code stringlengths 3 6.57k |
|---|
len(src_invalid) |
len(src_sent) |
float("-inf") |
attn_valid.max(dim=1) |
zip(tgt_valid, src_indices) |
src_idx.item() |
tgt_idx.item() |
new_arange(x, *size) |
len(size) |
x.size() |
torch.arange(size[-1], device=x.device) |
expand(*size) |
contiguous() |
get_tpu_device(args) |
xm.xla_device() |
logging_multiple_line_messages(msg) |
msg.split("\n") |
logger.info(line) |
CudaEnvironment(object) |
__init__(self) |
torch.cuda.current_device() |
torch.cuda.get_device_properties("cuda:{}".format(cur_device) |
pretty_print_cuda_env_list(cuda_env_list) |
len(cuda_env_list) |
format(num_workers) |
len(center) |
enumerate(cuda_env_list) |
format(r) |
format(env.major, env.minor) |
format(env.total_memory_in_GB) |
format(env.name) |
msg_arr.append(first_line) |
logging_multiple_line_messages("\n".join(msg_arr) |
analyze_episodic(model, test_data, args) |
model.eval() |
m.to(args.device) |
to(args.device) |
type(torch.long) |
to(args.device) |
y.squeeze() |
torch.no_grad() |
model(x, m) |
first (only) |
np.squeeze(attention) |
len(train) |
len(test) |
ids (train samples) |
np.zeros_like(attention) |
range(n_test) |
np.argsort(attention[i]) |
np.zeros([n_train]) |
np.arange(n_train) |
range(n_test) |
range(n_train) |
relevant.append(attn_ranks[i,j]) |
irrelevant.append(attn_ranks[i,j]) |
weights (k = 8 means 5 percent) |
range(n_test) |
np.argsort(attention[i]) |
hubs.append(train_f2) |
hubs.append(train_f1) |
list(set(hubs) |
len(hubs) |
append(sample[1]) |
append(sample[1]) |
append(sample[0]) |
append(sample[0]) |
used_hub.append(True) |
used_hub.append(True) |
used_hub.append(False) |
np.mean(used_hub) |
print("Proportion that episodic system retrieved a hub path:", p_used_hub) |
analyze_cortical(model, test_data, analyze_loader, args) |
loc2idx.items() |
range(n_states) |
model.eval() |
face_embedding.to(args.device) |
range(2) |
range(2) |
range(2) |
range(args.N_contexts) |
range(2) |
range(args.N_contexts) |
range(args.N_contexts) |
range(args.N_contexts) |
range(args.N_contexts) |
torch.no_grad() |
range(n_states) |
unsqueeze(0) |
to(args.device) |
face_embedding(face_tensor) |
embedding.cpu() |
numpy() |
embeddings.append(embedding) |
np.concatenate(embeddings, axis=0) |
samples.append(batch) |
f1.to(args.device) |
f2.to(args.device) |
ctx.to(args.device) |
np.arctan2((y2-y1) |
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