From 1f99fc6c4dc0d814cc51d285742cb4516f197535 Mon Sep 17 00:00:00 2001
From: Anastasia Murzova <anastasia.murzova@xperience.ai>
Date: Mon, 8 Feb 2021 00:04:40 +0300
Subject: [PATCH 1/3] Added TF sum behaviour alignment

---
 testdata/dnn/tensorflow/eltwise_add_vec_in.npy  | Bin 0 -> 1128 bytes
 testdata/dnn/tensorflow/eltwise_add_vec_net.pb  | Bin 0 -> 283 bytes
 testdata/dnn/tensorflow/eltwise_add_vec_out.npy | Bin 0 -> 1128 bytes
 testdata/dnn/tensorflow/generate_tf_models.py   |   6 ++++++
 4 files changed, 6 insertions(+)
 create mode 100644 testdata/dnn/tensorflow/eltwise_add_vec_in.npy
 create mode 100644 testdata/dnn/tensorflow/eltwise_add_vec_net.pb
 create mode 100644 testdata/dnn/tensorflow/eltwise_add_vec_out.npy

diff --git a/testdata/dnn/tensorflow/eltwise_add_vec_in.npy b/testdata/dnn/tensorflow/eltwise_add_vec_in.npy
new file mode 100644
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diff --git a/testdata/dnn/tensorflow/eltwise_add_vec_net.pb b/testdata/dnn/tensorflow/eltwise_add_vec_net.pb
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diff --git a/testdata/dnn/tensorflow/eltwise_add_vec_out.npy b/testdata/dnn/tensorflow/eltwise_add_vec_out.npy
new file mode 100644
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diff --git a/testdata/dnn/tensorflow/generate_tf_models.py b/testdata/dnn/tensorflow/generate_tf_models.py
index ef0be0990..dd001f76c 100644
--- a/testdata/dnn/tensorflow/generate_tf_models.py
+++ b/testdata/dnn/tensorflow/generate_tf_models.py
@@ -786,6 +786,12 @@ def pad_depth(x, desired_channels):
 sub = conv - inp
 save(inp, sub, 'eltwise_sub')
 ################################################################################
+inp = tf.placeholder(tf.float32, [1, 5, 5, 10], 'input')
+out1 = tf.keras.layers.MaxPool2D((2, 2), 5, "SAME", name="pooling")(inp)
+out2 = tf.keras.layers.ReLU(name="relu")(inp)
+final_out = tf.math.add(out1, out2, name='tf_sum')
+save(inp, final_out, 'eltwise_add_vec')
+################################################################################
 inp = tf.placeholder(tf.float32, [None, 2, 3, 4], 'input')
 conv = tf.layers.conv2d(inp, filters=3, kernel_size=[1, 1])
 softmax = tf.contrib.slim.softmax(conv)

From 7f5d272444d0c7a38818396d8c4b1039fa2ea1fc Mon Sep 17 00:00:00 2001
From: Anastasia Murzova <anastasia.murzova@xperience.ai>
Date: Mon, 22 Mar 2021 22:41:41 +0300
Subject: [PATCH 2/3] Corrected DNN elementwise multiplication

---
 testdata/dnn/tensorflow/eltwise_mul_vec_in.npy  | Bin 0 -> 320 bytes
 testdata/dnn/tensorflow/eltwise_mul_vec_net.pb  | Bin 0 -> 287 bytes
 testdata/dnn/tensorflow/eltwise_mul_vec_out.npy | Bin 0 -> 320 bytes
 testdata/dnn/tensorflow/generate_tf_models.py   |   6 ++++++
 4 files changed, 6 insertions(+)
 create mode 100644 testdata/dnn/tensorflow/eltwise_mul_vec_in.npy
 create mode 100644 testdata/dnn/tensorflow/eltwise_mul_vec_net.pb
 create mode 100644 testdata/dnn/tensorflow/eltwise_mul_vec_out.npy

diff --git a/testdata/dnn/tensorflow/eltwise_mul_vec_in.npy b/testdata/dnn/tensorflow/eltwise_mul_vec_in.npy
new file mode 100644
index 0000000000000000000000000000000000000000..3094cee03efb2b61d99d2837761e58ed63b1f0cb
GIT binary patch
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diff --git a/testdata/dnn/tensorflow/eltwise_mul_vec_net.pb b/testdata/dnn/tensorflow/eltwise_mul_vec_net.pb
new file mode 100644
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diff --git a/testdata/dnn/tensorflow/eltwise_mul_vec_out.npy b/testdata/dnn/tensorflow/eltwise_mul_vec_out.npy
new file mode 100644
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GIT binary patch
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diff --git a/testdata/dnn/tensorflow/generate_tf_models.py b/testdata/dnn/tensorflow/generate_tf_models.py
index dd001f76c..f1c98cbc7 100644
--- a/testdata/dnn/tensorflow/generate_tf_models.py
+++ b/testdata/dnn/tensorflow/generate_tf_models.py
@@ -792,6 +792,12 @@ def pad_depth(x, desired_channels):
 final_out = tf.math.add(out1, out2, name='tf_sum')
 save(inp, final_out, 'eltwise_add_vec')
 ################################################################################
+inp = tf.placeholder(tf.float32, [1, 4, 4, 3], 'input')
+out1 = tf.keras.layers.MaxPool2D((2, 2), 4, "SAME", name="pooling")(inp)
+out2 = tf.keras.layers.ReLU(name="relu")(inp)
+final_out = tf.keras.layers.Multiply(name='tf_mul')([out1, out2])
+save(inp, final_out, 'eltwise_mul_vec')
+################################################################################
 inp = tf.placeholder(tf.float32, [None, 2, 3, 4], 'input')
 conv = tf.layers.conv2d(inp, filters=3, kernel_size=[1, 1])
 softmax = tf.contrib.slim.softmax(conv)

From f79a6f0e320937595b466280babde7efe0d378b8 Mon Sep 17 00:00:00 2001
From: Anastasia Murzova <anastasia.murzova@xperience.ai>
Date: Tue, 23 Mar 2021 22:26:17 +0300
Subject: [PATCH 3/3] Added reshape corrections

---
 testdata/dnn/tensorflow/generate_tf_models.py |  10 ++++++++++
 testdata/dnn/tensorflow/reshape_conv_in.npy   | Bin 0 -> 428 bytes
 testdata/dnn/tensorflow/reshape_conv_net.pb   | Bin 0 -> 791 bytes
 testdata/dnn/tensorflow/reshape_conv_out.npy  | Bin 0 -> 144 bytes
 4 files changed, 10 insertions(+)
 create mode 100644 testdata/dnn/tensorflow/reshape_conv_in.npy
 create mode 100644 testdata/dnn/tensorflow/reshape_conv_net.pb
 create mode 100644 testdata/dnn/tensorflow/reshape_conv_out.npy

diff --git a/testdata/dnn/tensorflow/generate_tf_models.py b/testdata/dnn/tensorflow/generate_tf_models.py
index f1c98cbc7..1d933d006 100644
--- a/testdata/dnn/tensorflow/generate_tf_models.py
+++ b/testdata/dnn/tensorflow/generate_tf_models.py
@@ -416,6 +416,16 @@ def my_dropout(x):
 out = tf.reshape(conv2, [1, 2, 3, 6], 'reshaped')
 save(inp, out, 'reshape_nchw')
 ################################################################################
+inp = tf.placeholder(tf.float32, [1, 5, 5, 3], 'input')
+out = tf.keras.layers.MaxPool2D((2, 2), 4, "SAME", name='pooling')(inp)
+reshape = tf.reshape(out, [-1, 1, 1, 12], 'reshaped')
+conv_filter = tf.get_variable('filter', [1, 1, 12, 4],
+                              initializer=tf.truncated_normal_initializer(),
+                              dtype=tf.float32)
+conv = tf.nn.conv2d(input=reshape, filters=conv_filter, strides=[1, 1, 1, 1],
+                    padding='SAME', name='conv2d')
+save(inp, conv, 'reshape_conv')
+################################################################################
 inp = tf.placeholder(tf.float32, [1, 6, 5, 3], 'input')
 conv = tf.layers.conv2d(inputs=inp, filters=3, kernel_size=[1, 1],
                         activation=tf.nn.relu,
diff --git a/testdata/dnn/tensorflow/reshape_conv_in.npy b/testdata/dnn/tensorflow/reshape_conv_in.npy
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diff --git a/testdata/dnn/tensorflow/reshape_conv_out.npy b/testdata/dnn/tensorflow/reshape_conv_out.npy
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