mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-07 12:21:27 +01:00
Summary: This fixes ASAN test issues with https://github.com/pytorch/pytorch/pull/21786 seen at https://circleci.com/api/v1.1/project/github/pytorch/pytorch/2212325/output/105/0?file=true and lands it again. This cleans up the `torch.utils.tensorboard` API to remove all kwargs usage (which isn't clear to the user) and removes the "experimental" warning in prep for our 1.2 release. We also don't need the additional PyTorch version checks now that we are in the codebase itself. cc yf225, lanpa, natalialunova Pull Request resolved: https://github.com/pytorch/pytorch/pull/23000 Reviewed By: sanekmelnikov Differential Revision: D16349734 Pulled By: orionr fbshipit-source-id: 604a9cad56868a55e08b509a0c6f42b84f68de95
320 lines
4.4 KiB
Plaintext
320 lines
4.4 KiB
Plaintext
node {
|
|
name: "conv1/XavierFill"
|
|
op: "XavierFill"
|
|
attr {
|
|
key: "_output_shapes"
|
|
value {
|
|
list {
|
|
shape {
|
|
dim {
|
|
size: 96
|
|
}
|
|
dim {
|
|
size: 3
|
|
}
|
|
dim {
|
|
size: 11
|
|
}
|
|
dim {
|
|
size: 11
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
node {
|
|
name: "conv1/ConstantFill"
|
|
op: "ConstantFill"
|
|
attr {
|
|
key: "_output_shapes"
|
|
value {
|
|
list {
|
|
shape {
|
|
dim {
|
|
size: 96
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
node {
|
|
name: "classifier/XavierFill"
|
|
op: "XavierFill"
|
|
attr {
|
|
key: "_output_shapes"
|
|
value {
|
|
list {
|
|
shape {
|
|
dim {
|
|
size: 1000
|
|
}
|
|
dim {
|
|
size: 4096
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
node {
|
|
name: "classifier/ConstantFill"
|
|
op: "ConstantFill"
|
|
attr {
|
|
key: "_output_shapes"
|
|
value {
|
|
list {
|
|
shape {
|
|
dim {
|
|
size: 1000
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
node {
|
|
name: "conv1/Conv"
|
|
op: "Conv"
|
|
input: "conv1/data"
|
|
input: "conv1/conv1_w"
|
|
input: "conv1/conv1_b"
|
|
attr {
|
|
key: "exhaustive_search"
|
|
value {
|
|
i: 0
|
|
}
|
|
}
|
|
attr {
|
|
key: "kernel"
|
|
value {
|
|
i: 11
|
|
}
|
|
}
|
|
attr {
|
|
key: "order"
|
|
value {
|
|
s: "NCHW"
|
|
}
|
|
}
|
|
attr {
|
|
key: "stride"
|
|
value {
|
|
i: 4
|
|
}
|
|
}
|
|
}
|
|
node {
|
|
name: "conv1/Relu"
|
|
op: "Relu"
|
|
input: "conv1/conv1"
|
|
attr {
|
|
key: "cudnn_exhaustive_search"
|
|
value {
|
|
i: 0
|
|
}
|
|
}
|
|
attr {
|
|
key: "order"
|
|
value {
|
|
s: "NCHW"
|
|
}
|
|
}
|
|
}
|
|
node {
|
|
name: "conv1/MaxPool"
|
|
op: "MaxPool"
|
|
input: "conv1/conv1_1"
|
|
attr {
|
|
key: "cudnn_exhaustive_search"
|
|
value {
|
|
i: 0
|
|
}
|
|
}
|
|
attr {
|
|
key: "kernel"
|
|
value {
|
|
i: 2
|
|
}
|
|
}
|
|
attr {
|
|
key: "order"
|
|
value {
|
|
s: "NCHW"
|
|
}
|
|
}
|
|
attr {
|
|
key: "stride"
|
|
value {
|
|
i: 2
|
|
}
|
|
}
|
|
}
|
|
node {
|
|
name: "classifier/FC"
|
|
op: "FC"
|
|
input: "conv1/pool1"
|
|
input: "classifier/fc_w"
|
|
input: "classifier/fc_b"
|
|
attr {
|
|
key: "cudnn_exhaustive_search"
|
|
value {
|
|
i: 0
|
|
}
|
|
}
|
|
attr {
|
|
key: "order"
|
|
value {
|
|
s: "NCHW"
|
|
}
|
|
}
|
|
attr {
|
|
key: "use_cudnn"
|
|
value {
|
|
i: 1
|
|
}
|
|
}
|
|
}
|
|
node {
|
|
name: "classifier/Softmax"
|
|
op: "Softmax"
|
|
input: "classifier/fc"
|
|
attr {
|
|
key: "cudnn_exhaustive_search"
|
|
value {
|
|
i: 0
|
|
}
|
|
}
|
|
attr {
|
|
key: "order"
|
|
value {
|
|
s: "NCHW"
|
|
}
|
|
}
|
|
}
|
|
node {
|
|
name: "classifier/LabelCrossEntropy"
|
|
op: "LabelCrossEntropy"
|
|
input: "classifier/pred"
|
|
input: "classifier/label"
|
|
}
|
|
node {
|
|
name: "classifier/AveragedLoss"
|
|
op: "AveragedLoss"
|
|
input: "classifier/xent"
|
|
}
|
|
node {
|
|
name: "conv1/conv1_w"
|
|
op: "Blob"
|
|
input: "conv1/XavierFill:0"
|
|
}
|
|
node {
|
|
name: "conv1/conv1_b"
|
|
op: "Blob"
|
|
input: "conv1/ConstantFill:0"
|
|
}
|
|
node {
|
|
name: "classifier/fc_w"
|
|
op: "Blob"
|
|
input: "classifier/XavierFill:0"
|
|
}
|
|
node {
|
|
name: "classifier/fc_b"
|
|
op: "Blob"
|
|
input: "classifier/ConstantFill:0"
|
|
}
|
|
node {
|
|
name: "conv1/data"
|
|
op: "Placeholder"
|
|
}
|
|
node {
|
|
name: "conv1/conv1_w"
|
|
op: "Blob"
|
|
input: "conv1/XavierFill:0"
|
|
}
|
|
node {
|
|
name: "conv1/conv1_b"
|
|
op: "Blob"
|
|
input: "conv1/ConstantFill:0"
|
|
}
|
|
node {
|
|
name: "conv1/conv1"
|
|
op: "Blob"
|
|
input: "conv1/Conv:0"
|
|
}
|
|
node {
|
|
name: "conv1/conv1"
|
|
op: "Blob"
|
|
input: "conv1/Conv:0"
|
|
}
|
|
node {
|
|
name: "conv1/conv1_1"
|
|
op: "Blob"
|
|
input: "conv1/Relu:0"
|
|
}
|
|
node {
|
|
name: "conv1/conv1_1"
|
|
op: "Blob"
|
|
input: "conv1/Relu:0"
|
|
}
|
|
node {
|
|
name: "conv1/pool1"
|
|
op: "Blob"
|
|
input: "conv1/MaxPool:0"
|
|
}
|
|
node {
|
|
name: "conv1/pool1"
|
|
op: "Blob"
|
|
input: "conv1/MaxPool:0"
|
|
}
|
|
node {
|
|
name: "classifier/fc_w"
|
|
op: "Blob"
|
|
input: "classifier/XavierFill:0"
|
|
}
|
|
node {
|
|
name: "classifier/fc_b"
|
|
op: "Blob"
|
|
input: "classifier/ConstantFill:0"
|
|
}
|
|
node {
|
|
name: "classifier/fc"
|
|
op: "Blob"
|
|
input: "classifier/FC:0"
|
|
}
|
|
node {
|
|
name: "classifier/fc"
|
|
op: "Blob"
|
|
input: "classifier/FC:0"
|
|
}
|
|
node {
|
|
name: "classifier/pred"
|
|
op: "Blob"
|
|
input: "classifier/Softmax:0"
|
|
}
|
|
node {
|
|
name: "classifier/pred"
|
|
op: "Blob"
|
|
input: "classifier/Softmax:0"
|
|
}
|
|
node {
|
|
name: "classifier/label"
|
|
op: "Placeholder"
|
|
}
|
|
node {
|
|
name: "classifier/xent"
|
|
op: "Blob"
|
|
input: "classifier/LabelCrossEntropy:0"
|
|
}
|
|
node {
|
|
name: "classifier/xent"
|
|
op: "Blob"
|
|
input: "classifier/LabelCrossEntropy:0"
|
|
}
|
|
node {
|
|
name: "classifier/loss"
|
|
op: "Blob"
|
|
input: "classifier/AveragedLoss:0"
|
|
}
|