wehub-resource-sync
7254f7b4d1
Build / Build (macos-latest) (push) Has been cancelled
Build / Build (ubuntu-latest) (push) Has been cancelled
Build / Build (windows-latest) (push) Has been cancelled
Build / Analyze (javascript) (push) Has been cancelled
Build / Analyze (python) (push) Has been cancelled
91 lines
3.1 KiB
Python
Executable File
91 lines
3.1 KiB
Python
Executable File
#!/usr/bin/env python
|
|
|
|
""" Expermiental Python Server backend test """
|
|
|
|
import logging
|
|
import os
|
|
import sys
|
|
|
|
root_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
|
|
sys.path.append(root_dir)
|
|
sys.pycache_prefix = os.path.join(root_dir, "dist", "pycache", "backend")
|
|
netron = __import__("source")
|
|
|
|
third_party_dir = os.path.join(root_dir, "third_party")
|
|
test_data_dir = os.path.join(third_party_dir, "test")
|
|
|
|
logger = logging.getLogger(__name__)
|
|
logging.basicConfig(level=logging.INFO, format="%(message)s")
|
|
|
|
def _test_onnx():
|
|
file = os.path.join(test_data_dir, "onnx", "candy.onnx")
|
|
onnx = __import__("onnx")
|
|
model = onnx.load(file)
|
|
netron.serve(None, model)
|
|
|
|
def _test_onnx_iterate():
|
|
logging.getLogger(netron.__name__).setLevel(logging.WARNING)
|
|
folder = os.path.join(test_data_dir, "onnx")
|
|
for item in os.listdir(folder):
|
|
file = os.path.join(folder, item)
|
|
skip = (
|
|
"super_resolution.onnx",
|
|
"arcface-resnet100.onnx",
|
|
"aten_sum_dim_onnx_inlined.onnx",
|
|
"phi3-mini-128k-instruct-cuda-fp16.onnx",
|
|
"if_k1.onnx"
|
|
)
|
|
if file.endswith(".onnx") and item not in skip:
|
|
logger.info(item)
|
|
onnx = __import__("onnx")
|
|
model = onnx.load(file)
|
|
address = netron.serve(file, model)
|
|
netron.stop(address)
|
|
|
|
def _test_torchscript(file):
|
|
torch = __import__("torch")
|
|
path = os.path.join(test_data_dir, "pytorch", file)
|
|
model = torch.load(path, weights_only=False)
|
|
torch._C._jit_pass_inline(model.graph)
|
|
netron.serve(file, model)
|
|
|
|
def _test_torchscript_transformer():
|
|
torch = __import__("torch")
|
|
model = torch.nn.Transformer(nhead=16, num_encoder_layers=12)
|
|
module = torch.jit.trace(model, (torch.rand(10, 32, 512), torch.rand(20, 32, 512)))
|
|
# module = torch.jit.script(model)
|
|
torch._C._jit_pass_inline(module.graph)
|
|
netron.serve("transformer", module)
|
|
|
|
def _test_torchscript_resnet34():
|
|
torch = __import__("torch")
|
|
torchvision = __import__("torchvision")
|
|
model = torchvision.models.resnet34()
|
|
file = os.path.join(test_data_dir, "pytorch", "resnet34-333f7ec4.pth")
|
|
state_dict = torch.load(file)
|
|
model.load_state_dict(state_dict)
|
|
trace = torch.jit.trace(model, torch.zeros([1, 3, 224, 224]), strict=True)
|
|
torch._C._jit_pass_inline(trace.graph)
|
|
netron.serve("resnet34", trace)
|
|
|
|
def _test_torchscript_quantized():
|
|
torch = __import__("torch")
|
|
__import__("torchvision")
|
|
torch.backends.quantized.engine = "qnnpack"
|
|
trace = torch.jit.load(os.path.join(test_data_dir, "pytorch", "d2go.pt"))
|
|
torch._C._jit_pass_inline(trace.graph)
|
|
netron.serve("d2go", trace)
|
|
|
|
# _test_onnx()
|
|
# _test_onnx_iterate()
|
|
|
|
# _test_torchscript('alexnet.pt')
|
|
_test_torchscript("gpt2.pt")
|
|
# _test_torchscript('inception_v3_traced.pt')
|
|
# _test_torchscript('netron_issue_920.pt') # scalar
|
|
# _test_torchscript('fasterrcnn_resnet50_fpn.pt') # tuple
|
|
# _test_torchscript('mobilenetv2-quant_full-nnapi.pt') # nnapi
|
|
# _test_torchscript_quantized()
|
|
# _test_torchscript_resnet34()
|
|
# _test_torchscript_transformer()
|