repo_name stringlengths 9 75 | topic stringclasses 30
values | issue_number int64 1 203k | title stringlengths 1 976 | body stringlengths 0 254k | state stringclasses 2
values | created_at stringlengths 20 20 | updated_at stringlengths 20 20 | url stringlengths 38 105 | labels listlengths 0 9 | user_login stringlengths 1 39 | comments_count int64 0 452 |
|---|---|---|---|---|---|---|---|---|---|---|---|
mwaskom/seaborn | data-visualization | 3,000 | Release v0.12.0 | Release tracker issue for v0.12.0.
Mostly opening so that it gets issue #3000, which is satisfying. | closed | 2022-09-05T16:53:42Z | 2022-09-06T22:24:08Z | https://github.com/mwaskom/seaborn/issues/3000 | [] | mwaskom | 2 |
kornia/kornia | computer-vision | 2,812 | warp_affine buggy | ### Describe the bug
warp_affine doesn't work properly (a bug?). see code snipped and images (cv2's result showed as RGB, Kornia's as greyscaled for simplicity).
I met this on latest 0.7.1, also tried to downgrade to 0.5. didn't helped.
### Reproduction steps
```bash
center = torch.zeros((B, 2,)) + torch.tensor([w / 2, h / 2]) # xy
affine_matrix = KT.get_rotation_matrix2d(center, angle*0, torch.tensor([[0.5, 0.5]]))
with torch.no_grad():
image_ = KT.warp_affine(torch.tensor(image[None]).float(), affine_matrix, (512, 512)).squeeze(0)
plt.imshow(image_.mean(2)); plt.show()
plt.imshow(cv2.warpAffine(image.astype(np.float32)/255, affine_matrix.float().numpy().squeeze(0), (512, 512))); plt.title('cv2'); plt.show()
```
### Expected behavior
Warped image as cv2 returns.
Got something strange
### Environment
```shell
wget https://raw.githubusercontent.com/pytorch/pytorch/main/torch/utils/collect_env.py
# For security purposes, please check the contents of collect_env.py before running it.
python collect_env.py
```
- PyTorch Version (e.g., 1.0): 2.1 or 2.0 or 1.13 or 1.11
- OS (e.g., Linux): Linux
- How you installed PyTorch (`conda`, `pip`, source): pip
- Python version: 3.11
- CUDA/cuDNN version: 11.7
- GPU models and configuration: Tesla T4
- Any other relevant information:
```
### Additional context
_No response_ | closed | 2024-02-22T12:14:08Z | 2024-02-22T13:15:08Z | https://github.com/kornia/kornia/issues/2812 | [
"help wanted"
] | korabelnikov | 1 |
onnx/onnx | tensorflow | 6,571 | Reduce session create time with Loop. | # Ask a Question
When exporting a PyTorch model with `torch.onnx.export`, it seems there is no function like `jax.lax.fori_loop` such that
```
for block in blocks:
x = block(x)
````
would be translated into an ONNX [Loop](https://onnx.ai/onnx/operators/onnx__Loop.html) operation (inspecting with Netron shows above for-loop unrolled).
**Question.** Is there a way I can do manually by modify the ONNX file so the unrolled for-loop uses [Loop](https://onnx.ai/onnx/operators/onnx__Loop.html) operation (inspecting with Netron shows above for-loop unrolled)?
Why would I want this? The time to create a session with the resulting ONNX model increasing linearly with `len(blocks)` from <1s to 10s. This should be possible in <1s. To see why, I could just `block(x, weights)` and move the forloop out of the ONNX model - I'm then only loading one block which can be done in <1s. This is ofcause quite hacky, and I'd love any trick that prevents me from doing that. | open | 2024-12-03T22:44:49Z | 2025-01-25T15:41:16Z | https://github.com/onnx/onnx/issues/6571 | [
"question"
] | AlexanderMath | 2 |
iMerica/dj-rest-auth | rest-api | 236 | Limit social login to only a certain email address domain | Hello,
I'm getting started with dj-rest-auth and I have an app which allows user to only sign in via google. However, I want users to only be able to access my service with a specific google apps email domain.
I'm using dj-rest-auth in conjunction with django-allauth.
This is what I tried: I created allauth adapters like this:
```
class CustomAccountAdapter(DefaultAccountAdapter):
def is_open_for_signup(self, request):
return False # No email/password signups allowed
class CustomSocialAccountAdapter(DefaultSocialAccountAdapter):
def is_open_for_signup(self, request, socialaccount):
u = socialaccount.user
return u.email.split("@")[1] == "mydomain.com"
```
and in my settings I have
```
ACCOUNT_ADAPTER = "users.adapters.CustomAccountAdapter"
SOCIALACCOUNT_ADAPTER = "users.adapters.CustomSocialAccountAdapter"
```
With this solution, if the user tries to sign in with a valid email, everything works, whereas if they use a non-allowed email domain, I get this error in my console:
```
Traceback (most recent call last):
File "/home/my_user/.local/share/virtualenvs/my_project-mzTAT1MS/lib/python3.8/site-packages/django/core/handlers/exception.py", line 47, in inner
response = get_response(request)
File "/home/my_user/.local/share/virtualenvs/my_project-mzTAT1MS/lib/python3.8/site-packages/django/core/handlers/base.py", line 181, in _get_response
response = wrapped_callback(request, *callback_args, **callback_kwargs)
File "/home/my_user/.local/share/virtualenvs/my_project-mzTAT1MS/lib/python3.8/site-packages/django/views/decorators/csrf.py", line 54, in wrapped_view
return view_func(*args, **kwargs)
File "/home/my_user/.local/share/virtualenvs/my_project-mzTAT1MS/lib/python3.8/site-packages/django/views/generic/base.py", line 70, in view
return self.dispatch(request, *args, **kwargs)
File "/home/my_user/.local/share/virtualenvs/my_project-mzTAT1MS/lib/python3.8/site-packages/django/utils/decorators.py", line 43, in _wrapper
return bound_method(*args, **kwargs)
File "/home/my_user/.local/share/virtualenvs/my_project-mzTAT1MS/lib/python3.8/site-packages/django/views/decorators/debug.py", line 89, in sensitive_post_parameters_wrapper
return view(request, *args, **kwargs)
File "/home/my_user/.local/share/virtualenvs/my_project-mzTAT1MS/lib/python3.8/site-packages/dj_rest_auth/views.py", line 48, in dispatch
return super(LoginView, self).dispatch(*args, **kwargs)
File "/home/my_user/.local/share/virtualenvs/my_project-mzTAT1MS/lib/python3.8/site-packages/rest_framework/views.py", line 505, in dispatch
response = self.handle_exception(exc)
File "/home/my_user/.local/share/virtualenvs/my_project-mzTAT1MS/lib/python3.8/site-packages/rest_framework/views.py", line 465, in handle_exception
self.raise_uncaught_exception(exc)
File "/home/my_user/.local/share/virtualenvs/my_project-mzTAT1MS/lib/python3.8/site-packages/rest_framework/views.py", line 476, in raise_uncaught_exception
raise exc
File "/home/my_user/.local/share/virtualenvs/my_project-mzTAT1MS/lib/python3.8/site-packages/rest_framework/views.py", line 502, in dispatch
response = handler(request, *args, **kwargs)
File "/home/my_user/.local/share/virtualenvs/my_project-mzTAT1MS/lib/python3.8/site-packages/dj_rest_auth/views.py", line 138, in post
self.serializer.is_valid(raise_exception=True)
File "/home/my_user/.local/share/virtualenvs/my_project-mzTAT1MS/lib/python3.8/site-packages/rest_framework/serializers.py", line 234, in is_valid
self._validated_data = self.run_validation(self.initial_data)
File "/home/my_user/.local/share/virtualenvs/my_project-mzTAT1MS/lib/python3.8/site-packages/rest_framework/serializers.py", line 436, in run_validation
value = self.validate(value)
File "/home/my_user/.local/share/virtualenvs/my_project-mzTAT1MS/lib/python3.8/site-packages/dj_rest_auth/registration/serializers.py", line 134, in validate
complete_social_login(request, login)
File "/home/my_user/.local/share/virtualenvs/my_project-mzTAT1MS/lib/python3.8/site-packages/allauth/socialaccount/helpers.py", line 151, in complete_social_login
return _complete_social_login(request, sociallogin)
File "/home/my_user/.local/share/virtualenvs/my_project-mzTAT1MS/lib/python3.8/site-packages/allauth/socialaccount/helpers.py", line 172, in _complete_social_login
ret = _process_signup(request, sociallogin)
File "/home/my_user/.local/share/virtualenvs/my_project-mzTAT1MS/lib/python3.8/site-packages/allauth/socialaccount/helpers.py", line 38, in _process_signup
return render(
File "/home/my_user/.local/share/virtualenvs/my_project-mzTAT1MS/lib/python3.8/site-packages/django/shortcuts.py", line 19, in render
content = loader.render_to_string(template_name, context, request, using=using)
File "/home/my_user/.local/share/virtualenvs/my_project-mzTAT1MS/lib/python3.8/site-packages/django/template/loader.py", line 62, in render_to_string
return template.render(context, request)
File "/home/my_user/.local/share/virtualenvs/my_project-mzTAT1MS/lib/python3.8/site-packages/django/template/backends/django.py", line 61, in render
return self.template.render(context)
File "/home/my_user/.local/share/virtualenvs/my_project-mzTAT1MS/lib/python3.8/site-packages/django/template/base.py", line 170, in render
return self._render(context)
File "/home/my_user/.local/share/virtualenvs/my_project-mzTAT1MS/lib/python3.8/site-packages/django/test/utils.py", line 96, in instrumented_test_render
return self.nodelist.render(context)
File "/home/my_user/.local/share/virtualenvs/my_project-mzTAT1MS/lib/python3.8/site-packages/django/template/base.py", line 938, in render
bit = node.render_annotated(context)
File "/home/my_user/.local/share/virtualenvs/my_project-mzTAT1MS/lib/python3.8/site-packages/django/template/base.py", line 905, in render_annotated
return self.render(context)
File "/home/my_user/.local/share/virtualenvs/my_project-mzTAT1MS/lib/python3.8/site-packages/django/template/loader_tags.py", line 150, in render
return compiled_parent._render(context)
File "/home/my_user/.local/share/virtualenvs/my_project-mzTAT1MS/lib/python3.8/site-packages/django/test/utils.py", line 96, in instrumented_test_render
return self.nodelist.render(context)
File "/home/my_user/.local/share/virtualenvs/my_project-mzTAT1MS/lib/python3.8/site-packages/django/template/base.py", line 938, in render
bit = node.render_annotated(context)
File "/home/my_user/.local/share/virtualenvs/my_project-mzTAT1MS/lib/python3.8/site-packages/django/template/base.py", line 905, in render_annotated
return self.render(context)
File "/home/my_user/.local/share/virtualenvs/my_project-mzTAT1MS/lib/python3.8/site-packages/django/template/loader_tags.py", line 62, in render
result = block.nodelist.render(context)
File "/home/my_user/.local/share/virtualenvs/my_project-mzTAT1MS/lib/python3.8/site-packages/django/template/base.py", line 938, in render
bit = node.render_annotated(context)
File "/home/my_user/.local/share/virtualenvs/my_project-mzTAT1MS/lib/python3.8/site-packages/django/template/base.py", line 905, in render_annotated
return self.render(context)
File "/home/my_user/.local/share/virtualenvs/my_project-mzTAT1MS/lib/python3.8/site-packages/django/template/defaulttags.py", line 312, in render
return nodelist.render(context)
File "/home/my_user/.local/share/virtualenvs/my_project-mzTAT1MS/lib/python3.8/site-packages/django/template/base.py", line 938, in render
bit = node.render_annotated(context)
File "/home/my_user/.local/share/virtualenvs/my_project-mzTAT1MS/lib/python3.8/site-packages/django/template/base.py", line 905, in render_annotated
return self.render(context)
File "/home/my_user/.local/share/virtualenvs/my_project-mzTAT1MS/lib/python3.8/site-packages/django/template/defaulttags.py", line 446, in render
url = reverse(view_name, args=args, kwargs=kwargs, current_app=current_app)
File "/home/my_user/.local/share/virtualenvs/my_project-mzTAT1MS/lib/python3.8/site-packages/django/urls/base.py", line 87, in reverse
return iri_to_uri(resolver._reverse_with_prefix(view, prefix, *args, **kwargs))
File "/home/my_user/.local/share/virtualenvs/my_project-mzTAT1MS/lib/python3.8/site-packages/django/urls/resolvers.py", line 685, in _reverse_with_prefix
raise NoReverseMatch(msg)
django.urls.exceptions.NoReverseMatch: Reverse for 'account_login' not found. 'account_login' is not a valid view function or pattern name.
[17/Mar/2021 18:49:42] "POST /dj-rest-auth/google HTTP/1.1" 500 246781
```
What I would like to do is for my django app to send a 403 response to the user (or similar) with a message like "You can only access this service with an [at] whatever email address".
I'm trying to figure out at which point the dj-rest-auth views call the user adapters and what to do to handle the failure and achieve what I'm after.
I appreciate any guidance. | open | 2021-03-17T17:52:28Z | 2021-08-15T22:55:19Z | https://github.com/iMerica/dj-rest-auth/issues/236 | [] | samul-1 | 1 |
stanford-oval/storm | nlp | 259 | [BUG] How to generate my own report outline? | **Describe the bug**
A clear and concise description of what the bug is.
NOT A BUG: I want to generate my own report outline. How to do this?
**To Reproduce**
Report following things
1. Input topic name
2. All output files generated for this topic as a zip file.
**Screenshots**
If applicable, add screenshots to help explain your problem.
**Environment:**
- OS: [e.g. iOS, Windows] MacOS
- Browser [e.g. chrome, safari] if the bug report is UI problem
| closed | 2024-11-28T00:36:33Z | 2025-01-04T23:15:44Z | https://github.com/stanford-oval/storm/issues/259 | [] | parthchandak02 | 1 |
Zeyi-Lin/HivisionIDPhotos | machine-learning | 142 | Why does my Python 3.10 run with this error | File "/Users/oceanfly/.conda/envs/python3.10.0_env/lib/python3.10/site-packages/anyio/to_thread.py", line 56, in run_sync
return await get_async_backend().run_sync_in_worker_thread(
File "/Users/oceanfly/.conda/envs/python3.10.0_env/lib/python3.10/site-packages/anyio/_backends/_asyncio.py", line 2177, in run_sync_in_worker_thread
return await future
File "/Users/oceanfly/.conda/envs/python3.10.0_env/lib/python3.10/site-packages/anyio/_backends/_asyncio.py", line 859, in run
result = context.run(func, *args)
File "/Users/oceanfly/.conda/envs/python3.10.0_env/lib/python3.10/site-packages/gradio/utils.py", line 826, in wrapper
response = f(*args, **kwargs)
File "/Users/oceanfly/workspace/ai/HivisionIDPhotos/demo/processor.py", line 129, in process
return self._process_generated_photo(
File "/Users/oceanfly/workspace/ai/HivisionIDPhotos/demo/processor.py", line 327, in _process_generated_photo
return self._create_response(
TypeError: IDPhotoProcessor._create_response() takes 6 positional arguments but 7 were given
| closed | 2024-09-17T13:44:06Z | 2024-09-23T00:53:16Z | https://github.com/Zeyi-Lin/HivisionIDPhotos/issues/142 | [] | zhhOceanfly | 1 |
521xueweihan/HelloGitHub | python | 2,148 | 基于 Fetch API 的洋葱模型 http 客户端。 | ## 项目推荐
- 项目地址：https://github.com/molvqingtai/resreq
- 类别：JS
- 项目后续更新计划：持续更新
- 项目描述：
它是一个基于 [fetch](https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API/Using_Fetch) 的现代 http 客户端，因为它在内部使用洋葱模型实现，因此您可以使用中间件优雅地拦截请求和响应。
- 推荐理由：
目前大部分前端请求库使用两个钩子函数来处理请求和响应，当我们在请求中添加过多的业务逻辑时，这变得难以维护。
洋葱模型是一种非常优雅的处理请求和响应的套路，我们在 node 开发中有非常成熟的应用，那我为什么不让前端的请求也改成这种方式呢？
- 示例代码：
```typescript
import Resreq from 'resreq'
const resreq = new Resreq({
baseUrl: 'https://example.com'
})
// Intercepting responses and requests using middleware
resreq.use((next) => async (req) => {
try {
console.log(req) // Request can be changed here
const res = await next(req)
console.log(res) // Response can be changed here
return res
} catch (error) {
console.log(error) // Catch errors here
throw error
}
})
const res = await resreq.get('/api', {
params: { foo: 'bar' }
})
console.log(res.json())
```
- 截图：
| closed | 2022-03-29T17:23:33Z | 2022-04-25T06:37:03Z | https://github.com/521xueweihan/HelloGitHub/issues/2148 | [
"JavaScript 项目"
] | molvqingtai | 0 |
pyg-team/pytorch_geometric | deep-learning | 9,879 | Add ExecuTorch support | ### 🚀 The feature, motivation and pitch
Hi! I'm trying to deploy a GNN built with PyG on edge devices (mobile), and I'd like it to be supported with ExecuTorch.
### Alternatives
_No response_
### Additional context
Hi! I'm trying to deploy a GNN built with PyG (following [this blog](https://medium.com/stanford-cs224w/simulating-complex-physics-with-graph-networks-step-by-step-177354cb9b05)) on edge devices (mobile), and I've been exploring ExecuTorch.
However, it seems that PyG can't even be lowered to aten. And I suspect it has something to do with TorchDynamo not supporting the pyg.data.Data structure. This is because when I tried to export to ONNX using dynamo, I saw similar errors.
I attached the full code and full error messages at the end:
```python
aten_dialect = export(simulator, example_inputs) # Error occurred on this line
```
```python
graph = pyg.data.Data(...) # Error occurred on this line in the user code
```
I'm using PyTorch==2.5.0, PyG==2.6.1, torch_scatter=2.1.2
[export_gnn_executorch.py.txt](https://github.com/user-attachments/files/18200670/export_gnn_executorch.py.txt):
```python
# -*- coding: utf-8 -*-
import torch
print(f"PyTorch has version {torch.__version__} with cuda {torch.version.cuda}")
import numpy as np
import torch_geometric as pyg
"""## GNN helper layers
"""
import math
import torch_scatter
class MLP(torch.nn.Module):
"""Multi-Layer perceptron"""
def __init__(self, input_size, hidden_size, output_size, layers, layernorm=True):
super().__init__()
self.layers = torch.nn.ModuleList()
for i in range(layers):
self.layers.append(torch.nn.Linear(
input_size if i == 0 else hidden_size,
output_size if i == layers - 1 else hidden_size,
))
if i != layers - 1:
self.layers.append(torch.nn.ReLU())
if layernorm:
self.layers.append(torch.nn.LayerNorm(output_size))
self.reset_parameters()
def reset_parameters(self):
for layer in self.layers:
if isinstance(layer, torch.nn.Linear):
layer.weight.data.normal_(0, 1 / math.sqrt(layer.in_features))
layer.bias.data.fill_(0)
def forward(self, x):
for layer in self.layers:
x = layer(x)
return x
def find_connectivity(positions, radius):
""" Find all edges connecting to all nodes within the radius
Args:
positions (Tensor): [N_particles, N_Dim] Most recent positions
radius (float): Radius
Return:
edge_index: [2, X], containing all X edges in the graph in the form of (source node index, target node index).
Node index is the same as the indices in the node positions
"""
squared_norm = torch.sum(positions*positions, 1) # [N_particles]
squared_norm = torch.reshape(squared_norm, [-1, 1]) # [N_particles, 1]
distance_tensor = squared_norm - 2*torch.matmul(positions, torch.transpose(positions, 0, 1)) + torch.transpose(squared_norm, 0, 1) # [N_particles, N_particles] Pair-wise square distance matrix
# Find index pairs where the distance is less-than or equal to the radius
# equivalent to torch.where (but as_tuple=false)
edge_index = torch.nonzero(torch.less_equal(distance_tensor, radius * radius), as_tuple=False)
# Expected shape: [2, X]
return edge_index.T
def preprocess(particle_type, position_seq, metadata):
"""Preprocess a trajectory and construct the graph
particle_type: [N, dtype=int64], particle type
position_seq: [N, Sequence length, dim, dtype=float32], position sequence
metadata: dict, meta data
"""
# calculate the velocities of particles
recent_position = position_seq[:, -1]
velocity_seq = position_seq[:, 1:] - position_seq[:, :-1]
# construct the graph based on the distances between particles
# edge_index = pyg.nn.radius_graph(recent_position, metadata["default_connectivity_radius"], loop=True, max_num_neighbors=n_particle)
edge_index = find_connectivity(recent_position, metadata["default_connectivity_radius"])
# node-level features: velocity, distance to the boundary
boundary = torch.tensor(metadata["bounds"])
distance_to_lower_boundary = recent_position - boundary[:, 0]
distance_to_upper_boundary = boundary[:, 1] - recent_position
distance_to_boundary = torch.cat((distance_to_lower_boundary, distance_to_upper_boundary), dim=-1)
distance_to_boundary = torch.clip(distance_to_boundary / metadata["default_connectivity_radius"], -1.0, 1.0)
# edge-level features: displacement, distance
dim = recent_position.size(-1)
edge_displacement = (torch.gather(recent_position, dim=0, index=edge_index[0].unsqueeze(-1).expand(-1, dim)) -
torch.gather(recent_position, dim=0, index=edge_index[1].unsqueeze(-1).expand(-1, dim)))
edge_displacement /= metadata["default_connectivity_radius"]
edge_distance = torch.norm(edge_displacement, dim=-1, keepdim=True)
# return the graph with features
graph = pyg.data.Data(
x=particle_type,
edge_index=edge_index,
edge_attr=torch.cat((edge_displacement, edge_distance), dim=-1),
y=None, # Ground truth for training
pos=torch.cat((velocity_seq.reshape(velocity_seq.size(0), -1), distance_to_boundary), dim=-1),
recent_position=recent_position,
recent_velocity=velocity_seq[:, -1]
)
return graph
class BuildGraph(torch.nn.Module):
"""Preprocessing unit. Build graphs from positions"""
def __init__(self, metadata):
super().__init__()
self.metadata = metadata
def forward(self, particle_type, position_sequence):
graph = preprocess(particle_type, position_sequence, self.metadata)
return graph
class Postprocess(torch.nn.Module):
"""Preprocessing unit. Build graphs from positions"""
def __init__(self, metadata):
super().__init__()
self.metadata = metadata
def forward(self, graph, acceleration):
acceleration = acceleration * torch.sqrt(torch.tensor(self.metadata["acc_std"]) ** 2) + torch.tensor(self.metadata["acc_mean"])
recent_position = graph.recent_position
recent_velocity = graph.recent_velocity
new_velocity = recent_velocity + acceleration
new_position = recent_position + new_velocity
return new_position
class InteractionNetwork(pyg.nn.MessagePassing):
"""Interaction Network as proposed in this paper:
https://proceedings.neurips.cc/paper/2016/hash/3147da8ab4a0437c15ef51a5cc7f2dc4-Abstract.html"""
def __init__(self, hidden_size, layers):
super().__init__()
self.lin_edge = MLP(hidden_size * 3, hidden_size, hidden_size, layers)
self.lin_node = MLP(hidden_size * 2, hidden_size, hidden_size, layers)
def forward(self, x, edge_index, edge_feature):
edge_out, aggr = self.propagate(edge_index, x=(x, x), edge_feature=edge_feature)
node_out = self.lin_node(torch.cat((x, aggr), dim=-1))
edge_out = edge_feature + edge_out
node_out = x + node_out
return node_out, edge_out
def message(self, x_i, x_j, edge_feature):
x = torch.cat((x_i, x_j, edge_feature), dim=-1)
x = self.lin_edge(x)
return x
def aggregate(self, inputs, index, dim_size=None):
out = torch_scatter.scatter(inputs, index, dim=self.node_dim, dim_size=dim_size, reduce="sum")
return (inputs, out)
"""### The GNN
"""
class LearnedSimulatorFull(torch.nn.Module):
"""Graph Network-based Simulators(GNS) full pipeline (with preprocessor and postprocessor)"""
def __init__(
self,
metadata,
hidden_size=128,
n_mp_layers=10, # number of GNN layers
num_particle_types=9,
particle_type_dim=16, # embedding dimension of particle types
dim=2, # dimension of the world, typical 2D or 3D
window_size=5, # the model looks into W frames before the frame to be predicted
):
super().__init__()
self.window_size = window_size
self.embed_type = torch.nn.Embedding(num_particle_types, particle_type_dim)
self.node_in = MLP(particle_type_dim + dim * (window_size + 2), hidden_size, hidden_size, 3)
self.edge_in = MLP(dim + 1, hidden_size, hidden_size, 3)
self.node_out = MLP(hidden_size, hidden_size, dim, 3, layernorm=False)
self.n_mp_layers = n_mp_layers
self.layers = torch.nn.ModuleList([InteractionNetwork(
hidden_size, 3
) for _ in range(n_mp_layers)])
self.build_graph = BuildGraph(metadata=metadata)
self.postproc = Postprocess(metadata=metadata)
self.reset_parameters()
def reset_parameters(self):
torch.nn.init.xavier_uniform_(self.embed_type.weight)
def forward(self, particle_type, position_sequence):
# pre-processing: graph building
data = self.build_graph(particle_type, position_sequence)
# node feature: combine categorial feature data.x and contiguous feature data.pos.
embedded = self.embed_type(data.x)
node_feature = torch.cat((embedded, data.pos), dim=-1)
node_feature = self.node_in(node_feature)
edge_feature = self.edge_in(data.edge_attr)
# stack of GNN layers
for i in range(self.n_mp_layers):
node_feature, edge_feature = self.layers[i](node_feature, data.edge_index, edge_feature=edge_feature)
# post-processing
norm_acceleration = self.node_out(node_feature)
# Post_processing
new_pos = self.postproc(data, norm_acceleration)
return new_pos
N_PARTICLES = 858
N_DIM = 2
metadata = {"bounds": [[0.1, 0.9], [0.1, 0.9]], "sequence_length": 1000, "default_connectivity_radius": 0.015, "dim": 2, "dt": 0.0025, "vel_mean": [-4.906372733478189e-06, -0.0003581614249505887], "vel_std": [0.0018492343327724738, 0.0018154400863548657], "acc_mean": [-1.3758095862050814e-08, 1.114232425851392e-07], "acc_std": [0.0001279824304831018, 0.0001388316140032424]}
simulator = LearnedSimulatorFull(metadata=metadata)
device = next(simulator.parameters()).device
window_size = simulator.window_size + 1
# Prepare example inputs
particle_type = np.zeros((N_PARTICLES), dtype=np.int64)
particle_type = torch.from_numpy(particle_type)
position_sequence = np.random.random((N_PARTICLES, window_size, N_DIM)).astype(np.float32)
position_sequence = torch.from_numpy(position_sequence)
example_inputs = (particle_type, position_sequence)
with torch.no_grad():
simulator.eval()
acc = simulator(*example_inputs).cpu()
# ============== Export to Executorch (float)
import torch
import torch_scatter
from torch.export import export
from executorch.exir import to_edge
# 1. Lower to aten
aten_dialect = export(simulator, example_inputs)
# 2. to_edge: Make optimizations for Edge devices
edge_program = to_edge(aten_dialect)
# 3. to_executorch: Convert the graph to an ExecuTorch program
executorch_program = edge_program.to_executorch()
# 4. Save the compiled .pte program
with open("physics_gnn_2d.pte", "wb") as file:
file.write(executorch_program.buffer)
```
[export_gnn_executorch_error.txt](https://github.com/user-attachments/files/18200672/export_gnn_executorch_error.txt):
```
PyTorch has version 2.5.0+cu124 with cuda 12.4
/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/onnxscript/converter.py:820: FutureWarning: 'onnxscript.values.Op.param_schemas' is deprecated in version 0.1 and will be removed in the future. Please use '.op_signature' instea
d. param_schemas = callee.param_schemas()
/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/onnxscript/converter.py:820: FutureWarning: 'onnxscript.values.OnnxFunction.param_schemas' is deprecated in version 0.1 and will be removed in the future. Please use '.op_signatu
re' instead. param_schemas = callee.param_schemas()
Traceback (most recent call last):
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/export_gnn_executorch.py", line 227, in <module>
aten_dialect = export(simulator, example_inputs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/export/__init__.py", line 270, in export
return _export(
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/export/_trace.py", line 1017, in wrapper
raise e
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/export/_trace.py", line 990, in wrapper
ep = fn(*args, **kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/export/exported_program.py", line 114, in wrapper
return fn(*args, **kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/export/_trace.py", line 1880, in _export
export_artifact = export_func( # type: ignore[operator]
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/export/_trace.py", line 1224, in _strict_export
return _strict_export_lower_to_aten_ir(
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/export/_trace.py", line 1252, in _strict_export_lower_to_aten_ir
gm_torch_level = _export_to_torch_ir(
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/export/_trace.py", line 560, in _export_to_torch_ir
gm_torch_level, _ = torch._dynamo.export(
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 1432, in inner
result_traced = opt_f(*args, **kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
return forward_call(*args, **kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 465, in _fn
return fn(*args, **kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
return forward_call(*args, **kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 1269, in __call__
return self._torchdynamo_orig_callable(
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 526, in __call__
return _compile(
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 924, in _compile
guarded_code = compile_inner(code, one_graph, hooks, transform)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 666, in compile_inner
return _compile_inner(code, one_graph, hooks, transform)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_utils_internal.py", line 87, in wrapper_function
return function(*args, **kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 699, in _compile_inner
out_code = transform_code_object(code, transform)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/bytecode_transformation.py", line 1322, in transform_code_object
transformations(instructions, code_options)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 219, in _fn
return fn(*args, **kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 634, in transform
tracer.run()
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 2796, in run
super().run()
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 983, in run
while self.step():
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 895, in step
self.dispatch_table[inst.opcode](self, inst)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 582, in wrapper
return inner_fn(self, inst)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 1602, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 830, in call_function
self.push(fn.call_function(self, args, kwargs)) # type: ignore[arg-type]
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/variables/nn_module.py", line 442, in call_function
return tx.inline_user_function_return(
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 836, in inline_user_function_return
return InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 3011, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 3139, in inline_call_
tracer.run()
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 983, in run
while self.step():
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 895, in step
self.dispatch_table[inst.opcode](self, inst)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 582, in wrapper
return inner_fn(self, inst)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 1680, in CALL_FUNCTION_EX
self.call_function(fn, argsvars.items, kwargsvars)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 830, in call_function
self.push(fn.call_function(self, args, kwargs)) # type: ignore[arg-type]
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/variables/functions.py", line 385, in call_function
return super().call_function(tx, args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/variables/functions.py", line 324, in call_function
return super().call_function(tx, args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/variables/functions.py", line 111, in call_function
return tx.inline_user_function_return(self, [*self.self_args(), *args], kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 836, in inline_user_function_return
return InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 3011, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 3139, in inline_call_
tracer.run()
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 983, in run
while self.step():
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 895, in step
self.dispatch_table[inst.opcode](self, inst)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 582, in wrapper
return inner_fn(self, inst)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 1602, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 830, in call_function
self.push(fn.call_function(self, args, kwargs)) # type: ignore[arg-type]
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/variables/functions.py", line 324, in call_function
return super().call_function(tx, args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/variables/functions.py", line 111, in call_function
return tx.inline_user_function_return(self, [*self.self_args(), *args], kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 836, in inline_user_function_return
return InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 3011, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 3139, in inline_call_
tracer.run()
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 983, in run
while self.step():
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 895, in step
self.dispatch_table[inst.opcode](self, inst)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 582, in wrapper
return inner_fn(self, inst)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 1692, in CALL_FUNCTION_KW
self.call_function(fn, args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 830, in call_function
self.push(fn.call_function(self, args, kwargs)) # type: ignore[arg-type]
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/variables/user_defined.py", line 496, in call_function
var.call_method(tx, "__init__", args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/variables/user_defined.py", line 788, in call_method
return UserMethodVariable(method, self, source=source).call_function(
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/variables/functions.py", line 385, in call_function
return super().call_function(tx, args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/variables/functions.py", line 324, in call_function
return super().call_function(tx, args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/variables/functions.py", line 111, in call_function
return tx.inline_user_function_return(self, [*self.self_args(), *args], kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 836, in inline_user_function_return
return InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 3011, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 3139, in inline_call_
tracer.run()
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 983, in run
while self.step():
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 895, in step
self.dispatch_table[inst.opcode](self, inst)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 582, in wrapper
return inner_fn(self, inst)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 1692, in CALL_FUNCTION_KW
self.call_function(fn, args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 830, in call_function
self.push(fn.call_function(self, args, kwargs)) # type: ignore[arg-type]
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/variables/misc.py", line 1024, in call_function
return self.obj.call_method(tx, self.name, args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/variables/misc.py", line 195, in call_method
).call_function(tx, [self.objvar] + args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/variables/functions.py", line 324, in call_function
return super().call_function(tx, args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/variables/functions.py", line 111, in call_function
return tx.inline_user_function_return(self, [*self.self_args(), *args], kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 836, in inline_user_function_return
return InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 3011, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 3139, in inline_call_
tracer.run()
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 983, in run
while self.step():
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 895, in step
self.dispatch_table[inst.opcode](self, inst)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 582, in wrapper
return inner_fn(self, inst)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 1602, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 830, in call_function
self.push(fn.call_function(self, args, kwargs)) # type: ignore[arg-type]
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/variables/misc.py", line 1024, in call_function
return self.obj.call_method(tx, self.name, args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/variables/misc.py", line 195, in call_method
).call_function(tx, [self.objvar] + args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/variables/functions.py", line 324, in call_function
return super().call_function(tx, args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/variables/functions.py", line 111, in call_function
return tx.inline_user_function_return(self, [*self.self_args(), *args], kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 836, in inline_user_function_return
return InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 3011, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 3139, in inline_call_
tracer.run()
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 983, in run
while self.step():
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 895, in step
self.dispatch_table[inst.opcode](self, inst)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 582, in wrapper
return inner_fn(self, inst)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 1803, in STORE_SUBSCR
result = obj.call_method(self, "__setitem__", [key, val], {})
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/variables/misc.py", line 1082, in call_method
return super().call_method(tx, name, args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/variables/base.py", line 343, in call_method
unimplemented(f"call_method {self} {name} {args} {kwargs}")
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/exc.py", line 297, in unimplemented
raise Unsupported(msg, case_name=case_name)
torch._dynamo.exc.Unsupported: call_method GetAttrVariable(UserDefinedObjectVariable(Data), __dict__) __setitem__ [ConstantVariable(), UserDefinedClassVariable()] {}
from user code:
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/export_gnn_executorch.py", line 185, in forward
data = self.build_graph(particle_type, position_sequence)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
return forward_call(*args, **kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/export_gnn_executorch.py", line 108, in forward
graph = preprocess(particle_type, position_sequence, self.metadata)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/export_gnn_executorch.py", line 90, in preprocess
graph = pyg.data.Data(
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch_geometric/data/data.py", line 530, in __init__
super().__init__(tensor_attr_cls=DataTensorAttr)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch_geometric/data/feature_store.py", line 278, in __init__
super().__init__()
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch_geometric/data/graph_store.py", line 111, in __init__
self.__dict__['_edge_attr_cls'] = edge_attr_cls or EdgeAttr
Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information
```
[export_gnn_executorch_error_detailed.txt](https://github.com/user-attachments/files/18200671/export_gnn_executorch_error_detailed.txt):
```
V1219 16:33:18.212000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py:1234] skipping: _wrapped_call_impl (reason: in skipfiles, file: /home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/nn/modules/module.py)
V1219 16:33:18.212000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py:1234] skipping: _call_impl (reason: in skipfiles, file: /home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/nn/modules/module.py)
V1219 16:33:18.215000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py:864] [0/0] torchdynamo start compiling forward /home/sicli01/Projects/FluidML/gnn-physics-pytorch/export_gnn_executorch.py:183, stack (elided 4 frames):
V1219 16:33:18.215000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py:864] [0/0] File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/export_gnn_executorch.py", line 227, in <module>
V1219 16:33:18.215000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py:864] [0/0] aten_dialect = export(simulator, example_inputs)
V1219 16:33:18.215000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py:864] [0/0] File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/export/__init__.py", line 270, in export
V1219 16:33:18.215000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py:864] [0/0] return _export(
V1219 16:33:18.215000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py:864] [0/0] File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/export/_trace.py", line 990, in wrapper
V1219 16:33:18.215000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py:864] [0/0] ep = fn(*args, **kwargs)
V1219 16:33:18.215000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py:864] [0/0] File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/export/exported_program.py", line 114, in wrapper
V1219 16:33:18.215000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py:864] [0/0] return fn(*args, **kwargs)
V1219 16:33:18.215000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py:864] [0/0] File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/export/_trace.py", line 1880, in _export
V1219 16:33:18.215000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py:864] [0/0] export_artifact = export_func( # type: ignore[operator]
V1219 16:33:18.215000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py:864] [0/0] File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/export/_trace.py", line 1224, in _strict_export
V1219 16:33:18.215000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py:864] [0/0] return _strict_export_lower_to_aten_ir(
V1219 16:33:18.215000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py:864] [0/0] File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/export/_trace.py", line 1252, in _strict_export_lower_to_aten_ir
V1219 16:33:18.215000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py:864] [0/0] gm_torch_level = _export_to_torch_ir(
V1219 16:33:18.215000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py:864] [0/0] File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/export/_trace.py", line 560, in _export_to_torch_ir
V1219 16:33:18.215000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py:864] [0/0] gm_torch_level, _ = torch._dynamo.export(
V1219 16:33:18.215000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py:864] [0/0] File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 1432, in inner
V1219 16:33:18.215000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py:864] [0/0] result_traced = opt_f(*args, **kwargs)
V1219 16:33:18.215000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py:864] [0/0] File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
V1219 16:33:18.215000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py:864] [0/0] return self._call_impl(*args, **kwargs)
V1219 16:33:18.215000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py:864] [0/0] File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
V1219 16:33:18.215000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py:864] [0/0] return forward_call(*args, **kwargs)
V1219 16:33:18.215000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py:864] [0/0] File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 465, in _fn
V1219 16:33:18.215000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py:864] [0/0] return fn(*args, **kwargs)
V1219 16:33:18.215000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py:864] [0/0] File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
V1219 16:33:18.215000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py:864] [0/0] return self._call_impl(*args, **kwargs)
V1219 16:33:18.215000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py:864] [0/0]
I1219 16:33:18.215000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/utils.py:859] [0/0] ChromiumEventLogger initialized with id d4706065-cfc9-4744-8922-8c3f00f352a2
I1219 16:33:18.231000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/logging.py:57] [0/0] Step 1: torchdynamo start tracing forward /home/sicli01/Projects/FluidML/gnn-physics-pytorch/export_gnn_executorch.py:183
V1219 16:33:18.231000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:2498] [0/0] create_env
/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/onnxscript/converter.py:820: FutureWarning: 'onnxscript.values.Op.param_schemas' is deprecated in version 0.1 and will be removed in the future. Please use '.op_signature' instead.
param_schemas = callee.param_schemas()
/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/onnxscript/converter.py:820: FutureWarning: 'onnxscript.values.OnnxFunction.param_schemas' is deprecated in version 0.1 and will be removed in the future. Please use '.op_signature' instead.
param_schemas = callee.param_schemas()
V1219 16:33:18.585000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/output_graph.py:2076] [0/0] create_graph_input L_particle_type_ L['particle_type']
V1219 16:33:18.586000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/variables/builder.py:2702] [0/0] wrap_to_fake L['particle_type'] (858,) StatefulSymbolicContext(dynamic_sizes=[<DimDynamic.DYNAMIC: 0>], dynamic_strides=[<DimDynamic.INFER_STRIDE: 4>], constraint_sizes=[StrictMinMaxConstraint(warn_only=False, vr=VR[858, 858])], constraint_strides=[None], view_base_context=None, tensor_source=LocalSource(local_name='particle_type', cell_or_freevar=False), shape_env_to_source_to_symbol_cache={}) <class 'torch.Tensor'>
V1219 16:33:18.587000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/output_graph.py:2076] [0/0] create_graph_input L_position_sequence_ L['position_sequence']
V1219 16:33:18.587000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/variables/builder.py:2702] [0/0] wrap_to_fake L['position_sequence'] (858, 6, 2) StatefulSymbolicContext(dynamic_sizes=[<DimDynamic.DYNAMIC: 0>, <DimDynamic.DYNAMIC: 0>, <DimDynamic.DYNAMIC: 0>], dynamic_strides=[<DimDynamic.INFER_STRIDE: 4>, <DimDynamic.INFER_STRIDE: 4>, <DimDynamic.INFER_STRIDE: 4>], constraint_sizes=[StrictMinMaxConstraint(warn_only=False, vr=VR[858, 858]), StrictMinMaxConstraint(warn_only=False, vr=VR[6, 6]), StrictMinMaxConstraint(warn_only=False, vr=VR[2, 2])], constraint_strides=[None, None, None], view_base_context=None, tensor_source=LocalSource(local_name='position_sequence', cell_or_freevar=False), shape_env_to_source_to_symbol_cache={}) <class 'torch.Tensor'>
V1219 16:33:18.588000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] TRACE starts_line /home/sicli01/Projects/FluidML/gnn-physics-pytorch/export_gnn_executorch.py:185 in forward (LearnedSimulatorFull.forward)
V1219 16:33:18.588000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] data = self.build_graph(particle_type, position_sequence)
V1219 16:33:18.591000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST self []
V1219 16:33:18.591000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_ATTR build_graph [NNModuleVariable()]
V1219 16:33:18.592000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST particle_type [NNModuleVariable()]
V1219 16:33:18.592000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST position_sequence [NNModuleVariable(), TensorVariable()]
V1219 16:33:18.592000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE CALL_FUNCTION 2 [NNModuleVariable(), TensorVariable(), TensorVariable()]
V1219 16:33:18.593000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:3099] [0/0] INLINING <code object _call_impl at 0x78f885d08f50, file "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1740>, inlined according trace_rules.lookup MOD_INLINELIST
V1219 16:33:18.593000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] TRACE starts_line /home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/nn/modules/module.py:1741 in _call_impl (Module._call_impl) (inline depth: 1)
V1219 16:33:18.593000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] forward_call = (self._slow_forward if torch._C._get_tracing_state() else self.forward)
V1219 16:33:18.612000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_GLOBAL torch []
V1219 16:33:18.613000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_ATTR _C [PythonModuleVariable(<module 'torch' from '/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/__init__.py'>)]
V1219 16:33:18.613000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_ATTR _get_tracing_state [PythonModuleVariable(<module 'torch._C' from '/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_C.cpython-310-x86_64-linux-gnu.so'>)]
V1219 16:33:18.613000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE CALL_FUNCTION 0 [TorchInGraphFunctionVariable(<built-in method _get_tracing_state of PyCapsule object at 0x78f8d131b000>)]
V1219 16:33:18.613000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE POP_JUMP_IF_FALSE 16 [ConstantVariable()]
V1219 16:33:18.613000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_DEREF self []
V1219 16:33:18.614000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_ATTR forward [NNModuleVariable()]
V1219 16:33:18.614000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE STORE_DEREF forward_call [UserMethodVariable()]
V1219 16:33:18.614000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] TRACE starts_line /home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/nn/modules/module.py:1744 in _call_impl (Module._call_impl) (inline depth: 1)
V1219 16:33:18.614000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
V1219 16:33:18.614000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_DEREF self []
V1219 16:33:18.614000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_ATTR _backward_hooks [NNModuleVariable()]
V1219 16:33:18.614000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE POP_JUMP_IF_TRUE 76 [ConstDictVariable()]
V1219 16:33:18.614000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_DEREF self []
V1219 16:33:18.614000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_ATTR _backward_pre_hooks [NNModuleVariable()]
V1219 16:33:18.614000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE POP_JUMP_IF_TRUE 76 [ConstDictVariable()]
V1219 16:33:18.614000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_DEREF self []
V1219 16:33:18.614000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_ATTR _forward_hooks [NNModuleVariable()]
V1219 16:33:18.615000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE POP_JUMP_IF_TRUE 76 [ConstDictVariable()]
V1219 16:33:18.615000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_DEREF self []
V1219 16:33:18.615000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_ATTR _forward_pre_hooks [NNModuleVariable()]
V1219 16:33:18.615000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE POP_JUMP_IF_TRUE 76 [ConstDictVariable()]
V1219 16:33:18.615000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] TRACE starts_line /home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/nn/modules/module.py:1745 in _call_impl (Module._call_impl) (inline depth: 1)
V1219 16:33:18.615000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] or _global_backward_pre_hooks or _global_backward_hooks
V1219 16:33:18.615000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_GLOBAL _global_backward_pre_hooks []
V1219 16:33:18.615000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] TRACE starts_line /home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/nn/modules/module.py:1744 in _call_impl (Module._call_impl) (inline depth: 1)
V1219 16:33:18.615000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
V1219 16:33:18.615000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE POP_JUMP_IF_TRUE 76 [ConstDictVariable()]
V1219 16:33:18.615000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] TRACE starts_line /home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/nn/modules/module.py:1745 in _call_impl (Module._call_impl) (inline depth: 1)
V1219 16:33:18.615000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] or _global_backward_pre_hooks or _global_backward_hooks
V1219 16:33:18.615000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_GLOBAL _global_backward_hooks []
V1219 16:33:18.616000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] TRACE starts_line /home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/nn/modules/module.py:1744 in _call_impl (Module._call_impl) (inline depth: 1)
V1219 16:33:18.616000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
V1219 16:33:18.616000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE POP_JUMP_IF_TRUE 76 [ConstDictVariable()]
V1219 16:33:18.616000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] TRACE starts_line /home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/nn/modules/module.py:1746 in _call_impl (Module._call_impl) (inline depth: 1)
V1219 16:33:18.616000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] or _global_forward_hooks or _global_forward_pre_hooks):
V1219 16:33:18.616000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_GLOBAL _global_forward_hooks []
V1219 16:33:18.616000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] TRACE starts_line /home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/nn/modules/module.py:1744 in _call_impl (Module._call_impl) (inline depth: 1)
V1219 16:33:18.616000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
V1219 16:33:18.616000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE POP_JUMP_IF_TRUE 76 [ConstDictVariable()]
V1219 16:33:18.616000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] TRACE starts_line /home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/nn/modules/module.py:1746 in _call_impl (Module._call_impl) (inline depth: 1)
V1219 16:33:18.616000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] or _global_forward_hooks or _global_forward_pre_hooks):
V1219 16:33:18.616000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_GLOBAL _global_forward_pre_hooks []
V1219 16:33:18.616000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] TRACE starts_line /home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/nn/modules/module.py:1744 in _call_impl (Module._call_impl) (inline depth: 1)
V1219 16:33:18.616000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
V1219 16:33:18.616000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE POP_JUMP_IF_TRUE 76 [ConstDictVariable()]
V1219 16:33:18.616000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] TRACE starts_line /home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/nn/modules/module.py:1747 in _call_impl (Module._call_impl) (inline depth: 1)
V1219 16:33:18.616000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] return forward_call(*args, **kwargs)
V1219 16:33:18.616000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_DEREF forward_call []
V1219 16:33:18.617000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_DEREF args [UserMethodVariable()]
V1219 16:33:18.617000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE BUILD_MAP 0 [UserMethodVariable(), TupleVariable(length=2)]
V1219 16:33:18.617000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_DEREF kwargs [UserMethodVariable(), TupleVariable(length=2), ConstDictVariable()]
V1219 16:33:18.617000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE DICT_MERGE 1 [UserMethodVariable(), TupleVariable(length=2), ConstDictVariable(), ConstDictVariable()]
V1219 16:33:18.617000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE CALL_FUNCTION_EX 1 [UserMethodVariable(), TupleVariable(length=2), ConstDictVariable()]
V1219 16:33:18.617000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:3099] [0/0] INLINING <code object forward at 0x78f8d25a8190, file "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/export_gnn_executorch.py", line 107>, inlined according trace_rules.lookup inlined by default
V1219 16:33:18.617000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] TRACE starts_line /home/sicli01/Projects/FluidML/gnn-physics-pytorch/export_gnn_executorch.py:108 in forward (BuildGraph.forward) (inline depth: 2)
V1219 16:33:18.617000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] graph = preprocess(particle_type, position_sequence, self.metadata)
V1219 16:33:18.617000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_GLOBAL preprocess []
V1219 16:33:18.617000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST particle_type [UserFunctionVariable()]
V1219 16:33:18.618000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST position_sequence [UserFunctionVariable(), TensorVariable()]
V1219 16:33:18.618000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST self [UserFunctionVariable(), TensorVariable(), TensorVariable()]
V1219 16:33:18.618000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_ATTR metadata [UserFunctionVariable(), TensorVariable(), TensorVariable(), NNModuleVariable()]
V1219 16:33:18.618000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE CALL_FUNCTION 3 [UserFunctionVariable(), TensorVariable(), TensorVariable(), ConstDictVariable()]
V1219 16:33:18.618000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:3099] [0/0] INLINING <code object preprocess at 0x78f8d25a8030, file "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/export_gnn_executorch.py", line 61>, inlined according trace_rules.lookup inlined by default
V1219 16:33:18.619000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] TRACE starts_line /home/sicli01/Projects/FluidML/gnn-physics-pytorch/export_gnn_executorch.py:68 in preprocess (preprocess) (inline depth: 3)
V1219 16:33:18.619000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] recent_position = position_seq[:, -1]
V1219 16:33:18.619000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST position_seq []
V1219 16:33:18.619000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST None [TensorVariable()]
V1219 16:33:18.619000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST None [TensorVariable(), ConstantVariable()]
V1219 16:33:18.619000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE BUILD_SLICE 2 [TensorVariable(), ConstantVariable(), ConstantVariable()]
V1219 16:33:18.619000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST -1 [TensorVariable(), SliceVariable()]
V1219 16:33:18.619000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE BUILD_TUPLE 2 [TensorVariable(), SliceVariable(), ConstantVariable()]
V1219 16:33:18.619000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE BINARY_SUBSCR None [TensorVariable(), TupleVariable(length=2)]
V1219 16:33:18.621000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE STORE_FAST recent_position [TensorVariable()]
V1219 16:33:18.621000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] TRACE starts_line /home/sicli01/Projects/FluidML/gnn-physics-pytorch/export_gnn_executorch.py:69 in preprocess (preprocess) (inline depth: 3)
V1219 16:33:18.621000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] velocity_seq = position_seq[:, 1:] - position_seq[:, :-1]
V1219 16:33:18.621000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST position_seq []
V1219 16:33:18.621000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST None [TensorVariable()]
V1219 16:33:18.622000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST None [TensorVariable(), ConstantVariable()]
V1219 16:33:18.622000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE BUILD_SLICE 2 [TensorVariable(), ConstantVariable(), ConstantVariable()]
V1219 16:33:18.622000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST 1 [TensorVariable(), SliceVariable()]
V1219 16:33:18.622000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST None [TensorVariable(), SliceVariable(), ConstantVariable()]
V1219 16:33:18.622000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE BUILD_SLICE 2 [TensorVariable(), SliceVariable(), ConstantVariable(), ConstantVariable()]
V1219 16:33:18.622000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE BUILD_TUPLE 2 [TensorVariable(), SliceVariable(), SliceVariable()]
V1219 16:33:18.622000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE BINARY_SUBSCR None [TensorVariable(), TupleVariable(length=2)]
V1219 16:33:18.623000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST position_seq [TensorVariable()]
V1219 16:33:18.623000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST None [TensorVariable(), TensorVariable()]
V1219 16:33:18.623000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST None [TensorVariable(), TensorVariable(), ConstantVariable()]
V1219 16:33:18.623000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE BUILD_SLICE 2 [TensorVariable(), TensorVariable(), ConstantVariable(), ConstantVariable()]
V1219 16:33:18.623000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST None [TensorVariable(), TensorVariable(), SliceVariable()]
V1219 16:33:18.624000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST -1 [TensorVariable(), TensorVariable(), SliceVariable(), ConstantVariable()]
V1219 16:33:18.624000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE BUILD_SLICE 2 [TensorVariable(), TensorVariable(), SliceVariable(), ConstantVariable(), ConstantVariable()]
V1219 16:33:18.624000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE BUILD_TUPLE 2 [TensorVariable(), TensorVariable(), SliceVariable(), SliceVariable()]
V1219 16:33:18.624000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE BINARY_SUBSCR None [TensorVariable(), TensorVariable(), TupleVariable(length=2)]
V1219 16:33:18.625000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE BINARY_SUBTRACT None [TensorVariable(), TensorVariable()]
V1219 16:33:18.626000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE STORE_FAST velocity_seq [TensorVariable()]
V1219 16:33:18.626000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] TRACE starts_line /home/sicli01/Projects/FluidML/gnn-physics-pytorch/export_gnn_executorch.py:73 in preprocess (preprocess) (inline depth: 3)
V1219 16:33:18.626000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] edge_index = find_connectivity(recent_position, metadata["default_connectivity_radius"])
V1219 16:33:18.626000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_GLOBAL find_connectivity []
V1219 16:33:18.626000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST recent_position [UserFunctionVariable()]
V1219 16:33:18.626000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST metadata [UserFunctionVariable(), TensorVariable()]
V1219 16:33:18.627000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST default_connectivity_radius [UserFunctionVariable(), TensorVariable(), ConstDictVariable()]
V1219 16:33:18.627000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE BINARY_SUBSCR None [UserFunctionVariable(), TensorVariable(), ConstDictVariable(), ConstantVariable()]
V1219 16:33:18.627000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE CALL_FUNCTION 2 [UserFunctionVariable(), TensorVariable(), LazyVariableTracker()]
V1219 16:33:18.627000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:3099] [0/0] INLINING <code object find_connectivity at 0x78f8d257fec0, file "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/export_gnn_executorch.py", line 40>, inlined according trace_rules.lookup inlined by default
V1219 16:33:18.627000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] TRACE starts_line /home/sicli01/Projects/FluidML/gnn-physics-pytorch/export_gnn_executorch.py:51 in find_connectivity (find_connectivity) (inline depth: 4)
V1219 16:33:18.627000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] squared_norm = torch.sum(positions*positions, 1) # [N_particles]
V1219 16:33:18.627000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_GLOBAL torch []
V1219 16:33:18.627000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_ATTR sum [PythonModuleVariable(<module 'torch' from '/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/__init__.py'>)]
V1219 16:33:18.628000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST positions [TorchInGraphFunctionVariable(<built-in method sum of type object at 0x78f8d10bf1c0>)]
V1219 16:33:18.628000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST positions [TorchInGraphFunctionVariable(<built-in method sum of type object at 0x78f8d10bf1c0>), TensorVariable()]
V1219 16:33:18.628000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE BINARY_MULTIPLY None [TorchInGraphFunctionVariable(<built-in method sum of type object at 0x78f8d10bf1c0>), TensorVariable(), TensorVariable()]
V1219 16:33:18.629000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST 1 [TorchInGraphFunctionVariable(<built-in method sum of type object at 0x78f8d10bf1c0>), TensorVariable()]
V1219 16:33:18.629000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE CALL_FUNCTION 2 [TorchInGraphFunctionVariable(<built-in method sum of type object at 0x78f8d10bf1c0>), TensorVariable(), ConstantVariable()]
V1219 16:33:18.630000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE STORE_FAST squared_norm [TensorVariable()]
V1219 16:33:18.630000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] TRACE starts_line /home/sicli01/Projects/FluidML/gnn-physics-pytorch/export_gnn_executorch.py:52 in find_connectivity (find_connectivity) (inline depth: 4)
V1219 16:33:18.630000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] squared_norm = torch.reshape(squared_norm, [-1, 1]) # [N_particles, 1]
V1219 16:33:18.630000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_GLOBAL torch []
V1219 16:33:18.630000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_ATTR reshape [PythonModuleVariable(<module 'torch' from '/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/__init__.py'>)]
V1219 16:33:18.630000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST squared_norm [TorchInGraphFunctionVariable(<built-in method reshape of type object at 0x78f8d10bf1c0>)]
V1219 16:33:18.630000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST -1 [TorchInGraphFunctionVariable(<built-in method reshape of type object at 0x78f8d10bf1c0>), TensorVariable()]
V1219 16:33:18.630000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST 1 [TorchInGraphFunctionVariable(<built-in method reshape of type object at 0x78f8d10bf1c0>), TensorVariable(), ConstantVariable()]
V1219 16:33:18.630000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE BUILD_LIST 2 [TorchInGraphFunctionVariable(<built-in method reshape of type object at 0x78f8d10bf1c0>), TensorVariable(), ConstantVariable(), ConstantVariable()]
V1219 16:33:18.630000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE CALL_FUNCTION 2 [TorchInGraphFunctionVariable(<built-in method reshape of type object at 0x78f8d10bf1c0>), TensorVariable(), ListVariable(length=2)]
V1219 16:33:18.631000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE STORE_FAST squared_norm [TensorVariable()]
V1219 16:33:18.632000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] TRACE starts_line /home/sicli01/Projects/FluidML/gnn-physics-pytorch/export_gnn_executorch.py:53 in find_connectivity (find_connectivity) (inline depth: 4)
V1219 16:33:18.632000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] distance_tensor = squared_norm - 2*torch.matmul(positions, torch.transpose(positions, 0, 1)) + torch.transpose(squared_norm, 0, 1) # [N_particles, N_particles] Pair-wise square distance matrix
V1219 16:33:18.632000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST squared_norm []
V1219 16:33:18.632000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST 2 [TensorVariable()]
V1219 16:33:18.632000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_GLOBAL torch [TensorVariable(), ConstantVariable()]
V1219 16:33:18.632000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_ATTR matmul [TensorVariable(), ConstantVariable(), PythonModuleVariable(<module 'torch' from '/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/__init__.py'>)]
V1219 16:33:18.632000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST positions [TensorVariable(), ConstantVariable(), TorchInGraphFunctionVariable(<built-in method matmul of type object at 0x78f8d10bf1c0>)]
V1219 16:33:18.632000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_GLOBAL torch [TensorVariable(), ConstantVariable(), TorchInGraphFunctionVariable(<built-in method matmul of type object at 0x78f8d10bf1c0>), TensorVariable()]
V1219 16:33:18.632000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_ATTR transpose [TensorVariable(), ConstantVariable(), TorchInGraphFunctionVariable(<built-in method matmul of type object at 0x78f8d10bf1c0>), TensorVariable(), PythonModuleVariable(<module 'torch' from '/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/__init__.py'>)]
V1219 16:33:18.632000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST positions [TensorVariable(), ConstantVariable(), TorchInGraphFunctionVariable(<built-in method matmul of type object at 0x78f8d10bf1c0>), TensorVariable(), TorchInGraphFunctionVariable(<built-in method transpose of type object at 0x78f8d10bf1c0>)]
V1219 16:33:18.633000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST 0 [TensorVariable(), ConstantVariable(), TorchInGraphFunctionVariable(<built-in method matmul of type object at 0x78f8d10bf1c0>), TensorVariable(), TorchInGraphFunctionVariable(<built-in method transpose of type object at 0x78f8d10bf1c0>), TensorVariable()]
V1219 16:33:18.633000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST 1 [TensorVariable(), ConstantVariable(), TorchInGraphFunctionVariable(<built-in method matmul of type object at 0x78f8d10bf1c0>), TensorVariable(), TorchInGraphFunctionVariable(<built-in method transpose of type object at 0x78f8d10bf1c0>), TensorVariable(), ConstantVariable()]
V1219 16:33:18.633000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE CALL_FUNCTION 3 [TensorVariable(), ConstantVariable(), TorchInGraphFunctionVariable(<built-in method matmul of type object at 0x78f8d10bf1c0>), TensorVariable(), TorchInGraphFunctionVariable(<built-in method transpose of type object at 0x78f8d10bf1c0>), TensorVariable(), ConstantVariable(), ConstantVariable()]
V1219 16:33:18.634000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE CALL_FUNCTION 2 [TensorVariable(), ConstantVariable(), TorchInGraphFunctionVariable(<built-in method matmul of type object at 0x78f8d10bf1c0>), TensorVariable(), TensorVariable()]
V1219 16:33:18.635000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE BINARY_MULTIPLY None [TensorVariable(), ConstantVariable(), TensorVariable()]
V1219 16:33:18.635000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE BINARY_SUBTRACT None [TensorVariable(), TensorVariable()]
V1219 16:33:18.636000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_GLOBAL torch [TensorVariable()]
V1219 16:33:18.636000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_ATTR transpose [TensorVariable(), PythonModuleVariable(<module 'torch' from '/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/__init__.py'>)]
V1219 16:33:18.636000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST squared_norm [TensorVariable(), TorchInGraphFunctionVariable(<built-in method transpose of type object at 0x78f8d10bf1c0>)]
V1219 16:33:18.637000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST 0 [TensorVariable(), TorchInGraphFunctionVariable(<built-in method transpose of type object at 0x78f8d10bf1c0>), TensorVariable()]
V1219 16:33:18.637000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST 1 [TensorVariable(), TorchInGraphFunctionVariable(<built-in method transpose of type object at 0x78f8d10bf1c0>), TensorVariable(), ConstantVariable()]
V1219 16:33:18.637000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE CALL_FUNCTION 3 [TensorVariable(), TorchInGraphFunctionVariable(<built-in method transpose of type object at 0x78f8d10bf1c0>), TensorVariable(), ConstantVariable(), ConstantVariable()]
V1219 16:33:18.637000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE BINARY_ADD None [TensorVariable(), TensorVariable()]
V1219 16:33:18.638000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE STORE_FAST distance_tensor [TensorVariable()]
V1219 16:33:18.638000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] TRACE starts_line /home/sicli01/Projects/FluidML/gnn-physics-pytorch/export_gnn_executorch.py:56 in find_connectivity (find_connectivity) (inline depth: 4)
V1219 16:33:18.638000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] edge_index = torch.nonzero(torch.less_equal(distance_tensor, radius * radius), as_tuple=False)
V1219 16:33:18.639000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_GLOBAL torch []
V1219 16:33:18.639000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_ATTR nonzero [PythonModuleVariable(<module 'torch' from '/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/__init__.py'>)]
V1219 16:33:18.639000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_GLOBAL torch [TorchInGraphFunctionVariable(<built-in method nonzero of type object at 0x78f8d10bf1c0>)]
V1219 16:33:18.639000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_ATTR less_equal [TorchInGraphFunctionVariable(<built-in method nonzero of type object at 0x78f8d10bf1c0>), PythonModuleVariable(<module 'torch' from '/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/__init__.py'>)]
V1219 16:33:18.639000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST distance_tensor [TorchInGraphFunctionVariable(<built-in method nonzero of type object at 0x78f8d10bf1c0>), TorchInGraphFunctionVariable(<built-in method less_equal of type object at 0x78f8d10bf1c0>)]
V1219 16:33:18.639000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST radius [TorchInGraphFunctionVariable(<built-in method nonzero of type object at 0x78f8d10bf1c0>), TorchInGraphFunctionVariable(<built-in method less_equal of type object at 0x78f8d10bf1c0>), TensorVariable()]
V1219 16:33:18.639000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST radius [TorchInGraphFunctionVariable(<built-in method nonzero of type object at 0x78f8d10bf1c0>), TorchInGraphFunctionVariable(<built-in method less_equal of type object at 0x78f8d10bf1c0>), TensorVariable(), LazyVariableTracker()]
V1219 16:33:18.639000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE BINARY_MULTIPLY None [TorchInGraphFunctionVariable(<built-in method nonzero of type object at 0x78f8d10bf1c0>), TorchInGraphFunctionVariable(<built-in method less_equal of type object at 0x78f8d10bf1c0>), TensorVariable(), LazyVariableTracker(), LazyVariableTracker()]
V1219 16:33:18.640000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE CALL_FUNCTION 2 [TorchInGraphFunctionVariable(<built-in method nonzero of type object at 0x78f8d10bf1c0>), TorchInGraphFunctionVariable(<built-in method less_equal of type object at 0x78f8d10bf1c0>), TensorVariable(), ConstantVariable()]
V1219 16:33:18.641000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST False [TorchInGraphFunctionVariable(<built-in method nonzero of type object at 0x78f8d10bf1c0>), TensorVariable()]
V1219 16:33:18.641000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST ('as_tuple',) [TorchInGraphFunctionVariable(<built-in method nonzero of type object at 0x78f8d10bf1c0>), TensorVariable(), ConstantVariable()]
V1219 16:33:18.641000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE CALL_FUNCTION_KW 2 [TorchInGraphFunctionVariable(<built-in method nonzero of type object at 0x78f8d10bf1c0>), TensorVariable(), ConstantVariable(), TupleVariable(length=1)]
I1219 16:33:18.642000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:3317] [0/0] create_unbacked_symint u0 [-int_oo, int_oo] at export_gnn_executorch.py:56 in find_connectivity (_subclasses/fake_impls.py:426 in nonzero)
V1219 16:33:18.643000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:4734] [0/0] _update_var_to_range u0 = VR[0, 736164] (update)
I1219 16:33:18.643000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:5481] [0/0] constrain_symbol_range u0 [0, 736164]
V1219 16:33:18.659000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:5358] [0/0] runtime_assert u0 >= 0 == True [statically known]
V1219 16:33:18.661000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:5358] [0/0] runtime_assert u0 >= 0 == True [statically known]
V1219 16:33:18.662000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:5201] [0/0] eval Eq(u0, 0) == False [statically known]
V1219 16:33:18.663000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:5358] [0/0] runtime_assert u0 >= 0 == True [statically known]
I1219 16:33:18.663000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:604] [0/0] compute_unbacked_bindings [u0]
V1219 16:33:18.664000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE STORE_FAST edge_index [TensorVariable()]
V1219 16:33:18.664000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] TRACE starts_line /home/sicli01/Projects/FluidML/gnn-physics-pytorch/export_gnn_executorch.py:58 in find_connectivity (find_connectivity) (inline depth: 4)
V1219 16:33:18.664000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] return edge_index.T
V1219 16:33:18.664000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST edge_index []
V1219 16:33:18.664000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_ATTR T [TensorVariable()]
V1219 16:33:18.665000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE RETURN_VALUE None [TensorVariable()]
V1219 16:33:18.665000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:3164] [0/0] DONE INLINING <code object find_connectivity at 0x78f8d257fec0, file "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/export_gnn_executorch.py", line 40>
V1219 16:33:18.665000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE STORE_FAST edge_index [TensorVariable()]
V1219 16:33:18.665000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] TRACE starts_line /home/sicli01/Projects/FluidML/gnn-physics-pytorch/export_gnn_executorch.py:76 in preprocess (preprocess) (inline depth: 3)
V1219 16:33:18.665000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] boundary = torch.tensor(metadata["bounds"])
V1219 16:33:18.665000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_GLOBAL torch []
V1219 16:33:18.665000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_ATTR tensor [PythonModuleVariable(<module 'torch' from '/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/__init__.py'>)]
V1219 16:33:18.666000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST metadata [TorchInGraphFunctionVariable(<built-in method tensor of type object at 0x78f8d10bf1c0>)]
V1219 16:33:18.666000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST bounds [TorchInGraphFunctionVariable(<built-in method tensor of type object at 0x78f8d10bf1c0>), ConstDictVariable()]
V1219 16:33:18.666000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE BINARY_SUBSCR None [TorchInGraphFunctionVariable(<built-in method tensor of type object at 0x78f8d10bf1c0>), ConstDictVariable(), ConstantVariable()]
V1219 16:33:18.666000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE CALL_FUNCTION 1 [TorchInGraphFunctionVariable(<built-in method tensor of type object at 0x78f8d10bf1c0>), LazyVariableTracker()]
V1219 16:33:18.667000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE STORE_FAST boundary [TensorVariable()]
V1219 16:33:18.667000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] TRACE starts_line /home/sicli01/Projects/FluidML/gnn-physics-pytorch/export_gnn_executorch.py:77 in preprocess (preprocess) (inline depth: 3)
V1219 16:33:18.667000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] distance_to_lower_boundary = recent_position - boundary[:, 0]
V1219 16:33:18.667000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST recent_position []
V1219 16:33:18.667000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST boundary [TensorVariable()]
V1219 16:33:18.667000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST None [TensorVariable(), TensorVariable()]
V1219 16:33:18.668000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST None [TensorVariable(), TensorVariable(), ConstantVariable()]
V1219 16:33:18.668000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE BUILD_SLICE 2 [TensorVariable(), TensorVariable(), ConstantVariable(), ConstantVariable()]
V1219 16:33:18.668000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST 0 [TensorVariable(), TensorVariable(), SliceVariable()]
V1219 16:33:18.668000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE BUILD_TUPLE 2 [TensorVariable(), TensorVariable(), SliceVariable(), ConstantVariable()]
V1219 16:33:18.668000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE BINARY_SUBSCR None [TensorVariable(), TensorVariable(), TupleVariable(length=2)]
V1219 16:33:18.669000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE BINARY_SUBTRACT None [TensorVariable(), TensorVariable()]
V1219 16:33:18.670000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE STORE_FAST distance_to_lower_boundary [TensorVariable()]
V1219 16:33:18.670000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] TRACE starts_line /home/sicli01/Projects/FluidML/gnn-physics-pytorch/export_gnn_executorch.py:78 in preprocess (preprocess) (inline depth: 3)
V1219 16:33:18.670000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] distance_to_upper_boundary = boundary[:, 1] - recent_position
V1219 16:33:18.670000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST boundary []
V1219 16:33:18.670000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST None [TensorVariable()]
V1219 16:33:18.670000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST None [TensorVariable(), ConstantVariable()]
V1219 16:33:18.670000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE BUILD_SLICE 2 [TensorVariable(), ConstantVariable(), ConstantVariable()]
V1219 16:33:18.671000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST 1 [TensorVariable(), SliceVariable()]
V1219 16:33:18.671000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE BUILD_TUPLE 2 [TensorVariable(), SliceVariable(), ConstantVariable()]
V1219 16:33:18.671000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE BINARY_SUBSCR None [TensorVariable(), TupleVariable(length=2)]
V1219 16:33:18.672000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST recent_position [TensorVariable()]
V1219 16:33:18.672000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE BINARY_SUBTRACT None [TensorVariable(), TensorVariable()]
V1219 16:33:18.672000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE STORE_FAST distance_to_upper_boundary [TensorVariable()]
V1219 16:33:18.673000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] TRACE starts_line /home/sicli01/Projects/FluidML/gnn-physics-pytorch/export_gnn_executorch.py:79 in preprocess (preprocess) (inline depth: 3)
V1219 16:33:18.673000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] distance_to_boundary = torch.cat((distance_to_lower_boundary, distance_to_upper_boundary), dim=-1)
V1219 16:33:18.673000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_GLOBAL torch []
V1219 16:33:18.673000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_ATTR cat [PythonModuleVariable(<module 'torch' from '/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/__init__.py'>)]
V1219 16:33:18.673000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST distance_to_lower_boundary [TorchInGraphFunctionVariable(<built-in method cat of type object at 0x78f8d10bf1c0>)]
V1219 16:33:18.673000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST distance_to_upper_boundary [TorchInGraphFunctionVariable(<built-in method cat of type object at 0x78f8d10bf1c0>), TensorVariable()]
V1219 16:33:18.673000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE BUILD_TUPLE 2 [TorchInGraphFunctionVariable(<built-in method cat of type object at 0x78f8d10bf1c0>), TensorVariable(), TensorVariable()]
V1219 16:33:18.673000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST -1 [TorchInGraphFunctionVariable(<built-in method cat of type object at 0x78f8d10bf1c0>), TupleVariable(length=2)]
V1219 16:33:18.673000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST ('dim',) [TorchInGraphFunctionVariable(<built-in method cat of type object at 0x78f8d10bf1c0>), TupleVariable(length=2), ConstantVariable()]
V1219 16:33:18.673000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE CALL_FUNCTION_KW 2 [TorchInGraphFunctionVariable(<built-in method cat of type object at 0x78f8d10bf1c0>), TupleVariable(length=2), ConstantVariable(), TupleVariable(length=1)]
V1219 16:33:18.674000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE STORE_FAST distance_to_boundary [TensorVariable()]
V1219 16:33:18.675000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] TRACE starts_line /home/sicli01/Projects/FluidML/gnn-physics-pytorch/export_gnn_executorch.py:80 in preprocess (preprocess) (inline depth: 3)
V1219 16:33:18.675000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] distance_to_boundary = torch.clip(distance_to_boundary / metadata["default_connectivity_radius"], -1.0, 1.0)
V1219 16:33:18.675000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_GLOBAL torch []
V1219 16:33:18.675000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_ATTR clip [PythonModuleVariable(<module 'torch' from '/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/__init__.py'>)]
V1219 16:33:18.675000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST distance_to_boundary [TorchInGraphFunctionVariable(<built-in method clip of type object at 0x78f8d10bf1c0>)]
V1219 16:33:18.675000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST metadata [TorchInGraphFunctionVariable(<built-in method clip of type object at 0x78f8d10bf1c0>), TensorVariable()]
V1219 16:33:18.675000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST default_connectivity_radius [TorchInGraphFunctionVariable(<built-in method clip of type object at 0x78f8d10bf1c0>), TensorVariable(), ConstDictVariable()]
V1219 16:33:18.675000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE BINARY_SUBSCR None [TorchInGraphFunctionVariable(<built-in method clip of type object at 0x78f8d10bf1c0>), TensorVariable(), ConstDictVariable(), ConstantVariable()]
V1219 16:33:18.675000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE BINARY_TRUE_DIVIDE None [TorchInGraphFunctionVariable(<built-in method clip of type object at 0x78f8d10bf1c0>), TensorVariable(), LazyVariableTracker()]
V1219 16:33:18.676000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST -1.0 [TorchInGraphFunctionVariable(<built-in method clip of type object at 0x78f8d10bf1c0>), TensorVariable()]
V1219 16:33:18.676000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST 1.0 [TorchInGraphFunctionVariable(<built-in method clip of type object at 0x78f8d10bf1c0>), TensorVariable(), ConstantVariable()]
V1219 16:33:18.676000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE CALL_FUNCTION 3 [TorchInGraphFunctionVariable(<built-in method clip of type object at 0x78f8d10bf1c0>), TensorVariable(), ConstantVariable(), ConstantVariable()]
V1219 16:33:18.678000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE STORE_FAST distance_to_boundary [TensorVariable()]
V1219 16:33:18.678000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] TRACE starts_line /home/sicli01/Projects/FluidML/gnn-physics-pytorch/export_gnn_executorch.py:83 in preprocess (preprocess) (inline depth: 3)
V1219 16:33:18.678000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] dim = recent_position.size(-1)
V1219 16:33:18.678000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST recent_position []
V1219 16:33:18.678000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_ATTR size [TensorVariable()]
V1219 16:33:18.678000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST -1 [GetAttrVariable()]
V1219 16:33:18.679000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE CALL_FUNCTION 1 [GetAttrVariable(), ConstantVariable()]
V1219 16:33:18.679000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE STORE_FAST dim [ConstantVariable()]
V1219 16:33:18.679000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] TRACE starts_line /home/sicli01/Projects/FluidML/gnn-physics-pytorch/export_gnn_executorch.py:84 in preprocess (preprocess) (inline depth: 3)
V1219 16:33:18.679000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] edge_displacement = (torch.gather(recent_position, dim=0, index=edge_index[0].unsqueeze(-1).expand(-1, dim)) -
V1219 16:33:18.679000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_GLOBAL torch []
V1219 16:33:18.679000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_ATTR gather [PythonModuleVariable(<module 'torch' from '/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/__init__.py'>)]
V1219 16:33:18.679000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST recent_position [TorchInGraphFunctionVariable(<built-in method gather of type object at 0x78f8d10bf1c0>)]
V1219 16:33:18.679000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST 0 [TorchInGraphFunctionVariable(<built-in method gather of type object at 0x78f8d10bf1c0>), TensorVariable()]
V1219 16:33:18.679000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST edge_index [TorchInGraphFunctionVariable(<built-in method gather of type object at 0x78f8d10bf1c0>), TensorVariable(), ConstantVariable()]
V1219 16:33:18.679000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST 0 [TorchInGraphFunctionVariable(<built-in method gather of type object at 0x78f8d10bf1c0>), TensorVariable(), ConstantVariable(), TensorVariable()]
V1219 16:33:18.679000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE BINARY_SUBSCR None [TorchInGraphFunctionVariable(<built-in method gather of type object at 0x78f8d10bf1c0>), TensorVariable(), ConstantVariable(), TensorVariable(), ConstantVariable()]
V1219 16:33:18.680000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_ATTR unsqueeze [TorchInGraphFunctionVariable(<built-in method gather of type object at 0x78f8d10bf1c0>), TensorVariable(), ConstantVariable(), TensorVariable()]
V1219 16:33:18.680000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST -1 [TorchInGraphFunctionVariable(<built-in method gather of type object at 0x78f8d10bf1c0>), TensorVariable(), ConstantVariable(), GetAttrVariable()]
V1219 16:33:18.680000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE CALL_FUNCTION 1 [TorchInGraphFunctionVariable(<built-in method gather of type object at 0x78f8d10bf1c0>), TensorVariable(), ConstantVariable(), GetAttrVariable(), ConstantVariable()]
V1219 16:33:18.682000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:5201] [0/0] eval Eq(u0, 1) == False [statically known]
V1219 16:33:18.682000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:5358] [0/0] runtime_assert True == True [statically known]
V1219 16:33:18.683000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:5201] [0/0] eval False == False [statically known]
V1219 16:33:18.683000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_ATTR expand [TorchInGraphFunctionVariable(<built-in method gather of type object at 0x78f8d10bf1c0>), TensorVariable(), ConstantVariable(), TensorVariable()]
V1219 16:33:18.683000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST -1 [TorchInGraphFunctionVariable(<built-in method gather of type object at 0x78f8d10bf1c0>), TensorVariable(), ConstantVariable(), GetAttrVariable()]
V1219 16:33:18.683000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST dim [TorchInGraphFunctionVariable(<built-in method gather of type object at 0x78f8d10bf1c0>), TensorVariable(), ConstantVariable(), GetAttrVariable(), ConstantVariable()]
V1219 16:33:18.683000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE CALL_FUNCTION 2 [TorchInGraphFunctionVariable(<built-in method gather of type object at 0x78f8d10bf1c0>), TensorVariable(), ConstantVariable(), GetAttrVariable(), ConstantVariable(), ConstantVariable()]
V1219 16:33:18.685000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:5201] [0/0] eval Eq(u0, -1) == False [statically known]
V1219 16:33:18.686000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:5358] [0/0] runtime_assert u0 >= 0 == True [statically known]
V1219 16:33:18.686000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:5358] [0/0] runtime_assert True == True [statically known]
V1219 16:33:18.687000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST ('dim', 'index') [TorchInGraphFunctionVariable(<built-in method gather of type object at 0x78f8d10bf1c0>), TensorVariable(), ConstantVariable(), TensorVariable()]
V1219 16:33:18.687000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE CALL_FUNCTION_KW 3 [TorchInGraphFunctionVariable(<built-in method gather of type object at 0x78f8d10bf1c0>), TensorVariable(), ConstantVariable(), TensorVariable(), TupleVariable(length=2)]
V1219 16:33:18.688000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:5201] [0/0] eval Eq(2*u0, 0) == False [statically known]
V1219 16:33:18.689000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:5358] [0/0] runtime_assert u0 >= 0 == True [statically known]
V1219 16:33:18.689000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] TRACE starts_line /home/sicli01/Projects/FluidML/gnn-physics-pytorch/export_gnn_executorch.py:85 in preprocess (preprocess) (inline depth: 3)
V1219 16:33:18.689000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] torch.gather(recent_position, dim=0, index=edge_index[1].unsqueeze(-1).expand(-1, dim)))
V1219 16:33:18.689000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_GLOBAL torch [TensorVariable()]
V1219 16:33:18.690000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_ATTR gather [TensorVariable(), PythonModuleVariable(<module 'torch' from '/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/__init__.py'>)]
V1219 16:33:18.690000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST recent_position [TensorVariable(), TorchInGraphFunctionVariable(<built-in method gather of type object at 0x78f8d10bf1c0>)]
V1219 16:33:18.690000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST 0 [TensorVariable(), TorchInGraphFunctionVariable(<built-in method gather of type object at 0x78f8d10bf1c0>), TensorVariable()]
V1219 16:33:18.690000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST edge_index [TensorVariable(), TorchInGraphFunctionVariable(<built-in method gather of type object at 0x78f8d10bf1c0>), TensorVariable(), ConstantVariable()]
V1219 16:33:18.690000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST 1 [TensorVariable(), TorchInGraphFunctionVariable(<built-in method gather of type object at 0x78f8d10bf1c0>), TensorVariable(), ConstantVariable(), TensorVariable()]
V1219 16:33:18.690000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE BINARY_SUBSCR None [TensorVariable(), TorchInGraphFunctionVariable(<built-in method gather of type object at 0x78f8d10bf1c0>), TensorVariable(), ConstantVariable(), TensorVariable(), ConstantVariable()]
V1219 16:33:18.691000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_ATTR unsqueeze [TensorVariable(), TorchInGraphFunctionVariable(<built-in method gather of type object at 0x78f8d10bf1c0>), TensorVariable(), ConstantVariable(), TensorVariable()]
V1219 16:33:18.691000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST -1 [TensorVariable(), TorchInGraphFunctionVariable(<built-in method gather of type object at 0x78f8d10bf1c0>), TensorVariable(), ConstantVariable(), GetAttrVariable()]
V1219 16:33:18.691000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE CALL_FUNCTION 1 [TensorVariable(), TorchInGraphFunctionVariable(<built-in method gather of type object at 0x78f8d10bf1c0>), TensorVariable(), ConstantVariable(), GetAttrVariable(), ConstantVariable()]
V1219 16:33:18.691000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:5358] [0/0] runtime_assert True == True [statically known]
V1219 16:33:18.692000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_ATTR expand [TensorVariable(), TorchInGraphFunctionVariable(<built-in method gather of type object at 0x78f8d10bf1c0>), TensorVariable(), ConstantVariable(), TensorVariable()]
V1219 16:33:18.692000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST -1 [TensorVariable(), TorchInGraphFunctionVariable(<built-in method gather of type object at 0x78f8d10bf1c0>), TensorVariable(), ConstantVariable(), GetAttrVariable()]
V1219 16:33:18.692000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST dim [TensorVariable(), TorchInGraphFunctionVariable(<built-in method gather of type object at 0x78f8d10bf1c0>), TensorVariable(), ConstantVariable(), GetAttrVariable(), ConstantVariable()]
V1219 16:33:18.692000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE CALL_FUNCTION 2 [TensorVariable(), TorchInGraphFunctionVariable(<built-in method gather of type object at 0x78f8d10bf1c0>), TensorVariable(), ConstantVariable(), GetAttrVariable(), ConstantVariable(), ConstantVariable()]
V1219 16:33:18.693000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:5358] [0/0] runtime_assert u0 >= 0 == True [statically known]
V1219 16:33:18.693000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:5358] [0/0] runtime_assert True == True [statically known]
V1219 16:33:18.694000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST ('dim', 'index') [TensorVariable(), TorchInGraphFunctionVariable(<built-in method gather of type object at 0x78f8d10bf1c0>), TensorVariable(), ConstantVariable(), TensorVariable()]
V1219 16:33:18.694000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE CALL_FUNCTION_KW 3 [TensorVariable(), TorchInGraphFunctionVariable(<built-in method gather of type object at 0x78f8d10bf1c0>), TensorVariable(), ConstantVariable(), TensorVariable(), TupleVariable(length=2)]
V1219 16:33:18.694000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:5358] [0/0] runtime_assert u0 >= 0 == True [statically known]
V1219 16:33:18.695000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] TRACE starts_line /home/sicli01/Projects/FluidML/gnn-physics-pytorch/export_gnn_executorch.py:84 in preprocess (preprocess) (inline depth: 3)
V1219 16:33:18.695000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] edge_displacement = (torch.gather(recent_position, dim=0, index=edge_index[0].unsqueeze(-1).expand(-1, dim)) -
V1219 16:33:18.695000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE BINARY_SUBTRACT None [TensorVariable(), TensorVariable()]
V1219 16:33:18.695000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:5358] [0/0] runtime_assert True == True [statically known]
V1219 16:33:18.696000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:5358] [0/0] runtime_assert True == True [statically known]
V1219 16:33:18.696000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:5201] [0/0] eval True == True [statically known]
V1219 16:33:18.696000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:5358] [0/0] runtime_assert u0 >= 0 == True [statically known]
V1219 16:33:18.697000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE STORE_FAST edge_displacement [TensorVariable()]
V1219 16:33:18.697000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] TRACE starts_line /home/sicli01/Projects/FluidML/gnn-physics-pytorch/export_gnn_executorch.py:86 in preprocess (preprocess) (inline depth: 3)
V1219 16:33:18.697000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] edge_displacement /= metadata["default_connectivity_radius"]
V1219 16:33:18.697000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST edge_displacement []
V1219 16:33:18.697000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST metadata [TensorVariable()]
V1219 16:33:18.697000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST default_connectivity_radius [TensorVariable(), ConstDictVariable()]
V1219 16:33:18.697000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE BINARY_SUBSCR None [TensorVariable(), ConstDictVariable(), ConstantVariable()]
V1219 16:33:18.697000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE INPLACE_TRUE_DIVIDE None [TensorVariable(), LazyVariableTracker()]
V1219 16:33:18.698000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE STORE_FAST edge_displacement [TensorVariable()]
V1219 16:33:18.698000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] TRACE starts_line /home/sicli01/Projects/FluidML/gnn-physics-pytorch/export_gnn_executorch.py:87 in preprocess (preprocess) (inline depth: 3)
V1219 16:33:18.698000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] edge_distance = torch.norm(edge_displacement, dim=-1, keepdim=True)
V1219 16:33:18.698000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_GLOBAL torch []
V1219 16:33:18.698000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_ATTR norm [PythonModuleVariable(<module 'torch' from '/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/__init__.py'>)]
V1219 16:33:18.698000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST edge_displacement [TorchInGraphFunctionVariable(<function norm at 0x78f87368b9a0>)]
V1219 16:33:18.698000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST -1 [TorchInGraphFunctionVariable(<function norm at 0x78f87368b9a0>), TensorVariable()]
V1219 16:33:18.698000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST True [TorchInGraphFunctionVariable(<function norm at 0x78f87368b9a0>), TensorVariable(), ConstantVariable()]
V1219 16:33:18.698000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST ('dim', 'keepdim') [TorchInGraphFunctionVariable(<function norm at 0x78f87368b9a0>), TensorVariable(), ConstantVariable(), ConstantVariable()]
V1219 16:33:18.698000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE CALL_FUNCTION_KW 3 [TorchInGraphFunctionVariable(<function norm at 0x78f87368b9a0>), TensorVariable(), ConstantVariable(), ConstantVariable(), TupleVariable(length=2)]
V1219 16:33:18.700000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:5201] [0/0] eval u0 < 0 == False [statically known]
V1219 16:33:18.700000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:5358] [0/0] runtime_assert True == True [statically known]
V1219 16:33:18.700000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:5358] [0/0] runtime_assert True == True [statically known]
V1219 16:33:18.701000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:5358] [0/0] runtime_assert u0 >= 0 == True [statically known]
V1219 16:33:18.701000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:5358] [0/0] runtime_assert u0 >= 0 == True [statically known]
V1219 16:33:18.703000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:5358] [0/0] runtime_assert u0 >= 0 == True [statically known]
V1219 16:33:18.704000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:5358] [0/0] runtime_assert u0 >= 0 == True [statically known]
V1219 16:33:18.704000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:5358] [0/0] runtime_assert True == True [statically known]
V1219 16:33:18.705000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:5201] [0/0] eval True == True [statically known]
V1219 16:33:18.706000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:5201] [0/0] eval Ne(u0, 1) == True [statically known]
V1219 16:33:18.707000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:5358] [0/0] runtime_assert u0 >= 0 == True [statically known]
V1219 16:33:18.707000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:5358] [0/0] runtime_assert u0 >= 0 == True [statically known]
V1219 16:33:18.708000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE STORE_FAST edge_distance [TensorVariable()]
V1219 16:33:18.708000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] TRACE starts_line /home/sicli01/Projects/FluidML/gnn-physics-pytorch/export_gnn_executorch.py:90 in preprocess (preprocess) (inline depth: 3)
V1219 16:33:18.708000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] graph = pyg.data.Data(
V1219 16:33:18.708000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_GLOBAL pyg []
V1219 16:33:18.708000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_ATTR data [PythonModuleVariable(<module 'torch_geometric' from '/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch_geometric/__init__.py'>)]
V1219 16:33:18.708000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_ATTR Data [PythonModuleVariable(<module 'torch_geometric.data' from '/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch_geometric/data/__init__.py'>)]
V1219 16:33:18.709000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] TRACE starts_line /home/sicli01/Projects/FluidML/gnn-physics-pytorch/export_gnn_executorch.py:91 in preprocess (preprocess) (inline depth: 3)
V1219 16:33:18.709000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] x=particle_type,
V1219 16:33:18.709000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST particle_type [UserDefinedClassVariable()]
V1219 16:33:18.709000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] TRACE starts_line /home/sicli01/Projects/FluidML/gnn-physics-pytorch/export_gnn_executorch.py:92 in preprocess (preprocess) (inline depth: 3)
V1219 16:33:18.709000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] edge_index=edge_index,
V1219 16:33:18.709000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST edge_index [UserDefinedClassVariable(), TensorVariable()]
V1219 16:33:18.709000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] TRACE starts_line /home/sicli01/Projects/FluidML/gnn-physics-pytorch/export_gnn_executorch.py:93 in preprocess (preprocess) (inline depth: 3)
V1219 16:33:18.709000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] edge_attr=torch.cat((edge_displacement, edge_distance), dim=-1),
V1219 16:33:18.709000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_GLOBAL torch [UserDefinedClassVariable(), TensorVariable(), TensorVariable()]
V1219 16:33:18.709000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_ATTR cat [UserDefinedClassVariable(), TensorVariable(), TensorVariable(), PythonModuleVariable(<module 'torch' from '/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/__init__.py'>)]
V1219 16:33:18.709000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST edge_displacement [UserDefinedClassVariable(), TensorVariable(), TensorVariable(), TorchInGraphFunctionVariable(<built-in method cat of type object at 0x78f8d10bf1c0>)]
V1219 16:33:18.709000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST edge_distance [UserDefinedClassVariable(), TensorVariable(), TensorVariable(), TorchInGraphFunctionVariable(<built-in method cat of type object at 0x78f8d10bf1c0>), TensorVariable()]
V1219 16:33:18.709000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE BUILD_TUPLE 2 [UserDefinedClassVariable(), TensorVariable(), TensorVariable(), TorchInGraphFunctionVariable(<built-in method cat of type object at 0x78f8d10bf1c0>), TensorVariable(), TensorVariable()]
V1219 16:33:18.709000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST -1 [UserDefinedClassVariable(), TensorVariable(), TensorVariable(), TorchInGraphFunctionVariable(<built-in method cat of type object at 0x78f8d10bf1c0>), TupleVariable(length=2)]
V1219 16:33:18.710000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST ('dim',) [UserDefinedClassVariable(), TensorVariable(), TensorVariable(), TorchInGraphFunctionVariable(<built-in method cat of type object at 0x78f8d10bf1c0>), TupleVariable(length=2), ConstantVariable()]
V1219 16:33:18.710000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE CALL_FUNCTION_KW 2 [UserDefinedClassVariable(), TensorVariable(), TensorVariable(), TorchInGraphFunctionVariable(<built-in method cat of type object at 0x78f8d10bf1c0>), TupleVariable(length=2), ConstantVariable(), TupleVariable(length=1)]
V1219 16:33:18.710000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:5358] [0/0] runtime_assert True == True [statically known]
V1219 16:33:18.710000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:5358] [0/0] runtime_assert True == True [statically known]
V1219 16:33:18.711000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:5358] [0/0] runtime_assert u0 >= 0 == True [statically known]
V1219 16:33:18.712000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:5358] [0/0] runtime_assert u0 >= 0 == True [statically known]
V1219 16:33:18.713000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:5358] [0/0] runtime_assert u0 >= 0 == True [statically known]
V1219 16:33:18.714000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:5358] [0/0] runtime_assert u0 >= 0 == True [statically known]
V1219 16:33:18.714000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:5358] [0/0] runtime_assert u0 >= 0 == True [statically known]
V1219 16:33:18.715000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] TRACE starts_line /home/sicli01/Projects/FluidML/gnn-physics-pytorch/export_gnn_executorch.py:94 in preprocess (preprocess) (inline depth: 3)
V1219 16:33:18.715000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] y=None, # Ground truth for training
V1219 16:33:18.715000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST None [UserDefinedClassVariable(), TensorVariable(), TensorVariable(), TensorVariable()]
V1219 16:33:18.715000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] TRACE starts_line /home/sicli01/Projects/FluidML/gnn-physics-pytorch/export_gnn_executorch.py:95 in preprocess (preprocess) (inline depth: 3)
V1219 16:33:18.715000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] pos=torch.cat((velocity_seq.reshape(velocity_seq.size(0), -1), distance_to_boundary), dim=-1),
V1219 16:33:18.715000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_GLOBAL torch [UserDefinedClassVariable(), TensorVariable(), TensorVariable(), TensorVariable(), ConstantVariable()]
V1219 16:33:18.715000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_ATTR cat [UserDefinedClassVariable(), TensorVariable(), TensorVariable(), TensorVariable(), ConstantVariable(), PythonModuleVariable(<module 'torch' from '/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/__init__.py'>)]
V1219 16:33:18.715000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST velocity_seq [UserDefinedClassVariable(), TensorVariable(), TensorVariable(), TensorVariable(), ConstantVariable(), TorchInGraphFunctionVariable(<built-in method cat of type object at 0x78f8d10bf1c0>)]
V1219 16:33:18.715000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_ATTR reshape [UserDefinedClassVariable(), TensorVariable(), TensorVariable(), TensorVariable(), ConstantVariable(), TorchInGraphFunctionVariable(<built-in method cat of type object at 0x78f8d10bf1c0>), TensorVariable()]
V1219 16:33:18.715000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST velocity_seq [UserDefinedClassVariable(), TensorVariable(), TensorVariable(), TensorVariable(), ConstantVariable(), TorchInGraphFunctionVariable(<built-in method cat of type object at 0x78f8d10bf1c0>), GetAttrVariable()]
V1219 16:33:18.715000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_ATTR size [UserDefinedClassVariable(), TensorVariable(), TensorVariable(), TensorVariable(), ConstantVariable(), TorchInGraphFunctionVariable(<built-in method cat of type object at 0x78f8d10bf1c0>), GetAttrVariable(), TensorVariable()]
V1219 16:33:18.716000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST 0 [UserDefinedClassVariable(), TensorVariable(), TensorVariable(), TensorVariable(), ConstantVariable(), TorchInGraphFunctionVariable(<built-in method cat of type object at 0x78f8d10bf1c0>), GetAttrVariable(), GetAttrVariable()]
V1219 16:33:18.716000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE CALL_FUNCTION 1 [UserDefinedClassVariable(), TensorVariable(), TensorVariable(), TensorVariable(), ConstantVariable(), TorchInGraphFunctionVariable(<built-in method cat of type object at 0x78f8d10bf1c0>), GetAttrVariable(), GetAttrVariable(), ConstantVariable()]
V1219 16:33:18.716000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST -1 [UserDefinedClassVariable(), TensorVariable(), TensorVariable(), TensorVariable(), ConstantVariable(), TorchInGraphFunctionVariable(<built-in method cat of type object at 0x78f8d10bf1c0>), GetAttrVariable(), ConstantVariable()]
V1219 16:33:18.716000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE CALL_FUNCTION 2 [UserDefinedClassVariable(), TensorVariable(), TensorVariable(), TensorVariable(), ConstantVariable(), TorchInGraphFunctionVariable(<built-in method cat of type object at 0x78f8d10bf1c0>), GetAttrVariable(), ConstantVariable(), ConstantVariable()]
V1219 16:33:18.717000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST distance_to_boundary [UserDefinedClassVariable(), TensorVariable(), TensorVariable(), TensorVariable(), ConstantVariable(), TorchInGraphFunctionVariable(<built-in method cat of type object at 0x78f8d10bf1c0>), TensorVariable()]
V1219 16:33:18.717000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE BUILD_TUPLE 2 [UserDefinedClassVariable(), TensorVariable(), TensorVariable(), TensorVariable(), ConstantVariable(), TorchInGraphFunctionVariable(<built-in method cat of type object at 0x78f8d10bf1c0>), TensorVariable(), TensorVariable()]
V1219 16:33:18.717000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST -1 [UserDefinedClassVariable(), TensorVariable(), TensorVariable(), TensorVariable(), ConstantVariable(), TorchInGraphFunctionVariable(<built-in method cat of type object at 0x78f8d10bf1c0>), TupleVariable(length=2)]
V1219 16:33:18.717000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST ('dim',) [UserDefinedClassVariable(), TensorVariable(), TensorVariable(), TensorVariable(), ConstantVariable(), TorchInGraphFunctionVariable(<built-in method cat of type object at 0x78f8d10bf1c0>), TupleVariable(length=2), ConstantVariable()]
V1219 16:33:18.717000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE CALL_FUNCTION_KW 2 [UserDefinedClassVariable(), TensorVariable(), TensorVariable(), TensorVariable(), ConstantVariable(), TorchInGraphFunctionVariable(<built-in method cat of type object at 0x78f8d10bf1c0>), TupleVariable(length=2), ConstantVariable(), TupleVariable(length=1)]
V1219 16:33:18.718000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] TRACE starts_line /home/sicli01/Projects/FluidML/gnn-physics-pytorch/export_gnn_executorch.py:96 in preprocess (preprocess) (inline depth: 3)
V1219 16:33:18.718000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] recent_position=recent_position,
V1219 16:33:18.718000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST recent_position [UserDefinedClassVariable(), TensorVariable(), TensorVariable(), TensorVariable(), ConstantVariable(), TensorVariable()]
V1219 16:33:18.718000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] TRACE starts_line /home/sicli01/Projects/FluidML/gnn-physics-pytorch/export_gnn_executorch.py:97 in preprocess (preprocess) (inline depth: 3)
V1219 16:33:18.718000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] recent_velocity=velocity_seq[:, -1]
V1219 16:33:18.718000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST velocity_seq [UserDefinedClassVariable(), TensorVariable(), TensorVariable(), TensorVariable(), ConstantVariable(), TensorVariable(), TensorVariable()]
V1219 16:33:18.718000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST None [UserDefinedClassVariable(), TensorVariable(), TensorVariable(), TensorVariable(), ConstantVariable(), TensorVariable(), TensorVariable(), TensorVariable()]
V1219 16:33:18.718000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST None [UserDefinedClassVariable(), TensorVariable(), TensorVariable(), TensorVariable(), ConstantVariable(), TensorVariable(), TensorVariable(), TensorVariable(), ConstantVariable()]
V1219 16:33:18.718000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE BUILD_SLICE 2 [UserDefinedClassVariable(), TensorVariable(), TensorVariable(), TensorVariable(), ConstantVariable(), TensorVariable(), TensorVariable(), TensorVariable(), ConstantVariable(), ConstantVariable()]
V1219 16:33:18.718000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST -1 [UserDefinedClassVariable(), TensorVariable(), TensorVariable(), TensorVariable(), ConstantVariable(), TensorVariable(), TensorVariable(), TensorVariable(), SliceVariable()]
V1219 16:33:18.719000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE BUILD_TUPLE 2 [UserDefinedClassVariable(), TensorVariable(), TensorVariable(), TensorVariable(), ConstantVariable(), TensorVariable(), TensorVariable(), TensorVariable(), SliceVariable(), ConstantVariable()]
V1219 16:33:18.719000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE BINARY_SUBSCR None [UserDefinedClassVariable(), TensorVariable(), TensorVariable(), TensorVariable(), ConstantVariable(), TensorVariable(), TensorVariable(), TensorVariable(), TupleVariable(length=2)]
V1219 16:33:18.720000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] TRACE starts_line /home/sicli01/Projects/FluidML/gnn-physics-pytorch/export_gnn_executorch.py:90 in preprocess (preprocess) (inline depth: 3)
V1219 16:33:18.720000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] graph = pyg.data.Data(
V1219 16:33:18.720000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST ('x', 'edge_index', 'edge_attr', 'y', 'pos', 'recent_position', 'recent_velocity') [UserDefinedClassVariable(), TensorVariable(), TensorVariable(), TensorVariable(), ConstantVariable(), TensorVariable(), TensorVariable(), TensorVariable()]
V1219 16:33:18.720000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE CALL_FUNCTION_KW 7 [UserDefinedClassVariable(), TensorVariable(), TensorVariable(), TensorVariable(), ConstantVariable(), TensorVariable(), TensorVariable(), TensorVariable(), TupleVariable(length=7)]
V1219 16:33:18.721000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:3099] [0/0] INLINING <code object __init__ at 0x78f782f0c190, file "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch_geometric/data/data.py", line 518>, inlined according trace_rules.lookup inlined by default
V1219 16:33:18.721000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] TRACE starts_line /home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch_geometric/data/data.py:530 in __init__ (Data.__init__) (inline depth: 4)
V1219 16:33:18.721000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] super().__init__(tensor_attr_cls=DataTensorAttr)
V1219 16:33:18.732000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_GLOBAL super []
V1219 16:33:18.733000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_DEREF __class__ [BuiltinVariable()]
V1219 16:33:18.733000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST self [BuiltinVariable(), UserDefinedClassVariable()]
V1219 16:33:18.733000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE CALL_FUNCTION 2 [BuiltinVariable(), UserDefinedClassVariable(), UserDefinedObjectVariable(Data)]
V1219 16:33:18.733000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_ATTR __init__ [SuperVariable()]
V1219 16:33:18.733000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_GLOBAL DataTensorAttr [GetAttrVariable()]
V1219 16:33:18.733000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST ('tensor_attr_cls',) [GetAttrVariable(), UserDefinedClassVariable()]
V1219 16:33:18.733000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE CALL_FUNCTION_KW 1 [GetAttrVariable(), UserDefinedClassVariable(), TupleVariable(length=1)]
V1219 16:33:18.734000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:3099] [0/0] INLINING <code object __init__ at 0x78f7839df940, file "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch_geometric/data/feature_store.py", line 277>, inlined according trace_rules.lookup inlined by default
V1219 16:33:18.734000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] TRACE starts_line /home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch_geometric/data/feature_store.py:278 in __init__ (FeatureStore.__init__) (inline depth: 5)
V1219 16:33:18.734000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] super().__init__()
V1219 16:33:18.737000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_GLOBAL super []
V1219 16:33:18.737000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_DEREF __class__ [BuiltinVariable()]
V1219 16:33:18.737000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST self [BuiltinVariable(), UserDefinedClassVariable()]
V1219 16:33:18.737000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE CALL_FUNCTION 2 [BuiltinVariable(), UserDefinedClassVariable(), UserDefinedObjectVariable(Data)]
V1219 16:33:18.737000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_ATTR __init__ [SuperVariable()]
V1219 16:33:18.738000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE CALL_FUNCTION 0 [GetAttrVariable()]
V1219 16:33:18.738000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:3099] [0/0] INLINING <code object __init__ at 0x78f7839fa290, file "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch_geometric/data/graph_store.py", line 109>, inlined according trace_rules.lookup inlined by default
V1219 16:33:18.738000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] TRACE starts_line /home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch_geometric/data/graph_store.py:110 in __init__ (GraphStore.__init__) (inline depth: 6)
V1219 16:33:18.738000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] super().__init__()
V1219 16:33:18.741000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_GLOBAL super []
V1219 16:33:18.741000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_DEREF __class__ [BuiltinVariable()]
V1219 16:33:18.741000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST self [BuiltinVariable(), UserDefinedClassVariable()]
V1219 16:33:18.741000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE CALL_FUNCTION 2 [BuiltinVariable(), UserDefinedClassVariable(), UserDefinedObjectVariable(Data)]
V1219 16:33:18.741000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_ATTR __init__ [SuperVariable()]
V1219 16:33:18.741000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE CALL_FUNCTION 0 [GetAttrVariable()]
V1219 16:33:18.741000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE POP_TOP None [LambdaVariable()]
V1219 16:33:18.741000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] TRACE starts_line /home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch_geometric/data/graph_store.py:111 in __init__ (GraphStore.__init__) (inline depth: 6)
V1219 16:33:18.741000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:865] [0/0] [__trace_source] self.__dict__['_edge_attr_cls'] = edge_attr_cls or EdgeAttr
V1219 16:33:18.741000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST edge_attr_cls []
V1219 16:33:18.741000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE JUMP_IF_TRUE_OR_POP 16 [ConstantVariable()]
V1219 16:33:18.742000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_GLOBAL EdgeAttr []
V1219 16:33:18.742000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_FAST self [UserDefinedClassVariable()]
V1219 16:33:18.742000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_ATTR __dict__ [UserDefinedClassVariable(), UserDefinedObjectVariable(Data)]
V1219 16:33:18.742000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE LOAD_CONST _edge_attr_cls [UserDefinedClassVariable(), GetAttrVariable()]
V1219 16:33:18.742000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:888] [0/0] [__trace_bytecode] TRACE STORE_SUBSCR None [UserDefinedClassVariable(), GetAttrVariable(), ConstantVariable()]
V1219 16:33:18.742000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:904] [0/0] empty checkpoint
V1219 16:33:18.742000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:3153] [0/0] FAILED INLINING <code object __init__ at 0x78f7839fa290, file "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch_geometric/data/graph_store.py", line 109>
V1219 16:33:18.742000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:904] [0/0] empty checkpoint
V1219 16:33:18.742000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:3153] [0/0] FAILED INLINING <code object __init__ at 0x78f7839df940, file "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch_geometric/data/feature_store.py", line 277>
V1219 16:33:18.742000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:904] [0/0] empty checkpoint
V1219 16:33:18.742000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:3153] [0/0] FAILED INLINING <code object __init__ at 0x78f782f0c190, file "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch_geometric/data/data.py", line 518>
V1219 16:33:18.742000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:904] [0/0] empty checkpoint
V1219 16:33:18.743000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:3153] [0/0] FAILED INLINING <code object preprocess at 0x78f8d25a8030, file "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/export_gnn_executorch.py", line 61>
V1219 16:33:18.743000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:904] [0/0] empty checkpoint
V1219 16:33:18.743000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:3153] [0/0] FAILED INLINING <code object forward at 0x78f8d25a8190, file "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/export_gnn_executorch.py", line 107>
V1219 16:33:18.743000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:904] [0/0] empty checkpoint
V1219 16:33:18.743000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:3153] [0/0] FAILED INLINING <code object _call_impl at 0x78f885d08f50, file "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1740>
V1219 16:33:18.743000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py:904] [0/0] empty checkpoint
PyTorch has version 2.5.0+cu124 with cuda 12.4
-1 0
-1 0
Traceback (most recent call last):
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/export_gnn_executorch.py", line 227, in <module>
aten_dialect = export(simulator, example_inputs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/export/__init__.py", line 270, in export
return _export(
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/export/_trace.py", line 1017, in wrapper
raise e
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/export/_trace.py", line 990, in wrapper
ep = fn(*args, **kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/export/exported_program.py", line 114, in wrapper
return fn(*args, **kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/export/_trace.py", line 1880, in _export
export_artifact = export_func( # type: ignore[operator]
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/export/_trace.py", line 1224, in _strict_export
return _strict_export_lower_to_aten_ir(
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/export/_trace.py", line 1252, in _strict_export_lower_to_aten_ir
gm_torch_level = _export_to_torch_ir(
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/export/_trace.py", line 560, in _export_to_torch_ir
gm_torch_level, _ = torch._dynamo.export(
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 1432, in inner
result_traced = opt_f(*args, **kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
return forward_call(*args, **kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 465, in _fn
return fn(*args, **kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
return forward_call(*args, **kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 1269, in __call__
return self._torchdynamo_orig_callable(
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 526, in __call__
return _compile(
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 924, in _compile
guarded_code = compile_inner(code, one_graph, hooks, transform)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 666, in compile_inner
return _compile_inner(code, one_graph, hooks, transform)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_utils_internal.py", line 87, in wrapper_function
return function(*args, **kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 699, in _compile_inner
out_code = transform_code_object(code, transform)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/bytecode_transformation.py", line 1322, in transform_code_object
transformations(instructions, code_options)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 219, in _fn
return fn(*args, **kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 634, in transform
tracer.run()
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 2796, in run
super().run()
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 983, in run
while self.step():
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 895, in step
self.dispatch_table[inst.opcode](self, inst)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 582, in wrapper
return inner_fn(self, inst)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 1602, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 830, in call_function
self.push(fn.call_function(self, args, kwargs)) # type: ignore[arg-type]
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/variables/nn_module.py", line 442, in call_function
return tx.inline_user_function_return(
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 836, in inline_user_function_return
return InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 3011, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 3139, in inline_call_
tracer.run()
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 983, in run
while self.step():
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 895, in step
self.dispatch_table[inst.opcode](self, inst)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 582, in wrapper
return inner_fn(self, inst)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 1680, in CALL_FUNCTION_EX
self.call_function(fn, argsvars.items, kwargsvars)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 830, in call_function
self.push(fn.call_function(self, args, kwargs)) # type: ignore[arg-type]
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/variables/functions.py", line 385, in call_function
return super().call_function(tx, args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/variables/functions.py", line 324, in call_function
return super().call_function(tx, args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/variables/functions.py", line 111, in call_function
return tx.inline_user_function_return(self, [*self.self_args(), *args], kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 836, in inline_user_function_return
return InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 3011, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 3139, in inline_call_
tracer.run()
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 983, in run
while self.step():
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 895, in step
self.dispatch_table[inst.opcode](self, inst)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 582, in wrapper
return inner_fn(self, inst)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 1602, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 830, in call_function
self.push(fn.call_function(self, args, kwargs)) # type: ignore[arg-type]
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/variables/functions.py", line 324, in call_function
return super().call_function(tx, args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/variables/functions.py", line 111, in call_function
return tx.inline_user_function_return(self, [*self.self_args(), *args], kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 836, in inline_user_function_return
return InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 3011, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 3139, in inline_call_
tracer.run()
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 983, in run
while self.step():
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 895, in step
self.dispatch_table[inst.opcode](self, inst)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 582, in wrapper
return inner_fn(self, inst)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 1692, in CALL_FUNCTION_KW
self.call_function(fn, args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 830, in call_function
self.push(fn.call_function(self, args, kwargs)) # type: ignore[arg-type]
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/variables/user_defined.py", line 496, in call_function
var.call_method(tx, "__init__", args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/variables/user_defined.py", line 788, in call_method
return UserMethodVariable(method, self, source=source).call_function(
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/variables/functions.py", line 385, in call_function
return super().call_function(tx, args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/variables/functions.py", line 324, in call_function
return super().call_function(tx, args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/variables/functions.py", line 111, in call_function
return tx.inline_user_function_return(self, [*self.self_args(), *args], kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 836, in inline_user_function_return
return InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 3011, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 3139, in inline_call_
tracer.run()
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 983, in run
while self.step():
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 895, in step
self.dispatch_table[inst.opcode](self, inst)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 582, in wrapper
return inner_fn(self, inst)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 1692, in CALL_FUNCTION_KW
self.call_function(fn, args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 830, in call_function
self.push(fn.call_function(self, args, kwargs)) # type: ignore[arg-type]
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/variables/misc.py", line 1024, in call_function
return self.obj.call_method(tx, self.name, args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/variables/misc.py", line 195, in call_method
).call_function(tx, [self.objvar] + args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/variables/functions.py", line 324, in call_function
return super().call_function(tx, args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/variables/functions.py", line 111, in call_function
return tx.inline_user_function_return(self, [*self.self_args(), *args], kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 836, in inline_user_function_return
return InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 3011, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 3139, in inline_call_
tracer.run()
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 983, in run
while self.step():
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 895, in step
self.dispatch_table[inst.opcode](self, inst)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 582, in wrapper
return inner_fn(self, inst)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 1602, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 830, in call_function
self.push(fn.call_function(self, args, kwargs)) # type: ignore[arg-type]
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/variables/misc.py", line 1024, in call_function
return self.obj.call_method(tx, self.name, args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/variables/misc.py", line 195, in call_method
).call_function(tx, [self.objvar] + args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/variables/functions.py", line 324, in call_function
return super().call_function(tx, args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/variables/functions.py", line 111, in call_function
return tx.inline_user_function_return(self, [*self.self_args(), *args], kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 836, in inline_user_function_return
return InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 3011, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 3139, in inline_call_
tracer.run()
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 983, in run
while self.step():
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 895, in step
self.dispatch_table[inst.opcode](self, inst)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 582, in wrapper
return inner_fn(self, inst)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 1803, in STORE_SUBSCR
result = obj.call_method(self, "__setitem__", [key, val], {})
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/variables/misc.py", line 1082, in call_method
return super().call_method(tx, name, args, kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/variables/base.py", line 343, in call_method
unimplemented(f"call_method {self} {name} {args} {kwargs}")
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/_dynamo/exc.py", line 297, in unimplemented
raise Unsupported(msg, case_name=case_name)
torch._dynamo.exc.Unsupported: call_method GetAttrVariable(UserDefinedObjectVariable(Data), __dict__) __setitem__ [ConstantVariable(), UserDefinedClassVariable()] {}
from user code:
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/export_gnn_executorch.py", line 185, in forward
data = self.build_graph(particle_type, position_sequence)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
return forward_call(*args, **kwargs)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/export_gnn_executorch.py", line 108, in forward
graph = preprocess(particle_type, position_sequence, self.metadata)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/export_gnn_executorch.py", line 90, in preprocess
graph = pyg.data.Data(
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch_geometric/data/data.py", line 530, in __init__
super().__init__(tensor_attr_cls=DataTensorAttr)
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch_geometric/data/feature_store.py", line 278, in __init__
super().__init__()
File "/home/sicli01/Projects/FluidML/gnn-physics-pytorch/gnn_env/lib/python3.10/site-packages/torch_geometric/data/graph_store.py", line 111, in __init__
self.__dict__['_edge_attr_cls'] = edge_attr_cls or EdgeAttr
I1219 16:33:18.755000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/utils.py:399] TorchDynamo compilation metrics:
I1219 16:33:18.755000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/utils.py:399] Function Runtimes (s)
I1219 16:33:18.755000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/utils.py:399] ---------------------- --------------
I1219 16:33:18.755000 2552752 gnn_env/lib/python3.10/site-packages/torch/_dynamo/utils.py:399] _compile.compile_inner 0
V1219 16:33:18.755000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:122] lru_cache_stats constrain_symbol_range: CacheInfo(hits=18, misses=2, maxsize=None, currsize=2)
V1219 16:33:18.755000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:122] lru_cache_stats evaluate_expr: CacheInfo(hits=39, misses=9, maxsize=256, currsize=9)
V1219 16:33:18.755000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:122] lru_cache_stats _simplify_floor_div: CacheInfo(hits=0, misses=0, maxsize=None, currsize=0)
V1219 16:33:18.755000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:122] lru_cache_stats _maybe_guard_rel: CacheInfo(hits=0, misses=0, maxsize=256, currsize=0)
V1219 16:33:18.755000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:122] lru_cache_stats _find: CacheInfo(hits=20, misses=1, maxsize=None, currsize=1)
V1219 16:33:18.755000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:122] lru_cache_stats has_hint: CacheInfo(hits=0, misses=0, maxsize=256, currsize=0)
V1219 16:33:18.755000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:122] lru_cache_stats size_hint: CacheInfo(hits=0, misses=0, maxsize=256, currsize=0)
V1219 16:33:18.755000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:122] lru_cache_stats simplify: CacheInfo(hits=4, misses=9, maxsize=None, currsize=9)
V1219 16:33:18.755000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:122] lru_cache_stats _update_divisible: CacheInfo(hits=0, misses=0, maxsize=None, currsize=0)
V1219 16:33:18.755000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:122] lru_cache_stats replace: CacheInfo(hits=1208, misses=30, maxsize=None, currsize=30)
V1219 16:33:18.756000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:122] lru_cache_stats _maybe_evaluate_static: CacheInfo(hits=49, misses=13, maxsize=None, currsize=13)
V1219 16:33:18.756000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:122] lru_cache_stats get_implications: CacheInfo(hits=0, misses=0, maxsize=None, currsize=0)
V1219 16:33:18.756000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:122] lru_cache_stats get_axioms: CacheInfo(hits=10, misses=3, maxsize=None, currsize=3)
V1219 16:33:18.756000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:122] lru_cache_stats safe_expand: CacheInfo(hits=182, misses=30, maxsize=256, currsize=30)
V1219 16:33:18.756000 2552752 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:122] lru_cache_stats uninteresting_files: CacheInfo(hits=4, misses=1, maxsize=None, currsize=1)
I1219 16:33:19.704000 2552920 gnn_env/lib/python3.10/site-packages/torch/_dynamo/utils.py:399] TorchDynamo compilation metrics:
I1219 16:33:19.704000 2552920 gnn_env/lib/python3.10/site-packages/torch/_dynamo/utils.py:399] Function Runtimes (s)
I1219 16:33:19.704000 2552920 gnn_env/lib/python3.10/site-packages/torch/_dynamo/utils.py:399] ---------- --------------
V1219 16:33:19.704000 2552920 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:122] lru_cache_stats constrain_symbol_range: CacheInfo(hits=0, misses=0, maxsize=None, currsize=0)
V1219 16:33:19.704000 2552920 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:122] lru_cache_stats evaluate_expr: CacheInfo(hits=0, misses=0, maxsize=256, currsize=0)
V1219 16:33:19.704000 2552920 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:122] lru_cache_stats _simplify_floor_div: CacheInfo(hits=0, misses=0, maxsize=None, currsize=0)
V1219 16:33:19.704000 2552920 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:122] lru_cache_stats _maybe_guard_rel: CacheInfo(hits=0, misses=0, maxsize=256, currsize=0)
V1219 16:33:19.705000 2552920 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:122] lru_cache_stats _find: CacheInfo(hits=0, misses=0, maxsize=None, currsize=0)
V1219 16:33:19.705000 2552920 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:122] lru_cache_stats has_hint: CacheInfo(hits=0, misses=0, maxsize=256, currsize=0)
V1219 16:33:19.705000 2552920 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:122] lru_cache_stats size_hint: CacheInfo(hits=0, misses=0, maxsize=256, currsize=0)
V1219 16:33:19.705000 2552920 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:122] lru_cache_stats simplify: CacheInfo(hits=0, misses=0, maxsize=None, currsize=0)
V1219 16:33:19.705000 2552920 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:122] lru_cache_stats _update_divisible: CacheInfo(hits=0, misses=0, maxsize=None, currsize=0)
V1219 16:33:19.705000 2552920 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:122] lru_cache_stats replace: CacheInfo(hits=0, misses=0, maxsize=None, currsize=0)
V1219 16:33:19.705000 2552920 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:122] lru_cache_stats _maybe_evaluate_static: CacheInfo(hits=0, misses=0, maxsize=None, currsize=0)
V1219 16:33:19.705000 2552920 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:122] lru_cache_stats get_implications: CacheInfo(hits=0, misses=0, maxsize=None, currsize=0)
V1219 16:33:19.705000 2552920 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:122] lru_cache_stats get_axioms: CacheInfo(hits=0, misses=0, maxsize=None, currsize=0)
V1219 16:33:19.705000 2552920 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:122] lru_cache_stats safe_expand: CacheInfo(hits=0, misses=0, maxsize=256, currsize=0)
V1219 16:33:19.705000 2552920 gnn_env/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:122] lru_cache_stats uninteresting_files: CacheInfo(hits=0, misses=0, maxsize=None, currsize=0)
```
@akihironitta | open | 2024-12-19T16:35:53Z | 2025-01-18T13:46:05Z | https://github.com/pyg-team/pytorch_geometric/issues/9879 | [
"feature",
"compile"
] | sicong-li-arm | 3 |
hbldh/bleak | asyncio | 790 | Adding the scan.pattern filter, introduced in Bluez 5.54 | * bleak version: 0.14.2
* Python version: 3.9
* Operating System: Debian
* BlueZ version (`bluetoothctl -v`) in case of Linux: 5.55
### Description
Would you be open to a PR that would introduce the scan.pattern filter that was introduced in Bluez 5.54 (https://git.kernel.org/pub/scm/bluetooth/bluez.git/log/?h=5.54). I'm interested in offloading some of the responsibility of MAC address filtering onto Bluez rather than on my Python code.
### What I Did
Using bluetoothctl, by entering the following commands, you can see the type of functionality that I am interested in
```
bluetoothctl
menu scan
pattern AB:12 # only discover devices with a MAC address starting w/ AB:12
back
scan on
```
EDIT: added URL To 5.54 commits
| closed | 2022-03-22T14:21:59Z | 2022-03-22T18:09:55Z | https://github.com/hbldh/bleak/issues/790 | [
"enhancement",
"Backend: BlueZ"
] | ayemiller | 5 |
axnsan12/drf-yasg | rest-api | 812 | Current DRF-YASG version is not compatible with drf latest version 3.14.0 | # Bug Report
## Description
I have been facing an issue with `drf-yasg==1.23.1` after the release of latest `djangorestframework==3.14.0`. They have removed the support for `NullBooleanField` in the latest version of `rest_framework.serializers`. Please update the drf-yasg version accordingly since this is a breaking change.
## Is this a regression?
No, it's caused by django_rest_framework version 3.14.0.
## Minimal Reproduction
```code
from drf_yasg.generators import OpenAPISchemaGenerator
```
in any django app.
## Stack trace / Error message
```code
File "/home/runner/work/Shoonya-Backend/Shoonya-Backend/backend/shoonya_backend/urls.py", line 8, in <module>
from drf_yasg.generators import OpenAPISchemaGenerator
File "/opt/hostedtoolcache/Python/3.8.13/x[64](https://github.com/AI4Bharat/Shoonya-Backend/actions/runs/3110830209/jobs/5042426356#step:6:65)/lib/python3.8/site-packages/drf_yasg/generators.py", line 19, in <module>
from .inspectors.field import get_basic_type_info, get_queryset_field, get_queryset_from_view
File "/opt/hostedtoolcache/Python/3.8.13/x64/lib/python3.8/site-packages/drf_yasg/inspectors/__init__.py", line 5, in <module>
from .field import (
File "/opt/hostedtoolcache/Python/3.8.13/x64/lib/python3.8/site-packages/drf_yasg/inspectors/field.py", line 406, in <module>
(serializers.NullBooleanField, (openapi.TYPE_BOOLEAN, None)),
AttributeError: module 'rest_framework.serializers' has no attribute 'NullBooleanField'
```
More details can be found here - https://github.com/AI4Bharat/Shoonya-Backend/actions/runs/3110830209/jobs/5042426356
<!-- If the issue is accompanied by an exception or an error, please share it below: -->
## Your Environment
This is failing in pytest.
| closed | 2022-09-23T07:24:22Z | 2022-09-24T10:27:19Z | https://github.com/axnsan12/drf-yasg/issues/812 | [] | prakharrathi25 | 2 |
falconry/falcon | api | 1,390 | almost a year since last version | The lastest version of falcon, version 1.4.1, was released January of 2018. That's almost a year ago. Is there any intention of creating a new release in the foreseeable future? | closed | 2018-12-13T21:25:14Z | 2019-02-12T23:14:14Z | https://github.com/falconry/falcon/issues/1390 | [
"question"
] | bpmason1 | 1 |
ranaroussi/yfinance | pandas | 1,304 | Extremely minor changes, but would be really useful for me | 1. Would be nice to have the following properties included in (say) `info`
- `regularMarketTime` - market's time stamp of the price given
- `marketState` - shows the trading status of the market, e.g. open, closed etc
I use both of these in my s/w
2. Would be nice if it supported getting quotes on currency prices. I have shares in foreign currencies & crypto assets, so I have them included in my portfolio tracker, e.g. `GBPUSD=X` or `BTC-USD`
So far I've got round both of these issues by simply adding an `all` property to the `Quote` class to make the original JSON available externally (which might be a nice feature to include anyway), but in my experience custom mods to open source are always unsatisfactory.
If anybody is interested in my portfolio tracker its - https://github.com/james-stevens/diamond-hands - its hard coded to price everything in GBP, but that should be easy to change.
It requires a MySQL backend & comes with a WebUI with a few graphs. Right now, it just tracks the portfolio value & provides some graphs. Right now, adding trades & closing trades needs to be done manually in SQL!
Warning: its *very* insecure cos the rest/api gives direct access to the SQL database, so use with caution, like don't make it publicly accessible. | closed | 2023-01-15T14:10:49Z | 2023-03-27T15:45:03Z | https://github.com/ranaroussi/yfinance/issues/1304 | [] | james-stevens | 3 |
ydataai/ydata-profiling | data-science | 795 | Profile contains incorrect types and rejected columns | **Description:**
Profile contains incorrect types and rejected columns.
**Reproduction:**
```python
import datetime
import pandas
import pandas_profiling
df = pandas.DataFrame({
"time": [datetime.datetime(2021, 5, 11)] * 4,
"symbol": ["AAPL", "AMZN", "GOOG", "TSLA"],
"price": [125.91, 3223.91, 2308.76, 617.20],
})
profile = pandas_profiling.ProfileReport(df)
# `time` column is rejected.
# `price` column has type Categorical not Numeric.
```
**Version:**
* _Python version_: Python 3.8
* _Environment_: Jupyter Lab
<details><summary>Click to expand <strong><em>requirements.txt</em></strong></summary>
<p>
```
beautifulsoup4==4.9.3
croniter==1.0.13
defopt==6.1.0
findspark==1.4.2
folium==0.12.1
fsspec==2021.5.0
google-cloud-bigquery==2.16.1
humanize==3.5.0
imagehash==4.2.0
ipython==7.23.1
jedi==0.18.0
jinja2==3.0.1
jupyter-contrib-nbextensions==0.5.1
jupyter-nbextensions-configurator==0.4.1
jupyterlab==3.0.15
jupytext==1.11.2
koalas==1.5.0
marshmallow-pyspark==0.2.2
notebook==6.4.0
numpy==1.20.3
openpyxl==3.0.7
overrides==6.1.0
pandas==1.2.4
pandas-profiling==3.0.0
paramiko==2.7.2
pdpyras==4.2.1
pebble==4.6.1
praw==7.2.0
premailer==3.8.0
psutil==5.8.0
pyarrow==4.0.0
pydeequ==0.1.6
pyspark==3.0.2
pytest==6.2.4
pytest-cov==2.12.0
pytest-icdiff==0.5
pytest-securestore==0.1.3
pytest-timeout==1.4.2
python-dateutil==2.8.1
pytimeparse==1.1.8
ratelimit==2.2.1
requests==2.25.1
rich==10.2.1
scipy==1.6.3
sentry-sdk==1.1.0
slack-sdk==3.5.1
structlog==21.1.0
testing.postgresql==1.3.0
typeguard==2.12.0
typing-utils==0.0.3
xlrd==2.0.1
```
</p>
</details> | closed | 2021-05-19T15:06:14Z | 2021-06-30T10:16:26Z | https://github.com/ydataai/ydata-profiling/issues/795 | [] | ashwin153 | 1 |
miguelgrinberg/python-socketio | asyncio | 845 | client connect: url parameter is always truncated to hostname:port ? | **Describe the bug**
Using simple code:
```
sio = socketio.Client(logger=True, engineio_logger=True)
sio.connect('https://my.host.name/chat/')
```
I got 404 from my server because library changed URL to be just `https://my.host.name` and appends `/socket.io/?transport=polling&EIO=4`
Resulting:
```
Attempting polling connection to https://my.host.name/socket.io/?transport=polling&EIO=4
The connection failed!
Traceback (most recent call last):
File "chat-client.py", line 34, in <module>
sio.connect('https://my.host.name/chat/')
File "sio-chat-test\py-client\venv\lib\site-packages\socketio\client.py", line 338, in connect
raise exceptions.ConnectionError(exc.args[0]) from None
socketio.exceptions.ConnectionError: Unexpected status code 404 in server response
```
**To Reproduce**
Steps to reproduce the behavior:
1. Go to '...'
2. Click on '....'
3. Scroll down to '....'
4. See error
**Expected behavior**
A clear and concise description of what you expected to happen.
**Logs**
Please provide relevant logs from the server and the client. On the Python server and client, add the `logger=True` and `engineio_logger=True` arguments to your `Server()` or `Client()` objects to get logs dumped on your terminal. If you are using the JavaScript client, see [here](https://socket.io/docs/logging-and-debugging/) for how to enable logs.
**Additional context**
Add any other context about the problem here.
| closed | 2022-01-10T16:51:35Z | 2022-01-10T17:21:38Z | https://github.com/miguelgrinberg/python-socketio/issues/845 | [] | bialix | 2 |
LibrePhotos/librephotos | django | 1,530 | Need to restrict the "demo" account update password functionality | 📝 Description of issue:
After login using the "Demo" Account, navigate to the profile section, there we have text fields which will help us to change the password for the particular account. since it's "Demo" account multiple peoples can be able to login and change the password and block other from accessing the LibrePhotos. Please make some change to block the "Demo" account user from changing the account password.
🔁 How to reproduce it:
1) Visit: https://demo1.librephotos.com/login
2) Enter the "Demo" account credentials: Username: "demo" and password: "demo1234"
3) Click "Login" and Click the "profile-icon" top right corner and Click to the "Profile" section
4) Click the "Change the password" Button and Enter new password on the both text fields and Click the "Update" Button
5) Succssfully updated the "Demo" Account Password, this action will block the new user from accessing the "Demo Account. | open | 2025-02-13T09:01:01Z | 2025-03-13T08:32:36Z | https://github.com/LibrePhotos/librephotos/issues/1530 | [
"bug"
] | sundaresan0502 | 2 |
d2l-ai/d2l-en | tensorflow | 2,123 | Typo in Ch.2 introduction. | In the second paragraph on the intro to 2. Preliminaries, change "basic" to "basics" (see the image below).
| closed | 2022-05-10T15:08:49Z | 2022-05-10T17:37:44Z | https://github.com/d2l-ai/d2l-en/issues/2123 | [] | jbritton6 | 1 |
unit8co/darts | data-science | 2,434 | [Question] Help understanding warning message when trying to iteratively forecast | I am currently using the Darts library for time series forecasting with the XGBModel and have encountered a warning message that I need some clarification on. I am using a dataset with daily frequency and the following columns: ds (date), cases (predictor), var1 (past cov), var2 (past cov).
As I have been trying to use your library, I received the following warning message when I change output_chunk_length from 7 to 1:
'predict' was called with 'n > output_chunk_length': using auto regression to forecast the values after 'output_chunk_length' points. The model will access '(n-output_chunk_length)' future values of your 'past covariates' (relative to the first predicted time step).
I understand that this warning is related to the auto-regressive nature of the forecasting when n is greater than output_chunk_length. However, I am trying to achieve a setup where the model retrains on each iteration with an output chunk length of 1, allowing me to evaluate the performance iteratively.
Could you please provide guidance on how to properly configure the model to avoid this warning while ensuring it retrains on each iteration with the desired output_chunk_length? Additionally, any recommendations on best practices for this type of iterative forecasting would be greatly appreciated.
Below is a snippet of my code:
import pandas as pd
import numpy as np
from darts import TimeSeries
from darts.models import XGBModel
from darts.metrics import mape, rmse
# Assuming df is my dataframe imported from SQL with columns ds, cases, var1, var2
series = TimeSeries.from_dataframe(df, fill_missing_values=True, freq='D', time_col='ds', fillna_value=0.0001)
# Define forecast period and other parameters
forecast = 7
traininglen = forecast * 2
lag = 7
results = []
for i in range(len(series) - traininglen - forecast):
date = pd.to_datetime(series.time_index[i + traininglen])
# Define training and test data
traindata = series[:i + traininglen]
testdata = series[i + traininglen:i + traininglen + forecast]
past_cov = series[['cases', 'var1', 'var2']][:i + traininglen]
future_cov = series[['cases', 'var1', 'var2']][i + traininglen:i + traininglen + forecast]
model = XGBModel(
lags_past_covariates=lag,
output_chunk_length= 1 #had it 7 previously
)
model.fit(traindata, past_covariates=past_cov)
pred = model.predict(n=forecast, past_covariates=past_cov)
# Evaluate the predictions
mapepred = mape(testdata, pred)
rmsepred = rmse(testdata, pred)
# Store Results
results.append({‘Date’: date, ‘MAPE’: mapepred, ‘RMSE’: rmsepred
, ‘Prediction’: pred.values(), ‘Actual’: test.data.values()})
results_df = pd.DataFrame(results) | closed | 2024-06-28T20:42:26Z | 2024-08-16T07:09:49Z | https://github.com/unit8co/darts/issues/2434 | [
"question"
] | christian-dalton | 1 |
stanford-oval/storm | nlp | 112 | ValueError: Expected 2D array, got 1D array instead: | Thank you very much for your contribution! I encountered the same problem as [#14 ](https://github.com/stanford-oval/storm/issues/14)when running the command
```shell
-- python -m scripts.run_writing --input-source console --input-path ../FreshWiki/topic_list.csv --engine gpt-4 --do-polish-article --remove-duplicate.
```
The error is
```shell
ValueError: Expected 2D array, got 1D array instead: array=[]. Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.
```
Then I checked what you said a bunch of
```shell
root : ERROR : Error occurs when searching query xxx: 'hits'?
```
and found that mine is
```shell
Error occurs when searching query Taylor Hawkins musical influences: 'url'
Error occurs when searching query Taylor Hawkins musical background: 'url'
Error occurs when searching query Music inspirations of Taylor Hawkins: 'url'
...
...
```
Can you help me check it? | closed | 2024-07-30T00:37:09Z | 2024-07-31T18:25:25Z | https://github.com/stanford-oval/storm/issues/112 | [
"Bug Report - User Environment"
] | Stubborn-z | 2 |
biolab/orange3 | numpy | 6,206 | exception with Load Model widget | 1) double-click the Load Model widget to select a pkcls file to load, sometimes (not always), an exception is raised immediately
2) if it isn't and you get to select the pkcls file, the attached model (saved from a previous neural network training session, see the DB link) consistently raises an exception and fails to load. Also, this model fails to load directly from python, so maybe there are 2 problems, with the Save Model and Load Model?
Orange v3.33
macOS 10.14.6 (happens on different Macs, all running Mojave)
How you installed Orange: from your dmg (but also happens when I installed via Anaconda)
The models are too large to upload to github so you can find them and the ows file here:
https://www.dropbox.com/s/2cwcthf7uuqs3ui/orangeBug.zip?dl=0
The smaller model loads OK, the larger one generated by a more complex NN fails every time. | closed | 2022-11-16T20:58:12Z | 2023-01-27T19:37:32Z | https://github.com/biolab/orange3/issues/6206 | [
"bug",
"follow up"
] | pkstys | 10 |
sunscrapers/djoser | rest-api | 103 | How does /auth/me/ and /auth/logout/ works? | @konradhalas
Hi, may I ask how does /auth/me/ check whether a user is logged in or not? Do i need to include anything in the HTTP request header? Also, do i need to include anything in my HTTP request for /logout/?
| closed | 2015-12-09T10:17:50Z | 2015-12-10T04:46:13Z | https://github.com/sunscrapers/djoser/issues/103 | [] | teddyhartanto | 0 |
cvat-ai/cvat | pytorch | 8,237 | Which container uses Elastic Search? | Current: CVAT v2.15.0 in use, CVAT Analytics related questions
I want to install a container using Elastic Search on another EC2 in preparation for a lot of annotation work.
However, I don't know which container uses Elastic Search. (I heard it somewhere, so I don't know if it actually uses Elastic Search.)
In addition, are there any containers that would be good to distribute to avoid server overload? (ex. cvat_db) | closed | 2024-07-30T10:14:37Z | 2024-07-30T10:14:49Z | https://github.com/cvat-ai/cvat/issues/8237 | [] | dc-noh | 0 |
microsoft/nni | deep-learning | 5,407 | stop being disrespectful to real Engineers (Mechanical Engineering is the ONLY Engineering) | Death to you microsoft [TRASHrosoft] devs and employees, you cheap loser shills. Death to you for openly promoting donkeys who work for the garbage & useless so called "health" "sector". Death to you for being such stupid slaves and praising those daft morons. Death to you for promoting those stupid childish ego pigs, who work for such a garbage "sector". Death to every moron who works for or supports that dumpster "sector". Death to you for promoting those dumb clowns in your trashy office 365 calendar app and your garbage OS. Death to you microsoft clown employees, you donkey scums.
Death to you for openly promoting and praising "universities" and praising those disgusting 2/6 letter stupid "titles" those monkeys use. Death to you and death to every loser donkey who uses that disgusting & racist 6 letter donkey word used by the trasholes ["univeristies"].
Death to you for openly promoting donkeys who work for the garbage & useless so called "health" "sector". Death to you for being such stupid mainstream shills and praising those daft morons. Death to you for promoting those stupid childish ego pigs, who work for such a garbage "sector". Death to you for openly praising and promoting the UK's ugly & useless & trashhole "national" clown "service". Death to EVERY donkey who supports those donkeys. You're all nothing but useless slaves, you TRASHrosoft scumbags. Microsoft is utter trash, and so is windows and every garbage trashware that those monkey micorsoft devs have ever touched or made.
Microsoft has absolutely no potential, and is a complete and utter laughable failure. Death to you stupid microTRASH script kiddos. Death to your trashy software. Death to every idiot who uses the stupid 6-letter "prefixes" when mentioning first names. Death to all "universities" [DUMPSTERversities].
Death to you microsoft [microGARBAGE] devs and employees. Have a sad life and painful death, microsoft workers, you clown slaves.
And one other thing....
Death to you scums for being disrespectful to engineering and claiming to be engineers. None of you microsoft devs/employees are engineers, never were and you'll never be close. Microsoft knows ABSOLUTELY NOTHING about what engineering is or what it entails. You microsoft scriptkiddos clowns are just scummy wannabes who will never even come close to an engineer. There is no such thing as "software" "engineering". That is the stupidest and most pathetic lie ever. Quit being disrespectful to Engineering, you dirty rats. You know nothing about what Engineering entails. Engineering means designing, building and producing real mechanical systems, like turbojets and vehicular engines. It has nothing to do with scriptkiddies like you microsoft [TRASHrosoft] monkeys sitting behind a keyboard and writing code.
Software dev is easy and for dumb kids. And it has absolutely nothing to do with a serious and real subject like Engineering (Mechanical Engineering is the ONLY Engineering). Quit making false claims against engineering. And stop being disrespectful to real Engineers (Mechanical Engineering is the ONLY Engineering). Death to you dumb kids for making lies against engineering.
Hope your dirty and cheap throats get slit, microsoft loser employees and devs.
@MicrosoftIsGarbage
| closed | 2023-02-27T18:01:29Z | 2023-02-28T02:41:28Z | https://github.com/microsoft/nni/issues/5407 | [] | ghost | 0 |
apify/crawlee-python | automation | 861 | Add local caching for crawled pages to enhance development efficiency | It would be a huge time saver during development if the website only needed to be crawled once. While iterating on my data extraction code, I wish I could load the pages from my local disk instead of the Internet.
During development, repeatedly crawling the website to test data extraction code is time-consuming and inefficient. A caching mechanism that stores previously downloaded pages on the local disk would be a huge time saver. Instead of fetching the pages from the Internet, the development environment could simply load them from local storage.
This approach would not only speed up the iterative development process but also reduce load on the website's server. Such a feature would be particularly useful for debugging and refining data extraction scripts. | open | 2025-01-05T17:26:34Z | 2025-01-06T11:36:50Z | https://github.com/apify/crawlee-python/issues/861 | [
"enhancement",
"t-tooling"
] | matecsaj | 1 |
RayVentura/ShortGPT | automation | 112 | ❓ [Question]: is there any way to use Open AI for free ?? | ### Your Question
so i tried revered proxy but it's not working, it's showing me this error after 33%
ERROR : Exception : GPT3 error: Too many requests, you can send 3 requests per 15 seconds, please wait and try again later.
i know GPT4 is not that expensive but i am a student i just want it for free !! XD
is there any way or should i just buy it? | open | 2023-10-24T06:14:01Z | 2025-01-23T09:50:49Z | https://github.com/RayVentura/ShortGPT/issues/112 | [
"question"
] | enkii07 | 10 |
alteryx/featuretools | scikit-learn | 2,567 | Dask outputs missing from dependency checkers | Dask versions are no longer included in the output from the latest dependency checker or the minimum dependency checker. We should update these workflows to make sure dask outputs are included so we can be made aware of potentially breaking changes that come from updates to dask/distributed. | closed | 2023-04-26T20:11:37Z | 2024-02-09T20:55:42Z | https://github.com/alteryx/featuretools/issues/2567 | [] | thehomebrewnerd | 0 |
quokkaproject/quokka | flask | 622 | cli: init should copy secrets file | Ensure init will copy .secrets file
https://github.com/rochacbruno/quokka_ng/issues/49 | open | 2018-02-07T01:51:07Z | 2018-02-07T01:51:07Z | https://github.com/quokkaproject/quokka/issues/622 | [
"1.0.0",
"hacktoberfest"
] | rochacbruno | 0 |
huggingface/pytorch-image-models | pytorch | 2,290 | [BUG] Wrong model.num_features for mobilenetv4 | **Describe the bug**
`model.num_features` return 960, but correct number is 1280.
**To Reproduce**
```
import timm
model = timm.create_model('mobilenetv4_conv_large.e500_r256_in1k', pretrained=True, num_classes=0)
print(model.num_features)
960
```
**Expected behavior**
```
print(model.num_features)
1280
```
| closed | 2024-10-01T09:34:58Z | 2024-10-01T15:25:53Z | https://github.com/huggingface/pytorch-image-models/issues/2290 | [
"bug"
] | MichaelMonashev | 1 |
timkpaine/lantern | plotly | 71 | mpl not showing bar when combining bar+line/area in chart | closed | 2017-10-16T18:29:54Z | 2017-10-19T04:48:46Z | https://github.com/timkpaine/lantern/issues/71 | [
"bug",
"matplotlib/seaborn"
] | timkpaine | 0 | |
ageitgey/face_recognition | machine-learning | 1,521 | "Invalid image path: {}".format(X_img_path)) | raise Exception("Invalid image path: {}".format(X_img_path))
Exception: Invalid image path: knn_examples/test/.DS_Store
im using .jpeg format
| open | 2023-08-08T14:18:12Z | 2023-08-08T14:18:12Z | https://github.com/ageitgey/face_recognition/issues/1521 | [] | vamsidharreddyyeddula | 0 |
dsdanielpark/Bard-API | api | 36 | Provide Links For Images Generated by Google's Bard AI | Hello,
It seems BardAI now supports image generation as per below snapshot. However the Bard-API only returns the alt-image-text for the generated image.
Is there a way to include the links of the generated images?
| closed | 2023-05-23T22:34:16Z | 2024-01-18T15:57:11Z | https://github.com/dsdanielpark/Bard-API/issues/36 | [] | GordonLevy | 16 |
charlesq34/pointnet | tensorflow | 275 | Doesn't visualize the color for semantic segmentation | I was opening .obj files in CloudCompare but it doesn't visualize the color. How to solve this problem?
Thank you! | open | 2021-04-03T17:56:53Z | 2022-01-11T14:18:32Z | https://github.com/charlesq34/pointnet/issues/275 | [] | noridayu1998 | 1 |
microsoft/nni | deep-learning | 5,732 | How nni performs qat quantization on leaky relu | I used nni-2.10.1 and pytorch2.0
How nni performs qat quantization on leaky relu? | open | 2024-01-09T09:06:18Z | 2024-01-09T09:06:18Z | https://github.com/microsoft/nni/issues/5732 | [] | 11923303233 | 0 |
mwaskom/seaborn | data-visualization | 3,689 | Future deprecation of palette without hue - the proposed solution does not yield the same result | Hi,
I have a comment on the proposed change and on the Future warning that seaborn started providing:
```python
/var/folders/bx/tb4883l53hdd3zp2y0nyy_4m0000gp/T/ipykernel_19815/3866600701.py:9: FutureWarning:
Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.
sns.barplot(x='day', y='total_bill', data=tips, edgecolor='k', palette=cmap, ax=axs[0])
```
I'd like to illustrate on an example that the proposed solution yields substantially different results. The code to reproduce it is the following:
```python
import seaborn as sns
print(sns.__version__)
tips = sns.load_dataset("tips")
cmap = sns.color_palette('Greys', n_colors=4)
fig, axs = plt.subplots(1,2, figsize=(10,5))
sns.barplot(x='day', y='total_bill', data=tips, edgecolor='k', palette=cmap, ax=axs[0])
sns.barplot(hue='day', y='total_bill', data=tips, edgecolor='k', palette=cmap, legend=False, ax=axs[1])
plt.show()
```
and the result looks like this:
As you can see, there are a few differences and issues. The major one is that the x-axis in the old version contains 4 datapoints while in the new only 1 as there is only one point on the x-axis. This leads to the visual issues. You now cannot label the x-axis like before unless you start playing with it manually. The bars are squeezed without any space between them. Again, you may play with it manually, but the beauty is in simplicity and this change makes you work twice as hard.
I guess that you might object to the fact that either the colour or the x-axis tick labels are redundant as they convey the same information. That is, however, not true. You may want to preserve the colours because you have another plot where the exact same categories appear because it is more visual, or for other reasons. Even if this is the argument for the change, the warning doesn't give instructions on visually reproducing the same plot.
Related (but different) issue:
https://github.com/mwaskom/seaborn/issues/3536 | closed | 2024-05-09T08:08:02Z | 2024-10-23T02:26:39Z | https://github.com/mwaskom/seaborn/issues/3689 | [] | jankaWIS | 5 |
ultralytics/ultralytics | deep-learning | 18,849 | Object Detection When Using Image Size 1280 in YOLOv8x Training | ### Search before asking
- [x] I have searched the Ultralytics YOLO [issues](https://github.com/ultralytics/ultralytics/issues) and found no similar bug report.
### Ultralytics YOLO Component
_No response_
### Bug
I am training the YOLOv8x model using the VisDrone dataset. I set the image size to 1280 because I want the model to detect both large and small objects.
My training code is as follows:
```
from ultralytics import YOLO
model = YOLO('yolov8x.pt')
model.train(
data='data/VisDrone.yaml',
epochs=50,
imgsz=1280,
batch=4,
device=0,
workers=8,
)
```
By using an image size of 1280 instead of the default 640, the model was able to detect smaller objects, and the mAP values increased as desired. The results are as follows:
```
Class Images Instances Box(P) R mAP50 mAP50-95:
all 548 38759 0.677 0.579 0.616 0.398
pedestrian 520 8844 0.76 0.644 0.719 0.385
people 482 5125 0.711 0.538 0.594 0.279
bicycle 364 1287 0.54 0.41 0.435 0.231
car 515 14064 0.857 0.868 0.906 0.69
van 421 1975 0.635 0.598 0.627 0.472
truck 266 750 0.659 0.552 0.573 0.415
tricycle 337 1045 0.6 0.534 0.525 0.329
awning-tricycle 220 532 0.42 0.31 0.294 0.199
bus 131 251 0.866 0.669 0.775 0.601
motor 485 4886 0.72 0.669 0.708 0.379
Speed: 0.6ms preprocess, 18.8ms inference, 0.0ms loss, 1.5ms postprocess per image
```
When making predictions, my code is as follows. I resize any input image to 1280x720. I know that YOLO expects square images by default, but I let Ultralytics handle the padding.
```
from ultralytics import YOLO
model = YOLO("best.pt")
img = cv2.resize(img, (1280, 720), interpolation=cv2.INTER_AREA)
results = model.predict(img, imgsz=1280)
```
The code seems correct, but in some images, it fails to detect objects that should be easily detectable, and sometimes it doesn't detect any objects at all.
When training and performing inference with the default image size of 640 on the same dataset, there are no issues, and object detection generally works fine. The reason I increased the image size to 1280 was to increase the number of detected objects.
Are we sure that the imgsz parameter is working correctly?
### Environment
Ultralytics 8.3.59 🚀 Python-3.9.21 torch-2.0.1+cu118 CUDA:0 (NVIDIA RTX A5000, 24564MiB)
Setup complete ✅ (64 CPUs, 63.7 GB RAM, 4721.2/5588.9 GB disk)
OS Windows-10-10.0.19045-SP0
Environment Windows
Python 3.9.21
Install pip
RAM 63.70 GB
Disk 4721.2/5588.9 GB
CPU Intel Xeon Gold 6226R 2.90GHz
CPU count 64
GPU NVIDIA RTX A5000, 24564MiB
GPU count 1
CUDA 11.8
### Minimal Reproducible Example
from ultralytics import YOLO
model = YOLO('yolov8x.pt')
model.train(
data='data/VisDrone.yaml',
epochs=50,
imgsz=1280,
batch=4,
device=0,
workers=8,
)
model = YOLO("best.pt")
img = cv2.resize(img, (1280, 720), interpolation=cv2.INTER_AREA)
results = model.predict(img, imgsz=1280)
### Additional
_No response_
### Are you willing to submit a PR?
- [ ] Yes I'd like to help by submitting a PR! | open | 2025-01-23T15:00:22Z | 2025-01-24T06:11:59Z | https://github.com/ultralytics/ultralytics/issues/18849 | [
"detect"
] | uzunkadir | 4 |
plotly/dash | plotly | 2,251 | [BUG] Dropdown takes too much time to render | **Describe your context**
Please provide us your environment, so we can easily reproduce the issue.
- replace the result of `pip list | grep dash` below
```
dash==2.6.2
dash-bootstrap-components==1.2.1
dash-core-components==2.0.0
dash-html-components==2.0.0
dash-table==5.0.0
```
- if frontend related, tell us your Browser, Version and OS
- OS: Manjaro Linux x86_64
- Browser brave-browser
- Version 1.43.93-1
> The problem appears since **dash 2.6**, it didn't exist in 2.5
**Describe the bug**
I have a Dropdown with about 1000 items. Because it uses 'react-virtualized-select', I didn't have any problems with it.
Nevertheless, a problem appeared with the 2.6 version, where the dropdown takes a lot of time to appear on the page (even if the HTML is there). According to the "Performance" tab, this is because react is trying to "sort" something, of the 4 seconds of the render task, most of them are on the `UnorderedSearch.js`.
Also, if you do a keystroke on the dropdown, it takes about a second to process, making the UX very clunky.
**Expected behavior**
For the dropdown to appear swiftly and the keystrokes to be responsive
**Screenshots**
> 
> Screenshot of the Profiled application, showing the long, 3 second task
### Previous research and other information
This problem appears on dash 2.6. I also tried creating a custom component using the ReactDropdown from 'react-virtualized-select', and there are no problems. | closed | 2022-09-26T08:44:40Z | 2024-07-24T15:10:17Z | https://github.com/plotly/dash/issues/2251 | [] | daviddavo | 2 |
PeterL1n/BackgroundMattingV2 | computer-vision | 168 | 如何去除VideoMatte240K数据集视频人像周围马赛克 | 看了下数据集介绍，发布的VideoMatte240K视频是HEVC编码的。
在fgr文件夹下，视频人像周围那一圈类似马赛克的轮廓，应该怎么去掉？
如果是用ffmpeg的话，方便给一下完整的ffmpeg命令吗？
 | open | 2021-12-22T07:56:26Z | 2021-12-22T08:18:06Z | https://github.com/PeterL1n/BackgroundMattingV2/issues/168 | [] | zhifanlight | 2 |
CorentinJ/Real-Time-Voice-Cloning | python | 929 | Pretrained.pt (Bluefish models link ) seems missing? | Hi, I am testing / running this code in a class project and noticed today that the pretrained.pt file in synth, vocoder and encoder (previously stored in /saved_models and /saved_models/pretrained/ ) seems missing? | closed | 2021-12-01T14:09:20Z | 2021-12-10T07:47:24Z | https://github.com/CorentinJ/Real-Time-Voice-Cloning/issues/929 | [] | RobbeW | 18 |
hankcs/HanLP | nlp | 1,338 | 修改：加载自定义停用词文件无效 | <!--
注意事项和版本号必填，否则不回复。若希望尽快得到回复，请按模板认真填写，谢谢合作。
-->
## 注意事项
请确认下列注意事项：
* 我已仔细阅读下列文档，都没有找到答案：
- [首页文档](https://github.com/hankcs/HanLP)
- [wiki](https://github.com/hankcs/HanLP/wiki)
- [常见问题](https://github.com/hankcs/HanLP/wiki/FAQ)
* 我已经通过[Google](https://www.google.com/#newwindow=1&q=HanLP)和[issue区检索功能](https://github.com/hankcs/HanLP/issues)搜索了我的问题，也没有找到答案。
* 我明白开源社区是出于兴趣爱好聚集起来的自由社区，不承担任何责任或义务。我会礼貌发言，向每一个帮助我的人表示感谢。
* [x] 我在此括号内输入x打钩，代表上述事项确认完毕。
## 版本号
<!-- 发行版请注明jar文件名去掉拓展名的部分；GitHub仓库版请注明master还是portable分支 -->
当前最新版本号是：1.7.5
我使用的版本是：1.7.5
<!--以上属于必填项，以下可自由发挥-->
## 我的问题
加载自定义停用词文件无效
```
CoreStopWordDictionary.load("data/dictionary/custom/CustomStopWords.txt", false);
```
### 触发代码
```
/**
* 加载另一部停用词词典
* @param coreStopWordDictionaryPath 词典路径
* @param loadCacheIfPossible 是否优先加载缓存（速度更快）
*/
public static void load(String coreStopWordDictionaryPath, boolean loadCacheIfPossible)
{
ByteArray byteArray = loadCacheIfPossible ? ByteArray.createByteArray(coreStopWordDictionaryPath + Predefine.BIN_EXT) : null;
if (byteArray == null)
{
try
{
dictionary = new StopWordDictionary(HanLP.Config.CoreStopWordDictionaryPath);
DataOutputStream out = new DataOutputStream(new BufferedOutputStream(IOUtil.newOutputStream(HanLP.Config.CoreStopWordDictionaryPath + Predefine.BIN_EXT)));
dictionary.save(out);
out.close();
}
catch (Exception e)
{
logger.severe("载入停用词词典" + HanLP.Config.CoreStopWordDictionaryPath + "失败" + TextUtility.exceptionToString(e));
throw new RuntimeException("载入停用词词典" + HanLP.Config.CoreStopWordDictionaryPath + "失败");
}
}
else
{
dictionary = new StopWordDictionary();
dictionary.load(byteArray);
}
}
```
### 修改后的代码
```
/**
* 加载另一部停用词词典
* @param coreStopWordDictionaryPath 词典路径
* @param loadCacheIfPossible 是否优先加载缓存（速度更快）
*/
public static void load(String coreStopWordDictionaryPath, boolean loadCacheIfPossible)
{
ByteArray byteArray = loadCacheIfPossible ? ByteArray.createByteArray(coreStopWordDictionaryPath + Predefine.BIN_EXT) : null;
if (byteArray == null)
{
try
{
// HanLP.Config.CoreStopWordDictionaryPath 改为 coreStopWordDictionaryPath
dictionary = new StopWordDictionary(coreStopWordDictionaryPath);
DataOutputStream out = new DataOutputStream(new BufferedOutputStream(IOUtil.newOutputStream(coreStopWordDictionaryPath + Predefine.BIN_EXT)));
dictionary.save(out);
out.close();
}
catch (Exception e)
{
logger.severe("载入停用词词典" + coreStopWordDictionaryPath + "失败" + TextUtility.exceptionToString(e));
throw new RuntimeException("载入停用词词典" + coreStopWordDictionaryPath + "失败");
}
}
else
{
dictionary = new StopWordDictionary();
dictionary.load(byteArray);
}
}
```
| closed | 2019-12-05T03:53:50Z | 2020-01-01T10:48:07Z | https://github.com/hankcs/HanLP/issues/1338 | [
"ignored"
] | allen615 | 3 |
mwaskom/seaborn | data-visualization | 2,769 | Is the units parameter currently functional? | First, thank you so much @mwaskom for this amazing package -- I use it constantly in my own work. I recently noticed that categorical plots have a `units` parameter that is supposed to control the level of bootstrapping. If I am understanding the purpose of this correctly, using this parameter should result in error bars that reflect only within-unit variation (in the style of [Loftus and Masson, 1994](https://link.springer.com/article/10.3758/BF03210951)). This would be incredibly useful but I so far have not been able to get it to work. Using this parameter seems to result in plots that look essentially the same as without it specified. E.g.
> exercise = sns.load_dataset("exercise")
> g = sns.catplot(x="time", y="pulse", hue="kind", units="id", data=exercise, kind="point")
> g = sns.catplot(x="time", y="pulse", hue="kind", data=exercise, kind="point")
both result in the same figure (as far as I can tell). I can post a reprex where I know what the within-subject plot is supposed to look like but figured I'd first check whether I was using this correctly or perhaps this hadn't been implemented yet. Thanks so much for the help!
I am using seaboard version 0.11.2 and python version
3.7.9 (default, Aug 31 2020, 12:42:55) | closed | 2022-03-22T18:56:17Z | 2022-03-28T15:32:48Z | https://github.com/mwaskom/seaborn/issues/2769 | [] | dhalpern | 2 |
apache/airflow | machine-learning | 48,034 | @dag imported from airflow.sdk fails with `AttributeError: 'DAG' object has no attribute 'get_run_data_interval'` | ### Apache Airflow version
from main. 3.0.0b4
### What happened?
(This bug does not exist in beta3, only got it in the nightly from last night, can't get current main to run so could not test there)
Tried to add this dag:
```python
from airflow.decorators import task
from pendulum import datetime
from airflow.sdk import dag
@dag(
start_date=datetime(2025, 1, 1),
schedule="@daily",
catchup=False,
)
def test_dag():
@task
def test_task():
pass
test_task()
test_dag()
```
Getting the import error:
```
[2025-03-20T19:44:44.482+0000] {dag.py:1866} INFO - Sync 1 DAGs
Traceback (most recent call last):
File "/usr/local/bin/airflow", line 10, in <module>
sys.exit(main())
^^^^^^
File "/usr/local/lib/python3.12/site-packages/airflow/__main__.py", line 58, in main
args.func(args)
File "/usr/local/lib/python3.12/site-packages/airflow/cli/cli_config.py", line 49, in command
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/airflow/utils/cli.py", line 111, in wrapper
return f(*args, **kwargs)
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/airflow/utils/providers_configuration_loader.py", line 55, in wrapped_function
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/airflow/utils/session.py", line 101, in wrapper
return func(*args, session=session, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/airflow/cli/commands/remote_commands/dag_command.py", line 711, in dag_reserialize
dag_bag.sync_to_db(bundle.name, bundle_version=bundle.get_current_version(), session=session)
File "/usr/local/lib/python3.12/site-packages/airflow/utils/session.py", line 98, in wrapper
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/airflow/models/dagbag.py", line 649, in sync_to_db
update_dag_parsing_results_in_db(
File "/usr/local/lib/python3.12/site-packages/airflow/dag_processing/collection.py", line 326, in update_dag_parsing_results_in_db
for attempt in run_with_db_retries(logger=log):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/tenacity/__init__.py", line 443, in __iter__
do = self.iter(retry_state=retry_state)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/tenacity/__init__.py", line 376, in iter
result = action(retry_state)
^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/tenacity/__init__.py", line 398, in <lambda>
self._add_action_func(lambda rs: rs.outcome.result())
^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/concurrent/futures/_base.py", line 449, in result
return self.__get_result()
^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/concurrent/futures/_base.py", line 401, in __get_result
raise self._exception
File "/usr/local/lib/python3.12/site-packages/airflow/dag_processing/collection.py", line 336, in update_dag_parsing_results_in_db
DAG.bulk_write_to_db(bundle_name, bundle_version, dags, session=session)
File "/usr/local/lib/python3.12/site-packages/airflow/utils/session.py", line 98, in wrapper
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/airflow/models/dag.py", line 1872, in bulk_write_to_db
dag_op.update_dags(orm_dags, session=session)
File "/usr/local/lib/python3.12/site-packages/airflow/dag_processing/collection.py", line 471, in update_dags
last_automated_data_interval = dag.get_run_data_interval(last_automated_run)
^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'DAG' object has no attribute 'get_run_data_interval'
```
### What you think should happen instead?
The same dag works when using `from airflow.decorators import dag`.
### How to reproduce
1. Add the dag above
2. run airflow dags reserialize
3. see the error
### Operating System
Mac M1 Pro 15.3.1 (24D70)
### Versions of Apache Airflow Providers
None
### Deployment
Other
### Deployment details
Astro CLI
### Anything else?
_No response_
### Are you willing to submit PR?
- [ ] Yes I am willing to submit a PR!
### Code of Conduct
- [x] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)
| closed | 2025-03-20T20:17:29Z | 2025-03-21T08:11:39Z | https://github.com/apache/airflow/issues/48034 | [
"kind:bug",
"area:core",
"needs-triage",
"affected_version:3.0.0beta"
] | TJaniF | 4 |
flairNLP/fundus | web-scraping | 12 | Discussion: Convenience attribute access and human-readable Article printouts | It would be helpful if a print on an Article object resulted in a nicely readable string representation of the article without full information on all attributes.
I.e.:
```python
crawler = Crawler(PublisherCollection.de_de)
for article in crawler.crawl(max_articles=5):
print(article)
```
would print something like:
```console
--------------------
"IT-Schule von VW und SAP: Der Staat scheitert am Fachkräftemangel"
- by Daniel Zwick (WELT.DE)
"Während die Bundesregierung ausländischen
Fachkräften den Zuzug erleichtern will, helfen
sich Unternehmen wie SAP und Volkswagen selbst.
Im WELT-Interview erklären die Personalchefs der
beiden Dax-Konzerne, warum sie ihren IT- [...]"
from: https://www.welt.de/wirtschaft/article242481623/IT-Schule-von-VW-und-SAP-Der-Staat-scheitert-am-Fachkraeftemangel.html (crawled 2022-12-06 08:09:44.059753)
--------------------
"Zweiter Weltkrieg: Polen will im Streit um Reparationen weiter eskalieren"
- by WELT (WELT.DE)
"Zu seinem Antrittsbesuch in Berlin reist Polens
neuer Vize-Außenminister Arkadiusz Mularczyk mit
einem Bericht im Gepäck. Darin fordert Warschau
von Deutschland Reparationen in Höhe von 1,3
Billionen Euro – und droht mit weiterer [...]"
from: https://www.welt.de/politik/ausland/article242511375/Zweiter-Weltkrieg-Polen-will-im-Streit-um-Reparationen-weiter-eskalieren.html (crawled 2022-12-06 08:09:43.338912)
--------------------
```
Here is my attempt at a script for this:
```python
de_de = PublisherCollection.de_de
crawler = Crawler(de_de)
for article in crawler.crawl(max_articles=2, error_handling='raise'):
title = article.extracted['title']
author = article.extracted['authors']
# Wrap this text.
wrapper = textwrap.TextWrapper(width=50, max_lines=5, initial_indent= '"', subsequent_indent=' ')
word_list = wrapper.wrap(text=article.extracted['plaintext'])
text_sample = '\n'.join(word_list) + '"'
print('-' * 20)
print(f'"{title}"\n - by {author[0]} (WELT.DE)')
print("\n" + text_sample + "\n")
print(f'from: {article.url} (crawled {article.crawl_date})')
print('-' * 20)
```
However, building this script I noticed some small issues:
- it would be nice to get a direct access to some feature we expect most articles to have. E.g. `article.title` instead of `article.extracted['title']`
- the `article.extracted['plaintext']` is sometimes None.
- the `Article` should know the proper name of its "newspaper". I am thinking of a field like `article.source` that just prints "Die WELT" if crawled from there.
- `article.extracted['authors']` is sometimes should the newspaper name | closed | 2022-12-06T07:15:08Z | 2023-04-19T23:22:12Z | https://github.com/flairNLP/fundus/issues/12 | [] | alanakbik | 4 |
allenai/allennlp | data-science | 4,666 | When running multiple distributed runs on the same box, AllenNLP says "RuntimeError address already in use” | The workaround is to set `"distributed" { "master_port": 29501 }` in the training config, but we can do better and automatically find a free port. | closed | 2020-09-23T22:31:39Z | 2020-10-07T18:14:35Z | https://github.com/allenai/allennlp/issues/4666 | [
"bug"
] | dirkgr | 0 |
mlfoundations/open_clip | computer-vision | 538 | Intermediate Checkpoints | Hi, thanks for the great repo!
I was wondering if there is a possibility to get intermediate checkpoints (for the n(=32) epochs) for some common clip models. for eg. (any/some of) CLIP-VIT-B32 pre-trained on laion/yfcc/cc?
Thanks! | closed | 2023-05-21T04:12:08Z | 2023-10-24T16:10:29Z | https://github.com/mlfoundations/open_clip/issues/538 | [] | HarmanDotpy | 1 |
modin-project/modin | data-science | 6,946 | Remove `needs: [lint-black-isort, ...]` | closed | 2024-02-19T16:45:24Z | 2024-02-19T17:21:51Z | https://github.com/modin-project/modin/issues/6946 | [
"Testing 📈",
"P0"
] | dchigarev | 0 | |
JoeanAmier/XHS-Downloader | api | 112 | 需求：能否迁移到微信小程序中 | open | 2024-07-03T15:18:41Z | 2024-07-04T13:01:53Z | https://github.com/JoeanAmier/XHS-Downloader/issues/112 | [] | NoMoonAndStar | 1 | |
benbusby/whoogle-search | flask | 253 | [BUG] Whoogle returns a broken Page on search | **Describe the bug**
Recently, whoogle searches started returning more and more "broken" Pages on search. After waking up today, i found that my hosted whoogle-instance doesn't return ANY non-broken Pages anymore.
**To Reproduce**
I don't know if these steps create the problem specifically, but:
1. Set up whoogle on a VPS
2. Set whoogle as your default search engine
3. Search with whoogle every day
4. (Maybe) Encounter the problem
**Deployment Method**
- [ ] Heroku (one-click deploy)
- [ ] Docker
- [ ] `run` executable
- [x] pip/pipx
- [ ] Other: [describe setup]
**Version of Whoogle Search**
- [ ] Latest build from [source] (i.e. GitHub, Docker Hub, pip, etc)
- [x] Version [0.3.1]
- [ ] Not sure
**Desktop (please complete the following information):**
- OS: [macOS 11 on MacBook Pro 2021 (M1)]
- Browser [Firefox, Brave]
- Version [Firefox 87, Brave 1.22.70]
**Additional context**
This is what I had in the console of my VPS after waking up:
`WARNING:waitress.queue:Task queue depth is 1
WARNING:waitress.queue:Task queue depth is 2
WARNING:waitress.queue:Task queue depth is 1
WARNING:waitress.queue:Task queue depth is 2
WARNING:waitress.queue:Task queue depth is 1
WARNING:waitress.queue:Task queue depth is 1
WARNING:waitress.queue:Task queue depth is 2
WARNING:waitress.queue:Task queue depth is 1
WARNING:waitress.queue:Task queue depth is 2
ERROR:app:Exception on / [GET]
Traceback (most recent call last):
File "/root/.local/pipx/venvs/whoogle-search/lib/python3.6/site-packages/flask/app.py", line 2446, in wsgi_app
response = self.full_dispatch_request()
File "/root/.local/pipx/venvs/whoogle-search/lib/python3.6/site-packages/flask/app.py", line 1951, in full_dispatch_request
rv = self.handle_user_exception(e)
File "/root/.local/pipx/venvs/whoogle-search/lib/python3.6/site-packages/flask/app.py", line 1820, in handle_user_exception
reraise(exc_type, exc_value, tb)
File "/root/.local/pipx/venvs/whoogle-search/lib/python3.6/site-packages/flask/_compat.py", line 39, in reraise
raise value
File "/root/.local/pipx/venvs/whoogle-search/lib/python3.6/site-packages/flask/app.py", line 1947, in full_dispatch_request
rv = self.preprocess_request()
File "/root/.local/pipx/venvs/whoogle-search/lib/python3.6/site-packages/flask/app.py", line 2241, in preprocess_request
rv = func()
File "/root/.local/pipx/venvs/whoogle-search/lib/python3.6/site-packages/app/routes.py", line 85, in before_request_func
config=g.user_config)
File "/root/.local/pipx/venvs/whoogle-search/lib/python3.6/site-packages/app/request.py", line 147, in __init__
self.mobile = 'Android' in normal_ua or 'iPhone' in normal_ua
TypeError: argument of type 'NoneType' is not iterable
WARNING:waitress.queue:Task queue depth is 1`
Even after restarting, it still seems to return broken search results that look like this:
<img width="1440" alt="grafik" src="https://user-images.githubusercontent.com/39242991/113280384-7e0e5780-92e4-11eb-8042-9b745b1a716b.png">
| closed | 2021-04-01T10:23:13Z | 2021-04-01T14:13:19Z | https://github.com/benbusby/whoogle-search/issues/253 | [
"bug"
] | bitfl0wer | 2 |
AUTOMATIC1111/stable-diffusion-webui | pytorch | 15,902 | [Bug]: getting an import error about can't import name packaging from pkg_resources | ### Checklist
- [ ] The issue exists after disabling all extensions
- [X] The issue exists on a clean installation of webui
- [ ] The issue is caused by an extension, but I believe it is caused by a bug in the webui
- [X] The issue exists in the current version of the webui
- [X] The issue has not been reported before recently
- [ ] The issue has been reported before but has not been fixed yet
### What happened?
actually not sure if this is supposed to be in bug reports or not, but I didn't see another option that fits what my issue is so I went with it. but anyways I'm getting an error that says import error when I try to run the run.bat for the first time on a fresh install of automatic1111. I'll paste my error log from my command terminal on my windows desktop PC.
also how do I go about getting xformers, because I believe I had it before I reinstalled my automatic1111, but now I clearly don't according to the log.
any help would be great. thanks in advance.
### Steps to reproduce the problem
1. download the sd.webui.zip
2. extract it to wherever you want
3. run update.bat
4. run run.bat and wait for the import error to appear.
### What should have happened?
the run.bat should have finished launching the web ui after it installed all the necessary files.
### What browsers do you use to access the UI ?
Mozilla Firefox
### Sysinfo
if I try to type in the command line after the import error appears it just closes, and just typing --dump-sysinfo into the command line after reopening it returns,
'--dump-sysinfo' is not recognized as an internal or external command,
operable program or batch file.
so I don't believe I can get the sysinfo file
### Console logs
```Shell
Python 3.10.6 (tags/v3.10.6:9c7b4bd, Aug 1 2022, 21:53:49) [MSC v.1932 64 bit (AMD64)]
Version: v1.9.3
Commit hash: 1c0a0c4c26f78c32095ebc7f8af82f5c04fca8c0
Launching Web UI with arguments:
no module 'xformers'. Processing without...
no module 'xformers'. Processing without...
No module 'xformers'. Proceeding without it.
Traceback (most recent call last):
File "B:\_StableDiff\stable-diffusion-webui\webui\launch.py", line 48, in <module>
main()
File "B:\_StableDiff\stable-diffusion-webui\webui\launch.py", line 44, in main
start()
File "B:\_StableDiff\stable-diffusion-webui\webui\modules\launch_utils.py", line 465, in start
import webui
File "B:\_StableDiff\stable-diffusion-webui\webui\webui.py", line 13, in <module>
initialize.imports()
File "B:\_StableDiff\stable-diffusion-webui\webui\modules\initialize.py", line 39, in imports
from modules import processing, gradio_extensons, ui # noqa: F401
File "B:\_StableDiff\stable-diffusion-webui\webui\modules\processing.py", line 18, in <module>
import modules.sd_hijack
File "B:\_StableDiff\stable-diffusion-webui\webui\modules\sd_hijack.py", line 5, in <module>
from modules import devices, sd_hijack_optimizations, shared, script_callbacks, errors, sd_unet, patches
File "B:\_StableDiff\stable-diffusion-webui\webui\modules\sd_hijack_optimizations.py", line 13, in <module>
from modules.hypernetworks import hypernetwork
File "B:\_StableDiff\stable-diffusion-webui\webui\modules\hypernetworks\hypernetwork.py", line 8, in <module>
import modules.textual_inversion.dataset
File "B:\_StableDiff\stable-diffusion-webui\webui\modules\textual_inversion\dataset.py", line 12, in <module>
from modules import devices, shared, images
File "B:\_StableDiff\stable-diffusion-webui\webui\modules\images.py", line 22, in <module>
from modules import sd_samplers, shared, script_callbacks, errors
File "B:\_StableDiff\stable-diffusion-webui\webui\modules\sd_samplers.py", line 5, in <module>
from modules import sd_samplers_kdiffusion, sd_samplers_timesteps, sd_samplers_lcm, shared, sd_samplers_common, sd_schedulers
File "B:\_StableDiff\stable-diffusion-webui\webui\modules\sd_samplers_kdiffusion.py", line 3, in <module>
import k_diffusion.sampling
File "B:\_StableDiff\stable-diffusion-webui\webui\repositories\k-diffusion\k_diffusion\__init__.py", line 1, in <module>
from . import augmentation, config, evaluation, external, gns, layers, models, sampling, utils
File "B:\_StableDiff\stable-diffusion-webui\webui\repositories\k-diffusion\k_diffusion\evaluation.py", line 6, in <module>
import clip
File "B:\_StableDiff\stable-diffusion-webui\system\python\lib\site-packages\clip\__init__.py", line 1, in <module>
from .clip import *
File "B:\_StableDiff\stable-diffusion-webui\system\python\lib\site-packages\clip\clip.py", line 6, in <module>
from pkg_resources import packaging
ImportError: cannot import name 'packaging' from 'pkg_resources' (B:\_StableDiff\stable-diffusion-webui\system\python\lib\site-packages\pkg_resources\__init__.py)
Press any key to continue . . .
```
### Additional information
I did check the import error that is similar to mine for a solution and tried something from there, however it didn't fix my issue. and mine says site-packages while the other one says dist-packages | closed | 2024-05-28T13:21:07Z | 2024-05-28T21:15:15Z | https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/15902 | [
"bug-report"
] | deathtome1998 | 7 |
axnsan12/drf-yasg | rest-api | 747 | Failed to create schema for ListField(chield=FileField) | # Bug Report
## Description
Hi, I'm trying to upload files via swagger + drf but got an error. Can you please help?
## serializers.py
```code
class PictureInputSerializer(serializers.Serializer):
is_title = serializers.BooleanField(required=False)
file = serializers.FileField(
max_length=1000,
allow_empty_file=False,
use_url=False,
validators=[validate_image_size]
)
class UpdatePhotoSerializer(serializers.Serializer):
obj_id = serializers.SlugRelatedField(queryset=SomeModel.objects.all(), slug_field='id')
pictures = serializers.ListField(child=PictureInputSerializer())
```
## view.py
```code
@action(
detail=False,
methods=["POST"],
serializer_class=UpdatePhotoSerializer,
parser_classes=(MultiPartParser, FormParser),
pagination_class=None
)
def upload_photo(self, request):
with transaction.atomic():
serializer = self.get_serializer(data=request.data)
serializer.is_valid(raise_exception=True)
serializer.custom_create(serializer.validated_data)
return Response(status=status.HTTP_201_CREATED)
```
## Stack trace / Error message
```code
File "../venv/lib/python3.8/site-packages/drf_yasg/generators.py", line 404, in get_paths
operation = self.get_operation(view, path, prefix, method, components, request)
File "../venv/lib/python3.8/site-packages/drf_yasg/generators.py", line 446, in get_operation
operation = view_inspector.get_operation(operation_keys)
File "../venv/lib/python3.8/site-packages/drf_yasg/inspectors/view.py", line 32, in get_operation
body = self.get_request_body_parameters(consumes)
File "../venv/lib/python3.8/site-packages/drf_yasg/inspectors/view.py", line 82, in get_request_body_parameters
return self.get_request_form_parameters(serializer)
File "../venv/lib/python3.8/site-packages/drf_yasg/inspectors/view.py", line 134, in get_request_form_parameters
return self.serializer_to_parameters(serializer, in_=openapi.IN_FORM)
File "../venv/lib/python3.8/site-packages/drf_yasg/inspectors/base.py", line 448, in serializer_to_parameters
return self.probe_inspectors(
File "../venv/lib/python3.8/site-packages/drf_yasg/inspectors/base.py", line 110, in probe_inspectors
result = method(obj, **kwargs)
File "../venv/lib/python3.8/site-packages/drf_yasg/inspectors/field.py", line 48, in get_request_parameters
parameters = [
File "../venv/lib/python3.8/site-packages/drf_yasg/inspectors/field.py", line 49, in <listcomp>
self.probe_field_inspectors(
File "..venv/lib/python3.8/site-packages/drf_yasg/inspectors/base.py", line 228, in probe_field_inspectors
return self.probe_inspectors(
File "../venv/lib/python3.8/site-packages/drf_yasg/inspectors/base.py", line 110, in probe_inspectors
result = method(obj, **kwargs)
File "../venv/lib/python3.8/site-packages/drf_yasg/inspectors/field.py", line 77, in field_to_swagger_object
child_schema = self.probe_field_inspectors(field.child, ChildSwaggerType, use_references)
File "../venv/lib/python3.8/site-packages/drf_yasg/inspectors/base.py", line 228, in probe_field_inspectors
return self.probe_inspectors(
File "../venv/lib/python3.8/site-packages/drf_yasg/inspectors/base.py", line 110, in probe_inspectors
result = method(obj, **kwargs)
File "../venv/lib/python3.8/site-packages/drf_yasg/inspectors/field.py", line 86, in field_to_swagger_object
raise SwaggerGenerationError("cannot instantiate nested serializer as " + swagger_object_type.__name__)
drf_yasg.errors.SwaggerGenerationError: cannot instantiate nested serializer as Items
[05/Oct/2021 15:49:51] "GET /swagger/?format=openapi HTTP/1.1" 500 215395
```
<!-- If the issue is accompanied by an exception or an error, please share it below: -->
## Your Environment
```code
Django==3.2.7
django-rest-framework==0.1.0
djangorestframework==3.12.4
drf-yasg==1.20.0
```
| open | 2021-10-05T15:57:12Z | 2025-03-07T12:11:14Z | https://github.com/axnsan12/drf-yasg/issues/747 | [
"triage"
] | Panter7777 | 1 |
mouredev/Hello-Python | fastapi | 424 | 网赌被黑怎么办？账号被冻结怎么办？提款一直不到账失败怎么办？ | 澳门平台（威尼斯人，出黑拿回微信：xiaolu460570飞机：@lc15688金沙，巴黎人，美高梅，银河，新葡京，太阳城等等）不给出款怎么办？
关于网上网赌娱乐平台赢钱了各种借口不给出款最新解决方法
切记，只要你赢钱了，遇到任何不给你提现的借口，基本表明你已经被黑了。
第一：提款被拒绝，各种借口理由，就是不让出款，让打倍投流水等等！
第二：限制你账号的部分功能！出款和入款端口关闭，以及让你充值解开取款通道等等！
第三：客服找一些借口说什么系统维护，风控审核等等借口，就是不让取款！
第四：确认被黑了，应该怎么做？ （找专业团队教你怎么办挽回损失，出款不成功不收取任何前期费用） | closed | 2025-03-02T07:58:31Z | 2025-03-02T11:12:18Z | https://github.com/mouredev/Hello-Python/issues/424 | [] | 376838 | 0 |
ageitgey/face_recognition | python | 1,356 | Is there a way to recognize a face with a mask on? | Can the software recognize faces with masks on? I think its important in these times. Any suggestions on how to fix it? | open | 2021-08-15T18:16:29Z | 2021-08-15T18:17:28Z | https://github.com/ageitgey/face_recognition/issues/1356 | [] | Elon120 | 0 |
biolab/orange3 | scikit-learn | 6,023 | LSOpenURLsWithRole() failed with error -10810 for the file /Applications/Orange3.app | <!--
Thanks for taking the time to report a bug!
If you're raising an issue about an add-on (i.e., installed via Options > Add-ons), raise an issue in the relevant add-on's issue tracker instead. See: https://github.com/biolab?q=orange3
To fix the bug, we need to be able to reproduce it. Please answer the following questions to the best of your ability.
-->
**What's wrong?**
<!-- Be specific, clear, and concise. Include screenshots if relevant. -->
<!-- If you're getting an error message, copy it, and enclose it with three backticks (```). -->
```
$ open /Applications/Orange3.app
LSOpenURLsWithRole() failed with error -10810 for the file /Applications/Orange3.app.
```
**How can we reproduce the problem?**
<!-- Upload a zip with the .ows file and data. -->
<!-- Describe the steps (open this widget, click there, then add this...) -->
Download Orange3-3.32.0-Python3.8.8.dmg from https://orangedatamining.com/download/#macos
double click downloaded *.dmg file, then put in /Applications
open terminal then execute
`open /Applications/Orange3.app`
if you double click /Applications/Orange3.app from Finder, nothing happen
**What's your environment?**
<!-- To find your Orange version, see "Help → About → Version" or `Orange.version.full_version` in code -->
- Operating system:
```
$ system_profiler SPSoftwareDataType
Software:
System Software Overview:
System Version: macOS 10.12.6 (16G2136)
Kernel Version: Darwin 16.7.0
```
- Orange version:
Orange3-3.32.0-Python3.8.8.dmg
- How you installed Orange:
From *.dmg file above then put the Orange3.app to /Applications/
| closed | 2022-06-14T01:29:10Z | 2022-10-05T08:22:11Z | https://github.com/biolab/orange3/issues/6023 | [
"bug report"
] | sugizo | 4 |
recommenders-team/recommenders | deep-learning | 1,851 | [BUG] Error in wide&deep tests | ### Description
<!--- Describe your issue/bug/request in detail -->
```
@pytest.mark.parametrize(
"size, steps, expected_values, seed",
[
(
"1m",
10000,
***
"rmse": 0.924958,
"mae": 0.741425,
"rsquared": 0.316534,
"exp_var": 0.322202,
"ndcg_at_k": 0.118114,
"map_at_k": 0.0139213,
"precision_at_k": 0.107087,
"recall_at_k": 0.0328638,
***,
42,
)
],
)
def test_wide_deep_integration(
notebooks, output_notebook, kernel_name, size, steps, expected_values, seed, tmp
):
notebook_path = notebooks["wide_deep"]
params = ***
"MOVIELENS_DATA_SIZE": size,
"STEPS": steps,
"EVALUATE_WHILE_TRAINING": False,
"MODEL_DIR": tmp,
"EXPORT_DIR_BASE": tmp,
"RATING_METRICS": ["rmse", "mae", "rsquared", "exp_var"],
"RANKING_METRICS": ["ndcg_at_k", "map_at_k", "precision_at_k", "recall_at_k"],
"RANDOM_SEED": seed,
***
pm.execute_notebook(
notebook_path, output_notebook, kernel_name=kernel_name, parameters=params
)
results = sb.read_notebook(output_notebook).scraps.dataframe.set_index("name")[
"data"
]
for key, value in expected_values.items():
> assert results[key] == pytest.approx(value, rel=TOL, abs=ABS_TOL)
E assert 0.2426806154811335 == 0.316534 ± 5.0e-02
E comparison failed
E Obtained: 0.2426806154811335
E Expected: 0.316534 ± 5.0e-02
tests/integration/examples/test_notebooks_gpu.py:248: AssertionError
```
### In which platform does it happen?
<!--- Describe the platform where the issue is happening (use a list if needed) -->
<!--- For example: -->
<!--- * Azure Data Science Virtual Machine. -->
<!--- * Azure Databricks. -->
<!--- * Other platforms. -->
### How do we replicate the issue?
<!--- Please be specific as possible (use a list if needed). -->
<!--- For example: -->
<!--- * Create a conda environment for pyspark -->
<!--- * Run unit test `test_sar_pyspark.py` with `pytest -m 'spark'` -->
<!--- * ... -->
### Expected behavior (i.e. solution)
<!--- For example: -->
<!--- * The tests for SAR PySpark should pass successfully. -->
### Other Comments
| closed | 2022-11-17T08:43:25Z | 2022-11-17T10:36:07Z | https://github.com/recommenders-team/recommenders/issues/1851 | [
"bug"
] | miguelgfierro | 0 |
polakowo/vectorbt | data-visualization | 151 | Additional subplot for portfolio.plot() | I have the following strategy:
```python
import vectorbt as vbt
btc_prices = vbt.BinanceData.download('BTCUSDT', interval='1d')
rsi = vbt.RSI.run(btc_prices.get('Close'))
long_entries = rsi.rsi_above(50.0, crossover=True)
long_exits = rsi.rsi_above(70.0)
portfolio = vbt.Portfolio.from_signals(btc_prices.get('Close'), long_entries, long_exits, init_cash=1000)
portfolio.plot().show()
rsi.plot().show()
```
As it is written now, the `plot().show()` methods create two plots: one of the returns and orders of the portfolio and another of the rsi. I was wondering if it were possible to have the RSI plot included somehow in `portfolio.plot()` so that when the browser launches, I see the RSI plot underneath the Orders plot with the same red and green order markers plotted on the RSI plot? | closed | 2021-05-26T18:17:46Z | 2021-05-27T17:17:49Z | https://github.com/polakowo/vectorbt/issues/151 | [] | aclifton314 | 8 |
pyeve/eve | flask | 983 | AttributeErrors on defaults.py | AttributeError occurs when testing basic app from quickstart guide using all defaults.
**Using:**
virtualenv 15.1.0
Eve v0.7
Python3.5
```
from eve import Eve
app = Eve()
if __name__ =='__main__':
app.run(debug=True)
```
The following is the Traceback
```Traceback (most recent call last):
File "server.py", line 9, in <module>
app = Eve()
File "/mnt/c/Users/Justin/CodeProjects/fabric_management/unix_env/lib/python3.4/site-packages/eve/flaskapp.py", line 176, in __init__
self.register_resource(resource, settings)
File "/mnt/c/Users/Justin/CodeProjects/fabric_management/unix_env/lib/python3.4/site-packages/eve/flaskapp.py", line 891, in register_resource
self._set_resource_defaults(resource, settings)
File "/mnt/c/Users/Justin/CodeProjects/fabric_management/unix_env/lib/python3.4/site-packages/eve/flaskapp.py", line 626, in _set_resource_defaults
settings['defaults'] = build_defaults(schema)
File "/mnt/c/Users/Justin/CodeProjects/fabric_management/unix_env/lib/python3.4/site-packages/eve/defaults.py", line 47, in build_defaults
elif value.get('type') == 'dict' and 'schema' in value:
AttributeError: 'str' object has no attribute 'get'```
| closed | 2017-02-08T16:08:24Z | 2018-05-18T17:19:39Z | https://github.com/pyeve/eve/issues/983 | [
"stale"
] | jbool24 | 5 |
paulpierre/RasaGPT | fastapi | 3 | Could you show you arch of this projects? | I read your code, can't find what rasa do except connecting to telegram. Is it necessary?
Tks | closed | 2023-05-09T07:54:17Z | 2023-05-09T19:47:37Z | https://github.com/paulpierre/RasaGPT/issues/3 | [] | GingerNg | 2 |
lepture/authlib | flask | 62 | RFC7636 | Implement RFC7636: Proof Key for Code Exchange by OAuth Public Clients
- [x] code implementation
- [x] tests
- [x] documentation | closed | 2018-06-17T05:56:26Z | 2018-06-19T15:50:34Z | https://github.com/lepture/authlib/issues/62 | [] | lepture | 0 |
flasgger/flasgger | rest-api | 155 | feature request - logger handle | It would be great if there was a logger handle that I could grab for Swagger to log accesses, errors, and debug information to my api system log.
I would hope for something similar to adding these other handlers to my app.
```
# Set the log file name and log level with command line options
logHandler = RotatingFileHandler(logPath, maxBytes=10000000, backupCount=10)
logHandler.setLevel(logLevel)
logHandler.setFormatter(Formatter('%(asctime)s %(levelname)s %(module)s.%(funcName)s: %(message)s '))
werkzeugLogger = getLogger('werkzeug')
werkzeugLogger.addHandler(logHandler)
werkzeugLogger.setLevel(logLevel)
waitressLogger = getLogger('waitress')
waitressLogger.addHandler(logHandler)
waitressLogger.setLevel(logLevel)
app.logger.addHandler(logHandler)
app.logger.setLevel(logLevel)
# Prevent the log messages from going to stdout
app.logger.propagate = False
if logLevel == logging.DEBUG:
app.config['SQLALCHEMY_ECHO'] = True
app.debug = True
```
| open | 2017-09-13T20:15:08Z | 2017-10-02T16:31:06Z | https://github.com/flasgger/flasgger/issues/155 | [
"hacktoberfest"
] | rotten | 0 |
cvat-ai/cvat | pytorch | 8,803 | Upgrading from 2.22.0 to 2.23.1 fails with broken assets. | ### Actions before raising this issue
- [X] I searched the existing issues and did not find anything similar.
- [X] I read/searched [the docs](https://docs.cvat.ai/docs/)
### Steps to Reproduce
1. Created a backup following https://docs.cvat.ai/docs/administration/advanced/backup_guide/
2. Updated from github from 2.22.0 to the latest being 2.23.0 following the postgres upgrade just in case described in https://docs.cvat.ai/docs/administration/advanced/upgrade_guide/
Images and video data are missing. Reverting to 2.22.0 and restoring from backup works fine.
### Expected Behavior
I would expect to be able upgrade without loosing all the images/videos.
### Possible Solution
_No response_
### Context
_No response_
### Environment
```Markdown
Local installation.
- Git was on `v2.22.0` tag as well as the `v2.23.1` tag.
- Ubuntu 24
- Updated from github from 2.22.0 to the latest being 2.23.0
```
| open | 2024-12-09T13:55:30Z | 2025-03-04T23:16:26Z | https://github.com/cvat-ai/cvat/issues/8803 | [
"bug",
"documentation"
] | ggozad | 20 |
home-assistant/core | asyncio | 140,632 | Trigger template entity attribute exceptions cause entire entity to go unavailable | ### The problem
When adding trigger based lights to template entities, I noticed odd behavior and fixed it. When any template has syntax errors, it causes the entire entity to go unavailable instead of the single template.
The broken function:
```
def _render_templates(self, variables: dict[str, Any]) -> None:
"""Render templates."""
try:
rendered = dict(self._static_rendered)
for key in self._to_render_simple:
rendered[key] = self._config[key].async_render(
variables,
parse_result=key in self._parse_result,
)
for key in self._to_render_complex:
rendered[key] = render_complex(
self._config[key],
variables,
)
if CONF_ATTRIBUTES in self._config:
rendered[CONF_ATTRIBUTES] = render_complex(
self._config[CONF_ATTRIBUTES],
variables,
)
self._rendered = rendered
except TemplateError as err:
logging.getLogger(f"{__package__}.{self.entity_id.split('.')[0]}").error(
"Error rendering %s template for %s: %s", key, self.entity_id, err
)
self._rendered = self._static_rendered
```
The fix
```
def _render_single_template(
self, key: str, variables: dict[str, Any], is_complex: bool
) -> Any:
"""Render a single template."""
try:
if is_complex:
return render_complex(self._config[key], variables)
return self._config[key].async_render(
variables,
parse_result=key in self._parse_result,
)
except TemplateError as err:
logging.getLogger(f"{__package__}.{self.entity_id.split('.')[0]}").error(
"Error rendering %s template for %s: %s",
key,
self.entity_id,
err,
)
return _SENTINEL
def _render_templates(self, variables: dict[str, Any]) -> None:
"""Render templates."""
rendered = dict(self._static_rendered)
# Check availability first
available = True
if CONF_AVAILABILITY in self._to_render_simple:
if (
result := self._render_single_template(
CONF_AVAILABILITY, variables, False
)
) is not _SENTINEL:
rendered[CONF_AVAILABILITY] = available = result
# State is special, do not add it to rendered if there's an exception.
# This will avoid unwanted state `unknowns`.
if CONF_STATE in self._to_render_simple:
if (
result := self._render_single_template(CONF_STATE, variables, False)
) is not _SENTINEL:
rendered[CONF_STATE] = result
if not available:
self._rendered = rendered
return
for key in self._to_render_simple:
# Skip availability because we already handled it before.
if key in (CONF_AVAILABILITY, CONF_STATE):
continue
if (
result := self._render_single_template(key, variables, False)
) is not _SENTINEL:
rendered[key] = result
for key in self._to_render_complex:
if (
result := self._render_single_template(key, variables, True)
) is not _SENTINEL:
rendered[key] = result
if CONF_ATTRIBUTES in self._config:
if (
result := self._render_single_template(CONF_ATTRIBUTES, variables, True)
) is not _SENTINEL:
rendered[CONF_ATTRIBUTES] = result
else:
rendered[CONF_ATTRIBUTES] = {}
self._rendered = rendered
```
This is purely for myself, notes on what to fix.
### What version of Home Assistant Core has the issue?
all
### What was the last working version of Home Assistant Core?
never worked
### What type of installation are you running?
Home Assistant OS
### Integration causing the issue
template
### Link to integration documentation on our website
_No response_
### Diagnostics information
_No response_
### Example YAML snippet
```yaml
```
### Anything in the logs that might be useful for us?
```txt
```
### Additional information
_No response_ | open | 2025-03-15T00:03:17Z | 2025-03-15T00:03:17Z | https://github.com/home-assistant/core/issues/140632 | [] | Petro31 | 0 |
ivy-llc/ivy | tensorflow | 28,844 | [Bug]: `ivy.vector_norm` and `ivy.lgamma` when backend=numpy return rounded values | ### Bug Explanation
```shell
> python -c "import ivy; ivy.set_backend('numpy'); print(ivy.lgamma(5)); print(ivy.lgamma(5.))"
ivy.array(3)
ivy.array(3.1780539)
> python -c "import ivy; ivy.set_backend('numpy'); print(ivy.vector_norm([1, 1])); print(ivy.vector_norm([1., 1.]))"
ivy.array(1)
ivy.array(1.4142135)
```
https://data-apis.org/array-api/latest/extensions/generated/array_api.linalg.vector_norm.html
According to array-api, there is no specified behavior when a non-floating point type is given for `vector_norm`, but I would like to suggest, with all due respect, to stop this casting or to output an error, for the following reasons. (For `lgamma`, not even defined in array-api.)
- There are very limited situations where such behavior is needed.
- It does not match the behavior of other functions in numpy; `np.sin(1)` returns `0.8414709848078965` instead of `0`.
- This behavior can be easily achieved by `ivy.astype(ivy.lgamma(x), dtype=x.dtype)`
- Package maintainers using ivy need to worry about the type of input, and casts need to be made.
Thanks for this great project anyway.
### Steps to Reproduce Bug
```shell
python -c "import ivy; ivy.set_backend('numpy'); print(ivy.lgamma(5)); print(ivy.lgamma(5.))"
python -c "import ivy; ivy.set_backend('numpy'); print(ivy.vector_norm([1, 1])); print(ivy.vector_norm([1., 1.]))"
```
### Environment
NixOS 24.05, Python 3.11
### Ivy Version
1.0.0.1
### Backend
- [X] NumPy
- [ ] TensorFlow
- [ ] PyTorch
- [ ] JAX
### Device
CPU | open | 2024-11-27T14:20:19Z | 2024-11-29T05:14:02Z | https://github.com/ivy-llc/ivy/issues/28844 | [
"Bug Report"
] | 34j | 1 |
fastapi/sqlmodel | pydantic | 340 | IAM authentication token does not work in Postgres connection string | ### First Check
- [X] I added a very descriptive title to this issue.
- [X] I used the GitHub search to find a similar issue and didn't find it.
- [X] I searched the SQLModel documentation, with the integrated search.
- [X] I already searched in Google "How to X in SQLModel" and didn't find any information.
- [X] I already read and followed all the tutorial in the docs and didn't find an answer.
- [X] I already checked if it is not related to SQLModel but to [Pydantic](https://github.com/samuelcolvin/pydantic).
- [X] I already checked if it is not related to SQLModel but to [SQLAlchemy](https://github.com/sqlalchemy/sqlalchemy).
### Commit to Help
- [X] I commit to help with one of those options 👆
### Example Code
```python
import os
import boto3
from sqlmodel import create_engine, Session
# postgres url variables from environment
PG_HOST = os.environ.get('PG_HOST', False)
PG_USER = os.environ.get('PG_USER', False)
PG_PORT = os.environ.get('PG_PORT', '5432')
PG_REGION = os.environ.get('PG_REGION', False)
PG_DBNAME = os.environ.get('PG_DBNAME', False)
assert PG_HOST is not False
assert PG_USER is not False
assert PG_PORT == '5432'
assert PG_REGION is not False
assert PG_DBNAME is not False
# IAM authentication with user profile
# NOTE: requires that the runtime has access to an aws credential profile with the appropriate permissions to access an RDS instance or Proxy (e.g. 'postgres_admin_role')
# create token via IAM role
session = boto3.session.Session(profile_name='postgres_admin_role', region_name='us-east-1')
token = session.client('rds').generate_db_auth_token(
DBHostname=PG_HOST,
Port=PG_PORT,
DBUsername=PG_USER,
Region=PG_REGION)
# insert token into password section of postgres url
url = f'postgresql://{PG_USER}:{token}@{PG_HOST}{PG_PORT}/{PG_DBNAME}'
DATABASE = create_engine(url)
# get connection and exec command
with Session(DATABASE) as session:
print(session.exec('SELECT NOW()'))
# RESULT: Error "The IAM authentication failed for the role postgres. Check the IAM token for this role and try again."
```
### Description
The above code results in a runtime error:
```
The IAM authentication failed for the role postgres. Check the IAM token for this role and try again.
```
However, the URL is correct. I can login in from the same environment using the `psql` command.
### Wanted Solution
The above code should run as is.
I did however come up with a solution which I posted [here](https://stackoverflow.com/questions/71771441/the-iam-authentication-failed-for-the-role-postgres-check-the-iam-token-for-thi) on stackoverflow.
The solution is to use the 'creator' argument to the `create_engine` function which returns a raw psycopg2 connection. (see alternatives below)
### Wanted Code
```python
# SAME CODE AS ABOVE
url = f'postgresql://{PG_USER}:{token}@{PG_HOST}{PG_PORT}/{PG_DBNAME}'
DATABASE = create_engine(url)
# get connection and exec command
with Session(DATABASE) as session:
print(session.exec('SELECT NOW()'))
```
### Alternatives
The following code works as a workaround
```python
# the url
def get_pg_url(token) -> str:
ssl = '?sslmode=require'
return f'postgresql://{PG_USER}{token}@{PG_HOST}:{PG_PORT}/{PG_DB}{ssl}'
# the connection
def get_connection(token):
return psycopg2.connect(host=PG_HOST, user=PG_USER, port=PG_PORT, database=PG_DBNAME, password=token)
# Create the database engine
DATABASE = create_engine(get_pg_url(token), creator=get_connection)
with Session(DATABASE) as session:
session.exec('SELECT NOW()')
```
### Operating System
Linux
### Operating System Details
ubuntu EC2 image
### SQLModel Version
0.0.6
### Python Version
3.9
### Additional Context
It can be a lot of setup to recreate this problem as it has to run in an AWS VPC (and I'd be happy to help ). Roughly the steps are
1 - Create a VPC
2 -Security Group 1 - Accept TCP traffic on port 5432 from Security Group 2
3 - Security Group 2 - Accept all incoming traffic and allow outgoing TCP traffic on port 5432
4 - RDS postgres instance and attach to security group 1 (same vpc and all subnets)
4a - add a new role with login and password (in addition to the master username and password)
5 - save master and new user username/password in secrets manager
6 - Create RDS proxy and attach Security Group 2 (same vpc and all subnets)
6a - proxy needs a role with policy to get secrets and decrypt secret
7 - Create an ec2 instance (same vpc)
7a - ec2 needs a role with policy to connect to rds-proxy and for both users (i.e. master user and new user)
7b - pip install psycopg2-binary
7c - pip install sqlmodel boto3
7d - run example code from python terminal on this instance | open | 2022-05-12T15:15:42Z | 2024-06-26T16:42:22Z | https://github.com/fastapi/sqlmodel/issues/340 | [
"feature"
] | ejmolinelli | 1 |
graphdeco-inria/gaussian-splatting | computer-vision | 942 | Installed SIBR successful but cannot run (My server doesn't have display screen) | Hi! Thanks for your great work
But I have some problem when I try to run SIBR_viewers after I installed successfully
This error is :
(base) vinai@Fisheye2:~/Workspace/phat-intern-dev/VinAI/hierarchical-3d-gaussians$ xvfb-run -a bash SIBR_viewer.sh
[SIBR] -- INFOS --: Initialization of GLFW
[SIBR] -- INFOS --: OpenGL Version: 4.5 (Compatibility Profile) Mesa 23.2.1-1ubuntu3.1~22.04.2[major: 4, minor: 5]
Number of input Images to read: 1443
Number of Cameras set up: 1443
[SIBR] -- INFOS --: Error: can't load mesh '/home/vinai/Workspace/phat-intern-dev/VinAI/hierarchical-3d-gaussians/data/example_dataset/camera_calibration/aligned/sparse/0/points3d.txt.
[SIBR] -- INFOS --: Error: can't load mesh '/home/vinai/Workspace/phat-intern-dev/VinAI/hierarchical-3d-gaussians/data/example_dataset/camera_calibration/aligned/sparse/0/points3d.ply.
[SIBR] -- INFOS --: Error: can't load mesh '/home/vinai/Workspace/phat-intern-dev/VinAI/hierarchical-3d-gaussians/data/example_dataset/camera_calibration/aligned/sparse/0/points3d.obj.
LOADSFM: Try to open /home/vinai/Workspace/phat-intern-dev/VinAI/hierarchical-3d-gaussians/data/example_dataset/camera_calibration/aligned/sparse/0/points3D.bin
Num 3D pts 243088
[SIBR] -- INFOS --: SfM Mesh '/home/vinai/Workspace/phat-intern-dev/VinAI/hierarchical-3d-gaussians/data/example_dataset/camera_calibration/aligned/sparse/0/points3d.txt successfully loaded. (243088) vertices detected. Init GL ...
[SIBR] -- INFOS --: Init GL mesh complete
USED RES (1025,691) scene w h 1025 : 691 NAME cam3/pass1_0712.jpg
Warning: GLParameter user_color does not exist in shader PointBased
[SIBR] -- INFOS --: Allowing up to 6708512 Gaussians in VRAM
SIBR_viewer.sh: line 6: 160696 Segmentation fault (core dumped) ./SIBR_viewers/install/bin/SIBR_gaussianHierarchyViewer_app --path ${DATASET_DIR}/camera_calibration/aligned --scaffold ${DATASET_DIR}/output/scaffold/point_cloud/iteration_30000 --model-path ${DATASET_DIR}/output/merged.hier --images-path ${DATASET_DIR}/camera_calibration/rectified/images
I ssh to server so in this server is headless, can SIBR_viewer can be used at headless server ?
Thanks for your help | open | 2024-08-22T16:29:17Z | 2025-01-03T01:14:52Z | https://github.com/graphdeco-inria/gaussian-splatting/issues/942 | [] | RyanPham19092002 | 3 |
WZMIAOMIAO/deep-learning-for-image-processing | deep-learning | 244 | Loss is nan, stopping training . I use your faster-rcnn code to train my own dateset that I have transfered it into VOC2012 mode ,then I run 'train_mobilenetv2' in my pc, it got the result 'loss is nan'. | platform：win10 and Linux（I try both）
Using cuda device training.
Using 4 dataloader workers
Epoch: [0] [ 0/1985] eta: 1:08:57.381961 lr: 0.000500 loss: 2.7721 (2.7721) loss_classifier: 1.9666 (1.9666) loss_box_reg: 0.0388 (0.0388) loss_objectness: 0.6976 (0.6976) loss_rpn_box_reg: 0.0691 (0.0691) time: 2.0843 data: 0.1230 max mem: 1212
Epoch: [0] [ 50/1985] eta: 0:09:10.796689 lr: 0.000500 loss: 0.6686 (1.0468) loss_classifier: 0.2676 (0.4887) loss_box_reg: 0.0100 (0.0280) loss_objectness: 0.3348 (0.4657) loss_rpn_box_reg: 0.0498 (0.0644) time: 0.2467 data: 0.1051 max mem: 1792
Epoch: [0] [ 100/1985] eta: 0:08:22.285547 lr: 0.000500 loss: 0.5700 (0.8217) loss_classifier: 0.2172 (0.3628) loss_box_reg: 0.0152 (0.0238) loss_objectness: 0.2737 (0.3753) loss_rpn_box_reg: 0.0571 (0.0597) time: 0.2487 data: 0.1067 max mem: 1792
Epoch: [0] [ 150/1985] eta: 0:07:57.971424 lr: 0.000500 loss: 0.5219 (0.7306) loss_classifier: 0.1708 (0.3102) loss_box_reg: 0.0147 (0.0212) loss_objectness: 0.2607 (0.3394) loss_rpn_box_reg: 0.0590 (0.0599) time: 0.2477 data: 0.1055 max mem: 1792
Loss is nan, stopping training | closed | 2021-05-05T02:07:05Z | 2021-05-10T03:06:06Z | https://github.com/WZMIAOMIAO/deep-learning-for-image-processing/issues/244 | [] | littledeep | 1 |
netbox-community/netbox | django | 18,217 | unable to assign ip to interface (by PATCH method) | ### Deployment Type
Self-hosted
### Triage priority
N/A
### NetBox Version
4.1.7
### Python Version
3.11
### Steps to Reproduce
1. Create IP (id 1)
2. Create Device with Interface (id 1)
3. (API) curl -H @.env -X PATCH https://netbox/api/ipam/ip-addresses/1/ -d '[{"assigned_object_type":"dcim.interface","assigned_object_id":1}]'
### Expected Behavior
Assign IP to device interface. Same task done manually by webUI works as expected.
### Observed Behavior
Throws error:
{"error": "Serializers with many=True do not support multiple update by default, only multiple create. For updates it is unclear how to deal with insertions and deletions. If you need to support multiple update, use a `ListSerializer` class and override `.update()` so you can specify the behavior exactly.", "exception": "NotImplementedError", "netbox_version": "4.1.7", "python_version": "3.11.2"} | closed | 2024-12-12T11:40:08Z | 2025-03-13T03:10:04Z | https://github.com/netbox-community/netbox/issues/18217 | [] | yanecisco | 2 |
allenai/allennlp | pytorch | 4,931 | Investigate plotext as alternative to matplotlib for find-lr command output | See https://pypi.org/project/plotext/.
Plots right in terminal with ASCII. Would be nice to see the results right in the terminal, especially when you're working on a remote server. | open | 2021-01-26T15:19:55Z | 2021-04-22T17:03:23Z | https://github.com/allenai/allennlp/issues/4931 | [] | epwalsh | 6 |
pytest-dev/pytest-flask | pytest | 118 | Package documentation outdated | Your documentation at https://pytest-flask.readthedocs.io/en/latest/ hasn't [built in over two years](https://readthedocs.org/projects/pytest-flask/builds/) and the changelog ends with 0.10.
I'm surprised I'm the only one noticing (or I'm bad at using the search function) – is there some other documentation somewhere else? This is the once linked from the README. | closed | 2020-03-10T07:50:08Z | 2020-12-21T20:41:24Z | https://github.com/pytest-dev/pytest-flask/issues/118 | [
"bug",
"docs"
] | hynek | 8 |
paperless-ngx/paperless-ngx | machine-learning | 8,865 | [BUG] PAPERLESS_SUPERVISORD_WORKING_DIR has no effect on latest version | ### Description
I'm trying to setup paperless-ngx as application container on Incus. However the environment variable PAPERLESS_SUPERVISORD_WORKING_DIR has no effect, as the current config always defaults to /run/supervisord/. Am I missing something?
Thank you!!
`# cat /etc/supervisord.conf
[supervisord]
nodaemon=true ; start in foreground if true; default false
logfile=/var/log/supervisord/supervisord.log ; main log file; default $CWD/supervisord.log
pidfile=/var/run/supervisord/supervisord.pid ; supervisord pidfile; default supervisord.pid
logfile_maxbytes=50MB ; max main logfile bytes b4 rotation; default 50MB
logfile_backups=10 ; # of main logfile backups; 0 means none, default 10
loglevel=info ; log level; default info; others: debug,warn,trace
user=root
[program:gunicorn]
command=gunicorn -c /usr/src/paperless/gunicorn.conf.py paperless.asgi:application
user=paperless
priority = 1
stdout_logfile=/dev/stdout
stdout_logfile_maxbytes=0
stderr_logfile=/dev/stderr
stderr_logfile_maxbytes=0
environment = HOME="/usr/src/paperless",USER="paperless"
[program:consumer]
command=python3 manage.py document_consumer
user=paperless
stopsignal=INT
priority = 20
stdout_logfile=/dev/stdout
stdout_logfile_maxbytes=0
stderr_logfile=/dev/stderr
stderr_logfile_maxbytes=0
environment = HOME="/usr/src/paperless",USER="paperless"
[program:celery]
command = celery --app paperless worker --loglevel INFO --without-mingle --without-gossip
user=paperless
stopasgroup = true
stopwaitsecs = 60
priority = 5
stdout_logfile=/dev/stdout
stdout_logfile_maxbytes=0
stderr_logfile=/dev/stderr
stderr_logfile_maxbytes=0
environment = HOME="/usr/src/paperless",USER="paperless"
[program:celery-beat]
command = celery --app paperless beat --loglevel INFO
user=paperless
stopasgroup = true
priority = 10
stdout_logfile=/dev/stdout
stdout_logfile_maxbytes=0
stderr_logfile=/dev/stderr
stderr_logfile_maxbytes=0
environment = HOME="/usr/src/paperless",USER="paperless"
[program:celery-flower]
command = /usr/local/bin/flower-conditional.sh
user = paperless
startsecs = 0
priority = 40
stdout_logfile=/dev/stdout
stdout_logfile_maxbytes=0
stderr_logfile=/dev/stderr
stderr_logfile_maxbytes=0
environment = HOME="/usr/src/paperless",USER="paperless"`
### Steps to reproduce
1 - create redis and paperless-ngx container according to the dockerfile
2 - add persistent disks
3 - start container
4 - watch console while boot
### Webserver logs
```bash
Connected to Redis broker.
Apply database migrations...
Operations to perform:
Apply all migrations: account, admin, auditlog, auth, authtoken, contenttypes, django_celery_results, documents, guardian, mfa, paperless, paperless_mail, sessions, socialaccount
Running migrations:
Applying contenttypes.0001_initial... OK
Applying auth.0001_initial... OK
Applying account.0001_initial... OK
Applying account.0002_email_max_length... OK
Applying account.0003_alter_emailaddress_create_unique_verified_email... OK
Applying account.0004_alter_emailaddress_drop_unique_email... OK
Applying account.0005_emailaddress_idx_upper_email... OK
Applying account.0006_emailaddress_lower... OK
Applying account.0007_emailaddress_idx_email... OK
Applying account.0008_emailaddress_unique_primary_email_fixup... OK
Applying account.0009_emailaddress_unique_primary_email... OK
Applying admin.0001_initial... OK
Applying admin.0002_logentry_remove_auto_add... OK
Applying admin.0003_logentry_add_action_flag_choices... OK
Applying auditlog.0001_initial... OK
Applying auditlog.0002_auto_support_long_primary_keys... OK
Applying auditlog.0003_logentry_remote_addr... OK
Applying auditlog.0004_logentry_detailed_object_repr... OK
Applying auditlog.0005_logentry_additional_data_verbose_name... OK
Applying auditlog.0006_object_pk_index... OK
Applying auditlog.0007_object_pk_type... OK
Applying auditlog.0008_action_index... OK
Applying auditlog.0009_alter_logentry_additional_data... OK
Applying auditlog.0010_alter_logentry_timestamp... OK
Applying auditlog.0011_logentry_serialized_data... OK
Applying auditlog.0012_add_logentry_action_access... OK
Applying auditlog.0013_alter_logentry_timestamp... OK
Applying auditlog.0014_logentry_cid... OK
Applying auditlog.0015_alter_logentry_changes... OK
Applying contenttypes.0002_remove_content_type_name... OK
Applying auth.0002_alter_permission_name_max_length... OK
Applying auth.0003_alter_user_email_max_length... OK
Applying auth.0004_alter_user_username_opts... OK
Applying auth.0005_alter_user_last_login_null... OK
Applying auth.0006_require_contenttypes_0002... OK
Applying auth.0007_alter_validators_add_error_messages... OK
Applying auth.0008_alter_user_username_max_length... OK
Applying auth.0009_alter_user_last_name_max_length... OK
Applying auth.0010_alter_group_name_max_length... OK
Applying auth.0011_update_proxy_permissions... OK
Applying auth.0012_alter_user_first_name_max_length... OK
Applying authtoken.0001_initial... OK
Applying authtoken.0002_auto_20160226_1747... OK
Applying authtoken.0003_tokenproxy... OK
Applying authtoken.0004_alter_tokenproxy_options... OK
Applying django_celery_results.0001_initial... OK
Applying django_celery_results.0002_add_task_name_args_kwargs... OK
Applying django_celery_results.0003_auto_20181106_1101... OK
Applying django_celery_results.0004_auto_20190516_0412... OK
Applying django_celery_results.0005_taskresult_worker... OK
Applying django_celery_results.0006_taskresult_date_created... OK
Applying django_celery_results.0007_remove_taskresult_hidden... OK
Applying django_celery_results.0008_chordcounter... OK
Applying django_celery_results.0009_groupresult... OK
Applying django_celery_results.0010_remove_duplicate_indices... OK
Applying django_celery_results.0011_taskresult_periodic_task_name... OK
Applying documents.0001_initial... OK
Applying documents.0002_auto_20151226_1316... OK
Applying documents.0003_sender... OK
Applying documents.0004_auto_20160114_1844_squashed_0011_auto_20160303_1929... OK
Applying documents.0012_auto_20160305_0040... OK
Applying documents.0013_auto_20160325_2111... OK
Applying documents.0014_document_checksum... OK
Applying documents.0015_add_insensitive_to_match_squashed_0018_auto_20170715_1712... OK
Applying documents.0019_add_consumer_user... OK
Applying documents.0020_document_added... OK
Applying documents.0021_document_storage_type... OK
Applying documents.0022_auto_20181007_1420... OK
Applying documents.0023_document_current_filename... OK
Applying documents.1000_update_paperless_all... OK
Applying documents.1001_auto_20201109_1636... OK
Applying documents.1002_auto_20201111_1105... OK
Applying documents.1003_mime_types... OK
Applying documents.1004_sanity_check_schedule... OK
Applying documents.1005_checksums... OK
Applying documents.1006_auto_20201208_2209_squashed_1011_auto_20210101_2340... OK
Applying paperless_mail.0001_initial_squashed_0009_mailrule_assign_tags... OK
Applying paperless_mail.0010_auto_20220311_1602... OK
Applying paperless_mail.0011_remove_mailrule_assign_tag_squashed_0024_alter_mailrule_name_and_more... OK
Applying documents.1012_fix_archive_files... OK
Applying documents.1013_migrate_tag_colour... OK
Applying documents.1014_auto_20210228_1614... OK
Applying documents.1015_remove_null_characters... OK
Applying documents.1016_auto_20210317_1351_squashed_1020_merge_20220518_1839... OK
Applying documents.1021_webp_thumbnail_conversion... OK
Applying documents.1022_paperlesstask_squashed_1036_alter_savedviewfilterrule_rule_type... OK
Applying documents.1037_webp_encrypted_thumbnail_conversion... OK
Applying documents.1038_sharelink... OK
Applying documents.1039_consumptiontemplate... OK
Applying documents.1040_customfield_customfieldinstance_and_more... OK
Applying documents.1041_alter_consumptiontemplate_sources... OK
Applying documents.1042_consumptiontemplate_assign_custom_fields_and_more... OK
Applying documents.1043_alter_savedviewfilterrule_rule_type... OK
Applying documents.1044_workflow_workflowaction_workflowtrigger_and_more... OK
Applying documents.1045_alter_customfieldinstance_value_monetary_squashed_1049_document_deleted_at_document_restored_at... OK
Applying documents.1050_customfield_extra_data_and_more... OK
Applying documents.1051_alter_correspondent_owner_alter_document_owner_and_more... OK
Applying documents.1052_document_transaction_id... OK
Applying documents.1053_document_page_count... OK
Applying documents.1054_customfieldinstance_value_monetary_amount_and_more... OK
Applying documents.1055_alter_storagepath_path... OK
Applying documents.1056_customfieldinstance_deleted_at_and_more... OK
Applying documents.1057_paperlesstask_owner... OK
Applying documents.1058_workflowtrigger_schedule_date_custom_field_and_more... OK
Applying documents.1059_workflowactionemail_workflowactionwebhook_and_more... OK
Applying documents.1060_alter_customfieldinstance_value_select... OK
Applying documents.1061_workflowactionwebhook_as_json... OK
Applying guardian.0001_initial... OK
Applying guardian.0002_generic_permissions_index... OK
Applying mfa.0001_initial... OK
Applying mfa.0002_authenticator_timestamps... OK
Applying mfa.0003_authenticator_type_uniq... OK
Applying paperless.0001_initial... OK
Applying paperless.0002_applicationconfiguration_app_logo_and_more... OK
Applying paperless.0003_alter_applicationconfiguration_max_image_pixels... OK
Applying paperless_mail.0025_alter_mailaccount_owner_alter_mailrule_owner_and_more... OK
Applying paperless_mail.0026_mailrule_enabled... OK
Applying paperless_mail.0027_mailaccount_expiration_mailaccount_account_type_and_more... OK
Applying paperless_mail.0028_alter_mailaccount_password_and_more... OK
Applying sessions.0001_initial... OK
Applying socialaccount.0001_initial... OK
Applying socialaccount.0002_token_max_lengths... OK
Applying socialaccount.0003_extra_data_default_dict... OK
Applying socialaccount.0004_app_provider_id_settings... OK
Applying socialaccount.0005_socialtoken_nullable_app... OK
Applying socialaccount.0006_alter_socialaccount_extra_data... OK
Running Django checks
System check identified no issues (0 silenced).
Search index out of date. Updating...
Executing /usr/local/bin/paperless_cmd.sh
Error: The directory named as part of the path /var/run/supervisord/supervisord.pid does not exist
For help, use /usr/local/bin/supervisord -h
```
### Browser logs
```bash
```
### Paperless-ngx version
stable
### Host OS
Debian 12 amd64
### Installation method
Other (please describe above)
### System status
```json
```
### Browser
_No response_
### Configuration changes
_No response_
### Please confirm the following
- [x] I believe this issue is a bug that affects all users of Paperless-ngx, not something specific to my installation.
- [x] This issue is not about the OCR or archive creation of a specific file(s). Otherwise, please see above regarding OCR tools.
- [x] I have already searched for relevant existing issues and discussions before opening this report.
- [x] I have updated the title field above with a concise description. | closed | 2025-01-22T14:51:35Z | 2025-02-22T03:05:40Z | https://github.com/paperless-ngx/paperless-ngx/issues/8865 | [
"not a bug"
] | msperl-github | 4 |
ipython/ipython | jupyter | 14,367 | Problem with %store | I do:
```
In [1]: a=8
In [2]: %store a
C:\Users\GBattaglia\AppData\Local\Programs\Python\Python311\Lib\site-packages\IPython\extensions\storemagic.py:229: UserWarning: This is now an optional IPython functionality, setting autorestore/a requires you to install the `pickleshare` library.
db[ 'autorestore/' + arg ] = obj
Stored 'a' (int)
In [3]: %store
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In[3], line 1
----> 1 get_ipython().run_line_magic('store', '')
File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\IPython\core\interactiveshell.py:2480, in InteractiveShell.run_line_magic(self, magic_name, line, _stack_depth)
2478 kwargs['local_ns'] = self.get_local_scope(stack_depth)
2479 with self.builtin_trap:
-> 2480 result = fn(*args, **kwargs)
2482 # The code below prevents the output from being displayed
2483 # when using magics with decorator @output_can_be_silenced
2484 # when the last Python token in the expression is a ';'.
2485 if getattr(fn, magic.MAGIC_OUTPUT_CAN_BE_SILENCED, False):
File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\IPython\extensions\storemagic.py:161, in StoreMagics.store(self, parameter_s)
159 # run without arguments -> list variables & values
160 elif not args:
--> 161 vars = db.keys('autorestore/*')
162 vars.sort()
163 if vars:
AttributeError: 'PickleShareDB' object has no attribute 'keys'
In [4]:
```
Is it something goes wrong on my configuration or an ipython issue? Thanks.
| open | 2024-03-11T11:28:44Z | 2024-10-05T00:59:13Z | https://github.com/ipython/ipython/issues/14367 | [] | GabrieleBattaglia | 2 |
aminalaee/sqladmin | sqlalchemy | 137 | Lazy support | ### Checklist
- [x] There are no similar issues or pull requests for this yet.
### Is your feature related to a problem? Please describe.
Hello. In my project I have a lot relationships with `lazy="dynamic"`. But sqladmin don't supports it
### Describe the solution you would like.
I would like to see setting in config like load_lazys . If if is True load all relationships
### Describe alternatives you considered
_No response_
### Additional context
_No response_ | open | 2022-04-18T18:16:25Z | 2022-07-10T11:00:07Z | https://github.com/aminalaee/sqladmin/issues/137 | [
"hold"
] | badger-py | 1 |
donnemartin/data-science-ipython-notebooks | scikit-learn | 33 | "Error 503 No healthy backends" | Hello,
When I try to open the hyperlinks which should direct me to the correct ipython notebook, it returns me "Error 503 No healthy backends"
"No healthy backends
Guru Mediation:
Details: cache-fra1236-FRA 1462794681 3780339426
Varnish cache server"
<img width="833" alt="capture" src="https://cloud.githubusercontent.com/assets/14320144/15112809/3e3a020c-15f9-11e6-9440-bfed7debac08.PNG">
<img width="350" alt="capture2" src="https://cloud.githubusercontent.com/assets/14320144/15112808/3e391b62-15f9-11e6-86b0-cf57a5d2e16e.PNG">
Thanks
Jiahong Wang
| closed | 2016-05-09T12:19:03Z | 2016-05-10T09:55:50Z | https://github.com/donnemartin/data-science-ipython-notebooks/issues/33 | [
"question"
] | wangjiahong | 1 |
pywinauto/pywinauto | automation | 1,089 | wait_not error | ## Expected Behavior
I have a short function that checks whether 'loading' dialog doesn't exist and isn't visible. It is expected to recheck it every 0.3 seconds.
## Actual Behavior
Sometimes it raises an "unable to invoke any subscribers" error ("_ctypes.COMError: (-2147220991, 'Событие не смогло вызвать ни одного из абонентов', (None, None, None, 0, None))").
Here's fuller version of exception log:
File "pywinauto\application.py", line 566, in wait_not
File "pywinauto\timings.py", line 359, in wait_until
File "pywinauto\application.py", line 567, in <lambda>
File "pywinauto\application.py", line 476, in __check_all_conditions
File "pywinauto\application.py", line 427, in exists
File "pywinauto\application.py", line 250, in __resolve_control
File "pywinauto\timings.py", line 436, in wait_until_passes
File "pywinauto\application.py", line 222, in __get_ctrl
File "pywinauto\findwindows.py", line 84, in find_element
File "pywinauto\findwindows.py", line 305, in find_elements
File "pywinauto\findbestmatch.py", line 495, in find_best_control_matches
File "pywinauto\findbestmatch.py", line 474, in build_unique_dict
File "pywinauto\findbestmatch.py", line 320, in get_control_names
File "pywinauto\findbestmatch.py", line 219, in get_non_text_control_name
File "pywinauto\base_wrapper.py", line 367, in rectangle
File "pywinauto\uia_element_info.py", line 326, in rectangle
_ctypes.COMError: (-2147220991, 'Событие не смогло вызвать ни одного из абонентов', (None, None, None, 0, None))
## Short Example of Code to Demonstrate the Problem
def check_loading(dlg):
dlg['Please wait...'].wait_not("exists visible", 3, 0.3)
## Specifications
- Pywinauto version: 0.6.8
- Python version and bitness: 3.9.4 64bit
- Platform and OS: Windows 10
| open | 2021-06-21T08:20:56Z | 2021-06-21T08:20:56Z | https://github.com/pywinauto/pywinauto/issues/1089 | [] | bananoviiblinchik | 0 |
ansible/awx | django | 15,062 | AWX Community Meeting Agenda - April 2024 | # AWX Office Hours
## Proposed agenda based on topics
@TheRealHaoLiu fall out of the postgres 15 upgrade
- [Data lost after restarting postgres-15 pod after upgrading to awx-operator 2.13.0 ](https://github.com/ansible/awx-operator/issues/1768)
- [Postgres 15 pod: cannot create directory '/var/lib/pgsql/data/userdata': Permission](https://github.com/ansible/awx-operator/issues/1770)
- [postgres_data_path not respected by postgres-15 causing data lost](https://github.com/ansible/awx-operator/issues/1790)
- [awx migration pod issue with custom ca ](https://github.com/ansible/awx-operator/issues/1782)
@TheRealHaoLiu ARM64 AWX image release coming in future release https://github.com/ansible/awx/pull/15053
This afternoon's release will also contain fixes for the `postgres_data_path` parameter issue as well as the `/var/lib/pgsql/data/userdata` permissions issue, both of which are related to the sclorg postgresql image change. Please refer to the following docs for more information on that:
* https://ansible.readthedocs.io/projects/awx-operator/en/latest/user-guide/database-configuration.html?h=databas#note-about-overriding-the-postgres-image
## What
After a successful Contributor Summit in October 2023, one of the bits of feedback we got was to host a regular time for the Automation Controller (AWX) Team to be available for your folks in the AWX Community, so we are happy to announce a new regular video meeting.
This kind of feedback loop is vital to the success of AWX and the AWX team wants to make it as easy as possible for you - our community - to get involved.
## Where & When
Our next meeting will be held on Tuesday, April 9th, 2024 at [1500 UTC](https://dateful.com/time-zone-converter?t=15:00&tz=UTC)
* [Google Meet](https://meet.google.com/vyk-dfow-cfi)
* Via Phone PIN: 842522378 [Guide](https://support.google.com/meet/answer/9518557)
This meeting is held once a month, on the second Tuesday of the month, at [1500 UTC](https://dateful.com/time-zone-converter?t=15:00&tz=UTC)
## How
Add one topic per comment in this GitHub issue
If you don't have a GitHub account, jump on [#awx:ansible.com](https://matrix.to/#/#awx:ansible.com) on Matrix and we can add the topic for you
## Talk with us
As well as the fortnightly video meeting you can join the Community (inc development team) on Matrix Chat.
* Matrix: [#awx:ansible.com](https://matrix.to/#/#awx:ansible.com) (recomended)
* libera.chat IRC: `#ansible-awx` (If you are already setup on IRC)
The Matrix & IRC channels are bridged, you'll just have a better experience on Matrix
## Links
[AWX YouTube Chanel](https://www.youtube.com/@ansible-awx)
[Previous Meeting](https://github.com/ansible/awx/issues/14969)
Meeting recording
Next Meeting
See you soon!
| closed | 2024-04-03T17:41:51Z | 2024-04-10T19:20:47Z | https://github.com/ansible/awx/issues/15062 | [
"needs_triage"
] | TheRealHaoLiu | 1 |
lux-org/lux | jupyter | 462 | [Feature Request] Commit lint checker should only look at first commit | **Is your feature request related to a problem? Please describe.**
A clear and concise description of what the problem is.
**Describe the solution you'd like**
A clear and concise description of what you want to happen.
**Describe alternatives you've considered**
A clear and concise description of any alternative solutions or features you've considered.
**Additional context**
Add any other context or screenshots about the feature request here.
| closed | 2022-03-02T18:28:25Z | 2022-03-02T18:32:45Z | https://github.com/lux-org/lux/issues/462 | [] | dorisjlee | 1 |
jupyter-book/jupyter-book | jupyter | 1,433 | Latest `thebe` loads CSS that breaks most paragraph CSS | ### Describe the problem
After updating [this jb site](http://vote.maxghenis.com) ([repo](https://github.com/MaxGhenis/vote/)) for the first time since January, the paragraph breaks and bold fonts stopped showing up. They render correctly when I build locally (using the latest jb). I serve it with GitHub Pages & Actions.
For example, here's how [this page](https://vote.maxghenis.com/nov2020/san-francisco.html#no-on-prop-f) looks locally and on the web:
## Locally
## On the web
I've tried a few tweaks on the [workflow file](https://github.com/MaxGhenis/vote/blob/main/.github/workflows/deploy_jupyterbook.yml), which is essentially what's in the docs.
### Link to your repository or website
https://github.com/MaxGhenis/vote
### Steps to reproduce
Build a Jupyter Book with the latest `sphinx-thebe` (which loads the latest `thebe/`)
1. Trigger a jb build with GitHub Action
2. Visit the site
### The version of Python you're using
3.7.7
### Your operating system
Linux
### Versions of your packages
Jupyter Book : 0.11.2
External ToC : 0.2.2
MyST-Parser : 0.13.7
MyST-NB : 0.12.3
Sphinx Book Theme : 0.1.0
Jupyter-Cache : 0.4.2
NbClient : 0.5.3
### Additional context
When I load/reload the page on the web, it shows the page with the correct paragraph break and bold font for a split second before removing them.
# Updates
- Tracking this in https://github.com/executablebooks/thebe/issues/464 | closed | 2021-08-22T04:54:32Z | 2021-08-25T00:17:13Z | https://github.com/jupyter-book/jupyter-book/issues/1433 | [
"bug",
"state: needs info"
] | MaxGhenis | 9 |
plotly/dash | flask | 2,601 | [Feature Request] Improve build process by decoupling from MDN | We should remove our dependency on MDN for building Dash locally, since updates to MDN can prevent those builds from succeeding.
Instead, we ought to run the checks against MDN on a CI pipeline where local development won't be impeded.
| closed | 2023-07-13T21:36:44Z | 2023-07-28T12:14:22Z | https://github.com/plotly/dash/issues/2601 | [] | KoolADE85 | 0 |
huggingface/datasets | machine-learning | 6,736 | Mosaic Streaming (MDS) Support | ### Feature request
I'm a huge fan of the current HF Datasets `webdataset` integration (especially the built-in streaming support). However, I'd love to upload some robotics and multimodal datasets I've processed for use with [Mosaic Streaming](https://docs.mosaicml.com/projects/streaming/en/stable/), specifically their [MDS Format](https://docs.mosaicml.com/projects/streaming/en/stable/fundamentals/dataset_format.html#mds).
Because the shard files have similar semantics to WebDataset, I'm hoping that adding such support won't be too much trouble?
### Motivation
One of the downsides with WebDataset is a lack of out-of-the-box determinism (especially for large-scale training and reproducibility), easy job resumption, and the ability to quickly debug / visualize individual examples.
Mosaic Streaming provides a [great interface for this out of the box](https://docs.mosaicml.com/projects/streaming/en/stable/#key-features), so I'd love to see it supported in HF Datasets.
### Your contribution
Happy to help test things / provide example data. Can potentially submit a PR if maintainers could point me to the necessary WebDataset logic / steps for adding a new streaming format! | open | 2024-03-16T18:42:04Z | 2024-03-18T15:13:34Z | https://github.com/huggingface/datasets/issues/6736 | [
"enhancement"
] | siddk | 1 |
ansible/ansible | python | 84,430 | agent roles failed when multiple addresses set to zabbix_agent_listenip | ### Summary
Role fails when zabbix_agent_listenip is set to multiple IPs seperated by comma. According to the Zabbix Documentation it is possible to set multiple IPs. And since the role defaults to 0.0.0.0 when zabbix_agent_listenip is not defined it comes with alot of side effects. So either the zabbix_agent_listenip has to support multiple IPs or the default behavior should be changed when not defined ListenIP is not set in the agent config as that then would result in the expect bahavior during Autoregistration of a host that the Host IP is used and not the ListenIP as Agent IP on the server.
### Issue Type
Bug Report
### Component Name
Zabbix-agent role
### Ansible Version
```console
$ ansible --version
core 2.15.0
```
### Configuration
```console
# if using a version older than ansible-core 2.12 you should omit the '-t all'
$ ansible-config dump --only-changed -t all
```
### OS / Environment
RHEL8
### Steps to Reproduce
<!--- Paste example playbooks or commands between quotes below -->
```yaml (paste below)
- set zabbix_agent_listenip to 192.168.1.101,127.0.0.1
```
### Expected Results
The ListenIP value in config is set to the value given. As per Zabbix Documentation the ListenIP value can have multiple IPs seperated by comma. Role shouldn't fail when the variable is set to multiple ListenIPs
### Actual Results
```console
The Role fails and says zabbix_agen_listenip isn't set
```
### Code of Conduct
- [X] I agree to follow the Ansible Code of Conduct | closed | 2024-12-05T10:22:17Z | 2024-12-05T14:37:02Z | https://github.com/ansible/ansible/issues/84430 | [
"bug",
"migrated_to_collection_repo"
] | bufanda | 2 |
plotly/dash | plotly | 2,708 | Make Pages `dcc.Location` id public | The `id` of the `dcc.Location` component used in Pages is currently marked as non-public. However it's fairly common to see this id used in apps. Are there any issues with making the id public? What is the best practice for using `dcc.Location` in a Pages app?
Here are some options:
1. Add your own `dcc.Location` to the app
2. Use `"_pages_location"` in a callback
3. Import the id variable name:
```
from dash.dash import _LOCATION_ID
```
4. Create a new variable in Dash so it can be used like this:
```
from dash import <new variable name>
```
May need to update this [example the docs](https://github.com/plotly/ddk-dash-docs/pull/2055#discussion_r1389793771) after this is resolved
| open | 2023-12-03T18:03:12Z | 2024-11-20T16:34:18Z | https://github.com/plotly/dash/issues/2708 | [
"feature",
"P2"
] | AnnMarieW | 2 |
supabase/supabase-py | fastapi | 225 | Setting timeout for postgrest-py client | postgrest-py accepts a timeout parameter from [v0.8.0](https://github.com/supabase-community/postgrest-py/commit/1ea965a6cb32dacb5f41cd1198f8a970a24731b6)
We don't have an option to set this from supabase-py. So, if the queries run longer than 5s it throws an exception. | closed | 2022-06-21T05:37:16Z | 2022-10-07T07:41:04Z | https://github.com/supabase/supabase-py/issues/225 | [
"enhancement"
] | tbrains | 0 |
junyanz/pytorch-CycleGAN-and-pix2pix | deep-learning | 1,503 | L1 loss needs to be pushed to CUDA? | https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/e2c7618a2f2bf4ee012f43f96d1f62fd3c3bec89/models/pix2pix_model.py#L66 | closed | 2022-11-08T06:21:05Z | 2022-11-08T07:06:54Z | https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/1503 | [] | lkqnaruto | 1 |
unionai-oss/pandera | pandas | 1,755 | Allowing column with type list to be optional | #### Question about pandera
Hi Pandera folks! I am using a DataFrameModel and am trying to mark a column of type list[float] as optional, following the documentation [here](https://pandera.readthedocs.io/en/stable/dataframe_models.html#required-columns).
```python
class FakeCreatureSchema(pa.DataFrameModel):
class Config:
strict = True
idx: Index[str]
num_legs: int | None
leg_lengths: list[float] | None
```
I get the following error: `pandera.errors.SchemaInitError: Invalid annotation 'leg_lengths: list[float] | None'`.
Do you have an example of how to do this? Thank you!
| closed | 2024-07-18T21:06:53Z | 2024-08-08T15:01:38Z | https://github.com/unionai-oss/pandera/issues/1755 | [
"question"
] | kamillatekiela | 4 |
kochlisGit/ProphitBet-Soccer-Bets-Predictor | seaborn | 26 | Parsing HTML Error | Hello,
Wish you a happy day!
When i am trying to import fixtures i am recieving an issue with html parsing.Any idea of how to fix this issue? I have tried it both on windows and Linux. | open | 2023-06-04T11:56:58Z | 2023-06-04T15:17:30Z | https://github.com/kochlisGit/ProphitBet-Soccer-Bets-Predictor/issues/26 | [] | noriedum | 2 |
huggingface/text-generation-inference | nlp | 2,246 | can't start server with small --max-total-tokens. But works fine with big stting | when I try to run CUDA_VISIBLE_DEVICES=0,1,2,3 text-generation-launcher --port 6634 --model-id /models/ --max-concurrent-requests 128 --max-input-length 64--max-total-tokens 128 --max-batch-prefill-tokens 128 --cuda-memory-fraction 0.95. It says
torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 MiB. GPU has a total capacity of 44.53 GiB of which 1.94 MiB is free. Process 123210 has 44.52 GiB memory in use. Of the allocated memory 40.92 GiB is allocated by PyTorch, and 754.08 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management
But for sitting big max tokens. CUDA_VISIBLE_DEVICES=0,1,2,3 text-generation-launcher --port 6634 --model-id /models/ --max-concurrent-requests 128 --max-input-length 1024 --max-total-tokens 2048 --max-batch-prefill-tokens 2048 --cuda-memory-fraction 0.95. it works fine.
i don't get it why small max tokens cause CUDA out of memory but large max tokens works fine. Can someone answer my questions?
| closed | 2024-07-18T07:03:31Z | 2024-08-24T01:52:30Z | https://github.com/huggingface/text-generation-inference/issues/2246 | [
"question",
"Stale"
] | rooooc | 5 |
aiortc/aiortc | asyncio | 475 | Query: Is any implementation availble for receiving a WebRTC Stream published from WOWZA | We have to implement decoding of webrtc stream using python, We are kind of stuck in implementation.
Could any one provide some help on this so we can proceed?
Thanks in advance.
| closed | 2021-02-19T13:24:12Z | 2021-03-07T14:46:00Z | https://github.com/aiortc/aiortc/issues/475 | [
"invalid"
] | amitvikram1985 | 1 |
lanpa/tensorboardX | numpy | 213 | `add_histogram` fails when passing a python list | Hi, I noticed a problem when I passed a python list as the argument for `add_histogram` and the error output is less intuitive:
> AttributeError: 'NoneType' object has no attribute 'astype'
When passing a python list of floats instead of numpy/torch arrays, it will fail because `make_np` returns `None` at the end when all the conditions are not satisfied:
https://github.com/lanpa/tensorboardX/blob/9d2cbeb26778cc9784a6a7028e75b7fd0950ce87/tensorboardX/x2num.py#L10-L24
Will python lists of floats be supported? If not, would it be possible to add a type check to make debugging potentially easier?
Thanks. | closed | 2018-08-15T16:25:42Z | 2019-01-22T17:25:52Z | https://github.com/lanpa/tensorboardX/issues/213 | [] | ydawei | 0 |
jupyterhub/jupyterhub-deploy-docker | jupyter | 78 | `Permission denied` when saving notebooks after volumes transferred to new machine | I've been using this implementation on an AWS EC2 instance and everything works fine. However, we just had to move our services to a different AWS region and I had to transfer jupyterhub to a new instance. I followed the same steps as when I first installed it as per the instructions, yet in order to retain the users's files I copied the docker volumes from `/var/lib/docker/volumes` to the new machine and I made sure that the file permissions are the same as in the source instance.
Everything seems to be fine. I have access to all the notebooks in the volumes and I can run them without problems. But any time I try to save the work (or start a new notebook), I get a `Permission denied` error message. Since the permissions in the volumes have not changed, I'm at a loss about what the problem is and how I can fix it. Any help you can provide would be most appreciated. | closed | 2018-11-25T22:40:51Z | 2018-11-25T23:51:08Z | https://github.com/jupyterhub/jupyterhub-deploy-docker/issues/78 | [] | oriolmirosa | 1 |
microsoft/nni | data-science | 5,673 | 如何获得QAT后的模型参数？ | **Describe the issue**:
在pytroch中，可以通过conv2d.weight的方式获得卷积层的权重值
请问，通过nni的QAT之后，应该如何获得再训练后的权重值？
```python
quantizer = QAT_Quantizer(model, config_list, optimizer, dummy_input)
quantizer.compress()
for epoch in range(1, epochs + 1):
train(model, device, train_loader, optimizer, epoch)
scheduler.step()
```
通过`model.conv1.module.weight`，可以获得FP32的权重。
1. 通过该方法获得的权重是否即为当前网络中用来计算`output=model(input)`的权重了？
2. 如何获得量化为int8的权重值，是否只能通过`model.conv1.module.weight_scale`以及`model.conv1.module.zero_point`自己计算？
3. 为什么在训练过程中，`model.conv1.module.old_weight`也会不停迭代？我以为这个属性是用来存放量化之前的权重的
**Environment**:
- NNI version: 2.9
- Training service (local|remote|pai|aml|etc): local
- Client OS: windows
- Server OS (for remote mode only):
- Python version: 3.7.5
- PyTorch/TensorFlow version: 1.8.1+cu101
- Is conda/virtualenv/venv used?:
- Is running in Docker?:
| open | 2023-08-31T08:09:08Z | 2023-08-31T08:09:08Z | https://github.com/microsoft/nni/issues/5673 | [] | kucher-web | 0 |
AntonOsika/gpt-engineer | python | 1,048 | Implement Vector Store for Similarity-Based Code Generation (RAG on Codebase) | ## Feature description
I've been exploring your repository and I'm fascinated by the capabilities of the agent as a software engineer, particularly in executing commands and calling functions. However, I believe there's a significant opportunity to enhance its capabilities by integrating a vector store that contains embedded codebases. This would enable the agent to perform similarity searches on code snippets, allowing it to generate code similar to existing codebases.
## Motivation/Application
Why is this feature useful?
The agent will be able to produce code that closely resembles existing codebases, improving the quality and relevance of generated code. By leveraging similarity-based code generation, the agent can adapt to different coding styles and conventions present in the embedded codebases. | closed | 2024-03-06T15:50:04Z | 2024-10-10T21:48:58Z | https://github.com/AntonOsika/gpt-engineer/issues/1048 | [
"enhancement",
"triage"
] | moamen270 | 2 |
sqlalchemy/sqlalchemy | sqlalchemy | 11,875 | sqlalchemy.ext.asyncio can't use transcation, when connect mysql | ### Describe the bug
In an asynchronous environment, many methods have been tried but the transaction rollback does not work, but in the synchronous method it works.
### Optional link from https://docs.sqlalchemy.org which documents the behavior that is expected
_No response_
### SQLAlchemy Version in Use
2.0.32
### DBAPI (i.e. the database driver)
aiomysql 0.2.0
### Database Vendor and Major Version
mysql 5.7.36
### Python Version
3.8
### Operating system
linux, osx
### To Reproduce
```python
self.db is :AsyncSession
async with self.db.begin(): # 开始一个新的事务
try:
if obj_in.parent_id != 0:
await self.get(id=obj_in.parent_id)
new_obj = await self.create(obj_in=obj_in)
await self.db.commit()
raise Exception("xxxxx")
except Exception as e:
print(e)
await self.db.rollback()
or :
try:
if obj_in.parent_id != 0:
await self.get(id=obj_in.parent_id)
new_obj = await self.create(obj_in=obj_in)
await self.db.commit()
raise Exception("xxxxx")
except Exception as e:
print(e)
await self.db.rollback()
```
### Error
```
# Copy the complete stack trace and error message here, including SQL log output if applicable.
```
no error, but the db table have a record, can't rollback()
### Additional context
_No response_ | closed | 2024-09-15T04:03:58Z | 2024-09-15T06:51:38Z | https://github.com/sqlalchemy/sqlalchemy/issues/11875 | [] | lishu2006ll | 0 |
tiangolo/uwsgi-nginx-flask-docker | flask | 30 | net::ERR_INCOMPLETE_CHUNKED_ENCODING | I have a jquery front end that is trying to send images to an app inside the uwsgi-nginx-flask-docker container.
The javascript console is saying this:
jquery.min.js:4 POST http://localhost:8000/predict/ net::ERR_INCOMPLETE_CHUNKED_ENCODING
If I use the same app with only gunicorn it works ok, so I'm suspecting this is related to nginix configuration inside the container?
more Dockerfile
FROM tiangolo/uwsgi-nginx-flask:python3.6
COPY ./app /app
RUN pip install -r requirements.txt
On the client side I'm using this plugin to submit the form using ajax http://malsup.com/jquery/form/ | closed | 2017-11-06T12:48:38Z | 2018-01-15T10:31:08Z | https://github.com/tiangolo/uwsgi-nginx-flask-docker/issues/30 | [] | mbernico | 2 |
strawberry-graphql/strawberry | asyncio | 3,785 | `@oneOf` inputs do not support nullability | When an input class is marked as [`@oneOf`](https://www.apollographql.com/blog/more-expressive-schemas-with-oneof) and has a nullable field, it is not possible to actually provide a null value in the query, and the query completely fails.
## Describe the Bug
This bug relates to the usage of input classes that implement the [`@oneOf`](https://www.apollographql.com/blog/more-expressive-schemas-with-oneof) directive using [`one_of=True`](https://strawberry.rocks/docs/types/input-types#one-of-input-types):
```python
@strawberry.input(one_of=True)
class FilterInput:
name: str | None
```
As shown, it should be possible to pass a `null` value in the query for the `name` field. However, when executing this query:
```graphql
query MyQuery {
test(filterInput: {name: null})
}
```
You get this response:
```json
{
"data": null,
"errors": [
{
"message": "Field 'FilterInput.name' must be non-null.",
"locations": [
{
"line": 2,
"column": 21
}
]
}
]
}
```
I would assume that the `one_of` parameter directs Strawberry to evaluate if any of the given fields are `None` and fail if so, even though this is a legitimate input value.
---
I have provided this minimum working example, which runs using FastAPI. I have also included Poetry's `pyproject.toml` file so this issue can be replicated with ease:
```toml
[tool.poetry]
name = "strawberry-testing"
version = "0.1.0"
description = ""
authors = []
[tool.poetry.dependencies]
python = ">=3.11,<3.12"
strawberry-graphql = "0.260.2"
fastapi = "0.115.8"
uvicorn = "0.34.0"
[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"
```
```python
from fastapi import FastAPI
from strawberry.fastapi import GraphQLRouter
import strawberry
@strawberry.input(one_of=True)
class FilterInput:
name: str | None
@strawberry.input
class Query:
@strawberry.field
async def test(self, filter_input: FilterInput) -> str | None:
return filter_input.name
app = FastAPI()
schema = strawberry.Schema(query=Query)
graphql_app = GraphQLRouter(schema=schema)
app.include_router(graphql_app, prefix="/graphql")
```
| open | 2025-02-17T16:29:05Z | 2025-02-17T16:29:05Z | https://github.com/strawberry-graphql/strawberry/issues/3785 | [
"bug"
] | DevJake | 0 |
coqui-ai/TTS | deep-learning | 2,530 | ONNX and Tensor RT export for Tacotron 2 DDC [Feature request] | <!-- Welcome to the 🐸TTS project!
We are excited to see your interest, and appreciate your support! --->
🚀 In your roadmap from 2021 you mentioned" ONNX and TorchScript exports". I believe ONNX and Tensor RT are crucially important for the performance of many ML projects. I tried to convert a Tacotron DDC model to ONNX, but it did not work. Below you see the error I received. Is there a wrong configuration or is the DDC model now meant to be exported to ONNX. Is there a way to achieve it? I would appreciate an easy solution, export possibility and ofc, short-term I appreciate every helpful input for solving the conversion problem.
<!--A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] -->
Here you can find the code I used to convert a Tacotron DDC model to ONNX:
```
config_path = './config.json'
config = load_config(config_path)
ckpt = './model_file.pth'
model = Tacotron2.init_from_config(config)
model.load_checkpoint(config, ckpt, eval=True)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
sequences = torch.randint(low=1, high=131, size=(1, 50),
dtype=torch.long)
sequence_lengths = torch.IntTensor([sequences.size(0)]).long()
dummy_input=(sequences, sequence_lengths)
input_names = ["sequences", "sequence_lengths"]
output_names = ["output"]
#dynamic_axes={"sequences": {1: "text_seq"}, "output": {2: "mel_seq"}}
#dynamic_axes={"sequences": {0: "batch", 2: "text_seq"}, "output": {0: "batch", 2: "mel_seq", 3: "frame_channels"}}
#dynamic_axes={"sequences": {0: "batch", 1: "text"}, "output": {0: "model_outputs", 1: "decoder_outputs"}}
torch.onnx.export(
model,
dummy_input,
"test.onnx",
input_names=input_names,
output_names=output_names,
#dynamic_axes = dynamic_axes,
export_params=True,
opset_version=13,
verbose=True,
)
```
And this is the error I got. I tried to debug it and found out that in the teacher forcing forward step of the decoder it gets "None" values for the memories and that's why it throws the following error:
```
File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/torch/nn/modules/module.py:1113, in Module._call_impl(self, *input, **kwargs)
1109 # If we don't have any hooks, we want to skip the rest of the logic in
1110 # this function, and just call forward.
1111 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1112 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1113 return forward_call(*input, **kwargs)
1114 # Do not call functions when jit is used
1115 full_backward_hooks, non_full_backward_hooks = [], []
File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/torch/nn/modules/module.py:1100, in Module._slow_forward(self, *input, **kwargs)
1098 recording_scopes = False
1099 try:
-> 1100 result = self.forward(*input, **kwargs)
1101 print("FORWARD RESULT", result)
1102 finally:
File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/TTS/tts/layers/tacotron/tacotron2.py:314, in Decoder.forward(self, inputs, memories, mask)
312 memory = self.get_go_frame(inputs).unsqueeze(0)
313 print("IN forward memory", memory)
--> 314 memories = self._reshape_memory(memories)
315 print("after reshape", memories)
316 memories = torch.cat((memory, memories), dim=0)
File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/TTS/tts/layers/tacotron/tacotron2.py:239, in Decoder._reshape_memory(self, memory)
237 print("MEMORY",memory)
238 # Grouping multiple frames if necessary
--> 239 if memory.size(-1) == self.frame_channels:
240 memory = memory.view(memory.shape[0], memory.size(1) // self.r, -1)
241 # Time first (T_decoder, B, frame_channels)
AttributeError: 'NoneType' object has no attribute 'size'
```
<!-- A clear and concise description of what you want to happen. -->
I want an exported ONNX version of the tacotron DDC model.
<!-- A clear and concise description of any alternative solutions or features you've considered. -->
**Additional context**
<!-- Add any other context or screenshots about the feature request here. -->
| closed | 2023-04-17T07:15:19Z | 2023-05-25T00:33:25Z | https://github.com/coqui-ai/TTS/issues/2530 | [
"help wanted",
"wontfix",
"feature request"
] | huks0 | 2 |
piccolo-orm/piccolo | fastapi | 711 | Make the migration files valid python modules | Currently, the migration filenames are something like this:
```
2022-12-03T21-34-11-480928.py
```
You can import this file using `importlib`, but not using a Python `import` statement.
```python
# Error! Invalid syntax
from 2022-12-03T21-34-11-480928 import something
```
In order for it to be valid, it must begin with a letter, and we have to use underscores instead of `-`.
The migration file names have no special significance to Piccolo, so we can safely change the format without breaking anything.
The reason for this change is there's an edge case where we need to import something from one migration file into another migration file.
The proposed new migration file name, for an app called `blog`, is:
```
blog_2022_12_03T21_34_11_480928.py
```
| closed | 2022-12-09T19:23:43Z | 2022-12-09T20:22:09Z | https://github.com/piccolo-orm/piccolo/issues/711 | [
"enhancement"
] | dantownsend | 0 |
paulpierre/RasaGPT | fastapi | 70 | Using 'make install', occur error: sqlalchemy.exc.ArgumentError | FATAL: sqlalchemy.exc.ArgumentError: Textual SQL expression 'CREATE EXTENSION IF NOT E...' should be explicitly declared as text('CREATE EXTENSION IF NOT E...')
Hi,
I have a problem when install RasaGPT == make install.
The error is :
```
wait-for-it.sh: waiting 60 seconds for db:5432 to be available (tcp)
wait-for-it.sh: db:5432 is available after 0 seconds
INFO:config:...Enabling pgvector and creating database tables
...Enabling pgvector and creating database tables
Traceback (most recent call last):
File "/app/api/models.py", line 660, in <module>
create_db()
File "/app/api/models.py", line 586, in create_db
enable_vector()
File "/app/api/models.py", line 619, in enable_vector
session.execute(query)
File "/usr/local/lib/python3.9/site-packages/sqlmodel/orm/session.py", line 129, in execute
return super().execute(
File "/usr/local/lib/python3.9/site-packages/sqlalchemy/orm/session.py", line 2308, in execute
return self._execute_internal(
File "/usr/local/lib/python3.9/site-packages/sqlalchemy/orm/session.py", line 2088, in _execute_internal
statement = coercions.expect(roles.StatementRole, statement)
File "/usr/local/lib/python3.9/site-packages/sqlalchemy/sql/coercions.py", line 413, in expect
resolved = impl._literal_coercion(
File "/usr/local/lib/python3.9/site-packages/sqlalchemy/sql/coercions.py", line 638, in _literal_coercion
return self._text_coercion(element, argname, **kw)
File "/usr/local/lib/python3.9/site-packages/sqlalchemy/sql/coercions.py", line 631, in _text_coercion
return _no_text_coercion(element, argname)
File "/usr/local/lib/python3.9/site-packages/sqlalchemy/sql/coercions.py", line 601, in _no_text_coercion
raise exc_cls(
sqlalchemy.exc.ArgumentError: Textual SQL expression 'CREATE EXTENSION IF NOT E...' should be explicitly declared as text('CREATE EXTENSION IF NOT E...')
make[1]: *** [models] Error 1
make: *** [install] Error 2
```
| open | 2024-01-26T09:22:22Z | 2024-01-31T07:52:36Z | https://github.com/paulpierre/RasaGPT/issues/70 | [] | whitedogedev | 3 |
sqlalchemy/sqlalchemy | sqlalchemy | 10,237 | Request to make NullPool the default - fixes massive footgun with Render.com and DigitalOcean | ### Describe the bug
Hi there, I found a nasty bug that is affecting literally ALL of our projects for the org that I work for. I did it, the contractor that work for us did it. So I wanted to bring this up for discussion.
# SQL Alchemy uses a connection Pool by default
So what's the big deal? Well, both DigitalOcean and Render.com kill these connections at the os level after a certain amount of time has passed. SQLAlchemy then *blindly* attempts to use these dead connections and bombs out the server request with an error 500. My expectation as a developer would be that SQLAlchemy could auto-recover from this but it doesn't, and this is surprising.
# This bug is nasty and sporadic and infrequently happens during development
During an app redeploy, all connections are cleared out and the pool is torn down and then reinstantiated when the app is brought back online. As a result, these dead connections are largely hidden from the developer and go away as soon as they start pushing code, for example adding logging to investigate. Then after a length of time the app starts to degrade again. I caught this issue because I added logging in the proper place and was able to see the server logs to see the error:
```
(psycopg2.OperationalError) SSL connection has been closed unexpectedly
```
# New apps don't need a connection pool until they start to scale
And when apps need to scale, developers are willing to put in the time and energy to find these optimizations. In my opinion, a connection pool should be thought of in 2023 as **dangerous**, due to the number of Docker hosts coming online that auto-kill these connections after not too much time. Maybe a decade ago everyone had a default DB config with long running connections that could remain open, but this certainly isn't the case now.
# Does adding pre_ping=True as the default solve the issue?
Mostly, but I'm still getting 500 errors. I have to use very aggressive settings to make them go away. This is what I found works:
```python
engine = create_engine(
DB_URL,
pool_pre_ping=True,
pool_recycle=60,
pool_size=10,
max_overflow=2,
pool_use_lifo=True,
)
```
However, the app is early stage so I just opted for NullPool instead because I don't need the extra 50 ms or so that a connection pool gives. And when I re-introduce a connection pool in the future - I'll thoroughly test it.
# I can only imagine how many people this bug is affecting
Given that both I and a contractor made this mistake in *every* backend microservice (we have about 6), I'm going to go ahead and speculate that this is happening to **most** of the people using sqlalchemy for Render.com/DigitalOcean. That's a *lot* of broken code out there, right at the beginning of an app when the developer is in the learning curve. This is nasty footgun.
I urge the developers to change the defaults somehow so that sqlalchemy works out of the box. It's been pretty amazing for us to use this tool and we applaud all the hard work that has been put into this middlewear. But wow - this one was a doozy.
### Optional link from https://docs.sqlalchemy.org which documents the behavior that is expected
_No response_
### SQLAlchemy Version in Use
2.0.17
### DBAPI (i.e. the database driver)
psycopg2 (but I think but can't confirm it's happening in mysql)
### Database Vendor and Major Version
Latest PostgreSQL from Render.com
### Python Version
python 3.10, 3.11 (probably exists in all versions)
### Operating system
Ubuntu, Alpine
### To Reproduce
```python
Create a PostgreSQL db on Render.com.
Make a fastapi python project with a simple table.
Have the front end python app do a request on the DB. Doesn't matter what it is, it can be anything.
Launch the CRUD app.
Let it run for a while.
Randomly hit it with a request. You'll see a 500 error that occurs. If you log it you'll see:
(psycopg2.OperationalError) SSL connection has been closed unexpectedly
```
The exception will be caught in a default handler and the request will return HTTP error code 500.
Now change the pool class to NullPool and re-deploy.
The error will now go away.
```
### Error
```
# Copy the complete stack trace and error message here, including SQL log output if applicable.
```
### Additional context
_No response_ | closed | 2023-08-15T18:53:13Z | 2023-08-15T19:16:25Z | https://github.com/sqlalchemy/sqlalchemy/issues/10237 | [] | zackees | 0 |
tox-dev/tox | automation | 3,301 | `tox devenv`: Integration with running virtualenvs | ## What's the problem this feature will solve?
This is really just out of laziness; I've got the `pyenv-virtualenv` plugin configured and am running a PyEnv-managed virtual environment, and when I run `tox devenv`, it creates a new virtualenv in my current working directory, which I find a bit unpractical.
## Describe the solution you'd like
Could `tox devenv` detect any running virtual environment and create its developer environment within the same? Probably via a flag?
## Alternative Solutions
Of course I can always pass it the path as a positional argument.
## Additional context
| closed | 2024-06-26T08:37:59Z | 2024-06-26T13:43:56Z | https://github.com/tox-dev/tox/issues/3301 | [
"enhancement"
] | chrysle | 4 |
dynaconf/dynaconf | fastapi | 913 | Pytest is considering key DYNACONF_SQLALCHEMY_DATABASE_URI on my .env file | I noticed when I run pytest on my flask application, the string connection DYNACONF_SQLALCHEMY_DATABASE_URI on my env file is considered on test. So I want to use pytest and get "sqlite:///test.sqlite3". I always have to comment DYNACONF_SQLALCHEMY_DATABASE_URI on my env file to have that behavior. | open | 2023-04-06T14:16:01Z | 2023-08-21T19:47:49Z | https://github.com/dynaconf/dynaconf/issues/913 | [
"question"
] | Leonardoperrella | 6 |
pyg-team/pytorch_geometric | deep-learning | 9,831 | Implementing Bag of Tricks for Node Classification with Graph Neural Networks | ### 🚀 The feature, motivation and pitch
Hello PyG team,
@chriskynguyen, @liuvince, and I are CS224W students and have been working on implementing tricks from https://arxiv.org/abs/2103.13355.
We hope PyG users will find the following helpful to “level the playing field” for future research and squeeze extra performance out for production models. We’ll be linking separate pull requests for each of the tricks they discuss that are not already implemented:
* “Label Usage”
* GAT with Symmetric Normalized Adjacency Matrix
* “Non-interactive” GAT
* $\text{Log }\epsilon$ loss
We would much appreciate your consideration and review of any of these.
Additional descriptions and pull requests to come shortly.
Thank you!
### Alternatives
To be discussed in individual PRs.
### Additional context
_No response_ | open | 2024-12-09T04:31:30Z | 2024-12-13T00:43:50Z | https://github.com/pyg-team/pytorch_geometric/issues/9831 | [
"feature"
] | mattjhayes3 | 0 |
dynaconf/dynaconf | fastapi | 1,209 | fix: _recursive evaluation must not create new objects | Instead of creating a new instance of a list, that method must mutate the existing object to keep the same reference. | open | 2024-12-20T18:28:04Z | 2025-01-15T17:30:30Z | https://github.com/dynaconf/dynaconf/issues/1209 | [
"bug",
"in progress",
"aap"
] | rochacbruno | 1 |
shaikhsajid1111/twitter-scraper-selenium | web-scraping | 27 | Feature request - conversation ID | Hi thanks for putting this together. Been looking around for active scrapping projects since twint has been archived and was happy to find this!
I want to request getting the `conversaion_id`. I didn't that's not in this project's docs. More info here: https://developer.twitter.com/en/docs/twitter-api/conversation-id | open | 2022-07-17T18:28:19Z | 2022-08-06T12:43:35Z | https://github.com/shaikhsajid1111/twitter-scraper-selenium/issues/27 | [] | batmanscode | 1 |
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