update docs. cleanup scripts

README.md

@@ -13,7 +13,7 @@ It's Yet Another TF wrapper, but different in:
 
 1. Focus on __training speed__.
 	+	Speed comes for free with tensorpack -- it uses TensorFlow in the __efficient way__ with no extra overhead.
-	  On various CNNs, it runs 1.5~1.7x faster than the equivalent Keras code.
+	  On various CNNs, it runs 1.1~2x faster than the equivalent Keras code.
 
 	+ Data-parallel multi-GPU training is off-the-shelf to use. It runs as fast as Google's [official benchmark](https://www.tensorflow.org/performance/benchmarks).
 

examples/HED/README.md

@@ -12,10 +12,10 @@ for other FCN tasks such as semantic segmentation and detection.
 
 ## Usage
 
-This script only needs the original BSDS dataset and applies augmentation on the fly.
+This script needs the original BSDS dataset and applies augmentation on the fly.
 It will automatically download the dataset to `$TENSORPACK_DATASET/` if not there.
 
-It requires pretrained vgg16 model. See the docs in [examples/load-vgg16.py](../load-vgg16.py)
+It requires pretrained vgg16 model. See the docs in [examples/CaffeModels](../CaffeModels)
 for instructions to convert from vgg16 caffe model.
 
 To view augmented training images:
@@ -34,11 +34,3 @@ To inference (produce a heatmap at each level at out*.png):
 ./hed.py --load pretrained.model --run a.jpg
 ```
 Models I trained can be downloaded [here](http://models.tensorpack.com/HED/).
-
-To view the loss curve:
-```bash
-cat train_log/hed/stat.json | jq '.[] |
-"\(.xentropy1)\t\(.xentropy2)\t\(.xentropy3)\t\(.xentropy4)\t\(.xentropy5)\t\(.xentropy6)"' -r | \
-				tpk-plot-point --legend 1,2,3,4,5,final --decay 0.8
-```
-Or just open tensorboard.

scripts/dump-dataflow.py

-#!/usr/bin/env python
-# -*- coding: UTF-8 -*-
-# File: dump-dataflow.py
-# Author: Yuxin Wu <ppwwyyxx@gmail.com>
-
-import argparse
-import cv2
-import imp
-import tqdm
-import os
-from tensorpack.utils import logger
-from tensorpack.utils.fs import mkdir_p
-from tensorpack.dataflow import RepeatedData
-
-
-parser = argparse.ArgumentParser()
-parser.add_argument(dest='config')
-parser.add_argument('-o', '--output',
-                    help='output directory to dump dataset image. If not given, will not dump images.')
-parser.add_argument('-s', '--scale',
-                    help='scale the image data (maybe by 255)', default=1, type=int)
-parser.add_argument('--index',
-                    help='index of the image component in datapoint',
-                    default=0, type=int)
-parser.add_argument('-n', '--number', help='number of images to dump',
-                    default=10, type=int)
-args = parser.parse_args()
-logger.auto_set_dir(action='d')
-
-get_config_func = imp.load_source('config_script', args.config).get_config
-config = get_config_func()
-config.dataset.reset_state()
-
-if args.output:
-    mkdir_p(args.output)
-    cnt = 0
-    index = args.index   # TODO: as an argument?
-    for dp in config.dataset.get_data():
-        imgbatch = dp[index]
-        if cnt > args.number:
-            break
-        for bi, img in enumerate(imgbatch):
-            cnt += 1
-            fname = os.path.join(args.output, '{:03d}-{}.png'.format(cnt, bi))
-            cv2.imwrite(fname, img * args.scale)
-
-NR_DP_TEST = args.number
-logger.info("Testing dataflow speed:")
-ds = RepeatedData(config.dataset, -1)
-with tqdm.tqdm(total=NR_DP_TEST, leave=True, unit='data points') as pbar:
-    for idx, dp in enumerate(ds.get_data()):
-        del dp
-        if idx > NR_DP_TEST:
-            break
-        pbar.update()

scripts/serve-data.py

-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-# File: serve-data.py
-# Author: Yuxin Wu <ppwwyyxxc@gmail.com>
-
-import argparse
-import imp
-from tensorpack.dataflow import serve_data
-
-parser = argparse.ArgumentParser()
-parser.add_argument(dest='config')
-parser.add_argument('-p', '--port', help='port', type=int, required=True)
-args = parser.parse_args()
-
-get_config_func = imp.load_source('config_script', args.config).get_config
-config = get_config_func()
-
-ds = config.dataset
-serve_data(ds, "tcp://*:{}".format(args.port))

tensorpack/input_source/input_source.py

@@ -515,7 +515,8 @@ class StagingInput(FeedfreeInput):
                     element should be sufficient.
             towers: deprecated
             device (str or None): if not None, place the StagingArea on a specific device. e.g., '/cpu:0'.
-                Otherwise, they are placed under where `get_inputs_tensors` gets called.
+                Otherwise, they are placed under where `get_inputs_tensors`
+                gets called, which could be unspecified in case of simple trainers.
         """
         assert isinstance(input, FeedfreeInput), input
         self._input = input