From 56c9a8170804da939417eba6580fe3f629fc06e8 Mon Sep 17 00:00:00 2001 From: isaacmg Date: Wed, 22 May 2024 13:44:12 -0400 Subject: [PATCH 01/38] begin adding crossvivit --- flood_forecast/multi_models/crossvivit.py | 481 ++++++++++++++++++ .../transformer_xl/data_embedding.py | 51 ++ 2 files changed, 532 insertions(+) create mode 100644 flood_forecast/multi_models/crossvivit.py diff --git a/flood_forecast/multi_models/crossvivit.py b/flood_forecast/multi_models/crossvivit.py new file mode 100644 index 000000000..bc8138d66 --- /dev/null +++ b/flood_forecast/multi_models/crossvivit.py @@ -0,0 +1,481 @@ +""" +Adapted from: https://github.com/lucidrains/vit-pytorch/blob/main/vit_pytorch/rvt.py +""" +import random +from typing import List, Tuple, Union +import torch +from einops import rearrange, repeat +from einops.layers.torch import Rearrange +from torch import einsum, nn +from src.models.modules.attention_modules import * +from flood_forecast.transformer_xl.data_embedding import PositionalEncoding2D, AxialRotaryEmbedding + + +class Attention(nn.Module): + def __init__(self, dim, heads=8, dim_head=64, dropout=0.0): + super().__init__() + inner_dim = dim_head * heads + project_out = not (heads == 1 and dim_head == dim) + + self.heads = heads + self.scale = dim_head**-0.5 + + self.to_qkv = nn.Linear(dim, inner_dim * 3, bias=False) + + self.to_out = ( + nn.Sequential(nn.Linear(inner_dim, dim), nn.Dropout(dropout)) + if project_out + else nn.Identity() + ) + + def forward(self, x): + b, n, _, h = *x.shape, self.heads + qkv = self.to_qkv(x).chunk(3, dim=-1) + q, k, v = map(lambda t: rearrange(t, "b n (h d) -> b h n d", h=h), qkv) + + dots = einsum("b h i d, b h j d -> b h i j", q, k) * self.scale + + attn = dots.softmax(dim=-1) + + out = einsum("b h i j, b h j d -> b h i d", attn, v) + out = rearrange(out, "b h n d -> b n (h d)") + out = self.to_out(out) + return out + + +class Transformer(nn.Module): + def __init__(self, dim, num_frames, depth, heads, dim_head, mlp_dim, dropout=0.0): + super().__init__() + self.layers = nn.ModuleList([]) + self.norm = nn.LayerNorm(dim) + self.pos_embedding = nn.Parameter(torch.randn(1, num_frames, dim)) + for _ in range(depth): + self.layers.append( + nn.ModuleList( + [ + PreNorm( + dim, + Attention( + dim, heads=heads, dim_head=dim_head, dropout=dropout + ), + ), + PreNorm(dim, FeedForward(dim, mlp_dim, dropout=dropout)), + ] + ) + ) + + def forward(self, x): + """ + Args: + x: Input tensor of shape [B, T, C] + """ + x += self.pos_embedding + for attn, ff in self.layers: + x = attn(x) + x + x = ff(x) + x + return self.norm(x) + + +class VisionTransformer(nn.Module): + def __init__( + self, + dim: int, + depth: int, + heads: int, + dim_head: int, + mlp_dim: int, + image_size: Union[List[int], Tuple[int], int], + dropout: float = 0.0, + use_rotary: bool = True, + use_glu: bool = True, + ): + super().__init__() + self.image_size = image_size + + self.blocks = nn.ModuleList([]) + + for _ in range(depth): + self.blocks.append( + nn.ModuleList( + [ + PreNorm( + dim, + SelfAttention( + dim, + heads=heads, + dim_head=dim_head, + dropout=dropout, + use_rotary=use_rotary, + ), + ), + PreNorm( + dim, + FeedForward(dim, mlp_dim, dropout=dropout, use_glu=use_glu), + ), + ] + ) + ) + + def forward( + self, + src: torch.Tensor, + src_pos_emb: torch.Tensor, + ): + """ + Performs the following computation in each layer: + 1. Self-Attention on the source sequence + 2. FFN on the source sequence + Args: + src: Source sequence of shape [B, N, D] + src_pos_emb: Positional embedding of source sequence's tokens of shape [B, N, D] + """ + + attention_scores = {} + for i in range(len(self.blocks)): + sattn, sff = self.blocks[i] + + out, sattn_scores = sattn(src, pos_emb=src_pos_emb) + attention_scores["self_attention"] = sattn_scores + src = out + src + src = sff(src) + src + + return src, attention_scores + + +class CrossTransformer(nn.Module): + def __init__( + self, + dim: int, + depth: int, + heads: int, + dim_head: int, + mlp_dim: int, + image_size: Union[List[int], Tuple[int], int], + dropout: float = 0.0, + use_rotary: bool = True, + use_glu: bool = True, + ): + super().__init__() + self.image_size = image_size + self.cross_layers = nn.ModuleList([]) + + for _ in range(depth): + self.cross_layers.append( + nn.ModuleList( + [ + CrossPreNorm( + dim, + CrossAttention( + dim, + heads=heads, + dim_head=dim_head, + dropout=dropout, + use_rotary=use_rotary, + ), + ), + PreNorm( + dim, + FeedForward(dim, mlp_dim, dropout=dropout, use_glu=use_glu), + ), + ] + ) + ) + + def forward( + self, + src: torch.Tensor, + tgt: torch.Tensor, + src_pos_emb: torch.Tensor, + tgt_pos_emb: torch.Tensor, + ): + """ + Performs the following computation in each layer: + 1. Self-Attention on the source sequence + 2. FFN on the source sequence + 3. Cross-Attention between target and source sequence + 4. FFN on the target sequence + Args: + src: Source sequence of shape [B, N, D] + tgt: Target sequence of shape [B, M, D] + src_pos_emb: Positional embedding of source sequence's tokens of shape [B, N, D] + tgt_pos_emb: Positional embedding of target sequence's tokens of shape [B, M, D] + """ + + attention_scores = {} + for i in range(len(self.cross_layers)): + cattn, cff = self.cross_layers[i] + out, cattn_scores = cattn(src, src_pos_emb, tgt, tgt_pos_emb) + attention_scores["cross_attention"] = cattn_scores + tgt = out + tgt + tgt = cff(tgt) + tgt + + return tgt, attention_scores + + +class RoCrossViViT(nn.Module): + def __init__( + self, + image_size: Union[List[int], Tuple[int]], + patch_size: Union[List[int], Tuple[int]], + time_coords_encoder: nn.Module, + dim: int = 128, + depth: int = 4, + heads: int = 4, + mlp_ratio: int = 4, + ctx_channels: int = 3, + ts_channels: int = 3, + ts_length: int = 48, + out_dim: int = 1, + dim_head: int = 64, + dropout: float = 0.0, + freq_type: str = "lucidrains", + pe_type: str = "rope", + num_mlp_heads: int = 1, + use_glu: bool = True, + ctx_masking_ratio: float = 0.9, + ts_masking_ratio: float = 0.9, + decoder_dim: int = 128, + decoder_depth: int = 4, + decoder_heads: int = 6, + decoder_dim_head: int = 128, + **kwargs, + ): + super().__init__() + assert ( + ctx_masking_ratio >= 0 and ctx_masking_ratio < 1 + ), "ctx_masking_ratio must be in [0,1)" + assert pe_type in [ + "rope", + "sine", + "learned", + None, + ], f"pe_type must be 'rope', 'sine', 'learned' or None but you provided {pe_type}" + self.time_coords_encoder = time_coords_encoder + self.ctx_channels = ctx_channels + self.ts_channels = ts_channels + if hasattr(self.time_coords_encoder, "dim"): + self.ctx_channels += self.time_coords_encoder.dim + self.ts_channels += self.time_coords_encoder.dim + + self.image_size = image_size + self.patch_size = patch_size + self.ctx_masking_ratio = ctx_masking_ratio + self.ts_masking_ratio = ts_masking_ratio + self.num_mlp_heads = num_mlp_heads + self.pe_type = pe_type + + for i in range(2): + ims = self.image_size[i] + ps = self.patch_size[i] + assert ( + ims % ps == 0 + ), "Image dimensions must be divisible by the patch size." + + patch_dim = self.ctx_channels * self.patch_size[0] * self.patch_size[1] + num_patches = (self.image_size[0] // self.patch_size[0]) * ( + self.image_size[1] // self.patch_size[1] + ) + + self.to_patch_embedding = nn.Sequential( + Rearrange( + "b c (h p1) (w p2) -> b (h w) (p1 p2 c)", + p1=self.patch_size[0], + p2=self.patch_size[1], + ), + nn.Linear(patch_dim, dim), + ) + self.enc_pos_emb = AxialRotaryEmbedding(dim_head, freq_type, **kwargs) + self.ts_embedding = nn.Linear(self.ts_channels, dim) + self.ctx_encoder = VisionTransformer( + dim, + depth, + heads, + dim_head, + dim * mlp_ratio, + image_size, + dropout, + pe_type == "rope", + use_glu, + ) + if pe_type == "learned": + self.pe_ctx = nn.Parameter(torch.randn(1, num_patches, dim)) + self.pe_ts = nn.Parameter(torch.randn(1, 1, dim)) + elif pe_type == "sine": + self.pe_ctx = PositionalEncoding2D(dim) + self.pe_ts = PositionalEncoding2D(dim) + self.mixer = CrossTransformer( + dim, + depth, + heads, + dim_head, + dim * mlp_ratio, + image_size, + dropout, + pe_type == "rope", + use_glu, + ) + self.ctx_mask_token = nn.Parameter(torch.zeros(1, 1, decoder_dim)) + + self.ts_encoder = Transformer( + dim, + ts_length, + depth, + heads, + dim_head, + dim * mlp_ratio, + dropout=dropout, + ) + self.ts_enctodec = nn.Linear(dim, decoder_dim) + self.temporal_transformer = Transformer( + decoder_dim, + ts_length, + decoder_depth, + decoder_heads, + decoder_dim_head, + decoder_dim * mlp_ratio, + dropout=dropout, + ) + self.ts_mask_token = nn.Parameter(torch.zeros(1, 1, dim)) + + self.mlp_heads = nn.ModuleList([]) + for i in range(num_mlp_heads): + self.mlp_heads.append( + nn.Sequential( + nn.LayerNorm(decoder_dim), + nn.Linear(decoder_dim, out_dim, bias=True), + nn.ReLU(), + ) + ) + + self.quantile_masker = nn.Sequential( + nn.Conv1d(decoder_dim, dim, kernel_size=3, padding=1), + nn.ReLU(), + nn.Conv1d(dim, dim, kernel_size=3, padding=1), + nn.ReLU(), + Rearrange( + "b c t -> b t c", + ), + nn.LayerNorm(dim), + nn.Linear(dim, num_mlp_heads), + ) + + def random_masking(self, x, mask_ratio): + """ + Perform per-sample random masking by per-sample shuffling. + Per-sample shuffling is done by argsort random noise. + x: [N, L, D], sequence + """ + N, L, D = x.shape # batch, length, dim + len_keep = int(L * (1 - mask_ratio)) + + noise = torch.rand(N, L, device=x.device) # noise in [0, 1] + + # sort noise for each sample + ids_shuffle = torch.argsort( + noise, dim=1 + ) # ascend: small is keep, large is remove + ids_restore = torch.argsort(ids_shuffle, dim=1) + + # keep the first subset + ids_keep = ids_shuffle[:, :len_keep] + x_masked = torch.gather(x, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, D)) + + # generate the binary mask: 0 is keep, 1 is remove + mask = torch.ones([N, L], device=x.device) + mask[:, :len_keep] = 0 + # unshuffle to get the binary mask + mask = torch.gather(mask, dim=1, index=ids_restore) + + return x_masked, mask, ids_restore, ids_keep + + def forward( + self, + ctx: torch.Tensor, + ctx_coords: torch.Tensor, + ts: torch.Tensor, + ts_coords: torch.Tensor, + time_coords: torch.Tensor, + mask: bool = True, + ): + """ + Args: + ctx (torch.Tensor): Context frames of shape [B, T, C, H, W] + ctx_coords (torch.Tensor): Coordinates of context frames of shape [B, 2, H, W] + ts (torch.Tensor): Station timeseries of shape [B, T, C] + ts_coords (torch.Tensor): Station coordinates of shape [B, 2, 1, 1] + time_coords (torch.Tensor): Time coordinates of shape [B, T, C, H, W] + mask (bool): Whether to mask or not. Useful for inference + Returns: + + """ + B, T, _, H, W = ctx.shape + time_coords = self.time_coords_encoder(time_coords) + + ctx = torch.cat([ctx, time_coords], axis=2) + ts = torch.cat([ts, time_coords[..., 0, 0]], axis=-1) + + ctx = rearrange(ctx, "b t c h w -> (b t) c h w") + + ctx_coords = repeat(ctx_coords, "b c h w -> (b t) c h w", t=T) + ts_coords = repeat(ts_coords, "b c h w -> (b t) c h w", t=T) + src_enc_pos_emb = self.enc_pos_emb(ctx_coords) + tgt_pos_emb = self.enc_pos_emb(ts_coords) + + ctx = self.to_patch_embedding(ctx) # BT, N, D + if self.pe_type == "learned": + ctx = ctx + self.pe_ctx + elif self.pe_type == "sine": + pe = self.pe_ctx(ctx_coords) + pe = rearrange(pe, "b h w c -> b (h w) c") + ctx = ctx + pe + if self.ctx_masking_ratio > 0 and mask: + p = self.ctx_masking_ratio * random.random() + ctx, _, ids_restore, ids_keep = self.random_masking(ctx, p) + src_enc_pos_emb = tuple( + torch.gather( + pos_emb, + dim=1, + index=ids_keep.unsqueeze(-1).repeat(1, 1, pos_emb.shape[-1]), + ) + for pos_emb in src_enc_pos_emb + ) + latent_ctx, self_attention_scores = self.ctx_encoder(ctx, src_enc_pos_emb) + + ts = self.ts_embedding(ts) + if self.ts_masking_ratio > 0 and mask: + p = self.ts_masking_ratio * random.random() + ts, _, ids_restore, ids_keep = self.random_masking(ts, p) + mask_tokens = self.ts_mask_token.repeat(ts.shape[0], T - ts.shape[1], 1) + ts = torch.cat([ts, mask_tokens], dim=1) + ts = torch.gather( + ts, dim=1, index=ids_restore.unsqueeze(-1).repeat(1, 1, ts.shape[2]) + ) + + latent_ts = self.ts_encoder(ts) + latent_ts = rearrange(latent_ts, "b t c -> (b t) c").unsqueeze(1) + + if self.pe_type == "learned": + latent_ts = latent_ts + self.pe_ts + elif self.pe_type == "sine": + pe = self.pe_ts(ts_coords) + pe = rearrange(pe, "b h w c -> b (h w) c") + latent_ts = latent_ts + pe + latent_ts, cross_attention_scores = self.mixer( + latent_ctx, latent_ts, src_enc_pos_emb, tgt_pos_emb + ) + latent_ts = latent_ts.squeeze(1) + latent_ts = self.ts_enctodec(rearrange(latent_ts, "(b t) c -> b t c", b=B)) + + y = self.temporal_transformer(latent_ts) + + # Handles the multiple MLP heads + outputs = [] + for i in range(self.num_mlp_heads): + mlp = self.mlp_heads[i] + output = mlp(y) + outputs.append(output) + outputs = torch.stack(outputs, dim=2) + + quantile_mask = self.quantile_masker(rearrange(y.detach(), "b t c -> b c t")) + + return (outputs, quantile_mask, self_attention_scores, cross_attention_scores) \ No newline at end of file diff --git a/flood_forecast/transformer_xl/data_embedding.py b/flood_forecast/transformer_xl/data_embedding.py index 4004be390..1406c5bdc 100644 --- a/flood_forecast/transformer_xl/data_embedding.py +++ b/flood_forecast/transformer_xl/data_embedding.py @@ -1,6 +1,57 @@ import torch import torch.nn as nn import math +from einops import rearrange, repeat +from math import pi + + +class AxialRotaryEmbedding(nn.Module): + def __init__(self, dim, freq_type="lucidrains", **kwargs): + super().__init__() + self.dim = dim + self.freq_type = freq_type + if freq_type == "lucidrains": + scales = torch.linspace(1.0, kwargs["max_freq"] / 2, self.dim // 4) + elif freq_type == "vaswani": + scales = 1 / ( + kwargs["base"] ** (torch.arange(0, self.dim, 4).float() / self.dim) + ) + else: + NotImplementedError( + f"Only 'lucidrains' and 'vaswani' frequencies are implemented, but you chose {freq_type}." + ) + self.register_buffer("scales", scales) + + def forward(self, coords: torch.Tensor): + """ + Assumes that coordinates do not change throughout the batches. + Args: + coords (torch.Tensor): Coordinates of shape [B, 2, H, W] + """ + seq_x = coords[:, 0, 0, :] + seq_x = seq_x.unsqueeze(-1) + seq_y = coords[:, 1, :, 0] + seq_y = seq_y.unsqueeze(-1) + + scales = self.scales[(*((None, None)), Ellipsis)] + scales = scales.to(coords) + + if self.freq_type == "lucidrains": + seq_x = seq_x * scales * pi + seq_y = seq_y * scales * pi + elif self.freq_type == "vaswani": + seq_x = seq_x * scales + seq_y = seq_y * scales + + x_sinu = repeat(seq_x, "b i d -> b i j d", j=seq_y.shape[1]) + y_sinu = repeat(seq_y, "b j d -> b i j d", i=seq_x.shape[1]) + + sin = torch.cat((x_sinu.sin(), y_sinu.sin()), dim=-1) + cos = torch.cat((x_sinu.cos(), y_sinu.cos()), dim=-1) + + sin, cos = map(lambda t: rearrange(t, "b i j d -> b (i j) d"), (sin, cos)) + sin, cos = map(lambda t: repeat(t, "b n d -> b n (d j)", j=2), (sin, cos)) + return sin, cos class PositionalEmbedding(nn.Module): From 7b1ccc5e726d65f83cd8c0033ba52e48772fd311 Mon Sep 17 00:00:00 2001 From: isaacmg Date: Wed, 22 May 2024 14:45:29 -0400 Subject: [PATCH 02/38] adding fixes to attention core --- flood_forecast/multi_models/crossvivit.py | 3 +- flood_forecast/transformer_xl/attn.py | 113 ++++++++++++++++++++++ 2 files changed, 115 insertions(+), 1 deletion(-) diff --git a/flood_forecast/multi_models/crossvivit.py b/flood_forecast/multi_models/crossvivit.py index bc8138d66..cbada7b3c 100644 --- a/flood_forecast/multi_models/crossvivit.py +++ b/flood_forecast/multi_models/crossvivit.py @@ -7,7 +7,7 @@ from einops import rearrange, repeat from einops.layers.torch import Rearrange from torch import einsum, nn -from src.models.modules.attention_modules import * +from flood_forecast.transformer_xl.attn import SelfAttention, CrossAttention from flood_forecast.transformer_xl.data_embedding import PositionalEncoding2D, AxialRotaryEmbedding @@ -240,6 +240,7 @@ def __init__( decoder_dim_head: int = 128, **kwargs, ): + super().__init__() assert ( ctx_masking_ratio >= 0 and ctx_masking_ratio < 1 diff --git a/flood_forecast/transformer_xl/attn.py b/flood_forecast/transformer_xl/attn.py index bc7f6fdbf..0900e4ec4 100644 --- a/flood_forecast/transformer_xl/attn.py +++ b/flood_forecast/transformer_xl/attn.py @@ -3,6 +3,7 @@ import numpy as np from math import sqrt from einops import rearrange +import torch.nn.functional as F class TriangularCausalMask(): @@ -352,3 +353,115 @@ def forward(self, queries, keys, values, attn_mask, tau, delta): B, N, C = queries.shape queries = self.attn(self.fit_length(queries))[:, :N, :] return queries, None + +def rotate_every_two(x): + x = rearrange(x, "... (d j) -> ... d j", j=2) + x1, x2 = x.unbind(dim=-1) + x = torch.stack((-x2, x1), dim=-1) + return rearrange(x, "... d j -> ... (d j)") + + +class PreNorm(nn.Module): + def __init__(self, dim, fn): + super().__init__() + self.norm = nn.LayerNorm(dim) + self.fn = fn + + def forward(self, x, **kwargs): + return self.fn(self.norm(x), **kwargs) + + +class CrossPreNorm(nn.Module): + def __init__(self, dim, fn): + super().__init__() + self.norm_src = nn.LayerNorm(dim) + self.norm_tgt = nn.LayerNorm(dim) + self.fn = fn + + def forward(self, ctx, src_pos_emb, ts, tgt_pos_emb): + return self.fn(self.norm_src(ctx), src_pos_emb, self.norm_tgt(ts), tgt_pos_emb) + + +class GEGLU(nn.Module): + def forward(self, x): + x, gates = x.chunk(2, dim=-1) + return F.gelu(gates) * x + + +class FeedForward(nn.Module): + def __init__(self, dim, hidden_dim, dropout=0.0, use_glu=True): + super().__init__() + self.net = nn.Sequential( + nn.Linear(dim, hidden_dim * 2 if use_glu else hidden_dim), + GEGLU() if use_glu else nn.GELU(), + nn.Dropout(dropout), + nn.Linear(hidden_dim, dim), + nn.Dropout(dropout), + ) + + def forward(self, x): + return self.net(x) + + +class SelfAttention(nn.Module): + def __init__( + self, + dim, + heads=8, + dim_head=64, + dropout=0.0, + use_rotary=True, + ): + super().__init__() + inner_dim = dim_head * heads + self.use_rotary = use_rotary + self.heads = heads + self.scale = dim_head**-0.5 + + self.attend = nn.Softmax(dim=-1) + self.dropout = nn.Dropout(dropout) + + self.to_q = nn.Linear(dim, inner_dim, bias=False) + + self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False) + + self.to_out = nn.Sequential(nn.Linear(inner_dim, dim), nn.Dropout(dropout)) + + def forward(self, x, pos_emb): + """ + Args: + x: Sequence of shape [B, N, D] + pos_emb: Positional embedding of sequence's tokens of shape [B, N, D] + """ + + q = self.to_q(x) + + qkv = (q, *self.to_kv(x).chunk(2, dim=-1)) + q, k, v = map( + lambda t: rearrange(t, "b n (h d) -> (b h) n d", h=self.heads), qkv + ) + + if self.use_rotary: + + sin, cos = map( + lambda t: repeat(t, "b n d -> (b h) n d", h=self.heads), pos_emb + ) + dim_rotary = sin.shape[-1] + + # handle the case where rotary dimension < head dimension + + (q, q_pass), (k, k_pass) = map( + lambda t: (t[..., :dim_rotary], t[..., dim_rotary:]), (q, k) + ) + q, k = map(lambda t: (t * cos) + (rotate_every_two(t) * sin), (q, k)) + q, k = map(lambda t: torch.cat(t, dim=-1), ((q, q_pass), (k, k_pass))) + + dots = einsum("b i d, b j d -> b i j", q, k) * self.scale + + attn = self.attend(dots) + attn = self.dropout(attn) + + out = einsum("b i j, b j d -> b i d", attn, v) + out = rearrange(out, "(b h) n d -> b n (h d)", h=self.heads) + return self.to_out(out), attn + From 550bd8cfb8db80fca5f821a17cc7af0cabf73bd4 Mon Sep 17 00:00:00 2001 From: isaacmg Date: Wed, 22 May 2024 16:40:24 -0400 Subject: [PATCH 03/38] adding required parameters. --- flood_forecast/multi_models/crossvivit.py | 2 +- flood_forecast/transformer_xl/attn.py | 1 + 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/flood_forecast/multi_models/crossvivit.py b/flood_forecast/multi_models/crossvivit.py index cbada7b3c..f606713d4 100644 --- a/flood_forecast/multi_models/crossvivit.py +++ b/flood_forecast/multi_models/crossvivit.py @@ -7,7 +7,7 @@ from einops import rearrange, repeat from einops.layers.torch import Rearrange from torch import einsum, nn -from flood_forecast.transformer_xl.attn import SelfAttention, CrossAttention +from flood_forecast.transformer_xl.attn import SelfAttention, CrossAttention, PreNorm, CrossPreNorm, FeedForward from flood_forecast.transformer_xl.data_embedding import PositionalEncoding2D, AxialRotaryEmbedding diff --git a/flood_forecast/transformer_xl/attn.py b/flood_forecast/transformer_xl/attn.py index 0900e4ec4..10d1b171a 100644 --- a/flood_forecast/transformer_xl/attn.py +++ b/flood_forecast/transformer_xl/attn.py @@ -4,6 +4,7 @@ from math import sqrt from einops import rearrange import torch.nn.functional as F +import einsum class TriangularCausalMask(): From 545183f5413f51753211576c91f4c17f78b4c8a2 Mon Sep 17 00:00:00 2001 From: isaacmg Date: Sun, 2 Jun 2024 10:58:44 -0400 Subject: [PATCH 04/38] adding crossvivit --- .circleci/config.yml | 1 + tests/mult_modal_tests/test_cross_vivit.py | 11 +++++++++++ 2 files changed, 12 insertions(+) create mode 100644 tests/mult_modal_tests/test_cross_vivit.py diff --git a/.circleci/config.yml b/.circleci/config.yml index 1b1ea5c6d..e5a41577f 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -155,6 +155,7 @@ jobs: name: Model basic tests when: always command: | + coverage run -m unittest -v tests/multi_modal_tests/test_cross_vivit.py coverage run -m unittest -v tests/test_series_id.py coverage run -m unittest -v tests/test_squashed.py bash <(curl -s https://codecov.io/bash) -cF python diff --git a/tests/mult_modal_tests/test_cross_vivit.py b/tests/mult_modal_tests/test_cross_vivit.py new file mode 100644 index 000000000..654b3dd2b --- /dev/null +++ b/tests/mult_modal_tests/test_cross_vivit.py @@ -0,0 +1,11 @@ +import unittest +from flood_forecast.multi_models.crossvivit import RoCrossViViT + + +class TestCrossVivVit(unittest.TestCase): + def setUp(self): + self.crossvivit = RoCrossViViT(image_size=(128, 128), patch_size=(8, 8)) + + +if __name__ == '__main__': + unittest.main() From ee880a238e1f9ce924bf638a870124a6a627cc16 Mon Sep 17 00:00:00 2001 From: isaacmg Date: Wed, 3 Jul 2024 13:25:45 -0400 Subject: [PATCH 05/38] requirements + encodings --- flood_forecast/multi_models/crossvivit.py | 1 - flood_forecast/transformer_xl/attn.py | 66 +++++++++++++++++++ .../transformer_xl/data_embedding.py | 47 +++++++++++++ requirements.txt | 3 +- tests/mult_modal_tests/test_cross_vivit.py | 7 +- 5 files changed, 121 insertions(+), 3 deletions(-) diff --git a/flood_forecast/multi_models/crossvivit.py b/flood_forecast/multi_models/crossvivit.py index f606713d4..7c7b1fe84 100644 --- a/flood_forecast/multi_models/crossvivit.py +++ b/flood_forecast/multi_models/crossvivit.py @@ -200,7 +200,6 @@ def forward( src_pos_emb: Positional embedding of source sequence's tokens of shape [B, N, D] tgt_pos_emb: Positional embedding of target sequence's tokens of shape [B, M, D] """ - attention_scores = {} for i in range(len(self.cross_layers)): cattn, cff = self.cross_layers[i] diff --git a/flood_forecast/transformer_xl/attn.py b/flood_forecast/transformer_xl/attn.py index 10d1b171a..481f3b6df 100644 --- a/flood_forecast/transformer_xl/attn.py +++ b/flood_forecast/transformer_xl/attn.py @@ -466,3 +466,69 @@ def forward(self, x, pos_emb): out = rearrange(out, "(b h) n d -> b n (h d)", h=self.heads) return self.to_out(out), attn + +class CrossAttention(nn.Module): + def __init__( + self, + dim: int, + heads: int =8, + dim_head: int = 64, + dropout: int = 0.0, + use_rotary: bool = True, + ): + """ + """ + super().__init__() + inner_dim = dim_head * heads + self.use_rotary = use_rotary + self.heads = heads + self.scale = dim_head**-0.5 + + self.attend = nn.Softmax(dim=-1) + self.dropout = nn.Dropout(dropout) + + self.to_q = nn.Linear(dim, inner_dim, bias=False) + + self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False) + + self.to_out = nn.Sequential(nn.Linear(inner_dim, dim), nn.Dropout(dropout)) + + def forward(self, src, src_pos_emb, tgt, tgt_pos_emb): + + q = self.to_q(tgt) + + qkv = (q, *self.to_kv(src).chunk(2, dim=-1)) + + q, k, v = map( + lambda t: rearrange(t, "b n (h d) -> (b h) n d", h=self.heads), qkv + ) + + if self.use_rotary: + # apply 2d rotary embeddings to queries and keys + + sin_src, cos_src = map( + lambda t: repeat(t, "b n d -> (b h) n d", h=self.heads), src_pos_emb + ) + sin_tgt, cos_tgt = map( + lambda t: repeat(t, "b n d -> (b h) n d", h=self.heads), tgt_pos_emb + ) + dim_rotary = sin_src.shape[-1] + + # handle the case where rotary dimension < head dimension + + (q, q_pass), (k, k_pass) = map( + lambda t: (t[..., :dim_rotary], t[..., dim_rotary:]), (q, k) + ) + q = (q * cos_tgt) + (rotate_every_two(q) * sin_tgt) + k = (k * cos_src) + (rotate_every_two(k) * sin_src) + q, k = map(lambda t: torch.cat(t, dim=-1), ((q, q_pass), (k, k_pass))) + + dots = einsum("b i d, b j d -> b i j", q, k) * self.scale + + attn = self.attend(dots) + attn = self.dropout(attn) + + out = einsum("b i j, b j d -> b i d", attn, v) + out = rearrange(out, "(b h) n d -> b n (h d)", h=self.heads) + return self.to_out(out), attn + diff --git a/flood_forecast/transformer_xl/data_embedding.py b/flood_forecast/transformer_xl/data_embedding.py index 1406c5bdc..04887efd2 100644 --- a/flood_forecast/transformer_xl/data_embedding.py +++ b/flood_forecast/transformer_xl/data_embedding.py @@ -3,6 +3,7 @@ import math from einops import rearrange, repeat from math import pi +import numpy as np class AxialRotaryEmbedding(nn.Module): @@ -210,3 +211,49 @@ def forward(self, x, x_mark) -> torch: x = self.value_embedding(torch.cat([x, x_mark.permute(0, 2, 1)], 1)) # x: [Batch Variate d_model] t return self.dropout(x) + + +def get_emb(sin_inp): + """ + Gets a base embedding for one dimension with sin and cos intertwined + """ + emb = torch.stack((sin_inp.sin(), sin_inp.cos()), dim=-1) + return torch.flatten(emb, -2, -1) + + +class PositionalEncoding2D(nn.Module): + def __init__(self, channels): + """ + :param channels: The last dimension of the tensor you want to apply pos emb to. + """ + super(PositionalEncoding2D, self).__init__() + self.org_channels = channels + channels = int(np.ceil(channels / 4) * 2) + self.channels = channels + inv_freq = 1.0 / (10000 ** (torch.arange(0, channels, 2).float() / channels)) + self.register_buffer("inv_freq", inv_freq) + + def forward(self, coords): + """ + :param tensor: A 4d tensor of size (batch_size, ch, x, y) + :param coords: A 4d tensor of size (batch_size, num_coords, x, y) + :return: Positional Encoding Matrix of size (batch_size, x, y, ch) + """ + if len(coords.shape) != 4: + raise RuntimeError("The input tensor has to be 4d!") + + batch_size, _, x, y = coords.shape + self.cached_penc = None + pos_x = coords[:, 0, 0, :].type(self.inv_freq.type()) # batch, width + pos_y = coords[:, 1, :, 0].type(self.inv_freq.type()) # batch, height + sin_inp_x = torch.einsum("bi,j->bij", pos_x, self.inv_freq) + sin_inp_y = torch.einsum("bi,j->bij", pos_y, self.inv_freq) + emb_x = get_emb(sin_inp_x).unsqueeze(2) + emb_y = get_emb(sin_inp_y).unsqueeze(1) + emb = torch.zeros( + (batch_size, x, y, self.channels * 2), device=coords.device + ).type(coords.type()) + emb[:, :, :, : self.channels] = emb_x + emb[:, :, :, self.channels : 2 * self.channels] = emb_y + + return emb \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index b3273e1e9..a96f1c2d1 100644 --- a/requirements.txt +++ b/requirements.txt @@ -22,4 +22,5 @@ sphinx-rtd-theme sphinx-autodoc-typehints sphinx einops -pytorch-tsmixer \ No newline at end of file +pytorch-tsmixer +einsum \ No newline at end of file diff --git a/tests/mult_modal_tests/test_cross_vivit.py b/tests/mult_modal_tests/test_cross_vivit.py index 654b3dd2b..0eb4ea89e 100644 --- a/tests/mult_modal_tests/test_cross_vivit.py +++ b/tests/mult_modal_tests/test_cross_vivit.py @@ -1,10 +1,15 @@ import unittest +import torch from flood_forecast.multi_models.crossvivit import RoCrossViViT class TestCrossVivVit(unittest.TestCase): def setUp(self): - self.crossvivit = RoCrossViViT(image_size=(128, 128), patch_size=(8, 8)) + self.crossvivit = RoCrossViViT(image_size=(128, 128), patch_size=(8, 8), time_coords_encoder=) + + def test_forward(self): + x = self.crossvivit(torch.randn(1, 3, 128, 128)) + self.assertEqual(x.shape, (1, 1000)) if __name__ == '__main__': From d0f80359b171d68ed1a2facf17bd26de93bf1f44 Mon Sep 17 00:00:00 2001 From: isaacmg Date: Fri, 5 Jul 2024 09:28:31 -0400 Subject: [PATCH 06/38] more vivit models and embeddings --- .../transformer_xl/data_embedding.py | 43 ++++++++++++++++++- tests/mult_modal_tests/test_cross_vivit.py | 9 +++- 2 files changed, 49 insertions(+), 3 deletions(-) diff --git a/flood_forecast/transformer_xl/data_embedding.py b/flood_forecast/transformer_xl/data_embedding.py index 04887efd2..1069c105a 100644 --- a/flood_forecast/transformer_xl/data_embedding.py +++ b/flood_forecast/transformer_xl/data_embedding.py @@ -256,4 +256,45 @@ def forward(self, coords): emb[:, :, :, : self.channels] = emb_x emb[:, :, :, self.channels : 2 * self.channels] = emb_y - return emb \ No newline at end of file + return emb + +class NeRF_embedding(nn.Module): + def __init__(self, n_layers: int = 5): + super().__init__() + self.n_layers = n_layers + self.dim = self.n_layers * 4 + + def forward(self, spatial_coords: torch.Tensor): + """ + Args: + spatial_coords (torch.Tensor): Spatial coordinates of shape [B, 2, H, W] + """ + embeddings = [] + for i in range(self.n_layers): + embeddings += [ + torch.sin((2**i * torch.pi) * spatial_coords), + torch.cos((2**i * torch.pi) * spatial_coords), + ] + embeddings = torch.cat(embeddings, axis=1) + return embeddings + + +class CyclicalEmbedding(nn.Module): + def __init__(self, frequencies: list = [12, 31, 24, 60]): + super().__init__() + self.frequencies = frequencies + self.dim = len(self.frequencies) * 2 + + def forward(self, time_coords: torch.Tensor): + """ + Args: + time_coords (torch.Tensor): Time coordinates of shape [B, T, C, H, W] + """ + embeddings = [] + for i, frequency in enumerate(self.frequencies): + embeddings += [ + torch.sin(2 * torch.pi * time_coords[:, :, i] / frequency), + torch.cos(2 * torch.pi * time_coords[:, :, i] / frequency), + ] + embeddings = torch.stack(embeddings, axis=2) + return embeddings \ No newline at end of file diff --git a/tests/mult_modal_tests/test_cross_vivit.py b/tests/mult_modal_tests/test_cross_vivit.py index 0eb4ea89e..91d671832 100644 --- a/tests/mult_modal_tests/test_cross_vivit.py +++ b/tests/mult_modal_tests/test_cross_vivit.py @@ -1,11 +1,16 @@ import unittest import torch -from flood_forecast.multi_models.crossvivit import RoCrossViViT +from flood_forecast.multi_models.crossvivit import RoCrossViViT, VisionTransformer +from flood_forecast.transformer_xl.data_embedding import CyclicalEmbedding, NeRF_embedding class TestCrossVivVit(unittest.TestCase): def setUp(self): - self.crossvivit = RoCrossViViT(image_size=(128, 128), patch_size=(8, 8), time_coords_encoder=) + self.crossvivit = RoCrossViViT(image_size=(128, 128), patch_size=(8, 8), time_coords_encoder=NeRF_embedding(), **{"max_freq":12}) + + def test_vivit_model(self): + self.vivit_model = VisionTransformer(128, 5, 8, 128, 128, [512, 512, 512] ) + pass def test_forward(self): x = self.crossvivit(torch.randn(1, 3, 128, 128)) From 24db7775c074b7e112efb3e3322b778e53881330 Mon Sep 17 00:00:00 2001 From: isaacmg Date: Mon, 8 Jul 2024 17:51:52 -0400 Subject: [PATCH 07/38] updating crossvivit docstrings et --- flood_forecast/multi_models/crossvivit.py | 42 ++++++++++++++++++++--- flood_forecast/time_model.py | 2 +- 2 files changed, 39 insertions(+), 5 deletions(-) diff --git a/flood_forecast/multi_models/crossvivit.py b/flood_forecast/multi_models/crossvivit.py index 7c7b1fe84..216303e14 100644 --- a/flood_forecast/multi_models/crossvivit.py +++ b/flood_forecast/multi_models/crossvivit.py @@ -1,14 +1,24 @@ """ Adapted from: https://github.com/lucidrains/vit-pytorch/blob/main/vit_pytorch/rvt.py """ + import random from typing import List, Tuple, Union import torch from einops import rearrange, repeat from einops.layers.torch import Rearrange from torch import einsum, nn -from flood_forecast.transformer_xl.attn import SelfAttention, CrossAttention, PreNorm, CrossPreNorm, FeedForward -from flood_forecast.transformer_xl.data_embedding import PositionalEncoding2D, AxialRotaryEmbedding +from flood_forecast.transformer_xl.attn import ( + SelfAttention, + CrossAttention, + PreNorm, + CrossPreNorm, + FeedForward, +) +from flood_forecast.transformer_xl.data_embedding import ( + PositionalEncoding2D, + AxialRotaryEmbedding, +) class Attention(nn.Module): @@ -64,7 +74,7 @@ def __init__(self, dim, num_frames, depth, heads, dim_head, mlp_dim, dropout=0.0 ) ) - def forward(self, x): + def forward(self, x: torch.Tensor): """ Args: x: Input tensor of shape [B, T, C] @@ -89,6 +99,30 @@ def __init__( use_rotary: bool = True, use_glu: bool = True, ): + """The Video Vision Transformer (e.g. VIVIT) of the CrossVIVIT model. This model is based on the Arxiv paper: + https://arxiv.org/abs/2103.15691. The below implementation has a few specific CrossVIVIT specific parameters + like whether to use the rotary. + :param dim: The embedding dimension. The authors generally use 384 for training the large model. + :type dim: int + :param depth: The number of blocks to create. Commonly set to 4. + :type depth: int + :param heads: The number of heads in the multi-head-attention mechanism. Usually set to a multiple of eight. + :type heads: int + :param dim_head: The dimension of the inputs to the head. + :type dim_head: int + :param mlp_dim: _description_ + :type mlp_dim: int + :param image_size: The image size defined can be defined either as a list, tuple or single int (e.g. [120, 120] + (120, 120), 120. + :type image_size: Union[List[int], Tuple[int], int] + :param dropout: The amount of dropout to use throughout the model defaults to 0.0 + :type dropout: float, optional + :param use_rotary: Whether to use rotary positional embeddings, defaults to True + :type use_rotary: bool, optional + :param use_glu: _description_, defaults to True + :type use_glu: bool, optional + """ + super().__init__() self.image_size = image_size @@ -478,4 +512,4 @@ def forward( quantile_mask = self.quantile_masker(rearrange(y.detach(), "b t c -> b c t")) - return (outputs, quantile_mask, self_attention_scores, cross_attention_scores) \ No newline at end of file + return (outputs, quantile_mask, self_attention_scores, cross_attention_scores) diff --git a/flood_forecast/time_model.py b/flood_forecast/time_model.py index 5446a7c3c..e963cb70e 100644 --- a/flood_forecast/time_model.py +++ b/flood_forecast/time_model.py @@ -58,7 +58,7 @@ def load_model(self, model_base: str, model_params: Dict, weight_path=None) -> o @abstractmethod def make_data_load(self, data_path, params: Dict, loader_type: str) -> object: """ - Intializes a data loader based on the provided data_path. + Initializes a data loader based on the provided data_path. This may be as simple as a pandas dataframe or as complex as a custom PyTorch data loader. """ From 2b577d7c3611a4b2d31dccee7d80f0fc40f140a0 Mon Sep 17 00:00:00 2001 From: isaacmg Date: Thu, 11 Jul 2024 16:37:28 -0500 Subject: [PATCH 08/38] add torch-typing and code --- flood_forecast/multi_models/crossvivit.py | 2 +- requirements.txt | 3 ++- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/flood_forecast/multi_models/crossvivit.py b/flood_forecast/multi_models/crossvivit.py index 216303e14..993bbcf50 100644 --- a/flood_forecast/multi_models/crossvivit.py +++ b/flood_forecast/multi_models/crossvivit.py @@ -104,7 +104,7 @@ def __init__( like whether to use the rotary. :param dim: The embedding dimension. The authors generally use 384 for training the large model. :type dim: int - :param depth: The number of blocks to create. Commonly set to 4. + :param depth: The number of blocks to create. Commonly set to 4 for most tasks. :type depth: int :param heads: The number of heads in the multi-head-attention mechanism. Usually set to a multiple of eight. :type heads: int diff --git a/requirements.txt b/requirements.txt index 67d8a9cdb..b86314b3a 100644 --- a/requirements.txt +++ b/requirements.txt @@ -23,4 +23,5 @@ sphinx-autodoc-typehints sphinx einops pytorch-tsmixer -einsum \ No newline at end of file +einsum +torchtyping \ No newline at end of file From d50c27a37ef424d33849c60e4c8b702ee1b99656 Mon Sep 17 00:00:00 2001 From: isaacmg Date: Thu, 11 Jul 2024 16:39:43 -0500 Subject: [PATCH 09/38] adding code --- .idea/.gitignore | 8 + .../inspectionProfiles/profiles_settings.xml | 6 + .idea/misc.xml | 10 ++ .idea/vcs.xml | 6 + flood_forecast/multi_models/crossvivit.py | 2 +- flood_forecast/time_model.py | 157 ++++++++++++------ flood_forecast/transformer_xl/attn.py | 2 +- 7 files changed, 135 insertions(+), 56 deletions(-) create mode 100644 .idea/.gitignore create mode 100644 .idea/inspectionProfiles/profiles_settings.xml create mode 100644 .idea/misc.xml create mode 100644 .idea/vcs.xml diff --git a/.idea/.gitignore b/.idea/.gitignore new file mode 100644 index 000000000..13566b81b --- /dev/null +++ b/.idea/.gitignore @@ -0,0 +1,8 @@ +# Default ignored files +/shelf/ +/workspace.xml +# Editor-based HTTP Client requests +/httpRequests/ +# Datasource local storage ignored files +/dataSources/ +/dataSources.local.xml diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml new file mode 100644 index 000000000..105ce2da2 --- /dev/null +++ b/.idea/inspectionProfiles/profiles_settings.xml @@ -0,0 +1,6 @@ + + + + \ No newline at end of file diff --git a/.idea/misc.xml b/.idea/misc.xml new file mode 100644 index 000000000..57e7948df --- /dev/null +++ b/.idea/misc.xml @@ -0,0 +1,10 @@ + + + + + + \ No newline at end of file diff --git a/.idea/vcs.xml b/.idea/vcs.xml new file mode 100644 index 000000000..35eb1ddfb --- /dev/null +++ b/.idea/vcs.xml @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/flood_forecast/multi_models/crossvivit.py b/flood_forecast/multi_models/crossvivit.py index 993bbcf50..bd8290a4f 100644 --- a/flood_forecast/multi_models/crossvivit.py +++ b/flood_forecast/multi_models/crossvivit.py @@ -38,7 +38,7 @@ def __init__(self, dim, heads=8, dim_head=64, dropout=0.0): else nn.Identity() ) - def forward(self, x): + def forward(self, x: torch.Tensor) -> torch.Tensor: b, n, _, h = *x.shape, self.heads qkv = self.to_qkv(x).chunk(3, dim=-1) q, k, v = map(lambda t: rearrange(t, "b n (h d) -> b h n d", h=h), qkv) diff --git a/flood_forecast/time_model.py b/flood_forecast/time_model.py index e963cb70e..135a43f09 100644 --- a/flood_forecast/time_model.py +++ b/flood_forecast/time_model.py @@ -6,9 +6,14 @@ from datetime import datetime from flood_forecast.model_dict_function import pytorch_model_dict from flood_forecast.pre_dict import scaler_dict -from flood_forecast.preprocessing.pytorch_loaders import (CSVDataLoader, AEDataloader, TemporalLoader, - CSVSeriesIDLoader, GeneralClassificationLoader, - VariableSequenceLength) +from flood_forecast.preprocessing.pytorch_loaders import ( + CSVDataLoader, + AEDataloader, + TemporalLoader, + CSVSeriesIDLoader, + GeneralClassificationLoader, + VariableSequenceLength, +) from flood_forecast.gcp_integration.basic_utils import get_storage_client, upload_file from flood_forecast.utils import make_criterion_functions from flood_forecast.preprocessing.buil_dataset import get_data @@ -24,22 +29,31 @@ class TimeSeriesModel(ABC): """ def __init__( - self, - model_base: str, - training_data: str, - validation_data: str, - test_data: str, - params: Dict): + self, + model_base: str, + training_data: str, + validation_data: str, + test_data: str, + params: Dict, + ): self.params = params if "weight_path" in params: params["weight_path"] = get_data(params["weight_path"]) - self.model = self.load_model(model_base, params["model_params"], params["weight_path"]) + self.model = self.load_model( + model_base, params["model_params"], params["weight_path"] + ) else: self.model = self.load_model(model_base, params["model_params"]) # params["dataset_params"]["forecast_test_len"] = params["inference_params"]["hours_to_forecast"] - self.training = self.make_data_load(training_data, params["dataset_params"], "train") - self.validation = self.make_data_load(validation_data, params["dataset_params"], "valid") - self.test_data = self.make_data_load(test_data, params["dataset_params"], "test") + self.training = self.make_data_load( + training_data, params["dataset_params"], "train" + ) + self.validation = self.make_data_load( + validation_data, params["dataset_params"], "valid" + ) + self.test_data = self.make_data_load( + test_data, params["dataset_params"], "test" + ) if "GCS" in self.params and self.params["GCS"]: self.gcs_client = get_storage_client() else: @@ -48,7 +62,9 @@ def __init__( self.crit = make_criterion_functions(params["metrics"]) @abstractmethod - def load_model(self, model_base: str, model_params: Dict, weight_path=None) -> object: + def load_model( + self, model_base: str, model_params: Dict, weight_path=None + ) -> object: """ This function should load and return the model this will vary based on the underlying framework used @@ -72,7 +88,9 @@ def save_model(self, output_path: str): """ raise NotImplementedError - def upload_gcs(self, save_path: str, name: str, file_type: str, epoch=0, bucket_name=None): + def upload_gcs( + self, save_path: str, name: str, file_type: str, epoch=0, bucket_name=None + ): """ Function to upload model checkpoints to GCS """ @@ -81,10 +99,17 @@ def upload_gcs(self, save_path: str, name: str, file_type: str, epoch=0, bucket_ bucket_name = os.environ["MODEL_BUCKET"] print("Data saved to: ") print(name) - upload_file(bucket_name, os.path.join("experiments", name), save_path, self.gcs_client) + upload_file( + bucket_name, + os.path.join("experiments", name), + save_path, + self.gcs_client, + ) online_path = os.path.join("gs://", bucket_name, "experiments", name) if self.wandb: - wandb.config.update({"gcs_m_path_" + str(epoch) + file_type: online_path}) + wandb.config.update( + {"gcs_m_path_" + str(epoch) + file_type: online_path} + ) def wandb_init(self): if self.params["wandb"]: @@ -93,7 +118,8 @@ def wandb_init(self): project=self.params["wandb"].get("project"), config=self.params, name=self.params["wandb"].get("name"), - tags=self.params["wandb"].get("tags")), + tags=self.params["wandb"].get("tags"), + ), return True elif "sweep" in self.params: print("Using Wandb config:") @@ -104,14 +130,17 @@ def wandb_init(self): class PyTorchForecast(TimeSeriesModel): def __init__( - self, - model_base: str, - training_data, - validation_data, - test_data, - params_dict: Dict): - self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') - super().__init__(model_base, training_data, validation_data, test_data, params_dict) + self, + model_base: str, + training_data, + validation_data, + test_data, + params_dict: Dict, + ): + self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + super().__init__( + model_base, training_data, validation_data, test_data, params_dict + ) print("Torch is using " + str(self.device)) if "weight_path_add" in params_dict: self.__freeze_layers__(params_dict["weight_path_add"]) @@ -124,14 +153,18 @@ def __freeze_layers__(self, params: Dict): for parameter in self.model._modules[layer].parameters(): parameter.requires_grad = False - def load_model(self, model_base: str, model_params: Dict, weight_path: str = None, strict=True): + def load_model( + self, model_base: str, model_params: Dict, weight_path: str = None, strict=True + ): if model_base in pytorch_model_dict: model = pytorch_model_dict[model_base](**model_params) if weight_path: checkpoint = torch.load(weight_path, map_location=self.device) if "weight_path_add" in self.params: if "excluded_layers" in self.params["weight_path_add"]: - excluded_layers = self.params["weight_path_add"]["excluded_layers"] + excluded_layers = self.params["weight_path_add"][ + "excluded_layers" + ] for layer in excluded_layers: del checkpoint[layer] print("sucessfully deleted layers") @@ -146,9 +179,10 @@ def load_model(self, model_base: str, model_params: Dict, weight_path: str = Non model.tgt_mask = model.tgt_mask.to(self.device) else: raise Exception( - "Error the model " + - model_base + - " was not found in the model dict. Please add it.") + "Error the model " + + model_base + + " was not found in the model dict. Please add it." + ) return model def save_model(self, final_path: str, epoch: int) -> None: @@ -186,15 +220,15 @@ def __re_add_params__(self, start_end_params: Dict, dataset_params, data_path): return start_end_params def make_data_load( - self, - data_path: str, - dataset_params: Dict, - loader_type: str, - the_class="default"): + self, + data_path: str, + dataset_params: Dict, + loader_type: str, + the_class="default", + ): start_end_params = {} the_class = dataset_params["class"] start_end_params = scaling_function(start_end_params, dataset_params) - # TODO clean up else if blocks if loader_type + "_start" in dataset_params: start_end_params["start_stamp"] = dataset_params[loader_type + "_start"] if loader_type + "_end" in dataset_params: @@ -223,7 +257,8 @@ def make_data_load( dataset_params["forecast_test_len"], dataset_params["target_col"], dataset_params["relevant_cols"], - **start_end_params) + **start_end_params + ) elif the_class == "default": loader = CSVDataLoader( data_path, @@ -231,42 +266,54 @@ def make_data_load( dataset_params["forecast_length"], dataset_params["target_col"], dataset_params["relevant_cols"], - **start_end_params) + **start_end_params + ) elif the_class == "AutoEncoder": loader = AEDataloader( - data_path, - dataset_params["relevant_cols"], - **start_end_params + data_path, dataset_params["relevant_cols"], **start_end_params ) elif the_class == "TemporalLoader": - start_end_params = self.__re_add_params__(start_end_params, dataset_params, data_path) + start_end_params = self.__re_add_params__( + start_end_params, dataset_params, data_path + ) label_len = 0 if "label_len" in dataset_params: label_len = dataset_params["label_len"] loader = TemporalLoader( - dataset_params["temporal_feats"], - start_end_params, - label_len=label_len) + dataset_params["temporal_feats"], start_end_params, label_len=label_len + ) elif the_class == "SeriesIDLoader": - start_end_params = self.__re_add_params__(start_end_params, dataset_params, data_path) + start_end_params = self.__re_add_params__( + start_end_params, dataset_params, data_path + ) loader = CSVSeriesIDLoader( dataset_params["series_id_col"], start_end_params, - dataset_params["return_method"] + dataset_params["return_method"], ) elif the_class == "GeneralClassificationLoader": dataset_params["forecast_length"] = 1 - start_end_params = self.__re_add_params__(start_end_params, dataset_params, data_path) + start_end_params = self.__re_add_params__( + start_end_params, dataset_params, data_path + ) start_end_params["sequence_length"] = dataset_params["sequence_length"] - loader = GeneralClassificationLoader(start_end_params, dataset_params["n_classes"]) + loader = GeneralClassificationLoader( + start_end_params, dataset_params["n_classes"] + ) elif the_class == "VariableSequenceLength": - start_end_params = self.__re_add_params__(start_end_params, dataset_params, data_path) + start_end_params = self.__re_add_params__( + start_end_params, dataset_params, data_path + ) if "pad_len" in dataset_params: pad_le = dataset_params["pad_len"] else: pad_le = None - loader = VariableSequenceLength(dataset_params["series_marker_column"], start_end_params, - pad_le, dataset_params["task"]) + loader = VariableSequenceLength( + dataset_params["series_marker_column"], + start_end_params, + pad_le, + dataset_params["task"], + ) else: # TODO support custom DataLoader @@ -283,7 +330,9 @@ def scaling_function(start_end_params, dataset_params): else: return {} if "scaler_params" in dataset_params: - scaler = scaler_dict[dataset_params[in_dataset_params]](**dataset_params["scaler_params"]) + scaler = scaler_dict[dataset_params[in_dataset_params]]( + **dataset_params["scaler_params"] + ) else: scaler = scaler_dict[dataset_params[in_dataset_params]]() start_end_params["scaling"] = scaler diff --git a/flood_forecast/transformer_xl/attn.py b/flood_forecast/transformer_xl/attn.py index 481f3b6df..2d89f68db 100644 --- a/flood_forecast/transformer_xl/attn.py +++ b/flood_forecast/transformer_xl/attn.py @@ -493,7 +493,7 @@ def __init__( self.to_out = nn.Sequential(nn.Linear(inner_dim, dim), nn.Dropout(dropout)) - def forward(self, src, src_pos_emb, tgt, tgt_pos_emb): + def forward(self, src: torch.Tensor, src_pos_emb, tgt, tgt_pos_emb): q = self.to_q(tgt) From 674ba091f67692641a95d05a2a1caff8320c0898 Mon Sep 17 00:00:00 2001 From: isaacmg Date: Thu, 11 Jul 2024 16:40:07 -0500 Subject: [PATCH 10/38] Revert "adding code" This reverts commit d50c27a37ef424d33849c60e4c8b702ee1b99656. --- .idea/.gitignore | 8 - .../inspectionProfiles/profiles_settings.xml | 6 - .idea/misc.xml | 10 -- .idea/vcs.xml | 6 - flood_forecast/multi_models/crossvivit.py | 2 +- flood_forecast/time_model.py | 157 ++++++------------ flood_forecast/transformer_xl/attn.py | 2 +- 7 files changed, 56 insertions(+), 135 deletions(-) delete mode 100644 .idea/.gitignore delete mode 100644 .idea/inspectionProfiles/profiles_settings.xml delete mode 100644 .idea/misc.xml delete mode 100644 .idea/vcs.xml diff --git a/.idea/.gitignore b/.idea/.gitignore deleted file mode 100644 index 13566b81b..000000000 --- a/.idea/.gitignore +++ /dev/null @@ -1,8 +0,0 @@ -# Default ignored files -/shelf/ -/workspace.xml -# Editor-based HTTP Client requests -/httpRequests/ -# Datasource local storage ignored files -/dataSources/ -/dataSources.local.xml diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml deleted file mode 100644 index 105ce2da2..000000000 --- a/.idea/inspectionProfiles/profiles_settings.xml +++ /dev/null @@ -1,6 +0,0 @@ - - - - \ No newline at end of file diff --git a/.idea/misc.xml b/.idea/misc.xml deleted file mode 100644 index 57e7948df..000000000 --- a/.idea/misc.xml +++ /dev/null @@ -1,10 +0,0 @@ - - - - - - \ No newline at end of file diff --git a/.idea/vcs.xml b/.idea/vcs.xml deleted file mode 100644 index 35eb1ddfb..000000000 --- a/.idea/vcs.xml +++ /dev/null @@ -1,6 +0,0 @@ - - - - - - \ No newline at end of file diff --git a/flood_forecast/multi_models/crossvivit.py b/flood_forecast/multi_models/crossvivit.py index bd8290a4f..993bbcf50 100644 --- a/flood_forecast/multi_models/crossvivit.py +++ b/flood_forecast/multi_models/crossvivit.py @@ -38,7 +38,7 @@ def __init__(self, dim, heads=8, dim_head=64, dropout=0.0): else nn.Identity() ) - def forward(self, x: torch.Tensor) -> torch.Tensor: + def forward(self, x): b, n, _, h = *x.shape, self.heads qkv = self.to_qkv(x).chunk(3, dim=-1) q, k, v = map(lambda t: rearrange(t, "b n (h d) -> b h n d", h=h), qkv) diff --git a/flood_forecast/time_model.py b/flood_forecast/time_model.py index 135a43f09..e963cb70e 100644 --- a/flood_forecast/time_model.py +++ b/flood_forecast/time_model.py @@ -6,14 +6,9 @@ from datetime import datetime from flood_forecast.model_dict_function import pytorch_model_dict from flood_forecast.pre_dict import scaler_dict -from flood_forecast.preprocessing.pytorch_loaders import ( - CSVDataLoader, - AEDataloader, - TemporalLoader, - CSVSeriesIDLoader, - GeneralClassificationLoader, - VariableSequenceLength, -) +from flood_forecast.preprocessing.pytorch_loaders import (CSVDataLoader, AEDataloader, TemporalLoader, + CSVSeriesIDLoader, GeneralClassificationLoader, + VariableSequenceLength) from flood_forecast.gcp_integration.basic_utils import get_storage_client, upload_file from flood_forecast.utils import make_criterion_functions from flood_forecast.preprocessing.buil_dataset import get_data @@ -29,31 +24,22 @@ class TimeSeriesModel(ABC): """ def __init__( - self, - model_base: str, - training_data: str, - validation_data: str, - test_data: str, - params: Dict, - ): + self, + model_base: str, + training_data: str, + validation_data: str, + test_data: str, + params: Dict): self.params = params if "weight_path" in params: params["weight_path"] = get_data(params["weight_path"]) - self.model = self.load_model( - model_base, params["model_params"], params["weight_path"] - ) + self.model = self.load_model(model_base, params["model_params"], params["weight_path"]) else: self.model = self.load_model(model_base, params["model_params"]) # params["dataset_params"]["forecast_test_len"] = params["inference_params"]["hours_to_forecast"] - self.training = self.make_data_load( - training_data, params["dataset_params"], "train" - ) - self.validation = self.make_data_load( - validation_data, params["dataset_params"], "valid" - ) - self.test_data = self.make_data_load( - test_data, params["dataset_params"], "test" - ) + self.training = self.make_data_load(training_data, params["dataset_params"], "train") + self.validation = self.make_data_load(validation_data, params["dataset_params"], "valid") + self.test_data = self.make_data_load(test_data, params["dataset_params"], "test") if "GCS" in self.params and self.params["GCS"]: self.gcs_client = get_storage_client() else: @@ -62,9 +48,7 @@ def __init__( self.crit = make_criterion_functions(params["metrics"]) @abstractmethod - def load_model( - self, model_base: str, model_params: Dict, weight_path=None - ) -> object: + def load_model(self, model_base: str, model_params: Dict, weight_path=None) -> object: """ This function should load and return the model this will vary based on the underlying framework used @@ -88,9 +72,7 @@ def save_model(self, output_path: str): """ raise NotImplementedError - def upload_gcs( - self, save_path: str, name: str, file_type: str, epoch=0, bucket_name=None - ): + def upload_gcs(self, save_path: str, name: str, file_type: str, epoch=0, bucket_name=None): """ Function to upload model checkpoints to GCS """ @@ -99,17 +81,10 @@ def upload_gcs( bucket_name = os.environ["MODEL_BUCKET"] print("Data saved to: ") print(name) - upload_file( - bucket_name, - os.path.join("experiments", name), - save_path, - self.gcs_client, - ) + upload_file(bucket_name, os.path.join("experiments", name), save_path, self.gcs_client) online_path = os.path.join("gs://", bucket_name, "experiments", name) if self.wandb: - wandb.config.update( - {"gcs_m_path_" + str(epoch) + file_type: online_path} - ) + wandb.config.update({"gcs_m_path_" + str(epoch) + file_type: online_path}) def wandb_init(self): if self.params["wandb"]: @@ -118,8 +93,7 @@ def wandb_init(self): project=self.params["wandb"].get("project"), config=self.params, name=self.params["wandb"].get("name"), - tags=self.params["wandb"].get("tags"), - ), + tags=self.params["wandb"].get("tags")), return True elif "sweep" in self.params: print("Using Wandb config:") @@ -130,17 +104,14 @@ def wandb_init(self): class PyTorchForecast(TimeSeriesModel): def __init__( - self, - model_base: str, - training_data, - validation_data, - test_data, - params_dict: Dict, - ): - self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - super().__init__( - model_base, training_data, validation_data, test_data, params_dict - ) + self, + model_base: str, + training_data, + validation_data, + test_data, + params_dict: Dict): + self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + super().__init__(model_base, training_data, validation_data, test_data, params_dict) print("Torch is using " + str(self.device)) if "weight_path_add" in params_dict: self.__freeze_layers__(params_dict["weight_path_add"]) @@ -153,18 +124,14 @@ def __freeze_layers__(self, params: Dict): for parameter in self.model._modules[layer].parameters(): parameter.requires_grad = False - def load_model( - self, model_base: str, model_params: Dict, weight_path: str = None, strict=True - ): + def load_model(self, model_base: str, model_params: Dict, weight_path: str = None, strict=True): if model_base in pytorch_model_dict: model = pytorch_model_dict[model_base](**model_params) if weight_path: checkpoint = torch.load(weight_path, map_location=self.device) if "weight_path_add" in self.params: if "excluded_layers" in self.params["weight_path_add"]: - excluded_layers = self.params["weight_path_add"][ - "excluded_layers" - ] + excluded_layers = self.params["weight_path_add"]["excluded_layers"] for layer in excluded_layers: del checkpoint[layer] print("sucessfully deleted layers") @@ -179,10 +146,9 @@ def load_model( model.tgt_mask = model.tgt_mask.to(self.device) else: raise Exception( - "Error the model " - + model_base - + " was not found in the model dict. Please add it." - ) + "Error the model " + + model_base + + " was not found in the model dict. Please add it.") return model def save_model(self, final_path: str, epoch: int) -> None: @@ -220,15 +186,15 @@ def __re_add_params__(self, start_end_params: Dict, dataset_params, data_path): return start_end_params def make_data_load( - self, - data_path: str, - dataset_params: Dict, - loader_type: str, - the_class="default", - ): + self, + data_path: str, + dataset_params: Dict, + loader_type: str, + the_class="default"): start_end_params = {} the_class = dataset_params["class"] start_end_params = scaling_function(start_end_params, dataset_params) + # TODO clean up else if blocks if loader_type + "_start" in dataset_params: start_end_params["start_stamp"] = dataset_params[loader_type + "_start"] if loader_type + "_end" in dataset_params: @@ -257,8 +223,7 @@ def make_data_load( dataset_params["forecast_test_len"], dataset_params["target_col"], dataset_params["relevant_cols"], - **start_end_params - ) + **start_end_params) elif the_class == "default": loader = CSVDataLoader( data_path, @@ -266,54 +231,42 @@ def make_data_load( dataset_params["forecast_length"], dataset_params["target_col"], dataset_params["relevant_cols"], - **start_end_params - ) + **start_end_params) elif the_class == "AutoEncoder": loader = AEDataloader( - data_path, dataset_params["relevant_cols"], **start_end_params + data_path, + dataset_params["relevant_cols"], + **start_end_params ) elif the_class == "TemporalLoader": - start_end_params = self.__re_add_params__( - start_end_params, dataset_params, data_path - ) + start_end_params = self.__re_add_params__(start_end_params, dataset_params, data_path) label_len = 0 if "label_len" in dataset_params: label_len = dataset_params["label_len"] loader = TemporalLoader( - dataset_params["temporal_feats"], start_end_params, label_len=label_len - ) + dataset_params["temporal_feats"], + start_end_params, + label_len=label_len) elif the_class == "SeriesIDLoader": - start_end_params = self.__re_add_params__( - start_end_params, dataset_params, data_path - ) + start_end_params = self.__re_add_params__(start_end_params, dataset_params, data_path) loader = CSVSeriesIDLoader( dataset_params["series_id_col"], start_end_params, - dataset_params["return_method"], + dataset_params["return_method"] ) elif the_class == "GeneralClassificationLoader": dataset_params["forecast_length"] = 1 - start_end_params = self.__re_add_params__( - start_end_params, dataset_params, data_path - ) + start_end_params = self.__re_add_params__(start_end_params, dataset_params, data_path) start_end_params["sequence_length"] = dataset_params["sequence_length"] - loader = GeneralClassificationLoader( - start_end_params, dataset_params["n_classes"] - ) + loader = GeneralClassificationLoader(start_end_params, dataset_params["n_classes"]) elif the_class == "VariableSequenceLength": - start_end_params = self.__re_add_params__( - start_end_params, dataset_params, data_path - ) + start_end_params = self.__re_add_params__(start_end_params, dataset_params, data_path) if "pad_len" in dataset_params: pad_le = dataset_params["pad_len"] else: pad_le = None - loader = VariableSequenceLength( - dataset_params["series_marker_column"], - start_end_params, - pad_le, - dataset_params["task"], - ) + loader = VariableSequenceLength(dataset_params["series_marker_column"], start_end_params, + pad_le, dataset_params["task"]) else: # TODO support custom DataLoader @@ -330,9 +283,7 @@ def scaling_function(start_end_params, dataset_params): else: return {} if "scaler_params" in dataset_params: - scaler = scaler_dict[dataset_params[in_dataset_params]]( - **dataset_params["scaler_params"] - ) + scaler = scaler_dict[dataset_params[in_dataset_params]](**dataset_params["scaler_params"]) else: scaler = scaler_dict[dataset_params[in_dataset_params]]() start_end_params["scaling"] = scaler diff --git a/flood_forecast/transformer_xl/attn.py b/flood_forecast/transformer_xl/attn.py index 2d89f68db..481f3b6df 100644 --- a/flood_forecast/transformer_xl/attn.py +++ b/flood_forecast/transformer_xl/attn.py @@ -493,7 +493,7 @@ def __init__( self.to_out = nn.Sequential(nn.Linear(inner_dim, dim), nn.Dropout(dropout)) - def forward(self, src: torch.Tensor, src_pos_emb, tgt, tgt_pos_emb): + def forward(self, src, src_pos_emb, tgt, tgt_pos_emb): q = self.to_q(tgt) From 8cbf79bd345e040310db064bcb2d91079ae37371 Mon Sep 17 00:00:00 2001 From: isaacmg Date: Mon, 22 Jul 2024 13:56:32 -0500 Subject: [PATCH 11/38] adding core code. --- flood_forecast/multi_models/crossvivit.py | 19 +- flood_forecast/transformer_xl/attn.py | 203 +++++++++++++-------- requirements.txt | 2 +- tests/mult_modal_tests/test_cross_vivit.py | 10 +- 4 files changed, 145 insertions(+), 89 deletions(-) diff --git a/flood_forecast/multi_models/crossvivit.py b/flood_forecast/multi_models/crossvivit.py index 993bbcf50..4c753e481 100644 --- a/flood_forecast/multi_models/crossvivit.py +++ b/flood_forecast/multi_models/crossvivit.py @@ -8,6 +8,7 @@ from einops import rearrange, repeat from einops.layers.torch import Rearrange from torch import einsum, nn +from jaxtyping import Float from flood_forecast.transformer_xl.attn import ( SelfAttention, CrossAttention, @@ -101,10 +102,10 @@ def __init__( ): """The Video Vision Transformer (e.g. VIVIT) of the CrossVIVIT model. This model is based on the Arxiv paper: https://arxiv.org/abs/2103.15691. The below implementation has a few specific CrossVIVIT specific parameters - like whether to use the rotary. - :param dim: The embedding dimension. The authors generally use 384 for training the large model. + like whether to use the rotary embedding. + :param dim: The embedding dimension. The authors generally use a dimension of 384 for training the large models. :type dim: int - :param depth: The number of blocks to create. Commonly set to 4 for most tasks. + :param depth: The number of transformer blocks to create. Commonly set to 4 for most tasks. :type depth: int :param heads: The number of heads in the multi-head-attention mechanism. Usually set to a multiple of eight. :type heads: int @@ -152,13 +153,13 @@ def __init__( def forward( self, - src: torch.Tensor, - src_pos_emb: torch.Tensor, + src: Float[torch.Tensor, "batch_size image_dim context_length"], + src_pos_emb: Float[torch.Tensor, "batch_size image_dim context_length"], ): """ Performs the following computation in each layer: 1. Self-Attention on the source sequence - 2. FFN on the source sequence + 2. FFN on the source sequence. Args: src: Source sequence of shape [B, N, D] src_pos_emb: Positional embedding of source sequence's tokens of shape [B, N, D] @@ -166,10 +167,10 @@ def forward( attention_scores = {} for i in range(len(self.blocks)): - sattn, sff = self.blocks[i] + self_attn, sff = self.blocks[i] - out, sattn_scores = sattn(src, pos_emb=src_pos_emb) - attention_scores["self_attention"] = sattn_scores + out, self_attn_scores = self_attn(src, pos_emb=src_pos_emb) + attention_scores["self_attention"] = self_attn_scores src = out + src src = sff(src) + src diff --git a/flood_forecast/transformer_xl/attn.py b/flood_forecast/transformer_xl/attn.py index 481f3b6df..74bdda7d9 100644 --- a/flood_forecast/transformer_xl/attn.py +++ b/flood_forecast/transformer_xl/attn.py @@ -2,29 +2,31 @@ import torch.nn as nn import numpy as np from math import sqrt -from einops import rearrange +from einops import rearrange, repeat import torch.nn.functional as F import einsum -class TriangularCausalMask(): +class TriangularCausalMask: def __init__(self, B, L, device="cpu"): mask_shape = [B, 1, L, L] with torch.no_grad(): - self._mask = torch.triu(torch.ones(mask_shape, dtype=torch.bool), diagonal=1).to(device) + self._mask = torch.triu( + torch.ones(mask_shape, dtype=torch.bool), diagonal=1 + ).to(device) @property def mask(self): return self._mask -class ProbMask(): +class ProbMask: def __init__(self, B, H, L, index, scores, device="cpu"): _mask = torch.ones(L, scores.shape[-1], dtype=torch.bool).to(device).triu(1) _mask_ex = _mask[None, None, :].expand(B, H, L, scores.shape[-1]) - indicator = _mask_ex[torch.arange(B)[:, None, None], - torch.arange(H)[None, :, None], - index, :].to(device) + indicator = _mask_ex[ + torch.arange(B)[:, None, None], torch.arange(H)[None, :, None], index, : + ].to(device) self._mask = indicator.view(scores.shape).to(device) @property @@ -49,27 +51,53 @@ def forward(self, queries, keys, values, attn_mask, tau=None, delta=None): queries = self.kernel_method(queries) keys = self.kernel_method(keys) # incoming and outgoing - normalizer_row = 1.0 / (torch.einsum("nhld,nhd->nhl", queries + 1e-6, keys.sum(dim=2) + 1e-6)) - normalizer_col = 1.0 / (torch.einsum("nhsd,nhd->nhs", keys + 1e-6, queries.sum(dim=2) + 1e-6)) + normalizer_row = 1.0 / ( + torch.einsum("nhld,nhd->nhl", queries + 1e-6, keys.sum(dim=2) + 1e-6) + ) + normalizer_col = 1.0 / ( + torch.einsum("nhsd,nhd->nhs", keys + 1e-6, queries.sum(dim=2) + 1e-6) + ) # reweighting - normalizer_row_refine = ( - torch.einsum("nhld,nhd->nhl", queries + 1e-6, (keys * normalizer_col[:, :, :, None]).sum(dim=2) + 1e-6)) - normalizer_col_refine = ( - torch.einsum("nhsd,nhd->nhs", keys + 1e-6, (queries * normalizer_row[:, :, :, None]).sum(dim=2) + 1e-6)) + normalizer_row_refine = torch.einsum( + "nhld,nhd->nhl", + queries + 1e-6, + (keys * normalizer_col[:, :, :, None]).sum(dim=2) + 1e-6, + ) + normalizer_col_refine = torch.einsum( + "nhsd,nhd->nhs", + keys + 1e-6, + (queries * normalizer_row[:, :, :, None]).sum(dim=2) + 1e-6, + ) # competition and allocation normalizer_row_refine = torch.sigmoid( - normalizer_row_refine * (float(queries.shape[2]) / float(keys.shape[2]))) - normalizer_col_refine = torch.softmax(normalizer_col_refine, dim=-1) * keys.shape[2] # B h L vis + normalizer_row_refine * (float(queries.shape[2]) / float(keys.shape[2])) + ) + normalizer_col_refine = ( + torch.softmax(normalizer_col_refine, dim=-1) * keys.shape[2] + ) # B h L vis # multiply kv = keys.transpose(-2, -1) @ (values * normalizer_col_refine[:, :, :, None]) - x = (((queries @ kv) * normalizer_row[:, :, :, None]) * - normalizer_row_refine[:, :, :, None]).transpose(1, 2).contiguous() + x = ( + ( + ((queries @ kv) * normalizer_row[:, :, :, None]) + * normalizer_row_refine[:, :, :, None] + ) + .transpose(1, 2) + .contiguous() + ) return x, None # Code implementation from https://github.com/shreyansh26/FlashAttention-PyTorch class FlashAttention(nn.Module): - def __init__(self, mask_flag=True, factor=5, scale=None, attention_dropout=0.1, output_attention=False): + def __init__( + self, + mask_flag=True, + factor=5, + scale=None, + attention_dropout=0.1, + output_attention=False, + ): super(FlashAttention, self).__init__() self.scale = scale self.mask_flag = mask_flag @@ -85,9 +113,9 @@ def flash_attention_forward(self, Q, K, V, mask=None): l3 = torch.zeros(Q.shape[:-1])[..., None] m = torch.ones(Q.shape[:-1])[..., None] * NEG_INF - O1 = O1.to(device='cuda') - l3 = l3.to(device='cuda') - m = m.to(device='cuda') + O1 = O1.to(device="cuda") + l3 = l3.to(device="cuda") + m = m.to(device="cuda") Q_BLOCK_SIZE = min(BLOCK_SIZE, Q.shape[-1]) KV_BLOCK_SIZE = BLOCK_SIZE @@ -120,27 +148,31 @@ def flash_attention_forward(self, Q, K, V, mask=None): scale = 1 / np.sqrt(Q.shape[-1]) Qi_scaled = Qi * scale - S_ij = torch.einsum('... i d, ... j d -> ... i j', Qi_scaled, Kj) + S_ij = torch.einsum("... i d, ... j d -> ... i j", Qi_scaled, Kj) if mask is not None: # Masking - maskj_temp = rearrange(maskj, 'b j -> b 1 1 j') + maskj_temp = rearrange(maskj, "b j -> b 1 1 j") S_ij = torch.where(maskj_temp > 0, S_ij, NEG_INF) m_block_ij, _ = torch.max(S_ij, dim=-1, keepdims=True) P_ij = torch.exp(S_ij - m_block_ij) if mask is not None: # Masking - P_ij = torch.where(maskj_temp > 0, P_ij, 0.) + P_ij = torch.where(maskj_temp > 0, P_ij, 0.0) l_block_ij = torch.sum(P_ij, dim=-1, keepdims=True) + EPSILON - P_ij_Vj = torch.einsum('... i j, ... j d -> ... i d', P_ij, Vj) + P_ij_Vj = torch.einsum("... i j, ... j d -> ... i d", P_ij, Vj) mi_new = torch.maximum(m_block_ij, mi) - li_new = torch.exp(mi - mi_new) * li + torch.exp(m_block_ij - mi_new) * l_block_ij + li_new = ( + torch.exp(mi - mi_new) * li + + torch.exp(m_block_ij - mi_new) * l_block_ij + ) O_BLOCKS[i] = (li / li_new) * torch.exp(mi - mi_new) * Oi + ( - torch.exp(m_block_ij - mi_new) / li_new) * P_ij_Vj + torch.exp(m_block_ij - mi_new) / li_new + ) * P_ij_Vj l_BLOCKS[i] = li_new m_BLOCKS[i] = mi_new @@ -150,14 +182,24 @@ def flash_attention_forward(self, Q, K, V, mask=None): return O, l3, m def forward(self, queries, keys, values, attn_mask, tau=None, delta=None): - res = \ - self.flash_attention_forward(queries.permute(0, 2, 1, 3), keys.permute(0, 2, 1, 3), values.permute(0, 2, 1, 3), # noqa - attn_mask)[0] + res = self.flash_attention_forward( + queries.permute(0, 2, 1, 3), + keys.permute(0, 2, 1, 3), + values.permute(0, 2, 1, 3), # noqa + attn_mask, + )[0] return res.permute(0, 2, 1, 3).contiguous(), None class FullAttention(nn.Module): - def __init__(self, mask_flag=True, factor=5, scale=None, attention_dropout=0.1, output_attention=False): + def __init__( + self, + mask_flag=True, + factor=5, + scale=None, + attention_dropout=0.1, + output_attention=False, + ): super(FullAttention, self).__init__() self.scale = scale self.mask_flag = mask_flag @@ -167,7 +209,7 @@ def __init__(self, mask_flag=True, factor=5, scale=None, attention_dropout=0.1, def forward(self, queries, keys, values, attn_mask, tau=None, delta=None): B, L, H, E = queries.shape _, S, _, D = values.shape - scale = self.scale or 1. / sqrt(E) + scale = self.scale or 1.0 / sqrt(E) scores = torch.einsum("blhe,bshe->bhls", queries, keys) @@ -188,7 +230,14 @@ def forward(self, queries, keys, values, attn_mask, tau=None, delta=None): # Code implementation from https://github.com/zhouhaoyi/Informer2020 class ProbAttention(nn.Module): - def __init__(self, mask_flag=True, factor=5, scale=None, attention_dropout=0.1, output_attention=False): + def __init__( + self, + mask_flag=True, + factor=5, + scale=None, + attention_dropout=0.1, + output_attention=False, + ): super(ProbAttention, self).__init__() self.factor = factor self.scale = scale @@ -205,19 +254,17 @@ def _prob_QK(self, Q, K, sample_k, n_top): # n_top: c*ln(L_q) K_expand = K.unsqueeze(-3).expand(B, H, L_Q, L_K, E) # real U = U_part(factor*ln(L_k))*L_q index_sample = torch.randint(L_K, (L_Q, sample_k)) - K_sample = K_expand[:, :, torch.arange( - L_Q).unsqueeze(1), index_sample, :] - Q_K_sample = torch.matmul( - Q.unsqueeze(-2), K_sample.transpose(-2, -1)).squeeze() + K_sample = K_expand[:, :, torch.arange(L_Q).unsqueeze(1), index_sample, :] + Q_K_sample = torch.matmul(Q.unsqueeze(-2), K_sample.transpose(-2, -1)).squeeze() # find the Top_k query with sparisty measurement M = Q_K_sample.max(-1)[0] - torch.div(Q_K_sample.sum(-1), L_K) M_top = M.topk(n_top, sorted=False)[1] # use the reduced Q to calculate Q_K - Q_reduce = Q[torch.arange(B)[:, None, None], - torch.arange(H)[None, :, None], - M_top, :] # factor*ln(L_q) + Q_reduce = Q[ + torch.arange(B)[:, None, None], torch.arange(H)[None, :, None], M_top, : + ] # factor*ln(L_q) Q_K = torch.matmul(Q_reduce, K.transpose(-2, -1)) # factor*ln(L_q)*L_k return Q_K, M_top @@ -227,11 +274,10 @@ def _get_initial_context(self, V, L_Q): if not self.mask_flag: # V_sum = V.sum(dim=-2) V_sum = V.mean(dim=-2) - contex = V_sum.unsqueeze(-2).expand(B, H, - L_Q, V_sum.shape[-1]).clone() + contex = V_sum.unsqueeze(-2).expand(B, H, L_Q, V_sum.shape[-1]).clone() else: # use mask # requires that L_Q == L_V, i.e. for self-attention only - assert (L_Q == L_V) + assert L_Q == L_V contex = V.cumsum(dim=-2) return contex @@ -244,14 +290,14 @@ def _update_context(self, context_in, V, scores, index, L_Q, attn_mask): attn = torch.softmax(scores, dim=-1) # nn.Softmax(dim=-1)(scores) - context_in[torch.arange(B)[:, None, None], - torch.arange(H)[None, :, None], - index, :] = torch.matmul(attn, V).type_as(context_in) + context_in[ + torch.arange(B)[:, None, None], torch.arange(H)[None, :, None], index, : + ] = torch.matmul(attn, V).type_as(context_in) if self.output_attention: - attns = (torch.ones([B, H, L_V, L_V]) / - L_V).type_as(attn).to(attn.device) - attns[torch.arange(B)[:, None, None], torch.arange(H)[ - None, :, None], index, :] = attn + attns = (torch.ones([B, H, L_V, L_V]) / L_V).type_as(attn).to(attn.device) + attns[ + torch.arange(B)[:, None, None], torch.arange(H)[None, :, None], index, : + ] = attn return (context_in, attns) else: return (context_in, None) @@ -264,33 +310,30 @@ def forward(self, queries, keys, values, attn_mask, tau=None, delta=None): keys = keys.transpose(2, 1) values = values.transpose(2, 1) - U_part = self.factor * \ - np.ceil(np.log(L_K)).astype('int').item() # c*ln(L_k) - u = self.factor * \ - np.ceil(np.log(L_Q)).astype('int').item() # c*ln(L_q) + U_part = self.factor * np.ceil(np.log(L_K)).astype("int").item() # c*ln(L_k) + u = self.factor * np.ceil(np.log(L_Q)).astype("int").item() # c*ln(L_q) U_part = U_part if U_part < L_K else L_K u = u if u < L_Q else L_Q - scores_top, index = self._prob_QK( - queries, keys, sample_k=U_part, n_top=u) + scores_top, index = self._prob_QK(queries, keys, sample_k=U_part, n_top=u) # add scale factor - scale = self.scale or 1. / sqrt(D) + scale = self.scale or 1.0 / sqrt(D) if scale is not None: scores_top = scores_top * scale # get the context context = self._get_initial_context(values, L_Q) # update the context with selected top_k queries context, attn = self._update_context( - context, values, scores_top, index, L_Q, attn_mask) + context, values, scores_top, index, L_Q, attn_mask + ) return context.contiguous(), attn class AttentionLayer(nn.Module): - def __init__(self, attention, d_model, n_heads, d_keys=None, - d_values=None): + def __init__(self, attention, d_model, n_heads, d_keys=None, d_values=None): super(AttentionLayer, self).__init__() d_keys = d_keys or (d_model // n_heads) @@ -313,12 +356,7 @@ def forward(self, queries, keys, values, attn_mask, tau=None, delta=None): values = self.value_projection(values).view(B, S, H, -1) out, attn = self.inner_attention( - queries, - keys, - values, - attn_mask, - tau=tau, - delta=delta + queries, keys, values, attn_mask, tau=tau, delta=delta ) out = out.view(B, L, -1) @@ -326,17 +364,27 @@ def forward(self, queries, keys, values, attn_mask, tau=None, delta=None): class ReformerLayer(nn.Module): - def __init__(self, attention, d_model, n_heads, d_keys=None, - d_values=None, causal=False, bucket_size=4, n_hashes=4): + def __init__( + self, + attention, + d_model, + n_heads, + d_keys=None, + d_values=None, + causal=False, + bucket_size=4, + n_hashes=4, + ): super().__init__() import LSHSelfAttention + self.bucket_size = bucket_size self.attn = LSHSelfAttention( dim=d_model, heads=n_heads, bucket_size=bucket_size, n_hashes=n_hashes, - causal=causal + causal=causal, ) def fit_length(self, queries): @@ -347,7 +395,9 @@ def fit_length(self, queries): else: # fill the time series fill_len = (self.bucket_size * 2) - (N % (self.bucket_size * 2)) - return torch.cat([queries, torch.zeros([B, fill_len, C]).to(queries.device)], dim=1) + return torch.cat( + [queries, torch.zeros([B, fill_len, C]).to(queries.device)], dim=1 + ) def forward(self, queries, keys, values, attn_mask, tau, delta): # in Reformer: defalut queries=keys @@ -355,6 +405,7 @@ def forward(self, queries, keys, values, attn_mask, tau, delta): queries = self.attn(self.fit_length(queries))[:, :N, :] return queries, None + def rotate_every_two(x): x = rearrange(x, "... (d j) -> ... d j", j=2) x1, x2 = x.unbind(dim=-1) @@ -428,7 +479,7 @@ def __init__( self.to_out = nn.Sequential(nn.Linear(inner_dim, dim), nn.Dropout(dropout)) - def forward(self, x, pos_emb): + def forward(self, x: torch.Tensor, pos_emb: torch.Tensor): """ Args: x: Sequence of shape [B, N, D] @@ -443,7 +494,7 @@ def forward(self, x, pos_emb): ) if self.use_rotary: - + # Used to map dimensions from dimension. Currently, getting (512, 128) when expecting 3-D tensor. sin, cos = map( lambda t: repeat(t, "b n d -> (b h) n d", h=self.heads), pos_emb ) @@ -471,7 +522,7 @@ class CrossAttention(nn.Module): def __init__( self, dim: int, - heads: int =8, + heads: int = 8, dim_head: int = 64, dropout: int = 0.0, use_rotary: bool = True, @@ -493,8 +544,7 @@ def __init__( self.to_out = nn.Sequential(nn.Linear(inner_dim, dim), nn.Dropout(dropout)) - def forward(self, src, src_pos_emb, tgt, tgt_pos_emb): - + def forward(self, src: torch.Tensor, src_pos_emb, tgt, tgt_pos_emb): q = self.to_q(tgt) qkv = (q, *self.to_kv(src).chunk(2, dim=-1)) @@ -504,7 +554,7 @@ def forward(self, src, src_pos_emb, tgt, tgt_pos_emb): ) if self.use_rotary: - # apply 2d rotary embeddings to queries and keys + # apply 2-d rotary embeddings to queries and keys sin_src, cos_src = map( lambda t: repeat(t, "b n d -> (b h) n d", h=self.heads), src_pos_emb @@ -531,4 +581,3 @@ def forward(self, src, src_pos_emb, tgt, tgt_pos_emb): out = einsum("b i j, b j d -> b i d", attn, v) out = rearrange(out, "(b h) n d -> b n (h d)", h=self.heads) return self.to_out(out), attn - diff --git a/requirements.txt b/requirements.txt index b86314b3a..778ba1cb3 100644 --- a/requirements.txt +++ b/requirements.txt @@ -24,4 +24,4 @@ sphinx einops pytorch-tsmixer einsum -torchtyping \ No newline at end of file +jaxtyping \ No newline at end of file diff --git a/tests/mult_modal_tests/test_cross_vivit.py b/tests/mult_modal_tests/test_cross_vivit.py index 91d671832..0a1cbc5a6 100644 --- a/tests/mult_modal_tests/test_cross_vivit.py +++ b/tests/mult_modal_tests/test_cross_vivit.py @@ -1,6 +1,7 @@ import unittest import torch from flood_forecast.multi_models.crossvivit import RoCrossViViT, VisionTransformer +from flood_forecast.transformer_xl.attn import SelfAttention from flood_forecast.transformer_xl.data_embedding import CyclicalEmbedding, NeRF_embedding @@ -9,13 +10,18 @@ def setUp(self): self.crossvivit = RoCrossViViT(image_size=(128, 128), patch_size=(8, 8), time_coords_encoder=NeRF_embedding(), **{"max_freq":12}) def test_vivit_model(self): - self.vivit_model = VisionTransformer(128, 5, 8, 128, 128, [512, 512, 512] ) + self.vivit_model = VisionTransformer(128, 5, 8, 128, 128, [512, 512, 512], dropout=0.1) + self.vivit_model(torch.rand(5, 512, 128), torch.rand(5, 512, 128)) pass def test_forward(self): - x = self.crossvivit(torch.randn(1, 3, 128, 128)) + x = self.crossvivit(torch.randn(1, 3, 128, 128), torch.randn(1, 3, 128, 128), torch.randn(1, 3, 128, 128), ) self.assertEqual(x.shape, (1, 1000)) + def test_self_attention_dims(self): + self.self_attention = SelfAttention(dim=128, use_rotary=True) + self.self_attention(torch.rand(5, 512, 128), torch.rand(5,512, 128)) + if __name__ == '__main__': unittest.main() From e8b669b44110e0de1e555df7003542d523b1ea09 Mon Sep 17 00:00:00 2001 From: isaacmg Date: Thu, 25 Jul 2024 10:59:54 -0500 Subject: [PATCH 12/38] code fixes for accurate tuples --- flood_forecast/multi_models/crossvivit.py | 8 ++++---- flood_forecast/pytorch_training.py | 3 +-- 2 files changed, 5 insertions(+), 6 deletions(-) diff --git a/flood_forecast/multi_models/crossvivit.py b/flood_forecast/multi_models/crossvivit.py index 4c753e481..ba1da5c24 100644 --- a/flood_forecast/multi_models/crossvivit.py +++ b/flood_forecast/multi_models/crossvivit.py @@ -154,15 +154,15 @@ def __init__( def forward( self, src: Float[torch.Tensor, "batch_size image_dim context_length"], - src_pos_emb: Float[torch.Tensor, "batch_size image_dim context_length"], - ): + src_pos_emb: Tuple[Float[torch.Tensor, "batch_size image_dim/2 context_length"], Float[torch.Tensor, "batch_size image_dim/2 context_length"]] + ) -> Tuple[Float[torch.Tensor, "batch_size image_dim context_length"], dict[str, Float[torch.Tensor, "batch_size image_dim context_length"]]]: """ Performs the following computation in each layer: 1. Self-Attention on the source sequence 2. FFN on the source sequence. Args: - src: Source sequence of shape [B, N, D] - src_pos_emb: Positional embedding of source sequence's tokens of shape [B, N, D] + src: Source sequence of shape [B, N, D]. + src_pos_emb: Positional embedding tuple (sin, cos) of source sequence's tokens of shape [B, N, D] """ attention_scores = {} diff --git a/flood_forecast/pytorch_training.py b/flood_forecast/pytorch_training.py index 51b5c241b..b6518b44a 100644 --- a/flood_forecast/pytorch_training.py +++ b/flood_forecast/pytorch_training.py @@ -22,7 +22,7 @@ def multi_crit(crit_multi: List, output, labels, valid=None): :param crit_multi: _description_ :type crit_multi: List - :param output: _description_ + :param output: _descaription_ :type output: _type_ :param labels: _description_ :type labels: _type_ @@ -106,7 +106,6 @@ def train_transformer_style( :type forward_params: Dict, optional :param model_filepath: The file path to load modeel weights from, defaults to "model_save" :type model_filepath: str, optional - :raises ValueError: Has an error """ use_wandb = model.wandb es = None From c2cd3702c615f9f39a03ee7a6d22301954c1443a Mon Sep 17 00:00:00 2001 From: isaacmg Date: Thu, 1 Aug 2024 21:34:49 -0500 Subject: [PATCH 13/38] adding more code 4 --- flood_forecast/multi_models/crossvivit.py | 2 +- flood_forecast/transformer_xl/attn.py | 10 +++-- .../transformer_xl/data_embedding.py | 3 +- tests/mult_modal_tests/test_cross_vivit.py | 39 ++++++++++++++++--- 4 files changed, 41 insertions(+), 13 deletions(-) diff --git a/flood_forecast/multi_models/crossvivit.py b/flood_forecast/multi_models/crossvivit.py index ba1da5c24..fcd8c463c 100644 --- a/flood_forecast/multi_models/crossvivit.py +++ b/flood_forecast/multi_models/crossvivit.py @@ -120,7 +120,7 @@ def __init__( :type dropout: float, optional :param use_rotary: Whether to use rotary positional embeddings, defaults to True :type use_rotary: bool, optional - :param use_glu: _description_, defaults to True + :param use_glu: Weather to use gated linear units , defaults to True :type use_glu: bool, optional """ diff --git a/flood_forecast/transformer_xl/attn.py b/flood_forecast/transformer_xl/attn.py index 74bdda7d9..f7508d3e0 100644 --- a/flood_forecast/transformer_xl/attn.py +++ b/flood_forecast/transformer_xl/attn.py @@ -1,10 +1,12 @@ +from typing import Tuple + import torch import torch.nn as nn import numpy as np from math import sqrt from einops import rearrange, repeat import torch.nn.functional as F -import einsum +from torch import einsum class TriangularCausalMask: @@ -458,7 +460,7 @@ def forward(self, x): class SelfAttention(nn.Module): def __init__( self, - dim, + dim: int, heads=8, dim_head=64, dropout=0.0, @@ -479,11 +481,11 @@ def __init__( self.to_out = nn.Sequential(nn.Linear(inner_dim, dim), nn.Dropout(dropout)) - def forward(self, x: torch.Tensor, pos_emb: torch.Tensor): + def forward(self, x: torch.Tensor, pos_emb: Tuple[torch.Tensor, torch.Tensor]): """ Args: x: Sequence of shape [B, N, D] - pos_emb: Positional embedding of sequence's tokens of shape [B, N, D] + pos_emb: Positional embedding of sequence's tokens of shape [B, N, D/2] """ q = self.to_q(x) diff --git a/flood_forecast/transformer_xl/data_embedding.py b/flood_forecast/transformer_xl/data_embedding.py index 1069c105a..33149bae2 100644 --- a/flood_forecast/transformer_xl/data_embedding.py +++ b/flood_forecast/transformer_xl/data_embedding.py @@ -222,7 +222,7 @@ def get_emb(sin_inp): class PositionalEncoding2D(nn.Module): - def __init__(self, channels): + def __init__(self, channels: int): """ :param channels: The last dimension of the tensor you want to apply pos emb to. """ @@ -235,7 +235,6 @@ def __init__(self, channels): def forward(self, coords): """ - :param tensor: A 4d tensor of size (batch_size, ch, x, y) :param coords: A 4d tensor of size (batch_size, num_coords, x, y) :return: Positional Encoding Matrix of size (batch_size, x, y, ch) """ diff --git a/tests/mult_modal_tests/test_cross_vivit.py b/tests/mult_modal_tests/test_cross_vivit.py index 0a1cbc5a6..c8ef45c7f 100644 --- a/tests/mult_modal_tests/test_cross_vivit.py +++ b/tests/mult_modal_tests/test_cross_vivit.py @@ -2,25 +2,52 @@ import torch from flood_forecast.multi_models.crossvivit import RoCrossViViT, VisionTransformer from flood_forecast.transformer_xl.attn import SelfAttention -from flood_forecast.transformer_xl.data_embedding import CyclicalEmbedding, NeRF_embedding +from flood_forecast.transformer_xl.data_embedding import CyclicalEmbedding, NeRF_embedding, PositionalEncoding2D class TestCrossVivVit(unittest.TestCase): def setUp(self): - self.crossvivit = RoCrossViViT(image_size=(128, 128), patch_size=(8, 8), time_coords_encoder=NeRF_embedding(), **{"max_freq":12}) + self.crossvivit = RoCrossViViT(image_size=(128, 128), patch_size=(8, 8), time_coords_encoder=CyclicalEmbedding(), **{"max_freq":12}) + + def test_positional_encoding_forward(self): + """ + Test the positional encoding forward pass. + """ + positional_encoding = PositionalEncoding2D(128) + coords = torch.rand(5, 2, 32, 32) + output = positional_encoding(coords) + self.assertEqual(output.shape, (5, 32, 32, 128)) def test_vivit_model(self): self.vivit_model = VisionTransformer(128, 5, 8, 128, 128, [512, 512, 512], dropout=0.1) - self.vivit_model(torch.rand(5, 512, 128), torch.rand(5, 512, 128)) - pass + out = self.vivit_model(torch.rand(5, 512, 128), (torch.rand(5, 512, 64), torch.rand(5, 512, 64))) + assert out[0].shape == (5, 512, 128) def test_forward(self): - x = self.crossvivit(torch.randn(1, 3, 128, 128), torch.randn(1, 3, 128, 128), torch.randn(1, 3, 128, 128), ) + """ + ctx (torch.Tensor): Context frames of shape [B, T, C, H, W] + ctx_coords (torch.Tensor): Coordinates of context frames of shape [B, 2, H, W] + ts (torch.Tensor): Station timeseries of shape [B, T, C] + ts_coords (torch.Tensor): Station coordinates of shape [B, 2, 1, 1] + time_coords (torch.Tensor): Time coordinates of shape [B, T, C, H, W] + mask (bool): Whether to mask or not. Useful for inference. + """ + # The context tensor + ctx_tensor = torch.rand(5, 10, 12, 120, 120) + ctx_coords = torch.rand(5, 2, 120, 120) + ts = torch.rand(5, 10, 12) + time_coords = torch.rand(5, 10, 12, 120, 120) + ts_coords = torch.rand(5, 2, 1, 1) + mask = True + x = self.crossvivit(ctx_tensor, ctx_coords, ts, ts_coords, time_coords=time_coords, mask=True) self.assertEqual(x.shape, (1, 1000)) def test_self_attention_dims(self): + """ + Test the self attention layer with the correct dimensions. + """ self.self_attention = SelfAttention(dim=128, use_rotary=True) - self.self_attention(torch.rand(5, 512, 128), torch.rand(5,512, 128)) + self.self_attention(torch.rand(5, 512, 128), (torch.rand(5, 512, 64), torch.rand(5, 512, 64))) if __name__ == '__main__': From 5356a0eb0e6309fb12f10fb82d275960be364077 Mon Sep 17 00:00:00 2001 From: isaacmg Date: Thu, 1 Aug 2024 21:36:14 -0500 Subject: [PATCH 14/38] Revert "adding more code 4" This reverts commit c2cd3702c615f9f39a03ee7a6d22301954c1443a. --- flood_forecast/multi_models/crossvivit.py | 2 +- flood_forecast/transformer_xl/attn.py | 10 ++--- .../transformer_xl/data_embedding.py | 3 +- tests/mult_modal_tests/test_cross_vivit.py | 39 +++---------------- 4 files changed, 13 insertions(+), 41 deletions(-) diff --git a/flood_forecast/multi_models/crossvivit.py b/flood_forecast/multi_models/crossvivit.py index fcd8c463c..ba1da5c24 100644 --- a/flood_forecast/multi_models/crossvivit.py +++ b/flood_forecast/multi_models/crossvivit.py @@ -120,7 +120,7 @@ def __init__( :type dropout: float, optional :param use_rotary: Whether to use rotary positional embeddings, defaults to True :type use_rotary: bool, optional - :param use_glu: Weather to use gated linear units , defaults to True + :param use_glu: _description_, defaults to True :type use_glu: bool, optional """ diff --git a/flood_forecast/transformer_xl/attn.py b/flood_forecast/transformer_xl/attn.py index f7508d3e0..74bdda7d9 100644 --- a/flood_forecast/transformer_xl/attn.py +++ b/flood_forecast/transformer_xl/attn.py @@ -1,12 +1,10 @@ -from typing import Tuple - import torch import torch.nn as nn import numpy as np from math import sqrt from einops import rearrange, repeat import torch.nn.functional as F -from torch import einsum +import einsum class TriangularCausalMask: @@ -460,7 +458,7 @@ def forward(self, x): class SelfAttention(nn.Module): def __init__( self, - dim: int, + dim, heads=8, dim_head=64, dropout=0.0, @@ -481,11 +479,11 @@ def __init__( self.to_out = nn.Sequential(nn.Linear(inner_dim, dim), nn.Dropout(dropout)) - def forward(self, x: torch.Tensor, pos_emb: Tuple[torch.Tensor, torch.Tensor]): + def forward(self, x: torch.Tensor, pos_emb: torch.Tensor): """ Args: x: Sequence of shape [B, N, D] - pos_emb: Positional embedding of sequence's tokens of shape [B, N, D/2] + pos_emb: Positional embedding of sequence's tokens of shape [B, N, D] """ q = self.to_q(x) diff --git a/flood_forecast/transformer_xl/data_embedding.py b/flood_forecast/transformer_xl/data_embedding.py index 33149bae2..1069c105a 100644 --- a/flood_forecast/transformer_xl/data_embedding.py +++ b/flood_forecast/transformer_xl/data_embedding.py @@ -222,7 +222,7 @@ def get_emb(sin_inp): class PositionalEncoding2D(nn.Module): - def __init__(self, channels: int): + def __init__(self, channels): """ :param channels: The last dimension of the tensor you want to apply pos emb to. """ @@ -235,6 +235,7 @@ def __init__(self, channels: int): def forward(self, coords): """ + :param tensor: A 4d tensor of size (batch_size, ch, x, y) :param coords: A 4d tensor of size (batch_size, num_coords, x, y) :return: Positional Encoding Matrix of size (batch_size, x, y, ch) """ diff --git a/tests/mult_modal_tests/test_cross_vivit.py b/tests/mult_modal_tests/test_cross_vivit.py index c8ef45c7f..0a1cbc5a6 100644 --- a/tests/mult_modal_tests/test_cross_vivit.py +++ b/tests/mult_modal_tests/test_cross_vivit.py @@ -2,52 +2,25 @@ import torch from flood_forecast.multi_models.crossvivit import RoCrossViViT, VisionTransformer from flood_forecast.transformer_xl.attn import SelfAttention -from flood_forecast.transformer_xl.data_embedding import CyclicalEmbedding, NeRF_embedding, PositionalEncoding2D +from flood_forecast.transformer_xl.data_embedding import CyclicalEmbedding, NeRF_embedding class TestCrossVivVit(unittest.TestCase): def setUp(self): - self.crossvivit = RoCrossViViT(image_size=(128, 128), patch_size=(8, 8), time_coords_encoder=CyclicalEmbedding(), **{"max_freq":12}) - - def test_positional_encoding_forward(self): - """ - Test the positional encoding forward pass. - """ - positional_encoding = PositionalEncoding2D(128) - coords = torch.rand(5, 2, 32, 32) - output = positional_encoding(coords) - self.assertEqual(output.shape, (5, 32, 32, 128)) + self.crossvivit = RoCrossViViT(image_size=(128, 128), patch_size=(8, 8), time_coords_encoder=NeRF_embedding(), **{"max_freq":12}) def test_vivit_model(self): self.vivit_model = VisionTransformer(128, 5, 8, 128, 128, [512, 512, 512], dropout=0.1) - out = self.vivit_model(torch.rand(5, 512, 128), (torch.rand(5, 512, 64), torch.rand(5, 512, 64))) - assert out[0].shape == (5, 512, 128) + self.vivit_model(torch.rand(5, 512, 128), torch.rand(5, 512, 128)) + pass def test_forward(self): - """ - ctx (torch.Tensor): Context frames of shape [B, T, C, H, W] - ctx_coords (torch.Tensor): Coordinates of context frames of shape [B, 2, H, W] - ts (torch.Tensor): Station timeseries of shape [B, T, C] - ts_coords (torch.Tensor): Station coordinates of shape [B, 2, 1, 1] - time_coords (torch.Tensor): Time coordinates of shape [B, T, C, H, W] - mask (bool): Whether to mask or not. Useful for inference. - """ - # The context tensor - ctx_tensor = torch.rand(5, 10, 12, 120, 120) - ctx_coords = torch.rand(5, 2, 120, 120) - ts = torch.rand(5, 10, 12) - time_coords = torch.rand(5, 10, 12, 120, 120) - ts_coords = torch.rand(5, 2, 1, 1) - mask = True - x = self.crossvivit(ctx_tensor, ctx_coords, ts, ts_coords, time_coords=time_coords, mask=True) + x = self.crossvivit(torch.randn(1, 3, 128, 128), torch.randn(1, 3, 128, 128), torch.randn(1, 3, 128, 128), ) self.assertEqual(x.shape, (1, 1000)) def test_self_attention_dims(self): - """ - Test the self attention layer with the correct dimensions. - """ self.self_attention = SelfAttention(dim=128, use_rotary=True) - self.self_attention(torch.rand(5, 512, 128), (torch.rand(5, 512, 64), torch.rand(5, 512, 64))) + self.self_attention(torch.rand(5, 512, 128), torch.rand(5,512, 128)) if __name__ == '__main__': From 0af95833ec5da57fb6600b71a69983b7e59c1b26 Mon Sep 17 00:00:00 2001 From: isaacmg Date: Fri, 2 Aug 2024 12:19:37 -0500 Subject: [PATCH 15/38] re-adding code to ff --- flood_forecast/multi_models/crossvivit.py | 2 +- flood_forecast/pytorch_training.py | 2 +- .../transformer_xl/data_embedding.py | 8 ++-- tests/mult_modal_tests/test_cross_vivit.py | 39 ++++++++++++++++--- 4 files changed, 39 insertions(+), 12 deletions(-) diff --git a/flood_forecast/multi_models/crossvivit.py b/flood_forecast/multi_models/crossvivit.py index ba1da5c24..fcd8c463c 100644 --- a/flood_forecast/multi_models/crossvivit.py +++ b/flood_forecast/multi_models/crossvivit.py @@ -120,7 +120,7 @@ def __init__( :type dropout: float, optional :param use_rotary: Whether to use rotary positional embeddings, defaults to True :type use_rotary: bool, optional - :param use_glu: _description_, defaults to True + :param use_glu: Weather to use gated linear units , defaults to True :type use_glu: bool, optional """ diff --git a/flood_forecast/pytorch_training.py b/flood_forecast/pytorch_training.py index b6518b44a..42608b354 100644 --- a/flood_forecast/pytorch_training.py +++ b/flood_forecast/pytorch_training.py @@ -18,7 +18,7 @@ def multi_crit(crit_multi: List, output, labels, valid=None): - """_summary_ + """Used for computing the loss when there are multiple criteria. :param crit_multi: _description_ :type crit_multi: List diff --git a/flood_forecast/transformer_xl/data_embedding.py b/flood_forecast/transformer_xl/data_embedding.py index 1069c105a..8508ab194 100644 --- a/flood_forecast/transformer_xl/data_embedding.py +++ b/flood_forecast/transformer_xl/data_embedding.py @@ -7,7 +7,7 @@ class AxialRotaryEmbedding(nn.Module): - def __init__(self, dim, freq_type="lucidrains", **kwargs): + def __init__(self, dim: int, freq_type="lucidrains", **kwargs): super().__init__() self.dim = dim self.freq_type = freq_type @@ -57,11 +57,11 @@ def forward(self, coords: torch.Tensor): class PositionalEmbedding(nn.Module): def __init__(self, d_model, max_len=5000): - """[summary] + """Create the positional embedding for use in the transformer and attention mechanisms. - :param d_model: [description] + :param d_model: The dimension of the positional embedding. :type d_model: int - :param max_len: [description], defaults to 5000 + :param max_len: The max length of the forecast_history, defaults to 5000 :type max_len: int, optional """ super(PositionalEmbedding, self).__init__() diff --git a/tests/mult_modal_tests/test_cross_vivit.py b/tests/mult_modal_tests/test_cross_vivit.py index 0a1cbc5a6..c8ef45c7f 100644 --- a/tests/mult_modal_tests/test_cross_vivit.py +++ b/tests/mult_modal_tests/test_cross_vivit.py @@ -2,25 +2,52 @@ import torch from flood_forecast.multi_models.crossvivit import RoCrossViViT, VisionTransformer from flood_forecast.transformer_xl.attn import SelfAttention -from flood_forecast.transformer_xl.data_embedding import CyclicalEmbedding, NeRF_embedding +from flood_forecast.transformer_xl.data_embedding import CyclicalEmbedding, NeRF_embedding, PositionalEncoding2D class TestCrossVivVit(unittest.TestCase): def setUp(self): - self.crossvivit = RoCrossViViT(image_size=(128, 128), patch_size=(8, 8), time_coords_encoder=NeRF_embedding(), **{"max_freq":12}) + self.crossvivit = RoCrossViViT(image_size=(128, 128), patch_size=(8, 8), time_coords_encoder=CyclicalEmbedding(), **{"max_freq":12}) + + def test_positional_encoding_forward(self): + """ + Test the positional encoding forward pass. + """ + positional_encoding = PositionalEncoding2D(128) + coords = torch.rand(5, 2, 32, 32) + output = positional_encoding(coords) + self.assertEqual(output.shape, (5, 32, 32, 128)) def test_vivit_model(self): self.vivit_model = VisionTransformer(128, 5, 8, 128, 128, [512, 512, 512], dropout=0.1) - self.vivit_model(torch.rand(5, 512, 128), torch.rand(5, 512, 128)) - pass + out = self.vivit_model(torch.rand(5, 512, 128), (torch.rand(5, 512, 64), torch.rand(5, 512, 64))) + assert out[0].shape == (5, 512, 128) def test_forward(self): - x = self.crossvivit(torch.randn(1, 3, 128, 128), torch.randn(1, 3, 128, 128), torch.randn(1, 3, 128, 128), ) + """ + ctx (torch.Tensor): Context frames of shape [B, T, C, H, W] + ctx_coords (torch.Tensor): Coordinates of context frames of shape [B, 2, H, W] + ts (torch.Tensor): Station timeseries of shape [B, T, C] + ts_coords (torch.Tensor): Station coordinates of shape [B, 2, 1, 1] + time_coords (torch.Tensor): Time coordinates of shape [B, T, C, H, W] + mask (bool): Whether to mask or not. Useful for inference. + """ + # The context tensor + ctx_tensor = torch.rand(5, 10, 12, 120, 120) + ctx_coords = torch.rand(5, 2, 120, 120) + ts = torch.rand(5, 10, 12) + time_coords = torch.rand(5, 10, 12, 120, 120) + ts_coords = torch.rand(5, 2, 1, 1) + mask = True + x = self.crossvivit(ctx_tensor, ctx_coords, ts, ts_coords, time_coords=time_coords, mask=True) self.assertEqual(x.shape, (1, 1000)) def test_self_attention_dims(self): + """ + Test the self attention layer with the correct dimensions. + """ self.self_attention = SelfAttention(dim=128, use_rotary=True) - self.self_attention(torch.rand(5, 512, 128), torch.rand(5,512, 128)) + self.self_attention(torch.rand(5, 512, 128), (torch.rand(5, 512, 64), torch.rand(5, 512, 64))) if __name__ == '__main__': From b539b32d0e1c2af5350c026f1f714422b2718edc Mon Sep 17 00:00:00 2001 From: isaacmg Date: Mon, 5 Aug 2024 11:38:07 -0500 Subject: [PATCH 16/38] more important changes 3 --- .idea/workspace.xml | 200 ++++++++++++++++++ .../preprocessing/pytorch_loaders.py | 5 +- flood_forecast/pytorch_training.py | 2 +- flood_forecast/transformer_xl/attn.py | 20 +- .../transformer_xl/data_embedding.py | 4 +- tests/mult_modal_tests/test_cross_vivit.py | 29 ++- 6 files changed, 240 insertions(+), 20 deletions(-) create mode 100644 .idea/workspace.xml diff --git a/.idea/workspace.xml b/.idea/workspace.xml new file mode 100644 index 000000000..e4214a33b --- /dev/null +++ b/.idea/workspace.xml @@ -0,0 +1,200 @@ + + + + + + + + + + + + + + + + + + + + { + "customColor": "", + "associatedIndex": 1 +} + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 1720330545131 + + + + + + + + + + file://$PROJECT_DIR$/flood_forecast/multi_models/crossvivit.py + 171 + + + file://$PROJECT_DIR$/flood_forecast/multi_models/crossvivit.py + 448 + + + + + + + + + + \ No newline at end of file diff --git a/flood_forecast/preprocessing/pytorch_loaders.py b/flood_forecast/preprocessing/pytorch_loaders.py index fa061dc28..06cdf8ec6 100644 --- a/flood_forecast/preprocessing/pytorch_loaders.py +++ b/flood_forecast/preprocessing/pytorch_loaders.py @@ -164,7 +164,7 @@ class CSVSeriesIDLoader(CSVDataLoader): def __init__(self, series_id_col: str, main_params: dict, return_method: str, return_all=True): """A data-loader for a CSV file that contains a series ID column. - :param series_id_col: The id + :param series_id_col: The id column of the series you want to forecast. :type series_id_col: str :param main_params: The central set of parameters :type main_params: dict @@ -241,8 +241,7 @@ def __getitem__(self, idx: int) -> Tuple[Dict, Dict]: targ_list[self.unique_dict[idx2]] = targ return src_list, targ_list else: - raise NotImplementedError - return super().__getitem__(idx) + raise NotImplementedError("Current code only supports returning all the series at once at each iteration") def __sample_series_id__(idx, series_id): pass diff --git a/flood_forecast/pytorch_training.py b/flood_forecast/pytorch_training.py index 42608b354..aa3c9e419 100644 --- a/flood_forecast/pytorch_training.py +++ b/flood_forecast/pytorch_training.py @@ -22,7 +22,7 @@ def multi_crit(crit_multi: List, output, labels, valid=None): :param crit_multi: _description_ :type crit_multi: List - :param output: _descaription_ + :param output: :type output: _type_ :param labels: _description_ :type labels: _type_ diff --git a/flood_forecast/transformer_xl/attn.py b/flood_forecast/transformer_xl/attn.py index 74bdda7d9..556ea6a54 100644 --- a/flood_forecast/transformer_xl/attn.py +++ b/flood_forecast/transformer_xl/attn.py @@ -4,7 +4,7 @@ from math import sqrt from einops import rearrange, repeat import torch.nn.functional as F -import einsum +from torch import einsum class TriangularCausalMask: @@ -458,12 +458,20 @@ def forward(self, x): class SelfAttention(nn.Module): def __init__( self, - dim, - heads=8, - dim_head=64, - dropout=0.0, - use_rotary=True, + dim: int, + heads: int = 8, + dim_head: int = 64, + dropout: float = 0.0, + use_rotary: bool = True, ): + """ + The self-attention mechanism used in the CrossVIVIT model. It is currently not used in other models and could + likely be consolidated with those self-attention mechanisms. + :param dim: [description] + :type dim: [type] + :param heads: [description] + :type heads: [type] + """ super().__init__() inner_dim = dim_head * heads self.use_rotary = use_rotary diff --git a/flood_forecast/transformer_xl/data_embedding.py b/flood_forecast/transformer_xl/data_embedding.py index 8508ab194..02e69d6d9 100644 --- a/flood_forecast/transformer_xl/data_embedding.py +++ b/flood_forecast/transformer_xl/data_embedding.py @@ -233,9 +233,9 @@ def __init__(self, channels): inv_freq = 1.0 / (10000 ** (torch.arange(0, channels, 2).float() / channels)) self.register_buffer("inv_freq", inv_freq) - def forward(self, coords): + def forward(self, coords: torch.Tensor)-> torch.Tensor: """ - :param tensor: A 4d tensor of size (batch_size, ch, x, y) + :param coords: A 4d tensor of size (batch_size, ch, x, y) :param coords: A 4d tensor of size (batch_size, num_coords, x, y) :return: Positional Encoding Matrix of size (batch_size, x, y, ch) """ diff --git a/tests/mult_modal_tests/test_cross_vivit.py b/tests/mult_modal_tests/test_cross_vivit.py index c8ef45c7f..6ac66ac3c 100644 --- a/tests/mult_modal_tests/test_cross_vivit.py +++ b/tests/mult_modal_tests/test_cross_vivit.py @@ -7,13 +7,26 @@ class TestCrossVivVit(unittest.TestCase): def setUp(self): - self.crossvivit = RoCrossViViT(image_size=(128, 128), patch_size=(8, 8), time_coords_encoder=CyclicalEmbedding(), **{"max_freq":12}) - + self.crossvivit = RoCrossViViT( + image_size=(120, 120), + patch_size=(8, 8), + time_coords_encoder=CyclicalEmbedding(), + ctx_channels=12, + ts_channels=12, + dim=128, + depth=4, + heads=4, + mlp_ratio=4, + ts_length=10, + out_dim=1, + dropout=0.0, + **{"max_freq": 12} + ) def test_positional_encoding_forward(self): """ - Test the positional encoding forward pass. + Test the positional encoding forward pass with a PositionalEncoding2D layer. """ - positional_encoding = PositionalEncoding2D(128) + positional_encoding = PositionalEncoding2D(dim=128) coords = torch.rand(5, 2, 32, 32) output = positional_encoding(coords) self.assertEqual(output.shape, (5, 32, 32, 128)) @@ -25,22 +38,22 @@ def test_vivit_model(self): def test_forward(self): """ - ctx (torch.Tensor): Context frames of shape [B, T, C, H, W] + This tests the forward pass of the VIVIT model from the CrossVIVIT paper. + ctx (torch.Tensor): Context frames of shape [batch_size, number_time_stamps, number_channels, height, wid] ctx_coords (torch.Tensor): Coordinates of context frames of shape [B, 2, H, W] ts (torch.Tensor): Station timeseries of shape [B, T, C] ts_coords (torch.Tensor): Station coordinates of shape [B, 2, 1, 1] time_coords (torch.Tensor): Time coordinates of shape [B, T, C, H, W] mask (bool): Whether to mask or not. Useful for inference. """ - # The context tensor + # Construct a context tensor this tensor will ctx_tensor = torch.rand(5, 10, 12, 120, 120) ctx_coords = torch.rand(5, 2, 120, 120) ts = torch.rand(5, 10, 12) time_coords = torch.rand(5, 10, 12, 120, 120) ts_coords = torch.rand(5, 2, 1, 1) - mask = True x = self.crossvivit(ctx_tensor, ctx_coords, ts, ts_coords, time_coords=time_coords, mask=True) - self.assertEqual(x.shape, (1, 1000)) + self.assertEqual(x[0].shape, (1, 1000)) def test_self_attention_dims(self): """ From dc556b3c5f9676420d84120f0540b004a8db476c Mon Sep 17 00:00:00 2001 From: isaacmg Date: Thu, 8 Aug 2024 13:19:53 -0500 Subject: [PATCH 17/38] add file --- flood_forecast/multi_models/crossvivit.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/flood_forecast/multi_models/crossvivit.py b/flood_forecast/multi_models/crossvivit.py index fcd8c463c..d0e30b3bf 100644 --- a/flood_forecast/multi_models/crossvivit.py +++ b/flood_forecast/multi_models/crossvivit.py @@ -105,13 +105,13 @@ def __init__( like whether to use the rotary embedding. :param dim: The embedding dimension. The authors generally use a dimension of 384 for training the large models. :type dim: int - :param depth: The number of transformer blocks to create. Commonly set to 4 for most tasks. + :param depth: The number of transformer blocks to create. Commonly set to four for most tasks. :type depth: int :param heads: The number of heads in the multi-head-attention mechanism. Usually set to a multiple of eight. :type heads: int :param dim_head: The dimension of the inputs to the head. :type dim_head: int - :param mlp_dim: _description_ + :param mlp_dim: The dimension that the multi-head perceptron should output. :type mlp_dim: int :param image_size: The image size defined can be defined either as a list, tuple or single int (e.g. [120, 120] (120, 120), 120. @@ -431,7 +431,7 @@ def forward( ts_coords: torch.Tensor, time_coords: torch.Tensor, mask: bool = True, - ): + ) -> Tuple[Float[torch.Tensor, "batch_size image_dim num_mlp_heads"], Float[torch.Tensor, "batch_size image_dim num_mlp_heads"], dict[str, Float[torch.Tensor, "batch_size image_dim context_length"]], dict[str, Float[torch.Tensor, "batch_size image_dim context_length"]]]: """ Args: ctx (torch.Tensor): Context frames of shape [B, T, C, H, W] From 6846f2d653de201a40e32ab43df046ac6b836a6e Mon Sep 17 00:00:00 2001 From: isaacmg Date: Sat, 24 Aug 2024 22:17:56 -0400 Subject: [PATCH 18/38] fixing the core code --- .gitignore | 8 +++- flood_forecast/basic/linear_regression.py | 2 +- flood_forecast/deployment/inference.py | 2 +- flood_forecast/evaluator.py | 23 +++++----- flood_forecast/multi_models/crossvivit.py | 45 +++++++++++++------ .../preprocessing/pytorch_loaders.py | 9 ---- flood_forecast/transformer_xl/attn.py | 2 +- .../test_cross_vivit.py | 2 +- 8 files changed, 54 insertions(+), 39 deletions(-) rename tests/{mult_modal_tests => multi_modal_tests}/test_cross_vivit.py (98%) diff --git a/.gitignore b/.gitignore index 2c108f3fa..2f9d3ef1f 100644 --- a/.gitignore +++ b/.gitignore @@ -12,4 +12,10 @@ mypy .mypy_cache *.png *.svf -*.svg \ No newline at end of file +*.svg +.idea/flow-forecast.iml +.idea/inspectionProfiles/profiles_settings.xml +.idea/misc.xml +.idea/vcs.xml +.idea/workspace.xml +.idea/workspace.xml diff --git a/flood_forecast/basic/linear_regression.py b/flood_forecast/basic/linear_regression.py index 39b7b49e5..a88bd9d8c 100755 --- a/flood_forecast/basic/linear_regression.py +++ b/flood_forecast/basic/linear_regression.py @@ -4,7 +4,7 @@ class SimpleLinearModel(torch.nn.Module): """ - A very simple baseline model to resolve some of the + A very simple baseline linear model to resolve some of the difficulties with bugs in the various train/validation loops in code. Has only two layers. """ diff --git a/flood_forecast/deployment/inference.py b/flood_forecast/deployment/inference.py index 79347e558..4435cceef 100644 --- a/flood_forecast/deployment/inference.py +++ b/flood_forecast/deployment/inference.py @@ -18,7 +18,7 @@ def __init__(self, forecast_steps: int, n_samp: int, model_params, csv_path: Uni wandb_proj: str = None, torch_script=False): """Class to handle inference for models, - :param forecasts_steps: Number of time-steps to forecast (doesn't have to be hours) + :param forecast_steps: Number of time-steps to forecast (doesn't have to be hours) :type forecast_steps: int :param num_prediction_samples: Number of prediction samples :type num_prediction_samples: int diff --git a/flood_forecast/evaluator.py b/flood_forecast/evaluator.py index 489e77bfb..791fe2edf 100644 --- a/flood_forecast/evaluator.py +++ b/flood_forecast/evaluator.py @@ -1,3 +1,13 @@ +""" +Author: Isaac Godfried +Description: + This module contains functions for evaluating models. The basic logic flow is as follows: + 1. `evaluate_model` is called from `trainer.py` at the end of training. It calls `infer_on_torch_model` which does the actual inference. # noqa + 2. `infer_on_torch_model` calls `generate_predictions` which calls `generate_decoded_predictions` or `generate_predictions_non_decoded` depending on whether the model uses a decoder or not. + 3. `generate_decoded_predictions` calls `decoding_functions` which calls `greedy_decode` or `beam_decode` depending on the decoder function specified in the config file. + 4. The returned value from `generate_decoded_predictions` is then used to calculate the evaluation metrics in `run_evaluation`. + 5. `run_evaluation` returns the evaluation metrics to `evaluate_model` which returns them to `trainer.py`. +""" from datetime import datetime from typing import Callable, Dict, List, Tuple, Type, Union @@ -17,15 +27,6 @@ from flood_forecast.utils import flatten_list_function from flood_forecast.temporal_decoding import decoding_function -""" -This module contains functions for evaluating models. Basic logic flow: -1. `evaluate_model` is called from `trainer.py` at the end of training. It calls `infer_on_torch_model` which does the actual inference. # noqa -2. `infer_on_torch_model` calls `generate_predictions` which calls `generate_decoded_predictions` or `generate_predictions_non_decoded` depending on whether the model uses a decoder or not. -3. `generate_decoded_predictions` calls `decoding_functions` which calls `greedy_decode` or `beam_decode` depending on the decoder function specified in the config file. -4. The returned value from `generate_decoded_predictions` is then used to calculate the evaluation metrics in `run_evaluation`. -5. `run_evaluation` returns the evaluation metrics to `evaluate_model` which returns them to `trainer.py`. -""" - def stream_baseline( river_flow_df: pd.DataFrame, forecast_column: str, hours_forecast=336 @@ -61,7 +62,7 @@ def get_model_r2_score( ): """ - model_evaluate_function should call any necessary preprocessing. + model_evaluate_function should call any necessary preprocessing """ test_river_data, baseline_mse = stream_baseline(river_flow_df, forecast_column) @@ -334,7 +335,7 @@ def infer_on_torch_model( forecast_start_idx, history, datetime_start) -def handle_later_ev(model, df_train_and_test, end_tensor, params, csv_test_loader, multi_params, forecast_start_idx, +def handle_laxer_ev(model, df_train_and_test, end_tensor, params, csv_test_loader, multi_params, forecast_start_idx, history, datetime_start): targ = False decoder_params = None diff --git a/flood_forecast/multi_models/crossvivit.py b/flood_forecast/multi_models/crossvivit.py index d0e30b3bf..a5df24fa4 100644 --- a/flood_forecast/multi_models/crossvivit.py +++ b/flood_forecast/multi_models/crossvivit.py @@ -3,7 +3,7 @@ """ import random -from typing import List, Tuple, Union +from typing import List, Tuple, Union, Any, Dict import torch from einops import rearrange, repeat from einops.layers.torch import Rearrange @@ -24,6 +24,9 @@ class Attention(nn.Module): def __init__(self, dim, heads=8, dim_head=64, dropout=0.0): + """ + + """ super().__init__() inner_dim = dim_head * heads project_out = not (heads == 1 and dim_head == dim) @@ -168,7 +171,6 @@ def forward( attention_scores = {} for i in range(len(self.blocks)): self_attn, sff = self.blocks[i] - out, self_attn_scores = self_attn(src, pos_emb=src_pos_emb) attention_scores["self_attention"] = self_attn_scores src = out + src @@ -190,6 +192,11 @@ def __init__( use_rotary: bool = True, use_glu: bool = True, ): + """ + Computes the Cross-Attention between the source and target sequences. + :param dim: The embedding dimension. The authors generally use a dimension of 384 for training the large models. + :type dim: int + """ super().__init__() self.image_size = image_size self.cross_layers = nn.ModuleList([]) @@ -272,8 +279,17 @@ def __init__( decoder_depth: int = 4, decoder_heads: int = 6, decoder_dim_head: int = 128, - **kwargs, + axial_kwargs: Dict[str, Any] = {}, ): + """ + The CrossViViT model from the CrossVIVIT paper. This model is based on the Arxiv paper: https://arxiv.org/abs/2103.14899 + :param image_size: The image size defined can be defined either as a list, tuple or single int (e.g. [120, 120] + (120, 120), 120. + :type image_size: Union[List[int], Tuple[int], int] + :param patch_size: The patch size defined can be defined either as a list or a tuple (e.g. [8, 8]) this could allow + you to have patches of varying sizes such as (8, 16). + :type patch_size: Union[List[int], Tuple[int]] + """ super().__init__() assert ( @@ -319,7 +335,7 @@ def __init__( ), nn.Linear(patch_dim, dim), ) - self.enc_pos_emb = AxialRotaryEmbedding(dim_head, freq_type, **kwargs) + self.enc_pos_emb = AxialRotaryEmbedding(dim_head, freq_type, **axial_kwargs) self.ts_embedding = nn.Linear(self.ts_channels, dim) self.ctx_encoder = VisionTransformer( dim, @@ -425,7 +441,7 @@ def random_masking(self, x, mask_ratio): def forward( self, - ctx: torch.Tensor, + video_ctx: torch.Tensor, ctx_coords: torch.Tensor, ts: torch.Tensor, ts_coords: torch.Tensor, @@ -434,7 +450,7 @@ def forward( ) -> Tuple[Float[torch.Tensor, "batch_size image_dim num_mlp_heads"], Float[torch.Tensor, "batch_size image_dim num_mlp_heads"], dict[str, Float[torch.Tensor, "batch_size image_dim context_length"]], dict[str, Float[torch.Tensor, "batch_size image_dim context_length"]]]: """ Args: - ctx (torch.Tensor): Context frames of shape [B, T, C, H, W] + video_ctx (torch.Tensor): Context frames of shape [B, T, C, H, W] ctx_coords (torch.Tensor): Coordinates of context frames of shape [B, 2, H, W] ts (torch.Tensor): Station timeseries of shape [B, T, C] ts_coords (torch.Tensor): Station coordinates of shape [B, 2, 1, 1] @@ -443,29 +459,30 @@ def forward( Returns: """ - B, T, _, H, W = ctx.shape + B, T, _, H, W = video_ctx.shape time_coords = self.time_coords_encoder(time_coords) - ctx = torch.cat([ctx, time_coords], axis=2) + video_ctx = torch.cat([video_ctx, time_coords], axis=2) ts = torch.cat([ts, time_coords[..., 0, 0]], axis=-1) - ctx = rearrange(ctx, "b t c h w -> (b t) c h w") + # Rearranges the video_ctx to the format + video_ctx = rearrange(video_ctx, "b t c h w -> (b t) c h w") ctx_coords = repeat(ctx_coords, "b c h w -> (b t) c h w", t=T) ts_coords = repeat(ts_coords, "b c h w -> (b t) c h w", t=T) src_enc_pos_emb = self.enc_pos_emb(ctx_coords) tgt_pos_emb = self.enc_pos_emb(ts_coords) - ctx = self.to_patch_embedding(ctx) # BT, N, D + video_ctx = self.to_patch_embedding(video_ctx) # BT, N, D if self.pe_type == "learned": - ctx = ctx + self.pe_ctx + video_ctx = video_ctx + self.pe_ctx elif self.pe_type == "sine": pe = self.pe_ctx(ctx_coords) pe = rearrange(pe, "b h w c -> b (h w) c") - ctx = ctx + pe + video_ctx = video_ctx + pe if self.ctx_masking_ratio > 0 and mask: p = self.ctx_masking_ratio * random.random() - ctx, _, ids_restore, ids_keep = self.random_masking(ctx, p) + video_ctx, _, ids_restore, ids_keep = self.random_masking(video_ctx, p) src_enc_pos_emb = tuple( torch.gather( pos_emb, @@ -474,7 +491,7 @@ def forward( ) for pos_emb in src_enc_pos_emb ) - latent_ctx, self_attention_scores = self.ctx_encoder(ctx, src_enc_pos_emb) + latent_ctx, self_attention_scores = self.ctx_encoder(video_ctx, src_enc_pos_emb) ts = self.ts_embedding(ts) if self.ts_masking_ratio > 0 and mask: diff --git a/flood_forecast/preprocessing/pytorch_loaders.py b/flood_forecast/preprocessing/pytorch_loaders.py index 06cdf8ec6..1f2825259 100644 --- a/flood_forecast/preprocessing/pytorch_loaders.py +++ b/flood_forecast/preprocessing/pytorch_loaders.py @@ -21,13 +21,11 @@ def __init__( scaling=None, start_stamp: int = 0, end_stamp: int = None, - gcp_service_key: Optional[str] = None, interpolate_param: bool = False, sort_column=None, scaled_cols=None, feature_params=None, no_scale=False, - preformatted_df=False ): """ @@ -282,8 +280,6 @@ def __init__( print(df_path) self.forecast_total = forecast_total # TODO these are antiquated delete them - self.use_real_temp = use_real_temp - self.use_real_precip = use_real_precip self.target_supplied = target_supplied # Convert back to datetime and save index sort_col1 = sort_column_clone if sort_column_clone else "datetime" @@ -356,11 +352,6 @@ def __len__(self) -> int: len(self.df.index) - self.forecast_history - self.forecast_total - 1 ) - -class TestLoaderABC(CSVTestLoader): - pass - - class AEDataloader(CSVDataLoader): def __init__( self, diff --git a/flood_forecast/transformer_xl/attn.py b/flood_forecast/transformer_xl/attn.py index 556ea6a54..4a079f2e4 100644 --- a/flood_forecast/transformer_xl/attn.py +++ b/flood_forecast/transformer_xl/attn.py @@ -532,7 +532,7 @@ def __init__( dim: int, heads: int = 8, dim_head: int = 64, - dropout: int = 0.0, + dropout: float = 0.0, use_rotary: bool = True, ): """ diff --git a/tests/mult_modal_tests/test_cross_vivit.py b/tests/multi_modal_tests/test_cross_vivit.py similarity index 98% rename from tests/mult_modal_tests/test_cross_vivit.py rename to tests/multi_modal_tests/test_cross_vivit.py index 6ac66ac3c..f80899a7f 100644 --- a/tests/mult_modal_tests/test_cross_vivit.py +++ b/tests/multi_modal_tests/test_cross_vivit.py @@ -20,7 +20,7 @@ def setUp(self): ts_length=10, out_dim=1, dropout=0.0, - **{"max_freq": 12} + axial_kwargs={"max_freq": 12} ) def test_positional_encoding_forward(self): """ From d4d672b1a8942988e8104debcefd2d0fea5699b8 Mon Sep 17 00:00:00 2001 From: isaacmg Date: Wed, 28 Aug 2024 12:12:29 -0400 Subject: [PATCH 19/38] adding functions the things stub --- flood_forecast/time_model.py | 27 +++++++++++++++------ tests/multi_modal_tests/test_cross_vivit.py | 11 ++++++++- 2 files changed, 30 insertions(+), 8 deletions(-) diff --git a/flood_forecast/time_model.py b/flood_forecast/time_model.py index e963cb70e..c8c231220 100644 --- a/flood_forecast/time_model.py +++ b/flood_forecast/time_model.py @@ -17,10 +17,8 @@ class TimeSeriesModel(ABC): """ - An abstract class used to handle different configurations - of models + hyperparams for training, test, and predict functions. - This class assumes that data is already split into test train - and validation at this point. + An abstract class used to handle different configurations of models + hyperparams for training, test, and predict + functions. This class assumes that data is already split into test train and validation at this point. """ def __init__( @@ -30,6 +28,19 @@ def __init__( validation_data: str, test_data: str, params: Dict): + """ + Initializes the TimeSeriesModel class with certain attributes + :param model_base: The name of the model to load. This should be a key in the model_dict in the + pytorch_model_dict located in model_dict_function.py + :type model_base: str + :param training_data: The path to the training data file + :type training_data: str + :param validation_data: The path to the validation data file + :type validation_data: str + :param test_data: The path to the test data file + :type test_data: str + :param params: A dictionary of parameters to pass to the model + """ self.params = params if "weight_path" in params: params["weight_path"] = get_data(params["weight_path"]) @@ -50,8 +61,11 @@ def __init__( @abstractmethod def load_model(self, model_base: str, model_params: Dict, weight_path=None) -> object: """ - This function should load and return the model - this will vary based on the underlying framework used + This function should load and return the model. This will vary based on the underlying framework used. + :param model_base: The name of the model to load + :type model_base: str + :param model_params: A dictionary of parameters to pass to the model + :param weight_path: The path to the weights to load """ raise NotImplementedError @@ -269,7 +283,6 @@ def make_data_load( pad_le, dataset_params["task"]) else: - # TODO support custom DataLoader loader = None return loader diff --git a/tests/multi_modal_tests/test_cross_vivit.py b/tests/multi_modal_tests/test_cross_vivit.py index f80899a7f..1c6c2fd8a 100644 --- a/tests/multi_modal_tests/test_cross_vivit.py +++ b/tests/multi_modal_tests/test_cross_vivit.py @@ -26,7 +26,8 @@ def test_positional_encoding_forward(self): """ Test the positional encoding forward pass with a PositionalEncoding2D layer. """ - positional_encoding = PositionalEncoding2D(dim=128) + positional_encoding = PositionalEncoding2D(channels=2) + # Coordinates with format [B, 2, H, W] coords = torch.rand(5, 2, 32, 32) output = positional_encoding(coords) self.assertEqual(output.shape, (5, 32, 32, 128)) @@ -62,6 +63,14 @@ def test_self_attention_dims(self): self.self_attention = SelfAttention(dim=128, use_rotary=True) self.self_attention(torch.rand(5, 512, 128), (torch.rand(5, 512, 64), torch.rand(5, 512, 64))) + def test_neRF_embedding(self): + """ + Test the NeRF embedding layer. + """ + nerf_embedding = NeRF_embedding(n_layers=128) + coords = torch.rand(5, 2, 32, 32) + output = nerf_embedding(coords) + self.assertEqual(output.shape, (5, 32, 32, 128)) if __name__ == '__main__': unittest.main() From 6fe50c0172b0e616c9d494a88995b284baf7249c Mon Sep 17 00:00:00 2001 From: isaacmg Date: Wed, 28 Aug 2024 17:10:57 -0400 Subject: [PATCH 20/38] all file --- flood_forecast/evaluator.py | 2 +- flood_forecast/model_dict_function.py | 6 +- flood_forecast/multi_models/crossvivit.py | 208 +++++++++++------- .../transformer_xl/data_embedding.py | 81 ++++--- 4 files changed, 184 insertions(+), 113 deletions(-) diff --git a/flood_forecast/evaluator.py b/flood_forecast/evaluator.py index 791fe2edf..17a03c78b 100644 --- a/flood_forecast/evaluator.py +++ b/flood_forecast/evaluator.py @@ -335,7 +335,7 @@ def infer_on_torch_model( forecast_start_idx, history, datetime_start) -def handle_laxer_ev(model, df_train_and_test, end_tensor, params, csv_test_loader, multi_params, forecast_start_idx, +def handle_later_ev(model, df_train_and_test, end_tensor, params, csv_test_loader, multi_params, forecast_start_idx, history, datetime_start): targ = False decoder_params = None diff --git a/flood_forecast/model_dict_function.py b/flood_forecast/model_dict_function.py index acf2cd2bb..c00f22399 100644 --- a/flood_forecast/model_dict_function.py +++ b/flood_forecast/model_dict_function.py @@ -1,3 +1,4 @@ +from flood_forecast.multi_models.crossvivit import RoCrossViViT from flood_forecast.transformer_xl.multi_head_base import MultiAttnHeadSimple from flood_forecast.transformer_xl.transformer_basic import SimpleTransformer, CustomTransformerDecoder from flood_forecast.transformer_xl.informer import Informer @@ -27,7 +28,7 @@ """ -Utility dictionaries to map a string to a class +Utility dictionaries to map a string to a class. """ pytorch_model_dict = { "MultiAttnHeadSimple": MultiAttnHeadSimple, @@ -48,7 +49,8 @@ "NLinear": NLinear, "TSMixer": TSMixer, "TSMixerExt": TSMixerExt, - "ITransformer": ITransformer + "ITransformer": ITransformer, + "CrossVIVIT": RoCrossViViT, } pytorch_criterion_dict = { diff --git a/flood_forecast/multi_models/crossvivit.py b/flood_forecast/multi_models/crossvivit.py index a5df24fa4..b02ab38ce 100644 --- a/flood_forecast/multi_models/crossvivit.py +++ b/flood_forecast/multi_models/crossvivit.py @@ -8,7 +8,7 @@ from einops import rearrange, repeat from einops.layers.torch import Rearrange from torch import einsum, nn -from jaxtyping import Float +from jaxtyping import Float, Bool from flood_forecast.transformer_xl.attn import ( SelfAttention, CrossAttention, @@ -18,7 +18,7 @@ ) from flood_forecast.transformer_xl.data_embedding import ( PositionalEncoding2D, - AxialRotaryEmbedding, + AxialRotaryEmbedding, CyclicalEmbedding, ) @@ -258,7 +258,7 @@ def __init__( self, image_size: Union[List[int], Tuple[int]], patch_size: Union[List[int], Tuple[int]], - time_coords_encoder: nn.Module, + time_coords_encoder: CyclicalEmbedding, dim: int = 128, depth: int = 4, heads: int = 4, @@ -282,13 +282,18 @@ def __init__( axial_kwargs: Dict[str, Any] = {}, ): """ - The CrossViViT model from the CrossVIVIT paper. This model is based on the Arxiv paper: https://arxiv.org/abs/2103.14899 + The CrossViViT model from the CrossVIVIT paper. This model is based on the Arxiv paper: https://arxiv.org/abs/2103.14899. + In order to simplify understanding we have included comments in the forward pass detailing the different sections of the + paper that the code corresponds to. :param image_size: The image size defined can be defined either as a list, tuple or single int (e.g. [120, 120] (120, 120), 120. :type image_size: Union[List[int], Tuple[int], int] - :param patch_size: The patch size defined can be defined either as a list or a tuple (e.g. [8, 8]) this could allow + :param patch_size: The patch size defined can be defined either as a list or a tuple (e.g. [8, 8]) this could allow. you to have patches of varying sizes such as (8, 16). :type patch_size: Union[List[int], Tuple[int]] + :param time_coords_encoder: The time coordinates encoder to use for the model. + :type time_coords_encoder: CyclicalEmbedding + :param dim: The embedding dimension. The authors generally use a dimension of 384 for training the large models. """ super().__init__() @@ -410,10 +415,11 @@ def __init__( nn.Linear(dim, num_mlp_heads), ) - def random_masking(self, x, mask_ratio): + @staticmethod + def random_masking(x, mask_ratio): """ Perform per-sample random masking by per-sample shuffling. - Per-sample shuffling is done by argsort random noise. + Per-sample shuffling is done by arg-sort random noise. x: [N, L, D], sequence """ N, L, D = x.shape # batch, length, dim @@ -434,100 +440,136 @@ def random_masking(self, x, mask_ratio): # generate the binary mask: 0 is keep, 1 is remove mask = torch.ones([N, L], device=x.device) mask[:, :len_keep] = 0 - # unshuffle to get the binary mask + # un-shuffle to get the binary mask mask = torch.gather(mask, dim=1, index=ids_restore) return x_masked, mask, ids_restore, ids_keep def forward( self, - video_ctx: torch.Tensor, - ctx_coords: torch.Tensor, - ts: torch.Tensor, - ts_coords: torch.Tensor, - time_coords: torch.Tensor, - mask: bool = True, - ) -> Tuple[Float[torch.Tensor, "batch_size image_dim num_mlp_heads"], Float[torch.Tensor, "batch_size image_dim num_mlp_heads"], dict[str, Float[torch.Tensor, "batch_size image_dim context_length"]], dict[str, Float[torch.Tensor, "batch_size image_dim context_length"]]]: + video_context: Float[torch.Tensor, "batch time ctx_channels height width"], + context_coords: Float[torch.Tensor, "batch 2 height width"], + timeseries: Float[torch.Tensor, "batch time ts_channels"], + timeseries_spatial_coordinates: Float[torch.Tensor, "batch 2 1 1"], + ts_positional_encoding: Float[torch.Tensor, "batch time time_encoding_dim height width"], + apply_masking: Bool[torch.Tensor, "1"] = True, + ) -> Tuple[ + Float[torch.Tensor, "batch time num_mlp_heads out_dim"], + Float[torch.Tensor, "batch time num_mlp_heads"], + Dict[str, Float[torch.Tensor, "batch num_heads seq_len seq_len"]], + Dict[str, Float[torch.Tensor, "batch num_heads seq_len seq_len"]] + ]: """ - Args: - video_ctx (torch.Tensor): Context frames of shape [B, T, C, H, W] - ctx_coords (torch.Tensor): Coordinates of context frames of shape [B, 2, H, W] - ts (torch.Tensor): Station timeseries of shape [B, T, C] - ts_coords (torch.Tensor): Station coordinates of shape [B, 2, 1, 1] - time_coords (torch.Tensor): Time coordinates of shape [B, T, C, H, W] - mask (bool): Whether to mask or not. Useful for inference - Returns: - + Forward pass of the RoCrossViViT model. + + :param video_context: PyTorch tensor of the video context frames + :type video_context: Float[torch.Tensor, "batch time ctx_channels height width"] + :param context_coords: PyTorch tensor of coordinates of the context frames + :type context_coords: Float[torch.Tensor, "batch 2 height width"] + :param timeseries: The timeseries measurements + :type timeseries: Float[torch.Tensor, "batch time ts_channels"] + :param timeseries_spatial_coordinates: The coordinates of the station where the timeseries measurement was taken + :param ts_positional_encoding: Time coordinates + :param apply_masking: Whether to apply masking (useful for inference) + :return: Tuple of (outputs, quantile_mask, self_attention_scores, cross_attention_scores) """ - B, T, _, H, W = video_ctx.shape - time_coords = self.time_coords_encoder(time_coords) - - video_ctx = torch.cat([video_ctx, time_coords], axis=2) - ts = torch.cat([ts, time_coords[..., 0, 0]], axis=-1) - - # Rearranges the video_ctx to the format - video_ctx = rearrange(video_ctx, "b t c h w -> (b t) c h w") - - ctx_coords = repeat(ctx_coords, "b c h w -> (b t) c h w", t=T) - ts_coords = repeat(ts_coords, "b c h w -> (b t) c h w", t=T) - src_enc_pos_emb = self.enc_pos_emb(ctx_coords) - tgt_pos_emb = self.enc_pos_emb(ts_coords) - - video_ctx = self.to_patch_embedding(video_ctx) # BT, N, D + batch_size, time_steps, _, height, width = video_context.shape + # (Likely discussed in Section 3.1 or 3.2, where the authors describe input preprocessing) + encoded_time = self.time_coords_encoder(ts_positional_encoding) + + # Concatenate encoded time to video context and timeseries + # (Likely discussed in Section 3.2, where the authors describe how different inputs are combined) + video_context_with_time = torch.cat([video_context, encoded_time], dim=2) + timeseries_with_time = torch.cat([timeseries, encoded_time[..., 0, 0]], dim=-1) + + # Reshape video context for processing + # (Likely discussed in Section 3.2, where the authors describe the tokenization process) + flattened_video_context = rearrange(video_context_with_time, 'b t c h w -> (b t) c h w') + + # Repeat coordinates for each time step + # (Likely discussed in Section 3.1, where the authors describe how spatial information is incorporated) + repeated_context_coords = repeat(context_coords, 'b c h w -> (b t) c h w', t=time_steps) + repeated_ts_coords = repeat(timeseries_spatial_coordinates, 'b c h w -> (b t) c h w', t=time_steps) + + # Generate positional embeddings + # (Likely discussed in Section 3.1, subsection on Rotary Positional Embedding) + context_pos_embedding = self.enc_pos_emb(repeated_context_coords) + timeseries_pos_embedding = self.enc_pos_emb(repeated_ts_coords) + + # Embed video context + # (Likely discussed in Section 3.2, subsection on context encoding) + embedded_video_context = self.to_patch_embedding(flattened_video_context) + + # Apply positional encoding + # (Likely discussed in Section 3.1, subsection on positional encoding types) if self.pe_type == "learned": - video_ctx = video_ctx + self.pe_ctx + embedded_video_context = embedded_video_context + self.pe_ctx elif self.pe_type == "sine": - pe = self.pe_ctx(ctx_coords) - pe = rearrange(pe, "b h w c -> b (h w) c") - video_ctx = video_ctx + pe - if self.ctx_masking_ratio > 0 and mask: - p = self.ctx_masking_ratio * random.random() - video_ctx, _, ids_restore, ids_keep = self.random_masking(video_ctx, p) - src_enc_pos_emb = tuple( - torch.gather( - pos_emb, - dim=1, - index=ids_keep.unsqueeze(-1).repeat(1, 1, pos_emb.shape[-1]), - ) - for pos_emb in src_enc_pos_emb + pe = self.pe_ctx(repeated_context_coords) + pe = rearrange(pe, 'b h w c -> b (h w) c') + embedded_video_context = embedded_video_context + pe + + # Apply masking to video context if specified + # (Likely discussed in Section 3.2, subsection on regularization techniques) + if self.ctx_masking_ratio > 0 and apply_masking: + mask_ratio = self.ctx_masking_ratio * torch.rand(1).item() + embedded_video_context, _, _, keep_indices = self.random_masking(embedded_video_context, mask_ratio) + context_pos_embedding = tuple( + torch.gather(pos_emb, dim=1, index=keep_indices.unsqueeze(-1).repeat(1, 1, pos_emb.shape[-1])) + for pos_emb in context_pos_embedding ) - latent_ctx, self_attention_scores = self.ctx_encoder(video_ctx, src_enc_pos_emb) - - ts = self.ts_embedding(ts) - if self.ts_masking_ratio > 0 and mask: - p = self.ts_masking_ratio * random.random() - ts, _, ids_restore, ids_keep = self.random_masking(ts, p) - mask_tokens = self.ts_mask_token.repeat(ts.shape[0], T - ts.shape[1], 1) - ts = torch.cat([ts, mask_tokens], dim=1) - ts = torch.gather( - ts, dim=1, index=ids_restore.unsqueeze(-1).repeat(1, 1, ts.shape[2]) + + # Encode video context + # (Likely discussed in Section 3.2, subsection on context encoding) + encoded_context, self_attention_scores = self.ctx_encoder(embedded_video_context, context_pos_embedding) + + # Embed and potentially mask timeseries + # (Likely discussed in Section 3.2, subsection on timeseries encoding) + embedded_timeseries = self.ts_embedding(timeseries_with_time) + if self.ts_masking_ratio > 0 and apply_masking: + mask_ratio = self.ts_masking_ratio * torch.rand(1).item() + embedded_timeseries, _, restore_indices, _ = self.random_masking(embedded_timeseries, mask_ratio) + mask_tokens = self.ts_mask_token.repeat(embedded_timeseries.shape[0], + time_steps - embedded_timeseries.shape[1], 1) + embedded_timeseries = torch.cat([embedded_timeseries, mask_tokens], dim=1) + embedded_timeseries = torch.gather( + embedded_timeseries, dim=1, + index=restore_indices.unsqueeze(-1).repeat(1, 1, embedded_timeseries.shape[2]) ) - latent_ts = self.ts_encoder(ts) - latent_ts = rearrange(latent_ts, "b t c -> (b t) c").unsqueeze(1) + # Encode timeseries + # (Likely discussed in Section 3.2, subsection on timeseries encoding) + encoded_timeseries = self.ts_encoder(embedded_timeseries) + encoded_timeseries = rearrange(encoded_timeseries, 'b t c -> (b t) c').unsqueeze(1) + # Apply positional encoding to encoded timeseries + # (Likely discussed in Section 3.1, subsection on positional encoding types) if self.pe_type == "learned": - latent_ts = latent_ts + self.pe_ts + encoded_timeseries = encoded_timeseries + self.pe_ts elif self.pe_type == "sine": - pe = self.pe_ts(ts_coords) - pe = rearrange(pe, "b h w c -> b (h w) c") - latent_ts = latent_ts + pe - latent_ts, cross_attention_scores = self.mixer( - latent_ctx, latent_ts, src_enc_pos_emb, tgt_pos_emb + pe = self.pe_ts(repeated_ts_coords) + pe = rearrange(pe, 'b h w c -> b (h w) c') + encoded_timeseries = encoded_timeseries + pe + + # Mix context and timeseries + # (Likely discussed in Section 3.2, subsection on cross-attention or mixing) + mixed_timeseries, cross_attention_scores = self.mixer( + encoded_context, encoded_timeseries, context_pos_embedding, timeseries_pos_embedding ) - latent_ts = latent_ts.squeeze(1) - latent_ts = self.ts_enctodec(rearrange(latent_ts, "(b t) c -> b t c", b=B)) + mixed_timeseries = mixed_timeseries.squeeze(1) + decoder_input = self.ts_enctodec(rearrange(mixed_timeseries, '(b t) c -> b t c', b=batch_size)) + + # Apply temporal transformer + # (Likely discussed in Section 3.2, subsection on temporal modeling) + transformed_timeseries = self.temporal_transformer(decoder_input) - y = self.temporal_transformer(latent_ts) + # Generate outputs for each MLP head + # (Likely discussed in Section 3.3, subsection on output generation) + outputs = torch.stack([mlp(transformed_timeseries) for mlp in self.mlp_heads], dim=2) - # Handles the multiple MLP heads - outputs = [] - for i in range(self.num_mlp_heads): - mlp = self.mlp_heads[i] - output = mlp(y) - outputs.append(output) - outputs = torch.stack(outputs, dim=2) + # Generate quantile mask + # (Likely discussed in Section 3.3, subsection on uncertainty estimation) + quantile_mask = self.quantile_masker(rearrange(transformed_timeseries.detach(), 'b t c -> b c t')) - quantile_mask = self.quantile_masker(rearrange(y.detach(), "b t c -> b c t")) + return outputs, quantile_mask, self_attention_scores, cross_attention_scores - return (outputs, quantile_mask, self_attention_scores, cross_attention_scores) diff --git a/flood_forecast/transformer_xl/data_embedding.py b/flood_forecast/transformer_xl/data_embedding.py index 02e69d6d9..fff1706d8 100644 --- a/flood_forecast/transformer_xl/data_embedding.py +++ b/flood_forecast/transformer_xl/data_embedding.py @@ -1,9 +1,12 @@ +from typing import List + import torch import torch.nn as nn import math from einops import rearrange, repeat from math import pi import numpy as np +from jaxtyping import Float class AxialRotaryEmbedding(nn.Module): @@ -222,40 +225,64 @@ def get_emb(sin_inp): class PositionalEncoding2D(nn.Module): - def __init__(self, channels): - """ - :param channels: The last dimension of the tensor you want to apply pos emb to. - """ + """ + Applies 2D positional encoding to a 4D input tensor. + + This module generates a positional encoding for 2D data (like images or feature maps) + using a combination of sine and cosine functions at different frequencies. + + :param channels: The last dimension of the tensor you want to apply positional embedding to. + :type channels: int + """ + + def __init__(self, channels: int): super(PositionalEncoding2D, self).__init__() self.org_channels = channels - channels = int(np.ceil(channels / 4) * 2) - self.channels = channels - inv_freq = 1.0 / (10000 ** (torch.arange(0, channels, 2).float() / channels)) + self.channels = int(np.ceil(channels / 4) * 2) + + # Calculate inverse frequencies + inv_freq = 1.0 / (10000 ** (torch.arange(0, self.channels, 2).float() / self.channels)) self.register_buffer("inv_freq", inv_freq) - def forward(self, coords: torch.Tensor)-> torch.Tensor: + def forward(self, coords: torch.Tensor) -> torch.Tensor: """ - :param coords: A 4d tensor of size (batch_size, ch, x, y) - :param coords: A 4d tensor of size (batch_size, num_coords, x, y) - :return: Positional Encoding Matrix of size (batch_size, x, y, ch) + Forward pass of the module. + + :param coords: A 4D tensor of size (batch_size, num_coords, x, y) representing the coordinates. + :type coords: torch.Tensor + :return: Positional Encoding Matrix of size (batch_size, x, y, channels*2) + :rtype: torch.Tensor + :raises RuntimeError: If the input tensor is not 4D. """ if len(coords.shape) != 4: - raise RuntimeError("The input tensor has to be 4d!") + raise RuntimeError("The input tensor must be 4D!") + + batch_size, _, height, width = coords.shape - batch_size, _, x, y = coords.shape - self.cached_penc = None - pos_x = coords[:, 0, 0, :].type(self.inv_freq.type()) # batch, width - pos_y = coords[:, 1, :, 0].type(self.inv_freq.type()) # batch, height + # Extract x and y coordinates + # Shape: (batch_size, width) + pos_x = coords[:, 0, 0, :].type(self.inv_freq.type()) + # Shape: (batch_size, height) + pos_y = coords[:, 1, :, 0].type(self.inv_freq.type()) + + # Calculate sin of scaled coordinates + # Shape: (batch_size, width, channels/2) sin_inp_x = torch.einsum("bi,j->bij", pos_x, self.inv_freq) + # Shape: (batch_size, height, channels/2) sin_inp_y = torch.einsum("bi,j->bij", pos_y, self.inv_freq) + + # Get embeddings for x and y + # Shape: (batch_size, width, 1, channels) emb_x = get_emb(sin_inp_x).unsqueeze(2) + # Shape: (batch_size, 1, height, channels) emb_y = get_emb(sin_inp_y).unsqueeze(1) - emb = torch.zeros( - (batch_size, x, y, self.channels * 2), device=coords.device - ).type(coords.type()) - emb[:, :, :, : self.channels] = emb_x - emb[:, :, :, self.channels : 2 * self.channels] = emb_y + # Combine x and y embeddings + # Shape: (batch_size, height, width, channels*2) + emb = torch.zeros((batch_size, height, width, self.channels * 2), + device=coords.device).type(coords.type()) + emb[:, :, :, :self.channels] = emb_x + emb[:, :, :, self.channels:2*self.channels] = emb_y return emb class NeRF_embedding(nn.Module): @@ -280,21 +307,21 @@ def forward(self, spatial_coords: torch.Tensor): class CyclicalEmbedding(nn.Module): - def __init__(self, frequencies: list = [12, 31, 24, 60]): + def __init__(self, frequencies: List[int] = [12, 31, 24, 60]): super().__init__() self.frequencies = frequencies self.dim = len(self.frequencies) * 2 - def forward(self, time_coords: torch.Tensor): + def forward(self, time_series_data: torch.Tensor) -> Float[torch.Tensor, "batch_size, time_steps, n_time_series"]: """ Args: - time_coords (torch.Tensor): Time coordinates of shape [B, T, C, H, W] + time_series_data (torch.Tensor): Time coordinates of shape [B, T, C, H, W] """ embeddings = [] for i, frequency in enumerate(self.frequencies): embeddings += [ - torch.sin(2 * torch.pi * time_coords[:, :, i] / frequency), - torch.cos(2 * torch.pi * time_coords[:, :, i] / frequency), + torch.sin(2 * torch.pi * time_series_data[:, :, i] / frequency), + torch.cos(2 * torch.pi * time_series_data[:, :, i] / frequency), ] embeddings = torch.stack(embeddings, axis=2) - return embeddings \ No newline at end of file + return embeddings From 100a28b1ce32a9bfa59f5073defa2c79161491f7 Mon Sep 17 00:00:00 2001 From: isaacmg Date: Wed, 28 Aug 2024 22:41:50 -0400 Subject: [PATCH 21/38] fixing the bug e --- flood_forecast/deployment/inference.py | 8 ++++---- flood_forecast/multi_models/crossvivit.py | 4 ++-- flood_forecast/transformer_xl/data_embedding.py | 2 +- 3 files changed, 7 insertions(+), 7 deletions(-) diff --git a/flood_forecast/deployment/inference.py b/flood_forecast/deployment/inference.py index 4435cceef..9f29e7142 100644 --- a/flood_forecast/deployment/inference.py +++ b/flood_forecast/deployment/inference.py @@ -14,15 +14,15 @@ class InferenceMode(object): - def __init__(self, forecast_steps: int, n_samp: int, model_params, csv_path: Union[str, pd.DataFrame], weight_path, + def __init__(self, forecast_steps: int, num_prediction_samples: int, model_params, csv_path: Union[str, pd.DataFrame], weight_path, wandb_proj: str = None, torch_script=False): """Class to handle inference for models, :param forecast_steps: Number of time-steps to forecast (doesn't have to be hours) :type forecast_steps: int - :param num_prediction_samples: Number of prediction samples + :param num_prediction_samples: The number of prediction samples :type num_prediction_samples: int - :param model_params: A dictionary of model parameters (ideally this should come from saved JSON config file) + :param model_params: A dictionafry of model parameters (ideally this should come from saved JSON config file) :type model_params: Dict :param csv_path: Path to the CSV test file you want to be used for inference or a Pandas dataframe. :type csv_path: str @@ -43,7 +43,7 @@ def __init__(self, forecast_steps: int, n_samp: int, model_params, csv_path: Uni s = scaling_function({}, self.inference_params["dataset_params"])["scaling"] self.inference_params["dataset_params"]["scaling"] = s self.inference_params["hours_to_forecast"] = forecast_steps - self.inference_params["num_prediction_samples"] = n_samp + self.inference_params["num_prediction_samples"] = num_prediction_samples if wandb_proj: date = datetime.now() wandb.init(name=date.strftime("%H-%M-%D-%Y") + "_prod", project=wandb_proj) diff --git a/flood_forecast/multi_models/crossvivit.py b/flood_forecast/multi_models/crossvivit.py index b02ab38ce..f5e70afa8 100644 --- a/flood_forecast/multi_models/crossvivit.py +++ b/flood_forecast/multi_models/crossvivit.py @@ -560,7 +560,7 @@ def forward( decoder_input = self.ts_enctodec(rearrange(mixed_timeseries, '(b t) c -> b t c', b=batch_size)) # Apply temporal transformer - # (Likely discussed in Section 3.2, subsection on temporal modeling) + # (Discussed in Section 3.2, subsection on temporal modeling) transformed_timeseries = self.temporal_transformer(decoder_input) # Generate outputs for each MLP head @@ -568,7 +568,7 @@ def forward( outputs = torch.stack([mlp(transformed_timeseries) for mlp in self.mlp_heads], dim=2) # Generate quantile mask - # (Likely discussed in Section 3.3, subsection on uncertainty estimation) + # (Discussed in Section 3.3, subsection on uncertainty estimation) quantile_mask = self.quantile_masker(rearrange(transformed_timeseries.detach(), 'b t c -> b c t')) return outputs, quantile_mask, self_attention_scores, cross_attention_scores diff --git a/flood_forecast/transformer_xl/data_embedding.py b/flood_forecast/transformer_xl/data_embedding.py index fff1706d8..8555dc6ff 100644 --- a/flood_forecast/transformer_xl/data_embedding.py +++ b/flood_forecast/transformer_xl/data_embedding.py @@ -312,7 +312,7 @@ def __init__(self, frequencies: List[int] = [12, 31, 24, 60]): self.frequencies = frequencies self.dim = len(self.frequencies) * 2 - def forward(self, time_series_data: torch.Tensor) -> Float[torch.Tensor, "batch_size, time_steps, n_time_series"]: + def forward(self, time_series_data: torch.Tensor) -> Float[torch.Tensor, "batch_size time_steps n_time_series"]: """ Args: time_series_data (torch.Tensor): Time coordinates of shape [B, T, C, H, W] From 400863cdd18cc0075f75c3bc3dfc93ef5937b15b Mon Sep 17 00:00:00 2001 From: isaacmg Date: Sat, 7 Sep 2024 20:44:40 -0400 Subject: [PATCH 22/38] refactoring the code + updates to core --- flood_forecast/model_dict_function.py | 2 +- flood_forecast/multi_models/crossvivit.py | 34 +++++++++++-------- flood_forecast/pytorch_training.py | 2 +- flood_forecast/time_model.py | 16 ++++++--- .../transformer_xl/data_embedding.py | 16 ++++++--- tests/multi_modal_tests/test_cross_vivit.py | 16 ++++++--- 6 files changed, 55 insertions(+), 31 deletions(-) diff --git a/flood_forecast/model_dict_function.py b/flood_forecast/model_dict_function.py index c00f22399..1aac8326d 100644 --- a/flood_forecast/model_dict_function.py +++ b/flood_forecast/model_dict_function.py @@ -28,7 +28,7 @@ """ -Utility dictionaries to map a string to a class. +Utility dictionaries to map a string to a class in the flood_forecast package. """ pytorch_model_dict = { "MultiAttnHeadSimple": MultiAttnHeadSimple, diff --git a/flood_forecast/multi_models/crossvivit.py b/flood_forecast/multi_models/crossvivit.py index f5e70afa8..e8f9bcad4 100644 --- a/flood_forecast/multi_models/crossvivit.py +++ b/flood_forecast/multi_models/crossvivit.py @@ -264,8 +264,8 @@ def __init__( heads: int = 4, mlp_ratio: int = 4, ctx_channels: int = 3, - ts_channels: int = 3, - ts_length: int = 48, + num_time_series: int = 3, + forecast_history: int = 48, out_dim: int = 1, dim_head: int = 64, dropout: float = 0.0, @@ -288,7 +288,7 @@ def __init__( :param image_size: The image size defined can be defined either as a list, tuple or single int (e.g. [120, 120] (120, 120), 120. :type image_size: Union[List[int], Tuple[int], int] - :param patch_size: The patch size defined can be defined either as a list or a tuple (e.g. [8, 8]) this could allow. + :param patch_size: The patch size defined can be defined either as a list or a tuple (e.g. [8, 8]) this could allow you to have patches of varying sizes such as (8, 16). :type patch_size: Union[List[int], Tuple[int]] :param time_coords_encoder: The time coordinates encoder to use for the model. @@ -308,7 +308,7 @@ def __init__( ], f"pe_type must be 'rope', 'sine', 'learned' or None but you provided {pe_type}" self.time_coords_encoder = time_coords_encoder self.ctx_channels = ctx_channels - self.ts_channels = ts_channels + self.ts_channels = num_time_series if hasattr(self.time_coords_encoder, "dim"): self.ctx_channels += self.time_coords_encoder.dim self.ts_channels += self.time_coords_encoder.dim @@ -374,7 +374,7 @@ def __init__( self.ts_encoder = Transformer( dim, - ts_length, + forecast_history, depth, heads, dim_head, @@ -384,7 +384,7 @@ def __init__( self.ts_enctodec = nn.Linear(dim, decoder_dim) self.temporal_transformer = Transformer( decoder_dim, - ts_length, + forecast_history, decoder_depth, decoder_heads, decoder_dim_head, @@ -447,11 +447,11 @@ def random_masking(x, mask_ratio): def forward( self, - video_context: Float[torch.Tensor, "batch time ctx_channels height width"], + video_context: Float[torch.Tensor, "batch time_steps ctx_channels height width"], context_coords: Float[torch.Tensor, "batch 2 height width"], - timeseries: Float[torch.Tensor, "batch time ts_channels"], + timeseries: Float[torch.Tensor, "batch time_steps num_time_series"], timeseries_spatial_coordinates: Float[torch.Tensor, "batch 2 1 1"], - ts_positional_encoding: Float[torch.Tensor, "batch time time_encoding_dim height width"], + ts_positional_encoding: Float[torch.Tensor, "batch time_steps time_encoding_dim height width"], apply_masking: Bool[torch.Tensor, "1"] = True, ) -> Tuple[ Float[torch.Tensor, "batch time num_mlp_heads out_dim"], @@ -462,14 +462,18 @@ def forward( """ Forward pass of the RoCrossViViT model. - :param video_context: PyTorch tensor of the video context frames - :type video_context: Float[torch.Tensor, "batch time ctx_channels height width"] - :param context_coords: PyTorch tensor of coordinates of the context frames + :param video_context: PyTorch tensor of the video context frames. It will have shape [B, T, C, H, W] where B is + the batch_size, T is the number of time steps, C is the number of channels (generally 3 red, green, and blue), + H is the height of the image and W is the width image. + :type video_context: Float[torch.Tensor, "batch time_steps ctx_channels height width"] + :param context_coords: PyTorch tensor of coordinates of the context frames. :type context_coords: Float[torch.Tensor, "batch 2 height width"] - :param timeseries: The timeseries measurements - :type timeseries: Float[torch.Tensor, "batch time ts_channels"] + :param timeseries: The timeseries measurements themselves. + :type timeseries: Float[torch.Tensor, "batch time num_time_series"] :param timeseries_spatial_coordinates: The coordinates of the station where the timeseries measurement was taken - :param ts_positional_encoding: Time coordinates + :param ts_positional_encoding: Time coordinates for the temporal component of the time series (e.g. month, day, + hour, minute). Therefore, shape will be [batch_size, time_steps, 4, height, width]. As the time encoding dim + will be 4. :param apply_masking: Whether to apply masking (useful for inference) :return: Tuple of (outputs, quantile_mask, self_attention_scores, cross_attention_scores) """ diff --git a/flood_forecast/pytorch_training.py b/flood_forecast/pytorch_training.py index aa3c9e419..e851b9490 100644 --- a/flood_forecast/pytorch_training.py +++ b/flood_forecast/pytorch_training.py @@ -20,7 +20,7 @@ def multi_crit(crit_multi: List, output, labels, valid=None): """Used for computing the loss when there are multiple criteria. - :param crit_multi: _description_ + :param crit_multi: The list of criteria to use :type crit_multi: List :param output: :type output: _type_ diff --git a/flood_forecast/time_model.py b/flood_forecast/time_model.py index c8c231220..6d860c69e 100644 --- a/flood_forecast/time_model.py +++ b/flood_forecast/time_model.py @@ -29,7 +29,7 @@ def __init__( test_data: str, params: Dict): """ - Initializes the TimeSeriesModel class with certain attributes + Initializes the TimeSeriesModel class with certain attributes. :param model_base: The name of the model to load. This should be a key in the model_dict in the pytorch_model_dict located in model_dict_function.py :type model_base: str @@ -81,8 +81,9 @@ def make_data_load(self, data_path, params: Dict, loader_type: str) -> object: @abstractmethod def save_model(self, output_path: str): """ - Saves a model to a specific path along with a configuration report - of the parameters and data info. + Saves a model to a specific path along with a configuration report of the parameters and data info. + :param output_path: The path to save the model to (should be a directory) + :type output_path: str """ raise NotImplementedError @@ -101,6 +102,9 @@ def upload_gcs(self, save_path: str, name: str, file_type: str, epoch=0, bucket_ wandb.config.update({"gcs_m_path_" + str(epoch) + file_type: online_path}) def wandb_init(self): + """ + Initializes wandb if the params dict contains the wandb key or if sweep is present. + """ if self.params["wandb"]: wandb.init( id=wandb.util.generate_id(), @@ -287,8 +291,10 @@ def make_data_load( return loader -def scaling_function(start_end_params, dataset_params): - in_dataset_params = False +def scaling_function(start_end_params: Dict, dataset_params: Dict) -> Dict: + """ + + """ if "scaler" in dataset_params: in_dataset_params = "scaler" elif "scaling" in dataset_params: diff --git a/flood_forecast/transformer_xl/data_embedding.py b/flood_forecast/transformer_xl/data_embedding.py index 8555dc6ff..5e55c0786 100644 --- a/flood_forecast/transformer_xl/data_embedding.py +++ b/flood_forecast/transformer_xl/data_embedding.py @@ -244,15 +244,18 @@ def __init__(self, channels: int): inv_freq = 1.0 / (10000 ** (torch.arange(0, self.channels, 2).float() / self.channels)) self.register_buffer("inv_freq", inv_freq) - def forward(self, coords: torch.Tensor) -> torch.Tensor: + def forward(self, coords: Float[torch.Tensor, "batch_size x y channels"]) -> Float[torch.Tensor, "batch_size height width channels"]: """ - Forward pass of the module. + Forward pass of the PositionalEncoding2D module. :param coords: A 4D tensor of size (batch_size, num_coords, x, y) representing the coordinates. :type coords: torch.Tensor :return: Positional Encoding Matrix of size (batch_size, x, y, channels*2) :rtype: torch.Tensor :raises RuntimeError: If the input tensor is not 4D. + + :return a positional encoding for the input coordinate tensor also. + :rtype: Float[torch.Tensor, "batch_size height width channels"] """ if len(coords.shape) != 4: raise RuntimeError("The input tensor must be 4D!") @@ -308,14 +311,19 @@ def forward(self, spatial_coords: torch.Tensor): class CyclicalEmbedding(nn.Module): def __init__(self, frequencies: List[int] = [12, 31, 24, 60]): + """ + Creates a cyclical embedding for time series data. + """ super().__init__() self.frequencies = frequencies self.dim = len(self.frequencies) * 2 def forward(self, time_series_data: torch.Tensor) -> Float[torch.Tensor, "batch_size time_steps n_time_series"]: """ - Args: - time_series_data (torch.Tensor): Time coordinates of shape [B, T, C, H, W] + :param time_series_data: A tensor of the time series data [batch_size, time_steps, n_time_series] + :type time_series_data: torch.Tensor + :return: The embeddings of the time series data in cyclical form [batch_size, time_steps, n_time_series, dim] + :rtype: torch.Tensor """ embeddings = [] for i, frequency in enumerate(self.frequencies): diff --git a/tests/multi_modal_tests/test_cross_vivit.py b/tests/multi_modal_tests/test_cross_vivit.py index 1c6c2fd8a..fed56fd64 100644 --- a/tests/multi_modal_tests/test_cross_vivit.py +++ b/tests/multi_modal_tests/test_cross_vivit.py @@ -12,12 +12,12 @@ def setUp(self): patch_size=(8, 8), time_coords_encoder=CyclicalEmbedding(), ctx_channels=12, - ts_channels=12, + num_time_series=12, dim=128, depth=4, heads=4, mlp_ratio=4, - ts_length=10, + forecast_history=10, out_dim=1, dropout=0.0, axial_kwargs={"max_freq": 12} @@ -30,7 +30,7 @@ def test_positional_encoding_forward(self): # Coordinates with format [B, 2, H, W] coords = torch.rand(5, 2, 32, 32) output = positional_encoding(coords) - self.assertEqual(output.shape, (5, 32, 32, 128)) + self.assertEqual(output.shape, (5, 32, 32, 4)) def test_vivit_model(self): self.vivit_model = VisionTransformer(128, 5, 8, 128, 128, [512, 512, 512], dropout=0.1) @@ -46,14 +46,20 @@ def test_forward(self): ts_coords (torch.Tensor): Station coordinates of shape [B, 2, 1, 1] time_coords (torch.Tensor): Time coordinates of shape [B, T, C, H, W] mask (bool): Whether to mask or not. Useful for inference. + video_context: Float[torch.Tensor, "batch time ctx_channels height width"], + context_coords: Float[torch.Tensor, "batch 2 height width"], + timeseries: Float[torch.Tensor, "batch time num_time_series"], + timeseries_spatial_coordinates: Float[torch.Tensor, "batch 2 1 1"], + ts_positional_encoding """ # Construct a context tensor this tensor will ctx_tensor = torch.rand(5, 10, 12, 120, 120) ctx_coords = torch.rand(5, 2, 120, 120) ts = torch.rand(5, 10, 12) - time_coords = torch.rand(5, 10, 12, 120, 120) + time_coords1 = torch.rand(5, 10, 4, 120, 120) ts_coords = torch.rand(5, 2, 1, 1) - x = self.crossvivit(ctx_tensor, ctx_coords, ts, ts_coords, time_coords=time_coords, mask=True) + x = self.crossvivit(video_context=ctx_tensor, context_coords=ctx_coords, timeseries=ts, timeseries_spatial_coordinates=ts_coords, + ts_positional_encoding=time_coords1) self.assertEqual(x[0].shape, (1, 1000)) def test_self_attention_dims(self): From a2d452158031405c71a3c36fc63e172e07971b16 Mon Sep 17 00:00:00 2001 From: isaacmg Date: Sat, 14 Sep 2024 23:42:00 -0400 Subject: [PATCH 23/38] update some tests --- flood_forecast/evaluator.py | 11 +------- flood_forecast/multi_models/crossvivit.py | 12 +++++++-- .../preprocessing/pytorch_loaders.py | 3 +++ flood_forecast/pytorch_training.py | 2 +- flood_forecast/transformer_xl/attn.py | 27 ++++++++++++------- tests/multi_modal_tests/test_cross_vivit.py | 4 ++- 6 files changed, 35 insertions(+), 24 deletions(-) diff --git a/flood_forecast/evaluator.py b/flood_forecast/evaluator.py index 17a03c78b..202e097c1 100644 --- a/flood_forecast/evaluator.py +++ b/flood_forecast/evaluator.py @@ -32,8 +32,7 @@ def stream_baseline( river_flow_df: pd.DataFrame, forecast_column: str, hours_forecast=336 ) -> Tuple[pd.DataFrame, float]: """ - Function to compute the baseline MSE - by using the mean value from the train data. + Function to compute the baseline MSE by using the mean value from the train data. """ total_length = len(river_flow_df.index) train_river_data = river_flow_df[: total_length - hours_forecast] @@ -46,14 +45,6 @@ def stream_baseline( return test_river_data, round(mse_baseline, ndigits=3) -def plot_r2(river_flow_preds: pd.DataFrame) -> float: - """ - We assume at this point river_flow_preds already has - a predicted_baseline and a predicted_model column - """ - pass - - def get_model_r2_score( river_flow_df: pd.DataFrame, model_evaluate_function: Callable, diff --git a/flood_forecast/multi_models/crossvivit.py b/flood_forecast/multi_models/crossvivit.py index e8f9bcad4..4ab419466 100644 --- a/flood_forecast/multi_models/crossvivit.py +++ b/flood_forecast/multi_models/crossvivit.py @@ -23,8 +23,9 @@ class Attention(nn.Module): - def __init__(self, dim, heads=8, dim_head=64, dropout=0.0): + def __init__(self, dim: int, heads: int = 8, dim_head: int = 64, dropout: float = 0.0): """ + The attention mechanism for CrossVIVIT model. """ super().__init__() @@ -280,6 +281,7 @@ def __init__( decoder_heads: int = 6, decoder_dim_head: int = 128, axial_kwargs: Dict[str, Any] = {}, + video_cat_dim: int = 1, ): """ The CrossViViT model from the CrossVIVIT paper. This model is based on the Arxiv paper: https://arxiv.org/abs/2103.14899. @@ -294,6 +296,11 @@ def __init__( :param time_coords_encoder: The time coordinates encoder to use for the model. :type time_coords_encoder: CyclicalEmbedding :param dim: The embedding dimension. The authors generally use a dimension of 384 for training the large models. + :type dim: int + :param depth: The number of transformer blocks to create. Commonly set to four for most tasks. + :type depth: int + :param heads: The number of heads in the multi-head-attention mechanism. Usually set to a multiple of eight. + :type heads: int """ super().__init__() @@ -319,6 +326,7 @@ def __init__( self.ts_masking_ratio = ts_masking_ratio self.num_mlp_heads = num_mlp_heads self.pe_type = pe_type + self.video_cat_dim = video_cat_dim for i in range(2): ims = self.image_size[i] @@ -483,7 +491,7 @@ def forward( # Concatenate encoded time to video context and timeseries # (Likely discussed in Section 3.2, where the authors describe how different inputs are combined) - video_context_with_time = torch.cat([video_context, encoded_time], dim=2) + video_context_with_time = torch.cat([video_context, encoded_time], dim=self.video_cat_dim) timeseries_with_time = torch.cat([timeseries, encoded_time[..., 0, 0]], dim=-1) # Reshape video context for processing diff --git a/flood_forecast/preprocessing/pytorch_loaders.py b/flood_forecast/preprocessing/pytorch_loaders.py index 1f2825259..6b2bb319b 100644 --- a/flood_forecast/preprocessing/pytorch_loaders.py +++ b/flood_forecast/preprocessing/pytorch_loaders.py @@ -266,6 +266,7 @@ def __init__( """ :param str df_path: The path to the CSV file you want to use (GCS compatible) or a Pandas DataFrame A data loader for the test data. + :type df_path: str """ if "file_path" not in kwargs: kwargs["file_path"] = df_path @@ -280,6 +281,8 @@ def __init__( print(df_path) self.forecast_total = forecast_total # TODO these are antiquated delete them + self.use_real_precip = use_real_precip + self.use_real_temp = use_real_temp self.target_supplied = target_supplied # Convert back to datetime and save index sort_col1 = sort_column_clone if sort_column_clone else "datetime" diff --git a/flood_forecast/pytorch_training.py b/flood_forecast/pytorch_training.py index e851b9490..49fe7d673 100644 --- a/flood_forecast/pytorch_training.py +++ b/flood_forecast/pytorch_training.py @@ -20,7 +20,7 @@ def multi_crit(crit_multi: List, output, labels, valid=None): """Used for computing the loss when there are multiple criteria. - :param crit_multi: The list of criteria to use + :param crit_multi: The list of criteria to use for training. :type crit_multi: List :param output: :type output: _type_ diff --git a/flood_forecast/transformer_xl/attn.py b/flood_forecast/transformer_xl/attn.py index 4a079f2e4..36bf6722b 100644 --- a/flood_forecast/transformer_xl/attn.py +++ b/flood_forecast/transformer_xl/attn.py @@ -5,6 +5,7 @@ from einops import rearrange, repeat import torch.nn.functional as F from torch import einsum +from typing import Tuple class TriangularCausalMask: @@ -198,15 +199,24 @@ def __init__( factor=5, scale=None, attention_dropout=0.1, - output_attention=False, ): + """ + The full attention mechanism currently used by the Informer and ITransformer models. + :param mask_flag: Whether to mask the attention mechanism. + :type mask_flag: bool + :param factor: The factor to use in the attention mechanism. + :type factor: int + :param scale: The scale to use in the attention mechanism. + :type scale: Union[float, None] + :param attention_dropout: The dropout to use in the attention mechanism. + :type attention_dropout: float + """ super(FullAttention, self).__init__() self.scale = scale self.mask_flag = mask_flag - self.output_attention = output_attention self.dropout = nn.Dropout(attention_dropout) - def forward(self, queries, keys, values, attn_mask, tau=None, delta=None): + def forward(self, queries, keys, values, attn_mask, tau=None, delta=None) -> Tuple[torch.Tensor, torch.Tensor]: B, L, H, E = queries.shape _, S, _, D = values.shape scale = self.scale or 1.0 / sqrt(E) @@ -222,10 +232,7 @@ def forward(self, queries, keys, values, attn_mask, tau=None, delta=None): A = self.dropout(torch.softmax(scale * scores, dim=-1)) V = torch.einsum("bhls,bshd->blhd", A, values) - if self.output_attention: - return (V.contiguous(), A) - else: - return (V.contiguous(), None) + return V.contiguous(), A # Code implementation from https://github.com/zhouhaoyi/Informer2020 @@ -467,7 +474,7 @@ def __init__( """ The self-attention mechanism used in the CrossVIVIT model. It is currently not used in other models and could likely be consolidated with those self-attention mechanisms. - :param dim: [description] + :param dim: The input dimension of the sequence. :type dim: [type] :param heads: [description] :type heads: [type] @@ -487,7 +494,7 @@ def __init__( self.to_out = nn.Sequential(nn.Linear(inner_dim, dim), nn.Dropout(dropout)) - def forward(self, x: torch.Tensor, pos_emb: torch.Tensor): + def forward(self, x: torch.Tensor, pos_emb: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: """ Args: x: Sequence of shape [B, N, D] @@ -502,7 +509,7 @@ def forward(self, x: torch.Tensor, pos_emb: torch.Tensor): ) if self.use_rotary: - # Used to map dimensions from dimension. Currently, getting (512, 128) when expecting 3-D tensor. + # Used to map dimensions from dimension sin, cos = map( lambda t: repeat(t, "b n d -> (b h) n d", h=self.heads), pos_emb ) diff --git a/tests/multi_modal_tests/test_cross_vivit.py b/tests/multi_modal_tests/test_cross_vivit.py index fed56fd64..686d14736 100644 --- a/tests/multi_modal_tests/test_cross_vivit.py +++ b/tests/multi_modal_tests/test_cross_vivit.py @@ -20,6 +20,7 @@ def setUp(self): forecast_history=10, out_dim=1, dropout=0.0, + video_cat_dim=2, axial_kwargs={"max_freq": 12} ) def test_positional_encoding_forward(self): @@ -60,7 +61,7 @@ def test_forward(self): ts_coords = torch.rand(5, 2, 1, 1) x = self.crossvivit(video_context=ctx_tensor, context_coords=ctx_coords, timeseries=ts, timeseries_spatial_coordinates=ts_coords, ts_positional_encoding=time_coords1) - self.assertEqual(x[0].shape, (1, 1000)) + self.assertEqual(x[0].shape, (5, 10, 1, 1)) def test_self_attention_dims(self): """ @@ -78,5 +79,6 @@ def test_neRF_embedding(self): output = nerf_embedding(coords) self.assertEqual(output.shape, (5, 32, 32, 128)) + if __name__ == '__main__': unittest.main() From e19702c76f926198e0675fde6148c07942ecdb95 Mon Sep 17 00:00:00 2001 From: isaacmg Date: Wed, 18 Sep 2024 22:58:35 -0400 Subject: [PATCH 24/38] fixing core code. --- flood_forecast/transformer_xl/itransformer.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/flood_forecast/transformer_xl/itransformer.py b/flood_forecast/transformer_xl/itransformer.py index 9519ca97b..da7dcbc31 100644 --- a/flood_forecast/transformer_xl/itransformer.py +++ b/flood_forecast/transformer_xl/itransformer.py @@ -56,8 +56,8 @@ def __init__(self, forecast_history, forecast_length, d_model, embed, dropout, n [ EncoderLayer( AttentionLayer( - FullAttention(False, factor, attention_dropout=dropout, - output_attention=output_attention), d_model, n_heads), + FullAttention(mask_flag=False, factor=factor, attention_dropout=dropout, + ), d_model=d_model, n_heads=n_heads), d_model, d_ff, dropout=dropout, From b9750c30eb03ce9d0ad3a724b0a809e4c8b7c72c Mon Sep 17 00:00:00 2001 From: isaacmg Date: Wed, 18 Sep 2024 22:58:47 -0400 Subject: [PATCH 25/38] Revert "fixing core code." This reverts commit e19702c76f926198e0675fde6148c07942ecdb95. --- flood_forecast/transformer_xl/itransformer.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/flood_forecast/transformer_xl/itransformer.py b/flood_forecast/transformer_xl/itransformer.py index da7dcbc31..9519ca97b 100644 --- a/flood_forecast/transformer_xl/itransformer.py +++ b/flood_forecast/transformer_xl/itransformer.py @@ -56,8 +56,8 @@ def __init__(self, forecast_history, forecast_length, d_model, embed, dropout, n [ EncoderLayer( AttentionLayer( - FullAttention(mask_flag=False, factor=factor, attention_dropout=dropout, - ), d_model=d_model, n_heads=n_heads), + FullAttention(False, factor, attention_dropout=dropout, + output_attention=output_attention), d_model, n_heads), d_model, d_ff, dropout=dropout, From 271a5cf5267b7e66902a8454aff0a11678f4f860 Mon Sep 17 00:00:00 2001 From: isaacmg Date: Sat, 21 Sep 2024 23:40:10 -0400 Subject: [PATCH 26/38] clean up le --- .circleci/config.yml | 9 +- .github/dependabot.yml | 2 +- .github/workflows/codesee-arch-diagram.yml | 2 +- .idea/workspace.xml | 74 ++++----- .pre-commit-config.yaml | 7 + .readthedocs.yaml | 2 +- README.md | 29 ++-- docs/requirements.txt | 2 +- docs/source/crossformer.rst | 2 +- docs/source/index.rst | 2 +- docs/source/inference.rst | 8 +- docs/source/informer.rst | 4 +- docs/source/transformer_bottleneck.rst | 2 +- flood_forecast/da_rnn/README.md | 2 +- flood_forecast/da_rnn/checkpoint/.gitkeep | 1 - flood_forecast/da_rnn/config/dec_kwargs.json | 2 +- flood_forecast/da_rnn/config/enc_kwargs.json | 2 +- flood_forecast/multi_models/crossvivit.py | 144 +++++++++++------- .../preprocessing/pytorch_loaders.py | 4 +- flood_forecast/trainer.py | 14 +- requirements.txt | 2 +- tests/24_May_202202_25PM_1.json | 2 +- tests/auto_encoder.json | 7 +- tests/classification_test.json | 11 +- tests/config.json | 2 +- tests/cross_former.json | 19 +-- tests/custom_encode.json | 25 ++- tests/da_meta.json | 15 +- tests/da_rnn.json | 17 +-- tests/decoder_test.json | 19 ++- tests/dlinear.json | 17 +-- tests/dsanet.json | 21 ++- tests/dsanet_3.json | 21 ++- tests/full_transformer.json | 19 ++- tests/gru_vanilla.json | 17 +-- tests/lstm_test.json | 21 ++- tests/meta_data_test.json | 27 ++-- tests/multi_config.json | 2 +- tests/multi_decoder_test.json | 21 ++- tests/multi_modal_tests/test_cross_vivit.py | 45 ++++-- tests/multi_test.json | 11 +- tests/multitask_decoder.json | 19 ++- tests/nlinear.json | 19 +-- .../probabilistic_linear_regression_test.json | 2 +- tests/scaling_json.json | 19 ++- tests/test2.csv | 2 +- tests/test_data/big_black_md.json | 2 +- tests/test_data/farm_ex.csv | 2 +- tests/test_data/full_out.json | 2 +- tests/test_data/imputation_test.csv | 2 +- tests/test_data/small_test.csv | 2 +- tests/test_data/test2.csv | 2 +- tests/test_data/test_format_data.csv | 2 +- tests/test_dual.json | 21 ++- tests/test_iTransformer.json | 15 +- tests/test_inf_single.json | 13 +- tests/test_informer.json | 19 ++- tests/test_init/keag_small.csv | 2 +- tests/transformer_b_series.json | 18 +-- tests/transformer_bottleneck.json | 17 +-- tests/transformer_gaussian.json | 19 ++- tests/tsmixer_test.json | 19 +-- tests/variable_autoencoderl.json | 9 +- 63 files changed, 443 insertions(+), 439 deletions(-) create mode 100644 .pre-commit-config.yaml diff --git a/.circleci/config.yml b/.circleci/config.yml index e5a41577f..37247f006 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -284,7 +284,7 @@ jobs: - store_artifacts: path: test-results destination: test-results-trainer - + - run: name: upload results when: always @@ -341,14 +341,14 @@ jobs: coverage run flood_forecast/trainer.py -p tests/test_dual.json echo -e 'running dsanet \n' coverage run flood_forecast/trainer.py -p tests/dsanet.json - + - store_test_results: path: test-results - store_artifacts: path: test-results destination: test-results-trainer2 - + - run: name: upload results when: always @@ -396,7 +396,7 @@ jobs: - store_artifacts: path: test-results destination: test-results-trainer - + - run: name: upload results when: always @@ -476,4 +476,3 @@ workflows: - trainer_test2: requires: - setup_and_install - diff --git a/.github/dependabot.yml b/.github/dependabot.yml index 777fdd91f..66ee0c8d5 100644 --- a/.github/dependabot.yml +++ b/.github/dependabot.yml @@ -11,4 +11,4 @@ updates: - 0.5.2 - dependency-name: torchvision versions: - - 0.8.2 \ No newline at end of file + - 0.8.2 diff --git a/.github/workflows/codesee-arch-diagram.yml b/.github/workflows/codesee-arch-diagram.yml index 3f564ab8e..c752d6033 100644 --- a/.github/workflows/codesee-arch-diagram.yml +++ b/.github/workflows/codesee-arch-diagram.yml @@ -18,4 +18,4 @@ steps: - uses: Codesee-io/codesee-action@v2 with: - codesee-token: ${{ secrets.CODESEE_ARCH_DIAG_API_TOKEN }} \ No newline at end of file + codesee-token: ${{ secrets.CODESEE_ARCH_DIAG_API_TOKEN }} diff --git a/.idea/workspace.xml b/.idea/workspace.xml index e4214a33b..f03c85e74 100644 --- a/.idea/workspace.xml +++ b/.idea/workspace.xml @@ -5,12 +5,10 @@ + + - - - - - + - { + "keyToString": { + "Python tests.Python tests for test_cross_vivit.TestCrossVivVit.executor": "Run", + "Python tests.Python tests for test_cross_vivit.TestCrossVivVit.test_forward.executor": "Run", + "Python tests.Python tests for test_cross_vivit.TestCrossVivVit.test_positional_encoding.executor": "Run", + "Python tests.Python tests for test_cross_vivit.TestCrossVivVit.test_positional_encoding_forward.executor": "Run", + "Python tests.Python tests for test_cross_vivit.TestCrossVivVit.test_self_attention_dims.executor": "Run", + "Python tests.Python tests for test_cross_vivit.TestCrossVivVit.test_vivit_model.executor": "Run", + "Python tests.Python tests for test_ro_crossvivit.TestRoCrossViViT.executor": "Run", + "Python tests.Python tests for test_variable_length.TestVariableLength.executor": "Run", + "Python tests.Python tests in test_cross_vivit.py.executor": "Run", + "RunOnceActivity.ShowReadmeOnStart": "true", + "com.google.cloudcode.ide_session_index": "20240803_0000", + "git-widget-placeholder": "multimodal__models", + "node.js.detected.package.eslint": "true", + "node.js.detected.package.stylelint": "true", + "node.js.detected.package.tslint": "true", + "node.js.selected.package.eslint": "(autodetect)", + "node.js.selected.package.stylelint": "", + "node.js.selected.package.tslint": "(autodetect)", + "nodejs_package_manager_path": "npm", + "settings.editor.selected.configurable": "com.jetbrains.python.black.configuration.BlackFormatterConfigurable", + "vue.rearranger.settings.migration": "true" } -}]]> +} @@ -176,24 +174,8 @@ - - - - - file://$PROJECT_DIR$/flood_forecast/multi_models/crossvivit.py - 171 - - - file://$PROJECT_DIR$/flood_forecast/multi_models/crossvivit.py - 448 - - - - - + diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 000000000..a2fcade86 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,7 @@ +repos: + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.4.0 + hooks: + - id: trailing-whitespace + - id: end-of-file-fixer + - id: check-yaml diff --git a/.readthedocs.yaml b/.readthedocs.yaml index 2ac16ee4c..f6160c356 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -32,4 +32,4 @@ sphinx: # See https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html python: install: - - requirements: docs/requirements.txt \ No newline at end of file + - requirements: docs/requirements.txt diff --git a/README.md b/README.md index 1d276fd44..1b9cd8fb3 100644 --- a/README.md +++ b/README.md @@ -11,21 +11,21 @@ For additional tutorials and examples please see our [tutorials repository](http | Documentation | [![Documentation Status](https://readthedocs.org/projects/flow-forecast/badge/?version=latest)](https://flow-forecast.readthedocs.io/en/latest/)| | CodeCov| [![codecov](https://codecov.io/gh/AIStream-Peelout/flow-forecast/branch/master/graph/badge.svg)](https://codecov.io/gh/AIStream-Peelout/flow-forecast)| | CodeFactor| [![CodeFactor](https://www.codefactor.io/repository/github/aistream-peelout/flow-forecast/badge)](https://www.codefactor.io/repository/github/aistream-peelout/flow-forecast)| -## Getting Started +## Getting Started Using the library 1. Run `pip install flood-forecast` 2. Detailed info on training models can be found on the [Wiki](https://flow-forecast.atlassian.net/wiki/spaces/FF/pages/364019713/Training+Models). -3. Check out our [Confluence Documentation](https://flow-forecast.atlassian.net/wiki/spaces/FF/overview) +3. Check out our [Confluence Documentation](https://flow-forecast.atlassian.net/wiki/spaces/FF/overview) -**Models currently supported** +**Models currently supported** -1. Vanilla LSTM (LSTM): A basic LSTM that is suitable for multivariate time series forecasting and transfer learning. -2. Full transformer (SimpleTransformer in model_dict): The full original transformer with all 8 encoder and decoder blocks. Requires passing the target in at inference. +1. Vanilla LSTM (LSTM): A basic LSTM that is suitable for multivariate time series forecasting and transfer learning. +2. Full transformer (SimpleTransformer in model_dict): The full original transformer with all 8 encoder and decoder blocks. Requires passing the target in at inference. 3. Simple Multi-Head Attention (MultiHeadSimple): A simple multi-head attention block and linear embedding layers. Suitable for transfer learning. 4. Transformer with a linear decoder (CustomTransformerDecoder in model_dict): A transformer with n-encoder blocks (this is tunable) and a linear decoder. Suitable for forecasting, classification or anomaly detection. -5. [DA-RNN](https://arxiv.org/abs/1704.02971): (DARNN) A well rounded model with which utilizes a LSTM + attention. -6. [Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting](https://arxiv.org/abs/1907.00235) (called DecoderTransformer in model_dict): +5. [DA-RNN](https://arxiv.org/abs/1704.02971): (DARNN) A well rounded model with which utilizes a LSTM + attention. +6. [Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting](https://arxiv.org/abs/1907.00235) (called DecoderTransformer in model_dict): 7. [Transformer XL](https://arxiv.org/abs/1901.02860): Porting Transformer XL for time series. 8. [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://arxiv.org/abs/2012.07436) (Informer) 9. [DeepAR](https://arxiv.org/abs/1704.04110) @@ -44,19 +44,19 @@ We have a number of models we are planning on releasing soon. [Please check our **Integrations** -[Google Cloud Platform](https://github.com/AIStream-Peelout/flow-forecast/wiki/Cloud-Provider-Integration) +[Google Cloud Platform](https://github.com/AIStream-Peelout/flow-forecast/wiki/Cloud-Provider-Integration) [Weights and Biases](https://www.wandb.com/) -## Contributing +## Contributing -For instructions on contributing please see our [contributions page](https://flow-forecast.atlassian.net/wiki/spaces/FF/pages/11403276/Contributing) and our [project board](https://github.com/AIStream-Peeloutt/flow-forecast/projects/5). +For instructions on contributing please see our [contributions page](https://flow-forecast.atlassian.net/wiki/spaces/FF/pages/11403276/Contributing) and our [project board](https://github.com/AIStream-Peeloutt/flow-forecast/projects/5). -## Historical River Flow Data +## Historical River Flow Data -### Task 1 Stream Flow Forecasting -This task focuses on forecasting a stream's future flow/height (in either cfs or feet respectively) given factors such as current flow, temperature, and precipitation. In the future we plan on adding more variables that help with the stream flow prediction such as snow pack data and the surrounding soil moisture index. +### Task 1 Stream Flow Forecasting +This task focuses on forecasting a stream's future flow/height (in either cfs or feet respectively) given factors such as current flow, temperature, and precipitation. In the future we plan on adding more variables that help with the stream flow prediction such as snow pack data and the surrounding soil moisture index. ### Task 2 Flood severity forecasting Task two focuses on predicting the severity of the flood based on the flood forecast, population information, and topography. Flood severity is defined based on several factors including the number of injuires, property damage, and crop damage. @@ -64,7 +64,7 @@ Task two focuses on predicting the severity of the flood based on the flood fore If you use either the data or code from this repository please use the citation below. Additionally please cite the original authors of the models. ``` @misc{godfried2020flowdb, - title={FlowDB a large scale precipitation, river, and flash flood dataset}, + title={FlowDB a large scale precipitation, river, and flash flood dataset}, author={Isaac Godfried and Kriti Mahajan and Maggie Wang and Kevin Li and Pranjalya Tiwari}, year={2020}, eprint={2012.11154}, @@ -72,4 +72,3 @@ If you use either the data or code from this repository please use the citation primaryClass={cs.AI} } ``` - diff --git a/docs/requirements.txt b/docs/requirements.txt index 2ed7ed671..a39c791bb 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -8,4 +8,4 @@ plotly google-cloud-storage scikit-learn wandb -shap \ No newline at end of file +shap diff --git a/docs/source/crossformer.rst b/docs/source/crossformer.rst index ea77456e5..ebca59529 100644 --- a/docs/source/crossformer.rst +++ b/docs/source/crossformer.rst @@ -1,4 +1,4 @@ Crossformer ========================= .. automodule:: flood_forecast.transformer_xl.crossformer - :members: \ No newline at end of file + :members: diff --git a/docs/source/index.rst b/docs/source/index.rst index b169c3b7f..f10f1711d 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -7,7 +7,7 @@ Welcome to Flow Forecast's documentation! ========================================= Flow Forecast is a deep learning for time series forecasting framework written in PyTorch. Flow Forecast makes it easy to train PyTorch Forecast models on a wide variety -of datasets. This documentation describes the internal Python code that makes up Flow Forecast. +of datasets. This documentation describes the internal Python code that makes up Flow Forecast. .. automodule:: flood_forecast diff --git a/docs/source/inference.rst b/docs/source/inference.rst index 1fc82cf97..e3b5c4ff8 100644 --- a/docs/source/inference.rst +++ b/docs/source/inference.rst @@ -1,13 +1,13 @@ Inference ========================= -This API makes it easy to run inference on trained PyTorchForecast modules. To use this code you -need three main files: your model's configuration file, a CSV containing your data, and a path to +This API makes it easy to run inference on trained PyTorchForecast modules. To use this code you +need three main files: your model's configuration file, a CSV containing your data, and a path to your model weights. .. code-block:: python :caption: example initialization - + import json from datetime import datetime from flood_forecast.deployment.inference import InferenceMode @@ -17,7 +17,7 @@ your model weights. config_test = json.load(y) infer_model = InferenceMode(336, 30, config_test, new_water_data_path, weight_path, "river") -.. code-block:: python +.. code-block:: python :caption: example plotting .. automodule:: flood_forecast.deployment.inference diff --git a/docs/source/informer.rst b/docs/source/informer.rst index da0f4509b..1cfc3a48d 100644 --- a/docs/source/informer.rst +++ b/docs/source/informer.rst @@ -1,4 +1,4 @@ -Informer +Informer ========================= .. automodule:: flood_forecast.transformer_xl.informer - :members: search \ No newline at end of file + :members: search diff --git a/docs/source/transformer_bottleneck.rst b/docs/source/transformer_bottleneck.rst index 0dd03c822..115692fe5 100644 --- a/docs/source/transformer_bottleneck.rst +++ b/docs/source/transformer_bottleneck.rst @@ -1,7 +1,7 @@ Convolutional Transformer ================== -This is an implementation of the code from this paper +This is an implementation of the code from this paper .. automodule:: flood_forecast.transformer_xl.transformer_bottleneck :members: diff --git a/flood_forecast/da_rnn/README.md b/flood_forecast/da_rnn/README.md index e62924e32..45d722920 100755 --- a/flood_forecast/da_rnn/README.md +++ b/flood_forecast/da_rnn/README.md @@ -1 +1 @@ -This code is taken pretty much verbatim from [Seanny123](https://github.com/Seanny123/da-rnn). It is a simple Dual-Stage Attention-Based Recurrent Neural Net for Time Series Prediction. \ No newline at end of file +This code is taken pretty much verbatim from [Seanny123](https://github.com/Seanny123/da-rnn). It is a simple Dual-Stage Attention-Based Recurrent Neural Net for Time Series Prediction. diff --git a/flood_forecast/da_rnn/checkpoint/.gitkeep b/flood_forecast/da_rnn/checkpoint/.gitkeep index 8b1378917..e69de29bb 100644 --- a/flood_forecast/da_rnn/checkpoint/.gitkeep +++ b/flood_forecast/da_rnn/checkpoint/.gitkeep @@ -1 +0,0 @@ - diff --git a/flood_forecast/da_rnn/config/dec_kwargs.json b/flood_forecast/da_rnn/config/dec_kwargs.json index a7c925690..5c23988eb 100644 --- a/flood_forecast/da_rnn/config/dec_kwargs.json +++ b/flood_forecast/da_rnn/config/dec_kwargs.json @@ -3,4 +3,4 @@ "decoder_hidden_size": 64, "T": 10, "out_feats": 1 -} \ No newline at end of file +} diff --git a/flood_forecast/da_rnn/config/enc_kwargs.json b/flood_forecast/da_rnn/config/enc_kwargs.json index e4a6b70ab..7576937f7 100644 --- a/flood_forecast/da_rnn/config/enc_kwargs.json +++ b/flood_forecast/da_rnn/config/enc_kwargs.json @@ -2,4 +2,4 @@ "input_size": 2, "hidden_size": 64, "T": 10 -} \ No newline at end of file +} diff --git a/flood_forecast/multi_models/crossvivit.py b/flood_forecast/multi_models/crossvivit.py index 4ab419466..6cc0d6879 100644 --- a/flood_forecast/multi_models/crossvivit.py +++ b/flood_forecast/multi_models/crossvivit.py @@ -18,12 +18,15 @@ ) from flood_forecast.transformer_xl.data_embedding import ( PositionalEncoding2D, - AxialRotaryEmbedding, CyclicalEmbedding, + AxialRotaryEmbedding, + CyclicalEmbedding, ) class Attention(nn.Module): - def __init__(self, dim: int, heads: int = 8, dim_head: int = 64, dropout: float = 0.0): + def __init__( + self, dim: int, heads: int = 8, dim_head: int = 64, dropout: float = 0.0 + ): """ The attention mechanism for CrossVIVIT model. @@ -99,9 +102,8 @@ def __init__( heads: int, dim_head: int, mlp_dim: int, - image_size: Union[List[int], Tuple[int], int], dropout: float = 0.0, - use_rotary: bool = True, + use_rope: bool = True, use_glu: bool = True, ): """The Video Vision Transformer (e.g. VIVIT) of the CrossVIVIT model. This model is based on the Arxiv paper: @@ -117,19 +119,15 @@ def __init__( :type dim_head: int :param mlp_dim: The dimension that the multi-head perceptron should output. :type mlp_dim: int - :param image_size: The image size defined can be defined either as a list, tuple or single int (e.g. [120, 120] - (120, 120), 120. - :type image_size: Union[List[int], Tuple[int], int] :param dropout: The amount of dropout to use throughout the model defaults to 0.0 :type dropout: float, optional - :param use_rotary: Whether to use rotary positional embeddings, defaults to True - :type use_rotary: bool, optional + :param use_rope: Whether to use rotary positional embeddings, defaults to True + :type use_rope: bool, optional :param use_glu: Weather to use gated linear units , defaults to True :type use_glu: bool, optional """ super().__init__() - self.image_size = image_size self.blocks = nn.ModuleList([]) @@ -144,7 +142,7 @@ def __init__( heads=heads, dim_head=dim_head, dropout=dropout, - use_rotary=use_rotary, + use_rotary=use_rope, ), ), PreNorm( @@ -157,16 +155,23 @@ def __init__( def forward( self, - src: Float[torch.Tensor, "batch_size image_dim context_length"], - src_pos_emb: Tuple[Float[torch.Tensor, "batch_size image_dim/2 context_length"], Float[torch.Tensor, "batch_size image_dim/2 context_length"]] - ) -> Tuple[Float[torch.Tensor, "batch_size image_dim context_length"], dict[str, Float[torch.Tensor, "batch_size image_dim context_length"]]]: + src: Float[torch.Tensor, "batch_size variable_sequence_length model_dim"], + src_pos_emb: Tuple[ + Float[torch.Tensor, "batch_t_steps variable_sequence_length model_dim/2"], + Float[torch.Tensor, "batch_size image_dim/2 context_length"], + ], + ) -> Tuple[ + Float[torch.Tensor, "batch_size image_dim context_length"], + dict[str, Float[torch.Tensor, "batch_size image_dim context_length"]], + ]: """ Performs the following computation in each layer: 1. Self-Attention on the source sequence 2. FFN on the source sequence. - Args: - src: Source sequence of shape [B, N, D]. - src_pos_emb: Positional embedding tuple (sin, cos) of source sequence's tokens of shape [B, N, D] + :param src: Source sequence. By this point the shape of the code will be + :type src: Float[torch.Tensor, "batch_t_steps variable_sequence_length model_dim"] + :param src_pos_emb: Positional embedding of source sequence's tokens of shape [batch_t_steps, variable_sequence_length, model_dim/2] + """ attention_scores = {} @@ -351,15 +356,14 @@ def __init__( self.enc_pos_emb = AxialRotaryEmbedding(dim_head, freq_type, **axial_kwargs) self.ts_embedding = nn.Linear(self.ts_channels, dim) self.ctx_encoder = VisionTransformer( - dim, - depth, - heads, - dim_head, - dim * mlp_ratio, - image_size, - dropout, - pe_type == "rope", - use_glu, + dim=dim, + depth=depth, + heads=heads, + dim_head=dim_head, + mlp_dim=dim * mlp_ratio, + dropout=dropout, + use_rope=True if pe_type == "rope" else False, + use_glu=use_glu, ) if pe_type == "learned": self.pe_ctx = nn.Parameter(torch.randn(1, num_patches, dim)) @@ -455,17 +459,21 @@ def random_masking(x, mask_ratio): def forward( self, - video_context: Float[torch.Tensor, "batch time_steps ctx_channels height width"], + video_context: Float[ + torch.Tensor, "batch time_steps ctx_channels height width" + ], context_coords: Float[torch.Tensor, "batch 2 height width"], timeseries: Float[torch.Tensor, "batch time_steps num_time_series"], timeseries_spatial_coordinates: Float[torch.Tensor, "batch 2 1 1"], - ts_positional_encoding: Float[torch.Tensor, "batch time_steps time_encoding_dim height width"], + ts_positional_encoding: Float[ + torch.Tensor, "batch time_steps time_encoding_dim height width" + ], apply_masking: Bool[torch.Tensor, "1"] = True, ) -> Tuple[ Float[torch.Tensor, "batch time num_mlp_heads out_dim"], Float[torch.Tensor, "batch time num_mlp_heads"], Dict[str, Float[torch.Tensor, "batch num_heads seq_len seq_len"]], - Dict[str, Float[torch.Tensor, "batch num_heads seq_len seq_len"]] + Dict[str, Float[torch.Tensor, "batch num_heads seq_len seq_len"]], ]: """ Forward pass of the RoCrossViViT model. @@ -490,18 +498,25 @@ def forward( encoded_time = self.time_coords_encoder(ts_positional_encoding) # Concatenate encoded time to video context and timeseries - # (Likely discussed in Section 3.2, where the authors describe how different inputs are combined) - video_context_with_time = torch.cat([video_context, encoded_time], dim=self.video_cat_dim) + video_context_with_time = torch.cat( + [video_context, encoded_time], dim=self.video_cat_dim + ) timeseries_with_time = torch.cat([timeseries, encoded_time[..., 0, 0]], dim=-1) # Reshape video context for processing # (Likely discussed in Section 3.2, where the authors describe the tokenization process) - flattened_video_context = rearrange(video_context_with_time, 'b t c h w -> (b t) c h w') + flattened_video_context = rearrange( + video_context_with_time, "b t c h w -> (b t) c h w" + ) # Repeat coordinates for each time step # (Likely discussed in Section 3.1, where the authors describe how spatial information is incorporated) - repeated_context_coords = repeat(context_coords, 'b c h w -> (b t) c h w', t=time_steps) - repeated_ts_coords = repeat(timeseries_spatial_coordinates, 'b c h w -> (b t) c h w', t=time_steps) + repeated_context_coords = repeat( + context_coords, "b c h w -> (b t) c h w", t=time_steps + ) + repeated_ts_coords = repeat( + timeseries_spatial_coordinates, "b c h w -> (b t) c h w", t=time_steps + ) # Generate positional embeddings # (Likely discussed in Section 3.1, subsection on Rotary Positional Embedding) @@ -509,7 +524,8 @@ def forward( timeseries_pos_embedding = self.enc_pos_emb(repeated_ts_coords) # Embed video context - # (Likely discussed in Section 3.2, subsection on context encoding) + # This is the uniform sampling described in the paper for the video context. It would be here that we would + # likely substitute to tublet. embedded_video_context = self.to_patch_embedding(flattened_video_context) # Apply positional encoding @@ -518,41 +534,59 @@ def forward( embedded_video_context = embedded_video_context + self.pe_ctx elif self.pe_type == "sine": pe = self.pe_ctx(repeated_context_coords) - pe = rearrange(pe, 'b h w c -> b (h w) c') + pe = rearrange(pe, "b h w c -> b (h w) c") embedded_video_context = embedded_video_context + pe # Apply masking to video context if specified # (Likely discussed in Section 3.2, subsection on regularization techniques) if self.ctx_masking_ratio > 0 and apply_masking: mask_ratio = self.ctx_masking_ratio * torch.rand(1).item() - embedded_video_context, _, _, keep_indices = self.random_masking(embedded_video_context, mask_ratio) + embedded_video_context, _, _, keep_indices = self.random_masking( + embedded_video_context, mask_ratio + ) context_pos_embedding = tuple( - torch.gather(pos_emb, dim=1, index=keep_indices.unsqueeze(-1).repeat(1, 1, pos_emb.shape[-1])) + torch.gather( + pos_emb, + dim=1, + index=keep_indices.unsqueeze(-1).repeat(1, 1, pos_emb.shape[-1]), + ) for pos_emb in context_pos_embedding ) # Encode video context # (Likely discussed in Section 3.2, subsection on context encoding) - encoded_context, self_attention_scores = self.ctx_encoder(embedded_video_context, context_pos_embedding) + encoded_context, self_attention_scores = self.ctx_encoder( + embedded_video_context, context_pos_embedding + ) # Embed and potentially mask timeseries # (Likely discussed in Section 3.2, subsection on timeseries encoding) embedded_timeseries = self.ts_embedding(timeseries_with_time) if self.ts_masking_ratio > 0 and apply_masking: mask_ratio = self.ts_masking_ratio * torch.rand(1).item() - embedded_timeseries, _, restore_indices, _ = self.random_masking(embedded_timeseries, mask_ratio) - mask_tokens = self.ts_mask_token.repeat(embedded_timeseries.shape[0], - time_steps - embedded_timeseries.shape[1], 1) + embedded_timeseries, _, restore_indices, _ = self.random_masking( + embedded_timeseries, mask_ratio + ) + mask_tokens = self.ts_mask_token.repeat( + embedded_timeseries.shape[0], + time_steps - embedded_timeseries.shape[1], + 1, + ) embedded_timeseries = torch.cat([embedded_timeseries, mask_tokens], dim=1) embedded_timeseries = torch.gather( - embedded_timeseries, dim=1, - index=restore_indices.unsqueeze(-1).repeat(1, 1, embedded_timeseries.shape[2]) + embedded_timeseries, + dim=1, + index=restore_indices.unsqueeze(-1).repeat( + 1, 1, embedded_timeseries.shape[2] + ), ) # Encode timeseries # (Likely discussed in Section 3.2, subsection on timeseries encoding) encoded_timeseries = self.ts_encoder(embedded_timeseries) - encoded_timeseries = rearrange(encoded_timeseries, 'b t c -> (b t) c').unsqueeze(1) + encoded_timeseries = rearrange( + encoded_timeseries, "b t c -> (b t) c" + ).unsqueeze(1) # Apply positional encoding to encoded timeseries # (Likely discussed in Section 3.1, subsection on positional encoding types) @@ -560,16 +594,21 @@ def forward( encoded_timeseries = encoded_timeseries + self.pe_ts elif self.pe_type == "sine": pe = self.pe_ts(repeated_ts_coords) - pe = rearrange(pe, 'b h w c -> b (h w) c') + pe = rearrange(pe, "b h w c -> b (h w) c") encoded_timeseries = encoded_timeseries + pe # Mix context and timeseries # (Likely discussed in Section 3.2, subsection on cross-attention or mixing) mixed_timeseries, cross_attention_scores = self.mixer( - encoded_context, encoded_timeseries, context_pos_embedding, timeseries_pos_embedding + encoded_context, + encoded_timeseries, + context_pos_embedding, + timeseries_pos_embedding, ) mixed_timeseries = mixed_timeseries.squeeze(1) - decoder_input = self.ts_enctodec(rearrange(mixed_timeseries, '(b t) c -> b t c', b=batch_size)) + decoder_input = self.ts_enctodec( + rearrange(mixed_timeseries, "(b t) c -> b t c", b=batch_size) + ) # Apply temporal transformer # (Discussed in Section 3.2, subsection on temporal modeling) @@ -577,11 +616,14 @@ def forward( # Generate outputs for each MLP head # (Likely discussed in Section 3.3, subsection on output generation) - outputs = torch.stack([mlp(transformed_timeseries) for mlp in self.mlp_heads], dim=2) + outputs = torch.stack( + [mlp(transformed_timeseries) for mlp in self.mlp_heads], dim=2 + ) # Generate quantile mask # (Discussed in Section 3.3, subsection on uncertainty estimation) - quantile_mask = self.quantile_masker(rearrange(transformed_timeseries.detach(), 'b t c -> b c t')) + quantile_mask = self.quantile_masker( + rearrange(transformed_timeseries.detach(), "b t c -> b c t") + ) return outputs, quantile_mask, self_attention_scores, cross_attention_scores - diff --git a/flood_forecast/preprocessing/pytorch_loaders.py b/flood_forecast/preprocessing/pytorch_loaders.py index 6b2bb319b..6d30b263d 100644 --- a/flood_forecast/preprocessing/pytorch_loaders.py +++ b/flood_forecast/preprocessing/pytorch_loaders.py @@ -455,7 +455,7 @@ def __getitem__(self, idx: int): targ_labs = torch.zeros(self.n_classes) casted_shit = int(targ.data.tolist()) if casted_shit > self.n_classes: - raise ValueError("The class " + str(casted_shit) + " is greater than the number of classes " + str(self.n_classes)) # noqa + raise ValueError("The class " + str(casted_shit) + " is greater than the number of classes " + str(self.n_classes)) # noqa targ_labs[casted_shit] = 1 return src.float(), targ_labs.float().unsqueeze(0) @@ -624,7 +624,7 @@ def get_item_classification(self, idx: int): targ_labs = torch.zeros(self.n_classes) casted_shit = int(targ.data.tolist()) if casted_shit > self.n_classes - 1: # -1 because counting starts at zero - raise ValueError("The class " + str(casted_shit) + " is greater than the number of classes " + str(self.n_classes)) # noqa + raise ValueError("The class " + str(casted_shit) + " is greater than the number of classes " + str(self.n_classes)) # noqa targ_labs[casted_shit] = 1 return src.float(), targ_labs.float().unsqueeze(0) diff --git a/flood_forecast/trainer.py b/flood_forecast/trainer.py index afdf2e848..d407631a2 100644 --- a/flood_forecast/trainer.py +++ b/flood_forecast/trainer.py @@ -16,7 +16,7 @@ def handle_model_evaluation1(test_acc, params: Dict) -> None: """Utility function to help handle model evaluation. Primarily used at the moment for forecasting models. - :param trained_model: A PyTorchForecast model that has already been trained. + :param trained_model: A PyTorchForecast model that has already been trained. :type trained_model: PyTorchForecast :param params: A dictionary of the trained model parameters. :type params: Dict @@ -99,22 +99,22 @@ def handle_core_eval(trained_model, params: Dict, model_type: str): def train_function(model_type: str, params: Dict) -> PyTorchForecast: """Function to train a Model(TimeSeriesModel) or da_rnn. Will return the trained model - + :param model_type: Type of the model. In almost all cases this will be 'PyTorch' :type model_type: str :param params: Dictionary containing all the parameters needed to run the model. :type Dict: :return: A trained model - - .. code-block:: python - - with open("model_config.json") as f: + + .. code-block:: python + + with open("model_config.json") as f: params_dict = json.load(f) train_function("PyTorch", params_dict) ... - For information on what this params_dict should include see `Confluence pages `_ on training models. + For information on what this params_dict should include see `Confluence pages `_ on training models. """ dataset_params = params["dataset_params"] if model_type == "da_rnn": diff --git a/requirements.txt b/requirements.txt index b271aa834..3a33b7de3 100644 --- a/requirements.txt +++ b/requirements.txt @@ -24,4 +24,4 @@ sphinx einops pytorch-tsmixer einsum -jaxtyping \ No newline at end of file +jaxtyping diff --git a/tests/24_May_202202_25PM_1.json b/tests/24_May_202202_25PM_1.json index 3e6504963..a2f26fdd3 100644 --- a/tests/24_May_202202_25PM_1.json +++ b/tests/24_May_202202_25PM_1.json @@ -1 +1 @@ -{"model_name": "CustomTransformerDecoder", "n_targets": 2, "model_type": "PyTorch", "model_params": {"n_time_series": 4, "seq_length": 26, "output_seq_length": 1, "output_dim": 2, "n_layers_encoder": 6}, "dataset_params": {"class": "GeneralClassificationLoader", "n_classes": 2, "training_path": "tests/test_data/ff_test.csv", "validation_path": "tests/test_data/ff_test.csv", "test_path": "tests/test_data/ff_test.csv", "sequence_length": 26, "batch_size": 101, "forecast_history": 26, "train_end": 80, "valid_start": 4, "valid_end": 90, "target_col": ["anomalous_rain"], "relevant_cols": ["anomalous_rain", "tmpf", "cfs", "dwpf", "height"], "scaler": "StandardScaler", "interpolate": {"method": "back_forward_generic", "params": {"relevant_columns": ["cfs", "tmpf", "p01m", "dwpf"]}}, "forecast_length": 1}, "training_params": {"criterion": "CrossEntropyLoss", "optimizer": "Adam", "optim_params": {}, "lr": 0.03, "epochs": 4, "batch_size": 100, "shuffle": false}, "GCS": false, "wandb": {"name": "flood_forecast_circleci", "tags": ["dummy_run", "circleci", "multi_head", "classification"], "project": "repo-flood_forecast"}, "forward_params": {}, "metrics": ["CrossEntropyLoss"], "run": [{"epoch": 0, "train_loss": "0.0680028973509454", "validation_loss": "113.27512431330979"}, {"epoch": 1, "train_loss": "0.05458420396058096", "validation_loss": "108.99862844124436"}, {"epoch": 2, "train_loss": "0.054659905693390305", "validation_loss": "106.30307429283857"}, {"epoch": 3, "train_loss": "0.054730736438391936", "validation_loss": "104.98548858333379"}]} \ No newline at end of file +{"model_name": "CustomTransformerDecoder", "n_targets": 2, "model_type": "PyTorch", "model_params": {"n_time_series": 4, "seq_length": 26, "output_seq_length": 1, "output_dim": 2, "n_layers_encoder": 6}, "dataset_params": {"class": "GeneralClassificationLoader", "n_classes": 2, "training_path": "tests/test_data/ff_test.csv", "validation_path": "tests/test_data/ff_test.csv", "test_path": "tests/test_data/ff_test.csv", "sequence_length": 26, "batch_size": 101, "forecast_history": 26, "train_end": 80, "valid_start": 4, "valid_end": 90, "target_col": ["anomalous_rain"], "relevant_cols": ["anomalous_rain", "tmpf", "cfs", "dwpf", "height"], "scaler": "StandardScaler", "interpolate": {"method": "back_forward_generic", "params": {"relevant_columns": ["cfs", "tmpf", "p01m", "dwpf"]}}, "forecast_length": 1}, "training_params": {"criterion": "CrossEntropyLoss", "optimizer": "Adam", "optim_params": {}, "lr": 0.03, "epochs": 4, "batch_size": 100, "shuffle": false}, "GCS": false, "wandb": {"name": "flood_forecast_circleci", "tags": ["dummy_run", "circleci", "multi_head", "classification"], "project": "repo-flood_forecast"}, "forward_params": {}, "metrics": ["CrossEntropyLoss"], "run": [{"epoch": 0, "train_loss": "0.0680028973509454", "validation_loss": "113.27512431330979"}, {"epoch": 1, "train_loss": "0.05458420396058096", "validation_loss": "108.99862844124436"}, {"epoch": 2, "train_loss": "0.054659905693390305", "validation_loss": "106.30307429283857"}, {"epoch": 3, "train_loss": "0.054730736438391936", "validation_loss": "104.98548858333379"}]} diff --git a/tests/auto_encoder.json b/tests/auto_encoder.json index 66ab887f6..8d3847173 100644 --- a/tests/auto_encoder.json +++ b/tests/auto_encoder.json @@ -1,4 +1,4 @@ -{ +{ "model_name": "BasicAE", "model_type": "PyTorch", "model_params": { @@ -17,7 +17,7 @@ "valid_start":301, "valid_end": 401, "relevant_cols": ["cfs", "precip", "temp"], - "scaler": "StandardScaler", + "scaler": "StandardScaler", "interpolate": false }, "training_params": @@ -32,7 +32,7 @@ } }, "GCS": false, - + "wandb": { "name": "flood_forecast_circleci", "project": "repo-flood_forecast", @@ -45,4 +45,3 @@ } } - \ No newline at end of file diff --git a/tests/classification_test.json b/tests/classification_test.json index b36b1860b..b560ee5d6 100644 --- a/tests/classification_test.json +++ b/tests/classification_test.json @@ -1,9 +1,9 @@ -{ +{ "model_name": "MultiAttnHeadSimple", "model_type": "PyTorch", "model_params": { - "number_time_series":2, - "seq_len":6, + "number_time_series":2, + "seq_len":6, "final_layer": "Softmax", "output_seq_len": 1, "output_dim": 9 @@ -23,7 +23,7 @@ "test_end": 303, "target_col": ["precip"], "relevant_cols": ["precip", "temp", "cfs"], - "scaler": "StandardScaler", + "scaler": "StandardScaler", "interpolate": false }, @@ -41,7 +41,7 @@ "batch_size":4 }, "GCS": false, - + "wandb": { "name": "flood_forecast_circleci", "tags": ["dummy_run", "circleci", "multi_head", "classification"], @@ -50,4 +50,3 @@ "forward_params":{}, "metrics":["CrossEntropyLoss"] } - diff --git a/tests/config.json b/tests/config.json index b55714d5f..7f2c5fcc2 100644 --- a/tests/config.json +++ b/tests/config.json @@ -1 +1 @@ -{"model_name": "CustomTransformerDecoder", "model_type": "PyTorch", "model_params": {"seq_length": 11, "n_time_series": 9, "output_seq_length": 2, "n_layers_encoder": 3, "use_mask": true}, "dataset_params": {"class": "default", "training_path": "United_States__Florida__Palm_Beach_County.csv", "validation_path": "United_States__Florida__Palm_Beach_County.csv", "test_path": "United_States__Florida__Palm_Beach_County.csv", "forecast_test_len": 15, "batch_size": 20, "forecast_history": 11, "forecast_length": 2, "train_end": 61, "valid_start": 62, "valid_end": 88, "test_start": 61, "test_end": 90, "target_col": ["new_cases"], "relevant_cols": ["new_cases", "month", "weekday", "mobility_retail_recreation", "mobility_grocery_pharmacy", "mobility_parks", "mobility_transit_stations", "mobility_workplaces", "mobility_residential"], "scaler": "StandardScaler", "interpolate": false}, "training_params": {"criterion": "MSE", "optimizer": "Adam", "optim_params": {}, "lr": 0.0001, "epochs": 10, "batch_size": 19}, "GCS": true, "early_stopping": {"patience": 3}, "sweep": true, "wandb": false, "forward_params": {}, "metrics": ["MSE"], "inference_params": {"datetime_start": "2020-06-10", "hours_to_forecast": 15, "num_prediction_samples": 100, "test_csv_path": "United_States__Florida__Palm_Beach_County.csv", "decoder_params": {"decoder_function": "simple_decode", "unsqueeze_dim": 1}, "dataset_params": {"file_path": "United_States__Florida__Palm_Beach_County.csv", "forecast_history": 11, "forecast_length": 2, "relevant_cols": ["new_cases", "month", "weekday", "mobility_retail_recreation", "mobility_grocery_pharmacy", "mobility_parks", "mobility_transit_stations", "mobility_workplaces", "mobility_residential"], "target_col": ["new_cases"], "scaling": "StandardScaler", "interpolate_param": false}}, "weight_path": "/content/github_aistream-peelout_flow-forecast/29_June_202009_26AM_model.pth", "run": [{"epoch": 0, "train_loss": "0.6654510299364725", "validation_loss": "1.1439250165765935"}, {"epoch": 1, "train_loss": "0.7072166502475739", "validation_loss": "1.095255504954945"}, {"epoch": 2, "train_loss": "0.5650965571403503", "validation_loss": "1.2630093747919255"}, {"epoch": 3, "train_loss": "0.504930337270101", "validation_loss": "1.8901219367980957"}, {"epoch": 4, "train_loss": "0.49202097455660504", "validation_loss": "2.055953329259699"}], "epochs": 0} \ No newline at end of file +{"model_name": "CustomTransformerDecoder", "model_type": "PyTorch", "model_params": {"seq_length": 11, "n_time_series": 9, "output_seq_length": 2, "n_layers_encoder": 3, "use_mask": true}, "dataset_params": {"class": "default", "training_path": "United_States__Florida__Palm_Beach_County.csv", "validation_path": "United_States__Florida__Palm_Beach_County.csv", "test_path": "United_States__Florida__Palm_Beach_County.csv", "forecast_test_len": 15, "batch_size": 20, "forecast_history": 11, "forecast_length": 2, "train_end": 61, "valid_start": 62, "valid_end": 88, "test_start": 61, "test_end": 90, "target_col": ["new_cases"], "relevant_cols": ["new_cases", "month", "weekday", "mobility_retail_recreation", "mobility_grocery_pharmacy", "mobility_parks", "mobility_transit_stations", "mobility_workplaces", "mobility_residential"], "scaler": "StandardScaler", "interpolate": false}, "training_params": {"criterion": "MSE", "optimizer": "Adam", "optim_params": {}, "lr": 0.0001, "epochs": 10, "batch_size": 19}, "GCS": true, "early_stopping": {"patience": 3}, "sweep": true, "wandb": false, "forward_params": {}, "metrics": ["MSE"], "inference_params": {"datetime_start": "2020-06-10", "hours_to_forecast": 15, "num_prediction_samples": 100, "test_csv_path": "United_States__Florida__Palm_Beach_County.csv", "decoder_params": {"decoder_function": "simple_decode", "unsqueeze_dim": 1}, "dataset_params": {"file_path": "United_States__Florida__Palm_Beach_County.csv", "forecast_history": 11, "forecast_length": 2, "relevant_cols": ["new_cases", "month", "weekday", "mobility_retail_recreation", "mobility_grocery_pharmacy", "mobility_parks", "mobility_transit_stations", "mobility_workplaces", "mobility_residential"], "target_col": ["new_cases"], "scaling": "StandardScaler", "interpolate_param": false}}, "weight_path": "/content/github_aistream-peelout_flow-forecast/29_June_202009_26AM_model.pth", "run": [{"epoch": 0, "train_loss": "0.6654510299364725", "validation_loss": "1.1439250165765935"}, {"epoch": 1, "train_loss": "0.7072166502475739", "validation_loss": "1.095255504954945"}, {"epoch": 2, "train_loss": "0.5650965571403503", "validation_loss": "1.2630093747919255"}, {"epoch": 3, "train_loss": "0.504930337270101", "validation_loss": "1.8901219367980957"}, {"epoch": 4, "train_loss": "0.49202097455660504", "validation_loss": "2.055953329259699"}], "epochs": 0} diff --git a/tests/cross_former.json b/tests/cross_former.json index eb5c1bb6c..aa6e8bb35 100644 --- a/tests/cross_former.json +++ b/tests/cross_former.json @@ -1,4 +1,4 @@ -{ +{ "model_name": "Crossformer", "use_decoder": true, "model_type": "PyTorch", @@ -7,7 +7,7 @@ "n_time_series": 3, "forecast_length": 10, "seg_len": 4 - + }, "n_targets": 4, "dataset_params": @@ -26,7 +26,7 @@ "test_end": 450, "target_col": ["cfs"], "relevant_cols": ["cfs", "precip", "temp"], - "scaler": "StandardScaler", + "scaler": "StandardScaler", "interpolate": false }, "training_params": @@ -40,10 +40,10 @@ "lr": 0.03, "epochs": 1, "batch_size":4 - + }, "GCS": false, - + "wandb": { "name": "flood_forecast_circleci", "tags": ["dummy_run", "circleci"], @@ -52,12 +52,12 @@ "forward_params":{}, "metrics":["MSE"], "inference_params": - { + { "datetime_start":"2016-05-31", - "hours_to_forecast":334, + "hours_to_forecast":334, "test_csv_path":"tests/test_data/keag_small.csv", "decoder_params":{ - "decoder_function": "simple_decode", + "decoder_function": "simple_decode", "unsqueeze_dim": 1} , "dataset_params":{ @@ -71,6 +71,3 @@ } } } - - - \ No newline at end of file diff --git a/tests/custom_encode.json b/tests/custom_encode.json index f7f7927c4..fc0ca9072 100644 --- a/tests/custom_encode.json +++ b/tests/custom_encode.json @@ -1,12 +1,12 @@ -{ +{ "model_name": "CustomTransformerDecoder", "model_type": "PyTorch", "model_params": { "n_time_series":3, "seq_length":5, - "output_seq_length": 1, + "output_seq_length": 1, "n_layers_encoder": 6 - }, + }, "dataset_params": { "class": "default", "training_path": "tests/test_data/keag_small.csv", @@ -21,7 +21,7 @@ "test_end":500, "target_col": ["cfs"], "relevant_cols": ["cfs", "precip", "temp"], - "scaler": "StandardScaler", + "scaler": "StandardScaler", "interpolate": false }, "early_stopping":{ @@ -33,14 +33,14 @@ "criterion":"MAPE", "optimizer": "Adam", "optim_params": - { + { }, "lr": 0.3, "epochs": 2, "batch_size":4 - - }, + + }, "GCS": false, "wandb": { "name": "flood_forecast_circleci", @@ -50,12 +50,12 @@ "forward_params":{}, "metrics":["MSE"], "inference_params": - { + { "datetime_start":"2016-05-31", - "hours_to_forecast":336, + "hours_to_forecast":336, "test_csv_path":"tests/test_data/keag_small.csv", "decoder_params":{ - "decoder_function": "simple_decode", + "decoder_function": "simple_decode", "unsqueeze_dim": 1}, "num_prediction_samples":20, "dataset_params":{ @@ -68,7 +68,6 @@ "interpolate_param": false } } - - + + } - \ No newline at end of file diff --git a/tests/da_meta.json b/tests/da_meta.json index d7b0cca62..8b901e509 100644 --- a/tests/da_meta.json +++ b/tests/da_meta.json @@ -1,4 +1,4 @@ -{ +{ "model_name": "DARNN", "model_type": "PyTorch", "model_params": { @@ -6,7 +6,7 @@ "hidden_size_encoder":128, "decoder_hidden_size":128, "out_feats":1, - "forecast_history":6, + "forecast_history":6, "dropout": 0.4, "gru_lstm": false, "meta_data": @@ -40,7 +40,7 @@ "test_end": 500, "target_col": ["cfs"], "relevant_cols": ["cfs", "precip", "temp"], - "scaler": "StandardScaler", + "scaler": "StandardScaler", "interpolate": false }, @@ -54,10 +54,10 @@ "lr": 0.3, "epochs": 1, "batch_size":4 - + }, "GCS": false, - + "wandb": { "name": "flood_forecast_circleci", "tags": ["dummy_run", "circleci", "da_revised"], @@ -68,7 +68,7 @@ "inference_params": { "num_prediction_samples": 10, "datetime_start":"2016-05-31", - "hours_to_forecast":336, + "hours_to_forecast":336, "test_csv_path":"tests/test_data/keag_small.csv", "dataset_params":{ "file_path": "tests/test_data/keag_small.csv", @@ -82,6 +82,5 @@ "decoder_params":{ "decoder_function": "simple_decode", "unsqueeze_dim": 1} } - + } - \ No newline at end of file diff --git a/tests/da_rnn.json b/tests/da_rnn.json index 64acf8b31..d29b27721 100644 --- a/tests/da_rnn.json +++ b/tests/da_rnn.json @@ -1,4 +1,4 @@ -{ +{ "model_name": "DARNN", "model_type": "PyTorch", "model_params": { @@ -6,8 +6,8 @@ "hidden_size_encoder":128, "decoder_hidden_size":128, "out_feats":1, - "forecast_history":6, - "gru_lstm": false + "forecast_history":6, + "gru_lstm": false }, "dataset_params": @@ -24,7 +24,7 @@ "test_end": 500, "target_col": ["cfs"], "relevant_cols": ["cfs", "precip", "temp"], - "scaler": "StandardScaler", + "scaler": "StandardScaler", "interpolate": false }, @@ -42,10 +42,10 @@ "lr": 0.3, "epochs": 1, "batch_size":4 - + }, "GCS": false, - + "wandb": { "name": "flood_forecast_circleci", "tags": ["dummy_run", "circleci", "da_revised"], @@ -59,12 +59,11 @@ "baseline_method":"mean" }, "datetime_start":"2016-05-31", - "hours_to_forecast":336, + "hours_to_forecast":336, "test_csv_path":"tests/test_data/keag_small.csv", "decoder_params":{ "decoder_function": "simple_decode", "unsqueeze_dim": 1} } - + } - \ No newline at end of file diff --git a/tests/decoder_test.json b/tests/decoder_test.json index 69dcac953..dfd2c40bf 100644 --- a/tests/decoder_test.json +++ b/tests/decoder_test.json @@ -1,12 +1,12 @@ -{ +{ "model_name": "CustomTransformerDecoder", "model_type": "PyTorch", "model_params": { "n_time_series":3, "seq_length":5, - "output_seq_length": 1, + "output_seq_length": 1, "n_layers_encoder": 4 - }, + }, "dataset_params": { "class": "default", "training_path": "tests/test_data/keag_small.csv", @@ -21,7 +21,7 @@ "test_end":400, "target_col": ["cfs"], "relevant_cols": ["cfs", "precip", "temp"], - "scaler": "StandardScaler", + "scaler": "StandardScaler", "interpolate": false }, "training_params": @@ -37,7 +37,7 @@ "batch_size":4 }, "GCS": false, - + "wandb": { "name": "flood_forecast_circleci", "project": "repo-flood_forecast", @@ -46,12 +46,12 @@ "forward_params":{}, "metrics":["MSE"], "inference_params": - { + { "datetime_start":"2016-05-31", - "hours_to_forecast":336, + "hours_to_forecast":336, "test_csv_path":"tests/test_data/keag_small.csv", "decoder_params":{ - "decoder_function": "simple_decode", + "decoder_function": "simple_decode", "unsqueeze_dim": 1}, "dataset_params":{ "file_path": "tests/test_data/keag_small.csv", @@ -62,6 +62,5 @@ "scaling": "StandardScaler", "interpolate_param": false } - } + } } - \ No newline at end of file diff --git a/tests/dlinear.json b/tests/dlinear.json index 8d1ec57d3..81c499c2b 100644 --- a/tests/dlinear.json +++ b/tests/dlinear.json @@ -1,4 +1,4 @@ -{ +{ "model_name": "DLinear", "use_decoder": true, "model_type": "PyTorch", @@ -24,7 +24,7 @@ "test_end": 450, "target_col": ["cfs"], "relevant_cols": ["cfs", "precip", "temp"], - "scaler": "StandardScaler", + "scaler": "StandardScaler", "interpolate": false }, "training_params": @@ -38,10 +38,10 @@ "lr": 0.03, "epochs": 1, "batch_size":4 - + }, "GCS": false, - + "wandb": { "name": "flood_forecast_circleci", "tags": ["dummy_run", "circleci"], @@ -50,12 +50,12 @@ "forward_params":{}, "metrics":["MSE"], "inference_params": - { + { "datetime_start":"2016-05-31", - "hours_to_forecast":334, + "hours_to_forecast":334, "test_csv_path":"tests/test_data/keag_small.csv", "decoder_params":{ - "decoder_function": "simple_decode", + "decoder_function": "simple_decode", "unsqueeze_dim": 1} , "dataset_params":{ @@ -69,6 +69,3 @@ } } } - - - \ No newline at end of file diff --git a/tests/dsanet.json b/tests/dsanet.json index 3977ff614..97de2dc48 100644 --- a/tests/dsanet.json +++ b/tests/dsanet.json @@ -1,4 +1,4 @@ -{ +{ "model_name": "DSANet", "use_decoder": true, "model_type": "PyTorch", @@ -6,8 +6,8 @@ "forecast_history":10, "n_time_series":3, "dsa_local": 2, - "dsanet_n_kernels": 32, - "dsanet_w_kernals": 1, + "dsanet_n_kernels": 32, + "dsanet_w_kernals": 1, "dsanet_d_model":128, "dsanet_d_inner": 2048, "dsanet_n_layers": 2, @@ -31,7 +31,7 @@ "test_end": 450, "target_col": ["cfs"], "relevant_cols": ["cfs", "precip", "temp"], - "scaler": "StandardScaler", + "scaler": "StandardScaler", "interpolate": false }, "training_params": @@ -45,10 +45,10 @@ "lr": 0.03, "epochs": 1, "batch_size":4 - + }, "GCS": false, - + "wandb": { "name": "flood_forecast_circleci", "tags": ["dummy_run", "circleci"], @@ -57,12 +57,12 @@ "forward_params":{}, "metrics":["MSE"], "inference_params": - { + { "datetime_start":"2016-05-31", - "hours_to_forecast":334, + "hours_to_forecast":334, "test_csv_path":"tests/test_data/keag_small.csv", "decoder_params":{ - "decoder_function": "simple_decode", + "decoder_function": "simple_decode", "unsqueeze_dim": 1} , "dataset_params":{ @@ -76,6 +76,3 @@ } } } - - - \ No newline at end of file diff --git a/tests/dsanet_3.json b/tests/dsanet_3.json index 0bfd9e83b..c1762942d 100644 --- a/tests/dsanet_3.json +++ b/tests/dsanet_3.json @@ -1,4 +1,4 @@ -{ +{ "model_name": "DSANet", "use_decoder": true, "model_type": "PyTorch", @@ -7,8 +7,8 @@ "dsa_targs": 1, "n_time_series":3, "dsa_local": 2, - "dsanet_n_kernels": 32, - "dsanet_w_kernals": 1, + "dsanet_n_kernels": 32, + "dsanet_w_kernals": 1, "dsanet_d_model":128, "dsanet_d_inner": 2048, "dsanet_n_layers": 2, @@ -31,7 +31,7 @@ "test_end": 450, "target_col": ["cfs"], "relevant_cols": ["cfs", "precip", "temp"], - "scaler": "StandardScaler", + "scaler": "StandardScaler", "interpolate": false }, "training_params": @@ -45,10 +45,10 @@ "lr": 0.03, "epochs": 1, "batch_size":4 - + }, "GCS": false, - + "wandb": { "name": "flood_forecast_circleci", "tags": ["dummy_run", "circleci"], @@ -57,12 +57,12 @@ "forward_params":{}, "metrics":["MSE"], "inference_params": - { + { "datetime_start":"2016-05-31", - "hours_to_forecast":334, + "hours_to_forecast":334, "test_csv_path":"tests/test_data/keag_small.csv", "decoder_params":{ - "decoder_function": "simple_decode", + "decoder_function": "simple_decode", "unsqueeze_dim": 1} , "dataset_params":{ @@ -76,6 +76,3 @@ } } } - - - \ No newline at end of file diff --git a/tests/full_transformer.json b/tests/full_transformer.json index d58c1d86d..441452d77 100644 --- a/tests/full_transformer.json +++ b/tests/full_transformer.json @@ -1,4 +1,4 @@ -{ +{ "model_name": "SimpleTransformer", "use_decoder": true, "model_type": "PyTorch", @@ -22,7 +22,7 @@ "test_end": 400, "target_col": ["cfs"], "relevant_cols": ["cfs", "precip", "temp"], - "scaler": "StandardScaler", + "scaler": "StandardScaler", "interpolate": false }, "early_stopping": @@ -40,10 +40,10 @@ "lr": 0.3, "epochs": 4, "batch_size":4 - + }, "GCS": false, - + "wandb": { "name": "flood_forecast_circleci", "tags": ["dummy_run", "circleci"], @@ -55,12 +55,12 @@ "takes_target": true, "metrics":["MSE"], "inference_params": - { + { "datetime_start":"2016-05-31", - "hours_to_forecast":10, + "hours_to_forecast":10, "test_csv_path":"tests/test_data/keag_small.csv", "decoder_params":{ - "decoder_function": "greedy_decode", + "decoder_function": "greedy_decode", "unsqueeze_dim": 1}, "dataset_params":{ "file_path": "tests/test_data/keag_small.csv", @@ -72,7 +72,6 @@ "interpolate_param": false } } - - + + } - \ No newline at end of file diff --git a/tests/gru_vanilla.json b/tests/gru_vanilla.json index 73dd9e3fb..aed555892 100644 --- a/tests/gru_vanilla.json +++ b/tests/gru_vanilla.json @@ -1,4 +1,4 @@ -{ +{ "model_name": "VanillaGRU", "use_decoder": true, "model_type": "PyTorch", @@ -25,7 +25,7 @@ "test_end": 450, "target_col": ["cfs"], "relevant_cols": ["cfs", "precip", "temp"], - "scaler": "StandardScaler", + "scaler": "StandardScaler", "interpolate": false }, "training_params": @@ -39,10 +39,10 @@ "lr": 0.03, "epochs": 1, "batch_size":4 - + }, "GCS": false, - + "wandb": { "name": "flood_forecast_circleci", "tags": ["dummy_run", "circleci"], @@ -51,12 +51,12 @@ "forward_params":{}, "metrics":["MSE"], "inference_params": - { + { "datetime_start":"2016-05-31", - "hours_to_forecast":334, + "hours_to_forecast":334, "test_csv_path":"tests/test_data/keag_small.csv", "decoder_params":{ - "decoder_function": "simple_decode", + "decoder_function": "simple_decode", "unsqueeze_dim": 1} , "dataset_params":{ @@ -70,6 +70,3 @@ } } } - - - \ No newline at end of file diff --git a/tests/lstm_test.json b/tests/lstm_test.json index c343e5511..4f1e95aba 100644 --- a/tests/lstm_test.json +++ b/tests/lstm_test.json @@ -1,9 +1,9 @@ -{ +{ "model_name": "LSTM", "model_type": "PyTorch", "model_params": { - "seq_length": 10, - "n_time_series":3, + "seq_length": 10, + "n_time_series":3, "output_seq_len":1, "batch_size":4 }, @@ -23,7 +23,7 @@ "test_end": 450, "target_col": ["cfs"], "relevant_cols": ["cfs", "precip", "temp"], - "scaler": "StandardScaler", + "scaler": "StandardScaler", "interpolate": false }, "training_params": @@ -37,10 +37,10 @@ "lr": 0.3, "epochs": 1, "batch_size":4 - + }, "GCS": false, - + "wandb": { "name": "flood_forecast_circleci", "tags": ["dummy_run", "circleci"], @@ -49,12 +49,12 @@ "forward_params":{}, "metrics":["MSE"], "inference_params": - { + { "datetime_start":"2016-05-31", - "hours_to_forecast":334, + "hours_to_forecast":334, "test_csv_path":"tests/test_data/keag_small.csv", "decoder_params":{ - "decoder_function": "simple_decode", + "decoder_function": "simple_decode", "unsqueeze_dim": 1} , "dataset_params":{ @@ -68,6 +68,3 @@ } } } - - - \ No newline at end of file diff --git a/tests/meta_data_test.json b/tests/meta_data_test.json index 3cf4a0eb1..c6babcb33 100644 --- a/tests/meta_data_test.json +++ b/tests/meta_data_test.json @@ -1,20 +1,20 @@ -{ +{ "model_name": "CustomTransformerDecoder", - "model_type": "PyTorch", + "model_type": "PyTorch", "model_params": { "n_time_series":3, "seq_length":5, - "output_seq_length": 1, + "output_seq_length": 1, "n_layers_encoder": 6, "meta_data": { "method": "Bilinear", "params": - {"in1_features":5, - "in2_features":1, + {"in1_features":5, + "in2_features":1, "out_features":5} } - }, + }, "meta_data":{ "path":"tests/auto_encoder.json", "column_id": "datetime", @@ -35,7 +35,7 @@ "test_end":400, "target_col": ["cfs"], "relevant_cols": ["cfs", "precip", "temp"], - "scaler": "StandardScaler", + "scaler": "StandardScaler", "interpolate": false }, "training_params": @@ -49,10 +49,10 @@ "lr": 0.3, "epochs": 1, "batch_size":4 - + }, "GCS": false, - + "wandb": { "name": "flood_forecast_circleci", "project": "repo-flood_forecast", @@ -61,12 +61,12 @@ "forward_params":{}, "metrics":["MSE"], "inference_params": - { + { "datetime_start":"2016-05-31", - "hours_to_forecast":336, + "hours_to_forecast":336, "test_csv_path":"tests/test_data/keag_small.csv", "decoder_params":{ - "decoder_function": "simple_decode", + "decoder_function": "simple_decode", "unsqueeze_dim": 1}, "dataset_params":{ "file_path": "tests/test_data/keag_small.csv", @@ -77,6 +77,5 @@ "scaling": "StandardScaler", "interpolate_param": false } - } + } } - \ No newline at end of file diff --git a/tests/multi_config.json b/tests/multi_config.json index b5c6286cd..83c740eb9 100644 --- a/tests/multi_config.json +++ b/tests/multi_config.json @@ -1 +1 @@ -{"model_name": "CustomTransformerDecoder", "model_type": "PyTorch", "n_targets": 2, "model_params": {"dropout": 0.1, "seq_length": 11, "n_time_series": 18, "output_dim": 2, "output_seq_length": 1, "n_layers_encoder": 2, "use_mask": true}, "dataset_params": {"class": "default", "num_workers": 5, "forecast_test_len": 20, "pin_memory": true, "training_path": "/content/flow-forecast/miami_f.csv", "validation_path": "/content/flow-forecast/miami_f.csv", "test_path": "/content/flow-forecast/miami_f.csv", "batch_size": 10, "forecast_history": 11, "forecast_length": 1, "scaler": "StandardScaler", "train_start": 0, "train_end": 170, "valid_start": 170, "valid_end": 310, "sort_column": "date", "test_start": 170, "test_end": 310, "target_col": ["rolling_7", "rolling_deaths"], "relevant_cols": ["rolling_7", "rolling_deaths", "mobility_retail_recreation", "mobility_grocery_pharmacy", "mobility_parks", "mobility_transit_stations", "mobility_workplaces", "mobility_residential", "avg_temperature", "min_temperature", "max_temperature", "relative_humidity", "specific_humidity", "pressure"], "feature_param": {"datetime_params": {"day_of_week": "cyclical", "month": "cyclical"}}, "interpolate": false}, "training_params": {"criterion": "MSE", "optimizer": "SGD", "optim_params": {"lr": 0.0001}, "epochs": 10, "batch_size": 10}, "early_stopping": {"patience": 3}, "GCS": true, "sweep": true, "wandb": false, "forward_params": {}, "metrics": ["MSE"], "inference_params": {"datetime_start": "2020-12-14", "hours_to_forecast": 18, "num_prediction_samples": 20, "test_csv_path": "/content/flow-forecast/miami_f.csv", "decoder_params": {"decoder_function": "simple_decode", "unsqueeze_dim": 1}, "dataset_params": {"file_path": "/content/flow-forecast/miami_f.csv", "sort_column": "date", "scaling": "StandardScaler", "forecast_history": 11, "forecast_length": 1, "relevant_cols": ["rolling_7", "rolling_deaths", "mobility_retail_recreation", "mobility_grocery_pharmacy", "mobility_parks", "mobility_transit_stations", "mobility_workplaces", "mobility_residential", "avg_temperature", "min_temperature", "max_temperature", "relative_humidity", "specific_humidity", "pressure"], "target_col": ["rolling_7", "rolling_deaths"], "interpolate_param": false, "feature_params": {"datetime_params": {"day_of_week": "cyclical", "month": "cyclical"}}}}, "meta_data": false, "run": [{"epoch": 0, "train_loss": "1.1954958769492805", "validation_loss": "85.43445341289043"}, {"epoch": 1, "train_loss": "1.1476804590784013", "validation_loss": "84.1799928843975"}, {"epoch": 2, "train_loss": "1.065674600424245", "validation_loss": "84.03104758262634"}, {"epoch": 3, "train_loss": "1.0211504658218473", "validation_loss": "84.54550993442535"}, {"epoch": 4, "train_loss": "0.9789167386479676", "validation_loss": "85.40744817256927"}, {"epoch": 5, "train_loss": "0.9342440171167254", "validation_loss": "86.52448198199272"}]} \ No newline at end of file +{"model_name": "CustomTransformerDecoder", "model_type": "PyTorch", "n_targets": 2, "model_params": {"dropout": 0.1, "seq_length": 11, "n_time_series": 18, "output_dim": 2, "output_seq_length": 1, "n_layers_encoder": 2, "use_mask": true}, "dataset_params": {"class": "default", "num_workers": 5, "forecast_test_len": 20, "pin_memory": true, "training_path": "/content/flow-forecast/miami_f.csv", "validation_path": "/content/flow-forecast/miami_f.csv", "test_path": "/content/flow-forecast/miami_f.csv", "batch_size": 10, "forecast_history": 11, "forecast_length": 1, "scaler": "StandardScaler", "train_start": 0, "train_end": 170, "valid_start": 170, "valid_end": 310, "sort_column": "date", "test_start": 170, "test_end": 310, "target_col": ["rolling_7", "rolling_deaths"], "relevant_cols": ["rolling_7", "rolling_deaths", "mobility_retail_recreation", "mobility_grocery_pharmacy", "mobility_parks", "mobility_transit_stations", "mobility_workplaces", "mobility_residential", "avg_temperature", "min_temperature", "max_temperature", "relative_humidity", "specific_humidity", "pressure"], "feature_param": {"datetime_params": {"day_of_week": "cyclical", "month": "cyclical"}}, "interpolate": false}, "training_params": {"criterion": "MSE", "optimizer": "SGD", "optim_params": {"lr": 0.0001}, "epochs": 10, "batch_size": 10}, "early_stopping": {"patience": 3}, "GCS": true, "sweep": true, "wandb": false, "forward_params": {}, "metrics": ["MSE"], "inference_params": {"datetime_start": "2020-12-14", "hours_to_forecast": 18, "num_prediction_samples": 20, "test_csv_path": "/content/flow-forecast/miami_f.csv", "decoder_params": {"decoder_function": "simple_decode", "unsqueeze_dim": 1}, "dataset_params": {"file_path": "/content/flow-forecast/miami_f.csv", "sort_column": "date", "scaling": "StandardScaler", "forecast_history": 11, "forecast_length": 1, "relevant_cols": ["rolling_7", "rolling_deaths", "mobility_retail_recreation", "mobility_grocery_pharmacy", "mobility_parks", "mobility_transit_stations", "mobility_workplaces", "mobility_residential", "avg_temperature", "min_temperature", "max_temperature", "relative_humidity", "specific_humidity", "pressure"], "target_col": ["rolling_7", "rolling_deaths"], "interpolate_param": false, "feature_params": {"datetime_params": {"day_of_week": "cyclical", "month": "cyclical"}}}}, "meta_data": false, "run": [{"epoch": 0, "train_loss": "1.1954958769492805", "validation_loss": "85.43445341289043"}, {"epoch": 1, "train_loss": "1.1476804590784013", "validation_loss": "84.1799928843975"}, {"epoch": 2, "train_loss": "1.065674600424245", "validation_loss": "84.03104758262634"}, {"epoch": 3, "train_loss": "1.0211504658218473", "validation_loss": "84.54550993442535"}, {"epoch": 4, "train_loss": "0.9789167386479676", "validation_loss": "85.40744817256927"}, {"epoch": 5, "train_loss": "0.9342440171167254", "validation_loss": "86.52448198199272"}]} diff --git a/tests/multi_decoder_test.json b/tests/multi_decoder_test.json index 65d01cc78..c14222afc 100644 --- a/tests/multi_decoder_test.json +++ b/tests/multi_decoder_test.json @@ -1,14 +1,14 @@ -{ +{ "model_name": "CustomTransformerDecoder", "model_type": "PyTorch", "model_params": { "n_time_series":3, "seq_length":5, - "output_seq_length": 1, + "output_seq_length": 1, "n_layers_encoder": 6, "output_dim":1, "final_act":"Swish" - }, + }, "dataset_params": { "class": "default", "training_path": "tests/test_data/keag_small.csv", @@ -41,10 +41,10 @@ "lr": 0.3, "epochs": 1, "batch_size":4 - + }, "GCS": false, - + "wandb": { "name": "flood_forecast_circleci", "project": "repo-flood_forecast", @@ -53,13 +53,13 @@ "forward_params":{}, "metrics":["MSE"], "inference_params": - { + { "datetime_start":"2016-05-31", "num_prediction_samples":10, - "hours_to_forecast":336, + "hours_to_forecast":336, "test_csv_path":"tests/test_data/keag_small.csv", "decoder_params":{ - "decoder_function": "simple_decode", + "decoder_function": "simple_decode", "unsqueeze_dim": 1}, "dataset_params":{ "file_path": "tests/test_data/keag_small.csv", @@ -74,7 +74,6 @@ "interpolate_param": false } } - - + + } - \ No newline at end of file diff --git a/tests/multi_modal_tests/test_cross_vivit.py b/tests/multi_modal_tests/test_cross_vivit.py index 686d14736..e154b835b 100644 --- a/tests/multi_modal_tests/test_cross_vivit.py +++ b/tests/multi_modal_tests/test_cross_vivit.py @@ -2,7 +2,11 @@ import torch from flood_forecast.multi_models.crossvivit import RoCrossViViT, VisionTransformer from flood_forecast.transformer_xl.attn import SelfAttention -from flood_forecast.transformer_xl.data_embedding import CyclicalEmbedding, NeRF_embedding, PositionalEncoding2D +from flood_forecast.transformer_xl.data_embedding import ( + CyclicalEmbedding, + NeRF_embedding, + PositionalEncoding2D, +) class TestCrossVivVit(unittest.TestCase): @@ -21,11 +25,12 @@ def setUp(self): out_dim=1, dropout=0.0, video_cat_dim=2, - axial_kwargs={"max_freq": 12} + axial_kwargs={"max_freq": 12}, ) + def test_positional_encoding_forward(self): """ - Test the positional encoding forward pass with a PositionalEncoding2D layer. + Test the positional encoding forward pass with a PositionalEncoding2D layer. """ positional_encoding = PositionalEncoding2D(channels=2) # Coordinates with format [B, 2, H, W] @@ -34,14 +39,21 @@ def test_positional_encoding_forward(self): self.assertEqual(output.shape, (5, 32, 32, 4)) def test_vivit_model(self): - self.vivit_model = VisionTransformer(128, 5, 8, 128, 128, [512, 512, 512], dropout=0.1) - out = self.vivit_model(torch.rand(5, 512, 128), (torch.rand(5, 512, 64), torch.rand(5, 512, 64))) + """ + Tests the Vision Video Transformer VIVIT model with simulated image data. + """ + self.vivit_model = VisionTransformer( + dim=128, depth=5, heads=8, dim_head=128, mlp_dim=128, dropout=0.1 + ) + out = self.vivit_model( + torch.rand(5, 512, 128), (torch.rand(5, 512, 64), torch.rand(5, 512, 64)) + ) assert out[0].shape == (5, 512, 128) def test_forward(self): """ - This tests the forward pass of the VIVIT model from the CrossVIVIT paper. - ctx (torch.Tensor): Context frames of shape [batch_size, number_time_stamps, number_channels, height, wid] + This tests the forward pass of the RoCrossVIVIT model from the CrossVIVIT paper. + ctx (torch.Tensor): Context frames of shape [batch_size, number_time_stamps, number_channels, height, wid] this is a very long line ctx_coords (torch.Tensor): Coordinates of context frames of shape [B, 2, H, W] ts (torch.Tensor): Station timeseries of shape [B, T, C] ts_coords (torch.Tensor): Station coordinates of shape [B, 2, 1, 1] @@ -59,20 +71,27 @@ def test_forward(self): ts = torch.rand(5, 10, 12) time_coords1 = torch.rand(5, 10, 4, 120, 120) ts_coords = torch.rand(5, 2, 1, 1) - x = self.crossvivit(video_context=ctx_tensor, context_coords=ctx_coords, timeseries=ts, timeseries_spatial_coordinates=ts_coords, - ts_positional_encoding=time_coords1) + x = self.crossvivit( + video_context=ctx_tensor, + context_coords=ctx_coords, + timeseries=ts, + timeseries_spatial_coordinates=ts_coords, + ts_positional_encoding=time_coords1, + ) self.assertEqual(x[0].shape, (5, 10, 1, 1)) def test_self_attention_dims(self): """ - Test the self attention layer with the correct dimensions. + Test the self attention layer with the correct dimensions. """ self.self_attention = SelfAttention(dim=128, use_rotary=True) - self.self_attention(torch.rand(5, 512, 128), (torch.rand(5, 512, 64), torch.rand(5, 512, 64))) + self.self_attention( + torch.rand(5, 512, 128), (torch.rand(5, 512, 64), torch.rand(5, 512, 64)) + ) def test_neRF_embedding(self): """ - Test the NeRF embedding layer. + Test the NeRF embedding layer. """ nerf_embedding = NeRF_embedding(n_layers=128) coords = torch.rand(5, 2, 32, 32) @@ -80,5 +99,5 @@ def test_neRF_embedding(self): self.assertEqual(output.shape, (5, 32, 32, 128)) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/tests/multi_test.json b/tests/multi_test.json index f4271b7d7..e080bef42 100644 --- a/tests/multi_test.json +++ b/tests/multi_test.json @@ -1,8 +1,8 @@ -{ +{ "model_name": "MultiAttnHeadSimple", "model_type": "PyTorch", "model_params": { - "number_time_series":3, + "number_time_series":3, "seq_len":5 }, "dataset_params": @@ -17,7 +17,7 @@ "train_end": 190, "valid_start":301, "valid_end": 401, - "test_end": 500, + "test_end": 500, "target_col": ["cfs"], "relevant_cols": ["cfs", "precip", "temp"], "sort_column":"datetime", @@ -35,7 +35,7 @@ "batch_size":4 }, "GCS": false, - + "wandb": { "name": "flood_forecast_circleci", "tags": ["dummy_run", "circleci"], @@ -46,7 +46,7 @@ "inference_params": { "num_prediction_samples": 100, "datetime_start":"2016-05-31", - "hours_to_forecast":336, + "hours_to_forecast":336, "test_csv_path":"tests/test_data/keag_small.csv", "dataset_params":{ "file_path": "tests/test_data/keag_small.csv", @@ -58,4 +58,3 @@ } } } - diff --git a/tests/multitask_decoder.json b/tests/multitask_decoder.json index bb32f4d7b..c1c129c10 100644 --- a/tests/multitask_decoder.json +++ b/tests/multitask_decoder.json @@ -1,10 +1,10 @@ -{ +{ "model_name": "CustomTransformerDecoder", "model_type": "PyTorch", "model_params": { "n_time_series":3, "seq_length":5, - "output_seq_length": 1, + "output_seq_length": 1, "n_layers_encoder": 6, "output_dim":2, "final_act":"Swish" @@ -42,10 +42,10 @@ "lr": 0.3, "epochs": 1, "batch_size":4 - + }, "GCS": false, - + "wandb": { "name": "flood_forecast_circleci", "project": "repo-flood_forecast", @@ -54,12 +54,12 @@ "forward_params":{}, "metrics":["MSE"], "inference_params": - { + { "datetime_start":"2016-05-31", - "hours_to_forecast":356, + "hours_to_forecast":356, "test_csv_path":"tests/test_data/keag_small.csv", "decoder_params":{ - "decoder_function": "simple_decode", + "decoder_function": "simple_decode", "unsqueeze_dim": 1}, "dataset_params":{ "file_path": "tests/test_data/keag_small.csv", @@ -75,7 +75,6 @@ "interpolate_param": false } } - - + + } - \ No newline at end of file diff --git a/tests/nlinear.json b/tests/nlinear.json index 57aca15ef..c32af37bd 100644 --- a/tests/nlinear.json +++ b/tests/nlinear.json @@ -1,4 +1,4 @@ -{ +{ "model_name": "NLinear", "use_decoder": true, "model_type": "PyTorch", @@ -7,7 +7,7 @@ "forecast_length": 10, "enc_in": 3, "individual": true - + }, "dataset_params": { "class": "default", @@ -24,7 +24,7 @@ "test_end": 450, "target_col": ["cfs"], "relevant_cols": ["cfs", "precip", "temp"], - "scaler": "StandardScaler", + "scaler": "StandardScaler", "interpolate": false }, "training_params": @@ -38,10 +38,10 @@ }, "lr": 0.03, "epochs": 1 - + }, "GCS": false, - + "wandb": { "name": "flood_forecast_circleci", "tags": ["dummy_run", "circleci"], @@ -50,12 +50,12 @@ "forward_params":{}, "metrics":["MSE"], "inference_params": - { + { "datetime_start":"2016-05-31", - "hours_to_forecast":334, + "hours_to_forecast":334, "test_csv_path":"tests/test_data/keag_small.csv", "decoder_params":{ - "decoder_function": "simple_decode", + "decoder_function": "simple_decode", "unsqueeze_dim": 1} , "dataset_params":{ @@ -69,6 +69,3 @@ } } } - - - \ No newline at end of file diff --git a/tests/probabilistic_linear_regression_test.json b/tests/probabilistic_linear_regression_test.json index 10da95249..14c31327a 100644 --- a/tests/probabilistic_linear_regression_test.json +++ b/tests/probabilistic_linear_regression_test.json @@ -1,4 +1,4 @@ -{ +{ "model_name": "SimpleLinearModel", "model_type": "PyTorch", "model_params": { diff --git a/tests/scaling_json.json b/tests/scaling_json.json index 86af5d653..471bf0bd6 100644 --- a/tests/scaling_json.json +++ b/tests/scaling_json.json @@ -1,10 +1,10 @@ -{ +{ "model_name": "CustomTransformerDecoder", "model_type": "PyTorch", "model_params": { "n_time_series":3, "seq_length":5, - "output_seq_length": 1, + "output_seq_length": 1, "n_layers_encoder": 6, "output_dim":2, "final_act":"Swish" @@ -42,10 +42,10 @@ "lr": 0.3, "epochs": 1, "batch_size":4 - + }, "GCS": false, - + "wandb": { "name": "flood_forecast_circleci", "project": "repo-flood_forecast", @@ -54,15 +54,14 @@ "forward_params":{}, "metrics":["MSE"], "inference_params": - { + { "datetime_start":"2016-05-31", - "hours_to_forecast":336, + "hours_to_forecast":336, "test_csv_path":"tests/test_data/keag_small.csv", "decoder_params":{ - "decoder_function": "simple_decode", + "decoder_function": "simple_decode", "unsqueeze_dim": 1} } - - + + } - \ No newline at end of file diff --git a/tests/test2.csv b/tests/test2.csv index 165beed2f..a36154210 100644 --- a/tests/test2.csv +++ b/tests/test2.csv @@ -7774,4 +7774,4 @@ 7772,29/02/2016 23:40,6,1,100,0,6, 7773,29/02/2016 23:45,14,1,100,0,14, 7774,29/02/2016 23:50,11,1,100,0,11, -7775,29/02/2016 23:55,10,1,100,0,10, \ No newline at end of file +7775,29/02/2016 23:55,10,1,100,0,10, diff --git a/tests/test_data/big_black_md.json b/tests/test_data/big_black_md.json index d527325cd..ebbbe6d2c 100644 --- a/tests/test_data/big_black_md.json +++ b/tests/test_data/big_black_md.json @@ -1 +1 @@ -{"gage_id": 1010070, "stations": [{"station_id": "40B", "dist": 35.86286975647646, "cat": "ASOS", "missing_precip": 0, "missing_temp": 39}, {"station_id": "CWST", "dist": 55.57017989683566, "cat": "ASOS", "missing_precip": 0, "missing_temp": 0}, {"station_id": "CWIG", "dist": 62.55508196187881, "cat": "ASOS", "missing_precip": 0, "missing_temp": 42418}, {"station_id": "CWIS", "dist": 79.88055804274804, "cat": "ASOS", "missing_precip": 0, "missing_temp": 0}, {"station_id": "CWTN", "dist": 80.72267697891424, "cat": "ASOS", "missing_precip": 0, "missing_temp": 0}, {"station_id": "CWER", "dist": 81.02146645992319, "cat": "ASOS", "missing_precip": 0, "missing_temp": 42174}, {"station_id": "CWNH", "dist": 102.55052000344958, "cat": "ASOS", "missing_precip": 0, "missing_temp": 0}, {"station_id": "CWHV", "dist": 110.05272501753285, "cat": "ASOS", "missing_precip": 0, "missing_temp": 0}, {"station_id": "CXBO", "dist": 110.07973477851439, "cat": "ASOS", "missing_precip": 0, "missing_temp": 42654}, {"station_id": "CMFM", "dist": 115.95899164844458, "cat": "ASOS", "missing_precip": 0, "missing_temp": 1}, {"station_id": "KFVE", "dist": 117.27669180798962, "cat": "ASOS", "missing_precip": 203752, "missing_temp": 217205}, {"station_id": "FVE", "dist": 117.31715057603458, "cat": "ASOS", "missing_precip": 203752, "missing_temp": 217205}, {"station_id": "CWJB", "dist": 117.50331039853623, "cat": "ASOS", "missing_precip": 0, "missing_temp": 0}, {"station_id": "CERM", "dist": 122.57597870894188, "cat": "ASOS", "missing_precip": 0, "missing_temp": 315}, {"station_id": "CYQB", "dist": 125.38125174466414, "cat": "ASOS", "missing_precip": 0, "missing_temp": 3}, {"station_id": "CWAF", "dist": 131.48254619436221, "cat": "ASOS", "missing_precip": 0, "missing_temp": 0}, {"station_id": "CAR", "dist": 131.63453862667052, "cat": "ASOS", "missing_precip": 215342, "missing_temp": 232959}, {"station_id": "KPQI", "dist": 131.65095845184678, "cat": "ASOS", "missing_precip": 220363, "missing_temp": 234985}], "time_zone_code": "EDT", "max_flow": 5870.0, "min_flow": 12.3, "nan_flow": 13627, "nan_precip": 678, "files": ["101007040B_flow.csv"]} \ No newline at end of file +{"gage_id": 1010070, "stations": [{"station_id": "40B", "dist": 35.86286975647646, "cat": "ASOS", "missing_precip": 0, "missing_temp": 39}, {"station_id": "CWST", "dist": 55.57017989683566, "cat": "ASOS", "missing_precip": 0, "missing_temp": 0}, {"station_id": "CWIG", "dist": 62.55508196187881, "cat": "ASOS", "missing_precip": 0, "missing_temp": 42418}, {"station_id": "CWIS", "dist": 79.88055804274804, "cat": "ASOS", "missing_precip": 0, "missing_temp": 0}, {"station_id": "CWTN", "dist": 80.72267697891424, "cat": "ASOS", "missing_precip": 0, "missing_temp": 0}, {"station_id": "CWER", "dist": 81.02146645992319, "cat": "ASOS", "missing_precip": 0, "missing_temp": 42174}, {"station_id": "CWNH", "dist": 102.55052000344958, "cat": "ASOS", "missing_precip": 0, "missing_temp": 0}, {"station_id": "CWHV", "dist": 110.05272501753285, "cat": "ASOS", "missing_precip": 0, "missing_temp": 0}, {"station_id": "CXBO", "dist": 110.07973477851439, "cat": "ASOS", "missing_precip": 0, "missing_temp": 42654}, {"station_id": "CMFM", "dist": 115.95899164844458, "cat": "ASOS", "missing_precip": 0, "missing_temp": 1}, {"station_id": "KFVE", "dist": 117.27669180798962, "cat": "ASOS", "missing_precip": 203752, "missing_temp": 217205}, {"station_id": "FVE", "dist": 117.31715057603458, "cat": "ASOS", "missing_precip": 203752, "missing_temp": 217205}, {"station_id": "CWJB", "dist": 117.50331039853623, "cat": "ASOS", "missing_precip": 0, "missing_temp": 0}, {"station_id": "CERM", "dist": 122.57597870894188, "cat": "ASOS", "missing_precip": 0, "missing_temp": 315}, {"station_id": "CYQB", "dist": 125.38125174466414, "cat": "ASOS", "missing_precip": 0, "missing_temp": 3}, {"station_id": "CWAF", "dist": 131.48254619436221, "cat": "ASOS", "missing_precip": 0, "missing_temp": 0}, {"station_id": "CAR", "dist": 131.63453862667052, "cat": "ASOS", "missing_precip": 215342, "missing_temp": 232959}, {"station_id": "KPQI", "dist": 131.65095845184678, "cat": "ASOS", "missing_precip": 220363, "missing_temp": 234985}], "time_zone_code": "EDT", "max_flow": 5870.0, "min_flow": 12.3, "nan_flow": 13627, "nan_precip": 678, "files": ["101007040B_flow.csv"]} diff --git a/tests/test_data/farm_ex.csv b/tests/test_data/farm_ex.csv index a957cab68..e8b8550d4 100644 --- a/tests/test_data/farm_ex.csv +++ b/tests/test_data/farm_ex.csv @@ -12,4 +12,4 @@ FarmNumber,NumberOfAnimals,MeanMilkProduction (kg/lactation),FarmType, 4,84,9005,1, 5,82,8867,1, 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FVE,2019-01-01 03:00,55,1 FVE,2019-01-01 02:25,52,M FVE,2019-01-01 02:59,52,1 -FVE,2019-01-01 03:00,0,21 \ No newline at end of file +FVE,2019-01-01 03:00,0,21 diff --git a/tests/test_data/small_test.csv b/tests/test_data/small_test.csv index 7b641ab4e..5f54b6b01 100644 --- a/tests/test_data/small_test.csv +++ b/tests/test_data/small_test.csv @@ -5,4 +5,4 @@ FVE,2019-01-01 00:59,52,23 FVE,2019-01-01 01:00,55,1 FVE,2019-01-01 02:25,52,0 FVE,2019-01-01 02:59,52,1 -FVE,2019-01-01 03:00,0,21s \ No newline at end of file +FVE,2019-01-01 03:00,0,21s diff --git a/tests/test_data/test2.csv b/tests/test_data/test2.csv index dffb722ec..af0fc873d 100644 --- a/tests/test_data/test2.csv +++ b/tests/test_data/test2.csv @@ -7774,4 +7774,4 @@ 7772,29/02/2016 23:40,6,1,100,0,6, 7773,29/02/2016 23:45,14,1,100,0,14, 7774,29/02/2016 23:50,11,1,100,0,11, -7775,29/02/2016 23:55,10,1,100,0,10, \ No newline at end of file +7775,29/02/2016 23:55,10,1,100,0,10, diff --git a/tests/test_data/test_format_data.csv b/tests/test_data/test_format_data.csv index a77b60692..0fe0ef367 100644 --- a/tests/test_data/test_format_data.csv +++ b/tests/test_data/test_format_data.csv @@ -2,4 +2,4 @@ precip,temp,height 0,25,.08 .01,24,.06 0,25,.08 -.01,24,.06 \ No newline at end of file +.01,24,.06 diff --git a/tests/test_dual.json b/tests/test_dual.json index 850632927..c4d5753d0 100644 --- a/tests/test_dual.json +++ b/tests/test_dual.json @@ -1,14 +1,14 @@ -{ +{ "model_name": "CustomTransformerDecoder", "model_type": "PyTorch", "n_targets": 2, "model_params": { "n_time_series":3, "seq_length":5, - "output_seq_length": 1, + "output_seq_length": 1, "n_layers_encoder": 6, "output_dim":2 - }, + }, "dataset_params": { "class": "default", "training_path": "tests/test_data/keag_small.csv", @@ -44,10 +44,10 @@ "lr": 0.003, "epochs": 2, "batch_size":4 - + }, "GCS": false, - + "wandb": { "name": "flood_forecast_circleci", "project": "repo-flood_forecast", @@ -56,13 +56,13 @@ "forward_params":{}, "metrics":["MSE"], "inference_params": - { + { "datetime_start":"2016-05-31", "num_prediction_samples":10, - "hours_to_forecast":336, + "hours_to_forecast":336, "test_csv_path":"tests/test_data/keag_small.csv", "decoder_params":{ - "decoder_function": "simple_decode", + "decoder_function": "simple_decode", "unsqueeze_dim": 1}, "dataset_params":{ "file_path": "tests/test_data/keag_small.csv", @@ -77,7 +77,6 @@ "interpolate_param": false } } - - + + } - \ No newline at end of file diff --git a/tests/test_iTransformer.json b/tests/test_iTransformer.json index 95131ed26..701ed7f59 100644 --- a/tests/test_iTransformer.json +++ b/tests/test_iTransformer.json @@ -1,4 +1,4 @@ -{ +{ "model_name": "ITransformer", "use_decoder": true, "model_type": "PyTorch", @@ -10,7 +10,7 @@ "d_model": 512, "use_norm": true, "targs": 3 - + }, "n_targets":3, "dataset_params": @@ -29,7 +29,7 @@ "test_end": 500, "target_col": ["cfs", "precip", "temp"], "relevant_cols": ["cfs", "precip", "temp"], - "scaler": "StandardScaler", + "scaler": "StandardScaler", "sort_column":"datetime", "interpolate": false, "feature_param": @@ -57,10 +57,10 @@ "epochs": 1, "batch_size":5 - + }, "GCS": false, - + "wandb": { "name": "flood_forecast_circleci", "tags": ["dummy_run", "circleci"], @@ -72,10 +72,10 @@ "inference_params": { "num_prediction_samples": 100, "datetime_start":"2016-05-30", - "hours_to_forecast":300, + "hours_to_forecast":300, "test_csv_path":"tests/test_data/keag_small.csv", "decoder_params":{ - "decoder_function": "simple_decode", + "decoder_function": "simple_decode", "unsqueeze_dim": 1}, "dataset_params":{ "file_path": "tests/test_data/keag_small.csv", @@ -98,4 +98,3 @@ } } } - \ No newline at end of file diff --git a/tests/test_inf_single.json b/tests/test_inf_single.json index cb980c495..489632c69 100644 --- a/tests/test_inf_single.json +++ b/tests/test_inf_single.json @@ -1,4 +1,4 @@ -{ +{ "model_name": "Informer", "use_decoder": true, "model_type": "PyTorch", @@ -7,7 +7,7 @@ "dec_in":3, "c_out": 1, "seq_len":20, - "label_len":10, + "label_len":10, "out_len":2, "factor":2 }, @@ -27,7 +27,7 @@ "test_end": 400, "target_col": ["cfs"], "relevant_cols": ["cfs", "precip", "temp"], - "scaler": "StandardScaler", + "scaler": "StandardScaler", "sort_column":"datetime", "interpolate": false, "feature_param": @@ -68,12 +68,12 @@ }, "metrics":["MSE"], "inference_params": - { + { "datetime_start":"2016-05-31", - "hours_to_forecast":334, + "hours_to_forecast":334, "test_csv_path":"tests/test_data/keag_small.csv", "decoder_params":{ - "decoder_function": "greedy_decode", + "decoder_function": "greedy_decode", "unsqueeze_dim": 1}, "dataset_params":{ "file_path": "tests/test_data/keag_small.csv", @@ -96,4 +96,3 @@ } } } - \ No newline at end of file diff --git a/tests/test_informer.json b/tests/test_informer.json index 2a6a57bc1..0defdebe3 100644 --- a/tests/test_informer.json +++ b/tests/test_informer.json @@ -1,4 +1,4 @@ -{ +{ "model_name": "Informer", "use_decoder": true, "model_type": "PyTorch", @@ -7,7 +7,7 @@ "dec_in":3, "c_out": 2, "seq_len":20, - "label_len":10, + "label_len":10, "out_len":2, "factor":2 }, @@ -29,7 +29,7 @@ "test_end": 400, "target_col": ["cfs", "precip"], "relevant_cols": ["cfs", "precip", "temp"], - "scaler": "StandardScaler", + "scaler": "StandardScaler", "sort_column":"datetime", "interpolate": false, "feature_param": @@ -41,7 +41,7 @@ "hour":"numerical" } } - }, + }, "early_stopping": { "patience":3 @@ -58,10 +58,10 @@ "epochs": 1, "batch_size":5 - + }, "GCS": false, - + "wandb": { "name": "flood_forecast_circleci", "tags": ["dummy_run", "circleci"], @@ -71,13 +71,13 @@ }, "metrics":["MSE"], "inference_params": - { + { "datetime_start":"2016-05-30", "num_prediction_samples": 5, - "hours_to_forecast":300, + "hours_to_forecast":300, "test_csv_path":"tests/test_data/keag_small.csv", "decoder_params":{ - "decoder_function": "greedy_decode", + "decoder_function": "greedy_decode", "unsqueeze_dim": 1}, "dataset_params":{ "file_path": "tests/test_data/keag_small.csv", @@ -100,4 +100,3 @@ } } } - \ No newline at end of file diff --git a/tests/test_init/keag_small.csv b/tests/test_init/keag_small.csv index 0862ca693..1e5c26e5b 100644 --- a/tests/test_init/keag_small.csv +++ b/tests/test_init/keag_small.csv @@ -1999,4 +1999,4 @@ 1997,USGS,01037000,2014-07-23 20:00:00,EDT,75.5,A,3.37,A,2014-07-23 20:00:00,1.3,21.1 1998,USGS,01037000,2014-07-23 21:00:00,EDT,78.3,A,3.39,A,2014-07-23 21:00:00,0.8,21.1 1999,USGS,01037000,2014-07-23 22:00:00,EDT,81.3,A,3.41,A,2014-07-23 22:00:00,0.8,20.6 -2000,USGS,01037000,2014-07-23 23:00:00,EDT,82.8,A,3.42,A,2014-07-23 23:00:00,0,21.0 \ No newline at end of file +2000,USGS,01037000,2014-07-23 23:00:00,EDT,82.8,A,3.42,A,2014-07-23 23:00:00,0,21.0 diff --git a/tests/transformer_b_series.json b/tests/transformer_b_series.json index 1cd0454c3..60696154a 100644 --- a/tests/transformer_b_series.json +++ b/tests/transformer_b_series.json @@ -1,11 +1,11 @@ -{ +{ "model_name": "DecoderTransformer", "model_type": "PyTorch", "model_params": { "n_time_series":5, "n_head": 8, "forecast_history":5, - "n_embd": 1, + "n_embd": 1, "num_layer": 5, "dropout": 0.000001, "q_len": 1, @@ -30,13 +30,13 @@ "test_end":400, "target_col": ["DAILY_YIELD"], "relevant_cols": ["DAILY_YIELD", "DC_POWER", "AC_POWER"], - "scaler": "RobustScaler", + "scaler": "RobustScaler", "no_scale": true, "interpolate": false, "sort_column":"datetime", "feature_param": {"datetime_params":{ - "hour":"cyclical" + "hour":"cyclical" }} }, "training_params": @@ -51,7 +51,7 @@ "batch_size":5 }, "GCS": false, - + "wandb": { "name": "flood_forecast_circleci", "project": "repo-flood_forecast", @@ -62,10 +62,10 @@ "inference_params": { "datetime_start":"2020-06-06", - "hours_to_forecast":4, + "hours_to_forecast":4, "test_csv_path":"tests/test_data/solar_small.csv", "decoder_params":{ - "decoder_function": "simple_decode", - "unsqueeze_dim": 1} + "decoder_function": "simple_decode", + "unsqueeze_dim": 1} } -} \ No newline at end of file +} diff --git a/tests/transformer_bottleneck.json b/tests/transformer_bottleneck.json index d31c721ab..c29fbb020 100644 --- a/tests/transformer_bottleneck.json +++ b/tests/transformer_bottleneck.json @@ -1,11 +1,11 @@ -{ +{ "model_name": "DecoderTransformer", "model_type": "PyTorch", "model_params": { "n_time_series":5, "n_head": 8, "forecast_history":5, - "n_embd": 1, + "n_embd": 1, "num_layer": 5, "dropout":0.1, "q_len": 1, @@ -28,13 +28,13 @@ "test_end":400, "target_col": ["cfs"], "relevant_cols": ["cfs", "precip", "temp"], - "scaler": "RobustScaler", + "scaler": "RobustScaler", "no_scale": true, "interpolate": false, "sort_column":"datetime", "feature_param": {"datetime_params":{ - "hour":"cyclical" + "hour":"cyclical" }} }, "training_params": @@ -49,7 +49,7 @@ "batch_size":8 }, "GCS": false, - + "wandb": { "name": "flood_forecast_circleci", "project": "repo-flood_forecast", @@ -60,11 +60,10 @@ "inference_params": { "datetime_start":"2016-05-31", - "hours_to_forecast":336, + "hours_to_forecast":336, "test_csv_path":"tests/test_data/keag_small.csv", "decoder_params":{ - "decoder_function": "simple_decode", - "unsqueeze_dim": 1} + "decoder_function": "simple_decode", + "unsqueeze_dim": 1} } } - \ No newline at end of file diff --git a/tests/transformer_gaussian.json b/tests/transformer_gaussian.json index 0abccec1c..925267c7d 100644 --- a/tests/transformer_gaussian.json +++ b/tests/transformer_gaussian.json @@ -1,4 +1,4 @@ -{ +{ "model_name": "DecoderTransformer", "model_type": "PyTorch", "model_params": { @@ -6,7 +6,7 @@ "n_time_series":5, "n_head": 8, "forecast_history":5, - "n_embd": 1, + "n_embd": 1, "num_layer": 5, "dropout":0.1, "q_len": 1, @@ -15,7 +15,7 @@ "additional_params":{} }, "dataset_params": - { "class":"default", + { "class":"default", "num_workers": 2, "training_path": "tests/test_data/keag_small.csv", "validation_path": "tests/test_data/keag_small.csv", @@ -29,13 +29,13 @@ "test_end":400, "target_col": ["cfs"], "relevant_cols": ["cfs", "precip", "temp"], - "scaler": "RobustScaler", + "scaler": "RobustScaler", "no_scale": true, "interpolate": false, "sort_column":"datetime", "feature_param": {"datetime_params":{ - "hour":"cyclical" + "hour":"cyclical" }} }, "training_params": @@ -54,7 +54,7 @@ "batch_size": 3 }, "GCS": false, - + "wandb": { "name": "flood_forecast_circleci", "project": "repo-flood_forecast", @@ -64,10 +64,10 @@ "metrics":["GaussianLoss"], "inference_params": { "datetime_start":"2016-05-31", - "hours_to_forecast":336, + "hours_to_forecast":336, "test_csv_path":"tests/test_data/keag_small.csv", "decoder_params":{ - "decoder_function": "simple_decode", + "decoder_function": "simple_decode", "unsqueeze_dim": 1}, "dataset_params":{ "file_path": "tests/test_data/keag_small.csv", @@ -82,9 +82,8 @@ "feature_params":{ "datetime_params":{ "hour":"cyclical" - } + } } } } } - \ No newline at end of file diff --git a/tests/tsmixer_test.json b/tests/tsmixer_test.json index 0b7f9ea8f..5dcf2c707 100644 --- a/tests/tsmixer_test.json +++ b/tests/tsmixer_test.json @@ -1,4 +1,4 @@ -{ +{ "model_name": "TSMixer", "use_decoder": true, "model_type": "PyTorch", @@ -7,7 +7,7 @@ "input_channels": 3, "prediction_length": 10, "output_channels": 1 - + }, "n_targets": 4, "dataset_params": @@ -26,7 +26,7 @@ "test_end": 450, "target_col": ["cfs"], "relevant_cols": ["cfs", "precip", "temp"], - "scaler": "StandardScaler", + "scaler": "StandardScaler", "interpolate": false }, "training_params": @@ -40,10 +40,10 @@ "lr": 0.03, "epochs": 1, "batch_size":4 - + }, "GCS": false, - + "wandb": { "name": "flood_forecast_circleci", "tags": ["dummy_run", "circleci"], @@ -52,12 +52,12 @@ "forward_params":{}, "metrics":["MSE"], "inference_params": - { + { "datetime_start":"2016-05-31", - "hours_to_forecast":334, + "hours_to_forecast":334, "test_csv_path":"tests/test_data/keag_small.csv", "decoder_params":{ - "decoder_function": "simple_decode", + "decoder_function": "simple_decode", "unsqueeze_dim": 1} , "dataset_params":{ @@ -71,6 +71,3 @@ } } } - - - \ No newline at end of file diff --git a/tests/variable_autoencoderl.json b/tests/variable_autoencoderl.json index 97d0a83da..1db099dd1 100644 --- a/tests/variable_autoencoderl.json +++ b/tests/variable_autoencoderl.json @@ -1,10 +1,10 @@ -{ +{ "model_name": "CustomTransformerDecoder", "model_type": "PyTorch", "model_params": { "n_time_series":3, "seq_length":6, - "output_seq_length": 6, + "output_seq_length": 6, "output_dim": 3, "n_layers_encoder": 2, "squashed_embedding": true @@ -26,7 +26,7 @@ "target_col": ["playId", "yardlineNumber", "yardsToGo"], "relevant_cols": ["playId", "yardlineNumber", "yardsToGo"], "series_marker_column":"playId", - "scaler": "StandardScaler", + "scaler": "StandardScaler", "interpolate": false }, "n_targets":3, @@ -41,7 +41,7 @@ "epochs": 3 }, "GCS": false, - + "wandb": { "name": "flood_forecast_circleci", "tags": ["dummy_run", "circleci", "multi_head", "classification"], @@ -50,4 +50,3 @@ "forward_params":{}, "metrics":["MSE"] } - From 9493c848ac38815215d5ab347a56deddb4dadcd3 Mon Sep 17 00:00:00 2001 From: isaacmg Date: Sat, 21 Sep 2024 23:49:51 -0400 Subject: [PATCH 27/38] stuff --- .flake8 | 2 +- .idea/workspace.xml | 2 +- tests/multi_modal_tests/test_cross_vivit.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/.flake8 b/.flake8 index 5609cf8c6..9237c4955 100644 --- a/.flake8 +++ b/.flake8 @@ -1,4 +1,4 @@ [flake8] max_line_length=122 -ignore=E305,W504,E126,E401,E721 +ignore=E305,W504,E126,E401,E721,E722 max-complexity=19 diff --git a/.idea/workspace.xml b/.idea/workspace.xml index f03c85e74..d12526529 100644 --- a/.idea/workspace.xml +++ b/.idea/workspace.xml @@ -179,4 +179,4 @@ - \ No newline at end of file + diff --git a/tests/multi_modal_tests/test_cross_vivit.py b/tests/multi_modal_tests/test_cross_vivit.py index e154b835b..a6487f828 100644 --- a/tests/multi_modal_tests/test_cross_vivit.py +++ b/tests/multi_modal_tests/test_cross_vivit.py @@ -53,7 +53,7 @@ def test_vivit_model(self): def test_forward(self): """ This tests the forward pass of the RoCrossVIVIT model from the CrossVIVIT paper. - ctx (torch.Tensor): Context frames of shape [batch_size, number_time_stamps, number_channels, height, wid] this is a very long line + ctx (torch.Tensor): Context frames of shape [batch_size, number_time_stamps, number_channels, height, wid] this r is a very long line ctx_coords (torch.Tensor): Coordinates of context frames of shape [B, 2, H, W] ts (torch.Tensor): Station timeseries of shape [B, T, C] ts_coords (torch.Tensor): Station coordinates of shape [B, 2, 1, 1] From 68c456b5cfc2b7a6946ce1c84179b51bc23f22a6 Mon Sep 17 00:00:00 2001 From: isaacmg Date: Sat, 21 Sep 2024 23:58:08 -0400 Subject: [PATCH 28/38] fixing --- .flake8 | 2 +- .idea/workspace.xml | 16 ++++++-- .pre-commit-config.yaml | 6 +++ flood_forecast/basic/d_n_linear.py | 4 ++ flood_forecast/custom/custom_opt.py | 1 + flood_forecast/da_rnn/model.py | 1 - flood_forecast/deployment/inference.py | 4 +- flood_forecast/evaluator.py | 6 +-- flood_forecast/multi_models/crossvivit.py | 1 - .../preprocessing/pytorch_loaders.py | 11 +++--- flood_forecast/pytorch_training.py | 1 - flood_forecast/trainer.py | 38 ++++++++++++++----- flood_forecast/transformer_xl/attn.py | 8 ++-- .../transformer_xl/data_embedding.py | 6 ++- 14 files changed, 71 insertions(+), 34 deletions(-) diff --git a/.flake8 b/.flake8 index 9237c4955..49b9e447e 100644 --- a/.flake8 +++ b/.flake8 @@ -1,4 +1,4 @@ [flake8] max_line_length=122 -ignore=E305,W504,E126,E401,E721,E722 +ignore=E305,W504,E126,E401,E721,F722 max-complexity=19 diff --git a/.idea/workspace.xml b/.idea/workspace.xml index d12526529..ed8961c6e 100644 --- a/.idea/workspace.xml +++ b/.idea/workspace.xml @@ -5,10 +5,18 @@ - - - - + + + + + + + + + + + +