import torch import torchvision import tqdm from ignite.metrics.gan import FID from PIL import Image from pytorch_fid.inception import InceptionV3 device = "cpu" dims = 2048 block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims] model = InceptionV3([block_idx]).to(device) import torch.nn as nn...
runtime as ov import torch import numpy as np from torchvision import datasets, transforms from pathlib import Path ROOT = Path(__file__).parent.resolve() # Instantiate your uncompressed model model = ov.Core().read_model(r"db0_mobilenetv2_704x480_fp32.xml") # Pro...
import torch.optim as optim from torchvision import datasets, transforms from sklearn.metrics import confusion_matrix import numpy as np # from vit import VisionTransformer def evaluate(model): # model = VisionTransformer(28, 2, 2, 2, 64, 256, num_classes=10, representation_size=32) dev...
Training from scratch is not a significant requirement nowadays in most cases, even for semantic segmentation models. Libraries like Torchvision already provide a host of pretrained models which we can easily fine tune and get exceptional results. But Torchvision and many other libraries do not a pr...
nn.functional as F from torchvision import transforms from torchvision.datasets import MNIST from torch.utils.data import DataLoader, random_split import pytorch_lightning as pl from torchmetrics import ( RetrievalMAP, RetrievalPrecision, MeanAbsoluteError, ) class MNISTDataModule(pl.LightningDataModule)...
import torch from torch.nn import functional as F from torch import nn from pytorch_lightning.core.lightning import LightningModule from torchmetrics.functional import accuracy from torch.utils.data import DataLoader, random_split from torchvision.datasets import MNIST import os from torchvision import da...
from torchvision.datasets import ImageFolder from tqdm import tqdm URLS = { 'matched-frequency': 'https://imagenetv2public.s3-us-west-2.amazonaws.com/imagenetv2-matched-frequency.tar.gz', 'threshold-0.7': 'https://imagenetv2public.s3-us-west-2.amazonaws.com/imagenetv2-threshold0.7.tar.gz...
from typing import Any, Dict import numpy as np import torch import torchvision from nncore.core.learner.supervisedlearner import SupervisedLearner from nncore.core.metrics.metric_template import Metric from nncore.utils.device import get_device from nncore.utils.utils import inverse_normalize_batch,...
│ 19 import torchvision # pylint: disable=W0611,C0411 │ │ > 20 import pytorch_lightning # pytorch_lightning should be imported after torch, but it re-e │ │ 21 logging.getLogger("xformers").addFilter(lambda record: 'A matching Triton is not availab │ ...
from torchvision.transforms.functional import to_pil_image from tqdm.auto import tqdm class ImageGenerator: """Image generator that generates images from a dataset and saves them. Args: model (torch.nn.Module): The model to evaluate. dataset (Dataset): The dataset to use the prompts from. lo...