plotting in scratch.py and trying TCN

models/modules/causalinception.py

@@ -0,0 +1,140 @@
+"""
+Modifie from https://github.com/timeseriesAI/tsai/blob/main/tsai/models/InceptionTimePlus.py
+"""
+from tsai.models.InceptionTimePlus import Conv, Module, noop, Integral, nn, is_listy, SimpleSelfAttention, Concat, SqueezeExciteBlock, Norm, BN1d, delegates, ConvBlock, Add, np, random, ifnone, OrderedDict, Flatten, SigmoidRange, LinBnDrop, GACP1d, GAP1d, named_partial, F, torch, CausalConv1d, Noop
+
+Conv = named_partial('Conv', ConvBlock, norm=None, act=None, padding='causal')
+# CausalConvBlock = named_partial('CausalConv', ConvBlock, padding='causal')
+
+class CausalMaxPool1d(torch.nn.MaxPool1d):
+    def __init__(self, ks, stride=1, padding=0, dilation=1):
+        super().__init__(kernel_size=ks, stride=stride, padding=0, dilation=dilation)
+        self.__padding = (ks - 1) * dilation
+    def forward(self, input):
+        return super().forward(F.pad(input, (self.__padding, 0)))
+
+class InceptionModulePlus(Module):
+    def __init__(self, ni, nf, ks=40, bottleneck=True, padding='causal', coord=False, separable=False, dilation=1, stride=1, conv_dropout=0., sa=False, se=None,
+                 norm='Batch', zero_norm=False, bn_1st=True, act=nn.ReLU, act_kwargs={}):
+
+        dilation = max(1, dilation)
+
+        if not (is_listy(ks) and len(ks) == 3):
+            if isinstance(ks, Integral): ks = [ks // (2**i) for i in range(3)]
+            ks = [ksi if ksi % 2 != 0 else ksi - 1 for ksi in ks]  # ensure odd ks for padding='same'
+
+        bottleneck = False if ni == nf else bottleneck
+        self.bottleneck = Conv(ni, nf, 1, coord=coord, bias=False) if bottleneck else noop #
+        self.convs = nn.ModuleList()
+        for i in range(len(ks)): self.convs.append(Conv(nf if bottleneck else ni, nf, ks[i], padding=padding, coord=coord, separable=separable,
+                                                         dilation=dilation**i, stride=stride, bias=False))
+        self.mp_conv = nn.Sequential(*[Conv(ni, nf, 1, coord=coord, bias=False)])
+        self.concat = Concat()
+        if norm is not None:
+            self.norm = Norm(nf * 4, norm=norm, zero_norm=zero_norm)
+        else:
+            self.norm = noop
+        self.conv_dropout = nn.Dropout(conv_dropout) if conv_dropout else noop
+        self.sa = SimpleSelfAttention(nf * 4) if sa else noop
+        self.act = act(**act_kwargs) if act else noop
+        self.se = nn.Sequential(SqueezeExciteBlock(nf * 4, reduction=se), BN1d(nf * 4)) if se else noop
+
+        self._init_cnn(self)
+    
+    def _init_cnn(self, m):
+        if getattr(self, 'bias', None) is not None: nn.init.constant_(self.bias, 0)
+        if isinstance(self, (nn.Conv1d,nn.Conv2d,nn.Conv3d,nn.Linear)): nn.init.kaiming_normal_(self.weight)
+        for l in m.children(): self._init_cnn(l)
+
+    def forward(self, x):
+        input_tensor = x
+        x = self.bottleneck(x)
+        x = self.concat([l(x) for l in self.convs] + [self.mp_conv(input_tensor)])
+        x = self.norm(x)
+        x = self.conv_dropout(x)
+        x = self.sa(x)
+        x = self.act(x)
+        x = self.se(x)
+        return x
+
+
+@delegates(InceptionModulePlus.__init__)
+class InceptionBlockPlus(Module):
+    def __init__(self, ni, nf, residual=True, depth=6, coord=False, norm=None, zero_norm=False, act=nn.ReLU, act_kwargs={}, sa=False, se=None, dilation=1,
+                 stoch_depth=1., **kwargs):
+        self.residual, self.depth = residual, depth
+        self.inception, self.shortcut, self.act = nn.ModuleList(), nn.ModuleList(), nn.ModuleList()
+        for d in range(depth):
+            self.inception.append(InceptionModulePlus(ni if d == 0 else nf * 4, nf, coord=coord, norm=norm,
+                                                      zero_norm=zero_norm if d % 3 == 2 else False,
+                                                      act=act if d % 3 != 2 else None, act_kwargs=act_kwargs,
+                                                      sa=sa if d % 3 == 2 else False,
+                                                      se=se if d % 3 != 2 else None,
+                                                      dilation=dilation*d*(dilation>1),
+                                                      **kwargs))
+            if self.residual and d % 3 == 2:
+                n_in, n_out = ni if d == 2 else nf * 4, nf * 4
+                if norm is not None:
+                    n = Norm(n_in, norm=norm)
+                else:
+                    n = Noop
+                self.shortcut.append(n if n_in == n_out else ConvBlock(n_in, n_out, 1, coord=coord, bias=False, norm=norm, padding='causal', act=None))
+                self.act.append(act(**act_kwargs))
+        self.add = Add()
+        if stoch_depth != 0: keep_prob = np.linspace(1, stoch_depth, depth)
+        else: keep_prob = np.array([1] * depth)
+        self.keep_prob = keep_prob
+
+    def forward(self, x):
+        res = x
+        for i in range(self.depth):
+            if self.keep_prob[i] > random.random() or not self.training:
+                x = self.inception[i](x)
+            if self.residual and i % 3 == 2:
+                res = x = self.act[i//3](self.add(x, self.shortcut[i//3](res)))
+        return x
+
+# Cell
+@delegates(InceptionModulePlus.__init__)
+class CausalInceptionTimePlus(nn.Sequential):
+    def __init__(self, c_in, c_out, seq_len=None, nf=32, nb_filters=None,
+                 flatten=False, concat_pool=False, fc_dropout=0., bn=False, y_range=None, custom_head=None, **kwargs):
+
+        if nb_filters is not None: nf = nb_filters
+        else: nf = ifnone(nf, nb_filters) # for compatibility
+        backbone = InceptionBlockPlus(c_in, nf, **kwargs)
+
+        #head
+        self.head_nf = nf * 4
+        self.c_out = c_out
+        self.seq_len = seq_len
+        if custom_head: head = custom_head(self.head_nf, c_out, seq_len)
+        else: head = self.create_head(self.head_nf, c_out, seq_len, flatten=flatten, concat_pool=concat_pool,
+                                      fc_dropout=fc_dropout, bn=bn, y_range=y_range)
+
+        layers = OrderedDict([('backbone', nn.Sequential(backbone)), ('head', nn.Sequential(head))])
+        super().__init__(layers)
+
+        self.calc_receptive_field(kwargs.get('ks'), kwargs.get('depth'), kwargs.get('dilation', 1))
+
+    def calc_receptive_field(self, ks, depth, dilation):       
+        # receptive fields vs R
+        ks=np.array(ks)
+        d=np.array([dilation**i for i in range(3)])
+        rf = (ks-1)*d*depth
+
+        dilations = np.array([max(1, d*dilation) for d in range(depth)])
+        d=np.array([dilations**i for i in range(3)]).T
+        rf = ((ks-1)*d).sum(0)
+        print(f"receptive field {rf}={ks-1}*{d}")
+
+    def create_head(self, nf, c_out, seq_len, flatten=False, concat_pool=False, fc_dropout=0., bn=False, y_range=None):
+        if flatten:
+            nf *= seq_len
+            layers = [Flatten()]
+        else:
+            if concat_pool: nf *= 2
+            layers = [GACP1d(1) if concat_pool else GAP1d(1)]
+        layers += [LinBnDrop(nf, c_out, bn=bn, p=fc_dropout)]
+        if y_range: layers += [SigmoidRange(*y_range)]
+        return nn.Sequential(*layers)

models/modules/inrplus2.py

@@ -0,0 +1,43 @@
+# Copyright (c) 2022, salesforce.com, inc.
+# All rights reserved.
+# SPDX-License-Identifier: BSD-3-Clause
+# For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+
+from typing import Optional
+
+import torch
+import torch.nn as nn
+from torch import Tensor
+
+from models.modules.feature_transforms import GaussianFourierFeatureTransform
+
+from tsai.models.InceptionTimePlus import InceptionTimePlus
+from .causalinception import CausalInceptionTimePlus, CausalConv1d
+
+def custom_head(head_nf, c_out, seq_len):
+    return nn.Sequential(
+        CausalConv1d(head_nf, c_out, 1, bias=False)
+        
+    )
+
+class INRPlus2(nn.Module):
+    def __init__(self, in_feats: int, layers: int, layer_size: int, n_fourier_feats: int, scales: float,
+                 dropout: Optional[float] = 0.5, bn=False, *args, **kwargs):
+        super().__init__()
+        self.features = nn.Linear(in_feats, layer_size) if n_fourier_feats == 0 \
+            else GaussianFourierFeatureTransform(in_feats, n_fourier_feats, scales)
+        in_size = layer_size if n_fourier_feats == 0 \
+            else n_fourier_feats+in_feats
+        # import pdb; pdb.set_trace()
+        self.layers = CausalInceptionTimePlus(
+            in_size-1, layer_size, seq_len=None, nf=layer_size, depth=layers,
+                 flatten=False, concat_pool=False, fc_dropout=dropout, conv_dropout=0.05, bn=bn, y_range=None, custom_head=custom_head, ks=[139, 19, 3], dilation=2, *args, **kwargs
+        )
+        # layers = [INRPlusLayer(in_size, layer_size, dropout=dropout)] + \
+        #          [INRPlusLayer(layer_size, layer_size, dropout=dropout) for _ in range(layers - 1)]
+        # self.layers = nn.Sequential(*layers)
+
+    def forward(self, x: Tensor) -> Tensor:
+        x = self.features(x)
+        # import pdb; pdb.set_trace()
+        return self.layers(x.permute((0, 2, 1))).permute((0, 2, 1))

models/modules/regressors.py

@@ -29,15 +29,17 @@ class RidgeRegressor(nn.Module):
 
         if n_samples >= n_dim:
             # standard
-            A = torch.bmm(X.mT, X)
+            A = torch.bmm(X.transpose(-2, -1), X)
             A.diagonal(dim1=-2, dim2=-1).add_(reg_coeff)
-            B = torch.bmm(X.mT, Y)
+            B = torch.bmm(X.transpose(-2, -1), Y)
             weights = torch.linalg.solve(A, B)
         else:
             # Woodbury
-            A = torch.bmm(X, X.mT)
+            # A = torch.bmm(X, X.mT)
+            A = torch.bmm(X, X.transpose(-2, -1))
             A.diagonal(dim1=-2, dim2=-1).add_(reg_coeff)
-            weights = torch.bmm(X.mT, torch.linalg.solve(A, Y))
+            # weights = torch.bmm(X.mT, torch.linalg.solve(A, Y))
+            weights = torch.bmm(X.transpose(-2, -1), torch.linalg.solve(A, Y))
 
         return weights[:, :-1], weights[:, -1:]