steffen/Mel_Vision_Transformer_ComParE-21
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adjustment fot CCS, notebook folder

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Steffen Illium
2021-03-22 16:43:19 +01:00
parent 78b3139d1a
commit c12f3866c8
6 changed files with 156 additions and 29 deletions
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3
datasets/ccs_librosa_datamodule.py
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@@ -116,6 +116,7 @@ class CCSLibrosaDatamodule(_BaseDataModule):
for sub_dataset in results.get():
dataset.append(sub_dataset[0])
datasets[data_option] = ConcatDataset(dataset)
print(f'{data_option}-dataset prepared.')
self.datasets = datasets
return datasets
@@ -133,6 +134,7 @@ class CCSLibrosaDatamodule(_BaseDataModule):
for row in all_rows:
mel_dataset, class_id, _ = self._build_subdataset(row)
dataset.append(mel_dataset)
print(f'{data_option}-dataset prepared!')
datasets[data_option] = ConcatDataset(dataset)
# Build Weighted Sampler for train and val
@@ -158,6 +160,7 @@ class CCSLibrosaDatamodule(_BaseDataModule):
samplers[data_option] = None
self.datasets = datasets
self.samplers = samplers
print(f'Dataset {self.__class__.__name__} setup done.')
return datasets
def purge(self):

12
models/cnn_baseline.py
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@@ -3,6 +3,7 @@ from argparse import Namespace
from torch import nn
from ml_lib.metrics.binary_class_classifictaion import BinaryScores
from ml_lib.metrics.multi_class_classification import MultiClassScores
from ml_lib.modules.blocks import LinearModule
from ml_lib.modules.model_parts import CNNEncoder
@@ -36,9 +37,11 @@ class CNNBaseline(CombinedModelMixins,
# Modules with Parameters
self.encoder = CNNEncoder(in_shape=self.in_shape, **self.params.module_kwargs)
# Make Decision between binary and Multiclass Classification
logits = n_classes if n_classes > 2 else 1
module_kwargs = self.params.module_kwargs
module_kwargs.update(activation=nn.Softmax)
self.classifier = LinearModule(self.encoder.shape, n_classes, **module_kwargs)
module_kwargs.update(activation=(nn.Softmax if logits > 1 else nn.Sigmoid))
self.classifier = LinearModule(self.encoder.shape, logits, **module_kwargs)
def forward(self, x, mask=None, return_attn_weights=False):
"""
@@ -52,4 +55,7 @@ class CNNBaseline(CombinedModelMixins,
return Namespace(main_out=tensor)
def additional_scores(self, outputs):
return MultiClassScores(self)(outputs)
if self.params.n_classes > 2:
return MultiClassScores(self)(outputs)
else:
return BinaryScores(self)(outputs)

8
models/transformer_model.py
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@@ -1,8 +1,6 @@
import inspect
from argparse import Namespace
import warnings
import torch
from torch import nn
@@ -70,13 +68,15 @@ class VisualTransformer(CombinedModelMixins,
self.to_cls_token = nn.Identity()
logits = self.params.n_classes if self.params.n_classes > 2 else 1
self.mlp_head = nn.Sequential(
nn.LayerNorm(self.embed_dim),
nn.Linear(self.embed_dim, self.params.lat_dim),
nn.GELU(),
nn.Dropout(self.params.dropout),
nn.Linear(self.params.lat_dim, n_classes),
nn.Softmax()
nn.Linear(self.params.lat_dim, logits),
nn.Softmax() if logits > 1 else nn.Sigmoid()
)
def forward(self, x, mask=None, return_attn_weights=False):

7
multi_run.py
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@@ -11,13 +11,12 @@ if __name__ == '__main__':
# Set new values
hparams_dict = dict(seed=range(10),
model_name=['VisualTransformer'],
data_name=['CCSLibrosaDatamodule'],
model_name=['CNNBaseline'],
data_name=['CCSLibrosaDatamodule'], # 'CCSLibrosaDatamodule'],
batch_size=[50],
max_epochs=[200],
variable_length=[False],
sample_segment_len=[40],
sample_hop_len=[15],
target_mel_length_in_seconds=[0.5],
random_apply_chance=[0.5], # trial.suggest_float('random_apply_chance', 0.1, 0.5, step=0.1),
loudness_ratio=[0], # trial.suggest_float('loudness_ratio', 0.0, 0.5, step=0.1),
shift_ratio=[0.3], # trial.suggest_float('shift_ratio', 0.0, 0.5, step=0.1),

104
notebooks/reload model.ipynb Normal file
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@@ -0,0 +1,104 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": true,
"pycharm": {
"name": "#%% IMPORTS\n"
}
},
"outputs": [],
"source": [
"from pathlib import Path\n",
"from natsort import natsorted\n",
"from pytorch_lightning.core.saving import *\n",
"from ml_lib.utils.model_io import SavedLightningModels\n"
]
},
{
"cell_type": "code",
"execution_count": 48,
"outputs": [],
"source": [
"from ml_lib.utils.tools import locate_and_import_class\n",
"from models.transformer_model import VisualTransformer\n",
"_ROOT = Path('..')\n",
"out_path = 'output'\n",
"model_class = VisualTransformer\n",
"model_name = model_class.name()\n",
"\n",
"exp_name = 'VT_01123c93daaffa92d2ed341bda32426d'\n",
"version = 'version_2'"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%M Path resolving and variables\n"
}
}
},
{
"cell_type": "code",
"execution_count": 50,
"outputs": [
{
"ename": "ValueError",
"evalue": "When you set `reduce` as 'macro', you have to provide the number of classes.",
"output_type": "error",
"traceback": [
"\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
"\u001B[1;31mValueError\u001B[0m Traceback (most recent call last)",
"\u001B[1;32m<ipython-input-50-0216292a172f>\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[0;32m 6\u001B[0m \u001B[0madditional_kwargs\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mdict\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mvariable_length\u001B[0m \u001B[1;33m=\u001B[0m \u001B[1;32mFalse\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mc_classes\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;36m5\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 7\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m----> 8\u001B[1;33m \u001B[0mmodel\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mmodel_class\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mload_from_checkpoint\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mcheckpoint\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mhparams_file\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mstr\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mhparams_yaml\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m**\u001B[0m\u001B[0madditional_kwargs\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 9\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
"\u001B[1;32mc:\\users\\steff\\envs\\compare_21\\lib\\site-packages\\pytorch_lightning\\core\\saving.py\u001B[0m in \u001B[0;36mload_from_checkpoint\u001B[1;34m(cls, checkpoint_path, map_location, hparams_file, strict, **kwargs)\u001B[0m\n\u001B[0;32m 154\u001B[0m \u001B[0mcheckpoint\u001B[0m\u001B[1;33m[\u001B[0m\u001B[0mcls\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mCHECKPOINT_HYPER_PARAMS_KEY\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mupdate\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 155\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 156\u001B[1;33m \u001B[0mmodel\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mcls\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_load_model_state\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mcheckpoint\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mstrict\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mstrict\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 157\u001B[0m \u001B[1;32mreturn\u001B[0m \u001B[0mmodel\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 158\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
"\u001B[1;32mc:\\users\\steff\\envs\\compare_21\\lib\\site-packages\\pytorch_lightning\\core\\saving.py\u001B[0m in \u001B[0;36m_load_model_state\u001B[1;34m(cls, checkpoint, strict, **cls_kwargs_new)\u001B[0m\n\u001B[0;32m 196\u001B[0m \u001B[0m_cls_kwargs\u001B[0m \u001B[1;33m=\u001B[0m \u001B[1;33m{\u001B[0m\u001B[0mk\u001B[0m\u001B[1;33m:\u001B[0m \u001B[0mv\u001B[0m \u001B[1;32mfor\u001B[0m \u001B[0mk\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mv\u001B[0m \u001B[1;32min\u001B[0m \u001B[0m_cls_kwargs\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mitems\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m \u001B[1;32mif\u001B[0m \u001B[0mk\u001B[0m \u001B[1;32min\u001B[0m \u001B[0mcls_init_args_name\u001B[0m\u001B[1;33m}\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 197\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 198\u001B[1;33m \u001B[0mmodel\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mcls\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m**\u001B[0m\u001B[0m_cls_kwargs\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 199\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 200\u001B[0m \u001B[1;31m# give model a chance to load something\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
"\u001B[1;32m~\\projects\\compare_21\\models\\transformer_model.py\u001B[0m in \u001B[0;36m__init__\u001B[1;34m(self, in_shape, n_classes, weight_init, activation, embedding_size, heads, attn_depth, patch_size, use_residual, variable_length, use_bias, use_norm, dropout, lat_dim, loss, scheduler, mlp_dim, head_dim, lr, weight_decay, sto_weight_avg, lr_scheduler_parameter, opt_reset_interval)\u001B[0m\n\u001B[0;32m 27\u001B[0m \u001B[0ma\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mdict\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mlocals\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 28\u001B[0m \u001B[0mparams\u001B[0m \u001B[1;33m=\u001B[0m \u001B[1;33m{\u001B[0m\u001B[0marg\u001B[0m\u001B[1;33m:\u001B[0m \u001B[0ma\u001B[0m\u001B[1;33m[\u001B[0m\u001B[0marg\u001B[0m\u001B[1;33m]\u001B[0m \u001B[1;32mfor\u001B[0m \u001B[0marg\u001B[0m \u001B[1;32min\u001B[0m \u001B[0minspect\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0msignature\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m__init__\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mparameters\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mkeys\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m \u001B[1;32mif\u001B[0m \u001B[0marg\u001B[0m \u001B[1;33m!=\u001B[0m \u001B[1;34m'self'\u001B[0m\u001B[1;33m}\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m---> 29\u001B[1;33m \u001B[0msuper\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mVisualTransformer\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m__init__\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mparams\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 30\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 31\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0min_shape\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0min_shape\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
"\u001B[1;32m~\\projects\\compare_21\\ml_lib\\modules\\util.py\u001B[0m in \u001B[0;36m__init__\u001B[1;34m(self, model_parameters, weight_init)\u001B[0m\n\u001B[0;32m 112\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mparams\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mModelParameters\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mmodel_parameters\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 113\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 114\u001B[1;33m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mmetrics\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mPLMetrics\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mparams\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mn_classes\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mtag\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;34m'PL'\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 115\u001B[0m \u001B[1;32mpass\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 116\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
"\u001B[1;32m~\\projects\\compare_21\\ml_lib\\modules\\util.py\u001B[0m in \u001B[0;36m__init__\u001B[1;34m(self, n_classes, tag)\u001B[0m\n\u001B[0;32m 30\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 31\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0maccuracy_score\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mpl\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mmetrics\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mAccuracy\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mcompute_on_step\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;32mFalse\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m---> 32\u001B[1;33m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mprecision\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mpl\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mmetrics\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mPrecision\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mnum_classes\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mn_classes\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0maverage\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;34m'macro'\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mcompute_on_step\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;32mFalse\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 33\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mrecall\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mpl\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mmetrics\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mRecall\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mnum_classes\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mn_classes\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0maverage\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;34m'macro'\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mcompute_on_step\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;32mFalse\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 34\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mconfusion_matrix\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mpl\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mmetrics\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mConfusionMatrix\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mn_classes\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mnormalize\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;34m'true'\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mcompute_on_step\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;32mFalse\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
"\u001B[1;32mc:\\users\\steff\\envs\\compare_21\\lib\\site-packages\\pytorch_lightning\\metrics\\classification\\precision_recall.py\u001B[0m in \u001B[0;36m__init__\u001B[1;34m(self, num_classes, threshold, average, multilabel, mdmc_average, ignore_index, top_k, is_multiclass, compute_on_step, dist_sync_on_step, process_group, dist_sync_fn)\u001B[0m\n\u001B[0;32m 139\u001B[0m \u001B[1;32mraise\u001B[0m \u001B[0mValueError\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;34mf\"The `average` has to be one of {allowed_average}, got {average}.\"\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 140\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 141\u001B[1;33m super().__init__(\n\u001B[0m\u001B[0;32m 142\u001B[0m \u001B[0mreduce\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;34m\"macro\"\u001B[0m \u001B[1;32mif\u001B[0m \u001B[0maverage\u001B[0m \u001B[1;32min\u001B[0m \u001B[1;33m[\u001B[0m\u001B[1;34m\"weighted\"\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;34m\"none\"\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;32mNone\u001B[0m\u001B[1;33m]\u001B[0m \u001B[1;32melse\u001B[0m \u001B[0maverage\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 143\u001B[0m \u001B[0mmdmc_reduce\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mmdmc_average\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
"\u001B[1;32mc:\\users\\steff\\envs\\compare_21\\lib\\site-packages\\pytorch_lightning\\metrics\\classification\\stat_scores.py\u001B[0m in \u001B[0;36m__init__\u001B[1;34m(self, threshold, top_k, reduce, num_classes, ignore_index, mdmc_reduce, is_multiclass, compute_on_step, dist_sync_on_step, process_group, dist_sync_fn)\u001B[0m\n\u001B[0;32m 157\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 158\u001B[0m \u001B[1;32mif\u001B[0m \u001B[0mreduce\u001B[0m \u001B[1;33m==\u001B[0m \u001B[1;34m\"macro\"\u001B[0m \u001B[1;32mand\u001B[0m \u001B[1;33m(\u001B[0m\u001B[1;32mnot\u001B[0m \u001B[0mnum_classes\u001B[0m \u001B[1;32mor\u001B[0m \u001B[0mnum_classes\u001B[0m \u001B[1;33m<\u001B[0m \u001B[1;36m1\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 159\u001B[1;33m \u001B[1;32mraise\u001B[0m \u001B[0mValueError\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;34m\"When you set `reduce` as 'macro', you have to provide the number of classes.\"\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 160\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 161\u001B[0m \u001B[1;32mif\u001B[0m \u001B[0mnum_classes\u001B[0m \u001B[1;32mand\u001B[0m \u001B[0mignore_index\u001B[0m \u001B[1;32mis\u001B[0m \u001B[1;32mnot\u001B[0m \u001B[1;32mNone\u001B[0m \u001B[1;32mand\u001B[0m \u001B[1;33m(\u001B[0m\u001B[1;32mnot\u001B[0m \u001B[1;36m0\u001B[0m \u001B[1;33m<=\u001B[0m \u001B[0mignore_index\u001B[0m \u001B[1;33m<\u001B[0m \u001B[0mnum_classes\u001B[0m \u001B[1;32mor\u001B[0m \u001B[0mnum_classes\u001B[0m \u001B[1;33m==\u001B[0m \u001B[1;36m1\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
"\u001B[1;31mValueError\u001B[0m: When you set `reduce` as 'macro', you have to provide the number of classes."
]
}
],
"source": [
"exp_path = _ROOT / out_path / model_name / exp_name / version\n",
"checkpoint = natsorted(exp_path.glob('*.ckpt'))[-1]\n",
"hparams_yaml = next(exp_path.glob('*.yaml'))\n",
"\n",
"hparams_file = load_hparams_from_yaml(hparams_yaml)\n",
"additional_kwargs = dict(variable_length = False, c_classes=5)\n",
"\n",
"model = model_class.load_from_checkpoint(checkpoint, hparams_file=str(hparams_yaml), **additional_kwargs)\n"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
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"codemirror_mode": {
"name": "ipython",
"version": 2
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"file_extension": ".py",
"mimetype": "text/x-python",
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51
util/module_mixins.py
View File

@@ -17,13 +17,14 @@ class TrainMixin:
assert isinstance(self, LightningBaseModule)
batch_files, batch_x, batch_y = batch_xy
y = self(batch_x).main_out
if self.params.loss == 'focal_loss_rob':
labels_one_hot = torch.nn.functional.one_hot(batch_y, num_classes=self.params.n_classes)
loss = self.__getattribute__(self.params.loss)(y, labels_one_hot)
if self.params.n_classes <= 2:
loss = self.bce_loss(y, batch_y.long())
else:
loss = self.__getattribute__(self.params.loss)(y, batch_y.long())
if self.params.loss == 'focal_loss_rob':
labels_one_hot = torch.nn.functional.one_hot(batch_y, num_classes=self.params.n_classes)
loss = self.__getattribute__(self.params.loss)(y, labels_one_hot)
else:
loss = self.__getattribute__(self.params.loss)(y, batch_y.long())
return dict(loss=loss)
def training_epoch_end(self, outputs):
@@ -60,14 +61,17 @@ class ValMixin:
).squeeze()
y_one_hot = torch.nn.functional.one_hot(y_max, num_classes=self.params.n_classes).float()
target_y = torch.stack(tuple(sorted_batch_y.values())).long()
if y_one_hot.ndim == 1:
y_one_hot = y_one_hot.unsqueeze(0)
if target_y.ndim == 1:
target_y = target_y.unsqueeze(0)
if self.params.n_classes <= 2:
if y_one_hot.ndim == 1:
y_one_hot = y_one_hot.unsqueeze(0)
if target_y.ndim == 1:
target_y = target_y.unsqueeze(-1)
self.metrics.update(y_one_hot, target_y)
val_loss = self.ce_loss(y, batch_y.long())
if self.params.n_classes <= 2:
val_loss = self.bce_loss(y.squeeze().float(), batch_y.float())
else:
val_loss = self.ce_loss(y, batch_y.long())
return dict(batch_files=batch_files, val_loss=val_loss,
batch_idx=batch_idx, y=y, batch_y=batch_y)
@@ -93,17 +97,26 @@ class ValMixin:
for file_name in sorted_y:
sorted_y.update({file_name: torch.stack(sorted_y[file_name])})
y_mean = torch.stack(
[torch.mean(x, dim=0, keepdim=True) if x.shape[0] > 1 else x for x in sorted_y.values()]
).squeeze()
mean_vote_loss = self.ce_loss(y_mean, sorted_batch_y)
summary_dict.update(val_mean_vote_loss=mean_vote_loss)
#y_mean = torch.stack(
# [torch.mean(x, dim=0, keepdim=True) if x.shape[0] > 1 else x for x in sorted_y.values()]
#).squeeze()
#if y_mean.ndim == 1:
# y_mean = y_mean.unsqueeze(0)
#if sorted_batch_y.ndim == 1:
# sorted_batch_y = sorted_batch_y.unsqueeze(-1)
#
#mean_vote_loss = self.ce_loss(y_mean, sorted_batch_y)
#summary_dict.update(val_mean_vote_loss=mean_vote_loss)
y_max = torch.stack(
[torch.argmax(x.mean(dim=0)) if x.shape[0] > 1 else torch.argmax(x) for x in sorted_y.values()]
).squeeze()
y_one_hot = torch.nn.functional.one_hot(y_max, num_classes=self.params.n_classes).float()
max_vote_loss = self.ce_loss(y_one_hot, sorted_batch_y)
if self.params.n_classes >= 2:
max_vote_loss = self.ce_loss(y_one_hot, sorted_batch_y)
else:
max_vote_loss = self.bce_loss(y_one_hot, sorted_batch_y)
summary_dict.update(val_max_vote_loss=max_vote_loss)
summary_dict.update({f'mean_{key}': torch.mean(torch.stack([output[key]
@@ -156,6 +169,8 @@ class TestMixin:
enumerate(['background', 'chimpanze', 'geunon', 'mandrille', 'redcap'])}
elif self.params.n_classes == 2:
class_names = {val: key for val, key in ['negative', 'positive']}
else:
raise AttributeError(f'n_classes has to be any of: [2, 5]')
df = pd.DataFrame(data=dict(filename=[Path(x).name for x in sorted_y.keys()],
prediction=[class_names[x.item()] for x in y_max.cpu()]))