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infer.py
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infer.py
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# Sample code from the TorchVision 0.3 Object Detection Finetuning Tutorial
# http://pytorch.org/tutorials/intermediate/torchvision_tutorial.html
import os
import numpy as np
import torch
from PIL import Image
import torchvision
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
from torchvision.models.detection.mask_rcnn import MaskRCNNPredictor
from engine import evaluation_in_medical_cunet
import utils
import pdb
from datasets import MedicalDataset, MedicalDataset_Test
from configs import *
from models.unet import CUnet_Resnest
from collections import OrderedDict
from loss_factory import Loss_Factory
import glob
def main():
torch.manual_seed(0)
# train on the GPU or on the CPU, if a GPU is not available
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
# define dataset and dataloader
dataset_test = MedicalDataset_Test(test_path)
data_loader_test = torch.utils.data.DataLoader(
dataset_test, batch_size=1, shuffle=False, num_workers=1)
# define the model
model = CUnet_Resnest(encoder, encoder_weights='imagenet',couple_unet=True)
model = torch.nn.DataParallel(model)
model = model.to(device)
model.load_state_dict(torch.load(path_checkpoint))
evaluation_in_medical_cunet(model, data_loader_test, device=device, couple_unet=couple_unet)
if __name__ == "__main__":
main()