import torch from torch.nn import functional as F from funasr_detach.models.encoder.abs_encoder import AbsEncoder from typing import Tuple, Optional from funasr_detach.models.pooling.statistic_pooling import ( statistic_pooling, windowed_statistic_pooling, ) from collections import OrderedDict import logging import numpy as np class BasicLayer(torch.nn.Module): def __init__( self, in_filters: int, filters: int, stride: int, bn_momentum: float = 0.5 ): super().__init__() self.stride = stride self.in_filters = in_filters self.filters = filters self.bn1 = torch.nn.BatchNorm2d( in_filters, eps=1e-3, momentum=bn_momentum, affine=True ) self.relu1 = torch.nn.ReLU() self.conv1 = torch.nn.Conv2d(in_filters, filters, 3, stride, bias=False) self.bn2 = torch.nn.BatchNorm2d( filters, eps=1e-3, momentum=bn_momentum, affine=True ) self.relu2 = torch.nn.ReLU() self.conv2 = torch.nn.Conv2d(filters, filters, 3, 1, bias=False) if in_filters != filters or stride > 1: self.conv_sc = torch.nn.Conv2d(in_filters, filters, 1, stride, bias=False) self.bn_sc = torch.nn.BatchNorm2d( filters, eps=1e-3, momentum=bn_momentum, affine=True ) def proper_padding(self, x, stride): # align padding mode to tf.layers.conv2d with padding_mod="same" if stride == 1: return F.pad(x, (1, 1, 1, 1), "constant", 0) elif stride == 2: h, w = x.size(2), x.size(3) # (left, right, top, bottom) return F.pad(x, (w % 2, 1, h % 2, 1), "constant", 0) def forward(self, xs_pad, ilens): identity = xs_pad if self.in_filters != self.filters or self.stride > 1: identity = self.conv_sc(identity) identity = self.bn_sc(identity) xs_pad = self.relu1(self.bn1(xs_pad)) xs_pad = self.proper_padding(xs_pad, self.stride) xs_pad = self.conv1(xs_pad) xs_pad = self.relu2(self.bn2(xs_pad)) xs_pad = self.proper_padding(xs_pad, 1) xs_pad = self.conv2(xs_pad) if self.stride == 2: ilens = (ilens + 1) // self.stride return xs_pad + identity, ilens class BasicBlock(torch.nn.Module): def __init__(self, in_filters, filters, num_layer, stride, bn_momentum=0.5): super().__init__() self.num_layer = num_layer for i in range(num_layer): layer = BasicLayer( in_filters if i == 0 else filters, filters, stride if i == 0 else 1, bn_momentum, ) self.add_module("layer_{}".format(i), layer) def forward(self, xs_pad, ilens): for i in range(self.num_layer): xs_pad, ilens = self._modules["layer_{}".format(i)](xs_pad, ilens) return xs_pad, ilens class ResNet34(AbsEncoder): def __init__( self, input_size, use_head_conv=True, batchnorm_momentum=0.5, use_head_maxpool=False, num_nodes_pooling_layer=256, layers_in_block=(3, 4, 6, 3), filters_in_block=(32, 64, 128, 256), ): super(ResNet34, self).__init__() self.use_head_conv = use_head_conv self.use_head_maxpool = use_head_maxpool self.num_nodes_pooling_layer = num_nodes_pooling_layer self.layers_in_block = layers_in_block self.filters_in_block = filters_in_block self.input_size = input_size pre_filters = filters_in_block[0] if use_head_conv: self.pre_conv = torch.nn.Conv2d( 1, pre_filters, 3, 1, 1, bias=False, padding_mode="zeros" ) self.pre_conv_bn = torch.nn.BatchNorm2d( pre_filters, eps=1e-3, momentum=batchnorm_momentum ) if use_head_maxpool: self.head_maxpool = torch.nn.MaxPool2d(3, 1, padding=1) for i in range(len(layers_in_block)): if i == 0: in_filters = pre_filters if self.use_head_conv else 1 else: in_filters = filters_in_block[i - 1] block = BasicBlock( in_filters, filters=filters_in_block[i], num_layer=layers_in_block[i], stride=1 if i == 0 else 2, bn_momentum=batchnorm_momentum, ) self.add_module("block_{}".format(i), block) self.resnet0_dense = torch.nn.Conv2d( filters_in_block[-1], num_nodes_pooling_layer, 1 ) self.resnet0_bn = torch.nn.BatchNorm2d( num_nodes_pooling_layer, eps=1e-3, momentum=batchnorm_momentum ) self.time_ds_ratio = 8 def output_size(self) -> int: return self.num_nodes_pooling_layer def forward( self, xs_pad: torch.Tensor, ilens: torch.Tensor, prev_states: torch.Tensor = None, ) -> Tuple[torch.Tensor, torch.Tensor]: features = xs_pad assert ( features.size(-1) == self.input_size ), "Dimension of features {} doesn't match the input_size {}.".format( features.size(-1), self.input_size ) features = torch.unsqueeze(features, dim=1) if self.use_head_conv: features = self.pre_conv(features) features = self.pre_conv_bn(features) features = F.relu(features) if self.use_head_maxpool: features = self.head_maxpool(features) resnet_outs, resnet_out_lens = features, ilens for i in range(len(self.layers_in_block)): block = self._modules["block_{}".format(i)] resnet_outs, resnet_out_lens = block(resnet_outs, resnet_out_lens) features = self.resnet0_dense(resnet_outs) features = F.relu(features) features = self.resnet0_bn(features) return features, resnet_out_lens # Note: For training, this implement is not equivalent to tf because of the kernel_regularizer in tf.layers. # TODO: implement kernel_regularizer in torch with munal loss addition or weigth_decay in the optimizer class ResNet34_SP_L2Reg(AbsEncoder): def __init__( self, input_size, use_head_conv=True, batchnorm_momentum=0.5, use_head_maxpool=False, num_nodes_pooling_layer=256, layers_in_block=(3, 4, 6, 3), filters_in_block=(32, 64, 128, 256), tf2torch_tensor_name_prefix_torch="encoder", tf2torch_tensor_name_prefix_tf="EAND/speech_encoder", tf_train_steps=720000, ): super(ResNet34_SP_L2Reg, self).__init__() self.use_head_conv = use_head_conv self.use_head_maxpool = use_head_maxpool self.num_nodes_pooling_layer = num_nodes_pooling_layer self.layers_in_block = layers_in_block self.filters_in_block = filters_in_block self.input_size = input_size self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf self.tf_train_steps = tf_train_steps pre_filters = filters_in_block[0] if use_head_conv: self.pre_conv = torch.nn.Conv2d( 1, pre_filters, 3, 1, 1, bias=False, padding_mode="zeros" ) self.pre_conv_bn = torch.nn.BatchNorm2d( pre_filters, eps=1e-3, momentum=batchnorm_momentum ) if use_head_maxpool: self.head_maxpool = torch.nn.MaxPool2d(3, 1, padding=1) for i in range(len(layers_in_block)): if i == 0: in_filters = pre_filters if self.use_head_conv else 1 else: in_filters = filters_in_block[i - 1] block = BasicBlock( in_filters, filters=filters_in_block[i], num_layer=layers_in_block[i], stride=1 if i == 0 else 2, bn_momentum=batchnorm_momentum, ) self.add_module("block_{}".format(i), block) self.resnet0_dense = torch.nn.Conv1d( filters_in_block[-1] * input_size // 8, num_nodes_pooling_layer, 1 ) self.resnet0_bn = torch.nn.BatchNorm1d( num_nodes_pooling_layer, eps=1e-3, momentum=batchnorm_momentum ) self.time_ds_ratio = 8 def output_size(self) -> int: return self.num_nodes_pooling_layer def forward( self, xs_pad: torch.Tensor, ilens: torch.Tensor, prev_states: torch.Tensor = None, ) -> Tuple[torch.Tensor, torch.Tensor]: features = xs_pad assert ( features.size(-1) == self.input_size ), "Dimension of features {} doesn't match the input_size {}.".format( features.size(-1), self.input_size ) features = torch.unsqueeze(features, dim=1) if self.use_head_conv: features = self.pre_conv(features) features = self.pre_conv_bn(features) features = F.relu(features) if self.use_head_maxpool: features = self.head_maxpool(features) resnet_outs, resnet_out_lens = features, ilens for i in range(len(self.layers_in_block)): block = self._modules["block_{}".format(i)] resnet_outs, resnet_out_lens = block(resnet_outs, resnet_out_lens) # B, C, T, F bb, cc, tt, ff = resnet_outs.shape resnet_outs = torch.reshape(resnet_outs.permute(0, 3, 1, 2), [bb, ff * cc, tt]) features = self.resnet0_dense(resnet_outs) features = F.relu(features) features = self.resnet0_bn(features) return features, resnet_out_lens def gen_tf2torch_map_dict(self): tensor_name_prefix_torch = self.tf2torch_tensor_name_prefix_torch tensor_name_prefix_tf = self.tf2torch_tensor_name_prefix_tf train_steps = self.tf_train_steps map_dict_local = { # torch: conv1d.weight in "out_channel in_channel kernel_size" # tf : conv1d.weight in "kernel_size in_channel out_channel" # torch: linear.weight in "out_channel in_channel" # tf : dense.weight in "in_channel out_channel" "{}.pre_conv.weight".format(tensor_name_prefix_torch): { "name": "{}/pre_conv/kernel".format(tensor_name_prefix_tf), "squeeze": None, "transpose": (3, 2, 0, 1), }, "{}.pre_conv_bn.bias".format(tensor_name_prefix_torch): { "name": "{}/pre_conv_bn/beta".format(tensor_name_prefix_tf), "squeeze": None, "transpose": None, }, "{}.pre_conv_bn.weight".format(tensor_name_prefix_torch): { "name": "{}/pre_conv_bn/gamma".format(tensor_name_prefix_tf), "squeeze": None, "transpose": None, }, "{}.pre_conv_bn.running_mean".format(tensor_name_prefix_torch): { "name": "{}/pre_conv_bn/moving_mean".format(tensor_name_prefix_tf), "squeeze": None, "transpose": None, }, "{}.pre_conv_bn.running_var".format(tensor_name_prefix_torch): { "name": "{}/pre_conv_bn/moving_variance".format(tensor_name_prefix_tf), "squeeze": None, "transpose": None, }, "{}.pre_conv_bn.num_batches_tracked".format( tensor_name_prefix_torch ): train_steps, } for layer_idx in range(3): map_dict_local.update( { "{}.resnet{}_dense.weight".format( tensor_name_prefix_torch, layer_idx ): { "name": "{}/resnet{}_dense/kernel".format( tensor_name_prefix_tf, layer_idx ), "squeeze": None, "transpose": (2, 1, 0) if layer_idx == 0 else (1, 0), }, "{}.resnet{}_dense.bias".format( tensor_name_prefix_torch, layer_idx ): { "name": "{}/resnet{}_dense/bias".format( tensor_name_prefix_tf, layer_idx ), "squeeze": None, "transpose": None, }, "{}.resnet{}_bn.weight".format( tensor_name_prefix_torch, layer_idx ): { "name": "{}/resnet{}_bn/gamma".format( tensor_name_prefix_tf, layer_idx ), "squeeze": None, "transpose": None, }, "{}.resnet{}_bn.bias".format(tensor_name_prefix_torch, layer_idx): { "name": "{}/resnet{}_bn/beta".format( tensor_name_prefix_tf, layer_idx ), "squeeze": None, "transpose": None, }, "{}.resnet{}_bn.running_mean".format( tensor_name_prefix_torch, layer_idx ): { "name": "{}/resnet{}_bn/moving_mean".format( tensor_name_prefix_tf, layer_idx ), "squeeze": None, "transpose": None, }, "{}.resnet{}_bn.running_var".format( tensor_name_prefix_torch, layer_idx ): { "name": "{}/resnet{}_bn/moving_variance".format( tensor_name_prefix_tf, layer_idx ), "squeeze": None, "transpose": None, }, "{}.resnet{}_bn.num_batches_tracked".format( tensor_name_prefix_torch, layer_idx ): train_steps, } ) for block_idx in range(len(self.layers_in_block)): for layer_idx in range(self.layers_in_block[block_idx]): for i in ["1", "2", "_sc"]: map_dict_local.update( { "{}.block_{}.layer_{}.conv{}.weight".format( tensor_name_prefix_torch, block_idx, layer_idx, i ): { "name": "{}/block_{}/layer_{}/conv{}/kernel".format( tensor_name_prefix_tf, block_idx, layer_idx, i ), "squeeze": None, "transpose": (3, 2, 0, 1), }, "{}.block_{}.layer_{}.bn{}.weight".format( tensor_name_prefix_torch, block_idx, layer_idx, i ): { "name": "{}/block_{}/layer_{}/bn{}/gamma".format( tensor_name_prefix_tf, block_idx, layer_idx, i ), "squeeze": None, "transpose": None, }, "{}.block_{}.layer_{}.bn{}.bias".format( tensor_name_prefix_torch, block_idx, layer_idx, i ): { "name": "{}/block_{}/layer_{}/bn{}/beta".format( tensor_name_prefix_tf, block_idx, layer_idx, i ), "squeeze": None, "transpose": None, }, "{}.block_{}.layer_{}.bn{}.running_mean".format( tensor_name_prefix_torch, block_idx, layer_idx, i ): { "name": "{}/block_{}/layer_{}/bn{}/moving_mean".format( tensor_name_prefix_tf, block_idx, layer_idx, i ), "squeeze": None, "transpose": None, }, "{}.block_{}.layer_{}.bn{}.running_var".format( tensor_name_prefix_torch, block_idx, layer_idx, i ): { "name": "{}/block_{}/layer_{}/bn{}/moving_variance".format( tensor_name_prefix_tf, block_idx, layer_idx, i ), "squeeze": None, "transpose": None, }, "{}.block_{}.layer_{}.bn{}.num_batches_tracked".format( tensor_name_prefix_torch, block_idx, layer_idx, i ): train_steps, } ) return map_dict_local def convert_tf2torch( self, var_dict_tf, var_dict_torch, ): map_dict = self.gen_tf2torch_map_dict() var_dict_torch_update = dict() for name in sorted(var_dict_torch.keys(), reverse=False): if name.startswith(self.tf2torch_tensor_name_prefix_torch): if name in map_dict: if "num_batches_tracked" not in name: name_tf = map_dict[name]["name"] data_tf = var_dict_tf[name_tf] if map_dict[name]["squeeze"] is not None: data_tf = np.squeeze( data_tf, axis=map_dict[name]["squeeze"] ) if map_dict[name]["transpose"] is not None: data_tf = np.transpose(data_tf, map_dict[name]["transpose"]) data_tf = ( torch.from_numpy(data_tf).type(torch.float32).to("cpu") ) assert ( var_dict_torch[name].size() == data_tf.size() ), "{}, {}, {} != {}".format( name, name_tf, var_dict_torch[name].size(), data_tf.size() ) var_dict_torch_update[name] = data_tf logging.info( "torch tensor: {}, {}, loading from tf tensor: {}, {}".format( name, data_tf.size(), name_tf, var_dict_tf[name_tf].shape, ) ) else: var_dict_torch_update[name] = ( torch.Tensor(map_dict[name]).type(torch.int64).to("cpu") ) logging.info( "torch tensor: {}, manually assigning to: {}".format( name, map_dict[name] ) ) else: logging.warning("{} is missed from tf checkpoint".format(name)) return var_dict_torch_update class ResNet34Diar(ResNet34): def __init__( self, input_size, embedding_node="resnet1_dense", use_head_conv=True, batchnorm_momentum=0.5, use_head_maxpool=False, num_nodes_pooling_layer=256, layers_in_block=(3, 4, 6, 3), filters_in_block=(32, 64, 128, 256), num_nodes_resnet1=256, num_nodes_last_layer=256, pooling_type="window_shift", pool_size=20, stride=1, tf2torch_tensor_name_prefix_torch="encoder", tf2torch_tensor_name_prefix_tf="seq2seq/speech_encoder", ): """ Author: Speech Lab, Alibaba Group, China SOND: Speaker Overlap-aware Neural Diarization for Multi-party Meeting Analysis https://arxiv.org/abs/2211.10243 """ super(ResNet34Diar, self).__init__( input_size, use_head_conv=use_head_conv, batchnorm_momentum=batchnorm_momentum, use_head_maxpool=use_head_maxpool, num_nodes_pooling_layer=num_nodes_pooling_layer, layers_in_block=layers_in_block, filters_in_block=filters_in_block, ) self.embedding_node = embedding_node self.num_nodes_resnet1 = num_nodes_resnet1 self.num_nodes_last_layer = num_nodes_last_layer self.pooling_type = pooling_type self.pool_size = pool_size self.stride = stride self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf self.resnet1_dense = torch.nn.Linear( num_nodes_pooling_layer * 2, num_nodes_resnet1 ) self.resnet1_bn = torch.nn.BatchNorm1d( num_nodes_resnet1, eps=1e-3, momentum=batchnorm_momentum ) self.resnet2_dense = torch.nn.Linear(num_nodes_resnet1, num_nodes_last_layer) self.resnet2_bn = torch.nn.BatchNorm1d( num_nodes_last_layer, eps=1e-3, momentum=batchnorm_momentum ) def output_size(self) -> int: if self.embedding_node.startswith("resnet1"): return self.num_nodes_resnet1 elif self.embedding_node.startswith("resnet2"): return self.num_nodes_last_layer return self.num_nodes_pooling_layer def forward( self, xs_pad: torch.Tensor, ilens: torch.Tensor, prev_states: torch.Tensor = None, ) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]: endpoints = OrderedDict() res_out, ilens = super().forward(xs_pad, ilens) endpoints["resnet0_bn"] = res_out if self.pooling_type == "frame_gsp": features = statistic_pooling(res_out, ilens, (3,)) else: features, ilens = windowed_statistic_pooling( res_out, ilens, (2, 3), self.pool_size, self.stride ) features = features.transpose(1, 2) endpoints["pooling"] = features features = self.resnet1_dense(features) endpoints["resnet1_dense"] = features features = F.relu(features) endpoints["resnet1_relu"] = features features = self.resnet1_bn(features.transpose(1, 2)).transpose(1, 2) endpoints["resnet1_bn"] = features features = self.resnet2_dense(features) endpoints["resnet2_dense"] = features features = F.relu(features) endpoints["resnet2_relu"] = features features = self.resnet2_bn(features.transpose(1, 2)).transpose(1, 2) endpoints["resnet2_bn"] = features return endpoints[self.embedding_node], ilens, None def gen_tf2torch_map_dict(self): tensor_name_prefix_torch = self.tf2torch_tensor_name_prefix_torch tensor_name_prefix_tf = self.tf2torch_tensor_name_prefix_tf train_steps = 300000 map_dict_local = { # torch: conv1d.weight in "out_channel in_channel kernel_size" # tf : conv1d.weight in "kernel_size in_channel out_channel" # torch: linear.weight in "out_channel in_channel" # tf : dense.weight in "in_channel out_channel" "{}.pre_conv.weight".format(tensor_name_prefix_torch): { "name": "{}/pre_conv/kernel".format(tensor_name_prefix_tf), "squeeze": None, "transpose": (3, 2, 0, 1), }, "{}.pre_conv_bn.bias".format(tensor_name_prefix_torch): { "name": "{}/pre_conv_bn/beta".format(tensor_name_prefix_tf), "squeeze": None, "transpose": None, }, "{}.pre_conv_bn.weight".format(tensor_name_prefix_torch): { "name": "{}/pre_conv_bn/gamma".format(tensor_name_prefix_tf), "squeeze": None, "transpose": None, }, "{}.pre_conv_bn.running_mean".format(tensor_name_prefix_torch): { "name": "{}/pre_conv_bn/moving_mean".format(tensor_name_prefix_tf), "squeeze": None, "transpose": None, }, "{}.pre_conv_bn.running_var".format(tensor_name_prefix_torch): { "name": "{}/pre_conv_bn/moving_variance".format(tensor_name_prefix_tf), "squeeze": None, "transpose": None, }, "{}.pre_conv_bn.num_batches_tracked".format( tensor_name_prefix_torch ): train_steps, } for layer_idx in range(3): map_dict_local.update( { "{}.resnet{}_dense.weight".format( tensor_name_prefix_torch, layer_idx ): { "name": "{}/resnet{}_dense/kernel".format( tensor_name_prefix_tf, layer_idx ), "squeeze": None, "transpose": (3, 2, 0, 1) if layer_idx == 0 else (1, 0), }, "{}.resnet{}_dense.bias".format( tensor_name_prefix_torch, layer_idx ): { "name": "{}/resnet{}_dense/bias".format( tensor_name_prefix_tf, layer_idx ), "squeeze": None, "transpose": None, }, "{}.resnet{}_bn.weight".format( tensor_name_prefix_torch, layer_idx ): { "name": "{}/resnet{}_bn/gamma".format( tensor_name_prefix_tf, layer_idx ), "squeeze": None, "transpose": None, }, "{}.resnet{}_bn.bias".format(tensor_name_prefix_torch, layer_idx): { "name": "{}/resnet{}_bn/beta".format( tensor_name_prefix_tf, layer_idx ), "squeeze": None, "transpose": None, }, "{}.resnet{}_bn.running_mean".format( tensor_name_prefix_torch, layer_idx ): { "name": "{}/resnet{}_bn/moving_mean".format( tensor_name_prefix_tf, layer_idx ), "squeeze": None, "transpose": None, }, "{}.resnet{}_bn.running_var".format( tensor_name_prefix_torch, layer_idx ): { "name": "{}/resnet{}_bn/moving_variance".format( tensor_name_prefix_tf, layer_idx ), "squeeze": None, "transpose": None, }, "{}.resnet{}_bn.num_batches_tracked".format( tensor_name_prefix_torch, layer_idx ): train_steps, } ) for block_idx in range(len(self.layers_in_block)): for layer_idx in range(self.layers_in_block[block_idx]): for i in ["1", "2", "_sc"]: map_dict_local.update( { "{}.block_{}.layer_{}.conv{}.weight".format( tensor_name_prefix_torch, block_idx, layer_idx, i ): { "name": "{}/block_{}/layer_{}/conv{}/kernel".format( tensor_name_prefix_tf, block_idx, layer_idx, i ), "squeeze": None, "transpose": (3, 2, 0, 1), }, "{}.block_{}.layer_{}.bn{}.weight".format( tensor_name_prefix_torch, block_idx, layer_idx, i ): { "name": "{}/block_{}/layer_{}/bn{}/gamma".format( tensor_name_prefix_tf, block_idx, layer_idx, i ), "squeeze": None, "transpose": None, }, "{}.block_{}.layer_{}.bn{}.bias".format( tensor_name_prefix_torch, block_idx, layer_idx, i ): { "name": "{}/block_{}/layer_{}/bn{}/beta".format( tensor_name_prefix_tf, block_idx, layer_idx, i ), "squeeze": None, "transpose": None, }, "{}.block_{}.layer_{}.bn{}.running_mean".format( tensor_name_prefix_torch, block_idx, layer_idx, i ): { "name": "{}/block_{}/layer_{}/bn{}/moving_mean".format( tensor_name_prefix_tf, block_idx, layer_idx, i ), "squeeze": None, "transpose": None, }, "{}.block_{}.layer_{}.bn{}.running_var".format( tensor_name_prefix_torch, block_idx, layer_idx, i ): { "name": "{}/block_{}/layer_{}/bn{}/moving_variance".format( tensor_name_prefix_tf, block_idx, layer_idx, i ), "squeeze": None, "transpose": None, }, "{}.block_{}.layer_{}.bn{}.num_batches_tracked".format( tensor_name_prefix_torch, block_idx, layer_idx, i ): train_steps, } ) return map_dict_local def convert_tf2torch( self, var_dict_tf, var_dict_torch, ): map_dict = self.gen_tf2torch_map_dict() var_dict_torch_update = dict() for name in sorted(var_dict_torch.keys(), reverse=False): if name.startswith(self.tf2torch_tensor_name_prefix_torch): if name in map_dict: if "num_batches_tracked" not in name: name_tf = map_dict[name]["name"] data_tf = var_dict_tf[name_tf] if map_dict[name]["squeeze"] is not None: data_tf = np.squeeze( data_tf, axis=map_dict[name]["squeeze"] ) if map_dict[name]["transpose"] is not None: data_tf = np.transpose(data_tf, map_dict[name]["transpose"]) data_tf = ( torch.from_numpy(data_tf).type(torch.float32).to("cpu") ) assert ( var_dict_torch[name].size() == data_tf.size() ), "{}, {}, {} != {}".format( name, name_tf, var_dict_torch[name].size(), data_tf.size() ) var_dict_torch_update[name] = data_tf logging.info( "torch tensor: {}, {}, loading from tf tensor: {}, {}".format( name, data_tf.size(), name_tf, var_dict_tf[name_tf].shape, ) ) else: var_dict_torch_update[name] = ( torch.Tensor(map_dict[name]).type(torch.int64).to("cpu") ) logging.info( "torch tensor: {}, manually assigning to: {}".format( name, map_dict[name] ) ) else: logging.warning("{} is missed from tf checkpoint".format(name)) return var_dict_torch_update class ResNet34SpL2RegDiar(ResNet34_SP_L2Reg): def __init__( self, input_size, embedding_node="resnet1_dense", use_head_conv=True, batchnorm_momentum=0.5, use_head_maxpool=False, num_nodes_pooling_layer=256, layers_in_block=(3, 4, 6, 3), filters_in_block=(32, 64, 128, 256), num_nodes_resnet1=256, num_nodes_last_layer=256, pooling_type="window_shift", pool_size=20, stride=1, tf2torch_tensor_name_prefix_torch="encoder", tf2torch_tensor_name_prefix_tf="seq2seq/speech_encoder", ): """ Author: Speech Lab, Alibaba Group, China TOLD: A Novel Two-Stage Overlap-Aware Framework for Speaker Diarization https://arxiv.org/abs/2303.05397 """ super(ResNet34SpL2RegDiar, self).__init__( input_size, use_head_conv=use_head_conv, batchnorm_momentum=batchnorm_momentum, use_head_maxpool=use_head_maxpool, num_nodes_pooling_layer=num_nodes_pooling_layer, layers_in_block=layers_in_block, filters_in_block=filters_in_block, ) self.embedding_node = embedding_node self.num_nodes_resnet1 = num_nodes_resnet1 self.num_nodes_last_layer = num_nodes_last_layer self.pooling_type = pooling_type self.pool_size = pool_size self.stride = stride self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf self.resnet1_dense = torch.nn.Linear( num_nodes_pooling_layer * 2, num_nodes_resnet1 ) self.resnet1_bn = torch.nn.BatchNorm1d( num_nodes_resnet1, eps=1e-3, momentum=batchnorm_momentum ) self.resnet2_dense = torch.nn.Linear(num_nodes_resnet1, num_nodes_last_layer) self.resnet2_bn = torch.nn.BatchNorm1d( num_nodes_last_layer, eps=1e-3, momentum=batchnorm_momentum ) def output_size(self) -> int: if self.embedding_node.startswith("resnet1"): return self.num_nodes_resnet1 elif self.embedding_node.startswith("resnet2"): return self.num_nodes_last_layer return self.num_nodes_pooling_layer def forward( self, xs_pad: torch.Tensor, ilens: torch.Tensor, prev_states: torch.Tensor = None, ) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]: endpoints = OrderedDict() res_out, ilens = super().forward(xs_pad, ilens) endpoints["resnet0_bn"] = res_out if self.pooling_type == "frame_gsp": features = statistic_pooling(res_out, ilens, (2,)) else: features, ilens = windowed_statistic_pooling( res_out, ilens, (2,), self.pool_size, self.stride ) features = features.transpose(1, 2) endpoints["pooling"] = features features = self.resnet1_dense(features) endpoints["resnet1_dense"] = features features = F.relu(features) endpoints["resnet1_relu"] = features features = self.resnet1_bn(features.transpose(1, 2)).transpose(1, 2) endpoints["resnet1_bn"] = features features = self.resnet2_dense(features) endpoints["resnet2_dense"] = features features = F.relu(features) endpoints["resnet2_relu"] = features features = self.resnet2_bn(features.transpose(1, 2)).transpose(1, 2) endpoints["resnet2_bn"] = features return endpoints[self.embedding_node], ilens, None def gen_tf2torch_map_dict(self): tensor_name_prefix_torch = self.tf2torch_tensor_name_prefix_torch tensor_name_prefix_tf = self.tf2torch_tensor_name_prefix_tf train_steps = 720000 map_dict_local = { # torch: conv1d.weight in "out_channel in_channel kernel_size" # tf : conv1d.weight in "kernel_size in_channel out_channel" # torch: linear.weight in "out_channel in_channel" # tf : dense.weight in "in_channel out_channel" "{}.pre_conv.weight".format(tensor_name_prefix_torch): { "name": "{}/pre_conv/kernel".format(tensor_name_prefix_tf), "squeeze": None, "transpose": (3, 2, 0, 1), }, "{}.pre_conv_bn.bias".format(tensor_name_prefix_torch): { "name": "{}/pre_conv_bn/beta".format(tensor_name_prefix_tf), "squeeze": None, "transpose": None, }, "{}.pre_conv_bn.weight".format(tensor_name_prefix_torch): { "name": "{}/pre_conv_bn/gamma".format(tensor_name_prefix_tf), "squeeze": None, "transpose": None, }, "{}.pre_conv_bn.running_mean".format(tensor_name_prefix_torch): { "name": "{}/pre_conv_bn/moving_mean".format(tensor_name_prefix_tf), "squeeze": None, "transpose": None, }, "{}.pre_conv_bn.running_var".format(tensor_name_prefix_torch): { "name": "{}/pre_conv_bn/moving_variance".format(tensor_name_prefix_tf), "squeeze": None, "transpose": None, }, "{}.pre_conv_bn.num_batches_tracked".format( tensor_name_prefix_torch ): train_steps, } for layer_idx in range(3): map_dict_local.update( { "{}.resnet{}_dense.weight".format( tensor_name_prefix_torch, layer_idx ): { "name": "{}/resnet{}_dense/kernel".format( tensor_name_prefix_tf, layer_idx ), "squeeze": None, "transpose": (2, 1, 0) if layer_idx == 0 else (1, 0), }, "{}.resnet{}_dense.bias".format( tensor_name_prefix_torch, layer_idx ): { "name": "{}/resnet{}_dense/bias".format( tensor_name_prefix_tf, layer_idx ), "squeeze": None, "transpose": None, }, "{}.resnet{}_bn.weight".format( tensor_name_prefix_torch, layer_idx ): { "name": "{}/resnet{}_bn/gamma".format( tensor_name_prefix_tf, layer_idx ), "squeeze": None, "transpose": None, }, "{}.resnet{}_bn.bias".format(tensor_name_prefix_torch, layer_idx): { "name": "{}/resnet{}_bn/beta".format( tensor_name_prefix_tf, layer_idx ), "squeeze": None, "transpose": None, }, "{}.resnet{}_bn.running_mean".format( tensor_name_prefix_torch, layer_idx ): { "name": "{}/resnet{}_bn/moving_mean".format( tensor_name_prefix_tf, layer_idx ), "squeeze": None, "transpose": None, }, "{}.resnet{}_bn.running_var".format( tensor_name_prefix_torch, layer_idx ): { "name": "{}/resnet{}_bn/moving_variance".format( tensor_name_prefix_tf, layer_idx ), "squeeze": None, "transpose": None, }, "{}.resnet{}_bn.num_batches_tracked".format( tensor_name_prefix_torch, layer_idx ): train_steps, } ) for block_idx in range(len(self.layers_in_block)): for layer_idx in range(self.layers_in_block[block_idx]): for i in ["1", "2", "_sc"]: map_dict_local.update( { "{}.block_{}.layer_{}.conv{}.weight".format( tensor_name_prefix_torch, block_idx, layer_idx, i ): { "name": "{}/block_{}/layer_{}/conv{}/kernel".format( tensor_name_prefix_tf, block_idx, layer_idx, i ), "squeeze": None, "transpose": (3, 2, 0, 1), }, "{}.block_{}.layer_{}.bn{}.weight".format( tensor_name_prefix_torch, block_idx, layer_idx, i ): { "name": "{}/block_{}/layer_{}/bn{}/gamma".format( tensor_name_prefix_tf, block_idx, layer_idx, i ), "squeeze": None, "transpose": None, }, "{}.block_{}.layer_{}.bn{}.bias".format( tensor_name_prefix_torch, block_idx, layer_idx, i ): { "name": "{}/block_{}/layer_{}/bn{}/beta".format( tensor_name_prefix_tf, block_idx, layer_idx, i ), "squeeze": None, "transpose": None, }, "{}.block_{}.layer_{}.bn{}.running_mean".format( tensor_name_prefix_torch, block_idx, layer_idx, i ): { "name": "{}/block_{}/layer_{}/bn{}/moving_mean".format( tensor_name_prefix_tf, block_idx, layer_idx, i ), "squeeze": None, "transpose": None, }, "{}.block_{}.layer_{}.bn{}.running_var".format( tensor_name_prefix_torch, block_idx, layer_idx, i ): { "name": "{}/block_{}/layer_{}/bn{}/moving_variance".format( tensor_name_prefix_tf, block_idx, layer_idx, i ), "squeeze": None, "transpose": None, }, "{}.block_{}.layer_{}.bn{}.num_batches_tracked".format( tensor_name_prefix_torch, block_idx, layer_idx, i ): train_steps, } ) return map_dict_local def convert_tf2torch( self, var_dict_tf, var_dict_torch, ): map_dict = self.gen_tf2torch_map_dict() var_dict_torch_update = dict() for name in sorted(var_dict_torch.keys(), reverse=False): if name.startswith(self.tf2torch_tensor_name_prefix_torch): if name in map_dict: if "num_batches_tracked" not in name: name_tf = map_dict[name]["name"] data_tf = var_dict_tf[name_tf] if map_dict[name]["squeeze"] is not None: data_tf = np.squeeze( data_tf, axis=map_dict[name]["squeeze"] ) if map_dict[name]["transpose"] is not None: data_tf = np.transpose(data_tf, map_dict[name]["transpose"]) data_tf = ( torch.from_numpy(data_tf).type(torch.float32).to("cpu") ) assert ( var_dict_torch[name].size() == data_tf.size() ), "{}, {}, {} != {}".format( name, name_tf, var_dict_torch[name].size(), data_tf.size() ) var_dict_torch_update[name] = data_tf logging.info( "torch tensor: {}, {}, loading from tf tensor: {}, {}".format( name, data_tf.size(), name_tf, var_dict_tf[name_tf].shape, ) ) else: var_dict_torch_update[name] = ( torch.from_numpy(np.array(map_dict[name])) .type(torch.int64) .to("cpu") ) logging.info( "torch tensor: {}, manually assigning to: {}".format( name, map_dict[name] ) ) else: logging.warning("{} is missed from tf checkpoint".format(name)) return var_dict_torch_update