Merge branch 'MaximIntegratedAI:develop' into regression_test

.github/linters/.python-lint

@@ -7,7 +7,7 @@ ignored-classes = ModelProto
 max-line-length = 99
 [DESIGN]
 max-locals=100
-max-statements=300
+max-statements=350
 min-public-methods=1
 max-branches=120
 max-module-lines=5000

.gitmodules

@@ -2,6 +2,3 @@
 	path = distiller
 	url = https://github.com/MaximIntegratedAI/distiller.git
 	branch = pytorch-1.8
-[submodule "datasets/face_id/facenet_pytorch"]
-	path = datasets/face_id/facenet_pytorch
-	url = https://github.com/MaximIntegratedAI/facenet-pytorch.git

.pylintrc

@@ -7,7 +7,7 @@ ignored-classes = ModelProto
 max-line-length = 99
 [DESIGN]
 max-locals=100
-max-statements=300
+max-statements=350
 min-public-methods=1
 max-branches=120
 max-module-lines=5000

README.md

@@ -1,6 +1,6 @@
 # ADI MAX78000/MAX78002 Model Training and Synthesis
 
-November 13, 2023
+January 10, 2024
 
 ADI’s MAX78000/MAX78002 project is comprised of five repositories:
 
@@ -1534,6 +1534,11 @@ The following table describes the most important command line arguments for `tra
 | `--nas`                    | Enable network architecture search                           |                                 |
 | `--nas-policy`             | Define NAS policy in YAML file                               | `--nas-policy nas/nas_policy.yaml` |
 | `--regression` | Select regression instead of classification (changes Loss function, and log output) |  |
+| `--dr` | Set target embedding dimensionality for dimensionality reduction                |`--dr 64`                        |
+| `--scaf-lr` | Initial learning rate for sub-center ArcFace loss optimizer |  |
+| `--scaf-scale` |Scale hyperparameter for sub-center ArcFace loss |  |
+| `--scaf-margin` |Margin hyperparameter for sub-center ArcFace loss |  |
+| `--backbone-checkpoint` |Path to checkpoint from which to load backbone weights |  |
 | *Display and statistics*   |                                                              |                                 |
 | `--enable-tensorboard`     | Enable logging to TensorBoard (default: disabled)            |                                 |
 | `--confusion`              | Display the confusion matrix                                 |                                 |
@@ -1552,6 +1557,7 @@ The following table describes the most important command line arguments for `tra
 | `--summary onnx_simplified` | Export trained model to simplified [ONNX](https://onnx.ai/) file (default name: model.onnx) |                     |
 | `--summary-filename`       | Change the file name for the exported model                  | `--summary-filename mnist.onnx` |
 | `--save-sample`            | Save data[index] from the test set to a NumPy pickle for use as sample data | `--save-sample 10` |
+| `--slice-sample`  | For models that require RGB input, when the sample from the dataset has additional channels, slice the sample into 3 channels                                      |                                 |
 
 #### ONNX Model Export
 
@@ -3340,6 +3346,8 @@ Additional information about the evaluation kits, and the software development k
 
 [AHB Addresses for MAX78000 and MAX78002](docs/AHBAddresses.md)
 
+[Facial Recognition System](https://github.com/MaximIntegratedAI/ai8x-training/blob/develop/docs/FacialRecognitionSystem.md)
+
 
 ---
 

ai8x_blocks.py

@@ -1,6 +1,6 @@
 ###################################################################################################
 #
-# Copyright (C) 2020-2022 Maxim Integrated Products, Inc. All Rights Reserved.
+# Copyright (C) 2020-2023 Maxim Integrated Products, Inc. All Rights Reserved.
 #
 # Maxim Integrated Products, Inc. Default Copyright Notice:
 # https://www.maximintegrated.com/en/aboutus/legal/copyrights.html
@@ -116,6 +116,80 @@ def forward(self, x):  # pylint: disable=arguments-differ
         return self.resid(y, x)
 
 
+class ConvResidualBottleneck(nn.Module):
+    """
+    AI8X module based on Residual Bottleneck Layer.
+    Depthwise convolution is replaced with standard convolution.
+    This module uses ReLU activation not ReLU6 as the original study suggests [1],
+    because of MAX7800X capabilities.
+
+    Args:
+        in_channels: number of input channels
+        out_channels: number of output channels
+        expansion_factor: expansion_factor
+        stride: stirde size (default=1)
+        bias: determines if bias used at non-depthwise layers.
+        depthwise_bias: determines if bias used at depthwise layers.
+
+    References:
+        [1] https://arxiv.org/pdf/1801.04381.pdf (MobileNetV2)
+    """
+    def __init__(self, in_channels, out_channels, expansion_factor, stride=1, bias=False,
+                 depthwise_bias=False, **kwargs):
+        super().__init__()
+        self.stride = stride
+        hidden_channels = int(round(in_channels * expansion_factor))
+        if hidden_channels == in_channels:
+            self.conv1 = ai8x.Empty()
+        else:
+            self.conv1 = ai8x.FusedConv2dBNReLU(in_channels, hidden_channels, 1, padding=0,
+                                                bias=bias, **kwargs)
+        if stride == 1:
+            if depthwise_bias:
+                self.conv2 = ai8x.FusedConv2dBN(hidden_channels, out_channels, 3,
+                                                padding=1, stride=stride,
+                                                bias=depthwise_bias, **kwargs)
+
+            else:
+                self.conv2 = ai8x.Conv2d(hidden_channels, out_channels, 3,
+                                         padding=1, stride=stride,
+                                         bias=depthwise_bias, **kwargs)
+
+        else:
+            if depthwise_bias:
+                self.conv2 = ai8x.FusedMaxPoolConv2dBN(hidden_channels,
+                                                       out_channels, 3,
+                                                       padding=1, pool_size=stride,
+                                                       pool_stride=stride,
+                                                       bias=depthwise_bias, **kwargs)
+
+            else:
+                self.conv2 = ai8x.FusedMaxPoolConv2d(hidden_channels,
+                                                     out_channels, 3,
+                                                     padding=1, pool_size=stride,
+                                                     pool_stride=stride,
+                                                     bias=depthwise_bias, **kwargs)
+
+        if (stride == 1) and (in_channels == out_channels):
+            self.resid = ai8x.Add()
+        else:
+            self.resid = self.NoResidual()
+
+    class NoResidual(nn.Module):
+        """
+        Does nothing.
+        """
+        def forward(self, *x):  # pylint: disable=arguments-differ
+            """Forward prop"""
+            return x[0]
+
+    def forward(self, x):  # pylint: disable=arguments-differ
+        """Forward prop"""
+        y = self.conv1(x)
+        y = self.conv2(y)
+        return self.resid(y, x)
+
+
 class MBConvBlock(nn.Module):
     """Mobile Inverted Residual Bottleneck Block.