[RELAY][FRONTEND] Tensorflow frontend. (#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 <sivar.b@huawei.com>

* 	* review comments.

* 	CI fix.

* 	* review comments.

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 variables
 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 test_forward_ptb():
                                                       "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 test_forward_ptb():
         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 test_forward_ptb():
                                                     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 .onnx import from_onnx
 from .tflite import from_tflite
 from .coreml import from_coreml
 from .caffe2 import from_caffe2
+from .tensorflow import from_tensorflow

tests/python/frontend/tflite/test_forward.py

@@ -16,7 +16,7 @@ from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import variables
 from tensorflow.contrib.lite.python import interpreter as interpreter_wrapper
 
-import nnvm.testing.tf
+import tvm.relay.testing.tf as tf_testing
 
 #######################################################################
 # Generic run functions for TVM & TFLite
@@ -344,7 +344,7 @@ def test_forward_mobilenet():
     '''test mobilenet v1 tflite model'''
     # MobilenetV1
     temp = util.tempdir()
-    tflite_model_file = nnvm.testing.tf.get_workload_official(
+    tflite_model_file = tf_testing.get_workload_official(
         "http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224.tgz",
         "mobilenet_v1_1.0_224.tflite", temp)
     tflite_model_buf = open(tflite_model_file, "rb").read()

tests/scripts/task_python_frontend.sh

@@ -42,6 +42,9 @@ python3 -m nose -v tests/python/frontend/onnx || exit -1
 echo "Running relay CoreML frondend test..."
 python3 -m nose -v tests/python/frontend/coreml || exit -1
 
+echo "Running relay Tensorflow frontend test..."
+python3 -m nose -v tests/python/frontend/tensorflow || exit -1
+
 echo "Running nnvm to relay frontend test..."
 python3 -m nose -v tests/python/frontend/nnvm_to_relay || exit -1
 
@@ -50,4 +53,3 @@ python3 -m nose -v tests/python/frontend/tflite || exit -1
 
 echo "Running relay caffe2 frondend test..."
 python3 -m nose -v tests/python/frontend/caffe2 || exit -1
-

tutorials/frontend/from_tensorflow.py

+"""
+Compile Tensorflow Models
+=========================
+This article is an introductory tutorial to deploy tensorflow models with TVM.
+
+For us to begin with, tensorflow python module is required to be installed.
+
+Please refer to https://www.tensorflow.org/install
+"""
+
+# tvm, relay
+import tvm
+from tvm import relay
+
+# os and numpy
+import numpy as np
+import os.path
+
+# Tensorflow imports
+import tensorflow as tf
+
+# Tensorflow utility functions
+import tvm.relay.testing.tf as tf_testing
+
+# Base location for model related files.
+repo_base = 'https://github.com/dmlc/web-data/raw/master/tensorflow/models/InceptionV1/'
+
+# Test image
+img_name = 'elephant-299.jpg'
+image_url = os.path.join(repo_base, img_name)
+
+######################################################################
+# Tutorials
+# ---------
+# Please refer docs/frontend/tensorflow.md for more details for various models
+# from tensorflow.
+
+model_name = 'classify_image_graph_def-with_shapes.pb'
+model_url = os.path.join(repo_base, model_name)
+
+# Image label map
+map_proto = 'imagenet_2012_challenge_label_map_proto.pbtxt'
+map_proto_url = os.path.join(repo_base, map_proto)
+
+# Human readable text for labels
+lable_map = 'imagenet_synset_to_human_label_map.txt'
+lable_map_url = os.path.join(repo_base, lable_map)
+
+# Target settings
+# Use these commented settings to build for cuda.
+#target = 'cuda'
+#target_host = 'llvm'
+#layout = "NCHW"
+#ctx = tvm.gpu(0)
+target = 'llvm'
+target_host = 'llvm'
+layout = None
+ctx = tvm.cpu(0)
+
+######################################################################
+# Download required files
+# -----------------------
+# Download files listed above.
+from mxnet.gluon.utils import download
+
+download(image_url, img_name)
+download(model_url, model_name)
+download(map_proto_url, map_proto)
+download(lable_map_url, lable_map)
+
+######################################################################
+# Import model
+# ------------
+# Creates tensorflow graph definition from protobuf file.
+
+with tf.gfile.FastGFile(os.path.join("./", model_name), 'rb') as f:
+    graph_def = tf.GraphDef()
+    graph_def.ParseFromString(f.read())
+    graph = tf.import_graph_def(graph_def, name='')
+    # Call the utility to import the graph definition into default graph.
+    graph_def = tf_testing.ProcessGraphDefParam(graph_def)
+    # Add shapes to the graph.
+    with tf.Session() as sess:
+        graph_def = tf_testing.AddShapesToGraphDef(sess, 'softmax')
+
+######################################################################
+# Decode image
+# ------------
+# .. note::
+#
+#   tensorflow frontend import doesn't support preprocessing ops like JpegDecode.
+#   JpegDecode is bypassed (just return source node).
+#   Hence we supply decoded frame to TVM instead.
+#
+
+from PIL import Image
+image = Image.open(img_name).resize((299, 299))
+
+x = np.array(image)
+
+######################################################################
+# Import the graph to Relay
+# -------------------------
+# Import tensorflow graph definition to relay frontend.
+#
+# Results:
+#   sym: relay expr for given tensorflow protobuf.
+#   params: params converted from tensorflow params (tensor protobuf).
+shape_dict = {'DecodeJpeg/contents': x.shape}
+dtype_dict = {'DecodeJpeg/contents': 'uint8'}
+sym, params = relay.frontend.from_tensorflow(graph_def, layout=layout, shape=shape_dict)
+
+print ("Tensorflow protobuf imported to relay frontend.")
+######################################################################
+# Relay Build
+# -----------
+# Compile the graph to llvm target with given input specification.
+#
+# Results:
+#   graph: Final graph after compilation.
+#   params: final params after compilation.
+#   lib: target library which can be deployed on target with tvm runtime.
+
+with relay.build_config(opt_level=3):
+    graph, lib, params = relay.build(sym, target=target, target_host=target_host, params=params)
+
+######################################################################
+# Execute the portable graph on TVM
+# ---------------------------------
+# Now we can try deploying the compiled model on target.
+
+from tvm.contrib import graph_runtime
+dtype = 'uint8'
+m = graph_runtime.create(graph, lib, ctx)
+# set inputs
+m.set_input('DecodeJpeg/contents', tvm.nd.array(x.astype(dtype)))
+m.set_input(**params)
+# execute
+m.run()
+# get outputs
+tvm_output = m.get_output(0, tvm.nd.empty(((1, 1008)), 'float32'))
+
+######################################################################
+# Process the output
+# ------------------
+# Process the model output to human readable text for InceptionV1.
+predictions = tvm_output.asnumpy()
+predictions = np.squeeze(predictions)
+
+# Creates node ID --> English string lookup.
+node_lookup = tf_testing.NodeLookup(label_lookup_path=os.path.join("./", map_proto),
+                                         uid_lookup_path=os.path.join("./", lable_map))
+
+# Print top 5 predictions from TVM output.
+top_k = predictions.argsort()[-5:][::-1]
+for node_id in top_k:
+    human_string = node_lookup.id_to_string(node_id)
+    score = predictions[node_id]
+    print('%s (score = %.5f)' % (human_string, score))
+
+######################################################################
+# Inference on tensorflow
+# -----------------------
+# Run the corresponding model on tensorflow
+
+def create_graph():
+    """Creates a graph from saved GraphDef file and returns a saver."""
+    # Creates graph from saved graph_def.pb.
+    with tf.gfile.FastGFile(model_name, 'rb') as f:
+        graph_def = tf.GraphDef()
+        graph_def.ParseFromString(f.read())
+        graph = tf.import_graph_def(graph_def, name='')
+        # Call the utility to import the graph definition into default graph.
+        graph_def = tf_testing.ProcessGraphDefParam(graph_def)
+
+def run_inference_on_image(image):
+    """Runs inference on an image.
+
+    Parameters
+    ----------
+    image: String
+        Image file name.
+
+    Returns
+    -------
+        Nothing
+    """
+    if not tf.gfile.Exists(image):
+        tf.logging.fatal('File does not exist %s', image)
+    image_data = tf.gfile.FastGFile(image, 'rb').read()
+
+    # Creates graph from saved GraphDef.
+    create_graph()
+
+    with tf.Session() as sess:
+        softmax_tensor = sess.graph.get_tensor_by_name('softmax:0')
+        predictions = sess.run(softmax_tensor,
+                               {'DecodeJpeg/contents:0': image_data})
+
+        predictions = np.squeeze(predictions)
+
+        # Creates node ID --> English string lookup.
+        node_lookup = tf_testing.NodeLookup(label_lookup_path=os.path.join("./", map_proto),
+                                                 uid_lookup_path=os.path.join("./", lable_map))
+
+        # Print top 5 predictions from tensorflow.
+        top_k = predictions.argsort()[-5:][::-1]
+        print ("===== TENSORFLOW RESULTS =======")
+        for node_id in top_k:
+            human_string = node_lookup.id_to_string(node_id)
+            score = predictions[node_id]
+            print('%s (score = %.5f)' % (human_string, score))
+
+run_inference_on_image (img_name)

tutorials/nnvm/from_tensorflow.py

@@ -23,7 +23,7 @@ from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import tensor_util
 
 # Tensorflow utility functions
-import nnvm.testing.tf
+import tvm.relay.testing.tf as tf_testing
 
 # Base location for model related files.
 repo_base = 'https://github.com/dmlc/web-data/raw/master/tensorflow/models/InceptionV1/'
@@ -87,10 +87,10 @@ with tf.gfile.FastGFile(os.path.join("./", model_name), 'rb') as f:
     graph_def.ParseFromString(f.read())
     graph = tf.import_graph_def(graph_def, name='')
     # 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)
     # Add shapes to the graph.
     with tf.Session() as sess:
-        graph_def = nnvm.testing.tf.AddShapesToGraphDef(sess, 'softmax')
+        graph_def = tf_testing.AddShapesToGraphDef(sess, 'softmax')
 
 ######################################################################
 # Decode image
@@ -157,7 +157,7 @@ predictions = tvm_output.asnumpy()
 predictions = np.squeeze(predictions)
 
 # Creates node ID --> English string lookup.
-node_lookup = nnvm.testing.tf.NodeLookup(label_lookup_path=os.path.join("./", map_proto),
+node_lookup = tf_testing.NodeLookup(label_lookup_path=os.path.join("./", map_proto),
                                          uid_lookup_path=os.path.join("./", lable_map))
 
 # Print top 5 predictions from TVM output.
@@ -180,7 +180,7 @@ def create_graph():
         graph_def.ParseFromString(f.read())
         graph = tf.import_graph_def(graph_def, name='')
         # 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)
 
 def run_inference_on_image(image):
     """Runs inference on an image.
@@ -209,7 +209,7 @@ def run_inference_on_image(image):
         predictions = np.squeeze(predictions)
 
         # Creates node ID --> English string lookup.
-        node_lookup = nnvm.testing.tf.NodeLookup(label_lookup_path=os.path.join("./", map_proto),
+        node_lookup = tf_testing.NodeLookup(label_lookup_path=os.path.join("./", map_proto),
                                                  uid_lookup_path=os.path.join("./", lable_map))
 
         # Print top 5 predictions from tensorflow.