diff --git a/.gitattributes b/.gitattributes
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--- /dev/null
+++ b/.gitattributes
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+# Auto detect text files and perform LF normalization
+* text=auto
diff --git a/config.py b/config.py
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+++ b/config.py
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+Dataset_Path = dict(
+    Tusimple = "dataset/Tusimple",
+)
diff --git a/dataset/Tusimple.py b/dataset/Tusimple.py
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+++ b/dataset/Tusimple.py
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+import json
+import os
+
+import cv2
+import numpy as np
+import torch
+from torch.utils.data import Dataset
+
+
+class Tusimple(Dataset):
+    """
+    image_set is splitted into three partitions: train, val, test.
+    train includes label_data_0313.json, label_data_0601.json
+    val includes label_data_0531.json
+    test includes test_label.json
+    """
+    TRAIN_SET = ['label_data_0313.json', 'label_data_0601.json']
+    VAL_SET = ['label_data_0531.json']
+    TEST_SET = ['test_label.json']
+
+    def __init__(self, path, image_set, transforms=None):
+        super(Tusimple, self).__init__()
+        assert image_set in ('train', 'val', 'test'), "image_set is not valid!"
+        self.data_dir_path = path
+        self.image_set = image_set
+        self.transforms = transforms
+
+        if not os.path.exists(os.path.join(path, "seg_label")):
+            print("Label is going to get generated into dir: {} ...".format(os.path.join(path, "seg_label")))
+            self.generate_label()
+        self.createIndex()
+
+    def createIndex(self):
+        self.img_list = []
+        self.segLabel_list = []
+        self.exist_list = []
+
+        listfile = os.path.join(self.data_dir_path, "seg_label", "list", "{}_gt.txt".format(self.image_set))
+        if not os.path.exists(listfile):
+            raise FileNotFoundError("List file doesn't exist. Label has to be generated! ...")
+
+        with open(listfile) as f:
+            for line in f:
+                line = line.strip()
+                l = line.split(" ")
+                self.img_list.append(os.path.join(self.data_dir_path, l[0][1:]))  # l[0][1:]  get rid of the first '/' so as for os.path.join
+                self.segLabel_list.append(os.path.join(self.data_dir_path, l[1][1:]))
+                self.exist_list.append([int(x) for x in l[2:]])
+
+    def __getitem__(self, idx):
+        img = cv2.imread(self.img_list[idx])
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        if self.image_set != 'test':
+            segLabel = cv2.imread(self.segLabel_list[idx])[:, :, 0]
+            exist = np.array(self.exist_list[idx])
+        else:
+            segLabel = None
+            exist = None
+
+        sample = {'img': img,
+                  'segLabel': segLabel,
+                  'exist': exist,
+                  'img_name': self.img_list[idx]}
+        if self.transforms is not None:
+            sample = self.transforms(sample)
+        return sample
+
+    def __len__(self):
+        return len(self.img_list)
+
+    def generate_label(self):
+        save_dir = os.path.join(self.data_dir_path, "seg_label")
+        os.makedirs(save_dir, exist_ok=True)
+
+        # --------- merge json into one file ---------
+        with open(os.path.join(save_dir, "train.json"), "w") as outfile:
+            for json_name in self.TRAIN_SET:
+                with open(os.path.join(self.data_dir_path, json_name)) as infile:
+                    for line in infile:
+                        outfile.write(line)
+
+        with open(os.path.join(save_dir, "val.json"), "w") as outfile:
+            for json_name in self.VAL_SET:
+                with open(os.path.join(self.data_dir_path, json_name)) as infile:
+                    for line in infile:
+                        outfile.write(line)
+
+        with open(os.path.join(save_dir, "test.json"), "w") as outfile:
+            for json_name in self.TEST_SET:
+                with open(os.path.join(self.data_dir_path, json_name)) as infile:
+                    for line in infile:
+                        outfile.write(line)
+
+        self._gen_label_for_json('train')
+        print("train set is done")
+        self._gen_label_for_json('val')
+        print("val set is done")
+        self._gen_label_for_json('test')
+        print("test set is done")
+
+    def _gen_label_for_json(self, image_set):
+        H, W = 720, 1280
+        SEG_WIDTH = 30
+        save_dir = "seg_label"
+
+        os.makedirs(os.path.join(self.data_dir_path, save_dir, "list"), exist_ok=True)
+        list_f = open(os.path.join(self.data_dir_path, save_dir, "list", "{}_gt.txt".format(image_set)), "w")
+
+        json_path = os.path.join(self.data_dir_path, save_dir, "{}.json".format(image_set))
+        with open(json_path) as f:
+            for line in f:
+                label = json.loads(line)
+
+                # ---------- clean and sort lanes -------------
+                lanes = []
+                _lanes = []
+                slope = [] # identify 1st, 2nd, 3rd, 4th lane through slope
+                for i in range(len(label['lanes'])):
+                    l = [(x, y) for x, y in zip(label['lanes'][i], label['h_samples']) if x >= 0]
+                    if (len(l)>1):
+                        _lanes.append(l)
+                        slope.append(np.arctan2(l[-1][1]-l[0][1], l[0][0]-l[-1][0]) / np.pi * 180)
+                _lanes = [_lanes[i] for i in np.argsort(slope)]
+                slope = [slope[i] for i in np.argsort(slope)]
+
+                idx_1 = None
+                idx_2 = None
+                idx_3 = None
+                idx_4 = None
+                for i in range(len(slope)):
+                    if slope[i]<=90:
+                        idx_2 = i
+                        idx_1 = i-1 if i>0 else None
+                    else:
+                        idx_3 = i
+                        idx_4 = i+1 if i+1 < len(slope) else None
+                        break
+                lanes.append([] if idx_1 is None else _lanes[idx_1])
+                lanes.append([] if idx_2 is None else _lanes[idx_2])
+                lanes.append([] if idx_3 is None else _lanes[idx_3])
+                lanes.append([] if idx_4 is None else _lanes[idx_4])
+                # ---------------------------------------------
+
+                img_path = label['raw_file']
+                seg_img = np.zeros((H, W, 3))
+                list_str = []  # str to be written to list.txt
+                for i in range(len(lanes)):
+                    coords = lanes[i]
+                    if len(coords) < 4:
+                        list_str.append('0')
+                        continue
+                    for j in range(len(coords)-1):
+                        cv2.line(seg_img, coords[j], coords[j+1], (i+1, i+1, i+1), SEG_WIDTH//2)
+                    list_str.append('1')
+
+                seg_path = img_path.split("/")
+                seg_path, img_name = os.path.join(self.data_dir_path, save_dir, seg_path[1], seg_path[2]), seg_path[3]
+                os.makedirs(seg_path, exist_ok=True)
+                seg_path = os.path.join(seg_path, img_name[:-3]+"png")
+                cv2.imwrite(seg_path, seg_img)
+
+                seg_path = "/".join([save_dir, *img_path.split("/")[1:3], img_name[:-3]+"png"])
+                if seg_path[0] != '/':
+                    seg_path = '/' + seg_path
+                if img_path[0] != '/':
+                    img_path = '/' + img_path
+                list_str.insert(0, seg_path)
+                list_str.insert(0, img_path)
+                list_str = " ".join(list_str) + "\n"
+                list_f.write(list_str)
+
+        list_f.close()
+
+    @staticmethod
+    def collate(batch):
+        if isinstance(batch[0]['img'], torch.Tensor):
+            img = torch.stack([b['img'] for b in batch])
+        else:
+            img = [b['img'] for b in batch]
+
+        if batch[0]['segLabel'] is None:
+            segLabel = None
+            exist = None
+        elif isinstance(batch[0]['segLabel'], torch.Tensor):
+            segLabel = torch.stack([b['segLabel'] for b in batch])
+            exist = torch.stack([b['exist'] for b in batch])
+        else:
+            segLabel = [b['segLabel'] for b in batch]
+            exist = [b['exist'] for b in batch]
+
+        samples = {'img': img,
+                   'segLabel': segLabel,
+                   'exist': exist,
+                   'img_name': [x['img_name'] for x in batch]}
+
+        return samples
\ No newline at end of file
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diff --git a/dataset/Tusimple/label_data_0531.json b/dataset/Tusimple/label_data_0531.json
new file mode 100644
index 0000000..9df1a8c
--- /dev/null
+++ b/dataset/Tusimple/label_data_0531.json
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diff --git a/dataset/Tusimple/label_data_0601.json b/dataset/Tusimple/label_data_0601.json
new file mode 100644
index 0000000..76801f2
--- /dev/null
+++ b/dataset/Tusimple/label_data_0601.json
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diff --git a/dataset/Tusimple/seg_label/train.json b/dataset/Tusimple/seg_label/train.json
new file mode 100644
index 0000000..303111c
--- /dev/null
+++ b/dataset/Tusimple/seg_label/train.json
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diff --git a/dataset/Tusimple/seg_label/val.json b/dataset/Tusimple/seg_label/val.json
new file mode 100644
index 0000000..9df1a8c
--- /dev/null
+++ b/dataset/Tusimple/seg_label/val.json
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diff --git a/dataset/Tusimple/test_label.json b/dataset/Tusimple/test_label.json
new file mode 100644
index 0000000..5302553
--- /dev/null
+++ b/dataset/Tusimple/test_label.json
@@ -0,0 +1,47 @@
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diff --git a/dataset/Tusimple/test_tasks_0627.json b/dataset/Tusimple/test_tasks_0627.json
new file mode 100644
index 0000000..b7004bc
--- /dev/null
+++ b/dataset/Tusimple/test_tasks_0627.json
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+{"h_samples":[160,170,180,190,200,210,220,230,240,250,260,270,280,290,300,310,320,330,340,350,360,370,380,390,400,410,420,430,440,450,460,470,480,490,500,510,520,530,540,550,560,570,580,590,600,610,620,630,640,650,660,670,680,690,700,710],"lanes":[],"run_time":1000,"raw_file":"clips\/0601\/1494452393588330446\/20.jpg"}
+{"h_samples":[160,170,180,190,200,210,220,230,240,250,260,270,280,290,300,310,320,330,340,350,360,370,380,390,400,410,420,430,440,450,460,470,480,490,500,510,520,530,540,550,560,570,580,590,600,610,620,630,640,650,660,670,680,690,700,710],"lanes":[],"run_time":1000,"raw_file":"clips\/0601\/1494452395587533775\/20.jpg"}
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+{"h_samples":[160,170,180,190,200,210,220,230,240,250,260,270,280,290,300,310,320,330,340,350,360,370,380,390,400,410,420,430,440,450,460,470,480,490,500,510,520,530,540,550,560,570,580,590,600,610,620,630,640,650,660,670,680,690,700,710],"lanes":[],"run_time":1000,"raw_file":"clips\/0601\/1494452413579633881\/20.jpg"}
+{"h_samples":[160,170,180,190,200,210,220,230,240,250,260,270,280,290,300,310,320,330,340,350,360,370,380,390,400,410,420,430,440,450,460,470,480,490,500,510,520,530,540,550,560,570,580,590,600,610,620,630,640,650,660,670,680,690,700,710],"lanes":[],"run_time":1000,"raw_file":"clips\/0601\/1494452417580383008\/20.jpg"}
+{"h_samples":[160,170,180,190,200,210,220,230,240,250,260,270,280,290,300,310,320,330,340,350,360,370,380,390,400,410,420,430,440,450,460,470,480,490,500,510,520,530,540,550,560,570,580,590,600,610,620,630,640,650,660,670,680,690,700,710],"lanes":[],"run_time":1000,"raw_file":"clips\/0601\/1494452419576967330\/20.jpg"}
diff --git a/dataset/__init__.py b/dataset/__init__.py
new file mode 100644
index 0000000..705d988
--- /dev/null
+++ b/dataset/__init__.py
@@ -0,0 +1 @@
+from .Tusimple import Tusimple
\ No newline at end of file
diff --git a/demo/demo.jpg b/demo/demo.jpg
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diff --git a/experiments/exp0/cfg.json b/experiments/exp0/cfg.json
new file mode 100644
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--- /dev/null
+++ b/experiments/exp0/cfg.json
@@ -0,0 +1,25 @@
+{
+  "device": "cuda:0",
+  "MAX_EPOCHES": 1,
+  "model": "enet_sad",
+
+  "dataset": {
+    "dataset_name": "Tusimple",
+    "batch_size": 4,
+    "resize_shape": [800, 288]
+  },
+
+  "optim": {
+    "lr": 1e-2,
+    "momentum": 0.9,
+    "weight_decay": 2e-5,
+    "nesterov": true
+  },
+
+  "lr_scheduler": {
+    "warmup": 50,
+    "max_iter": 60000
+  },
+
+  "sad_start_iter": 40000
+}
\ No newline at end of file
diff --git a/experiments/exp0/events.out.tfevents.1624084648.DESKTOP-F01MSO8 b/experiments/exp0/events.out.tfevents.1624084648.DESKTOP-F01MSO8
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diff --git a/experiments/exp1/cfg.json b/experiments/exp1/cfg.json
new file mode 100644
index 0000000..2f9c989
--- /dev/null
+++ b/experiments/exp1/cfg.json
@@ -0,0 +1,25 @@
+{
+  "device": "cuda:0",
+  "MAX_EPOCHES": 80,
+  "model": "enet_sad",
+
+  "dataset": {
+    "dataset_name": "CULane",
+    "batch_size": 12,
+    "resize_shape": [800, 288]
+  },
+
+  "optim": {
+    "lr": 1e-2,
+    "momentum": 0.9,
+    "weight_decay": 2e-5,
+    "nesterov": true
+  },
+
+  "lr_scheduler": {
+    "warmup": 50,
+    "max_iter": 60000
+  },
+
+  "sad_start_iter": 40000
+}
\ No newline at end of file
diff --git a/experiments/exp1/exp1.pth b/experiments/exp1/exp1.pth
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diff --git a/model_ENET_SAD.py b/model_ENET_SAD.py
new file mode 100644
index 0000000..7197c45
--- /dev/null
+++ b/model_ENET_SAD.py
@@ -0,0 +1,903 @@
+# Enet pytorch code retrieved from https://github.com/davidtvs/PyTorch-ENet/blob/master/models/enet.py
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn.parameter import Parameter
+
+from utils.utils import mIoULoss, to_one_hot
+
+
+class InitialBlock(nn.Module):
+    """The initial block is composed of two branches:
+    1. a main branch which performs a regular convolution with stride 2;
+    2. an extension branch which performs max-pooling.
+    Doing both operations in parallel and concatenating their results
+    allows for efficient downsampling and expansion. The main branch
+    outputs 13 feature maps while the extension branch outputs 3, for a
+    total of 16 feature maps after concatenation.
+    Keyword arguments:
+    - in_channels (int): the number of input channels.
+    - out_channels (int): the number output channels.
+    - kernel_size (int, optional): the kernel size of the filters used in
+    the convolution layer. Default: 3.
+    - padding (int, optional): zero-padding added to both sides of the
+    input. Default: 0.
+    - bias (bool, optional): Adds a learnable bias to the output if
+    ``True``. Default: False.
+    - relu (bool, optional): When ``True`` ReLU is used as the activation
+    function; otherwise, PReLU is used. Default: True.
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 bias=False,
+                 relu=True):
+        super().__init__()
+
+        if relu:
+            activation = nn.ReLU
+        else:
+            activation = nn.PReLU
+
+        # Main branch - As stated above the number of output channels for this
+        # branch is the total minus 3, since the remaining channels come from
+        # the extension branch
+        self.main_branch = nn.Conv2d(
+            in_channels,
+            out_channels - 3,
+            kernel_size=3,
+            stride=2,
+            padding=1,
+            bias=bias)
+
+        # Extension branch
+        self.ext_branch = nn.MaxPool2d(3, stride=2, padding=1)
+
+        # Initialize batch normalization to be used after concatenation
+        self.batch_norm = nn.BatchNorm2d(out_channels)
+
+        # PReLU layer to apply after concatenating the branches
+        self.out_activation = activation()
+
+    def forward(self, x):
+        main = self.main_branch(x)
+        ext = self.ext_branch(x)
+
+        # Concatenate branches
+        out = torch.cat((main, ext), 1)
+
+        # Apply batch normalization
+        out = self.batch_norm(out)
+
+        return self.out_activation(out)
+
+
+class RegularBottleneck(nn.Module):
+    """Regular bottlenecks are the main building block of ENet.
+    Main branch:
+    1. Shortcut connection.
+    Extension branch:
+    1. 1x1 convolution which decreases the number of channels by
+    ``internal_ratio``, also called a projection;
+    2. regular, dilated or asymmetric convolution;
+    3. 1x1 convolution which increases the number of channels back to
+    ``channels``, also called an expansion;
+    4. dropout as a regularizer.
+    Keyword arguments:
+    - channels (int): the number of input and output channels.
+    - internal_ratio (int, optional): a scale factor applied to
+    ``channels`` used to compute the number of
+    channels after the projection. eg. given ``channels`` equal to 128 and
+    internal_ratio equal to 2 the number of channels after the projection
+    is 64. Default: 4.
+    - kernel_size (int, optional): the kernel size of the filters used in
+    the convolution layer described above in item 2 of the extension
+    branch. Default: 3.
+    - padding (int, optional): zero-padding added to both sides of the
+    input. Default: 0.
+    - dilation (int, optional): spacing between kernel elements for the
+    convolution described in item 2 of the extension branch. Default: 1.
+    asymmetric (bool, optional): flags if the convolution described in
+    item 2 of the extension branch is asymmetric or not. Default: False.
+    - dropout_prob (float, optional): probability of an element to be
+    zeroed. Default: 0 (no dropout).
+    - bias (bool, optional): Adds a learnable bias to the output if
+    ``True``. Default: False.
+    - relu (bool, optional): When ``True`` ReLU is used as the activation
+    function; otherwise, PReLU is used. Default: True.
+    """
+
+    def __init__(self,
+                 channels,
+                 internal_ratio=4,
+                 kernel_size=3,
+                 padding=0,
+                 dilation=1,
+                 asymmetric=False,
+                 dropout_prob=0,
+                 bias=False,
+                 relu=True):
+        super().__init__()
+
+        # Check in the internal_scale parameter is within the expected range
+        # [1, channels]
+        if internal_ratio <= 1 or internal_ratio > channels:
+            raise RuntimeError("Value out of range. Expected value in the "
+                               "interval [1, {0}], got internal_scale={1}."
+                               .format(channels, internal_ratio))
+
+        internal_channels = channels // internal_ratio
+
+        if relu:
+            activation = nn.ReLU
+        else:
+            activation = nn.PReLU
+
+        # Main branch - shortcut connection
+
+        # Extension branch - 1x1 convolution, followed by a regular, dilated or
+        # asymmetric convolution, followed by another 1x1 convolution, and,
+        # finally, a regularizer (spatial dropout). Number of channels is constant.
+
+        # 1x1 projection convolution
+        self.ext_conv1 = nn.Sequential(
+            nn.Conv2d(
+                channels,
+                internal_channels,
+                kernel_size=1,
+                stride=1,
+                bias=bias), nn.BatchNorm2d(internal_channels), activation())
+
+        # If the convolution is asymmetric we split the main convolution in
+        # two. Eg. for a 5x5 asymmetric convolution we have two convolution:
+        # the first is 5x1 and the second is 1x5.
+        if asymmetric:
+            self.ext_conv2 = nn.Sequential(
+                nn.Conv2d(
+                    internal_channels,
+                    internal_channels,
+                    kernel_size=(kernel_size, 1),
+                    stride=1,
+                    padding=(padding, 0),
+                    dilation=dilation,
+                    bias=bias), nn.BatchNorm2d(internal_channels), activation(),
+                nn.Conv2d(
+                    internal_channels,
+                    internal_channels,
+                    kernel_size=(1, kernel_size),
+                    stride=1,
+                    padding=(0, padding),
+                    dilation=dilation,
+                    bias=bias), nn.BatchNorm2d(internal_channels), activation())
+        else:
+            self.ext_conv2 = nn.Sequential(
+                nn.Conv2d(
+                    internal_channels,
+                    internal_channels,
+                    kernel_size=kernel_size,
+                    stride=1,
+                    padding=padding,
+                    dilation=dilation,
+                    bias=bias), nn.BatchNorm2d(internal_channels), activation())
+
+        # 1x1 expansion convolution
+        self.ext_conv3 = nn.Sequential(
+            nn.Conv2d(
+                internal_channels,
+                channels,
+                kernel_size=1,
+                stride=1,
+                bias=bias), nn.BatchNorm2d(channels), activation())
+
+        self.ext_regul = nn.Dropout2d(p=dropout_prob)
+
+        # PReLU layer to apply after adding the branches
+        self.out_activation = activation()
+
+    def forward(self, x):
+        # Main branch shortcut
+        main = x
+
+        # Extension branch
+        ext = self.ext_conv1(x)
+        ext = self.ext_conv2(ext)
+        ext = self.ext_conv3(ext)
+        ext = self.ext_regul(ext)
+
+        # Add main and extension branches
+        out = main + ext
+
+        return self.out_activation(out)
+
+
+class DownsamplingBottleneck(nn.Module):
+    """Downsampling bottlenecks further downsample the feature map size.
+    Main branch:
+    1. max pooling with stride 2; indices are saved to be used for
+    unpooling later.
+    Extension branch:
+    1. 2x2 convolution with stride 2 that decreases the number of channels
+    by ``internal_ratio``, also called a projection;
+    2. regular convolution (by default, 3x3);
+    3. 1x1 convolution which increases the number of channels to
+    ``out_channels``, also called an expansion;
+    4. dropout as a regularizer.
+    Keyword arguments:
+    - in_channels (int): the number of input channels.
+    - out_channels (int): the number of output channels.
+    - internal_ratio (int, optional): a scale factor applied to ``channels``
+    used to compute the number of channels after the projection. eg. given
+    ``channels`` equal to 128 and internal_ratio equal to 2 the number of
+    channels after the projection is 64. Default: 4.
+    - return_indices (bool, optional):  if ``True``, will return the max
+    indices along with the outputs. Useful when unpooling later.
+    - dropout_prob (float, optional): probability of an element to be
+    zeroed. Default: 0 (no dropout).
+    - bias (bool, optional): Adds a learnable bias to the output if
+    ``True``. Default: False.
+    - relu (bool, optional): When ``True`` ReLU is used as the activation
+    function; otherwise, PReLU is used. Default: True.
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 internal_ratio=4,
+                 return_indices=False,
+                 dropout_prob=0,
+                 bias=False,
+                 relu=True):
+        super().__init__()
+
+        # Store parameters that are needed later
+        self.return_indices = return_indices
+
+        # Check in the internal_scale parameter is within the expected range
+        # [1, channels]
+        if internal_ratio <= 1 or internal_ratio > in_channels:
+            raise RuntimeError("Value out of range. Expected value in the "
+                               "interval [1, {0}], got internal_scale={1}. "
+                               .format(in_channels, internal_ratio))
+
+        internal_channels = in_channels // internal_ratio
+
+        if relu:
+            activation = nn.ReLU
+        else:
+            activation = nn.PReLU
+
+        # Main branch - max pooling followed by feature map (channels) padding
+        self.main_max1 = nn.MaxPool2d(
+            2,
+            stride=2,
+            return_indices=return_indices)
+
+        # Extension branch - 2x2 convolution, followed by a regular, dilated or
+        # asymmetric convolution, followed by another 1x1 convolution. Number
+        # of channels is doubled.
+
+        # 2x2 projection convolution with stride 2
+        self.ext_conv1 = nn.Sequential(
+            nn.Conv2d(
+                in_channels,
+                internal_channels,
+                kernel_size=2,
+                stride=2,
+                bias=bias), nn.BatchNorm2d(internal_channels), activation())
+
+        # Convolution
+        self.ext_conv2 = nn.Sequential(
+            nn.Conv2d(
+                internal_channels,
+                internal_channels,
+                kernel_size=3,
+                stride=1,
+                padding=1,
+                bias=bias), nn.BatchNorm2d(internal_channels), activation())
+
+        # 1x1 expansion convolution
+        self.ext_conv3 = nn.Sequential(
+            nn.Conv2d(
+                internal_channels,
+                out_channels,
+                kernel_size=1,
+                stride=1,
+                bias=bias), nn.BatchNorm2d(out_channels), activation())
+
+        self.ext_regul = nn.Dropout2d(p=dropout_prob)
+
+        # PReLU layer to apply after concatenating the branches
+        self.out_activation = activation()
+
+    def forward(self, x):
+        # Main branch shortcut
+        if self.return_indices:
+            main, max_indices = self.main_max1(x)
+        else:
+            main = self.main_max1(x)
+
+        # Extension branch
+        ext = self.ext_conv1(x)
+        ext = self.ext_conv2(ext)
+        ext = self.ext_conv3(ext)
+        ext = self.ext_regul(ext)
+
+        # Main branch channel padding
+        n, ch_ext, h, w = ext.size()
+        ch_main = main.size()[1]
+        padding = torch.zeros(n, ch_ext - ch_main, h, w)
+
+        # Before concatenating, check if main is on the CPU or GPU and
+        # convert padding accordingly
+        if main.is_cuda:
+            padding = padding.cuda()
+
+        # Concatenate
+        main = torch.cat((main, padding), 1)
+
+        # Add main and extension branches
+        out = main + ext
+
+        return self.out_activation(out), max_indices
+
+
+class UpsamplingBottleneck(nn.Module):
+    """The upsampling bottlenecks upsample the feature map resolution using max
+    pooling indices stored from the corresponding downsampling bottleneck.
+    Main branch:
+    1. 1x1 convolution with stride 1 that decreases the number of channels by
+    ``internal_ratio``, also called a projection;
+    2. max unpool layer using the max pool indices from the corresponding
+    downsampling max pool layer.
+    Extension branch:
+    1. 1x1 convolution with stride 1 that decreases the number of channels by
+    ``internal_ratio``, also called a projection;
+    2. transposed convolution (by default, 3x3);
+    3. 1x1 convolution which increases the number of channels to
+    ``out_channels``, also called an expansion;
+    4. dropout as a regularizer.
+    Keyword arguments:
+    - in_channels (int): the number of input channels.
+    - out_channels (int): the number of output channels.
+    - internal_ratio (int, optional): a scale factor applied to ``in_channels``
+     used to compute the number of channels after the projection. eg. given
+     ``in_channels`` equal to 128 and ``internal_ratio`` equal to 2 the number
+     of channels after the projection is 64. Default: 4.
+    - dropout_prob (float, optional): probability of an element to be zeroed.
+    Default: 0 (no dropout).
+    - bias (bool, optional): Adds a learnable bias to the output if ``True``.
+    Default: False.
+    - relu (bool, optional): When ``True`` ReLU is used as the activation
+    function; otherwise, PReLU is used. Default: True.
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 internal_ratio=4,
+                 dropout_prob=0,
+                 bias=False,
+                 relu=True):
+        super().__init__()
+
+        # Check in the internal_scale parameter is within the expected range
+        # [1, channels]
+        if internal_ratio <= 1 or internal_ratio > in_channels:
+            raise RuntimeError("Value out of range. Expected value in the "
+                               "interval [1, {0}], got internal_scale={1}. "
+                               .format(in_channels, internal_ratio))
+
+        internal_channels = in_channels // internal_ratio
+
+        if relu:
+            activation = nn.ReLU
+        else:
+            activation = nn.PReLU
+
+        # Main branch - max pooling followed by feature map (channels) padding
+        self.main_conv1 = nn.Sequential(
+            nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=bias),
+            nn.BatchNorm2d(out_channels))
+
+        # Remember that the stride is the same as the kernel_size, just like
+        # the max pooling layers
+        self.main_unpool1 = nn.MaxUnpool2d(kernel_size=2)
+
+        # Extension branch - 1x1 convolution, followed by a regular, dilated or
+        # asymmetric convolution, followed by another 1x1 convolution. Number
+        # of channels is doubled.
+
+        # 1x1 projection convolution with stride 1
+        self.ext_conv1 = nn.Sequential(
+            nn.Conv2d(
+                in_channels, internal_channels, kernel_size=1, bias=bias),
+            nn.BatchNorm2d(internal_channels), activation())
+
+        # Transposed convolution
+        self.ext_tconv1 = nn.ConvTranspose2d(
+            internal_channels,
+            internal_channels,
+            kernel_size=2,
+            stride=2,
+            bias=bias)
+        self.ext_tconv1_bnorm = nn.BatchNorm2d(internal_channels)
+        self.ext_tconv1_activation = activation()
+
+        # 1x1 expansion convolution
+        self.ext_conv2 = nn.Sequential(
+            nn.Conv2d(
+                internal_channels, out_channels, kernel_size=1, bias=bias),
+            nn.BatchNorm2d(out_channels), activation())
+
+        self.ext_regul = nn.Dropout2d(p=dropout_prob)
+
+        # PReLU layer to apply after concatenating the branches
+        self.out_activation = activation()
+
+    def forward(self, x, max_indices, output_size):
+        # Main branch shortcut
+        main = self.main_conv1(x)
+        main = self.main_unpool1(
+            main, max_indices, output_size=output_size)
+
+        # Extension branch
+        ext = self.ext_conv1(x)
+        ext = self.ext_tconv1(ext, output_size=output_size)
+        ext = self.ext_tconv1_bnorm(ext)
+        ext = self.ext_tconv1_activation(ext)
+        ext = self.ext_conv2(ext)
+        ext = self.ext_regul(ext)
+
+        # Add main and extension branches
+        out = main + ext
+
+        return self.out_activation(out)
+
+
+class ENet(nn.Module):
+    """Generate the ENet model.
+    Keyword arguments:
+    - num_classes (int): the number of classes to segment.
+    - encoder_relu (bool, optional): When ``True`` ReLU is used as the
+    activation function in the encoder blocks/layers; otherwise, PReLU
+    is used. Default: False.
+    - decoder_relu (bool, optional): When ``True`` ReLU is used as the
+    activation function in the decoder blocks/layers; otherwise, PReLU
+    is used. Default: True.
+    """
+
+    def __init__(self, num_classes, encoder_relu=False, decoder_relu=True):
+        super().__init__()
+
+        self.initial_block = InitialBlock(3, 16, relu=encoder_relu)
+
+        # Stage 1 - Encoder
+        self.downsample1_0 = DownsamplingBottleneck(
+            16,
+            64,
+            return_indices=True,
+            dropout_prob=0.01,
+            relu=encoder_relu)
+        self.regular1_1 = RegularBottleneck(
+            64, padding=1, dropout_prob=0.01, relu=encoder_relu)
+        self.regular1_2 = RegularBottleneck(
+            64, padding=1, dropout_prob=0.01, relu=encoder_relu)
+        self.regular1_3 = RegularBottleneck(
+            64, padding=1, dropout_prob=0.01, relu=encoder_relu)
+        self.regular1_4 = RegularBottleneck(
+            64, padding=1, dropout_prob=0.01, relu=encoder_relu)
+
+        # Stage 2 - Encoder
+        self.downsample2_0 = DownsamplingBottleneck(
+            64,
+            128,
+            return_indices=True,
+            dropout_prob=0.1,
+            relu=encoder_relu)
+        self.regular2_1 = RegularBottleneck(
+            128, padding=1, dropout_prob=0.1, relu=encoder_relu)
+        self.dilated2_2 = RegularBottleneck(
+            128, dilation=2, padding=2, dropout_prob=0.1, relu=encoder_relu)
+        self.asymmetric2_3 = RegularBottleneck(
+            128,
+            kernel_size=5,
+            padding=2,
+            asymmetric=True,
+            dropout_prob=0.1,
+            relu=encoder_relu)
+        self.dilated2_4 = RegularBottleneck(
+            128, dilation=4, padding=4, dropout_prob=0.1, relu=encoder_relu)
+        self.regular2_5 = RegularBottleneck(
+            128, padding=1, dropout_prob=0.1, relu=encoder_relu)
+        self.dilated2_6 = RegularBottleneck(
+            128, dilation=8, padding=8, dropout_prob=0.1, relu=encoder_relu)
+        self.asymmetric2_7 = RegularBottleneck(
+            128,
+            kernel_size=5,
+            asymmetric=True,
+            padding=2,
+            dropout_prob=0.1,
+            relu=encoder_relu)
+        self.dilated2_8 = RegularBottleneck(
+            128, dilation=16, padding=16, dropout_prob=0.1, relu=encoder_relu)
+
+        # Stage 3 - Encoder
+        self.regular3_0 = RegularBottleneck(
+            128, padding=1, dropout_prob=0.1, relu=encoder_relu)
+        self.dilated3_1 = RegularBottleneck(
+            128, dilation=2, padding=2, dropout_prob=0.1, relu=encoder_relu)
+        self.asymmetric3_2 = RegularBottleneck(
+            128,
+            kernel_size=5,
+            padding=2,
+            asymmetric=True,
+            dropout_prob=0.1,
+            relu=encoder_relu)
+        self.dilated3_3 = RegularBottleneck(
+            128, dilation=4, padding=4, dropout_prob=0.1, relu=encoder_relu)
+        self.regular3_4 = RegularBottleneck(
+            128, padding=1, dropout_prob=0.1, relu=encoder_relu)
+        self.dilated3_5 = RegularBottleneck(
+            128, dilation=8, padding=8, dropout_prob=0.1, relu=encoder_relu)
+        self.asymmetric3_6 = RegularBottleneck(
+            128,
+            kernel_size=5,
+            asymmetric=True,
+            padding=2,
+            dropout_prob=0.1,
+            relu=encoder_relu)
+        self.dilated3_7 = RegularBottleneck(
+            128, dilation=16, padding=16, dropout_prob=0.1, relu=encoder_relu)
+
+        # Stage 4 - Decoder
+        self.upsample4_0 = UpsamplingBottleneck(
+            128, 64, dropout_prob=0.1, relu=decoder_relu)
+        self.regular4_1 = RegularBottleneck(
+            64, padding=1, dropout_prob=0.1, relu=decoder_relu)
+        self.regular4_2 = RegularBottleneck(
+            64, padding=1, dropout_prob=0.1, relu=decoder_relu)
+
+        # Stage 5 - Decoder
+        self.upsample5_0 = UpsamplingBottleneck(
+            64, 16, dropout_prob=0.1, relu=decoder_relu)
+        self.regular5_1 = RegularBottleneck(
+            16, padding=1, dropout_prob=0.1, relu=decoder_relu)
+        self.transposed_conv = nn.ConvTranspose2d(
+            16,
+            num_classes,
+            kernel_size=3,
+            stride=2,
+            padding=1,
+            bias=False)
+
+    def forward(self, x):
+        # Initial block
+        input_size = x.size()
+        x = self.initial_block(x)
+
+        # Stage 1 - Encoder
+        stage1_input_size = x.size()
+        x, max_indices1_0 = self.downsample1_0(x)
+        x = self.regular1_1(x)
+        x = self.regular1_2(x)
+        x = self.regular1_3(x)
+        x = self.regular1_4(x)
+
+        # Stage 2 - Encoder
+        stage2_input_size = x.size()
+        x, max_indices2_0 = self.downsample2_0(x)
+        x = self.regular2_1(x)
+        x = self.dilated2_2(x)
+        x = self.asymmetric2_3(x)
+        x = self.dilated2_4(x)
+        x = self.regular2_5(x)
+        x = self.dilated2_6(x)
+        x = self.asymmetric2_7(x)
+        x = self.dilated2_8(x)
+
+        # Stage 3 - Encoder
+        x = self.regular3_0(x)
+        x = self.dilated3_1(x)
+        x = self.asymmetric3_2(x)
+        x = self.dilated3_3(x)
+        x = self.regular3_4(x)
+        x = self.dilated3_5(x)
+        x = self.asymmetric3_6(x)
+        x = self.dilated3_7(x)
+
+        # Stage 4 - Decoder
+        x = self.upsample4_0(x, max_indices2_0, output_size=stage2_input_size)
+        x = self.regular4_1(x)
+        x = self.regular4_2(x)
+
+        # Stage 5 - Decoder
+        x = self.upsample5_0(x, max_indices1_0, output_size=stage1_input_size)
+        x = self.regular5_1(x)
+        x = self.transposed_conv(x, output_size=input_size)
+
+        return x
+
+
+class SpatialSoftmax(nn.Module):
+    def __init__(self, temperature=1, device='cpu'):
+        super(SpatialSoftmax, self).__init__()
+
+        if temperature:
+            self.temperature = Parameter(torch.ones(1) * temperature).to(device)
+        else:
+            self.temperature = 1.
+
+    def forward(self, feature):
+        feature = feature.view(feature.shape[0], -1, feature.shape[1] * feature.shape[2])
+        softmax_attention = F.softmax(feature / self.temperature, dim=-1)
+
+        return softmax_attention
+
+
+class ENet_SAD(nn.Module):
+    """Generate the ENet model.
+    Keyword arguments:
+    - num_classes (int): the number of classes to segment.
+    - encoder_relu (bool, optional): When ``True`` ReLU is used as the
+    activation function in the encoder blocks/layers; otherwise, PReLU
+    is used. Default: False.
+    - decoder_relu (bool, optional): When ``True`` ReLU is used as the
+    activation function in the decoder blocks/layers; otherwise, PReLU
+    is used. Default: True.
+    - sad (bool, optional): When ``True``, SAD is added to model
+    . If False, SAD is removed.
+    """
+
+    def __init__(self, input_size, pretrained=False, sad=False, encoder_relu=False, decoder_relu=True, weight_share=True):
+        super().__init__()
+
+        # Init parameter
+        input_w, input_h = input_size
+        self.fc_input_feature = 5 * int(input_w / 16) * int(input_h / 16)
+
+        self.num_classes = 5
+        self.pretrained = pretrained
+
+        self.scale_background = 0.4
+
+        # Loss scale factor for ENet w/o SAD
+        self.scale_seg = 1.0
+        self.scale_exist = 0.1
+
+        # Loss scale factor for ENet w SAD
+        self.scale_sad_seg = 1.0
+        self.scale_sad_iou = 0.1
+        self.scale_sad_exist = 0.1
+        self.scale_sad_distill = 0.1
+
+        # Loss function
+        self.ce_loss = nn.CrossEntropyLoss(weight=torch.tensor([self.scale_background, 1, 1, 1, 1]))
+        self.bce_loss = nn.BCELoss()
+        self.iou_loss = mIoULoss(n_classes=4)
+
+        # Stage 0 - Initial block
+        self.initial_block = InitialBlock(3, 16, relu=encoder_relu)
+        self.sad = sad
+
+        # Stage 1 - Encoder (E1)
+        self.downsample1_0 = DownsamplingBottleneck(16, 64, return_indices=True, dropout_prob=0.01, relu=encoder_relu)
+        self.regular1_1 = RegularBottleneck(64, padding=1, dropout_prob=0.01, relu=encoder_relu)
+        self.regular1_2 = RegularBottleneck(64, padding=1, dropout_prob=0.01, relu=encoder_relu)
+        self.regular1_3 = RegularBottleneck(64, padding=1, dropout_prob=0.01, relu=encoder_relu)
+        self.regular1_4 = RegularBottleneck(64, padding=1, dropout_prob=0.01, relu=encoder_relu)
+
+        # Shared Encoder (E2~E4)
+        # Stage 2 - Encoder (E2)
+        self.downsample2_0 = DownsamplingBottleneck(64, 128, return_indices=True, dropout_prob=0.1, relu=encoder_relu)
+        self.regular2_1 = RegularBottleneck(128, padding=1, dropout_prob=0.1, relu=encoder_relu)
+        self.dilated2_2 = RegularBottleneck(128, dilation=2, padding=2, dropout_prob=0.1, relu=encoder_relu)
+        self.asymmetric2_3 = RegularBottleneck(128, kernel_size=5, padding=2, asymmetric=True, dropout_prob=0.1, relu=encoder_relu)
+        self.dilated2_4 = RegularBottleneck(128, dilation=4, padding=4, dropout_prob=0.1, relu=encoder_relu)
+        self.regular2_5 = RegularBottleneck(128, padding=1, dropout_prob=0.1, relu=encoder_relu)
+        self.dilated2_6 = RegularBottleneck(128, dilation=8, padding=8, dropout_prob=0.1, relu=encoder_relu)
+        self.asymmetric2_7 = RegularBottleneck(128, kernel_size=5, asymmetric=True, padding=2, dropout_prob=0.1, relu=encoder_relu)
+        self.dilated2_8 = RegularBottleneck(128, dilation=16, padding=16, dropout_prob=0.1, relu=encoder_relu)
+
+        # Stage 3 - Encoder (E3)
+        if weight_share:
+            self.regular3_0 = self.regular2_1
+            self.dilated3_1 = self.dilated2_2
+            self.asymmetric3_2 = self.asymmetric2_3
+            self.dilated3_3 = self.dilated2_4
+            self.regular3_4 = self.regular2_5
+            self.dilated3_5 = self.dilated2_6
+            self.asymmetric3_6 = self.asymmetric2_7
+            self.dilated3_7 = self.dilated2_8
+        else:
+            self.regular3_0 = RegularBottleneck(128, padding=1, dropout_prob=0.1, relu=encoder_relu)
+            self.dilated3_1 = RegularBottleneck(128, dilation=2, padding=2, dropout_prob=0.1, relu=encoder_relu)
+            self.asymmetric3_2 = RegularBottleneck(128, kernel_size=5, padding=2, asymmetric=True, dropout_prob=0.1, relu=encoder_relu)
+            self.dilated3_3 = RegularBottleneck(128, dilation=4, padding=4, dropout_prob=0.1, relu=encoder_relu)
+            self.regular3_4 = RegularBottleneck(128, padding=1, dropout_prob=0.1, relu=encoder_relu)
+            self.dilated3_5 = RegularBottleneck(128, dilation=8, padding=8, dropout_prob=0.1, relu=encoder_relu)
+            self.asymmetric3_6 = RegularBottleneck(128, kernel_size=5, asymmetric=True, padding=2, dropout_prob=0.1, relu=encoder_relu)
+            self.dilated3_7 = RegularBottleneck(128, dilation=16, padding=16, dropout_prob=0.1, relu=encoder_relu)
+
+        # Stage 4 - Encoder (E4)
+        if weight_share:
+            self.regular4_0 = self.regular2_1
+            self.dilated4_1 = self.dilated2_2
+            self.asymmetric4_2 = self.asymmetric2_3
+            self.dilated4_3 = self.dilated2_4
+            self.regular4_4 = self.regular2_5
+            self.dilated4_5 = self.dilated2_6
+            self.asymmetric4_6 = self.asymmetric2_7
+            self.dilated4_7 = self.dilated2_8
+        else:
+            self.regular4_0 = RegularBottleneck(128, padding=1, dropout_prob=0.1, relu=encoder_relu)
+            self.dilated4_1 = RegularBottleneck(128, dilation=2, padding=2, dropout_prob=0.1, relu=encoder_relu)
+            self.asymmetric4_2 = RegularBottleneck(128, kernel_size=5, padding=2, asymmetric=True, dropout_prob=0.1, relu=encoder_relu)
+            self.dilated4_3 = RegularBottleneck(128, dilation=4, padding=4, dropout_prob=0.1, relu=encoder_relu)
+            self.regular4_4 = RegularBottleneck(128, padding=1, dropout_prob=0.1, relu=encoder_relu)
+            self.dilated4_5 = RegularBottleneck(128, dilation=8, padding=8, dropout_prob=0.1, relu=encoder_relu)
+            self.asymmetric4_6 = RegularBottleneck(128, kernel_size=5, asymmetric=True, padding=2, dropout_prob=0.1, relu=encoder_relu)
+            self.dilated4_7 = RegularBottleneck(128, dilation=16, padding=16, dropout_prob=0.1, relu=encoder_relu)
+
+        # Stage 5 - Decoder (D1)
+        # self.upsample4_0 = UpsamplingBottleneck(128, 64, dropout_prob=0.1, relu=decoder_relu)
+        self.upsample4_0 = UpsamplingBottleneck(256, 64, dropout_prob=0.1, relu=decoder_relu)
+        self.regular4_1 = RegularBottleneck(64, padding=1, dropout_prob=0.1, relu=decoder_relu)
+        self.regular4_2 = RegularBottleneck(64, padding=1, dropout_prob=0.1, relu=decoder_relu)
+
+        # Stage 6 - Decoder (D2)
+        self.upsample5_0 = UpsamplingBottleneck(64, 16, dropout_prob=0.1, relu=decoder_relu)
+        self.regular5_1 = RegularBottleneck(16, padding=1, dropout_prob=0.1, relu=decoder_relu)
+        self.transposed_conv = nn.ConvTranspose2d(16, self.num_classes, kernel_size=3, stride=2, padding=1, bias=False)
+
+        # AT_GEN
+        if self.sad:
+            self.at_gen_upsample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False)
+            self.at_gen_l2_loss = nn.MSELoss(reduction='mean')
+
+        # Lane exist (P1)
+        self.layer3 = nn.Sequential(
+            nn.Conv2d(128, 5, 1),
+            nn.Softmax(dim=1),
+            nn.AvgPool2d(2, 2),
+        )
+        self.fc = nn.Sequential(
+            nn.Linear(self.fc_input_feature, 128),
+            nn.ReLU(),
+            nn.Linear(128, 4),
+            nn.Sigmoid()
+        )
+
+    def at_gen(self, x1, x2):
+        """
+        x1 - previous encoder step feature map
+        x2 - current encoder step feature map
+        """
+
+        # G^2_sum
+        sps = SpatialSoftmax(device=x1.device)
+
+        if x1.size() != x2.size():
+            x1 = torch.sum(x1 * x1, dim=1)
+            x1 = sps(x1)
+            x2 = torch.sum(x2 * x2, dim=1, keepdim=True)
+            x2 = torch.squeeze(self.at_gen_upsample(x2), dim=1)
+            x2 = sps(x2)
+        else:
+            x1 = torch.sum(x1 * x1, dim=1)
+            x1 = sps(x1)
+            x2 = torch.sum(x2 * x2, dim=1)
+            x2 = sps(x2)
+
+        loss = self.at_gen_l2_loss(x1, x2)
+        return loss
+
+    def forward(self, img, seg_gt=None, exist_gt=None, sad_loss=False):
+        # Stage 0 - Initial block
+        input_size = img.size()
+        x_0 = self.initial_block(img)
+
+        # AT-GEN after each E2, E3, E4
+        # Stage 1 - Encoder (E1)
+        stage1_input_size = x_0.size()
+        x, max_indices1_0 = self.downsample1_0(x_0)
+        x = self.regular1_1(x)
+        x = self.regular1_2(x)
+        x = self.regular1_3(x)
+        x_1 = self.regular1_4(x)
+        # if self.sad:
+        #     loss_1 = self.at_gen(x_0, x_1)
+
+        # Stage 2 - Encoder (E2)
+        stage2_input_size = x_1.size()
+        x, max_indices2_0 = self.downsample2_0(x_1)
+        x = self.regular2_1(x)
+        x = self.dilated2_2(x)
+        x = self.asymmetric2_3(x)
+        x = self.dilated2_4(x)
+        x = self.regular2_5(x)
+        x = self.dilated2_6(x)
+        x = self.asymmetric2_7(x)
+        x_2 = self.dilated2_8(x)
+        if self.sad:
+            loss_2 = self.at_gen(x_1, x_2)
+
+        # Stage 3 - Encoder (E3)
+        x = self.regular3_0(x_2)
+        x = self.dilated3_1(x)
+        x = self.asymmetric3_2(x)
+        x = self.dilated3_3(x)
+        x = self.regular3_4(x)
+        x = self.dilated3_5(x)
+        x = self.asymmetric3_6(x)
+        x_3 = self.dilated3_7(x)
+        if self.sad:
+            loss_3 = self.at_gen(x_2, x_3)
+
+        # Stage 4 - Encoder (E4)
+        x = self.regular3_0(x_3)
+        x = self.dilated3_1(x)
+        x = self.asymmetric3_2(x)
+        x = self.dilated3_3(x)
+        x = self.regular3_4(x)
+        x = self.dilated3_5(x)
+        x = self.asymmetric3_6(x)
+        x_4 = self.dilated3_7(x)
+        if self.sad:
+            loss_4 = self.at_gen(x_3, x_4)
+
+        # Concatenate E3, E4
+        x_34 = torch.cat((x_3, x_4), dim=1)
+
+        # Stage 4 - Decoder (D1)
+        x = self.upsample4_0(x_34, max_indices2_0, output_size=stage2_input_size)
+        x = self.regular4_1(x)
+        x = self.regular4_2(x)
+
+        # Stage 5 - Decoder (D2)
+        x = self.upsample5_0(x, max_indices1_0, output_size=stage1_input_size)
+        x = self.regular5_1(x)
+        seg_pred = self.transposed_conv(x, output_size=input_size)
+
+        # lane exist
+        y = self.layer3(x_4)
+        y = y.view(-1, self.fc_input_feature)
+        exist_pred = self.fc(y)
+
+        # loss calculation
+        if seg_gt is not None and exist_gt is not None:
+            # L = L_seg + a * L_iou + b * L_exist + c * L_distill
+            if self.sad:
+                loss_seg = self.ce_loss(seg_pred, seg_gt)
+                seg_gt_onehot = to_one_hot(seg_gt, 5)
+                loss_iou = self.iou_loss(seg_pred[:, 1:self.num_classes, :, :], seg_gt_onehot[:, 1:self.num_classes, :, :])
+                loss_exist = self.bce_loss(exist_pred, exist_gt)
+                loss_distill = loss_2 + loss_3 + loss_4
+                loss = loss_seg * self.scale_sad_seg + loss_iou * self.scale_sad_iou + loss_exist * self.scale_sad_exist
+
+                # Add SAD loss after 40K episodes
+                if sad_loss:
+                    loss += loss_distill * self.scale_sad_distill
+
+            else:
+                loss_seg = self.ce_loss(seg_pred, seg_gt)
+                loss_exist = self.bce_loss(exist_pred, exist_gt)
+                loss = loss_seg * self.scale_seg + loss_exist * self.scale_exist
+
+        else:
+            loss_seg = torch.tensor(0, dtype=img.dtype, device=img.device)
+            loss_exist = torch.tensor(0, dtype=img.dtype, device=img.device)
+            loss = torch.tensor(0, dtype=img.dtype, device=img.device)
+
+        return seg_pred, exist_pred, loss_seg, loss_exist, loss
+
+
+if __name__ == '__main__':
+    tensor = torch.ones((8, 3, 288, 800)).cuda()
+    seg_gt = torch.zeros((8, 288, 800)).long().cuda()
+    exist_gt = torch.ones((8, 4)).cuda()
+    enet_sad = ENet_SAD((800, 288), sad=True)
+    enet_sad.cuda()
+    enet_sad.train(mode=True)
+    result = enet_sad(tensor, seg_gt, exist_gt, sad_loss=True)
diff --git a/predict.py b/predict.py
new file mode 100644
index 0000000..adda8e4
--- /dev/null
+++ b/predict.py
@@ -0,0 +1,34 @@
+import argparse
+
+from model_ENET_SAD import ENet_SAD
+from utils.prob2lines import getLane
+from utils.transforms import *
+
+img_size = (800, 288)
+
+net = ENet_SAD(img_size, sad=False)
+mean = (0.485, 0.456, 0.406)
+std = (0.229, 0.224, 0.225)
+transform_img = Resize(img_size)
+transform_to_net = Compose(ToTensor(), Normalize(mean=mean, std=std))
+
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--img_path", '-i', type=str, default=r"demo/demo.jpg",
+                        help="Path to demo image")
+    parser.add_argument("--weight_path", '-w', type=str, default=r"exp1_best.pth",
+                        help="Path to model weights")
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    ########## Begin ##########
+
+    ########## End ##########
+
+
+
+if __name__ == "__main__":
+    main()
diff --git a/train.py b/train.py
new file mode 100644
index 0000000..6ca679d
--- /dev/null
+++ b/train.py
@@ -0,0 +1,235 @@
+import argparse
+import json
+import os
+import shutil
+import time
+
+import torch.optim as optim
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+
+from config import *
+import dataset
+from model_ENET_SAD import ENet_SAD
+# from utils.tensorboard import TensorBoard
+from utils.transforms import *
+from utils.lr_scheduler import PolyLR
+
+
+from multiprocessing import Process, JoinableQueue
+from threading import Lock
+import pickle
+#from torch.multiprocessing import Process, SimpleQueue
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--exp_dir", type=str, default="./experiments/exp0")
+    parser.add_argument("--resume", "-r", action="store_true")
+    args = parser.parse_args()
+    return args
+args = parse_args()
+
+# ------------ config ------------
+exp_dir = args.exp_dir
+while exp_dir[-1]=='/':
+    exp_dir = exp_dir[:-1]
+exp_name = exp_dir.split('/')[-1]
+
+with open(os.path.join(exp_dir, "cfg.json")) as f:
+    exp_cfg = json.load(f)
+resize_shape = tuple(exp_cfg['dataset']['resize_shape'])
+
+device = torch.device(exp_cfg['device'])
+# tensorboard = TensorBoard(exp_dir)
+
+# ------------ train data ------------
+# # CULane mean, std
+mean=(0.3598, 0.3653, 0.3662)
+std=(0.2573, 0.2663, 0.2756)
+# Imagenet mean, std
+# mean=(0.485, 0.456, 0.406)
+# std=(0.229, 0.224, 0.225)
+transform_train = Compose(Resize(resize_shape), Rotation(2), ToTensor(),
+                          Normalize(mean=mean, std=std))
+dataset_name = exp_cfg['dataset'].pop('dataset_name')
+Dataset_Type = getattr(dataset, dataset_name)
+train_dataset = Dataset_Type(Dataset_Path[dataset_name], "train", transform_train)
+train_loader = DataLoader(train_dataset, batch_size=exp_cfg['dataset']['batch_size'], shuffle=True, collate_fn=train_dataset.collate, num_workers=0)
+
+# ------------ val data ------------
+transform_val_img = Resize(resize_shape)
+transform_val_x = Compose(ToTensor(), Normalize(mean=mean, std=std))
+transform_val = Compose(transform_val_img, transform_val_x)
+val_dataset = Dataset_Type(Dataset_Path[dataset_name], "val", transform_val)
+val_loader = DataLoader(val_dataset, batch_size=8, collate_fn=val_dataset.collate, num_workers=0)
+
+# ------------ preparation ------------
+net = ENet_SAD(resize_shape, sad=True)
+
+net = net.to(device)
+net = torch.nn.DataParallel(net)
+
+optimizer = optim.SGD(net.parameters(), **exp_cfg['optim'])
+lr_scheduler = PolyLR(optimizer, 0.9, **exp_cfg['lr_scheduler'])
+best_val_loss = 1e6
+
+"""
+def batch_processor(arg):
+    b_queue, data_loader = arg
+    while True:
+        if b_queue.empty():
+            sample = next(data_loader)
+            b_queue.put(sample)
+            b_queue.join()
+"""
+
+def train(epoch):
+    print("Train Epoch: {}".format(epoch))
+    net.train()
+    train_loss = 0
+    train_loss_seg = 0
+    train_loss_exist = 0
+    progressbar = tqdm(range(len(train_loader)))
+
+    for batch_idx, sample in enumerate(train_loader):
+
+        img = sample['img'].to(device)
+        segLabel = sample['segLabel'].to(device)
+        exist = sample['exist'].to(device)
+
+        optimizer.zero_grad()
+
+        if exp_cfg['model'] == "scnn":
+            seg_pred, exist_pred, loss_seg, loss_exist, loss = net(img, segLabel, exist)
+        elif exp_cfg['model'] == "enet_sad":
+            if (epoch * len(train_loader) + batch_idx) < exp_cfg['sad_start_iter']:
+                seg_pred, exist_pred, loss_seg, loss_exist, loss = net(img, segLabel, exist, False)
+            else:
+                print("sad activated")
+                seg_pred, exist_pred, loss_seg, loss_exist, loss = net(img, segLabel, exist, True)
+
+        if isinstance(net, torch.nn.DataParallel):
+            loss_seg = loss_seg.sum()
+            loss_exist = loss_exist.sum()
+            loss = loss.sum()
+        loss.backward()
+        optimizer.step()
+        lr_scheduler.step()
+
+        iter_idx = epoch * len(train_loader) + batch_idx
+        train_loss = loss.item()
+        train_loss_seg = loss_seg.item()
+        train_loss_exist = loss_exist.item()
+        progressbar.set_description("batch loss: {:.3f}".format(loss.item()))
+        progressbar.update(1)
+
+        lr = optimizer.param_groups[0]['lr']
+    progressbar.close()
+
+    if epoch % 1 == 0:
+        save_dict = {
+            "epoch": epoch,
+            "net": net.module.state_dict() if isinstance(net, torch.nn.DataParallel) else net.state_dict(),
+            "optim": optimizer.state_dict(),
+            "lr_scheduler": lr_scheduler.state_dict(),
+            "best_val_loss": best_val_loss
+        }
+        save_name = os.path.join(exp_dir, exp_name + '.pth')
+        torch.save(save_dict, save_name)
+        print("model is saved: {}".format(save_name))
+
+    print("------------------------\n")
+
+
+def val(epoch):
+    global best_val_loss
+
+    print("Val Epoch: {}".format(epoch))
+
+    net.eval()
+    val_loss = 0
+    val_loss_seg = 0
+    val_loss_exist = 0
+    progressbar = tqdm(range(len(val_loader)))
+
+    with torch.no_grad():
+        for batch_idx, sample in enumerate(val_loader):
+            img = sample['img'].to(device)
+            segLabel = sample['segLabel'].to(device)
+            exist = sample['exist'].to(device)
+
+            seg_pred, exist_pred, loss_seg, loss_exist, loss = net(img, segLabel, exist)
+            if isinstance(net, torch.nn.DataParallel):
+                loss_seg = loss_seg.sum()
+                loss_exist = loss_exist.sum()
+                loss = loss.sum()
+
+            # visualize validation every 5 frame, 50 frames in all
+            gap_num = 5
+            if batch_idx%gap_num == 0 and batch_idx < 50 * gap_num:
+                origin_imgs = []
+                seg_pred = seg_pred.detach().cpu().numpy()
+                exist_pred = exist_pred.detach().cpu().numpy()
+
+                for b in range(len(img)):
+                    img_name = sample['img_name'][b]
+                    img = cv2.imread(img_name)
+                    img = transform_val_img({'img': img})['img']
+
+                    lane_img = np.zeros_like(img)
+                    color = np.array([[255, 125, 0], [0, 255, 0], [0, 0, 255], [0, 255, 255]], dtype='uint8')
+
+                    coord_mask = np.argmax(seg_pred[b], axis=0)
+                    for i in range(0, 4):
+                        if exist_pred[b, i] > 0.5:
+                            lane_img[coord_mask==(i+1)] = color[i]
+                    img = cv2.addWeighted(src1=lane_img, alpha=0.8, src2=img, beta=1., gamma=0.)
+                    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+                    lane_img = cv2.cvtColor(lane_img, cv2.COLOR_BGR2RGB)
+                    cv2.putText(lane_img, "{}".format([1 if exist_pred[b, i]>0.5 else 0 for i in range(4)]), (20, 20), cv2.FONT_HERSHEY_SIMPLEX, 1.1, (255, 255, 255), 2)
+                    origin_imgs.append(img)
+                    origin_imgs.append(lane_img)
+
+            val_loss += loss.item()
+            val_loss_seg += loss_seg.item()
+            val_loss_exist += loss_exist.item()
+
+            progressbar.set_description("batch loss: {:.3f}".format(loss.item()))
+            progressbar.update(1)
+
+    progressbar.close()
+    iter_idx = (epoch + 1) * len(train_loader)  # keep align with training process iter_idx
+
+    print("------------------------\n")
+    if val_loss < best_val_loss:
+        best_val_loss = val_loss
+        save_name = os.path.join(exp_dir, exp_name + '.pth')
+        copy_name = os.path.join(exp_dir, exp_name + '_best.pth')
+        shutil.copyfile(save_name, copy_name)
+
+
+def main():
+    global best_val_loss
+    if args.resume:
+        save_dict = torch.load(os.path.join(exp_dir, exp_name + '.pth'))
+        if isinstance(net, torch.nn.DataParallel):
+            net.module.load_state_dict(save_dict['net'])
+        else:
+            net.load_state_dict(save_dict['net'])
+        optimizer.load_state_dict(save_dict['optim'])
+        lr_scheduler.load_state_dict(save_dict['lr_scheduler'])
+        start_epoch = save_dict['epoch'] + 1
+        best_val_loss = save_dict.get("best_val_loss", 1e6)
+    else:
+        start_epoch = 0
+
+    for epoch in range(start_epoch, exp_cfg['MAX_EPOCHES']):
+        train(epoch)
+        if epoch % 1 == 0:
+            print("\nValidation For Experiment: ", exp_dir)
+            print(time.strftime('%H:%M:%S', time.localtime()))
+            val(epoch)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/utils/lane_evaluation/tusimple/lane.py b/utils/lane_evaluation/tusimple/lane.py
new file mode 100644
index 0000000..362b272
--- /dev/null
+++ b/utils/lane_evaluation/tusimple/lane.py
@@ -0,0 +1,101 @@
+import numpy as np
+from sklearn.linear_model import LinearRegression
+import json as json
+
+
+class LaneEval(object):
+    lr = LinearRegression()
+    pixel_thresh = 20
+    pt_thresh = 0.85
+
+    @staticmethod
+    def get_angle(xs, y_samples):
+        xs, ys = xs[xs >= 0], y_samples[xs >= 0]
+        if len(xs) > 1:
+            LaneEval.lr.fit(ys[:, None], xs)
+            k = LaneEval.lr.coef_[0]
+            theta = np.arctan(k)
+        else:
+            theta = 0
+        return theta
+
+    @staticmethod
+    def line_accuracy(pred, gt, thresh):
+        pred = np.array([p if p >= 0 else -100 for p in pred])
+        gt = np.array([g if g >= 0 else -100 for g in gt])
+        return np.sum(np.where(np.abs(pred - gt) < thresh, 1., 0.)) / len(gt)
+
+    @staticmethod
+    def bench(pred, gt, y_samples, running_time):
+        if any(len(p) != len(y_samples) for p in pred):
+            raise Exception('Format of lanes error.')
+        if running_time > 200 or len(gt) + 2 < len(pred):
+            return 0., 0., 1.
+        angles = [LaneEval.get_angle(np.array(x_gts), np.array(y_samples)) for x_gts in gt]
+        threshs = [LaneEval.pixel_thresh / np.cos(angle) for angle in angles]
+        line_accs = []
+        fp, fn = 0., 0.
+        matched = 0.
+        for x_gts, thresh in zip(gt, threshs):
+            accs = [LaneEval.line_accuracy(np.array(x_preds), np.array(x_gts), thresh) for x_preds in pred]
+            max_acc = np.max(accs) if len(accs) > 0 else 0.
+            if max_acc < LaneEval.pt_thresh:
+                fn += 1
+            else:
+                matched += 1
+            line_accs.append(max_acc)
+        fp = len(pred) - matched
+        if len(gt) > 4 and fn > 0:
+            fn -= 1
+        s = sum(line_accs)
+        if len(gt) > 4:
+            s -= min(line_accs)
+        return s / max(min(4.0, len(gt)), 1.), fp / len(pred) if len(pred) > 0 else 0., fn / max(min(len(gt), 4.) , 1.)
+
+    @staticmethod
+    def bench_one_submit(pred_file, gt_file):
+        try:
+            json_pred = [json.loads(line) for line in open(pred_file).readlines()]
+        except BaseException as e:
+            raise Exception('Fail to load json file of the prediction.')
+        json_gt = [json.loads(line) for line in open(gt_file).readlines()]
+        if len(json_gt) != len(json_pred):
+            raise Exception('We do not get the predictions of all the test tasks')
+        gts = {l['raw_file']: l for l in json_gt}
+        accuracy, fp, fn = 0., 0., 0.
+        for pred in json_pred:
+            if 'raw_file' not in pred or 'lanes' not in pred or 'run_time' not in pred:
+                raise Exception('raw_file or lanes or run_time not in some predictions.')
+            raw_file = pred['raw_file']
+            pred_lanes = pred['lanes']
+            run_time = pred['run_time']
+            if raw_file not in gts:
+                raise Exception('Some raw_file from your predictions do not exist in the test tasks.')
+            gt = gts[raw_file]
+            gt_lanes = gt['lanes']
+            y_samples = gt['h_samples']
+            try:
+                a, p, n = LaneEval.bench(pred_lanes, gt_lanes, y_samples, run_time)
+            except BaseException as e:
+                raise Exception('Format of lanes error.')
+            accuracy += a
+            fp += p
+            fn += n
+        num = len(gts)
+        # the first return parameter is the default ranking parameter
+        return json.dumps([
+            {'name': 'Accuracy', 'value': accuracy / num, 'order': 'desc'},
+            {'name': 'FP', 'value': fp / num, 'order': 'asc'},
+            {'name': 'FN', 'value': fn / num, 'order': 'asc'}
+        ])
+
+
+if __name__ == '__main__':
+    import sys
+    try:
+        if len(sys.argv) != 3:
+            raise Exception('Invalid input arguments')
+        print(LaneEval.bench_one_submit(sys.argv[1], sys.argv[2]))
+    except Exception as e:
+        print(e.message)
+        sys.exit(e.message)
diff --git a/utils/lr_scheduler.py b/utils/lr_scheduler.py
new file mode 100644
index 0000000..6040e7f
--- /dev/null
+++ b/utils/lr_scheduler.py
@@ -0,0 +1,28 @@
+from torch.optim.lr_scheduler import _LRScheduler
+
+
+class PolyLR(_LRScheduler):
+    def __init__(self, optimizer, pow, max_iter, min_lrs=1e-20, last_epoch=-1, warmup=0):
+        """
+        :param warmup: how many steps for linearly warmup lr
+        """
+        self.pow = pow
+        self.max_iter = max_iter
+        if not isinstance(min_lrs, list) and not isinstance(min_lrs, tuple):
+            self.min_lrs = [min_lrs] * len(optimizer.param_groups)
+
+        assert isinstance(warmup, int), "The type of warmup is incorrect, got {}".format(type(warmup))
+        self.warmup = max(warmup, 0)
+
+        super(PolyLR, self).__init__(optimizer, last_epoch)
+
+    def get_lr(self):
+        if self.last_epoch < self.warmup:
+            return [base_lr / self.warmup * (self.last_epoch+1) for base_lr in self.base_lrs]
+
+        if self.last_epoch < self.max_iter:
+            coeff = (1 - (self.last_epoch-self.warmup) / (self.max_iter-self.warmup)) ** self.pow
+        else:
+            coeff = 0
+        return [(base_lr - min_lr) * coeff + min_lr
+                for base_lr, min_lr in zip(self.base_lrs, self.min_lrs)]
diff --git a/utils/prob2lines/getLane.py b/utils/prob2lines/getLane.py
new file mode 100644
index 0000000..fe10605
--- /dev/null
+++ b/utils/prob2lines/getLane.py
@@ -0,0 +1,136 @@
+import cv2
+import numpy as np
+
+
+def getLane_tusimple(prob_map, y_px_gap, pts, thresh, resize_shape=None):
+    """
+    Arguments:
+    ----------
+    prob_map: prob map for single lane, np array size (h, w)
+    resize_shape:  reshape size target, (H, W)
+
+    Return:
+    ----------
+    coords: x coords bottom up every y_px_gap px, 0 for non-exist, in resized shape
+    """
+    if resize_shape is None:
+        resize_shape = prob_map.shape
+    h, w = prob_map.shape
+    H, W = resize_shape
+
+    coords = np.zeros(pts)
+    for i in range(pts):
+        y = int((H - 10 - i * y_px_gap) * h / H)
+        if y < 0:
+            break
+        line = prob_map[y, :]
+        id = np.argmax(line)
+        if line[id] > thresh:
+            coords[i] = int(id / w * W)
+    if (coords > 0).sum() < 2:
+        coords = np.zeros(pts)
+    return coords
+
+
+def prob2lines_tusimple(seg_pred, exist, resize_shape=None, smooth=True, y_px_gap=10, pts=None, thresh=0.3):
+    """
+    Arguments:
+    ----------
+    seg_pred:      np.array size (5, h, w)
+    resize_shape:  reshape size target, (H, W)
+    exist:       list of existence, e.g. [0, 1, 1, 0]
+    smooth:      whether to smooth the probability or not
+    y_px_gap:    y pixel gap for sampling
+    pts:     how many points for one lane
+    thresh:  probability threshold
+
+    Return:
+    ----------
+    coordinates: [x, y] list of lanes, e.g.: [ [[9, 569], [50, 549]] ,[[630, 569], [647, 549]] ]
+    """
+    if resize_shape is None:
+        resize_shape = seg_pred.shape[1:]  # seg_pred (5, h, w)
+    _, h, w = seg_pred.shape
+    H, W = resize_shape
+    coordinates = []
+
+    if pts is None:
+        pts = round(H / 2 / y_px_gap)
+
+    seg_pred = np.ascontiguousarray(np.transpose(seg_pred, (1, 2, 0)))
+    for i in range(4):
+        prob_map = seg_pred[..., i + 1]
+        if smooth:
+            prob_map = cv2.blur(prob_map, (9, 9), borderType=cv2.BORDER_REPLICATE)
+        if exist[i] > 0:
+            coords = getLane_tusimple(prob_map, y_px_gap, pts, thresh, resize_shape)
+            coordinates.append(
+                [[coords[j], H - 10 - j * y_px_gap] if coords[j] > 0 else [-1, H - 10 - j * y_px_gap] for j in
+                 range(pts)])
+
+    return coordinates
+
+
+def getLane_CULane(prob_map, y_px_gap, pts, thresh, resize_shape=None):
+    """
+    Arguments:
+    ----------
+    prob_map: prob map for single lane, np array size (h, w)
+    resize_shape:  reshape size target, (H, W)
+    Return:
+    ----------
+    coords: x coords bottom up every y_px_gap px, 0 for non-exist, in resized shape
+    """
+    if resize_shape is None:
+        resize_shape = prob_map.shape
+    h, w = prob_map.shape
+    H, W = resize_shape
+
+    coords = np.zeros(pts)
+    for i in range(pts):
+        y = int(h - i * y_px_gap / H * h - 1)
+        if y < 0:
+            break
+        line = prob_map[y, :]
+        id = np.argmax(line)
+        if line[id] > thresh:
+            coords[i] = int(id / w * W)
+    if (coords > 0).sum() < 2:
+        coords = np.zeros(pts)
+    return coords
+
+
+def prob2lines_CULane(seg_pred, exist, resize_shape=None, smooth=True, y_px_gap=20, pts=None, thresh=0.3):
+    """
+    Arguments:
+    ----------
+    seg_pred: np.array size (5, h, w)
+    resize_shape:  reshape size target, (H, W)
+    exist:   list of existence, e.g. [0, 1, 1, 0]
+    smooth:  whether to smooth the probability or not
+    y_px_gap: y pixel gap for sampling
+    pts:     how many points for one lane
+    thresh:  probability threshold
+    Return:
+    ----------
+    coordinates: [x, y] list of lanes, e.g.: [ [[9, 569], [50, 549]] ,[[630, 569], [647, 549]] ]
+    """
+    if resize_shape is None:
+        resize_shape = seg_pred.shape[1:]  # seg_pred (5, h, w)
+    _, h, w = seg_pred.shape
+    H, W = resize_shape
+    coordinates = []
+
+    if pts is None:
+        pts = round(H / 2 / y_px_gap)
+
+    seg_pred = np.ascontiguousarray(np.transpose(seg_pred, (1, 2, 0)))
+    for i in range(4):
+        prob_map = seg_pred[..., i + 1]
+        if smooth:
+            prob_map = cv2.blur(prob_map, (9, 9), borderType=cv2.BORDER_REPLICATE)
+        if exist[i] > 0:
+            coords = getLane_CULane(prob_map, y_px_gap, pts, thresh, resize_shape)
+            coordinates.append([[coords[j], H - 1 - j * y_px_gap] for j in range(pts) if coords[j] > 0])
+
+    return coordinates
diff --git a/utils/transforms/__init__.py b/utils/transforms/__init__.py
new file mode 100644
index 0000000..3d25303
--- /dev/null
+++ b/utils/transforms/__init__.py
@@ -0,0 +1,2 @@
+from .transforms import *
+from .data_augmentation import *
diff --git a/utils/transforms/data_augmentation.py b/utils/transforms/data_augmentation.py
new file mode 100644
index 0000000..0ac4d43
--- /dev/null
+++ b/utils/transforms/data_augmentation.py
@@ -0,0 +1,55 @@
+import random
+import numpy as np
+import cv2
+
+from utils.transforms.transforms import CustomTransform
+
+
+class RandomFlip(CustomTransform):
+    def __init__(self, prob_x=0, prob_y=0):
+        """
+        Arguments:
+        ----------
+        prob_x: range [0, 1], probability to use horizontal flip, setting to 0 means disabling flip
+        prob_y: range [0, 1], probability to use vertical flip
+        """
+        self.prob_x = prob_x
+        self.prob_y = prob_y
+
+    def __call__(self, sample):
+        img = sample.get('img').copy()
+        segLabel = sample.get('segLabel', None)
+        if segLabel is not None:
+            segLabel = segLabel.copy()
+
+        flip_x = np.random.choice([False, True], p=(1 - self.prob_x, self.prob_x))
+        flip_y = np.random.choice([False, True], p=(1 - self.prob_y, self.prob_y))
+        if flip_x:
+            img = np.ascontiguousarray(np.flip(img, axis=1))
+            if segLabel is not None:
+                segLabel = np.ascontiguousarray(np.flip(segLabel, axis=1))
+
+        if flip_y:
+            img = np.ascontiguousarray(np.flip(img, axis=0))
+            if segLabel is not None:
+                segLabel = np.ascontiguousarray(np.flip(segLabel, axis=0))
+
+        _sample = sample.copy()
+        _sample['img'] = img
+        _sample['segLabel'] = segLabel
+        return _sample
+
+
+class Darkness(CustomTransform):
+    def __init__(self, coeff):
+        assert coeff >= 1., "Darkness coefficient must be greater than 1"
+        self.coeff = coeff
+
+    def __call__(self, sample):
+        img = sample.get('img')
+        coeff = np.random.uniform(1., self.coeff)
+        img = (img.astype('float32') / coeff).astype('uint8')
+
+        _sample = sample.copy()
+        _sample['img'] = img
+        return _sample
\ No newline at end of file
diff --git a/utils/transforms/transforms.py b/utils/transforms/transforms.py
new file mode 100644
index 0000000..677dc05
--- /dev/null
+++ b/utils/transforms/transforms.py
@@ -0,0 +1,164 @@
+import cv2
+
+import numpy as np
+import torch
+from torchvision.transforms import Normalize as Normalize_th
+
+
+class CustomTransform:
+    def __call__(self, *args, **kwargs):
+        raise NotImplementedError
+
+    def __str__(self):
+        return self.__class__.__name__
+
+    def __eq__(self, name):
+        return str(self) == name
+
+    def __iter__(self):
+        def iter_fn():
+            for t in [self]:
+                yield t
+        return iter_fn()
+
+    def __contains__(self, name):
+        for t in self.__iter__():
+            if isinstance(t, Compose):
+                if name in t:
+                    return True
+            elif name == t:
+                return True
+        return False
+
+
+class Compose(CustomTransform):
+    """
+    All transform in Compose should be able to accept two non None variable, img and boxes
+    """
+    def __init__(self, *transforms):
+        self.transforms = [*transforms]
+
+    def __call__(self, sample):
+        for t in self.transforms:
+            sample = t(sample)
+        return sample
+
+    def __iter__(self):
+        return iter(self.transforms)
+
+    def modules(self):
+        yield self
+        for t in self.transforms:
+            if isinstance(t, Compose):
+                for _t in t.modules():
+                    yield _t
+            else:
+                yield t
+
+
+class Resize(CustomTransform):
+    def __init__(self, size):
+        if isinstance(size, int):
+            size = (size, size)
+        self.size = size  #(W, H)
+
+    def __call__(self, sample):
+        img = sample.get('img')
+        segLabel = sample.get('segLabel', None)
+
+        img = cv2.resize(img, self.size, interpolation=cv2.INTER_CUBIC)
+        if segLabel is not None:
+            segLabel = cv2.resize(segLabel, self.size, interpolation=cv2.INTER_NEAREST)
+
+        _sample = sample.copy()
+        _sample['img'] = img
+        _sample['segLabel'] = segLabel
+        return _sample
+
+    def reset_size(self, size):
+        if isinstance(size, int):
+            size = (size, size)
+        self.size = size
+
+
+class RandomResize(Resize):
+    """
+    Resize to (w, h), where w randomly samples from (minW, maxW) and h randomly samples from (minH, maxH)
+    """
+    def __init__(self, minW, maxW, minH=None, maxH=None, batch=False):
+        if minH is None or maxH is None:
+            minH, maxH = minW, maxW
+        super(RandomResize, self).__init__((minW, minH))
+        self.minW = minW
+        self.maxW = maxW
+        self.minH = minH
+        self.maxH = maxH
+        self.batch = batch
+
+    def random_set_size(self):
+        w = np.random.randint(self.minW, self.maxW+1)
+        h = np.random.randint(self.minH, self.maxH+1)
+        self.reset_size((w, h))
+
+
+class Rotation(CustomTransform):
+    def __init__(self, theta):
+        self.theta = theta
+
+    def __call__(self, sample):
+        img = sample.get('img')
+        segLabel = sample.get('segLabel', None)
+
+        u = np.random.uniform()
+        degree = (u-0.5) * self.theta
+        R = cv2.getRotationMatrix2D((img.shape[1]//2, img.shape[0]//2), degree, 1)
+        img = cv2.warpAffine(img, R, (img.shape[1], img.shape[0]), flags=cv2.INTER_LINEAR)
+        if segLabel is not None:
+            segLabel = cv2.warpAffine(segLabel, R, (segLabel.shape[1], segLabel.shape[0]), flags=cv2.INTER_NEAREST)
+
+        _sample = sample.copy()
+        _sample['img'] = img
+        _sample['segLabel'] = segLabel
+        return _sample
+
+    def reset_theta(self, theta):
+        self.theta = theta
+
+
+class Normalize(CustomTransform):
+    def __init__(self, mean, std):
+        self.transform = Normalize_th(mean, std)
+
+    def __call__(self, sample):
+        img = sample.get('img')
+
+        img = self.transform(img)
+
+        _sample = sample.copy()
+        _sample['img'] = img
+        return _sample
+
+
+class ToTensor(CustomTransform):
+    def __init__(self, dtype=torch.float):
+        self.dtype=dtype
+
+    def __call__(self, sample):
+        img = sample.get('img')
+        segLabel = sample.get('segLabel', None)
+        exist = sample.get('exist', None)
+
+        img = img.transpose(2, 0, 1)
+        img = torch.from_numpy(img).type(self.dtype) / 255.
+        if segLabel is not None:
+            segLabel = torch.from_numpy(segLabel).type(torch.long)
+        if exist is not None:
+            exist = torch.from_numpy(exist).type(torch.float32)  # BCEloss requires float tensor
+
+        _sample = sample.copy()
+        _sample['img'] = img
+        _sample['segLabel'] = segLabel
+        _sample['exist'] = exist
+        return _sample
+
+
diff --git a/utils/utils.py b/utils/utils.py
new file mode 100644
index 0000000..40e072d
--- /dev/null
+++ b/utils/utils.py
@@ -0,0 +1,169 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+
+np.random.seed(42)
+
+
+class Metrics:
+    '''Compute tpr, fpr, fpr, fnr and balanced accuracy'''
+
+    @classmethod
+    def compute_tpr(cls, y_true, y_pred):
+        y_true = y_true.to('cpu').numpy()
+        y_pred = y_pred.to('cpu').numpy()
+
+        y_pred_pos = y_pred
+        y_pred_neg = 1 - y_pred
+        y_true_pos = y_true
+
+        tp = np.sum(y_pred_pos * y_true_pos + 1e-10)
+        fn = np.sum(y_pred_neg * y_true_pos + 1e-10)
+        return tp / (tp + fn)
+
+    @staticmethod
+    def _compute_tpr(y_true, y_pred):
+        y_true = y_true.to('cpu').numpy()
+        y_pred = y_pred.to('cpu').numpy()
+
+        y_pred_pos = y_pred
+        y_pred_neg = 1 - y_pred
+        y_true_pos = y_true
+
+        tp = np.sum(y_pred_pos * y_true_pos + 1e-10)
+        fn = np.sum(y_pred_neg * y_true_pos + 1e-10)
+        return tp / (tp + fn)
+
+    @classmethod
+    def compute_tnr(cls, y_true, y_pred):
+        y_true = y_true.to('cpu').numpy()
+        y_pred = y_pred.to('cpu').numpy()
+
+        y_pred_pos = y_pred
+        y_pred_neg = 1 - y_pred
+        y_true_neg = 1 - y_true
+
+        tn = np.sum(y_pred_neg * y_true_neg + 1e-10)
+        fp = np.sum(y_pred_pos * y_true_neg + 1e-10)
+        return tn / (tn + fp)
+
+    @staticmethod
+    def _compute_tnr(y_true, y_pred):
+        y_true = y_true.to('cpu').numpy()
+        y_pred = y_pred.to('cpu').numpy()
+
+        y_pred_pos = y_pred
+        y_pred_neg = 1 - y_pred
+        y_true_neg = 1 - y_true
+
+        tn = np.sum(y_pred_neg * y_true_neg + 1e-10)
+        fp = np.sum(y_pred_pos * y_true_neg + 1e-10)
+        return tn / (tn + fp)
+
+    @classmethod
+    def compute_ppv(cls, y_true, y_pred):
+        y_true = y_true.to('cpu').numpy()
+        y_pred = y_pred.to('cpu').numpy()
+
+        y_pred_pos = y_pred
+        y_true_pos = y_true
+        y_true_neg = 1 - y_true
+
+        tp = np.sum(y_pred_pos * y_true_pos + 1e-10)
+        fp = np.sum(y_pred_pos * y_true_neg + 1e-10)
+        return tp / (tp + fp)
+
+    @classmethod
+    def compute_npv(cls, y_true, y_pred):
+        y_true = y_true.to('cpu').numpy()
+        y_pred = y_pred.to('cpu').numpy()
+
+        y_pred_neg = 1 - y_pred
+        y_true_pos = y_true
+        y_true_neg = 1 - y_true
+
+        tn = np.sum(y_pred_neg * y_true_neg + 1e-10)
+        fn = np.sum(y_pred_neg * y_true_pos + 1e-10)
+        return tn / (tn + fn)
+
+    @classmethod
+    def balanced_accuracy(cls, y_true, y_pred):
+        tpr = cls._compute_tpr(y_true, y_pred)
+        tnr = cls._compute_tnr(y_true, y_pred)
+        return (tpr + tnr) / 2
+
+
+def to_one_hot(tensor, nClasses):
+    n, h, w = tensor.size()
+    one_hot = torch.zeros(n, nClasses, h, w).to(tensor.device).scatter_(1, tensor.view(n, 1, h, w), 1)
+    return one_hot
+
+
+class mIoULoss(nn.Module):
+    def __init__(self, weight=None, size_average=True, n_classes=4):
+        super(mIoULoss, self).__init__()
+        self.classes = n_classes
+
+    def forward(self, inputs, target_oneHot):
+        """
+        IoU Loss for individual examples
+        inputs - N x Classes x H x W
+        target_oneHot - N x Classes x H x W
+        """
+
+        N = inputs.size()[0]
+
+        # predicted probabilities for each pixel along channel
+        inputs = F.softmax(inputs, dim=1)
+
+        # Numerator Product
+        inter = inputs * target_oneHot
+        # Sum over all pixels N x C x H x W => N x C
+        inter = inter.view(N, self.classes, -1).sum(2)
+
+        # Denominator
+        union = inputs + target_oneHot - (inputs * target_oneHot)
+        # Sum over all pixels N x C x H x W => N x C
+        union = union.view(N, self.classes, -1).sum(2)
+
+        loss = inter / union
+
+        ## Return average loss over classes and batch
+        # return 1 - loss.mean()
+        return -(loss.mean() - 1.)
+
+
+'''
+def IoULoss(pred, target):
+    epsilon = 0.0001
+    inter = torch.dot(pred.view(-1), target.view(-1))
+    union = torch.sum(pred) + torch.sum(target)
+    l_iou = 1 - inter / (union + epsilon)
+    l_iou = torch.mean(l_iou)
+    return l_iou
+'''
+
+
+class DCLoss(torch.autograd.Function):
+    """Dice coeff for individual examples"""
+
+    @staticmethod
+    def forward(ctx, pred, target):
+        #target = target.type(torch.FloatTensor).to(cfg.device)
+        #target = target.type(torch.FloatTensor).cuda()
+        target = target.type(torch.FloatTensor).to(pred.device)
+        pred = torch.abs(pred)
+        eps = 0.0001
+        inter = torch.dot(pred.view(-1), target.view(-1))
+        union = torch.sum(pred) + torch.sum(target) + eps
+        ctx.save_for_backward(pred, target, inter, union)
+        t = (2 * inter + eps) / union
+        return t
+
+    # This function has only a single output, so it gets only one gradient
+    @staticmethod
+    def backward(ctx, grad_output):
+        pred, target, inter, union = ctx.saved_variables
+        grad_input = grad_output * 2 * (target * union - inter) / (union * union)
+        return grad_input, None