From 46fec41ad7279d6cce17a93b2d992320d8272ebd Mon Sep 17 00:00:00 2001
From: "Daniel.Frisinghelli" <daniel.frisinghelli@eurac.edu>
Date: Mon, 17 Aug 2020 10:18:30 +0200
Subject: [PATCH] Adjusted prediction modules to changes in core.trainer.py
---
pysegcnn/core/predict.py | 28 +++++--------------------
pysegcnn/main/eval.py | 45 +++++++++++++---------------------------
2 files changed, 19 insertions(+), 54 deletions(-)
diff --git a/pysegcnn/core/predict.py b/pysegcnn/core/predict.py
index ae53905..eebade8 100644
--- a/pysegcnn/core/predict.py
+++ b/pysegcnn/core/predict.py
@@ -31,8 +31,7 @@ def get_scene_tiles(ds, scene_id):
return indices
-def predict_samples(ds, model, optimizer, state_file, cm=False,
- plot=False, **kwargs):
+def predict_samples(ds, model, cm=False, plot=False, **kwargs):
# check whether the dataset is a valid subset, i.e.
# an instance of pysegcnn.core.split.SceneSubset or
@@ -41,24 +40,16 @@ def predict_samples(ds, model, optimizer, state_file, cm=False,
raise TypeError('ds should be an instance of {} or of {}.'
.format(repr(RandomSubset), repr(SceneSubset)))
- # convert state file to pathlib.Path object
- state_file = pathlib.Path(state_file)
-
# the device to compute on, use gpu if available
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
- # load the pretrained model state
- if not state_file.exists():
- raise FileNotFoundError('{} does not exist.'.format(state_file))
- _ = model.load(state_file.name, optimizer, state_file.parent)
-
# set the model to evaluation mode
LOGGER.info('Setting model to evaluation mode ...')
model.eval()
model.to(device)
# base filename for each sample
- fname = state_file.name.split('.pt')[0]
+ fname = model.state_file.name.split('.pt')[0]
# initialize confusion matrix
cmm = np.zeros(shape=(model.nclasses, model.nclasses))
@@ -107,8 +98,7 @@ def predict_samples(ds, model, optimizer, state_file, cm=False,
return output, cmm
-def predict_scenes(ds, model, optimizer, state_file,
- scene_id=None, cm=False, plot_scenes=False, **kwargs):
+def predict_scenes(ds, model, scene_id=None, cm=False, plot=False, **kwargs):
# check whether the dataset is a valid subset, i.e. an instance of
# pysegcnn.core.split.SceneSubset
@@ -116,24 +106,16 @@ def predict_scenes(ds, model, optimizer, state_file,
raise TypeError('ds should be an instance of {}.'
.format(repr(SceneSubset)))
- # convert state file to pathlib.Path object
- state_file = pathlib.Path(state_file)
-
# the device to compute on, use gpu if available
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
- # load the pretrained model state
- if not state_file.exists():
- raise FileNotFoundError('{} does not exist.'.format(state_file))
- _ = model.load(state_file.name, optimizer, state_file.parent)
-
# set the model to evaluation mode
LOGGER.info('Setting model to evaluation mode ...')
model.eval()
model.to(device)
# base filename for each scene
- fname = state_file.name.split('.pt')[0]
+ fname = model.state_file.name.split('.pt')[0]
# initialize confusion matrix
cmm = np.zeros(shape=(model.nclasses, model.nclasses))
@@ -196,7 +178,7 @@ def predict_scenes(ds, model, optimizer, state_file,
scenes[sid] = {'input': inputs, 'labels': labels, 'prediction': prdtcn}
# plot current scene
- if plot_scenes:
+ if plot:
fig, ax = plot_sample(inputs.clip(0, 1),
labels,
ds.dataset.use_bands,
diff --git a/pysegcnn/main/eval.py b/pysegcnn/main/eval.py
index 2479340..82fe4e3 100644
--- a/pysegcnn/main/eval.py
+++ b/pysegcnn/main/eval.py
@@ -9,7 +9,7 @@ import os
# locals
from pysegcnn.core.trainer import (DatasetConfig, SplitConfig, ModelConfig,
- TrainConfig, EvalConfig)
+ StateConfig, EvalConfig)
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)
@@ -22,24 +22,21 @@ if __name__ == '__main__':
dc = DatasetConfig(**dataset_config)
sc = SplitConfig(**split_config)
mc = ModelConfig(**model_config)
- tc = TrainConfig(**train_config)
ec = EvalConfig(**eval_config)
# (ii) instanciate the dataset
ds = dc.init_dataset()
- ds
# (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 files
- state_file, loss_state = mc.init_state(ds, sc, tc)
+ # (iv) instanciate the model state
+ state = StateConfig(ds, sc, mc)
+ state_file, loss_state = state.init_state()
- # (v) instanciate the model
- model = mc.init_model(ds)
-
- # (vi) instanciate the optimizer
- optimizer = tc.init_optimizer(model)
+ # (vii) load pretrained model weights
+ model, _ = mc.load_pretrained(state_file)
+ model.state_file = state_file
# plot loss and accuracy
plot_loss(loss_state, outpath=os.path.join(HERE, '_graphics/'))
@@ -54,37 +51,23 @@ if __name__ == '__main__':
# keyword arguments for plotting
kwargs = {'bands': ec.plot_bands,
'outpath': os.path.join(HERE, '_scenes/'),
- 'stretch': True,
- 'alpha': 5}
+ 'alpha': ec.alpha,
+ 'figsize': ec.figsize}
# whether to predict each sample or each scene individually
if ec.predict_scene:
# reconstruct and predict the scenes in the validation/test set
- scenes, cm = predict_scenes(ds,
- model,
- optimizer,
- state_file,
- scene_id=None,
- cm=ec.cm,
- plot_scenes=ec.plot_scenes,
- **kwargs
- )
+ scenes, cm = predict_scenes(ds, model, scene_id=None, cm=ec.cm,
+ plot=ec.plot_scenes, **kwargs)
else:
# predict the samples in the validation/test set
- samples, cm = predict_samples(ds,
- model,
- optimizer,
- state_file,
- cm=ec.cm,
- plot_scenes=ec.plot_scenes,
- **kwargs)
+ samples, cm = predict_samples(ds, model, cm=ec.cm,
+ plot=ec.plot_samples, **kwargs)
# whether to plot the confusion matrix
if ec.cm:
- plot_confusion_matrix(cm,
- ds.dataset.labels,
- normalize=True,
+ plot_confusion_matrix(cm, ds.dataset.labels,
state=state_file.name.replace('.pt', '.png'),
outpath=os.path.join(HERE, '_graphics/')
)
--
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