inference_and_visualization_swin.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Notes\n",
    "\n",
    "This notebook is for inference and visualizations. \n",
    "\n",
    "You can either load images from a dataloader(see Sec. 1) or from a local path(see Sec. 2).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, sys\n",
    "import json\n",
    "import numpy as np\n",
    "import mlx.core as mx\n",
    "from main import build_model_main\n",
    "from util.slconfig import SLConfig\n",
    "from datasets import build_dataset\n",
    "from util.visualizer import COCOVisualizer\n",
    "from util import box_ops\n",
    "import util.misc as utils\n",
    "from pprint import pprint"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Initialize and Load Pre-trained Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_config_path = \"config/DINO/DINO_4scale_swin.py\" # change the path of the model config file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sachinraja/Code/DINO_DETR_MLX/util/pytorch_weights_to_mlx.py:198: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  pytorch_weights_flattened = torch.load(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DINO(\n",
       "  (transformer): DeformableTransformer(\n",
       "    (encoder): TransformerEncoder(\n",
       "      (params.encoder_layer): DeformableTransformerEncoderLayer(\n",
       "        (self_attn): MSDeformAttn(\n",
       "          (sampling_offsets): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "          (attention_weights): Linear(input_dims=256, output_dims=128, bias=True)\n",
       "          (value_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "          (output_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "        )\n",
       "        (dropout1): Dropout(p=0.0)\n",
       "        (norm1): LayerNorm(256, eps=1e-05, affine=True)\n",
       "        (linear1): Linear(input_dims=256, output_dims=2048, bias=True)\n",
       "        (dropout2): Dropout(p=0.0)\n",
       "        (linear2): Linear(input_dims=2048, output_dims=256, bias=True)\n",
       "        (dropout3): Dropout(p=0.0)\n",
       "        (norm2): LayerNorm(256, eps=1e-05, affine=True)\n",
       "      )\n",
       "      (layers.0): DeformableTransformerEncoderLayer(\n",
       "        (self_attn): MSDeformAttn(\n",
       "          (sampling_offsets): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "          (attention_weights): Linear(input_dims=256, output_dims=128, bias=True)\n",
       "          (value_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "          (output_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "        )\n",
       "        (dropout1): Dropout(p=0.0)\n",
       "        (norm1): LayerNorm(256, eps=1e-05, affine=True)\n",
       "        (linear1): Linear(input_dims=256, output_dims=2048, bias=True)\n",
       "        (dropout2): Dropout(p=0.0)\n",
       "        (linear2): Linear(input_dims=2048, output_dims=256, bias=True)\n",
       "        (dropout3): Dropout(p=0.0)\n",
       "        (norm2): LayerNorm(256, eps=1e-05, affine=True)\n",
       "      )\n",
       "      (layers.1): DeformableTransformerEncoderLayer(\n",
       "        (self_attn): MSDeformAttn(\n",
       "          (sampling_offsets): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "          (attention_weights): Linear(input_dims=256, output_dims=128, bias=True)\n",
       "          (value_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "          (output_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "        )\n",
       "        (dropout1): Dropout(p=0.0)\n",
       "        (norm1): LayerNorm(256, eps=1e-05, affine=True)\n",
       "        (linear1): Linear(input_dims=256, output_dims=2048, bias=True)\n",
       "        (dropout2): Dropout(p=0.0)\n",
       "        (linear2): Linear(input_dims=2048, output_dims=256, bias=True)\n",
       "        (dropout3): Dropout(p=0.0)\n",
       "        (norm2): LayerNorm(256, eps=1e-05, affine=True)\n",
       "      )\n",
       "      (layers.2): DeformableTransformerEncoderLayer(\n",
       "        (self_attn): MSDeformAttn(\n",
       "          (sampling_offsets): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "          (attention_weights): Linear(input_dims=256, output_dims=128, bias=True)\n",
       "          (value_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "          (output_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "        )\n",
       "        (dropout1): Dropout(p=0.0)\n",
       "        (norm1): LayerNorm(256, eps=1e-05, affine=True)\n",
       "        (linear1): Linear(input_dims=256, output_dims=2048, bias=True)\n",
       "        (dropout2): Dropout(p=0.0)\n",
       "        (linear2): Linear(input_dims=2048, output_dims=256, bias=True)\n",
       "        (dropout3): Dropout(p=0.0)\n",
       "        (norm2): LayerNorm(256, eps=1e-05, affine=True)\n",
       "      )\n",
       "      (layers.3): DeformableTransformerEncoderLayer(\n",
       "        (self_attn): MSDeformAttn(\n",
       "          (sampling_offsets): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "          (attention_weights): Linear(input_dims=256, output_dims=128, bias=True)\n",
       "          (value_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "          (output_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "        )\n",
       "        (dropout1): Dropout(p=0.0)\n",
       "        (norm1): LayerNorm(256, eps=1e-05, affine=True)\n",
       "        (linear1): Linear(input_dims=256, output_dims=2048, bias=True)\n",
       "        (dropout2): Dropout(p=0.0)\n",
       "        (linear2): Linear(input_dims=2048, output_dims=256, bias=True)\n",
       "        (dropout3): Dropout(p=0.0)\n",
       "        (norm2): LayerNorm(256, eps=1e-05, affine=True)\n",
       "      )\n",
       "      (layers.4): DeformableTransformerEncoderLayer(\n",
       "        (self_attn): MSDeformAttn(\n",
       "          (sampling_offsets): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "          (attention_weights): Linear(input_dims=256, output_dims=128, bias=True)\n",
       "          (value_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "          (output_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "        )\n",
       "        (dropout1): Dropout(p=0.0)\n",
       "        (norm1): LayerNorm(256, eps=1e-05, affine=True)\n",
       "        (linear1): Linear(input_dims=256, output_dims=2048, bias=True)\n",
       "        (dropout2): Dropout(p=0.0)\n",
       "        (linear2): Linear(input_dims=2048, output_dims=256, bias=True)\n",
       "        (dropout3): Dropout(p=0.0)\n",
       "        (norm2): LayerNorm(256, eps=1e-05, affine=True)\n",
       "      )\n",
       "      (layers.5): DeformableTransformerEncoderLayer(\n",
       "        (self_attn): MSDeformAttn(\n",
       "          (sampling_offsets): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "          (attention_weights): Linear(input_dims=256, output_dims=128, bias=True)\n",
       "          (value_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "          (output_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "        )\n",
       "        (dropout1): Dropout(p=0.0)\n",
       "        (norm1): LayerNorm(256, eps=1e-05, affine=True)\n",
       "        (linear1): Linear(input_dims=256, output_dims=2048, bias=True)\n",
       "        (dropout2): Dropout(p=0.0)\n",
       "        (linear2): Linear(input_dims=2048, output_dims=256, bias=True)\n",
       "        (dropout3): Dropout(p=0.0)\n",
       "        (norm2): LayerNorm(256, eps=1e-05, affine=True)\n",
       "      )\n",
       "    )\n",
       "    (decoder): TransformerDecoder(\n",
       "      (params.decoder_layer): DeformableTransformerDecoderLayer(\n",
       "        (cross_attn): MSDeformAttn(\n",
       "          (sampling_offsets): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "          (attention_weights): Linear(input_dims=256, output_dims=128, bias=True)\n",
       "          (value_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "          (output_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "        )\n",
       "        (dropout1): Dropout(p=0.0)\n",
       "        (norm1): LayerNorm(256, eps=1e-05, affine=True)\n",
       "        (self_attn): MultiHeadAttention(\n",
       "          (query_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "          (key_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "          (value_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "          (out_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "        )\n",
       "        (dropout2): Dropout(p=0.0)\n",
       "        (norm2): LayerNorm(256, eps=1e-05, affine=True)\n",
       "        (linear1): Linear(input_dims=256, output_dims=2048, bias=True)\n",
       "        (dropout3): Dropout(p=0.0)\n",
       "        (linear2): Linear(input_dims=2048, output_dims=256, bias=True)\n",
       "        (dropout4): Dropout(p=0.0)\n",
       "        (norm3): LayerNorm(256, eps=1e-05, affine=True)\n",
       "      )\n",
       "      (params.norm): LayerNorm(256, eps=1e-05, affine=True)\n",
       "      (layers.0): DeformableTransformerDecoderLayer(\n",
       "        (cross_attn): MSDeformAttn(\n",
       "          (sampling_offsets): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "          (attention_weights): Linear(input_dims=256, output_dims=128, bias=True)\n",
       "          (value_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "          (output_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "        )\n",
       "        (dropout1): Dropout(p=0.0)\n",
       "        (norm1): LayerNorm(256, eps=1e-05, affine=True)\n",
       "        (self_attn): MultiHeadAttention(\n",
       "          (query_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "          (key_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "          (value_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "          (out_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "        )\n",
       "        (dropout2): Dropout(p=0.0)\n",
       "        (norm2): LayerNorm(256, eps=1e-05, affine=True)\n",
       "        (linear1): Linear(input_dims=256, output_dims=2048, bias=True)\n",
       "        (dropout3): Dropout(p=0.0)\n",
       "        (linear2): Linear(input_dims=2048, output_dims=256, bias=True)\n",
       "        (dropout4): Dropout(p=0.0)\n",
       "        (norm3): LayerNorm(256, eps=1e-05, affine=True)\n",
       "      )\n",
       "      (layers.1): DeformableTransformerDecoderLayer(\n",
       "        (cross_attn): MSDeformAttn(\n",
       "          (sampling_offsets): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "          (attention_weights): Linear(input_dims=256, output_dims=128, bias=True)\n",
       "          (value_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "          (output_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "        )\n",
       "        (dropout1): Dropout(p=0.0)\n",
       "        (norm1): LayerNorm(256, eps=1e-05, affine=True)\n",
       "        (self_attn): MultiHeadAttention(\n",
       "          (query_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "          (key_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "          (value_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "          (out_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "        )\n",
       "        (dropout2): Dropout(p=0.0)\n",
       "        (norm2): LayerNorm(256, eps=1e-05, affine=True)\n",
       "        (linear1): Linear(input_dims=256, output_dims=2048, bias=True)\n",
       "        (dropout3): Dropout(p=0.0)\n",
       "        (linear2): Linear(input_dims=2048, output_dims=256, bias=True)\n",
       "        (dropout4): Dropout(p=0.0)\n",
       "        (norm3): LayerNorm(256, eps=1e-05, affine=True)\n",
       "      )\n",
       "      (layers.2): DeformableTransformerDecoderLayer(\n",
       "        (cross_attn): MSDeformAttn(\n",
       "          (sampling_offsets): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "          (attention_weights): Linear(input_dims=256, output_dims=128, bias=True)\n",
       "          (value_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "          (output_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "        )\n",
       "        (dropout1): Dropout(p=0.0)\n",
       "        (norm1): LayerNorm(256, eps=1e-05, affine=True)\n",
       "        (self_attn): MultiHeadAttention(\n",
       "          (query_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "          (key_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "          (value_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "          (out_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "        )\n",
       "        (dropout2): Dropout(p=0.0)\n",
       "        (norm2): LayerNorm(256, eps=1e-05, affine=True)\n",
       "        (linear1): Linear(input_dims=256, output_dims=2048, bias=True)\n",
       "        (dropout3): Dropout(p=0.0)\n",
       "        (linear2): Linear(input_dims=2048, output_dims=256, bias=True)\n",
       "        (dropout4): Dropout(p=0.0)\n",
       "        (norm3): LayerNorm(256, eps=1e-05, affine=True)\n",
       "      )\n",
       "      (layers.3): DeformableTransformerDecoderLayer(\n",
       "        (cross_attn): MSDeformAttn(\n",
       "          (sampling_offsets): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "          (attention_weights): Linear(input_dims=256, output_dims=128, bias=True)\n",
       "          (value_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "          (output_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "        )\n",
       "        (dropout1): Dropout(p=0.0)\n",
       "        (norm1): LayerNorm(256, eps=1e-05, affine=True)\n",
       "        (self_attn): MultiHeadAttention(\n",
       "          (query_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "          (key_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "          (value_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "          (out_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "        )\n",
       "        (dropout2): Dropout(p=0.0)\n",
       "        (norm2): LayerNorm(256, eps=1e-05, affine=True)\n",
       "        (linear1): Linear(input_dims=256, output_dims=2048, bias=True)\n",
       "        (dropout3): Dropout(p=0.0)\n",
       "        (linear2): Linear(input_dims=2048, output_dims=256, bias=True)\n",
       "        (dropout4): Dropout(p=0.0)\n",
       "        (norm3): LayerNorm(256, eps=1e-05, affine=True)\n",
       "      )\n",
       "      (layers.4): DeformableTransformerDecoderLayer(\n",
       "        (cross_attn): MSDeformAttn(\n",
       "          (sampling_offsets): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "          (attention_weights): Linear(input_dims=256, output_dims=128, bias=True)\n",
       "          (value_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "          (output_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "        )\n",
       "        (dropout1): Dropout(p=0.0)\n",
       "        (norm1): LayerNorm(256, eps=1e-05, affine=True)\n",
       "        (self_attn): MultiHeadAttention(\n",
       "          (query_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "          (key_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "          (value_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "          (out_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "        )\n",
       "        (dropout2): Dropout(p=0.0)\n",
       "        (norm2): LayerNorm(256, eps=1e-05, affine=True)\n",
       "        (linear1): Linear(input_dims=256, output_dims=2048, bias=True)\n",
       "        (dropout3): Dropout(p=0.0)\n",
       "        (linear2): Linear(input_dims=2048, output_dims=256, bias=True)\n",
       "        (dropout4): Dropout(p=0.0)\n",
       "        (norm3): LayerNorm(256, eps=1e-05, affine=True)\n",
       "      )\n",
       "      (layers.5): DeformableTransformerDecoderLayer(\n",
       "        (cross_attn): MSDeformAttn(\n",
       "          (sampling_offsets): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "          (attention_weights): Linear(input_dims=256, output_dims=128, bias=True)\n",
       "          (value_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "          (output_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "        )\n",
       "        (dropout1): Dropout(p=0.0)\n",
       "        (norm1): LayerNorm(256, eps=1e-05, affine=True)\n",
       "        (self_attn): MultiHeadAttention(\n",
       "          (query_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "          (key_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "          (value_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "          (out_proj): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "        )\n",
       "        (dropout2): Dropout(p=0.0)\n",
       "        (norm2): LayerNorm(256, eps=1e-05, affine=True)\n",
       "        (linear1): Linear(input_dims=256, output_dims=2048, bias=True)\n",
       "        (dropout3): Dropout(p=0.0)\n",
       "        (linear2): Linear(input_dims=2048, output_dims=256, bias=True)\n",
       "        (dropout4): Dropout(p=0.0)\n",
       "        (norm3): LayerNorm(256, eps=1e-05, affine=True)\n",
       "      )\n",
       "      (norm): LayerNorm(256, eps=1e-05, affine=True)\n",
       "      (ref_point_head): MLP(\n",
       "        (layers.0): Linear(input_dims=512, output_dims=256, bias=True)\n",
       "        (layers.1): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "      )\n",
       "      (bbox_embed.0): MLP(\n",
       "        (layers.0): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "        (layers.1): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "        (layers.2): Linear(input_dims=256, output_dims=4, bias=True)\n",
       "      )\n",
       "      (bbox_embed.1): MLP(\n",
       "        (layers.0): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "        (layers.1): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "        (layers.2): Linear(input_dims=256, output_dims=4, bias=True)\n",
       "      )\n",
       "      (bbox_embed.2): MLP(\n",
       "        (layers.0): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "        (layers.1): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "        (layers.2): Linear(input_dims=256, output_dims=4, bias=True)\n",
       "      )\n",
       "      (bbox_embed.3): MLP(\n",
       "        (layers.0): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "        (layers.1): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "        (layers.2): Linear(input_dims=256, output_dims=4, bias=True)\n",
       "      )\n",
       "      (bbox_embed.4): MLP(\n",
       "        (layers.0): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "        (layers.1): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "        (layers.2): Linear(input_dims=256, output_dims=4, bias=True)\n",
       "      )\n",
       "      (bbox_embed.5): MLP(\n",
       "        (layers.0): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "        (layers.1): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "        (layers.2): Linear(input_dims=256, output_dims=4, bias=True)\n",
       "      )\n",
       "      (class_embed.0): Linear(input_dims=256, output_dims=91, bias=True)\n",
       "      (class_embed.1): Linear(input_dims=256, output_dims=91, bias=True)\n",
       "      (class_embed.2): Linear(input_dims=256, output_dims=91, bias=True)\n",
       "      (class_embed.3): Linear(input_dims=256, output_dims=91, bias=True)\n",
       "      (class_embed.4): Linear(input_dims=256, output_dims=91, bias=True)\n",
       "      (class_embed.5): Linear(input_dims=256, output_dims=91, bias=True)\n",
       "    )\n",
       "    (tgt_embed): Embedding(900, 256)\n",
       "    (enc_output): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "    (enc_output_norm): LayerNorm(256, eps=1e-05, affine=True)\n",
       "    (enc_out_bbox_embed): MLP(\n",
       "      (layers.0): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "      (layers.1): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "      (layers.2): Linear(input_dims=256, output_dims=4, bias=True)\n",
       "    )\n",
       "    (enc_out_class_embed): Linear(input_dims=256, output_dims=91, bias=True)\n",
       "  )\n",
       "  (label_enc): Embedding(92, 256)\n",
       "  (dn_encoder): DNEncoder(\n",
       "    (label_enc): Embedding(92, 256)\n",
       "  )\n",
       "  (input_proj.0): Sequential(\n",
       "    (layers.0): Conv2d(384, 256, kernel_size=(1,), stride=(1, 1), padding=(0, 0), dilation=1, bias=True)\n",
       "    (layers.1): GroupNorm(32, 256, eps=1e-05, affine=True, pytorch_compatible=False)\n",
       "  )\n",
       "  (input_proj.1): Sequential(\n",
       "    (layers.0): Conv2d(768, 256, kernel_size=(1,), stride=(1, 1), padding=(0, 0), dilation=1, bias=True)\n",
       "    (layers.1): GroupNorm(32, 256, eps=1e-05, affine=True, pytorch_compatible=False)\n",
       "  )\n",
       "  (input_proj.2): Sequential(\n",
       "    (layers.0): Conv2d(1536, 256, kernel_size=(1,), stride=(1, 1), padding=(0, 0), dilation=1, bias=True)\n",
       "    (layers.1): GroupNorm(32, 256, eps=1e-05, affine=True, pytorch_compatible=False)\n",
       "  )\n",
       "  (input_proj.3): Sequential(\n",
       "    (layers.0): Conv2d(1536, 256, kernel_size=(3,), stride=(2, 2), padding=(1, 1), dilation=1, bias=True)\n",
       "    (layers.1): GroupNorm(32, 256, eps=1e-05, affine=True, pytorch_compatible=False)\n",
       "  )\n",
       "  (backbone): Joiner(\n",
       "    (layers.0): Backbone(\n",
       "      (body): SwinTransformer(\n",
       "        (patch_embed): Sequential(\n",
       "          (layers.0): Conv2d(3, 192, kernel_size=(4,), stride=(4, 4), padding=(0, 0), dilation=1, bias=True)\n",
       "          (layers.1): Identity()\n",
       "          (layers.2): LayerNorm(192, eps=1e-05, affine=True)\n",
       "        )\n",
       "        (layers.0): Sequential(\n",
       "          (layers.0): SwinTransformerBlock(\n",
       "            (norm1): LayerNorm(192, eps=1e-05, affine=True)\n",
       "            (attn): ShiftedWindowAttention(\n",
       "              (qkv): Linear(input_dims=192, output_dims=576, bias=True)\n",
       "              (proj): Linear(input_dims=192, output_dims=192, bias=True)\n",
       "            )\n",
       "            (stochastic_depth): StochasticDepth()\n",
       "            (norm2): LayerNorm(192, eps=1e-05, affine=True)\n",
       "            (mlp): MLP(\n",
       "              (layers.0): Linear(input_dims=192, output_dims=768, bias=True)\n",
       "              (layers.1): GELU()\n",
       "              (layers.2): Dropout(p=0.0)\n",
       "              (layers.3): Linear(input_dims=768, output_dims=192, bias=True)\n",
       "              (layers.4): Dropout(p=0.0)\n",
       "            )\n",
       "          )\n",
       "          (layers.1): SwinTransformerBlock(\n",
       "            (norm1): LayerNorm(192, eps=1e-05, affine=True)\n",
       "            (attn): ShiftedWindowAttention(\n",
       "              (qkv): Linear(input_dims=192, output_dims=576, bias=True)\n",
       "              (proj): Linear(input_dims=192, output_dims=192, bias=True)\n",
       "            )\n",
       "            (stochastic_depth): StochasticDepth()\n",
       "            (norm2): LayerNorm(192, eps=1e-05, affine=True)\n",
       "            (mlp): MLP(\n",
       "              (layers.0): Linear(input_dims=192, output_dims=768, bias=True)\n",
       "              (layers.1): GELU()\n",
       "              (layers.2): Dropout(p=0.0)\n",
       "              (layers.3): Linear(input_dims=768, output_dims=192, bias=True)\n",
       "              (layers.4): Dropout(p=0.0)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (layers.1): PatchMerging(\n",
       "          (reduction): Linear(input_dims=768, output_dims=384, bias=False)\n",
       "          (norm): LayerNorm(768, eps=1e-05, affine=True)\n",
       "        )\n",
       "        (layers.2): Sequential(\n",
       "          (layers.0): SwinTransformerBlock(\n",
       "            (norm1): LayerNorm(384, eps=1e-05, affine=True)\n",
       "            (attn): ShiftedWindowAttention(\n",
       "              (qkv): Linear(input_dims=384, output_dims=1152, bias=True)\n",
       "              (proj): Linear(input_dims=384, output_dims=384, bias=True)\n",
       "            )\n",
       "            (stochastic_depth): StochasticDepth()\n",
       "            (norm2): LayerNorm(384, eps=1e-05, affine=True)\n",
       "            (mlp): MLP(\n",
       "              (layers.0): Linear(input_dims=384, output_dims=1536, bias=True)\n",
       "              (layers.1): GELU()\n",
       "              (layers.2): Dropout(p=0.0)\n",
       "              (layers.3): Linear(input_dims=1536, output_dims=384, bias=True)\n",
       "              (layers.4): Dropout(p=0.0)\n",
       "            )\n",
       "          )\n",
       "          (layers.1): SwinTransformerBlock(\n",
       "            (norm1): LayerNorm(384, eps=1e-05, affine=True)\n",
       "            (attn): ShiftedWindowAttention(\n",
       "              (qkv): Linear(input_dims=384, output_dims=1152, bias=True)\n",
       "              (proj): Linear(input_dims=384, output_dims=384, bias=True)\n",
       "            )\n",
       "            (stochastic_depth): StochasticDepth()\n",
       "            (norm2): LayerNorm(384, eps=1e-05, affine=True)\n",
       "            (mlp): MLP(\n",
       "              (layers.0): Linear(input_dims=384, output_dims=1536, bias=True)\n",
       "              (layers.1): GELU()\n",
       "              (layers.2): Dropout(p=0.0)\n",
       "              (layers.3): Linear(input_dims=1536, output_dims=384, bias=True)\n",
       "              (layers.4): Dropout(p=0.0)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (layers.3): PatchMerging(\n",
       "          (reduction): Linear(input_dims=1536, output_dims=768, bias=False)\n",
       "          (norm): LayerNorm(1536, eps=1e-05, affine=True)\n",
       "        )\n",
       "        (layers.4): Sequential(\n",
       "          (layers.0): SwinTransformerBlock(\n",
       "            (norm1): LayerNorm(768, eps=1e-05, affine=True)\n",
       "            (attn): ShiftedWindowAttention(\n",
       "              (qkv): Linear(input_dims=768, output_dims=2304, bias=True)\n",
       "              (proj): Linear(input_dims=768, output_dims=768, bias=True)\n",
       "            )\n",
       "            (stochastic_depth): StochasticDepth()\n",
       "            (norm2): LayerNorm(768, eps=1e-05, affine=True)\n",
       "            (mlp): MLP(\n",
       "              (layers.0): Linear(input_dims=768, output_dims=3072, bias=True)\n",
       "              (layers.1): GELU()\n",
       "              (layers.2): Dropout(p=0.0)\n",
       "              (layers.3): Linear(input_dims=3072, output_dims=768, bias=True)\n",
       "              (layers.4): Dropout(p=0.0)\n",
       "            )\n",
       "          )\n",
       "          (layers.1): SwinTransformerBlock(\n",
       "            (norm1): LayerNorm(768, eps=1e-05, affine=True)\n",
       "            (attn): ShiftedWindowAttention(\n",
       "              (qkv): Linear(input_dims=768, output_dims=2304, bias=True)\n",
       "              (proj): Linear(input_dims=768, output_dims=768, bias=True)\n",
       "            )\n",
       "            (stochastic_depth): StochasticDepth()\n",
       "            (norm2): LayerNorm(768, eps=1e-05, affine=True)\n",
       "            (mlp): MLP(\n",
       "              (layers.0): Linear(input_dims=768, output_dims=3072, bias=True)\n",
       "              (layers.1): GELU()\n",
       "              (layers.2): Dropout(p=0.0)\n",
       "              (layers.3): Linear(input_dims=3072, output_dims=768, bias=True)\n",
       "              (layers.4): Dropout(p=0.0)\n",
       "            )\n",
       "          )\n",
       "          (layers.2): SwinTransformerBlock(\n",
       "            (norm1): LayerNorm(768, eps=1e-05, affine=True)\n",
       "            (attn): ShiftedWindowAttention(\n",
       "              (qkv): Linear(input_dims=768, output_dims=2304, bias=True)\n",
       "              (proj): Linear(input_dims=768, output_dims=768, bias=True)\n",
       "            )\n",
       "            (stochastic_depth): StochasticDepth()\n",
       "            (norm2): LayerNorm(768, eps=1e-05, affine=True)\n",
       "            (mlp): MLP(\n",
       "              (layers.0): Linear(input_dims=768, output_dims=3072, bias=True)\n",
       "              (layers.1): GELU()\n",
       "              (layers.2): Dropout(p=0.0)\n",
       "              (layers.3): Linear(input_dims=3072, output_dims=768, bias=True)\n",
       "              (layers.4): Dropout(p=0.0)\n",
       "            )\n",
       "          )\n",
       "          (layers.3): SwinTransformerBlock(\n",
       "            (norm1): LayerNorm(768, eps=1e-05, affine=True)\n",
       "            (attn): ShiftedWindowAttention(\n",
       "              (qkv): Linear(input_dims=768, output_dims=2304, bias=True)\n",
       "              (proj): Linear(input_dims=768, output_dims=768, bias=True)\n",
       "            )\n",
       "            (stochastic_depth): StochasticDepth()\n",
       "            (norm2): LayerNorm(768, eps=1e-05, affine=True)\n",
       "            (mlp): MLP(\n",
       "              (layers.0): Linear(input_dims=768, output_dims=3072, bias=True)\n",
       "              (layers.1): GELU()\n",
       "              (layers.2): Dropout(p=0.0)\n",
       "              (layers.3): Linear(input_dims=3072, output_dims=768, bias=True)\n",
       "              (layers.4): Dropout(p=0.0)\n",
       "            )\n",
       "          )\n",
       "          (layers.4): SwinTransformerBlock(\n",
       "            (norm1): LayerNorm(768, eps=1e-05, affine=True)\n",
       "            (attn): ShiftedWindowAttention(\n",
       "              (qkv): Linear(input_dims=768, output_dims=2304, bias=True)\n",
       "              (proj): Linear(input_dims=768, output_dims=768, bias=True)\n",
       "            )\n",
       "            (stochastic_depth): StochasticDepth()\n",
       "            (norm2): LayerNorm(768, eps=1e-05, affine=True)\n",
       "            (mlp): MLP(\n",
       "              (layers.0): Linear(input_dims=768, output_dims=3072, bias=True)\n",
       "              (layers.1): GELU()\n",
       "              (layers.2): Dropout(p=0.0)\n",
       "              (layers.3): Linear(input_dims=3072, output_dims=768, bias=True)\n",
       "              (layers.4): Dropout(p=0.0)\n",
       "            )\n",
       "          )\n",
       "          (layers.5): SwinTransformerBlock(\n",
       "            (norm1): LayerNorm(768, eps=1e-05, affine=True)\n",
       "            (attn): ShiftedWindowAttention(\n",
       "              (qkv): Linear(input_dims=768, output_dims=2304, bias=True)\n",
       "              (proj): Linear(input_dims=768, output_dims=768, bias=True)\n",
       "            )\n",
       "            (stochastic_depth): StochasticDepth()\n",
       "            (norm2): LayerNorm(768, eps=1e-05, affine=True)\n",
       "            (mlp): MLP(\n",
       "              (layers.0): Linear(input_dims=768, output_dims=3072, bias=True)\n",
       "              (layers.1): GELU()\n",
       "              (layers.2): Dropout(p=0.0)\n",
       "              (layers.3): Linear(input_dims=3072, output_dims=768, bias=True)\n",
       "              (layers.4): Dropout(p=0.0)\n",
       "            )\n",
       "          )\n",
       "          (layers.6): SwinTransformerBlock(\n",
       "            (norm1): LayerNorm(768, eps=1e-05, affine=True)\n",
       "            (attn): ShiftedWindowAttention(\n",
       "              (qkv): Linear(input_dims=768, output_dims=2304, bias=True)\n",
       "              (proj): Linear(input_dims=768, output_dims=768, bias=True)\n",
       "            )\n",
       "            (stochastic_depth): StochasticDepth()\n",
       "            (norm2): LayerNorm(768, eps=1e-05, affine=True)\n",
       "            (mlp): MLP(\n",
       "              (layers.0): Linear(input_dims=768, output_dims=3072, bias=True)\n",
       "              (layers.1): GELU()\n",
       "              (layers.2): Dropout(p=0.0)\n",
       "              (layers.3): Linear(input_dims=3072, output_dims=768, bias=True)\n",
       "              (layers.4): Dropout(p=0.0)\n",
       "            )\n",
       "          )\n",
       "          (layers.7): SwinTransformerBlock(\n",
       "            (norm1): LayerNorm(768, eps=1e-05, affine=True)\n",
       "            (attn): ShiftedWindowAttention(\n",
       "              (qkv): Linear(input_dims=768, output_dims=2304, bias=True)\n",
       "              (proj): Linear(input_dims=768, output_dims=768, bias=True)\n",
       "            )\n",
       "            (stochastic_depth): StochasticDepth()\n",
       "            (norm2): LayerNorm(768, eps=1e-05, affine=True)\n",
       "            (mlp): MLP(\n",
       "              (layers.0): Linear(input_dims=768, output_dims=3072, bias=True)\n",
       "              (layers.1): GELU()\n",
       "              (layers.2): Dropout(p=0.0)\n",
       "              (layers.3): Linear(input_dims=3072, output_dims=768, bias=True)\n",
       "              (layers.4): Dropout(p=0.0)\n",
       "            )\n",
       "          )\n",
       "          (layers.8): SwinTransformerBlock(\n",
       "            (norm1): LayerNorm(768, eps=1e-05, affine=True)\n",
       "            (attn): ShiftedWindowAttention(\n",
       "              (qkv): Linear(input_dims=768, output_dims=2304, bias=True)\n",
       "              (proj): Linear(input_dims=768, output_dims=768, bias=True)\n",
       "            )\n",
       "            (stochastic_depth): StochasticDepth()\n",
       "            (norm2): LayerNorm(768, eps=1e-05, affine=True)\n",
       "            (mlp): MLP(\n",
       "              (layers.0): Linear(input_dims=768, output_dims=3072, bias=True)\n",
       "              (layers.1): GELU()\n",
       "              (layers.2): Dropout(p=0.0)\n",
       "              (layers.3): Linear(input_dims=3072, output_dims=768, bias=True)\n",
       "              (layers.4): Dropout(p=0.0)\n",
       "            )\n",
       "          )\n",
       "          (layers.9): SwinTransformerBlock(\n",
       "            (norm1): LayerNorm(768, eps=1e-05, affine=True)\n",
       "            (attn): ShiftedWindowAttention(\n",
       "              (qkv): Linear(input_dims=768, output_dims=2304, bias=True)\n",
       "              (proj): Linear(input_dims=768, output_dims=768, bias=True)\n",
       "            )\n",
       "            (stochastic_depth): StochasticDepth()\n",
       "            (norm2): LayerNorm(768, eps=1e-05, affine=True)\n",
       "            (mlp): MLP(\n",
       "              (layers.0): Linear(input_dims=768, output_dims=3072, bias=True)\n",
       "              (layers.1): GELU()\n",
       "              (layers.2): Dropout(p=0.0)\n",
       "              (layers.3): Linear(input_dims=3072, output_dims=768, bias=True)\n",
       "              (layers.4): Dropout(p=0.0)\n",
       "            )\n",
       "          )\n",
       "          (layers.10): SwinTransformerBlock(\n",
       "            (norm1): LayerNorm(768, eps=1e-05, affine=True)\n",
       "            (attn): ShiftedWindowAttention(\n",
       "              (qkv): Linear(input_dims=768, output_dims=2304, bias=True)\n",
       "              (proj): Linear(input_dims=768, output_dims=768, bias=True)\n",
       "            )\n",
       "            (stochastic_depth): StochasticDepth()\n",
       "            (norm2): LayerNorm(768, eps=1e-05, affine=True)\n",
       "            (mlp): MLP(\n",
       "              (layers.0): Linear(input_dims=768, output_dims=3072, bias=True)\n",
       "              (layers.1): GELU()\n",
       "              (layers.2): Dropout(p=0.0)\n",
       "              (layers.3): Linear(input_dims=3072, output_dims=768, bias=True)\n",
       "              (layers.4): Dropout(p=0.0)\n",
       "            )\n",
       "          )\n",
       "          (layers.11): SwinTransformerBlock(\n",
       "            (norm1): LayerNorm(768, eps=1e-05, affine=True)\n",
       "            (attn): ShiftedWindowAttention(\n",
       "              (qkv): Linear(input_dims=768, output_dims=2304, bias=True)\n",
       "              (proj): Linear(input_dims=768, output_dims=768, bias=True)\n",
       "            )\n",
       "            (stochastic_depth): StochasticDepth()\n",
       "            (norm2): LayerNorm(768, eps=1e-05, affine=True)\n",
       "            (mlp): MLP(\n",
       "              (layers.0): Linear(input_dims=768, output_dims=3072, bias=True)\n",
       "              (layers.1): GELU()\n",
       "              (layers.2): Dropout(p=0.0)\n",
       "              (layers.3): Linear(input_dims=3072, output_dims=768, bias=True)\n",
       "              (layers.4): Dropout(p=0.0)\n",
       "            )\n",
       "          )\n",
       "          (layers.12): SwinTransformerBlock(\n",
       "            (norm1): LayerNorm(768, eps=1e-05, affine=True)\n",
       "            (attn): ShiftedWindowAttention(\n",
       "              (qkv): Linear(input_dims=768, output_dims=2304, bias=True)\n",
       "              (proj): Linear(input_dims=768, output_dims=768, bias=True)\n",
       "            )\n",
       "            (stochastic_depth): StochasticDepth()\n",
       "            (norm2): LayerNorm(768, eps=1e-05, affine=True)\n",
       "            (mlp): MLP(\n",
       "              (layers.0): Linear(input_dims=768, output_dims=3072, bias=True)\n",
       "              (layers.1): GELU()\n",
       "              (layers.2): Dropout(p=0.0)\n",
       "              (layers.3): Linear(input_dims=3072, output_dims=768, bias=True)\n",
       "              (layers.4): Dropout(p=0.0)\n",
       "            )\n",
       "          )\n",
       "          (layers.13): SwinTransformerBlock(\n",
       "            (norm1): LayerNorm(768, eps=1e-05, affine=True)\n",
       "            (attn): ShiftedWindowAttention(\n",
       "              (qkv): Linear(input_dims=768, output_dims=2304, bias=True)\n",
       "              (proj): Linear(input_dims=768, output_dims=768, bias=True)\n",
       "            )\n",
       "            (stochastic_depth): StochasticDepth()\n",
       "            (norm2): LayerNorm(768, eps=1e-05, affine=True)\n",
       "            (mlp): MLP(\n",
       "              (layers.0): Linear(input_dims=768, output_dims=3072, bias=True)\n",
       "              (layers.1): GELU()\n",
       "              (layers.2): Dropout(p=0.0)\n",
       "              (layers.3): Linear(input_dims=3072, output_dims=768, bias=True)\n",
       "              (layers.4): Dropout(p=0.0)\n",
       "            )\n",
       "          )\n",
       "          (layers.14): SwinTransformerBlock(\n",
       "            (norm1): LayerNorm(768, eps=1e-05, affine=True)\n",
       "            (attn): ShiftedWindowAttention(\n",
       "              (qkv): Linear(input_dims=768, output_dims=2304, bias=True)\n",
       "              (proj): Linear(input_dims=768, output_dims=768, bias=True)\n",
       "            )\n",
       "            (stochastic_depth): StochasticDepth()\n",
       "            (norm2): LayerNorm(768, eps=1e-05, affine=True)\n",
       "            (mlp): MLP(\n",
       "              (layers.0): Linear(input_dims=768, output_dims=3072, bias=True)\n",
       "              (layers.1): GELU()\n",
       "              (layers.2): Dropout(p=0.0)\n",
       "              (layers.3): Linear(input_dims=3072, output_dims=768, bias=True)\n",
       "              (layers.4): Dropout(p=0.0)\n",
       "            )\n",
       "          )\n",
       "          (layers.15): SwinTransformerBlock(\n",
       "            (norm1): LayerNorm(768, eps=1e-05, affine=True)\n",
       "            (attn): ShiftedWindowAttention(\n",
       "              (qkv): Linear(input_dims=768, output_dims=2304, bias=True)\n",
       "              (proj): Linear(input_dims=768, output_dims=768, bias=True)\n",
       "            )\n",
       "            (stochastic_depth): StochasticDepth()\n",
       "            (norm2): LayerNorm(768, eps=1e-05, affine=True)\n",
       "            (mlp): MLP(\n",
       "              (layers.0): Linear(input_dims=768, output_dims=3072, bias=True)\n",
       "              (layers.1): GELU()\n",
       "              (layers.2): Dropout(p=0.0)\n",
       "              (layers.3): Linear(input_dims=3072, output_dims=768, bias=True)\n",
       "              (layers.4): Dropout(p=0.0)\n",
       "            )\n",
       "          )\n",
       "          (layers.16): SwinTransformerBlock(\n",
       "            (norm1): LayerNorm(768, eps=1e-05, affine=True)\n",
       "            (attn): ShiftedWindowAttention(\n",
       "              (qkv): Linear(input_dims=768, output_dims=2304, bias=True)\n",
       "              (proj): Linear(input_dims=768, output_dims=768, bias=True)\n",
       "            )\n",
       "            (stochastic_depth): StochasticDepth()\n",
       "            (norm2): LayerNorm(768, eps=1e-05, affine=True)\n",
       "            (mlp): MLP(\n",
       "              (layers.0): Linear(input_dims=768, output_dims=3072, bias=True)\n",
       "              (layers.1): GELU()\n",
       "              (layers.2): Dropout(p=0.0)\n",
       "              (layers.3): Linear(input_dims=3072, output_dims=768, bias=True)\n",
       "              (layers.4): Dropout(p=0.0)\n",
       "            )\n",
       "          )\n",
       "          (layers.17): SwinTransformerBlock(\n",
       "            (norm1): LayerNorm(768, eps=1e-05, affine=True)\n",
       "            (attn): ShiftedWindowAttention(\n",
       "              (qkv): Linear(input_dims=768, output_dims=2304, bias=True)\n",
       "              (proj): Linear(input_dims=768, output_dims=768, bias=True)\n",
       "            )\n",
       "            (stochastic_depth): StochasticDepth()\n",
       "            (norm2): LayerNorm(768, eps=1e-05, affine=True)\n",
       "            (mlp): MLP(\n",
       "              (layers.0): Linear(input_dims=768, output_dims=3072, bias=True)\n",
       "              (layers.1): GELU()\n",
       "              (layers.2): Dropout(p=0.0)\n",
       "              (layers.3): Linear(input_dims=3072, output_dims=768, bias=True)\n",
       "              (layers.4): Dropout(p=0.0)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (layers.5): PatchMerging(\n",
       "          (reduction): Linear(input_dims=3072, output_dims=1536, bias=False)\n",
       "          (norm): LayerNorm(3072, eps=1e-05, affine=True)\n",
       "        )\n",
       "        (layers.6): Sequential(\n",
       "          (layers.0): SwinTransformerBlock(\n",
       "            (norm1): LayerNorm(1536, eps=1e-05, affine=True)\n",
       "            (attn): ShiftedWindowAttention(\n",
       "              (qkv): Linear(input_dims=1536, output_dims=4608, bias=True)\n",
       "              (proj): Linear(input_dims=1536, output_dims=1536, bias=True)\n",
       "            )\n",
       "            (stochastic_depth): StochasticDepth()\n",
       "            (norm2): LayerNorm(1536, eps=1e-05, affine=True)\n",
       "            (mlp): MLP(\n",
       "              (layers.0): Linear(input_dims=1536, output_dims=6144, bias=True)\n",
       "              (layers.1): GELU()\n",
       "              (layers.2): Dropout(p=0.0)\n",
       "              (layers.3): Linear(input_dims=6144, output_dims=1536, bias=True)\n",
       "              (layers.4): Dropout(p=0.0)\n",
       "            )\n",
       "          )\n",
       "          (layers.1): SwinTransformerBlock(\n",
       "            (norm1): LayerNorm(1536, eps=1e-05, affine=True)\n",
       "            (attn): ShiftedWindowAttention(\n",
       "              (qkv): Linear(input_dims=1536, output_dims=4608, bias=True)\n",
       "              (proj): Linear(input_dims=1536, output_dims=1536, bias=True)\n",
       "            )\n",
       "            (stochastic_depth): StochasticDepth()\n",
       "            (norm2): LayerNorm(1536, eps=1e-05, affine=True)\n",
       "            (mlp): MLP(\n",
       "              (layers.0): Linear(input_dims=1536, output_dims=6144, bias=True)\n",
       "              (layers.1): GELU()\n",
       "              (layers.2): Dropout(p=0.0)\n",
       "              (layers.3): Linear(input_dims=6144, output_dims=1536, bias=True)\n",
       "              (layers.4): Dropout(p=0.0)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (norm): LayerNorm(1536, eps=1e-05, affine=True)\n",
       "        (avgpool): AdaptiveAvgPool2d()\n",
       "        (norm_layers.0): LayerNorm(384, eps=1e-05, affine=True)\n",
       "        (norm_layers.1): LayerNorm(768, eps=1e-05, affine=True)\n",
       "        (norm_layers.2): LayerNorm(1536, eps=1e-05, affine=True)\n",
       "        (head): Linear(input_dims=1536, output_dims=1000, bias=True)\n",
       "      )\n",
       "    )\n",
       "    (layers.1): PositionEmbeddingSine()\n",
       "  )\n",
       "  (bbox_embed.0): MLP(\n",
       "    (layers.0): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "    (layers.1): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "    (layers.2): Linear(input_dims=256, output_dims=4, bias=True)\n",
       "  )\n",
       "  (bbox_embed.1): MLP(\n",
       "    (layers.0): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "    (layers.1): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "    (layers.2): Linear(input_dims=256, output_dims=4, bias=True)\n",
       "  )\n",
       "  (bbox_embed.2): MLP(\n",
       "    (layers.0): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "    (layers.1): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "    (layers.2): Linear(input_dims=256, output_dims=4, bias=True)\n",
       "  )\n",
       "  (bbox_embed.3): MLP(\n",
       "    (layers.0): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "    (layers.1): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "    (layers.2): Linear(input_dims=256, output_dims=4, bias=True)\n",
       "  )\n",
       "  (bbox_embed.4): MLP(\n",
       "    (layers.0): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "    (layers.1): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "    (layers.2): Linear(input_dims=256, output_dims=4, bias=True)\n",
       "  )\n",
       "  (bbox_embed.5): MLP(\n",
       "    (layers.0): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "    (layers.1): Linear(input_dims=256, output_dims=256, bias=True)\n",
       "    (layers.2): Linear(input_dims=256, output_dims=4, bias=True)\n",
       "  )\n",
       "  (class_embed.0): Linear(input_dims=256, output_dims=91, bias=True)\n",
       "  (class_embed.1): Linear(input_dims=256, output_dims=91, bias=True)\n",
       "  (class_embed.2): Linear(input_dims=256, output_dims=91, bias=True)\n",
       "  (class_embed.3): Linear(input_dims=256, output_dims=91, bias=True)\n",
       "  (class_embed.4): Linear(input_dims=256, output_dims=91, bias=True)\n",
       "  (class_embed.5): Linear(input_dims=256, output_dims=91, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "args = SLConfig.fromfile(model_config_path) \n",
    "args.device = 'metal' \n",
    "model, criterion, postprocessors = build_model_main(args)\n",
    "model = utils.load_mlx_model_with_pytorch_weights(model, args.pytorch_weights_path, args.backbone, None)\n",
    "model.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load coco names\n",
    "with open('util/coco_id2name.json') as f:\n",
    "    id2name = json.load(f)\n",
    "    id2name = {int(k):v for k,v in id2name.items()}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize Custom Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "import datasets.transforms as T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "image = Image.open(\"./figs/idea.jpg\").convert(\"RGB\") # load image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# transform images\n",
    "transform = T.Compose([\n",
    "    T.RandomResize([800], max_size=1333),\n",
    "    T.ToTensor(),\n",
    "    T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
    "])\n",
    "image, _ = transform(image, None)\n",
    "image = mx.array(image.numpy()).transpose(1, 2, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1199, 800, 3)\n"
     ]
    }
   ],
   "source": [
    "print(image.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predict images\n",
    "output = model(image[None])\n",
    "output = postprocessors['bbox'](output, mx.array([[1.0, 1.0]]))[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'boxes': array([[0.753456, 0.300464, 0.99937, 0.497483],\n",
      "       [0.74538, 0.646396, 1, 0.885837],\n",
      "       [0.382414, 0.3095, 0.748125, 0.577949],\n",
      "       ...,\n",
      "       [0.81931, 0.527312, 0.946801, 0.561951],\n",
      "       [0.304267, 0.564361, 0.499272, 0.648051],\n",
      "       [0.42822, 0.482882, 0.533913, 0.577725]], dtype=float32),\n",
      " 'labels': array([72, 62, 64, ..., 84, 2, 64], dtype=uint32),\n",
      " 'scores': array([0.844652, 0.799851, 0.77425, ..., 0.0219006, 0.0218595, 0.0218471], dtype=float32)}\n"
     ]
    }
   ],
   "source": [
    "pprint(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1199, 800, 3)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize outputs\n",
    "thershold = 0.3 # set a thershold\n",
    "\n",
    "vslzr = COCOVisualizer()\n",
    "\n",
    "scores = output['scores']\n",
    "labels = output['labels']\n",
    "boxes = box_ops.box_xyxy_to_cxcywh(output['boxes'])\n",
    "select_mask = scores > thershold\n",
    "labels = np.asarray(labels)\n",
    "boxes = np.asarray(boxes)\n",
    "select_mask = np.asarray(select_mask)\n",
    "# image = np.asarray(image)\n",
    "print(image.shape)\n",
    "box_label = [id2name[int(item)] for item in labels[select_mask]]\n",
    "pred_dict = {\n",
    "    'boxes': boxes[select_mask],\n",
    "    'size': mx.array([image.shape[0], image.shape[1]]),\n",
    "    'box_label': box_label\n",
    "}\n",
    "vslzr.visualize(image, pred_dict, savedir=None, dpi=100)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.18"
  },
  "vscode": {
   "interpreter": {
    "hash": "0ad18b9cce5171a92b1ace78d675cb7cfe7b38ef1dfda11fe1bc29cba1874dd4"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}