Move config script, reformat data_transforms

openfold/features/data_transforms.py

@@ -5,7 +5,7 @@ import numpy as np
 import torch
 from operator import add
 
-from config import NUM_RES, NUM_EXTRA_SEQ, NUM_TEMPLATES, NUM_MSA_SEQ
+from openfold.config import NUM_RES, NUM_EXTRA_SEQ, NUM_TEMPLATES, NUM_MSA_SEQ
 from openfold.np import residue_constants
 
 MSA_FEATURE_NAMES = [
@@ -29,7 +29,9 @@ def make_seq_mask(protein):
     return protein
 
 def make_template_mask(protein):
-    protein['template_mask'] = torch.ones(protein['template_aatype'].shape[0], dtype=torch.float32)
+    protein['template_mask'] = torch.ones(
+        protein['template_aatype'].shape[0], dtype=torch.float32
+    )
     return protein
 
 def curry1(f):
@@ -42,7 +44,9 @@ def curry1(f):
 
 @curry1
 def add_distillation_flag(protein, distillation):
-    protein['is_distillation'] = torch.tensor(float(distillation), dtype=torch.float32)
+    protein['is_distillation'] = torch.tensor(
+        float(distillation), dtype=torch.float32
+    )
     return protein
 
 def make_all_atom_aatype(protein):
@@ -55,14 +59,20 @@ def fix_templates_aatype(protein):
     protein['template_aatype'] = torch.argmax(protein['template_aatype'], dim=-1)
     # Map hhsearch-aatype to our aatype.
     new_order_list = residue_constants.MAP_HHBLITS_AATYPE_TO_OUR_AATYPE
-    new_order = torch.tensor(new_order_list, dtype=torch.int32).expand(num_templates, -1)
-    protein['template_aatype'] = torch.gather(new_order, 1, index=protein['template_aatype'])
+    new_order = torch.tensor(
+        new_order_list, dtype=torch.int32
+    ).expand(num_templates, -1)
+    protein['template_aatype'] = torch.gather(
+        new_order, 1, index=protein['template_aatype']
+    )
     return protein
 
 def correct_msa_restypes(protein):
     """Correct MSA restype to have the same order as residue_constants."""
     new_order_list = residue_constants.MAP_HHBLITS_AATYPE_TO_OUR_AATYPE
-    new_order = torch.tensor([new_order_list]*protein['msa'].shape[1], dtype=protein['msa'].dtype).transpose(0,1)
+    new_order = torch.tensor(
+        [new_order_list]*protein['msa'].shape[1], dtype=protein['msa'].dtype
+    ).transpose(0,1)
     protein['msa'] = torch.gather(new_order, 0, protein['msa'])
 
     perm_matrix = np.zeros((22, 22), dtype=np.float32)
@@ -94,7 +104,9 @@ def squeeze_features(protein):
     return protein
 
 def make_protein_crop_to_size_seed(protein):
-    protein['random_crop_to_size_seed'] = torch.distributions.Uniform(low=torch.int32, high=torch.int32).sample((2))
+    protein['random_crop_to_size_seed'] = torch.distributions.Uniform(
+        low=torch.int32, high=torch.int32).sample((2)
+    )
     return protein
 
 @curry1
@@ -110,8 +122,10 @@ def randomly_replace_msa_with_unknown(protein, replace_proportion):
         torch.rand(protein['aatype'].shape) < replace_proportion
     )
 
-    protein['aatype'] = torch.where(aatype_mask, torch.ones_like(protein['aatype']) * x_idx,
-                                    protein['aatype'])
+    protein['aatype'] = torch.where(
+        aatype_mask, torch.ones_like(protein['aatype']) * x_idx,
+        protein['aatype']
+    )
     return protein
 
 @curry1
@@ -151,7 +165,11 @@ def delete_extra_msa(protein):
 @curry1
 def block_delete_msa(protein, config):
     num_seq = protein['msa'].shape[0]
-    block_num_seq = torch.floor(torch.tensor(num_seq, dtype=torch.float32) * config.msa_fraction_per_block).to(torch.int32)
+    block_num_seq = torch.floor(
+        torch.tensor(
+            num_seq, dtype=torch.float32
+        ) * config.msa_fraction_per_block
+    ).to(torch.int32)
 
     if config.randomize_num_blocks:
         nb = torch.distributions.uniform.Uniform(0, config.num_blocks+1).sample()
@@ -195,8 +213,11 @@ def nearest_neighbor_clusters(protein, gap_agreement_weight=0.):
 
     # Compute tf.einsum('mrc,nrc,c->mn', sample_one_hot, extra_one_hot, weights)
     # in an optimized fashion to avoid possible memory or computation blowup.
-    agreement = torch.matmul(torch.reshape(extra_one_hot, [extra_num_seq, num_res*23]),
-                             torch.reshape(sample_one_hot * weights, [num_seq, num_res * 23]).transpose(0, 1),
+    agreement = torch.matmul(
+        torch.reshape(extra_one_hot, [extra_num_seq, num_res*23]),
+        torch.reshape(
+            sample_one_hot * weights, [num_seq, num_res * 23]
+        ).transpose(0, 1),
     )
 
     # Assign each sequence in the extra sequences to the closest MSA sample
@@ -213,14 +234,18 @@ def unsorted_segment_sum(data, segment_ids, num_segments):
     :param num_segments: The number of segments.
     :return: A tensor of same data type as the data argument.
     """
-    assert all([i in data.shape for i in segment_ids.shape]), "segment_ids.shape should be a prefix of data.shape"
+    # segment_ids.shape should be a prefix of data.shape
+    assert all([i in data.shape for i in segment_ids.shape])
 
     # segment_ids is a 1-D tensor repeat it to have the same shape as data
     if len(segment_ids.shape) == 1:
         s = torch.prod(torch.tensor(data.shape[1:])).long()
-        segment_ids = segment_ids.repeat_interleave(s).view(segment_ids.shape[0], *data.shape[1:])
+        segment_ids = segment_ids.repeat_interleave(s).view(
+            segment_ids.shape[0], *data.shape[1:]
+        )
 
-    assert data.shape == segment_ids.shape, "data.shape and segment_ids.shape should be equal"
+    # data.shape and segment_ids.shape should be equal
+    assert data.shape == segment_ids.shape
 
     shape = [num_segments] + list(data.shape[1:])
     tensor = torch.zeros(*shape).scatter_add(0, segment_ids, data.float())
@@ -232,7 +257,9 @@ def summarize_clusters(protein):
     """Produce profile and deletion_matrix_mean within each cluster."""
     num_seq = protein['msa'].shape[0]
     def csum(x):
-        return unsorted_segment_sum(x, protein['extra_cluster_assignment'], num_seq)
+        return unsorted_segment_sum(
+            x, protein['extra_cluster_assignment'], num_seq
+        )
 
     mask = protein['extra_msa_mask']
     mask_counts = 1e-6 + protein['msa_mask'] + csum(mask)   # Include center
@@ -292,7 +319,9 @@ def add_constant_field(protein, key, value):
 def shaped_categorical(probs, epsilon=1e-10):
     ds = probs.shape
     num_classes = ds[-1]
-    distribution = torch.distributions.categorical.Categorical(torch.reshape(probs+epsilon,[-1, num_classes]))
+    distribution = torch.distributions.categorical.Categorical(
+        torch.reshape(probs+epsilon,[-1, num_classes])
+    )
     counts = distribution.sample()
     return torch.reshape(counts, ds[:-1])
 
@@ -323,7 +352,9 @@ def make_masked_msa(protein, config, replace_fraction):
     pad_shapes[1] = 1
     mask_prob = 1. - config.profile_prob - config.same_prob - config.uniform_prob
     assert mask_prob >= 0.
-    categorical_probs = torch.nn.functional.pad(categorical_probs, pad_shapes, value=mask_prob)
+    categorical_probs = torch.nn.functional.pad(
+        categorical_probs, pad_shapes, value=mask_prob
+    )
 
     sh = protein['msa'].shape
     mask_position = torch.rand(sh) < replace_fraction
@@ -339,7 +370,14 @@ def make_masked_msa(protein, config, replace_fraction):
     return protein
 
 @curry1
-def make_fixed_size(protein, shape_schema, msa_cluster_size, extra_msa_size, num_res=0, num_templates=0):
+def make_fixed_size(
+    protein, 
+    shape_schema, 
+    msa_cluster_size, 
+    extra_msa_size, 
+    num_res=0, 
+    num_templates=0
+):
     """Guess at the MSA and sequence dimension to make fixed size."""
 
     pad_size_map = {
@@ -355,9 +393,13 @@ def make_fixed_size(protein, shape_schema, msa_cluster_size, extra_msa_size, num
             continue
         shape = list(v.shape)
         schema = shape_schema[k]
+        msd = "Rank mismatch between shape and shape schema for"
         assert len(shape) == len(schema), (
-            f'Rank mismatch between shape and shape schema for {k}: {shape} vs {schema}')
-        pad_size = [pad_size_map.get(s2, None) or s1 for (s1, s2) in zip(shape, schema)]
+            f'{msg} {k}: {shape} vs {schema}'
+        )
+        pad_size = [
+            pad_size_map.get(s2, None) or s1 for (s1, s2) in zip(shape, schema)
+        ]
 
         padding = [(0, p - v.shape[i]) for i, p in enumerate(pad_size)]
         padding.reverse()
@@ -371,8 +413,11 @@ def make_fixed_size(protein, shape_schema, msa_cluster_size, extra_msa_size, num
 @curry1
 def make_msa_feat(protein):
     """Create and concatenate MSA features."""
-    # Whether there is a domain break. Always zero for chains, but keeping for compatibility with domain datasets.
-    has_break = torch.clip(protein['between_segment_residues'].to(torch.float32), 0, 1)
+    # Whether there is a domain break. Always zero for chains, but keeping for 
+    # compatibility with domain datasets.
+    has_break = torch.clip(
+        protein['between_segment_residues'].to(torch.float32), 0, 1
+    )
     aatype_1hot = make_one_hot(protein['aatype'], 21)
 
     target_feat = [
@@ -391,14 +436,20 @@ def make_msa_feat(protein):
     ]
 
     if 'cluster_profile' in protein:
-        deletion_mean_value = (torch.atan(protein['cluster_deletion_mean'] / 3.) * (2. / np.pi))
+        deletion_mean_value = (
+            torch.atan(protein['cluster_deletion_mean'] / 3.) * (2. / np.pi)
+        )
         msa_feat.extend([protein['cluster_profile'],
                          torch.unsqueeze(deletion_mean_value, dim=-1),
         ])
 
     if 'extra_deletion_matrix' in protein:
-        protein['extra_has_deletion'] = torch.clip(protein['extra_deletion_matrix'], 0., 1.)
-        protein['extra_deletion_value'] = torch.atan(protein['extra_deletion_matrix'] / 3.) * (2. / np.pi)
+        protein['extra_has_deletion'] = torch.clip(
+            protein['extra_deletion_matrix'], 0., 1.
+        )
+        protein['extra_deletion_value'] = torch.atan(
+            protein['extra_deletion_matrix'] / 3.
+        ) * (2. / np.pi)
 
     protein['msa_feat'] = torch.cat(msa_feat, dim=-1)
     protein['target_feat'] = torch.cat(target_feat, dim=-1)
@@ -422,35 +473,51 @@ def make_atom14_masks(protein):
     restype_atom14_mask = []
 
     for rt in residue_constants.restypes:
-        atom_names = residue_constants.restype_name_to_atom14_names[residue_constants.restype_1to3[rt]]
+        atom_names = residue_constants.restype_name_to_atom14_names[
+            residue_constants.restype_1to3[rt]
+        ]
         restype_atom14_to_atom37.append([
-            (residue_constants.atom_order[name] if name else 0) for name in atom_names
+            (residue_constants.atom_order[name] if name else 0) 
+            for name in atom_names
         ])
         atom_name_to_idx14 = {name: i for i, name in enumerate(atom_names)}
         restype_atom37_to_atom14.append([
             (atom_name_to_idx14[name] if name in atom_name_to_idx14 else 0)
             for name in residue_constants.atom_types
         ])
-        # Since all 14 atoms are not present in every residue, use this mask to tell which atom is there in this residue
+        # Since all 14 atoms are not present in every residue, use this mask to 
+        # tell which atom is there in this residue
         restype_atom14_mask.append([(1. if name else 0.) for name in atom_names])
 
     # Add dummy mapping for restype 'UNK'
     restype_atom14_to_atom37.append([0] * 14)
     restype_atom37_to_atom14.append([0] * 37)
-    restype_atom14_to_atom37 = torch.tensor(restype_atom14_to_atom37, dtype=torch.int32)
-    restype_atom37_to_atom14 = torch.tensor(restype_atom37_to_atom14, dtype=torch.int32)
-    restype_atom14_mask = torch.tensor(restype_atom14_mask, dtype=torch.float32)
+    restype_atom14_to_atom37 = torch.tensor(
+        restype_atom14_to_atom37, dtype=torch.int32
+    )
+    restype_atom37_to_atom14 = torch.tensor(
+        restype_atom37_to_atom14, dtype=torch.int32
+    )
+    restype_atom14_mask = torch.tensor(
+        restype_atom14_mask, dtype=torch.float32
+    )
 
     # create the mapping for (residx, atom14) --> atom37, i.e. an array
     # with shape (num_res, 14) containing the atom37 indices for this protein
-    residx_atom14_to_atom37 = torch.index_select(restype_atom14_to_atom37, 0, protein['aatype'])
-    residx_atom14_mask = torch.index_select(restype_atom14_mask, 0, protein['aatype'])
+    residx_atom14_to_atom37 = torch.index_select(
+        restype_atom14_to_atom37, 0, protein['aatype']
+    )
+    residx_atom14_mask = torch.index_select(
+        restype_atom14_mask, 0, protein['aatype']
+    )
 
     protein['atom14_atom_exists'] = residx_atom14_mask
     protein['residx_atom14_to_atom37'] = residx_atom14_to_atom37
 
     # create the gather indices for mapping back
-    residx_atom37_to_atom14 = torch.index_select(restype_atom37_to_atom14, 0, protein['aatype'])
+    residx_atom37_to_atom14 = torch.index_select(
+        restype_atom37_to_atom14, 0, protein['aatype']
+    )
     protein['residx_atom37_to_atom14'] = residx_atom37_to_atom14
 
     # create the corresponding mask
@@ -462,7 +529,9 @@ def make_atom14_masks(protein):
             atom_type = residue_constants.atom_order[atom_name]
             restype_atom37_mask[restype, atom_type] = 1
 
-    residx_atom37_mask = torch.index_select(restype_atom37_mask, 0, protein['aatype'])
+    residx_atom37_mask = torch.index_select(
+        restype_atom37_mask, 0, protein['aatype']
+    )
     protein['atom37_atom_exists'] = residx_atom37_mask
 
     return protein

run_pretrained_alphafold.py

@@ -31,7 +31,7 @@ import time
 import numpy as np
 import torch
 
-from config import model_config
+from openfold.config import model_config
 from openfold.model.model import AlphaFold
 from openfold.np import residue_constants, protein
 import openfold.np.relax.relax as relax

tests/compare_utils.py

@@ -6,7 +6,7 @@ import unittest
 
 import numpy as np
 
-from config import model_config
+from openfold.config import model_config
 from openfold.model.model import AlphaFold
 from openfold.utils.import_weights import import_jax_weights_
 from tests.config import consts

tests/test_import_weights.py

@@ -16,7 +16,7 @@ import torch
 import numpy as np
 import unittest
 
-from config import model_config
+from openfold.config import model_config
 from openfold.model.model import AlphaFold
 from openfold.utils.import_weights import import_jax_weights_
 

tests/test_model.py

@@ -17,7 +17,7 @@ import torch
 import torch.nn as nn
 import numpy as np
 import unittest
-from config import *
+from openfold.config import model_config
 from openfold.model.model import AlphaFold
 import openfold.utils.feats as feats
 from openfold.utils.tensor_utils import tree_map, tensor_tree_map