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Consider to add emulation to support multiple axes case, e.g. input shape is [2, 3, 4, 5] and scale shape is [1, 3, 4, 1].
The text was updated successfully, but these errors were encountered:
Note blockwise broadcasting via emulation is just a ONNX resampling call (https://onnx.ai/onnx/operators/onnx__Resize.html) with mode "nearest" (treat as nop if there isn't any scaling).
function dequantizeLinear(input:uint8, scale:float32, zeroPoint:uint8) upsampledScale = resample(scale, sizes=input.shape) upsampledZeroPoint = resample(zeroPoint, sizes=input.shape) return mul(sub(input, upsampledZeroPoint).cast(float32), upsampledScale) endfunction
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Consider to add emulation to support multiple axes case, e.g. input shape is [2, 3, 4, 5] and scale shape is [1, 3, 4, 1].
The text was updated successfully, but these errors were encountered: