diff --git a/testdata/cv/cvtcolor/bayerVNG_gold.png b/testdata/cv/cvtcolor/bayerVNG_gold.png index d7cd144df..188726360 100644 Binary files a/testdata/cv/cvtcolor/bayerVNG_gold.png and b/testdata/cv/cvtcolor/bayerVNG_gold.png differ diff --git a/testdata/cv/cvtcolor_strict/bglena.png b/testdata/cv/cvtcolor_strict/bglena.png index eb06f8ce3..2d8550df9 100644 Binary files a/testdata/cv/cvtcolor_strict/bglena.png and b/testdata/cv/cvtcolor_strict/bglena.png differ diff --git a/testdata/cv/cvtcolor_strict/gblena.png b/testdata/cv/cvtcolor_strict/gblena.png index 7d0b3efd6..c96fc208e 100644 Binary files a/testdata/cv/cvtcolor_strict/gblena.png and b/testdata/cv/cvtcolor_strict/gblena.png differ diff --git a/testdata/cv/cvtcolor_strict/grlena.png b/testdata/cv/cvtcolor_strict/grlena.png index 235a69814..5f4c326fb 100644 Binary files a/testdata/cv/cvtcolor_strict/grlena.png and b/testdata/cv/cvtcolor_strict/grlena.png differ diff --git a/testdata/cv/cvtcolor_strict/rglena.png b/testdata/cv/cvtcolor_strict/rglena.png index 216f8f7cc..31780ab05 100644 Binary files a/testdata/cv/cvtcolor_strict/rglena.png and b/testdata/cv/cvtcolor_strict/rglena.png differ diff --git a/testdata/dnn/download_models.py b/testdata/dnn/download_models.py index 4c0216792..29c5b36da 100755 --- a/testdata/dnn/download_models.py +++ b/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', diff --git a/testdata/dnn/layers/prior_box.prototxt b/testdata/dnn/layers/prior_box.prototxt new file mode 100644 index 000000000..38cc182c7 --- /dev/null +++ b/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 + } +} diff --git a/testdata/dnn/layers/priorbox_output.npy b/testdata/dnn/layers/priorbox_output.npy new file mode 100644 index 000000000..cf685d275 Binary files /dev/null and b/testdata/dnn/layers/priorbox_output.npy differ diff --git a/testdata/dnn/onnx/data/input_split_max.npy b/testdata/dnn/onnx/data/input_split_max.npy new file mode 100644 index 000000000..74095fed4 Binary files /dev/null and b/testdata/dnn/onnx/data/input_split_max.npy differ diff --git a/testdata/dnn/onnx/data/output_split_max.npy b/testdata/dnn/onnx/data/output_split_max.npy new file mode 100644 index 000000000..856f0f718 Binary files /dev/null and b/testdata/dnn/onnx/data/output_split_max.npy differ diff --git a/testdata/dnn/onnx/generate_onnx_models.py b/testdata/dnn/onnx/generate_onnx_models.py index 422082404..7cfd3a4d2 100644 --- a/testdata/dnn/onnx/generate_onnx_models.py +++ b/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) diff --git a/testdata/dnn/onnx/models/split_max.onnx b/testdata/dnn/onnx/models/split_max.onnx new file mode 100644 index 000000000..bdc3ccb74 --- /dev/null +++ b/testdata/dnn/onnx/models/split_max.onnx @@ -0,0 +1,27 @@ +pytorch1.1:â +9 +tensor.11tensor"Split* +axis * +split@@  +2 +tensor34"Split* +axis * +split@@  + +3 +45"Max + +1 +56"Maxtorch-jit-exportZ" +tensor.1 + + + + +b +6 + + + + +B \ No newline at end of file diff --git a/testdata/dnn/opencv_face_detector.pbtxt b/testdata/dnn/opencv_face_detector.pbtxt index 5f498aad5..60daa5e6b 100644 --- a/testdata/dnn/opencv_face_detector.pbtxt +++ 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" diff --git a/testdata/dnn/opencv_face_detector.prototxt b/testdata/dnn/opencv_face_detector.prototxt index 8b7ba1aa2..a12851566 100644 --- a/testdata/dnn/opencv_face_detector.prototxt +++ b/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 } } diff --git a/testdata/dnn/ssd_mobilenet_v1_coco_2017_11_17.pbtxt b/testdata/dnn/ssd_mobilenet_v1_coco_2017_11_17.pbtxt index 5e1dc10c6..cc26fd7a5 100644 --- a/testdata/dnn/ssd_mobilenet_v1_coco_2017_11_17.pbtxt +++ b/testdata/dnn/ssd_mobilenet_v1_coco_2017_11_17.pbtxt @@ -45,6 +45,12 @@ node { input: "Preprocessor/sub" input: "FeatureExtractor/MobilenetV1/Conv2d_0/weights/read/_104__cf__107" input: "^FeatureExtractor/Assert/Assert" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "padding" value { @@ -79,6 +85,12 @@ node { op: "DepthwiseConv2dNative" input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/Relu6" input: "FeatureExtractor/MobilenetV1/Conv2d_1_depthwise/depthwise_weights/read/_101__cf__104" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "padding" value { @@ -119,6 +131,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_depthwise/Relu6" input: "FeatureExtractor/MobilenetV1/Conv2d_1_pointwise/weights/read/_98__cf__101" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "padding" value { @@ -153,6 +171,12 @@ node { op: "DepthwiseConv2dNative" input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_pointwise/Relu6" input: "FeatureExtractor/MobilenetV1/Conv2d_2_depthwise/depthwise_weights/read/_95__cf__98" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "padding" value { @@ -193,6 +217,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_depthwise/Relu6" input: "FeatureExtractor/MobilenetV1/Conv2d_2_pointwise/weights/read/_92__cf__95" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "padding" value { @@ -227,6 +257,12 @@ node { op: "DepthwiseConv2dNative" input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_pointwise/Relu6" input: "FeatureExtractor/MobilenetV1/Conv2d_3_depthwise/depthwise_weights/read/_89__cf__92" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "padding" value { @@ -267,6 +303,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_depthwise/Relu6" input: "FeatureExtractor/MobilenetV1/Conv2d_3_pointwise/weights/read/_86__cf__89" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "padding" value { @@ -301,6 +343,12 @@ node { op: "DepthwiseConv2dNative" input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_pointwise/Relu6" input: "FeatureExtractor/MobilenetV1/Conv2d_4_depthwise/depthwise_weights/read/_83__cf__86" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "padding" value { @@ -341,6 +389,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_depthwise/Relu6" input: "FeatureExtractor/MobilenetV1/Conv2d_4_pointwise/weights/read/_80__cf__83" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "padding" value { @@ -375,6 +429,12 @@ node { op: "DepthwiseConv2dNative" input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_pointwise/Relu6" input: "FeatureExtractor/MobilenetV1/Conv2d_5_depthwise/depthwise_weights/read/_77__cf__80" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "padding" value { @@ -415,6 +475,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_depthwise/Relu6" input: "FeatureExtractor/MobilenetV1/Conv2d_5_pointwise/weights/read/_74__cf__77" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "padding" value { @@ -449,6 +515,12 @@ node { op: "DepthwiseConv2dNative" input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_pointwise/Relu6" input: "FeatureExtractor/MobilenetV1/Conv2d_6_depthwise/depthwise_weights/read/_71__cf__74" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "padding" value { @@ -489,6 +561,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_depthwise/Relu6" input: "FeatureExtractor/MobilenetV1/Conv2d_6_pointwise/weights/read/_68__cf__71" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "padding" value { @@ -523,6 +601,12 @@ node { op: "DepthwiseConv2dNative" input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_pointwise/Relu6" input: "FeatureExtractor/MobilenetV1/Conv2d_7_depthwise/depthwise_weights/read/_65__cf__68" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "padding" value { @@ -563,6 +647,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_depthwise/Relu6" input: "FeatureExtractor/MobilenetV1/Conv2d_7_pointwise/weights/read/_62__cf__65" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "padding" value { @@ -597,6 +687,12 @@ node { op: "DepthwiseConv2dNative" input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_pointwise/Relu6" input: "FeatureExtractor/MobilenetV1/Conv2d_8_depthwise/depthwise_weights/read/_59__cf__62" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "padding" value { @@ -637,6 +733,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_depthwise/Relu6" input: "FeatureExtractor/MobilenetV1/Conv2d_8_pointwise/weights/read/_56__cf__59" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "padding" value { @@ -671,6 +773,12 @@ node { op: "DepthwiseConv2dNative" input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_pointwise/Relu6" input: "FeatureExtractor/MobilenetV1/Conv2d_9_depthwise/depthwise_weights/read/_53__cf__56" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "padding" value { @@ -711,6 +819,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_depthwise/Relu6" input: "FeatureExtractor/MobilenetV1/Conv2d_9_pointwise/weights/read/_50__cf__53" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "padding" value { @@ -745,6 +859,12 @@ node { op: "DepthwiseConv2dNative" input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_pointwise/Relu6" input: "FeatureExtractor/MobilenetV1/Conv2d_10_depthwise/depthwise_weights/read/_47__cf__50" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "padding" value { @@ -785,6 +905,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_depthwise/Relu6" input: "FeatureExtractor/MobilenetV1/Conv2d_10_pointwise/weights/read/_44__cf__47" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "padding" value { @@ -819,6 +945,12 @@ node { op: "DepthwiseConv2dNative" input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_pointwise/Relu6" input: "FeatureExtractor/MobilenetV1/Conv2d_11_depthwise/depthwise_weights/read/_41__cf__44" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "padding" value { @@ -859,6 +991,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_depthwise/Relu6" input: "FeatureExtractor/MobilenetV1/Conv2d_11_pointwise/weights/read/_38__cf__41" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "padding" value { @@ -893,6 +1031,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_pointwise/Relu6" input: "BoxPredictor_0/ClassPredictor/weights/read/_178__cf__181" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "padding" value { @@ -916,12 +1060,24 @@ node { op: "BiasAdd" input: "BoxPredictor_0/ClassPredictor/Conv2D" input: "BoxPredictor_0/ClassPredictor/biases/read/_177__cf__180" + attr { + key: "data_format" + value { + s: "NHWC" + } + } } node { name: "BoxPredictor_0/BoxEncodingPredictor/Conv2D" op: "Conv2D" input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_pointwise/Relu6" input: "BoxPredictor_0/BoxEncodingPredictor/weights/read/_116__cf__119" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "loc_pred_transposed" value { @@ -951,12 +1107,24 @@ node { op: "BiasAdd" input: "BoxPredictor_0/BoxEncodingPredictor/Conv2D" input: "BoxPredictor_0/BoxEncodingPredictor/biases/read/_115__cf__118" + attr { + key: "data_format" + value { + s: "NHWC" + } + } } node { name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_depthwise/depthwise" op: "DepthwiseConv2dNative" input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_pointwise/Relu6" input: "FeatureExtractor/MobilenetV1/Conv2d_12_depthwise/depthwise_weights/read/_35__cf__38" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "padding" value { @@ -997,6 +1165,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_depthwise/Relu6" input: "FeatureExtractor/MobilenetV1/Conv2d_12_pointwise/weights/read/_32__cf__35" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "padding" value { @@ -1031,6 +1205,12 @@ node { op: "DepthwiseConv2dNative" input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_pointwise/Relu6" input: "FeatureExtractor/MobilenetV1/Conv2d_13_depthwise/depthwise_weights/read/_29__cf__32" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "padding" value { @@ -1071,6 +1251,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_depthwise/Relu6" input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise/weights/read/_26__cf__29" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "padding" value { @@ -1105,6 +1291,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_pointwise/Relu6" input: "BoxPredictor_1/ClassPredictor/weights/read/_176__cf__179" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "padding" value { @@ -1128,12 +1320,24 @@ node { op: "BiasAdd" input: "BoxPredictor_1/ClassPredictor/Conv2D" input: "BoxPredictor_1/ClassPredictor/biases/read/_175__cf__178" + attr { + key: "data_format" + value { + s: "NHWC" + } + } } node { name: "BoxPredictor_1/BoxEncodingPredictor/Conv2D" op: "Conv2D" input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_pointwise/Relu6" input: "BoxPredictor_1/BoxEncodingPredictor/weights/read/_114__cf__117" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "loc_pred_transposed" value { @@ -1163,12 +1367,24 @@ node { op: "BiasAdd" input: "BoxPredictor_1/BoxEncodingPredictor/Conv2D" input: "BoxPredictor_1/BoxEncodingPredictor/biases/read/_113__cf__116" + attr { + key: "data_format" + value { + s: "NHWC" + } + } } node { name: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_2_1x1_256/BatchNorm/batchnorm/mul_1" op: "Conv2D" input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_pointwise/Relu6" input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_2_1x1_256/weights/read/_23__cf__26" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "padding" value { @@ -1203,6 +1419,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_2_1x1_256/Relu6" input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_2_3x3_s2_512/weights/read/_20__cf__23" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "padding" value { @@ -1237,6 +1459,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_2_3x3_s2_512/Relu6" input: "BoxPredictor_2/ClassPredictor/weights/read/_174__cf__177" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "padding" value { @@ -1260,12 +1488,24 @@ node { op: "BiasAdd" input: "BoxPredictor_2/ClassPredictor/Conv2D" input: "BoxPredictor_2/ClassPredictor/biases/read/_173__cf__176" + attr { + key: "data_format" + value { + s: "NHWC" + } + } } node { name: "BoxPredictor_2/BoxEncodingPredictor/Conv2D" op: "Conv2D" input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_2_3x3_s2_512/Relu6" input: "BoxPredictor_2/BoxEncodingPredictor/weights/read/_112__cf__115" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "loc_pred_transposed" value { @@ -1295,12 +1535,24 @@ node { op: "BiasAdd" input: "BoxPredictor_2/BoxEncodingPredictor/Conv2D" input: "BoxPredictor_2/BoxEncodingPredictor/biases/read/_111__cf__114" + attr { + key: "data_format" + value { + s: "NHWC" + } + } } node { name: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_3_1x1_128/BatchNorm/batchnorm/mul_1" op: "Conv2D" input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_2_3x3_s2_512/Relu6" input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_3_1x1_128/weights/read/_17__cf__20" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "padding" value { @@ -1335,6 +1587,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_3_1x1_128/Relu6" input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_3_3x3_s2_256/weights/read/_14__cf__17" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "padding" value { @@ -1369,6 +1627,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_3_3x3_s2_256/Relu6" input: "BoxPredictor_3/ClassPredictor/weights/read/_172__cf__175" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "padding" value { @@ -1392,12 +1656,24 @@ node { op: "BiasAdd" input: "BoxPredictor_3/ClassPredictor/Conv2D" input: "BoxPredictor_3/ClassPredictor/biases/read/_171__cf__174" + attr { + key: "data_format" + value { + s: "NHWC" + } + } } node { name: "BoxPredictor_3/BoxEncodingPredictor/Conv2D" op: "Conv2D" input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_3_3x3_s2_256/Relu6" input: "BoxPredictor_3/BoxEncodingPredictor/weights/read/_110__cf__113" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "loc_pred_transposed" value { @@ -1427,12 +1703,24 @@ node { op: "BiasAdd" input: "BoxPredictor_3/BoxEncodingPredictor/Conv2D" input: "BoxPredictor_3/BoxEncodingPredictor/biases/read/_109__cf__112" + attr { + key: "data_format" + value { + s: "NHWC" + } + } } node { name: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_4_1x1_128/BatchNorm/batchnorm/mul_1" op: "Conv2D" input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_3_3x3_s2_256/Relu6" input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_4_1x1_128/weights/read/_11__cf__14" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "padding" value { @@ -1467,6 +1755,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_4_1x1_128/Relu6" input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_4_3x3_s2_256/weights/read/_8__cf__11" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "padding" value { @@ -1501,6 +1795,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_4_3x3_s2_256/Relu6" input: "BoxPredictor_4/ClassPredictor/weights/read/_170__cf__173" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "padding" value { @@ -1524,12 +1824,24 @@ node { op: "BiasAdd" input: "BoxPredictor_4/ClassPredictor/Conv2D" input: "BoxPredictor_4/ClassPredictor/biases/read/_169__cf__172" + attr { + key: "data_format" + value { + s: "NHWC" + } + } } node { name: "BoxPredictor_4/BoxEncodingPredictor/Conv2D" op: "Conv2D" input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_4_3x3_s2_256/Relu6" input: "BoxPredictor_4/BoxEncodingPredictor/weights/read/_108__cf__111" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "loc_pred_transposed" value { @@ -1559,12 +1871,24 @@ node { op: "BiasAdd" input: "BoxPredictor_4/BoxEncodingPredictor/Conv2D" input: "BoxPredictor_4/BoxEncodingPredictor/biases/read/_107__cf__110" + attr { + key: "data_format" + value { + s: "NHWC" + } + } } node { name: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_5_1x1_64/BatchNorm/batchnorm/mul_1" op: "Conv2D" input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_4_3x3_s2_256/Relu6" input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_5_1x1_64/weights/read/_5__cf__8" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "padding" value { @@ -1599,6 +1923,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_5_1x1_64/Relu6" input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_5_3x3_s2_128/weights/read/_2__cf__5" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "padding" value { @@ -1633,6 +1963,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_5_3x3_s2_128/Relu6" input: "BoxPredictor_5/ClassPredictor/weights/read/_168__cf__171" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "padding" value { @@ -1656,12 +1992,24 @@ node { op: "BiasAdd" input: "BoxPredictor_5/ClassPredictor/Conv2D" input: "BoxPredictor_5/ClassPredictor/biases/read/_167__cf__170" + attr { + key: "data_format" + value { + s: "NHWC" + } + } } node { name: "BoxPredictor_5/BoxEncodingPredictor/Conv2D" op: "Conv2D" input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_5_3x3_s2_128/Relu6" input: "BoxPredictor_5/BoxEncodingPredictor/weights/read/_106__cf__109" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "loc_pred_transposed" value { @@ -1691,6 +2039,12 @@ node { op: "BiasAdd" input: "BoxPredictor_5/BoxEncodingPredictor/Conv2D" input: "BoxPredictor_5/BoxEncodingPredictor/biases/read/_105__cf__108" + attr { + key: "data_format" + value { + s: "NHWC" + } + } } node { name: "concat/axis_flatten" @@ -1700,12 +2054,12 @@ node { value { tensor { dtype: DT_INT32 + int_val: -1 tensor_shape { dim { size: 1 } } - int_val: -1 } } } @@ -1814,14 +2168,14 @@ node { value { tensor { dtype: DT_FLOAT + float_val: 30.0 + float_val: 42.4264068712 + float_val: 84.8528137424 tensor_shape { dim { size: 3 } } - float_val: 30.0 - float_val: 42.4264068604 - float_val: 84.8528137207 } } } @@ -1830,15 +2184,15 @@ node { value { tensor { dtype: DT_FLOAT + float_val: 0.1 + float_val: 0.1 + float_val: 0.2 + float_val: 0.2 tensor_shape { dim { size: 4 } } - float_val: 0.10000000149 - float_val: 0.10000000149 - float_val: 0.20000000298 - float_val: 0.20000000298 } } } @@ -1847,14 +2201,14 @@ node { value { tensor { dtype: DT_FLOAT + float_val: 30.0 + float_val: 84.8528137424 + float_val: 42.4264068712 tensor_shape { dim { size: 3 } } - float_val: 30.0 - float_val: 84.8528137207 - float_val: 42.4264068604 } } } @@ -1881,17 +2235,17 @@ node { value { tensor { dtype: DT_FLOAT + float_val: 105.0 + float_val: 74.2462120246 + float_val: 148.492424049 + float_val: 60.6217782649 + float_val: 181.874428744 + float_val: 125.49900398 tensor_shape { dim { size: 6 } } - float_val: 105.0 - float_val: 74.2462158203 - float_val: 148.492431641 - float_val: 60.6217765808 - float_val: 181.956329346 - float_val: 125.499000549 } } } @@ -1900,15 +2254,15 @@ node { value { tensor { dtype: DT_FLOAT + float_val: 0.1 + float_val: 0.1 + float_val: 0.2 + float_val: 0.2 tensor_shape { dim { size: 4 } } - float_val: 0.10000000149 - float_val: 0.10000000149 - float_val: 0.20000000298 - float_val: 0.20000000298 } } } @@ -1917,17 +2271,17 @@ node { value { tensor { dtype: DT_FLOAT + float_val: 105.0 + float_val: 148.492424049 + float_val: 74.2462120246 + float_val: 181.865334795 + float_val: 60.6187471002 + float_val: 125.49900398 tensor_shape { dim { size: 6 } } - float_val: 105.0 - float_val: 148.492431641 - float_val: 74.2462158203 - float_val: 181.865341187 - float_val: 60.5914611816 - float_val: 125.499000549 } } } @@ -1954,17 +2308,17 @@ node { value { tensor { dtype: DT_FLOAT + float_val: 150.0 + float_val: 106.066017178 + float_val: 212.132034356 + float_val: 86.6025403784 + float_val: 259.820612491 + float_val: 171.026313765 tensor_shape { dim { size: 6 } } - float_val: 150.0 - float_val: 106.066017151 - float_val: 212.132034302 - float_val: 86.6025390625 - float_val: 259.93762207 - float_val: 171.026306152 } } } @@ -1973,15 +2327,15 @@ node { value { tensor { dtype: DT_FLOAT + float_val: 0.1 + float_val: 0.1 + float_val: 0.2 + float_val: 0.2 tensor_shape { dim { size: 4 } } - float_val: 0.10000000149 - float_val: 0.10000000149 - float_val: 0.20000000298 - float_val: 0.20000000298 } } } @@ -1990,17 +2344,17 @@ node { value { tensor { dtype: DT_FLOAT + float_val: 150.0 + float_val: 212.132034356 + float_val: 106.066017178 + float_val: 259.807621135 + float_val: 86.5982101432 + float_val: 171.026313765 tensor_shape { dim { size: 6 } } - float_val: 150.0 - float_val: 212.132034302 - float_val: 106.066017151 - float_val: 259.807617188 - float_val: 86.5592269897 - float_val: 171.026306152 } } } @@ -2027,17 +2381,17 @@ node { value { tensor { dtype: DT_FLOAT + float_val: 195.0 + float_val: 137.885822331 + float_val: 275.771644663 + float_val: 112.583302492 + float_val: 337.766796238 + float_val: 216.333076528 tensor_shape { dim { size: 6 } } - float_val: 195.0 - float_val: 137.885818481 - float_val: 275.771636963 - float_val: 112.583305359 - float_val: 337.918914795 - float_val: 216.333084106 } } } @@ -2046,15 +2400,15 @@ node { value { tensor { dtype: DT_FLOAT + float_val: 0.1 + float_val: 0.1 + float_val: 0.2 + float_val: 0.2 tensor_shape { dim { size: 4 } } - float_val: 0.10000000149 - float_val: 0.10000000149 - float_val: 0.20000000298 - float_val: 0.20000000298 } } } @@ -2063,17 +2417,17 @@ node { value { tensor { dtype: DT_FLOAT + float_val: 195.0 + float_val: 275.771644663 + float_val: 137.885822331 + float_val: 337.749907476 + float_val: 112.577673186 + float_val: 216.333076528 tensor_shape { dim { size: 6 } } - float_val: 195.0 - float_val: 275.771636963 - float_val: 137.885818481 - float_val: 337.749908447 - float_val: 112.527000427 - float_val: 216.333084106 } } } @@ -2100,17 +2454,17 @@ node { value { tensor { dtype: DT_FLOAT + float_val: 240.0 + float_val: 169.705627485 + float_val: 339.41125497 + float_val: 138.564064606 + float_val: 415.712979985 + float_val: 261.533936612 tensor_shape { dim { size: 6 } } - float_val: 240.0 - float_val: 169.705627441 - float_val: 339.411254883 - float_val: 138.564071655 - float_val: 415.90020752 - float_val: 261.533935547 } } } @@ -2119,15 +2473,15 @@ node { value { tensor { dtype: DT_FLOAT + float_val: 0.1 + float_val: 0.1 + float_val: 0.2 + float_val: 0.2 tensor_shape { dim { size: 4 } } - float_val: 0.10000000149 - float_val: 0.10000000149 - float_val: 0.20000000298 - float_val: 0.20000000298 } } } @@ -2136,17 +2490,17 @@ node { value { tensor { dtype: DT_FLOAT + float_val: 240.0 + float_val: 339.41125497 + float_val: 169.705627485 + float_val: 415.692193817 + float_val: 138.557136229 + float_val: 261.533936612 tensor_shape { dim { size: 6 } } - float_val: 240.0 - float_val: 339.411254883 - float_val: 169.705627441 - float_val: 415.692199707 - float_val: 138.494766235 - float_val: 261.533935547 } } } @@ -2173,17 +2527,17 @@ node { value { tensor { dtype: DT_FLOAT + float_val: 285.0 + float_val: 201.525432638 + float_val: 403.050865276 + float_val: 164.544826719 + float_val: 493.659163732 + float_val: 292.403830344 tensor_shape { dim { size: 6 } } - float_val: 285.0 - float_val: 201.525436401 - float_val: 403.050872803 - float_val: 164.544830322 - float_val: 493.881469727 - float_val: 292.403839111 } } } @@ -2192,15 +2546,15 @@ node { value { tensor { dtype: DT_FLOAT + float_val: 0.1 + float_val: 0.1 + float_val: 0.2 + float_val: 0.2 tensor_shape { dim { size: 4 } } - float_val: 0.10000000149 - float_val: 0.10000000149 - float_val: 0.20000000298 - float_val: 0.20000000298 } } } @@ -2209,17 +2563,17 @@ node { value { tensor { dtype: DT_FLOAT + float_val: 285.0 + float_val: 403.050865276 + float_val: 201.525432638 + float_val: 493.634480157 + float_val: 164.536599272 + float_val: 292.403830344 tensor_shape { dim { size: 6 } } - float_val: 285.0 - float_val: 403.050872803 - float_val: 201.525436401 - float_val: 493.634490967 - float_val: 164.462539673 - float_val: 292.403839111 } } } @@ -2235,16 +2589,47 @@ node { input: "PriorBox_5" input: "concat/axis_flatten" } +node { + name: "ClassPredictor/concat/reshape/shape" + op: "Const" + attr { + key: "value" + value { + tensor { + dtype: DT_INT32 + int_val: 0 + int_val: -1 + int_val: 91 + tensor_shape { + dim { + size: 3 + } + } + } + } + } +} +node { + name: "ClassPredictor/concat/reshape" + op: "Reshape" + input: "ClassPredictor/concat" + input: "ClassPredictor/concat/reshape/shape" +} node { name: "ClassPredictor/concat/sigmoid" op: "Sigmoid" - input: "ClassPredictor/concat" + input: "ClassPredictor/concat/reshape" +} +node { + name: "ClassPredictor/concat/sigmoid/flatten" + op: "Flatten" + input: "ClassPredictor/concat/sigmoid" } node { name: "detection_out" op: "DetectionOutput" input: "BoxEncodingPredictor/concat" - input: "ClassPredictor/concat/sigmoid" + input: "ClassPredictor/concat/sigmoid/flatten" input: "PriorBox/concat" attr { key: "background_label_id" @@ -2261,7 +2646,7 @@ node { attr { key: "confidence_threshold" value { - f: 0.00999999977648 + f: 0.01 } } attr { @@ -2273,7 +2658,7 @@ node { attr { key: "nms_threshold" value { - f: 0.600000023842 + f: 0.6 } } attr { @@ -2295,5 +2680,3 @@ node { } } } -library { -} diff --git a/testdata/dnn/ssd_mobilenet_v1_ppn_coco.pbtxt b/testdata/dnn/ssd_mobilenet_v1_ppn_coco.pbtxt index 3c0d38f46..3376e482b 100644 --- a/testdata/dnn/ssd_mobilenet_v1_ppn_coco.pbtxt +++ b/testdata/dnn/ssd_mobilenet_v1_ppn_coco.pbtxt @@ -2750,12 +2750,12 @@ node { value { tensor { dtype: DT_FLOAT - float_val: 45.000001788 - float_val: 31.8198064177 - float_val: 63.6396128354 - float_val: 25.9807631458 - float_val: 77.946187491 - float_val: 64.6915774925 + float_val: 45.0 + float_val: 31.8198051534 + float_val: 63.6396103068 + float_val: 25.9807621135 + float_val: 77.9461837472 + float_val: 64.6915759585 tensor_shape { dim { size: 6 @@ -2786,12 +2786,12 @@ node { value { tensor { dtype: DT_FLOAT - float_val: 45.000001788 - float_val: 63.6396128354 - float_val: 31.8198064177 - float_val: 77.9422894375 - float_val: 25.9794638596 - float_val: 64.6915774925 + float_val: 45.0 + float_val: 63.6396103068 + float_val: 31.8198051534 + float_val: 77.9422863406 + float_val: 25.9794630429 + float_val: 64.6915759585 tensor_shape { dim { size: 6 @@ -2823,12 +2823,12 @@ node { value { tensor { dtype: DT_FLOAT - float_val: 93.0000007151 - float_val: 65.760931156 - float_val: 131.521862312 - float_val: 53.6935754475 - float_val: 161.08878232 - float_val: 114.512008399 + float_val: 93.0 + float_val: 65.7609306503 + float_val: 131.521861301 + float_val: 53.6935750346 + float_val: 161.088779744 + float_val: 114.512008104 tensor_shape { dim { size: 6 @@ -2859,12 +2859,12 @@ node { value { tensor { dtype: DT_FLOAT - float_val: 93.0000007151 - float_val: 131.521862312 - float_val: 65.760931156 - float_val: 161.080726343 - float_val: 53.6908902561 - float_val: 114.512008399 + float_val: 93.0 + float_val: 131.521861301 + float_val: 65.7609306503 + float_val: 161.080725104 + float_val: 53.6908902888 + float_val: 114.512008104 tensor_shape { dim { size: 6 @@ -2896,12 +2896,12 @@ node { value { tensor { dtype: DT_FLOAT - float_val: 140.999999642 - float_val: 99.7020558944 - float_val: 199.404111789 - float_val: 81.4063877492 - float_val: 244.231377148 - float_val: 163.2452135 + float_val: 141.0 + float_val: 99.7020561473 + float_val: 199.404112295 + float_val: 81.4063879557 + float_val: 244.231375741 + float_val: 163.245214325 tensor_shape { dim { size: 6 @@ -2932,12 +2932,12 @@ node { value { tensor { dtype: DT_FLOAT - float_val: 140.999999642 - float_val: 199.404111789 - float_val: 99.7020558944 - float_val: 244.219163248 - float_val: 81.4023166526 - float_val: 163.2452135 + float_val: 141.0 + float_val: 199.404112295 + float_val: 99.7020561473 + float_val: 244.219163867 + float_val: 81.4023175346 + float_val: 163.245214325 tensor_shape { dim { size: 6 @@ -2969,12 +2969,12 @@ node { value { tensor { dtype: DT_FLOAT - float_val: 188.999998569 - float_val: 133.643180633 - float_val: 267.286361265 - float_val: 109.119200051 - float_val: 327.373971977 - float_val: 211.643566375 + float_val: 189.0 + float_val: 133.643181644 + float_val: 267.286363289 + float_val: 109.119200877 + float_val: 327.373971738 + float_val: 211.643568293 tensor_shape { dim { size: 6 @@ -3005,12 +3005,12 @@ node { value { tensor { dtype: DT_FLOAT - float_val: 188.999998569 - float_val: 267.286361265 - float_val: 133.643180633 - float_val: 327.357600153 - float_val: 109.113743049 - float_val: 211.643566375 + float_val: 189.0 + float_val: 267.286363289 + float_val: 133.643181644 + float_val: 327.357602631 + float_val: 109.11374478 + float_val: 211.643568293 tensor_shape { dim { size: 6 @@ -3042,12 +3042,12 @@ node { value { tensor { dtype: DT_FLOAT - float_val: 236.999997497 - float_val: 167.584305371 - float_val: 335.168610742 - float_val: 136.832012353 - float_val: 410.516566805 - float_val: 259.894206243 + float_val: 237.0 + float_val: 167.584307141 + float_val: 335.168614282 + float_val: 136.832013798 + float_val: 410.516567735 + float_val: 259.894209247 tensor_shape { dim { size: 6 @@ -3078,12 +3078,12 @@ node { value { tensor { dtype: DT_FLOAT - float_val: 236.999997497 - float_val: 335.168610742 - float_val: 167.584305371 - float_val: 410.496037058 - float_val: 136.825169446 - float_val: 259.894206243 + float_val: 237.0 + float_val: 335.168614282 + float_val: 167.584307141 + float_val: 410.496041394 + float_val: 136.825172026 + float_val: 259.894209247 tensor_shape { dim { size: 6 @@ -3115,12 +3115,12 @@ node { value { tensor { dtype: DT_FLOAT - float_val: 284.999996424 - float_val: 201.525430109 - float_val: 403.050860219 - float_val: 164.544824654 - float_val: 493.659161634 - float_val: 292.40382851 + float_val: 285.0 + float_val: 201.525432638 + float_val: 403.050865276 + float_val: 164.544826719 + float_val: 493.659163732 + float_val: 292.403830344 tensor_shape { dim { size: 6 @@ -3151,12 +3151,12 @@ node { value { tensor { dtype: DT_FLOAT - float_val: 284.999996424 - float_val: 403.050860219 - float_val: 201.525430109 - float_val: 493.634473963 - float_val: 164.536595842 - float_val: 292.40382851 + float_val: 285.0 + float_val: 403.050865276 + float_val: 201.525432638 + float_val: 493.634480157 + float_val: 164.536599272 + float_val: 292.403830344 tensor_shape { dim { size: 6 @@ -3177,16 +3177,47 @@ node { input: "PriorBox_5" input: "concat/axis_flatten" } +node { + name: "ClassPredictor/concat3d/shape" + op: "Const" + attr { + key: "value" + value { + tensor { + dtype: DT_INT32 + int_val: 0 + int_val: -1 + int_val: 91 + tensor_shape { + dim { + size: 3 + } + } + } + } + } +} +node { + name: "ClassPredictor/concat3d" + op: "Reshape" + input: "ClassPredictor/concat" + input: "ClassPredictor/concat3d/shape" +} node { name: "ClassPredictor/concat/sigmoid" op: "Sigmoid" - input: "ClassPredictor/concat" + input: "ClassPredictor/concat3d" +} +node { + name: "ClassPredictor/concat/sigmoid/Flatten" + op: "Flatten" + input: "ClassPredictor/concat/sigmoid" } node { name: "detection_out" op: "DetectionOutput" input: "BoxPredictor/concat" - input: "ClassPredictor/concat/sigmoid" + input: "ClassPredictor/concat/sigmoid/Flatten" input: "PriorBox/concat" attr { key: "background_label_id" diff --git a/testdata/dnn/ssd_mobilenet_v2_coco_2018_03_29.pbtxt b/testdata/dnn/ssd_mobilenet_v2_coco_2018_03_29.pbtxt index 9b9672a62..dfc168520 100644 --- a/testdata/dnn/ssd_mobilenet_v2_coco_2018_03_29.pbtxt +++ b/testdata/dnn/ssd_mobilenet_v2_coco_2018_03_29.pbtxt @@ -44,6 +44,12 @@ node { op: "Conv2D" input: "Preprocessor/sub" input: "FeatureExtractor/MobilenetV2/Conv/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -84,7 +90,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -98,6 +104,12 @@ node { op: "DepthwiseConv2dNative" input: "FeatureExtractor/MobilenetV2/Conv/Relu6" input: "FeatureExtractor/MobilenetV2/expanded_conv/depthwise/depthwise_weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -138,7 +150,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -152,6 +164,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV2/expanded_conv/depthwise/Relu6" input: "FeatureExtractor/MobilenetV2/expanded_conv/project/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -192,7 +210,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -206,6 +224,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV2/expanded_conv/output" input: "FeatureExtractor/MobilenetV2/expanded_conv_1/expand/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -246,7 +270,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -260,6 +284,12 @@ node { op: "DepthwiseConv2dNative" input: "FeatureExtractor/MobilenetV2/expanded_conv_1/expand/Relu6" input: "FeatureExtractor/MobilenetV2/expanded_conv_1/depthwise/depthwise_weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -300,7 +330,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -314,6 +344,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV2/expanded_conv_1/depthwise/Relu6" input: "FeatureExtractor/MobilenetV2/expanded_conv_1/project/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -354,7 +390,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -368,6 +404,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV2/expanded_conv_1/output" input: "FeatureExtractor/MobilenetV2/expanded_conv_2/expand/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -408,7 +450,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -422,6 +464,12 @@ node { op: "DepthwiseConv2dNative" input: "FeatureExtractor/MobilenetV2/expanded_conv_2/expand/Relu6" input: "FeatureExtractor/MobilenetV2/expanded_conv_2/depthwise/depthwise_weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -462,7 +510,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -476,6 +524,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV2/expanded_conv_2/depthwise/Relu6" input: "FeatureExtractor/MobilenetV2/expanded_conv_2/project/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -516,7 +570,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -536,6 +590,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV2/expanded_conv_3/input" input: "FeatureExtractor/MobilenetV2/expanded_conv_3/expand/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -576,7 +636,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -590,6 +650,12 @@ node { op: "DepthwiseConv2dNative" input: "FeatureExtractor/MobilenetV2/expanded_conv_3/expand/Relu6" input: "FeatureExtractor/MobilenetV2/expanded_conv_3/depthwise/depthwise_weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -630,7 +696,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -644,6 +710,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV2/expanded_conv_3/depthwise/Relu6" input: "FeatureExtractor/MobilenetV2/expanded_conv_3/project/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -684,7 +756,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -698,6 +770,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV2/expanded_conv_3/output" input: "FeatureExtractor/MobilenetV2/expanded_conv_4/expand/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -738,7 +816,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -752,6 +830,12 @@ node { op: "DepthwiseConv2dNative" input: "FeatureExtractor/MobilenetV2/expanded_conv_4/expand/Relu6" input: "FeatureExtractor/MobilenetV2/expanded_conv_4/depthwise/depthwise_weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -792,7 +876,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -806,6 +890,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV2/expanded_conv_4/depthwise/Relu6" input: "FeatureExtractor/MobilenetV2/expanded_conv_4/project/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -846,7 +936,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -866,6 +956,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV2/expanded_conv_5/input" input: "FeatureExtractor/MobilenetV2/expanded_conv_5/expand/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -906,7 +1002,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -920,6 +1016,12 @@ node { op: "DepthwiseConv2dNative" input: "FeatureExtractor/MobilenetV2/expanded_conv_5/expand/Relu6" input: "FeatureExtractor/MobilenetV2/expanded_conv_5/depthwise/depthwise_weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -960,7 +1062,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -974,6 +1076,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV2/expanded_conv_5/depthwise/Relu6" input: "FeatureExtractor/MobilenetV2/expanded_conv_5/project/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -1014,7 +1122,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -1034,6 +1142,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV2/expanded_conv_6/input" input: "FeatureExtractor/MobilenetV2/expanded_conv_6/expand/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -1074,7 +1188,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -1088,6 +1202,12 @@ node { op: "DepthwiseConv2dNative" input: "FeatureExtractor/MobilenetV2/expanded_conv_6/expand/Relu6" input: "FeatureExtractor/MobilenetV2/expanded_conv_6/depthwise/depthwise_weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -1128,7 +1248,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -1142,6 +1262,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV2/expanded_conv_6/depthwise/Relu6" input: "FeatureExtractor/MobilenetV2/expanded_conv_6/project/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -1182,7 +1308,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -1196,6 +1322,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV2/expanded_conv_6/output" input: "FeatureExtractor/MobilenetV2/expanded_conv_7/expand/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -1236,7 +1368,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -1250,6 +1382,12 @@ node { op: "DepthwiseConv2dNative" input: "FeatureExtractor/MobilenetV2/expanded_conv_7/expand/Relu6" input: "FeatureExtractor/MobilenetV2/expanded_conv_7/depthwise/depthwise_weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -1290,7 +1428,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -1304,6 +1442,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV2/expanded_conv_7/depthwise/Relu6" input: "FeatureExtractor/MobilenetV2/expanded_conv_7/project/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -1344,7 +1488,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -1364,6 +1508,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV2/expanded_conv_8/input" input: "FeatureExtractor/MobilenetV2/expanded_conv_8/expand/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -1404,7 +1554,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -1418,6 +1568,12 @@ node { op: "DepthwiseConv2dNative" input: "FeatureExtractor/MobilenetV2/expanded_conv_8/expand/Relu6" input: "FeatureExtractor/MobilenetV2/expanded_conv_8/depthwise/depthwise_weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -1458,7 +1614,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -1472,6 +1628,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV2/expanded_conv_8/depthwise/Relu6" input: "FeatureExtractor/MobilenetV2/expanded_conv_8/project/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -1512,7 +1674,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -1532,6 +1694,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV2/expanded_conv_9/input" input: "FeatureExtractor/MobilenetV2/expanded_conv_9/expand/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -1572,7 +1740,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -1586,6 +1754,12 @@ node { op: "DepthwiseConv2dNative" input: "FeatureExtractor/MobilenetV2/expanded_conv_9/expand/Relu6" input: "FeatureExtractor/MobilenetV2/expanded_conv_9/depthwise/depthwise_weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -1626,7 +1800,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -1640,6 +1814,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV2/expanded_conv_9/depthwise/Relu6" input: "FeatureExtractor/MobilenetV2/expanded_conv_9/project/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -1680,7 +1860,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -1700,6 +1880,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV2/expanded_conv_10/input" input: "FeatureExtractor/MobilenetV2/expanded_conv_10/expand/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -1740,7 +1926,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -1754,6 +1940,12 @@ node { op: "DepthwiseConv2dNative" input: "FeatureExtractor/MobilenetV2/expanded_conv_10/expand/Relu6" input: "FeatureExtractor/MobilenetV2/expanded_conv_10/depthwise/depthwise_weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -1794,7 +1986,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -1808,6 +2000,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV2/expanded_conv_10/depthwise/Relu6" input: "FeatureExtractor/MobilenetV2/expanded_conv_10/project/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -1848,7 +2046,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -1862,6 +2060,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV2/expanded_conv_10/output" input: "FeatureExtractor/MobilenetV2/expanded_conv_11/expand/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -1902,7 +2106,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -1916,6 +2120,12 @@ node { op: "DepthwiseConv2dNative" input: "FeatureExtractor/MobilenetV2/expanded_conv_11/expand/Relu6" input: "FeatureExtractor/MobilenetV2/expanded_conv_11/depthwise/depthwise_weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -1956,7 +2166,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -1970,6 +2180,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV2/expanded_conv_11/depthwise/Relu6" input: "FeatureExtractor/MobilenetV2/expanded_conv_11/project/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -2010,7 +2226,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -2030,6 +2246,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV2/expanded_conv_12/input" input: "FeatureExtractor/MobilenetV2/expanded_conv_12/expand/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -2070,7 +2292,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -2084,6 +2306,12 @@ node { op: "DepthwiseConv2dNative" input: "FeatureExtractor/MobilenetV2/expanded_conv_12/expand/Relu6" input: "FeatureExtractor/MobilenetV2/expanded_conv_12/depthwise/depthwise_weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -2124,7 +2352,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -2138,6 +2366,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV2/expanded_conv_12/depthwise/Relu6" input: "FeatureExtractor/MobilenetV2/expanded_conv_12/project/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -2178,7 +2412,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -2198,6 +2432,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV2/expanded_conv_13/input" input: "FeatureExtractor/MobilenetV2/expanded_conv_13/expand/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -2238,7 +2478,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -2252,6 +2492,12 @@ node { op: "DepthwiseConv2dNative" input: "FeatureExtractor/MobilenetV2/expanded_conv_13/expand/Relu6" input: "FeatureExtractor/MobilenetV2/expanded_conv_13/depthwise/depthwise_weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -2292,7 +2538,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -2306,6 +2552,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV2/expanded_conv_13/depthwise/Relu6" input: "FeatureExtractor/MobilenetV2/expanded_conv_13/project/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -2346,7 +2598,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -2360,6 +2612,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV2/expanded_conv_13/output" input: "FeatureExtractor/MobilenetV2/expanded_conv_14/expand/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -2400,7 +2658,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -2414,6 +2672,12 @@ node { op: "DepthwiseConv2dNative" input: "FeatureExtractor/MobilenetV2/expanded_conv_14/expand/Relu6" input: "FeatureExtractor/MobilenetV2/expanded_conv_14/depthwise/depthwise_weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -2454,7 +2718,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -2468,6 +2732,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV2/expanded_conv_14/depthwise/Relu6" input: "FeatureExtractor/MobilenetV2/expanded_conv_14/project/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -2508,7 +2778,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -2528,6 +2798,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV2/expanded_conv_15/input" input: "FeatureExtractor/MobilenetV2/expanded_conv_15/expand/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -2568,7 +2844,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -2582,6 +2858,12 @@ node { op: "DepthwiseConv2dNative" input: "FeatureExtractor/MobilenetV2/expanded_conv_15/expand/Relu6" input: "FeatureExtractor/MobilenetV2/expanded_conv_15/depthwise/depthwise_weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -2622,7 +2904,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -2636,6 +2918,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV2/expanded_conv_15/depthwise/Relu6" input: "FeatureExtractor/MobilenetV2/expanded_conv_15/project/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -2676,7 +2964,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -2696,6 +2984,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV2/expanded_conv_16/input" input: "FeatureExtractor/MobilenetV2/expanded_conv_16/expand/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -2736,7 +3030,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -2750,6 +3044,12 @@ node { op: "DepthwiseConv2dNative" input: "FeatureExtractor/MobilenetV2/expanded_conv_16/expand/Relu6" input: "FeatureExtractor/MobilenetV2/expanded_conv_16/depthwise/depthwise_weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -2790,7 +3090,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -2804,6 +3104,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV2/expanded_conv_16/depthwise/Relu6" input: "FeatureExtractor/MobilenetV2/expanded_conv_16/project/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -2844,7 +3150,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -2858,6 +3164,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV2/expanded_conv_16/output" input: "FeatureExtractor/MobilenetV2/Conv_1/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -2898,7 +3210,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -2912,6 +3224,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV2/Conv_1/Relu6" input: "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_2_1x1_256/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -2952,7 +3270,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -2966,6 +3284,12 @@ node { op: "DepthwiseConv2dNative" input: "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_2_1x1_256/Relu6" input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_2_3x3_s2_512_depthwise/depthwise_weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -3006,7 +3330,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -3020,6 +3344,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_2_3x3_s2_512_depthwise/Relu6" input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_2_3x3_s2_512/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -3060,7 +3390,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -3074,6 +3404,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_2_3x3_s2_512/Relu6" input: "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_3_1x1_128/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -3114,7 +3450,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -3128,6 +3464,12 @@ node { op: "DepthwiseConv2dNative" input: "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_3_1x1_128/Relu6" input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_3_3x3_s2_256_depthwise/depthwise_weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -3168,7 +3510,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -3182,6 +3524,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_3_3x3_s2_256_depthwise/Relu6" input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_3_3x3_s2_256/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -3222,7 +3570,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -3236,6 +3584,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_3_3x3_s2_256/Relu6" input: "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_4_1x1_128/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -3276,7 +3630,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -3290,6 +3644,12 @@ node { op: "DepthwiseConv2dNative" input: "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_4_1x1_128/Relu6" input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_4_3x3_s2_256_depthwise/depthwise_weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -3330,7 +3690,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -3344,6 +3704,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_4_3x3_s2_256_depthwise/Relu6" input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_4_3x3_s2_256/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -3384,7 +3750,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -3398,6 +3764,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_4_3x3_s2_256/Relu6" input: "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_5_1x1_64/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -3438,7 +3810,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -3452,6 +3824,12 @@ node { op: "DepthwiseConv2dNative" input: "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_5_1x1_64/Relu6" input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_5_3x3_s2_128_depthwise/depthwise_weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -3492,7 +3870,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -3506,6 +3884,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_5_3x3_s2_128_depthwise/Relu6" input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_5_3x3_s2_128/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -3546,7 +3930,7 @@ node { attr { key: "epsilon" value { - f: 0.0010000000475 + f: 0.001 } } } @@ -3560,6 +3944,12 @@ node { op: "Conv2D" input: "FeatureExtractor/MobilenetV2/expanded_conv_13/expand/Relu6" input: "BoxPredictor_0/BoxEncodingPredictor/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -3600,12 +3990,24 @@ node { op: "BiasAdd" input: "BoxPredictor_0/BoxEncodingPredictor/Conv2D" input: "BoxPredictor_0/BoxEncodingPredictor/biases" + attr { + key: "data_format" + value { + s: "NHWC" + } + } } node { name: "BoxPredictor_0/ClassPredictor/Conv2D" op: "Conv2D" input: "FeatureExtractor/MobilenetV2/expanded_conv_13/expand/Relu6" input: "BoxPredictor_0/ClassPredictor/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -3640,12 +4042,24 @@ node { op: "BiasAdd" input: "BoxPredictor_0/ClassPredictor/Conv2D" input: "BoxPredictor_0/ClassPredictor/biases" + attr { + key: "data_format" + value { + s: "NHWC" + } + } } node { name: "BoxPredictor_1/BoxEncodingPredictor/Conv2D" op: "Conv2D" input: "FeatureExtractor/MobilenetV2/Conv_1/Relu6" input: "BoxPredictor_1/BoxEncodingPredictor/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -3686,12 +4100,24 @@ node { op: "BiasAdd" input: "BoxPredictor_1/BoxEncodingPredictor/Conv2D" input: "BoxPredictor_1/BoxEncodingPredictor/biases" + attr { + key: "data_format" + value { + s: "NHWC" + } + } } node { name: "BoxPredictor_1/ClassPredictor/Conv2D" op: "Conv2D" input: "FeatureExtractor/MobilenetV2/Conv_1/Relu6" input: "BoxPredictor_1/ClassPredictor/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -3726,12 +4152,24 @@ node { op: "BiasAdd" input: "BoxPredictor_1/ClassPredictor/Conv2D" input: "BoxPredictor_1/ClassPredictor/biases" + attr { + key: "data_format" + value { + s: "NHWC" + } + } } node { name: "BoxPredictor_2/BoxEncodingPredictor/Conv2D" op: "Conv2D" input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_2_3x3_s2_512/Relu6" input: "BoxPredictor_2/BoxEncodingPredictor/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -3772,12 +4210,24 @@ node { op: "BiasAdd" input: "BoxPredictor_2/BoxEncodingPredictor/Conv2D" input: "BoxPredictor_2/BoxEncodingPredictor/biases" + attr { + key: "data_format" + value { + s: "NHWC" + } + } } node { name: "BoxPredictor_2/ClassPredictor/Conv2D" op: "Conv2D" input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_2_3x3_s2_512/Relu6" input: "BoxPredictor_2/ClassPredictor/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -3812,12 +4262,24 @@ node { op: "BiasAdd" input: "BoxPredictor_2/ClassPredictor/Conv2D" input: "BoxPredictor_2/ClassPredictor/biases" + attr { + key: "data_format" + value { + s: "NHWC" + } + } } node { name: "BoxPredictor_3/BoxEncodingPredictor/Conv2D" op: "Conv2D" input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_3_3x3_s2_256/Relu6" input: "BoxPredictor_3/BoxEncodingPredictor/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -3858,12 +4320,24 @@ node { op: "BiasAdd" input: "BoxPredictor_3/BoxEncodingPredictor/Conv2D" input: "BoxPredictor_3/BoxEncodingPredictor/biases" + attr { + key: "data_format" + value { + s: "NHWC" + } + } } node { name: "BoxPredictor_3/ClassPredictor/Conv2D" op: "Conv2D" input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_3_3x3_s2_256/Relu6" input: "BoxPredictor_3/ClassPredictor/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -3898,12 +4372,24 @@ node { op: "BiasAdd" input: "BoxPredictor_3/ClassPredictor/Conv2D" input: "BoxPredictor_3/ClassPredictor/biases" + attr { + key: "data_format" + value { + s: "NHWC" + } + } } node { name: "BoxPredictor_4/BoxEncodingPredictor/Conv2D" op: "Conv2D" input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_4_3x3_s2_256/Relu6" input: "BoxPredictor_4/BoxEncodingPredictor/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -3944,12 +4430,24 @@ node { op: "BiasAdd" input: "BoxPredictor_4/BoxEncodingPredictor/Conv2D" input: "BoxPredictor_4/BoxEncodingPredictor/biases" + attr { + key: "data_format" + value { + s: "NHWC" + } + } } node { name: "BoxPredictor_4/ClassPredictor/Conv2D" op: "Conv2D" input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_4_3x3_s2_256/Relu6" input: "BoxPredictor_4/ClassPredictor/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -3984,12 +4482,24 @@ node { op: "BiasAdd" input: "BoxPredictor_4/ClassPredictor/Conv2D" input: "BoxPredictor_4/ClassPredictor/biases" + attr { + key: "data_format" + value { + s: "NHWC" + } + } } node { name: "BoxPredictor_5/BoxEncodingPredictor/Conv2D" op: "Conv2D" input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_5_3x3_s2_128/Relu6" input: "BoxPredictor_5/BoxEncodingPredictor/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -4030,12 +4540,24 @@ node { op: "BiasAdd" input: "BoxPredictor_5/BoxEncodingPredictor/Conv2D" input: "BoxPredictor_5/BoxEncodingPredictor/biases" + attr { + key: "data_format" + value { + s: "NHWC" + } + } } node { name: "BoxPredictor_5/ClassPredictor/Conv2D" op: "Conv2D" input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_5_3x3_s2_128/Relu6" input: "BoxPredictor_5/ClassPredictor/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } attr { key: "dilations" value { @@ -4070,6 +4592,12 @@ node { op: "BiasAdd" input: "BoxPredictor_5/ClassPredictor/Conv2D" input: "BoxPredictor_5/ClassPredictor/biases" + attr { + key: "data_format" + value { + s: "NHWC" + } + } } node { name: "concat/axis_flatten" @@ -4079,12 +4607,12 @@ node { value { tensor { dtype: DT_INT32 + int_val: -1 tensor_shape { dim { size: 1 } } - int_val: -1 } } } @@ -4193,14 +4721,14 @@ node { value { tensor { dtype: DT_FLOAT + float_val: 30.0 + float_val: 42.4264075033 + float_val: 84.8528150067 tensor_shape { dim { size: 3 } } - float_val: 30.0 - float_val: 42.4264068604 - float_val: 84.8528137207 } } } @@ -4209,15 +4737,15 @@ node { value { tensor { dtype: DT_FLOAT + float_val: 0.1 + float_val: 0.1 + float_val: 0.2 + float_val: 0.2 tensor_shape { dim { size: 4 } } - float_val: 0.10000000149 - float_val: 0.10000000149 - float_val: 0.20000000298 - float_val: 0.20000000298 } } } @@ -4226,14 +4754,14 @@ node { value { tensor { dtype: DT_FLOAT + float_val: 30.0 + float_val: 84.8528150067 + float_val: 42.4264075033 tensor_shape { dim { size: 3 } } - float_val: 30.0 - float_val: 84.8528137207 - float_val: 42.4264068604 } } } @@ -4260,17 +4788,17 @@ node { value { tensor { dtype: DT_FLOAT + float_val: 105.0 + float_val: 74.2462120245 + float_val: 148.492424049 + float_val: 60.6217782649 + float_val: 181.874430252 + float_val: 125.499003606 tensor_shape { dim { size: 6 } } - float_val: 105.0 - float_val: 74.2462158203 - float_val: 148.492431641 - float_val: 60.6217765808 - float_val: 181.956329346 - float_val: 125.499000549 } } } @@ -4279,15 +4807,15 @@ node { value { tensor { dtype: DT_FLOAT + float_val: 0.1 + float_val: 0.1 + float_val: 0.2 + float_val: 0.2 tensor_shape { dim { size: 4 } } - float_val: 0.10000000149 - float_val: 0.10000000149 - float_val: 0.20000000298 - float_val: 0.20000000298 } } } @@ -4296,17 +4824,17 @@ node { value { tensor { dtype: DT_FLOAT + float_val: 105.0 + float_val: 148.492424049 + float_val: 74.2462120245 + float_val: 181.865334795 + float_val: 60.6187465972 + float_val: 125.499003606 tensor_shape { dim { size: 6 } } - float_val: 105.0 - float_val: 148.492431641 - float_val: 74.2462158203 - float_val: 181.865341187 - float_val: 60.5914611816 - float_val: 125.499000549 } } } @@ -4333,17 +4861,17 @@ node { value { tensor { dtype: DT_FLOAT + float_val: 149.999999106 + float_val: 106.066016546 + float_val: 212.132033091 + float_val: 86.6025398622 + float_val: 259.820613098 + float_val: 171.026312471 tensor_shape { dim { size: 6 } } - float_val: 150.0 - float_val: 106.066017151 - float_val: 212.132034302 - float_val: 86.6025390625 - float_val: 259.93762207 - float_val: 171.026306152 } } } @@ -4352,15 +4880,15 @@ node { value { tensor { dtype: DT_FLOAT + float_val: 0.1 + float_val: 0.1 + float_val: 0.2 + float_val: 0.2 tensor_shape { dim { size: 4 } } - float_val: 0.10000000149 - float_val: 0.10000000149 - float_val: 0.20000000298 - float_val: 0.20000000298 } } } @@ -4369,17 +4897,17 @@ node { value { tensor { dtype: DT_FLOAT + float_val: 149.999999106 + float_val: 212.132033091 + float_val: 106.066016546 + float_val: 259.807619587 + float_val: 86.5982089084 + float_val: 171.026312471 tensor_shape { dim { size: 6 } } - float_val: 150.0 - float_val: 212.132034302 - float_val: 106.066017151 - float_val: 259.807617188 - float_val: 86.5592269897 - float_val: 171.026306152 } } } @@ -4406,17 +4934,17 @@ node { value { tensor { dtype: DT_FLOAT + float_val: 194.999998212 + float_val: 137.885821067 + float_val: 275.771642134 + float_val: 112.58330146 + float_val: 337.766795943 + float_val: 216.333074327 tensor_shape { dim { size: 6 } } - float_val: 195.0 - float_val: 137.885818481 - float_val: 275.771636963 - float_val: 112.583305359 - float_val: 337.918914795 - float_val: 216.333084106 } } } @@ -4425,15 +4953,15 @@ node { value { tensor { dtype: DT_FLOAT + float_val: 0.1 + float_val: 0.1 + float_val: 0.2 + float_val: 0.2 tensor_shape { dim { size: 4 } } - float_val: 0.10000000149 - float_val: 0.10000000149 - float_val: 0.20000000298 - float_val: 0.20000000298 } } } @@ -4442,17 +4970,17 @@ node { value { tensor { dtype: DT_FLOAT + float_val: 194.999998212 + float_val: 275.771642134 + float_val: 137.885821067 + float_val: 337.749904379 + float_val: 112.57767122 + float_val: 216.333074327 tensor_shape { dim { size: 6 } } - float_val: 195.0 - float_val: 275.771636963 - float_val: 137.885818481 - float_val: 337.749908447 - float_val: 112.527000427 - float_val: 216.333084106 } } } @@ -4479,17 +5007,17 @@ node { value { tensor { dtype: DT_FLOAT + float_val: 239.999997318 + float_val: 169.705625588 + float_val: 339.411251176 + float_val: 138.564063057 + float_val: 415.712978788 + float_val: 261.53393351 tensor_shape { dim { size: 6 } } - float_val: 240.0 - float_val: 169.705627441 - float_val: 339.411254883 - float_val: 138.564071655 - float_val: 415.90020752 - float_val: 261.533935547 } } } @@ -4498,15 +5026,15 @@ node { value { tensor { dtype: DT_FLOAT + float_val: 0.1 + float_val: 0.1 + float_val: 0.2 + float_val: 0.2 tensor_shape { dim { size: 4 } } - float_val: 0.10000000149 - float_val: 0.10000000149 - float_val: 0.20000000298 - float_val: 0.20000000298 } } } @@ -4515,17 +5043,17 @@ node { value { tensor { dtype: DT_FLOAT + float_val: 239.999997318 + float_val: 339.411251176 + float_val: 169.705625588 + float_val: 415.692189171 + float_val: 138.557133531 + float_val: 261.53393351 tensor_shape { dim { size: 6 } } - float_val: 240.0 - float_val: 339.411254883 - float_val: 169.705627441 - float_val: 415.692199707 - float_val: 138.494766235 - float_val: 261.533935547 } } } @@ -4552,17 +5080,17 @@ node { value { tensor { dtype: DT_FLOAT + float_val: 284.999996424 + float_val: 201.525430109 + float_val: 403.050860219 + float_val: 164.544824654 + float_val: 493.659161634 + float_val: 292.40382851 tensor_shape { dim { size: 6 } } - float_val: 285.0 - float_val: 201.525436401 - float_val: 403.050872803 - float_val: 164.544830322 - float_val: 493.881469727 - float_val: 292.403839111 } } } @@ -4571,15 +5099,15 @@ node { value { tensor { dtype: DT_FLOAT + float_val: 0.1 + float_val: 0.1 + float_val: 0.2 + float_val: 0.2 tensor_shape { dim { size: 4 } } - float_val: 0.10000000149 - float_val: 0.10000000149 - float_val: 0.20000000298 - float_val: 0.20000000298 } } } @@ -4588,17 +5116,17 @@ node { value { tensor { dtype: DT_FLOAT + float_val: 284.999996424 + float_val: 403.050860219 + float_val: 201.525430109 + float_val: 493.634473963 + float_val: 164.536595842 + float_val: 292.40382851 tensor_shape { dim { size: 6 } } - float_val: 285.0 - float_val: 403.050872803 - float_val: 201.525436401 - float_val: 493.634490967 - float_val: 164.462539673 - float_val: 292.403839111 } } } @@ -4614,16 +5142,47 @@ node { input: "PriorBox_5" input: "concat/axis_flatten" } +node { + name: "ClassPredictor/concat3d/shape" + op: "Const" + attr { + key: "value" + value { + tensor { + dtype: DT_INT32 + int_val: 0 + int_val: -1 + int_val: 91 + tensor_shape { + dim { + size: 3 + } + } + } + } + } +} +node { + name: "ClassPredictor/concat3d" + op: "Reshape" + input: "ClassPredictor/concat" + input: "ClassPredictor/concat3d/shape" +} node { name: "ClassPredictor/concat/sigmoid" op: "Sigmoid" - input: "ClassPredictor/concat" + input: "ClassPredictor/concat3d" +} +node { + name: "ClassPredictor/concat/sigmoid/Flatten" + op: "Flatten" + input: "ClassPredictor/concat/sigmoid" } node { name: "detection_out" op: "DetectionOutput" input: "BoxEncodingPredictor/concat" - input: "ClassPredictor/concat/sigmoid" + input: "ClassPredictor/concat/sigmoid/Flatten" input: "PriorBox/concat" attr { key: "background_label_id" @@ -4640,7 +5199,7 @@ node { attr { key: "confidence_threshold" value { - f: 0.00999999977648 + f: 0.01 } } attr { @@ -4652,7 +5211,7 @@ node { attr { key: "nms_threshold" value { - f: 0.600000023842 + f: 0.6 } } attr { @@ -4674,5 +5233,3 @@ node { } } } -library { -}