Merge 3.4

testdata/dnn/download_models.py

@@ -110,8 +110,8 @@ def save(self, r):
         sha='c8a5a000ee8d8dd75886f152a50a9c5b53d726a5',
         filename='tensorflow_inception_graph.pb'),
     Model(
-        name='Enet',
-        url='https://www.dropbox.com/sh/dywzk3gyb12hpe5/AABoUwqQGWvClUu27Z1EWeu9a/model-best.net?dl=1',
+        name='Enet',  # https://github.com/e-lab/ENet-training
+        url='https://www.dropbox.com/s/tdde0mawbi5dugq/Enet-model-best.net?dl=1',
         sha='b4123a73bf464b9ebe9cfc4ab9c2d5c72b161315',
         filename='Enet-model-best.net'),
     Model(
@@ -437,29 +437,6 @@ def save(self, r):
         member='zfnet512/test_data_set_0/output_0.pb',
         sha='a74974096088954ca4e4e89bec212c1ac2ab0745',
         filename='onnx/data/output_zfnet512.pb'),
-    Model(
-        name='VGG16 (ONNX)',
-        url='https://s3.amazonaws.com/onnx-model-zoo/vgg/vgg16/vgg16.tar.gz',
-        sha='39bc0f689388a32227d804e97ea9f562467ed863',
-        filename='vgg16.tar.gz'),
-    Model(
-        name='VGG16 (ONNX)',
-        archive='vgg16.tar.gz',
-        member='vgg16/vgg16.onnx',
-        sha='effed6f2bbe6c3a2f7705da4c0002ec6250827dd',
-        filename='onnx/models/vgg16.onnx'),
-    Model(
-        name='VGG16 (ONNX)',
-        archive='vgg16.tar.gz',
-        member='vgg16/test_data_set_0/input_0.pb',
-        sha='55c285cfbc4d61e3c026302a3af9e7d220b82d0a',
-        filename='onnx/data/input_vgg16.pb'),
-    Model(
-        name='VGG16 (ONNX)',
-        archive='vgg16.tar.gz',
-        member='vgg16/test_data_set_0/output_0.pb',
-        sha='dcd5945025906ff4ea2270963b795c8fa29d4cd8',
-        filename='onnx/data/output_vgg16.pb'),
     Model(
         name='VGG16_bn (ONNX)',
         url='https://s3.amazonaws.com/onnx-model-zoo/vgg/vgg16-bn/vgg16-bn.tar.gz',

testdata/dnn/layers/prior_box.prototxt

@@ -0,0 +1,37 @@
+name: "prior_box"
+input: "data"
+input_shape {
+  dim: 1
+  dim: 3
+  dim: 10
+  dim: 10
+}
+
+input: "shape"
+input_shape {
+  dim: 1
+  dim: 2
+  dim: 3
+  dim: 4
+}
+
+layer {
+  name: "priorbox"
+  type: "PriorBox"
+  bottom: "shape"
+  bottom: "data"
+  top: "priorbox"
+  prior_box_param {
+    min_size: 2.0
+    min_size: 3.0
+    max_size: 6.0
+    max_size: 7.0
+    aspect_ratio: 2
+    flip: true
+    clip: false
+    variance: 0.1
+    variance: 0.1
+    variance: 0.2
+    variance: 0.2
+  }
+}

testdata/dnn/onnx/generate_onnx_models.py

@@ -425,3 +425,17 @@ def forward(self, x):
 input = np.random.rand(1, 3, 4, 2, 3)
 output = np.mean(input, axis=(3, 4), keepdims=True)
 save_onnx_data_and_model(input, output, 'reduce_mean3d', 'ReduceMean', axes=(3, 4), keepdims=True)
+
+class SplitMax(nn.Module):
+
+    def __init__(self):
+        super(SplitMax, self).__init__()
+
+    def forward(self, x):
+        first, second = torch.split(x, (2, 4), dim=1)
+        second, third = torch.split(second, (2, 2), dim=1)
+        return torch.max(first, torch.max(second, third))
+
+model = SplitMax()
+input = Variable(torch.randn(1, 6, 2, 3))
+save_data_and_model("split_max", input, model)

testdata/dnn/onnx/models/split_max.onnx

@@ -0,0 +1,27 @@
+pytorch1.1:�

+9
+tensor.1
1tensor"Split*
+axis
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+split@@�

+2
+tensor
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+axis
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+split@@�

+
+
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+
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+
+
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+
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+tensor.1
+

+

+
+
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+
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+

+

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+
+B	

testdata/dnn/opencv_face_detector.pbtxt

@@ -1221,7 +1221,7 @@ node {
   attr {
     key: "padding"
     value {
-      s: "SAME"
+      s: "VALID"
     }
   }
   attr {
@@ -1311,7 +1311,7 @@ node {
   attr {
     key: "padding"
     value {
-      s: "SAME"
+      s: "VALID"
     }
   }
   attr {
@@ -2337,6 +2337,12 @@ node {
       i: 400
     }
   }
+  attr {
+    key: "clip"
+    value {
+      b: true
+    }
+  }
 }
 node {
   name: "reshape_before_softmax"

testdata/dnn/opencv_face_detector.prototxt

@@ -1785,5 +1785,6 @@ layer {
     code_type: CENTER_SIZE
     keep_top_k: 200
     confidence_threshold: 0.01
+    clip: 1
   }
 }