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Cleaning up more.
author
Francois Fleuret
<francois@fleuret.org>
Wed, 27 Jul 2022 14:15:39 +0000
(16:15 +0200)
committer
Francois Fleuret
<francois@fleuret.org>
Wed, 27 Jul 2022 14:15:39 +0000
(16:15 +0200)
main.py
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diff --git
a/main.py
b/main.py
index
77b1b22
..
339d185
100755
(executable)
--- a/
main.py
+++ b/
main.py
@@
-18,7
+18,6
@@
import mygpt
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
######################################################################
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
######################################################################
-
parser = argparse.ArgumentParser(description = 'My own GPT.')
parser.add_argument('--log_filename',
parser = argparse.ArgumentParser(description = 'My own GPT.')
parser.add_argument('--log_filename',
@@
-148,7
+147,7
@@
class Task:
def vocabulary_size(self):
pass
def vocabulary_size(self):
pass
- def produce_results(self, n_epoch, model
, nb_tokens = 50
):
+ def produce_results(self, n_epoch, model):
pass
######################################################################
pass
######################################################################
@@
-202,12
+201,9
@@
class TaskPicoCLVR(Task):
def batches(self, split = 'train'):
assert split in { 'train', 'test' }
def batches(self, split = 'train'):
assert split in { 'train', 'test' }
- if split == 'train':
- for batch in tqdm.tqdm(self.train_input.split(self.batch_size), desc = f'epoch-{split}'):
- yield batch
- else:
- for batch in tqdm.tqdm(self.test_input.split(self.batch_size), desc = f'epoch-{split}'):
- yield batch
+ input = self.train_input if split == 'train' else self.test_input
+ for batch in tqdm.tqdm(input.split(self.batch_size), desc = f'epoch-{split}'):
+ yield batch
def vocabulary_size(self):
return len(self.token2id)
def vocabulary_size(self):
return len(self.token2id)
@@
-215,14
+211,13
@@
class TaskPicoCLVR(Task):
def generate(self, primer_descr, model, nb_tokens):
results = autoregression(
model, self.batch_size,
def generate(self, primer_descr, model, nb_tokens):
results = autoregression(
model, self.batch_size,
-
1,
nb_tokens, primer = descr2tensor(primer_descr),
+
nb_samples = 1, nb_tokens =
nb_tokens, primer = descr2tensor(primer_descr),
device = self.device
)
return ' '.join([ self.id2token[t.item()] for t in results.flatten() ])
device = self.device
)
return ' '.join([ self.id2token[t.item()] for t in results.flatten() ])
- def produce_results(self, n_epoch, model, nb_tokens = None):
- if nb_tokens is None:
- nb_tokens = self.height * self.width + 3
+ def produce_results(self, n_epoch, model):
+ nb_tokens = self.height * self.width + 3
result_descr = [ ]
nb_per_primer = 8
result_descr = [ ]
nb_per_primer = 8
@@
-316,7
+311,8
@@
class TaskWiki103(Task):
def vocabulary_size(self):
return len(self.vocab)
def vocabulary_size(self):
return len(self.vocab)
- def produce_results(self, n_epoch, model, nb_tokens = 50):
+ def produce_results(self, n_epoch, model):
+ nb_tokens = 50
file_name = f'result_wiki103_{n_epoch:04d}.txt'
with open(file_name, 'w') as outfile:
file_name = f'result_wiki103_{n_epoch:04d}.txt'
with open(file_name, 'w') as outfile:
@@
-377,7
+373,8
@@
class TaskMNIST(Task):
def vocabulary_size(self):
return 256
def vocabulary_size(self):
return 256
- def produce_results(self, n_epoch, model, nb_samples = 64):
+ def produce_results(self, n_epoch, model):
+ nb_samples = 64
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.,
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.,