diff --git a/Boat_MNIST/10/AdemAtmaca_AdrianSauter_Challenge.pdf b/Boat_MNIST/10/AdemAtmaca_AdrianSauter_Challenge.pdf
new file mode 100644
index 0000000..df404c2
Binary files /dev/null and b/Boat_MNIST/10/AdemAtmaca_AdrianSauter_Challenge.pdf differ
diff --git a/Boat_MNIST/10/challenge_nn_big.py b/Boat_MNIST/10/challenge_nn_big.py
new file mode 100644
index 0000000..3e870b4
--- /dev/null
+++ b/Boat_MNIST/10/challenge_nn_big.py
@@ -0,0 +1,216 @@
+"""
+TODO
+- team member 1: Adem Atmaca
+- team member 2: Adrian Sauter
+tasks:
+ - add your team members' names at the top of the file
+ - Take part in the challenge :)
+"""
+
+import argparse
+import copy
+import json
+import os
+from matplotlib.image import imread
+
+import numpy
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+from torchvision import transforms
+from torch.utils.data import Dataset
+
+
+class Boats(Dataset):
+
+ def __init__(self, root_dir, transform=None, gt_json_path=''):
+ self.root_dir = root_dir
+ self.transform = transform
+ self.gt_json_path = gt_json_path
+ self.labels = json.load(open(gt_json_path, 'r'))
+ self.image_list = sorted(os.listdir(root_dir))
+ self.image_ids = dict(enumerate(self.image_list, start=0))
+
+ def __len__(self):
+ return len(self.image_ids)
+
+ def __getitem__(self, idx):
+ img = self.load_image(idx)
+ img_name = self.image_ids[idx]
+ label = self.labels[img_name]
+ if self.transform:
+ img = self.transform(img)
+ sample = (img, label)
+ return sample
+
+ def load_image(self, image_index):
+ image_name = self.image_ids[image_index]
+ path = os.path.join(self.root_dir, image_name)
+ img = imread(path)
+ return img
+
+
+class Net(nn.Module):
+ def __init__(self):
+ super(Net, self).__init__()
+ self.conv1 = nn.Conv2d(3, 6, 3, padding=1)
+ self.conv2 = nn.Conv2d(6, 9, 3, padding=1)
+ self.conv3 = nn.Conv2d(9, 12, 3, padding=1)
+ self.conv4 = nn.Conv2d(12, 24, 3, padding=1)
+ self.dropout = nn.Dropout(0.5)
+ self.fc1 = nn.Linear(24*4*7, 200) #12*54*96
+ self.fc2 = nn.Linear(200, 50)
+ self.fc3 = nn.Linear(50, 5)
+ self.fc4 = nn.Linear(50, 1)
+ self.maxPooling = nn.MaxPool2d(3,3)
+
+ def forward(self, x):
+ x = self.maxPooling(F.relu(self.conv1(x)))
+ x = self.maxPooling(F.relu(self.conv2(x)))
+ x = self.maxPooling(F.relu(self.conv3(x)))
+ x = F.relu(self.conv4(x))
+ #print(x.shape)
+ x = torch.flatten(x, start_dim=1)
+ x = self.dropout(F.relu(self.fc1(x)))
+ x = self.dropout(F.relu(self.fc2(x)))
+ #x = self.dropout(F.relu(self.fc3(x)))
+ x = self.fc4(x)
+ output = torch.sigmoid(x)
+
+ return output
+
+
+def train(args, model, device, train_loader, optimizer, criterion, epoch):
+ """
+ Train a network
+ You can find example code here: https://pytorch.org/tutorials/beginner/basics/quickstart_tutorial.html
+ """
+ model.train()
+ for batch_idx, (data, target) in enumerate(train_loader):
+ data, target = data.to(device), target.to(device).float()
+ optimizer.zero_grad()
+ output = model(data)
+ #print(f"output: {output}")
+ loss = criterion(output, torch.unsqueeze(target, 1))
+ loss.backward()
+ optimizer.step()
+ if batch_idx % args.log_interval == 0:
+ print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
+ epoch, batch_idx * len(data), len(train_loader.dataset),
+ 100. * batch_idx / len(train_loader), loss.item()))
+ if args.dry_run:
+ break
+
+
+def test(model, device, test_loader, criterion):
+ model.eval()
+ test_loss = 0
+ correct = 0
+ with torch.no_grad():
+ for data, target in test_loader:
+ data, target = data.to(device), target.to(device).float()
+ output = model(data)
+ test_loss += criterion(output, torch.unsqueeze(target, 1)).item() # sum up batch loss
+ pred = torch.round(output) # get the index of the max log-probability
+ correct += pred.eq(target.view_as(pred)).sum().item()
+
+ test_loss /= len(test_loader.dataset)
+
+ print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
+ test_loss, correct, len(test_loader.dataset),
+ 100. * correct / len(test_loader.dataset)))
+ return 100. * correct / len(test_loader.dataset)
+
+
+def main():
+ # Training settings
+ parser = argparse.ArgumentParser(description='PyTorch Ship Detection')
+ parser.add_argument('--batch-size', type=int, default=64, metavar='N',
+ help='input batch size for training (default: 64)')
+ parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
+ help='input batch size for testing (default: 1000)')
+ parser.add_argument('--epochs', type=int, default=14, metavar='N',
+ help='number of epochs to train (default: 14)')
+ parser.add_argument('--lr', type=float, default=0.1, metavar='LR',
+ help='learning rate (default: 0.1)')
+ parser.add_argument('--no-cuda', action='store_true', default=False,
+ help='disables CUDA training')
+ parser.add_argument('--dry-run', action='store_true', default=False,
+ help='quickly check a single pass')
+ parser.add_argument('--seed', type=int, default=1, metavar='S',
+ help='random seed (default: 1)')
+ parser.add_argument('--log-interval', type=int, default=10, metavar='N',
+ help='how many batches to wait before logging training status')
+ parser.add_argument('--save-model', action='store_true', default=False,
+ help='For Saving the current Model')
+ args = parser.parse_args()
+ torch.manual_seed(args.seed)
+ use_cuda = not args.no_cuda and torch.cuda.is_available()
+ #use_cuda = True
+ device = torch.device("cuda" if use_cuda else "cpu")
+ #device = torch.device("cpu")
+ train_kwargs = {'batch_size': args.batch_size}
+ val_kwargs = {'batch_size': args.test_batch_size}
+
+
+ if use_cuda:
+ cuda_kwargs = {'num_workers': 1,
+ 'pin_memory': True,
+ 'shuffle': True}
+ train_kwargs.update(cuda_kwargs)
+ val_kwargs.update(cuda_kwargs)
+
+
+ # Create transform
+ transform = transforms.Compose([
+ transforms.ToTensor(),
+ # This normalization is used on the test server
+ transforms.Normalize([0.2404, 0.2967, 0.3563], [0.0547, 0.0527, 0.0477])
+ ])
+
+ # Create train and test set
+ path_to_dataset = "Boat_MNIST" # TODO Set correct path
+ train_set = Boats(root_dir=f'{path_to_dataset}/train', transform=transform,
+ gt_json_path=f'{path_to_dataset}/boat_mnist_labels_trainval.json')
+ val_set = Boats(root_dir=f'{path_to_dataset}/val', transform=transform,
+ gt_json_path=f'{path_to_dataset}/boat_mnist_labels_trainval.json')
+
+ # Create data loaders
+ train_loader = torch.utils.data.DataLoader(train_set, **train_kwargs)
+ test_loader = torch.utils.data.DataLoader(val_set, **val_kwargs)
+
+ # Create network, optimizer and loss
+ model = Net().to(device)
+ #optimizer = optim.SGD(model.parameters(), lr=1e-4) #lr=args.lr)
+ optimizer = optim.Adam(model.parameters(), lr=1e-3)
+ criterion = nn.MSELoss()
+
+ # Train and validate
+ best_acc = 0
+ best_model_wts = copy.deepcopy(model.state_dict())
+ for epoch in range(1, args.epochs + 1):
+ train(args, model, device, train_loader, optimizer, criterion, epoch)
+ acc = test(model, device, test_loader, criterion)
+ if acc > best_acc:
+ best_acc = acc
+ best_model_wts = copy.deepcopy(model.state_dict())
+
+ # Load best model weights
+ model.load_state_dict(best_model_wts)
+ print(f"Best accuracy (val): {best_acc}")
+
+ if args.save_model:
+ torch.save(model.state_dict(), "model.pth")
+
+ # --- Do not touch -----
+ # Save model as onnx file
+ dummy_input = torch.randn(1, 3, 108, 192, device=device)
+ input_names = ["img_1"]
+ output_names = ["output1"]
+ torch.onnx.export(model, dummy_input, "ship_example.onnx", input_names=input_names, output_names=output_names)
+ # ----------------------
+
+
+if __name__ == '__main__':
+ main()
\ No newline at end of file
diff --git a/Boat_MNIST/10/challenge_nn_small.py b/Boat_MNIST/10/challenge_nn_small.py
new file mode 100644
index 0000000..c71a9cf
--- /dev/null
+++ b/Boat_MNIST/10/challenge_nn_small.py
@@ -0,0 +1,223 @@
+"""
+TODO
+- team member 1: Adem Atmaca
+- team member 2: Adrian Sauter
+tasks:
+ - add your team members' names at the top of the file
+ - Take part in the challenge :)
+"""
+
+import argparse
+import copy
+import json
+import os
+from matplotlib.image import imread
+
+import numpy
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+from torchvision import transforms
+from torch.utils.data import Dataset
+
+
+class Boats(Dataset):
+
+ def __init__(self, root_dir, transform=None, gt_json_path=''):
+ self.root_dir = root_dir
+ self.transform = transform
+ self.gt_json_path = gt_json_path
+ self.labels = json.load(open(gt_json_path, 'r'))
+ self.image_list = sorted(os.listdir(root_dir))
+ self.image_ids = dict(enumerate(self.image_list, start=0))
+
+ def __len__(self):
+ return len(self.image_ids)
+
+ def __getitem__(self, idx):
+ img = self.load_image(idx)
+ img_name = self.image_ids[idx]
+ label = self.labels[img_name]
+ if self.transform:
+ img = self.transform(img)
+ sample = (img, label)
+ return sample
+
+ def load_image(self, image_index):
+ image_name = self.image_ids[image_index]
+ path = os.path.join(self.root_dir, image_name)
+ img = imread(path)
+ return img
+
+class Net(nn.Module):
+ def __init__(self):
+ super(Net, self).__init__()
+ self.conv1 = nn.Conv2d(3, 4, 3, padding=1)
+ self.conv2 = nn.Conv2d(4, 6, 3, padding=1)
+ self.conv3 = nn.Conv2d(6, 6, 3, padding=1)
+ self.conv4 = nn.Conv2d(6, 6, 3, padding=1)
+ self.conv5 = nn.Conv2d(6, 6, 3, padding=1)
+ self.conv6 = nn.Conv2d(6, 8, 3, padding=1)
+ #self.dropout = nn.Dropout(0.5)
+ self.fc1 = nn.Linear(8*3*6, 1) #12*54*96
+ #self.fc2 = nn.Linear(5, 1) # 12*54*96
+ #self.fc2 = nn.Linear(200, 50)
+ #self.fc3 = nn.Linear(50, 5)
+ #self.fc4 = nn.Linear(50, 1)
+ self.maxPooling = nn.MaxPool2d(2, 2)
+ #self.maxPooling3 = nn.MaxPool2d(3, 4)
+
+ def forward(self, x):
+ x = self.maxPooling(F.relu(self.conv1(x)))
+ x = self.maxPooling(F.relu(self.conv2(x)))
+ x = self.maxPooling(F.relu(self.conv3(x)))
+ x = self.maxPooling(F.relu(self.conv4(x)))
+ x = self.maxPooling(F.relu(self.conv5(x)))
+ x = F.relu(self.conv6(x))
+ #print(x.shape)
+ x = torch.flatten(x, start_dim=1)
+ #x = self.dropout(F.relu(self.fc1(x)))
+ #x = self.dropout(F.relu(self.fc2(x)))
+ #x = self.dropout(F.relu(self.fc3(x)))
+ #x = F.relu(self.fc1(x))
+ x = self.fc1(x)
+ output = torch.sigmoid(x)
+
+ return output
+
+def train(args, model, device, train_loader, optimizer, criterion, epoch):
+ """
+ Train a network
+ You can find example code here: https://pytorch.org/tutorials/beginner/basics/quickstart_tutorial.html
+ """
+ model.train()
+ for batch_idx, (data, target) in enumerate(train_loader):
+ data, target = data.to(device), target.to(device).float()
+ optimizer.zero_grad()
+ output = model(data)
+ #print(f"output: {output}")
+ loss = criterion(output, torch.unsqueeze(target, 1))
+ loss.backward()
+ optimizer.step()
+ if batch_idx % args.log_interval == 0:
+ print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
+ epoch, batch_idx * len(data), len(train_loader.dataset),
+ 100. * batch_idx / len(train_loader), loss.item()))
+ if args.dry_run:
+ break
+
+
+def test(model, device, test_loader, criterion):
+ model.eval()
+ test_loss = 0
+ correct = 0
+ with torch.no_grad():
+ for data, target in test_loader:
+ data, target = data.to(device), target.to(device).float()
+ output = model(data)
+ test_loss += criterion(output, torch.unsqueeze(target, 1)).item() # sum up batch loss
+ pred = torch.round(output) # get the index of the max log-probability
+ correct += pred.eq(target.view_as(pred)).sum().item()
+
+ test_loss /= len(test_loader.dataset)
+
+ print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
+ test_loss, correct, len(test_loader.dataset),
+ 100. * correct / len(test_loader.dataset)))
+ return 100. * correct / len(test_loader.dataset)
+
+
+def main():
+ # Training settings
+ parser = argparse.ArgumentParser(description='PyTorch Ship Detection')
+ parser.add_argument('--batch-size', type=int, default=64, metavar='N',
+ help='input batch size for training (default: 64)')
+ parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
+ help='input batch size for testing (default: 1000)')
+ parser.add_argument('--epochs', type=int, default=200, metavar='N',
+ help='number of epochs to train (default: 14)')
+ parser.add_argument('--lr', type=float, default=0.1, metavar='LR',
+ help='learning rate (default: 0.1)')
+ parser.add_argument('--no-cuda', action='store_true', default=False,
+ help='disables CUDA training')
+ parser.add_argument('--dry-run', action='store_true', default=False,
+ help='quickly check a single pass')
+ parser.add_argument('--seed', type=int, default=1, metavar='S',
+ help='random seed (default: 1)')
+ parser.add_argument('--log-interval', type=int, default=10, metavar='N',
+ help='how many batches to wait before logging training status')
+ parser.add_argument('--save-model', action='store_true', default=False,
+ help='For Saving the current Model')
+ args = parser.parse_args()
+ torch.manual_seed(args.seed)
+ use_cuda = not args.no_cuda and torch.cuda.is_available()
+ #use_cuda = True
+ device = torch.device("cuda" if use_cuda else "cpu")
+ #device = torch.device("cpu")
+ train_kwargs = {'batch_size': args.batch_size}
+ val_kwargs = {'batch_size': args.test_batch_size}
+
+
+ if use_cuda:
+ cuda_kwargs = {'num_workers': 1,
+ 'pin_memory': True,
+ 'shuffle': True}
+ train_kwargs.update(cuda_kwargs)
+ val_kwargs.update(cuda_kwargs)
+
+
+ # Create transform
+ transform = transforms.Compose([
+ transforms.ToTensor(),
+ # This normalization is used on the test server
+ transforms.Normalize([0.2404, 0.2967, 0.3563], [0.0547, 0.0527, 0.0477])
+ ])
+
+ # Create train and test set
+ path_to_dataset = "Boat_MNIST" # TODO Set correct path
+ train_set = Boats(root_dir=f'{path_to_dataset}/train', transform=transform,
+ gt_json_path=f'{path_to_dataset}/boat_mnist_labels_trainval.json')
+ val_set = Boats(root_dir=f'{path_to_dataset}/val', transform=transform,
+ gt_json_path=f'{path_to_dataset}/boat_mnist_labels_trainval.json')
+
+ # Create data loaders
+ train_loader = torch.utils.data.DataLoader(train_set, **train_kwargs)
+ test_loader = torch.utils.data.DataLoader(val_set, **val_kwargs)
+
+ # Create network, optimizer and loss
+ model = Net().to(device)
+ #optimizer = optim.SGD(model.parameters(), lr=1e-4) #lr=args.lr)
+ optimizer = optim.Adam(model.parameters(), lr=1e-3)
+ criterion = nn.MSELoss()
+
+ # Train and validate
+ best_acc = 0
+ best_model_wts = copy.deepcopy(model.state_dict())
+ for epoch in range(1, args.epochs + 1):
+ train(args, model, device, train_loader, optimizer, criterion, epoch)
+ acc = test(model, device, test_loader, criterion)
+ if acc > best_acc:
+ best_acc = acc
+ best_model_wts = copy.deepcopy(model.state_dict())
+
+ # Load best model weights
+ model.load_state_dict(best_model_wts)
+ print(f"Best accuracy (val): {best_acc}")
+ pytorch_total_params = sum(p.numel() for p in model.parameters())
+ print("number of Parameters: " + str(pytorch_total_params))
+
+ if args.save_model:
+ torch.save(model.state_dict(), "model.pth")
+
+ # --- Do not touch -----
+ # Save model as onnx file
+ dummy_input = torch.randn(1, 3, 108, 192, device=device)
+ input_names = ["img_1"]
+ output_names = ["output1"]
+ torch.onnx.export(model, dummy_input, "ship_example.onnx", input_names=input_names, output_names=output_names)
+ # ----------------------
+
+
+if __name__ == '__main__':
+ main()
diff --git a/Boat_MNIST/106/challenge_nn.py b/Boat_MNIST/106/challenge_nn.py
new file mode 100644
index 0000000..a61a2ca
--- /dev/null
+++ b/Boat_MNIST/106/challenge_nn.py
@@ -0,0 +1,294 @@
+"""
+- team member 1: Finn Fassbender
+- team member 2: Mathis Welker
+"""
+
+"""
+model size: 2459 Parameters 0.01MB
+==========================================================================================
+Layer (type:depth-idx) Output Shape Param #
+==========================================================================================
+Net [1, 1] 33
+├─Conv2d: 1-1 [1, 16, 54, 96] 1,216
+├─Conv2d: 1-2 [1, 4, 55, 97] 1,028
+├─Dropout: 1-3 [1, 4, 27, 48] --
+├─Conv2d: 1-4 [1, 1, 27, 48] 37
+├─Dropout: 1-5 [1, 1, 9, 16] --
+├─Linear: 1-6 [1, 1] 145
+==========================================================================================
+Total params: 2,459
+Trainable params: 2,459
+Non-trainable params: 0
+Total mult-adds (M): 11.84
+==========================================================================================
+Input size (MB): 0.25
+Forward/backward pass size (MB): 0.84
+Params size (MB): 0.01
+Estimated Total Size (MB): 1.10
+==========================================================================================
+"""
+
+import argparse
+import copy
+import json
+import os
+import pathlib
+from matplotlib.image import imread
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+from torchvision import transforms
+from torch.utils.data import Dataset
+from torchinfo import summary
+
+class Boats(Dataset):
+
+ def __init__(self, root_dir, transform=None, gt_json_path=''):
+ self.root_dir = root_dir
+ self.transform = transform
+ self.gt_json_path = gt_json_path
+ self.labels = json.load(open(gt_json_path, 'r'))
+ self.image_list = sorted(os.listdir(root_dir))
+ self.image_ids = dict(enumerate(self.image_list, start=0))
+
+ def __len__(self):
+ return len(self.image_ids)
+
+ def __getitem__(self, idx):
+ img = self.load_image(idx)
+ img_name = self.image_ids[idx]
+ label = self.labels[img_name]
+ if self.transform:
+ img = self.transform(img)
+ sample = (img, label)
+ return sample
+
+ def load_image(self, image_index):
+ image_name = self.image_ids[image_index]
+ path = os.path.join(self.root_dir, image_name)
+ img = imread(path)
+ return img
+
+
+class Net(nn.Module):
+ def __init__(self):
+ super(Net, self).__init__()
+ # First 2D convolutional layer, taking in 3 input channels (image),
+ # outputting 16 convolutional features, with a square kernel size of 5
+ self.conv1 = nn.Conv2d( 3, 16, 5, stride=2, padding=2)
+ self.conv2 = nn.Conv2d(16, 4, 4, stride=1, padding=2)
+ self.conv3 = nn.Conv2d( 4, 1, 3, stride=1, padding=1)
+
+ # Designed to ensure that adjacent pixels are either all 0s or all
+ # active with an input probability
+ self.dropout1 = nn.Dropout(0.25)
+ self.dropout2 = nn.Dropout(0.5)
+
+ # Fully connected layer that outputs the label
+ self.fc1 = nn.Linear(144, 1)
+
+ def forward(self, x):
+ # Pass data through convolutional layers
+ x = self.conv1(x)
+ x = F.relu(x)
+ x = self.conv2(x)
+ x = F.relu(x)
+ x = F.max_pool2d(x, 2)
+ x = self.dropout2(x)
+ x = self.conv3(x)
+ x = F.relu(x)
+ x = F.max_pool2d(x, 3)
+ x = self.dropout1(x)
+ # Prepare data for fully connected layers
+ x = torch.flatten(x, 1)
+ # Pass data through fully connected layers
+ x = self.fc1(x)
+ output = torch.sigmoid(x)
+ return output
+
+
+def train(args, model, device, train_loader, optimizer, criterion, epoch):
+ """
+ Train a network
+ You can find example code here: https://pytorch.org/tutorials/beginner/basics/quickstart_tutorial.html
+ """
+ model.train()
+ for batch_idx, (data, target) in enumerate(train_loader):
+ data, target = data.to(device), target.to(device).float()
+ optimizer.zero_grad()
+ output = model(data)
+ loss = criterion(output, torch.unsqueeze(target, 1))
+ loss.backward()
+ optimizer.step()
+ if batch_idx % args.log_interval == 0:
+ print('Train Epoch: {:2.0f} [{:4.0f}/{} ({:3.0f}%)]\tLoss: {:.6f}'.format(
+ epoch, batch_idx * len(data), len(train_loader.dataset),
+ 100. * batch_idx / len(train_loader), loss.item()))
+ if args.dry_run:
+ break
+
+
+def test(model, device, test_loader, criterion, name):
+ model.eval()
+ test_loss = 0
+ correct = 0
+ with torch.no_grad():
+ for data, target in test_loader:
+ data, target = data.to(device), target.to(device).float()
+ output = model(data)
+ test_loss += criterion(output, torch.unsqueeze(target, 1)).item() # sum up batch loss
+ pred = torch.round(output) # get the index of the max log-probability
+ correct += pred.eq(target.view_as(pred)).sum().item()
+
+ test_loss /= len(test_loader.dataset)
+
+ print(name + ' set: Average loss: {:.4f}, Accuracy: {}/{} ({:.1f}%)'.format(
+ test_loss, correct, len(test_loader.dataset),
+ 100. * correct / len(test_loader.dataset)))
+ return 100. * correct / len(test_loader.dataset)
+
+def size(model):
+ # Print size of model
+ param_size, param_num = 0, 0
+ for param in model.parameters():
+ param_num += param.nelement()
+ param_size += param.nelement() * param.element_size()
+ buffer_size = 0
+ for buffer in model.buffers():
+ buffer_size += buffer.nelement() * buffer.element_size()
+
+ size_all_mb = (param_size + buffer_size) / 1024**2
+ print('model size: {:.0f} Parameters\t{:.2f}MB'.format(param_num, size_all_mb))
+
+
+def main():
+ # Training settings
+ parser = argparse.ArgumentParser(description='PyTorch Ship Detection')
+ parser.add_argument('--batch-size', type=int, default=64, metavar='N',
+ help='input batch size for training (default: 64)')
+ parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
+ help='input batch size for testing (default: 1000)')
+ parser.add_argument('--epochs', type=int, default=14, metavar='N',
+ help='number of epochs to train (default: 14)')
+ parser.add_argument('--lr', type=float, default=0.1, metavar='LR',
+ help='learning rate (default: 0.1)')
+ parser.add_argument('--no-cuda', action='store_true', default=False,
+ help='disables CUDA training')
+ parser.add_argument('--dry-run', action='store_true', default=False,
+ help='quickly check a single pass')
+ parser.add_argument('--seed', type=int, default=1, metavar='S',
+ help='random seed (default: 1)')
+ parser.add_argument('--log-interval', type=int, default=10, metavar='N',
+ help='how many batches to wait before logging training status')
+ parser.add_argument('--save-model', action='store_true', default=True,
+ help='For Saving the current Model')
+ parser.add_argument('--test-model', type=pathlib.Path, default='',
+ metavar='PATH', help='For Testing the last saved Model')
+ parser.add_argument('--export-model', type=pathlib.Path, default='',
+ metavar='PATH', help='For exporting the specified Model to .onnx')
+ parser.add_argument('--load-model', type=pathlib.Path, default='',
+ metavar='PATH', help='For loading a previous Model state dict')
+ parser.add_argument('--load-bestacc', type=float, default=0, metavar='N',
+ help='the previous best accuracy to use')
+ parser.add_argument('--vis-model', action='store_true', default=False,
+ help='For Visualizing the current Model')
+
+ args = parser.parse_args()
+ torch.manual_seed(args.seed)
+ use_cuda = not args.no_cuda and torch.cuda.is_available()
+ torch.set_num_threads(8)
+ device = torch.device("cuda" if use_cuda else "cpu")
+ train_kwargs = {'batch_size': args.batch_size}
+ val_kwargs = {'batch_size': args.test_batch_size}
+ more_kwargs = {'num_workers': 1,
+ 'pin_memory': True,
+ 'shuffle': True}
+ train_kwargs.update(more_kwargs)
+ val_kwargs.update(more_kwargs)
+
+ # Create transform
+ transform = transforms.Compose([
+ transforms.ToTensor(),
+ # This normalization is used on the test server
+ transforms.Normalize([0.2404, 0.2967, 0.3563], [0.0547, 0.0527, 0.0477])
+ ])
+
+ # Create train and test set
+ path_to_dataset = "../boat-mnist-dataset"
+ train_set = Boats(root_dir=f'{path_to_dataset}/train', transform=transform,
+ gt_json_path=f'{path_to_dataset}/boat_mnist_labels_trainval.json')
+ val_set = Boats(root_dir=f'{path_to_dataset}/val', transform=transform,
+ gt_json_path=f'{path_to_dataset}/boat_mnist_labels_trainval.json')
+
+ # Create data loaders
+ train_loader = torch.utils.data.DataLoader(train_set, **train_kwargs)
+ test_loader = torch.utils.data.DataLoader(val_set, **val_kwargs)
+
+ # Create network, optimizer and loss
+ model = Net().to(device)
+ size(model)
+ optimizer = optim.SGD(model.parameters(), lr=args.lr)
+ criterion = nn.L1Loss()
+
+ # Load a previous model state (continues after)
+ if args.load_model != pathlib.PurePath():
+ model.load_state_dict(torch.load(args.load_model))
+
+ # Test last saved model (stops after)
+ if args.test_model != pathlib.PurePath():
+ model.load_state_dict(torch.load(args.test_model))
+ test(model, device, test_loader, criterion, 'Test ')
+ test(model, device, train_loader, criterion, 'Train')
+ return
+
+ # Export specified model (stops after)
+ if args.export_model != pathlib.PurePath():
+ model.load_state_dict(torch.load(args.export_model))
+ test(model, device, test_loader, criterion, 'Test ')
+ test(model, device, train_loader, criterion, 'Train')
+ # Save model as onnx file
+ dummy_input = torch.randn(1, 3, 108, 192, device=device)
+ input_names = ["img_1"]
+ output_names = ["output1"]
+ torch.onnx.export(model, dummy_input, "ship_example-" + str(args.export_model) + ".onnx", input_names=input_names, output_names=output_names)
+ print("ship_example-" + str(args.export_model) + ".onnx")
+ return
+
+ # Visualize model (stops after)
+ if args.vis_model:
+ summary(model, input_size=(1, 3, 108, 192))
+ return
+
+ # Train and validate
+ best_acc = args.load_bestacc if args.load_bestacc != 0 else 0
+ best_model_wts = copy.deepcopy(model.state_dict())
+ for epoch in range(1, args.epochs + 1):
+ train(args, model, device, train_loader, optimizer, criterion, epoch)
+ acc = test(model, device, test_loader, criterion, 'Test ')
+ #test(model, device, train_loader, criterion, 'Train')
+ print('\n')
+ if acc > best_acc:
+ best_acc = acc
+ best_model_wts = copy.deepcopy(model.state_dict())
+ print(f"Current best accuracy (val): {best_acc:.2f}")
+
+ # Load best model weights
+ model.load_state_dict(best_model_wts)
+ print(f"Best accuracy (val): {best_acc}")
+
+ # Save the model weights
+ if args.save_model:
+ torch.save(model.state_dict(), "model.pth")
+
+ # --- Do not touch -----
+ # Save model as onnx file
+ dummy_input = torch.randn(1, 3, 108, 192, device=device)
+ input_names = ["img_1"]
+ output_names = ["output1"]
+ torch.onnx.export(model, dummy_input, "ship_example.onnx", input_names=input_names, output_names=output_names)
+ # ----------------------
+
+if __name__ == '__main__':
+ main()
diff --git a/Boat_MNIST/106/documentation.pdf b/Boat_MNIST/106/documentation.pdf
new file mode 100644
index 0000000..610f59c
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diff --git a/Boat_MNIST/106/model.pth b/Boat_MNIST/106/model.pth
new file mode 100644
index 0000000..0be3bad
Binary files /dev/null and b/Boat_MNIST/106/model.pth differ
diff --git a/Boat_MNIST/106/ship_example.onnx b/Boat_MNIST/106/ship_example.onnx
new file mode 100644
index 0000000..844ffda
Binary files /dev/null and b/Boat_MNIST/106/ship_example.onnx differ
diff --git a/Boat_MNIST/107/Documentation.pdf b/Boat_MNIST/107/Documentation.pdf
new file mode 100644
index 0000000..1ea57f1
Binary files /dev/null and b/Boat_MNIST/107/Documentation.pdf differ
diff --git a/Boat_MNIST/107/challenge_nn.py b/Boat_MNIST/107/challenge_nn.py
new file mode 100644
index 0000000..a8551bb
--- /dev/null
+++ b/Boat_MNIST/107/challenge_nn.py
@@ -0,0 +1,879 @@
+"""
+TODO
+- Group 107
+- [Names removed, see moodle version]
+
+tasks:
+ - add your team members' names at the top of the file
+ - Take part in the challenge :)
+"""
+
+import argparse
+import copy
+import json
+import os
+from matplotlib.image import imread
+import matplotlib.pyplot as plt
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+from torchvision import transforms
+from torch.utils.data import Dataset
+
+class Analyzer():
+ def __init__(self):
+ self.training_loss = []
+ self.validation_loss = []
+ self.percentage = []
+
+class Boats(Dataset):
+
+ def __init__(self, root_dir, transform=None, gt_json_path=''):
+ self.root_dir = root_dir
+ self.transform = transform
+ self.gt_json_path = gt_json_path
+ self.labels = json.load(open(gt_json_path, 'r'))
+ self.image_list = sorted(os.listdir(root_dir))
+ self.image_ids = dict(enumerate(self.image_list, start=0))
+
+ def __len__(self):
+ return len(self.image_ids)
+
+ def __getitem__(self, idx):
+ img = self.load_image(idx)
+ # remove alpha channel if needed
+ if img.shape[2] == 4:
+ img = img[:,:,:3]
+ img_name = self.image_ids[idx]
+ label = self.labels[img_name]
+ if self.transform:
+ img = self.transform(img)
+ sample = (img, label)
+ return sample
+
+ def load_image(self, image_index):
+ image_name = self.image_ids[image_index]
+ path = os.path.join(self.root_dir, image_name)
+ img = imread(path)
+ return img
+
+
+class Net(nn.Module):
+ def __init__(self):
+ super(Net, self).__init__()
+ #self.fc1 = nn.Linear(3 * 192 * 108, 1)
+ self.network_stack = nn.Sequential(
+ # convlutional processing
+ nn.Conv2d(3, 14, 14, stride = 2),
+ nn.ReLU(),
+ nn.MaxPool2d(3,3),
+ nn.Conv2d(14, 14, 9),
+ nn.ReLU(),
+ nn.MaxPool2d(2,2),
+ nn.Flatten(),
+
+ # FFNN
+ nn.Linear(616, 616),
+ nn.ReLU(),
+ nn.Linear(616, 616),
+ nn.ReLU(),
+ nn.Linear(616, 616),
+
+ nn.ReLU(),
+ nn.Linear(616, 32),
+ nn.Dropout(p=0.5),
+ nn.ReLU(),
+ nn.Linear(32, 1),
+ nn.Sigmoid()
+ )
+
+
+
+ """
+ 99.40%
+
+ self.network_stack = nn.Sequential(
+ # convlutional processing
+ nn.Conv2d(3, 12, 14, stride = 2),
+ nn.ReLU(),
+ nn.MaxPool2d(3,3),
+ nn.Conv2d(12, 12, 9),
+ nn.ReLU(),
+ nn.MaxPool2d(2,2),
+ nn.Flatten(),
+
+ # FFNN
+ nn.Linear(528, 528),
+ nn.ReLU(),
+ nn.Linear(528, 528),
+ nn.ReLU(),
+ nn.Linear(528, 528),
+ nn.ReLU(),
+ nn.Linear(528, 32),
+ nn.Dropout(p=0.5),
+ nn.ReLU(),
+ nn.Linear(32, 1),
+ nn.Sigmoid()
+ )
+
+
+ epochs = 150
+
+ -------------------------------------
+
+ 99.26958831341301%
+
+ self.network_stack = nn.Sequential(
+ # convlutional processing
+ nn.Conv2d(3, 8, 14, stride = 2),
+ nn.ReLU(),
+ nn.MaxPool2d(3,3),
+ nn.Conv2d(8, 8, 9),
+ nn.ReLU(),
+ nn.MaxPool2d(2,2),
+ nn.Flatten(),
+
+ # FFNN
+ nn.Linear(352, 352),
+ nn.ReLU(),
+ nn.Linear(352, 352),
+ nn.ReLU(),
+ nn.Linear(352, 352),
+ nn.ReLU(),
+ nn.Linear(352, 32),
+ nn.Dropout(p=0.5),
+ nn.ReLU(),
+ nn.Linear(32, 1),
+ nn.Sigmoid()
+ )
+
+ epochs = 200
+
+
+
+ ---------------------------------------------
+
+ 99.003% (1491/1506)
+
+ self.network_stack = nn.Sequential(
+ # convlutional processing
+ nn.Conv2d(3, 4, 14, stride = 2),
+ nn.ReLU(),
+ nn.MaxPool2d(3,3),
+ nn.Conv2d(4, 4, 9),
+ nn.ReLU(),
+ nn.MaxPool2d(2,2),
+ nn.Flatten(),
+
+ # FFNN
+ nn.Linear(176, 176),
+ nn.ReLU(),
+ nn.Linear(176, 176),
+ nn.ReLU(),
+ nn.Linear(176, 176),
+ nn.ReLU(),
+ nn.Linear(176, 32),
+ nn.Dropout(p=0.5),
+ nn.ReLU(),
+ nn.Linear(32, 1),
+ nn.Sigmoid()
+ )
+
+ epochs = 100
+
+
+
+
+
+
+
+
+
+
+
+
+ -------------------------------------------------
+
+ VV This is the network documented in the pdf VV
+
+ -------------------------------------------------
+ 98.937%
+
+
+ self.network_stack = nn.Sequential(
+ # convlutional processing
+ nn.Conv2d(3, 4, 14, stride = 2),
+ nn.ReLU(),
+ nn.MaxPool2d(3,3),
+ nn.Conv2d(4, 4, 9),
+ nn.ReLU(),
+ nn.MaxPool2d(2,2),
+ nn.Flatten(),
+
+ # FFNN
+ nn.Linear(176, 176),
+ nn.Dropout(p=0.05),
+ nn.ReLU(),
+ nn.Linear(176, 32),
+ nn.Dropout(p=0.5),
+ nn.ReLU(),
+ nn.Linear(32, 1),
+ nn.Sigmoid()
+ )
+
+
+
+ optimizer = optim.SGD(model.parameters(), lr=0.05, weight_decay=0.005, momentum=0.0)
+ scheduler = optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer,T_0 = args.epochs // 6, eta_min = 0.0001, verbose=True)
+ criterion = nn.BCELoss()
+
+ ----------------------------------------------------
+ 98,605%
+
+ self.network_stack = nn.Sequential(
+ # convlutional processing
+ nn.Conv2d(3, 3, 14, stride = 2),
+ nn.ReLU(),
+ nn.MaxPool2d(3,3),
+ nn.Conv2d(3, 3, 9),
+ nn.ReLU(),
+ nn.MaxPool2d(2,2),
+ nn.Flatten(),
+
+ # FFNN
+ nn.Linear(132, 132),
+ nn.Dropout(p=0.5),
+ nn.ReLU(),
+ nn.Linear(132, 64),
+ nn.Dropout(p=0.5),
+ nn.ReLU(),
+ nn.Linear(64, 1),
+ nn.Sigmoid()
+ )
+
+ optimizer = optim.SGD(model.parameters(), lr=0.05, weight_decay=0.005, momentum=0.0)
+ scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=[int(args.epochs*0.4),int(args.epochs*0.83)], gamma=0.1, verbose=True)
+ criterion = nn.BCELoss()
+
+ original set only(?)
+ epochs = 20(?)
+
+
+ -----------------------------------------
+
+
+
+ 98,804%
+
+ self.network_stack = nn.Sequential(
+ # convlutional processing
+ nn.Conv2d(3, 3, 14, stride = 2),
+ nn.ReLU(),
+ nn.MaxPool2d(3,3),
+ nn.Conv2d(3, 3, 9),
+ nn.ReLU(),
+ nn.MaxPool2d(2,2),
+ nn.Flatten(),
+
+ # FFNN
+ nn.Linear(132, 132),
+ nn.Dropout(p=0.05),
+ nn.ReLU(),
+ nn.Linear(132, 132),
+ nn.Dropout(p=0.5),
+ nn.ReLU(),
+ nn.Linear(132, 1),
+ nn.Sigmoid()
+ )
+
+ optimizer = optim.SGD(model.parameters(), lr=0.05, weight_decay=0.005, momentum=0.0)
+ scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=[15,48,70], gamma=0.3, verbose=True)
+ criterion = nn.BCELoss()
+ epochs = 80
+
+ -----------------------------------------------------------------
+ 98,738%
+
+ self.network_stack = nn.Sequential(
+ # convlutional processing
+ nn.Conv2d(3, 3, 14, stride = 2),
+ nn.ReLU(),
+ nn.MaxPool2d(3,3),
+ nn.Conv2d(3, 3, 9),
+ nn.ReLU(),
+ nn.MaxPool2d(2,2),
+ nn.Flatten(),
+
+ # FFNN
+ nn.Linear(132, 132),
+ nn.Dropout(p=0.05),
+ nn.ReLU(),
+ nn.Linear(132, 132),
+ nn.Dropout(p=0.5),
+ nn.ReLU(),
+ nn.Linear(132, 1),
+ nn.Sigmoid()
+ )
+
+ optimizer = optim.SGD(model.parameters(), lr=0.05, weight_decay=0.005, momentum=0.0)
+ scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=[12,25], gamma=0.1, verbose=True)
+ criterion = nn.BCELoss()
+ epochs=30
+
+
+ 98.671%
+
+ self.network_stack = nn.Sequential(
+ # convlutional processing
+ nn.Conv2d(3, 3, 14, stride = 2),
+ nn.ReLU(),
+ nn.MaxPool2d(3,3),
+ nn.Conv2d(3, 3, 9),
+ nn.ReLU(),
+ nn.MaxPool2d(2,2),
+ nn.Flatten(),
+
+ # FFNN
+ nn.Linear(132, 132),
+ nn.Dropout(p=0.05),
+ nn.ReLU(),
+ nn.Linear(132, 132),
+ nn.Dropout(p=0.5),
+ nn.ReLU(),
+ nn.Linear(132, 1),
+ nn.Sigmoid()
+ )
+
+ optimizer = optim.SGD(model.parameters(), lr=0.05, weight_decay=0.005, momentum=0.0)
+ scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=[10,20], gamma=0.1, verbose=True)
+ criterion = nn.BCELoss()
+
+ epochs=30
+
+
+
+
+ -----------------------------------------------------
+ 98.605%
+
+ self.network_stack = nn.Sequential(
+ # convlutional processing
+ nn.Conv2d(3, 3, 14, stride = 2),
+ nn.ReLU(),
+ nn.MaxPool2d(3,3),
+ nn.Conv2d(3, 3, 9),
+ nn.ReLU(),
+ nn.MaxPool2d(2,2),
+ nn.Flatten(),
+
+ # FFNN
+ nn.Linear(132, 132),
+ #nn.Dropout(p=0.05),
+ nn.ReLU(),
+ nn.Linear(132, 132),
+ nn.Dropout(p=0.5),
+ nn.ReLU(),
+ nn.Linear(132, 1),
+ nn.Sigmoid()
+ )
+ epochs=50, all datasets
+
+ optimizer = optim.Adagrad(model.parameters(), lr=0.06, weight_decay=0.005)
+ criterion = nn.BCELoss()
+
+
+ ----------------------------------------------------
+ double dataset added
+ -----------------------------------------------------
+ 98,53%
+
+ self.network_stack = nn.Sequential(
+ # convlutional processing
+ nn.Conv2d(3, 3, 14, stride = 2),
+ nn.ReLU(),
+ nn.MaxPool2d(3,3),
+ nn.Conv2d(3, 3, 9),
+ nn.ReLU(),
+ nn.MaxPool2d(2,2),
+ nn.Flatten(),
+
+ # FFNN
+ nn.Linear(132, 132),
+ nn.Dropout(p=0.05),
+ nn.ReLU(),
+ nn.Linear(132, 132),
+ nn.Dropout(p=0.05),
+ nn.ReLU(),
+ nn.Linear(132, 1),
+ nn.Sigmoid()
+ )
+
+ epochs:15
+ optimizer = optim.SGD(model.parameters(), lr=0.05, weight_decay=0.005, momentum=0.0)
+ criterion = nn.BCELoss()
+ orig, hor, vert
+
+
+ ---------------------------------------------
+ Custom Datasets Added: Hor, Ver
+ --------------------------------------------
+ 98.14%
+ self.network_stack = nn.Sequential(
+ # convlutional processing
+ nn.Conv2d(3, 3, 14),
+ nn.ReLU(),
+ nn.MaxPool2d(3,3),
+ nn.Conv2d(3, 3, 9),
+ nn.ReLU(),
+ nn.MaxPool2d(2,2),
+ nn.Flatten(),
+
+ # FFNN
+ nn.Linear(825, 825),
+ nn.Dropout(p=0.5),
+ nn.ReLU(),
+ nn.Linear(825, 825),
+ nn.Dropout(p=0.5),
+ nn.ReLU(),
+ nn.Linear(825, 1),
+ nn.Sigmoid()
+ )
+
+ optimizer = optim.SGD(model.parameters(), lr=0.05, weight_decay=0.005, momentum=0.0)
+ criterion = nn.BCELoss()
+
+
+ --------------------------------------------
+ 97.54%
+ self.network_stack = nn.Sequential(
+ # convlutional processing
+ nn.Conv2d(3, 3, 14),
+ nn.ReLU(),
+ nn.MaxPool2d(3,3),
+ nn.Conv2d(3, 3, 9),
+ nn.ReLU(),
+ nn.MaxPool2d(2,2),
+ nn.Flatten(),
+
+ # FFNN
+ nn.Linear(825, 825),
+ nn.Dropout(p=0.5),
+ nn.ReLU(),
+ nn.Linear(825, 825),
+ nn.Dropout(p=0.5),
+ nn.ReLU(),
+ nn.Linear(825, 1),
+ nn.Sigmoid()
+ )
+
+ optimizer = optim.SGD(model.parameters(), lr=0.02, weight_decay=0.01, momentum=0.00)
+ criterion = nn.BCELoss()
+
+ ---------------------------------------------
+ 97.34%
+
+ self.network_stack = nn.Sequential(
+ # convlutional processing
+ nn.Conv2d(3, 3, 14),
+ nn.ReLU(),
+ nn.MaxPool2d(3,3),
+ nn.Conv2d(3, 3, 9),
+ nn.ReLU(),
+ nn.MaxPool2d(2,2),
+ nn.Flatten(),
+
+ # FFNN
+ nn.Linear(825, 825),
+ nn.Dropout(p=0.2)
+ nn.ReLU(),
+ nn.Linear(825, 825),
+ nn.Dropout(p=0.2)
+ nn.ReLU(),
+ nn.Linear(825, 1),
+ nn.Sigmoid()
+ )
+
+ optimizer = optim.SGD(model.parameters(), lr=0.01, weight_decay=0.01, momentum=0.0)
+ criterion = nn.BCELoss()
+ ------------------------------------------------
+ 97.07%
+
+ self.network_stack = nn.Sequential(
+ # convlutional processing
+ nn.Conv2d(3, 3, 14),
+ nn.ReLU(),
+ nn.MaxPool2d(2,2),
+ nn.Conv2d(3, 3, 9),
+ nn.ReLU(),
+ nn.MaxPool2d(2,2),
+ nn.Flatten(),
+
+ # FFNN
+ nn.Linear(2280, 2280),
+ nn.ReLU(),
+ nn.Linear(2280, 1028),
+ nn.ReLU(),
+ nn.Linear(1028, 1),
+ nn.Sigmoid()
+ )
+
+ optimizer = optim.SGD(model.parameters(), lr=0.01, weight_decay=0.01, momentum=0.0)
+ criterion = nn.BCELoss()
+ --------------------------------------------------------------------------------------
+
+ 97.34%
+ super(Net, self).__init__()
+ #self.fc1 = nn.Linear(3 * 192 * 108, 1)
+ self.network_stack = nn.Sequential(
+ # convlutional processing
+ nn.Conv2d(3, 3, 9),
+ nn.ReLU(),
+ nn.MaxPool2d(2,2),
+ nn.Conv2d(3, 3, 4),
+ nn.ReLU(),
+ nn.MaxPool2d(2,2),
+ nn.Flatten(),
+
+ # FFNN
+ nn.Linear(3036, 1028),
+ nn.ReLU(),
+ nn.Linear(1028, 1028),
+ nn.ReLU(),
+ nn.Linear(1028, 1),
+ nn.Sigmoid()
+ )
+
+ optimizer = optim.SGD(model.parameters(), lr=0.01, weight_decay=0.01, momentum=0.0)
+ criterion = nn.BCELoss()
+----------------------------------------------------
+ 96.74%
+
+ self.network_stack = nn.Sequential(
+ # convlutional processing
+ nn.Conv2d(3, 3, 9),
+ nn.ReLU(),
+ nn.MaxPool2d(2,2),
+ nn.Conv2d(3, 3, 4),
+ nn.ReLU(),
+ nn.MaxPool2d(2,2),
+ nn.Flatten(),
+
+ # FFNN
+ nn.Linear(3036, 1028),
+ nn.ReLU(),
+ nn.Linear(1028, 32),
+ nn.ReLU(),
+ nn.Linear(32, 1),
+ nn.Sigmoid()
+ )
+
+ optimizer = optim.SGD(model.parameters(), lr=0.01, weight_decay=0.01, momentum=0.0)
+ criterion = nn.BCELoss()
+ -------------------------------------------------------------
+
+ 95,949%
+ self.network_stack = nn.Sequential(
+ # convlutional processing
+ nn.Conv2d(3, 3, 9),
+ nn.ReLU(),
+ nn.MaxPool2d(2,2),
+ nn.Conv2d(3, 3, 4),
+ nn.ReLU(),
+ nn.MaxPool2d(2,2),
+ nn.Flatten(),
+
+ # FFNN
+ nn.Linear(3036, 64),
+ nn.ReLU(),
+ nn.Linear(64, 1),
+ nn.Sigmoid()
+ )
+
+
+ optimizer = optim.SGD(model.parameters(), lr=0.01, weight_decay=0.01, momentum=0.0)
+ criterion = nn.BCELoss()
+ ---------------------------------------------------
+ 95.21%
+
+ self.network_stack = nn.Sequential(
+ # convlutional processing
+ nn.Conv2d(3, 3, 9),
+ nn.ReLU(),
+ nn.MaxPool2d(2,2),
+ nn.Flatten(),
+
+ # FFNN
+ nn.Linear(3 * 50 * 92, 64),
+ nn.ReLU(),
+ nn.Linear(64, 1),
+ nn.Sigmoid()
+ )
+
+ optimizer = optim.SGD(model.parameters(), lr=0.01, weight_decay=0.01, momentum=0.0)
+ criterion = nn.BCELoss()
+
+
+ -----------------------------------------------------
+
+ 85,32%
+ self.network_stack = nn.Sequential(
+ nn.Linear(3 * 192 * 108, 64),
+ nn.ELU(),
+ nn.Linear(64, 64),
+ nn.ELU(),
+ nn.Linear(64, 64),
+ nn.ELU(),
+ nn.Linear(64, 64),
+ nn.ELU(),
+ nn.Linear(64, 32),
+ nn.ELU(),
+ nn.Linear(32, 1),
+ nn.Sigmoid()
+ )
+
+ With: batch_size 14, lr 0.1,
+ optimizer = optim.SGD(model.parameters(), lr=args.lr, weight_decay=0.02, momentum=0.01)
+ criterion = nn.MSELoss()
+
+ --------------
+
+ 84.52%
+ self.network_stack = nn.Sequential(
+ nn.Linear(3 * 192 * 108, 64),
+ nn.ELU(),
+ nn.Linear(64, 64),
+ nn.ELU(),
+ nn.Linear(64, 64),
+ nn.ELU(),
+ nn.Linear(64, 64),
+ nn.Tanh(),
+ nn.Linear(64, 32),
+ nn.Tanh(),
+ nn.Linear(32, 1),
+ nn.Sigmoid()
+ )
+
+
+ with: batch_size 16, lr 0.08
+ optimizer = optim.SGD(model.parameters(), lr=args.lr, weight_decay=0.02, momentum=0.01)
+ criterion = nn.MSELoss()
+
+ --------------------------
+
+ 84.98%
+ self.network_stack = nn.Sequential(
+ nn.Linear(3 * 192 * 108, 1028),
+ nn.Tanh(),
+ nn.Linear(1028, 256),
+ nn.Tanh(),
+ nn.Linear(256, 256),
+ nn.Tanh(),
+ nn.Linear(256, 256),
+ nn.Tanh(),
+ nn.Linear(256, 1),
+ nn.Sigmoid()
+ )
+
+
+ with:
+
+ optimizer = optim.SGD(model.parameters(), lr=0.1)
+ criterion = nn.MSELoss()
+
+ """
+
+ def forward(self, image):
+ #image = torch.flatten(image, start_dim=1)
+ #output = self.fc1(image)
+ #image = transforms.functional.adjust_saturation(image, 0.8)
+ output = self.network_stack(image)
+ return output
+
+
+def train(args, model, device, train_loader, optimizer, criterion, epoch, scheduler, analyzer):
+ """
+ Train a network
+ You can find example code here: https://pytorch.org/tutorials/beginner/basics/quickstart_tutorial.html
+ """
+ model.train()
+ average_loss = 0
+ for batch_idx, (data, target) in enumerate(train_loader):
+ data, target = data.to(device), target.to(device).float()
+ optimizer.zero_grad()
+ output = model(data)
+ loss = criterion(output, torch.unsqueeze(target, 1))
+ loss.backward()
+ average_loss += float(loss)
+ optimizer.step()
+ if batch_idx % args.log_interval == 0:
+ print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
+ epoch, batch_idx * len(data), len(train_loader.dataset),
+ 100. * batch_idx / len(train_loader), loss.item()))
+ if args.dry_run:
+ break
+ analyzer.training_loss.append(average_loss/len(train_loader))
+ print(f"Average training loss: {average_loss/len(train_loader)}")
+
+
+def test(model, device, test_loader, criterion, analyzer):
+ model.eval()
+ test_loss = 0
+ correct = 0
+ with torch.no_grad():
+ for data, target in test_loader:
+ data, target = data.to(device), target.to(device).float()
+ output = model(data)
+ loss = criterion(output, torch.unsqueeze(target, 1)).item() # sum up batch loss
+ test_loss += loss
+ pred = torch.round(output) # get the index of the max log-probability
+ correct += pred.eq(target.view_as(pred)).sum().item()
+
+ test_loss /= len(test_loader.dataset)
+ analyzer.validation_loss.append(float(test_loss))
+ print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
+ test_loss, correct, len(test_loader.dataset),
+ 100. * correct / len(test_loader.dataset)))
+ analyzer.percentage.append(correct / len(test_loader.dataset))
+ return 100. * correct / len(test_loader.dataset)
+
+
+def main():
+ # Training settings
+ parser = argparse.ArgumentParser(description='PyTorch Ship Detection')
+ parser.add_argument('--batch-size', type=int, default=16, metavar='N',
+ help='input batch size for training (default: 64)')
+ parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
+ help='input batch size for testing (default: 1000)')
+ parser.add_argument('--epochs', type=int, default=14, metavar='N',
+ help='number of epochs to train (default: 14)')
+ parser.add_argument('--lr', type=float, default=0.1, metavar='LR',
+ help='learning rate (default: 0.1)')
+ parser.add_argument('--no-cuda', action='store_true', default=False,
+ help='disables CUDA training')
+ parser.add_argument('--dry-run', action='store_true', default=False,
+ help='quickly check a single pass')
+ parser.add_argument('--seed', type=int, default=1, metavar='S',
+ help='random seed (default: 1)')
+ parser.add_argument('--log-interval', type=int, default=10, metavar='N',
+ help='how many batches to wait before logging training status')
+ parser.add_argument('--save-model', action='store_true', default=False,
+ help='For Saving the current Model')
+ args = parser.parse_args()
+ torch.manual_seed(args.seed)
+ use_cuda = not args.no_cuda and torch.cuda.is_available()
+ device = torch.device("cuda" if use_cuda else "cpu")
+ print(f"using {device}")
+ train_kwargs = {'batch_size': args.batch_size}
+ val_kwargs = {'batch_size': args.test_batch_size}
+ if use_cuda:
+ cuda_kwargs = {'num_workers': 1,
+ 'pin_memory': True,
+ 'shuffle': True}
+ train_kwargs.update(cuda_kwargs)
+ val_kwargs.update(cuda_kwargs)
+
+ # Create transform
+ transform = transforms.Compose([
+ transforms.ToTensor(),
+ # This normalization is used on the test server
+ transforms.Normalize([0.2404, 0.2967, 0.3563], [0.0547, 0.0527, 0.0477])
+ ])
+
+ # Create train and test set
+ path_to_dataset = "./Boat-MNIST" # TODO Set correct path
+ train_set = Boats(root_dir=f'{path_to_dataset}/train', transform=transform,
+ gt_json_path=f'{path_to_dataset}/boat_mnist_labels_trainval.json')
+ train_set_hor_mirrored = Boats(root_dir=f'{path_to_dataset}/custom_dataset/hor_mirrored', transform=transform,
+ gt_json_path=f'{path_to_dataset}/boat_mnist_labels_trainval.json')
+ train_set_vert_mirrored = Boats(root_dir=f'{path_to_dataset}/custom_dataset/vert_mirrored', transform=transform,
+ gt_json_path=f'{path_to_dataset}/boat_mnist_labels_trainval.json')
+ train_set_double_mirrored = Boats(root_dir=f'{path_to_dataset}/custom_dataset/double_mirrored', transform=transform,
+ gt_json_path=f'{path_to_dataset}/boat_mnist_labels_trainval.json')
+ val_set = Boats(root_dir=f'{path_to_dataset}/val', transform=transform,
+ gt_json_path=f'{path_to_dataset}/boat_mnist_labels_trainval.json')
+
+ # Create data loaders
+ train_loader = torch.utils.data.DataLoader(train_set, **train_kwargs)
+ train_loader_hor_mirrored = torch.utils.data.DataLoader(train_set_hor_mirrored, **train_kwargs)
+ train_loader_vert_mirrored = torch.utils.data.DataLoader(train_set_vert_mirrored, **train_kwargs)
+ train_loader_double_mirrored = torch.utils.data.DataLoader(train_set_double_mirrored, **train_kwargs)
+ train_loader_val_hor_mirrored = torch.utils.data.DataLoader(train_set_hor_mirrored, **train_kwargs)
+ train_loader_val_vert_mirrored = torch.utils.data.DataLoader(train_set_vert_mirrored, **train_kwargs)
+ test_loader = torch.utils.data.DataLoader(val_set, **val_kwargs)
+
+ # Create network, optimizer and loss
+ model = Net().to(device)
+ optimizer = optim.SGD(model.parameters(), lr=0.05, weight_decay=0.005, momentum=0.0)
+ #optimizer = optim.Adagrad(model.parameters(), lr=0.05, weight_decay=0.005)
+ #scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=[int(args.epochs*0.4),int(args.epochs*0.83)], gamma=0.1, verbose=True)
+ scheduler = optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer,T_0 = 10, eta_min = 0.0001, verbose=True)
+
+ #criterion = nn.MSELoss()
+ #Change loss function
+ criterion = nn.BCELoss()
+ #criterion = nn.CrossEntropyLoss()
+
+ # Train and validate
+ best_acc = 0
+ best_epoch = 0
+ analyzer = Analyzer()
+ best_model_wts = copy.deepcopy(model.state_dict())
+ for epoch in range(1, args.epochs + 1):
+ if (epoch %6 == 0):
+ print("Original set")
+ train(args, model, device, train_loader, optimizer, criterion, epoch, scheduler, analyzer)
+ elif (epoch %6== 1):
+ print("Horizontally Mirrored set")
+ train(args, model, device, train_loader_hor_mirrored, optimizer, criterion, epoch, scheduler, analyzer)
+ elif (epoch %6== 2):
+ print("Vertically Mirrored set")
+ train(args, model, device, train_loader_vert_mirrored, optimizer, criterion, epoch, scheduler, analyzer)
+ elif (epoch %6 == 3):
+ print("Double Mirrored set")
+ train(args, model, device, train_loader_double_mirrored, optimizer, criterion, epoch, scheduler, analyzer)
+ elif (epoch %6 == 4):
+ print("Horizontally Mirrored Verification set")
+ train(args, model, device, train_loader_val_hor_mirrored, optimizer, criterion, epoch, scheduler, analyzer)
+ else:
+ print("Vertically Mirrored Verification set")
+ train(args, model, device, train_loader_val_vert_mirrored, optimizer, criterion, epoch, scheduler, analyzer)
+
+
+ acc = test(model, device, test_loader, criterion, analyzer)
+ if acc > best_acc:
+ best_acc = acc
+ best_epoch = epoch
+ best_model_wts = copy.deepcopy(model.state_dict())
+ scheduler.step(epoch)
+
+ # Load best model weights
+ model.load_state_dict(best_model_wts)
+ print(f"Best accuracy (val): {best_acc} in epoch {best_epoch}")
+
+ if args.save_model:
+ torch.save(model.state_dict(), "model.pth")
+
+ # --- Do not touch -----
+ # Save model as onnx file
+ dummy_input = torch.randn(1, 3, 108, 192, device=device)
+ input_names = ["img_1"]
+ output_names = ["output1"]
+ torch.onnx.export(model, dummy_input, "ship_example.onnx", input_names=input_names, output_names=output_names)
+ # ----------------------
+
+ plt.plot(list(range(len(analyzer.training_loss))), analyzer.training_loss, label = "Training loss")
+ plt.plot(list(range(len(analyzer.validation_loss))), analyzer.validation_loss, label = "Validation loss")
+ plt.plot(list(range(len(analyzer.percentage))), analyzer.percentage, label = "percentage")
+ plt.legend()
+ plt.show()
+
+
+if __name__ == '__main__':
+ try:
+ main()
+ except(KeyboardInterrupt):
+ print("-"*50)
+ print("KeyboardInterrupt")
+ print("-"*50)
\ No newline at end of file
diff --git a/Boat_MNIST/11/Anstett_Luebbing05.pdf b/Boat_MNIST/11/Anstett_Luebbing05.pdf
new file mode 100644
index 0000000..576f228
Binary files /dev/null and b/Boat_MNIST/11/Anstett_Luebbing05.pdf differ
diff --git a/Boat_MNIST/11/boat_classifier.py b/Boat_MNIST/11/boat_classifier.py
new file mode 100644
index 0000000..404b647
--- /dev/null
+++ b/Boat_MNIST/11/boat_classifier.py
@@ -0,0 +1,367 @@
+"""
+TODO
+- team member 1: Raphael Anstett
+- team member 2: Timo Lübbing
+
+tasks:
+ - add your team members' names at the top of the file
+ - Take part in the challenge :)
+"""
+
+import argparse
+import copy
+import json
+import os
+from matplotlib.image import imread
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+from torchvision import transforms
+from torch.utils.data import Dataset
+
+
+class Boats(Dataset):
+
+ def __init__(self, root_dir, transform=None, gt_json_path=''):
+ self.root_dir = root_dir
+ self.transform = transform
+ self.gt_json_path = gt_json_path
+ self.labels = json.load(open(gt_json_path, 'r'))
+ self.image_list = sorted(os.listdir(root_dir))
+ self.image_ids = dict(enumerate(self.image_list, start=0))
+
+ def __len__(self):
+ return len(self.image_ids)
+
+ def __getitem__(self, idx):
+ img = self.load_image(idx)
+ img_name = self.image_ids[idx]
+ label = self.labels[img_name]
+ if self.transform:
+ img = self.transform(img)
+ return img, label
+
+ def load_image(self, image_index):
+ image_name = self.image_ids[image_index]
+ path = os.path.join(self.root_dir, image_name)
+ return imread(path)
+
+
+class Net(nn.Module):
+ def __init__(self):
+ super(Net, self).__init__()
+ self.fc1 = nn.Linear(3 * 192 * 108, 1)
+
+ def forward(self, x):
+ x = torch.flatten(x, start_dim=1)
+ x = self.fc1(x)
+ return torch.sigmoid(x)
+
+class Net1(nn.Module):
+ def __init__(self):
+ super(Net1, self).__init__()
+ self.fc1 = nn.Linear(3 * 192 * 108, 128)
+ self.fc2 = nn.Linear(128, 64)
+ self.fc3 = nn.Linear(64, 1)
+ self.drop = nn.Dropout(0.1)
+
+ def forward(self, x):
+ x = torch.flatten(x, start_dim=1)
+ x = F.relu(self.fc1(x))
+ x = F.relu(self.fc2(x))
+ x = self.drop(x)
+ x = F.relu(self.fc3(x))
+ return torch.sigmoid(x)
+
+class Net2(nn.Module):
+ def __init__(self):
+ super(Net2, self).__init__()
+
+ # Output size after conv filter:
+ # ((w-f+2p) / s) + 1
+ self.conv1 = nn.Conv2d(in_channels=3, out_channels=12, kernel_size=3, stride=1, padding=1)
+ # Shape = (batch, 12, 108, 192)
+ self.bn1 = nn.BatchNorm2d(num_features=12)
+ # Shape = (batch, 12, 108, 192)
+ self.pool1 = nn.MaxPool2d(kernel_size=2)
+ # Shape = (batch, 12, 54, 96)
+
+ self.conv2 = nn.Conv2d(in_channels=12, out_channels=20, kernel_size=3, stride=1, padding=1)
+ # Shape = (batch, 20, 54, 96)
+ self.bn2 = nn.BatchNorm2d(num_features=20)
+ # Shape = (batch, 20, 54, 96)
+ self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
+ # Shape = (batch, 20, 27, 48)
+
+ self.fc1 = nn.Linear(20 * 27 * 48, 128)
+ self.fc2 = nn.Linear(128, 1)
+
+ def forward(self, x):
+ # print(f"Input: {x.shape}")
+ x = F.relu(self.bn1(self.conv1(x)))
+ # print(f"Conv1: {x.shape}")
+ x = self.pool1(x)
+ # print(f"Pool1: {x.shape}")
+ x = F.relu(self.bn2(self.conv2(x)))
+ # print(f"Conv2: {x.shape}")
+ x = self.pool2(x)
+ # print(f"Pool2: {x.shape}")
+ x = torch.flatten(x, start_dim=1)
+ # print(f"Flatten: {x.shape}")
+ x = F.relu(self.fc1(x))
+ # print(f"fc1: {x.shape}")
+ x = F.relu(self.fc2(x))
+ return torch.sigmoid(x)
+
+class Net3(nn.Module):
+ def __init__(self):
+ super(Net3, self).__init__()
+ # Output size after conv filter:
+ # ((w-f+2p) / s) + 1
+ self.conv1 = nn.Conv2d(in_channels=3, out_channels=12, kernel_size=3, stride=1, padding=1)
+ self.bn1 = nn.BatchNorm2d(num_features=12)
+ # Shape = (batch, 12, 108, 192)
+
+ self.conv2 = nn.Conv2d(in_channels=12, out_channels=20, kernel_size=3, stride=1, padding=1)
+ self.bn2 = nn.BatchNorm2d(num_features=20)
+ self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
+ # Shape = (batch, 20, 54, 96)
+
+ self.conv3 = nn.Conv2d(in_channels=20, out_channels=32, kernel_size=3, stride=1, padding=1)
+ self.bn3 = nn.BatchNorm2d(num_features=32)
+ self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
+ # Shape = (batch, 32, 27, 48)
+
+ self.fc1 = nn.Linear(32 * 27 * 48, 128)
+ self.fc2 = nn.Linear(128, 64)
+ self.fc3 = nn.Linear(64, 1)
+
+ def forward(self, x):
+ # print(f"Input: {x.shape}")
+ x = F.relu(self.bn1(self.conv1(x)))
+ # print(f"Conv1: {x.shape}")
+ x = F.relu(self.bn2(self.conv2(x)))
+ # print(f"Conv2: {x.shape}")
+ x = self.pool1(x)
+ # print(f"Pool1: {x.shape}")
+ x = F.relu(self.bn3(self.conv3(x)))
+ # print(f"Conv3: {x.shape}")
+ x = self.pool2(x)
+ # print(f"Pool2: {x.shape}")
+ x = torch.flatten(x, start_dim=1)
+ # print(f"Flatten: {x.shape}")
+ x = F.relu(self.fc1(x))
+ # print(f"fc1: {x.shape}")
+ x = F.relu(self.fc2(x))
+ x = F.relu(self.fc3(x))
+ return torch.sigmoid(x)
+
+class Net4(nn.Module):
+ def __init__(self):
+ super(Net4, self).__init__()
+ # Output size after conv filter:
+ # ((w-f+2p) / s) + 1
+ self.conv1 = nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3, stride=1, padding=1)
+ self.bn1 = nn.BatchNorm2d(num_features=16)
+ # Shape = (batch, 16, 108, 192)
+
+ self.conv2 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, stride=1, padding=1)
+ self.bn2 = nn.BatchNorm2d(num_features=32)
+ self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
+ # Shape = (batch, 32, 54, 96)
+
+ self.conv3 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1)
+ self.bn3 = nn.BatchNorm2d(num_features=64)
+ self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
+ # Shape = (batch, 64, 27, 48)
+
+ self.fc1 = nn.Linear(64 * 27 * 48, 256)
+ self.fc2 = nn.Linear(256, 128)
+ self.fc3 = nn.Linear(128, 1)
+
+ def forward(self, x):
+ # print(f"Input: {x.shape}")
+ x = F.relu(self.bn1(self.conv1(x)))
+ # print(f"Conv1: {x.shape}")
+ x = F.relu(self.bn2(self.conv2(x)))
+ # print(f"Conv2: {x.shape}")
+ x = self.pool1(x)
+ # print(f"Pool1: {x.shape}")
+ x = F.relu(self.bn3(self.conv3(x)))
+ # print(f"Conv3: {x.shape}")
+ x = self.pool2(x)
+ # print(f"Pool2: {x.shape}")
+ x = torch.flatten(x, start_dim=1)
+ # print(f"Flatten: {x.shape}")
+ x = F.relu(self.fc1(x))
+ # print(f"fc1: {x.shape}")
+ x = F.relu(self.fc2(x))
+ x = F.relu(self.fc3(x))
+ return torch.sigmoid(x)
+
+def get_lr(optimizer):
+ for param_group in optimizer.param_groups:
+ return param_group['lr']
+
+def train(args, model, device, train_loader, optimizer, scheduler, criterion, epoch):
+ """
+ Train a network
+ You can find example code here: https://pytorch.org/tutorials/beginner/basics/quickstart_tutorial.html
+ """
+ model.train()
+ processed = 0
+ correct = 0
+ for batch_idx, (data, target) in enumerate(train_loader):
+ data, target = data.to(device), target.to(device).float()
+ optimizer.zero_grad()
+ output = model(data)
+ loss = criterion(output, torch.unsqueeze(target, 1))
+ loss.backward()
+ optimizer.step()
+ scheduler.step()
+
+ # batch accuracy during training
+ pred = torch.round(output)
+ processed += len(data)
+ correct += pred.eq(target.view_as(pred)).sum().item()
+ accuracy = 100. * correct / processed
+
+ if batch_idx % args.log_interval == 0:
+ print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}\tLR: {:.6f}\tAccuracy: {:.6f}%'.format(
+ epoch, batch_idx * len(data), len(train_loader.dataset),
+ 100. * batch_idx / len(train_loader), loss.item(),
+ get_lr(optimizer), accuracy))
+ if args.dry_run:
+ break
+
+
+def test(model, device, test_loader, criterion):
+ model.eval()
+ test_loss = 0
+ correct = 0
+ with torch.no_grad():
+ for data, target in test_loader:
+ data, target = data.to(device), target.to(device).float()
+ output = model(data)
+ loss = criterion(output, torch.unsqueeze(target, 1)).item()
+ test_loss += float(loss) # sum up batch loss
+ pred = torch.round(output) # get the index of the max log-probability
+ correct += pred.eq(target.view_as(pred)).sum().item()
+
+ test_loss /= len(test_loader.dataset)
+
+ print('\nVal set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
+ test_loss, correct, len(test_loader.dataset),
+ 100. * correct / len(test_loader.dataset)))
+ return 100. * correct / len(test_loader.dataset)
+
+
+def main():
+ # Training settings
+ parser = argparse.ArgumentParser(description='PyTorch Ship Detection')
+ parser.add_argument('--batch-size', type=int, default=64, metavar='N',
+ help='input batch size for training (default: 64)')
+ parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
+ help='input batch size for testing (default: 1000)')
+ parser.add_argument('--epochs', type=int, default=35, metavar='N',
+ help='number of epochs to train (default: 14)')
+ parser.add_argument('--lr', type=float, default=0.0008, metavar='LR',
+ help='learning rate (default: 0.1)')
+ parser.add_argument('--momentum', type=float, default=0.95, metavar='M',
+ help='momentum (default: 0.95)'),
+ parser.add_argument('--weightdecay', type=float, default=0.01, metavar='LR',
+ help='weight decay (default: 0.1)'),
+ parser.add_argument('--no-cuda', action='store_true', default=False,
+ help='disables CUDA training')
+ parser.add_argument('--dry-run', action='store_true', default=False,
+ help='quickly check a single pass')
+ parser.add_argument('--seed', type=int, default=1, metavar='S',
+ help='random seed (default: 1)')
+ parser.add_argument('--log-interval', type=int, default=1, metavar='N',
+ help='how many batches to wait before logging training status')
+ parser.add_argument('--save-model', action='store_true', default=True,
+ help='For Saving the current Model')
+ args = parser.parse_args()
+ torch.manual_seed(args.seed)
+ use_cuda = not args.no_cuda and torch.cuda.is_available()
+ device = torch.device("cuda" if use_cuda else "cpu")
+ train_kwargs = {'batch_size': args.batch_size}
+ val_kwargs = {'batch_size': args.test_batch_size}
+ print(f"Cuda? {use_cuda}")
+ if use_cuda:
+ cuda_kwargs = {'num_workers': 4,
+ 'pin_memory': True,
+ 'shuffle': True}
+ train_kwargs.update(cuda_kwargs)
+ val_kwargs.update(cuda_kwargs)
+
+ # Create transform
+ transform = transforms.Compose([
+ transforms.ToTensor(),
+ transforms.RandomHorizontalFlip(),
+ transforms.RandomRotation(6),
+ # This normalization is used on the test server
+ transforms.Normalize([0.2404, 0.2967, 0.3563], [0.0547, 0.0527, 0.0477])
+ ])
+
+ transform_val = transforms.Compose([
+ transforms.ToTensor(),
+ transforms.Normalize([0.2404, 0.2967, 0.3563], [0.0547, 0.0527, 0.0477])
+ ])
+
+ # Create train and test set
+ path_to_dataset = "Boat_MNIST" # TODO Set correct path
+ train_set = Boats(root_dir=f'{path_to_dataset}/train', transform=transform,
+ gt_json_path=f'{path_to_dataset}/boat_mnist_labels_trainval.json')
+ val_set = Boats(root_dir=f'{path_to_dataset}/val', transform=transform_val,
+ gt_json_path=f'{path_to_dataset}/boat_mnist_labels_trainval.json')
+
+ # Create data loaders
+ train_loader = torch.utils.data.DataLoader(train_set, **train_kwargs)
+ val_loader = torch.utils.data.DataLoader(val_set, **val_kwargs)
+
+ # Create network, optimizer and loss
+ model = Net().to(device)
+ optimizer = optim.SGD(model.parameters(), lr=args.lr,
+ momentum=args.momentum,
+ weight_decay=args.weightdecay)
+ scheduler = torch.optim.lr_scheduler.CyclicLR(optimizer,
+ max_lr=0.1,
+ base_lr=args.lr,
+ mode="exp_range",
+ step_size_up=1500,
+ gamma=0.99)
+ criterion = nn.MSELoss()
+
+ print('Training of the model: \n', repr(model))
+
+ # Train and validate
+ best_acc = 0
+ best_model_wts = copy.deepcopy(model.state_dict())
+ for epoch in range(1, args.epochs + 1):
+ train(args, model, device, train_loader, optimizer, scheduler, criterion, epoch)
+ acc = test(model, device, val_loader, criterion)
+ if acc > best_acc:
+ best_acc = acc
+ best_model_wts = copy.deepcopy(model.state_dict())
+
+ # Load best model weights
+ model.load_state_dict(best_model_wts)
+ print(f"Best accuracy (val): {best_acc}")
+
+ if args.save_model:
+ torch.save(model.state_dict(), "boat_classifier.pth")
+
+ # --- Do not touch -----
+ # Save model as onnx file
+ dummy_input = torch.randn(1, 3, 108, 192, device=device)
+ input_names = ["img_1"]
+ output_names = ["output1"]
+ torch.onnx.export(model, dummy_input, "ship_example.onnx", input_names=input_names, output_names=output_names)
+ # ----------------------
+
+
+if __name__ == '__main__':
+ main()
diff --git a/Boat_MNIST/11/ship_example_conv_98.onnx b/Boat_MNIST/11/ship_example_conv_98.onnx
new file mode 100644
index 0000000..e9d7edb
Binary files /dev/null and b/Boat_MNIST/11/ship_example_conv_98.onnx differ
diff --git a/Boat_MNIST/120/Challenge_Group120.pdf b/Boat_MNIST/120/Challenge_Group120.pdf
new file mode 100644
index 0000000..879af65
Binary files /dev/null and b/Boat_MNIST/120/Challenge_Group120.pdf differ
diff --git a/Boat_MNIST/120/challenge_nn.py b/Boat_MNIST/120/challenge_nn.py
new file mode 100644
index 0000000..7642338
--- /dev/null
+++ b/Boat_MNIST/120/challenge_nn.py
@@ -0,0 +1,211 @@
+"""
+TODO
+- team member 1: Joud Zakharia
+- team member 2: Emilie Ferdinand
+
+tasks:
+ - add your team members' names at the top of the file
+ - Take part in the challenge :)
+"""
+
+import argparse
+import copy
+import json
+import os
+from traceback import print_tb
+from matplotlib.image import imread
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+from torchvision import transforms
+from torch.utils.data import Dataset
+
+
+class Boats(Dataset):
+
+ def __init__(self, root_dir, transform=None, gt_json_path=''):
+ self.root_dir = root_dir
+ self.transform = transform
+ self.gt_json_path = gt_json_path
+ self.labels = json.load(open(gt_json_path, 'r'))
+ self.image_list = sorted(os.listdir(root_dir))
+ self.image_ids = dict(enumerate(self.image_list, start=0))
+
+ def __len__(self):
+ return len(self.image_ids)
+
+ def __getitem__(self, idx):
+ img = self.load_image(idx)
+ img_name = self.image_ids[idx]
+ label = self.labels[img_name]
+ if self.transform:
+ img = self.transform(img)
+ sample = (img, label)
+ return sample
+
+ def load_image(self, image_index):
+ image_name = self.image_ids[image_index]
+ path = os.path.join(self.root_dir, image_name)
+ img = imread(path)
+ return img
+
+
+class Net(nn.Module):
+ def __init__(self):
+ super(Net, self).__init__()
+ # self.fc1 = nn.Linear(3 * 192 * 108, 1)
+ self.model = nn.Sequential(
+ nn.Conv2d(3, 11, 5),
+ nn.ReLU(),
+ nn.MaxPool2d(3),
+ nn.Conv2d(11,13, 5),
+ nn.ReLU(),
+ nn.MaxPool2d(3),
+ nn.Conv2d(13,15,5),
+ nn.ReLU(),
+ nn.MaxPool2d(3),
+ nn.Flatten(),
+ nn.Linear(150, 1),
+ )
+
+ def forward(self, x):
+ # x = torch.flatten(x, start_dim=1)
+ # print(x.shape)
+ x = self.model(x)
+ output = torch.sigmoid(x)
+ return output
+
+
+def train(args, model, device, train_loader, optimizer, criterion, epoch):
+ """
+ Train a network
+ You can find example code here: https://pytorch.org/tutorials/beginner/basics/quickstart_tutorial.html
+ """
+ model.train()
+ for batch_idx, (data, target) in enumerate(train_loader):
+ data, target = data.to(device), target.to(device).float()
+ optimizer.zero_grad()
+ output = model(data)
+ loss = criterion(output, torch.unsqueeze(target, 1))
+ loss.backward()
+ optimizer.step()
+ if batch_idx % args.log_interval == 0:
+ print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
+ epoch, batch_idx * len(data), len(train_loader.dataset),
+ 100. * batch_idx / len(train_loader), loss.item()))
+ if args.dry_run:
+ break
+
+
+def test(model, device, test_loader, criterion):
+ model.eval()
+ test_loss = 0
+ correct = 0
+ with torch.no_grad():
+ for data, target in test_loader:
+ data, target = data.to(device), target.to(device).float()
+ output = model(data)
+ test_loss += criterion(output, torch.unsqueeze(target, 1)).item() # sum up batch loss
+ pred = torch.round(output) # get the index of the max log-probability
+ correct += pred.eq(target.view_as(pred)).sum().item()
+
+ test_loss /= len(test_loader.dataset)
+
+ print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
+ test_loss, correct, len(test_loader.dataset),
+ 100. * correct / len(test_loader.dataset)))
+ return 100. * correct / len(test_loader.dataset)
+
+
+def main():
+ # Training settings
+ parser = argparse.ArgumentParser(description='PyTorch Ship Detection')
+ parser.add_argument('--batch-size', type=int, default=64, metavar='N',
+ help='input batch size for training (default: 64)')
+ parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
+ help='input batch size for testing (default: 1000)')
+ parser.add_argument('--epochs', type=int, default=50, metavar='N',
+ help='number of epochs to train (default: 20)')
+ parser.add_argument('--lr', type=float, default=0.1, metavar='LR',
+ help='learning rate (default: 0.1)')
+ parser.add_argument('--no-cuda', action='store_true', default=False,
+ help='disables CUDA training')
+ parser.add_argument('--dry-run', action='store_true', default=False,
+ help='quickly check a single pass')
+ parser.add_argument('--seed', type=int, default=1, metavar='S',
+ help='random seed (default: 1)')
+ parser.add_argument('--log-interval', type=int, default=10, metavar='N',
+ help='how many batches to wait before logging training status')
+ parser.add_argument('--save-model', action='store_true', default=False,
+ help='For Saving the current Model')
+ args = parser.parse_args()
+ torch.manual_seed(args.seed)
+ use_cuda = not args.no_cuda and torch.cuda.is_available()
+ device = torch.device("cuda:1" if use_cuda else "cpu")
+ train_kwargs = {'batch_size': args.batch_size}
+ val_kwargs = {'batch_size': args.test_batch_size}
+ if use_cuda:
+ cuda_kwargs = {'num_workers': 8,
+ 'pin_memory': True,
+ 'shuffle': True}
+ train_kwargs.update(cuda_kwargs)
+ val_kwargs.update(cuda_kwargs)
+
+ # Create transform
+ transform = transforms.Compose([
+ transforms.ToTensor(),
+ # This normalization is used on the test server
+ transforms.RandomHorizontalFlip(),
+ transforms.RandomVerticalFlip(),
+ transforms.Normalize([0.2404, 0.2967, 0.3563], [0.0547, 0.0527, 0.0477])
+ ])
+
+ # Create train and test set
+ path_to_dataset = "/home/zakharia/See/Challenge_Intro_Ship_Classification/" # TODO Set correct path
+ train_set = Boats(root_dir=f'{path_to_dataset}/train', transform=transform,
+ gt_json_path=f'{path_to_dataset}/boat_mnist_labels_trainval.json')
+ val_set = Boats(root_dir=f'{path_to_dataset}/val', transform=transform,
+ gt_json_path=f'{path_to_dataset}/boat_mnist_labels_trainval.json')
+
+
+
+ # Create data loaders
+ train_loader = torch.utils.data.DataLoader(train_set, **train_kwargs)
+ test_loader = torch.utils.data.DataLoader(val_set, **val_kwargs)
+
+ # Create network, optimizer and loss
+ model = Net().to(device)
+ optimizer = optim.SGD(model.parameters(), lr=args.lr)
+ # criterion = nn.BCELoss()
+ criterion = nn.MSELoss()
+
+ # Train and validate
+ best_acc = 0
+ best_model_wts = copy.deepcopy(model.state_dict())
+ for epoch in range(1, args.epochs + 1):
+ train(args, model, device, train_loader, optimizer, criterion, epoch)
+ acc = test(model, device, test_loader, criterion)
+ if acc > best_acc:
+ best_acc = acc
+ best_model_wts = copy.deepcopy(model.state_dict())
+
+ # Load best model weights
+ model.load_state_dict(best_model_wts)
+ print(f"Best accuracy (val): {best_acc}")
+
+ if args.save_model:
+ torch.save(model.state_dict(), "model.pth")
+
+ # --- Do not touch -----
+ # Save model as onnx file
+ dummy_input = torch.randn(1, 3, 108, 192, device=device)
+ input_names = ["img_1"]
+ output_names = ["output1"]
+ torch.onnx.export(model, dummy_input, "ship_example.onnx", input_names=input_names, output_names=output_names)
+ # ----------------------
+
+
+if __name__ == '__main__':
+ main()
diff --git a/Boat_MNIST/120/ship_example.onnx b/Boat_MNIST/120/ship_example.onnx
new file mode 100644
index 0000000..7da6b69
Binary files /dev/null and b/Boat_MNIST/120/ship_example.onnx differ
diff --git a/Boat_MNIST/123/challenge_nn.py b/Boat_MNIST/123/challenge_nn.py
new file mode 100644
index 0000000..3346799
--- /dev/null
+++ b/Boat_MNIST/123/challenge_nn.py
@@ -0,0 +1,219 @@
+"""
+TODO
+- team member 1: Leonard Zieger
+- team member 2: Daniele Locatelli
+tasks:
+ - add your team members' names at the top of the file
+ - Take part in the challenge :)
+"""
+
+import argparse
+import copy
+import json
+import os
+from matplotlib.image import imread
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+from torchvision import transforms
+from torch.utils.data import Dataset
+
+
+class Boats(Dataset):
+
+ def __init__(self, root_dir, transform=None, gt_json_path=''):
+ self.root_dir = root_dir
+ self.transform = transform
+ self.gt_json_path = gt_json_path
+ self.labels = json.load(open(gt_json_path, 'r'))
+ self.image_list = sorted(os.listdir(root_dir))
+ self.image_ids = dict(enumerate(self.image_list, start=0))
+
+ def __len__(self):
+ return len(self.image_ids)
+
+ def __getitem__(self, idx):
+ img = self.load_image(idx)
+ img_name = self.image_ids[idx]
+ label = self.labels[img_name]
+ if self.transform:
+ img = self.transform(img)
+ sample = (img, label)
+ return sample
+
+ def load_image(self, image_index):
+ image_name = self.image_ids[image_index]
+ path = os.path.join(self.root_dir, image_name)
+ img = imread(path)
+ return img
+
+
+class Net(nn.Module):
+ def __init__(self):
+ super(Net, self).__init__()
+
+ input_layer = nn.Linear(in_features=3 * 192 * 108, out_features=30)
+ hidden_layer = nn.Linear(in_features=30, out_features=30)
+ hidden_layer2 = nn.Linear(in_features=30, out_features=30)
+ hidden_layer3 = nn.Linear(in_features=30, out_features=30)
+ hidden_layer4 = nn.Linear(in_features=30, out_features=20)
+ hidden_layer5 = nn.Linear(in_features=20, out_features=20)
+ hidden_layer6 = nn.Linear(in_features=20, out_features=20)
+ last_hidden_layer = nn.Linear(in_features=20, out_features=1)
+
+ self.stack = nn.Sequential(
+ input_layer,
+ nn.CELU(),
+ hidden_layer,
+ nn.CELU(),
+ hidden_layer2,
+ nn.CELU(),
+ hidden_layer3,
+ nn.CELU(),
+ hidden_layer4,
+ nn.CELU(),
+ hidden_layer5,
+ nn.CELU(),
+ hidden_layer6,
+ nn.CELU(),
+ last_hidden_layer
+ )
+
+
+
+
+ def forward(self, x):
+ x = torch.flatten(x, start_dim=1)
+ x = self.stack(x)
+ output = torch.sigmoid(x)
+ return output
+
+
+def train(args, model, device, train_loader, optimizer, criterion, epoch):
+ """
+ Train a network
+ You can find example code here: https://pytorch.org/tutorials/beginner/basics/quickstart_tutorial.html
+ """
+ model.train()
+ for batch_idx, (data, target) in enumerate(train_loader):
+ data, target = data.to(device), target.to(device).float()
+ optimizer.zero_grad()
+ output = model(data)
+ loss = criterion(output, torch.unsqueeze(target, 1))
+ loss.backward()
+ optimizer.step()
+ if batch_idx % args.log_interval == 0:
+ print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
+ epoch, batch_idx * len(data), len(train_loader.dataset),
+ 100. * batch_idx / len(train_loader), loss.item()))
+ if args.dry_run:
+ break
+
+
+def test(model, device, test_loader, criterion):
+ model.eval()
+ test_loss = 0
+ correct = 0
+ with torch.no_grad():
+ for data, target in test_loader:
+ data, target = data.to(device), target.to(device).float()
+ output = model(data)
+ test_loss += criterion(output, torch.unsqueeze(target, 1)).item() # sum up batch loss
+ pred = torch.round(output) # get the index of the max log-probability
+ correct += pred.eq(target.view_as(pred)).sum().item()
+
+ test_loss /= len(test_loader.dataset)
+
+ print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
+ test_loss, correct, len(test_loader.dataset),
+ 100. * correct / len(test_loader.dataset)))
+ return 100. * correct / len(test_loader.dataset)
+
+
+def main():
+ # Training settings
+ parser = argparse.ArgumentParser(description='PyTorch Ship Detection')
+ parser.add_argument('--batch-size', type=int, default=64, metavar='N',
+ help='input batch size for training (default: 64)')
+ parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
+ help='input batch size for testing (default: 1000)')
+ parser.add_argument('--epochs', type=int, default=14, metavar='N',
+ help='number of epochs to train (default: 14)')
+ parser.add_argument('--lr', type=float, default=0.1, metavar='LR',
+ help='learning rate (default: 0.1)')
+ parser.add_argument('--no-cuda', action='store_true', default=False,
+ help='disables CUDA training')
+ parser.add_argument('--dry-run', action='store_true', default=False,
+ help='quickly check a single pass')
+ parser.add_argument('--seed', type=int, default=1, metavar='S',
+ help='random seed (default: 1)')
+ parser.add_argument('--log-interval', type=int, default=10, metavar='N',
+ help='how many batches to wait before logging training status')
+ parser.add_argument('--save-model', action='store_true', default=False,
+ help='For Saving the current Model')
+ args = parser.parse_args()
+ torch.manual_seed(args.seed)
+ use_cuda = not args.no_cuda and torch.cuda.is_available()
+ device = torch.device("cuda" if use_cuda else "cpu")
+ train_kwargs = {'batch_size': args.batch_size}
+ val_kwargs = {'batch_size': args.test_batch_size}
+ if use_cuda:
+ cuda_kwargs = {'num_workers': 1,
+ 'pin_memory': True,
+ 'shuffle': True}
+ train_kwargs.update(cuda_kwargs)
+ val_kwargs.update(cuda_kwargs)
+
+ # Create transform
+ transform = transforms.Compose([
+ transforms.ToTensor(),
+ # This normalization is used on the test server
+ transforms.Normalize([0.2404, 0.2967, 0.3563], [0.0547, 0.0527, 0.0477])
+ ])
+
+ # Create train and test set
+ path_to_dataset = "dataset/" #os.path.dirname(__file__)
+ train_set = Boats(root_dir=f'{path_to_dataset}/train', transform=transform,
+ gt_json_path=f'{path_to_dataset}/boat_mnist_labels_trainval.json')
+ val_set = Boats(root_dir=f'{path_to_dataset}/val', transform=transform,
+ gt_json_path=f'{path_to_dataset}/boat_mnist_labels_trainval.json')
+
+ # Create data loaders
+ train_loader = torch.utils.data.DataLoader(train_set, **train_kwargs)
+ test_loader = torch.utils.data.DataLoader(val_set, **val_kwargs)
+
+ # Create network, optimizer and loss
+ model = Net().to(device)
+ optimizer = optim.SGD(model.parameters(), lr=args.lr)
+ criterion = nn.MSELoss()
+
+ # Train and validate
+ best_acc = 0
+ best_model_wts = copy.deepcopy(model.state_dict())
+ for epoch in range(1, args.epochs + 1):
+ train(args, model, device, train_loader, optimizer, criterion, epoch)
+ acc = test(model, device, test_loader, criterion)
+ if acc > best_acc:
+ best_acc = acc
+ best_model_wts = copy.deepcopy(model.state_dict())
+
+ # Load best model weights
+ model.load_state_dict(best_model_wts)
+ print(f"Best accuracy (val): {best_acc}")
+
+ if args.save_model:
+ torch.save(model.state_dict(), "model.pth")
+
+ # --- Do not touch -----
+ # Save model as onnx file
+ dummy_input = torch.randn(1, 3, 108, 192, device=device)
+ input_names = ["img_1"]
+ output_names = ["output1"]
+ torch.onnx.export(model, dummy_input, "ship_example.onnx", input_names=input_names, output_names=output_names)
+ # ----------------------
+
+
+if __name__ == '__main__':
+ main()
\ No newline at end of file
diff --git a/Boat_MNIST/123/documentation.pdf b/Boat_MNIST/123/documentation.pdf
new file mode 100644
index 0000000..d103892
Binary files /dev/null and b/Boat_MNIST/123/documentation.pdf differ
diff --git a/Boat_MNIST/14/challenge_nn.py b/Boat_MNIST/14/challenge_nn.py
new file mode 100644
index 0000000..ee499cf
--- /dev/null
+++ b/Boat_MNIST/14/challenge_nn.py
@@ -0,0 +1,228 @@
+"""
+TODO
+- team member 1: Aslihan Özkan
+- team member 2: Kevin Weiss
+
+tasks:
+ - add your team members' names at the top of the file
+ - Take part in the challenge :)
+"""
+
+import argparse
+import copy
+import json
+import os
+from matplotlib.image import imread
+#from OD.od import ROOT_DIR
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+from torchvision import transforms
+from torch.utils.data import Dataset
+import torchvision.transforms as transforms
+from torch.optim import lr_scheduler
+from torch.backends import cudnn
+from torchvision.datasets import ImageFolder
+from torchvision import datasets
+
+
+class Boats(Dataset):
+
+ def __init__(self, root_dir, transform=None, gt_json_path=''):
+ self.root_dir = root_dir
+ self.transform = transform
+ self.gt_json_path = gt_json_path
+ self.labels = json.load(open(gt_json_path, 'r'))
+ self.image_list = sorted(os.listdir(root_dir))
+ self.image_ids = dict(enumerate(self.image_list, start=0))
+
+ def __len__(self):
+ return len(self.image_ids)
+
+ def __getitem__(self, idx):
+ img = self.load_image(idx)
+ img_name = self.image_ids[idx]
+ label = self.labels[img_name]
+ if self.transform:
+ img = self.transform(img)
+ sample = (img, label)
+ return sample
+
+ def load_image(self, image_index):
+ image_name = self.image_ids[image_index]
+ path = os.path.join(self.root_dir, image_name)
+ img = imread(path)
+ return img
+
+
+
+class Net(nn.Module):
+ def __init__(self):
+ super(Net, self).__init__()
+ self.fc1 = nn.Linear(3 * 192 * 108, 100)
+ self.bn1 = nn.BatchNorm1d(100)
+ self.fc2 = nn.Linear(100, 50)
+ self.bn2 = nn.BatchNorm1d(50)
+ self.fc3 = nn.Linear(50, 20)
+ self.bn3 = nn.BatchNorm1d(20)
+ self.fc4 = nn.Linear(20, 1)
+ #self.bn4 = nn.BatchNorm1d(1)
+ self.dropout = nn.Dropout()
+
+ def forward(self, x):
+ x = torch.flatten(x, start_dim=1)
+ #x = self.dropout(x)
+ x = torch.relu(self.bn1(self.fc1(x)))
+ x = self.dropout(x)
+ x = torch.relu(self.bn2(self.fc2(x)))
+ x = self.dropout(x)
+ x = torch.tanh(self.bn3(self.fc3(x)))
+ x = self.dropout(x)
+ output = torch.sigmoid(self.fc4(x))
+ return output
+
+
+def train(args, model, device ,train_loader, optimizer, criterion, epoch):
+ """
+ Train a network
+ You can find example code here: https://pytorch.org/tutorials/beginner/basics/quickstart_tutorial.html
+ """
+
+ model.train()
+ for batch_idx, (data, target) in enumerate(train_loader):
+ data, target = data.to(device), target.to(device).float()
+ optimizer.zero_grad()
+ output = model(data)
+ loss = criterion(output, torch.unsqueeze(target, 1))
+ l2_lambda = 0.001
+ l2_norm = sum(p.pow(2.0).sum()
+ for p in model.parameters())
+ loss = loss + l2_lambda * l2_norm
+ loss.backward()
+ optimizer.step()
+ if batch_idx % args.log_interval == 0:
+ print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
+ epoch, batch_idx * len(data), len(train_loader.dataset),
+ 100. * batch_idx / len(train_loader), loss.item()))
+ if args.dry_run:
+ break
+
+
+def test(model, device, test_loader, criterion):
+ model.eval()
+ test_loss = 0
+ correct = 0
+ with torch.no_grad():
+ for data, target in test_loader:
+ data, target = data.to(device), target.to(device).float()
+ output = model(data)
+ test_loss += criterion(output, torch.unsqueeze(target, 1)).item() # sum up batch loss
+ pred = torch.round(output) # get the index of the max log-probability
+ correct += pred.eq(target.view_as(pred)).sum().item()
+
+ test_loss /= len(test_loader.dataset)
+
+ print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
+ test_loss, correct, len(test_loader.dataset),
+ 100. * correct / len(test_loader.dataset)))
+ return 100. * correct / len(test_loader.dataset)
+
+
+def main():
+ # Training settings
+ parser = argparse.ArgumentParser(description='PyTorch Ship Detection')
+ parser.add_argument('--batch-size', type=int, default=32, metavar='N',
+ help='input batch size for training (default: 64)')
+ parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
+ help='input batch size for testing (default: 1000)')
+ parser.add_argument('--epochs', type=int, default=14, metavar='N',
+ help='number of epochs to train (default: 14)')
+ parser.add_argument('--lr', type=float, default=0.201, metavar='LR',
+ help='learning rate (default: 0.5)')
+ parser.add_argument('--no-cuda', action='store_true', default=False,
+ help='disables CUDA training')
+ parser.add_argument('--dry-run', action='store_true', default=False,
+ help='quickly check a single pass')
+ parser.add_argument('--seed', type=int, default=1, metavar='S',
+ help='random seed (default: 1)')
+ parser.add_argument('--log-interval', type=int, default=10, metavar='N',
+ help='how many batches to wait before logging training status')
+ parser.add_argument('--save-model', action='store_true', default=False,
+ help='For Saving the current Model')
+ parser.add_argument('--step_size', default=30, type=int,
+ help='learning rate decay interval')
+ parser.add_argument('--gamma', default=0.1, type=float,
+ help='learning rate decay scope')
+
+ args = parser.parse_args()
+ torch.manual_seed(args.seed)
+ use_cuda = not args.no_cuda and torch.cuda.is_available()
+ device = torch.device("cuda" if use_cuda else "cpu")
+ train_kwargs = {'batch_size': args.batch_size}
+ val_kwargs = {'batch_size': args.test_batch_size}
+ if use_cuda:
+ cuda_kwargs = {'num_workers': 3,
+ 'pin_memory': True,
+ 'shuffle': True}
+ train_kwargs.update(cuda_kwargs)
+ val_kwargs.update(cuda_kwargs)
+
+ # Create transform
+ transform = transforms.Compose([
+ transforms.ToTensor(),
+ transforms.RandomHorizontalFlip(),
+ transforms.RandomVerticalFlip(),
+ transforms.RandomRotation(15),
+ # This normalization is used on the test server
+ transforms.Normalize([0.2404, 0.2967, 0.3563], [0.0547, 0.0527, 0.0477])
+ ])
+
+ # Create train and test set
+ path_to_dataset = "/Users/asliozkan/Downloads/Boat-MNIST" # TODO Set correct path
+ train_set = Boats(root_dir=f'{path_to_dataset}/train', transform=transform,
+ gt_json_path=f'{path_to_dataset}/boat_mnist_labels_trainval.json')
+ val_set = Boats(root_dir=f'{path_to_dataset}/val', transform=transform,
+ gt_json_path=f'{path_to_dataset}/boat_mnist_labels_trainval.json')
+
+ # Create data loaders
+ train_loader = torch.utils.data.DataLoader(train_set ,shuffle=True, **train_kwargs) #shuffle=True
+ test_loader = torch.utils.data.DataLoader(val_set,shuffle=True, **val_kwargs) #shuffle=False,
+
+ # Create network, optimizer and loss
+ model = Net().to(device)
+ optimizer = optim.SGD(model.parameters(), lr=args.lr )
+ cudnn.benchmark = True
+ criterion = nn.SmoothL1Loss().to(device)
+
+ # Train and validate
+ exp_lr_scheduler = lr_scheduler.StepLR(optimizer, step_size=args.step_size, gamma=args.gamma)
+ best_acc = 0
+ best_model_wts = copy.deepcopy(model.state_dict())
+ for epoch in range(1, args.epochs + 1):
+ train(args, model, device, train_loader, optimizer, criterion, epoch)
+ exp_lr_scheduler.step()
+ acc = test(model, device, test_loader, criterion)
+ if acc > best_acc:
+ best_acc = acc
+ best_model_wts = copy.deepcopy(model.state_dict())
+
+ # Load best model weights
+ model.load_state_dict(best_model_wts)
+ print(f"Best accuracy (val): {best_acc}")
+
+ if args.save_model:
+ torch.save(model.state_dict(), "model.pth")
+
+ # --- Do not touch -----
+ # Save model as onnx file
+ dummy_input = torch.randn(1, 3, 108, 192, device=device)
+ input_names = ["img_1"]
+ output_names = ["output1"]
+ torch.onnx.export(model, dummy_input, "ship_example.onnx", input_names=input_names, output_names=output_names)
+ # ----------------------
+
+
+if __name__ == '__main__':
+ main()
diff --git "a/Boat_MNIST/2/Assignment_5_Abaza_R\303\244ther.pdf" "b/Boat_MNIST/2/Assignment_5_Abaza_R\303\244ther.pdf"
new file mode 100644
index 0000000..4fdcc87
Binary files /dev/null and "b/Boat_MNIST/2/Assignment_5_Abaza_R\303\244ther.pdf" differ
diff --git a/Boat_MNIST/2/challenge_nn.py b/Boat_MNIST/2/challenge_nn.py
new file mode 100644
index 0000000..081f25c
--- /dev/null
+++ b/Boat_MNIST/2/challenge_nn.py
@@ -0,0 +1,201 @@
+"""
+TODO
+- team member 1: Ibrahim Abaza
+- team member 2: Marius Räther
+tasks:
+ - add your team members' names at the top of the file
+ - Take part in the challenge :)
+"""
+
+import argparse
+import copy
+import json
+import os
+from matplotlib.image import imread
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+from torchvision import transforms
+from torch.utils.data import Dataset
+
+
+class Boats(Dataset):
+
+ def __init__(self, root_dir, transform=None, gt_json_path=''):
+ self.root_dir = root_dir
+ self.transform = transform
+ self.gt_json_path = gt_json_path
+ self.labels = json.load(open(gt_json_path, 'r'))
+ self.image_list = sorted(os.listdir(root_dir))
+ self.image_ids = dict(enumerate(self.image_list, start=0))
+
+ def __len__(self):
+ return len(self.image_ids)
+
+ def __getitem__(self, idx):
+ img = self.load_image(idx)
+ img_name = self.image_ids[idx]
+ label = self.labels[img_name]
+ if self.transform:
+ img = self.transform(img)
+ sample = (img, label)
+ return sample
+
+ def load_image(self, image_index):
+ image_name = self.image_ids[image_index]
+ path = os.path.join(self.root_dir, image_name)
+ img = imread(path)
+ return img
+
+
+class Net(nn.Module):
+ def __init__(self):
+ super(Net, self).__init__()
+ self.input_size = 3 * 192 * 108
+ self.relu_stack = nn.Sequential(
+ nn.Linear(self.input_size, 64),
+ nn.ReLU(),
+ nn.Linear(64, 64),
+ nn.Tanh(),
+ nn.Linear(64, 64),
+ nn.ReLU(),
+ nn.Linear(64,64),
+ nn.Tanh(),
+ nn.Linear(64,1)
+ )
+
+ def forward(self, x):
+ x = torch.flatten(x, start_dim=1)
+ x = self.relu_stack(x)
+ output = torch.sigmoid(x)
+ return output
+
+
+def train(args, model, device, train_loader, optimizer, criterion, epoch):
+ """
+ Train a network
+ You can find example code here: https://pytorch.org/tutorials/beginner/basics/quickstart_tutorial.html
+ """
+ model.train()
+ for batch_idx, (data, target) in enumerate(train_loader):
+ data, target = data.to(device), target.to(device).float()
+ optimizer.zero_grad()
+ output = model(data)
+ loss = criterion(output, torch.unsqueeze(target, 1))
+ loss.backward()
+ optimizer.step()
+ if batch_idx % args.log_interval == 0:
+ print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
+ epoch, batch_idx * len(data), len(train_loader.dataset),
+ 100. * batch_idx / len(train_loader), loss.item()))
+ if args.dry_run:
+ break
+
+
+def test(model, device, test_loader, criterion):
+ model.eval()
+ test_loss = 0
+ correct = 0
+ with torch.no_grad():
+ for data, target in test_loader:
+ data, target = data.to(device), target.to(device).float()
+ output = model(data)
+ test_loss += criterion(output, torch.unsqueeze(target, 1)).item() # sum up batch loss
+ pred = torch.round(output) # get the index of the max log-probability
+ correct += pred.eq(target.view_as(pred)).sum().item()
+
+ test_loss /= len(test_loader.dataset)
+
+ print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
+ test_loss, correct, len(test_loader.dataset),
+ 100. * correct / len(test_loader.dataset)))
+ return 100. * correct / len(test_loader.dataset)
+
+
+def main():
+ # Training settings
+ parser = argparse.ArgumentParser(description='PyTorch Ship Detection')
+ parser.add_argument('--batch-size', type=int, default=64, metavar='N',
+ help='input batch size for training (default: 64)')
+ parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
+ help='input batch size for testing (default: 1000)')
+ parser.add_argument('--epochs', type=int, default=200, metavar='N',
+ help='number of epochs to train (default: 14)')
+ parser.add_argument('--lr', type=float, default=0.05, metavar='LR',
+ help='learning rate (default: 0.1)')
+ parser.add_argument('--no-cuda', action='store_true', default=False,
+ help='disables CUDA training')
+ parser.add_argument('--dry-run', action='store_true', default=False,
+ help='quickly check a single pass')
+ parser.add_argument('--seed', type=int, default=1, metavar='S',
+ help='random seed (default: 1)')
+ parser.add_argument('--log-interval', type=int, default=10, metavar='N',
+ help='how many batches to wait before logging training status')
+ parser.add_argument('--save-model', action='store_true', default=False,
+ help='For Saving the current Model')
+ args = parser.parse_args()
+ torch.manual_seed(args.seed)
+ use_cuda = not args.no_cuda and torch.cuda.is_available()
+ device = torch.device("cuda" if use_cuda else "cpu")
+ train_kwargs = {'batch_size': args.batch_size}
+ val_kwargs = {'batch_size': args.test_batch_size}
+ if use_cuda:
+ cuda_kwargs = {'num_workers': 1,
+ 'pin_memory': True,
+ 'shuffle': True}
+ train_kwargs.update(cuda_kwargs)
+ val_kwargs.update(cuda_kwargs)
+
+ # Create transform
+ transform = transforms.Compose([
+ transforms.ToTensor(),
+ # This normalization is used on the test server
+ transforms.Normalize([0.2404, 0.2967, 0.3563], [0.0547, 0.0527, 0.0477])
+ ])
+
+ # Create train and test set
+ path_to_dataset = os.path.join("dataset","Boat-MNIST")
+ train_set = Boats(root_dir=f'{path_to_dataset}/train', transform=transform,
+ gt_json_path=f'{path_to_dataset}/boat_mnist_labels_trainval.json')
+ val_set = Boats(root_dir=f'{path_to_dataset}/val', transform=transform,
+ gt_json_path=f'{path_to_dataset}/boat_mnist_labels_trainval.json')
+
+ # Create data loaders
+ train_loader = torch.utils.data.DataLoader(train_set, **train_kwargs)
+ test_loader = torch.utils.data.DataLoader(val_set, **val_kwargs)
+
+ # Create network, optimizer and loss
+ model = Net().to(device)
+ optimizer = optim.SGD(model.parameters(), lr=args.lr)
+ criterion = nn.MSELoss()
+
+ # Train and validate
+ best_acc = 0
+ best_model_wts = copy.deepcopy(model.state_dict())
+ for epoch in range(1, args.epochs + 1):
+ train(args, model, device, train_loader, optimizer, criterion, epoch)
+ acc = test(model, device, test_loader, criterion)
+ if acc > best_acc:
+ best_acc = acc
+ best_model_wts = copy.deepcopy(model.state_dict())
+
+ # Load best model weights
+ model.load_state_dict(best_model_wts)
+ print(f"Best accuracy (val): {best_acc}")
+
+ if args.save_model:
+ torch.save(model.state_dict(), "model.pth")
+
+ # --- Do not touch -----
+ # Save model as onnx file
+ dummy_input = torch.randn(1, 3, 108, 192, device=device)
+ input_names = ["img_1"]
+ output_names = ["output1"]
+ torch.onnx.export(model, dummy_input, "ship_example.onnx", input_names=input_names, output_names=output_names)
+ # ----------------------
+
+
+if __name__ == '__main__':
+ main()
\ No newline at end of file
diff --git a/Boat_MNIST/2/dataset_augment.py b/Boat_MNIST/2/dataset_augment.py
new file mode 100644
index 0000000..912d3a4
--- /dev/null
+++ b/Boat_MNIST/2/dataset_augment.py
@@ -0,0 +1,33 @@
+from PIL import Image
+import os
+import json
+path = os.path.join("dataset","Boat-MNIST",)
+imagedir_path = os.path.join(path,"train")
+json_path = os.path.join(path,"boat_mnist_labels_trainval.json")
+img_list = os.listdir(imagedir_path)
+
+labels_dict = {}
+with open(json_path) as file:
+ labels_dict = json.load(file)
+
+for img in img_list:
+
+ label = labels_dict[img]
+ img_name = img.split('.')[0]
+ vertical_path = os.path.join(imagedir_path,img_name+"_vertical.png")
+ horizontal_path = os.path.join(imagedir_path,img_name+"_horizontal.png")
+ labels_dict[vertical_path] = label
+ labels_dict[horizontal_path] = label
+ original_img = Image.open(os.path.join(imagedir_path,img))
+ vertical_img = original_img.transpose(method=Image.FLIP_TOP_BOTTOM)
+ vertical_img.save(vertical_path)
+ horizontal_img = original_img.transpose(method=Image.FLIP_LEFT_RIGHT)
+ horizontal_img.save(horizontal_path)
+ original_img.close()
+ vertical_img.close()
+ horizontal_img.close()
+
+with open(os.path.join(path,"boat_mnist_labels_trainval_augmented.json"), "w") as outfile:
+ json.dump(labels_dict, outfile)
+
+#vertical_img = original_img.transpose(method=Image.FLIP_TOP_BOTTOM)
\ No newline at end of file
diff --git a/Boat_MNIST/50/AR_CH_Boat.py b/Boat_MNIST/50/AR_CH_Boat.py
new file mode 100644
index 0000000..d0e7759
--- /dev/null
+++ b/Boat_MNIST/50/AR_CH_Boat.py
@@ -0,0 +1,498 @@
+"""
+
+- team member 1: Aaron Riedling
+- team member 2: Christoph Hoffmann
+- Additional Learning Tutorial on Pytorch: "The Morpheus Tutorials" on Youtube
+
+tasks:
+ - add your team members' names at the top of the file
+ - Take part in the challenge :)
+"""
+
+import argparse
+import copy
+import json
+import os
+import random
+from matplotlib.image import imread
+import shutil
+from PIL import Image
+import json
+#from tqdm import tqdm
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+from torchvision import transforms
+from torch.utils.data import Dataset
+
+# ========================================= classes ======================================================
+class Boats(Dataset):
+
+ def __init__(self, root_dir, transform=None, gt_json_path=''):
+ self.root_dir = root_dir
+ self.transform = transform
+ self.gt_json_path = gt_json_path
+ self.labels = json.load(open(gt_json_path, 'r'))
+ self.image_list = sorted(os.listdir(root_dir))
+ self.image_ids = dict(enumerate(self.image_list, start=0))
+
+ def __len__(self):
+ return len(self.image_ids)
+
+ def __getitem__(self, idx):
+ img = self.load_image(idx)
+ img_name = self.image_ids[idx]
+ label = self.labels[img_name]
+ if self.transform:
+ img = self.transform(img)
+ sample = (img, label)
+ return sample
+
+ def load_image(self, image_index):
+ image_name = self.image_ids[image_index]
+ path = os.path.join(self.root_dir, image_name)
+ img = imread(path)
+ return img
+
+
+class Net(nn.Module):
+ def __init__(self, dropout):
+ super(Net, self).__init__()
+ self.conv1a = nn.Conv2d(3, 12, kernel_size=5) # 3 input images: RGB
+ self.conv2a = nn.Conv2d(12, 24, kernel_size=5) # 2 conv layers for feature extraction
+ self.conv_DOa = nn.Dropout2d(dropout)
+ self.conv1b = nn.Conv2d(3, 12, kernel_size=7) # 3 input images: RGB
+ self.conv2b = nn.Conv2d(12, 24, kernel_size=7) # 2 conv layers for feature extraction
+ self.conv_DOb = nn.Dropout2d(dropout)
+ self.fc1 = nn.Linear(24*24*45 + 24*14*28, 512)
+ self.fc2 = nn.Linear(512, 128)
+ #self.fc3 = nn.Linear(256, 128)
+ self.fc4 = nn.Linear(128, 1)
+
+ def forward(self, x):
+ # a-arm of CNN --> smaller features
+ xa = F.max_pool2d(self.conv1a(x),2)
+ xa = F.leaky_relu(xa,0.05)
+ xa = self.conv2a(xa)
+ xa = self.conv_DOa(xa)
+ xa = F.max_pool2d(xa , 2)
+ xa = F.leaky_relu(xa , 0.05) # x.size -> BS*24*24*45 (BS: BatchSize)
+ #print(xa.size())
+ #exit()
+ xa = xa.view(-1,24*24*45) # changes data format to 1 neuron per subimage
+
+ # b-arm of CNN --> larger features
+ xb = F.max_pool2d(self.conv1b(x) , 3)
+ xb = F.leaky_relu(xb,0.05)
+ xb = self.conv2b(xb)
+ xb = self.conv_DOb(xb)
+ xb = F.max_pool2d(xb , 2)
+ xb = F.leaky_relu(xb , 0.05) # x.size -> BS*24*10*19 (BS: BatchSize)
+ #print(xb.size())
+ #exit()
+ xb = xb.view(-1,24*14*28) # changes data format to 1 neuron per subimage
+
+ # combine
+ x = torch.cat((xa, xb),1)
+ x = F.leaky_relu(self.fc1(x), 0.05)
+ x = F.leaky_relu(self.fc2(x), 0.05)
+ #x = F.leaky_relu(self.fc3(x), 0.05)
+ output = torch.sigmoid(self.fc4(x))
+ return output
+
+# =============================================== file methods ===========================================
+
+def make_splitlist(input_path, kfolds):
+ temp = os.listdir(input_path+"original_split/train")
+ files_in_directory = ["train/" + file for file in temp]
+ temp = os.listdir(input_path + "original_split/val")
+ files_in_directory = files_in_directory + ["val/" + file for file in temp]
+
+ # shuffle order of files since it might be ordered
+ # random.shuffle(files_in_directory) shuffle activated in train loader
+
+ file_split_dict= {}
+
+ for file in files_in_directory:
+ if file.endswith(".png"):
+ file_split_dict[file] = random.randrange(0, kfolds , 1)
+
+ return file_split_dict
+
+
+def remove_split_files(path, extension):
+ files_in_directory = os.listdir(path)
+ filtered_files = [file for file in files_in_directory if file.endswith(extension)]
+ for file in filtered_files:
+ path_to_file = os.path.join(path, file)
+ os.remove(path_to_file)
+
+
+def split_files(path, split_list, k):
+ """
+ does a train/test split at the location
+ creates two folders: val and train
+ """
+
+ train_path = path + "train/"
+ val_path = path + "val/"
+ label_path = path + "label/"
+
+ print("clearing out old splits")
+ if os.path.exists(train_path):
+ remove_split_files(train_path, ".png")
+ os.rmdir(train_path)
+
+ if os.path.exists(val_path):
+ remove_split_files(val_path, ".png")
+ os.rmdir(val_path)
+
+ if os.path.exists(label_path):
+ remove_split_files(label_path, ".json")
+ os.rmdir(label_path)
+
+ print("creating new split")
+ os.makedirs(train_path)
+ os.makedirs(val_path)
+ os.makedirs(label_path)
+
+ for key in split_list.keys():
+ if split_list[key] == k:
+ shutil.copy(path +"original_split/"+ key, path + "val/" + key.split("/")[-1])
+ else:
+ shutil.copy(path +"original_split/"+ key, path + "train/" + key.split("/")[-1])
+
+ shutil.copy(path+'original_split/boat_mnist_labels_trainval.json', label_path + 'boat_mnist_labels_trainval.json')
+
+def inflate_train_data(train_path, label_file):
+ '''
+ Inflates train data by different transformations, might be worth testing multiple sets
+ Currently Mirroring should be safe
+ '''
+ files_in_folder = os.listdir(train_path)
+
+ # handle JSON
+ label_list = {}
+ with open(label_file) as fp:
+ label_list = json.load(fp)
+
+ # iterate over train images
+ print("Creating inflated dataset...")
+ count = 0
+ for file in files_in_folder:
+ count +=1
+ if count % 500 == 0:
+ print("Image " + str(count) + "...")
+
+ Yval = label_list[file]
+ orig_image = Image.open(train_path+file)
+
+ # make and save changed items
+ #hf_image = transforms.RandomHorizontalFlip(1.0)(orig_image)
+ #hf_image.save(train_path + "HF_" + file)
+ #label_list["HF_" + file] = Yval
+
+ #fl_image = transforms.RandomHorizontalFlip(1.0)(orig_image)
+ #fl_image = transforms.RandomVerticalFlip(1.0)(fl_image)
+ #fl_image.save(train_path + "FL_" + file)
+ #label_list["FL_" + file] = Yval
+
+ gs_image = transforms.Grayscale(3)(orig_image) # three channels are needed to match the input dimensions
+ gs_image.save(train_path + "GS_" + file)
+ label_list["GS_" + file] = Yval
+
+ #eq_image = transforms.RandomEqualize(1.0)(orig_image)
+ #eq_image.save(train_path + "EQ_" + file)
+ #label_list["EQ_" + file] = Yval
+
+ #rp_image = transforms.RandomPerspective(distortion_scale=0.8, p=1.0, fill=(0,) * len(orig_image.getbands()))(orig_image)
+ #rp_image.save(train_path+"RP_" + file)
+ #T.functional.rotate(img=rgb, angle=degree_to_rotate, fill=(0,) * len(rgb.getbands()))
+ #label_list["RP_" + file] = Yval
+
+ #rr_image = transforms.RandomRotation(degrees=(0, 180))(orig_image)
+ #rr_image.save(train_path+"RR_" + file)
+ #label_list["RR_" + file] = Yval
+
+ #ri_image = transforms.RandomInvert(1)(orig_image)
+ #ri_image.save(train_path+"RI_" + file)
+ #label_list["RI_" + file] = Yval
+
+
+ # save modified JSON
+ with open(label_file, 'w') as json_file:
+ json.dump(label_list, json_file,
+ indent=None,
+ separators=(',', ': '))
+
+def setup_standard_split(path):
+ train_path = path + "train/"
+ val_path = path + "val/"
+ label_path = path + "label/"
+
+ print("clearing out old splits")
+ if os.path.exists(train_path):
+ remove_split_files(train_path, ".png")
+ os.rmdir(train_path)
+
+ if os.path.exists(val_path):
+ remove_split_files(val_path, ".png")
+ os.rmdir(val_path)
+
+ if os.path.exists(label_path):
+ remove_split_files(label_path, ".json")
+ os.rmdir(label_path)
+
+ print("importing standard split")
+ os.makedirs(label_path)
+ shutil.copy(path + 'original_split/boat_mnist_labels_trainval.json', label_path + 'boat_mnist_labels_trainval.json')
+ shutil.copytree(path + "original_split/train" , train_path)
+ shutil.copytree(path + "original_split/val" , val_path)
+
+# =============================================== training methods ===========================================
+
+def train(args, model, device, train_loader, optimizer, criterion, epoch):
+ """
+ Train a network
+ You can find example code here: https://pytorch.org/tutorials/beginner/basics/quickstart_tutorial.html
+ """
+ train_loss = []
+ model.train() # model is the Net loaded on the device, now set to train
+ for batch_idx, (data, target) in enumerate(train_loader):
+ data, target = data.to(device), target.to(device).float()
+ optimizer.zero_grad() # reset optimizer
+ output = model(data)
+ loss = criterion(output, torch.unsqueeze(target, 1))
+ loss.backward() # does the backward propagation
+ optimizer.step() # this is the learning step. Optimiser has been loaded with the net parameters
+
+ if batch_idx % args.log_interval == 0:
+ train_loss.append(loss.item())
+ print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.7f}'.format(
+ epoch, batch_idx * len(data), len(train_loader.dataset),
+ 100. * batch_idx / len(train_loader), loss.item()))
+ if args.dry_run:
+ break
+
+ return sum(train_loss)/len(train_loss)
+
+def test(model, device, test_loader, criterion):
+ model.eval() # model set to eval, now it does not learn, parameters are frozen
+ test_loss = 0
+ correct = 0
+ with torch.no_grad():
+ for data, target in test_loader:
+ data, target = data.to(device), target.to(device).float()
+ output = model(data)
+ test_loss += criterion(output, torch.unsqueeze(target, 1)).item() # sum up batch loss
+ pred = torch.round(output) # get the index of the max log-probability
+ correct += pred.eq(target.view_as(pred)).sum().item()
+
+
+ test_loss /= len(test_loader.dataset)
+
+ print('\nTest set: Average loss: {:.7f}, Accuracy: {}/{} ({:.2f}%)\n'.format(
+ test_loss, correct, len(test_loader.dataset),
+ 100. * correct / len(test_loader.dataset)))
+ return 100. * correct / len(test_loader.dataset)
+
+# =============================================== main function ===========================================
+
+def main():
+ kfolds = 4
+ input_path = "/home/christoph/Documents/Semester_4/Intro_to_ANN/A05/Code_Boat_MNIST/input/"
+ run_splits = kfolds
+
+ # Training settings
+ parser = argparse.ArgumentParser(description='PyTorch Ship Detection')
+
+ parser.add_argument('-i', type=str, metavar='i',
+ help='input path to all files')
+ parser.add_argument('-k', type=int, default=4, metavar='k',
+ help='k-fold CV (default:4)')
+ parser.add_argument('--nsplits', type=int, metavar='nsplits',
+ help='run number of splits, (default = k)')
+ parser.add_argument('--reuse-split', action='store_true', default=False,
+ help='reuse the last existing train/test split (!Will double-inflate inflated data!)')
+ parser.add_argument('--standard-split', action='store_true', default=False,
+ help='use the standard train/test split')
+ #parser.add_argument('--final-train', action='store_true', default=False,
+ # help='does the final training run with all data and saves the model')
+ parser.add_argument('--batch-size', type=int, default=64, metavar='N',
+ help='input batch size for training (default: 64)')
+ parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
+ help='input batch size for testing (default: 1000)')
+ parser.add_argument('--epochs', type=int, default=14, metavar='N',
+ help='number of epochs to train (default: 14)')
+ parser.add_argument('--lr', type=float, default=0.1, metavar='LR',
+ help='learning rate (default: 0.1)')
+ parser.add_argument('--momentum', type=float, default=0, metavar='M',
+ help='learning momentum (default: 0)')
+ parser.add_argument('--lr-redux', type=float, default=0, metavar='LRR',
+ help='learning rate reduction per epoch, decrease reduces when getting closer to 0 lr')
+ parser.add_argument('--dropout', type=float, default=0.2, metavar='DO',
+ help='dropout layer (default: 0.2)')
+ parser.add_argument('--full-lr-epochs', type=int, default=0, metavar='EPs',
+ help='epochs at full lr before reduction starts')
+ parser.add_argument('--no-cuda', action='store_true', default=False,
+ help='disables CUDA training')
+ parser.add_argument('--inflate-train', action='store_true', default=False,
+ help='creates more training images by using transforms')
+ parser.add_argument('--dry-run', action='store_true', default=False,
+ help='quickly check a single pass')
+ parser.add_argument('--seed', type=int, default=1, metavar='S',
+ help='random seed (default: 1)')
+ parser.add_argument('--log-interval', type=int, default=10, metavar='N',
+ help='how many batches to wait before logging training status')
+ parser.add_argument('--save-model', action='store_true', default=False,
+ help='For Saving the current Model')
+
+ args = parser.parse_args()
+ torch.manual_seed(args.seed)
+
+ # decide if to use cuda or not, then pick device accordingly
+ use_cuda = not args.no_cuda and torch.cuda.is_available()
+ device = torch.device("cuda" if use_cuda else "cpu")
+
+ # update arguments for training, including batch size and cuda
+ train_kwargs = {'batch_size': args.batch_size}
+ val_kwargs = {'batch_size': args.test_batch_size}
+ if use_cuda:
+ cuda_kwargs = {'num_workers': 1,
+ 'pin_memory': True,
+ 'shuffle': True}
+ train_kwargs.update(cuda_kwargs)
+ val_kwargs.update(cuda_kwargs)
+
+ # Create transform, randomly do some flipping
+ transform = transforms.Compose([
+ transforms.ToTensor(),
+ #transforms.RandomGrayscale(0.5),
+ transforms.RandomHorizontalFlip(0.5),
+ transforms.RandomVerticalFlip(0.5),
+ # This normalization is used on the test server
+ transforms.Normalize([0.2404, 0.2967, 0.3563], [0.0547, 0.0527, 0.0477])
+ ])
+
+ # Create train and test set
+ if not args.i == None:
+ input_path = args.i
+ if not input_path.endswith(os.sep):
+ input_path = input_path + os.sep
+
+ if not args.k == None:
+ kfolds = args.k
+ if not args.nsplits == None:
+ run_splits = args.nsplits
+ if run_splits > kfolds:
+ run_splits = kfolds
+ print("Maximal run splits is the number of folds")
+
+ if args.standard_split:
+ run_splits = 1
+
+ print("\n======================================== new training run ========================================\nArgs for this run:")
+ print(args)
+
+ # create a train-test split
+ split_list = make_splitlist(input_path, kfolds)
+
+
+ for current_k in range(run_splits):
+ print("\n======================================= running split " + str(current_k+1) + " =======================================\n")
+ train_path = input_path + "train/"
+ val_path = input_path + "val/"
+ label_path = input_path + "label/"
+
+
+ if not args.standard_split:
+ if not args.reuse_split:
+ split_files(input_path,split_list,current_k)
+ else:
+ setup_standard_split(input_path)
+
+ # create additional training data
+ if args.inflate_train:
+ inflate_train_data(train_path,label_path + 'boat_mnist_labels_trainval.json')
+
+ # create sets of images and Y variables
+ train_set = Boats(root_dir=train_path, transform=transform,
+ gt_json_path= label_path + 'boat_mnist_labels_trainval.json')
+ val_set = Boats(root_dir=val_path, transform=transform,
+ gt_json_path= label_path + 'boat_mnist_labels_trainval.json')
+
+ # Create data loaders, contain both images and Y variables
+ if use_cuda:
+ print("training with cuda")
+ train_loader = torch.utils.data.DataLoader(train_set, **train_kwargs)
+ else:# ensure data shuffling
+ print("training with CPU")
+ train_loader = torch.utils.data.DataLoader(train_set, **train_kwargs, shuffle=True)
+
+ test_loader = torch.utils.data.DataLoader(val_set, **val_kwargs)
+
+ # Create network, optimizer and loss
+ model = Net(args.dropout).to(device)
+
+ # train with lr = learning rate
+ #optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum) # stochastic gradient descent
+ #criterion = nn.MSELoss() # maybe a class-balanced loss function is better?
+ criterion = F.binary_cross_entropy # write our own balanced loss function?
+
+ prev_lr = args.lr
+ # Train and validate
+ best_acc = 0
+ best_trainloss = 10
+ best_model_wts = copy.deepcopy(model.state_dict())
+ for epoch in range(1, args.epochs + 1):
+ # lr reduction
+ new_lr = prev_lr
+
+ if epoch > args.full_lr_epochs+1:
+ if new_lr <= args.lr / 250 and args.lr_redux > 0:
+ print("LR at final floor, ending decrease")
+ elif new_lr <= args.lr / 50 and args.lr_redux > 0:
+ print("LR at final floor, decreasing further")
+ new_lr = prev_lr-args.lr_redux/25
+ elif new_lr <= args.lr / 10 and args.lr_redux > 0:
+ print("LR at first floor, slowing decrease")
+ new_lr = prev_lr-args.lr_redux/5
+ else:
+ new_lr = prev_lr-args.lr_redux
+
+ prev_lr = new_lr
+
+ print("\nStarting new epoch, current LR: " + str(round(new_lr,ndigits=8)))
+
+ #optimizer = optim.SGD(model.parameters(), lr=new_lr, momentum=args.momentum)
+ optimizer = optim.Adam(model.parameters(), lr=new_lr)
+
+ train_loss = train(args, model, device, train_loader, optimizer, criterion, epoch)
+ print("Average train loss: " + str(train_loss))
+ acc = test(model, device, test_loader, criterion)
+ if acc > best_acc:
+ best_acc = acc
+ best_model_wts = copy.deepcopy(model.state_dict())
+ elif acc == best_acc and train_loss < best_trainloss:
+ best_trainloss = train_loss
+ best_model_wts = copy.deepcopy(model.state_dict())
+
+ # Load best model weights
+ model.load_state_dict(best_model_wts)
+ print(f"Best accuracy (val): {best_acc}")
+
+ if args.save_model:
+ torch.save(model.state_dict(), "model.pth")
+
+ # --- Do not touch -----
+ # Save model as onnx file
+ dummy_input = torch.randn(1, 3, 108, 192, device=device)
+ input_names = ["img_1"]
+ output_names = ["output1"]
+ torch.onnx.export(model, dummy_input, "ship_example.onnx", input_names=input_names, output_names=output_names)
+ # ----------------------
+
+if __name__ == '__main__':
+ main()
diff --git a/Boat_MNIST/50/ARiedel_CHoffmann_Boats.pdf b/Boat_MNIST/50/ARiedel_CHoffmann_Boats.pdf
new file mode 100644
index 0000000..08e0ec5
Binary files /dev/null and b/Boat_MNIST/50/ARiedel_CHoffmann_Boats.pdf differ
diff --git a/Boat_MNIST/50/REAMDE.MD b/Boat_MNIST/50/REAMDE.MD
new file mode 100644
index 0000000..74269ed
--- /dev/null
+++ b/Boat_MNIST/50/REAMDE.MD
@@ -0,0 +1,16 @@
+Here are the pdf file describing our skript as well as the python file we created:
+
+We submitted 3 Models to the leaderboard. All use an Adam optimiser.
+
+CrystalBall makes use of a split ratio of 6/1 and MSE loss as well as additional grayscale pictures in 30 Epochs of training.
+
+ python3 ./AR_CH_Boat.py -k 6 --nsplits 1 --lr 0.001 --lr-redux 0.00005 --dropout 0.3 --epochs 30 --inflate-train
+
+Tarotkarten uses a 5/1 split, BCE loss and additional grayscale pictures in 30 Epochs of training.
+
+ python3 ./AR_CH_Boat.py -k 5 --nsplits 1 --lr 0.001 --lr-redux 0.00005 --dropout 0.3 --epochs 30 --inflate-train
+
+Wuenschelrute uses a 7/1 split, BCE loss and 50 Epochs and slower lr decrease, but no grayscale images.
+We also reduced the number of parallel ins and outs in the conv layers and inplemented train loss as a tiebreaker.
+
+ python3 ./AR_CH_Boat.py -k 7 --nsplits 1 --lr 0.001 --lr-redux 0.00003 --dropout 0.3 --epochs 50
diff --git a/MOT/visualize_bbox_on_video.py b/MOT/visualize_bbox_on_video.py
new file mode 100644
index 0000000..9ec92b9
--- /dev/null
+++ b/MOT/visualize_bbox_on_video.py
@@ -0,0 +1,78 @@
+import argparse
+import json
+
+import cv2
+import time
+import os
+
+'''
+one color for each category (excluding category nr 7)
+ 1: swimmer
+ 2: floater
+ 3: boat
+ 4: swimmer on boat
+ 5: floater on boat
+ 6: life jacket
+ 7: ignored
+'''
+colors = {1: (0,255,0), 2: (0,0,255), 3: (255,255,51), 4: (0,255,255), 5: (255, 102, 255), 6: (51, 153, 255)}
+
+if __name__ == '__main__':
+ scriptTime = time.time()
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--annotation', type=str, default=None, help='path to the annotations json file')
+ parser.add_argument('--pictures', type=str, default=None, help='path to the pictures')
+ parser.add_argument('--output', type=str, default=None, help='Path to output')
+ options = parser.parse_args()
+ if (options.annotation == None):
+ print("Please enter a valid path to the annotation json file ")
+ exit()
+ if (options.pictures == None):
+ print("Please enter a valid path to folder where the pictures are located")
+ exit()
+ if (options.output == None):
+ print("Please enter a valid path for the output directory, where the pictures will be saved")
+ exit()
+
+ with open(options.annotation) as f:
+ data = json.load(f)
+
+ # needed to show the current progression
+ numberOfImages = len(data['images'])
+ currentImage = 0
+
+ # currently the video has fixed 30 frames per second
+ width = data['images'][0]['width']
+ height = data['images'][0]['height']
+ fourcc = cv2.VideoWriter_fourcc(*'DIVX')
+ fps = 30
+ count = 0
+ videoName = options.output + "\\video_" + str(fps) + "fps_" + str(count) + ".avi"
+ # avoid overriding existing videos
+ # is not really needed because a new output folder will be generated each time the script runs
+ while os.path.isfile(videoName):
+ count += 1
+ videoName = options.output + "\\video_" + str(fps) + "fps_" + str(count) + ".avi"
+ out = cv2.VideoWriter(videoName, fourcc, fps, (width, height))
+ for image in data['images']:
+ currentImage += 1
+ img = cv2.imread(options.pictures + "\\" + image['file_name'])
+ start = time.time()
+ # draw all boundingBoxes, which belong to one image, on it with the corresponding colors of the categories
+ for annotation in data['annotations']:
+ if annotation['image_id'] == image['id']:
+ topLeftX = annotation['bbox'][0]
+ topLeftY = annotation['bbox'][1]
+ width = annotation['bbox'][2]
+ height = annotation['bbox'][3]
+ for category in data['categories']:
+ if category['id'] == annotation['category_id']:
+ label = category['name']
+ cv2.rectangle(img, (topLeftX, topLeftY), (topLeftX + width, topLeftY + height), colors[annotation['category_id']], 3)
+ out.write(img)
+ print('Finished: {0}%'.format(round((currentImage / numberOfImages) * 100, 2)))
+ # save video
+ out.release()
+ scriptRunTime = time.time() - scriptTime
+ print('\nScript run time in seconds: {0}'.format(scriptRunTime))
+ print("mean time per image: {0}".format(scriptRunTime / numberOfImages))
\ No newline at end of file
diff --git a/OD/evaluation of OD V2/required files/instances_val_iscrowd.json b/OD/evaluation of OD V2/required files/instances_val_iscrowd.json
new file mode 100644
index 0000000..41ff2ae
--- /dev/null
+++ b/OD/evaluation of OD V2/required files/instances_val_iscrowd.json
@@ -0,0 +1 @@
+{"info": {"year": "2022", "version": "2", "description": "", "contributor": "", "url": "localhost", "date_created": "2022-08-15T15:17:20.683314"}, "licenses": [{"id": 1, "url": "", "name": "Unknown"}], "categories": [{"supercategory": "ignored", "id": 0, "name": "ignored"}, {"supercategory": "person", "id": 1, "name": "swimmer"}, {"supercategory": "boat", "id": 2, "name": "boat"}, {"supercategory": "boat", "id": 3, "name": "jetski"}, {"supercategory": "object", "id": 4, "name": "life_saving_appliances"}, {"supercategory": "object", "id": 5, "name": "buoy"}], "images": [{"id": 3464, "file_name": "3464.jpg", "height": 2160, "width": 3840, "source": {"drone": "mavic", "folder_name": "DJI_0032", "video": "DJI_0032.MP4", "frame_no": 5415}, "date_time": "2020-08-25T14:19:21.647133", "meta": {"image_name": "DJI_0032_merged.MP4_image5316.png", "datetime(utc)": "2020-08-25 12:19:22", "latitude": 47.67209, "longitude": 9.267889, "height_above_takeoff(meter)": 9.999512343797623, "speed(m/s)": 2.8019248457755923, "xspeed(m/s)": -0.09999731783638802, "yspeed(m/s)": -2.7999248994188646, "zspeed(m/s)": 0.0, "compass_heading(degrees)": 116.5, "gimbal_heading(degrees)": 99.3, "gimbal_pitch(degrees)": 13.7}}, {"id": 3465, "file_name": "3465.jpg", "height": 2160, "width": 3840, "source": {"drone": "mavic", "folder_name": "DJI_0032", "video": "DJI_0032.MP4", "frame_no": 5430}, "date_time": "2020-08-25T14:19:22.147633", "meta": {"image_name": "DJI_0032_merged.MP4_image5316.png", "datetime(utc)": "2020-08-25 12:19:22", "latitude": 47.67209, "longitude": 9.267889, "height_above_takeoff(meter)": 9.999512343797623, "speed(m/s)": 2.8019248457755923, "xspeed(m/s)": -0.09999731783638802, "yspeed(m/s)": -2.7999248994188646, "zspeed(m/s)": 0.0, "compass_heading(degrees)": 116.5, "gimbal_heading(degrees)": 99.3, "gimbal_pitch(degrees)": 13.7}}, {"id": 3466, "file_name": "3466.jpg", "height": 2160, "width": 3840, "source": {"drone": "mavic", "folder_name": "DJI_0032", "video": "DJI_0032.MP4", 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\ No newline at end of file
diff --git a/OD/evaluation of OD V2/required files/od.py b/OD/evaluation of OD V2/required files/od.py
index df1f248..496a78d 100644
--- a/OD/evaluation of OD V2/required files/od.py
+++ b/OD/evaluation of OD V2/required files/od.py
@@ -52,39 +52,20 @@ def get_args():
def v1_od():
# swimmer, person, boat, swimmer on boat, floater on boat, life jacket
- # categories = [1, 2, 3, 4, 5, 6]
- # categories = [1, 2, 3, 6]
- categories = [0]
- #MOT
- # categories = [0]
- # [{"supercategory": "person", "id": 1, "name": "swimmer"},
- # {"supercategory": "person", "id": 2, "name": "swimmer with life jacket"},
- # {"supercategory": "boat", "id": 3, "name": "boat"},
- # {"supercategory": "lifejacket", "id": 6, "name": "life jacket"}],
- # ground_truth = os.path.join(ROOT_DIR, 'annotations', 'instances_' + 'val_iscrowd' + '.json')
- # ground_truth = '/home/kiefer/PycharmProjects/3D_Tracking/gt_single_classs.json'
- # ground_truth = '/home/kiefer/TCML/yolov7_tiny/yolov7_tiny_mit_early_stopping/result/gt_single_class.json'
- # ground_truth = '/cshome/share/avalon/dataset/lake_constance_v2021_tracking/annotations/instances_test_objects_in_water_gt_iscrowd.json'
- # ground_truth = '/home/kiefer/TCML/yolov7_tiny/yolov7_tiny_mit_early_stopping/result/gt_single_class.json'
- ground_truth = '/cshome/share/avalon/dataset/seadronessee_august_splitted_challenge/annotations/instances_test.json'
- print("using ground truth: ", ground_truth)
+ categories = [1, 2, 3, 4, 5, 6]
+ ground_truth = os.path.join(ROOT_DIR, 'annotations', 'instances_' + 'val_iscrowd' + '.json')
return evaluate_coco(opt.file_name, categories, ground_truth)
def v2_od_subtrack1():
# swimmer, boat, jetski, life_saving_appliances, buoy
- # categories = [1, 2, 3, 4, 5]
- categories = [0]
+ categories = [1, 2, 3, 4, 5]
ground_truth = os.path.join(ROOT_DIR, 'annotations', 'od_v2_annotations', 'instances_' + 'val_iscrowd' + '.json')
- ground_truth = '/home/kiefer/Documents/SeaDronesSee/OD/evaluation of OD V2/required files/new_instances_shuffled_test_iscrowd.json'
- ground_truth = '/home/kiefer/PycharmProjects/3D_Tracking/gt_single_classs.json'
return evaluate_coco(opt.file_name, categories, ground_truth)
def v2_od_subtrack2(filename):
# non-water
categories = [0]
ground_truth = os.path.join(ROOT_DIR, 'annotations', 'od_v2_annotations', 'instances_' + 'val_iscrowd' + '_non_water' + '.json')
- ground_truth = '/home/kiefer/Documents/SeaDronesSee/OD/evaluation of OD V2/required files/new_instances_test_iscrowd_non_water.json'
- ground_truth = '/home/kiefer/PycharmProjects/3D_Tracking/gt_single_classs.json'
return evaluate_coco(filename, categories, ground_truth)
if __name__ == '__main__':
diff --git a/OD/visualize/README.md b/OD/visualize/README.md
index 26cc3bb..e5c1bdc 100644
--- a/OD/visualize/README.md
+++ b/OD/visualize/README.md
@@ -1,4 +1,10 @@
# Visualization Scripts
-This subfolder contains useful scripts to visualize SeaDronesSee bounding boxes on images as a standard Python file
-(visualize_bbox_on_img.py) or as Jupyter notebook (visualize_bbox_on_img.ipynb).
\ No newline at end of file
+
+This subfolder contains useful scripts to visualize SeaDronesSee bounding boxes on images as a standard Python file
+(visualize_bbox_on_img.py) or as Jupyter notebook (visualize_bbox_on_img.ipynb).
+
+
+
+
+
diff --git a/OD/visualize/md_bbox_on_images.py b/OD/visualize/md_bbox_on_images.py
index 24248be..2628393 100644
--- a/OD/visualize/md_bbox_on_images.py
+++ b/OD/visualize/md_bbox_on_images.py
@@ -8,7 +8,7 @@
import os
-colors = {1: (0,255,0), 2: (0,0,255), 3: (255,255,51), 4: (0,255,255), 5: (255, 102, 255), 0: (51, 153, 255)}
+colors = {1: (0,255,0), 2: (0,0,255), 3: (255,255,51), 4: (0,255,255), 5: (255, 102, 255), 6: (51, 153, 255)}
if __name__ == '__main__':
scriptTime = time.time()
@@ -28,7 +28,7 @@
raise ValueError("Please enter a valid path for the output directory, where the images will be saved")
if not os.path.isdir(options.output):
raise ValueError("The output dir was not found.")
-
+
drone_65 = "drone_65.png"
drone_65= cv2.imread(drone_65,-1)
drone_65= cv2.resize(drone_65,(150,150))
@@ -39,16 +39,13 @@
drone_35_65= cv2.imread(drone_35_65,-1)
drone_35_65= cv2.resize(drone_35_65,(150,150))
with open(options.annotation) as f:
-
data = json.load(f)
- lenimg_annots = len(data['images'])
total_number_of_images = len(os.listdir(options.images))
for i in range(options.i):
- idx = np.random.randint(0,min(total_number_of_images,lenimg_annots))
+ idx = np.random.randint(0,total_number_of_images)
image = data['images'][idx]
id = image['id']
file_name = image['file_name']
- print(options.images + '/' + file_name)
img = cv2.imread(options.images + '/' + file_name)
img_width = data['images'][0]['width']
img_height = data['images'][0]['height']
@@ -56,8 +53,8 @@
annotations = data['annotations']
altitude = round(image['meta']['altitude'])
saltitude = 'altitude= ' + str("%.2f" % (image['meta']['altitude'])) + ' m'
- speed = round(image['meta']['speed(mph)'])
- sspeed = 'speed= ' + str("%.2f" % (image['meta']['speed(mph)'])) + ' mi/h'
+ speed = round(image['meta']['speed'])
+ sspeed = 'speed= ' + str("%.2f" % (image['meta']['speed'])) + ' km/h'
sangle = 'angle= ' + str("%.2f" %(image['meta']['gimbal_pitch'])) + ' degrees'
angle = round(image['meta']['gimbal_pitch'])
@@ -89,6 +86,4 @@
if angle >= 65:
img = cvzone.overlayPNG(img, drone_65,[40,drone_y_position])
- cv2.imwrite(os.path.join(options.output,file_name).replace('png','jpg'), img)
-
-
+ cv2.imwrite(os.path.join(options.output,file_name), img)
\ No newline at end of file
diff --git a/OD/visualize/md_bbox_on_video.py b/OD/visualize/md_bbox_on_video.py
index b3cd2e5..c9c2184 100644
--- a/OD/visualize/md_bbox_on_video.py
+++ b/OD/visualize/md_bbox_on_video.py
@@ -8,7 +8,7 @@
import cvzone
-colors = {1: (0,255,0), 2: (0,0,255), 3: (255,255,51), 4: (0,255,255), 5: (255, 102, 255), 0: (51, 153, 255)}
+colors = {1: (0,255,0), 2: (0,0,255), 3: (255,255,51), 4: (0,255,255), 5: (255, 102, 255), 6: (51, 153, 255)}
if __name__ == '__main__':
scriptTime = time.time()
@@ -53,13 +53,12 @@
out = cv2.VideoWriter(videoName, fourcc, fps, (img_width, img_height))
for image in data['images']:
currentImage += 1
- print(options.images + '/' +image['file_name'])
img = cv2.imread(options.images + '/' +image['file_name'])
start = time.time()
altitude = round(image['meta']['altitude'])
saltitude = 'altitude= ' + str("%.2f" % (image['meta']['altitude'])) + ' m'
- speed = round(image['meta']['speed(mph)'])
- sspeed = 'speed= ' + str("%.2f" % (image['meta']['speed(mph)'])) + ' mi/h'
+ speed = round(image['meta']['speed'])
+ sspeed = 'speed= ' + str("%.2f" % (image['meta']['speed'])) + ' km/h'
sangle = 'angle= ' + str("%.2f" %(image['meta']['gimbal_pitch'])) + ' degrees'
angle = round(image['meta']['gimbal_pitch'])
# draw all boundingBoxes, which belong to one image, on it with the corresponding colors of the categories
@@ -100,4 +99,4 @@
out.release()
scriptRunTime = time.time() - scriptTime
print('\nScript run time in seconds: {0}'.format(scriptRunTime))
- print("mean time per image: {0}".format(scriptRunTime / numberOfImages))
+ print("mean time per image: {0}".format(scriptRunTime / numberOfImages))
\ No newline at end of file
diff --git a/OD/visualize/visualize_bbox_on_img.py b/OD/visualize/visualize_bbox_on_img.py
new file mode 100644
index 0000000..1100230
--- /dev/null
+++ b/OD/visualize/visualize_bbox_on_img.py
@@ -0,0 +1,71 @@
+import argparse
+import json
+
+import cv2
+import time
+import os
+
+'''
+one color for each category (excluding category nr 7)
+ 1: swimmer
+ 2: floater
+ 3: boat
+ 4: swimmer on boat
+ 5: floater on boat
+ 6: life jacket
+ 7: ignored
+'''
+colors = {1: (0,255,0), 2: (0,0,255), 3: (255,255,51), 4: (0,255,255), 5: (255, 102, 255), 6: (51, 153, 255)}
+
+if __name__ == '__main__':
+ scriptTime = time.time()
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--annotation', type=str, default=None, help='path to the annotations json file')
+ parser.add_argument('--pictures', type=str, default=None, help='path to the pictures')
+ parser.add_argument('--output', type=str, default=None, help='Path to output')
+ options = parser.parse_args()
+ if (options.annotation == None):
+ print("Please enter a valid path to the annotation json file ")
+ exit()
+ if (options.pictures == None):
+ print("Please enter a valid path to folder where the pictures are located")
+ exit()
+ if (options.output == None):
+ print("Please enter a valid path for the output directory")
+ exit()
+
+ # create Output Folder, avoid overriding any existing ones
+ num = 0
+ outputPath = options.output + "\\Output" + str(num)
+ while os.path.exists(outputPath):
+ num += 1
+ outputPath = options.output + "\\Output" + str(num)
+ os.mkdir(outputPath)
+
+ with open(options.annotation) as f:
+ data = json.load(f)
+
+ # needed to show the current progression
+ numberOfImages = len(data['images'])
+ currentImage = 0
+
+ for image in data['images']:
+ currentImage += 1
+ img = cv2.imread(options.pictures + "\\" + image['file_name'])
+ start = time.time()
+ # draw all boundingBoxes, which belong to one image, on it with the corresponding colors of the categories
+ for annotation in data['annotations']:
+ if annotation['image_id'] == image['id']:
+ topLeftX = annotation['bbox'][0]
+ topLeftY = annotation['bbox'][1]
+ width = annotation['bbox'][2]
+ height = annotation['bbox'][3]
+ for category in data['categories']:
+ if category['id'] == annotation['category_id']:
+ label = category['name']
+ cv2.rectangle(img, (topLeftX, topLeftY), (topLeftX + width, topLeftY + height), colors[annotation['category_id']], 3)
+ cv2.imwrite(outputPath + "\\" + image['file_name'], img)
+ print('Picture generation took {0} second! Finished: {1}%'.format(time.time() - start, round((currentImage / numberOfImages) * 100, 2)))
+ scriptRunTime = time.time() - scriptTime
+ print('\nScript run time in seconds: {0}'.format(scriptRunTime))
+ print("mean time per image: {0}".format(scriptRunTime / numberOfImages))
diff --git a/README.md b/README.md
index 593a4c6..1db7258 100644
--- a/README.md
+++ b/README.md
@@ -1,28 +1,28 @@
-
-
-
+
+
+
# SeaDronesSee
-
Vision Benchmark for Maritime Search and Rescue
+
-
-
-SeaDronesSee is a large-scale data set aimed at helping develop systems for Search and Rescue (SAR) using Unmanned Aerial Vehicles (UAVs) in maritime scenarios. Building highly complex autonomous UAV systems that aid in SAR missions requires robust computer vision algorithms to detect and track objects or persons of interest. This data set provides three sets of tracks: object detection, single-object tracking and multi-object tracking. Each track consists of its own data set and leaderboard.
+
+
+
+SeaDronesSee is a large-scale data set aimed at helping develop systems for Search and Rescue (SAR) using Unmanned Aerial Vehicles (UAVs) in maritime scenarios. Building highly complex autonomous UAV systems that aid in SAR missions requires robust computer vision algorithms to detect and track objects or persons of interest. This data set provides three sets of tracks: object detection, single-object tracking and multi-object tracking. Each track consists of its own data set and leaderboard.
This repository contains evaluation kits, code and model samples and links to the SeaDronesSee benchmark and evaluation webserver.
## MaCVi 2023 - Information
-SeaDronesSee is featured as part of two challenges at [MaCVi '23](https://seadronessee.cs.uni-tuebingen.de/wacv23):
+SeaDronesSee is featured as part of two challenges at [MaCVi '23](https://seadronessee.cs.uni-tuebingen.de/wacv23):
1. Object Detection v2 (standard Object Detection and binary Object Detection)
2. Multi-Object Tracking
-For Object Detection v2, find the [dataset](https://seadronessee.cs.uni-tuebingen.de/dataset), [visualization](OD/visualize/) and [evaluation](OD/evaluation%20of%20OD%20V2/) scripts and [training and testing tutorials](OD/OD_Tut/) here and on the [workshop page](https://seadronessee.cs.uni-tuebingen.de/wacv23).
+For Object Detection v2, find the [dataset](https://seadronessee.cs.uni-tuebingen.de/dataset), [visualization](OD/visualize/) and [evaluation]() scripts and [training and testing tutorials](OD/OD_Tut/) here and on the [workshop page](https://seadronessee.cs.uni-tuebingen.de/wacv23).
## Data Sets
@@ -46,7 +46,7 @@ Soon, we will update the SeaDronesSee benchmark and add additional data - stay t
### Object Detection
-You need so submit a json-file in COCO format. That is, a list of dictionaries, each containing a single prediction of the form
+ You need so submit a json-file in COCO format. That is, a list of dictionaries, each containing a single prediction of the form
```
{ "image_id": 6503, "category_id": 4, "score": 0.6774558424949646,
@@ -55,9 +55,10 @@ You need so submit a json-file in COCO format. That is, a list of dictionaries,
The predictions are evaluated on AP50:0.05:95, AP50, AP75, AR1, AR10. You can upload your prediction [here](https://seadronessee.cs.uni-tuebingen.de/upload) upon registration. If you need, you can test your json-style COCO prediction with the evaluation script [here](OD/od.py). You have to replace GROUND_TRUTH_COCO_JSON.json with the corresponding json file from the webserver.
+
### Single-Object Tracking
-There are 80 testing video clips on which the performance is measured. The protocol is based on the implementation of [PyTracking](https://github.com/visionml/pytracking). Note that this is a short-term Single-Object Tracking task, meaning that clips only feature objects that are present in the video during the whole video and do not disappear and reappear.
+ There are 80 testing video clips on which the performance is measured. The protocol is based on the implementation of [PyTracking](https://github.com/visionml/pytracking). Note that this is a short-term Single-Object Tracking task, meaning that clips only feature objects that are present in the video during the whole video and do not disappear and reappear.
You need to submit a zip-file containing exactly 80 text-files, each corresponding to the respective clip. Each text file has to be named j.txt where j is the number corresponding to the respective clip (1,...,80). Each text file has as many rows as its corresponding clip has frames. Each row has 4 comma separated numbers (x,y,w,h), where x is the left-most pixel value of the tracked object's bounding box, y the top-most pixel value and w and h the width and height of the bounding box in pixels.
@@ -71,11 +72,11 @@ Furthermore, you find three folder: train_annotations, val_annotations, test_ann
See also the compressed folder [sample_submission.zip](SOT/sample_submission.zip). This zip-archive could be uploaded right away but will naturally yield bad results.
-The predictions are evaluated on precision and success numbers.
+The predictions are evaluated on precision and success numbers.
### Multi-Object Tracking
-There are 22 video clips in the data on which you can test your trained tracker. The mapping from images to video clips can be done via the files 'instances_test_objects_in_water.json' and 'instances_test_swimmer.json', respectively. They can be found via the link above.
+ There are 22 video clips in the data on which you can test your trained tracker. The mapping from images to video clips can be done via the files 'instances_test_objects_in_water.json' and 'instances_test_swimmer.json', respectively. They can be found via the link above.
For example, '410.png' from the test set can be assigned to video clip 'DJI_0057.MP4' because its entry in the annotation file looks like this:
{'id': 410, 'file_name': '410.png', 'height': 2160, 'width': 3840, 'source': {'drone': 'mavic', 'folder_name': 'DJI_0057', 'video': 'DJI_0057.MP4', 'frame_no': 715}, 'video_id': 0, 'frame_index': 715, 'date_time': '2020-08-27T14:18:35.823800', 'meta': {'date_time': '2020-08-27T12:18:36', 'gps_latitude': 47.671949, 'gps_latitude_ref': 'N', 'gps_longitude': 9.269724, 'gps_longitude_ref': 'E', 'altitude': 8.599580615665955, 'gimbal_pitch': 45.4, 'compass_heading': 138.2, 'gimbal_heading': 140.9, 'speed': 0.6399828341528834, 'xspeed': -0.39998927134555207, 'yspeed': 0.39998927134555207, 'zspeed': 0.299991953509164}}
@@ -85,7 +86,7 @@ The submission format is similar to the one for the [MOT-challenge](https://motc
{'id': 0, 'height': 2160, 'width': 3840, 'name:': '/data/input/recordings/mavic/DJI_0057.MP4'}
Inside any of the .txt files there has to be one line per object per frame. Each line is formatted like: [frame_id],[object_id],x,y,w,h
-frame_id and object_id are supposed to be integers, the rest of the numbers may be floats. The frame_id can be obtained from the .json file while the object_id can be assigned by your tracker. Coordinates x and y are the upper left coordinate of the bounding box while w and h are its width and height, respectively. All of these are expressed in pixels.
+frame_id and object_id are supposed to be integers, the rest of the numbers may be floats. The frame_id can be obtained from the .json file while the object_id can be assigned by your tracker. Coordinates x and y are the upper left coordinate of the bounding box while w and h are its width and height, respectively. All of these are expressed in pixels.
### Multi-Spektral Object Detection
@@ -93,7 +94,7 @@ Currently, there is no challenge or leaderboard for this track.
### Boat-MNIST
-This is a toy data set for the task of binary image classification. It aims at providing a simple hands-on benchmark to test small neural networks. There are the following two classes:
+This is a toy data set for the task of binary image classification. It aims at providing a simple hands-on benchmark to test small neural networks. There are the following two classes:
1 - if the image contains any watercraft instance including boats, ships, surfboards, ... ON the water
@@ -105,6 +106,7 @@ As metrics, we employ the prediction accuracy (number of correctly predicted ima
You can also find some sample solutions of some groups in the folders under Boat-MNIST with their respective group number.
+
#### Citation
If you find the data set, the benchmark or this repo useful, please consider citing
@@ -118,4 +120,14 @@ pages={2260--2270},
year={2022} }
```
+#### License
+
+Code is MIT license, dataset is Creative Commons CC0 1.0 Universal (CC0 1.0).
+
+Essentially, do what you want!
+
+
+
+
+