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convert_full_model.py
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convert_full_model.py
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import tensorflow
from models import dense_embedding
from tensorflow.keras.layers import Input, Concatenate
from tensorflow.keras.models import Model
import numpy as np
import hls4ml
import pandas as pd
from qkeras.utils import _add_supported_quantized_objects
from models import dense_embedding, dense_embedding_quantized
from utils import preProcessing
import h5py
import scipy
co = {}
_add_supported_quantized_objects(co)
def print_dict(d, indent=0):
align = 20
for key, value in d.items():
print(' ' * indent + str(key), end='')
if isinstance(value, dict):
print()
print_dict(value, indent+1)
else:
print(':' + ' ' * (20 - len(key) - 2 * indent) + str(value))
# load full model:
model_name = 'trained_DeepMET'
# model_name = 'trained_quantized_DeepMET'
# model_name = 'trained_quantized_DeepMET_normfac1000'
model = tensorflow.keras.models.load_model(f'models/baseline_DeepMET{"_quantized" if "quantized" in model_name else ""}/{model_name}.h5', compile=False, custom_objects=co)
reuse_factor = 1
precision = 'ap_fixed<32,16>'
io_type = 'io_parallel'
strategy = 'Latency'
output_dir = 'hls_output_{}_{}_{}_rf{}_{}'.format(model_name ,io_type, strategy, reuse_factor, precision)
batch_size = 1
synth = False
trace = True
normFac = 1
# check everthing works
model.summary()
model.save('{}/model.h5'.format(output_dir))
config = hls4ml.utils.config_from_keras_model(model,
granularity='name',
default_reuse_factor=reuse_factor,
default_precision=precision)
config['Model']['Strategy'] = strategy
for name in config['LayerName'].keys():
config['LayerName'][name]['Trace'] = trace
config['LayerName']['input_cat0']['Precision']['result'] = 'ap_uint<4>'
config['LayerName']['input_cat1']['Precision']['result'] = 'ap_uint<4>'
# config['LayerName']['input_cont']['Precision']['result'] = 'ap_fixed<20,10>'
#if 'q_dense' in config['LayerName']:
# config['LayerName']['q_dense']['Precision']['accum'] = 'ap_fixed<32,16>'
# config['LayerName']['q_dense']['Precision']['weight'] = 'ap_fixed<32,16>'
# config['LayerName']['q_dense']['Precision']['bias'] = 'ap_fixed<32,16>'
# config['LayerName']['q_dense_1']['Precision']['accum'] = 'ap_fixed<32,16>'
# config['LayerName']['q_dense_1']['Precision']['weight'] = 'ap_fixed<32,16>'
# config['LayerName']['q_dense_1']['Precision']['bias'] = 'ap_fixed<32,16>'
config['LayerName']['multiply']['n_elem'] = 100
config['LayerName']['output']['n_filt'] = 2
# skip optimize_pointwise_conv
# config['SkipOptimizers'] = ['optimize_pointwise_conv']
# for layer in config['LayerName'].keys():
# config['LayerName'][layer]['Trace'] = True
print("-----------------------------------")
print_dict(config)
print("-----------------------------------")
hls_model = hls4ml.converters.convert_from_keras_model(model,
hls_config=config,
io_type=io_type,
output_dir=output_dir,
part='xcvu13p-flga2577-2-e',
clock_period=5,
project_name='L1METML_v1',
)
hls_model.compile()
hls4ml.utils.plot_model(hls_model, show_shapes=True, show_precision=True, to_file='{}/model_hls4ml.png'.format(output_dir))
if synth:
hls_model.build(synth=synth)
hls4ml.report.read_vivado_report(output_dir)
f = h5py.File('data/test_data.h5')
# 1000 test events is good enough
X = f['X'][:1000]
y = -f['Y'][:1000]
# preprocessing
X_pre = list(preProcessing(X, normFac=normFac))
X_pre = [np.ascontiguousarray(x) for x in X_pre]
y_pred = model.predict(X_pre)
y_hls = hls_model.predict(X_pre)
met = np.hypot(y[:, 0], y[:, 1])
met_pred = np.hypot(y_pred[:, 0], y_pred[:, 1]) * normFac
met_hls = np.hypot(y_hls[:, 0], y_hls[:, 1]) * normFac
met_pup_x = np.sum(X[:, :, 1], axis=-1)
met_pup_y = np.sum(X[:, :, 2], axis=-1)
met_pup = np.hypot(met_pup_x, met_pup_y)
import seaborn
import pandas as pd
import matplotlib.pyplot as plt
df = pd.DataFrame.from_dict({'Gen MET': met, 'PUPPI MET': met_pup, 'QKeras MET': met_pred, 'hls4ml MET': met_hls})
plt.figure()
seaborn.pairplot(df, corner=True)
plt.savefig(f'{output_dir}/profiling_MET.png', dpi=300)
df = pd.DataFrame.from_dict({'Gen MET x': y[:, 0], 'PUPPI MET x': met_pup_x, 'QKeras MET x': y_pred[:, 0], 'hls4ml MET x': y_hls[:, 0]})
plt.figure()
seaborn.pairplot(df, corner=True)
plt.savefig(f'{output_dir}/profiling_MET_x.png', dpi=300)
df = pd.DataFrame.from_dict({'Gen MET y': y[:, 1], 'PUPPI MET y': met_pup_y, 'QKeras MET y': y_pred[:, 1], 'hls4ml MET y': y_hls[:, 1]})
plt.figure()
seaborn.pairplot(df, corner=True)
plt.savefig(f'{output_dir}/profiling_MET_y.png', dpi=300)
response_pup = met_pup / met
response_pred = met_pred / met
response_hls = met_hls / met
bins = np.linspace(0, 2, 25)
plt.figure(figsize=(12, 5))
plt.subplot(1, 3, 1)
plt.hist(response_pup, bins=bins, label=f'PUPPI, median={np.median(response_pup):0.2f}, IQR={scipy.stats.iqr(response_pup):0.2f}')
plt.legend()
plt.xlabel("MET response $\hat{y}/y$")
plt.ylabel("Events")
plt.subplot(1, 3, 2)
plt.hist(response_pred, bins=bins, label=f'QKeras, median={np.median(response_pred):0.2f}, IQR={scipy.stats.iqr(response_pred):0.2f}')
plt.legend()
plt.xlabel("MET response $\hat{y}/y$")
plt.ylabel("Events")
plt.subplot(1, 3, 3)
plt.hist(response_hls, bins=bins, label=f'hls4ml, median={np.median(response_hls):0.2f}, IQR={scipy.stats.iqr(response_hls):0.2f}')
plt.legend()
plt.xlabel("MET response $\hat{y}/y$")
plt.ylabel("Events")
plt.tight_layout()
plt.savefig(f"{output_dir}/response_MET.png", dpi=300)
y_hls, hls4ml_trace = hls_model.trace(X_pre)
keras_trace = hls4ml.model.profiling.get_ymodel_keras(model, X_pre)
for layer in hls4ml_trace.keys():
plt.figure()
if layer not in keras_trace: continue
plt.scatter(hls4ml_trace[layer].flatten(), keras_trace[layer].flatten(), s=0.2)
min_x = min(np.amin(hls4ml_trace[layer]), np.amin(keras_trace[layer]))
max_x = max(np.amax(hls4ml_trace[layer]), np.amax(keras_trace[layer]))
plt.plot([min_x, max_x], [min_x, max_x], c='gray')
plt.xlabel(f'hls4ml {layer}')
plt.ylabel(f'QKeras {layer}')
plt.savefig(f'{output_dir}/profiling_{layer}.png', dpi=300)