From 2f7063b70d493c29d72babdcbe3f55e182a2332d Mon Sep 17 00:00:00 2001
From: Victor Mylle <victor.mylle@hotmail.com>
Date: Mon, 17 Apr 2023 15:52:19 +0000
Subject: [PATCH] Some fixes

---
 export_model.py                      | 66 ++++++++++++++++++++++++++
 hyperparam_opt.py                    | 45 ++++++++++++++----
 notebooks/visualize_embeddings.ipynb | 69 ++++++++++++----------------
 requirements.txt                     |  4 +-
 train.py                             | 25 +++++++---
 train.sh                             | 10 ++--
 6 files changed, 158 insertions(+), 61 deletions(-)
 create mode 100644 export_model.py

diff --git a/export_model.py b/export_model.py
new file mode 100644
index 0000000..126a6d8
--- /dev/null
+++ b/export_model.py
@@ -0,0 +1,66 @@
+import numpy as np
+import onnx
+import torch
+import torchvision
+
+from models.spoter_embedding_model import SPOTER_EMBEDDINGS
+
+# set parameters of the model
+model_name = 'embedding_model'
+output=32
+
+# load PyTorch model from .pth file
+
+device = torch.device("cpu")
+if torch.cuda.is_available():
+    device = torch.device("cuda")
+
+CHECKPOINT_PATH = "out-checkpoints/augment_rotate_75_x8/checkpoint_embed_1105.pth"
+checkpoint = torch.load(CHECKPOINT_PATH, map_location=device)
+
+model = SPOTER_EMBEDDINGS(
+    features=checkpoint["config_args"].vector_length,
+    hidden_dim=checkpoint["config_args"].hidden_dim,
+    norm_emb=checkpoint["config_args"].normalize_embeddings,
+).to(device)
+model.load_state_dict(checkpoint["state_dict"])
+# set model to evaluation mode
+model.eval()
+
+model_export = "onnx"
+if model_export == "coreml":
+    dummy_input = torch.randn(1, 10, 54, 2)
+    traced_model = torch.jit.trace(model, dummy_input)
+    out = traced_model(dummy_input)
+    import coremltools as ct
+
+    # Convert to Core ML
+    coreml_model = ct.convert(
+        traced_model,
+        inputs=[ct.TensorType(name="input", shape=dummy_input.shape)],
+    )
+
+    # Save Core ML model
+    coreml_model.save("models/" + model_name + ".mlmodel")
+else:
+    # create dummy input tensor
+    dummy_input = torch.randn(1, 10, 54, 2)
+
+    # export model to ONNX format
+    output_file = 'models/' + model_name + '.onnx'
+    torch.onnx.export(model, dummy_input, output_file, input_names=['input'], output_names=['output'])
+
+    torch.onnx.export(model,                                # model being run
+                    dummy_input,                          # model input (or a tuple for multiple inputs)
+                    'output-models/' + model_name + '.onnx',     # where to save the model (can be a file or file-like object)
+                    export_params=True,                   # store the trained parameter weights inside the model file
+                    opset_version=9,                      # the ONNX version to export the model to
+                    do_constant_folding=True,             # whether to execute constant folding for optimization
+                    input_names = ['X'],                  # the model's input names
+                    output_names = ['Y']                  # the model's output names
+                    )
+
+
+    # load exported ONNX model for verification
+    onnx_model = onnx.load(output_file)
+    onnx.checker.check_model(onnx_model)
\ No newline at end of file
diff --git a/hyperparam_opt.py b/hyperparam_opt.py
index d46098d..a7caabe 100644
--- a/hyperparam_opt.py
+++ b/hyperparam_opt.py
@@ -1,7 +1,7 @@
 from clearml.automation import UniformParameterRange, UniformIntegerParameterRange, DiscreteParameterRange
 from clearml.automation import HyperParameterOptimizer
 from clearml.automation.optuna import OptimizerOptuna
-
+from optuna.pruners import HyperbandPruner, MedianPruner
 from clearml import Task
 
 task = Task.init(
@@ -17,19 +17,19 @@ optimizer = HyperParameterOptimizer(
       base_task_id="4504e0b3ec6745249d3d4c94d3d40652",  
       # setting the hyperparameters to optimize
       hyper_parameters=[
-        # epochs
-        UniformIntegerParameterRange('Args/epochs', 200, 800),
+        # epochs:
+        DiscreteParameterRange('Args/epochs', [200]),
         # learning rate
-        UniformParameterRange('Args/lr', 0.000001, 0.1),
+        UniformParameterRange('Args/lr', 0.000001, 0.01),
         # optimizer
         DiscreteParameterRange('Args/optimizer', ['ADAM', 'SGD']),
         # vector length
         UniformIntegerParameterRange('Args/vector_length', 10, 100),
           ],
       # setting the objective metric we want to maximize/minimize
-      objective_metric_title='silhouette_coefficient',
-      objective_metric_series='val',
-      objective_metric_sign='max',  
+      objective_metric_title='train_loss',
+      objective_metric_series='loss',
+      objective_metric_sign='min',  
 
       # setting optimizer  
       optimizer_class=OptimizerOptuna,
@@ -40,7 +40,34 @@ optimizer = HyperParameterOptimizer(
       compute_time_limit=480, 
       total_max_jobs=20,
       min_iteration_per_job=0,
-      max_iteration_per_job=150000,
+      max_iteration_per_job=150000, 
+      pool_period_min=0.1,
+
+      save_top_k_tasks_only=3,
+      optuna_pruner=MedianPruner(),
+
       )
 
-task.execute_remotely(queue_name='default', exit_process=True)
+def job_complete_callback(
+    job_id,                 # type: str
+    objective_value,        # type: float
+    objective_iteration,    # type: int
+    job_parameters,         # type: dict
+    top_performance_job_id  # type: str
+):
+    print('Job completed!', job_id, objective_value, objective_iteration, job_parameters)
+    if job_id == top_performance_job_id:
+        print('WOOT WOOT we broke the record! Objective reached {}'.format(objective_value))
+
+task.execute_remotely(queue_name='hypertuning', exit_process=True)
+
+optimizer.set_report_period(0.3)
+
+optimizer.start(job_complete_callback=job_complete_callback)
+
+optimizer.wait()
+
+top_exp = optimizer.get_top_experiments(top_k=3)
+print([t.id for t in top_exp])
+
+optimizer.stop()
\ No newline at end of file
diff --git a/notebooks/visualize_embeddings.ipynb b/notebooks/visualize_embeddings.ipynb
index 84ebdff..b0d979c 100644
--- a/notebooks/visualize_embeddings.ipynb
+++ b/notebooks/visualize_embeddings.ipynb
@@ -2,19 +2,10 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 1,
    "id": "8ef5cd92",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%load_ext autoreload\n",
     "%autoreload 2"
@@ -22,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 2,
    "id": "78c4643a",
    "metadata": {},
    "outputs": [],
@@ -37,7 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 3,
    "id": "ffba4333",
    "metadata": {},
    "outputs": [],
@@ -47,7 +38,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 4,
    "id": "5bc81f71",
    "metadata": {},
    "outputs": [],
@@ -57,7 +48,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 5,
    "id": "3de8bcf2",
    "metadata": {
     "lines_to_next_cell": 0
@@ -73,7 +64,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 6,
    "id": "91a045ba",
    "metadata": {},
    "outputs": [],
@@ -93,17 +84,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 7,
    "id": "bc50c296",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<torch._C.Generator at 0x7f2d4c10efd0>"
+       "<torch._C.Generator at 0x7fc80f62ff70>"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -124,7 +115,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 8,
    "id": "82766a17",
    "metadata": {},
    "outputs": [],
@@ -138,7 +129,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 41,
    "id": "ead15a36",
    "metadata": {},
    "outputs": [
@@ -148,7 +139,7 @@
        "<All keys matched successfully>"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -160,7 +151,7 @@
     "# checkpoint = torch.load(model.get_weights())\n",
     "\n",
     "\n",
-    "CHECKPOINT_PATH = \"../out-checkpoints/augment_rotate_75_x8/checkpoint_embed_1105.pth\"\n",
+    "CHECKPOINT_PATH = \"../out-checkpoints/augment_rotate_75_x8/checkpoint_embed_197.pth\"\n",
     "checkpoint = torch.load(CHECKPOINT_PATH, map_location=device)\n",
     "\n",
     "\n",
@@ -175,7 +166,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 42,
    "id": "20f8036d",
    "metadata": {},
    "outputs": [],
@@ -190,13 +181,13 @@
     "    split_dataset_path = \"WLASL100_{}.csv\"\n",
     "elif SL_DATASET == 'basic-signs':\n",
     "    dataset_name = \"basic-signs\"\n",
-    "    split_dataset_path = \"basic-signs_{}.csv\"\n",
+    "    split_dataset_path = \"{}.csv\"\n",
     "    "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 43,
    "id": "758716b6",
    "metadata": {},
    "outputs": [],
@@ -216,7 +207,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 44,
    "id": "f1527959",
    "metadata": {},
    "outputs": [
@@ -266,7 +257,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 45,
    "id": "3c3af5bf",
    "metadata": {
     "lines_to_next_cell": 0
@@ -275,10 +266,10 @@
     {
      "data": {
       "text/plain": [
-       "164"
+       "143"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -303,7 +294,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 46,
    "id": "dccbe1b9",
    "metadata": {},
    "outputs": [],
@@ -326,7 +317,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 47,
    "id": "904298f0",
    "metadata": {},
    "outputs": [],
@@ -340,7 +331,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 48,
    "id": "42832f7c",
    "metadata": {
     "scrolled": false
@@ -351,7 +342,7 @@
       "text/html": [
        "<div class=\"bk-root\">\n",
        "        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
-       "        <span id=\"1368\">Loading BokehJS ...</span>\n",
+       "        <span id=\"1506\">Loading BokehJS ...</span>\n",
        "    </div>\n"
       ]
      },
@@ -360,7 +351,7 @@
     },
     {
      "data": {
-      "application/javascript": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\nconst JS_MIME_TYPE = 'application/javascript';\n  const HTML_MIME_TYPE = 'text/html';\n  const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n  const CLASS_NAME = 'output_bokeh rendered_html';\n\n  /**\n   * Render data to the DOM node\n   */\n  function render(props, node) {\n    const script = document.createElement(\"script\");\n    node.appendChild(script);\n  }\n\n  /**\n   * Handle when an output is cleared or removed\n   */\n  function handleClearOutput(event, handle) {\n    const cell = handle.cell;\n\n    const id = cell.output_area._bokeh_element_id;\n    const server_id = cell.output_area._bokeh_server_id;\n    // Clean up Bokeh references\n    if (id != null && id in Bokeh.index) {\n      Bokeh.index[id].model.document.clear();\n      delete Bokeh.index[id];\n    }\n\n    if (server_id !== undefined) {\n      // Clean up Bokeh references\n      const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n      cell.notebook.kernel.execute(cmd_clean, {\n        iopub: {\n          output: function(msg) {\n            const id = msg.content.text.trim();\n            if (id in Bokeh.index) {\n              Bokeh.index[id].model.document.clear();\n              delete Bokeh.index[id];\n            }\n          }\n        }\n      });\n      // Destroy server and session\n      const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n      cell.notebook.kernel.execute(cmd_destroy);\n    }\n  }\n\n  /**\n   * Handle when a new output is added\n   */\n  function handleAddOutput(event, handle) {\n    const output_area = handle.output_area;\n    const output = handle.output;\n\n    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n    if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n      return\n    }\n\n    const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n\n    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n      // store reference to embed id on output_area\n      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n    }\n    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n      const bk_div = document.createElement(\"div\");\n      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n      const script_attrs = bk_div.children[0].attributes;\n      for (let i = 0; i < script_attrs.length; i++) {\n        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n      }\n      // store reference to server id on output_area\n      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n    }\n  }\n\n  function register_renderer(events, OutputArea) {\n\n    function append_mime(data, metadata, element) {\n      // create a DOM node to render to\n      const toinsert = this.create_output_subarea(\n        metadata,\n        CLASS_NAME,\n        EXEC_MIME_TYPE\n      );\n      this.keyboard_manager.register_events(toinsert);\n      // Render to node\n      const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n      render(props, toinsert[toinsert.length - 1]);\n      element.append(toinsert);\n      return toinsert\n    }\n\n    /* Handle when an output is cleared or removed */\n    events.on('clear_output.CodeCell', handleClearOutput);\n    events.on('delete.Cell', handleClearOutput);\n\n    /* Handle when a new output is added */\n    events.on('output_added.OutputArea', handleAddOutput);\n\n    /**\n     * Register the mime type and append_mime function with output_area\n     */\n    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n      /* Is output safe? */\n      safe: true,\n      /* Index of renderer in `output_area.display_order` */\n      index: 0\n    });\n  }\n\n  // register the mime type if in Jupyter Notebook environment and previously unregistered\n  if (root.Jupyter !== undefined) {\n    const events = require('base/js/events');\n    const OutputArea = require('notebook/js/outputarea').OutputArea;\n\n    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n      register_renderer(events, OutputArea);\n    }\n  }\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"1368\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"1368\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));",
+      "application/javascript": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\nconst JS_MIME_TYPE = 'application/javascript';\n  const HTML_MIME_TYPE = 'text/html';\n  const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n  const CLASS_NAME = 'output_bokeh rendered_html';\n\n  /**\n   * Render data to the DOM node\n   */\n  function render(props, node) {\n    const script = document.createElement(\"script\");\n    node.appendChild(script);\n  }\n\n  /**\n   * Handle when an output is cleared or removed\n   */\n  function handleClearOutput(event, handle) {\n    const cell = handle.cell;\n\n    const id = cell.output_area._bokeh_element_id;\n    const server_id = cell.output_area._bokeh_server_id;\n    // Clean up Bokeh references\n    if (id != null && id in Bokeh.index) {\n      Bokeh.index[id].model.document.clear();\n      delete Bokeh.index[id];\n    }\n\n    if (server_id !== undefined) {\n      // Clean up Bokeh references\n      const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n      cell.notebook.kernel.execute(cmd_clean, {\n        iopub: {\n          output: function(msg) {\n            const id = msg.content.text.trim();\n            if (id in Bokeh.index) {\n              Bokeh.index[id].model.document.clear();\n              delete Bokeh.index[id];\n            }\n          }\n        }\n      });\n      // Destroy server and session\n      const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n      cell.notebook.kernel.execute(cmd_destroy);\n    }\n  }\n\n  /**\n   * Handle when a new output is added\n   */\n  function handleAddOutput(event, handle) {\n    const output_area = handle.output_area;\n    const output = handle.output;\n\n    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n    if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n      return\n    }\n\n    const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n\n    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n      // store reference to embed id on output_area\n      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n    }\n    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n      const bk_div = document.createElement(\"div\");\n      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n      const script_attrs = bk_div.children[0].attributes;\n      for (let i = 0; i < script_attrs.length; i++) {\n        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n      }\n      // store reference to server id on output_area\n      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n    }\n  }\n\n  function register_renderer(events, OutputArea) {\n\n    function append_mime(data, metadata, element) {\n      // create a DOM node to render to\n      const toinsert = this.create_output_subarea(\n        metadata,\n        CLASS_NAME,\n        EXEC_MIME_TYPE\n      );\n      this.keyboard_manager.register_events(toinsert);\n      // Render to node\n      const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n      render(props, toinsert[toinsert.length - 1]);\n      element.append(toinsert);\n      return toinsert\n    }\n\n    /* Handle when an output is cleared or removed */\n    events.on('clear_output.CodeCell', handleClearOutput);\n    events.on('delete.Cell', handleClearOutput);\n\n    /* Handle when a new output is added */\n    events.on('output_added.OutputArea', handleAddOutput);\n\n    /**\n     * Register the mime type and append_mime function with output_area\n     */\n    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n      /* Is output safe? */\n      safe: true,\n      /* Index of renderer in `output_area.display_order` */\n      index: 0\n    });\n  }\n\n  // register the mime type if in Jupyter Notebook environment and previously unregistered\n  if (root.Jupyter !== undefined) {\n    const events = require('base/js/events');\n    const OutputArea = require('notebook/js/outputarea').OutputArea;\n\n    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n      register_renderer(events, OutputArea);\n    }\n  }\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"1506\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"1506\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));",
       "application/vnd.bokehjs_load.v0+json": ""
      },
      "metadata": {},
@@ -403,7 +394,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 49,
    "id": "ead4daf7",
    "metadata": {
     "scrolled": false
@@ -413,7 +404,7 @@
      "data": {
       "text/html": [
        "\n",
-       "  <div class=\"bk-root\" id=\"1a2f2ca2-cdb2-4df2-ba23-85018cca17d9\" data-root-id=\"1369\"></div>\n"
+       "  <div class=\"bk-root\" id=\"c95f81fb-7cf0-4c10-b8a3-a9e77c772aef\" data-root-id=\"1507\"></div>\n"
       ]
      },
      "metadata": {},
@@ -421,12 +412,12 @@
     },
     {
      "data": {
-      "application/javascript": "(function(root) {\n  function embed_document(root) {\n  const docs_json = 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</div>\\n\"},\"id\":\"1533\",\"type\":\"HoverTool\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"field\":\"labels\",\"transform\":{\"id\":\"1505\"}},\"hatch_alpha\":{\"value\":0.1},\"hatch_color\":{\"field\":\"labels\",\"transform\":{\"id\":\"1505\"}},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"field\":\"labels\",\"transform\":{\"id\":\"1505\"}},\"size\":{\"value\":10},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1545\",\"type\":\"Scatter\"},{\"attributes\":{\"axis\":{\"id\":\"1518\"},\"coordinates\":null,\"group\":null,\"ticker\":null},\"id\":\"1521\",\"type\":\"Grid\"},{\"attributes\":{},\"id\":\"1557\",\"type\":\"Selection\"},{\"attributes\":{},\"id\":\"1530\",\"type\":\"ResetTool\"},{\"attributes\":{\"overlay\":{\"id\":\"1532\"}},\"id\":\"1528\",\"type\":\"BoxZoomTool\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"field\":\"labels\",\"transform\":{\"id\":\"1505\"}},\"hatch_alpha\":{\"value\":0.5},\"hatch_color\":{\"field\":\"labels\",\"transform\":{\"id\":\"1505\"}},\"line_alpha\":{\"value\":0.5},\"line_color\":{\"field\":\"labels\",\"transform\":{\"id\":\"1505\"}},\"size\":{\"value\":10},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1544\",\"type\":\"Scatter\"},{\"attributes\":{},\"id\":\"1516\",\"type\":\"LinearScale\"},{\"attributes\":{\"tools\":[{\"id\":\"1526\"},{\"id\":\"1527\"},{\"id\":\"1528\"},{\"id\":\"1529\"},{\"id\":\"1530\"},{\"id\":\"1531\"},{\"id\":\"1533\"}]},\"id\":\"1534\",\"type\":\"Toolbar\"},{\"attributes\":{\"coordinates\":null,\"formatter\":{\"id\":\"1551\"},\"group\":null,\"major_label_policy\":{\"id\":\"1552\"},\"ticker\":{\"id\":\"1523\"}},\"id\":\"1522\",\"type\":\"LinearAxis\"}],\"root_ids\":[\"1507\"]},\"title\":\"Bokeh Application\",\"version\":\"2.4.3\"}};\n  const render_items = [{\"docid\":\"49e50715-c814-427d-91f3-7faabca8d9a1\",\"root_ids\":[\"1507\"],\"roots\":{\"1507\":\"c95f81fb-7cf0-4c10-b8a3-a9e77c772aef\"}}];\n  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n  }\n  if (root.Bokeh !== undefined) {\n    embed_document(root);\n  } else {\n    let attempts = 0;\n    const timer = setInterval(function(root) {\n      if (root.Bokeh !== undefined) {\n        clearInterval(timer);\n        embed_document(root);\n      } else {\n        attempts++;\n        if (attempts > 100) {\n          clearInterval(timer);\n          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n        }\n      }\n    }, 10, root)\n  }\n})(window);",
       "application/vnd.bokehjs_exec.v0+json": ""
      },
      "metadata": {
       "application/vnd.bokehjs_exec.v0+json": {
-       "id": "1369"
+       "id": "1507"
       }
      },
      "output_type": "display_data"
diff --git a/requirements.txt b/requirements.txt
index 2e09c25..d83a933 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -9,7 +9,7 @@ opencv-python==4.6.0.66
 plotly==5.11.0
 scikit-learn==1.0.2
 clearml==1.10.3
-torch==2.0.0+cu118
-torchvision==0.15.1+cu118
+torch==2.0.0
+torchvision==0.15.1
 tqdm==4.54.1
 optuna==3.1.1
\ No newline at end of file
diff --git a/train.py b/train.py
index 1d27a79..c7038b1 100644
--- a/train.py
+++ b/train.py
@@ -75,12 +75,25 @@ def train(args, tracker: Tracker):
 
     # Construct the model
     if not args.classification_model:
-        slrt_model = SPOTER_EMBEDDINGS(
-            features=args.vector_length,
-            hidden_dim=args.hidden_dim,
-            norm_emb=args.normalize_embeddings,
-            dropout=args.dropout
-        )
+        # if finetune, load the weights from the classification model
+        if args.finetune:
+            checkpoint = torch.load(args.checkpoint, map_location=device)
+
+            slrt_model = SPOTER_EMBEDDINGS(
+                features=checkpoint["config_args"].vector_length,
+                hidden_dim=checkpoint["config_args"].hidden_dim,
+                norm_emb=checkpoint["config_args"].normalize_embeddings,
+                dropout=checkpoint["config_args"].dropout,
+            )
+        else:
+            slrt_model = SPOTER_EMBEDDINGS(
+                features=args.vector_length,
+                hidden_dim=args.hidden_dim,
+                norm_emb=args.normalize_embeddings,
+                dropout=args.dropout,
+            )
+            
+
         model_type = 'embed'
         if args.hard_triplet_mining == "None":
             cel_criterion = nn.TripletMarginLoss(margin=args.triplet_loss_margin, p=2)
diff --git a/train.sh b/train.sh
index b6eb7ef..08ecc1f 100755
--- a/train.sh
+++ b/train.sh
@@ -1,10 +1,10 @@
 #!/bin/sh
-python -m train \
+python3 -m train \
 	--save_checkpoints_every 10 \
     --experiment_name "wlasl" \
-    --epochs 300 \
-	--optimizer "ADAM" \
-	--lr 0.0001 \
+    --epochs 600 \
+	--optimizer "SGD" \
+	--lr 0.001 \
     --batch_size 16 \
 	--dataset_name "WLASL" \
 	--training_set_path "WLASL100_train.csv" \
@@ -12,7 +12,7 @@ python -m train \
 	--vector_length 32 \
 	--epoch_iters -1 \
 	--scheduler_factor 0.2 \
-	--hard_triplet_mining "None" \
+	--hard_triplet_mining "in_batch" \
 	--filter_easy_triplets \
 	--triplet_loss_margin 2 \
 	--dropout 0.2 \