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  1. 06 Jul, 2022 1 commit
  3. 02 Jul, 2022 1 commit
  5. 29 Jun, 2022 1 commit
  7. 24 Jun, 2022 1 commit
    • Yanghan Wang's avatar
      use runner class instead of instance outside of main · 8051775c
      Yanghan Wang authored 
      Summary:
Pull Request resolved: https://github.com/facebookresearch/d2go/pull/312

As discussed, we decided to not use runner instance outside of `main`, previous diffs already solved the prerequisites, this diff mainly does the renaming.
- Use runner name (str) in the fblearner, ML pipeline.
- Use runner name (str) in FBL operator, MAST and binary operator.
- Use runner class as the interface of main, it can be either the name of class (str) or actual class. The main usage should be using `str`, so that the importing of class happens inside `main`. But it's also a common use case to import runner class and call `main` for things like ad-hoc scripts or tests, supporting actual class makes it easier modify code for those cases (eg. some local test class doesn't have a name, so it's not feasible to use runner name).

Reviewed By: newstzpz

Differential Revision: D37060338

fbshipit-source-id: 879852d41902b87d6db6cb9d7b3e8dc55dc4b976
      8051775c
  9. 21 Jun, 2022 1 commit
  11. 20 Jun, 2022 1 commit
  13. 17 Jun, 2022 1 commit
  15. 16 Jun, 2022 1 commit
    • Matthew Yu's avatar
      add modeling hook algo and helper · f3fc01aa
      Matthew Yu authored 
      Summary:
Pull Request resolved: https://github.com/facebookresearch/d2go/pull/299

This implements the first iteration of generalized distillation in D2 (https://github.com/facebookresearch/d2go/commit/87374efb134e539090e0b5c476809dc35bf6aedb)Go. The functionality is separated into the following:

=> Adding distillation functionality without user changing their meta architecture:

class DistillationModelingHook
* This is an implementation detail that we hide from the user.
* We use dynamic mixin to specify additional functionality to the user's model. In this way, the original (student) model retains all attributes but the mixin class will override the forward (and provide more functionality like teacher updates).
* Teacher build currently only supports loading a torchscript model, pytorch compatiblity in later diffs

=> Implementing distillation methods

class DistillationAlgorithm
* The user can use some default algorithm (e.g., LabelDistillation) or create their own. This is where we specify the overrided forward func of the model and any other distillation requirements (updating the weights of the teacher model).
* The basic LabelDistillation allows a user to use a teacher model during training to relabel the gt

=> User customization

class DistillationHelper
* This is what we expect the user to customize. As an example the user probably needs to write their own pseudo_labeler to take batched_inputs and relabel this with the teacher

Both DistillationHelper and DistillationAlgorithm use a registry so that users can add their customization in their own code and use these customizations by specifying in the config

Reviewed By: newstzpz

Differential Revision: D36708227

fbshipit-source-id: bc427c5d42d0c7ff4d839bf10782efac24dea107
      f3fc01aa
  17. 10 Jun, 2022 1 commit
    • John Reese's avatar
      apply new formatting config · 0900aeba
      John Reese authored 
      Summary:
pyfmt now specifies a target Python version of 3.8 when formatting
with black. With this new config, black adds trailing commas to all
multiline function calls. This applies the new formatting as part
of rolling out the linttool-integration for pyfmt.

paintitblack

Reviewed By: zertosh, lisroach

Differential Revision: D37084377

fbshipit-source-id: 781a1b883a381a172e54d6e447137657977876b4
      0900aeba
  19. 09 Jun, 2022 1 commit
  21. 05 Jun, 2022 2 commits
  23. 02 Jun, 2022 2 commits
  25. 28 May, 2022 1 commit
  27. 27 May, 2022 1 commit
  29. 25 May, 2022 1 commit
  31. 21 May, 2022 1 commit
  33. 20 May, 2022 1 commit
  35. 17 May, 2022 3 commits
  37. 15 May, 2022 1 commit
    • John Reese's avatar
      apply import merging for fbcode (7 of 11) · b3a9204c
      John Reese authored 
      Summary:
Applies new import merging and sorting from µsort v1.0.

When merging imports, µsort will make a best-effort to move associated
comments to match merged elements, but there are known limitations due to
the diynamic nature of Python and developer tooling. These changes should
not produce any dangerous runtime changes, but may require touch-ups to
satisfy linters and other tooling.

Note that µsort uses case-insensitive, lexicographical sorting, which
results in a different ordering compared to isort. This provides a more
consistent sorting order, matching the case-insensitive order used when
sorting import statements by module name, and ensures that "frog", "FROG",
and "Frog" always sort next to each other.

For details on µsort's sorting and merging semantics, see the user guide:
https://usort.readthedocs.io/en/stable/guide.html#sorting

Reviewed By: lisroach

Differential Revision: D36402205

fbshipit-source-id: a4efc688d02da80c6e96685aa8eb00411615a366
      b3a9204c
  39. 14 May, 2022 1 commit
  41. 12 May, 2022 1 commit
    • John Reese's avatar
      formatting changes from black 22.3.0 · e1623106
      John Reese authored 
      Summary:
Applies the black-fbsource codemod with the new build of pyfmt.

paintitblack

Reviewed By: lisroach

Differential Revision: D36324783

fbshipit-source-id: 280c09e88257e5e569ab729691165d8dedd767bc
      e1623106
  43. 29 Apr, 2022 1 commit
  45. 26 Apr, 2022 2 commits
  47. 25 Apr, 2022 1 commit
  49. 19 Apr, 2022 2 commits
    • Yanghan Wang's avatar
      consolidate the creation of qconfig · 3204f147
      Yanghan Wang authored 
      Summary: Pull Request resolved: https://github.com/facebookresearch/d2go/pull/210

Reviewed By: kimishpatel

Differential Revision: D35631192

fbshipit-source-id: a713d86734c6937c16c7ced705171db9ea2f0894
      3204f147
    • Lisa Roach's avatar
      apply import merging for fbcode/mobile-vision/d2go (3 of 4) · ae2f2f64
      Lisa Roach authored 
      Summary:
Pull Request resolved: https://github.com/facebookresearch/d2go/pull/212

Applies new import merging and sorting from µsort v1.0.

When merging imports, µsort will make a best-effort to move associated
comments to match merged elements, but there are known limitations due to
the diynamic nature of Python and developer tooling. These changes should
not produce any dangerous runtime changes, but may require touch-ups to
satisfy linters and other tooling.

Note that µsort uses case-insensitive, lexicographical sorting, which
results in a different ordering compared to isort. This provides a more
consistent sorting order, matching the case-insensitive order used when
sorting import statements by module name, and ensures that "frog", "FROG",
and "Frog" always sort next to each other.

For details on µsort's sorting and merging semantics, see the user guide:
https://usort.readthedocs.io/en/stable/guide.html#sorting

Reviewed By: jreese, wat3rBro

Differential Revision: D35559673

fbshipit-source-id: feeae2465ac2b62c44a0e92dc566e9a386567c9d
      ae2f2f64
  51. 15 Apr, 2022 1 commit
    • Yanghan Wang's avatar
      enable moving traced model between devices · 2235f180
      Yanghan Wang authored 
      Summary:
X-link: https://github.com/facebookresearch/detectron2/pull/4132

X-link: https://github.com/fairinternal/detectron2/pull/568

Pull Request resolved: https://github.com/facebookresearch/d2go/pull/203

For full discussion: https://fb.workplace.com/groups/1405155842844877/posts/5744470455580039

Tracing the `.to(device)` will cause problem when moving the traced torchscript to another device (eg. from cpu to gpu, or even, from `cuda:0` to `cuda:1`). The reason is that `device` is not a `torch.Tensor`, so the tracer just hardcode the value during tracing. The solution is scripting the casting operation.

Here's the code snippet illustrating this:
```
# define the MyModel similar to GeneralizedRCNN, which casts the input to the model's device
class MyModel(nn.Module):
    def __init__(self):
        super().__init__()

        self.conv1 = nn.Conv2d(3, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        # cast the input to the same device as this model, this makes it possible to
        # take a cpu tensor as input when the model is on GPU.
        x = x.to(self.conv1.weight.device)

        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

# export the model by tracing
model = MyModel()
x = torch.zeros([1, 3, 32, 32])
ts = torch.jit.trace(model, x)
print(ts.graph)

# =====================================================
graph(%self.1 : __torch__.MyModel,
      %x : Float(1, 3, 32, 32, strides=[3072, 1024, 32, 1], requires_grad=0, device=cpu)):
  %conv2 : __torch__.torch.nn.modules.conv.___torch_mangle_0.Conv2d = prim::GetAttr[name="conv2"](%self.1)
  %conv1 : __torch__.torch.nn.modules.conv.Conv2d = prim::GetAttr[name="conv1"](%self.1)
  %14 : int = prim::Constant[value=6]() # <ipython-input-2-5abde0efc36f>:11:0
  %15 : int = prim::Constant[value=0]() # <ipython-input-2-5abde0efc36f>:11:0
  %16 : Device = prim::Constant[value="cpu"]() # <ipython-input-2-5abde0efc36f>:11:0
  %17 : NoneType = prim::Constant()
  %18 : bool = prim::Constant[value=0]() # <ipython-input-2-5abde0efc36f>:11:0
  %19 : bool = prim::Constant[value=0]() # <ipython-input-2-5abde0efc36f>:11:0
  %20 : NoneType = prim::Constant()
  %input.1 : Float(1, 3, 32, 32, strides=[3072, 1024, 32, 1], requires_grad=0, device=cpu) = aten::to(%x, %14, %15, %16, %17, %18, %19, %20) # <ipython-input-2-5abde0efc36f>:11:0
  %72 : Tensor = prim::CallMethod[name="forward"](%conv1, %input.1)
  %input.5 : Float(1, 20, 28, 28, strides=[15680, 784, 28, 1], requires_grad=1, device=cpu) = aten::relu(%72) # /mnt/xarfuse/uid-20293/a90d1698-seed-nspid4026533681_cgpid21128615-ns-4026533618/torch/nn/functional.py:1406:0
  %73 : Tensor = prim::CallMethod[name="forward"](%conv2, %input.5)
  %61 : Float(1, 20, 24, 24, strides=[11520, 576, 24, 1], requires_grad=1, device=cpu) = aten::relu(%73) # /mnt/xarfuse/uid-20293/a90d1698-seed-nspid4026533681_cgpid21128615-ns-4026533618/torch/nn/functional.py:1406:0
  return (%61)
# =====================================================

# PyTorch cuda works
model = copy.deepcopy(model)
model.to("cuda")
y = model(x)
# torchscript cpu works
y = ts(x)
# torchscript cuda doesn't work
ts = ts.to("cuda")
y = ts(x)

# =====================================================
RuntimeError: Input type (torch.FloatTensor) and weight type (torch.cuda.FloatTensor) should be the same or input should be a MKLDNN tensor and weight is a dense tensor
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-4-2aece3ad6c9a> in <module>
      7 # torchscript cuda doesn't work
      8 ts = ts.to("cuda")
----> 9 y = ts(x)
/mnt/xarfuse/uid-20293/a90d1698-seed-nspid4026533681_cgpid21128615-ns-4026533618/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
   1108         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1109                 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1110             return forward_call(*input, **kwargs)
   1111         # Do not call functions when jit is used
   1112         full_backward_hooks, non_full_backward_hooks = [], []
RuntimeError: The following operation failed in the TorchScript interpreter.
# =====================================================

# One solution is scripting the casting instead of tracing it, the folloing code demonstrate how to do it. We need to use mixed scripting/tracing
torch.jit.script_if_tracing
def cast_device_like(src: torch.Tensor, dst: torch.Tensor) -> torch.Tensor:
    return src.to(dst.device)

class MyModel2(nn.Module):
    def __init__(self):
        super().__init__()

        self.conv1 = nn.Conv2d(3, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        # cast the input to the same device as this model, this makes it possible to
        # take a cpu tensor as input when the model is on GPU.
        x = cast_device_like(x, self.conv1.weight)

        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

# export the model by tracing
model = MyModel2()
x = torch.zeros([1, 3, 32, 32])
ts = torch.jit.trace(model, x)
print(ts.graph)

# =====================================================
graph(%self.1 : __torch__.MyModel2,
      %x : Float(1, 3, 32, 32, strides=[3072, 1024, 32, 1], requires_grad=0, device=cpu)):
  %conv2 : __torch__.torch.nn.modules.conv.___torch_mangle_5.Conv2d = prim::GetAttr[name="conv2"](%self.1)
  %conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_4.Conv2d = prim::GetAttr[name="conv1"](%self.1)
  %conv1.1 : __torch__.torch.nn.modules.conv.___torch_mangle_4.Conv2d = prim::GetAttr[name="conv1"](%self.1)
  %weight.5 : Tensor = prim::GetAttr[name="weight"](%conv1.1)
  %14 : Function = prim::Constant[name="cast_device_like"]()
  %input.1 : Tensor = prim::CallFunction(%14, %x, %weight.5)
  %68 : Tensor = prim::CallMethod[name="forward"](%conv1, %input.1)
  %input.5 : Float(1, 20, 28, 28, strides=[15680, 784, 28, 1], requires_grad=1, device=cpu) = aten::relu(%68) # /mnt/xarfuse/uid-20293/a90d1698-seed-nspid4026533681_cgpid21128615-ns-4026533618/torch/nn/functional.py:1406:0
  %69 : Tensor = prim::CallMethod[name="forward"](%conv2, %input.5)
  %55 : Float(1, 20, 24, 24, strides=[11520, 576, 24, 1], requires_grad=1, device=cpu) = aten::relu(%69) # /mnt/xarfuse/uid-20293/a90d1698-seed-nspid4026533681_cgpid21128615-ns-4026533618/torch/nn/functional.py:1406:0
  return (%55)
# =====================================================

# PyTorch cuda works
model = copy.deepcopy(model)
model.to("cuda")
y = model(x)
# torchscript cpu works
y = ts(x)
# Note that now torchscript cuda works
ts = ts.to("cuda")
y = ts(x)
print(y.device)

# =====================================================
cuda:0
# =====================================================
```

For D2 (https://github.com/facebookresearch/d2go/commit/87374efb134e539090e0b5c476809dc35bf6aedb), this diff creates a `move_tensor_device_same_as_another(A, B)` function to replace `A.to(B.device)`. This diff updates the `rcnn.py` and all its utils.

For D2 (https://github.com/facebookresearch/d2go/commit/87374efb134e539090e0b5c476809dc35bf6aedb)Go, since the exported model will become device-agnostic, we can remove the "_gpu" from predictor-type.

Update (April 11):
Add test to cover tracing on one device and move traced model to another device for inference. When GPU is available, it'll trace on `cuda:0` and run inference on `cpu`, `cuda:0` (and `cuda:N-1` if available).

Summary of the device related patterns
- The usage of `.to(dtype=another_dype)` won't affect device.
- Explicit device casting like `.to(device)` can be generally replaced by `move_device_like`.
- For creating variable directly on device (eg. `torch.zeros`, `torch.arange`), we can replace then with ScriptModule to avoid first create on CPU and then move to new device.
    - Creating things on tracing device and then moving to new device is dangerous, because tracing device (eg. `cuda:0`) might not be available (eg. running on CPU-only machine).
    - It's hard to write `image_list.py` in this pattern because the size behaves differently during tracing (int vs. scalar tensor), in this diff, still create on CPU first and then move to target device.

Reviewed By: tglik

Differential Revision: D35367772

fbshipit-source-id: 02d07e3d96da85f4cfbeb996e3c14c2a6f619beb
      2235f180
  53. 07 Apr, 2022 1 commit
    • Owen Wang's avatar
      add metal GPU to d2go export · 6b4dbb31
      Owen Wang authored 
      Summary: Allow string name of export type to indicate which mobile opt backend user wants to trigger.

Reviewed By: wat3rBro

Differential Revision: D35375928

fbshipit-source-id: dc3f91564681344e1d43862423ab5dc63b6644d3
      6b4dbb31
  55. 05 Apr, 2022 2 commits
    • Yanghan Wang's avatar
      support do_postprocess when tracing rcnn model in D2 style · 647a3fdf
      Yanghan Wang authored 
      Summary:
Pull Request resolved: https://github.com/facebookresearch/d2go/pull/200

Currently when exporting the RCNN model, we call it with `self.model.inference(inputs, do_postprocess=False)[0]`, therefore the output of exported model is not post-processed, eg. the mask is in the squared shape. This diff adds the option to include postprocess in the exported model.

Worth noting that since the input is a single tensor, the post-process doesn't resize the output to original resolution, and we can't apply the post-process twice to further resize it in the Predictor's PostProcessFunc, add an assertion to raise error in this case. But this is fine for most production use cases where the input is not resized.

Set `RCNN_EXPORT.INCLUDE_POSTPROCESS` to `True` to enable this.

Reviewed By: tglik

Differential Revision: D34904058

fbshipit-source-id: 65f120eadc9747e9918d26ce0bd7dd265931cfb5
      647a3fdf
    • Yanghan Wang's avatar
      refactor create_fake_detection_data_loader · 312c6b62
      Yanghan Wang authored 
      Summary:
Pull Request resolved: https://github.com/facebookresearch/d2go/pull/199

- `create_fake_detection_data_loader` currently doesn't take `cfg` as input, sometimes we need to test the augmentation that needs more complicated different cfg.
- name is a bit bad, rename it to `create_detection_data_loader_on_toy_dataset`.
- width/height were the resized size previously, we want to change it to the size of data source (image files) and use `cfg` to control resized size.

Update V3:
In V2 there're some test failures, the reason is that V2 is building data loader (via GeneralizedRCNN runner) using actual test config instead of default config before this diff + dataset name change. In V3 we uses the test's runner instead of default runner for the consistency. This reveals some real bugs that we didn't test before.

Reviewed By: omkar-fb

Differential Revision: D35238890

fbshipit-source-id: 28a6037374e74f452f91b494bd455b38d3a48433
      312c6b62
  57. 24 Mar, 2022 2 commits
  59. 16 Mar, 2022 2 commits
  61. 08 Mar, 2022 1 commit