--- a
+++ b/eval_base.py
@@ -0,0 +1,91 @@
+#!/usr/bin/env python
+# coding: utf-8
+'''Subject-independent model evaluator.
+'''
+import argparse
+import json
+import logging
+import sys
+from os.path import join as pjoin
+
+import h5py
+import numpy as np
+import torch
+import torch.nn.functional as F
+from braindecode.models.deep4 import Deep4Net
+from braindecode.torch_ext.optimizers import AdamW
+from braindecode.torch_ext.util import set_random_seeds
+
+logging.basicConfig(format='%(asctime)s %(levelname)s : %(message)s',
+                    level=logging.INFO, stream=sys.stdout)
+
+parser = argparse.ArgumentParser(
+    description='Subject independent model evaluator.')
+parser.add_argument('datapath', type=str, help='Path to KU data')
+parser.add_argument('modelpath', type=str, help='Path to base model')
+parser.add_argument('outpath', type=str, help='Path to output')
+parser.add_argument('-gpu', type=int, help='The gpu device to use', default=0)
+
+args = parser.parse_args()
+datapath = args.datapath
+outpath = args.outpath
+modelpath = args.modelpath
+dfile = h5py.File(datapath, 'r')
+torch.cuda.set_device(args.gpu)
+set_random_seeds(seed=20200205, cuda=True)
+BATCH_SIZE = 16
+
+# Randomly shuffled subject.
+subjs = [35, 47, 46, 37, 13, 27, 12, 32, 53, 54, 4, 40, 19, 41, 18, 42, 34, 7,
+         49, 9, 5, 48, 29, 15, 21, 17, 31, 45, 1, 38, 51, 8, 11, 16, 28, 44, 24,
+         52, 3, 26, 39, 50, 6, 23, 2, 14, 25, 20, 10, 33, 22, 43, 36, 30]
+
+
+# Get data from single subject.
+def get_data(subj):
+    dpath = '/s' + str(subj)
+    X = dfile[pjoin(dpath, 'X')]
+    Y = dfile[pjoin(dpath, 'Y')]
+    return X[:], Y[:]
+
+
+X, Y = get_data(subjs[0])
+n_classes = 2
+in_chans = X.shape[1]
+# final_conv_length = auto ensures we only get a single output in the time dimension
+model = Deep4Net(in_chans=in_chans, n_classes=n_classes,
+                 input_time_length=X.shape[2],
+                 final_conv_length='auto').cuda()
+
+# Dummy train data to set up the model.
+X_train = np.zeros(X[:2].shape).astype(np.float32)
+Y_train = np.zeros(Y[:2].shape).astype(np.int64)
+
+
+def reset_model(checkpoint):
+    # Load the state dict of the model.
+    model.network.load_state_dict(checkpoint['model_state_dict'])
+
+    # Only optimize parameters that requires gradient.
+    optimizer = AdamW(filter(lambda p: p.requires_grad, model.network.parameters()),
+                      lr=1*0.01, weight_decay=0.5*0.001)
+    model.compile(loss=F.nll_loss, optimizer=optimizer,
+                  iterator_seed=20200205, )
+
+
+for fold, subj in enumerate(subjs):
+    suffix = '_s' + str(subj) + '_f' + str(fold)
+    checkpoint = torch.load(pjoin(modelpath, 'model_f' + str(fold) + '.pt'),
+                            map_location='cuda:' + str(args.gpu))
+
+    # Set up the model.
+    reset_model(checkpoint)
+    model.fit(X_train, Y_train, 0, BATCH_SIZE)
+
+    X, Y = get_data(subj)
+    X_test, Y_test = X[300:], Y[300:]
+    test_loss = model.evaluate(X_test, Y_test)
+    with open(pjoin(outpath, 'test_base' + suffix + '.json'), 'w') as f:
+        json.dump(test_loss, f)
+
+dfile.close()