Examples (mobilenetv1): update to r2 w/ 4b first layer

Signed-off-by: Alessandro Pappalardo <volcacius@users.noreply.github.com>
Xilinx · Aug 11, 2020 · 7703823 · 7703823
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diff --git a/README.md b/README.md
@@ -127,7 +127,7 @@ Below a list of relevant pretrained models available, currently for evaluation o
 
 | Name         | Scaling Type               | First layer weights | Weights | Activations | Avg pool | Top1  | Top5  | Pretrained model                                                                                | Retrained from                                                |
 |--------------|----------------------------|---------------------|---------|-------------|----------|-------|-------|-------------------------------------------------------------------------------------------------|---------------------------------------------------------------|
-| MobileNet V1 | Floating-point per channel | 8 bit               | 4 bit   | 4 bit       | 4 bit    | 71.14 | 90.10 | [Download](https://github.com/Xilinx/brevitas/releases/download/quant_mobilenet_v1_4b-r1/quant_mobilenet_v1_4b-0100a667.pth) | [link](https://github.com/osmr/imgclsmob/tree/master/pytorch) |
+| MobileNet V1 | Floating-point per channel | 4 bit               | 4 bit   | 4 bit       | 4 bit    | 70.36 | 89.61 | [Download](https://github.com/Xilinx/brevitas/releases/download/quant_mobilenet_v1_4b-r2/quant_mobilenet_v1_4b-e75a094f.pth) | [link](https://github.com/osmr/imgclsmob/tree/master/pytorch) |
 | ProxylessNAS Mobile14 w/ Hadamard classifier | Floating-point per channel | 8 bit               | 4 bit  | 4 bit       | 4 bit    | 73.52 | 91.46 | [Download](https://github.com/Xilinx/brevitas/releases/download/quant_proxylessnas_mobile14_hadamard_4b-r0/quant_proxylessnas_mobile14_hadamard_4b-4acbfa9f.pth) | [link](https://github.com/osmr/imgclsmob/tree/master/pytorch) |
 | ProxylessNAS Mobile14 | Floating-point per channel | 8 bit               | 4 bit  | 4 bit       | 4 bit    | 74.42 | 92.04 | [Download](https://github.com/Xilinx/brevitas/releases/download/quant_proxylessnas_mobile14_4b-r0/quant_proxylessnas_mobile14_4b-e10882e1.pth) | [link](https://github.com/osmr/imgclsmob/tree/master/pytorch) |
 | ProxylessNAS Mobile14 | Floating-point per channel | 8 bit               | 4 bit, 5 bit  | 4 bit, 5 bit       | 4 bit    | 75.01 | 92.33 | [Download](https://github.com/Xilinx/brevitas/releases/download/quant_proxylessnas_mobile14_4b5b-r0/quant_proxylessnas_mobile14_4b5b-2bdf7f8d.pth) | [link](https://github.com/osmr/imgclsmob/tree/master/pytorch) |

diff --git a/brevitas_examples/imagenet_classification/README.md b/brevitas_examples/imagenet_classification/README.md
@@ -7,7 +7,7 @@ Below in the table is a list of example pretrained models made available for ref
 
 | Name         | Cfg                   | Scaling Type               | First layer weights | Weights | Activations | Avg pool | Top1  | Top5  | Pretrained model                                                                                | Retrained from                                                |
 |--------------|-----------------------|----------------------------|---------------------|---------|-------------|----------|-------|-------|-------------------------------------------------------------------------------------------------|---------------------------------------------------------------|
-| MobileNet V1 | quant_mobilenet_v1_4b | Floating-point per channel | 8 bit               | 4 bit   | 4 bit       | 4 bit    | 71.14 | 90.10 | [Download](https://github.com/Xilinx/brevitas/releases/download/quant_mobilenet_v1_4b-r1/quant_mobilenet_v1_4b-0100a667.pth) | [link](https://github.com/osmr/imgclsmob/tree/master/pytorch) |
+| MobileNet V1 | quant_mobilenet_v1_4b | Floating-point per channel | 4 bit               | 4 bit   | 4 bit       | 4 bit    | 70.36 | 89.61 | [Download](https://github.com/Xilinx/brevitas/releases/download/quant_mobilenet_v1_4b-r2/quant_mobilenet_v1_4b-e75a094f.pth) | [link](https://github.com/osmr/imgclsmob/tree/master/pytorch) |
 | ProxylessNAS Mobile14 w/ Hadamard classifier | quant_proxylessnas_mobile14_hadamard_4b  | Floating-point per channel | 8 bit               | 4 bit  | 4 bit       | 4 bit    | 73.52 | 91.46 | [Download](https://github.com/Xilinx/brevitas/releases/download/quant_proxylessnas_mobile14_hadamard_4b-r0/quant_proxylessnas_mobile14_hadamard_4b-4acbfa9f.pth) | [link](https://github.com/osmr/imgclsmob/tree/master/pytorch) |
 | ProxylessNAS Mobile14 | quant_proxylessnas_mobile14_4b | Floating-point per channel | 8 bit               | 4 bit  | 4 bit       | 4 bit    | 74.42 | 92.04 | [Download](https://github.com/Xilinx/brevitas/releases/download/quant_proxylessnas_mobile14_4b-r0/quant_proxylessnas_mobile14_4b-e10882e1.pth) | [link](https://github.com/osmr/imgclsmob/tree/master/pytorch) |
 | ProxylessNAS Mobile14 | quant_proxylessnas_mobile14_4b5b | Floating-point per channel | 8 bit               | 4 bit, 5 bit  | 4 bit, 5 bit       | 4 bit    | 75.01 | 92.33 | [Download](https://github.com/Xilinx/brevitas/releases/download/quant_proxylessnas_mobile14_4b5b-r0/quant_proxylessnas_mobile14_4b5b-2bdf7f8d.pth) | [link](https://github.com/osmr/imgclsmob/tree/master/pytorch) |

diff --git a/brevitas_examples/imagenet_classification/cfg/quant_mobilenet_v1_4b.ini b/brevitas_examples/imagenet_classification/cfg/quant_mobilenet_v1_4b.ini
@@ -1,10 +1,11 @@
 [MODEL]
 ARCH: quant_mobilenet_v1
 WIDTH_SCALE: 1.0
-PRETRAINED_URL: https://github.com/Xilinx/brevitas/releases/download/quant_mobilenet_v1_4b-r1/quant_mobilenet_v1_4b-0100a667.pth
+PRETRAINED_URL: https://github.com/Xilinx/brevitas/releases/download/quant_mobilenet_v1_4b-r2/quant_mobilenet_v1_4b-e75a094f.pth
 
 [QUANT]
 BIT_WIDTH: 4
+FIRST_LAYER_BIT_WIDTH: 4
 
 [PREPROCESS]
 MEAN_0: 0.485

diff --git a/brevitas_examples/imagenet_classification/models/mobilenetv1.py b/brevitas_examples/imagenet_classification/models/mobilenetv1.py
@@ -36,8 +36,6 @@
 from .common import *
 
 
-FIRST_LAYER_BIT_WIDTH = 8
-
 
 class DwsConvBlock(nn.Module):
     def __init__(self,
@@ -109,6 +107,7 @@ class MobileNet(nn.Module):
     def __init__(self,
                  channels,
                  first_stage_stride,
+                 first_layer_bit_width,
                  bit_width,
                  in_channels=3,
                  num_classes=1000):
@@ -120,7 +119,7 @@ def __init__(self,
                                out_channels=init_block_channels,
                                kernel_size=3,
                                stride=2,
-                               weight_bit_width=FIRST_LAYER_BIT_WIDTH,
+                               weight_bit_width=first_layer_bit_width,
                                activation_scaling_per_channel=True,
                                act_bit_width=bit_width)
         self.features.add_module('init_block', init_block)
@@ -162,13 +161,15 @@ def quant_mobilenet_v1(cfg):
     first_stage_stride = False
     width_scale = float(cfg.get('MODEL', 'WIDTH_SCALE'))
     bit_width = cfg.getint('QUANT', 'BIT_WIDTH')
+    first_layer_bit_width = cfg.getint('QUANT', 'FIRST_LAYER_BIT_WIDTH')
 
     if width_scale != 1.0:
         channels = [[int(cij * width_scale) for cij in ci] for ci in channels]
 
     net = MobileNet(channels=channels,
                     first_stage_stride=first_stage_stride,
-                    bit_width=bit_width)
+                    bit_width=bit_width,
+                    first_layer_bit_width=first_layer_bit_width)
     return net