Upload sd_token_similarity_calculator.ipynb
Browse files
Google Colab Notebooks/sd_token_similarity_calculator.ipynb
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],
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"metadata": {
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"id": "rUXQ73IbonHY",
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"outputId": "0be95ebc-c6b0-4141-d299-d1e33c7a2e00",
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"colab": {
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"base_uri": "https://localhost:8080/"
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"metadata": {
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"id": "ZMG4CThUAmwW",
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"outputId": "d5ec8719-a0d7-4bff-f87c-379fed3a5d20",
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"colab": {
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"base_uri": "https://localhost:8080/"
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"outputs": [
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" text_features = text_encodings[f'{index}']\n",
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"\n",
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" torch.matmul(text_features, image_features.t()) * logit_scale\n",
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" sims[index] = torch.
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" if name_NEG != '':\n",
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" torch.matmul(text_features_NEG, image_features.t()) * logit_scale\n",
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" sims[index] = sims[index] - neg_strength* torch.
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" if image_NEG != '':\n",
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" sims[index] = sims[index] - neg_strength* torch.
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"#-------#\n",
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"sorted , indices = torch.sort(sims,dim=0 , descending=True)"
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],
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"metadata": {
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"id": "rebogpoyOG8k",
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"outputId": "c2a9083b-3b49-4693-bb17-4b829182462f",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 273,
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"9b8be4e7d91a47c2852ca202528744cf",
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"metadata": {
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"id": "JkzncP8SgKtS",
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"outputId": "d47b9b06-982a-457e-ef50-60664ba6c11f",
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"colab": {
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"base_uri": "https://localhost:8080/"
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"execution_count": 6,
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"outputs": [
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"metadata": {
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"id": "rUXQ73IbonHY",
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"colab": {
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"base_uri": "https://localhost:8080/"
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"outputId": "0be95ebc-c6b0-4141-d299-d1e33c7a2e00"
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"execution_count": 1,
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"outputs": [
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"metadata": {
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"id": "ZMG4CThUAmwW",
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"colab": {
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"base_uri": "https://localhost:8080/"
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"outputId": "d5ec8719-a0d7-4bff-f87c-379fed3a5d20"
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"execution_count": 2,
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"outputs": [
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" text_features = text_encodings[f'{index}']\n",
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"\n",
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" torch.matmul(text_features, image_features.t()) * logit_scale\n",
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" sims[index] = torch.nn.functional.cosine_similarity(text_features, image_features)\n",
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" if name_NEG != '':\n",
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" torch.matmul(text_features_NEG, image_features.t()) * logit_scale\n",
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" sims[index] = sims[index] - neg_strength* torch.nn.functional.cosine_similarity(text_features_NEG, image_features)\n",
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" if image_NEG != '':\n",
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" sims[index] = sims[index] - neg_strength* torch.nn.functional.cosine_similarity(image_features, image_features_NEG)\n",
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"#-------#\n",
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"sorted , indices = torch.sort(sims,dim=0 , descending=True)"
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],
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"metadata": {
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"id": "rebogpoyOG8k",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 273,
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"9b8be4e7d91a47c2852ca202528744cf",
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"outputId": "c2a9083b-3b49-4693-bb17-4b829182462f"
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"execution_count": 4,
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"outputs": [
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"metadata": {
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"id": "JkzncP8SgKtS",
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"colab": {
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"base_uri": "https://localhost:8080/"
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"outputId": "d47b9b06-982a-457e-ef50-60664ba6c11f"
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"execution_count": 6,
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"outputs": [
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