update caffe case (huaweicloud#1709)

* Delete train.py * Delete lenet_train_test.prototxt * Delete lenet_solver.prototxt * Create README.md * Add files via upload * Delete README.md * Add files via upload
Letian-Fu · Feb 11, 2020 · d1e8dca · d1e8dca
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diff --git a/..._examples/Using_Caffe_to_Create_a_MNIST_Dataset_Recognition_Application/codes/config.json b/..._examples/Using_Caffe_to_Create_a_MNIST_Dataset_Recognition_Application/codes/config.json
@@ -0,0 +1 @@
+{"apis": [{"url": "/", "request": {"Content-type": "multipart/form-data", "data": {"type": "object", "properties": {"images": {"type": "file"}}}}, "protocol": "http", "method": "post", "response": {"Content-type": "multipart/form-data", "data": {"required": ["predicted_label", "scores"], "type": "object", "properties": {"predicted_label": {"type": "string"}, "scores": {"items": {"minItems": 2, "items": [{"type": "string"}, {"type": "number"}], "type": "array", "maxItems": 2}, "type": "array"}}}}}], "service_name": "builtin_algorithms", "model_algorithm": "image_classification", "metrics": {"f1": 1.0, "accuracy": 1.0, "precision": 1.0, "recall": 1.0}, "tunable": false, "model_source": "algos", "model_type": "Caffe"}
diff --git a/.../Using_Caffe_to_Create_a_MNIST_Dataset_Recognition_Application/codes/customize_service.py b/.../Using_Caffe_to_Create_a_MNIST_Dataset_Recognition_Application/codes/customize_service.py
@@ -0,0 +1,33 @@
+import caffe
+from model_service.caffe_model_service import CaffeBaseService
+LABELS = {'0': '0', '1': '1', '2': '2', '3': '3', '4': '4',
+          '5': '5', '6': '6', '7': '7', '8': '8', '9': '9'}
+
+class ResnetService(CaffeBaseService):
+
+    def __init__(self, model_name, model_path):
+        super(ResnetService, self).__init__(model_name, model_path)
+        # load input and configure preprocessing
+        transformer = caffe.io.Transformer({'data': self.net.blobs['data'].data.shape})
+        transformer.set_transpose('data', (2, 0, 1))
+        self.transformer = transformer
+        self.num_classes = len(LABELS)
+
+    def _preprocess(self, data):
+        for _, v in data.items():
+            for _, file_content in v.items():
+                im = caffe.io.load_image(file_content, color=False)
+                self.net.blobs['data'].data[...] = self.transformer.preprocess('data', im)
+        return
+
+    def _postprocess(self, data):
+        data = self.net.blobs['prob'].data[0]
+        end_idx = -6 if self.num_classes >= 5 else -self.num_classes - 1
+        top_k = data.argsort()[-1:end_idx:-1]
+        print(top_k)
+        return {
+            "predicted_label":
+            LABELS[str(top_k[0])],
+            "scores":
+            [[LABELS[str(idx)], float(data[idx])] for idx in top_k]
+        }
diff --git a/...Using_Caffe_to_Create_a_MNIST_Dataset_Recognition_Application/codes/lenet_deploy.prototxt b/...Using_Caffe_to_Create_a_MNIST_Dataset_Recognition_Application/codes/lenet_deploy.prototxt
@@ -0,0 +1,139 @@
+name: "LeNet"
+layer {
+  name: "mnist"
+  type: "Input"
+  top: "data"
+  transform_param {
+    scale: 0.00390625
+  }
+  input_param {
+    shape {
+      dim: 1
+      dim: 1
+      dim: 28
+      dim: 28
+    }
+  }
+}
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  convolution_param {
+    num_output: 20
+    kernel_size: 5
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "conv1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  convolution_param {
+    num_output: 50
+    kernel_size: 5
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "conv2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "ip1"
+  type: "InnerProduct"
+  bottom: "pool2"
+  top: "ip1"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  inner_product_param {
+    num_output: 500
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "ip1"
+  top: "ip1"
+}
+layer {
+  name: "ip2"
+  type: "InnerProduct"
+  bottom: "ip1"
+  top: "ip2"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  inner_product_param {
+    num_output: 10
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "prob"
+  type: "Softmax"
+  bottom: "ip2"
+  top: "prob"
+}
diff --git a/...Using_Caffe_to_Create_a_MNIST_Dataset_Recognition_Application/codes/lenet_solver.prototxt b/...Using_Caffe_to_Create_a_MNIST_Dataset_Recognition_Application/codes/lenet_solver.prototxt
@@ -1,5 +1,5 @@
 # The train/test net protocol buffer definition
-net: "/home/work/user-job-dir/codes/lenet_train_test.prototxt"
+net: "/cache/lenet_train_test.prototxt"
 # test_iter specifies how many forward passes the test should carry out.
 # In the case of MNIST, we have test batch size 100 and 100 test iterations,
 # covering the full 10,000 testing images.
@@ -20,6 +20,6 @@ display: 100
 max_iter: 10000
 # snapshot intermediate results
 snapshot: 500
-snapshot_prefix: "/home/work/lenet_mnist/mnist_lenet"
+snapshot_prefix: "/cache/lenet_mnist/mnist_lenet"
 # solver mode: CPU or GPU
 solver_mode: GPU
diff --git a/...g_Caffe_to_Create_a_MNIST_Dataset_Recognition_Application/codes/lenet_train_test.prototxt b/...g_Caffe_to_Create_a_MNIST_Dataset_Recognition_Application/codes/lenet_train_test.prototxt
@@ -11,7 +11,7 @@ layer {
     scale: 0.00390625
   }
   data_param {
-    source: "/home/work/dataset/mnist_train_lmdb"
+    source: "/cache/dataset/mnist_train_lmdb"
     batch_size: 1024
     backend: LMDB
   }
@@ -28,7 +28,7 @@ layer {
     scale: 0.00390625
   }
   data_param {
-    source: "/home/work/dataset/mnist_test_lmdb"
+    source: "/cache/dataset/mnist_test_lmdb"
     batch_size: 512
     backend: LMDB
   }

diff --git a/...ial_examples/Using_Caffe_to_Create_a_MNIST_Dataset_Recognition_Application/codes/train.py b/...ial_examples/Using_Caffe_to_Create_a_MNIST_Dataset_Recognition_Application/codes/train.py
Original file line number	Diff line number	Diff line change
		@@ -0,0 +1 @@
		{"apis": [{"url": "/", "request": {"Content-type": "multipart/form-data", "data": {"type": "object", "properties": {"images": {"type": "file"}}}}, "protocol": "http", "method": "post", "response": {"Content-type": "multipart/form-data", "data": {"required": ["predicted_label", "scores"], "type": "object", "properties": {"predicted_label": {"type": "string"}, "scores": {"items": {"minItems": 2, "items": [{"type": "string"}, {"type": "number"}], "type": "array", "maxItems": 2}, "type": "array"}}}}}], "service_name": "builtin_algorithms", "model_algorithm": "image_classification", "metrics": {"f1": 1.0, "accuracy": 1.0, "precision": 1.0, "recall": 1.0}, "tunable": false, "model_source": "algos", "model_type": "Caffe"}