modify reading data way and add inference test (#50)

inference.py

@@ -32,6 +32,7 @@ from fastfold.config import model_config
 from fastfold.model.fastnn import set_chunk_size
 from fastfold.data import data_pipeline, feature_pipeline, templates
 from fastfold.utils import inject_fastnn
+from fastfold.data.parsers import parse_fasta
 from fastfold.utils.import_weights import import_jax_weights_
 from fastfold.utils.tensor_utils import tensor_tree_map
 
@@ -141,10 +142,8 @@ def main(args):
 
     # Gather input sequences
     with open(args.fasta_path, "r") as fp:
-        lines = [l.strip() for l in fp.readlines()]
-
-    tags, seqs = lines[::2], lines[1::2]
-    tags = [l[1:] for l in tags]
+        fasta = fp.read()
+    seqs, tags = parse_fasta(fasta)
 
     for tag, seq in zip(tags, seqs):
         batch = [None]

tests/test_data_utils.py

+# Copyright 2021 AlQuraishi Laboratory
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import numpy as np
+
+
+def random_template_feats(n_templ, n, batch_size=None):
+    b = []
+    if batch_size is not None:
+        b.append(batch_size)
+    batch = {
+        "template_mask": np.random.randint(0, 2, (*b, n_templ)),
+        "template_pseudo_beta_mask": np.random.randint(0, 2, (*b, n_templ, n)),
+        "template_pseudo_beta": np.random.rand(*b, n_templ, n, 3),
+        "template_aatype": np.random.randint(0, 22, (*b, n_templ, n)),
+        "template_all_atom_mask": np.random.randint(
+            0, 2, (*b, n_templ, n, 37)
+        ),
+        "template_all_atom_positions": 
+            np.random.rand(*b, n_templ, n, 37, 3) * 10,
+        "template_torsion_angles_sin_cos": 
+            np.random.rand(*b, n_templ, n, 7, 2),
+        "template_alt_torsion_angles_sin_cos": 
+            np.random.rand(*b, n_templ, n, 7, 2),
+        "template_torsion_angles_mask": 
+            np.random.rand(*b, n_templ, n, 7),
+    }
+    batch = {k: v.astype(np.float32) for k, v in batch.items()}
+    batch["template_aatype"] = batch["template_aatype"].astype(np.int64)
+    return batch
+
+
+def random_extra_msa_feats(n_extra, n, batch_size=None):
+    b = []
+    if batch_size is not None:
+        b.append(batch_size)
+    batch = {
+        "extra_msa": np.random.randint(0, 22, (*b, n_extra, n)).astype(
+            np.int64
+        ),
+        "extra_has_deletion": np.random.randint(0, 2, (*b, n_extra, n)).astype(
+            np.float32
+        ),
+        "extra_deletion_value": np.random.rand(*b, n_extra, n).astype(
+            np.float32
+        ),
+        "extra_msa_mask": np.random.randint(0, 2, (*b, n_extra, n)).astype(
+            np.float32
+        ),
+    }
+    return batch
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tests/test_inference.py

+# Copyright 2021 AlQuraishi Laboratory
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import time
+import torch
+import ml_collections as mlc
+
+import fastfold
+from fastfold.model.hub import AlphaFold
+from fastfold.config import model_config
+from fastfold.model.fastnn import set_chunk_size
+from fastfold.utils import inject_fastnn
+from test_data_utils import random_extra_msa_feats, random_template_feats
+from fastfold.data import data_transforms
+from fastfold.utils.tensor_utils import tensor_tree_map
+
+
+consts = mlc.ConfigDict(
+    {
+        "n_res": 11,
+        "n_seq": 13,
+        "n_templ": 3,
+        "n_extra": 17,
+    }
+)
+
+def inference():
+    fastfold.distributed.init_dap()
+
+    n_seq = consts.n_seq
+    n_templ = consts.n_templ
+    n_res = consts.n_res
+    n_extra_seq = consts.n_extra
+
+
+    config = model_config('model_1')
+    model = AlphaFold(config)
+    model = inject_fastnn(model)
+    model.eval()
+    model.cuda()
+
+    set_chunk_size(model.globals.chunk_size)
+
+    batch = {}
+    tf = torch.randint(config.model.input_embedder.tf_dim - 1, size=(n_res,))
+    batch["target_feat"] = torch.nn.functional.one_hot(
+        tf, config.model.input_embedder.tf_dim).float()
+    batch["aatype"] = torch.argmax(batch["target_feat"], dim=-1)
+    batch["residue_index"] = torch.arange(n_res)
+    batch["msa_feat"] = torch.rand((n_seq, n_res, config.model.input_embedder.msa_dim))
+    t_feats = random_template_feats(n_templ, n_res)
+    batch.update({k: torch.tensor(v) for k, v in t_feats.items()})
+    extra_feats = random_extra_msa_feats(n_extra_seq, n_res)
+    batch.update({k: torch.tensor(v) for k, v in extra_feats.items()})
+    batch["msa_mask"] = torch.randint(low=0, high=2, size=(n_seq, n_res)).float()
+    batch["seq_mask"] = torch.randint(low=0, high=2, size=(n_res,)).float()
+    batch.update(data_transforms.make_atom14_masks(batch))
+    batch["no_recycling_iters"] = torch.tensor(2.)
+    add_recycling_dims = lambda t: (
+            t.unsqueeze(-1).expand(*t.shape, config.data.common.max_recycling_iters))
+    batch = tensor_tree_map(add_recycling_dims, batch)
+
+
+    with torch.no_grad():
+        batch = {k: torch.as_tensor(v).cuda() for k, v in batch.items()}
+        t = time.perf_counter()
+        out = model(batch)
+        print(f"Inference time: {time.perf_counter() - t}")
+
+if __name__ == "__main__":
+    inference()
+    print("Inference Test Passed!")
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