Simplify argparser. Adding histogram. ToDo: change option reverse. Cl…

…arify rescale.

dwi_ml/io_utils.py

@@ -43,7 +43,7 @@ def add_memory_args(p: ArgumentParser, add_lazy_options=False,
         add_processes_arg(ram_options)
         ram_options.add_argument(
             '--use_gpu', action='store_true',
-            help="If set, use GPU for processing. Cannot be used \ntogether "
+            help="If set, use GPU for processing. Cannot be used together "
                  "with --processes.")
     else:
         p.add_argument('--use_gpu', action='store_true',

dwi_ml/models/projects/transformer_models.py

@@ -11,7 +11,8 @@
 
 from dwi_ml.data.processing.streamlines.sos_eos_management import \
     add_label_as_last_dim, convert_dirs_to_class
-from dwi_ml.data.processing.streamlines.post_processing import compute_directions
+from dwi_ml.data.processing.streamlines.post_processing import \
+    compute_directions
 from dwi_ml.data.spheres import TorchSphere
 from dwi_ml.models.embeddings import keys_to_embeddings
 from dwi_ml.models.main_models import (ModelWithDirectionGetter,
@@ -993,3 +994,18 @@ def merge_batches_weights(self, weights, new_weights, device):
             weights.extend(new_weights)
             return (weights,)
 
+
+def find_transformer_class(model_type: str):
+    """
+    model_type: returned by verify_which_model_in_path.
+    """
+    transformers_dict = {
+        OriginalTransformerModel.__name__: OriginalTransformerModel,
+        TransformerSrcAndTgtModel.__name__: TransformerSrcAndTgtModel,
+        TransformerSrcOnlyModel.__name: TransformerSrcOnlyModel
+    }
+    if model_type not in transformers_dict.keys():
+        raise ValueError("Model type is not a recognized Transformer"
+                         "({})".format(model_type))
+
+    return transformers_dict[model_type]

dwi_ml/models/projects/transformers_utils.py

@@ -26,8 +26,8 @@ def add_transformers_model_args(p):
 
     gx = p.add_argument_group(
         "Embedding of the input (X)",
-        "Input embedding size defines the d_model. The d_model must be divisible "
-        "by the number of heads.\n"
+        "Input embedding size defines the d_model. The d_model must be "
+        "divisible by the number of heads.\n"
         "Note that for TTST, total d_model will rather be "
         "input_embedded_size + target_embedded_size.\n")
     AbstractTransformerModel.add_args_input_embedding(
@@ -56,7 +56,8 @@ def add_transformers_model_args(p):
     gt.add_argument(
         '--target_embedded_size', type=int, metavar='n',
         help="Embedding size for targets (for TTST only). \n"
-             "Total d_model will be input_embedded_size + target_embedded_size.")
+             "Total d_model will be input_embedded_size + "
+             "target_embedded_size.")
 
     gtt = p.add_argument_group(title='Transformer: main layers')
     gtt.add_argument(
@@ -119,20 +120,3 @@ def add_transformers_model_args(p):
 
     g = p.add_argument_group("Output")
     AbstractTransformerModel.add_args_tracking_model(g)
-
-
-def find_transformer_class(model_type):
-    """
-    model_type: returned by verify_which_model_in_path.
-    """
-    if model_type == 'OriginalTransformerModel':
-        model_cls = OriginalTransformerModel
-    elif model_type == 'TransformerSrcAndTgtModel':
-        model_cls = TransformerSrcAndTgtModel
-    elif model_type == 'TransformerSrcOnlyModel':
-        model_cls = TransformerSrcOnlyModel
-    else:
-        raise ValueError("Model type is not a recognized Transformer"
-                         "({})".format(model_type))
-
-    return model_cls

dwi_ml/testing/projects/tt_visu_main.py

@@ -1,30 +1,29 @@
 # -*- coding: utf-8 -*-
 
 """
-Main part for the tt_visualize_weights, separated to be callable by the
-jupyter notebook.
+Main part for the tt_visualize_weights, separated to be callable by the jupyter
+notebook.
 """
 import argparse
 import glob
 import logging
 import os
-from typing import List
 
+from dipy.io.streamline import save_tractogram
 import numpy as np
 import torch
-from dipy.io.streamline import save_tractogram
 from matplotlib import pyplot as plt
 
-from scilpy.io.fetcher import get_home as get_scilpy_folder
 from scilpy.io.streamlines import load_tractogram_with_reference
 from scilpy.io.utils import (add_overwrite_arg, assert_inputs_exist,
                              assert_outputs_exist, add_reference_arg)
 
-from dwi_ml.io_utils import add_logging_arg, verify_which_model_in_path, add_memory_args, \
-    add_arg_existing_experiment_path
+from dwi_ml.io_utils import (add_logging_arg, add_memory_args,
+                             verify_which_model_in_path,
+                             add_arg_existing_experiment_path)
 from dwi_ml.models.projects.transformer_models import (
-    OriginalTransformerModel, TransformerSrcAndTgtModel)
-from dwi_ml.models.projects.transformers_utils import find_transformer_class
+    OriginalTransformerModel, TransformerSrcAndTgtModel,
+    find_transformer_class)
 from dwi_ml.testing.projects.tt_visu_bertviz import (
     encoder_decoder_show_head_view, encoder_decoder_show_model_view,
     encoder_show_model_view, encoder_show_head_view)
@@ -33,32 +32,11 @@
 from dwi_ml.testing.projects.tt_visu_matrix import show_model_view_as_imshow
 from dwi_ml.testing.projects.tt_visu_utils import (
     prepare_encoder_tokens, prepare_decoder_tokens,
-    reshape_attention_to4d_tocpu, unpad_rescale_attention, resample_attention_one_line)
+    reshape_attention_to4d_tocpu, unpad_rescale_attention,
+    resample_attention_one_line)
 from dwi_ml.testing.utils import add_args_testing_subj_hdf5
 
 
-def set_out_dir_visu_weights_and_create_if_not_exists(args):
-    if args.out_dir is None:
-        args.out_dir = os.path.join(args.experiment_path, 'visu_weights')
-    if not os.path.isdir(args.out_dir):
-        os.mkdir(args.out_dir)
-
-    return args
-
-
-def get_config_filename():
-    """
-    File that will be saved by the python script with all the args. The
-    jupyter notebook can then load them again.
-    """
-    # We choose to add it in the hidden .scilpy folder in our home.
-    # (Where our test data also is).
-    hidden_folder = get_scilpy_folder()
-    config_filename = os.path.join(
-        hidden_folder, 'ipynb_tt_visualize_weights.config')
-    return config_filename
-
-
 def build_argparser_transformer_visu():
     """
     This needs to be in a module, to be imported in the jupyter notebook. Do
@@ -68,27 +46,29 @@ def build_argparser_transformer_visu():
         formatter_class=argparse.RawTextHelpFormatter,
         description=__doc__)
 
+    # Main dataset arguments:
     add_arg_existing_experiment_path(p)
     add_args_testing_subj_hdf5(p, ask_input_group=True)
-
     p.add_argument('in_sft',
-                   help="A small tractogram; a bundle of streamlines whose "
-                        "attention mask we will average.")
+                   help="A small tractogram; a bundle of streamlines that "
+                        "should be \nuniformized. Else, see option "
+                        "--align_endpoints")
     p.add_argument(
         '--out_prefix', metavar='name',
         help="Prefix of the all output files. Do not include a path. "
              "Suffixes are: \n"
              "   1) 'as_matrix': tt_matrix_[encoder|decoder|cross].png.\n"
              "   2) 'bertviz': tt_bertviz.html, tt_bertviz.ipynb, "
              "tt_bertviz.config.\n"
-             "   3) 'colored_sft': colored_sft.trk."
+             "   3) 'colored_sft': colored_sft.trk.\n"
              "   4) 'bertviz_locally': None")
     p.add_argument(
         '--out_dir', metavar='d',
         help="Output directory where to save the output files.\n"
              "Default: experiment_path/visu_weights")
 
-    p.add_argument(
+    g = p.add_argument_group("Visualization options")
+    g.add_argument(
         '--visu_type', required=True, nargs='+',
         choices=['as_matrix', 'bertviz', 'colored_sft', 'bertviz_locally'],
         help="Output option. Choose any number (at least one). \n"
@@ -99,34 +79,31 @@ def build_argparser_transformer_visu():
              "      Will create a html file that can be viewed (see "
              "--out_dir)\n"
              "   3) 'colored_sft': Save a colored sft.\n"
-             "   4) 'bertviz_locally': Run the bertviz without using jupyter "
-             "(debug purposes).\n"
-             "      Output will not not show, but html stuff will print in "
-             "the terminal.")
-    p.add_argument(
-        '--rescale', action='store_true',
-        help="If true, rescale to max 1 per row.")
-
-    g = p.add_mutually_exclusive_group()
+             "   4) 'bertviz_locally': Run the bertviz without using jupyter\n"
+             "      (Debugging purposes. Output will not not show, but html\n"
+             "       stuff will print in the terminal.")
+    g.add_argument('--rescale', action='store_true',
+                   help="If true, rescale to max 1 per row.")
     g.add_argument('--align_endpoints', action='store_true',
-                   help="If set, align endpoints of the batch. Either this "
-                        "or --inverse_align_endpoints. \nProbably helps"
-                        "visualisation with option --visu_type 'colored_sft'.")
-    g.add_argument('--inverse_align_endpoints', action='store_true',
-                   help="If set, aligns endpoints and then reverses the "
-                        "bundle.")
-    p.add_argument('--resample_attention', type=int,
-                   help="Streamline will be sampled as decided by the model."
-                        "However, \nattention will be resampled to fit better "
-                        "in the html page. \n (Resampling is done by "
-                        "averaging the attention every N points).\n"
-                        "(only for bertviz and as_matrix")
-    p.add_argument('--average_heads', action='store_true',
+                   help="If set, try aligning endpoints of the sft. Will use "
+                        "the automatic \nalignment. For more options, align "
+                        "your streamlines first, using \n"
+                        "  >> scil_tractogram_uniformize_endpoints.py.\n")
+    g.add_argument('--reverse', action='store_true',
+                   help="If set, reverses all streamlines first.\n"
+                        "(With option --align_endpoints, reversing is done "
+                        "after.)")
+    g.add_argument('--resample_matrix', type=int, metavar='n',
+                   help="Streamlines will be sampled (nb points) as decided "
+                        "by the model. \nHowever, attention shown as a matrix "
+                        "can be resampled \nto better fit in the html page.")
+    g.add_argument('--average_heads', action='store_true',
                    help="If true, resample all heads (per layer per "
                         "attention type).")
 
-    p.add_argument('--batch_size', type=int)
-    add_memory_args(p)
+    g = add_memory_args(p)
+    g.add_argument('--batch_size', metavar='s', type=int,
+                   help="The batch size in number of streamlines.")
 
     p.add_argument('--show_now', action='store_true',
                    help="If set, shows the matrices on screen. Else, only "
@@ -138,6 +115,16 @@ def build_argparser_transformer_visu():
     return p
 
 
+def create_out_dir_visu_weights(args):
+    # Define out_dir as experiment_path/visu_weights if not defined.
+    # Create it if it does not exist.
+    if args.out_dir is None:
+        args.out_dir = os.path.join(args.experiment_path, 'visu_weights')
+    if not os.path.isdir(args.out_dir):
+        os.mkdir(args.out_dir)
+    return args
+
+
 def tt_visualize_weights_main(args, parser):
     """
     Main part of the script: verifies with type of Transformer we have,
@@ -166,7 +153,7 @@ def tt_visualize_weights_main(args, parser):
         parser.error("Experiment {} not found.".format(args.experiment_path))
 
     # Out files: jupyter stuff already managed in main script. Remains the sft.
-    args = set_out_dir_visu_weights_and_create_if_not_exists(args)
+    args = create_out_dir_visu_weights(args)
     out_files = []
     out_sft = None
     prefix_total = os.path.join(args.out_dir, args.out_prefix)
@@ -244,7 +231,7 @@ def tt_visualize_weights_main(args, parser):
     else:  # TransformerSrcOnlyModel
         visu_fct = visu_encoder_only
 
-    visu_fct(weights, sft, args.resample_attention, args.rescale,
+    visu_fct(weights, sft, args.resample_matrix, args.rescale,
              model.direction_getter.add_eos, save_colored_sft,
              run_bertviz, show_as_matrices, colored_sft_name=out_sft,
              matrices_prefix=prefix_total)