aicps / hw2vec Goto Github PK

we have to create tests/ folder and add test cases back to guarantee code quality.

num_layers hyperparameter is not used in GCN model build.

try to implement a timing profiling framework for the unit tests. (use the functions implemented in utilities.py)

Hi,

My environment is an ubuntu virtual machine, and I have installed the pytorch cpu version.
I tried to run use_case_1.py, and changed line 105 of models.py.
torch.load(model_weight_path, map_location='cpu')
However, I get the error in the figure below:

Then I changed the key in the dictionary to make the names match, and ran it with the following error:

Then I used the reshape function to convert (37, 200) to (200,37).
The program can output the data, but the output data is different every time I run it.

Is there a step I'm missing?

Looking forward to your reply.

Benoit

Normalization should be on by default, and this can help reduce some code in DataProcessor

Utility functions in hw2graph.py etc should be moved to utilities.py for cleaner, easier imports

PreprocessVerilog should be merged with HW2GRAPH class

python -m pip install hw2vec
ERROR: Could not find a version that satisfies the requirement hw2vec (from versions: none)

I also checked this https://libraries.io/pypi/hw2vec/0.1.1 and it mentions that Repository is not available.

followed the sequence of commands given in readme.md
and after running the command python -m pip install hw2vec got this error
"ERROR: Could not find a version that satisfies the requirement hw2vec-contrib-python (from versions: none) ERROR: No matching distribution found for hw2vec-contrib-python"

align use_case_2 with the paper.

def train_epoch_tj(self, data):
    output, _ = self.model.embed_graph(data.x, data.edge_index, data.batch)
    output = self.model.mlp(output)
    output = F.log_softmax(output, dim=1)

    loss_train = self.loss_func(output, data.label)
    return loss_train

def __init__(self, cfg, class_weights=None):
    super().__init__(cfg)
    self.task = "TJ"
    if class_weights.shape[0] < 2:
        self.loss_func = nn.CrossEntropyLoss()
    else:    
        self.loss_func = nn.CrossEntropyLoss(weight=class_weights.float().to(cfg.device))

That means using double logsoftmax, which might introduce some negative effects.

Use the below url to install pygraphviz on Mac (tested and working).

https://ports.macports.org/port/py38-pygraphviz/

`(hw2vec) rahulvishwakarma@Rahuls-Air examples % python
Python 3.6.15 | packaged by conda-forge | (default, Dec 3 2021, 18:49:43)
[GCC Clang 11.1.0] on darwin
Type "help", "copyright", "credits" or "license" for more information.

import pygraphviz as pgv
G = pgv.AGraph()
G.add_node("a")
G.add_edge("b", "c")
print(G)
strict graph "" {
a;
b -- c;
}

`

Parameters not related to model building or training should be moved to config.py

Implement a node-level task trainer in trainers.py for tasks at the node level.

align use_case_3 with the paper.

Hi, I cloned the repo and then run python3 setup.py install and it throws these errors.

Can someone help me out?

align use_case_1 with paper.

Use_Case_1.py" in GPU mode throws the same error as when ran with only CPU.

!python use_case_1.py

Traceback (most recent call last):
  File "/content/hw2vec/examples/use_case_1.py", line 33, in <module>
    graph_emb = use_case_1(cfg, hw_design_dir_path,\
  File "/content/hw2vec/examples/use_case_1.py", line 18, in use_case_1
    model.load_model(pretrained_model_cfg_path, pretrained_model_weight_path)
  File "/content/hw2vec/hw2vec/graph2vec/models.py", line 105, in load_model
    self.load_state_dict(torch.load(model_weight_path, map_location=torch.device('cpu')))
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 2041, in load_state_dict
    raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for GRAPH2VEC:
	Missing key(s) in state_dict: "layers.0.graph_conv.lin.weight", "layers.1.graph_conv.lin.weight", "pool1.graph_pool.gnn.lin_rel.weight", "pool1.graph_pool.gnn.lin_rel.bias", "pool1.graph_pool.gnn.lin_root.weight". 
	Unexpected key(s) in state_dict: "layers.0.graph_conv.weight", "layers.1.graph_conv.weight", "pool1.graph_pool.gnn.lin_l.weight", "pool1.graph_pool.gnn.lin_l.bias", "pool1.graph_pool.gnn.lin_r.weight". 

Can you please help with this issue?

use_case_2.py and use_case_3.py works fine as documented on the GitHub page.