DispNet-TensorFlow
TensorFlow implementation of A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation by Zhijian Jiang.
Dataset
Tutorials
TensorFlow
Network
Convolutional Network
| Name | Kernel | Strides | Channels I/O | Input Resolution | Output Resolution | Input |
|---|---|---|---|---|---|---|
| conv1 | 7 * 7 | 1 | 6/64 | 1536 * 768 | 768 * 384 | Images |
| max_pool1 | 2 * 2 | 2 | 64/64 | 1536 * 768 | 768 * 384 | conv1 |
| conv2 | 5 * 5 | 1 | 64/128 | 768 * 384 | 384 * 192 | max_pool1 |
| max_pool2 | 2 * 2 | 2 | 128/128 | 768 * 384 | 384 * 192 | conv2 |
| conv3a | 5 * 5 | 1 | 128/256 | 384 * 192 | 192 * 96 | max_pool2 |
| max_pool3 | 2 * 2 | 2 | 256/256 | 384 * 192 | 192 * 96 | conv3a |
| conv3b | 3 * 3 | 1 | 256/256 | 192 * 96 | 192 * 96 | max_pool3 |
| conv4a | 5 * 5 | 1 | 256/512 | 192 * 96 | 96 * 48 | conv3b |
| max_pool4 | 2 * 2 | 2 | 512/512 | 96 * 48 | 96 * 48 | conv4a |
| conv4b | 3 * 3 | 1 | 512/512 | 96 * 48 | 96 * 48 | max_pool4 |
| conv5a | 5 * 5 | 1 | 512/512 | 96 * 48 | 48 * 24 | conv4b |
| max_pool5 | 2 * 2 | 2 | 512/512 | 48 * 24 | 48 * 24 | conv5a |
| conv5b | 3 * 3 | 1 | 512/512 | 48 * 24 | 48 * 24 | max_pool5 |
| conv6a | 5 * 5 | 1 | 512/512 | 48 * 24 | 24 * 12 | conv5b |
| max_pool6 | 2 * 2 | 2 | 1024/1024 | 24 * 12 | 24 * 12 | conv6a |
| conv6b | 3 * 3 | 1 | 1024/1024 | 24 * 12 | 24 * 12 | max_pool6 |
| pr6 + loss6 | 3 * 3 | 1 | 1024/1 | 24 * 12 | 24 * 12 | conv6b |
Upconvolutional Network
| Name | Kernel | Strides | Channels I/O | Input Resolution | Output Resolution | Input |
|---|---|---|---|---|---|---|
| upconv5 | 4 * 4 | 2 | 1024/512 | 24 * 12 | 48 * 24 | conv6b |
| iconv5 | 3 * 3 | 1 | 1024/512 | 48 * 24 | 48 * 24 | upconv5 + conv5b |
| pr5+loss5 | 3 * 3 | 1 | 512/1 | 48 * 24 | 48 * 24 | iconv5 |
| upconv4 | 4 * 4 | 2 | 512/256 | 48 * 24 | 96 * 48 | iconv5 |
| iconv4 | 3 * 3 | 1 | 768/256 | 96 * 48 | 96 * 48 | upconv4 + conv4b |
| pr4+loss4 | 3 * 3 | 1 | 512/1 | 96 * 48 | 96 * 48 | iconv4 |
| upconv3 | 4 * 4 | 2 | 256/128 | 96 * 48 | 192 * 96 | iconv4 |
| iconv3 | 3 * 3 | 1 | 384/128 | 192 * 96 | 192 * 96 | upconv3 + conv3b |
| pr3+loss3 | 3 * 3 | 1 | 128/1 | 192 * 96 | 192 * 96 | iconv3 |
| upconv2 | 4 * 4 | 2 | 128/64 | 192 * 96 | 384 * 192 | iconv3 |
| iconv2 | 3 * 3 | 1 | 192/64 | 384 * 192 | 384 * 192 | upconv2 + conv2 |
| pr2+loss2 | 3 * 3 | 1 | 64/1 | 384 * 192 | 384 * 192 | iconv2 |
| upconv1 | 4 * 4 | 2 | 64/32 | 384 * 192 | 768 * 384 | iconv2 |
| iconv1 | 3 * 3 | 1 | 96/32 | 768 * 384 | 768 * 384 | upconv1 + conv1 |
| pr1+loss1 | 3 * 3 | 1 | 32/1 | 768 * 384 | 768 * 384 | iconv1 |
Issues
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How to input png images:
contents = '' with open('path/to/image.jpeg') as f: contents = f.read() tf.image.decode_jpeg(contents)reader = tf.WholeFileReader(http://stackoverflow.com/questions/34340489/tensorflow-read-images-with-labels) key, value = reader.read(filename_queue) example = tf.image.decode_png(value)file_contents = tf.read_file(input_queue[0]) example = tf.image.decode_png(file_contents, channels=3)