From d8194aeb890267e9c6bf2b8f2e25cce30e23425f Mon Sep 17 00:00:00 2001
From: Misaka <oncwnuC6OQ1Rqu2BCB_PqQoI92xY@git.weixin.qq.com>
Date: Sun, 17 Dec 2023 18:15:34 +0800
Subject: [PATCH] KNC NBC

---
 .gitignore                                    |      7 +
 ads/data/Night.csv                            | 191508 +++++++++++++++
 ads/data/Rainy_result.csv                     |   5614 -
 ads/data/Sunny_result.csv                     |   5614 -
 ads/data/night_result.csv                     |   5614 -
 ads/data/snow_night_result.csv                |   5614 -
 ads/data/snowy_result.csv                     |   5614 -
 .../test_outputs/knc_coverage.pkl             |    Bin 577456 -> 577456 bytes
 .../test_outputs/steering_angles.pkl          |    Bin 84330 -> 84330 bytes
 .../train_outputs/layer_bounds.pkl            |    Bin 17580 -> 17580 bytes
 .../train_outputs/layer_bounds_bin.pkl        |    Bin 4636421 -> 4636421 bytes
 munit/MUNIT/networks.py                       |     55 +-
 munit/MUNIT/trainer.py                        |      5 +-
 munit/MUNIT/utils.py                          |      4 +-
 test_knc.py                                   |    319 +
 test_nbc.py                                   |    309 +
 16 files changed, 192182 insertions(+), 28095 deletions(-)
 create mode 100644 .gitignore
 delete mode 100644 ads/data/Rainy_result.csv
 delete mode 100644 ads/data/Sunny_result.csv
 delete mode 100644 ads/data/night_result.csv
 delete mode 100644 ads/data/snow_night_result.csv
 delete mode 100644 ads/data/snowy_result.csv
 create mode 100644 test_knc.py
 create mode 100644 test_nbc.py

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..3bb1e46
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,7 @@
+ads/data
+cache
+logger
+ads/features_40
+ads/labels
+.idea
+*.h5
\ No newline at end of file
diff --git a/ads/data/Night.csv b/ads/data/Night.csv
index d607b44..d6e51f7 100644
--- a/ads/data/Night.csv
+++ b/ads/data/Night.csv
@@ -69062,3 +69062,191511 @@ night_5614,-6.4891040325164795,"[[ 0.00504502 -0.01388478 -0.02097037 -0.0115975
   -0.01523501 -0.00631004 -0.007242    0.0349333   0.04253223 -0.02966989
   -0.00804439 -0.01632835  0.00402375  0.00264166  0.03100077  0.01157373
   -0.04505073 -0.01896249  0.0055962  -0.02018874]]"
+night_1,12.110389024019241,"[[-3.01800147e-02, -1.86566096e-02, -2.50223149e-02,  1.26045430e-02,
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+   6.94826292e-03, -3.43588516e-02,  3.29585723e-03,  1.64878890e-02,
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diff --git a/ads/data/Rainy_result.csv b/ads/data/Rainy_result.csv
deleted file mode 100644
index 11f387a..0000000
--- a/ads/data/Rainy_result.csv
+++ /dev/null
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diff --git a/ads/data/Sunny_result.csv b/ads/data/Sunny_result.csv
deleted file mode 100644
index dd9d073..0000000
--- a/ads/data/Sunny_result.csv
+++ /dev/null
@@ -1,5614 +0,0 @@
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diff --git a/ads/data/night_result.csv b/ads/data/night_result.csv
deleted file mode 100644
index 157a9b8..0000000
--- a/ads/data/night_result.csv
+++ /dev/null
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diff --git a/ads/data/snow_night_result.csv b/ads/data/snow_night_result.csv
deleted file mode 100644
index 3e3bf4c..0000000
--- a/ads/data/snow_night_result.csv
+++ /dev/null
@@ -1,5614 +0,0 @@
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diff --git a/ads/data/snowy_result.csv b/ads/data/snowy_result.csv
deleted file mode 100644
index 0fadcfd..0000000
--- a/ads/data/snowy_result.csv
+++ /dev/null
@@ -1,5614 +0,0 @@
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diff --git a/cache/Dave_dropout/test_outputs/knc_coverage.pkl b/cache/Dave_dropout/test_outputs/knc_coverage.pkl
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diff --git a/cache/Dave_dropout/train_outputs/layer_bounds_bin.pkl b/cache/Dave_dropout/train_outputs/layer_bounds_bin.pkl
index ca47a7d85b7aa5301bec7183e305cb53ba56b684..6a00ceb04be66dec6c1fa5b866d838d6ab36fde6 100644
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diff --git a/munit/MUNIT/networks.py b/munit/MUNIT/networks.py
index 099877f..fad1c70 100644
--- a/munit/MUNIT/networks.py
+++ b/munit/MUNIT/networks.py
@@ -6,11 +6,13 @@ from torch import nn
 from torch.autograd import Variable
 import torch
 import torch.nn.functional as F
+
 try:
     from itertools import izip as zip
-except ImportError: # will be 3.x series
+except ImportError:  # will be 3.x series
     pass
 
+
 ##################################################################################
 # Discriminator
 ##################################################################################
@@ -58,7 +60,7 @@ class MsImageDis(nn.Module):
 
         for it, (out0, out1) in enumerate(zip(outs0, outs1)):
             if self.gan_type == 'lsgan':
-                loss += torch.mean((out0 - 0)**2) + torch.mean((out1 - 1)**2)
+                loss += torch.mean((out0 - 0) ** 2) + torch.mean((out1 - 1) ** 2)
             elif self.gan_type == 'nsgan':
                 all0 = Variable(torch.zeros_like(out0.data).cuda(), requires_grad=False)
                 all1 = Variable(torch.ones_like(out1.data).cuda(), requires_grad=False)
@@ -74,7 +76,7 @@ class MsImageDis(nn.Module):
         loss = 0
         for it, (out0) in enumerate(outs0):
             if self.gan_type == 'lsgan':
-                loss += torch.mean((out0 - 1)**2) # LSGAN
+                loss += torch.mean((out0 - 1) ** 2)  # LSGAN
             elif self.gan_type == 'nsgan':
                 all1 = Variable(torch.ones_like(out0.data).cuda(), requires_grad=False)
                 loss += torch.mean(F.binary_cross_entropy(F.sigmoid(out0), all1))
@@ -82,6 +84,7 @@ class MsImageDis(nn.Module):
                 assert 0, "Unsupported GAN type: {}".format(self.gan_type)
         return loss
 
+
 ##################################################################################
 # Generator
 ##################################################################################
@@ -103,7 +106,8 @@ class AdaINGen(nn.Module):
 
         # content encoder
         self.enc_content = ContentEncoder(n_downsample, n_res, input_dim, dim, 'in', activ, pad_type=pad_type)
-        self.dec = Decoder(n_downsample, n_res, self.enc_content.output_dim, input_dim, res_norm='adain', activ=activ, pad_type=pad_type)
+        self.dec = Decoder(n_downsample, n_res, self.enc_content.output_dim, input_dim, res_norm='adain', activ=activ,
+                           pad_type=pad_type)
 
         # MLP to generate AdaIN parameters
         self.mlp = MLP(style_dim, self.get_num_adain_params(self.dec), mlp_dim, 3, norm='none', activ=activ)
@@ -132,18 +136,18 @@ class AdaINGen(nn.Module):
         for m in model.modules():
             if m.__class__.__name__ == "AdaptiveInstanceNorm2d":
                 mean = adain_params[:, :m.num_features]
-                std = adain_params[:, m.num_features:2*m.num_features]
+                std = adain_params[:, m.num_features:2 * m.num_features]
                 m.bias = mean.contiguous().view(-1)
                 m.weight = std.contiguous().view(-1)
-                if adain_params.size(1) > 2*m.num_features:
-                    adain_params = adain_params[:, 2*m.num_features:]
+                if adain_params.size(1) > 2 * m.num_features:
+                    adain_params = adain_params[:, 2 * m.num_features:]
 
     def get_num_adain_params(self, model):
         # return the number of AdaIN parameters needed by the model
         num_adain_params = 0
         for m in model.modules():
             if m.__class__.__name__ == "AdaptiveInstanceNorm2d":
-                num_adain_params += 2*m.num_features
+                num_adain_params += 2 * m.num_features
         return num_adain_params
 
 
@@ -159,7 +163,8 @@ class VAEGen(nn.Module):
 
         # content encoder
         self.enc = ContentEncoder(n_downsample, n_res, input_dim, dim, 'in', activ, pad_type=pad_type)
-        self.dec = Decoder(n_downsample, n_res, self.enc.output_dim, input_dim, res_norm='in', activ=activ, pad_type=pad_type)
+        self.dec = Decoder(n_downsample, n_res, self.enc.output_dim, input_dim, res_norm='in', activ=activ,
+                           pad_type=pad_type)
 
     def forward(self, images):
         # This is a reduced VAE implementation where we assume the outputs are multivariate Gaussian distribution with mean = hiddens and std_dev = all ones.
@@ -195,7 +200,7 @@ class StyleEncoder(nn.Module):
             dim *= 2
         for i in range(n_downsample - 2):
             self.model += [Conv2dBlock(dim, dim, 4, 2, 1, norm=norm, activation=activ, pad_type=pad_type)]
-        self.model += [nn.AdaptiveAvgPool2d(1)] # global average pooling
+        self.model += [nn.AdaptiveAvgPool2d(1)]  # global average pooling
         self.model += [nn.Conv2d(dim, style_dim, 1, 1, 0)]
         self.model = nn.Sequential(*self.model)
         self.output_dim = dim
@@ -203,6 +208,7 @@ class StyleEncoder(nn.Module):
     def forward(self, x):
         return self.model(x)
 
+
 class ContentEncoder(nn.Module):
     def __init__(self, n_downsample, n_res, input_dim, dim, norm, activ, pad_type):
         super(ContentEncoder, self).__init__()
@@ -220,6 +226,7 @@ class ContentEncoder(nn.Module):
     def forward(self, x):
         return self.model(x)
 
+
 class Decoder(nn.Module):
     def __init__(self, n_upsample, n_res, dim, output_dim, res_norm='adain', activ='relu', pad_type='zero'):
         super(Decoder, self).__init__()
@@ -239,6 +246,7 @@ class Decoder(nn.Module):
     def forward(self, x):
         return self.model(x)
 
+
 ##################################################################################
 # Sequential Models
 ##################################################################################
@@ -253,20 +261,21 @@ class ResBlocks(nn.Module):
     def forward(self, x):
         return self.model(x)
 
+
 class MLP(nn.Module):
     def __init__(self, input_dim, output_dim, dim, n_blk, norm='none', activ='relu'):
-
         super(MLP, self).__init__()
         self.model = []
         self.model += [LinearBlock(input_dim, dim, norm=norm, activation=activ)]
         for i in range(n_blk - 2):
             self.model += [LinearBlock(dim, dim, norm=norm, activation=activ)]
-        self.model += [LinearBlock(dim, output_dim, norm='none', activation='none')] # no output activations
+        self.model += [LinearBlock(dim, output_dim, norm='none', activation='none')]  # no output activations
         self.model = nn.Sequential(*self.model)
 
     def forward(self, x):
         return self.model(x.view(x.size(0), -1))
 
+
 ##################################################################################
 # Basic Blocks
 ##################################################################################
@@ -275,8 +284,8 @@ class ResBlock(nn.Module):
         super(ResBlock, self).__init__()
 
         model = []
-        model += [Conv2dBlock(dim ,dim, 3, 1, 1, norm=norm, activation=activation, pad_type=pad_type)]
-        model += [Conv2dBlock(dim ,dim, 3, 1, 1, norm=norm, activation='none', pad_type=pad_type)]
+        model += [Conv2dBlock(dim, dim, 3, 1, 1, norm=norm, activation=activation, pad_type=pad_type)]
+        model += [Conv2dBlock(dim, dim, 3, 1, 1, norm=norm, activation='none', pad_type=pad_type)]
         self.model = nn.Sequential(*model)
 
     def forward(self, x):
@@ -285,8 +294,9 @@ class ResBlock(nn.Module):
         out += residual
         return out
 
+
 class Conv2dBlock(nn.Module):
-    def __init__(self, input_dim ,output_dim, kernel_size, stride,
+    def __init__(self, input_dim, output_dim, kernel_size, stride,
                  padding=0, norm='none', activation='relu', pad_type='zero'):
         super(Conv2dBlock, self).__init__()
         self.use_bias = True
@@ -305,7 +315,7 @@ class Conv2dBlock(nn.Module):
         if norm == 'bn':
             self.norm = nn.BatchNorm2d(norm_dim)
         elif norm == 'in':
-            #self.norm = nn.InstanceNorm2d(norm_dim, track_running_stats=True)
+            # self.norm = nn.InstanceNorm2d(norm_dim, track_running_stats=True)
             self.norm = nn.InstanceNorm2d(norm_dim)
         elif norm == 'ln':
             self.norm = LayerNorm(norm_dim)
@@ -346,6 +356,7 @@ class Conv2dBlock(nn.Module):
             x = self.activation(x)
         return x
 
+
 class LinearBlock(nn.Module):
     def __init__(self, input_dim, output_dim, norm='none', activation='relu'):
         super(LinearBlock, self).__init__()
@@ -393,6 +404,7 @@ class LinearBlock(nn.Module):
             out = self.activation(out)
         return out
 
+
 ##################################################################################
 # VGG network definition
 ##################################################################################
@@ -447,6 +459,7 @@ class Vgg16(nn.Module):
         return relu5_3
         # return [relu1_2, relu2_2, relu3_3, relu4_3]
 
+
 ##################################################################################
 # Normalization layers
 ##################################################################################
@@ -511,6 +524,7 @@ class LayerNorm(nn.Module):
             x = x * self.gamma.view(*shape) + self.beta.view(*shape)
         return x
 
+
 def l2normalize(v, eps=1e-12):
     return v / (v.norm() + eps)
 
@@ -520,6 +534,7 @@ class SpectralNorm(nn.Module):
     Based on the paper "Spectral Normalization for Generative Adversarial Networks" by Takeru Miyato, Toshiki Kataoka, Masanori Koyama, Yuichi Yoshida
     and the Pytorch implementation https://github.com/christiancosgrove/pytorch-spectral-normalization-gan
     """
+
     def __init__(self, module, name='weight', power_iterations=1):
         super(SpectralNorm, self).__init__()
         self.module = module
@@ -535,8 +550,8 @@ class SpectralNorm(nn.Module):
 
         height = w.data.shape[0]
         for _ in range(self.power_iterations):
-            v.data = l2normalize(torch.mv(torch.t(w.view(height,-1).data), u.data))
-            u.data = l2normalize(torch.mv(w.view(height,-1).data, v.data))
+            v.data = l2normalize(torch.mv(torch.t(w.view(height, -1).data), u.data))
+            u.data = l2normalize(torch.mv(w.view(height, -1).data, v.data))
 
         # sigma = torch.dot(u.data, torch.mv(w.view(height,-1).data, v.data))
         sigma = u.dot(w.view(height, -1).mv(v))
@@ -551,7 +566,6 @@ class SpectralNorm(nn.Module):
         except AttributeError:
             return False
 
-
     def _make_params(self):
         w = getattr(self.module, self.name)
 
@@ -570,7 +584,6 @@ class SpectralNorm(nn.Module):
         self.module.register_parameter(self.name + "_v", v)
         self.module.register_parameter(self.name + "_bar", w_bar)
 
-
     def forward(self, *args):
         self._update_u_v()
-        return self.module.forward(*args)
\ No newline at end of file
+        return self.module.forward(*args)
diff --git a/munit/MUNIT/trainer.py b/munit/MUNIT/trainer.py
index 87577bd..e2cef9c 100644
--- a/munit/MUNIT/trainer.py
+++ b/munit/MUNIT/trainer.py
@@ -2,13 +2,14 @@
 Copyright (C) 2017 NVIDIA Corporation.  All rights reserved.
 Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
 """
-from networks import AdaINGen, MsImageDis, VAEGen
-from utils import weights_init, get_model_list, vgg_preprocess, load_vgg16, get_scheduler
+from munit.MUNIT.networks import AdaINGen, MsImageDis, VAEGen
+from munit.MUNIT.utils import weights_init, get_model_list, vgg_preprocess, load_vgg16, get_scheduler
 from torch.autograd import Variable
 import torch
 import torch.nn as nn
 import os
 
+
 class MUNIT_Trainer(nn.Module):
     def __init__(self, hyperparameters):
         super(MUNIT_Trainer, self).__init__()
diff --git a/munit/MUNIT/utils.py b/munit/MUNIT/utils.py
index a5a3504..b06f0ab 100644
--- a/munit/MUNIT/utils.py
+++ b/munit/MUNIT/utils.py
@@ -4,11 +4,11 @@ Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses
 """
 import torchfile
 from torch.utils.data import DataLoader
-from networks import Vgg16
+from munit.MUNIT.networks import Vgg16
 from torch.autograd import Variable
 from torch.optim import lr_scheduler
 from torchvision import transforms
-from data import ImageFilelist, ImageFolder
+from munit.MUNIT.data import ImageFilelist, ImageFolder
 import torch
 import torch.nn as nn
 import os
diff --git a/test_knc.py b/test_knc.py
new file mode 100644
index 0000000..7298e91
--- /dev/null
+++ b/test_knc.py
@@ -0,0 +1,319 @@
+import logging
+import os
+import pickle
+from copy import deepcopy
+
+import numpy as np
+import torch
+from PIL import Image
+from keras import backend as K
+from keras.applications.imagenet_utils import preprocess_input
+from keras.models import Model
+from keras.preprocessing import image
+from torch.autograd import Variable
+from torchvision import transforms
+
+from ads.TrainModel import dave_dropout
+from engine import EAEngine
+from munit.MUNIT.trainer import MUNIT_Trainer
+from munit.MUNIT.utils import get_config
+
+K.set_learning_phase(0)
+####################
+# parameters
+####################
+# the image path
+# train_image_paths = '/home/test/program/self-driving/dataset/train/center/'
+test_image_path = './ads/data/sunny/'
+# munit model path
+# config_path = '/home/test/program/self-driving/munit/configs/snowy.yaml'
+# checkpoint_path = '/home/test/program/self-driving/munit/checkpoints/snowy/gen_01000000.pt'
+# config_path = '/home/test/program/self-driving/munit/configs/night.yaml'
+# checkpoint_path = '/home/test/program/self-driving/munit/checkpoints/night/gen_01000000.pt'
+# config_path = '/home/test/program/self-driving/munit/configs/rainy.yaml'
+# checkpoint_path = '/home/test/program/self-driving/munit/checkpoints/rainy/gen_01000000.pt'
+# config_path = '/home/test/program/self-driving/munit/configs/sunny.yaml'
+# checkpoint_path = '/home/test/program/self-driving/munit/checkpoints/sunny/gen_01250000.pt'
+config_path = 'munit/MUNIT/configs/snow_night.yaml'
+checkpoint_path = 'munit/MUNIT/checkpoints/snow_night/gen_01000000.pt'
+# the self-driving system's weight file
+weights_path = './ads/Autopilot.h5'
+target_size = (40, 40)
+nb_part = 1000
+
+###################
+# set logger
+###################
+logger = logging.getLogger(__name__)
+logger.setLevel(level=logging.INFO)
+handler = logging.FileHandler("./logger/knc_snow_night_ES_time_cost.txt")
+handler.setLevel(logging.INFO)
+formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+handler.setFormatter(formatter)
+logger.addHandler(handler)
+
+model, callback_list = dave_dropout(load_weights=True, weights_path=weights_path)
+model.summary()
+
+layer_to_compute = [layer for layer in model.layers
+                    if all(ex not in layer.name for ex in ['flatten', 'input', 'pool', 'dropout'])][0:-2]
+
+outputs_layer = [layer.output for layer in layer_to_compute]
+outputs_layer.append(model.layers[-1].output)
+intermediate_model = Model(inputs=model.input, outputs=outputs_layer)
+
+with open('cache/Dave_dropout/train_outputs/layer_bounds_bin.pkl', 'rb') as f:
+    layer_bounds_bins = pickle.load(f)
+with open('cache/Dave_dropout/test_outputs/knc_coverage.pkl', 'rb') as f:
+     knc_cov_dict = pickle.load(f)
+#with open('cache/Dave_dropout/test_outputs/knc_coverage_cache_snowy_random.pkl', 'rb') as f:
+ #   knc_cov_dict = pickle.load(f)
+with open('cache/Dave_dropout/test_outputs/steering_angles.pkl', 'rb') as f:
+    original_steering_angles = pickle.load(f)
+
+#####################
+# build MUNIT model
+#####################
+config = get_config(config_path)
+
+munit = MUNIT_Trainer(config)
+
+# try:
+#     state_dict = torch.load(checkpoint_path)
+#     munit.gen_a.load_state_dict(state_dict['a'])
+#     munit.gen_b.load_state_dict(state_dict['b'])
+# except Exception:
+#     raise RuntimeError('load model failed')
+
+munit.cuda()
+new_size = config['new_size']  # the GAN's input size is 256*256
+style_dim = config['gen']['style_dim']
+encode = munit.gen_a.encode
+style_encode = munit.gen_b.encode
+decode = munit.gen_b.decode
+
+
+# process the munit's input
+transform = transforms.Compose([transforms.Resize(new_size),
+                                transforms.ToTensor(),
+                                transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
+
+
+# function generator use to generate transformed images by MUNIT
+def generator(img, style):
+    with torch.no_grad():
+        img = Variable(transform(img).unsqueeze(0).cuda())
+        s = Variable(style.unsqueeze(0).cuda())
+        content, _ = encode(img)
+
+        outputs = decode(content, s)
+        outputs = (outputs + 1) / 2.
+        del img
+        del s
+        del content
+        return outputs.data
+
+
+# process the generated image from munit
+def preprocess_transformed_images(original_image):
+    tensor = original_image.view(1, original_image.size(0), original_image.size(1), original_image.size(2))
+    tensor = tensor.clone()
+
+    def norm_ip(img, min, max):
+        img.clamp_(min=min, max=max)
+        img.add_(-min).div_(max - min + 1e-5)
+
+    def norm_range(img):
+        norm_ip(img, float(img.min()), float(img.max()))
+
+    norm_range(tensor)
+    tensor = tensor.squeeze()
+    ndarr = tensor.mul(255).clamp(0, 255).byte().permute(1, 2, 0).cpu().numpy()
+    img = Image.fromarray(ndarr)
+    img = img.resize((target_size[1], target_size[0])).convert('L')
+    input_img_data = image.image_utils.img_to_array(img)
+    input_img_data = np.expand_dims(input_img_data, axis=0)
+    input_img_data = preprocess_input(input_img_data, mode='tf')
+    return input_img_data
+
+
+######################
+# create some functions to calculate
+######################
+def calculate_uncovered_knc_sections():
+    """
+    Calculate the number of uncovered sections on the KNC criterion
+    :return:
+    """
+    return np.sum([np.count_nonzero(knc_cov_dict[layer.name] == 0) for layer in layer_to_compute])
+
+
+def get_new_covered_knc_sections(intermediate_layer_outputs, cov_dict):
+    # cov_dict = deepcopy(knc_cov_dict)
+    new_covered_sections = 0
+    for i, intermediate_layer_output in enumerate(intermediate_layer_outputs):
+        output = intermediate_layer_output[0]
+        layer = layer_to_compute[i]
+        bins = layer_bounds_bins[layer.name]
+        for id_neuron in range(output.shape[-1]):
+            _bin = np.digitize(np.mean(output[..., id_neuron]), bins[id_neuron]) - 1
+            if _bin >= nb_part or cov_dict[layer.name][id_neuron][_bin]:
+                continue
+            new_covered_sections = new_covered_sections + 1
+            cov_dict[layer.name][id_neuron][_bin] = True
+    return new_covered_sections
+
+
+def update_knc(intermediate_layer_outputs):
+    for i, intermediate_layer_output in enumerate(intermediate_layer_outputs):
+        layer = layer_to_compute[i]
+        bins = layer_bounds_bins[layer.name]
+        output = intermediate_layer_output[0]
+        for id_neuron in range(output.shape[-1]):
+            _bin = np.digitize(np.mean(output[..., id_neuron]), bins[id_neuron]) - 1
+            if _bin >= nb_part or knc_cov_dict[layer.name][id_neuron][_bin]:
+                continue
+            knc_cov_dict[layer.name][id_neuron][_bin] = True
+
+
+def current_knc_coverage():
+    """
+    Calculate the current K-Multi Section Neuron Coverage
+    :return:
+    """
+    covered = 0
+    total = 0
+    for layer in layer_to_compute:
+        covered = covered + np.count_nonzero(knc_cov_dict[layer.name])
+        total = total + np.size(knc_cov_dict[layer.name])
+    return covered / float(total)
+
+
+# the fitness function
+def fitness_function(original_images, original_preds, theoretical_uncovered_sections, style):
+    preds = []
+    cov_dict = deepcopy(knc_cov_dict)
+    new_covered_sections = 0
+    logger.info("do prediction")
+    img_num = 0
+    for img in original_images:
+        img_num += 1
+        logger.info("begin generate driving scenes {i}".format(i=img_num))
+        transformed_image = generator(img, style)[0]
+        transformed_image = preprocess_transformed_images(transformed_image)
+        logger.info("finish generating driving scenes")
+
+        logger.info("obtain internal outputs")
+        internal_outputs = intermediate_model.predict(transformed_image)
+        intermediate_outputs = internal_outputs[0:-1]
+        preds.append(internal_outputs[-1][0][0])
+        logger.info("finish obtaining internal outputs")
+
+        logger.info("calculate coverage")
+        new_covered_sections += get_new_covered_knc_sections(intermediate_outputs, cov_dict)
+        logger.info("finish calculating coverage")
+    # for img in original_images:
+    #     transformed_image = generator(img, style)[0]
+    #     transformed_image = preprocess_transformed_images(transformed_image)
+    #
+    #     internal_outputs = intermediate_model.predict(transformed_image)
+    #     intermediate_outputs = internal_outputs[0:-1]
+    #     preds.append(internal_outputs[-1][0][0])
+    #
+    #     new_covered_sections += get_new_covered_knc_sections(intermediate_outputs, cov_dict)
+
+    logger.info(new_covered_sections)
+    # logger.info(len(transformed_preds))
+    transformed_preds = np.asarray(preds)
+    o1 = float(new_covered_sections) / float(theoretical_uncovered_sections)
+    o2 = np.average(np.abs(transformed_preds - original_preds))
+    o2 = o2 / (o2 + 1)  # normalize
+    logger.info("the o1 is {}".format(o1))
+    logger.info("the o2 is {}".format(o2))
+    del new_covered_sections
+    del transformed_preds
+    del cov_dict
+    return o1 + o2
+
+
+# the wrapper of fitness function
+def fitness_function_wrapper(original_images, original_preds, theoretical_uncovered_sections):
+    def decorator(func):
+        def wrapper(*args, **kwargs):
+            return fitness_function(original_images, original_preds, theoretical_uncovered_sections, *args, **kwargs)
+        return wrapper
+    return decorator
+
+
+def count_error_behaviors(ori_preds, preds):
+    count = 0
+    for i in range(len(ori_preds)):
+        if np.abs(ori_preds[i] - preds[i]) > 0.2 \
+                or (ori_preds[i] < 0 and preds[i] > 0) or (ori_preds[i] > 0 and preds[i] < 0):
+            count = count + 1
+    return count
+
+
+def update_history(original_images, style):
+    preds = []
+    for img in original_images:
+        transformed_image = generator(img, style)[0]
+        transformed_image = preprocess_transformed_images(transformed_image)
+
+        internal_outputs = intermediate_model.predict(transformed_image)
+        intermediate_outputs = internal_outputs[0:-1]
+        preds.append(internal_outputs[-1][0][0])
+
+        update_knc(intermediate_outputs)
+    return preds
+
+
+def testing():
+    images_path = [(test_image_path + image_file) for image_file in sorted(os.listdir(test_image_path))
+                   if image_file.endswith(".jpg")]
+    orig_images_for_transform = [Image.open(path).convert('RGB') for path in images_path]
+
+    iteration = 0
+
+    print('## current_knc_coverage: {}'.format(current_knc_coverage()))
+
+    while True:
+        logger.info("the {nb_iter} begin".format(nb_iter=iteration))
+        # theoretical_uncovered_sections = nb_neurons * nb_images
+        nb_uncovered_sections = calculate_uncovered_knc_sections()
+        theoretical_uncovered_sections = calculate_uncovered_knc_sections()
+
+        theoretical_uncovered_sections = theoretical_uncovered_sections \
+            if theoretical_uncovered_sections <= nb_uncovered_sections else nb_uncovered_sections
+
+        print('## theoretical_uncovered_sections: '.format(theoretical_uncovered_sections))
+
+        @fitness_function_wrapper(orig_images_for_transform, original_steering_angles, theoretical_uncovered_sections)
+        def fitness(style):
+            pass
+
+        search_handler = EAEngine(style_dim=style_dim, fitness_func=fitness, logger=logger)
+        best = search_handler.run(150)
+
+        transformed_preds = update_history(orig_images_for_transform, best)
+
+        logger.info("the {nb_iter} finish".format(nb_iter=iteration))
+        logger.info("current k-multi section coverage is {}".format(current_knc_coverage()))
+        # logger.info("current neuron boundary coverage is {}".format(current_knc_coverage()))
+        logger.info("the best style code is {}".format(best))
+        logger.info("the number of error behaviors is {}".format(count_error_behaviors(original_steering_angles, transformed_preds)))
+
+        with open('cache/Dave_dropout/test_outputs/knc_coverage_cache_snow_night_random.pkl', 'wb') \
+                as f:
+            pickle.dump(knc_cov_dict, f, pickle.HIGHEST_PROTOCOL)
+
+        iteration += 1
+
+        if iteration == 4 or current_knc_coverage() >= 0.80:
+            # Terminate the algorithm when the coverage is greater than threshold
+            # or the number of iterations is euqal to four
+            break
+
+
+if __name__ == '__main__':
+    testing()
diff --git a/test_nbc.py b/test_nbc.py
new file mode 100644
index 0000000..23d0163
--- /dev/null
+++ b/test_nbc.py
@@ -0,0 +1,309 @@
+import logging
+import os
+import pickle
+from copy import deepcopy
+
+import numpy as np
+import torch
+from PIL import Image
+from keras.applications.imagenet_utils import preprocess_input
+from keras.models import Model
+from keras.preprocessing import image
+from torch.autograd import Variable
+from torchvision import transforms
+
+from ads.TrainModel import dave_dropout
+from engine import RandomSearch
+from munit.MUNIT.trainer import MUNIT_Trainer
+from munit.MUNIT.utils import get_config
+
+####################
+# parameters
+####################
+# the image path
+# train_image_paths = '/home/test/program/self-driving/dataset/train/center/'
+test_image_path = './ads/data/sunny/'
+# the cache of original images steering angles
+# munit model path
+# config_path = '/home/test/program/self-driving/munit/configs/snowy.yaml'
+# checkpoint_path = '/home/test/program/self-driving/munit/checkpoints/snowy/gen_01000000.pt'
+# config_path = '/home/test/program/self-driving/munit/configs/night.yaml'
+# checkpoint_path = '/home/test/program/self-driving/munit/checkpoints/night/gen_01000000.pt'
+# config_path = '/home/test/program/self-driving/munit/configs/rainy.yaml'
+# checkpoint_path = '/home/test/program/self-driving/munit/checkpoints/rainy/gen_01000000.pt'
+# config_path = '/home/test/program/self-driving/munit/configs/sunny.yaml'
+# checkpoint_path = '/home/test/program/self-driving/munit/checkpoints/sunny/gen_01250000.pt'
+config_path = 'munit/MUNIT/configs/snow_night.yaml'
+checkpoint_path = 'munit/MUNIT/checkpoints/snow_night/gen_01000000.pt'
+# the self-driving system's weight file
+weights_path = './ads/Autopilot.h5'
+target_size = (40, 40)
+nb_part = 1000
+
+###################
+# set logger
+###################
+logger = logging.getLogger(__name__)
+logger.setLevel(level=logging.INFO)
+handler = logging.FileHandler("./logger/nbc_snow_night_random.txt")
+handler.setLevel(logging.INFO)
+formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+handler.setFormatter(formatter)
+logger.addHandler(handler)
+
+model, callback_list = dave_dropout(load_weights=True, weights_path=weights_path)
+model.summary()
+
+layer_to_compute = [layer for layer in model.layers
+                    if all(ex not in layer.name for ex in ['flatten', 'input', 'pool', 'dropout'])][0:-2]
+
+outputs_layer = [layer.output for layer in layer_to_compute]
+outputs_layer.append(model.layers[-1].output)
+intermediate_model = Model(inputs=model.input, outputs=outputs_layer)
+
+with open('cache/Dave_dropout/train_outputs/layer_bounds.pkl', 'rb') as f:
+    layer_bounds = pickle.load(f)
+with open('cache/Dave_dropout/test_outputs/nbc_coverage.pkl', 'rb') as f:
+    nbc_cov_dict = pickle.load(f)
+# with open('cache/Dave_dropout/test_outputs/nbc_coverage_cache_night_random.pkl', 'rb') as f:
+#     nbc_cov_dict = pickle.load(f)
+with open('cache/Dave_dropout/test_outputs/steering_angles.pkl', 'rb') as f:
+    original_steering_angles = pickle.load(f)
+
+#####################
+# build MUNIT model
+#####################
+config = get_config(config_path)
+
+munit = MUNIT_Trainer(config)
+
+# try:
+#     state_dict = torch.load(checkpoint_path)
+#     munit.gen_a.load_state_dict(state_dict['a'])
+#     munit.gen_b.load_state_dict(state_dict['b'])
+# except Exception:
+#     raise RuntimeError('load model failed')
+
+munit.cuda()
+new_size = config['new_size']  # the GAN's input size is 256*256
+style_dim = config['gen']['style_dim']
+encode = munit.gen_a.encode
+style_encode = munit.gen_b.encode
+decode = munit.gen_b.decode
+
+# process the munit's input
+# warning: this is very import configuration
+# transform = transforms.Compose([transforms.Resize((new_size, new_size)),
+#                                 transforms.ToTensor(),
+#                                 transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
+transform = transforms.Compose([transforms.Resize(new_size),
+                                transforms.ToTensor(),
+                                transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
+
+
+# function generator use to generate transformed images by MUNIT
+def generator(img, style):
+    with torch.no_grad():
+        img = Variable(transform(img).unsqueeze(0).cuda())
+        s = Variable(style.unsqueeze(0).cuda())
+        content, _ = encode(img)
+
+        outputs = decode(content, s)
+        outputs = (outputs + 1) / 2.
+        del img
+        del s
+        del content
+        return outputs.data
+
+
+# process the generated image from munit
+def preprocess_transformed_images(original_image):
+    tensor = original_image.view(1, original_image.size(0), original_image.size(1), original_image.size(2))
+    tensor = tensor.clone()
+
+    def norm_ip(img, min, max):
+        img.clamp_(min=min, max=max)
+        img.add_(-min).div_(max - min + 1e-5)
+
+    def norm_range(img):
+        norm_ip(img, float(img.min()), float(img.max()))
+
+    norm_range(tensor)
+    tensor = tensor.squeeze()
+    ndarr = tensor.mul(255).clamp(0, 255).byte().permute(1, 2, 0).cpu().numpy()
+    img = Image.fromarray(ndarr)
+    img = img.resize((target_size[1], target_size[0])).convert('L')
+    input_img_data = image.image_utils.img_to_array(img)
+    input_img_data = np.expand_dims(input_img_data, axis=0)
+    input_img_data = preprocess_input(input_img_data, mdoe='tf')
+    return input_img_data
+
+
+######################
+# create some functions to calculate
+######################
+def calculate_uncovered_nbc_sections():
+    """
+    Calculate the number of uncovered sections on the KNC criterion
+    :return:
+    """
+    return np.sum([np.count_nonzero(nbc_cov_dict[layer.name] == 0) for layer in layer_to_compute])
+
+
+def get_new_covered_nbc_sections(intermediate_layer_outputs, cov_dict):
+    # cov_dict = deepcopy(knc_cov_dict)
+    new_covered_sections = 0
+    for i, intermediate_layer_output in enumerate(intermediate_layer_outputs):
+        output = intermediate_layer_output[0]
+        layer = layer_to_compute[i]
+        (low_bound, high_bound) = layer_bounds[layer.name]
+        for id_neuron in range(output.shape[-1]):
+            val = np.mean(output[..., id_neuron])
+            if val < low_bound[id_neuron] and not cov_dict[layer.name][id_neuron][0]:
+                new_covered_sections = new_covered_sections + 1
+                cov_dict[layer.name][id_neuron][0] = True
+            elif val > high_bound[id_neuron] and not cov_dict[layer.name][id_neuron][1]:
+                new_covered_sections = new_covered_sections + 1
+                cov_dict[layer.name][id_neuron][1] = True
+            else:
+                continue
+    return new_covered_sections
+
+
+def update_nbc(intermediate_layer_outputs):
+    for i, intermediate_layer_output in enumerate(intermediate_layer_outputs):
+        output = intermediate_layer_output[0]
+        layer = layer_to_compute[i]
+        (low_bound, high_bound) = layer_bounds[layer.name]
+        for id_neuron in range(output.shape[-1]):
+            val = np.mean(output[..., id_neuron])
+            if val < low_bound[id_neuron] and not nbc_cov_dict[layer.name][id_neuron][0]:
+                nbc_cov_dict[layer.name][id_neuron][0] = True
+            elif val > high_bound[id_neuron] and not nbc_cov_dict[layer.name][id_neuron][1]:
+                nbc_cov_dict[layer.name][id_neuron][1] = True
+            else:
+                continue
+
+
+def current_nbc_coverage():
+    """
+    Calculate the current Neuron Boundary Coverage
+    :return:
+    """
+    covered = 0
+    total = 0
+    for layer in layer_to_compute:
+        covered = covered + np.count_nonzero(nbc_cov_dict[layer.name])
+        total = total + np.size(nbc_cov_dict[layer.name])
+    return covered / float(total)
+
+
+# the fitness function
+def fitness_function(original_images, original_preds, theoretical_uncovered_sections, style):
+    preds = []
+    cov_dict = deepcopy(nbc_cov_dict)
+    new_covered_sections = 0
+    for img in original_images:
+        transformed_image = generator(img, style)[0]
+        transformed_image = preprocess_transformed_images(transformed_image)
+
+        internal_outputs = intermediate_model.predict(transformed_image)
+        intermediate_outputs = internal_outputs[0:-1]
+        preds.append(internal_outputs[-1][0][0])
+
+        new_covered_sections += get_new_covered_nbc_sections(intermediate_outputs, cov_dict)
+
+    logger.info(new_covered_sections)
+    # logger.info(len(transformed_preds))
+    transformed_preds = np.asarray(preds)
+    o1 = float(new_covered_sections) / float(theoretical_uncovered_sections)
+    o2 = np.average(np.abs(transformed_preds - original_preds))
+    o2 = o2 / (o2 + 1)  # normalize
+    logger.info("the o1 is {}".format(o1))
+    logger.info("the o2 is {}".format(o2))
+    del new_covered_sections
+    del transformed_preds
+    del cov_dict
+    return (10 * o1) + o2
+
+
+# the wrapper of fitness function
+def fitness_function_wrapper(original_images, original_preds, theoretical_uncovered_sections):
+    def decorator(func):
+        def wrapper(*args, **kwargs):
+            return fitness_function(original_images, original_preds, theoretical_uncovered_sections, *args, **kwargs)
+
+        return wrapper
+
+    return decorator
+
+
+def count_error_behaviors(ori_preds, preds):
+    count = 0
+    for i in range(len(ori_preds)):
+        if np.abs(ori_preds[i] - preds[i]) > 0.2 \
+                or (ori_preds[i] < 0 and preds[i] > 0) or (ori_preds[i] > 0 and preds[i] < 0):
+            count = count + 1
+    return count
+
+
+def update_history(original_images, style):
+    preds = []
+    for img in original_images:
+        transformed_image = generator(img, style)[0]
+        transformed_image = preprocess_transformed_images(transformed_image)
+
+        internal_outputs = intermediate_model.predict(transformed_image)
+        intermediate_outputs = internal_outputs[0:-1]
+        preds.append(internal_outputs[-1][0][0])
+
+        update_nbc(intermediate_outputs)
+    return preds
+
+
+def testing():
+    images_path = [(test_image_path + image_file) for image_file in sorted(os.listdir(test_image_path))
+                   if image_file.endswith(".jpg")]
+    orig_images_for_transform = [Image.open(path).convert('RGB') for path in images_path]
+
+    iteration = 0
+
+    print(current_nbc_coverage())
+
+    while True:
+        logger.info("the {nb_iter} begin".format(nb_iter=iteration))
+        # theoretical_uncovered_sections = nb_neurons * nb_images
+        theoretical_uncovered_sections = calculate_uncovered_nbc_sections()
+
+        print(theoretical_uncovered_sections)
+
+        @fitness_function_wrapper(orig_images_for_transform, original_steering_angles, theoretical_uncovered_sections)
+        def fitness(style):
+            pass
+
+        search_handler = RandomSearch(style_dim=style_dim, fitness_func=fitness, logger=logger)
+        best = search_handler.run(150)
+
+        transformed_preds = update_history(orig_images_for_transform, best)
+
+        logger.info("the {nb_iter} finish".format(nb_iter=iteration))
+        # logger.info("current k-multi section coverage is {}".format(current_nbc_coverage()))
+        logger.info("current neuron boundary coverage is {}".format(current_nbc_coverage()))
+        logger.info("the best style code is {}".format(best))
+        logger.info("the number of error behaviors is {}".format(
+            count_error_behaviors(original_steering_angles, transformed_preds)))
+
+        with open('cache/Dave_dropout/test_outputs/nbc_coverage_cache_snow_night_random.pkl', 'wb') \
+                as f:
+            pickle.dump(nbc_cov_dict, f, pickle.HIGHEST_PROTOCOL)
+
+        iteration += 1
+
+        if iteration == 4 or current_nbc_coverage() >= 0.40:
+            # Terminate the algorithm when the coverage is greater than threshold
+            # or the number of iterations is euqal to ten
+            break
+
+
+if __name__ == '__main__':
+    testing()
-- 
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