def _compute_distance_scores(obs1, obs2, segments1, segments2):
scores = [[] for _ in range(len(segments1))]
base_dist = utils.euclidean_dist(obs1, obs2)
for i, segment1 in enumerate(segments1):
projection1 = utils.get_projection(segment1['endpoints'], obs1)
for segment2 in segments2:
projection2 = utils.get_projection(segment2['endpoints'], obs2)
dist = utils.euclidean_dist(projection1, projection2)
dist_diff = abs(dist - base_dist)
scores[i].append(1.0 / (1.0 + dist_diff))
return scores
def test(test_loader, model, args):
print('Testing...')
losses = AverageMeter()
accuracy = AverageMeter()
# Switch to evaluate mode
model.eval()
with torch.no_grad():
for n_episode, batch in enumerate(test_loader, 1):
data, _ = [_.cuda(non_blocking=True) for _ in batch]
p = args.n_support * args.n_way
data_support, data_query = data[:p], data[p:]
# Compute class prototypes (n_way, output_dim)
class_prototypes = model(data_support).reshape(args.n_support, args.n_way, -1).mean(dim=0)
# Generate labels (n_way, n_query)
labels = torch.arange(args.n_way).repeat(args.n_query)
labels = labels.type(torch.cuda.LongTensor)
# Compute loss and metrics
logits = euclidean_dist(model(data_query), class_prototypes)
loss = F.cross_entropy(logits, labels)
acc = compute_accuracy(logits, labels)
# Record loss and accuracy
losses.update(loss.item(), data_query.size(0))
accuracy.update(acc, data_query.size(0))
print('Test Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Test Accuracy {accuracy.val:.3f} ({accuracy.avg:.3f})\t'.format(loss=losses, accuracy=accuracy))
return losses.avg, accuracy.avg
def train_loop(model, epoch, device, train_loader, optimizer):
avg_loss = 0
avg_acc = 0
for i, batch in enumerate(train_loader):
data, label = [_.to(device) for _ in batch]
data_shot, data_query = data[:, :params.
n_shot, :, :, :], data[:, params.
n_shot:, :, :, :]
data_shot = data_shot.reshape(-1, 3, 84, 84)
embedding = model(data_shot)
proto = embedding.reshape(params.train_n_way, params.n_shot,
-1).mean(1)
label = torch.from_numpy(
np.repeat(range(params.train_n_way), params.query))
label = label.type(torch.cuda.LongTensor)
data_query = data_query.reshape(-1, 3, 84, 84)
inference = model(data_query)
inference = inference.reshape(params.train_n_way * params.query, -1)
scores = -euclidean_dist(inference, proto)
optimizer.zero_grad()
loss = nn.CrossEntropyLoss()(scores, label)
loss.backward()
optimizer.step()
avg_loss = avg_loss + loss.item()
pred = scores.argmax(dim=1)
avg_acc += (pred == label).type(torch.FloatTensor).mean().item()
print('Epoch {:d} | loss={:.4f} | acc={:.4f}'.format(
epoch, avg_loss / len(train_loader), avg_acc / len(train_loader)))
def test_loop(model, device, val_loader):
iter_num = len(val_loader)
acc_all = []
for i, batch in enumerate(val_loader):
data, _ = [_.to(device) for _ in batch]
data_shot, data_query = data[:, :params.
n_shot, :, :, :], data[:, params.
n_shot:, :, :, :]
data_shot = data_shot.reshape(-1, 3, 84, 84)
proto = model(data_shot)
proto = proto.reshape(params.test_n_way, params.n_shot, -1).mean(dim=1)
label = torch.from_numpy(
np.repeat(range(params.train_n_way), params.query))
#if device=='cuda:0':
label = label.type(torch.cuda.LongTensor)
data_query = data_query.reshape(-1, 3, 84, 84)
scores = -euclidean_dist(model(data_query), proto)
#loss = nn.CrossEntropyLoss()(scores, label)
# F.cross_entropy(scores, label)
pred = scores.argmax(dim=1)
acc = (pred == label).type(torch.FloatTensor).mean().item()
acc_all.append(acc)
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 * 100, 100 * 1.96 * acc_std / np.sqrt(iter_num)))
return acc_mean, acc_std
def find_k_nearest(k, point_star):
"""
Function to loop through each line in the stdin and keep track of the k
closest points
Args:
k: the number of closest neighbors to find
point_star: the star for which to find the k closest neighbors
Returns:
max_heap: a heap containing the k closest distances to the point_star
star_dist: a key value pari of {dist-d1: [list of stars at dist-d1 to point_star]}
"""
max_heap = []
star_dist = defaultdict(list)
# skip header and sun row
utils.skip_line(2)
for line in sys.stdin:
star_name, x, y, z = utils.parse_line(line=line)
dist = utils.euclidean_dist(x=(x, y, z), y=point_star)
new_star = star(x=x, y=y, z=z, star_name=star_name)
if len(max_heap) < k:
heappush(max_heap, -1 * dist)
star_dist[dist].append(new_star)
elif dist <= -1 * max_heap[0]:
heappushpop(max_heap, -1 * dist)
star_dist[dist].append(new_star)
return max_heap, star_dist
def extract_features(des, codebook):
"""
Construct the Bag-of-visual-Words histogram features for images using the codebook.
HINT: Refer to helper functions.
:param des(numpy.array): Descriptors. shape:[num_images, num_des_of_each_img, 128]
:param codebook(numpy.array): Bag of visual words. shape:[k, 128]
:return(numpy.array): Bag of visual words shape:[num_images, k]
"""
# YOUR CODE HERE
bow_hist = []
k = codebook.shape[0]
for i in range(len(des)):
# data = copy.deepcopy(des[i])
dist = euclidean_dist(des[i], codebook)
cluster_idx = np.argmin(dist, axis=1)
targets = cluster_idx.reshape(-1)
one_hot_targets = np.eye(k)[targets]
bow_hist.append(onehot_targets)
return np.array(bow_hist)
# features = np.zeros((2472, 1024))
# dist = cdist(obs, code_book)
# code = dist.argmin(axis=1)
# min_dist = dist[np.arange(len(code)), code]
# return code, min_dist
def forward_pred(self, data_shot, data_query, n_way, k_shot):
proto = self.forward(data_shot)
proto = proto.view(n_way, k_shot, -1).mean(dim=1)
query = self.forward(data_query)
logits = euclidean_dist(query, proto)
return logits
def _calc_num_steps_to_next_node(self):
dist = utils.euclidean_dist(
self.current_node['x'], self.current_node['y'],
self.next_node['x'], self.next_node['y']
)
num_of_steps_to_next_node = int(round(dist / STEP_LENGTH))
self.log.info("%d steps to next node", num_of_steps_to_next_node)
return num_of_steps_to_next_node
def _get_dist_matrix(data, eps):
dist_matrix = [[True] * len(data) for i in range(len(data))]
for p in range(len(data)):
for q in range(p + 1, len(data)):
dist = euclidean_dist(data[p], data[q])
if (dist > eps):
dist_matrix[p][q] = dist_matrix[q][p] = False
return dist_matrix
def calc_distances(nn_tree):
n_centroids = nn_tree.get_arrays()[0].shape[0]
distances = np.ndarray((n_centroids, n_centroids))
for i in range(n_centroids):
for j in range(n_centroids):
distances[i, j] = euclidean_dist(nn_tree.get_arrays()[0][i],
nn_tree.get_arrays()[0][j])
return distances
def closest_cluster_to(target, clusters):
min_dist = float('inf')
min_idx = None
# clusters = self._enemy_clusters if enemy else self._clusters
for idx, cluster in clusters.items():
curr_dist = euclidean_dist(cluster.get_center(), target)
if curr_dist < min_dist and curr_dist != 0:
min_dist = curr_dist
min_idx = idx
return min_idx
def score(self, x):
preds = []
for l in range(self.encoder.depth):
enc = lambda x: self.encoder.intermediate_forward(x, l, avg_pool=True).view(x.size(0),-1)
zq = enc(x)
zs = enc(self.s)
preds.append(-euclidean_dist(zs, zq)) # (S, N)
preds = torch.stack(preds) # (L, C, N)
preds = preds.sum(0).t() # (N, C)
return preds.max(1)[0]
def validate(val_loader, model, att, args):
print('Validating...')
losses = AverageMeter()
accuracy = AverageMeter()
# Switch to evaluate mode
model.eval()
att.eval()
with torch.no_grad():
for n_episode, batch in enumerate(val_loader, 1):
data, _ = [_.cuda(non_blocking=True) for _ in batch]
p = args.n_support * args.n_way_val
data_support, data_query = data[:p], data[p:]
# Compute class prototypes (n_way, output_dim)
# Calculate weighted averages for class prototypes
# (n_support, n_way_val feature_dimension)
latent_vecs_val = model(data_support).reshape(
args.n_support, args.n_way_val, -1)
# (n_way_train, n_val, feature_dimension)
latent_vecs_val = latent_vecs_val.transpose(0, 1)
# _, scores_val = att(latent_vecs_val)
scores_val = F.softmax(att(latent_vecs_val), dim=1)
# scores_val = scores_val.unsqueeze(-1).expand_as(latent_vecs_val)
scores_val = scores_val.expand_as(latent_vecs_val)
# class_prototypes = torch.sum(
# torch.matmul(scores_val, latent_vecs_val), 1)
class_prototypes = torch.sum(
torch.mul(scores_val, latent_vecs_val), 1)
# class_prototypes = att(model(data_support)).reshape(
# args.n_support, args.n_way_val, -1).mean(dim=0)
# Generate labels (n_way, n_query)
labels = torch.arange(args.n_way_val).repeat(args.n_query_val)
labels = labels.type(torch.cuda.LongTensor)
# Compute loss and metrics
logits = euclidean_dist(model(data_query), class_prototypes)
loss = F.cross_entropy(logits, labels)
acc = compute_accuracy(logits, labels)
# Record loss and accuracy
losses.update(loss.item(), data_query.size(0))
accuracy.update(acc, data_query.size(0))
print('Validation Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Validation Accuracy {accuracy.val:.3f} ({accuracy.avg:.3f})\t'.
format(loss=losses, accuracy=accuracy))
return losses.avg, accuracy.avg
def is_near_depot(self, lat_lng):
''' Calcola distanza dal deposito in base a lat e lng,
se inferiore a config.depot_radius ogni due secondi
prova a connettersi al wifi chiamando try_wireless_connection.
Se si allontata interrompe l'azione
'''
dist = euclidean_dist(lat_lng, self.config.depot_location)
# 0.00001 degrees is about a meter
# print("Is near depot", dist, lat_lng)
if dist <= self.config.depot_radius and not self.checking_edge_connection:
self.checking_edge_connection = setInterval(
self.config.retry_connection_delay, self.try_wireless_connection)
elif dist > self.config.depot_radius and self.checking_edge_connection:
self.checking_edge_connection.cancel()
def hello_render(request):
# today = date.today()
dist = euclidean_dist(0, 0, 2, 2)
area = rectanglearea(0, 0, 2, 2)
context = {
'person': "Jhon Doe",
'date': today,
'distance': dist,
'area': area
}
return render(request, "catalog/templates/hello.html", context)
def forward(self, data, mode='test'):
args = self.args
data = data.view(args.n_way * (args.n_shot + args.n_query),
*data.size()[2:])
feature = self.encoder(data)
feature = feature.view(args.n_way, args.n_shot + args.n_query, -1)
z_support = feature[:, :args.n_shot]
z_query = feature[:, args.n_shot:]
proto = z_support.view(args.n_way, args.n_shot, -1).mean(1)
z_query = z_query.contiguous().view(args.n_way * args.n_query, -1)
logits = -euclidean_dist(z_query, proto) / self.args.temperature
return logits
def predict_batch(self, img_paths_list, top_k):
# load inference samples
infer_imgs = list()
for path in img_paths_list:
infer_imgs.append(torch.tensor(load_img(path))) # list of tensor
X = torch.stack(infer_imgs)
# load model
model = ProtoNet().cpu()
model.load_state_dict(torch.load(self.model_path, map_location='cpu'))
model.eval()
# start inferring
pred_label_list = list()
pred_class_name = list()
pred_class_sku = list()
pred_class_prob = list()
model_output = model(X) # [batch_size,128]
dists = euclidean_dist(
model_output.to('cpu'),
self.prototypes.to('cpu')) # [batch_size,num_classes]
dists = dists.data.cpu().numpy()
sorted_dists = np.sort(dists, axis=1)
sorted_idxs = np.argsort(dists, axis=1)
# whether reject
threshold = 15.0
mask = sorted_dists < threshold
for i in range(len(infer_imgs)):
pred_class_prob.append(sorted_dists[i][mask[i]][:top_k].tolist())
pred_label_list.append(
self.labels[sorted_idxs[i]][mask[i]][:top_k].tolist())
pred_class_sku.append(
[self.idx2sku[idx] for idx in pred_label_list[i]])
pred_class_name.append(
[self.sku2name[idx] for idx in pred_class_sku[i]])
result = [] # list of dict for each image
for i in range(len(infer_imgs)):
cur_img_result = {
'name': pred_class_name[i],
'prob': pred_class_prob[i],
'sku': pred_class_sku[i]
}
result.append(cur_img_result)
return result
def cheapest_insertion(i, seq):
'''
returns (cost, j, k)
where cost, int -- the cost of the insertion (positive is bad)
j, int -- the index of the element that will go to j
k, int -- the index of the element that i will go to
(so we end up with something like this: ... -> j -> i -> k -> ...
'''
L = []
for j, k in zip(seq, seq[1:]):
old_edge = utils.euclidean_dist(X[[j, k]])
new_edge = D[j, i] + D[i, k]
cost = -old_edge + new_edge
L.append((cost, j, k))
return min(L, key=lambda x: x[0])
def retrain(self, img_paths_list, class_name, sku):
self.labelID += 1
infer_imgs = []
for p in img_paths_list:
infer_imgs += [
transforms.ToTensor()(im) for im in image_enforce(p)
]
X = torch.stack(infer_imgs)
# load model
model = ProtoNet().cpu()
model.load_state_dict(torch.load(self.model_path, map_location='cpu'))
model.eval()
# compute new prototype
model_output = model(X) # [batch_size,128]
batch_prototype = model_output.mean(0)
batch_prototype = batch_prototype.unsqueeze(0)
# whether fail to map to a distinguishing emmbedding
threshold = 0.0
dists = euclidean_dist(
batch_prototype.to('cpu'),
self.prototypes.to('cpu')) # [batch_size,num_classes]
min_dist = torch.min(dists).item()
if min_dist < threshold:
index = np.argmin(dists)
sim_lblid = self.labels[index]
info = {
'msg': 'fail',
'similar_object_name': self.sku2name[self.idx2sku[sim_lblid]],
'similar_object_sku': self.idx2sku[sim_lblid]
}
return info
# add new class info
self.prototypes = torch.cat([self.prototypes, batch_prototype], 0)
self.labels = np.concatenate((self.labels, [self.labelID]), axis=0)
self.idx2sku[self.labelID] = sku
self.sku2name[sku] = class_name
info = {'msg': 'success'}
return info
def forward(self, data, mode='train'):
args = self.args
if mode in ['val', 'test']:
data = data.view(args.n_way * (args.n_shot + args.n_query),
*data.size()[2:])
feature = self.encoder(data)
feature = feature.view(args.n_way, args.n_shot + args.n_query, -1)
z_support = feature[:, :args.n_shot]
z_query = feature[:, args.n_shot:]
proto = z_support.view(args.n_way, args.n_shot, -1).mean(1)
z_query = z_query.contiguous().view(args.n_way * args.n_query, -1)
scores = -euclidean_dist(z_query, proto) / self.args.temperature
else:
feature = self.encoder(data)
return feature
# scores = self.classifier(feature)
return scores
def _get_dists(data, centers):
dists = [None] * len(data)
for p in range(len(data)):
dists[p] = [euclidean_dist(data[p], center) for center in centers]
return dists
def evaluation(data_loader, class_prototypes, model, classes, args):
queries_data = {n: [] for n in range(len(classes))}
losses = AverageMeter()
accuracy_1 = AverageMeter()
accuracy_5 = AverageMeter()
metrics_class = {}
with torch.no_grad():
print('Forwarding queries...')
for data, targets in data_loader:
data = data.cuda(non_blocking=True)
outputs = model(data)
for i, output in enumerate(outputs):
queries_data[targets[i].item()].append(output)
mean_accuracy = []
for key, values in queries_data.items():
if len(values) > 0:
logits = euclidean_dist(torch.stack(values), class_prototypes)
labels = torch.Tensor([key]).repeat(len(values)).type(
torch.cuda.LongTensor)
loss = F.cross_entropy(logits, labels).item()
acc = accuracy_top_k(logits, labels, top_k=(1, 2, 3, 4, 5))
acc = [a.item() for a in acc]
metrics_class[key] = {
'class_name': classes[key],
'accuracy': acc[0],
'loss': loss,
'n_samples': len(values)
}
# Record loss and accuracy
losses.update(loss, len(values))
accuracy_1.update(acc[0], len(values))
accuracy_5.update(acc[4], len(values))
mean_accuracy.append(acc[0])
# print('Class ' + classes[key])
printer.pprint(metrics_class[key])
else:
metrics_class[key] = {
'class_name': classes[key],
'accuracy': np.nan,
'loss': np.nan,
'n_samples': 0
}
# print('Class ' + classes[key] + ' is empty')
mean_accuracy = sum(mean_accuracy) / len(mean_accuracy)
print('Total Weighted Top-1 Accuracy %.4f\n'
'Total Weighted Top-5 Accuracy %.4f\n'
'Mean Class Top-1 Accuracy %.4f\n'
'Total Avg Loss %.4f' %
(accuracy_1.avg, accuracy_5.avg, mean_accuracy, losses.avg))
class_metrics_df = pd.DataFrame.from_dict(metrics_class, 'index')
class_metrics_df.round(4)
class_metrics_df.to_csv(os.path.join(results_path, args.results_name +
'_individual.csv'),
index=False,
float_format='%.4f')
average_df = pd.DataFrame({
'accuracy_top_1': [accuracy_1.avg],
'accuracy_top_5': [accuracy_5.avg],
'sensitivity': [mean_accuracy]
})
average_df.round(4)
average_df.to_csv(os.path.join(results_path,
args.results_name + '_average.csv'),
index=False,
float_format='%.4f')
def forward(self, input):
x, labels, idx_train, idx_val, idx_test = input # x is N * L, where L is the time-series feature dimension
if True:
N = x.size(0)
# LSTM
if self.use_lstm:
x_lstm = self.lstm(x)[0]
x_lstm = x_lstm.mean(1)
x_lstm = x_lstm.view(N, -1)
if self.use_cnn:
# Covolutional Network
# input ts: # N * C * L
if self.use_rp:
for i in range(len(self.conv_1_models)):
#x_conv = x
x_conv = self.conv_1_models[i](x[:, self.idx[i], :])
x_conv = self.conv_bn_1(x_conv)
x_conv = F.leaky_relu(x_conv)
x_conv = self.conv_2(x_conv)
x_conv = self.conv_bn_2(x_conv)
x_conv = F.leaky_relu(x_conv)
x_conv = self.conv_3(x_conv)
x_conv = self.conv_bn_3(x_conv)
x_conv = F.leaky_relu(x_conv)
x_conv = torch.mean(x_conv, 2)
if i == 0:
x_conv_sum = x_conv
else:
x_conv_sum = torch.cat([x_conv_sum, x_conv], dim=1)
x_conv = x_conv_sum
else:
x_conv = x
x_conv = self.conv_1(x_conv) # N * C * L
x_conv = self.conv_bn_1(x_conv)
x_conv = F.leaky_relu(x_conv)
x_conv = self.conv_2(x_conv)
x_conv = self.conv_bn_2(x_conv)
x_conv = F.leaky_relu(x_conv)
x_conv = self.conv_3(x_conv)
x_conv = self.conv_bn_3(x_conv)
x_conv = F.leaky_relu(x_conv)
x_conv = x_conv.view(N, -1)
if self.use_lstm and self.use_cnn:
x = torch.cat([x_conv, x_lstm], dim=1)
elif self.use_lstm:
x = x_lstm
elif self.use_cnn:
x = x_conv
#
# linear mapping to low-dimensional space
x = self.mapping(x)
# generate the class protocal with dimension C * D (nclass * dim)
proto_list = []
for i in range(self.nclass):
idx = (labels[idx_train].squeeze() == i).nonzero().squeeze(1)
if self.use_att:
A = self.att_models[i](x[idx_train][idx]) # N_k * 1
A = torch.transpose(A, 1, 0) # 1 * N_k
A = F.softmax(A, dim=1) # softmax over N_k
class_repr = torch.mm(A, x[idx_train][idx]) # 1 * L
class_repr = torch.transpose(class_repr, 1, 0) # L * 1
else: # if do not use attention, simply use the mean of training samples with the same labels.
class_repr = x[idx_train][idx].mean(0) # L * 1
proto_list.append(class_repr.view(1, -1))
x_proto = torch.cat(proto_list, dim=0)
# prototype distance
proto_dists = euclidean_dist(x_proto, x_proto)
proto_dists = torch.exp(-0.5 * proto_dists)
num_proto_pairs = int(self.nclass * (self.nclass - 1) / 2)
proto_dist = torch.sum(proto_dists) / num_proto_pairs
dists = euclidean_dist(x, x_proto)
dump_embedding(x_proto, x, labels)
return torch.exp(-0.5 * dists), proto_dist
def is_on_target(self):
return euclidean_dist(self.get_center(), self.target_loc) < 1
def log_p_y(self, xs, xq, no_grad=False, mask=None):
"""log_p_y
Args:
xs: support set (#way, #shot, ...)
xq: support set (#query classes, #queries, ...)
return_target_inds: set to True only if #way and #query index over the same classes
Return:
a dict of {
'log_p_y': log p(y) (#query classes, #queries, #way)
'target_inds': ...
'logits': ...
}
"""
ret_dict = {}
# xs.size() == (50, 3, 1, 28, 28) for 50-way, 3-shot classification
# xq.size() == (50, 5, 1, 28, 28) for 50-way, 5 query points for each of the 50 classes
n_class = xs.size(0)
n_query_class = xq.size(0)
n_support = xs.size(1)
n_query = xq.size(1)
# Combines the support and query points into one "minibatch" to get embeddings
x = torch.cat([xs.reshape(n_class * n_support, *xs.size()[2:]),
xq.reshape(n_query_class * n_query, *xq.size()[2:])], 0)
try:
z = self.encoder(x, no_grad=no_grad)
except:
z = self.encoder(x) # If the encoder doesn't support option no_grad
z_dim = z.size(-1)
supports = z[:n_class*n_support].view(n_class, n_support, z_dim)
ret_dict['supports'] = supports
# Prototype representations of the support images in each class: (50, 64)
if mask is not None:
mask = mask.unsqueeze(-1)
# ipdb.set_trace()
z_proto = (supports * mask).sum(1) / mask.sum(1)
else:
z_proto = supports.mean(1)
z_proto_var = supports.var(1)
ret_dict['z'] = z # z.shape == (50, 256)
# Embeddings of the query points: (500, 64)
zq = z[n_class*n_support:]
ret_dict['zq'] = zq
def _vectorized_mog_logp(zq, z_proto_m, z_proto_var):
res = []
for ci, (_m, _v) in enumerate(zip(z_proto_m, z_proto_var)):
_m, _v = _m.unsqueeze(0), _v.unsqueeze(0)
_v = _v + 1e-7
logp = -1 * (torch.log(_v).sum() +
(torch.pow(zq - _m , 2) / _v).sum(-1))
res.append(logp)
return torch.transpose(torch.stack(res), 0, 1)
if self.decision == 'baseline':
# Distances between each query embedding and each class prototype
dists = euclidean_dist(zq, z_proto)
# Class logits for each query point and each class: (50, 10, 50) [#query classes, #queries per class, #support classes] ?
log_p_y = F.log_softmax(-dists, dim=1).view(n_query_class, n_query, -1)
# Prepare logits
ret_dict['logits'] = (-dists).view(n_query_class, n_query, -1)
elif self.decision == 'maha':
raise "Well, we cannot fit a full-covariance b/c N < D"
# tied full-covariance
diff = supports - z_proto.unsqueeze(1) # (way, shot, dim)
diff = diff.view(-1, diff.shape[-1]) # (way*shot, dim) b/c shared across way
cov = torch.mean(torch.stack([torch.ger(vec,vec) for vec in diff]),0)
mahas = []
for _zq in zq:
mahas.append()
elif self.decision == 'tied-var':
if mask is not None:
raise # implement the masking part
z_proto_var = supports.view(-1, supports.shape[-1]).var(0).unsqueeze(0).repeat(5,1)
log_p_y = _vectorized_mog_logp(zq, z_proto, z_proto_var).view(n_query_class, n_query, -1)
ret_dict['logits'] = log_p_y
ret_dict['log_p_y'] = log_p_y
# Prepare target_inds
if n_class == n_query_class:
target_inds = torch.arange(0, n_class).view(n_class, 1, 1).expand(n_class, n_query, 1).long()
target_inds.requires_grad = False
if xq.is_cuda:
target_inds = target_inds.cuda()
ret_dict['target_inds'] = target_inds
return ret_dict
def _no_shift(centers, new_centers, tol):
shifts = [
euclidean_dist(centers[i], new_centers[i]) for i in range(len(centers))
]
no_shift = all([shift <= tol for shift in shifts])
return no_shift
def _get_shifts(centers, new_centers):
shifts = [
euclidean_dist(centers[i], new_centers[i]) for i in range(len(centers))
]
return shifts
with torch.no_grad():
for i, batch in enumerate(novel_loader):
data, _ = [_.to(device) for _ in batch]
data_shot, data_query = data[:, :params.
n_shot, :, :, :], data[:, params.
n_shot:, :, :, :]
data_shot = data_shot.reshape(-1, 3, 84, 84)
proto = model(data_shot)
proto = proto.reshape(params.test_n_way, params.n_shot,
-1).mean(dim=1)
label = torch.from_numpy(
np.repeat(range(params.train_n_way), params.query))
# if device=='cuda:0':
label = label.type(torch.cuda.LongTensor)
data_query = data_query.reshape(-1, 3, 84, 84)
scores = -euclidean_dist(model(data_query), proto)
#loss = nn.CrossEntropyLoss()(scores, label)
# F.cross_entropy(scores, label)
pred = scores.argmax(dim=1)
acc = (pred == label).type(torch.FloatTensor).mean().item()
acc_all.append(acc)
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 * 100,
100 * 1.96 * acc_std / np.sqrt(iter_num)))
def train(train_loader, model, optimizer, epoch, args):
print("Training epoch %d" % epoch)
episode_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
accuracy = AverageMeter()
# Switch to train mode
model.train()
end = time.time()
optimizer.zero_grad()
# Iterate over episodes
for n_episode, batch in enumerate(train_loader, 1):
data_time.update(time.time() - end)
data, _ = [_.cuda(non_blocking=True) for _ in batch]
p = args.n_support * args.n_way_train
data_support, data_query = data[:p], data[p:]
# Compute class prototypes (n_way, output_dim)
if n_episode > 1 and args.alpha > 0.0:
class_prototypes = args.alpha * class_prototypes + (1 - args.alpha) * \
model(data_support).reshape(args.n_support, args.n_way_train, -1).mean(dim=0)
else:
class_prototypes = model(data_support).reshape(
args.n_support, args.n_way_train, -1).mean(dim=0)
# Generate labels (n_way, n_query)
labels = torch.arange(args.n_way_train).repeat(args.n_query_train)
labels = labels.type(torch.cuda.LongTensor)
# Compute loss and metrics
logits = euclidean_dist(model(data_query), class_prototypes)
loss = F.cross_entropy(logits, labels)
acc = compute_accuracy(logits, labels)
# Record loss and accuracy
losses.update(loss.item(), data_query.size(0))
accuracy.update(acc, data_query.size(0))
# Compute gradient and do SGD step
loss.backward()
optimizer.step()
optimizer.zero_grad()
# Free the graph
if args.alpha > 0.0:
class_prototypes = class_prototypes.detach()
else:
class_prototypes = None
# Measure elapsed time
episode_time.update(time.time() - end)
end = time.time()
if n_episode % args.print_freq == 0:
print(
'Epoch: [{0}][{1}/{2}]\t'
'Episode Time {episode_time.val:.3f} ({episode_time.avg:.3f})\t'
'Data Time {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Accuracy {accuracy.val:.3f} ({accuracy.avg:.3f})\t'.format(
epoch,
n_episode,
args.n_episodes_train,
episode_time=episode_time,
data_time=data_time,
loss=losses,
accuracy=accuracy))
return losses.avg, accuracy.avg
sum_loss = 0
sum_tr_loss = 0
sum_clf_loss = 0
correct = 0
overall = 0
for n, (x, y) in enumerate(trainloader):
classes = y.clone()
if gpu:
x = x.to('cuda')
y = y.to('cuda')
features, logits = model(x, y)
dist = euclidean_dist(features, features)
labels = torch.cuda.LongTensor(
np.repeat(np.arange(dist.shape[0] // 3), 3)).unsqueeze(1)
pos = labels.eq(labels.t())
neg = labels.ne(labels.t())
dist_ap = torch.max(dist[pos].view(dist.shape[0], -1), 1)[0]
dist_an = torch.min(dist[neg].view(dist.shape[0], -1), 1)[0]
optimizer.zero_grad()
tr_loss = triplet_loss(dist_ap=dist_ap, dist_an=dist_an)
clf_loss = F.cross_entropy(logits, y)
loss = tr_loss + clf_loss