Pytorch

PyTorch

Use PyTorch when model artifacts are tensors first and everything else second.

When to use PyTorch

Use PyTorch when you need to:

• load .pt or .pth checkpoints safely onto CPU or GPU
• inspect state_dict keys, module hierarchy, and parameter shapes
• switch a model into stable inference mode for probing
• script quick tensor statistics or output checks against a checkpoint

Quick Start

import torch

checkpoint = torch.load("model.pt", map_location="cpu")
print(type(checkpoint))
print(checkpoint.keys() if isinstance(checkpoint, dict) else "non-dict checkpoint")

High-Value Workflows

Inspect a state dict

state = torch.load("weights.pth", map_location="cpu")
for name, tensor in state.items():
    print(name, tuple(tensor.shape), tensor.dtype)

Inspect a loaded model

model.eval()
for name, module in model.named_modules():
    print(name, module.__class__.__name__)

for name, param in model.named_parameters():
    print(name, tuple(param.shape))

Safe inference baseline

model.eval()
with torch.no_grad():
    output = model(sample_input)

Practical Notes

• Use map_location="cpu" first unless you explicitly need GPU execution.
• Prefer state_dict-style loading and inspection over whole-model pickle blobs when possible.
• PyTorch ≥ 2.6 defaults torch.load(weights_only=True). State-dicts referencing extra globals (NumPy reconstructors, torchvision classes, omegaconf.ListConfig, etc.) raise WeightsUnpickler error: Unsupported global: GLOBAL ... — allowlist them explicitly:

import torch
from torch.serialization import add_safe_globals, safe_globals
import numpy as np

add_safe_globals([np.core.multiarray._reconstruct])         # process-wide
# or scope it:
with safe_globals([np.core.multiarray._reconstruct]):
    state = torch.load("weights.pth", map_location="cpu")

Pass weights_only=False only when the file is trusted — it re-enables arbitrary code execution via pickle. For untrusted weights, prefer safetensors files (safetensors.torch.load_file), which cannot execute code.

• eval() and torch.no_grad() belong together for stable inspection and reduced memory noise.
• For structural inspection without materializing tensors, load under torch._subclasses.fake_tensor.FakeTensorMode — gives shape/dtype/stride from the checkpoint without allocating memory.

Caveats

• torch.load uses pickle under the hood; do not trust untrusted checkpoint files.
• Whole-model checkpoints require the original class code to be importable.
• Version drift can break deserialization or subtly alter behavior.

Resources

No bundled scripts/, references/, or assets/. Use the official PyTorch docs for torch.load, module introspection, and checkpoint best practices.