generated from fastai/nbdev_template
-
Notifications
You must be signed in to change notification settings - Fork 3
/
infer_weightedRegress_wClusterEcut.py
194 lines (156 loc) · 7.54 KB
/
infer_weightedRegress_wClusterEcut.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
import numpy as np
import os
import sys
import glob
import uproot as ur
import matplotlib.pyplot as plt
import time
import seaborn as sns
import tensorflow as tf
from graph_nets import utils_np
from graph_nets import utils_tf
from graph_nets.graphs import GraphsTuple
import sonnet as snt
import argparse
import yaml
import logging
import tensorflow as tf
import pandas as pd
from gn4pions.modules.data_infer_wClusterEcuts import MPGraphDataGenerator
from gn4pions.modules.models import MultiOutWeightedRegressModel
from gn4pions.modules.utils import convert_to_tuple
sns.set_context('poster')
def get_batch(data_iter):
for graphs, targets, meta in data_iter:
graphs = convert_to_tuple(graphs)
targets = tf.convert_to_tensor(targets)
yield graphs, targets, meta
index_to_class = {0: 'pi0', 1: 'pion'}
if __name__=="__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--save_dir', default='results/Block_multiJob_20220218_simult_wClusterEcut/')
args = parser.parse_args()
config = yaml.load(open(args.save_dir + '/config.yaml'))
data_config = config['data']
model_config = config['model']
train_config = config['training']
data_dir = data_config['data_dir']
num_train_files = data_config['num_train_files']
num_val_files = data_config['num_val_files']
batch_size = data_config['batch_size']
shuffle = data_config['shuffle']
cluster_E_cut = data_config['cluster_E_cut']
num_procs = data_config['num_procs']
preprocess = data_config['preprocess']
output_dir = '/p/vast1/karande1/heavyIon/data/preprocessed_data/infer/geo_wClusterEcut/'
already_preprocessed = False # data_config['already_preprocessed']
concat_input = model_config['concat_input']
epochs = train_config['epochs']
learning_rate = train_config['learning_rate']
alpha = train_config['alpha']
os.environ['CUDA_VISIBLE_DEVICES'] = str(train_config['gpu'])
log_freq = train_config['log_freq']
save_dir = args.save_dir
logging.basicConfig(level=logging.INFO,
format='%(message)s',
filename=save_dir + '/infer_output.log')
logging.info('Using config file from {}'.format(args.save_dir))
pi0_files = np.sort(glob.glob(data_dir+'*graphs.v01*/*pi0*/*root'))
pion_files = np.sort(glob.glob(data_dir+'*graphs.v01*/*pion*/*root'))
train_start = 0
train_end = train_start + num_train_files
val_end = train_end + num_val_files
pi0_val_files = pi0_files[train_end:val_end]
pion_val_files = pion_files[train_end:val_end]
val_output_dir = None
# Get Data
if preprocess:
val_output_dir = output_dir + '/val/'
if already_preprocessed:
val_files = np.sort(glob.glob(val_output_dir+'*.p'))[:num_val_files]
pi0_val_files = val_files
pion_val_files = None
val_output_dir = None
data_gen_val = MPGraphDataGenerator(pi0_file_list=pi0_val_files,
pion_file_list=pion_val_files,
cellGeo_file=data_dir+'graph_examples/cell_geo.root',
batch_size=batch_size,
shuffle=shuffle,
cluster_E_cut=cluster_E_cut,
num_procs=num_procs,
preprocess=preprocess,
output_dir=val_output_dir)
# Optimizer.
optimizer = tf.keras.optimizers.Adam(learning_rate)
model = MultiOutWeightedRegressModel(global_output_size=1, num_outputs=2, model_config=model_config)
mae_loss = tf.keras.losses.MeanAbsoluteError()
bce_loss = tf.keras.losses.BinaryCrossentropy(from_logits=True)
def loss_fn(targets, regress_preds, class_preds):
regress_loss = mae_loss(targets[:,:1], regress_preds)
class_loss = bce_loss(targets[:,1:], class_preds)
combined_loss = alpha*regress_loss + (1 - alpha)*class_loss
return regress_loss, class_loss, combined_loss
samp_graph, samp_target, _ = next(get_batch(data_gen_val.generator()))
data_gen_val.kill_procs()
graph_spec = utils_tf.specs_from_graphs_tuple(samp_graph, True, True, True)
@tf.function(input_signature=[graph_spec, tf.TensorSpec(shape=[None,2], dtype=tf.float32)])
def val_step(graphs, targets):
regress_output, class_output = model(graphs)
regress_preds = regress_output.globals
class_preds = class_output.globals
regress_loss, class_loss, loss = loss_fn(targets, regress_preds, class_preds)
return regress_loss, class_loss, loss, regress_preds, class_preds
val_loss = []
val_loss_regress = []
val_loss_class = []
checkpoint = tf.train.Checkpoint(module=model)
checkpoint_prefix = os.path.join(save_dir, 'best_model')
latest = tf.train.latest_checkpoint(save_dir)
logging.info(f'Restoring checkpoint from {latest}')
print(f'Restoring checkpoint from {latest}')
checkpoint.restore(latest)
meta_cols = data_gen_val.meta_features
meta_cols.extend(['pred_cluster_ENG_CALIB_TOT', 'pred_type', 'pred_prob'])
# validate
df = pd.DataFrame(columns=meta_cols)
df.to_csv(save_dir+'/validation_data_predictions.csv', index=False)
logging.info('\nStarting inference on validation data..')
print('\nStarting inference on validation data..')
i = 1
start = time.time()
for graph_data_val, targets_val, meta_vals in get_batch(data_gen_val.generator()):#val_iter):
losses_val_rg, losses_val_cl, losses_val, regress_vals, class_vals = val_step(graph_data_val, targets_val)
targets_val = targets_val.numpy()
regress_vals = regress_vals.numpy()
class_vals = class_vals.numpy()
targets_val[:,0] = 10**targets_val[:,0]
regress_vals = 10**regress_vals
class_vals = tf.math.sigmoid(class_vals) # 1 / (1 + np.exp(class_vals))
class_pred_vals = [index_to_class[int(c>.5)] for c in class_vals]
class_pred_vals = np.array([class_pred_vals]).reshape(-1, 1)
class_vals = np.array([class_vals]).reshape(-1, 1)
meta_vals = np.array(meta_vals)
df = df.append(pd.DataFrame(np.hstack([meta_vals, regress_vals, class_pred_vals, class_vals]),
columns=meta_cols))
val_loss.append(losses_val.numpy())
val_loss_regress.append(losses_val_rg.numpy())
val_loss_class.append(losses_val_cl.numpy())
if not (i-1)%log_freq:
end = time.time()
logging.info('Iter: {:04d}, Val_loss_mean: {:.4f}, Val_loss_rg_mean: {:.4f}, Val_loss_cl_mean: {:.4f}, Took {:.3f}secs'. \
format(i,
np.mean(val_loss),
np.mean(val_loss_regress),
np.mean(val_loss_class),
end-start))
print('Iter: {:04d}, Val_loss_mean: {:.4f}, Val_loss_rg_mean: {:.4f}, Val_loss_cl_mean: {:.4f}, Took {:.3f}secs'. \
format(i,
np.mean(val_loss),
np.mean(val_loss_regress),
np.mean(val_loss_class),
end-start))
start = time.time()
df.to_csv(save_dir+'/validation_data_predictions.csv', mode='a', index=False, header=False)
df = pd.DataFrame(columns=meta_cols)
i += 1
df.to_csv(save_dir+'/validation_data_predictions.csv', mode='a', index=False, header=False)