[examples] switch examples + models to Input layers

shelhamer · shelhamer · commit 2cc3844cb2a4 · 2016-02-25T17:29:42.000-08:00
diff --git a/examples/cifar10/cifar10_full.prototxt b/examples/cifar10/cifar10_full.prototxt
@@ -1,12 +1,11 @@
 name: "CIFAR10_full_deploy"
 # N.B. input image must be in CIFAR-10 format
 # as described at http://www.cs.toronto.edu/~kriz/cifar.html
-input: "data"
-input_shape {
-  dim: 1
-  dim: 3
-  dim: 32
-  dim: 32
+layer {
+  name: "data"
+  type: "Input"
+  top: "data"
+  input_param { shape: { dim: 1 dim: 3 dim: 32 dim: 32 } }
 }
 layer {
   name: "conv1"
diff --git a/examples/cifar10/cifar10_quick.prototxt b/examples/cifar10/cifar10_quick.prototxt
@@ -1,10 +1,9 @@
 name: "CIFAR10_quick_test"
-input: "data"
-input_shape {
-  dim: 1
-  dim: 3
-  dim: 32
-  dim: 32
+layer {
+  name: "data"
+  type: "Input"
+  top: "data"
+  input_param { shape: { dim: 1 dim: 3 dim: 32 dim: 32 } }
 }
 layer {
   name: "conv1"
diff --git a/examples/cpp_classification/classification.cpp b/examples/cpp_classification/classification.cpp
@@ -159,7 +159,7 @@ std::vector<float> Classifier::Predict(const cv::Mat& img) {
 
   Preprocess(img, &input_channels);
 
-  net_->ForwardPrefilled();
+  net_->Forward();
 
   /* Copy the output layer to a std::vector */
   Blob<float>* output_layer = net_->output_blobs()[0];
diff --git a/examples/mnist/lenet.prototxt b/examples/mnist/lenet.prototxt
@@ -1,10 +1,9 @@
 name: "LeNet"
-input: "data"
-input_shape {
-  dim: 64
-  dim: 1
-  dim: 28
-  dim: 28
+layer {
+  name: "data"
+  type: "Input"
+  top: "data"
+  input_param { shape: { dim: 64 dim: 1 dim: 28 dim: 28 } }
 }
 layer {
   name: "conv1"
diff --git a/examples/net_surgery.ipynb b/examples/net_surgery.ipynb
@@ -5494,48 +5494,47 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1,2c1\r\n",
+      "1,2c1,2\r\n",
       "< # Fully convolutional network version of CaffeNet.\r\n",
       "< name: \"CaffeNetConv\"\r\n",
       "---\r\n",
       "> name: \"CaffeNet\"\r\n",
-      "4c3\r\n",
-      "< input_dim: 1\r\n",
+      "> input: \"data\"\r\n",
+      "7,11c7\r\n",
+      "<   input_param {\r\n",
+      "<     # initial shape for a fully convolutional network:\r\n",
+      "<     # the shape can be set for each input by reshape.\r\n",
+      "<     shape: { dim: 1 dim: 3 dim: 451 dim: 451 }\r\n",
+      "<   }\r\n",
       "---\r\n",
-      "> input_dim: 10\r\n",
-      "6,7c5,6\r\n",
-      "< input_dim: 451\r\n",
-      "< input_dim: 451\r\n",
-      "---\r\n",
-      "> input_dim: 227\r\n",
-      "> input_dim: 227\r\n",
-      "152,153c151,152\r\n",
+      ">   input_param { shape: { dim: 10 dim: 3 dim: 227 dim: 227 } }\r\n",
+      "157,158c153,154\r\n",
       "<   name: \"fc6-conv\"\r\n",
       "<   type: \"Convolution\"\r\n",
       "---\r\n",
       ">   name: \"fc6\"\r\n",
       ">   type: \"InnerProduct\"\r\n",
-      "155,156c154,155\r\n",
+      "160,161c156,157\r\n",
       "<   top: \"fc6-conv\"\r\n",
       "<   convolution_param {\r\n",
       "---\r\n",
       ">   top: \"fc6\"\r\n",
       ">   inner_product_param {\r\n",
-      "158d156\r\n",
+      "163d158\r\n",
       "<     kernel_size: 6\r\n",
-      "164,165c162,163\r\n",
+      "169,170c164,165\r\n",
       "<   bottom: \"fc6-conv\"\r\n",
       "<   top: \"fc6-conv\"\r\n",
       "---\r\n",
       ">   bottom: \"fc6\"\r\n",
       ">   top: \"fc6\"\r\n",
-      "170,171c168,169\r\n",
+      "175,176c170,171\r\n",
       "<   bottom: \"fc6-conv\"\r\n",
       "<   top: \"fc6-conv\"\r\n",
       "---\r\n",
       ">   bottom: \"fc6\"\r\n",
       ">   top: \"fc6\"\r\n",
-      "177,181c175,179\r\n",
+      "182,186c177,181\r\n",
       "<   name: \"fc7-conv\"\r\n",
       "<   type: \"Convolution\"\r\n",
       "<   bottom: \"fc6-conv\"\r\n",
@@ -5547,21 +5546,21 @@
       ">   bottom: \"fc6\"\r\n",
       ">   top: \"fc7\"\r\n",
       ">   inner_product_param {\r\n",
-      "183d180\r\n",
+      "188d182\r\n",
       "<     kernel_size: 1\r\n",
-      "189,190c186,187\r\n",
+      "194,195c188,189\r\n",
       "<   bottom: \"fc7-conv\"\r\n",
       "<   top: \"fc7-conv\"\r\n",
       "---\r\n",
       ">   bottom: \"fc7\"\r\n",
       ">   top: \"fc7\"\r\n",
-      "195,196c192,193\r\n",
+      "200,201c194,195\r\n",
       "<   bottom: \"fc7-conv\"\r\n",
       "<   top: \"fc7-conv\"\r\n",
       "---\r\n",
       ">   bottom: \"fc7\"\r\n",
       ">   top: \"fc7\"\r\n",
-      "202,206c199,203\r\n",
+      "207,211c201,205\r\n",
       "<   name: \"fc8-conv\"\r\n",
       "<   type: \"Convolution\"\r\n",
       "<   bottom: \"fc7-conv\"\r\n",
@@ -5573,9 +5572,9 @@
       ">   bottom: \"fc7\"\r\n",
       ">   top: \"fc8\"\r\n",
       ">   inner_product_param {\r\n",
-      "208d204\r\n",
+      "213d206\r\n",
       "<     kernel_size: 1\r\n",
-      "214c210\r\n",
+      "219c212\r\n",
       "<   bottom: \"fc8-conv\"\r\n",
       "---\r\n",
       ">   bottom: \"fc8\"\r\n"
diff --git a/examples/net_surgery/bvlc_caffenet_full_conv.prototxt b/examples/net_surgery/bvlc_caffenet_full_conv.prototxt
@@ -1,11 +1,14 @@
 # Fully convolutional network version of CaffeNet.
 name: "CaffeNetConv"
-input: "data"
-input_shape {
-  dim: 1
-  dim: 3
-  dim: 451
-  dim: 451
+layer {
+  name: "data"
+  type: "Input"
+  top: "data"
+  input_param {
+    # initial shape for a fully convolutional network:
+    # the shape can be set for each input by reshape.
+    shape: { dim: 1 dim: 3 dim: 451 dim: 451 }
+  }
 }
 layer {
   name: "conv1"
diff --git a/examples/net_surgery/conv.prototxt b/examples/net_surgery/conv.prototxt
@@ -1,11 +1,10 @@
 # Simple single-layer network to showcase editing model parameters.
 name: "convolution"
-input: "data"
-input_shape {
-  dim: 1
-  dim: 1
-  dim: 100
-  dim: 100
+layer {
+  name: "data"
+  type: "Input"
+  top: "data"
+  input_param { shape: { dim: 1 dim: 1 dim: 100 dim: 100 } }
 }
 layer {
   name: "conv"
diff --git a/examples/siamese/mnist_siamese.prototxt b/examples/siamese/mnist_siamese.prototxt
@@ -1,10 +1,11 @@
 name: "mnist_siamese"
-input: "data"
-input_shape {
-  dim: 10000
-  dim: 1
-  dim: 28
-  dim: 28
+layer {
+  name: "data"
+  type: "Input"
+  top: "data"
+  input_param {
+    shape: { dim: 10000 dim: 1 dim: 28 dim: 28 }
+  }
 }
 layer {
   name: "conv1"
diff --git a/models/bvlc_alexnet/deploy.prototxt b/models/bvlc_alexnet/deploy.prototxt
@@ -1,10 +1,9 @@
 name: "AlexNet"
-input: "data"
-input_shape {
-  dim: 10
-  dim: 3
-  dim: 227
-  dim: 227
+layer {
+  name: "data"
+  type: "Input"
+  top: "data"
+  input_param { shape: { dim: 10 dim: 3 dim: 227 dim: 227 } }
 }
 layer {
   name: "conv1"
diff --git a/models/bvlc_googlenet/deploy.prototxt b/models/bvlc_googlenet/deploy.prototxt
@@ -1,10 +1,9 @@
 name: "GoogleNet"
-input: "data"
-input_shape {
-  dim: 10
-  dim: 3
-  dim: 224
-  dim: 224
+layer {
+  name: "data"
+  type: "Input"
+  top: "data"
+  input_param { shape: { dim: 10 dim: 3 dim: 224 dim: 224 } }
 }
 layer {
   name: "conv1/7x7_s2"
diff --git a/models/bvlc_reference_caffenet/deploy.prototxt b/models/bvlc_reference_caffenet/deploy.prototxt
@@ -1,10 +1,9 @@
 name: "CaffeNet"
-input: "data"
-input_shape {
-  dim: 10
-  dim: 3
-  dim: 227
-  dim: 227
+layer {
+  name: "data"
+  type: "Input"
+  top: "data"
+  input_param { shape: { dim: 10 dim: 3 dim: 227 dim: 227 } }
 }
 layer {
   name: "conv1"
diff --git a/models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt b/models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt
@@ -1,10 +1,9 @@
 name: "R-CNN-ilsvrc13"
-input: "data"
-input_shape {
-  dim: 10
-  dim: 3
-  dim: 227
-  dim: 227
+layer {
+  name: "data"
+  type: "Input"
+  top: "data"
+  input_param { shape: { dim: 10 dim: 3 dim: 227 dim: 227 } }
 }
 layer {
   name: "conv1"
diff --git a/models/finetune_flickr_style/deploy.prototxt b/models/finetune_flickr_style/deploy.prototxt
@@ -1,10 +1,9 @@
 name: "FlickrStyleCaffeNet"
-input: "data"
-input_shape {
-  dim: 10
-  dim: 3
-  dim: 227
-  dim: 227
+layer {
+  name: "data"
+  type: "Input"
+  top: "data"
+  input_param { shape: { dim: 10 dim: 3 dim: 227 dim: 227 } }
 }
 layer {
   name: "conv1"
