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| # Copyright 2018 Johns Hopkins University (Author: Dan Povey) | |
| # 2016 Vijayaditya Peddinti | |
| # Apache 2.0. | |
| """ This module has the implementation of convolutional layers. | |
| """ | |
| from __future__ import print_function | |
| from __future__ import division | |
| import math | |
| import re | |
| import sys | |
| from libs.nnet3.xconfig.basic_layers import XconfigLayerBase | |
| # This class is for lines like the following: | |
| # | |
| # conv-batchnorm-layer name=conv2 height-in=40 height-out=40 \ | |
| # num-filters-out=64 height-offsets=-1,0,1 time-offsets=-1,0,1 \ | |
| # required-time-offsets=0 | |
| # or (with NormalizeLayer instead of batch-norm, and with subsampling on the height axis): | |
| # conv-renorm-layer name=conv3 height-in=40 height-out=20 \ | |
| # height-subsample-out=2 num-filters-out=128 height-offsets=-1,0,1 \ | |
| # time-offsets=-1,0,1 required-time-offsets=0 | |
| # | |
| # You don't specify subsampling on the time axis explicitly, it's implicit | |
| # in the 'time-offsets' which are the same as the splicing indexes in a TDNN, | |
| # and which, unlike the height offsets, operate relative to a fixed clock, | |
| # so that after subsampling by a factor of 2, we'd expect all time-offsets | |
| # of subsequent layers to be a factor of 2. You don't specify the input | |
| # num-filters either; it's worked out from the input height and the input dim. | |
| # | |
| # The layer-name encodes the use (or not) of batch normalization, so that if you | |
| # want to skip batch normalization you could just call it 'conv-layer'. | |
| # | |
| # If batch-normalization is used, it's *spatial* batch-normalization, meaning | |
| # that the offset and scale is specific to the output filter, but shared across | |
| # all time and height offsets. | |
| # | |
| # Most of the configuration values mirror same-named values in class | |
| # TimeHeightConvolutionComponent, and for a deeper understanding of what's going | |
| # on you should look at the comment by its declaration, in | |
| # src/nnet3/nnet-convolutional-component.h. | |
| # | |
| # Parameters of the class, and their defaults if they have defaults: | |
| # | |
| # input='[-1]' Descriptor giving the input of the layer. | |
| # height-in The height of the input image, e.g. 40 if the input | |
| # is MFCCs. The num-filters-in is worked out as | |
| # (dimension of input) / height-in. If the preceding | |
| # layer is a convolutional layer, height-in should be | |
| # the same as the height-out of the preceding layer. | |
| # height-subsample-out=1 The height subsampling factor, will be e.g. 2 if you | |
| # want to subsample by a factor of 2 on the height | |
| # axis. | |
| # height-out The height of the output image. This will normally | |
| # be <= (height-in / height-subsample-out). | |
| # Zero-padding on the height axis may be implied by a | |
| # combination of this and height-offsets-in, e.g. if | |
| # height-out==height-in and height-subsample-out=1 | |
| # and height-offsets=-2,-1,0,1 then we'd be padding | |
| # by 2 pixels on the bottom and 1 on the top; see | |
| # comments in nnet-convolutional-layers.h for more | |
| # details. | |
| # height-offsets The offsets on the height axis that define what | |
| # inputs require for each output pixel; will | |
| # often be something like -1,0,1 (if zero-padding | |
| # on height axis) or 0,1,2 otherwise. These are | |
| # comparable to TDNN splicing offsets; e.g. if | |
| # height-offsets=-1,0,1 then height 10 at the output | |
| # would take input from heights 9,10,11 at the input. | |
| # num-filters-out The number of output filters. The output dimension | |
| # of this layer is num-filters-out * height-out; the | |
| # filter dim varies the fastest (filter-stride == 1). | |
| # time-offsets The input offsets on the time axis; these are | |
| # interpreted just like the splicing indexes in TDNNs. | |
| # E.g. if time-offsets=-2,0,2 then time 100 at the | |
| # output would require times 98,100,102 at the input. | |
| # required-time-offsets The subset of 'time-offsets' that are required in | |
| # order to produce an output; if the set has fewer | |
| # elements than 'time-offsets' then it implies some | |
| # kind of zero-padding on the time axis is allowed. | |
| # Defaults to the same as 'time-offsets'. For speech | |
| # tasks we recommend not to set this, as the normal | |
| # padding approach is to pad with copies of the | |
| # first/last frame, which is handled automatically in | |
| # the calling code. | |
| # target-rms=1.0 Only applicable if the layer type is | |
| # conv-batchnorm-layer or | |
| # conv-normalize-layer. This will affect the | |
| # scaling of the output features (larger -> larger), | |
| # and sometimes we set target-rms=0.5 for the layer | |
| # prior to the final layer to make the final layer | |
| # train more slowly. | |
| # self-repair-scale=2.0e-05 This affects the ReLu's. It is a scale on the | |
| # 'self-repair' mechanism that nudges the inputs to the | |
| # ReLUs into the appropriate range in cases where | |
| # the unit is active either too little of the time | |
| # (<10%) or too much of the time (>90%). | |
| # | |
| # The following initialization and natural-gradient related options are, if | |
| # provided, passed through to the config file; if not, they are left at the | |
| # defaults in the code. See nnet-convolutional-component.h for more information. | |
| # | |
| # param-stddev, bias-stddev, max-change, learning-rate-factor (float) | |
| # use-natural-gradient (bool) | |
| # rank-in, rank-out (int) | |
| # num-minibatches-history (float) | |
| # alpha-in, alpha-out (float) | |
| # the following is also passed into the convolution components, if specified: | |
| # l2-regularize (float) | |
| class XconfigConvLayer(XconfigLayerBase): | |
| def __init__(self, first_token, key_to_value, prev_names = None): | |
| for operation in first_token.split('-')[:-1]: | |
| assert operation in ['conv', 'renorm', 'batchnorm', 'relu', | |
| 'noconv', 'dropout', 'so'] | |
| XconfigLayerBase.__init__(self, first_token, key_to_value, prev_names) | |
| def set_default_configs(self): | |
| self.config = {'input':'[-1]', | |
| 'height-in':-1, | |
| 'height-subsample-out':1, | |
| 'height-out':-1, | |
| 'height-offsets':'', | |
| 'num-filters-out':-1, | |
| 'time-offsets':'', | |
| 'required-time-offsets':'', | |
| 'target-rms':1.0, | |
| 'self-repair-scale': 2.0e-05, | |
| 'self-repair-lower-threshold': 0.05, | |
| # the following are not really inspected by this level of | |
| # code, just passed through (but not if left at ''). | |
| 'param-stddev':'', 'bias-stddev':'', | |
| 'max-change': 0.75, 'learning-rate-factor':'', | |
| 'use-natural-gradient':'', | |
| 'rank-in':'', 'rank-out':'', 'num-minibatches-history':'', | |
| 'alpha-in':'', 'alpha-out':'', 'l2-regularize':'', | |
| 'dropout-proportion': 0.5} | |
| def set_derived_configs(self): | |
| # sets 'num-filters-in'. | |
| input_dim = self.descriptors['input']['dim'] | |
| height_in = self.config['height-in'] | |
| if height_in <= 0: | |
| raise RuntimeError("height-in must be specified"); | |
| if input_dim % height_in != 0: | |
| raise RuntimeError("Input dimension {0} is not a multiple of height-in={1}".format( | |
| input_dim, height_in)) | |
| self.config['num-filters-in'] = input_dim // height_in | |
| # Check whether 'str' is a sorted, unique, nonempty list of integers, like -1,0,1., | |
| # returns true if so. | |
| def check_offsets_var(self, str): | |
| try: | |
| a = [ int(x) for x in str.split(",") ] | |
| if len(a) == 0: | |
| return False | |
| for i in range(len(a) - 1): | |
| if a[i] >= a[i+1]: | |
| return False | |
| return True | |
| except: | |
| return False | |
| def check_configs(self): | |
| # Do some basic checking of the configs. The component-level code does | |
| # some more thorough checking, but if you set the height-out too small it | |
| # prints it as a warning, which the user may not see, so at a minimum we | |
| # want to check for that here. | |
| height_subsample_out = self.config['height-subsample-out'] | |
| height_in = self.config['height-in'] | |
| height_out = self.config['height-out'] | |
| if height_subsample_out <= 0: | |
| raise RuntimeError("height-subsample-out has invalid value {0}.".format( | |
| height_subsample_out)) | |
| # we already checked height-in in set_derived_configs. | |
| if height_out <= 0: | |
| raise RuntimeError("height-out has invalid value {0}.".format( | |
| height_out)) | |
| if height_out * height_subsample_out > height_in: | |
| raise RuntimeError("The combination height-in={0}, height-out={1} and " | |
| "height-subsample-out={2} does not look right " | |
| "(height-out too large).".format( | |
| height_in, height_out, height_subsample_out)) | |
| height_offsets = self.config['height-offsets'] | |
| time_offsets = self.config['time-offsets'] | |
| required_time_offsets = self.config['required-time-offsets'] | |
| if not 'noconv' in self.layer_type.split('-'): | |
| # only check height-offsets, time-offsets and required-time-offsets if there | |
| # is actually a convolution in this layer. | |
| if not self.check_offsets_var(height_offsets): | |
| raise RuntimeError("height-offsets={0} is not valid".format(height_offsets)) | |
| if not self.check_offsets_var(time_offsets): | |
| raise RuntimeError("time-offsets={0} is not valid".format(time_offsets)) | |
| if required_time_offsets != "" and not self.check_offsets_var(required_time_offsets): | |
| raise RuntimeError("required-time-offsets={0} is not valid".format( | |
| required_time_offsets)) | |
| if height_out * height_subsample_out < \ | |
| height_in - len(height_offsets.split(',')): | |
| raise RuntimeError("The combination height-in={0}, height-out={1} and " | |
| "height-subsample-out={2} and height-offsets={3} " | |
| "does not look right (height-out too small).") | |
| if self.config['target-rms'] <= 0.0: | |
| raise RuntimeError("Config value target-rms={0} is not valid".format( | |
| self.config['target_rms'])) | |
| def auxiliary_outputs(self): | |
| return [] | |
| def output_name(self, auxiliary_output = None): | |
| assert auxiliary_output is None | |
| # note: the [:-1] is to remove the '-layer'. | |
| operations = self.layer_type.split('-')[:-1] | |
| if operations[-1] == 'noconv': | |
| operations = operations[:-1] | |
| assert len(operations) >= 1 | |
| last_operation = operations[-1] | |
| assert last_operation in ['relu', 'conv', 'renorm', 'batchnorm', 'dropout', 'so'] | |
| # we'll return something like 'layer1.batchnorm'. | |
| return '{0}.{1}'.format(self.name, last_operation) | |
| def output_dim(self, auxiliary_output = None): | |
| assert auxiliary_output is None | |
| return self.config['num-filters-out'] * self.config['height-out'] | |
| def get_full_config(self): | |
| ans = [] | |
| config_lines = self._generate_cnn_config() | |
| for line in config_lines: | |
| for config_name in ['ref', 'final']: | |
| # we do not support user specified matrices in CNN initialization | |
| # so 'ref' and 'final' configs are the same. | |
| ans.append((config_name, line)) | |
| return ans | |
| # convenience function to generate the CNN config | |
| def _generate_cnn_config(self): | |
| configs = [] | |
| name = self.name | |
| # These 3 variables will be updated as we add components. | |
| cur_num_filters = self.config['num-filters-in'] | |
| cur_height = self.config['height-in'] | |
| cur_descriptor = self.descriptors['input']['final-string'] | |
| # note: the [:-1] is to remove the '-layer'. | |
| operations = self.layer_type.split('-')[:-1] | |
| if operations[-1] == 'noconv': | |
| operations = operations[:-1] | |
| # e.g.: | |
| # operations = [ 'conv', 'relu', 'batchnorm' ] | |
| # or: | |
| # operations = [ 'relu', 'conv', 'renorm' ] | |
| for operation in operations: | |
| if operation == 'conv': | |
| a = [] | |
| for opt_name in [ | |
| 'param-stddev', 'bias-stddev', 'use-natural-gradient', | |
| 'max-change', 'rank-in', 'rank-out', 'num-minibatches-history', | |
| 'alpha-in', 'alpha-out', 'num-filters-in', 'num-filters-out', | |
| 'height-in','height-out', 'height-subsample-out', | |
| 'height-offsets', 'time-offsets', 'required-time-offsets', | |
| 'learning-rate-factor', 'l2-regularize' ]: | |
| value = self.config[opt_name] | |
| if value != '': | |
| a.append('{0}={1}'.format(opt_name, value)) | |
| conv_opts = ' '.join(a) | |
| configs.append('component name={0}.conv type=TimeHeightConvolutionComponent ' | |
| '{1}'.format(name, conv_opts)) | |
| configs.append('component-node name={0}.conv component={0}.conv ' | |
| 'input={1}'.format(name, cur_descriptor)) | |
| cur_num_filters = self.config['num-filters-out'] | |
| cur_height = self.config['height-out'] | |
| elif operation == 'batchnorm': | |
| configs.append('component name={0}.batchnorm type=BatchNormComponent dim={1} ' | |
| 'block-dim={2} target-rms={3}'.format( | |
| name, cur_num_filters * cur_height, cur_num_filters, | |
| self.config['target-rms'])) | |
| configs.append('component-node name={0}.batchnorm component={0}.batchnorm ' | |
| 'input={1}'.format(name, cur_descriptor)) | |
| elif operation == 'renorm': | |
| configs.append('component name={0}.renorm type=NormalizeComponent ' | |
| 'dim={1} target-rms={2}'.format( | |
| name, cur_num_filters * cur_height, | |
| self.config['target-rms'])) | |
| configs.append('component-node name={0}.renorm component={0}.renorm ' | |
| 'input={1}'.format(name, cur_descriptor)) | |
| elif operation == 'relu': | |
| configs.append('component name={0}.relu type=RectifiedLinearComponent ' | |
| 'dim={1} block-dim={2} self-repair-scale={3} ' | |
| 'self-repair-lower-threshold={4}'.format( | |
| name, cur_num_filters * cur_height, cur_num_filters, | |
| self.config['self-repair-scale'], | |
| self.config['self-repair-lower-threshold'])) | |
| configs.append('component-node name={0}.relu component={0}.relu ' | |
| 'input={1}'.format(name, cur_descriptor)) | |
| elif operation == 'dropout': | |
| configs.append('component name={0}.dropout type=DropoutComponent ' | |
| 'dim={1} dropout-proportion={2}'.format( | |
| name, cur_num_filters * cur_height, | |
| self.config['dropout-proportion'])) | |
| configs.append('component-node name={0}.dropout component={0}.dropout ' | |
| 'input={1}'.format(name, cur_descriptor)) | |
| elif operation == 'so': | |
| configs.append('component name={0}.so type=ScaleAndOffsetComponent ' | |
| 'dim={1} block-dim={2}'.format( | |
| name, cur_num_filters * cur_height, cur_num_filters)) | |
| configs.append('component-node name={0}.so component={0}.so ' | |
| 'input={1}'.format(name, cur_descriptor)) | |
| else: | |
| raise RuntimeError("Un-handled operation type: " + operation) | |
| cur_descriptor = '{0}.{1}'.format(name, operation) | |
| return configs | |
| # This class is for lines like the following: | |
| # | |
| # res-block name=res1 num-filters=64 height=32 time-period=1 | |
| # | |
| # It implements a residual block as in ResNets, with pre-activation, and with | |
| # some small differences-- basically, instead of adding the input to the output, | |
| # we put a convolutional layer in there but initialize it to the unit matrix and | |
| # if you want you can give it a relatively small (or even zero) learning rate | |
| # and max-change. And there is batch-norm in that path also. | |
| # | |
| # The number of filters is the same on the input and output; it is actually | |
| # redundant to write it in the config file, because given that we know the | |
| # height, we can work it out from the dimension of the input (as dimension = | |
| # height * num-filters). But we allow it to be specified anyway, for clarity. | |
| # | |
| # Note: the res-block does not support subsampling or changing the number of | |
| # filters. If you want to do that, we recommend that you should do it with a | |
| # single relu-batchnorm-conv-layer. | |
| # | |
| # Here are the most important configuration values, with defaults shown if | |
| # defaults exist: | |
| # | |
| # input='[-1]' Descriptor giving the input of the layer. | |
| # height The input and output height of the image, e.g. 40. Note: the width | |
| # is associated with the time dimension and is dealt with | |
| # implicitly, so it's not specified here. | |
| # num-filters The number of filters on the input and output, e.g. 64. | |
| # It does not have to be specified; if it is not specified, | |
| # we work it out from the input dimension. | |
| # num-bottleneck-filters If specified then this will be a 'bottleneck' | |
| # ResBlock, in which there is a 1x1 convolution from | |
| # num-filters->num-bottleneck-filters, a 3x3 convolution | |
| # from num-bottleneck-filters->num-bottleneck-filters, and | |
| # a 1x1 convolution from num-bottleneck-filters->num-filters. | |
| # | |
| # time-period=1 Think of this as the stride in the time dimension. At the | |
| # input of the network will always have time-period=1; then | |
| # after subsampling once in time we'd have time-period=2; then | |
| # after subsampling again we'd have time-period=4. Because of | |
| # the way nnet3 works, subsampling on the time axis is an | |
| # implicit, not explicit, operation. | |
| # height-period=1 This will almost always be left at the default (1). It is | |
| # analogous to time-period, but because the height, unlike the | |
| # time, is explicitly subsampled, in normal topologies this should | |
| # be left at 1. | |
| # | |
| # bypass-source=noop | |
| # The output of this component is Sum(convolution, x), and | |
| # this option controls what 'x' is. There are 3 options | |
| # here: 'noop', 'input', 'relu' or 'batchnorm'. 'noop' is | |
| # equivalent to 'input' in what it computes; it just | |
| # inserts a 'noop' component in order to make the | |
| # computation more efficient. For both 'noop' and | |
| # 'input', x is the input to this component. If | |
| # bypass-source=relu then we use the relu of the | |
| # input; if 'batchnorm', then we use the relu+batchnorm of | |
| # the input. | |
| # allow-zero-padding=true By default this will allow zero-padding in the time | |
| # dimension, meaning that you don't need extra frames at | |
| # the input to compute the output. There may be ASR | |
| # applications where you want to pad in the time dimension | |
| # with repeats of the first or last frame (as we do for | |
| # TDNNs), where it would be appropriate to write | |
| # allow-zero-padding=false. Note: the way we have | |
| # set it up, it does zero-padding on the height axis | |
| # regardless | |
| # | |
| # Less important config variables: | |
| # self-repair-scale=2.0e-05 This affects the ReLu's. It is a scale on the | |
| # 'self-repair' mechanism that nudges the inputs to the | |
| # ReLUs into the appropriate range in cases where | |
| # the unit is active either too little of the time | |
| # (<10%) or too much of the time (>90%). | |
| # max-change=0.75 Max-parameter-change constant (per minibatch) | |
| # used for convolutional components. | |
| # | |
| # | |
| # The following natural-gradient-related configuration variables are passed in | |
| # to the convolution components, if specified: | |
| # use-natural-gradient (bool) | |
| # rank-in, rank-out (int) | |
| # num-minibatches-history (float) | |
| # alpha-in, alpha-out (float) | |
| # the following is also passed into the convolution components, if specified: | |
| # l2-regularize (float) | |
| # | |
| class XconfigResBlock(XconfigLayerBase): | |
| def __init__(self, first_token, key_to_value, prev_names = None): | |
| assert first_token == 'res-block' | |
| XconfigLayerBase.__init__(self, first_token, key_to_value, prev_names) | |
| def set_default_configs(self): | |
| self.config = {'input':'[-1]', | |
| 'height':-1, | |
| 'num-filters':-1, | |
| 'num-bottleneck-filters':-1, | |
| 'time-period':1, | |
| 'height-period':1, | |
| 'self-repair-scale': 2.0e-05, | |
| 'self-repair-lower-threshold1': 0.05, | |
| 'self-repair-lower-threshold2': 0.05, | |
| 'self-repair-lower-threshold3': 0.05, | |
| 'max-change': 0.75, | |
| 'allow-zero-padding': True, | |
| 'bypass-source' : 'noop', | |
| # the following are not really inspected by this level of | |
| # code, just passed through (but not if left at ''). | |
| 'param-stddev':'', 'bias-stddev':'', | |
| 'use-natural-gradient':'', | |
| 'rank-in':'', 'rank-out':'', | |
| 'num-minibatches-history':'', | |
| 'alpha-in':'', 'alpha-out':'', 'l2-regularize':'' } | |
| def set_derived_configs(self): | |
| # set 'num-filters' or check it.. | |
| input_dim = self.descriptors['input']['dim'] | |
| height = self.config['height'] | |
| cur_num_filters = self.config['num-filters'] | |
| if cur_num_filters == -1: | |
| if input_dim % height != 0: | |
| raise RuntimeError("Specified image height {0} does not " | |
| "divide the input dim {1}".format( | |
| height, input_dim)) | |
| self.config['num-filters'] = input_dim / height | |
| elif input_dim != cur_num_filters * height: | |
| raise RuntimeError("Expected the input-dim to equal " | |
| "height={0} * num-filters={1} = {2}, but " | |
| "it is {3}".format( | |
| height, cur_num_filters, | |
| height * cur_num_filters, | |
| input_dim)); | |
| def check_configs(self): | |
| # we checked the dimensions in set_derived_configs. | |
| if not self.config['bypass-source'] in [ | |
| 'input', 'noop', 'relu', 'batchnorm' ]: | |
| raise RuntimeError("Expected direct-convolution-source to " | |
| "be input, relu or batchnorm, got: {1}".format( | |
| self.config['direct-convolution-source'])) | |
| def auxiliary_outputs(self): | |
| return [] | |
| def output_name(self, auxiliary_output = None): | |
| bypass_source = self.config['bypass-source'] | |
| b = self.config['num-bottleneck-filters'] | |
| conv = ('{0}.conv2' if b <= 0 else '{0}.conv3').format(self.name) | |
| if bypass_source == 'input': | |
| residual = self.descriptors['input']['final-string'] | |
| elif bypass_source == 'noop': | |
| # we let the noop be the sum of the convolutional part and the | |
| # input, so just return the output of the no-op component. | |
| return '{0}.noop'.format(self.name) | |
| elif bypass_source == 'relu': | |
| residual = '{0}.relu1'.format(self.name) | |
| else: | |
| assert bypass_source == 'batchnorm' | |
| residual = '{0}.batchnorm1'.format(self.name) | |
| return 'Sum({0}, {1})'.format(conv, residual) | |
| def output_dim(self, auxiliary_output = None): | |
| assert auxiliary_output is None | |
| input_dim = self.descriptors['input']['dim'] | |
| return input_dim | |
| def get_full_config(self): | |
| ans = [] | |
| b = self.config['num-bottleneck-filters'] | |
| if b <= 0: | |
| config_lines = self._generate_normal_resblock_config() | |
| else: | |
| config_lines = self._generate_bottleneck_resblock_config() | |
| for line in config_lines: | |
| for config_name in ['ref', 'final']: | |
| # we do not support user specified matrices in CNN initialization | |
| # so 'ref' and 'final' configs are the same. | |
| ans.append((config_name, line)) | |
| return ans | |
| # _generate_normal_resblock_config is a convenience function to generate the | |
| # res-block config (the non-bottleneck version). | |
| # | |
| # The main path inside the res-block in the non-bottleneck case is as | |
| # follows: | |
| # | |
| # input -> relu1 -> batchnorm1 -> conv1 -> relu2 -> batchnorm2 -> conv2 | |
| # | |
| # We put the relu before the batchnorm because we think it makes more sense; | |
| # because the Torch people seemed to find that this works better | |
| # (https://github.com/gcr/torch-residual-networks/issues/5); | |
| # and because in our batchnorm component we haven't implemented the beta and | |
| # gamma; these would be essential to having it work before relu, but | |
| # when before a convolution or linear component, they add no extra modeling | |
| # power. | |
| # | |
| # The output of the res-block can be the sum of the last convolutional | |
| # component (conv2), with the input. However, the option ('bypass-source') | |
| # controls whether we sum with the raw input, or its relu or relu+batchnorm. | |
| # If the term is going to be the raw input, we give the option ('noop') and | |
| # to cache the output sum via a NoOpComponent)-- because due to how nnet3 | |
| # works, if we didn't do this, redundant summing operations would take | |
| # place. | |
| def _generate_normal_resblock_config(self): | |
| configs = [] | |
| name = self.name | |
| num_filters = self.config['num-filters'] | |
| assert self.config['num-bottleneck-filters'] == -1 | |
| height = self.config['height'] | |
| input_descriptor = self.descriptors['input']['final-string'] | |
| allow_zero_padding = self.config['allow-zero-padding'] | |
| height_period = self.config['height-period'] | |
| time_period = self.config['time-period'] | |
| # input -> relu1 -> batchnorm1 -> conv1 -> relu2 -> batchnorm2 -> conv2 | |
| cur_descriptor = input_descriptor | |
| for n in [1, 2]: | |
| # the ReLU | |
| configs.append('component name={0}.relu{1} type=RectifiedLinearComponent ' | |
| 'dim={2} block-dim={3} self-repair-scale={4} ' | |
| 'self-repair-lower-threshold={5}'.format( | |
| name, n, num_filters * height, num_filters, | |
| self.config['self-repair-scale'], | |
| self.config['self-repair-lower-threshold{0}'.format(n)])) | |
| configs.append('component-node name={0}.relu{1} component={0}.relu{1} ' | |
| 'input={2}'.format(name, n, cur_descriptor)) | |
| cur_descriptor = '{0}.relu{1}'.format(name, n) | |
| # the batch-norm | |
| configs.append('component name={0}.batchnorm{1} type=BatchNormComponent dim={2} ' | |
| 'block-dim={3}'.format( | |
| name, n, num_filters * height, | |
| num_filters)) | |
| configs.append('component-node name={0}.batchnorm{1} component={0}.batchnorm{1} ' | |
| 'input={2}'.format(name, n, cur_descriptor)) | |
| cur_descriptor = '{0}.batchnorm{1}'.format(name, n) | |
| # the convolution. | |
| a = [] | |
| for opt_name in [ | |
| 'param-stddev', 'bias-stddev', 'use-natural-gradient', | |
| 'max-change', 'rank-in', 'rank-out', 'num-minibatches-history', | |
| 'alpha-in', 'alpha-out', 'l2-regularize' ]: | |
| value = self.config[opt_name] | |
| if value != '': | |
| a.append('{0}={1}'.format(opt_name, value)) | |
| conv_opts = ('height-in={h} height-out={h} height-offsets=-{hp},0,{hp} ' | |
| 'time-offsets=-{p},0,{p} ' | |
| 'num-filters-in={f} num-filters-out={f} {r} {o}'.format( | |
| h=height, hp=height_period, p=time_period, f=num_filters, | |
| r=('required-time-offsets=0' if allow_zero_padding else ''), | |
| o=' '.join(a))) | |
| configs.append('component name={0}.conv{1} type=TimeHeightConvolutionComponent ' | |
| '{2}'.format(name, n, conv_opts)) | |
| configs.append('component-node name={0}.conv{1} component={0}.conv{1} ' | |
| 'input={2}'.format(name, n, cur_descriptor)) | |
| cur_descriptor = '{0}.conv{1}'.format(name, n) | |
| if self.config['bypass-source'] == 'noop': | |
| dim = self.descriptors['input']['dim'] | |
| configs.append('component name={0}.noop dim={1} type=NoOpComponent'.format( | |
| name, dim)) | |
| configs.append('component-node name={0}.noop component={0}.noop ' | |
| 'input=Sum({1}, {0}.conv2)'.format(name, | |
| input_descriptor)) | |
| # Note: the function 'output_name' is responsible for returning the | |
| # descriptor corresponding to the output of the network. | |
| return configs | |
| # _generate_bottleneck_resblock_config is a convenience function to generate the | |
| # res-block config (this is the bottleneck version, where there is | |
| # a 3x3 kernel with a smaller number of filters than at the input and output, | |
| # sandwiched between two 1x1 kernels. | |
| # | |
| # The main path inside the res-block in the bottleneck case is as follows: | |
| # | |
| # input -> relu1 -> batchnorm1 -> conv1 -> relu2 -> batchnorm2 -> conv2 -> | |
| # relu3 -> batchnorm3 -> conv3 | |
| # | |
| # power. | |
| # | |
| # The output of the res-block can be the sum of the last convolutional | |
| # component (conv3), with the input. However we give the option | |
| # ('bypass-source') to sum with the raw input, or its relu or | |
| # relu+batchnorm. If the term is going to be the raw input, we give the | |
| # option ('noop') and to cache the output sum via a NoOpComponent)-- because | |
| # due to how nnet3 works, if we didn't do this, redundant summing operations | |
| # would take place. | |
| def _generate_bottleneck_resblock_config(self): | |
| configs = [] | |
| name = self.name | |
| num_filters = self.config['num-filters'] | |
| num_bottleneck_filters = self.config['num-bottleneck-filters'] | |
| assert num_bottleneck_filters > 0 | |
| height = self.config['height'] | |
| input_descriptor = self.descriptors['input']['final-string'] | |
| allow_zero_padding = self.config['allow-zero-padding'] | |
| height_period = self.config['height-period'] | |
| time_period = self.config['time-period'] | |
| # input -> relu1 -> batchnorm1 -> conv1 -> relu2 -> batchnorm2 -> conv2 | |
| cur_descriptor = input_descriptor | |
| cur_num_filters = num_filters | |
| for n in [1, 2, 3]: | |
| # the ReLU | |
| configs.append('component name={0}.relu{1} type=RectifiedLinearComponent ' | |
| 'dim={2} block-dim={3} self-repair-scale={4} ' | |
| 'self-repair-lower-threshold={5}'.format( | |
| name, n, cur_num_filters * height, cur_num_filters, | |
| self.config['self-repair-scale'], | |
| self.config['self-repair-lower-threshold{0}'.format(n)])) | |
| configs.append('component-node name={0}.relu{1} component={0}.relu{1} ' | |
| 'input={2}'.format(name, n, cur_descriptor)) | |
| cur_descriptor = '{0}.relu{1}'.format(name, n) | |
| # the batch-norm | |
| configs.append('component name={0}.batchnorm{1} type=BatchNormComponent dim={2} ' | |
| 'block-dim={3}'.format( | |
| name, n, cur_num_filters * height, | |
| cur_num_filters)) | |
| configs.append('component-node name={0}.batchnorm{1} component={0}.batchnorm{1} ' | |
| 'input={2}'.format(name, n, cur_descriptor)) | |
| cur_descriptor = '{0}.batchnorm{1}'.format(name, n) | |
| # the convolution. | |
| a = [] | |
| for opt_name in [ | |
| 'param-stddev', 'bias-stddev', 'use-natural-gradient', | |
| 'max-change', 'rank-in', 'rank-out', 'num-minibatches-history', | |
| 'alpha-in', 'alpha-out', 'l2-regularize' ]: | |
| value = self.config[opt_name] | |
| if value != '': | |
| a.append('{0}={1}'.format(opt_name, value)) | |
| height_offsets = ('-{hp},0,{hp}'.format(hp=height_period) if n == 2 else '0') | |
| time_offsets = ('-{t},0,{t}'.format(t=time_period) if n == 2 else '0') | |
| next_num_filters = (num_filters if n == 3 else num_bottleneck_filters) | |
| conv_opts = ('height-in={h} height-out={h} height-offsets={ho} time-offsets={to} ' | |
| 'num-filters-in={fi} num-filters-out={fo} {r} {o}'.format( | |
| h=height, ho=height_offsets, to=time_offsets, | |
| fi=cur_num_filters, fo=next_num_filters, | |
| r=('required-time-offsets=0' if allow_zero_padding else ''), | |
| o=' '.join(a))) | |
| configs.append('component name={0}.conv{1} type=TimeHeightConvolutionComponent ' | |
| '{2}'.format(name, n, conv_opts)) | |
| configs.append('component-node name={0}.conv{1} component={0}.conv{1} ' | |
| 'input={2}'.format(name, n, cur_descriptor)) | |
| cur_descriptor = '{0}.conv{1}'.format(name, n) | |
| cur_num_filters = next_num_filters | |
| if self.config['bypass-source'] == 'noop': | |
| dim = self.descriptors['input']['dim'] | |
| configs.append('component name={0}.noop dim={1} type=NoOpComponent'.format( | |
| name, dim)) | |
| configs.append('component-node name={0}.noop component={0}.noop ' | |
| 'input=Sum({1}, {0}.conv3)'.format(name, | |
| input_descriptor)) | |
| # Note: the function 'output_name' is responsible for returning the | |
| # descriptor corresponding to the output of the network. | |
| return configs | |
| # This class is for lines like the following: | |
| # | |
| # res2-block name=res1 num-filters=64 height=32 time-period=1 | |
| # | |
| # It is a residual block with post-activations, which does not support | |
| # downsampling (strided convolution) or changing the number of filters; | |
| # for that, see res2-downsample-block. | |
| # It's a pretty standard res-block, more standard than "res-block" (XconfigResBlock). | |
| # | |
| # The number of filters is the same on the input and output; it is actually | |
| # redundant to write it in the config file, because given that we know the | |
| # height, we can work it out from the dimension of the input (as dimension = | |
| # height * num-filters). But we allow it to be specified anyway, for clarity. | |
| # | |
| # Here are the most important configuration values, with defaults shown if | |
| # defaults exist: | |
| # | |
| # input='[-1]' Descriptor giving the input of the layer. | |
| # height The input and output height of the image, e.g. 40. Note: the width | |
| # is associated with the time dimension and is dealt with | |
| # implicitly, so it's not specified here. | |
| # num-filters The number of filters on the input and output, e.g. 64. | |
| # It does not have to be specified; if it is not specified, | |
| # we work it out from the input dimension. | |
| # num-bottleneck-filters If specified then this will be a 'bottleneck' | |
| # ResBlock, in which there is a 1x1 convolution from | |
| # num-filters->num-bottleneck-filters, a 3x3 convolution | |
| # from num-bottleneck-filters->num-bottleneck-filters, and | |
| # a 1x1 convolution from num-bottleneck-filters->num-filters. | |
| # time-period=1 Think of this as the stride in the time dimension. At the | |
| # input of the network will always have time-period=1; then | |
| # after subsampling once in time we'd have time-period=2; then | |
| # after subsampling again we'd have time-period=4. Because of | |
| # the way nnet3 works, subsampling on the time axis is an | |
| # implicit, not explicit, operation. | |
| # allow-zero-padding=true By default this will allow zero-padding in the time | |
| # dimension, meaning that you don't need extra frames at | |
| # the input to compute the output. There may be ASR | |
| # applications where you want to pad in the time dimension | |
| # with repeats of the first or last frame (as we do for | |
| # TDNNs), where it would be appropriate to write | |
| # allow-zero-padding=false. Note: the way we have | |
| # set it up, it does zero-padding on the height axis | |
| # regardless | |
| # | |
| # Less important config variables: | |
| # self-repair-scale=2.0e-05 This affects the ReLu's. It is a scale on the | |
| # 'self-repair' mechanism that nudges the inputs to the | |
| # ReLUs into the appropriate range in cases where | |
| # the unit is active either too little of the time | |
| # (<10%) or too much of the time (>90%). | |
| # max-change=0.75 Max-parameter-change constant (per minibatch) | |
| # used for convolutional components. | |
| # | |
| # | |
| # The following natural-gradient-related configuration variables are passed in | |
| # to the convolution components, if specified: | |
| # use-natural-gradient (bool) | |
| # rank-in, rank-out (int) | |
| # num-minibatches-history (float) | |
| # alpha-in, alpha-out (float) | |
| # the following is also passed into the convolution components, if specified: | |
| # l2-regularize (float) | |
| class XconfigRes2Block(XconfigLayerBase): | |
| def __init__(self, first_token, key_to_value, prev_names = None): | |
| assert first_token == 'res2-block' | |
| XconfigLayerBase.__init__(self, first_token, key_to_value, prev_names) | |
| def set_default_configs(self): | |
| self.config = {'input':'[-1]', | |
| 'height':-1, # sets height-in and height-out | |
| 'height-in':-1, | |
| 'height-out':-1, | |
| 'num-filters':-1, # interpreted as num-filters-out. | |
| 'num-bottleneck-filters':-1, | |
| 'time-period':1, | |
| 'self-repair-scale': 2.0e-05, | |
| 'self-repair-lower-threshold1': 0.05, | |
| 'self-repair-lower-threshold2': 0.05, | |
| 'self-repair-lower-threshold3': 0.05, | |
| 'max-change': 0.75, | |
| 'allow-zero-padding': True, | |
| # the following are not really inspected by this level of | |
| # code, just passed through (but not if left at ''). | |
| 'param-stddev':'', 'bias-stddev':'', | |
| 'use-natural-gradient':'', | |
| 'rank-in':'', 'rank-out':'', | |
| 'num-minibatches-history':'', | |
| 'alpha-in':'', 'alpha-out':'', | |
| 'l2-regularize':'' } | |
| def set_derived_configs(self): | |
| input_dim = self.descriptors['input']['dim'] | |
| if not ((self.config['height'] > 0 and self.config['height-in'] == -1 and | |
| self.config['height-out'] == -1) or | |
| (self.config['height-out'] > 0 and self.config['height-in'] > 0)): | |
| raise RuntimeError("You must specify height, or height-in and height-out, for res2-block.") | |
| if not (self.config['height-in'] > 0 and self.config['height-out'] > 0): | |
| height = self.config['height'] | |
| if not height > 0: | |
| raise RuntimeError("You must specify either height, or height-in and height-out, for " | |
| "res2-block.") | |
| self.config['height-in'] = height | |
| self.config['height-out'] = height | |
| height_in = self.config['height-in'] | |
| if input_dim % height_in != 0: | |
| raise RuntimeError("Specified input image height {0} does not " | |
| "divide the input dim {1}".format( | |
| height_in, input_dim)) | |
| self.config['num-filters'] = input_dim / height | |
| def check_configs(self): | |
| if self.config['num-filters'] == -1: | |
| raise RuntimeError("You must specify num-filters for res2-block.") | |
| def auxiliary_outputs(self): | |
| return [] | |
| def output_name(self, auxiliary_output = None): | |
| b = self.config['num-bottleneck-filters'] | |
| return ('{0}.relu2' if b <= 0 else '{0}.relu3').format(self.name) | |
| def output_dim(self, auxiliary_output = None): | |
| assert auxiliary_output is None | |
| return self.config['height-out'] * self.config['num-filters'] | |
| def get_full_config(self): | |
| ans = [] | |
| b = self.config['num-bottleneck-filters'] | |
| if b <= 0: | |
| config_lines = self._generate_normal_resblock_config() | |
| else: | |
| config_lines = self._generate_bottleneck_resblock_config() | |
| for line in config_lines: | |
| for config_name in ['ref', 'final']: | |
| # we do not support user specified matrices in CNN initialization | |
| # so 'ref' and 'final' configs are the same. | |
| ans.append((config_name, line)) | |
| return ans | |
| # _generate_normal_resblock_config is a convenience function to generate the | |
| # res-block config (the non-bottleneck version). | |
| # | |
| # The main path inside the res-block in the non-bottleneck case is as | |
| # follows: | |
| # | |
| # input -> conv1 -> batchnorm1 -> scaleoffset1 -> relu1 -> conv2 -> batchnorm2 -> scaleoffset2 -> relu2 | |
| # | |
| # where the 'scaleoffsetN' are ScaleAndOffsetComponent, which conventionally would be | |
| # considered part of the BatchNorm. | |
| # | |
| # The relu2 actually sees the sum of the input and 'scaleoffset2'-- which gives us the bypass | |
| # connection. | |
| def _generate_normal_resblock_config(self): | |
| configs = [] | |
| name = self.name | |
| assert self.config['num-bottleneck-filters'] == -1 | |
| input_dim = self.descriptors['input']['dim'] | |
| height_in = self.config['height-in'] | |
| height_out = self.config['height-out'] | |
| time_period_out = self.config['time-period'] | |
| if not input_dim % height_in == 0: | |
| raise RuntimeError("input-dim {0} does not divide height-in {1}".format( | |
| input_dim, height_in)) | |
| num_filters_in = input_dim / height_in | |
| num_filters_out = self.config['num-filters'] | |
| if height_out != height_in: | |
| if height_out < height_in / 2 - 1 or height_out > height_in / 2 + 1: | |
| raise RuntimeError("Expected height-out to be about half height-in, or the same: " | |
| "height-in={0} height-out={1}".format(height_in, height_out)) | |
| if not time_period_out % 2 == 0: | |
| raise RuntimeError("Expected time-period to be a multiple of 2 if you are subsampling " | |
| "on height.") | |
| time_period_in = time_period_out / 2 | |
| height_subsample = 2 | |
| else: | |
| time_period_in = time_period_out | |
| height_subsample = 1 | |
| cur_time_period = time_period_in | |
| cur_num_filters = num_filters_in | |
| cur_height = height_in | |
| input_descriptor = self.descriptors['input']['final-string'] | |
| allow_zero_padding = self.config['allow-zero-padding'] | |
| if height_subsample == 1 and num_filters_in == num_filters_out: | |
| bypass_descriptor = input_descriptor | |
| else: | |
| bypass_descriptor = '{0}.conv_bypass'.format(name) | |
| cur_descriptor = input_descriptor | |
| # get miscellaneous convolution options passed in from the xconfig line | |
| a = [] | |
| for opt_name in [ | |
| 'param-stddev', 'bias-stddev', 'use-natural-gradient', | |
| 'max-change', 'rank-in', 'rank-out', 'num-minibatches-history', | |
| 'alpha-in', 'alpha-out', 'l2-regularize' ]: | |
| value = self.config[opt_name] | |
| if value != '': | |
| a.append('{0}={1}'.format(opt_name, value)) | |
| misc_conv_opts = ' '.join(a) | |
| for n in [1, 2]: | |
| # the convolution. | |
| conv_opts = ('height-in={hi} height-out={ho} height-offsets=-1,0,1 ' | |
| 'height-subsample-out={hs} ' | |
| 'time-offsets=-{p},0,{p} ' | |
| 'num-filters-in={fi} num-filters-out={fo} {r} {o}'.format( | |
| hi=cur_height, ho=height_out, | |
| p=cur_time_period, | |
| hs=(height_subsample if n == 1 else 1), | |
| fi=cur_num_filters, | |
| fo=num_filters_out, | |
| r=('required-time-offsets=0' if allow_zero_padding else ''), | |
| o=misc_conv_opts)) | |
| configs.append('component name={0}.conv{1} type=TimeHeightConvolutionComponent ' | |
| '{2}'.format(name, n, conv_opts)) | |
| configs.append('component-node name={0}.conv{1} component={0}.conv{1} ' | |
| 'input={2}'.format(name, n, cur_descriptor)) | |
| cur_descriptor = '{0}.conv{1}'.format(name, n) | |
| cur_num_filters = num_filters_out | |
| cur_height = height_out | |
| cur_time_period = time_period_out | |
| # the batch-norm | |
| configs.append('component name={0}.batchnorm{1} type=BatchNormComponent dim={2} ' | |
| 'block-dim={3}'.format( | |
| name, n, cur_num_filters * cur_height, | |
| cur_num_filters)) | |
| configs.append('component-node name={0}.batchnorm{1} component={0}.batchnorm{1} ' | |
| 'input={2}'.format(name, n, cur_descriptor)) | |
| cur_descriptor = '{0}.batchnorm{1}'.format(name, n) | |
| # the scale-and-offset | |
| configs.append('component name={0}.scaleoffset{1} type=ScaleAndOffsetComponent dim={2} ' | |
| 'block-dim={3}'.format( | |
| name, n, cur_num_filters * cur_height, | |
| cur_num_filters)) | |
| configs.append('component-node name={0}.scaleoffset{1} component={0}.scaleoffset{1} ' | |
| 'input={2}'.format(name, n, cur_descriptor)) | |
| cur_descriptor = '{0}.scaleoffset{1}'.format(name, n) | |
| if n == 2: | |
| # the bypass connection | |
| cur_descriptor = 'Sum({0}, {1})'.format(cur_descriptor, bypass_descriptor) | |
| # the ReLU | |
| configs.append('component name={0}.relu{1} type=RectifiedLinearComponent ' | |
| 'dim={2} block-dim={3} self-repair-scale={4} ' | |
| 'self-repair-lower-threshold={5}'.format( | |
| name, n, cur_num_filters * cur_height, cur_num_filters, | |
| self.config['self-repair-scale'], | |
| self.config['self-repair-lower-threshold{0}'.format(n)])) | |
| configs.append('component-node name={0}.relu{1} component={0}.relu{1} ' | |
| 'input={2}'.format(name, n, cur_descriptor)) | |
| cur_descriptor = '{0}.relu{1}'.format(name, n) | |
| if bypass_descriptor != input_descriptor: | |
| # We need to add the 1x1 bypass convolution because we're either doing height | |
| # subsampling or changing the number of filters. | |
| conv_opts = ('height-in={hi} height-out={ho} height-offsets=0 ' | |
| 'time-offsets=0 height-subsample-out={hs} ' | |
| 'num-filters-in={fi} num-filters-out={fo} {o}'.format( | |
| hi=height_in, ho=height_out, hs=height_subsample, | |
| fi=num_filters_in, fo=num_filters_out, o=misc_conv_opts)) | |
| configs.append('component name={0}.conv_bypass type=TimeHeightConvolutionComponent ' | |
| '{1}'.format(name, conv_opts)) | |
| configs.append('component-node name={0}.conv_bypass component={0}.conv_bypass ' | |
| 'input={1}'.format(name, input_descriptor)) | |
| # Note: the function 'output_name' is responsible for returning the | |
| # descriptor corresponding to the output of the network, which in | |
| # this case would be '{0}.relu2'.format(name). | |
| return configs | |
| # _generate_bottleneck_resblock_config is a convenience function to generate the | |
| # res-block config (this is the bottleneck version, where there is | |
| # a 3x3 kernel with a smaller number of filters than at the input and output, | |
| # sandwiched between two 1x1 kernels. | |
| # | |
| # The main path inside the res-block in the bottleneck case is as follows: | |
| # | |
| # input -> conv1 -> batchnorm1 -> scaleoffset1 -> relu1 -> | |
| # conv2 -> batchnorm2 -> scaleoffset2 -> relu2 -> | |
| # conv3 -> batchnorm3 -> scaleoffset3 -> relu3 | |
| # | |
| # but the relu3 takes as its input the sum of 'input' and 'scaleoffset3'. | |
| # | |
| def _generate_bottleneck_resblock_config(self): | |
| configs = [] | |
| name = self.name | |
| num_bottleneck_filters = self.config['num-bottleneck-filters'] | |
| assert num_bottleneck_filters > 0 | |
| input_dim = self.descriptors['input']['dim'] | |
| height_in = self.config['height-in'] | |
| height_out = self.config['height-out'] | |
| input_descriptor = self.descriptors['input']['final-string'] | |
| allow_zero_padding = self.config['allow-zero-padding'] | |
| time_period_out = self.config['time-period'] | |
| if not input_dim % height_in == 0: | |
| raise RuntimeError("input-dim={0} does not divide height-in={1}".format( | |
| input_dim, height_in)) | |
| num_filters_in = input_dim / height_in | |
| num_filters_out = self.config['num-filters'] | |
| if height_out != height_in: | |
| if height_out < height_in / 2 - 1 or height_out > height_in / 2 + 1: | |
| raise RuntimeError("Expected height-out to be about half height-in, or the same: " | |
| "height-in={0} height-out={1}".format(height_in, height_out)) | |
| height_subsample = 2 | |
| else: | |
| height_subsample = 1 | |
| cur_descriptor = input_descriptor | |
| cur_num_filters = num_filters_in | |
| cur_height = height_in | |
| if height_subsample == 1 and num_filters_in == num_filters_out: | |
| bypass_descriptor = input_descriptor | |
| else: | |
| bypass_descriptor = '{0}.conv_bypass'.format(name) | |
| # get miscellaneous convolution options passed in from the xconfig line | |
| a = [] | |
| for opt_name in [ | |
| 'param-stddev', 'bias-stddev', 'use-natural-gradient', | |
| 'max-change', 'rank-in', 'rank-out', 'num-minibatches-history', | |
| 'alpha-in', 'alpha-out', 'l2-regularize' ]: | |
| value = self.config[opt_name] | |
| if value != '': | |
| a.append('{0}={1}'.format(opt_name, value)) | |
| misc_conv_opts = ' '.join(a) | |
| for n in [1, 2, 3]: | |
| # the convolution. | |
| height_offsets = ('-1,0,1' if n == 2 else '0') | |
| this_height_subsample = height_subsample if n == 1 else 1 | |
| time_offsets = ('-{t},0,{t}'.format(t=time_period_out) if n == 2 else '0') | |
| next_num_filters = (num_filters_out if n == 3 else num_bottleneck_filters) | |
| conv_opts = ('height-in={h_in} height-out={h_out} height-offsets={ho} time-offsets={to} ' | |
| 'num-filters-in={fi} num-filters-out={fo} height-subsample-out={hs} ' | |
| '{r} {o}'.format( | |
| h_in=cur_height, h_out=height_out, | |
| to=time_offsets, ho=height_offsets, | |
| hs=this_height_subsample, | |
| fi=cur_num_filters, fo=next_num_filters, | |
| r=('required-time-offsets=0' if allow_zero_padding else ''), | |
| o=misc_conv_opts)) | |
| configs.append('component name={0}.conv{1} type=TimeHeightConvolutionComponent ' | |
| '{2}'.format(name, n, conv_opts)) | |
| configs.append('component-node name={0}.conv{1} component={0}.conv{1} ' | |
| 'input={2}'.format(name, n, cur_descriptor)) | |
| cur_num_filters = next_num_filters | |
| cur_height = height_out | |
| cur_descriptor = '{0}.conv{1}'.format(name, n) | |
| # the batch-norm | |
| configs.append('component name={0}.batchnorm{1} type=BatchNormComponent dim={2} ' | |
| 'block-dim={3}'.format( | |
| name, n, cur_num_filters * cur_height, | |
| cur_num_filters)) | |
| configs.append('component-node name={0}.batchnorm{1} component={0}.batchnorm{1} ' | |
| 'input={2}'.format(name, n, cur_descriptor)) | |
| cur_descriptor = '{0}.batchnorm{1}'.format(name, n) | |
| # the scale and offset | |
| configs.append('component name={0}.scaleoffset{1} type=ScaleAndOffsetComponent dim={2} ' | |
| 'block-dim={3}'.format( | |
| name, n, cur_num_filters * cur_height, | |
| cur_num_filters)) | |
| configs.append('component-node name={0}.scaleoffset{1} component={0}.scaleoffset{1} ' | |
| 'input={2}'.format(name, n, cur_descriptor)) | |
| cur_descriptor = '{0}.scaleoffset{1}'.format(name, n) | |
| if n == 3: | |
| # the bypass connection | |
| cur_descriptor = 'Sum({0}, {1})'.format(cur_descriptor, bypass_descriptor) | |
| # the ReLU | |
| configs.append('component name={0}.relu{1} type=RectifiedLinearComponent ' | |
| 'dim={2} block-dim={3} self-repair-scale={4} ' | |
| 'self-repair-lower-threshold={5}'.format( | |
| name, n, cur_num_filters * cur_height, cur_num_filters, | |
| self.config['self-repair-scale'], | |
| self.config['self-repair-lower-threshold{0}'.format(n)])) | |
| configs.append('component-node name={0}.relu{1} component={0}.relu{1} ' | |
| 'input={2}'.format(name, n, cur_descriptor)) | |
| cur_descriptor = '{0}.relu{1}'.format(name, n) | |
| if bypass_descriptor != input_descriptor: | |
| # We need to add the 1x1 bypass convolution because we're either doing height | |
| # subsampling or changing the number of filters. | |
| conv_opts = ('height-in={hi} height-out={ho} height-offsets=0 ' | |
| 'time-offsets=0 height-subsample-out={hs} ' | |
| 'num-filters-in={fi} num-filters-out={fo} {o}'.format( | |
| hi=height_in, ho=height_out, hs=height_subsample, | |
| fi=num_filters_in, fo=num_filters_out, o=misc_conv_opts)) | |
| configs.append('component name={0}.conv_bypass type=TimeHeightConvolutionComponent ' | |
| '{1}'.format(name, conv_opts)) | |
| configs.append('component-node name={0}.conv_bypass component={0}.conv_bypass ' | |
| 'input={1}'.format(name, input_descriptor)) | |
| # Note: the function 'output_name' is responsible for returning the | |
| # descriptor corresponding to the output of the network, which | |
| # in this case will be '{0}.relu3'.format(name). | |
| return configs | |
| # This layer just maps to a single component, a SumBlockComponent. It's for | |
| # doing channel averaging at the end of neural networks. See scripts for | |
| # examples of how to use it. | |
| # An example line using this layer is: | |
| # channel-average-layer name=channel-average input=Append(2, 4, 6, 8) dim=64 | |
| # the configuration value 'dim' is the output dimension of this layer. | |
| # The input dimension is expected to be a multiple of 'dim'. The output | |
| # will be the average of 'dim'-sized blocks of the input. | |
| class ChannelAverageLayer(XconfigLayerBase): | |
| def __init__(self, first_token, key_to_value, prev_names = None): | |
| assert first_token == "channel-average-layer" | |
| XconfigLayerBase.__init__(self, first_token, key_to_value, prev_names) | |
| def set_default_configs(self): | |
| self.config = {'input':'[-1]', | |
| 'dim': -1 } | |
| def set_derived_configs(self): | |
| pass | |
| def check_configs(self): | |
| input_dim = self.descriptors['input']['dim'] | |
| dim = self.config['dim'] | |
| if dim <= 0: | |
| raise RuntimeError("dim must be specified and > 0.") | |
| if input_dim % dim != 0: | |
| raise RuntimeError("input-dim={0} is not a multiple of dim={1}".format( | |
| input_dim, dim)) | |
| def auxiliary_outputs(self): | |
| return [] | |
| def output_name(self, auxiliary_output = None): | |
| assert auxiliary_output is None | |
| return self.name | |
| def output_dim(self, auxiliary_output = None): | |
| assert auxiliary_output is None | |
| return self.config['dim'] | |
| def get_full_config(self): | |
| ans = [] | |
| config_lines = self._generate_channel_average_config() | |
| for line in config_lines: | |
| for config_name in ['ref', 'final']: | |
| ans.append((config_name, line)) | |
| return ans | |
| def _generate_channel_average_config(self): | |
| configs = [] | |
| name = self.name | |
| input_dim = self.descriptors['input']['dim'] | |
| input_descriptor = self.descriptors['input']['final-string'] | |
| dim = self.config['dim'] | |
| # choose the scale that makes it an average rather than a sum. | |
| scale = dim * 1.0 / input_dim | |
| configs.append('component name={0} type=SumBlockComponent input-dim={1} ' | |
| 'output-dim={2} scale={3}'.format(name, input_dim, | |
| dim, scale)) | |
| configs.append('component-node name={0} component={0} input={1}'.format( | |
| name, input_descriptor)) | |
| return configs |