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import argparse
import torch
import os
os.chdir('..')
from dataloader import CellLoader
from matplotlib import pyplot as plt
from celle_main import instantiate_from_config
from omegaconf import OmegaConf
from celle.utils import process_image
def run_model(mode, sequence,
nucleus_image_path,
protein_image_path,
model_ckpt_path,
model_config_path,
device):
if mode == "image":
run_image_prediction(
sequence,
nucleus_image_path,
protein_image_path,
model_ckpt_path,
model_config_path,
device
)
elif mode == "sequence":
run_sequence_prediction(
sequence,
nucleus_image_path,
protein_image_path,
model_ckpt_path,
model_config_path,
device
)
def run_sequence_prediction(
sequence_input,
nucleus_image_path,
protein_image_path,
model_ckpt_path,
model_config_path,
device
):
"""
Run Celle model with provided inputs and display results.
:param sequence: Path to sequence file
:param nucleus_image_path: Path to nucleus image
:param protein_image_path: Path to protein image (optional)
:param model_ckpt_path: Path to model checkpoint
:param model_config_path: Path to model config
"""
# Instantiate dataset object
dataset = CellLoader(
sequence_mode="embedding",
vocab="esm2",
split_key="val",
crop_method="center",
resize=600,
crop_size=256,
text_seq_len=1000,
pad_mode="end",
threshold="median",
)
# Check if sequence is provided and valid
if len(sequence_input) == 0:
raise ValueError("Sequence must be provided.")
if "<mask>" not in sequence_input:
print("Warning: Sequence does not contain any masked positions to predict.")
# Convert SEQUENCE to sequence using dataset.tokenize_sequence()
sequence = dataset.tokenize_sequence(sequence_input)
# Check if nucleus image path is provided and valid
if not os.path.exists(nucleus_image_path):
# Use default nucleus image from dataset and print warning
nucleus_image_path = 'images/nucleus.jpg'
print(
"Warning: No nucleus image provided. Using default nucleus image from dataset."
)
else:
# Load nucleus image from provided path
nucleus_image = process_image(nucleus_image_path)
# Check if protein image path is provided and valid
if not os.path.exists(protein_image_path):
# Use default nucleus image from dataset and print warning
protein_image_path = 'images/protein.jpg'
print(
"Warning: No nucleus image provided. Using default protein image from dataset."
)
else:
# Load protein image from provided path
protein_image = process_image(protein_image_path)
protein_image = (protein_image > torch.median(protein_image,dim=0))*1.0
# Load model config and set ckpt_path if not provided in config
config = OmegaConf.load(model_config_path)
if config["model"]["params"]["ckpt_path"] is None:
config["model"]["params"]["ckpt_path"] = model_ckpt_path
# Set condition_model_path and vqgan_model_path to None
config["model"]["params"]["condition_model_path"] = None
config["model"]["params"]["vqgan_model_path"] = None
# Instantiate model from config and move to device
model = instantiate_from_config(config).to(device)
# Sample from model using provided sequence and nucleus image
_, predicted_sequence, _ = model.celle.sample_text(
text=sequence,
condition=nucleus_image,
image=protein_image,
force_aas=True,
timesteps=1,
temperature=1,
progress=True,
)
formatted_predicted_sequence = ""
for i in range(min(len(predicted_sequence), len(sequence))):
if predicted_sequence[i] != sequence[i]:
formatted_predicted_sequence += f"**{predicted_sequence[i]}**"
else:
formatted_predicted_sequence += predicted_sequence[i]
if len(predicted_sequence) > len(sequence):
formatted_predicted_sequence += f"**{predicted_sequence[len(sequence):]}**"
print("predicted_sequence:", formatted_predicted_sequence)
def run_image_prediction(
sequence_input,
nucleus_image_path,
protein_image_path,
model_ckpt_path,
model_config_path,
device
):
"""
Run Celle model with provided inputs and display results.
:param sequence: Path to sequence file
:param nucleus_image_path: Path to nucleus image
:param protein_image_path: Path to protein image (optional)
:param model_ckpt_path: Path to model checkpoint
:param model_config_path: Path to model config
"""
# Instantiate dataset object
dataset = CellLoader(
sequence_mode="embedding",
vocab="esm2",
split_key="val",
crop_method="center",
resize=600,
crop_size=256,
text_seq_len=1000,
pad_mode="end",
threshold="median",
)
# Check if sequence is provided and valid
if len(sequence_input) == 0:
sequence = "MSKGEELFTGVVPILVELDGDVNGHKFSVSGEGEGDATYGKLTLKFICTTGKLPVPWPTLVTTFSYGVQCFSRYPDHMKQHDFFKSAMPEGYVQERTIFFKDDGNYKTRAEVKFEGDTLVNRIELKGIDFKEDGNILGHKLEYNYNSHNVYIMADKQKNGIKVNFKIRHNIEDGSVQLADHYQQNTPIGDGPVLLPDNHYLSTQSALSKDPNEKRDHMVLLEFVTAAGITHGMDELYK"
# Use default sequence for GFP and print warning
print("Warning: No sequence provided. Using default sequence for GFP.")
# Convert SEQUENCE to sequence using dataset.tokenize_sequence()
sequence = dataset.tokenize_sequence(sequence_input)
# Check if nucleus image path is provided and valid
if not os.path.exists(nucleus_image_path):
# Use default nucleus image from dataset and print warning
nucleus_image = dataset[0]["nucleus"]
print(
"Warning: No nucleus image provided. Using default nucleus image from dataset."
)
else:
# Load nucleus image from provided path
nucleus_image = process_image(nucleus_image_path)
# Load model config and set ckpt_path if not provided in config
config = OmegaConf.load(model_config_path)
if config["model"]["params"]["ckpt_path"] is None:
config["model"]["params"]["ckpt_path"] = model_ckpt_path
# Set condition_model_path and vqgan_model_path to None
config["model"]["params"]["condition_model_path"] = None
config["model"]["params"]["vqgan_model_path"] = None
# Instantiate model from config and move to device
model = instantiate_from_config(config).to(device)
# Sample from model using provided sequence and nucleus image
_, _, _, predicted_threshold, predicted_heatmap = model.celle.sample(
text=sequence,
condition=nucleus_image,
timesteps=1,
temperature=1,
progress=True,
)
# Move predicted_threshold and predicted_heatmap to CPU and select first element of batch
predicted_threshold = predicted_threshold.cpu()[0, 0]
predicted_heatmap = predicted_heatmap.cpu()[0, 0]
# Create 3 or 4 panel plot depending on whether protein image path is provided
fig, axs = plt.subplots(1, 3 if protein_image_path is None else 4)
axs[0].imshow(nucleus_image)
axs[0].set_title("Nucleus Input")
axs[1].imshow(predicted_threshold)
axs[1].set_title("Predicted Threshold")
if protein_image_path is not None:
protein_image = process_image(protein_image_path)
axs[2].imshow(protein_image)
axs[2].set_title("Protein Image")
axs[-1].imshow(predicted_heatmap)
axs[-1].set_title("Predicted Heatmap")
plt.show()
if __name__ == "__main__":
# Parse command line arguments for input parameters
parser = argparse.ArgumentParser(
description="Run Celle model with provided inputs."
)
parser.add_argument("--mode", type=str, default="", help="Sequence or Image")
parser.add_argument(
"--sequence", type=str, default="", help="Path to sequence file"
)
parser.add_argument(
"--nucleus_image_path",
type=str,
default="images/nucleus.jpg",
help="Path to nucleus image",
)
parser.add_argument(
"--protein_image_path",
type=str,
default=None,
help="Path to protein image (optional)",
)
parser.add_argument(
"--model_ckpt_path", type=str, required=True, help="Path to model checkpoint"
)
parser.add_argument(
"--model_config_path", type=str, required=True, help="Path to model config"
)
parser.add_argument(
"--device", type=str, default="cpu", required=True, help="device"
)
args = parser.parse_args()
run_model(
args.mode,
args.sequence,
args.nucleus_image_path,
args.protein_image_path,
args.model_ckpt_path,
args.model_config_path,
args.device
)
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