def _init_sess_serial(self) :
self.sess = tf.Session(
config=tf.ConfigProto(intra_op_parallelism_threads=self.run_opt.num_intra_threads,
inter_op_parallelism_threads=self.run_opt.num_inter_threads
))
self.saver = tf.train.Saver()
saver = self.saver
if self.run_opt.init_mode == 'init_from_scratch' :
self._message("initialize model from scratch")
init_op = tf.global_variables_initializer()
self.sess.run(init_op)
fp = open(self.disp_file, "w")
fp.close ()
elif self.run_opt.init_mode == 'init_from_model' :
self._message("initialize from model %s" % self.run_opt.init_model)
init_op = tf.global_variables_initializer()
self.sess.run(init_op)
saver.restore (self.sess, self.run_opt.init_model)
self.sess.run(self.global_step.assign(0))
fp = open(self.disp_file, "w")
fp.close ()
elif self.run_opt.init_mode == 'restart' :
self._message("restart from model %s" % self.run_opt.restart)
init_op = tf.global_variables_initializer()
self.sess.run(init_op)
saver.restore (self.sess, self.run_opt.restart)
else :
raise RuntimeError ("unkown init mode")
def _init_sess_distrib(self):
ckpt_dir = os.path.join(os.getcwd(), self.save_ckpt)
assert (_is_subdir(ckpt_dir, os.getcwd())
), "the checkpoint dir must be a subdir of the current dir"
if self.run_opt.init_mode == 'init_from_scratch':
self._message("initialize model from scratch")
if self.run_opt.is_chief:
if os.path.exists(ckpt_dir):
shutil.rmtree(ckpt_dir)
if not os.path.exists(ckpt_dir):
os.makedirs(ckpt_dir)
fp = open(self.disp_file, "w")
fp.close()
elif self.run_opt.init_mode == 'init_from_model':
raise RuntimeError("distributed training does not support %s" %
self.run_opt.init_mode)
elif self.run_opt.init_mode == 'restart':
self._message("restart from model %s" % ckpt_dir)
if self.run_opt.is_chief:
assert (os.path.isdir(ckpt_dir)
), "the checkpoint dir %s should exists" % ckpt_dir
else:
raise RuntimeError("unkown init mode")
saver = tf.train.Saver(max_to_keep=1)
self.saver = None
# gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.5)
# config = tf.ConfigProto(allow_soft_placement=True,
# gpu_options = gpu_options,
# intra_op_parallelism_threads=self.run_opt.num_intra_threads,
# inter_op_parallelism_threads=self.run_opt.num_inter_threads)
config = tf.ConfigProto(
intra_op_parallelism_threads=self.run_opt.num_intra_threads,
inter_op_parallelism_threads=self.run_opt.num_inter_threads)
# The stop_hook handles stopping after running given steps
# stop_hook = tf.train.StopAtStepHook(last_step = stop_batch)
# hooks = [self.sync_replicas_hook, stop_hook]
hooks = [self.sync_replicas_hook]
scaffold = tf.train.Scaffold(saver=saver)
# Use monitor session for distributed computation
self.sess = tf.train.MonitoredTrainingSession(
master=self.run_opt.server.target,
is_chief=self.run_opt.is_chief,
config=config,
hooks=hooks,
scaffold=scaffold,
checkpoint_dir=ckpt_dir)
def setUp(self):
config = tf.ConfigProto()
if int(os.environ.get("DP_AUTO_PARALLELIZATION", 0)):
config.graph_options.rewrite_options.custom_optimizers.add(
).name = "dpparallel"
self.sess = self.test_session(config=config).__enter__()
self.nframes = 2
self.dcoord = [
12.83, 2.56, 2.18, 12.09, 2.87, 2.74, 00.25, 3.32, 1.68, 3.36,
3.00, 1.81, 3.51, 2.51, 2.60, 4.27, 3.22, 1.56
]
self.dtype = [0, 1, 1, 0, 1, 1]
self.dbox = [13., 0., 0., 0., 13., 0., 0., 0., 13.]
self.dnlist = [
33, -1, -1, -1, -1, 1, 32, 34, 35, -1, 0, 33, -1, -1, -1, 32, 34,
35, -1, -1, 6, 3, -1, -1, -1, 7, 4, 5, -1, -1, 6, -1, -1, -1, -1,
4, 5, 2, 7, -1, 3, 6, -1, -1, -1, 5, 2, 7, -1, -1, 3, 6, -1, -1,
-1, 4, 2, 7, -1, -1
]
self.dem_deriv = [
0.13227682739491875, 0.01648776318803519, -0.013864709953575083,
0.12967498112414713, 0.0204174282700489, -0.017169201045268437,
0.0204174282700489, -0.031583528930688706, -0.0021400703852459233,
-0.01716920104526844, -0.0021400703852459233, -0.03232887285478848,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.7946522798827726,
0.33289487400494444, 0.6013584820734476, 0.15412158847174678,
-0.502001299580599, -0.9068410573068878, -0.502001299580599,
-0.833906252681877, 0.3798928753582899, -0.9068410573068878,
0.3798928753582899, -0.3579459969766471, 0.4206262499369199,
0.761133214171572, -0.5007455356391932, -0.6442543005863454,
0.635525177045359, -0.4181086691087898, 0.6355251770453592,
0.15453235677768898, -0.75657759172067, -0.4181086691087898,
-0.75657759172067, -0.49771716703202185, 0.12240657396947655,
-0.0016631327984983461, 0.013970315507385892, 0.12123416269111335,
-0.0020346719145638054, 0.017091244082335703,
-0.002034671914563806, -0.028490045221941415,
-0.00023221799024912971, 0.017091244082335703,
-0.00023221799024912971, -0.026567059102687942,
0.057945707686107975, 0.008613551142529565, -0.008091517739952026,
0.056503423854730866, 0.009417127630974357, -0.008846392623036528,
0.009417127630974357, -0.005448318729873151,
-0.0013150043088297543, -0.008846392623036528,
-0.0013150043088297541, -0.005612854948377751, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.7946522798827726,
-0.33289487400494444, -0.6013584820734476, 0.15412158847174678,
-0.502001299580599, -0.9068410573068878, -0.502001299580599,
-0.833906252681877, 0.3798928753582899, -0.9068410573068878,
0.3798928753582899, -0.3579459969766471, 0.06884320605436924,
0.002095928989945659, -0.01499395354345747, 0.0668001797461137,
0.0023216922720068383, -0.016609029330510533,
0.0023216922720068383, -0.009387797963986713,
-0.0005056613145120282, -0.016609029330510533,
-0.0005056613145120282, -0.005841058553679004, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.3025931001933299,
0.11738525438534331, -0.2765074881076981, 0.034913562192579815,
0.15409432322878, -0.3629777391611269, 0.15409432322878003,
-0.30252938969021487, -0.14081032984698866, -0.3629777391611269,
-0.14081032984698866, -0.030620805157591004, 0.06555082496658332,
-0.005338981218997747, -0.002076270474054677, 0.06523884623439505,
-0.00599162877720186, -0.0023300778578007205, -0.00599162877720186,
-0.007837034455273667, 0.00018978009701544363,
-0.0023300778578007205, 0.00018978009701544363,
-0.008251237047966105, 0.014091999096200191, 0.0009521621010946066,
-0.00321014651226182, 0.013676554858123476, 0.0009667394698497006,
-0.0032592930697789946, 0.0009667394698497006,
-0.0005658690612028018, -0.00022022250471479668,
-0.0032592930697789937, -0.00022022250471479666,
0.00011127514881492382, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, -0.4206262499369199, -0.761133214171572,
0.5007455356391932, -0.6442543005863454, 0.635525177045359,
-0.4181086691087898, 0.6355251770453592, 0.15453235677768898,
-0.75657759172067, -0.4181086691087898, -0.75657759172067,
-0.49771716703202185, 0.17265177804411166, -0.01776481317495682,
0.007216955352326217, 0.1708538944675734, -0.023853120077098278,
0.009690330031321191, -0.02385312007709828, -0.05851427595224925,
-0.0009970757588497682, 0.00969033003132119,
-0.0009970757588497682, -0.06056355425469288, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.3025931001933299,
-0.11738525438534331, 0.2765074881076981, 0.034913562192579815,
0.15409432322878, -0.3629777391611269, 0.15409432322878003,
-0.30252938969021487, -0.14081032984698866, -0.3629777391611269,
-0.14081032984698866, -0.030620805157591004, 0.13298898711407747,
-0.03304327593938735, 0.03753063440029181, 0.11967949867634801,
-0.0393666881596552, 0.044712781613435545, -0.0393666881596552,
-0.02897797727002851, -0.01110961751744871, 0.044712781613435545,
-0.011109617517448708, -0.026140939946396612, 0.09709214772325653,
-0.00241522755530488, -0.0028982730663658636, 0.09699249715361474,
-0.0028489422636695603, -0.0034187307164034813,
-0.00284894226366956, -0.017464112635362926, 8.504305264685245e-05,
-0.003418730716403481, 8.504305264685245e-05,
-0.017432930182725747, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, -0.1322768273949186, -0.016487763188035173,
0.013864709953575069, 0.12967498112414702, 0.020417428270048884,
-0.017169201045268423, 0.02041742827004888, -0.03158352893068868,
-0.002140070385245921, -0.017169201045268423,
-0.002140070385245921, -0.03232887285478844, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.1802999914938216, -0.5889799722131493,
0.9495799552007915, -1.070225697321266, -0.18728687322613707,
0.30195230581356786, -0.18728687322613707, -0.5157546277429348,
-0.9863775323243197, 0.30195230581356786, -0.9863775323243197,
0.4627237303364723, 1.0053013143052718, 0.24303987818369216,
-0.2761816797541954, 0.8183357773897718, 0.45521877564245394,
-0.517294063230061, 0.45521877564245394, -0.9545617219529918,
-0.1250601031984763, -0.517294063230061, -0.1250601031984763,
-0.922500859133019, -0.17265177804411166, 0.01776481317495682,
-0.007216955352326217, 0.1708538944675734, -0.023853120077098278,
0.009690330031321191, -0.02385312007709828, -0.05851427595224925,
-0.0009970757588497682, 0.00969033003132119,
-0.0009970757588497682, -0.06056355425469288, -0.06884320605436924,
-0.002095928989945659, 0.01499395354345747, 0.0668001797461137,
0.0023216922720068383, -0.016609029330510533,
0.0023216922720068383, -0.009387797963986713,
-0.0005056613145120282, -0.016609029330510533,
-0.0005056613145120282, -0.005841058553679004, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.1802999914938216,
0.5889799722131493, -0.9495799552007915, -1.070225697321266,
-0.18728687322613707, 0.30195230581356786, -0.18728687322613707,
-0.5157546277429348, -0.9863775323243197, 0.30195230581356786,
-0.9863775323243197, 0.4627237303364723, -0.12240657396947667,
0.0016631327984983487, -0.013970315507385913, 0.12123416269111348,
-0.002034671914563809, 0.01709124408233573, -0.002034671914563809,
-0.028490045221941467, -0.00023221799024913015,
0.01709124408233573, -0.00023221799024913015,
-0.026567059102687987, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.2602591506940697, 0.24313683814840728, -0.3561441009497795,
-0.19841405298242495, 0.23891499072173572, -0.3499599864093028,
0.23891499072173572, -0.23095714382387694, -0.32693630309290145,
-0.34995998640930287, -0.32693630309290145, 0.02473856993038946,
-0.13298898711407747, 0.03304327593938735, -0.03753063440029181,
0.11967949867634801, -0.0393666881596552, 0.044712781613435545,
-0.0393666881596552, -0.02897797727002851, -0.01110961751744871,
0.044712781613435545, -0.011109617517448708, -0.026140939946396612,
-0.0655508249665835, 0.005338981218997763, 0.002076270474054683,
0.0652388462343952, -0.005991628777201879, -0.0023300778578007283,
-0.005991628777201879, -0.007837034455273709,
0.0001897800970154443, -0.002330077857800728,
0.0001897800970154443, -0.008251237047966148, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -1.0053013143052718,
-0.24303987818369216, 0.2761816797541954, 0.8183357773897718,
0.45521877564245394, -0.517294063230061, 0.45521877564245394,
-0.9545617219529918, -0.1250601031984763, -0.517294063230061,
-0.1250601031984763, -0.922500859133019, -0.057945707686107864,
-0.008613551142529548, 0.00809151773995201, 0.05650342385473076,
0.009417127630974336, -0.00884639262303651, 0.009417127630974336,
-0.005448318729873148, -0.0013150043088297515,
-0.00884639262303651, -0.0013150043088297513,
-0.005612854948377747, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, -0.2602591506940697, -0.24313683814840728, 0.3561441009497795,
-0.19841405298242495, 0.23891499072173572, -0.3499599864093028,
0.23891499072173572, -0.23095714382387694, -0.32693630309290145,
-0.34995998640930287, -0.32693630309290145, 0.02473856993038946,
-0.09709214772325653, 0.00241522755530488, 0.0028982730663658636,
0.09699249715361474, -0.0028489422636695603,
-0.0034187307164034813, -0.00284894226366956,
-0.017464112635362926, 8.504305264685245e-05,
-0.003418730716403481, 8.504305264685245e-05,
-0.017432930182725747, -0.014091999096200191,
-0.0009521621010946064, 0.0032101465122618194,
0.013676554858123474, 0.0009667394698497003,
-0.0032592930697789933, 0.0009667394698497003,
-0.0005658690612028016, -0.0002202225047147966,
-0.0032592930697789933, -0.0002202225047147966,
0.00011127514881492362, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0
]
self.dcoord = np.reshape(self.dcoord, [1, -1])
self.dtype = np.reshape(self.dtype, [1, -1])
self.dbox = np.reshape(self.dbox, [1, -1])
self.dnlist = np.reshape(self.dnlist, [1, -1])
self.dem_deriv = np.reshape(self.dem_deriv, [1, -1])
self.dcoord = np.tile(self.dcoord, [self.nframes, 1])
self.dtype = np.tile(self.dtype, [self.nframes, 1])
self.dbox = np.tile(self.dbox, [self.nframes, 1])
self.dnlist = np.tile(self.dnlist, [self.nframes, 1])
self.dem_deriv = np.tile(self.dem_deriv, [self.nframes, 1])
self.expected_force = [
9.44498, -13.86254, 10.52884, -19.42688, 8.09273, 19.64478,
4.81771, 11.39255, 12.38830, -16.65832, 6.65153, -10.15585,
1.16660, -14.43259, 22.97076, 22.86479, 7.42726, -11.41943,
-7.67893, -7.23287, -11.33442, -4.51184, -3.80588, -2.44935,
0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000,
0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000,
0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000,
0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000,
0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000,
0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000,
0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000,
0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000,
0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000,
0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000,
0.00000, 0.00000, 1.16217, 6.16192, -28.79094, 3.81076, -0.01986,
-1.01629, 3.65869, -0.49195, -0.07437, 1.35028, 0.11969, -0.29201,
0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000,
0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000,
0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000,
0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000,
0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000,
0.00000
]
self.sel = [5, 5]
self.sec = np.array([0, 0, 0], dtype=int)
self.sec[1:3] = np.cumsum(self.sel)
self.rcut = 6.
self.rcut_smth = 0.8
self.dnatoms = [6, 48, 2, 4]
self.nloc = self.dnatoms[0]
self.nall = self.dnatoms[1]
self.nnei = self.sec[-1]
self.ndescrpt = 4 * self.nnei
self.ntypes = np.max(self.dtype) + 1
self.dnet_deriv = []
for ii in range(self.nloc * self.ndescrpt):
self.dnet_deriv.append(10 - ii * 0.01)
self.dnet_deriv = np.reshape(self.dnet_deriv, [1, -1])
self.dnet_deriv = np.tile(self.dnet_deriv, [self.nframes, 1])
self.tnet_deriv = tf.placeholder(
GLOBAL_TF_FLOAT_PRECISION, [None, self.dnatoms[0] * self.ndescrpt],
name='t_net_deriv')
self.tem_deriv = tf.placeholder(
GLOBAL_TF_FLOAT_PRECISION,
[None, self.dnatoms[0] * self.ndescrpt * 3],
name='t_em_deriv')
self.tnlist = tf.placeholder(tf.int32,
[None, self.dnatoms[0] * self.nnei],
name="t_nlist")
self.tnatoms = tf.placeholder(tf.int32, [None], name="t_natoms")