(model, blob_in, blob_out, dim_in, order="NCHW", **kwargs)
| 45 | |
| 46 | |
| 47 | def instance_norm(model, blob_in, blob_out, dim_in, order="NCHW", **kwargs): |
| 48 | blob_out = blob_out or model.net.NextName() |
| 49 | # Input: input, scale, bias |
| 50 | # Output: output, saved_mean, saved_inv_std |
| 51 | # scale: initialize with ones |
| 52 | # bias: initialize with zeros |
| 53 | |
| 54 | def init_blob(value, suffix): |
| 55 | return model.param_init_net.ConstantFill( |
| 56 | [], blob_out + "_" + suffix, shape=[dim_in], value=value) |
| 57 | scale, bias = init_blob(1.0, "s"), init_blob(0.0, "b") |
| 58 | |
| 59 | model.AddParameter(scale, ParameterTags.WEIGHT) |
| 60 | model.AddParameter(bias, ParameterTags.BIAS) |
| 61 | blob_outs = [blob_out, blob_out + "_sm", blob_out + "_siv"] |
| 62 | if 'is_test' in kwargs and kwargs['is_test']: |
| 63 | blob_outputs = model.net.InstanceNorm( |
| 64 | [blob_in, scale, bias], [blob_out], |
| 65 | order=order, **kwargs) |
| 66 | return blob_outputs |
| 67 | else: |
| 68 | blob_outputs = model.net.InstanceNorm( |
| 69 | [blob_in, scale, bias], blob_outs, |
| 70 | order=order, **kwargs) |
| 71 | # Return the output |
| 72 | return blob_outputs[0] |
| 73 | |
| 74 | |
| 75 | def spatial_bn(model, blob_in, blob_out, dim_in, |
nothing calls this directly
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