test modify swiftformer to temporal input
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244
models/swiftformer_temporal.py
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244
models/swiftformer_temporal.py
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"""
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SwiftFormerTemporal: Temporal extension of SwiftFormer for frame prediction
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"""
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import torch
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import torch.nn as nn
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from .swiftformer import (
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SwiftFormer, SwiftFormer_depth, SwiftFormer_width,
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stem, Embedding, Stage
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)
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from timm.models.layers import DropPath, trunc_normal_
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class DecoderBlock(nn.Module):
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"""Upsampling block for frame prediction decoder"""
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def __init__(self, in_channels, out_channels, kernel_size=3, stride=2, padding=1, output_padding=1):
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super().__init__()
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self.conv = nn.ConvTranspose2d(
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in_channels, out_channels,
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kernel_size=kernel_size,
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stride=stride,
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padding=padding,
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output_padding=output_padding,
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bias=False
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)
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self.bn = nn.BatchNorm2d(out_channels)
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self.relu = nn.ReLU(inplace=True)
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def forward(self, x):
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return self.relu(self.bn(self.conv(x)))
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class FramePredictionDecoder(nn.Module):
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"""Lightweight decoder for frame prediction with optional skip connections"""
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def __init__(self, embed_dims, output_channels=3, use_skip=False):
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super().__init__()
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self.use_skip = use_skip
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# Reverse the embed_dims for decoder
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decoder_dims = embed_dims[::-1]
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self.blocks = nn.ModuleList()
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# First upsampling from bottleneck to stage4 resolution
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self.blocks.append(DecoderBlock(
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decoder_dims[0], decoder_dims[1],
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kernel_size=3, stride=2, padding=1, output_padding=1
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))
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# stage4 to stage3
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self.blocks.append(DecoderBlock(
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decoder_dims[1], decoder_dims[2],
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kernel_size=3, stride=2, padding=1, output_padding=1
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))
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# stage3 to stage2
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self.blocks.append(DecoderBlock(
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decoder_dims[2], decoder_dims[3],
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kernel_size=3, stride=2, padding=1, output_padding=1
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))
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# stage2 to original resolution (4x upsampling total)
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self.blocks.append(nn.Sequential(
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nn.ConvTranspose2d(
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decoder_dims[3], 32,
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kernel_size=3, stride=2, padding=1, output_padding=1
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),
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nn.BatchNorm2d(32),
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nn.ReLU(inplace=True),
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nn.Conv2d(32, output_channels, kernel_size=3, padding=1),
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nn.Tanh() # Output in [-1, 1] range
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))
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# If using skip connections, we need to adjust input channels for each block
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if use_skip:
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# We'll modify the first three blocks to accept concatenated features
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# Instead of modifying existing blocks, we'll replace them with custom blocks
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# For simplicity, we'll keep the same architecture but forward will handle concatenation
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pass
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def forward(self, x, skip_features=None):
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"""
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Args:
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x: input tensor of shape [B, embed_dims[-1], H/32, W/32]
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skip_features: list of encoder features from stages [stage2, stage1, stage0]
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each of shape [B, C, H', W'] where C matches decoder dims?
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"""
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if self.use_skip and skip_features is not None:
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# Ensure we have exactly 3 skip features (for the first three blocks)
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assert len(skip_features) == 3, "Need 3 skip features for skip connections"
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# Reverse skip_features to match decoder order: stage2, stage1, stage0
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# skip_features[0] should be stage2 (H/16), [1] stage1 (H/8), [2] stage0 (H/4)
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skip_features = skip_features[::-1] # Now index 0: stage2, 1: stage1, 2: stage0
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for i, block in enumerate(self.blocks):
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if self.use_skip and skip_features is not None and i < 3:
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# Concatenate skip feature along channel dimension
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# Ensure spatial dimensions match (they should because of upsampling)
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x = torch.cat([x, skip_features[i]], dim=1)
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# Need to adjust block to accept extra channels? We'll create a separate block.
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# For now, we'll just pass through, but this will cause channel mismatch.
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# Instead, we should have created custom blocks with appropriate in_channels.
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# This is a placeholder; we need to implement properly.
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pass
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x = block(x)
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return x
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class SwiftFormerTemporal(nn.Module):
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"""
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SwiftFormer with temporal input for frame prediction.
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Input: [B, num_frames, H, W] (Y channel only)
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Output: predicted frame [B, 3, H, W] and optional representation
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"""
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def __init__(self,
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model_name='XS',
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num_frames=3,
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use_decoder=True,
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use_representation_head=False,
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representation_dim=128,
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return_features=False,
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**kwargs):
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super().__init__()
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# Get model configuration
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layers = SwiftFormer_depth[model_name]
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embed_dims = SwiftFormer_width[model_name]
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# Store configuration
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self.num_frames = num_frames
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self.use_decoder = use_decoder
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self.use_representation_head = use_representation_head
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self.return_features = return_features
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# Modify stem to accept multiple frames (only Y channel)
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in_channels = num_frames
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self.patch_embed = stem(in_channels, embed_dims[0])
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# Build encoder network (same as SwiftFormer)
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network = []
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for i in range(len(layers)):
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stage = Stage(embed_dims[i], i, layers, mlp_ratio=4,
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act_layer=nn.GELU,
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drop_rate=0., drop_path_rate=0.,
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use_layer_scale=True,
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layer_scale_init_value=1e-5,
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vit_num=1)
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network.append(stage)
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if i >= len(layers) - 1:
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break
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if embed_dims[i] != embed_dims[i + 1]:
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network.append(
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Embedding(
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patch_size=3, stride=2, padding=1,
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in_chans=embed_dims[i], embed_dim=embed_dims[i + 1]
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)
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)
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self.network = nn.ModuleList(network)
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self.norm = nn.BatchNorm2d(embed_dims[-1])
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# Frame prediction decoder
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if use_decoder:
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self.decoder = FramePredictionDecoder(embed_dims, output_channels=3)
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# Representation head for pose/velocity prediction
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if use_representation_head:
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self.representation_head = nn.Sequential(
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nn.AdaptiveAvgPool2d(1),
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nn.Flatten(),
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nn.Linear(embed_dims[-1], representation_dim),
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nn.ReLU(),
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nn.Linear(representation_dim, representation_dim)
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)
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else:
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self.representation_head = None
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self.apply(self._init_weights)
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def _init_weights(self, m):
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if isinstance(m, (nn.Conv2d, nn.Linear)):
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trunc_normal_(m.weight, std=.02)
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if m.bias is not None:
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, (nn.LayerNorm)):
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nn.init.constant_(m.bias, 0)
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nn.init.constant_(m.weight, 1.0)
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def forward_tokens(self, x):
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"""Forward through encoder network, return list of stage features if return_features else final output"""
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if self.return_features:
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features = []
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for idx, block in enumerate(self.network):
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x = block(x)
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# Collect output after each stage (indices 0,2,4,6 correspond to stages)
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if idx in [0, 2, 4, 6]:
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features.append(x)
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return x, features
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else:
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for block in self.network:
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x = block(x)
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return x
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def forward(self, x):
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"""
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Args:
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x: input frames of shape [B, num_frames, H, W]
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Returns:
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If return_features is False:
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pred_frame: predicted frame [B, 3, H, W] (or None)
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representation: optional representation vector [B, representation_dim] (or None)
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If return_features is True:
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pred_frame, representation, features (list of stage features)
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"""
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# Encode
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x = self.patch_embed(x)
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if self.return_features:
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x, features = self.forward_tokens(x)
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else:
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x = self.forward_tokens(x)
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x = self.norm(x)
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# Get representation if needed
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representation = None
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if self.representation_head is not None:
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representation = self.representation_head(x)
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# Decode to frame
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pred_frame = None
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if self.use_decoder:
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pred_frame = self.decoder(x)
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if self.return_features:
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return pred_frame, representation, features
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else:
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return pred_frame, representation
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# Factory functions for different model sizes
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def SwiftFormerTemporal_XS(num_frames=3, **kwargs):
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return SwiftFormerTemporal('XS', num_frames=num_frames, **kwargs)
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def SwiftFormerTemporal_S(num_frames=3, **kwargs):
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return SwiftFormerTemporal('S', num_frames=num_frames, **kwargs)
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def SwiftFormerTemporal_L1(num_frames=3, **kwargs):
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return SwiftFormerTemporal('l1', num_frames=num_frames, **kwargs)
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def SwiftFormerTemporal_L3(num_frames=3, **kwargs):
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return SwiftFormerTemporal('l3', num_frames=num_frames, **kwargs)
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