Update README (#256)

README.md

@@ -1,6 +1,6 @@
 # ADI MAX78000/MAX78002 Model Training and Synthesis
 
-September 12, 2023
+September 20, 2023
 
 ADI’s MAX78000/MAX78002 project is comprised of five repositories:
 
@@ -2288,6 +2288,9 @@ The following table describes the most important command line arguments for `ai8
 
 The [quick-start guide](https://github.com/MaximIntegratedAI/MaximAI_Documentation/blob/master/Guides/YAML%20Quickstart.md) provides a short overview of the purpose and structure of the YAML network description file.
 
+If `yamllint` is installed and available in the shell path, it is automatically run against the configuration file and all warnings and errors are reported.
+*Note: The name of the linter can be changed using the `--yamllint` command line argument.*
+
 The following is a detailed guide into all supported configuration options.
 
 An example network description for the ai85net5 architecture and MNIST is shown below:
@@ -2357,7 +2360,7 @@ To generate an RTL simulation for the same network and sample data in the direct
 
 Network descriptions are written in YAML (see <https://en.wikipedia.org/wiki/YAML>). There are two sections in each file — global statements and a sequence of layer descriptions.
 
-*Note: The network loader automatically checks the configuration file for syntax errors and prints warnings for non-fatal errors. To perform the same checks manually, run:* `yamllint configfile.yaml`
+*Note: The network loader automatically checks the configuration file for syntax errors and prints warnings for non-fatal errors if `yamllint` is installed in the shell search path. To perform the same checks manually, run:* `yamllint configfile.yaml` (to use a different linter, specify `--yamllint mylinter`).
 
 #### Purpose of the YAML Network Description
 
@@ -2625,12 +2628,24 @@ Example:
 
 `in_dim` specifies the dimensions of the input data. This is usually automatically computed based on the output of the previous layer or the layer(s) referenced by `in_sequences`. This key allows overriding of the automatically calculated dimensions. `in_dim` must be used when changing from 1D to 2D data or vice versa. 1D dimensions can be specified as a tuple `[L, 1]` or as an integer `L`.
 
-See also: `in_channels`.
+See also: `in_channels`, `in_crop`.
 
 Examples:
         `in_dim: [64, 64]`
         `in_dim: 32`
 
+##### `in_crop` (Optional)
+
+`in_crop` specifies a number of rows (2D) or data bytes (1D) to skip (crop) when using the previous layer's output as input. By also adjusting `in_offset`, this provides the means to crop the top/bottom of an image or the beginning/end of 1D data. The dimensions and offsets are validated to match (minus the crop amount).
+
+See also: `in_dim`, `in_offset`.
+
+Example (1D cropping):
+        `# Output data had L=512`
+        `in_offset: 0x000c  # Skip 3 (x4 processors) at beginning`
+        `in_dim: 506  # Target length = 506`
+        `in_crop: [3, 3]  # Crop 3 at the beginning, 3 at the end`
+
 ##### `in_sequences` (Optional)
 
 By default, a layer’s input is the output of the previous layer. `in_sequences` can be used to point to the output of one or more arbitrary previous layers, for example when processing the same data using two different kernel sizes, or when combining the outputs of several prior layers. `in_sequences` can be specified as a single item (for a single input) or as a list (for multiple inputs). Both layer sequence numbers as well as layer names can be used. As a special case, `-1` or `input` refer to the input data. The `in_offset` and `out_offset` must be set to match the specified sequence.

models/ai85net-bayer2rgbnet.py

@@ -1,7 +1,7 @@
 ###################################################################################################
 #
-# Copyright © 2023 Analog Devices, Inc. All Rights Reserved.
-# This software is proprietary and confidential to Analog Devices, Inc. and its licensors.
+# Copyright (C) 2023 Analog Devices, Inc. All Rights Reserved.
+# This software is proprietary to Analog Devices, Inc. and its licensors.
 #
 ###################################################################################################
 """