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use local cached model for dynas ut test #258

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Merged
merged 4 commits into from
Dec 12, 2022
Merged

use local cached model for dynas ut test #258

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b213dca
copy local cached model for dynas ut test
chensuyue Dec 12, 2022
61a0bf2
update ut
chensuyue Dec 12, 2022
687df83
fix ut issue
chensuyue Dec 12, 2022
a4bbf87
update fix
chensuyue Dec 12, 2022
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4 changes: 4 additions & 0 deletions .azure-pipelines/scripts/ut/run_basic_others.sh
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Expand Up @@ -22,6 +22,10 @@ sed -i '/ neural_coder\//d' run.sh
sed -i '/ ipex\//d' run.sh
sed -i '/ itex\//d' run.sh

echo "copy model for dynas..."
mkdir -p .torch/ofa_nets || true
cp -r /tf_dataset/ut-localfile/ofa_mbv3_d234_e346_k357_w1.2 .torch/ofa_nets || true

LOG_DIR=/neural-compressor/log_dir
mkdir -p ${LOG_DIR}
ut_log_name=${LOG_DIR}/ut_tf_${tensorflow_version}_pt_${pytorch_version}.log
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1 change: 1 addition & 0 deletions .azure-pipelines/ut-basic.yml
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Expand Up @@ -11,6 +11,7 @@ pr:
- neural_compressor
- test
- setup.py
- .azure-pipelines/scripts/ut
exclude:
- neural_compressor/ux
- test/ux
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1 change: 1 addition & 0 deletions .azure-pipelines/ut-ncoder.yml
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Expand Up @@ -11,6 +11,7 @@ pr:
- neural_coder
- test/neural_coder
- setup.py
- .azure-pipelines/scripts/ut

pool: ICX-16C

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1 change: 1 addition & 0 deletions .azure-pipelines/ut-ux.yml
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Expand Up @@ -11,6 +11,7 @@ pr:
- neural_compressor/ux
- test/ux
- setup.py
- .azure-pipelines/scripts/ut

pool: ICX-16C

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3 changes: 1 addition & 2 deletions test/itex/test_tensorflow_itex_basic.py
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Expand Up @@ -22,6 +22,7 @@ def build_fake_yaml(fake_yaml, save_path, **kwargs):
class TestItexEnabling(unittest.TestCase):
@classmethod
def setUpClass(self):
os.system("rm *.log")
fake_yaml_1 = '''
model:
name: fake_model_cpu
Expand Down Expand Up @@ -238,8 +239,6 @@ def test_itex_benchmark_gpu(self):
relu = tf.nn.relu(add)
relu6 = tf.nn.relu6(relu, name='op_to_store')
out_name = relu6.name.split(':')[0]
num_of_instance = 1
cores_per_instance = 1
with tf.compat.v1.Session() as sess:
sess.run(tf.compat.v1.global_variables_initializer())
output_graph_def = graph_util.convert_variables_to_constants(
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