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+train_40025 +train_40041 +train_40050 +train_40052 +train_40056 +train_40066 +train_40071 +train_40083 +train_40101 +train_40121 +train_40139 +train_40157 +train_40203 +train_40210 +train_40214 +train_40222 +train_40227 +train_40231 +train_40236 +train_40264 +train_40271 +train_40283 +train_40287 +train_40310 +train_40316 +train_40319 +train_40353 +train_40363 +train_40369 +train_40385 +train_40402 +train_40411 +train_40416 +train_40420 +train_40429 +train_40434 +train_40439 +train_40474 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 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100644 index 0000000..5206d5b Binary files /dev/null and b/planet_models/__pycache__/simplenet.cpython-35.pyc differ 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) diff --git a/trainers/__init__.py b/trainers/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/trainers/__pycache__/__init__.cpython-35.pyc b/trainers/__pycache__/__init__.cpython-35.pyc new file mode 100644 index 0000000..5c9ded8 Binary files /dev/null and b/trainers/__pycache__/__init__.cpython-35.pyc differ diff --git a/trainers/__pycache__/train_simplenet.cpython-35.pyc b/trainers/__pycache__/train_simplenet.cpython-35.pyc new file mode 100644 index 0000000..17540c9 Binary files /dev/null and b/trainers/__pycache__/train_simplenet.cpython-35.pyc differ 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) +