tgt_encoder = LeNetEncoder().to(DEVICE)
discriminator = Discriminator(input_dims=500, hidden_dims=500,
output_dims=2).to(DEVICE)
# In[5]:
#Print models for source
print(src_encoder)
print(src_classifier)
# In[6]:
try:
src_encoder.load_state_dict(torch.load('src_encoder.pth'))
src_classifier.load_state_dict(torch.load('src_classifier.pth'))
except FileNotFoundError:
pretrain.train_src(src_encoder, src_classifier, source_train_loader,
DEVICE)
# In[7]:
#Evaluate pretrained model
pretrain.eval_src(src_encoder, src_classifier, source_train_loader, DEVICE)
# In[8]:
#Evaluate pretrained model
pretrain.eval_src(src_encoder, src_classifier, target_test_loader, DEVICE)
# In[9]:
target_encoder_pth = os.path.join(opt.save_dir, target_encoder_name)
source_classifier_pth = os.path.join(opt.save_dir, source_classifier_name)
else:
print("ERROR: unrecognized target dataset name. Please input 'mnistm' or 'svhn', lower case only.")
# Load models
if os.path.exists(target_encoder_pth):
print("---> found previously saved {}, loading checkpoint...".format(target_encoder_name))
target_encoder.load_state_dict( torch.load(target_encoder_pth) )
else:
print("Error: target encoder not loaded")
if os.path.exists(source_classifier_pth):
print("---> found previously saved {}, loading checkpoint...".format(source_classifier_name))
source_classifier.load_state_dict( torch.load(source_classifier_pth) )
else:
print("Error: source classifier not loaded")
# Move to GPU
if cuda:
target_encoder = target_encoder.cuda()
source_classifier = source_classifier.cuda()
# -------------
# Predictions
# -------------
print("---> generating predictions...")
def main(args):
# read from args
test_path = args.test_path
d_target = args.d_target
output_predict_path = args.output_predict_path
########## Arguments ##########
batch_size = 128
# svhn, usps, mnistm
if d_target == "mnistm":
d_source = "usps"
elif d_target == "svhn":
d_source = "mnistm"
else:
d_source = "svhn"
output_src_classifier_path = "./hw3-4/models/src_classifier_{}_{}.pth".format(
d_source, d_target)
output_tgt_encoder_path = "./hw3-4//models/tgt_encoder_{}_{}.pth".format(
d_source, d_target)
#############################
dataset = ReproduceDataset(test_path)
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=False)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# init models
tgt_encoder = LeNetEncoder()
src_classifier = LeNetClassifier()
# to device
tgt_encoder.to(device)
src_classifier.to(device)
# init weights
tgt_encoder.load_state_dict(torch.load(
output_tgt_encoder_path, map_location=device))
src_classifier.load_state_dict(torch.load(
output_src_classifier_path, map_location=device))
tgt_encoder.eval()
src_classifier.eval()
all_pred = []
for idx, targets in enumerate(dataloader):
target_images = targets.to(device)
target_bs = target_images.shape[0]
with torch.no_grad():
preds = src_classifier(tgt_encoder(target_images))
# calculate label acc
_, pred_labels = torch.max(preds, 1)
all_pred.append(pred_labels)
# save to predict
pred = torch.cat(all_pred).cpu().numpy()
image_names = ['{:05}.png'.format(i) for i in range(len(pred))]
pd.DataFrame({
'image_name': image_names,
'label': pred
}).to_csv(output_predict_path, index=False)