From 541c86f16f7c5eb26a528db53885f96cd89782c5 Mon Sep 17 00:00:00 2001
From: Nathan Zhao <nz11@tyler.cs.illinois.edu>
Date: Sun, 31 Jan 2021 18:57:00 -0600
Subject: [PATCH] update expected accuracy, fix frontend causing mobilenet
problem
---
hpvm/projects/keras/README.md | 19 ++-----------------
hpvm/projects/keras/frontend/weight_utils.py | 10 +++++++---
2 files changed, 9 insertions(+), 20 deletions(-)
diff --git a/hpvm/projects/keras/README.md b/hpvm/projects/keras/README.md
index f96a0afa01..638657a064 100644
--- a/hpvm/projects/keras/README.md
+++ b/hpvm/projects/keras/README.md
@@ -2,21 +2,6 @@
## Installing Dependencies
-### Updating pip
-The pip version required in this subproject must be >= `19.3`.
-
-To upgrade pip:
-
-```
-pip install --upgrade pip
-```
-
-To check installed pip version:
-
-```
-pip -V
-```
-
### Importing Conda Environment:
```
@@ -55,11 +40,11 @@ List of benchmarks and the expected accuracies:
| ResNet50-ImageNet | 74.50 |
| VGG16-CIFAR10 | 89.42 |
| VGG16-CIFAR100 | 66.20 |
-| VGG16-ImageNet | 72.50 |
+| VGG16-ImageNet | 72.50 | todo: fix broken
Activate conda environment (above) before running benchmarks
-### Synopsis
+### Synopsys
```
python src/${BENCH_NAME}.py [hpvm_reload|keras_reload] [frontend]
diff --git a/hpvm/projects/keras/frontend/weight_utils.py b/hpvm/projects/keras/frontend/weight_utils.py
index 18ceb433c6..83a13c8135 100644
--- a/hpvm/projects/keras/frontend/weight_utils.py
+++ b/hpvm/projects/keras/frontend/weight_utils.py
@@ -151,8 +151,9 @@ def reloadHPVMWeights(model, reload_dir, output_model):
#-- print ("** w_path = ", w_path)
w_arr = np.fromfile(w_path, dtype='float32')
- b_path = reload_dir + layer_name + "_b.bin"
- b_arr = np.fromfile(b_path, dtype='float32')
+ if layer.use_bias:
+ b_path = reload_dir + layer_name + "_b.bin"
+ b_arr = np.fromfile(b_path, dtype='float32')
w_shape = layer.get_weights()[0].shape
if "conv" in layer_name:
@@ -163,7 +164,10 @@ def reloadHPVMWeights(model, reload_dir, output_model):
if "dense" in layer_name:
w_arr = np.reshape(w_arr, w_shape)
- weights = [w_arr, b_arr]
+ if layer.use_bias:
+ weights = [w_arr, b_arr]
+ else:
+ weights = [w_arr]
layer.set_weights(weights)
--
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