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hed.py

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+# -*- coding: utf-8 -*-
+"""HED.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1sxAQYKOi_hJozIVNX80ChnznWExGtWXd
+
+# IMPORTS
+"""
+
+import tensorflow as tf # For tensorflow
+import numpy as np # For mathematical computations
+import matplotlib.pyplot as plt # For plotting and Visualization
+import seaborn as sns
+from tensorflow.keras.layers import Input, Layer, Resizing, Rescaling, InputLayer, Conv2D, BatchNormalization, MaxPooling2D, Dropout, Flatten, Dense, RandomRotation, RandomFlip, RandomContrast, ReLU, Add, GlobalAveragePooling2D, Permute
+from tensorflow.keras import Model
+from tensorflow.keras.regularizers import L2
+from tensorflow.keras.losses import CategoricalCrossentropy
+from tensorflow.keras.metrics import CategoricalAccuracy, TopKCategoricalAccuracy
+from tensorflow.keras.optimizers import Adam
+from sklearn.metrics import confusion_matrix
+from tensorflow.keras.callbacks import ModelCheckpoint, Callback
+import cv2
+
+"""# ONNX"""
+
+!pip install onnx
+!pip install onnxruntime
+
+import onnx
+import onnxruntime as ort
+
+# from google.colab import drive
+# drive.mount('/content/drive')
+
+
+
+"""## Predicting Using Onnx Model"""
+
+!pip install onnx
+!pip install onnxruntime
+
+import onnxruntime as rt
+import onnx
+
+"""# Creating Web Interface Using Gradio"""
+
+!pip install gradio
+
+import gradio as gr
+
+!pip install onnx
+!pip install onnxruntime
+
+import onnxruntime as rt
+
+# !cp -r /content/drive/MyDrive/vit_onnx.onnx /content/vit_onnx.onnx
+
+import onnx
+model = onnx.load("vit_onnx.onnx")
+
+import onnxruntime as ort
+session = ort.InferenceSession("vit_onnx.onnx")
+
+input_name = session.get_inputs()[0].name
+output_name = session.get_outputs()[0].name
+
+CLASS_NAMES = ["Angry", "Happy", "Sad"]
+def predict_image(im):
+  im = tf.expand_dims(tf.cast(im, tf.float32), axis=0).numpy()
+  prediction = session.run([output_name], {input_name: im})
+  return {CLASS_NAMES[i]: float(prediction[0][0][i]) for i in range(3)}
+
+image = gr.inputs.Image(shape=(224, 224))
+label = gr.outputs.Label(num_top_classes=3)
+iface = gr.Interface(fn=predict_image, inputs=image, outputs=label, capture_session=True)
+iface.launch(debug="True")
+

vit_onnx.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a24c81c8da0f4c831414b6bca27320ed6f632681eddace226611db5a276e5fa2
+size 343610599