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Merge pull request #122 from wilhelm-lab/feature/prosit_cit
Feature/prosit cit
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77 changes: 77 additions & 0 deletions
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clients/python/test/Prosit/test_Prosit_2024_intensity_cit.py
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from test.server_config import SERVER_GRPC, SERVER_HTTP | ||
import tritonclient.grpc as grpcclient | ||
import numpy as np | ||
from pathlib import Path | ||
import requests | ||
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# To ensure MODEL_NAME == test_<filename>.py | ||
MODEL_NAME = Path(__file__).stem.replace("test_", "") | ||
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def test_available_http(): | ||
req = requests.get(f"{SERVER_HTTP}/v2/models/{MODEL_NAME}", timeout=1) | ||
assert req.status_code == 200 | ||
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def test_available_grpc(): | ||
triton_client = grpcclient.InferenceServerClient(url=SERVER_GRPC) | ||
assert triton_client.is_model_ready(MODEL_NAME) | ||
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def test_inference(): | ||
SEQUENCES = np.array( | ||
[ | ||
["AA"], | ||
["PEPTIPEPTIR[UNIMOD:7]EPTIPEPTIPEPTIPEPT"], | ||
["HKDER[UNIMOD:7]STNQCGAVILMFYW"], | ||
["R[UNIMOD:7]HKDESTNQC[UNIMOD:4]GPAVILMFYW"], | ||
["R[UNIMOD:7]HKDESTNQCGPAVILM[UNIMOD:35]FYW"], | ||
], | ||
dtype=np.object_, | ||
) | ||
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charge = np.array([[3] for _ in range(len(SEQUENCES))], dtype=np.int32) | ||
ces = np.array([[30] for _ in range(len(SEQUENCES))], dtype=np.float32) | ||
frag = np.array([["HCD"] for _ in range(len(SEQUENCES))], dtype=np.object_) | ||
# frag = np.load("test/Prosit/arr_Prosit_2020_intensityTMT_frag.npy").reshape([5,1]) | ||
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triton_client = grpcclient.InferenceServerClient(url=SERVER_GRPC) | ||
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in_pep_seq = grpcclient.InferInput("peptide_sequences", SEQUENCES.shape, "BYTES") | ||
in_pep_seq.set_data_from_numpy(SEQUENCES) | ||
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in_charge = grpcclient.InferInput("precursor_charges", charge.shape, "INT32") | ||
in_charge.set_data_from_numpy(charge) | ||
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in_ces = grpcclient.InferInput("collision_energies", ces.shape, "FP32") | ||
in_ces.set_data_from_numpy(ces) | ||
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in_frag = grpcclient.InferInput("fragmentation_types", frag.shape, "BYTES") | ||
in_frag.set_data_from_numpy(frag) | ||
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result = triton_client.infer( | ||
MODEL_NAME, | ||
inputs=[in_pep_seq, in_charge, in_ces, in_frag], | ||
outputs=[ | ||
grpcclient.InferRequestedOutput("intensities"), | ||
grpcclient.InferRequestedOutput("mz"), | ||
grpcclient.InferRequestedOutput("annotation"), | ||
], | ||
) | ||
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intensities = result.as_numpy("intensities") | ||
fragmentmz = result.as_numpy("mz") | ||
annotation = result.as_numpy("annotation") | ||
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assert intensities.shape == (5, 174) | ||
assert fragmentmz.shape == (5, 174) | ||
assert annotation.shape == (5, 174) | ||
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# Assert intensities consistent | ||
assert np.allclose( | ||
intensities, | ||
np.load("test/Prosit/arr_Prosit_2024_intensity_cit_int.npy"), | ||
rtol=0, | ||
atol=1e-5, | ||
equal_nan=True, | ||
) |
57 changes: 57 additions & 0 deletions
57
clients/python/test/Prosit/test_Prosit_2024_intensity_cit_core.py
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from test.server_config import SERVER_GRPC, SERVER_HTTP | ||
import tritonclient.grpc as grpcclient | ||
import numpy as np | ||
from pathlib import Path | ||
import requests | ||
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# To ensure MODEL_NAME == test_<filename>.py | ||
MODEL_NAME = Path(__file__).stem.replace("test_", "") | ||
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def test_available_http(): | ||
req = requests.get(f"{SERVER_HTTP}/v2/models/{MODEL_NAME}", timeout=1) | ||
assert req.status_code == 200 | ||
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def test_available_grpc(): | ||
triton_client = grpcclient.InferenceServerClient(url=SERVER_GRPC) | ||
assert triton_client.is_model_ready(MODEL_NAME) | ||
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def test_inference(): | ||
seq = np.load("test/Prosit/arr_Prosit_2019_intensity_seq.npy") | ||
charge = np.load("test/Prosit/arr_Prosit_2019_intensity_charge.npy") | ||
ces = np.load("test/Prosit/arr_Prosit_2019_intensity_ces.npy") | ||
frag = np.load("test/Prosit/arr_Prosit_2020_intensityTMT_frag.npy").reshape([5, 1]) | ||
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triton_client = grpcclient.InferenceServerClient(url=SERVER_GRPC) | ||
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in_pep_seq = grpcclient.InferInput("modified_sequence", seq.shape, "INT64") | ||
in_pep_seq.set_data_from_numpy(seq.astype(np.int64)) | ||
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in_charge = grpcclient.InferInput("precursor_charge_onehot", charge.shape, "INT64") | ||
in_charge.set_data_from_numpy(charge.astype(np.int64)) | ||
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in_ces = grpcclient.InferInput("aligned_collision_energy", ces.shape, "FP32") | ||
in_ces.set_data_from_numpy(ces) | ||
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in_frag = grpcclient.InferInput("method_nbr", frag.shape, "INT64") | ||
in_frag.set_data_from_numpy(frag.astype(np.int64)) | ||
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result = triton_client.infer( | ||
MODEL_NAME, | ||
inputs=[in_pep_seq, in_charge, in_ces, in_frag], | ||
outputs=[ | ||
grpcclient.InferRequestedOutput("output_1"), | ||
], | ||
) | ||
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intensities = result.as_numpy("output_1") | ||
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assert intensities.shape == (5, 174) | ||
assert np.allclose( | ||
intensities, | ||
np.load("test/Prosit/arr_Prosit_2024_intensity_cit_int_raw.npy"), | ||
rtol=0, | ||
atol=1e-4, | ||
) |
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from test.server_config import SERVER_GRPC, SERVER_HTTP | ||
import tritonclient.grpc as grpcclient | ||
import numpy as np | ||
from pathlib import Path | ||
import requests | ||
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# To ensure MODEL_NAME == test_<filename>.py | ||
MODEL_NAME = Path(__file__).stem.replace("test_", "") | ||
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def test_available_http(): | ||
req = requests.get(f"{SERVER_HTTP}/v2/models/{MODEL_NAME}", timeout=1) | ||
assert req.status_code == 200 | ||
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def test_available_grpc(): | ||
triton_client = grpcclient.InferenceServerClient(url=SERVER_GRPC) | ||
assert triton_client.is_model_ready(MODEL_NAME) | ||
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def test_inference(): | ||
SEQUENCES = np.array( | ||
[ | ||
["AA"], | ||
["PEPTIPEPTIR[UNIMOD:7]EPTIPEPTIPEPTIPEPT"], | ||
["R[UNIMOD:7]HKDESTNQCGAVILMFYW"], | ||
["R[UNIMOD:7]HKDESTNQC[UNIMOD:4]GPAVILMFYW"], | ||
["R[UNIMOD:7]HKDESTNQCGPAVILM[UNIMOD:35]FYW"], | ||
], | ||
dtype=np.object_, | ||
) | ||
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triton_client = grpcclient.InferenceServerClient(url=SERVER_GRPC) | ||
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in_pep_seq = grpcclient.InferInput("peptide_sequences", [5, 1], "BYTES") | ||
in_pep_seq.set_data_from_numpy(SEQUENCES) | ||
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result = triton_client.infer( | ||
MODEL_NAME, | ||
inputs=[in_pep_seq], | ||
outputs=[ | ||
grpcclient.InferRequestedOutput("irt"), | ||
], | ||
) | ||
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irt = result.as_numpy("irt") | ||
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assert irt.shape == (5, 1) | ||
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# Assert intensities consistent | ||
assert np.allclose( | ||
irt, | ||
np.load("test/Prosit/arr_Prosit_2024_irt_cit_irt.npy"), | ||
rtol=0, | ||
atol=1e-4, | ||
) |
46 changes: 46 additions & 0 deletions
46
clients/python/test/Prosit/test_Prosit_2024_irt_cit_core.py
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from test.server_config import SERVER_GRPC, SERVER_HTTP | ||
import tritonclient.grpc as grpcclient | ||
import numpy as np | ||
from pathlib import Path | ||
import requests | ||
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# To ensure MODEL_NAME == test_<filename>.py | ||
MODEL_NAME = Path(__file__).stem.replace("test_", "") | ||
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def test_available_http(): | ||
req = requests.get(f"{SERVER_HTTP}/v2/models/{MODEL_NAME}", timeout=1) | ||
assert req.status_code == 200 | ||
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def test_available_grpc(): | ||
triton_client = grpcclient.InferenceServerClient(url=SERVER_GRPC) | ||
assert triton_client.is_model_ready(MODEL_NAME) | ||
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def test_inference(): | ||
seq = np.load("test/Prosit/arr_Prosit_2019_intensity_seq.npy") | ||
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triton_client = grpcclient.InferenceServerClient(url=SERVER_GRPC) | ||
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in_pep_seq = grpcclient.InferInput("input_1", seq.shape, "INT64") | ||
in_pep_seq.set_data_from_numpy(seq.astype(np.int64)) | ||
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result = triton_client.infer( | ||
MODEL_NAME, | ||
inputs=[in_pep_seq], | ||
outputs=[ | ||
grpcclient.InferRequestedOutput("output_1"), | ||
], | ||
) | ||
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irt = result.as_numpy("output_1") | ||
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assert irt.shape == (5, 1) | ||
print(irt) | ||
assert np.allclose( | ||
irt, | ||
np.load("test/Prosit/arr_Prosit_2024_irt_cit_irt_raw.npy"), | ||
rtol=0, | ||
atol=1e-4, | ||
) |
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max_batch_size: 1000 | ||
platform: "ensemble" | ||
input [ | ||
{ | ||
name: 'peptide_sequences', | ||
data_type: TYPE_STRING, | ||
dims: [-1] | ||
}, | ||
{ | ||
name: 'precursor_charges', | ||
data_type: TYPE_INT32, | ||
dims: [1], | ||
}, | ||
{ | ||
name: 'collision_energies', | ||
data_type: TYPE_FP32, | ||
dims: [1], | ||
}, | ||
{ | ||
name: 'fragmentation_types', | ||
data_type: TYPE_STRING, | ||
dims: [1], | ||
} | ||
] | ||
output [ | ||
{ | ||
name: 'intensities', | ||
data_type: TYPE_FP32, | ||
dims: [174] | ||
}, | ||
{ | ||
name: 'mz', | ||
data_type: TYPE_FP32, | ||
dims: [174] | ||
}, | ||
{ | ||
name: 'annotation', | ||
data_type: TYPE_STRING, | ||
dims: [174] | ||
} | ||
] | ||
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ensemble_scheduling { | ||
step [ | ||
{ | ||
model_name: "Prosit_Preprocess_charge_cit" | ||
model_version: 1 | ||
input_map { | ||
key: "precursor_charges" | ||
value: "precursor_charges" | ||
}, | ||
output_map { | ||
key: "precursor_charges_in:0" | ||
value: "precursor_charges_in_preprocessed:0" | ||
} | ||
}, | ||
{ | ||
model_name: "Prosit_Preprocess_charge" | ||
model_version: 1 | ||
input_map { | ||
key: "precursor_charges" | ||
value: "precursor_charges" | ||
}, | ||
output_map { | ||
key: "precursor_charges_in:0" | ||
value: "precursor_charges_in_preprocessed_FP" | ||
} | ||
}, | ||
{ | ||
model_name: "Prosit_Preprocess_fragmentation_types_cit" | ||
model_version: 1 | ||
input_map { | ||
key: "fragmentation_types" | ||
value: "fragmentation_types" | ||
}, | ||
output_map { | ||
key: "fragmentation_types_encoding" | ||
value: "fragmentation_types_processed:0" | ||
} | ||
}, | ||
{ | ||
model_name: "Prosit_Preprocess_peptide_no_termini" | ||
model_version: 1 | ||
input_map { | ||
key: "peptide_sequences" | ||
value: "peptide_sequences" | ||
}, | ||
output_map { | ||
key: "peptides_in:0" | ||
value: "peptides_in:0" | ||
} | ||
}, | ||
{ | ||
model_name: "Prosit_2024_intensity_cit_core" | ||
model_version: 1 | ||
input_map { | ||
key: "modified_sequence" | ||
value: "peptides_in:0" | ||
}, | ||
input_map { | ||
key: "aligned_collision_energy" | ||
value: "collision_energies" | ||
}, | ||
input_map { | ||
key: "precursor_charge_onehot" | ||
value: "precursor_charges_in_preprocessed:0" | ||
} | ||
input_map { | ||
key: "method_nbr" | ||
value: "fragmentation_types_processed:0" | ||
} | ||
output_map { | ||
key: "output_1" | ||
value: "output_1" | ||
} | ||
}, | ||
{ | ||
model_name: "Prosit_2019_intensity_postprocess" | ||
model_version: 1 | ||
input_map { | ||
key: "peptides_in:0" | ||
value: "peptide_sequences" | ||
}, | ||
input_map{ | ||
key: "precursor_charges_in:0" | ||
value: "precursor_charges_in_preprocessed_FP" | ||
} | ||
input_map{ | ||
key: "peaks_in:0", | ||
value: "output_1" | ||
} | ||
output_map { | ||
key: "intensities" | ||
value: "intensities" | ||
} | ||
output_map { | ||
key: "mz" | ||
value: "mz" | ||
} | ||
}, | ||
{ | ||
model_name: "Prosit_Helper_annotation" | ||
model_version: 1 | ||
input_map { | ||
key: "precursor_charges" | ||
value: "precursor_charges" | ||
}, | ||
output_map { | ||
key: "annotation" | ||
value: "annotation" | ||
} | ||
} | ||
] | ||
} |
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