controller_action_in = pa_pruned[-1] -2planner_img_feats = torch.from_numpy(raw_img_feats[pq_idx_pruned].copy()) planner_actions_in = torch.from_numpy(np.array(pa_pruned[:-1]) -1)returnplanner_actions_in, planner_img_feats, controller_step, controller_action_in, controller_img_feat...
a = np.random.random((2, 4)) # 返回 0到 1之间的数 # help(np.random.random) a = np.arange(4).reshape((2, 2)) print(a) print(np.sum(a, axis=0)) # 在第一个维度中的元素间进行求和 print(np.max(a, axis=0)) # 在第一个维度中的元素间的每个位置上取最大值(列) print(np....
fuzzy_count += 1 else: self.oov_count += 1 sum_col = np.sum(embeddings, axis=0) / len(inword_list) # avg for i in range(len(words_dict)): if i not in inword_list and i != self.padID: embeddings[i] = sum_col final_embed = torch.from_numpy(embeddings).float() return ...
By design, NumPy APIs are built around vector operations that have ample data parallelism. cuPyNumeric uses this inherent data parallelism by partitioning arrays and performing computations in parallel on the subsets using multiple GPUs. While doing so, cuPyNumeric performs necessary communication when G...
for i in range(2, int(num**0.5) + 1): if num % i == 0: return False return True def print_primes(n): for num in range(2, n + 1): if is_prime(num): print(num) # Example usage N = 50 print_primes(N) In this example, theis_primefunction checks if a number is prime...
# Importing the NumPy library and aliasing it as 'np' import numpy as np # Creating a NumPy array 'x' containing floating-point values x = np.array([10, 10, 20, 30, 30], float) # Printing the original array 'x' print(x)
deftest_frompyfunc_sig_broadcast(self):importsysfromnumpyimportfrompyfunc, dtype, arangeif"__pypy__"notinsys.builtin_module_names: skip("PyPy onlyfrompyfuncextension")defsum_along_0(in_array, out_array):out_array[...] = in_array.sum(axis=0)defadd_two(in0, in1, out):out[...] ...
for epoch in range(args.num_train_epochs): accelerator.print(f"epoch: {epoch}") for data in dataloader: update += 1 global_step += args.micro_batch_size query_responses = torch.cat((data["query_chosen_token"], data["query_rejected_token"]), dim=0) ...
(fullgraph=False)deffn():opt.step()# Warmup runs to compile the functionfor_inrange(5):fn()eager_runtime=benchmark_torch_function_in_microseconds(opt.step)compiled_runtime=benchmark_torch_function_in_microseconds(fn)asserteager_runtime>compiled_runtimeprint(f"eager runtime:{eager_runtime}...
device = torch.device('cuda'iftorch.cuda.is_available()else'cpu')print(f"Device: '{device}'") model = model.to(device) optimizer = torch.optim.Adam(model.parameters(), lr=0.001)forepochinrange(1,6): total_loss = total_examples =0forsampled_dataintqdm.tqdm(train_loader): ...