rm tf.contrib

dataset_tool.py

@@ -16,6 +16,7 @@
 import traceback
 import numpy as np
 import tensorflow as tf
+tf = tf.compat.v1
 import PIL.Image
 import dnnlib.tflib as tflib
 

dnnlib/tflib/autosummary.py

@@ -24,6 +24,8 @@
 from collections import OrderedDict
 import numpy as np
 import tensorflow as tf
+tf = tf.compat.v1
+
 from tensorboard import summary as summary_lib
 from tensorboard.plugins.custom_scalar import layout_pb2
 

dnnlib/tflib/custom_ops.py

@@ -14,6 +14,8 @@
 import tempfile
 import shutil
 import tensorflow as tf
+tf = tf.compat.v1
+
 from tensorflow.python.client import device_lib # pylint: disable=no-name-in-module
 
 #----------------------------------------------------------------------------

dnnlib/tflib/network.py

@@ -13,6 +13,8 @@
 import sys
 import numpy as np
 import tensorflow as tf
+tf = tf.compat.v1
+
 
 from collections import OrderedDict
 from typing import Any, List, Tuple, Union

dnnlib/tflib/ops/fused_bias_act.py

@@ -9,6 +9,8 @@
 import os
 import numpy as np
 import tensorflow as tf
+tf = tf.compat.v1
+
 from .. import custom_ops
 from ...util import EasyDict
 

dnnlib/tflib/ops/upfirdn_2d.py

@@ -9,6 +9,8 @@
 import os
 import numpy as np
 import tensorflow as tf
+tf = tf.compat.v1
+
 from .. import custom_ops
 
 def _get_plugin():

dnnlib/tflib/optimizer.py

@@ -9,6 +9,8 @@
 import platform
 import numpy as np
 import tensorflow as tf
+tf = tf.compat.v1
+
 
 from collections import OrderedDict
 from typing import List, Union
@@ -131,13 +133,13 @@ def register_gradients(self, loss: TfExpression, trainable_vars: Union[List, dic
 
         # Report memory usage if requested.
         deps = []
-        if self._report_mem_usage:
-            self._report_mem_usage = False
-            try:
-                with tf.name_scope(self.id + '_mem'), tf.device(device.name), tf.control_dependencies([loss]):
-                    deps.append(autosummary.autosummary(self.id + "/mem_usage_gb", tf.contrib.memory_stats.BytesInUse() / 2**30))
-            except tf.errors.NotFoundError:
-                pass
+        # if self._report_mem_usage:
+        #     self._report_mem_usage = False
+        #     try:
+        #         with tf.name_scope(self.id + '_mem'), tf.device(device.name), tf.control_dependencies([loss]):
+        #             deps.append(autosummary.autosummary(self.id + "/mem_usage_gb", tf.contrib.memory_stats.BytesInUse() / 2**30))
+        #     except tf.errors.NotFoundError:
+        #         pass
 
         # Compute gradients.
         with tf.name_scope(self.id + "_grad"), tf.device(device.name), tf.control_dependencies(deps):

dnnlib/tflib/tfutil.py

@@ -9,12 +9,13 @@
 import os
 import numpy as np
 import tensorflow as tf
+tf = tf.compat.v1
 
 # Silence deprecation warnings from TensorFlow 1.13 onwards
 import logging
 logging.getLogger('tensorflow').setLevel(logging.ERROR)
-import tensorflow.contrib   # requires TensorFlow 1.x!
-tf.contrib = tensorflow.contrib
+# import tensorflow.contrib   # requires TensorFlow 1.x!
+# tf.contrib = tensorflow.contrib
 
 from typing import Any, Iterable, List, Union
 

metrics/frechet_inception_distance.py

@@ -10,6 +10,8 @@
 import numpy as np
 import scipy
 import tensorflow as tf
+tf = tf.compat.v1
+
 import dnnlib.tflib as tflib
 
 from metrics import metric_base

metrics/inception_score.py

@@ -8,6 +8,8 @@
 
 import numpy as np
 import tensorflow as tf
+tf = tf.compat.v1
+
 import dnnlib.tflib as tflib
 
 from metrics import metric_base

metrics/linear_separability.py

@@ -10,6 +10,8 @@
 import numpy as np
 import sklearn.svm
 import tensorflow as tf
+tf = tf.compat.v1
+
 import dnnlib.tflib as tflib
 
 from metrics import metric_base

metrics/metric_base.py

@@ -11,6 +11,8 @@
 import hashlib
 import numpy as np
 import tensorflow as tf
+tf = tf.compat.v1
+
 import dnnlib
 import dnnlib.tflib as tflib
 

metrics/perceptual_path_length.py

@@ -8,6 +8,8 @@
 
 import numpy as np
 import tensorflow as tf
+tf = tf.compat.v1
+
 import dnnlib.tflib as tflib
 
 from metrics import metric_base

metrics/precision_recall.py

@@ -9,6 +9,8 @@
 import os
 import numpy as np
 import tensorflow as tf
+tf = tf.compat.v1
+
 import dnnlib
 import dnnlib.tflib as tflib
 

projector.py

@@ -6,6 +6,8 @@
 
 import numpy as np
 import tensorflow as tf
+tf = tf.compat.v1
+
 import dnnlib
 import dnnlib.tflib as tflib
 

training/dataset.py

@@ -10,6 +10,8 @@
 import glob
 import numpy as np
 import tensorflow as tf
+tf = tf.compat.v1
+
 import dnnlib
 import dnnlib.tflib as tflib
 

training/loss.py

@@ -8,6 +8,8 @@
 
 import numpy as np
 import tensorflow as tf
+tf = tf.compat.v1
+
 import dnnlib.tflib as tflib
 from dnnlib.tflib.autosummary import autosummary
 

training/networks_stylegan.py

@@ -8,6 +8,8 @@
 
 import numpy as np
 import tensorflow as tf
+tf = tf.compat.v1
+
 import dnnlib
 import dnnlib.tflib as tflib
 

training/networks_stylegan2.py

@@ -8,6 +8,8 @@
 
 import numpy as np
 import tensorflow as tf
+tf = tf.compat.v1
+
 import dnnlib
 import dnnlib.tflib as tflib
 from dnnlib.tflib.ops.upfirdn_2d import upsample_2d, downsample_2d, upsample_conv_2d, conv_downsample_2d

training/training_loop.py

@@ -8,6 +8,7 @@
 
 import numpy as np
 import tensorflow as tf
+tf = tf.compat.v1
 import dnnlib
 import dnnlib.tflib as tflib
 from dnnlib.tflib.autosummary import autosummary
@@ -240,11 +241,11 @@ def training_loop(
     Gs_update_op = Gs.setup_as_moving_average_of(G, beta=Gs_beta)
 
     # Finalize graph.
-    with tf.device('/gpu:0'):
-        try:
-            peak_gpu_mem_op = tf.contrib.memory_stats.MaxBytesInUse()
-        except tf.errors.NotFoundError:
-            peak_gpu_mem_op = tf.constant(0)
+    # with tf.device('/gpu:0'):
+    #     try:
+    #         peak_gpu_mem_op = tf.contrib.memory_stats.MaxBytesInUse()
+    #     except tf.errors.NotFoundError:
+    #         peak_gpu_mem_op = tf.constant(0)
     tflib.init_uninitialized_vars()
 
     print('Initializing logs...')
@@ -327,7 +328,7 @@ def training_loop(
                 autosummary('Timing/sec_per_tick', tick_time),
                 autosummary('Timing/sec_per_kimg', tick_time / tick_kimg),
                 autosummary('Timing/maintenance_sec', maintenance_time),
-                autosummary('Resources/peak_gpu_mem_gb', peak_gpu_mem_op.eval() / 2**30)))
+                # autosummary('Resources/peak_gpu_mem_gb', peak_gpu_mem_op.eval() / 2**30)))
             autosummary('Timing/total_hours', total_time / (60.0 * 60.0))
             autosummary('Timing/total_days', total_time / (24.0 * 60.0 * 60.0))