diff --git a/practice/1_OpenCV/cat_passport.py b/practice/1_OpenCV/cat_passport.py
index ca73b6e..674040d 100644
--- a/practice/1_OpenCV/cat_passport.py
+++ b/practice/1_OpenCV/cat_passport.py
@@ -1,26 +1,38 @@
import argparse
import cv2
+import sys
def make_cat_passport_image(input_image_path, haar_model_path):
# Read image
-
+ img = cv2.imread(input_image_path)
# Convert image to grayscale
-
+ gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Normalize image intensity
-
+ gray = cv2.equalizeHist(gray)
# Resize image
# Detect cat faces using Haar Cascade
+ detector = cv2.CascadeClassifier(haar_model_path)
+ rects = detector.detectMultiScale(img, scaleFactor=1.1, minNeighbors=5,
+ minSize=(75, 75))
# Draw bounding box
+ for (i, (x, y, w, h)) in enumerate(rects):
+ cv2.rectangle(img, (x, y), (x + w, y + h), (0, 0, 255), 2)
+ cv2.putText(img, f"Cat #{i + 1}", (x, y - 10),
+ cv2.FONT_HERSHEY_SIMPLEX, 0.55, (0, 0, 255), 2)
# Display result image
-
+ cv2.imshow("window_name", img)
+ cv2.waitKey(0)
+ cv2.destroyAllWindows()
# Crop image
-
+ x, y, w, h = rects[0]
+ image = img[y:y + h, x:x + w]
# Save result image to file
+ cv2.imwrite('out.jpg', img)
return
diff --git a/practice/1_OpenCV/out.jpg b/practice/1_OpenCV/out.jpg
new file mode 100644
index 0000000..68754c2
Binary files /dev/null and b/practice/1_OpenCV/out.jpg differ
diff --git a/practice/2_Classification/doge_classifier.py b/practice/2_Classification/doge_classifier.py
index ba07e00..ab48a74 100644
--- a/practice/2_Classification/doge_classifier.py
+++ b/practice/2_Classification/doge_classifier.py
@@ -20,16 +20,23 @@ class InferenceEngineClassifier:
def __init__(self, model_path, device='CPU', classes_path=None):
# Add code for Inference Engine initialization
+ self.core = Core()
# Add code for model loading
+ self.model = self.core.read_model(model=model_path)
# Add code for classes names loading
+ self.exec_model = self.core.compile_model(model=self.model,
+ device_name=device)
+ if classes_path:
+ self.classes = [line.rstrip('\n') for line in open(classes_path, encoding='UTF-8')]
return
def get_top(self, prob, topN=1):
- result = []
-
+ result = prob
+ result = np.squeeze(result)
+ result = np.argsort(result)[-topN:][::-1]
# Add code for getting top predictions
return result
@@ -37,15 +44,20 @@ def get_top(self, prob, topN=1):
def _prepare_image(self, image, h, w):
# Add code for image preprocessing
-
+ image = cv2.resize(image, (w, h))
+ image = image.transpose((2, 0, 1))
+ image = np.expand_dims(image, axis = 0)
return image
def classify(self, image):
- probabilities = None
-
+ result = None
+ input_layer = self.exec_model.input(0)
+ output_layer = self.exec_model.output(0)
+ n, c, h, w = input_layer.shape
+ image = self._prepare_image(image, h, w)
# Add code for image classification using Inference Engine
-
- return probabilities
+ result = self.exec_model([image])[output_layer]
+ return result
def build_argparser():
@@ -71,15 +83,17 @@ def main():
log.info("Start IE classification sample")
# Create InferenceEngineClassifier object
-
+ test = InferenceEngineClassifier(model_path=args.model, classes_path=args.classes)
# Read image
-
+ image = cv2.imread(args.input)
# Classify image
-
+ classfer = test.classify(image)
# Get top 5 predictions
-
+ predictions = test.get_top(classfer, 5)
+ classfer = np.squeeze(classfer)
# print result
-
+ predictions = [str(test.classes[predictions[i]]) + ', ' + str(classfer[predictions[i]]) for i in range(5)]
+ log.info("Predictions: " + str(predictions))
return
diff --git a/practice/2_Classification/public/mobilenet-v2-pytorch/FP16/mobilenet-v2-pytorch.bin b/practice/2_Classification/public/mobilenet-v2-pytorch/FP16/mobilenet-v2-pytorch.bin
new file mode 100644
index 0000000..0c18172
Binary files /dev/null and b/practice/2_Classification/public/mobilenet-v2-pytorch/FP16/mobilenet-v2-pytorch.bin differ
diff --git a/practice/2_Classification/public/mobilenet-v2-pytorch/FP16/mobilenet-v2-pytorch.mapping b/practice/2_Classification/public/mobilenet-v2-pytorch/FP16/mobilenet-v2-pytorch.mapping
new file mode 100644
index 0000000..0228391
--- /dev/null
+++ b/practice/2_Classification/public/mobilenet-v2-pytorch/FP16/mobilenet-v2-pytorch.mapping
@@ -0,0 +1,563 @@
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diff --git a/practice/2_Classification/public/mobilenet-v2-pytorch/FP16/mobilenet-v2-pytorch.xml b/practice/2_Classification/public/mobilenet-v2-pytorch/FP16/mobilenet-v2-pytorch.xml
new file mode 100644
index 0000000..e5333b9
--- /dev/null
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diff --git a/practice/2_Classification/public/mobilenet-v2-pytorch/FP32/mobilenet-v2-pytorch.bin b/practice/2_Classification/public/mobilenet-v2-pytorch/FP32/mobilenet-v2-pytorch.bin
new file mode 100644
index 0000000..c7019c7
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diff --git a/practice/2_Classification/public/mobilenet-v2-pytorch/FP32/mobilenet-v2-pytorch.mapping b/practice/2_Classification/public/mobilenet-v2-pytorch/FP32/mobilenet-v2-pytorch.mapping
new file mode 100644
index 0000000..6fb9dd4
--- /dev/null
+++ b/practice/2_Classification/public/mobilenet-v2-pytorch/FP32/mobilenet-v2-pytorch.mapping
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diff --git a/practice/2_Classification/public/mobilenet-v2-pytorch/FP32/mobilenet-v2-pytorch.xml b/practice/2_Classification/public/mobilenet-v2-pytorch/FP32/mobilenet-v2-pytorch.xml
new file mode 100644
index 0000000..36e0570
--- /dev/null
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diff --git a/practice/2_Classification/public/mobilenet-v2-pytorch/mobilenet-v2.onnx b/practice/2_Classification/public/mobilenet-v2-pytorch/mobilenet-v2.onnx
new file mode 100644
index 0000000..b763b8e
Binary files /dev/null and b/practice/2_Classification/public/mobilenet-v2-pytorch/mobilenet-v2.onnx differ
diff --git a/practice/2_Classification/public/mobilenet-v2-pytorch/mobilenet_v2-b0353104.pth b/practice/2_Classification/public/mobilenet-v2-pytorch/mobilenet_v2-b0353104.pth
new file mode 100644
index 0000000..36508b3
Binary files /dev/null and b/practice/2_Classification/public/mobilenet-v2-pytorch/mobilenet_v2-b0353104.pth differ
diff --git a/practice/2_Classification/public/mobilenet-v2/mobilenet-v2.caffemodel b/practice/2_Classification/public/mobilenet-v2/mobilenet-v2.caffemodel
new file mode 100644
index 0000000..901c2b5
Binary files /dev/null and b/practice/2_Classification/public/mobilenet-v2/mobilenet-v2.caffemodel differ
diff --git a/practice/2_Classification/public/mobilenet-v2/mobilenet-v2.prototxt b/practice/2_Classification/public/mobilenet-v2/mobilenet-v2.prototxt
new file mode 100644
index 0000000..a6465e8
--- /dev/null
+++ b/practice/2_Classification/public/mobilenet-v2/mobilenet-v2.prototxt
@@ -0,0 +1,3417 @@
+name: "MOBILENET_V2"
+# transform_param {
+# scale: 0.017
+# mirror: false
+# crop_size: 224
+# mean_value: [103.94,116.78,123.68]
+# }
+input: "data"
+input_dim: 1
+input_dim: 3
+input_dim: 224
+input_dim: 224
+layer {
+ name: "conv1"
+ type: "Convolution"
+ bottom: "data"
+ top: "conv1"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ convolution_param {
+ num_output: 32
+ bias_term: false
+ pad: 1
+ kernel_size: 3
+ stride: 2
+ weight_filler {
+ type: "msra"
+ }
+ }
+}
+layer {
+ name: "conv1/bn"
+ type: "BatchNorm"
+ bottom: "conv1"
+ top: "conv1/bn"
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-5
+ }
+}
+layer {
+ name: "conv1/scale"
+ type: "Scale"
+ bottom: "conv1/bn"
+ top: "conv1/bn"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "relu1"
+ type: "ReLU"
+ bottom: "conv1/bn"
+ top: "conv1/bn"
+}
+layer {
+ name: "conv2_1/expand"
+ type: "Convolution"
+ bottom: "conv1/bn"
+ top: "conv2_1/expand"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ convolution_param {
+ num_output: 32
+ bias_term: false
+ kernel_size: 1
+ weight_filler {
+ type: "msra"
+ }
+ }
+}
+layer {
+ name: "conv2_1/expand/bn"
+ type: "BatchNorm"
+ bottom: "conv2_1/expand"
+ top: "conv2_1/expand/bn"
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-5
+ }
+}
+layer {
+ name: "conv2_1/expand/scale"
+ type: "Scale"
+ bottom: "conv2_1/expand/bn"
+ top: "conv2_1/expand/bn"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "relu2_1/expand"
+ type: "ReLU"
+ bottom: "conv2_1/expand/bn"
+ top: "conv2_1/expand/bn"
+}
+layer {
+ name: "conv2_1/dwise"
+ type: "Convolution"
+ bottom: "conv2_1/expand/bn"
+ top: "conv2_1/dwise"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ convolution_param {
+ num_output: 32
+ bias_term: false
+ pad: 1
+ kernel_size: 3
+ group: 32
+ weight_filler {
+ type: "msra"
+ }
+ engine: CAFFE
+ }
+}
+layer {
+ name: "conv2_1/dwise/bn"
+ type: "BatchNorm"
+ bottom: "conv2_1/dwise"
+ top: "conv2_1/dwise/bn"
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-5
+ }
+}
+layer {
+ name: "conv2_1/dwise/scale"
+ type: "Scale"
+ bottom: "conv2_1/dwise/bn"
+ top: "conv2_1/dwise/bn"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "relu2_1/dwise"
+ type: "ReLU"
+ bottom: "conv2_1/dwise/bn"
+ top: "conv2_1/dwise/bn"
+}
+layer {
+ name: "conv2_1/linear"
+ type: "Convolution"
+ bottom: "conv2_1/dwise/bn"
+ top: "conv2_1/linear"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ convolution_param {
+ num_output: 16
+ bias_term: false
+ kernel_size: 1
+ weight_filler {
+ type: "msra"
+ }
+ }
+}
+layer {
+ name: "conv2_1/linear/bn"
+ type: "BatchNorm"
+ bottom: "conv2_1/linear"
+ top: "conv2_1/linear/bn"
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-5
+ }
+}
+layer {
+ name: "conv2_1/linear/scale"
+ type: "Scale"
+ bottom: "conv2_1/linear/bn"
+ top: "conv2_1/linear/bn"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "conv2_2/expand"
+ type: "Convolution"
+ bottom: "conv2_1/linear/bn"
+ top: "conv2_2/expand"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ convolution_param {
+ num_output: 96
+ bias_term: false
+ kernel_size: 1
+ weight_filler {
+ type: "msra"
+ }
+ }
+}
+layer {
+ name: "conv2_2/expand/bn"
+ type: "BatchNorm"
+ bottom: "conv2_2/expand"
+ top: "conv2_2/expand/bn"
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-5
+ }
+}
+layer {
+ name: "conv2_2/expand/scale"
+ type: "Scale"
+ bottom: "conv2_2/expand/bn"
+ top: "conv2_2/expand/bn"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "relu2_2/expand"
+ type: "ReLU"
+ bottom: "conv2_2/expand/bn"
+ top: "conv2_2/expand/bn"
+}
+layer {
+ name: "conv2_2/dwise"
+ type: "Convolution"
+ bottom: "conv2_2/expand/bn"
+ top: "conv2_2/dwise"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ convolution_param {
+ num_output: 96
+ bias_term: false
+ pad: 1
+ kernel_size: 3
+ group: 96
+ stride: 2
+ weight_filler {
+ type: "msra"
+ }
+ engine: CAFFE
+ }
+}
+layer {
+ name: "conv2_2/dwise/bn"
+ type: "BatchNorm"
+ bottom: "conv2_2/dwise"
+ top: "conv2_2/dwise/bn"
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-5
+ }
+}
+layer {
+ name: "conv2_2/dwise/scale"
+ type: "Scale"
+ bottom: "conv2_2/dwise/bn"
+ top: "conv2_2/dwise/bn"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "relu2_2/dwise"
+ type: "ReLU"
+ bottom: "conv2_2/dwise/bn"
+ top: "conv2_2/dwise/bn"
+}
+layer {
+ name: "conv2_2/linear"
+ type: "Convolution"
+ bottom: "conv2_2/dwise/bn"
+ top: "conv2_2/linear"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ convolution_param {
+ num_output: 24
+ bias_term: false
+ kernel_size: 1
+ weight_filler {
+ type: "msra"
+ }
+ }
+}
+layer {
+ name: "conv2_2/linear/bn"
+ type: "BatchNorm"
+ bottom: "conv2_2/linear"
+ top: "conv2_2/linear/bn"
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-5
+ }
+}
+layer {
+ name: "conv2_2/linear/scale"
+ type: "Scale"
+ bottom: "conv2_2/linear/bn"
+ top: "conv2_2/linear/bn"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "conv3_1/expand"
+ type: "Convolution"
+ bottom: "conv2_2/linear/bn"
+ top: "conv3_1/expand"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ convolution_param {
+ num_output: 144
+ bias_term: false
+ kernel_size: 1
+ weight_filler {
+ type: "msra"
+ }
+ }
+}
+layer {
+ name: "conv3_1/expand/bn"
+ type: "BatchNorm"
+ bottom: "conv3_1/expand"
+ top: "conv3_1/expand/bn"
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-5
+ }
+}
+layer {
+ name: "conv3_1/expand/scale"
+ type: "Scale"
+ bottom: "conv3_1/expand/bn"
+ top: "conv3_1/expand/bn"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "relu3_1/expand"
+ type: "ReLU"
+ bottom: "conv3_1/expand/bn"
+ top: "conv3_1/expand/bn"
+}
+layer {
+ name: "conv3_1/dwise"
+ type: "Convolution"
+ bottom: "conv3_1/expand/bn"
+ top: "conv3_1/dwise"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ convolution_param {
+ num_output: 144
+ bias_term: false
+ pad: 1
+ kernel_size: 3
+ group: 144
+ weight_filler {
+ type: "msra"
+ }
+ engine: CAFFE
+ }
+}
+layer {
+ name: "conv3_1/dwise/bn"
+ type: "BatchNorm"
+ bottom: "conv3_1/dwise"
+ top: "conv3_1/dwise/bn"
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-5
+ }
+}
+layer {
+ name: "conv3_1/dwise/scale"
+ type: "Scale"
+ bottom: "conv3_1/dwise/bn"
+ top: "conv3_1/dwise/bn"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "relu3_1/dwise"
+ type: "ReLU"
+ bottom: "conv3_1/dwise/bn"
+ top: "conv3_1/dwise/bn"
+}
+layer {
+ name: "conv3_1/linear"
+ type: "Convolution"
+ bottom: "conv3_1/dwise/bn"
+ top: "conv3_1/linear"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ convolution_param {
+ num_output: 24
+ bias_term: false
+ kernel_size: 1
+ weight_filler {
+ type: "msra"
+ }
+ }
+}
+layer {
+ name: "conv3_1/linear/bn"
+ type: "BatchNorm"
+ bottom: "conv3_1/linear"
+ top: "conv3_1/linear/bn"
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-5
+ }
+}
+layer {
+ name: "conv3_1/linear/scale"
+ type: "Scale"
+ bottom: "conv3_1/linear/bn"
+ top: "conv3_1/linear/bn"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "block_3_1"
+ type: "Eltwise"
+ bottom: "conv2_2/linear/bn"
+ bottom: "conv3_1/linear/bn"
+ top: "block_3_1"
+}
+layer {
+ name: "conv3_2/expand"
+ type: "Convolution"
+ bottom: "block_3_1"
+ top: "conv3_2/expand"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ convolution_param {
+ num_output: 144
+ bias_term: false
+ kernel_size: 1
+ weight_filler {
+ type: "msra"
+ }
+ }
+}
+layer {
+ name: "conv3_2/expand/bn"
+ type: "BatchNorm"
+ bottom: "conv3_2/expand"
+ top: "conv3_2/expand/bn"
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-5
+ }
+}
+layer {
+ name: "conv3_2/expand/scale"
+ type: "Scale"
+ bottom: "conv3_2/expand/bn"
+ top: "conv3_2/expand/bn"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "relu3_2/expand"
+ type: "ReLU"
+ bottom: "conv3_2/expand/bn"
+ top: "conv3_2/expand/bn"
+}
+layer {
+ name: "conv3_2/dwise"
+ type: "Convolution"
+ bottom: "conv3_2/expand/bn"
+ top: "conv3_2/dwise"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ convolution_param {
+ num_output: 144
+ bias_term: false
+ pad: 1
+ kernel_size: 3
+ group: 144
+ stride: 2
+ weight_filler {
+ type: "msra"
+ }
+ engine: CAFFE
+ }
+}
+layer {
+ name: "conv3_2/dwise/bn"
+ type: "BatchNorm"
+ bottom: "conv3_2/dwise"
+ top: "conv3_2/dwise/bn"
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-5
+ }
+}
+layer {
+ name: "conv3_2/dwise/scale"
+ type: "Scale"
+ bottom: "conv3_2/dwise/bn"
+ top: "conv3_2/dwise/bn"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "relu3_2/dwise"
+ type: "ReLU"
+ bottom: "conv3_2/dwise/bn"
+ top: "conv3_2/dwise/bn"
+}
+layer {
+ name: "conv3_2/linear"
+ type: "Convolution"
+ bottom: "conv3_2/dwise/bn"
+ top: "conv3_2/linear"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ convolution_param {
+ num_output: 32
+ bias_term: false
+ kernel_size: 1
+ weight_filler {
+ type: "msra"
+ }
+ }
+}
+layer {
+ name: "conv3_2/linear/bn"
+ type: "BatchNorm"
+ bottom: "conv3_2/linear"
+ top: "conv3_2/linear/bn"
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-5
+ }
+}
+layer {
+ name: "conv3_2/linear/scale"
+ type: "Scale"
+ bottom: "conv3_2/linear/bn"
+ top: "conv3_2/linear/bn"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "conv4_1/expand"
+ type: "Convolution"
+ bottom: "conv3_2/linear/bn"
+ top: "conv4_1/expand"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ convolution_param {
+ num_output: 192
+ bias_term: false
+ kernel_size: 1
+ weight_filler {
+ type: "msra"
+ }
+ }
+}
+layer {
+ name: "conv4_1/expand/bn"
+ type: "BatchNorm"
+ bottom: "conv4_1/expand"
+ top: "conv4_1/expand/bn"
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-5
+ }
+}
+layer {
+ name: "conv4_1/expand/scale"
+ type: "Scale"
+ bottom: "conv4_1/expand/bn"
+ top: "conv4_1/expand/bn"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "relu4_1/expand"
+ type: "ReLU"
+ bottom: "conv4_1/expand/bn"
+ top: "conv4_1/expand/bn"
+}
+layer {
+ name: "conv4_1/dwise"
+ type: "Convolution"
+ bottom: "conv4_1/expand/bn"
+ top: "conv4_1/dwise"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ convolution_param {
+ num_output: 192
+ bias_term: false
+ pad: 1
+ kernel_size: 3
+ group: 192
+ weight_filler {
+ type: "msra"
+ }
+ engine: CAFFE
+ }
+}
+layer {
+ name: "conv4_1/dwise/bn"
+ type: "BatchNorm"
+ bottom: "conv4_1/dwise"
+ top: "conv4_1/dwise/bn"
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-5
+ }
+}
+layer {
+ name: "conv4_1/dwise/scale"
+ type: "Scale"
+ bottom: "conv4_1/dwise/bn"
+ top: "conv4_1/dwise/bn"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "relu4_1/dwise"
+ type: "ReLU"
+ bottom: "conv4_1/dwise/bn"
+ top: "conv4_1/dwise/bn"
+}
+layer {
+ name: "conv4_1/linear"
+ type: "Convolution"
+ bottom: "conv4_1/dwise/bn"
+ top: "conv4_1/linear"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ convolution_param {
+ num_output: 32
+ bias_term: false
+ kernel_size: 1
+ weight_filler {
+ type: "msra"
+ }
+ }
+}
+layer {
+ name: "conv4_1/linear/bn"
+ type: "BatchNorm"
+ bottom: "conv4_1/linear"
+ top: "conv4_1/linear/bn"
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-5
+ }
+}
+layer {
+ name: "conv4_1/linear/scale"
+ type: "Scale"
+ bottom: "conv4_1/linear/bn"
+ top: "conv4_1/linear/bn"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "block_4_1"
+ type: "Eltwise"
+ bottom: "conv3_2/linear/bn"
+ bottom: "conv4_1/linear/bn"
+ top: "block_4_1"
+}
+layer {
+ name: "conv4_2/expand"
+ type: "Convolution"
+ bottom: "block_4_1"
+ top: "conv4_2/expand"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ convolution_param {
+ num_output: 192
+ bias_term: false
+ kernel_size: 1
+ weight_filler {
+ type: "msra"
+ }
+ }
+}
+layer {
+ name: "conv4_2/expand/bn"
+ type: "BatchNorm"
+ bottom: "conv4_2/expand"
+ top: "conv4_2/expand/bn"
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-5
+ }
+}
+layer {
+ name: "conv4_2/expand/scale"
+ type: "Scale"
+ bottom: "conv4_2/expand/bn"
+ top: "conv4_2/expand/bn"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "relu4_2/expand"
+ type: "ReLU"
+ bottom: "conv4_2/expand/bn"
+ top: "conv4_2/expand/bn"
+}
+layer {
+ name: "conv4_2/dwise"
+ type: "Convolution"
+ bottom: "conv4_2/expand/bn"
+ top: "conv4_2/dwise"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ convolution_param {
+ num_output: 192
+ bias_term: false
+ pad: 1
+ kernel_size: 3
+ group: 192
+ weight_filler {
+ type: "msra"
+ }
+ engine: CAFFE
+ }
+}
+layer {
+ name: "conv4_2/dwise/bn"
+ type: "BatchNorm"
+ bottom: "conv4_2/dwise"
+ top: "conv4_2/dwise/bn"
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-5
+ }
+}
+layer {
+ name: "conv4_2/dwise/scale"
+ type: "Scale"
+ bottom: "conv4_2/dwise/bn"
+ top: "conv4_2/dwise/bn"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "relu4_2/dwise"
+ type: "ReLU"
+ bottom: "conv4_2/dwise/bn"
+ top: "conv4_2/dwise/bn"
+}
+layer {
+ name: "conv4_2/linear"
+ type: "Convolution"
+ bottom: "conv4_2/dwise/bn"
+ top: "conv4_2/linear"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ convolution_param {
+ num_output: 32
+ bias_term: false
+ kernel_size: 1
+ weight_filler {
+ type: "msra"
+ }
+ }
+}
+layer {
+ name: "conv4_2/linear/bn"
+ type: "BatchNorm"
+ bottom: "conv4_2/linear"
+ top: "conv4_2/linear/bn"
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-5
+ }
+}
+layer {
+ name: "conv4_2/linear/scale"
+ type: "Scale"
+ bottom: "conv4_2/linear/bn"
+ top: "conv4_2/linear/bn"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "block_4_2"
+ type: "Eltwise"
+ bottom: "block_4_1"
+ bottom: "conv4_2/linear/bn"
+ top: "block_4_2"
+}
+layer {
+ name: "conv4_3/expand"
+ type: "Convolution"
+ bottom: "block_4_2"
+ top: "conv4_3/expand"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ convolution_param {
+ num_output: 192
+ bias_term: false
+ kernel_size: 1
+ weight_filler {
+ type: "msra"
+ }
+ }
+}
+layer {
+ name: "conv4_3/expand/bn"
+ type: "BatchNorm"
+ bottom: "conv4_3/expand"
+ top: "conv4_3/expand/bn"
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-5
+ }
+}
+layer {
+ name: "conv4_3/expand/scale"
+ type: "Scale"
+ bottom: "conv4_3/expand/bn"
+ top: "conv4_3/expand/bn"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "relu4_3/expand"
+ type: "ReLU"
+ bottom: "conv4_3/expand/bn"
+ top: "conv4_3/expand/bn"
+}
+layer {
+ name: "conv4_3/dwise"
+ type: "Convolution"
+ bottom: "conv4_3/expand/bn"
+ top: "conv4_3/dwise"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ convolution_param {
+ num_output: 192
+ bias_term: false
+ pad: 1
+ kernel_size: 3
+ group: 192
+ weight_filler {
+ type: "msra"
+ }
+ engine: CAFFE
+ }
+}
+layer {
+ name: "conv4_3/dwise/bn"
+ type: "BatchNorm"
+ bottom: "conv4_3/dwise"
+ top: "conv4_3/dwise/bn"
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-5
+ }
+}
+layer {
+ name: "conv4_3/dwise/scale"
+ type: "Scale"
+ bottom: "conv4_3/dwise/bn"
+ top: "conv4_3/dwise/bn"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "relu4_3/dwise"
+ type: "ReLU"
+ bottom: "conv4_3/dwise/bn"
+ top: "conv4_3/dwise/bn"
+}
+layer {
+ name: "conv4_3/linear"
+ type: "Convolution"
+ bottom: "conv4_3/dwise/bn"
+ top: "conv4_3/linear"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ convolution_param {
+ num_output: 64
+ bias_term: false
+ kernel_size: 1
+ weight_filler {
+ type: "msra"
+ }
+ }
+}
+layer {
+ name: "conv4_3/linear/bn"
+ type: "BatchNorm"
+ bottom: "conv4_3/linear"
+ top: "conv4_3/linear/bn"
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-5
+ }
+}
+layer {
+ name: "conv4_3/linear/scale"
+ type: "Scale"
+ bottom: "conv4_3/linear/bn"
+ top: "conv4_3/linear/bn"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "conv4_4/expand"
+ type: "Convolution"
+ bottom: "conv4_3/linear/bn"
+ top: "conv4_4/expand"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ convolution_param {
+ num_output: 384
+ bias_term: false
+ kernel_size: 1
+ weight_filler {
+ type: "msra"
+ }
+ }
+}
+layer {
+ name: "conv4_4/expand/bn"
+ type: "BatchNorm"
+ bottom: "conv4_4/expand"
+ top: "conv4_4/expand/bn"
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-5
+ }
+}
+layer {
+ name: "conv4_4/expand/scale"
+ type: "Scale"
+ bottom: "conv4_4/expand/bn"
+ top: "conv4_4/expand/bn"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "relu4_4/expand"
+ type: "ReLU"
+ bottom: "conv4_4/expand/bn"
+ top: "conv4_4/expand/bn"
+}
+layer {
+ name: "conv4_4/dwise"
+ type: "Convolution"
+ bottom: "conv4_4/expand/bn"
+ top: "conv4_4/dwise"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ convolution_param {
+ num_output: 384
+ bias_term: false
+ pad: 1
+ kernel_size: 3
+ group: 384
+ weight_filler {
+ type: "msra"
+ }
+ engine: CAFFE
+ }
+}
+layer {
+ name: "conv4_4/dwise/bn"
+ type: "BatchNorm"
+ bottom: "conv4_4/dwise"
+ top: "conv4_4/dwise/bn"
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-5
+ }
+}
+layer {
+ name: "conv4_4/dwise/scale"
+ type: "Scale"
+ bottom: "conv4_4/dwise/bn"
+ top: "conv4_4/dwise/bn"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "relu4_4/dwise"
+ type: "ReLU"
+ bottom: "conv4_4/dwise/bn"
+ top: "conv4_4/dwise/bn"
+}
+layer {
+ name: "conv4_4/linear"
+ type: "Convolution"
+ bottom: "conv4_4/dwise/bn"
+ top: "conv4_4/linear"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ convolution_param {
+ num_output: 64
+ bias_term: false
+ kernel_size: 1
+ weight_filler {
+ type: "msra"
+ }
+ }
+}
+layer {
+ name: "conv4_4/linear/bn"
+ type: "BatchNorm"
+ bottom: "conv4_4/linear"
+ top: "conv4_4/linear/bn"
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-5
+ }
+}
+layer {
+ name: "conv4_4/linear/scale"
+ type: "Scale"
+ bottom: "conv4_4/linear/bn"
+ top: "conv4_4/linear/bn"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "block_4_4"
+ type: "Eltwise"
+ bottom: "conv4_3/linear/bn"
+ bottom: "conv4_4/linear/bn"
+ top: "block_4_4"
+}
+layer {
+ name: "conv4_5/expand"
+ type: "Convolution"
+ bottom: "block_4_4"
+ top: "conv4_5/expand"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ convolution_param {
+ num_output: 384
+ bias_term: false
+ kernel_size: 1
+ weight_filler {
+ type: "msra"
+ }
+ }
+}
+layer {
+ name: "conv4_5/expand/bn"
+ type: "BatchNorm"
+ bottom: "conv4_5/expand"
+ top: "conv4_5/expand/bn"
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-5
+ }
+}
+layer {
+ name: "conv4_5/expand/scale"
+ type: "Scale"
+ bottom: "conv4_5/expand/bn"
+ top: "conv4_5/expand/bn"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "relu4_5/expand"
+ type: "ReLU"
+ bottom: "conv4_5/expand/bn"
+ top: "conv4_5/expand/bn"
+}
+layer {
+ name: "conv4_5/dwise"
+ type: "Convolution"
+ bottom: "conv4_5/expand/bn"
+ top: "conv4_5/dwise"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ convolution_param {
+ num_output: 384
+ bias_term: false
+ pad: 1
+ kernel_size: 3
+ group: 384
+ weight_filler {
+ type: "msra"
+ }
+ engine: CAFFE
+ }
+}
+layer {
+ name: "conv4_5/dwise/bn"
+ type: "BatchNorm"
+ bottom: "conv4_5/dwise"
+ top: "conv4_5/dwise/bn"
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-5
+ }
+}
+layer {
+ name: "conv4_5/dwise/scale"
+ type: "Scale"
+ bottom: "conv4_5/dwise/bn"
+ top: "conv4_5/dwise/bn"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "relu4_5/dwise"
+ type: "ReLU"
+ bottom: "conv4_5/dwise/bn"
+ top: "conv4_5/dwise/bn"
+}
+layer {
+ name: "conv4_5/linear"
+ type: "Convolution"
+ bottom: "conv4_5/dwise/bn"
+ top: "conv4_5/linear"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ convolution_param {
+ num_output: 64
+ bias_term: false
+ kernel_size: 1
+ weight_filler {
+ type: "msra"
+ }
+ }
+}
+layer {
+ name: "conv4_5/linear/bn"
+ type: "BatchNorm"
+ bottom: "conv4_5/linear"
+ top: "conv4_5/linear/bn"
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-5
+ }
+}
+layer {
+ name: "conv4_5/linear/scale"
+ type: "Scale"
+ bottom: "conv4_5/linear/bn"
+ top: "conv4_5/linear/bn"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "block_4_5"
+ type: "Eltwise"
+ bottom: "block_4_4"
+ bottom: "conv4_5/linear/bn"
+ top: "block_4_5"
+}
+layer {
+ name: "conv4_6/expand"
+ type: "Convolution"
+ bottom: "block_4_5"
+ top: "conv4_6/expand"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ convolution_param {
+ num_output: 384
+ bias_term: false
+ kernel_size: 1
+ weight_filler {
+ type: "msra"
+ }
+ }
+}
+layer {
+ name: "conv4_6/expand/bn"
+ type: "BatchNorm"
+ bottom: "conv4_6/expand"
+ top: "conv4_6/expand/bn"
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-5
+ }
+}
+layer {
+ name: "conv4_6/expand/scale"
+ type: "Scale"
+ bottom: "conv4_6/expand/bn"
+ top: "conv4_6/expand/bn"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "relu4_6/expand"
+ type: "ReLU"
+ bottom: "conv4_6/expand/bn"
+ top: "conv4_6/expand/bn"
+}
+layer {
+ name: "conv4_6/dwise"
+ type: "Convolution"
+ bottom: "conv4_6/expand/bn"
+ top: "conv4_6/dwise"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ convolution_param {
+ num_output: 384
+ bias_term: false
+ pad: 1
+ kernel_size: 3
+ group: 384
+ weight_filler {
+ type: "msra"
+ }
+ engine: CAFFE
+ }
+}
+layer {
+ name: "conv4_6/dwise/bn"
+ type: "BatchNorm"
+ bottom: "conv4_6/dwise"
+ top: "conv4_6/dwise/bn"
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-5
+ }
+}
+layer {
+ name: "conv4_6/dwise/scale"
+ type: "Scale"
+ bottom: "conv4_6/dwise/bn"
+ top: "conv4_6/dwise/bn"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "relu4_6/dwise"
+ type: "ReLU"
+ bottom: "conv4_6/dwise/bn"
+ top: "conv4_6/dwise/bn"
+}
+layer {
+ name: "conv4_6/linear"
+ type: "Convolution"
+ bottom: "conv4_6/dwise/bn"
+ top: "conv4_6/linear"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ convolution_param {
+ num_output: 64
+ bias_term: false
+ kernel_size: 1
+ weight_filler {
+ type: "msra"
+ }
+ }
+}
+layer {
+ name: "conv4_6/linear/bn"
+ type: "BatchNorm"
+ bottom: "conv4_6/linear"
+ top: "conv4_6/linear/bn"
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-5
+ }
+}
+layer {
+ name: "conv4_6/linear/scale"
+ type: "Scale"
+ bottom: "conv4_6/linear/bn"
+ top: "conv4_6/linear/bn"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "block_4_6"
+ type: "Eltwise"
+ bottom: "block_4_5"
+ bottom: "conv4_6/linear/bn"
+ top: "block_4_6"
+}
+layer {
+ name: "conv4_7/expand"
+ type: "Convolution"
+ bottom: "block_4_6"
+ top: "conv4_7/expand"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ convolution_param {
+ num_output: 384
+ bias_term: false
+ kernel_size: 1
+ weight_filler {
+ type: "msra"
+ }
+ }
+}
+layer {
+ name: "conv4_7/expand/bn"
+ type: "BatchNorm"
+ bottom: "conv4_7/expand"
+ top: "conv4_7/expand/bn"
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-5
+ }
+}
+layer {
+ name: "conv4_7/expand/scale"
+ type: "Scale"
+ bottom: "conv4_7/expand/bn"
+ top: "conv4_7/expand/bn"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "relu4_7/expand"
+ type: "ReLU"
+ bottom: "conv4_7/expand/bn"
+ top: "conv4_7/expand/bn"
+}
+layer {
+ name: "conv4_7/dwise"
+ type: "Convolution"
+ bottom: "conv4_7/expand/bn"
+ top: "conv4_7/dwise"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ convolution_param {
+ num_output: 384
+ bias_term: false
+ pad: 1
+ kernel_size: 3
+ group: 384
+ stride: 2
+ weight_filler {
+ type: "msra"
+ }
+ engine: CAFFE
+ }
+}
+layer {
+ name: "conv4_7/dwise/bn"
+ type: "BatchNorm"
+ bottom: "conv4_7/dwise"
+ top: "conv4_7/dwise/bn"
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-5
+ }
+}
+layer {
+ name: "conv4_7/dwise/scale"
+ type: "Scale"
+ bottom: "conv4_7/dwise/bn"
+ top: "conv4_7/dwise/bn"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "relu4_7/dwise"
+ type: "ReLU"
+ bottom: "conv4_7/dwise/bn"
+ top: "conv4_7/dwise/bn"
+}
+layer {
+ name: "conv4_7/linear"
+ type: "Convolution"
+ bottom: "conv4_7/dwise/bn"
+ top: "conv4_7/linear"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ convolution_param {
+ num_output: 96
+ bias_term: false
+ kernel_size: 1
+ weight_filler {
+ type: "msra"
+ }
+ }
+}
+layer {
+ name: "conv4_7/linear/bn"
+ type: "BatchNorm"
+ bottom: "conv4_7/linear"
+ top: "conv4_7/linear/bn"
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-5
+ }
+}
+layer {
+ name: "conv4_7/linear/scale"
+ type: "Scale"
+ bottom: "conv4_7/linear/bn"
+ top: "conv4_7/linear/bn"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "conv5_1/expand"
+ type: "Convolution"
+ bottom: "conv4_7/linear/bn"
+ top: "conv5_1/expand"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ convolution_param {
+ num_output: 576
+ bias_term: false
+ kernel_size: 1
+ weight_filler {
+ type: "msra"
+ }
+ }
+}
+layer {
+ name: "conv5_1/expand/bn"
+ type: "BatchNorm"
+ bottom: "conv5_1/expand"
+ top: "conv5_1/expand/bn"
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-5
+ }
+}
+layer {
+ name: "conv5_1/expand/scale"
+ type: "Scale"
+ bottom: "conv5_1/expand/bn"
+ top: "conv5_1/expand/bn"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "relu5_1/expand"
+ type: "ReLU"
+ bottom: "conv5_1/expand/bn"
+ top: "conv5_1/expand/bn"
+}
+layer {
+ name: "conv5_1/dwise"
+ type: "Convolution"
+ bottom: "conv5_1/expand/bn"
+ top: "conv5_1/dwise"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ convolution_param {
+ num_output: 576
+ bias_term: false
+ pad: 1
+ kernel_size: 3
+ group: 576
+ weight_filler {
+ type: "msra"
+ }
+ engine: CAFFE
+ }
+}
+layer {
+ name: "conv5_1/dwise/bn"
+ type: "BatchNorm"
+ bottom: "conv5_1/dwise"
+ top: "conv5_1/dwise/bn"
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-5
+ }
+}
+layer {
+ name: "conv5_1/dwise/scale"
+ type: "Scale"
+ bottom: "conv5_1/dwise/bn"
+ top: "conv5_1/dwise/bn"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "relu5_1/dwise"
+ type: "ReLU"
+ bottom: "conv5_1/dwise/bn"
+ top: "conv5_1/dwise/bn"
+}
+layer {
+ name: "conv5_1/linear"
+ type: "Convolution"
+ bottom: "conv5_1/dwise/bn"
+ top: "conv5_1/linear"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ convolution_param {
+ num_output: 96
+ bias_term: false
+ kernel_size: 1
+ weight_filler {
+ type: "msra"
+ }
+ }
+}
+layer {
+ name: "conv5_1/linear/bn"
+ type: "BatchNorm"
+ bottom: "conv5_1/linear"
+ top: "conv5_1/linear/bn"
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-5
+ }
+}
+layer {
+ name: "conv5_1/linear/scale"
+ type: "Scale"
+ bottom: "conv5_1/linear/bn"
+ top: "conv5_1/linear/bn"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "block_5_1"
+ type: "Eltwise"
+ bottom: "conv4_7/linear/bn"
+ bottom: "conv5_1/linear/bn"
+ top: "block_5_1"
+}
+layer {
+ name: "conv5_2/expand"
+ type: "Convolution"
+ bottom: "block_5_1"
+ top: "conv5_2/expand"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ convolution_param {
+ num_output: 576
+ bias_term: false
+ kernel_size: 1
+ weight_filler {
+ type: "msra"
+ }
+ }
+}
+layer {
+ name: "conv5_2/expand/bn"
+ type: "BatchNorm"
+ bottom: "conv5_2/expand"
+ top: "conv5_2/expand/bn"
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-5
+ }
+}
+layer {
+ name: "conv5_2/expand/scale"
+ type: "Scale"
+ bottom: "conv5_2/expand/bn"
+ top: "conv5_2/expand/bn"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "relu5_2/expand"
+ type: "ReLU"
+ bottom: "conv5_2/expand/bn"
+ top: "conv5_2/expand/bn"
+}
+layer {
+ name: "conv5_2/dwise"
+ type: "Convolution"
+ bottom: "conv5_2/expand/bn"
+ top: "conv5_2/dwise"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ convolution_param {
+ num_output: 576
+ bias_term: false
+ pad: 1
+ kernel_size: 3
+ group: 576
+ weight_filler {
+ type: "msra"
+ }
+ engine: CAFFE
+ }
+}
+layer {
+ name: "conv5_2/dwise/bn"
+ type: "BatchNorm"
+ bottom: "conv5_2/dwise"
+ top: "conv5_2/dwise/bn"
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-5
+ }
+}
+layer {
+ name: "conv5_2/dwise/scale"
+ type: "Scale"
+ bottom: "conv5_2/dwise/bn"
+ top: "conv5_2/dwise/bn"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "relu5_2/dwise"
+ type: "ReLU"
+ bottom: "conv5_2/dwise/bn"
+ top: "conv5_2/dwise/bn"
+}
+layer {
+ name: "conv5_2/linear"
+ type: "Convolution"
+ bottom: "conv5_2/dwise/bn"
+ top: "conv5_2/linear"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ convolution_param {
+ num_output: 96
+ bias_term: false
+ kernel_size: 1
+ weight_filler {
+ type: "msra"
+ }
+ }
+}
+layer {
+ name: "conv5_2/linear/bn"
+ type: "BatchNorm"
+ bottom: "conv5_2/linear"
+ top: "conv5_2/linear/bn"
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-5
+ }
+}
+layer {
+ name: "conv5_2/linear/scale"
+ type: "Scale"
+ bottom: "conv5_2/linear/bn"
+ top: "conv5_2/linear/bn"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "block_5_2"
+ type: "Eltwise"
+ bottom: "block_5_1"
+ bottom: "conv5_2/linear/bn"
+ top: "block_5_2"
+}
+layer {
+ name: "conv5_3/expand"
+ type: "Convolution"
+ bottom: "block_5_2"
+ top: "conv5_3/expand"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ convolution_param {
+ num_output: 576
+ bias_term: false
+ kernel_size: 1
+ weight_filler {
+ type: "msra"
+ }
+ }
+}
+layer {
+ name: "conv5_3/expand/bn"
+ type: "BatchNorm"
+ bottom: "conv5_3/expand"
+ top: "conv5_3/expand/bn"
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-5
+ }
+}
+layer {
+ name: "conv5_3/expand/scale"
+ type: "Scale"
+ bottom: "conv5_3/expand/bn"
+ top: "conv5_3/expand/bn"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "relu5_3/expand"
+ type: "ReLU"
+ bottom: "conv5_3/expand/bn"
+ top: "conv5_3/expand/bn"
+}
+layer {
+ name: "conv5_3/dwise"
+ type: "Convolution"
+ bottom: "conv5_3/expand/bn"
+ top: "conv5_3/dwise"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ convolution_param {
+ num_output: 576
+ bias_term: false
+ pad: 1
+ kernel_size: 3
+ group: 576
+ stride: 2
+ weight_filler {
+ type: "msra"
+ }
+ engine: CAFFE
+ }
+}
+layer {
+ name: "conv5_3/dwise/bn"
+ type: "BatchNorm"
+ bottom: "conv5_3/dwise"
+ top: "conv5_3/dwise/bn"
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-5
+ }
+}
+layer {
+ name: "conv5_3/dwise/scale"
+ type: "Scale"
+ bottom: "conv5_3/dwise/bn"
+ top: "conv5_3/dwise/bn"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "relu5_3/dwise"
+ type: "ReLU"
+ bottom: "conv5_3/dwise/bn"
+ top: "conv5_3/dwise/bn"
+}
+layer {
+ name: "conv5_3/linear"
+ type: "Convolution"
+ bottom: "conv5_3/dwise/bn"
+ top: "conv5_3/linear"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ convolution_param {
+ num_output: 160
+ bias_term: false
+ kernel_size: 1
+ weight_filler {
+ type: "msra"
+ }
+ }
+}
+layer {
+ name: "conv5_3/linear/bn"
+ type: "BatchNorm"
+ bottom: "conv5_3/linear"
+ top: "conv5_3/linear/bn"
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-5
+ }
+}
+layer {
+ name: "conv5_3/linear/scale"
+ type: "Scale"
+ bottom: "conv5_3/linear/bn"
+ top: "conv5_3/linear/bn"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "conv6_1/expand"
+ type: "Convolution"
+ bottom: "conv5_3/linear/bn"
+ top: "conv6_1/expand"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ convolution_param {
+ num_output: 960
+ bias_term: false
+ kernel_size: 1
+ weight_filler {
+ type: "msra"
+ }
+ }
+}
+layer {
+ name: "conv6_1/expand/bn"
+ type: "BatchNorm"
+ bottom: "conv6_1/expand"
+ top: "conv6_1/expand/bn"
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-5
+ }
+}
+layer {
+ name: "conv6_1/expand/scale"
+ type: "Scale"
+ bottom: "conv6_1/expand/bn"
+ top: "conv6_1/expand/bn"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "relu6_1/expand"
+ type: "ReLU"
+ bottom: "conv6_1/expand/bn"
+ top: "conv6_1/expand/bn"
+}
+layer {
+ name: "conv6_1/dwise"
+ type: "Convolution"
+ bottom: "conv6_1/expand/bn"
+ top: "conv6_1/dwise"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ convolution_param {
+ num_output: 960
+ bias_term: false
+ pad: 1
+ kernel_size: 3
+ group: 960
+ weight_filler {
+ type: "msra"
+ }
+ engine: CAFFE
+ }
+}
+layer {
+ name: "conv6_1/dwise/bn"
+ type: "BatchNorm"
+ bottom: "conv6_1/dwise"
+ top: "conv6_1/dwise/bn"
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-5
+ }
+}
+layer {
+ name: "conv6_1/dwise/scale"
+ type: "Scale"
+ bottom: "conv6_1/dwise/bn"
+ top: "conv6_1/dwise/bn"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "relu6_1/dwise"
+ type: "ReLU"
+ bottom: "conv6_1/dwise/bn"
+ top: "conv6_1/dwise/bn"
+}
+layer {
+ name: "conv6_1/linear"
+ type: "Convolution"
+ bottom: "conv6_1/dwise/bn"
+ top: "conv6_1/linear"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ convolution_param {
+ num_output: 160
+ bias_term: false
+ kernel_size: 1
+ weight_filler {
+ type: "msra"
+ }
+ }
+}
+layer {
+ name: "conv6_1/linear/bn"
+ type: "BatchNorm"
+ bottom: "conv6_1/linear"
+ top: "conv6_1/linear/bn"
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-5
+ }
+}
+layer {
+ name: "conv6_1/linear/scale"
+ type: "Scale"
+ bottom: "conv6_1/linear/bn"
+ top: "conv6_1/linear/bn"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "block_6_1"
+ type: "Eltwise"
+ bottom: "conv5_3/linear/bn"
+ bottom: "conv6_1/linear/bn"
+ top: "block_6_1"
+}
+layer {
+ name: "conv6_2/expand"
+ type: "Convolution"
+ bottom: "block_6_1"
+ top: "conv6_2/expand"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ convolution_param {
+ num_output: 960
+ bias_term: false
+ kernel_size: 1
+ weight_filler {
+ type: "msra"
+ }
+ }
+}
+layer {
+ name: "conv6_2/expand/bn"
+ type: "BatchNorm"
+ bottom: "conv6_2/expand"
+ top: "conv6_2/expand/bn"
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-5
+ }
+}
+layer {
+ name: "conv6_2/expand/scale"
+ type: "Scale"
+ bottom: "conv6_2/expand/bn"
+ top: "conv6_2/expand/bn"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "relu6_2/expand"
+ type: "ReLU"
+ bottom: "conv6_2/expand/bn"
+ top: "conv6_2/expand/bn"
+}
+layer {
+ name: "conv6_2/dwise"
+ type: "Convolution"
+ bottom: "conv6_2/expand/bn"
+ top: "conv6_2/dwise"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ convolution_param {
+ num_output: 960
+ bias_term: false
+ pad: 1
+ kernel_size: 3
+ group: 960
+ weight_filler {
+ type: "msra"
+ }
+ engine: CAFFE
+ }
+}
+layer {
+ name: "conv6_2/dwise/bn"
+ type: "BatchNorm"
+ bottom: "conv6_2/dwise"
+ top: "conv6_2/dwise/bn"
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-5
+ }
+}
+layer {
+ name: "conv6_2/dwise/scale"
+ type: "Scale"
+ bottom: "conv6_2/dwise/bn"
+ top: "conv6_2/dwise/bn"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "relu6_2/dwise"
+ type: "ReLU"
+ bottom: "conv6_2/dwise/bn"
+ top: "conv6_2/dwise/bn"
+}
+layer {
+ name: "conv6_2/linear"
+ type: "Convolution"
+ bottom: "conv6_2/dwise/bn"
+ top: "conv6_2/linear"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ convolution_param {
+ num_output: 160
+ bias_term: false
+ kernel_size: 1
+ weight_filler {
+ type: "msra"
+ }
+ }
+}
+layer {
+ name: "conv6_2/linear/bn"
+ type: "BatchNorm"
+ bottom: "conv6_2/linear"
+ top: "conv6_2/linear/bn"
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-5
+ }
+}
+layer {
+ name: "conv6_2/linear/scale"
+ type: "Scale"
+ bottom: "conv6_2/linear/bn"
+ top: "conv6_2/linear/bn"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "block_6_2"
+ type: "Eltwise"
+ bottom: "block_6_1"
+ bottom: "conv6_2/linear/bn"
+ top: "block_6_2"
+}
+layer {
+ name: "conv6_3/expand"
+ type: "Convolution"
+ bottom: "block_6_2"
+ top: "conv6_3/expand"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ convolution_param {
+ num_output: 960
+ bias_term: false
+ kernel_size: 1
+ weight_filler {
+ type: "msra"
+ }
+ }
+}
+layer {
+ name: "conv6_3/expand/bn"
+ type: "BatchNorm"
+ bottom: "conv6_3/expand"
+ top: "conv6_3/expand/bn"
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-5
+ }
+}
+layer {
+ name: "conv6_3/expand/scale"
+ type: "Scale"
+ bottom: "conv6_3/expand/bn"
+ top: "conv6_3/expand/bn"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "relu6_3/expand"
+ type: "ReLU"
+ bottom: "conv6_3/expand/bn"
+ top: "conv6_3/expand/bn"
+}
+layer {
+ name: "conv6_3/dwise"
+ type: "Convolution"
+ bottom: "conv6_3/expand/bn"
+ top: "conv6_3/dwise"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ convolution_param {
+ num_output: 960
+ bias_term: false
+ pad: 1
+ kernel_size: 3
+ group: 960
+ weight_filler {
+ type: "msra"
+ }
+ engine: CAFFE
+ }
+}
+layer {
+ name: "conv6_3/dwise/bn"
+ type: "BatchNorm"
+ bottom: "conv6_3/dwise"
+ top: "conv6_3/dwise/bn"
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-5
+ }
+}
+layer {
+ name: "conv6_3/dwise/scale"
+ type: "Scale"
+ bottom: "conv6_3/dwise/bn"
+ top: "conv6_3/dwise/bn"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "relu6_3/dwise"
+ type: "ReLU"
+ bottom: "conv6_3/dwise/bn"
+ top: "conv6_3/dwise/bn"
+}
+layer {
+ name: "conv6_3/linear"
+ type: "Convolution"
+ bottom: "conv6_3/dwise/bn"
+ top: "conv6_3/linear"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ convolution_param {
+ num_output: 320
+ bias_term: false
+ kernel_size: 1
+ weight_filler {
+ type: "msra"
+ }
+ }
+}
+layer {
+ name: "conv6_3/linear/bn"
+ type: "BatchNorm"
+ bottom: "conv6_3/linear"
+ top: "conv6_3/linear/bn"
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-5
+ }
+}
+layer {
+ name: "conv6_3/linear/scale"
+ type: "Scale"
+ bottom: "conv6_3/linear/bn"
+ top: "conv6_3/linear/bn"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "conv6_4"
+ type: "Convolution"
+ bottom: "conv6_3/linear/bn"
+ top: "conv6_4"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ convolution_param {
+ num_output: 1280
+ bias_term: false
+ kernel_size: 1
+ weight_filler {
+ type: "msra"
+ }
+ }
+}
+layer {
+ name: "conv6_4/bn"
+ type: "BatchNorm"
+ bottom: "conv6_4"
+ top: "conv6_4/bn"
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 0
+ decay_mult: 0
+ }
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-5
+ }
+}
+layer {
+ name: "conv6_4/scale"
+ type: "Scale"
+ bottom: "conv6_4/bn"
+ top: "conv6_4/bn"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "relu6_4"
+ type: "ReLU"
+ bottom: "conv6_4/bn"
+ top: "conv6_4/bn"
+}
+layer {
+ name: "pool6"
+ type: "Pooling"
+ bottom: "conv6_4/bn"
+ top: "pool6"
+ pooling_param {
+ pool: AVE
+ global_pooling: true
+ }
+}
+layer {
+ name: "fc7"
+ type: "Convolution"
+ bottom: "pool6"
+ top: "fc7"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ param {
+ lr_mult: 2
+ decay_mult: 0
+ }
+ convolution_param {
+ num_output: 1000
+ kernel_size: 1
+ weight_filler {
+ type: "msra"
+ }
+ bias_filler {
+ type: "constant"
+ value: 0
+ }
+ }
+}
+layer {
+ name: "prob"
+ type: "Softmax"
+ bottom: "fc7"
+ top: "prob"
+}
\ No newline at end of file
diff --git a/practice/openvino-env/README.md b/practice/openvino-env/README.md
new file mode 100644
index 0000000..e69de29
diff --git a/practice/openvino-env/openvino_env/__init__.py b/practice/openvino-env/openvino_env/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/practice/openvino-env/poetry.lock b/practice/openvino-env/poetry.lock
new file mode 100644
index 0000000..414d624
--- /dev/null
+++ b/practice/openvino-env/poetry.lock
@@ -0,0 +1,1606 @@
+[[package]]
+name = "addict"
+version = "2.4.0"
+description = "Addict is a dictionary whose items can be set using both attribute and item syntax."
+category = "main"
+optional = false
+python-versions = "*"
+
+[[package]]
+name = "certifi"
+version = "2022.9.24"
+description = "Python package for providing Mozilla's CA Bundle."
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
+[[package]]
+name = "charset-normalizer"
+version = "2.1.1"
+description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
+category = "main"
+optional = false
+python-versions = ">=3.6.0"
+
+[package.extras]
+unicode-backport = ["unicodedata2"]
+
+[[package]]
+name = "click"
+version = "8.1.3"
+description = "Composable command line interface toolkit"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+colorama = {version = "*", markers = "platform_system == \"Windows\""}
+
+[[package]]
+name = "colorama"
+version = "0.4.5"
+description = "Cross-platform colored terminal text."
+category = "main"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+
+[[package]]
+name = "defusedxml"
+version = "0.7.1"
+description = "XML bomb protection for Python stdlib modules"
+category = "main"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+
+[[package]]
+name = "fast-ctc-decode"
+version = "0.3.2"
+description = ""
+category = "main"
+optional = false
+python-versions = "*"
+
+[[package]]
+name = "filelock"
+version = "3.8.0"
+description = "A platform independent file lock."
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.extras]
+docs = ["furo (>=2022.6.21)", "sphinx (>=5.1.1)", "sphinx-autodoc-typehints (>=1.19.1)"]
+testing = ["covdefaults (>=2.2)", "coverage (>=6.4.2)", "pytest (>=7.1.2)", "pytest-cov (>=3)", "pytest-timeout (>=2.1)"]
+
+[[package]]
+name = "huggingface-hub"
+version = "0.10.1"
+description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
+category = "main"
+optional = false
+python-versions = ">=3.7.0"
+
+[package.dependencies]
+filelock = "*"
+packaging = ">=20.9"
+pyyaml = ">=5.1"
+requests = "*"
+tqdm = "*"
+typing-extensions = ">=3.7.4.3"
+
+[package.extras]
+all = ["InquirerPy (==0.3.4)", "Jinja2", "black (==22.3)", "flake8 (>=3.8.3)", "flake8-bugbear", "isort (>=5.5.4)", "jedi", "mypy", "pytest", "pytest-cov", "soundfile"]
+cli = ["InquirerPy (==0.3.4)"]
+dev = ["InquirerPy (==0.3.4)", "Jinja2", "black (==22.3)", "flake8 (>=3.8.3)", "flake8-bugbear", "isort (>=5.5.4)", "jedi", "mypy", "pytest", "pytest-cov", "soundfile"]
+fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
+quality = ["black (==22.3)", "flake8 (>=3.8.3)", "flake8-bugbear", "isort (>=5.5.4)", "mypy"]
+tensorflow = ["graphviz", "pydot", "tensorflow"]
+testing = ["InquirerPy (==0.3.4)", "Jinja2", "isort (>=5.5.4)", "jedi", "pytest", "pytest-cov", "soundfile"]
+torch = ["torch"]
+
+[[package]]
+name = "idna"
+version = "3.4"
+description = "Internationalized Domain Names in Applications (IDNA)"
+category = "main"
+optional = false
+python-versions = ">=3.5"
+
+[[package]]
+name = "imagecodecs"
+version = "2022.2.22"
+description = "Image transformation, compression, and decompression codecs"
+category = "main"
+optional = false
+python-versions = ">=3.8"
+
+[package.dependencies]
+numpy = ">=1.19.2"
+
+[package.extras]
+all = ["matplotlib (>=3.3)", "numcodecs", "tifffile (>=2021.11.2)"]
+
+[[package]]
+name = "imageio"
+version = "2.22.2"
+description = "Library for reading and writing a wide range of image, video, scientific, and volumetric data formats."
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+numpy = "*"
+pillow = ">=8.3.2"
+
+[package.extras]
+all-plugins = ["astropy", "av", "imageio-ffmpeg", "opencv-python", "psutil", "tifffile"]
+all-plugins-pypy = ["av", "imageio-ffmpeg", "psutil", "tifffile"]
+build = ["wheel"]
+dev = ["black", "flake8", "fsspec[github]", "invoke", "pytest", "pytest-cov"]
+docs = ["numpydoc", "pydata-sphinx-theme", "sphinx"]
+ffmpeg = ["imageio-ffmpeg", "psutil"]
+fits = ["astropy"]
+full = ["astropy", "av", "black", "flake8", "fsspec[github]", "gdal", "imageio-ffmpeg", "invoke", "itk", "numpydoc", "opencv-python", "psutil", "pydata-sphinx-theme", "pytest", "pytest-cov", "sphinx", "tifffile", "wheel"]
+gdal = ["gdal"]
+itk = ["itk"]
+linting = ["black", "flake8"]
+opencv = ["opencv-python"]
+pyav = ["av"]
+test = ["fsspec[github]", "invoke", "pytest", "pytest-cov"]
+tifffile = ["tifffile"]
+
+[[package]]
+name = "joblib"
+version = "1.2.0"
+description = "Lightweight pipelining with Python functions"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[[package]]
+name = "jstyleson"
+version = "0.0.2"
+description = "Library to parse JSON with js-style comments."
+category = "main"
+optional = false
+python-versions = "*"
+
+[[package]]
+name = "lmdb"
+version = "1.3.0"
+description = "Universal Python binding for the LMDB 'Lightning' Database"
+category = "main"
+optional = false
+python-versions = "*"
+
+[[package]]
+name = "networkx"
+version = "2.8"
+description = "Python package for creating and manipulating graphs and networks"
+category = "main"
+optional = false
+python-versions = ">=3.8"
+
+[package.extras]
+default = ["matplotlib (>=3.4)", "numpy (>=1.19)", "pandas (>=1.3)", "scipy (>=1.8)"]
+developer = ["mypy (>=0.942)", "pre-commit (>=2.18)"]
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diff --git a/practice/openvino-env/pyproject.toml b/practice/openvino-env/pyproject.toml
new file mode 100644
index 0000000..e04b828
--- /dev/null
+++ b/practice/openvino-env/pyproject.toml
@@ -0,0 +1,18 @@
+[tool.poetry]
+name = "openvino-env"
+version = "0.1.0"
+description = ""
+authors = ["VankaVstanka03 "]
+readme = "README.md"
+packages = [{include = "openvino_env"}]
+
+[tool.poetry.dependencies]
+python = "^3.9"
+opencv-python = "^4.6.0.66"
+openvino-dev = "^2022.2.0"
+onnx = "^1.12.0"
+
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
diff --git a/practice/openvino-env/tests/__init__.py b/practice/openvino-env/tests/__init__.py
new file mode 100644
index 0000000..e69de29