def plot_detection_image(self, training_context):
tile_images_list = []
input = None
target = None
output = None
data_feed = training_context['data_feed']
data = training_context['train_data']
model = training_context['current_model']
output = try_map_args_and_call(model, data, data_feed)
target = data['bbox']
input = data[data_feed[model.signature.inputs.key_list[0]]]
input_image = self.reverse_image_transform(to_numpy(input))
targetmask = (target[:, 4] > 0.9)
input_image1 = input_image.copy()
target_boxes = to_numpy(xywh2xyxy(target[targetmask, :]))
for box in target_boxes:
plot_one_box(box, input_image1, (255, 128, 128),
self.labels[box[5:]])
# input_arr=np.asarray(input_arr)
tile_images_list.append(input_image1)
input_image = self.reverse_image_transform(to_numpy(input))
mask = (output[:, :, 4] > 0.7)
if len(output[:, :, 4]) > 0:
mask2 = (argmax(softmax(output[:, :, 5:], -1), -1) != 0)
mask = (mask.float() + mask2.float() == 2)
output = output[mask, :]
input_image2 = input_image.copy()
output_boxes = to_numpy(xywh2xyxy(output[mask, :]))
for box in output_boxes:
plot_one_box(box, input_image2, (255, 255, 128),
self.labels[np.argmax(box[5:])])
tile_images_list.append(input_image2)
fig = tile_rgb_images(*tile_images_list,
save_path=os.path.join(
self.save_path, self.tile_image_name_prefix),
imshow=True)
if ctx.enable_tensorboard and ctx.summary_writer is not None:
ctx.summary_writer.add_figure(
training_context['training_name'] + '/plot/detection_plot',
fig,
global_step=training_context['steps'],
close=True,
walltime=time.time())
plt.close()
def plot_tile_image(self, training_context):
tile_images_list = []
input = None
target = None
output = None
is_label_mask = False
data_feed = training_context['data_feed']
data = training_context['train_data']
model = training_context['current_model']
if model.output_shape[0] > 2:
is_label_mask = True
# if len(data) >= 3:
for data_key in data.key_list:
if data_key == data_feed[model.signature.inputs.key_list[0]]:
input = data[data_feed[model.signature.inputs.key_list[0]]]
training_context['current_model'].eval()
output = model(input)
training_context['current_model'].train()
elif (
'target' in data_key or 'label' in data_key
or 'mask' in data_key
) and not 'output' in data_key and data_key in data_feed.value_list:
target = to_numpy(data[data_key])
if 'alpha' not in data:
output = np.argmax(to_numpy(output), 1)
if is_label_mask:
target = label2color(target, self.palette)
output = label2color(output, self.palette)
else:
output = to_numpy(output[:, 1, :, :] * argmax(output, 1))
target = to_numpy(data['alpha'])
input_arr = []
input = to_numpy(input)
for i in range(len(input)):
input_arr.append(self.reverse_image_transform(input[i]))
# input_arr=np.asarray(input_arr)
tile_images_list.append(input_arr)
if is_label_mask:
tile_images_list.append(target)
tile_images_list.append(output)
else:
target_arr = np.expand_dims(target, -1)
output_arr = np.expand_dims(output, -1)
if 'alpha' not in data:
target_arr[target_arr > 0] = 1
background = np.ones_like(target_arr) * self.background
tile_images_list.append(target_arr * input_arr +
(1 - target_arr) * background)
output_arr = np.expand_dims(output, -1)
if 'alpha' not in data:
output_arr[output_arr > 0] = 1
tile_images_list.append(output_arr * input_arr +
(1 - output_arr) * background)
# if self.tile_image_include_mask:
# tile_images_list.append(input*127.5+127.5)
tile_rgb_images(*tile_images_list,
save_path=os.path.join(self.save_path,
self.tile_image_name_prefix),
imshow=True)
def plot_tile_image(self, training_context):
axis = 1
if get_backend() == 'tensorflow':
axis = -1
tile_images_list = []
input = None
target = None
output = None
is_label_mask = False
data_feed = training_context['data_feed']
data = training_context['train_data']
model = training_context['current_model']
if model.output_shape[model.filter_index] > 2:
is_label_mask = True
# if len(data) >= 3:
for data_key in data.key_list:
if data_key == data_feed[model.signature.inputs.key_list[0]]:
input = data[data_feed[model.signature.inputs.key_list[0]]]
model.eval()
if is_label_mask:
output = to_numpy(argmax(model(input), axis=axis))
else:
output = to_numpy(
expand_dims(cast(argmax(model(input), axis=axis),
input.dtype),
axis=-1))
model.train()
# elif data_key == data_feed[model.signature.outputs.key_list[0]]:
# output = data[data_feed[model.signature.outputs.key_list[0]]]
# if output.max() < 0:
# output = exp(output)
elif (
'target' in data_key or 'label' in data_key
or 'mask' in data_key
) and not 'output' in data_key and data_key in data_feed.value_list:
target = to_numpy(data[data_key])
output_arr = None
if 'alpha' not in data:
output_arr = output.copy()
if is_label_mask:
target = label2color(target, self.palette)
output = label2color(output, self.palette)
else:
if get_backend() == 'tensorflow':
output = output[:, :, :, 1:2] * argmax(output, axis)
else:
output = (output[:, 1:2, :, :] *
argmax(output, axis)).transpose(0, 2, 3, 1)
target = to_numpy(data['alpha'])
input_arr = []
input = to_numpy(input)
for i in range(len(input)):
input_arr.append(self.reverse_image_transform(input[i]))
# input_arr=np.asarray(input_arr)
tile_images_list.append(input_arr)
if is_label_mask:
tile_images_list.append(target)
tile_images_list.append(output)
else:
target_arr = target
if len(target.shape) < len(int_shape(input)):
if get_backend() == 'tensorflow':
target_arr = np.expand_dims(target, -1)
else:
target_arr = np.expand_dims(target, 1)
if 'alpha' not in data:
target_arr[target_arr > 0] = 1
background = np.ones_like(target_arr) * self.background
tile_images_list.append(target_arr * input_arr +
(1 - target_arr) * background)
tile_images_list.append(output_arr * input_arr +
(1 - output_arr) * background)
# if self.tile_image_include_mask:
# tile_images_list.append(input*127.5+127.5)
fig = tile_rgb_images(*tile_images_list,
save_path=os.path.join(
self.save_path, self.tile_image_name_prefix),
imshow=True)
if ctx.enable_tensorboard and ctx.summary_writer is not None:
ctx.summary_writer.add_figure(
training_context['training_name'] + '/plot/segtile_image',
fig,
global_step=training_context['steps'],
close=True,
walltime=time.time())
plt.close()