WIP. Fix dropout transformer

setup.py

@@ -29,7 +29,7 @@
         ]},
     install_requires=[
         'Jinja2',
-        'tensorflow',
+        'tensorflow==1.13.1',
         'idx2numpy',
         'attrs',
         'click',

tests/test_transformer/test_dropout/conftest.py

@@ -1,7 +1,10 @@
+from random import sample
+
 import numpy as np
 import pytest
 
 import tensorflow as tf
+from utensor_cgen.frontend.tensorflow import GraphDefParser
 from utensor_cgen.utils import random_str
 
 
@@ -28,3 +31,41 @@ def dropout_graph_tuple2():
         rate = tf.placeholder(dtype=tf.float32, name='rate')
         drop = tf.nn.dropout(x, rate=rate, name=random_str(10))
     return graph.as_graph_def(), [drop.op.name]
+
+
+@pytest.fixture(name='vgg_ugraph')
+def gen_vgg_graph():
+    graph = tf.Graph()
+    with graph.as_default():
+        x = tf.placeholder(dtype=tf.float32, shape=[None, 2048, 2048, 3], name='input_x')
+        rate = tf.placeholder(dtype=tf.float32, name='rate')
+        in_feat = x
+        num_layers = sample([3, 4, 5], 1)[0]
+        for i in range(1, num_layers+1):
+            ksize = sample([2, 3, 5], 1)[0]
+            in_channel = in_feat.shape.as_list()[-1]
+            out_channel = sample([3, 5, 10], 1)[0]
+            stride = sample([1, 2], 1)[0]
+            kernel = tf.constant(
+                np.random.rand(ksize, ksize, in_channel, out_channel),
+                dtype=tf.float32,
+                name='kernel_{}'.format(i)
+            )
+            in_feat = tf.nn.conv2d(
+                in_feat, 
+                kernel,
+                strides=[1, stride, stride, 1],
+                padding='VALID',
+                name='feat_map_{}'.format(i)
+            )
+            in_feat = tf.nn.relu(in_feat, name='relu_{}'.format(i))
+            in_feat = tf.nn.max_pool(
+                in_feat,
+                ksize=[1, ksize, ksize, 1],
+                strides=[1, stride, stride, 1],
+                name='pool_{}'.format(i),
+                padding='SAME',
+            )
+            if i != num_layers:
+                in_feat = tf.nn.dropout(in_feat, rate=rate, name='dropout_{}'.format(i))   
+    return GraphDefParser.parse(graph.as_graph_def(), output_nodes=[in_feat.op.name])

tests/test_transformer/test_dropout/test_dropout_transormer.py

@@ -42,3 +42,12 @@ def test_dropout_trans_2(dropout_graph_tuple2):
     new_ugraph = trans.transform(ugraph)
     assert len(new_ugraph.ops_info) == 1
     assert 'x' in new_ugraph.ops_info
+
+
+def test_dropout_vgg(vgg_ugraph):
+    trans = DropoutTransformer()
+    from utensor_cgen.ir.misc.graph_viz import viz_graph
+    viz_graph(trans.pattern_ugraph, 'dropout_pattern')
+    new_ugraph = trans.transform(vgg_ugraph)
+    for op_name in new_ugraph.ops_info:
+        assert not op_name.startswith('dropout')

utensor_cgen/backend/operators.py

@@ -21,7 +21,7 @@ class OperatorFactory():
   def createOperatorSnippet(self, op_info, **kwargs):
     op_type = op_info.op_type
     if op_type not in self._operators:
-      err_msg = "unsupported op type in uTensor: {}".format(op_type)
+      err_msg = "unsupported op type in uTensor: {op.name}, {op.op_type}".format(op=op_info)
       raise ValueError(err_msg)
 
     op = self._operators[op_type](op_info, **kwargs)  # Create desired object

utensor_cgen/ir/base.py

@@ -122,7 +122,7 @@ def move_into(self, ugraph):
     """
     self._ugraph = ugraph
 
-@attr.s(cmp=False)
+@attr.s(cmp=False, repr=False)
 class OperationInfo(IRBase, _NoShallowCopyMixin):
   """
   name : str
@@ -281,6 +281,8 @@ def __getitem__(self, tensor_idx):
       )
     return self.output_tensors[tensor_idx]
 
+  def __repr__(self):
+    return str((self.name, self.op_type))
 
 class MetaOperationInfo(OperationInfo):
 

utensor_cgen/logger.py

@@ -1,12 +1,13 @@
 #-*- coding: utf8 -*-
 import logging
+import os
 import sys
 
 __all__ = ['logger']
 
 
 logger = logging.getLogger(name='utensor-cli')
-logger.setLevel(logging.INFO)
+logger.setLevel(os.environ.get('UTENSOR_LOG_LEVEL', logging.INFO))
 _fmt = logging.Formatter(fmt='[%(levelname)s %(filename)s %(funcName)s @ %(lineno)s] %(message)s')
 _handler = logging.StreamHandler(sys.stdout)
 _handler.formatter = _fmt

utensor_cgen/matcher/_matcher_impl.py

@@ -8,6 +8,7 @@
 
 from utensor_cgen.ir import (MetaOperationInfo, OperationInfo, uTensorGraph,
                              uTensorGraphView)
+from utensor_cgen.logger import logger
 from utensor_cgen.matcher._morphism import Morphism
 from utensor_cgen.utils import (ops_bfs_queue, prune_graph, random_str,
                                 topologic_order_graph)
@@ -462,4 +463,4 @@ def is_done(self):
     a state is done, if
     1. the patrn_bfs_queue is empty
     """
-    return not self.patrn_bfs_queue
+    return not (self.patrn_bfs_queue and self.sub_bfs_queue)

utensor_cgen/transformer/ns_transformer.py

@@ -31,8 +31,6 @@ def transform(self, ugraph):
       elif op_type == 'BiasAdd':
         op_info = ugraph.ops_info[node_name]
         op_info.op_type = 'Add'
-
-
     return ugraph
 
 
@@ -59,12 +57,18 @@ class DropoutTransformer(Transformer):
   def pattern_ugraph(self):
     graph = tf.Graph()
     with graph.as_default():
-        dummy_x = tf.constant(np.random.rand(10), dtype=tf.float32, name='dummy_x')
-        dummy_rate = tf.constant(0.5, dtype=tf.float32, name='dummy_rate')
+        dummy_x = tf.constant(np.random.rand(10, 10), dtype=tf.float32, name='dummy_x')
+        dummy_rate = tf.placeholder(dtype=tf.float32, name='dummy_rate')
         dropout = tf.nn.dropout(dummy_x, rate=dummy_rate, name='dropout')
     patrn_ugraph = GraphDefParser.parse(graph.as_graph_def(), output_nodes=[dropout.op.name])
-    patrn_ugraph['dropout/truediv'].replace_with_null_input_tensor(0) # replace dummy_x
-    patrn_ugraph['dropout/sub'].replace_with_null_input_tensor(1) # replce dummy_rate
+    from utensor_cgen.ir.misc.graph_viz import viz_graph
+    viz_graph(patrn_ugraph, 'ori_pattrn')
+    # replace dummy_x
+    patrn_ugraph['dropout/truediv'].replace_with_null_input_tensor(0)
+    # # replace dummy_rate
+    patrn_ugraph['dropout/sub'].replace_with_null_input_tensor(1)
+    # # replace other tensors
+    patrn_ugraph['dropout/random_uniform/RandomUniform'].replace_with_null_input_tensor(0)
     patrn_ugraph = prune_graph(patrn_ugraph)
     topologic_order_graph(patrn_ugraph)
     return patrn_ugraph