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--- cs501r_f2018:lab4 [2018/09/24 21:52]
shreeya
+++ cs501r_f2018:lab4 [2021/06/30 23:42] (current)
@@ 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 104: / Line 104: @@
 <code python>
+import torchvision
+import os
+import gzip
+import tarfile
+import gc
+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):
@@ Line 134: / Line 142: @@
     img = self.dataset_folder[index]
     label = self.label_folder[index]
-    return img[0] * 255,label[0][0]
+    return img[0],label[0][0]
   def __len__(self):
@@ Line 144: / Line 152: @@
 down to 512x512.
-You are welcome (and encouraged) to use the built-in
+You will need to add some lines of code for memory management:
-dropout layer.
+<code python>
+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>
+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:
+<code python>
+class CancerDetection(nn.Module):
+  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). This is the result I got after 1 hour or training.
+{{:cs501r_f2016:training_accuracy.png?400|}}
+{{:cs501r_f2016:training_loss.png?400|}}

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