--- a
+++ b/utils/evalF.py
@@ -0,0 +1,213 @@
+import sys
+import os
+
+import evalMetrics as METRICS
+import PP
+import numpy as np
+import torch
+import torch.nn as nn
+from torch.autograd import Variable
+
+import augmentations as AUG
+
+#---------------------------------------------
+#Evaluation functions
+#---------------------------------------------
+
+def evalModelX(model, num_labels, postfix, main_folder_path, eval_method, gpu0, useGPU,
+                    patch_size = 70, eval_metric = 'iou', test_augm = False, extra_patch = 30):
+    eval_list = main_folder_path + 'val' + postfix + '.txt'
+    img_list = open(eval_list).readlines()
+    v = 0
+    v_priv = 0
+    for img_str in img_list:
+        img_str = img_str.rstrip()
+        _, gt, out, _ = predict(os.path.join(main_folder_path, img_str), model, num_labels, postfix,
+                                            main_folder_path, eval_method, gpu0, useGPU, patch_size=patch_size,
+                                                test_augm = test_augm, extra_patch = extra_patch)
+        curr_eval = METRICS.metricEval(eval_metric, out, gt, num_labels)
+        v+=curr_eval
+    return v / len(img_list)
+
+def testPredict(img, model, num_labels, eval_method, gpu0, useGPU, stride= 50, patch_size = 70, test_augm = True, extra_patch = 30, get_soft = False):
+    if eval_method == 0:
+        if useGPU:
+            out = model(Variable(torch.from_numpy(img).float(),volatile = True).cuda(gpu0))
+        else:
+            out = model(Variable(torch.from_numpy(img).float(),volatile = True))
+        out = out.data[0].cpu().numpy()
+    elif eval_method == 1:
+            out = predictByPatches(img, model, num_labels, useGPU, gpu0, 
+                stride = stride, patch_size = patch_size, 
+                test_augm = test_augm, extra_patch = extra_patch)
+    out = out.squeeze()
+    if get_soft:
+        return out
+    #take argmax to get predictions
+    out = np.argmax(out, axis = 0)
+    #remove batch and label dimension
+    out = out.squeeze()
+    return out
+
+#returns the image as numpy, the ground truth and the prediction given model and input path
+#affine = True, returns the affine transformation from loading the scan
+def predict(img_path, model, num_labels, postfix, main_folder_path, eval_method, gpu0, useGPU, 
+                        stride = 50, patch_size = 70, test_augm = True, extra_patch = 30):  
+    #read image
+    img = PP.numpyFromScan(img_path)
+    #read wmh
+    gt_path = img_path.replace('slices', 'gt_slices').replace('FLAIR', 'wmh').replace('/pre','')
+    gt, affine = PP.numpyFromScan(gt_path, get_affine = True, makebin = (num_labels == 2))
+
+    img = img.transpose((3,0,1,2))
+    img = img[np.newaxis, :]
+    gt = gt.transpose((3,0,1,2))
+
+    if eval_method == 0:
+        if useGPU:
+            out_v = model(Variable(torch.from_numpy(img).float(),volatile = True).cuda(gpu0))
+        else:
+            out_v = model(Variable(torch.from_numpy(img).float(),volatile = True))
+        out = out_v.data[0].cpu().numpy()
+        #FIX?
+        del out_v
+        out_v = Variable(torch.from_numpy(np.array([1])).float())
+        out_v = Variable(torch.from_numpy(np.array([1])).float())
+    elif eval_method == 1:
+            out = predictByPatches(img, model, num_labels, useGPU, gpu0, stride = stride, patch_size = patch_size, test_augm = test_augm, extra_patch = extra_patch)
+    out = out.squeeze()
+    #take argmax to get predictions
+    out = np.argmax(out, axis = 0)
+    #remove batch and label dimension
+    img = img.squeeze()
+    out = out.squeeze()
+    gt = gt.squeeze()
+
+    return img, gt, out, affine
+
+def predictByPatches(img, model, num_labels, useGPU, gpu0, patch_size = 70, test_augm = False, stride = 50, extra_pad = 0, extra_patch = 30):    
+    batch_num, num_channels, dim1, dim2, dim3 = img.shape
+    p_size = patch_size
+    #add padding to each dim s.t. % stride = 0
+    dim1_pad = (stride - ((dim1-p_size) % stride)) % stride
+    dim2_pad = (stride - ((dim2-p_size) % stride)) % stride
+    dim3_pad = (stride - ((dim3-p_size) % stride)) % stride
+
+    x_1_off, x_2_off = int(round(dim1_pad/2.0)), dim1_pad//2
+    y_1_off, y_2_off = int(round(dim2_pad/2.0)), dim2_pad//2
+    z_1_off, z_2_off = int(round(dim3_pad/2.0)), dim3_pad//2
+
+    img = np.lib.pad(img, ((0,0),(0,0), (x_1_off, x_2_off), (y_1_off, y_2_off), (z_1_off, z_2_off)), mode='minimum')
+    _, _, padded_dim1, padded_dim2, padded_dim3 = img.shape
+
+    out_shape = (img.shape[0], num_labels, img.shape[2], img.shape[3], img.shape[4])
+    out_total = np.zeros(out_shape)
+    out_counter = np.zeros(out_shape)
+
+    extra_p = extra_patch / 2
+    for i in range(0, padded_dim1 - p_size + 1, stride):
+        for j in range(0, padded_dim2 - p_size + 1, stride):
+            for k in range(0, padded_dim3 - p_size + 1, stride):
+                if extra_p != 0:
+                    i_l, i_r = getExtraPatchOffsets(i, 0, padded_dim1 - p_size, extra_p)
+                    j_l, j_r = getExtraPatchOffsets(j, 0, padded_dim2 - p_size, extra_p)
+                    k_l, k_r = getExtraPatchOffsets(k, 0, padded_dim3 - p_size, extra_p)
+
+                    img_patch = img[:,:, (i-i_l):(i+p_size+i_r),(j-j_l):(j+p_size+j_r),(k-k_l):(k+p_size+k_r)]
+                    out_np = getPatchPrediction(img_patch, model, useGPU, gpu0, extra_pad = extra_pad, test_augm = test_augm)
+                    out_np = removePatchOffset(out_np, i_l, i_r, j_l, j_r, k_l, k_r)
+                    out_total[:,:, i:i+p_size,j:j+p_size,k:k+p_size] += out_np
+                    out_counter[:, :, i:i+p_size, j:j+p_size, k:k+p_size] += 1
+                else:
+                    img_patch = img[:, :, i:i+p_size, j:j+p_size, k:k+p_size]
+                    #make a prediction on this image patch, adding extra padding during prediction and augmenting
+                    #the result is of the same shape and size as the original img patch
+                    out_np = getPatchPrediction(img_patch, model, useGPU, gpu0, extra_pad = extra_pad, test_augm = test_augm)
+
+                    out_total[:, :, i:i+p_size, j:j+p_size, k:k+p_size] += out_np
+                    out_counter[:, :, i:i+p_size, j:j+p_size, k:k+p_size] += 1
+    out_total = out_total / out_counter
+    #remove padding from predictions
+    nb, c, i_size, j_size, k_size = out_total.shape
+    out_total = out_total[:, :, x_1_off:i_size-x_2_off, y_1_off:j_size-y_2_off, z_1_off:k_size-z_2_off]
+
+    return out_total
+
+def getExtraPatchOffsets(v, low_bound, upper_bound, extra_p):
+    v_left = 0
+    v_right = 0
+    if v - extra_p > low_bound:
+        v_left = extra_p
+    if v + extra_p < upper_bound:
+        v_right = extra_p  
+    return v_left, v_right
+
+#list of tuple [(i_l, i_r), (j_l, j_r)]
+def removePatchOffset(np_arr, i_l, i_r, j_l, j_r, k_l, k_r):
+    bn, c, s_i, s_j, s_k = np_arr.shape
+    return np_arr[:,:,(i_l):(s_i-i_r), (j_l):(s_j-j_r), (k_l):(s_k-k_r)]
+
+def getPatchPrediction(img_patch, model, useGPU, gpu0, extra_pad = 0, test_augm = False):
+    pd = extra_pad/2
+    padding = ((0,0), (0,0), (pd, pd), (pd, pd), (pd,pd))
+    img_patch = np.pad(img_patch, padding, 'constant')
+
+    num_augm = 1
+    if test_augm:
+        num_augm = 3
+
+    out_np_total = None
+    for i in range(num_augm):
+        img_patch_cp = np.copy(img_patch)
+        #AUGMENT IMAGE
+        if test_augm and i != 0:
+            pass
+            #apply augmentation
+            rot_x, rot_y, rot_z = AUG.getRotationVal([10,10,10])
+            zoom_val = AUG.getScalingVal(0.8, 1.1)
+
+            img_patch_cp = AUG.applyScale([img_patch_cp], zoom_val, [3])[0]
+            img_patch_cp = AUG.applyRotation([img_patch_cp], [rot_x, rot_y, rot_z], [3])[0]
+
+        #MAKE PREDICTION
+        if useGPU:
+            out = model(Variable(torch.from_numpy(img_patch_cp).float(),volatile = True).cuda(gpu0))
+        else:
+            out = model(Variable(torch.from_numpy(img_patch_cp).float(),volatile = True))
+        out_np = out.data[0].cpu().numpy()
+        #output is (1 x 3 x dim1 x dim2 x dim3)
+        out_np = out_np[np.newaxis,:]
+        if test_augm and i != 0:
+            temp = np.copy(out_np)
+            out_np = None
+            #reverse augmentation on predictions
+            rev_zoom_i = float(img_patch.shape[2]) / img_patch_cp.shape[2]
+            rev_zoom_j = float(img_patch.shape[3]) / img_patch_cp.shape[3]
+            rev_zoom_k = float(img_patch.shape[4]) / img_patch_cp.shape[4]
+
+            for j in range(temp.shape[1]):
+                r = AUG.applyRotation([temp[:,j:j+1,:,:,:]], [-rot_x, -rot_y, -rot_z], [3])[0]
+                r = AUG.applyScale(r, [rev_zoom_i,rev_zoom_j,rev_zoom_k], [3])[0]
+
+                if not isinstance(out_np, np.ndarray):
+                    out_np = np.zeros([1, temp.shape[1], r.shape[2], r.shape[3], r.shape[4]])
+                out_np[:, j,:,:,:] = r
+        out_np = numpySoftmax(out_np, 1)
+        if not isinstance(out_np_total, np.ndarray):
+            if pd == 0:
+                out_np_total = out_np
+            else:
+                out_np_total = out_np[:,:,pd:-pd, pd:-pd, pd:-pd]
+        else:
+            if pd ==0:
+                out_np_total += out_np
+            else:
+                out_np_total += out_np[:,:,pd:-pd, pd:-pd, pd:-pd]
+
+    return out_np_total / num_augm
+
+
+
+def numpySoftmax(x, axis_):
+    e_x = np.exp(x - np.max(x))
+    return e_x / (e_x.sum(axis=axis_) + 0.00001)