mirror of
https://github.com/zebrajr/pytorch.git
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a28b01c155
Summary: Update rnn_cell.py and char_rnn.py example with new `brew` model. - Deprecated CNNModelHelper - replace all helper functions with brew helper functions - Use `model.net.<SingleOp>` format to create bare bone Operator for better clarity. Reviewed By: salexspb Differential Revision: D5062963 fbshipit-source-id: 254f7b9059a29621027d2b09e932f3f81db2e0ce
115 lines
3.8 KiB
Python
115 lines
3.8 KiB
Python
## @package normalization
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# Module caffe2.python.helpers.normalization
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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from caffe2.python import core
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def lrn(model, blob_in, blob_out, order="NCHW", use_cudnn=False, **kwargs):
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"""LRN"""
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if use_cudnn:
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kwargs['engine'] = 'CUDNN'
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blobs_out = blob_out
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else:
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blobs_out = [blob_out, "_" + blob_out + "_scale"]
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lrn = model.net.LRN(
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blob_in,
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blobs_out,
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order=order,
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**kwargs
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)
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if use_cudnn:
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return lrn
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else:
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return lrn[0]
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def softmax(model, blob_in, blob_out=None, use_cudnn=False, **kwargs):
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"""Softmax."""
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if use_cudnn:
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kwargs['engine'] = 'CUDNN'
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if blob_out is not None:
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return model.net.Softmax(blob_in, blob_out, **kwargs)
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else:
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return model.net.Softmax(blob_in, **kwargs)
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def instance_norm(model, blob_in, blob_out, dim_in, order="NCHW", **kwargs):
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blob_out = blob_out or model.net.NextName()
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# Input: input, scale, bias
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# Output: output, saved_mean, saved_inv_std
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# scale: initialize with ones
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# bias: initialize with zeros
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def init_blob(value, suffix):
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return model.param_init_net.ConstantFill(
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[], blob_out + "_" + suffix, shape=[dim_in], value=value)
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scale, bias = init_blob(1.0, "s"), init_blob(0.0, "b")
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model.params.extend([scale, bias])
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model.weights.append(scale)
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model.biases.append(bias)
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blob_outs = [blob_out, blob_out + "_sm", blob_out + "_siv"]
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if 'is_test' in kwargs and kwargs['is_test']:
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blob_outputs = model.net.InstanceNorm(
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[blob_in, scale, bias], [blob_out],
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order=order, **kwargs)
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return blob_outputs
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else:
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blob_outputs = model.net.InstanceNorm(
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[blob_in, scale, bias], blob_outs,
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order=order, **kwargs)
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# Return the output
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return blob_outputs[0]
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def spatial_bn(model, blob_in, blob_out, dim_in, order="NCHW", **kwargs):
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blob_out = blob_out or model.net.NextName()
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# Input: input, scale, bias, est_mean, est_inv_var
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# Output: output, running_mean, running_inv_var, saved_mean,
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# saved_inv_var
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# scale: initialize with ones
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# bias: initialize with zeros
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# est mean: zero
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# est var: ones
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def init_blob(value, suffix):
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return model.param_init_net.ConstantFill(
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[], blob_out + "_" + suffix, shape=[dim_in], value=value)
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if model.init_params:
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scale, bias = init_blob(1.0, "s"), init_blob(0.0, "b")
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running_mean = init_blob(0.0, "rm")
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running_inv_var = init_blob(1.0, "riv")
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else:
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scale = core.ScopedBlobReference(
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blob_out + '_s', model.param_init_net)
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bias = core.ScopedBlobReference(
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blob_out + '_b', model.param_init_net)
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running_mean = core.ScopedBlobReference(
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blob_out + '_rm', model.param_init_net)
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running_inv_var = core.ScopedBlobReference(
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blob_out + '_riv', model.param_init_net)
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model.params.extend([scale, bias])
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model.computed_params.extend([running_mean, running_inv_var])
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model.weights.append(scale)
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model.biases.append(bias)
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blob_outs = [blob_out, running_mean, running_inv_var,
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blob_out + "_sm", blob_out + "_siv"]
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if 'is_test' in kwargs and kwargs['is_test']:
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blob_outputs = model.net.SpatialBN(
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[blob_in, scale, bias, blob_outs[1], blob_outs[2]], [blob_out],
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order=order, **kwargs)
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return blob_outputs
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else:
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blob_outputs = model.net.SpatialBN(
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[blob_in, scale, bias, blob_outs[1], blob_outs[2]], blob_outs,
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order=order, **kwargs)
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# Return the output
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return blob_outputs[0]
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