# Copyright 2019-2020 Stanislav Pidhorskyi
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
import math
import numpy as np
[docs]def make_grid(tensor, nrow=8, padding=2):
nmaps = tensor.shape[0]
xmaps = min(nrow, nmaps)
ymaps = int(math.ceil(float(nmaps) / xmaps))
height, width = int(tensor.shape[2] + padding), int(tensor.shape[3] + padding)
grid = np.zeros((3, height * ymaps + padding, width * xmaps + padding), dtype=np.uint8)
k = 0
for y in range(ymaps):
for x in range(xmaps):
if k >= nmaps:
break
grid[:, y * height + padding : y * height + padding + height - padding,
x * width + padding : x * width + padding + width - padding] = tensor[k]
k = k + 1
return np.transpose(grid, (1, 2, 0))
[docs]def display_grid(tensor, nrow=8, padding=2):
import IPython.display as display
import PIL.Image
display.display(PIL.Image.fromarray(make_grid(tensor, nrow, padding)))