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DOC: change the input patch size in the example

Merged DOC: change the input patch size in the example
docfix-2 into develop
Merged Cresson Remi requested to merge docfix-2 into develop
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doc/api_tutorial.md
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@@ -258,10 +258,7 @@ and the estimated values.
out_tconv1 = _tconv(out_conv4, 64, "tconv1") + out_conv3
out_tconv2 = _tconv(out_tconv1, 32, "tconv2") + out_conv2
out_tconv3 = _tconv(out_tconv2, 16, "tconv3") + out_conv1
out_tconv4 = _tconv(out_tconv3, N_CLASSES, "classifier", None)
softmax_op = tf.keras.layers.Softmax(name=OUTPUT_SOFTMAX_NAME)
predictions = softmax_op(out_tconv4)
predictions = _tconv(out_tconv3, N_CLASSES, OUTPUT_SOFTMAX_NAME, "softmax")
return {TARGET_NAME: predictions}
@@ -375,36 +372,36 @@ polluted by the convolutional padding.
For a 2D convolution of stride \(s\) and kernel size \(k\), we can deduce the
valid output size \(y\) from input size \(x\) using this expression:
$$
y = \left[\frac{x - k + 1}{s}\right]
y = \left[\frac{x - k }{s}\right] + 1
$$
For a 2D transposed convolution of stride \(s\) and kernel size \(k\), we can
deduce the valid output size \(y\) from input size \(x\) using this expression:
$$
y = (x * s) - k + 1
y = x * s - k + 2
$$
Let's consider a chunk of input image of size 128, and check the valid output
Let's consider a chunk of input image of size 64, and check the valid output
size of our model:
| Conv. name | Conv. type | Kernel | Stride | Out. size | Valid out. size |
|----------------|-------------------|--------|--------|-----------|-----------------|
| *input* | / | / | / | 128 | 128 |
| *conv1* | Conv2D | 3 | 2 | 64 | 63 |
| *conv2* | Conv2D | 3 | 2 | 32 | 30 |
| *conv3* | Conv2D | 3 | 2 | 16 | 14 |
| *conv4* | Conv2D | 3 | 2 | 8 | 6 |
| *tconv1* | Transposed Conv2D | 3 | 2 | 16 | 10 |
| *tconv2* | Transposed Conv2D | 3 | 2 | 32 | 18 |
| *tconv3* | Transposed Conv2D | 3 | 2 | 64 | 34 |
| *classifier* | Transposed Conv2D | 3 | 2 | 128 | 66 |
| *input* | / | / | / | 64 | 64 |
| *conv1* | Conv2D | 3 | 2 | 32 | 31 |
| *conv2* | Conv2D | 3 | 2 | 16 | 15 |
| *conv3* | Conv2D | 3 | 2 | 8 | 7 |
| *conv4* | Conv2D | 3 | 2 | 4 | 3 |
| *tconv1* | Transposed Conv2D | 3 | 2 | 8 | 5 |
| *tconv2* | Transposed Conv2D | 3 | 2 | 16 | 9 |
| *tconv3* | Transposed Conv2D | 3 | 2 | 32 | 17 |
| *classifier* | Transposed Conv2D | 3 | 2 | 64 | 33 |
This shows that our model can be applied in a fully convolutional fashion
without generating blocking artifacts, using the central part of the output of
size 66. This is equivalent to remove \((128 - 66)/2 = 31\) pixels from
size 33. This is equivalent to remove \((64 - 33)/2 = 15\) pixels from
the borders of the output. We keep the upper nearest power of 2 to keep the
convolutions consistent between two adjacent image chunks, hence we can remove 32
pixels from the borders. We can hence use the output cropped with **32** pixels,
named ***predictions_crop32*** in the model outputs.
convolutions consistent between two adjacent image chunks, hence we can remove 16
pixels from the borders. We can hence use the output cropped with **16** pixels,
named ***predictions_crop16*** in the model outputs.
By default, cropped outputs in `otbtf.ModelBase` are generated for the following
values: `[16, 32, 64, 96, 128]` but that can be changed setting `inference_cropping`
in the model `__init__()` (see the reference API documentation for details).
@@ -434,10 +431,10 @@ In the following subsections, we run `TensorflowModelServe` over the input
image, with the following parameters:
- the input name is ***input_xs***
- the output name is ***predictions_crop32*** (cropping margin of 32 pixels)
- we choose a receptive field of ***128*** and an expression field of
***64*** so that they match the cropping margin of 32 pixels (since we remove
32 pixels from each side in x and y dimensions, we remove a total of 64 pixels
- the output name is ***predictions_crop16*** (cropping margin of 16 pixels)
- we choose a receptive field of ***64*** and an expression field of
***32*** so that they match the cropping margin of 16 pixels (since we remove
16 pixels from each side in x and y dimensions, we remove a total of 32 pixels
from each borders in x/y dimensions).
### Command Line Interface
@@ -447,14 +444,14 @@ Open a terminal and run the following command:
```commandline
otbcli_TensorflowModelServe \
-source1.il $DATADIR/fake_spot6.jp2 \
-source1.rfieldx 128 \
-source1.rfieldy 128 \
-source1.rfieldx 64 \
-source1.rfieldy 64 \
-source1.placeholder "input_xs" \
-model.dir /tmp/my_1st_savedmodel \
-model.fullyconv on \
-output.names "predictions_crop32" \
-output.efieldx 64 \
-output.efieldy 64 \
-output.names "predictions_crop16" \
-output.efieldx 32 \
-output.efieldy 32 \
-out softmax.tif
```
@@ -467,14 +464,14 @@ python wrapper:
import otbApplication
app = otbApplication.Registry.CreateApplication("TensorflowModelServe")
app.SetParameterStringList("source1.il", ["fake_spot6.jp2"])
app.SetParameterInt("source1.rfieldx", 128)
app.SetParameterInt("source1.rfieldy", 128)
app.SetParameterInt("source1.rfieldx", 64)
app.SetParameterInt("source1.rfieldy", 64)
app.SetParameterString("source1.placeholder", "input_xs")
app.SetParameterString("model.dir", "/tmp/my_1st_savedmodel")
app.EnableParameter("fullyconv")
app.SetParameterStringList("output.names", ["predictions_crop32"])
app.SetParameterInt("output.efieldx", 64)
app.SetParameterInt("output.efieldy", 64)
app.SetParameterStringList("output.names", ["predictions_crop16"])
app.SetParameterInt("output.efieldx", 32)
app.SetParameterInt("output.efieldy", 32)
app.SetParameterString("out", "softmax.tif")
app.ExecuteAndWriteOutput()
```
@@ -487,14 +484,14 @@ Using PyOTB is nicer:
import pyotb
pyotb.TensorflowModelServe({
"source1.il": "fake_spot6.jp2",
"source1.rfieldx": 128,
"source1.rfieldy": 128,
"source1.rfieldx": 64,
"source1.rfieldy": 64,
"source1.placeholder": "input_xs",
"model.dir": "/tmp/my_1st_savedmodel",
"fullyconv": True,
"output.names": ["predictions_crop32"],
"output.efieldx": 64,
"output.efieldy": 64,
"output.names": ["predictions_crop16"],
"output.efieldx": 32,
"output.efieldy": 32,
"out": "softmax.tif",
})
```