| from model import model_init, BraggNN |
| from torch.utils.data import DataLoader |
| from dataset import PatchWiseDataset |
| import torch, argparse, os |
| from util import str2bool, str2tuple, s2ituple |
| from plot import plot_loss, plot_error |
| import numpy as np |
|
|
| parser = argparse.ArgumentParser(description='Bragg peak finding for HEDM.') |
| parser.add_argument('-p_file', type=str, default="debug", help='frame/patch metadata h5 file') |
| parser.add_argument('-f_file', type=str, default="debug", help='frame/patch h5 file') |
| parser.add_argument('-m_file', type=str, help='model state_dict file') |
| parser.add_argument('-psz', type=int, default=15, help='working patch size') |
| parser.add_argument('-fcsz', type=s2ituple, default='16_8_4_2', help='size of dense layers') |
| parser.add_argument('-expName',type=str, default="debug", help='Experiment name') |
| args, unparsed = parser.parse_known_args() |
|
|
| def main(args): |
| device = torch.device("cpu") |
| |
| if device.type == 'cpu': |
| model=torch.load(args.m_file,map_location=torch.device('cpu')) |
| else: |
| model=torch.load(args.m_file) |
| model.eval() |
|
|
| ds_valid = PatchWiseDataset(psz=args.psz, rnd_shift=0, use='validation', pfile=args.p_file, ffile=args.f_file) |
| dl_valid = DataLoader(dataset=ds_valid, batch_size=len(ds_valid), shuffle=True, \ |
| num_workers=4, drop_last=False, pin_memory=True) |
| mse=0.0 |
| with torch.no_grad(): |
| for i, (inputs, labels) in enumerate(dl_valid): |
| inputs = inputs.to(device) |
| y_pred = model(inputs) |
| y_pred = y_pred.cpu().numpy() |
| labels = labels.cpu().numpy() |
| plot_error(y_pred,labels,args.expName) |
| mse += np.power(y_pred[:,0] - labels[:,0],2) + np.power(y_pred[:,1] - labels[:,1],2) |
| if __name__ == "__main__": |
| main(args) |
|
|
|
|
