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--- cs501r_f2018:lab4 [2018/09/24 17:56]
wingated
+++ cs501r_f2018:lab4 [2018/09/26 00:36]
shreeya
@@ Line 28: / Line 28: @@
   * 40% Proper design, creation and debugging of a dense prediction network
-  * 40% Proper design of a loss function and test set accuracy measure
+  * 40% Proper implementation of a loss function and train/test set accuracy measure
   * 10% Tidy visualizations of loss of your dense predictor during training
   * 10% Test image output
@@ Line 56: / Line 56: @@
 ----
 ====Description:====
+For a video including some tips and tricks that can help with this lab: [[https://youtu.be/Ms19kgK_D8w|https://youtu.be/Ms19kgK_D8w]]
 For this lab, you will implement a virtual radiologist.  You are given
@@ Line 75: / Line 77: @@
 {{ :cs501r_f2016:screen_shot_2017-10-10_at_10.11.55_am.png?direct&200|}}
-Use the "Deep Convolution U-Net" from this paper: [[https://arxiv.org/pdf/1505.04597.pdf|U-Net: Convolutional Networks for Biomedical Image Segmentation]] (See figure 1, replicated at the right).  This should be fairly easy to implement given the
+Use the "Deep Convolution U-Net" from this paper: [[https://arxiv.org/pdf/1505.04597.pdf|U-Net: Convolutional Networks for Biomedical Image Segmentation]] (See figure 1, replicated at the right).  You should use existing pytorch functions (not your own Conv2D module), such as ''nn.Conv2d''; you will also need the pytorch function ''torch.cat'' and ''nn.ConvTranspose2d''
-''conv'' helper functions that you implemented previously; you
-may also need the pytorch function ''torch.cat'' and ''nn.ConvTranspose2d''
 ''torch.cat'' allows you to concatenate tensors. ''nn.ConvTranspose2d'' is the opposite of ''nn.Conv2d''. It is used to bring an image from low res to higher res. [[https://towardsdatascience.com/up-sampling-with-transposed-convolution-9ae4f2df52d0|This blog]] should help you understand this function in detail.
@@ Line 91: / Line 91: @@
 **Part 2: Plot performance over time**
+Please generate a plot that shows loss on the training set as a function of training time.  Make sure your axes are labeled!
 **Part 3: Generate a prediction on the ''pos_test_000072.png'' image**
+Calculate the output of your trained network on the ''pos_test_000072.png'' image, then make a hard decision (cancerous/not-cancerous) for each pixel.  The resulting image should be black-and-white, where white pixels represent things you think are probably cancerous.
 ----
 ====Hints:====
-Importing data is a little tricky for this lab. There are a few ways to handle data. I downloaded the dataset and divided it into train_input, train_output, test_input and test_output. I uploaded this to my google drive and wrote the follwoing in colab to use the dataset
+The intention of this lab is to learn how to make deep neural nets and implement loss function. Therefore we'll help you with the implementation of Dataset. This code will download the dataset for you so that you are ready to use it and focus on network implementation, losses and accuracies.
 <code python>
-    # Load the Drive helper and mount
+import torchvision
-    from google.colab import drive
+import os
-    # This will prompt for authorization.
+import gzip
-    drive.mount('/content/drive')
+import tarfile
-    print('done')
+import gc
-</code>
+from IPython.core.ultratb import AutoFormattedTB
+__ITB__ = AutoFormattedTB(mode = 'Verbose',color_scheme='LightBg', tb_offset = 1)
+class CancerDataset(Dataset):
+  def __init__(self, root, download=True, size=512, train=True):
+    if download and not os.path.exists(os.path.join(root, 'cancer_data')):
+      datasets.utils.download_url('http://liftothers.org/cancer_data.tar.gz', root, 'cancer_data.tar.gz', None)
+      self.extract_gzip(os.path.join(root, 'cancer_data.tar.gz'))
+      self.extract_tar(os.path.join(root, 'cancer_data.tar'))
+    postfix = 'train' if train else 'test'
+    root = os.path.join(root, 'cancer_data', 'cancer_data')
+    self.dataset_folder = torchvision.datasets.ImageFolder(os.path.join(root, 'inputs_' + postfix) ,transform = transforms.Compose([transforms.Resize(size),transforms.ToTensor()]))
+    self.label_folder = torchvision.datasets.ImageFolder(os.path.join(root, 'outputs_' + postfix) ,transform = transforms.Compose([transforms.Resize(size),transforms.ToTensor()]))
-This will ask you for an authentication so please copy paste the code that it gives you. Next make your dataset class and you will be using ''ImageFolder' to build your dataset in init:
+  @staticmethod
+  def extract_gzip(gzip_path, remove_finished=False):
+    print('Extracting {}'.format(gzip_path))
+    with open(gzip_path.replace('.gz', ''), 'wb') as out_f, gzip.GzipFile(gzip_path) as zip_f:
+      out_f.write(zip_f.read())
+    if remove_finished:
+      os.unlink(gzip_path)
-<code python>
+  @staticmethod
+  def extract_tar(tar_path):
+    print('Untarring {}'.format(tar_path))
+    z = tarfile.TarFile(tar_path)
+    z.extractall(tar_path.replace('.tar', ''))
-    class CancerDataset(Dataset):
-      def __init__(self, dataset_folder):
-        self.dataset_folder = torchvision.datasets.ImageFolder(dataset_folder + 'train_input' ,transform = transforms.ToTensor())
-        self.label_folder = torchvision.datasets.ImageFolder(dataset_folder + 'train_output' ,transform = transforms.ToTensor())
-      def __getitem__(self,index):
+  def __getitem__(self,index):
-        img = self.dataset_folder[index]
+    img = self.dataset_folder[index]
-        label = self.label_folder[index]
+    label = self.label_folder[index]
-        return img[0],label[0]
+    return img[0],label[0][0]
-      def __len__(self):
+  def __len__(self):
-        return len(self.dataset_folder)
+    return len(self.dataset_folder)
 </code>
-Finally you can call your dataset like this:
+You are welcome to resize your input images, although don't make them
+so small that the essential details are blurred!  I resized my images
+down to 512x512.
+You will need to add some lines of code for memory management:
 <code python>
-    trainset = CancerDataset(dataset_folder = '/content/drive/My Drive/cancer_data/')
+def scope():
+  try:
+    #your code for calling dataset and dataloader
+    gc.collect()
+    print(torch.cuda.memory_allocated(0) / 1e9)
+    #for epochs:
+    #  Call your model,loss and accuracy
+  except:
+    __ITB__()
+scope()
 </code>
-The tricky part here is that Imagefolder takes a folder of images. When you have both inputs and outputs folder inside cancer_data, it does not know that outputs is label. To get over this problem, I created a layer of folders that looks like this: cancer_data -> train_input -> train_input->all image files and similar for all other folders.(we are looking at other options of making the dataset...)
+Since you will be using the output of one network in two places(convolution and maxpooling), you can't use nn.Sequential. Instead you will write up the network like normal variable assignment as the example shown below:
-You are welcome to resize your input images, although don't make them
+<code python>
-so small that the essential details are blurred!  I resized my images
+class CancerDetection(nn.Module):
-down to 512x512.
+  def __init__(self):
+    super(CancerDetection, self).__init__()
+    self.conv1 = nn.Conv2d(3,64,kernel_size = 3, stride = 1, padding = 1)
+    self.relu2 = nn.ReLU()
+    self.conv3 = nn.Conv2d(64,128,kernel_size = 3, stride = 1, padding = 1)
+    self.relu4 = nn.ReLU()
+  def forward(self, input):
+    conv1_out = self.conv1(input)
+    relu2_out = self.relu2(conv1_out)
+    conv3_out = self.conv3(relu2_out)
+    relu4_out = self.relu4(conv3_out)
+    return relu4_out
+</code>
+You are welcome (and encouraged) to use the built-in batch normalization and dropout layer.
+Guessing that the pixel is not cancerous every single time will give you an accuracy of ~ 85%. Your trained network should be able to do better than that (but you will not be graded on accuracy).
-You are welcome (and encouraged) to use the built-in
+{{:cs501r_f2016:training_accuracy.png?400|}}
-dropout layer.
+{{:cs501r_f2016:training_loss.png?400|}}

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