Update README.md

README.md

@@ -1,10 +1,11 @@
-latitude_mean:  39.95184413388056
+atitude_mean:  39.95184413388056
 latitude_std:  0.0006308700565432299
 longitude_mean:  -75.19147985909444
 longitude_std:  0.0006379960634765379
 
 To run input tensors to `predict_from_model(input_tensor)`:
 
+
 ```
 import torch
 import torch.nn as nn
@@ -24,10 +25,13 @@ def predict_from_model(input_tensor):
     from datasets import load_dataset
     from huggingface_hub import hf_hub_download
     import numpy as np
+
+    torch.cuda.empty_cache()
+
 #############
     path_map = {"best region models/region_model_lr_0.0002_step_10_gamma_0.1_epochs_15.pth" : hf_hub_download(repo_id="IanAndJohn/region_ensemble_model", filename="best region models/region_model_lr_0.0002_step_10_gamma_0.1_epochs_15.pth"),
                 "best region models/region_model_lr_0.00035_step_10_gamma_0.1_epochs_50.pth" : hf_hub_download(repo_id="IanAndJohn/region_ensemble_model", filename="best region models/region_model_lr_0.00035_step_10_gamma_0.1_epochs_50.pth"),
-                "best region models/region_model_lr_0.0005_step_10_gamma_0.1_epochs_50.pth" : hf_hub_download(repo_id="IanAndJohn/region_ensemble_model", filename="best region models/region_model_lr_0.0005_step_10_gamma_0.1_epochs_50.pth"), 
+                "best region models/region_model_lr_0.0005_step_10_gamma_0.1_epochs_50.pth" : hf_hub_download(repo_id="IanAndJohn/region_ensemble_model", filename="best region models/region_model_lr_0.0005_step_10_gamma_0.1_epochs_50.pth"),
                 "best region models/region_model_lr_0.0005_step_10_gamma_0.1_epochs_60.pth" : hf_hub_download(repo_id="IanAndJohn/region_ensemble_model", filename="best region models/region_model_lr_0.0005_step_10_gamma_0.1_epochs_60.pth"),
                 "best region models/region_model_lr_0.002_step_10_gamma_0.1_epochs_100.pth" : hf_hub_download(repo_id="IanAndJohn/region_ensemble_model", filename="best region models/region_model_lr_0.002_step_10_gamma_0.1_epochs_100.pth"),
                 "best region models/model_histories.json" : hf_hub_download(repo_id="IanAndJohn/region_ensemble_model", filename="best region models/model_histories.json"),
@@ -138,7 +142,7 @@ def predict_from_model(input_tensor):
         def __init__(self, tensors, transform=None, lat_mean=None, lat_std=None, lon_mean=None, lon_std=None, useRegions=False, give_originals=False):
             # self.hf_dataset = hf_dataset.map(
             self.tensors = tensors
-            
+
         def __len__(self):
             return len(self.tensors)
 
@@ -204,7 +208,7 @@ def predict_from_model(input_tensor):
     class_weights = [0.2839, 0.4268, 0.5583, 0.3873, 1.0000, 0.6036, 0.6009]
 #####################
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    print(f'Using device: {device}')
+    # print(f'Using device: {device}')
 
     per_model_weights = []
     with open(path_map['region_ensemble_weights.json'], 'r') as file:
@@ -279,6 +283,7 @@ def predict_from_model(input_tensor):
         po_predicted_region_lst[po.predicted_region].append(po)
 
     po_datasets = [PredictionObjectDataset(x, give_originals=True) for x in po_predicted_region_lst]
+    # print([len(ds) for ds in po_datasets])
     po_loaders = [DataLoader(x, batch_size=32, shuffle=False) for x in po_datasets]
 
     # lat_mean_lst = [x.latitude_mean for x in po_datasets]
@@ -305,6 +310,8 @@ def predict_from_model(input_tensor):
         # model_all_regions = []
 
         val_dataloader = po_loaders[i]
+        if (len(val_dataloader) == 0):
+            continue
 
         state_dict = torch.load(path_map[f'models/location_model_{i}.pth'])
 
@@ -360,6 +367,7 @@ def predict_from_model(input_tensor):
         # all_preds.append([model_all_preds])
         # all_actuals.append([model_all_actuals])
         all_preds_norm.append([model_all_preds_norm])
+        # print("images predicted: ", len(all_preds_norm))
         # all_actuals_norm.append([model_all_actuals_norm])
         # all_regions.append(model_all_regions)
 
@@ -550,7 +558,4 @@ def predict_from_model(input_tensor):
 #     plt.legend()
 #     plt.grid(True)
 #     plt.show()
-
-torch.cuda.empty_cache()
-predict_from_model(input)
-```
+```