put image to tensorboard (fix #239)

README.md

@@ -20,7 +20,7 @@ See some [examples](examples) to learn about the framework:
 + [Asynchronous Advantage Actor-Critic(A3C) with demos on OpenAI Gym](examples/A3C-Gym)
 
 ### Unsupervised Learning:
-+ [Generative Adversarial Network(GAN) variants](examples/GAN), including DCGAN, InfoGAN, Conditional GAN, WGAN, DiscoGAN, Image to Image.
++ [Generative Adversarial Network(GAN) variants](examples/GAN), including DCGAN, InfoGAN, Conditional GAN, WGAN, BEGAN, DiscoGAN, Image to Image.
 
 ### Speech / NLP:
 + [LSTM-CTC for speech recognition](examples/CTC-TIMIT)

docs/tutorial/efficient-dataflow.md

@@ -41,7 +41,7 @@ for filename, label in filelist:
 
 And `ds1` batch the datapoints from `ds0`, so that we can measure the speed of this DataFlow in terms of "batch per second".
 By default, `BatchData`
-will stack the datapoints into an `numpy.ndarray`, but since images are original of different shapes, we use
+will stack the datapoints into an `numpy.ndarray`, but since original images are of different shapes, we use
 `use_list=True` so that it just produces lists.
 
 On an SSD you probably can already observe good speed here (e.g. 5 it/s, that is 1280 samples/s), but on HDD the speed may be just 1 it/s,

tensorpack/callbacks/dump.py

@@ -23,7 +23,7 @@ class DumpParamAsImage(Callback):
     the input pipeline).
     """
 
-    def __init__(self, tensor_name, prefix=None, map_func=None, scale=255, clip=False):
+    def __init__(self, tensor_name, prefix=None, map_func=None, scale=255):
         """
         Args:
             tensor_name (str): the name of the tensor.
@@ -31,7 +31,6 @@ class DumpParamAsImage(Callback):
             map_func: map the value of the tensor to an image or list of
                  images of shape [h, w] or [h, w, c]. If None, will use identity.
             scale (float): a multiplier on pixel values, applied after map_func.
-            clip (bool): whether to clip the result to [0, 255].
         """
         op_name, self.tensor_name = get_op_tensor_name(tensor_name)
         self.func = map_func
@@ -41,7 +40,6 @@ class DumpParamAsImage(Callback):
             self.prefix = prefix
         self.log_dir = logger.LOG_DIR
         self.scale = scale
-        self.clip = clip
 
     def _before_train(self):
         # TODO might not work for multiGPU?
@@ -56,6 +54,7 @@ class DumpParamAsImage(Callback):
                 self._dump_image(im, idx)
         else:
             self._dump_image(val)
+        self.trainer.monitors.put_image(self.prefix, val)
 
     def _dump_image(self, im, idx=None):
         assert im.ndim in [2, 3], str(im.ndim)
@@ -64,6 +63,5 @@ class DumpParamAsImage(Callback):
             self.prefix + '-ep{:03d}{}.png'.format(
                 self.epoch_num, '-' + str(idx) if idx else ''))
         res = im * self.scale
-        if self.clip:
-            res = np.clip(res, 0, 255)
+        res = np.clip(res, 0, 255)
         cv2.imwrite(fname, res.astype('uint8'))

tensorpack/callbacks/inference_runner.py

@@ -47,10 +47,10 @@ def summary_inferencer(trainer, infs):
         for k, v in six.iteritems(ret):
             try:
                 v = float(v)
+                trainer.monitors.put_scalar(k, v)
             except:
                 logger.warn("{} returns a non-scalar statistics!".format(type(inf).__name__))
                 continue
-            trainer.monitors.put(k, v)
 
 
 @six.add_metaclass(ABCMeta)

tensorpack/callbacks/monitor.py

@@ -4,6 +4,7 @@
 # Author: Yuxin Wu <ppwwyyxxc@gmail.com>
 
 import os
+import numpy as np
 import shutil
 import operator
 from collections import defaultdict
@@ -13,12 +14,28 @@ import re
 
 import tensorflow as tf
 from ..utils import logger
+from ..tfutils.summary import create_scalar_summary, create_image_summary
 from .base import Callback
 
 __all__ = ['TrainingMonitor', 'Monitors',
            'TFSummaryWriter', 'JSONWriter', 'ScalarPrinter', 'SendMonitorData']
 
 
+def image_to_nhwc(arr):
+    if arr.ndim == 4:
+        pass
+    elif arr.ndim == 3:
+        if arr.shape[-1] in [1, 3, 4]:
+            arr = arr[np.newaxis, :]
+        else:
+            arr = arr[:, :, :, np.newaxis]
+    elif arr.ndim == 2:
+        arr = arr[np.newaxis, :, :, np.newaxis]
+    else:
+        raise ValueError("Array of shape {} is not an image!".format(arr.shape))
+    return arr
+
+
 class TrainingMonitor(Callback):
     """
     Monitor a training progress, by processing different types of
@@ -48,7 +65,15 @@ class TrainingMonitor(Callback):
         pass
 
     def put_scalar(self, name, val):
-        self.put(name, val)
+        pass
+
+    def put_image(self, name, val):
+        """
+        Args:
+            val (np.ndarray): 4D (NHWC) numpy array of images.
+                If channel is 3, assumed to be RGB.
+        """
+        pass
 
     # TODO put other types
 
@@ -76,11 +101,16 @@ class Monitors(TrainingMonitor):
         for m in self._monitors:
             m.put_scalar(name, val)
 
+    def _dispatch_put_image(self, name, val):
+        for m in self._monitors:
+            m.put_image(name, val)
+
     def put_summary(self, summary):
         if isinstance(summary, six.binary_type):
             summary = tf.Summary.FromString(summary)
         assert isinstance(summary, tf.Summary), type(summary)
 
+        # TODO remove -summary suffix for summary
         self._dispatch_put_summary(summary)
 
         # TODO other types
@@ -98,8 +128,20 @@ class Monitors(TrainingMonitor):
 
     def put_scalar(self, name, val):
         self._dispatch_put_scalar(name, val)
-        s = tf.Summary()
-        s.value.add(tag=name, simple_value=val)
+        s = create_scalar_summary(name, val)
+        self._dispatch_put_summary(s)
+
+    def put_image(self, name, val):
+        """
+        Args:
+            name (str):
+            val (np.ndarray): 2D, 3D (HWC) or 4D (NHWC) numpy array of images.
+                If channel is 3, assumed to be RGB.
+        """
+        assert isinstance(val, np.ndarray)
+        arr = image_to_nhwc(val)
+        self._dispatch_put_image(name, arr)
+        s = create_image_summary(name, arr)
         self._dispatch_put_summary(s)
 
     def get_latest(self, name):

tensorpack/tfutils/summary.py

@@ -4,7 +4,9 @@
 
 import six
 import tensorflow as tf
+import cv2
 import re
+from six.moves import range
 
 from ..utils import logger
 from ..utils.develop import log_deprecated
@@ -29,6 +31,33 @@ def create_scalar_summary(name, v):
     return s
 
 
+def create_image_summary(name, val):
+    """
+    Args:
+        name(str):
+        val(np.ndarray): 4D tensor of NHWC
+
+    Returns:
+        tf.Summary:
+    """
+    assert isinstance(name, six.string_types), type(name)
+    n, h, w, c = val.shape
+    s = tf.Summary()
+    for k in range(n):
+        tag = name if n == 1 else '{}/{}'.format(name, k)
+        ret, buf = cv2.imencode('.png', val[k])
+        assert ret, "imencode failed!"
+
+        img = tf.Summary.Image()
+        img.height = h
+        img.width = w
+        # 1 - grayscale 3 - RGB 4 - RGBA
+        img.colorspace = c
+        img.encoded_image_string = buf.tostring()
+        s.value.add(tag=tag, image=img)
+    return s
+
+
 def add_activation_summary(x, name=None):
     """
     Add summary for an activation tensor x.  If name is None, use x.name.

tensorpack/utils/fs.py

@@ -87,7 +87,7 @@ def get_dataset_path(*args):
         old_d = os.path.abspath(os.path.join(
             os.path.dirname(__file__), '..', 'dataflow', 'dataset'))
         old_d_ret = os.path.join(old_d, *args)
-        new_d = os.path.expanduser('~/tensorpack_data')
+        new_d = os.path.join(os.path.expanduser('~'), 'tensorpack_data')
         if os.path.isdir(old_d_ret):
             # there is an old dir containing data, use it for back-compat
             logger.warn("You seem to have old data at {}. This is no longer \