def test_external_delegate_options_memory_import(delegate_dir,
test_data_folder):
# create armnn delegate with memory-import option
armnn_delegate = tflite.load_delegate(delegate_dir,
options={
'backends': 'CpuAcc,CpuRef',
'memory-import': '1'
})
model_file_name = 'fallback_model.tflite'
tensor_shape = [1, 2, 2, 1]
input0 = np.array([1, 2, 3, 4], dtype=np.uint8).reshape(tensor_shape)
input1 = np.array([2, 2, 3, 4], dtype=np.uint8).reshape(tensor_shape)
inputs = [input0, input0, input1]
expected_output = np.array([1, 2, 2, 2],
dtype=np.uint8).reshape(tensor_shape)
# run the inference
armnn_outputs = run_inference(test_data_folder, model_file_name, inputs,
[armnn_delegate])
# check results
compare_outputs(armnn_outputs, [expected_output])
def test_external_delegate_options_fp32_to_fp16(capfd, delegate_dir,
test_data_folder):
# create armnn delegate with reduce-fp32-to-fp16 option
armnn_delegate = tflite.load_delegate(delegate_dir,
options={
'backends': 'CpuRef',
'debug-data': '1',
'reduce-fp32-to-fp16': '1'
})
model_file_name = 'fp32_model.tflite'
tensor_shape = [1, 2, 2, 1]
input0 = np.array([1, 2, 3, 4], dtype=np.float32).reshape(tensor_shape)
input1 = np.array([2, 2, 3, 4], dtype=np.float32).reshape(tensor_shape)
inputs = [input0, input0, input1]
expected_output = np.array([1, 2, 2, 2],
dtype=np.float32).reshape(tensor_shape)
# run the inference
armnn_outputs = run_inference(test_data_folder, model_file_name, inputs,
[armnn_delegate])
# check results
compare_outputs(armnn_outputs, [expected_output])
captured = capfd.readouterr()
assert 'convert_fp32_to_fp16' in captured.out
assert 'convert_fp16_to_fp32' in captured.out
def test_external_delegate_options_fp32_to_bf16(capfd, delegate_dir,
test_data_folder):
# create armnn delegate with reduce-fp32-to-bf16 option
armnn_delegate = tflite.load_delegate(delegate_dir,
options={
'backends': 'CpuRef',
'debug-data': '1',
'reduce-fp32-to-bf16': '1'
})
model_file_name = 'conv2d.tflite'
inputShape = [1, 5, 5, 1]
outputShape = [1, 3, 3, 1]
inputValues = [
1, 5, 2, 3, 5, 8, 7, 3, 6, 3, 3, 3, 9, 1, 9, 4, 1, 8, 1, 3, 6, 8, 1, 9,
2
]
expectedResult = [28, 38, 29, 96, 104, 53, 31, 55, 24]
input = np.array(inputValues, dtype=np.float32).reshape(inputShape)
expected_output = np.array(expectedResult,
dtype=np.float32).reshape(outputShape)
# run the inference
armnn_outputs = run_inference(test_data_folder, model_file_name, [input],
[armnn_delegate])
# check results
compare_outputs(armnn_outputs, [expected_output])
captured = capfd.readouterr()
assert 'convert_fp32_to_bf16' in captured.out
def test_external_delegate_gpu_fastmath(delegate_dir, test_data_folder):
# create armnn delegate with enable-fast-math
# fast-math is only enabled on Conv2d layer, so use conv2d model.
armnn_delegate = tflite.load_delegate(delegate_dir,
options={
'backends': 'GpuAcc',
'enable-fast-math': '1',
"logging-severity": "info"
})
model_file_name = 'conv2d.tflite'
inputShape = [1, 5, 5, 1]
outputShape = [1, 3, 3, 1]
inputValues = [
1, 5, 2, 3, 5, 8, 7, 3, 6, 3, 3, 3, 9, 1, 9, 4, 1, 8, 1, 3, 6, 8, 1, 9,
2
]
expectedResult = [28, 38, 29, 96, 104, 53, 31, 55, 24]
input = np.array(inputValues, dtype=np.float32).reshape(inputShape)
expected_output = np.array(expectedResult,
dtype=np.float32).reshape(outputShape)
# run the inference
armnn_outputs = run_inference(test_data_folder, model_file_name, [input],
[armnn_delegate])
# check results
compare_outputs(armnn_outputs, [expected_output])
def evaluate(vocab, vqa, data_loader, criterion, epoch, args):
"""Calculates vqg average loss on data_loader.
Args:
vocab: questions and answers vocabulary.
vqa: visual question answering model.
data_loader: Iterator for the data.
criterion: The criterion function used to evaluate the loss.
args: ArgumentParser object.
Returns:
A float value of average loss.
"""
gts, gens, qs = [], [], []
vqa.eval()
total_loss = 0.0
total_correct = 0.0
iterations = 0
total_steps = len(data_loader)
if args.eval_steps is not None:
total_steps = min(len(data_loader), args.eval_steps)
start_time = time.time()
for i, (feats, questions, categories) in enumerate(data_loader):
# Set mini-batch dataset.
if torch.cuda.is_available():
feats = feats.cuda()
questions = questions.cuda()
categories = categories.cuda()
qlengths = process_lengths(questions)
# Forward.
outputs = vqa(feats, questions, qlengths)
loss = criterion(outputs, categories)
preds = outputs.max(1)[1]
# Backprop and optimize.
total_loss += loss.item()
total_correct += accuracy(preds, categories)
iterations += 1
# Quit after eval_steps.
if args.eval_steps is not None and i >= args.eval_steps:
break
q, gen, gt = parse_outputs(preds, questions, categories, vocab)
gts.extend(gt)
gens.extend(gen)
qs.extend(q)
# Print logs.
if i % args.log_step == 0:
delta_time = time.time() - start_time
start_time = time.time()
logging.info('Time: %.4f, Step [%d/%d], '
'Avg Loss: %.4f, Avg Acc: %.4f' %
(delta_time, i, total_steps, total_loss / iterations,
total_correct / iterations))
# Compare model reconstruction to target
compare_outputs(gens, qs, gts, logging)
return total_loss / iterations
def main():
reference_id = open("input/reference.fasta",
"r").readline().split(' ')[0][1:] # NC_045512.2
cleanUpOutputDir(folder='output')
# # ClustalJ = file json from clustal alignment; MuscleJ = file json from muscle alignment
# # Iran
# ClustalJ = runClustal('analysis/iran-ref.txt', reference_id, 3)
# MuscleJ = runMuscle('analysis/muscle-I20200523-084930-0610-44096621-p1m.clw', reference_id, 3)
# diff = utils.compare_outputs(clustal='output/'+ ClustalJ, muscle='output/'+ MuscleJ)
# utils.saveCompareFile("differences.txt", "Iran", diff)
# # Israel
# ClustalJ = runClustal('analysis/israel-ref.txt', reference_id, 4)
# MuscleJ = runMuscle('analysis/muscle-I20200523-085708-0753-28920419-p1m.clw', reference_id, 4)
# diff = utils.compare_outputs(clustal='output/'+ ClustalJ, muscle='output/'+ MuscleJ)
# utils.saveCompareFile("differences.txt", "Israel ", diff)
# # GISAID only
# ClustalJ = runClustal('analysis/GISAID-all.txt', reference_id, 7)
# MuscleJ = runMuscle('analysis/muscle-I20200523-090216-0023-41230765-p1m.clw', reference_id, 7)
# diff = utils.compare_outputs(clustal='output/'+ ClustalJ, muscle='output/'+ MuscleJ)
# utils.saveCompareFile("differences.txt", "GISAID ", diff)
# # NCBI only
# ClustalJ = runClustal('analysis/all.txt', reference_id, 3)
# MuscleJ = runMuscle('analysis/muscle-I20200512-170208-0225-69454386-p1m.clw', reference_id, 8)
# diff = utils.compare_outputs(clustal='output/'+ ClustalJ, muscle='output/'+ MuscleJ)
# utils.saveCompareFile("differences.txt", "ncbi ", diff)
# # All sequences
# ClustalJ = runClustal('analysis/global.txt', reference_id, 14)
# MuscleJ = runMuscle('analysis/muscle-I20200523-090837-0910-95910164-p1m.clw', reference_id, 15)
# diff = utils.compare_outputs(clustal='output/'+ ClustalJ, muscle='output/'+ MuscleJ)
# utils.saveCompareFile("differences.txt","Global ", diff)
# Compare global alignments
print('[...] Parsing ClustalW analysis...')
ClustalJ = runClustal('analysis/clustal-global.clustal_num', reference_id,
14)
print('[DONE] Parsing ClustalW analysis')
print('[...] Parsing Muscle analysis...')
MuscleJ = runMuscle('analysis/muscle-global.clw', reference_id, 14)
print('[DONE] Parsing Muscle analysis ')
print('[...] Comparing alignments...')
diff = utils.compare_outputs(clustal='output/' + ClustalJ,
muscle='output/' + MuscleJ,
path='output')
print('[DONE] Comparing alignments')
# utils.saveCompareFile("differences.txt","Global ", diff)
analysis(ClustalJ)
def test_external_delegate_cpu_options(capfd, delegate_dir, test_data_folder):
# create armnn delegate with enable-fast-math and number-of-threads options
# fast-math is only enabled on Conv2d layer, so use conv2d model.
armnn_delegate = tflite.load_delegate(delegate_dir,
options={
'backends': 'CpuAcc',
'enable-fast-math': '1',
'number-of-threads': '4',
"logging-severity": "info"
})
model_file_name = 'conv2d.tflite'
inputShape = [1, 5, 5, 1]
outputShape = [1, 3, 3, 1]
inputValues = [
1, 5, 2, 3, 5, 8, 7, 3, 6, 3, 3, 3, 9, 1, 9, 4, 1, 8, 1, 3, 6, 8, 1, 9,
2
]
expectedResult = [28, 38, 29, 96, 104, 53, 31, 55, 24]
input = np.array(inputValues, dtype=np.float32).reshape(inputShape)
expected_output = np.array(expectedResult,
dtype=np.float32).reshape(outputShape)
# run the inference
armnn_outputs = run_inference(test_data_folder, model_file_name, [input],
[armnn_delegate])
# check results
compare_outputs(armnn_outputs, [expected_output])
captured = capfd.readouterr()
assert 'Set CPPScheduler to Linear mode, with 4 threads to use' in captured.out
def test_codegen(compiler_path, cool_file):
compare_outputs(compiler_path, tests_dir + cool_file, tests_dir + cool_file[:-3] + '_input.txt',\
tests_dir + cool_file[:-3] + '_output.txt')
def test_initial_gen(compiler_path, cool_file):
compare_outputs(compiler_path, tests_dir + cool_file, tests_dir + cool_file[:-3] + '_input.txt',\
tests_dir + cool_file[:-3] + '_output.txt', timeout=2)
# if __name__ == "__main__":
# pytest.main(["-m", "initialgen"])
c1tm = executer.run_cpp_bin(code1_bin, tc_out, code1_out)
elif ".py" in code1_file:
c1tm = executer.run_py_code(code1_fio, tc_out, code1_out)
print("Code1 executed in %.5f sec" % c1tm)
utils.copy_file_to_folder_group(i, code1_out)
stats.append({'code1_time': c1tm})
if code2_file is not None:
if ".cpp" in code2_file:
c2tm = executer.run_cpp_bin(code2_bin, tc_out, code2_out)
elif ".py" in code2_file:
c2tm = executer.run_py_code(code2_fio, tc_out, code2_out)
print("Code2 executed in %.5f sec" % c2tm)
utils.copy_file_to_folder_group(i, code2_out)
diffs = utils.compare_outputs(code1_out, code2_out, result)
if diffs == 0:
print("Success : both outputs are same")
elif diffs == -1:
print("Failure : invalid output generated")
else:
print("Failure : output different at %d positions" % diffs)
if diffs != -1:
utils.copy_file_to_folder_group(i, result)
stats[-1].update({'code2_time': c2tm, 'diff': diffs})
print()
except KeyboardInterrupt as _:
foldr = utils.copy_to_grp(i, tc1_out, **grp_args)
created_folders.add(foldr)
stats.append({'code1_time': c1tm})
if code2_file is not None:
if ".cpp" in code2_file or ".c" in code2_file:
c2tm = executer.run_c_cpp_bin(code2_bin, ttc_out, tc2_out)
elif ".py" in code2_file:
c2tm = executer.run_py_code(code2_fio, ttc_out, tc2_out)
print("Code2 executed in %.5f sec" % c2tm)
foldr = utils.copy_to_grp(i, tc2_out, **grp_args)
created_folders.add(foldr)
diffs = utils.compare_outputs(tc1_out, tc2_out, tresult)
if diffs == 0:
print("Success : both outputs are same")
elif diffs == -1:
print("Failure : invalid output generated")
else:
print("Failure : output different at %d positions" % diffs)
if diffs != -1:
foldr = utils.copy_to_grp(i, tresult, **grp_args)
created_folders.add(foldr)
stats[-1].update({'code2_time': c2tm, 'diff': diffs})
print()