[Relay][Frontend] CoreML Support

docker/install/ubuntu_install_python_package.sh

@@ -1,3 +1,3 @@
 # install libraries for python package on ubuntu
-pip2 install nose pylint numpy nose-timer cython decorator scipy tornado typing antlr4-python2-runtime attrs
-pip3 install nose pylint numpy nose-timer cython decorator scipy tornado typed_ast pytest mypy orderedset antlr4-python3-runtime attrs
+pip2 install nose pylint six numpy nose-timer cython decorator scipy tornado typing antlr4-python2-runtime attrs
+pip3 install nose pylint six numpy nose-timer cython decorator scipy tornado typed_ast pytest mypy orderedset antlr4-python3-runtime attrs

nnvm/python/nnvm/frontend/coreml.py

@@ -68,14 +68,15 @@ def ConvolutionLayerParams(op, insym, symtab):
     else:
         pos = [insym, weights]
 
-    if op.isDeconvolution:
-        ret = _sym.conv2d_transpose(*pos, **params)
-    else:
-        ret = _sym.conv2d(*pos, **params)
     # consume padding layer
     if symtab.in_padding:
         params['padding'] = [sum(x) for x in zip(params.get('padding', [0, 0]), symtab.paddings)]
         symtab.clear_padding()
+
+    if op.isDeconvolution:
+        ret = _sym.conv2d_transpose(*pos, **params)
+    else:
+        ret = _sym.conv2d(*pos, **params)
     return ret
 
 def BatchnormLayerParams(op, insym, symtab):

nnvm/tests/python/frontend/coreml/model_zoo/__init__.py

@@ -1,4 +1,4 @@
-import urllib
+from six.moves import urllib
 import os
 from PIL import Image
 import numpy as np
@@ -7,7 +7,7 @@ def download(url, path, overwrite=False):
     if os.path.exists(path) and not overwrite:
         return
     print('Downloading {} to {}.'.format(url, path))
-    urllib.URLopener().retrieve(url, path)
+    urllib.request.urlretrieve(url, path)
 
 def get_mobilenet():
     url = 'https://docs-assets.developer.apple.com/coreml/models/MobileNet.mlmodel'

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

@@ -15,9 +15,9 @@
 import model_zoo
 
 def get_tvm_output(symbol, x, params, target, ctx,
-                   out_shape=(1000,), input_name='image', dtype='float32'):
+                   out_shape=(1, 1000), input_name='image', dtype='float32'):
     shape_dict = {input_name : x.shape}
-    with nnvm.compiler.build_config(opt_level=3):
+    with nnvm.compiler.build_config(opt_level=2):
         graph, lib, params = nnvm.compiler.build(symbol, target, shape_dict, params=params)
     m = graph_runtime.create(graph, lib, ctx)
     # set inputs
@@ -28,7 +28,7 @@ def get_tvm_output(symbol, x, params, target, ctx,
     out = m.get_output(0, tvm.nd.empty(out_shape, dtype))
     return out.asnumpy()
 
-def test_model_checkonly(model_file, model_name=''):
+def run_model_checkonly(model_file, model_name=''):
     model = cm.models.MLModel(model_file)
     sym, params = nnvm.frontend.from_coreml(model)
     x = model_zoo.get_cat_image()
@@ -38,11 +38,11 @@ def test_model_checkonly(model_file, model_name=''):
 
 def test_mobilenet_checkonly():
     model_file = model_zoo.get_mobilenet()
-    test_model_checkonly(model_file, 'mobilenet')
+    run_model_checkonly(model_file, 'mobilenet')
 
 def test_resnet50_checkonly():
     model_file = model_zoo.get_resnet50()
-    test_model_checkonly(model_file, 'resnet50')
+    run_model_checkonly(model_file, 'resnet50')
 
 def run_tvm_graph(graph_def, input_data, input_name, output_shape, output_dtype='float32'):
     """ Generic function to compile on nnvm and execute on tvm """

python/tvm/relay/expr.py

@@ -231,7 +231,7 @@ def __init__(self,
             _make.Function, params, body, ret_type, type_params, attrs)
 
     def __call__(self, *args):
-        """Invoke the gobal function.
+        """Invoke the global function.
 
         Parameters
         ----------

python/tvm/relay/frontend/__init__.py

@@ -11,3 +11,4 @@
 from .keras import from_keras
 from .onnx import from_onnx
 from .tflite import from_tflite
+from .coreml import from_coreml

python/tvm/relay/frontend/common.py

@@ -240,6 +240,7 @@ def __init__(self):
         self.exprs = {}
         self.params = {}
         self.const_ctr = 1
+        self.in_padding = False
 
     def new_const(self, value, shape=None, dtype="float32"):
         name = "_param_%d" % (self.const_ctr)
@@ -257,6 +258,13 @@ def set_expr(self, name, expr):
         assert isinstance(expr, _expr.Expr)
         self.exprs[name] = expr
 
+    def set_padding(self, paddings):
+        self.paddings = paddings
+        self.in_padding = True
+
+    def clear_padding(self):
+        self.in_padding = False
+
 
 class AttrCvt(object):
     """Common attribute conveter. An AttrConverter instance is a callable: