[RELAY][FRONTEND] Tensorflow frontend. (apache#2216)

* [RELAY][FRONTEND] Tensorflow frontend support.

* 	* LSTM removed for a while.

* 	* basic ops are good.

* 	* nn wip

* 	* wip

* 	* python2.7 corrections.

* * NN ops are good.

* * e2e models working good

* 	* all good except LSTM

* 	* rebase, tutorials and CI trigger.

* 	* CI errors.

* 	* enable opt_level=3

* 	* Docstrings cleanup. testing.tf utils moved to relay from nnvm.

* 	* tutorials update.

* 	* LSTM work good now.

* 	* Rebase

* 	* CI error

* 	* enable PTB.

* 	* rebase.

* 	* tutorials

* Update python/tvm/relay/frontend/tensorflow.py

Co-Authored-By: srkreddy1238 <[email protected]>

* 	* review comments.

* 	CI fix.

* 	* review comments.

Author: srkreddy1238

docs/frontend/tensorflow.md

@@ -21,7 +21,7 @@ instructions to generate protobuf from checkpoint.
 
 ### Add Shapes:
 While freezing of protobuf add additional option ```add_shapes=True``` to embed output shapes of each node into graph.
-You may use ```nnvm.testing.tf.AddShapesToGraphDef``` from nnvm for the same.
+You may use ```tvm.relay.testing.tf.AddShapesToGraphDef``` from nnvm for the same.
 Please refer to [tensorflow tutorial](https://github.com/dmlc/tvm/blob/master/tutorials/nnvm/from_tensorflow.py).
 
 ### Explicit Shape:

nnvm/tests/python/frontend/tensorflow/test_forward.py

@@ -21,7 +21,7 @@
 from tensorflow.python.ops import init_ops
 from tensorflow.core.framework import graph_pb2
 
-import nnvm.testing.tf
+import tvm.relay.testing.tf as tf_testing
 
 #######################################################################
 # Generic run functions for TVM & tensorflow
@@ -784,9 +784,9 @@ def test_forward_pad():
 def test_forward_inception_v3():
     '''test inception V3 model'''
     with tf.Graph().as_default():
-        graph_def = nnvm.testing.tf.get_workload('InceptionV3/inception_v3_2016_08_28_frozen-with_shapes.pb')
+        graph_def = tf_testing.get_workload('InceptionV3/inception_v3_2016_08_28_frozen-with_shapes.pb')
         # Call the utility to import the graph definition into default graph.
-        graph_def = nnvm.testing.tf.ProcessGraphDefParam(graph_def)
+        graph_def = tf_testing.ProcessGraphDefParam(graph_def)
 
         data = np.random.uniform(size=(1, 299, 299, 3)).astype('float32')
 
@@ -801,9 +801,9 @@ def test_forward_inception_v3():
 def test_forward_inception_v1():
     '''test inception V1 model'''
     with tf.Graph().as_default():
-        graph_def = nnvm.testing.tf.get_workload("InceptionV1/classify_image_graph_def-with_shapes.pb")
+        graph_def = tf_testing.get_workload("InceptionV1/classify_image_graph_def-with_shapes.pb")
         # Call the utility to import the graph definition into default graph.
-        graph_def = nnvm.testing.tf.ProcessGraphDefParam(graph_def)
+        graph_def = tf_testing.ProcessGraphDefParam(graph_def)
 
         # Build an image from random data.
         from PIL import Image
@@ -838,18 +838,18 @@ def test_forward_mobilenet():
     '''test mobilenet model'''
     # MobilenetV2
     with tf.Graph().as_default():
-        graph_def = nnvm.testing.tf.get_workload(
+        graph_def = tf_testing.get_workload(
             "https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.4_224.tgz",
             "mobilenet_v2_1.4_224_frozen.pb")
         # Call the utility to import the graph definition into default graph.
-        graph_def = nnvm.testing.tf.ProcessGraphDefParam(graph_def)
+        graph_def = tf_testing.ProcessGraphDefParam(graph_def)
 
         data = np.random.uniform(size=(1, 224, 224, 3)).astype('float32')
         out_node = 'MobilenetV2/Predictions/Reshape_1'
 
         with tf.Session() as sess:
             # Add shapes to the graph.
-            graph_def = nnvm.testing.tf.AddShapesToGraphDef(sess, out_node)
+            graph_def = tf_testing.AddShapesToGraphDef(sess, out_node)
             tf_output = run_tf_graph(sess, data, 'input:0', out_node + ':0')
             tvm_output = run_tvm_graph(graph_def, data, 'input')
             tvm.testing.assert_allclose(np.squeeze(tvm_output[0]), np.squeeze(tf_output[0]), rtol=1e-5, atol=1e-5)
@@ -861,9 +861,9 @@ def test_forward_resnetv2():
     '''test resnet model'''
     if is_gpu_available():
         with tf.Graph().as_default():
-            graph_def = nnvm.testing.tf.get_workload("ResnetV2/resnet-20180601_resnet_v2_imagenet-shapes.pb")
+            graph_def = tf_testing.get_workload("ResnetV2/resnet-20180601_resnet_v2_imagenet-shapes.pb")
             # Call the utility to import the graph definition into default graph.
-            graph_def = nnvm.testing.tf.ProcessGraphDefParam(graph_def)
+            graph_def = tf_testing.ProcessGraphDefParam(graph_def)
 
             data = np.random.uniform(size=(128, 224, 224, 3)).astype('float32')
             out_node = 'ArgMax'
@@ -879,7 +879,7 @@ def test_forward_resnetv2():
 dir(tf.contrib)
 def test_forward_ptb():
     '''test ptb model'''
-    config = nnvm.testing.tf.get_config()
+    config = tf_testing.get_config()
     num_steps = config.num_steps
     num_hidden = config.hidden_size
     num_layers = config.num_layers
@@ -936,7 +936,7 @@ def _get_sample(data, state):
                                                       "float32")).asnumpy()
             state_output = model.get_output(1, tvm.nd.empty(out_state_shape,
                                                         "float32")).asnumpy()
-            sample = nnvm.testing.tf.pick_from_weight(tvm_output[0])
+            sample = tf_testing.pick_from_weight(tvm_output[0])
 
             return sample, state_output
 
@@ -956,10 +956,10 @@ def _get_sample(data, state):
         return samples, state
 
     with tf.Graph().as_default():
-        word_to_id, id_to_word, graph_def = nnvm.testing.tf.get_workload_ptb()
+        word_to_id, id_to_word, graph_def = tf_testing.get_workload_ptb()
         vocab_size = len(word_to_id)
         # Call the utility to import the graph definition into default graph.
-        graph_def = nnvm.testing.tf.ProcessGraphDefParam(graph_def)
+        graph_def = tf_testing.ProcessGraphDefParam(graph_def)
         sess = tf.Session()
 
     #TVM graph module creation
@@ -975,7 +975,7 @@ def _get_sample(data, state):
                                                     for word in seed_for_sample],
                                                 in_state, params, cnt_sample)
         tvm_sample_str = _pretty_print(tvm_samples, False, id_to_word)
-        tf_samples, tf_state = nnvm.testing.tf.do_tf_sample(sess,
+        tf_samples, tf_state = tf_testing.do_tf_sample(sess,
                                 [word_to_id[word] for word in seed_for_sample],
                                 in_state, cnt_sample)
         tf_sample_str = _pretty_print(tf_samples, False, id_to_word)

python/tvm/relay/frontend/__init__.py

@@ -13,3 +13,4 @@
 from .tflite import from_tflite
 from .coreml import from_coreml
 from .caffe2 import from_caffe2
+from .tensorflow import from_tensorflow