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README.md

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+---
+license: apache-2.0
+language:
+- en
+library_name: pytorch
+tags:
+- autonomous-driving
+- multi-modal
+- transformer
+- interfuser
+---
+
+# InterFuser: Multi-modal Transformer for Autonomous Driving
+
+This is a Hugging Face repository for the InterFuser model, designed for end-to-end autonomous driving tasks. It processes multi-view camera images and LiDAR data to predict driving waypoints and perceive the surrounding traffic environment.
+
+This model was trained by [Your Name or Alias].
+
+## Model Architecture
+
+The model, `Interfuser`, is a Transformer-based architecture that:
+- Uses a CNN backbone (ResNet-50 for RGB, ResNet-18 for LiDAR) to extract features.
+- A Transformer Encoder fuses these multi-modal features.
+- A Transformer Decoder predicts waypoints, junction status, and a Bird's-Eye-View (BEV) map of traffic.
+
+## How to Use
+
+First, make sure you have `timm` installed: `pip install timm`.
+
+You can then use the model with `AutoModel`. Remember to pass `trust_remote_code=True`.
+
+```python
+import torch
+from transformers import AutoModel, AutoConfig
+
+# === 1. Load Model from Hugging Face Hub ===
+model_id = "your-username/interfuser-driving-model" # Replace with your REPO_ID
+config = AutoConfig.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModel.from_pretrained(model_id, trust_remote_code=True)
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+model.eval()
+
+# === 2. Prepare Dummy Input Data ===
+batch_size = 1
+dummy_inputs = {
+    'rgb': torch.randn(batch_size, 3 * 4, 224, 224, device=device),
+    'rgb_left': torch.randn(batch_size, 3, 224, 224, device=device),
+    'rgb_right': torch.randn(batch_size, 3, 224, 224, device=device),
+    'rgb_center': torch.randn(batch_size, 3, 224, 224, device=device),
+    'lidar': torch.randn(batch_size, 3, 112, 112, device=device),
+    'measurements': torch.randn(batch_size, 10, device=device),
+    'target_point': torch.randn(batch_size, 2, device=device)
+}
+
+# === 3. Run Inference ===
+with torch.no_grad():
+    outputs = model(**dummy_inputs)
+
+# === 4. Interpret the Outputs ===
+traffic, waypoints, is_junc, light, stop, _ = outputs
+print("Inference successful!")
+print(f"Waypoints shape: {waypoints.shape}")
+print(f"Traffic BEV map shape: {traffic.shape}")

config.json

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+{
+  "model_type": "interfuser",
+  "architectures": [
+    "Interfuser"
+  ],
+  "embed_dim": 256,
+  "enc_depth": 6,
+  "dec_depth": 6,
+  "num_heads": 8,
+  "dim_feedforward": 2048,
+  "dropout": 0.1,
+  "rgb_backbone_name": "r50",
+  "lidar_backbone_name": "r18",
+  "use_different_backbone": true,
+  "waypoints_pred_head": "gru"
+}

model.py

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+import math
+import copy
+import logging
+from collections import OrderedDict
+from functools import partial
+from typing import Optional, List
+
+import torch
+from torch import nn, Tensor
+import torch.nn.functional as F
+from torch.nn.parameter import Parameter
+
+try:
+    from timm.models.layers import to_2tuple
+    from timm.models.resnet import resnet26d, resnet50d, resnet18d
+except ImportError:
+    print("Please install timm: pip install timm")
+    raise
+
+_logger = logging.getLogger(__name__)
+
+# --- All the class definitions from the previous response go here ---
+# HybridEmbed, PositionEmbeddingSine, Transformers, GRUWaypointsPredictor, etc.
+
+
+class HybridEmbed(nn.Module):
+    def __init__(
+        self,
+        backbone,
+        img_size=224,
+        patch_size=1,
+        feature_size=None,
+        in_chans=3,
+        embed_dim=768,
+    ):
+        super().__init__()
+        assert isinstance(backbone, nn.Module)
+        img_size = to_2tuple(img_size)
+        patch_size = to_2tuple(patch_size)
+        self.img_size = img_size
+        self.patch_size = patch_size
+        self.backbone = backbone
+        if feature_size is None:
+            with torch.no_grad():
+                training = backbone.training
+                if training:
+                    backbone.eval()
+                o = self.backbone(torch.zeros(1, in_chans, img_size[0], img_size[1]))
+                if isinstance(o, (list, tuple)):
+                    o = o[-1]  # last feature if backbone outputs list/tuple of features
+                feature_size = o.shape[-2:]
+                feature_dim = o.shape[1]
+                backbone.train(training)
+        else:
+            feature_size = to_2tuple(feature_size)
+            if hasattr(self.backbone, "feature_info"):
+                feature_dim = self.backbone.feature_info.channels()[-1]
+            else:
+                feature_dim = self.backbone.num_features
+
+        self.proj = nn.Conv2d(feature_dim, embed_dim, kernel_size=1, stride=1)
+
+    def forward(self, x):
+        x = self.backbone(x)
+        if isinstance(x, (list, tuple)):
+            x = x[-1]  # last feature if backbone outputs list/tuple of features
+        x = self.proj(x)
+        global_x = torch.mean(x, [2, 3], keepdim=False)[:, :, None]
+        return x, global_x
+
+
+class PositionEmbeddingSine(nn.Module):
+    """
+    This is a more standard version of the position embedding, very similar to the one
+    used by the Attention is all you need paper, generalized to work on images.
+    """
+
+    def __init__(
+        self, num_pos_feats=64, temperature=10000, normalize=False, scale=None
+    ):
+        super().__init__()
+        self.num_pos_feats = num_pos_feats
+        self.temperature = temperature
+        self.normalize = normalize
+        if scale is not None and normalize is False:
+            raise ValueError("normalize should be True if scale is passed")
+        if scale is None:
+            scale = 2 * math.pi
+        self.scale = scale
+
+    def forward(self, tensor):
+        x = tensor
+        bs, _, h, w = x.shape
+        not_mask = torch.ones((bs, h, w), device=x.device)
+        y_embed = not_mask.cumsum(1, dtype=torch.float32)
+        x_embed = not_mask.cumsum(2, dtype=torch.float32)
+        if self.normalize:
+            eps = 1e-6
+            y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
+            x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
+
+        dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
+        dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
+
+        pos_x = x_embed[:, :, :, None] / dim_t
+        pos_y = y_embed[:, :, :, None] / dim_t
+        pos_x = torch.stack(
+            (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4
+        ).flatten(3)
+        pos_y = torch.stack(
+            (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4
+        ).flatten(3)
+        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
+        return pos
+
+
+class TransformerEncoder(nn.Module):
+    def __init__(self, encoder_layer, num_layers, norm=None):
+        super().__init__()
+        self.layers = _get_clones(encoder_layer, num_layers)
+        self.num_layers = num_layers
+        self.norm = norm
+
+    def forward(
+        self,
+        src,
+        mask: Optional[Tensor] = None,
+        src_key_padding_mask: Optional[Tensor] = None,
+        pos: Optional[Tensor] = None,
+    ):
+        output = src
+
+        for layer in self.layers:
+            output = layer(
+                output,
+                src_mask=mask,
+                src_key_padding_mask=src_key_padding_mask,
+                pos=pos,
+            )
+
+        if self.norm is not None:
+            output = self.norm(output)
+
+        return output
+
+
+class SpatialSoftmax(nn.Module):
+    def __init__(self, height, width, channel, temperature=None, data_format="NCHW"):
+        super().__init__()
+
+        self.data_format = data_format
+        self.height = height
+        self.width = width
+        self.channel = channel
+
+        if temperature:
+            self.temperature = Parameter(torch.ones(1) * temperature)
+        else:
+            self.temperature = 1.0
+
+        pos_x, pos_y = np.meshgrid(
+            np.linspace(-1.0, 1.0, self.height), np.linspace(-1.0, 1.0, self.width)
+        )
+        pos_x = torch.from_numpy(pos_x.reshape(self.height * self.width)).float()
+        pos_y = torch.from_numpy(pos_y.reshape(self.height * self.width)).float()
+        self.register_buffer("pos_x", pos_x)
+        self.register_buffer("pos_y", pos_y)
+
+    def forward(self, feature):
+        # Output:
+        #   (N, C*2) x_0 y_0 ...
+
+        if self.data_format == "NHWC":
+            feature = (
+                feature.transpose(1, 3)
+                .tranpose(2, 3)
+                .view(-1, self.height * self.width)
+            )
+        else:
+            feature = feature.view(-1, self.height * self.width)
+
+        weight = F.softmax(feature / self.temperature, dim=-1)
+        expected_x = torch.sum(
+            torch.autograd.Variable(self.pos_x) * weight, dim=1, keepdim=True
+        )
+        expected_y = torch.sum(
+            torch.autograd.Variable(self.pos_y) * weight, dim=1, keepdim=True
+        )
+        expected_xy = torch.cat([expected_x, expected_y], 1)
+        feature_keypoints = expected_xy.view(-1, self.channel, 2)
+        feature_keypoints[:, :, 1] = (feature_keypoints[:, :, 1] - 1) * 12
+        feature_keypoints[:, :, 0] = feature_keypoints[:, :, 0] * 12
+        return feature_keypoints
+
+
+class MultiPath_Generator(nn.Module):
+    def __init__(self, in_channel, embed_dim, out_channel):
+        super().__init__()
+        self.spatial_softmax = SpatialSoftmax(100, 100, out_channel)
+        self.tconv0 = nn.Sequential(
+            nn.ConvTranspose2d(in_channel, 256, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(256),
+            nn.ReLU(True),
+        )
+        self.tconv1 = nn.Sequential(
+            nn.ConvTranspose2d(256, 256, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(256),
+            nn.ReLU(True),
+        )
+        self.tconv2 = nn.Sequential(
+            nn.ConvTranspose2d(256, 192, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(192),
+            nn.ReLU(True),
+        )
+        self.tconv3 = nn.Sequential(
+            nn.ConvTranspose2d(192, 64, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(64),
+            nn.ReLU(True),
+        )
+        self.tconv4_list = torch.nn.ModuleList(
+            [
+                nn.Sequential(
+                    nn.ConvTranspose2d(64, out_channel, 8, 2, 3, bias=False),
+                    nn.Tanh(),
+                )
+                for _ in range(6)
+            ]
+        )
+
+        self.upsample = nn.Upsample(size=(50, 50), mode="bilinear")
+
+    def forward(self, x, measurements):
+        mask = measurements[:, :6]
+        mask = mask.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1).repeat(1, 1, 1, 100, 100)
+        velocity = measurements[:, 6:7].unsqueeze(-1).unsqueeze(-1)
+        velocity = velocity.repeat(1, 32, 2, 2)
+
+        n, d, c = x.shape
+        x = x.transpose(1, 2)
+        x = x.view(n, -1, 2, 2)
+        x = torch.cat([x, velocity], dim=1)
+        x = self.tconv0(x)
+        x = self.tconv1(x)
+        x = self.tconv2(x)
+        x = self.tconv3(x)
+        x = self.upsample(x)
+        xs = []
+        for i in range(6):
+            xt = self.tconv4_list[i](x)
+            xs.append(xt)
+        xs = torch.stack(xs, dim=1)
+        x = torch.sum(xs * mask, dim=1)
+        x = self.spatial_softmax(x)
+        return x
+
+
+class LinearWaypointsPredictor(nn.Module):
+    def __init__(self, input_dim, cumsum=True):
+        super().__init__()
+        self.cumsum = cumsum
+        self.rank_embed = nn.Parameter(torch.zeros(1, 10, input_dim))
+        self.head_fc1_list = nn.ModuleList([nn.Linear(input_dim, 64) for _ in range(6)])
+        self.head_relu = nn.ReLU(inplace=True)
+        self.head_fc2_list = nn.ModuleList([nn.Linear(64, 2) for _ in range(6)])
+
+    def forward(self, x, measurements):
+        # input shape: n 10 embed_dim
+        bs, n, dim = x.shape
+        x = x + self.rank_embed
+        x = x.reshape(-1, dim)
+
+        mask = measurements[:, :6]
+        mask = torch.unsqueeze(mask, -1).repeat(n, 1, 2)
+
+        rs = []
+        for i in range(6):
+            res = self.head_fc1_list[i](x)
+            res = self.head_relu(res)
+            res = self.head_fc2_list[i](res)
+            rs.append(res)
+        rs = torch.stack(rs, 1)
+        x = torch.sum(rs * mask, dim=1)
+
+        x = x.view(bs, n, 2)
+        if self.cumsum:
+            x = torch.cumsum(x, 1)
+        return x
+
+
+class GRUWaypointsPredictor(nn.Module):
+    def __init__(self, input_dim, waypoints=10):
+        super().__init__()
+        # self.gru = torch.nn.GRUCell(input_size=input_dim, hidden_size=64)
+        self.gru = torch.nn.GRU(input_size=input_dim, hidden_size=64, batch_first=True)
+        self.encoder = nn.Linear(2, 64)
+        self.decoder = nn.Linear(64, 2)
+        self.waypoints = waypoints
+
+    def forward(self, x, target_point):
+        bs = x.shape[0]
+        z = self.encoder(target_point).unsqueeze(0)
+        output, _ = self.gru(x, z)
+        output = output.reshape(bs * self.waypoints, -1)
+        output = self.decoder(output).reshape(bs, self.waypoints, 2)
+        output = torch.cumsum(output, 1)
+        return output
+
+class GRUWaypointsPredictorWithCommand(nn.Module):
+    def __init__(self, input_dim, waypoints=10):
+        super().__init__()
+        # self.gru = torch.nn.GRUCell(input_size=input_dim, hidden_size=64)
+        self.grus = nn.ModuleList([torch.nn.GRU(input_size=input_dim, hidden_size=64, batch_first=True) for _ in range(6)])
+        self.encoder = nn.Linear(2, 64)
+        self.decoders = nn.ModuleList([nn.Linear(64, 2) for _ in range(6)])
+        self.waypoints = waypoints
+
+    def forward(self, x, target_point, measurements):
+        bs, n, dim = x.shape
+        mask = measurements[:, :6, None, None]
+        mask = mask.repeat(1, 1, self.waypoints, 2)
+
+        z = self.encoder(target_point).unsqueeze(0)
+        outputs = []
+        for i in range(6):
+            output, _ = self.grus[i](x, z)
+            output = output.reshape(bs * self.waypoints, -1)
+            output = self.decoders[i](output).reshape(bs, self.waypoints, 2)
+            output = torch.cumsum(output, 1)
+            outputs.append(output)
+        outputs = torch.stack(outputs, 1)
+        output = torch.sum(outputs * mask, dim=1)
+        return output
+
+
+class TransformerDecoder(nn.Module):
+    def __init__(self, decoder_layer, num_layers, norm=None, return_intermediate=False):
+        super().__init__()
+        self.layers = _get_clones(decoder_layer, num_layers)
+        self.num_layers = num_layers
+        self.norm = norm
+        self.return_intermediate = return_intermediate
+
+    def forward(
+        self,
+        tgt,
+        memory,
+        tgt_mask: Optional[Tensor] = None,
+        memory_mask: Optional[Tensor] = None,
+        tgt_key_padding_mask: Optional[Tensor] = None,
+        memory_key_padding_mask: Optional[Tensor] = None,
+        pos: Optional[Tensor] = None,
+        query_pos: Optional[Tensor] = None,
+    ):
+        output = tgt
+
+        intermediate = []
+
+        for layer in self.layers:
+            output = layer(
+                output,
+                memory,
+                tgt_mask=tgt_mask,
+                memory_mask=memory_mask,
+                tgt_key_padding_mask=tgt_key_padding_mask,
+                memory_key_padding_mask=memory_key_padding_mask,
+                pos=pos,
+                query_pos=query_pos,
+            )
+            if self.return_intermediate:
+                intermediate.append(self.norm(output))
+
+        if self.norm is not None:
+            output = self.norm(output)
+            if self.return_intermediate:
+                intermediate.pop()
+                intermediate.append(output)
+
+        if self.return_intermediate:
+            return torch.stack(intermediate)
+
+        return output.unsqueeze(0)
+
+
+class TransformerEncoderLayer(nn.Module):
+    def __init__(
+        self,
+        d_model,
+        nhead,
+        dim_feedforward=2048,
+        dropout=0.1,
+        activation=nn.ReLU(),
+        normalize_before=False,
+    ):
+        super().__init__()
+        self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
+        # Implementation of Feedforward model
+        self.linear1 = nn.Linear(d_model, dim_feedforward)
+        self.dropout = nn.Dropout(dropout)
+        self.linear2 = nn.Linear(dim_feedforward, d_model)
+
+        self.norm1 = nn.LayerNorm(d_model)
+        self.norm2 = nn.LayerNorm(d_model)
+        self.dropout1 = nn.Dropout(dropout)
+        self.dropout2 = nn.Dropout(dropout)
+
+        self.activation = activation()
+        self.normalize_before = normalize_before
+
+    def with_pos_embed(self, tensor, pos: Optional[Tensor]):
+        return tensor if pos is None else tensor + pos
+
+    def forward_post(
+        self,
+        src,
+        src_mask: Optional[Tensor] = None,
+        src_key_padding_mask: Optional[Tensor] = None,
+        pos: Optional[Tensor] = None,
+    ):
+        q = k = self.with_pos_embed(src, pos)
+        src2 = self.self_attn(
+            q, k, value=src, attn_mask=src_mask, key_padding_mask=src_key_padding_mask
+        )[0]
+        src = src + self.dropout1(src2)
+        src = self.norm1(src)
+        src2 = self.linear2(self.dropout(self.activation(self.linear1(src))))
+        src = src + self.dropout2(src2)
+        src = self.norm2(src)
+        return src
+
+    def forward_pre(
+        self,
+        src,
+        src_mask: Optional[Tensor] = None,
+        src_key_padding_mask: Optional[Tensor] = None,
+        pos: Optional[Tensor] = None,
+    ):
+        src2 = self.norm1(src)
+        q = k = self.with_pos_embed(src2, pos)
+        src2 = self.self_attn(
+            q, k, value=src2, attn_mask=src_mask, key_padding_mask=src_key_padding_mask
+        )[0]
+        src = src + self.dropout1(src2)
+        src2 = self.norm2(src)
+        src2 = self.linear2(self.dropout(self.activation(self.linear1(src2))))
+        src = src + self.dropout2(src2)
+        return src
+
+    def forward(
+        self,
+        src,
+        src_mask: Optional[Tensor] = None,
+        src_key_padding_mask: Optional[Tensor] = None,
+        pos: Optional[Tensor] = None,
+    ):
+        if self.normalize_before:
+            return self.forward_pre(src, src_mask, src_key_padding_mask, pos)
+        return self.forward_post(src, src_mask, src_key_padding_mask, pos)
+
+
+class TransformerDecoderLayer(nn.Module):
+    def __init__(
+        self,
+        d_model,
+        nhead,
+        dim_feedforward=2048,
+        dropout=0.1,
+        activation=nn.ReLU(),
+        normalize_before=False,
+    ):
+        super().__init__()
+        self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
+        self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
+        # Implementation of Feedforward model
+        self.linear1 = nn.Linear(d_model, dim_feedforward)
+        self.dropout = nn.Dropout(dropout)
+        self.linear2 = nn.Linear(dim_feedforward, d_model)
+
+        self.norm1 = nn.LayerNorm(d_model)
+        self.norm2 = nn.LayerNorm(d_model)
+        self.norm3 = nn.LayerNorm(d_model)
+        self.dropout1 = nn.Dropout(dropout)
+        self.dropout2 = nn.Dropout(dropout)
+        self.dropout3 = nn.Dropout(dropout)
+
+        self.activation = activation()
+        self.normalize_before = normalize_before
+
+    def with_pos_embed(self, tensor, pos: Optional[Tensor]):
+        return tensor if pos is None else tensor + pos
+
+    def forward_post(
+        self,
+        tgt,
+        memory,
+        tgt_mask: Optional[Tensor] = None,
+        memory_mask: Optional[Tensor] = None,
+        tgt_key_padding_mask: Optional[Tensor] = None,
+        memory_key_padding_mask: Optional[Tensor] = None,
+        pos: Optional[Tensor] = None,
+        query_pos: Optional[Tensor] = None,
+    ):
+        q = k = self.with_pos_embed(tgt, query_pos)
+        tgt2 = self.self_attn(
+            q, k, value=tgt, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask
+        )[0]
+        tgt = tgt + self.dropout1(tgt2)
+        tgt = self.norm1(tgt)
+        tgt2 = self.multihead_attn(
+            query=self.with_pos_embed(tgt, query_pos),
+            key=self.with_pos_embed(memory, pos),
+            value=memory,
+            attn_mask=memory_mask,
+            key_padding_mask=memory_key_padding_mask,
+        )[0]
+        tgt = tgt + self.dropout2(tgt2)
+        tgt = self.norm2(tgt)
+        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))
+        tgt = tgt + self.dropout3(tgt2)
+        tgt = self.norm3(tgt)
+        return tgt
+
+    def forward_pre(
+        self,
+        tgt,
+        memory,
+        tgt_mask: Optional[Tensor] = None,
+        memory_mask: Optional[Tensor] = None,
+        tgt_key_padding_mask: Optional[Tensor] = None,
+        memory_key_padding_mask: Optional[Tensor] = None,
+        pos: Optional[Tensor] = None,
+        query_pos: Optional[Tensor] = None,
+    ):
+        tgt2 = self.norm1(tgt)
+        q = k = self.with_pos_embed(tgt2, query_pos)
+        tgt2 = self.self_attn(
+            q, k, value=tgt2, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask
+        )[0]
+        tgt = tgt + self.dropout1(tgt2)
+        tgt2 = self.norm2(tgt)
+        tgt2 = self.multihead_attn(
+            query=self.with_pos_embed(tgt2, query_pos),
+            key=self.with_pos_embed(memory, pos),
+            value=memory,
+            attn_mask=memory_mask,
+            key_padding_mask=memory_key_padding_mask,
+        )[0]
+        tgt = tgt + self.dropout2(tgt2)
+        tgt2 = self.norm3(tgt)
+        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
+        tgt = tgt + self.dropout3(tgt2)
+        return tgt
+
+    def forward(
+        self,
+        tgt,
+        memory,
+        tgt_mask: Optional[Tensor] = None,
+        memory_mask: Optional[Tensor] = None,
+        tgt_key_padding_mask: Optional[Tensor] = None,
+        memory_key_padding_mask: Optional[Tensor] = None,
+        pos: Optional[Tensor] = None,
+        query_pos: Optional[Tensor] = None,
+    ):
+        if self.normalize_before:
+            return self.forward_pre(
+                tgt,
+                memory,
+                tgt_mask,
+                memory_mask,
+                tgt_key_padding_mask,
+                memory_key_padding_mask,
+                pos,
+                query_pos,
+            )
+        return self.forward_post(
+            tgt,
+            memory,
+            tgt_mask,
+            memory_mask,
+            tgt_key_padding_mask,
+            memory_key_padding_mask,
+            pos,
+            query_pos,
+        )
+
+
+def _get_clones(module, N):
+    return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
+
+
+def _get_activation_fn(activation):
+    """Return an activation function given a string"""
+    if activation == "relu":
+        return F.relu
+    if activation == "gelu":
+        return F.gelu
+    if activation == "glu":
+        return F.glu
+    raise RuntimeError(f"activation should be relu/gelu, not {activation}.")
+
+
+def build_attn_mask(mask_type):
+    mask = torch.ones((151, 151), dtype=torch.bool).cuda()
+    if mask_type == "seperate_all":
+        mask[:50, :50] = False
+        mask[50:67, 50:67] = False
+        mask[67:84, 67:84] = False
+        mask[84:101, 84:101] = False
+        mask[101:151, 101:151] = False
+    elif mask_type == "seperate_view":
+        mask[:50, :50] = False
+        mask[50:67, 50:67] = False
+        mask[67:84, 67:84] = False
+        mask[84:101, 84:101] = False
+        mask[101:151, :] = False
+        mask[:, 101:151] = False
+    return mask
+
+class Interfuser(nn.Module):
+    def __init__(
+        self,
+        img_size=224,
+        multi_view_img_size=112,
+        patch_size=8,
+        in_chans=3,
+        embed_dim=768,
+        enc_depth=6,
+        dec_depth=6,
+        dim_feedforward=2048,
+        normalize_before=False,
+        rgb_backbone_name="r26",
+        lidar_backbone_name="r26",
+        num_heads=8,
+        norm_layer=None,
+        dropout=0.1,
+        end2end=False,
+        direct_concat=True,
+        separate_view_attention=False,
+        separate_all_attention=False,
+        act_layer=None,
+        weight_init="",
+        freeze_num=-1,
+        with_lidar=False,
+        with_right_left_sensors=True,
+        with_center_sensor=False,
+        traffic_pred_head_type="det",
+        waypoints_pred_head="heatmap",
+        reverse_pos=True,
+        use_different_backbone=False,
+        use_view_embed=True,
+        use_mmad_pretrain=None,
+    ):
+        super().__init__()
+        self.traffic_pred_head_type = traffic_pred_head_type
+        self.num_features = (
+            self.embed_dim
+        ) = embed_dim  # num_features for consistency with other models
+        norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
+        act_layer = act_layer or nn.GELU
+
+        self.reverse_pos = reverse_pos
+        self.waypoints_pred_head = waypoints_pred_head
+        self.with_lidar = with_lidar
+        self.with_right_left_sensors = with_right_left_sensors
+        self.with_center_sensor = with_center_sensor
+
+        self.direct_concat = direct_concat
+        self.separate_view_attention = separate_view_attention
+        self.separate_all_attention = separate_all_attention
+        self.end2end = end2end
+        self.use_view_embed = use_view_embed
+
+        if self.direct_concat:
+            in_chans = in_chans * 4
+            self.with_center_sensor = False
+            self.with_right_left_sensors = False
+
+        if self.separate_view_attention:
+            self.attn_mask = build_attn_mask("seperate_view")
+        elif self.separate_all_attention:
+            self.attn_mask = build_attn_mask("seperate_all")
+        else:
+            self.attn_mask = None
+
+        if use_different_backbone:
+            if rgb_backbone_name == "r50":
+                self.rgb_backbone = resnet50d(
+                    pretrained=True,
+                    in_chans=in_chans,
+                    features_only=True,
+                    out_indices=[4],
+                )
+            elif rgb_backbone_name == "r26":
+                self.rgb_backbone = resnet26d(
+                    pretrained=True,
+                    in_chans=in_chans,
+                    features_only=True,
+                    out_indices=[4],
+                )
+            elif rgb_backbone_name == "r18":
+                self.rgb_backbone = resnet18d(
+                    pretrained=True,
+                    in_chans=in_chans,
+                    features_only=True,
+                    out_indices=[4],
+                )
+            if lidar_backbone_name == "r50":
+                self.lidar_backbone = resnet50d(
+                    pretrained=False,
+                    in_chans=in_chans,
+                    features_only=True,
+                    out_indices=[4],
+                )
+            elif lidar_backbone_name == "r26":
+                self.lidar_backbone = resnet26d(
+                    pretrained=False,
+                    in_chans=in_chans,
+                    features_only=True,
+                    out_indices=[4],
+                )
+            elif lidar_backbone_name == "r18":
+                self.lidar_backbone = resnet18d(
+                    pretrained=False, in_chans=3, features_only=True, out_indices=[4]
+                )
+            rgb_embed_layer = partial(HybridEmbed, backbone=self.rgb_backbone)
+            lidar_embed_layer = partial(HybridEmbed, backbone=self.lidar_backbone)
+
+            if use_mmad_pretrain:
+                params = torch.load(use_mmad_pretrain)["state_dict"]
+                updated_params = OrderedDict()
+                for key in params:
+                    if "backbone" in key:
+                        updated_params[key.replace("backbone.", "")] = params[key]
+                self.rgb_backbone.load_state_dict(updated_params)
+
+            self.rgb_patch_embed = rgb_embed_layer(
+                img_size=img_size,
+                patch_size=patch_size,
+                in_chans=in_chans,
+                embed_dim=embed_dim,
+            )
+            self.lidar_patch_embed = lidar_embed_layer(
+                img_size=img_size,
+                patch_size=patch_size,
+                in_chans=3,
+                embed_dim=embed_dim,
+            )
+        else:
+            if rgb_backbone_name == "r50":
+                self.rgb_backbone = resnet50d(
+                    pretrained=True, in_chans=3, features_only=True, out_indices=[4]
+                )
+            elif rgb_backbone_name == "r101":
+                self.rgb_backbone = resnet101d(
+                    pretrained=True, in_chans=3, features_only=True, out_indices=[4]
+                )
+            elif rgb_backbone_name == "r26":
+                self.rgb_backbone = resnet26d(
+                    pretrained=True, in_chans=3, features_only=True, out_indices=[4]
+                )
+            elif rgb_backbone_name == "r18":
+                self.rgb_backbone = resnet18d(
+                    pretrained=True, in_chans=3, features_only=True, out_indices=[4]
+                )
+            embed_layer = partial(HybridEmbed, backbone=self.rgb_backbone)
+
+            self.rgb_patch_embed = embed_layer(
+                img_size=img_size,
+                patch_size=patch_size,
+                in_chans=in_chans,
+                embed_dim=embed_dim,
+            )
+            self.lidar_patch_embed = embed_layer(
+                img_size=img_size,
+                patch_size=patch_size,
+                in_chans=in_chans,
+                embed_dim=embed_dim,
+            )
+
+        self.global_embed = nn.Parameter(torch.zeros(1, embed_dim, 5))
+        self.view_embed = nn.Parameter(torch.zeros(1, embed_dim, 5, 1))
+
+        if self.end2end:
+            self.query_pos_embed = nn.Parameter(torch.zeros(1, embed_dim, 4))
+            self.query_embed = nn.Parameter(torch.zeros(4, 1, embed_dim))
+        elif self.waypoints_pred_head == "heatmap":
+            self.query_pos_embed = nn.Parameter(torch.zeros(1, embed_dim, 5))
+            self.query_embed = nn.Parameter(torch.zeros(400 + 5, 1, embed_dim))
+        else:
+            self.query_pos_embed = nn.Parameter(torch.zeros(1, embed_dim, 11))
+            self.query_embed = nn.Parameter(torch.zeros(400 + 11, 1, embed_dim))
+
+        if self.end2end:
+            self.waypoints_generator = GRUWaypointsPredictor(embed_dim, 4)
+        elif self.waypoints_pred_head == "heatmap":
+            self.waypoints_generator = MultiPath_Generator(
+                embed_dim + 32, embed_dim, 10
+            )
+        elif self.waypoints_pred_head == "gru":
+            self.waypoints_generator = GRUWaypointsPredictor(embed_dim)
+        elif self.waypoints_pred_head == "gru-command":
+            self.waypoints_generator = GRUWaypointsPredictorWithCommand(embed_dim)
+        elif self.waypoints_pred_head == "linear":
+            self.waypoints_generator = LinearWaypointsPredictor(embed_dim)
+        elif self.waypoints_pred_head == "linear-sum":
+            self.waypoints_generator = LinearWaypointsPredictor(embed_dim, cumsum=True)
+
+        self.junction_pred_head = nn.Linear(embed_dim, 2)
+        self.traffic_light_pred_head = nn.Linear(embed_dim, 2)
+        self.stop_sign_head = nn.Linear(embed_dim, 2)
+
+        if self.traffic_pred_head_type == "det":
+            self.traffic_pred_head = nn.Sequential(
+                *[
+                    nn.Linear(embed_dim + 32, 64),
+                    nn.ReLU(),
+                    nn.Linear(64, 7),
+                    nn.Sigmoid(),
+                ]
+            )
+        elif self.traffic_pred_head_type == "seg":
+            self.traffic_pred_head = nn.Sequential(
+                *[nn.Linear(embed_dim, 64), nn.ReLU(), nn.Linear(64, 1), nn.Sigmoid()]
+            )
+
+        self.position_encoding = PositionEmbeddingSine(embed_dim // 2, normalize=True)
+
+        encoder_layer = TransformerEncoderLayer(
+            embed_dim, num_heads, dim_feedforward, dropout, act_layer, normalize_before
+        )
+        self.encoder = TransformerEncoder(encoder_layer, enc_depth, None)
+
+        decoder_layer = TransformerDecoderLayer(
+            embed_dim, num_heads, dim_feedforward, dropout, act_layer, normalize_before
+        )
+        decoder_norm = nn.LayerNorm(embed_dim)
+        self.decoder = TransformerDecoder(
+            decoder_layer, dec_depth, decoder_norm, return_intermediate=False
+        )
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        nn.init.uniform_(self.global_embed)
+        nn.init.uniform_(self.view_embed)
+        nn.init.uniform_(self.query_embed)
+        nn.init.uniform_(self.query_pos_embed)
+
+    def forward_features(
+        self,
+        front_image,
+        left_image,
+        right_image,
+        front_center_image,
+        lidar,
+        measurements,
+    ):
+        features = []
+
+        # Front view processing
+        front_image_token, front_image_token_global = self.rgb_patch_embed(front_image)
+        if self.use_view_embed:
+            front_image_token = (
+                front_image_token
+                + self.view_embed[:, :, 0:1, :]
+                + self.position_encoding(front_image_token)
+            )
+        else:
+            front_image_token = front_image_token + self.position_encoding(
+                front_image_token
+            )
+        front_image_token = front_image_token.flatten(2).permute(2, 0, 1)
+        front_image_token_global = (
+            front_image_token_global
+            + self.view_embed[:, :, 0, :]
+            + self.global_embed[:, :, 0:1]
+        )
+        front_image_token_global = front_image_token_global.permute(2, 0, 1)
+        features.extend([front_image_token, front_image_token_global])
+
+        if self.with_right_left_sensors:
+            # Left view processing
+            left_image_token, left_image_token_global = self.rgb_patch_embed(left_image)
+            if self.use_view_embed:
+                left_image_token = (
+                    left_image_token
+                    + self.view_embed[:, :, 1:2, :]
+                    + self.position_encoding(left_image_token)
+                )
+            else:
+                left_image_token = left_image_token + self.position_encoding(
+                    left_image_token
+                )
+            left_image_token = left_image_token.flatten(2).permute(2, 0, 1)
+            left_image_token_global = (
+                left_image_token_global
+                + self.view_embed[:, :, 1, :]
+                + self.global_embed[:, :, 1:2]
+            )
+            left_image_token_global = left_image_token_global.permute(2, 0, 1)
+
+            # Right view processing
+            right_image_token, right_image_token_global = self.rgb_patch_embed(
+                right_image
+            )
+            if self.use_view_embed:
+                right_image_token = (
+                    right_image_token
+                    + self.view_embed[:, :, 2:3, :]
+                    + self.position_encoding(right_image_token)
+                )
+            else:
+                right_image_token = right_image_token + self.position_encoding(
+                    right_image_token
+                )
+            right_image_token = right_image_token.flatten(2).permute(2, 0, 1)
+            right_image_token_global = (
+                right_image_token_global
+                + self.view_embed[:, :, 2, :]
+                + self.global_embed[:, :, 2:3]
+            )
+            right_image_token_global = right_image_token_global.permute(2, 0, 1)
+
+            features.extend(
+                [
+                    left_image_token,
+                    left_image_token_global,
+                    right_image_token,
+                    right_image_token_global,
+                ]
+            )
+
+        if self.with_center_sensor:
+            # Front center view processing
+            (
+                front_center_image_token,
+                front_center_image_token_global,
+            ) = self.rgb_patch_embed(front_center_image)
+            if self.use_view_embed:
+                front_center_image_token = (
+                    front_center_image_token
+                    + self.view_embed[:, :, 3:4, :]
+                    + self.position_encoding(front_center_image_token)
+                )
+            else:
+                front_center_image_token = (
+                    front_center_image_token
+                    + self.position_encoding(front_center_image_token)
+                )
+
+            front_center_image_token = front_center_image_token.flatten(2).permute(
+                2, 0, 1
+            )
+            front_center_image_token_global = (
+                front_center_image_token_global
+                + self.view_embed[:, :, 3, :]
+                + self.global_embed[:, :, 3:4]
+            )
+            front_center_image_token_global = front_center_image_token_global.permute(
+                2, 0, 1
+            )
+            features.extend([front_center_image_token, front_center_image_token_global])
+
+        if self.with_lidar:
+            lidar_token, lidar_token_global = self.lidar_patch_embed(lidar)
+            if self.use_view_embed:
+                lidar_token = (
+                    lidar_token
+                    + self.view_embed[:, :, 4:5, :]
+                    + self.position_encoding(lidar_token)
+                )
+            else:
+                lidar_token = lidar_token + self.position_encoding(lidar_token)
+            lidar_token = lidar_token.flatten(2).permute(2, 0, 1)
+            lidar_token_global = (
+                lidar_token_global
+                + self.view_embed[:, :, 4, :]
+                + self.global_embed[:, :, 4:5]
+            )
+            lidar_token_global = lidar_token_global.permute(2, 0, 1)
+            features.extend([lidar_token, lidar_token_global])
+
+        features = torch.cat(features, 0)
+        return features
+
+    def forward(self, x):
+        front_image = x["rgb"]
+        left_image = x["rgb_left"]
+        right_image = x["rgb_right"]
+        front_center_image = x["rgb_center"]
+        measurements = x["measurements"]
+        target_point = x["target_point"]
+        lidar = x["lidar"]
+
+        if self.direct_concat:
+            img_size = front_image.shape[-1]
+            left_image = torch.nn.functional.interpolate(
+                left_image, size=(img_size, img_size)
+            )
+            right_image = torch.nn.functional.interpolate(
+                right_image, size=(img_size, img_size)
+            )
+            front_center_image = torch.nn.functional.interpolate(
+                front_center_image, size=(img_size, img_size)
+            )
+            front_image = torch.cat(
+                [front_image, left_image, right_image, front_center_image], dim=1
+            )
+        features = self.forward_features(
+            front_image,
+            left_image,
+            right_image,
+            front_center_image,
+            lidar,
+            measurements,
+        )
+
+        bs = front_image.shape[0]
+
+        if self.end2end:
+            tgt = self.query_pos_embed.repeat(bs, 1, 1)
+        else:
+            tgt = self.position_encoding(
+                torch.ones((bs, 1, 20, 20), device=x["rgb"].device)
+            )
+            tgt = tgt.flatten(2)
+            tgt = torch.cat([tgt, self.query_pos_embed.repeat(bs, 1, 1)], 2)
+        tgt = tgt.permute(2, 0, 1)
+
+        memory = self.encoder(features, mask=self.attn_mask)
+        hs = self.decoder(self.query_embed.repeat(1, bs, 1), memory, query_pos=tgt)[0]
+
+        hs = hs.permute(1, 0, 2)  # Batchsize ,  N, C
+        if self.end2end:
+            waypoints = self.waypoints_generator(hs, target_point)
+            return waypoints
+
+        if self.waypoints_pred_head != "heatmap":
+            traffic_feature = hs[:, :400]
+            is_junction_feature = hs[:, 400]
+            traffic_light_state_feature = hs[:, 400]
+            stop_sign_feature = hs[:, 400]
+            waypoints_feature = hs[:, 401:411]
+        else:
+            traffic_feature = hs[:, :400]
+            is_junction_feature = hs[:, 400]
+            traffic_light_state_feature = hs[:, 400]
+            stop_sign_feature = hs[:, 400]
+            waypoints_feature = hs[:, 401:405]
+
+        if self.waypoints_pred_head == "heatmap":
+            waypoints = self.waypoints_generator(waypoints_feature, measurements)
+        elif self.waypoints_pred_head == "gru":
+            waypoints = self.waypoints_generator(waypoints_feature, target_point)
+        elif self.waypoints_pred_head == "gru-command":
+            waypoints = self.waypoints_generator(waypoints_feature, target_point, measurements)
+        elif self.waypoints_pred_head == "linear":
+            waypoints = self.waypoints_generator(waypoints_feature, measurements)
+        elif self.waypoints_pred_head == "linear-sum":
+            waypoints = self.waypoints_generator(waypoints_feature, measurements)
+
+        is_junction = self.junction_pred_head(is_junction_feature)
+        traffic_light_state = self.traffic_light_pred_head(traffic_light_state_feature)
+        stop_sign = self.stop_sign_head(stop_sign_feature)
+
+        velocity = measurements[:, 6:7].unsqueeze(-1)
+        velocity = velocity.repeat(1, 400, 32)
+        traffic_feature_with_vel = torch.cat([traffic_feature, velocity], dim=2)
+        traffic = self.traffic_pred_head(traffic_feature_with_vel)
+        return traffic, waypoints, is_junction, traffic_light_state, stop_sign, traffic_feature

pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5ef39ec4197f3e9ed8b659709b331a2cd63c30b6019c61eb155eb438135e3dd7
+size 212334062