Issue regarding adding custom model to menu list

README.md

@@ -1,5 +1,5 @@
 # CFU Playground
-
+  
 Want a faster ML processor?   Do it yourself!
 
 This project provides a framework that an engineer, intern, or student can use to design and evaluate **enhancements** to an FPGA-based “soft” processor, specifically to increase the performance of machine learning (ML) tasks.   The goal is to abstract away most infrastructure details so that the user can get up to speed quickly and focus solely on adding new processor instructions, exploiting them in the computation, and measuring the results.

common/src/models/models.c

@@ -28,6 +28,8 @@
 #include "models/mlcommons_tiny_v01/vww/vww.h"
 #include "models/mnv2/mnv2.h"
 #include "models/pdti8/pdti8.h"
+#include "models/neural_net1/neural_net1.h"
+#include "models/neural_net_svhn/neural_net_svhn.h"
 
 inline void no_menu() {}
 
@@ -40,6 +42,14 @@ static struct Menu MENU = {
     "TfLM Models",
     "models",
     {
+#if defined(INCLUDE_MODEL_NEURAL_NET1) || defined(INCLUDE_ALL_TFLM_EXAMPLES)
+        MENU_ITEM(AUTO_INC_CHAR, "Neural_net1 model", neural_net1_menu),
+#endif
+
+#if defined(INCLUDE_MODEL_NEURAL_NET_SVHN) || defined(INCLUDE_ALL_TFLM_EXAMPLES)
+        MENU_ITEM(AUTO_INC_CHAR, "Neural_net_svhn model", neural_net_svhn_menu),
+#endif
+
 #if defined(INCLUDE_MODEL_PDTI8) || defined(INCLUDE_ALL_TFLM_EXAMPLES)
         MENU_ITEM(AUTO_INC_CHAR, "Person Detection int8 model", pdti8_menu),
 #endif

common/src/models/neural_net1/neural_net1.cc

@@ -0,0 +1,114 @@
+/*
+ * Copyright 2021 The CFU-Playground Authors
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "models/neural_net1/neural_net1.h"
+
+#include <stdio.h>
+
+#include "menu.h"
+#include "models/neural_net1/model_neural_net1.h"
+#include "/home/shivaubuntu/Tensorflow/mnist_test_data/mnist_img_data.cc"
+#include "tflite.h"
+
+
+extern "C" {
+#include "fb_util.h"
+};
+
+
+// Initialize everything once
+// deallocate tensors when done
+static void neural_net1_init(void) {
+  tflite_load_model(model_neural_net1_tflite, model_neural_net1_tflite_len);
+}
+
+// Run classification, after input has been loaded
+static int32_t neural_net1_classify() {
+  printf("Running neural_net1\n");
+  tflite_classify();
+
+  // Process the inference results.
+  int8_t* output = tflite_get_output();
+  int8_t max_conf=0;
+  for(int i=0;i<10;i++)
+  {
+    if(output[i]>=output[max_conf])
+      max_conf = i;
+    printf("conf lvl for %d : %d\n",i,output[i]);
+  }
+  printf("OUT: %d\n",max_conf);
+  return max_conf;
+
+}
+
+
+#define NUM_GOLDEN 2
+static int32_t golden_results[NUM_GOLDEN] = {0, 2};
+
+static void do_golden_tests() {
+  int32_t actual[NUM_GOLDEN];
+
+
+  tflite_set_input_zeros();
+  actual[0] = neural_net1_classify();
+  printf("output @ all 0s inp: %ld", actual[0]);
+  tflite_set_input(g_mnist_img_data);
+  actual[1] = neural_net1_classify();
+
+  #ifdef CSR_VIDEO_FRAMEBUFFER_BASE
+  char msg_buff[256] = { 0 };
+  fb_clear();
+  memset(msg_buff, 0x00, sizeof(msg_buff));
+  snprintf(msg_buff, sizeof(msg_buff), "Result is %ld, Expected is %ld", actual[1],  golden_results[1]);
+  flush_cpu_dcache();
+  flush_l2_cache();
+#endif 
+
+  if(actual[1]!= golden_results[1])
+    printf("Failed golden test..check again");
+
+
+
+}
+
+
+static void test() {
+printf("hi");
+
+}
+static struct Menu MENU = {
+    "Tests for MNIST neural_net1 model",
+    "neural_net1",
+    {
+        MENU_ITEM('1', "Test- say hi", test),
+        MENU_ITEM('g', "Run golden tests (check for expected outputs)",
+                  do_golden_tests),
+        MENU_END,
+    },
+};
+
+// For integration into menu system
+void neural_net1_menu() {
+  neural_net1_init();
+
+  #ifdef CSR_VIDEO_FRAMEBUFFER_BASE
+  fb_init();
+  flush_cpu_dcache();
+  flush_l2_cache();
+#endif
+
+  menu_run(&MENU);
+}

common/src/models/neural_net1/neural_net1.h

@@ -0,0 +1,31 @@
+/*
+ * Copyright 2021 The CFU-Playground Authors
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef _NEURAL_NET1_H
+#define _NEURAL_NET1_H
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+// For integration into menu system
+void neural_net1_menu();
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif  // _NEURAL_NET1_H

common/src/models/neural_net1/neural_net1.py

@@ -0,0 +1,77 @@
+import tensorflow as tf
+import numpy as np
+import tensorflow_datasets as tfds
+(ds_train, ds_test), ds_info = tfds.load(
+    'mnist',
+    split=['train', 'test'],
+    shuffle_files=True,
+    as_supervised=True,
+    with_info=True,
+)
+def normalize_img(image, label):
+  """Normalize image"""
+  return tf.cast(image, tf.float32) / 255., label
+
+ds_train = ds_train.map( normalize_img, num_parallel_calls=tf.data.AUTOTUNE)
+ds_train = ds_train.cache()
+ds_train = ds_train.shuffle(ds_info.splits['train'].num_examples)
+ds_train = ds_train.batch(128)
+ds_train = ds_train.prefetch(tf.data.AUTOTUNE)
+
+""" Testing pipeline"""
+ds_test = ds_test.map(
+    normalize_img, num_parallel_calls=tf.data.AUTOTUNE)
+ds_test = ds_test.batch(128)
+ds_test = ds_test.cache()
+ds_test = ds_test.prefetch(tf.data.AUTOTUNE)
+
+
+model = tf.keras.models.Sequential([
+  tf.keras.layers.Flatten(input_shape=(28, 28)),
+  tf.keras.layers.Dense(128, activation='relu'),
+  tf.keras.layers.Dense(10)
+])
+model.compile(
+    optimizer=tf.keras.optimizers.Adam(0.001),
+    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+    metrics=[tf.keras.metrics.SparseCategoricalAccuracy()],
+    run_eagerly = True
+)
+model.fit(
+    ds_train,
+    epochs=6,
+    validation_data=ds_test,
+)
+
+"""Custom Inference test"""
+model.summary
+count = 0
+#for data in ds_train:
+#  print(model(data[0]))
+
+""" Converting to TFlite"""
+converter = tf.lite.TFLiteConverter.from_keras_model(model)
+tflite_model = converter.convert()
+
+with open('model.tflite', 'wb') as f:
+  f.write(tflite_model)
+
+interpreter = tf.lite.Interpreter(model_path="model.tflite")
+interpreter.allocate_tensors()
+
+input_details = interpreter.get_input_details()
+output_details = interpreter.get_output_details()
+
+input_shape = input_details[0]['shape']
+""" Giving random input to model to see if it is computing properly"""
+input_data = np.array(np.random.random_sample(input_shape), dtype=np.float32)
+interpreter.set_tensor(input_details[0]['index'], input_data)
+
+interpreter.invoke()
+output_data = interpreter.get_tensor(output_details[0]['index'])
+print(output_data)
+
+print("Evaluate on test data")
+results = model.evaluate(ds_test, batch_size=128)
+print("test loss, test acc:", results)
+