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trainCaloX_IDEA_CNN_RI1p8.py
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trainCaloX_IDEA_CNN_RI1p8.py
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import pandas as pd
import numpy as np
import awkward
import itertools
import copy
import logging
logging.basicConfig(level=logging.INFO, format='[%(asctime)s] %(levelname)s: %(message)s')
batch_size = 28
batch_sizeV = 6
nfiles = 27000
#nfiles = 110
epochs = 100
def generator(batch_size,nfiles):
samples_per_file = 30
number_of_batches = int(samples_per_file/batch_size)
counter=0
fcnt = 101
df = pd.read_pickle('/lustre/research/hep/jdamgov/idea_ntpl_v1/IDEA_pi_pkl3_3D_2C_RI2p2_noSci_noProp_1cmBS/GNN.pi150GeV_100.pkl.gz')
df["Label"]=df["Label"]/1000.
nhits = len(df["Points"][0])
samples_per_file = len(df)
number_of_batches = int(samples_per_file/batch_size)
while 1:
try:
np1 = np.array(df["Features"].to_list(),dtype="int16")[batch_size*counter:batch_size*(counter+1),:,:1].sum(axis=-1)
np2 = np.array(df["Features"].to_list(),dtype="int16")[batch_size*counter:batch_size*(counter+1),:,1:].sum(axis=-1)
np3=np.concatenate([np1.reshape(batch_size,nhits,1),
np2.reshape(batch_size,nhits,1)],axis=2)
X_batch = {'points':np.array(df["Points"][batch_size*counter:batch_size*(counter+1)].to_list(),dtype="float32"),
#'features':np.array(df["Features"][batch_size*counter:batch_size*(counter+1)].to_list(),dtype="int16"),
'features':np2,
'mask':np.array(df["Mask"][batch_size*counter:batch_size*(counter+1)].to_list(),dtype="int16").reshape(batch_size,nhits,1)}
y_batch=np.array(df["Label"][batch_size*counter:batch_size*(counter+1)].to_list(),dtype="float32")
counter += 1
points = X_batch['points']
energies = X_batch['features']
X_b = []
for evt in range(0,len(points)):
point=points[evt]
energy=energies[evt]
var = np.zeros((200,35,35),dtype=np.float32)
for ihit in range(0,len(point)):
hitP=point[ihit]
hitE=energy[ihit]
X=int(hitP[0]+49)
Y=int(hitP[1]+15)
Z=int(hitP[2]+10)
if X>-1 and Y>-1 and Z>-1 and Z<35 and Y<35 and X<200: var[X][Y][Z]+=hitE
#else:
# if(y_batch[evt]>8.): print("--- ",y_batch[evt], X,Y,Z,hitE)
#R = np.sum(var)/np.sum(energies[evt])
#if(y_batch[evt]>8.): print(y_batch[evt],R)
#print(var.shape)
X_b.append(var)
X_b = np.asarray(X_b).reshape(len(points), 200, 35, 35, 1)
#print(X_b.shape)
#print(y_batch.shape)
#yield X_batch, y_batch
X_b_sum = X_b.sum(1).sum(1).sum(1)[:,0]
yield [X_b,X_b_sum], y_batch
except:
print("Something went wrong with the data")
counter += 1
#restart counter to yeild data in the next epoch as well
if fcnt > nfiles :
fcnt = 100
if counter >= number_of_batches:
counter = 0
df = pd.read_pickle('/lustre/research/hep/jdamgov/idea_ntpl_v1/IDEA_pi_pkl3_3D_2C_RI2p2_noSci_noProp_1cmBS/GNN.pi150GeV_'+str(fcnt)+'.pkl.gz')
df["Label"]=df["Label"]/1000.
samples_per_file = len(df)
number_of_batches = int(samples_per_file/batch_size)
fcnt += 1
def val_generator(batch_size,nfiles):
counter=0
fcnt = nfiles +1
df = pd.read_pickle('/lustre/research/hep/jdamgov/idea_ntpl_v1/IDEA_pi_pkl3_3D_2C_RI2p2_noSci_noProp_1cmBS/GNN.pi150GeV_'+str(fcnt)+'.pkl.gz')
nhits = len(df["Points"][0])
samples_per_file = len(df)
number_of_batches = samples_per_file/batch_size
df["Label"]=df["Label"]/1000.
while 1:
try:
np1 = np.array(df["Features"].to_list(),dtype="int16")[batch_size*counter:batch_size*(counter+1),:,:1].sum(axis=-1)
np2 = np.array(df["Features"].to_list(),dtype="int16")[batch_size*counter:batch_size*(counter+1),:,1:].sum(axis=-1)
np3=np.concatenate([np1.reshape(batch_size,nhits,1),
np2.reshape(batch_size,nhits,1)],axis=2)
X_batch = {'points':np.array(df["Points"][batch_size*counter:batch_size*(counter+1)].to_list(),dtype="float32"),
'features':np2,
#'features':np.array(df["Features"][batch_size*counter:batch_size*(counter+1)].to_list(),dtype="int16"),
'mask':np.array(df["Mask"][batch_size*counter:batch_size*(counter+1)].to_list(),dtype="int16").reshape(batch_size,nhits,1)}
y_batch=np.array(df["Label"][batch_size*counter:batch_size*(counter+1)].to_list(),dtype="float32")
counter += 1
points = X_batch['points']
energies = X_batch['features']
X_b = []
for evt in range(0,len(points)):
point=points[evt]
energy=energies[evt]
var = np.zeros((200,35,35),dtype=np.float32)
for ihit in range(0,len(point)):
hitP=point[ihit]
hitE=energy[ihit]
X=int(hitP[0]+49)
Y=int(hitP[1]+15)
Z=int(hitP[2]+10)
if X>-1 and Y>-1 and Z>-1 and Z<35 and Y<35 and X<200: var[X][Y][Z]+=hitE
#else:
# if(y_batch[evt]>8.): print("--- ",y_batch[evt], X,Y,Z,hitE)
#R = np.sum(var)/np.sum(energies[evt])
#if(y_batch[evt]>8.): print(y_batch[evt],R)
#print(var.shape)
X_b.append(var)
X_b = np.asarray(X_b).reshape(len(points), 200, 35, 35, 1)
#print(X_b.shape)
#print(y_batch.shape)
X_b_sum = X_b.sum(1).sum(1).sum(1)[:,0]
yield [X_b,X_b_sum], y_batch
except:
print("Something went wrong with the data")
counter += 1
#restart counter to yeild data in the next epoch as well
if fcnt > nfiles+3000 :
fcnt = nfiles +1
if counter >= number_of_batches:
counter = 0
df = pd.read_pickle('/lustre/research/hep/jdamgov/idea_ntpl_v1/IDEA_pi_pkl3_3D_2C_RI2p2_noSci_noProp_1cmBS/GNN.pi150GeV_'+str(fcnt)+'.pkl.gz')
df["Label"]=df["Label"]/1000.
samples_per_file = len(df)
number_of_batches = int(samples_per_file/batch_size)
fcnt += 1
import tensorflow as tf
from tensorflow import keras
#from tf_keras_model import get_particle_net, get_particle_net_lite
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
try:
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
logical_gpus = tf.config.experimental.list_logical_devices('GPU')
print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
except RuntimeError as e:
print(e)
#strategy = tf.distribute.MirroredStrategy()
strategy = tf.distribute.MirroredStrategy(cross_device_ops=tf.distribute.HierarchicalCopyAllReduce())
with strategy.scope():
input_layer1 = tf.keras.layers.Input((200, 35, 35, 1))
input_layer2 = tf.keras.layers.Input((1))
conv_layer1 = tf.keras.layers.Conv3D(64, kernel_size=(7,3,3) , activation='relu')(input_layer1)
conv_layer2 = tf.keras.layers.Conv3D(32, kernel_size=(5,3,3) , activation='relu')(conv_layer1)
pooling_layer1 = tf.keras.layers.MaxPool3D()(conv_layer2)
conv_layer3 = tf.keras.layers.Conv3D(32, kernel_size=(5,3,3) , activation='relu')(pooling_layer1)
conv_layer4 = tf.keras.layers.Conv3D(32, kernel_size=(5,3,3) , activation='relu')(conv_layer3)
bnorm_layer2 = tf.keras.layers.BatchNormalization()(conv_layer4)
pooling_layer2 = tf.keras.layers.MaxPool3D()(bnorm_layer2)
conv_layer5 = tf.keras.layers.Conv3D(32, 3 , activation='relu')(pooling_layer2)
conv_layer6 = tf.keras.layers.Conv3D(32, 3 , activation='relu')(conv_layer5)
conv_layer7 = tf.keras.layers.Conv3D(8, kernel_size=(5,1,1) , activation='relu')(conv_layer6)
#pooling_layer3 = tf.keras.layers.MaxPool3D()(conv_layer6)
#flatn_layer = tf.keras.layers.Flatten()(pooling_layer3)
flatn_layer = tf.keras.layers.Flatten()(conv_layer7)
conc_layer = tf.keras.layers.Concatenate(axis=1)([flatn_layer,input_layer2])
#dense_layer1 = tf.keras.layers.Dense(units=1024, activation='relu')(conc_layer)
dense_layer2 = tf.keras.layers.Dense(units=768, activation='relu')(conc_layer)
dense_layer2 = tf.keras.layers.Dropout(0.4)(dense_layer2)
dense_layer3 = tf.keras.layers.Dense(units=128, activation='relu')(dense_layer2)
dense_layer4 = tf.keras.layers.Dense(units=32, activation='relu')(dense_layer3)
dense_layer4 = tf.keras.layers.Dropout(0.3)(dense_layer4)
output_layer = tf.keras.layers.Dense(units=1, activation='linear')(dense_layer4)
model = tf.keras.Model(inputs=[input_layer1,input_layer2], outputs=output_layer)
model.compile(loss='mean_squared_logarithmic_error',
optimizer=keras.optimizers.Ftrl() )
model.summary()
# Prepare model model saving directory.
import os
save_dir = 'model_checkpoints'
model_name = 'CNN_3D_3cm_50ps_noProp_2C_RI2p20.loss_{val_loss:01.6f}.e{epoch:03d}_pre.h5'
if not os.path.isdir(save_dir):
os.makedirs(save_dir)
filepath = os.path.join(save_dir, model_name)
# Prepare callbacks for model saving and for learning rate adjustment.
checkpoint = keras.callbacks.ModelCheckpoint(filepath=filepath,
monitor='val_loss',
verbose=1,
save_best_only=True)
progress_bar = keras.callbacks.ProgbarLogger()
early = keras.callbacks.EarlyStopping(monitor="val_loss",
mode="min", patience=12)
callbacks = [checkpoint,early]
model.fit(
generator(batch_size,nfiles),
steps_per_epoch = (nfiles-100)*(30/batch_size),
epochs=1,
validation_data=val_generator(batch_sizeV,nfiles),
validation_steps=3000*int(30/batch_sizeV),
callbacks=callbacks
,use_multiprocessing=True, workers=4, max_queue_size=60
)
model.compile(loss='mean_squared_logarithmic_error',
optimizer=keras.optimizers.Adam() )
model.summary()
model.fit(
generator(batch_size,nfiles),
steps_per_epoch = (nfiles-100)*(30/batch_size),
epochs=epochs,
validation_data=val_generator(batch_sizeV,nfiles),
validation_steps=3000*int(30/batch_sizeV),
callbacks=callbacks
,use_multiprocessing=True, workers=4, max_queue_size=60
)