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"execution_count": 19,
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"execution_count": 18,
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"id": "2cd46569",
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"metadata": {},
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"outputs": [
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@ -428,6 +374,7 @@
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}
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],
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"source": [
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"# pytorch CTCLoss\n",
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"probs = torch.FloatTensor([[\n",
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" [0.1, 0.6, 0.1, 0.1, 0.1], [0.1, 0.1, 0.6, 0.1, 0.1]\n",
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" ]]).transpose(0, 1).contiguous()\n",
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@ -449,7 +396,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"execution_count": 27,
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"id": "85c3461a",
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"metadata": {},
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"outputs": [
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@ -467,6 +414,7 @@
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}
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],
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"source": [
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"# Paddle CTCLoss\n",
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"paddle.set_device('cpu')\n",
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"probs = paddle.to_tensor([[\n",
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" [0.1, 0.6, 0.1, 0.1, 0.1], [0.1, 0.1, 0.6, 0.1, 0.1],\n",
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@ -490,7 +438,55 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d390cd91",
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"id": "8cdf76c2",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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"id": "2c305eaf",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"torch.Size([2, 1, 5])\n",
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"2.4628584384918213\n",
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"[[[ 0.17703117 -0.7081247 0.17703117 0.17703117 0.17703117]]\n",
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"\n",
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" [[ 0.17703117 0.17703117 -0.7081247 0.17703117 0.17703117]]]\n"
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]
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}
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],
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"source": [
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"# warpctc_pytorch CTCLoss, log_softmax idempotent\n",
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"probs = torch.FloatTensor([[\n",
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" [0.1, 0.6, 0.1, 0.1, 0.1], [0.1, 0.1, 0.6, 0.1, 0.1]\n",
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" ]]).transpose(0, 1).contiguous()\n",
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"print(probs.size())\n",
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"labels = torch.IntTensor([1, 2])\n",
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"label_sizes = torch.IntTensor([2])\n",
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"probs_sizes = torch.IntTensor([2])\n",
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"probs.requires_grad_(True)\n",
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"bs = probs.size(1)\n",
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"\n",
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"ctc_loss = wp.CTCLoss(size_average=False, length_average=False)\n",
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"\n",
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"log_probs = torch.log_softmax(probs, axis=-1)\n",
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"cost = ctc_loss(log_probs, labels, probs_sizes, label_sizes)\n",
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"cost = cost.sum() / bs\n",
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"print(cost.item())\n",
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"cost.backward()\n",
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"print(probs.grad.numpy())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "443336f0",
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"metadata": {},
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"outputs": [],
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"source": []
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lym0302
4 years ago