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testdata.py
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from __future__ import absolute_import, division, print_function, unicode_literals
from cgitb import html
import tensorflow as tf
import tensorflow_hub as hub
from tensorflow import lite
from tensorflow.keras.utils import to_categorical
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from keras.models import load_model
# from tensorflow.keras.callbacks import TensorBoard
import numpy as np
import cv2
import numpy as np
import pandas as pd
import os
import time
import mediapipe as mp
DATA_PATH = os.path.join('ISL_Data')
actions = np.array(['A', 'B', 'C'])
# no of videos
no_sequences = 30
# no of frames in each video
sequence_length = 30
label_map = {label: num for num, label in enumerate(actions)}
# print(label_map)
sequences, labels = [], []
for action in actions:
for sequence in range(no_sequences):
window = []
for frame_num in range(sequence_length):
res = np.load(os.path.join(DATA_PATH, action, str(
sequence), "{}.npy".format(frame_num)))
window.append(res)
sequences.append(window)
labels.append(label_map[action])
X = np.array(sequences)
# Step 6 - preprocess data create labels and features
y = to_categorical(labels).astype(int)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
model = load_model('action3.h5')
yhat = model.predict(X_test)
ytrue = np.argmax(y_test, axis=1).tolist()
yhat = np.argmax(yhat, axis=1).tolist()
def check():
return accuracy_score(yhat, ytrue)
SAVED_MODEL = "saved_models"
# tf.saved_model.save(model, SAVED_MODEL)
sign_model = hub.load(SAVED_MODEL)
TFLITE_MODEL = "tflite_models/sign.tflite"
TFLITE_QUANT_MODEL = "tflite_models/sign_quant.tflite"
X_test = np.float32(X_test)
def convert_to_tflite():
converter = lite.TFLiteConverter.from_keras_model(model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.experimental_new_converter = True
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS,
tf.lite.OpsSet.SELECT_TF_OPS]
converted_tflite_model = converter.convert()
open(TFLITE_MODEL, "wb").write(converted_tflite_model)
converter = lite.TFLiteConverter.from_keras_model(model)
converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]
converter.experimental_new_converter = True
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS,
tf.lite.OpsSet.SELECT_TF_OPS]
tflite_quant_model = converter.convert()
open(TFLITE_QUANT_MODEL, "wb").write(tflite_quant_model)
tflite_interpreter = tf.lite.Interpreter(model_path=TFLITE_MODEL)
input_details = tflite_interpreter.get_input_details()
output_details = tflite_interpreter.get_output_details()
tflite_interpreter.resize_tensor_input(
input_details[0]['index'], X_test.shape)
tflite_interpreter.resize_tensor_input(
output_details[0]['index'], y_test.shape)
tflite_interpreter.allocate_tensors()
input_details = tflite_interpreter.get_input_details()
output_details = tflite_interpreter.get_output_details()
# Load quantized TFLite model
tflite_interpreter_quant = tf.lite.Interpreter(
model_path=TFLITE_QUANT_MODEL)
# Learn about its input and output details
input_details = tflite_interpreter_quant.get_input_details()
output_details = tflite_interpreter_quant.get_output_details()
# Resize input and output tensors
tflite_interpreter_quant.resize_tensor_input(
input_details[0]['index'], X_test.shape)
tflite_interpreter_quant.resize_tensor_input(
output_details[0]['index'], y_test.shape)
tflite_interpreter_quant.allocate_tensors()
input_details = tflite_interpreter_quant.get_input_details()
output_details = tflite_interpreter_quant.get_output_details()
# Run inference
tflite_interpreter_quant.set_tensor(input_details[0]['index'], X_test)
tflite_interpreter_quant.invoke()
tflite_q_model_predictions = tflite_interpreter_quant.get_tensor(
output_details[0]['index'])
#print("\nPrediction results shape:", tflite_q_model_predictions.shape)
def TestDataComparison():
tflite_interpreter = tf.lite.Interpreter(model_path=TFLITE_MODEL)
input_details = tflite_interpreter.get_input_details()
output_details = tflite_interpreter.get_output_details()
tflite_interpreter.resize_tensor_input(
input_details[0]['index'], X_test.shape)
tflite_interpreter.resize_tensor_input(
output_details[0]['index'], y_test.shape)
tflite_interpreter.allocate_tensors()
input_details = tflite_interpreter.get_input_details()
output_details = tflite_interpreter.get_output_details()
tflite_interpreter.set_tensor(input_details[0]['index'], X_test)
tflite_interpreter.invoke()
tflite_model_predictions = tflite_interpreter.get_tensor(
output_details[0]['index'])
#print("Prediction results shape:", tflite_model_predictions.shape)
# Convert prediction results to Pandas dataframe, for better visualization
tflite_pred_dataframe = pd.DataFrame(tflite_model_predictions)
tflite_pred_dataframe.columns = actions
#print("TFLite prediction results: ")
# print(tflite_pred_dataframe)
tflite_interpreter_quant = tf.lite.Interpreter(
model_path=TFLITE_QUANT_MODEL)
input_details = tflite_interpreter_quant.get_input_details()
output_details = tflite_interpreter_quant.get_output_details()
tflite_interpreter_quant.resize_tensor_input(
input_details[0]['index'], X_test.shape)
tflite_interpreter_quant.resize_tensor_input(
output_details[0]['index'], y_test.shape)
tflite_interpreter_quant.allocate_tensors()
input_details = tflite_interpreter_quant.get_input_details()
output_details = tflite_interpreter_quant.get_output_details()
# Run inference
tflite_interpreter_quant.set_tensor(input_details[0]['index'], X_test)
tflite_interpreter_quant.invoke()
tflite_q_model_predictions = tflite_interpreter_quant.get_tensor(
output_details[0]['index'])
#print("\nPrediction results shape:", tflite_q_model_predictions.shape)
# Convert prediction results to Pandas dataframe, for better visualization
tflite_q_pred_dataframe = pd.DataFrame(tflite_q_model_predictions)
tflite_q_pred_dataframe.columns = actions
# print("Quantized TFLite model prediction results")
# print(tflite_q_pred_dataframe)
tf_model_predictions = sign_model(X_test)
#print("Prediction results shape:", tf_model_predictions.shape)
tf_pred_dataframe = pd.DataFrame(tf_model_predictions.numpy())
tf_pred_dataframe.columns = actions
text_file = open("./templates/testdata.html", "w")
text_file.write("<h2>TF model prediction results: </h2>")
text_file = open("./templates/testdata.html", "a")
text_file.write(tf_pred_dataframe.to_html())
text_file.write("<h2>TFLite model prediction results: </h2>")
text_file.write(tflite_pred_dataframe.to_html())
text_file.write("<h2>Quantized TFLite model prediction results: </h2>")
text_file.write(tflite_q_pred_dataframe.to_html())
# Concatenate results from all models
all_models_dataframe = pd.concat([tf_pred_dataframe,
tflite_pred_dataframe,
tflite_q_pred_dataframe],
keys=['TF Model', 'TFLite',
'Quantized TFLite'],
axis='columns')
all_models_dataframe = all_models_dataframe.swaplevel(
axis='columns')[tflite_pred_dataframe.columns]
def highlight_diff(data, color='yellow'):
attr = 'background-color: {}'.format(color)
other = data.xs('TF Model', axis='columns', level=-1)
return pd.DataFrame(np.where(data.ne(other, level=0), attr, ''),
index=data.index, columns=data.columns)
final_df = all_models_dataframe.style.apply(highlight_diff, axis=None)
text_file.write("<h2>Comparison between models: </h2>")
text_file.write(final_df.to_html())
# Concatenation of argmax and max value for each row
def max_values_only(data):
argmax_col = np.argmax(data, axis=1).reshape(-1, 1)
max_col = np.max(data, axis=1).reshape(-1, 1)
return np.concatenate([argmax_col, max_col], axis=1)
# Build simplified prediction tables
tf_model_pred_simplified = max_values_only(tf_model_predictions)
tflite_model_pred_simplified = max_values_only(tflite_model_predictions)
tflite_q_model_pred_simplified = max_values_only(
tflite_q_model_predictions)
# Build DataFrames and present example
columns_names = ["Label_id", "Confidence"]
tf_model_simple_dataframe = pd.DataFrame(tf_model_pred_simplified)
tf_model_simple_dataframe.columns = columns_names
tf_confidence = np.mean(tf_model_simple_dataframe["Confidence"])
tflite_model_simple_dataframe = pd.DataFrame(tflite_model_pred_simplified)
tflite_model_simple_dataframe.columns = columns_names
tflite_confidence = np.mean(tflite_model_simple_dataframe["Confidence"])
tflite_q_model_simple_dataframe = pd.DataFrame(
tflite_q_model_pred_simplified)
tflite_q_model_simple_dataframe.columns = columns_names
tflite_q_confidence = np.mean(
tflite_q_model_simple_dataframe["Confidence"])
# print("Confidence in TF Model: ", tf_confidence)
# print(tf_model_simple_dataframe)
# print("Confidence in TF Lite Model: ", tflite_confidence)
# print(tflite_model_simple_dataframe)
# print("Confidence in TF Lite Quant Model: ", tflite_q_confidence)
# print(tflite_q_model_simple_dataframe)
text_file.write("<h2>Confidence in TF Model: </h2>")
text_file.write(tf_model_simple_dataframe.to_html())
text_file.write("<h2>Confidence in TF Lite Model: </h2>")
text_file.write(tflite_model_simple_dataframe.to_html())
text_file.write("<h2>Confidence in Quantized TF Lite Model: </h2>")
text_file.write(tflite_q_model_simple_dataframe.to_html())
text_file.close()