import torch from typing import Dict from PIL import Image import torch.nn as nn import pytorch_lightning as pl from torch_utils import im2tensor from quickvision.models.detection import detr from quickvision.models.detection.detr import create_detr_backbone from quickvision.losses import detr_loss from quickvision.models.detection.detr import engine from dataset_utils import DummyDetectionDataset if(torch.cuda.is_available()): from torch.cuda import amp train_dataset = DummyDetectionDataset(img_shape=(3, 256, 256), num_classes=3, num_samples=10, box_fmt="cxcywh") val_dataset = DummyDetectionDataset(img_shape=(3, 256, 256), num_classes=3, num_samples=10, box_fmt="cxcywh") supported_detr_backbones = ["resnet50", "resnet50_dc5", "resnet101", "resnet101_dc5"] error_bbone = "invalid_model" some_supported_backbones = ["resnet50"] def collate_fn(batch): return tuple(zip(*batch)) train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=2, shuffle=False, collate_fn=collate_fn)
from PIL import Image import torch.nn as nn import pytorch_lightning as pl from torch_utils import im2tensor from quickvision.models.detection import detr from quickvision.models.detection.detr import create_detr_backbone from quickvision.losses import detr_loss from quickvision.models.detection.detr import engine from dataset_utils import DummyDetectionDataset if (torch.cuda.is_available()): from torch.cuda import amp train_dataset = DummyDetectionDataset(img_shape=(3, 256, 256), num_classes=3, class_start=0, normalize=True, num_samples=10, box_fmt="cxcywh") val_dataset = DummyDetectionDataset(img_shape=(3, 256, 256), num_classes=3, class_start=0, normalize=True, num_samples=10, box_fmt="cxcywh") supported_detr_backbones = [ "resnet50", "resnet50_dc5", "resnet101", "resnet101_dc5" ] error_bbone = "invalid_model" some_supported_backbones = ["resnet50"]
if (torch.cuda.is_available()): from torch.cuda import amp fpn_supported_models = [ "resnet18", ] # "resnet34","resnet50", "resnet101", "resnet152", # "resnext50_32x4d", "resnext101_32x8d", "wide_resnet50_2", "wide_resnet101_2" non_fpn_supported_models = ["mobilenet_v2"] # "resnet18", "resnet34", "resnet50","resnet101", # "resnet152", "resnext101_32x8d", "mobilenet_v2", "vgg11", "vgg13", "vgg16", "vgg19" train_dataset = DummyDetectionDataset(img_shape=(3, 256, 256), num_classes=2, class_start=1, num_samples=10, box_fmt="xyxy") val_dataset = DummyDetectionDataset(img_shape=(3, 256, 256), num_classes=2, class_start=1, num_samples=10, box_fmt="xyxy") def collate_fn(batch): return tuple(zip(*batch)) train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=2,