Merge pull request #152 from geometric-intelligence/fix_serialization

Fix serialization

neurometry/curvature/datasets/utils.py

@@ -6,7 +6,18 @@
 import torch
 from scipy.signal import savgol_filter
 
-import neurometry.curvature.datasets as datasets
+from neurometry.curvature.datasets.experimental import load_neural_activity
+from neurometry.curvature.datasets.gridcells import load_grid_cells_synthetic
+from neurometry.curvature.datasets.synthetic import (
+    load_images,
+    load_place_cells,
+    load_points,
+    load_projected_images,
+    load_s1_synthetic,
+    load_s2_synthetic,
+    load_t2_synthetic,
+    load_three_place_cells,
+)
 
 
 def load(config):
@@ -31,7 +42,7 @@ def load(config):
         test dataset.
     """
     if config.dataset_name == "experimental":
-        dataset, labels = datasets.experimental.load_neural_activity(
+        dataset, labels = load_neural_activity(
             expt_id=config.expt_id, timestep_microsec=config.timestep_microsec
         )
         dataset = dataset[labels["velocities"] > 5]
@@ -72,24 +83,22 @@ def load(config):
         labels = labels[labels["gains"] == gain]
 
     elif config.dataset_name == "synthetic":
-        dataset, labels = datasets.synthetic.load_place_cells()
+        dataset, labels = load_place_cells()
         dataset = np.log(dataset.astype(np.float32) + 1)
         dataset = (dataset - np.min(dataset)) / (np.max(dataset) - np.min(dataset))
     elif config.dataset_name == "images":
-        dataset, labels = datasets.synthetic.load_images(img_size=config.img_size)
+        dataset, labels = load_images(img_size=config.img_size)
         dataset = (dataset - np.min(dataset)) / (np.max(dataset) - np.min(dataset))
         height, width = dataset.shape[1:3]
         dataset = dataset.reshape((-1, height * width))
     elif config.dataset_name == "projected_images":
-        dataset, labels = datasets.synthetic.load_projected_images(
-            img_size=config.img_size
-        )
+        dataset, labels = load_projected_images(img_size=config.img_size)
         dataset = (dataset - np.min(dataset)) / (np.max(dataset) - np.min(dataset))
     elif config.dataset_name == "points":
-        dataset, labels = datasets.synthetic.load_points()
+        dataset, labels = load_points()
         dataset = dataset.astype(np.float32)
     elif config.dataset_name == "s1_synthetic":
-        dataset, labels = datasets.synthetic.load_s1_synthetic(
+        dataset, labels = load_s1_synthetic(
             synthetic_rotation=config.synthetic_rotation,
             n_times=config.n_times,
             radius=config.radius,
@@ -100,7 +109,7 @@ def load(config):
             geodesic_distortion_func=config.geodesic_distortion_func,
         )
     elif config.dataset_name == "s2_synthetic":
-        dataset, labels = datasets.synthetic.load_s2_synthetic(
+        dataset, labels = load_s2_synthetic(
             synthetic_rotation=config.synthetic_rotation,
             n_times=config.n_times,
             radius=config.radius,
@@ -109,7 +118,7 @@ def load(config):
             noise_var=config.noise_var,
         )
     elif config.dataset_name == "t2_synthetic":
-        dataset, labels = datasets.synthetic.load_t2_synthetic(
+        dataset, labels = load_t2_synthetic(
             synthetic_rotation=config.synthetic_rotation,
             n_times=config.n_times,
             major_radius=config.major_radius,
@@ -119,7 +128,7 @@ def load(config):
             noise_var=config.noise_var,
         )
     elif config.dataset_name == "grid_cells":
-        dataset, labels = datasets.gridcells.load_grid_cells_synthetic(
+        dataset, labels = load_grid_cells_synthetic(
             grid_scale=config.grid_scale,
             arena_dims=config.arena_dims,
             n_cells=config.n_cells,
@@ -129,7 +138,7 @@ def load(config):
             resolution=config.resolution,
         )
     elif config.dataset_name == "three_place_cells_synthetic":
-        dataset, labels = datasets.synthetic.load_three_place_cells()
+        dataset, labels = load_three_place_cells()
     print(f"Dataset shape: {dataset.shape}.")
     if type(dataset) == np.ndarray:
         dataset_torch = torch.from_numpy(dataset)

neurometry/curvature/default_config.py

@@ -11,7 +11,7 @@
 # WANDB API KEY
 # Find it here: https://wandb.ai/authorize
 # Story it in file: api_key.txt (without extra line break)
-api_key_path = os.path.join(os.getcwd(),"api_key.txt")
+api_key_path = os.path.join(os.getcwd(), "api_key.txt")
 with open(api_key_path) as f:
     api_key = f.read()
 
@@ -28,18 +28,14 @@
 if not os.path.exists(curvature_profiles_dir):
     os.makedirs(curvature_profiles_dir)
 
-print(configs_dir)
-print(trained_models_dir)
-
-
 # Hardware
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
 # Can be replaced by logging.DEBUG or logging.WARNING
 logging.basicConfig(level=logging.INFO)
 
 # Results
-project = "neurometry"
+project = "topo-vae"
 trained_model_path = None
 
 ### Fixed experiment parameters ###
@@ -164,10 +160,10 @@
 
 # Only used of dataset_name in ["s1_synthetic", "s2_synthetic", "t2_synthetic"]
 n_times = [2500]  # , 2000]  # actual number of times is sqrt_ntimes ** 2
-embedding_dim = [5]  # for s1 stopped at 5 (not done, but 3 was done)
+embedding_dim = [3]  # for s1 stopped at 5 (not done, but 3 was done)
 geodesic_distortion_amp = [0.4]
 # TODO: Add 0.03, possibly 0,000[1
-noise_var = [0.1]  # , 1e-2, 1e-1] 0.075, 0.1] #[
+noise_var = [1e-5]  # , 1e-2, 1e-1] 0.075, 0.1] #[
 
 # Only used if dataset_name == "grid_cells"
 grid_scale = [1.0]
@@ -188,7 +184,7 @@
 scheduler = False
 log_interval = 20
 checkpt_interval = 20
-n_epochs = 60  # 00  # 00  # 50  # 200  # 150  # 240
+n_epochs = 400  # 00  # 00  # 50  # 200  # 150  # 240
 sftbeta = 4.5  # beta parameter for softplus
 alpha = 1.0  # weight for the reconstruction loss
 beta = 0.03  # 0.03  # weight for KL loss
@@ -202,14 +198,14 @@
 ### Ray sweep hyperparameters ###
 # --> Lists of values to sweep for each hyperparameter
 # Except for lr_min and lr_max which are floats
-lr_min = 0.0001
+lr_min = [0.001]  # 0.0001
 lr_max = 0.1
-batch_size = [16, 64, 128]  # [16,32,64]
-encoder_width = [200, 400]  # [100,400]  # , 100, 200, 300]
-encoder_depth = [4, 10, 12]  # [4,6,8]  # , 10, 20, 50, 100]
-decoder_width = [200, 400]  # [100,400]  # , 100, 200, 300]
-decoder_depth = [4, 6, 8]  # [4,6,8]  # , 10, 20, 50, 100]
-drop_out_p = [0, 0.1]  # [0,0.1,0.2]  # put probability p at 0. for no drop out
+batch_size = [64]  # [16,32,64]
+encoder_width = [400]  # [100,400]  # , 100, 200, 300]
+encoder_depth = [10]  # [4,6,8]  # , 10, 20, 50, 100]
+decoder_width = [200]  # [100,400]  # , 100, 200, 300]
+decoder_depth = [6]  # [4,6,8]  # , 10, 20, 50, 100]
+drop_out_p = [0]  # [0,0.1,0.2]  # put probability p at 0. for no drop out
 for p in drop_out_p:
     assert p >= 0.0 and p <= 1, "Probability needs to be in [0, 1]"
 

neurometry/curvature/evaluate.py

@@ -6,15 +6,15 @@
 
 os.environ["GEOMSTATS_BACKEND"] = "pytorch"
 import geomstats.backend as gs  # noqa: E402
+from geomstats.geometry.pullback_metric import PullbackMetric  # noqa: E402
+from geomstats.geometry.special_orthogonal import SpecialOrthogonal  # noqa: E402
 
 # import gph
 from neurometry.curvature.datasets.synthetic import (  # noqa: E402
     get_s1_synthetic_immersion,
     get_s2_synthetic_immersion,
     get_t2_synthetic_immersion,
 )
-from geomstats.geometry.pullback_metric import PullbackMetric  # noqa: E402
-from geomstats.geometry.special_orthogonal import SpecialOrthogonal  # noqa: E402
 
 
 def get_learned_immersion(model, config):
@@ -106,7 +106,8 @@ def get_z_grid(config, n_grid_points=100):
         z_grid = torch.cartesian_prod(thetas, phis)
     return z_grid
 
-#TODO: change instantiation of PullbackMetric to match latest geomstats version
+
+# TODO: change instantiation of PullbackMetric to match latest geomstats version
 def _compute_curvature(z_grid, immersion, dim, embedding_dim):
     """Compute mean curvature vector and its norm at each point."""
     neural_metric = PullbackMetric(

neurometry/curvature/grid-cells-curvature/models/xu_rnn/LSTM.py

@@ -1,4 +1,3 @@
-
 import torch
 from labml_helpers.module import Module
 from torch import nn

neurometry/curvature/grid-cells-curvature/models/xu_rnn/input_pipeline.py

@@ -18,6 +18,7 @@ def __init__(
         self.dx_list = self._generate_dx_list(config.max_dr_trans)
         # self.dx_list = self._generate_dx_list_continous(config.max_dr_trans)
         self.scale_vector = np.zeros(self.num_blocks) + config.max_dr_isometry
+        self.rng = self.rng.default_rng()
 
     def __iter__(self):
         while True:
@@ -42,9 +43,9 @@ def _gen_data_kernel(self):
         batch_size = self.config.batch_size
         config = self.config
 
-        theta = np.random.random(size=int(batch_size * 1.5)) * 2 * np.pi
+        theta = self.rng.random(size=int(batch_size * 1.5)) * 2 * np.pi
         dr = (
-            np.abs(np.random.normal(size=int(batch_size * 1.5)) * config.sigma_data)
+            np.abs(self.rng.normal(size=int(batch_size * 1.5)) * config.sigma_data)
             * self.num_grid
         )
         dx = _dr_theta_to_dx(dr, theta)
@@ -57,7 +58,7 @@ def _gen_data_kernel(self):
         x_max, x_min, dx = x_max[select_idx], x_min[select_idx], dx[select_idx]
         assert len(dx) == batch_size
 
-        x = np.random.random(size=(batch_size, 2)) * (x_max - x_min) + x_min
+        x = self.rng.random(size=(batch_size, 2)) * (x_max - x_min) + x_min
         x_prime = x + dx
 
         return {"x": x, "x_prime": x_prime}
@@ -69,7 +70,7 @@ def _gen_data_trans_rnn(self):
         n_steps = self.rnn_step
         dx_list = self.dx_list
 
-        dx_idx = np.random.choice(len(dx_list), size=[n_traj * 10, n_steps])
+        dx_idx = self.rng.choice(len(dx_list), size=[n_traj * 10, n_steps])
         dx = dx_list[dx_idx]  # [N, T, 2]
         dx_cumsum = np.cumsum(dx, axis=1)  # [N, T, 2]
 
@@ -86,7 +87,7 @@ def _gen_data_trans_rnn(self):
         x_start_max, x_start_min = x_start_max[select_idx], x_start_min[select_idx]
         dx_cumsum = dx_cumsum[select_idx]
         x_start = (
-            np.random.random((n_traj, 2)) * (x_start_max - x_start_min) + x_start_min
+         self.rng.random((n_traj, 2)) * (x_start_max - x_start_min) + x_start_min
         )
         x_start = x_start[:, None]  # [N, 1, 2]
         x_start = np.round(x_start - 0.5)
@@ -99,13 +100,13 @@ def _gen_data_iso_numerical(self):
         batch_size = self.config.batch_size
         config = self.config
 
-        theta = np.random.random(size=(batch_size, 2)) * 2 * np.pi
-        dr = np.sqrt(np.random.random(size=(batch_size, 1))) * config.max_dr_isometry
+        theta = self.rng.random(size=(batch_size, 2)) * 2 * np.pi
+        dr = np.sqrt(self.rng.random(size=(batch_size, 1))) * config.max_dr_isometry
         dx = _dr_theta_to_dx(dr, theta)  # [N, 2, 2]
 
         x_max = np.fmin(self.num_grid - 0.5, np.min(self.num_grid - 0.5 - dx, axis=1))
         x_min = np.fmax(-0.5, np.max(-0.5 - dx, axis=1))
-        x = np.random.random(size=(batch_size, 2)) * (x_max - x_min) + x_min
+        x = self.rng.random(size=(batch_size, 2)) * (x_max - x_min) + x_min
         x_plus_dx1 = x + dx[:, 0]
         x_plus_dx2 = x + dx[:, 1]
 
@@ -117,18 +118,18 @@ def _gen_data_iso_numerical_adaptive(self):
         config = self.config
 
         theta = (
-            np.random.random(size=(batch_size, num_blocks, 2)) * 2 * np.pi
+         self.rng.random(size=(batch_size, num_blocks, 2)) * 2 * np.pi
         )  # (batch_size, num_blocks, 2)
         dr = (
-            np.sqrt(np.random.random(size=(batch_size, num_blocks, 1)))
+            np.sqrt(self.rng.random(size=(batch_size, num_blocks, 1)))
             * np.tile(self.scale_vector, (batch_size, 1))[:, :, None]
         )  # (batch_size, num_blocks, 1)
         dx = _dr_theta_to_dx(dr, theta)  # [N, num_blocks, 2, 2]
 
         x_max = np.fmin(self.num_grid - 0.5, np.min(self.num_grid - 0.5 - dx, axis=2))
         x_min = np.fmax(-0.5, np.max(-0.5 - dx, axis=2))
         x = (
-            np.random.random(size=(batch_size, num_blocks, 2)) * (x_max - x_min) + x_min
+         self.rng.random(size=(batch_size, num_blocks, 2)) * (x_max - x_min) + x_min
         )  # (batch_size, num_blocks, 2)
         x_plus_dx1 = x + dx[:, :, 0]
         x_plus_dx2 = x + dx[:, :, 1]
@@ -157,9 +158,9 @@ def _generate_dx_list_continous(self, max_dr):
         dx_list = []
         batch_size = self.config.batch_size
 
-        dr = np.sqrt(np.random.random(size=(batch_size,))) * max_dr
-        np.random.shuffle(dr)
-        theta = np.random.random(size=(batch_size,)) * 2 * np.pi
+        dr = np.sqrt(self.rng.random(size=(batch_size,))) * max_dr
+        self.rng.shuffle(dr)
+        theta = self.rng.random(size=(batch_size,)) * 2 * np.pi
 
         dx = _dr_theta_to_dx(dr, theta)
 
@@ -202,7 +203,7 @@ def _gen_trajectory_vis(self, n_traj, n_steps):
         x_start = np.reshape([5, 5], newshape=(1, 1, 2))  # [1, 1, 2]
         dx_idx_pool = np.where((dx_list[:, 0] >= -1) & (dx_list[:, 1] >= -1))[0]
         # dx_idx_pool = np.where((dx_list[:, 0] >= 0) & (dx_list[:, 1] >= -1))[0]
-        dx_idx = np.random.choice(dx_idx_pool, size=[n_traj * 50, n_steps])
+        dx_idx = self.rng.choice(dx_idx_pool, size=[n_traj * 50, n_steps])
         dx = dx_list[dx_idx]
         dx_cumsum = np.cumsum(dx, axis=1)  # [N, T, 2]
 
@@ -224,7 +225,7 @@ def _gen_trajectory(self, n_traj, n_steps):
         # uniformly wihtin the whole region.
         dx_list = self.dx_list
 
-        dx_idx = np.random.choice(len(dx_list), size=[n_traj * 10, n_steps])
+        dx_idx = self.rng.choice(len(dx_list), size=[n_traj * 10, n_steps])
         dx = dx_list[dx_idx]  # [N, T, 2]
         dx_cumsum = np.cumsum(dx, axis=1)  # [N, T, 2]
 
@@ -241,7 +242,7 @@ def _gen_trajectory(self, n_traj, n_steps):
         x_start_max, x_start_min = x_start_max[select_idx], x_start_min[select_idx]
         dx_cumsum = dx_cumsum[select_idx]
         x_start = (
-            np.random.random((n_traj, 2)) * (x_start_max - x_start_min) + x_start_min
+         self.rng.random((n_traj, 2)) * (x_start_max - x_start_min) + x_start_min
         )
         x_start = x_start[:, None]  # [N, 1, 2]
         x_start = np.round(x_start - 0.5)

neurometry/curvature/grid-cells-curvature/models/xu_rnn/model.py

@@ -507,10 +507,9 @@ def get_grid_code(codebook, x, num_grid):
         align_corners=False,
     )  # [1, C, 1, N]
 
-    v_x = torch.squeeze(torch.squeeze(v_x, 0), 1).transpose(0, 1)  # [N, C]
     # v_x = v_x.squeeze().transpose(0, 1)
 
-    return v_x
+    return torch.squeeze(torch.squeeze(v_x, 0), 1).transpose(0, 1)  # [N, C]
 
 
 def get_grid_code_block(codebook, x, num_grid, block_size):
@@ -537,7 +536,10 @@ def get_grid_code_int(codebook, x, num_grid):
 
     # query the 2D codebook, no interpolation
     v_x = torch.vstack(
-        [codebook[:, i, j] for i, j in zip(x_normalized[:, 0], x_normalized[:, 1], strict=False)]
+        [
+            codebook[:, i, j]
+            for i, j in zip(x_normalized[:, 0], x_normalized[:, 1], strict=False)
+        ]
     )
     # v_x = v_x.squeeze().transpose(0, 1)  # [N, C]
 

neurometry/curvature/grid-cells-curvature/models/xu_rnn/scores.py

@@ -15,7 +15,6 @@
 
 """Grid score calculations."""
 
-
 import math
 
 import matplotlib.pyplot as plt