From 6440bedd94ddd8830b03732483ff240e4bddbd82 Mon Sep 17 00:00:00 2001
From: jxu7 <jxu7@bsu.edu>
Date: Wed, 10 May 2017 14:17:18 -0400
Subject: [PATCH] first commit

---
 dataset/train.csv                             | 37479 ++++++++++++++++
 dataset/validation.csv                        |  3000 ++
 datasets.py                                   |   153 +
 labels.py                                     |    22 +
 log/resnet-50.csv                             |   511 +
 log/resnet-50_fcscore.jpg                     |   Bin 0 -> 44638 bytes
 log/resnet-50_losses.jpg                      |   Bin 0 -> 53945 bytes
 log/simplenet.csv                             |   601 +
 log/simplenet_fcscore.jpg                     |   Bin 0 -> 36053 bytes
 log/simplenet_losses.jpg                      |   Bin 0 -> 57039 bytes
 planet_models/__init__.py                     |     0
 .../__pycache__/__init__.cpython-35.pyc       |   Bin 0 -> 147 bytes
 .../__pycache__/resnet_planet.cpython-35.pyc  |   Bin 0 -> 857 bytes
 .../__pycache__/simplenet.cpython-35.pyc      |   Bin 0 -> 2060 bytes
 planet_models/resnet_planet.py                |    18 +
 planet_models/resnetxt.py                     |     9 +
 planet_models/simplenet.py                    |    66 +
 test.py                                       |    57 +
 trainers/__init__.py                          |     0
 trainers/__pycache__/__init__.cpython-35.pyc  |   Bin 0 -> 142 bytes
 .../train_simplenet.cpython-35.pyc            |   Bin 0 -> 2035 bytes
 trainers/resnet_forest.py                     |    73 +
 trainers/train_simplenet.py                   |    57 +
 util.py                                       |   138 +
 24 files changed, 42184 insertions(+)
 create mode 100644 dataset/train.csv
 create mode 100644 dataset/validation.csv
 create mode 100644 datasets.py
 create mode 100644 labels.py
 create mode 100644 log/resnet-50.csv
 create mode 100644 log/resnet-50_fcscore.jpg
 create mode 100644 log/resnet-50_losses.jpg
 create mode 100644 log/simplenet.csv
 create mode 100644 log/simplenet_fcscore.jpg
 create mode 100644 log/simplenet_losses.jpg
 create mode 100644 planet_models/__init__.py
 create mode 100644 planet_models/__pycache__/__init__.cpython-35.pyc
 create mode 100644 planet_models/__pycache__/resnet_planet.cpython-35.pyc
 create mode 100644 planet_models/__pycache__/simplenet.cpython-35.pyc
 create mode 100644 planet_models/resnet_planet.py
 create mode 100644 planet_models/resnetxt.py
 create mode 100644 planet_models/simplenet.py
 create mode 100644 test.py
 create mode 100644 trainers/__init__.py
 create mode 100644 trainers/__pycache__/__init__.cpython-35.pyc
 create mode 100644 trainers/__pycache__/train_simplenet.cpython-35.pyc
 create mode 100644 trainers/resnet_forest.py
 create mode 100644 trainers/train_simplenet.py
 create mode 100644 util.py

diff --git a/dataset/train.csv b/dataset/train.csv
new file mode 100644
index 0000000..393d407
--- /dev/null
+++ b/dataset/train.csv
@@ -0,0 +1,37479 @@
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diff --git a/datasets.py b/datasets.py
new file mode 100644
index 0000000..ddc7ddc
--- /dev/null
+++ b/datasets.py
@@ -0,0 +1,153 @@
+import os
+import time
+import pandas as pd
+from torch.utils.data import Dataset, DataLoader
+from torchvision.transforms import *
+from labels import *
+from skimage.external.tifffile import *
+
+
+def is_image_file(filename):
+    return any(filename.endswith(extension) for extension in ['.png', 'jpg', '.jpeg'])
+
+
+def load_img(filepath):
+    if is_image_file(filepath):
+        image = Image.open(filepath)
+        image = image.convert('RGB')
+    elif '.tif' in filepath:
+        image = imread(filepath)
+    else:
+        raise OSError('File is not either a .tif file or an image file.')
+    return image
+
+
+def input_transform(crop_size):
+    return Compose(
+        [
+            RandomCrop(crop_size),
+            RandomHorizontalFlip(),
+            ToTensor(),
+        ]
+    )
+
+
+class PlanetDataSet(Dataset):
+    def __init__(self, image_dir, label_dir=None, num_labels=17, mode='Train', input_transform=ToTensor(),
+                 target_transform=None, tif=False):
+        super(PlanetDataSet, self).__init__()
+        self.mode = mode
+        self.tif = tif
+        self.images = []
+        suffix = '.jpg' if tif is False else '.tif'
+        print('[*]Loading Dataset {}'.format(image_dir))
+        t = time.time()
+        if mode == 'Train' or mode == 'Validation':
+            self.targets = []
+            self.labels = pd.read_csv(label_dir)
+            image_names = pd.read_csv('../dataset/train.csv' if mode == 'Train' else '../dataset/validation.csv')
+            image_names = image_names.as_matrix().flatten()
+            self.image_filenames = image_names
+            for image in image_names:
+                str_target = self.labels.loc[self.labels['image_name'] == image]
+                image = os.path.join(image_dir, '{}{}'.format(image, suffix))
+                target = np.zeros(num_labels, dtype=np.float32)
+                target_index = [label_to_idx[l] for l in str_target['tags'].values[0].split(' ')]
+                target[target_index] = 1
+                image = load_img(image)
+                self.images.append(image)
+                self.targets.append(target)
+        elif mode == 'Test':
+            self.image_filenames = sorted([os.path.join(image_dir, filename) for filename in os.listdir(image_dir)
+                                           if is_image_file(filename)])
+            for image in self.image_filenames:
+                image = load_img(image)
+                self.images.append(image)
+
+        print('[*]Dataset loading completed, total time elisped {}'.format(time.time() - t))
+        print('[*]Total number of data is {}'.format(len(self)))
+        self.input_transform = input_transform
+        self.target_transform = target_transform
+
+    def __getitem__(self, index):
+        if self.mode == 'Test':
+            image = load_img(self.image_filenames[index])
+            if '.tif' in self.image_filenames[index]:
+                im_id = self.image_filenames[index].split('/')[-1].strip('.tif')
+            else:
+                im_id = self.image_filenames[index].split('/')[-1].strip('.jpg')
+            if self.input_transform is not None:
+                image = self.input_transform(image)
+            return image, im_id
+        else:
+            image = self.images[index]
+            target = self.targets[index]
+            if self.input_transform is not None:
+                image = self.input_transform(image)
+            return image, torch.from_numpy(target)
+
+    def __len__(self):
+        return len(self.image_filenames)
+
+
+def train_tif_loader(batch_size=64, transform=ToTensor()):
+    dataset = PlanetDataSet(
+        '/media/jxu7/BACK-UP/Data/AmazonPlanet/train/train-tif',
+        '/media/jxu7/BACK-UP/Data/AmazonPlanet/train/train.csv',
+        mode='Train',
+        input_transform=transform,
+        tif=True
+    )
+    return DataLoader(dataset=dataset, batch_size=batch_size, shuffle=True, )
+
+
+def validation_tif_loader(batch_size=64, transform=ToTensor()):
+    dataset = PlanetDataSet(
+        '/media/jxu7/BACK-UP/Data/AmazonPlanet/train/train-tif',
+        '/media/jxu7/BACK-UP/Data/AmazonPlanet/train/train.csv',
+        mode='Validation',
+        input_transform=transform,
+        tif=True
+    )
+    return DataLoader(dataset=dataset, batch_size=batch_size, shuffle=True, )
+
+
+def test_tif_loader(batch_size=64, transform=ToTensor()):
+    dataset = PlanetDataSet(
+        '/media/jxu7/BACK-UP/Data/AmazonPlanet/test/test-tif',
+        mode='Test',
+        input_transform=transform,
+        tif=True
+    )
+    return DataLoader(dataset=dataset, batch_size=batch_size, shuffle=True, )
+
+
+def train_jpg_loader(batch_size=64, transform=ToTensor()):
+    dataset = PlanetDataSet(
+        '/media/jxu7/BACK-UP/Data/AmazonPlanet/train/train-jpg',
+        '/media/jxu7/BACK-UP/Data/AmazonPlanet/train/train.csv',
+        mode='Train',
+        input_transform=transform
+    )
+    return DataLoader(dataset=dataset, batch_size=batch_size, shuffle=True, )
+
+
+def validation_jpg_loader(batch_size=64, transform=ToTensor()):
+    dataset = PlanetDataSet(
+        '/media/jxu7/BACK-UP/Data/AmazonPlanet/train/train-jpg',
+        '/media/jxu7/BACK-UP/Data/AmazonPlanet/train/train.csv',
+        mode='Validation',
+        input_transform=transform
+    )
+    return DataLoader(dataset=dataset, batch_size=batch_size, shuffle=True)
+
+
+def test_jpg_loader(batch_size=128, transform=ToTensor()):
+    dataset = PlanetDataSet(
+        '/media/jxu7/BACK-UP/Data/AmazonPlanet/test/test-jpg',
+        mode='Test',
+        input_transform=transform
+    )
+    return DataLoader(dataset=dataset, batch_size=batch_size, shuffle=True)
+
+
diff --git a/labels.py b/labels.py
new file mode 100644
index 0000000..fc25066
--- /dev/null
+++ b/labels.py
@@ -0,0 +1,22 @@
+label_to_idx = {
+    'primary':0,
+    'clear':1,
+    'agriculture':2,
+    'road':3,
+    'water':4,
+    'partly_cloudy':5,
+    'slash_burn':6,
+    'haze':7,
+    'cloudy':8,
+    'blooming':9,
+    'bare_ground':10,
+    'cultivation':11,
+    'selective_logging':12,
+    'conventional_mine':13,
+    'habitation':14,
+    'blow_down':15,
+    'artisinal_mine':16
+}
+
+idx_to_label = {key: value for value, key in label_to_idx.items()}
+
diff --git a/log/resnet-50.csv b/log/resnet-50.csv
new file mode 100644
index 0000000..1d90a03
--- /dev/null
+++ b/log/resnet-50.csv
@@ -0,0 +1,511 @@
+evaluation_loss,f2_score,train_loss
+0.6436094253782242,0.1128716436086035,0.797487199306488
+0.27973577711317277,0.6555395389173008,0.19168153405189514
+0.21013768513997397,0.7084352298793082,0.18092931807041168
+0.19673082563612196,0.7441281845450978,0.18346332013607025
+0.20803931402781653,0.7210509496551517,0.17028364539146423
+0.2171267554873512,0.7608697358378738,0.18354135751724243
+0.19555181170266772,0.7416990676453927,0.151983380317688
+0.20959079076373388,0.7239260114297861,0.16962146759033203
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diff --git a/log/simplenet.csv b/log/simplenet.csv
new file mode 100644
index 0000000..3886572
--- /dev/null
+++ b/log/simplenet.csv
@@ -0,0 +1,601 @@
+evaluation_loss,f2_score,train_loss
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diff --git a/planet_models/resnet_planet.py b/planet_models/resnet_planet.py
new file mode 100644
index 0000000..9a7d72a
--- /dev/null
+++ b/planet_models/resnet_planet.py
@@ -0,0 +1,18 @@
+from torchvision.models import resnet34, resnet50, resnet101, resnet152
+
+
+def resnet34_planet():
+    return resnet34(False, num_classes=17)
+
+
+def resnet101_planet():
+    return resnet101(pretrained=False, num_classes=17)
+
+
+def resnet50_planet():
+    return resnet50(pretrained=False, num_classes=17)
+
+
+def resnet152_planet():
+    resnet152(pretrained=False, num_classes=17)
+
diff --git a/planet_models/resnetxt.py b/planet_models/resnetxt.py
new file mode 100644
index 0000000..2be6e4d
--- /dev/null
+++ b/planet_models/resnetxt.py
@@ -0,0 +1,9 @@
+import torch
+from torch.nn import Module
+from torch.nn import functional as F
+
+
+class Block(Module):
+    def __init__(self):
+        super(Block, self).__init__()
+
diff --git a/planet_models/simplenet.py b/planet_models/simplenet.py
new file mode 100644
index 0000000..11b2512
--- /dev/null
+++ b/planet_models/simplenet.py
@@ -0,0 +1,66 @@
+from torch.nn import *
+from torch.nn import functional as F
+
+
+class MultiLabelCNN(Module):
+    def __init__(self, num_labels, dropout_rate=0.3):
+        super(MultiLabelCNN, self).__init__()
+        self.conv1 = Conv2d(in_channels=3, out_channels=32, padding=1, kernel_size=3, bias=False)
+        self.bn_conv1 = BatchNorm2d(32)
+        self.conv2 = Conv2d(in_channels=32, out_channels=64, padding=1, kernel_size=3, bias=False)
+        self.bn_conv2 = BatchNorm2d(64)
+        self.conv3 = Conv2d(in_channels=64, out_channels=128, padding=1, kernel_size=3, bias=False)
+        self.bn_conv3 = BatchNorm2d(128)
+        self.conv4 = Conv2d(in_channels=128, out_channels=128, padding=1, kernel_size=3, bias=False)
+        self.bn_conv4 = BatchNorm2d(128)
+        # Here comes the fully connected layer
+        # Because we are using max pooling after each conv operation,
+        # the dimension of the last conv layer should be 16*16*128=32768
+        self.fc1 = Linear(in_features=32768, out_features=256, bias=False)
+        self.fc1_bn = BatchNorm1d(256)
+        self.fc1_dropout = Dropout(dropout_rate)
+        self.fc2 = Linear(in_features=256, out_features=num_labels)
+
+        for m in self.modules():
+            if isinstance(m, Conv2d):
+                m.weight.data.normal_(0, 0.02)
+            elif isinstance(m, BatchNorm2d):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()
+
+    def forward(self, x):
+        # conv1 computation
+        x = self.conv1(x)
+        x = self.bn_conv1(x)
+        x = F.elu(x)
+        x = F.max_pool2d(x, 2, stride=2)
+
+
+        # conv2 computation
+        x = self.conv2(x)
+        x = self.bn_conv2(x)
+        x = F.elu(x)
+        x = F.max_pool2d(x, 2, stride=2)
+
+        # conv3 computation
+        x = self.conv3(x)
+        x = self.bn_conv3(x)
+        x = F.elu(x)
+        x = F.max_pool2d(x, 2, stride=2)
+
+        # conv 4 computation
+        x = self.conv4(x)
+        x = self.bn_conv4(x)
+        x = F.elu(x)
+        x = F.max_pool2d(x, 2, stride=2)
+
+        # fc1
+        x = x.view(-1, 32768)
+        x = self.fc1(x)
+        x = self.fc1_bn(x)
+        x = F.elu(x)
+        x = self.fc1_dropout(x)
+
+        # output
+        x = self.fc2(x)
+        return x
diff --git a/test.py b/test.py
new file mode 100644
index 0000000..b3832b5
--- /dev/null
+++ b/test.py
@@ -0,0 +1,57 @@
+import numpy as np
+import pandas as pd
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+from torchvision.transforms import *
+
+from datasets import input_transform
+from datasets import test_jpg_loader
+from labels import *
+from planet_models.simplenet import MultiLabelCNN
+from planet_models.resnet_planet import *
+from trainers.train_simplenet import evaluate
+
+MODEL='models/resnet-101.pth'
+
+
+def test(model_dir, transform):
+    name = model_dir.split('/')[-1][:-4]
+    test_loader = test_jpg_loader(256, transform=Compose(
+        [
+            Scale(224),
+            ToTensor()
+         ]
+    ))
+
+    if 'resnet' in model_dir:
+        model = nn.DataParallel(resnet101_planet())
+    else:
+        model = MultiLabelCNN(17)
+    model.load_state_dict(torch.load(model_dir))
+    model.eval()
+
+    imid_to_label = {}
+    if torch.cuda.is_available():
+        model.cuda()
+    for batch_idx, (images, im_ids) in enumerate(test_loader):
+        result = evaluate(model, images)
+        result = F.sigmoid(result)
+        result = result.data.cpu().numpy()
+        for r, id in zip(result, im_ids):
+            r = np.where(r >= 0.15)[0]
+            labels = [idx_to_label[index] for index in r]
+            imid_to_label[id] = sorted(labels)
+        print('Batch Index {}'.format(batch_idx))
+    sample_submission = pd.read_csv('/media/jxu7/BACK-UP/Data/AmazonPlanet/sample_submission.csv')
+    for key, value in imid_to_label.items():
+        sample_submission.loc[sample_submission['image_name'] == key,'tags'] = ' '.join(str(x) for x in value)
+    sample_submission.to_csv('submissions/%s.csv' % name, index=False)
+
+
+if __name__ == '__main__':
+    if 'resnet' in MODEL:
+        transform = input_transform(227)
+    else:
+        transform = ToTensor()
+    test(MODEL, transform=transform)
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diff --git a/trainers/resnet_forest.py b/trainers/resnet_forest.py
new file mode 100644
index 0000000..07af050
--- /dev/null
+++ b/trainers/resnet_forest.py
@@ -0,0 +1,73 @@
+from torch.nn import *
+from util import *
+from torch import optim
+from planet_models.resnet_planet import resnet50_planet, resnet101_planet, resnet152_planet, resnet34_planet
+from datasets import *
+import torch
+
+
+is_cuda_availible = torch.cuda.is_available()
+
+
+def train_resnet_forest(epoch=50):
+    criterion = MultiLabelSoftMarginLoss()
+    resnet = resnet34_planet()
+    logger = Logger('../log/', 'resnet-34')
+    optimizer = optim.Adam(lr=1e-4, params=resnet.parameters(), weight_decay=1e-4)
+    resnet.cuda()
+    resnet = torch.nn.DataParallel(resnet, device_ids=[0, 1])
+    train_data_set = train_jpg_loader(256, transform=Compose(
+        [
+            RandomHorizontalFlip(),
+            RandomCrop(224),
+            ToTensor()
+        ]
+    ))
+    validation_data_set = validation_jpg_loader(64, transform=Compose(
+        [
+            Scale(224),
+            ToTensor()
+         ]
+    ))
+
+    for i in range(epoch):
+        # save the model for every epoch
+        torch.save(resnet.state_dict(), '../models/resnet-34.pth')
+        for batch_index, (target_x, target_y) in enumerate(train_data_set):
+            if is_cuda_availible:
+                target_x, target_y = target_x.cuda(), target_y.cuda()
+            resnet.train()
+            target_x, target_y = Variable(target_x), Variable(target_y)
+            optimizer.zero_grad()
+            output = resnet(target_x)
+            loss = criterion(output, target_y)
+            loss.backward()
+            optimizer.step()
+
+            if batch_index % 50 == 0:
+                val_loss = 0.0
+                f2_scores = 0.0
+                resnet.eval()
+                for batch_index, (val_x, val_y) in enumerate(validation_data_set):
+                    if is_cuda_availible:
+                        val_y = val_y.cuda()
+                    val_y = Variable(val_y, volatile=True)
+                    val_output = evaluate(resnet, val_x)
+                    val_loss += criterion(val_output, val_y)
+                    binary_y = threshold_labels(val_output.data.cpu().numpy())
+                    f2 = f2_score(val_y.data.cpu().numpy(), binary_y)
+                    f2_scores += f2
+                print('Evaluation loss is {}, Training loss is {}'.format(val_loss.data[0]/batch_index,
+                                                                          loss.data[0]))
+                print('F2 Score is %s' % (f2_scores/batch_index))
+                logger.add_record('train_loss', loss.data[0])
+                logger.add_record('evaluation_loss', val_loss.data[0]/batch_index)
+                logger.add_record('f2_score', f2_scores/batch_index)
+        print('Finished epoch {}'.format(i))
+    logger.save()
+    logger.save_plot()
+
+
+if __name__ == '__main__':
+    train_resnet_forest(epoch=85)
+
diff --git a/trainers/train_simplenet.py b/trainers/train_simplenet.py
new file mode 100644
index 0000000..aac2ae2
--- /dev/null
+++ b/trainers/train_simplenet.py
@@ -0,0 +1,57 @@
+from util import *
+from planet_models.simplenet import MultiLabelCNN
+from torch.nn import *
+from torch import optim
+from torch.autograd import Variable
+from dataset import *
+import torch
+
+
+def train(epoch):
+    train_loader = test_jpg_loader(batch_size=64)
+    val_loader = test_jpg_loader(batch_size=128)
+    logger = Logger('../log/', 'simplenet')
+    criterion = MultiLabelSoftMarginLoss()
+    model = MultiLabelCNN(17)
+    optimizer = optim.Adam(model.parameters(), lr=8e-4, weight_decay=5e-6)
+    if torch.cuda.is_available():
+        model.cuda()
+    for e in range(epoch):
+        torch.save(model.state_dict(), 'models/net.pth')
+        for batch_idx, (image, target) in enumerate(train_loader):
+            model.train()
+            if torch.cuda.is_available():
+                image, target = image.cuda(), target.cuda()
+            image, target = Variable(image), Variable(target)
+            optimizer.zero_grad()
+            output = model(image)
+            loss = criterion(output, target)
+            loss.backward()
+            optimizer.step()
+            if batch_idx % 50 == 0:
+                model.eval()
+                eval_loss = 0
+                f2_scores = 0
+                for eval_batch_idx, (eval_image, eval_target) in enumerate(val_loader):
+                    if torch.cuda.is_available():
+                        target = target.cuda()
+                    target = Variable(target, volatile=True)
+                    output = evaluate(model, eval_image)
+                    eval_loss += criterion(output, eval_target)
+                    binary_y = threshold_labels(output.data.cpu().numpy())
+                    f2 = f2_score(eval_target.data.cpu().numpy(), binary_y)
+                    f2_scores += f2
+                print('Evaluation loss is {}, training loss is {}'.format(eval_loss.data[0]/eval_batch_idx,
+                                                                          loss.data[0]))
+                print('F2 score {}'.format(f2_scores/eval_batch_idx))
+                logger.add_record('train_loss', loss.data[0])
+                logger.add_record('evaluation_loss', eval_loss.data[0]/eval_batch_idx)
+                logger.add_record('f2_score', f2_scores/eval_batch_idx)
+        print('Finished epoch {}'.format(e))
+    logger.save()
+    logger.save_plot()
+
+
+
+if __name__ == '__main__':
+    train(100)
diff --git a/util.py b/util.py
new file mode 100644
index 0000000..d94f326
--- /dev/null
+++ b/util.py
@@ -0,0 +1,138 @@
+import torch.nn as nn
+from torch.autograd import Variable
+from sklearn.metrics import fbeta_score
+from torch.nn import functional as F
+from matplotlib import pyplot as plt
+import numpy as np
+import pandas as pds
+from datasets import *
+import torch
+import os
+
+
+def evaluate(model, image):
+    """Evaluate the model given evaluation images and labels"""
+    model.eval()
+    if torch.cuda.is_available():
+        image = image.cuda()
+    image = Variable(image, volatile=True)
+    output = model(image)
+    return output
+
+
+def split_train_validation(num_val=3000):
+    """
+    Save train image names and validation image names to csv files
+    """
+    train_image_idx = np.sort(np.random.choice(40479, 40479-3000, replace=False))
+    all_idx = np.arange(40479)
+    validation_image_idx = np.zeros(num_val, dtype=np.int32)
+    val_idx = 0
+    train_idx = 0
+    for i in all_idx:
+        if not i in train_image_idx:
+            validation_image_idx[val_idx] = i
+            val_idx += 1
+        else:
+            train_idx += 1
+    # save train
+    train = []
+    for name in train_image_idx:
+        train.append('train_%s' % name)
+
+    eval = []
+    for name in validation_image_idx:
+        eval.append('train_%s' % name)
+
+    df = pds.DataFrame(train)
+    df.to_csv('train.csv', index=False, header=False)
+
+    df = pds.DataFrame(eval)
+    df.to_csv('validation.csv', index=False, header=False)
+
+
+def threshold_labels(y, threshold=0.2):
+    """
+        y is a numpy array of shape N, num_classes, threshold can either be a float or a numpy array
+    """
+
+    if hasattr(threshold, '__iter__'):
+        for i in range(y.shape[-1]):
+            y[:, i] = (y[:, i] > threshold[i]).astype(np.int)
+    else:
+        y[y >= threshold] = 1
+        y[y <= threshold] = 0
+    return y
+
+
+def f2_score(y_true, y_pred):
+    return fbeta_score(y_true, y_pred, beta=2, average='samples')
+
+
+def optimize_threshold(model, mode_dir, resolution=10000):
+    """
+    This function takes the validation set and find the best threshold for each class.
+    """
+    model = nn.DataParallel(model)
+    model.load_state_dict(torch.load(mode_dir))
+    model.cuda(0)
+    data = validation_jpg_loader(256, transform=input_transform(227))
+    num_class = 17
+    pred = []
+    targets = []
+    # predict
+    for batch_index, (images, target) in enumerate(data):
+        output = evaluate(model, images)
+        output = F.sigmoid(output)
+        pred.append(output.data.cpu().numpy())
+        targets.append(target.cpu().numpy())
+    pred = np.vstack(pred)
+    targets = np.vstack(targets)
+    threshold = [0.1] * 17
+    # optimize
+    for i in range(num_class):
+        best_thresh = 0.0
+        best_score = 0.0
+        for r in range(resolution):
+            r /= resolution
+            threshold[i] = r
+            labels = threshold_labels(pred, threshold)
+            score = f2_score(targets, labels)
+            if score > best_score:
+                best_thresh = r
+                best_score = score
+        threshold[i] = best_thresh
+        print(i, best_score, best_thresh)
+    return threshold
+
+
+class Logger(object):
+    def __init__(self, save_dir, name):
+        self.save_dir = save_dir
+        self.name = name
+        if not os.path.exists(self.save_dir):
+            os.makedirs(self.save_dir)
+        self.save_dict = {'train_loss': [], "evaluation_loss": [], 'f2_score': []}
+
+    def add_record(self, key, value):
+        self.save_dict[key].append(value)
+
+    def save(self):
+        df = pd.DataFrame.from_dict(self.save_dict)
+        df.to_csv(os.path.join(self.save_dir, '%s.csv' % self.name), header=True, index=False)
+
+    def save_plot(self):
+        train_loss = self.save_dict['train_loss']
+        eval_loss = self.save_dict['evaluation_loss']
+        f2_scores = self.save_dict['f2_score']
+        plt.figure()
+        plt.plot(np.arange(len(train_loss)), train_loss, color='red', label='train_loss')
+        plt.plot(np.arange(len(eval_loss)), eval_loss, color='blue', label='eval_loss')
+        plt.legend(loc='best')
+
+        plt.savefig('log/%s_losses.jpg' % self.name)
+
+        plt.figure()
+        plt.plot(np.arange(len(f2_scores)), f2_scores)
+        plt.savefig('log/%s_fcscore.jpg' % self.name)
+