import torch
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from torch.nn import functional as F
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from funasr.models.encoder.abs_encoder import AbsEncoder
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from typing import Tuple
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class BasicLayer(torch.nn.Module):
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def __init__(self, in_filters: int, filters: int, stride: int, bn_momentum: float = 0.5):
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super().__init__()
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self.stride = stride
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self.in_filters = in_filters
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self.filters = filters
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self.bn1 = torch.nn.BatchNorm2d(in_filters, eps=1e-3, momentum=bn_momentum, affine=True)
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self.relu1 = torch.nn.ReLU()
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self.conv1 = torch.nn.Conv2d(in_filters, filters, 3, stride, bias=False)
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self.bn2 = torch.nn.BatchNorm2d(filters, eps=1e-3, momentum=bn_momentum, affine=True)
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self.relu2 = torch.nn.ReLU()
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self.conv2 = torch.nn.Conv2d(filters, filters, 3, 1, bias=False)
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if in_filters != filters or stride > 1:
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self.conv_sc = torch.nn.Conv2d(in_filters, filters, 1, stride, bias=False)
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self.bn_sc = torch.nn.BatchNorm2d(filters, eps=1e-3, momentum=bn_momentum, affine=True)
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def proper_padding(self, x, stride):
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# align padding mode to tf.layers.conv2d with padding_mod="same"
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if stride == 1:
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return F.pad(x, (1, 1, 1, 1), "constant", 0)
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elif stride == 2:
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h, w = x.size(2), x.size(3)
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# (left, right, top, bottom)
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return F.pad(x, (w % 2, 1, h % 2, 1), "constant", 0)
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def forward(self, xs_pad, ilens):
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identity = xs_pad
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if self.in_filters != self.filters or self.stride > 1:
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identity = self.conv_sc(identity)
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identity = self.bn_sc(identity)
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xs_pad = self.relu1(self.bn1(xs_pad))
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xs_pad = self.proper_padding(xs_pad, self.stride)
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xs_pad = self.conv1(xs_pad)
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xs_pad = self.relu2(self.bn2(xs_pad))
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xs_pad = self.proper_padding(xs_pad, 1)
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xs_pad = self.conv2(xs_pad)
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if self.stride == 2:
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ilens = (ilens + 1) // self.stride
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return xs_pad + identity, ilens
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class BasicBlock(torch.nn.Module):
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def __init__(self, in_filters, filters, num_layer, stride, bn_momentum=0.5):
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super().__init__()
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self.num_layer = num_layer
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for i in range(num_layer):
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layer = BasicLayer(in_filters if i == 0 else filters, filters,
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stride if i == 0 else 1, bn_momentum)
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self.add_module("layer_{}".format(i), layer)
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def forward(self, xs_pad, ilens):
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for i in range(self.num_layer):
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xs_pad, ilens = self._modules["layer_{}".format(i)](xs_pad, ilens)
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return xs_pad, ilens
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class ResNet34(AbsEncoder):
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def __init__(
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self,
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input_size,
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use_head_conv=True,
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batchnorm_momentum=0.5,
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use_head_maxpool=False,
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num_nodes_pooling_layer=256,
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layers_in_block=(3, 4, 6, 3),
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filters_in_block=(32, 64, 128, 256),
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):
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super(ResNet34, self).__init__()
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self.use_head_conv = use_head_conv
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self.use_head_maxpool = use_head_maxpool
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self.num_nodes_pooling_layer = num_nodes_pooling_layer
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self.layers_in_block = layers_in_block
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self.filters_in_block = filters_in_block
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self.input_size = input_size
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pre_filters = filters_in_block[0]
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if use_head_conv:
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self.pre_conv = torch.nn.Conv2d(1, pre_filters, 3, 1, 1, bias=False, padding_mode="zeros")
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self.pre_conv_bn = torch.nn.BatchNorm2d(pre_filters, eps=1e-3, momentum=batchnorm_momentum)
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if use_head_maxpool:
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self.head_maxpool = torch.nn.MaxPool2d(3, 1, padding=1)
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for i in range(len(layers_in_block)):
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if i == 0:
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in_filters = pre_filters if self.use_head_conv else 1
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else:
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in_filters = filters_in_block[i-1]
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block = BasicBlock(in_filters,
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filters=filters_in_block[i],
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num_layer=layers_in_block[i],
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stride=1 if i == 0 else 2,
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bn_momentum=batchnorm_momentum)
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self.add_module("block_{}".format(i), block)
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self.resnet0_dense = torch.nn.Conv2d(filters_in_block[-1], num_nodes_pooling_layer, 1)
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self.resnet0_bn = torch.nn.BatchNorm2d(num_nodes_pooling_layer, eps=1e-3, momentum=batchnorm_momentum)
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def output_size(self) -> int:
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return self.num_nodes_pooling_layer
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def forward(self, xs_pad: torch.Tensor, ilens: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
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features = xs_pad
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assert features.size(-1) == self.input_size, \
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"Dimension of features {} doesn't match the input_size {}.".format(features.size(-1), self.input_size)
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features = torch.unsqueeze(features, dim=1)
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if self.use_head_conv:
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features = self.pre_conv(features)
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features = self.pre_conv_bn(features)
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features = F.relu(features)
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if self.use_head_maxpool:
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features = self.head_maxpool(features)
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resnet_outs, resnet_out_lens = features, ilens
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for i in range(len(self.layers_in_block)):
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block = self._modules["block_{}".format(i)]
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resnet_outs, resnet_out_lens = block(resnet_outs, resnet_out_lens)
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features = self.resnet0_dense(resnet_outs)
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features = F.relu(features)
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features = self.resnet0_bn(features)
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return features, ilens // 8
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