update template code

mmp/a5/main.py

@@ -0,0 +1,39 @@
+import torch
+import torch.optim as optim
+
+from .model import MmpNet
+
+
+def step(
+    model: MmpNet,
+    criterion,
+    optimizer: optim.Optimizer,
+    img_batch: torch.Tensor,
+    lbl_batch: torch.Tensor,
+) -> float:
+    """Performs one update step for the model
+
+    @return: The loss for the specified batch. Return a float and not a PyTorch tensor
+    """
+    raise NotImplementedError()
+
+
+def get_random_sampling_mask(labels: torch.Tensor, neg_ratio: float) -> torch.Tensor:
+    """
+    @param labels: The label tensor that is returned by your data loader.
+    The values are either 0 (negative label) or 1 (positive label).
+    @param neg_ratio: The desired negative/positive ratio.
+    Hint: after computing the mask, check if the neg_ratio is fulfilled.
+    @return: A tensor with the same shape as labels
+    """
+    assert labels.min() >= 0 and labels.max() <= 1  # remove this line if you want
+    raise NotImplementedError()
+
+
+def main():
+    """Put your training code for exercises 5.2 and 5.3 here"""
+    raise NotImplementedError()
+
+
+if __name__ == "__main__":
+    main()

mmp/a5/model.py

@@ -0,0 +1,9 @@
+import torch
+
+
+class MmpNet(torch.nn.Module):
+    def __init__(self, num_widths: int, num_aspect_ratios: int):
+        raise NotImplementedError()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        raise NotImplementedError()