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authorGravatar Patrick Nguyen <drpng@google.com>2018-06-05 10:20:13 -0700
committerGravatar Patrick Nguyen <drpng@google.com>2018-06-05 10:20:13 -0700
commitbf1c227340e02b2a1a5ba5eacdeb1990c21f8f8b (patch)
treecf6c61f001495bcf2551a0b5e8e587b4f072a7a0 /tensorflow/contrib/receptive_field
parent83543deedb68fef61ea7e709de3f462a1edd13ce (diff)
parent8c9afdf9c6c2e8139e2a0526bc41d5220be3b164 (diff)
Merge commit for internal changes
Diffstat (limited to 'tensorflow/contrib/receptive_field')
-rw-r--r--tensorflow/contrib/receptive_field/README.md32
-rw-r--r--tensorflow/contrib/receptive_field/RECEPTIVE_FIELD_TABLE.md629
-rw-r--r--tensorflow/contrib/receptive_field/python/util/examples/csv_to_markdown_table.py82
3 files changed, 740 insertions, 3 deletions
diff --git a/tensorflow/contrib/receptive_field/README.md b/tensorflow/contrib/receptive_field/README.md
index 3ff85faf61..79b015a916 100644
--- a/tensorflow/contrib/receptive_field/README.md
+++ b/tensorflow/contrib/receptive_field/README.md
@@ -6,6 +6,32 @@ region your output features depend on. Better yet, using the parameters computed
by the library, you can easily find the exact image region which is used to
compute each convnet feature.
+This library can be used to compute receptive field parameters of popular
+convnets:
+
+<center>
+
+convnet model | receptive field | effective stride | effective padding
+:-----------------: | :-------------: | :--------------: | :---------------:
+alexnet_v2 | 195 | 32 | 64
+vgg_16 | 212 | 32 | 90
+inception_v2 | 699 | 32 | 318
+inception_v3 | 1311 | 32 | 618
+inception_v4 | 2071 | 32 | 998
+inception_resnet_v2 | 3039 | 32 | 1482
+mobilenet_v1 | 315 | 32 | 126
+mobilenet_v1_075 | 315 | 32 | 126
+resnet_v1_50 | 483 | 32 | 241
+resnet_v1_101 | 1027 | 32 | 513
+resnet_v1_152 | 1507 | 32 | 753
+resnet_v1_200 | 1763 | 32 | 881
+
+</center>
+
+A comprehensive table with pre-computed receptive field parameters for different
+end-points, input resolutions, and other variants of these networks can be found
+[here](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/receptive_field/RECEPTIVE_FIELD_TABLE.md).
+
## Basic usage
The main function to be called is `compute_receptive_field_from_graph_def`,
@@ -96,9 +122,9 @@ The script will write to stdout the receptive field parameters for many variants
of several popular convnets: AlexNet, VGG, ResNet, Inception, Mobilenet. They
are also written to the file `/tmp/rf_benchmark_results.csv`.
-TODO: include here a plot for receptive field sizes of different convnets.
-
-TODO: include table/link to pre-computed RF parameters.
+A comprehensive table with pre-computed receptive field parameters for different
+networks can be found
+[here](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/receptive_field/RECEPTIVE_FIELD_TABLE.md).
## Compute RF parameters from a graph pbtxt
diff --git a/tensorflow/contrib/receptive_field/RECEPTIVE_FIELD_TABLE.md b/tensorflow/contrib/receptive_field/RECEPTIVE_FIELD_TABLE.md
new file mode 100644
index 0000000000..736fbef6e7
--- /dev/null
+++ b/tensorflow/contrib/receptive_field/RECEPTIVE_FIELD_TABLE.md
@@ -0,0 +1,629 @@
+# Pre-computed receptive field parameters
+
+## Table with results
+
+The table below presents the receptive field parameters for several popular
+convolutional neural networks. These are computed using the models from the
+[TF-Slim
+repository](https://github.com/tensorflow/models/tree/master/research/slim),
+by using the [rf_benchmark
+script](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/receptive_field/python/util/examples/rf_benchmark.py).
+
+Questions? See the [FAQ](#faq).
+
+CNN | resolution | end-point | RF | effective stride | effective padding
+:----------------------------: | :--------: | :------------------: | :--: | :--------------: | :---------------:
+alexnet_v2 | None | alexnet_v2/conv1 | 11 | 4 | 0
+alexnet_v2 | None | alexnet_v2/pool1 | 19 | 8 | 0
+alexnet_v2 | None | alexnet_v2/conv2 | 51 | 8 | 16
+alexnet_v2 | None | alexnet_v2/conv3 | 99 | 16 | 32
+alexnet_v2 | None | alexnet_v2/conv4 | 131 | 16 | 48
+alexnet_v2 | None | alexnet_v2/conv5 | 163 | 16 | 64
+alexnet_v2 | None | alexnet_v2/pool5 | 195 | 32 | 64
+alexnet_v2 | 224 | alexnet_v2/conv1 | 11 | 4 | 0
+alexnet_v2 | 224 | alexnet_v2/pool1 | 19 | 8 | 0
+alexnet_v2 | 224 | alexnet_v2/conv2 | 51 | 8 | 16
+alexnet_v2 | 224 | alexnet_v2/conv3 | 99 | 16 | 32
+alexnet_v2 | 224 | alexnet_v2/conv4 | 131 | 16 | 48
+alexnet_v2 | 224 | alexnet_v2/conv5 | 163 | 16 | 64
+alexnet_v2 | 224 | alexnet_v2/pool5 | 195 | 32 | 64
+alexnet_v2 | 321 | alexnet_v2/conv1 | 11 | 4 | 0
+alexnet_v2 | 321 | alexnet_v2/pool1 | 19 | 8 | 0
+alexnet_v2 | 321 | alexnet_v2/conv2 | 51 | 8 | 16
+alexnet_v2 | 321 | alexnet_v2/conv3 | 99 | 16 | 32
+alexnet_v2 | 321 | alexnet_v2/conv4 | 131 | 16 | 48
+alexnet_v2 | 321 | alexnet_v2/conv5 | 163 | 16 | 64
+alexnet_v2 | 321 | alexnet_v2/pool5 | 195 | 32 | 64
+vgg_a | None | vgg_a/conv1/conv1_1 | 3 | 1 | 1
+vgg_a | None | vgg_a/pool1 | 4 | 2 | 1
+vgg_a | None | vgg_a/conv2/conv2_1 | 8 | 2 | 3
+vgg_a | None | vgg_a/pool2 | 10 | 4 | 3
+vgg_a | None | vgg_a/conv3/conv3_1 | 18 | 4 | 7
+vgg_a | None | vgg_a/conv3/conv3_2 | 26 | 4 | 11
+vgg_a | None | vgg_a/pool3 | 30 | 8 | 11
+vgg_a | None | vgg_a/conv4/conv4_1 | 46 | 8 | 19
+vgg_a | None | vgg_a/conv4/conv4_2 | 62 | 8 | 27
+vgg_a | None | vgg_a/pool4 | 70 | 16 | 27
+vgg_a | None | vgg_a/conv5/conv5_1 | 102 | 16 | 43
+vgg_a | None | vgg_a/conv5/conv5_2 | 134 | 16 | 59
+vgg_a | None | vgg_a/pool5 | 150 | 32 | 59
+vgg_a | 224 | vgg_a/conv1/conv1_1 | 3 | 1 | 1
+vgg_a | 224 | vgg_a/pool1 | 4 | 2 | 1
+vgg_a | 224 | vgg_a/conv2/conv2_1 | 8 | 2 | 3
+vgg_a | 224 | vgg_a/pool2 | 10 | 4 | 3
+vgg_a | 224 | vgg_a/conv3/conv3_1 | 18 | 4 | 7
+vgg_a | 224 | vgg_a/conv3/conv3_2 | 26 | 4 | 11
+vgg_a | 224 | vgg_a/pool3 | 30 | 8 | 11
+vgg_a | 224 | vgg_a/conv4/conv4_1 | 46 | 8 | 19
+vgg_a | 224 | vgg_a/conv4/conv4_2 | 62 | 8 | 27
+vgg_a | 224 | vgg_a/pool4 | 70 | 16 | 27
+vgg_a | 224 | vgg_a/conv5/conv5_1 | 102 | 16 | 43
+vgg_a | 224 | vgg_a/conv5/conv5_2 | 134 | 16 | 59
+vgg_a | 224 | vgg_a/pool5 | 150 | 32 | 59
+vgg_a | 321 | vgg_a/conv1/conv1_1 | 3 | 1 | 1
+vgg_a | 321 | vgg_a/pool1 | 4 | 2 | 1
+vgg_a | 321 | vgg_a/conv2/conv2_1 | 8 | 2 | 3
+vgg_a | 321 | vgg_a/pool2 | 10 | 4 | 3
+vgg_a | 321 | vgg_a/conv3/conv3_1 | 18 | 4 | 7
+vgg_a | 321 | vgg_a/conv3/conv3_2 | 26 | 4 | 11
+vgg_a | 321 | vgg_a/pool3 | 30 | 8 | 11
+vgg_a | 321 | vgg_a/conv4/conv4_1 | 46 | 8 | 19
+vgg_a | 321 | vgg_a/conv4/conv4_2 | 62 | 8 | 27
+vgg_a | 321 | vgg_a/pool4 | 70 | 16 | 27
+vgg_a | 321 | vgg_a/conv5/conv5_1 | 102 | 16 | 43
+vgg_a | 321 | vgg_a/conv5/conv5_2 | 134 | 16 | 59
+vgg_a | 321 | vgg_a/pool5 | 150 | 32 | 59
+vgg_16 | None | vgg_16/conv1/conv1_1 | 3 | 1 | 1
+vgg_16 | None | vgg_16/pool1 | 6 | 2 | 2
+vgg_16 | None | vgg_16/conv2/conv2_1 | 10 | 2 | 4
+vgg_16 | None | vgg_16/pool2 | 16 | 4 | 6
+vgg_16 | None | vgg_16/conv3/conv3_1 | 24 | 4 | 10
+vgg_16 | None | vgg_16/conv3/conv3_2 | 32 | 4 | 14
+vgg_16 | None | vgg_16/pool3 | 44 | 8 | 18
+vgg_16 | None | vgg_16/conv4/conv4_1 | 60 | 8 | 26
+vgg_16 | None | vgg_16/conv4/conv4_2 | 76 | 8 | 34
+vgg_16 | None | vgg_16/pool4 | 100 | 16 | 42
+vgg_16 | None | vgg_16/conv5/conv5_1 | 132 | 16 | 58
+vgg_16 | None | vgg_16/conv5/conv5_2 | 164 | 16 | 74
+vgg_16 | None | vgg_16/pool5 | 212 | 32 | 90
+vgg_16 | 224 | vgg_16/conv1/conv1_1 | 3 | 1 | 1
+vgg_16 | 224 | vgg_16/pool1 | 6 | 2 | 2
+vgg_16 | 224 | vgg_16/conv2/conv2_1 | 10 | 2 | 4
+vgg_16 | 224 | vgg_16/pool2 | 16 | 4 | 6
+vgg_16 | 224 | vgg_16/conv3/conv3_1 | 24 | 4 | 10
+vgg_16 | 224 | vgg_16/conv3/conv3_2 | 32 | 4 | 14
+vgg_16 | 224 | vgg_16/pool3 | 44 | 8 | 18
+vgg_16 | 224 | vgg_16/conv4/conv4_1 | 60 | 8 | 26
+vgg_16 | 224 | vgg_16/conv4/conv4_2 | 76 | 8 | 34
+vgg_16 | 224 | vgg_16/pool4 | 100 | 16 | 42
+vgg_16 | 224 | vgg_16/conv5/conv5_1 | 132 | 16 | 58
+vgg_16 | 224 | vgg_16/conv5/conv5_2 | 164 | 16 | 74
+vgg_16 | 224 | vgg_16/pool5 | 212 | 32 | 90
+vgg_16 | 321 | vgg_16/conv1/conv1_1 | 3 | 1 | 1
+vgg_16 | 321 | vgg_16/pool1 | 6 | 2 | 2
+vgg_16 | 321 | vgg_16/conv2/conv2_1 | 10 | 2 | 4
+vgg_16 | 321 | vgg_16/pool2 | 16 | 4 | 6
+vgg_16 | 321 | vgg_16/conv3/conv3_1 | 24 | 4 | 10
+vgg_16 | 321 | vgg_16/conv3/conv3_2 | 32 | 4 | 14
+vgg_16 | 321 | vgg_16/pool3 | 44 | 8 | 18
+vgg_16 | 321 | vgg_16/conv4/conv4_1 | 60 | 8 | 26
+vgg_16 | 321 | vgg_16/conv4/conv4_2 | 76 | 8 | 34
+vgg_16 | 321 | vgg_16/pool4 | 100 | 16 | 42
+vgg_16 | 321 | vgg_16/conv5/conv5_1 | 132 | 16 | 58
+vgg_16 | 321 | vgg_16/conv5/conv5_2 | 164 | 16 | 74
+vgg_16 | 321 | vgg_16/pool5 | 212 | 32 | 90
+inception_v2 | None | Conv2d_1a_7x7 | 7 | 2 | None
+inception_v2 | None | MaxPool_2a_3x3 | 11 | 4 | None
+inception_v2 | None | Conv2d_2b_1x1 | 11 | 4 | None
+inception_v2 | None | Conv2d_2c_3x3 | 19 | 4 | None
+inception_v2 | None | MaxPool_3a_3x3 | 27 | 8 | None
+inception_v2 | None | Mixed_3b | 59 | 8 | None
+inception_v2 | None | Mixed_3c | 91 | 8 | None
+inception_v2 | None | Mixed_4a | 123 | 16 | None
+inception_v2 | None | Mixed_4b | 187 | 16 | None
+inception_v2 | None | Mixed_4c | 251 | 16 | None
+inception_v2 | None | Mixed_4d | 315 | 16 | None
+inception_v2 | None | Mixed_4e | 379 | 16 | None
+inception_v2 | None | Mixed_5a | 443 | 32 | None
+inception_v2 | None | Mixed_5b | 571 | 32 | None
+inception_v2 | None | Mixed_5c | 699 | 32 | None
+inception_v2 | 224 | Conv2d_1a_7x7 | 7 | 2 | 2
+inception_v2 | 224 | MaxPool_2a_3x3 | 11 | 4 | 2
+inception_v2 | 224 | Conv2d_2b_1x1 | 11 | 4 | 2
+inception_v2 | 224 | Conv2d_2c_3x3 | 19 | 4 | 6
+inception_v2 | 224 | MaxPool_3a_3x3 | 27 | 8 | 6
+inception_v2 | 224 | Mixed_3b | 59 | 8 | 22
+inception_v2 | 224 | Mixed_3c | 91 | 8 | 38
+inception_v2 | 224 | Mixed_4a | 123 | 16 | 46
+inception_v2 | 224 | Mixed_4b | 187 | 16 | 78
+inception_v2 | 224 | Mixed_4c | 251 | 16 | 110
+inception_v2 | 224 | Mixed_4d | 315 | 16 | 142
+inception_v2 | 224 | Mixed_4e | 379 | 16 | 174
+inception_v2 | 224 | Mixed_5a | 443 | 32 | 190
+inception_v2 | 224 | Mixed_5b | 571 | 32 | 254
+inception_v2 | 224 | Mixed_5c | 699 | 32 | 318
+inception_v2 | 321 | Conv2d_1a_7x7 | 7 | 2 | 3
+inception_v2 | 321 | MaxPool_2a_3x3 | 11 | 4 | 5
+inception_v2 | 321 | Conv2d_2b_1x1 | 11 | 4 | 5
+inception_v2 | 321 | Conv2d_2c_3x3 | 19 | 4 | 9
+inception_v2 | 321 | MaxPool_3a_3x3 | 27 | 8 | 13
+inception_v2 | 321 | Mixed_3b | 59 | 8 | 29
+inception_v2 | 321 | Mixed_3c | 91 | 8 | 45
+inception_v2 | 321 | Mixed_4a | 123 | 16 | 61
+inception_v2 | 321 | Mixed_4b | 187 | 16 | 93
+inception_v2 | 321 | Mixed_4c | 251 | 16 | 125
+inception_v2 | 321 | Mixed_4d | 315 | 16 | 157
+inception_v2 | 321 | Mixed_4e | 379 | 16 | 189
+inception_v2 | 321 | Mixed_5a | 443 | 32 | 221
+inception_v2 | 321 | Mixed_5b | 571 | 32 | 285
+inception_v2 | 321 | Mixed_5c | 699 | 32 | 349
+inception_v2-no-separable-conv | None | Conv2d_1a_7x7 | 7 | 2 | None
+inception_v2-no-separable-conv | None | MaxPool_2a_3x3 | 11 | 4 | None
+inception_v2-no-separable-conv | None | Conv2d_2b_1x1 | 11 | 4 | None
+inception_v2-no-separable-conv | None | Conv2d_2c_3x3 | 19 | 4 | None
+inception_v2-no-separable-conv | None | MaxPool_3a_3x3 | 27 | 8 | None
+inception_v2-no-separable-conv | None | Mixed_3b | 59 | 8 | None
+inception_v2-no-separable-conv | None | Mixed_3c | 91 | 8 | None
+inception_v2-no-separable-conv | None | Mixed_4a | 123 | 16 | None
+inception_v2-no-separable-conv | None | Mixed_4b | 187 | 16 | None
+inception_v2-no-separable-conv | None | Mixed_4c | 251 | 16 | None
+inception_v2-no-separable-conv | None | Mixed_4d | 315 | 16 | None
+inception_v2-no-separable-conv | None | Mixed_4e | 379 | 16 | None
+inception_v2-no-separable-conv | None | Mixed_5a | 443 | 32 | None
+inception_v2-no-separable-conv | None | Mixed_5b | 571 | 32 | None
+inception_v2-no-separable-conv | None | Mixed_5c | 699 | 32 | None
+inception_v2-no-separable-conv | 224 | Conv2d_1a_7x7 | 7 | 2 | 2
+inception_v2-no-separable-conv | 224 | MaxPool_2a_3x3 | 11 | 4 | 2
+inception_v2-no-separable-conv | 224 | Conv2d_2b_1x1 | 11 | 4 | 2
+inception_v2-no-separable-conv | 224 | Conv2d_2c_3x3 | 19 | 4 | 6
+inception_v2-no-separable-conv | 224 | MaxPool_3a_3x3 | 27 | 8 | 6
+inception_v2-no-separable-conv | 224 | Mixed_3b | 59 | 8 | 22
+inception_v2-no-separable-conv | 224 | Mixed_3c | 91 | 8 | 38
+inception_v2-no-separable-conv | 224 | Mixed_4a | 123 | 16 | 46
+inception_v2-no-separable-conv | 224 | Mixed_4b | 187 | 16 | 78
+inception_v2-no-separable-conv | 224 | Mixed_4c | 251 | 16 | 110
+inception_v2-no-separable-conv | 224 | Mixed_4d | 315 | 16 | 142
+inception_v2-no-separable-conv | 224 | Mixed_4e | 379 | 16 | 174
+inception_v2-no-separable-conv | 224 | Mixed_5a | 443 | 32 | 190
+inception_v2-no-separable-conv | 224 | Mixed_5b | 571 | 32 | 254
+inception_v2-no-separable-conv | 224 | Mixed_5c | 699 | 32 | 318
+inception_v2-no-separable-conv | 321 | Conv2d_1a_7x7 | 7 | 2 | 3
+inception_v2-no-separable-conv | 321 | MaxPool_2a_3x3 | 11 | 4 | 5
+inception_v2-no-separable-conv | 321 | Conv2d_2b_1x1 | 11 | 4 | 5
+inception_v2-no-separable-conv | 321 | Conv2d_2c_3x3 | 19 | 4 | 9
+inception_v2-no-separable-conv | 321 | MaxPool_3a_3x3 | 27 | 8 | 13
+inception_v2-no-separable-conv | 321 | Mixed_3b | 59 | 8 | 29
+inception_v2-no-separable-conv | 321 | Mixed_3c | 91 | 8 | 45
+inception_v2-no-separable-conv | 321 | Mixed_4a | 123 | 16 | 61
+inception_v2-no-separable-conv | 321 | Mixed_4b | 187 | 16 | 93
+inception_v2-no-separable-conv | 321 | Mixed_4c | 251 | 16 | 125
+inception_v2-no-separable-conv | 321 | Mixed_4d | 315 | 16 | 157
+inception_v2-no-separable-conv | 321 | Mixed_4e | 379 | 16 | 189
+inception_v2-no-separable-conv | 321 | Mixed_5a | 443 | 32 | 221
+inception_v2-no-separable-conv | 321 | Mixed_5b | 571 | 32 | 285
+inception_v2-no-separable-conv | 321 | Mixed_5c | 699 | 32 | 349
+inception_v3 | None | Conv2d_1a_3x3 | 3 | 2 | 0
+inception_v3 | None | Conv2d_2a_3x3 | 7 | 2 | 0
+inception_v3 | None | Conv2d_2b_3x3 | 11 | 2 | 2
+inception_v3 | None | MaxPool_3a_3x3 | 15 | 4 | 2
+inception_v3 | None | Conv2d_3b_1x1 | 15 | 4 | 2
+inception_v3 | None | Conv2d_4a_3x3 | 23 | 4 | 2
+inception_v3 | None | MaxPool_5a_3x3 | 31 | 8 | 2
+inception_v3 | None | Mixed_5b | 63 | 8 | 18
+inception_v3 | None | Mixed_5c | 95 | 8 | 34
+inception_v3 | None | Mixed_5d | 127 | 8 | 50
+inception_v3 | None | Mixed_6a | 159 | 16 | 58
+inception_v3 | None | Mixed_6b | 351 | 16 | 154
+inception_v3 | None | Mixed_6c | 543 | 16 | 250
+inception_v3 | None | Mixed_6d | 735 | 16 | 346
+inception_v3 | None | Mixed_6e | 927 | 16 | 442
+inception_v3 | None | Mixed_7a | 1055 | 32 | 490
+inception_v3 | None | Mixed_7b | 1183 | 32 | 554
+inception_v3 | None | Mixed_7c | 1311 | 32 | 618
+inception_v3 | 224 | Conv2d_1a_3x3 | 3 | 2 | 0
+inception_v3 | 224 | Conv2d_2a_3x3 | 7 | 2 | 0
+inception_v3 | 224 | Conv2d_2b_3x3 | 11 | 2 | 2
+inception_v3 | 224 | MaxPool_3a_3x3 | 15 | 4 | 2
+inception_v3 | 224 | Conv2d_3b_1x1 | 15 | 4 | 2
+inception_v3 | 224 | Conv2d_4a_3x3 | 23 | 4 | 2
+inception_v3 | 224 | MaxPool_5a_3x3 | 31 | 8 | 2
+inception_v3 | 224 | Mixed_5b | 63 | 8 | 18
+inception_v3 | 224 | Mixed_5c | 95 | 8 | 34
+inception_v3 | 224 | Mixed_5d | 127 | 8 | 50
+inception_v3 | 224 | Mixed_6a | 159 | 16 | 58
+inception_v3 | 224 | Mixed_6b | 351 | 16 | 154
+inception_v3 | 224 | Mixed_6c | 543 | 16 | 250
+inception_v3 | 224 | Mixed_6d | 735 | 16 | 346
+inception_v3 | 224 | Mixed_6e | 927 | 16 | 442
+inception_v3 | 224 | Mixed_7a | 1055 | 32 | 490
+inception_v3 | 224 | Mixed_7b | 1183 | 32 | 554
+inception_v3 | 224 | Mixed_7c | 1311 | 32 | 618
+inception_v3 | 321 | Conv2d_1a_3x3 | 3 | 2 | 0
+inception_v3 | 321 | Conv2d_2a_3x3 | 7 | 2 | 0
+inception_v3 | 321 | Conv2d_2b_3x3 | 11 | 2 | 2
+inception_v3 | 321 | MaxPool_3a_3x3 | 15 | 4 | 2
+inception_v3 | 321 | Conv2d_3b_1x1 | 15 | 4 | 2
+inception_v3 | 321 | Conv2d_4a_3x3 | 23 | 4 | 2
+inception_v3 | 321 | MaxPool_5a_3x3 | 31 | 8 | 2
+inception_v3 | 321 | Mixed_5b | 63 | 8 | 18
+inception_v3 | 321 | Mixed_5c | 95 | 8 | 34
+inception_v3 | 321 | Mixed_5d | 127 | 8 | 50
+inception_v3 | 321 | Mixed_6a | 159 | 16 | 58
+inception_v3 | 321 | Mixed_6b | 351 | 16 | 154
+inception_v3 | 321 | Mixed_6c | 543 | 16 | 250
+inception_v3 | 321 | Mixed_6d | 735 | 16 | 346
+inception_v3 | 321 | Mixed_6e | 927 | 16 | 442
+inception_v3 | 321 | Mixed_7a | 1055 | 32 | 490
+inception_v3 | 321 | Mixed_7b | 1183 | 32 | 554
+inception_v3 | 321 | Mixed_7c | 1311 | 32 | 618
+inception_v4 | None | Conv2d_1a_3x3 | 3 | 2 | 0
+inception_v4 | None | Conv2d_2a_3x3 | 7 | 2 | 0
+inception_v4 | None | Conv2d_2b_3x3 | 11 | 2 | 2
+inception_v4 | None | Mixed_3a | 15 | 4 | 2
+inception_v4 | None | Mixed_4a | 47 | 4 | 14
+inception_v4 | None | Mixed_5a | 55 | 8 | 14
+inception_v4 | None | Mixed_5b | 87 | 8 | 30
+inception_v4 | None | Mixed_5c | 119 | 8 | 46
+inception_v4 | None | Mixed_5d | 151 | 8 | 62
+inception_v4 | None | Mixed_5e | 183 | 8 | 78
+inception_v4 | None | Mixed_6a | 215 | 16 | 86
+inception_v4 | None | Mixed_6b | 407 | 16 | 182
+inception_v4 | None | Mixed_6c | 599 | 16 | 278
+inception_v4 | None | Mixed_6d | 791 | 16 | 374
+inception_v4 | None | Mixed_6e | 983 | 16 | 470
+inception_v4 | None | Mixed_6f | 1175 | 16 | 566
+inception_v4 | None | Mixed_6g | 1367 | 16 | 662
+inception_v4 | None | Mixed_6h | 1559 | 16 | 758
+inception_v4 | None | Mixed_7a | 1687 | 32 | 806
+inception_v4 | None | Mixed_7b | 1815 | 32 | 870
+inception_v4 | None | Mixed_7c | 1943 | 32 | 934
+inception_v4 | None | Mixed_7d | 2071 | 32 | 998
+inception_v4 | 224 | Conv2d_1a_3x3 | 3 | 2 | 0
+inception_v4 | 224 | Conv2d_2a_3x3 | 7 | 2 | 0
+inception_v4 | 224 | Conv2d_2b_3x3 | 11 | 2 | 2
+inception_v4 | 224 | Mixed_3a | 15 | 4 | 2
+inception_v4 | 224 | Mixed_4a | 47 | 4 | 14
+inception_v4 | 224 | Mixed_5a | 55 | 8 | 14
+inception_v4 | 224 | Mixed_5b | 87 | 8 | 30
+inception_v4 | 224 | Mixed_5c | 119 | 8 | 46
+inception_v4 | 224 | Mixed_5d | 151 | 8 | 62
+inception_v4 | 224 | Mixed_5e | 183 | 8 | 78
+inception_v4 | 224 | Mixed_6a | 215 | 16 | 86
+inception_v4 | 224 | Mixed_6b | 407 | 16 | 182
+inception_v4 | 224 | Mixed_6c | 599 | 16 | 278
+inception_v4 | 224 | Mixed_6d | 791 | 16 | 374
+inception_v4 | 224 | Mixed_6e | 983 | 16 | 470
+inception_v4 | 224 | Mixed_6f | 1175 | 16 | 566
+inception_v4 | 224 | Mixed_6g | 1367 | 16 | 662
+inception_v4 | 224 | Mixed_6h | 1559 | 16 | 758
+inception_v4 | 224 | Mixed_7a | 1687 | 32 | 806
+inception_v4 | 224 | Mixed_7b | 1815 | 32 | 870
+inception_v4 | 224 | Mixed_7c | 1943 | 32 | 934
+inception_v4 | 224 | Mixed_7d | 2071 | 32 | 998
+inception_v4 | 321 | Conv2d_1a_3x3 | 3 | 2 | 0
+inception_v4 | 321 | Conv2d_2a_3x3 | 7 | 2 | 0
+inception_v4 | 321 | Conv2d_2b_3x3 | 11 | 2 | 2
+inception_v4 | 321 | Mixed_3a | 15 | 4 | 2
+inception_v4 | 321 | Mixed_4a | 47 | 4 | 14
+inception_v4 | 321 | Mixed_5a | 55 | 8 | 14
+inception_v4 | 321 | Mixed_5b | 87 | 8 | 30
+inception_v4 | 321 | Mixed_5c | 119 | 8 | 46
+inception_v4 | 321 | Mixed_5d | 151 | 8 | 62
+inception_v4 | 321 | Mixed_5e | 183 | 8 | 78
+inception_v4 | 321 | Mixed_6a | 215 | 16 | 86
+inception_v4 | 321 | Mixed_6b | 407 | 16 | 182
+inception_v4 | 321 | Mixed_6c | 599 | 16 | 278
+inception_v4 | 321 | Mixed_6d | 791 | 16 | 374
+inception_v4 | 321 | Mixed_6e | 983 | 16 | 470
+inception_v4 | 321 | Mixed_6f | 1175 | 16 | 566
+inception_v4 | 321 | Mixed_6g | 1367 | 16 | 662
+inception_v4 | 321 | Mixed_6h | 1559 | 16 | 758
+inception_v4 | 321 | Mixed_7a | 1687 | 32 | 806
+inception_v4 | 321 | Mixed_7b | 1815 | 32 | 870
+inception_v4 | 321 | Mixed_7c | 1943 | 32 | 934
+inception_v4 | 321 | Mixed_7d | 2071 | 32 | 998
+inception_resnet_v2 | None | Conv2d_1a_3x3 | 3 | 2 | 0
+inception_resnet_v2 | None | Conv2d_2a_3x3 | 7 | 2 | 0
+inception_resnet_v2 | None | Conv2d_2b_3x3 | 11 | 2 | 2
+inception_resnet_v2 | None | MaxPool_3a_3x3 | 15 | 4 | 2
+inception_resnet_v2 | None | Conv2d_3b_1x1 | 15 | 4 | 2
+inception_resnet_v2 | None | Conv2d_4a_3x3 | 23 | 4 | 2
+inception_resnet_v2 | None | MaxPool_5a_3x3 | 31 | 8 | 2
+inception_resnet_v2 | None | Mixed_5b | 63 | 8 | 18
+inception_resnet_v2 | None | Mixed_6a | 415 | 16 | 186
+inception_resnet_v2 | None | PreAuxLogits | 2335 | 16 | 1146
+inception_resnet_v2 | None | Mixed_7a | 2399 | 32 | 1162
+inception_resnet_v2 | None | Conv2d_7b_1x1 | 3039 | 32 | 1482
+inception_resnet_v2 | 224 | Conv2d_1a_3x3 | 3 | 2 | 0
+inception_resnet_v2 | 224 | Conv2d_2a_3x3 | 7 | 2 | 0
+inception_resnet_v2 | 224 | Conv2d_2b_3x3 | 11 | 2 | 2
+inception_resnet_v2 | 224 | MaxPool_3a_3x3 | 15 | 4 | 2
+inception_resnet_v2 | 224 | Conv2d_3b_1x1 | 15 | 4 | 2
+inception_resnet_v2 | 224 | Conv2d_4a_3x3 | 23 | 4 | 2
+inception_resnet_v2 | 224 | MaxPool_5a_3x3 | 31 | 8 | 2
+inception_resnet_v2 | 224 | Mixed_5b | 63 | 8 | 18
+inception_resnet_v2 | 224 | Mixed_6a | 415 | 16 | 186
+inception_resnet_v2 | 224 | PreAuxLogits | 2335 | 16 | 1146
+inception_resnet_v2 | 224 | Mixed_7a | 2399 | 32 | 1162
+inception_resnet_v2 | 224 | Conv2d_7b_1x1 | 3039 | 32 | 1482
+inception_resnet_v2 | 321 | Conv2d_1a_3x3 | 3 | 2 | 0
+inception_resnet_v2 | 321 | Conv2d_2a_3x3 | 7 | 2 | 0
+inception_resnet_v2 | 321 | Conv2d_2b_3x3 | 11 | 2 | 2
+inception_resnet_v2 | 321 | MaxPool_3a_3x3 | 15 | 4 | 2
+inception_resnet_v2 | 321 | Conv2d_3b_1x1 | 15 | 4 | 2
+inception_resnet_v2 | 321 | Conv2d_4a_3x3 | 23 | 4 | 2
+inception_resnet_v2 | 321 | MaxPool_5a_3x3 | 31 | 8 | 2
+inception_resnet_v2 | 321 | Mixed_5b | 63 | 8 | 18
+inception_resnet_v2 | 321 | Mixed_6a | 415 | 16 | 186
+inception_resnet_v2 | 321 | PreAuxLogits | 2335 | 16 | 1146
+inception_resnet_v2 | 321 | Mixed_7a | 2399 | 32 | 1162
+inception_resnet_v2 | 321 | Conv2d_7b_1x1 | 3039 | 32 | 1482
+inception_resnet_v2-same | None | Conv2d_1a_3x3 | 3 | 2 | None
+inception_resnet_v2-same | None | Conv2d_2a_3x3 | 7 | 2 | None
+inception_resnet_v2-same | None | Conv2d_2b_3x3 | 11 | 2 | None
+inception_resnet_v2-same | None | MaxPool_3a_3x3 | 15 | 4 | None
+inception_resnet_v2-same | None | Conv2d_3b_1x1 | 15 | 4 | None
+inception_resnet_v2-same | None | Conv2d_4a_3x3 | 23 | 4 | None
+inception_resnet_v2-same | None | MaxPool_5a_3x3 | 31 | 8 | None
+inception_resnet_v2-same | None | Mixed_5b | 63 | 8 | None
+inception_resnet_v2-same | None | Mixed_6a | 415 | 16 | None
+inception_resnet_v2-same | None | PreAuxLogits | 2335 | 16 | None
+inception_resnet_v2-same | None | Mixed_7a | 2399 | 32 | None
+inception_resnet_v2-same | None | Conv2d_7b_1x1 | 3039 | 32 | None
+inception_resnet_v2-same | 224 | Conv2d_1a_3x3 | 3 | 2 | 0
+inception_resnet_v2-same | 224 | Conv2d_2a_3x3 | 7 | 2 | 2
+inception_resnet_v2-same | 224 | Conv2d_2b_3x3 | 11 | 2 | 4
+inception_resnet_v2-same | 224 | MaxPool_3a_3x3 | 15 | 4 | 4
+inception_resnet_v2-same | 224 | Conv2d_3b_1x1 | 15 | 4 | 4
+inception_resnet_v2-same | 224 | Conv2d_4a_3x3 | 23 | 4 | 8
+inception_resnet_v2-same | 224 | MaxPool_5a_3x3 | 31 | 8 | 8
+inception_resnet_v2-same | 224 | Mixed_5b | 63 | 8 | 24
+inception_resnet_v2-same | 224 | Mixed_6a | 415 | 16 | 192
+inception_resnet_v2-same | 224 | PreAuxLogits | 2335 | 16 | 1152
+inception_resnet_v2-same | 224 | Mixed_7a | 2399 | 32 | 1168
+inception_resnet_v2-same | 224 | Conv2d_7b_1x1 | 3039 | 32 | 1488
+inception_resnet_v2-same | 321 | Conv2d_1a_3x3 | 3 | 2 | 1
+inception_resnet_v2-same | 321 | Conv2d_2a_3x3 | 7 | 2 | 3
+inception_resnet_v2-same | 321 | Conv2d_2b_3x3 | 11 | 2 | 5
+inception_resnet_v2-same | 321 | MaxPool_3a_3x3 | 15 | 4 | 7
+inception_resnet_v2-same | 321 | Conv2d_3b_1x1 | 15 | 4 | 7
+inception_resnet_v2-same | 321 | Conv2d_4a_3x3 | 23 | 4 | 11
+inception_resnet_v2-same | 321 | MaxPool_5a_3x3 | 31 | 8 | 15
+inception_resnet_v2-same | 321 | Mixed_5b | 63 | 8 | 31
+inception_resnet_v2-same | 321 | Mixed_6a | 415 | 16 | 207
+inception_resnet_v2-same | 321 | PreAuxLogits | 2335 | 16 | 1167
+inception_resnet_v2-same | 321 | Mixed_7a | 2399 | 32 | 1199
+inception_resnet_v2-same | 321 | Conv2d_7b_1x1 | 3039 | 32 | 1519
+mobilenet_v1 | None | Conv2d_0 | 3 | 2 | None
+mobilenet_v1 | None | Conv2d_1_pointwise | 7 | 2 | None
+mobilenet_v1 | None | Conv2d_2_pointwise | 11 | 4 | None
+mobilenet_v1 | None | Conv2d_3_pointwise | 19 | 4 | None
+mobilenet_v1 | None | Conv2d_4_pointwise | 27 | 8 | None
+mobilenet_v1 | None | Conv2d_5_pointwise | 43 | 8 | None
+mobilenet_v1 | None | Conv2d_6_pointwise | 59 | 16 | None
+mobilenet_v1 | None | Conv2d_7_pointwise | 91 | 16 | None
+mobilenet_v1 | None | Conv2d_8_pointwise | 123 | 16 | None
+mobilenet_v1 | None | Conv2d_9_pointwise | 155 | 16 | None
+mobilenet_v1 | None | Conv2d_10_pointwise | 187 | 16 | None
+mobilenet_v1 | None | Conv2d_11_pointwise | 219 | 16 | None
+mobilenet_v1 | None | Conv2d_12_pointwise | 251 | 32 | None
+mobilenet_v1 | None | Conv2d_13_pointwise | 315 | 32 | None
+mobilenet_v1 | 224 | Conv2d_0 | 3 | 2 | 0
+mobilenet_v1 | 224 | Conv2d_1_pointwise | 7 | 2 | 2
+mobilenet_v1 | 224 | Conv2d_2_pointwise | 11 | 4 | 2
+mobilenet_v1 | 224 | Conv2d_3_pointwise | 19 | 4 | 6
+mobilenet_v1 | 224 | Conv2d_4_pointwise | 27 | 8 | 6
+mobilenet_v1 | 224 | Conv2d_5_pointwise | 43 | 8 | 14
+mobilenet_v1 | 224 | Conv2d_6_pointwise | 59 | 16 | 14
+mobilenet_v1 | 224 | Conv2d_7_pointwise | 91 | 16 | 30
+mobilenet_v1 | 224 | Conv2d_8_pointwise | 123 | 16 | 46
+mobilenet_v1 | 224 | Conv2d_9_pointwise | 155 | 16 | 62
+mobilenet_v1 | 224 | Conv2d_10_pointwise | 187 | 16 | 78
+mobilenet_v1 | 224 | Conv2d_11_pointwise | 219 | 16 | 94
+mobilenet_v1 | 224 | Conv2d_12_pointwise | 251 | 32 | 94
+mobilenet_v1 | 224 | Conv2d_13_pointwise | 315 | 32 | 126
+mobilenet_v1 | 321 | Conv2d_0 | 3 | 2 | 1
+mobilenet_v1 | 321 | Conv2d_1_pointwise | 7 | 2 | 3
+mobilenet_v1 | 321 | Conv2d_2_pointwise | 11 | 4 | 5
+mobilenet_v1 | 321 | Conv2d_3_pointwise | 19 | 4 | 9
+mobilenet_v1 | 321 | Conv2d_4_pointwise | 27 | 8 | 13
+mobilenet_v1 | 321 | Conv2d_5_pointwise | 43 | 8 | 21
+mobilenet_v1 | 321 | Conv2d_6_pointwise | 59 | 16 | 29
+mobilenet_v1 | 321 | Conv2d_7_pointwise | 91 | 16 | 45
+mobilenet_v1 | 321 | Conv2d_8_pointwise | 123 | 16 | 61
+mobilenet_v1 | 321 | Conv2d_9_pointwise | 155 | 16 | 77
+mobilenet_v1 | 321 | Conv2d_10_pointwise | 187 | 16 | 93
+mobilenet_v1 | 321 | Conv2d_11_pointwise | 219 | 16 | 109
+mobilenet_v1 | 321 | Conv2d_12_pointwise | 251 | 32 | 125
+mobilenet_v1 | 321 | Conv2d_13_pointwise | 315 | 32 | 157
+mobilenet_v1_075 | None | Conv2d_0 | 3 | 2 | None
+mobilenet_v1_075 | None | Conv2d_1_pointwise | 7 | 2 | None
+mobilenet_v1_075 | None | Conv2d_2_pointwise | 11 | 4 | None
+mobilenet_v1_075 | None | Conv2d_3_pointwise | 19 | 4 | None
+mobilenet_v1_075 | None | Conv2d_4_pointwise | 27 | 8 | None
+mobilenet_v1_075 | None | Conv2d_5_pointwise | 43 | 8 | None
+mobilenet_v1_075 | None | Conv2d_6_pointwise | 59 | 16 | None
+mobilenet_v1_075 | None | Conv2d_7_pointwise | 91 | 16 | None
+mobilenet_v1_075 | None | Conv2d_8_pointwise | 123 | 16 | None
+mobilenet_v1_075 | None | Conv2d_9_pointwise | 155 | 16 | None
+mobilenet_v1_075 | None | Conv2d_10_pointwise | 187 | 16 | None
+mobilenet_v1_075 | None | Conv2d_11_pointwise | 219 | 16 | None
+mobilenet_v1_075 | None | Conv2d_12_pointwise | 251 | 32 | None
+mobilenet_v1_075 | None | Conv2d_13_pointwise | 315 | 32 | None
+mobilenet_v1_075 | 224 | Conv2d_0 | 3 | 2 | 0
+mobilenet_v1_075 | 224 | Conv2d_1_pointwise | 7 | 2 | 2
+mobilenet_v1_075 | 224 | Conv2d_2_pointwise | 11 | 4 | 2
+mobilenet_v1_075 | 224 | Conv2d_3_pointwise | 19 | 4 | 6
+mobilenet_v1_075 | 224 | Conv2d_4_pointwise | 27 | 8 | 6
+mobilenet_v1_075 | 224 | Conv2d_5_pointwise | 43 | 8 | 14
+mobilenet_v1_075 | 224 | Conv2d_6_pointwise | 59 | 16 | 14
+mobilenet_v1_075 | 224 | Conv2d_7_pointwise | 91 | 16 | 30
+mobilenet_v1_075 | 224 | Conv2d_8_pointwise | 123 | 16 | 46
+mobilenet_v1_075 | 224 | Conv2d_9_pointwise | 155 | 16 | 62
+mobilenet_v1_075 | 224 | Conv2d_10_pointwise | 187 | 16 | 78
+mobilenet_v1_075 | 224 | Conv2d_11_pointwise | 219 | 16 | 94
+mobilenet_v1_075 | 224 | Conv2d_12_pointwise | 251 | 32 | 94
+mobilenet_v1_075 | 224 | Conv2d_13_pointwise | 315 | 32 | 126
+mobilenet_v1_075 | 321 | Conv2d_0 | 3 | 2 | 1
+mobilenet_v1_075 | 321 | Conv2d_1_pointwise | 7 | 2 | 3
+mobilenet_v1_075 | 321 | Conv2d_2_pointwise | 11 | 4 | 5
+mobilenet_v1_075 | 321 | Conv2d_3_pointwise | 19 | 4 | 9
+mobilenet_v1_075 | 321 | Conv2d_4_pointwise | 27 | 8 | 13
+mobilenet_v1_075 | 321 | Conv2d_5_pointwise | 43 | 8 | 21
+mobilenet_v1_075 | 321 | Conv2d_6_pointwise | 59 | 16 | 29
+mobilenet_v1_075 | 321 | Conv2d_7_pointwise | 91 | 16 | 45
+mobilenet_v1_075 | 321 | Conv2d_8_pointwise | 123 | 16 | 61
+mobilenet_v1_075 | 321 | Conv2d_9_pointwise | 155 | 16 | 77
+mobilenet_v1_075 | 321 | Conv2d_10_pointwise | 187 | 16 | 93
+mobilenet_v1_075 | 321 | Conv2d_11_pointwise | 219 | 16 | 109
+mobilenet_v1_075 | 321 | Conv2d_12_pointwise | 251 | 32 | 125
+mobilenet_v1_075 | 321 | Conv2d_13_pointwise | 315 | 32 | 157
+resnet_v1_50 | None | resnet_v1_50/block1 | 35 | 8 | None
+resnet_v1_50 | None | resnet_v1_50/block2 | 99 | 16 | None
+resnet_v1_50 | None | resnet_v1_50/block3 | 291 | 32 | None
+resnet_v1_50 | None | resnet_v1_50/block4 | 483 | 32 | None
+resnet_v1_50 | 224 | resnet_v1_50/block1 | 35 | 8 | 15
+resnet_v1_50 | 224 | resnet_v1_50/block2 | 99 | 16 | 47
+resnet_v1_50 | 224 | resnet_v1_50/block3 | 291 | 32 | 143
+resnet_v1_50 | 224 | resnet_v1_50/block4 | 483 | 32 | 239
+resnet_v1_50 | 321 | resnet_v1_50/block1 | 35 | 8 | 17
+resnet_v1_50 | 321 | resnet_v1_50/block2 | 99 | 16 | 49
+resnet_v1_50 | 321 | resnet_v1_50/block3 | 291 | 32 | 145
+resnet_v1_50 | 321 | resnet_v1_50/block4 | 483 | 32 | 241
+resnet_v1_101 | None | resnet_v1_101/block1 | 35 | 8 | None
+resnet_v1_101 | None | resnet_v1_101/block2 | 99 | 16 | None
+resnet_v1_101 | None | resnet_v1_101/block3 | 835 | 32 | None
+resnet_v1_101 | None | resnet_v1_101/block4 | 1027 | 32 | None
+resnet_v1_101 | 224 | resnet_v1_101/block1 | 35 | 8 | 15
+resnet_v1_101 | 224 | resnet_v1_101/block2 | 99 | 16 | 47
+resnet_v1_101 | 224 | resnet_v1_101/block3 | 835 | 32 | 415
+resnet_v1_101 | 224 | resnet_v1_101/block4 | 1027 | 32 | 511
+resnet_v1_101 | 321 | resnet_v1_101/block1 | 35 | 8 | 17
+resnet_v1_101 | 321 | resnet_v1_101/block2 | 99 | 16 | 49
+resnet_v1_101 | 321 | resnet_v1_101/block3 | 835 | 32 | 417
+resnet_v1_101 | 321 | resnet_v1_101/block4 | 1027 | 32 | 513
+resnet_v1_152 | None | resnet_v1_152/block1 | 35 | 8 | None
+resnet_v1_152 | None | resnet_v1_152/block2 | 163 | 16 | None
+resnet_v1_152 | None | resnet_v1_152/block3 | 1315 | 32 | None
+resnet_v1_152 | None | resnet_v1_152/block4 | 1507 | 32 | None
+resnet_v1_152 | 224 | resnet_v1_152/block1 | 35 | 8 | 15
+resnet_v1_152 | 224 | resnet_v1_152/block2 | 163 | 16 | 79
+resnet_v1_152 | 224 | resnet_v1_152/block3 | 1315 | 32 | 655
+resnet_v1_152 | 224 | resnet_v1_152/block4 | 1507 | 32 | 751
+resnet_v1_152 | 321 | resnet_v1_152/block1 | 35 | 8 | 17
+resnet_v1_152 | 321 | resnet_v1_152/block2 | 163 | 16 | 81
+resnet_v1_152 | 321 | resnet_v1_152/block3 | 1315 | 32 | 657
+resnet_v1_152 | 321 | resnet_v1_152/block4 | 1507 | 32 | 753
+resnet_v1_200 | None | resnet_v1_200/block1 | 35 | 8 | None
+resnet_v1_200 | None | resnet_v1_200/block2 | 419 | 16 | None
+resnet_v1_200 | None | resnet_v1_200/block3 | 1571 | 32 | None
+resnet_v1_200 | None | resnet_v1_200/block4 | 1763 | 32 | None
+resnet_v1_200 | 224 | resnet_v1_200/block1 | 35 | 8 | 15
+resnet_v1_200 | 224 | resnet_v1_200/block2 | 419 | 16 | 207
+resnet_v1_200 | 224 | resnet_v1_200/block3 | 1571 | 32 | 783
+resnet_v1_200 | 224 | resnet_v1_200/block4 | 1763 | 32 | 879
+resnet_v1_200 | 321 | resnet_v1_200/block1 | 35 | 8 | 17
+resnet_v1_200 | 321 | resnet_v1_200/block2 | 419 | 16 | 209
+resnet_v1_200 | 321 | resnet_v1_200/block3 | 1571 | 32 | 785
+resnet_v1_200 | 321 | resnet_v1_200/block4 | 1763 | 32 | 881
+resnet_v2_50 | None | resnet_v2_50/block1 | 35 | 8 | None
+resnet_v2_50 | None | resnet_v2_50/block2 | 99 | 16 | None
+resnet_v2_50 | None | resnet_v2_50/block3 | 291 | 32 | None
+resnet_v2_50 | None | resnet_v2_50/block4 | 483 | 32 | None
+resnet_v2_50 | 224 | resnet_v2_50/block1 | 35 | 8 | 15
+resnet_v2_50 | 224 | resnet_v2_50/block2 | 99 | 16 | 47
+resnet_v2_50 | 224 | resnet_v2_50/block3 | 291 | 32 | 143
+resnet_v2_50 | 224 | resnet_v2_50/block4 | 483 | 32 | 239
+resnet_v2_50 | 321 | resnet_v2_50/block1 | 35 | 8 | 17
+resnet_v2_50 | 321 | resnet_v2_50/block2 | 99 | 16 | 49
+resnet_v2_50 | 321 | resnet_v2_50/block3 | 291 | 32 | 145
+resnet_v2_50 | 321 | resnet_v2_50/block4 | 483 | 32 | 241
+resnet_v2_101 | None | resnet_v2_101/block1 | 35 | 8 | None
+resnet_v2_101 | None | resnet_v2_101/block2 | 99 | 16 | None
+resnet_v2_101 | None | resnet_v2_101/block3 | 835 | 32 | None
+resnet_v2_101 | None | resnet_v2_101/block4 | 1027 | 32 | None
+resnet_v2_101 | 224 | resnet_v2_101/block1 | 35 | 8 | 15
+resnet_v2_101 | 224 | resnet_v2_101/block2 | 99 | 16 | 47
+resnet_v2_101 | 224 | resnet_v2_101/block3 | 835 | 32 | 415
+resnet_v2_101 | 224 | resnet_v2_101/block4 | 1027 | 32 | 511
+resnet_v2_101 | 321 | resnet_v2_101/block1 | 35 | 8 | 17
+resnet_v2_101 | 321 | resnet_v2_101/block2 | 99 | 16 | 49
+resnet_v2_101 | 321 | resnet_v2_101/block3 | 835 | 32 | 417
+resnet_v2_101 | 321 | resnet_v2_101/block4 | 1027 | 32 | 513
+resnet_v2_152 | None | resnet_v2_152/block1 | 35 | 8 | None
+resnet_v2_152 | None | resnet_v2_152/block2 | 163 | 16 | None
+resnet_v2_152 | None | resnet_v2_152/block3 | 1315 | 32 | None
+resnet_v2_152 | None | resnet_v2_152/block4 | 1507 | 32 | None
+resnet_v2_152 | 224 | resnet_v2_152/block1 | 35 | 8 | 15
+resnet_v2_152 | 224 | resnet_v2_152/block2 | 163 | 16 | 79
+resnet_v2_152 | 224 | resnet_v2_152/block3 | 1315 | 32 | 655
+resnet_v2_152 | 224 | resnet_v2_152/block4 | 1507 | 32 | 751
+resnet_v2_152 | 321 | resnet_v2_152/block1 | 35 | 8 | 17
+resnet_v2_152 | 321 | resnet_v2_152/block2 | 163 | 16 | 81
+resnet_v2_152 | 321 | resnet_v2_152/block3 | 1315 | 32 | 657
+resnet_v2_152 | 321 | resnet_v2_152/block4 | 1507 | 32 | 753
+resnet_v2_200 | None | resnet_v2_200/block1 | 35 | 8 | None
+resnet_v2_200 | None | resnet_v2_200/block2 | 419 | 16 | None
+resnet_v2_200 | None | resnet_v2_200/block3 | 1571 | 32 | None
+resnet_v2_200 | None | resnet_v2_200/block4 | 1763 | 32 | None
+resnet_v2_200 | 224 | resnet_v2_200/block1 | 35 | 8 | 15
+resnet_v2_200 | 224 | resnet_v2_200/block2 | 419 | 16 | 207
+resnet_v2_200 | 224 | resnet_v2_200/block3 | 1571 | 32 | 783
+resnet_v2_200 | 224 | resnet_v2_200/block4 | 1763 | 32 | 879
+resnet_v2_200 | 321 | resnet_v2_200/block1 | 35 | 8 | 17
+resnet_v2_200 | 321 | resnet_v2_200/block2 | 419 | 16 | 209
+resnet_v2_200 | 321 | resnet_v2_200/block3 | 1571 | 32 | 785
+resnet_v2_200 | 321 | resnet_v2_200/block4 | 1763 | 32 | 881
+
+## FAQ
+
+### What does a resolution of 'None' mean?
+
+In this case, the input resolution is undefined. For most models, the receptive
+field parameters can be computed even without knowing the input resolution.
+
+### For some networks, effective_padding shows as 'None' (eg, for Inception_v2 or Mobilenet_v1 when input size is not specified). Why is that?
+
+This means that the padding for these networks depends on the input size. So,
+unless we know exactly the input image dimensionality to be used, it is not
+possible to determine the padding applied at the different layers. Look at the
+other entries where the input size is fixed; for those cases, effective_padding
+is not None.
+
+This happens due to Tensorflow's implementation of the 'SAME' padding mode,
+which may depend on the input feature map size to a given layer. For background
+on this, see [these notes from the TF
+documentation](https://www.tensorflow.org/versions/master/api_guides/python/nn#Notes_on_SAME_Convolution_Padding).
+
+Also, note that in this case the program is not able to check if the network is
+aligned (ie, it could be that the different paths from input to output have
+receptive fields which are not consistently centered at the same position in the
+input image).
+
+So you should be aware that such networks might not be aligned -- the program
+has no way of checking it when the padding cannot be determined.
+
+### The receptive field parameters for network X seem different from what I expected... maybe your calculation is incorrect?
+
+First, note that the results presented here are based on the tensorflow
+implementations from the [TF-Slim model
+library](https://github.com/tensorflow/models/tree/master/research/slim).
+
+So, it is possible that due to some implementation details the RF parameters are
+different.
+
+One common case of confusion is the TF-Slim Resnet implementation, which applies
+stride in the last residual unit of each block, instead of at the input
+activations in the first residual unit of each block (which is what is described
+in the Resnet paper) -- see [this
+comment](https://github.com/tensorflow/models/blob/master/research/slim/nets/resnet_utils.py#L30).
+This makes the stride with respect to each convolution block potentially
+different. In this case, though, note that a
+[flag](https://github.com/tensorflow/models/blob/master/research/slim/nets/resnet_v1.py#L150)
+may be used to recover the original striding convention.
+
+Second, it could be that we have a bug somewhere. While we include [many
+tests](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/receptive_field/python/util/receptive_field_test.py)
+in our library, it is always possible that we missed something. If you suspect
+this is happening, please file a GitHub issue
+[here](https://github.com/tensorflow/tensorflow/issues).
diff --git a/tensorflow/contrib/receptive_field/python/util/examples/csv_to_markdown_table.py b/tensorflow/contrib/receptive_field/python/util/examples/csv_to_markdown_table.py
new file mode 100644
index 0000000000..4495d74bbf
--- /dev/null
+++ b/tensorflow/contrib/receptive_field/python/util/examples/csv_to_markdown_table.py
@@ -0,0 +1,82 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed 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.
+# ==============================================================================
+"""Simple script to convert CSV output from rf_benchmark to Markdown format.
+
+The input CSV should have the following fields:
+- CNN
+- input resolution
+- end_point
+- RF size hor
+- RF size ver
+- effective stride hor
+- effective stride ver
+- effective padding hor
+- effective padding ver
+
+Since usually in all cases the parameters in the horizontal and vertical
+directions are the same, this is assumed by this script, which only prints one
+of them to the Markdown file.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+import csv
+import sys
+
+from tensorflow.python.platform import app
+
+cmd_args = None
+
+
+def main(unused_argv):
+ with open(cmd_args.markdown_path, 'w') as f:
+ # Write table header and field size.
+ f.write('CNN | resolution | end-point | RF | effective stride | '
+ 'effective padding|\n')
+ f.write(
+ ':--------------------: | :----------: | :---------------: | :-----: |'
+ ' :----: | :----:|\n')
+ with open(cmd_args.csv_path) as csvfile:
+ reader = csv.DictReader(csvfile)
+ for row in reader:
+ # Make sure horizontal and parameters are the same.
+ assert row['RF size hor'] == row['RF size ver']
+ assert row['effective stride hor'] == row['effective stride ver']
+ assert row['effective padding hor'] == row['effective padding ver']
+
+ f.write('%s|%s|%s|%s|%s|%s\n' %
+ (row['CNN'], row['input resolution'], row['end_point'],
+ row['RF size hor'], row['effective stride hor'],
+ row['effective padding hor']))
+
+
+if __name__ == '__main__':
+ parser = argparse.ArgumentParser()
+ parser.register('type', 'bool', lambda v: v.lower() == 'true')
+ parser.add_argument(
+ '--csv_path',
+ type=str,
+ default='/tmp/rf.csv',
+ help='Path where CSV output of rf_benchmark was saved.')
+ parser.add_argument(
+ '--markdown_path',
+ type=str,
+ default='/tmp/rf.md',
+ help='Path where Markdown output will be saved.')
+ cmd_args, unparsed = parser.parse_known_args()
+ app.run(main=main, argv=[sys.argv[0]] + unparsed)
generated by cgit v1.2.3 (git 2.39.1) at 2024-06-14 23:59:26 +0000