Analyze channel compression with precise tensor controls. Switch modes and inspect dequantized outputs with confidence. Export clean summaries for optimization reviews and deployment planning.
| Channel | Min | Max | Sample | Scale | Zero Point | Quantized | Dequantized |
|---|---|---|---|---|---|---|---|
| filter_0 | -1.2 | 1 | 0.73 | 0.00862745 | 11 | 96 | 0.73333333 |
| filter_1 | -0.8 | 0.9 | -0.21 | 0.00666667 | -8 | -40 | -0.21333333 |
| filter_2 | -1.5 | 1.7 | 1.11 | 0.01254902 | -8 | 80 | 1.10431373 |
| filter_3 | -0.45 | 0.6 | 0.18 | 0.00411765 | -19 | 25 | 0.18117647 |
Affine scale: scale = (max - min) / (qmax - qmin)
Affine zero point: zero_point = round(qmin - min / scale)
Symmetric scale: scale = max(|min|, |max|) / max(|qmin|, |qmax|)
Quantization: q = clamp(round(sample / scale) + zero_point, qmin, qmax)
Dequantization: dequantized = (q - zero_point) × scale
This calculator also reports clipping, absolute error, percentage error, and a simple SQNR estimate for every channel.
Per channel quantization is widely used when one tensor range is not enough. A single global scale can hide channel level variation. That can increase reconstruction error. Per channel settings solve that by assigning independent scales and zero points. This often preserves more useful signal in convolution weights, projection layers, and other model parameters.
This calculator estimates per channel parameters from min and max calibration values or from direct manual inputs. It then maps the sample into the selected integer domain. After that, it dequantizes the value back to float space. You can review raw integer output, final clamped integer output, reconstructed float output, absolute error, percentage error, and SQNR. It is useful for quick audits and repeatable engineering reviews. That creates a practical inspection step before model export.
ML engineers often compare affine and symmetric modes before freezing a model. They also compare qint8 and quint8 behavior. If one or more channels clip too often, calibration may be weak. The tensor distribution may also be drifting. Teams sometimes use this review to flag outlier channels, narrow observer settings, or revisit representative data. A better scale fit usually reduces clipping and improves dequantized fidelity. That matters for stable inference, repeatable benchmarking, and cleaner deployment validation.
Scale tells you how much real value one integer step carries. Zero point tells you which stored integer represents real zero. Together they define the quantization grid. If the grid is too coarse, small detail disappears. If the grid is poorly centered, error grows faster. Careful parameter choice helps compressed tensors stay numerically useful.
Use this page during PyTorch quantization planning, tensor debugging, edge deployment review, and QA signoff. It works well for CNN filters, transformer projections, and custom research tensors. You can test channel behavior before committing to observer settings, calibration batches, or deployment thresholds. The CSV and PDF options also help teams archive results, compare experiments, and document why one scheme was selected over another. That makes the workflow easier to audit later.
It assigns separate quantization parameters to each channel instead of using one shared scale for the full tensor. This often preserves weight detail better.
Use affine mode when ranges are not centered around zero. It can fit asymmetric distributions more tightly and reduce clipping.
Use symmetric mode when values are roughly balanced around zero and you want zero point fixed at zero. It is common for weight tensors.
Clipping means the raw quantized value exceeded the allowed integer range. The value was forced to qmin or qmax.
It shows how far the dequantized value moved from the original sample. Lower error usually means better numeric fidelity.
Reduce range narrows the integer interval by one bit. Some deployment flows use it for compatibility or stability checks.
Yes. Switch the input mode to manual parameters. Then paste channel, scale, zero point, and sample on each line.
The exports include the main calculator summary and channel level results. They are useful for sharing quantization reviews and validation notes.
Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.