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add config path

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xmuhanma
2024-09-22 15:47:51 +02:00
parent f75657ac3b
commit 9360839a35
3 changed files with 49 additions and 27 deletions
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17
graph_dit/diffusion_model.py
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@@ -24,7 +24,7 @@ class Graph_DiT(pl.LightningModule):
self.guidance_target = getattr(cfg.dataset, 'guidance_target', None)
from nas_201_api import NASBench201API as API
self.api = API('/nfs/data3/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth')
self.api = API(cfg.general.nas_201)
input_dims = dataset_infos.input_dims
output_dims = dataset_infos.output_dims
@@ -44,7 +44,7 @@ class Graph_DiT(pl.LightningModule):
self.args.batch_size = 128
self.args.GPU = '0'
self.args.dataset = 'cifar10-valid'
self.args.api_loc = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
self.args.api_loc = cfg.general.nas_201
self.args.data_loc = '../cifardata/'
self.args.seed = 777
self.args.init = ''
@@ -177,7 +177,7 @@ class Graph_DiT(pl.LightningModule):
rewards = []
if reward_model == 'swap':
import csv
with open('/nfs/data3/hanzhang/nasbenchDiT/graph_dit/swap_results.csv', 'r') as f:
with open(self.cfg.general.swap_result, 'r') as f:
reader = csv.reader(f)
header = next(reader)
data = [row for row in reader]
@@ -345,10 +345,15 @@ class Graph_DiT(pl.LightningModule):
num_examples = self.val_y_collection.size(0)
batch_y = self.val_y_collection[start_index:start_index + to_generate]
all_ys.append(batch_y)
samples.extend(self.sample_batch(batch_id=ident, batch_size=to_generate, y=batch_y,
cur_sample, logprobs = self.sample_batch(batch_id=ident, batch_size=to_generate, y=batch_y,
save_final=to_save,
keep_chain=chains_save,
number_chain_steps=self.number_chain_steps))
number_chain_steps=self.number_chain_steps)
samples.extend(cur_sample)
# samples.extend(self.sample_batch(batch_id=ident, batch_size=to_generate, y=batch_y,
# save_final=to_save,
# keep_chain=chains_save,
# number_chain_steps=self.number_chain_steps))
ident += to_generate
start_index += to_generate
@@ -423,7 +428,7 @@ class Graph_DiT(pl.LightningModule):
cur_sample, log_probs = self.sample_batch(batch_id, to_generate, batch_y, save_final=to_save,
keep_chain=chains_save, number_chain_steps=self.number_chain_steps)
samples.append(cur_sample)
samples.extend(cur_sample)
all_ys.append(batch_y)
batch_id += to_generate