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model.py
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AVHubertSeq2Seq(
(encoder): HubertEncoderWrapper(
(w2v_model): AVHubertModel(
(feature_extractor_audio): SubModel(
(proj): Linear(in_features=104, out_features=768, bias=True)
)
(feature_extractor_video): SubModel(
(resnet): ResEncoder(
(frontend3D): Sequential(
(0): Conv3d(1, 64, kernel_size=(5, 7, 7), stride=(1, 2, 2), padding=(2, 3, 3), bias=False)
(1): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): PReLU(num_parameters=64)
(3): MaxPool3d(kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 1, 1), dilation=1, ceil_mode=False)
)
(trunk): ResNet(
(layer1): Sequential(
(0): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): PReLU(num_parameters=64)
(relu2): PReLU(num_parameters=64)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(1): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): PReLU(num_parameters=64)
(relu2): PReLU(num_parameters=64)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(layer2): Sequential(
(0): BasicBlock(
(conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): PReLU(num_parameters=128)
(relu2): PReLU(num_parameters=128)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): PReLU(num_parameters=128)
(relu2): PReLU(num_parameters=128)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(layer3): Sequential(
(0): BasicBlock(
(conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): PReLU(num_parameters=256)
(relu2): PReLU(num_parameters=256)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): PReLU(num_parameters=256)
(relu2): PReLU(num_parameters=256)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(layer4): Sequential(
(0): BasicBlock(
(conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): PReLU(num_parameters=512)
(relu2): PReLU(num_parameters=512)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): PReLU(num_parameters=512)
(relu2): PReLU(num_parameters=512)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(avgpool): AdaptiveAvgPool2d(output_size=1)
)
)
(proj): Linear(in_features=512, out_features=768, bias=True)
)
(post_extract_proj): Linear(in_features=1536, out_features=768, bias=True)
(dropout_input): Dropout(p=0.1, inplace=False)
(dropout_features): Dropout(p=0.1, inplace=False)
(encoder): TransformerEncoder(
(pos_conv): Sequential(
(0): Conv1d(768, 768, kernel_size=(128,), stride=(1,), padding=(64,), groups=16)
(1): SamePad()
(2): GELU()
)
(layers): ModuleList(
(0): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=768, out_features=768, bias=True)
(v_proj): Linear(in_features=768, out_features=768, bias=True)
(q_proj): Linear(in_features=768, out_features=768, bias=True)
(out_proj): Linear(in_features=768, out_features=768, bias=True)
)
(dropout1): Dropout(p=0.1, inplace=False)
(dropout2): Dropout(p=0.0, inplace=False)
(dropout3): Dropout(p=0.1, inplace=False)
(self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
)
(1): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=768, out_features=768, bias=True)
(v_proj): Linear(in_features=768, out_features=768, bias=True)
(q_proj): Linear(in_features=768, out_features=768, bias=True)
(out_proj): Linear(in_features=768, out_features=768, bias=True)
)
(dropout1): Dropout(p=0.1, inplace=False)
(dropout2): Dropout(p=0.0, inplace=False)
(dropout3): Dropout(p=0.1, inplace=False)
(self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
)
(2): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=768, out_features=768, bias=True)
(v_proj): Linear(in_features=768, out_features=768, bias=True)
(q_proj): Linear(in_features=768, out_features=768, bias=True)
(out_proj): Linear(in_features=768, out_features=768, bias=True)
)
(dropout1): Dropout(p=0.1, inplace=False)
(dropout2): Dropout(p=0.0, inplace=False)
(dropout3): Dropout(p=0.1, inplace=False)
(self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
)
(3): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=768, out_features=768, bias=True)
(v_proj): Linear(in_features=768, out_features=768, bias=True)
(q_proj): Linear(in_features=768, out_features=768, bias=True)
(out_proj): Linear(in_features=768, out_features=768, bias=True)
)
(dropout1): Dropout(p=0.1, inplace=False)
(dropout2): Dropout(p=0.0, inplace=False)
(dropout3): Dropout(p=0.1, inplace=False)
(self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
)
(4): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=768, out_features=768, bias=True)
(v_proj): Linear(in_features=768, out_features=768, bias=True)
(q_proj): Linear(in_features=768, out_features=768, bias=True)
(out_proj): Linear(in_features=768, out_features=768, bias=True)
)
(dropout1): Dropout(p=0.1, inplace=False)
(dropout2): Dropout(p=0.0, inplace=False)
(dropout3): Dropout(p=0.1, inplace=False)
(self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
)
(5): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=768, out_features=768, bias=True)
(v_proj): Linear(in_features=768, out_features=768, bias=True)
(q_proj): Linear(in_features=768, out_features=768, bias=True)
(out_proj): Linear(in_features=768, out_features=768, bias=True)
)
(dropout1): Dropout(p=0.1, inplace=False)
(dropout2): Dropout(p=0.0, inplace=False)
(dropout3): Dropout(p=0.1, inplace=False)
(self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
)
(6): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=768, out_features=768, bias=True)
(v_proj): Linear(in_features=768, out_features=768, bias=True)
(q_proj): Linear(in_features=768, out_features=768, bias=True)
(out_proj): Linear(in_features=768, out_features=768, bias=True)
)
(dropout1): Dropout(p=0.1, inplace=False)
(dropout2): Dropout(p=0.0, inplace=False)
(dropout3): Dropout(p=0.1, inplace=False)
(self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
)
(7): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=768, out_features=768, bias=True)
(v_proj): Linear(in_features=768, out_features=768, bias=True)
(q_proj): Linear(in_features=768, out_features=768, bias=True)
(out_proj): Linear(in_features=768, out_features=768, bias=True)
)
(dropout1): Dropout(p=0.1, inplace=False)
(dropout2): Dropout(p=0.0, inplace=False)
(dropout3): Dropout(p=0.1, inplace=False)
(self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
)
(8): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=768, out_features=768, bias=True)
(v_proj): Linear(in_features=768, out_features=768, bias=True)
(q_proj): Linear(in_features=768, out_features=768, bias=True)
(out_proj): Linear(in_features=768, out_features=768, bias=True)
)
(dropout1): Dropout(p=0.1, inplace=False)
(dropout2): Dropout(p=0.0, inplace=False)
(dropout3): Dropout(p=0.1, inplace=False)
(self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
)
(9): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=768, out_features=768, bias=True)
(v_proj): Linear(in_features=768, out_features=768, bias=True)
(q_proj): Linear(in_features=768, out_features=768, bias=True)
(out_proj): Linear(in_features=768, out_features=768, bias=True)
)
(dropout1): Dropout(p=0.1, inplace=False)
(dropout2): Dropout(p=0.0, inplace=False)
(dropout3): Dropout(p=0.1, inplace=False)
(self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
)
(10): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=768, out_features=768, bias=True)
(v_proj): Linear(in_features=768, out_features=768, bias=True)
(q_proj): Linear(in_features=768, out_features=768, bias=True)
(out_proj): Linear(in_features=768, out_features=768, bias=True)
)
(dropout1): Dropout(p=0.1, inplace=False)
(dropout2): Dropout(p=0.0, inplace=False)
(dropout3): Dropout(p=0.1, inplace=False)
(self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
)
(11): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=768, out_features=768, bias=True)
(v_proj): Linear(in_features=768, out_features=768, bias=True)
(q_proj): Linear(in_features=768, out_features=768, bias=True)
(out_proj): Linear(in_features=768, out_features=768, bias=True)
)
(dropout1): Dropout(p=0.1, inplace=False)
(dropout2): Dropout(p=0.0, inplace=False)
(dropout3): Dropout(p=0.1, inplace=False)
(self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
)
)
(layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
)
(layer_norm): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)
(final_proj): None
)
)
(decoder): TransformerDecoder(
(embed_tokens): Embedding(1000, 768, padding_idx=1)
(embed_positions): SinusoidalPositionalEmbedding()
(layers): ModuleList(
(0): TransformerDecoderLayer(
(dropout_module): FairseqDropout()
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=768, out_features=768, bias=True)
(v_proj): Linear(in_features=768, out_features=768, bias=True)
(q_proj): Linear(in_features=768, out_features=768, bias=True)
(out_proj): Linear(in_features=768, out_features=768, bias=True)
)
(activation_dropout_module): FairseqDropout()
(self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(encoder_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=768, out_features=768, bias=True)
(v_proj): Linear(in_features=768, out_features=768, bias=True)
(q_proj): Linear(in_features=768, out_features=768, bias=True)
(out_proj): Linear(in_features=768, out_features=768, bias=True)
)
(encoder_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
)
(1): TransformerDecoderLayer(
(dropout_module): FairseqDropout()
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=768, out_features=768, bias=True)
(v_proj): Linear(in_features=768, out_features=768, bias=True)
(q_proj): Linear(in_features=768, out_features=768, bias=True)
(out_proj): Linear(in_features=768, out_features=768, bias=True)
)
(activation_dropout_module): FairseqDropout()
(self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(encoder_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=768, out_features=768, bias=True)
(v_proj): Linear(in_features=768, out_features=768, bias=True)
(q_proj): Linear(in_features=768, out_features=768, bias=True)
(out_proj): Linear(in_features=768, out_features=768, bias=True)
)
(encoder_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
)
(2): TransformerDecoderLayer(
(dropout_module): FairseqDropout()
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=768, out_features=768, bias=True)
(v_proj): Linear(in_features=768, out_features=768, bias=True)
(q_proj): Linear(in_features=768, out_features=768, bias=True)
(out_proj): Linear(in_features=768, out_features=768, bias=True)
)
(activation_dropout_module): FairseqDropout()
(self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(encoder_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=768, out_features=768, bias=True)
(v_proj): Linear(in_features=768, out_features=768, bias=True)
(q_proj): Linear(in_features=768, out_features=768, bias=True)
(out_proj): Linear(in_features=768, out_features=768, bias=True)
)
(encoder_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
)
(3): TransformerDecoderLayer(
(dropout_module): FairseqDropout()
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=768, out_features=768, bias=True)
(v_proj): Linear(in_features=768, out_features=768, bias=True)
(q_proj): Linear(in_features=768, out_features=768, bias=True)
(out_proj): Linear(in_features=768, out_features=768, bias=True)
)
(activation_dropout_module): FairseqDropout()
(self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(encoder_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=768, out_features=768, bias=True)
(v_proj): Linear(in_features=768, out_features=768, bias=True)
(q_proj): Linear(in_features=768, out_features=768, bias=True)
(out_proj): Linear(in_features=768, out_features=768, bias=True)
)
(encoder_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
)
(4): TransformerDecoderLayer(
(dropout_module): FairseqDropout()
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=768, out_features=768, bias=True)
(v_proj): Linear(in_features=768, out_features=768, bias=True)
(q_proj): Linear(in_features=768, out_features=768, bias=True)
(out_proj): Linear(in_features=768, out_features=768, bias=True)
)
(activation_dropout_module): FairseqDropout()
(self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(encoder_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=768, out_features=768, bias=True)
(v_proj): Linear(in_features=768, out_features=768, bias=True)
(q_proj): Linear(in_features=768, out_features=768, bias=True)
(out_proj): Linear(in_features=768, out_features=768, bias=True)
)
(encoder_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
)
(5): TransformerDecoderLayer(
(dropout_module): FairseqDropout()
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=768, out_features=768, bias=True)
(v_proj): Linear(in_features=768, out_features=768, bias=True)
(q_proj): Linear(in_features=768, out_features=768, bias=True)
(out_proj): Linear(in_features=768, out_features=768, bias=True)
)
(activation_dropout_module): FairseqDropout()
(self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(encoder_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=768, out_features=768, bias=True)
(v_proj): Linear(in_features=768, out_features=768, bias=True)
(q_proj): Linear(in_features=768, out_features=768, bias=True)
(out_proj): Linear(in_features=768, out_features=768, bias=True)
)
(encoder_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
)
)
(layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
)
)