CI test file updated & CI error fixed

nnvm/python/nnvm/testing/__init__.py

@@ -13,5 +13,4 @@
 from . import inception_v3
 from . import dcgan
 from . import dqn
-from . import yolo_detection
 from . import check_computation

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

@@ -27,8 +27,8 @@
 from tvm.contrib.download import download_testdata
 download_testdata.__test__ = False
 from nnvm import frontend
-from nnvm.testing.darknet import LAYERTYPE
-from nnvm.testing.darknet import __darknetffi__
+from tvm.relay.testing.darknet import LAYERTYPE
+from tvm.relay.testing.darknet import __darknetffi__
 import nnvm.compiler
 
 DARKNET_LIB = 'libdarknet2.0.so'

nnvm/tutorials/from_darknet.py

@@ -33,16 +33,16 @@
 
 import nnvm
 import nnvm.frontend.darknet
-import nnvm.testing.yolo_detection
-import nnvm.testing.darknet
+import tvm.relay.testing.yolo_detection
+import tvm.relay.testing.darknet
 import matplotlib.pyplot as plt
 import numpy as np
 import tvm
 import sys
 
 from ctypes import *
 from tvm.contrib.download import download_testdata
-from nnvm.testing.darknet import __darknetffi__
+from tvm.relay.testing.darknet import __darknetffi__
 
 # Model name
 MODEL_NAME = 'yolov3'
@@ -104,7 +104,7 @@
           test_image + '?raw=true'
 img_path = download_testdata(img_url, test_image, "data")
 
-data = nnvm.testing.darknet.load_image(img_path, netw, neth)
+data = tvm.relay.testing.darknet.load_image(img_path, netw, neth)
 ######################################################################
 # Execute on TVM Runtime
 # ----------------------
@@ -153,12 +153,12 @@
 # do the detection and bring up the bounding boxes
 thresh = 0.5
 nms_thresh = 0.45
-img = nnvm.testing.darknet.load_image_color(test_image)
+img = tvm.relay.testing.darknet.load_image_color(test_image)
 _, im_h, im_w = img.shape
-dets = nnvm.testing.yolo_detection.fill_network_boxes((netw, neth), (im_w, im_h), thresh,
+dets = tvm.relay.testing.yolo_detection.fill_network_boxes((netw, neth), (im_w, im_h), thresh,
                                                       1, tvm_out)
 last_layer = net.layers[net.n - 1]
-nnvm.testing.yolo_detection.do_nms_sort(dets, last_layer.classes, nms_thresh)
+tvm.relay.testing.yolo_detection.do_nms_sort(dets, last_layer.classes, nms_thresh)
 
 coco_name = 'coco.names'
 coco_url = 'https://github.com/siju-samuel/darknet/blob/master/data/' + coco_name + '?raw=true'
@@ -172,6 +172,6 @@
 
 names = [x.strip() for x in content]
 
-nnvm.testing.yolo_detection.draw_detections(img, dets, thresh, names, last_layer.classes)
+tvm.relay.testing.yolo_detection.draw_detections(img, dets, thresh, names, last_layer.classes)
 plt.imshow(img.transpose(1, 2, 0))
 plt.show()

python/tvm/relay/frontend/darknet.py

@@ -1,3 +1,19 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
 # pylint: disable=unused-argument
 """
 DarkNet symbol frontend for Relay.
@@ -61,15 +77,6 @@ def _darknet_avgpooling(inputs, params, attrs, prefix):
     new_attrs['padding'] = (pads, pads)
     return get_relay_op('avg_pool2d')(*inputs, **new_attrs)
 
-def _darknet_batch_norm(inputs, params, attrs, prefix):
-    """Process the batchnormalization operation."""
-    new_attrs = {}
-    new_attrs['axis'] = attrs.get('axis', 1)
-    new_attrs['epsilon'] = attrs.get('eps', 0.000001)
-    new_attrs['center'] = True
-    new_attrs['scale'] = True
-    return get_relay_op('batch_norm')(*inputs, **new_attrs)
-
 def _darknet_conv2d(inputs, params, attrs, prefix):
     """Process the convolution 2d operation."""
     new_attrs = {}
@@ -85,14 +92,14 @@ def _darknet_conv2d(inputs, params, attrs, prefix):
     new_attrs['groups'] = attrs.get('num_group', 1)
 
     weight = _get_param_var(params, prefix, 'weight')
-    out = get_relay_op('conv2d')(*inputs, weight, **new_attrs)
+    out = get_relay_op('conv2d')(*inputs, weight=weight, **new_attrs)
 
     use_bias = not attrs.get('use_batchNorm', False)
     if use_bias:
         new_attrs = {}
         new_attrs['axis'] = 1
         bias = _get_param_var(params, prefix, 'bias')
-        out = get_relay_op('bias_add')(out, bias, **new_attrs)
+        out = get_relay_op('bias_add')(out, bias=bias, **new_attrs)
     else:
         new_attrs = {}
         new_attrs['epsilon'] = 0.000001
@@ -109,32 +116,6 @@ def _darknet_conv2d(inputs, params, attrs, prefix):
         out = _darknet_activations(out, None, new_attrs)
     return out
 
-def _darknet_conv2d_transpose(inputs, params, attrs, prefix):
-    """Process the convolution 2d transpose operation."""
-    new_attrs = {}
-    kernel = attrs.get('kernel')
-    strides = attrs.get('stride', 1)
-    pads = attrs.get('pad', 0)
-
-    new_attrs['channels'] = attrs.get('num_filter')
-    new_attrs['kernel_size'] = (kernel, kernel)
-    new_attrs['strides'] = (strides, strides)
-    new_attrs['padding'] = (pads, pads)
-    new_attrs['dilation'] = attrs.get('dilate', (1, 1))
-    new_attrs['groups'] = attrs.get('num_group', 1)
-    new_attrs['output_padding'] = attrs.get('adj', (0, 0))
-
-    weight = _get_param_var(params, prefix, 'weight')
-    out = get_relay_op('conv2d_transpose')(*inputs, weight, **new_attrs)
-
-    use_bias = not attrs.get(attrs, 'no_bias')
-    if use_bias:
-        new_attrs = {}
-        new_attrs['axis'] = 1
-        bias = _get_param_var(params, prefix, 'bias')
-        out = get_relay_op('bias_add')(out, bias, **new_attrs)
-    return out
-
 def _darknet_shortcut(inputs, params, attrs, prefix):
     """Process the shortcut operation."""
     input_0 = inputs[0]
@@ -266,12 +247,8 @@ def _darknet_region(inputs, params, attrs, prefix):
     split_indices = (2, 4, 5)
     split_res = get_relay_op('split')(data_block, indices_or_sections=split_indices, axis=2)
     split_res0 = get_relay_op('sigmoid')(split_res[0])
-    if not background:
-        split_res2 = get_relay_op('sigmoid')(split_res[2])
-    else:
-        split_res2 = split_res[2]
-    if softmax:
-        split_res3 = get_relay_op('softmax')(split_res[3], axis=2)
+    split_res2 = split_res[2] if background else get_relay_op('sigmoid')(split_res[2])
+    split_res3 = get_relay_op('softmax')(split_res[3], axis=2) if softmax else split_res[3]
     out = get_relay_op('concatenate')((split_res0, split_res[1], split_res2, split_res3), axis=2)
     return get_relay_op('reshape')(out, newshape=input_shape)
 
@@ -385,20 +362,20 @@ class LAYERTYPE(Enum):
 
 _DARKNET_CONVERT_MAP = {
     LAYERTYPE.CONVOLUTIONAL   : _darknet_conv2d,
-    LAYERTYPE.DECONVOLUTIONAL : _darknet_conv2d_transpose,
     LAYERTYPE.CONNECTED       : _darknet_dense,
     LAYERTYPE.MAXPOOL         : _darknet_maxpooling,
     LAYERTYPE.SOFTMAX         : _darknet_softmax_output,
     LAYERTYPE.DROPOUT         : _darknet_dropout,
     LAYERTYPE.AVGPOOL         : _darknet_avgpooling,
-    LAYERTYPE.BATCHNORM       : _darknet_batch_norm,
     LAYERTYPE.ROUTE           : _darknet_route,
     LAYERTYPE.REORG           : _darknet_reorg,
     LAYERTYPE.REGION          : _darknet_region,
     LAYERTYPE.SHORTCUT        : _darknet_shortcut,
     LAYERTYPE.UPSAMPLE        : _darknet_upsampling,
     LAYERTYPE.L2NORM          : _darknet_l2normalize,
     LAYERTYPE.YOLO            : _darknet_yolo,
+    LAYERTYPE.DECONVOLUTIONAL : _darknet_not_support,
+    LAYERTYPE.BATCHNORM       : _darknet_not_support,
     LAYERTYPE.DETECTION       : _darknet_not_support,
     LAYERTYPE.CROP            : _darknet_not_support,
     LAYERTYPE.COST            : _darknet_not_support,
@@ -757,7 +734,6 @@ def _handle_darknet_rnn_layers(self, layer_num, sym):
             attr.update({'n' : layer.n})
             attr.update({'batch' : layer.batch})
             attr.update({'num_hidden' : str(layer.outputs)})
-
             state = self._get_rnn_state_buffer(layer, 'rnn')
             for _ in range(layer.steps):
                 input_layer = layer.input_layer

python/tvm/relay/testing/__init__.py

@@ -27,6 +27,7 @@
 from . import squeezenet
 from . import vgg
 from . import densenet
+from . import yolo_detection
 
 from .config import ctx_list
 from .init import create_workload