fix BGR for CAM

examples/ConvolutionalPoseMachines/load-cpm.py

@@ -9,6 +9,7 @@ import numpy as np
 import argparse
 
 from tensorpack import *
+from tensorpack.utils import viz
 from tensorpack.utils.argtools import memoized
 
 """

examples/Saliency/CAM-resnet.py

@@ -12,9 +12,11 @@ import multiprocessing
 import tensorflow as tf
 from tensorflow.contrib.layers import variance_scaling_initializer
 from tensorpack import *
+from tensorpack.dataflow import dataset
 from tensorpack.tfutils import optimizer
 from tensorpack.tfutils.symbolic_functions import *
 from tensorpack.tfutils.summary import *
+from tensorpack.utils import viz
 
 from imagenet_resnet_utils import (
     fbresnet_augmentor, resnet_basicblock, resnet_bottleneck, resnet_group,
@@ -33,7 +35,7 @@ class Model(ModelDesc):
 
     def _build_graph(self, inputs):
         image, label = inputs
-        image = image_preprocess(image, bgr=False)
+        image = image_preprocess(image, bgr=True)
         image = tf.transpose(image, [0, 3, 1, 2])
 
         cfg = {
@@ -85,13 +87,14 @@ def get_data(train_or_test):
 
     ds = AugmentImageComponent(ds, augmentors, copy=False)
     if isTrain:
-        ds = PrefetchDataZMQ(ds, min(20, multiprocessing.cpu_count()))
+        ds = PrefetchDataZMQ(ds, min(25, multiprocessing.cpu_count()))
     ds = BatchData(ds, BATCH_SIZE, remainder=not isTrain)
     return ds
 
 
 def get_config():
     nr_gpu = get_nr_gpu()
+    global BATCH_SIZE
     BATCH_SIZE = TOTAL_BATCH_SIZE // nr_gpu
 
     dataset_train = get_data('train')
@@ -102,14 +105,15 @@ def get_config():
         dataflow=dataset_train,
         callbacks=[
             ModelSaver(),
-            InferenceRunner(dataset_val, [
+            PeriodicTrigger(InferenceRunner(dataset_val, [
                 ClassificationError('wrong-top1', 'val-error-top1'),
                 ClassificationError('wrong-top5', 'val-error-top5')]),
+                every_k_epochs=2),
             ScheduledHyperParamSetter('learning_rate',
-                                      [(30, 1e-2), (60, 1e-3), (85, 1e-4), (95, 1e-5)]),
+                                      [(30, 1e-2), (55, 1e-3), (75, 1e-4), (95, 1e-5)]),
         ],
         steps_per_epoch=5000,
-        max_epoch=110,
+        max_epoch=105,
         nr_tower=nr_gpu
     )
 
@@ -151,7 +155,7 @@ def viz_cam(model_file, data_dir):
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
-    parser.add_argument('--gpu', help='comma separated list of GPU(s) to use.', required=True)
+    parser.add_argument('--gpu', help='comma separated list of GPU(s) to use.')
     parser.add_argument('--data', help='ILSVRC dataset dir')
     parser.add_argument('--depth', type=int, default=18)
     parser.add_argument('--load', help='load model')
@@ -159,7 +163,8 @@ if __name__ == '__main__':
     args = parser.parse_args()
 
     DEPTH = args.depth
-    os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
+    if args.gpu:
+        os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
 
     if args.cam:
         BATCH_SIZE = 128    # something that can run on one gpu
@@ -170,4 +175,4 @@ if __name__ == '__main__':
     config = get_config()
     if args.load:
         config.session_init = get_model_loader(args.load)
-    SyncMultiGPUTrainer(config).train()
+    SyncMultiGPUTrainerParameterServer(config).train()

tensorpack/dataflow/tf_func.py

-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-# File: tf_func.py
-# Author: Yuxin Wu <ppwwyyxxc@gmail.com>
-
-import tensorflow as tf
-from .base import ProxyDataFlow
-
-
-""" This file was deprecated """
-
-__all__ = []
-
-
-class TFFuncMapper(ProxyDataFlow):
-    def __init__(self, ds,
-                 get_placeholders, symbf, apply_symbf_on_dp, device='/cpu:0'):
-        """
-        :param get_placeholders: a function returning the placeholders
-        :param symbf: a symbolic function taking the placeholders
-        :param apply_symbf_on_dp: apply the above function to datapoint
-        """
-        super(TFFuncMapper, self).__init__(ds)
-        self.get_placeholders = get_placeholders
-        self.symbf = symbf
-        self.apply_symbf_on_dp = apply_symbf_on_dp
-        self.device = device
-
-    def reset_state(self):
-        super(TFFuncMapper, self).reset_state()
-        self.graph = tf.Graph()
-        with self.graph.as_default(), \
-                tf.device(self.device):
-            self.placeholders = self.get_placeholders()
-            self.output_vars = self.symbf(self.placeholders)
-            self.sess = tf.Session()
-
-            def run_func(vals):
-                return self.sess.run(self.output_vars,
-                                     feed_dict=dict(zip(self.placeholders, vals)))
-            self.run_func = run_func
-
-    def get_data(self):
-        for dp in self.ds.get_data():
-            dp = self.apply_symbf_on_dp(dp, self.run_func)
-            if dp:
-                yield dp
-
-
-if __name__ == '__main__':
-    from .raw import FakeData
-    ds = FakeData([[224, 224, 3]], 100000, random=False)
-
-    def tf_aug(v):
-        v = v[0]
-        v = tf.image.random_brightness(v, 0.1)
-        v = tf.image.random_contrast(v, 0.8, 1.2)
-        v = tf.image.random_flip_left_right(v)
-        return v
-    ds = TFFuncMapper(ds,
-                      lambda: [tf.placeholder(tf.float32, [224, 224, 3], name='img')],
-                      tf_aug,
-                      lambda dp, f: [f([dp[0]])[0]]
-                      )
-    # from .prefetch import PrefetchDataZMQ
-    # from .image import AugmentImageComponent
-    # from . import imgaug
-    # ds = AugmentImageComponent(ds,
-    #   [imgaug.Brightness(0.1, clip=False),
-    #    imgaug.Contrast((0.8, 1.2), clip=False),
-    #    imgaug.Flip(horiz=True)
-    #   ])
-    # ds = PrefetchDataZMQ(ds, 4)
-    ds.reset_state()
-
-    import tqdm
-    itr = ds.get_data()
-    for k in tqdm.trange(100000):
-        next(itr)