move config to strings

progres/chainsaw/get_predictions.py

@@ -7,6 +7,7 @@
 """
 
 import hashlib
+import json
 import logging
 import os
 import sys
@@ -17,10 +18,92 @@
 from progres.chainsaw.src.domain_assignment.util import convert_domain_dict_strings
 from progres.chainsaw.src.factories import pairwise_predictor
 from progres.chainsaw.src.models.results import PredictionResult
-from .src.utils import common as common_utils
 
 LOG = logging.getLogger(__name__)
 
+config_str = """
+{
+    "experiment_name": "C_mul65_do30",
+    "experiment_group": "cath_new",
+    "lr": 0.0002,
+    "weight_decay": 0.001,
+    "val_freq": 1,
+    "epochs": 15,
+    "lr_scheduler": {
+        "type": "exponential",
+        "gamma": 0.9
+    },
+    "accumulation_steps": 16,
+    "data": {
+        "splits_file": "splits_new_cath_featurized.json",
+        "validation_splits": [
+            "validation",
+            "test"
+        ],
+        "crop_size": null,
+        "crop_type": null,
+        "batch_size": 1,
+        "feature_dir": "../features/new_cath/2d_features",
+        "label_dir": "../features/new_cath/pairwise",
+        "chains_csv": null,
+        "evaluate_test": false,
+        "eval_casp_10_plus": false,
+        "remove_missing_residues": false,
+        "using_alphafold_features": false,
+        "recycling": false,
+        "add_padding_mask": false,
+        "training_exclusion_json": null,
+        "multi_proportion": 0.65,
+        "train_ids": "splits_new_cath_featurized.json",
+        "exclude_test_topology": false,
+        "cluster_sampling_training": true,
+        "dist_transform": "unidoc_exponent",
+        "distance_denominator": 10,
+        "merizo_train_data": false,
+        "redundancy_level": "S60_comb"
+    },
+    "learner": {
+        "uncertainty_model": false,
+        "save_every_epoch": true,
+        "model": {
+            "type": "trrosetta",
+            "kwargs": {
+                "filters": 32,
+                "kernel": 3,
+                "num_layers": 31,
+                "in_channels": 5,
+                "dropout": 0.3,
+                "symmetrise_output": true
+            }
+        },
+        "assignment": {
+            "type": "sparse_lowrank",
+            "kwargs": {
+                "N_iters": 3,
+                "K_init": 4,
+                "linker_threshold": 30
+            }
+        },
+        "max_recycles": 0,
+        "save_val_best": true,
+        "x_has_padding_mask": false
+    },
+    "num_trainable_params": 577889
+}
+"""
+
+feature_config_str = """
+{
+    "description": "alpha distance only, keep the start and end boundaries in one channel but use -1",
+    "alpha_distances": true,
+    "beta_distances": false,
+    "ss_bounds": true,
+    "negative_ss_end": true,
+    "separate_channel_ss_start_end": false,
+    "same_channel_boundaries_and_ss": false
+}
+"""
+
 def setup_logging(loglevel):
     # log all messages to stderr so results can be sent to stdout
     logging.basicConfig(level=loglevel,
@@ -35,8 +118,8 @@ def load_model(*,
                min_domain_length: int = 30,
                post_process_domains: bool = True,
                device: str = "cpu"):
-    config = common_utils.load_json(os.path.join(model_dir, "config.json"))
-    feature_config = common_utils.load_json(os.path.join(model_dir, "feature_config.json"))
+    config = json.loads(config_str)
+    feature_config = json.loads(feature_config_str)
     config["learner"]["remove_disordered_domain_threshold"] = remove_disordered_domain_threshold
     config["learner"]["post_process_domains"] = post_process_domains
     config["learner"]["min_ss_components"] = min_ss_components