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Commit 541c86f1 authored by Nathan Zhao's avatar Nathan Zhao
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update expected accuracy, fix frontend causing mobilenet problem

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......@@ -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]
......
......@@ -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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