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support Conv [BiasAdd, Sum, LeakyRelu] int8 (#1118)
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test/tfnewapi/test_tensorflow_graph_qdq_new_conv_fusion.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,97 @@ | ||
| # | ||
| # -*- coding: utf-8 -*- | ||
| # | ||
| import unittest | ||
| import os | ||
| import yaml | ||
| import tensorflow as tf | ||
| import logging | ||
|
|
||
| from tensorflow.python.framework import graph_util | ||
| from tensorflow.python.framework import function | ||
| from neural_compressor.adaptor.tf_utils.util import disable_random | ||
|
|
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| def build_fake_yaml(): | ||
| fake_yaml = ''' | ||
| model: | ||
| name: fake_yaml | ||
| framework: inteltensorflow | ||
| inputs: input | ||
| device: cpu | ||
| quantization: | ||
| model_wise: | ||
| weight: | ||
| granularity: per_tensor | ||
| scheme: sym | ||
| dtype: int8 | ||
| algorithm: minmax | ||
| evaluation: | ||
| accuracy: | ||
| metric: | ||
| topk: 1 | ||
| tuning: | ||
| strategy: | ||
| name: basic | ||
| accuracy_criterion: | ||
| relative: 0.1 | ||
| exit_policy: | ||
| performance_only: True | ||
| workspace: | ||
| path: saved | ||
| ''' | ||
|
|
||
| y = yaml.load(fake_yaml, Loader=yaml.SafeLoader) | ||
|
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| with open('fake_yaml.yaml', "w", encoding="utf-8") as f: | ||
| yaml.dump(y, f) | ||
|
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| f.close() | ||
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| class TestTensorflowNewQdqConvFusion(unittest.TestCase): | ||
|
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| @classmethod | ||
| def setUpClass(self): | ||
| build_fake_yaml() | ||
|
|
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| @classmethod | ||
| def tearDownClass(self): | ||
| os.remove('fake_yaml.yaml') | ||
|
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| @disable_random() | ||
| def test_conv_biasadd_add_leakyrelu_fusion(self): | ||
| logging.getLogger().info("test_conv_biasadd_add_leakyrelu_fusion") | ||
| x = tf.compat.v1.placeholder(tf.float32, [1, 56, 56, 16], name="input") | ||
| conv_weights = tf.compat.v1.get_variable("weight", [3, 3, 16, 16], | ||
| initializer=tf.compat.v1.random_normal_initializer()) | ||
| conv = tf.nn.conv2d(x, conv_weights, strides=[1, 2, 2, 1], padding="SAME") | ||
| normed = tf.compat.v1.layers.batch_normalization(conv) | ||
| conv2_weights = tf.compat.v1.get_variable("weight_conv2", [3, 3, 16, 16], | ||
| initializer=tf.compat.v1.random_normal_initializer()) | ||
| conv2 = tf.nn.conv2d(x, conv2_weights, strides=[1, 2, 2, 1], padding="SAME") | ||
| sumadd = tf.raw_ops.AddV2(x=normed, y=conv2, name='addv2') | ||
| leaky_relu = tf.nn.leaky_relu(sumadd, name='op_to_store') | ||
|
|
||
| out_name = leaky_relu.name.split(':')[0] | ||
| with tf.compat.v1.Session() as sess: | ||
| sess.run(tf.compat.v1.global_variables_initializer()) | ||
| output_graph_def = graph_util.convert_variables_to_constants( | ||
| sess=sess, | ||
| input_graph_def=sess.graph_def, | ||
| output_node_names=[out_name]) | ||
| from neural_compressor.experimental import Quantization, common | ||
| quantizer = Quantization('fake_yaml.yaml') | ||
| dataset = quantizer.dataset('dummy', shape=(100, 56, 56, 16), label=True) | ||
| quantizer.eval_dataloader = common.DataLoader(dataset) | ||
| quantizer.calib_dataloader = common.DataLoader(dataset) | ||
| quantizer.model = output_graph_def | ||
| output_graph = quantizer.fit() | ||
| found_conv_fusion = True | ||
|
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| for i in output_graph.graph_def.node: | ||
| if i.op == 'LeakyRelu': | ||
| found_conv_fusion = False | ||
| break | ||
|
|
||
| if __name__ == '__main__': | ||
| unittest.main() |