def make_debug_plots(self,
dataloader,
model,
classes,
output_dir,
max_plots=25,
score_thresh=0.3):
preds_dir = join(output_dir, 'preds')
zip_path = join(output_dir, 'preds.zip')
make_dir(preds_dir, force_empty=True)
model.eval()
for batch_x, batch_y in dataloader:
with torch.no_grad():
device = list(model.parameters())[0].device
batch_x = batch_x.to(device=device)
batch_sz = batch_x.shape[0]
batch_boxlist = model(batch_x)
for img_ind in range(batch_sz):
x = batch_x[img_ind].cpu()
y = batch_y[img_ind].cpu()
boxlist = batch_boxlist[img_ind].score_filter(
score_thresh).cpu()
# Plot image, ground truth, and predictions
fig = self.plot_image_preds(x, y, boxlist, classes)
plt.savefig(join(preds_dir, '{}-images.png'.format(img_ind)),
bbox_inches='tight')
plt.close(fig)
break
zipdir(preds_dir, zip_path)
shutil.rmtree(preds_dir)
def _plot_data(split):
debug_chips_dir = join(output_dir, '{}-debug-chips'.format(split))
zip_path = join(output_dir, '{}-debug-chips.zip'.format(split))
make_dir(debug_chips_dir, force_empty=True)
ds = data.train_ds if split == 'train' else data.valid_ds
for i, (x, y) in enumerate(ds):
if i == max_per_split:
break
x.show(y=y)
plt.savefig(join(debug_chips_dir, '{}.png'.format(i)),
figsize=(3, 3))
plt.close()
zipdir(debug_chips_dir, zip_path)
shutil.rmtree(debug_chips_dir)
def plot_preds(data, learn, output_dir, max_plots=50):
preds_dir = join(output_dir, 'preds')
zip_path = join(output_dir, 'preds.zip')
make_dir(preds_dir, force_empty=True)
ds = data.valid_ds
for i, (x, y) in enumerate(ds):
if i == max_plots:
break
z = learn.predict(x)
x.show(y=z[0])
plt.savefig(join(preds_dir, '{}.png'.format(i)), figsize=(3, 3))
plt.close()
zipdir(preds_dir, zip_path)
shutil.rmtree(preds_dir)
def make_debug_plots(self,
dataset,
model,
classes,
output_dir,
max_plots=25,
score_thresh=0.25):
preds_dir = join(output_dir, 'preds')
zip_path = join(output_dir, 'preds.zip')
make_dir(preds_dir, force_empty=True)
model.eval()
for img_id, (x, y) in enumerate(dataset):
if img_id == max_plots:
break
# Get predictions
boxlist, head_out = self.get_pred(x, model, score_thresh)
# Plot image, ground truth, and predictions
fig = self.plot_image_preds(x, y, boxlist, classes)
plt.savefig(join(preds_dir, '{}.png'.format(img_id)),
dpi=200,
bbox_inches='tight')
plt.close(fig)
# Plot raw output of network at each level.
for level, level_out in enumerate(head_out):
stride = model.fpn.strides[level]
reg_arr, label_arr, center_arr = level_out
# Plot label_arr
label_arr = label_arr[0].detach().cpu()
label_probs = torch.sigmoid(label_arr)
fig = self.plot_label_arr(label_probs, classes, stride)
plt.savefig(join(preds_dir,
'{}-{}-label-arr.png'.format(img_id, stride)),
dpi=100,
bbox_inches='tight')
plt.close(fig)
# Plot top, left, bottom, right from reg_arr and center_arr.
reg_arr = reg_arr[0].detach().cpu()
center_arr = center_arr[0][0].detach().cpu()
center_probs = torch.sigmoid(center_arr)
fig = plot_reg_center_arr(reg_arr, center_probs, stride)
plt.savefig(join(
preds_dir,
'{}-{}-reg-center-arr.png'.format(img_id, stride)),
dpi=100,
bbox_inches='tight')
plt.close(fig)
# Get encoding of ground truth targets.
h, w = x.shape[1:]
targets = encode_single_targets(y.boxes, y.get_field('labels'),
model.pyramid_shape,
model.num_labels)
# Plot encoding of ground truth at each level.
for level, level_targets in enumerate(targets):
stride = model.fpn.strides[level]
reg_arr, label_arr, center_arr = level_targets
# Plot label_arr
label_probs = label_arr.detach().cpu()
fig = self.plot_label_arr(label_probs, classes, stride)
plt.savefig(join(
preds_dir, '{}-{}-label-arr-gt.png'.format(img_id,
stride)),
dpi=100,
bbox_inches='tight')
plt.close(fig)
# Plot top, left, bottom, right from reg_arr and center_arr.
reg_arr = reg_arr.detach().cpu()
center_arr = center_arr[0].detach().cpu()
center_probs = center_arr
fig = plot_reg_center_arr(reg_arr, center_probs, stride)
plt.savefig(join(
preds_dir,
'{}-{}-reg-center-arr-gt.png'.format(img_id, stride)),
dpi=100,
bbox_inches='tight')
plt.close(fig)
zipdir(preds_dir, zip_path)
shutil.rmtree(preds_dir)
def make_debug_plots(self,
dataloader,
model,
classes,
output_path,
max_plots=25,
score_thresh=0.3):
with tempfile.TemporaryDirectory() as preds_dir:
model.eval()
for batch_x, batch_y in dataloader:
with torch.no_grad():
device = list(model.parameters())[0].device
batch_x = batch_x.to(device=device)
batch_sz = batch_x.shape[0]
batch_boxlist, batch_head_out = model(batch_x,
get_head_out=True)
for img_ind in range(batch_sz):
x = batch_x[img_ind].cpu()
y = batch_y[img_ind].cpu()
boxlist = batch_boxlist[img_ind].score_filter(
score_thresh).cpu()
head_out = (batch_head_out[0][img_ind],
batch_head_out[1][img_ind])
# Plot image, ground truth, and predictions
fig = self.plot_image_preds(x, y, boxlist, classes)
fig.savefig(join(preds_dir,
'{}-images.png'.format(img_ind)),
bbox_inches='tight')
plt.close(fig)
# Plot raw output of network.
keypoint, reg = head_out
keypoint, reg = keypoint.cpu(), reg.cpu()
stride = model.stride
fig = plot_encoded(boxlist,
stride,
keypoint,
reg,
classes=classes)
fig.savefig(join(preds_dir,
'{}-output.png'.format(img_ind)),
bbox_inches='tight')
plt.close(fig)
# Plot encoding of ground truth targets.
h, w = x.shape[1:]
positions = get_positions(h, w, stride, y.boxes.device)
keypoint, reg = encode([y], positions, stride,
len(classes), self.cfg)
fig = plot_encoded(y,
stride,
keypoint[0],
reg[0],
classes=classes)
fig.savefig(join(preds_dir,
'{}-targets.png'.format(img_ind)),
bbox_inches='tight')
plt.close(fig)
break
zipdir(preds_dir, output_path)