Python utils.INF属性代码示例

# 需要导入模块: import utils [as 别名]
# 或者: from utils import INF [as 别名]
def wastar_search(start_v, end_v, neighbors_fn, cost_fn=unit_cost_fn,
                  heuristic_fn=zero_heuristic_fn, w=1, max_cost=INF, max_time=INF):
    # TODO: lazy wastar to get different paths
    #heuristic_fn = lambda v: cost_fn(v, end_v)
    priority_fn = lambda g, h: g + w*h
    goal_test = lambda v: v == end_v

    start_time = time.time()
    start_g, start_h = 0, heuristic_fn(start_v)
    visited = {start_v: Node(start_g, None)}
    queue = [(priority_fn(start_g, start_h), start_g, start_v)]
    while queue and (elapsed_time(start_time) < max_time):
        _, current_g, current_v = heappop(queue)
        if visited[current_v].g < current_g:
            continue
        if goal_test(current_v):
            return retrace_path(visited, current_v)
        for next_v in neighbors_fn(current_v):
            next_g = current_g + cost_fn(current_v, next_v)
            if (next_v not in visited) or (next_g < visited[next_v].g):
                visited[next_v] = Node(next_g, current_v)
                next_h = heuristic_fn(next_v)
                if priority_fn(next_g, next_h) < max_cost:
                    heappush(queue, (priority_fn(next_g, next_h), next_g, next_v))
    return None 

# 需要导入模块: import utils [as 别名]
# 或者: from utils import INF [as 别名]
def __init__(self):
        LTContainer.__init__(self, (+INF,+INF,-INF,-INF))
        return 

# 需要导入模块: import utils [as 别名]
# 或者: from utils import INF [as 别名]
def __init__(self):
        LTContainer.__init__(self, (+INF, +INF, -INF, -INF))
        return 

# 需要导入模块: import utils [as 别名]
# 或者: from utils import INF [as 别名]
def forward(self, seq, mask):
        in_c = seq.size()[1]
        seq = torch.transpose(seq, 1, 2)  # (N, L, C)
        queries = seq
        keys = seq
        num_heads = self.num_heads

        # T_q = T_k = L
        Q = F.relu(self.linear_q(seq))  # (N, T_q, C)
        K = F.relu(self.linear_k(seq))  # (N, T_k, C)
        V = F.relu(self.linear_v(seq))  # (N, T_k, C)

        # Split and concat
        Q_ = torch.cat(torch.split(Q, in_c // num_heads, dim=2), dim=0)  # (h*N, T_q, C/h)
        K_ = torch.cat(torch.split(K, in_c // num_heads, dim=2), dim=0)  # (h*N, T_k, C/h)
        V_ = torch.cat(torch.split(V, in_c // num_heads, dim=2), dim=0)  # (h*N, T_k, C/h)

        # Multiplication
        outputs = torch.matmul(Q_, K_.transpose(1, 2))  # (h*N, T_q, T_k)
        # Scale
        outputs = outputs / (K_.size()[-1] ** 0.5)
        # Key Masking
        key_masks = mask.repeat(num_heads, 1)  # (h*N, T_k)
        key_masks = torch.unsqueeze(key_masks, 1)  # (h*N, 1, T_k)
        key_masks = key_masks.repeat(1, queries.size()[1], 1)  # (h*N, T_q, T_k)

        paddings = torch.ones_like(outputs) * (-INF)  # extremely small value
        outputs = torch.where(torch.eq(key_masks, 0), paddings, outputs)

        query_masks = mask.repeat(num_heads, 1)  # (h*N, T_q)
        query_masks = torch.unsqueeze(query_masks, -1)  # (h*N, T_q, 1)
        query_masks = query_masks.repeat(1, 1, keys.size()[1]).float()  # (h*N, T_q, T_k)

        att_scores = F.softmax(outputs, dim=-1) * query_masks  # (h*N, T_q, T_k)
        att_scores = self.dropout(att_scores)

        # Weighted sum
        x_outputs = torch.matmul(att_scores, V_)  # (h*N, T_q, C/h)
        # Restore shape
        x_outputs = torch.cat(
            torch.split(x_outputs, x_outputs.size()[0] // num_heads, dim=0),
            dim=2)  # (N, T_q, C)

        x = torch.transpose(x_outputs, 1, 2)  # (N, C, L)
        x = self.bn(x, mask)

        return x