[NNVM][TENSORFLOW] bug fix on bilinear and resize op integration in frontend.

nnvm/python/nnvm/frontend/tensorflow.py

@@ -332,26 +332,14 @@ def _impl(inputs, attr, params):
 
 def _resize_bilinear():
     def _impl(inputs, attr, params):
-        # Change this when we have corresponding resize bilinear operation.
-        print("ResizeBilinear:Only NN (nearest neighbor) supported in symetric mode of dimensions")
-        print("Change this when we have corresponding resize bilinear operation")
-
-        # NHWC
-        input_shape = attr['_input_shapes'][inputs[0]][0]
-        in_hw = (input_shape[1], input_shape[2])
-        out_hw = params[inputs[1].list_output_names()[0]]
+        attr['size'] = attr['_output_shapes'][0][1:3]
         inputs.pop(1)
+        # NHWC
         attr['layout'] = 'NHWC'
 
-        if in_hw[0] < 0 or in_hw[1] < 0:
-            scale = 1
-        else:
-            # Considering height alone for scale
-            scale = out_hw[0] / in_hw[0]
-
-        return AttrCvt(op_name="upsampling",
-                       ignores=['Tdim', 'align_corners'],
-                       extras={'scale': scale})(inputs, attr)
+        return AttrCvt(op_name="resize",
+                       ignores=['Tdim'],
+                       extras={'method': "BILINEAR"})(inputs, attr)
     return _impl
 
 def _check_numerics():

nnvm/python/nnvm/testing/tf.py

@@ -6,7 +6,6 @@
 """
 import re
 import os.path
-import numpy as np
 
 # Tensorflow imports
 import tensorflow as tf
@@ -107,52 +106,6 @@ def id_to_string(self, node_id):
             return ''
         return self.node_lookup[node_id]
 
-def read_normalized_tensor_from_image_file(file_name,
-                                           input_height=299,
-                                           input_width=299,
-                                           input_mean=0,
-                                           input_std=255):
-    """ Preprocessing of image
-    Parameters
-    ----------
-
-    file_name: String
-        Image filename.
-
-    input_height: int
-        model input height.
-
-    input_width: int
-        model input width
-
-    input_mean: int
-        Mean to be substracted in normalization.
-
-    input_std: int
-        Standard deviation used in normalization.
-
-    Returns
-    -------
-
-    np_array: Numpy array
-        Normalized image data as a numpy array.
-
-    """
-
-    input_name = "file_reader"
-    output_name = "normalized"
-    file_reader = tf.read_file(file_name, input_name)
-
-    image_reader = tf.image.decode_jpeg(file_reader, channels=3,
-                                        name='jpeg_reader')
-    float_caster = tf.cast(image_reader, tf.float32)
-    dims_expander = tf.expand_dims(float_caster, 0)
-    resized = tf.image.resize_bilinear(dims_expander, [input_height, input_width])
-    normalized = tf.divide(tf.subtract(resized, [input_mean]), [input_std])
-    tf.InteractiveSession()
-    np_array = normalized.eval()
-    return np_array
-
 def get_workload(model_path):
     """ Import workload from frozen protobuf
 
@@ -181,79 +134,3 @@ def get_workload(model_path):
         graph_def.ParseFromString(f.read())
         graph = tf.import_graph_def(graph_def, name='')
         return graph_def
-
-def get_workload_inception_v3():
-    """ Import Inception V3 workload from frozen protobuf
-
-    Parameters
-    ----------
-        Nothing.
-
-    Returns
-    -------
-    (normalized, graph_def) : Tuple
-        normalized is normalized input for graph testing.
-        graph_def is the tensorflow workload for Inception V3.
-    """
-
-    repo_base = 'https://github.com/dmlc/web-data/raw/master/tensorflow/models/InceptionV3/'
-    model_path = 'InceptionV3/inception_v3_2016_08_28_frozen-with_shapes.pb'
-
-    image_name = 'elephant-299.jpg'
-    image_url = os.path.join(repo_base, image_name)
-    from mxnet.gluon.utils import download
-    download(image_url, image_name)
-    normalized = read_normalized_tensor_from_image_file(os.path.join("./", image_name))
-
-    return (normalized, get_workload(model_path))
-
-def get_workload_inception_v1():
-    """ Import Inception V1 workload from frozen protobuf
-
-    Parameters
-    ----------
-        Nothing.
-
-    Returns
-    -------
-    (image_data, tvm_data, graph_def) : Tuple
-        image_data is raw encoded image data for TF input.
-        tvm_data is the decoded image data for TVM input.
-        graph_def is the tensorflow workload for Inception V1.
-
-    """
-
-    repo_base = 'https://github.com/dmlc/web-data/raw/master/tensorflow/models/InceptionV1/'
-    model_path = 'InceptionV1/classify_image_graph_def-with_shapes.pb'
-    image_name = 'elephant-299.jpg'
-    image_url = os.path.join(repo_base, image_name)
-
-    from mxnet.gluon.utils import download
-    download(image_url, image_name)
-
-    if not tf.gfile.Exists(os.path.join("./", image_name)):
-        tf.logging.fatal('File does not exist %s', image)
-    image_data = tf.gfile.FastGFile(os.path.join("./", image_name), 'rb').read()
-
-    # TVM doesn't handle decode, hence decode it.
-    from PIL import Image
-    tvm_data = Image.open(os.path.join("./", image_name)).resize((299, 299))
-    tvm_data = np.array(tvm_data)
-
-    return (image_data, tvm_data, get_workload(model_path))
-
-def get_workload_mobilenet():
-    """ Import mobilenet workload from frozen protobuf
-
-    Parameters
-    ----------
-        Nothing.
-
-    Returns
-    -------
-    graph_def: graphdef
-        graph_def is the tensorflow workload for mobilenet.
-
-    """
-
-    return get_workload("MobilenetV1/mobilenet_v1_1.0_224_frozen-with-shapes.pb")

nnvm/src/top/image/resize.cc

@@ -40,13 +40,9 @@ inline bool ResizeInferShape(const nnvm::NodeAttrs& attrs,
   dshape = ConvertLayout(dshape, param.layout, kNCHW);
 
   TShape oshape = dshape;
-  if (param.layout == "NCHW") {
-    oshape[2] = param.size[0];
-    oshape[3] = param.size[1];
-  } else {
-    oshape[1] = param.size[0];
-    oshape[2] = param.size[1];
-  }
+  oshape[2] = param.size[0];
+  oshape[3] = param.size[1];
+
   oshape = ConvertLayout(oshape, kNCHW, param.layout);
   NNVM_ASSIGN_OUTPUT_SHAPE(attrs, *out_shape, 0, oshape);
 

nnvm/tests/python/compiler/test_top_level2.py

@@ -300,11 +300,10 @@ def test_upsampling_bilinear():
 
 def test_resize_bilinear():
     x = sym.Variable("x")
-    scale = 2
-    y = sym.upsampling(x, scale=scale, method="BILINEAR", name="y", layout="NHWC")
+    y = sym.resize(x, size=(60, 60), method="BILINEAR", name="y", layout="NHWC")
     dtype = "float32"
     dshape = (1, 32, 32, 4)
-    oshape = (1, 32*scale, 32*scale, 4)
+    oshape = (1, 60, 60, 4)
     shape_dict = {"x": dshape}
     dtype_dict = {"x": dtype}
     for target, ctx in ctx_list():
@@ -314,7 +313,7 @@ def test_resize_bilinear():
         data = tvm.nd.array(a_np)
         m.run(x=data)
         out = m.get_output(0, tvm.nd.empty(oshape, dtype))
-        b_np = topi.testing.bilinear_resize_python(a_np, (32*scale, 32*scale), "NHWC")
+        b_np = topi.testing.bilinear_resize_python(a_np, (60, 60), "NHWC")
         np.testing.assert_allclose(out.asnumpy(), b_np, rtol=1e-5, atol=1e-5)
 
 if __name__ == "__main__":

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

@@ -469,49 +469,109 @@ def test_forward_multi_input():
 
             sess.close()
 
+#######################################################################
+# Resize Bilinear
+# ---------------
+
+def _test_resize_bilinear(in_shape, to_shape, align_corners):
+    """ One iteration of resize bilinear """
+
+    data = np.random.uniform(size=in_shape).astype('float32')
+    shape_data = np.array(to_shape).astype('int32')
+
+    with tf.Graph().as_default():
+        in_data = constant_op.constant(data, shape=data.shape, dtype=data.dtype)
+        shape_data = constant_op.constant(shape_data, shape=shape_data.shape, dtype=shape_data.dtype)
+
+        # pylint: disable=unused-variable
+        resize_out = tf.image.resize_bilinear(in_data, shape_data, align_corners=align_corners)
+        # pylint: enable=unused-variable
+
+        with tf.Session() as sess:
+            graph_def = tf.graph_util.convert_variables_to_constants(
+                sess,
+                sess.graph.as_graph_def(add_shapes=True),
+                ['ResizeBilinear'],
+                )
+
+            tf_output = run_tf_graph(sess, data,
+                    'Const:0', 'ResizeBilinear:0')
+
+            tvm_output = run_tvm_graph(graph_def,
+                                       data,
+                                       "Const", tf_output.shape, data.dtype)
+
+            np.testing.assert_allclose(tf_output, tvm_output, atol=1e-3, rtol=1e-3)
+
+            sess.close()
+
+def test_forward_resize_bilinear():
+    """ Resize Bilinear """
+
+    _test_resize_bilinear((4, 16, 32, 32), [50, 50], False)
+    _test_resize_bilinear((6, 32, 64, 64), [20, 20], True)
+
+
 #######################################################################
 # Inception V3
 # ------------
 def test_forward_inception_v3():
     '''test inception V3 model'''
     with tf.Graph().as_default():
-        (data, graph_def) = nnvm.testing.tf.get_workload_inception_v3()
+        graph_def = nnvm.testing.tf.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)
 
-        tvm_output = run_tvm_graph(graph_def, data, 'input', (1, 1001), 'float32')
+        data = np.random.uniform(size=(1, 299, 299, 3)).astype('float32')
+
         with tf.Session() as sess:
             tf_output = run_tf_graph(sess, data, 'input:0', 'InceptionV3/Predictions/Reshape_1:0')
-
-            top_tvm = np.squeeze(tvm_output).argsort()[-3:][::-1]
-            top_tf = np.squeeze(tf_output).argsort()[-3:][::-1]
-
-            np.testing.assert_allclose(top_tf, top_tvm, rtol=1e-5, atol=1e-5)
+            tvm_output = run_tvm_graph(graph_def, data, 'input', tf_output.shape, 'float32')
+            np.testing.assert_allclose(tf_output, tvm_output, rtol=1e-5, atol=1e-5)
 
 #######################################################################
 # Inception V1
 # ------------
 def test_forward_inception_v1():
     '''test inception V1 model'''
     with tf.Graph().as_default():
-        (data, tvm_data, graph_def) = nnvm.testing.tf.get_workload_inception_v1()
+        graph_def = nnvm.testing.tf.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)
 
-        tvm_output = run_tvm_graph(graph_def, tvm_data, 'DecodeJpeg/contents', (1, 1008), 'float32')
+        # Build an image from random data.
+        from PIL import Image
+        from tvm.contrib import util
+
+        img_array = np.random.uniform(size=(1, 600, 600, 3)).astype("uint8")
+        img = Image.frombuffer('RGB', (600, 600), img_array.tostring(), 'raw', 'RGB', 0, 1)
+        temp = util.tempdir()
+        img_path = temp.relpath("tf-test.jpg")
+        img.save(img_path);
+
+        import os.path
+        if not tf.gfile.Exists(os.path.join(img_path)):
+            tf.logging.fatal('File does not exist %s', image)
+        data = tf.gfile.FastGFile(os.path.join(img_path), 'rb').read()
+
+        temp.remove()
 
+        # Extract tensorflow decoded image frame for tvm input
         with tf.Session() as sess:
-            tf_output = run_tf_graph(sess, data, 'DecodeJpeg/contents:0', 'softmax:0')
+            tvm_data = run_tf_graph(sess, data, 'DecodeJpeg/contents:0', 'DecodeJpeg:0')
 
-        np.testing.assert_allclose(tf_output, tvm_output, rtol=2e-2, atol=2e-2)
+        with tf.Session() as sess:
+            tf_output = run_tf_graph(sess, data, 'DecodeJpeg/contents:0', 'softmax:0')
+            tvm_output = run_tvm_graph(graph_def, tvm_data, 'DecodeJpeg/contents', tf_output.shape, 'float32')
+            np.testing.assert_allclose(tf_output, tvm_output, rtol=1e-5, atol=1e-5)
 
 #######################################################################
 # Mobilenet
 # ---------
 def test_forward_mobilenet():
     '''test mobilenet model'''
     with tf.Graph().as_default():
-        graph_def = nnvm.testing.tf.get_workload_mobilenet()
+        graph_def = nnvm.testing.tf.get_workload("MobilenetV1/mobilenet_v1_1.0_224_frozen-with-shapes.pb")
         # Call the utility to import the graph definition into default graph.
         graph_def = nnvm.testing.tf.ProcessGraphDefParam(graph_def)
 
@@ -520,12 +580,7 @@ def test_forward_mobilenet():
 
         with tf.Session() as sess:
             tf_output = run_tf_graph(sess, data, 'input:0', out_node + ':0')
-
-            out_shape = tf_output.shape
-            tvm_output = run_tvm_graph(graph_def, data, 'input', out_shape, 'float32')
-            top_tvm = np.squeeze(tvm_output).argsort()[-10:][::-1]
-            top_tf = np.squeeze(tf_output).argsort()[-10:][::-1]
-
+            tvm_output = run_tvm_graph(graph_def, data, 'input', tf_output.shape, 'float32')
             np.testing.assert_allclose(np.squeeze(tvm_output), np.squeeze(tf_output), rtol=1e-5, atol=1e-5)
 
 #######################################################################
@@ -544,3 +599,4 @@ def test_forward_mobilenet():
     test_forward_inception_v1()
     test_forward_mobilenet()
     test_forward_variable()
+    test_forward_resize_bilinear()