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###########################################################################
# Computer vision - Binary neural networks demo software by HyperbeeAI. #
# Copyrights © 2023 Hyperbee.AI Inc. All rights reserved. hello@hyperbee.ai #
###########################################################################
import torch, matplotlib, os, sys, argparse
import numpy as np
import matplotlib.pyplot as plt
matplotlib.use('Agg')
def generate_histogram_for_quantized_layer(layer_key, layer_weight, layer_bias, checkpoint_type, histograms_folderpath):
histogram_folder_exists = os.path.isdir(histograms_folderpath)
if not histogram_folder_exists:
os.makedirs(histograms_folderpath)
matplotlib.rcParams.update({'font.size': 16})
fig, axs = plt.subplots(1, 2, tight_layout=True, figsize=(20, 10))
ww = layer_weight.flatten();
bb = layer_bias.flatten();
ww_max = np.amax(ww)
ww_min = np.amin(ww)
ww_unq = len(np.unique(ww))
bb_max = np.amax(bb)
bb_min = np.amin(bb)
bb_unq = len(np.unique(bb))
if checkpoint_type=='hardware':
ww_num_bins = ww_unq*3
bb_num_bins = bb_unq*3
ww_max_lim = ww_max+1;
bb_max_lim = bb_max+1/16384;
elif checkpoint_type=='training':
ww_num_bins = min(ww_unq*3,800)
bb_num_bins = min(bb_unq*3,800)
ww_max_lim = ww_max+1/128;
bb_max_lim = bb_max+1/128;
axs[0].grid(True)
axs[0].set_title('weight', fontdict={'fontsize': 22, 'fontweight': 'medium'})
axs[0].hist(ww, range=(ww_min, ww_max_lim), bins=ww_num_bins, align='left')
axs[1].grid(True)
axs[1].set_title('bias', fontdict={'fontsize': 22, 'fontweight': 'medium'})
axs[1].hist(bb, range=(bb_min, bb_max_lim), bins=bb_num_bins, align='left')
filename = os.path.join(histograms_folderpath,layer_key + '.jpg')
plt.savefig(filename)
plt.close()
def main():
parser = argparse.ArgumentParser(description='Print out model statistics file and optionally also save weight/bias histogram figures for each layer')
parser.add_argument('-c','--checkpoint-name', help='Name of folder under the checkpoints folder for which you want to generate a model statistics file', required=True)
parser.add_argument('-q','--checkpoint-type', help='checkpoint type can be either a hardware or training checkpoint.', required=True)
parser.add_argument('-g','--generate-histograms', help='Add this flag if you want to save jpg figures inside the checkpoint folder for histograms of bias and weight values of each layer in the network', action='store_true', default=False, required=False)
args = vars(parser.parse_args())
checkpoint_folder = os.path.join('checkpoints',args['checkpoint_name']);
if(os.path.isdir(checkpoint_folder)):
print('')
print('Found checkpoint folder')
else:
print('')
print('Could not find checkpoint folder. Please check that:')
print('1- you are running this script from the top level of the repository, and')
print('2- the checkpoint folder you gave the name for exists (needs to be created manually)')
sys.exit();
checkpoint_type = args['checkpoint_type']
if(checkpoint_type=='hardware'):
print('')
print('Searching for a hardware_checkpoint.pth.tar')
print('')
check_for_bit_errors = True;
elif(checkpoint_type=='training'):
print('')
print('Searching for a training_checkpoint.pth.tar')
print('')
check_for_bit_errors = False;
else:
print('')
print('Something is wrong, we dont know of a',checkpoint_type, 'checkpoint. Perhaps a misspelling?' )
print('')
sys.exit()
checkpoint_filename = checkpoint_type+'_checkpoint.pth.tar';
a = torch.load(os.path.join(checkpoint_folder,checkpoint_filename))
flag_generate_histograms = args['generate_histograms']
if(flag_generate_histograms):
print('[INFO]: Will generate histograms')
with open(os.path.join(checkpoint_folder,'statistics_'+checkpoint_type+'_checkpoint'), 'w') as f:
print('[INFO]: Generating statistics file')
print('Top:', file=f)
for key in a.keys():
print(' ', key, file=f)
if( 'arch' not in a.keys()):
print('[ERROR]: there is no key named arch in this checkpoint', file=f)
print('[ERROR]: there is no key named arch in this checkpoint')
#sys.exit()
if( 'state_dict' not in a.keys()):
print('[ERROR]: there is no key named state_dict in this checkpoint', file=f)
print('[ERROR]: there is no key named state_dict in this checkpoint')
#sys.exit()
if( 'extras' not in a.keys()):
print('[ERROR]: there is no key named extras in this checkpoint', file=f)
print('[ERROR]: there is no key named extras in this checkpoint')
#sys.exit()
print('-------------------------------------', file=f)
print('arch:', a['arch'], file=f)
print('-------------------------------------', file=f)
print('extras:', a['extras'], file=f)
print('-------------------------------------', file=f)
print('state_dict:', file=f)
layer_keys = []
layers = []
for key in a['state_dict'].keys():
fields = key.split('.')
if(fields[0] not in layer_keys):
layer_keys.append(fields[0])
layers.append({'key': fields[0],
'weight_bits':None,
'bias_bits':None,
'adjust_output_shift':None,
'output_shift':None,
'quantize_activation':None,
'shift_quantile':None,
'weight': None,
'bias':None })
idx = -1
else:
idx = layer_keys.index(fields[0])
if((fields[1]=='weight_bits') or \
(fields[1]=='output_shift') or \
(fields[1]=='bias_bits') or \
(fields[1]=='quantize_activation') \
or (fields[1]=='adjust_output_shift') \
or (fields[1]=='shift_quantile')):
layers[idx][fields[1]] = a['state_dict'][key].cpu().numpy();
elif(fields[1]=='op'):
layers[idx][fields[2]] = a['state_dict'][key].cpu().numpy();
else:
print('[ERROR]: Unknown field. Exiting', file=f)
print('[ERROR]: Unknown field. Exiting')
sys.exit()
for layer in layers:
print(' ', layer['key'], file=f)
print(' output_shift: ', layer['output_shift'], file=f)
print(' adjust_output_shift: ', layer['adjust_output_shift'], file=f)
print(' quantize_activation: ', layer['quantize_activation'], file=f)
print(' shift_quantile: ', layer['shift_quantile'], file=f)
print(' weight bits: ', layer['weight_bits'], file=f)
print(' bias_bits: ', layer['bias_bits'], file=f)
print(' bias', file=f)
print(' total # of elements, shape:', np.size(layer['bias']), ',', list(layer['bias'].shape), file=f)
print(' # of unique elements: ', len(np.unique(layer['bias'])), file=f)
print(' min, max, mean:', np.amin(layer['bias']), ', ', np.amax(layer['bias']), ', ', np.mean(layer['bias']), file=f)
if((len(np.unique(layer['bias'])) > 2**layer['bias_bits']) and (check_for_bit_errors)):
print('', file=f)
print('[WARNING]: # of unique elements in bias tensor is more than that allowed by bias_bits.', file=f)
print(' This might be OK, since Maxim deployment repository right shifts these.', file=f)
print('', file=f)
print('')
print('[WARNING]: # of unique elements in bias tensor is more than that allowed by bias_bits.')
print(' This might be OK, since Maxim deployment repository right shifts these.')
print(' Check stats file for details.')
print('')
print(' weight', file=f)
print(' total # of elements, shape:', np.size(layer['weight']), ',', list(layer['weight'].shape), file=f)
print(' # of unique elements: ', len(np.unique(layer['weight'])), file=f)
print(' min, max, mean:', np.amin(layer['weight']), ', ', np.amax(layer['weight']), ', ', np.mean(layer['weight']), file=f)
if((len(np.unique(layer['weight'])) > 2**layer['weight_bits']) and (check_for_bit_errors)):
print('', file=f)
print('[ERROR]: # of unique elements in weight tensor is more than that allowed by weight_bits.', file=f)
print(' This is definitely not OK, weights are used in HW as is.', file=f)
print(' Exiting.', file=f)
print('', file=f)
print('')
print('[ERROR]: # of unique elements in weight tensor is more than that allowed by weight_bits.')
print(' This is definitely not OK, weights are used in HW as is.')
print(' Exiting.')
print('')
sys.exit()
if(flag_generate_histograms):
generate_histogram_for_quantized_layer(layer['key'], layer['weight'], layer['bias'], checkpoint_type, os.path.join(checkpoint_folder, 'histograms_'+checkpoint_type+'_checkpoint'))
print('[INFO]: saved histograms for layer', layer['key'])
if __name__ == '__main__':
main()
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