Merge remote-tracking branch 'upstream/master' into amp_resnet50

research/lstm_object_detection/test_tflite_model.py

+# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+#
+# 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.
+# ==============================================================================
+
+"""Test a tflite model using random input data."""
+
+from __future__ import print_function
+from absl import flags
+import numpy as np
+import tensorflow as tf
+
+flags.DEFINE_string('model_path', None, 'Path to model.')
+FLAGS = flags.FLAGS
+
+
+def main(_):
+
+  flags.mark_flag_as_required('model_path')
+
+  # Load TFLite model and allocate tensors.
+  interpreter = tf.lite.Interpreter(model_path=FLAGS.model_path)
+  interpreter.allocate_tensors()
+
+  # Get input and output tensors.
+  input_details = interpreter.get_input_details()
+  print('input_details:', input_details)
+  output_details = interpreter.get_output_details()
+  print('output_details:', output_details)
+
+  # Test model on random input data.
+  input_shape = input_details[0]['shape']
+  # change the following line to feed into your own data.
+  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)
+
+
+if __name__ == '__main__':
+  tf.app.run()

research/lstm_object_detection/tflite/BUILD

@@ -59,12 +59,19 @@ cc_library(
     name = "mobile_lstd_tflite_client",
     srcs = ["mobile_lstd_tflite_client.cc"],
     hdrs = ["mobile_lstd_tflite_client.h"],
+    defines = select({
+        "//conditions:default": [],
+        "enable_edgetpu": ["ENABLE_EDGETPU"],
+    }),
     deps = [
         ":mobile_ssd_client",
         ":mobile_ssd_tflite_client",
-        "@com_google_absl//absl/base:core_headers",
         "@com_google_glog//:glog",
+        "@com_google_absl//absl/base:core_headers",
         "@org_tensorflow//tensorflow/lite/kernels:builtin_ops",
-    ],
+    ] + select({
+        "//conditions:default": [],
+        "enable_edgetpu": ["@libedgetpu//libedgetpu:header"],
+    }),
     alwayslink = 1,
 )

research/lstm_object_detection/tflite/WORKSPACE

@@ -90,13 +90,6 @@ http_archive(
     sha256 = "79d102c61e2a479a0b7e5fc167bcfaa4832a0c6aad4a75fa7da0480564931bcc",
 )
 
-#
-# http_archive(
-#     name = "com_google_protobuf",
-#     strip_prefix = "protobuf-master",
-#     urls = ["https://github.com/protocolbuffers/protobuf/archive/master.zip"],
-# )
-
 # Needed by TensorFlow
 http_archive(
     name = "io_bazel_rules_closure",

research/lstm_object_detection/tflite/mobile_lstd_tflite_client.cc

@@ -66,6 +66,11 @@ bool MobileLSTDTfLiteClient::InitializeInterpreter(
     interpreter_->UseNNAPI(false);
   }
 
+#ifdef ENABLE_EDGETPU
+  interpreter_->SetExternalContext(kTfLiteEdgeTpuContext,
+                                   edge_tpu_context_.get());
+#endif
+
   // Inputs are: normalized_input_image_tensor, raw_inputs/init_lstm_c,
   // raw_inputs/init_lstm_h
   if (interpreter_->inputs().size() != 3) {

research/lstm_object_detection/tflite/mobile_ssd_tflite_client.h

@@ -26,7 +26,7 @@ limitations under the License.
 #include "mobile_ssd_client.h"
 #include "protos/anchor_generation_options.pb.h"
 #ifdef ENABLE_EDGETPU
-#include "libedgetpu/libedgetpu.h"
+#include "libedgetpu/edgetpu.h"
 #endif  // ENABLE_EDGETPU
 
 namespace lstm_object_detection {
@@ -76,6 +76,10 @@ class MobileSSDTfLiteClient : public MobileSSDClient {
   std::unique_ptr<::tflite::MutableOpResolver> resolver_;
   std::unique_ptr<::tflite::Interpreter> interpreter_;
 
+#ifdef ENABLE_EDGETPU
+  std::unique_ptr<edgetpu::EdgeTpuContext> edge_tpu_context_;
+#endif
+
  private:
   // MobileSSDTfLiteClient is neither copyable nor movable.
   MobileSSDTfLiteClient(const MobileSSDTfLiteClient&) = delete;
@@ -103,10 +107,6 @@ class MobileSSDTfLiteClient : public MobileSSDClient {
   bool FloatInference(const uint8_t* input_data);
   bool QuantizedInference(const uint8_t* input_data);
   void GetOutputBoxesAndScoreTensorsFromUInt8();
-
-#ifdef ENABLE_EDGETPU
-  std::unique_ptr<edgetpu::EdgeTpuContext> edge_tpu_context_;
-#endif
 };
 
 }  // namespace tflite

research/lstm_object_detection/utils/config_util.py

@@ -37,7 +37,7 @@ def get_configs_from_pipeline_file(pipeline_config_path):
 
   Returns:
     Dictionary of configuration objects. Keys are `model`, `train_config`,
-      `train_input_config`, `eval_config`, `eval_input_config`, `lstm_confg`.
+      `train_input_config`, `eval_config`, `eval_input_config`, `lstm_model`.
       Value are the corresponding config objects.
   """
   pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()