def _evaluate(data_loader,
model,
criterion,
observer,
observer_criterion,
writer,
epoch,
logging_label,
no_cuda=False,
log_interval=10,
**kwargs):
"""
The evaluation routine
Parameters
----------
:param data_loader : torch.utils.data.DataLoader
The dataloader of the evaluation set
:param model : torch.nn.module
The network model being used
:param criterion: torch.nn.loss
The loss function used to compute the loss of the model
:param writer : tensorboardX.writer.SummaryWriter
The tensorboard writer object. Used to log values on file for the tensorboard visualization.
:param epoch : int
Number of the epoch (for logging purposes)
:param logging_label : string
Label for logging purposes. Typically 'test' or 'valid'. Its prepended to the logging output path and messages.
:param no_cuda : boolean
Specifies whether the GPU should be used or not. A value of 'True' means the CPU will be used.
:param log_interval : int
Interval limiting the logging of mini-batches. Default value of 10.
:return:
None
"""
multi_run = kwargs['run'] if 'run' in kwargs else None
# Instantiate the counters
batch_time = AverageMeter()
losses = AverageMeter()
observer_loss_meter = AverageMeter()
top1 = AverageMeter()
observer_acc_meter = AverageMeter()
data_time = AverageMeter()
# Switch to evaluate mode (turn off dropout & such )
model.eval()
# Iterate over whole evaluation set
end = time.time()
# Empty lists to store the predictions and target values
preds = []
targets = []
pbar = tqdm(enumerate(data_loader),
total=len(data_loader),
unit='batch',
ncols=150,
leave=False)
for batch_idx, (input, target) in pbar:
# Measure data loading time
data_time.update(time.time() - end)
# Moving data to GPU
if not no_cuda:
input = input.cuda(async=True)
target = target.cuda(async=True)
# Convert the input and its labels to Torch Variables
input_var = torch.autograd.Variable(input, volatile=True)
target_var = torch.autograd.Variable(target, volatile=True)
# Compute output
output = model(input_var)
# Get the features from second last layer
input_features_var = torch.autograd.Variable(
model.module.features.data)
# Use observer on the features
observer_acc, observer_loss = evaluate_one_mini_batch(
observer, observer_criterion, input_features_var, target_var,
observer_loss_meter, observer_acc_meter)
# Compute and record the loss
loss = criterion(output, target_var)
losses.update(loss.data[0], input.size(0))
# Compute and record the accuracy
acc1 = accuracy(output.data, target, topk=(1, ))[0]
top1.update(acc1[0], input.size(0))
# Get the predictions
_ = [
preds.append(item) for item in
[np.argmax(item) for item in output.data.cpu().numpy()]
]
_ = [targets.append(item) for item in target.cpu().numpy()]
# Add loss and accuracy to Tensorboard
if multi_run is None:
writer.add_scalar(logging_label + '/mb_loss', loss.data[0],
epoch * len(data_loader) + batch_idx)
writer.add_scalar(logging_label + '/mb_accuracy',
acc1.cpu().numpy(),
epoch * len(data_loader) + batch_idx)
writer.add_scalar(logging_label + '/obs_mb_loss',
observer_loss.data[0],
epoch * len(data_loader) + batch_idx)
writer.add_scalar(logging_label + '/obs_mb_accuracy',
observer_acc.cpu().numpy(),
epoch * len(data_loader) + batch_idx)
else:
writer.add_scalar(logging_label + '/mb_loss_{}'.format(multi_run),
loss.data[0],
epoch * len(data_loader) + batch_idx)
writer.add_scalar(
logging_label + '/mb_accuracy_{}'.format(multi_run),
acc1.cpu().numpy(),
epoch * len(data_loader) + batch_idx)
writer.add_scalar(
logging_label + '/obs_mb_loss_{}'.format(multi_run),
observer_loss.data[0],
epoch * len(data_loader) + batch_idx)
writer.add_scalar(
logging_label + '/obs_mb_accuracy_{}'.format(multi_run),
observer_acc.cpu().numpy(),
epoch * len(data_loader) + batch_idx)
# Measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if batch_idx % log_interval == 0:
pbar.set_description(logging_label +
' epoch [{0}][{1}/{2}]\t'.format(
epoch, batch_idx, len(data_loader)))
pbar.set_postfix(
Time='{batch_time.avg:.3f}\t'.format(batch_time=batch_time),
Loss='{loss.avg:.4f}\t'.format(loss=losses),
Acc1='{top1.avg:.3f}\t'.format(top1=top1),
Data='{data_time.avg:.3f}\t'.format(data_time=data_time))
# Make a confusion matrix
try:
cm = confusion_matrix(y_true=targets, y_pred=preds)
confusion_matrix_heatmap = make_heatmap(cm,
data_loader.dataset.classes)
except ValueError:
logging.warning('Confusion Matrix did not work as expected')
confusion_matrix_heatmap = np.zeros((10, 10, 3))
# Logging the epoch-wise accuracy and confusion matrix
if multi_run is None:
writer.add_scalar(logging_label + '/accuracy', top1.avg, epoch)
writer.add_scalar(logging_label + '/obs_accuracy',
observer_acc_meter.avg, epoch)
save_image_and_log_to_tensorboard(
writer,
tag=logging_label + '/confusion_matrix',
image_tensor=confusion_matrix_heatmap,
global_step=epoch)
else:
writer.add_scalar(logging_label + '/accuracy_{}'.format(multi_run),
top1.avg, epoch)
writer.add_scalar(logging_label + '/obs_accuracy_{}'.format(multi_run),
observer_acc_meter.avg, epoch)
save_image_and_log_to_tensorboard(
writer,
tag=logging_label + '/confusion_matrix_{}'.format(multi_run),
image_tensor=confusion_matrix_heatmap,
global_step=epoch)
logging.info(
_prettyprint_logging_label(logging_label) + ' epoch[{}]: '
'Acc@1={top1.avg:.3f}\t'
'Loss={loss.avg:.4f}\t'
'Batch time={batch_time.avg:.3f} ({data_time.avg:.3f} to load data)'.
format(epoch,
batch_time=batch_time,
data_time=data_time,
loss=losses,
top1=top1))
# Generate a classification report for each epoch
_log_classification_report(data_loader, epoch, preds, targets, writer)
return top1.avg
def _evaluate(data_loader,
model,
criterion,
writer,
epoch,
logging_label,
no_cuda=False,
log_interval=10,
**kwargs):
"""
The evaluation routine
Parameters
----------
data_loader : torch.utils.data.DataLoader
The dataloader of the evaluation set
model : torch.nn.module
The network model being used
criterion: torch.nn.loss
The loss function used to compute the loss of the model
writer : tensorboardX.writer.SummaryWriter
The tensorboard writer object. Used to log values on file for the tensorboard visualization.
epoch : int
Number of the epoch (for logging purposes)
logging_label : string
Label for logging purposes. Typically 'test' or 'valid'. Its prepended to the logging output path and messages.
no_cuda : boolean
Specifies whether the GPU should be used or not. A value of 'True' means the CPU will be used.
log_interval : int
Interval limiting the logging of mini-batches. Default value of 10.
Returns
-------
top1.avg : float
Accuracy of the model of the evaluated split
"""
multi_run = kwargs['run'] if 'run' in kwargs else None
# Instantiate the counters
batch_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
data_time = AverageMeter()
# Switch to evaluate mode (turn off dropout & such )
model.eval()
# Iterate over whole evaluation set
end = time.time()
# Empty lists to store the predictions and target values
preds = []
targets = []
multi_run = False
pbar = tqdm(enumerate(data_loader),
total=len(data_loader),
unit='batch',
ncols=150,
leave=False)
for batch_idx, (input, target) in pbar:
# todo: how to you implement sliding window accross batches
if len(input.size()) == 5:
multi_run = True
# input [64, 5, 3, 299, 299]
bs, ncrops, c, h, w = input.size()
# input.view leaves the 3rd 4th and 5th dimension as is, but multiplies the 1st and 2nd together
# result [320, 3, 299, 299]
# result = input.view(-1, c, h, w) # fuse batch size and ncrops
# result_avg = input.view(bs, -1, c, h, w).mean(1)
input = input.view(-1, c, h, w)
# If you are using tensor.max(1) then you get a tupel with two tensors, choose the first one
# which is a floattensor and what you need.
# input = result_avg[0]
# Measure data loading time
data_time.update(time.time() - end)
# Moving data to GPU
if not no_cuda:
input = input.cuda(async=True)
target = target.cuda(async=True)
# Convert the input and its labels to Torch Variables
# todo: check them out in debugger
input_var = torch.autograd.Variable(input, volatile=True)
target_var = torch.autograd.Variable(target, volatile=True)
# Compute output
output = model(input_var)
if multi_run:
output = output.view(bs, ncrops, -1).mean(1)
# Compute and record the loss
loss = criterion(output, target_var)
losses.update(loss.data[0], input.size(0))
# Compute and record the accuracy
acc1 = accuracy(output.data, target, topk=(1, ))[0]
top1.update(acc1[0], input.size(0))
# Get the predictions
_ = [
preds.append(item) for item in
[np.argmax(item) for item in output.data.cpu().numpy()]
]
_ = [targets.append(item) for item in target.cpu().numpy()]
# Add loss and accuracy to Tensorboard
if multi_run is None:
writer.add_scalar(logging_label + '/mb_loss', loss.data[0],
epoch * len(data_loader) + batch_idx)
writer.add_scalar(logging_label + '/mb_accuracy',
acc1.cpu().numpy(),
epoch * len(data_loader) + batch_idx)
else:
writer.add_scalar(logging_label + '/mb_loss_{}'.format(multi_run),
loss.data[0],
epoch * len(data_loader) + batch_idx)
writer.add_scalar(
logging_label + '/mb_accuracy_{}'.format(multi_run),
acc1.cpu().numpy(),
epoch * len(data_loader) + batch_idx)
# Measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if batch_idx % log_interval == 0:
pbar.set_description(logging_label +
' epoch [{0}][{1}/{2}]\t'.format(
epoch, batch_idx, len(data_loader)))
pbar.set_postfix(
Time='{batch_time.avg:.3f}\t'.format(batch_time=batch_time),
Loss='{loss.avg:.4f}\t'.format(loss=losses),
Acc1='{top1.avg:.3f}\t'.format(top1=top1),
Data='{data_time.avg:.3f}\t'.format(data_time=data_time))
# Make a confusion matrix
try:
cm = confusion_matrix(y_true=targets, y_pred=preds)
confusion_matrix_heatmap = make_heatmap(cm,
data_loader.dataset.classes)
except ValueError:
logging.warning('Confusion Matrix did not work as expected')
confusion_matrix_heatmap = np.zeros((10, 10, 3))
# Logging the epoch-wise accuracy and confusion matrix
if multi_run is None:
writer.add_scalar(logging_label + '/accuracy', top1.avg, epoch)
# ERROR: save_image_and_log_tensorboard() got an unexpected keyword argument 'image_tensore'
# changed 'image_tensor=confusion_matrix_heattmap' to 'image=confusion_mastrix_heatmap'
save_image_and_log_to_tensorboard(writer,
tag=logging_label +
'/confusion_matrix',
image=confusion_matrix_heatmap,
global_step=epoch)
else:
writer.add_scalar(logging_label + '/accuracy_{}'.format(multi_run),
top1.avg, epoch)
save_image_and_log_to_tensorboard(
writer,
tag=logging_label + '/confusion_matrix_{}'.format(multi_run),
image=confusion_matrix_heatmap,
global_step=epoch)
logging.info(
_prettyprint_logging_label(logging_label) + ' epoch[{}]: '
'Acc@1={top1.avg:.3f}\t'
'Loss={loss.avg:.4f}\t'
'Batch time={batch_time.avg:.3f} ({data_time.avg:.3f} to load data)'.
format(epoch,
batch_time=batch_time,
data_time=data_time,
loss=losses,
top1=top1))
# Generate a classification report for each epoch
_log_classification_report(data_loader, epoch, preds, targets, writer)
return top1.avg
def _evaluate(data_loader,
model,
criterion,
writer,
epoch,
logging_label,
no_cuda=False,
log_interval=10,
**kwargs):
"""
The evaluation routine
Parameters
----------
data_loader : torch.utils.data.DataLoader
The dataloader of the evaluation set
model : torch.nn.module
The network model being used
criterion: torch.nn.loss
The loss function used to compute the loss of the model
writer : tensorboardX.writer.SummaryWriter
The tensorboard writer object. Used to log values on file for the tensorboard visualization.
epoch : int
Number of the epoch (for logging purposes)
logging_label : string
Label for logging purposes. Typically 'test' or 'valid'. Its prepended to the logging output path and messages.
no_cuda : boolean
Specifies whether the GPU should be used or not. A value of 'True' means the CPU will be used.
log_interval : int
Interval limiting the logging of mini-batches. Default value of 10.
Returns
-------
top1.avg : float
Accuracy of the model of the evaluated split
"""
#TODO All parts computing the accuracy are commented out. It is necessary to
#TODO implement a 2D softmax and instead of regressing the output class have it
#TODO work with class labels. Notice that, however, it would be
#TODO of interest leaving open the possibility to work with soft labels
#TODO (e.g. the ground truth for pixel X,Y is an array of probabilities instead
#TODO of an integer.
multi_run = kwargs['run'] if 'run' in kwargs else None
# Instantiate the counters
batch_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
data_time = AverageMeter()
# Switch to evaluate mode (turn off dropout & such )
model.eval()
# Iterate over whole evaluation set
end = time.time()
# Empty lists to store the predictions and target values
preds = []
targets = []
pbar = tqdm(enumerate(data_loader),
total=len(data_loader),
unit='batch',
ncols=150,
leave=False)
for batch_idx, (input, _) in pbar:
# Measure data loading time
data_time.update(time.time() - end)
# Moving data to GPU
if not no_cuda:
input = input.cuda(async=True)
# Split the data into halves to separate the input from the GT
satel_image, map_image = torch.chunk(input, chunks=2, dim=3)
# Convert the input and its labels to Torch Variables
input_var = torch.autograd.Variable(satel_image)
target_var = torch.autograd.Variable(map_image)
# Compute output
output = model(input_var)
# Compute and record the loss
loss = criterion(output, target_var)
losses.update(loss.data[0], input.size(0))
# Compute and record the accuracy
# acc1 = accuracy(output.data, target, topk=(1,))[0]
# top1.update(acc1[0], input.size(0))
# Get the predictions
# _ = [preds.append(item) for item in [np.argmax(item) for item in output.data.cpu().numpy()]]
# _ = [targets.append(item) for item in target.cpu().numpy()]
# Add loss and accuracy to Tensorboard
if multi_run is None:
writer.add_scalar(logging_label + '/mb_loss', loss.data[0],
epoch * len(data_loader) + batch_idx)
# writer.add_scalar(logging_label + '/mb_accuracy', acc1.cpu().numpy(), epoch * len(data_loader) + batch_idx)
else:
writer.add_scalar(logging_label + '/mb_loss_{}'.format(multi_run),
loss.data[0],
epoch * len(data_loader) + batch_idx)
# writer.add_scalar(logging_label + '/mb_accuracy_{}'.format(multi_run), acc1.cpu().numpy(),
# epoch * len(data_loader) + batch_idx)
# Measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if batch_idx % log_interval == 0:
pbar.set_description(logging_label +
' epoch [{0}][{1}/{2}]\t'.format(
epoch, batch_idx, len(data_loader)))
pbar.set_postfix(
Time='{batch_time.avg:.3f}\t'.format(batch_time=batch_time),
Loss='{loss.avg:.4f}\t'.format(loss=losses),
# Acc1='{top1.avg:.3f}\t'.format(top1=top1),
Data='{data_time.avg:.3f}\t'.format(data_time=data_time))
# Logging the epoch-wise accuracy
if multi_run is None:
# writer.add_scalar(logging_label + '/accuracy', top1.avg, epoch)
save_image_and_log_to_tensorboard(writer,
tag=logging_label + '/output',
image=output[:1],
global_step=epoch)
save_image_and_log_to_tensorboard(writer,
tag=logging_label + '/input',
image=satel_image[:1])
save_image_and_log_to_tensorboard(writer,
tag=logging_label + '/target',
image=map_image[:1])
else:
# writer.add_scalar(logging_label + '/accuracy_{}'.format(multi_run), top1.avg, epoch)
save_image_and_log_to_tensorboard(writer,
tag=logging_label +
'/output_{}'.format(multi_run),
image=output[:1],
global_step=epoch)
save_image_and_log_to_tensorboard(writer,
tag=logging_label +
'/input_{}'.format(multi_run),
image=satel_image[:1])
save_image_and_log_to_tensorboard(writer,
tag=logging_label + '/target',
image=map_image[:1])
logging.info(
_prettyprint_logging_label(logging_label) + ' epoch[{}]: '
# 'Acc@1={top1.avg:.3f}\t'
'Loss={loss.avg:.4f}\t'
'Batch time={batch_time.avg:.3f} ({data_time.avg:.3f} to load data)'.
format(epoch,
batch_time=batch_time,
data_time=data_time,
loss=losses,
top1=top1))
def validate(val_loader, model, criterion, writer, epoch, class_encodings, no_cuda=False, log_interval=10, **kwargs):
"""
The evaluation routine
Parameters
----------
val_loader : torch.utils.data.DataLoader
The dataloader of the evaluation set
model : torch.nn.module
The network model being used
criterion: torch.nn.loss
The loss function used to compute the loss of the model
writer : tensorboardX.writer.SummaryWriter
The tensorboard writer object. Used to log values on file for the tensorboard visualization.
epoch : int
Number of the epoch (for logging purposes)
class_encodings : List
Contains the classes (range of ints)
no_cuda : boolean
Specifies whether the GPU should be used or not. A value of 'True' means the CPU will be used.
log_interval : int
Interval limiting the logging of mini-batches. Default value of 10.
Returns
-------
meanIU.avg : float
MeanIU of the model of the evaluated split
"""
# 'Run' is injected in kwargs at runtime IFF it is a multi-run event
multi_run = kwargs['run'] if 'run' in kwargs else None
num_classes = len(class_encodings)
# Instantiate the counters
batch_time = AverageMeter()
losses = AverageMeter()
meanIU = AverageMeter()
data_time = AverageMeter()
# Switch to evaluate mode (turn off dropout & such )
model.eval()
# Iterate over whole evaluation set
end = time.time()
pbar = tqdm(enumerate(val_loader), total=len(val_loader), unit='batch', ncols=150, leave=False)
for batch_idx, (input, target) in pbar:
# Measure data loading time
data_time.update(time.time() - end)
# Moving data to GPU
if not no_cuda:
input = input.cuda(non_blocking=True)
target = target.cuda(non_blocking=True)
# Compute output
output = model(input)
# Compute and record the loss
loss = criterion(output, target)
losses.update(loss.item(), input.size(0))
# Compute and record the accuracy
_, _, mean_iu_batch, _ = accuracy_segmentation(target.cpu().numpy(), get_argmax(output), num_classes)
meanIU.update(mean_iu_batch, input.size(0))
# Add loss and meanIU to Tensorboard
scalar_label = 'val/mb_loss' if multi_run is None else 'val/mb_loss_{}'.format(multi_run)
writer.add_scalar(scalar_label, loss.item(), epoch * len(val_loader) + batch_idx)
scalar_label = 'val/mb_meanIU' if multi_run is None else 'val/mb_meanIU_{}'.format(multi_run)
writer.add_scalar(scalar_label, mean_iu_batch, epoch * len(val_loader) + batch_idx)
# Measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if batch_idx % log_interval == 0:
pbar.set_description('val epoch [{0}][{1}/{2}]\t'.format(epoch, batch_idx, len(val_loader)))
pbar.set_postfix(Time='{batch_time.avg:.3f}\t'.format(batch_time=batch_time),
Loss='{loss.avg:.4f}\t'.format(loss=losses),
meanIU='{meanIU.avg:.3f}\t'.format(meanIU=meanIU),
Data='{data_time.avg:.3f}\t'.format(data_time=data_time))
# Logging the epoch-wise meanIU
scalar_label = 'val/meanIU' if multi_run is None else 'val/meanIU_{}'.format(multi_run)
writer.add_scalar(scalar_label, meanIU.avg, epoch)
logging.info(_prettyprint_logging_label("val") +
' epoch[{}]: '
'MeanIU={meanIU.avg:.3f}\t'
'Loss={loss.avg:.4f}\t'
'Batch time={batch_time.avg:.3f} ({data_time.avg:.3f} to load data)'
.format(epoch, batch_time=batch_time, data_time=data_time, loss=losses, meanIU=meanIU))
return meanIU.avg
def test(test_loader, model, criterion, writer, epoch, class_encodings, img_names_sizes_dict, dataset_folder,
post_process, no_cuda=False, log_interval=10, **kwargs):
"""
The evaluation routine
Parameters
----------
img_names_sizes_dict: dictionary {str: (int, int)}
Key: gt image name (with extension), Value: image size
test_loader : torch.utils.data.DataLoader
The dataloader of the evaluation set
model : torch.nn.module
The network model being used
criterion: torch.nn.loss
The loss function used to compute the loss of the model
writer : tensorboardX.writer.SummaryWriter
The tensorboard writer object. Used to log values on file for the tensorboard visualization.
epoch : int
Number of the epoch (for logging purposes)
class_encodings : List
Contains the range of encoded classes
img_names_sizes_dict
# TODO
dataset_folder : str
# TODO
post_process : Boolean
apply post-processing to the output of the network
no_cuda : boolean
Specifies whether the GPU should be used or not. A value of 'True' means the CPU will be used.
log_interval : int
Interval limiting the logging of mini-batches. Default value of 10.
Returns
-------
meanIU.avg : float
MeanIU of the model of the evaluated split
"""
# 'Run' is injected in kwargs at runtime IFF it is a multi-run event
multi_run = kwargs['run'] if 'run' in kwargs else None
num_classes = len(class_encodings)
# Instantiate the counters
batch_time = AverageMeter()
losses = AverageMeter()
meanIU = AverageMeter()
data_time = AverageMeter()
# Switch to evaluate mode (turn off dropout & such )
model.eval()
# Iterate over whole evaluation set
end = time.time()
# Need to store the images currently being processes
canvas = {}
pbar = tqdm(enumerate(test_loader), total=len(test_loader), unit='batch', ncols=150, leave=False)
for batch_idx, (input, target) in pbar:
# Unpack input
input, top_left_coordinates, test_img_names = input
# Measure data loading time
data_time.update(time.time() - end)
# Moving data to GPU
if not no_cuda:
input = input.cuda(non_blocking=True)
target = target.cuda(non_blocking=True)
# Compute output
output = model(input)
# Compute and record the loss
loss = criterion(output, target)
losses.update(loss.item(), input.size(0))
# Compute and record the batch meanIU
_, _, mean_iu_batch, _ = accuracy_segmentation(target.cpu().numpy(), get_argmax(output), num_classes)
# Add loss and meanIU to Tensorboard
scalar_label = 'test/mb_loss' if multi_run is None else 'test/mb_loss_{}'.format(multi_run)
writer.add_scalar(scalar_label, loss.item(), epoch * len(test_loader) + batch_idx)
scalar_label = 'test/mb_meanIU' if multi_run is None else 'test/mb_meanIU_{}'.format(multi_run)
writer.add_scalar(scalar_label, mean_iu_batch, epoch * len(test_loader) + batch_idx)
# Measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if batch_idx % log_interval == 0:
pbar.set_description('test epoch [{0}][{1}/{2}]\t'.format(epoch, batch_idx, len(test_loader)))
pbar.set_postfix(Time='{batch_time.avg:.3f}\t'.format(batch_time=batch_time),
Loss='{loss.avg:.4f}\t'.format(loss=losses),
meanIU='{meanIU.avg:.3f}\t'.format(meanIU=meanIU),
Data='{data_time.avg:.3f}\t'.format(data_time=data_time))
# Output needs to be patched together to form the complete output of the full image
# patches are returned as a sliding window over the full image, overlapping sections are averaged
for patch, x, y, img_name in zip(output.data.cpu().numpy(), top_left_coordinates[0].numpy(), top_left_coordinates[1].numpy(), test_img_names):
# Is a new image?
if not img_name in canvas:
# Create a new image of the right size filled with NaNs
canvas[img_name] = np.empty((num_classes, *img_names_sizes_dict[img_name]))
canvas[img_name].fill(np.nan)
# Add the patch to the image
canvas[img_name] = merge_patches(patch, (x, y), canvas[img_name])
# Save the image when done
if not np.isnan(np.sum(canvas[img_name])):
# Save the final image
mean_iu = process_full_image(img_name, canvas[img_name], multi_run, dataset_folder, class_encodings, post_process)
# Update the meanIU
meanIU.update(mean_iu, 1)
# Remove the entry
canvas.pop(img_name)
logging.info("\nProcessed image {} with mean IU={}".format(img_name, mean_iu))
# Canvas MUST be empty or something was wrong with coverage of all images
assert len(canvas) == 0
# Logging the epoch-wise meanIU
scalar_label = 'test/mb_meanIU' if multi_run is None else 'test/mb_meanIU_{}'.format(multi_run)
writer.add_scalar(scalar_label, meanIU.avg, epoch)
logging.info(_prettyprint_logging_label("test") +
' epoch[{}]: '
'MeanIU={meanIU.avg:.3f}\t'
'Loss={loss.avg:.4f}\t'
'Batch time={batch_time.avg:.3f} ({data_time.avg:.3f} to load data)'
.format(epoch, batch_time=batch_time, data_time=data_time, loss=losses, meanIU=meanIU))
return meanIU.avg
def _evaluate(data_loader,
model,
criterion,
writer,
epoch,
logging_label,
no_cuda=False,
log_interval=10,
**kwargs):
"""
The evaluation routine
Parameters
----------
data_loader : torch.utils.data.DataLoader
The dataloader of the evaluation set
model : torch.nn.module
The network model being used
criterion: torch.nn.loss
The loss function used to compute the loss of the model
writer : tensorboardX.writer.SummaryWriter
The tensorboard writer object. Used to log values on file for the tensorboard visualization.
epoch : int
Number of the epoch (for logging purposes)
logging_label : string
Label for logging purposes. Typically 'test' or 'valid'. Its prepended to the logging output path and messages.
no_cuda : boolean
Specifies whether the GPU should be used or not. A value of 'True' means the CPU will be used.
log_interval : int
Interval limiting the logging of mini-batches. Default value of 10.
Returns
-------
top1.avg : float
Accuracy of the model of the evaluated split
"""
multi_run = kwargs['run'] if 'run' in kwargs else None
# Instantiate the counters
batch_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
data_time = AverageMeter()
# Switch to evaluate mode (turn off dropout & such )
model.eval()
# Iterate over whole evaluation set
end = time.time()
# Empty lists to store the predictions and target values
preds = []
targets = []
pbar = tqdm(enumerate(data_loader),
total=len(data_loader),
unit='batch',
ncols=150,
leave=False)
for batch_idx, (input, target) in pbar:
# Measure data loading time
data_time.update(time.time() - end)
# Moving data to GPU
if not no_cuda:
input = input.cuda(async=True)
target = target.cuda(async=True)
# Convert the input and its labels to Torch Variables
input_var = torch.autograd.Variable(input, volatile=True)
target_var = torch.autograd.Variable(target, volatile=True)
# Compute output
output = model(input_var)
# Compute and record the loss
loss = criterion(output, target_var)
losses.update(loss.data[0], input.size(0))
# Apply sigmoid and take everything above a threshold of 0.5
squashed_output = torch.nn.Sigmoid()(output).data.cpu().numpy()
target_vals = target.cpu().numpy().astype(np.int)
# jss = compute_jss(target_vals, get_preds_from_minibatch(squashed_output))
# top1.update(jss, input.size(0))
# Store results of each minibatch
_ = [
preds.append(item)
for item in get_preds_from_minibatch(squashed_output)
]
_ = [targets.append(item) for item in target.cpu().numpy()]
# Add loss and accuracy to Tensorboard
if multi_run is None:
writer.add_scalar(logging_label + '/mb_loss', loss.data[0],
epoch * len(data_loader) + batch_idx)
# writer.add_scalar(logging_label + '/mb_jaccard_similarity', jss, epoch * len(data_loader) + batch_idx)
else:
writer.add_scalar(logging_label + '/mb_loss_{}'.format(multi_run),
loss.data[0],
epoch * len(data_loader) + batch_idx)
# writer.add_scalar(logging_label + '/mb_jaccard_similarity_{}'.format(multi_run), jss,
# epoch * len(data_loader) + batch_idx)
# Measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if batch_idx % log_interval == 0:
pbar.set_description(logging_label +
' epoch [{0}][{1}/{2}]\t'.format(
epoch, batch_idx, len(data_loader)))
pbar.set_postfix(
Time='{batch_time.avg:.3f}\t'.format(batch_time=batch_time),
Loss='{loss.avg:.4f}\t'.format(loss=losses),
# JSS='{top1.avg:.3f}\t'.format(top1=top1),
Data='{data_time.avg:.3f}\t'.format(data_time=data_time))
# Generate a classification report for each epoch
targets = np.array(targets).astype(np.int)
preds = np.array(preds).astype(np.int)
_log_classification_report(data_loader, epoch, preds, targets, writer)
jss_epoch = compute_jss(targets, preds)
# try:
# np.testing.assert_approx_equal(jss_epoch, top1.avg)
# except:
# logging.error('Computed JSS scores do not match')
# logging.error('JSS: {} Avg: {}'.format(jss_epoch, top1.avg))
# # Logging the epoch-wise JSS
if multi_run is None:
writer.add_scalar(logging_label + '/loss', losses.avg, epoch)
writer.add_scalar(logging_label + '/jaccard_similarity', jss_epoch,
epoch)
else:
writer.add_scalar(logging_label + '/loss_{}'.format(multi_run),
losses.avg, epoch)
writer.add_scalar(
logging_label + '/jaccard_similarity_{}'.format(multi_run),
jss_epoch, epoch)
logging.info(
_prettyprint_logging_label(logging_label) + ' epoch[{}]: '
'JSS={jss_epoch:.3f}\t'
'Loss={loss.avg:.4f}\t'
'Batch time={batch_time.avg:.3f} ({data_time.avg:.3f} to load data)'.
format(epoch,
batch_time=batch_time,
data_time=data_time,
loss=losses,
jss_epoch=jss_epoch))
return jss_epoch
save_image_and_log_to_tensorboard(
writer,
tag=logging_label + '/confusion_matrix',
image_tensor=confusion_matrix_heatmap,
global_step=epoch)
else:
writer.add_scalar(logging_label + '/accuracy_{}'.format(multi_run),
top1.avg, epoch)
save_image_and_log_to_tensorboard(
writer,
tag=logging_label + '/confusion_matrix_{}'.format(multi_run),
image_tensor=confusion_matrix_heatmap,
global_step=epoch)
logging.info(
_prettyprint_logging_label(logging_label) + ' epoch[{}]: '
'Acc@1={top1.avg:.3f}\t'
'Loss={loss.avg:.4f}\t'
'Batch time={batch_time.avg:.3f} ({data_time.avg:.3f} to load data)'.
format(epoch,
batch_time=batch_time,
data_time=data_time,
loss=losses,
top1=top1))
# Generate a classification report for each epoch
_log_classification_report(data_loader, epoch, preds, targets, writer)
return top1.avg