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012_Fast_Accurate_and_Lightweight_Super-Resolution/download.sh create mode 100755 013_ml-sound-classifier/download.sh create mode 100755 014_tf-monodepth2/download.sh create mode 100755 015_Faster-Grad-CAM/download.sh create mode 100755 016_EfficientNet-lite/download.sh create mode 100755 017_Artistic-Style-Transfer/download.sh create mode 100755 018_EfficientDet/download.sh create mode 100755 019_White-box-Cartoonization/download.sh create mode 100755 022_Learning_to_See_Moving_Objects_in_the_Dark/download.sh create mode 100755 023_yolov3-nano/download.sh create mode 100755 024_yolov3-lite/download.sh create mode 100755 025_head_pose_estimation/download.sh create mode 100755 026_mobile-deeplabv3-plus/download.sh create mode 100755 028_struct2depth/download.sh create mode 100755 032_FaceMesh/download.sh create mode 100755 034_ssd_mobilenet_v2_mnasfpn_shared_box_predictor/download.sh diff --git a/005_one_class_anomaly_detection/01_float32/01_freeze_the_saved_model.py b/005_one_class_anomaly_detection/01_float32/01_freeze_the_saved_model.py deleted file mode 100644 index c90bea72a7..0000000000 --- a/005_one_class_anomaly_detection/01_float32/01_freeze_the_saved_model.py +++ /dev/null @@ -1,35 +0,0 @@ -import tensorflow as tf -import os -import shutil -from tensorflow.python.saved_model import tag_constants -from tensorflow.python.tools import freeze_graph -from tensorflow.python import ops -from tensorflow.tools.graph_transforms import TransformGraph - - -def get_graph_def_from_file(graph_filepath): - tf.reset_default_graph() - with ops.Graph().as_default(): - with tf.gfile.GFile(graph_filepath, 'rb') as f: - graph_def = tf.GraphDef() - graph_def.ParseFromString(f.read()) - return graph_def - -def convert_graph_def_to_saved_model(export_dir, graph_filepath, input_name, outputs): - graph_def = get_graph_def_from_file(graph_filepath) - with tf.Session(graph=tf.Graph()) as session: - tf.import_graph_def(graph_def, name='') - tf.compat.v1.saved_model.simple_save( - session, - export_dir,# change input_image to node.name if you know the name - inputs={input_name: session.graph.get_tensor_by_name('{}:0'.format(node.name)) - for node in graph_def.node if node.op=='Placeholder'}, - outputs={t.rstrip(":0"):session.graph.get_tensor_by_name(t) for t in outputs} - ) - print('Optimized graph converted to SavedModel!') - -tf.compat.v1.enable_eager_execution() - -# convert this to a TF Serving compatible mode -shutil.rmtree('./saved_model', ignore_errors=True) -convert_graph_def_to_saved_model('./saved_model', '../weights.pb', 'input_1', ['global_average_pooling2d_1/Mean:0']) diff --git a/005_one_class_anomaly_detection/01_float32/02_confirm_saved_model_structure.txt b/005_one_class_anomaly_detection/01_float32/02_confirm_saved_model_structure.txt deleted file mode 100644 index d11a0d01c6..0000000000 --- a/005_one_class_anomaly_detection/01_float32/02_confirm_saved_model_structure.txt +++ /dev/null @@ -1 +0,0 @@ -$ saved_model_cli show --dir ./saved_model --all diff --git a/005_one_class_anomaly_detection/01_float32/03_quantization.py b/005_one_class_anomaly_detection/01_float32/03_quantization.py deleted file mode 100644 index d9b2cc7cf4..0000000000 --- a/005_one_class_anomaly_detection/01_float32/03_quantization.py +++ /dev/null @@ -1,52 +0,0 @@ -import tensorflow as tf -import numpy as np -from PIL import Image -import os -import glob - -def representative_dataset_gen(): - folder = ["images"] - raw_test_data = [] - for name in folder: - dir = "./" + name - files = glob.glob(dir + "/*.jpg") - for file in files: - image = Image.open(file) - image = image.convert("RGB") - image = np.asarray(image).astype(np.float32) - image = image[np.newaxis,:,:,:] - raw_test_data.append(image) - - for data in raw_test_data: - yield [data] - -tf.compat.v1.enable_eager_execution() - -# Weight Quantization - Input/Output=float32 -converter = tf.lite.TFLiteConverter.from_saved_model('./saved_model') -converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE] -tflite_quant_model = converter.convert() -with open('./weights_weight_quant.tflite', 'wb') as w: - w.write(tflite_quant_model) -print("Weight Quantization complete! - weights_weight_quant.tflite") - -# Integer Quantization - Input/Output=float32 -converter = tf.lite.TFLiteConverter.from_saved_model('./saved_model') -converter.optimizations = [tf.lite.Optimize.DEFAULT] -converter.representative_dataset = representative_dataset_gen -tflite_quant_model = converter.convert() -with open('./weights_integer_quant.tflite', 'wb') as w: - w.write(tflite_quant_model) -print("Integer Quantization complete! - weights_integer_quant.tflite") - -# Full Integer Quantization - Input/Output=int8 -converter = tf.lite.TFLiteConverter.from_saved_model('./saved_model') -converter.optimizations = [tf.lite.Optimize.DEFAULT] -converter.representative_dataset = representative_dataset_gen -converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8] -converter.inference_input_type = tf.uint8 -converter.inference_output_type = tf.uint8 -tflite_quant_model = converter.convert() -with open('./weights_full_integer_quant.tflite', 'wb') as w: - w.write(tflite_quant_model) -print("Full Integer Quantization complete! - weights_full_integer_quant.tflite") \ No newline at end of file diff --git a/005_one_class_anomaly_detection/01_float32/04_benchmark.txt b/005_one_class_anomaly_detection/01_float32/04_benchmark.txt deleted file mode 100644 index 75ac568857..0000000000 --- a/005_one_class_anomaly_detection/01_float32/04_benchmark.txt +++ /dev/null @@ -1,5 +0,0 @@ -bazel run -c opt tensorflow/lite/tools/benchmark:benchmark_model -- \ - --graph=${HOME}/weights_integer_quant.tflite \ - --num_threads=4 \ - --warmup_runs=1 \ - --enable_op_profiling=true diff --git a/005_one_class_anomaly_detection/01_float32/images/000001.jpg b/005_one_class_anomaly_detection/01_float32/images/000001.jpg deleted file mode 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