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segment_and_test.py
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import os
import subprocess
import time
import shutil
import re
#'''
fn_list = [
#'tf2_ssd_mobilenet_v2_coco17_ptq',
#'ssd_mobilenet_v2_coco_quant_postprocess',
#'ssdlite_mobiledet_coco_qat_postprocess',
#'ssd_mobilenet_v1_coco_quant_postprocess',
#'tf2_ssd_mobilenet_v1_fpn_640x640_coco17_ptq',
#'efficientdet_lite0_320_ptq',
#'efficientdet_lite1_384_ptq',
#'efficientdet_lite2_448_ptq',
#'efficientdet_lite3_512_ptq',
#'efficientdet_lite3x_640_ptq',
#'yolov5n-int8',
#'yolov5s-int8',
#'yolov5m-int8',
#'yolov5l-int8',
#'yolov8n_416_640px', # lg 1st seg
#'yolov8s_416_640px', # lg 1st seg
#'yolov8m_416_640px', # lg 1st seg
#'yolov8l_416_640px', # lg 1st seg
#'yolov8n_640px',
#'yolov8s_640px',
#'yolov8m_640px', # lg 1st seg
#'yolov8l_640px', # lg 1st seg
'ipcam-general-v8']
custom_args = {
'tf2_ssd_mobilenet_v2_coco17_ptq': {
2: ["--diff_threshold_ns","100000"]},
'ssd_mobilenet_v2_coco_quant_postprocess': {
5: ["--undefok=enable_multiple_subgraphs","--enable_multiple_subgraphs","--partition_search_step","3"]},
'ssdlite_mobiledet_coco_qat_postprocess': {
2: ["--diff_threshold_ns","100000"]},
'efficientdet_lite3_512_ptq': {
2: ["--undefok=enable_multiple_subgraphs","--enable_multiple_subgraphs"],
3: ["--undefok=enable_multiple_subgraphs","--enable_multiple_subgraphs"],
4: ["--undefok=enable_multiple_subgraphs","--enable_multiple_subgraphs"],
5: ["--undefok=enable_multiple_subgraphs","--enable_multiple_subgraphs"],
6: ["--undefok=enable_multiple_subgraphs","--enable_multiple_subgraphs"],
7: ["--undefok=enable_multiple_subgraphs","--enable_multiple_subgraphs"]},
'efficientdet_lite3x_640_ptq': {
5: ["--undefok=enable_multiple_subgraphs","--enable_multiple_subgraphs","--partition_search_step","2"],
6: ["--undefok=enable_multiple_subgraphs","--enable_multiple_subgraphs","--partition_search_step","3"]},
'yolov5n-int8': {
5: ["--partition_search_step","2"],
6: ["--partition_search_step","2"],
7: ["--partition_search_step","2"],
8: ["--partition_search_step","2"]},
'yolov5s-int8': {
5: ["--partition_search_step","2"],
6: ["--partition_search_step","2"],
7: ["--partition_search_step","2"],
8: ["--partition_search_step","2"]},
'yolov5m-int8': {
5: ["--partition_search_step","2"],
6: ["--partition_search_step","2"],
7: ["--partition_search_step","2"],
8: ["--partition_search_step","2"]},
'yolov5l-int8': {
5: ["--undefok=enable_multiple_subgraphs","--enable_multiple_subgraphs","--partition_search_step","2"],
6: ["--partition_search_step","2"],
7: ["--partition_search_step","2"],
8: ["--partition_search_step","2"]},
'yolov8m_416_640px': {
5: ["--partition_search_step","2"],
6: ["--partition_search_step","3"],
7: ["--partition_search_step","4"],
8: ["--partition_search_step","5"]},
'yolov8l_416_640px': {
4: ["--partition_search_step","2"],
5: ["--partition_search_step","2"],
6: ["--partition_search_step","3"],
7: ["--partition_search_step","4"],
8: ["--partition_search_step","5"]}}#'''
'''
fn_list = [
# 'yolov5n-int8',
# 'yolov5s-int8',
# 'yolov5m-int8',
# 'yolov5l-int8',
# 'yolov8n_full_integer_quant',
# 'yolov8s_full_integer_quant',
# 'yolov8m_full_integer_quant',
# 'yolov8l_full_integer_quant',
# 'yolov8n_480px',
# 'yolov8s_480px',
# 'yolov8m_480px',
# 'yolov8l_480px',
# 'yolov8n_512px',
# 'yolov8s_512px',
# 'yolov8m_512px',
# 'yolov8l_512px',
# 'yolov8s_544px',
# 'yolov8m_544px', # lg 1st seg
# 'yolov8l_544px', # lg 1st seg
# 'yolov8s_576px',
# 'yolov8m_576px', # lg 1st seg
# 'yolov8l_576px', # lg 1st seg
# 'yolov8s_608px',
# 'yolov8m_608px', # lg 1st seg
# 'yolov8l_608px',
# 'yolov8n_640px',
# 'yolov8s_640px',
# 'yolov8m_640px', # lg 1st seg
# 'yolov8l_640px', # lg 1st seg
# 'yolov8n_416_640px', # lg 1st seg
'yolov8s_416_640px', # lg 1st seg
'yolov8m_416_640px', # lg 1st seg
'yolov8l_416_640px'] # lg 1st seg
# 'ipcam-general-v8'] #'''
'''
custom_args = {
'yolov8n_full_integer_quant': {
2: ["--diff_threshold_ns","100000"],
3: ["--diff_threshold_ns","200000"]},
'yolov8s_full_integer_quant': {
2: ["--diff_threshold_ns","200000"]},
'yolov8l_full_integer_quant': {
5: ["--partition_search_step","2"]},
'yolov8n_480px': {
2: ["--diff_threshold_ns","100000"],
3: ["--diff_threshold_ns","200000"]},
'yolov8s_480px': {
2: ["--diff_threshold_ns","200000"]},
'yolov8m_480px': {
5: ["--partition_search_step","2"]},
'yolov8n_512px': {
2: ["--diff_threshold_ns","1200000"],
3: ["--diff_threshold_ns","600000"]},
'yolov8s_512px': {
2: ["--diff_threshold_ns","200000"]},
'yolov8m_640px': {
2: ["--diff_threshold_ns","200000", "--undefok=timeout_sec","--timeout_sec=360"]},
'yolov8l_640px': {
2: ["--undefok=timeout_sec","--timeout_sec=360"]},
'yolov8n_416_640px': {
5: ["--partition_search_step","2"]},
'yolov8s_416_640px': {
5: ["--partition_search_step","2"]},
'yolov8m_416_640px': {
5: ["--initial_lower_bound_ns","44658311","--initial_upper_bound_ns","45466138","--partition_search_step","2"],
6: ["--initial_lower_bound_ns","39444004","--initial_upper_bound_ns","40071927","--partition_search_step","3"],
7: ["--initial_lower_bound_ns","36028652","--initial_upper_bound_ns","37012866","--partition_search_step","4"],
8: ["--initial_lower_bound_ns","33892323","--initial_upper_bound_ns","34856571","--partition_search_step","5"]},
'yolov8l_416_640px': {
5: ["--initial_lower_bound_ns","82297482","--initial_upper_bound_ns","82892528","--partition_search_step","2"],
6: ["--initial_lower_bound_ns","69966647","--initial_upper_bound_ns","70757195","--partition_search_step","3"],
7: ["--initial_lower_bound_ns","69067450","--initial_upper_bound_ns","69599451","--partition_search_step","4"],
8: ["--initial_lower_bound_ns","55889854","--initial_upper_bound_ns","56444625","--partition_search_step","5"]}}#'''
'''
diff_threshold_ns = {
'yolov8s_416_640px': {
2: 4000000},
'yolov8m_416_640px': {
4: 40000000,
5: 30000000},
'yolov8l_416_640px': {
7: 90000000,
8: 70000000}}#'''
'''
custom_args = {
'yolov8m_416_640px': {
5: ["--partition_search_step","2"],
6: ["--partition_search_step","3"],
7: ["--partition_search_step","4"],
8: ["--partition_search_step","5"]},
'yolov8l_416_640px': {
4: ["--partition_search_step","2"],
5: ["--partition_search_step","2"],
6: ["--partition_search_step","3"],
7: ["--partition_search_step","4"],
8: ["--partition_search_step","5"]}}#'''
seg_dir = "/media/seth/FAT_THUMB/all_segments/"
seg_types = ['', '2x_first_seg/', '15x_first_seg/', '166x_first_seg/', '3x_first_seg/', '4x_first_seg/', 'inc_seg/', 'dumb/']
def seg_exists(filename, segment_type, segment_count):
if segment_type == 'orig_code':
segment_type = ''
if segment_count == 1:
seg_list = [seg_dir+segment_type+filename+'_edgetpu.tflite']
else:
seg_list = [seg_dir+segment_type+filename+'_segment_{}_of_{}_edgetpu.tflite'.format(i, segment_count) for i in range(segment_count)]
return (seg_list, any([True for s in seg_list if not os.path.exists(s)]))
MAX_TPU_COUNT = 4
'''
# Generate segment files
for sn in range(1,MAX_TPU_COUNT+1):
for fn in fn_list:
for seg_type in seg_types:
seg_list, file_missing = seg_exists(fn, seg_type, sn)
if not file_missing:
continue
if sn == 1:
cmd = ["/usr/bin/edgetpu_compiler","-s","-d","--out_dir",seg_dir+seg_type,seg_dir+fn+".tflite"]
elif 'dumb' in seg_type:
cmd = ["/usr/bin/edgetpu_compiler","-s","-d","-n",str(sn),"--out_dir",seg_dir+seg_type,seg_dir+fn+".tflite"]
elif 'saturated' in seg_type:
try:
cmd = ["libcoral/out/k8/tools/partitioner/partition_with_profiling","--output_dir",seg_dir+seg_type,"--edgetpu_compiler_binary",
"/usr/bin/edgetpu_compiler","--model_path",seg_dir+fn+".tflite","--num_segments",str(sn),
"--diff_threshold_ns", str(diff_threshold_ns[fn][sn])]
except:
# Note: "Saturated segments" is an attempt to load as much of the model as possible onto segments
# while ignoring the latency incurred by slower segments. We assume we'll be able to "speed up"
# these slower segments simply by running more copies of them. The faster segments ideally will
# be optimized to all run at roughly the same speed. Thus the overall inference throughput will
# be limited by how many multiples of the slowest segment we can run.
#
# diff_threshold_ns key entries only exist where we want to create "saturated segments". More would
# mean the model is too sparse across segments. We create saturated segments by adjusting the
# diff_threshold_ns until the compiler just starts pushing parameters off of the TPUs. Ideally
# this will result in one or two slow segments and the rest of the segments are roughly equally
# fast.
continue
else:
if '2x_first_seg' in seg_type:
#+++ b/coral/tools/partitioner/profiling_based_partitioner.cc
#@@ -190,6 +190,8 @@ int64_t ProfilingBasedPartitioner::PartitionCompileAndAnalyze(
# latencies = std::get<2>(coral::BenchmarkPartitionedModel(
# tmp_edgetpu_segment_paths, &edgetpu_contexts(), kNumInferences));
#+ latencies[0] /= 2;
# if (kUseCache) {
# for (int i = 0; i < num_segments_; ++i) {
# segment_latency_cache_[{segment_starts[i], num_ops[i]}] = latencies[i];
#@@ -211,10 +213,11 @@ std::pair<int64_t, int64_t> ProfilingBasedPartitioner::GetBounds(
# num_segments_, /*search_delegate=*/true,
# delegate_search_step))
# << "Can not compile initial partition.";
#- const auto latencies = std::get<2>(coral::BenchmarkPartitionedModel(
#+ auto latencies = std::get<2>(coral::BenchmarkPartitionedModel(
# tmp_edgetpu_segment_paths, &edgetpu_contexts(), kNumInferences));
#
# DeleteFolder(tmp_dir);
#+ latencies[0] /= 4;
#
# int64_t lower_bound = std::numeric_limits<int64_t>::max(), upper_bound = 0;
# for (auto latency : latencies) {
#
# sudo make DOCKER_IMAGE="ubuntu:20.04" DOCKER_CPUS="k8" DOCKER_TARGETS="tools" docker-build
#// Encourage each segment slower than the previous to spread out the bottlenecks
#double latency_adjust = 1.0;
#for (int i = 1; i < num_segments_; ++i)
#{
# if (latencies[i-1] < latencies[i])
# latency_adjust *= 0.97;
# latencies[i-1] *= latency_adjust;
#}
#latencies[num_segments_-1] *= latency_adjust;
partition_with_profiling_dir = "libcoral/tools.2"
elif '15x_first_seg' in seg_type:
partition_with_profiling_dir = "libcoral/tools.15"
elif '133x_first_seg' in seg_type:
partition_with_profiling_dir = "libcoral/tools.133"
elif '166x_first_seg' in seg_type:
partition_with_profiling_dir = "libcoral/tools.166"
elif '3x_first_seg' in seg_type:
partition_with_profiling_dir = "libcoral/tools.3"
elif '4x_first_seg' in seg_type:
partition_with_profiling_dir = "libcoral/tools.4"
elif '15x_last_seg' in seg_type:
partition_with_profiling_dir = "libcoral/tools.last15"
elif '2x_last_seg' in seg_type:
partition_with_profiling_dir = "libcoral/tools.last2"
elif 'inc_seg' == seg_type:
partition_with_profiling_dir = "libcoral/tools.inc_seg"
else:
partition_with_profiling_dir = "libcoral/tools.orig"
cmd = [partition_with_profiling_dir+"/partitioner/partition_with_profiling","--output_dir",seg_dir+seg_type,"--edgetpu_compiler_binary",
"/usr/bin/edgetpu_compiler","--model_path",seg_dir+fn+".tflite","--num_segments",str(sn)]
try:
cmd += custom_args[fn][sn]
except:
pass
print(cmd)
subprocess.run(cmd)#'''
seg_types += ['133x_first_seg/', '15x_last_seg/', '2x_last_seg/']
# Test timings
fin_timings = {}
fin_fnames = {}
for fn in fn_list:
timings = []
fin_timings[fn] = {}
fin_fnames[fn] = {}
for num_tpus in range(2,MAX_TPU_COUNT+1):
for seg_type in seg_types:
max_seg = 0
for sn in range(1,num_tpus+1):
# Test against orig code
exe_file = "/home/seth/CodeProject.AI-Server/src/modules/ObjectDetectionCoral/objectdetection_coral_multitpu.py"
# Get file types
seg_list, file_missing = seg_exists(fn, seg_type, sn)
if file_missing:
continue
max_seg = sn
cmd = ["python3",exe_file,"--model"] + \
seg_list + ["--labels","coral/pycoral/test_data/coco_labels.txt","--input","/home/seth/coral/pycoral/test_data/grace_hopper.bmp",
"--count","2000","--num-tpus",str(num_tpus)]
print(cmd)
c = subprocess.run(cmd, check=True, universal_newlines=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
print(c.stdout)
print(c.stderr)
ms_time = float(re.compile(r'threads; ([\d\.]+)ms ea').findall(c.stderr)[0])
timings.append((ms_time, num_tpus, fn, seg_type, sn))
timings = sorted(timings, key=lambda t: t[0])
# Print the top three
print(f"TIMINGS FOR {num_tpus} TPUs AND {fn} MODEL:")
for t in range(min(10,len(timings))):
print(timings[t])
# Get best segments, but
# Skip if it's not 'orig_code' and > 1 segment
t = [t for t in timings if t[3] != 'orig_code'][0]
if t[4] == 1:
continue
# Add segment to the final list
fin_timings[fn][num_tpus] = timings[0]
# Copy best to local dir
seg_list, _ = seg_exists(t[2], t[3], t[4])
fin_fnames[fn][num_tpus] = []
for s in seg_list:
file_components = os.path.normpath(s).split("/")
out_fname = file_components[-2]+"_"+file_components[-1]
shutil.copyfile(s, out_fname)
fin_fnames[fn][num_tpus].append(out_fname)
# Create archive for this model / TPU count
if any(fin_fnames[fn][num_tpus]):
cmd = ['zip', '-9', f'objectdetection-{fn}-{num_tpus}-edgetpu.zip'] + fin_fnames[fn][num_tpus]
print(cmd)
subprocess.run(cmd)
print(fin_timings)
print(fin_fnames)
# Pretty print all of the segments we've timed and selected
for fn, v in fin_fnames.items():
print(" '%s': {" % fn)
for tpu_count, out_fnames in v.items():
print(f" # {fin_timings[fn][tpu_count][0]:6.1f} ms per inference")
print(f" {tpu_count}: "+str(out_fnames)+",")
print(" },")