Download Built-in model parameters from ASE_ANI (#481)

* download resources from ASE_ANI * update gitignore * get resources with request * fix flke8 * rm svn checkout for resources * update * fix flake8

Download Built-in model parameters from ASE_ANI (#481)
* download resources from ASE_ANI * update gitignore * get resources with request * fix flke8 * rm svn checkout for resources * update * fix flake8
9e7baa6e · Farhad Ramezanghorbani · GitHub · e08bb3d5 · 9e7baa6e · 9e7baa6e
Unverified Commit 9e7baa6e authored Jun 10, 2020 by Farhad Ramezanghorbani Committed by GitHub Jun 10, 2020
20 changed files
--- a/.gitignore
+++ b/.gitignore
@@ -30,6 +30,9 @@ dist
 *.swo
 /download
 /download.tar.xz
+/torchani/resources/*.info
+/torchani/resources/ani-1ccx_8x
+/torchani/resources/ani-2x_8x
 *.qdrep
 *.qdstrm
 *.zip

--- a/download.sh
+++ b/download.sh
 #!/bin/bash

+# download the data
 wget https://www.dropbox.com/sh/2c8zdqc1hrqsgwy/AAD6l24ngoiFa6DRapF6HPk5a/ -O download.zip
 unzip download.zip -d download || [[ $? == 2 ]]  # unzip return 2 for dropbox created zip file
--- a/torchani/models.py
+++ b/torchani/models.py
@@ -24,10 +24,12 @@ directly calculate energies or get an ASE calculator. For example:
    model0.species_to_tensor(['C', 'H', 'H', 'H', 'H'])
 """
 import os
+import io
+import requests
+import zipfile
 import torch
 from torch import Tensor
 from typing import Tuple, Optional
-from pkg_resources import resource_filename
 from . import neurochem
 from .nn import SpeciesConverter, SpeciesEnergies
 from .aev import AEVComputer
@@ -70,11 +72,26 @@ class BuiltinModel(torch.nn.Module):

    @staticmethod
    def _parse_neurochem_resources(info_file_path):
-        def get_resource(file_path):
-            package_name = '.'.join(__name__.split('.')[:-1])
-            return resource_filename(package_name, 'resources/' + file_path)
-
-        info_file = get_resource(info_file_path)
+        def get_resource(resource_path, file_path):
+            return os.path.join(resource_path, 'resources/' + file_path)
+
+        resource_path = os.path.dirname(__file__)
+        local_dir = os.path.expanduser('~/.local/torchani')
+
+        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_zip = zipfile.ZipFile(io.BytesIO(resource_res.content))
+                try:
+                    resource_zip.extractall(resource_path)
+                except PermissionError:
+                    resource_zip.extractall(local_dir)
+                    resource_path = local_dir
+            else:
+                resource_path = local_dir
+
+        info_file = get_resource(resource_path, info_file_path)

        with open(info_file) as f:
            # const_file: Path to the file with the builtin constants.
@@ -82,9 +99,9 @@ class BuiltinModel(torch.nn.Module):
            # ensemble_prefix: Prefix of the neurochem resource directories.
            lines = [x.strip() for x in f.readlines()][:4]
            const_file_path, sae_file_path, ensemble_prefix_path, ensemble_size = lines
-            const_file = get_resource(const_file_path)
-            sae_file = get_resource(sae_file_path)
-            ensemble_prefix = get_resource(ensemble_prefix_path)
+            const_file = get_resource(resource_path, const_file_path)
+            sae_file = get_resource(resource_path, sae_file_path)
+            ensemble_prefix = get_resource(resource_path, ensemble_prefix_path)
            ensemble_size = int(ensemble_size)
            consts = neurochem.Constants(const_file)
        return consts, sae_file, ensemble_prefix, ensemble_size

--- a/torchani/resources/ani-1ccx_8x.info
+++ b/torchani/resources/ani-1ccx_8x.info
-ani-1ccx_8x/rHCNO-5.2R_16-3.5A_a4-8.params
-ani-1ccx_8x/sae_linfit.dat
-ani-1ccx_8x/train
-8
-
--- a/torchani/resources/ani-1ccx_8x/rHCNO-5.2R_16-3.5A_a4-8.params
+++ b/torchani/resources/ani-1ccx_8x/rHCNO-5.2R_16-3.5A_a4-8.params
-TM = 1
-Rcr = 5.2000e+00
-Rca = 3.5000e+00
-EtaR = [1.6000000e+01]
-ShfR = [9.0000000e-01,1.1687500e+00,1.4375000e+00,1.7062500e+00,1.9750000e+00,2.2437500e+00,2.5125000e+00,2.7812500e+00,3.0500000e+00,3.3187500e+00,3.5875000e+00,3.8562500e+00,4.1250000e+00,4.3937500e+00,4.6625000e+00,4.9312500e+00]
-Zeta = [3.2000000e+01]
-ShfZ = [1.9634954e-01,5.8904862e-01,9.8174770e-01,1.3744468e+00,1.7671459e+00,2.1598449e+00,2.5525440e+00,2.9452431e+00]
-EtaA = [8.0000000e+00]
-ShfA = [9.0000000e-01,1.5500000e+00,2.2000000e+00,2.8500000e+00]
-Atyp = [H,C,N,O]
--- a/torchani/resources/ani-1ccx_8x/sae_linfit.dat
+++ b/torchani/resources/ani-1ccx_8x/sae_linfit.dat
-H,0=-0.5991501324919538
-C,1=-38.03750806057356
-N,2=-54.67448347695333
-O,3=-75.16043537275567
--- a/torchani/resources/ani-1ccx_8x/train
+++ b/torchani/resources/ani-1ccx_8x/train
--- a/torchani/resources/ani-1ccx_8x/train0/cost.dat
+++ b/torchani/resources/ani-1ccx_8x/train0/cost.dat
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-2 1.2222e-05 0.000241391 1.79769e+308 1.79769e+308 inf inf
-3 1.11759e-05 1.66028e-05 0.000241391 1.79769e+308 9.74946 inf
-4 1.335e-05 1.66687e-05 1.66028e-05 1.79769e+308 2.55688 inf
-5 5.44115e-05 2.59782e-05 1.66028e-05 1.79769e+308 2.55688 inf
-6 4.55619e-05 0.000357282 1.66028e-05 1.79769e+308 2.55688 inf
-7 2.59048e-05 0.000149242 1.66028e-05 1.79769e+308 2.55688 inf
-8 2.00976e-05 2.27834e-05 1.66028e-05 1.79769e+308 2.55688 inf
-9 8.92743e-05 2.68523e-05 1.66028e-05 1.79769e+308 2.55688 inf
-10 1.71051e-05 2.34208e-05 1.66028e-05 1.79769e+308 2.55688 inf
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-13 1.86511e-05 2.2338e-05 1.66028e-05 1.79769e+308 2.55688 inf
-14 3.46589e-05 0.00016045 1.66028e-05 1.79769e+308 2.55688 inf
-15 3.0222e-05 3.76193e-05 1.66028e-05 1.79769e+308 2.55688 inf
-16 1.64391e-05 2.46422e-05 1.66028e-05 1.79769e+308 2.55688 inf
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-20 1.19781e-05 2.05404e-05 1.66028e-05 1.79769e+308 2.55688 inf
-21 1.74969e-05 2.16347e-05 1.66028e-05 1.79769e+308 2.55688 inf
-22 1.19209e-05 2.57904e-05 1.66028e-05 1.79769e+308 2.55688 inf
-23 1.23027e-05 0.000133314 1.66028e-05 1.79769e+308 2.55688 inf
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-28 1.21865e-05 2.07102e-05 1.66028e-05 1.79769e+308 2.55688 inf
-29 1.10196e-05 0.000193286 1.66028e-05 1.79769e+308 2.55688 inf
-30 1.47525e-05 0.000254176 1.66028e-05 1.79769e+308 2.55688 inf
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-33 1.35587e-05 2.15521e-05 1.66028e-05 1.79769e+308 2.55688 inf
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-2363 1.56965e-05 0.00174866 1.51788e-05 1.79769e+308 2.44478 inf
-2364 1.50864e-05 0.000155369 1.51788e-05 1.79769e+308 2.44478 inf
-2365 1.48594e-05 1.96233e-05 1.51788e-05 1.79769e+308 2.44478 inf
-2366 1.47109e-05 0.00173932 1.51788e-05 1.79769e+308 2.44478 inf
-2367 1.56948e-05 1.86799e-05 1.51788e-05 1.79769e+308 2.44478 inf
-2368 1.54305e-05 0.000150538 1.51788e-05 1.79769e+308 2.44478 inf
-2369 2.21132e-05 2.11632e-05 1.51788e-05 1.79769e+308 2.44478 inf
-2370 1.51885e-05 0.000134581 1.51788e-05 1.79769e+308 2.44478 inf
-2371 1.4652e-05 0.00013803 1.51788e-05 1.79769e+308 2.44478 inf
-2372 1.57049e-05 0.000136946 1.51788e-05 1.79769e+308 2.44478 inf
-2373 1.63035e-05 1.91327e-05 1.51788e-05 1.79769e+308 2.44478 inf
-2374 1.60175e-05 2.0458e-05 1.51788e-05 1.79769e+308 2.44478 inf
-2375 1.5585e-05 2.01355e-05 1.51788e-05 1.79769e+308 2.44478 inf
-2376 1.55976e-05 2.11099e-05 1.51788e-05 1.79769e+308 2.44478 inf
-2377 1.60337e-05 0.000142457 1.51788e-05 1.79769e+308 2.44478 inf
-2378 1.53482e-05 0.000143661 1.51788e-05 1.79769e+308 2.44478 inf
-2379 1.64799e-05 0.000145269 1.51788e-05 1.79769e+308 2.44478 inf
-2380 1.45352e-05 2.05818e-05 1.51788e-05 1.79769e+308 2.44478 inf
-2381 1.4876e-05 1.98657e-05 1.51788e-05 1.79769e+308 2.44478 inf
-2382 1.52041e-05 1.92527e-05 1.51788e-05 1.79769e+308 2.44478 inf
-2383 1.49424e-05 2.00548e-05 1.51788e-05 1.79769e+308 2.44478 inf
-2384 1.49548e-05 1.9646e-05 1.51788e-05 1.79769e+308 2.44478 inf
-2385 1.56911e-05 0.000148889 1.51788e-05 1.79769e+308 2.44478 inf
-2386 1.53617e-05 0.000280007 1.51788e-05 1.79769e+308 2.44478 inf
-2387 1.53907e-05 2.24229e-05 1.51788e-05 1.79769e+308 2.44478 inf
-2388 1.54362e-05 0.000150204 1.51788e-05 1.79769e+308 2.44478 inf
-2389 1.57051e-05 0.000148312 1.51788e-05 1.79769e+308 2.44478 inf
-2390 1.50206e-05 1.99797e-05 1.51788e-05 1.79769e+308 2.44478 inf
-2391 1.55806e-05 2.08035e-05 1.51788e-05 1.79769e+308 2.44478 inf
-2392 1.6002e-05 0.000146853 1.51788e-05 1.79769e+308 2.44478 inf
-2393 1.56592e-05 2.00851e-05 1.51788e-05 1.79769e+308 2.44478 inf
-2394 1.62569e-05 0.000149228 1.51788e-05 1.79769e+308 2.44478 inf
-2395 1.58366e-05 2.0145e-05 1.51788e-05 1.79769e+308 2.44478 inf
-2396 1.53361e-05 1.92002e-05 1.51788e-05 1.79769e+308 2.44478 inf
-2397 1.70338e-05 0.000148942 1.51788e-05 1.79769e+308 2.44478 inf
-2398 1.52919e-05 2.02852e-05 1.51788e-05 1.79769e+308 2.44478 inf
-2399 1.53597e-05 2.10431e-05 1.51788e-05 1.79769e+308 2.44478 inf
-2400 1.61368e-05 0.000150768 1.51788e-05 1.79769e+308 2.44478 inf
-2401 1.54616e-05 0.000151748 1.51788e-05 1.79769e+308 2.44478 inf
-2402 1.56511e-05 2.01107e-05 1.51788e-05 1.79769e+308 2.44478 inf
-2403 1.57223e-05 2.01535e-05 1.51788e-05 1.79769e+308 2.44478 inf
-2404 3.09268e-05 1.98497e-05 1.51788e-05 1.79769e+308 2.44478 inf
-2405 1.7908e-05 2.11303e-05 1.51788e-05 1.79769e+308 2.44478 inf
-2406 1.59881e-05 0.000153648 1.51788e-05 1.79769e+308 2.44478 inf
-2407 1.61958e-05 1.91998e-05 1.51788e-05 1.79769e+308 2.44478 inf
-2408 1.52019e-05 0.000154725 1.51788e-05 1.79769e+308 2.44478 inf
-2409 1.57626e-05 0.000152079 1.51788e-05 1.79769e+308 2.44478 inf
-2410 1.58239e-05 0.000152985 1.51788e-05 1.79769e+308 2.44478 inf
-2411 1.51159e-05 0.000152278 1.51788e-05 1.79769e+308 2.44478 inf
-2412 1.54106e-05 0.000151192 1.51788e-05 1.79769e+308 2.44478 inf
-2413 1.5669e-05 2.00567e-05 1.51788e-05 1.79769e+308 2.44478 inf
-2414 1.63929e-05 0.000149907 1.51788e-05 1.79769e+308 2.44478 inf
--- a/torchani/resources/ani-1ccx_8x/train0/inputtrain.ipt
+++ b/torchani/resources/ani-1ccx_8x/train0/inputtrain.ipt
-!InputFile for Force Prediction Network
-sflparamsfile=rHCNO-5.2R_16-3.5A_a4-8.params
-ntwkStoreDir=networks/
-atomEnergyFile=sae_linfit.dat
-nmax=0! Maximum number of iterations (0 = inf)
-tolr=200! Tolerance - early stopping
-emult=0.5!Multiplier by eta after tol switch
-eta=0.001! Eta -- Learning rate
-tcrit=1.0E-6! Eta termination criterion
-tmax=0! Maximum time (0 = inf)
-tbtchsz=2560
-vbtchsz=2560
-gpuid=0
-ntwshr=0
-nkde=2
-energy=1
-freezelayer=0,2
-force=0
-fmult=0.0
-pbc=0
-cmult =0.001
-runtype=ANNP_CREATE_HDNN_AND_TRAIN!Create and train a HDN network
-network_setup {
-  inputsize=384;
-  atom_net H $
-    layer [
-          nodes=160;
-          activation=9;
-          type=0;
-    ]
-    layer [
-          nodes=128;
-          activation=9;
-          type=0;
-          l2norm=1;
-          l2valu=0.00001;
-    ]
-    layer [
-          nodes=96;
-          activation=9;
-          type=0;
-          l2norm=1;
-          l2valu=0.000001;
-    ]
-    layer [
-          nodes=1;
-          activation=6;
-          type=0;
-    ]
-    $
-  atom_net C $
-    layer [
-          nodes=144;
-          activation=9;
-          type=0;
-    ]
-    layer [
-          nodes=112;
-          activation=9;
-          type=0;
-          l2norm=1;
-          l2valu=0.00001;
-    ]
-    layer [
-          nodes=96;
-          activation=9;
-          type=0;
-          l2norm=1;
-          l2valu=0.000001;
-    ]
-    layer [
-          nodes=1;
-          activation=6;
-          type=0;
-    ]
-    $
-  atom_net N $
-    layer [
-          nodes=128;
-          activation=9;
-          type=0;
-    ]
-    layer [
-          nodes=112;
-          activation=9;
-          type=0;
-          l2norm=1;
-          l2valu=0.00001;
-    ]
-    layer [
-          nodes=96;
-          activation=9;
-          type=0;
-          l2norm=1;
-          l2valu=0.000001;
-    ]
-    layer [
-          nodes=1;
-          activation=6;
-          type=0;
-    ]
-    $
-  atom_net O $
-    layer [
-          nodes=128;
-          activation=9;
-          type=0;
-    ]
-    layer [
-          nodes=112;
-          activation=9;
-          type=0;
-          l2norm=1;
-          l2valu=0.00001;
-    ]
-    layer [
-          nodes=96;
-          activation=9;
-          type=0;
-          l2norm=1;
-          l2valu=0.000001;
-    ]
-    layer [
-          nodes=1;
-          activation=6;
-          type=0;
-    ]
-    $
-
-}
-adptlrn=OFF ! Adaptive learning (OFF,RMSPROP)
-decrate=0.9 !Decay rate of RMSPROP
-moment=ADAM! Turn on momentum or nesterov momentum (OFF,CNSTTEMP,TMANNEAL,REGULAR,NESTEROV)
-mu=0.99 ! Mu factor for momentum
-
--- a/torchani/resources/ani-1ccx_8x/train0/networks/ANN-C.nnf
+++ b/torchani/resources/ani-1ccx_8x/train0/networks/ANN-C.nnf
--- a/torchani/resources/ani-1ccx_8x/train0/networks/ANN-C.nnf-l1.bparam
+++ b/torchani/resources/ani-1ccx_8x/train0/networks/ANN-C.nnf-l1.bparam
--- a/torchani/resources/ani-1ccx_8x/train0/networks/ANN-C.nnf-l1.wparam
+++ b/torchani/resources/ani-1ccx_8x/train0/networks/ANN-C.nnf-l1.wparam
--- a/torchani/resources/ani-1ccx_8x/train0/networks/ANN-C.nnf-l2.bparam
+++ b/torchani/resources/ani-1ccx_8x/train0/networks/ANN-C.nnf-l2.bparam
--- a/torchani/resources/ani-1ccx_8x/train0/networks/ANN-C.nnf-l2.wparam
+++ b/torchani/resources/ani-1ccx_8x/train0/networks/ANN-C.nnf-l2.wparam
--- a/torchani/resources/ani-1ccx_8x/train0/networks/ANN-C.nnf-l3.bparam
+++ b/torchani/resources/ani-1ccx_8x/train0/networks/ANN-C.nnf-l3.bparam
--- a/torchani/resources/ani-1ccx_8x/train0/networks/ANN-C.nnf-l3.wparam
+++ b/torchani/resources/ani-1ccx_8x/train0/networks/ANN-C.nnf-l3.wparam
--- a/torchani/resources/ani-1ccx_8x/train0/networks/ANN-C.nnf-l4.bparam
+++ b/torchani/resources/ani-1ccx_8x/train0/networks/ANN-C.nnf-l4.bparam
-iU
\ No newline at end of file
--- a/torchani/resources/ani-1ccx_8x/train0/networks/ANN-C.nnf-l4.wparam
+++ b/torchani/resources/ani-1ccx_8x/train0/networks/ANN-C.nnf-l4.wparam
-9l=a9vw<<޽e;&=˼Th<eKRІ=-=AܽjT==b><
-=8<KΧ;VCyd
-p=xn'<n%X > *<<u$(켭S=oX;n=˄۾<5Kz5_LJW:=P9ܼ!)3ֳ=ƽ>fʼŐ=H?AX=72M	w+p(=;=7#%=<y۽to@qT=WbEq c@7Q۴捋=4.==|EZ=>?f>.f=dk<Mk<y<Ba=-B썽=5^=r=
\ No newline at end of file
--- a/torchani/resources/ani-1ccx_8x/train0/networks/ANN-H.nnf
+++ b/torchani/resources/ani-1ccx_8x/train0/networks/ANN-H.nnf
--- a/torchani/resources/ani-1ccx_8x/train0/networks/ANN-H.nnf-l1.bparam
+++ b/torchani/resources/ani-1ccx_8x/train0/networks/ANN-H.nnf-l1.bparam