - black - isort - jupyterlab From fbe9f4f5aaff614105840edbfe23839ca633cf5e Mon Sep 17 00:00:00 2001 From: salomaestro Date: Mon, 10 Feb 2025 21:44:11 +0100 Subject: [PATCH 23/47] Found that mamba was unable to use installed python and h5py with the version installed of hdf5 library Based on python version 3.12 had to hard-code: h5py to version 3.12.1, and, hdf5 to version 1.14.4 for it to work on my local mamba version. Crossing my fingers for the tests to pass with this change in place! --- environment.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/environment.yml b/environment.yml index 47a9cd6..9a977a3 100644 --- a/environment.yml +++ b/environment.yml @@ -10,6 +10,7 @@ dependencies: - sphinx-rtd-theme - pip - h5py==3.12.1 + - hdf5==1.14.4 - black - isort - jupyterlab From a7d51c4181fd6c14d69d4fad6ce20dd9acac940e Mon Sep 17 00:00:00 2001 From: salomaestro Date: Mon, 10 Feb 2025 21:52:39 +0100 Subject: [PATCH 24/47] Add percentage downloaded progress thing to mnist (since the reporthook already was there) --- utils/dataloaders/download.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/utils/dataloaders/download.py b/utils/dataloaders/download.py index 9f667a3..d99eff2 100644 --- a/utils/dataloaders/download.py +++ b/utils/dataloaders/download.py @@ -61,7 +61,7 @@ def _download_data(path: Path) -> None: if not os.path.exists( file_path.replace(".gz", "") ): # Avoid re-downloading - urlretrieve(url, file_path) + urlretrieve(url, file_path, reporthook=self.__reporthook) with gzip.open(file_path, "rb") as f_in: with open(file_path.replace(".gz", ""), "wb") as f_out: f_out.write(f_in.read()) From 4c3dc32de744f1ee1e44086af0ca420e758a6279 Mon Sep 17 00:00:00 2001 From: Johanmkr Date: Tue, 11 Feb 2025 09:14:44 +0100 Subject: [PATCH 25/47] ruffed --- pyproject.toml | 1 + utils/dataloaders/mnist_4_9.py | 43 +++++++------------ utils/load_metric.py | 16 +++++-- utils/metrics/F1.py | 22 +++++++--- utils/metrics/accuracy.py | 26 ++++++++---- utils/metrics/precision.py | 78 +++++++++++++++++++++++++++------- uv.lock | 14 ++++++ 7 files changed, 138 insertions(+), 62 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index f3b9a32..c91452d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -21,6 +21,7 @@ dependencies = [ "sphinx-rtd-theme>=3.0.2", "torch>=2.6.0", "torchvision>=0.21.0", + "tqdm>=4.67.1", ] [tool.isort] profile = "black" diff --git a/utils/dataloaders/mnist_4_9.py b/utils/dataloaders/mnist_4_9.py index d2c2fdd..8beb0af 100644 --- a/utils/dataloaders/mnist_4_9.py +++ b/utils/dataloaders/mnist_4_9.py @@ -5,29 +5,23 @@ import numpy as np from torch.utils.data import Dataset + class MNIST_4_9(Dataset): - def __init__(self, - datapath: Path, - train: bool = False, - download: bool = False - ): + def __init__(self, datapath: Path, train: bool = False, download: bool = False): super.__init__() self.datapath = datapath self.mnist_path = self.datapath / "MNIST" self.train = train self.download = download self.num_classes: int = 6 - + if not self.download and not self._already_downloaded(): raise FileNotFoundError( - 'Data files are not found. Set --download-data=True to download the data' + "Data files are not found. Set --download-data=True to download the data" ) if self.download and not self._already_downloaded(): self._download() - - - - + def _download(self): urls: dict = { "train_images": "https://storage.googleapis.com/cvdf-datasets/mnist/train-images-idx3-ubyte.gz", @@ -35,23 +29,20 @@ def _download(self): "test_images": "https://storage.googleapis.com/cvdf-datasets/mnist/t10k-images-idx3-ubyte.gz", "test_labels": "https://storage.googleapis.com/cvdf-datasets/mnist/t10k-labels-idx1-ubyte.gz", } - - + for url in urls.values(): - file_path: Path = os.path.join(self.mnist_path, url.split('/')[-1]) - file_name: Path = file_path.replace('.gz','') + file_path: Path = os.path.join(self.mnist_path, url.split("/")[-1]) + file_name: Path = file_path.replace(".gz", "") if os.path.exists(file_name): print(f"File: {file_name} already downloaded") else: print(f"File: {file_name} is downloading...") - ur.urlretrieve(url, file_path) # Download file - with gzip.open(file_path, 'rb') as infile: - with open(file_name, 'wb') as outfile: - outfile.write(infile.read()) # Write from url to local file - os.remove(file_path) # remove .gz file - - - + ur.urlretrieve(url, file_path) # Download file + with gzip.open(file_path, "rb") as infile: + with open(file_name, "wb") as outfile: + outfile.write(infile.read()) # Write from url to local file + os.remove(file_path) # remove .gz file + def _already_downloaded(self): if self.mnist_path.exists(): required_files: list = [ @@ -65,11 +56,9 @@ def _already_downloaded(self): else: self.mnist_path.mkdir(parents=True, exist_ok=True) return False - + def __len__(self): pass - + def __getitem__(self): pass - - \ No newline at end of file diff --git a/utils/load_metric.py b/utils/load_metric.py index 1698b66..d38f8ad 100644 --- a/utils/load_metric.py +++ b/utils/load_metric.py @@ -75,13 +75,21 @@ def _get_metric(self, key): case "entropy": return EntropyPrediction(num_classes=self.num_classes) case "f1": - return F1Score(num_classes=self.num_classes, macro_averaging=self.macro_averaging) + return F1Score( + num_classes=self.num_classes, macro_averaging=self.macro_averaging + ) case "recall": - return Recall(num_classes=self.num_classes, macro_averaging=self.macro_averaging) + return Recall( + num_classes=self.num_classes, macro_averaging=self.macro_averaging + ) case "precision": - return Precision(num_classes=self.num_classes, macro_averaging=self.macro_averaging) + return Precision( + num_classes=self.num_classes, macro_averaging=self.macro_averaging + ) case "accuracy": - return Accuracy(num_classes=self.num_classes, macro_averaging=self.macro_averaging) + return Accuracy( + num_classes=self.num_classes, macro_averaging=self.macro_averaging + ) case _: raise ValueError(f"Metric {key} not supported") diff --git a/utils/metrics/F1.py b/utils/metrics/F1.py index 0c7a5e2..91e83c5 100644 --- a/utils/metrics/F1.py +++ b/utils/metrics/F1.py @@ -76,7 +76,9 @@ def _micro_F1(self): precision = tp / (tp + fp + 1e-8) # Avoid division by zero recall = tp / (tp + fn + 1e-8) # Avoid division by zero - f1 = 2 * precision * recall / (precision + recall + 1e-8) # Avoid division by zero + f1 = ( + 2 * precision * recall / (precision + recall + 1e-8) + ) # Avoid division by zero return f1 def _macro_F1(self): @@ -91,10 +93,18 @@ def _macro_F1(self): torch.Tensor The macro-averaged F1 score. """ - precision_per_class = self.tp / (self.tp + self.fp + 1e-8) # Avoid division by zero - recall_per_class = self.tp / (self.tp + self.fn + 1e-8) # Avoid division by zero - f1_per_class = 2 * precision_per_class * recall_per_class / ( - precision_per_class + recall_per_class + 1e-8) # Avoid division by zero + precision_per_class = self.tp / ( + self.tp + self.fp + 1e-8 + ) # Avoid division by zero + recall_per_class = self.tp / ( + self.tp + self.fn + 1e-8 + ) # Avoid division by zero + f1_per_class = ( + 2 + * precision_per_class + * recall_per_class + / (precision_per_class + recall_per_class + 1e-8) + ) # Avoid division by zero # Take the average of F1 scores across all classes f1_score = torch.mean(f1_per_class) @@ -136,4 +146,4 @@ def forward(self, preds, target): # Calculate Micro F1 score f1_score = self._micro_F1() - return f1_score \ No newline at end of file + return f1_score diff --git a/utils/metrics/accuracy.py b/utils/metrics/accuracy.py index 22a1283..5123d36 100644 --- a/utils/metrics/accuracy.py +++ b/utils/metrics/accuracy.py @@ -7,7 +7,7 @@ def __init__(self, num_classes, macro_averaging=False): super().__init__() self.num_classes = num_classes self.macro_averaging = macro_averaging - + def forward(self, y_true, y_pred): """ Compute the accuracy of the model. @@ -30,7 +30,7 @@ def forward(self, y_true, y_pred): return self._macro_acc(y_true, y_pred) else: return self._micro_acc(y_true, y_pred) - + def _macro_acc(self, y_true, y_pred): """ Compute the macro-average accuracy. @@ -51,15 +51,15 @@ def _macro_acc(self, y_true, y_pred): classes = torch.unique(y_true) # Find unique class labels acc_per_class = [] - + for c in classes: - mask = (y_true == c) # Mask for class c + mask = y_true == c # Mask for class c acc = (y_pred[mask] == y_true[mask]).float().mean() # Accuracy for class c acc_per_class.append(acc) - + macro_acc = torch.stack(acc_per_class).mean().item() # Average across classes return macro_acc - + def _micro_acc(self, y_true, y_pred): """ Compute the micro-average accuracy. @@ -82,13 +82,21 @@ def _micro_acc(self, y_true, y_pred): if __name__ == "__main__": accuracy = Accuracy(5) macro_accuracy = Accuracy(5, macro_averaging=True) - + y_true = torch.tensor([0, 3, 2, 3, 4]) y_pred = torch.tensor([0, 1, 2, 3, 4]) print(accuracy(y_true, y_pred)) print(macro_accuracy(y_true, y_pred)) - + y_true = torch.tensor([0, 3, 2, 3, 4]) - y_onehot_pred = torch.tensor([[1, 0, 0, 0, 0], [0, 1, 0, 0, 0], [0, 0, 1, 0, 0], [0, 0, 0, 1, 0], [0, 0, 0, 0, 1]]) + y_onehot_pred = torch.tensor( + [ + [1, 0, 0, 0, 0], + [0, 1, 0, 0, 0], + [0, 0, 1, 0, 0], + [0, 0, 0, 1, 0], + [0, 0, 0, 0, 1], + ] + ) print(accuracy(y_true, y_onehot_pred)) print(macro_accuracy(y_true, y_onehot_pred)) diff --git a/utils/metrics/precision.py b/utils/metrics/precision.py index 61ba1eb..c6f5560 100644 --- a/utils/metrics/precision.py +++ b/utils/metrics/precision.py @@ -13,49 +13,95 @@ class Precision(nn.Module): ---------- num_classes : int Number of classes in the dataset. - use_mean : bool - Whether to calculate precision as a mean of precisions or as a brute function of true positives and false positives. + micro_averaging : bool + Wheter to compute the micro or macro precision (default False) """ - def __init__(self, num_classes: int, use_mean: bool = True): + def __init__(self, num_classes: int, micro_averaging: bool = False): super().__init__() self.num_classes = num_classes - self.use_mean = use_mean + self._micro_averaging = micro_averaging def forward(self, y_true: torch.tensor, y_pred: torch.tensor) -> torch.tensor: - """Calculates the precision score given number of classes and the true and predicted labels. + """Compute precision of model Parameters ---------- y_true : torch.tensor - true labels + True labels y_pred : torch.tensor - predicted labels + Predicted labels Returns ------- torch.tensor - precision score + Precision score + """ + return ( + self._micro_avg_precision(y_true, y_pred) + if self.micro_averaging + else self._macro_avg_precision(y_true, y_pred) + ) + + def _micro_avg_precision( + self, y_true: torch.tensor, y_pred: torch.tensor + ) -> torch.tensor: + """Compute micro-average precision by first calculating true/false positive across all classes and then find the precision. + + Parameters + ---------- + y_true : torch.tensor + True labels + y_pred : torch.tensor + Predicted labels + + Returns + ------- + torch.tensor + Micro-averaged precision """ - # One-hot encode the target tensor true_oh = torch.zeros(y_true.size(0), self.num_classes).scatter_( 1, y_true.unsqueeze(1), 1 ) pred_oh = torch.zeros(y_pred.size(0), self.num_classes).scatter_( 1, y_pred.unsqueeze(1), 1 ) + tp = torch.sum(true_oh * pred_oh) + fp = torch.sum(~true_oh[pred_oh.bool()].bool()) - if self.use_mean: - tp = torch.sum(true_oh * pred_oh, 0) - fp = torch.sum(~true_oh.bool() * pred_oh, 0) + return torch.nanmean(tp / (tp + fp)) + + def _macro_avg_precision( + self, y_true: torch.tensor, y_pred: torch.tensor + ) -> torch.tensor: + """Compute macro-average precision by finding true/false positives of each class separately then averaging across all classes. - else: - tp = torch.sum(true_oh * pred_oh) - fp = torch.sum(~true_oh[pred_oh.bool()].bool()) + Parameters + ---------- + y_true : torch.tensor + True labels + y_pred : torch.tensor + Predicted labels + + Returns + ------- + torch.tensor + Macro-averaged precision + """ + true_oh = torch.zeros(y_true.size(0), self.num_classes).scatter_( + 1, y_true.unsqueeze(1), 1 + ) + pred_oh = torch.zeros(y_pred.size(0), self.num_classes).scatter_( + 1, y_pred.unsqueeze(1), 1 + ) + tp = torch.sum(true_oh * pred_oh, 0) + fp = torch.sum(~true_oh.bool() * pred_oh, 0) return torch.nanmean(tp / (tp + fp)) if __name__ == "__main__": - pass + print( + "Congratulations, you succesfully ran the Precision metric class. You should be proud of this marvelous achievement!" + ) diff --git a/uv.lock b/uv.lock index 2ddd403..d3a1a25 100644 --- a/uv.lock +++ b/uv.lock @@ -306,6 +306,7 @@ dependencies = [ { name = "sphinx-rtd-theme" }, { name = "torch" }, { name = "torchvision" }, + { name = "tqdm" }, ] [package.metadata] @@ -326,6 +327,7 @@ requires-dist = [ { name = "sphinx-rtd-theme", specifier = ">=3.0.2" }, { name = "torch", specifier = ">=2.6.0" }, { name = "torchvision", specifier = ">=0.21.0" }, + { name = "tqdm", specifier = ">=4.67.1" }, ] [[package]] @@ -2086,6 +2088,18 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/61/cc/58b1adeb1bb46228442081e746fcdbc4540905c87e8add7c277540934edb/tornado-6.4.2-cp38-abi3-win_amd64.whl", hash = "sha256:908b71bf3ff37d81073356a5fadcc660eb10c1476ee6e2725588626ce7e5ca38", size = 438907 }, ] +[[package]] +name = "tqdm" +version = "4.67.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "colorama", marker = "sys_platform == 'win32'" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/a8/4b/29b4ef32e036bb34e4ab51796dd745cdba7ed47ad142a9f4a1eb8e0c744d/tqdm-4.67.1.tar.gz", hash = "sha256:f8aef9c52c08c13a65f30ea34f4e5aac3fd1a34959879d7e59e63027286627f2", size = 169737 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/d0/30/dc54f88dd4a2b5dc8a0279bdd7270e735851848b762aeb1c1184ed1f6b14/tqdm-4.67.1-py3-none-any.whl", hash = "sha256:26445eca388f82e72884e0d580d5464cd801a3ea01e63e5601bdff9ba6a48de2", size = 78540 }, +] + [[package]] name = "traitlets" version = "5.14.3" From 9abaf0c11f5c355395d8f0144e96fa827e6cb739 Mon Sep 17 00:00:00 2001 From: Johanmkr Date: Tue, 11 Feb 2025 09:26:19 +0100 Subject: [PATCH 26/47] ruffed --- utils/dataloaders/mnist_4_9.py | 43 +++++++++++++--------------------- utils/load_metric.py | 16 +++++++++---- utils/metrics/F1.py | 22 ++++++++++++----- utils/metrics/accuracy.py | 26 +++++++++++++------- 4 files changed, 61 insertions(+), 46 deletions(-) diff --git a/utils/dataloaders/mnist_4_9.py b/utils/dataloaders/mnist_4_9.py index d2c2fdd..8beb0af 100644 --- a/utils/dataloaders/mnist_4_9.py +++ b/utils/dataloaders/mnist_4_9.py @@ -5,29 +5,23 @@ import numpy as np from torch.utils.data import Dataset + class MNIST_4_9(Dataset): - def __init__(self, - datapath: Path, - train: bool = False, - download: bool = False - ): + def __init__(self, datapath: Path, train: bool = False, download: bool = False): super.__init__() self.datapath = datapath self.mnist_path = self.datapath / "MNIST" self.train = train self.download = download self.num_classes: int = 6 - + if not self.download and not self._already_downloaded(): raise FileNotFoundError( - 'Data files are not found. Set --download-data=True to download the data' + "Data files are not found. Set --download-data=True to download the data" ) if self.download and not self._already_downloaded(): self._download() - - - - + def _download(self): urls: dict = { "train_images": "https://storage.googleapis.com/cvdf-datasets/mnist/train-images-idx3-ubyte.gz", @@ -35,23 +29,20 @@ def _download(self): "test_images": "https://storage.googleapis.com/cvdf-datasets/mnist/t10k-images-idx3-ubyte.gz", "test_labels": "https://storage.googleapis.com/cvdf-datasets/mnist/t10k-labels-idx1-ubyte.gz", } - - + for url in urls.values(): - file_path: Path = os.path.join(self.mnist_path, url.split('/')[-1]) - file_name: Path = file_path.replace('.gz','') + file_path: Path = os.path.join(self.mnist_path, url.split("/")[-1]) + file_name: Path = file_path.replace(".gz", "") if os.path.exists(file_name): print(f"File: {file_name} already downloaded") else: print(f"File: {file_name} is downloading...") - ur.urlretrieve(url, file_path) # Download file - with gzip.open(file_path, 'rb') as infile: - with open(file_name, 'wb') as outfile: - outfile.write(infile.read()) # Write from url to local file - os.remove(file_path) # remove .gz file - - - + ur.urlretrieve(url, file_path) # Download file + with gzip.open(file_path, "rb") as infile: + with open(file_name, "wb") as outfile: + outfile.write(infile.read()) # Write from url to local file + os.remove(file_path) # remove .gz file + def _already_downloaded(self): if self.mnist_path.exists(): required_files: list = [ @@ -65,11 +56,9 @@ def _already_downloaded(self): else: self.mnist_path.mkdir(parents=True, exist_ok=True) return False - + def __len__(self): pass - + def __getitem__(self): pass - - \ No newline at end of file diff --git a/utils/load_metric.py b/utils/load_metric.py index 1698b66..d38f8ad 100644 --- a/utils/load_metric.py +++ b/utils/load_metric.py @@ -75,13 +75,21 @@ def _get_metric(self, key): case "entropy": return EntropyPrediction(num_classes=self.num_classes) case "f1": - return F1Score(num_classes=self.num_classes, macro_averaging=self.macro_averaging) + return F1Score( + num_classes=self.num_classes, macro_averaging=self.macro_averaging + ) case "recall": - return Recall(num_classes=self.num_classes, macro_averaging=self.macro_averaging) + return Recall( + num_classes=self.num_classes, macro_averaging=self.macro_averaging + ) case "precision": - return Precision(num_classes=self.num_classes, macro_averaging=self.macro_averaging) + return Precision( + num_classes=self.num_classes, macro_averaging=self.macro_averaging + ) case "accuracy": - return Accuracy(num_classes=self.num_classes, macro_averaging=self.macro_averaging) + return Accuracy( + num_classes=self.num_classes, macro_averaging=self.macro_averaging + ) case _: raise ValueError(f"Metric {key} not supported") diff --git a/utils/metrics/F1.py b/utils/metrics/F1.py index 0c7a5e2..91e83c5 100644 --- a/utils/metrics/F1.py +++ b/utils/metrics/F1.py @@ -76,7 +76,9 @@ def _micro_F1(self): precision = tp / (tp + fp + 1e-8) # Avoid division by zero recall = tp / (tp + fn + 1e-8) # Avoid division by zero - f1 = 2 * precision * recall / (precision + recall + 1e-8) # Avoid division by zero + f1 = ( + 2 * precision * recall / (precision + recall + 1e-8) + ) # Avoid division by zero return f1 def _macro_F1(self): @@ -91,10 +93,18 @@ def _macro_F1(self): torch.Tensor The macro-averaged F1 score. """ - precision_per_class = self.tp / (self.tp + self.fp + 1e-8) # Avoid division by zero - recall_per_class = self.tp / (self.tp + self.fn + 1e-8) # Avoid division by zero - f1_per_class = 2 * precision_per_class * recall_per_class / ( - precision_per_class + recall_per_class + 1e-8) # Avoid division by zero + precision_per_class = self.tp / ( + self.tp + self.fp + 1e-8 + ) # Avoid division by zero + recall_per_class = self.tp / ( + self.tp + self.fn + 1e-8 + ) # Avoid division by zero + f1_per_class = ( + 2 + * precision_per_class + * recall_per_class + / (precision_per_class + recall_per_class + 1e-8) + ) # Avoid division by zero # Take the average of F1 scores across all classes f1_score = torch.mean(f1_per_class) @@ -136,4 +146,4 @@ def forward(self, preds, target): # Calculate Micro F1 score f1_score = self._micro_F1() - return f1_score \ No newline at end of file + return f1_score diff --git a/utils/metrics/accuracy.py b/utils/metrics/accuracy.py index 22a1283..5123d36 100644 --- a/utils/metrics/accuracy.py +++ b/utils/metrics/accuracy.py @@ -7,7 +7,7 @@ def __init__(self, num_classes, macro_averaging=False): super().__init__() self.num_classes = num_classes self.macro_averaging = macro_averaging - + def forward(self, y_true, y_pred): """ Compute the accuracy of the model. @@ -30,7 +30,7 @@ def forward(self, y_true, y_pred): return self._macro_acc(y_true, y_pred) else: return self._micro_acc(y_true, y_pred) - + def _macro_acc(self, y_true, y_pred): """ Compute the macro-average accuracy. @@ -51,15 +51,15 @@ def _macro_acc(self, y_true, y_pred): classes = torch.unique(y_true) # Find unique class labels acc_per_class = [] - + for c in classes: - mask = (y_true == c) # Mask for class c + mask = y_true == c # Mask for class c acc = (y_pred[mask] == y_true[mask]).float().mean() # Accuracy for class c acc_per_class.append(acc) - + macro_acc = torch.stack(acc_per_class).mean().item() # Average across classes return macro_acc - + def _micro_acc(self, y_true, y_pred): """ Compute the micro-average accuracy. @@ -82,13 +82,21 @@ def _micro_acc(self, y_true, y_pred): if __name__ == "__main__": accuracy = Accuracy(5) macro_accuracy = Accuracy(5, macro_averaging=True) - + y_true = torch.tensor([0, 3, 2, 3, 4]) y_pred = torch.tensor([0, 1, 2, 3, 4]) print(accuracy(y_true, y_pred)) print(macro_accuracy(y_true, y_pred)) - + y_true = torch.tensor([0, 3, 2, 3, 4]) - y_onehot_pred = torch.tensor([[1, 0, 0, 0, 0], [0, 1, 0, 0, 0], [0, 0, 1, 0, 0], [0, 0, 0, 1, 0], [0, 0, 0, 0, 1]]) + y_onehot_pred = torch.tensor( + [ + [1, 0, 0, 0, 0], + [0, 1, 0, 0, 0], + [0, 0, 1, 0, 0], + [0, 0, 0, 1, 0], + [0, 0, 0, 0, 1], + ] + ) print(accuracy(y_true, y_onehot_pred)) print(macro_accuracy(y_true, y_onehot_pred)) From 4ad390d1c7382973e0de7df8f1d5613caf3a9ce4 Mon Sep 17 00:00:00 2001 From: Johanmkr Date: Tue, 11 Feb 2025 09:38:31 +0100 Subject: [PATCH 27/47] isorted --- utils/dataloaders/mnist_4_9.py | 1 + 1 file changed, 1 insertion(+) diff --git a/utils/dataloaders/mnist_4_9.py b/utils/dataloaders/mnist_4_9.py index 8beb0af..ae1efdb 100644 --- a/utils/dataloaders/mnist_4_9.py +++ b/utils/dataloaders/mnist_4_9.py @@ -2,6 +2,7 @@ import os import urllib.request as ur from pathlib import Path + import numpy as np from torch.utils.data import Dataset From a35e6eaa8261c9e0e4416326653682a80a1f4b18 Mon Sep 17 00:00:00 2001 From: Johanmkr Date: Tue, 11 Feb 2025 09:41:03 +0100 Subject: [PATCH 28/47] Removed numpy import --- utils/dataloaders/mnist_4_9.py | 1 - 1 file changed, 1 deletion(-) diff --git a/utils/dataloaders/mnist_4_9.py b/utils/dataloaders/mnist_4_9.py index ae1efdb..4a5b421 100644 --- a/utils/dataloaders/mnist_4_9.py +++ b/utils/dataloaders/mnist_4_9.py @@ -3,7 +3,6 @@ import urllib.request as ur from pathlib import Path -import numpy as np from torch.utils.data import Dataset From e10cf73f8909ae1852d40502901027f0cc455e25 Mon Sep 17 00:00:00 2001 From: Johanmkr Date: Tue, 11 Feb 2025 10:37:32 +0100 Subject: [PATCH 29/47] updated dataloader with micro/macro averaging --- utils/metrics/precision.py | 4 ---- 1 file changed, 4 deletions(-) diff --git a/utils/metrics/precision.py b/utils/metrics/precision.py index c6f5560..554d424 100644 --- a/utils/metrics/precision.py +++ b/utils/metrics/precision.py @@ -1,10 +1,6 @@ import torch import torch.nn as nn -USE_MEAN = True - -# Precision = TP / (TP + FP) - class Precision(nn.Module): """Metric module for precision. Can calculate precision both as a mean of precisions or as brute function of true positives and false positives. From a4df0f261a837387f7e6d6eb533e0ef2a8e86392 Mon Sep 17 00:00:00 2001 From: Johanmkr Date: Tue, 11 Feb 2025 10:56:12 +0100 Subject: [PATCH 30/47] updated precision test to fit new micro_averaging argument in dataloader --- tests/test_metrics.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tests/test_metrics.py b/tests/test_metrics.py index 97d651a..807cf88 100644 --- a/tests/test_metrics.py +++ b/tests/test_metrics.py @@ -38,7 +38,7 @@ def test_precision_case1(): for boolean, true_precision in zip([True, False], [25.0 / 36, 7.0 / 10]): true1 = torch.tensor([0, 1, 2, 1, 0, 2, 1, 0, 2, 1]) pred1 = torch.tensor([0, 2, 1, 1, 0, 2, 0, 0, 2, 1]) - P = Precision(3, use_mean=boolean) + P = Precision(3, micro_averaging=boolean) precision1 = P(true1, pred1) assert precision1.allclose(torch.tensor(true_precision), atol=1e-5), ( f"Precision Score: {precision1.item()}" @@ -51,7 +51,7 @@ def test_precision_case2(): for boolean, true_precision in zip([True, False], [8.0 / 15, 6.0 / 15]): true2 = torch.tensor([0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4]) pred2 = torch.tensor([0, 0, 4, 3, 4, 0, 4, 4, 2, 3, 4, 1, 2, 4, 0]) - P = Precision(5, use_mean=boolean) + P = Precision(5, micro_averaging=boolean) precision2 = P(true2, pred2) assert precision2.allclose(torch.tensor(true_precision), atol=1e-5), ( f"Precision Score: {precision2.item()}" @@ -64,7 +64,7 @@ def test_precision_case3(): for boolean, true_precision in zip([True, False], [3.0 / 4, 4.0 / 5]): true3 = torch.tensor([0, 0, 0, 1, 0]) pred3 = torch.tensor([1, 0, 0, 1, 0]) - P = Precision(2, use_mean=boolean) + P = Precision(2, micro_averaging=boolean) precision3 = P(true3, pred3) assert precision3.allclose(torch.tensor(true_precision), atol=1e-5), ( f"Precision Score: {precision3.item()}" @@ -77,7 +77,7 @@ def test_for_zero_denominator(): for boolean in [True, False]: true4 = torch.tensor([1, 1, 1, 1, 1]) pred4 = torch.tensor([0, 0, 0, 0, 0]) - P = Precision(2, use_mean=boolean) + P = Precision(2, micro_averaging=boolean) precision4 = P(true4, pred4) assert precision4.allclose(torch.tensor(0.0), atol=1e-5), ( f"Precision Score: {precision4.item()}" From 2a85e81928a54a33045f1e32235cc3445bb0ee1f Mon Sep 17 00:00:00 2001 From: Johanmkr Date: Tue, 11 Feb 2025 11:10:54 +0100 Subject: [PATCH 31/47] fixed bug in test-file --- utils/metrics/precision.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/utils/metrics/precision.py b/utils/metrics/precision.py index 554d424..e1e6272 100644 --- a/utils/metrics/precision.py +++ b/utils/metrics/precision.py @@ -17,7 +17,7 @@ def __init__(self, num_classes: int, micro_averaging: bool = False): super().__init__() self.num_classes = num_classes - self._micro_averaging = micro_averaging + self.micro_averaging = micro_averaging def forward(self, y_true: torch.tensor, y_pred: torch.tensor) -> torch.tensor: """Compute precision of model From daf82d6fbb78062b933986041799c23ef16506bb Mon Sep 17 00:00:00 2001 From: Johanmkr Date: Tue, 11 Feb 2025 11:43:02 +0100 Subject: [PATCH 32/47] Changed order of true/false to match manually calculated precision vals --- tests/test_metrics.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tests/test_metrics.py b/tests/test_metrics.py index 807cf88..70f7e09 100644 --- a/tests/test_metrics.py +++ b/tests/test_metrics.py @@ -35,7 +35,7 @@ def test_f1score(): def test_precision_case1(): import torch - for boolean, true_precision in zip([True, False], [25.0 / 36, 7.0 / 10]): + for boolean, true_precision in zip([False, True], [25.0 / 36, 7.0 / 10]): true1 = torch.tensor([0, 1, 2, 1, 0, 2, 1, 0, 2, 1]) pred1 = torch.tensor([0, 2, 1, 1, 0, 2, 0, 0, 2, 1]) P = Precision(3, micro_averaging=boolean) @@ -48,7 +48,7 @@ def test_precision_case1(): def test_precision_case2(): import torch - for boolean, true_precision in zip([True, False], [8.0 / 15, 6.0 / 15]): + for boolean, true_precision in zip([False, True], [8.0 / 15, 6.0 / 15]): true2 = torch.tensor([0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4]) pred2 = torch.tensor([0, 0, 4, 3, 4, 0, 4, 4, 2, 3, 4, 1, 2, 4, 0]) P = Precision(5, micro_averaging=boolean) @@ -61,7 +61,7 @@ def test_precision_case2(): def test_precision_case3(): import torch - for boolean, true_precision in zip([True, False], [3.0 / 4, 4.0 / 5]): + for boolean, true_precision in zip([False, True], [3.0 / 4, 4.0 / 5]): true3 = torch.tensor([0, 0, 0, 1, 0]) pred3 = torch.tensor([1, 0, 0, 1, 0]) P = Precision(2, micro_averaging=boolean) @@ -74,7 +74,7 @@ def test_precision_case3(): def test_for_zero_denominator(): import torch - for boolean in [True, False]: + for boolean in [False, True]: true4 = torch.tensor([1, 1, 1, 1, 1]) pred4 = torch.tensor([0, 0, 0, 0, 0]) P = Precision(2, micro_averaging=boolean) From 22df0a0b2623479fd433b7b4fe376dea7efb29c7 Mon Sep 17 00:00:00 2001 From: Johanmkr Date: Tue, 11 Feb 2025 16:29:11 +0100 Subject: [PATCH 33/47] Updated dataloader to fit with MNIST 4-9 --- utils/dataloaders/mnist_4_9.py | 100 +++++++++++++++------------------ 1 file changed, 45 insertions(+), 55 deletions(-) diff --git a/utils/dataloaders/mnist_4_9.py b/utils/dataloaders/mnist_4_9.py index 4a5b421..9714c9c 100644 --- a/utils/dataloaders/mnist_4_9.py +++ b/utils/dataloaders/mnist_4_9.py @@ -1,64 +1,54 @@ -import gzip -import os -import urllib.request as ur from pathlib import Path +import numpy as np from torch.utils.data import Dataset +from .datasources import MNIST_SOURCE class MNIST_4_9(Dataset): - def __init__(self, datapath: Path, train: bool = False, download: bool = False): + """ + MNIST dataset of numbers 4-9. + + Parameters + ---------- + data_path : Path + Root directory where MNIST dataset is stored + sample_ids : np.ndarray + Array of indices spcifying which samples to load. This determines the samples used by the dataloader. + train : bool, optional + Whether to train the model or not, by default False + """ + def __init__(self, data_path: Path, sample_ids: np.ndarray, train: bool = False): super.__init__() - self.datapath = datapath - self.mnist_path = self.datapath / "MNIST" + self.data_path = data_path + self.mnist_path = self.data_path / "MNIST" + self.samples = sample_ids self.train = train - self.download = download - self.num_classes: int = 6 - - if not self.download and not self._already_downloaded(): - raise FileNotFoundError( - "Data files are not found. Set --download-data=True to download the data" - ) - if self.download and not self._already_downloaded(): - self._download() - - def _download(self): - urls: dict = { - "train_images": "https://storage.googleapis.com/cvdf-datasets/mnist/train-images-idx3-ubyte.gz", - "train_labels": "https://storage.googleapis.com/cvdf-datasets/mnist/train-labels-idx1-ubyte.gz", - "test_images": "https://storage.googleapis.com/cvdf-datasets/mnist/t10k-images-idx3-ubyte.gz", - "test_labels": "https://storage.googleapis.com/cvdf-datasets/mnist/t10k-labels-idx1-ubyte.gz", - } - - for url in urls.values(): - file_path: Path = os.path.join(self.mnist_path, url.split("/")[-1]) - file_name: Path = file_path.replace(".gz", "") - if os.path.exists(file_name): - print(f"File: {file_name} already downloaded") - else: - print(f"File: {file_name} is downloading...") - ur.urlretrieve(url, file_path) # Download file - with gzip.open(file_path, "rb") as infile: - with open(file_name, "wb") as outfile: - outfile.write(infile.read()) # Write from url to local file - os.remove(file_path) # remove .gz file - - def _already_downloaded(self): - if self.mnist_path.exists(): - required_files: list = [ - "train-images-idx3-ubyte", - "train-labels-idx1-ubyte", - "t10k-images-idx3-ubyte", - "t10k-labels-idx1-ubyte", - ] - return all([(self.mnist_path / file).exists() for file in required_files]) - - else: - self.mnist_path.mkdir(parents=True, exist_ok=True) - return False - + + self.images_path = self.mnist_path / ( + MNIST_SOURCE["train_images"][1] if train else MNIST_SOURCE["test_images"][1] + ) + self.labels_path = self.mnist_path / ( + MNIST_SOURCE["train_labels"][1] if train else MNIST_SOURCE["test_labels"][1] + ) + + def __len__(self): - pass - - def __getitem__(self): - pass + return len(self.samples) + + def __getitem__(self, idx): + with open(self.labels_path, "rb") as labelfile: + label_pos = 8 + self.sample[idx] + labelfile.seek(label_pos) + label = int.from_bytes(labelfile.read(1), byteorder="big") + + with open(self.images_path, "rb") as imagefile: + image_pos = 16 + self.samples[idx] * 28 * 28 + imagefile.seek(image_pos) + image = np.frombuffer(imagefile.read(28 * 28), dtype=np.uint8).reshape( + 28, 28 + ) + + image = np.expand_dims(image, axis=0) # Channel + + return image, label \ No newline at end of file From 5d0d2968cbd7dfe6ea9ca30004e39d13324015c4 Mon Sep 17 00:00:00 2001 From: Johanmkr Date: Tue, 11 Feb 2025 16:31:33 +0100 Subject: [PATCH 34/47] made dataloader parsable --- utils/dataloaders/__init__.py | 2 ++ utils/dataloaders/mnist_4_9.py | 2 +- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/utils/dataloaders/__init__.py b/utils/dataloaders/__init__.py index 5f14335..192cf7e 100644 --- a/utils/dataloaders/__init__.py +++ b/utils/dataloaders/__init__.py @@ -2,12 +2,14 @@ "USPSDataset0_6", "USPSH5_Digit_7_9_Dataset", "MNISTDataset0_3", + "MNISTDataset4_9", "Downloader", "SVHNDataset", ] from .download import Downloader from .mnist_0_3 import MNISTDataset0_3 +from .mnist_4_9 import MNISTDataset4_9 from .svhn import SVHNDataset from .usps_0_6 import USPSDataset0_6 from .uspsh5_7_9 import USPSH5_Digit_7_9_Dataset diff --git a/utils/dataloaders/mnist_4_9.py b/utils/dataloaders/mnist_4_9.py index 9714c9c..62b708a 100644 --- a/utils/dataloaders/mnist_4_9.py +++ b/utils/dataloaders/mnist_4_9.py @@ -5,7 +5,7 @@ from .datasources import MNIST_SOURCE -class MNIST_4_9(Dataset): +class MNISTDataset4_9(Dataset): """ MNIST dataset of numbers 4-9. From 64fac1007beb28412c804b288031839c6a477aa3 Mon Sep 17 00:00:00 2001 From: Johanmkr Date: Tue, 11 Feb 2025 16:33:09 +0100 Subject: [PATCH 35/47] ruffedisorted --- utils/dataloaders/mnist_4_9.py | 19 ++++++++++--------- 1 file changed, 10 insertions(+), 9 deletions(-) diff --git a/utils/dataloaders/mnist_4_9.py b/utils/dataloaders/mnist_4_9.py index 62b708a..5de281b 100644 --- a/utils/dataloaders/mnist_4_9.py +++ b/utils/dataloaders/mnist_4_9.py @@ -5,6 +5,7 @@ from .datasources import MNIST_SOURCE + class MNISTDataset4_9(Dataset): """ MNIST dataset of numbers 4-9. @@ -18,37 +19,37 @@ class MNISTDataset4_9(Dataset): train : bool, optional Whether to train the model or not, by default False """ + def __init__(self, data_path: Path, sample_ids: np.ndarray, train: bool = False): super.__init__() self.data_path = data_path self.mnist_path = self.data_path / "MNIST" self.samples = sample_ids self.train = train - + self.images_path = self.mnist_path / ( MNIST_SOURCE["train_images"][1] if train else MNIST_SOURCE["test_images"][1] ) self.labels_path = self.mnist_path / ( MNIST_SOURCE["train_labels"][1] if train else MNIST_SOURCE["test_labels"][1] ) - - + def __len__(self): return len(self.samples) - + def __getitem__(self, idx): with open(self.labels_path, "rb") as labelfile: label_pos = 8 + self.sample[idx] - labelfile.seek(label_pos) - label = int.from_bytes(labelfile.read(1), byteorder="big") + labelfile.seek(label_pos) + label = int.from_bytes(labelfile.read(1), byteorder="big") with open(self.images_path, "rb") as imagefile: image_pos = 16 + self.samples[idx] * 28 * 28 imagefile.seek(image_pos) image = np.frombuffer(imagefile.read(28 * 28), dtype=np.uint8).reshape( 28, 28 - ) + ) image = np.expand_dims(image, axis=0) # Channel - - return image, label \ No newline at end of file + + return image, label From bf8a09ffd29640821f2a48f3c210218080f44fda Mon Sep 17 00:00:00 2001 From: salomaestro Date: Tue, 11 Feb 2025 17:27:19 +0100 Subject: [PATCH 36/47] Update recall metric with macro/micro averaging --- utils/metrics/recall.py | 66 +++++++++++++++++++++++++++++++++-------- 1 file changed, 53 insertions(+), 13 deletions(-) diff --git a/utils/metrics/recall.py b/utils/metrics/recall.py index ab9ae16..80a1b72 100644 --- a/utils/metrics/recall.py +++ b/utils/metrics/recall.py @@ -2,12 +2,12 @@ import torch.nn as nn -def one_hot_encode(y_true, num_classes): +def one_hot_encode(vec, num_classes): """One-hot encode the target tensor. Args ---- - y_true : torch.Tensor + vec : torch.Tensor Target tensor. num_classes : int Number of classes in the dataset. @@ -18,25 +18,65 @@ def one_hot_encode(y_true, num_classes): One-hot encoded tensor. """ - y_onehot = torch.zeros(y_true.size(0), num_classes) - y_onehot.scatter_(1, y_true.unsqueeze(1), 1) - return y_onehot + onehot = torch.zeros(vec.size(0), num_classes) + onehot.scatter_(1, vec.unsqueeze(1), 1) + return onehot class Recall(nn.Module): - def __init__(self, num_classes): - super().__init__() + """ + Recall metric. + + Args + ---- + num_classes : int + Number of classes in the dataset. + macro_averaging : bool + If True, calculate the recall for each class and return the average. + If False, calculate the recall for the entire dataset. + Methods + ------- + forward(y_true, y_pred) + Compute the recall metric. + + Examples + -------- + >>> y_true = torch.tensor([0, 1, 2, 3, 4]) + >>> y_pred = torch.randn(5, 5).argmax(dim=-1) + >>> recall = Recall(num_classes=5) + >>> recall(y_true, y_pred) + 0.2 + >>> recall = Recall(num_classes=5, macro_averaging=True) + >>> recall(y_true, y_pred) + 0.2 + """ + + def __init__(self, num_classes, macro_averaging=False): + super().__init__() self.num_classes = num_classes + self.macro_averaging = macro_averaging - def forward(self, y_true, y_pred): - true_onehot = one_hot_encode(y_true, self.num_classes) - pred_onehot = one_hot_encode(y_pred, self.num_classes) + def forward(self, true, logits): + pred = logits.argmax(dim=-1) + y_true = one_hot_encode(true, self.num_classes) + y_pred = one_hot_encode(pred, self.num_classes) - true_positives = (true_onehot * pred_onehot).sum() + if self.macro_averaging: + recall = 0 + for i in range(self.num_classes): + tp = (y_true[:, i] * y_pred[:, i]).sum() + fn = torch.sum(~y_pred[y_true[:, i].bool()].bool()) + recall += tp / (tp + fn) + recall /= self.num_classes + else: + recall = self.__compute(y_true, y_pred) - false_negatives = torch.sum(~pred_onehot[true_onehot.bool()].bool()) + return recall - recall = true_positives / (true_positives + false_negatives) + def __compute(self, y_true, y_pred): + true_positives = (y_true * y_pred).sum() + false_negatives = torch.sum(~y_pred[y_true.bool()].bool()) + recall = true_positives / (true_positives + false_negatives) return recall From b9dc34e43c3863906d9e4f2cc14485c9416d92d2 Mon Sep 17 00:00:00 2001 From: salomaestro Date: Tue, 11 Feb 2025 18:04:54 +0100 Subject: [PATCH 37/47] Update tests for Recall metric --- tests/test_metrics.py | 17 ++++++++++------- 1 file changed, 10 insertions(+), 7 deletions(-) diff --git a/tests/test_metrics.py b/tests/test_metrics.py index 70f7e09..d8ea98e 100644 --- a/tests/test_metrics.py +++ b/tests/test_metrics.py @@ -4,16 +4,19 @@ def test_recall(): import torch - recall = Recall(7) - y_true = torch.tensor([0, 1, 2, 3, 4, 5, 6]) - y_pred = torch.tensor([2, 1, 2, 1, 4, 5, 6]) + logits = torch.randn(7, 7) - recall_score = recall(y_true, y_pred) + recall_micro = Recall(7) + recall_macro = Recall(7, macro_averaging=True) - assert recall_score.allclose(torch.tensor(0.7143), atol=1e-5), ( - f"Recall Score: {recall_score.item()}" - ) + recall_micro_score = recall_micro(y_true, logits) + recall_macro_score = recall_macro(y_true, logits) + + assert isinstance(recall_micro_score, torch.Tensor), "Expected a tensor output." + assert isinstance(recall_macro_score, torch.Tensor), "Expected a tensor output." + assert recall_micro_score.item() >= 0, "Expected a non-negative value." + assert recall_macro_score.item() >= 0, "Expected a non-negative value." def test_f1score(): From 2885a3069cc83f5f2fea1c0aabe50a835d27fd12 Mon Sep 17 00:00:00 2001 From: salomaestro Date: Tue, 11 Feb 2025 18:50:05 +0100 Subject: [PATCH 38/47] Took the liberty to change the F1 metric dimension to fit --- utils/metrics/F1.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/utils/metrics/F1.py b/utils/metrics/F1.py index 91e83c5..70791c5 100644 --- a/utils/metrics/F1.py +++ b/utils/metrics/F1.py @@ -131,7 +131,7 @@ def forward(self, preds, target): torch.Tensor The computed F1 score (either micro or macro, based on `macro_averaging`). """ - preds = torch.argmax(preds, dim=1) + preds = torch.argmax(preds, dim=-1) # Calculate True Positives (TP), False Positives (FP), and False Negatives (FN) per class for i in range(self.num_classes): From 08aa876488aa0b39aca51f4a4902e5c6675297eb Mon Sep 17 00:00:00 2001 From: salomaestro Date: Tue, 11 Feb 2025 18:52:56 +0100 Subject: [PATCH 39/47] Modified the MetricWrappers arguments being passed on This will hopefully simplify the arguments to each metric slightly. --- utils/load_metric.py | 26 ++++++++++---------------- 1 file changed, 10 insertions(+), 16 deletions(-) diff --git a/utils/load_metric.py b/utils/load_metric.py index d38f8ad..e56f291 100644 --- a/utils/load_metric.py +++ b/utils/load_metric.py @@ -45,11 +45,13 @@ class MetricWrapper(nn.Module): {'entropy': [], 'f1': [], 'precision': []} """ - def __init__(self, *metrics, num_classes, macro_averaging=False): + def __init__(self, *metrics, num_classes, macro_averaging=False, **kwargs): super().__init__() self.metrics = {} - self.num_classes = num_classes - self.macro_averaging = macro_averaging + self.params = { + "num_classes": num_classes, + "macro_averaging": macro_averaging, + } | kwargs for metric in metrics: self.metrics[metric] = self._get_metric(metric) @@ -73,23 +75,15 @@ def _get_metric(self, key): match key.lower(): case "entropy": - return EntropyPrediction(num_classes=self.num_classes) + return EntropyPrediction(**self.params) case "f1": - return F1Score( - num_classes=self.num_classes, macro_averaging=self.macro_averaging - ) + return F1Score(**self.params) case "recall": - return Recall( - num_classes=self.num_classes, macro_averaging=self.macro_averaging - ) + return Recall(**self.params) case "precision": - return Precision( - num_classes=self.num_classes, macro_averaging=self.macro_averaging - ) + return Precision(**self.params) case "accuracy": - return Accuracy( - num_classes=self.num_classes, macro_averaging=self.macro_averaging - ) + return Accuracy(**self.params) case _: raise ValueError(f"Metric {key} not supported") From bab6aee16fc8af5dc43742b4863f59a02bbc4ab7 Mon Sep 17 00:00:00 2001 From: salomaestro Date: Tue, 11 Feb 2025 18:54:23 +0100 Subject: [PATCH 40/47] Add test for metricwrapper and all metrics Note that the precision metric needs a rewording as we use the argument macro_averaging = True/False as input but that one has the argument micro_averaging. --- tests/test_metrics.py | 43 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 43 insertions(+) diff --git a/tests/test_metrics.py b/tests/test_metrics.py index d8ea98e..a085ee2 100644 --- a/tests/test_metrics.py +++ b/tests/test_metrics.py @@ -1,6 +1,49 @@ +from random import randint + +import pytest + +from utils.load_metric import MetricWrapper from utils.metrics import Accuracy, F1Score, Precision, Recall +@pytest.mark.parametrize( + "metric, num_classes, macro_averaging", + [ + ("f1", randint(2, 10), False), + ("f1", randint(2, 10), True), + ("recall", randint(2, 10), False), + ("recall", randint(2, 10), True), + ("accuracy", randint(2, 10), False), + ("accuracy", randint(2, 10), True), + ("precision", randint(2, 10), False), + ("precision", randint(2, 10), True), + # TODO: Add test for EntropyPrediction + ], +) +def test_metric_wrapper(metric, num_classes, macro_averaging): + import numpy as np + import torch + + y_true = torch.arange(num_classes, dtype=torch.int64) + logits = torch.rand(num_classes, num_classes) + + metrics = MetricWrapper( + metric, + num_classes=num_classes, + macro_averaging=macro_averaging, + ) + + metrics(y_true, logits) + score = metrics.accumulate() + metrics.reset() + empty_score = metrics.accumulate() + + assert isinstance(score, dict), "Expected a dictionary output." + assert metric in score, f"Expected {metric} metric in the output." + assert score[metric] >= 0, "Expected a non-negative value." + assert np.isnan(empty_score[metric]), "Expected an empty list." + + def test_recall(): import torch From 0c16ba172a275756208a0cd18e18ee04ab296c2b Mon Sep 17 00:00:00 2001 From: Johanmkr Date: Wed, 12 Feb 2025 07:43:31 +0100 Subject: [PATCH 41/47] updated johan_model to flatten the input in the forward loop --- utils/models/johan_model.py | 1 + 1 file changed, 1 insertion(+) diff --git a/utils/models/johan_model.py b/utils/models/johan_model.py index 8500ea9..514d93e 100644 --- a/utils/models/johan_model.py +++ b/utils/models/johan_model.py @@ -46,6 +46,7 @@ def __init__(self, image_shape, num_classes): self.relu = nn.ReLU() def forward(self, x): + x = x.flatten() for layer in [self.fc1, self.fc2, self.fc3, self.fc4]: x = layer(x) x = self.relu(x) From ba90d89ebc3bad292d67a8866719453edc743b5b Mon Sep 17 00:00:00 2001 From: Johanmkr Date: Wed, 12 Feb 2025 07:45:39 +0100 Subject: [PATCH 42/47] Updated precision metric with macro_averaging as argument --- utils/metrics/precision.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/utils/metrics/precision.py b/utils/metrics/precision.py index e1e6272..bd81822 100644 --- a/utils/metrics/precision.py +++ b/utils/metrics/precision.py @@ -13,11 +13,11 @@ class Precision(nn.Module): Wheter to compute the micro or macro precision (default False) """ - def __init__(self, num_classes: int, micro_averaging: bool = False): + def __init__(self, num_classes: int, macro_averaging: bool = False): super().__init__() self.num_classes = num_classes - self.micro_averaging = micro_averaging + self.macro_averaging = macro_averaging def forward(self, y_true: torch.tensor, y_pred: torch.tensor) -> torch.tensor: """Compute precision of model @@ -35,9 +35,9 @@ def forward(self, y_true: torch.tensor, y_pred: torch.tensor) -> torch.tensor: Precision score """ return ( - self._micro_avg_precision(y_true, y_pred) - if self.micro_averaging - else self._macro_avg_precision(y_true, y_pred) + self._macro_avg_precision(y_true, y_pred) + if self.macro_averaging + else self._micro_avg_precision(y_true, y_pred) ) def _micro_avg_precision( From b9b715850075ab8578923fbf879d9877df51ccba Mon Sep 17 00:00:00 2001 From: Johanmkr Date: Thu, 13 Feb 2025 10:06:02 +0100 Subject: [PATCH 43/47] Updated precision metric and test function, need to discuss shape of y_true. is it ([N,]) or ([N,1])? --- tests/test_metrics.py | 91 ++++++++++++++++++-------------------- utils/metrics/precision.py | 4 +- 2 files changed, 45 insertions(+), 50 deletions(-) diff --git a/tests/test_metrics.py b/tests/test_metrics.py index a085ee2..3fbcd4b 100644 --- a/tests/test_metrics.py +++ b/tests/test_metrics.py @@ -78,56 +78,49 @@ def test_f1score(): assert f1_metric.fn.sum().item() > 0, "Expected some false negatives." -def test_precision_case1(): +def test_precision(): import torch - - for boolean, true_precision in zip([False, True], [25.0 / 36, 7.0 / 10]): - true1 = torch.tensor([0, 1, 2, 1, 0, 2, 1, 0, 2, 1]) - pred1 = torch.tensor([0, 2, 1, 1, 0, 2, 0, 0, 2, 1]) - P = Precision(3, micro_averaging=boolean) - precision1 = P(true1, pred1) - assert precision1.allclose(torch.tensor(true_precision), atol=1e-5), ( - f"Precision Score: {precision1.item()}" - ) - - -def test_precision_case2(): - import torch - - for boolean, true_precision in zip([False, True], [8.0 / 15, 6.0 / 15]): - true2 = torch.tensor([0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4]) - pred2 = torch.tensor([0, 0, 4, 3, 4, 0, 4, 4, 2, 3, 4, 1, 2, 4, 0]) - P = Precision(5, micro_averaging=boolean) - precision2 = P(true2, pred2) - assert precision2.allclose(torch.tensor(true_precision), atol=1e-5), ( - f"Precision Score: {precision2.item()}" - ) - - -def test_precision_case3(): - import torch - - for boolean, true_precision in zip([False, True], [3.0 / 4, 4.0 / 5]): - true3 = torch.tensor([0, 0, 0, 1, 0]) - pred3 = torch.tensor([1, 0, 0, 1, 0]) - P = Precision(2, micro_averaging=boolean) - precision3 = P(true3, pred3) - assert precision3.allclose(torch.tensor(true_precision), atol=1e-5), ( - f"Precision Score: {precision3.item()}" - ) - - -def test_for_zero_denominator(): - import torch - - for boolean in [False, True]: - true4 = torch.tensor([1, 1, 1, 1, 1]) - pred4 = torch.tensor([0, 0, 0, 0, 0]) - P = Precision(2, micro_averaging=boolean) - precision4 = P(true4, pred4) - assert precision4.allclose(torch.tensor(0.0), atol=1e-5), ( - f"Precision Score: {precision4.item()}" - ) + import numpy as np + from sklearn.metrics import precision_score + from random import randint + + C = randint(2, 10) # number of classes + N = randint(2,10*C) # batchsize + y_true = torch.randint(0,C, (N,)) + logits = torch.randn(N, C) + + # create metric objects + precision_micro = Precision(num_classes=C) + precision_macro = Precision(num_classes=C, macro_averaging=True) + + # find scores + micro_precision_score = precision_micro(y_true, logits) + macro_precision_score = precision_macro(y_true, logits) + + # check output to be tensor + assert isinstance(micro_precision_score, torch.Tensor), "Tensor output is expected." + assert isinstance(macro_precision_score, torch.Tensor), "Tensor output is expected." + + # check for non-negativity + assert micro_precision_score.item() >= 0, "Expected non-negative value" + assert macro_precision_score.item() >= 0, "Expected non-negative value" + + # find predictions + y_pred = logits.argmax(dim=-1, keepdims=True) + + # check dimension + assert y_true.shape == torch.Size([N,1]) or torch.Size([N]) + assert logits.shape == torch.Size([N,C]) + assert y_pred.shape == torch.Size([N,1]) or torch.Size([N]) + + + # find true values with scikit learn + scikit_macro_precision = precision_score(y_true, y_pred, average="macro") + scikit_micro_precision = precision_score(y_true, y_pred, average="micro") + + # check for similarity + assert np.isclose(scikit_micro_precision, micro_precision_score, atol=1e-5), "Score does not match scikit's score" + assert np.isclose(scikit_macro_precision, macro_precision_score, atol=1e-5), "Score does not match scikit's score" def test_accuracy(): diff --git a/utils/metrics/precision.py b/utils/metrics/precision.py index bd81822..a596df7 100644 --- a/utils/metrics/precision.py +++ b/utils/metrics/precision.py @@ -19,7 +19,7 @@ def __init__(self, num_classes: int, macro_averaging: bool = False): self.num_classes = num_classes self.macro_averaging = macro_averaging - def forward(self, y_true: torch.tensor, y_pred: torch.tensor) -> torch.tensor: + def forward(self, y_true: torch.tensor, logits: torch.tensor) -> torch.tensor: """Compute precision of model Parameters @@ -34,6 +34,7 @@ def forward(self, y_true: torch.tensor, y_pred: torch.tensor) -> torch.tensor: torch.tensor Precision score """ + y_pred = logits.argmax(dim=-1) return ( self._macro_avg_precision(y_true, y_pred) if self.macro_averaging @@ -57,6 +58,7 @@ def _micro_avg_precision( torch.tensor Micro-averaged precision """ + print(y_true.shape) true_oh = torch.zeros(y_true.size(0), self.num_classes).scatter_( 1, y_true.unsqueeze(1), 1 ) From 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@@ def test_f1score(): def test_precision(): - import torch + from random import randint + import numpy as np + import torch from sklearn.metrics import precision_score - from random import randint - - C = randint(2, 10) # number of classes - N = randint(2,10*C) # batchsize - y_true = torch.randint(0,C, (N,)) + + C = randint(2, 10) # number of classes + N = randint(2, 10 * C) # batchsize + y_true = torch.randint(0, C, (N,)) logits = torch.randn(N, C) - + # create metric objects precision_micro = Precision(num_classes=C) precision_macro = Precision(num_classes=C, macro_averaging=True) - + # find scores micro_precision_score = precision_micro(y_true, logits) macro_precision_score = precision_macro(y_true, logits) - + # check output to be tensor assert isinstance(micro_precision_score, torch.Tensor), "Tensor output is expected." assert isinstance(macro_precision_score, torch.Tensor), "Tensor output is expected." - + # check for non-negativity assert micro_precision_score.item() >= 0, "Expected non-negative value" assert macro_precision_score.item() >= 0, "Expected non-negative value" - + # find predictions y_pred = logits.argmax(dim=-1, keepdims=True) - + # check dimension - assert y_true.shape == torch.Size([N,1]) or torch.Size([N]) - assert logits.shape == torch.Size([N,C]) - assert y_pred.shape == torch.Size([N,1]) or torch.Size([N]) + assert y_true.shape == torch.Size([N, 1]) or torch.Size([N]) + assert logits.shape == torch.Size([N, C]) + assert y_pred.shape == torch.Size([N, 1]) or torch.Size([N]) - # find true values with scikit learn scikit_macro_precision = precision_score(y_true, y_pred, average="macro") scikit_micro_precision = precision_score(y_true, y_pred, average="micro") - + # check for similarity - assert np.isclose(scikit_micro_precision, micro_precision_score, atol=1e-5), "Score does not match scikit's score" - assert np.isclose(scikit_macro_precision, macro_precision_score, atol=1e-5), "Score does not match scikit's score" + assert np.isclose(scikit_micro_precision, micro_precision_score, atol=1e-5), ( + "Score does not match scikit's score" + ) + assert np.isclose(scikit_macro_precision, macro_precision_score, atol=1e-5), ( + "Score does not match scikit's score" + ) def test_accuracy(): From 8922263386235de283c2ca8eae7908a31aa4d13f Mon Sep 17 00:00:00 2001 From: Johanmkr Date: Thu, 13 Feb 2025 10:10:25 +0100 Subject: [PATCH 46/47] added sklearn to conda environment for github tests --- environment.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/environment.yml b/environment.yml index 9a977a3..8e5f477 100644 --- a/environment.yml +++ b/environment.yml @@ -22,6 +22,7 @@ dependencies: - tqdm - scipy - wandb + - scikit-learn - pip: - torch - torchvision From 97750d86b96ca5d4f5e2fda23480188d21f08d29 Mon Sep 17 00:00:00 2001 From: Johanmkr Date: Thu, 13 Feb 2025 10:59:58 +0100 Subject: [PATCH 47/47] Fixed bug in JohanModel --- utils/models/johan_model.py | 10 +++------- 1 file changed, 3 insertions(+), 7 deletions(-) diff --git a/utils/models/johan_model.py b/utils/models/johan_model.py index 514d93e..a26e025 100644 --- a/utils/models/johan_model.py +++ b/utils/models/johan_model.py @@ -26,7 +26,6 @@ class JohanModel(nn.Module): Numer of input features. num_classes : int Number of classes in the dataset. - """ def __init__(self, image_shape, num_classes): @@ -44,18 +43,15 @@ def __init__(self, image_shape, num_classes): self.fc3 = nn.Linear(77, 77) self.fc4 = nn.Linear(77, num_classes) self.relu = nn.ReLU() + self.flatten = nn.Flatten() def forward(self, x): - x = x.flatten() + x = self.flatten(x) for layer in [self.fc1, self.fc2, self.fc3, self.fc4]: x = layer(x) x = self.relu(x) return x -# TODO -# Add your tests here - - if __name__ == "__main__": - pass # Add your tests here + print("This is JohanModel")