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import torch | ||
import torch.nn as nn | ||
from mmdet.models.backbones.swin import WindowMSA, ShiftWindowMSA | ||
from mmdet3d.ops.spconv import SparseConv3d, SubMConv3d | ||
from mmdet3d.models.utils.transformer import MultiheadAttention | ||
from typing import Union | ||
from thop import profile | ||
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__all__ = ["flops_counter"] | ||
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# TODO: no need to consider ShiftWindowMSA since it contains WindowMSA | ||
def count_window_msa(m: Union[WindowMSA, ShiftWindowMSA], x, y): | ||
if isinstance(m, WindowMSA): | ||
embed_dims = m.embed_dims | ||
num_heads = m.num_heads | ||
else: | ||
embed_dims = m.w_msa.embed_dims | ||
num_heads = m.w_msa.num_heads | ||
B, N, C = x[0].shape | ||
# qkv = model.qkv(x) | ||
m.total_ops += B * N * embed_dims * 3 * embed_dims | ||
# attn = (q @ k.transpose(-2, -1)) | ||
m.total_ops += B * num_heads * N * (embed_dims // num_heads) * N | ||
# x = (attn @ v) | ||
m.total_ops += num_heads * B * N * N * (embed_dims // num_heads) | ||
# x = m.proj(x) | ||
m.total_ops += B * N * embed_dims * embed_dims | ||
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def count_sparseconv(m: Union[SparseConv3d, SubMConv3d], x, y): | ||
indice_dict = y.indice_dict[m.indice_key] | ||
kmap_size = indice_dict[-2].sum().item() | ||
m.total_ops += kmap_size * x[0].features.shape[1] * y.features.shape[1] | ||
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def count_mha(m: Union[MultiheadAttention, nn.MultiheadAttention], x, y): | ||
flops = 0 | ||
if len(x) == 3: | ||
q, k, v = x | ||
elif len(x) == 2: | ||
q, k = x | ||
v = k | ||
elif len(x) == 1: | ||
q = x[0] | ||
k = v = q | ||
else: | ||
return | ||
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batch_first = m.batch_first \ | ||
if hasattr(m, 'batch_first') else False | ||
if batch_first: | ||
batch_size = q.shape[0] | ||
len_idx = 1 | ||
else: | ||
batch_size = q.shape[1] | ||
len_idx = 0 | ||
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dim_idx = 2 | ||
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qdim = q.shape[dim_idx] | ||
kdim = k.shape[dim_idx] | ||
vdim = v.shape[dim_idx] | ||
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qlen = q.shape[len_idx] | ||
klen = k.shape[len_idx] | ||
vlen = v.shape[len_idx] | ||
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num_heads = m.num_heads | ||
assert qdim == m.embed_dim | ||
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if m.kdim is None: | ||
assert kdim == qdim | ||
if m.vdim is None: | ||
assert vdim == qdim | ||
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flops = 0 | ||
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# Q scaling | ||
flops += qlen * qdim | ||
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# Initial projections | ||
flops += ( | ||
(qlen * qdim * qdim) # QW | ||
+ (klen * kdim * kdim) # KW | ||
+ (vlen * vdim * vdim) # VW | ||
) | ||
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if m.in_proj_bias is not None: | ||
flops += (qlen + klen + vlen) * qdim | ||
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# attention heads: scale, matmul, softmax, matmul | ||
qk_head_dim = qdim // num_heads | ||
v_head_dim = vdim // num_heads | ||
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head_flops = ( | ||
(qlen * klen * qk_head_dim) # QK^T | ||
+ (qlen * klen) # softmax | ||
+ (qlen * klen * v_head_dim) # AV | ||
) | ||
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flops += num_heads * head_flops | ||
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# final projection, bias is always enabled | ||
flops += qlen * vdim * (vdim + 1) | ||
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flops *= batch_size | ||
m.total_ops += flops | ||
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def flops_counter(model, inputs): | ||
macs, params = profile( | ||
model, | ||
inputs, | ||
custom_ops={ | ||
WindowMSA: count_window_msa, | ||
#ShiftWindowMSA: count_window_msa, | ||
SparseConv3d: count_sparseconv, | ||
SubMConv3d: count_sparseconv, | ||
MultiheadAttention: count_mha | ||
}, | ||
verbose=False | ||
) | ||
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return macs, params |