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config.py reduced min_after_dequeue slight modification in train.py #1

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config.py reduced min_after_dequeue slight modification in train.py #1

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ec6ea56
config.py reduced min_after_dequeue slight modification in train.py
SwastiKh Oct 26, 2020
0d88b0c
Merge pull request #1 from tyagi-iiitv/main
SwastiKh Nov 21, 2020
b1e68e4
trainer_step.py data path changed for trained model
SwastiKh Nov 21, 2020
50bf1ab
config.py changed argument back to 5000
SwastiKh Nov 21, 2020
820c0af
reverted changes in train.py
SwastiKh Nov 21, 2020
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1 change: 1 addition & 0 deletions train.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,7 @@
import config
import trainer_step


def main():
cfg = config.Config(filenamequeue="../data/dataset/layout_1205.tfrecords")
t = trainer_step.Trainer(cfg)
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19 changes: 10 additions & 9 deletions trainer_step.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,7 +11,7 @@
tf.compat.v1.disable_eager_execution()
# slim = tf.contrib.slim

DATA_DIR = os.path.join('..','data')
DATA_DIR = os.path.join('..')
class Trainer(object):
def __init__(self, config):
filenamequeue = tf.compat.v1.train.string_input_producer([config.filenamequeue])
Expand Down Expand Up @@ -58,7 +58,8 @@ def _build_model(self, filenamequeue, config):
randomz = tf.random.normal([config.batch_size, config.z_dim])
# testing case
testLayout, testImgfea, testSemvec, testTexfea = self.inputs(config)
randomz_val = np.load(os.path.join(DATA_DIR,'sample','noiseVector_128.npy'))
# randomz_val = np.load(os.path.join(DATA_DIR,'/drive/MyDrive/LayoutNetme/sample','noiseVector_128.npy'))
randomz_val = np.load('../drive/MyDrive/LayoutNetme/sample/noiseVector_128.npy')
testSemvec, _ = model.embeddingSemvec(testSemvec, training_, reuse=True)
testImgfea, _ = model.embeddingImg(testImgfea, training_, reuse=True)
testTexfea, _ = model.embeddingTex(testTexfea, training_, reuse=True)
Expand Down Expand Up @@ -223,21 +224,21 @@ def sample(self):
return (inputdata + 1) / 2.0, (gen + 1) / 2.0, fea

def testing(self):
new_saver = tf.compat.v1.train.import_meta_graph('./log/layoutNet-100.meta')
new_saver.restore(self.sess, './log/layoutNet-100')
new_saver = tf.compat.v1.train.import_meta_graph('/layoutNet-100.meta')
new_saver.restore(self.sess, '/layoutNet-100')
gen, fea = self.sess.run([self.model["Gtest"],
self.model["Etest"]],
feed_dict={self.model["is_training"]: False})

return (gen + 1) / 2.0, fea

def inputs(self, config):
layoutpath = DATA_DIR + '/sample/layout/'
imgfeapath = DATA_DIR + '/sample/visfea/'
texfeapath = DATA_DIR + '/sample/texfea/'
semvecpath = DATA_DIR + '/sample/semvec/'
layoutpath = DATA_DIR + '/drive/MyDrive/LayoutNetme/validation/layout/'
imgfeapath = DATA_DIR + '/drive/MyDrive/LayoutNetme/validation/visfea/'
texfeapath = DATA_DIR + '/drive/MyDrive/LayoutNetme/validation/texfea/'
semvecpath = DATA_DIR + '/drive/MyDrive/LayoutNetme/validation/semvec/'

f = open((DATA_DIR + '/sample/imgSel_128.txt'), 'r')
f = open((DATA_DIR + '/drive/MyDrive/LayoutNetme/validation/imgSel_60.txt'), 'r')
name = f.read()
namelist = name.split()
n_samples = len(namelist)
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