added inference.py

inference.py

@@ -0,0 +1,41 @@
+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")#"cuda" if torch.cuda.is_available() else "cpu")
+    #model = BraggNN(imgsz=args.psz, fcsz=args.fcsz)
+    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)
+