docs update

README.md

@@ -64,7 +64,7 @@ See [tutorials](http://tensorpack.readthedocs.io/en/latest/tutorial/index.html)
 
 Dependencies:
 
-+ Python 2 or 3
++ Python 2.7 or 3
 + TensorFlow >= 1.0.0 (>=1.1.0 for Multi-GPU)
 + Python bindings for OpenCV (Optional, but required by a lot of features)
 ```

examples/FasterRCNN/model.py

@@ -78,7 +78,7 @@ def rpn_losses(anchor_labels, anchor_boxes, label_logits, box_logits):
         with tf.device('/cpu:0'):
             for th in [0.5, 0.2, 0.1]:
                 valid_prediction = tf.cast(valid_label_prob > th, tf.int32)
-                prediction_corr = tf.count_nonzero(tf.equal(valid_prediction, valid_anchor_labels))
+                nr_pos_prediction = tf.count_nonzero(valid_prediction, name='num_pos_prediction')
                 pos_prediction_corr = tf.count_nonzero(tf.logical_and(
                     valid_label_prob > th,
                     tf.equal(valid_prediction, valid_anchor_labels)))
@@ -86,8 +86,8 @@ def rpn_losses(anchor_labels, anchor_boxes, label_logits, box_logits):
                     pos_prediction_corr,
                     nr_pos, name='recall_th{}'.format(th)))
                 summaries.append(tf.truediv(
-                    prediction_corr,
-                    nr_valid, name='accuracy_th{}'.format(th)))
+                    pos_prediction_corr,
+                    nr_pos_prediction, name='precision_th{}'.format(th)))
 
     label_loss = tf.nn.sigmoid_cross_entropy_with_logits(
         labels=tf.to_float(valid_anchor_labels), logits=valid_label_logits)
@@ -403,9 +403,6 @@ def fastrcnn_losses(labels, label_logits, fg_boxes, fg_box_logits):
     label_loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
         labels=labels, logits=label_logits)
     label_loss = tf.reduce_mean(label_loss, name='label_loss')
-    prediction = tf.argmax(label_logits, axis=1, name='label_prediction')
-    correct = tf.to_float(tf.equal(prediction, labels))  # boolean/integer gather is unavailable on GPU
-    accuracy = tf.reduce_mean(correct, name='accuracy')
 
     fg_inds = tf.where(labels > 0)[:, 0]
     fg_labels = tf.gather(labels, fg_inds)
@@ -415,7 +412,10 @@ def fastrcnn_losses(labels, label_logits, fg_boxes, fg_box_logits):
          tf.to_int32(fg_labels) - 1], axis=1)  # #fgx2
     fg_box_logits = tf.gather_nd(fg_box_logits, indices)
 
-    # some metrics to summarize
+    with tf.name_scope('label_metrics'), tf.device('/cpu:0'):
+        prediction = tf.argmax(label_logits, axis=1, name='label_prediction')
+        correct = tf.to_float(tf.equal(prediction, labels))  # boolean/integer gather is unavailable on GPU
+        accuracy = tf.reduce_mean(correct, name='accuracy')
         fg_label_pred = tf.argmax(tf.gather(label_logits, fg_inds), axis=1)
         num_zero = tf.reduce_sum(tf.to_int32(tf.equal(fg_label_pred, 0)), name='num_zero')
         false_negative = tf.truediv(num_zero, num_fg, name='false_negative')

examples/load-alexnet.py

@@ -17,6 +17,10 @@ import tensorflow as tf
 
 """
 Usage:
+    Download caffe models at https://github.com/BVLC/caffe/tree/master/models/bvlc_alexnet
+
+    Install caffe python bindings.
+
     python -m tensorpack.utils.loadcaffe PATH/TO/CAFFE/{deploy.prototxt,bvlc_alexnet.caffemodel} alexnet.npy
     ./load-alexnet.py --load alexnet.npy --input cat.png
 """

examples/load-vgg16.py

@@ -17,6 +17,11 @@ from tensorpack.dataflow.dataset import ILSVRCMeta
 
 """
 Usage:
+    Download original caffe models at:
+    https://gist.github.com/ksimonyan/211839e770f7b538e2d8
+
+    Install caffe python bindings.
+
     python -m tensorpack.utils.loadcaffe \
             PATH/TO/VGG/{VGG_ILSVRC_16_layers_deploy.prototxt,VGG_ILSVRC_16_layers.caffemodel} vgg16.npy
     ./load-vgg16.py --load vgg16.npy --input cat.png