New Model, Many Changes
This commit is contained in:
Si11ium
parent
13812b83b5
commit
14ed4e0117
@ -61,10 +61,11 @@ class BaseTrainMixin:
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assert isinstance(self, LightningBaseModule)
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keys = list(outputs[0].keys())
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summary_dict = dict(log={f'mean_{key}': torch.mean(torch.stack([output[key]
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for output in outputs]))
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for key in keys if 'loss' in key})
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return summary_dict
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summary_dict = {f'mean_{key}': torch.mean(torch.stack([output[key]
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for output in outputs]))
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for key in keys if 'loss' in key}
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for key in summary_dict.keys():
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self.log(key, summary_dict[key])
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class BaseValMixin:
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@ -83,16 +84,16 @@ class BaseValMixin:
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def validation_epoch_end(self, outputs, *_, **__):
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assert isinstance(self, LightningBaseModule)
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summary_dict = dict(log=dict())
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summary_dict = dict()
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# In case of Multiple given dataloader this will outputs will be: list[list[dict[]]]
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# for output_idx, output in enumerate(outputs):
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# else:list[dict[]]
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keys = list(outputs.keys())
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# Add Every Value das has a "loss" in it, by calc. mean over all occurences.
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summary_dict['log'].update({f'mean_{key}': torch.mean(torch.stack([output[key]
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for output in outputs]))
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for key in keys if 'loss' in key}
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)
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summary_dict.update({f'mean_{key}': torch.mean(torch.stack([output[key]
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for output in outputs]))
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for key in keys if 'loss' in key}
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)
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"""
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# Additional Score like the unweighted Average Recall:
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# UnweightedAverageRecall
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@ -107,7 +108,8 @@ class BaseValMixin:
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summary_dict['log'].update({f'uar_score': uar_score})
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"""
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return summary_dict
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for key in summary_dict.keys():
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self.log(key, summary_dict[key])
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class BinaryMaskDatasetMixin:
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@ -1,8 +1,5 @@
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from argparse import Namespace
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from ml_lib.utils.config import Config
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class GlobalVar(Namespace):
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# Labels for classes
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LEFT = 1
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@ -21,10 +18,3 @@ class GlobalVar(Namespace):
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train='train',
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vali='vali',
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test='test'
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class ThisConfig(Config):
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@property
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def _model_map(self):
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return dict()
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@ -12,6 +12,7 @@ class Speed(object):
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def __init__(self, max_amount=0.3, speed_min=1, speed_max=1):
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self.speed_max = speed_max if speed_max else 1
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self.speed_min = speed_min if speed_min else 1
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# noinspection PyTypeChecker
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self.max_amount = min(max(0, max_amount), 1)
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def __call__(self, x):
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@ -1,16 +1,18 @@
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import math
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import warnings
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from pathlib import Path
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from typing import Union
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import torch
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import warnings
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from torch import nn
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from torch.nn import functional as F
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from einops import rearrange
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import sys
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sys.path.append(str(Path(__file__).parent))
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from .util import AutoPad, Interpolate, ShapeMixin, F_x, Flatten
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from .util import AutoPad, Interpolate, ShapeMixin, F_x, Flatten, ResidualBlock, PreNorm
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DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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@ -212,81 +214,81 @@ class RecurrentModule(ShapeMixin, nn.Module):
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tensor = self.rnn(x)
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return tensor
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class AttentionModule(ShapeMixin, nn.Module):
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def __init__(self,in_shape, features, dropout=0.1):
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class FeedForward(nn.Module):
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def __init__(self, dim, hidden_dim, dropout = 0.):
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super().__init__()
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self.in_shape = in_shape
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self.dropout = dropout
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self.features = features
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raise NotImplementedError
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self.net = nn.Sequential(
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nn.Linear(dim, hidden_dim),
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nn.GELU(),
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nn.Dropout(dropout),
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nn.Linear(hidden_dim, dim),
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nn.Dropout(dropout)
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)
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def forward(self, x):
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pass
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return self.net(x)
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class MultiHeadAttentionModule(ShapeMixin, nn.Module):
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def __init__(self, in_shape, heads, features, dropout=0.1):
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class Attention(nn.Module):
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def __init__(self, dim, heads = 8, dropout = 0.):
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super().__init__()
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self.in_shape = in_shape
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self.features = features
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self.heads = heads
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self.final_dim = self.features // self.heads
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self.scale = dim ** -0.5
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self.linear_q = LinearModule(self.features, self.features)
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self.linear_v = LinearModule(self.features, self.features)
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self.linear_k = LinearModule(self.features, self.features)
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self.dropout = nn.Dropout(dropout) if dropout else F_x(self.features)
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self.linear_out = nn.Linear(self.features, self.features)
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self.to_qkv = nn.Linear(dim, dim * 3, bias = False)
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self.to_out = nn.Sequential(
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nn.Linear(dim, dim),
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nn.Dropout(dropout)
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)
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def forward(self, q, k, v, mask=None):
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def forward(self, x, mask = None):
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b, n, _, h = *x.shape, self.heads
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qkv = self.to_qkv(x).chunk(3, dim = -1)
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q, k, v = [rearrange(t, 'b n (h d) -> b h n d', h = h) for t in qkv]
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batch_size = q.size(0)
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dots = torch.einsum('bhid,bhjd->bhij', q, k) * self.scale
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mask_value = -torch.finfo(dots.dtype).max
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# perform linear operation and split into h heads
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k = self.linear_k(k).view(batch_size, -1, self.heads, self.final_dim)
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q = self.linear_q(q).view(batch_size, -1, self.heads, self.final_dim)
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v = self.linear_v(v).view(batch_size, -1, self.heads, self.final_dim)
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# transpose to get dimensions bs * h * sl * features
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# ToDo: Do we need this?
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k = k.transpose(1, 2)
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q = q.transpose(1, 2)
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v = v.transpose(1, 2)
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# calculate attention
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scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.final_dim)
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if mask is not None:
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mask = mask.unsqueeze(1)
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scores = scores.masked_fill(mask == 0, -1e9)
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scores = F.softmax(scores, dim=-1)
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scores = self.dropout(scores)
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scores = torch.matmul(scores, v)
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mask = F.pad(mask.flatten(1), [1, 0], value = True)
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assert mask.shape[-1] == dots.shape[-1], 'mask has incorrect dimensions'
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mask = mask[:, None, :] * mask[:, :, None]
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dots.masked_fill_(~mask, mask_value)
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del mask
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# concatenate heads and apply final linear transformation
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# ToDo: This seems to be old coding style. Do we Need this?
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concat = scores.transpose(1, 2).contiguous().view(batch_size, -1, self.features)
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attn = dots.softmax(dim=-1)
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output = self.out(concat)
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return output
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out = torch.einsum('bhij,bhjd->bhid', attn, v)
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out = rearrange(out, 'b h n d -> b n (h d)')
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out = self.to_out(out)
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return out
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class Transformer(nn.Module):
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def __init__(self, dim, depth, heads, mlp_dim, dropout):
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super().__init__()
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self.layers = nn.ModuleList([])
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for _ in range(depth):
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self.layers.append(nn.ModuleList([
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ResidualBlock(PreNorm(dim, Attention(dim, heads = heads, dropout = dropout))),
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ResidualBlock(PreNorm(dim, FeedForward(dim, mlp_dim, dropout = dropout)))
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]))
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def forward(self, x, mask = None, *_, **__):
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for attn, ff in self.layers:
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x = attn(x, mask = mask)
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x = ff(x)
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return x
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class TransformerModule(ShapeMixin, nn.Module):
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def __init__(self, in_shape, hidden_size, n_heads, num_layers=1, dropout=None, use_norm=False, **kwargs):
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def __init__(self, in_shape, hidden_size, n_heads, num_layers=1, dropout=None, use_norm=False, activation='gelu'):
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super(TransformerModule, self).__init__()
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self.in_shape = in_shape
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self.flat = Flatten(self.in_shape) if isinstance(self.in_shape, (tuple, list)) else F_x(in_shape)
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encoder_layer = nn.TransformerEncoderLayer(self.flat_shape, n_heads, dim_feedforward=hidden_size,
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dropout=dropout, activation=kwargs.get('activation')
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)
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self.norm = nn.LayerNorm(hidden_size) if use_norm else F_x(hidden_size)
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self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=num_layers, )
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self.transformer = Transformer(dim=self.flat.flat_shape, depth=num_layers, heads=n_heads,
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mlp_dim=hidden_size, dropout=dropout)
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def forward(self, x, mask=None, key_padding_mask=None):
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tensor = self.flat(x)
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@ -11,7 +11,7 @@ from operator import mul
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from torch import nn
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from torch.utils.data import DataLoader
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from .blocks import ConvModule, DeConvModule, LinearModule, MultiHeadAttentionModule
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from .blocks import ConvModule, DeConvModule, LinearModule
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from .util import ShapeMixin, LightningBaseModule, Flatten
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@ -112,6 +112,7 @@ class Generator(ShapeMixin, nn.Module):
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last_shape = re_shape
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for conv_filter, conv_kernel, interpolation in zip(reversed(filters), kernels, interpolations):
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# noinspection PyTypeChecker
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self.de_conv_list.append(DeConvModule(last_shape, conv_filters=conv_filter,
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conv_kernel=conv_kernel,
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conv_padding=conv_kernel-2,
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@ -275,16 +276,3 @@ class Encoder(BaseEncoder):
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tensor = self.l1(tensor)
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tensor = self.latent_activation(tensor) if self.latent_activation else tensor
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return tensor
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class TransformerEncoder(ShapeMixin, nn.Module):
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def __init__(self, in_shape):
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super(TransformerEncoder, self).__init__()
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# MultiheadSelfAttention
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self.msa = MultiHeadAttentionModule()
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def forward(self, x):
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@ -1,3 +1,5 @@
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from typing import List
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from functools import reduce
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from abc import ABC
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@ -6,7 +8,7 @@ from pathlib import Path
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import torch
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from operator import mul
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from torch import nn
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from torch.nn import functional as F
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from torch.nn import functional as F, Unfold
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# Utility - Modules
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###################
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@ -38,6 +40,7 @@ try:
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################################
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self.hparams = hparams
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self.params = ModelParameters(hparams)
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self.lr = self.params.lr or 1e-4
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def size(self):
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return self.shape
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@ -76,10 +79,10 @@ try:
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weight_initializer = WeightInit(in_place_init_function=in_place_init_func_)
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self.apply(weight_initializer)
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modules = [LightningBaseModule, nn.Module]
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module_types = (LightningBaseModule, nn.Module,)
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except ImportError:
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modules = [nn.Module, ]
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module_types = (nn.Module,)
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pass # Maybe post a hint to install pytorch-lightning.
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@ -88,7 +91,7 @@ class ShapeMixin:
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@property
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def shape(self):
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assert isinstance(self, modules)
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assert isinstance(self, module_types)
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def get_out_shape(output):
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return output.shape[1:] if len(output.shape[1:]) > 1 else output.shape[-1]
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@ -135,6 +138,41 @@ class F_x(ShapeMixin, nn.Module):
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return x
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class ResidualBlock(nn.Module):
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def __init__(self, fn):
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super().__init__()
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self.fn = fn
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def forward(self, x, **kwargs):
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return self.fn(x, **kwargs) + x
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class PreNorm(nn.Module):
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def __init__(self, dim, fn):
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super().__init__()
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self.norm = nn.LayerNorm(dim)
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self.fn = fn
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def forward(self, x, **kwargs):
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return self.fn(self.norm(x), **kwargs)
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class SlidingWindow(nn.Module):
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def __init__(self, kernel, stride=1, padding=0, keepdim=False):
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super(SlidingWindow, self).__init__()
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self.kernel = kernel if not isinstance(kernel, int) else (kernel, kernel)
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self.padding = padding
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self.stride = stride
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self.keepdim = keepdim
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self._unfolder = Unfold(self.kernel, dilation=1, padding=self.padding, stride=self.stride)
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def forward(self, x):
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tensor = self._unfolder(x)
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tensor = tensor.transpose(-1, -2)
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if self.keepdim:
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shape = *x.shape[:2], -1, *self.kernel
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tensor = tensor.reshape(shape)
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return tensor
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# Utility - Modules
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###################
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class Flatten(ShapeMixin, nn.Module):
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@ -232,14 +270,13 @@ class AutoPadToShape(object):
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def __call__(self, x):
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if not torch.is_tensor(x):
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x = torch.as_tensor(x)
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if x.shape[1:] == self.shape or x.shape == self.shape:
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if x.shape[-len(self.shape):] == self.shape or x.shape == self.shape:
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return x
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for i in range(-1, -len(self.shape), -1):
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idx = [0] * len(x.shape)
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idx[i] = self.shape[i] - x.shape[i]
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idx = tuple(idx)
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x = torch.nn.functional.pad(x, idx)
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idx = [0] * (len(self.shape) * 2)
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for i, j in zip(range(-1, -(len(self.shape)+1), -1), range(0, len(idx), 2)):
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idx[j] = self.shape[i] - x.shape[i]
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x = torch.nn.functional.pad(x, idx)
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return x
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def __repr__(self):
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@ -94,7 +94,7 @@ class Config(ConfigParser, ABC):
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try:
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return locate_and_import_class(self.model.type)
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except AttributeError as e:
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raise AttributeError(f'The model alias you provided ("{self.get("model", "type")}")' +
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raise AttributeError(f'The model alias you provided ("{self.get("model", "type")}") ' +
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f'was not found!\n' +
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f'{e}')
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@ -13,6 +13,10 @@ from torch import nn
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# Hyperparamter Object
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class ModelParameters(Namespace, Mapping):
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@property
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def activation_as_string(self):
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return self['activation'].lower()
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@property
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def module_kwargs(self):
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@ -56,6 +60,7 @@ class ModelParameters(Namespace, Mapping):
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_activations = dict(
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leaky_relu=nn.LeakyReLU,
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gelu=nn.GELU,
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elu=nn.ELU,
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relu=nn.ReLU,
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sigmoid=nn.Sigmoid,
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