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import torch
import torch.nn as nn
from typing import Type, Optional, Tuple
import numpy as np
from .modeling.transformer import Attention
from .modeling.common import MLPBlock
# from modeling.transformer import Attention
# from modeling.common import MLPBlock
class MutualCrossAttention(nn.Module):
def __init__(
self,
embedding_dim: int = 1024,
num_heads: int = 8,
mlp_dim: int = 1024,
activation: Type[nn.Module] = nn.GELU,
attention_downsample_rate: int = 4,
) -> None:
super().__init__()
self.cross_attn_token_to_image = Attention(
embedding_dim, num_heads, downsample_rate=attention_downsample_rate
)
self.norm1 = nn.LayerNorm(embedding_dim)
self.mlp = MLPBlock(embedding_dim, mlp_dim, activation)
self.norm2 = nn.LayerNorm(embedding_dim)
self.norm3 = nn.LayerNorm(embedding_dim)
self.cross_attn_image_to_token = Attention(
embedding_dim, num_heads, downsample_rate=attention_downsample_rate
)
def forward(self, queries, keys, query_pe=None, key_pe=None):
# Cross attention block, tokens attending to image embedding
q = queries + query_pe if query_pe is not None else queries
k = keys + key_pe if key_pe is not None else keys
attn_out = self.cross_attn_token_to_image(q=q, k=k, v=keys)
queries = queries + attn_out
queries = self.norm1(queries)
# MLP block
mlp_out = self.mlp(queries)
queries = queries + mlp_out
queries = self.norm2(queries)
# Cross attention block, image embedding attending to tokens
q = queries + query_pe if query_pe is not None else queries
k = keys + key_pe if key_pe is not None else keys
attn_out = self.cross_attn_image_to_token(q=k, k=q, v=queries)
keys = keys + attn_out
keys = self.norm3(keys)
return queries, keys
class PositionEmbeddingRandom(nn.Module):
"""
Positional encoding using random spatial frequencies.
"""
def __init__(self, num_pos_feats: int = 64, scale: Optional[float] = None) -> None:
super().__init__()
if scale is None or scale <= 0.0:
scale = 1.0
self.register_buffer(
"positional_encoding_gaussian_matrix",
scale * torch.randn((2, num_pos_feats)),
)
def _pe_encoding(self, coords: torch.Tensor) -> torch.Tensor:
"""Positionally encode points that are normalized to [0,1]."""
# assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape
coords = 2 * coords - 1
coords = coords @ self.positional_encoding_gaussian_matrix
coords = 2 * np.pi * coords
# outputs d_1 x ... x d_n x C shape
return torch.cat([torch.sin(coords), torch.cos(coords)], dim=-1)
def forward(self, size: Tuple[int, int]) -> torch.Tensor:
"""Generate positional encoding for a grid of the specified size."""
h, w = size
device = self.positional_encoding_gaussian_matrix.device
grid = torch.ones((h, w), device=device, dtype=torch.float32)
y_embed = grid.cumsum(dim=0) - 0.5
x_embed = grid.cumsum(dim=1) - 0.5
y_embed = y_embed / h
x_embed = x_embed / w
pe = self._pe_encoding(torch.stack([x_embed, y_embed], dim=-1))
# return pe.permute(2, 0, 1) # C x H x W
return pe.reshape(h * w, -1)[None] # 1 x (H x W) x C
class FeatureFusion(nn.Module):
def __init__(
self,
in_channels=1024,
input_compression_ratio=1,
attn_compression_ratio=4,
features_num=4,
w_pe=True,
):
super().__init__()
self.input_compression_ratio = input_compression_ratio
if self.input_compression_ratio != 1:
self.mlp_in = nn.ModuleList([nn.Sequential(
nn.Linear(in_channels, in_channels // input_compression_ratio),
# activation(),
# nn.Linear(embedding_dim // compression_ratio, embedding_dim // compression_ratio)
) for _ in range(features_num)])
self.mlp_out = nn.ModuleList([nn.Sequential(
nn.Linear(in_channels // input_compression_ratio, in_channels),
# activation(),
# nn.Linear(embedding_dim, embedding_dim)
) for _ in range(features_num)])
in_channels = in_channels // input_compression_ratio
self.mutual_cross_attn = nn.ModuleList([
MutualCrossAttention(embedding_dim=in_channels, mlp_dim=in_channels // attn_compression_ratio, attention_downsample_rate=attn_compression_ratio) for _ in range(features_num - 1)
])
self.w_pe = w_pe
if self.w_pe:
# no grad
self.get_pe = PositionEmbeddingRandom(in_channels // 2)
with torch.no_grad():
self.pe = self.get_pe(size=(64, 64))
def forward(self, features):
# [B, 64, 64, 1024] x 4
b, h, w, _ = features[0].shape
for i in range(len(features)):
features[i] = features[i].reshape(b, h * w, -1)
if self.input_compression_ratio != 1:
features[i] = self.mlp_in[i](features[i])
for i in range(len(features) - 1):
features[i], features[i + 1] = self.mutual_cross_attn[i](features[i], features[i + 1], self.pe, self.pe)
for i in range(len(features)):
features[i] = features[i].reshape(b, h, w, -1)
if self.input_compression_ratio != 1:
features[i] = self.mlp_out[i](features[i])
return features
if __name__ == '__main__':
import typing
from collections import defaultdict
import tabulate
from torch import nn
def parameter_count(model: nn.Module, trainable_only: bool = False) -> typing.DefaultDict[str, int]:
"""
Count parameters of a model and its submodules.
Args:
model: a torch module
Returns:
dict (str-> int): the key is either a parameter name or a module name.
The value is the number of elements in the parameter, or in all
parameters of the module. The key "" corresponds to the total
number of parameters of the model.
"""
r = defaultdict(int)
for name, prm in model.named_parameters():
if trainable_only:
if not prm.requires_grad:
continue
size = prm.numel()
name = name.split(".")
for k in range(0, len(name) + 1):
prefix = ".".join(name[:k])
r[prefix] += size
return r
def parameter_count_table(
model: nn.Module, max_depth: int = 3, trainable_only: bool = False
) -> str:
"""
Format the parameter count of the model (and its submodules or parameters)
in a nice table. It looks like this:
::
| name | #elements or shape |
|:--------------------------------|:---------------------|
| model | 37.9M |
| backbone | 31.5M |
| backbone.fpn_lateral3 | 0.1M |
| backbone.fpn_lateral3.weight | (256, 512, 1, 1) |
| backbone.fpn_lateral3.bias | (256,) |
| backbone.fpn_output3 | 0.6M |
| backbone.fpn_output3.weight | (256, 256, 3, 3) |
| backbone.fpn_output3.bias | (256,) |
| backbone.fpn_lateral4 | 0.3M |
| backbone.fpn_lateral4.weight | (256, 1024, 1, 1) |
| backbone.fpn_lateral4.bias | (256,) |
| backbone.fpn_output4 | 0.6M |
| backbone.fpn_output4.weight | (256, 256, 3, 3) |
| backbone.fpn_output4.bias | (256,) |
| backbone.fpn_lateral5 | 0.5M |
| backbone.fpn_lateral5.weight | (256, 2048, 1, 1) |
| backbone.fpn_lateral5.bias | (256,) |
| backbone.fpn_output5 | 0.6M |
| backbone.fpn_output5.weight | (256, 256, 3, 3) |
| backbone.fpn_output5.bias | (256,) |
| backbone.top_block | 5.3M |
| backbone.top_block.p6 | 4.7M |
| backbone.top_block.p7 | 0.6M |
| backbone.bottom_up | 23.5M |
| backbone.bottom_up.stem | 9.4K |
| backbone.bottom_up.res2 | 0.2M |
| backbone.bottom_up.res3 | 1.2M |
| backbone.bottom_up.res4 | 7.1M |
| backbone.bottom_up.res5 | 14.9M |
| ...... | ..... |
Args:
model: a torch module
max_depth (int): maximum depth to recursively print submodules or
parameters
Returns:
str: the table to be printed
"""
count: typing.DefaultDict[str, int] = parameter_count(model, trainable_only)
# pyre-fixme[24]: Generic type `tuple` expects at least 1 type parameter.
param_shape: typing.Dict[str, typing.Tuple] = {
k: tuple(v.shape) for k, v in model.named_parameters()
}
# pyre-fixme[24]: Generic type `tuple` expects at least 1 type parameter.
table: typing.List[typing.Tuple] = []
def format_size(x: int) -> str:
if x > 1e8:
return "{:.1f}G".format(x / 1e9)
if x > 1e5:
return "{:.1f}M".format(x / 1e6)
if x > 1e2:
return "{:.1f}K".format(x / 1e3)
return str(x)
def fill(lvl: int, prefix: str) -> None:
if lvl >= max_depth:
return
for name, v in count.items():
if name.count(".") == lvl and name.startswith(prefix):
indent = " " * (lvl + 1)
if name in param_shape:
table.append((indent + name, indent + str(param_shape[name])))
else:
table.append((indent + name, indent + format_size(v)))
fill(lvl + 1, name + ".")
table.append(("model", format_size(count.pop(""))))
fill(0, "")
old_ws = tabulate.PRESERVE_WHITESPACE
tabulate.PRESERVE_WHITESPACE = True
tab = tabulate.tabulate(table, headers=["name", "#elements or shape"], tablefmt="pipe")
tabulate.PRESERVE_WHITESPACE = old_ws
return tab
feature_fusion = FeatureFusion(in_channels=1024, attn_compression_ratio=8)
print("All parameters: \n" + parameter_count_table(feature_fusion, max_depth=8))
features = [torch.randn(2, 64, 64, 1024) for _ in range(4)]
out = feature_fusion(features)
for i in out:
print(i.shape)
print('done')
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