Delete modeling_yield.py

modeling_yield.py

@@ -1,88 +0,0 @@
-import sys
-from pathlib import Path
-
-PROJECT_ROOT = Path(__file__).resolve().parents[1]
-sys.path.insert(0, str(PROJECT_ROOT))
-
-import torch
-from torch import nn
-from transformers import PreTrainedModel
-from transformers.modeling_outputs import ModelOutput
-from dataclasses import dataclass
-
-from .configuration_yield import YieldConfig
-from .yield_transformer import YieldTransformer
-
-
-@dataclass
-class YieldModelOutput(ModelOutput):
-    loss: torch.Tensor | None = None
-    logits: torch.Tensor | None = None
-    predictions: torch.Tensor | None = None
-
-
-class YieldForRegression(PreTrainedModel):
-    config_class = YieldConfig
-    base_model_prefix = "yield_model"
-
-    def __init__(self, config: YieldConfig):
-        super().__init__(config)
-
-        self.yield_model = YieldTransformer(
-            w_dim=config.W,
-            soil_dim=config.S,
-            d_model=config.d_model,
-            nhead=config.nhead,
-            num_layers=config.num_layers,
-            dim_ff=config.dim_ff,
-            dropout=config.dropout,
-            use_crop=config.use_crop,
-            crop_emb_dim=config.crop_emb_dim,
-            max_weeks=max(32, config.K),
-            pool=config.pool,
-        )
-
-        self.post_init()
-
-    def forward(
-        self,
-        weather,
-        soil,
-        crop_id,
-        labels=None,
-        horizon_idx=None,
-        causal=True,
-        return_sequence=False,
-        return_dict=True,
-    ):
-        if horizon_idx is None:
-            horizon_idx = weather.shape[1]
-
-        logits = self.yield_model(
-            weather,
-            soil,
-            crop_id,
-            horizon_idx=horizon_idx,
-            causal=causal,
-            return_sequence=return_sequence,
-        )
-
-        y_mean = torch.tensor(self.config.y_mean, device=logits.device, dtype=logits.dtype)
-        y_std = torch.tensor(self.config.y_std, device=logits.device, dtype=logits.dtype)
-
-        #predictions = torch.expm1(logits * y_std + y_mean)
-        predictions = logits * y_std + y_mean
-
-        loss = None
-        if labels is not None:
-            labels_norm = (labels - y_mean) / y_std
-            loss = nn.functional.mse_loss(logits, labels_norm)
-
-        if not return_dict:
-            return (loss, logits, predictions)
-
-        return YieldModelOutput(
-            loss=loss,
-            logits=logits,
-            predictions=predictions,
-        )