def loadmodel(hook_fn):
if settings.MODEL_FILE is None:
model = torchvision.models.__dict__[settings.MODEL](pretrained=True)
else:
checkpoint = torch.load(settings.MODEL_FILE)
if type(checkpoint).__name__ == 'OrderedDict' or type(
checkpoint).__name__ == 'dict':
if settings.MODEL == 'genotype':
backbone.geneC = settings.CHANNELS #params.channels
backbone.geneLayers = settings.LAYERS #params.layers
backbone.geneName = settings.GENE_NAME #params.gene_name
model = ProtoNet(Genotype, settings.N_WAY, settings.N_SHOT)
state_dict = checkpoint['state']
model.load_state_dict(state_dict)
else:
model = torchvision.models.__dict__[settings.MODEL](
num_classes=settings.NUM_CLASSES)
if settings.MODEL_PARALLEL:
state_dict = {
str.replace(k, 'module.', ''): v
for k, v in checkpoint['state_dict'].items()
} # the data parallel layer will add 'module' before each layer name
else:
state_dict = checkpoint
model.load_state_dict(state_dict)
else:
model = checkpoint
for name in settings.FEATURE_NAMES:
if settings.MODEL == 'genotype':
model._modules.get('feature')._modules.get('cells')._modules.get(
'%d' % (settings.LAYER - 1)).register_forward_hook(hook_fn)
else:
model._modules.get(name).register_forward_hook(hook_fn)
if settings.GPU:
model.cuda()
model.eval()
return model
def meta_test(novel_loader,
n_query=15,
pretrained_dataset='miniImageNet',
freeze_backbone=False,
n_pseudo=100,
n_way=5,
n_support=5):
#few_shot_params={"n_way":5, "n_support":5}
#pretrained_dataset = "miniImageNet"
#n_pseudo=100
#n_way=5 # five class
#n_support=5 # each class contain 5 support images. Thus, 25 query images in total
#freeze_backbone=True
#n_query=15 # each class contains 15 query images. Thus, 75 query images in total
correct = 0
count = 0
iter_num = len(novel_loader) #600
acc_all = []
for ti, (x, y) in enumerate(novel_loader): #600 "ti"mes
###############################################################################################
# load pretrained model on miniImageNet
if params.method == 'protonet':
pretrained_model = ProtoNet(model_dict[params.model],
n_way=n_way,
n_support=n_support)
elif 'mytpn' in params.method:
pretrained_model = MyTPN(model_dict[params.model],
n_way=n_way,
n_support=n_support)
checkpoint_dir = '%s/checkpoints/%s/%s_%s' % (
configs.save_dir, pretrained_dataset, params.model, params.method)
if params.train_aug:
checkpoint_dir += '_aug'
checkpoint_dir += '_5way_5shot'
params.save_iter = -1
if params.save_iter != -1:
modelfile = get_assigned_file(checkpoint_dir, params.save_iter)
else:
modelfile = get_best_file(checkpoint_dir)
print(
"load from %s" % (modelfile)
) #logs/checkpoints/miniImageNet/ResNet10_protonet_aug_5way_5shot/best_model.tar
tmp = torch.load(modelfile)
state = tmp['state']
pretrained_model.load_state_dict(state) #load checkpoints to model
pretrained_model.cuda()
###############################################################################################
# split data into support set and query set
n_query = x.size(1) - n_support #20-5=15
x = x.cuda() ##torch.Size([5, 20, 3, 224, 224])
x_var = Variable(x)
support_size = n_way * n_support #25
y_a_i = Variable(torch.from_numpy(np.repeat(
range(n_way), n_support))).cuda() # (25,)
x_b_i = x_var[:, n_support:, :, :, :].contiguous().view(
n_way * n_query,
*x.size()[2:]) # query set (75,3,224,224)
x_a_i = x_var[:, :n_support, :, :, :].contiguous().view(
n_way * n_support,
*x.size()[2:]) # support set (25,3,224,224)
if freeze_backbone == False:
###############################################################################################
# Finetune components initialization
pseudo_q_genrator = PseudoQeuryGenerator(n_way, n_support,
n_pseudo)
delta_opt = torch.optim.Adam(
filter(lambda p: p.requires_grad,
pretrained_model.parameters()))
###############################################################################################
# finetune process
finetune_epoch = 100
fine_tune_n_query = n_pseudo // n_way # 100//5 =20
pretrained_model.n_query = fine_tune_n_query #20
pretrained_model.train()
z_support = x_a_i.view(n_way, n_support,
*x_a_i.size()[1:]) #(5,5,3,224,224)
for epoch in range(finetune_epoch): #100 EPOCH
delta_opt.zero_grad() #clear feature extractor gradient
# generate pseudo query images
psedo_query_set, _ = pseudo_q_genrator.generate(x_a_i)
psedo_query_set = psedo_query_set.cuda().view(
n_way, fine_tune_n_query,
*x_a_i.size()[1:]) #(5,20,3,224,224)
x = torch.cat((z_support, psedo_query_set), dim=1)
loss = pretrained_model.set_forward_loss(x)
loss.backward()
delta_opt.step()
###############################################################################################
# inference
pretrained_model.eval()
pretrained_model.n_query = n_query #15
with torch.no_grad():
scores = pretrained_model.set_forward(
x_var.cuda()) #set_forward in protonet.py
y_query = np.repeat(range(n_way),
n_query) #[0,...0, ...4,...4] with shape (75)
topk_scores, topk_labels = scores.data.topk(1, 1, True, True)
#the 1st argument means return top-1
#the 2nd argument dim=1 means return the value row-wisely
#the 3rd arguemtn is largest=True
#the 4th argument is sorted=True
topk_ind = topk_labels.cpu().numpy()
top1_correct = np.sum(topk_ind[:, 0] == y_query)
correct_this, count_this = float(top1_correct), len(y_query)
acc_all.append((correct_this / count_this * 100))
print("Task %d : %4.2f%% Now avg: %4.2f%%" %
(ti, correct_this / count_this * 100, np.mean(acc_all)))
###############################################################################################
acc_all = np.asarray(acc_all)
acc_mean = np.mean(acc_all)
acc_std = np.std(acc_all)
print('%d Test Acc = %4.2f%% +- %4.2f%%' %
(iter_num, acc_mean, 1.96 * acc_std / np.sqrt(iter_num)))
def meta_test(novel_loader,
n_query=15,
pretrained_dataset='miniImageNet',
freeze_backbone=False,
n_pseudo=100,
n_way=5,
n_support=5):
correct = 0
count = 0
iter_num = len(novel_loader)
acc_all = []
for ti, (x, y) in enumerate(novel_loader):
###############################################################################################
# load pretrained model on miniImageNet
if params.method == 'protonet':
pretrained_model = ProtoNet(model_dict[params.model],
n_way=n_way,
n_support=n_support)
checkpoint_dir = '%s/checkpoints/%s/%s_%s' % (
configs.save_dir, pretrained_dataset, params.model, params.method)
if params.train_aug:
checkpoint_dir += '_aug'
checkpoint_dir += '_5way_5shot'
params.save_iter = -1
if params.save_iter != -1:
modelfile = get_assigned_file(checkpoint_dir, params.save_iter)
else:
modelfile = get_best_file(checkpoint_dir)
tmp = torch.load(modelfile)
state = tmp['state']
pretrained_model.load_state_dict(state)
pretrained_model.cuda()
###############################################################################################
# split data into support set and query set
n_query = x.size(1) - n_support
x = x.cuda()
x_var = Variable(x)
support_size = n_way * n_support
y_a_i = Variable(torch.from_numpy(np.repeat(
range(n_way), n_support))).cuda() # (25,)
x_b_i = x_var[:, n_support:, :, :, :].contiguous().view(
n_way * n_query,
*x.size()[2:]) # query set
x_a_i = x_var[:, :n_support, :, :, :].contiguous().view(
n_way * n_support,
*x.size()[2:]) # support set
if freeze_backbone == False:
###############################################################################################
# Finetune components initialization
pseudo_q_genrator = PseudoQeuryGenerator(n_way, n_support,
n_pseudo)
delta_opt = torch.optim.Adam(
filter(lambda p: p.requires_grad,
pretrained_model.parameters()))
###############################################################################################
# finetune process
finetune_epoch = 100
fine_tune_n_query = n_pseudo // n_way
pretrained_model.n_query = fine_tune_n_query
pretrained_model.train()
z_support = x_a_i.view(n_way, n_support, *x_a_i.size()[1:])
for epoch in range(finetune_epoch):
delta_opt.zero_grad()
# generate pseudo query images
psedo_query_set, _ = pseudo_q_genrator.generate(x_a_i)
psedo_query_set = psedo_query_set.cuda().view(
n_way, fine_tune_n_query,
*x_a_i.size()[1:])
x = torch.cat((z_support, psedo_query_set), dim=1)
loss = pretrained_model.set_forward_loss(x)
loss.backward()
delta_opt.step()
###############################################################################################
# inference
pretrained_model.eval()
pretrained_model.n_query = n_query
with torch.no_grad():
scores = pretrained_model.set_forward(x_var.cuda())
y_query = np.repeat(range(n_way), n_query)
topk_scores, topk_labels = scores.data.topk(1, 1, True, True)
topk_ind = topk_labels.cpu().numpy()
top1_correct = np.sum(topk_ind[:, 0] == y_query)
correct_this, count_this = float(top1_correct), len(y_query)
acc_all.append((correct_this / count_this * 100))
print("Task %d : %4.2f%% Now avg: %4.2f%%" %
(ti, correct_this / count_this * 100, np.mean(acc_all)))
###############################################################################################
acc_all = np.asarray(acc_all)
acc_mean = np.mean(acc_all)
acc_std = np.std(acc_all)
print('%d Test Acc = %4.2f%% +- %4.2f%%' %
(iter_num, acc_mean, 1.96 * acc_std / np.sqrt(iter_num)))
def calculate_dist(n_way, n_shot, task_num, episode, batch_size=64):
model = ProtoNet(model_dict["ResNet18"], n_way, n_shot)
device = "cuda:0"
with torch.cuda.device(device):
model = model.cuda()
resume_file = "/home/takumi/research/CloserLookFewShot/instance_selection/feature_space/{0}.tar".format(
episode)
tmp = torch.load(resume_file)
model.load_state_dict(tmp['state'])
base_file = "/home/takumi/research/CloserLookFewShot/filelists/full_Imagenet_except_testclass.json"
candidate_data_manager = SimpleDataManager(224, batch_size)
candidate_data_loader = candidate_data_manager.get_data_loader(
base_file, aug=False, shuffle=False)
task_file = "/home/takumi/research/CloserLookFewShot/instance_selection/task/few_shot_task{0}.json".format(
task_num)
few_image_list = task_train_reader(task_file, n_shot, n_way)
few_image_feature_list = []
for images in few_image_list:
images = images.to(device)
features2 = model.feature(images)
features = torch.mean(features2, dim=0)
few_image_feature_list.append(features)
#print(euclidean_dist(features2, features2))
dim = few_image_feature_list[0].size()[0]
features_ave = torch.zeros(0, dim).to(device)
for x in few_image_feature_list:
features_ave = torch.cat([features_ave, x.unsqueeze(0)], dim=0)
with open(base_file) as f:
base = json.load(f)
image_dists = []
image_id = 0
for x, labels in tqdm.tqdm(candidate_data_loader):
x = x.to(device)
y = model.feature(x)
#print(euclidean_dist(y, y))
#print(euclidean_dist(features_ave, features_ave))
dist = euclidean_dist(y, features_ave)
#print(dist)
dist_min, _ = torch.min(dist, dim=1)
dist_min = dist_min.tolist()
labels = labels.tolist()
for i in range(len(dist_min)):
image_dists.append(
[dist_min[i], labels[i], base["image_names"][image_id]])
image_id += 1
image_dists.sort()
task_image_dist = dict()
task_image_dist["label_names"] = copy.deepcopy(base["label_names"])
task_image_dist["image_names"] = []
task_image_dist["image_labels"] = []
task_image_dist["distance"] = []
for i in range(len(image_dists)):
task_image_dist["image_names"].append(image_dists[i][2])
task_image_dist["image_labels"].append(image_dists[i][1])
task_image_dist["distance"].append(image_dists[i][0])
with open(
"/home/takumi/research/CloserLookFewShot/instance_selection/task/task{0}_dataset_dist_{1}.json"
.format(task_num, episode), "w") as f:
json.dump(task_image_dist, f)
print(len(task_image_dist["image_names"]),
len(task_image_dist["image_labels"]),
len(task_image_dist["distance"]))
n_query=params.n_query,
**train_few_shot_params,
num_views=params.num_views)
base_loader = base_datamgr.get_data_loader(base_file, aug=params.train_aug)
test_few_shot_params = dict(n_way=params.test_n_way,
n_support=params.n_shot)
val_datamgr = SetDataManager(image_size,
n_query=params.n_query,
**test_few_shot_params,
num_views=params.num_views)
val_loader = val_datamgr.get_data_loader(val_file, aug=False)
backbone = model_dict[params.model]
model = ProtoNet(backbone, params.num_views, **train_few_shot_params)
model = model.cuda()
# model = torch.nn.DataParallel(model).cuda()
params.checkpoint_dir = '%s/checkpoints/%s/%s_%s' % (
configs.save_dir, params.dataset, params.model, params.method)
if params.train_aug:
params.checkpoint_dir += '_aug'
params.checkpoint_dir += '_%dway_%dshot_%dviews_lr%f' % (
params.train_n_way, params.n_shot, params.num_views, params.lr)
if not os.path.isdir(params.checkpoint_dir):
os.makedirs(params.checkpoint_dir)
if params.resume:
resume_file = get_resume_file(params.checkpoint_dir)
if resume_file is not None:
tmp = torch.load(resume_file)
def explain_gnnnet():
params = options.parse_args('test')
feature_model = backbone.model_dict['ResNet10']
params.method = 'gnnnet'
params.dataset = 'miniImagenet' # name relationnet --testset miniImagenet
params.name = 'gnn'
params.testset = 'miniImagenet'
params.data_dir = '/home/sunjiamei/work/fewshotlearning/dataset/'
params.save_dir = '/home/sunjiamei/work/fewshotlearning/CrossDomainFewShot-master/output'
if 'Conv' in params.model:
image_size = 84
else:
image_size = 224
split = params.split
n_query = 1
loadfile = os.path.join(params.data_dir, params.testset, split + '.json')
few_shot_params = dict(n_way=params.test_n_way, n_support=params.n_shot)
data_datamgr = SetDataManager(image_size,
n_query=n_query,
**few_shot_params)
data_loader = data_datamgr.get_data_loader(loadfile, aug=False)
# model
print(' build metric-based model')
if params.method == 'protonet':
model = ProtoNet(backbone.model_dict[params.model], **few_shot_params)
elif params.method == 'matchingnet':
model = MatchingNet(backbone.model_dict[params.model],
**few_shot_params)
elif params.method == 'gnnnet':
model = GnnNet(backbone.model_dict[params.model], **few_shot_params)
elif params.method in ['relationnet', 'relationnet_softmax']:
if params.model == 'Conv4':
feature_model = backbone.Conv4NP
elif params.model == 'Conv6':
feature_model = backbone.Conv6NP
else:
feature_model = backbone.model_dict[params.model]
loss_type = 'LRP'
model = RelationNet(feature_model,
loss_type=loss_type,
**few_shot_params)
else:
raise ValueError('Unknown method')
checkpoint_dir = '%s/checkpoints/%s' % (params.save_dir, params.name)
# print(checkpoint_dir)
if params.save_epoch != -1:
modelfile = get_assigned_file(checkpoint_dir, params.save_epoch)
else:
modelfile = get_best_file(checkpoint_dir)
# print(modelfile)
if modelfile is not None:
tmp = torch.load(modelfile)
try:
model.load_state_dict(tmp['state'])
print('loaded pretrained model')
except RuntimeError:
print('warning! RuntimeError when load_state_dict()!')
model.load_state_dict(tmp['state'], strict=False)
except KeyError:
for k in tmp['model_state']: ##### revise latter
if 'running' in k:
tmp['model_state'][k] = tmp['model_state'][k].squeeze()
model.load_state_dict(tmp['model_state'], strict=False)
except:
raise
model = model.cuda()
model.eval()
model.n_query = n_query
# for module in model.modules():
# print(type(module))
lrp_preset = lrp_presets.SequentialPresetA()
feature_model = model.feature
fc_encoder = model.fc
gnn_net = model.gnn
lrp_wrapper.add_lrp(fc_encoder, lrp_preset)
# lrp_wrapper.add_lrp(feature_model, lrp_preset)
# lrp_wrapper.add_lrp(fc_encoder,lrp_preset)
# lrp_wrapper.add_lrp(feature_model, lrp_preset)
# acc = 0
# count = 0
# tested the forward pass is correct by observing the accuracy
# for i, (x, _, _) in enumerate(data_loader):
# x = x.cuda()
# support_label = torch.from_numpy(np.repeat(range(model.n_way), model.n_support)).unsqueeze(1)
# support_label = torch.zeros(model.n_way*model.n_support, model.n_way).scatter(1, support_label, 1).view(model.n_way, model.n_support, model.n_way)
# support_label = torch.cat([support_label, torch.zeros(model.n_way, 1, model.n_way)], dim=1)
# support_label = support_label.view(1, -1, model.n_way)
# support_label = support_label.cuda()
# x = x.view(-1, *x.size()[2:])
#
# x_feature = feature_model(x)
# x_fc_encoded = fc_encoder(x_feature)
# z = x_fc_encoded.view(model.n_way, -1, x_fc_encoded.size(1))
# gnn_feature = [
# torch.cat([z[:, :model.n_support], z[:, model.n_support + i:model.n_support + i + 1]], dim=1).view(1, -1, z.size(2))
# for i in range(model.n_query)]
# gnn_nodes = torch.cat([torch.cat([z, support_label], dim=2) for z in gnn_feature], dim=0)
# scores = gnn_net(gnn_nodes)
# scores = scores.view(model.n_query, model.n_way, model.n_support + 1, model.n_way)[:, :, -1].permute(1, 0,
# 2).contiguous().view(
# -1, model.n_way)
# pred = scores.data.cpu().numpy().argmax(axis=1)
# y = np.repeat(range(model.n_way), n_query)
# acc += np.sum(pred == y)
# count += len(y)
# # print(1.0*acc/count)
# print(1.0*acc/count)
with open(
'/home/sunjiamei/work/fewshotlearning/dataset/miniImagenet/class_to_readablelabel.json',
'r') as f:
class_to_readable = json.load(f)
explanation_save_dir = os.path.join(params.save_dir, 'explanations',
params.name)
if not os.path.isdir(explanation_save_dir):
os.makedirs(explanation_save_dir)
for batch_idx, (x, y, p) in enumerate(data_loader):
print(p)
label_to_readableclass, query_img_path, query_gt_class = LRPutil.get_class_label(
p, class_to_readable, model.n_query)
x = x.cuda()
support_label = torch.from_numpy(
np.repeat(range(model.n_way),
model.n_support)).unsqueeze(1) #torch.Size([25, 1])
support_label = torch.zeros(model.n_way * model.n_support,
model.n_way).scatter(1, support_label,
1).view(
model.n_way,
model.n_support,
model.n_way)
support_label = torch.cat(
[support_label,
torch.zeros(model.n_way, 1, model.n_way)], dim=1)
support_label = support_label.view(1, -1, model.n_way)
support_label = support_label.cuda() #torch.Size([1, 30, 5])
x = x.contiguous()
x = x.view(-1, *x.size()[2:]) #torch.Size([30, 3, 224, 224])
x_feature = feature_model(x) #torch.Size([30, 512])
x_fc_encoded = fc_encoder(x_feature) #torch.Size([30, 128])
z = x_fc_encoded.view(model.n_way, -1,
x_fc_encoded.size(1)) # (5,6,128)
gnn_feature = [
torch.cat([
z[:, :model.n_support],
z[:, model.n_support + i:model.n_support + i + 1]
],
dim=1).view(1, -1, z.size(2))
for i in range(model.n_query)
] # model.n_query is the number of query images for each class
# gnn_feature is grouped into n_query groups. each group contains the support image features concatenated with one query image features.
# print(len(gnn_feature), gnn_feature[0].shape)
gnn_nodes = torch.cat(
[torch.cat([z, support_label], dim=2) for z in gnn_feature], dim=0
) # the features are concatenated with the one hot label. for the unknow image the one hot label is all zero
# perform gnn_net step by step
# the first iteration
print('x', gnn_nodes.shape)
W_init = torch.eye(
gnn_nodes.size(1), device=gnn_nodes.device
).unsqueeze(0).repeat(gnn_nodes.size(0), 1, 1).unsqueeze(
3
) # (n_querry, n_way*(num_support + 1), n_way*(num_support + 1), 1)
# print(W_init.shape)
W1 = gnn_net._modules['layer_w{}'.format(0)](
gnn_nodes, W_init
) # (n_querry, n_way*(num_support + 1), n_way*(num_support + 1), 2)
# print(Wi.shape)
x_new1 = F.leaky_relu(gnn_net._modules['layer_l{}'.format(0)](
[W1, gnn_nodes])[1]) # (num_querry, num_support + 1, num_outputs)
# print(x_new1.shape) #torch.Size([1, 30, 48])
gnn_nodes_1 = torch.cat([gnn_nodes, x_new1],
2) # (concat more features)
# print('gn1',gnn_nodes_1.shape) #torch.Size([1, 30, 181])
# the second iteration
W2 = gnn_net._modules['layer_w{}'.format(1)](
gnn_nodes_1, W_init
) # (n_querry, n_way*(num_support + 1), n_way*(num_support + 1), 2)
x_new2 = F.leaky_relu(gnn_net._modules['layer_l{}'.format(1)](
[W2,
gnn_nodes_1])[1]) # (num_querry, num_support + 1, num_outputs)
# print(x_new2.shape)
gnn_nodes_2 = torch.cat([gnn_nodes_1, x_new2],
2) # (concat more features)
# print('gn2', gnn_nodes_2.shape) #torch.Size([1, 30, 229])
Wl = gnn_net.w_comp_last(gnn_nodes_2, W_init)
# print(Wl.shape) #torch.Size([1, 30, 30, 2])
scores = gnn_net.layer_last(
[Wl, gnn_nodes_2])[1] # (num_querry, num_support + 1, num_way)
print(scores.shape)
scores_sf = torch.softmax(scores, dim=-1)
# print(scores_sf)
gnn_logits = torch.log(LRPutil.LOGIT_BETA * scores_sf /
(1 - scores_sf))
gnn_logits = gnn_logits.view(-1, model.n_way,
model.n_support + n_query, model.n_way)
# print(gnn_logits)
query_scores = scores.view(
model.n_query, model.n_way, model.n_support + 1,
model.n_way)[:, :,
-1].permute(1, 0,
2).contiguous().view(-1, model.n_way)
preds = query_scores.data.cpu().numpy().argmax(axis=-1)
# print(preds.shape)
for k in range(model.n_way):
mask = torch.zeros(5).cuda()
mask[k] = 1
gnn_logits_cls = gnn_logits.clone()
gnn_logits_cls[:, :, -1] = gnn_logits_cls[:, :, -1] * mask
# print(gnn_logits_cls)
# print(gnn_logits_cls.shape)
gnn_logits_cls = gnn_logits_cls.view(-1, model.n_way)
relevance_gnn_nodes_2 = explain_Gconv(gnn_logits_cls,
gnn_net.layer_last, Wl,
gnn_nodes_2)
relevance_x_new2 = relevance_gnn_nodes_2.narrow(-1, 181, 48)
# relevance_gnn_nodes = relevance_gnn_nodes_2
relevance_gnn_nodes_1 = explain_Gconv(
relevance_x_new2, gnn_net._modules['layer_l{}'.format(1)], W2,
gnn_nodes_1)
relevance_x_new1 = relevance_gnn_nodes_1.narrow(-1, 133, 48)
relevance_gnn_nodes = explain_Gconv(
relevance_x_new1, gnn_net._modules['layer_l{}'.format(0)], W1,
gnn_nodes)
relevance_gnn_features = relevance_gnn_nodes.narrow(-1, 0, 128)
print(relevance_gnn_features.shape)
relevance_gnn_features += relevance_gnn_nodes_1.narrow(-1, 0, 128)
relevance_gnn_features += relevance_gnn_nodes_2.narrow(
-1, 0, 128) #[2, 30, 128]
relevance_gnn_features = relevance_gnn_features.view(
n_query, model.n_way, model.n_support + 1, 128)
for i in range(n_query):
query_i = relevance_gnn_features[i][:, model.
n_support:model.n_support +
1]
if i == 0:
relevance_z = query_i
else:
relevance_z = torch.cat((relevance_z, query_i), 1)
relevance_z = relevance_z.view(-1, 128)
query_feature = x_feature.view(model.n_way, -1,
512)[:, model.n_support:]
# print(query_feature.shape)
query_feature = query_feature.contiguous()
query_feature = query_feature.view(n_query * model.n_way, 512)
# print(query_feature.shape)
relevance_query_features = fc_encoder.compute_lrp(
query_feature, target=relevance_z)
# print(relevance_query_features.shape)
# print(relevance_gnn_features.shape)
# explain the fc layer and the image encoder
query_images = x.view(model.n_way, -1,
*x.size()[1:])[:, model.n_support:]
query_images = query_images.contiguous()
query_images = query_images.view(-1, *x.size()[1:]).detach()
# print(query_images.shape)
lrp_wrapper.add_lrp(feature_model, lrp_preset)
relevance_query_images = feature_model.compute_lrp(
query_images, target=relevance_query_features)
print(relevance_query_images.shape)
for j in range(n_query * model.n_way):
predict_class = label_to_readableclass[preds[j]]
true_class = query_gt_class[int(j % model.n_way)][int(
j // model.n_way)]
explain_class = label_to_readableclass[k]
img_name = query_img_path[int(j % model.n_way)][int(
j // model.n_way)].split('/')[-1]
if not os.path.isdir(
os.path.join(explanation_save_dir, 'episode' +
str(batch_idx), img_name.strip('.jpg'))):
os.makedirs(
os.path.join(explanation_save_dir,
'episode' + str(batch_idx),
img_name.strip('.jpg')))
save_path = os.path.join(explanation_save_dir,
'episode' + str(batch_idx),
img_name.strip('.jpg'))
if not os.path.exists(
os.path.join(
save_path,
true_class + '_' + predict_class + img_name)):
original_img = Image.fromarray(
np.uint8(
project(query_images[j].permute(
1, 2, 0).detach().cpu().numpy())))
original_img.save(
os.path.join(
save_path,
true_class + '_' + predict_class + img_name))
img_relevance = relevance_query_images.narrow(0, j, 1)
print(predict_class, true_class, explain_class)
# assert relevance_querry_cls[j].sum() != 0
# assert img_relevance.sum()!=0
hm = img_relevance.permute(0, 2, 3, 1).cpu().detach().numpy()
hm = LRPutil.gamma(hm)
hm = LRPutil.heatmap(hm)[0]
hm = project(hm)
hp_img = Image.fromarray(np.uint8(hm))
hp_img.save(
os.path.join(
save_path,
true_class + '_' + explain_class + '_lrp_hm.jpg'))
break
def explain_relationnet():
# print(sys.path)
params = options.parse_args('test')
feature_model = backbone.model_dict['ResNet10']
params.method = 'relationnet'
params.dataset = 'miniImagenet' # name relationnet --testset miniImagenet
params.name = 'relationnet'
params.testset = 'miniImagenet'
params.data_dir = '/home/sunjiamei/work/fewshotlearning/dataset/'
params.save_dir = '/home/sunjiamei/work/fewshotlearning/CrossDomainFewShot-master/output'
if 'Conv' in params.model:
image_size = 84
else:
image_size = 224
split = params.split
n_query = 1
loadfile = os.path.join(params.data_dir, params.testset, split + '.json')
few_shot_params = dict(n_way=params.test_n_way, n_support=params.n_shot)
data_datamgr = SetDataManager(image_size,
n_query=n_query,
**few_shot_params)
data_loader = data_datamgr.get_data_loader(loadfile, aug=False)
acc_all = []
iter_num = 1000
# model
print(' build metric-based model')
if params.method == 'protonet':
model = ProtoNet(backbone.model_dict[params.model], **few_shot_params)
elif params.method == 'matchingnet':
model = MatchingNet(backbone.model_dict[params.model],
**few_shot_params)
elif params.method == 'gnnnet':
model = GnnNet(backbone.model_dict[params.model], **few_shot_params)
elif params.method in ['relationnet', 'relationnet_softmax']:
if params.model == 'Conv4':
feature_model = backbone.Conv4NP
elif params.model == 'Conv6':
feature_model = backbone.Conv6NP
else:
feature_model = backbone.model_dict[params.model]
loss_type = 'LRPmse'
model = RelationNet(feature_model,
loss_type=loss_type,
**few_shot_params)
else:
raise ValueError('Unknown method')
checkpoint_dir = '%s/checkpoints/%s' % (params.save_dir, params.name)
# print(checkpoint_dir)
if params.save_epoch != -1:
modelfile = get_assigned_file(checkpoint_dir, params.save_epoch)
else:
modelfile = get_best_file(checkpoint_dir)
# print(modelfile)
if modelfile is not None:
tmp = torch.load(modelfile)
try:
model.load_state_dict(tmp['state'])
except RuntimeError:
print('warning! RuntimeError when load_state_dict()!')
model.load_state_dict(tmp['state'], strict=False)
except KeyError:
for k in tmp['model_state']: ##### revise latter
if 'running' in k:
tmp['model_state'][k] = tmp['model_state'][k].squeeze()
model.load_state_dict(tmp['model_state'], strict=False)
except:
raise
model = model.cuda()
model.eval()
model.n_query = n_query
# ---test the accuracy on the test set to verify the model is loaded----
acc = 0
count = 0
# for i, (x, y) in enumerate(data_loader):
# scores = model.set_forward(x)
# pred = scores.data.cpu().numpy().argmax(axis=1)
# y = np.repeat(range(model.n_way), n_query)
# acc += np.sum(pred == y)
# count += len(y)
# # print(1.0*acc/count)
# print(1.0*acc/count)
preset = lrp_presets.SequentialPresetA()
feature_model = copy.deepcopy(model.feature)
lrp_wrapper.add_lrp(feature_model, preset=preset)
relation_model = copy.deepcopy(model.relation_module)
# print(relation_model)
lrp_wrapper.add_lrp(relation_model, preset=preset)
with open(
'/home/sunjiamei/work/fewshotlearning/dataset/miniImagenet/class_to_readablelabel.json',
'r') as f:
class_to_readable = json.load(f)
explanation_save_dir = os.path.join(params.save_dir, 'explanations',
params.name)
if not os.path.isdir(explanation_save_dir):
os.makedirs(explanation_save_dir)
for i, (x, y, p) in enumerate(data_loader):
'''x is the images with shape as n_way, n_support + n_querry, 3, img_size, img_size
y is the global labels of the images with shape as (n_way, n_support + n_query)
p is the image path as a list of tuples, length is n_query+n_support, each tuple element is with length n_way'''
if i >= 3:
break
label_to_readableclass, query_img_path, query_gt_class = LRPutil.get_class_label(
p, class_to_readable, model.n_query)
z_support, z_query = model.parse_feature(x, is_feature=False)
z_support = z_support.contiguous()
z_proto = z_support.view(model.n_way, model.n_support,
*model.feat_dim).mean(1)
# print(z_proto.shape)
z_query = z_query.contiguous().view(model.n_way * model.n_query,
*model.feat_dim)
# print(z_query.shape)
# get relations with metric function
z_proto_ext = z_proto.unsqueeze(0).repeat(model.n_query * model.n_way,
1, 1, 1, 1)
# print(z_proto_ext.shape)
z_query_ext = z_query.unsqueeze(0).repeat(model.n_way, 1, 1, 1, 1)
z_query_ext = torch.transpose(z_query_ext, 0, 1)
# print(z_query_ext.shape)
extend_final_feat_dim = model.feat_dim.copy()
extend_final_feat_dim[0] *= 2
relation_pairs = torch.cat((z_proto_ext, z_query_ext),
2).view(-1, *extend_final_feat_dim)
# print(relation_pairs.shape)
relations = relation_model(relation_pairs)
# print(relations)
scores = relations.view(-1, model.n_way)
preds = scores.data.cpu().numpy().argmax(axis=1)
# print(preds.shape)
relations = relations.view(-1, model.n_way)
# print(relations)
relations_sf = torch.softmax(relations, dim=-1)
# print(relations_sf)
relations_logits = torch.log(LRPutil.LOGIT_BETA * relations_sf /
(1 - relations_sf))
# print(relations_logits)
# print(preds)
relations_logits = relations_logits.view(-1, 1)
relevance_relations = relation_model.compute_lrp(
relation_pairs, target=relations_logits)
# print(relevance_relations.shape)
# print(model.feat_dim)
relevance_z_query = torch.narrow(relevance_relations, 1,
model.feat_dim[0], model.feat_dim[0])
# print(relevance_z_query.shape)
relevance_z_query = relevance_z_query.view(
model.n_query * model.n_way, model.n_way,
*relevance_z_query.size()[1:])
# print(relevance_z_query.shape)
query_img = x.narrow(1, model.n_support,
model.n_query).view(model.n_way * model.n_query,
*x.size()[2:])
# query_img_copy = query_img.view(model.n_way, model.n_query, *x.size()[2:])
# print(query_img.shape)
for k in range(model.n_way):
relevance_querry_cls = torch.narrow(relevance_z_query, 1, k,
1).squeeze(1)
# print(relevance_querry_cls.shape)
relevance_querry_img = feature_model.compute_lrp(
query_img.cuda(), target=relevance_querry_cls)
# print(relevance_querry_img.max(), relevance_querry_img.min())
# print(relevance_querry_img.shape)
for j in range(model.n_query * model.n_way):
predict_class = label_to_readableclass[preds[j]]
true_class = query_gt_class[int(j % model.n_way)][int(
j // model.n_way)]
explain_class = label_to_readableclass[k]
img_name = query_img_path[int(j % model.n_way)][int(
j // model.n_way)].split('/')[-1]
if not os.path.isdir(
os.path.join(explanation_save_dir, 'episode' + str(i),
img_name.strip('.jpg'))):
os.makedirs(
os.path.join(explanation_save_dir, 'episode' + str(i),
img_name.strip('.jpg')))
save_path = os.path.join(explanation_save_dir,
'episode' + str(i),
img_name.strip('.jpg'))
if not os.path.exists(
os.path.join(
save_path,
true_class + '_' + predict_class + img_name)):
original_img = Image.fromarray(
np.uint8(
project(query_img[j].permute(1, 2,
0).cpu().numpy())))
original_img.save(
os.path.join(
save_path,
true_class + '_' + predict_class + img_name))
img_relevance = relevance_querry_img.narrow(0, j, 1)
print(predict_class, true_class, explain_class)
# assert relevance_querry_cls[j].sum() != 0
# assert img_relevance.sum()!=0
hm = img_relevance.permute(0, 2, 3, 1).cpu().detach().numpy()
hm = LRPutil.gamma(hm)
hm = LRPutil.heatmap(hm)[0]
hm = project(hm)
hp_img = Image.fromarray(np.uint8(hm))
hp_img.save(
os.path.join(
save_path,
true_class + '_' + explain_class + '_lrp_hm.jpg'))
if params.method == 'manifold_mixup':
model = wrn_mixup_model.wrn28_10(64)
elif params.method == 'S2M2_R':
model = ProtoNet(model_dict[params.model], params.train_n_way,
params.n_shot)
elif params.method == 'rotation':
model = BaselineTrain(model_dict[params.model], 64, loss_type='dist')
if params.method == 'S2M2_R':
if use_gpu:
if torch.cuda.device_count() > 1:
model = torch.nn.DataParallel(model,
device_ids=range(
torch.cuda.device_count()))
model.cuda()
if params.resume:
resume_file = get_resume_file(params.checkpoint_dir)
print("resume_file", resume_file)
tmp = torch.load(resume_file)
start_epoch = tmp['epoch'] + 1
print("restored epoch is", tmp['epoch'])
state = tmp['state']
model.load_state_dict(state)
else:
resume_file = get_resume_file(params.checkpoint_dir)
# resume_file = './checkpoints/cifar/ProtoNet_from_S2M2_R_SGD_lr0.0001/best_model.tar'
print("resume_file", resume_file)
tmp = torch.load(resume_file)
