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support gsam2 image predictor model

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rentainhe
2024-08-01 17:05:01 +08:00
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import argparse
import json
import warnings
from collections import OrderedDict
from copy import deepcopy
from typing import Any, Dict, List
import numpy as np
import torch
from transformers import AutoTokenizer
from grounding_dino.groundingdino.util.slconfig import SLConfig
def slprint(x, name="x"):
if isinstance(x, (torch.Tensor, np.ndarray)):
print(f"{name}.shape:", x.shape)
elif isinstance(x, (tuple, list)):
print("type x:", type(x))
for i in range(min(10, len(x))):
slprint(x[i], f"{name}[{i}]")
elif isinstance(x, dict):
for k, v in x.items():
slprint(v, f"{name}[{k}]")
else:
print(f"{name}.type:", type(x))
def clean_state_dict(state_dict):
new_state_dict = OrderedDict()
for k, v in state_dict.items():
if k[:7] == "module.":
k = k[7:] # remove `module.`
new_state_dict[k] = v
return new_state_dict
def renorm(
img: torch.FloatTensor, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
) -> torch.FloatTensor:
# img: tensor(3,H,W) or tensor(B,3,H,W)
# return: same as img
assert img.dim() == 3 or img.dim() == 4, "img.dim() should be 3 or 4 but %d" % img.dim()
if img.dim() == 3:
assert img.size(0) == 3, 'img.size(0) shoule be 3 but "%d". (%s)' % (
img.size(0),
str(img.size()),
)
img_perm = img.permute(1, 2, 0)
mean = torch.Tensor(mean)
std = torch.Tensor(std)
img_res = img_perm * std + mean
return img_res.permute(2, 0, 1)
else: # img.dim() == 4
assert img.size(1) == 3, 'img.size(1) shoule be 3 but "%d". (%s)' % (
img.size(1),
str(img.size()),
)
img_perm = img.permute(0, 2, 3, 1)
mean = torch.Tensor(mean)
std = torch.Tensor(std)
img_res = img_perm * std + mean
return img_res.permute(0, 3, 1, 2)
class CocoClassMapper:
def __init__(self) -> None:
self.category_map_str = {
"1": 1,
"2": 2,
"3": 3,
"4": 4,
"5": 5,
"6": 6,
"7": 7,
"8": 8,
"9": 9,
"10": 10,
"11": 11,
"13": 12,
"14": 13,
"15": 14,
"16": 15,
"17": 16,
"18": 17,
"19": 18,
"20": 19,
"21": 20,
"22": 21,
"23": 22,
"24": 23,
"25": 24,
"27": 25,
"28": 26,
"31": 27,
"32": 28,
"33": 29,
"34": 30,
"35": 31,
"36": 32,
"37": 33,
"38": 34,
"39": 35,
"40": 36,
"41": 37,
"42": 38,
"43": 39,
"44": 40,
"46": 41,
"47": 42,
"48": 43,
"49": 44,
"50": 45,
"51": 46,
"52": 47,
"53": 48,
"54": 49,
"55": 50,
"56": 51,
"57": 52,
"58": 53,
"59": 54,
"60": 55,
"61": 56,
"62": 57,
"63": 58,
"64": 59,
"65": 60,
"67": 61,
"70": 62,
"72": 63,
"73": 64,
"74": 65,
"75": 66,
"76": 67,
"77": 68,
"78": 69,
"79": 70,
"80": 71,
"81": 72,
"82": 73,
"84": 74,
"85": 75,
"86": 76,
"87": 77,
"88": 78,
"89": 79,
"90": 80,
}
self.origin2compact_mapper = {int(k): v - 1 for k, v in self.category_map_str.items()}
self.compact2origin_mapper = {int(v - 1): int(k) for k, v in self.category_map_str.items()}
def origin2compact(self, idx):
return self.origin2compact_mapper[int(idx)]
def compact2origin(self, idx):
return self.compact2origin_mapper[int(idx)]
def to_device(item, device):
if isinstance(item, torch.Tensor):
return item.to(device)
elif isinstance(item, list):
return [to_device(i, device) for i in item]
elif isinstance(item, dict):
return {k: to_device(v, device) for k, v in item.items()}
else:
raise NotImplementedError(
"Call Shilong if you use other containers! type: {}".format(type(item))
)
#
def get_gaussian_mean(x, axis, other_axis, softmax=True):
"""
Args:
x (float): Input images(BxCxHxW)
axis (int): The index for weighted mean
other_axis (int): The other index
Returns: weighted index for axis, BxC
"""
mat2line = torch.sum(x, axis=other_axis)
# mat2line = mat2line / mat2line.mean() * 10
if softmax:
u = torch.softmax(mat2line, axis=2)
else:
u = mat2line / (mat2line.sum(2, keepdim=True) + 1e-6)
size = x.shape[axis]
ind = torch.linspace(0, 1, size).to(x.device)
batch = x.shape[0]
channel = x.shape[1]
index = ind.repeat([batch, channel, 1])
mean_position = torch.sum(index * u, dim=2)
return mean_position
def get_expected_points_from_map(hm, softmax=True):
"""get_gaussian_map_from_points
B,C,H,W -> B,N,2 float(0, 1) float(0, 1)
softargmax function
Args:
hm (float): Input images(BxCxHxW)
Returns:
weighted index for axis, BxCx2. float between 0 and 1.
"""
# hm = 10*hm
B, C, H, W = hm.shape
y_mean = get_gaussian_mean(hm, 2, 3, softmax=softmax) # B,C
x_mean = get_gaussian_mean(hm, 3, 2, softmax=softmax) # B,C
# return torch.cat((x_mean.unsqueeze(-1), y_mean.unsqueeze(-1)), 2)
return torch.stack([x_mean, y_mean], dim=2)
# Positional encoding (section 5.1)
# borrow from nerf
class Embedder:
def __init__(self, **kwargs):
self.kwargs = kwargs
self.create_embedding_fn()
def create_embedding_fn(self):
embed_fns = []
d = self.kwargs["input_dims"]
out_dim = 0
if self.kwargs["include_input"]:
embed_fns.append(lambda x: x)
out_dim += d
max_freq = self.kwargs["max_freq_log2"]
N_freqs = self.kwargs["num_freqs"]
if self.kwargs["log_sampling"]:
freq_bands = 2.0 ** torch.linspace(0.0, max_freq, steps=N_freqs)
else:
freq_bands = torch.linspace(2.0**0.0, 2.0**max_freq, steps=N_freqs)
for freq in freq_bands:
for p_fn in self.kwargs["periodic_fns"]:
embed_fns.append(lambda x, p_fn=p_fn, freq=freq: p_fn(x * freq))
out_dim += d
self.embed_fns = embed_fns
self.out_dim = out_dim
def embed(self, inputs):
return torch.cat([fn(inputs) for fn in self.embed_fns], -1)
def get_embedder(multires, i=0):
import torch.nn as nn
if i == -1:
return nn.Identity(), 3
embed_kwargs = {
"include_input": True,
"input_dims": 3,
"max_freq_log2": multires - 1,
"num_freqs": multires,
"log_sampling": True,
"periodic_fns": [torch.sin, torch.cos],
}
embedder_obj = Embedder(**embed_kwargs)
embed = lambda x, eo=embedder_obj: eo.embed(x)
return embed, embedder_obj.out_dim
class APOPMeter:
def __init__(self) -> None:
self.tp = 0
self.fp = 0
self.tn = 0
self.fn = 0
def update(self, pred, gt):
"""
Input:
pred, gt: Tensor()
"""
assert pred.shape == gt.shape
self.tp += torch.logical_and(pred == 1, gt == 1).sum().item()
self.fp += torch.logical_and(pred == 1, gt == 0).sum().item()
self.tn += torch.logical_and(pred == 0, gt == 0).sum().item()
self.tn += torch.logical_and(pred == 1, gt == 0).sum().item()
def update_cm(self, tp, fp, tn, fn):
self.tp += tp
self.fp += fp
self.tn += tn
self.tn += fn
def inverse_sigmoid(x, eps=1e-5):
x = x.clamp(min=0, max=1)
x1 = x.clamp(min=eps)
x2 = (1 - x).clamp(min=eps)
return torch.log(x1 / x2)
def get_raw_dict(args):
"""
return the dicf contained in args.
e.g:
>>> with open(path, 'w') as f:
json.dump(get_raw_dict(args), f, indent=2)
"""
if isinstance(args, argparse.Namespace):
return vars(args)
elif isinstance(args, dict):
return args
elif isinstance(args, SLConfig):
return args._cfg_dict
else:
raise NotImplementedError("Unknown type {}".format(type(args)))
def stat_tensors(tensor):
assert tensor.dim() == 1
tensor_sm = tensor.softmax(0)
entropy = (tensor_sm * torch.log(tensor_sm + 1e-9)).sum()
return {
"max": tensor.max(),
"min": tensor.min(),
"mean": tensor.mean(),
"var": tensor.var(),
"std": tensor.var() ** 0.5,
"entropy": entropy,
}
class NiceRepr:
"""Inherit from this class and define ``__nice__`` to "nicely" print your
objects.
Defines ``__str__`` and ``__repr__`` in terms of ``__nice__`` function
Classes that inherit from :class:`NiceRepr` should redefine ``__nice__``.
If the inheriting class has a ``__len__``, method then the default
``__nice__`` method will return its length.
Example:
>>> class Foo(NiceRepr):
... def __nice__(self):
... return 'info'
>>> foo = Foo()
>>> assert str(foo) == '<Foo(info)>'
>>> assert repr(foo).startswith('<Foo(info) at ')
Example:
>>> class Bar(NiceRepr):
... pass
>>> bar = Bar()
>>> import pytest
>>> with pytest.warns(None) as record:
>>> assert 'object at' in str(bar)
>>> assert 'object at' in repr(bar)
Example:
>>> class Baz(NiceRepr):
... def __len__(self):
... return 5
>>> baz = Baz()
>>> assert str(baz) == '<Baz(5)>'
"""
def __nice__(self):
"""str: a "nice" summary string describing this module"""
if hasattr(self, "__len__"):
# It is a common pattern for objects to use __len__ in __nice__
# As a convenience we define a default __nice__ for these objects
return str(len(self))
else:
# In all other cases force the subclass to overload __nice__
raise NotImplementedError(f"Define the __nice__ method for {self.__class__!r}")
def __repr__(self):
"""str: the string of the module"""
try:
nice = self.__nice__()
classname = self.__class__.__name__
return f"<{classname}({nice}) at {hex(id(self))}>"
except NotImplementedError as ex:
warnings.warn(str(ex), category=RuntimeWarning)
return object.__repr__(self)
def __str__(self):
"""str: the string of the module"""
try:
classname = self.__class__.__name__
nice = self.__nice__()
return f"<{classname}({nice})>"
except NotImplementedError as ex:
warnings.warn(str(ex), category=RuntimeWarning)
return object.__repr__(self)
def ensure_rng(rng=None):
"""Coerces input into a random number generator.
If the input is None, then a global random state is returned.
If the input is a numeric value, then that is used as a seed to construct a
random state. Otherwise the input is returned as-is.
Adapted from [1]_.
Args:
rng (int | numpy.random.RandomState | None):
if None, then defaults to the global rng. Otherwise this can be an
integer or a RandomState class
Returns:
(numpy.random.RandomState) : rng -
a numpy random number generator
References:
.. [1] https://gitlab.kitware.com/computer-vision/kwarray/blob/master/kwarray/util_random.py#L270 # noqa: E501
"""
if rng is None:
rng = np.random.mtrand._rand
elif isinstance(rng, int):
rng = np.random.RandomState(rng)
else:
rng = rng
return rng
def random_boxes(num=1, scale=1, rng=None):
"""Simple version of ``kwimage.Boxes.random``
Returns:
Tensor: shape (n, 4) in x1, y1, x2, y2 format.
References:
https://gitlab.kitware.com/computer-vision/kwimage/blob/master/kwimage/structs/boxes.py#L1390
Example:
>>> num = 3
>>> scale = 512
>>> rng = 0
>>> boxes = random_boxes(num, scale, rng)
>>> print(boxes)
tensor([[280.9925, 278.9802, 308.6148, 366.1769],
[216.9113, 330.6978, 224.0446, 456.5878],
[405.3632, 196.3221, 493.3953, 270.7942]])
"""
rng = ensure_rng(rng)
tlbr = rng.rand(num, 4).astype(np.float32)
tl_x = np.minimum(tlbr[:, 0], tlbr[:, 2])
tl_y = np.minimum(tlbr[:, 1], tlbr[:, 3])
br_x = np.maximum(tlbr[:, 0], tlbr[:, 2])
br_y = np.maximum(tlbr[:, 1], tlbr[:, 3])
tlbr[:, 0] = tl_x * scale
tlbr[:, 1] = tl_y * scale
tlbr[:, 2] = br_x * scale
tlbr[:, 3] = br_y * scale
boxes = torch.from_numpy(tlbr)
return boxes
class ModelEma(torch.nn.Module):
def __init__(self, model, decay=0.9997, device=None):
super(ModelEma, self).__init__()
# make a copy of the model for accumulating moving average of weights
self.module = deepcopy(model)
self.module.eval()
# import ipdb; ipdb.set_trace()
self.decay = decay
self.device = device # perform ema on different device from model if set
if self.device is not None:
self.module.to(device=device)
def _update(self, model, update_fn):
with torch.no_grad():
for ema_v, model_v in zip(
self.module.state_dict().values(), model.state_dict().values()
):
if self.device is not None:
model_v = model_v.to(device=self.device)
ema_v.copy_(update_fn(ema_v, model_v))
def update(self, model):
self._update(model, update_fn=lambda e, m: self.decay * e + (1.0 - self.decay) * m)
def set(self, model):
self._update(model, update_fn=lambda e, m: m)
class BestMetricSingle:
def __init__(self, init_res=0.0, better="large") -> None:
self.init_res = init_res
self.best_res = init_res
self.best_ep = -1
self.better = better
assert better in ["large", "small"]
def isbetter(self, new_res, old_res):
if self.better == "large":
return new_res > old_res
if self.better == "small":
return new_res < old_res
def update(self, new_res, ep):
if self.isbetter(new_res, self.best_res):
self.best_res = new_res
self.best_ep = ep
return True
return False
def __str__(self) -> str:
return "best_res: {}\t best_ep: {}".format(self.best_res, self.best_ep)
def __repr__(self) -> str:
return self.__str__()
def summary(self) -> dict:
return {
"best_res": self.best_res,
"best_ep": self.best_ep,
}
class BestMetricHolder:
def __init__(self, init_res=0.0, better="large", use_ema=False) -> None:
self.best_all = BestMetricSingle(init_res, better)
self.use_ema = use_ema
if use_ema:
self.best_ema = BestMetricSingle(init_res, better)
self.best_regular = BestMetricSingle(init_res, better)
def update(self, new_res, epoch, is_ema=False):
"""
return if the results is the best.
"""
if not self.use_ema:
return self.best_all.update(new_res, epoch)
else:
if is_ema:
self.best_ema.update(new_res, epoch)
return self.best_all.update(new_res, epoch)
else:
self.best_regular.update(new_res, epoch)
return self.best_all.update(new_res, epoch)
def summary(self):
if not self.use_ema:
return self.best_all.summary()
res = {}
res.update({f"all_{k}": v for k, v in self.best_all.summary().items()})
res.update({f"regular_{k}": v for k, v in self.best_regular.summary().items()})
res.update({f"ema_{k}": v for k, v in self.best_ema.summary().items()})
return res
def __repr__(self) -> str:
return json.dumps(self.summary(), indent=2)
def __str__(self) -> str:
return self.__repr__()
def targets_to(targets: List[Dict[str, Any]], device):
"""Moves the target dicts to the given device."""
excluded_keys = [
"questionId",
"tokens_positive",
"strings_positive",
"tokens",
"dataset_name",
"sentence_id",
"original_img_id",
"nb_eval",
"task_id",
"original_id",
"token_span",
"caption",
"dataset_type",
]
return [
{k: v.to(device) if k not in excluded_keys else v for k, v in t.items()} for t in targets
]
def get_phrases_from_posmap(
posmap: torch.BoolTensor, tokenized: Dict, tokenizer: AutoTokenizer, left_idx: int = 0, right_idx: int = 255
):
assert isinstance(posmap, torch.Tensor), "posmap must be torch.Tensor"
if posmap.dim() == 1:
posmap[0: left_idx + 1] = False
posmap[right_idx:] = False
non_zero_idx = posmap.nonzero(as_tuple=True)[0].tolist()
token_ids = [tokenized["input_ids"][i] for i in non_zero_idx]
return tokenizer.decode(token_ids)
else:
raise NotImplementedError("posmap must be 1-dim")