mirror of
https://github.com/wassname/DeepTime.git
synced 2026-06-27 18:40:49 +08:00
partially converted to M2S (multi inputs)
This commit is contained in:
wassname
+31
-17
@@ -43,14 +43,15 @@ class ForecastDataset(Dataset):
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"""
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assert flag in ('train', 'val', 'test'), \
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f"flag should be one of (train, val, test)"
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assert features in ('M', 'S'), \
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f"features should be one of (M: multivar, S: univar)"
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assert features in ('M', 'S', 'M2S'), \
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f"features should be one of (M: multivar, S: univar, M2S: multi inputs 2 single output)"
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assert (lookback_len is not None) ^ (lookback_mult is not None), \
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f"only 'lookback_len' xor 'lookback_mult' should be specified"
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self.flag = flag
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self.lookback_len = int(horizon_len * lookback_mult) if lookback_mult is not None else lookback_len
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self.lookback_aux_len = lookback_aux_len
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self.gap = 0
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self.horizon_len = horizon_len
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self.scale = scale
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self.cross_learn = cross_learn
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@@ -84,8 +85,11 @@ class ForecastDataset(Dataset):
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elif self.features == 'S':
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df_data = df_raw[[self.target]]
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self.n_dims = 1
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elif self.features == 'M2S':
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df_data = df_raw[cols + [self.target]]
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self.n_dims = 1 # len(cols + [self.target])
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else:
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raise ValueError
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raise NotImplementedError(self.features)
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self.scaler = StandardScaler()
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if self.scale:
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@@ -95,13 +99,15 @@ class ForecastDataset(Dataset):
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else:
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data = df_data.values
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self.data_x = data[border1:border2]
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self.data_y = data[border1:border2]
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# is will be our past data, including the y col
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self.data_x = data[border1:border2]
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# y is just the col we predict
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self.data_y = data[border1:border2][:, [-1]]
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self.timestamps = get_time_features(pd.to_datetime(df_raw.date[border1:border2].values),
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normalise=self.normalise_time_features,
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features=self.time_features)
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self.n_time = len(self.data_x)
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self.n_time_samples = self.n_time - self.lookback_len - self.horizon_len + 1
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self.n_time_samples = self.n_time - self.lookback_len * 2 - self.horizon_len + 1 + self.gap
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def get_borders(self, df_raw: pd.DataFrame) -> Tuple[List[int], List[int], List[int], List[int]]:
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set_type = {'train': 0, 'val': 1, 'test': 2}[self.flag]
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@@ -133,18 +139,26 @@ class ForecastDataset(Dataset):
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idx = idx % self.n_time_samples
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else:
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dim_slice = slice(None)
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cx_start = idx
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cx_end = cx_start + self.lookback_len
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c_start = cx_end + self.gap
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c_end = c_start + self.horizon_len
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qx_start = cx_end + self.gap
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qx_end = qx_start + self.lookback_len
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q_start = qx_end + self.gap
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q_end = q_start + self.horizon_len
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x_start = idx
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x_end = x_start + self.lookback_len
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y_start = x_end - self.lookback_aux_len
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y_end = y_start + self.lookback_aux_len + self.horizon_len
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x = self.data_x[x_start:x_end, dim_slice]
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y = self.data_y[y_start:y_end, dim_slice]
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x_time = self.timestamps[x_start:x_end]
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y_time = self.timestamps[y_start:y_end]
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return x, y, x_time, y_time
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context_past_x = self.data_x[cx_start:cx_end, dim_slice]
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context_y = self.data_y[c_start:c_end, dim_slice]
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context_time = self.timestamps[c_start:c_end]
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query_past_x = self.data_x[qx_start:qx_end, dim_slice]
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query_y = self.data_y[q_start:q_end, dim_slice]
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query_time = self.timestamps[q_start:q_end]
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return context_past_x, context_y, query_past_x, query_y, context_time, query_time
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def inverse_transform(self, data):
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return self.scaler.inverse_transform(data)
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@@ -0,0 +1,37 @@
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build.experiment_name = 'Stocks/96M2S'
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build.module = 'experiments.forecast'
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build.repeat = 1
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build.variables_dict = {
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}
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instance.model_type = 'deeptime3'
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instance.save_vals = False
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get_optimizer.lr = 1e-3
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get_optimizer.lambda_lr = 1.
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get_optimizer.weight_decay = 0.
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get_scheduler.warmup_epochs = 5
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get_data.batch_size = 256
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train.loss_name = 'mse'
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train.epochs = 50
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train.clip = 10.
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Checkpoint.patience = 7
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deeptime3.layer_size = 32
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deeptime3.inr_layers = 5
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deeptime3.n_fourier_feats = 4096
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deeptime3.scales = [0.01, 0.1, 1, 5, 10, 20, 50, 100]
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ForecastDataset.data_path = 'stocks/OXY_2019.csv.gz'
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ForecastDataset.target = 'RSMKs_18_144_72'
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ForecastDataset.scale = True
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ForecastDataset.cross_learn = False
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ForecastDataset.time_features = []
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ForecastDataset.normalise_time_features = True
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ForecastDataset.features = 'M2S'
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ForecastDataset.horizon_len = 96
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ForecastDataset.lookback_mult = 1
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+13
-12
@@ -139,16 +139,16 @@ def train(model: nn.Module,
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model.train()
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for it, data in enumerate(train_loader):
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optimizer.zero_grad()
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x, y, x_time, y_time = map(to_tensor, data)
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forecast = model(x, x_time, y_time)
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data2 = list(map(to_tensor, data))
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context_past_x, context_y, query_past_x, query_y, context_time, query_time = data2
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forecast = model(*data2)
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if isinstance(forecast, tuple):
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# for models which require reconstruction + forecast loss
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loss = training_loss_fn(forecast[0], x) + \
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training_loss_fn(forecast[1], y)
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loss = training_loss_fn(forecast[0], context_y) + \
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training_loss_fn(forecast[1], query_y)
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else:
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loss = training_loss_fn(forecast, y)
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loss = training_loss_fn(forecast, query_y)
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loss.backward()
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nn.utils.clip_grad_norm_(model.parameters(), clip)
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optimizer.step()
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@@ -188,22 +188,23 @@ def validate(model: nn.Module,
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inps = []
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total_loss = []
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for it, data in enumerate(loader):
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x, y, x_time, y_time = map(to_tensor, data)
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data2 = list(map(to_tensor, data))
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context_past_x, context_y, query_past_x, query_y, context_time, query_time = data2
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if x.shape[0] == 1:
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if context_past_x.shape[0] == 1:
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# skip final batch if batch_size == 1
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# due to bug in torch.linalg.solve which raises error when batch_size == 1
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continue
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forecast = model(x, x_time, y_time)
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forecast = model(*data2)
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if report_metrics:
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preds.append(forecast)
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trues.append(y)
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trues.append(query_y)
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if save_path is not None:
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inps.append(x)
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inps.append(context_y)
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else:
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loss = loss_fn(forecast, y, reduction='none')
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loss = loss_fn(forecast, query_y, reduction='none')
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total_loss.append(loss)
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if report_metrics:
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@@ -0,0 +1,88 @@
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# Copyright (c) 2022, salesforce.com, inc.
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# All rights reserved.
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# SPDX-License-Identifier: BSD-3-Clause
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# For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
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from typing import Optional
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import gin
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import torch
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import torch.nn as nn
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from torch import Tensor
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from einops import rearrange, repeat, reduce
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from models.modules.causalinception import CausalInceptionTimePlus, CausalConv1d
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from models.modules.inrplus2 import INRPlus2
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from models.modules.regressors import RidgeRegressor
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@gin.configurable()
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def deeptime3(dim_size:int, datetime_feats: int, layer_size: int, inr_layers: int, n_fourier_feats: int, scales: float):
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return DeepTIMe3(dim_size, datetime_feats, layer_size, inr_layers, n_fourier_feats, scales)
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class DeepTIMe3(nn.Module):
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def __init__(self, dim_size: int, datetime_feats: int, layer_size: int, inr_layers: int, n_fourier_feats: int, scales: float, dropout: float=0.3):
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super().__init__()
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# encode the past
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encoded_size = layer_size//2
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self.encoder = CausalInceptionTimePlus(
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c_in=dim_size, c_out=encoded_size,
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# nf=32, depth=6,
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nf=17, depth=3,
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bn=True,
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ks=[39, 19, 3],
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coord=True, fc_dropout=dropout,
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)
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# translate coords to a representation, given a summary of the past
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coord_size = 1
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in_feats=datetime_feats+encoded_size+coord_size
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self.inr = INRPlus2(in_feats=in_feats, layers=inr_layers, layer_size=layer_size,
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n_fourier_feats=n_fourier_feats, scales=scales, dropout=dropout)
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# meta learn y given a representation
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self.adaptive_weights = RidgeRegressor()
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self.datetime_feats = datetime_feats
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self.inr_layers = inr_layers
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self.layer_size = layer_size
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self.n_fourier_feats = n_fourier_feats
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self.scales = scales
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def encode_and_decode(self, past_x, time, offset=0):
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"""
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h_past = encode(past) # get representation of past
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representation = decode(h_past, coords)
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i = length of past, so we can offset the coords
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"""
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encoded_x = self.encoder(past_x.transpose(2, 1))
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# relative coordinates are the same for each batch, so we make them once and repeat them
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past_len = time.shape[1]
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encoded_x = repeat(encoded_x, "b f -> b t f", t=past_len)
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coords = self.get_coords(past_len).to(time.device) + offset
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coords = repeat(coords, "1 t 1 -> b t 1", b=time.shape[0])
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context_input = torch.cat([encoded_x, coords, time], dim=-1)
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context_repr = self.inr(context_input)
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return context_repr
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def forward(self, context_past_x, context_y, query_past_x, query_y, context_time, query_time) -> Tensor:
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context_reprs = self.encode_and_decode(context_past_x, context_time)
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query_reprs = self.encode_and_decode(query_past_x, query_time, offset=context_reprs.shape[1])
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w, b = self.adaptive_weights(context_reprs, context_y)
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preds = self.forecast(query_reprs, w, b)
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return preds
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def forecast(self, inp: Tensor, w: Tensor, b: Tensor) -> Tensor:
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return torch.einsum('... d o, ... t d -> ... t o', [w, inp]) + b
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def get_coords(self, lookback_len: int) -> Tensor:
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coords = torch.linspace(0, 1, lookback_len)
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return rearrange(coords, 't -> 1 t 1')
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+5
-2
@@ -4,13 +4,16 @@ import torch
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from .DeepTIMe import deeptime
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from .DeepTIMe2 import deeptime2
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from .DeepTIMe3 import deeptime3
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def get_model(model_type: str, **kwargs: Union[int, float]) -> torch.nn.Module:
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if model_type == 'deeptime':
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model = deeptime(datetime_feats=kwargs['datetime_feats'])
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model = deeptime(datetime_feats=kwargs['datetime_feats'], dim_size=kwargs['dim_size'])
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elif model_type=="deeptime2":
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model = deeptime2(datetime_feats=kwargs['datetime_feats'])
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model = deeptime2(datetime_feats=kwargs['datetime_feats'], dim_size=kwargs['dim_size'])
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elif model_type=="deeptime3":
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model = deeptime3(datetime_feats=kwargs['datetime_feats'], dim_size=kwargs['dim_size'])
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else:
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raise ValueError(f"Unknown model type {model_type}")
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return model
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@@ -24,13 +24,14 @@ class INRPlus2(nn.Module):
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def __init__(self, in_feats: int, layers: int, layer_size: int, n_fourier_feats: int, scales: float,
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dropout: Optional[float] = 0.5, bn=False, *args, **kwargs):
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super().__init__()
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self.features = nn.Linear(in_feats, layer_size) if n_fourier_feats == 0 \
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self.n_fourier_feats = n_fourier_feats
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self.features = nn.Linear(in_feats, in_feats) if n_fourier_feats == 0 \
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else GaussianFourierFeatureTransform(in_feats, n_fourier_feats, scales)
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in_size = layer_size if n_fourier_feats == 0 \
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in_size = in_feats if n_fourier_feats == 0 \
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else n_fourier_feats+in_feats
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# import pdb; pdb.set_trace()
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self.layers = CausalInceptionTimePlus(
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in_size-1, layer_size, seq_len=None, nf=layer_size, depth=layers,
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in_size, layer_size, seq_len=None, nf=layer_size, depth=layers,
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flatten=False, concat_pool=False, fc_dropout=dropout, conv_dropout=0.05, bn=bn, y_range=None, custom_head=custom_head, ks=[139, 19, 3], dilation=2, *args, **kwargs
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)
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# layers = [INRPlusLayer(in_size, layer_size, dropout=dropout)] + \
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@@ -38,6 +39,7 @@ class INRPlus2(nn.Module):
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# self.layers = nn.Sequential(*layers)
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|
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def forward(self, x: Tensor) -> Tensor:
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x = self.features(x)
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# import pdb; pdb.set_trace()
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return self.layers(x.permute((0, 2, 1))).permute((0, 2, 1))
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f = self.features(x)
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if self.n_fourier_feats>0:
|
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f = torch.concat([f, x], -1)
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return self.layers(f.permute((0, 2, 1))).permute((0, 2, 1))
|
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|
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@@ -0,0 +1,464 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "b1e031e3",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"- [x] try just one predictor\n",
|
||||
" - [ ] multi input, single output\n",
|
||||
"- [x] comparem ulti\n",
|
||||
"- losses:\n",
|
||||
" - try logp? nah\n",
|
||||
" - mae?\n",
|
||||
"- [x] make my own csv with 5m data (maybe 10k rows)\n",
|
||||
"- [ ] backtest?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "7f9e3d73",
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2022-11-22T08:32:51.601212Z",
|
||||
"start_time": "2022-11-22T08:32:51.590095Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import warnings\n",
|
||||
"warnings.simplefilter(\"ignore\")\n",
|
||||
"\n",
|
||||
"# autoreload import your package\n",
|
||||
"%load_ext autoreload\n",
|
||||
"%autoreload 2"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "4e09086b",
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2022-11-22T08:32:52.773863Z",
|
||||
"start_time": "2022-11-22T08:32:51.602648Z"
|
||||
},
|
||||
"lines_to_next_cell": 0
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import os\n",
|
||||
"from os.path import join\n",
|
||||
"import math\n",
|
||||
"import logging\n",
|
||||
"from typing import Callable, Optional, Union, Dict, Tuple\n",
|
||||
"\n",
|
||||
"from matplotlib import pyplot as plt\n",
|
||||
"from pathlib import Path\n",
|
||||
"import matplotlib.colors as mcolors\n",
|
||||
"\n",
|
||||
"import gin\n",
|
||||
"from fire import Fire\n",
|
||||
"import numpy as np\n",
|
||||
"import torch\n",
|
||||
"from torch.utils.data import DataLoader\n",
|
||||
"from torch import optim\n",
|
||||
"from torch import nn\n",
|
||||
"\n",
|
||||
"from experiments.base import Experiment\n",
|
||||
"from data.datasets import ForecastDataset\n",
|
||||
"from models import get_model\n",
|
||||
"from utils.checkpoint import Checkpoint\n",
|
||||
"from utils.ops import default_device, to_tensor\n",
|
||||
"from utils.losses import get_loss_fn\n",
|
||||
"from utils.metrics import calc_metrics\n",
|
||||
"\n",
|
||||
"from experiments.forecast import get_data\n",
|
||||
"gin.enter_interactive_mode()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "66d7f095",
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2022-11-19T23:55:34.939075Z",
|
||||
"start_time": "2022-11-19T23:55:34.820277Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "d4df5270",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# auto"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "04499bef",
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2022-11-22T08:32:52.793863Z",
|
||||
"start_time": "2022-11-22T08:32:52.776011Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"\n",
|
||||
"\n",
|
||||
"def plot(model_name=\"deeptime\", save_path=Path(\"storage/experiments/Exchange/96M/repeat=0\"), i=200, title=None, plot=True):\n",
|
||||
"\n",
|
||||
" gin.clear_config()\n",
|
||||
" gin.parse_config(open(save_path/\"config.gin\"))\n",
|
||||
"\n",
|
||||
" train_set, train_loader = get_data(flag='train', batch_size=2)\n",
|
||||
"\n",
|
||||
" model = get_model(model_name,\n",
|
||||
" dim_size=train_set.data_x.shape[1],\n",
|
||||
" datetime_feats=train_set.timestamps.shape[-1]).to(default_device())\n",
|
||||
" model.load_state_dict(torch.load(save_path/'model.pth'))\n",
|
||||
" model = model.eval()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
" b = train_set[i]\n",
|
||||
" b = [bb[None, :] for bb in b]\n",
|
||||
" x, y, x_time, y_time = map(to_tensor, b)\n",
|
||||
" with torch.no_grad():\n",
|
||||
" forecast = model(x, x_time, y_time)\n",
|
||||
"\n",
|
||||
" if title is None:\n",
|
||||
" title = str(save_path).split('/')[-3:]\n",
|
||||
" title = \"-\".join(title)\n",
|
||||
" \n",
|
||||
" colors = list(mcolors.BASE_COLORS.keys())\n",
|
||||
" l = x.shape[1]\n",
|
||||
" forecast2 = forecast[0].detach().cpu().numpy()\n",
|
||||
" x2 = x[0].cpu()\n",
|
||||
" y2 = y[0].cpu()\n",
|
||||
" l2 = y.shape[1]\n",
|
||||
" i_past = list(range(l))\n",
|
||||
" i_future = list(range(l, l+l2))\n",
|
||||
" \n",
|
||||
" if plot:\n",
|
||||
" plt.title(title)\n",
|
||||
" for i in range(x.shape[-1]):\n",
|
||||
" plt.plot(i_past, x2[:, i], c=colors[i])\n",
|
||||
" for i in range(x.shape[-1]):\n",
|
||||
" plt.plot(i_future, y2[:, i], c=colors[i])\n",
|
||||
" for i in range(x.shape[-1]):\n",
|
||||
" plt.plot(i_future, forecast2[:, i], c=colors[i], linestyle='--')\n",
|
||||
" return x2, y2, forecast2, i_past, i_future\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "768530be",
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2022-11-22T08:32:52.819217Z",
|
||||
"start_time": "2022-11-22T08:32:52.794951Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"\n",
|
||||
"\n",
|
||||
"def plot_multi(save_paths=[Path(\"storage/experiments/Exchange/96M/repeat=0\")], i=200, title=None, plot=True):\n",
|
||||
" for j in range(len(save_paths)):\n",
|
||||
" save_path = save_paths[j]\n",
|
||||
"\n",
|
||||
" gin.clear_config()\n",
|
||||
" gin.parse_config(open(save_path/\"config.gin\"))\n",
|
||||
" model_name = gin.query_parameter(\"instance.model_type\")\n",
|
||||
"\n",
|
||||
" train_set, train_loader = get_data(flag='test', batch_size=3)\n",
|
||||
"\n",
|
||||
" model = get_model(model_name,\n",
|
||||
" dim_size=train_set.data_x.shape[1],\n",
|
||||
" datetime_feats=train_set.timestamps.shape[-1]).to(default_device())\n",
|
||||
" model.load_state_dict(torch.load(save_path/'model.pth'))\n",
|
||||
" model = model.eval()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
" b = train_set[i]\n",
|
||||
" b = [bb[None, :] for bb in b]\n",
|
||||
" \n",
|
||||
" b = next(iter(train_loader))\n",
|
||||
" print([s.shape for s in b])\n",
|
||||
" \n",
|
||||
" x, y, x_time, y_time = map(to_tensor, b)\n",
|
||||
"# print(b)\n",
|
||||
" with torch.no_grad():\n",
|
||||
" forecast = model(x, x_time, y_time)\n",
|
||||
" \n",
|
||||
" colors = list(mcolors.BASE_COLORS.keys())\n",
|
||||
" l = x.shape[1]\n",
|
||||
" forecast2 = forecast[0].detach().cpu().numpy()\n",
|
||||
" x2 = x[0].cpu()\n",
|
||||
" y2 = y[0].cpu()\n",
|
||||
" l2 = y.shape[1]\n",
|
||||
" i_past = list(range(l))\n",
|
||||
" i_future = list(range(l, l+l2))\n",
|
||||
"\n",
|
||||
" if plot:\n",
|
||||
" plt.plot(i_past, x2[:, 0], c=colors[0], label=f\"past\")\n",
|
||||
" plt.plot(i_future, y2[:, 0], c=colors[0], label=\"future true\", alpha=0.3)\n",
|
||||
" \n",
|
||||
" mtitle = str(save_path).split('/')[-2:-1]\n",
|
||||
" mtitle = \"-\".join(mtitle)\n",
|
||||
" plt.plot(i_future, forecast2[:, 0], c=colors[j], linestyle='--', label=f\"{mtitle}\")\n",
|
||||
" plt.legend()\n",
|
||||
" plt.title(title)\n",
|
||||
" return x2, y2, forecast2, i_past, i_future\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "739ee5e3",
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2022-11-22T08:32:52.843086Z",
|
||||
"start_time": "2022-11-22T08:32:52.820267Z"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[Path('storage/experiments/Stocks/96M/repeat=0/_SUCCESS'), Path('storage/experiments/Stocks/96S/repeat=0/_SUCCESS'), Path('storage/experiments/Stocks/96Splus/repeat=0/_SUCCESS'), Path('storage/experiments/Stocks/96Splusshort/repeat=0/_SUCCESS'), Path('storage/experiments/Stocks/96Sshort/repeat=0/_SUCCESS')]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# list the models we have run...\n",
|
||||
"m=sorted(Path(\"storage/experiments/Stocks\").glob(\"**/_SUCCESS\"))\n",
|
||||
"print(m)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "0529c377",
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2022-11-22T08:32:52.857950Z",
|
||||
"start_time": "2022-11-22T08:32:52.844134Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"save_path = Path('storage/experiments/Stocks/96M2S/repeat=0')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"id": "2ce3c75a",
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2022-11-22T08:32:52.879200Z",
|
||||
"start_time": "2022-11-22T08:32:52.859136Z"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'deeptime3'"
|
||||
]
|
||||
},
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"gin.clear_config()\n",
|
||||
"config_path = save_path/\"config.gin\"\n",
|
||||
"gin.parse_config(open(config_path))\n",
|
||||
"model_name = gin.query_parameter(\"instance.model_type\")\n",
|
||||
"model_name"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"id": "ef1989b7",
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2022-11-22T08:32:52.904019Z",
|
||||
"start_time": "2022-11-22T08:32:52.880345Z"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<experiments.forecast.ForecastExperiment at 0x7f4044fc3fa0>"
|
||||
]
|
||||
},
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from experiments.forecast import ForecastExperiment\n",
|
||||
"exp = ForecastExperiment(config_path=config_path)\n",
|
||||
"exp"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"id": "915e5648",
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2022-11-22T08:32:56.783272Z",
|
||||
"start_time": "2022-11-22T08:32:52.905696Z"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"receptive field [114 72 12]=[38 18 2]*[[1 1 1]\n",
|
||||
" [1 1 1]\n",
|
||||
" [1 2 4]]\n",
|
||||
"129 in_feats\n",
|
||||
"receptive field [690 378 242]=[138 18 2]*[[ 1 1 1]\n",
|
||||
" [ 1 2 4]\n",
|
||||
" [ 1 4 16]\n",
|
||||
" [ 1 6 36]\n",
|
||||
" [ 1 8 64]]\n",
|
||||
"torch.Size([256, 96, 129])\n",
|
||||
"torch.Size([256, 96, 129])\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"ename": "RuntimeError",
|
||||
"evalue": "CUDA out of memory. Tried to allocate 24.00 MiB (GPU 0; 10.74 GiB total capacity; 8.00 GiB already allocated; 50.12 MiB free; 8.16 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF\n In call to configurable 'train' (<function train at 0x7f4045284ee0>)\n In call to configurable 'instance' (<function ForecastExperiment.instance at 0x7f4045284550>)\n In call to configurable 'run' (<function Experiment.run at 0x7f4092a49550>)",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
|
||||
"Cell \u001b[0;32mIn [9], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mexp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"File \u001b[0;32m~/miniforge3/envs/deeptime/lib/python3.8/site-packages/gin/config.py:1605\u001b[0m, in \u001b[0;36m_make_gin_wrapper.<locals>.gin_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 1603\u001b[0m scope_info \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m in scope \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(scope_str) \u001b[38;5;28;01mif\u001b[39;00m scope_str \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 1604\u001b[0m err_str \u001b[38;5;241m=\u001b[39m err_str\u001b[38;5;241m.\u001b[39mformat(name, fn_or_cls, scope_info)\n\u001b[0;32m-> 1605\u001b[0m \u001b[43mutils\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maugment_exception_message_and_reraise\u001b[49m\u001b[43m(\u001b[49m\u001b[43me\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merr_str\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"File \u001b[0;32m~/miniforge3/envs/deeptime/lib/python3.8/site-packages/gin/utils.py:41\u001b[0m, in \u001b[0;36maugment_exception_message_and_reraise\u001b[0;34m(exception, message)\u001b[0m\n\u001b[1;32m 39\u001b[0m proxy \u001b[38;5;241m=\u001b[39m ExceptionProxy()\n\u001b[1;32m 40\u001b[0m ExceptionProxy\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtype\u001b[39m(exception)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m\n\u001b[0;32m---> 41\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m proxy\u001b[38;5;241m.\u001b[39mwith_traceback(exception\u001b[38;5;241m.\u001b[39m__traceback__) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28mNone\u001b[39m\n",
|
||||
"File \u001b[0;32m~/miniforge3/envs/deeptime/lib/python3.8/site-packages/gin/config.py:1582\u001b[0m, in \u001b[0;36m_make_gin_wrapper.<locals>.gin_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 1579\u001b[0m new_kwargs\u001b[38;5;241m.\u001b[39mupdate(kwargs)\n\u001b[1;32m 1581\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1582\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnew_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnew_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1583\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e: \u001b[38;5;66;03m# pylint: disable=broad-except\u001b[39;00m\n\u001b[1;32m 1584\u001b[0m err_str \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m\n",
|
||||
"File \u001b[0;32m/media/wassname/SGIronWolf/projects5/investing/DeepTime/experiments/base.py:96\u001b[0m, in \u001b[0;36mExperiment.run\u001b[0;34m(self, timer)\u001b[0m\n\u001b[1;32m 94\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 95\u001b[0m Path(running_flag)\u001b[38;5;241m.\u001b[39munlink()\n\u001b[0;32m---> 96\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m:\n\u001b[1;32m 98\u001b[0m Path(running_flag)\u001b[38;5;241m.\u001b[39munlink()\n",
|
||||
"File \u001b[0;32m/media/wassname/SGIronWolf/projects5/investing/DeepTime/experiments/base.py:93\u001b[0m, in \u001b[0;36mExperiment.run\u001b[0;34m(self, timer)\u001b[0m\n\u001b[1;32m 90\u001b[0m Path(running_flag)\u001b[38;5;241m.\u001b[39mtouch()\n\u001b[1;32m 92\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 93\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minstance\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 94\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 95\u001b[0m Path(running_flag)\u001b[38;5;241m.\u001b[39munlink()\n",
|
||||
"File \u001b[0;32m~/miniforge3/envs/deeptime/lib/python3.8/site-packages/gin/config.py:1605\u001b[0m, in \u001b[0;36m_make_gin_wrapper.<locals>.gin_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 1603\u001b[0m scope_info \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m in scope \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(scope_str) \u001b[38;5;28;01mif\u001b[39;00m scope_str \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 1604\u001b[0m err_str \u001b[38;5;241m=\u001b[39m err_str\u001b[38;5;241m.\u001b[39mformat(name, fn_or_cls, scope_info)\n\u001b[0;32m-> 1605\u001b[0m \u001b[43mutils\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maugment_exception_message_and_reraise\u001b[49m\u001b[43m(\u001b[49m\u001b[43me\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merr_str\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/miniforge3/envs/deeptime/lib/python3.8/site-packages/gin/utils.py:41\u001b[0m, in \u001b[0;36maugment_exception_message_and_reraise\u001b[0;34m(exception, message)\u001b[0m\n\u001b[1;32m 39\u001b[0m proxy \u001b[38;5;241m=\u001b[39m ExceptionProxy()\n\u001b[1;32m 40\u001b[0m ExceptionProxy\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtype\u001b[39m(exception)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m\n\u001b[0;32m---> 41\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m proxy\u001b[38;5;241m.\u001b[39mwith_traceback(exception\u001b[38;5;241m.\u001b[39m__traceback__) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28mNone\u001b[39m\n",
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"File \u001b[0;32m~/miniforge3/envs/deeptime/lib/python3.8/site-packages/gin/config.py:1582\u001b[0m, in \u001b[0;36m_make_gin_wrapper.<locals>.gin_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 1579\u001b[0m new_kwargs\u001b[38;5;241m.\u001b[39mupdate(kwargs)\n\u001b[1;32m 1581\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1582\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnew_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnew_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1583\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e: \u001b[38;5;66;03m# pylint: disable=broad-except\u001b[39;00m\n\u001b[1;32m 1584\u001b[0m err_str \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m\n",
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"File \u001b[0;32m/media/wassname/SGIronWolf/projects5/investing/DeepTime/experiments/forecast.py:40\u001b[0m, in \u001b[0;36mForecastExperiment.instance\u001b[0;34m(self, model_type, save_vals)\u001b[0m\n\u001b[1;32m 37\u001b[0m checkpoint \u001b[38;5;241m=\u001b[39m Checkpoint(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mroot)\n\u001b[1;32m 39\u001b[0m \u001b[38;5;66;03m# train forecasting task\u001b[39;00m\n\u001b[0;32m---> 40\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheckpoint\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_loader\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval_loader\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtest_loader\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 42\u001b[0m \u001b[38;5;66;03m# testing\u001b[39;00m\n\u001b[1;32m 43\u001b[0m val_metrics \u001b[38;5;241m=\u001b[39m validate(model, loader\u001b[38;5;241m=\u001b[39mval_loader, report_metrics\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
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"File \u001b[0;32m~/miniforge3/envs/deeptime/lib/python3.8/site-packages/gin/config.py:1605\u001b[0m, in \u001b[0;36m_make_gin_wrapper.<locals>.gin_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 1603\u001b[0m scope_info \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m in scope \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(scope_str) \u001b[38;5;28;01mif\u001b[39;00m scope_str \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 1604\u001b[0m err_str \u001b[38;5;241m=\u001b[39m err_str\u001b[38;5;241m.\u001b[39mformat(name, fn_or_cls, scope_info)\n\u001b[0;32m-> 1605\u001b[0m \u001b[43mutils\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maugment_exception_message_and_reraise\u001b[49m\u001b[43m(\u001b[49m\u001b[43me\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merr_str\u001b[49m\u001b[43m)\u001b[49m\n",
|
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"File \u001b[0;32m~/miniforge3/envs/deeptime/lib/python3.8/site-packages/gin/utils.py:41\u001b[0m, in \u001b[0;36maugment_exception_message_and_reraise\u001b[0;34m(exception, message)\u001b[0m\n\u001b[1;32m 39\u001b[0m proxy \u001b[38;5;241m=\u001b[39m ExceptionProxy()\n\u001b[1;32m 40\u001b[0m ExceptionProxy\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtype\u001b[39m(exception)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m\n\u001b[0;32m---> 41\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m proxy\u001b[38;5;241m.\u001b[39mwith_traceback(exception\u001b[38;5;241m.\u001b[39m__traceback__) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28mNone\u001b[39m\n",
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"File \u001b[0;32m~/miniforge3/envs/deeptime/lib/python3.8/site-packages/gin/config.py:1582\u001b[0m, in \u001b[0;36m_make_gin_wrapper.<locals>.gin_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 1579\u001b[0m new_kwargs\u001b[38;5;241m.\u001b[39mupdate(kwargs)\n\u001b[1;32m 1581\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1582\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnew_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnew_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1583\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e: \u001b[38;5;66;03m# pylint: disable=broad-except\u001b[39;00m\n\u001b[1;32m 1584\u001b[0m err_str \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m\n",
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"File \u001b[0;32m/media/wassname/SGIronWolf/projects5/investing/DeepTime/experiments/forecast.py:144\u001b[0m, in \u001b[0;36mtrain\u001b[0;34m(model, checkpoint, train_loader, val_loader, test_loader, loss_name, epochs, clip)\u001b[0m\n\u001b[1;32m 142\u001b[0m data2 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmap\u001b[39m(to_tensor, data)\n\u001b[1;32m 143\u001b[0m context_past_x, context_y, query_past_x, query_y, context_time, query_time \u001b[38;5;241m=\u001b[39m data2\n\u001b[0;32m--> 144\u001b[0m forecast \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcontext_past_x\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcontext_y\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mquery_past_x\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcontext_time\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mquery_time\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 146\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(forecast, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[1;32m 147\u001b[0m \u001b[38;5;66;03m# for models which require reconstruction + forecast loss\u001b[39;00m\n\u001b[1;32m 148\u001b[0m loss \u001b[38;5;241m=\u001b[39m training_loss_fn(forecast[\u001b[38;5;241m0\u001b[39m], context_y) \u001b[38;5;241m+\u001b[39m \\\n\u001b[1;32m 149\u001b[0m training_loss_fn(forecast[\u001b[38;5;241m1\u001b[39m], query_y)\n",
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"File \u001b[0;32m~/miniforge3/envs/deeptime/lib/python3.8/site-packages/torch/nn/modules/module.py:1102\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1098\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1099\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1101\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1103\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1104\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
|
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"File \u001b[0;32m/media/wassname/SGIronWolf/projects5/investing/DeepTime/models/DeepTIMe3.py:77\u001b[0m, in \u001b[0;36mDeepTIMe3.forward\u001b[0;34m(self, context_past_x, context_y, query_past_x, context_time, query_time)\u001b[0m\n\u001b[1;32m 74\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, context_past_x, context_y, query_past_x, context_time, query_time) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[1;32m 76\u001b[0m context_reprs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mencode_and_decode(context_past_x, context_time)\n\u001b[0;32m---> 77\u001b[0m query_reprs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencode_and_decode\u001b[49m\u001b[43m(\u001b[49m\u001b[43mquery_past_x\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mquery_time\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moffset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcontext_reprs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshape\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 79\u001b[0m w, b \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39madaptive_weights(context_reprs, context_y)\n\u001b[1;32m 80\u001b[0m preds \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mforecast(query_reprs, w, b)\n",
|
||||
"File \u001b[0;32m/media/wassname/SGIronWolf/projects5/investing/DeepTime/models/DeepTIMe3.py:71\u001b[0m, in \u001b[0;36mDeepTIMe3.encode_and_decode\u001b[0;34m(self, past_x, time, offset)\u001b[0m\n\u001b[1;32m 68\u001b[0m context_input \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mcat([encoded_x, coords, time], dim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 70\u001b[0m \u001b[38;5;28mprint\u001b[39m(context_input\u001b[38;5;241m.\u001b[39mshape)\n\u001b[0;32m---> 71\u001b[0m context_repr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minr\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcontext_input\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m context_repr\n",
|
||||
"File \u001b[0;32m~/miniforge3/envs/deeptime/lib/python3.8/site-packages/torch/nn/modules/module.py:1102\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1098\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1099\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1101\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1103\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1104\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
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"File \u001b[0;32m/media/wassname/SGIronWolf/projects5/investing/DeepTime/models/modules/inrplus2.py:45\u001b[0m, in \u001b[0;36mINRPlus2.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_fourier_feats\u001b[38;5;241m>\u001b[39m\u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 44\u001b[0m f \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mconcat([f, x], \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m---> 45\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlayers\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpermute\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mpermute((\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m1\u001b[39m))\n",
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"File \u001b[0;32m~/miniforge3/envs/deeptime/lib/python3.8/site-packages/torch/nn/modules/module.py:1102\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1098\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1099\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1101\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1103\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1104\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
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"File \u001b[0;32m~/miniforge3/envs/deeptime/lib/python3.8/site-packages/torch/nn/modules/container.py:141\u001b[0m, in \u001b[0;36mSequential.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 139\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m):\n\u001b[1;32m 140\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m module \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[0;32m--> 141\u001b[0m \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[43mmodule\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 142\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28minput\u001b[39m\n",
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"File \u001b[0;32m~/miniforge3/envs/deeptime/lib/python3.8/site-packages/torch/nn/modules/module.py:1102\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1098\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1099\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1101\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1103\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1104\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
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"File \u001b[0;32m~/miniforge3/envs/deeptime/lib/python3.8/site-packages/torch/nn/modules/container.py:141\u001b[0m, in \u001b[0;36mSequential.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 139\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m):\n\u001b[1;32m 140\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m module \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[0;32m--> 141\u001b[0m \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[43mmodule\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 142\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28minput\u001b[39m\n",
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"File \u001b[0;32m~/miniforge3/envs/deeptime/lib/python3.8/site-packages/torch/nn/modules/module.py:1102\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1098\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1099\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1101\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1103\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1104\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
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"File \u001b[0;32m/media/wassname/SGIronWolf/projects5/investing/DeepTime/models/modules/causalinception.py:92\u001b[0m, in \u001b[0;36mInceptionBlockPlus.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 90\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdepth):\n\u001b[1;32m 91\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkeep_prob[i] \u001b[38;5;241m>\u001b[39m random\u001b[38;5;241m.\u001b[39mrandom() \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtraining:\n\u001b[0;32m---> 92\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minception\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 93\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mresidual \u001b[38;5;129;01mand\u001b[39;00m i \u001b[38;5;241m%\u001b[39m \u001b[38;5;241m3\u001b[39m \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[1;32m 94\u001b[0m res \u001b[38;5;241m=\u001b[39m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mact[i\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m3\u001b[39m](\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39madd(x, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mshortcut[i\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m3\u001b[39m](res)))\n",
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"File \u001b[0;32m~/miniforge3/envs/deeptime/lib/python3.8/site-packages/torch/nn/modules/module.py:1102\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1098\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1099\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1101\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1103\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1104\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
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"File \u001b[0;32m/media/wassname/SGIronWolf/projects5/investing/DeepTime/models/modules/causalinception.py:52\u001b[0m, in \u001b[0;36mInceptionModulePlus.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 50\u001b[0m input_tensor \u001b[38;5;241m=\u001b[39m x\n\u001b[1;32m 51\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbottleneck(x)\n\u001b[0;32m---> 52\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconcat([l(x) \u001b[38;5;28;01mfor\u001b[39;00m l \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvs] \u001b[38;5;241m+\u001b[39m [\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmp_conv\u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_tensor\u001b[49m\u001b[43m)\u001b[49m])\n\u001b[1;32m 53\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnorm(x)\n\u001b[1;32m 54\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconv_dropout(x)\n",
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"File \u001b[0;32m~/miniforge3/envs/deeptime/lib/python3.8/site-packages/torch/nn/modules/module.py:1102\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1098\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1099\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1101\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1103\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1104\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
|
||||
"File \u001b[0;32m~/miniforge3/envs/deeptime/lib/python3.8/site-packages/torch/nn/modules/container.py:141\u001b[0m, in \u001b[0;36mSequential.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 139\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m):\n\u001b[1;32m 140\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m module \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[0;32m--> 141\u001b[0m \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[43mmodule\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 142\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28minput\u001b[39m\n",
|
||||
"File \u001b[0;32m~/miniforge3/envs/deeptime/lib/python3.8/site-packages/torch/nn/modules/module.py:1102\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1098\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1099\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1101\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1103\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1104\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
|
||||
"File \u001b[0;32m~/miniforge3/envs/deeptime/lib/python3.8/site-packages/torch/nn/modules/container.py:141\u001b[0m, in \u001b[0;36mSequential.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 139\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m):\n\u001b[1;32m 140\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m module \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[0;32m--> 141\u001b[0m \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[43mmodule\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 142\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28minput\u001b[39m\n",
|
||||
"File \u001b[0;32m~/miniforge3/envs/deeptime/lib/python3.8/site-packages/torch/nn/modules/module.py:1102\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1098\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1099\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1101\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1103\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1104\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
|
||||
"File \u001b[0;32m~/miniforge3/envs/deeptime/lib/python3.8/site-packages/tsai/models/layers.py:148\u001b[0m, in \u001b[0;36mCausalConv1d.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 147\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m):\n\u001b[0;32m--> 148\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mCausalConv1d\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mforward\u001b[49m\u001b[43m(\u001b[49m\u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpad\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__padding\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"File \u001b[0;32m~/miniforge3/envs/deeptime/lib/python3.8/site-packages/torch/nn/modules/conv.py:301\u001b[0m, in \u001b[0;36mConv1d.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 300\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[0;32m--> 301\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_conv_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbias\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"File \u001b[0;32m~/miniforge3/envs/deeptime/lib/python3.8/site-packages/torch/nn/modules/conv.py:297\u001b[0m, in \u001b[0;36mConv1d._conv_forward\u001b[0;34m(self, input, weight, bias)\u001b[0m\n\u001b[1;32m 293\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpadding_mode \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mzeros\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m 294\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m F\u001b[38;5;241m.\u001b[39mconv1d(F\u001b[38;5;241m.\u001b[39mpad(\u001b[38;5;28minput\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reversed_padding_repeated_twice, mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpadding_mode),\n\u001b[1;32m 295\u001b[0m weight, bias, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstride,\n\u001b[1;32m 296\u001b[0m _single(\u001b[38;5;241m0\u001b[39m), \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdilation, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgroups)\n\u001b[0;32m--> 297\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconv1d\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbias\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstride\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 298\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpadding\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdilation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroups\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"File \u001b[0;32m~/miniforge3/envs/deeptime/lib/python3.8/site-packages/fastai/torch_core.py:378\u001b[0m, in \u001b[0;36mTensorBase.__torch_function__\u001b[0;34m(cls, func, types, args, kwargs)\u001b[0m\n\u001b[1;32m 376\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mdebug \u001b[38;5;129;01mand\u001b[39;00m func\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m (\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m__str__\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m__repr__\u001b[39m\u001b[38;5;124m'\u001b[39m): \u001b[38;5;28mprint\u001b[39m(func, types, args, kwargs)\n\u001b[1;32m 377\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _torch_handled(args, \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_opt, func): types \u001b[38;5;241m=\u001b[39m (torch\u001b[38;5;241m.\u001b[39mTensor,)\n\u001b[0;32m--> 378\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__torch_function__\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtypes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mifnone\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 379\u001b[0m dict_objs \u001b[38;5;241m=\u001b[39m _find_args(args) \u001b[38;5;28;01mif\u001b[39;00m args \u001b[38;5;28;01melse\u001b[39;00m _find_args(\u001b[38;5;28mlist\u001b[39m(kwargs\u001b[38;5;241m.\u001b[39mvalues()))\n\u001b[1;32m 380\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28missubclass\u001b[39m(\u001b[38;5;28mtype\u001b[39m(res),TensorBase) \u001b[38;5;129;01mand\u001b[39;00m dict_objs: res\u001b[38;5;241m.\u001b[39mset_meta(dict_objs[\u001b[38;5;241m0\u001b[39m],as_copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
|
||||
"File \u001b[0;32m~/miniforge3/envs/deeptime/lib/python3.8/site-packages/torch/_tensor.py:1051\u001b[0m, in \u001b[0;36mTensor.__torch_function__\u001b[0;34m(cls, func, types, args, kwargs)\u001b[0m\n\u001b[1;32m 1048\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mNotImplemented\u001b[39m\n\u001b[1;32m 1050\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _C\u001b[38;5;241m.\u001b[39mDisableTorchFunction():\n\u001b[0;32m-> 1051\u001b[0m ret \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1052\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m func \u001b[38;5;129;01min\u001b[39;00m get_default_nowrap_functions():\n\u001b[1;32m 1053\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ret\n",
|
||||
"\u001b[0;31mRuntimeError\u001b[0m: CUDA out of memory. Tried to allocate 24.00 MiB (GPU 0; 10.74 GiB total capacity; 8.00 GiB already allocated; 50.12 MiB free; 8.16 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF\n In call to configurable 'train' (<function train at 0x7f4045284ee0>)\n In call to configurable 'instance' (<function ForecastExperiment.instance at 0x7f4045284550>)\n In call to configurable 'run' (<function Experiment.run at 0x7f4092a49550>)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"exp.run()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "66b15b6a",
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2022-11-22T08:32:56.784632Z",
|
||||
"start_time": "2022-11-22T08:32:56.784625Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%debug"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "374d6ca6",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"jupytext": {
|
||||
"cell_metadata_filter": "-all",
|
||||
"main_language": "python",
|
||||
"notebook_metadata_filter": "-all"
|
||||
},
|
||||
"kernelspec": {
|
||||
"display_name": "deeptime",
|
||||
"language": "python",
|
||||
"name": "deeptime"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.8.13"
|
||||
},
|
||||
"toc": {
|
||||
"base_numbering": 1,
|
||||
"nav_menu": {},
|
||||
"number_sections": true,
|
||||
"sideBar": true,
|
||||
"skip_h1_title": false,
|
||||
"title_cell": "Table of Contents",
|
||||
"title_sidebar": "Contents",
|
||||
"toc_cell": false,
|
||||
"toc_position": {},
|
||||
"toc_section_display": true,
|
||||
"toc_window_display": false
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
Reference in New Issue
Block a user