diff --git a/scripts/calculate_chamfer.py b/scripts/calculate_chamfer.py
new file mode 100644
index 0000000..8ead6c6
--- /dev/null
+++ b/scripts/calculate_chamfer.py
@@ -0,0 +1,134 @@
+import os
+import sys
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/..')
+
+
+os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
+os.environ["CUDA_VISIBLE_DEVICES"] = '0'
+
+
+import time
+import argparse
+import wandb
+import tqdm
+import random
+import numpy as np
+import plotly.graph_objects as go
+import plotly.express as ex
+from PIL import Image
+
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torch.nn.functional as F
+from tqdm import tqdm
+from data.dataset import Dataset
+from data.utils import Frame
+from data.cloth3d_io import readPC2Frame, readPC2
+from data.preprocessing import deorient
+from utils.checkpoints import CheckpointSaver, load_model
+from torch.utils.data import DataLoader
+from utils.utils import text_to_html, pickle_load
+from utils.postprocess import fix_collision
+from losses.losses import collision_loss, normal_loss, kl_loss
+from custom_utils.custom_utils import UV2Mesh, Accumulator, plot_3d, get_summary_html, maskFacesBatch, get_summary_str, plot_3d_collision, group_inputs_for_levels
+from plotly.subplots import make_subplots
+import pytorch3d.loss
+from scipy.spatial.distance import cdist
+from scipy.spatial import cKDTree
+from smpl.smpl_torch import SMPL
+from scipy.spatial.transform import Rotation as R
+
+
+def chamfer(points1, points2, points2_tree: cKDTree):
+    tree1 = cKDTree(points1)
+    dist1, _ = tree1.query(points2, workers=40)
+    dist2, _ = points2_tree.query(points1, workers=40)
+    chamfer_dist = np.mean(dist1) + np.mean(dist2)
+    return chamfer_dist
+
+
+smpl = SMPL(path='smpl', device='cpu')
+def deorient(verts, pose, inverse=True):
+    assert pose.shape[1] == 72
+    rodr = smpl.rodrigues(torch.tensor(pose, device=smpl.device).reshape(-1, 1, 3)).reshape(verts.shape[0], -1, 3, 3).numpy()
+    rotated = np.zeros_like(verts)
+    for frame in range(verts.shape[0]):
+        r = R.from_matrix(rodr[frame, 0, :, :])
+        rotated[frame] = r.apply(verts[frame], inverse=inverse)
+
+    return rotated
+
+def calculate():
+    errors_hood = []
+    errors_ours = []
+    garments = sorted(os.listdir(hood_folder))
+    garments = [g[:-4] for g in garments]
+
+    for garment in tqdm(garments):
+        picker = 'skirt' if garment.split('_')[1] in ['Dress', 'Skirt'] else 'nonskirt'
+        id, garment_type = garment.split('_')
+
+        pred_hood = pickle_load(f'{hood_folder}/{garment}.pkl')['verts']
+        gt = readPC2(os.path.join(cloth3d_folder, id, garment_type + '.pc16'), True)['V'][2:]
+        mask = np.load(f'/data-local/127_2/cloth3d_processed_test/mesh_fixed/{id}_{garment_type}.npz')['mask']
+        pose = np.load(f'/data-local/127_2/cloth3d_processed_test/pose/{id}.npy')[2:]
+        gt = deorient(gt, pose, True)
+
+
+        for i in range(pred_hood.shape[0]):
+            gt_tree = cKDTree(gt[i])
+
+            try:
+                pred_ours = np.load(f'{ours_folder[picker]}/{id}_{i+2}_{garment_type}.npz', allow_pickle=True)['arr_0'].item()['lvl3']
+            except:
+                print('missed one')
+                continue
+
+            errors_hood.append((f'{id}_{i}_{garment_type}', chamfer(pred_hood[i], gt[i], gt_tree)))
+            errors_ours.append((f'{id}_{i}_{garment_type}', chamfer(pred_ours[mask,:], gt[i], gt_tree)))
+
+            # fig = go.Figure()
+            # fig.add_trace(go.Scatter3d(x=gt[i][:, 0], y=gt[i][:, 2], z=gt[i][:, 1], mode='markers', marker=dict(size=1)))
+            # fig.add_trace(go.Scatter3d(x=pred_hood[i][:, 0], y=pred_hood[i][:, 2], z=pred_hood[i][:, 1], mode='markers', marker=dict(size=1)))
+            # fig.update_layout(scene_aspectmode='cube')
+            # fig.update_layout(scene=dict(xaxis=dict(range=[-1, 1]), yaxis=dict(range=[-1, 1]), zaxis=dict(range=[-1, 1])))
+            # fig.show()
+
+            # fig = go.Figure()
+            # fig.add_trace(go.Scatter3d(x=gt[i][:, 0], y=gt[i][:, 2], z=gt[i][:, 1], mode='markers', marker=dict(size=1)))
+            # fig.add_trace(go.Scatter3d(x=pred_ours[i,mask,:][:, 0], y=pred_ours[i,mask,:][:, 2], z=pred_ours[i,mask,:][:, 1], mode='markers', marker=dict(size=1)))
+            # fig.update_layout(scene_aspectmode='cube')
+            # fig.update_layout(scene=dict(xaxis=dict(range=[-1, 1]), yaxis=dict(range=[-1, 1]), zaxis=dict(range=[-1, 1])))
+            # fig.show()
+
+
+    # save errors to a csv file
+    with open(f'{results_folder}/hood/{name}_hood.csv', 'w') as f:
+        for frame, error in errors_hood:
+            frame_str = ','.join(str(frame).split('_'))
+            f.write(f'{frame_str},{error}\n')
+
+    with open(f'{results_folder}/hood/{name}_ours.csv', 'w') as f:
+        for frame, error in errors_ours:
+            frame_str = ','.join(str(frame).split('_'))
+            f.write(f'{frame_str},{error}\n')
+
+
+
+if __name__ == '__main__':
+
+    name = 'hood_2'
+
+    hood_folder = '/data-local/127_2/results/hood/all_2'
+    ours_folder = dict()
+    ours_folder['skirt'] = '/data-local/127_2/results/pyr_iccvw_64_128_256_512_skirt_e2e_200000'
+    ours_folder['nonskirt'] = '/data-local/127_2/results/pyr_iccvw_64_128_256_512_e2e_2_250000'
+    cloth3d_folder = '/data-net/dataset/cloth3d/test'
+
+    results_folder = '/data-local/127_2/results'
+    out_folder = f'{results_folder}/errors/{name}'
+
+
+    calculate()
\ No newline at end of file
diff --git a/scripts/calculate_error_all.py b/scripts/calculate_error_all.py
new file mode 100644
index 0000000..60ce969
--- /dev/null
+++ b/scripts/calculate_error_all.py
@@ -0,0 +1,261 @@
+import os
+import sys
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/..')
+
+import importlib
+import argparse
+parser = argparse.ArgumentParser(description='')
+parser.add_argument('--name', type=str)
+parser.add_argument('--config', type=str)
+parser.add_argument('--debug', action='store_true')
+parser.add_argument('--iteration', type=str, default='200000')
+parser.add_argument('-g', '--gpu', type=str, default='0')
+args = vars(parser.parse_args())
+
+if args['debug']:
+    from configs.iccvw.pyr_64_128_256_512_skirt_e2e import config as config_1
+else:
+    config_module = importlib.import_module(args['config'])
+    config_1 = config_module.config
+
+os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
+os.environ["CUDA_VISIBLE_DEVICES"] = args['gpu']
+if args['debug']:
+    os.environ["CUDA_LAUNCH_BLOCKING"] = '1'
+
+import time
+import argparse
+import wandb
+import tqdm
+import random
+import numpy as np
+import plotly.graph_objects as go
+import plotly.express as ex
+from PIL import Image
+import trimesh as tm
+
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torch.nn.functional as F
+from tqdm import tqdm
+from data.dataset import Dataset
+from data.utils import Frame
+from data.cloth3d_io import readPC2Frame, readPC2
+from utils.checkpoints import CheckpointSaver, load_model
+from torch.utils.data import DataLoader
+from utils.utils import text_to_html
+from utils.postprocess import fix_collision
+from losses.losses import collision_loss, normal_loss, kl_loss
+from custom_utils.custom_utils import UV2Mesh, Accumulator, plot_3d, get_summary_html, maskFacesBatch, get_summary_str, plot_3d_collision, group_inputs_for_levels
+from plotly.subplots import make_subplots
+
+if config_1['is_skirt']:
+    from losses.losses import edge_loss_skirt as edge_loss
+else:
+    from losses.losses import edge_loss
+from losses.losses import collision_loss, normal_loss, kl_loss
+
+device = 'cuda'
+
+def get_edge_loss(pred, gt, mask):
+    e_loss = torch.mean(edge_loss(torch.from_numpy(pred).unsqueeze(0).to(device), torch.from_numpy(gt).unsqueeze(0).to(device), torch.from_numpy(mask).unsqueeze(0).to(device)))
+    return e_loss.cpu().numpy()
+
+def get_normal_loss(pred, gt, mask):
+    n_loss = normal_loss(torch.from_numpy(pred).unsqueeze(0).to(device), torch.from_numpy(gt).unsqueeze(0).to(device), torch.from_numpy(mask).unsqueeze(0).to(device), is_skirt=config_1['is_skirt'], is_smpl_orig=config_1['is_cape'])
+    return n_loss.cpu().numpy()
+
+def get_collision_loss(pred, body, mask):
+    c_loss, c_ratio = collision_loss(torch.from_numpy(pred).unsqueeze(0).to(device), torch.from_numpy(body).unsqueeze(0).to(device), torch.from_numpy(mask).unsqueeze(0).to(device))
+    return c_loss.cpu().numpy()[0], c_ratio.cpu().numpy()
+
+
+def calculate():
+    errors_collision = []
+    errors_collision_ratio = []
+    errors_normal = []
+    errors_edge = []
+
+    for frame in tqdm(frames):
+
+        # import plotly.graph_objects as go
+        # mesh_pred = mesh_pred.cpu().numpy()
+        # mask = batch['mesh_mask'].cpu().numpy()
+        # i = 3
+        # fig = go.Figure(data=[go.Scatter3d(x=mesh_pred[i,mask[i],0], y=mesh_pred[i,mask[i],2], z=mesh_pred[i,mask[i],1], mode='markers', marker=dict(size=1))])
+        # fig.update_layout(scene_aspectmode='cube')
+        # fig.show()
+
+        pred = np.load(f'{out_folder}/{frame}.npz', allow_pickle=True)['arr_0']
+
+        try:
+            pred.item()['lvl3']
+            all_levels = True
+        except:
+            all_levels = False
+
+        gt = readPC2Frame(f'{config_1["test_dir"]}/mesh_posed_no_orient/{frame.id}_{frame.garment}.pc16', frame.frame_nr, True)
+        body = readPC2Frame(f'{config_1["test_dir"]}/mesh_body_posed_no_orient/{frame.id}.pc16', frame.frame_nr, True)
+
+        fix_data = np.load(f'{config_1["test_dir"]}/mesh_fixed/{frame.id}_{frame.garment}.npz')
+        mask_convert = fix_data['mas_convert']
+        new_mask = fix_data['mask']
+        gt = gt[mask_convert]
+        gt_full = np.zeros((14475, 3), dtype=np.float32)
+        gt_full[new_mask] = gt
+        gt = gt_full
+
+        if not all_levels:
+            c_loss, c_ratio = get_collision_loss(pred, body, new_mask)
+            errors_collision.append((str(frame), c_loss))
+            errors_collision_ratio.append((str(frame), c_ratio))
+            errors_normal.append((str(frame), get_normal_loss(pred, gt, new_mask)))
+            errors_edge.append((str(frame), get_edge_loss(pred, gt, new_mask)))
+
+        else:
+            for i in range(4):
+                _pred = pred.item()[f'lvl{i}']
+                c_loss, c_ratio = get_collision_loss(_pred, body, new_mask)
+
+
+                # faces = fix_data['faces']
+                # body_faces = np.load('smpl/smpl_faces.npy').astype(np.int32)
+                # m1 = tm.Trimesh(vertices=body, faces=body_faces, process=False, validate=False)
+                # m2 = tm.Trimesh(vertices=_pred[new_mask], faces=faces, process=False, validate=False)
+                # # m2 = tm.Trimesh(vertices=gt[new_mask], faces=faces, process=False, validate=False)
+
+                # cm = tm.collision.CollisionManager()
+                # cm.add_object('m1', m1)
+                # cm.add_object('m2', m2)
+                # data = cm.in_collision_internal(return_data=True)[1]
+                # c = np.asarray([d.index('m2') for d in data])
+                # count = np.unique(c).shape[0]
+                # depth_sum = np.sum([d.depth for d in data])
+
+                # _pred = gt
+
+                # fig = go.Figure(data=[go.Mesh3d(x=_pred[new_mask,0], y=_pred[new_mask,2], z=_pred[new_mask,1], i=faces[:,0], j=faces[:,1], k=faces[:,2])])
+                # fig.add_trace(go.Mesh3d(x=body[:,0], y=body[:,2], z=body[:,1], i=body_faces[:,0], j=body_faces[:,1], k=body_faces[:,2]))
+                # fig.update_layout(scene=dict(xaxis=dict(range=[-1, 1], ), yaxis=dict(range=[-1, 1], ), zaxis=dict(range=[-1, 1], ), ), )
+                # fig.update_layout(scene_aspectmode='cube', scene_aspectratio=dict(x=1, y=1, z=1))
+                # fig.show()
+
+
+                errors_collision.append((f'{frame}_lvl{i}', c_loss))
+                errors_collision_ratio.append((f'{frame}_lvl{i}', c_ratio))
+                errors_normal.append((f'{frame}_lvl{i}', get_normal_loss(_pred, gt, new_mask)))
+                errors_edge.append((f'{frame}_lvl{i}', get_edge_loss(_pred, gt, new_mask)))
+
+
+    # save errors to a csv file
+    with open(f'{results_folder}/errors/{name}_collision.csv', 'w') as f:
+        for frame, error in errors_collision:
+            frame_str = ','.join(str(frame).split('_'))
+            f.write(f'{frame_str},{error}\n')
+
+    with open(f'{results_folder}/errors/{name}_collision_ratio.csv', 'w') as f:
+        for frame, error in errors_collision_ratio:
+            frame_str = ','.join(str(frame).split('_'))
+            f.write(f'{frame_str},{error}\n')
+
+    with open(f'{results_folder}/errors/{name}_normal.csv', 'w') as f:
+        for frame, error in errors_normal:
+            frame_str = ','.join(str(frame).split('_'))
+            f.write(f'{frame_str},{error}\n')
+
+    with open(f'{results_folder}/errors/{name}_edge.csv', 'w') as f:
+        for frame, error in errors_edge:
+            frame_str = ','.join(str(frame).split('_'))
+            f.write(f'{frame_str},{error}\n')
+
+
+def calculate_cape():
+    from smpl.smpl_torch import SMPL
+    errors = []
+
+    clothing_verts_idx = np.load(os.path.join('custom_data', 'clothing_verts_idx.npy'))
+    cape_mask = np.zeros((6890), bool)
+    cape_mask[clothing_verts_idx] = True
+
+    smpl = SMPL(path='smpl', orig_smpl=True, device='cpu')
+
+    for frame in tqdm(frames):
+
+        # import plotly.graph_objects as go
+        # mesh_pred = mesh_pred.cpu().numpy()
+        # mask = batch['mesh_mask'].cpu().numpy()
+        # i = 3
+        # fig = go.Figure(data=[go.Scatter3d(x=mesh_pred[i,mask[i],0], y=mesh_pred[i,mask[i],2], z=mesh_pred[i,mask[i],1], mode='markers', marker=dict(size=1))])
+        # fig.update_layout(scene_aspectmode='cube')
+        # fig.show()
+
+        if not os.path.exists(f'{out_folder}/{frame}.npz'):
+            print('missed one')
+            continue
+
+
+
+        pred = np.load(f'{out_folder}/{frame}.npz')['arr_0']
+        gt = np.load(f'{config_1["test_dir"]}/mesh_unposed_no_shape/{frame.id}.{frame.frame_nr}.npz')['mesh_unposed_no_shape']
+        pose_shape = np.load(f'{config_1["test_dir"]}/pose_shape/{frame.id}.{frame.frame_nr}.npy').astype(np.float32)
+        pose, shape = pose_shape[:72], pose_shape[72:]
+        gender = int(shape[10] >= 0.5)
+        shape_offsets = torch.tensordot(torch.from_numpy(shape[:10][None, :]), smpl.shapedirs[gender], dims=[[1], [2]])[0,smpl.to_keep,:].numpy()
+
+        gt_shaped = gt + shape_offsets 
+        
+        min_body = np.load(os.path.join('/data-local/127_2/cape_processed', 'minimal_body_shape', frame.id.split('_')[0], frame.id.split('_')[0] + '_minimal.npy'))
+        offsets_pred = pred[cape_mask[smpl.to_keep],:] - min_body[clothing_verts_idx,:]
+        offsets_orig =  gt_shaped[cape_mask[smpl.to_keep],:] - min_body[clothing_verts_idx,:]
+
+        error = np.linalg.norm(offsets_pred - offsets_orig, axis=1).mean()
+        errors.append((str(frame), error))
+
+        # import plotly.graph_objects as go
+        # fig = go.Figure(data=[go.Scatter3d(x=pred[:,0], y=pred[:,2], z=pred[:,1], mode='markers', marker=dict(size=1))])
+        # fig.add_trace(go.Scatter3d(x=gt[:,0], y=gt[:,2], z=gt[:,1], mode='markers', marker=dict(size=1)))
+        # fig.update_layout(scene_aspectmode='cube')
+        # fig.update_layout(scene=dict(xaxis=dict(range=[-1,1]), yaxis=dict(range=[-1,1]), zaxis=dict(range=[-1,1])))
+        # fig.show()
+
+    # save errors to a csv file
+    with open(f'{results_folder}/errors/{name}.csv', 'w') as f:
+        for frame, error in errors:
+            split = str(frame).split('_')
+            if not config_1['is_cape']:
+                frame_str = ','.join(split)
+            else:
+                id = '_'.join(split[:-2])
+                frame_str = f'{id},{split[-2]},{split[-1]}'
+            f.write(f'{frame_str},{error}\n')
+
+
+if __name__ == '__main__':
+    skirt_modifier = '_skirt' if config_1['is_skirt'] else ''
+
+    name = config_1['name']
+
+    results_folder = '/data-local/127_2/results'
+    if args['iteration'] == '':
+        folders = [f for f in os.listdir(results_folder) if os.path.isdir(os.path.join(results_folder, f)) and f.startswith(config_1['name'])]
+        assert len(folders) == 1
+        name = folders[0]
+    else:
+        name = f'{name}_{args["iteration"]}'
+    out_folder = f'{results_folder}/{name}'
+
+    os.makedirs(f'{results_folder}/errors', exist_ok=True)
+
+    if config_1['is_cape']:
+        from data.dataset_cape import read_frames
+        frames = read_frames(config_1['test_dir'], shuffle=False, is_cape=True)
+        calculate_cape()
+    else:
+        frames_file = config_1['test_frames']
+        with open(os.path.join(config_1['test_dir'], frames_file), 'r') as f:
+            frames = f.readlines()
+        frames = [Frame(frames.split()[0], int(frames.split()[1]), frames.split()[2]) for frames in frames]
+
+        calculate()
\ No newline at end of file
diff --git a/scripts/get_error_all.py b/scripts/get_error_all.py
new file mode 100644
index 0000000..1a0bbfd
--- /dev/null
+++ b/scripts/get_error_all.py
@@ -0,0 +1,102 @@
+import os
+import sys
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/..')
+
+import importlib
+import argparse
+parser = argparse.ArgumentParser(description='')
+parser.add_argument('--name', type=str)
+parser.add_argument('--config', type=str)
+parser.add_argument('--debug', action='store_true')
+parser.add_argument('--iteration', type=str, default='190000')
+parser.add_argument('-g', '--gpu', type=str)
+parser.add_argument('-a', '--all', action='store_true')
+args = vars(parser.parse_args())
+
+if args['debug']:
+    from configs.iccvw.pyr_64_128_256_512_cape_f_e2e import config as config_1
+else:
+    config_module = importlib.import_module(args['config'])
+    config_1 = config_module.config
+
+
+import time
+import argparse
+import tqdm
+import random
+import numpy as np
+import plotly.graph_objects as go
+import plotly.express as ex
+from data.utils import Frame
+
+
+
+
+def calculate():
+    all_levels = args['all']
+    if all_levels:
+        errors = {0: dict(), 1: dict(), 2: dict(), 3: dict()}
+        errors_mean = {0: dict(), 1: dict(), 2: dict(), 3: dict()}
+    else:
+        errors = dict()
+        errors_mean = dict()
+
+
+    with open(f'{results_folder}/errors/{name}.csv', 'r') as f:
+        lines = f.readlines()
+
+    for line in lines:
+        line = line.split(',')
+        category = line[2]
+        if all_levels:
+            level = int(line[3][-1])
+            if category not in errors:
+                errors[level][category] = []
+            errors[level][category].append(float(line[4]))
+        else:
+            if category not in errors:
+                errors[category] = []
+            errors[category].append(float(line[3]))
+
+    if all_levels:
+        for i in range(4):
+            for category in errors[i]:
+                errors_mean[i][category] = round(np.mean(errors[i][category]), 5)
+            print(f'Level {i}: {errors_mean[i]}')
+
+            mean = np.mean(list(errors_mean[i].values()))
+            print(f'Final error level {i}: {mean}')
+    else:
+        for category in errors:
+            errors_mean[category] = round(np.mean(errors[category]), 5)
+        print(errors_mean) 
+
+        mean = np.mean(list(errors_mean.values()))
+        print(f'Final error: {mean}')
+
+
+
+if __name__ == '__main__':
+    skirt_modifier = '_skirt' if config_1['is_skirt'] else ''
+
+    name = config_1['name']
+    if not config_1['is_cape']:
+        frames_file = config_1['test_frames']
+        with open(os.path.join(config_1['test_dir'], frames_file), 'r') as f:
+            frames = f.readlines()
+        frames = [Frame(frames.split()[0], int(frames.split()[1]), frames.split()[2]) for frames in frames]
+    else:
+        from data.dataset_cape import read_frames
+        frames = read_frames(config_1['test_dir'], shuffle=False, is_cape=True)
+
+    results_folder = '/data-local/127_2/results'
+    if args['iteration'] == '':
+        folders = [f for f in os.listdir(results_folder) if os.path.isdir(os.path.join(results_folder, f)) and f.startswith(config_1['name'])]
+        assert len(folders) == 1
+        name = folders[0]
+    else:
+        name = f'{name}_{args["iteration"]}'
+    out_folder = f'{results_folder}/{name}'
+
+
+    calculate()
\ No newline at end of file
diff --git a/scripts/get_error_hood.py b/scripts/get_error_hood.py
new file mode 100644
index 0000000..9221939
--- /dev/null
+++ b/scripts/get_error_hood.py
@@ -0,0 +1,84 @@
+import os
+import sys
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/..')
+
+import importlib
+import argparse
+parser = argparse.ArgumentParser(description='')
+parser.add_argument('--name', type=str)
+parser.add_argument('--config', type=str)
+parser.add_argument('--debug', action='store_true')
+parser.add_argument('--iteration', type=str, default='')
+parser.add_argument('-g', '--gpu', type=str)
+parser.add_argument('-n', '--normal', action='store_true')
+parser.add_argument('-e', '--edge', action='store_true')
+parser.add_argument('-cr', '--collision_ratio', action='store_true')
+parser.add_argument('-a', '--all', action='store_true')
+args = vars(parser.parse_args())
+
+if args['debug']:
+    from configs.iccvw.pyr_64_128_256_512_cape_f_e2e import config as config_1
+else:
+    config_module = importlib.import_module(args['config'])
+    config_1 = config_module.config
+
+
+import time
+import argparse
+import tqdm
+import random
+import numpy as np
+import plotly.graph_objects as go
+import plotly.express as ex
+from data.utils import Frame
+
+
+
+
+def calculate(type=''):
+    errors = dict()
+    errors_mean = dict()
+
+    with open(f'{folder}/{hood_csv}', 'r') as f:
+        lines_hood = f.readlines()
+    with open(f'{folder}/{ours_csv}', 'r') as f:
+        lines_ours = f.readlines()
+
+    for line in lines_hood:
+        line = line.split(',')
+        category = line[2]
+
+        if category not in errors:
+            errors[category] = []
+        errors[category].append(float(line[3]))
+
+    for category in errors:
+        errors_mean[category] = round(np.mean(errors[category]), 5)
+    print(errors_mean) 
+
+    mean = round(np.mean(list(errors_mean.values())), 5)
+    print(f'Final HOOD error{type}: {mean}')
+
+    for line in lines_ours:
+        line = line.split(',')
+        category = line[2]
+
+        if category not in errors:
+            errors[category] = []
+        errors[category].append(float(line[3]))
+
+    for category in errors:
+        errors_mean[category] = round(np.mean(errors[category]), 5)
+    print(errors_mean) 
+
+    mean = round(np.mean(list(errors_mean.values())), 5)
+    print(f'Final OURS error{type}: {mean}')
+
+
+if __name__ == '__main__':
+
+    folder = '/data-local/127_2/results/hood'
+    hood_csv = 'hood_2_hood.csv'
+    ours_csv = 'hood_2_ours.csv'
+
+    calculate()
\ No newline at end of file