Summary
The discrepancy in the reported number of parameters between the UNETR paper and the MONAI implementation is a critical issue that needs to be addressed. Parameter count accuracy is essential in deep learning research, as it affects the comparison of different models. The UNETR paper reports approximately 92.58M parameters, while the MONAI implementation reports around 121.1M parameters.
Root Cause
The root cause of this discrepancy can be attributed to several factors, including:
- Different implementation details: The MONAI implementation might have additional layers or components that are not present in the original UNETR paper.
- Variations in input size and formatting: The input size and formatting used in the MONAI implementation might differ from those used in the UNETR paper.
- Differences in PyTorch versions: The PyTorch version used in the MONAI implementation might have different parameter counting mechanisms than the version used in the UNETR paper.
Why This Happens in Real Systems
This discrepancy can occur in real systems due to:
- Lack of standardization: Different implementations and frameworks might have varying ways of counting parameters, leading to inconsistencies.
- Insufficient documentation: The original paper or implementation might not provide detailed information about the parameter count, making it challenging to reproduce the results.
- Versioning issues: Changes in framework or library versions can introduce differences in parameter counting.
Real-World Impact
The impact of this discrepancy can be significant, including:
- Inaccurate comparisons: Comparing models based on incorrect parameter counts can lead to misleading conclusions.
- Reproducibility issues: The discrepancy can make it challenging to reproduce the results, hindering the progress of research.
- Trust in research: Inaccurate reporting can erode trust in research and the scientific community.
Example or Code
from monai.networks.nets import UNETR
model = UNETR(
in_channels=1,
out_channels=13,
img_size=(96, 96, 96),
feature_size=16,
hidden_size=768,
mlp_dim=3072,
num_heads=12,
proj_type="perceptron",
norm_name="instance",
res_block=True,
dropout_rate=0.0,
)
params = sum(p.numel() for p in model.parameters())
print(params / 1e6)How Senior Engineers Fix It
Senior engineers can address this issue by:
- Carefully reviewing the implementation: Verifying that the implementation matches the original paper and accounting for any differences.
- Using standardized parameter counting methods: Ensuring that the parameter count is calculated consistently across different implementations and frameworks.
- Providing detailed documentation: Including clear information about the parameter count and any assumptions made during the implementation.
Why Juniors Miss It
Junior engineers might miss this issue due to:
- Lack of experience: Inadequate familiarity with the implementation details and parameter counting mechanisms.
- Insufficient attention to detail: Overlooking critical differences between the original paper and the implementation.
- Limited understanding of the framework: Not being aware of the framework’s specificities and potential pitfalls.