Update model URL (#484)

* Update model URL

* Update models.py

* Update models.py

* fix

* cleanup

* save

examples/energy_force.py

@@ -26,7 +26,7 @@ device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 # The ``periodic_table_index`` arguments tells TorchANI to use element index
 # in periodic table to index species. If not specified, you need to use
 # 0, 1, 2, 3, ... to index species
-model = torchani.models.ANI1ccx(periodic_table_index=True).to(device)
+model = torchani.models.ANI2x(periodic_table_index=True).to(device)
 
 ###############################################################################
 # Now let's define the coordinate and species. If you just want to compute the

torchani/models.py

@@ -26,7 +26,9 @@ directly calculate energies or get an ASE calculator. For example:
 import os
 import io
 import requests
+import glob
 import zipfile
+import shutil
 import torch
 from torch import Tensor
 from typing import Tuple, Optional
@@ -73,15 +75,19 @@ class BuiltinModel(torch.nn.Module):
     @staticmethod
     def _parse_neurochem_resources(info_file_path):
         def get_resource(resource_path, file_path):
-            return os.path.join(resource_path, 'resources/' + file_path)
+            return os.path.join(resource_path, file_path)
 
-        resource_path = os.path.dirname(__file__)
-        local_dir = os.path.expanduser('~/.local/torchani')
+        resource_path = os.path.join(os.path.dirname(__file__), 'resources/')
+        local_dir = os.path.expanduser('~/.local/torchani/')
+        repo_name = "ani-model-zoo"
+        tag_name = "ani-2x"
+        extracted_name = '{}-{}'.format(repo_name, tag_name)
+        url = "https://github.com/aiqm/{}/archive/{}.zip".format(repo_name, tag_name)
 
         if not os.path.isfile(get_resource(resource_path, info_file_path)):
             if not os.path.isfile(get_resource(local_dir, info_file_path)):
                 print('Downloading ANI model parameters ...')
-                resource_res = requests.get("https://www.dropbox.com/sh/otrzul6yuye8uzs/AABuaihE22vtaB_rdrI0r6TUa?dl=1")
+                resource_res = requests.get(url)
                 resource_zip = zipfile.ZipFile(io.BytesIO(resource_res.content))
                 try:
                     resource_zip.extractall(resource_path)
@@ -91,6 +97,14 @@ class BuiltinModel(torch.nn.Module):
             else:
                 resource_path = local_dir
 
+            files = glob.glob(os.path.join(resource_path, extracted_name, "resources", "*"))
+            for f in files:
+                try:
+                    shutil.move(f, resource_path)
+                except shutil.Error:
+                    pass
+            shutil.rmtree(os.path.join(resource_path, extracted_name))
+
         info_file = get_resource(resource_path, info_file_path)
 
         with open(info_file) as f: