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53 lines
1.5 KiB
53 lines
1.5 KiB
2 years ago
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from abc import ABC, abstractmethod
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from typing import Any, Dict, Optional
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import torch
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from shap_e.models.query import Query
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from shap_e.models.renderer import append_tensor
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from shap_e.util.collections import AttrDict
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class Model(ABC):
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@abstractmethod
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def forward(
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self,
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query: Query,
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params: Optional[Dict[str, torch.Tensor]] = None,
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options: Optional[Dict[str, Any]] = None,
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) -> AttrDict[str, Any]:
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"""
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Predict an attribute given position
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"""
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def forward_batched(
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self,
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query: Query,
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query_batch_size: int = 4096,
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params: Optional[Dict[str, torch.Tensor]] = None,
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options: Optional[Dict[str, Any]] = None,
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) -> AttrDict[str, Any]:
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if not query.position.numel():
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# Avoid torch.cat() of zero tensors.
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return self(query, params=params, options=options)
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if options.cache is None:
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created_cache = True
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options.cache = AttrDict()
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else:
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created_cache = False
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results_list = AttrDict()
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for i in range(0, query.position.shape[1], query_batch_size):
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out = self(
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query=query.map_tensors(lambda x, i=i: x[:, i : i + query_batch_size]),
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params=params,
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options=options,
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)
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results_list = results_list.combine(out, append_tensor)
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if created_cache:
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del options["cache"]
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return results_list.map(lambda key, tensor_list: torch.cat(tensor_list, dim=1))
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