From f8a96422f7b126d86171d0b0639804443e4a8d85 Mon Sep 17 00:00:00 2001
From: "Daniel.Frisinghelli" <daniel.frisinghelli@eurac.edu>
Date: Thu, 30 Jul 2020 16:49:21 +0200
Subject: [PATCH] Included type handling and improved console prints
---
pysegcnn/core/predict.py | 62 +++++++++++++++++++++++-----------------
1 file changed, 36 insertions(+), 26 deletions(-)
diff --git a/pysegcnn/core/predict.py b/pysegcnn/core/predict.py
index fe9a33e..b1e69cd 100644
--- a/pysegcnn/core/predict.py
+++ b/pysegcnn/core/predict.py
@@ -9,8 +9,9 @@ from torch.utils.data.dataset import Subset
import torch.nn.functional as F
# locals
-from pysegcnn.core.utils import reconstruct_scene
+from pysegcnn.core.utils import reconstruct_scene, accuracy_function
from pysegcnn.core.graphics import plot_sample
+from pysegcnn.core.split import RandomSubset, SceneSubset
def get_scene_tiles(ds, scene_id):
@@ -28,9 +29,18 @@ def get_scene_tiles(ds, scene_id):
def predict_samples(ds, model, optimizer, state_path, state_file, cm=False,
plot=False, **kwargs):
- # check whether the dataset is a subset
- if not isinstance(ds, Subset):
- raise TypeError('ds should be of type {}'.format(Subset))
+ # check whether the dataset is a valid subset, i.e.
+ # an instance of pysegcnn.core.split.SceneSubset or
+ # an instance of pysegcnn.core.split.RandomSubset
+ _name = type(ds).__name__
+ if _name is not RandomSubset.__name__ or _name is not SceneSubset.__name__:
+ raise TypeError('ds should be an instance of {} or of {}'
+ .format('.'.join([RandomSubset.__module__,
+ RandomSubset.__name__]),
+ '.'.join([SceneSubset.__module__,
+ SceneSubset.__name__])
+ )
+ )
# the device to compute on, use gpu if available
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
@@ -58,6 +68,7 @@ def predict_samples(ds, model, optimizer, state_path, state_file, cm=False,
# iterate over the samples and plot inputs, ground truth and
# model predictions
output = {}
+ print('Predicting samples of the {} dataset ...'.format(ds.name))
for batch, (inputs, labels) in enumerate(dataloader):
# send inputs and labels to device
@@ -71,6 +82,10 @@ def predict_samples(ds, model, optimizer, state_path, state_file, cm=False,
# store output for current batch
output[batch] = {'input': inputs, 'labels': labels, 'prediction': prd}
+ print('Sample: {:d}/{:d}, Accuracy: {:.2f}'
+ .format(batch + 1, len(dataloader),
+ accuracy_function(prd, labels)))
+
# update confusion matrix
if cm:
for ytrue, ypred in zip(labels.view(-1), prd.view(-1)):
@@ -95,9 +110,11 @@ def predict_samples(ds, model, optimizer, state_path, state_file, cm=False,
def predict_scenes(ds, model, optimizer, state_path, state_file,
scene_id=None, cm=False, plot_scenes=False, **kwargs):
- # check if the dataset is an instance of torch.data.dataset.Subset
- if not isinstance(ds, Subset):
- raise TypeError('ds should be of type {}'.format(Subset))
+ # check whether the dataset is a valid subset, i.e. an instance of
+ # pysegcnn.core.split.SceneSubset
+ if not type(ds).__name__ is SceneSubset.__name__:
+ raise TypeError('ds should be an instance of {}'.format(
+ '.'.join([SceneSubset.__module__, SceneSubset.__name__])))
# the device to compute on, use gpu if available
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
@@ -121,16 +138,8 @@ def predict_scenes(ds, model, optimizer, state_path, state_file,
# check whether a scene id is provided
if scene_id is None:
-
- # get the names of the scenes
- try:
- scene_ids = ds.ids
- except AttributeError:
- raise TypeError('predict_scenes does only work for datasets split '
- 'by "scene" or by "date".')
-
+ scene_ids = ds.ids
else:
-
# the name of the selected scene
scene_ids = [scene_id]
@@ -138,12 +147,12 @@ def predict_scenes(ds, model, optimizer, state_path, state_file,
scene_size = (ds.dataset.height, ds.dataset.width)
# iterate over the scenes
- print('Predicting scenes of the subset ...')
- scene = {}
- for sid in scene_ids:
+ print('Predicting scenes of the {} dataset ...'.format(ds.name))
+ scenes = {}
+ for i, sid in enumerate(scene_ids):
# filename for the current scene
- sname = fname + '_' + sid + '.pt'
+ sname = fname + '_{}_{}.pt'.format(ds.name, sid)
# get the indices of the tiles of the scene
indices = get_scene_tiles(ds, sid)
@@ -157,10 +166,7 @@ def predict_scenes(ds, model, optimizer, state_path, state_file,
shuffle=False, drop_last=False)
# predict the current scene
- for i, (inp, lab) in enumerate(scene_dl):
- print('Predicting scene ({}/{}), id: {}'.format(i + 1,
- len(scene_ids),
- sid))
+ for b, (inp, lab) in enumerate(scene_dl):
# send inputs and labels to device
inp = inp.to(device)
@@ -180,8 +186,12 @@ def predict_scenes(ds, model, optimizer, state_path, state_file,
labels = reconstruct_scene(lab, scene_size, nbands=1)
prdtcn = reconstruct_scene(prd, scene_size, nbands=1)
+ # print progress
+ print('Scene {:d}/{:d}, Id: {}, Accuracy: {:.2f}'.format(
+ i + 1, len(scene_ids), sid, accuracy_function(prdtcn, labels)))
+
# save outputs to dictionary
- scene[sid] = {'input': inputs, 'labels': labels, 'prediction': prdtcn}
+ scenes[sid] = {'input': inputs, 'labels': labels, 'prediction': prdtcn}
# plot current scene
if plot_scenes:
@@ -193,4 +203,4 @@ def predict_scenes(ds, model, optimizer, state_path, state_file,
state=sname,
**kwargs)
- return scene, cmm
+ return scenes, cmm
--
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