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janus/lib/python3.10/site-packages/distutils-precedence.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2638ce9e2500e572a5e0de7faed6661eb569d1b696fcba07b0dd223da5f5d224
+size 151

janus/lib/python3.10/site-packages/einops/experimental/indexing.py

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+"""
+
+Indexing one array with the other(s).
+
+Concept for discussion.
+
+Notation targets hard cases, not simple ones, like indexing of 1d-array with another 1d-array
+(notation supports that, but you can't simplify arr[ind], and there is no reason to)
+
+Examples
+
+1. query for every token in sequence a token in the image. Images and sequences are paired
+   einindex('b t c <- b h w c, [h, w] b t', arr_bhwc, [h_indices_bt, w_indices_bt])
+
+   this is equivalent, so you can pass indexers idependently or together
+   einindex('b t c <- b h w c, [h, w] b t', arr_bhwc, np.asarray([h_indices_bt, w_indices_bt]))
+
+   after some thinking I decided that having first axis for indexing variable is not too restrictive,
+   but should simplify mapping of such cases.
+   For this reason [...] part should always go first in indexer.
+
+   This makes the largest difference with einindex https://github.com/malmaud/einindex,
+   which has almost identical grammar, but puts special dimension last, while we put it first.
+   This trick allows naturally decomposing multiindex into individual dimensions or visa versa.
+
+
+2. query for every token in the video the most suitable word in a (matching) sentence
+   einindex('b t h w <- seq b, [seq] t b h w', arr_tbc, [t_indices_bhw])
+
+   note, that only one indexer is used, but still it has to be enclosed in the list.
+   That's a price for being generic. Alternatively leading singleton dimension can be added.
+
+
+3. (not supported now, future planning)
+   for every timeframe in a video, find the token with the highest norm (across h and w), and compose a new stack of them
+   indices_2bt = argmax(x_bthwc.norm(dim=-1), 'b t h w -> [h, w] b t')
+   selected_embeddings_btc = einindex('b t c <- b t h w c, [h, w] b t', x_bthwc, indices_2bt)
+
+   while currently question is around 'how do we index',
+   it is important to pre-align that with a question 'what are natural ways to get indices'.
+   Most common are min/max. less common options: topk (works here), random sampling.
+
+
+
+Some important properties of this notation:
+- support for multiple indexers, including using a single tensor to keep multiple indexers
+- 'batch' indexing, when some axes of indexer and array should be matched
+- universal (one-indexing-to-rule-them-all)
+- extensible for (named) ellipses, including variadic number of indexers
+- extensible for einops-style compositions and decompositions
+- extensible for outer indexing when indexers are not aligned
+
+Current implementation based on python array api and uses loops,
+because no appropriate indexing available in the standard.
+
+"""
+
+from typing import List, Union, TypeVar, Tuple
+
+from einops import EinopsError
+
+T = TypeVar("T")
+
+
+class CompositionDecomposition:
+    def __init__(
+        self,
+        decomposed_shape: List[str],
+        composed_shape: List[List[str]],
+    ):
+        flat_shape = []
+        for x in composed_shape:
+            flat_shape.extend(x)
+
+        self.compose_transposition: Tuple[int, ...] = tuple([decomposed_shape.index(x) for x in flat_shape])
+        self.decompose_transposition: Tuple[int, ...] = tuple([flat_shape.index(x) for x in decomposed_shape])
+        self.composed_shape = composed_shape
+        self.decomposed_shape = decomposed_shape
+
+    def decompose(self, x, known_axes_lengths: dict[str, int]):
+        xp = x.__array_namespace__()
+        shape = x.shape
+
+        flat_shape = []
+
+        for i, axis_group in enumerate(self.composed_shape):
+            unknown_axis_name = None
+            known_sizes_prod = 1
+            for axis_name in axis_group:
+                if axis_name in known_axes_lengths:
+                    known_sizes_prod *= known_axes_lengths[axis_name]
+                else:
+                    if unknown_axis_name is None:
+                        unknown_axis_name = axis_name
+                    else:
+                        raise EinopsError("Can't infer the size")
+
+            if unknown_axis_name is None:
+                assert shape[i] == known_sizes_prod
+            else:
+                known_axes_lengths[unknown_axis_name] = shape[i] // known_sizes_prod
+
+            for axis in axis_group:
+                flat_shape.append(known_axes_lengths[axis])
+
+        x = xp.reshape(x, flat_shape)
+        return xp.permute_dims(x, self.decompose_transposition)
+
+    def compose(self, x, known_axes_lengths: dict[str, int]):
+        xp = x.__array_namespace__()
+
+        for axis_len, axis_name in zip(x.shape, self.decomposed_shape):
+            if axis_name in known_axes_lengths:
+                assert known_axes_lengths[axis_name] == axis_len
+            else:
+                known_axes_lengths[axis_name] = axis_len
+
+        x = xp.permute_dims(x, self.compose_transposition)
+        new_shape = []
+        for axis_group in self.composed_shape:
+            composed_axis_size = 1
+            for axis_name in axis_group:
+                composed_axis_size *= known_axes_lengths[axis_name]
+            new_shape.append(composed_axis_size)
+
+        return xp.reshape(x, tuple(new_shape))
+
+
+def arange_at_position(xp, n_axes, axis, axis_len, device=None):
+    x = xp.arange(axis_len, dtype=xp.int64, device=device)
+    shape = [1] * n_axes
+    shape[axis] = axis_len
+    x = xp.reshape(x, shape)
+    return x
+
+
+class IndexingFormula:
+    def __init__(self, pattern: str):
+        """
+        :param pattern: example 'b t c <- b hsel wsel c, [hsel, wsel] b t'
+        """
+        self.pattern = pattern
+        left, right = pattern.split("<-")
+        arg_split = right.index(",")
+        arr_pattern, ind_pattern = right[:arg_split], right[arg_split + 1 :]
+        ind_pattern = ind_pattern.strip()
+        # print(
+        #     arr_pattern, '\n',
+        #     ind_pattern,
+        # )
+        assert ind_pattern.startswith("["), "composition axis should go first in indexer (second argument) [h w] i j k"
+        composition_start = ind_pattern.index("[")
+        composition_end = ind_pattern.index("]")
+        composition = ind_pattern[composition_start + 1 : composition_end]
+        ind_other_axes = ind_pattern[composition_end + 1 :]
+
+        self.result_axes_names = left.split()
+        self.array_axes_names = arr_pattern.split()
+        self.indexing_axes_names = [x.strip() for x in composition.split(",")]
+        self.indexer_other_axes_names = ind_other_axes.split()
+
+        for group_name, group in [
+            ("result", self.result_axes_names),
+            ("array", self.array_axes_names),
+            ("indexer", self.indexing_axes_names + self.indexer_other_axes_names),
+        ]:
+            if len(set(group)) != len(group):
+                # need more verbosity, which axis, raise
+                raise EinopsError(f"{group_name} pattern ({group}) contains a duplicated axis")
+
+        axis_groups = [
+            self.result_axes_names,
+            self.array_axes_names,
+            self.indexing_axes_names,
+            self.indexer_other_axes_names,
+        ]
+
+        all_axes = set()
+        for group in axis_groups:
+            all_axes.update(group)
+
+        self.indexer_axes = []
+        self.batch_axes = []
+        self.result_and_index_axes = []
+        self.result_and_array_axes = []
+
+        for axis in all_axes:
+            presence = tuple(axis in g for g in axis_groups)
+            # want match-case here. sweet dreams
+            if presence == (False, True, True, False):
+                self.indexer_axes.append(axis)
+            elif presence[2]:
+                raise EinopsError(f"Wrong usage of indexer variable {axis}")
+            elif presence == (True, True, False, True):
+                self.batch_axes.append(axis)
+            elif presence == (True, False, False, True):
+                self.result_and_index_axes.append(axis)
+            elif presence == (True, True, False, False):
+                self.result_and_array_axes.append(axis)
+            else:
+                # TODO better categorization of wrong usage patterns
+                raise EinopsError(f"{axis} is used incorrectly in {pattern}")
+
+        assert set(self.indexer_axes) == set(self.indexing_axes_names)
+        # order of these variables matters, since we can't lose mapping here
+        self.indexer_axes = self.indexing_axes_names
+
+        self.array_composition = CompositionDecomposition(
+            decomposed_shape=self.array_axes_names,
+            composed_shape=[self.batch_axes + self.indexer_axes, self.result_and_array_axes],
+        )
+
+        self.index_composition = CompositionDecomposition(
+            decomposed_shape=self.indexer_other_axes_names,
+            # single axis after composition
+            composed_shape=[self.batch_axes + self.result_and_index_axes],
+        )
+
+        self.result_composition = CompositionDecomposition(
+            decomposed_shape=self.result_axes_names,
+            composed_shape=[self.batch_axes + self.result_and_index_axes, self.result_and_array_axes],
+        )
+
+    def apply_to_array_api(self, arr: T, ind: Union[T, List[T]]):
+        known_axes_sizes: dict[str, int] = {}
+        xp = arr.__array_namespace__()
+
+        if not isinstance(ind, list):
+            ind = [ind[i, ...] for i in range(ind.shape[0])]
+
+        for indexer in ind:
+            assert len(indexer.shape) == len(self.indexer_other_axes_names)
+
+        # step 1. transpose, reshapes of arr; learn its dimensions
+        arr_2d = self.array_composition.compose(arr, known_axes_sizes)
+
+        # step 2. compute shifts and create an actual indexing array
+        shift = 1
+        full_index = xp.zeros([1] * len(ind[0].shape), dtype=xp.int64, device=arr.device)
+
+        # original order: [*batch-like axes, *indexing_axes,]
+        # now we need to traverse them in the opposite direction
+
+        for axis_name, indexer in list(zip(self.indexing_axes_names, ind))[::-1]:
+            full_index = full_index + shift * (indexer % known_axes_sizes[axis_name])
+            shift *= known_axes_sizes[axis_name]
+
+        for axis_name in self.batch_axes[::-1]:
+            axis_id = self.indexer_other_axes_names.index(axis_name)
+            full_index = (
+                full_index
+                + arange_at_position(
+                    xp,
+                    len(self.indexer_other_axes_names),
+                    axis=axis_id,
+                    axis_len=known_axes_sizes[axis_name],
+                    device=arr.device,
+                )
+                * shift
+            )
+            shift *= known_axes_sizes[axis_name]
+
+        assert shift == arr_2d.shape[0]
+
+        # step 3. Flatten index
+        full_index = self.index_composition.compose(full_index, known_axes_sizes)
+
+        # step 4. indexing
+        # python array api lacks any integer indexing, so... I use loops.
+        # did you know that there is conceptual programming ... just like art?
+        # result_2d = arr_2d[full_index]
+        result_2d = xp.stack([arr_2d[full_index[i], :] for i in range(full_index.shape[0])])
+
+        # step 5. doing resulting
+        result = self.result_composition.decompose(result_2d, known_axes_sizes)
+        return result
+
+
+def einindex(pattern: str, arr: T, /, ind: Union[T, List[T]]):
+    """
+    Demonstrates how einindex should work.
+    Supports data-api compliant arrays.
+    """
+    formula = IndexingFormula(pattern)
+    return formula.apply_to_array_api(arr, ind)
+
+
+def test_composition_and_decomposition():
+    import numpy.array_api as np
+
+    x = np.arange(2 * 3 * 5 * 7)
+    x = np.reshape(x, (2, 3, 5, 7))
+    comp = CompositionDecomposition(
+        decomposed_shape=["a", "b", "c", "d"],
+        composed_shape=[["a", "b"], ["c", "d"]],
+    )
+    assert comp.compose(x, known_axes_lengths={}).shape == (2 * 3, 5 * 7)
+
+    y = CompositionDecomposition(
+        decomposed_shape=["a", "b", "c", "d"],
+        composed_shape=[["a", "b"], [], ["c", "d"]],
+    ).compose(x, {})
+    assert y.shape == (2 * 3, 1, 5 * 7)
+    assert np.all(np.reshape(x, (-1,)) == np.reshape(y, (-1,)))
+
+    comp = CompositionDecomposition(
+        decomposed_shape=["a", "b", "e", "c", "d"],
+        composed_shape=[["e", "c"], ["b"], ["a", "d"]],
+    )
+    x = np.arange(2 * 3 * 5 * 7 * 3)
+    x = np.reshape(x, (2, 3, 5, 7, 3))
+
+    axes = {}
+    y = comp.compose(x, axes)
+    x2 = comp.decompose(y, axes)
+    assert np.all(x == x2)
+
+
+def test_simple_indexing():
+    import numpy.array_api as np
+
+    # simple 2d test
+    arr = np.reshape(np.arange(5 * 7), (5, 7))
+    ind = np.arange(7) % 5
+    x = einindex("j <- i j, [i] j", arr, [ind])
+    for j, i in enumerate(ind):
+        assert arr[i, j] == x[j]
+
+    y = einindex("j <- j i, [i] j", np.permute_dims(arr, (1, 0)), [ind])
+    for j, i in enumerate(ind):
+        assert arr[i, j] == y[j]
+
+
+def test_multidimensional_indexing():
+    import numpy.array_api as np
+
+    embedding_bhwc = (
+        +arange_at_position(np, 4, 0, 2) * 1000
+        + arange_at_position(np, 4, 1, 3) * 100
+        + arange_at_position(np, 4, 2, 5) * 10
+        + arange_at_position(np, 4, 3, 7) * 1
+    )
+
+    hindices_bt = np.reshape(np.arange(6), (2, 3)) % 3
+    windices_bt = np.reshape(np.arange(6), (2, 3)) % 5
+
+    # imagine that you have pairs of image <> sentence
+    # your goal is to get most suitable token from image for every token in sentence
+    # thus for every token in sentence you compute best k and v
+
+    result = einindex("c t b <- b h w c, [h, w] b t", embedding_bhwc, [hindices_bt, windices_bt])
+    # example of using a single array for indexing multiple axes
+    hw_indices_bt = np.stack([hindices_bt, windices_bt])
+    result2 = einindex("c t b <- b h w c, [h, w] b t", embedding_bhwc, hw_indices_bt)
+    assert np.all(result == result2)
+
+    # check vs manual element computation
+    result_manual = result * 0
+    for b in range(2):
+        for t in range(3):
+            for c in range(7):
+                h = hindices_bt[b, t]
+                w = windices_bt[b, t]
+                result_manual[c, t, b] = embedding_bhwc[b, h, w, c]
+
+    assert np.all(result == result_manual)
+
+
+def test_reverse_indexing():
+    import numpy.array_api as np
+
+    C, T, B = 2, 3, 5
+    # G = GPU, batch-like varaible
+    G = 4
+    H = 7
+    W = 9
+
+    arr_gtbc = (
+        +arange_at_position(np, 4, 0, G) * 1000
+        + arange_at_position(np, 4, 1, T) * 100
+        + arange_at_position(np, 4, 2, B) * 10
+        + arange_at_position(np, 4, 3, C) * 1
+    )
+
+    t_indices_gbhw = np.reshape(np.arange(G * B * H * W), (G, B, H, W)) % T
+
+    result = einindex("g b c h w <- g t b c, [t] g b h w", arr_gtbc, [t_indices_gbhw])
+
+    result_manual = result * 0
+    for g in range(G):
+        for b in range(B):
+            for c in range(C):
+                for h in range(H):
+                    for w in range(W):
+                        t = t_indices_gbhw[g, b, h, w]
+                        result_manual[g, b, c, h, w] = arr_gtbc[g, t, b, c]
+
+    assert np.all(result == result_manual)

janus/lib/python3.10/site-packages/einops/layers/oneflow.py

@@ -0,0 +1,54 @@
+from typing import Optional, Dict, cast
+
+import oneflow as flow
+
+from . import RearrangeMixin, ReduceMixin
+from ._einmix import _EinmixMixin
+
+__author__ = "Tianhe Ren & Depeng Liang"
+
+
+class Rearrange(RearrangeMixin, flow.nn.Module):
+    def forward(self, input):
+        return self._apply_recipe(input)
+
+
+class Reduce(ReduceMixin, flow.nn.Module):
+    def forward(self, input):
+        return self._apply_recipe(input)
+
+
+class EinMix(_EinmixMixin, flow.nn.Module):
+    def _create_parameters(self, weight_shape, weight_bound, bias_shape, bias_bound):
+        self.weight = flow.nn.Parameter(
+            flow.zeros(weight_shape).uniform_(-weight_bound, weight_bound), requires_grad=True
+        )
+        if bias_shape is not None:
+            self.bias = flow.nn.Parameter(flow.zeros(bias_shape).uniform_(-bias_bound, bias_bound), requires_grad=True)
+        else:
+            self.bias = None
+
+    def _create_rearrange_layers(
+        self,
+        pre_reshape_pattern: Optional[str],
+        pre_reshape_lengths: Optional[Dict],
+        post_reshape_pattern: Optional[str],
+        post_reshape_lengths: Optional[Dict],
+    ):
+        self.pre_rearrange = None
+        if pre_reshape_pattern is not None:
+            self.pre_rearrange = Rearrange(pre_reshape_pattern, **cast(dict, pre_reshape_lengths))
+
+        self.post_rearrange = None
+        if post_reshape_pattern is not None:
+            self.post_rearrange = Rearrange(post_reshape_pattern, **cast(dict, post_reshape_lengths))
+
+    def forward(self, input):
+        if self.pre_rearrange is not None:
+            input = self.pre_rearrange(input)
+        result = flow.einsum(self.einsum_pattern, input, self.weight)
+        if self.bias is not None:
+            result += self.bias
+        if self.post_rearrange is not None:
+            result = self.post_rearrange(result)
+        return result

janus/lib/python3.10/site-packages/einops/parsing.py

@@ -0,0 +1,152 @@
+from einops import EinopsError
+import keyword
+import warnings
+from typing import List, Optional, Set, Tuple, Union
+
+_ellipsis: str = "…"  # NB, this is a single unicode symbol. String is used as it is not a list, but can be iterated
+
+
+class AnonymousAxis(object):
+    """Important thing: all instances of this class are not equal to each other"""
+
+    def __init__(self, value: str):
+        self.value = int(value)
+        if self.value <= 1:
+            if self.value == 1:
+                raise EinopsError("No need to create anonymous axis of length 1. Report this as an issue")
+            else:
+                raise EinopsError("Anonymous axis should have positive length, not {}".format(self.value))
+
+    def __repr__(self):
+        return "{}-axis".format(str(self.value))
+
+
+class ParsedExpression:
+    """
+    non-mutable structure that contains information about one side of expression (e.g. 'b c (h w)')
+    and keeps some information important for downstream
+    """
+
+    def __init__(self, expression: str, *, allow_underscore: bool = False, allow_duplicates: bool = False):
+        self.has_ellipsis: bool = False
+        self.has_ellipsis_parenthesized: Optional[bool] = None
+        self.identifiers: Set[str] = set()
+        # that's axes like 2, 3, 4 or 5. Axes with size 1 are exceptional and replaced with empty composition
+        self.has_non_unitary_anonymous_axes: bool = False
+        # composition keeps structure of composite axes, see how different corner cases are handled in tests
+        self.composition: List[Union[List[str], str]] = []
+        if "." in expression:
+            if "..." not in expression:
+                raise EinopsError("Expression may contain dots only inside ellipsis (...)")
+            if str.count(expression, "...") != 1 or str.count(expression, ".") != 3:
+                raise EinopsError(
+                    "Expression may contain dots only inside ellipsis (...); only one ellipsis for tensor "
+                )
+            expression = expression.replace("...", _ellipsis)
+            self.has_ellipsis = True
+
+        bracket_group: Optional[List[str]] = None
+
+        def add_axis_name(x):
+            if x in self.identifiers:
+                if not (allow_underscore and x == "_") and not allow_duplicates:
+                    raise EinopsError('Indexing expression contains duplicate dimension "{}"'.format(x))
+            if x == _ellipsis:
+                self.identifiers.add(_ellipsis)
+                if bracket_group is None:
+                    self.composition.append(_ellipsis)
+                    self.has_ellipsis_parenthesized = False
+                else:
+                    bracket_group.append(_ellipsis)
+                    self.has_ellipsis_parenthesized = True
+            else:
+                is_number = str.isdecimal(x)
+                if is_number and int(x) == 1:
+                    # handling the case of anonymous axis of length 1
+                    if bracket_group is None:
+                        self.composition.append([])
+                    else:
+                        pass  # no need to think about 1s inside parenthesis
+                    return
+                is_axis_name, reason = self.check_axis_name_return_reason(x, allow_underscore=allow_underscore)
+                if not (is_number or is_axis_name):
+                    raise EinopsError("Invalid axis identifier: {}\n{}".format(x, reason))
+                if is_number:
+                    x = AnonymousAxis(x)
+                self.identifiers.add(x)
+                if is_number:
+                    self.has_non_unitary_anonymous_axes = True
+                if bracket_group is None:
+                    self.composition.append([x])
+                else:
+                    bracket_group.append(x)
+
+        current_identifier = None
+        for char in expression:
+            if char in "() ":
+                if current_identifier is not None:
+                    add_axis_name(current_identifier)
+                current_identifier = None
+                if char == "(":
+                    if bracket_group is not None:
+                        raise EinopsError("Axis composition is one-level (brackets inside brackets not allowed)")
+                    bracket_group = []
+                elif char == ")":
+                    if bracket_group is None:
+                        raise EinopsError("Brackets are not balanced")
+                    self.composition.append(bracket_group)
+                    bracket_group = None
+            elif str.isalnum(char) or char in ["_", _ellipsis]:
+                if current_identifier is None:
+                    current_identifier = char
+                else:
+                    current_identifier += char
+            else:
+                raise EinopsError("Unknown character '{}'".format(char))
+
+        if bracket_group is not None:
+            raise EinopsError('Imbalanced parentheses in expression: "{}"'.format(expression))
+        if current_identifier is not None:
+            add_axis_name(current_identifier)
+
+    def flat_axes_order(self) -> List:
+        result = []
+        for composed_axis in self.composition:
+            assert isinstance(composed_axis, list), "does not work with ellipsis"
+            for axis in composed_axis:
+                result.append(axis)
+        return result
+
+    def has_composed_axes(self) -> bool:
+        # this will ignore 1 inside brackets
+        for axes in self.composition:
+            if isinstance(axes, list) and len(axes) > 1:
+                return True
+        return False
+
+    @staticmethod
+    def check_axis_name_return_reason(name: str, allow_underscore: bool = False) -> Tuple[bool, str]:
+        if not str.isidentifier(name):
+            return False, "not a valid python identifier"
+        elif name[0] == "_" or name[-1] == "_":
+            if name == "_" and allow_underscore:
+                return True, ""
+            return False, "axis name should should not start or end with underscore"
+        else:
+            if keyword.iskeyword(name):
+                warnings.warn("It is discouraged to use axes names that are keywords: {}".format(name), RuntimeWarning)
+            if name in ["axis"]:
+                warnings.warn(
+                    "It is discouraged to use 'axis' as an axis name " "and will raise an error in future",
+                    FutureWarning,
+                )
+            return True, ""
+
+    @staticmethod
+    def check_axis_name(name: str) -> bool:
+        """
+        Valid axes names are python identifiers except keywords,
+        and additionally should not start or end with underscore
+        """
+        is_valid, _reason = ParsedExpression.check_axis_name_return_reason(name)
+        return is_valid

janus/lib/python3.10/site-packages/timm/layers/__init__.py

@@ -0,0 +1,62 @@
+from .activations import *
+from .adaptive_avgmax_pool import \
+    adaptive_avgmax_pool2d, select_adaptive_pool2d, AdaptiveAvgMaxPool2d, SelectAdaptivePool2d
+from .attention2d import MultiQueryAttention2d, Attention2d, MultiQueryAttentionV2
+from .attention_pool import AttentionPoolLatent
+from .attention_pool2d import AttentionPool2d, RotAttentionPool2d, RotaryEmbedding
+from .blur_pool import BlurPool2d, create_aa
+from .classifier import create_classifier, ClassifierHead, NormMlpClassifierHead, ClNormMlpClassifierHead
+from .cond_conv2d import CondConv2d, get_condconv_initializer
+from .config import is_exportable, is_scriptable, is_no_jit, use_fused_attn, \
+    set_exportable, set_scriptable, set_no_jit, set_layer_config, set_fused_attn, \
+    set_reentrant_ckpt, use_reentrant_ckpt
+from .conv2d_same import Conv2dSame, conv2d_same
+from .conv_bn_act import ConvNormAct, ConvNormActAa, ConvBnAct
+from .create_act import create_act_layer, get_act_layer, get_act_fn
+from .create_attn import get_attn, create_attn
+from .create_conv2d import create_conv2d
+from .create_norm import get_norm_layer, create_norm_layer
+from .create_norm_act import get_norm_act_layer, create_norm_act_layer, get_norm_act_layer
+from .drop import DropBlock2d, DropPath, drop_block_2d, drop_path
+from .eca import EcaModule, CecaModule, EfficientChannelAttn, CircularEfficientChannelAttn
+from .evo_norm import EvoNorm2dB0, EvoNorm2dB1, EvoNorm2dB2,\
+    EvoNorm2dS0, EvoNorm2dS0a, EvoNorm2dS1, EvoNorm2dS1a, EvoNorm2dS2, EvoNorm2dS2a
+from .fast_norm import is_fast_norm, set_fast_norm, fast_group_norm, fast_layer_norm
+from .filter_response_norm import FilterResponseNormTlu2d, FilterResponseNormAct2d
+from .format import Format, get_channel_dim, get_spatial_dim, nchw_to, nhwc_to
+from .gather_excite import GatherExcite
+from .global_context import GlobalContext
+from .grid import ndgrid, meshgrid
+from .helpers import to_ntuple, to_2tuple, to_3tuple, to_4tuple, make_divisible, extend_tuple
+from .hybrid_embed import HybridEmbed, HybridEmbedWithSize
+from .inplace_abn import InplaceAbn
+from .layer_scale import LayerScale, LayerScale2d
+from .linear import Linear
+from .mixed_conv2d import MixedConv2d
+from .mlp import Mlp, GluMlp, GatedMlp, SwiGLU, SwiGLUPacked, ConvMlp, GlobalResponseNormMlp
+from .non_local_attn import NonLocalAttn, BatNonLocalAttn
+from .norm import GroupNorm, GroupNorm1, LayerNorm, LayerNorm2d, RmsNorm, RmsNorm2d, SimpleNorm, SimpleNorm2d
+from .norm_act import BatchNormAct2d, GroupNormAct, GroupNorm1Act, LayerNormAct, LayerNormAct2d,\
+    SyncBatchNormAct, convert_sync_batchnorm, FrozenBatchNormAct2d, freeze_batch_norm_2d, unfreeze_batch_norm_2d
+from .padding import get_padding, get_same_padding, pad_same
+from .patch_dropout import PatchDropout
+from .patch_embed import PatchEmbed, PatchEmbedWithSize, resample_patch_embed
+from .pool2d_same import AvgPool2dSame, create_pool2d
+from .pos_embed import resample_abs_pos_embed, resample_abs_pos_embed_nhwc
+from .pos_embed_rel import RelPosMlp, RelPosBias, RelPosBiasTf, gen_relative_position_index, gen_relative_log_coords, \
+    resize_rel_pos_bias_table, resize_rel_pos_bias_table_simple, resize_rel_pos_bias_table_levit
+from .pos_embed_sincos import pixel_freq_bands, freq_bands, build_sincos2d_pos_embed, build_fourier_pos_embed, \
+    build_rotary_pos_embed, apply_rot_embed, apply_rot_embed_cat, apply_rot_embed_list, apply_keep_indices_nlc, \
+    FourierEmbed, RotaryEmbedding, RotaryEmbeddingCat
+from .squeeze_excite import SEModule, SqueezeExcite, EffectiveSEModule, EffectiveSqueezeExcite
+from .selective_kernel import SelectiveKernel
+from .separable_conv import SeparableConv2d, SeparableConvNormAct
+from .space_to_depth import SpaceToDepth, DepthToSpace
+from .split_attn import SplitAttn
+from .split_batchnorm import SplitBatchNorm2d, convert_splitbn_model
+from .std_conv import StdConv2d, StdConv2dSame, ScaledStdConv2d, ScaledStdConv2dSame
+from .test_time_pool import TestTimePoolHead, apply_test_time_pool
+from .trace_utils import _assert, _float_to_int
+from .typing import LayerType, PadType
+from .weight_init import trunc_normal_, trunc_normal_tf_, variance_scaling_, lecun_normal_, \
+    init_weight_jax, init_weight_vit

janus/lib/python3.10/site-packages/timm/layers/attention2d.py

@@ -0,0 +1,351 @@
+from typing import List, Optional, Type, Union
+
+import torch
+from torch import nn as nn
+from torch.nn import functional as F
+
+from .config import use_fused_attn
+from .create_conv2d import create_conv2d
+from .helpers import to_2tuple
+from .pool2d_same import create_pool2d
+
+
+class MultiQueryAttentionV2(nn.Module):
+    """Multi Query Attention.
+
+    Fast Transformer Decoding: One Write-Head is All You Need
+    https://arxiv.org/pdf/1911.02150.pdf
+
+    This is an acceletor optimized version - removing multiple unnecessary
+    tensor transpose by re-arranging indices according to the following rules: 1)
+    contracted indices are at the end, 2) other indices have the same order in the
+    input and output tensores.
+
+    Compared to V1, this gives 3x speed up.
+    """
+
+    def __init__(
+            self,
+            dim: int,
+            dim_out: Optional[int] = None,
+            num_heads: int = 8,
+            key_dim: int = 64,
+            value_dim: int = 64,
+            attn_drop: float = 0.,
+            proj_drop: float = 0.,
+    ):
+        """Initializer."""
+        super().__init__()
+        dim_out = dim_out or dim
+        self.num_heads = num_heads
+        self.key_dim = key_dim
+        self.value_dim = value_dim
+        self.scale = key_dim ** -0.5
+
+        self.query_proj = nn.Parameter(torch.randn([self.num_heads, self.key_dim, dim]))
+        self.key_proj = nn.Parameter(torch.randn([dim, self.key_dim]))
+        self.value_proj = nn.Parameter(torch.randn([dim, self.value_dim]))
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.out_proj = nn.Parameter(torch.randn([dim_out, self.num_heads, self.value_dim]))
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def _reshape_input(self, t):
+        """Reshapes a tensor to three dimensions, keeping the first and last."""
+        s = t.shape
+        # Propagate the shape statically where possible.
+        #num = t.shape[1:-1].numel()
+        #return t.reshape(s[0], num, s[-1])
+        return t.reshape(s[0], s[1], -1).transpose(1, 2)
+
+    def forward(self, x, m: Optional[torch.Tensor] = None):
+        """Run layer computation."""
+        b, _, h, w = x.shape
+        m = m if m is not None else x
+
+        reshaped_x = self._reshape_input(x)
+        reshaped_m = self._reshape_input(m)
+
+        q = torch.einsum('bnd,hkd->bnhk', reshaped_x, self.query_proj)
+        k = torch.einsum('bmd,dk->bmk', reshaped_m, self.key_proj)
+
+        attn = torch.einsum('bnhk,bmk->bnhm', q, k) * self.scale
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        v = torch.einsum('bmd,dv->bmv', reshaped_m, self.value_proj)
+        o = torch.einsum('bnhm,bmv->bnhv', attn, v)
+        result = torch.einsum('bnhv,dhv->bdn', o, self.out_proj)
+        result = self.proj_drop(result)
+        return result.reshape(b, -1, h, w)
+
+
+class MultiQueryAttention2d(nn.Module):
+    """Multi Query Attention with spatial downsampling.
+
+     3 parameters are introduced for the spatial downsampling:
+     1. kv_stride: downsampling factor on Key and Values only.
+     2. query_strides: horizontal & vertical strides on Query only.
+
+    This is an optimized version.
+    1. Projections in Attention is explicit written out as 1x1 Conv2D.
+    2. Additional reshapes are introduced to bring a up to 3x speed up.
+    """
+    fused_attn: torch.jit.Final[bool]
+
+    def __init__(
+            self,
+            dim: int,
+            dim_out: Optional[int] = None,
+            num_heads: int = 8,
+            key_dim: Optional[int] = None,
+            value_dim: Optional[int] = None,
+            query_strides: int = 1,
+            kv_stride: int = 1,
+            dw_kernel_size: int = 3,
+            dilation: int = 1,
+            padding: Union[str, int, List[int]] = '',
+            attn_drop: float = 0.,
+            proj_drop: float = 0.,
+            norm_layer: Type[nn.Module] = nn.BatchNorm2d,
+            use_bias: bool = False,
+    ):
+        """Initializer.
+
+        Args:
+          num_heads: Number of attention heads.
+          key_dim: Size of the attention key dimension.
+          value_dim: Size of the attention value dimension.
+          query_strides: Vertical stride size for query only.
+          kv_stride: Key and value stride size.
+          dw_kernel_size: Spatial dimension of the depthwise kernel.
+        """
+        super().__init__()
+        dim_out = dim_out or dim
+        self.num_heads = num_heads
+        self.key_dim = key_dim or dim // num_heads
+        self.value_dim = value_dim or dim // num_heads
+        self.query_strides = to_2tuple(query_strides)
+        self.kv_stride = kv_stride
+        self.has_query_strides = any([s > 1 for s in self.query_strides])
+        self.scale = self.key_dim ** -0.5
+        self.fused_attn = use_fused_attn()
+        self.drop = attn_drop
+
+        self.query = nn.Sequential()
+        if self.has_query_strides:
+            # FIXME dilation
+            if padding == 'same':
+                self.query.add_module('down_pool', create_pool2d(
+                        'avg',
+                        kernel_size=self.query_strides,
+                        padding='same',
+                ))
+            else:
+                # no pad if not 'same' as kern=stride=even
+                self.query.add_module('down_pool', nn.AvgPool2d(kernel_size=query_strides))
+            self.query.add_module('norm', norm_layer(dim))
+        self.query.add_module('proj', create_conv2d(
+            dim,
+            self.num_heads * self.key_dim,
+            kernel_size=1,
+            bias=use_bias,
+        ))
+
+        self.key = nn.Sequential()
+        if kv_stride > 1:
+            self.key.add_module('down_conv', create_conv2d(
+                dim,
+                dim,
+                kernel_size=dw_kernel_size,
+                stride=kv_stride,
+                dilation=dilation,
+                padding=padding,
+                depthwise=True,
+            ))
+            self.key.add_module('norm', norm_layer(dim))
+        self.key.add_module('proj', create_conv2d(
+            dim,
+            self.key_dim,
+            kernel_size=1,
+            padding=padding,
+            bias=use_bias,
+        ))
+
+        self.value = nn.Sequential()
+        if kv_stride > 1:
+            self.value.add_module('down_conv', create_conv2d(
+                dim,
+                dim,
+                kernel_size=dw_kernel_size,
+                stride=kv_stride,
+                dilation=dilation,
+                padding=padding,
+                depthwise=True,
+            ))
+            self.value.add_module('norm', norm_layer(dim))
+        self.value.add_module('proj', create_conv2d(
+            dim,
+            self.value_dim,
+            kernel_size=1,
+            bias=use_bias,
+        ))
+
+        self.attn_drop = nn.Dropout(attn_drop)
+
+        self.output = nn.Sequential()
+        if self.has_query_strides:
+            self.output.add_module('upsample', nn.Upsample(scale_factor=self.query_strides, mode='bilinear', align_corners=False))
+        self.output.add_module('proj', create_conv2d(
+            self.value_dim * self.num_heads,
+            dim_out,
+            kernel_size=1,
+            bias=use_bias,
+        ))
+        self.output.add_module('drop',  nn.Dropout(proj_drop))
+
+        self.einsum = False
+
+    def init_weights(self):
+        # using xavier appeared to improve stability for mobilenetv4 hybrid w/ this layer
+        nn.init.xavier_uniform_(self.query.proj.weight)
+        nn.init.xavier_uniform_(self.key.proj.weight)
+        nn.init.xavier_uniform_(self.value.proj.weight)
+        if self.kv_stride > 1:
+            nn.init.xavier_uniform_(self.key.down_conv.weight)
+            nn.init.xavier_uniform_(self.value.down_conv.weight)
+        nn.init.xavier_uniform_(self.output.proj.weight)
+
+    def _reshape_input(self, t: torch.Tensor):
+        """Reshapes a tensor to three dimensions, keeping the batch and channels."""
+        s = t.shape
+        t = t.reshape(s[0], s[1], -1).transpose(1, 2)
+        if self.einsum:
+            return t
+        else:
+            return t.unsqueeze(1).contiguous()
+
+    def _reshape_projected_query(self, t: torch.Tensor, num_heads: int, key_dim: int):
+        """Reshapes projected query: [b, n, n, h x k] -> [b, n x n, h, k]."""
+        s = t.shape
+        t = t.reshape(s[0], num_heads, key_dim, -1)
+        if self.einsum:
+            return t.permute(0, 3, 1, 2).contiguous()
+        else:
+            return t.transpose(-1, -2).contiguous()
+
+    def _reshape_output(self, t: torch.Tensor, num_heads: int, h_px: int, w_px: int):
+        """Reshape output:[b, n x n x h, k] -> [b, n, n, hk]."""
+        s = t.shape
+        feat_dim = s[-1] * num_heads
+        if not self.einsum:
+            t = t.transpose(1, 2)
+        return t.reshape(s[0], h_px, w_px, feat_dim).permute(0, 3, 1, 2).contiguous()
+
+    def forward(self, x, attn_mask: Optional[torch.Tensor] = None):
+        """Run layer computation."""
+        B, C, H, W = s = x.shape
+
+        q = self.query(x)
+        # desired q shape: [b, h, k, n x n] - [b, l, h, k]
+        q = self._reshape_projected_query(q, self.num_heads, self.key_dim)
+
+        k = self.key(x)
+        # output shape of k: [b, k, p], p = m x m
+        k = self._reshape_input(k)
+
+        v = self.value(x)
+        # output shape of v: [ b, p, k], p = m x m
+        v = self._reshape_input(v)
+
+        # desired q shape: [b, n x n, h, k]
+        # desired k shape: [b, m x m, k]
+        # desired logits shape: [b, n x n, h, m x m]
+        if self.einsum:
+            attn = torch.einsum('blhk,bpk->blhp', q, k) * self.scale
+            if attn_mask is not None:
+                # NOTE: assumes mask is float and in correct shape
+                attn = attn + attn_mask
+            attn = attn.softmax(dim=-1)
+            attn = self.attn_drop(attn)
+            o = torch.einsum('blhp,bpk->blhk', attn, v)
+        else:
+            if self.fused_attn:
+                o = F.scaled_dot_product_attention(
+                    q, k, v,
+                    attn_mask=attn_mask,
+                    dropout_p=self.attn_drop.p if self.training else 0.
+                )
+            else:
+                q = q * self.scale
+                attn = q @ k.transpose(-1, -2)
+                if attn_mask is not None:
+                    # NOTE: assumes mask is float and in correct shape
+                    attn = attn + attn_mask
+                attn = attn.softmax(dim=-1)
+                attn = self.attn_drop(attn)
+                o = attn @ v
+
+        # reshape o into [b, hk, n, n,]
+        o = self._reshape_output(o, self.num_heads, H // self.query_strides[0], W // self.query_strides[1])
+        x = self.output(o)
+        return x
+
+
+class Attention2d(nn.Module):
+    fused_attn: torch.jit.Final[bool]
+
+    """ multi-head attention for 2D NCHW tensors"""
+    def __init__(
+            self,
+            dim: int,
+            dim_out: Optional[int] = None,
+            num_heads: int = 32,
+            bias: bool = True,
+            expand_first: bool = False,
+            head_first: bool = False,
+            attn_drop: float = 0.,
+            proj_drop: float = 0.
+    ):
+        super().__init__()
+        dim_out = dim_out or dim
+        dim_attn = dim_out if expand_first else dim
+        self.num_heads = num_heads
+        self.dim_head = dim_attn // num_heads
+        self.head_first = head_first
+        self.fused_attn = use_fused_attn()
+
+        self.qkv = nn.Conv2d(dim, dim_attn * 3, 1, bias=bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Conv2d(dim_attn, dim_out, 1, bias=bias)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(self, x, attn_mask: Optional[torch.Tensor] = None):
+        B, C, H, W = x.shape
+
+        if self.head_first:
+            q, k, v = self.qkv(x).view(B, self.num_heads, self.dim_head * 3, -1).chunk(3, dim=2)
+        else:
+            q, k, v = self.qkv(x).reshape(B, 3, self.num_heads, self.dim_head, -1).unbind(1)
+
+        if self.fused_attn:
+            x = torch.nn.functional.scaled_dot_product_attention(
+                q.transpose(-1, -2).contiguous(),
+                k.transpose(-1, -2).contiguous(),
+                v.transpose(-1, -2).contiguous(),
+                attn_mask=attn_mask,
+                dropout_p=self.attn_drop.p if self.training else 0.,
+            ).transpose(-1, -2).reshape(B, -1, H, W)
+        else:
+            q = q.transpose(-1, -2)
+            v = v.transpose(-1, -2)
+            attn = q @ k * q.size(-1) ** -0.5
+            if attn_mask is not None:
+                # NOTE: assumes mask is float and in correct shape
+                attn = attn + attn_mask
+            attn = attn.softmax(dim=-1)
+            attn = self.attn_drop(attn)
+            x = (attn @ v).transpose(-1, -2).reshape(B, -1, H, W)
+
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x

janus/lib/python3.10/site-packages/timm/layers/attention_pool.py

@@ -0,0 +1,105 @@
+from typing import Optional
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .config import use_fused_attn
+from .mlp import Mlp
+from .weight_init import trunc_normal_tf_
+
+
+class AttentionPoolLatent(nn.Module):
+    """ Attention pooling w/ latent query
+    """
+    fused_attn: torch.jit.Final[bool]
+
+    def __init__(
+            self,
+            in_features: int,
+            out_features: int = None,
+            embed_dim: int = None,
+            num_heads: int = 8,
+            feat_size: Optional[int] = None,
+            mlp_ratio: float = 4.0,
+            qkv_bias: bool = True,
+            qk_norm: bool = False,
+            latent_len: int = 1,
+            latent_dim: int = None,
+            pos_embed: str = '',
+            pool_type: str = 'token',
+            norm_layer: Optional[nn.Module] = None,
+            drop: float = 0.0,
+    ):
+        super().__init__()
+        embed_dim = embed_dim or in_features
+        out_features = out_features or in_features
+        assert embed_dim % num_heads == 0
+        self.num_heads = num_heads
+        self.head_dim = embed_dim // num_heads
+        self.feat_size = feat_size
+        self.scale = self.head_dim ** -0.5
+        self.pool = pool_type
+        self.fused_attn = use_fused_attn()
+
+        if pos_embed == 'abs':
+            assert feat_size is not None
+            self.pos_embed = nn.Parameter(torch.zeros(feat_size, in_features))
+        else:
+            self.pos_embed = None
+
+        self.latent_dim = latent_dim or embed_dim
+        self.latent_len = latent_len
+        self.latent = nn.Parameter(torch.zeros(1, self.latent_len, embed_dim))
+
+        self.q = nn.Linear(embed_dim, embed_dim, bias=qkv_bias)
+        self.kv = nn.Linear(embed_dim, embed_dim * 2, bias=qkv_bias)
+        self.q_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
+        self.k_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
+        self.proj = nn.Linear(embed_dim, embed_dim)
+        self.proj_drop = nn.Dropout(drop)
+
+        self.norm = norm_layer(out_features) if norm_layer is not None else nn.Identity()
+        self.mlp = Mlp(embed_dim, int(embed_dim * mlp_ratio))
+
+        self.init_weights()
+
+    def init_weights(self):
+        if self.pos_embed is not None:
+            trunc_normal_tf_(self.pos_embed, std=self.pos_embed.shape[1] ** -0.5)
+        trunc_normal_tf_(self.latent, std=self.latent_dim ** -0.5)
+
+    def forward(self, x):
+        B, N, C = x.shape
+
+        if self.pos_embed is not None:
+            # FIXME interpolate
+            x = x + self.pos_embed.unsqueeze(0).to(x.dtype)
+
+        q_latent = self.latent.expand(B, -1, -1)
+        q = self.q(q_latent).reshape(B, self.latent_len, self.num_heads, self.head_dim).transpose(1, 2)
+
+        kv = self.kv(x).reshape(B, N, 2, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
+        k, v = kv.unbind(0)
+
+        q, k = self.q_norm(q), self.k_norm(k)
+
+        if self.fused_attn:
+            x = F.scaled_dot_product_attention(q, k, v)
+        else:
+            q = q * self.scale
+            attn = q @ k.transpose(-2, -1)
+            attn = attn.softmax(dim=-1)
+            x = attn @ v
+        x = x.transpose(1, 2).reshape(B, self.latent_len, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+
+        x = x + self.mlp(self.norm(x))
+
+        # optional pool if latent seq_len > 1 and pooled output is desired
+        if self.pool == 'token':
+            x = x[:, 0]
+        elif self.pool == 'avg':
+            x = x.mean(1)
+        return x

janus/lib/python3.10/site-packages/timm/layers/bottleneck_attn.py

@@ -0,0 +1,157 @@
+""" Bottleneck Self Attention (Bottleneck Transformers)
+
+Paper: `Bottleneck Transformers for Visual Recognition` - https://arxiv.org/abs/2101.11605
+
+@misc{2101.11605,
+Author = {Aravind Srinivas and Tsung-Yi Lin and Niki Parmar and Jonathon Shlens and Pieter Abbeel and Ashish Vaswani},
+Title = {Bottleneck Transformers for Visual Recognition},
+Year = {2021},
+}
+
+Based on ref gist at: https://gist.github.com/aravindsrinivas/56359b79f0ce4449bcb04ab4b56a57a2
+
+This impl is a WIP but given that it is based on the ref gist likely not too far off.
+
+Hacked together by / Copyright 2021 Ross Wightman
+"""
+from typing import List
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .helpers import to_2tuple, make_divisible
+from .weight_init import trunc_normal_
+from .trace_utils import _assert
+
+
+def rel_logits_1d(q, rel_k, permute_mask: List[int]):
+    """ Compute relative logits along one dimension
+
+    As per: https://gist.github.com/aravindsrinivas/56359b79f0ce4449bcb04ab4b56a57a2
+    Originally from: `Attention Augmented Convolutional Networks` - https://arxiv.org/abs/1904.09925
+
+    Args:
+        q: (batch, heads, height, width, dim)
+        rel_k: (2 * width - 1, dim)
+        permute_mask: permute output dim according to this
+    """
+    B, H, W, dim = q.shape
+    x = (q @ rel_k.transpose(-1, -2))
+    x = x.reshape(-1, W, 2 * W -1)
+
+    # pad to shift from relative to absolute indexing
+    x_pad = F.pad(x, [0, 1]).flatten(1)
+    x_pad = F.pad(x_pad, [0, W - 1])
+
+    # reshape and slice out the padded elements
+    x_pad = x_pad.reshape(-1, W + 1, 2 * W - 1)
+    x = x_pad[:, :W, W - 1:]
+
+    # reshape and tile
+    x = x.reshape(B, H, 1, W, W).expand(-1, -1, H, -1, -1)
+    return x.permute(permute_mask)
+
+
+class PosEmbedRel(nn.Module):
+    """ Relative Position Embedding
+    As per: https://gist.github.com/aravindsrinivas/56359b79f0ce4449bcb04ab4b56a57a2
+    Originally from: `Attention Augmented Convolutional Networks` - https://arxiv.org/abs/1904.09925
+    """
+    def __init__(self, feat_size, dim_head, scale):
+        super().__init__()
+        self.height, self.width = to_2tuple(feat_size)
+        self.dim_head = dim_head
+        self.height_rel = nn.Parameter(torch.randn(self.height * 2 - 1, dim_head) * scale)
+        self.width_rel = nn.Parameter(torch.randn(self.width * 2 - 1, dim_head) * scale)
+
+    def forward(self, q):
+        B, HW, _ = q.shape
+
+        # relative logits in width dimension.
+        q = q.reshape(B, self.height, self.width, -1)
+        rel_logits_w = rel_logits_1d(q, self.width_rel, permute_mask=(0, 1, 3, 2, 4))
+
+        # relative logits in height dimension.
+        q = q.transpose(1, 2)
+        rel_logits_h = rel_logits_1d(q, self.height_rel, permute_mask=(0, 3, 1, 4, 2))
+
+        rel_logits = rel_logits_h + rel_logits_w
+        rel_logits = rel_logits.reshape(B, HW, HW)
+        return rel_logits
+
+
+class BottleneckAttn(nn.Module):
+    """ Bottleneck Attention
+    Paper: `Bottleneck Transformers for Visual Recognition` - https://arxiv.org/abs/2101.11605
+
+    The internal dimensions of the attention module are controlled by the interaction of several arguments.
+      * the output dimension of the module is specified by dim_out, which falls back to input dim if not set
+      * the value (v) dimension is set to dim_out // num_heads, the v projection determines the output dim
+      * the query and key (qk) dimensions are determined by
+        * num_heads * dim_head if dim_head is not None
+        * num_heads * (dim_out * attn_ratio // num_heads) if dim_head is None
+      * as seen above, attn_ratio determines the ratio of q and k relative to the output if dim_head not used
+
+    Args:
+        dim (int): input dimension to the module
+        dim_out (int): output dimension of the module, same as dim if not set
+        stride (int): output stride of the module, avg pool used if stride == 2 (default: 1).
+        num_heads (int): parallel attention heads (default: 4)
+        dim_head (int): dimension of query and key heads, calculated from dim_out * attn_ratio // num_heads if not set
+        qk_ratio (float): ratio of q and k dimensions to output dimension when dim_head not set. (default: 1.0)
+        qkv_bias (bool): add bias to q, k, and v projections
+        scale_pos_embed (bool): scale the position embedding as well as Q @ K
+    """
+    def __init__(
+            self, dim, dim_out=None, feat_size=None, stride=1, num_heads=4, dim_head=None,
+            qk_ratio=1.0, qkv_bias=False, scale_pos_embed=False):
+        super().__init__()
+        assert feat_size is not None, 'A concrete feature size matching expected input (H, W) is required'
+        dim_out = dim_out or dim
+        assert dim_out % num_heads == 0
+        self.num_heads = num_heads
+        self.dim_head_qk = dim_head or make_divisible(dim_out * qk_ratio, divisor=8) // num_heads
+        self.dim_head_v = dim_out // self.num_heads
+        self.dim_out_qk = num_heads * self.dim_head_qk
+        self.dim_out_v = num_heads * self.dim_head_v
+        self.scale = self.dim_head_qk ** -0.5
+        self.scale_pos_embed = scale_pos_embed
+
+        self.qkv = nn.Conv2d(dim, self.dim_out_qk * 2 + self.dim_out_v, 1, bias=qkv_bias)
+
+        # NOTE I'm only supporting relative pos embedding for now
+        self.pos_embed = PosEmbedRel(feat_size, dim_head=self.dim_head_qk, scale=self.scale)
+
+        self.pool = nn.AvgPool2d(2, 2) if stride == 2 else nn.Identity()
+
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        trunc_normal_(self.qkv.weight, std=self.qkv.weight.shape[1] ** -0.5)  # fan-in
+        trunc_normal_(self.pos_embed.height_rel, std=self.scale)
+        trunc_normal_(self.pos_embed.width_rel, std=self.scale)
+
+    def forward(self, x):
+        B, C, H, W = x.shape
+        _assert(H == self.pos_embed.height, '')
+        _assert(W == self.pos_embed.width, '')
+
+        x = self.qkv(x)  # B, (2 * dim_head_qk + dim_head_v) * num_heads, H, W
+
+        # NOTE head vs channel split ordering in qkv projection was decided before I allowed qk to differ from v
+        # So, this is more verbose than if heads were before qkv splits, but throughput is not impacted.
+        q, k, v = torch.split(x, [self.dim_out_qk, self.dim_out_qk, self.dim_out_v], dim=1)
+        q = q.reshape(B * self.num_heads, self.dim_head_qk, -1).transpose(-1, -2)
+        k = k.reshape(B * self.num_heads, self.dim_head_qk, -1)  # no transpose, for q @ k
+        v = v.reshape(B * self.num_heads, self.dim_head_v, -1).transpose(-1, -2)
+
+        if self.scale_pos_embed:
+            attn = (q @ k + self.pos_embed(q)) * self.scale  # B * num_heads, H * W, H * W
+        else:
+            attn = (q @ k) * self.scale + self.pos_embed(q)
+        attn = attn.softmax(dim=-1)
+
+        out = (attn @ v).transpose(-1, -2).reshape(B, self.dim_out_v, H, W)  # B, dim_out, H, W
+        out = self.pool(out)
+        return out

janus/lib/python3.10/site-packages/timm/layers/classifier.py

@@ -0,0 +1,283 @@
+""" Classifier head and layer factory
+
+Hacked together by / Copyright 2020 Ross Wightman
+"""
+from collections import OrderedDict
+from functools import partial
+from typing import Optional, Union, Callable
+
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+
+from .adaptive_avgmax_pool import SelectAdaptivePool2d
+from .create_act import get_act_layer
+from .create_norm import get_norm_layer
+
+
+def _create_pool(
+        num_features: int,
+        num_classes: int,
+        pool_type: str = 'avg',
+        use_conv: bool = False,
+        input_fmt: Optional[str] = None,
+):
+    flatten_in_pool = not use_conv  # flatten when we use a Linear layer after pooling
+    if not pool_type:
+        flatten_in_pool = False  # disable flattening if pooling is pass-through (no pooling)
+    global_pool = SelectAdaptivePool2d(
+        pool_type=pool_type,
+        flatten=flatten_in_pool,
+        input_fmt=input_fmt,
+    )
+    num_pooled_features = num_features * global_pool.feat_mult()
+    return global_pool, num_pooled_features
+
+
+def _create_fc(num_features, num_classes, use_conv=False):
+    if num_classes <= 0:
+        fc = nn.Identity()  # pass-through (no classifier)
+    elif use_conv:
+        fc = nn.Conv2d(num_features, num_classes, 1, bias=True)
+    else:
+        fc = nn.Linear(num_features, num_classes, bias=True)
+    return fc
+
+
+def create_classifier(
+        num_features: int,
+        num_classes: int,
+        pool_type: str = 'avg',
+        use_conv: bool = False,
+        input_fmt: str = 'NCHW',
+        drop_rate: Optional[float] = None,
+):
+    global_pool, num_pooled_features = _create_pool(
+        num_features,
+        num_classes,
+        pool_type,
+        use_conv=use_conv,
+        input_fmt=input_fmt,
+    )
+    fc = _create_fc(
+        num_pooled_features,
+        num_classes,
+        use_conv=use_conv,
+    )
+    if drop_rate is not None:
+        dropout = nn.Dropout(drop_rate)
+        return global_pool, dropout, fc
+    return global_pool, fc
+
+
+class ClassifierHead(nn.Module):
+    """Classifier head w/ configurable global pooling and dropout."""
+
+    def __init__(
+            self,
+            in_features: int,
+            num_classes: int,
+            pool_type: str = 'avg',
+            drop_rate: float = 0.,
+            use_conv: bool = False,
+            input_fmt: str = 'NCHW',
+    ):
+        """
+        Args:
+            in_features: The number of input features.
+            num_classes:  The number of classes for the final classifier layer (output).
+            pool_type: Global pooling type, pooling disabled if empty string ('').
+            drop_rate: Pre-classifier dropout rate.
+        """
+        super(ClassifierHead, self).__init__()
+        self.in_features = in_features
+        self.use_conv = use_conv
+        self.input_fmt = input_fmt
+
+        global_pool, fc = create_classifier(
+            in_features,
+            num_classes,
+            pool_type,
+            use_conv=use_conv,
+            input_fmt=input_fmt,
+        )
+        self.global_pool = global_pool
+        self.drop = nn.Dropout(drop_rate)
+        self.fc = fc
+        self.flatten = nn.Flatten(1) if use_conv and pool_type else nn.Identity()
+
+    def reset(self, num_classes: int, pool_type: Optional[str] = None):
+        if pool_type is not None and pool_type != self.global_pool.pool_type:
+            self.global_pool, self.fc = create_classifier(
+                self.in_features,
+                num_classes,
+                pool_type=pool_type,
+                use_conv=self.use_conv,
+                input_fmt=self.input_fmt,
+            )
+            self.flatten = nn.Flatten(1) if self.use_conv and pool_type else nn.Identity()
+        else:
+            num_pooled_features = self.in_features * self.global_pool.feat_mult()
+            self.fc = _create_fc(
+                num_pooled_features,
+                num_classes,
+                use_conv=self.use_conv,
+            )
+
+    def forward(self, x, pre_logits: bool = False):
+        x = self.global_pool(x)
+        x = self.drop(x)
+        if pre_logits:
+            return self.flatten(x)
+        x = self.fc(x)
+        return self.flatten(x)
+
+
+class NormMlpClassifierHead(nn.Module):
+    """ A Pool -> Norm -> Mlp Classifier Head for '2D' NCHW tensors
+    """
+    def __init__(
+            self,
+            in_features: int,
+            num_classes: int,
+            hidden_size: Optional[int] = None,
+            pool_type: str = 'avg',
+            drop_rate: float = 0.,
+            norm_layer: Union[str, Callable] = 'layernorm2d',
+            act_layer: Union[str, Callable] = 'tanh',
+    ):
+        """
+        Args:
+            in_features: The number of input features.
+            num_classes:  The number of classes for the final classifier layer (output).
+            hidden_size: The hidden size of the MLP (pre-logits FC layer) if not None.
+            pool_type: Global pooling type, pooling disabled if empty string ('').
+            drop_rate: Pre-classifier dropout rate.
+            norm_layer: Normalization layer type.
+            act_layer: MLP activation layer type (only used if hidden_size is not None).
+        """
+        super().__init__()
+        self.in_features = in_features
+        self.hidden_size = hidden_size
+        self.num_features = in_features
+        self.use_conv = not pool_type
+        norm_layer = get_norm_layer(norm_layer)
+        act_layer = get_act_layer(act_layer)
+        linear_layer = partial(nn.Conv2d, kernel_size=1) if self.use_conv else nn.Linear
+
+        self.global_pool = SelectAdaptivePool2d(pool_type=pool_type)
+        self.norm = norm_layer(in_features)
+        self.flatten = nn.Flatten(1) if pool_type else nn.Identity()
+        if hidden_size:
+            self.pre_logits = nn.Sequential(OrderedDict([
+                ('fc', linear_layer(in_features, hidden_size)),
+                ('act', act_layer()),
+            ]))
+            self.num_features = hidden_size
+        else:
+            self.pre_logits = nn.Identity()
+        self.drop = nn.Dropout(drop_rate)
+        self.fc = linear_layer(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
+
+    def reset(self, num_classes: int, pool_type: Optional[str] = None):
+        if pool_type is not None:
+            self.global_pool = SelectAdaptivePool2d(pool_type=pool_type)
+            self.flatten = nn.Flatten(1) if pool_type else nn.Identity()
+        self.use_conv = self.global_pool.is_identity()
+        linear_layer = partial(nn.Conv2d, kernel_size=1) if self.use_conv else nn.Linear
+        if self.hidden_size:
+            if ((isinstance(self.pre_logits.fc, nn.Conv2d) and not self.use_conv) or
+                    (isinstance(self.pre_logits.fc, nn.Linear) and self.use_conv)):
+                with torch.no_grad():
+                    new_fc = linear_layer(self.in_features, self.hidden_size)
+                    new_fc.weight.copy_(self.pre_logits.fc.weight.reshape(new_fc.weight.shape))
+                    new_fc.bias.copy_(self.pre_logits.fc.bias)
+                    self.pre_logits.fc = new_fc
+        self.fc = linear_layer(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
+
+    def forward(self, x, pre_logits: bool = False):
+        x = self.global_pool(x)
+        x = self.norm(x)
+        x = self.flatten(x)
+        x = self.pre_logits(x)
+        x = self.drop(x)
+        if pre_logits:
+            return x
+        x = self.fc(x)
+        return x
+
+
+class ClNormMlpClassifierHead(nn.Module):
+    """ A Pool -> Norm -> Mlp Classifier Head for n-D NxxC tensors
+    """
+    def __init__(
+            self,
+            in_features: int,
+            num_classes: int,
+            hidden_size: Optional[int] = None,
+            pool_type: str = 'avg',
+            drop_rate: float = 0.,
+            norm_layer: Union[str, Callable] = 'layernorm',
+            act_layer: Union[str, Callable] = 'gelu',
+            input_fmt: str = 'NHWC',
+    ):
+        """
+        Args:
+            in_features: The number of input features.
+            num_classes:  The number of classes for the final classifier layer (output).
+            hidden_size: The hidden size of the MLP (pre-logits FC layer) if not None.
+            pool_type: Global pooling type, pooling disabled if empty string ('').
+            drop_rate: Pre-classifier dropout rate.
+            norm_layer: Normalization layer type.
+            act_layer: MLP activation layer type (only used if hidden_size is not None).
+        """
+        super().__init__()
+        self.in_features = in_features
+        self.hidden_size = hidden_size
+        self.num_features = in_features
+        assert pool_type in ('', 'avg', 'max', 'avgmax')
+        self.pool_type = pool_type
+        assert input_fmt in ('NHWC', 'NLC')
+        self.pool_dim = 1 if input_fmt == 'NLC' else (1, 2)
+        norm_layer = get_norm_layer(norm_layer)
+        act_layer = get_act_layer(act_layer)
+
+        self.norm = norm_layer(in_features)
+        if hidden_size:
+            self.pre_logits = nn.Sequential(OrderedDict([
+                ('fc', nn.Linear(in_features, hidden_size)),
+                ('act', act_layer()),
+            ]))
+            self.num_features = hidden_size
+        else:
+            self.pre_logits = nn.Identity()
+        self.drop = nn.Dropout(drop_rate)
+        self.fc = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
+
+    def reset(self, num_classes: int, pool_type: Optional[str] = None, reset_other: bool = False):
+        if pool_type is not None:
+            self.pool_type = pool_type
+        if reset_other:
+            self.pre_logits = nn.Identity()
+            self.norm = nn.Identity()
+        self.fc = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
+
+    def _global_pool(self, x):
+        if self.pool_type:
+            if self.pool_type == 'avg':
+                x = x.mean(dim=self.pool_dim)
+            elif self.pool_type == 'max':
+                x = x.amax(dim=self.pool_dim)
+            elif self.pool_type == 'avgmax':
+                x = 0.5 * (x.amax(dim=self.pool_dim) + x.mean(dim=self.pool_dim))
+        return x
+
+    def forward(self, x, pre_logits: bool = False):
+        x = self._global_pool(x)
+        x = self.norm(x)
+        x = self.pre_logits(x)
+        x = self.drop(x)
+        if pre_logits:
+            return x
+        x = self.fc(x)
+        return x

janus/lib/python3.10/site-packages/timm/layers/conv2d_same.py

@@ -0,0 +1,110 @@
+""" Conv2d w/ Same Padding
+
+Hacked together by / Copyright 2020 Ross Wightman
+"""
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from typing import Tuple, Optional
+
+from .config import is_exportable, is_scriptable
+from .padding import pad_same, pad_same_arg, get_padding_value
+
+
+_USE_EXPORT_CONV = False
+
+
+def conv2d_same(
+        x,
+        weight: torch.Tensor,
+        bias: Optional[torch.Tensor] = None,
+        stride: Tuple[int, int] = (1, 1),
+        padding: Tuple[int, int] = (0, 0),
+        dilation: Tuple[int, int] = (1, 1),
+        groups: int = 1,
+):
+    x = pad_same(x, weight.shape[-2:], stride, dilation)
+    return F.conv2d(x, weight, bias, stride, (0, 0), dilation, groups)
+
+
+class Conv2dSame(nn.Conv2d):
+    """ Tensorflow like 'SAME' convolution wrapper for 2D convolutions
+    """
+
+    def __init__(
+            self,
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=1,
+            padding=0,
+            dilation=1,
+            groups=1,
+            bias=True,
+    ):
+        super(Conv2dSame, self).__init__(
+            in_channels, out_channels, kernel_size,
+            stride, 0, dilation, groups, bias,
+        )
+
+    def forward(self, x):
+        return conv2d_same(
+            x, self.weight, self.bias,
+            self.stride, self.padding, self.dilation, self.groups,
+        )
+
+
+class Conv2dSameExport(nn.Conv2d):
+    """ ONNX export friendly Tensorflow like 'SAME' convolution wrapper for 2D convolutions
+
+    NOTE: This does not currently work with torch.jit.script
+    """
+
+    # pylint: disable=unused-argument
+    def __init__(
+            self,
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=1,
+            padding=0,
+            dilation=1,
+            groups=1,
+            bias=True,
+    ):
+        super(Conv2dSameExport, self).__init__(
+            in_channels, out_channels, kernel_size,
+            stride, 0, dilation, groups, bias,
+        )
+        self.pad = None
+        self.pad_input_size = (0, 0)
+
+    def forward(self, x):
+        input_size = x.size()[-2:]
+        if self.pad is None:
+            pad_arg = pad_same_arg(input_size, self.weight.size()[-2:], self.stride, self.dilation)
+            self.pad = nn.ZeroPad2d(pad_arg)
+            self.pad_input_size = input_size
+
+        x = self.pad(x)
+        return F.conv2d(
+            x, self.weight, self.bias,
+            self.stride, self.padding, self.dilation, self.groups,
+        )
+
+
+def create_conv2d_pad(in_chs, out_chs, kernel_size, **kwargs):
+    padding = kwargs.pop('padding', '')
+    kwargs.setdefault('bias', False)
+    padding, is_dynamic = get_padding_value(padding, kernel_size, **kwargs)
+    if is_dynamic:
+        if _USE_EXPORT_CONV and is_exportable():
+            # older PyTorch ver needed this to export same padding reasonably
+            assert not is_scriptable()  # Conv2DSameExport does not work with jit
+            return Conv2dSameExport(in_chs, out_chs, kernel_size, **kwargs)
+        else:
+            return Conv2dSame(in_chs, out_chs, kernel_size, **kwargs)
+    else:
+        return nn.Conv2d(in_chs, out_chs, kernel_size, padding=padding, **kwargs)
+
+

janus/lib/python3.10/site-packages/timm/layers/create_conv2d.py

@@ -0,0 +1,36 @@
+""" Create Conv2d Factory Method
+
+Hacked together by / Copyright 2020 Ross Wightman
+"""
+
+from .mixed_conv2d import MixedConv2d
+from .cond_conv2d import CondConv2d
+from .conv2d_same import create_conv2d_pad
+
+
+def create_conv2d(in_channels, out_channels, kernel_size, **kwargs):
+    """ Select a 2d convolution implementation based on arguments
+    Creates and returns one of torch.nn.Conv2d, Conv2dSame, MixedConv2d, or CondConv2d.
+
+    Used extensively by EfficientNet, MobileNetv3 and related networks.
+    """
+    if isinstance(kernel_size, list):
+        assert 'num_experts' not in kwargs  # MixNet + CondConv combo not supported currently
+        if 'groups' in kwargs:
+            groups = kwargs.pop('groups')
+            if groups == in_channels:
+                kwargs['depthwise'] = True
+            else:
+                assert groups == 1
+        # We're going to use only lists for defining the MixedConv2d kernel groups,
+        # ints, tuples, other iterables will continue to pass to normal conv and specify h, w.
+        m = MixedConv2d(in_channels, out_channels, kernel_size, **kwargs)
+    else:
+        depthwise = kwargs.pop('depthwise', False)
+        # for DW out_channels must be multiple of in_channels as must have out_channels % groups == 0
+        groups = in_channels if depthwise else kwargs.pop('groups', 1)
+        if 'num_experts' in kwargs and kwargs['num_experts'] > 0:
+            m = CondConv2d(in_channels, out_channels, kernel_size, groups=groups, **kwargs)
+        else:
+            m = create_conv2d_pad(in_channels, out_channels, kernel_size, groups=groups, **kwargs)
+    return m

janus/lib/python3.10/site-packages/timm/layers/create_norm.py

@@ -0,0 +1,60 @@
+""" Norm Layer Factory
+
+Create norm modules by string (to mirror create_act and creat_norm-act fns)
+
+Copyright 2022 Ross Wightman
+"""
+import functools
+import types
+from typing import Type
+
+import torch.nn as nn
+
+from .norm import GroupNorm, GroupNorm1, LayerNorm, LayerNorm2d, RmsNorm, RmsNorm2d, SimpleNorm, SimpleNorm2d
+from torchvision.ops.misc import FrozenBatchNorm2d
+
+_NORM_MAP = dict(
+    batchnorm=nn.BatchNorm2d,
+    batchnorm2d=nn.BatchNorm2d,
+    batchnorm1d=nn.BatchNorm1d,
+    groupnorm=GroupNorm,
+    groupnorm1=GroupNorm1,
+    layernorm=LayerNorm,
+    layernorm2d=LayerNorm2d,
+    rmsnorm=RmsNorm,
+    rmsnorm2d=RmsNorm2d,
+    simplenorm=SimpleNorm,
+    simplenorm2d=SimpleNorm2d,
+    frozenbatchnorm2d=FrozenBatchNorm2d,
+)
+_NORM_TYPES = {m for n, m in _NORM_MAP.items()}
+
+
+def create_norm_layer(layer_name, num_features, **kwargs):
+    layer = get_norm_layer(layer_name)
+    layer_instance = layer(num_features, **kwargs)
+    return layer_instance
+
+
+def get_norm_layer(norm_layer):
+    if norm_layer is None:
+        return None
+    assert isinstance(norm_layer, (type, str, types.FunctionType, functools.partial))
+    norm_kwargs = {}
+
+    # unbind partial fn, so args can be rebound later
+    if isinstance(norm_layer, functools.partial):
+        norm_kwargs.update(norm_layer.keywords)
+        norm_layer = norm_layer.func
+
+    if isinstance(norm_layer, str):
+        if not norm_layer:
+            return None
+        layer_name = norm_layer.replace('_', '').lower()
+        norm_layer = _NORM_MAP[layer_name]
+    else:
+        norm_layer = norm_layer
+
+    if norm_kwargs:
+        norm_layer = functools.partial(norm_layer, **norm_kwargs)  # bind/rebind args
+    return norm_layer

janus/lib/python3.10/site-packages/timm/layers/drop.py

@@ -0,0 +1,182 @@
+""" DropBlock, DropPath
+
+PyTorch implementations of DropBlock and DropPath (Stochastic Depth) regularization layers.
+
+Papers:
+DropBlock: A regularization method for convolutional networks (https://arxiv.org/abs/1810.12890)
+
+Deep Networks with Stochastic Depth (https://arxiv.org/abs/1603.09382)
+
+Code:
+DropBlock impl inspired by two Tensorflow impl that I liked:
+ - https://github.com/tensorflow/tpu/blob/master/models/official/resnet/resnet_model.py#L74
+ - https://github.com/clovaai/assembled-cnn/blob/master/nets/blocks.py
+
+Hacked together by / Copyright 2020 Ross Wightman
+"""
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .grid import ndgrid
+
+
+def drop_block_2d(
+        x,
+        drop_prob: float = 0.1,
+        block_size: int = 7,
+        gamma_scale: float = 1.0,
+        with_noise: bool = False,
+        inplace: bool = False,
+        batchwise: bool = False
+):
+    """ DropBlock. See https://arxiv.org/pdf/1810.12890.pdf
+
+    DropBlock with an experimental gaussian noise option. This layer has been tested on a few training
+    runs with success, but needs further validation and possibly optimization for lower runtime impact.
+    """
+    B, C, H, W = x.shape
+    total_size = W * H
+    clipped_block_size = min(block_size, min(W, H))
+    # seed_drop_rate, the gamma parameter
+    gamma = gamma_scale * drop_prob * total_size / clipped_block_size ** 2 / (
+            (W - block_size + 1) * (H - block_size + 1))
+
+    # Forces the block to be inside the feature map.
+    w_i, h_i = ndgrid(torch.arange(W, device=x.device), torch.arange(H, device=x.device))
+    valid_block = ((w_i >= clipped_block_size // 2) & (w_i < W - (clipped_block_size - 1) // 2)) & \
+                  ((h_i >= clipped_block_size // 2) & (h_i < H - (clipped_block_size - 1) // 2))
+    valid_block = torch.reshape(valid_block, (1, 1, H, W)).to(dtype=x.dtype)
+
+    if batchwise:
+        # one mask for whole batch, quite a bit faster
+        uniform_noise = torch.rand((1, C, H, W), dtype=x.dtype, device=x.device)
+    else:
+        uniform_noise = torch.rand_like(x)
+    block_mask = ((2 - gamma - valid_block + uniform_noise) >= 1).to(dtype=x.dtype)
+    block_mask = -F.max_pool2d(
+        -block_mask,
+        kernel_size=clipped_block_size,  # block_size,
+        stride=1,
+        padding=clipped_block_size // 2)
+
+    if with_noise:
+        normal_noise = torch.randn((1, C, H, W), dtype=x.dtype, device=x.device) if batchwise else torch.randn_like(x)
+        if inplace:
+            x.mul_(block_mask).add_(normal_noise * (1 - block_mask))
+        else:
+            x = x * block_mask + normal_noise * (1 - block_mask)
+    else:
+        normalize_scale = (block_mask.numel() / block_mask.to(dtype=torch.float32).sum().add(1e-7)).to(x.dtype)
+        if inplace:
+            x.mul_(block_mask * normalize_scale)
+        else:
+            x = x * block_mask * normalize_scale
+    return x
+
+
+def drop_block_fast_2d(
+        x: torch.Tensor,
+        drop_prob: float = 0.1,
+        block_size: int = 7,
+        gamma_scale: float = 1.0,
+        with_noise: bool = False,
+        inplace: bool = False,
+):
+    """ DropBlock. See https://arxiv.org/pdf/1810.12890.pdf
+
+    DropBlock with an experimental gaussian noise option. Simplied from above without concern for valid
+    block mask at edges.
+    """
+    B, C, H, W = x.shape
+    total_size = W * H
+    clipped_block_size = min(block_size, min(W, H))
+    gamma = gamma_scale * drop_prob * total_size / clipped_block_size ** 2 / (
+            (W - block_size + 1) * (H - block_size + 1))
+
+    block_mask = torch.empty_like(x).bernoulli_(gamma)
+    block_mask = F.max_pool2d(
+        block_mask.to(x.dtype), kernel_size=clipped_block_size, stride=1, padding=clipped_block_size // 2)
+
+    if with_noise:
+        normal_noise = torch.empty_like(x).normal_()
+        if inplace:
+            x.mul_(1. - block_mask).add_(normal_noise * block_mask)
+        else:
+            x = x * (1. - block_mask) + normal_noise * block_mask
+    else:
+        block_mask = 1 - block_mask
+        normalize_scale = (block_mask.numel() / block_mask.to(dtype=torch.float32).sum().add(1e-6)).to(dtype=x.dtype)
+        if inplace:
+            x.mul_(block_mask * normalize_scale)
+        else:
+            x = x * block_mask * normalize_scale
+    return x
+
+
+class DropBlock2d(nn.Module):
+    """ DropBlock. See https://arxiv.org/pdf/1810.12890.pdf
+    """
+
+    def __init__(
+            self,
+            drop_prob: float = 0.1,
+            block_size: int = 7,
+            gamma_scale: float = 1.0,
+            with_noise: bool = False,
+            inplace: bool = False,
+            batchwise: bool = False,
+            fast: bool = True):
+        super(DropBlock2d, self).__init__()
+        self.drop_prob = drop_prob
+        self.gamma_scale = gamma_scale
+        self.block_size = block_size
+        self.with_noise = with_noise
+        self.inplace = inplace
+        self.batchwise = batchwise
+        self.fast = fast  # FIXME finish comparisons of fast vs not
+
+    def forward(self, x):
+        if not self.training or not self.drop_prob:
+            return x
+        if self.fast:
+            return drop_block_fast_2d(
+                x, self.drop_prob, self.block_size, self.gamma_scale, self.with_noise, self.inplace)
+        else:
+            return drop_block_2d(
+                x, self.drop_prob, self.block_size, self.gamma_scale, self.with_noise, self.inplace, self.batchwise)
+
+
+def drop_path(x, drop_prob: float = 0., training: bool = False, scale_by_keep: bool = True):
+    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
+
+    This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
+    the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
+    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
+    changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
+    'survival rate' as the argument.
+
+    """
+    if drop_prob == 0. or not training:
+        return x
+    keep_prob = 1 - drop_prob
+    shape = (x.shape[0],) + (1,) * (x.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
+    random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
+    if keep_prob > 0.0 and scale_by_keep:
+        random_tensor.div_(keep_prob)
+    return x * random_tensor
+
+
+class DropPath(nn.Module):
+    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
+    """
+    def __init__(self, drop_prob: float = 0., scale_by_keep: bool = True):
+        super(DropPath, self).__init__()
+        self.drop_prob = drop_prob
+        self.scale_by_keep = scale_by_keep
+
+    def forward(self, x):
+        return drop_path(x, self.drop_prob, self.training, self.scale_by_keep)
+
+    def extra_repr(self):
+        return f'drop_prob={round(self.drop_prob,3):0.3f}'

janus/lib/python3.10/site-packages/timm/layers/eca.py

@@ -0,0 +1,145 @@
+"""
+ECA module from ECAnet
+
+paper: ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
+https://arxiv.org/abs/1910.03151
+
+Original ECA model borrowed from https://github.com/BangguWu/ECANet
+
+Modified circular ECA implementation and adaption for use in timm package
+by Chris Ha https://github.com/VRandme
+
+Original License:
+
+MIT License
+
+Copyright (c) 2019 BangguWu, Qilong Wang
+
+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 math
+from torch import nn
+import torch.nn.functional as F
+
+
+from .create_act import create_act_layer
+from .helpers import make_divisible
+
+
+class EcaModule(nn.Module):
+    """Constructs an ECA module.
+
+    Args:
+        channels: Number of channels of the input feature map for use in adaptive kernel sizes
+            for actual calculations according to channel.
+            gamma, beta: when channel is given parameters of mapping function
+            refer to original paper https://arxiv.org/pdf/1910.03151.pdf
+            (default=None. if channel size not given, use k_size given for kernel size.)
+        kernel_size: Adaptive selection of kernel size (default=3)
+        gamm: used in kernel_size calc, see above
+        beta: used in kernel_size calc, see above
+        act_layer: optional non-linearity after conv, enables conv bias, this is an experiment
+        gate_layer: gating non-linearity to use
+    """
+    def __init__(
+            self, channels=None, kernel_size=3, gamma=2, beta=1, act_layer=None, gate_layer='sigmoid',
+            rd_ratio=1/8, rd_channels=None, rd_divisor=8, use_mlp=False):
+        super(EcaModule, self).__init__()
+        if channels is not None:
+            t = int(abs(math.log(channels, 2) + beta) / gamma)
+            kernel_size = max(t if t % 2 else t + 1, 3)
+        assert kernel_size % 2 == 1
+        padding = (kernel_size - 1) // 2
+        if use_mlp:
+            # NOTE 'mlp' mode is a timm experiment, not in paper
+            assert channels is not None
+            if rd_channels is None:
+                rd_channels = make_divisible(channels * rd_ratio, divisor=rd_divisor)
+            act_layer = act_layer or nn.ReLU
+            self.conv = nn.Conv1d(1, rd_channels, kernel_size=1, padding=0, bias=True)
+            self.act = create_act_layer(act_layer)
+            self.conv2 = nn.Conv1d(rd_channels, 1, kernel_size=kernel_size, padding=padding, bias=True)
+        else:
+            self.conv = nn.Conv1d(1, 1, kernel_size=kernel_size, padding=padding, bias=False)
+            self.act = None
+            self.conv2 = None
+        self.gate = create_act_layer(gate_layer)
+
+    def forward(self, x):
+        y = x.mean((2, 3)).view(x.shape[0], 1, -1)  # view for 1d conv
+        y = self.conv(y)
+        if self.conv2 is not None:
+            y = self.act(y)
+            y = self.conv2(y)
+        y = self.gate(y).view(x.shape[0], -1, 1, 1)
+        return x * y.expand_as(x)
+
+
+EfficientChannelAttn = EcaModule  # alias
+
+
+class CecaModule(nn.Module):
+    """Constructs a circular ECA module.
+
+    ECA module where the conv uses circular padding rather than zero padding.
+    Unlike the spatial dimension, the channels do not have inherent ordering nor
+    locality. Although this module in essence, applies such an assumption, it is unnecessary
+    to limit the channels on either "edge" from being circularly adapted to each other.
+    This will fundamentally increase connectivity and possibly increase performance metrics
+    (accuracy, robustness), without significantly impacting resource metrics
+    (parameter size, throughput,latency, etc)
+
+    Args:
+        channels: Number of channels of the input feature map for use in adaptive kernel sizes
+            for actual calculations according to channel.
+            gamma, beta: when channel is given parameters of mapping function
+            refer to original paper https://arxiv.org/pdf/1910.03151.pdf
+            (default=None. if channel size not given, use k_size given for kernel size.)
+        kernel_size: Adaptive selection of kernel size (default=3)
+        gamm: used in kernel_size calc, see above
+        beta: used in kernel_size calc, see above
+        act_layer: optional non-linearity after conv, enables conv bias, this is an experiment
+        gate_layer: gating non-linearity to use
+    """
+
+    def __init__(self, channels=None, kernel_size=3, gamma=2, beta=1, act_layer=None, gate_layer='sigmoid'):
+        super(CecaModule, self).__init__()
+        if channels is not None:
+            t = int(abs(math.log(channels, 2) + beta) / gamma)
+            kernel_size = max(t if t % 2 else t + 1, 3)
+        has_act = act_layer is not None
+        assert kernel_size % 2 == 1
+
+        # PyTorch circular padding mode is buggy as of pytorch 1.4
+        # see https://github.com/pytorch/pytorch/pull/17240
+        # implement manual circular padding
+        self.padding = (kernel_size - 1) // 2
+        self.conv = nn.Conv1d(1, 1, kernel_size=kernel_size, padding=0, bias=has_act)
+        self.gate = create_act_layer(gate_layer)
+
+    def forward(self, x):
+        y = x.mean((2, 3)).view(x.shape[0], 1, -1)
+        # Manually implement circular padding, F.pad does not seemed to be bugged
+        y = F.pad(y, (self.padding, self.padding), mode='circular')
+        y = self.conv(y)
+        y = self.gate(y).view(x.shape[0], -1, 1, 1)
+        return x * y.expand_as(x)
+
+
+CircularEfficientChannelAttn = CecaModule

janus/lib/python3.10/site-packages/timm/layers/evo_norm.py

@@ -0,0 +1,352 @@
+""" EvoNorm in PyTorch
+
+Based on `Evolving Normalization-Activation Layers` - https://arxiv.org/abs/2004.02967
+@inproceedings{NEURIPS2020,
+ author = {Liu, Hanxiao and Brock, Andy and Simonyan, Karen and Le, Quoc},
+ booktitle = {Advances in Neural Information Processing Systems},
+ editor = {H. Larochelle and M. Ranzato and R. Hadsell and M. F. Balcan and H. Lin},
+ pages = {13539--13550},
+ publisher = {Curran Associates, Inc.},
+ title = {Evolving Normalization-Activation Layers},
+ url = {https://proceedings.neurips.cc/paper/2020/file/9d4c03631b8b0c85ae08bf05eda37d0f-Paper.pdf},
+ volume = {33},
+ year = {2020}
+}
+
+An attempt at getting decent performing EvoNorms running in PyTorch.
+While faster than other PyTorch impl, still quite a ways off the built-in BatchNorm
+in terms of memory usage and throughput on GPUs.
+
+I'm testing these modules on TPU w/ PyTorch XLA. Promising start but
+currently working around some issues with builtin torch/tensor.var/std. Unlike
+GPU, similar train speeds for EvoNormS variants and BatchNorm.
+
+Hacked together by / Copyright 2020 Ross Wightman
+"""
+from typing import Sequence, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .create_act import create_act_layer
+from .trace_utils import _assert
+
+
+def instance_std(x, eps: float = 1e-5):
+    std = x.float().var(dim=(2, 3), unbiased=False, keepdim=True).add(eps).sqrt().to(x.dtype)
+    return std.expand(x.shape)
+
+
+def instance_std_tpu(x, eps: float = 1e-5):
+    std = manual_var(x, dim=(2, 3)).add(eps).sqrt()
+    return std.expand(x.shape)
+# instance_std = instance_std_tpu
+
+
+def instance_rms(x, eps: float = 1e-5):
+    rms = x.float().square().mean(dim=(2, 3), keepdim=True).add(eps).sqrt().to(x.dtype)
+    return rms.expand(x.shape)
+
+
+def manual_var(x, dim: Union[int, Sequence[int]], diff_sqm: bool = False):
+    xm = x.mean(dim=dim, keepdim=True)
+    if diff_sqm:
+        # difference of squared mean and mean squared, faster on TPU can be less stable
+        var = ((x * x).mean(dim=dim, keepdim=True) - (xm * xm)).clamp(0)
+    else:
+        var = ((x - xm) * (x - xm)).mean(dim=dim, keepdim=True)
+    return var
+
+
+def group_std(x, groups: int = 32, eps: float = 1e-5, flatten: bool = False):
+    B, C, H, W = x.shape
+    x_dtype = x.dtype
+    _assert(C % groups == 0, '')
+    if flatten:
+        x = x.reshape(B, groups, -1)  # FIXME simpler shape causing TPU / XLA issues
+        std = x.float().var(dim=2, unbiased=False, keepdim=True).add(eps).sqrt().to(x_dtype)
+    else:
+        x = x.reshape(B, groups, C // groups, H, W)
+        std = x.float().var(dim=(2, 3, 4), unbiased=False, keepdim=True).add(eps).sqrt().to(x_dtype)
+    return std.expand(x.shape).reshape(B, C, H, W)
+
+
+def group_std_tpu(x, groups: int = 32, eps: float = 1e-5, diff_sqm: bool = False, flatten: bool = False):
+    # This is a workaround for some stability / odd behaviour of .var and .std
+    # running on PyTorch XLA w/ TPUs. These manual var impl are producing much better results
+    B, C, H, W = x.shape
+    _assert(C % groups == 0, '')
+    if flatten:
+        x = x.reshape(B, groups, -1)  # FIXME simpler shape causing TPU / XLA issues
+        var = manual_var(x, dim=-1, diff_sqm=diff_sqm)
+    else:
+        x = x.reshape(B, groups, C // groups, H, W)
+        var = manual_var(x, dim=(2, 3, 4), diff_sqm=diff_sqm)
+    return var.add(eps).sqrt().expand(x.shape).reshape(B, C, H, W)
+#group_std = group_std_tpu  # FIXME TPU temporary
+
+
+def group_rms(x, groups: int = 32, eps: float = 1e-5):
+    B, C, H, W = x.shape
+    _assert(C % groups == 0, '')
+    x_dtype = x.dtype
+    x = x.reshape(B, groups, C // groups, H, W)
+    rms = x.float().square().mean(dim=(2, 3, 4), keepdim=True).add(eps).sqrt_().to(x_dtype)
+    return rms.expand(x.shape).reshape(B, C, H, W)
+
+
+class EvoNorm2dB0(nn.Module):
+    def __init__(self, num_features, apply_act=True, momentum=0.1, eps=1e-3, **_):
+        super().__init__()
+        self.apply_act = apply_act  # apply activation (non-linearity)
+        self.momentum = momentum
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(num_features))
+        self.bias = nn.Parameter(torch.zeros(num_features))
+        self.v = nn.Parameter(torch.ones(num_features)) if apply_act else None
+        self.register_buffer('running_var', torch.ones(num_features))
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        nn.init.ones_(self.weight)
+        nn.init.zeros_(self.bias)
+        if self.v is not None:
+            nn.init.ones_(self.v)
+
+    def forward(self, x):
+        _assert(x.dim() == 4, 'expected 4D input')
+        x_dtype = x.dtype
+        v_shape = (1, -1, 1, 1)
+        if self.v is not None:
+            if self.training:
+                var = x.float().var(dim=(0, 2, 3), unbiased=False)
+                # var = manual_var(x, dim=(0, 2, 3)).squeeze()
+                n = x.numel() / x.shape[1]
+                self.running_var.copy_(
+                    self.running_var * (1 - self.momentum) +
+                    var.detach() * self.momentum * (n / (n - 1)))
+            else:
+                var = self.running_var
+            left = var.add(self.eps).sqrt_().to(x_dtype).view(v_shape).expand_as(x)
+            v = self.v.to(x_dtype).view(v_shape)
+            right = x * v + instance_std(x, self.eps)
+            x = x / left.max(right)
+        return x * self.weight.to(x_dtype).view(v_shape) + self.bias.to(x_dtype).view(v_shape)
+
+
+class EvoNorm2dB1(nn.Module):
+    def __init__(self, num_features, apply_act=True, momentum=0.1, eps=1e-5, **_):
+        super().__init__()
+        self.apply_act = apply_act  # apply activation (non-linearity)
+        self.momentum = momentum
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(num_features))
+        self.bias = nn.Parameter(torch.zeros(num_features))
+        self.register_buffer('running_var', torch.ones(num_features))
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        nn.init.ones_(self.weight)
+        nn.init.zeros_(self.bias)
+
+    def forward(self, x):
+        _assert(x.dim() == 4, 'expected 4D input')
+        x_dtype = x.dtype
+        v_shape = (1, -1, 1, 1)
+        if self.apply_act:
+            if self.training:
+                var = x.float().var(dim=(0, 2, 3), unbiased=False)
+                n = x.numel() / x.shape[1]
+                self.running_var.copy_(
+                    self.running_var * (1 - self.momentum) +
+                    var.detach().to(self.running_var.dtype) * self.momentum * (n / (n - 1)))
+            else:
+                var = self.running_var
+            var = var.to(x_dtype).view(v_shape)
+            left = var.add(self.eps).sqrt_()
+            right = (x + 1) * instance_rms(x, self.eps)
+            x = x / left.max(right)
+        return x * self.weight.view(v_shape).to(x_dtype) + self.bias.view(v_shape).to(x_dtype)
+
+
+class EvoNorm2dB2(nn.Module):
+    def __init__(self, num_features, apply_act=True, momentum=0.1, eps=1e-5, **_):
+        super().__init__()
+        self.apply_act = apply_act  # apply activation (non-linearity)
+        self.momentum = momentum
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(num_features))
+        self.bias = nn.Parameter(torch.zeros(num_features))
+        self.register_buffer('running_var', torch.ones(num_features))
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        nn.init.ones_(self.weight)
+        nn.init.zeros_(self.bias)
+
+    def forward(self, x):
+        _assert(x.dim() == 4, 'expected 4D input')
+        x_dtype = x.dtype
+        v_shape = (1, -1, 1, 1)
+        if self.apply_act:
+            if self.training:
+                var = x.float().var(dim=(0, 2, 3), unbiased=False)
+                n = x.numel() / x.shape[1]
+                self.running_var.copy_(
+                    self.running_var * (1 - self.momentum) +
+                    var.detach().to(self.running_var.dtype) * self.momentum * (n / (n - 1)))
+            else:
+                var = self.running_var
+            var = var.to(x_dtype).view(v_shape)
+            left = var.add(self.eps).sqrt_()
+            right = instance_rms(x, self.eps) - x
+            x = x / left.max(right)
+        return x * self.weight.view(v_shape).to(x_dtype) + self.bias.view(v_shape).to(x_dtype)
+
+
+class EvoNorm2dS0(nn.Module):
+    def __init__(self, num_features, groups=32, group_size=None, apply_act=True, eps=1e-5, **_):
+        super().__init__()
+        self.apply_act = apply_act  # apply activation (non-linearity)
+        if group_size:
+            assert num_features % group_size == 0
+            self.groups = num_features // group_size
+        else:
+            self.groups = groups
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(num_features))
+        self.bias = nn.Parameter(torch.zeros(num_features))
+        self.v = nn.Parameter(torch.ones(num_features)) if apply_act else None
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        nn.init.ones_(self.weight)
+        nn.init.zeros_(self.bias)
+        if self.v is not None:
+            nn.init.ones_(self.v)
+
+    def forward(self, x):
+        _assert(x.dim() == 4, 'expected 4D input')
+        x_dtype = x.dtype
+        v_shape = (1, -1, 1, 1)
+        if self.v is not None:
+            v = self.v.view(v_shape).to(x_dtype)
+            x = x * (x * v).sigmoid() / group_std(x, self.groups, self.eps)
+        return x * self.weight.view(v_shape).to(x_dtype) + self.bias.view(v_shape).to(x_dtype)
+
+
+class EvoNorm2dS0a(EvoNorm2dS0):
+    def __init__(self, num_features, groups=32, group_size=None, apply_act=True, eps=1e-3, **_):
+        super().__init__(
+            num_features, groups=groups, group_size=group_size, apply_act=apply_act, eps=eps)
+
+    def forward(self, x):
+        _assert(x.dim() == 4, 'expected 4D input')
+        x_dtype = x.dtype
+        v_shape = (1, -1, 1, 1)
+        d = group_std(x, self.groups, self.eps)
+        if self.v is not None:
+            v = self.v.view(v_shape).to(x_dtype)
+            x = x * (x * v).sigmoid()
+        x = x / d
+        return x * self.weight.view(v_shape).to(x_dtype) + self.bias.view(v_shape).to(x_dtype)
+
+
+class EvoNorm2dS1(nn.Module):
+    def __init__(
+            self, num_features, groups=32, group_size=None,
+            apply_act=True, act_layer=None, eps=1e-5, **_):
+        super().__init__()
+        act_layer = act_layer or nn.SiLU
+        self.apply_act = apply_act  # apply activation (non-linearity)
+        if act_layer is not None and apply_act:
+            self.act = create_act_layer(act_layer)
+        else:
+            self.act = nn.Identity()
+        if group_size:
+            assert num_features % group_size == 0
+            self.groups = num_features // group_size
+        else:
+            self.groups = groups
+        self.eps = eps
+        self.pre_act_norm = False
+        self.weight = nn.Parameter(torch.ones(num_features))
+        self.bias = nn.Parameter(torch.zeros(num_features))
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        nn.init.ones_(self.weight)
+        nn.init.zeros_(self.bias)
+
+    def forward(self, x):
+        _assert(x.dim() == 4, 'expected 4D input')
+        x_dtype = x.dtype
+        v_shape = (1, -1, 1, 1)
+        if self.apply_act:
+            x = self.act(x) / group_std(x, self.groups, self.eps)
+        return x * self.weight.view(v_shape).to(x_dtype) + self.bias.view(v_shape).to(x_dtype)
+
+
+class EvoNorm2dS1a(EvoNorm2dS1):
+    def __init__(
+            self, num_features, groups=32, group_size=None,
+            apply_act=True, act_layer=None, eps=1e-3, **_):
+        super().__init__(
+            num_features, groups=groups, group_size=group_size, apply_act=apply_act, act_layer=act_layer, eps=eps)
+
+    def forward(self, x):
+        _assert(x.dim() == 4, 'expected 4D input')
+        x_dtype = x.dtype
+        v_shape = (1, -1, 1, 1)
+        x = self.act(x) / group_std(x, self.groups, self.eps)
+        return x * self.weight.view(v_shape).to(x_dtype) + self.bias.view(v_shape).to(x_dtype)
+
+
+class EvoNorm2dS2(nn.Module):
+    def __init__(
+            self, num_features, groups=32, group_size=None,
+            apply_act=True, act_layer=None, eps=1e-5, **_):
+        super().__init__()
+        act_layer = act_layer or nn.SiLU
+        self.apply_act = apply_act  # apply activation (non-linearity)
+        if act_layer is not None and apply_act:
+            self.act = create_act_layer(act_layer)
+        else:
+            self.act = nn.Identity()
+        if group_size:
+            assert num_features % group_size == 0
+            self.groups = num_features // group_size
+        else:
+            self.groups = groups
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(num_features))
+        self.bias = nn.Parameter(torch.zeros(num_features))
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        nn.init.ones_(self.weight)
+        nn.init.zeros_(self.bias)
+
+    def forward(self, x):
+        _assert(x.dim() == 4, 'expected 4D input')
+        x_dtype = x.dtype
+        v_shape = (1, -1, 1, 1)
+        if self.apply_act:
+            x = self.act(x) / group_rms(x, self.groups, self.eps)
+        return x * self.weight.view(v_shape).to(x_dtype) + self.bias.view(v_shape).to(x_dtype)
+
+
+class EvoNorm2dS2a(EvoNorm2dS2):
+    def __init__(
+            self, num_features, groups=32, group_size=None,
+            apply_act=True, act_layer=None, eps=1e-3, **_):
+        super().__init__(
+            num_features, groups=groups, group_size=group_size, apply_act=apply_act, act_layer=act_layer, eps=eps)
+
+    def forward(self, x):
+        _assert(x.dim() == 4, 'expected 4D input')
+        x_dtype = x.dtype
+        v_shape = (1, -1, 1, 1)
+        x = self.act(x) / group_rms(x, self.groups, self.eps)
+        return x * self.weight.view(v_shape).to(x_dtype) + self.bias.view(v_shape).to(x_dtype)

janus/lib/python3.10/site-packages/timm/layers/filter_response_norm.py

@@ -0,0 +1,68 @@
+""" Filter Response Norm in PyTorch
+
+Based on `Filter Response Normalization Layer` - https://arxiv.org/abs/1911.09737
+
+Hacked together by / Copyright 2021 Ross Wightman
+"""
+import torch
+import torch.nn as nn
+
+from .create_act import create_act_layer
+from .trace_utils import _assert
+
+
+def inv_instance_rms(x, eps: float = 1e-5):
+    rms = x.square().float().mean(dim=(2, 3), keepdim=True).add(eps).rsqrt().to(x.dtype)
+    return rms.expand(x.shape)
+
+
+class FilterResponseNormTlu2d(nn.Module):
+    def __init__(self, num_features, apply_act=True, eps=1e-5, rms=True, **_):
+        super(FilterResponseNormTlu2d, self).__init__()
+        self.apply_act = apply_act  # apply activation (non-linearity)
+        self.rms = rms
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(num_features))
+        self.bias = nn.Parameter(torch.zeros(num_features))
+        self.tau = nn.Parameter(torch.zeros(num_features)) if apply_act else None
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        nn.init.ones_(self.weight)
+        nn.init.zeros_(self.bias)
+        if self.tau is not None:
+            nn.init.zeros_(self.tau)
+
+    def forward(self, x):
+        _assert(x.dim() == 4, 'expected 4D input')
+        x_dtype = x.dtype
+        v_shape = (1, -1, 1, 1)
+        x = x * inv_instance_rms(x, self.eps)
+        x = x * self.weight.view(v_shape).to(dtype=x_dtype) + self.bias.view(v_shape).to(dtype=x_dtype)
+        return torch.maximum(x, self.tau.reshape(v_shape).to(dtype=x_dtype)) if self.tau is not None else x
+
+
+class FilterResponseNormAct2d(nn.Module):
+    def __init__(self, num_features, apply_act=True, act_layer=nn.ReLU, inplace=None, rms=True, eps=1e-5, **_):
+        super(FilterResponseNormAct2d, self).__init__()
+        if act_layer is not None and apply_act:
+            self.act = create_act_layer(act_layer, inplace=inplace)
+        else:
+            self.act = nn.Identity()
+        self.rms = rms
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(num_features))
+        self.bias = nn.Parameter(torch.zeros(num_features))
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        nn.init.ones_(self.weight)
+        nn.init.zeros_(self.bias)
+
+    def forward(self, x):
+        _assert(x.dim() == 4, 'expected 4D input')
+        x_dtype = x.dtype
+        v_shape = (1, -1, 1, 1)
+        x = x * inv_instance_rms(x, self.eps)
+        x = x * self.weight.view(v_shape).to(dtype=x_dtype) + self.bias.view(v_shape).to(dtype=x_dtype)
+        return self.act(x)

janus/lib/python3.10/site-packages/timm/layers/format.py

@@ -0,0 +1,58 @@
+from enum import Enum
+from typing import Union
+
+import torch
+
+
+class Format(str, Enum):
+    NCHW = 'NCHW'
+    NHWC = 'NHWC'
+    NCL = 'NCL'
+    NLC = 'NLC'
+
+
+FormatT = Union[str, Format]
+
+
+def get_spatial_dim(fmt: FormatT):
+    fmt = Format(fmt)
+    if fmt is Format.NLC:
+        dim = (1,)
+    elif fmt is Format.NCL:
+        dim = (2,)
+    elif fmt is Format.NHWC:
+        dim = (1, 2)
+    else:
+        dim = (2, 3)
+    return dim
+
+
+def get_channel_dim(fmt: FormatT):
+    fmt = Format(fmt)
+    if fmt is Format.NHWC:
+        dim = 3
+    elif fmt is Format.NLC:
+        dim = 2
+    else:
+        dim = 1
+    return dim
+
+
+def nchw_to(x: torch.Tensor, fmt: Format):
+    if fmt == Format.NHWC:
+        x = x.permute(0, 2, 3, 1)
+    elif fmt == Format.NLC:
+        x = x.flatten(2).transpose(1, 2)
+    elif fmt == Format.NCL:
+        x = x.flatten(2)
+    return x
+
+
+def nhwc_to(x: torch.Tensor, fmt: Format):
+    if fmt == Format.NCHW:
+        x = x.permute(0, 3, 1, 2)
+    elif fmt == Format.NLC:
+        x = x.flatten(1, 2)
+    elif fmt == Format.NCL:
+        x = x.flatten(1, 2).transpose(1, 2)
+    return x

janus/lib/python3.10/site-packages/timm/layers/global_context.py

@@ -0,0 +1,67 @@
+""" Global Context Attention Block
+
+Paper: `GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond`
+    - https://arxiv.org/abs/1904.11492
+
+Official code consulted as reference: https://github.com/xvjiarui/GCNet
+
+Hacked together by / Copyright 2021 Ross Wightman
+"""
+from torch import nn as nn
+import torch.nn.functional as F
+
+from .create_act import create_act_layer, get_act_layer
+from .helpers import make_divisible
+from .mlp import ConvMlp
+from .norm import LayerNorm2d
+
+
+class GlobalContext(nn.Module):
+
+    def __init__(self, channels, use_attn=True, fuse_add=False, fuse_scale=True, init_last_zero=False,
+                 rd_ratio=1./8, rd_channels=None, rd_divisor=1, act_layer=nn.ReLU, gate_layer='sigmoid'):
+        super(GlobalContext, self).__init__()
+        act_layer = get_act_layer(act_layer)
+
+        self.conv_attn = nn.Conv2d(channels, 1, kernel_size=1, bias=True) if use_attn else None
+
+        if rd_channels is None:
+            rd_channels = make_divisible(channels * rd_ratio, rd_divisor, round_limit=0.)
+        if fuse_add:
+            self.mlp_add = ConvMlp(channels, rd_channels, act_layer=act_layer, norm_layer=LayerNorm2d)
+        else:
+            self.mlp_add = None
+        if fuse_scale:
+            self.mlp_scale = ConvMlp(channels, rd_channels, act_layer=act_layer, norm_layer=LayerNorm2d)
+        else:
+            self.mlp_scale = None
+
+        self.gate = create_act_layer(gate_layer)
+        self.init_last_zero = init_last_zero
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        if self.conv_attn is not None:
+            nn.init.kaiming_normal_(self.conv_attn.weight, mode='fan_in', nonlinearity='relu')
+        if self.mlp_add is not None:
+            nn.init.zeros_(self.mlp_add.fc2.weight)
+
+    def forward(self, x):
+        B, C, H, W = x.shape
+
+        if self.conv_attn is not None:
+            attn = self.conv_attn(x).reshape(B, 1, H * W)  # (B, 1, H * W)
+            attn = F.softmax(attn, dim=-1).unsqueeze(3)  # (B, 1, H * W, 1)
+            context = x.reshape(B, C, H * W).unsqueeze(1) @ attn
+            context = context.view(B, C, 1, 1)
+        else:
+            context = x.mean(dim=(2, 3), keepdim=True)
+
+        if self.mlp_scale is not None:
+            mlp_x = self.mlp_scale(context)
+            x = x * self.gate(mlp_x)
+        if self.mlp_add is not None:
+            mlp_x = self.mlp_add(context)
+            x = x + mlp_x
+
+        return x

janus/lib/python3.10/site-packages/timm/layers/grid.py

@@ -0,0 +1,49 @@
+from typing import Tuple
+
+import torch
+
+
+def ndgrid(*tensors) -> Tuple[torch.Tensor, ...]:
+    """generate N-D grid in dimension order.
+
+    The ndgrid function is like meshgrid except that the order of the first two input arguments are switched.
+
+    That is, the statement
+    [X1,X2,X3] = ndgrid(x1,x2,x3)
+
+    produces the same result as
+
+    [X2,X1,X3] = meshgrid(x2,x1,x3)
+
+    This naming is based on MATLAB, the purpose is to avoid confusion due to torch's change to make
+    torch.meshgrid behaviour move from matching ndgrid ('ij') indexing to numpy meshgrid defaults of ('xy').
+
+    """
+    try:
+        return torch.meshgrid(*tensors, indexing='ij')
+    except TypeError:
+        # old PyTorch < 1.10 will follow this path as it does not have indexing arg,
+        # the old behaviour of meshgrid was 'ij'
+        return torch.meshgrid(*tensors)
+
+
+def meshgrid(*tensors) -> Tuple[torch.Tensor, ...]:
+    """generate N-D grid in spatial dim order.
+
+    The meshgrid function is similar to ndgrid except that the order of the
+    first two input and output arguments is switched.
+
+    That is, the statement
+
+    [X,Y,Z] = meshgrid(x,y,z)
+    produces the same result as
+
+    [Y,X,Z] = ndgrid(y,x,z)
+    Because of this, meshgrid is better suited to problems in two- or three-dimensional Cartesian space,
+    while ndgrid is better suited to multidimensional problems that aren't spatially based.
+    """
+
+    # NOTE: this will throw in PyTorch < 1.10 as meshgrid did not support indexing arg or have
+    # capability of generating grid in xy order before then.
+    return torch.meshgrid(*tensors, indexing='xy')
+

janus/lib/python3.10/site-packages/timm/layers/grn.py

@@ -0,0 +1,39 @@
+""" Global Response Normalization Module
+
+Based on the GRN layer presented in
+`ConvNeXt-V2 - Co-designing and Scaling ConvNets with Masked Autoencoders` - https://arxiv.org/abs/2301.00808
+
+This implementation
+* works for both NCHW and NHWC tensor layouts
+* uses affine param names matching existing torch norm layers
+* slightly improves eager mode performance via fused addcmul
+
+Hacked together by / Copyright 2023 Ross Wightman
+"""
+
+import torch
+from torch import nn as nn
+
+
+class GlobalResponseNorm(nn.Module):
+    """ Global Response Normalization layer
+    """
+    def __init__(self, dim, eps=1e-6, channels_last=True):
+        super().__init__()
+        self.eps = eps
+        if channels_last:
+            self.spatial_dim = (1, 2)
+            self.channel_dim = -1
+            self.wb_shape = (1, 1, 1, -1)
+        else:
+            self.spatial_dim = (2, 3)
+            self.channel_dim = 1
+            self.wb_shape = (1, -1, 1, 1)
+
+        self.weight = nn.Parameter(torch.zeros(dim))
+        self.bias = nn.Parameter(torch.zeros(dim))
+
+    def forward(self, x):
+        x_g = x.norm(p=2, dim=self.spatial_dim, keepdim=True)
+        x_n = x_g / (x_g.mean(dim=self.channel_dim, keepdim=True) + self.eps)
+        return x + torch.addcmul(self.bias.view(self.wb_shape), self.weight.view(self.wb_shape), x * x_n)

janus/lib/python3.10/site-packages/timm/layers/halo_attn.py

@@ -0,0 +1,233 @@
+""" Halo Self Attention
+
+Paper: `Scaling Local Self-Attention for Parameter Efficient Visual Backbones`
+    - https://arxiv.org/abs/2103.12731
+
+@misc{2103.12731,
+Author = {Ashish Vaswani and Prajit Ramachandran and Aravind Srinivas and Niki Parmar and Blake Hechtman and
+    Jonathon Shlens},
+Title = {Scaling Local Self-Attention for Parameter Efficient Visual Backbones},
+Year = {2021},
+}
+
+Status:
+This impl is a WIP, there is no official ref impl and some details in paper weren't clear to me.
+The attention mechanism works but it's slow as implemented.
+
+Hacked together by / Copyright 2021 Ross Wightman
+"""
+from typing import List
+
+import torch
+from torch import nn
+import torch.nn.functional as F
+
+from .helpers import make_divisible
+from .weight_init import trunc_normal_
+from .trace_utils import _assert
+
+
+def rel_logits_1d(q, rel_k, permute_mask: List[int]):
+    """ Compute relative logits along one dimension
+
+    As per: https://gist.github.com/aravindsrinivas/56359b79f0ce4449bcb04ab4b56a57a2
+    Originally from: `Attention Augmented Convolutional Networks` - https://arxiv.org/abs/1904.09925
+
+    Args:
+        q: (batch, height, width, dim)
+        rel_k: (2 * window - 1, dim)
+        permute_mask: permute output dim according to this
+    """
+    B, H, W, dim = q.shape
+    rel_size = rel_k.shape[0]
+    win_size = (rel_size + 1) // 2
+
+    x = (q @ rel_k.transpose(-1, -2))
+    x = x.reshape(-1, W, rel_size)
+
+    # pad to shift from relative to absolute indexing
+    x_pad = F.pad(x, [0, 1]).flatten(1)
+    x_pad = F.pad(x_pad, [0, rel_size - W])
+
+    # reshape and slice out the padded elements
+    x_pad = x_pad.reshape(-1, W + 1, rel_size)
+    x = x_pad[:, :W, win_size - 1:]
+
+    # reshape and tile
+    x = x.reshape(B, H, 1, W, win_size).expand(-1, -1, win_size, -1, -1)
+    return x.permute(permute_mask)
+
+
+class PosEmbedRel(nn.Module):
+    """ Relative Position Embedding
+    As per: https://gist.github.com/aravindsrinivas/56359b79f0ce4449bcb04ab4b56a57a2
+    Originally from: `Attention Augmented Convolutional Networks` - https://arxiv.org/abs/1904.09925
+
+    """
+    def __init__(self, block_size, win_size, dim_head, scale):
+        """
+        Args:
+            block_size (int): block size
+            win_size (int): neighbourhood window size
+            dim_head (int): attention head dim
+            scale (float): scale factor (for init)
+        """
+        super().__init__()
+        self.block_size = block_size
+        self.dim_head = dim_head
+        self.height_rel = nn.Parameter(torch.randn(win_size * 2 - 1, dim_head) * scale)
+        self.width_rel = nn.Parameter(torch.randn(win_size * 2 - 1, dim_head) * scale)
+
+    def forward(self, q):
+        B, BB, HW, _ = q.shape
+
+        # relative logits in width dimension.
+        q = q.reshape(-1, self.block_size, self.block_size, self.dim_head)
+        rel_logits_w = rel_logits_1d(q, self.width_rel, permute_mask=(0, 1, 3, 2, 4))
+
+        # relative logits in height dimension.
+        q = q.transpose(1, 2)
+        rel_logits_h = rel_logits_1d(q, self.height_rel, permute_mask=(0, 3, 1, 4, 2))
+
+        rel_logits = rel_logits_h + rel_logits_w
+        rel_logits = rel_logits.reshape(B, BB, HW, -1)
+        return rel_logits
+
+
+class HaloAttn(nn.Module):
+    """ Halo Attention
+
+    Paper: `Scaling Local Self-Attention for Parameter Efficient Visual Backbones`
+        - https://arxiv.org/abs/2103.12731
+
+    The internal dimensions of the attention module are controlled by the interaction of several arguments.
+      * the output dimension of the module is specified by dim_out, which falls back to input dim if not set
+      * the value (v) dimension is set to dim_out // num_heads, the v projection determines the output dim
+      * the query and key (qk) dimensions are determined by
+        * num_heads * dim_head if dim_head is not None
+        * num_heads * (dim_out * attn_ratio // num_heads) if dim_head is None
+      * as seen above, attn_ratio determines the ratio of q and k relative to the output if dim_head not used
+
+    Args:
+        dim (int): input dimension to the module
+        dim_out (int): output dimension of the module, same as dim if not set
+        feat_size (Tuple[int, int]): size of input feature_map (not used, for arg compat with bottle/lambda)
+        stride: output stride of the module, query downscaled if > 1 (default: 1).
+        num_heads: parallel attention heads (default: 8).
+        dim_head: dimension of query and key heads, calculated from dim_out * attn_ratio // num_heads if not set
+        block_size (int): size of blocks. (default: 8)
+        halo_size (int): size of halo overlap. (default: 3)
+        qk_ratio (float): ratio of q and k dimensions to output dimension when dim_head not set. (default: 1.0)
+        qkv_bias (bool) : add bias to q, k, and v projections
+        avg_down (bool): use average pool downsample instead of strided query blocks
+        scale_pos_embed (bool): scale the position embedding as well as Q @ K
+    """
+    def __init__(
+            self, dim, dim_out=None, feat_size=None, stride=1, num_heads=8, dim_head=None, block_size=8, halo_size=3,
+            qk_ratio=1.0, qkv_bias=False, avg_down=False, scale_pos_embed=False):
+        super().__init__()
+        dim_out = dim_out or dim
+        assert dim_out % num_heads == 0
+        assert stride in (1, 2)
+        self.num_heads = num_heads
+        self.dim_head_qk = dim_head or make_divisible(dim_out * qk_ratio, divisor=8) // num_heads
+        self.dim_head_v = dim_out // self.num_heads
+        self.dim_out_qk = num_heads * self.dim_head_qk
+        self.dim_out_v = num_heads * self.dim_head_v
+        self.scale = self.dim_head_qk ** -0.5
+        self.scale_pos_embed = scale_pos_embed
+        self.block_size = self.block_size_ds = block_size
+        self.halo_size = halo_size
+        self.win_size = block_size + halo_size * 2  # neighbourhood window size
+        self.block_stride = 1
+        use_avg_pool = False
+        if stride > 1:
+            use_avg_pool = avg_down or block_size % stride != 0
+            self.block_stride = 1 if use_avg_pool else stride
+            self.block_size_ds = self.block_size // self.block_stride
+
+        # FIXME not clear if this stride behaviour is what the paper intended
+        # Also, the paper mentions using a 3D conv for dealing with the blocking/gather, and leaving
+        # data in unfolded block form. I haven't wrapped my head around how that'd look.
+        self.q = nn.Conv2d(dim, self.dim_out_qk, 1, stride=self.block_stride, bias=qkv_bias)
+        self.kv = nn.Conv2d(dim, self.dim_out_qk + self.dim_out_v, 1, bias=qkv_bias)
+
+        self.pos_embed = PosEmbedRel(
+            block_size=self.block_size_ds, win_size=self.win_size, dim_head=self.dim_head_qk, scale=self.scale)
+
+        self.pool = nn.AvgPool2d(2, 2) if use_avg_pool else nn.Identity()
+
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        std = self.q.weight.shape[1] ** -0.5  # fan-in
+        trunc_normal_(self.q.weight, std=std)
+        trunc_normal_(self.kv.weight, std=std)
+        trunc_normal_(self.pos_embed.height_rel, std=self.scale)
+        trunc_normal_(self.pos_embed.width_rel, std=self.scale)
+
+    def forward(self, x):
+        B, C, H, W = x.shape
+        _assert(H % self.block_size == 0, '')
+        _assert(W % self.block_size == 0, '')
+        num_h_blocks = H // self.block_size
+        num_w_blocks = W // self.block_size
+        num_blocks = num_h_blocks * num_w_blocks
+
+        q = self.q(x)
+        # unfold
+        q = q.reshape(
+            -1, self.dim_head_qk,
+            num_h_blocks, self.block_size_ds, num_w_blocks, self.block_size_ds).permute(0, 1, 3, 5, 2, 4)
+        # B, num_heads * dim_head * block_size ** 2, num_blocks
+        q = q.reshape(B * self.num_heads, self.dim_head_qk, -1, num_blocks).transpose(1, 3)
+        # B * num_heads, num_blocks, block_size ** 2, dim_head
+
+        kv = self.kv(x)
+        # Generate overlapping windows for kv. This approach is good for GPU and CPU. However, unfold() is not
+        # lowered for PyTorch XLA so it will be very slow. See code at bottom of file for XLA friendly approach.
+        # FIXME figure out how to switch impl between this and conv2d if XLA being used.
+        kv = F.pad(kv, [self.halo_size, self.halo_size, self.halo_size, self.halo_size])
+        kv = kv.unfold(2, self.win_size, self.block_size).unfold(3, self.win_size, self.block_size).reshape(
+            B * self.num_heads, self.dim_head_qk + self.dim_head_v, num_blocks, -1).permute(0, 2, 3, 1)
+        k, v = torch.split(kv, [self.dim_head_qk, self.dim_head_v], dim=-1)
+        # B * num_heads, num_blocks, win_size ** 2, dim_head_qk or dim_head_v
+
+        if self.scale_pos_embed:
+            attn = (q @ k.transpose(-1, -2) + self.pos_embed(q)) * self.scale
+        else:
+            attn = (q @ k.transpose(-1, -2)) * self.scale + self.pos_embed(q)
+        # B * num_heads, num_blocks, block_size ** 2, win_size ** 2
+        attn = attn.softmax(dim=-1)
+
+        out = (attn @ v).transpose(1, 3)  # B * num_heads, dim_head_v, block_size ** 2, num_blocks
+        # fold
+        out = out.reshape(-1, self.block_size_ds, self.block_size_ds, num_h_blocks, num_w_blocks)
+        out = out.permute(0, 3, 1, 4, 2).contiguous().view(
+            B, self.dim_out_v, H // self.block_stride, W // self.block_stride)
+        # B, dim_out, H // block_stride, W // block_stride
+        out = self.pool(out)
+        return out
+
+
+""" Three alternatives for overlapping windows.
+
+`.unfold().unfold()` is same speed as stride tricks with similar clarity as F.unfold()
+
+    if is_xla:
+        # This code achieves haloing on PyTorch XLA with reasonable runtime trade-off, it is
+        # EXTREMELY slow for backward on a GPU though so I need a way of selecting based on environment.
+        WW = self.win_size ** 2
+        pw = torch.eye(WW, dtype=x.dtype, device=x.device).reshape(WW, 1, self.win_size, self.win_size)
+        kv = F.conv2d(kv.reshape(-1, 1, H, W), pw, stride=self.block_size, padding=self.halo_size)
+    elif self.stride_tricks:
+        kv = F.pad(kv, [self.halo_size, self.halo_size, self.halo_size, self.halo_size]).contiguous()
+        kv = kv.as_strided((
+            B, self.dim_out_qk + self.dim_out_v, self.win_size, self.win_size, num_h_blocks, num_w_blocks),
+            stride=(kv.stride(0), kv.stride(1), kv.shape[-1], 1, self.block_size * kv.shape[-1], self.block_size))
+    else:
+        kv = F.unfold(kv, kernel_size=self.win_size, stride=self.block_size, padding=self.halo_size)
+
+    kv = kv.reshape(
+       B * self.num_heads, self.dim_head_qk + self.dim_head_v, -1, num_blocks).transpose(1, 3)
+"""

janus/lib/python3.10/site-packages/timm/layers/helpers.py

@@ -0,0 +1,43 @@
+""" Layer/Module Helpers
+
+Hacked together by / Copyright 2020 Ross Wightman
+"""
+from itertools import repeat
+import collections.abc
+
+
+# From PyTorch internals
+def _ntuple(n):
+    def parse(x):
+        if isinstance(x, collections.abc.Iterable) and not isinstance(x, str):
+            return tuple(x)
+        return tuple(repeat(x, n))
+    return parse
+
+
+to_1tuple = _ntuple(1)
+to_2tuple = _ntuple(2)
+to_3tuple = _ntuple(3)
+to_4tuple = _ntuple(4)
+to_ntuple = _ntuple
+
+
+def make_divisible(v, divisor=8, min_value=None, round_limit=.9):
+    min_value = min_value or divisor
+    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
+    # Make sure that round down does not go down by more than 10%.
+    if new_v < round_limit * v:
+        new_v += divisor
+    return new_v
+
+
+def extend_tuple(x, n):
+    # pads a tuple to specified n by padding with last value
+    if not isinstance(x, (tuple, list)):
+        x = (x,)
+    else:
+        x = tuple(x)
+    pad_n = n - len(x)
+    if pad_n <= 0:
+        return x[:n]
+    return x + (x[-1],) * pad_n

janus/lib/python3.10/site-packages/timm/layers/interpolate.py

@@ -0,0 +1,68 @@
+""" Interpolation helpers for timm layers
+
+RegularGridInterpolator from https://github.com/sbarratt/torch_interpolations
+Copyright Shane Barratt, Apache 2.0 license
+"""
+import torch
+from itertools import product
+
+
+class RegularGridInterpolator:
+    """ Interpolate data defined on a rectilinear grid with even or uneven spacing.
+    Produces similar results to scipy RegularGridInterpolator or interp2d
+    in 'linear' mode.
+
+    Taken from https://github.com/sbarratt/torch_interpolations
+    """
+
+    def __init__(self, points, values):
+        self.points = points
+        self.values = values
+
+        assert isinstance(self.points, tuple) or isinstance(self.points, list)
+        assert isinstance(self.values, torch.Tensor)
+
+        self.ms = list(self.values.shape)
+        self.n = len(self.points)
+
+        assert len(self.ms) == self.n
+
+        for i, p in enumerate(self.points):
+            assert isinstance(p, torch.Tensor)
+            assert p.shape[0] == self.values.shape[i]
+
+    def __call__(self, points_to_interp):
+        assert self.points is not None
+        assert self.values is not None
+
+        assert len(points_to_interp) == len(self.points)
+        K = points_to_interp[0].shape[0]
+        for x in points_to_interp:
+            assert x.shape[0] == K
+
+        idxs = []
+        dists = []
+        overalls = []
+        for p, x in zip(self.points, points_to_interp):
+            idx_right = torch.bucketize(x, p)
+            idx_right[idx_right >= p.shape[0]] = p.shape[0] - 1
+            idx_left = (idx_right - 1).clamp(0, p.shape[0] - 1)
+            dist_left = x - p[idx_left]
+            dist_right = p[idx_right] - x
+            dist_left[dist_left < 0] = 0.
+            dist_right[dist_right < 0] = 0.
+            both_zero = (dist_left == 0) & (dist_right == 0)
+            dist_left[both_zero] = dist_right[both_zero] = 1.
+
+            idxs.append((idx_left, idx_right))
+            dists.append((dist_left, dist_right))
+            overalls.append(dist_left + dist_right)
+
+        numerator = 0.
+        for indexer in product([0, 1], repeat=self.n):
+            as_s = [idx[onoff] for onoff, idx in zip(indexer, idxs)]
+            bs_s = [dist[1 - onoff] for onoff, dist in zip(indexer, dists)]
+            numerator += self.values[as_s] * \
+                torch.prod(torch.stack(bs_s), dim=0)
+        denominator = torch.prod(torch.stack(overalls), dim=0)
+        return numerator / denominator

janus/lib/python3.10/site-packages/timm/layers/layer_scale.py

@@ -0,0 +1,38 @@
+import torch
+from torch import nn
+
+
+class LayerScale(nn.Module):
+    """ LayerScale on tensors with channels in last-dim.
+    """
+    def __init__(
+            self,
+            dim: int,
+            init_values: float = 1e-5,
+            inplace: bool = False,
+    ) -> None:
+        super().__init__()
+        self.inplace = inplace
+        self.gamma = nn.Parameter(init_values * torch.ones(dim))
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return x.mul_(self.gamma) if self.inplace else x * self.gamma
+
+
+class LayerScale2d(nn.Module):
+    """ LayerScale for tensors with torch 2D NCHW layout.
+    """
+    def __init__(
+            self,
+            dim: int,
+            init_values: float = 1e-5,
+            inplace: bool = False,
+    ):
+        super().__init__()
+        self.inplace = inplace
+        self.gamma = nn.Parameter(init_values * torch.ones(dim))
+
+    def forward(self, x):
+        gamma = self.gamma.view(1, -1, 1, 1)
+        return x.mul_(gamma) if self.inplace else x * gamma
+

janus/lib/python3.10/site-packages/timm/layers/mixed_conv2d.py

@@ -0,0 +1,51 @@
+""" PyTorch Mixed Convolution
+
+Paper: MixConv: Mixed Depthwise Convolutional Kernels (https://arxiv.org/abs/1907.09595)
+
+Hacked together by / Copyright 2020 Ross Wightman
+"""
+
+import torch
+from torch import nn as nn
+
+from .conv2d_same import create_conv2d_pad
+
+
+def _split_channels(num_chan, num_groups):
+    split = [num_chan // num_groups for _ in range(num_groups)]
+    split[0] += num_chan - sum(split)
+    return split
+
+
+class MixedConv2d(nn.ModuleDict):
+    """ Mixed Grouped Convolution
+
+    Based on MDConv and GroupedConv in MixNet impl:
+      https://github.com/tensorflow/tpu/blob/master/models/official/mnasnet/mixnet/custom_layers.py
+    """
+    def __init__(self, in_channels, out_channels, kernel_size=3,
+                 stride=1, padding='', dilation=1, depthwise=False, **kwargs):
+        super(MixedConv2d, self).__init__()
+
+        kernel_size = kernel_size if isinstance(kernel_size, list) else [kernel_size]
+        num_groups = len(kernel_size)
+        in_splits = _split_channels(in_channels, num_groups)
+        out_splits = _split_channels(out_channels, num_groups)
+        self.in_channels = sum(in_splits)
+        self.out_channels = sum(out_splits)
+        for idx, (k, in_ch, out_ch) in enumerate(zip(kernel_size, in_splits, out_splits)):
+            conv_groups = in_ch if depthwise else 1
+            # use add_module to keep key space clean
+            self.add_module(
+                str(idx),
+                create_conv2d_pad(
+                    in_ch, out_ch, k, stride=stride,
+                    padding=padding, dilation=dilation, groups=conv_groups, **kwargs)
+            )
+        self.splits = in_splits
+
+    def forward(self, x):
+        x_split = torch.split(x, self.splits, 1)
+        x_out = [c(x_split[i]) for i, c in enumerate(self.values())]
+        x = torch.cat(x_out, 1)
+        return x

janus/lib/python3.10/site-packages/timm/layers/padding.py

@@ -0,0 +1,87 @@
+""" Padding Helpers
+
+Hacked together by / Copyright 2020 Ross Wightman
+"""
+import math
+from typing import List, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+
+from .helpers import to_2tuple
+
+
+# Calculate symmetric padding for a convolution
+def get_padding(kernel_size: int, stride: int = 1, dilation: int = 1, **_) -> Union[int, List[int]]:
+    if any([isinstance(v, (tuple, list)) for v in [kernel_size, stride, dilation]]):
+        kernel_size, stride, dilation = to_2tuple(kernel_size), to_2tuple(stride), to_2tuple(dilation)
+        return [get_padding(*a) for a in zip(kernel_size, stride, dilation)]
+    padding = ((stride - 1) + dilation * (kernel_size - 1)) // 2
+    return padding
+
+
+# Calculate asymmetric TensorFlow-like 'SAME' padding for a convolution
+def get_same_padding(x: int, kernel_size: int, stride: int, dilation: int):
+    if isinstance(x, torch.Tensor):
+        return torch.clamp(((x / stride).ceil() - 1) * stride + (kernel_size - 1) * dilation + 1 - x, min=0)
+    else:
+        return max((math.ceil(x / stride) - 1) * stride + (kernel_size - 1) * dilation + 1 - x, 0)
+
+
+# Can SAME padding for given args be done statically?
+def is_static_pad(kernel_size: int, stride: int = 1, dilation: int = 1, **_):
+    if any([isinstance(v, (tuple, list)) for v in [kernel_size, stride, dilation]]):
+        kernel_size, stride, dilation = to_2tuple(kernel_size), to_2tuple(stride), to_2tuple(dilation)
+        return all([is_static_pad(*a) for a in zip(kernel_size, stride, dilation)])
+    return stride == 1 and (dilation * (kernel_size - 1)) % 2 == 0
+
+
+def pad_same_arg(
+        input_size: List[int],
+        kernel_size: List[int],
+        stride: List[int],
+        dilation: List[int] = (1, 1),
+) -> List[int]:
+    ih, iw = input_size
+    kh, kw = kernel_size
+    pad_h = get_same_padding(ih, kh, stride[0], dilation[0])
+    pad_w = get_same_padding(iw, kw, stride[1], dilation[1])
+    return [pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2]
+
+
+# Dynamically pad input x with 'SAME' padding for conv with specified args
+def pad_same(
+        x,
+        kernel_size: List[int],
+        stride: List[int],
+        dilation: List[int] = (1, 1),
+        value: float = 0,
+):
+    ih, iw = x.size()[-2:]
+    pad_h = get_same_padding(ih, kernel_size[0], stride[0], dilation[0])
+    pad_w = get_same_padding(iw, kernel_size[1], stride[1], dilation[1])
+    x = F.pad(x, (pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2), value=value)
+    return x
+
+
+def get_padding_value(padding, kernel_size, **kwargs) -> Tuple[Tuple, bool]:
+    dynamic = False
+    if isinstance(padding, str):
+        # for any string padding, the padding will be calculated for you, one of three ways
+        padding = padding.lower()
+        if padding == 'same':
+            # TF compatible 'SAME' padding, has a performance and GPU memory allocation impact
+            if is_static_pad(kernel_size, **kwargs):
+                # static case, no extra overhead
+                padding = get_padding(kernel_size, **kwargs)
+            else:
+                # dynamic 'SAME' padding, has runtime/GPU memory overhead
+                padding = 0
+                dynamic = True
+        elif padding == 'valid':
+            # 'VALID' padding, same as padding=0
+            padding = 0
+        else:
+            # Default to PyTorch style 'same'-ish symmetric padding
+            padding = get_padding(kernel_size, **kwargs)
+    return padding, dynamic

janus/lib/python3.10/site-packages/timm/layers/patch_embed.py

@@ -0,0 +1,307 @@
+""" Image to Patch Embedding using Conv2d
+
+A convolution based approach to patchifying a 2D image w/ embedding projection.
+
+Based on code in:
+  * https://github.com/google-research/vision_transformer
+  * https://github.com/google-research/big_vision/tree/main/big_vision
+
+Hacked together by / Copyright 2020 Ross Wightman
+"""
+import logging
+import math
+from typing import Callable, List, Optional, Tuple, Union
+
+import torch
+from torch import nn as nn
+import torch.nn.functional as F
+
+from .format import Format, nchw_to
+from .helpers import to_2tuple
+from .trace_utils import _assert
+
+_logger = logging.getLogger(__name__)
+
+
+class PatchEmbed(nn.Module):
+    """ 2D Image to Patch Embedding
+    """
+    output_fmt: Format
+    dynamic_img_pad: torch.jit.Final[bool]
+
+    def __init__(
+            self,
+            img_size: Optional[int] = 224,
+            patch_size: int = 16,
+            in_chans: int = 3,
+            embed_dim: int = 768,
+            norm_layer: Optional[Callable] = None,
+            flatten: bool = True,
+            output_fmt: Optional[str] = None,
+            bias: bool = True,
+            strict_img_size: bool = True,
+            dynamic_img_pad: bool = False,
+    ):
+        super().__init__()
+        self.patch_size = to_2tuple(patch_size)
+        self.img_size, self.grid_size, self.num_patches = self._init_img_size(img_size)
+
+        if output_fmt is not None:
+            self.flatten = False
+            self.output_fmt = Format(output_fmt)
+        else:
+            # flatten spatial dim and transpose to channels last, kept for bwd compat
+            self.flatten = flatten
+            self.output_fmt = Format.NCHW
+        self.strict_img_size = strict_img_size
+        self.dynamic_img_pad = dynamic_img_pad
+
+        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias)
+        self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
+
+    def _init_img_size(self, img_size: Union[int, Tuple[int, int]]):
+        assert self.patch_size
+        if img_size is None:
+            return None, None, None
+        img_size = to_2tuple(img_size)
+        grid_size = tuple([s // p for s, p in zip(img_size, self.patch_size)])
+        num_patches = grid_size[0] * grid_size[1]
+        return img_size, grid_size, num_patches
+
+    def set_input_size(
+            self,
+            img_size: Optional[Union[int, Tuple[int, int]]] = None,
+            patch_size: Optional[Union[int, Tuple[int, int]]] = None,
+    ):
+        new_patch_size = None
+        if patch_size is not None:
+            new_patch_size = to_2tuple(patch_size)
+        if new_patch_size is not None and new_patch_size != self.patch_size:
+            with torch.no_grad():
+                new_proj = nn.Conv2d(
+                    self.proj.in_channels,
+                    self.proj.out_channels,
+                    kernel_size=new_patch_size,
+                    stride=new_patch_size,
+                    bias=self.proj.bias is not None,
+                )
+                new_proj.weight.copy_(resample_patch_embed(self.proj.weight, new_patch_size, verbose=True))
+                if self.proj.bias is not None:
+                    new_proj.bias.copy_(self.proj.bias)
+                self.proj = new_proj
+            self.patch_size = new_patch_size
+        img_size = img_size or self.img_size
+        if img_size != self.img_size or new_patch_size is not None:
+            self.img_size, self.grid_size, self.num_patches = self._init_img_size(img_size)
+
+    def feat_ratio(self, as_scalar=True) -> Union[Tuple[int, int], int]:
+        if as_scalar:
+            return max(self.patch_size)
+        else:
+            return self.patch_size
+
+    def dynamic_feat_size(self, img_size: Tuple[int, int]) -> Tuple[int, int]:
+        """ Get grid (feature) size for given image size taking account of dynamic padding.
+        NOTE: must be torchscript compatible so using fixed tuple indexing
+        """
+        if self.dynamic_img_pad:
+            return math.ceil(img_size[0] / self.patch_size[0]), math.ceil(img_size[1] / self.patch_size[1])
+        else:
+            return img_size[0] // self.patch_size[0], img_size[1] // self.patch_size[1]
+
+    def forward(self, x):
+        B, C, H, W = x.shape
+        if self.img_size is not None:
+            if self.strict_img_size:
+                _assert(H == self.img_size[0], f"Input height ({H}) doesn't match model ({self.img_size[0]}).")
+                _assert(W == self.img_size[1], f"Input width ({W}) doesn't match model ({self.img_size[1]}).")
+            elif not self.dynamic_img_pad:
+                _assert(
+                    H % self.patch_size[0] == 0,
+                    f"Input height ({H}) should be divisible by patch size ({self.patch_size[0]})."
+                )
+                _assert(
+                    W % self.patch_size[1] == 0,
+                    f"Input width ({W}) should be divisible by patch size ({self.patch_size[1]})."
+                )
+        if self.dynamic_img_pad:
+            pad_h = (self.patch_size[0] - H % self.patch_size[0]) % self.patch_size[0]
+            pad_w = (self.patch_size[1] - W % self.patch_size[1]) % self.patch_size[1]
+            x = F.pad(x, (0, pad_w, 0, pad_h))
+        x = self.proj(x)
+        if self.flatten:
+            x = x.flatten(2).transpose(1, 2)  # NCHW -> NLC
+        elif self.output_fmt != Format.NCHW:
+            x = nchw_to(x, self.output_fmt)
+        x = self.norm(x)
+        return x
+
+
+class PatchEmbedWithSize(PatchEmbed):
+    """ 2D Image to Patch Embedding
+    """
+    output_fmt: Format
+
+    def __init__(
+            self,
+            img_size: Optional[int] = 224,
+            patch_size: int = 16,
+            in_chans: int = 3,
+            embed_dim: int = 768,
+            norm_layer: Optional[Callable] = None,
+            flatten: bool = True,
+            output_fmt: Optional[str] = None,
+            bias: bool = True,
+    ):
+        super().__init__(
+            img_size=img_size,
+            patch_size=patch_size,
+            in_chans=in_chans,
+            embed_dim=embed_dim,
+            norm_layer=norm_layer,
+            flatten=flatten,
+            output_fmt=output_fmt,
+            bias=bias,
+        )
+
+    def forward(self, x) -> Tuple[torch.Tensor, List[int]]:
+        B, C, H, W = x.shape
+        if self.img_size is not None:
+            _assert(H % self.patch_size[0] == 0, f"Input image height ({H}) must be divisible by patch size ({self.patch_size[0]}).")
+            _assert(W % self.patch_size[1] == 0, f"Input image width ({W}) must be divisible by patch size ({self.patch_size[1]}).")
+
+        x = self.proj(x)
+        feat_size = x.shape[-2:]
+        if self.flatten:
+            x = x.flatten(2).transpose(1, 2)  # NCHW -> NLC
+        elif self.output_fmt != Format.NCHW:
+            x = nchw_to(x, self.output_fmt)
+        x = self.norm(x)
+        return x, feat_size
+
+
+def resample_patch_embed(
+        patch_embed,
+        new_size: List[int],
+        interpolation: str = 'bicubic',
+        antialias: bool = True,
+        verbose: bool = False,
+):
+    """Resample the weights of the patch embedding kernel to target resolution.
+    We resample the patch embedding kernel by approximately inverting the effect
+    of patch resizing.
+
+    Code based on:
+      https://github.com/google-research/big_vision/blob/b00544b81f8694488d5f36295aeb7972f3755ffe/big_vision/models/proj/flexi/vit.py
+
+    With this resizing, we can for example load a B/8 filter into a B/16 model
+    and, on 2x larger input image, the result will match.
+
+    Args:
+        patch_embed: original parameter to be resized.
+        new_size (tuple(int, int): target shape (height, width)-only.
+        interpolation (str): interpolation for resize
+        antialias (bool): use anti-aliasing filter in resize
+        verbose (bool): log operation
+    Returns:
+        Resized patch embedding kernel.
+    """
+    import numpy as np
+    try:
+        from torch import vmap
+    except ImportError:
+        from functorch import vmap
+
+    assert len(patch_embed.shape) == 4, "Four dimensions expected"
+    assert len(new_size) == 2, "New shape should only be hw"
+    old_size = patch_embed.shape[-2:]
+    if tuple(old_size) == tuple(new_size):
+        return patch_embed
+
+    if verbose:
+        _logger.info(f"Resize patch embedding {patch_embed.shape} to {new_size}, w/ {interpolation} interpolation.")
+
+    def resize(x_np, _new_size):
+        x_tf = torch.Tensor(x_np)[None, None, ...]
+        x_upsampled = F.interpolate(
+            x_tf, size=_new_size, mode=interpolation, antialias=antialias)[0, 0, ...].numpy()
+        return x_upsampled
+
+    def get_resize_mat(_old_size, _new_size):
+        mat = []
+        for i in range(np.prod(_old_size)):
+            basis_vec = np.zeros(_old_size)
+            basis_vec[np.unravel_index(i, _old_size)] = 1.
+            mat.append(resize(basis_vec, _new_size).reshape(-1))
+        return np.stack(mat).T
+
+    resize_mat = get_resize_mat(old_size, new_size)
+    resize_mat_pinv = torch.tensor(np.linalg.pinv(resize_mat.T), device=patch_embed.device)
+
+    def resample_kernel(kernel):
+        resampled_kernel = resize_mat_pinv @ kernel.reshape(-1)
+        return resampled_kernel.reshape(new_size)
+
+    v_resample_kernel = vmap(vmap(resample_kernel, 0, 0), 1, 1)
+    orig_dtype = patch_embed.dtype
+    patch_embed = patch_embed.float()
+    patch_embed = v_resample_kernel(patch_embed)
+    patch_embed = patch_embed.to(orig_dtype)
+    return patch_embed
+
+
+# def divs(n, m=None):
+#     m = m or n // 2
+#     if m == 1:
+#         return [1]
+#     if n % m == 0:
+#         return [m] + divs(n, m - 1)
+#     return divs(n, m - 1)
+#
+#
+# class FlexiPatchEmbed(nn.Module):
+#     """ 2D Image to Patch Embedding w/ Flexible Patch sizes (FlexiViT)
+#     FIXME WIP
+#     """
+#     def __init__(
+#             self,
+#             img_size=240,
+#             patch_size=16,
+#             in_chans=3,
+#             embed_dim=768,
+#             base_img_size=240,
+#             base_patch_size=32,
+#             norm_layer=None,
+#             flatten=True,
+#             bias=True,
+#     ):
+#         super().__init__()
+#         self.img_size = to_2tuple(img_size)
+#         self.patch_size = to_2tuple(patch_size)
+#         self.num_patches = 0
+#
+#         # full range for 240 = (5, 6, 8, 10, 12, 14, 15, 16, 20, 24, 30, 40, 48)
+#         self.seqhw = (6, 8, 10, 12, 14, 15, 16, 20, 24, 30)
+#
+#         self.base_img_size = to_2tuple(base_img_size)
+#         self.base_patch_size = to_2tuple(base_patch_size)
+#         self.base_grid_size = tuple([i // p for i, p in zip(self.base_img_size, self.base_patch_size)])
+#         self.base_num_patches = self.base_grid_size[0] * self.base_grid_size[1]
+#
+#         self.flatten = flatten
+#         self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=self.patch_size, stride=self.patch_size, bias=bias)
+#         self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
+#
+#     def forward(self, x):
+#         B, C, H, W = x.shape
+#
+#         if self.patch_size == self.base_patch_size:
+#             weight = self.proj.weight
+#         else:
+#             weight = resample_patch_embed(self.proj.weight, self.patch_size)
+#         patch_size = self.patch_size
+#         x = F.conv2d(x, weight, bias=self.proj.bias, stride=patch_size)
+#         if self.flatten:
+#             x = x.flatten(2).transpose(1, 2)  # BCHW -> BNC
+#         x = self.norm(x)
+#         return x

janus/lib/python3.10/site-packages/timm/layers/pool2d_same.py

@@ -0,0 +1,73 @@
+""" AvgPool2d w/ Same Padding
+
+Hacked together by / Copyright 2020 Ross Wightman
+"""
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from typing import List, Tuple, Optional
+
+from .helpers import to_2tuple
+from .padding import pad_same, get_padding_value
+
+
+def avg_pool2d_same(x, kernel_size: List[int], stride: List[int], padding: List[int] = (0, 0),
+                    ceil_mode: bool = False, count_include_pad: bool = True):
+    # FIXME how to deal with count_include_pad vs not for external padding?
+    x = pad_same(x, kernel_size, stride)
+    return F.avg_pool2d(x, kernel_size, stride, (0, 0), ceil_mode, count_include_pad)
+
+
+class AvgPool2dSame(nn.AvgPool2d):
+    """ Tensorflow like 'SAME' wrapper for 2D average pooling
+    """
+    def __init__(self, kernel_size: int, stride=None, padding=0, ceil_mode=False, count_include_pad=True):
+        kernel_size = to_2tuple(kernel_size)
+        stride = to_2tuple(stride)
+        super(AvgPool2dSame, self).__init__(kernel_size, stride, (0, 0), ceil_mode, count_include_pad)
+
+    def forward(self, x):
+        x = pad_same(x, self.kernel_size, self.stride)
+        return F.avg_pool2d(
+            x, self.kernel_size, self.stride, self.padding, self.ceil_mode, self.count_include_pad)
+
+
+def max_pool2d_same(
+        x, kernel_size: List[int], stride: List[int], padding: List[int] = (0, 0),
+        dilation: List[int] = (1, 1), ceil_mode: bool = False):
+    x = pad_same(x, kernel_size, stride, value=-float('inf'))
+    return F.max_pool2d(x, kernel_size, stride, (0, 0), dilation, ceil_mode)
+
+
+class MaxPool2dSame(nn.MaxPool2d):
+    """ Tensorflow like 'SAME' wrapper for 2D max pooling
+    """
+    def __init__(self, kernel_size: int, stride=None, padding=0, dilation=1, ceil_mode=False):
+        kernel_size = to_2tuple(kernel_size)
+        stride = to_2tuple(stride)
+        dilation = to_2tuple(dilation)
+        super(MaxPool2dSame, self).__init__(kernel_size, stride, (0, 0), dilation, ceil_mode)
+
+    def forward(self, x):
+        x = pad_same(x, self.kernel_size, self.stride, value=-float('inf'))
+        return F.max_pool2d(x, self.kernel_size, self.stride, (0, 0), self.dilation, self.ceil_mode)
+
+
+def create_pool2d(pool_type, kernel_size, stride=None, **kwargs):
+    stride = stride or kernel_size
+    padding = kwargs.pop('padding', '')
+    padding, is_dynamic = get_padding_value(padding, kernel_size, stride=stride, **kwargs)
+    if is_dynamic:
+        if pool_type == 'avg':
+            return AvgPool2dSame(kernel_size, stride=stride, **kwargs)
+        elif pool_type == 'max':
+            return MaxPool2dSame(kernel_size, stride=stride, **kwargs)
+        else:
+            assert False, f'Unsupported pool type {pool_type}'
+    else:
+        if pool_type == 'avg':
+            return nn.AvgPool2d(kernel_size, stride=stride, padding=padding, **kwargs)
+        elif pool_type == 'max':
+            return nn.MaxPool2d(kernel_size, stride=stride, padding=padding, **kwargs)
+        else:
+            assert False, f'Unsupported pool type {pool_type}'

janus/lib/python3.10/site-packages/timm/layers/pos_embed_sincos.py

@@ -0,0 +1,443 @@
+""" Sin-cos, fourier, rotary position embedding modules and functions
+
+Hacked together by / Copyright 2022 Ross Wightman
+"""
+import math
+from typing import List, Tuple, Optional, Union
+
+import torch
+from torch import nn as nn
+
+from .grid import ndgrid
+from .trace_utils import _assert
+
+
+def pixel_freq_bands(
+        num_bands: int,
+        max_freq: float = 224.,
+        linear_bands: bool = True,
+        device: Optional[torch.device] = None,
+):
+    if linear_bands:
+        bands = torch.linspace(1.0, max_freq / 2, num_bands, dtype=torch.float32, device=device)
+    else:
+        bands = 2 ** torch.linspace(0, math.log(max_freq, 2) - 1, num_bands, dtype=torch.float32, device=device)
+    return bands * torch.pi
+
+
+def freq_bands(
+        num_bands: int,
+        temperature: float = 10000.,
+        step: int = 2,
+        device: Optional[torch.device] = None,
+) -> torch.Tensor:
+    exp = torch.arange(0, num_bands, step, dtype=torch.int64, device=device).to(torch.float32) / num_bands
+    bands = 1. / (temperature ** exp)
+    return bands
+
+
+def build_sincos2d_pos_embed(
+        feat_shape: List[int],
+        dim: int = 64,
+        temperature: float = 10000.,
+        reverse_coord: bool = False,
+        interleave_sin_cos: bool = False,
+        dtype: torch.dtype = torch.float32,
+        device: Optional[torch.device] = None
+) -> torch.Tensor:
+    """
+
+    Args:
+        feat_shape:
+        dim:
+        temperature:
+        reverse_coord: stack grid order W, H instead of H, W
+        interleave_sin_cos: sin, cos, sin, cos stack instead of sin, sin, cos, cos
+        dtype:
+        device:
+
+    Returns:
+
+    """
+    assert dim % 4 == 0, 'Embed dimension must be divisible by 4 for sin-cos 2D position embedding'
+    pos_dim = dim // 4
+    bands = freq_bands(pos_dim, temperature=temperature, step=1, device=device)
+
+    if reverse_coord:
+        feat_shape = feat_shape[::-1]  # stack W, H instead of H, W
+    grid = torch.stack(ndgrid([
+        torch.arange(s, device=device, dtype=torch.int64).to(torch.float32)
+        for s in feat_shape
+    ])).flatten(1).transpose(0, 1)
+    pos2 = grid.unsqueeze(-1) * bands.unsqueeze(0)
+    # FIXME add support for unflattened spatial dim?
+
+    stack_dim = 2 if interleave_sin_cos else 1  # stack sin, cos, sin, cos  instead of sin sin cos cos
+    pos_emb = torch.stack([torch.sin(pos2), torch.cos(pos2)], dim=stack_dim).flatten(1)
+    return pos_emb.to(dtype=dtype)
+
+
+def build_fourier_pos_embed(
+        feat_shape: List[int],
+        bands: Optional[torch.Tensor] = None,
+        num_bands: int = 64,
+        max_res: int = 224,
+        temperature: float = 10000.,
+        linear_bands: bool = False,
+        include_grid: bool = False,
+        in_pixels: bool = True,
+        ref_feat_shape: Optional[List[int]] = None,
+        dtype: torch.dtype = torch.float32,
+        device: Optional[torch.device] = None,
+) -> List[torch.Tensor]:
+    """
+
+    Args:
+        feat_shape: Feature shape for embedding.
+        bands: Pre-calculated frequency bands.
+        num_bands: Number of frequency bands (determines output dim).
+        max_res: Maximum resolution for pixel based freq.
+        temperature: Temperature for non-pixel freq.
+        linear_bands: Linear band spacing for pixel based freq.
+        include_grid: Include the spatial grid in output.
+        in_pixels: Output in pixel freq.
+        ref_feat_shape: Reference feature shape for resize / fine-tune.
+        dtype: Output dtype.
+        device: Output device.
+
+    Returns:
+
+    """
+    if bands is None:
+        if in_pixels:
+            bands = pixel_freq_bands(
+                num_bands,
+                float(max_res),
+                linear_bands=linear_bands,
+                device=device,
+            )
+        else:
+            bands = freq_bands(
+                num_bands,
+                temperature=temperature,
+                step=1,
+                device=device,
+            )
+    else:
+        if device is None:
+            device = bands.device
+        if dtype is None:
+            dtype = bands.dtype
+
+    if in_pixels:
+        t = [torch.linspace(-1., 1., steps=s, device=device, dtype=torch.float32) for s in feat_shape]
+    else:
+        t = [torch.arange(s, device=device, dtype=torch.int64).to(torch.float32) for s in feat_shape]
+
+    if ref_feat_shape is not None:
+        # eva's scheme for resizing rope embeddings (ref shape = pretrain)
+        t = [x / f * r for x, f, r in zip(t, feat_shape, ref_feat_shape)]
+
+    grid = torch.stack(ndgrid(t), dim=-1)
+    grid = grid.unsqueeze(-1)
+    pos = grid * bands
+
+    pos_sin, pos_cos = pos.sin().to(dtype=dtype), pos.cos().to(dtype)
+    out = [grid, pos_sin, pos_cos] if include_grid else [pos_sin, pos_cos]
+    return out
+
+
+class FourierEmbed(nn.Module):
+
+    def __init__(
+            self,
+            max_res: int = 224,
+            num_bands: int = 64,
+            concat_grid=True,
+            keep_spatial=False,
+    ):
+        super().__init__()
+        self.max_res = max_res
+        self.num_bands = num_bands
+        self.concat_grid = concat_grid
+        self.keep_spatial = keep_spatial
+        self.register_buffer(
+            'bands',
+            pixel_freq_bands(max_res, num_bands),
+            persistent=False,
+        )
+
+    def forward(self, x):
+        B, C = x.shape[:2]
+        feat_shape = x.shape[2:]
+        emb = build_fourier_pos_embed(
+            feat_shape,
+            self.bands,
+            include_grid=self.concat_grid,
+            dtype=x.dtype,
+            device=x.device,
+        )
+        emb = torch.cat(emb, dim=-1)
+        emb = emb.transpose(-1, -2).flatten(len(feat_shape))
+        batch_expand = (B,) + (-1,) * (x.ndim - 1)
+
+        # FIXME support nD
+        if self.keep_spatial:
+            x = torch.cat([x, emb.unsqueeze(0).expand(batch_expand).permute(0, 3, 1, 2)], dim=1)
+        else:
+            x = torch.cat([x.permute(0, 2, 3, 1), emb.unsqueeze(0).expand(batch_expand)], dim=-1)
+            x = x.reshape(B, feat_shape.numel(), -1)
+
+        return x
+
+
+def rot(x):
+    return torch.stack([-x[..., 1::2], x[..., ::2]], -1).reshape(x.shape)
+
+
+def apply_rot_embed(x: torch.Tensor, sin_emb, cos_emb):
+    if sin_emb.ndim == 3:
+        return x * cos_emb.unsqueeze(1).expand_as(x) + rot(x) * sin_emb.unsqueeze(1).expand_as(x)
+    return x * cos_emb + rot(x) * sin_emb
+
+
+def apply_rot_embed_list(x: List[torch.Tensor], sin_emb, cos_emb):
+    if isinstance(x, torch.Tensor):
+        x = [x]
+    return [t * cos_emb + rot(t) * sin_emb for t in x]
+
+
+def apply_rot_embed_cat(x: torch.Tensor, emb):
+    sin_emb, cos_emb = emb.tensor_split(2, -1)
+    if sin_emb.ndim == 3:
+        return x * cos_emb.unsqueeze(1).expand_as(x) + rot(x) * sin_emb.unsqueeze(1).expand_as(x)
+    return x * cos_emb + rot(x) * sin_emb
+
+
+def apply_keep_indices_nlc(x, pos_embed, keep_indices):
+    pos_embed = pos_embed.unsqueeze(0).expand(x.shape[0], -1, -1)
+    pos_embed = pos_embed.gather(1, keep_indices.unsqueeze(-1).expand(-1, -1, pos_embed.shape[-1]))
+    return pos_embed
+
+
+def build_rotary_pos_embed(
+        feat_shape: List[int],
+        bands: Optional[torch.Tensor] = None,
+        dim: int = 64,
+        max_res: int = 224,
+        temperature: float = 10000.,
+        linear_bands: bool = False,
+        in_pixels: bool = True,
+        ref_feat_shape: Optional[List[int]] = None,
+        dtype: torch.dtype = torch.float32,
+        device: Optional[torch.device] = None,
+):
+    """
+
+    Args:
+        feat_shape: Spatial shape of the target tensor for embedding.
+        bands: Optional pre-generated frequency bands
+        dim: Output dimension of embedding tensor.
+        max_res: Maximum resolution for pixel mode.
+        temperature: Temperature (inv freq) for non-pixel mode
+        linear_bands: Linearly (instead of log) spaced bands for pixel mode
+        in_pixels: Pixel vs language (inv freq) mode.
+        dtype: Output dtype.
+        device: Output device.
+
+    Returns:
+
+    """
+    sin_emb, cos_emb = build_fourier_pos_embed(
+        feat_shape,
+        bands=bands,
+        num_bands=dim // 4,
+        max_res=max_res,
+        temperature=temperature,
+        linear_bands=linear_bands,
+        in_pixels=in_pixels,
+        ref_feat_shape=ref_feat_shape,
+        device=device,
+        dtype=dtype,
+    )
+    num_spatial_dim = 1
+    # this would be much nicer as a .numel() call to torch.Size(), but torchscript sucks
+    for x in feat_shape:
+        num_spatial_dim *= x
+    sin_emb = sin_emb.reshape(num_spatial_dim, -1).repeat_interleave(2, -1)
+    cos_emb = cos_emb.reshape(num_spatial_dim, -1).repeat_interleave(2, -1)
+    return sin_emb, cos_emb
+
+
+class RotaryEmbedding(nn.Module):
+    """ Rotary position embedding
+
+    NOTE: This is my initial attempt at impl rotary embedding for spatial use, it has not
+    been well tested, and will likely change. It will be moved to its own file.
+
+    The following impl/resources were referenced for this impl:
+    * https://github.com/lucidrains/vit-pytorch/blob/6f3a5fcf0bca1c5ec33a35ef48d97213709df4ba/vit_pytorch/rvt.py
+    * https://blog.eleuther.ai/rotary-embeddings/
+    """
+
+    def __init__(
+            self,
+            dim,
+            max_res=224,
+            temperature=10000,
+            in_pixels=True,
+            linear_bands: bool = False,
+            feat_shape: Optional[List[int]] = None,
+            ref_feat_shape: Optional[List[int]] = None,
+    ):
+        super().__init__()
+        self.dim = dim
+        self.max_res = max_res
+        self.temperature = temperature
+        self.in_pixels = in_pixels
+        self.feat_shape = feat_shape
+        self.ref_feat_shape = ref_feat_shape
+
+        if feat_shape is None:
+            # only cache bands
+            if in_pixels:
+                bands = pixel_freq_bands(
+                    dim // 4,
+                    float(max_res),
+                    linear_bands=linear_bands,
+                )
+            else:
+                bands = freq_bands(
+                    dim // 4,
+                    temperature=temperature,
+                    step=1,
+                )
+            self.register_buffer(
+                'bands',
+                bands,
+                persistent=False,
+            )
+            self.pos_embed_sin = None
+            self.pos_embed_cos = None
+        else:
+            # cache full sin/cos embeddings if shape provided up front
+            emb_sin, emb_cos = build_rotary_pos_embed(
+                feat_shape=feat_shape,
+                dim=dim,
+                max_res=max_res,
+                linear_bands=linear_bands,
+                in_pixels=in_pixels,
+                ref_feat_shape=self.ref_feat_shape,
+            )
+            self.bands = None
+            self.register_buffer(
+                'pos_embed_sin',
+                emb_sin,
+                persistent=False,
+            )
+            self.register_buffer(
+                'pos_embed_cos',
+                emb_cos,
+                persistent=False,
+            )
+
+    def get_embed(self, shape: Optional[List[int]] = None):
+        if self.bands is not None:
+            # rebuild embeddings every call, use if target shape changes
+            assert shape is not None
+            return build_rotary_pos_embed(
+                shape,
+                self.bands,
+                in_pixels=self.in_pixels,
+            )
+        else:
+            return self.pos_embed_sin, self.pos_embed_cos
+
+    def forward(self, x):
+        # assuming channel-first tensor where spatial dim are >= 2
+        sin_emb, cos_emb = self.get_embed(x.shape[2:])
+        return apply_rot_embed(x, sin_emb, cos_emb)
+
+
+class RotaryEmbeddingCat(nn.Module):
+    """ Rotary position embedding w/ concatenatd sin & cos
+
+    The following impl/resources were referenced for this impl:
+    * https://github.com/lucidrains/vit-pytorch/blob/6f3a5fcf0bca1c5ec33a35ef48d97213709df4ba/vit_pytorch/rvt.py
+    * https://blog.eleuther.ai/rotary-embeddings/
+    """
+
+    def __init__(
+            self,
+            dim,
+            max_res=224,
+            temperature=10000,
+            in_pixels=True,
+            linear_bands: bool = False,
+            feat_shape: Optional[List[int]] = None,
+            ref_feat_shape: Optional[List[int]] = None,
+    ):
+        super().__init__()
+        self.dim = dim
+        self.max_res = max_res
+        self.temperature = temperature
+        self.in_pixels = in_pixels
+        self.feat_shape = feat_shape
+        self.ref_feat_shape = ref_feat_shape
+
+        if feat_shape is None:
+            # only cache bands
+            if in_pixels:
+                bands = pixel_freq_bands(
+                    dim // 4,
+                    float(max_res),
+                    linear_bands=linear_bands,
+                )
+            else:
+                bands = freq_bands(
+                    dim // 4,
+                    temperature=temperature,
+                    step=1,
+                )
+            self.register_buffer(
+                'bands',
+                bands,
+                persistent=False,
+            )
+            self.pos_embed = None
+        else:
+            # cache full sin/cos embeddings if shape provided up front
+            embeds = build_rotary_pos_embed(
+                feat_shape=feat_shape,
+                dim=dim,
+                max_res=max_res,
+                linear_bands=linear_bands,
+                in_pixels=in_pixels,
+                ref_feat_shape=self.ref_feat_shape,
+            )
+            self.bands = None
+            self.register_buffer(
+                'pos_embed',
+                torch.cat(embeds, -1),
+                persistent=False,
+            )
+
+    def get_embed(self, shape: Optional[List[int]] = None):
+        if self.bands is not None and shape is not None:
+            # rebuild embeddings every call, use if target shape changes
+            embeds = build_rotary_pos_embed(
+                shape,
+                self.bands,
+                in_pixels=self.in_pixels,
+                ref_feat_shape=self.ref_feat_shape,
+            )
+            return torch.cat(embeds, -1)
+        elif self.pos_embed is not None:
+            return self.pos_embed
+        else:
+            assert False, "get_embed() requires pre-computed pos_embed or valid shape w/ pre-computed bands"
+
+    def forward(self, x):
+        # assuming channel-first tensor where spatial dim are >= 2
+        pos_embed = self.get_embed(x.shape[2:])
+        return apply_rot_embed_cat(x, pos_embed)

janus/lib/python3.10/site-packages/timm/layers/split_batchnorm.py

@@ -0,0 +1,75 @@
+""" Split BatchNorm
+
+A PyTorch BatchNorm layer that splits input batch into N equal parts and passes each through
+a separate BN layer. The first split is passed through the parent BN layers with weight/bias
+keys the same as the original BN. All other splits pass through BN sub-layers under the '.aux_bn'
+namespace.
+
+This allows easily removing the auxiliary BN layers after training to efficiently
+achieve the 'Auxiliary BatchNorm' as described in the AdvProp Paper, section 4.2,
+'Disentangled Learning via An Auxiliary BN'
+
+Hacked together by / Copyright 2020 Ross Wightman
+"""
+import torch
+import torch.nn as nn
+
+
+class SplitBatchNorm2d(torch.nn.BatchNorm2d):
+
+    def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True,
+                 track_running_stats=True, num_splits=2):
+        super().__init__(num_features, eps, momentum, affine, track_running_stats)
+        assert num_splits > 1, 'Should have at least one aux BN layer (num_splits at least 2)'
+        self.num_splits = num_splits
+        self.aux_bn = nn.ModuleList([
+            nn.BatchNorm2d(num_features, eps, momentum, affine, track_running_stats) for _ in range(num_splits - 1)])
+
+    def forward(self, input: torch.Tensor):
+        if self.training:  # aux BN only relevant while training
+            split_size = input.shape[0] // self.num_splits
+            assert input.shape[0] == split_size * self.num_splits, "batch size must be evenly divisible by num_splits"
+            split_input = input.split(split_size)
+            x = [super().forward(split_input[0])]
+            for i, a in enumerate(self.aux_bn):
+                x.append(a(split_input[i + 1]))
+            return torch.cat(x, dim=0)
+        else:
+            return super().forward(input)
+
+
+def convert_splitbn_model(module, num_splits=2):
+    """
+    Recursively traverse module and its children to replace all instances of
+    ``torch.nn.modules.batchnorm._BatchNorm`` with `SplitBatchnorm2d`.
+    Args:
+        module (torch.nn.Module): input module
+        num_splits: number of separate batchnorm layers to split input across
+    Example::
+        >>> # model is an instance of torch.nn.Module
+        >>> model = timm.models.convert_splitbn_model(model, num_splits=2)
+    """
+    mod = module
+    if isinstance(module, torch.nn.modules.instancenorm._InstanceNorm):
+        return module
+    if isinstance(module, torch.nn.modules.batchnorm._BatchNorm):
+        mod = SplitBatchNorm2d(
+            module.num_features, module.eps, module.momentum, module.affine,
+            module.track_running_stats, num_splits=num_splits)
+        mod.running_mean = module.running_mean
+        mod.running_var = module.running_var
+        mod.num_batches_tracked = module.num_batches_tracked
+        if module.affine:
+            mod.weight.data = module.weight.data.clone().detach()
+            mod.bias.data = module.bias.data.clone().detach()
+        for aux in mod.aux_bn:
+            aux.running_mean = module.running_mean.clone()
+            aux.running_var = module.running_var.clone()
+            aux.num_batches_tracked = module.num_batches_tracked.clone()
+            if module.affine:
+                aux.weight.data = module.weight.data.clone().detach()
+                aux.bias.data = module.bias.data.clone().detach()
+    for name, child in module.named_children():
+        mod.add_module(name, convert_splitbn_model(child, num_splits=num_splits))
+    del module
+    return mod

janus/lib/python3.10/site-packages/timm/layers/squeeze_excite.py

@@ -0,0 +1,102 @@
+""" Squeeze-and-Excitation Channel Attention
+
+An SE implementation originally based on PyTorch SE-Net impl.
+Has since evolved with additional functionality / configuration.
+
+Paper: `Squeeze-and-Excitation Networks` - https://arxiv.org/abs/1709.01507
+
+Also included is Effective Squeeze-Excitation (ESE).
+Paper: `CenterMask : Real-Time Anchor-Free Instance Segmentation` - https://arxiv.org/abs/1911.06667
+
+Hacked together by / Copyright 2021 Ross Wightman
+"""
+from torch import nn as nn
+
+from .create_act import create_act_layer
+from .helpers import make_divisible
+
+
+class SEModule(nn.Module):
+    """ SE Module as defined in original SE-Nets with a few additions
+    Additions include:
+        * divisor can be specified to keep channels % div == 0 (default: 8)
+        * reduction channels can be specified directly by arg (if rd_channels is set)
+        * reduction channels can be specified by float rd_ratio (default: 1/16)
+        * global max pooling can be added to the squeeze aggregation
+        * customizable activation, normalization, and gate layer
+    """
+    def __init__(
+            self, channels, rd_ratio=1. / 16, rd_channels=None, rd_divisor=8, add_maxpool=False,
+            bias=True, act_layer=nn.ReLU, norm_layer=None, gate_layer='sigmoid'):
+        super(SEModule, self).__init__()
+        self.add_maxpool = add_maxpool
+        if not rd_channels:
+            rd_channels = make_divisible(channels * rd_ratio, rd_divisor, round_limit=0.)
+        self.fc1 = nn.Conv2d(channels, rd_channels, kernel_size=1, bias=bias)
+        self.bn = norm_layer(rd_channels) if norm_layer else nn.Identity()
+        self.act = create_act_layer(act_layer, inplace=True)
+        self.fc2 = nn.Conv2d(rd_channels, channels, kernel_size=1, bias=bias)
+        self.gate = create_act_layer(gate_layer)
+
+    def forward(self, x):
+        x_se = x.mean((2, 3), keepdim=True)
+        if self.add_maxpool:
+            # experimental codepath, may remove or change
+            x_se = 0.5 * x_se + 0.5 * x.amax((2, 3), keepdim=True)
+        x_se = self.fc1(x_se)
+        x_se = self.act(self.bn(x_se))
+        x_se = self.fc2(x_se)
+        return x * self.gate(x_se)
+
+
+SqueezeExcite = SEModule  # alias
+
+
+class EffectiveSEModule(nn.Module):
+    """ 'Effective Squeeze-Excitation
+    From `CenterMask : Real-Time Anchor-Free Instance Segmentation` - https://arxiv.org/abs/1911.06667
+    """
+    def __init__(self, channels, add_maxpool=False, gate_layer='hard_sigmoid', **_):
+        super(EffectiveSEModule, self).__init__()
+        self.add_maxpool = add_maxpool
+        self.fc = nn.Conv2d(channels, channels, kernel_size=1, padding=0)
+        self.gate = create_act_layer(gate_layer)
+
+    def forward(self, x):
+        x_se = x.mean((2, 3), keepdim=True)
+        if self.add_maxpool:
+            # experimental codepath, may remove or change
+            x_se = 0.5 * x_se + 0.5 * x.amax((2, 3), keepdim=True)
+        x_se = self.fc(x_se)
+        return x * self.gate(x_se)
+
+
+EffectiveSqueezeExcite = EffectiveSEModule  # alias
+
+
+class SqueezeExciteCl(nn.Module):
+    """ SE Module as defined in original SE-Nets with a few additions
+    Additions include:
+        * divisor can be specified to keep channels % div == 0 (default: 8)
+        * reduction channels can be specified directly by arg (if rd_channels is set)
+        * reduction channels can be specified by float rd_ratio (default: 1/16)
+        * global max pooling can be added to the squeeze aggregation
+        * customizable activation, normalization, and gate layer
+    """
+    def __init__(
+            self, channels, rd_ratio=1. / 16, rd_channels=None, rd_divisor=8,
+            bias=True, act_layer=nn.ReLU, gate_layer='sigmoid'):
+        super().__init__()
+        if not rd_channels:
+            rd_channels = make_divisible(channels * rd_ratio, rd_divisor, round_limit=0.)
+        self.fc1 = nn.Linear(channels, rd_channels, bias=bias)
+        self.act = create_act_layer(act_layer, inplace=True)
+        self.fc2 = nn.Linear(rd_channels, channels, bias=bias)
+        self.gate = create_act_layer(gate_layer)
+
+    def forward(self, x):
+        x_se = x.mean((1, 2), keepdims=True)  # FIXME avg dim [1:n-1], don't assume 2D NHWC
+        x_se = self.fc1(x_se)
+        x_se = self.act(x_se)
+        x_se = self.fc2(x_se)
+        return x * self.gate(x_se)

janus/lib/python3.10/site-packages/timm/layers/trace_utils.py

@@ -0,0 +1,13 @@
+try:
+    from torch import _assert
+except ImportError:
+    def _assert(condition: bool, message: str):
+        assert condition, message
+
+
+def _float_to_int(x: float) -> int:
+    """
+    Symbolic tracing helper to substitute for inbuilt `int`.
+    Hint: Inbuilt `int` can't accept an argument of type `Proxy`
+    """
+    return int(x)