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Running
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Zero
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import torch
from torch import nn, Tensor
from einops import rearrange
from typing import Tuple, Union, Dict, Optional, List
from functools import partial
from .cannet import _cannet, _cannet_bn
from .csrnet import _csrnet, _csrnet_bn
from .vgg import _vgg_encoder_decoder, _vgg_encoder
from .vit import _vit, supported_vit_backbones
from .timm_models import _timm_model
from .timm_models import regular_models as timm_regular_models, heavy_models as timm_heavy_models, light_models as timm_light_models, lighter_models as timm_lighter_models
from .hrnet import _hrnet, available_hrnets
from ..utils import conv1x1
regular_models = [
"csrnet", "csrnet_bn",
"cannet", "cannet_bn",
"vgg11", "vgg11_bn", "vgg13", "vgg13_bn", "vgg16", "vgg16_bn", "vgg19", "vgg19_bn",
"vgg11_ae", "vgg11_bn_ae", "vgg13_ae", "vgg13_bn_ae", "vgg16_ae", "vgg16_bn_ae", "vgg19_ae", "vgg19_bn_ae",
*timm_regular_models,
*available_hrnets,
]
heavy_models = timm_heavy_models
light_models = timm_light_models
lighter_models = timm_lighter_models
transformer_models = supported_vit_backbones
supported_models = regular_models + heavy_models + light_models + lighter_models + transformer_models
class EBC(nn.Module):
def __init__(
self,
model_name: str,
block_size: int,
bins: List[Tuple[float, float]],
bin_centers: List[float],
zero_inflated: bool = True,
num_vpt: Optional[int] = None,
vpt_drop: Optional[float] = None,
input_size: Optional[int] = None,
norm: str = "none",
act: str = "none"
) -> None:
super().__init__()
assert model_name in supported_models, f"Model name should be one of {supported_models}, but got {model_name}."
self.model_name = model_name
if input_size is not None:
input_size = (input_size, input_size) if isinstance(input_size, int) else input_size
assert len(input_size) == 2 and input_size[0] > 0 and input_size[1] > 0, f"Expected input_size to be a tuple of two positive integers, got {input_size}"
self.input_size = input_size
assert len(bins) == len(bin_centers), f"Expected bins and bin_centers to have the same length, got {len(bins)} and {len(bin_centers)}"
assert len(bins) >= 2, f"Expected at least 2 bins, got {len(bins)}"
assert all(len(b) == 2 for b in bins), f"Expected bins to be a list of tuples of length 2, got {bins}"
bins = [(float(b[0]), float(b[1])) for b in bins]
assert all(bin[0] <= p <= bin[1] for bin, p in zip(bins, bin_centers)), f"Expected bin_centers to be within the range of the corresponding bin, got {bins} and {bin_centers}"
self.block_size = block_size
self.bins = bins
self.register_buffer("bin_centers", torch.tensor(bin_centers, dtype=torch.float32, requires_grad=False).view(1, -1, 1, 1))
self.zero_inflated = zero_inflated
self.num_vpt = num_vpt
self.vpt_drop = vpt_drop
self.input_size = input_size
self.norm = norm
self.act = act
self._build_backbone()
self._build_head()
def _build_backbone(self) -> None:
model_name = self.model_name
if model_name == "csrnet":
self.backbone = _csrnet(self.block_size, self.norm, self.act)
elif model_name == "csrnet_bn":
self.backbone = _csrnet_bn(self.block_size, self.norm, self.act)
elif model_name == "cannet":
self.backbone = _cannet(self.block_size, self.norm, self.act)
elif model_name == "cannet_bn":
self.backbone = _cannet_bn(self.block_size, self.norm, self.act)
elif model_name == "vgg11":
self.backbone = _vgg_encoder("vgg11", self.block_size, self.norm, self.act)
elif model_name == "vgg11_ae":
self.backbone = _vgg_encoder_decoder("vgg11", self.block_size, self.norm, self.act)
elif model_name == "vgg11_bn":
self.backbone = _vgg_encoder("vgg11_bn", self.block_size, self.norm, self.act)
elif model_name == "vgg11_bn_ae":
self.backbone = _vgg_encoder_decoder("vgg11_bn", self.block_size, self.norm, self.act)
elif model_name == "vgg13":
self.backbone = _vgg_encoder("vgg13", self.block_size, self.norm, self.act)
elif model_name == "vgg13_ae":
self.backbone = _vgg_encoder_decoder("vgg13", self.block_size, self.norm, self.act)
elif model_name == "vgg13_bn":
self.backbone = _vgg_encoder("vgg13_bn", self.block_size, self.norm, self.act)
elif model_name == "vgg13_bn_ae":
self.backbone = _vgg_encoder_decoder("vgg13_bn", self.block_size, self.norm, self.act)
elif model_name == "vgg16":
self.backbone = _vgg_encoder("vgg16", self.block_size, self.norm, self.act)
elif model_name == "vgg16_ae":
self.backbone = _vgg_encoder_decoder("vgg16", self.block_size, self.norm, self.act)
elif model_name == "vgg16_bn":
self.backbone = _vgg_encoder("vgg16_bn", self.block_size, self.norm, self.act)
elif model_name == "vgg16_bn_ae":
self.backbone = _vgg_encoder_decoder("vgg16_bn", self.block_size, self.norm, self.act)
elif model_name == "vgg19":
self.backbone = _vgg_encoder("vgg19", self.block_size, self.norm, self.act)
elif model_name == "vgg19_ae":
self.backbone = _vgg_encoder_decoder("vgg19", self.block_size, self.norm, self.act)
elif model_name == "vgg19_bn":
self.backbone = _vgg_encoder("vgg19_bn", self.block_size, self.norm, self.act)
elif model_name == "vgg19_bn_ae":
self.backbone = _vgg_encoder_decoder("vgg19_bn", self.block_size, self.norm, self.act)
elif model_name in supported_vit_backbones:
self.backbone = _vit(model_name, block_size=self.block_size, num_vpt=self.num_vpt, vpt_drop=self.vpt_drop, input_size=self.input_size, norm=self.norm, act=self.act)
elif model_name in available_hrnets:
self.backbone = _hrnet(model_name, block_size=self.block_size, norm=self.norm, act=self.act)
else:
self.backbone = _timm_model(model_name, self.block_size, self.norm, self.act)
def _build_head(self) -> None:
channels = self.backbone.decoder_channels
if self.zero_inflated:
self.bin_head = conv1x1(
in_channels=channels,
out_channels=len(self.bins) - 1,
)
self.pi_head = conv1x1(
in_channels=channels,
out_channels=2,
) # this models structural 0s.
else:
self.bin_head = conv1x1(
in_channels=channels,
out_channels=len(self.bins),
)
def forward(self, x: Tensor) -> Union[Tensor, Tuple[Tensor, Tensor]]:
x = self.backbone(x)
if self.zero_inflated:
logit_pi_maps = self.pi_head(x) # shape: (B, 2, H, W)
logit_maps = self.bin_head(x) # shape: (B, C, H, W)
lambda_maps = (logit_maps.softmax(dim=1) * self.bin_centers[:, 1:]).sum(dim=1, keepdim=True) # shape: (B, 1, H, W)
# logit_pi_maps.softmax(dim=1)[:, 0] is the probability of zeros
den_maps = logit_pi_maps.softmax(dim=1)[:, 1:] * lambda_maps # expectation of the Poisson distribution
if self.training:
return logit_pi_maps, logit_maps, lambda_maps, den_maps
else:
return den_maps
else:
logit_maps = self.bin_head(x)
den_maps = (logit_maps.softmax(dim=1) * self.bin_centers).sum(dim=1, keepdim=True)
if self.training:
return logit_maps, den_maps
else:
return den_maps
def _ebc(
model_name: str,
block_size: int,
bins: List[Tuple[float, float]],
bin_centers: List[float],
zero_inflated: bool = True,
num_vpt: Optional[int] = None,
vpt_drop: Optional[float] = None,
input_size: Optional[int] = None,
norm: str = "none",
act: str = "none"
) -> EBC:
return EBC(
model_name=model_name,
block_size=block_size,
bins=bins,
bin_centers=bin_centers,
zero_inflated=zero_inflated,
num_vpt=num_vpt,
vpt_drop=vpt_drop,
input_size=input_size,
norm=norm,
act=act
)
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