update mobilenets (#48)

LICENSE

@@ -1,29 +1,202 @@
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README.md

@@ -4,14 +4,6 @@ MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicatio
 
 MobileNetV2: Inverted Residuals and Linear Bottlenecks
 
-Models are taken from https://github.com/shicai/MobileNet-Caffe and converted to ONNX format using [caffe2onnx](https://github.com/asiryan/caffe2onnx):
-```
-python -m caffe2onnx.convert --prototxt mobilenet_deploy.prototxt --caffemodel mobilenet.caffemodel --onnx mobilenet_v1.onnx
-python -m caffe2onnx.convert --prototxt mobilenet_v2_deploy.prototxt --caffemodel mobilenet_v2.caffemodel --onnx mobilenet_v2.onnx
-```
-
-NOTE: Quantized MobileNet V1 & V2 have a great drop in accuracy. We are working on producing higher accuracy MobileNets.
-
 ## Demo
 
 Run the following command to try the demo:
@@ -24,12 +16,11 @@ python demo.py --input /path/to/image --model v2
 
 ## License
 
-Model weights are licensed under [BSD-3-Clause License](./LICENSE).
-Scripts are licensed unser [Apache 2.0 License](../../LICENSE).
+All files in this directory are licensed under [Apache 2.0 License](./LICENSE).
 
 ## Reference
 
 - MobileNet V1: https://arxiv.org/abs/1704.04861
 - MobileNet V2: https://arxiv.org/abs/1801.04381
-- https://github.com/shicai/MobileNet-Caffe
-
+- MobileNet V1 weight and scripts for training: https://github.com/wjc852456/pytorch-mobilenet-v1
+- MobileNet V2 weight: https://github.com/onnx/models/tree/main/vision/classification/mobilenet

mobilenet_v1.py

@@ -15,8 +15,8 @@ class MobileNetV1:
         self.input_names = ''
         self.output_names = ''
         self.input_size = [224, 224]
-        self.mean = [103.94,116.78,123.68]
-        self.scale = 0.017
+        self.mean=[0.485, 0.456, 0.406]
+        self.std=[0.229, 0.224, 0.225]
 
         # load labels
         self.labels = self._load_labels()
@@ -41,7 +41,11 @@ class MobileNetV1:
         self.model.setPreferableTarget(self.target_id)
 
     def _preprocess(self, image):
-        return cv.dnn.blobFromImage(image, scalefactor=self.scale, size=self.input_size, mean=self.mean)
+        input_blob = (image / 255.0 - self.mean) / self.std
+        input_blob = input_blob.transpose(2, 0, 1)
+        input_blob = input_blob[np.newaxis, :, :, :]
+        input_blob = input_blob.astype(np.float32)
+        return input_blob
 
     def infer(self, image):
         # Preprocess

mobilenet_v2.py

@@ -15,8 +15,8 @@ class MobileNetV2:
         self.input_names = ''
         self.output_names = ''
         self.input_size = [224, 224]
-        self.mean = [103.94,116.78,123.68]
-        self.scale = 0.017
+        self.mean=[0.485, 0.456, 0.406]
+        self.std=[0.229, 0.224, 0.225]
 
         # load labels
         self.labels = self._load_labels()
@@ -41,7 +41,11 @@ class MobileNetV2:
         self.model.setPreferableTarget(self.target_id)
 
     def _preprocess(self, image):
-        return cv.dnn.blobFromImage(image, scalefactor=self.scale, size=self.input_size, mean=self.mean)
+        input_blob = (image / 255.0 - self.mean) / self.std
+        input_blob = input_blob.transpose(2, 0, 1)
+        input_blob = input_blob[np.newaxis, :, :, :]
+        input_blob = input_blob.astype(np.float32)
+        return input_blob
 
     def infer(self, image):
         # Preprocess