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bin_packing_app.py
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bin_packing_app.py
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# Copyright 2022 D-Wave Systems Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from io import StringIO
import numpy as np
import sys
import streamlit as st
from typing import Optional
from packing3d import (Cases,
Bins,
Variables,
build_cqm,
call_solver)
from utils import (print_cqm_stats,
plot_cuboids,
read_instance,
write_solution_to_file,
write_input_data)
def _get_cqm_stats(cqm) -> str:
cqm_info_stream = StringIO()
sys.stdout = cqm_info_stream
print_cqm_stats(cqm)
sys.stdout = sys.__stdout__
return cqm_info_stream.getvalue()
def _solve_bin_packing_instance(data: dict,
write_to_file: bool,
solution_filename: Optional[str],
use_cqm_solver: bool = True,
**st_plotly_kwargs):
cases = Cases(data)
bins = Bins(data, cases=cases)
model_variables = Variables(cases, bins)
cqm, effective_dimensions = build_cqm(model_variables, bins, cases)
best_feasible = call_solver(cqm, time_limit, use_cqm_solver)
plotly_fig = plot_cuboids(best_feasible, model_variables, cases,
bins, effective_dimensions, color_coded)
st.plotly_chart(plotly_fig, **st_plotly_kwargs)
st.code(_get_cqm_stats(cqm))
if write_to_file:
write_solution_to_file(solution_filename, cqm,
model_variables, best_feasible,
cases, bins, effective_dimensions)
st.set_page_config(layout="wide")
st.markdown(
"<h1 style='text-align: center;'>3D Bin Packing Demo</h1>",
unsafe_allow_html=True
)
run_type = st.sidebar.radio(label="Choose run type:",
options=["Random", "File upload"])
solver_type = st.sidebar.radio(label="Choose solver to run problems on:",
options=["Constrained Quadratic Model",
"CBC (Python-MIP)",
])
if solver_type == "Constrained Quadratic Model":
use_cqm_solver = True
else:
use_cqm_solver = False
if run_type == "File upload":
problem_filepath = st.sidebar.text_input(label="Problem instance file",
value="input/sample_data_1.txt")
time_limit = st.sidebar.number_input(label="Hybrid solver time limit (S)",
value=20)
color_coded = st.sidebar.checkbox("Color coded cases")
display_input = st.sidebar.checkbox("Display input data")
write_to_file = st.sidebar.checkbox("Write solution to file")
if write_to_file:
solution_filename = st.sidebar.text_input("Solution filename")
else:
solution_filename = None
run_button = st.sidebar.button("Run")
if display_input:
col1, col2 = st.columns([1, 2])
with col1:
if problem_filepath:
with open(problem_filepath) as f:
for line in f:
st.text(line)
with col2:
if run_button:
data = read_instance(problem_filepath)
_solve_bin_packing_instance(data,
write_to_file,
solution_filename,
use_cqm_solver,
**{"use_container_width": True})
else:
if run_button:
data = read_instance(problem_filepath)
_solve_bin_packing_instance(data,
write_to_file,
solution_filename,
use_cqm_solver,
**{"use_container_width": True})
elif run_type == "Random":
color_coded = st.sidebar.checkbox("Color coded cases")
display_input = st.sidebar.checkbox("Display input data")
random_seed = st.sidebar.checkbox("Set random seed")
if random_seed:
seed = st.sidebar.number_input("Random seed", value=0)
save_input_to_file = st.sidebar.checkbox("Save input data to file")
if save_input_to_file:
input_filename = st.sidebar.text_input("input filename")
else:
input_filename = None
write_to_file = st.sidebar.checkbox("Write solution to file")
if write_to_file:
solution_filename = st.sidebar.text_input("Solution filename")
else:
solution_filename = None
col1, col2 = st.columns([1, 2])
with col1:
with st.form(key="problem_config"):
time_limit = st.number_input(label="Hybrid solver time limit(S)",
value=20)
num_bins = st.number_input("Number of bins", min_value=1,
max_value=5)
num_cases = st.number_input("Number of cases",
min_value=1, max_value=75, value=20)
case_size_range = st.slider("Case dimension range", min_value=1,
max_value=30, value=(1, 15))
bin_length = st.number_input("Bin length", min_value=1,
max_value=200, value=50)
bin_width = st.number_input("Bin width", min_value=1,
max_value=200, value=50)
bin_height = st.number_input("Bin height", min_value=1,
max_value=200, value=50)
form_submit = st.form_submit_button("Run")
if form_submit:
if random_seed:
rng = np.random.default_rng(seed)
else:
rng = np.random.default_rng()
data = {
"num_bins": num_bins,
"bin_dimensions": [bin_length, bin_width, bin_height],
"case_length": rng.integers(
case_size_range[0], case_size_range[1],
num_cases, endpoint=True
),
"case_width": rng.integers(
case_size_range[0], case_size_range[1],
num_cases, endpoint=True
),
"case_height": rng.integers(
case_size_range[0], case_size_range[1],
num_cases, endpoint=True
),
}
# Determine quantities and case_ids
case_dimensions = np.vstack(
[data["case_length"], data["case_width"], data["case_height"]]
)
unique_dimensions, data["quantity"] = np.unique(case_dimensions,
axis=1,
return_counts=True)
data["case_length"] = unique_dimensions[0,:]
data["case_width"] = unique_dimensions[1,:]
data["case_height"] = unique_dimensions[2,:]
data["case_ids"] = np.array(range(len(data["quantity"])))
input_data_string = write_input_data(data, input_filename)
if display_input:
for line in input_data_string.split(sep='\n'):
st.text(line)
with col2:
_solve_bin_packing_instance(data,
write_to_file,
solution_filename,
use_cqm_solver,
**{"use_container_width": True})