Update.

[mygpt.git] / main.py

diff --git a/main.py b/main.py
index ac1e2e3..b2adf98 100755 (executable)
--- a/main.py
+++ b/main.py
@@ -24,9 +24,6 @@ parser = argparse.ArgumentParser(description = 'My own GPT.')
 parser.add_argument('--log_filename',
                     type = str, default = 'train.log')
 
-parser.add_argument('--download',
-                    action='store_true', default = False)
-
 parser.add_argument('--seed',
                     type = int, default = 0)
 
@@ -114,7 +111,7 @@ for n in vars(args):
 ######################################################################
 
 def autoregression(
-        model,
+        model, batch_size,
         nb_samples, nb_tokens_to_generate, starting_input = None,
         device = torch.device('cpu')
 ):
@@ -129,7 +126,7 @@ def autoregression(
         first = starting_input.size(1)
         results = torch.cat((starting_input, results), 1)
 
-    for input in results.split(args.batch_size):
+    for input in results.split(batch_size):
         for s in tqdm.tqdm(range(first, input.size(1)), desc = 'synth'):
             output = model(input)
             logits = output[:, s]
@@ -173,6 +170,7 @@ class TaskPicoCLVR(Task):
 
             descr = [ s.strip().split(' ') for s in descr ]
             l = max([ len(s) for s in descr ])
+            #descr = [ [ '<unk>' ] * (l - len(s)) + s for s in descr ]
             descr = [ s + [ '<unk>' ] * (l - len(s)) for s in descr ]
 
             return descr
@@ -194,6 +192,7 @@ class TaskPicoCLVR(Task):
         self.token2id = dict([ (t, n) for n, t in enumerate(tokens) ])
         self.id2token = dict([ (n, t) for n, t in enumerate(tokens) ])
 
+        # Tokenize the train and test sets
         t = [ [ self.token2id[u] for u in s ] for s in self.train_descr ]
         self.train_input = torch.tensor(t, device = self.device)
         t = [ [ self.token2id[u] for u in s ] for s in self.test_descr ]
@@ -255,14 +254,14 @@ class TaskPicoCLVR(Task):
         )
         log_string(f'wrote {image_name}')
 
-        nb_missing = sum( [
-            x[2] for x in picoclvr.nb_missing_properties(
-                descr,
-                height = self.height, width = self.width
-            )
-        ] )
+        np = picoclvr.nb_properties(
+            descr,
+            height = self.height, width = self.width
+        )
+
+        nb_requested_properties, _, nb_missing_properties = zip(*np)
 
-        log_string(f'nb_missing {nb_missing / len(descr):.02f}')
+        log_string(f'nb_requested_properties {sum(nb_requested_properties) / len(descr):.02f} nb_missing_properties {sum(nb_missing_properties) / len(descr):.02f}')
 
 ######################################################################
 
@@ -387,7 +386,7 @@ class TaskMNIST(Task):
         return 256
 
     def produce_results(self, n_epoch, model, nb_samples = 64):
-        results = autoregression(model, nb_samples, 28 * 28, device = self.device)
+        results = autoregression(model, self.batch_size, nb_samples, 28 * 28, device = self.device)
         image_name = f'result_mnist_{n_epoch:04d}.png'
         torchvision.utils.save_image(1 - results.reshape(-1, 1, 28, 28) / 255.,
                                      image_name, nrow = 16, pad_value = 0.8)