Finish some TODOs in comments

src/gz21_ocean_momentum/analysis/analysis.py

@@ -14,6 +14,7 @@
 import numpy as np
 import matplotlib.pyplot as plt
 from os.path import join
+from functools import wraps
 
 data_location = "/data/ag7531/"
 figures_directory = "figures"
@@ -23,15 +24,28 @@ def allow_hold_on(f):
     """Decorator that allows to specify a hold_on parameter that makes the
     plotting use the current figure instead of creating a new one."""
 
-    def wrapper_f(*args, **kargs):
-        if "hold_on" in kargs and kargs["hold_on"]:
+    @wraps(f)  # preserves the name and docstring of the function
+    def wrapped(*args, **kargs):
+        if kargs.pop("hold_on", False):
             plt.gcf()
-            del kargs["hold_on"]
         else:
             plt.figure()
         f(*args, **kargs)
 
-    return wrapper_f
+    return wrapped
+
+
+def allow_save_fig(f):
+    """Decorator that allows to specify a save_file parameter that saves the plot."""
+
+    @wraps(f)
+    def wrapped(*args, **kargs):
+        save_file = kargs.pop("save_file", None)
+        f(*args, **kargs)
+        if save_file:  # save_file gives the filename of the saved figure
+            plt.savefig(join(data_location, figures_directory, save_file))
+
+    return wrapped
 
 
 class TimeSeriesForPoint:
@@ -71,6 +85,7 @@ def true_values(self):
         return self._time_series["true values"]
 
     @allow_hold_on
+    @allow_save_fig
     def plot_pred_vs_true(self):
         """Plots the predictions and the true target accross time for the
         instance's point."""
@@ -87,4 +102,4 @@ def plot_pred_vs_true(self):
     def save_fig(self):
         if not self._fig:
             self.plot_pred_vs_true()
-        plt.savefig(join(data_location, figures_directory, self.name))
+        plt.savefig(join(data_location, figures_directory, self._name))

src/gz21_ocean_momentum/analysis/utils.py

@@ -14,6 +14,7 @@
 import pandas as pd
 from analysis.analysis import TimeSeriesForPoint
 import xarray as xr
+from typing import Optional
 from scipy.ndimage import gaussian_filter
 from data.pangeo_catalog import get_patch, get_whole_data
 from cartopy.crs import PlateCarree
@@ -165,7 +166,7 @@ def onClick(event):
     fig.canvas.mpl_connect("button_press_event", onClick)
 
 
-def sample(data: np.ndarray, step_time: int = 1, nb_per_time: int = 5):
+def sample(data: np.ndarray, step_time: int = 1, nb_per_time: int = 5, random_state: Optional[int] = None):
     """Samples points from the data, where it is assumed that the data
     is 4-D, with the first dimension representing time , the second
     the channel, and the others representing spatial dimensions.
@@ -185,6 +186,9 @@ def sample(data: np.ndarray, step_time: int = 1, nb_per_time: int = 5):
     :nb_per_time: int,
         Number of points used (chosen randomly according to a uniform
         distribution over the spatial domain) for each image.
+
+    :random_state: int, optional,
+        Random state used for the random number generator.
 
 
     Returns
@@ -194,6 +198,7 @@ def sample(data: np.ndarray, step_time: int = 1, nb_per_time: int = 5):
     """
     if data.ndim != 4:
         raise ValueError("The data is expected to have 4 dimensions.")
+    np.random.seed(random_state)
     n_times, n_channels, n_x, n_y = data.shape
     time_indices = np.arange(0, n_times, step_time)
     x_indices = np.random.randint(0, n_x, (time_indices.shape[0], 2, nb_per_time))

src/gz21_ocean_momentum/models/base.py

@@ -19,7 +19,7 @@
 class DetectOutputSizeMixin:
     """Class to detect the shape of a neural net."""
 
-    # TODO: protect this with `@no_grad` decorator to conserve memory/time etc.
+    @torch.no_grad()
     def output_width(self, input_height, input_width):
         """
         Generate a tensor and run forward model to get output width.
@@ -34,17 +34,14 @@ def output_width(self, input_height, input_width):
         dummy_out.size(3) : int
             width of the output tensor
         """
-        # TODO: following 2 lines can be combined for speedup as
-        #       e.g. `torch.zeros(10, 10, device=self.device)`
-        dummy_in = torch.zeros((1, self.n_in_channels, input_height, input_width))
-        dummy_in = dummy_in.to(device=self.device)
+        dummy_in = torch.zeros((1, self.n_in_channels, input_height, input_width), device=self.device)
         # AB - Self here is assuming access to a neural net forward method?
         #      If so I think this should really be contained in FullyCNN.
         #      We can discuss and I am happy to perform the refactor.
         dummy_out = self(dummy_in)
         return dummy_out.size(3)
 
-    # TODO: protect this with `@no_grad` decorator to conserve memory/time etc.
+    @torch.no_grad()
     def output_height(self, input_height, input_width):
         """
         Generate a tensor and run forward model to get output height.
@@ -59,10 +56,7 @@ def output_height(self, input_height, input_width):
         dummy_out.size(2) : int
             height of the output tensor
         """
-        # TODO: following 2 lines can be combined for speedup as
-        #       e.g. `torch.zeros(10, 10, device=self.device)`
-        dummy_in = torch.zeros((1, self.n_in_channels, input_height, input_width))
-        dummy_in = dummy_in.to(device=self.device)
+        dummy_in = torch.zeros((1, self.n_in_channels, input_height, input_width), device=self.device)
         dummy_out = self(dummy_in)
         return dummy_out.size(2)
 

src/gz21_ocean_momentum/models/fully_conv_net.py

@@ -76,6 +76,8 @@ def __init__(
         # store in_chans as attribute
         self._n_in_channels = in_chans
 
+        self._final_transformation = lambda x: x
+
 
     @staticmethod
     def _process_padding(padding: Optional[str] = None) -> Tuple[int, int]:

src/gz21_ocean_momentum/trainScript.py

@@ -153,6 +153,9 @@ def check_str_is_None(string_in: str):
     "models.transforms.",
 )
 parser.add_argument("--submodel", type=str, default="transform1")
+parser.add_argument(
+    "--device", type=str, default="auto", help="Device to use for training."
+)
 parser.add_argument(
     "--features_transform_cls_name", type=str, default="None", help="Depreciated"
 )
@@ -216,9 +219,11 @@ def check_str_is_None(string_in: str):
     _check_dir(os.path.join(data_location, directory))
 
 # Device selection. If available we use the GPU.
-# TODO Allow CLI argument to select the GPU
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-device_type = DEVICE_TYPE.GPU if torch.cuda.is_available() else DEVICE_TYPE.CPU
+device = torch.device(
+    params.device if params.device != "auto" else
+    "cuda:0" if torch.cuda.is_available() else "cpu"
+)
+device_type = DEVICE_TYPE.CPU if device.type == 'cpu' else DEVICE_TYPE.GPU
 print("Selected device type: ", device_type.value)
 
 

src/gz21_ocean_momentum/utils.py

@@ -6,8 +6,12 @@
 @author: arthur
 """
 
+import os
 import mlflow
 from mlflow.tracking import client
+import torch
+import random
+import numpy as np
 import pandas as pd
 import pickle
 import gz21_ocean_momentum.models as models
@@ -126,3 +130,16 @@ def pickle_artifact(run_id: str, path: str):
     file = client.download_artifacts(run_id, path)
     f = open(file, "rb")
     return pickle.load(f)
+
+
+def seed_all(seed: int = 0):
+    random.seed(seed)
+    # seed hash
+    os.environ['PYTHONHASHSEED'] = str(seed)
+    # seed numpy
+    np.random.seed(seed)
+    # seed torch
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = True

tests/models/test_fully_conv_net.py

@@ -10,7 +10,6 @@ def test_construct_valid():
     Simple check migrated from `models.models1`.
     """
     net = FullyCNN()
-    net._final_transformation = lambda x: x
     input_ = torch.randint(0, 10, (17, 2, 35, 30)).to(dtype=torch.float)
     input_[0, 0, 0, 0] = np.nan
     output = net(input_)