upgrade detection / some v2 compat fix

.github/workflows/workflow.yml

@@ -29,8 +29,7 @@ jobs:
       max-parallel: 6
       matrix:
         python-version: [3.6]
-        # TF-version: [1.3.0, 1.14.0, nightly] # TODO make nightly work
-        TF-version: [1.3.0, 1.14.0]
+        TF-version: [1.5.0, 1.15.0]
     steps:
       - uses: actions/checkout@v1
       - name: Set up Python ${{ matrix.python-version }}

examples/FasterRCNN/README.md

@@ -15,7 +15,7 @@ with the support of:
 + Training from scratch (from [Rethinking ImageNet Pre-training](https://arxiv.org/abs/1811.08883))
 
 ## Dependencies
-+ OpenCV, TensorFlow ≥ 1.6
++ OpenCV, TensorFlow ≥ 1.14
 + pycocotools/scipy: `for i in cython 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI' scipy; do pip install $i; done`
 + Pre-trained [ImageNet ResNet model](http://models.tensorpack.com/#FasterRCNN)
   from tensorpack model zoo

examples/FasterRCNN/modeling/generalized_rcnn.py

@@ -1,8 +1,7 @@
 # -*- coding: utf-8 -*-
 # File:
 
-import tensorflow as tf
-
+from tensorpack.compat import tfv1 as tf
 from tensorpack import ModelDesc
 from tensorpack.models import GlobalAvgPooling, l2_regularizer, regularize_cost
 from tensorpack.tfutils import optimizer

examples/FasterRCNN/modeling/model_box.py

@@ -74,7 +74,7 @@ def encode_bbox_target(boxes, anchors):
 
     # Note that here not all boxes are valid. Some may be zero
     txty = (xbyb - xaya) / waha
-    twth = tf.log(wbhb / waha)  # may contain -inf for invalid boxes
+    twth = tf.math.log(wbhb / waha)  # may contain -inf for invalid boxes
     encoded = tf.concat([txty, twth], axis=1)  # (-1x2x2)
     return tf.reshape(encoded, tf.shape(boxes))
 

examples/FasterRCNN/modeling/model_fpn.py

@@ -84,7 +84,7 @@ def fpn_map_rois_to_levels(boxes):
     """
     sqrtarea = tf.sqrt(tf_area(boxes))
     level = tf.cast(tf.floor(
-        4 + tf.log(sqrtarea * (1. / 224) + 1e-6) * (1.0 / np.log(2))), tf.int32)
+        4 + tf.math.log(sqrtarea * (1. / 224) + 1e-6) * (1.0 / np.log(2))), tf.int32)
 
     # RoI levels range from 2~5 (not 6)
     level_ids = [
@@ -127,7 +127,7 @@ def multilevel_roi_align(features, rcnn_boxes, resolution):
     all_rois = tf.concat(all_rois, axis=0)  # NCHW
     # Unshuffle to the original order, to match the original samples
     level_id_perm = tf.concat(level_ids, axis=0)  # A permutation of 1~N
-    level_id_invert_perm = tf.invert_permutation(level_id_perm)
+    level_id_invert_perm = tf.math.invert_permutation(level_id_perm)
     all_rois = tf.gather(all_rois, level_id_invert_perm, name="output")
     return all_rois
 

examples/FasterRCNN/modeling/model_frcnn.py

@@ -5,7 +5,6 @@
 
 from tensorpack.models import Conv2D, FullyConnected, layer_register
 from tensorpack.tfutils.argscope import argscope
-from tensorpack.tfutils.common import get_tf_version_tuple
 from tensorpack.tfutils.scope_utils import under_name_scope
 from tensorpack.tfutils.summary import add_moving_summary
 from tensorpack.utils.argtools import memoized_method
@@ -74,13 +73,13 @@ def sample_fg_bg(iou):
         num_fg = tf.minimum(int(
             cfg.FRCNN.BATCH_PER_IM * cfg.FRCNN.FG_RATIO),
             tf.size(fg_inds), name='num_fg')
-        fg_inds = tf.random_shuffle(fg_inds)[:num_fg]
+        fg_inds = tf.random.shuffle(fg_inds)[:num_fg]
 
         bg_inds = tf.reshape(tf.where(tf.logical_not(fg_mask)), [-1])
         num_bg = tf.minimum(
             cfg.FRCNN.BATCH_PER_IM - num_fg,
             tf.size(bg_inds), name='num_bg')
-        bg_inds = tf.random_shuffle(bg_inds)[:num_bg]
+        bg_inds = tf.random.shuffle(bg_inds)[:num_bg]
 
         add_moving_summary(num_fg, num_bg)
         return fg_inds, bg_inds
@@ -151,12 +150,8 @@ def fastrcnn_losses(labels, label_logits, fg_boxes, fg_box_logits):
     num_fg = tf.size(fg_inds, out_type=tf.int64)
     empty_fg = tf.equal(num_fg, 0)
     if int(fg_box_logits.shape[1]) > 1:
-        if get_tf_version_tuple() >= (1, 14):
-            fg_labels = tf.expand_dims(fg_labels, axis=1)  # nfg x 1
-            fg_box_logits = tf.gather(fg_box_logits, fg_labels, batch_dims=1)
-        else:
-            indices = tf.stack([tf.range(num_fg), fg_labels], axis=1)  # nfgx2
-            fg_box_logits = tf.gather_nd(fg_box_logits, indices)
+        fg_labels = tf.expand_dims(fg_labels, axis=1)  # nfg x 1
+        fg_box_logits = tf.gather(fg_box_logits, fg_labels, batch_dims=1)
     fg_box_logits = tf.reshape(fg_box_logits, [-1, 4])  # nfg x 4
 
     with tf.name_scope('label_metrics'), tf.device('/cpu:0'):
@@ -253,7 +248,7 @@ def fastrcnn_Xconv1fc_head(feature, num_convs, norm=None):
     with argscope(Conv2D, data_format='channels_first',
                   kernel_initializer=tf.variance_scaling_initializer(
                       scale=2.0, mode='fan_out',
-                      distribution='untruncated_normal' if get_tf_version_tuple() >= (1, 12) else 'normal')):
+                      distribution='untruncated_normal')):
         for k in range(num_convs):
             l = Conv2D('conv{}'.format(k), l, cfg.FPN.FRCNN_CONV_HEAD_DIM, 3, activation=tf.nn.relu)
             if norm is not None:

examples/FasterRCNN/modeling/model_mrcnn.py

@@ -4,7 +4,6 @@
 
 from tensorpack.models import Conv2D, Conv2DTranspose, layer_register
 from tensorpack.tfutils.argscope import argscope
-from tensorpack.tfutils.common import get_tf_version_tuple
 from tensorpack.tfutils.scope_utils import under_name_scope
 from tensorpack.tfutils.summary import add_moving_summary
 
@@ -20,14 +19,9 @@ def maskrcnn_loss(mask_logits, fg_labels, fg_target_masks):
         fg_labels: #fg, in 1~#class, int64
         fg_target_masks: #fgxhxw, float32
     """
-    if get_tf_version_tuple() >= (1, 14):
-        mask_logits = tf.gather(
-            mask_logits, tf.reshape(fg_labels - 1, [-1, 1]), batch_dims=1)
-        mask_logits = tf.squeeze(mask_logits, axis=1)
-    else:
-        indices = tf.stack([tf.range(tf.size(fg_labels, out_type=tf.int64)),
-                            fg_labels - 1], axis=1)  # #fgx2
-        mask_logits = tf.gather_nd(mask_logits, indices)  # #fg x h x w
+    mask_logits = tf.gather(
+        mask_logits, tf.reshape(fg_labels - 1, [-1, 1]), batch_dims=1)
+    mask_logits = tf.squeeze(mask_logits, axis=1)
 
     mask_probs = tf.sigmoid(mask_logits)
 
@@ -74,7 +68,7 @@ def maskrcnn_upXconv_head(feature, num_category, num_convs, norm=None):
     with argscope([Conv2D, Conv2DTranspose], data_format='channels_first',
                   kernel_initializer=tf.variance_scaling_initializer(
                       scale=2.0, mode='fan_out',
-                      distribution='untruncated_normal' if get_tf_version_tuple() >= (1, 12) else 'normal')):
+                      distribution='untruncated_normal')):
         # c2's MSRAFill is fan_out
         for k in range(num_convs):
             l = Conv2D('fcn{}'.format(k), l, cfg.MRCNN.HEAD_DIM, 3, activation=tf.nn.relu)

examples/FasterRCNN/train.py

@@ -1,12 +1,10 @@
 #!/usr/bin/env python
-# -*- coding: utf-8 -*-
 # File: train.py
 
 import argparse
 
 from tensorpack import *
 from tensorpack.tfutils import collect_env_info
-from tensorpack.tfutils.common import get_tf_version_tuple
 
 from dataset import register_coco, register_balloon
 from config import config as cfg
@@ -34,10 +32,6 @@
                         default='train_log/maskrcnn')
     parser.add_argument('--config', help="A list of KEY=VALUE to overwrite those defined in config.py", nargs='+')
 
-    if get_tf_version_tuple() < (1, 6):
-        # https://github.com/tensorflow/tensorflow/issues/14657
-        logger.warn("TF<1.6 has a bug which may lead to crash in FasterRCNN if you're unlucky.")
-
     args = parser.parse_args()
     if args.config:
         cfg.update_args(args.config)

tensorpack/graph_builder/training.py

@@ -316,9 +316,9 @@ def get_post_init_ops():
         Copy values of variables on GPU 0 to other GPUs.
         """
         # literally all variables, because it's better to sync optimizer-internal variables as well
-        all_vars = tf.global_variables() + tf.local_variables()
+        all_vars = tfv1.global_variables() + tfv1.local_variables()
         var_by_name = {v.name: v for v in all_vars}
-        trainable_names = {x.name for x in tf.trainable_variables()}
+        trainable_names = {x.name for x in tfv1.trainable_variables()}
         post_init_ops = []
 
         def log_failure(name, reason):

tensorpack/graph_builder/utils.py

@@ -25,11 +25,11 @@ def _replace_global_by_local(kwargs):
     if 'collections' in kwargs:
         collections = kwargs['collections']
     if not collections:
-        collections = {tf.GraphKeys.GLOBAL_VARIABLES}
+        collections = {tfv1.GraphKeys.GLOBAL_VARIABLES}
     else:
         collections = set(collections.copy())
-    collections.remove(tf.GraphKeys.GLOBAL_VARIABLES)
-    collections.add(tf.GraphKeys.LOCAL_VARIABLES)
+    collections.remove(tfv1.GraphKeys.GLOBAL_VARIABLES)
+    collections.add(tfv1.GraphKeys.LOCAL_VARIABLES)
     kwargs['collections'] = list(collections)
 
 

tensorpack/train/tower.py

@@ -289,9 +289,9 @@ def xla_func():
 
                 grads_no_vars = xla.compile(xla_func)
                 if ctx.has_own_variables:
-                    varlist = ctx.get_collection_in_tower(tf.GraphKeys.TRAINABLE_VARIABLES)
+                    varlist = ctx.get_collection_in_tower(tfv1.GraphKeys.TRAINABLE_VARIABLES)
                 else:
-                    varlist = tf.trainable_variables()
+                    varlist = tfv1.trainable_variables()
                 return list(zip(grads_no_vars, varlist))
 
         return get_grad_fn