<jupyter_start><jupyter_text>Assess Performance: Get Perfomance data from files<jupyter_code>import matplotlib.pyplot as plt

precision_list = ['FP16', 'FP32', 'FP32-INT8']
inference_time = []
model_load_time = []
fps = []

for precision in precision_list:
    with open('results/'+precision+'/stats.txt', 'r') as f:
        inference_time.append(float(f.readline().split('\n')[0]))
        fps.append(float(f.readline().split('\n')[0]))
        model_load_time.append(float(f.readline().split('\n')[0]))

print("Inference Time :",inference_time)
print("fps :",fps)
print("Model Load Time :",model_load_time)
plt.bar(precision_list, inference_time)
plt.xlabel('Model Precision Value')
plt.ylabel('Inference Time in seconds')
plt.show()
plt.bar(precision_list, fps)
plt.xlabel('Model Precision Value')
plt.ylabel('Frames per Second')
plt.show()
plt.bar(precision_list, model_load_time)
plt.xlabel('Model Precision Value')
plt.ylabel('Model Load Time')
plt.show()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.16750756811301715, 'fraction_numerical': 0.010090817356205853, 'mean_word_length': 6.458646616541353, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '4276', 'license': 'MIT', 'n_tokens_mistral': 376, 'n_tokens_neox': 357, 'n_words': 66, 'path': 'src/.ipynb_checkpoints/PrecisionComparision-checkpoint.ipynb', 'repo_name': 'bhadreshpsavani/Computer-Pointer-Controller', 'size': '991'}