Merge pull request BVLC#2667 from BVLC/tutorial

bundle CVPR15 tutorial notebooks

examples/detection.ipynb

@@ -8385,7 +8385,7 @@
    "pygments_lexer": "ipython2",
    "version": "2.7.9"
   },
-  "priority": 3
+  "priority": 6
  },
  "nbformat": 4,
  "nbformat_minor": 0

examples/finetune_flickr_style/assemble_data.py

@@ -9,6 +9,7 @@
 import argparse
 import numpy as np
 import pandas as pd
+from skimage import io
 import multiprocessing
 
 # Flickr returns a special image if the request is unavailable.
@@ -27,6 +28,7 @@ def download_image(args_tuple):
             urllib.urlretrieve(url, filename)
         with open(filename) as f:
             assert hashlib.sha1(f.read()).hexdigest() != MISSING_IMAGE_SHA1
+        test_read_image = io.imread(filename)
         return True
     except KeyboardInterrupt:
         raise Exception()  # multiprocessing doesn't catch keyboard exceptions
@@ -48,6 +50,10 @@ def download_image(args_tuple):
         '-w', '--workers', type=int, default=-1,
         help="num workers used to download images. -x uses (all - x) cores [-1 default]."
     )
+    parser.add_argument(
+        '-l', '--labels', type=int, default=0,
+        help="if set to a positive value, only sample images from the first number of labels."
+    )
 
     args = parser.parse_args()
     np.random.seed(args.seed)
@@ -56,6 +62,8 @@ def download_image(args_tuple):
     csv_filename = os.path.join(example_dirname, 'flickr_style.csv.gz')
     df = pd.read_csv(csv_filename, index_col=0, compression='gzip')
     df = df.iloc[np.random.permutation(df.shape[0])]
+    if args.labels > 0:
+        df = df.loc[df['label'] < args.labels]
     if args.images > 0 and args.images < df.shape[0]:
         df = df.iloc[:args.images]
 

examples/hdf5_classification/nonlinear_auto_test.prototxt

@@ -0,0 +1,54 @@
+layer {
+  name: "data"
+  type: "HDF5Data"
+  top: "data"
+  top: "label"
+  hdf5_data_param {
+    source: "examples/hdf5_classification/data/test.txt"
+    batch_size: 10
+  }
+}
+layer {
+  name: "ip1"
+  type: "InnerProduct"
+  bottom: "data"
+  top: "ip1"
+  inner_product_param {
+    num_output: 40
+    weight_filler {
+      type: "xavier"
+    }
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "ip1"
+  top: "ip1"
+}
+layer {
+  name: "ip2"
+  type: "InnerProduct"
+  bottom: "ip1"
+  top: "ip2"
+  inner_product_param {
+    num_output: 2
+    weight_filler {
+      type: "xavier"
+    }
+  }
+}
+layer {
+  name: "accuracy"
+  type: "Accuracy"
+  bottom: "ip2"
+  bottom: "label"
+  top: "accuracy"
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "ip2"
+  bottom: "label"
+  top: "loss"
+}

examples/hdf5_classification/nonlinear_auto_train.prototxt

@@ -0,0 +1,54 @@
+layer {
+  name: "data"
+  type: "HDF5Data"
+  top: "data"
+  top: "label"
+  hdf5_data_param {
+    source: "examples/hdf5_classification/data/train.txt"
+    batch_size: 10
+  }
+}
+layer {
+  name: "ip1"
+  type: "InnerProduct"
+  bottom: "data"
+  top: "ip1"
+  inner_product_param {
+    num_output: 40
+    weight_filler {
+      type: "xavier"
+    }
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "ip1"
+  top: "ip1"
+}
+layer {
+  name: "ip2"
+  type: "InnerProduct"
+  bottom: "ip1"
+  top: "ip2"
+  inner_product_param {
+    num_output: 2
+    weight_filler {
+      type: "xavier"
+    }
+  }
+}
+layer {
+  name: "accuracy"
+  type: "Accuracy"
+  bottom: "ip2"
+  bottom: "label"
+  top: "accuracy"
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "ip2"
+  bottom: "label"
+  top: "loss"
+}

examples/hdf5_classification/nonlinear_solver.prototxt

@@ -0,0 +1,15 @@
+train_net: "examples/hdf5_classification/nonlinear_auto_train.prototxt"
+test_net: "examples/hdf5_classification/nonlinear_auto_test.prototxt"
+test_iter: 250
+test_interval: 1000
+base_lr: 0.01
+lr_policy: "step"
+gamma: 0.1
+stepsize: 5000
+display: 1000
+max_iter: 10000
+momentum: 0.9
+weight_decay: 0.0005
+snapshot: 10000
+snapshot_prefix: "examples/hdf5_classification/data/train"
+solver_mode: CPU

examples/hdf5_classification/train_val2.prototxt → examples/hdf5_classification/nonlinear_train_val.prototxt

@@ -8,7 +8,7 @@ layer {
     phase: TRAIN
   }
   hdf5_data_param {
-    source: "hdf5_classification/data/train.txt"
+    source: "examples/hdf5_classification/data/train.txt"
     batch_size: 10
   }
 }
@@ -21,7 +21,7 @@ layer {
     phase: TEST
   }
   hdf5_data_param {
-    source: "hdf5_classification/data/test.txt"
+    source: "examples/hdf5_classification/data/test.txt"
     batch_size: 10
   }
 }
@@ -41,8 +41,7 @@ layer {
   inner_product_param {
     num_output: 40
     weight_filler {
-      type: "gaussian"
-      std: 0.01
+      type: "xavier"
     }
     bias_filler {
       type: "constant"
@@ -72,8 +71,7 @@ layer {
   inner_product_param {
     num_output: 2
     weight_filler {
-      type: "gaussian"
-      std: 0.01
+      type: "xavier"
     }
     bias_filler {
       type: "constant"

examples/hdf5_classification/solver.prototxt

@@ -1,4 +1,5 @@
-net: "hdf5_classification/train_val.prototxt"
+train_net: "examples/hdf5_classification/logreg_auto_train.prototxt"
+test_net: "examples/hdf5_classification/logreg_auto_test.prototxt"
 test_iter: 250
 test_interval: 1000
 base_lr: 0.01
@@ -10,5 +11,5 @@ max_iter: 10000
 momentum: 0.9
 weight_decay: 0.0005
 snapshot: 10000
-snapshot_prefix: "hdf5_classification/data/train"
+snapshot_prefix: "examples/hdf5_classification/data/train"
 solver_mode: CPU

examples/hdf5_classification/train_val.prototxt

@@ -8,7 +8,7 @@ layer {
     phase: TRAIN
   }
   hdf5_data_param {
-    source: "hdf5_classification/data/train.txt"
+    source: "examples/hdf5_classification/data/train.txt"
     batch_size: 10
   }
 }
@@ -21,7 +21,7 @@ layer {
     phase: TEST
   }
   hdf5_data_param {
-    source: "hdf5_classification/data/test.txt"
+    source: "examples/hdf5_classification/data/test.txt"
     batch_size: 10
   }
 }
@@ -41,8 +41,7 @@ layer {
   inner_product_param {
     num_output: 2
     weight_filler {
-      type: "gaussian"
-      std: 0.01
+      type: "xavier"
     }
     bias_filler {
       type: "constant"

examples/mnist/lenet_auto_solver.prototxt

@@ -0,0 +1,24 @@
+# The train/test net protocol buffer definition
+train_net: "examples/mnist/lenet_auto_train.prototxt"
+test_net: "examples/mnist/lenet_auto_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.
+test_iter: 100
+# Carry out testing every 500 training iterations.
+test_interval: 500
+# The base learning rate, momentum and the weight decay of the network.
+base_lr: 0.01
+momentum: 0.9
+weight_decay: 0.0005
+# The learning rate policy
+lr_policy: "inv"
+gamma: 0.0001
+power: 0.75
+# Display every 100 iterations
+display: 100
+# The maximum number of iterations
+max_iter: 10000
+# snapshot intermediate results
+snapshot: 5000
+snapshot_prefix: "examples/mnist/lenet"

examples/net_surgery.ipynb

@@ -6884,7 +6884,7 @@
   }
  ],
  "metadata": {
-  "description": "How to do net surgery and manually change model parameters, making a fully-convolutional classifier for dense feature extraction.",
+  "description": "How to do net surgery and manually change model parameters for custom use.",
   "example_name": "Editing model parameters",
   "include_in_docs": true,
   "kernelspec": {

examples/pycaffe/layers/pyloss.py

@@ -0,0 +1,37 @@
+import caffe
+import numpy as np
+
+
+class EuclideanLossLayer(caffe.Layer):
+    """
+    Compute the Euclidean Loss in the same manner as the C++ EuclideanLossLayer
+    to demonstrate the class interface for developing layers in Python.
+    """
+
+    def setup(self, bottom, top):
+        # check input pair
+        if len(bottom) != 2:
+            raise Exception("Need two inputs to compute distance.")
+
+    def reshape(self, bottom, top):
+        # check input dimensions match
+        if bottom[0].count != bottom[1].count:
+            raise Exception("Inputs must have the same dimension.")
+        # difference is shape of inputs
+        self.diff = np.zeros_like(bottom[0].data, dtype=np.float32)
+        # loss output is scalar
+        top[0].reshape(1)
+
+    def forward(self, bottom, top):
+        self.diff[...] = bottom[0].data - bottom[1].data
+        top[0].data[...] = np.sum(self.diff**2) / bottom[0].num / 2.
+
+    def backward(self, top, propagate_down, bottom):
+        for i in range(2):
+            if not propagate_down[i]:
+                continue
+            if i == 0:
+                sign = 1
+            else:
+                sign = -1
+            bottom[i].diff[...] = sign * self.diff / bottom[i].num

examples/pycaffe/linreg.prototxt

@@ -0,0 +1,60 @@
+name: 'LinearRegressionExample'
+# define a simple network for linear regression on dummy data
+# that computes the loss by a PythonLayer.
+layer {
+  type: 'DummyData'
+  name: 'x'
+  top: 'x'
+  dummy_data_param {
+    shape: { dim: 10 dim: 3 dim: 2 }
+    data_filler: { type: 'gaussian' }
+  }
+}
+layer {
+  type: 'DummyData'
+  name: 'y'
+  top: 'y'
+  dummy_data_param {
+    shape: { dim: 10 dim: 3 dim: 2 }
+    data_filler: { type: 'gaussian' }
+  }
+}
+# include InnerProduct layers for parameters
+# so the net will need backward
+layer {
+  type: 'InnerProduct'
+  name: 'ipx'
+  top: 'ipx'
+  bottom: 'x'
+  inner_product_param {
+    num_output: 10
+    weight_filler { type: 'xavier' }
+  }
+}
+layer {
+  type: 'InnerProduct'
+  name: 'ipy'
+  top: 'ipy'
+  bottom: 'y'
+  inner_product_param {
+    num_output: 10
+    weight_filler { type: 'xavier' }
+  }
+}
+layer {
+  type: 'Python'
+  name: 'loss'
+  top: 'loss'
+  bottom: 'ipx'
+  bottom: 'ipy'
+  python_param {
+    # the module name -- usually the filename -- that needs to be in $PYTHONPATH
+    module: 'pyloss'
+    # the layer name -- the class name in the module
+    layer: 'EuclideanLossLayer'
+  }
+  # set loss weight so Caffe knows this is a loss layer.
+  # since PythonLayer inherits directly from Layer, this isn't automatically
+  # known to Caffe
+  loss_weight: 1
+}