LOG(ERROR)->LOG(FATAL), and misc script changes.

examples/cifar10/readme.md

@@ -42,7 +42,7 @@ Training the model is simple after you have written the network definition proto
     cd $CAFFE_ROOT/examples/cifar10
     ./train_quick.sh
 
-`train_quick.sh` is a simple script, so have a look inside. `GLOG_logtostderr=1` is the google logging flag that prints all the logging messages directly to stderr. The main tool for training is `train_net.bin`, with the solver protobuf text file as its argument.
+`train_quick.sh` is a simple script, so have a look inside. `GLOG_logtostderr=1` is the google logging flag that prints all the logging messages directly to stderr. The main tool for training is `caffe.bin` with the `train` action, and the solver protobuf text file as its argument.
 
 When you run the code, you will see a lot of messages flying by like this:
 

examples/cifar10/train_full.sh

@@ -2,15 +2,15 @@
 
 TOOLS=../../build/tools
 
-GLOG_logtostderr=1 $TOOLS/train_net.bin \
-    cifar10_full_solver.prototxt
+GLOG_logtostderr=1 $TOOLS/caffe.bin train \
+    --solver_proto_file=cifar10_full_solver.prototxt
 
 #reduce learning rate by factor of 10
-GLOG_logtostderr=1 $TOOLS/train_net.bin \
-    cifar10_full_solver_lr1.prototxt \
-    cifar10_full_iter_60000.solverstate
+GLOG_logtostderr=1 $TOOLS/caffe.bin train \
+    --solver_proto_file=cifar10_full_solver_lr1.prototxt \
+    --resume_point_file=cifar10_full_iter_60000.solverstate
 
 #reduce learning rate by factor of 10
-GLOG_logtostderr=1 $TOOLS/train_net.bin \
-    cifar10_full_solver_lr2.prototxt \
-    cifar10_full_iter_65000.solverstate
+GLOG_logtostderr=1 $TOOLS/caffe.bin train \
+    --solver_proto_file=cifar10_full_solver_lr2.prototxt \
+    --resume_point_file=cifar10_full_iter_65000.solverstate

examples/cifar10/train_quick.sh

@@ -2,7 +2,10 @@
 
 TOOLS=../../build/tools
 
-GLOG_logtostderr=1 $TOOLS/train_net.bin cifar10_quick_solver.prototxt
+GLOG_logtostderr=1 $TOOLS/caffe.bin train \
+  --solver_proto_file=cifar10_quick_solver.prototxt
 
 #reduce learning rate by fctor of 10 after 8 epochs
-GLOG_logtostderr=1 $TOOLS/train_net.bin cifar10_quick_solver_lr1.prototxt cifar10_quick_iter_4000.solverstate
+GLOG_logtostderr=1 $TOOLS/caffe.bin train \
+  --solver_proto_file=cifar10_quick_solver_lr1.prototxt \
+  --resume_point_file=cifar10_quick_iter_4000.solverstate

examples/imagenet/resume_training.sh

@@ -2,7 +2,8 @@
 
 TOOLS=../../build/tools
 
-GLOG_logtostderr=1 $TOOLS/train_net.bin \
-    imagenet_solver.prototxt caffe_imagenet_train_10000.solverstate
+GLOG_logtostderr=1 $TOOLS/caffe.bin train\
+    --solver_proto_file=imagenet_solver.prototxt \
+    --resume_point_file=caffe_imagenet_train_10000.solverstate
 
 echo "Done."

examples/imagenet/train_alexnet.sh

@@ -2,6 +2,7 @@
 
 TOOLS=../../build/tools
 
-GLOG_logtostderr=1 $TOOLS/train_net.bin alexnet_solver.prototxt
+GLOG_logtostderr=1 $TOOLS/caffe.bin train \
+  --solver_proto_file=alexnet_solver.prototxt
 
 echo "Done."

examples/imagenet/train_imagenet.sh

@@ -2,6 +2,7 @@
 
 TOOLS=../../build/tools
 
-GLOG_logtostderr=1 $TOOLS/train_net.bin imagenet_solver.prototxt
+GLOG_logtostderr=1 $TOOLS/caffe.bin train \
+  --solver_proto_file=imagenet_solver.prototxt
 
 echo "Done."

examples/mnist/readme.md

@@ -212,7 +212,7 @@ Training the model is simple after you have written the network definition proto
     cd $CAFFE_ROOT/examples/mnist
     ./train_lenet.sh
 
-`train_lenet.sh` is a simple script, but here are a few explanations: `GLOG_logtostderr=1` is the google logging flag that prints all the logging messages directly to stderr. The main tool for training is `train_net.bin`, with the solver protobuf text file as its argument.
+`train_lenet.sh` is a simple script, but here are a few explanations: `GLOG_logtostderr=1` is the google logging flag that prints all the logging messages directly to stderr. The main tool for training is `caffe.bin` with action `train`, with the solver protobuf text file as its argument.
 
 When you run the code, you will see a lot of messages flying by like this:
 

examples/mnist/train_lenet.sh

@@ -2,4 +2,5 @@
 
 TOOLS=../../build/tools
 
-GLOG_logtostderr=1 $TOOLS/train_net.bin lenet_solver.prototxt
+GLOG_logtostderr=1 $TOOLS/caffe.bin train \
+  --solver_proto_file=lenet_solver.prototxt

examples/mnist/train_lenet_consolidated.sh

@@ -2,4 +2,5 @@
 
 TOOLS=../../build/tools
 
-GLOG_logtostderr=1 $TOOLS/train_net.bin lenet_consolidated_solver.prototxt
+GLOG_logtostderr=1 $TOOLS/caffe.bin train \
+  --solver_proto_file=lenet_consolidated_solver.prototxt

examples/mnist/train_mnist_autoencoder.sh

@@ -1,4 +1,5 @@
 #!/bin/bash
 TOOLS=../../build/tools
 
-GLOG_logtostderr=1 $TOOLS/train_net.bin mnist_autoencoder_solver.prototxt
+GLOG_logtostderr=1 $TOOLS/caffe.bin train \
+  --solver_proto_file=mnist_autoencoder_solver.prototxt

tools/device_query.cpp

@@ -3,7 +3,7 @@
 #include "caffe/common.hpp"
 
 int main(int argc, char** argv) {
-  LOG(ERROR) << "Deprecated. Use caffe.bin devicequery "
+  LOG(FATAL) << "Deprecated. Use caffe.bin devicequery "
                 "[--device_id=0] instead.";
   return 0;
 }

tools/finetune_net.cpp

@@ -3,7 +3,7 @@
 #include "caffe/caffe.hpp"
 
 int main(int argc, char** argv) {
-  LOG(ERROR) << "Deprecated. Use caffe.bin train --solver_proto_file=... "
+  LOG(FATAL) << "Deprecated. Use caffe.bin train --solver_proto_file=... "
                 "[--pretrained_net_file=...] instead.";
   return 0;
 }

tools/net_speed_benchmark.cpp

@@ -3,7 +3,7 @@
 #include "caffe/caffe.hpp"
 
 int main(int argc, char** argv) {
-  LOG(ERROR) << "Deprecated. Use caffe.bin speedtest --net_proto_file=... "
+  LOG(FATAL) << "Deprecated. Use caffe.bin speedtest --net_proto_file=... "
              "[--run_iterations=50] [--speedtest_with_gpu] [--device_id=0]";
   return 0;
 }

tools/train_net.cpp

@@ -3,7 +3,7 @@
 #include "caffe/caffe.hpp"
 
 int main(int argc, char** argv) {
-  LOG(ERROR) << "Deprecated. Use caffe.bin train --solver_proto_file=... "
+  LOG(FATAL) << "Deprecated. Use caffe.bin train --solver_proto_file=... "
                 "[--resume_point_file=...] instead.";
   return 0;
 }