Initial commit

ML/Projects/DeepSort/utils.py

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+import torch
+
+
+def ask_user():
+    print("Write your array as a list [i,j,k..] with arbitrary positive numbers")
+    array = input("Input q if you want to quit \n")
+    return array
+
+
+def sort_array(encoder, decoder, device, arr=None):
+    """
+    A very simple example of use of the model
+    Input: encoder nn.Module
+           decoder nn.Module
+           device
+           array to sort (optional)
+    """
+
+    if arr is None:
+        arr = ask_user()
+
+    with torch.no_grad():
+        while arr != "q":
+            # Avoid numerical errors by rounding to max_len
+            arr = eval(arr)
+            lengths = [
+                len(str(elem).split(".")[1]) if len(str(elem).split(".")) > 1 else 0
+                for elem in arr
+            ]
+            max_len = max(lengths)
+            source = torch.tensor(arr, dtype=torch.float).to(device).unsqueeze(1)
+            batch_size = source.shape[1]
+            target_len = source.shape[0] + 1
+
+            outputs = torch.zeros(target_len, batch_size, target_len - 1).to(device)
+            encoder_states, hidden, cell = encoder(source)
+
+            # First input will be <SOS> token
+            x = torch.tensor([-1], dtype=torch.float).to(device)
+            predictions = torch.zeros((target_len)).to(device)
+
+            for t in range(1, target_len):
+                # At every time step use encoder_states and update hidden, cell
+                attention, energy, hidden, cell = decoder(
+                    x, encoder_states, hidden, cell
+                )
+
+                # Store prediction for current time step
+                outputs[t] = energy.permute(1, 0)
+
+                # Get the best word the Decoder predicted (index in the vocabulary)
+                best_guess = attention.argmax(0)
+                predictions[t] = best_guess.item()
+                x = torch.tensor([best_guess.item()], dtype=torch.float).to(device)
+
+            output = [
+                round(source[predictions[1:].long()][i, :].item(), max_len)
+                for i in range(source.shape[0])
+            ]
+
+            print(f"Here's the result: {output}")
+            arr = ask_user()
+
+
+def save_checkpoint(state, filename="my_checkpoint.pth.tar"):
+    print("=> Saving checkpoint")
+    torch.save(state, filename)
+
+
+def load_checkpoint(checkpoint, model, optimizer):  # , steps):
+    print("=> Loading checkpoint")
+    model.load_state_dict(checkpoint["state_dict"])
+    optimizer.load_state_dict(checkpoint["optimizer"])
+    # steps = checkpoint['steps']
+    # return steps