Merge branch 'mpii-file' of https://github.com/tensorlayer/tensorlayer into mpii-file

Author: zsdonghao

docs/modules/prepro.rst

@@ -357,6 +357,10 @@ In practice, you may want to use threading method to process a batch of images a
                b_ann[i][0], b_ann[i][1], [], classes, True,
                save_name='_bbox_vis_%d.png' % i)
 
+Image Aug with TF Dataset API
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+- Example code for VOC `here <https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_tf_dataset_voc.py>`__.
 
 Coordinate pixel unit to percentage
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

example/tutorial_tf_dataset_voc.py

@@ -0,0 +1,95 @@
+#! /usr/bin/python
+# -*- coding: utf8 -*-
+
+# tf import data dataset.map https://www.tensorflow.org/programmers_guide/datasets#applying_arbitrary_python_logic_with_tfpy_func
+# tf.py_func https://www.tensorflow.org/api_docs/python/tf/py_func
+# tl ref: https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_imagenet_inceptionV3_distributed.py
+# cn ref: https://blog.csdn.net/dQCFKyQDXYm3F8rB0/article/details/79342369
+# cn ref: https://zhuanlan.zhihu.com/p/31466173
+
+import numpy as np
+import multiprocessing, random, json, time
+import tensorflow as tf
+import tensorlayer as tl
+
+imgs_file_list, _, _, _, classes, _, _,\
+    _, objs_info_list, _ = tl.files.load_voc_dataset(dataset="2007")
+
+ann_list = []
+for info in objs_info_list:
+    ann = tl.prepro.parse_darknet_ann_str_to_list(info)
+    c, b = tl.prepro.parse_darknet_ann_list_to_cls_box(ann)
+    ann_list.append([c, b])
+
+n_epoch = 10
+batch_size = 64
+im_size = [416, 416]
+jitter = 0.2
+shuffle_buffer_size = 100
+
+
+def generator():
+    inputs = imgs_file_list
+    targets = objs_info_list
+    assert len(inputs) == len(targets)
+    for _input, _target in zip(inputs, targets):
+        yield _input.encode('utf-8'), _target.encode('utf-8')
+
+
+def _data_aug_fn(im, ann):
+    ## parse annotation
+    ann = ann.decode()
+    ann = tl.prepro.parse_darknet_ann_str_to_list(ann)
+    clas, coords = tl.prepro.parse_darknet_ann_list_to_cls_box(ann)
+    ## random brightness, contrast and saturation
+    im = tl.prepro.brightness(im, gamma=0.5, gain=1, is_random=True)
+    # im = tl.prepro.illumination(im, gamma=(0.5, 1.5),
+    #          contrast=(0.5, 1.5), saturation=(0.5, 1.5), is_random=True)    # TypeError: Cannot handle this data type
+    ## random horizontal flip
+    im, coords = tl.prepro.obj_box_left_right_flip(im, coords, is_rescale=True, is_center=True, is_random=True)
+    ## random resize and crop
+    tmp0 = random.randint(1, int(im_size[0] * jitter))
+    tmp1 = random.randint(1, int(im_size[1] * jitter))
+    im, coords = tl.prepro.obj_box_imresize(im, coords, [im_size[0] + tmp0, im_size[1] + tmp1], is_rescale=True, interp='bicubic')
+    im, clas, coords = tl.prepro.obj_box_crop(im, clas, coords, wrg=im_size[1], hrg=im_size[0], is_rescale=True, is_center=True, is_random=True)
+    ## value [0, 255] to [-1, 1] (optional)
+    # im = im / 127.5 - 1
+    ## value [0, 255] to [0, 1] (optional)
+    im = im / 255
+    im = np.array(im, dtype=np.float32)  # important
+    return im, str([clas, coords]).encode('utf-8')
+
+
+def _map_fn(filename, annotation):
+    ## read image
+    image = tf.read_file(filename)
+    image = tf.image.decode_jpeg(image, channels=3)
+    image = tf.image.convert_image_dtype(image, dtype=tf.float32)
+    ## data augmentation
+    image, annotation = tf.py_func(_data_aug_fn, [image, annotation], [tf.float32, tf.string])
+    return image, annotation
+
+
+ds = tf.data.Dataset().from_generator(generator, output_types=(tf.string, tf.string))
+ds = ds.map(_map_fn, num_parallel_calls=multiprocessing.cpu_count())
+ds = ds.repeat(n_epoch)
+ds = ds.shuffle(shuffle_buffer_size)
+ds = ds.batch(batch_size)
+value = ds.make_one_shot_iterator().get_next()
+
+sess = tf.InteractiveSession()
+
+## get a batch of images (after data augmentation)
+_, _ = sess.run(value)  # 1st time takes time to compile
+st = time.time()
+im, annbyte = sess.run(value)
+print('took {}s'.format(time.time() - st))
+
+ann = []
+for a in annbyte:
+    a = a.decode()
+    ann.append(json.loads(a))
+
+## save all images
+for i in range(len(im)):
+    tl.vis.draw_boxes_and_labels_to_image(im[i] * 255, ann[i][0], ann[i][1], [], classes, True, save_name='_bbox_vis_%d.png' % i)

tensorlayer/iterate.py

@@ -59,7 +59,10 @@ def minibatches(inputs=None, targets=None, batch_size=None, shuffle=False):
             excerpt = indices[start_idx:start_idx + batch_size]
         else:
             excerpt = slice(start_idx, start_idx + batch_size)
-        yield inputs[excerpt], targets[excerpt]
+        if (isinstance(inputs, list) or isinstance(targets, list)) and (shuffle == True):
+            yield [inputs[i] for i in excerpt], [targets[i] for i in excerpt]  # zsdonghao: for list indexing when shuffle==True
+        else:
+            yield inputs[excerpt], targets[excerpt]
 
 
 def seq_minibatches(inputs, targets, batch_size, seq_length, stride=1):

tensorlayer/models/mobilenetv1.py

@@ -56,7 +56,7 @@ class MobileNetV1(Layer):
     >>> # restore pre-trained parameters
     >>> cnn.restore_params(sess)
     >>> # train your own classifier (only update the last layer)
-    >>> train_params = tl.layers.get_variables_with_name('output')
+    >>> train_params = tl.layers.get_variables_with_name('out')
 
     Reuse model
 

tensorlayer/models/vgg16.py

@@ -231,7 +231,7 @@ def restore_params(self, sess):
 
 
 class VGG16(VGG16Base):
-    """Pre-trained VGG-16 Model.
+    """Pre-trained VGG-16 model.
 
     Parameters
     ------------