added kaggle comp solution for facial keypoint

ML/Kaggles/Facial Keypoint Detection Competition/utils.py

@@ -0,0 +1,71 @@
+import torch
+import numpy as np
+import config
+import pandas as pd
+from tqdm import tqdm
+
+
+def get_submission(loader, dataset, model_15, model_4):
+    """
+    This can be done a lot faster.. but it didn't take
+    too much time to do it in this inefficient way
+    """
+    model_15.eval()
+    model_4.eval()
+    id_lookup = pd.read_csv("data/IdLookupTable.csv")
+    predictions = []
+    image_id = 1
+
+    for image, label in tqdm(loader):
+        image = image.to(config.DEVICE)
+        preds_15 = torch.clip(model_15(image).squeeze(0), 0.0, 96.0)
+        preds_4 = torch.clip(model_4(image).squeeze(0), 0.0, 96.0)
+        feature_names = id_lookup.loc[id_lookup["ImageId"] == image_id]["FeatureName"]
+
+        for feature_name in feature_names:
+            feature_index = dataset.category_names.index(feature_name)
+            if feature_names.shape[0] < 10:
+                predictions.append(preds_4[feature_index].item())
+            else:
+                predictions.append(preds_15[feature_index].item())
+
+        image_id += 1
+
+    df = pd.DataFrame({"RowId": np.arange(1, len(predictions)+1), "Location": predictions})
+    df.to_csv("submission.csv", index=False)
+    model_15.train()
+    model_4.train()
+
+
+def get_rmse(loader, model, loss_fn, device):
+    model.eval()
+    num_examples = 0
+    losses = []
+    for batch_idx, (data, targets) in enumerate(loader):
+        data = data.to(device=device)
+        targets = targets.to(device=device)
+
+        # forward
+        scores = model(data)
+        loss = loss_fn(scores[targets != -1], targets[targets != -1])
+        num_examples += scores[targets != -1].shape[0]
+        losses.append(loss.item())
+
+    model.train()
+    print(f"Loss on val: {(sum(losses)/num_examples)**0.5}")
+
+
+def save_checkpoint(state, filename="my_checkpoint.pth.tar"):
+    print("=> Saving checkpoint")
+    torch.save(state, filename)
+
+
+def load_checkpoint(checkpoint, model, optimizer, lr):
+    print("=> Loading checkpoint")
+    model.load_state_dict(checkpoint["state_dict"])
+    optimizer.load_state_dict(checkpoint["optimizer"])
+
+    # If we don't do this then it will just have learning rate of old checkpoint
+    # and it will lead to many hours of debugging \:
+    for param_group in optimizer.param_groups:
+        param_group["lr"] = lr