Upload anwm root and small dirs

.gitattributes

@@ -34,3 +34,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 master.mp4 filter=lfs diff=lfs merge=lfs -text
+git-lfs_3.3.0-1+deb12u1_amd64.deb filter=lfs diff=lfs merge=lfs -text

build/lib/prope/jax.py

@@ -0,0 +1,315 @@
+# MIT License
+#
+# Copyright (c) Authors of
+# "PRoPE: Projective Positional Encoding for Multiview Transformers"
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+import itertools
+from functools import partial
+from typing import Callable
+
+import jax
+import jax.nn
+import jax.numpy as jnp
+
+
+class PropeDotProductAttention:
+    """PRoPE attention with precomputed RoPE coefficients."""
+
+    def __init__(
+        self,
+        head_dim: int,
+        cameras: int,
+        patches_x: int,
+        patches_y: int,
+        image_width: int,
+        image_height: int,
+        freq_base: float = 100.0,
+        freq_scale: float = 1.0,
+    ):
+        self.head_dim = head_dim
+        self.cameras = cameras
+        self.patches_x = patches_x
+        self.patches_y = patches_y
+        self.image_width = image_width
+        self.image_height = image_height
+
+        # Precompute coefficients for x and y
+        self.coeffs_x = _rope_precompute_coeffs(
+            jnp.tile(jnp.arange(patches_x), patches_y * cameras),
+            freq_base=freq_base,
+            freq_scale=freq_scale,
+            feat_dim=head_dim // 4,
+        )
+        self.coeffs_y = _rope_precompute_coeffs(
+            jnp.tile(jnp.repeat(jnp.arange(patches_y), patches_x), cameras),
+            freq_base=freq_base,
+            freq_scale=freq_scale,
+            feat_dim=head_dim // 4,
+        )
+
+    def __call__(
+        self,
+        q: jax.Array,  # (batch, seqlen, num_heads, head_dim)
+        k: jax.Array,  # (batch, seqlen, num_heads, head_dim)
+        v: jax.Array,  # (batch, seqlen, num_heads, head_dim)
+        viewmats: jax.Array,  # (batch, cameras, 4, 4)
+        Ks: jax.Array | None = None,  # (batch, cameras, 3, 3)
+        **kwargs,
+    ) -> jax.Array:
+        """Apply PRoPE attention with precomputed coefficients."""
+        return prope_dot_product_attention(
+            q,
+            k,
+            v,
+            viewmats=viewmats,
+            Ks=Ks,
+            patches_x=self.patches_x,
+            patches_y=self.patches_y,
+            image_width=self.image_width,
+            image_height=self.image_height,
+            coeffs_x=self.coeffs_x,
+            coeffs_y=self.coeffs_y,
+            **kwargs,
+        )
+
+
+def prope_dot_product_attention(
+    q: jax.Array,  # (batch, seqlen, num_heads, head_dim)
+    k: jax.Array,  # (batch, seqlen, num_heads, head_dim)
+    v: jax.Array,  # (batch, seqlen, num_heads, head_dim)
+    *,
+    viewmats: jax.Array,  # (batch, cameras, 4, 4)
+    Ks: jax.Array | None = None,  # (batch, cameras, 3, 3)
+    patches_x: int,  # How many patches wide is each image?
+    patches_y: int,  # How many patches tall is each image?
+    image_width: int,  # Width of the image. Used to normalize intrinsics.
+    image_height: int,  # Height of the image. Used to normalize intrinsics.
+    coeffs_x: tuple[jax.Array, jax.Array] | None = None,
+    coeffs_y: tuple[jax.Array, jax.Array] | None = None,
+    **kwargs,
+) -> jax.Array:
+    """Similar to jax.nn.dot_product_attention, but applies PRoPE-style
+    positional encoding.
+
+    Currently, we assume that the sequence length is equal to:
+
+        cameras * patches_x * patches_y
+
+    And token ordering allows the `(seqlen,)` axis to be reshaped into
+    `(cameras, patches_x, patches_y)`.
+    """
+
+    # We're going to assume self-attention: all inputs are the same shape.
+    (batch, seqlen, num_heads, head_dim) = q.shape
+    cameras = viewmats.shape[1]
+    assert q.shape == k.shape == v.shape
+    assert viewmats.shape == (batch, cameras, 4, 4)
+    assert Ks is None or Ks.shape == (batch, cameras, 3, 3)
+    assert seqlen == cameras * patches_x * patches_y
+
+    # Normalize camera intrinsics.
+    if Ks is not None:
+        Ks_norm = jnp.zeros_like(Ks)
+        Ks_norm = Ks_norm.at[..., 0, 0].set(Ks[..., 0, 0] / image_width)
+        Ks_norm = Ks_norm.at[..., 1, 1].set(Ks[..., 1, 1] / image_height)
+        Ks_norm = Ks_norm.at[..., 0, 2].set(Ks[..., 0, 2] / image_width - 0.5)
+        Ks_norm = Ks_norm.at[..., 1, 2].set(Ks[..., 1, 2] / image_height - 0.5)
+        Ks_norm = Ks_norm.at[..., 2, 2].set(1.0)
+        del Ks
+
+        # Compute the camera projection matrices we use in PRoPE.
+        # - K is an `image<-camera` transform.
+        # - viewmats is a `camera<-world` transform.
+        # - P = lift(K) @ viewmats is an `image<-world` transform.
+        P = jnp.einsum("...ij,...jk->...ik", _lift_K(Ks_norm), viewmats)
+        P_T = P.swapaxes(-1, -2)
+        P_inv = jnp.einsum(
+            "...ij,...jk->...ik",
+            _invert_SE3(viewmats),
+            _lift_K(_invert_K(Ks_norm)),
+        )
+    else:
+        # GTA formula. P is `camera<-world` transform.
+        P = viewmats
+        P_T = P.swapaxes(-1, -2)
+        P_inv = _invert_SE3(viewmats)
+
+    assert P.shape == P_inv.shape == (batch, cameras, 4, 4)
+
+    # Precompute cos/sin terms for RoPE if not provided.
+    if coeffs_x is None:
+        coeffs_x = _rope_precompute_coeffs(
+            jnp.tile(jnp.arange(patches_x), patches_y * cameras),
+            freq_base=100.0,
+            freq_scale=1.0,
+            feat_dim=head_dim // 4,
+        )
+    if coeffs_y is None:
+        coeffs_y = _rope_precompute_coeffs(
+            jnp.tile(jnp.repeat(jnp.arange(patches_y), patches_x), cameras),
+            freq_base=100.0,
+            freq_scale=1.0,
+            feat_dim=head_dim // 4,
+        )
+
+    # Block-diagonal transforms to the inputs and outputs of the attention operator.
+    assert head_dim % 4 == 0
+    transforms_q = [
+        (partial(_apply_tiled_projmat, projmat=P_T), head_dim // 2),
+        (partial(_rope_apply_coeffs, coeffs=coeffs_x), head_dim // 4),
+        (partial(_rope_apply_coeffs, coeffs=coeffs_y), head_dim // 4),
+    ]
+    transforms_kv = [
+        (partial(_apply_tiled_projmat, projmat=P_inv), head_dim // 2),
+        (partial(_rope_apply_coeffs, coeffs=coeffs_x), head_dim // 4),
+        (partial(_rope_apply_coeffs, coeffs=coeffs_y), head_dim // 4),
+    ]
+    transforms_o = [
+        (partial(_apply_tiled_projmat, projmat=P), head_dim // 2),
+        (partial(_rope_apply_coeffs, coeffs=coeffs_x, inverse=True), head_dim // 4),
+        (partial(_rope_apply_coeffs, coeffs=coeffs_y, inverse=True), head_dim // 4),
+    ]
+    out = jax.nn.dot_product_attention(
+        query=_apply_block_diagonal(q, transforms_q),
+        key=_apply_block_diagonal(k, transforms_kv),
+        value=_apply_block_diagonal(v, transforms_kv),
+        **kwargs,
+    )
+    out = _apply_block_diagonal(out, transforms_o)
+    assert out.shape == (batch, seqlen, num_heads, head_dim)
+    return out
+
+
+def _apply_tiled_projmat(
+    feats: jax.Array,  # (batch, seqlen, num_heads, feat_dim)
+    projmat: jax.Array,  # (batch, cameras, 4, 4)
+) -> jax.Array:
+    """Apply projection matrix to features."""
+    # - seqlen => (cameras, patches_x * patches_y)
+    # - feat_dim => (feat_dim // 4, 4)
+    (batch, seqlen, num_heads, feat_dim) = feats.shape
+    cameras = projmat.shape[1]
+    assert seqlen > cameras and seqlen % cameras == 0
+    assert projmat.shape == (batch, cameras, 4, 4)
+    return jnp.einsum(
+        "bcij,bcpnkj->bcpnki",
+        projmat,
+        feats.reshape((batch, cameras, -1, num_heads, feat_dim // 4, 4)),
+    ).reshape(feats.shape)
+
+
+def _rope_precompute_coeffs(
+    positions: jax.Array,  # (seqlen,)
+    freq_base: float,
+    freq_scale: float,
+    feat_dim: int,
+) -> tuple[jax.Array, jax.Array]:
+    """Precompute RoPE coefficients."""
+    assert len(positions.shape) == 1
+    assert feat_dim % 2 == 0
+    num_freqs = feat_dim // 2
+    freqs = freq_scale * (
+        freq_base ** (-jnp.arange(num_freqs)[None, None, None, :] / num_freqs)
+    )
+    angles = positions[None, :, None, None] * freqs
+    # Shape should be: `(batch, seqlen, num_heads, num_freqs)`; we're
+    # broadcasting across `batch` and `num_heads`.
+    assert angles.shape == (1, positions.shape[0], 1, num_freqs)
+    return jnp.cos(angles), jnp.sin(angles)
+
+
+def _rope_apply_coeffs(
+    feats: jax.Array,  # (batch, seqlen, num_heads, feat_dim)
+    coeffs: tuple[jax.Array, jax.Array],
+    inverse: bool = False,
+) -> jax.Array:
+    """Apply RoPE coefficients to features. We adopt a 'split' ordering
+    convention. (in contrast to 'interleaved')"""
+    cos, sin = coeffs
+    assert len(feats.shape) == len(cos.shape) == len(sin.shape) == 4
+    assert cos.shape[-1] == sin.shape[-1] == feats.shape[-1] // 2
+    x_in = feats[..., : feats.shape[-1] // 2]
+    y_in = feats[..., feats.shape[-1] // 2 :]
+    return jnp.concatenate(
+        (
+            [cos * x_in + sin * y_in, -sin * x_in + cos * y_in]
+            if not inverse
+            else [cos * x_in - sin * y_in, sin * x_in + cos * y_in]
+        ),
+        axis=-1,
+    )
+
+
+def _apply_block_diagonal(
+    feats: jax.Array,  # (..., dim)
+    func_size_pairs: list[tuple[Callable[[jax.Array], jax.Array], int]],
+) -> jax.Array:
+    """Apply a block-diagonal function to an input array.
+
+    Each function is specified as a tuple with form:
+
+        ((Array) -> Array, int)
+
+    Where the integer is the size of the input to the function.
+    """
+    funcs, block_sizes = zip(*func_size_pairs)
+    assert feats.shape[-1] == sum(block_sizes)
+    x_blocks = jnp.split(feats, tuple(itertools.accumulate(block_sizes[:-1])), axis=-1)
+    out = jnp.concatenate(
+        [f(x_block) for f, x_block in zip(funcs, x_blocks)],
+        axis=-1,
+    )
+    assert out.shape == feats.shape, "Input/output shapes should match."
+    return out
+
+
+def _invert_SE3(transforms: jax.Array) -> jax.Array:
+    """Invert a 4x4 SE(3) matrix."""
+    assert transforms.shape[-2:] == (4, 4)
+    Rinv = transforms[..., :3, :3].swapaxes(-1, -2)
+    out = jnp.zeros_like(transforms)
+    out = out.at[..., :3, :3].set(Rinv)
+    out = out.at[..., :3, 3].set(
+        -jnp.einsum("...ij,...j->...i", Rinv, transforms[..., :3, 3])
+    )
+    out = out.at[..., 3, 3].set(1.0)
+    return out
+
+
+def _lift_K(Ks: jax.Array) -> jax.Array:
+    """Lift 3x3 matrices to homogeneous 4x4 matrices."""
+    assert Ks.shape[-2:] == (3, 3)
+    out = jnp.zeros(Ks.shape[:-2] + (4, 4))
+    out = out.at[..., :3, :3].set(Ks)
+    out = out.at[..., 3, 3].set(1.0)
+    return out
+
+
+def _invert_K(Ks: jax.Array) -> jax.Array:
+    """Invert 3x3 intrinsics matrices. Assumes no skew."""
+    assert Ks.shape[-2:] == (3, 3)
+    out = jnp.zeros(Ks.shape)
+    out = out.at[..., 0, 0].set(1.0 / Ks[..., 0, 0])
+    out = out.at[..., 1, 1].set(1.0 / Ks[..., 1, 1])
+    out = out.at[..., 0, 2].set(-Ks[..., 0, 2] / Ks[..., 0, 0])
+    out = out.at[..., 1, 2].set(-Ks[..., 1, 2] / Ks[..., 1, 1])
+    out = out.at[..., 2, 2].set(1.0)
+    return out

build/lib/prope/torch.py

@@ -0,0 +1,347 @@
+# MIT License
+#
+# Copyright (c) Authors of
+# "PRoPE: Projective Positional Encoding for Multiview Transformers"
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+from functools import partial
+from typing import Callable, Optional, Tuple
+
+import torch
+import torch.nn.functional as F
+
+
+class PropeDotProductAttention(torch.nn.Module):
+    """PRoPE attention with precomputed RoPE coefficients."""
+
+    coeffs_x_0: torch.Tensor
+    coeffs_x_1: torch.Tensor
+    coeffs_y_0: torch.Tensor
+    coeffs_y_1: torch.Tensor
+
+    def __init__(
+        self,
+        head_dim: int,
+        cameras: int,
+        patches_x: int,
+        patches_y: int,
+        image_width: int,
+        image_height: int,
+        freq_base: float = 100.0,
+        freq_scale: float = 1.0,
+    ):
+        super().__init__()
+        self.head_dim = head_dim
+        self.cameras = cameras
+        self.patches_x = patches_x
+        self.patches_y = patches_y
+        self.image_width = image_width
+        self.image_height = image_height
+
+        coeffs_x: Tuple[torch.Tensor, torch.Tensor] = _rope_precompute_coeffs(
+            torch.tile(torch.arange(patches_x), (patches_y * cameras,)),
+            freq_base=freq_base,
+            freq_scale=freq_scale,
+            feat_dim=head_dim // 4,
+        )
+        coeffs_y: Tuple[torch.Tensor, torch.Tensor] = _rope_precompute_coeffs(
+            torch.tile(
+                torch.repeat_interleave(torch.arange(patches_y), patches_x),
+                (cameras,),
+            ),
+            freq_base=freq_base,
+            freq_scale=freq_scale,
+            feat_dim=head_dim // 4,
+        )
+        # Do not save coeffs to checkpoint as `cameras` might change during testing.
+        self.register_buffer("coeffs_x_0", coeffs_x[0], persistent=False)
+        self.register_buffer("coeffs_x_1", coeffs_x[1], persistent=False)
+        self.register_buffer("coeffs_y_0", coeffs_y[0], persistent=False)
+        self.register_buffer("coeffs_y_1", coeffs_y[1], persistent=False)
+
+    # override load_state_dict to not load coeffs if they exist (for backward compatibility)
+    def load_state_dict(self, state_dict, strict=True):
+        # remove coeffs from state_dict
+        state_dict.pop("coeffs_x_0", None)
+        state_dict.pop("coeffs_x_1", None)
+        state_dict.pop("coeffs_y_0", None)
+        state_dict.pop("coeffs_y_1", None)
+        super().load_state_dict(state_dict, strict)
+
+    def forward(
+        self,
+        q: torch.Tensor,  # (batch, num_heads, seqlen, head_dim)
+        k: torch.Tensor,  # (batch, num_heads, seqlen, head_dim)
+        v: torch.Tensor,  # (batch, num_heads, seqlen, head_dim)
+        viewmats: torch.Tensor,  # (batch, cameras, 4, 4)
+        Ks: Optional[torch.Tensor],  # (batch, cameras, 3, 3)
+        **kwargs,
+    ) -> torch.Tensor:
+        return prope_dot_product_attention(
+            q,
+            k,
+            v,
+            viewmats=viewmats,
+            Ks=Ks,
+            patches_x=self.patches_x,
+            patches_y=self.patches_y,
+            image_width=self.image_width,
+            image_height=self.image_height,
+            coeffs_x=(self.coeffs_x_0, self.coeffs_x_1),
+            coeffs_y=(self.coeffs_y_0, self.coeffs_y_1),
+            **kwargs,
+        )
+
+
+def prope_dot_product_attention(
+    q: torch.Tensor,  # (batch, num_heads, seqlen, head_dim)
+    k: torch.Tensor,  # (batch, num_heads, seqlen, head_dim)
+    v: torch.Tensor,  # (batch, num_heads, seqlen, head_dim)
+    *,
+    viewmats: torch.Tensor,  # (batch, cameras, 4, 4)
+    Ks: Optional[torch.Tensor],  # (batch, cameras, 3, 3)
+    patches_x: int,  # How many patches wide is each image?
+    patches_y: int,  # How many patches tall is each image?
+    image_width: int,  # Width of the image. Used to normalize intrinsics.
+    image_height: int,  # Height of the image. Used to normalize intrinsics.
+    coeffs_x: Optional[torch.Tensor] = None,
+    coeffs_y: Optional[torch.Tensor] = None,
+    **kwargs,
+) -> torch.Tensor:
+    """Similar to torch.nn.functional.scaled_dot_product_attention, but applies PRoPE-style
+    positional encoding.
+
+    Currently, we assume that the sequence length is equal to:
+
+        cameras * patches_x * patches_y
+
+    And token ordering allows the `(seqlen,)` axis to be reshaped into
+    `(cameras, patches_x, patches_y)`.
+    """
+
+    # We're going to assume self-attention: all inputs are the same shape.
+    (batch, num_heads, seqlen, head_dim) = q.shape
+    cameras = viewmats.shape[1]
+    assert q.shape == k.shape == v.shape
+    assert viewmats.shape == (batch, cameras, 4, 4)
+    assert Ks is None or Ks.shape == (batch, cameras, 3, 3)
+    assert seqlen == cameras * patches_x * patches_y
+
+    # Normalize camera intrinsics.
+    if Ks is not None:
+        Ks_norm = torch.zeros_like(Ks)
+        Ks_norm[..., 0, 0] = Ks[..., 0, 0] / image_width
+        Ks_norm[..., 1, 1] = Ks[..., 1, 1] / image_height
+        Ks_norm[..., 0, 2] = Ks[..., 0, 2] / image_width - 0.5
+        Ks_norm[..., 1, 2] = Ks[..., 1, 2] / image_height - 0.5
+        Ks_norm[..., 2, 2] = 1.0
+        del Ks
+
+        # Compute the camera projection matrices we use in PRoPE.
+        # - K is an `image<-camera` transform.
+        # - viewmats is a `camera<-world` transform.
+        # - P = lift(K) @ viewmats is an `image<-world` transform.
+        P = torch.einsum("...ij,...jk->...ik", _lift_K(Ks_norm), viewmats)
+        P_T = P.transpose(-1, -2)
+        P_inv = torch.einsum(
+            "...ij,...jk->...ik",
+            _invert_SE3(viewmats),
+            _lift_K(_invert_K(Ks_norm)),
+        )
+
+    else:
+        # GTA formula. P is `camera<-world` transform.
+        P = viewmats
+        P_T = P.transpose(-1, -2)
+        P_inv = _invert_SE3(viewmats)
+
+    assert P.shape == P_inv.shape == (batch, cameras, 4, 4)
+
+    # Precompute cos/sin terms for RoPE. We use tiles/repeats for 'row-major'
+    # broadcasting.
+    if coeffs_x is None:
+        coeffs_x = _rope_precompute_coeffs(
+            torch.tile(
+                torch.arange(patches_x, device=q.device), (patches_y * cameras,)
+            ),
+            freq_base=100.0,
+            freq_scale=1.0,
+            feat_dim=head_dim // 4,
+        )
+    if coeffs_y is None:
+        coeffs_y = _rope_precompute_coeffs(
+            torch.tile(
+                torch.repeat_interleave(
+                    torch.arange(patches_y, device=q.device), patches_x
+                ),
+                (cameras,),
+            ),
+            freq_base=100.0,
+            freq_scale=1.0,
+            feat_dim=head_dim // 4,
+        )
+
+    # Block-diagonal transforms to the inputs and outputs of the attention operator.
+    assert head_dim % 4 == 0
+    transforms_q = [
+        (partial(_apply_tiled_projmat, matrix=P_T), head_dim // 2),
+        (partial(_rope_apply_coeffs, coeffs=coeffs_x), head_dim // 4),
+        (partial(_rope_apply_coeffs, coeffs=coeffs_y), head_dim // 4),
+    ]
+    transforms_kv = [
+        (partial(_apply_tiled_projmat, matrix=P_inv), head_dim // 2),
+        (partial(_rope_apply_coeffs, coeffs=coeffs_x), head_dim // 4),
+        (partial(_rope_apply_coeffs, coeffs=coeffs_y), head_dim // 4),
+    ]
+    transforms_o = [
+        (partial(_apply_tiled_projmat, matrix=P), head_dim // 2),
+        (partial(_rope_apply_coeffs, coeffs=coeffs_x, inverse=True), head_dim // 4),
+        (partial(_rope_apply_coeffs, coeffs=coeffs_y, inverse=True), head_dim // 4),
+    ]
+    out = F.scaled_dot_product_attention(
+        query=_apply_block_diagonal(q, transforms_q),
+        key=_apply_block_diagonal(k, transforms_kv),
+        value=_apply_block_diagonal(v, transforms_kv),
+        **kwargs,
+    )
+    out = _apply_block_diagonal(out, transforms_o)
+    assert out.shape == (batch, num_heads, seqlen, head_dim)
+    return out
+
+
+def _apply_tiled_projmat(
+    feats: torch.Tensor,  # (batch, num_heads, seqlen, feat_dim)
+    matrix: torch.Tensor,  # (batch, cameras, D, D)
+) -> torch.Tensor:
+    """Apply projection matrix to features."""
+    # - seqlen => (cameras, patches_x * patches_y)
+    # - feat_dim => (feat_dim // 4, 4)
+    (batch, num_heads, seqlen, feat_dim) = feats.shape
+    cameras = matrix.shape[1]
+    assert seqlen > cameras and seqlen % cameras == 0
+    D = matrix.shape[-1]
+    assert matrix.shape == (batch, cameras, D, D)
+    assert feat_dim % D == 0
+    return torch.einsum(
+        "bcij,bncpkj->bncpki",
+        matrix,
+        feats.reshape((batch, num_heads, cameras, -1, feat_dim // D, D)),
+    ).reshape(feats.shape)
+
+
+def _rope_precompute_coeffs(
+    positions: torch.Tensor,  # (seqlen,)
+    freq_base: float,
+    freq_scale: float,
+    feat_dim: int,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    """Precompute RoPE coefficients."""
+    assert len(positions.shape) == 1
+    assert feat_dim % 2 == 0
+    num_freqs = feat_dim // 2
+    freqs = freq_scale * (
+        freq_base
+        ** (
+            -torch.arange(num_freqs, device=positions.device)[None, None, None, :]
+            / num_freqs
+        )
+    )
+    angles = positions[None, None, :, None] * freqs
+    # Shape should be: `(batch, num_heads, seqlen, num_freqs)`; we're
+    # broadcasting across `batch` and `num_heads`.
+    assert angles.shape == (1, 1, positions.shape[0], num_freqs)
+    return torch.cos(angles), torch.sin(angles)
+
+
+def _rope_apply_coeffs(
+    feats: torch.Tensor,  # (batch, num_heads, seqlen, feat_dim)
+    coeffs: Tuple[torch.Tensor, torch.Tensor],
+    inverse: bool = False,
+) -> torch.Tensor:
+    """Apply RoPE coefficients to features. We adopt a 'split' ordering
+    convention. (in contrast to 'interleaved')"""
+    cos, sin = coeffs
+    assert len(feats.shape) == len(cos.shape) == len(sin.shape) == 4
+    assert cos.shape[-1] == sin.shape[-1] == feats.shape[-1] // 2
+    x_in = feats[..., : feats.shape[-1] // 2]
+    y_in = feats[..., feats.shape[-1] // 2 :]
+    return torch.cat(
+        (
+            [cos * x_in + sin * y_in, -sin * x_in + cos * y_in]
+            if not inverse
+            else [cos * x_in - sin * y_in, sin * x_in + cos * y_in]
+        ),
+        dim=-1,
+    )
+
+
+def _apply_block_diagonal(
+    feats: torch.Tensor,  # (..., dim)
+    func_size_pairs: list[tuple[Callable[[torch.Tensor], torch.Tensor], int]],
+) -> torch.Tensor:
+    """Apply a block-diagonal function to an input array.
+
+    Each function is specified as a tuple with form:
+
+        ((Tensor) -> Tensor, int)
+
+    Where the integer is the size of the input to the function.
+    """
+    funcs, block_sizes = zip(*func_size_pairs)
+    assert feats.shape[-1] == sum(block_sizes)
+    x_blocks = torch.split(feats, block_sizes, dim=-1)
+    out = torch.cat(
+        [f(x_block) for f, x_block in zip(funcs, x_blocks)],
+        dim=-1,
+    )
+    assert out.shape == feats.shape, "Input/output shapes should match."
+    return out
+
+
+def _invert_SE3(transforms: torch.Tensor) -> torch.Tensor:
+    """Invert a 4x4 SE(3) matrix."""
+    assert transforms.shape[-2:] == (4, 4)
+    Rinv = transforms[..., :3, :3].transpose(-1, -2)
+    out = torch.zeros_like(transforms)
+    out[..., :3, :3] = Rinv
+    out[..., :3, 3] = -torch.einsum("...ij,...j->...i", Rinv, transforms[..., :3, 3])
+    out[..., 3, 3] = 1.0
+    return out
+
+
+def _lift_K(Ks: torch.Tensor) -> torch.Tensor:
+    """Lift 3x3 matrices to homogeneous 4x4 matrices."""
+    assert Ks.shape[-2:] == (3, 3)
+    out = torch.zeros(Ks.shape[:-2] + (4, 4), device=Ks.device)
+    out[..., :3, :3] = Ks
+    out[..., 3, 3] = 1.0
+    return out
+
+
+def _invert_K(Ks: torch.Tensor) -> torch.Tensor:
+    """Invert 3x3 intrinsics matrices. Assumes no skew."""
+    assert Ks.shape[-2:] == (3, 3)
+    out = torch.zeros_like(Ks)
+    out[..., 0, 0] = 1.0 / Ks[..., 0, 0]
+    out[..., 1, 1] = 1.0 / Ks[..., 1, 1]
+    out[..., 0, 2] = -Ks[..., 0, 2] / Ks[..., 0, 0]
+    out[..., 1, 2] = -Ks[..., 1, 2] / Ks[..., 1, 1]
+    out[..., 2, 2] = 1.0
+    return out

data_splits/airvln/test/navigation_eval.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5a19c11ae615e506cc45f9109accb0e2824cc8b692aba6d3f3bd412873b0c8bc
+size 817

data_splits/airvln/test/rollout_traj_names.txt

@@ -0,0 +1 @@
+32UTUBMZ7PU1BPNAEP5B02AVF2CVBG_processed

data_splits/airvln/test/traj_names.txt

@@ -0,0 +1,230 @@
+32UTUBMZ7PU1BPNAEP5B02AVF2CVBG_processed
+3FE7TXL1LRLFI4RX9XIQTM8J8D6Q2N_processed
+3HOSI13XH8M8W5L810WNP6SOIGGDD2_processed
+36W0OB37H5CSBMV6FE4A7JQPFANZHL_processed
+3AZHRG4CUDIGFTIHWZ2GXQMTEYG03H_processed
+31LVTDXBLG8LVRS5WIPVD8HYPRGLRQ_processed
+3A9AA95AT5KMHP95R35ZROLLNHD5PU_processed
+33PPO7FEC4DP4G1UTEXYG381RMXDIW_processed
+3EG49X3513AN92BAIDPLD7LOLACX63_processed
+3ERET4BTVV747LE1H1D87N4OEFV9KG_processed
+34FNN24DCV7GQ316IQ1I4FEXO4UY5S_processed
+3E337GFOLI6KU1MCSKBYRW7FDJRNGF_processed
+36U2A8VAGAXJ8MM9PD5O64XFHXRYKH_processed
+3HMVI3QICSQH2L0NH3Z61P1GS6NY1O_processed
+3E1QT0TDFY7DNL53C6MIUBU0V1D8IP_processed
+3AQF3RZ55HG69GKPIJJZ70LHGUF6FX_processed
+3GD6L00D31VH84B9Y0BV7UE94QFM11_processed
+3EICBYG64DUBUZFMB0NN8Y5J50BCJS_processed
+3GD6L00D31VH84B9Y0BV7UE9Y6W1MN_processed
+326O153BMRWDOBZ8XV77VOA9M19DE9_processed
+33NF62TLXS0TDXRA3D4LWHIRAYYKJ5_processed
+3H7XDTSHKLPAHVVQKL2LNFK62NUWGU_processed
+3H8DHMCCWI9GABRITHIATJNE4M6DK4_processed
+3H7XDTSHKLPAHVVQKL2LNFK62NQWGQ_processed
+3C8HJ7UOPGSE30WH6BKNZOYLCWSZMG_processed
+3907X2AHF93UISQI9K31THI912GP2V_processed
+3ATTHHXXWJM32M6BKEYAAABT74AXIJ_processed
+36PW28KO48UFQ4WWDLG55N23ZJUAEQ_processed
+37M28K1J0ZBN1KIORMRW23FVYG9JAB_processed
+38F71OA9G2U8YJ5H5ZQGWWI3K1EMFZ_processed
+3EICBYG64DUBUZFMB0NN8Y5J6W1JCI_processed
+373ERPL3YX69E40A5RRMZZRE3H6RTB_processed
+37Z929RLGI6LFJ0NKGRRP9WA3QWTS0_processed
+36V4Q8R5ZTY5P8VQJF1A8VMITGRMQR_processed
+3A4NIXBJ7FXUYBFDRATER9NNW9DMLK_processed
+38F5OAUN5WA9RCP0WJCRGZXQLJWH7H_processed
+3D8YOU6S9NIVSYHFD8EYT0MKJ17U6U_processed
+37TRT2X24ZPSKUNGX0RSXEZ1PEEBJN_processed
+3IKZ72A5BDEEG1QHH05G9B73C6NFN8_processed
+3DYGAII7PU6BABS3BVT2PZGCIIRPQR_processed
+3IQ1VMJRY2IYV85F5A0JD91S2BB9AK_processed
+336YQZE834CGWMCSZND7AKRRXRKM58_processed
+3E337GFOLI6KU1MCSKBYRW7F5K6GNH_processed
+3I3WADAZ9Z24WPX4C3SK2OGVQYJO5J_processed
+3FPRZHYEP75W8UJXZWQ5IJCJTSI3VI_processed
+340UGXU9D7ZG7OWGEGM65S2JLPQVUP_processed
+36NEMU28XOBA95RCVO0G0K44IZRWMP_processed
+39DD6S19JY9GRC44PDW4MGTC894ZET_processed
+37TRT2X24ZPSKUNGX0RSXEZ1MBBJBJ_processed
+3IGI0VL64GI8ME3AG3O6W5GHW5IONV_processed
+39L1G8WVWZPGMI3ZSI6GBV536MW13A_processed
+3EF8EXOTTATR7W5OV3Y3R2MSV1Z1JW_processed
+33LKR6A5KNILLZ1T7XC7T891SBY1TO_processed
+3H7Z272LXG50JECQJQL68DHI2SHLP1_processed
+33FBRBDW6XXMAIJ3KWQWJ8W5QN28CM_processed
+37TRT2X24ZPSKUNGX0RSXEZ1MCXBJZ_processed
+3IFS6Q0HJRHV65KCQ4SNB2U1SCZISX_processed
+32Z9ZLUT1UIGCIHZD5TTRLJLGH3HOR_processed
+35H6S234SJYE7JR0C76QLOU45BI65V_processed
+3DHE4R9OC59O5FXJ61DEZ84HF8W2GG_processed
+352YTHGRO4BC8P7HULEKDNDG0LDH4I_processed
+35BLDD71IFVXTNQQK3LEMKHZQADVZX_processed
+3B3WTRP3DK09Q5ABSV747RIURQU92V_processed
+37U1UTWH94KQGK8M2TGR83VR3SW8R4_processed
+39ZSFO5CAHU7GTWMXR8N24RPQ1HJUO_processed
+3FE7TXL1LRLFI4RX9XIQTM8J6162Q9_processed
+36ZN444YT0WG8DSJJNBERXK0VJZOI1_processed
+3EKVH9QME72LTVNS4BC6TN1KNIP2D4_processed
+3DUZQ9U6SVM0SB4CPBR6QRKVGC9SVU_processed
+31Z0PCVWUTDZWLGVWDTGUI0E1NB7TQ_processed
+3IAS3U3I0OESEYP8QFL0YXDKZ0AB2O_processed
+39O5D9O872Q09LDX8FV0SROU8X0C3E_processed
+3FK0YFF9P8EGK3LGJ6WB1E42FV6VVZ_processed
+3CFVK00FWUJS98UL4UMX2DQ1EIYL6Z_processed
+39GXDJN2O2CI9RP3BDL190KUOH6V8J_processed
+3HVVDCPGTNQIB5CWJMJG7VKB6W8YTR_processed
+3F6KKYWMNKZHIHCLQUAQLB4I0J2DN6_processed
+3FE2ERCCZ668PZFNWI1Y0ULC25TOPD_processed
+37TRT2X24ZPSKUNGX0RSXEZ23XUBJL_processed
+39PAAFCODVY17BHREBWW0WGHMLCTVT_processed
+32SVAV9L3O7CG6GZO1Z9MWY627OA33_processed
+32M8BPYGA2KSG0BLV4Y6MOBY4X3IG3_processed
+31HQ4X3T318XW6SIB7P03IMUB89SLP_processed
+3BXQMRHWK8WXH1270OLFIWJ6AEDUMM_processed
+31QNSG6A50RGY1O7TFIL3M4R22878I_processed
+3FE7TXL1LRLFI4RX9XIQTM8J15PQ2J_processed
+3B3WTRP3DK09Q5ABSV747RIUWYP92B_processed
+3E1QT0TDFY7DNL53C6MIUBU020L8I2_processed
+39DD6S19JY9GRC44PDW4MGTCN83ZE5_processed
+32ZKVD547OLHZGLRUFZ5H7ICR6Y3BL_processed
+35USIKEBN0EIX8X6G72CF5UQJIEN6T_processed
+3A0EX8ZRNHMIFJIFJ0SV71EPZCDYBF_processed
+3HHRAGRYXH3NXCZGFT51E4LXRSEO9X_processed
+3A4TN5196TGF3TKWN0K2WLP4TD6CHX_processed
+358010RM5NR8OSQBJLXTPZ1N00BXVU_processed
+3IAEQB9FMNI75BLZIV1DHUHA91ZDWK_processed
+3II4UPYCOS52LEPD6M27P5ZOKE6DQV_processed
+37WLF8U1WYOHP2C3JUAWNDDMOAZ6K6_processed
+30LSNF2393T2163EWPDHKKHXUP42IW_processed
+3H8DHMCCWI9GABRITHIATJNE6TBKDW_processed
+3C8HJ7UOPGSE30WH6BKNZOYMYV0ZMA_processed
+34V1S5K3G1ZX86TCFXJ7X4Z6R9M96E_processed
+3A0EX8ZRNHMIFJIFJ0SV71EPZF1YB9_processed
+37QW5D2ZRPK2HZ8ZHIGWYD0N8RF8S5_processed
+35H6S234SJYE7JR0C76QLOU4LAH567_processed
+3HSYG7LRBSWOOHXSLY4XXWI4P9PKK3_processed
+34V1S5K3G1ZX86TCFXJ7X4Z6DVH963_processed
+39DD6S19JY9GRC44PDW4MGTC84HEZB_processed
+32M8BPYGA2KSG0BLV4Y6MOBZ7H8IGH_processed
+36AHBNMV10ABUGGZXWJ12R2QJC3YDD_processed
+3AWETUDC9BQS7H6QLWMV7VR40ZDIZH_processed
+32EYX73OY97OEHFZ5WHW4VDGNQ0RUG_processed
+3CFVK00FWUJS98UL4UMX2DQ1E2F6L5_processed
+3E4GGUZ1THPT71T2WSYTX0WA2URK25_processed
+38SKSKU7RAV82SPM6VVMZGG6MR8IL4_processed
+32KTQ2V7RMDZX9E42DQOM0WS1RJM93_processed
+3300DTYQTBF7DK3D2HDRVNH84UJEQR_processed
+3DYGAII7PU6BABS3BVT2PZGCHDSQPI_processed
+31JLPPHS23S1IAA0YK4RNRIBC9G3OA_processed
+3AMYWKA6YKK0FTXIHM2PUUUV72ZO67_processed
+37FMASSAYLPWPJ3AFI6SLDK19U4BID_processed
+32EYX73OY97OEHFZ5WHW4VDGKT0RUJ_processed
+3G2UL9A02MCTUN5J00LNJ880BPU67M_processed
+3DY46V3X3YGIOFRM7OWLTIG5BJ555M_processed
+3A1PQ49WVQFVGQAY73VIFP6D23WH1G_processed
+39LOEL67O13YXINKR3FVQ5SBIUM83N_processed
+30OG32W0S39MANQLMPLARTM3YZINEU_processed
+3180JW2OTDAQVE67WQ0FUSZ9EHB5JW_processed
+3FFJ6VRILAMV5W00H2931WOIIPL0IS_processed
+3AAPLD8UCLFWPAM79MFSLVP8FRRHTO_processed
+38JBBYETQX80OFGF4K6ESKG32CME48_processed
+39K0FND3AQDD2SO9UJ4OIV7C9U7AMK_processed
+35L9RVQFCXGBPN1W9R52QRBAY2LHUW_processed
+32VNZTT0AG2PXJLKQQQERRFN3LXR4B_processed
+3AMW0RGHOM0Q7EFBV31LL5L7RXEPN8_processed
+38JBBYETQX80OFGF4K6ESKG3M0JE41_processed
+3B837J3LDXU8Z4NVGRMWPUVSV5XRSZ_processed
+3CPLWGV3MXX5FRZ4EE5OZFLS9QMN9I_processed
+38JBBYETQX80OFGF4K6ESKG4PZKE45_processed
+3I7DHKZYGWYAQCKBD09UFU7HEYX5F1_processed
+30ZX6P7VFHTYWHNKE7YHWLWXUID2J7_processed
+354GIDR5ZK4KY1JKDCKXVSOMRZY00U_processed
+3FFJ6VRILAMV5W00H2931WOIIOHI04_processed
+3ERET4BTVV747LE1H1D87N4OET99KM_processed
+3GD6L00D31VH84B9Y0BV7UE9Y3S1MD_processed
+39ZSFO5CAHU7GTWMXR8N24RPCO2JU5_processed
+3018Q3ZVORO4Z811ZR054U1MTZWARC_processed
+34J10VATJOWJTP5ZY03IG2F0P7RIQ2_processed
+3FTOP5WAROMR07K6ZKU3DX3IV5U0JQ_processed
+339ANSOTRE0XIM74LVJLKG3RWDCKIJ_processed
+3G5W44VEUGGJMV19V7JIZ4QBCEWGKM_processed
+3D3VGR7TA9D4FN2GQCLVHMNG2BCR3S_processed
+33FBRBDW6XXMAIJ3KWQWJ8W5J9WC8L_processed
+358UUM7WR81MG633SOOLU3ZXVBPR7A_processed
+3BWI6RSP7P7X72YY6I4LVW0EFY27E3_processed
+32KTQ2V7RMDZX9E42DQOM0WSHWTM93_processed
+39GAF6DQW0Y0YR5ICSCMUQXMWRFV1W_processed
+3CFJTT4SX2O9N70K21UCGBY5L7NI79_processed
+39JEC75373ZKLUFGSP16BMGDZGPCV5_processed
+33OOO72IVQJ582BRNU1RA4SSHQSCTD_processed
+32XVDSJFP8VOXPT5H4B9CDOEYC22M5_processed
+37Q970SNZN6K6ZOEIX7HJ3DFROES1D_processed
+3018Q3ZVORO4Z811ZR054U1M9CVRAY_processed
+33TIN5LC0D8ZRQ3ILGEZGVJZBCM9YS_processed
+351SEKWQS9FB0ZRLETVQ8WFIRTFMDY_processed
+37WLF8U1WYOHP2C3JUAWNDDMNX0K6U_processed
+3CCZ6YKWRGH1P2TYAE925YL6ISH59F_processed
+36TFCYNS4D836RVREWGPAYAESMBXHH_processed
+33CKWXB73SIKCFPK5ESXN2Q6161112_processed
+39ASUFLU665RMH4POV8JPVD1V7UEXQ_processed
+3H7Z272LXG50JECQJQL68DHI7BLLPC_processed
+37C0GNLMHO19BW6TRDO9J0IW4ZDD67_processed
+3A0EX8ZRNHMIFJIFJ0SV71EPUGABYS_processed
+3BEFOD78WFRY038SLIUX5SLDYBU4MN_processed
+3IGI0VL64GI8ME3AG3O6W5GHHJDNO2_processed
+34PGFRQONX9K8XLRSRNOKSCMB6DJWM_processed
+373ERPL3YX69E40A5RRMZZREWASTRE_processed
+34S6N1K2Z4H86XE20DD1Z4FRMWMHL3_processed
+33CID5710DRTCP73LSOZVAEH29CL37_processed
+31LM9EDVOUQUL3CD0QW1O9ZVITNJN7_processed
+3IQ1VMJRY2IYV85F5A0JD91S2Y49AN_processed
+3CFJTT4SX2O9N70K21UCGBY5Q39I7S_processed
+30ZX6P7VFHTYWHNKE7YHWLWXS5ZJ2I_processed
+39U1BHVTDUPHV2F8U1QQH1OYV303TR_processed
+30LB5CDZNL8H0MPAH6XP1RR88F70ZT_processed
+3BDCF01OG6SUSS4RADHPL4AU6XILYT_processed
+3H8DHMCCWI9GABRITHIATJNFMJ2KDL_processed
+3F6KKYWMNKZHIHCLQUAQLB4IY1PND1_processed
+3COPXFW7XKAPZ8U8YQ95TD43CEZKPR_processed
+3A1PQ49WVQFVGQAY73VIFP6DI9QH12_processed
+34HJIJKLPEUHQQ21ZAVOSO4P73X4V9_processed
+3CP1TO84P2ZQPI8ZP5UNVBDSA2F25M_processed
+34S9DKFK7CNKGS7CMQX4YWEJVW1YNJ_processed
+3DY4FPOOAAMORW4ZAMR10HFZY22VR9_processed
+37XITHEIS57SSNYZJ5WWAPRAEBHCRJ_processed
+3GU1KF0O4RZO65QESDWCUALDVDSBPS_processed
+374TNBHA8KT5JPK42IPL2Q9ONS1YQC_processed
+33JKGHPFYLSKMBIY3N9T41RC2IGNMJ_processed
+345LHZDED6QWVFUXU6G0MXA0GU63U9_processed
+3137ONMDKP3G0V9ZZL8F19D6ZBSEGN_processed
+3H0W84IWBT07PLIDFW2R0DRDUEPERM_processed
+32AT8R96GU70FWFCKLH1U5IC7DZUSR_processed
+39LOEL67O13YXINKR3FVQ5SBJ5Q389_processed
+3I3WADAZ9Z24WPX4C3SK2OGV6A7O5B_processed
+3A4NIXBJ7FXUYBFDRATER9NN1HTMLL_processed
+373ERPL3YX69E40A5RRMZZREH4MRTF_processed
+3CP1TO84P2ZQPI8ZP5UNVBDSCHO25R_processed
+37Z929RLGI6LFJ0NKGRRP9WAJPYSTF_processed
+3AUQQEL7UER0R2K0G98MGLED7FGV0E_processed
+34FNN24DCV7GQ316IQ1I4FEX4CK5YL_processed
+323Q6SJS8REM6546E2A34RA7OE0HFP_processed
+3GU1KF0O4RZO65QESDWCUALDUQ6BPV_processed
+37U1UTWH94KQGK8M2TGR83VS6VJR8L_processed
+3GD6L00D31VH84B9Y0BV7UE9JYCM1T_processed
+32KTQ2V7RMDZX9E42DQOM0WSHU49MX_processed
+3907X2AHF93UISQI9K31THIAJX62P8_processed
+386CSBG1O8K305AYWZUTJZPVMU4Q6M_processed
+3B4YI393VIUK0JN8VGM1F40OMGOSSJ_processed
+3DI28L7YXJCJWGJWH2NCUM90X1T1EO_processed
+39JEC75373ZKLUFGSP16BMGD13IVCT_processed
+3FTYUGLFS3JDSS6FMUE4RU4W73HD53_processed
+3EQHHY4HQ1QG4CG6OKFCXF7TEXN5GT_processed
+3G5W44VEUGGJMV19V7JIZ4QBWCOKGY_processed
+3CN4LGXD56MYUKX64LI8ZN8JPTW4YX_processed
+3CFVK00FWUJS98UL4UMX2DQ16AL6LJ_processed
+304SM51WAC2LJX66KU06PFQOKL8SBC_processed
+34T446B1C9C4STE0PSDHQTQN6UT0C3_processed
+34T446B1C9C4STE0PSDHQTQNZYQ0C1_processed
+31LVTDXBLG8LVRS5WIPVD8HX9TELRA_processed
+3FTOP5WAROMR07K6ZKU3DX3IWL9J0L_processed

data_splits/airvln/train/traj_names.txt

@@ -0,0 +1,919 @@
+34PGFRQONX9K8XLRSRNOKSCMXO5WJD_processed
+392CY0QWGAPC4KI103W5DZEVTF3I4E_processed
+3F1567XTN53QIOCWU6X539GXR5Z9Q2_processed
+3FPRZHYEP75W8UJXZWQ5IJCJO8G3V7_processed
+30X31N5D6COG0N1EEG0AU46IV0NSA4_processed
+30LB5CDZNL8H0MPAH6XP1RR8FC90ZW_processed
+3AAPLD8UCLFWPAM79MFSLVP8HOFHT8_processed
+36AHBNMV10ABUGGZXWJ12R2PWBHYD0_processed
+3D3VGR7TA9D4FN2GQCLVHMNG868R3K_processed
+3A1PQ49WVQFVGQAY73VIFP6E4S4H16_processed
+39RP059MEQRIL2T10X4KD6RJ7N9BM1_processed
+33CID5710DRTCP73LSOZVAEHWIY3LN_processed
+3C5W7UE9COOPY5W25TW223IAOXSXM1_processed
+36TFCYNS4D836RVREWGPAYAER41HXQ_processed
+3F1567XTN53QIOCWU6X539GX6TR9QL_processed
+32UTUBMZ7PU1BPNAEP5B02AWYFDBV8_processed
+386CSBG1O8K305AYWZUTJZPVO7DQ6N_processed
+32Z9ZLUT1UIGCIHZD5TTRLJLIJAHO4_processed
+3GA6AFUKOXMRQTODU7913ILAM393HB_processed
+3EJPLAJKEVECEXBPJVD6AS1ZSHZZ64_processed
+3AMYWKA6YKK0FTXIHM2PUUUV7EY6OC_processed
+323Q6SJS8REM6546E2A34RA7J5ZFHZ_processed
+32KTQ2V7RMDZX9E42DQOM0WSHTUM9Y_processed
+3IAEQB9FMNI75BLZIV1DHUHA9KNDWA_processed
+3D4CH1LGEJRZ5ZIIRAST9VCV7QF9GY_processed
+3EO896NRA5TSGGHXXMIDX85NR1ATJJ_processed
+39U1BHVTDUPHV2F8U1QQH1OYRBST3L_processed
+37FMASSAYLPWPJ3AFI6SLDK1OU8BIW_processed
+31Q0U3WYDYDYN14MUU96OCHJ9C271F_processed
+31HQ4X3T318XW6SIB7P03IMTUO2SLV_processed
+37WLF8U1WYOHP2C3JUAWNDDMIOG6K9_processed
+31JLPPHS23S1IAA0YK4RNRIBYBU3OE_processed
+3EFVCAY5LC79IWP9UO7I6C9096UJ8L_processed
+34V1S5K3G1ZX86TCFXJ7X4Z6D7K69R_processed
+3180JW2OTDAQVE67WQ0FUSZAUOS5J9_processed
+32VNZTT0AG2PXJLKQQQERRFM1S74R8_processed
+3HRMW88U1FOH1FQR2H7VTPVZTIXM0K_processed
+30X31N5D6COG0N1EEG0AU46IZXOSA3_processed
+3IKZ72A5BDEEG1QHH05G9B73A1DNFU_processed
+34Q075JO16BK5Q429VOX9X7WWQC013_processed
+37Z929RLGI6LFJ0NKGRRP9WAPMHSTY_processed
+3AQF3RZ55HG69GKPIJJZ70LHFBVF6J_processed
+36WLNQG7888K9E1OJZG10YM9C4ZBE7_processed
+3IJXV6UZ16HJ5QKZBFSILSP5RFZRIW_processed
+3EO896NRA5TSGGHXXMIDX85NNNNTJ0_processed
+3BWI6RSP7P7X72YY6I4LVW0DD3O7EV_processed
+354GIDR5ZK4KY1JKDCKXVSOMROP00Z_processed
+35GCEFQ6IEMDHEO70TF83PPGYOOZ33_processed
+3F6KKYWMNKZHIHCLQUAQLB4IYNXDN7_processed
+333U7HK6IIDLZRL0BWIYS4N14S8DJC_processed
+3CP1TO84P2ZQPI8ZP5UNVBDS87M251_processed
+34BBWHLWHJ9ODM1LA92O8RXMJVNWIF_processed
+369J354OFM8CNGGKT3PHPLLTGGP6GJ_processed
+3DYGAII7PU6BABS3BVT2PZGCHB4QPQ_processed
+35H6S234SJYE7JR0C76QLOU4QDB65D_processed
+39GXDJN2O2CI9RP3BDL190KUBU6V8W_processed
+32Q90QCQ11JL2H5IPCTUZ15Z22MKEP_processed
+35K3O9HUAKBAMVD4O12XJODURRREF6_processed
+326O153BMRWDOBZ8XV77VOA9KN0DE6_processed
+3AWETUDC9BQS7H6QLWMV7VR4FQ4IZ5_processed
+32SVAV9L3O7CG6GZO1Z9MWY631DA3H_processed
+34T446B1C9C4STE0PSDHQTQN0C6C0M_processed
+39PAAFCODVY17BHREBWW0WGH2VJVT2_processed
+31T4R4OBO1EIA4EKARYQ580ADZS7CK_processed
+33M4IA01QPZGVL9UA1SJO2CKG73XRX_processed
+33LKR6A5KNILLZ1T7XC7T891Y8I1T8_processed
+3AZHRG4CUDIGFTIHWZ2GXQMT1JD30A_processed
+3I0BTBYZA6JHVWF5LKV6NKI6A6XY08_processed
+374TNBHA8KT5JPK42IPL2Q9OMHFQYV_processed
+3C44YUNSIANHL2OY69NIP09D08ADPT_processed
+32XVDSJFP8VOXPT5H4B9CDOEBOKM28_processed
+3137ONMDKP3G0V9ZZL8F19D6XF8EG9_processed
+3A1PQ49WVQFVGQAY73VIFP6DN361HV_processed
+3A1PQ49WVQFVGQAY73VIFP6DNAU1HX_processed
+3BGYGHDBB6IYQLPC7MSPX4AGPHU224_processed
+3F6HPJW4JMYK21NJL6HWLBQ5OFS2W2_processed
+3EJPLAJKEVECEXBPJVD6AS1Z6X26ZO_processed
+3BC8WZX3VCUWYPNI12MRZKQXVO7RRY_processed
+31T4R4OBO1EIA4EKARYQ580ADZGC7D_processed
+323Q6SJS8REM6546E2A34RA7N36FH6_processed
+336YQZE834CGWMCSZND7AKRQHF3M5L_processed
+3BDCF01OG6SUSS4RADHPL4AUQ8HLYY_processed
+31LVTDXBLG8LVRS5WIPVD8HX8J3LRE_processed
+31T4R4OBO1EIA4EKARYQ580AFI37CZ_processed
+36V4Q8R5ZTY5P8VQJF1A8VMHDB0QMC_processed
+33CKWXB73SIKCFPK5ESXN2Q66O1117_processed
+304SM51WAC2LJX66KU06PFQP036SBS_processed
+32SVAV9L3O7CG6GZO1Z9MWY6NV43A9_processed
+34J10VATJOWJTP5ZY03IG2F0AQWQI2_processed
+39ASUFLU665RMH4POV8JPVD2I2FEXQ_processed
+3B837J3LDXU8Z4NVGRMWPUVS1NTRS1_processed
+39U1BHVTDUPHV2F8U1QQH1OYY4B3T7_processed
+3EFVCAY5LC79IWP9UO7I6C908GT8JS_processed
+37M28K1J0ZBN1KIORMRW23FVZKYJA9_processed
+3BGYGHDBB6IYQLPC7MSPX4AH8TH220_processed
+32M8BPYGA2KSG0BLV4Y6MOBYP7CIGH_processed
+39O5D9O872Q09LDX8FV0SROU2B1C31_processed
+3F1567XTN53QIOCWU6X539GYUEX9QN_processed
+39GXDJN2O2CI9RP3BDL190KU35XV81_processed
+36W0OB37H5CSBMV6FE4A7JQP0J8ZH9_processed
+30OG32W0S39MANQLMPLARTM4F0ZENN_processed
+35DR22AR5MIOCJOSZBRN649445DX31_processed
+3E47SOBEYZUFZOVGTI2NWXQJ69FCIZ_processed
+3HUTX6F6V3LCXSEHUKQM4HDJQHH2OR_processed
+354P56DE9T1YHL3G7DOQKD4S04K7SP_processed
+37Z929RLGI6LFJ0NKGRRP9WAJ28TSG_processed
+36V4Q8R5ZTY5P8VQJF1A8VMHD3ZMQR_processed
+358010RM5NR8OSQBJLXTPZ1N09JXVK_processed
+33JKGHPFYLSKMBIY3N9T41RC8M8MNO_processed
+35L9RVQFCXGBPN1W9R52QRBAD0WUHV_processed
+34HJIJKLPEUHQQ21ZAVOSO4QPVPV4W_processed
+33CKWXB73SIKCFPK5ESXN2Q66FD111_processed
+34PGFRQONX9K8XLRSRNOKSCMBG0JWT_processed
+31N2WW6R90O7CXX7ZHL10X9X0BNF3K_processed
+3GDTJDAPV39ZJ4VUXLKAM41HD538M2_processed
+340UGXU9D7ZG7OWGEGM65S2JLLEVU5_processed
+3DR23U6WEECK50V5JL7M0SSV25UTEI_processed
+333U7HK6IIDLZRL0BWIYS4N1JJJJDQ_processed
+39U1BHVTDUPHV2F8U1QQH1OYXMGT31_processed
+3E13VNJ1NWTVCLLIDLD3F17SILF1IN_processed
+3ATPCQ38JH8DW9DNDMSZCQUJWOOAYV_processed
+33CKWXB73SIKCFPK5ESXN2Q6LK511I_processed
+39O5D9O872Q09LDX8FV0SROU8ZP3CY_processed
+3AQF3RZ55HG69GKPIJJZ70LHU3TF6G_processed
+35L9RVQFCXGBPN1W9R52QRBASG7UHH_processed
+3HFNH7HEMQC5XYZ48C3FVQ2KB89QGL_processed
+340UGXU9D7ZG7OWGEGM65S2J6HDVUH_processed
+30IQTZXKAT4SVRPVGO9446BXLUB0XD_processed
+3AUQQEL7UER0R2K0G98MGLECK8HV0C_processed
+3HVVDCPGTNQIB5CWJMJG7VKB78WYT4_processed
+3137ONMDKP3G0V9ZZL8F19D6RTLEG8_processed
+35USIKEBN0EIX8X6G72CF5UQHRG6NU_processed
+36AHBNMV10ABUGGZXWJ12R2PH1FYDZ_processed
+37XITHEIS57SSNYZJ5WWAPRAT5KRC4_processed
+3AQF3RZ55HG69GKPIJJZ70LHAUIF63_processed
+31IBVUNM91XRO6ZQJRR7JS1GUZRFV3_processed
+34MAJL3QPDLXEH0IF0PI9TC6GJC43K_processed
+384PI804X1ZKZA3WME3H5YU56N7S0B_processed
+32UTUBMZ7PU1BPNAEP5B02AVI55VBI_processed
+30ZX6P7VFHTYWHNKE7YHWLWXTHL2JC_processed
+3DZQRBDBSUDGG22T51L07WSCVYZS3Y_processed
+37W3JXSD6F6A3MGQERORUFRA7K9YWL_processed
+3DY4FPOOAAMORW4ZAMR10HF0G6XRVS_processed
+33L7PJKHCPW3WZLE6ILM8IW9C8UT8E_processed
+3GU1KF0O4RZO65QESDWCUALED39BP9_processed
+3IGI0VL64GI8ME3AG3O6W5GIK2UNOQ_processed
+3B1NLC6UG8UKXMYP8ZOQVLCADPIPGL_processed
+3E13VNJ1NWTVCLLIDLD3F17S2CL1IV_processed
+3E7TUJ2EGLKWTF8RPKRL3A9YTGED96_processed
+32ZKVD547OLHZGLRUFZ5H7ICLGSB31_processed
+3HMIGG0U4U4ZDLK8GO8LGZOOR7Y8YY_processed
+36V4Q8R5ZTY5P8VQJF1A8VMITM4MQG_processed
+3GNCZX450RLJNVLMMZWGZZ17ENPAPZ_processed
+3ATPCQ38JH8DW9DNDMSZCQUJW05AY0_processed
+3BEFOD78WFRY038SLIUX5SLCI5RM48_processed
+3AMYWKA6YKK0FTXIHM2PUUUV8MOO61_processed
+31UV0MXWNZAU03M1E8FFIRK9ERXI5Z_processed
+3EO896NRA5TSGGHXXMIDX85NSGEJT8_processed
+3E7TUJ2EGLKWTF8RPKRL3A9YVIYD9W_processed
+351SEKWQS9FB0ZRLETVQ8WFIOO6MDC_processed
+33C7UALJVUW2NF954Z1C2CDF149813_processed
+3570Y55XZYHE60QQZMK98C5MDV3YG4_processed
+36U2A8VAGAXJ8MM9PD5O64XFMAHYK2_processed
+36TFCYNS4D836RVREWGPAYAESNIHXA_processed
+30MVJZJNHVB9W18JZGDW684BXRO9JA_processed
+36W0OB37H5CSBMV6FE4A7JQP1EEHZO_processed
+3B837J3LDXU8Z4NVGRMWPUVSUTRSR5_processed
+3BWI6RSP7P7X72YY6I4LVW0D0VCE7X_processed
+3BEFOD78WFRY038SLIUX5SLCVCX4MN_processed
+32VNZTT0AG2PXJLKQQQERRFMGAZ4RF_processed
+3H7XDTSHKLPAHVVQKL2LNFK6456GWS_processed
+31IBVUNM91XRO6ZQJRR7JS1GB18VFL_processed
+37FMASSAYLPWPJ3AFI6SLDK19JKIBE_processed
+3B1NLC6UG8UKXMYP8ZOQVLCAEVGPGW_processed
+3GNA64GUZN27H1AKRGKORTBV50E5QN_processed
+358UUM7WR81MG633SOOLU3ZWD617R8_processed
+32RIADZIS121YYLX5FQJH1KI3RFS4J_processed
+3HWRJOOETE0JQ0IQUL2WQNW9GFYESY_processed
+37Q970SNZN6K6ZOEIX7HJ3DFWFXS1J_processed
+3DPNQGW4LUDWB0N0Y2NGGWOQRJW64T_processed
+36PW28KO48UFQ4WWDLG55N230IFEAE_processed
+38F71OA9G2U8YJ5H5ZQGWWI3626MFF_processed
+3G2UL9A02MCTUN5J00LNJ880IUN76X_processed
+3HUTX6F6V3LCXSEHUKQM4HDJLA52OW_processed
+3BEFOD78WFRY038SLIUX5SLCAN1M4A_processed
+324G5B4FBC6YGCJ4YB56A7Y956Y70N_processed
+38JBBYETQX80OFGF4K6ESKG39DGE4B_processed
+3AUQQEL7UER0R2K0G98MGLECLLRV0D_processed
+37Q970SNZN6K6ZOEIX7HJ3DGDMTS1C_processed
+30JNVC0ORIIJXUUFSILXGQELPV8HQ6_processed
+3HVVDCPGTNQIB5CWJMJG7VKB5N9TY4_processed
+3CN4LGXD56MYUKX64LI8ZN8JQ3X4YJ_processed
+3DQQ64TANPJGU8OPN4S8HOYYK0FPWE_processed
+3FTF2T8WL0GV2L8I6FWBVYYKIFE9WR_processed
+3EICBYG64DUBUZFMB0NN8Y5J4LRCJD_processed
+3EG49X3513AN92BAIDPLD7LOLDZ6X5_processed
+33C7UALJVUW2NF954Z1C2CDELHO81Q_processed
+3I02618YA94324ZKS4D7Z0D1LXHPU4_processed
+3ATTHHXXWJM32M6BKEYAAABTN02IX4_processed
+3AUQQEL7UER0R2K0G98MGLECQX90VT_processed
+3HYA4D4520HIRF1OVH7WHN45EQLF2D_processed
+3DR23U6WEECK50V5JL7M0SSW5Y7TEM_processed
+32Z9ZLUT1UIGCIHZD5TTRLJLVA6HOV_processed
+324G5B4FBC6YGCJ4YB56A7Y9ZHN07L_processed
+30BUDKLTXMTIKYKFIPPOXCMH9FEE5V_processed
+3907X2AHF93UISQI9K31THI9WVSP2O_processed
+3DBQWDE4YFWME4XSBOS7NZGQ0O7N5W_processed
+3F6KKYWMNKZHIHCLQUAQLB4IZVQNDR_processed
+345LHZDED6QWVFUXU6G0MXA0D2G3UW_processed
+304SM51WAC2LJX66KU06PFQOK2ISBK_processed
+32KTQ2V7RMDZX9E42DQOM0WSH2R9M0_processed
+3B2X28YI35DGWZ89DHX3OAUXIE56BG_processed
+32XVDSJFP8VOXPT5H4B9CDOEW232MK_processed
+35USIKEBN0EIX8X6G72CF5UQHVNN6Q_processed
+3HRMW88U1FOH1FQR2H7VTPVZZ3V0M8_processed
+3E1QT0TDFY7DNL53C6MIUBU1JP68IK_processed
+3BEFOD78WFRY038SLIUX5SLCBEHM49_processed
+3AQF3RZ55HG69GKPIJJZ70LHH0A6F5_processed
+3A7Y0R2P2XMZXXQH2WJAMBQT66DXJ5_processed
+3483FV8BENHM8M8DUO4SNHRS61O62C_processed
+38YMOXR4M3X8K26K88TZBO7H89N6WY_processed
+36NEMU28XOBA95RCVO0G0K43V81WMS_processed
+3DY4FPOOAAMORW4ZAMR10HFZTV8VRW_processed
+34FNN24DCV7GQ316IQ1I4FEX496Y5U_processed
+39DD6S19JY9GRC44PDW4MGTC2HEZED_processed
+3137ONMDKP3G0V9ZZL8F19D6XDSEGP_processed
+3C44YUNSIANHL2OY69NIP09D0RXPDU_processed
+3AZHRG4CUDIGFTIHWZ2GXQMTZ6J30O_processed
+3DYGAII7PU6BABS3BVT2PZGCJK0PQ5_processed
+37UQDCYH66TE9DKK18TVV0OZY5KV7H_processed
+3HSYG7LRBSWOOHXSLY4XXWI4OV1KKM_processed
+3IAEQB9FMNI75BLZIV1DHUH9MTHWDG_processed
+3F6HPJW4JMYK21NJL6HWLBQ5OCR2WV_processed
+33ISQZVXPYKOML9G1861LUSOTD0CCK_processed
+323Q6SJS8REM6546E2A34RA7RBRFHB_processed
+31JLPPHS23S1IAA0YK4RNRIBZBV3OG_processed
+3AMYWKA6YKK0FTXIHM2PUUUV8026OP_processed
+30H4UDGLTBGKPWUBJ2ML8QW71LWPM7_processed
+36H9ULYP6BSIXBZW22DOG4QM17HJFB_processed
+3AMW0RGHOM0Q7EFBV31LL5L7CLSNPH_processed
+35BLDD71IFVXTNQQK3LEMKHY983ZV4_processed
+3483FV8BENHM8M8DUO4SNHRS10762O_processed
+35LDD5557J2JE5XV6AHDC9A87BIMKV_processed
+3FE7TXL1LRLFI4RX9XIQTM8J64C2QL_processed
+3GDTJDAPV39ZJ4VUXLKAM41HDN1M8E_processed
+3BV8HQ2ZZ5ZBDP3H73X7TZAQITLA60_processed
+30JNVC0ORIIJXUUFSILXGQELWWIQHY_processed
+39K0FND3AQDD2SO9UJ4OIV7C2Y3MAT_processed
+3HPZF4IVNVR1POALX0OQILK8YL5CYN_processed
+32EYX73OY97OEHFZ5WHW4VDGMKCURI_processed
+3F6KKYWMNKZHIHCLQUAQLB4IYDYDNO_processed
+3ERMJ6L4D7QVJQQBNGS4845NP3F7ML_processed
+32AT8R96GU70FWFCKLH1U5IC1TYUSG_processed
+3B2X28YI35DGWZ89DHX3OAUW038B6I_processed
+33IZTU6J8AZSW097PQSOECHG356XSY_processed
+30X31N5D6COG0N1EEG0AU46IFCBASI_processed
+3D4CH1LGEJRZ5ZIIRAST9VCVLLA9GX_processed
+32UTUBMZ7PU1BPNAEP5B02AVCM9VBE_processed
+3IKZ72A5BDEEG1QHH05G9B73POPNFV_processed
+3H8DHMCCWI9GABRITHIATJNFMKRKDC_processed
+36PW28KO48UFQ4WWDLG55N23ZJWEAW_processed
+32M8BPYGA2KSG0BLV4Y6MOBY4B4GIU_processed
+38F5OAUN5WA9RCP0WJCRGZXQPDRH74_processed
+3IOEN3P9SGHFJ1QHBK4EIYHOV9T61P_processed
+392CY0QWGAPC4KI103W5DZEVTF9I4K_processed
+31QTRG6Q22BKJDJP25AW2LJEEM1PYA_processed
+37XITHEIS57SSNYZJ5WWAPRA8QCRCH_processed
+33SA9F9TR6SCXHZZEQCXNCZUM36WET_processed
+3IJXV6UZ16HJ5QKZBFSILSP5SG0IRR_processed
+30BUDKLTXMTIKYKFIPPOXCMH8MXE5R_processed
+374TNBHA8KT5JPK42IPL2Q9OLCJQYO_processed
+3HPZF4IVNVR1POALX0OQILK7WO8YCE_processed
+3634BBTX0XSM20FQKKSO43BMI55IFV_processed
+3B837J3LDXU8Z4NVGRMWPUVTI6HRSA_processed
+336YQZE834CGWMCSZND7AKRQI485MO_processed
+35H6S234SJYE7JR0C76QLOU4RHO65Z_processed
+3CN4LGXD56MYUKX64LI8ZN8JQQ24YY_processed
+3HMVI3QICSQH2L0NH3Z61P1G82P1Y1_processed
+35USIKEBN0EIX8X6G72CF5UQH0VN68_processed
+34T446B1C9C4STE0PSDHQTQNZYQC0D_processed
+35BLDD71IFVXTNQQK3LEMKHY83ZZVP_processed
+32VNZTT0AG2PXJLKQQQERRFMNSUR44_processed
+3HRMW88U1FOH1FQR2H7VTPVZE2N0MD_processed
+3AAJC4I4FPQO2SQW3E7VJW64S3EJZZ_processed
+35BLDD71IFVXTNQQK3LEMKHYA3DZV5_processed
+3F1567XTN53QIOCWU6X539GX7BDQ9P_processed
+32N49TQG3PGWSFII8B5F5RQPTRUVAY_processed
+3F6KKYWMNKZHIHCLQUAQLB4IXWHDN8_processed
+37M28K1J0ZBN1KIORMRW23FVJL0JAX_processed
+33IZTU6J8AZSW097PQSOECHG1YVXS7_processed
+34Q075JO16BK5Q429VOX9X7W2VX105_processed
+33PPUNGG3H350G99BJG4LNSVNWCRZC_processed
+358UUM7WR81MG633SOOLU3ZWD0CR7R_processed
+3CFVK00FWUJS98UL4UMX2DQ1RRX6LE_processed
+3I2PTA7R32SAZKVNY3OUTIUDPEIQK1_processed
+3DZQRBDBSUDGG22T51L07WSCU1R3S6_processed
+38SKSKU7RAV82SPM6VVMZGG6LP8ILZ_processed
+31HQ4X3T318XW6SIB7P03IMT9SULS3_processed
+3BF51CHDT48V91PGBKRI1KN3ICXH07_processed
+33CID5710DRTCP73LSOZVAEHWJ4L3D_processed
+31IBVUNM91XRO6ZQJRR7JS1GWU8VFS_processed
+32ZKVD547OLHZGLRUFZ5H7ICRIA3BL_processed
+39LOEL67O13YXINKR3FVQ5SBX1Y83S_processed
+384PI804X1ZKZA3WME3H5YU5MEE0SO_processed
+3IKZ72A5BDEEG1QHH05G9B7344XNFE_processed
+31Q0U3WYDYDYN14MUU96OCHJ92Y17L_processed
+324G5B4FBC6YGCJ4YB56A7Y95PL075_processed
+3BC8WZX3VCUWYPNI12MRZKQWHI6RR5_processed
+33L7PJKHCPW3WZLE6ILM8IW9ER0T8O_processed
+39LOEL67O13YXINKR3FVQ5SBJR838Z_processed
+31N2WW6R90O7CXX7ZHL10X9XKEXF3K_processed
+37FMASSAYLPWPJ3AFI6SLDK19V1BIC_processed
+31EUONYN241LUJCJIUAX6MAUJ2EOVV_processed
+3A4TN5196TGF3TKWN0K2WLP46XVCH3_processed
+35BLDD71IFVXTNQQK3LEMKHY995VZ4_processed
+34QN5IT0T8P2GQON8GXCO7U496W08K_processed
+36DSNE9QZEWC3OCP15WE3YZ082AOJ7_processed
+3D8YOU6S9NIVSYHFD8EYT0MKPQ16UK_processed
+386CSBG1O8K305AYWZUTJZPVO81Q6D_processed
+34YB12FSQ7MECJ1CGJWYEHIF6PTGMA_processed
+3GNCZX450RLJNVLMMZWGZZ17ELTAPZ_processed
+3BGYGHDBB6IYQLPC7MSPX4AGRR6222_processed
+3I33IC7ZWO0NVOKGK1L43MTE2GC2AO_processed
+3DYGAII7PU6BABS3BVT2PZGCCAVPQ9_processed
+38BQUHLA95Y24WIA4296JQRHS58OMC_processed
+3HYA4D4520HIRF1OVH7WHN46GJOF26_processed
+3IAS3U3I0OESEYP8QFL0YXDKMVEB25_processed
+3GS6S824SZVGRNCC7PFGDZT368BNW9_processed
+32SCWG5HIQ2I0T9JWMF4JUZLWY36PB_processed
+323Q6SJS8REM6546E2A34RA852FFHX_processed
+320DUZ38GGKOBBVRDMJ6YPQYHGUGJN_processed
+3CP1TO84P2ZQPI8ZP5UNVBDSB9J255_processed
+3HWRJOOETE0JQ0IQUL2WQNW9LSNESI_processed
+33CID5710DRTCP73LSOZVAEHHBI3LE_processed
+3DY4FPOOAAMORW4ZAMR10HFZEMBRVY_processed
+3018Q3ZVORO4Z811ZR054U1MT1ORAP_processed
+34FNN24DCV7GQ316IQ1I4FEX48S5YL_processed
+33L7PJKHCPW3WZLE6ILM8IW9ERZT8N_processed
+3EA3QWIZ4RTWL5XRF4XDL7QNGFPTIZ_processed
+3F1567XTN53QIOCWU6X539GYTROQ9K_processed
+34J10VATJOWJTP5ZY03IG2F1DOVIQU_processed
+3GFK2QRXXIFC1Y663PJUJITHFIV5WB_processed
+3ATTHHXXWJM32M6BKEYAAABTNPDXI8_processed
+3IKZ72A5BDEEG1QHH05G9B73BZOFNU_processed
+37XITHEIS57SSNYZJ5WWAPRA9DDCRE_processed
+3IKZ72A5BDEEG1QHH05G9B73PYMFN4_processed
+36AHBNMV10ABUGGZXWJ12R2P27NDYJ_processed
+3570Y55XZYHE60QQZMK98C5M93PGYK_processed
+37UQDCYH66TE9DKK18TVV0OZSI4V7L_processed
+37UEWGM5H2685KO1387CTT8J556R11_processed
+351SEKWQS9FB0ZRLETVQ8WFIUUIDMX_processed
+34PGFRQONX9K8XLRSRNOKSCMWABWJQ_processed
+308XBLVESR29II6T5501Z5LSV8CRBL_processed
+3E13VNJ1NWTVCLLIDLD3F17SPHF1IM_processed
+3CCZ6YKWRGH1P2TYAE925YL6G8O59G_processed
+32XVDSJFP8VOXPT5H4B9CDOER13M2X_processed
+3180JW2OTDAQVE67WQ0FUSZ9CGBJ56_processed
+3A7Y0R2P2XMZXXQH2WJAMBQTZOBJXI_processed
+38JBBYETQX80OFGF4K6ESKG38INE4R_processed
+352YTHGRO4BC8P7HULEKDNDGESW4HG_processed
+358010RM5NR8OSQBJLXTPZ1NV1OXV4_processed
+39O5D9O872Q09LDX8FV0SROU8XT3CY_processed
+3ATTHHXXWJM32M6BKEYAAABTM9VXIT_processed
+3H7Z272LXG50JECQJQL68DHI25GLPQ_processed
+3A0EX8ZRNHMIFJIFJ0SV71EPZEQYBW_processed
+3EWIJTFFVX5JPRYELJJCGHQQRET0EZ_processed
+3E1QT0TDFY7DNL53C6MIUBU01CH8IL_processed
+3DY4FPOOAAMORW4ZAMR10HFZ0P5VRO_processed
+3DQQ64TANPJGU8OPN4S8HOYY507PWR_processed
+38YMOXR4M3X8K26K88TZBO7HAD06WL_processed
+34HJIJKLPEUHQQ21ZAVOSO4P89Y4VN_processed
+338JKRMM2FXRAENY3MOBQQZGR2BHA8_processed
+3180JW2OTDAQVE67WQ0FUSZ9D4L5JF_processed
+3IKZ72A5BDEEG1QHH05G9B73ASGFN7_processed
+3A4NIXBJ7FXUYBFDRATER9NN2LBLMB_processed
+3E13VNJ1NWTVCLLIDLD3F17SHUNI1T_processed
+31HQ4X3T318XW6SIB7P03IMTVR4LSX_processed
+35USIKEBN0EIX8X6G72CF5URYB86N9_processed
+30ZX6P7VFHTYWHNKE7YHWLWX7YHJ21_processed
+3GLB5JMZF6TB8PVZ3QXVVRVMEKCGD4_processed
+358UUM7WR81MG633SOOLU3ZWB7ER75_processed
+30LB5CDZNL8H0MPAH6XP1RR8GI2Z01_processed
+3BQU611VFYIKQPVAD4MJHEQ9KZV999_processed
+34V1S5K3G1ZX86TCFXJ7X4Z67A196B_processed
+3BV8HQ2ZZ5ZBDP3H73X7TZARZ41A6L_processed
+3GLB5JMZF6TB8PVZ3QXVVRVLSUUGDI_processed
+384PI804X1ZKZA3WME3H5YU5TL30SY_processed
+37XITHEIS57SSNYZJ5WWAPRAEZ2CRG_processed
+3180JW2OTDAQVE67WQ0FUSZ9REZ5JR_processed
+3COPXFW7XKAPZ8U8YQ95TD43G8FKPZ_processed
+3DBQWDE4YFWME4XSBOS7NZGQFA7N5J_processed
+37QW5D2ZRPK2HZ8ZHIGWYD0N5PMS8P_processed
+38SKSKU7RAV82SPM6VVMZGG6LOFLI7_processed
+36W0OB37H5CSBMV6FE4A7JQPUZ7HZG_processed
+35GCEFQ6IEMDHEO70TF83PPFC5V3ZO_processed
+36U2A8VAGAXJ8MM9PD5O64XFNGSKYC_processed
+3FPRZHYEP75W8UJXZWQ5IJCJT69V3T_processed
+3GA6AFUKOXMRQTODU7913ILAMT9H35_processed
+37XITHEIS57SSNYZJ5WWAPRAEF1RCQ_processed
+31LVTDXBLG8LVRS5WIPVD8HX2Z0RL7_processed
+3A7Y0R2P2XMZXXQH2WJAMBQT6PZXJT_processed
+3EICBYG64DUBUZFMB0NN8Y5KL56JCM_processed
+3AUQQEL7UER0R2K0G98MGLECQGBV0S_processed
+36AHBNMV10ABUGGZXWJ12R2P2XLDYX_processed
+36WLNQG7888K9E1OJZG10YM9WYOBE4_processed
+3C8HJ7UOPGSE30WH6BKNZOYLIW8ZM2_processed
+30H4UDGLTBGKPWUBJ2ML8QW72A6MPT_processed
+33IZTU6J8AZSW097PQSOECHGG3XSXT_processed
+33LK57MYL23H1V9MQ8G9BXXROPHZSR_processed
+339ANSOTRE0XIM74LVJLKG3RXKVKIH_processed
+34X6J5FLP2WFO0P5GIOIS3U0NFOJQU_processed
+3A1PQ49WVQFVGQAY73VIFP6DO5ZH19_processed
+3GS6S824SZVGRNCC7PFGDZT35T6NW9_processed
+3EF8EXOTTATR7W5OV3Y3R2MS16B1JO_processed
+3E47SOBEYZUFZOVGTI2NWXQK97ZCIK_processed
+3HPZF4IVNVR1POALX0OQILK7C0MCYA_processed
+3IKZ72A5BDEEG1QHH05G9B738XDNFK_processed
+31UV0MXWNZAU03M1E8FFIRK91DOI5L_processed
+31QNSG6A50RGY1O7TFIL3M4RPNC87G_processed
+3AMW0RGHOM0Q7EFBV31LL5L7DQ1NP1_processed
+34HJIJKLPEUHQQ21ZAVOSO4P27X4VC_processed
+3B837J3LDXU8Z4NVGRMWPUVSVL6SR5_processed
+3C44YUNSIANHL2OY69NIP09DSIQPDX_processed
+3HYA4D4520HIRF1OVH7WHN450QHF2V_processed
+30BUDKLTXMTIKYKFIPPOXCMH2VYE54_processed
+374TNBHA8KT5JPK42IPL2Q9O0J0YQ6_processed
+392CY0QWGAPC4KI103W5DZEV03G4IW_processed
+36WLNQG7888K9E1OJZG10YM9IBNEBI_processed
+3137ONMDKP3G0V9ZZL8F19D6XNOEG5_processed
+34T446B1C9C4STE0PSDHQTQNKHNC0X_processed
+32VNZTT0AG2PXJLKQQQERRFM1OE4R7_processed
+337RC3OW0E0DOY9M52U5E560TRRVLW_processed
+3H8DHMCCWI9GABRITHIATJNE4H9KD4_processed
+3B1NLC6UG8UKXMYP8ZOQVLCACU8PGK_processed
+336KAV9KYZQOR6IJ07ZK68BLKYU2YA_processed
+3E47SOBEYZUFZOVGTI2NWXQK8OPICD_processed
+323Q6SJS8REM6546E2A34RA73JVFH7_processed
+30X31N5D6COG0N1EEG0AU46JHW2SAZ_processed
+3C6FJU71TZRXBIRLJR9QCR6DP7HUYA_processed
+3HFNH7HEMQC5XYZ48C3FVQ2KVMPQGD_processed
+3AWETUDC9BQS7H6QLWMV7VR425WIZE_processed
+39O5D9O872Q09LDX8FV0SROUNTS3C4_processed
+33PPO7FEC4DP4G1UTEXYG3817G0DI3_processed
+386CSBG1O8K305AYWZUTJZPVNCOQ67_processed
+3HHRAGRYXH3NXCZGFT51E4LYFTQO91_processed
+320DUZ38GGKOBBVRDMJ6YPQY3PQJGQ_processed
+39LNWE0K43UBLICGF9NNQ3CPWMWIUY_processed
+3BWI6RSP7P7X72YY6I4LVW0DY2T7EJ_processed
+39PAAFCODVY17BHREBWW0WGH2WQVTB_processed
+30BUDKLTXMTIKYKFIPPOXCMH5AD5E7_processed
+34QN5IT0T8P2GQON8GXCO7U4976804_processed
+3FFJ6VRILAMV5W00H2931WOIJV7I09_processed
+3IO1LGZLKIVXU1A2A5DTX84W9I168H_processed
+3DZQRBDBSUDGG22T51L07WSCVJU3SA_processed
+3F6HPJW4JMYK21NJL6HWLBQ5WXC2WU_processed
+3CFJTT4SX2O9N70K21UCGBY5RQLI7F_processed
+33PPUNGG3H350G99BJG4LNSVH6ARZO_processed
+333U7HK6IIDLZRL0BWIYS4N1Q57DJN_processed
+3DH6GAKTY7NEXHLVPE8YTJLSQGFZY4_processed
+30LSNF2393T2163EWPDHKKHXDTZI2Y_processed
+37FMASSAYLPWPJ3AFI6SLDK193SIBQ_processed
+345LHZDED6QWVFUXU6G0MXA09MRU3Y_processed
+34Z02EIMI1BLEAGBBE87F3QRU080T5_processed
+3AAJC4I4FPQO2SQW3E7VJW647OTZJF_processed
+3G5W44VEUGGJMV19V7JIZ4QBW72KG2_processed
+3DEL4X4ELFJPSMLGOWKDWY5HJCAXYR_processed
+326O153BMRWDOBZ8XV77VOA9E15EDY_processed
+35LDD5557J2JE5XV6AHDC9A8F2XMK0_processed
+3FPRZHYEP75W8UJXZWQ5IJCJSM23VP_processed
+3H7Z272LXG50JECQJQL68DHI8B3LPV_processed
+3BV8HQ2ZZ5ZBDP3H73X7TZAQIKVA6S_processed
+3EKVH9QME72LTVNS4BC6TN1KHVX2DW_processed
+36H9ULYP6BSIXBZW22DOG4QM199FJ3_processed
+3CP1TO84P2ZQPI8ZP5UNVBDSPWK25U_processed
+37WLF8U1WYOHP2C3JUAWNDDMNWNK6F_processed
+3A1PQ49WVQFVGQAY73VIFP6DHJS1H7_processed
+3EFE17QCRL3VAA9SKMA2BQTMEKZHSA_processed
+32EYX73OY97OEHFZ5WHW4VDH3CMRUS_processed
+384PI804X1ZKZA3WME3H5YU688TS07_processed
+3DUZQ9U6SVM0SB4CPBR6QRKVMEDSV8_processed
+3B2X28YI35DGWZ89DHX3OAUW1CHB6A_processed
+34PGFRQONX9K8XLRSRNOKSCMVY5JWI_processed
+3FIUS151D40EK95Q5GY31CYI47ZGGM_processed
+3EFE17QCRL3VAA9SKMA2BQTMS8WHSX_processed
+392CY0QWGAPC4KI103W5DZEVUCMI4S_processed
+3A7Y0R2P2XMZXXQH2WJAMBQT5DAJX1_processed
+3DBQWDE4YFWME4XSBOS7NZGQ0OV5N2_processed
+31Q0U3WYDYDYN14MUU96OCHJO3Q17U_processed
+36DSNE9QZEWC3OCP15WE3YZ080FOJ8_processed
+37QW5D2ZRPK2HZ8ZHIGWYD0NPGJ8S4_processed
+35BLDD71IFVXTNQQK3LEMKHYAEOZV2_processed
+31Q0U3WYDYDYN14MUU96OCHJAHN175_processed
+33LKR6A5KNILLZ1T7XC7T891D35T1S_processed
+39RP059MEQRIL2T10X4KD6RIOA7MBZ_processed
+32M8BPYGA2KSG0BLV4Y6MOBYKBSGIY_processed
+32Z9ZLUT1UIGCIHZD5TTRLJLVIQHOV_processed
+30H4UDGLTBGKPWUBJ2ML8QW7M30PMW_processed
+37U1UTWH94KQGK8M2TGR83VRJOOR8N_processed
+3A7Y0R2P2XMZXXQH2WJAMBQT5DWJXN_processed
+3FE2ERCCZ668PZFNWI1Y0ULCXYYPO0_processed
+3HYA4D4520HIRF1OVH7WHN450K7F29_processed
+3EA3QWIZ4RTWL5XRF4XDL7QNHG7TIK_processed
+339ANSOTRE0XIM74LVJLKG3RBIFKIB_processed
+3EO896NRA5TSGGHXXMIDX85NSG2TJ6_processed
+3HWRJOOETE0JQ0IQUL2WQNW9M1BESP_processed
+3FTYUGLFS3JDSS6FMUE4RU4W7W7D5F_processed
+33FOTY3KEVJ4ZIZILBHH4Y9XCXL1CG_processed
+308Q0PEVBHBD1QODHYSWU7PBGOLI9D_processed
+37QW5D2ZRPK2HZ8ZHIGWYD0NPIF8S4_processed
+3HHRAGRYXH3NXCZGFT51E4LXYFPO9P_processed
+3AQF3RZ55HG69GKPIJJZ70LHHLXF67_processed
+3HRMW88U1FOH1FQR2H7VTPVZZZ90ME_processed
+32VNZTT0AG2PXJLKQQQERRFM1ODR4T_processed
+3C44YUNSIANHL2OY69NIP09DYI0DP1_processed
+308XBLVESR29II6T5501Z5LSBU6BRN_processed
+337RC3OW0E0DOY9M52U5E5608UGLVW_processed
+3IAS3U3I0OESEYP8QFL0YXDKFQF2BG_processed
+32EYX73OY97OEHFZ5WHW4VDGLBDRUX_processed
+351SEKWQS9FB0ZRLETVQ8WFJABUMDY_processed
+3A7Y0R2P2XMZXXQH2WJAMBQT4TJXJJ_processed
+35LDD5557J2JE5XV6AHDC9A8B2VKMS_processed
+3EG49X3513AN92BAIDPLD7LODKLX6O_processed
+3FTOP5WAROMR07K6ZKU3DX3IW2EJ0O_processed
+35LDD5557J2JE5XV6AHDC9A88NXMKZ_processed
+3CCZ6YKWRGH1P2TYAE925YL6V19956_processed
+30H4UDGLTBGKPWUBJ2ML8QW78EIPMM_processed
+34HJIJKLPEUHQQ21ZAVOSO4P1HNV4C_processed
+3DR23U6WEECK50V5JL7M0SSVO2KTEO_processed
+339ANSOTRE0XIM74LVJLKG3RWPQIKJ_processed
+3D8YOU6S9NIVSYHFD8EYT0MKJ1J6UI_processed
+3907X2AHF93UISQI9K31THI91QDP24_processed
+3180JW2OTDAQVE67WQ0FUSZ9RBO5JA_processed
+3B3WTRP3DK09Q5ABSV747RIUW3V92R_processed
+36ZN444YT0WG8DSJJNBERXK0GLNIO8_processed
+3AMYWKA6YKK0FTXIHM2PUUUWPSHO6P_processed
+308XBLVESR29II6T5501Z5LSBVVBRE_processed
+3DQQ64TANPJGU8OPN4S8HOYY774PW4_processed
+38F71OA9G2U8YJ5H5ZQGWWI304MMFT_processed
+30LB5CDZNL8H0MPAH6XP1RR8FGV0ZQ_processed
+3FTOP5WAROMR07K6ZKU3DX3IQYX0JA_processed
+37UEWGM5H2685KO1387CTT8JB7PR1U_processed
+3IO1LGZLKIVXU1A2A5DTX84V73968R_processed
+3IJXV6UZ16HJ5QKZBFSILSP5TUJRIC_processed
+31N2WW6R90O7CXX7ZHL10X9X7HU3FY_processed
+3GDTJDAPV39ZJ4VUXLKAM41HHFB8MY_processed
+30IQTZXKAT4SVRPVGO9446BX86KX0U_processed
+37W3JXSD6F6A3MGQERORUFRBROWWY0_processed
+3I33IC7ZWO0NVOKGK1L43MTEHE42AR_processed
+30MVJZJNHVB9W18JZGDW684BYCWJ9Z_processed
+320DUZ38GGKOBBVRDMJ6YPQY3W8GJJ_processed
+338JKRMM2FXRAENY3MOBQQZH9A2HAZ_processed
+3D8YOU6S9NIVSYHFD8EYT0MKPPS6U9_processed
+3E7TUJ2EGLKWTF8RPKRL3A9YV87D9L_processed
+3E13VNJ1NWTVCLLIDLD3F17SIAWI1Z_processed
+3CN4LGXD56MYUKX64LI8ZN8JJZ14Y8_processed
+3CN4LGXD56MYUKX64LI8ZN8JKTF4YB_processed
+38F71OA9G2U8YJ5H5ZQGWWI350IFMF_processed
+323Q6SJS8REM6546E2A34RA7OIPFHK_processed
+34HJIJKLPEUHQQ21ZAVOSO4PMYOV4W_processed
+31LVTDXBLG8LVRS5WIPVD8HX886LRV_processed
+3A1PQ49WVQFVGQAY73VIFP6DPJJ1H6_processed
+3E4GGUZ1THPT71T2WSYTX0WA7SI2KF_processed
+3B3WTRP3DK09Q5ABSV747RIUB4I29O_processed
+3DZQRBDBSUDGG22T51L07WSDBE6S3J_processed
+36ZN444YT0WG8DSJJNBERXK1XGNOIN_processed
+39N5ACM9HNL5ICBHUTIG34QN96OP9J_processed
+31QNSG6A50RGY1O7TFIL3M4RIY178J_processed
+30H4UDGLTBGKPWUBJ2ML8QW79RWMPO_processed
+3HQUKB7LNOC6K1VC9SY37CMK4TKHHU_processed
+3B837J3LDXU8Z4NVGRMWPUVS0H8RS3_processed
+39GHHAVOMOP8ZV27ILH5P0TVCGH4JF_processed
+36V4Q8R5ZTY5P8VQJF1A8VMHD79QMD_processed
+3A1PQ49WVQFVGQAY73VIFP6DI9W1HS_processed
+3DL65MZB8MD3JJTUXZCWBISRTBWECL_processed
+3CCZ6YKWRGH1P2TYAE925YL6HHW95B_processed
+31EUONYN241LUJCJIUAX6MAU3VJVOD_processed
+3AQF3RZ55HG69GKPIJJZ70LHA2V6FN_processed
+32Z9ZLUT1UIGCIHZD5TTRLJMXPUOHO_processed
+3EICBYG64DUBUZFMB0NN8Y5J6ECJCT_processed
+3137ONMDKP3G0V9ZZL8F19D6CF3GEL_processed
+33TIN5LC0D8ZRQ3ILGEZGVJZQ7P9Y0_processed
+35GMH2SV3NF4S8QXN4LN6IM8NJXOE3_processed
+33IZTU6J8AZSW097PQSOECHG3A3SX0_processed
+35GCEFQ6IEMDHEO70TF83PPFWC7Z3U_processed
+324G5B4FBC6YGCJ4YB56A7Y95A607W_processed
+31UV0MXWNZAU03M1E8FFIRK9TEH5IV_processed
+3FUI0JHJP6WCWLHEFMKZZOK02TH33J_processed
+374TNBHA8KT5JPK42IPL2Q9O0GSYQS_processed
+386CSBG1O8K305AYWZUTJZPVK9J6Q9_processed
+33NF62TLXS0TDXRA3D4LWHISRDHKJ1_processed
+3AUQQEL7UER0R2K0G98MGLED8MDV0Q_processed
+38JBBYETQX80OFGF4K6ESKG38J8E4E_processed
+3GDTJDAPV39ZJ4VUXLKAM41HD1U8ML_processed
+34HJIJKLPEUHQQ21ZAVOSO4P7TSV4B_processed
+3EFVCAY5LC79IWP9UO7I6C90FDO8JO_processed
+3IAS3U3I0OESEYP8QFL0YXDKMIAB2B_processed
+36ZN444YT0WG8DSJJNBERXK0VL4OIA_processed
+3DPNQGW4LUDWB0N0Y2NGGWOQ58K649_processed
+3I3WADAZ9Z24WPX4C3SK2OGWTHUO51_processed
+337RC3OW0E0DOY9M52U5E560OXALVC_processed
+323Q6SJS8REM6546E2A34RA7J5FFHF_processed
+31EUONYN241LUJCJIUAX6MAV666VOR_processed
+36DSNE9QZEWC3OCP15WE3YZ0U0NJOX_processed
+336KAV9KYZQOR6IJ07ZK68BLKBJY2L_processed
+3G5F9DBFOYVB22QWEHF7MDDT6LEVH1_processed
+3AQF3RZ55HG69GKPIJJZ70LHAVLF68_processed
+339ANSOTRE0XIM74LVJLKG3SDUOKIC_processed
+34T446B1C9C4STE0PSDHQTQN70RC0Q_processed
+3GA6AFUKOXMRQTODU7913ILAGTV3H7_processed
+3IFS6Q0HJRHV65KCQ4SNB2U1F8TIS6_processed
+33JKGHPFYLSKMBIY3N9T41RC69VMNJ_processed
+38BQUHLA95Y24WIA4296JQRHX6MOMX_processed
+36PW28KO48UFQ4WWDLG55N23T8QEAY_processed
+358UUM7WR81MG633SOOLU3ZWSSGR7U_processed
+3AUQQEL7UER0R2K0G98MGLED82I0VW_processed
+3F0BG9B9MYLVD7FP6JGF2ZSTHVV7YU_processed
+37W3JXSD6F6A3MGQERORUFRA4IBWYE_processed
+37ZHEEHM65KUX4I1HYNPC5WG2Q1377_processed
+37UQDCYH66TE9DKK18TVV0OZY5AV77_processed
+3A0EX8ZRNHMIFJIFJ0SV71EPZM1BY0_processed
+33PPUNGG3H350G99BJG4LNSVOLNZRA_processed
+3D4CH1LGEJRZ5ZIIRAST9VCVLDI9GP_processed
+35BLDD71IFVXTNQQK3LEMKHY81JVZ1_processed
+3A1PQ49WVQFVGQAY73VIFP6DHUD1HE_processed
+36WLNQG7888K9E1OJZG10YMAZCJEBZ_processed
+37Z929RLGI6LFJ0NKGRRP9WAOZ4STA_processed
+3H0W84IWBT07PLIDFW2R0DRDZWVERX_processed
+31HQ4X3T318XW6SIB7P03IMTVMTLSC_processed
+34YB12FSQ7MECJ1CGJWYEHIF0T9MGY_processed
+31Q0U3WYDYDYN14MUU96OCHJ951710_processed
+345LHZDED6QWVFUXU6G0MXA0UQ9U39_processed
+38F71OA9G2U8YJ5H5ZQGWWI36VAFMY_processed
+3IO1LGZLKIVXU1A2A5DTX84VMAP862_processed
+31QNSG6A50RGY1O7TFIL3M4ROE887T_processed
+37W3JXSD6F6A3MGQERORUFRA9HUYW2_processed
+35BLDD71IFVXTNQQK3LEMKHYA4DVZ3_processed
+3G5F9DBFOYVB22QWEHF7MDDTRF7VH3_processed
+33JKGHPFYLSKMBIY3N9T41RDPPWMN1_processed
+30X31N5D6COG0N1EEG0AU46I2PLSAN_processed
+36DSNE9QZEWC3OCP15WE3YZ0NO2JOH_processed
+3CFJTT4SX2O9N70K21UCGBY5LUTI7P_processed
+3CN4LGXD56MYUKX64LI8ZN8JPKR4YA_processed
+34T446B1C9C4STE0PSDHQTQOM490CL_processed
+35USIKEBN0EIX8X6G72CF5UQH1BN6Q_processed
+33FOTY3KEVJ4ZIZILBHH4Y9XPFLC14_processed
+3I33IC7ZWO0NVOKGK1L43MTE4MO2AE_processed
+33CKWXB73SIKCFPK5ESXN2Q6LHH11O_processed
+324G5B4FBC6YGCJ4YB56A7Y96RC071_processed
+34BBWHLWHJ9ODM1LA92O8RXMJUJIWV_processed
+30LSNF2393T2163EWPDHKKHXUQWI26_processed
+3E7TUJ2EGLKWTF8RPKRL3A9YN2XD9R_processed
+3FE2ERCCZ668PZFNWI1Y0ULCWJWPO3_processed
+3HWRJOOETE0JQ0IQUL2WQNW9FEYESV_processed
+33PPUNGG3H350G99BJG4LNSV2PORZP_processed
+37M28K1J0ZBN1KIORMRW23FV695AJS_processed
+3GNA64GUZN27H1AKRGKORTBVPSHQ5F_processed
+31LVTDXBLG8LVRS5WIPVD8HXNAERLS_processed
+31T4R4OBO1EIA4EKARYQ580AFDOC7F_processed
+33CID5710DRTCP73LSOZVAEH4GYL39_processed
+382M9COHEQDZ5DAUJQXW5OEX1TPEUI_processed
+3EQHHY4HQ1QG4CG6OKFCXF7TNSL5GQ_processed
+3ATTHHXXWJM32M6BKEYAAABTSV7IX4_processed
+3GU1KF0O4RZO65QESDWCUALDW1TBP6_processed
+338JKRMM2FXRAENY3MOBQQZGR1HHAC_processed
+3G0WWMR1U4IB7QGQVGGALABHCJHNQA_processed
+336KAV9KYZQOR6IJ07ZK68BL57M2Y5_processed
+33F859I56FBWTOSQ90O7L1JMOO9BHX_processed
+32XVDSJFP8VOXPT5H4B9CDOEBR32MD_processed
+3CFVK00FWUJS98UL4UMX2DQ1A3X6LL_processed
+3DL65MZB8MD3JJTUXZCWBISREGJCE1_processed
+37U1UTWH94KQGK8M2TGR83VR3A8R8Z_processed
+336YQZE834CGWMCSZND7AKRQBBY5ML_processed
+32SVAV9L3O7CG6GZO1Z9MWY68SXA3O_processed
+3H7XDTSHKLPAHVVQKL2LNFK6YXJWGZ_processed
+3DI28L7YXJCJWGJWH2NCUM90TW0E1U_processed
+3HMVI3QICSQH2L0NH3Z61P1HUG3Y1S_processed
+3I3WADAZ9Z24WPX4C3SK2OGV5KF5OJ_processed
+31HQ4X3T318XW6SIB7P03IMTST1SL2_processed
+3C6FJU71TZRXBIRLJR9QCR6D33MUYL_processed
+3IOEN3P9SGHFJ1QHBK4EIYHNHYC161_processed
+3BQU611VFYIKQPVAD4MJHEQ841P99P_processed
+333U7HK6IIDLZRL0BWIYS4N1QTNJDL_processed
+3A0EX8ZRNHMIFJIFJ0SV71EP0MKBYK_processed
+3B3WTRP3DK09Q5ABSV747RIUWX192L_processed
+31IBVUNM91XRO6ZQJRR7JS1GY9CVFS_processed
+358UUM7WR81MG633SOOLU3ZXUAP7RN_processed
+3AUQQEL7UER0R2K0G98MGLECLJR0VE_processed
+31Z0PCVWUTDZWLGVWDTGUI0E1KX7T6_processed
+33C7UALJVUW2NF954Z1C2CDEKMW180_processed
+33CID5710DRTCP73LSOZVAEH26UL3J_processed
+36V4Q8R5ZTY5P8VQJF1A8VMHC3FQMA_processed
+39LOEL67O13YXINKR3FVQ5SBD8638P_processed
+39L1G8WVWZPGMI3ZSI6GBV53ZS513O_processed
+37XITHEIS57SSNYZJ5WWAPRAFHLRCF_processed
+39OWYR0EPTP8S0UVO2ERQR5ACHIYFM_processed
+35BLDD71IFVXTNQQK3LEMKHYNUBZVY_processed
+37TRT2X24ZPSKUNGX0RSXEZ1NFRJB8_processed
+37QW5D2ZRPK2HZ8ZHIGWYD0NAM48SM_processed
+3GD6L00D31VH84B9Y0BV7UE9JKYM1N_processed
+33PPUNGG3H350G99BJG4LNSVLYXZR7_processed
+3A9AA95AT5KMHP95R35ZROLLG2VP5V_processed
+32Z9ZLUT1UIGCIHZD5TTRLJMXUNOHR_processed
+30IQTZXKAT4SVRPVGO9446BX86EX0O_processed
+36H9ULYP6BSIXBZW22DOG4QM21LFJ0_processed
+3CCZ6YKWRGH1P2TYAE925YL6G76950_processed
+3G0WWMR1U4IB7QGQVGGALABHHUWNQG_processed
+382M9COHEQDZ5DAUJQXW5OEYNWQEUD_processed
+3B1NLC6UG8UKXMYP8ZOQVLCA5YMPGZ_processed
+351SEKWQS9FB0ZRLETVQ8WFIU1PDMI_processed
+37TRT2X24ZPSKUNGX0RSXEZ1MJMJBA_processed
+3FIJLY1B632EJM2UXFI8D0CYA6IFP2_processed
+3DOCMVPBTWCQ4T3YFOOTHGGCDRONNP_processed
+3GNA64GUZN27H1AKRGKORTBVQ885QI_processed
+34MAJL3QPDLXEH0IF0PI9TC7YU443I_processed
+3DYGAII7PU6BABS3BVT2PZGCH70PQD_processed
+33LKR6A5KNILLZ1T7XC7T891DRN1TU_processed
+37U1UTWH94KQGK8M2TGR83VRN408RG_processed
+386CSBG1O8K305AYWZUTJZPVGGE6QE_processed
+36U2A8VAGAXJ8MM9PD5O64XG3RFKY3_processed
+3HYA4D4520HIRF1OVH7WHN46GFJ2FG_processed
+3018Q3ZVORO4Z811ZR054U1MFKOARW_processed
+33LK57MYL23H1V9MQ8G9BXXR4NUZSG_processed
+30ZX6P7VFHTYWHNKE7YHWLWXS4ZJ2G_processed
+3B3WTRP3DK09Q5ABSV747RIUWY292O_processed
+3DL65MZB8MD3JJTUXZCWBISR1OUECH_processed
+33CUSNVVNWAKYFTQ3KARHM0MTVH887_processed
+3BWI6RSP7P7X72YY6I4LVW0DYAUE77_processed
+3EO896NRA5TSGGHXXMIDX85NRXPTJQ_processed
+3B2X28YI35DGWZ89DHX3OAUWVTT6B9_processed
+32Q90QCQ11JL2H5IPCTUZ15ZZLHKEJ_processed
+31N2WW6R90O7CXX7ZHL10X9XKBS3FX_processed
+3GDTJDAPV39ZJ4VUXLKAM41HXX58M8_processed
+37TRT2X24ZPSKUNGX0RSXEZ1HA6JB7_processed
+36W0OB37H5CSBMV6FE4A7JQP0U1HZ6_processed
+3FTYUGLFS3JDSS6FMUE4RU4W6E3D5A_processed
+32EYX73OY97OEHFZ5WHW4VDG06VRUK_processed
+3C2NJ6JBKJFUF7E5T5Q9PSDEGKTN2K_processed
+3IAEQB9FMNI75BLZIV1DHUH9LCFWDF_processed
+31QTRG6Q22BKJDJP25AW2LJEGHPYPZ_processed
+3HFNH7HEMQC5XYZ48C3FVQ2KEMKGQH_processed
+3GA6AFUKOXMRQTODU7913ILAMWC3H0_processed
+3D8YOU6S9NIVSYHFD8EYT0MK4G56UJ_processed
+3DBQWDE4YFWME4XSBOS7NZGQLY1N5V_processed
+34MAJL3QPDLXEH0IF0PI9TC6BQM433_processed
+37QW5D2ZRPK2HZ8ZHIGWYD0ORZH8S8_processed
+339ANSOTRE0XIM74LVJLKG3RREVKIZ_processed
+3HRMW88U1FOH1FQR2H7VTPVZZYXM0M_processed
+3HWRJOOETE0JQ0IQUL2WQNW9GFWSEA_processed
+36WLNQG7888K9E1OJZG10YM9HAWEBO_processed
+36DSNE9QZEWC3OCP15WE3YZ0VOZJOM_processed
+3A4NIXBJ7FXUYBFDRATER9NN04RMLS_processed
+3570Y55XZYHE60QQZMK98C5MURVGYN_processed
+3DQQ64TANPJGU8OPN4S8HOYY5UTWP8_processed
+3E47SOBEYZUFZOVGTI2NWXQJLS0IC7_processed
+38BQUHLA95Y24WIA4296JQRHRHXMOM_processed
+3018Q3ZVORO4Z811ZR054U1ME89ARS_processed
+33LK57MYL23H1V9MQ8G9BXXROTRSZ2_processed
+374TNBHA8KT5JPK42IPL2Q9OMC9YQN_processed
+33IZTU6J8AZSW097PQSOECHHIH4SXW_processed
+3C8HJ7UOPGSE30WH6BKNZOYLH50MZY_processed
+32SCWG5HIQ2I0T9JWMF4JUZLJ5PP6H_processed
+30OG32W0S39MANQLMPLARTM3TS9NE2_processed
+339ANSOTRE0XIM74LVJLKG3SEKLKIQ_processed
+324G5B4FBC6YGCJ4YB56A7Y93TD70A_processed
+3FE2ERCCZ668PZFNWI1Y0ULC1QYPOO_processed
+32VNZTT0AG2PXJLKQQQERRFMGAIR4L_processed
+3CPLWGV3MXX5FRZ4EE5OZFLTOGS9N7_processed
+3HFNH7HEMQC5XYZ48C3FVQ2LYRPQGS_processed
+3GDTJDAPV39ZJ4VUXLKAM41HCDKM8C_processed
+39LOEL67O13YXINKR3FVQ5SCZFC832_processed
+3EWIJTFFVX5JPRYELJJCGHQR7BA0ES_processed
+388U7OUMFGZ8YV3J7P3CFOP97JXR0E_processed
+3IOEN3P9SGHFJ1QHBK4EIYHNFK6161_processed
+34FNN24DCV7GQ316IQ1I4FEXR2C5YG_processed
+38YMOXR4M3X8K26K88TZBO7HPVC6WC_processed
+31Z0PCVWUTDZWLGVWDTGUI0EWDE7T4_processed
+3BEFOD78WFRY038SLIUX5SLCGAVM4K_processed
+3DQQ64TANPJGU8OPN4S8HOYY0CRWP1_processed
+32N49TQG3PGWSFII8B5F5RQPEIFVAM_processed
+33OOO72IVQJ582BRNU1RA4SSXPBCTA_processed
+3B3WTRP3DK09Q5ABSV747RIUQDA29D_processed
+31IBVUNM91XRO6ZQJRR7JS1GVD9VFU_processed
+36ZN444YT0WG8DSJJNBERXK0A64IOP_processed
+3A1COHJ8NSTDRQUJ6O4XUZTLVN1H86_processed
+3HPZF4IVNVR1POALX0OQILK7J8YYCV_processed
+354P56DE9T1YHL3G7DOQKD4SE45S79_processed
+3EG49X3513AN92BAIDPLD7LP0RM6X1_processed
+3GGAI1SQE4W1V7L7EX0FOQX02D2MCX_processed
+39GAF6DQW0Y0YR5ICSCMUQXNCQIV1F_processed
+3HMIGG0U4U4ZDLK8GO8LGZOO6B18YO_processed
+3BGYGHDBB6IYQLPC7MSPX4AGQSC229_processed
+30ZX6P7VFHTYWHNKE7YHWLWXQ85J2S_processed
+3DUZQ9U6SVM0SB4CPBR6QRKVLK7VSG_processed
+34S9DKFK7CNKGS7CMQX4YWEJVU6NY9_processed
+34MAJL3QPDLXEH0IF0PI9TC6VMG439_processed
+3BXQMRHWK8WXH1270OLFIWJ7XZDMU9_processed
+3DEL4X4ELFJPSMLGOWKDWY5HIPHXYN_processed
+39ZSFO5CAHU7GTWMXR8N24RPQU0JUT_processed
+3I33IC7ZWO0NVOKGK1L43MTE2HX2AB_processed
+30BUDKLTXMTIKYKFIPPOXCMHM3BE5H_processed
+3BGYGHDBB6IYQLPC7MSPX4AG5QO22W_processed
+32XVDSJFP8VOXPT5H4B9CDOEX4AM2G_processed
+384PI804X1ZKZA3WME3H5YU5RNS0SP_processed
+36H9ULYP6BSIXBZW22DOG4QMZCQJFS_processed
+3FE2ERCCZ668PZFNWI1Y0ULCWKKPOT_processed
+3F1567XTN53QIOCWU6X539GX6G9Q9U_processed
+3F1567XTN53QIOCWU6X539GX7VV9QU_processed
+33CUSNVVNWAKYFTQ3KARHM0MS5988I_processed
+35BLDD71IFVXTNQQK3LEMKHY33JVZ0_processed
+3FE2ERCCZ668PZFNWI1Y0ULC2AOOPI_processed
+3EJPLAJKEVECEXBPJVD6AS109HEZ62_processed
+36H9ULYP6BSIXBZW22DOG4QMGYGFJ3_processed
+3HYA4D4520HIRF1OVH7WHN45ESRF2N_processed
+3GNA64GUZN27H1AKRGKORTBV56VQ51_processed
+35LDD5557J2JE5XV6AHDC9A88Q4KMA_processed
+3FIJLY1B632EJM2UXFI8D0CYBCNFPK_processed
+340UGXU9D7ZG7OWGEGM65S2J0U1UVO_processed
+3HL8HNGX4EZTRZMNGQMBRACJ724F9D_processed
+3DI28L7YXJCJWGJWH2NCUM90AO41EM_processed
+3BXQMRHWK8WXH1270OLFIWJ7XJDMUD_processed
+3EKVH9QME72LTVNS4BC6TN1L5U1D2Z_processed
+37W3JXSD6F6A3MGQERORUFRAO5NWYK_processed
+3FTF2T8WL0GV2L8I6FWBVYYJVBYW91_processed
+3AMYWKA6YKK0FTXIHM2PUUUVMAO6O9_processed
+3IAS3U3I0OESEYP8QFL0YXDKL4P2BO_processed
+33CID5710DRTCP73LSOZVAEH2V63LR_processed
+388U7OUMFGZ8YV3J7P3CFOP96SZ0R6_processed
+3137ONMDKP3G0V9ZZL8F19D6CLSEGK_processed
+35GMH2SV3NF4S8QXN4LN6IM8IDIOE7_processed
+3DQQ64TANPJGU8OPN4S8HOYYK0UWP0_processed
+32UTUBMZ7PU1BPNAEP5B02AVHZ1BVH_processed
+32EYX73OY97OEHFZ5WHW4VDGKUCRUX_processed
+3FDJT1UU7D6L6YCPEB0RV7ZMT025KA_processed
+3E47SOBEYZUFZOVGTI2NWXQJR4WCIR_processed
+31Q0U3WYDYDYN14MUU96OCHJOW317T_processed
+3DEL4X4ELFJPSMLGOWKDWY5HJA9XYM_processed
+3FTF2T8WL0GV2L8I6FWBVYYJ2319W8_processed
+31JLPPHS23S1IAA0YK4RNRIBC6WO35_processed
+3CN4LGXD56MYUKX64LI8ZN8JKMZY4B_processed
+3IHR8NYAMGZ4L655DYMPMWF7NVEP4P_processed
+35L9RVQFCXGBPN1W9R52QRBATSEUHD_processed
+34T446B1C9C4STE0PSDHQTQN5KN0CC_processed
+36ZN444YT0WG8DSJJNBERXK0HLUOIM_processed
+3IGI0VL64GI8ME3AG3O6W5GHXISNOV_processed
+3B1NLC6UG8UKXMYP8ZOQVLCA6C7PGD_processed
+37WLF8U1WYOHP2C3JUAWNDDMIXO6KZ_processed
+3II4UPYCOS52LEPD6M27P5ZN3OZDQP_processed
+30IQTZXKAT4SVRPVGO9446BX485X0F_processed
+324G5B4FBC6YGCJ4YB56A7Y9Z09079_processed
+31Z0PCVWUTDZWLGVWDTGUI0EVY4T7L_processed
+358010RM5NR8OSQBJLXTPZ1N10OXV8_processed
+32EYX73OY97OEHFZ5WHW4VDGM4GURQ_processed
+3IJXV6UZ16HJ5QKZBFSILSP5LWNIR3_processed
+392CY0QWGAPC4KI103W5DZEVZ3F4IU_processed
+3CP1TO84P2ZQPI8ZP5UNVBDSP6P25J_processed
+3E7TUJ2EGLKWTF8RPKRL3A9Y8NP9D6_processed
+39GHHAVOMOP8ZV27ILH5P0TVR9XJ4B_processed
+3DEL4X4ELFJPSMLGOWKDWY5HCTJXYR_processed
+37C0GNLMHO19BW6TRDO9J0IW4YCD64_processed
+33PPUNGG3H350G99BJG4LNSVNWCZRK_processed
+3HMVI3QICSQH2L0NH3Z61P1HUPXY14_processed
+3G0WWMR1U4IB7QGQVGGALABHHZ5NQZ_processed
+3DBQWDE4YFWME4XSBOS7NZGQ0Q95NK_processed
+32M8BPYGA2KSG0BLV4Y6MOBY4FTGIR_processed
+3FIJLY1B632EJM2UXFI8D0CY9QFFP2_processed
+308XBLVESR29II6T5501Z5LSVADBRA_processed
+3IKZ72A5BDEEG1QHH05G9B739FPNFX_processed
+32AT8R96GU70FWFCKLH1U5IC91ASUE_processed
+3FE7TXL1LRLFI4RX9XIQTM8J6NW2Q7_processed
+339ANSOTRE0XIM74LVJLKG3RBSLKI1_processed
+33UKMF931IIMUJD29GWHZESWRM9TT0_processed
+36NEMU28XOBA95RCVO0G0K431LLMWY_processed
+320DUZ38GGKOBBVRDMJ6YPQZJ0GJGK_processed
+3G5F9DBFOYVB22QWEHF7MDDTR17VHB_processed
+3A7Y0R2P2XMZXXQH2WJAMBQT7ORJX6_processed
+3AMW0RGHOM0Q7EFBV31LL5L7E5SNPN_processed
+3I2PTA7R32SAZKVNY3OUTIUDHENKQS_processed
+36U2A8VAGAXJ8MM9PD5O64XFHB3KY7_processed
+358UUM7WR81MG633SOOLU3ZWE64R7W_processed
+3FE2ERCCZ668PZFNWI1Y0ULC2GHOPN_processed
+37WLF8U1WYOHP2C3JUAWNDDMI0K6K1_processed
+3HFNH7HEMQC5XYZ48C3FVQ2KVEMQGU_processed
+3DPNQGW4LUDWB0N0Y2NGGWOQQAX469_processed
+3HPZF4IVNVR1POALX0OQILK7JENYCW_processed
+34Q075JO16BK5Q429VOX9X7WZUB01D_processed
+36U2A8VAGAXJ8MM9PD5O64XFKD6KYH_processed
+3EFVCAY5LC79IWP9UO7I6C90E4Z8JG_processed
+35LDD5557J2JE5XV6AHDC9A8B2QKMN_processed
+358010RM5NR8OSQBJLXTPZ1N02UVXF_processed
+3EJPLAJKEVECEXBPJVD6AS1ZMAJ6ZB_processed
+34X6J5FLP2WFO0P5GIOIS3U06GBJQ2_processed
+35H6S234SJYE7JR0C76QLOU4Q9K56D_processed
+354GIDR5ZK4KY1JKDCKXVSOMWWB006_processed
+3E13VNJ1NWTVCLLIDLD3F17S2NCI1P_processed
+324G5B4FBC6YGCJ4YB56A7Y95G670F_processed
+3BV8HQ2ZZ5ZBDP3H73X7TZAQK0NA6I_processed
+3FDJT1UU7D6L6YCPEB0RV7ZMRAE5K4_processed
+369J354OFM8CNGGKT3PHPLLTVCI6GJ_processed
+39GXDJN2O2CI9RP3BDL190KU9QO8VH_processed
+3HFNH7HEMQC5XYZ48C3FVQ2KBIYQGU_processed
+3HMIGG0U4U4ZDLK8GO8LGZOOMAIY89_processed
+369J354OFM8CNGGKT3PHPLLTGGU6GO_processed
+3FIUS151D40EK95Q5GY31CYJSMTGG0_processed
+31LM9EDVOUQUL3CD0QW1O9ZV5OAJN7_processed
+3GS6S824SZVGRNCC7PFGDZT33ZHNWU_processed
+31Q0U3WYDYDYN14MUU96OCHJAXQ71A_processed
+32Q90QCQ11JL2H5IPCTUZ15Z6XNEKE_processed
+39RP059MEQRIL2T10X4KD6RIL6AMBR_processed
+3FE7TXL1LRLFI4RX9XIQTM8JLYB2QN_processed
+3II4UPYCOS52LEPD6M27P5ZN3EPDQV_processed
+36U2A8VAGAXJ8MM9PD5O64XG44KKYZ_processed
+39ZSFO5CAHU7GTWMXR8N24RPDVWUJP_processed
+32Z9ZLUT1UIGCIHZD5TTRLJLH91HOA_processed
+3H7Z272LXG50JECQJQL68DHI2SVPLJ_processed
+3I2PTA7R32SAZKVNY3OUTIUD22PKQR_processed
+3EJJQNKU903J9V9F5XAXLGOQT00RHH_processed
+31LVTDXBLG8LVRS5WIPVD8HXNBHRLX_processed
+3CTOC39K3GO5IIKQKJFCSCD55N07J7_processed
+35BLDD71IFVXTNQQK3LEMKHY3P2ZVV_processed

data_splits/airvln_16/test/airvln_16_3_trajectories_long.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dc0c0af90b9ef226afa84677ce1d9c83627c2998c4733d174f1daa8c0f1ed987
+size 47221

data_splits/airvln_16/test/airvln_16_3_trajectories_long_64.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dcc65355a6788dfcd96125f1beb37a0e52f8632e39561232380b3cf78cd91b3a
+size 85630

data_splits/airvln_16/test/airvln_16_4_trajectories_2d.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:271dad093374d4bbb3748782079f538315632415c2f00c922ae037c5c09aa8d9
+size 61621

data_splits/airvln_16/test/airvln_16_5_trajectories.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d309cab6e576e6b2ac27513ba323a23de3cda26a0bf107fe3f2c9e8290b4f810
+size 76030

data_splits/airvln_16/test/airvln_16_5_trajectories_long.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b644a06c285a7d897d279ee2c3210c9ff6b0c32bc5bd79a2921bb1d547ccad09
+size 140039

data_splits/airvln_16/test/navigation_eval.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b225e6a17ef3a6ea3b679ecc262b5ab6fd11ac83e12c72d570ae2e5b21369a84
+size 4547

data_splits/airvln_16/test/navigation_eval_16.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b041b67a175e2179a1a7fcfdbd41294544af013aa9fcceb8a15bd3f30dcdf044
+size 4630

data_splits/airvln_16/test/navigation_eval_16_2d.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:effe73444000bdd7b9dabcfb602a43ac76399904352e8ffcbca789f15a4adfc0
+size 4254

data_splits/airvln_16/test/navigation_eval_16_long.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:046bf2af6d9de194999cc7ab173a9dda4eb4da4d291cb3a76e07f18eb4de277f
+size 4695

data_splits/airvln_16/test/navigation_eval_16_o.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0680c5064c7971ec09144529615603e79087cf71106ca9155810d873e0697722
+size 4380

data_splits/airvln_16/test/navigation_eval_2d.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:27b4e47869b527ce61d5e435e3951e5829d32c546df2990d0e7cbf768950e40e
+size 4387

data_splits/airvln_16/test/navigation_eval_2d_all.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f1e2aa556be150ad49501e4069a78347d2b68e7740bf7a984d77896aaa35c4af
+size 32433

data_splits/airvln_16/test/rollout.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e9b6b53ab3017c7a9ecfd1ebd736c5c5d014b7874f49bbd420bf46dbee4f3cc5
+size 17572

data_splits/airvln_16/test/rollout_16.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b2c68a23c9c9e4c4fbcfdc764b53201743d6033b35324ffc0943344c348b6dce
+size 17586

data_splits/airvln_16/test/rollout_2d.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ae8008a6042b01995ae3a1d70239ba0e47ac8ac7c1d27cbe9bad0016f55ecda2
+size 4115

data_splits/airvln_16/test/rollout_2d_8s.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6e8ae214e7d61c8ba6e0a6d6acf85aacd0418c40da8f48e980e87f8cef868b41
+size 463

data_splits/airvln_16/test/rollout_traj_names.txt

@@ -0,0 +1 @@
+3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed

data_splits/airvln_16/test/rollout_turn_135deg.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:eef2a5e5af0de53f2e76df5fc33d545ad61d5bf7fb4f51a89e792da57eaf27dd
+size 6205

data_splits/airvln_16/test/rollout_turn_135deg_n4.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3bb18dbf5a0db18c65bb9ef2bae0549f45ece863f8c3b74d80c496092243fe44
+size 4047

data_splits/airvln_16/test/rollout_turn_15deg.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b5b3ccbfbc351d80b68694bc9956b3e39bbd7de19a64762cfc0ade6d47b708ca
+size 8609

data_splits/airvln_16/test/rollout_turn_30deg.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:12f32a23d582052addac583ab9ab11d562bb11f9b68ca16fdc287a0adb998a29
+size 8531

data_splits/airvln_16/test/rollout_turn_45deg.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7a61d33390c95dde6bda5e78d8befd960b000ac4a6a3d0ef0f31ca8571de4d90
+size 8192

data_splits/airvln_16/test/rollout_turn_45deg_n4.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fcc7be005c800affd279dfc3327f346a963b6d16b1cf2bba2ce7a0737a993cb2
+size 4627

data_splits/airvln_16/test/rollout_turn_90deg.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0214ef9b9c72a3cba118f851701a74a73621ab7f1d0644638e0f3288adeaa960
+size 8642

data_splits/airvln_16/test/rollout_turn_90deg_n4.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9a0e23fe4510e965528b1ae34501327ac53bb96692e998ede85a1a8751e339d9
+size 4353

data_splits/airvln_16/test/sampled_trajectories_5.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:78188f74b18d6700857cb60e65741d6b0626abce8ddc2b63ff589610314f3a81
+size 72994

data_splits/airvln_16/test/traj_names copy.txt

@@ -0,0 +1,5 @@
+3BDORL6HLSBP6316T2RUL6WMNHBCRJ_processed
+3XU80RHWI7EFATMTPBPZUF9OZ2M44B_processed
+3DZKABX20Q3HZAIN2MW4M3LP3VVVCD_processed
+3VEI3XUC0ZV80SAAY7D056VTIHMPR0_processed
+33CLA8O0NQ9W9FLGK4HVTCHPJAIRFJ_processed