Files from summer project.

applications/physics/HRRL/data/spectra_probies_test_data_info.py

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+import numpy as np
+import os
+
+data_file = '/p/vast1/lbann/datasets/HRRL/images_spectra_test_set.npy'
+
+sample_size = 90201 #size of one sample, flattened (For PROBIES, 300x300 image + 201 spectra array)
+nsamples = 701 
+
+samples = None
+
+# Sample access functions
+def get_sample(index):
+    global samples
+    if samples is None:
+      samples_raw = np.load(data_file, mmap_mode='r', allow_pickle=True)
+      samples = np.reshape(samples_raw,(nsamples,sample_size))  
+    return samples[index]
+
+def num_samples():
+    return nsamples
+
+def sample_dims():
+    return [sample_size]

applications/physics/HRRL/data/spectra_probies_train_data_info.py

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+import numpy as np
+import os
+
+data_file = '/p/vast1/lbann/datasets/HRRL/images_spectra_train_set.npy'
+
+sample_size = 90201 #size of one sample, flattened (For PROBIES, 300x300 image + 201 spectra array)
+nsamples = 5670 
+
+samples = None
+
+# Sample access functions
+def get_sample(index):
+    global samples
+    if samples is None:
+      samples_raw = np.load(data_file, mmap_mode='r', allow_pickle=True)
+      samples = np.reshape(samples_raw,(nsamples,sample_size))  
+    return samples[index]
+
+def num_samples():
+    return nsamples
+
+def sample_dims():
+    return [sample_size]

applications/physics/HRRL/data/spectra_probies_val_data_info.py

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+import numpy as np
+import os
+
+data_file = '/p/vast1/lbann/datasets/HRRL/images_spectra_val_set.npy'
+
+sample_size = 90201 #size of one sample, flattened (For PROBIES, 300x300 image + 201 spectra array)
+nsamples = 631 
+
+samples = None
+
+# Sample access functions
+def get_sample(index):
+    global samples
+    if samples is None:
+      samples_raw = np.load(data_file, mmap_mode='r', allow_pickle=True)
+      samples = np.reshape(samples_raw,(nsamples,sample_size))  
+    return samples[index]
+
+def num_samples():
+    return nsamples
+
+def sample_dims():
+    return [sample_size]

applications/physics/HRRL/images_to_scalers_grid_search.py

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+import lbann
+import torch
+import lbann.torch
+import lbann.contrib.launcher
+import lbann.contrib.args
+import google.protobuf.text_format as txtf
+import lbann.contrib.hyperparameter as hyper
+import argparse
+import sys
+import os
+from os.path import abspath, dirname, join
+
+# ==============================================
+# Setup 
+# ==============================================
+
+# Debugging
+torch._dynamo.config.verbose=True
+
+# Command-line arguments
+desc = ('Construct and a grid search on HRRL PROBIES data. ')
+parser = argparse.ArgumentParser(description=desc)
+lbann.contrib.args.add_scheduler_arguments(parser)
+parser.add_argument(
+    '--job-name', action='store', default='probiesNet', type=str,
+    help='scheduler job name (default: probiesNet)')
+parser.add_argument(
+    '--mini-batch-size', action='store', default=32, type=int,
+    help='mini-batch size (default: 32)', metavar='NUM')
+parser.add_argument(
+    '--reader-prototext', action='store', default='probies_v2.prototext', type=str,
+    help='data to use (default: probies_v2.prototext, 20K data)')
+parser.add_argument(
+    '--num-epochs', action='store', default=100, type=int, 
+    help='number of epochs (default: 100)', metavar='NUM')
+
+# Add reader prototext
+lbann.contrib.args.add_optimizer_arguments(parser)
+args = parser.parse_args()
+
+# Default data reader
+cur_dir = dirname(abspath(__file__))
+data_reader_prototext = join(cur_dir,
+                             'data',
+                             args.reader_prototext)
+
+print("DATA READER ", data_reader_prototext)
+
+# Make script with 2 nodes
+script = lbann.launcher.make_batch_script(nodes=2, procs_per_node=4) 
+
+# Run experiment
+kwargs = lbann.contrib.args.get_scheduler_kwargs(args)
+
+# Define the model for the search
+def make_search_model(
+        learning_rate,
+    	beta1, 
+        beta2,
+        eps,
+        intermed_fc_layers,
+        activation,
+        dropout_percent,
+        num_labels=5):
+
+    import models.probiesNetLBANN_grid_search as model
+    images = lbann.Input(data_field='samples')
+    responses = lbann.Input(data_field='responses')
+    num_labels = 5
+    images = lbann.Reshape(images, dims=[1, 300, 300])
+    pred = model.PROBIESNetLBANN(num_labels, intermed_fc_layers, activation, dropout_percent)(images)
+
+    # lbann slice layer 
+    pred_slice = lbann.Slice(pred, axis=0, slice_points=[0,1,2,3,4,5])
+    response_slice = lbann.Slice(responses, axis=0, slice_points=[0,1,2,3,4,5])
+
+    # ==============================================
+    # Metrics 
+    # ==============================================
+
+    # MSE loss between responses and preds 
+    mse = lbann.MeanSquaredError([responses, pred])
+
+    # Responses
+    epmax_response = lbann.Identity(response_slice)
+    etot_response = lbann.Identity(response_slice)
+    n_response = lbann.Identity(response_slice)
+    t_response = lbann.Identity(response_slice)
+    alpha_response = lbann.Identity(response_slice)
+
+    # Preds
+    epmax_pred = lbann.Identity(pred_slice)
+    etot_pred = lbann.Identity(pred_slice)
+    n_pred = lbann.Identity(pred_slice)
+    t_pred = lbann.Identity(pred_slice)
+    alpha_pred = lbann.Identity(pred_slice)
+
+    # MSEs
+    mse_epmax = lbann.MeanSquaredError([epmax_response, epmax_pred])
+    mse_etot = lbann.MeanSquaredError([etot_response, etot_pred])
+    mse_n = lbann.MeanSquaredError([n_response, n_pred])
+    mse_t = lbann.MeanSquaredError([t_response, t_pred])
+    mse_alpha = lbann.MeanSquaredError([alpha_response, alpha_pred])
+
+    layers = list(lbann.traverse_layer_graph([images, responses]))
+
+    # Append metrics
+    metrics = [lbann.Metric(mse, name='mse')]
+    metrics.append(lbann.Metric(mse_epmax, name='mse_epmax'))
+    metrics.append(lbann.Metric(mse_etot, name='mse_etot'))
+    metrics.append(lbann.Metric(mse_n, name='mse_n'))
+    metrics.append(lbann.Metric(mse_t, name='mse_t'))
+    metrics.append(lbann.Metric(mse_alpha, name='mse_alpha'))
+
+    callbacks = [lbann.CallbackPrint(),
+                lbann.CallbackTimer()]
+
+    layers = list(lbann.traverse_layer_graph([images, responses]))
+
+    model = lbann.Model(args.num_epochs,
+                        layers=layers, 
+                        objective_function=mse,  
+                        metrics=metrics,
+                        callbacks=callbacks) 
+
+    # Setup optimizer
+    opt = lbann.Adam(learn_rate=learning_rate,beta1=beta1,beta2=beta2,eps=eps)
+
+    # Load data reader from prototext
+    data_reader_proto = lbann.lbann_pb2.LbannPB()
+    with open(data_reader_prototext, 'r') as f:
+        txtf.Merge(f.read(), data_reader_proto)
+    data_reader_proto = data_reader_proto.data_reader
+
+    # Aliases for simplicity
+    SGD = lbann.BatchedIterativeOptimizer
+    RPE = lbann.RandomPairwiseExchange
+
+    # Construct the local training algorithm
+    local_sgd = SGD("local sgd", num_iterations=10)
+
+    # Construct the metalearning strategy. 
+    meta_learning = RPE(
+        metric_strategies={'mse': RPE.MetricStrategy.LOWER_IS_BETTER})
+
+    # Setup vanilla trainer
+    trainer = lbann.Trainer(mini_batch_size=args.mini_batch_size)
+    return model, opt, data_reader_proto, trainer
+
+# run the grid search using make_search_model.  Options below.
+hyper.grid_search(
+    script,
+    make_search_model,
+    use_data_store=True, # must be True for images to scalers training
+    procs_per_trainer=1,
+    learning_rate=[0.00001],
+    beta1=[0.9],
+    beta2=[0.9],
+    eps=[1e-8],
+    intermed_fc_layers = [[960,240]],
+    activation = [lbann.Relu],
+    dropout_percent = [0.7])
+
+# Sample syntax for a larger search:
+# hyper.grid_search(
+#     script,
+#     make_search_model,
+#     use_data_store=False,
+#     procs_per_trainer=1,
+#     learning_rate=[0.00001],
+#     beta1=[0.9,0.99],
+#     beta2=[0.9,0.99],
+#     eps=[1e-8],
+#     intermed_fc_layers = [[960,240],[1920,960,480,240],[480,240]],
+#     activation = [lbann.Relu,lbann.Softmax,lbann.LeakyRelu],
+#     dropout_percent = [0.3, 0.5, 0.7])