From 06b913606bae304026a9e542278906d8f6bf4dd7 Mon Sep 17 00:00:00 2001
From: Dmitry Kurtaev <dmitry.kurtaev@gmail.com>
Date: Mon, 17 Jul 2023 16:23:32 +0300
Subject: [PATCH 1/3] Add test for Pack from TFLite

---
 testdata/dnn/tflite/generate.py               |  31 ++++++++++++++++++
 testdata/dnn/tflite/replicate_by_pack.tflite  | Bin 0 -> 1788 bytes
 testdata/dnn/tflite/replicate_by_pack_inp.npy | Bin 0 -> 164 bytes
 ...eplicate_by_pack_out_PartitionedCall:0.npy | Bin 0 -> 164 bytes
 4 files changed, 31 insertions(+)
 create mode 100644 testdata/dnn/tflite/replicate_by_pack.tflite
 create mode 100644 testdata/dnn/tflite/replicate_by_pack_inp.npy
 create mode 100644 testdata/dnn/tflite/replicate_by_pack_out_PartitionedCall:0.npy

diff --git a/testdata/dnn/tflite/generate.py b/testdata/dnn/tflite/generate.py
index b1d5d9d67..0ca5fd53d 100644
--- a/testdata/dnn/tflite/generate.py
+++ b/testdata/dnn/tflite/generate.py
@@ -45,3 +45,34 @@ def run_mediapipe_solution(solution, inp_size):
 run_tflite_model("face_detection_short_range", (128, 128))
 
 run_mediapipe_solution(mp.solutions.selfie_segmentation.SelfieSegmentation(model_selection=0), (256, 256))
+
+# Save TensorFlow model as TFLite
+def save_tflite_model(model, inp, name):
+    func = model.__call__.get_concrete_function()
+    converter = tf.lite.TFLiteConverter.from_concrete_functions([func], model)
+    tflite_model = converter.convert()
+
+    with open(f'{name}.tflite', 'wb') as f:
+        f.write(tflite_model)
+
+    out = model(inp)
+
+    np.save(f'{name}_inp.npy', inp.transpose(0, 3, 1, 2))
+    np.save(f'{name}_out_PartitionedCall:0.npy', np.array(out).transpose(0, 3, 1, 2))
+
+
+class ReplicateByPack(tf.Module):
+    @tf.function(input_signature=[tf.TensorSpec(shape=[1, 3, 3, 1], dtype=tf.float32)])
+    def __call__(self, x):
+        pack_1 = tf.stack([x, x], axis=3)
+        reshape_1 = tf.reshape(pack_1, [1, 3, 6, 1])
+        pack_2 = tf.stack([reshape_1, reshape_1], axis=2)
+        reshape_2 = tf.reshape(pack_2, [1, 6, 6, 1])
+        scaled = tf.image.resize(reshape_2, size=(3, 3), method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
+        return scaled + x
+
+model = ReplicateByPack()
+inp = np.random.standard_normal((1, 3, 3, 1)).astype(np.float32)
+
+save_tflite_model(model, inp, 'replicate_by_pack')
+
diff --git a/testdata/dnn/tflite/replicate_by_pack.tflite b/testdata/dnn/tflite/replicate_by_pack.tflite
new file mode 100644
index 0000000000000000000000000000000000000000..c56f2e89274831994c18678ce6b25b9df7269d75
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diff --git a/testdata/dnn/tflite/replicate_by_pack_inp.npy b/testdata/dnn/tflite/replicate_by_pack_inp.npy
new file mode 100644
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diff --git a/testdata/dnn/tflite/replicate_by_pack_out_PartitionedCall:0.npy b/testdata/dnn/tflite/replicate_by_pack_out_PartitionedCall:0.npy
new file mode 100644
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From 89d03b0500d9e1b34b4e5f57418baf7da681ed21 Mon Sep 17 00:00:00 2001
From: Dmitry Kurtaev <dmitry.kurtaev@gmail.com>
Date: Tue, 18 Jul 2023 09:32:44 +0300
Subject: [PATCH 2/3] Update test data

---
 testdata/dnn/tflite/generate.py               |  29 +++++++++---------
 testdata/dnn/tflite/replicate_by_pack.tflite  | Bin 1788 -> 1664 bytes
 testdata/dnn/tflite/replicate_by_pack_inp.npy | Bin 164 -> 164 bytes
 ...npy => replicate_by_pack_out_Identity.npy} | Bin 164 -> 164 bytes
 4 files changed, 14 insertions(+), 15 deletions(-)
 rename testdata/dnn/tflite/{replicate_by_pack_out_PartitionedCall:0.npy => replicate_by_pack_out_Identity.npy} (78%)

diff --git a/testdata/dnn/tflite/generate.py b/testdata/dnn/tflite/generate.py
index 0ca5fd53d..9e7f5992f 100644
--- a/testdata/dnn/tflite/generate.py
+++ b/testdata/dnn/tflite/generate.py
@@ -48,31 +48,30 @@ def run_mediapipe_solution(solution, inp_size):
 
 # Save TensorFlow model as TFLite
 def save_tflite_model(model, inp, name):
-    func = model.__call__.get_concrete_function()
-    converter = tf.lite.TFLiteConverter.from_concrete_functions([func], model)
+    func = model.get_concrete_function()
+    converter = tf.lite.TFLiteConverter.from_concrete_functions([func])
     tflite_model = converter.convert()
 
+    interpreter = tf.lite.Interpreter(model_content=tflite_model)
+
     with open(f'{name}.tflite', 'wb') as f:
         f.write(tflite_model)
 
     out = model(inp)
 
     np.save(f'{name}_inp.npy', inp.transpose(0, 3, 1, 2))
-    np.save(f'{name}_out_PartitionedCall:0.npy', np.array(out).transpose(0, 3, 1, 2))
+    np.save(f'{name}_out_Identity.npy', np.array(out).transpose(0, 3, 1, 2))
 
 
-class ReplicateByPack(tf.Module):
-    @tf.function(input_signature=[tf.TensorSpec(shape=[1, 3, 3, 1], dtype=tf.float32)])
-    def __call__(self, x):
-        pack_1 = tf.stack([x, x], axis=3)
-        reshape_1 = tf.reshape(pack_1, [1, 3, 6, 1])
-        pack_2 = tf.stack([reshape_1, reshape_1], axis=2)
-        reshape_2 = tf.reshape(pack_2, [1, 6, 6, 1])
-        scaled = tf.image.resize(reshape_2, size=(3, 3), method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
-        return scaled + x
+@tf.function(input_signature=[tf.TensorSpec(shape=[1, 3, 3, 1], dtype=tf.float32)])
+def replicate_by_pack(x):
+    pack_1 = tf.stack([x, x], axis=3)
+    reshape_1 = tf.reshape(pack_1, [1, 3, 6, 1])
+    pack_2 = tf.stack([reshape_1, reshape_1], axis=2)
+    reshape_2 = tf.reshape(pack_2, [1, 6, 6, 1])
+    scaled = tf.image.resize(reshape_2, size=(3, 3), method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
+    return scaled + x
 
-model = ReplicateByPack()
 inp = np.random.standard_normal((1, 3, 3, 1)).astype(np.float32)
-
-save_tflite_model(model, inp, 'replicate_by_pack')
+save_tflite_model(replicate_by_pack, inp, 'replicate_by_pack')
 
diff --git a/testdata/dnn/tflite/replicate_by_pack.tflite b/testdata/dnn/tflite/replicate_by_pack.tflite
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diff --git a/testdata/dnn/tflite/replicate_by_pack_inp.npy b/testdata/dnn/tflite/replicate_by_pack_inp.npy
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diff --git a/testdata/dnn/tflite/replicate_by_pack_out_PartitionedCall:0.npy b/testdata/dnn/tflite/replicate_by_pack_out_Identity.npy
similarity index 78%
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rename to testdata/dnn/tflite/replicate_by_pack_out_Identity.npy
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From da67e69c869b86f16a8330e72066a0c336ce2e78 Mon Sep 17 00:00:00 2001
From: Dmitry Kurtaev <dmitry.kurtaev@gmail.com>
Date: Tue, 25 Jul 2023 20:25:03 +0300
Subject: [PATCH 3/3] Test data for PReLU from TensorFlow 2 with non-zero
 scales

---
 .../dnn/tensorflow/generate_tf2_models.py     |   4 ++--
 testdata/dnn/tensorflow/tf2_prelu_in.npy      | Bin 152 -> 224 bytes
 testdata/dnn/tensorflow/tf2_prelu_net.pb      | Bin 3513 -> 3282 bytes
 testdata/dnn/tensorflow/tf2_prelu_out.npy     | Bin 152 -> 224 bytes
 4 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/testdata/dnn/tensorflow/generate_tf2_models.py b/testdata/dnn/tensorflow/generate_tf2_models.py
index 0834ec85d..61928554b 100644
--- a/testdata/dnn/tensorflow/generate_tf2_models.py
+++ b/testdata/dnn/tensorflow/generate_tf2_models.py
@@ -76,9 +76,9 @@ def saveBroken(graph, name):
 save(model, 'tf2_dense', flatten_input=tf.TensorSpec(shape=[None, 1, 2, 3], dtype=tf.float32))
 ################################################################################
 model = tf.keras.models.Sequential([
-  tf.keras.layers.PReLU(input_shape=(1, 2, 3)),
+  tf.keras.layers.PReLU(input_shape=(1, 4, 6), alpha_initializer='random_normal'),
 ])
-save(model, 'tf2_prelu', p_re_lu_input=tf.TensorSpec(shape=[None, 1, 2, 3], dtype=tf.float32))
+save(model, 'tf2_prelu', p_re_lu_input=tf.TensorSpec(shape=[None, 1, 4, 6], dtype=tf.float32))
 ################################################################################
 model = tf.keras.models.Sequential([
   tf.keras.layers.AveragePooling2D(input_shape=(4, 6, 3), pool_size=(2, 2)),
diff --git a/testdata/dnn/tensorflow/tf2_prelu_in.npy b/testdata/dnn/tensorflow/tf2_prelu_in.npy
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diff --git a/testdata/dnn/tensorflow/tf2_prelu_out.npy b/testdata/dnn/tensorflow/tf2_prelu_out.npy
index 3f341818db5901df0f1aa9ecb4a82b30f6e46186..a8fa513f53e0eece994b23f2c32f6efdecc32395 100644
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