/
debug_profiling.py
95 lines (74 loc) · 2.66 KB
/
debug_profiling.py
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import os
import json
import timeit
import pandas as pd
import numpy as np
import tensorflow as tf
from sqlalchemy import create_engine
from rna_learn.alphabet import ALPHABET_DNA
from rna_learn.load_sequences import (
load_growth_temperatures,
compute_inverse_effective_sample,
assign_weight_to_batch_values,
SpeciesSequence,
)
from rna_learn.model import (
conv1d_densenet_regression_model,
compile_regression_model,
)
def main():
run_id = 'run_yb64o'
model_path = os.path.join(os.getcwd(), f'saved_models/{run_id}/model.h5')
metadata_path = os.path.join(os.getcwd(), f'saved_models/{run_id}/metadata.json')
db_path = os.path.join(os.getcwd(), 'data/condensed_traits/db/seq.db')
engine = create_engine(f'sqlite+pysqlite:///{db_path}')
with open(metadata_path) as f:
metadata = json.load(f)
temperatures, mean, std = load_growth_temperatures(engine)
model = conv1d_densenet_regression_model(
alphabet_size=len(metadata['alphabet']),
growth_rate=metadata['growth_rate'],
n_layers=metadata['n_layers'],
kernel_sizes=metadata['kernel_sizes'],
dilation_rates=metadata['dilation_rates'],
l2_reg=metadata['l2_reg'],
dropout=metadata['dropout'],
)
model.load_weights(model_path)
species_taxid = 167
max_sequence_length = 9999
species_seq = SpeciesSequence(
engine,
species_taxid=species_taxid,
batch_size=64,
temperatures=temperatures,
mean=mean,
std=std,
alphabet=ALPHABET_DNA,
max_sequence_length=max_sequence_length,
random_seed=metadata['seed'],
)
print('fast:', timeit.timeit(lambda: evaluate_seq_fast(model, species_seq), number=1))
print('semi-fast:', timeit.timeit(lambda: evaluate_seq_semi_fast(model, species_seq), number=1))
print('slow:', timeit.timeit(lambda: evaluate_seq(model, species_seq), number=1))
def evaluate_seq(model, species_seq):
iterator = species_seq.__iter__()
for _ in range(len(species_seq)):
batch_x, _, _ = next(iterator)
_ = model(batch_x)
@tf.function(experimental_relax_shapes=True)
def evaluate_seq_fast(model, species_seq):
iterator = species_seq.__iter__()
for _ in tf.range(len(species_seq)):
batch_x, _, _ = next(iterator)
_ = model(batch_x)
def evaluate_seq_semi_fast(model, species_seq):
def evaluate_batch(batch_x):
model(batch_x)
fn = tf.function(evaluate_batch, experimental_relax_shapes=True)
iterator = species_seq.__iter__()
for _ in range(len(species_seq)):
batch_x, _, _ = next(iterator)
fn(batch_x)
if __name__ == '__main__':
main()