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Commit fe57ae65 authored by Frisinghelli Daniel's avatar Frisinghelli Daniel
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Adjusting main executables to changes in core.trainer.py

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pysegcnn/main/config.py
+ 14
− 10
View file @ fe57ae65
......@@ -30,6 +30,12 @@ DATASET_PATH = os.path.join(DRIVE_PATH, DATASET_NAME)
# DATASET_PATH = os.path.join(DRIVE_PATH, DATASET_NAME, 'Training')
# DATASET_PATH = os.path.join(DRIVE_PATH, 'ProSnow', DATASET_NAME)
# path to store the model states
MODEL_PATH = os.path.join(HERE, '_models/')
# path to store model logs
LOG_PATH = os.path.join(HERE, '_logs/')
# the dataset configuration dictionary
dataset_config = {
......@@ -189,9 +195,6 @@ model_config = {
'dilation': 1 # the field of view of the kernel
},
# path to save trained models
'state_path': os.path.join(HERE, '_models/'),
# Transfer learning -------------------------------------------------------
# Use pretrained=True only if you wish to fine-tune a pre-trained model
......@@ -203,7 +206,7 @@ model_config = {
# was trained on
# whether to use a pretrained model for transfer learning
'transfer': True,
'transfer': False,
# name of the pretrained model to apply to a different dataset
'pretrained_model': 'UNet_SparcsDataset_t125_b64_rgbn.pt',
......@@ -226,6 +229,8 @@ model_config = {
# -------------------------------------------------------------------------
# whether to save the model state to disk
# model states are saved in: pysegcnn/main/_models
# model log files are saved in: pysegcnn/main/_logs
'save': True,
# whether to early stop training if the accuracy on the validation set
......@@ -260,6 +265,10 @@ eval_config = {
# these options are only used for evaluating a trained model using
# pysegcnn.main.eval.py
# the model to evaluate
'state_file': os.path.join(MODEL_PATH,
'UNet_SparcsDataset_Adam_SceneSplit_s0_t005v05_t125_b64_r4g3b2n5.pt'),
# the dataset to evaluate the model on
# test=False, 0 means evaluating on the validation set
# test=True, 1 means evaluating on the test set
......@@ -267,6 +276,7 @@ eval_config = {
'test': False,
# whether to compute and plot the confusion matrix
# output path is: pysegcnn/main/_graphics/
'cm': True,
# whether to predict each sample or each scene individually
......@@ -299,9 +309,3 @@ eval_config = {
'alpha': 5
}
# the complete configuration
config = {**dataset_config,
**split_config,
**model_config,
**eval_config}
pysegcnn/main/eval.py
+ 20
− 30
View file @ fe57ae65
......@@ -5,52 +5,41 @@ Created on Wed Jul 29 15:57:01 2020
@author: Daniel
"""
# builtins
import os
from logging.config import dictConfig
# locals
from pysegcnn.core.trainer import (DatasetConfig, SplitConfig, ModelConfig,
StateConfig, EvalConfig)
from pysegcnn.core.models import Network
from pysegcnn.core.trainer import EvalConfig, LogConfig
from pysegcnn.core.predict import predict_samples, predict_scenes
from pysegcnn.main.config import (dataset_config, split_config, model_config,
train_config, eval_config, HERE)
from pysegcnn.core.logging import log_conf
from pysegcnn.core.graphics import plot_confusion_matrix, plot_loss
from pysegcnn.main.config import eval_config
if __name__ == '__main__':
# (i) instanciate the configurations
dc = DatasetConfig(**dataset_config)
sc = SplitConfig(**split_config)
mc = ModelConfig(**model_config)
# instanciate the evaluation configuration
ec = EvalConfig(**eval_config)
# (ii) instanciate the dataset
ds = dc.init_dataset()
# initialize logging
log = LogConfig(ec.state_file)
dictConfig(log_conf(log.log_file))
# (iii) instanciate the training, validation and test datasets
train_ds, valid_ds, test_ds = sc.train_val_test_split(ds)
# (iv) instanciate the model state
state = StateConfig(ds, sc, mc)
state_file, loss_state = state.init_state()
# (vii) load pretrained model weights
model, _ = mc.load_pretrained(state_file)
model.state_file = state_file
# load the model state
model, _, model_state = Network.load(ec.state_file)
# plot loss and accuracy
plot_loss(loss_state, outpath=os.path.join(HERE, '_graphics/'))
plot_loss(ec.state_file, outpath=ec.models_path)
# check whether to evaluate the model on the training set, validation set
# or the test set
if ec.test is None:
ds = train_ds
ds = model_state['train_ds']
else:
ds = test_ds if ec.test else valid_ds
ds = model_state['test_ds'] if ec.test else model_state['valid_ds']
# keyword arguments for plotting
kwargs = {'bands': ec.plot_bands,
'outpath': os.path.join(HERE, '_scenes/'),
'alpha': ec.alpha,
'figsize': ec.figsize}
......@@ -58,16 +47,17 @@ if __name__ == '__main__':
if ec.predict_scene:
# reconstruct and predict the scenes in the validation/test set
scenes, cm = predict_scenes(ds, model, scene_id=None, cm=ec.cm,
plot=ec.plot_scenes, **kwargs)
plot=ec.plot_scenes,
outpath=ec.scenes_path, **kwargs)
else:
# predict the samples in the validation/test set
samples, cm = predict_samples(ds, model, cm=ec.cm,
plot=ec.plot_samples, **kwargs)
plot=ec.plot_samples,
outpath=ec.sample_path, **kwargs)
# whether to plot the confusion matrix
if ec.cm:
plot_confusion_matrix(cm, ds.dataset.labels,
state=state_file.name.replace('.pt', '.png'),
outpath=os.path.join(HERE, '_graphics/')
)
state=ec.state_file.name.replace('.pt', '.png'),
outpath=ec.models_path)
pysegcnn/main/train.py
+ 14
− 20
View file @ fe57ae65
......@@ -6,13 +6,14 @@ Created on Tue Jun 30 09:33:38 2020
@author: Daniel
"""
# builtins
import logging
from logging.config import dictConfig
# locals
from pysegcnn.core.trainer import (DatasetConfig, SplitConfig, ModelConfig,
StateConfig, NetworkTrainer)
StateConfig, LogConfig, NetworkTrainer)
from pysegcnn.core.logging import log_conf
from pysegcnn.main.config import (dataset_config, split_config, model_config)
from pysegcnn.main.config import (dataset_config, split_config, model_config,
LOG_PATH)
if __name__ == '__main__':
......@@ -26,49 +27,42 @@ if __name__ == '__main__':
# (ii) instanciate the dataset
ds = dc.init_dataset()
ds
# (iii) instanciate the model state
state = StateConfig(ds, sc, mc)
state_file, loss_state = state.init_state()
state_file = state.init_state()
# initialize logging
log_file = str(state_file).replace('.pt', '_train.log')
logging.config.dictConfig(log_conf(log_file))
# (iv) initialize logging
log = LogConfig(state_file)
dictConfig(log_conf(log.log_file))
# (iv) instanciate the training, validation and test datasets and
# (v) instanciate the training, validation and test datasets and
# dataloaders
train_ds, valid_ds, test_ds = sc.train_val_test_split(ds)
train_dl, valid_dl, test_dl = sc.dataloaders(
train_ds, valid_ds, test_ds, batch_size=mc.batch_size, shuffle=True,
drop_last=False)
# (iv) instanciate the model
model = mc.init_model(ds)
# (vi) instanciate the model
model, optimizer, checkpoint_state = mc.init_model(ds, state_file)
# (vi) instanciate the optimizer and the loss function
optimizer = mc.init_optimizer(model)
# (vii) instanciate the loss function
loss_function = mc.init_loss_function()
# (vii) resume training from an existing model checkpoint
(model, optimizer, checkpoint_state, max_accuracy) = mc.from_checkpoint(
model, optimizer, state_file, loss_state)
# (viii) initialize network trainer class for eays model training
# (viii) initialize network trainer class for easy model training
trainer = NetworkTrainer(model=model,
optimizer=optimizer,
loss_function=loss_function,
train_dl=train_dl,
valid_dl=valid_dl,
test_dl=test_dl,
state_file=state_file,
loss_state=loss_state,
epochs=mc.epochs,
nthreads=mc.nthreads,
early_stop=mc.early_stop,
mode=mc.mode,
delta=mc.delta,
patience=mc.patience,
max_accuracy=max_accuracy,
checkpoint_state=checkpoint_state,
save=mc.save
)
......
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