Update app.py

app.py

@@ -1,66 +1,60 @@
-import mxnet as mx
-import matplotlib.pyplot as plt
+import onnx
 import numpy as np
-from collections import namedtuple
-from mxnet.gluon.data.vision import transforms
+import onnxruntime as ort
+from PIL import Image
+import cv2
 import os
 import gradio as gr
 
-from PIL import Image
-import imageio
-import onnxruntime as ort
-
-def get_image(path):
-    '''
-        Using path to image, return the RGB load image
-    '''
-    img = imageio.imread(path, pilmode='RGB')
-    return img
-
-# Pre-processing function for ImageNet models using numpy
-def preprocess(img):
-    '''
-    Preprocessing required on the images for inference with mxnet gluon
-    The function takes loaded image and returns processed tensor
-    '''
-    img = np.array(Image.fromarray(img).resize((224, 224))).astype(np.float32)
-    img[:, :, 0] -= 123.68
-    img[:, :, 1] -= 116.779
-    img[:, :, 2] -= 103.939
-    img[:,:,[0,1,2]] = img[:,:,[2,1,0]]
-    img = img.transpose((2, 0, 1))
-    img = np.expand_dims(img, axis=0)
-
-    return img
+os.system("wget https://s3.amazonaws.com/onnx-model-zoo/synset.txt")
 
-mx.test_utils.download('https://s3.amazonaws.com/model-server/inputs/kitten.jpg')
 
-mx.test_utils.download('https://s3.amazonaws.com/onnx-model-zoo/synset.txt')
 with open('synset.txt', 'r') as f:
     labels = [l.rstrip() for l in f]
     
 os.system("wget https://github.com/AK391/models/raw/main/vision/classification/inception_and_googlenet/inception_v2/model/inception-v2-9.onnx")
 
-ort_session = ort.InferenceSession("inception-v2-9.onnx")
+os.system("wget https://s3.amazonaws.com/model-server/inputs/kitten.jpg")
+
 
-    
-def predict(path):
-    img_batch = preprocess(get_image(path))
 
-    outputs = ort_session.run(
-        None,
-        {"data_0": img_batch.astype(np.float32)},
-    )
+model_path = 'inception-v2-9.onnx'
+model = onnx.load(model_path)
+session = ort.InferenceSession(model.SerializeToString())
+
+def get_image(path):
+    with Image.open(path) as img:
+        img = np.array(img.convert('RGB'))
+    return img
+    
+def preprocess(img):
+    img = img / 255.
+    img = cv2.resize(img, (256, 256))
+    h, w = img.shape[0], img.shape[1]
+    y0 = (h - 224) // 2
+    x0 = (w - 224) // 2
+    img = img[y0 : y0+224, x0 : x0+224, :]
+    img = (img - [0.485, 0.456, 0.406]) / [0.229, 0.224, 0.225]
+    img = np.transpose(img, axes=[2, 0, 1])
+    img = img.astype(np.float32)
+    img = np.expand_dims(img, axis=0)
+    return img
 
-    a = np.argsort(outputs[0].flatten())[::-1]
+def predict(path):
+    img = get_image(path)
+    img = preprocess(img)
+    ort_inputs = {session.get_inputs()[0].name: img}
+    preds = session.run(None, ort_inputs)[0]
+    preds = np.squeeze(preds)
+    a = np.argsort(preds)[::-1]
     results = {}
-    for i in a[0:5]:
-        results[labels[i]]=float(outputs[0][0][i])
+    results[labels[a[0]]] = float(preds[a[0]]*0.1)
     return results
+
        
 
 title="Inception v2"
 description="Inception v2 is a deep convolutional networks for classification."
 
-examples=[['catonnx.jpg']]
+examples=[['kitten.jpg']]
 gr.Interface(predict,gr.inputs.Image(type='filepath'),"label",title=title,description=description,examples=examples).launch(enable_queue=True,debug=True)