onnx

funasr/export/models/predictor/cif.py

@@ -16,6 +16,11 @@ def sequence_mask(lengths, maxlen=None, dtype=torch.float32, device=None):
 	
 	return mask.type(dtype).to(device) if device is not None else mask.type(dtype)
 
+def sequence_mask_scripts(lengths, maxlen:int):
+	row_vector = torch.arange(0, maxlen, 1).type(lengths.dtype).to(lengths.device)
+	matrix = torch.unsqueeze(lengths, dim=-1)
+	mask = row_vector < matrix
+	return mask.type(torch.float32).to(lengths.device)
 
 class CifPredictorV2(nn.Module):
 	def __init__(self, model):
@@ -71,28 +76,131 @@ class CifPredictorV2(nn.Module):
 		
 		return hidden, alphas, token_num_floor
 
+# @torch.jit.script
+# def cif(hidden, alphas, threshold: float):
+# 	batch_size, len_time, hidden_size = hidden.size()
+# 	threshold = torch.tensor([threshold], dtype=alphas.dtype).to(alphas.device)
+#
+# 	# loop varss
+# 	integrate = torch.zeros([batch_size], device=hidden.device)
+# 	frame = torch.zeros([batch_size, hidden_size], device=hidden.device)
+# 	# intermediate vars along time
+# 	list_fires = []
+# 	list_frames = []
+#
+# 	for t in range(len_time):
+# 		alpha = alphas[:, t]
+# 		distribution_completion = torch.ones([batch_size], device=hidden.device) - integrate
+#
+# 		integrate += alpha
+# 		list_fires.append(integrate)
+#
+# 		fire_place = integrate >= threshold
+# 		integrate = torch.where(fire_place,
+# 		                        integrate - torch.ones([batch_size], device=hidden.device),
+# 		                        integrate)
+# 		cur = torch.where(fire_place,
+# 		                  distribution_completion,
+# 		                  alpha)
+# 		remainds = alpha - cur
+#
+# 		frame += cur[:, None] * hidden[:, t, :]
+# 		list_frames.append(frame)
+# 		frame = torch.where(fire_place[:, None].repeat(1, hidden_size),
+# 		                    remainds[:, None] * hidden[:, t, :],
+# 		                    frame)
+#
+# 	fires = torch.stack(list_fires, 1)
+# 	frames = torch.stack(list_frames, 1)
+# 	list_ls = []
+# 	len_labels = torch.round(alphas.sum(-1)).int()
+# 	max_label_len = len_labels.max().item()
+# 	# print("type: {}".format(type(max_label_len)))
+# 	for b in range(batch_size):
+# 		fire = fires[b, :]
+# 		l = torch.index_select(frames[b, :, :], 0, torch.nonzero(fire >= threshold).squeeze())
+# 		pad_l = torch.zeros([int(max_label_len - l.size(0)), int(hidden_size)], dtype=l.dtype, device=hidden.device)
+# 		list_ls.append(torch.cat([l, pad_l], 0))
+# 	return torch.stack(list_ls, 0), fires
+
+# @torch.jit.script
+# def cif(hidden, alphas, threshold: float):
+# 	batch_size, len_time, hidden_size = hidden.size()
+# 	threshold = torch.tensor([threshold], dtype=alphas.dtype).to(alphas.device)
+#
+# 	# loop varss
+# 	integrate = torch.zeros([batch_size], dtype=alphas.dtype, device=hidden.device)
+# 	frame = torch.zeros([batch_size, hidden_size], dtype=hidden.dtype, device=hidden.device)
+# 	# intermediate vars along time
+# 	list_fires = []
+# 	list_frames = []
+#
+# 	for t in range(len_time):
+# 		alpha = alphas[:, t]
+# 		distribution_completion = torch.ones([batch_size], dtype=alphas.dtype, device=hidden.device) - integrate
+#
+# 		integrate += alpha
+# 		list_fires.append(integrate)
+#
+# 		fire_place = integrate >= threshold
+# 		integrate = torch.where(fire_place,
+# 		                        integrate - torch.ones([batch_size], dtype=alphas.dtype, device=hidden.device),
+# 		                        integrate)
+# 		cur = torch.where(fire_place,
+# 		                  distribution_completion,
+# 		                  alpha)
+# 		remainds = alpha - cur
+#
+# 		frame += cur[:, None] * hidden[:, t, :]
+# 		list_frames.append(frame)
+# 		frame = torch.where(fire_place[:, None].repeat(1, hidden_size),
+# 		                    remainds[:, None] * hidden[:, t, :],
+# 		                    frame)
+#
+# 	fires = torch.stack(list_fires, 1)
+# 	frames = torch.stack(list_frames, 1)
+# 	len_labels = torch.floor(torch.sum(alphas, dim=1)).int()
+# 	max_label_len = torch.max(len_labels)
+# 	pad_num = max_label_len - len_labels
+# 	pad_num_max = torch.max(pad_num).item()
+# 	frames_pad_tensor = torch.zeros([int(batch_size), int(pad_num_max), int(hidden_size)], dtype=frames.dtype,
+# 	                                device=frames.device)
+# 	fires_pad_tensor = torch.ones([int(batch_size), int(pad_num_max)], dtype=fires.dtype, device=fires.device)
+# 	fires_pad_tensor_mask = sequence_mask_scripts(pad_num, maxlen=int(pad_num_max))
+# 	fires_pad_tensor *= fires_pad_tensor_mask
+# 	frames_pad = torch.cat([frames, frames_pad_tensor], dim=1)
+# 	fires_pad = torch.cat([fires, fires_pad_tensor], dim=1)
+# 	index_bool = fires_pad >= threshold
+# 	frames_fire = frames_pad[index_bool]
+# 	frames_fire = torch.reshape(frames_fire, (int(batch_size), -1, int(hidden_size)))
+# 	frames_fire_mask = sequence_mask_scripts(len_labels, maxlen=int(max_label_len))
+# 	frames_fire *= frames_fire_mask[:, :, None]
+#
+# 	return frames_fire, fires
+
+
 @torch.jit.script
 def cif(hidden, alphas, threshold: float):
 	batch_size, len_time, hidden_size = hidden.size()
 	threshold = torch.tensor([threshold], dtype=alphas.dtype).to(alphas.device)
 	
 	# loop varss
-	integrate = torch.zeros([batch_size], device=hidden.device)
-	frame = torch.zeros([batch_size, hidden_size], device=hidden.device)
+	integrate = torch.zeros([batch_size], dtype=alphas.dtype, device=hidden.device)
+	frame = torch.zeros([batch_size, hidden_size], dtype=hidden.dtype, device=hidden.device)
 	# intermediate vars along time
 	list_fires = []
 	list_frames = []
 	
 	for t in range(len_time):
 		alpha = alphas[:, t]
-		distribution_completion = torch.ones([batch_size], device=hidden.device) - integrate
+		distribution_completion = torch.ones([batch_size], dtype=alphas.dtype, device=hidden.device) - integrate
 		
 		integrate += alpha
 		list_fires.append(integrate)
 		
 		fire_place = integrate >= threshold
 		integrate = torch.where(fire_place,
-		                        integrate - torch.ones([batch_size], device=hidden.device),
+		                        integrate - torch.ones([batch_size], dtype=alphas.dtype, device=hidden.device),
 		                        integrate)
 		cur = torch.where(fire_place,
 		                  distribution_completion,
@@ -107,13 +215,20 @@ def cif(hidden, alphas, threshold: float):
 	
 	fires = torch.stack(list_fires, 1)
 	frames = torch.stack(list_frames, 1)
-	list_ls = []
-	len_labels = torch.round(alphas.sum(-1)).int()
-	max_label_len = len_labels.max().item()
-	print("type: {}".format(type(max_label_len)))
+	# list_ls = []
+	len_labels = torch.round(alphas.sum(-1)).type(torch.int32)
+	# max_label_len = int(torch.max(len_labels).item())
+	# print("type: {}".format(type(max_label_len)))
+	fire_idxs = fires >= threshold
+	frame_fires = torch.zeros_like(hidden)
+	max_label_len = frames[0, fire_idxs[0]].size(0)
 	for b in range(batch_size):
-		fire = fires[b, :]
-		l = torch.index_select(frames[b, :, :], 0, torch.nonzero(fire >= threshold).squeeze())
-		pad_l = torch.zeros([int(max_label_len - l.size(0)), int(hidden_size)], device=hidden.device)
-		list_ls.append(torch.cat([l, pad_l], 0))
-	return torch.stack(list_ls, 0), fires
+		# fire = fires[b, :]
+		frame_fire = frames[b, fire_idxs[b]]
+		frame_len = frame_fire.size(0)
+		frame_fires[b, :frame_len, :] = frame_fire
+	
+		if frame_len >= max_label_len:
+			max_label_len = frame_len
+	frame_fires = frame_fires[:, :max_label_len, :]
+	return frame_fires, fires