Modularise code

README.md

@@ -1,7 +1,7 @@
 FCN-semantic-segmentation
 =========================
 
-Simple end-to-end semantic segmentation using fully convolutional networks [[1]](#references). Takes a pretrained 34-layer ResNet [[2]](#references), removes the fully connected layers, and adds transposed convolution layers with skip residual connections from lower layers. Initialises upsampling convolutions with bilinear interpolation filters and zeros the final (classification) layer. Uses an independent cross-entropy loss per class.
+Simple end-to-end semantic segmentation using fully convolutional networks [[1]](#references). Takes a pretrained 34-layer ResNet [[2]](#references), removes the fully connected layers, and adds transposed convolution layers with skip connections from lower layers. Initialises upsampling convolutions with bilinear interpolation filters and zeros the final (classification) layer. Uses an independent cross-entropy loss per class.
 
 Calculates and plots class-wise and mean intersection-over-union. Checkpoints the network every epoch.
 

data.py

@@ -0,0 +1,63 @@
+import os
+import random
+from PIL import Image
+import torch
+from torch.utils.data import Dataset
+
+
+# Labels: -1 license plate, 0 unlabeled, 1 ego vehicle, 2 rectification border, 3 out of roi, 4 static, 5 dynamic, 6 ground, 7 road, 8 sidewalk, 9 parking, 10 rail track, 11 building, 12 wall, 13 fence, 14 guard rail, 15 bridge, 16 tunnel, 17 pole, 18 polegroup, 19 traffic light, 20 traffic sign, 21 vegetation, 22 terrain, 23 sky, 24 person, 25 rider, 26 car, 27 truck, 28 bus, 29 caravan, 30 trailer, 31 train, 32 motorcycle, 33 bicycle
+num_classes = 20
+full_to_train = {-1: 19, 0: 19, 1: 19, 2: 19, 3: 19, 4: 19, 5: 19, 6: 19, 7: 0, 8: 1, 9: 19, 10: 19, 11: 2, 12: 3, 13: 4, 14: 19, 15: 19, 16: 19, 17: 5, 18: 19, 19: 6, 20: 7, 21: 8, 22: 9, 23: 10, 24: 11, 25: 12, 26: 13, 27: 14, 28: 15, 29: 19, 30: 19, 31: 16, 32: 17, 33: 18}
+train_to_full = {0: 7, 1: 8, 2: 11, 3: 12, 4: 13, 5: 17, 6: 19, 7: 20, 8: 21, 9: 22, 10: 23, 11: 24, 12: 25, 13: 26, 14: 27, 15: 28, 16: 31, 17: 32, 18: 33, 19: 0}
+full_to_colour = {0: (0, 0, 0), 7: (128, 64, 128), 8: (244, 35, 232), 11: (70, 70, 70), 12: (102, 102, 156), 13: (190, 153, 153), 17: (153, 153, 153), 19: (250, 170, 30), 20: (220, 220, 0), 21: (107, 142, 35), 22: (152, 251, 152), 23: (70, 130, 180), 24: (220, 20, 60), 25: (255, 0, 0), 26: (0, 0, 142), 27: (0, 0, 70), 28: (0, 60,100), 31: (0, 80, 100), 32: (0, 0, 230), 33: (119, 11, 32)}
+
+
+class CityscapesDataset(Dataset):
+  def __init__(self, split='train', crop=None, flip=False):
+    super().__init__()
+    self.crop = crop
+    self.flip = flip
+    self.inputs = []
+    self.targets = []
+
+    for root, _, filenames in os.walk(os.path.join('leftImg8bit_trainvaltest', 'leftImg8bit', split)):
+      for filename in filenames:
+        if os.path.splitext(filename)[1] == '.png':
+          filename_base = '_'.join(filename.split('_')[:-1])
+          target_root = os.path.join('gtFine_trainvaltest', 'gtFine', split, os.path.basename(root))
+          self.inputs.append(os.path.join(root, filename_base + '_leftImg8bit.png'))
+          self.targets.append(os.path.join(target_root, filename_base + '_gtFine_labelIds.png'))
+
+  def __len__(self):
+    return len(self.inputs)
+
+  def __getitem__(self, i):
+    # Load images and perform augmentations with PIL
+    input, target = Image.open(self.inputs[i]), Image.open(self.targets[i])
+    # Random uniform crop
+    if self.crop is not None:
+      w, h = input.size
+      x1, y1 = random.randint(0, w - self.crop), random.randint(0, h - self.crop)
+      input, target = input.crop((x1, y1, x1 + self.crop, y1 + self.crop)), target.crop((x1, y1, x1 + self.crop, y1 + self.crop))
+    # Random horizontal flip
+    if self.flip:
+      if random.random() < 0.5:
+        input, target = input.transpose(Image.FLIP_LEFT_RIGHT), target.transpose(Image.FLIP_LEFT_RIGHT)
+
+    # Convert to tensors
+    w, h = input.size
+    input = torch.ByteTensor(torch.ByteStorage.from_buffer(input.tobytes())).view(h, w, 3).permute(2, 0, 1).float().div(255)
+    target = torch.ByteTensor(torch.ByteStorage.from_buffer(target.tobytes())).view(h, w).long()
+    # Normalise input
+    input[0].add_(-0.485).div_(0.229)
+    input[1].add_(-0.456).div_(0.224)
+    input[2].add_(-0.406).div_(0.225)
+    # Convert to training labels
+    remapped_target = target.clone()
+    for k, v in full_to_train.items():
+      remapped_target[target == k] = v
+    # Create one-hot encoding
+    target = torch.zeros(num_classes, h, w)
+    for c in range(num_classes):
+      target[c][remapped_target == c] = 1
+    return input, target, remapped_target  # Return x, y (one-hot), y (index)

main.py

@@ -1,18 +1,18 @@
 from argparse import ArgumentParser
 import os
 import random
+from matplotlib import pyplot as plt
 import torch
-from PIL import Image
 from torch import optim
 from torch import nn
-from torch.nn import init
 from torch.nn import functional as F
 from torch.autograd import Variable
-from torch.utils.data import DataLoader, Dataset
-from torchvision import models, transforms
-from torchvision.models.resnet import BasicBlock, ResNet
+from torch.utils.data import DataLoader
+from torchvision import models
 from torchvision.utils import save_image
-from matplotlib import pyplot as plt
+
+from data import CityscapesDataset, num_classes, full_to_colour, train_to_full
+from model import FeatureResNet, SegResNet
 
 
 # Setup
@@ -31,146 +31,14 @@
   os.makedirs('results')
 plt.switch_backend('agg')  # Allow plotting when running remotely
 
-# Labels: -1 license plate, 0 unlabeled, 1 ego vehicle, 2 rectification border, 3 out of roi, 4 static, 5 dynamic, 6 ground, 7 road, 8 sidewalk, 9 parking, 10 rail track, 11 building, 12 wall, 13 fence, 14 guard rail, 15 bridge, 16 tunnel, 17 pole, 18 polegroup, 19 traffic light, 20 traffic sign, 21 vegetation, 22 terrain, 23 sky, 24 person, 25 rider, 26 car, 27 truck, 28 bus, 29 caravan, 30 trailer, 31 train, 32 motorcycle, 33 bicycle
-num_classes = 20
-full_to_train = {-1: 19, 0: 19, 1: 19, 2: 19, 3: 19, 4: 19, 5: 19, 6: 19, 7: 0, 8: 1, 9: 19, 10: 19, 11: 2, 12: 3, 13: 4, 14: 19, 15: 19, 16: 19, 17: 5, 18: 19, 19: 6, 20: 7, 21: 8, 22: 9, 23: 10, 24: 11, 25: 12, 26: 13, 27: 14, 28: 15, 29: 19, 30: 19, 31: 16, 32: 17, 33: 18}
-train_to_full = {0: 7, 1: 8, 2: 11, 3: 12, 4: 13, 5: 17, 6: 19, 7: 20, 8: 21, 9: 22, 10: 23, 11: 24, 12: 25, 13: 26, 14: 27, 15: 28, 16: 31, 17: 32, 18: 33, 19: 0}
-full_to_colour = {0: (0, 0, 0), 7: (128, 64, 128), 8: (244, 35, 232), 11: (70, 70, 70), 12: (102, 102, 156), 13: (190, 153, 153), 17: (153, 153, 153), 19: (250, 170, 30), 20: (220, 220, 0), 21: (107, 142, 35), 22: (152, 251, 152), 23: (70, 130, 180), 24: (220, 20, 60), 25: (255, 0, 0), 26: (0, 0, 142), 27: (0, 0, 70), 28: (0, 60,100), 31: (0, 80, 100), 32: (0, 0, 230), 33: (119, 11, 32)}
-
 
 # Data
-class CityscapesDataset(Dataset):
-  def __init__(self, split='train', crop=None, flip=False):
-    super().__init__()
-    self.crop = crop
-    self.flip = flip
-    self.inputs = []
-    self.targets = []
-
-    for root, _, filenames in os.walk(os.path.join('leftImg8bit_trainvaltest', 'leftImg8bit', split)):
-      for filename in filenames:
-        if os.path.splitext(filename)[1] == '.png':
-          filename_base = '_'.join(filename.split('_')[:-1])
-          target_root = os.path.join('gtFine_trainvaltest', 'gtFine', split, os.path.basename(root))
-          self.inputs.append(os.path.join(root, filename_base + '_leftImg8bit.png'))
-          self.targets.append(os.path.join(target_root, filename_base + '_gtFine_labelIds.png'))
-
-  def __len__(self):
-    return len(self.inputs)
-
-  def __getitem__(self, i):
-    # Load images and perform augmentations with PIL
-    input, target = Image.open(self.inputs[i]), Image.open(self.targets[i])
-    # Random uniform crop
-    if self.crop is not None:
-      w, h = input.size
-      x1, y1 = random.randint(0, w - self.crop), random.randint(0, h - self.crop)
-      input, target = input.crop((x1, y1, x1 + self.crop, y1 + self.crop)), target.crop((x1, y1, x1 + self.crop, y1 + self.crop))
-    # Random horizontal flip
-    if self.flip:
-      if random.random() < 0.5:
-        input, target = input.transpose(Image.FLIP_LEFT_RIGHT), target.transpose(Image.FLIP_LEFT_RIGHT)
-
-    # Convert to tensors
-    w, h = input.size
-    input = torch.ByteTensor(torch.ByteStorage.from_buffer(input.tobytes())).view(h, w, 3).permute(2, 0, 1).float().div(255)
-    target = torch.ByteTensor(torch.ByteStorage.from_buffer(target.tobytes())).view(h, w).long()
-    # Normalise input
-    input[0].add_(-0.485).div_(0.229)
-    input[1].add_(-0.456).div_(0.224)
-    input[2].add_(-0.406).div_(0.225)
-    # Convert to training labels
-    remapped_target = target.clone()
-    for k, v in full_to_train.items():
-      remapped_target[target == k] = v
-    # Create one-hot encoding
-    target = torch.zeros(num_classes, h, w)
-    for c in range(num_classes):
-      target[c][remapped_target == c] = 1
-    return input, target, remapped_target  # Return x, y (one-hot), y (index)
-
-
 train_dataset = CityscapesDataset(split='train', crop=args.crop_size, flip=True)
 val_dataset = CityscapesDataset(split='val')
-weight_loader = DataLoader(train_dataset, batch_size=args.batch_size, num_workers=args.workers, pin_memory=True)
 train_loader = DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True, num_workers=args.workers, pin_memory=True)
 val_loader = DataLoader(val_dataset, batch_size=1, num_workers=args.workers, pin_memory=True)
 
 
-# Models
-# Returns 2D convolutional layer with space-preserving padding
-def conv(in_planes, out_planes, kernel_size=3, stride=1, dilation=1, bias=False, transposed=False):
-  if transposed:
-    layer = nn.ConvTranspose2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=1, output_padding=1, dilation=dilation, bias=bias)
-    # Bilinear interpolation init
-    w = torch.Tensor(kernel_size, kernel_size)
-    centre = kernel_size % 2 == 1 and stride - 1 or stride - 0.5
-    for y in range(kernel_size):
-      for x in range(kernel_size):
-        w[y, x] = (1 - abs((x - centre) / stride)) * (1 - abs((y - centre) / stride))
-    layer.weight.data.copy_(w.div(in_planes).repeat(out_planes, in_planes, 1, 1))
-  else:
-    padding = (kernel_size + 2 * (dilation - 1)) // 2
-    layer = nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, bias=bias)
-  if bias:
-    init.constant(layer.bias, 0)
-  return layer
-
-
-# Returns 2D batch normalisation layer
-def bn(planes):
-  layer = nn.BatchNorm2d(planes)
-  # Use mean 0, standard deviation 1 init
-  init.constant(layer.weight, 1)
-  init.constant(layer.bias, 0)
-  return layer
-
-
-class FeatureResNet(ResNet):
-  def __init__(self):
-    super().__init__(BasicBlock, [3, 4, 6, 3], 1000)
-
-  def forward(self, x):
-    x1 = self.conv1(x)
-    x = self.bn1(x1)
-    x = self.relu(x)
-    x2 = self.maxpool(x)
-    x = self.layer1(x2)
-    x3 = self.layer2(x)
-    x4 = self.layer3(x3)
-    x5 = self.layer4(x4)
-    return x1, x2, x3, x4, x5
-
-
-class SegResNet(nn.Module):
-  def __init__(self, num_classes, pretrained_net):
-    super().__init__()
-    self.pretrained_net = pretrained_net
-    self.relu = nn.ReLU(inplace=True)
-    self.conv5 = conv(512 + num_classes, 256, stride=2, transposed=True)
-    self.bn5 = bn(256)
-    self.conv6 = conv(256 + num_classes, 128, stride=2, transposed=True)
-    self.bn6 = bn(128)
-    self.conv7 = conv(128 + num_classes, 64, stride=2, transposed=True)
-    self.bn7 = bn(64)
-    self.conv8 = conv(64 + num_classes, 64, stride=2, transposed=True)
-    self.bn8 = bn(64)
-    self.conv9 = conv(64 + num_classes, 32, stride=2, transposed=True)
-    self.bn9 = bn(32)
-    self.conv10 = conv(32, num_classes, kernel_size=7, gain='linear')
-    init.constant(self.conv10.weight, 0)  # Zero init
-
-  def forward(self, x):
-    x1, x2, x3, x4, x5 = self.pretrained_net(x)
-    x = self.relu(self.bn5(self.conv5(x5)))
-    x = self.relu(self.bn6(self.conv6(x + x4)))
-    x = self.relu(self.bn7(self.conv7(x + x3)))
-    x = self.relu(self.bn8(self.conv8(x + x2)))
-    x = self.relu(self.bn9(self.conv9(x + x1)))
-    x = self.conv10(x)
-    return x
-
-
 # Training/Testing
 pretrained_net = FeatureResNet()
 pretrained_net.load_state_dict(models.resnet34(pretrained=True).state_dict())
@@ -256,8 +124,7 @@ def test(e):
   plt.plot(es, mean_scores, 'b-')
   plt.xlabel('Epoch')
   plt.ylabel('Mean IoU')
-  plt.xlim(xmin=0, xmax=args.epochs)
-  plt.savefig(os.path.join('results', 'ious.eps'))
+  plt.savefig(os.path.join('results', 'ious.png'))
   plt.close()
 
 

model.py

@@ -0,0 +1,77 @@
+import torch
+from torch import nn
+from torch.nn import init
+from torchvision.models.resnet import BasicBlock, ResNet
+
+
+# Returns 2D convolutional layer with space-preserving padding
+def conv(in_planes, out_planes, kernel_size=3, stride=1, dilation=1, bias=False, transposed=False):
+  if transposed:
+    layer = nn.ConvTranspose2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=1, output_padding=1, dilation=dilation, bias=bias)
+    # Bilinear interpolation init
+    w = torch.Tensor(kernel_size, kernel_size)
+    centre = kernel_size % 2 == 1 and stride - 1 or stride - 0.5
+    for y in range(kernel_size):
+      for x in range(kernel_size):
+        w[y, x] = (1 - abs((x - centre) / stride)) * (1 - abs((y - centre) / stride))
+    layer.weight.data.copy_(w.div(in_planes).repeat(out_planes, in_planes, 1, 1))
+  else:
+    padding = (kernel_size + 2 * (dilation - 1)) // 2
+    layer = nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, bias=bias)
+  if bias:
+    init.constant(layer.bias, 0)
+  return layer
+
+
+# Returns 2D batch normalisation layer
+def bn(planes):
+  layer = nn.BatchNorm2d(planes)
+  # Use mean 0, standard deviation 1 init
+  init.constant(layer.weight, 1)
+  init.constant(layer.bias, 0)
+  return layer
+
+
+class FeatureResNet(ResNet):
+  def __init__(self):
+    super().__init__(BasicBlock, [3, 4, 6, 3], 1000)
+
+  def forward(self, x):
+    x1 = self.conv1(x)
+    x = self.bn1(x1)
+    x = self.relu(x)
+    x2 = self.maxpool(x)
+    x = self.layer1(x2)
+    x3 = self.layer2(x)
+    x4 = self.layer3(x3)
+    x5 = self.layer4(x4)
+    return x1, x2, x3, x4, x5
+
+
+class SegResNet(nn.Module):
+  def __init__(self, num_classes, pretrained_net):
+    super().__init__()
+    self.pretrained_net = pretrained_net
+    self.relu = nn.ReLU(inplace=True)
+    self.conv5 = conv(512, 256, stride=2, transposed=True)
+    self.bn5 = bn(256)
+    self.conv6 = conv(256, 128, stride=2, transposed=True)
+    self.bn6 = bn(128)
+    self.conv7 = conv(128, 64, stride=2, transposed=True)
+    self.bn7 = bn(64)
+    self.conv8 = conv(64, 64, stride=2, transposed=True)
+    self.bn8 = bn(64)
+    self.conv9 = conv(64, 32, stride=2, transposed=True)
+    self.bn9 = bn(32)
+    self.conv10 = conv(32, num_classes, kernel_size=7)
+    init.constant(self.conv10.weight, 0)  # Zero init
+
+  def forward(self, x):
+    x1, x2, x3, x4, x5 = self.pretrained_net(x)
+    x = self.relu(self.bn5(self.conv5(x5)))
+    x = self.relu(self.bn6(self.conv6(x + x4)))
+    x = self.relu(self.bn7(self.conv7(x + x3)))
+    x = self.relu(self.bn8(self.conv8(x + x2)))
+    x = self.relu(self.bn9(self.conv9(x + x1)))
+    x = self.conv10(x)
+    return x