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train_tf.py
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import mymodel, mymodel_knn,seg_model
import tensorflow as tf
import numpy as np
import time,json
import os
os.environ["CUDA_VISIBLE_DEVICES"]="2"
def genData(cls,limit=None):
assert type(cls) is str
seg_classes = {'Earphone': [16, 17, 18], 'Motorbike': [30, 31, 32, 33, 34, 35], 'Rocket': [41, 42, 43],
'Car': [8, 9, 10, 11], 'Laptop': [28, 29], 'Cap': [6, 7], 'Skateboard': [44, 45, 46],
'Mug': [36, 37], 'Guitar': [19, 20, 21], 'Bag': [4, 5], 'Lamp': [24, 25, 26, 27],
'Table': [47, 48, 49], 'Airplane': [0, 1, 2, 3], 'Pistol': [38, 39, 40], 'Chair': [12, 13, 14, 15],
'Knife': [22, 23]}
data = np.load("/home/tegs/RGCNN/data_%s.npy" % cls)
label = np.load("/home/tegs/RGCNN/label_%s.npy" % cls)
data = data[:limit]
label = label[:limit]
seg = {}
name = {}
i = 0
for k,v in sorted(seg_classes.items()):
for value in v:
seg[value] = i
name[value] = k
i += 1
cnt = data.shape[0]
cat = np.zeros((cnt))
for i in range(cnt):
cat[i] = seg[label[i][0]]
return data,label,cat
def train():
train_data, train_label, train_cat = genData('train')
val_data, val_label, val_cat = genData('val')
test_data, test_label, test_cat = genData('test')
params = dict()
params['dir_name'] = 'model'
params['num_epochs'] = 50
params['batch_size'] = 26
params['eval_frequency'] = 30
# Building blocks.
params['filter'] = 'chebyshev5'
params['brelu'] = 'b1relu'
params['pool'] = 'apool1'
# Number of classes.
# C = y.max() + 1
# assert C == np.unique(y) .size
# Architecture.
params['F'] = [128, 512, 1024, 512, 128, 50] # Number of graph convolutional filters.
params['K'] = [6, 5, 3, 1, 1, 1] # Polynomial orders.
params['M'] = [384, 16, 1] # Output dimensionality of fully connected layers.
# Optimization.
params['regularization'] = 1e-9
params['dropout'] = 1
params['learning_rate'] = 1e-3
params['decay_rate'] = 0.95
params['momentum'] = 0
params['decay_steps'] = train_data.shape[0] / params['batch_size']
model = seg_model.rgcnn(2048, **params)
accuracy, loss, t_step = model.fit(train_data, train_cat, train_label, val_data, val_cat, val_label,
is_continue=False)
def test():
test_data, test_label, test_cat = genData('test')
params = dict()
params['dir_name'] = 'model'
params['num_epochs'] = 50
params['batch_size'] = 26
params['eval_frequency'] = 30
# Building blocks.
params['filter'] = 'chebyshev5'
params['brelu'] = 'b1relu'
params['pool'] = 'apool1'
# Number of classes.
# C = y.max() + 1
# assert C == np.unique(y) .size
# Architecture.
params['F'] = [128, 512, 1024, 512, 128, 50] # Number of graph convolutional filters.
params['K'] = [6, 5, 3, 1, 1, 1] # Polynomial orders.
params['M'] = [384, 16, 1] # Output dimensionality of fully connected layers. For classification only
# Optimization.
params['regularization'] = 1e-9
params['dropout'] = 1
params['learning_rate'] = 1e-3
params['decay_rate'] = 0.95
params['momentum'] = 0
params['decay_steps'] = test_data.shape[0] / params['batch_size']
model = seg_model.rgcnn(2048, **params)
model.evaluate(test_data,test_cat,test_label)
if __name__=="__main__":
train()