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@@ -91,7 +91,7 @@ class DSSMLayer(nn.Layer):
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)
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feature = paddle.cast(feature, dtype='int64')
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embedded = embeddings[i](feature)
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- paddle.static.Print(embedded, message="lqc debug _process_features embedded {i}:")
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+
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embedded_features.append(embedded)
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# 将所有embedding连接起来
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@@ -101,7 +101,8 @@ class DSSMLayer(nn.Layer):
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def forward(self, left_features, right_features):
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# 获取两个视频的特征表示
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left_vec, right_vec = self.get_vectors(left_features, right_features)
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-
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+ paddle.static.Print(left_vec, message="lqc left model output shape:")
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+ paddle.static.Print(right_vec, message="lqc right model output shape:")
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# 计算相似度
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sim_score = F.cosine_similarity(
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left_vec,
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@@ -116,7 +117,7 @@ class DSSMLayer(nn.Layer):
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# 处理左视频特征
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left_embedded = self._process_features(left_features, self.left_embeddings)
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- paddle.static.Print(left_embedded, message="lqc left_embedded shape:")
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+
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# left_vec = left_embedded
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left_vec = paddle.reshape(left_embedded, [-1, self.feature_num * self.embedding_dim])
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often