X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=qmlp.py;fp=qmlp.py;h=a7defe49d6244301cc091e9cc3e0b2b3236d8621;hb=26ef53ee3769c3b6b92b85d15b5a43cbd18ede07;hp=e12f0e1a6e16e16d9abf7a5ee18edde1e53d7763;hpb=f44ab6863f93ae348e66ffbf52251d96d3b5453c;p=picoclvr.git

diff --git a/qmlp.py b/qmlp.py
index e12f0e1..a7defe4 100755
--- a/qmlp.py
+++ b/qmlp.py
@@ -224,7 +224,7 @@ def generate_sequence_and_test_set(
     nb_mlps_per_batch=1024,
 ):
 
-    inputs, q_test_sets = [],[]
+    seqs, q_test_sets = [],[]
 
     for n in range(0,nb_mlps,nb_mlps_per_batch):
         q_train_set, q_test_set, q_params = generate_sets_and_params(
@@ -235,7 +235,7 @@ def generate_sequence_and_test_set(
             device=device,
         )
 
-        inputs.append(torch.cat(
+        seqs.append(torch.cat(
             [
                 q_train_set,
                 q_train_set.new_full(
@@ -252,10 +252,10 @@ def generate_sequence_and_test_set(
 
         q_test_sets.append(q_test_set)
 
-    input = torch.cat(inputs)
+    seq = torch.cat(seqs)
     q_test_set = torch.cat(q_test_sets)
 
-    return input, q_test_set
+    return seq, q_test_set
 
 
 ######################################################################
@@ -271,7 +271,7 @@ if __name__ == "__main__":
 
     data = []
 
-    input, q_test_set = generate_sequence_and_test_set(
+    seq, q_test_set = generate_sequence_and_test_set(
         nb_mlps=batch_nb_mlps,
         nb_samples=nb_samples,
         device=device,
@@ -281,11 +281,11 @@ if __name__ == "__main__":
     )
 
     end_time = time.perf_counter()
-    print(f"{input.size(0) / (end_time - start_time):.02f} samples per second")
+    print(f"{seq.size(0) / (end_time - start_time):.02f} samples per second")
 
-    q_train_set = input[:, : nb_samples * 3]
-    q_params = input[:, nb_samples * 3 + 1 :]
-    print(f"SANITY #2 {q_train_set.size()=} {q_params.size()=} {input.size()=}")
+    q_train_set = seq[:, : nb_samples * 3]
+    q_params = seq[:, nb_samples * 3 + 1 :]
+    print(f"SANITY #2 {q_train_set.size()=} {q_params.size()=} {seq.size()=}")
     error_train = evaluate_q_params(q_params, q_train_set, nb_mlps_per_batch=17)
     print(f"train {error_train*100}%")
     error_test = evaluate_q_params(q_params, q_test_set, nb_mlps_per_batch=17)