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Han Wang
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add unittests for input conversion. fix bugs
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7 files changed

+229
-7
lines changed

7 files changed

+229
-7
lines changed

‎examples/water/train/water_se_a.json‎

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@@ -24,7 +24,7 @@
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"learning_rate" :{
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"type": "exp",
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"start_lr": 0.005,
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"start_lr": 0.001,
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"decay_steps": 5000,
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"decay_rate": 0.95,
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"_comment": "that's all"

‎source/tests/compat_inputs/water_se_a_v0.json‎

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{
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"_comment": " model parameters",
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"use_smooth": true,
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"sel_a": [46, 92],
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"rcut_smth": 5.80,
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"rcut": 6.00,
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"filter_neuron": [25, 50, 100],
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"filter_resnet_dt": false,
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"axis_neuron": 16,
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"fitting_neuron": [240, 240, 240],
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"fitting_resnet_dt":true,
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"coord_norm": true,
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"type_fitting_net": false,
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"_comment": " traing controls",
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"systems": ["../data/"],
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"set_prefix": "set",
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"stop_batch": 1000000,
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"batch_size": [1],
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"start_lr": 0.001,
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"decay_steps": 5000,
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"decay_rate": 0.95,
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"start_pref_e": 0.02,
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"limit_pref_e": 1,
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"start_pref_f": 1000,
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"limit_pref_f": 1,
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"start_pref_v": 0,
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"limit_pref_v": 0,
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"seed": 1,
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"_comment": " display and restart",
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"_comment": " frequencies counted in batch",
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"disp_file": "lcurve.out",
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"disp_freq": 100,
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"numb_test": 10,
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"save_freq": 1000,
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"save_ckpt": "model.ckpt",
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"disp_training": true,
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"time_training": true,
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"profiling": true,
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"profiling_file": "timeline.json",
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"_comment": "that's all"
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}
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‎source/tests/compat_inputs/water_se_a_v1.json‎

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{
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"model": {
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"descriptor" :{
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"type": "se_a",
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"sel": [46, 92],
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"rcut_smth": 5.80,
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"rcut": 6.00,
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"neuron": [25, 50, 100],
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"axis_neuron": 16,
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"resnet_dt": false,
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"seed": 1
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},
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"fitting_net" : {
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"neuron": [240, 240, 240],
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"resnet_dt": true,
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"seed": 1
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}
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},
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"learning_rate" :{
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"type": "exp",
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"decay_steps": 5000,
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"decay_rate": 0.95,
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"start_lr": 0.001
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},
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"loss" :{
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"start_pref_e": 0.02,
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"limit_pref_e": 1,
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"start_pref_f": 1000,
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"limit_pref_f": 1,
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"start_pref_v": 0,
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"limit_pref_v": 0
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},
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"training" : {
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"systems": ["../data/"],
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"set_prefix": "set",
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"stop_batch": 1000000,
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"batch_size": [1],
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"seed": 1,
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"disp_file": "lcurve.out",
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"disp_freq": 100,
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"numb_test": 10,
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"save_freq": 1000,
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"save_ckpt": "model.ckpt",
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"disp_training":true,
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"time_training":true,
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"profiling": true,
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"profiling_file":"timeline.json"
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}
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}
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‎source/tests/compat_inputs/water_v0.json‎

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{
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"with_distrib": false,
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"_comment": " model parameters",
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"use_smooth": false,
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"sel_a": [16, 32],
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"sel_r": [30, 60],
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"rcut": 6.00,
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"axis_rule": [0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0],
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"_comment": " default rule: []",
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"_comment": " user defined rule: for each type provides two axes, ",
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"_comment": " for each axis: (a_or_r, type, idx)",
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"_comment": " if type < 0, exclude type -(type+1)",
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"_comment": " for water (O:0, H:1) it can be",
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"_comment": " [0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0]",
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"fitting_neuron": [240, 120, 60, 30, 10],
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"_comment": " traing controls",
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"systems": ["../data/"],
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"set_prefix": "set",
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"stop_batch": 1000000,
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"batch_size": [4],
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"start_lr": 0.001,
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"decay_steps": 5000,
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"decay_rate": 0.95,
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"start_pref_e": 0.02,
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"limit_pref_e": 8,
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"start_pref_f": 1000,
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"limit_pref_f": 1,
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"start_pref_v": 0,
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"limit_pref_v": 0,
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"seed": 1,
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"_comment": " display and restart",
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"_comment": " frequencies counted in batch",
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"disp_file": "lcurve.out",
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"disp_freq": 100,
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"numb_test": 10,
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"save_freq": 1000,
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"save_ckpt": "model.ckpt",
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"disp_training": true,
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"time_training": true,
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"_comment": "that's all"
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}
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‎source/tests/compat_inputs/water_v1.json‎

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{
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"with_distrib": false,
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"model":{
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"descriptor": {
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"type": "loc_frame",
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"sel_a": [16, 32],
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"sel_r": [30, 60],
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"rcut": 6.00,
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"axis_rule": [0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0]
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},
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"fitting_net": {
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"neuron": [240, 120, 60, 30, 10],
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"resnet_dt": true,
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"seed": 1
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}
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},
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"learning_rate" :{
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"type": "exp",
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"decay_steps": 5000,
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"decay_rate": 0.95,
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"start_lr": 0.001
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},
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"loss" : {
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"start_pref_e": 0.02,
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"limit_pref_e": 8,
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"start_pref_f": 1000,
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"limit_pref_f": 1,
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"start_pref_v": 0,
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"limit_pref_v": 0
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},
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"training": {
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"systems": ["../data/"],
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"set_prefix": "set",
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"stop_batch": 1000000,
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"batch_size": [4],
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"seed": 1,
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"disp_file": "lcurve.out",
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"disp_freq": 100,
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"numb_test": 10,
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"save_freq": 1000,
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"save_ckpt": "model.ckpt",
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"disp_training":true,
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"time_training":true
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}
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}
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‎source/tests/test_compat_input_v0_v1.py‎

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import os,sys,json
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import numpy as np
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import unittest
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from deepmd.compat import convert_input_v0_v1
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class TestConvertInputV0V1 (unittest.TestCase) :
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def test_convert_smth(self):
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with open(os.path.join('compat_inputs', 'water_se_a_v0.json')) as fp:
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jdata0 = json.load(fp)
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with open(os.path.join('compat_inputs', 'water_se_a_v1.json')) as fp:
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jdata1 = json.load(fp)
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jdata = convert_input_v0_v1(jdata0, warning = False, dump = None)
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self.assertEqual(jdata, jdata1)
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def test_convert_nonsmth(self):
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with open(os.path.join('compat_inputs', 'water_v0.json')) as fp:
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jdata0 = json.load(fp)
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with open(os.path.join('compat_inputs', 'water_v1.json')) as fp:
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jdata1 = json.load(fp)
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jdata = convert_input_v0_v1(jdata0, warning = False, dump = None)
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self.assertEqual(jdata, jdata1)
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‎source/train/compat.py‎

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Original file line numberDiff line numberDiff line change
@@ -57,11 +57,11 @@ def _smth_descriptor(jdata):
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seed = jdata['seed']
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descriptor['type'] = 'se_a'
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descriptor['sel'] = jdata['sel_a']
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descriptor['rcut'] = jdata['rcut']
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if j_have(jdata, 'rcut_smth') :
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descriptor['rcut_r_smth'] = jdata['rcut_smth']
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descriptor['rcut_smth'] = jdata['rcut_smth']
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else :
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descriptor['rcut_r_smth'] = descriptor['rcut']
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descriptor['rcut_smth'] = descriptor['rcut']
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descriptor['rcut'] = jdata['rcut']
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descriptor['neuron'] = j_must_have (jdata, 'filter_neuron')
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descriptor['axis_neuron'] = j_must_have_d (jdata, 'axis_neuron', ['n_axis_neuron'])
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descriptor['resnet_dt'] = False
@@ -128,9 +128,8 @@ def _training(jdata):
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training['numb_test'] = j_must_have (jdata, 'numb_test')
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training['save_freq'] = j_must_have (jdata, 'save_freq')
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training['save_ckpt'] = j_must_have (jdata, 'save_ckpt')
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training['display_in_training'] = j_must_have (jdata, 'disp_training')
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training['timing_in_training'] = j_must_have (jdata, 'time_training')
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training['profiling'] = False
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training['disp_training'] = j_must_have (jdata, 'disp_training')
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training['time_training'] = j_must_have (jdata, 'time_training')
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if j_have (jdata, 'profiling') :
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training['profiling'] = jdata['profiling']
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if training['profiling'] :

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