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9 Commits
TSDiff-S4
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174a82fab2
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1
.gitattributes
vendored
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1
.gitattributes
vendored
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@@ -0,0 +1 @@
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*.csv filter=lfs diff=lfs merge=lfs -text
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@@ -1,7 +1,5 @@
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FROM pytorch/pytorch:2.0.1-cuda11.7-cudnn8-runtime
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FROM pytorch/pytorch:1.13.1-cuda11.6-cudnn8-devel
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#FROM getkeops/keops-full:2.1-geomloss0.2.5-cuda11.8-pytorch2.0.0-python3.10
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# FROM pytorch/pytorch:2.1.0-cuda11.8-cudnn8-devel
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RUN apt-get update
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RUN apt-get install -y git
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@@ -144,5 +144,14 @@ Test data: 01-01-2023 until 08-10–2023
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- [x] Profit penalty parameter als over charge cycles voor een dag -> parameter bepalen op training data (convex probleem) (< 400 charge cycles per jaar) (over een dag kijken hoeveel charge cycles -> profit - penalty * charge cycles erover, (misschien belonen als eronder charge cycles))
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- [ ] Meer verschil bekijken tussen GRU en diffusion
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- [ ] Andere lagen voor diffusion model (GRU, kijken naar TSDiff)
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- [x] Policies met andere modellen (Linear, Non Linear)
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- [ ] (In Progress) Andere lagen voor diffusion model (GRU, kijken naar TSDiff)
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- [x] Policies met andere modellen (Linear, Non Linear)
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- [ ] Visualize the policies over the whole testr set -> thresholds plotten voor elke dag (elke policy) -> mss distribution om overzichtelijk te houden (mean and std)
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- [ ] Probleem met diffusion model (activation function? waarom direct grote waardes?)
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- [ ] Autoregressive confidence problem -> Quantiles zelf uit elkaar halen (helpt dit?)
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- [ ] time steps reducing for diffusion model (UNet activation functions?)
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- [ ] (State space model? S4)
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@@ -11,6 +11,4 @@ clearml
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properscoring
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nbconvert
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torchinfo
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tabulate
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einops
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opt_einsum
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tabulate
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@@ -25,12 +25,19 @@ class NrvDataset(Dataset):
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self.sequence_length = sequence_length
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self.predict_sequence_length = predict_sequence_length
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self.samples_to_skip = self.skip_samples(dataframe=dataframe)
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self.samples_to_skip = self.skip_samples(dataframe=dataframe, full_day_skip=self.full_day_skip)
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total_indices = set(
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range(len(dataframe) - self.sequence_length - self.predict_sequence_length)
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)
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self.valid_indices = sorted(list(total_indices - set(self.samples_to_skip)))
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# full day indices
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full_day_skipped_samples = self.skip_samples(dataframe=dataframe, full_day_skip=True)
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full_day_total_indices = set(
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range(len(dataframe) - self.sequence_length - self.predict_sequence_length)
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)
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self.full_day_valid_indices = sorted(list(full_day_total_indices - set(full_day_skipped_samples)))
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self.history_features = []
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if self.data_config.LOAD_HISTORY:
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self.history_features.append("total_load")
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@@ -73,7 +80,7 @@ class NrvDataset(Dataset):
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self.history_features, self.forecast_features = self.preprocess_data(dataframe)
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def skip_samples(self, dataframe):
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def skip_samples(self, dataframe, full_day_skip):
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nan_rows = dataframe[dataframe.isnull().any(axis=1)]
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nan_indices = nan_rows.index
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skip_indices = [
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@@ -91,7 +98,7 @@ class NrvDataset(Dataset):
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# add indices that are not the start of a day (00:15) to the skip indices (use datetime column)
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# get indices of all 00:15 timestamps
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if self.full_day_skip:
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if full_day_skip:
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start_of_day_indices = dataframe[
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dataframe["datetime"].dt.time != pd.Timestamp("00:00:00").time()
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].index
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@@ -45,3 +45,53 @@ class SimpleDiffusionModel(DiffusionModel):
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self.layers.append(nn.ReLU())
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self.layers.append(nn.Linear(hidden_sizes[-1] + time_dim + other_inputs_dim, input_size))
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class GRUDiffusionModel(DiffusionModel):
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def __init__(self, input_size: int, hidden_sizes: list, other_inputs_dim: int, gru_hidden_size: int, time_dim: int = 64):
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super(GRUDiffusionModel, self).__init__(time_dim)
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self.other_inputs_dim = other_inputs_dim
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self.gru_hidden_size = gru_hidden_size
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# GRU layer
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self.gru = nn.GRU(input_size=input_size + time_dim + other_inputs_dim,
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hidden_size=gru_hidden_size,
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num_layers=3,
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batch_first=True)
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# Fully connected layers after GRU
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self.fc_layers = nn.ModuleList()
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prev_size = gru_hidden_size
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for hidden_size in hidden_sizes:
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self.fc_layers.append(nn.Linear(prev_size, hidden_size))
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self.fc_layers.append(nn.ReLU())
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prev_size = hidden_size
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# Final output layer
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self.fc_layers.append(nn.Linear(prev_size, input_size))
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def forward(self, x, t, inputs):
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batch_size, seq_len = x.shape
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x = x.unsqueeze(-1).repeat(1, 1, seq_len)
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# Positional encoding for each time step
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t = t.unsqueeze(-1).type(torch.float)
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t = self.pos_encoding(t, self.time_dim) # Shape: [batch_size, seq_len, time_dim]
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# repeat time encoding for each time step t is shape [batch_size, time_dim], i want [batch_size, seq_len, time_dim]
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t = t.unsqueeze(1).repeat(1, seq_len, 1)
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# Concatenate x, t, and inputs along the feature dimension
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x = torch.cat((x, t, inputs), dim=-1) # Shape: [batch_size, seq_len, input_size + time_dim + other_inputs_dim]
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# Pass through GRU
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output, hidden = self.gru(x) # Hidden Shape: [batch_size, seq_len, 1]
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# Get last hidden state
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x = hidden[-1]
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# Process each time step's output with fully connected layers
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for layer in self.fc_layers:
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x = layer(x)
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return x
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@@ -1,172 +0,0 @@
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# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
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# SPDX-License-Identifier: Apache-2.0
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import math
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import torch
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from torch import nn
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from src.models.tsdiff_s4.s4 import S4
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class SinusoidalPositionEmbeddings(nn.Module):
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def __init__(self, dim):
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super().__init__()
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self.dim = dim
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def forward(self, time):
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device = time.device
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half_dim = self.dim // 2
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embeddings = math.log(10000) / (half_dim - 1)
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embeddings = torch.exp(
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torch.arange(half_dim, device=device) * -embeddings
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)
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embeddings = time[:, None] * embeddings[None, :]
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embeddings = torch.cat((embeddings.sin(), embeddings.cos()), dim=-1)
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return embeddings
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class S4Layer(nn.Module):
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def __init__(
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self,
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d_model,
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dropout=0.0,
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):
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super().__init__()
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self.layer = S4(
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d_model=d_model,
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d_state=128,
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bidirectional=True,
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dropout=dropout,
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transposed=True,
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postact=None,
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)
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self.norm = nn.LayerNorm(d_model)
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self.dropout = (
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nn.Dropout1d(dropout) if dropout > 0.0 else nn.Identity()
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)
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def forward(self, x):
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"""
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Input x is shape (B, d_input, L)
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"""
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z = x
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# Prenorm
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z = self.norm(z.transpose(-1, -2)).transpose(-1, -2)
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# Apply layer: we ignore the state input and output for training
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z, _ = self.layer(z)
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# Dropout on the output of the layer
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z = self.dropout(z)
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# Residual connection
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x = z + x
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return x, None
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def default_state(self, *args, **kwargs):
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return self.layer.default_state(*args, **kwargs)
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def step(self, x, state, **kwargs):
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z = x
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# Prenorm
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z = self.norm(z.transpose(-1, -2)).transpose(-1, -2)
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# Apply layer
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z, state = self.layer.step(z, state, **kwargs)
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# Residual connection
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x = z + x
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return x, state
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class S4Block(nn.Module):
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def __init__(self, d_model, dropout=0.0, expand=2, num_features=0):
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super().__init__()
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self.s4block = S4Layer(d_model, dropout=dropout)
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self.time_linear = nn.Linear(d_model, d_model)
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self.tanh = nn.Tanh()
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self.sigm = nn.Sigmoid()
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self.out_linear1 = nn.Conv1d(
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in_channels=d_model, out_channels=d_model, kernel_size=1
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)
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self.out_linear2 = nn.Conv1d(
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in_channels=d_model, out_channels=d_model, kernel_size=1
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)
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self.feature_encoder = nn.Conv1d(num_features, d_model, kernel_size=1)
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def forward(self, x, t, features=None):
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t = self.time_linear(t)[:, None, :].repeat(1, x.shape[2], 1)
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t = t.transpose(-1, -2)
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out, _ = self.s4block(x + t)
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if features is not None:
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out = out + self.feature_encoder(features)
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out = self.tanh(out) * self.sigm(out)
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out1 = self.out_linear1(out)
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out2 = self.out_linear2(out)
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return out1 + x, out2
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def Conv1dKaiming(in_channels, out_channels, kernel_size):
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layer = nn.Conv1d(in_channels, out_channels, kernel_size)
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nn.init.kaiming_normal_(layer.weight)
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return layer
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class BackboneModel(nn.Module):
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def __init__(
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self,
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input_dim,
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hidden_dim,
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output_dim,
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step_emb,
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num_residual_blocks,
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num_features,
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residual_block="s4",
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dropout=0.0,
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init_skip=True,
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):
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super().__init__()
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if residual_block == "s4":
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residual_block = S4Block
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else:
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raise ValueError(f"Unknown residual block {residual_block}")
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self.input_init = nn.Sequential(
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nn.Linear(input_dim, hidden_dim),
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nn.ReLU(),
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)
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self.time_init = nn.Sequential(
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nn.Linear(step_emb, hidden_dim),
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nn.SiLU(),
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nn.Linear(hidden_dim, hidden_dim),
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nn.SiLU(),
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)
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self.out_linear = nn.Sequential(
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nn.Linear(hidden_dim, hidden_dim),
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nn.ReLU(),
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nn.Linear(hidden_dim, output_dim),
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)
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residual_blocks = []
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for i in range(num_residual_blocks):
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residual_blocks.append(
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residual_block(
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hidden_dim, num_features=num_features, dropout=dropout
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)
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)
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self.residual_blocks = nn.ModuleList(residual_blocks)
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self.step_embedding = SinusoidalPositionEmbeddings(step_emb)
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self.init_skip = init_skip
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def forward(self, input, t, features=None):
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x = self.input_init(input) # B, L ,C
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step_emb = self.step_embedding(t)
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t = self.time_init(step_emb)
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x = x.transpose(-1, -2)
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if features is not None:
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features = features.transpose(-1, -2)
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skips = []
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for layer in self.residual_blocks:
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x, skip = layer(x, t, features)
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skips.append(skip)
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skip = torch.stack(skips).sum(0)
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skip = skip.transpose(-1, -2)
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out = self.out_linear(skip)
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if self.init_skip:
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out = out + input
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return out
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File diff suppressed because it is too large
Load Diff
@@ -4,39 +4,18 @@
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/opt/conda/lib/python3.10/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
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" from .autonotebook import tqdm as notebook_tqdm\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"import sys\n",
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"sys.path.append('../..')\n",
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"import torch\n",
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"\n",
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"%load_ext autoreload\n",
|
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"%autoreload 2"
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"import torch"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
|
||||
"The autoreload extension is already loaded. To reload it, use:\n",
|
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" %reload_ext autoreload\n"
|
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]
|
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}
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],
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"outputs": [],
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"source": [
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"from src.data import DataProcessor, DataConfig\n",
|
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"from src.trainers.quantile_trainer import AutoRegressiveQuantileTrainer, NonAutoRegressiveQuantileRegression\n",
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@@ -52,7 +31,7 @@
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"from datetime import datetime\n",
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"import torch.nn as nn\n",
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"from src.models.time_embedding_layer import TimeEmbedding\n",
|
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"from src.models.diffusion_model import SimpleDiffusionModel\n",
|
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"from src.models.diffusion_model import SimpleDiffusionModel, GRUDiffusionModel\n",
|
||||
"from src.trainers.diffusion_trainer import DiffusionTrainer\n",
|
||||
"from torchinfo import summary\n",
|
||||
"\n",
|
||||
@@ -65,7 +44,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
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||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -84,29 +63,98 @@
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||||
"data_config.NOMINAL_NET_POSITION = True\n",
|
||||
"\n",
|
||||
"data_processor = DataProcessor(data_config, path=\"../../\", lstm=True)\n",
|
||||
"data_processor.set_batch_size(128)\n",
|
||||
"data_processor.set_batch_size(1024)\n",
|
||||
"data_processor.set_full_day_skip(True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"ClearML Task: created new task id=b71216825809432682ea3c7841c07612\n",
|
||||
"ClearML results page: http://192.168.1.182:8080/projects/2e46d4af6f1e4c399cf9f5aa30bc8795/experiments/b71216825809432682ea3c7841c07612/output/log\n"
|
||||
"torch.Size([1024, 96, 96])\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"500 model found when searching for `file:///workspaces/Thesis/src/notebooks/checkpoint.pt`\n",
|
||||
"Selected model `Autoregressive Non Linear Quantile Regression + Quarter + DoW + Net` (id=bc0cb0d7fc614e2e8b0edf5b85348646)\n"
|
||||
"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py:536: UserWarning: Using a target size (torch.Size([1024, 96])) that is different to the input size (torch.Size([2, 1024, 96])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
|
||||
" return F.mse_loss(input, target, reduction=self.reduction)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"torch.Size([556, 96, 96])\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py:536: UserWarning: Using a target size (torch.Size([556, 96])) that is different to the input size (torch.Size([2, 556, 96])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
|
||||
" return F.mse_loss(input, target, reduction=self.reduction)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"torch.Size([1024, 96, 96])\n",
|
||||
"torch.Size([556, 96, 96])\n",
|
||||
"torch.Size([1024, 96, 96])\n",
|
||||
"torch.Size([556, 96, 96])\n",
|
||||
"torch.Size([1024, 96, 96])\n",
|
||||
"torch.Size([556, 96, 96])\n",
|
||||
"torch.Size([1024, 96, 96])\n",
|
||||
"torch.Size([556, 96, 96])\n",
|
||||
"torch.Size([1024, 96, 96])\n",
|
||||
"torch.Size([556, 96, 96])\n",
|
||||
"torch.Size([1024, 96, 96])\n",
|
||||
"torch.Size([556, 96, 96])\n",
|
||||
"torch.Size([1024, 96, 96])\n",
|
||||
"torch.Size([556, 96, 96])\n",
|
||||
"torch.Size([1024, 96, 96])\n",
|
||||
"torch.Size([556, 96, 96])\n",
|
||||
"torch.Size([1024, 96, 96])\n",
|
||||
"torch.Size([556, 96, 96])\n",
|
||||
"torch.Size([1024, 96, 96])\n",
|
||||
"torch.Size([556, 96, 96])\n",
|
||||
"torch.Size([1024, 96, 96])\n",
|
||||
"torch.Size([556, 96, 96])\n",
|
||||
"torch.Size([1024, 96, 96])\n",
|
||||
"torch.Size([556, 96, 96])\n",
|
||||
"torch.Size([1024, 96, 96])\n",
|
||||
"torch.Size([556, 96, 96])\n",
|
||||
"torch.Size([1024, 96, 96])\n",
|
||||
"torch.Size([556, 96, 96])\n",
|
||||
"torch.Size([1024, 96, 96])\n",
|
||||
"torch.Size([556, 96, 96])\n",
|
||||
"torch.Size([1024, 96, 96])\n",
|
||||
"torch.Size([556, 96, 96])\n",
|
||||
"torch.Size([1024, 96, 96])\n",
|
||||
"torch.Size([556, 96, 96])\n",
|
||||
"torch.Size([1024, 96, 96])\n",
|
||||
"torch.Size([556, 96, 96])\n",
|
||||
"torch.Size([1024, 96, 96])\n",
|
||||
"torch.Size([556, 96, 96])\n",
|
||||
"torch.Size([1024, 96, 96])\n",
|
||||
"torch.Size([556, 96, 96])\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"KeyboardInterrupt\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -116,14 +164,15 @@
|
||||
"epochs=150\n",
|
||||
"\n",
|
||||
"#### Model ####\n",
|
||||
"model = SimpleDiffusionModel(96, [512, 512, 512], other_inputs_dim=inputDim[1], time_dim=64)\n",
|
||||
"# model = SimpleDiffusionModel(96, [512, 512, 512], other_inputs_dim=inputDim[1], time_dim=64)\n",
|
||||
"model = GRUDiffusionModel(96, [256, 256], other_inputs_dim=inputDim[2], time_dim=64, gru_hidden_size=128)\n",
|
||||
"\n",
|
||||
"#### ClearML ####\n",
|
||||
"task = clearml_helper.get_task(task_name=\"Diffusion Model\")\n",
|
||||
"# task = clearml_helper.get_task(task_name=\"Diffusion Model\")\n",
|
||||
"\n",
|
||||
"#### Trainer ####\n",
|
||||
"trainer = DiffusionTrainer(model, data_processor, \"cuda\")\n",
|
||||
"trainer.train(epochs, learningRate, task)"
|
||||
"trainer.train(epochs, learningRate, None)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -243,165 +292,6 @@
|
||||
"sample_diffusion(new_model, 1, inputs)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Trying out BackboneModel using S4 state space model"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[KeOps] Compiling cuda jit compiler engine ... \n",
|
||||
"[KeOps] Warning : There were warnings or errors compiling formula :\n",
|
||||
"/usr/bin/ld: warning: /opt/conda/lib/libstdc++.so: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001\n",
|
||||
"/usr/bin/ld: warning: /opt/conda/lib/libstdc++.so: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002\n",
|
||||
"/usr/bin/ld: warning: /opt/conda/lib/libgcc_s.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001\n",
|
||||
"/usr/bin/ld: warning: /opt/conda/lib/libgcc_s.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002\n",
|
||||
"/usr/bin/ld: warning: /opt/conda/lib/libgcc_s.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001\n",
|
||||
"/usr/bin/ld: warning: /opt/conda/lib/libgcc_s.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002\n",
|
||||
"\n",
|
||||
"OK\n",
|
||||
"[pyKeOps] Compiling nvrtc binder for python ... \n",
|
||||
"[KeOps] Warning : There were warnings or errors compiling formula :\n",
|
||||
"/usr/bin/ld: warning: /opt/conda/lib/libstdc++.so: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001\n",
|
||||
"/usr/bin/ld: warning: /opt/conda/lib/libstdc++.so: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002\n",
|
||||
"/usr/bin/ld: warning: /opt/conda/lib/libgcc_s.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001\n",
|
||||
"/usr/bin/ld: warning: /opt/conda/lib/libgcc_s.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002\n",
|
||||
"/usr/bin/ld: warning: /opt/conda/lib/libgcc_s.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001\n",
|
||||
"/usr/bin/ld: warning: /opt/conda/lib/libgcc_s.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002\n",
|
||||
"\n",
|
||||
"OK\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import sys\n",
|
||||
"sys.path.append('../..')\n",
|
||||
"import torch\n",
|
||||
"\n",
|
||||
"%load_ext autoreload\n",
|
||||
"%autoreload 2\n",
|
||||
"from src.models.tsdiff_s4.backbones import BackboneModel\n",
|
||||
"from src.trainers.diffusion_trainer import DiffusionTrainer\n",
|
||||
"\n",
|
||||
"backbone = BackboneModel(\n",
|
||||
" input_dim=1,\n",
|
||||
" hidden_dim=512,\n",
|
||||
" output_dim=1,\n",
|
||||
" step_emb=128,\n",
|
||||
" num_residual_blocks=3,\n",
|
||||
" num_features=2\n",
|
||||
")\n",
|
||||
"backbone = backbone.to(\"cuda\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[KeOps] Generating code for formula Sum_Reduction(ComplexMult(Real2Complex(1/ComplexSquareAbs(ComplexMult(Var(1,2,0)-Var(2,2,1),Var(1,2,0)-Conj(Var(2,2,1))))),ComplexMult(Var(1,2,0)*ComplexReal(Var(0,2,1))-Real2Complex(Sum(Var(0,2,1)*Var(2,2,1))),Conj(ComplexMult(Var(1,2,0)-Var(2,2,1),Var(1,2,0)-Conj(Var(2,2,1)))))),0) ... "
|
||||
]
|
||||
},
|
||||
{
|
||||
"ename": "",
|
||||
"evalue": "",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. View Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# now lets find out what the input shape of the featues and input must be\n",
|
||||
"\n",
|
||||
"# input: (B, L, C)\n",
|
||||
"# features: (B, L, F)\n",
|
||||
"# time: (B, 1)\n",
|
||||
"\n",
|
||||
"# output: (B, L, C)? \n",
|
||||
"\n",
|
||||
"input = torch.randn(2, 96, 1).to(\"cuda\")\n",
|
||||
"features = torch.randn(2, 96, 2).to(\"cuda\")\n",
|
||||
"times = torch.randn(2).to(\"cuda\")\n",
|
||||
"\n",
|
||||
"backbone(input, times, features).shape"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "KeyError",
|
||||
"evalue": "'nvrtc'",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
|
||||
"Cell \u001b[0;32mIn[3], line 13\u001b[0m\n\u001b[1;32m 10\u001b[0m times \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mtensor([\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m2\u001b[39m)\u001b[38;5;241m.\u001b[39mto(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcuda\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 11\u001b[0m features \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mrandn(\u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m96\u001b[39m, \u001b[38;5;241m2\u001b[39m)\u001b[38;5;241m.\u001b[39mto(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcuda\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 13\u001b[0m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfeatures\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mshape\n",
|
||||
"File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1496\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 1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1498\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_backward_pre_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\n\u001b[1;32m 1499\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1500\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-> 1501\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[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 1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
|
||||
"File \u001b[0;32m/workspaces/Thesis/src/notebooks/../../src/models/tsdiff_s4/backbones.py:164\u001b[0m, in \u001b[0;36mBackboneModel.forward\u001b[0;34m(self, input, t, features)\u001b[0m\n\u001b[1;32m 162\u001b[0m skips \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m 163\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m layer \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mresidual_blocks:\n\u001b[0;32m--> 164\u001b[0m x, skip \u001b[38;5;241m=\u001b[39m \u001b[43mlayer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfeatures\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 165\u001b[0m skips\u001b[38;5;241m.\u001b[39mappend(skip)\n\u001b[1;32m 167\u001b[0m skip \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mstack(skips)\u001b[38;5;241m.\u001b[39msum(\u001b[38;5;241m0\u001b[39m)\n",
|
||||
"File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1496\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 1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1498\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_backward_pre_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\n\u001b[1;32m 1499\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1500\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-> 1501\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[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 1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
|
||||
"File \u001b[0;32m/workspaces/Thesis/src/notebooks/../../src/models/tsdiff_s4/backbones.py:96\u001b[0m, in \u001b[0;36mS4Block.forward\u001b[0;34m(self, x, t, features)\u001b[0m\n\u001b[1;32m 94\u001b[0m t \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtime_linear(t)[:, \u001b[38;5;28;01mNone\u001b[39;00m, :]\u001b[38;5;241m.\u001b[39mrepeat(\u001b[38;5;241m1\u001b[39m, x\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m2\u001b[39m], \u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 95\u001b[0m t \u001b[38;5;241m=\u001b[39m t\u001b[38;5;241m.\u001b[39mtranspose(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m2\u001b[39m)\n\u001b[0;32m---> 96\u001b[0m out, _ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43ms4block\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m features \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 98\u001b[0m out \u001b[38;5;241m=\u001b[39m out \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfeature_encoder(features)\n",
|
||||
"File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1496\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 1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1498\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_backward_pre_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\n\u001b[1;32m 1499\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1500\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-> 1501\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[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 1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
|
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"File \u001b[0;32m/workspaces/Thesis/src/notebooks/../../src/models/tsdiff_s4/backbones.py:56\u001b[0m, in \u001b[0;36mS4Layer.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 54\u001b[0m z \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnorm(z\u001b[38;5;241m.\u001b[39mtranspose(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m2\u001b[39m))\u001b[38;5;241m.\u001b[39mtranspose(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m2\u001b[39m)\n\u001b[1;32m 55\u001b[0m \u001b[38;5;66;03m# Apply layer: we ignore the state input and output for training\u001b[39;00m\n\u001b[0;32m---> 56\u001b[0m z, _ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlayer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mz\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 57\u001b[0m \u001b[38;5;66;03m# Dropout on the output of the layer\u001b[39;00m\n\u001b[1;32m 58\u001b[0m z \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdropout(z)\n",
|
||||
"File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1496\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 1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1498\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_backward_pre_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\n\u001b[1;32m 1499\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1500\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-> 1501\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[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 1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
|
||||
"File \u001b[0;32m/workspaces/Thesis/src/notebooks/../../src/models/tsdiff_s4/s4.py:1761\u001b[0m, in \u001b[0;36mS4.forward\u001b[0;34m(self, u, state, rate, lengths, **kwargs)\u001b[0m\n\u001b[1;32m 1759\u001b[0m \u001b[38;5;66;03m# Compute SS Kernel\u001b[39;00m\n\u001b[1;32m 1760\u001b[0m L_kernel \u001b[38;5;241m=\u001b[39m L \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mL \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mmin\u001b[39m(L, \u001b[38;5;28mround\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mL \u001b[38;5;241m/\u001b[39m rate))\n\u001b[0;32m-> 1761\u001b[0m k, k_state \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkernel\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1762\u001b[0m \u001b[43m \u001b[49m\u001b[43mL\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mL_kernel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstate\u001b[49m\n\u001b[1;32m 1763\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# (C H L) (B C H L)\u001b[39;00m\n\u001b[1;32m 1765\u001b[0m \u001b[38;5;66;03m# Convolution\u001b[39;00m\n\u001b[1;32m 1766\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbidirectional:\n",
|
||||
"File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1496\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 1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1498\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_backward_pre_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\n\u001b[1;32m 1499\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1500\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-> 1501\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[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 1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
|
||||
"File \u001b[0;32m/workspaces/Thesis/src/notebooks/../../src/models/tsdiff_s4/s4.py:1549\u001b[0m, in \u001b[0;36mSSKernel.forward\u001b[0;34m(self, state, L, rate)\u001b[0m\n\u001b[1;32m 1548\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, state\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, L\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, rate\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1.0\u001b[39m):\n\u001b[0;32m-> 1549\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[43mkernel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mL\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mL\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrate\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1496\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 1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1498\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_backward_pre_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\n\u001b[1;32m 1499\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1500\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-> 1501\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[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 1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
|
||||
"File \u001b[0;32m/workspaces/Thesis/src/notebooks/../../src/models/tsdiff_s4/s4.py:925\u001b[0m, in \u001b[0;36mSSKernelNPLR.forward\u001b[0;34m(self, state, rate, L)\u001b[0m\n\u001b[1;32m 923\u001b[0m r \u001b[38;5;241m=\u001b[39m cauchy_mult(v, z, w, symmetric\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 924\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m has_pykeops:\n\u001b[0;32m--> 925\u001b[0m r \u001b[38;5;241m=\u001b[39m \u001b[43mcauchy_conj\u001b[49m\u001b[43m(\u001b[49m\u001b[43mv\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mz\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mw\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 926\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 927\u001b[0m r \u001b[38;5;241m=\u001b[39m cauchy_naive(v, z, w)\n",
|
||||
"File \u001b[0;32m/workspaces/Thesis/src/notebooks/../../src/models/tsdiff_s4/s4.py:89\u001b[0m, in \u001b[0;36mcauchy_conj\u001b[0;34m(v, z, w)\u001b[0m\n\u001b[1;32m 86\u001b[0m z \u001b[38;5;241m=\u001b[39m _c2r(z)\n\u001b[1;32m 87\u001b[0m w \u001b[38;5;241m=\u001b[39m _c2r(w)\n\u001b[0;32m---> 89\u001b[0m r \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;241m*\u001b[39m \u001b[43mcauchy_mult\u001b[49m\u001b[43m(\u001b[49m\u001b[43mv\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mz\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mw\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbackend\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mGPU\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 90\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _r2c(r)\n",
|
||||
"File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pykeops/torch/generic/generic_red.py:688\u001b[0m, in \u001b[0;36mGenred.__call__\u001b[0;34m(self, backend, device_id, ranges, out, *args)\u001b[0m\n\u001b[1;32m 686\u001b[0m params\u001b[38;5;241m.\u001b[39mny \u001b[38;5;241m=\u001b[39m ny\n\u001b[1;32m 687\u001b[0m params\u001b[38;5;241m.\u001b[39mout \u001b[38;5;241m=\u001b[39m out\n\u001b[0;32m--> 688\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mGenredAutograd_fun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 690\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m postprocess(out, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtorch\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreduction_op, nout, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mopt_arg, dtype)\n",
|
||||
"File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pykeops/torch/generic/generic_red.py:384\u001b[0m, in \u001b[0;36mGenredAutograd_fun\u001b[0;34m(*inputs)\u001b[0m\n\u001b[1;32m 383\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mGenredAutograd_fun\u001b[39m(\u001b[38;5;241m*\u001b[39minputs):\n\u001b[0;32m--> 384\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mGenredAutograd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m[\u001b[38;5;241m0\u001b[39m]\n",
|
||||
"File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torch/autograd/function.py:506\u001b[0m, in \u001b[0;36mFunction.apply\u001b[0;34m(cls, *args, **kwargs)\u001b[0m\n\u001b[1;32m 503\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m torch\u001b[38;5;241m.\u001b[39m_C\u001b[38;5;241m.\u001b[39m_are_functorch_transforms_active():\n\u001b[1;32m 504\u001b[0m \u001b[38;5;66;03m# See NOTE: [functorch vjp and autograd interaction]\u001b[39;00m\n\u001b[1;32m 505\u001b[0m args \u001b[38;5;241m=\u001b[39m _functorch\u001b[38;5;241m.\u001b[39mutils\u001b[38;5;241m.\u001b[39munwrap_dead_wrappers(args)\n\u001b[0;32m--> 506\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \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[43mapply\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 \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 508\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39msetup_context \u001b[38;5;241m==\u001b[39m _SingleLevelFunction\u001b[38;5;241m.\u001b[39msetup_context:\n\u001b[1;32m 509\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m 510\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mIn order to use an autograd.Function with functorch transforms \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 511\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m(vmap, grad, jvp, jacrev, ...), it must override the setup_context \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 512\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstaticmethod. For more details, please see \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 513\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhttps://pytorch.org/docs/master/notes/extending.func.html\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
|
||||
"File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pykeops/torch/generic/generic_red.py:295\u001b[0m, in \u001b[0;36mGenredAutograd.forward\u001b[0;34m(*inputs)\u001b[0m\n\u001b[1;32m 293\u001b[0m \u001b[38;5;129m@staticmethod\u001b[39m\n\u001b[1;32m 294\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;241m*\u001b[39minputs):\n\u001b[0;32m--> 295\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mGenredAutograd_base\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pykeops/torch/generic/generic_red.py:91\u001b[0m, in \u001b[0;36mGenredAutograd_base._forward\u001b[0;34m(params, *args)\u001b[0m\n\u001b[1;32m 85\u001b[0m device_id, device_args \u001b[38;5;241m=\u001b[39m set_device(\n\u001b[1;32m 86\u001b[0m tagCPUGPU, tagHostDevice, params\u001b[38;5;241m.\u001b[39mdevice_id_request, \u001b[38;5;241m*\u001b[39margs\n\u001b[1;32m 87\u001b[0m )\n\u001b[1;32m 89\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpykeops\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcommon\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mkeops_io\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m keops_binder\n\u001b[0;32m---> 91\u001b[0m myconv \u001b[38;5;241m=\u001b[39m \u001b[43mkeops_binder\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mnvrtc\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mtagCPUGPU\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcpp\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m(\n\u001b[1;32m 92\u001b[0m tagCPUGPU,\n\u001b[1;32m 93\u001b[0m tag1D2D,\n\u001b[1;32m 94\u001b[0m tagHostDevice,\n\u001b[1;32m 95\u001b[0m use_ranges,\n\u001b[1;32m 96\u001b[0m device_id,\n\u001b[1;32m 97\u001b[0m params\u001b[38;5;241m.\u001b[39mformula,\n\u001b[1;32m 98\u001b[0m params\u001b[38;5;241m.\u001b[39maliases,\n\u001b[1;32m 99\u001b[0m \u001b[38;5;28mlen\u001b[39m(args),\n\u001b[1;32m 100\u001b[0m params\u001b[38;5;241m.\u001b[39mdtype,\n\u001b[1;32m 101\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtorch\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 102\u001b[0m params\u001b[38;5;241m.\u001b[39moptional_flags,\n\u001b[1;32m 103\u001b[0m )\u001b[38;5;241m.\u001b[39mimport_module()\n\u001b[1;32m 105\u001b[0m \u001b[38;5;66;03m# N.B.: KeOps C++ expects contiguous data arrays\u001b[39;00m\n\u001b[1;32m 106\u001b[0m test_contig \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mall\u001b[39m(arg\u001b[38;5;241m.\u001b[39mis_contiguous() \u001b[38;5;28;01mfor\u001b[39;00m arg \u001b[38;5;129;01min\u001b[39;00m args)\n",
|
||||
"\u001b[0;31mKeyError\u001b[0m: 'nvrtc'"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# inputDim = data_processor.get_input_size()\n",
|
||||
"learningRate = 0.0001\n",
|
||||
"epochs=150\n",
|
||||
"\n",
|
||||
"#### Model ####\n",
|
||||
"model = BackboneModel(1, 512, output_dim=1, step_emb=64, num_residual_blocks=4, num_features=2)\n",
|
||||
"model.to(\"cuda\")\n",
|
||||
"\n",
|
||||
"inputs = torch.randn(2, 96, 1).to(\"cuda\")\n",
|
||||
"times = torch.tensor([0]*2).to(\"cuda\")\n",
|
||||
"features = torch.randn(2, 96, 2).to(\"cuda\")\n",
|
||||
"\n",
|
||||
"model(inputs, times, features).shape\n",
|
||||
"\n",
|
||||
"#### Trainer ####\n",
|
||||
"# trainer = DiffusionTrainer(model, data_processor, \"cuda\")\n",
|
||||
"# trainer.train(epochs, learningRate, None)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
|
||||
@@ -1197,7 +1197,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.11"
|
||||
"version": "3.10.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
@@ -8,7 +8,8 @@ import pandas as pd
|
||||
import datetime
|
||||
from tqdm import tqdm
|
||||
from src.utils.imbalance_price_calculator import ImbalancePriceCalculator
|
||||
import time
|
||||
import seaborn as sns
|
||||
import matplotlib.pyplot as plt
|
||||
import plotly.express as px
|
||||
|
||||
### import functions ###
|
||||
@@ -16,7 +17,7 @@ from src.trainers.quantile_trainer import auto_regressive as quantile_auto_regre
|
||||
from src.trainers.diffusion_trainer import sample_diffusion
|
||||
from src.utils.clearml import ClearMLHelper
|
||||
|
||||
# argparse to parse task id and model type
|
||||
### Arguments ###
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--task_id', type=str, default=None)
|
||||
parser.add_argument('--model_type', type=str, default=None)
|
||||
@@ -27,6 +28,7 @@ assert args.task_id is not None, "Please specify task id"
|
||||
assert args.model_type is not None, "Please specify model type"
|
||||
assert args.model_name is not None, "Please specify model name"
|
||||
|
||||
### Baseline Policy ###
|
||||
battery = Battery(2, 1)
|
||||
baseline_policy = BaselinePolicy(battery, data_path="")
|
||||
|
||||
@@ -124,6 +126,9 @@ def get_next_day_profits_for_date(model, data_processor, test_loader, date, ipc,
|
||||
predicted_nrv_profits_cycles = {i: [0, 0] for i in penalties}
|
||||
baseline_profits_cycles = {i: [0, 0] for i in penalties}
|
||||
|
||||
_charge_thresholds = {}
|
||||
_discharge_thresholds = {}
|
||||
|
||||
initial, nrvs, target = predict_NRV(model, date, data_processor, test_loader)
|
||||
|
||||
initial = np.repeat(initial, nrvs.shape[0])
|
||||
@@ -139,6 +144,10 @@ def get_next_day_profits_for_date(model, data_processor, test_loader, date, ipc,
|
||||
|
||||
for penalty in penalties:
|
||||
found_charge_thresholds, found_discharge_thresholds = baseline_policy.get_optimal_thresholds(reconstructed_imbalance_prices, charge_thresholds, discharge_thresholds, penalty)
|
||||
|
||||
_charge_thresholds[penalty] = found_charge_thresholds
|
||||
_discharge_thresholds[penalty] = found_discharge_thresholds
|
||||
|
||||
next_day_charge_threshold = found_charge_thresholds.mean(axis=0)
|
||||
next_day_discharge_threshold = found_discharge_thresholds.mean(axis=0)
|
||||
yesterday_charge_thresholds, yesterday_discharge_thresholds = baseline_policy.get_optimal_thresholds(yesterday_imbalance_prices, charge_thresholds, discharge_thresholds, penalty)
|
||||
@@ -153,23 +162,26 @@ def get_next_day_profits_for_date(model, data_processor, test_loader, date, ipc,
|
||||
baseline_profits_cycles[penalty][0] += yesterday_profit.item()
|
||||
baseline_profits_cycles[penalty][1] += yesterday_charge_cycles.item()
|
||||
|
||||
return predicted_nrv_profits_cycles, baseline_profits_cycles
|
||||
return predicted_nrv_profits_cycles, baseline_profits_cycles, _charge_thresholds, _discharge_thresholds
|
||||
|
||||
def next_day_test_set(model, data_processor, test_loader, ipc, predict_NRV: callable):
|
||||
penalties = [0, 10, 50, 150, 300, 500, 600, 800, 1000, 1500, 2000, 2500]
|
||||
penalties = [0, 50, 250, 500, 1000, 1500]
|
||||
predicted_nrv_profits_cycles = {i: [0, 0] for i in penalties}
|
||||
baseline_profits_cycles = {i: [0, 0] for i in penalties}
|
||||
|
||||
# get all dates in test set
|
||||
dates = baseline_policy.test_data["DateTime"].dt.date.unique()
|
||||
charge_thresholds = {}
|
||||
discharge_thresholds = {}
|
||||
|
||||
# dates back to datetime
|
||||
dates = baseline_policy.test_data["DateTime"].dt.date.unique()
|
||||
dates = pd.to_datetime(dates)
|
||||
|
||||
for date in tqdm(dates):
|
||||
try:
|
||||
new_predicted_nrv_profits_cycles, new_baseline_profits_cycles = get_next_day_profits_for_date(model, data_processor, test_loader, date, ipc, predict_NRV, penalties)
|
||||
new_predicted_nrv_profits_cycles, new_baseline_profits_cycles, new_charge_thresholds, new_discharge_thresholds = get_next_day_profits_for_date(model, data_processor, test_loader, date, ipc, predict_NRV, penalties)
|
||||
|
||||
charge_thresholds[date] = new_charge_thresholds
|
||||
discharge_thresholds[date] = new_discharge_thresholds
|
||||
|
||||
for penalty in penalties:
|
||||
predicted_nrv_profits_cycles[penalty][0] += new_predicted_nrv_profits_cycles[penalty][0]
|
||||
predicted_nrv_profits_cycles[penalty][1] += new_predicted_nrv_profits_cycles[penalty][1]
|
||||
@@ -178,16 +190,15 @@ def next_day_test_set(model, data_processor, test_loader, ipc, predict_NRV: call
|
||||
baseline_profits_cycles[penalty][1] += new_baseline_profits_cycles[penalty][1]
|
||||
|
||||
except Exception as e:
|
||||
# print(f"Error for date {date}")
|
||||
continue
|
||||
print(f"Error for date {date}")
|
||||
|
||||
return predicted_nrv_profits_cycles, baseline_profits_cycles
|
||||
return predicted_nrv_profits_cycles, baseline_profits_cycles, charge_thresholds, discharge_thresholds
|
||||
|
||||
def main():
|
||||
clearml_helper = ClearMLHelper(project_name="Thesis/NrvForecast")
|
||||
task = clearml_helper.get_task(task_name="Policy Test")
|
||||
|
||||
task.execute_remotely(queue_name="default", exit_process=True)
|
||||
# task.execute_remotely(queue_name="default", exit_process=True)
|
||||
|
||||
configuration, model, data_processor, test_loader = load_model(args.task_id)
|
||||
|
||||
@@ -205,7 +216,92 @@ def main():
|
||||
|
||||
ipc = ImbalancePriceCalculator(data_path="")
|
||||
|
||||
predicted_nrv_profits_cycles, baseline_profits_cycles = next_day_test_set(model, data_processor, test_loader, ipc, predict_NRV)
|
||||
predicted_nrv_profits_cycles, baseline_profits_cycles, charge_thresholds, discharge_thresholds = next_day_test_set(model, data_processor, test_loader, ipc, predict_NRV)
|
||||
# the charge_thresholds is a dictionary with date as key. The values of the dictionary is another dictionary with keys as penalties and values as the charge thresholds
|
||||
# create density plot that shows a density plot of the charge thresholds for each penalty (use seaborn displot) (One plot with a different color for each penalty)
|
||||
|
||||
charge_thresholds_for_penalty = {}
|
||||
for d in charge_thresholds.values():
|
||||
for penalty, thresholds in d.items():
|
||||
if penalty not in charge_thresholds_for_penalty:
|
||||
charge_thresholds_for_penalty[penalty] = []
|
||||
charge_thresholds_for_penalty[penalty].extend(thresholds)
|
||||
|
||||
discharge_thresholds_for_penalty = {}
|
||||
for d in discharge_thresholds.values():
|
||||
for penalty, thresholds in d.items():
|
||||
if penalty not in discharge_thresholds_for_penalty:
|
||||
discharge_thresholds_for_penalty[penalty] = []
|
||||
discharge_thresholds_for_penalty[penalty].extend(thresholds)
|
||||
|
||||
def plot_threshold_distribution(thresholds: dict, title: str):
|
||||
data_to_plot = []
|
||||
for penalty, values in thresholds.items():
|
||||
for value in values:
|
||||
data_to_plot.append({'Penalty': penalty, 'Value': value.item()})
|
||||
df = pd.DataFrame(data_to_plot)
|
||||
palette = sns.color_palette("bright", len(thresholds.keys()))
|
||||
fig = sns.displot(data=df, x="Value", hue="Penalty", kind="kde", palette=palette)
|
||||
plt.title('Density of Charge Thresholds by Penalty')
|
||||
plt.xlabel('Charge Threshold')
|
||||
plt.ylabel('Density')
|
||||
plt.legend(title='Penalty')
|
||||
task.get_logger().report_matplotlib_figure(
|
||||
"Policy Results",
|
||||
title,
|
||||
iteration=0,
|
||||
figure=fig
|
||||
)
|
||||
plt.close()
|
||||
|
||||
### Plot charge thresholds distribution ###
|
||||
plot_threshold_distribution(charge_thresholds_for_penalty, "Charge Thresholds")
|
||||
|
||||
### Plot discharge thresholds distribution ###
|
||||
plot_threshold_distribution(discharge_thresholds_for_penalty, "Discharge Thresholds")
|
||||
|
||||
def plot_thresholds_per_day(thresholds: dict, title: str):
|
||||
# plot mean charge threshold per day (per penalty (other color))
|
||||
data_to_plot = []
|
||||
for date, values in thresholds.items():
|
||||
for penalty, value in values.items():
|
||||
mean_val = value.mean().item()
|
||||
std_val = value.std().item() # Calculate standard deviation
|
||||
data_to_plot.append({'Date': date, 'Penalty': penalty, 'Mean': mean_val, 'StdDev': std_val})
|
||||
print(f"Date: {date}, Penalty: {penalty}, Mean: {mean_val}, StdDev: {std_val}")
|
||||
df = pd.DataFrame(data_to_plot)
|
||||
df["Date"] = pd.to_datetime(df["Date"])
|
||||
|
||||
fig = px.line(
|
||||
df,
|
||||
x="Date",
|
||||
y="Mean",
|
||||
color="Penalty",
|
||||
title=title,
|
||||
labels={"Mean": "Threshold", "Date": "Date"},
|
||||
markers=True, # Adds markers to the lines
|
||||
hover_data=["Penalty"], # Adds additional hover information
|
||||
)
|
||||
|
||||
fig.update_layout(
|
||||
width=1000, # Set the width of the figure
|
||||
height=600, # Set the height of the figure
|
||||
title_x=0.5, # Center the title horizontally
|
||||
)
|
||||
|
||||
task.get_logger().report_plotly(
|
||||
"Thresholds per Day",
|
||||
title,
|
||||
iteration=0,
|
||||
figure=fig
|
||||
)
|
||||
|
||||
### Plot mean charge thresholds per day ###
|
||||
plot_thresholds_per_day(charge_thresholds, "Mean Charge Thresholds per Day")
|
||||
|
||||
### Plot mean discharge thresholds per day ###
|
||||
plot_thresholds_per_day(discharge_thresholds, "Mean Discharge Thresholds per Day")
|
||||
|
||||
|
||||
# create dataframe with columns "name", "penalty", "profit", "cycles"
|
||||
df = pd.DataFrame(columns=["name", "penalty", "profit", "cycles"])
|
||||
|
||||
@@ -33,67 +33,29 @@ class AutoRegressiveTrainer(Trainer):
|
||||
self.model.output_size = 1
|
||||
|
||||
def debug_plots(self, task, train: bool, data_loader, sample_indices, epoch):
|
||||
num_samples = len(sample_indices)
|
||||
rows = num_samples # One row per sample since we only want one column
|
||||
|
||||
# check if self has get_plot_error
|
||||
if hasattr(self, "get_plot_error"):
|
||||
cols = 2
|
||||
print("Using get_plot_error")
|
||||
else:
|
||||
cols = 1
|
||||
print("Using get_plot")
|
||||
|
||||
fig = make_subplots(
|
||||
rows=rows,
|
||||
cols=cols,
|
||||
subplot_titles=[f"Sample {i+1}" for i in range(num_samples)],
|
||||
)
|
||||
|
||||
for i, idx in enumerate(sample_indices):
|
||||
auto_regressive_output = self.auto_regressive(data_loader.dataset, [idx])
|
||||
for actual_idx, idx in sample_indices.items():
|
||||
auto_regressive_output = self.auto_regressive(data_loader.dataset, [idx]*1000)
|
||||
if len(auto_regressive_output) == 3:
|
||||
initial, predictions, target = auto_regressive_output
|
||||
else:
|
||||
initial, predictions, _, target = auto_regressive_output
|
||||
initial, _, predictions, target = auto_regressive_output
|
||||
|
||||
initial = initial.squeeze(0)
|
||||
predictions = predictions.squeeze(0)
|
||||
target = target.squeeze(0)
|
||||
|
||||
# keep one initial
|
||||
initial = initial[0]
|
||||
target = target[0]
|
||||
|
||||
sub_fig = self.get_plot(initial, target, predictions, show_legend=(i == 0))
|
||||
predictions = predictions
|
||||
|
||||
row = i + 1
|
||||
col = 1
|
||||
fig = self.get_plot(initial, target, predictions, show_legend=(0 == 0))
|
||||
|
||||
for trace in sub_fig.data:
|
||||
fig.add_trace(trace, row=row, col=col)
|
||||
|
||||
if cols == 2:
|
||||
error_sub_fig = self.get_plot_error(
|
||||
target, predictions
|
||||
)
|
||||
for trace in error_sub_fig.data:
|
||||
fig.add_trace(trace, row=row, col=col + 1)
|
||||
|
||||
loss = self.criterion(
|
||||
predictions.to(self.device), target.to(self.device)
|
||||
).item()
|
||||
|
||||
fig["layout"]["annotations"][i].update(
|
||||
text=f"{self.criterion.__class__.__name__}: {loss:.6f}"
|
||||
task.get_logger().report_matplotlib_figure(
|
||||
title="Training" if train else "Testing",
|
||||
series=f'Sample {actual_idx}',
|
||||
iteration=epoch,
|
||||
figure=fig,
|
||||
)
|
||||
|
||||
# y axis same for all plots
|
||||
# fig.update_yaxes(range=[-1, 1], col=1)
|
||||
|
||||
fig.update_layout(height=1000 * rows)
|
||||
task.get_logger().report_plotly(
|
||||
title=f"{'Training' if train else 'Test'} Samples",
|
||||
series="full_day",
|
||||
iteration=epoch,
|
||||
figure=fig,
|
||||
)
|
||||
|
||||
def auto_regressive(self, data_loader, idx, sequence_length: int = 96):
|
||||
self.model.eval()
|
||||
|
||||
@@ -19,7 +19,11 @@ def sample_diffusion(model: DiffusionModel, n: int, inputs: torch.tensor, noise_
|
||||
alpha = 1. - beta
|
||||
alpha_hat = torch.cumprod(alpha, dim=0)
|
||||
|
||||
inputs = inputs.repeat(n, 1).to(device)
|
||||
if len(inputs.shape) == 2:
|
||||
inputs = inputs.repeat(n, 1)
|
||||
elif len(inputs.shape) == 3:
|
||||
inputs = inputs.repeat(n, 1, 1)
|
||||
|
||||
model.eval()
|
||||
with torch.no_grad():
|
||||
x = torch.randn(inputs.shape[0], ts_length).to(device)
|
||||
@@ -36,17 +40,17 @@ def sample_diffusion(model: DiffusionModel, n: int, inputs: torch.tensor, noise_
|
||||
noise = torch.zeros_like(x)
|
||||
|
||||
x = 1/torch.sqrt(_alpha) * (x-((1-_alpha) / (torch.sqrt(1 - _alpha_hat))) * predicted_noise) + torch.sqrt(_beta) * noise
|
||||
x = torch.clamp(x, -1.0, 1.0)
|
||||
return x
|
||||
|
||||
|
||||
|
||||
class DiffusionTrainer:
|
||||
def __init__(self, model: nn.Module, data_processor: DataProcessor, device: torch.device):
|
||||
self.model = model
|
||||
self.device = device
|
||||
|
||||
self.noise_steps = 1000
|
||||
self.beta_start = 1e-4
|
||||
self.noise_steps = 30
|
||||
self.beta_start = 0.0001
|
||||
self.beta_end = 0.02
|
||||
self.ts_length = 96
|
||||
|
||||
@@ -92,7 +96,16 @@ class DiffusionTrainer:
|
||||
else:
|
||||
loader = test_loader
|
||||
|
||||
indices = np.random.randint(0, len(loader.dataset) - 1, size=num_samples)
|
||||
# set seed
|
||||
np.random.seed(42)
|
||||
|
||||
actual_indices = np.random.choice(loader.dataset.full_day_valid_indices, num_samples, replace=False)
|
||||
indices = {}
|
||||
for i in actual_indices:
|
||||
indices[i] = loader.dataset.valid_indices.index(i)
|
||||
|
||||
print(actual_indices)
|
||||
|
||||
return indices
|
||||
|
||||
def init_clearml_task(self, task):
|
||||
@@ -101,8 +114,12 @@ class DiffusionTrainer:
|
||||
|
||||
input_data = torch.randn(1024, 96).to(self.device)
|
||||
time_steps = torch.randn(1024).long().to(self.device)
|
||||
other_input_data = torch.randn(1024, self.model.other_inputs_dim).to(self.device)
|
||||
|
||||
if self.data_processor.lstm:
|
||||
inputDim = self.data_processor.get_input_size()
|
||||
other_input_data = torch.randn(1024, inputDim[1], self.model.other_inputs_dim).to(self.device)
|
||||
else:
|
||||
other_input_data = torch.randn(1024, self.model.other_inputs_dim).to(self.device)
|
||||
task.set_configuration_object("model", str(summary(self.model, input_data=[input_data, time_steps, other_input_data])))
|
||||
|
||||
self.data_processor = task.connect(self.data_processor, name="data_processor")
|
||||
@@ -120,8 +137,8 @@ class DiffusionTrainer:
|
||||
predict_sequence_length=self.ts_length
|
||||
)
|
||||
|
||||
train_sample_indices = self.random_samples(train=True, num_samples=10)
|
||||
test_sample_indices = self.random_samples(train=False, num_samples=10)
|
||||
train_sample_indices = self.random_samples(train=True, num_samples=5)
|
||||
test_sample_indices = self.random_samples(train=False, num_samples=5)
|
||||
|
||||
for epoch in range(epochs):
|
||||
running_loss = 0.0
|
||||
@@ -132,8 +149,6 @@ class DiffusionTrainer:
|
||||
|
||||
t = self.sample_timesteps(time_series.shape[0]).to(self.device)
|
||||
x_t, noise = self.noise_time_series(time_series, t)
|
||||
x_t = x_t.unsqueeze(-1)
|
||||
print(x_t.shape, t.shape, base_pattern.shape)
|
||||
predicted_noise = self.model(x_t, t, base_pattern)
|
||||
loss = criterion(predicted_noise, noise)
|
||||
|
||||
@@ -145,7 +160,7 @@ class DiffusionTrainer:
|
||||
|
||||
running_loss /= len(train_loader.dataset)
|
||||
|
||||
if epoch % 20 == 0 and epoch != 0:
|
||||
if epoch % 40 == 0 and epoch != 0:
|
||||
self.test(test_loader, epoch, task)
|
||||
|
||||
if task:
|
||||
@@ -156,7 +171,7 @@ class DiffusionTrainer:
|
||||
value=loss.item(),
|
||||
)
|
||||
|
||||
if epoch % 100 == 0 and epoch != 0:
|
||||
if epoch % 150 == 0 and epoch != 0:
|
||||
self.debug_plots(task, True, train_loader, train_sample_indices, epoch)
|
||||
self.debug_plots(task, False, test_loader, test_sample_indices, epoch)
|
||||
|
||||
@@ -165,26 +180,30 @@ class DiffusionTrainer:
|
||||
|
||||
|
||||
def debug_plots(self, task, training: bool, data_loader, sample_indices, epoch):
|
||||
for i, idx in enumerate(sample_indices):
|
||||
for actual_idx, idx in sample_indices.items():
|
||||
features, target, _ = data_loader.dataset[idx]
|
||||
|
||||
features = features.to(self.device)
|
||||
features = features.unsqueeze(0)
|
||||
|
||||
self.model.eval()
|
||||
with torch.no_grad():
|
||||
samples = self.sample(self.model, 100, features).cpu().numpy()
|
||||
samples = self.data_processor.inverse_transform(samples)
|
||||
target = self.data_processor.inverse_transform(target)
|
||||
|
||||
ci_99_upper = np.quantile(samples, 0.99, axis=0)
|
||||
ci_99_lower = np.quantile(samples, 0.01, axis=0)
|
||||
ci_99_upper = np.quantile(samples, 0.995, axis=0)
|
||||
ci_99_lower = np.quantile(samples, 0.005, axis=0)
|
||||
|
||||
ci_95_upper = np.quantile(samples, 0.95, axis=0)
|
||||
ci_95_lower = np.quantile(samples, 0.05, axis=0)
|
||||
ci_95_upper = np.quantile(samples, 0.975, axis=0)
|
||||
ci_95_lower = np.quantile(samples, 0.025, axis=0)
|
||||
|
||||
ci_90_upper = np.quantile(samples, 0.9, axis=0)
|
||||
ci_90_lower = np.quantile(samples, 0.1, axis=0)
|
||||
ci_90_upper = np.quantile(samples, 0.95, axis=0)
|
||||
ci_90_lower = np.quantile(samples, 0.05, axis=0)
|
||||
|
||||
ci_50_lower = np.quantile(samples, 0.25, axis=0)
|
||||
ci_50_upper = np.quantile(samples, 0.75, axis=0)
|
||||
|
||||
ci_50_upper = np.quantile(samples, 0.5, axis=0)
|
||||
ci_50_lower = np.quantile(samples, 0.5, axis=0)
|
||||
|
||||
sns.set_theme()
|
||||
time_steps = np.arange(0, 96)
|
||||
@@ -210,7 +229,7 @@ class DiffusionTrainer:
|
||||
|
||||
task.get_logger().report_matplotlib_figure(
|
||||
title="Training" if training else "Testing",
|
||||
series=f'Sample {i}',
|
||||
series=f'Sample {actual_idx}',
|
||||
iteration=epoch,
|
||||
figure=fig,
|
||||
)
|
||||
@@ -224,7 +243,7 @@ class DiffusionTrainer:
|
||||
|
||||
number_of_samples = 100
|
||||
sample = self.sample(self.model, number_of_samples, inputs)
|
||||
|
||||
|
||||
# reduce samples from (batch_size*number_of_samples, time_steps) to (batch_size, number_of_samples, time_steps)
|
||||
samples_batched = sample.reshape(inputs.shape[0], number_of_samples, 96)
|
||||
|
||||
|
||||
@@ -10,7 +10,9 @@ import plotly.graph_objects as go
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from scipy.interpolate import CubicSpline
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
import seaborn as sns
|
||||
import matplotlib.patches as mpatches
|
||||
|
||||
def sample_from_dist(quantiles, preds):
|
||||
if isinstance(preds, torch.Tensor):
|
||||
@@ -261,35 +263,35 @@ class AutoRegressiveQuantileTrainer(AutoRegressiveTrainer):
|
||||
name="test_CRPS_from_samples_transformed", value=np.mean(crps_from_samples_metric)
|
||||
)
|
||||
|
||||
def get_plot_error(
|
||||
self,
|
||||
next_day,
|
||||
predictions,
|
||||
):
|
||||
metric = PinballLoss(quantiles=self.quantiles)
|
||||
fig = go.Figure()
|
||||
# def get_plot_error(
|
||||
# self,
|
||||
# next_day,
|
||||
# predictions,
|
||||
# ):
|
||||
# metric = PinballLoss(quantiles=self.quantiles)
|
||||
# fig = go.Figure()
|
||||
|
||||
next_day_np = next_day.view(-1).cpu().numpy()
|
||||
predictions_np = predictions.cpu().numpy()
|
||||
# next_day_np = next_day.view(-1).cpu().numpy()
|
||||
# predictions_np = predictions.cpu().numpy()
|
||||
|
||||
if True:
|
||||
next_day_np = self.data_processor.inverse_transform(next_day_np)
|
||||
predictions_np = self.data_processor.inverse_transform(predictions_np)
|
||||
# if True:
|
||||
# next_day_np = self.data_processor.inverse_transform(next_day_np)
|
||||
# predictions_np = self.data_processor.inverse_transform(predictions_np)
|
||||
|
||||
# for each time step, calculate the error using the metric
|
||||
errors = []
|
||||
for i in range(96):
|
||||
# # for each time step, calculate the error using the metric
|
||||
# errors = []
|
||||
# for i in range(96):
|
||||
|
||||
target_tensor = torch.tensor(next_day_np[i]).unsqueeze(0)
|
||||
prediction_tensor = torch.tensor(predictions_np[i]).unsqueeze(0)
|
||||
# target_tensor = torch.tensor(next_day_np[i]).unsqueeze(0)
|
||||
# prediction_tensor = torch.tensor(predictions_np[i]).unsqueeze(0)
|
||||
|
||||
errors.append(metric(prediction_tensor, target_tensor))
|
||||
# errors.append(metric(prediction_tensor, target_tensor))
|
||||
|
||||
# plot the error
|
||||
fig.add_trace(go.Scatter(x=np.arange(96), y=errors, name=metric.__class__.__name__))
|
||||
fig.update_layout(title=f"Error of {metric.__class__.__name__} for each time step")
|
||||
# # plot the error
|
||||
# fig.add_trace(go.Scatter(x=np.arange(96), y=errors, name=metric.__class__.__name__))
|
||||
# fig.update_layout(title=f"Error of {metric.__class__.__name__} for each time step")
|
||||
|
||||
return fig
|
||||
# return fig
|
||||
|
||||
|
||||
def get_plot(
|
||||
@@ -312,26 +314,59 @@ class AutoRegressiveQuantileTrainer(AutoRegressiveTrainer):
|
||||
next_day_np = self.data_processor.inverse_transform(next_day_np)
|
||||
predictions_np = self.data_processor.inverse_transform(predictions_np)
|
||||
|
||||
ci_99_upper = np.quantile(predictions_np, 0.995, axis=0)
|
||||
ci_99_lower = np.quantile(predictions_np, 0.005, axis=0)
|
||||
|
||||
ci_95_upper = np.quantile(predictions_np, 0.975, axis=0)
|
||||
ci_95_lower = np.quantile(predictions_np, 0.025, axis=0)
|
||||
|
||||
ci_90_upper = np.quantile(predictions_np, 0.95, axis=0)
|
||||
ci_90_lower = np.quantile(predictions_np, 0.05, axis=0)
|
||||
|
||||
ci_50_lower = np.quantile(predictions_np, 0.25, axis=0)
|
||||
ci_50_upper = np.quantile(predictions_np, 0.75, axis=0)
|
||||
|
||||
# Add traces for current and next day
|
||||
fig.add_trace(go.Scatter(x=np.arange(96), y=current_day_np, name="Current Day"))
|
||||
fig.add_trace(go.Scatter(x=96 + np.arange(96), y=next_day_np, name="Next Day"))
|
||||
# fig.add_trace(go.Scatter(x=np.arange(96), y=current_day_np, name="Current Day"))
|
||||
# fig.add_trace(go.Scatter(x=96 + np.arange(96), y=next_day_np, name="Next Day"))
|
||||
|
||||
for i, q in enumerate(self.quantiles):
|
||||
fig.add_trace(
|
||||
go.Scatter(
|
||||
x=96 + np.arange(96),
|
||||
y=predictions_np[:, i],
|
||||
name=f"Prediction (Q={q})",
|
||||
line=dict(dash="dash"),
|
||||
)
|
||||
)
|
||||
# for i, q in enumerate(self.quantiles):
|
||||
# fig.add_trace(
|
||||
# go.Scatter(
|
||||
# x=96 + np.arange(96),
|
||||
# y=predictions_np[:, i],
|
||||
# name=f"Prediction (Q={q})",
|
||||
# line=dict(dash="dash"),
|
||||
# )
|
||||
# )
|
||||
|
||||
# Update the layout
|
||||
fig.update_layout(
|
||||
title="Predictions and Quantiles of the Linear Model",
|
||||
showlegend=show_legend,
|
||||
)
|
||||
# # Update the layout
|
||||
# fig.update_layout(
|
||||
# title="Predictions and Quantiles of the Linear Model",
|
||||
# showlegend=show_legend,
|
||||
# )
|
||||
|
||||
sns.set_theme()
|
||||
time_steps = np.arange(0, 96)
|
||||
|
||||
fig, ax = plt.subplots(figsize=(20, 10))
|
||||
ax.plot(time_steps, predictions_np.mean(axis=0), label="Mean of NRV samples", linewidth=3)
|
||||
# ax.fill_between(time_steps, ci_lower, ci_upper, color='b', alpha=0.2, label='Full Interval')
|
||||
|
||||
ax.fill_between(time_steps, ci_99_lower, ci_99_upper, color='b', alpha=0.2, label='99% Interval')
|
||||
ax.fill_between(time_steps, ci_95_lower, ci_95_upper, color='b', alpha=0.2, label='95% Interval')
|
||||
ax.fill_between(time_steps, ci_90_lower, ci_90_upper, color='b', alpha=0.2, label='90% Interval')
|
||||
ax.fill_between(time_steps, ci_50_lower, ci_50_upper, color='b', alpha=0.2, label='50% Interval')
|
||||
|
||||
ax.plot(next_day_np, label="Real NRV", linewidth=3)
|
||||
# full_interval_patch = mpatches.Patch(color='b', alpha=0.2, label='Full Interval')
|
||||
ci_99_patch = mpatches.Patch(color='b', alpha=0.3, label='99% Interval')
|
||||
ci_95_patch = mpatches.Patch(color='b', alpha=0.4, label='95% Interval')
|
||||
ci_90_patch = mpatches.Patch(color='b', alpha=0.5, label='90% Interval')
|
||||
ci_50_patch = mpatches.Patch(color='b', alpha=0.6, label='50% Interval')
|
||||
|
||||
|
||||
ax.legend(handles=[ci_99_patch, ci_95_patch, ci_90_patch, ci_50_patch, ax.lines[0], ax.lines[1]])
|
||||
return fig
|
||||
|
||||
def auto_regressive(self, dataset, idx_batch, sequence_length: int = 96):
|
||||
|
||||
@@ -86,7 +86,7 @@ class Trainer:
|
||||
|
||||
def random_samples(self, train: bool = True, num_samples: int = 10):
|
||||
train_loader, test_loader = self.data_processor.get_dataloaders(
|
||||
predict_sequence_length=self.model.output_size
|
||||
predict_sequence_length=96
|
||||
)
|
||||
|
||||
if train:
|
||||
@@ -94,7 +94,14 @@ class Trainer:
|
||||
else:
|
||||
loader = test_loader
|
||||
|
||||
indices = np.random.randint(0, len(loader.dataset) - 1, size=num_samples)
|
||||
np.random.seed(42)
|
||||
actual_indices = np.random.choice(loader.dataset.full_day_valid_indices, num_samples, replace=False)
|
||||
indices = {}
|
||||
for i in actual_indices:
|
||||
indices[i] = loader.dataset.valid_indices.index(i)
|
||||
|
||||
print(actual_indices)
|
||||
|
||||
return indices
|
||||
|
||||
def train(self, epochs: int, remotely: bool = False, task: Task = None):
|
||||
@@ -107,8 +114,8 @@ class Trainer:
|
||||
predict_sequence_length=self.model.output_size
|
||||
)
|
||||
|
||||
train_samples = self.random_samples(train=True)
|
||||
test_samples = self.random_samples(train=False)
|
||||
train_samples = self.random_samples(train=True, num_samples=5)
|
||||
test_samples = self.random_samples(train=False, num_samples=5)
|
||||
|
||||
self.init_clearml_task(task)
|
||||
|
||||
|
||||
@@ -10,7 +10,7 @@ from torch.nn import MSELoss, L1Loss
|
||||
from datetime import datetime
|
||||
import torch.nn as nn
|
||||
from src.models.time_embedding_layer import TimeEmbedding
|
||||
from src.models.diffusion_model import SimpleDiffusionModel
|
||||
from src.models.diffusion_model import GRUDiffusionModel, SimpleDiffusionModel
|
||||
from src.trainers.diffusion_trainer import DiffusionTrainer
|
||||
|
||||
|
||||
@@ -38,22 +38,24 @@ data_config.NOMINAL_NET_POSITION = True
|
||||
data_config = task.connect(data_config, name="data_features")
|
||||
|
||||
data_processor = DataProcessor(data_config, path="", lstm=False)
|
||||
data_processor.set_batch_size(8192)
|
||||
data_processor.set_batch_size(64)
|
||||
data_processor.set_full_day_skip(True)
|
||||
|
||||
inputDim = data_processor.get_input_size()
|
||||
print("Input dim: ", inputDim)
|
||||
|
||||
model_parameters = {
|
||||
"epochs": 5000,
|
||||
"learning_rate": 0.0001,
|
||||
"hidden_sizes": [512, 512, 512],
|
||||
"time_dim": 64,
|
||||
"hidden_sizes": [128, 128],
|
||||
"time_dim": 8,
|
||||
}
|
||||
|
||||
model_parameters = task.connect(model_parameters, name="model_parameters")
|
||||
|
||||
#### Model ####
|
||||
model = SimpleDiffusionModel(96, model_parameters["hidden_sizes"], other_inputs_dim=inputDim[1], time_dim=model_parameters["time_dim"])
|
||||
# model = GRUDiffusionModel(96, model_parameters["hidden_sizes"], other_inputs_dim=inputDim[2], time_dim=model_parameters["time_dim"], gru_hidden_size=128)
|
||||
|
||||
print("Starting training ...")
|
||||
|
||||
|
||||
@@ -10,6 +10,6 @@ class ClearMLHelper:
|
||||
Task.ignore_requirements("torchvision")
|
||||
Task.ignore_requirements("tensorboard")
|
||||
task = Task.init(project_name=self.project_name, task_name=task_name, continue_last_task=False)
|
||||
task.set_base_docker(f"docker.io/clearml/pytorch-cuda-gcc:2.0.0-cuda11.7-cudnn8-runtime --env GIT_SSL_NO_VERIFY=true --env CLEARML_AGENT_GIT_USER=VictorMylle --env CLEARML_AGENT_GIT_PASS=Voetballer1" )
|
||||
task.set_base_docker(f"docker.io/clearml/pytorch-cuda-gcc:2.0.0-cuda11.7-cudnn8-runtime")
|
||||
task.set_packages("requirements.txt")
|
||||
return task
|
||||
Reference in New Issue
Block a user