Release dev savedata

dannce/engine/processing.py

@@ -1476,3 +1476,4 @@ def write_npy(uri, gen):
             print(fname)
             np.save(os.path.join(imdir, fname + ".npy"), bch[0][0][j].astype("uint8"))
             np.save(os.path.join(griddir, fname + ".npy"), bch[0][1][j])
+

dannce/interface.py

@@ -740,11 +740,33 @@ def dannce_train(params: Dict):
 
     if params["use_npy"]:
         # Add all npy volume directories to list, to be used by generator
-        npydir = {}
+        dirnames = ["image_volumes", "grid_volumes", "targets"]
+        npydir, missing_npydir = {}, {}
         for e in range(num_experiments):
             npydir[e] = params["experiment"][e]["npy_vol_dir"]
+
+            if not os.path.exists(npydir[e]):
+                missing_npydir[e] = npydir[e]
+                for dir in dirnames:
+                    os.makedirs(os.path.join(npydir[e], dir))
+            else:
+                for dir in dirnames:
+                    dirpath = os.path.join(npydir[e], dir)
+                    if (not os.path.exists(dirpath)) or (len(os.listdir(dirpath)) == 0):
+                        missing_npydir[e] = npydir[e]
+                        os.makedirs(dirpath, exist_ok=True)
+
+        # samples = processing.remove_samples_npy(npydir, samples, params)
+        missing_samples = np.array([samp for samp in samples if int(samp.split("_")[0]) in list(missing_npydir.keys())])
+        if len(missing_samples) != 0:
+            print("{} npy files for experiments {} are missing.".format(len(missing_samples), list(missing_npydir.keys())))
+
+            vids = {}
+            for e in range(num_experiments):
+                vids = processing.initialize_vids(params, datadict, e, vids, pathonly=True)
+        else:
+            print("No missing npy files. Ready for training.")
 
-        samples = processing.remove_samples_npy(npydir, samples, params)
     else:
         # Initialize video objects
         vids = {}
@@ -778,6 +800,67 @@ def dannce_train(params: Dict):
         # mono conversion will happen from RGB npy files, and the generator
         # needs to b aware that the npy files contain RGB content
         params["chan_num"] = params["n_channels_in"]
+
+        if len(missing_samples) != 0:
+            valid_params = {
+                "dim_in": (
+                    params["crop_height"][1] - params["crop_height"][0],
+                    params["crop_width"][1] - params["crop_width"][0],
+                ),
+                "n_channels_in": params["n_channels_in"],
+                "batch_size": 1,
+                "n_channels_out": params["new_n_channels_out"],
+                "out_scale": params["sigma"],
+                "crop_width": params["crop_width"],
+                "crop_height": params["crop_height"],
+                "vmin": params["vmin"],
+                "vmax": params["vmax"],
+                "nvox": params["nvox"],
+                "interp": params["interp"],
+                "depth": params["depth"],
+                "channel_combo": None,
+                "mode": "coordinates",
+                "camnames": camnames,
+                "immode": params["immode"],
+                "shuffle": False,
+                "rotation": False,
+                "vidreaders": vids,
+                "distort": True,
+                "expval": True,
+                "crop_im": False,
+                "chunks": total_chunks,
+                "mono": params["mono"],
+                "mirror": params["mirror"],
+                "predict_flag": False,
+                "norm_im": False
+            }
+
+            tifdirs = []
+            npy_generator = generator.DataGenerator_3Dconv_torch(
+                missing_samples,
+                datadict,
+                datadict_3d,
+                cameras,
+                missing_samples,
+                com3d_dict,
+                tifdirs,
+                **valid_params
+            )
+            print("Generating missing npy files ...")
+            for i, samp in enumerate(missing_samples):
+                exp = int(samp.split("_")[0])
+                save_root = missing_npydir[exp]
+                fname = "0_{}.npy".format(samp.split("_")[1])
+
+                rr = npy_generator.__getitem__(i)
+                print(i, end="\r")
+                np.save(os.path.join(save_root, "image_volumes", fname), rr[0][0][0].astype("uint8"))
+                np.save(os.path.join(save_root, "grid_volumes", fname), rr[0][1][0])
+                np.save(os.path.join(save_root, "targets", fname), rr[1][0])
+
+            samples = processing.remove_samples_npy(npydir, samples, params)
+            print("{} samples ready for npy training.".format(len(samples)))
+
     else:
         # Used to initialize arrays for mono, and also in *frommem (the final generator)
         params["chan_num"] = 1 if params["mono"] else params["n_channels_in"]