Create app.py

app.py

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+import re
+import os, time, pickle
+import torch
+from omegaconf import OmegaConf
+import hydra
+import logging
+from rfdiffusion.util import writepdb_multi, writepdb
+from rfdiffusion.inference import utils as iu
+from hydra.core.hydra_config import HydraConfig
+import numpy as np
+import random
+import glob
+import gradio as gr
+def greet(mtf):
+    return "Hello " + name + "!!"
+def make_deterministic(seed=0):
+    torch.manual_seed(seed)
+    np.random.seed(seed)
+    random.seed(seed)
+
+
+@hydra.main(version_base=None, config_path="../config/inference", config_name="base")
+def main(conf: HydraConfig) -> None:
+    log = logging.getLogger(__name__)
+    if conf.inference.deterministic:
+        make_deterministic()
+
+    # Check for available GPU and print result of check
+    if torch.cuda.is_available():
+        device_name = torch.cuda.get_device_name(torch.cuda.current_device())
+        log.info(f"Found GPU with device_name {device_name}. Will run RFdiffusion on {device_name}")
+    else:
+        log.info("////////////////////////////////////////////////")
+        log.info("///// NO GPU DETECTED! Falling back to CPU /////")
+        log.info("////////////////////////////////////////////////")
+
+    # Initialize sampler and target/contig.
+    sampler = iu.sampler_selector(conf)
+
+    # Loop over number of designs to sample.
+    design_startnum = sampler.inf_conf.design_startnum
+    if sampler.inf_conf.design_startnum == -1:
+        existing = glob.glob(sampler.inf_conf.output_prefix + "*.pdb")
+        indices = [-1]
+        for e in existing:
+            print(e)
+            m = re.match(".*_(\d+)\.pdb$", e)
+            print(m)
+            if not m:
+                continue
+            m = m.groups()[0]
+            indices.append(int(m))
+        design_startnum = max(indices) + 1
+
+    for i_des in range(design_startnum, design_startnum + sampler.inf_conf.num_designs):
+        if conf.inference.deterministic:
+            make_deterministic(i_des)
+
+        start_time = time.time()
+        out_prefix = f"{sampler.inf_conf.output_prefix}_{i_des}"
+        log.info(f"Making design {out_prefix}")
+        if sampler.inf_conf.cautious and os.path.exists(out_prefix + ".pdb"):
+            log.info(
+                f"(cautious mode) Skipping this design because {out_prefix}.pdb already exists."
+            )
+            continue
+
+        x_init, seq_init = sampler.sample_init()
+        denoised_xyz_stack = []
+        px0_xyz_stack = []
+        seq_stack = []
+        plddt_stack = []
+
+        x_t = torch.clone(x_init)
+        seq_t = torch.clone(seq_init)
+        # Loop over number of reverse diffusion time steps.
+        for t in range(int(sampler.t_step_input), sampler.inf_conf.final_step - 1, -1):
+            px0, x_t, seq_t, plddt = sampler.sample_step(
+                t=t, x_t=x_t, seq_init=seq_t, final_step=sampler.inf_conf.final_step
+            )
+            px0_xyz_stack.append(px0)
+            denoised_xyz_stack.append(x_t)
+            seq_stack.append(seq_t)
+            plddt_stack.append(plddt[0])  # remove singleton leading dimension
+
+        # Flip order for better visualization in pymol
+        denoised_xyz_stack = torch.stack(denoised_xyz_stack)
+        denoised_xyz_stack = torch.flip(
+            denoised_xyz_stack,
+            [
+                0,
+            ],
+        )
+        px0_xyz_stack = torch.stack(px0_xyz_stack)
+        px0_xyz_stack = torch.flip(
+            px0_xyz_stack,
+            [
+                0,
+            ],
+        )
+
+        # For logging -- don't flip
+        plddt_stack = torch.stack(plddt_stack)
+
+        # Save outputs
+        os.makedirs(os.path.dirname(out_prefix), exist_ok=True)
+        final_seq = seq_stack[-1]
+
+        # Output glycines, except for motif region
+        final_seq = torch.where(
+            torch.argmax(seq_init, dim=-1) == 21, 7, torch.argmax(seq_init, dim=-1)
+        )  # 7 is glycine
+
+        bfacts = torch.ones_like(final_seq.squeeze())
+        # make bfact=0 for diffused coordinates
+        bfacts[torch.where(torch.argmax(seq_init, dim=-1) == 21, True, False)] = 0
+        # pX0 last step
+        out = f"{out_prefix}.pdb"
+
+        # Now don't output sidechains
+        writepdb(
+            out,
+            denoised_xyz_stack[0, :, :4],
+            final_seq,
+            sampler.binderlen,
+            chain_idx=sampler.chain_idx,
+            bfacts=bfacts,
+        )
+
+        # run metadata
+        trb = dict(
+            config=OmegaConf.to_container(sampler._conf, resolve=True),
+            plddt=plddt_stack.cpu().numpy(),
+            device=torch.cuda.get_device_name(torch.cuda.current_device())
+            if torch.cuda.is_available()
+            else "CPU",
+            time=time.time() - start_time,
+        )
+        if hasattr(sampler, "contig_map"):
+            for key, value in sampler.contig_map.get_mappings().items():
+                trb[key] = value
+        with open(f"{out_prefix}.trb", "wb") as f_out:
+            pickle.dump(trb, f_out)
+
+        if sampler.inf_conf.write_trajectory:
+            # trajectory pdbs
+            traj_prefix = (
+                os.path.dirname(out_prefix) + "/traj/" + os.path.basename(out_prefix)
+            )
+            os.makedirs(os.path.dirname(traj_prefix), exist_ok=True)
+
+            out = f"{traj_prefix}_Xt-1_traj.pdb"
+            writepdb_multi(
+                out,
+                denoised_xyz_stack,
+                bfacts,
+                final_seq.squeeze(),
+                use_hydrogens=False,
+                backbone_only=False,
+                chain_ids=sampler.chain_idx,
+            )
+
+            out = f"{traj_prefix}_pX0_traj.pdb"
+            writepdb_multi(
+                out,
+                px0_xyz_stack,
+                bfacts,
+                final_seq.squeeze(),
+                use_hydrogens=False,
+                backbone_only=False,
+                chain_ids=sampler.chain_idx,
+            )
+
+        log.info(f"Finished design in {(time.time()-start_time)/60:.2f} minutes")
+
+
+if __name__ == "__main__":
+    main()
+iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+iface.launch()