From d1926414d6d82bfd5ad81b9f8d8184f0a91f7f32 Mon Sep 17 00:00:00 2001 From: James Fulton Date: Fri, 18 Aug 2023 13:27:38 +0000 Subject: [PATCH 01/25] temporary store for ml_ids available in training and production --- .../datamodule/configuration/pv_ml_ids.csv | 357 ++++++++++++++++++ 1 file changed, 357 insertions(+) create mode 100644 configs/datamodule/configuration/pv_ml_ids.csv diff --git a/configs/datamodule/configuration/pv_ml_ids.csv b/configs/datamodule/configuration/pv_ml_ids.csv new file mode 100644 index 00000000..354f1659 --- /dev/null +++ b/configs/datamodule/configuration/pv_ml_ids.csv @@ -0,0 +1,357 @@ +,ml_id +0,154 +1,155 +2,156 +3,158 +4,159 +5,160 +6,162 +7,164 +8,165 +9,166 +10,167 +11,168 +12,169 +13,171 +14,173 +15,177 +16,178 +17,179 +18,181 +19,182 +20,185 +21,186 +22,187 +23,188 +24,189 +25,190 +26,191 +27,192 +28,193 +29,197 +30,198 +31,199 +32,200 +33,202 +34,204 +35,205 +36,206 +37,208 +38,209 +39,211 +40,214 +41,215 +42,216 +43,217 +44,218 +45,219 +46,220 +47,221 +48,225 +49,229 +50,230 +51,232 +52,233 +53,234 +54,236 +55,237 +56,242 +57,243 +58,245 +59,251 +60,252 +61,254 +62,255 +63,256 +64,257 +65,258 +66,260 +67,261 +68,262 +69,265 +70,267 +71,268 +72,272 +73,273 +74,275 +75,276 +76,277 +77,280 +78,281 +79,282 +80,283 +81,287 +82,289 +83,291 +84,292 +85,293 +86,294 +87,295 +88,296 +89,297 +90,298 +91,301 +92,302 +93,303 +94,304 +95,306 +96,307 +97,309 +98,310 +99,311 +100,317 +101,318 +102,319 +103,320 +104,321 +105,322 +106,323 +107,324 +108,325 +109,326 +110,329 +111,332 +112,333 +113,335 +114,336 +115,338 +116,340 +117,342 +118,344 +119,345 +120,346 +121,348 +122,349 +123,352 +124,354 +125,355 +126,356 +127,357 +128,360 +129,362 +130,363 +131,368 +132,369 +133,370 +134,371 +135,372 +136,374 +137,375 +138,376 +139,378 +140,380 +141,382 +142,384 +143,385 +144,387 +145,388 +146,390 +147,391 +148,393 +149,396 +150,397 +151,398 +152,399 +153,400 +154,401 +155,403 +156,404 +157,405 +158,406 +159,407 +160,409 +161,411 +162,412 +163,413 +164,414 +165,415 +166,416 +167,417 +168,418 +169,419 +170,420 +171,421 +172,422 +173,423 +174,424 +175,425 +176,426 +177,427 +178,429 +179,431 +180,435 +181,437 +182,438 +183,440 +184,441 +185,444 +186,447 +187,449 +188,450 +189,451 +190,453 +191,456 +192,457 +193,458 +194,459 +195,464 +196,465 +197,466 +198,467 +199,468 +200,470 +201,471 +202,473 +203,474 +204,476 +205,477 +206,479 +207,480 +208,481 +209,482 +210,485 +211,486 +212,488 +213,490 +214,491 +215,492 +216,493 +217,496 +218,498 +219,501 +220,503 +221,506 +222,507 +223,508 +224,509 +225,510 +226,511 +227,512 +228,513 +229,515 +230,516 +231,517 +232,519 +233,520 +234,521 +235,522 +236,524 +237,526 +238,527 +239,528 +240,531 +241,532 +242,536 +243,537 +244,538 +245,540 +246,541 +247,542 +248,543 +249,544 +250,545 +251,549 +252,550 +253,551 +254,552 +255,553 +256,554 +257,556 +258,557 +259,560 +260,561 +261,563 +262,566 +263,568 +264,571 +265,572 +266,575 +267,576 +268,577 +269,579 +270,580 +271,581 +272,582 +273,584 +274,585 +275,588 +276,590 +277,594 +278,595 +279,597 +280,600 +281,602 +282,603 +283,604 +284,606 +285,611 +286,613 +287,614 +288,616 +289,618 +290,620 +291,622 +292,623 +293,624 +294,625 +295,626 +296,628 +297,629 +298,630 +299,631 +300,636 +301,637 +302,638 +303,640 +304,641 +305,642 +306,644 +307,645 +308,646 +309,650 +310,651 +311,652 +312,653 +313,654 +314,655 +315,657 +316,660 +317,661 +318,662 +319,663 +320,665 +321,666 +322,667 +323,668 +324,670 +325,675 +326,676 +327,679 +328,681 +329,683 +330,684 +331,685 +332,687 +333,688 +334,696 +335,698 +336,701 +337,702 +338,703 +339,704 +340,706 +341,710 +342,722 +343,723 +344,724 +345,725 +346,727 +347,728 +348,729 +349,730 +350,732 +351,733 +352,734 +353,735 +354,736 +355,737 From 44900b7c4e11a3b8e0d0fb3459788fb35292b3cc Mon Sep 17 00:00:00 2001 From: James Fulton Date: Mon, 2 Oct 2023 10:20:40 +0000 Subject: [PATCH 02/25] add passiv to data config --- .../configuration/gcp_configuration.yaml | 27 ++++++++++++++++++- 1 file changed, 26 insertions(+), 1 deletion(-) diff --git a/configs/datamodule/configuration/gcp_configuration.yaml b/configs/datamodule/configuration/gcp_configuration.yaml index 16072bc5..31e98370 100644 --- a/configs/datamodule/configuration/gcp_configuration.yaml +++ b/configs/datamodule/configuration/gcp_configuration.yaml @@ -12,9 +12,34 @@ input_data: forecast_minutes: 480 time_resolution_minutes: 30 metadata_only: false + + pv: + pv_files_groups: + - label: solar_sheffield_passiv + pv_filename: /mnt/disks/nwp/passive/v0/passiv.netcdf + pv_metadata_filename: /mnt/disks/nwp/passive/v0/system_metadata_OCF_ONLY.csv + pv_ml_ids: [154,155,156,158,159,160,162,164,165,166,167,168,169,171,173,177,178,179,181,182,185, + 186,187,188,189,190,191,192,193,197,198,199,200,202,204,205,206,208,209,211,214,215,216,217, + 218,219,220,221,225,229,230,232,233,234,236,242,243,245,252,254,255,256,257,258,260,261,262, + 265,267,268,272,273,275,276,277,280,281,282,283,287,289,291,292,293,294,295,296,297,298,301, + 302,303,304,306,307,309,310,311,317,318,319,320,321,322,323,325,326,329,332,333,335,336,338, + 340,342,344,345,346,348,349,352,354,355,356,357,360,362,363,368,369,370,371,372,374,375,376, + 378,380,382,384,385,388,390,391,393,396,397,398,399,400,401,403,404,405,406,407,409,411,412, + 413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,429,431,435,437,438,440,441,444, + 447,450,451,453,456,457,458,459,464,465,466,467,468,470,471,473,474,476,477,479,480,481,482, + 485,486,488,490,491,492,493,496,498,501,503,506,507,508,509,510,511,512,513,515,516,517,519, + 520,521,522,524,526,527,528,531,532,536,537,538,540,541,542,543,544,545,549,550,551,552,553, + 554,556,557,560,561,563,566,568,571,572,575,576,577,579,580,581,582,584,585,588,590,594,595, + 597,600,602,603,604,606,611,613,614,616,618,620,622,623,624,625,626,628,629,630,631,636,637, + 638,640,641,642,644,645,646,650,651,652,653,654,655,657,660,661,662,663,666,667,668,670,675, + 676,679,681,683,684,685,687,696,698,701,702,703,704,706,710,722,723,724,725,727,728,729,730, + 732,733,734,735,736,737] + history_minutes: 180 + forecast_minutes: 0 + time_resolution_minutes: 5 nwp: - nwp_zarr_path: /mnt/disks/nwp/UKV_intermediate_version_7.zarr + nwp_zarr_path: /mnt/disks/nwp_rechunk/UKV_intermediate_version_7.1.zarr history_minutes: 120 forecast_minutes: 480 time_resolution_minutes: 60 From 811104e7dfd409c9061dc39e4b82d491186b9c6c Mon Sep 17 00:00:00 2001 From: James Fulton Date: Mon, 2 Oct 2023 10:22:05 +0000 Subject: [PATCH 03/25] add pv encoders --- .../multimodal/encoders/basic_blocks.py | 2 +- .../multimodal/site_encoders/__init__.py | 1 + .../multimodal/site_encoders/basic_blocks.py | 32 +++++ .../multimodal/site_encoders/encoders.py | 111 ++++++++++++++++++ pvnet/utils.py | 4 +- 5 files changed, 147 insertions(+), 3 deletions(-) create mode 100644 pvnet/models/multimodal/site_encoders/__init__.py create mode 100644 pvnet/models/multimodal/site_encoders/basic_blocks.py create mode 100644 pvnet/models/multimodal/site_encoders/encoders.py diff --git a/pvnet/models/multimodal/encoders/basic_blocks.py b/pvnet/models/multimodal/encoders/basic_blocks.py index 01072a4e..b78beb73 100644 --- a/pvnet/models/multimodal/encoders/basic_blocks.py +++ b/pvnet/models/multimodal/encoders/basic_blocks.py @@ -1,4 +1,4 @@ -"""Basic blocks for immage sequence encoders""" +"""Basic blocks for image sequence encoders""" from abc import ABCMeta, abstractmethod import torch diff --git a/pvnet/models/multimodal/site_encoders/__init__.py b/pvnet/models/multimodal/site_encoders/__init__.py new file mode 100644 index 00000000..225604d3 --- /dev/null +++ b/pvnet/models/multimodal/site_encoders/__init__.py @@ -0,0 +1 @@ +"""Submodels to encode site-level PV data""" diff --git a/pvnet/models/multimodal/site_encoders/basic_blocks.py b/pvnet/models/multimodal/site_encoders/basic_blocks.py new file mode 100644 index 00000000..0a080a96 --- /dev/null +++ b/pvnet/models/multimodal/site_encoders/basic_blocks.py @@ -0,0 +1,32 @@ +"""Basic blocks for PV-site encoders""" +from abc import ABCMeta, abstractmethod + +from torch import nn + + +class AbstractPVSitesEncoder(nn.Module, metaclass=ABCMeta): + """Abstract class for encoder for output data from multiple PV sites. + + The encoder will take an input of shape (batch_size, sequence_length, num_sites) + and return an output of shape (batch_size, out_features). + """ + + def __init__( + self, + sequence_length: int, + num_sites: int, + out_features: int, + ): + """Abstract class for PV site-level encoder. + + Args: + sequence_length: The time sequence length of the data. + num_sites: Number of PV sites in the input data. + out_features: Number of output features. + """ + super().__init__() + + @abstractmethod + def forward(self): + """Run model forward""" + pass \ No newline at end of file diff --git a/pvnet/models/multimodal/site_encoders/encoders.py b/pvnet/models/multimodal/site_encoders/encoders.py new file mode 100644 index 00000000..71698c41 --- /dev/null +++ b/pvnet/models/multimodal/site_encoders/encoders.py @@ -0,0 +1,111 @@ +"""Encoder modules for the site-level PV data. + +""" + +import torch +from torch import nn + +from ocf_datapipes.utils.consts import BatchKey + +from pvnet.models.multimodal.linear_networks.networks import ResFCNet2 +from pvnet.models.multimodal.site_encoders.basic_blocks import AbstractPVSitesEncoder + + +class SimpleLearnedAggregator(AbstractPVSitesEncoder): + """A simple model which learns a different weighted-average across all of the PV sites for each + GSP. + + Each sequence from each site is independently encodeded through some dense layers wih skip- + connections, then the encoded form of each sequence is aggregated through a learned weighted-sum + and finally put through more dense layers. + + This model was written to be a simplified version of a single-headed attention layer. + """ + + def __init__( + self, + sequence_length: int, + num_sites: int, + out_features: int, + value_dim: int = 10, + value_enc_resblocks: int = 2, + final_resblocks: int = 2, + ): + """A simple sequence encoder and weighted-average model. + + Args: + sequence_length: The time sequence length of the data. + num_sites: Number of PV sites in the input data. + out_features: Number of output features. + value_dim: The number of features in each encoded sequence. Similar to the value + dimension in single- or multi-head attention. + value_dim: The number of features in each encoded sequence. Similar to the value + dimension in single- or multi-head attention. + value_enc_resblocks: Number of residual blocks in the value-encoder sub-network. + final_resblocks: Number of residual blocks in the final sub-network. + """ + + super().__init__(sequence_length, num_sites, out_features) + + self.sequence_length = sequence_length + self.num_sites = num_sites + + # Network used to encode each PV site sequence + self._value_encoder = nn.Sequential( + ResFCNet2( + in_features=sequence_length, + out_features=value_dim, + fc_hidden_features=value_dim, + n_res_blocks=value_enc_resblocks, + res_block_layers=2, + dropout_frac=0, + ), + ) + + # The learned weighted average is stored in an embedding layer for ease of use + self._attention_network = nn.Sequential( + nn.Embedding(318, num_sites), + nn.Softmax(dim=1), + ) + + # Network used to process weighted average + self.output_network = ResFCNet2( + in_features=value_dim, + out_features=out_features, + fc_hidden_features=value_dim, + n_res_blocks=final_resblocks, + res_block_layers=2, + dropout_frac=0, + ) + + def _calculate_attention(self, x): + gsp_ids = x[BatchKey.gsp_id].squeeze().int() + attention = self._attention_network(gsp_ids) + return attention + + def _encode_value(self, x): + # Shape: [batch size, sequence length, PV site] + pv_site_seqs = x[BatchKey.pv] + batch_size = pv_site_seqs.shape[0] + + pv_site_seqs = pv_site_seqs.swapaxes(1,2).flatten(0,1) + + x_seq_enc = self._value_encoder(pv_site_seqs) + x_seq_out = x_seq_enc.unflatten(0, (batch_size, self.num_sites)) + return x_seq_out + + def forward(self, x): + """Run model forward""" + # Output has shape: [batch size, num_sites, value_dim] + encodeded_seqs = self.encode_value(x) + + # Calculate learned averaging weights + attn_avg_weights = self._calculate_attention(x) + + # Take weighted average across num_sites + value_weighted_avg = (encodeded_seqs*attn_avg_weights.unsqueeze(-1)).sum(dim=1) + + # Put through final processing layers + x_out = self.output_network(value_weighted_avg) + + return x_out diff --git a/pvnet/utils.py b/pvnet/utils.py index 337ea922..2c01316c 100644 --- a/pvnet/utils.py +++ b/pvnet/utils.py @@ -16,7 +16,7 @@ from lightning.pytorch.loggers import Logger from lightning.pytorch.utilities import rank_zero_only from ocf_datapipes.utils.consts import BatchKey, Location -from ocf_datapipes.utils.geospatial import osgb_to_lat_lon +from ocf_datapipes.utils.geospatial import osgb_to_lon_lat from omegaconf import DictConfig, OmegaConf import pvnet @@ -318,7 +318,7 @@ def _get_numpy(key): y_osgb_centre = _get_numpy(BatchKey.gsp_y_osgb) x_osgb_centre = _get_numpy(BatchKey.gsp_x_osgb) - latitude, longitude = osgb_to_lat_lon(x=x_osgb_centre, y=y_osgb_centre) + longitude, latitude = osgb_to_lon_lat(x=x_osgb_centre, y=y_osgb_centre) # Store df columns in dict df_dict = {} From 6d85f43fcc3c33608a5690ec3f964911bb3f9c7e Mon Sep 17 00:00:00 2001 From: James Fulton Date: Mon, 2 Oct 2023 11:21:21 +0000 Subject: [PATCH 04/25] refactor and add PV network --- configs/model/multimodal.yaml | 49 +++-- .../multimodal/encoders/basic_blocks.py | 2 + pvnet/models/multimodal/multimodal.py | 170 +++++++----------- .../multimodal/site_encoders/basic_blocks.py | 1 + 4 files changed, 108 insertions(+), 114 deletions(-) diff --git a/configs/model/multimodal.yaml b/configs/model/multimodal.yaml index 874a2d03..e03e4ba6 100644 --- a/configs/model/multimodal.yaml +++ b/configs/model/multimodal.yaml @@ -3,24 +3,43 @@ _target_: pvnet.models.multimodal.multimodal.Model output_quantiles: null #-------------------------------------------- -# Sat/NWP encoder settings +# NWP encoder #-------------------------------------------- -image_encoder: +nwp_encoder: _target_: pvnet.models.multimodal.encoders.encoders3d.DefaultPVNet _partial_: True -encoder_out_features: 256 -encoder_kwargs: + in_channels: 2 + out_features: 256 number_of_conv3d_layers: 6 conv3d_channels: 32 + image_size_pixels: 24 + +#-------------------------------------------- +# Sat encoder settings +#-------------------------------------------- + +sat_encoder: + _target_: pvnet.models.multimodal.encoders.encoders3d.DefaultPVNet + _partial_: True + in_channels: 11 + out_features: 256 + number_of_conv3d_layers: 6 + conv3d_channels: 32 + image_size_pixels: 24 -include_sat: True -include_nwp: True add_image_embedding_channel: True -sat_image_size_pixels: 24 -nwp_image_size_pixels: 24 -number_sat_channels: 11 -number_nwp_channels: 2 + +#-------------------------------------------- +# PV encoder settings +#-------------------------------------------- + +pv_encoder: + _target_: pvnet.models.multimodal.site_encoders.encoders.SimpleLearnedAggregator + _partial_: True + out_features: 256 + num_sites: 349 + #-------------------------------------------- # Tabular network settings @@ -29,7 +48,6 @@ number_nwp_channels: 2 output_network: _target_: pvnet.models.multimodal.linear_networks.networks.ResFCNet2 _partial_: True -output_network_kwargs: fc_hidden_features: 128 n_res_blocks: 6 res_block_layers: 2 @@ -39,6 +57,10 @@ embedding_dim: 16 include_sun: True include_gsp_yield_history: True +#-------------------------------------------- +# Times +#-------------------------------------------- + # Foreast and time settings forecast_minutes: 480 history_minutes: 120 @@ -46,8 +68,11 @@ history_minutes: 120 sat_history_minutes: 90 nwp_history_minutes: 120 nwp_forecast_minutes: 480 -# ---------------------------------------------- +pv_history_minutes: 180 +# ---------------------------------------------- +# Optimizer +# ---------------------------------------------- optimizer: _target_: pvnet.optimizers.AdamWReduceLROnPlateau lr: 0.0001 diff --git a/pvnet/models/multimodal/encoders/basic_blocks.py b/pvnet/models/multimodal/encoders/basic_blocks.py index b78beb73..14cc610f 100644 --- a/pvnet/models/multimodal/encoders/basic_blocks.py +++ b/pvnet/models/multimodal/encoders/basic_blocks.py @@ -28,6 +28,8 @@ def __init__( out_features: Number of output features. """ super().__init__() + self.out_features = out_features + self.image_size_pixels = image_size_pixels @abstractmethod def forward(self): diff --git a/pvnet/models/multimodal/multimodal.py b/pvnet/models/multimodal/multimodal.py index 027966d5..7657a035 100644 --- a/pvnet/models/multimodal/multimodal.py +++ b/pvnet/models/multimodal/multimodal.py @@ -9,11 +9,10 @@ import pvnet from pvnet.models.base_model import BaseModel -from pvnet.models.multimodal.basic_blocks import CompleteDropoutNd, ImageEmbedding +from pvnet.models.multimodal.basic_blocks import ImageEmbedding from pvnet.models.multimodal.encoders.basic_blocks import AbstractNWPSatelliteEncoder -from pvnet.models.multimodal.encoders.encoders3d import DefaultPVNet from pvnet.models.multimodal.linear_networks.basic_blocks import AbstractLinearNetwork -from pvnet.models.multimodal.linear_networks.networks import DefaultFCNet +from pvnet.models.multimodal.site_encoders.basic_blocks import AbstractPVSitesEncoder from pvnet.optimizers import AbstractOptimizer @@ -25,9 +24,11 @@ class Model(BaseModel): - Satellite data, if included, is put through an encoder which transforms it from 4D, with time, channel, height, and width dimensions to become a 1D feature vector. - NWP, if included, is put through a similar encoder. - - The satellite data*, NWP data*, GSP history*, GSP ID embedding*, and sun paramters* are - concatenated into a 1D feature vector and passed through another neural network to combine - them and produce a forecast. + - PV site-level data, if included, is put through an encoder which transforms it from 2D, with + time and system-ID dimensions, to become a 1D feature vector. + - The satellite features*, NWP features*, PV site-level features*, GSP ID embedding*, and sun + paramters* are concatenated into a 1D feature vector and passed through another neural + network to combine them and produce a forecast. * if included """ @@ -36,16 +37,11 @@ class Model(BaseModel): def __init__( self, + output_network: AbstractLinearNetwork, output_quantiles: Optional[list[float]] = None, - image_encoder: AbstractNWPSatelliteEncoder = DefaultPVNet, - encoder_out_features: int = 128, - encoder_kwargs: dict = dict(), + nwp_encoder: Optional[AbstractNWPSatelliteEncoder] = None, sat_encoder: Optional[AbstractNWPSatelliteEncoder] = None, - sat_encoder_kwargs: Optional[dict] = None, - output_network: AbstractLinearNetwork = DefaultFCNet, - output_network_kwargs: dict = dict(), - include_sat: bool = True, - include_nwp: bool = True, + pv_encoder: Optional[AbstractPVSitesEncoder] = None, add_image_embedding_channel: bool = False, include_gsp_yield_history: bool = True, include_sun: bool = True, @@ -56,105 +52,82 @@ def __init__( min_sat_delay_minutes: Optional[int] = 30, nwp_forecast_minutes: Optional[int] = None, nwp_history_minutes: Optional[int] = None, - sat_image_size_pixels: int = 64, - nwp_image_size_pixels: int = 64, - number_sat_channels: int = 12, - number_nwp_channels: int = 10, - source_dropout=0.0, + pv_history_minutes: Optional[int] = None, optimizer: AbstractOptimizer = pvnet.optimizers.Adam(), ): """Neural network which combines information from different sources. - Args: - output_quantiles: A list of float (0.0, 1.0) quantiles to predict values for. If set to - None the output is a single value. - image_encoder: Pytorch Module class used to encode the NWP data (and satellite data - unless sat_encoder is set) from 4D into an 1D feature vector. - encoder_out_features: Number of features of the 1D vector created by the - `encoder_out_features` class. - encoder_kwargs: Dictionary of optional kwargs for the `image_encoder` module. - sat_encoder: Pytorch Module class used to encode the satellite data from 4D - into an 1D feature vector. If not set `image_encoder` is used. - sat_encoder_kwargs: Dictionary of optional kwargs for the `sat_encoder` module. - output_network: Pytorch Module class used to combine the 1D features to produce the - forecast. - output_network_kwargs: Dictionary of optional kwargs for the `output_network` module. - include_sat: Include satellite data. - include_nwp: Include NWP data. - add_image_embedding_channel: Add a channel to the NWP and satellite data with the - embedding of the GSP ID. - include_gsp_yield_history: Include GSP yield data. - include_sun: Include sun azimuth and altitude data. - embedding_dim: Number of embedding dimensions to use for GSP ID. Not included if set to - `None`. - forecast_minutes: The amount of minutes that should be forecasted. - history_minutes: The default amount of historical minutes that are used. - sat_history_minutes: Period of historical data to use for satellite data. Defaults to - `history_minutes` if not provided. - min_sat_delay_minutes: Minimum delay with respect to t0 of the first available satellite - image. - nwp_forecast_minutes: Period of future NWP forecast data to use. Defaults to - `forecast_minutes` if not provided. - nwp_history_minutes: Period of historical data to use for NWP data. Defaults to - `history_minutes` if not provided. - sat_image_size_pixels: Image size (assumed square) of the satellite data. - nwp_image_size_pixels: Image size (assumed square) of the NWP data. - number_sat_channels: Number of satellite channels used. - number_nwp_channels: Number of NWP channels used. - - source_dropout: Fraction of samples where each data source will be completely dropped - out. - - optimizer: Optimizer factory function used for network. + """ self.include_gsp_yield_history = include_gsp_yield_history - self.include_sat = include_sat - self.include_nwp = include_nwp + self.include_sat = sat_encoder is not None + self.include_nwp = nwp_encoder is not None + self.include_pv = pv_encoder is not None self.include_sun = include_sun self.embedding_dim = embedding_dim self.add_image_embedding_channel = add_image_embedding_channel super().__init__(history_minutes, forecast_minutes, optimizer, output_quantiles) - - if include_sat: + + # Number of features expected by the output_network + # Add to this as network pices are constructed + fusion_input_features = 0 + + if self.include_sat: # We limit the history to have a delay of 15 mins in satellite data - if sat_encoder is None: - sat_encoder = image_encoder - sat_encoder_kwargs = encoder_kwargs if sat_history_minutes is None: sat_history_minutes = history_minutes + self.sat_sequence_len = (sat_history_minutes - min_sat_delay_minutes) // 5 + 1 self.sat_encoder = sat_encoder( sequence_length=self.sat_sequence_len, - image_size_pixels=sat_image_size_pixels, - in_channels=number_sat_channels + add_image_embedding_channel, - out_features=encoder_out_features, - **sat_encoder_kwargs, + in_channels=sat_encoder.keywords["in_channels"] + add_image_embedding_channel, ) if add_image_embedding_channel: - self.sat_embed = ImageEmbedding(318, self.sat_sequence_len, sat_image_size_pixels) + self.sat_embed = ImageEmbedding( + 318, self.sat_sequence_len, self.sat_encoder.image_size_pixels + ) + + # Update num features + fusion_input_features += self.sat_encoder.out_features - if include_nwp: + if self.include_nwp: if nwp_history_minutes is None: nwp_history_minutes = history_minutes if nwp_forecast_minutes is None: nwp_forecast_minutes = forecast_minutes nwp_sequence_len = nwp_history_minutes // 60 + nwp_forecast_minutes // 60 + 1 - self.nwp_encoder = image_encoder( + self.nwp_encoder = nwp_encoder( sequence_length=nwp_sequence_len, - image_size_pixels=nwp_image_size_pixels, - in_channels=number_nwp_channels + add_image_embedding_channel, - out_features=encoder_out_features, - **encoder_kwargs, + in_channels=nwp_encoder.keywords["in_channels"] + add_image_embedding_channel, ) if add_image_embedding_channel: - self.nwp_embed = ImageEmbedding(318, nwp_sequence_len, nwp_image_size_pixels) + self.nwp_embed = ImageEmbedding( + 318, nwp_sequence_len, self.nwp_encoder.image_size_pixels + ) + + # Update num features + fusion_input_features += self.nwp_encoder.out_features + + if self.include_pv: + if pv_history_minutes is None: + pv_history_minutes = history_minutes + + self.pv_encoder = pv_encoder( + sequence_length=pv_history_minutes//5 + 1, + ) + + # Update num features + fusion_input_features += self.pv_encoder.out_features if self.embedding_dim: - self.embed = nn.Embedding(num_embeddings=318, embedding_dim=self.embedding_dim) + self.embed = nn.Embedding(num_embeddings=318, embedding_dim=embedding_dim) + + # Update num features + fusion_input_features += embedding_dim if self.include_sun: # the minus 12 is bit of hard coded smudge for pvnet @@ -162,28 +135,19 @@ def __init__( in_features=2 * (self.forecast_len_30 + self.history_len_30 + 1), out_features=16, ) - - fc_in_features = 0 - if include_sat: - fc_in_features += encoder_out_features - if include_nwp: - fc_in_features += encoder_out_features + + # Update num features + fusion_input_features += 16 + if include_gsp_yield_history: - fc_in_features += self.history_len_30 - if embedding_dim: - fc_in_features += embedding_dim - if include_sun: - fc_in_features += 16 + # Update num features + fusion_input_features += self.history_len_30 self.output_network = output_network( - in_features=fc_in_features, + in_features=fusion_input_features, out_features=self.num_output_features, - **output_network_kwargs, ) - - self.source_dropout_0d = CompleteDropoutNd(0, p=source_dropout) - self.source_dropout_3d = CompleteDropoutNd(3, p=source_dropout) - + self.save_hyperparameters() def forward(self, x): @@ -192,12 +156,11 @@ def forward(self, x): # ******************* Satellite imagery ************************* if self.include_sat: # Shape: batch_size, seq_length, channel, height, width - sat_data = x[BatchKey.satellite_actual] + sat_data = x[BatchKey.satellite_actual][:, :self.sat_sequence_len] sat_data = torch.swapaxes(sat_data, 1, 2).float() # switch time and channels if self.add_image_embedding_channel: id = x[BatchKey.gsp_id][:, 0].int() sat_data = self.sat_embed(sat_data, id) - sat_data = self.source_dropout_3d(sat_data) modes["sat"] = self.sat_encoder(sat_data) # *********************** NWP Data ************************************ @@ -208,15 +171,19 @@ def forward(self, x): if self.add_image_embedding_channel: id = x[BatchKey.gsp_id][:, 0].int() nwp_data = self.nwp_embed(nwp_data, id) - nwp_data = self.source_dropout_3d(nwp_data) modes["nwp"] = self.nwp_encoder(nwp_data) + + + # *********************** PV Data ************************************* + # Add site-level PV yield + if self.include_pv: + modes["pv"] = self.pv_encoder(x) # *********************** GSP Data ************************************ # add gsp yield history if self.include_gsp_yield_history: gsp_history = x[BatchKey.gsp][:, : self.history_len_30].float() gsp_history = gsp_history.reshape(gsp_history.shape[0], -1) - gsp_history = self.source_dropout_0d(gsp_history) modes["gsp"] = gsp_history # ********************** Embedding of GSP ID ******************** @@ -229,7 +196,6 @@ def forward(self, x): sun = torch.cat( (x[BatchKey.gsp_solar_azimuth], x[BatchKey.gsp_solar_elevation]), dim=1 ).float() - sun = self.source_dropout_0d(sun) sun = self.sun_fc1(sun) modes["sun"] = sun diff --git a/pvnet/models/multimodal/site_encoders/basic_blocks.py b/pvnet/models/multimodal/site_encoders/basic_blocks.py index 0a080a96..b2d6c3b1 100644 --- a/pvnet/models/multimodal/site_encoders/basic_blocks.py +++ b/pvnet/models/multimodal/site_encoders/basic_blocks.py @@ -25,6 +25,7 @@ def __init__( out_features: Number of output features. """ super().__init__() + self.out_features = out_features @abstractmethod def forward(self): From ab7063649572c2163ce26cbb3e7007b15224014f Mon Sep 17 00:00:00 2001 From: James Fulton Date: Mon, 2 Oct 2023 11:28:25 +0000 Subject: [PATCH 05/25] update model config --- configs/model/multimodal.yaml | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) diff --git a/configs/model/multimodal.yaml b/configs/model/multimodal.yaml index e03e4ba6..c654694a 100644 --- a/configs/model/multimodal.yaml +++ b/configs/model/multimodal.yaml @@ -1,6 +1,6 @@ _target_: pvnet.models.multimodal.multimodal.Model -output_quantiles: null +output_quantiles: [0.02, 0.1, 0.25, 0.5, 0.75, 0.9, 0.98] #-------------------------------------------- # NWP encoder @@ -37,9 +37,11 @@ add_image_embedding_channel: True pv_encoder: _target_: pvnet.models.multimodal.site_encoders.encoders.SimpleLearnedAggregator _partial_: True - out_features: 256 num_sites: 349 - + out_features: 64 + value_dim: 64 + value_enc_resblocks: 2 + final_resblocks: 2 #-------------------------------------------- # Tabular network settings @@ -55,7 +57,7 @@ output_network: embedding_dim: 16 include_sun: True -include_gsp_yield_history: True +include_gsp_yield_history: False #-------------------------------------------- # Times @@ -64,6 +66,9 @@ include_gsp_yield_history: True # Foreast and time settings forecast_minutes: 480 history_minutes: 120 + +min_sat_delay_minutes: 60 + # --- set to null if same as history_minutes --- sat_history_minutes: 90 nwp_history_minutes: 120 From 049dcef69dd0719a22e114d9d88cd85ab656a4ca Mon Sep 17 00:00:00 2001 From: James Fulton Date: Tue, 3 Oct 2023 12:28:59 +0000 Subject: [PATCH 06/25] allow different learning rates for submodules + fixes --- configs/model/multimodal.yaml | 10 ++- pvnet/models/base_model.py | 2 +- .../multimodal/site_encoders/encoders.py | 4 +- pvnet/optimizers.py | 78 +++++++++++++++---- 4 files changed, 71 insertions(+), 23 deletions(-) diff --git a/configs/model/multimodal.yaml b/configs/model/multimodal.yaml index c654694a..9581c0da 100644 --- a/configs/model/multimodal.yaml +++ b/configs/model/multimodal.yaml @@ -6,7 +6,7 @@ output_quantiles: [0.02, 0.1, 0.25, 0.5, 0.75, 0.9, 0.98] # NWP encoder #-------------------------------------------- -nwp_encoder: +nwp_encoder: _target_: pvnet.models.multimodal.encoders.encoders3d.DefaultPVNet _partial_: True in_channels: 2 @@ -28,7 +28,7 @@ sat_encoder: conv3d_channels: 32 image_size_pixels: 24 -add_image_embedding_channel: True +add_image_embedding_channel: False #-------------------------------------------- # PV encoder settings @@ -80,8 +80,10 @@ pv_history_minutes: 180 # ---------------------------------------------- optimizer: _target_: pvnet.optimizers.AdamWReduceLROnPlateau - lr: 0.0001 - weight_decay: 0.25 + lr: + pv_encoder: 0.002 + default: 0.0001 + weight_decay: 0.02 amsgrad: True patience: 5 factor: 0.1 diff --git a/pvnet/models/base_model.py b/pvnet/models/base_model.py index 74cadd39..7561c893 100644 --- a/pvnet/models/base_model.py +++ b/pvnet/models/base_model.py @@ -448,4 +448,4 @@ def configure_optimizers(self): if self.lr is not None: # Use learning rate found by learning rate finder callback self._optimizer.lr = self.lr - return self._optimizer(self.parameters()) + return self._optimizer(self) diff --git a/pvnet/models/multimodal/site_encoders/encoders.py b/pvnet/models/multimodal/site_encoders/encoders.py index 71698c41..51549c83 100644 --- a/pvnet/models/multimodal/site_encoders/encoders.py +++ b/pvnet/models/multimodal/site_encoders/encoders.py @@ -85,7 +85,7 @@ def _calculate_attention(self, x): def _encode_value(self, x): # Shape: [batch size, sequence length, PV site] - pv_site_seqs = x[BatchKey.pv] + pv_site_seqs = x[BatchKey.pv].float() batch_size = pv_site_seqs.shape[0] pv_site_seqs = pv_site_seqs.swapaxes(1,2).flatten(0,1) @@ -97,7 +97,7 @@ def _encode_value(self, x): def forward(self, x): """Run model forward""" # Output has shape: [batch size, num_sites, value_dim] - encodeded_seqs = self.encode_value(x) + encodeded_seqs = self._encode_value(x) # Calculate learned averaging weights attn_avg_weights = self._calculate_attention(x) diff --git a/pvnet/optimizers.py b/pvnet/optimizers.py index 54d64af7..bf370ef7 100644 --- a/pvnet/optimizers.py +++ b/pvnet/optimizers.py @@ -11,7 +11,7 @@ class AbstractOptimizer(ABC): Optimizer classes will be used by model like: > OptimizerGenerator = AbstractOptimizer() - > optimizer = OptimizerGenerator(model.parameters()) + > optimizer = OptimizerGenerator(model) The returned object `optimizer` must be something that may be returned by `pytorch_lightning`'s `configure_optimizers()` method. See : @@ -33,9 +33,9 @@ def __init__(self, lr=0.0005, **kwargs): self.lr = lr self.kwargs = kwargs - def __call__(self, model_parameters): + def __call__(self, model): """Return optimizer""" - return torch.optim.Adam(model_parameters, lr=self.lr, **self.kwargs) + return torch.optim.Adam(model.parameters(), lr=self.lr, **self.kwargs) class AdamW(AbstractOptimizer): @@ -46,9 +46,9 @@ def __init__(self, lr=0.0005, **kwargs): self.lr = lr self.kwargs = kwargs - def __call__(self, model_parameters): + def __call__(self, model): """Return optimizer""" - return torch.optim.AdamW(model_parameters, lr=self.lr, **self.kwargs) + return torch.optim.AdamW(model.parameters(), lr=self.lr, **self.kwargs) class AdamWReduceLROnPlateau(AbstractOptimizer): @@ -56,20 +56,66 @@ class AdamWReduceLROnPlateau(AbstractOptimizer): def __init__(self, lr=0.0005, patience=3, factor=0.5, threshold=2e-4, **opt_kwargs): """AdamW optimizer and reduce on plateau scheduler""" - self.lr = lr + self._lr = lr self.patience = patience self.factor = factor self.threshold = threshold self.opt_kwargs = opt_kwargs - - def __call__(self, model_parameters): - """Return optimizer""" - opt = torch.optim.AdamW(model_parameters, lr=self.lr, **self.opt_kwargs) - sch = torch.optim.lr_scheduler.ReduceLROnPlateau( - opt, - factor=self.factor, - patience=self.patience, - threshold=self.threshold, + + def _call_multi(self, model): + + remaining_params = {k:p for k,p in model.named_parameters()} + + group_args = [] + + for key in self._lr.keys(): + if key=="default": + continue + + submodule_params = [] + for param_name in list(remaining_params.keys()): + if param_name.startswith(key): + submodule_params += [remaining_params.pop(param_name)] + + group_args += [{"params": submodule_params, "lr": self._lr[key]}] + + remaining_params = [p for k, p in remaining_params.items()] + group_args += [{"params": remaining_params}] + + opt = torch.optim.AdamW( + group_args, + lr=self._lr["default"] if model.lr is None else model.lr, + **self.opt_kwargs ) - sch = {"scheduler": sch, "monitor": "MAE/train"} + sch = { + "scheduler": torch.optim.lr_scheduler.ReduceLROnPlateau( + opt, + factor=self.factor, + patience=self.patience, + threshold=self.threshold, + ), + "monitor": "quantile_loss/val" if model.use_quantile_regression else "MAE/train" + } + return [opt], [sch] + + + def __call__(self, model): + """Return optimizer""" + if not isinstance(self._lr, float): + return self._call_multi(model) + else: + assert False + default_lr = self._lr if model.lr is None else model.lr + opt = torch.optim.AdamW(model.parameters(), lr=default_lr, **self.opt_kwargs) + sch = torch.optim.lr_scheduler.ReduceLROnPlateau( + opt, + factor=self.factor, + patience=self.patience, + threshold=self.threshold, + ) + sch = { + "scheduler": sch, + "monitor": "quantile_loss/train" if model.use_quantile_regression else "MAE/train", + } + return [opt], [sch] From d8883fa5441743a47e9f6df4281581101ffcc045 Mon Sep 17 00:00:00 2001 From: James Fulton Date: Mon, 23 Oct 2023 15:18:54 +0000 Subject: [PATCH 07/25] update PV site subnetwork to minimal attention mechanism --- configs/model/multimodal.yaml | 20 ++- .../multimodal/site_encoders/basic_blocks.py | 3 + .../multimodal/site_encoders/encoders.py | 148 +++++++++++++++++- pvnet/optimizers.py | 79 +++++++++- 4 files changed, 233 insertions(+), 17 deletions(-) diff --git a/configs/model/multimodal.yaml b/configs/model/multimodal.yaml index 9581c0da..2b2db1f1 100644 --- a/configs/model/multimodal.yaml +++ b/configs/model/multimodal.yaml @@ -35,13 +35,13 @@ add_image_embedding_channel: False #-------------------------------------------- pv_encoder: - _target_: pvnet.models.multimodal.site_encoders.encoders.SimpleLearnedAggregator + _target_: pvnet.models.multimodal.site_encoders.encoders.SingleAttentionNetwork _partial_: True num_sites: 349 - out_features: 64 - value_dim: 64 - value_enc_resblocks: 2 - final_resblocks: 2 + out_features: 40 + num_heads: 4 + kdim: 40 + pv_id_embed_dim: 20 #-------------------------------------------- # Tabular network settings @@ -79,12 +79,10 @@ pv_history_minutes: 180 # Optimizer # ---------------------------------------------- optimizer: - _target_: pvnet.optimizers.AdamWReduceLROnPlateau - lr: - pv_encoder: 0.002 - default: 0.0001 - weight_decay: 0.02 + _target_: pvnet.optimizers.EmbAdamWReduceLROnPlateau + lr: 0.0001 + weight_decay: 0.01 amsgrad: True patience: 5 factor: 0.1 - threshold: 0.002 + threshold: 0.002 \ No newline at end of file diff --git a/pvnet/models/multimodal/site_encoders/basic_blocks.py b/pvnet/models/multimodal/site_encoders/basic_blocks.py index b2d6c3b1..1fcd2056 100644 --- a/pvnet/models/multimodal/site_encoders/basic_blocks.py +++ b/pvnet/models/multimodal/site_encoders/basic_blocks.py @@ -25,8 +25,11 @@ def __init__( out_features: Number of output features. """ super().__init__() + self.sequence_length = sequence_length + self.num_sites = num_sites self.out_features = out_features + @abstractmethod def forward(self): """Run model forward""" diff --git a/pvnet/models/multimodal/site_encoders/encoders.py b/pvnet/models/multimodal/site_encoders/encoders.py index 51549c83..9fb2ea52 100644 --- a/pvnet/models/multimodal/site_encoders/encoders.py +++ b/pvnet/models/multimodal/site_encoders/encoders.py @@ -47,9 +47,6 @@ def __init__( super().__init__(sequence_length, num_sites, out_features) - self.sequence_length = sequence_length - self.num_sites = num_sites - # Network used to encode each PV site sequence self._value_encoder = nn.Sequential( ResFCNet2( @@ -109,3 +106,148 @@ def forward(self, x): x_out = self.output_network(value_weighted_avg) return x_out + + +class SingleAttentionNetwork(AbstractPVSitesEncoder): + """A simple attention-based model with a single multihead attention layer + + For the attention layer the query is based on the target GSP alone, the key is based on the PV + ID and the recent PV data, the value is based on the recent PV data. + + """ + def __init__( + self, + sequence_length: int, + num_sites: int, + out_features: int, + kdim: int = 10, + num_heads: int = 2, + pv_id_embed_dim: int = 10, + n_kv_res_blocks: int = 2, + kv_res_block_layers: int = 2, + use_pv_id_in_value: bool = False, + ): + """A simple attention-based model with a single multihead attention layer + + Args: + sequence_length: The time sequence length of the data. + num_sites: Number of PV sites in the input data. + out_features: Number of output features. In this network this is also the the value + dimension in the multi-head attention layer. + kdim: The dimensions used in both the keys and queries. + num_heads: Number of parallel attention heads. Note that `out_features` will be split + across `num_heads` so `out_features` must be a multiple of `num_heads`. + pv_id_embed_dim: The dimension of the PV ID embedding used in calculating the key. + n_kv_res_blocks: Number of residual blocks to use in the key and value encoders. + kv_res_block_layers: Number of fully-connected layers used in each residual block within + the key and value encoders. + use_pv_id_in_value: Whether to use the PV ID in network used to produce the value for + the attention layer. + + """ + super().__init__(sequence_length, num_sites, out_features) + + self.gsp_id_embedding = nn.Embedding(318, kdim) + self.pv_id_embedding = nn.Embedding(num_sites, pv_id_embed_dim) + self._pv_ids = nn.parameter.Parameter(torch.arange(num_sites), requires_grad=False) + self.use_pv_id_in_value = use_pv_id_in_value + + if use_pv_id_in_value: + self.value_pv_id_embedding = nn.Embedding(num_sites, pv_id_embed_dim) + + self._value_encoder = nn.Sequential( + ResFCNet2( + in_features=sequence_length+int(use_pv_id_in_value)*pv_id_embed_dim, + out_features=out_features, + fc_hidden_features=sequence_length, + n_res_blocks=n_kv_res_blocks, + res_block_layers=kv_res_block_layers, + dropout_frac=0, + ), + nn.Linear(out_features, kdim), + ) + + self._key_encoder = nn.Sequential( + ResFCNet2( + in_features=pv_id_embed_dim+sequence_length, + out_features=kdim, + fc_hidden_features=pv_id_embed_dim+sequence_length, + n_res_blocks=n_kv_res_blocks, + res_block_layers=kv_res_block_layers, + dropout_frac=0, + ), + nn.Linear(kdim, kdim) + ) + + self.multihead_attn = nn.MultiheadAttention( + embed_dim=kdim, + num_heads=num_heads, + batch_first=True, + vdim=out_features, + ) + + + def _encode_query(self, x): + gsp_ids = x[BatchKey.gsp_id].squeeze().int() + query = self.gsp_id_embedding(gsp_ids).unsqueeze(1) + return query + + def _encode_key(self, x): + # Shape: [batch size, sequence length, PV site] + pv_site_seqs = x[BatchKey.pv].float() + batch_size = pv_site_seqs.shape[0] + + # PV ID embeddings are the same for each sample + pv_id_embed = torch.tile(self.pv_id_embedding(self._pv_ids), (batch_size, 1, 1)) + + # Each concated (PV sequence, PV ID embedding) is processed with encoder + x_seq_in = torch.cat((pv_site_seqs.swapaxes(1,2), pv_id_embed), dim=2).flatten(0,1) + key = self._key_encoder(x_seq_in) + + # Reshape to [batch size, PV site, kdim] + key = key.unflatten(0, (batch_size, self.num_sites)) + return key + + def _encode_value(self, x): + # Shape: [batch size, sequence length, PV site] + pv_site_seqs = x[BatchKey.pv].float() + batch_size = pv_site_seqs.shape[0] + + if self.use_pv_id_in_value: + # PV ID embeddings are the same for each sample + pv_id_embed = torch.tile(self.value_pv_id_embedding(self._pv_ids), (batch_size, 1, 1)) + # Each concated (PV sequence, PV ID embedding) is processed with encoder + x_seq_in = torch.cat((pv_site_seqs.swapaxes(1,2), pv_id_embed), dim=2).flatten(0,1) + else: + # Encode each PV sequence independently + x_seq_in = pv_site_seqs.swapaxes(1,2).flatten(0,1) + + value = self._value_encoder(x_seq_in) + + # Reshape to [batch size, PV site, vdim] + value = value.unflatten(0, (batch_size, self.num_sites)) + return value + + def _attention_forward(self, x, average_attn_weights=True): + + query = self._encode_query(x) + key = self._encode_key(x) + value = self._encode_value(x) + + attn_output, attn_weights = self.multihead_attn( + query, + key, + value, + average_attn_weights=average_attn_weights + ) + + return attn_output, attn_weights + + def forward(self, x): + """Run model forward""" + attn_output, attn_output_weights = self._attention_forward(x) + + # Reshape from [batch_size, 1, vdim] to [batch_size, vdim] + x_out = attn_output.squeeze() + + return x_out \ No newline at end of file diff --git a/pvnet/optimizers.py b/pvnet/optimizers.py index bf370ef7..92d6fa40 100644 --- a/pvnet/optimizers.py +++ b/pvnet/optimizers.py @@ -50,16 +50,89 @@ def __call__(self, model): """Return optimizer""" return torch.optim.AdamW(model.parameters(), lr=self.lr, **self.kwargs) + +def find_submodule_parameters(model, search_modules): + if isinstance(model, search_modules): + return model.parameters() + + children = list(model.children()) + if len(children)==0: + return [] + else: + params = [] + for c in children: + params += find_submodule_parameters(c, search_modules) + return params + + +def find_other_than_submodule_parameters(model, ignore_modules): + if isinstance(model, ignore_modules): + return [] + + children = list(model.children()) + if len(children)==0: + return model.parameters() + else: + params = [] + for c in children: + params += find_other_than_submodule_parameters(c, ignore_modules) + return params + + +class EmbAdamWReduceLROnPlateau(AbstractOptimizer): + """AdamW optimizer and reduce on plateau scheduler""" + + def __init__(self, lr=0.0005, weight_decay=0.01, patience=3, factor=0.5, threshold=2e-4, + **opt_kwargs + ): + """AdamW optimizer and reduce on plateau scheduler""" + self.lr = lr + self.weight_decay = weight_decay + self.patience = patience + self.factor = factor + self.threshold = threshold + self.opt_kwargs = opt_kwargs + + + def __call__(self, model): + """Return optimizer""" + + search_modules = (torch.nn.Embedding, ) + + no_decay = find_submodule_parameters(model, search_modules) + decay = find_other_than_submodule_parameters(model, search_modules) + + optim_groups = [ + {"params": decay, "weight_decay": self.weight_decay}, + {"params": no_decay, "weight_decay": 0.0}, + ] + opt = torch.optim.AdamW(optim_groups, lr=self.lr, **self.opt_kwargs) + + sch = torch.optim.lr_scheduler.ReduceLROnPlateau( + opt, + factor=self.factor, + patience=self.patience, + threshold=self.threshold, + ) + sch = { + "scheduler": sch, + "monitor": "quantile_loss/val" if model.use_quantile_regression else "MAE/val", + } + return [opt], [sch] + class AdamWReduceLROnPlateau(AbstractOptimizer): """AdamW optimizer and reduce on plateau scheduler""" - def __init__(self, lr=0.0005, patience=3, factor=0.5, threshold=2e-4, **opt_kwargs): + def __init__(self, lr=0.0005, patience=3, factor=0.5, threshold=2e-4, step_freq=None, + **opt_kwargs + ): """AdamW optimizer and reduce on plateau scheduler""" self._lr = lr self.patience = patience self.factor = factor self.threshold = threshold + self.step_freq = step_freq self.opt_kwargs = opt_kwargs def _call_multi(self, model): @@ -94,7 +167,7 @@ def _call_multi(self, model): patience=self.patience, threshold=self.threshold, ), - "monitor": "quantile_loss/val" if model.use_quantile_regression else "MAE/train" + "monitor": "quantile_loss/val" if model.use_quantile_regression else "MAE/val", } return [opt], [sch] @@ -116,6 +189,6 @@ def __call__(self, model): ) sch = { "scheduler": sch, - "monitor": "quantile_loss/train" if model.use_quantile_regression else "MAE/train", + "monitor": "quantile_loss/val" if model.use_quantile_regression else "MAE/val", } return [opt], [sch] From ec922df3306221c354a58abd5154b5cd6bec3b9a Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 24 Oct 2023 10:46:39 +0000 Subject: [PATCH 08/25] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- .../configuration/gcp_configuration.yaml | 370 +++++++++++++++++- configs/model/multimodal.yaml | 4 +- pvnet/models/multimodal/multimodal.py | 54 ++- .../multimodal/site_encoders/basic_blocks.py | 3 +- .../multimodal/site_encoders/encoders.py | 155 ++++---- pvnet/optimizers.py | 65 ++- 6 files changed, 486 insertions(+), 165 deletions(-) diff --git a/configs/datamodule/configuration/gcp_configuration.yaml b/configs/datamodule/configuration/gcp_configuration.yaml index 31e98370..1d6dd67b 100644 --- a/configs/datamodule/configuration/gcp_configuration.yaml +++ b/configs/datamodule/configuration/gcp_configuration.yaml @@ -12,28 +12,364 @@ input_data: forecast_minutes: 480 time_resolution_minutes: 30 metadata_only: false - + pv: pv_files_groups: - label: solar_sheffield_passiv pv_filename: /mnt/disks/nwp/passive/v0/passiv.netcdf pv_metadata_filename: /mnt/disks/nwp/passive/v0/system_metadata_OCF_ONLY.csv - pv_ml_ids: [154,155,156,158,159,160,162,164,165,166,167,168,169,171,173,177,178,179,181,182,185, - 186,187,188,189,190,191,192,193,197,198,199,200,202,204,205,206,208,209,211,214,215,216,217, - 218,219,220,221,225,229,230,232,233,234,236,242,243,245,252,254,255,256,257,258,260,261,262, - 265,267,268,272,273,275,276,277,280,281,282,283,287,289,291,292,293,294,295,296,297,298,301, - 302,303,304,306,307,309,310,311,317,318,319,320,321,322,323,325,326,329,332,333,335,336,338, - 340,342,344,345,346,348,349,352,354,355,356,357,360,362,363,368,369,370,371,372,374,375,376, - 378,380,382,384,385,388,390,391,393,396,397,398,399,400,401,403,404,405,406,407,409,411,412, - 413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,429,431,435,437,438,440,441,444, - 447,450,451,453,456,457,458,459,464,465,466,467,468,470,471,473,474,476,477,479,480,481,482, - 485,486,488,490,491,492,493,496,498,501,503,506,507,508,509,510,511,512,513,515,516,517,519, - 520,521,522,524,526,527,528,531,532,536,537,538,540,541,542,543,544,545,549,550,551,552,553, - 554,556,557,560,561,563,566,568,571,572,575,576,577,579,580,581,582,584,585,588,590,594,595, - 597,600,602,603,604,606,611,613,614,616,618,620,622,623,624,625,626,628,629,630,631,636,637, - 638,640,641,642,644,645,646,650,651,652,653,654,655,657,660,661,662,663,666,667,668,670,675, - 676,679,681,683,684,685,687,696,698,701,702,703,704,706,710,722,723,724,725,727,728,729,730, - 732,733,734,735,736,737] + pv_ml_ids: + [ + 154, + 155, + 156, + 158, + 159, + 160, + 162, + 164, + 165, + 166, + 167, + 168, + 169, + 171, + 173, + 177, + 178, + 179, + 181, + 182, + 185, + 186, + 187, + 188, + 189, + 190, + 191, + 192, + 193, + 197, + 198, + 199, + 200, + 202, + 204, + 205, + 206, + 208, + 209, + 211, + 214, + 215, + 216, + 217, + 218, + 219, + 220, + 221, + 225, + 229, + 230, + 232, + 233, + 234, + 236, + 242, + 243, + 245, + 252, + 254, + 255, + 256, + 257, + 258, + 260, + 261, + 262, + 265, + 267, + 268, + 272, + 273, + 275, + 276, + 277, + 280, + 281, + 282, + 283, + 287, + 289, + 291, + 292, + 293, + 294, + 295, + 296, + 297, + 298, + 301, + 302, + 303, + 304, + 306, + 307, + 309, + 310, + 311, + 317, + 318, + 319, + 320, + 321, + 322, + 323, + 325, + 326, + 329, + 332, + 333, + 335, + 336, + 338, + 340, + 342, + 344, + 345, + 346, + 348, + 349, + 352, + 354, + 355, + 356, + 357, + 360, + 362, + 363, + 368, + 369, + 370, + 371, + 372, + 374, + 375, + 376, + 378, + 380, + 382, + 384, + 385, + 388, + 390, + 391, + 393, + 396, + 397, + 398, + 399, + 400, + 401, + 403, + 404, + 405, + 406, + 407, + 409, + 411, + 412, + 413, + 414, + 415, + 416, + 417, + 418, + 419, + 420, + 421, + 422, + 423, + 424, + 425, + 426, + 427, + 429, + 431, + 435, + 437, + 438, + 440, + 441, + 444, + 447, + 450, + 451, + 453, + 456, + 457, + 458, + 459, + 464, + 465, + 466, + 467, + 468, + 470, + 471, + 473, + 474, + 476, + 477, + 479, + 480, + 481, + 482, + 485, + 486, + 488, + 490, + 491, + 492, + 493, + 496, + 498, + 501, + 503, + 506, + 507, + 508, + 509, + 510, + 511, + 512, + 513, + 515, + 516, + 517, + 519, + 520, + 521, + 522, + 524, + 526, + 527, + 528, + 531, + 532, + 536, + 537, + 538, + 540, + 541, + 542, + 543, + 544, + 545, + 549, + 550, + 551, + 552, + 553, + 554, + 556, + 557, + 560, + 561, + 563, + 566, + 568, + 571, + 572, + 575, + 576, + 577, + 579, + 580, + 581, + 582, + 584, + 585, + 588, + 590, + 594, + 595, + 597, + 600, + 602, + 603, + 604, + 606, + 611, + 613, + 614, + 616, + 618, + 620, + 622, + 623, + 624, + 625, + 626, + 628, + 629, + 630, + 631, + 636, + 637, + 638, + 640, + 641, + 642, + 644, + 645, + 646, + 650, + 651, + 652, + 653, + 654, + 655, + 657, + 660, + 661, + 662, + 663, + 666, + 667, + 668, + 670, + 675, + 676, + 679, + 681, + 683, + 684, + 685, + 687, + 696, + 698, + 701, + 702, + 703, + 704, + 706, + 710, + 722, + 723, + 724, + 725, + 727, + 728, + 729, + 730, + 732, + 733, + 734, + 735, + 736, + 737, + ] history_minutes: 180 forecast_minutes: 0 time_resolution_minutes: 5 diff --git a/configs/model/multimodal.yaml b/configs/model/multimodal.yaml index 2b2db1f1..41af91e2 100644 --- a/configs/model/multimodal.yaml +++ b/configs/model/multimodal.yaml @@ -6,7 +6,7 @@ output_quantiles: [0.02, 0.1, 0.25, 0.5, 0.75, 0.9, 0.98] # NWP encoder #-------------------------------------------- -nwp_encoder: +nwp_encoder: _target_: pvnet.models.multimodal.encoders.encoders3d.DefaultPVNet _partial_: True in_channels: 2 @@ -85,4 +85,4 @@ optimizer: amsgrad: True patience: 5 factor: 0.1 - threshold: 0.002 \ No newline at end of file + threshold: 0.002 diff --git a/pvnet/models/multimodal/multimodal.py b/pvnet/models/multimodal/multimodal.py index 7657a035..d1faa2d8 100644 --- a/pvnet/models/multimodal/multimodal.py +++ b/pvnet/models/multimodal/multimodal.py @@ -26,8 +26,8 @@ class Model(BaseModel): - NWP, if included, is put through a similar encoder. - PV site-level data, if included, is put through an encoder which transforms it from 2D, with time and system-ID dimensions, to become a 1D feature vector. - - The satellite features*, NWP features*, PV site-level features*, GSP ID embedding*, and sun - paramters* are concatenated into a 1D feature vector and passed through another neural + - The satellite features*, NWP features*, PV site-level features*, GSP ID embedding*, and sun + paramters* are concatenated into a 1D feature vector and passed through another neural network to combine them and produce a forecast. * if included @@ -55,10 +55,7 @@ def __init__( pv_history_minutes: Optional[int] = None, optimizer: AbstractOptimizer = pvnet.optimizers.Adam(), ): - """Neural network which combines information from different sources. - - - """ + """Neural network which combines information from different sources.""" self.include_gsp_yield_history = include_gsp_yield_history self.include_sat = sat_encoder is not None self.include_nwp = nwp_encoder is not None @@ -68,17 +65,17 @@ def __init__( self.add_image_embedding_channel = add_image_embedding_channel super().__init__(history_minutes, forecast_minutes, optimizer, output_quantiles) - + # Number of features expected by the output_network # Add to this as network pices are constructed fusion_input_features = 0 - + if self.include_sat: # We limit the history to have a delay of 15 mins in satellite data if sat_history_minutes is None: sat_history_minutes = history_minutes - + self.sat_sequence_len = (sat_history_minutes - min_sat_delay_minutes) // 5 + 1 self.sat_encoder = sat_encoder( @@ -89,8 +86,8 @@ def __init__( self.sat_embed = ImageEmbedding( 318, self.sat_sequence_len, self.sat_encoder.image_size_pixels ) - - # Update num features + + # Update num features fusion_input_features += self.sat_encoder.out_features if self.include_nwp: @@ -108,25 +105,25 @@ def __init__( self.nwp_embed = ImageEmbedding( 318, nwp_sequence_len, self.nwp_encoder.image_size_pixels ) - - # Update num features + + # Update num features fusion_input_features += self.nwp_encoder.out_features - + if self.include_pv: if pv_history_minutes is None: pv_history_minutes = history_minutes - + self.pv_encoder = pv_encoder( - sequence_length=pv_history_minutes//5 + 1, + sequence_length=pv_history_minutes // 5 + 1, ) - - # Update num features + + # Update num features fusion_input_features += self.pv_encoder.out_features if self.embedding_dim: self.embed = nn.Embedding(num_embeddings=318, embedding_dim=embedding_dim) - - # Update num features + + # Update num features fusion_input_features += embedding_dim if self.include_sun: @@ -135,19 +132,19 @@ def __init__( in_features=2 * (self.forecast_len_30 + self.history_len_30 + 1), out_features=16, ) - - # Update num features + + # Update num features fusion_input_features += 16 - + if include_gsp_yield_history: - # Update num features + # Update num features fusion_input_features += self.history_len_30 self.output_network = output_network( in_features=fusion_input_features, out_features=self.num_output_features, ) - + self.save_hyperparameters() def forward(self, x): @@ -156,7 +153,7 @@ def forward(self, x): # ******************* Satellite imagery ************************* if self.include_sat: # Shape: batch_size, seq_length, channel, height, width - sat_data = x[BatchKey.satellite_actual][:, :self.sat_sequence_len] + sat_data = x[BatchKey.satellite_actual][:, : self.sat_sequence_len] sat_data = torch.swapaxes(sat_data, 1, 2).float() # switch time and channels if self.add_image_embedding_channel: id = x[BatchKey.gsp_id][:, 0].int() @@ -172,12 +169,11 @@ def forward(self, x): id = x[BatchKey.gsp_id][:, 0].int() nwp_data = self.nwp_embed(nwp_data, id) modes["nwp"] = self.nwp_encoder(nwp_data) - - + # *********************** PV Data ************************************* # Add site-level PV yield if self.include_pv: - modes["pv"] = self.pv_encoder(x) + modes["pv"] = self.pv_encoder(x) # *********************** GSP Data ************************************ # add gsp yield history diff --git a/pvnet/models/multimodal/site_encoders/basic_blocks.py b/pvnet/models/multimodal/site_encoders/basic_blocks.py index 1fcd2056..b20835f1 100644 --- a/pvnet/models/multimodal/site_encoders/basic_blocks.py +++ b/pvnet/models/multimodal/site_encoders/basic_blocks.py @@ -29,8 +29,7 @@ def __init__( self.num_sites = num_sites self.out_features = out_features - @abstractmethod def forward(self): """Run model forward""" - pass \ No newline at end of file + pass diff --git a/pvnet/models/multimodal/site_encoders/encoders.py b/pvnet/models/multimodal/site_encoders/encoders.py index 9fb2ea52..4821968f 100644 --- a/pvnet/models/multimodal/site_encoders/encoders.py +++ b/pvnet/models/multimodal/site_encoders/encoders.py @@ -3,9 +3,8 @@ """ import torch -from torch import nn - from ocf_datapipes.utils.consts import BatchKey +from torch import nn from pvnet.models.multimodal.linear_networks.networks import ResFCNet2 from pvnet.models.multimodal.site_encoders.basic_blocks import AbstractPVSitesEncoder @@ -14,11 +13,11 @@ class SimpleLearnedAggregator(AbstractPVSitesEncoder): """A simple model which learns a different weighted-average across all of the PV sites for each GSP. - + Each sequence from each site is independently encodeded through some dense layers wih skip- connections, then the encoded form of each sequence is aggregated through a learned weighted-sum and finally put through more dense layers. - + This model was written to be a simplified version of a single-headed attention layer. """ @@ -32,21 +31,21 @@ def __init__( final_resblocks: int = 2, ): """A simple sequence encoder and weighted-average model. - + Args: sequence_length: The time sequence length of the data. num_sites: Number of PV sites in the input data. out_features: Number of output features. - value_dim: The number of features in each encoded sequence. Similar to the value + value_dim: The number of features in each encoded sequence. Similar to the value dimension in single- or multi-head attention. - value_dim: The number of features in each encoded sequence. Similar to the value + value_dim: The number of features in each encoded sequence. Similar to the value dimension in single- or multi-head attention. value_enc_resblocks: Number of residual blocks in the value-encoder sub-network. final_resblocks: Number of residual blocks in the final sub-network. """ super().__init__(sequence_length, num_sites, out_features) - + # Network used to encode each PV site sequence self._value_encoder = nn.Sequential( ResFCNet2( @@ -58,84 +57,85 @@ def __init__( dropout_frac=0, ), ) - - # The learned weighted average is stored in an embedding layer for ease of use + + # The learned weighted average is stored in an embedding layer for ease of use self._attention_network = nn.Sequential( nn.Embedding(318, num_sites), nn.Softmax(dim=1), ) - + # Network used to process weighted average self.output_network = ResFCNet2( - in_features=value_dim, - out_features=out_features, - fc_hidden_features=value_dim, - n_res_blocks=final_resblocks, - res_block_layers=2, - dropout_frac=0, + in_features=value_dim, + out_features=out_features, + fc_hidden_features=value_dim, + n_res_blocks=final_resblocks, + res_block_layers=2, + dropout_frac=0, ) - + def _calculate_attention(self, x): gsp_ids = x[BatchKey.gsp_id].squeeze().int() attention = self._attention_network(gsp_ids) return attention - + def _encode_value(self, x): # Shape: [batch size, sequence length, PV site] pv_site_seqs = x[BatchKey.pv].float() batch_size = pv_site_seqs.shape[0] - - pv_site_seqs = pv_site_seqs.swapaxes(1,2).flatten(0,1) - + + pv_site_seqs = pv_site_seqs.swapaxes(1, 2).flatten(0, 1) + x_seq_enc = self._value_encoder(pv_site_seqs) x_seq_out = x_seq_enc.unflatten(0, (batch_size, self.num_sites)) - return x_seq_out - + return x_seq_out + def forward(self, x): """Run model forward""" - # Output has shape: [batch size, num_sites, value_dim] + # Output has shape: [batch size, num_sites, value_dim] encodeded_seqs = self._encode_value(x) - + # Calculate learned averaging weights attn_avg_weights = self._calculate_attention(x) - + # Take weighted average across num_sites - value_weighted_avg = (encodeded_seqs*attn_avg_weights.unsqueeze(-1)).sum(dim=1) - + value_weighted_avg = (encodeded_seqs * attn_avg_weights.unsqueeze(-1)).sum(dim=1) + # Put through final processing layers x_out = self.output_network(value_weighted_avg) - + return x_out - + class SingleAttentionNetwork(AbstractPVSitesEncoder): """A simple attention-based model with a single multihead attention layer - + For the attention layer the query is based on the target GSP alone, the key is based on the PV ID and the recent PV data, the value is based on the recent PV data. - + """ + def __init__( - self, - sequence_length: int, - num_sites: int, + self, + sequence_length: int, + num_sites: int, out_features: int, - kdim: int = 10, - num_heads: int = 2, + kdim: int = 10, + num_heads: int = 2, pv_id_embed_dim: int = 10, n_kv_res_blocks: int = 2, kv_res_block_layers: int = 2, use_pv_id_in_value: bool = False, - ): + ): """A simple attention-based model with a single multihead attention layer - + Args: sequence_length: The time sequence length of the data. num_sites: Number of PV sites in the input data. - out_features: Number of output features. In this network this is also the the value + out_features: Number of output features. In this network this is also the the value dimension in the multi-head attention layer. kdim: The dimensions used in both the keys and queries. - num_heads: Number of parallel attention heads. Note that `out_features` will be split + num_heads: Number of parallel attention heads. Note that `out_features` will be split across `num_heads` so `out_features` must be a multiple of `num_heads`. pv_id_embed_dim: The dimension of the PV ID embedding used in calculating the key. n_kv_res_blocks: Number of residual blocks to use in the key and value encoders. @@ -143,21 +143,21 @@ def __init__( the key and value encoders. use_pv_id_in_value: Whether to use the PV ID in network used to produce the value for the attention layer. - + """ super().__init__(sequence_length, num_sites, out_features) - + self.gsp_id_embedding = nn.Embedding(318, kdim) self.pv_id_embedding = nn.Embedding(num_sites, pv_id_embed_dim) self._pv_ids = nn.parameter.Parameter(torch.arange(num_sites), requires_grad=False) self.use_pv_id_in_value = use_pv_id_in_value - + if use_pv_id_in_value: self.value_pv_id_embedding = nn.Embedding(num_sites, pv_id_embed_dim) - + self._value_encoder = nn.Sequential( ResFCNet2( - in_features=sequence_length+int(use_pv_id_in_value)*pv_id_embed_dim, + in_features=sequence_length + int(use_pv_id_in_value) * pv_id_embed_dim, out_features=out_features, fc_hidden_features=sequence_length, n_res_blocks=n_kv_res_blocks, @@ -166,88 +166,83 @@ def __init__( ), nn.Linear(out_features, kdim), ) - - self._key_encoder = nn.Sequential( + + self._key_encoder = nn.Sequential( ResFCNet2( - in_features=pv_id_embed_dim+sequence_length, + in_features=pv_id_embed_dim + sequence_length, out_features=kdim, - fc_hidden_features=pv_id_embed_dim+sequence_length, + fc_hidden_features=pv_id_embed_dim + sequence_length, n_res_blocks=n_kv_res_blocks, res_block_layers=kv_res_block_layers, dropout_frac=0, ), - nn.Linear(kdim, kdim) + nn.Linear(kdim, kdim), ) - + self.multihead_attn = nn.MultiheadAttention( - embed_dim=kdim, + embed_dim=kdim, num_heads=num_heads, batch_first=True, vdim=out_features, ) - - + def _encode_query(self, x): gsp_ids = x[BatchKey.gsp_id].squeeze().int() query = self.gsp_id_embedding(gsp_ids).unsqueeze(1) return query - + def _encode_key(self, x): # Shape: [batch size, sequence length, PV site] pv_site_seqs = x[BatchKey.pv].float() batch_size = pv_site_seqs.shape[0] - + # PV ID embeddings are the same for each sample pv_id_embed = torch.tile(self.pv_id_embedding(self._pv_ids), (batch_size, 1, 1)) - + # Each concated (PV sequence, PV ID embedding) is processed with encoder - x_seq_in = torch.cat((pv_site_seqs.swapaxes(1,2), pv_id_embed), dim=2).flatten(0,1) + x_seq_in = torch.cat((pv_site_seqs.swapaxes(1, 2), pv_id_embed), dim=2).flatten(0, 1) key = self._key_encoder(x_seq_in) - + # Reshape to [batch size, PV site, kdim] key = key.unflatten(0, (batch_size, self.num_sites)) return key - + def _encode_value(self, x): # Shape: [batch size, sequence length, PV site] pv_site_seqs = x[BatchKey.pv].float() batch_size = pv_site_seqs.shape[0] - + if self.use_pv_id_in_value: # PV ID embeddings are the same for each sample pv_id_embed = torch.tile(self.value_pv_id_embedding(self._pv_ids), (batch_size, 1, 1)) # Each concated (PV sequence, PV ID embedding) is processed with encoder - x_seq_in = torch.cat((pv_site_seqs.swapaxes(1,2), pv_id_embed), dim=2).flatten(0,1) + x_seq_in = torch.cat((pv_site_seqs.swapaxes(1, 2), pv_id_embed), dim=2).flatten(0, 1) else: - # Encode each PV sequence independently - x_seq_in = pv_site_seqs.swapaxes(1,2).flatten(0,1) - + # Encode each PV sequence independently + x_seq_in = pv_site_seqs.swapaxes(1, 2).flatten(0, 1) + value = self._value_encoder(x_seq_in) - + # Reshape to [batch size, PV site, vdim] value = value.unflatten(0, (batch_size, self.num_sites)) return value - + def _attention_forward(self, x, average_attn_weights=True): - query = self._encode_query(x) key = self._encode_key(x) value = self._encode_value(x) attn_output, attn_weights = self.multihead_attn( - query, - key, - value, - average_attn_weights=average_attn_weights + query, key, value, average_attn_weights=average_attn_weights ) - - return attn_output, attn_weights - + + return attn_output, attn_weights + def forward(self, x): """Run model forward""" attn_output, attn_output_weights = self._attention_forward(x) - + # Reshape from [batch_size, 1, vdim] to [batch_size, vdim] x_out = attn_output.squeeze() - - return x_out \ No newline at end of file + + return x_out diff --git a/pvnet/optimizers.py b/pvnet/optimizers.py index 92d6fa40..55aa5493 100644 --- a/pvnet/optimizers.py +++ b/pvnet/optimizers.py @@ -50,41 +50,41 @@ def __call__(self, model): """Return optimizer""" return torch.optim.AdamW(model.parameters(), lr=self.lr, **self.kwargs) - + def find_submodule_parameters(model, search_modules): if isinstance(model, search_modules): return model.parameters() - + children = list(model.children()) - if len(children)==0: + if len(children) == 0: return [] else: params = [] for c in children: params += find_submodule_parameters(c, search_modules) return params - - + + def find_other_than_submodule_parameters(model, ignore_modules): if isinstance(model, ignore_modules): return [] - + children = list(model.children()) - if len(children)==0: + if len(children) == 0: return model.parameters() else: params = [] for c in children: params += find_other_than_submodule_parameters(c, ignore_modules) return params - - + + class EmbAdamWReduceLROnPlateau(AbstractOptimizer): """AdamW optimizer and reduce on plateau scheduler""" - def __init__(self, lr=0.0005, weight_decay=0.01, patience=3, factor=0.5, threshold=2e-4, - **opt_kwargs - ): + def __init__( + self, lr=0.0005, weight_decay=0.01, patience=3, factor=0.5, threshold=2e-4, **opt_kwargs + ): """AdamW optimizer and reduce on plateau scheduler""" self.lr = lr self.weight_decay = weight_decay @@ -92,13 +92,12 @@ def __init__(self, lr=0.0005, weight_decay=0.01, patience=3, factor=0.5, thresho self.factor = factor self.threshold = threshold self.opt_kwargs = opt_kwargs - def __call__(self, model): """Return optimizer""" - search_modules = (torch.nn.Embedding, ) - + search_modules = (torch.nn.Embedding,) + no_decay = find_submodule_parameters(model, search_modules) decay = find_other_than_submodule_parameters(model, search_modules) @@ -107,7 +106,7 @@ def __call__(self, model): {"params": no_decay, "weight_decay": 0.0}, ] opt = torch.optim.AdamW(optim_groups, lr=self.lr, **self.opt_kwargs) - + sch = torch.optim.lr_scheduler.ReduceLROnPlateau( opt, factor=self.factor, @@ -115,7 +114,7 @@ def __call__(self, model): threshold=self.threshold, ) sch = { - "scheduler": sch, + "scheduler": sch, "monitor": "quantile_loss/val" if model.use_quantile_regression else "MAE/val", } return [opt], [sch] @@ -124,9 +123,9 @@ def __call__(self, model): class AdamWReduceLROnPlateau(AbstractOptimizer): """AdamW optimizer and reduce on plateau scheduler""" - def __init__(self, lr=0.0005, patience=3, factor=0.5, threshold=2e-4, step_freq=None, - **opt_kwargs - ): + def __init__( + self, lr=0.0005, patience=3, factor=0.5, threshold=2e-4, step_freq=None, **opt_kwargs + ): """AdamW optimizer and reduce on plateau scheduler""" self._lr = lr self.patience = patience @@ -134,31 +133,28 @@ def __init__(self, lr=0.0005, patience=3, factor=0.5, threshold=2e-4, step_freq= self.threshold = threshold self.step_freq = step_freq self.opt_kwargs = opt_kwargs - + def _call_multi(self, model): - - remaining_params = {k:p for k,p in model.named_parameters()} - + remaining_params = {k: p for k, p in model.named_parameters()} + group_args = [] - + for key in self._lr.keys(): - if key=="default": + if key == "default": continue - + submodule_params = [] for param_name in list(remaining_params.keys()): if param_name.startswith(key): submodule_params += [remaining_params.pop(param_name)] - + group_args += [{"params": submodule_params, "lr": self._lr[key]}] remaining_params = [p for k, p in remaining_params.items()] group_args += [{"params": remaining_params}] - + opt = torch.optim.AdamW( - group_args, - lr=self._lr["default"] if model.lr is None else model.lr, - **self.opt_kwargs + group_args, lr=self._lr["default"] if model.lr is None else model.lr, **self.opt_kwargs ) sch = { "scheduler": torch.optim.lr_scheduler.ReduceLROnPlateau( @@ -166,12 +162,11 @@ def _call_multi(self, model): factor=self.factor, patience=self.patience, threshold=self.threshold, - ), + ), "monitor": "quantile_loss/val" if model.use_quantile_regression else "MAE/val", } return [opt], [sch] - def __call__(self, model): """Return optimizer""" @@ -188,7 +183,7 @@ def __call__(self, model): threshold=self.threshold, ) sch = { - "scheduler": sch, + "scheduler": sch, "monitor": "quantile_loss/val" if model.use_quantile_regression else "MAE/val", } return [opt], [sch] From 73d2f2d6da0792df9819fa81f305e62e1472f0cf Mon Sep 17 00:00:00 2001 From: James Fulton Date: Tue, 24 Oct 2023 10:47:19 +0000 Subject: [PATCH 09/25] drop id CSV --- .../datamodule/configuration/pv_ml_ids.csv | 357 ------------------ 1 file changed, 357 deletions(-) delete mode 100644 configs/datamodule/configuration/pv_ml_ids.csv diff --git a/configs/datamodule/configuration/pv_ml_ids.csv b/configs/datamodule/configuration/pv_ml_ids.csv deleted file mode 100644 index 354f1659..00000000 --- a/configs/datamodule/configuration/pv_ml_ids.csv +++ /dev/null @@ -1,357 +0,0 @@ -,ml_id -0,154 -1,155 -2,156 -3,158 -4,159 -5,160 -6,162 -7,164 -8,165 -9,166 -10,167 -11,168 -12,169 -13,171 -14,173 -15,177 -16,178 -17,179 -18,181 -19,182 -20,185 -21,186 -22,187 -23,188 -24,189 -25,190 -26,191 -27,192 -28,193 -29,197 -30,198 -31,199 -32,200 -33,202 -34,204 -35,205 -36,206 -37,208 -38,209 -39,211 -40,214 -41,215 -42,216 -43,217 -44,218 -45,219 -46,220 -47,221 -48,225 -49,229 -50,230 -51,232 -52,233 -53,234 -54,236 -55,237 -56,242 -57,243 -58,245 -59,251 -60,252 -61,254 -62,255 -63,256 -64,257 -65,258 -66,260 -67,261 -68,262 -69,265 -70,267 -71,268 -72,272 -73,273 -74,275 -75,276 -76,277 -77,280 -78,281 -79,282 -80,283 -81,287 -82,289 -83,291 -84,292 -85,293 -86,294 -87,295 -88,296 -89,297 -90,298 -91,301 -92,302 -93,303 -94,304 -95,306 -96,307 -97,309 -98,310 -99,311 -100,317 -101,318 -102,319 -103,320 -104,321 -105,322 -106,323 -107,324 -108,325 -109,326 -110,329 -111,332 -112,333 -113,335 -114,336 -115,338 -116,340 -117,342 -118,344 -119,345 -120,346 -121,348 -122,349 -123,352 -124,354 -125,355 -126,356 -127,357 -128,360 -129,362 -130,363 -131,368 -132,369 -133,370 -134,371 -135,372 -136,374 -137,375 -138,376 -139,378 -140,380 -141,382 -142,384 -143,385 -144,387 -145,388 -146,390 -147,391 -148,393 -149,396 -150,397 -151,398 -152,399 -153,400 -154,401 -155,403 -156,404 -157,405 -158,406 -159,407 -160,409 -161,411 -162,412 -163,413 -164,414 -165,415 -166,416 -167,417 -168,418 -169,419 -170,420 -171,421 -172,422 -173,423 -174,424 -175,425 -176,426 -177,427 -178,429 -179,431 -180,435 -181,437 -182,438 -183,440 -184,441 -185,444 -186,447 -187,449 -188,450 -189,451 -190,453 -191,456 -192,457 -193,458 -194,459 -195,464 -196,465 -197,466 -198,467 -199,468 -200,470 -201,471 -202,473 -203,474 -204,476 -205,477 -206,479 -207,480 -208,481 -209,482 -210,485 -211,486 -212,488 -213,490 -214,491 -215,492 -216,493 -217,496 -218,498 -219,501 -220,503 -221,506 -222,507 -223,508 -224,509 -225,510 -226,511 -227,512 -228,513 -229,515 -230,516 -231,517 -232,519 -233,520 -234,521 -235,522 -236,524 -237,526 -238,527 -239,528 -240,531 -241,532 -242,536 -243,537 -244,538 -245,540 -246,541 -247,542 -248,543 -249,544 -250,545 -251,549 -252,550 -253,551 -254,552 -255,553 -256,554 -257,556 -258,557 -259,560 -260,561 -261,563 -262,566 -263,568 -264,571 -265,572 -266,575 -267,576 -268,577 -269,579 -270,580 -271,581 -272,582 -273,584 -274,585 -275,588 -276,590 -277,594 -278,595 -279,597 -280,600 -281,602 -282,603 -283,604 -284,606 -285,611 -286,613 -287,614 -288,616 -289,618 -290,620 -291,622 -292,623 -293,624 -294,625 -295,626 -296,628 -297,629 -298,630 -299,631 -300,636 -301,637 -302,638 -303,640 -304,641 -305,642 -306,644 -307,645 -308,646 -309,650 -310,651 -311,652 -312,653 -313,654 -314,655 -315,657 -316,660 -317,661 -318,662 -319,663 -320,665 -321,666 -322,667 -323,668 -324,670 -325,675 -326,676 -327,679 -328,681 -329,683 -330,684 -331,685 -332,687 -333,688 -334,696 -335,698 -336,701 -337,702 -338,703 -339,704 -340,706 -341,710 -342,722 -343,723 -344,724 -345,725 -346,727 -347,728 -348,729 -349,730 -350,732 -351,733 -352,734 -353,735 -354,736 -355,737 From 05bf8a185481fe4eb54fe0d620616716706ea8ca Mon Sep 17 00:00:00 2001 From: James Fulton Date: Tue, 24 Oct 2023 10:56:16 +0000 Subject: [PATCH 10/25] linting --- pvnet/models/multimodal/site_encoders/encoders.py | 3 +-- pvnet/optimizers.py | 12 ++++++++++++ 2 files changed, 13 insertions(+), 2 deletions(-) diff --git a/pvnet/models/multimodal/site_encoders/encoders.py b/pvnet/models/multimodal/site_encoders/encoders.py index 4821968f..90588039 100644 --- a/pvnet/models/multimodal/site_encoders/encoders.py +++ b/pvnet/models/multimodal/site_encoders/encoders.py @@ -11,8 +11,7 @@ class SimpleLearnedAggregator(AbstractPVSitesEncoder): - """A simple model which learns a different weighted-average across all of the PV sites for each - GSP. + """A simple model which learns a different weighted-average across all PV sites for each GSP. Each sequence from each site is independently encodeded through some dense layers wih skip- connections, then the encoded form of each sequence is aggregated through a learned weighted-sum diff --git a/pvnet/optimizers.py b/pvnet/optimizers.py index 55aa5493..fda5c0fb 100644 --- a/pvnet/optimizers.py +++ b/pvnet/optimizers.py @@ -52,6 +52,12 @@ def __call__(self, model): def find_submodule_parameters(model, search_modules): + """Finds all parameters within given submodule types + + Args: + model: torch Module to search through + search_modules: List of submodule types to search for + """ if isinstance(model, search_modules): return model.parameters() @@ -66,6 +72,12 @@ def find_submodule_parameters(model, search_modules): def find_other_than_submodule_parameters(model, ignore_modules): + """Finds all parameters not with given submodule types + + Args: + model: torch Module to search through + search_modules: List of submodule types to ignore + """ if isinstance(model, ignore_modules): return [] From 784a47b7b921d6e591746ad0d9aa879cb38e57d5 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 24 Oct 2023 10:57:38 +0000 Subject: [PATCH 11/25] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- pvnet/optimizers.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pvnet/optimizers.py b/pvnet/optimizers.py index fda5c0fb..983a9d2e 100644 --- a/pvnet/optimizers.py +++ b/pvnet/optimizers.py @@ -53,7 +53,7 @@ def __call__(self, model): def find_submodule_parameters(model, search_modules): """Finds all parameters within given submodule types - + Args: model: torch Module to search through search_modules: List of submodule types to search for @@ -73,7 +73,7 @@ def find_submodule_parameters(model, search_modules): def find_other_than_submodule_parameters(model, ignore_modules): """Finds all parameters not with given submodule types - + Args: model: torch Module to search through search_modules: List of submodule types to ignore From 02336f04c0229da64f7e4e5caed5bce926ae64f0 Mon Sep 17 00:00:00 2001 From: James Fulton Date: Tue, 24 Oct 2023 11:00:58 +0000 Subject: [PATCH 12/25] doc mistake --- pvnet/optimizers.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pvnet/optimizers.py b/pvnet/optimizers.py index fda5c0fb..37f0e086 100644 --- a/pvnet/optimizers.py +++ b/pvnet/optimizers.py @@ -76,7 +76,7 @@ def find_other_than_submodule_parameters(model, ignore_modules): Args: model: torch Module to search through - search_modules: List of submodule types to ignore + ignore_modules: List of submodule types to ignore """ if isinstance(model, ignore_modules): return [] From 69c1884582ebf8c111bf711526d673f812af9543 Mon Sep 17 00:00:00 2001 From: James Fulton Date: Tue, 24 Oct 2023 11:02:51 +0000 Subject: [PATCH 13/25] update reqs --- requirements.txt | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 8024dae8..e6b5c6ea 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -ocf_datapipes==1.2.44 +ocf_datapipes==2.0.4 nowcasting_utils ocf_ml_metrics numpy @@ -24,3 +24,4 @@ omegaconf hydra-core python-dotenv hydra-optuna-sweeper +rich \ No newline at end of file From e052bdc0f003e76b24243efe3e507c1ba2cd5313 Mon Sep 17 00:00:00 2001 From: James Fulton Date: Tue, 24 Oct 2023 12:52:03 +0000 Subject: [PATCH 14/25] update tests and fix attention model --- .../multimodal/site_encoders/encoders.py | 29 +++---- tests/conftest.py | 81 ++++++++++++++---- tests/data/sample_batches/train/000000.pt | Bin 3057047 -> 3999223 bytes tests/data/sample_batches/train/000001.pt | Bin 3057047 -> 3999223 bytes .../multimodal/site_encoders/test_encoders.py | 41 +++++++++ .../multimodal/test_deep_supervision.py | 4 +- tests/models/multimodal/test_nwp_weighting.py | 4 +- .../multimodal/test_weather_residual.py | 4 +- 8 files changed, 125 insertions(+), 38 deletions(-) create mode 100644 tests/models/multimodal/site_encoders/test_encoders.py diff --git a/pvnet/models/multimodal/site_encoders/encoders.py b/pvnet/models/multimodal/site_encoders/encoders.py index 90588039..7ac7b686 100644 --- a/pvnet/models/multimodal/site_encoders/encoders.py +++ b/pvnet/models/multimodal/site_encoders/encoders.py @@ -120,8 +120,8 @@ def __init__( num_sites: int, out_features: int, kdim: int = 10, - num_heads: int = 2, pv_id_embed_dim: int = 10, + num_heads: int = 2, n_kv_res_blocks: int = 2, kv_res_block_layers: int = 2, use_pv_id_in_value: bool = False, @@ -131,22 +131,22 @@ def __init__( Args: sequence_length: The time sequence length of the data. num_sites: Number of PV sites in the input data. - out_features: Number of output features. In this network this is also the the value - dimension in the multi-head attention layer. - kdim: The dimensions used in both the keys and queries. + out_features: Number of output features. In this network this is also the embed and + value dimension in the multi-head attention layer. + kdim: The dimensions used the keys. + pv_id_embed_dim: Number of dimensiosn used in the PD ID embedding layer(s). num_heads: Number of parallel attention heads. Note that `out_features` will be split across `num_heads` so `out_features` must be a multiple of `num_heads`. - pv_id_embed_dim: The dimension of the PV ID embedding used in calculating the key. n_kv_res_blocks: Number of residual blocks to use in the key and value encoders. kv_res_block_layers: Number of fully-connected layers used in each residual block within the key and value encoders. - use_pv_id_in_value: Whether to use the PV ID in network used to produce the value for - the attention layer. + use_pv_id_in_value: Whether to use a PV ID embedding in network used to produce the + value for the attention layer. """ super().__init__(sequence_length, num_sites, out_features) - self.gsp_id_embedding = nn.Embedding(318, kdim) + self.gsp_id_embedding = nn.Embedding(318, out_features) self.pv_id_embedding = nn.Embedding(num_sites, pv_id_embed_dim) self._pv_ids = nn.parameter.Parameter(torch.arange(num_sites), requires_grad=False) self.use_pv_id_in_value = use_pv_id_in_value @@ -163,26 +163,25 @@ def __init__( res_block_layers=kv_res_block_layers, dropout_frac=0, ), - nn.Linear(out_features, kdim), ) self._key_encoder = nn.Sequential( ResFCNet2( - in_features=pv_id_embed_dim + sequence_length, + in_features=sequence_length + pv_id_embed_dim, out_features=kdim, fc_hidden_features=pv_id_embed_dim + sequence_length, n_res_blocks=n_kv_res_blocks, res_block_layers=kv_res_block_layers, dropout_frac=0, ), - nn.Linear(kdim, kdim), ) self.multihead_attn = nn.MultiheadAttention( - embed_dim=kdim, + embed_dim=out_features, + kdim=kdim, + vdim=out_features, num_heads=num_heads, batch_first=True, - vdim=out_features, ) def _encode_query(self, x): @@ -233,8 +232,8 @@ def _attention_forward(self, x, average_attn_weights=True): attn_output, attn_weights = self.multihead_attn( query, key, value, average_attn_weights=average_attn_weights - ) - + ) + return attn_output, attn_weights def forward(self, x): diff --git a/tests/conftest.py b/tests/conftest.py index 1adf90cd..721b1bd3 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -5,6 +5,7 @@ import numpy as np import xarray as xr import torch +import hydra from ocf_datapipes.utils.consts import BatchKey from datetime import timedelta @@ -12,6 +13,9 @@ import pvnet from pvnet.data.datamodule import DataModule +import pvnet.models.multimodal.encoders.encoders3d +import pvnet.models.multimodal.linear_networks.networks + xr.set_options(keep_attrs=True) @@ -114,6 +118,12 @@ def sample_satellite_batch(sample_batch): return torch.swapaxes(sat_image, 1, 2) +@pytest.fixture() +def sample_pv_batch(sample_batch): + pv_data = sample_batch[BatchKey.pv] + return pv_data + + @pytest.fixture() def model_minutes_kwargs(): kwargs = dict( @@ -134,37 +144,74 @@ def encoder_model_kwargs(): ) return kwargs +@pytest.fixture() +def site_encoder_model_kwargs(): + # Used to test site encoder model on PV data + kwargs = dict( + sequence_length=180 // 5 +1, + num_sites=349, + out_features=128, + ) + return kwargs @pytest.fixture() def multimodal_model_kwargs(model_minutes_kwargs): + kwargs = dict( - image_encoder=pvnet.models.multimodal.encoders.encoders3d.DefaultPVNet, - encoder_out_features=128, - encoder_kwargs=dict( - number_of_conv3d_layers=6, - conv3d_channels=32, + + sat_encoder=dict( + _target_=pvnet.models.multimodal.encoders.encoders3d.DefaultPVNet, + _partial_=True, + in_channels=11, + out_features=128, + number_of_conv3d_layers=6, + conv3d_channels=32, + image_size_pixels=24, + ), + + nwp_encoder=dict( + _target_=pvnet.models.multimodal.encoders.encoders3d.DefaultPVNet, + _partial_=True, + in_channels=2, + out_features=128, + number_of_conv3d_layers=6, + conv3d_channels=32, + image_size_pixels=24, ), - include_sat=True, - include_nwp=True, + add_image_embedding_channel=True, - sat_image_size_pixels=24, - nwp_image_size_pixels=24, - number_sat_channels=11, - number_nwp_channels=2, - output_network=pvnet.models.multimodal.linear_networks.networks.ResFCNet2, - output_network_kwargs=dict( - fc_hidden_features=128, - n_res_blocks=6, - res_block_layers=2, - dropout_frac=0.0, + + pv_encoder=dict( + _target_=pvnet.models.multimodal.site_encoders.encoders.SingleAttentionNetwork, + _partial_=True, + num_sites=349, + out_features=40, + num_heads=4, + kdim=40, + pv_id_embed_dim=20, + ), + + output_network=dict( + _target_=pvnet.models.multimodal.linear_networks.networks.ResFCNet2, + _partial_=True, + fc_hidden_features=128, + n_res_blocks=6, + res_block_layers=2, + dropout_frac=0.0, ), + embedding_dim=16, include_sun=True, include_gsp_yield_history=True, sat_history_minutes=90, nwp_history_minutes=120, nwp_forecast_minutes=480, + pv_history_minutes=180, min_sat_delay_minutes=30, + ) + + kwargs = hydra.utils.instantiate(kwargs) + kwargs.update(model_minutes_kwargs) return kwargs diff --git a/tests/data/sample_batches/train/000000.pt b/tests/data/sample_batches/train/000000.pt index 9ba57e729b7dff80db15df3e05afdd32f308943c..58f410f5016bc07d74b8df4bdc4e4ce4e0d94c97 100644 GIT binary patch literal 3999223 zcmaI91$b1)_y3>ZBxr(bDemssI~0lrU7$D=0u+ZE0tH$M1QOic-Mwf@b`~kcwUk0} zDNww)mjAvc9KN^t_w(an2&Yn5bFf?HzKfh$j{QiIcQu_t_l`#K`cWfWg zzF42`J!(`mAm)9Z&V?+NV?BV*Mhz_UKzItaq=z5q$%ywvPzw z6545CfbAceD#}jawiAX$^bQZ}QmmbME}&g_rw;wP_UPCyqEoNFy~Eq}FBM=XDo~(# zQXflweNVRkrjwmGu1P?R9=+Q~G>*F%U?*wr?|Y_%owRvEUmez`pPj6Z-#CB22s?SB zP`?79$wC7|6NF|8%@kn=6sR|}WWAw@>Q&)_e}o-4IxMVrj~<=EBD(tc3}_JEu~T@b zj$d{SiwLk&6m;7uBkWXeJ9QWT(9}_O8n>M`G=c9pO_Uwvwu8I)`0FA(ec8KThaR0i zF_q3RRnku1C0S_m2s?w16BvoMGy1skkG3<7?gD=12s?}0&g#QVA7y8A+u6f<_3PPZ 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