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https://github.com/saymrwulf/pytorch.git
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8d45f555d7
Rewrite Python built-in class `super()` calls. Only non-semantic changes should be applied. - #94587 - #94588 - #94592 Also, methods with only a `super()` call are removed: ```diff class MyModule(nn.Module): - def __init__(self): - super().__init__() - def forward(self, ...): ... ``` Some cases that change the semantics should be kept unchanged. E.g.:f152a79be9/caffe2/python/net_printer.py (L184-L190)f152a79be9/test/test_jit_fuser_te.py (L2628-L2635)Pull Request resolved: https://github.com/pytorch/pytorch/pull/94587 Approved by: https://github.com/ezyang
119 lines
3.4 KiB
Python
119 lines
3.4 KiB
Python
# @package batch_huber_loss
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# Module caffe2.python.layers.batch_huber_loss
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from caffe2.python import core, schema
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from caffe2.python.layers.layers import (
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ModelLayer,
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)
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from caffe2.python.layers.tags import (
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Tags
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)
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import numpy as np
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class BatchHuberLoss(ModelLayer):
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def __init__(self, model, input_record, name='batch_huber_loss', delta=1.0, **kwargs):
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super().__init__(model, name, input_record, **kwargs)
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assert delta > 0
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self._delta = delta
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assert schema.is_schema_subset(
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schema.Struct(
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('label', schema.Scalar()),
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('prediction', schema.Scalar())
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),
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input_record
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)
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self.tags.update([Tags.EXCLUDE_FROM_PREDICTION])
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self.output_schema = schema.Scalar(
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np.float32,
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self.get_next_blob_reference('output'))
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def add_ops(self, net):
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prediction = net.Squeeze(
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self.input_record.prediction(),
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net.NextScopedBlob('squeezed_prediction'),
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dims=[1]
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)
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label = self.input_record.label.field_blobs()
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if self.input_record.label.field_type().base != (
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self.input_record.prediction.field_type().base):
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label = net.Cast(
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label,
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net.NextScopedBlob('cast_label'),
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to=schema.data_type_for_dtype(
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self.input_record.prediction.field_type()
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)
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)
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const_delta = net.ConstantFill(
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label,
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net.NextScopedBlob("delta"),
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value=self._delta,
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dtype=core.DataType.FLOAT,
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)
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label = net.StopGradient(
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label,
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net.NextScopedBlob('stopped_label')
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)
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const_delta = net.StopGradient(
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const_delta,
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net.NextScopedBlob('stopped_delta')
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)
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# abs_error = np.abs(true - pred)
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abs_error = net.L1Distance(
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[label, prediction], net.NextScopedBlob("abs_error")
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)
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# quadratic = 0.5*min(abs_error, delta)^2, linear = delta*max(abs_error-delta, 0)
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min_error = net.Min(
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[abs_error, const_delta], net.NextScopedBlob("min_error_delta")
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)
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quadratic_term = net.Scale(
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net.Sqr(min_error), scale=float(0.5)
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)
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linear_term = net.Mul(
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[
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net.Sub([abs_error, min_error]),
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const_delta,
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],
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net.NextScopedBlob("huber_linear_term")
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)
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# huber = 0.5 * min(abs_error, delta)^2 + delta * max(abs_error-delta, 0)
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huber_dist = net.Add(
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[quadratic_term, linear_term], net.NextScopedBlob("huber_dist")
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)
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if 'weight' in self.input_record.fields:
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weight_blob = self.input_record.weight()
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if self.input_record.weight.field_type().base != np.float32:
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weight_blob = net.Cast(
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weight_blob,
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weight_blob + '_float32',
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to=core.DataType.FLOAT
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)
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weight_blob = net.StopGradient(
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[weight_blob],
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[net.NextScopedBlob('weight_stop_gradient')],
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)
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huber_dist = net.Mul(
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[huber_dist, weight_blob],
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net.NextScopedBlob("weighted_huber_distance"),
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)
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net.AveragedLoss(huber_dist, self.output_schema.field_blobs())
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