ZTWHHH commited on Apr 18, 2025

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f121d74

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llava/lib/python3.10/site-packages/setuptools/tests/__pycache__/mod_with_constant.cpython-310.pyc +0 -0
llava/lib/python3.10/site-packages/setuptools/tests/__pycache__/script-with-bom.cpython-310.pyc +0 -0
llava/lib/python3.10/site-packages/setuptools/tests/__pycache__/server.cpython-310.pyc +0 -0
llava/lib/python3.10/site-packages/setuptools/tests/__pycache__/test_archive_util.cpython-310.pyc +0 -0
llava/lib/python3.10/site-packages/setuptools/tests/__pycache__/test_bdist_wheel.cpython-310.pyc +0 -0
llava/lib/python3.10/site-packages/setuptools/tests/__pycache__/test_build_ext.cpython-310.pyc +0 -0
llava/lib/python3.10/site-packages/setuptools/tests/__pycache__/test_build_py.cpython-310.pyc +0 -0
llava/lib/python3.10/site-packages/setuptools/tests/__pycache__/test_core_metadata.cpython-310.pyc +0 -0
llava/lib/python3.10/site-packages/setuptools/tests/__pycache__/test_install_scripts.cpython-310.pyc +0 -0
llava/lib/python3.10/site-packages/setuptools/tests/__pycache__/test_manifest.cpython-310.pyc +0 -0
llava/lib/python3.10/site-packages/setuptools/tests/__pycache__/test_namespaces.cpython-310.pyc +0 -0
llava/lib/python3.10/site-packages/setuptools/tests/__pycache__/test_virtualenv.cpython-310.pyc +0 -0
llava/lib/python3.10/site-packages/setuptools/tests/__pycache__/test_wheel.cpython-310.pyc +0 -0
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llava/lib/python3.10/site-packages/setuptools/tests/config/__pycache__/test_expand.cpython-310.pyc +0 -0
llava/lib/python3.10/site-packages/setuptools/tests/config/__pycache__/test_pyprojecttoml.cpython-310.pyc +0 -0
llava/lib/python3.10/site-packages/setuptools/tests/config/__pycache__/test_pyprojecttoml_dynamic_deps.cpython-310.pyc +0 -0
llava/lib/python3.10/site-packages/setuptools/tests/indexes/test_links_priority/external.html +3 -0
llava/lib/python3.10/site-packages/setuptools/tests/indexes/test_links_priority/simple/foobar/index.html +4 -0
llava/lib/python3.10/site-packages/setuptools/tests/integration/__init__.py +0 -0
llava/lib/python3.10/site-packages/setuptools/tests/integration/__pycache__/__init__.cpython-310.pyc +0 -0
llava/lib/python3.10/site-packages/setuptools/tests/integration/__pycache__/helpers.cpython-310.pyc +0 -0
llava/lib/python3.10/site-packages/setuptools/tests/integration/__pycache__/test_pip_install_sdist.cpython-310.pyc +0 -0
llava/lib/python3.10/site-packages/setuptools/tests/integration/helpers.py +77 -0
llava/lib/python3.10/site-packages/setuptools/tests/integration/test_pip_install_sdist.py +223 -0
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minigpt2/lib/python3.10/site-packages/open_flamingo/eval/__pycache__/__init__.cpython-310.pyc +0 -0
minigpt2/lib/python3.10/site-packages/open_flamingo/eval/__pycache__/classification.cpython-310.pyc +0 -0
minigpt2/lib/python3.10/site-packages/open_flamingo/eval/__pycache__/coco_metric.cpython-310.pyc +0 -0
minigpt2/lib/python3.10/site-packages/open_flamingo/eval/__pycache__/eval_datasets.cpython-310.pyc +0 -0
minigpt2/lib/python3.10/site-packages/open_flamingo/eval/__pycache__/evaluate.cpython-310.pyc +0 -0
minigpt2/lib/python3.10/site-packages/open_flamingo/eval/__pycache__/imagenet_utils.cpython-310.pyc +0 -0
minigpt2/lib/python3.10/site-packages/open_flamingo/eval/__pycache__/ok_vqa_utils.cpython-310.pyc +0 -0
minigpt2/lib/python3.10/site-packages/open_flamingo/eval/__pycache__/vqa_metric.cpython-310.pyc +0 -0
minigpt2/lib/python3.10/site-packages/open_flamingo/eval/classification.py +147 -0
minigpt2/lib/python3.10/site-packages/open_flamingo/eval/coco_metric.py +22 -0
minigpt2/lib/python3.10/site-packages/open_flamingo/eval/imagenet_utils.py +1007 -0
minigpt2/lib/python3.10/site-packages/open_flamingo/src/__init__.py +0 -0
minigpt2/lib/python3.10/site-packages/open_flamingo/src/__pycache__/__init__.cpython-310.pyc +0 -0
minigpt2/lib/python3.10/site-packages/open_flamingo/src/__pycache__/flamingo.cpython-310.pyc +0 -0
minigpt2/lib/python3.10/site-packages/open_flamingo/src/__pycache__/helpers.cpython-310.pyc +0 -0
minigpt2/lib/python3.10/site-packages/open_flamingo/src/__pycache__/utils.cpython-310.pyc +0 -0
minigpt2/lib/python3.10/site-packages/open_flamingo/src/flamingo.py +198 -0
minigpt2/lib/python3.10/site-packages/open_flamingo/src/flamingo_lm.py +138 -0
minigpt2/lib/python3.10/site-packages/open_flamingo/src/helpers.py +275 -0
minigpt2/lib/python3.10/site-packages/open_flamingo/src/utils.py +31 -0
minigpt2/lib/python3.10/site-packages/open_flamingo/train/__init__.py +1 -0
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minigpt2/lib/python3.10/site-packages/open_flamingo/train/__pycache__/distributed.cpython-310.pyc +0 -0

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+<html><body>
+<a href="/foobar-0.1.tar.gz#md5=1__bad_md5___">bad old link</a>
+</body></html>

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+<html><body>
+<a href="/foobar-0.1.tar.gz#md5=0_correct_md5">foobar-0.1.tar.gz</a><br/>
+<a href="../../external.html" rel="homepage">external homepage</a><br/>
+</body></html>

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+"""Reusable functions and classes for different types of integration tests.
+For example ``Archive`` can be used to check the contents of distribution built
+with setuptools, and ``run`` will always try to be as verbose as possible to
+facilitate debugging.
+"""
+import os
+import subprocess
+import tarfile
+from pathlib import Path
+from zipfile import ZipFile
+def run(cmd, env=None):
+    r = subprocess.run(
+        cmd,
+        capture_output=True,
+        text=True,
+        encoding="utf-8",
+        env={**os.environ, **(env or {})},
+        # ^-- allow overwriting instead of discarding the current env
+    )
+    out = r.stdout + "\n" + r.stderr
+    # pytest omits stdout/err by default, if the test fails they help debugging
+    print("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")
+    print(f"Command: {cmd}\nreturn code: {r.returncode}\n\n{out}")
+    if r.returncode == 0:
+        return out
+    raise subprocess.CalledProcessError(r.returncode, cmd, r.stdout, r.stderr)
+class Archive:
+    """Compatibility layer for ZipFile/Info and TarFile/Info"""
+    def __init__(self, filename):
+        self._filename = filename
+        if filename.endswith("tar.gz"):
+            self._obj = tarfile.open(filename, "r:gz")
+        elif filename.endswith("zip"):
+            self._obj = ZipFile(filename)
+        else:
+            raise ValueError(f"{filename} doesn't seem to be a zip or tar.gz")
+    def __iter__(self):
+        if hasattr(self._obj, "infolist"):
+            return iter(self._obj.infolist())
+        return iter(self._obj)
+    def get_name(self, zip_or_tar_info):
+        if hasattr(zip_or_tar_info, "filename"):
+            return zip_or_tar_info.filename
+        return zip_or_tar_info.name
+    def get_content(self, zip_or_tar_info):
+        if hasattr(self._obj, "extractfile"):
+            content = self._obj.extractfile(zip_or_tar_info)
+            if content is None:
+                msg = f"Invalid {zip_or_tar_info.name} in {self._filename}"
+                raise ValueError(msg)
+            return str(content.read(), "utf-8")
+        return str(self._obj.read(zip_or_tar_info), "utf-8")
+def get_sdist_members(sdist_path):
+    with tarfile.open(sdist_path, "r:gz") as tar:
+        files = [Path(f) for f in tar.getnames()]
+    # remove root folder
+    relative_files = ("/".join(f.parts[1:]) for f in files)
+    return {f for f in relative_files if f}
+def get_wheel_members(wheel_path):
+    with ZipFile(wheel_path) as zipfile:
+        return set(zipfile.namelist())

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+# https://github.com/python/mypy/issues/16936
+# mypy: disable-error-code="has-type"
+"""Integration tests for setuptools that focus on building packages via pip.
+The idea behind these tests is not to exhaustively check all the possible
+combinations of packages, operating systems, supporting libraries, etc, but
+rather check a limited number of popular packages and how they interact with
+the exposed public API. This way if any change in API is introduced, we hope to
+identify backward compatibility problems before publishing a release.
+The number of tested packages is purposefully kept small, to minimise duration
+and the associated maintenance cost (changes in the way these packages define
+their build process may require changes in the tests).
+"""
+import json
+import os
+import shutil
+import sys
+from enum import Enum
+from glob import glob
+from hashlib import md5
+from urllib.request import urlopen
+import pytest
+from packaging.requirements import Requirement
+from .helpers import Archive, run
+pytestmark = pytest.mark.integration
+(LATEST,) = Enum("v", "LATEST")  # type: ignore[misc] # https://github.com/python/mypy/issues/16936
+"""Default version to be checked"""
+# There are positive and negative aspects of checking the latest version of the
+# packages.
+# The main positive aspect is that the latest version might have already
+# removed the use of APIs deprecated in previous releases of setuptools.
+# Packages to be tested:
+# (Please notice the test environment cannot support EVERY library required for
+# compiling binary extensions. In Ubuntu/Debian nomenclature, we only assume
+# that `build-essential`, `gfortran` and `libopenblas-dev` are installed,
+# due to their relevance to the numerical/scientific programming ecosystem)
+EXAMPLES = [
+    ("pip", LATEST),  # just in case...
+    ("pytest", LATEST),  # uses setuptools_scm
+    ("mypy", LATEST),  # custom build_py + ext_modules
+    # --- Popular packages: https://hugovk.github.io/top-pypi-packages/ ---
+    ("botocore", LATEST),
+    ("kiwisolver", LATEST),  # build_ext
+    ("brotli", LATEST),  # not in the list but used by urllib3
+    ("pyyaml", LATEST),  # cython + custom build_ext + custom distclass
+    ("charset-normalizer", LATEST),  # uses mypyc, used by aiohttp
+    ("protobuf", LATEST),
+    ("requests", LATEST),
+    ("celery", LATEST),
+    # When adding packages to this list, make sure they expose a `__version__`
+    # attribute, or modify the tests below
+]
+# Some packages have "optional" dependencies that modify their build behaviour
+# and are not listed in pyproject.toml, others still use `setup_requires`
+EXTRA_BUILD_DEPS = {
+    "pyyaml": ("Cython<3.0",),  # constraint to avoid errors
+    "charset-normalizer": ("mypy>=1.4.1",),  # no pyproject.toml available
+}
+EXTRA_ENV_VARS = {
+    "pyyaml": {"PYYAML_FORCE_CYTHON": "1"},
+    "charset-normalizer": {"CHARSET_NORMALIZER_USE_MYPYC": "1"},
+}
+IMPORT_NAME = {
+    "pyyaml": "yaml",
+    "protobuf": "google.protobuf",
+}
+VIRTUALENV = (sys.executable, "-m", "virtualenv")
+# By default, pip will try to build packages in isolation (PEP 517), which
+# means it will download the previous stable version of setuptools.
+# `pip` flags can avoid that (the version of setuptools under test
+# should be the one to be used)
+INSTALL_OPTIONS = (
+    "--ignore-installed",
+    "--no-build-isolation",
+    # Omit "--no-binary :all:" the sdist is supplied directly.
+    # Allows dependencies as wheels.
+)
+# The downside of `--no-build-isolation` is that pip will not download build
+# dependencies. The test script will have to also handle that.
+@pytest.fixture
+def venv_python(tmp_path):
+    run([*VIRTUALENV, str(tmp_path / ".venv")])
+    possible_path = (str(p.parent) for p in tmp_path.glob(".venv/*/python*"))
+    return shutil.which("python", path=os.pathsep.join(possible_path))
+@pytest.fixture(autouse=True)
+def _prepare(tmp_path, venv_python, monkeypatch):
+    download_path = os.getenv("DOWNLOAD_PATH", str(tmp_path))
+    os.makedirs(download_path, exist_ok=True)
+    # Environment vars used for building some of the packages
+    monkeypatch.setenv("USE_MYPYC", "1")
+    yield
+    # Let's provide the maximum amount of information possible in the case
+    # it is necessary to debug the tests directly from the CI logs.
+    print("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")
+    print("Temporary directory:")
+    map(print, tmp_path.glob("*"))
+    print("Virtual environment:")
+    run([venv_python, "-m", "pip", "freeze"])
+@pytest.mark.parametrize(("package", "version"), EXAMPLES)
+@pytest.mark.uses_network
+def test_install_sdist(package, version, tmp_path, venv_python, setuptools_wheel):
+    venv_pip = (venv_python, "-m", "pip")
+    sdist = retrieve_sdist(package, version, tmp_path)
+    deps = build_deps(package, sdist)
+    if deps:
+        print("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")
+        print("Dependencies:", deps)
+        run([*venv_pip, "install", *deps])
+    # Use a virtualenv to simulate PEP 517 isolation
+    # but install fresh setuptools wheel to ensure the version under development
+    env = EXTRA_ENV_VARS.get(package, {})
+    run([*venv_pip, "install", "--force-reinstall", setuptools_wheel])
+    run([*venv_pip, "install", *INSTALL_OPTIONS, sdist], env)
+    # Execute a simple script to make sure the package was installed correctly
+    pkg = IMPORT_NAME.get(package, package).replace("-", "_")
+    script = f"import {pkg}; print(getattr({pkg}, '__version__', 0))"
+    run([venv_python, "-c", script])
+# ---- Helper Functions ----
+def retrieve_sdist(package, version, tmp_path):
+    """Either use cached sdist file or download it from PyPI"""
+    # `pip download` cannot be used due to
+    # https://github.com/pypa/pip/issues/1884
+    # https://discuss.python.org/t/pep-625-file-name-of-a-source-distribution/4686
+    # We have to find the correct distribution file and download it
+    download_path = os.getenv("DOWNLOAD_PATH", str(tmp_path))
+    dist = retrieve_pypi_sdist_metadata(package, version)
+    # Remove old files to prevent cache to grow indefinitely
+    for file in glob(os.path.join(download_path, f"{package}*")):
+        if dist["filename"] != file:
+            os.unlink(file)
+    dist_file = os.path.join(download_path, dist["filename"])
+    if not os.path.exists(dist_file):
+        download(dist["url"], dist_file, dist["md5_digest"])
+    return dist_file
+def retrieve_pypi_sdist_metadata(package, version):
+    # https://warehouse.pypa.io/api-reference/json.html
+    id_ = package if version is LATEST else f"{package}/{version}"
+    with urlopen(f"https://pypi.org/pypi/{id_}/json") as f:
+        metadata = json.load(f)
+    if metadata["info"]["yanked"]:
+        raise ValueError(f"Release for {package} {version} was yanked")
+    version = metadata["info"]["version"]
+    release = metadata["releases"][version] if version is LATEST else metadata["urls"]
+    (sdist,) = filter(lambda d: d["packagetype"] == "sdist", release)
+    return sdist
+def download(url, dest, md5_digest):
+    with urlopen(url) as f:
+        data = f.read()
+    assert md5(data).hexdigest() == md5_digest
+    with open(dest, "wb") as f:
+        f.write(data)
+    assert os.path.exists(dest)
+def build_deps(package, sdist_file):
+    """Find out what are the build dependencies for a package.
+    "Manually" install them, since pip will not install build
+    deps with `--no-build-isolation`.
+    """
+    # delay importing, since pytest discovery phase may hit this file from a
+    # testenv without tomli
+    from setuptools.compat.py310 import tomllib
+    archive = Archive(sdist_file)
+    info = tomllib.loads(_read_pyproject(archive))
+    deps = info.get("build-system", {}).get("requires", [])
+    deps += EXTRA_BUILD_DEPS.get(package, [])
+    # Remove setuptools from requirements (and deduplicate)
+    requirements = {Requirement(d).name: d for d in deps}
+    return [v for k, v in requirements.items() if k != "setuptools"]
+def _read_pyproject(archive):
+    contents = (
+        archive.get_content(member)
+        for member in archive
+        if os.path.basename(archive.get_name(member)) == "pyproject.toml"
+    )
+    return next(contents, "")

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+from typing import Dict, Sequence, Tuple
+import re
+import numpy as np
+import torch
+def postprocess_classification_generation(predictions) -> str:
+    return re.split("Prompt|Completion", predictions, 1)[0]
+def compute_classification_accuracy(predictions: Sequence[Dict[str, str]]) -> float:
+    """Compute the accuracy of a sequence of predictions."""
+    def _preprocess_fn(s):
+        """Function to preprocess both targets and predictions."""
+        return s.lower()
+    is_correct = [
+        _preprocess_fn(x["prediction"]) == _preprocess_fn(x["class_label"])
+        for x in predictions
+    ]
+    return np.mean(is_correct).item()
+def compute_shifted_logits_and_labels(
+    logits: torch.Tensor, encodings, tokenizer, eoc_token_id
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    """Helper function to compute shifted logits and labels.
+    This allows for straightforward computation of the loss on shift_logits
+    and shift_labels such that the nth element of logits computes the n-1th
+    element of the original labels (in the outputs, the nth element of logits
+    corresponds to the nth element of the labels).
+    Elements in shift_labels that correspond to inputs are masked with values
+    of -100 (by default in hf, loss is only computed on token IDs >= 0).
+    Returns: tuple containing two elements:
+        shift_logits: a float Tensor of shape [batch_size, seq_len - 1].
+        shift_labels: an integer Tensor of shape [batch_size, seq_len - 1]
+    """
+    labels = encodings["input_ids"].clone()
+    # convert padding and EOC tokens to -100 so they are ignored in loss
+    labels[labels == tokenizer.pad_token_id] = -100
+    labels[labels == eoc_token_id] = -100
+    # Convert all tokens in prefix until separator to -100 so they are
+    # ignored in loss
+    for idx in range(len(labels)):
+        # Find the location of the last token of prefix *from right*,
+        # since the first non-padding token of the sequence will also be
+        # eos_token (because bos_token and eos_token are the same for
+        # the tokenizer).
+        end_of_prefix = -labels[idx].tolist()[::-1].index(tokenizer.eos_token_id) - 1
+        labels[idx, : end_of_prefix + 1] = -100
+    # Shift so that tokens < n predict n. The shifted tensors both have
+    # shape [batch_size, seq_len - 1].
+    shift_logits = logits[..., :-1, :].contiguous()
+    shift_labels = labels[..., 1:].contiguous()
+    return shift_logits, shift_labels
+def compute_per_sample_probs(
+    encodings, tokenizer, logits: torch.Tensor, eoc_token_id
+) -> torch.Tensor:
+    """Helper function to compute per-sample probability of the input sequence.
+    Assumes <eos token> is used to separate inputs from targets in the
+    prompt text
+    """
+    shift_logits, shift_labels = compute_shifted_logits_and_labels(
+        logits, encodings, tokenizer, eoc_token_id
+    )
+    # Tuple of tensors for unmasked label tokens. The first element of the
+    # tuple contains the batch indices; the second element contains the
+    # sequence indices.
+    unmasked_indices = torch.nonzero(shift_labels != -100, as_tuple=True)
+    # Tensor where the i^th element is the token_id corresponding to the i^th
+    # element of unmasked_indices
+    unmasked_token_ids = shift_labels[unmasked_indices]
+    # 3d tensor of [batch_idx, sequence_position, token_id] for unmasked tokens.
+    target_idxs = torch.column_stack([*unmasked_indices, unmasked_token_ids])
+    target_idxs = target_idxs.to(shift_logits.device)
+    # Sanity check that every element in batch has at least one unmasked
+    # target token
+    assert torch.all(
+        torch.bincount(target_idxs[:, 0]) != 0
+    ), "At least one element in batch has no unmasked target tokens."
+    # Renormalize over tokens to make sure they are proper probabilities via
+    # softmax over the token dimension.
+    shift_probs = torch.nn.functional.softmax(shift_logits, 2)
+    # Compute the probability of the target sequence (as the product of the
+    # probability of the individual tokens in the sequence).
+    target_probs = torch.ones(len(shift_labels), device=shift_logits.device)
+    for i, j, k in target_idxs:
+        target_probs[i] *= shift_probs[i, j, k]
+    return target_probs
+def compute_per_sample_loss(encodings, tokenizer, logits, eoc_token_id) -> torch.Tensor:
+    """Helper function to compute per-sample classification loss.
+    Assumes <eos token> is used to separate inputs from targets in the
+    prompt text
+    """
+    shift_logits, shift_labels = compute_shifted_logits_and_labels(
+        logits, encodings, tokenizer, eoc_token_id
+    )
+    device = shift_logits.device
+    # Loss is computed token-wise, on Tensors of shape
+    # [batch_size * (seq_len - 1), vocab_size]
+    # and returns a loss tensor of shape
+    # [batch_size * (seq_len - 1)]. Most of the tokens will be masked
+    # in this computation.
+    loss = torch.nn.functional.cross_entropy(
+        shift_logits.view(-1, shift_logits.size(-1)),
+        shift_labels.view(-1).to(device),
+        reduction="none",
+    )
+    # Reshape to [batch_size, seq_len - 1]
+    loss = loss.view(shift_logits.size(0), shift_logits.size(1)).cpu()
+    # loss_mask is 1 for tokens we want included in the loss, and 0 for tokens
+    # that should be ignored in the loss.
+    loss_mask = (shift_labels != -100).int().cpu()
+    loss *= loss_mask
+    # Compute per-element loss : sum loss over all (unmasked) tokens and
+    # divide by number of variable tokens to obtain tensor of
+    # shape [batch_size,]
+    loss = loss.sum(dim=1) / (shift_labels != -100).sum(dim=1).float()
+    return loss

minigpt2/lib/python3.10/site-packages/open_flamingo/eval/coco_metric.py ADDED Viewed

	@@ -0,0 +1,22 @@

+from pycocoevalcap.eval import COCOEvalCap
+from pycocotools.coco import COCO
+def compute_cider(
+    result_path,
+    annotations_path="/data/yfcc-tmp/data/mscoco/annotations/captions_train2017.json",
+):
+    # create coco object and coco_result object
+    coco = COCO(annotations_path)
+    coco_result = coco.loadRes(result_path)
+    # create coco_eval object by taking coco and coco_result
+    coco_eval = COCOEvalCap(coco, coco_result)
+    coco_eval.params["image_id"] = coco_result.getImgIds()
+    coco_eval.evaluate()
+    return coco_eval.eval
+def postprocess_captioning_generation(predictions):
+    return predictions.split("Output", 1)[0]

minigpt2/lib/python3.10/site-packages/open_flamingo/eval/imagenet_utils.py ADDED Viewed

	@@ -0,0 +1,1007 @@

+# classnames via https://github.com/mlfoundations/wise-ft/blob/master/src/datasets/imagenet_classnames.py#L1
+openai_imagenet_classnames = [
+    "tench",
+    "goldfish",
+    "great white shark",
+    "tiger shark",
+    "hammerhead shark",
+    "electric ray",
+    "stingray",
+    "rooster",
+    "hen",
+    "ostrich",
+    "brambling",
+    "goldfinch",
+    "house finch",
+    "junco",
+    "indigo bunting",
+    "American robin",
+    "bulbul",
+    "jay",
+    "magpie",
+    "chickadee",
+    "American dipper",
+    "kite (bird of prey)",
+    "bald eagle",
+    "vulture",
+    "great grey owl",
+    "fire salamander",
+    "smooth newt",
+    "newt",
+    "spotted salamander",
+    "axolotl",
+    "American bullfrog",
+    "tree frog",
+    "tailed frog",
+    "loggerhead sea turtle",
+    "leatherback sea turtle",
+    "mud turtle",
+    "terrapin",
+    "box turtle",
+    "banded gecko",
+    "green iguana",
+    "Carolina anole",
+    "desert grassland whiptail lizard",
+    "agama",
+    "frilled-necked lizard",
+    "alligator lizard",
+    "Gila monster",
+    "European green lizard",
+    "chameleon",
+    "Komodo dragon",
+    "Nile crocodile",
+    "American alligator",
+    "triceratops",
+    "worm snake",
+    "ring-necked snake",
+    "eastern hog-nosed snake",
+    "smooth green snake",
+    "kingsnake",
+    "garter snake",
+    "water snake",
+    "vine snake",
+    "night snake",
+    "boa constrictor",
+    "African rock python",
+    "Indian cobra",
+    "green mamba",
+    "sea snake",
+    "Saharan horned viper",
+    "eastern diamondback rattlesnake",
+    "sidewinder rattlesnake",
+    "trilobite",
+    "harvestman",
+    "scorpion",
+    "yellow garden spider",
+    "barn spider",
+    "European garden spider",
+    "southern black widow",
+    "tarantula",
+    "wolf spider",
+    "tick",
+    "centipede",
+    "black grouse",
+    "ptarmigan",
+    "ruffed grouse",
+    "prairie grouse",
+    "peafowl",
+    "quail",
+    "partridge",
+    "african grey parrot",
+    "macaw",
+    "sulphur-crested cockatoo",
+    "lorikeet",
+    "coucal",
+    "bee eater",
+    "hornbill",
+    "hummingbird",
+    "jacamar",
+    "toucan",
+    "duck",
+    "red-breasted merganser",
+    "goose",
+    "black swan",
+    "tusker",
+    "echidna",
+    "platypus",
+    "wallaby",
+    "koala",
+    "wombat",
+    "jellyfish",
+    "sea anemone",
+    "brain coral",
+    "flatworm",
+    "nematode",
+    "conch",
+    "snail",
+    "slug",
+    "sea slug",
+    "chiton",
+    "chambered nautilus",
+    "Dungeness crab",
+    "rock crab",
+    "fiddler crab",
+    "red king crab",
+    "American lobster",
+    "spiny lobster",
+    "crayfish",
+    "hermit crab",
+    "isopod",
+    "white stork",
+    "black stork",
+    "spoonbill",
+    "flamingo",
+    "little blue heron",
+    "great egret",
+    "bittern bird",
+    "crane bird",
+    "limpkin",
+    "common gallinule",
+    "American coot",
+    "bustard",
+    "ruddy turnstone",
+    "dunlin",
+    "common redshank",
+    "dowitcher",
+    "oystercatcher",
+    "pelican",
+    "king penguin",
+    "albatross",
+    "grey whale",
+    "killer whale",
+    "dugong",
+    "sea lion",
+    "Chihuahua",
+    "Japanese Chin",
+    "Maltese",
+    "Pekingese",
+    "Shih Tzu",
+    "King Charles Spaniel",
+    "Papillon",
+    "toy terrier",
+    "Rhodesian Ridgeback",
+    "Afghan Hound",
+    "Basset Hound",
+    "Beagle",
+    "Bloodhound",
+    "Bluetick Coonhound",
+    "Black and Tan Coonhound",
+    "Treeing Walker Coonhound",
+    "English foxhound",
+    "Redbone Coonhound",
+    "borzoi",
+    "Irish Wolfhound",
+    "Italian Greyhound",
+    "Whippet",
+    "Ibizan Hound",
+    "Norwegian Elkhound",
+    "Otterhound",
+    "Saluki",
+    "Scottish Deerhound",
+    "Weimaraner",
+    "Staffordshire Bull Terrier",
+    "American Staffordshire Terrier",
+    "Bedlington Terrier",
+    "Border Terrier",
+    "Kerry Blue Terrier",
+    "Irish Terrier",
+    "Norfolk Terrier",
+    "Norwich Terrier",
+    "Yorkshire Terrier",
+    "Wire Fox Terrier",
+    "Lakeland Terrier",
+    "Sealyham Terrier",
+    "Airedale Terrier",
+    "Cairn Terrier",
+    "Australian Terrier",
+    "Dandie Dinmont Terrier",
+    "Boston Terrier",
+    "Miniature Schnauzer",
+    "Giant Schnauzer",
+    "Standard Schnauzer",
+    "Scottish Terrier",
+    "Tibetan Terrier",
+    "Australian Silky Terrier",
+    "Soft-coated Wheaten Terrier",
+    "West Highland White Terrier",
+    "Lhasa Apso",
+    "Flat-Coated Retriever",
+    "Curly-coated Retriever",
+    "Golden Retriever",
+    "Labrador Retriever",
+    "Chesapeake Bay Retriever",
+    "German Shorthaired Pointer",
+    "Vizsla",
+    "English Setter",
+    "Irish Setter",
+    "Gordon Setter",
+    "Brittany dog",
+    "Clumber Spaniel",
+    "English Springer Spaniel",
+    "Welsh Springer Spaniel",
+    "Cocker Spaniel",
+    "Sussex Spaniel",
+    "Irish Water Spaniel",
+    "Kuvasz",
+    "Schipperke",
+    "Groenendael dog",
+    "Malinois",
+    "Briard",
+    "Australian Kelpie",
+    "Komondor",
+    "Old English Sheepdog",
+    "Shetland Sheepdog",
+    "collie",
+    "Border Collie",
+    "Bouvier des Flandres dog",
+    "Rottweiler",
+    "German Shepherd Dog",
+    "Dobermann",
+    "Miniature Pinscher",
+    "Greater Swiss Mountain Dog",
+    "Bernese Mountain Dog",
+    "Appenzeller Sennenhund",
+    "Entlebucher Sennenhund",
+    "Boxer",
+    "Bullmastiff",
+    "Tibetan Mastiff",
+    "French Bulldog",
+    "Great Dane",
+    "St. Bernard",
+    "husky",
+    "Alaskan Malamute",
+    "Siberian Husky",
+    "Dalmatian",
+    "Affenpinscher",
+    "Basenji",
+    "pug",
+    "Leonberger",
+    "Newfoundland dog",
+    "Great Pyrenees dog",
+    "Samoyed",
+    "Pomeranian",
+    "Chow Chow",
+    "Keeshond",
+    "brussels griffon",
+    "Pembroke Welsh Corgi",
+    "Cardigan Welsh Corgi",
+    "Toy Poodle",
+    "Miniature Poodle",
+    "Standard Poodle",
+    "Mexican hairless dog (xoloitzcuintli)",
+    "grey wolf",
+    "Alaskan tundra wolf",
+    "red wolf or maned wolf",
+    "coyote",
+    "dingo",
+    "dhole",
+    "African wild dog",
+    "hyena",
+    "red fox",
+    "kit fox",
+    "Arctic fox",
+    "grey fox",
+    "tabby cat",
+    "tiger cat",
+    "Persian cat",
+    "Siamese cat",
+    "Egyptian Mau",
+    "cougar",
+    "lynx",
+    "leopard",
+    "snow leopard",
+    "jaguar",
+    "lion",
+    "tiger",
+    "cheetah",
+    "brown bear",
+    "American black bear",
+    "polar bear",
+    "sloth bear",
+    "mongoose",
+    "meerkat",
+    "tiger beetle",
+    "ladybug",
+    "ground beetle",
+    "longhorn beetle",
+    "leaf beetle",
+    "dung beetle",
+    "rhinoceros beetle",
+    "weevil",
+    "fly",
+    "bee",
+    "ant",
+    "grasshopper",
+    "cricket insect",
+    "stick insect",
+    "cockroach",
+    "praying mantis",
+    "cicada",
+    "leafhopper",
+    "lacewing",
+    "dragonfly",
+    "damselfly",
+    "red admiral butterfly",
+    "ringlet butterfly",
+    "monarch butterfly",
+    "small white butterfly",
+    "sulphur butterfly",
+    "gossamer-winged butterfly",
+    "starfish",
+    "sea urchin",
+    "sea cucumber",
+    "cottontail rabbit",
+    "hare",
+    "Angora rabbit",
+    "hamster",
+    "porcupine",
+    "fox squirrel",
+    "marmot",
+    "beaver",
+    "guinea pig",
+    "common sorrel horse",
+    "zebra",
+    "pig",
+    "wild boar",
+    "warthog",
+    "hippopotamus",
+    "ox",
+    "water buffalo",
+    "bison",
+    "ram (adult male sheep)",
+    "bighorn sheep",
+    "Alpine ibex",
+    "hartebeest",
+    "impala (antelope)",
+    "gazelle",
+    "arabian camel",
+    "llama",
+    "weasel",
+    "mink",
+    "European polecat",
+    "black-footed ferret",
+    "otter",
+    "skunk",
+    "badger",
+    "armadillo",
+    "three-toed sloth",
+    "orangutan",
+    "gorilla",
+    "chimpanzee",
+    "gibbon",
+    "siamang",
+    "guenon",
+    "patas monkey",
+    "baboon",
+    "macaque",
+    "langur",
+    "black-and-white colobus",
+    "proboscis monkey",
+    "marmoset",
+    "white-headed capuchin",
+    "howler monkey",
+    "titi monkey",
+    "Geoffroy's spider monkey",
+    "common squirrel monkey",
+    "ring-tailed lemur",
+    "indri",
+    "Asian elephant",
+    "African bush elephant",
+    "red panda",
+    "giant panda",
+    "snoek fish",
+    "eel",
+    "silver salmon",
+    "rock beauty fish",
+    "clownfish",
+    "sturgeon",
+    "gar fish",
+    "lionfish",
+    "pufferfish",
+    "abacus",
+    "abaya",
+    "academic gown",
+    "accordion",
+    "acoustic guitar",
+    "aircraft carrier",
+    "airliner",
+    "airship",
+    "altar",
+    "ambulance",
+    "amphibious vehicle",
+    "analog clock",
+    "apiary",
+    "apron",
+    "trash can",
+    "assault rifle",
+    "backpack",
+    "bakery",
+    "balance beam",
+    "balloon",
+    "ballpoint pen",
+    "Band-Aid",
+    "banjo",
+    "baluster / handrail",
+    "barbell",
+    "barber chair",
+    "barbershop",
+    "barn",
+    "barometer",
+    "barrel",
+    "wheelbarrow",
+    "baseball",
+    "basketball",
+    "bassinet",
+    "bassoon",
+    "swimming cap",
+    "bath towel",
+    "bathtub",
+    "station wagon",
+    "lighthouse",
+    "beaker",
+    "military hat (bearskin or shako)",
+    "beer bottle",
+    "beer glass",
+    "bell tower",
+    "baby bib",
+    "tandem bicycle",
+    "bikini",
+    "ring binder",
+    "binoculars",
+    "birdhouse",
+    "boathouse",
+    "bobsleigh",
+    "bolo tie",
+    "poke bonnet",
+    "bookcase",
+    "bookstore",
+    "bottle cap",
+    "hunting bow",
+    "bow tie",
+    "brass memorial plaque",
+    "bra",
+    "breakwater",
+    "breastplate",
+    "broom",
+    "bucket",
+    "buckle",
+    "bulletproof vest",
+    "high-speed train",
+    "butcher shop",
+    "taxicab",
+    "cauldron",
+    "candle",
+    "cannon",
+    "canoe",
+    "can opener",
+    "cardigan",
+    "car mirror",
+    "carousel",
+    "tool kit",
+    "cardboard box / carton",
+    "car wheel",
+    "automated teller machine",
+    "cassette",
+    "cassette player",
+    "castle",
+    "catamaran",
+    "CD player",
+    "cello",
+    "mobile phone",
+    "chain",
+    "chain-link fence",
+    "chain mail",
+    "chainsaw",
+    "storage chest",
+    "chiffonier",
+    "bell or wind chime",
+    "china cabinet",
+    "Christmas stocking",
+    "church",
+    "movie theater",
+    "cleaver",
+    "cliff dwelling",
+    "cloak",
+    "clogs",
+    "cocktail shaker",
+    "coffee mug",
+    "coffeemaker",
+    "spiral or coil",
+    "combination lock",
+    "computer keyboard",
+    "candy store",
+    "container ship",
+    "convertible",
+    "corkscrew",
+    "cornet",
+    "cowboy boot",
+    "cowboy hat",
+    "cradle",
+    "construction crane",
+    "crash helmet",
+    "crate",
+    "infant bed",
+    "Crock Pot",
+    "croquet ball",
+    "crutch",
+    "cuirass",
+    "dam",
+    "desk",
+    "desktop computer",
+    "rotary dial telephone",
+    "diaper",
+    "digital clock",
+    "digital watch",
+    "dining table",
+    "dishcloth",
+    "dishwasher",
+    "disc brake",
+    "dock",
+    "dog sled",
+    "dome",
+    "doormat",
+    "drilling rig",
+    "drum",
+    "drumstick",
+    "dumbbell",
+    "Dutch oven",
+    "electric fan",
+    "electric guitar",
+    "electric locomotive",
+    "entertainment center",
+    "envelope",
+    "espresso machine",
+    "face powder",
+    "feather boa",
+    "filing cabinet",
+    "fireboat",
+    "fire truck",
+    "fire screen",
+    "flagpole",
+    "flute",
+    "folding chair",
+    "football helmet",
+    "forklift",
+    "fountain",
+    "fountain pen",
+    "four-poster bed",
+    "freight car",
+    "French horn",
+    "frying pan",
+    "fur coat",
+    "garbage truck",
+    "gas mask or respirator",
+    "gas pump",
+    "goblet",
+    "go-kart",
+    "golf ball",
+    "golf cart",
+    "gondola",
+    "gong",
+    "gown",
+    "grand piano",
+    "greenhouse",
+    "radiator grille",
+    "grocery store",
+    "guillotine",
+    "hair clip",
+    "hair spray",
+    "half-track",
+    "hammer",
+    "hamper",
+    "hair dryer",
+    "hand-held computer",
+    "handkerchief",
+    "hard disk drive",
+    "harmonica",
+    "harp",
+    "combine harvester",
+    "hatchet",
+    "holster",
+    "home theater",
+    "honeycomb",
+    "hook",
+    "hoop skirt",
+    "gymnastic horizontal bar",
+    "horse-drawn vehicle",
+    "hourglass",
+    "iPod",
+    "clothes iron",
+    "carved pumpkin",
+    "jeans",
+    "jeep",
+    "T-shirt",
+    "jigsaw puzzle",
+    "rickshaw",
+    "joystick",
+    "kimono",
+    "knee pad",
+    "knot",
+    "lab coat",
+    "ladle",
+    "lampshade",
+    "laptop computer",
+    "lawn mower",
+    "lens cap",
+    "letter opener",
+    "library",
+    "lifeboat",
+    "lighter",
+    "limousine",
+    "ocean liner",
+    "lipstick",
+    "slip-on shoe",
+    "lotion",
+    "music speaker",
+    "loupe magnifying glass",
+    "sawmill",
+    "magnetic compass",
+    "messenger bag",
+    "mailbox",
+    "tights",
+    "one-piece bathing suit",
+    "manhole cover",
+    "maraca",
+    "marimba",
+    "mask",
+    "matchstick",
+    "maypole",
+    "maze",
+    "measuring cup",
+    "medicine cabinet",
+    "megalith",
+    "microphone",
+    "microwave oven",
+    "military uniform",
+    "milk can",
+    "minibus",
+    "miniskirt",
+    "minivan",
+    "missile",
+    "mitten",
+    "mixing bowl",
+    "mobile home",
+    "ford model t",
+    "modem",
+    "monastery",
+    "monitor",
+    "moped",
+    "mortar and pestle",
+    "graduation cap",
+    "mosque",
+    "mosquito net",
+    "vespa",
+    "mountain bike",
+    "tent",
+    "computer mouse",
+    "mousetrap",
+    "moving van",
+    "muzzle",
+    "metal nail",
+    "neck brace",
+    "necklace",
+    "baby pacifier",
+    "notebook computer",
+    "obelisk",
+    "oboe",
+    "ocarina",
+    "odometer",
+    "oil filter",
+    "pipe organ",
+    "oscilloscope",
+    "overskirt",
+    "bullock cart",
+    "oxygen mask",
+    "product packet / packaging",
+    "paddle",
+    "paddle wheel",
+    "padlock",
+    "paintbrush",
+    "pajamas",
+    "palace",
+    "pan flute",
+    "paper towel",
+    "parachute",
+    "parallel bars",
+    "park bench",
+    "parking meter",
+    "railroad car",
+    "patio",
+    "payphone",
+    "pedestal",
+    "pencil case",
+    "pencil sharpener",
+    "perfume",
+    "Petri dish",
+    "photocopier",
+    "plectrum",
+    "Pickelhaube",
+    "picket fence",
+    "pickup truck",
+    "pier",
+    "piggy bank",
+    "pill bottle",
+    "pillow",
+    "ping-pong ball",
+    "pinwheel",
+    "pirate ship",
+    "drink pitcher",
+    "block plane",
+    "planetarium",
+    "plastic bag",
+    "plate rack",
+    "farm plow",
+    "plunger",
+    "Polaroid camera",
+    "pole",
+    "police van",
+    "poncho",
+    "pool table",
+    "soda bottle",
+    "plant pot",
+    "potter's wheel",
+    "power drill",
+    "prayer rug",
+    "printer",
+    "prison",
+    "missile",
+    "projector",
+    "hockey puck",
+    "punching bag",
+    "purse",
+    "quill",
+    "quilt",
+    "race car",
+    "racket",
+    "radiator",
+    "radio",
+    "radio telescope",
+    "rain barrel",
+    "recreational vehicle",
+    "fishing casting reel",
+    "reflex camera",
+    "refrigerator",
+    "remote control",
+    "restaurant",
+    "revolver",
+    "rifle",
+    "rocking chair",
+    "rotisserie",
+    "eraser",
+    "rugby ball",
+    "ruler measuring stick",
+    "sneaker",
+    "safe",
+    "safety pin",
+    "salt shaker",
+    "sandal",
+    "sarong",
+    "saxophone",
+    "scabbard",
+    "weighing scale",
+    "school bus",
+    "schooner",
+    "scoreboard",
+    "CRT monitor",
+    "screw",
+    "screwdriver",
+    "seat belt",
+    "sewing machine",
+    "shield",
+    "shoe store",
+    "shoji screen / room divider",
+    "shopping basket",
+    "shopping cart",
+    "shovel",
+    "shower cap",
+    "shower curtain",
+    "ski",
+    "balaclava ski mask",
+    "sleeping bag",
+    "slide rule",
+    "sliding door",
+    "slot machine",
+    "snorkel",
+    "snowmobile",
+    "snowplow",
+    "soap dispenser",
+    "soccer ball",
+    "sock",
+    "solar thermal collector",
+    "sombrero",
+    "soup bowl",
+    "keyboard space bar",
+    "space heater",
+    "space shuttle",
+    "spatula",
+    "motorboat",
+    "spider web",
+    "spindle",
+    "sports car",
+    "spotlight",
+    "stage",
+    "steam locomotive",
+    "through arch bridge",
+    "steel drum",
+    "stethoscope",
+    "scarf",
+    "stone wall",
+    "stopwatch",
+    "stove",
+    "strainer",
+    "tram",
+    "stretcher",
+    "couch",
+    "stupa",
+    "submarine",
+    "suit",
+    "sundial",
+    "sunglasses",
+    "sunglasses",
+    "sunscreen",
+    "suspension bridge",
+    "mop",
+    "sweatshirt",
+    "swim trunks / shorts",
+    "swing",
+    "electrical switch",
+    "syringe",
+    "table lamp",
+    "tank",
+    "tape player",
+    "teapot",
+    "teddy bear",
+    "television",
+    "tennis ball",
+    "thatched roof",
+    "front curtain",
+    "thimble",
+    "threshing machine",
+    "throne",
+    "tile roof",
+    "toaster",
+    "tobacco shop",
+    "toilet seat",
+    "torch",
+    "totem pole",
+    "tow truck",
+    "toy store",
+    "tractor",
+    "semi-trailer truck",
+    "tray",
+    "trench coat",
+    "tricycle",
+    "trimaran",
+    "tripod",
+    "triumphal arch",
+    "trolleybus",
+    "trombone",
+    "hot tub",
+    "turnstile",
+    "typewriter keyboard",
+    "umbrella",
+    "unicycle",
+    "upright piano",
+    "vacuum cleaner",
+    "vase",
+    "vaulted or arched ceiling",
+    "velvet fabric",
+    "vending machine",
+    "vestment",
+    "viaduct",
+    "violin",
+    "volleyball",
+    "waffle iron",
+    "wall clock",
+    "wallet",
+    "wardrobe",
+    "military aircraft",
+    "sink",
+    "washing machine",
+    "water bottle",
+    "water jug",
+    "water tower",
+    "whiskey jug",
+    "whistle",
+    "hair wig",
+    "window screen",
+    "window shade",
+    "Windsor tie",
+    "wine bottle",
+    "airplane wing",
+    "wok",
+    "wooden spoon",
+    "wool",
+    "split-rail fence",
+    "shipwreck",
+    "sailboat",
+    "yurt",
+    "website",
+    "comic book",
+    "crossword",
+    "traffic or street sign",
+    "traffic light",
+    "dust jacket",
+    "menu",
+    "plate",
+    "guacamole",
+    "consomme",
+    "hot pot",
+    "trifle",
+    "ice cream",
+    "popsicle",
+    "baguette",
+    "bagel",
+    "pretzel",
+    "cheeseburger",
+    "hot dog",
+    "mashed potatoes",
+    "cabbage",
+    "broccoli",
+    "cauliflower",
+    "zucchini",
+    "spaghetti squash",
+    "acorn squash",
+    "butternut squash",
+    "cucumber",
+    "artichoke",
+    "bell pepper",
+    "cardoon",
+    "mushroom",
+    "Granny Smith apple",
+    "strawberry",
+    "orange",
+    "lemon",
+    "fig",
+    "pineapple",
+    "banana",
+    "jackfruit",
+    "cherimoya (custard apple)",
+    "pomegranate",
+    "hay",
+    "carbonara",
+    "chocolate syrup",
+    "dough",
+    "meatloaf",
+    "pizza",
+    "pot pie",
+    "burrito",
+    "red wine",
+    "espresso",
+    "tea cup",
+    "eggnog",
+    "mountain",
+    "bubble",
+    "cliff",
+    "coral reef",
+    "geyser",
+    "lakeshore",
+    "promontory",
+    "sandbar",
+    "beach",
+    "valley",
+    "volcano",
+    "baseball player",
+    "bridegroom",
+    "scuba diver",
+    "rapeseed",
+    "daisy",
+    "yellow lady's slipper",
+    "corn",
+    "acorn",
+    "rose hip",
+    "horse chestnut seed",
+    "coral fungus",
+    "agaric",
+    "gyromitra",
+    "stinkhorn mushroom",
+    "earth star fungus",
+    "hen of the woods mushroom",
+    "bolete",
+    "corn cob",
+    "toilet paper",
+]
+# Maps numeric class ids to labels
+IMAGENET_1K_CLASS_ID_TO_LABEL = dict(
+    zip(range(len(openai_imagenet_classnames)), openai_imagenet_classnames)
+)

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minigpt2/lib/python3.10/site-packages/open_flamingo/src/flamingo.py ADDED Viewed

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+import torch
+from einops import rearrange
+from torch import nn
+from .helpers import PerceiverResampler
+class Flamingo(nn.Module):
+    def __init__(
+        self,
+        vision_encoder: nn.Module,
+        lang_encoder: nn.Module,
+        eoc_token_id: int,
+        media_token_id: int,
+        vis_dim: int,
+        cross_attn_every_n_layers: int = 1,
+        use_media_placement_augmentation: bool = False,
+    ):
+        """
+        Args:
+            vision_encoder (nn.Module): HF CLIPModel
+            lang_encoder (nn.Module): HF causal language model
+            eoc_token_id (int): Token id for <|endofchunk|>
+            media_token_id (int): Token id for <image>
+            vis_dim (int): Dimension of the visual features.
+                Visual features are projected to match this shape along the last dimension.
+            cross_attn_every_n_layers (int, optional): How often to apply cross attention after transformer layer. Defaults to 1.
+            use_media_placement_augmentation (bool, optional): Whether to randomly assign images to the preceding or following text in training. Defaults to False.
+        """
+        super().__init__()
+        self.eoc_token_id = eoc_token_id
+        self.media_token_id = media_token_id
+        self.use_media_placement_augmentation = use_media_placement_augmentation
+        self.vis_dim = vis_dim
+        self.vision_encoder = vision_encoder
+        self.perceiver = PerceiverResampler(dim=self.vis_dim)
+        self.lang_encoder = lang_encoder
+        self.lang_encoder.init_flamingo(
+            media_token_id=media_token_id,
+            vis_hidden_size=self.vis_dim,
+            cross_attn_every_n_layers=cross_attn_every_n_layers,
+            use_media_placement_augmentation=self.use_media_placement_augmentation,
+        )
+    def forward(
+        self,
+        vision_x: torch.Tensor,
+        lang_x: torch.Tensor,
+        attention_mask: torch.Tensor = None,
+        labels: torch.Tensor = None,
+        use_cached_vision_x: bool = False,
+        clear_conditioned_layers: bool = True,
+        past_key_values=None,
+        use_cache: bool = False,
+    ):
+        """
+        Forward pass of Flamingo.
+        Args:
+            vision_x (torch.Tensor): Vision input
+                shape (B, T_img, F, C, H, W) with F=1
+            lang_x (torch.Tensor): Language input ids
+                shape (B, T_txt)
+            attention_mask (torch.Tensor, optional): Attention mask. Defaults to None.
+            labels (torch.Tensor, optional): Labels. Defaults to None.
+            clear_conditioned_layers: if True, clear the conditioned layers
+                once the foward pass is completed. Set this to false if the
+                same set of images will be reused in another subsequent
+                forward pass.
+            past_key_values: pre-computed values to pass to language model.
+                See past_key_values documentation in Hugging Face
+                CausalLM models.
+            use_cache: whether to use cached key values. See use_cache
+                documentation in Hugging Face CausalLM models.
+        """
+        assert (
+            vision_x is not None
+        ) or use_cached_vision_x, (
+            "Must provide either vision_x or use_cached_vision_x to True."
+        )
+        if use_cached_vision_x:
+            # Case: use cached; vision_x should be cached and other
+            # vision-related inputs should not be provided.
+            assert (
+                vision_x is None
+            ), "Expect vision_x to be None when use_cached_vision_x is True."
+            assert self.lang_encoder.is_conditioned()
+        else:
+            # Case: do not use caching (i.e. this is a standard forward pass);
+            self._encode_vision_x(vision_x=vision_x)
+        output = self.lang_encoder(
+            input_ids=lang_x,
+            attention_mask=attention_mask,
+            labels=labels,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+        )
+        if clear_conditioned_layers:
+            self.lang_encoder.clear_conditioned_layers()
+        return output
+    def generate(
+        self,
+        vision_x: torch.Tensor,
+        lang_x: torch.Tensor,
+        attention_mask: torch.Tensor = None,
+        num_beams=1,
+        max_new_tokens=None,
+        temperature=1.0,
+        top_k=0,
+        top_p=1.0,
+        no_repeat_ngram_size=0,
+        prefix_allowed_tokens_fn=None,
+        length_penalty=1.0,
+        num_return_sequences=1,
+        do_sample=False,
+        early_stopping=False,
+    ):
+        """
+        Generate text conditioned on vision and language inputs.
+        Args:
+            vision_x (torch.Tensor): Vision input
+                shape (B, T_img, F, C, H, W)
+                images in the same chunk are collated along T_img, and frames are collated along F
+                currently only F=1 is supported (single-frame videos)
+            lang_x (torch.Tensor): Language input
+                shape (B, T_txt)
+            max_length (int, optional): Maximum length of the output. Defaults to None.
+            attention_mask (torch.Tensor, optional): Attention mask. Defaults to None.
+            num_beams (int, optional): Number of beams. Defaults to 1.
+            max_new_tokens (int, optional): Maximum new tokens. Defaults to None.
+            temperature (float, optional): Temperature. Defaults to 1.0.
+            top_k (int, optional): Top k. Defaults to 0.
+            top_p (float, optional): Top p. Defaults to 1.0.
+            no_repeat_ngram_size (int, optional): No repeat ngram size. Defaults to 0.
+            length_penalty (float, optional): Length penalty. Defaults to 1.0.
+            num_return_sequences (int, optional): Number of return sequences. Defaults to 1.
+            do_sample (bool, optional): Do sample. Defaults to False.
+            early_stopping (bool, optional): Early stopping. Defaults to False.
+        Returns:
+            torch.Tensor: lang_x with generated tokens appended to it
+        """
+        if num_beams > 1:
+            vision_x = vision_x.repeat_interleave(num_beams, dim=0)
+        self._encode_vision_x(vision_x=vision_x)
+        output = self.lang_encoder.generate(
+            lang_x,
+            attention_mask=attention_mask,
+            eos_token_id=self.eoc_token_id,
+            num_beams=num_beams,
+            max_new_tokens=max_new_tokens,
+            temperature=temperature,
+            top_k=top_k,
+            top_p=top_p,
+            prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
+            no_repeat_ngram_size=no_repeat_ngram_size,
+            length_penalty=length_penalty,
+            num_return_sequences=num_return_sequences,
+            do_sample=do_sample,
+            early_stopping=early_stopping,
+        )
+        self.lang_encoder.clear_conditioned_layers()
+        return output
+    def _encode_vision_x(self, vision_x: torch.Tensor):
+        """
+        Compute media tokens from vision input by passing it through vision encoder and conditioning language model.
+        Args:
+            vision_x (torch.Tensor): Vision input
+                shape (B, T_img, F, C, H, W)
+                Images in the same chunk are collated along T_img, and frames are collated along F
+                Currently only F=1 is supported (single-frame videos)
+        rearrange code based on https://github.com/dhansmair/flamingo-mini
+        """
+        assert vision_x.ndim == 6, "vision_x should be of shape (b, T_img, F, C, H, W)"
+        b, T, F = vision_x.shape[:3]
+        assert F == 1, "Only single frame supported"
+        vision_x = rearrange(vision_x, "b T F c h w -> (b T F) c h w")
+        with torch.no_grad():
+            vision_x = self.vision_encoder.visual(vision_x)[1]
+        vision_x = rearrange(vision_x, "(b T F) v d -> b T F v d", b=b, T=T, F=F)
+        vision_x = self.perceiver(vision_x)  # reshapes to (b, T, n, d)
+        for layer in self.lang_encoder._get_decoder_layers():
+            layer.condition_vis_x(vision_x)

minigpt2/lib/python3.10/site-packages/open_flamingo/src/flamingo_lm.py ADDED Viewed

	@@ -0,0 +1,138 @@

+import random
+import torch.nn as nn
+from .helpers import GatedCrossAttentionBlock
+from .utils import getattr_recursive, setattr_recursive
+class FlamingoLayer(nn.Module):
+    def __init__(self, gated_cross_attn_layer, decoder_layer):
+        super().__init__()
+        self.gated_cross_attn_layer = gated_cross_attn_layer
+        self.decoder_layer = decoder_layer
+        self.vis_x = None
+        self.media_locations = None
+    def is_conditioned(self) -> bool:
+        """Check whether the layer is conditioned."""
+        return self.vis_x is not None
+    # Used this great idea from this implementation of Flamingo (https://github.com/dhansmair/flamingo-mini/)
+    def condition_vis_x(self, vis_x):
+        self.vis_x = vis_x
+    def condition_media_locations(self, media_locations):
+        self.media_locations = media_locations
+    def condition_attend_previous(self, attend_previous):
+        self.attend_previous = attend_previous
+    def forward(
+        self,
+        lang_x,
+        attention_mask=None,
+        **decoder_layer_kwargs,
+    ):
+        if self.gated_cross_attn_layer is None:
+            return self.decoder_layer(
+                lang_x, attention_mask=attention_mask, **decoder_layer_kwargs
+            )
+        if self.vis_x is None:
+            raise ValueError("vis_x must be conditioned before forward pass")
+        if self.media_locations is None:
+            raise ValueError("media_locations must be conditioned before forward pass")
+        lang_x = self.gated_cross_attn_layer(
+            lang_x,
+            self.vis_x,
+            media_locations=self.media_locations,
+            attend_previous=self.attend_previous,
+        )
+        lang_x = self.decoder_layer(
+            lang_x, attention_mask=attention_mask, **decoder_layer_kwargs
+        )
+        return lang_x
+class FlamingoLMMixin(nn.Module):
+    """
+    Mixin to add cross-attention layers to a language model.
+    """
+    def set_decoder_layers_attr_name(self, decoder_layers_attr_name):
+        self.decoder_layers_attr_name = decoder_layers_attr_name
+    def _get_decoder_layers(self):
+        return getattr_recursive(self, self.decoder_layers_attr_name)
+    def _set_decoder_layers(self, value):
+        setattr_recursive(self, self.decoder_layers_attr_name, value)
+    def init_flamingo(
+        self,
+        media_token_id,
+        vis_hidden_size,
+        cross_attn_every_n_layers,
+        use_media_placement_augmentation,
+    ):
+        """
+        Initialize Flamingo by adding a new gated cross attn to the decoder. Store the media token id for computing the media locations.
+        """
+        self.gated_cross_attn_layers = nn.ModuleList(
+            [
+                GatedCrossAttentionBlock(
+                    dim=self.config.hidden_size, dim_visual=vis_hidden_size
+                )
+                if (layer_idx + 1) % cross_attn_every_n_layers == 0
+                else None
+                for layer_idx, _ in enumerate(self._get_decoder_layers())
+            ]
+        )
+        self._set_decoder_layers(
+            nn.ModuleList(
+                [
+                    FlamingoLayer(gated_cross_attn_layer, decoder_layer)
+                    for gated_cross_attn_layer, decoder_layer in zip(
+                        self.gated_cross_attn_layers, self._get_decoder_layers()
+                    )
+                ]
+            )
+        )
+        self.media_token_id = media_token_id
+        self.use_media_placement_augmentation = use_media_placement_augmentation
+        self.initialized_flamingo = True
+    def forward(self, *input, **kwargs):
+        """Condition the Flamingo layers on the media locations before forward()"""
+        if not self.initialized_flamingo:
+            raise ValueError(
+                "Flamingo layers are not initialized. Please call `init_flamingo` first."
+            )
+        input_ids = kwargs["input_ids"] if "input_ids" in kwargs else input[0]
+        media_locations = input_ids == self.media_token_id
+        attend_previous = (
+            (random.random() < 0.5) if self.use_media_placement_augmentation else False
+        )
+        for layer in self.get_decoder().layers:
+            layer.condition_media_locations(media_locations)
+            layer.condition_attend_previous(attend_previous)
+        return super().forward(
+            *input, **kwargs
+        )  # Call the other parent's forward method
+    def is_conditioned(self) -> bool:
+        """Check whether all decoder layers are already conditioned."""
+        return all(l.is_conditioned() for l in self._get_decoder_layers())
+    def clear_conditioned_layers(self):
+        for layer in self._get_decoder_layers():
+            layer.condition_vis_x(None)
+            layer.condition_media_locations(None)
+            layer.condition_attend_previous(None)

minigpt2/lib/python3.10/site-packages/open_flamingo/src/helpers.py ADDED Viewed

	@@ -0,0 +1,275 @@

+"""
+Taken from https://github.com/lucidrains/flamingo-pytorch
+"""
+import torch
+from einops import rearrange, repeat
+from einops_exts import rearrange_many
+from torch import einsum, nn
+def exists(val):
+    return val is not None
+def FeedForward(dim, mult=4):
+    inner_dim = int(dim * mult)
+    return nn.Sequential(
+        nn.LayerNorm(dim),
+        nn.Linear(dim, inner_dim, bias=False),
+        nn.GELU(),
+        nn.Linear(inner_dim, dim, bias=False),
+    )
+class PerceiverAttention(nn.Module):
+    def __init__(self, *, dim, dim_head=64, heads=8):
+        super().__init__()
+        self.scale = dim_head**-0.5
+        self.heads = heads
+        inner_dim = dim_head * heads
+        self.norm_media = nn.LayerNorm(dim)
+        self.norm_latents = nn.LayerNorm(dim)
+        self.to_q = nn.Linear(dim, inner_dim, bias=False)
+        self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False)
+        self.to_out = nn.Linear(inner_dim, dim, bias=False)
+    def forward(self, x, latents):
+        """
+        Args:
+            x (torch.Tensor): image features
+                shape (b, T, n1, D)
+            latent (torch.Tensor): latent features
+                shape (b, T, n2, D)
+        """
+        x = self.norm_media(x)
+        latents = self.norm_latents(latents)
+        h = self.heads
+        q = self.to_q(latents)
+        kv_input = torch.cat((x, latents), dim=-2)
+        k, v = self.to_kv(kv_input).chunk(2, dim=-1)
+        q, k, v = rearrange_many((q, k, v), "b t n (h d) -> b h t n d", h=h)
+        q = q * self.scale
+        # attention
+        sim = einsum("... i d, ... j d  -> ... i j", q, k)
+        sim = sim - sim.amax(dim=-1, keepdim=True).detach()
+        attn = sim.softmax(dim=-1)
+        out = einsum("... i j, ... j d -> ... i d", attn, v)
+        out = rearrange(out, "b h t n d -> b t n (h d)", h=h)
+        return self.to_out(out)
+class PerceiverResampler(nn.Module):
+    def __init__(
+        self,
+        *,
+        dim,
+        depth=6,
+        dim_head=64,
+        heads=8,
+        num_latents=64,
+        max_num_media=None,
+        max_num_frames=None,
+        ff_mult=4,
+    ):
+        super().__init__()
+        self.latents = nn.Parameter(torch.randn(num_latents, dim))
+        self.frame_embs = (
+            nn.Parameter(torch.randn(max_num_frames, dim))
+            if exists(max_num_frames)
+            else None
+        )
+        self.media_time_embs = (
+            nn.Parameter(torch.randn(max_num_media, 1, dim))
+            if exists(max_num_media)
+            else None
+        )
+        self.layers = nn.ModuleList([])
+        for _ in range(depth):
+            self.layers.append(
+                nn.ModuleList(
+                    [
+                        PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads),
+                        FeedForward(dim=dim, mult=ff_mult),
+                    ]
+                )
+            )
+        self.norm = nn.LayerNorm(dim)
+    def forward(self, x):
+        """
+        Args:
+            x (torch.Tensor): image features
+                shape (b, T, F, v, D)
+        Returns:
+            shape (b, T, n, D) where n is self.num_latents
+        """
+        b, T, F, v = x.shape[:4]
+        # frame and media time embeddings
+        if exists(self.frame_embs):
+            frame_embs = repeat(self.frame_embs[:F], "F d -> b T F v d", b=b, T=T, v=v)
+            x = x + frame_embs
+        x = rearrange(
+            x, "b T F v d -> b T (F v) d"
+        )  # flatten the frame and spatial dimensions
+        if exists(self.media_time_embs):
+            x = x + self.media_time_embs[:T]
+        # blocks
+        latents = repeat(self.latents, "n d -> b T n d", b=b, T=T)
+        for attn, ff in self.layers:
+            latents = attn(x, latents) + latents
+            latents = ff(latents) + latents
+        return self.norm(latents)
+# gated cross attention
+class MaskedCrossAttention(nn.Module):
+    def __init__(
+        self,
+        *,
+        dim,
+        dim_visual,
+        dim_head=64,
+        heads=8,
+        only_attend_immediate_media=True,
+    ):
+        super().__init__()
+        self.scale = dim_head**-0.5
+        self.heads = heads
+        inner_dim = dim_head * heads
+        self.norm = nn.LayerNorm(dim)
+        self.to_q = nn.Linear(dim, inner_dim, bias=False)
+        self.to_kv = nn.Linear(dim_visual, inner_dim * 2, bias=False)
+        self.to_out = nn.Linear(inner_dim, dim, bias=False)
+        # whether for text to only attend to immediate preceding image, or all previous images
+        self.only_attend_immediate_media = only_attend_immediate_media
+    def forward(self, x, media, media_locations=None, attend_previous=True):
+        """
+        Args:
+            x (torch.Tensor): text features
+                shape (B, T_txt, D_txt)
+            media (torch.Tensor): image features
+                shape (B, T_img, n, D_img) where n is the dim of the latents
+            media_locations: boolean mask identifying the media tokens in x
+                shape (B, T_txt)
+            attend_previous: bool
+                If false, ignores immediately preceding image and starts attending when following image
+        """
+        _, T_img, n = media.shape[:3]
+        h = self.heads
+        x = self.norm(x)
+        q = self.to_q(x)
+        media = rearrange(media, "b t n d -> b (t n) d")
+        k, v = self.to_kv(media).chunk(2, dim=-1)
+        q, k, v = rearrange_many((q, k, v), "b n (h d) -> b h n d", h=h)
+        q = q * self.scale
+        sim = einsum("... i d, ... j d -> ... i j", q, k)
+        if exists(media_locations):
+            # at each boolean of True, increment the time counter (relative to media time)
+            text_time = media_locations.cumsum(dim=-1)
+            media_time = torch.arange(T_img, device=x.device) + 1
+            if not attend_previous:
+                text_time[~media_locations] += 1
+                # make sure max is still the number of images in the sequence
+                text_time[
+                    text_time
+                    > repeat(
+                        torch.count_nonzero(media_locations, dim=1),
+                        "b -> b i",
+                        i=text_time.shape[1],
+                    )
+                ] = 0
+            # text time must equal media time if only attending to most immediate image
+            # otherwise, as long as text time is greater than media time (if attending to all previous images / media)
+            mask_op = torch.eq if self.only_attend_immediate_media else torch.ge
+            text_to_media_mask = mask_op(
+                rearrange(text_time, "b i -> b 1 i 1"),
+                repeat(media_time, "j -> 1 1 1 (j n)", n=n),
+            )
+            sim = sim.masked_fill(~text_to_media_mask, -torch.finfo(sim.dtype).max)
+        sim = sim - sim.amax(dim=-1, keepdim=True).detach()
+        attn = sim.softmax(dim=-1)
+        if exists(media_locations) and self.only_attend_immediate_media:
+            # any text without a preceding media needs to have attention zeroed out
+            text_without_media_mask = text_time == 0
+            text_without_media_mask = rearrange(
+                text_without_media_mask, "b i -> b 1 i 1"
+            )
+            attn = attn.masked_fill(text_without_media_mask, 0.0)
+        out = einsum("... i j, ... j d -> ... i d", attn, v)
+        out = rearrange(out, "b h n d -> b n (h d)")
+        return self.to_out(out)
+class GatedCrossAttentionBlock(nn.Module):
+    def __init__(
+        self,
+        *,
+        dim,
+        dim_visual,
+        dim_head=64,
+        heads=8,
+        ff_mult=4,
+        only_attend_immediate_media=True,
+    ):
+        super().__init__()
+        self.attn = MaskedCrossAttention(
+            dim=dim,
+            dim_visual=dim_visual,
+            dim_head=dim_head,
+            heads=heads,
+            only_attend_immediate_media=only_attend_immediate_media,
+        )
+        self.attn_gate = nn.Parameter(torch.tensor([0.0]))
+        self.ff = FeedForward(dim, mult=ff_mult)
+        self.ff_gate = nn.Parameter(torch.tensor([0.0]))
+    def forward(
+        self,
+        x,
+        media,
+        media_locations=None,
+        attend_previous=True,
+    ):
+        x = (
+            self.attn(
+                x,
+                media,
+                media_locations=media_locations,
+                attend_previous=attend_previous,
+            )
+            * self.attn_gate.tanh()
+            + x
+        )
+        x = self.ff(x) * self.ff_gate.tanh() + x
+        return x

minigpt2/lib/python3.10/site-packages/open_flamingo/src/utils.py ADDED Viewed

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+def extend_instance(obj, mixin):
+    """Apply mixins to a class instance after creation"""
+    base_cls = obj.__class__
+    base_cls_name = obj.__class__.__name__
+    obj.__class__ = type(
+        base_cls_name, (mixin, base_cls), {}
+    )  # mixin needs to go first for our forward() logic to work
+def getattr_recursive(obj, att):
+    """
+    Return nested attribute of obj
+    Example: getattr_recursive(obj, 'a.b.c') is equivalent to obj.a.b.c
+    """
+    if att == "":
+        return obj
+    i = att.find(".")
+    if i < 0:
+        return getattr(obj, att)
+    else:
+        return getattr_recursive(getattr(obj, att[:i]), att[i + 1 :])
+def setattr_recursive(obj, att, val):
+    """
+    Set nested attribute of obj
+    Example: setattr_recursive(obj, 'a.b.c', val) is equivalent to obj.a.b.c = val
+    """
+    if "." in att:
+        obj = getattr_recursive(obj, ".".join(att.split(".")[:-1]))
+    setattr(obj, att.split(".")[-1], val)

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1	+

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