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model.py
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model.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
def get_model(params):
return get_model_from_dict(params.to_dict())
def get_model_from_dict(params):
model_type = params.get('model', 'res-mlp')
if model_type == 'mlp':
return MLP(params)
elif model_type == 'res-mlp':
return ResMLP(params)
class MLP(nn.Module):
def __init__(self, params):
super(MLP, self).__init__()
stage_fn = self._bn_stage if params.get('batch_norm', True) else self._linear_stage
fusion_strategy = params.get('fusion', 'early')
depth = params.get('depth', 1)
if fusion_strategy == 'early':
pre, post = 0, depth
elif fusion_strategy == 'mid':
pre = depth // 2
post = depth - pre
elif fusion_strategy == 'late':
pre, post = depth, 0
op_dim = params.get('dim', 64)
pp_dim = op_dim * (op_dim - 1) // 2
post_dim = op_dim + pp_dim
self.pre_stages_op = nn.ModuleList([stage_fn(op_dim) for _ in range(pre)])
self.pre_stages_pp = nn.ModuleList([stage_fn(pp_dim) for _ in range(pre)])
self.post_stages = nn.ModuleList([stage_fn(post_dim) for _ in range(post)])
self.dropout = nn.Dropout(params.get('dropout', 0))
self.last = nn.Linear(post_dim, op_dim)
def forward(self, op, pp):
# object-pivot branch
for stage in self.pre_stages_op:
op = stage(op)
# pivot-pivot branch
if pp.dim() < 2:
pp = pp.unsqueeze(0)
for stage in self.pre_stages_pp:
pp = stage(pp)
# combined branch
pp = pp.expand(op.shape[0], -1) # expand to batch_size
x = torch.cat((op, pp), dim=1) # fusion (concatenate)
for stage in self.post_stages:
x = stage(x)
x = self.dropout(x)
x = self.last(x)
return x
@staticmethod
def _linear_stage(dim):
return nn.Sequential( nn.Linear(dim, dim), nn.ReLU() )
@staticmethod
def _bn_stage(dim):
return nn.Sequential(
nn.Linear(dim, dim),
nn.BatchNorm1d(dim),
nn.ReLU()
)
class ResMLP(nn.Module):
def __init__(self, params):
super(ResMLP, self).__init__()
block_fn = self._bn_res_block if params.get('batch_norm', True) else self._res_block
fusion_strategy = params.get('fusion', 'early')
depth = params.get('depth', 1)
if fusion_strategy == 'early':
pre, post = 0, depth
elif fusion_strategy == 'mid':
pre = depth // 2
post = depth - pre
elif fusion_strategy == 'late':
pre, post = depth, 0
op_dim = params.get('dim', 64)
pp_dim = op_dim * (op_dim - 1) // 2
post_dim = op_dim + pp_dim
self.pre_stages_op = nn.ModuleList([block_fn(op_dim) for _ in range(pre)])
self.pre_stages_pp = nn.ModuleList([block_fn(pp_dim) for _ in range(pre)])
self.post_stages = nn.ModuleList([block_fn(post_dim) for _ in range(post)])
self.dropout = nn.Dropout(params.get('dropout', 0))
self.last = nn.Linear(post_dim, op_dim)
def forward(self, op, pp):
# object-pivot branch
for stage in self.pre_stages_op:
op = op + stage(op)
# pivot-pivot branch
if pp.dim() < 2:
pp = pp.unsqueeze(0)
pp = pp.expand(op.shape[0], -1) # expand to batch_size
for stage in self.pre_stages_pp:
pp = pp + stage(pp)
# combined branch
x = torch.cat((op, pp), dim=1) # fusion (concatenate)
for stage in self.post_stages:
x = x + stage(x)
x = self.dropout(x)
x = self.last(x)
return x
@staticmethod
def _bn_res_block(dim):
return nn.Sequential(
nn.Linear(dim, dim),
nn.BatchNorm1d(dim),
nn.ReLU(),
nn.Linear(dim, dim),
nn.BatchNorm1d(dim)
)
@staticmethod
def _res_block(dim):
return nn.Sequential(
nn.Linear(dim, dim),
nn.ReLU(),
nn.Linear(dim, dim),
)