From f5021bf58b9d4f9e03e024047460cb45f46e97d3 Mon Sep 17 00:00:00 2001 From: Carlo Date: Tue, 1 Oct 2024 11:55:12 +0200 Subject: [PATCH 1/2] u --- Ch08_Attention/ec08_1_attention/attn.ipynb | 335 ++++ .../ec08_A_nlp_attn/eng-spa.txt | 0 .../ec08_A_nlp_attn/nlp_attn.ipynb | 371 +++- .../ec08_B_transformer}/images/vit-image.png | Bin .../ec08_B_transformer}/transformer.ipynb | 379 ++-- Ch08_Attention/ec08_C_ViT/ViT.ipynb | 1750 +++++++++++++++++ Ch08_Transformers/ec08_B_ViT/ViT.ipynb | 1349 ------------- 7 files changed, 2512 insertions(+), 1672 deletions(-) create mode 100644 Ch08_Attention/ec08_1_attention/attn.ipynb rename {Ch08_Transformers => Ch08_Attention}/ec08_A_nlp_attn/eng-spa.txt (100%) rename {Ch08_Transformers => Ch08_Attention}/ec08_A_nlp_attn/nlp_attn.ipynb (68%) rename {Ch08_Transformers/ec08_1_transformer => Ch08_Attention/ec08_B_transformer}/images/vit-image.png (100%) rename {Ch08_Transformers/ec08_1_transformer => Ch08_Attention/ec08_B_transformer}/transformer.ipynb (92%) create mode 100644 Ch08_Attention/ec08_C_ViT/ViT.ipynb delete mode 100644 Ch08_Transformers/ec08_B_ViT/ViT.ipynb diff --git a/Ch08_Attention/ec08_1_attention/attn.ipynb b/Ch08_Attention/ec08_1_attention/attn.ipynb new file mode 100644 index 000000000..b2390c8ac --- /dev/null +++ b/Ch08_Attention/ec08_1_attention/attn.ipynb @@ -0,0 +1,335 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Understanding Attention\n", + "\n", + "This notebook provides a complete code example that demonstrates how the attention mechanism works." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Obtaining a matrix representation of sentences\n", + "\n", + "Use randomly generated embeddings to represent each word ..." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import numpy as np\n", + "\n", + "in_sentence= \"The teacher praised the student because she improved\"\n", + "\n", + "in_tokens = in_sentence.split()\n", + "embeddings = {token: np.random.rand(300) for token in in_tokens}\n", + "in_embeddings = np.array([embeddings[token] for token in in_tokens])\n", + "in_embeddings = torch.tensor(in_embeddings, dtype=torch.float).unsqueeze(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Implement Dot-product Attention" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import torch.nn.functional as F\n", + "import deeplay as dl\n", + "\n", + "\n", + "class DotProductAttention(dl.DeeplayModule):\n", + " def __init__(self):\n", + " super().__init__()\n", + "\n", + " def forward(self, queries, keys, values):\n", + " attn_scores = torch.matmul(queries, keys.transpose(-2, -1)) \\\n", + " / (keys.size(-1) ** 0.5)\n", + " attn_weights = F.softmax(attn_scores, dim=-1) \n", + " attn_output = torch.matmul(attn_weights, values) \n", + " return attn_output, attn_weights" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calculate self-attention for the example" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "attention = DotProductAttention()\n", + "\n", + "queries = in_embeddings\n", + "keys = in_embeddings\n", + "values = in_embeddings\n", + "\n", + "attn_output, attn_weights = attention(queries, keys, values)\n", + "\n", + "attn_matrix = attn_weights.detach().squeeze()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Define a function to visualize the attention matrix as a heatmap. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from matplotlib import pyplot as plt \n", + "from matplotlib.ticker import FixedLocator\n", + "\n", + "def plot_attention(in_tokens, out_tokens, attn_matrix, specials = None):\n", + "\n", + " if specials is None:\n", + " specials = [\"\", \"\", \"\", \"\"]\n", + "\n", + " if specials[1] in in_tokens:\n", + " in_tokens = in_tokens[1:]\n", + " out_tokens = out_tokens[:-1]\n", + " attn_matrix = attn_matrix[:-1, 1:] \n", + "\n", + " eos_in_index = in_tokens.index(specials[2]) if specials[2] in in_tokens else len(in_tokens)\n", + " eos_out_index = out_tokens.index(specials[2]) if specials[2] in out_tokens else len(out_tokens)\n", + "\n", + " cut_index = max(eos_in_index, eos_out_index)\n", + " filtered_in_tokens = in_tokens[:cut_index]\n", + " filtered_out_tokens = out_tokens[:cut_index]\n", + " filtered_attn_matrix = attn_matrix[:cut_index, :cut_index]\n", + "\n", + " fig, ax = plt.subplots()\n", + " cax = ax.matshow(filtered_attn_matrix, cmap=\"bone\")\n", + " fig.colorbar(cax)\n", + "\n", + " ax.xaxis.set_major_locator(FixedLocator(range(len(filtered_in_tokens))))\n", + " ax.yaxis.set_major_locator(FixedLocator(range(len(filtered_out_tokens))))\n", + " ax.set_xticklabels(filtered_in_tokens, rotation=90)\n", + " ax.set_yticklabels(filtered_out_tokens)\n", + "\n", + " ### plt.savefig(\"fig_08_Xn.pdf\") \n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "... and plot the attention." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_attention(in_tokens, in_tokens, attn_matrix)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise\n", + "Shuffle and crop the sentence and calculate cross-attention with the original sentence " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "\n", + "crop_length = 6\n", + "indices = np.random.choice(len(in_tokens), crop_length, replace=False)\n", + "\n", + "shuffled_tokens = [in_tokens[i] for i in indices] \n", + "shuffled_embeddings = in_embeddings[:, indices, :]\n", + "\n", + "queries = shuffled_embeddings\n", + "\n", + "attn_output, attn_weights = attention(queries, keys, values)\n", + "attn_matrix = attn_weights.detach().squeeze()\n", + "\n", + "plot_attention(in_tokens, shuffled_tokens, attn_matrix)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Defining Other Forms of Attention\n", + "\n", + "Prepare a class implementing standard scaled dot-product attention ..." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "import torch.nn as nn\n", + "\n", + "\n", + "class StandardAttention(dl.DeeplayModule):\n", + "\n", + " def __init__(self, num_in_features=128, num_out_features=256):\n", + " super().__init__()\n", + " self.Wq = nn.Linear(num_in_features, num_out_features)\n", + " self.Wk = nn.Linear(num_in_features, num_out_features)\n", + " self.Wv = nn.Linear(num_in_features, num_out_features)\n", + "\n", + " def forward(self, queries, keys, values):\n", + " Q = self.Wq(queries)\n", + " K = self.Wk(keys) \n", + " V = self.Wv(values) \n", + " \n", + " attn_scores = torch.matmul(Q, K.transpose(-2, -1)) \\\n", + " / (K.size(-1) ** 0.5)\n", + " attn_weights = F.softmax(attn_scores, dim=-1)\n", + " attn_output = torch.matmul(attn_weights, V)\n", + " return attn_output, attn_weights" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "... general attention (Luong) ..." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "class GeneralAttention(dl.DeeplayModule):\n", + "\n", + "\n", + " def __init__(self, num_in_features=128, num_out_features=128):\n", + " super().__init__()\n", + " self.Wk = nn.Linear(num_in_features, num_out_features)\n", + "\n", + " def forward(self, queries, keys, values):\n", + " Q = queries\n", + " K = self.Wk(keys) \n", + " V = values\n", + " \n", + " attn_scores = torch.matmul(Q, K.transpose(-2, -1)) \\\n", + " / (K.size(-1) ** 0.5)\n", + " attn_weights = F.softmax(attn_scores, dim=-1)\n", + " attn_output = torch.matmul(attn_weights, V)\n", + " return attn_output, attn_weights" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "... and additive attention (Bahdanau)." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "class AdditiveAttention(dl.DeeplayModule):\n", + "\n", + "\n", + " def __init__(self, num_in_features=128, num_out_features=256):\n", + " super().__init__()\n", + " self.Wq = nn.Linear(num_in_features, num_out_features)\n", + " self.Wk = nn.Linear(num_in_features, num_out_features)\n", + " self.Ws = nn.Linear(num_out_features, 1)\n", + "\n", + " def forward(self, queries, keys, values):\n", + " Q = self.Wq(queries)\n", + " K = self.Wk(keys) \n", + " V = values\n", + "\n", + " attn_scores = torch.tanh(Q + K)\n", + " attn_scores = self.Ws(attn_scores)\n", + "\n", + " attn_scores = attn_scores.transpose(-2, -1)\n", + " attn_weights = F.softmax(attn_scores, dim=-1)\n", + "\n", + " attn_output = torch.matmul(attn_weights, V)\n", + " return attn_output, attn_weights" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "py_env_dlcc", + "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": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Ch08_Transformers/ec08_A_nlp_attn/eng-spa.txt b/Ch08_Attention/ec08_A_nlp_attn/eng-spa.txt similarity index 100% rename from Ch08_Transformers/ec08_A_nlp_attn/eng-spa.txt rename to Ch08_Attention/ec08_A_nlp_attn/eng-spa.txt diff --git a/Ch08_Transformers/ec08_A_nlp_attn/nlp_attn.ipynb b/Ch08_Attention/ec08_A_nlp_attn/nlp_attn.ipynb similarity index 68% rename from Ch08_Transformers/ec08_A_nlp_attn/nlp_attn.ipynb rename to Ch08_Attention/ec08_A_nlp_attn/nlp_attn.ipynb index 667acc0fc..91c823f6d 100644 --- a/Ch08_Transformers/ec08_A_nlp_attn/nlp_attn.ipynb +++ b/Ch08_Attention/ec08_A_nlp_attn/nlp_attn.ipynb @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -233,64 +233,27 @@ "source": [ "## Defining the Attention Mechanism\n", "\n", - "Prepare a class implementing multiplicative (dot-product) attention ..." + "Prepare a class implementing dot-product attention ..." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ + "import torch.nn.functional as F\n", "import deeplay as dl\n", "\n", "class DotProductAttention(dl.DeeplayModule):\n", - " \"\"\"Multiplicative attention.\"\"\"\n", - "\n", " def __init__(self):\n", - " \"\"\"Initialize multiplicative attention.\"\"\"\n", " super().__init__()\n", - " self.attn = dl.Layer(torch.nn.Softmax, dim=-1)\n", "\n", " def forward(self, queries, keys, values):\n", - " \"\"\"Perform forward pass.\"\"\"\n", - " scores = torch.sum(queries * keys, dim=2).unsqueeze(1)\n", - " weights = self.attn(scores)\n", - " context = torch.bmm(weights, values)\n", - " return context, weights" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "... and a class implementing additive attention." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "class AdditiveAttention(dl.DeeplayModule):\n", - " \"\"\"Additive attention.\"\"\"\n", - "\n", - " def __init__(self, in_features=128, emb_dim=300):\n", - " \"\"\"Initialize additive attention.\"\"\"\n", - " super().__init__()\n", - " self.Wa = dl.Layer(torch.nn.Linear, in_features, emb_dim)\n", - " self.Ua = dl.Layer(torch.nn.Linear, in_features, emb_dim)\n", - " self.Va = dl.Layer(torch.nn.Linear, emb_dim, 1)\n", - " self.attn = dl.Layer(torch.nn.Softmax, dim=-1)\n", - "\n", - " def forward(self, queries, keys, values):\n", - " \"\"\"Perform forward pass.\"\"\"\n", - " scores = self.Va(torch.tanh(self.Wa(queries) + self.Ua(keys)))\n", - " scores = scores.squeeze(2).unsqueeze(1)\n", - " weights = self.attn(scores)\n", - " context = torch.bmm(weights, values)\n", - " return context, weights" + " attn_scores = torch.matmul(queries, keys.transpose(-2, -1)) / (keys.size(-1) ** 0.5) # Calculate dot-product\n", + " attn_weights = F.softmax(attn_scores, dim=-1) # Apply softmax to get attention weights\n", + " attn_output = torch.matmul(attn_weights, values) # Compute weighted sum of values\n", + " return attn_output, attn_weights\n" ] }, { @@ -304,7 +267,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -333,7 +296,7 @@ " encoded_sequences, contexts = self.rnn(in_embeddings, contexts)\n", " encoded_sequences = (encoded_sequences[:, :, :self.hidden_features]\n", " + encoded_sequences[:, :, self.hidden_features:])\n", - " contexts = contexts[:self.hidden_layers]\n", + " contexts = contexts[:self.hidden_layers] # only forward\n", " return encoded_sequences, contexts" ] }, @@ -346,7 +309,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -354,29 +317,31 @@ " \"\"\"Sequence-to-sequence decoder with attention.\"\"\"\n", "\n", " def __init__(self, vocab_size, in_features=300, hidden_features=128, \n", - " hidden_layers=1, dropout=0.0, attn = DotProductAttention):\n", + " hidden_layers=1, dropout=0.0):\n", " \"\"\"Initialize sequence-to-sequence decoder.\"\"\"\n", " super().__init__()\n", "\n", " self.embedding = dl.Layer(torch.nn.Embedding, vocab_size, in_features)\n", " self.rnn = dl.Layer(torch.nn.GRU, \\\n", - " input_size=in_features + hidden_features, \\\n", - " hidden_size=hidden_features, num_layers=hidden_layers, \\\n", - " bidirectional=False, batch_first=True, \\\n", + " input_size=in_features, hidden_size=hidden_features, \\\n", + " num_layers=hidden_layers, bidirectional=False, batch_first=True, \\\n", " dropout=(0 if hidden_layers == 1 else dropout))\n", " self.dense = dl.Layer(torch.nn.Linear, hidden_features, vocab_size)\n", " self.softmax = dl.Layer(torch.nn.Softmax, dim=-1)\n", - " self.relu = dl.Layer(torch.nn.ReLU)\n", - " self.attn = attn\n", + " self.attn = DotProductAttention()\n", "\n", - " def forward(self, decoder_in_values, contexts):\n", - " \"\"\"Perform forward pass.\"\"\"\n", - " out_embeddings = self.embedding(decoder_in_values)\n", - " out_embeddings = self.relu(out_embeddings)\n", + " def forward(self, decoder_in_values, contexts, encoded_sequences):\n", + " \"\"\"Perform forward pass.\"\"\" \n", + " out_embeddings = self.embedding(decoder_in_values) \n", " decoder_outputs, contexts = self.rnn(out_embeddings, contexts)\n", + " attn_contexts, attn_weights = self.attn(queries = decoder_outputs, \\\n", + " keys = encoded_sequences, values = encoded_sequences)\n", + "\n", + " decoder_outputs = decoder_outputs + attn_contexts\n", " decoder_outputs = self.dense(decoder_outputs)\n", " decoder_outputs = self.softmax(decoder_outputs)\n", - " return decoder_outputs, contexts" + "\n", + " return decoder_outputs, contexts, attn_weights" ] }, { @@ -388,12 +353,12 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "class Seq2SeqModel(dl.DeeplayModule):\n", - " \"\"\"Sequence-to-sequence model.\"\"\"\n", + " \"\"\"Sequence-to-sequence model with attention.\"\"\"\n", "\n", " def __init__(self, in_vocab_size=None, out_vocab_size=None,\n", " teacher_prob=1.0, embedding_dim=300, hidden_features=128,\n", @@ -402,11 +367,12 @@ " super().__init__()\n", " \n", " self.in_vocab_size, self.out_vocab_size = in_vocab_size, out_vocab_size\n", - " self.teacher_prob = teacher_prob\n", " self.encoder = Seq2SeqEncoder(in_vocab_size, embedding_dim, \\\n", " hidden_features, hidden_layers, dropout)\n", " self.decoder = Seq2SeqDecoder(out_vocab_size, embedding_dim, \\\n", " hidden_features, hidden_layers, dropout)\n", + " self.teacher_prob = teacher_prob\n", + "\n", "\n", " def forward(self, batch):\n", " \"\"\"Perform forward pass.\"\"\"\n", @@ -414,24 +380,24 @@ " num_sequences, sequence_length = in_sequences.size()\n", " device = next(self.encoder.parameters()).device\n", " \n", - " _, contexts = self.encoder(in_sequences)\n", + " encoder_outputs, contexts = self.encoder(in_sequences)\n", " \n", " decoder_outputs_vec = torch.zeros(num_sequences, sequence_length,\n", " self.out_vocab_size).to(device)\n", - " decoder_in_sequences = torch.full(size=(num_sequences, 1), \n", + " decoder_in_values = torch.full(size=(num_sequences, 1), \n", " fill_value=1, device=device) # \n", " for t in range(sequence_length):\n", - " decoder_outputs, contexts = \\\n", - " self.decoder(decoder_in_sequences, contexts)\n", + " decoder_outputs, contexts, _ = \\\n", + " self.decoder(decoder_in_values, contexts, encoder_outputs)\n", " decoder_outputs_vec[:, t, :] = decoder_outputs.squeeze(1)\n", " \n", " if (np.random.rand() < self.teacher_prob \n", " and t < sequence_length - 1): # Teacher forcing.\n", - " decoder_in_sequences = \\\n", + " decoder_in_values = \\\n", " out_sequences[:, t + 1].unsqueeze(-1).to(device)\n", " else: # Model prediction.\n", " _, top_decoder_outputs = decoder_outputs.topk(1)\n", - " decoder_in_sequences = \\\n", + " decoder_in_values = \\\n", " top_decoder_outputs.squeeze(-1).detach().to(device) \n", " \n", " return decoder_outputs_vec\n", @@ -442,21 +408,24 @@ " device = next(self.encoder.parameters()).device\n", "\n", " with torch.no_grad():\n", - " _, contexts = self.encoder(in_sequences)\n", + " encoder_outputs, contexts = self.encoder(in_sequences)\n", " \n", - " out_sequences = torch.zeros(num_sequences, sequence_length).to(device)\n", - " decoder_in_sequences = torch.full(size=(num_sequences, 1), \n", - " fill_value=1, device=device) # \n", + " pred_sequences = torch.zeros(num_sequences, sequence_length).to(device)\n", + " decoder_in_values = torch.full(size=(num_sequences, 1), \n", + " fill_value=1, device=device) # \n", + " attn_matrices = torch.zeros(num_sequences, sequence_length, sequence_length).to(device) \n", " for t in range(sequence_length):\n", " with torch.no_grad():\n", - " decoder_outputs, contexts = \\\n", - " self.decoder(decoder_in_sequences.to(device), contexts)\n", + " decoder_outputs, contexts, attn_weights = \\\n", + " self.decoder(decoder_in_values, contexts, encoder_outputs)\n", + " attn_matrices[:, t, :] = attn_weights.squeeze(1)\n", " _, top_decoder_outputs = decoder_outputs.topk(1)\n", - " out_sequences[:, t] = top_decoder_outputs.squeeze()\n", + " pred_sequences[:, t] = top_decoder_outputs.squeeze()\n", + " \n", + " decoder_in_values = top_decoder_outputs.squeeze(-1).detach()\n", " \n", - " decoder_in_sequences = top_decoder_outputs.squeeze(-1).detach()\n", " \n", - " return out_sequences" + " return pred_sequences, attn_matrices" ] }, { @@ -468,7 +437,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -495,14 +464,14 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "class Seq2Seq(dl.Application):\n", " \"\"\"Application for the sequence-to-sequence model.\"\"\"\n", "\n", - " def __init__(self, in_vocab, out_vocab, teacher_prob=1.0):\n", + " def __init__(self, in_vocab, out_vocab, teacher_prob=1.0, attn=None):\n", " \"\"\"Initialize the application.\"\"\"\n", " super().__init__(loss=maskedNLL, optimizer=dl.Adam(lr=1e-3))\n", " self.model = Seq2SeqModel(in_vocab_size=len(in_vocab), \\\n", @@ -559,7 +528,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -572,6 +541,25 @@ "seq2seq.model.decoder.embedding.weight.requires_grad = False" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "... you might want to check the number of learnable parameters ..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def count_parameters(model):\n", + " return sum(p.numel() for p in model.parameters() if p.requires_grad)\n", + "\n", + "count_parameters(seq2seq)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -600,7 +588,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -623,42 +611,77 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "def translate(in_sentence, model, in_lang, in_vocab, out_vocab, specials):\n", " \"\"\"Translate a sentence.\"\"\"\n", + " in_sentence = unicodedata.normalize(\"NFC\", in_sentence)\n", " in_tokens = pad(tokenize(in_sentence, in_lang))\n", " in_sequence = (torch.tensor(in_vocab(in_tokens), dtype=torch.int)\n", " .unsqueeze(0).to(next(model.parameters()).device))\n", - " pred_sequence = model.evaluate(in_sequence)\n", + " pred_sequence, attn_matrix = model.evaluate(in_sequence)\n", " pred_sentence = unprocess(pred_sequence, out_vocab, specials)\n", - " print(f\"Predicted Translation: {pred_sentence[0]}\\n\")" + " pred_tokens = [out_vocab.lookup_token(idx) for idx in pred_sequence[0]]\n", + "\n", + " print(f\"Predicted Translation: {pred_sentence[0]}\\n\")\n", + "\n", + " return (in_tokens, pred_tokens, attn_matrix.squeeze())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "... try to translate a simple sentence ..." + "... and a function for plotting the attention matrix as a heatmap ..." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ - "in_sentence = \"I bought a book.\"\n", - "translate(in_sentence, seq2seq.model, in_lang, in_vocab, out_vocab, specials)" + "from matplotlib import pyplot as plt \n", + "from matplotlib.ticker import FixedLocator\n", + "\n", + "def plot_attention(in_tokens, out_tokens, attn_matrix, specials = None):\n", + "\n", + " if specials is None:\n", + " specials = [\"\", \"\", \"\", \"\"]\n", + "\n", + " if specials[1] in in_tokens:\n", + " in_tokens = in_tokens[1:]\n", + " out_tokens = out_tokens[:-1]\n", + " attn_matrix = attn_matrix[:-1, 1:] \n", + "\n", + " eos_in_index = in_tokens.index(specials[2]) if specials[2] in in_tokens else len(in_tokens)\n", + " eos_out_index = out_tokens.index(specials[2]) if specials[2] in out_tokens else len(out_tokens)\n", + "\n", + " cut_index = max(eos_in_index, eos_out_index)\n", + " filtered_in_tokens = in_tokens[:cut_index]\n", + " filtered_out_tokens = out_tokens[:cut_index]\n", + " filtered_attn_matrix = attn_matrix[:cut_index, :cut_index]\n", + "\n", + " fig, ax = plt.subplots()\n", + " cax = ax.matshow(filtered_attn_matrix, cmap=\"bone\")\n", + " fig.colorbar(cax)\n", + "\n", + " ax.xaxis.set_major_locator(FixedLocator(range(len(filtered_in_tokens))))\n", + " ax.yaxis.set_major_locator(FixedLocator(range(len(filtered_out_tokens))))\n", + " ax.set_xticklabels(filtered_in_tokens, rotation=90)\n", + " ax.set_yticklabels(filtered_out_tokens)\n", + "\n", + " ### plt.savefig(\"fig_08_Xn.pdf\") \n", + " plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "... another simple sentence ..." + "... try to translate a simple sentence ..." ] }, { @@ -668,14 +691,15 @@ "outputs": [], "source": [ "in_sentence = \"This book is very interesting.\"\n", - "translate(in_sentence, seq2seq.model, in_lang, in_vocab, out_vocab, specials)" + "out = translate(in_sentence, seq2seq.model, in_lang, in_vocab, out_vocab, specials)\n", + "plot_attention(*out)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "... and a more complex one ..." + "... and more complex ones ..." ] }, { @@ -685,7 +709,41 @@ "outputs": [], "source": [ "in_sentence = \"The book that I bought is very interesting.\"\n", - "translate(in_sentence, seq2seq.model, in_lang, in_vocab, out_vocab, specials)" + "out = translate(in_sentence, seq2seq.model, in_lang, in_vocab, out_vocab, specials)\n", + "plot_attention(*out)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "in_sentence = \"Do you think that we should go home?\"\n", + "out = translate(in_sentence, seq2seq.model, in_lang, in_vocab, out_vocab, specials)\n", + "plot_attention(*out)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "in_sentence = \"The man bought the book that I suggested.\"\n", + "out = translate(in_sentence, seq2seq.model, in_lang, in_vocab, out_vocab, specials)\n", + "plot_attention(*out)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "in_sentence = \"The policeman called the dad on the phone.\"\n", + "out = translate(in_sentence, seq2seq.model, in_lang, in_vocab, out_vocab, specials)\n", + "plot_attention(*out)" ] }, { @@ -708,7 +766,7 @@ "device = next(seq2seq.model.parameters()).device\n", "for batch_index, (in_sequences, out_sequences) in enumerate(test_loader):\n", " in_sentences = unprocess(in_sequences.to(device), in_vocab, specials)\n", - " pred_sequences = seq2seq.model.evaluate(in_sequences.to(device))\n", + " pred_sequences,_ = seq2seq.model.evaluate(in_sequences.to(device))\n", " pred_sentences = unprocess(pred_sequences, out_vocab, specials)\n", " out_sentences = unprocess(out_sequences.to(device), out_vocab, specials)\n", " \n", @@ -721,6 +779,131 @@ "final_bleu = bleu_score.compute()\n", "print(f\"Validation BLEU Score: {final_bleu:.3f}\")" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Experiment with Other Attention Mechanisms\n", + "\n", + "Prepare a class implementing standard scaled dot-product attention ..." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "import torch.nn as nn\n", + "\n", + "\n", + "class StandardAttention(dl.DeeplayModule):\n", + "\n", + " def __init__(self, num_in_features=128, num_out_features=256):\n", + " super().__init__()\n", + " self.Wq = nn.Linear(num_in_features, num_out_features)\n", + " self.Wk = nn.Linear(num_in_features, num_out_features)\n", + " self.Wv = nn.Linear(num_in_features, num_out_features)\n", + "\n", + " def forward(self, queries, keys, values):\n", + " Q = self.Wq(queries)\n", + " K = self.Wk(keys) \n", + " V = self.Wv(values) \n", + " \n", + " attn_scores = torch.matmul(Q, K.transpose(-2, -1)) \\\n", + " / (K.size(-1) ** 0.5)\n", + " attn_weights = F.softmax(attn_scores, dim=-1)\n", + " attn_output = torch.matmul(attn_weights, V)\n", + " return attn_output, attn_weights" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "... general attention (Luong) ..." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "class GeneralAttention(dl.DeeplayModule):\n", + "\n", + "\n", + " def __init__(self, num_in_features=128, num_out_features=128):\n", + " super().__init__()\n", + " self.Wk = nn.Linear(num_in_features, num_out_features)\n", + "\n", + " def forward(self, queries, keys, values):\n", + " Q = queries\n", + " K = self.Wk(keys) \n", + " V = values\n", + " \n", + " attn_scores = torch.matmul(Q, K.transpose(-2, -1)) \\\n", + " / (K.size(-1) ** 0.5)\n", + " attn_weights = F.softmax(attn_scores, dim=-1)\n", + " attn_output = torch.matmul(attn_weights, V)\n", + " return attn_output, attn_weights" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "... and additive attention (Bahdanau)." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "class AdditiveAttention(dl.DeeplayModule):\n", + "\n", + "\n", + " def __init__(self, num_in_features=128, num_out_features=256):\n", + " super().__init__()\n", + " self.Wq = nn.Linear(num_in_features, num_out_features)\n", + " self.Wk = nn.Linear(num_in_features, num_out_features)\n", + " self.Ws = nn.Linear(num_out_features, 1)\n", + "\n", + " def forward(self, queries, keys, values):\n", + " Q = self.Wq(queries)\n", + " K = self.Wk(keys) \n", + " V = values\n", + "\n", + " attn_scores = torch.tanh(Q + K)\n", + " attn_scores = self.Ws(attn_scores)\n", + "\n", + " attn_scores = attn_scores.transpose(-2, -1)\n", + " attn_weights = F.softmax(attn_scores, dim=-1)\n", + "\n", + " attn_output = torch.matmul(attn_weights, V)\n", + " return attn_output, attn_weights" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Substitute the attention in the model, change parameters accordingly and train..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "seq2seq.model.decoder.replace('attn',AdditiveAttention())\n", + "\n", + "seq2seq.model.decoder.attn.configure(num_out_features = 128)\n" + ] } ], "metadata": { diff --git a/Ch08_Transformers/ec08_1_transformer/images/vit-image.png b/Ch08_Attention/ec08_B_transformer/images/vit-image.png similarity index 100% rename from Ch08_Transformers/ec08_1_transformer/images/vit-image.png rename to Ch08_Attention/ec08_B_transformer/images/vit-image.png diff --git a/Ch08_Transformers/ec08_1_transformer/transformer.ipynb b/Ch08_Attention/ec08_B_transformer/transformer.ipynb similarity index 92% rename from Ch08_Transformers/ec08_1_transformer/transformer.ipynb rename to Ch08_Attention/ec08_B_transformer/transformer.ipynb index 0dba6f2a3..492b9f93c 100644 --- a/Ch08_Transformers/ec08_1_transformer/transformer.ipynb +++ b/Ch08_Attention/ec08_B_transformer/transformer.ipynb @@ -138,8 +138,8 @@ }, "outputs": [], "source": [ - "split = dataset[\"train\"] \\\n", - " .train_test_split(test_size=0.2, stratify_by_column=\"label\", seed=42)\n", + "split = dataset[\"train\"].train_test_split(test_size=0.2, \n", + " stratify_by_column=\"label\", seed=42)\n", "train_dataset, val_dataset = split[\"train\"], split[\"test\"]" ] }, @@ -168,8 +168,10 @@ "import numpy as np\n", "import pandas as pd\n", "\n", - "examples = train_dataset.select(np.random.randint(0, len(train_dataset), 3))\n", - "df = pd.DataFrame({\"Text\": examples[\"text\"], \"Label\": examples[\"label\"]})\n", + "samples = train_dataset.select(np.random.randint(0, len(train_dataset), 3))\n", + "texts, labels = samples[\"text\"], samples[\"label\"]\n", + "\n", + "df = pd.DataFrame({\"Text\": texts, \"Label\": labels})\n", "styled_df = df.style.set_properties(**{\"text-align\": \"left\"}).set_table_styles(\n", " [{\"selector\": \"th\", \"props\": [(\"text-align\", \"center\")]}]\n", ")\n", @@ -194,8 +196,7 @@ "metadata": {}, "outputs": [], "source": [ - "import contractions\n", - "import re\n", + "import contractions, re\n", "from torchtext.data.utils import get_tokenizer\n", "\n", "tokenizer = get_tokenizer(\"basic_english\")\n", @@ -235,8 +236,8 @@ "\n", "def imdb_iterator(dataset):\n", " \"\"\"Iterate over the IMBD dataset.\"\"\"\n", - " for data in dataset:\n", - " yield tokenize(data[\"text\"])\n", + " for sample in dataset:\n", + " yield tokenize(sample[\"text\"])\n", "\n", "vocab = build_vocab_from_iterator(imdb_iterator(train_dataset), \n", " specials=[\"\"])\n", @@ -301,10 +302,11 @@ "outputs": [], "source": [ "def preprocessing(sample):\n", - " \"\"\"Preprocess the input data.\"\"\"\n", - " tokens = tokenize(sample[\"text\"])\n", - " indices = vocab(tokens)\n", - " sample.update({\"x\": indices}) ### sample.update({\"sequences\": indices})\n", + " \"\"\"Preprocess a movie review.\"\"\"\n", + " sentence = sample[\"text\"]\n", + " tokens = tokenize(sentence)\n", + " sequence_of_indices = vocab(tokens)\n", + " sample.update({\"sequence\": sequence_of_indices}) \n", " return sample\n", "\n", "train_dataset = train_dataset.map(preprocessing)\n", @@ -326,34 +328,62 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "id": "rD3wrYfvMBYB" - }, + "metadata": {}, "outputs": [], "source": [ "import deeplay as dl\n", "import torch\n", "import torch.nn as nn\n", + "import torch.nn.functional as F\n", "\n", "class MultiHeadAttentionLayer(dl.DeeplayModule):\n", - " \"\"\"\"Multi-head attention layer.\"\"\"\n", - "\n", - " def __init__(self, features, num_heads): # features > num_features\n", - " \"\"\"Initialize multi-head attention layer.\"\"\"\n", + " \"\"\"\"Multi-head attention layer with masking.\"\"\"\n", + " \n", + " def __init__(self, num_features, num_heads):\n", " super().__init__()\n", - " self.features, self.num_heads = features, num_heads\n", - " self.layer = dl.Layer(nn.MultiheadAttention, features, num_heads)\n", - "\n", - " def forward(self, x, batch_indices): ### change x\n", - " \"\"\"Calculate forward pass.\"\"\"\n", - " attn_mask = self._fetch_attn_mask(batch_indices)\n", - " y, *_ = self.layer(x, x, x, attn_mask=attn_mask) ### change y. why 3 x?\n", - " return y ### same size as x, updated features\n", + " assert num_features % num_heads == 0, \\\n", + " \"num_features must be divisible by num_heads\"\n", + " \n", + " self.num_features = num_features\n", + " self.num_heads = num_heads\n", + " self.head_dim = num_features // num_heads\n", + " self.Wq = dl.Layer(nn.Linear, num_features, num_features)\n", + " self.Wk = dl.Layer(nn.Linear,num_features, num_features)\n", + " self.Wv = dl.Layer(nn.Linear, num_features, num_features)\n", + " \n", + " self.Wout = dl.Layer(nn.Linear, num_features, num_features)\n", + " \n", + " def forward(self, in_sequences, batch_indices):\n", + " \"\"\"Process sequences with multi-head attention.\"\"\"\n", + " \n", + " seq_len, embed_dim = in_sequences.shape\n", + " \n", + " Q = self.Wq(in_sequences)\n", + " K = self.Wk(in_sequences)\n", + " V = self.Wv(in_sequences)\n", + " \n", + " Q = Q.view(seq_len, self.num_heads, self.head_dim).permute(1, 0, 2)\n", + " K = K.view(seq_len, self.num_heads, self.head_dim).permute(1, 0, 2)\n", + " V = V.view(seq_len, self.num_heads, self.head_dim).permute(1, 0, 2)\n", + " \n", + " attn_scores = torch.matmul(Q, K.transpose(-2, -1)) \\\n", + " / (self.head_dim ** 0.5)\n", + " \n", + " attn_mask = torch.eq(batch_indices.unsqueeze(1), \\\n", + " batch_indices.unsqueeze(0))\n", + " attn_mask = attn_mask.unsqueeze(0) \n", + " attn_scores = attn_scores.masked_fill(attn_mask == False, \\\n", + " float('-inf'))\n", "\n", - " def _fetch_attn_mask(self, batch_indices): ### incorporate in the forward pass\n", - " \"\"\"Get attention mask.\"\"\"\n", - " return ~torch.eq(batch_indices.unsqueeze(1),\n", - " batch_indices.unsqueeze(0))" + " attn_weights = F.softmax(attn_scores, dim=-1)\n", + " attn_output = torch.matmul(attn_weights, V)\n", + " \n", + " attn_output = attn_output.permute(1, 0, 2).contiguous()\n", + " attn_output = attn_output.view(seq_len, self.num_features)\n", + " \n", + " output = self.Wout(attn_output)\n", + " \n", + " return output" ] }, { @@ -378,41 +408,37 @@ "class TransformerEncoderLayer(dl.DeeplayModule):\n", " \"\"\"Transformer encoder layer.\"\"\"\n", "\n", - " def __init__(self, d_model, num_heads, feedforward_dim, dropout_p=0.0): ### d_model > num_features. dropout_p > dropout\n", + " def __init__(self, num_features, num_heads, feedforward_dim, dropout=0.0):\n", " \"\"\"Initialize transformer encoder layer.\"\"\"\n", " super().__init__()\n", "\n", - " self.d_model = d_model ### 2 lines? ... = ...\n", - " self.num_heads = num_heads\n", - " self.feedforward_dim = feedforward_dim\n", - " self.dropout_p = dropout_p\n", - "\n", - " self.self_attn = MultiHeadAttentionLayer(d_model, num_heads) ### why d_model becomes features?\n", - " self.attn_dropout = dl.Layer(nn.Dropout, dropout_p)\n", + " self.self_attn = MultiHeadAttentionLayer(num_features, num_heads)\n", + " self.attn_dropout = dl.Layer(nn.Dropout, dropout)\n", " self.attn_skip = dl.Add()\n", - " self.attn_norm = dl.Layer(LayerNorm, d_model, eps=1e-6)\n", + " self.attn_norm = dl.Layer(LayerNorm, num_features, eps=1e-6)\n", "\n", " self.feedforward = dl.Sequential(\n", - " dl.Layer(nn.Linear, d_model, feedforward_dim),\n", + " dl.Layer(nn.Linear, num_features, feedforward_dim),\n", " dl.Layer(nn.ReLU),\n", - " dl.Layer(nn.Linear, feedforward_dim, d_model),\n", + " dl.Layer(nn.Linear, feedforward_dim, num_features),\n", " )\n", - " self.feedforward_dropout = dl.Layer(nn.Dropout, dropout_p)\n", + " self.feedforward_dropout = dl.Layer(nn.Dropout, dropout)\n", " self.feedforward_skip = dl.Add()\n", - " self.feedforward_norm = dl.Layer(LayerNorm, d_model, eps=1e-6)\n", + " self.feedforward_norm = dl.Layer(LayerNorm, num_features, eps=1e-6)\n", "\n", - " def forward(self, x, batch_index): # change x. Why batch_index and not batch_indices?\n", - " \"\"\"Calculate forward pass.\"\"\"\n", - " y_attn = self.self_attn(x, batch_index) # here and below, change x, y, ...\n", - " y_attn = self.attn_dropout(y_attn)\n", - " y_attn = self.attn_skip(x, y_attn) # conbination\n", - " y_attn = self.attn_norm(y_attn, batch_index)\n", + " def forward(self, in_sequences, batch_indices):\n", + " \"\"\"Refine sequence via attention and feedforward layers.\"\"\"\n", + " attns = self.self_attn(in_sequences, batch_indices)\n", + " attns = self.attn_dropout(attns)\n", + " attns = self.attn_skip(in_sequences, attns)\n", + " attns = self.attn_norm(attns, batch_indices)\n", "\n", - " y = self.feedforward(y_attn)\n", - " y = self.feedforward_dropout(y)\n", - " y = self.feedforward_skip(y_attn, y)\n", - " y = self.feedforward_norm(y, batch_index)\n", - " return y" + " out_sequences = self.feedforward(attns)\n", + " out_sequences = self.feedforward_dropout(out_sequences)\n", + " out_sequences = self.feedforward_skip(attns, out_sequences)\n", + " out_sequences = self.feedforward_norm(out_sequences, batch_indices)\n", + " \n", + " return out_sequences" ] }, { @@ -437,146 +463,53 @@ "class TransformerEncoderModel(dl.DeeplayModule):\n", " \"\"\"Transformer encoder model.\"\"\"\n", " \n", - " def __init__(self, vocab_size, d_model, num_heads, feedforward_dim,\n", - " num_layers, out_dim, dropout_p=0.0): ### model? dropout?\n", + " def __init__(self, vocab_size, num_features, num_heads, feedforward_dim,\n", + " num_layers, out_dim, dropout=0.0):\n", " \"\"\"Initialize transformer encoder model.\"\"\"\n", " super().__init__()\n", - " self.d_model, self.num_heads, self.feedforward_dim, self.num_layers, \\\n", - " self.dropout_p, self.out_dim = d_model, num_heads, \\\n", - " feedforward_dim, num_layers, dropout_p, out_dim\n", - "\n", - " self.embedding = dl.Layer(nn.Embedding, vocab_size, d_model)\n", + " self.num_features = num_features\n", + " self.embedding = dl.Layer(nn.Embedding, vocab_size, num_features)\n", " \n", - " self.pos_encoder = dl.IndexedPositionalEmbedding(d_model)\n", - " self.pos_encoder.dropout.configure(p=dropout_p)\n", + " self.pos_encoder = dl.IndexedPositionalEmbedding(num_features)\n", + " self.pos_encoder.dropout.configure(p=dropout)\n", "\n", - " self.blocks = dl.LayerList()\n", + " self.transformer_block = dl.LayerList()\n", " for _ in range(num_layers):\n", - " self.blocks.append(\n", + " self.transformer_block.append(\n", " TransformerEncoderLayer(\n", - " d_model, num_heads, feedforward_dim, dropout_p=dropout_p\n", + " num_features, num_heads, feedforward_dim, dropout=dropout\n", " )\n", " )\n", " \n", - " self.out = dl.Sequential(\n", - " dl.Layer(nn.Dropout, dropout_p),\n", - " dl.Layer(nn.Linear, d_model, d_model // 2), \n", + " self.out_block = dl.Sequential(\n", + " dl.Layer(nn.Dropout, dropout),\n", + " dl.Layer(nn.Linear, num_features, num_features // 2), \n", " dl.Layer(nn.ReLU),\n", - " dl.Layer(nn.Linear, d_model // 2, out_dim), \n", + " dl.Layer(nn.Linear, num_features // 2, out_dim), \n", " dl.Layer(nn.Sigmoid)\n", " )\n", "\n", - " def forward(self, seq): # seq = dictionario con indices y batch_indices\n", - " \"\"\"Calculate forward pass.\"\"\"\n", - " h = self.embedding(seq[\"x\"]) * self.d_model ** 0.5 # h = x\n", - " h = self.pos_encoder(h, seq[\"batch_indices\"])\n", - " \n", - " for layer in self.blocks:\n", - " h = layer(h, seq[\"batch_indices\"])\n", + " def forward(self, dict):\n", + " \"\"\"Predict sentiment of movie reviews.\"\"\"\n", + " in_sequences, batch_indices = dict[\"sequences\"], dict[\"batch_indices\"]\n", "\n", - " batch_size = torch.max(seq[\"batch_indices\"]) + 1 # show first as a for loop\n", - " g = torch.zeros(batch_size, self.d_model, device=h.device) \n", - " g = g.scatter_add(0, seq[\"batch_indices\"][:, None].expand_as(h), h) # split \n", - " g = g / torch.bincount(seq[\"batch_indices\"])[:, None]\n", + " embeddings = self.embedding(in_sequences) * self.num_features ** 0.5\n", + " pos_embeddings = self.pos_encoder(embeddings, batch_indices)\n", "\n", - " return self.out(g).squeeze()" - ] - }, - { - "cell_type": "raw", - "metadata": { - "vscode": { - "languageId": "raw" - } - }, - "source": [ - "class TransformerEncoderModel(dl.DeeplayModule):\n", - " \"\"\"Transformer encoder model.\"\"\"\n", - " \n", - " def __init__(self, vocab_size, d_model, num_heads, feedforward_dim,\n", - " num_layers, out_dim, dropout_p=0.0): ### model? dropout?\n", - " \"\"\"Initialize transformer encoder model.\"\"\"\n", - " super().__init__()\n", - " self.d_model, self.num_heads, self.feedforward_dim, self.num_layers, \\\n", - " self.dropout_p, self.out_dim = d_model, num_heads, \\\n", - " feedforward_dim, num_layers, dropout_p, out_dim\n", + " out_sequences = pos_embeddings\n", + " for transformer_layer in self.transformer_block:\n", + " out_sequences = transformer_layer(out_sequences, batch_indices)\n", "\n", - " self.embedding = dl.Layer(nn.Embedding, vocab_size, d_model)\n", - " \n", - " self.pos_encoder = dl.IndexedPositionalEmbedding(d_model)\n", - " self.pos_encoder.dropout.configure(p=dropout_p)\n", + " batch_size = torch.max(batch_indices) + 1\n", + " aggregates = torch.zeros(batch_size, self.num_features, \n", + " device=out_sequences.device) \n", + " for batch_index in torch.unique(batch_indices):\n", + " mask = batch_indices == batch_index\n", + " aggregates[batch_index] = out_sequences[mask].mean(dim=0)\n", "\n", - " self.blocks = dl.LayerList()\n", - " for _ in range(num_layers):\n", - " self.blocks.append(\n", - " TransformerEncoderLayer(\n", - " d_model, num_heads, feedforward_dim, dropout_p=dropout_p\n", - " )\n", - " )\n", - " \n", - " self.out = dl.Sequential(\n", - " dl.Layer(nn.Dropout, dropout_p),\n", - " dl.Layer(nn.Linear, d_model, d_model // 2), \n", - " dl.Layer(nn.ReLU),\n", - " dl.Layer(nn.Linear, d_model // 2, out_dim), \n", - " dl.Layer(nn.Sigmoid)\n", - " )\n", - "\n", - " def forward(self, seq): # seq = dictionario con indices y batch_indices\n", - " \"\"\"Calculate forward pass.\"\"\"\n", - " h = self.embedding(seq[\"x\"]) * self.d_model ** 0.5 # h = x\n", - " h = self.pos_encoder(h, seq[\"batch_indices\"])\n", - " \n", - " for layer in self.blocks:\n", - " h = layer(h, seq[\"batch_indices\"])\n", - "\n", - " batch_size = torch.max(seq[\"batch_indices\"]) + 1 # show first as a for loop\n", - " g = torch.zeros(batch_size, self.d_model, device=h.device) \n", - " \n", - " # THIS\n", - " # g = g.scatter_add(0, seq[\"batch_indices\"][:, None].expand_as(h), h) # split\n", - " # g = g / torch.bincount(seq[\"batch_indices\"])[:, None]\n", - "\n", - " # CAN BE REPLACED WITH THIS\n", - " for batch_index in torch.unique(seq[\"batch_indices\"]):\n", - " where = seq[\"batch_indices\"] == batch_index\n", - " g[batch_index] = h[where].mean(dim=0)\n", - " \n", - " return self.out(g).squeeze()" - ] - }, - { - "cell_type": "raw", - "metadata": { - "vscode": { - "languageId": "raw" - } - }, - "source": [ - "import torch \n", - "\n", - "\n", - "h = torch.rand(10, 128).to(\"cuda\")\n", - "\n", - "batch_indices = torch.Tensor([0, 0, 0, 0, 0, 1, 1, 1, 1, 1])\n", - "batch_indices = batch_indices.long().to(\"cuda\")\n", + " pred_sentiment = self.out_block(aggregates).squeeze()\n", "\n", - "\n", - "batch_size = torch.max(batch_indices) + 1 \n", - "\n", - "# METHOD 1\n", - "g1 = torch.zeros(int(batch_size), h.size(1), device=h.device)\n", - "g1 = g1.scatter_add(0, batch_indices[:, None].expand_as(h), h) \n", - "g1 = g1 / torch.bincount(batch_indices)[:, None]\n", - "\n", - "# METHOD 2\n", - "g2 = torch.zeros(batch_size, h.size(1), device=h.device)\n", - "for batch_index in torch.unique(batch_indices):\n", - " where = batch_indices == batch_index\n", - " g2[batch_index] = h[where].mean(dim=0)\n", - "\n", - "# check g and g2 are the same\n", - "torch.allclose(g1, g2)" + " return pred_sentiment" ] }, { @@ -601,8 +534,8 @@ "outputs": [], "source": [ "model = TransformerEncoderModel(\n", - " vocab_size=len(vocab), d_model=300, num_heads=12, feedforward_dim=512,\n", - " num_layers=4, out_dim=1, dropout_p=0.1\n", + " vocab_size=len(vocab), num_features=300, num_heads=12, feedforward_dim=512,\n", + " num_layers=4, out_dim=1, dropout=0.1,\n", ").create()\n", "\n", "print(model)" @@ -632,8 +565,8 @@ "from torchtext.vocab import GloVe\n", "\n", "glove = GloVe(name=\"42B\", dim=300, cache=\".glove_cache\")\n", - "model.embedding.weight.data = \\\n", - " glove.get_vecs_by_tokens(vocab.get_itos(), lower_case_backup=True)\n", + "model.embedding.weight.data = glove.get_vecs_by_tokens(vocab.get_itos(), \n", + " lower_case_backup=True)\n", "model.embedding.weight.requires_grad = False" ] }, @@ -658,14 +591,20 @@ "from torch_geometric.data import Data\n", "\n", "def collate(batch):\n", - " \"\"\"Combine data into a single batch that the model can process.\"\"\"\n", - " xs, ys, batch_indices = [], [], [] # same as before - x = indices, y = ground truth\n", - " for i, b in enumerate(batch):\n", - " x, label = torch.tensor(b[\"x\"]), torch.tensor(b[\"label\"])\n", - " xs.append(x), ys.append(label)\n", - " batch_indices.append(torch.ones_like(x, dtype=torch.long) * i)\n", - " return Data(x=torch.cat(xs), batch_indices=torch.cat(batch_indices), # change x, y\n", - " y=torch.Tensor(ys).float())\n", + " \"\"\"Prepare a batch of data for the model to process.\"\"\"\n", + " sequences, labels, batch_indices = [], [], []\n", + " for batch_index, sample in enumerate(batch):\n", + " sequence = torch.tensor(sample[\"sequence\"])\n", + " sequences.append(sequence)\n", + "\n", + " batch_indices.append(torch.ones_like(sequence, dtype=torch.long) \n", + " * batch_index)\n", + " \n", + " label = torch.tensor(sample[\"label\"])\n", + " labels.append(label)\n", + " return Data(sequences=torch.cat(sequences), \n", + " batch_indices=torch.cat(batch_indices),\n", + " y=torch.Tensor(labels).float())\n", "\n", "train_dataloader = \\\n", " DataLoader(train_dataset, batch_size=8, shuffle=True, collate_fn=collate)\n", @@ -689,33 +628,11 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "id": "LoqTxwCFMBYD" - }, + "metadata": {}, "outputs": [], "source": [ - "class AdamW(dl.Optimizer):\n", - " \"\"\"AdamW optimizer.\"\"\"\n", - " \n", - " def __pre_init__(self, **optimzer_kwargs):\n", - " \"\"\"Execute before initialization.\"\"\"\n", - " optimzer_kwargs.pop(\"classtype\", None)\n", - " super().__pre_init__(torch.optim.AdamW, **optimzer_kwargs)\n", - "\n", - "classifier = dl.BinaryClassifier(model=model, optimizer=AdamW(lr=1e-4)) \\\n", - " .create()" - ] - }, - { - "cell_type": "raw", - "metadata": { - "vscode": { - "languageId": "raw" - } - }, - "source": [ - "classifier = dl.BinaryClassifier(model=model, optimizer=dl.AdamW(lr=1e-4)) \\\n", - " .create()" + "classifier = dl.BinaryClassifier(model=model, \n", + " optimizer=dl.AdamW(lr=1e-4)).create()" ] }, { @@ -746,11 +663,12 @@ "source": [ "from lightning.pytorch.callbacks import ModelCheckpoint\n", "\n", - "checkpoint_callback = ModelCheckpoint(monitor=\"valBinaryAccuracy\", \\\n", - " dirpath=\"models\", \\\n", + "checkpoint_callback = ModelCheckpoint(\n", + " monitor=\"valBinaryAccuracy\", dirpath=\"models\",\n", " filename=\"ATT-model{epoch:02d}-val_accuracy{valBinaryAccuracy:.2f}\",\n", - " auto_insert_metric_name=False, mode=\"max\")\n", - "trainer = dl.Trainer(max_epochs=5, accelerator=\"cpu\", callbacks=[checkpoint_callback])\n", + " auto_insert_metric_name=False, mode=\"max\",\n", + ")\n", + "trainer = dl.Trainer(max_epochs=5, callbacks=[checkpoint_callback])\n", "trainer.fit(classifier, train_dataloader, val_dataloader)" ] }, @@ -767,7 +685,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": { "id": "d7PkWl8nMBYE" }, @@ -775,10 +693,11 @@ "source": [ "import glob, os\n", "\n", - "best_model = glob.glob(\"./models/ATT-model*\")\n", - "best_model = max(best_model, key=os.path.getctime)\n", - "best_classifier = dl.BinaryClassifier \\\n", - " .load_from_checkpoint(best_model, model=model).create()" + "saved_models = glob.glob(\"./models/ATT-model*\")\n", + "best_model = max(saved_models, key=os.path.getctime)\n", + "best_classifier = dl.BinaryClassifier.load_from_checkpoint(\n", + " best_model, model=model,\n", + ").create()" ] }, { @@ -840,16 +759,18 @@ "for idx in random.sample(range(len(test_dataset)), 3):\n", " sample = test_dataset[idx]\n", " \n", - " input_tensor = torch.Tensor(vocab(tokenize(sample[\"text\"]))).long()\n", - " test_input = {\"x\": input_tensor, \\ ### sequences\n", - " \"batch_indices\": torch.zeros_like(input_tensor, dtype=torch.long)}\n", + " input_sequence = torch.Tensor(vocab(tokenize(sample[\"text\"]))).long()\n", + " test_input = {\n", + " \"sequences\": input_sequence,\n", + " \"batch_indices\": torch.zeros_like(input_sequence, dtype=torch.long)\n", + " }\n", "\n", " probability = classifier.model(test_input)\n", - " pred = probability > 0.5\n", + " prediction = probability > 0.5\n", " \n", " texts.append(sample[\"text\"])\n", " labels.append(sample[\"label\"])\n", - " predictions.append(pred.item() * 1)\n", + " predictions.append(prediction.item() * 1)\n", "\n", "df = pd.DataFrame({\"text\": texts, \"label\": labels, \"prediction\": predictions})\n", "styled_df = df.style.set_properties(**{\"text-align\": \"left\"}).set_table_styles(\n", @@ -880,7 +801,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.2" + "version": "3.10.9" }, "widgets": { "application/vnd.jupyter.widget-state+json": { diff --git a/Ch08_Attention/ec08_C_ViT/ViT.ipynb b/Ch08_Attention/ec08_C_ViT/ViT.ipynb new file mode 100644 index 000000000..ec0c694e3 --- /dev/null +++ b/Ch08_Attention/ec08_C_ViT/ViT.ipynb @@ -0,0 +1,1750 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### **Introduction to Vision Transformer (ViT)**\n", + "\n", + "The [Vision Transformer](https://arxiv.org/abs/2010.11929) (ViT) represents an innovative breakthrough in how machines interpret images. Developed by Google Research, the ViT draws inspiration from the transformative impact of transformer models in Natural Language Processing (NLP). Traditionally, Convolutional Neural Networks (CNNs) dominated image analysis tasks. However, the introduction of ViT marked a paradigm shift, demonstrating that a model primarily designed for text could excel in visual domains as well. The ViT model has not only outperformed existing CNN-based methods across various image classification benchmarks but also shown remarkable scalability and generalization capabilities. Its success has led to its adoption in several leading-edge applications, including image segmentation in [Segment Anything](https://arxiv.org/abs/2304.02643) (SAM) and multimodal learning in [Contrastive Language-Image Pre-training](https://arxiv.org/pdf/2103.00020.pdf) (CLIP).\n", + "\n", + "This notebook provides an introduction to the Vision Transformer (ViT) model, explaining its architecture and how it can be implemented using PyTorch for image classification tasks.\n", + "\n", + "#### **CIFAR-10 Dataset**\n", + "\n", + "The CIFAR-10 dataset will serve as our primary playground for experimenting with ViT.\n", + "\n", + "Comprising 60,000 images, each 32x32 pixels and in full color, CIFAR-10 is categorized into 10 distinct classes, with each class containing 6,000 images. The dataset is divided into a training set of 50,000 images and a testing set of 10,000 images.\n", + "\n", + "Use the following code snippet to download and prepare the data:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Files already downloaded and verified\n", + "Files already downloaded and verified\n" + ] + } + ], + "source": [ + "from torchvision import transforms, datasets\n", + "\n", + "train_dataset = datasets.CIFAR10(\n", + " root=\"./data\", train=True, download=True, transform=transforms.ToTensor(), # (1)\n", + ")\n", + "test_dataset = datasets.CIFAR10(\n", + " root=\"./data\", train=False, download=True, transform=transforms.ToTensor() # (2)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This script initializes the CIFAR-10 dataset for training and testing purposes using PyTorch's `datasets.CIFAR10` class for seamless data handling. \n", + "\n", + "**(1)** To initiate the training dataset, the script specifies the root directory for storing the data, sets the `train` parameter to `True`, and enables the download option. Additionally, it uses the `transforms.ToTensor()` method to convert the images into PyTorch tensors.\n", + "\n", + "**(2)** To initialize the testing dataset, similar parameters are used except for the `train` parameter, which is set to `False`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's visualize images from the CIFAR-10 dataset by defining the function `plot_class_examples`." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "def plot_class_examples(dataset, n_images=5):\n", + " \"\"\"\n", + " Plot the first n_images for each class in the dataset.\n", + " \"\"\"\n", + " classes = dataset.classes # (1)\n", + " indices = {c: [] for c in range(len(classes))} # (2)\n", + "\n", + " for i, (_, label) in enumerate(dataset): # (3)\n", + " if all(len(v) == n_images for v in indices.values()): # (4)\n", + " break \n", + " if len(indices[label]) < n_images: # (5)\n", + " indices[label].append(i) # (6)\n", + "\n", + " fig, axes = plt \\\n", + " .subplots(n_images, len(classes), figsize=(16, 8), squeeze=False) # (7)\n", + "\n", + " for c, (class_idx, idx) in enumerate(indices.items()): # (9)\n", + " for j, index in enumerate(idx): # (10)\n", + " img = dataset[index][0].permute(1, 2, 0) # (11)\n", + " axes[j, c].imshow(img)\n", + " axes[j, c].set(xticks=[], yticks=[])\n", + " if j == 0:\n", + " axes[j, c].set_title(classes[class_idx], size=\"large\") \n", + "\n", + " plt.subplots_adjust(wspace=0.05, hspace=0.05)\n", + " ### plt.savefig(\"fig_08_Xn.pdf\") \n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This function displays the first `n_images` examples for each class from the dataset.\n", + "\n", + "**(1)** The process starts by extracting the classes from the dataset and **(2)** initializing a dictionary to hold indices of images for each class. **(3-4)** Then, as it iterates over the dataset, the function checks whether enough images have been collected for each class. **(5-6)** If a class still needs more images, the current index is added to its list of indices. **(7)** Once the desired number of images for each class is reached, the function prepares a matplotlib figure with a grid of subplots. The subplots are arranged to have one row per class and one column per image. This ensures that the layout supports the visualization of all selected images. \n", + "\n", + "**(8-10)** For each class, the function iterates over its collected image indices, retrieves each image, permutes it for correct display formatting, and plots them in the corresponding subplot cell." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_class_examples(train_dataset, 5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### **Preparing the data**\n", + "\n", + "Preparing the data involves more than just loading it; we also need to ensure that our model trains effectively and generalizes well to unseen data. To this end, the CIFAR-10 dataset, initially comprising only training and testing subsets, requires further partitioning to create a separate validation set.\n", + "\n", + "For this purpose, we implement the `split_train_val` function, which splits the training set into training and validation subsets given a specified validation split ratio. This split allows us to independently evaluate the model's performance and make adjustments as needed without compromising the integrity of the test dataset.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "from torch.utils.data import Subset\n", + "\n", + "import copy\n", + "\n", + "def split_train_val(dataset, val_split=0.2):\n", + " \"\"\"\n", + " Splits a dataset into training and validation sets.\n", + " \"\"\"\n", + "\n", + " # Generate random indices\n", + " indices = torch.randperm(len(dataset)).tolist() # (1)\n", + "\n", + " # Calculate split index\n", + " split = int(len(dataset) * val_split) # (2)\n", + "\n", + " # Split indices\n", + " train_indices, val_indices = indices[split:], indices[:split] # (3)\n", + "\n", + " # Create dataset subsets\n", + " train_subset = Subset(dataset, train_indices) # (4)\n", + " val_subset = Subset(copy.deepcopy(dataset), val_indices) # (5)\n", + "\n", + " return train_subset, val_subset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `split_train_val` function takes a dataset and a validation split ratio as inputs, and returns the training and validation subsets. The function performs the following steps:\n", + "\n", + "**(1)** It generates random indices for the dataset.\n", + "\n", + "**(2)** It calculates the split index based on the validation split ratio.\n", + "\n", + "**(3)** It splits the indices into training and validation indices.\n", + "\n", + "**(4)** It creates the training subset using the `Subset` class, which allows us to create a subset of the dataset using the specified indices.\n", + "\n", + "**(5)** It creates the validation subset using the same method as step 4. To ensure that the training and validation subsets are independent of each other, the function uses the `copy.deepcopy` function." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this example, we will use a validation split ratio of 0.2, which means that 20% of the training data will be reserved for validation." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "train_dataset, val_dataset = split_train_val(train_dataset, val_split=0.2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To enhance the model's ability to generalize and prevent overfitting, we will apply various data augmentation techniques to the training dataset. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "train_transform = transforms.Compose(\n", + " [\n", + " transforms.RandomCrop(32, padding=4), # (1)\n", + " transforms.RandomHorizontalFlip(), # (2) \n", + " transforms.ToTensor(),\n", + " transforms.Normalize(\n", + " [0.49139968, 0.48215841, 0.44653091], [0.24703223, 0.24348513, 0.26158784] # (3)\n", + " ),\n", + " ]\n", + ")\n", + "train_dataset.dataset.transform = train_transform" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These include **(1)** random cropping and padding, **(2)** horizontal flipping, and **(3)** normalization of the image tensors using the CIFAR-10 training set mean and standard deviation values for each channel." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the validation dataset, however, we'll restrict the preprocessing to just conversion to tensors and normalization, avoiding augmentation to maintain the validity of the evaluation process." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "val_transform = transforms.Compose(\n", + " [\n", + " transforms.ToTensor(),\n", + " transforms.Normalize(\n", + " [0.49139968, 0.48215841, 0.44653091], [0.24703223, 0.24348513, 0.26158784]\n", + " ),\n", + " ]\n", + ")\n", + "val_dataset.dataset.transform = val_transform" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### **Defining data loaders**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we will define data loaders that will feed the training, validation, and test data to the model during the training and evaluation processes." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "from torch.utils.data import DataLoader\n", + "\n", + "train_dataloader = DataLoader(train_dataset, batch_size=128, shuffle=True, num_workers=4)\n", + "val_dataloader = DataLoader(val_dataset, batch_size=128, shuffle=False, num_workers=4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### **Building a Vision Transformer model**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Having prepared our dataset, we define the Vision Transformer (ViT) model. \n", + "\n", + "**Figure 1** illustrates the ViT architecture. It is built around four pivotal components that work together to process and classify images.\n", + "\n", + "Firstly, **Image Patch Embeddings** play an essential role by slicing the input image into fixed-size, non-overlapping patches. These patches are linearly embedded, creating a series of flattened embeddings that serve as the input sequence for the transformer encoder (**Figure 1a**). To capture global information about the image, an additional learnable class token is integrated with these patch embeddings (**Figure 1b**). This class token, while not directly corresponding to any individual patch, symbolizes the collective image and its interactions with the embeddings, providing a holistic view for classification.\n", + "\n", + "Secondly, the model incorporates **Positional Encodings** to permeate the patch embeddings with spatial context (**Figure 1b**). Unlike sin/cos encoding, these positional encodings are initially randomized and subsequently refined through the learning process, ensuring the model comprehends the layout and position of each patch within the image.\n", + "\n", + "The **Transformer Encoder**, the third component, is the heart of the model (**Figure 1c**). It comprises several layers of transformer blocks that each feature a multi-head self-attention mechanism and a feedforward neural network. This structure allows the model to focus on different parts of the image simultaneously, facilitating a deeper understanding of the visual content.\n", + "\n", + "The **Classification Head** or **Dense Top** is the last component of ViT (**Figure 1d**). It takes the class token output from the transformer encoder and feeds it through a feedforward neural network to predict the image's class label.\n", + "\n", + "\n", + "

\n", + " \n", + "
\n", + " Figure 1. Vision Transformer (ViT) Architecture\n", + "

" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us now instantiate the ViT model" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "from deeplay import ViT \n", + "\n", + "model = ViT(\n", + " in_channels=3, # (1)\n", + " image_size=32, # (2)\n", + " patch_size=4, # (3)\n", + " hidden_features=[384,] * 7, # (4)\n", + " out_features=10, # (5)\n", + " num_heads=12, # (6)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Deeplay `ViT` class is responsible for managing the ViT (Vision Transformer) model architecture. The class constructor initializes the model components, including the patch embeddings, positional encodings, transformer encoder, and classification head. `ViT` accepts the following parameters:\n", + "\n", + "**(1)** `in_channels`: This parameter defines the number of input channels in the image. For the CIFAR-10 dataset, the number of channels is 3 (RGB).\n", + "\n", + "**(2)** `image_size`: This parameter defines the size of the input image, which is 32x32 for the CIFAR-10 dataset.\n", + "\n", + "**(3)** `patch_size`: This parameter defines the size of the image patches. By dividing the image into smaller patches, the model can analyze the image in segments and learn local features before integrating them for global understanding. This parameter is set to 4, resulting in a total of 64 patches of size 4x4.\n", + "\n", + "**(4)** `hidden_dim`: This parameter defines the dimension of the hidden layers in the transformer encoder. Seven transformer blocks (or layers) are defined in the model, each with a hidden size of 384 channels.\n", + "\n", + "**(5)** `out_channels`: This parameter defines the number of classes in the dataset, which is 10 for CIFAR-10.\n", + "\n", + "**(6)** `num_heads`: This parameter defines the number of attention heads in the multi-head self-attention mechanism. In this case, it is set to 12.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ViT(\n", + " (patch_embedder): Patchify(\n", + " (layer): Layer[Conv2d](in_channels=3, out_channels=384, kernel_size=4, stride=4)\n", + " (dropout): Layer[Dropout](p=0)\n", + " )\n", + " (positional_embedder): PositionalEmbedding(\n", + " (dropout): Layer[Dropout](p=0)\n", + " )\n", + " (transformer_encoder): TransformerEncoderLayer(\n", + " (blocks): LayerList(\n", + " (0-6): 7 x SequentialBlock(\n", + " (multihead): LayerDropoutSkipNormalization(\n", + " (normalization): Layer[LayerNorm](normalized_shape=384)\n", + " (layer): MultiheadSelfAttention(\n", + " (projection): Layer[Identity]()\n", + " (attention): Layer[MultiheadAttention](embed_dim=384, num_heads=12, batch_first=True)\n", + " )\n", + " (dropout): Layer[Dropout](p=0)\n", + " (skip): Add()\n", + " )\n", + " (feed_forward): LayerDropoutSkipNormalization(\n", + " (normalization): Layer[LayerNorm](normalized_shape=384)\n", + " (layer): MultiLayerPerceptron(\n", + " (blocks): LayerList(\n", + " (0-1): 2 x LinearBlock(\n", + " (layer): Layer[Linear](in_features=384, out_features=384, bias=True)\n", + " (activation): Layer[GELU]()\n", + " )\n", + " )\n", + " )\n", + " (dropout): Layer[Dropout](p=0)\n", + " (skip): Add()\n", + " )\n", + " )\n", + " )\n", + " )\n", + " (dense_top): MultiLayerPerceptron(\n", + " (blocks): LayerList(\n", + " (0): LinearBlock(\n", + " (layer): Layer[Linear](in_features=384, out_features=192, bias=True)\n", + " (activation): Layer[GELU]()\n", + " )\n", + " (1): LinearBlock(\n", + " (layer): Layer[Linear](in_features=192, out_features=96, bias=True)\n", + " (activation): Layer[GELU]()\n", + " )\n", + " (2): LinearBlock(\n", + " (layer): Layer[Linear](in_features=96, out_features=10, bias=True)\n", + " (activation): Layer[Identity]()\n", + " )\n", + " )\n", + " )\n", + ")\n" + ] + } + ], + "source": [ + "print(model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### **Training the model**\n", + "\n", + "The following create a `Trainer` object to train the model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will use the Adam optimizer with a learning rate of `1e-3` and weight decay of `1e-5`, and `CrossEntropyLoss` as the loss function." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "from deeplay import Classifier, Adam, Trainer\n", + "\n", + "from lightning.pytorch.callbacks import ModelCheckpoint\n", + "\n", + "classifier = Classifier(\n", + " model=model,\n", + " optimizer=Adam(\n", + " lr=1e-3,\n", + " weight_decay=5e-5,\n", + " betas=(0.9, 0.999),\n", + " ),\n", + " num_classes=10,\n", + ").create()\n", + "\n", + "checkpoint_callback = ModelCheckpoint( # (2)\n", + " monitor=\"valMulticlassAccuracy\",\n", + " mode=\"max\",\n", + " dirpath=\"checkpoint_classifier\",\n", + " filename=\"cifar10-{epoch:02d}-{valMulticlassAccuracy:.2f}\",\n", + " auto_insert_metric_name=False,\n", + ")\n", + "\n", + "trainer = Trainer(max_epochs=100, callbacks=[checkpoint_callback])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-10-01 11:31:24.422057: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "/Users/841602/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/lightning/pytorch/callbacks/model_checkpoint.py:654: Checkpoint directory /Users/841602/Documents/GitHub/DeepLearningCrashCourse/Ch08_Attention/ec08_C_ViT/checkpoint_classifier exists and is not empty.\n" + ] + }, + { + "data": { + "text/html": [ + "
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+       "Total params: 6.4 M                                                                                                \n",
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\u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtrainer_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 49\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m _TunerExitException:\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/lightning/pytorch/trainer/trainer.py:574\u001b[0m, in \u001b[0;36mTrainer._fit_impl\u001b[0;34m(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)\u001b[0m\n\u001b[1;32m 568\u001b[0m ckpt_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_checkpoint_connector\u001b[38;5;241m.\u001b[39m_select_ckpt_path(\n\u001b[1;32m 569\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mfn,\n\u001b[1;32m 570\u001b[0m ckpt_path,\n\u001b[1;32m 571\u001b[0m model_provided\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m 572\u001b[0m model_connected\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlightning_module \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 573\u001b[0m )\n\u001b[0;32m--> 574\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mckpt_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mckpt_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 576\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mstopped\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/lightning/pytorch/trainer/trainer.py:981\u001b[0m, in \u001b[0;36mTrainer._run\u001b[0;34m(self, model, ckpt_path)\u001b[0m\n\u001b[1;32m 978\u001b[0m \u001b[38;5;66;03m# ----------------------------\u001b[39;00m\n\u001b[1;32m 979\u001b[0m \u001b[38;5;66;03m# RUN THE TRAINER\u001b[39;00m\n\u001b[1;32m 980\u001b[0m \u001b[38;5;66;03m# ----------------------------\u001b[39;00m\n\u001b[0;32m--> 981\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run_stage\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 983\u001b[0m \u001b[38;5;66;03m# ----------------------------\u001b[39;00m\n\u001b[1;32m 984\u001b[0m \u001b[38;5;66;03m# POST-Training CLEAN UP\u001b[39;00m\n\u001b[1;32m 985\u001b[0m \u001b[38;5;66;03m# ----------------------------\u001b[39;00m\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/lightning/pytorch/trainer/trainer.py:1025\u001b[0m, in \u001b[0;36mTrainer._run_stage\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1024\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mautograd\u001b[38;5;241m.\u001b[39mset_detect_anomaly(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_detect_anomaly):\n\u001b[0;32m-> 1025\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_loop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1026\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/lightning/pytorch/loops/fit_loop.py:205\u001b[0m, in \u001b[0;36m_FitLoop.run\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 204\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mon_advance_start()\n\u001b[0;32m--> 205\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madvance\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 206\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mon_advance_end()\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/lightning/pytorch/loops/fit_loop.py:363\u001b[0m, in \u001b[0;36m_FitLoop.advance\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 362\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_data_fetcher \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 363\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mepoch_loop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_data_fetcher\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/lightning/pytorch/loops/training_epoch_loop.py:140\u001b[0m, in \u001b[0;36m_TrainingEpochLoop.run\u001b[0;34m(self, data_fetcher)\u001b[0m\n\u001b[1;32m 139\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 140\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madvance\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata_fetcher\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 141\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mon_advance_end(data_fetcher)\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/lightning/pytorch/loops/training_epoch_loop.py:250\u001b[0m, in \u001b[0;36m_TrainingEpochLoop.advance\u001b[0;34m(self, data_fetcher)\u001b[0m\n\u001b[1;32m 248\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mlightning_module\u001b[38;5;241m.\u001b[39mautomatic_optimization:\n\u001b[1;32m 249\u001b[0m \u001b[38;5;66;03m# in automatic optimization, there can only be one optimizer\u001b[39;00m\n\u001b[0;32m--> 250\u001b[0m batch_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautomatic_optimization\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptimizers\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 251\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/lightning/pytorch/loops/optimization/automatic.py:190\u001b[0m, in \u001b[0;36m_AutomaticOptimization.run\u001b[0;34m(self, optimizer, batch_idx, kwargs)\u001b[0m\n\u001b[1;32m 185\u001b[0m \u001b[38;5;66;03m# ------------------------------\u001b[39;00m\n\u001b[1;32m 186\u001b[0m \u001b[38;5;66;03m# BACKWARD PASS\u001b[39;00m\n\u001b[1;32m 187\u001b[0m \u001b[38;5;66;03m# ------------------------------\u001b[39;00m\n\u001b[1;32m 188\u001b[0m \u001b[38;5;66;03m# gradient update with accumulated gradients\u001b[39;00m\n\u001b[1;32m 189\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 190\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_optimizer_step\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclosure\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 192\u001b[0m result \u001b[38;5;241m=\u001b[39m closure\u001b[38;5;241m.\u001b[39mconsume_result()\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/lightning/pytorch/loops/optimization/automatic.py:268\u001b[0m, in \u001b[0;36m_AutomaticOptimization._optimizer_step\u001b[0;34m(self, batch_idx, train_step_and_backward_closure)\u001b[0m\n\u001b[1;32m 267\u001b[0m \u001b[38;5;66;03m# model hook\u001b[39;00m\n\u001b[0;32m--> 268\u001b[0m \u001b[43mcall\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_lightning_module_hook\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 269\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrainer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 270\u001b[0m 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\u001b[0;36m_call_lightning_module_hook\u001b[0;34m(trainer, hook_name, pl_module, *args, **kwargs)\u001b[0m\n\u001b[1;32m 166\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mprofile(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[LightningModule]\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpl_module\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mhook_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 167\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 169\u001b[0m \u001b[38;5;66;03m# restore current_fx when nested context\u001b[39;00m\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/lightning/pytorch/core/module.py:1306\u001b[0m, in \u001b[0;36mLightningModule.optimizer_step\u001b[0;34m(self, epoch, batch_idx, optimizer, optimizer_closure)\u001b[0m\n\u001b[1;32m 1282\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"Override this method to adjust the default way the :class:`~lightning.pytorch.trainer.trainer.Trainer` calls\u001b[39;00m\n\u001b[1;32m 1283\u001b[0m \u001b[38;5;124;03mthe optimizer.\u001b[39;00m\n\u001b[1;32m 1284\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1304\u001b[0m \n\u001b[1;32m 1305\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m-> 1306\u001b[0m \u001b[43moptimizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43mclosure\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptimizer_closure\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/lightning/pytorch/core/optimizer.py:153\u001b[0m, in \u001b[0;36mLightningOptimizer.step\u001b[0;34m(self, closure, **kwargs)\u001b[0m\n\u001b[1;32m 152\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_strategy \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 153\u001b[0m step_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_strategy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptimizer_step\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_optimizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclosure\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 155\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_on_after_step()\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/lightning/pytorch/strategies/strategy.py:238\u001b[0m, in \u001b[0;36mStrategy.optimizer_step\u001b[0;34m(self, optimizer, closure, model, **kwargs)\u001b[0m\n\u001b[1;32m 237\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(model, pl\u001b[38;5;241m.\u001b[39mLightningModule)\n\u001b[0;32m--> 238\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprecision_plugin\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptimizer_step\u001b[49m\u001b[43m(\u001b[49m\u001b[43moptimizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclosure\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclosure\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/lightning/pytorch/plugins/precision/precision.py:122\u001b[0m, in \u001b[0;36mPrecision.optimizer_step\u001b[0;34m(self, optimizer, model, closure, **kwargs)\u001b[0m\n\u001b[1;32m 121\u001b[0m closure \u001b[38;5;241m=\u001b[39m partial(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_wrap_closure, model, optimizer, closure)\n\u001b[0;32m--> 122\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43moptimizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43mclosure\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclosure\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/torch/optim/optimizer.py:385\u001b[0m, in \u001b[0;36mOptimizer.profile_hook_step..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 381\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m 382\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must return None or a tuple of (new_args, new_kwargs), but got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresult\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 383\u001b[0m )\n\u001b[0;32m--> 385\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 386\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_optimizer_step_code()\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/torch/optim/optimizer.py:76\u001b[0m, in \u001b[0;36m_use_grad_for_differentiable.._use_grad\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 75\u001b[0m torch\u001b[38;5;241m.\u001b[39m_dynamo\u001b[38;5;241m.\u001b[39mgraph_break()\n\u001b[0;32m---> 76\u001b[0m ret \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 77\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/torch/optim/adam.py:166\u001b[0m, in \u001b[0;36mAdam.step\u001b[0;34m(self, closure)\u001b[0m\n\u001b[1;32m 157\u001b[0m has_complex \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_init_group(\n\u001b[1;32m 158\u001b[0m group,\n\u001b[1;32m 159\u001b[0m params_with_grad,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 163\u001b[0m max_exp_avg_sqs,\n\u001b[1;32m 164\u001b[0m state_steps)\n\u001b[0;32m--> 166\u001b[0m \u001b[43madam\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 167\u001b[0m \u001b[43m \u001b[49m\u001b[43mparams_with_grad\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 168\u001b[0m \u001b[43m \u001b[49m\u001b[43mgrads\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 169\u001b[0m \u001b[43m \u001b[49m\u001b[43mexp_avgs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 170\u001b[0m \u001b[43m \u001b[49m\u001b[43mexp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 171\u001b[0m \u001b[43m \u001b[49m\u001b[43mmax_exp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 172\u001b[0m \u001b[43m \u001b[49m\u001b[43mstate_steps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 173\u001b[0m \u001b[43m \u001b[49m\u001b[43mamsgrad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mamsgrad\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 174\u001b[0m \u001b[43m \u001b[49m\u001b[43mhas_complex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhas_complex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 175\u001b[0m \u001b[43m \u001b[49m\u001b[43mbeta1\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta1\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 176\u001b[0m \u001b[43m \u001b[49m\u001b[43mbeta2\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta2\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 177\u001b[0m \u001b[43m \u001b[49m\u001b[43mlr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlr\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 178\u001b[0m \u001b[43m \u001b[49m\u001b[43mweight_decay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mweight_decay\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 179\u001b[0m \u001b[43m \u001b[49m\u001b[43meps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43meps\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 180\u001b[0m \u001b[43m \u001b[49m\u001b[43mmaximize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmaximize\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 181\u001b[0m \u001b[43m \u001b[49m\u001b[43mforeach\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mforeach\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 182\u001b[0m \u001b[43m \u001b[49m\u001b[43mcapturable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mcapturable\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 183\u001b[0m \u001b[43m \u001b[49m\u001b[43mdifferentiable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdifferentiable\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 184\u001b[0m \u001b[43m \u001b[49m\u001b[43mfused\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mfused\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 185\u001b[0m \u001b[43m \u001b[49m\u001b[43mgrad_scale\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mgrad_scale\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 186\u001b[0m \u001b[43m \u001b[49m\u001b[43mfound_inf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfound_inf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 187\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 189\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m loss\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/torch/optim/adam.py:316\u001b[0m, in \u001b[0;36madam\u001b[0;34m(params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, foreach, capturable, differentiable, fused, grad_scale, found_inf, has_complex, amsgrad, beta1, beta2, lr, weight_decay, eps, maximize)\u001b[0m\n\u001b[1;32m 314\u001b[0m func \u001b[38;5;241m=\u001b[39m _single_tensor_adam\n\u001b[0;32m--> 316\u001b[0m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 317\u001b[0m \u001b[43m \u001b[49m\u001b[43mgrads\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 318\u001b[0m \u001b[43m \u001b[49m\u001b[43mexp_avgs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 319\u001b[0m \u001b[43m \u001b[49m\u001b[43mexp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 320\u001b[0m \u001b[43m \u001b[49m\u001b[43mmax_exp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 321\u001b[0m \u001b[43m \u001b[49m\u001b[43mstate_steps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 322\u001b[0m \u001b[43m \u001b[49m\u001b[43mamsgrad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mamsgrad\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 323\u001b[0m \u001b[43m \u001b[49m\u001b[43mhas_complex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhas_complex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 324\u001b[0m \u001b[43m \u001b[49m\u001b[43mbeta1\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta1\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 325\u001b[0m \u001b[43m \u001b[49m\u001b[43mbeta2\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta2\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 326\u001b[0m \u001b[43m \u001b[49m\u001b[43mlr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlr\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 327\u001b[0m \u001b[43m \u001b[49m\u001b[43mweight_decay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mweight_decay\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 328\u001b[0m \u001b[43m \u001b[49m\u001b[43meps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43meps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 329\u001b[0m \u001b[43m \u001b[49m\u001b[43mmaximize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmaximize\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 330\u001b[0m \u001b[43m \u001b[49m\u001b[43mcapturable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcapturable\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 331\u001b[0m \u001b[43m \u001b[49m\u001b[43mdifferentiable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdifferentiable\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 332\u001b[0m \u001b[43m \u001b[49m\u001b[43mgrad_scale\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgrad_scale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 333\u001b[0m \u001b[43m \u001b[49m\u001b[43mfound_inf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfound_inf\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/torch/optim/adam.py:441\u001b[0m, in \u001b[0;36m_single_tensor_adam\u001b[0;34m(params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, grad_scale, found_inf, amsgrad, has_complex, beta1, beta2, lr, weight_decay, eps, maximize, capturable, differentiable)\u001b[0m\n\u001b[1;32m 439\u001b[0m denom \u001b[38;5;241m=\u001b[39m (exp_avg_sq\u001b[38;5;241m.\u001b[39msqrt() \u001b[38;5;241m/\u001b[39m bias_correction2_sqrt)\u001b[38;5;241m.\u001b[39madd_(eps)\n\u001b[0;32m--> 441\u001b[0m \u001b[43mparam\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maddcdiv_\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexp_avg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdenom\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43mstep_size\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 443\u001b[0m \u001b[38;5;66;03m# Lastly, switch back to complex view\u001b[39;00m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: ", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[12], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mclassifier\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_dataloader\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval_dataloader\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/lightning/pytorch/trainer/trainer.py:538\u001b[0m, in \u001b[0;36mTrainer.fit\u001b[0;34m(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)\u001b[0m\n\u001b[1;32m 536\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mstatus \u001b[38;5;241m=\u001b[39m TrainerStatus\u001b[38;5;241m.\u001b[39mRUNNING\n\u001b[1;32m 537\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtraining \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m--> 538\u001b[0m \u001b[43mcall\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_and_handle_interrupt\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 539\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit_impl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_dataloaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval_dataloaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdatamodule\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mckpt_path\u001b[49m\n\u001b[1;32m 540\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/lightning/pytorch/trainer/call.py:64\u001b[0m, in \u001b[0;36m_call_and_handle_interrupt\u001b[0;34m(trainer, trainer_fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(launcher, _SubprocessScriptLauncher):\n\u001b[1;32m 63\u001b[0m launcher\u001b[38;5;241m.\u001b[39mkill(_get_sigkill_signal())\n\u001b[0;32m---> 64\u001b[0m \u001b[43mexit\u001b[49m(\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 66\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exception:\n\u001b[1;32m 67\u001b[0m _interrupt(trainer, exception)\n", + "\u001b[0;31mNameError\u001b[0m: name 'exit' is not defined" + ] + } + ], + "source": [ + "trainer.fit(classifier, train_dataloader, val_dataloader)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use the following code snippet to visualize the training and validation loss and accuracy." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "ename": "UnboundLocalError", + "evalue": "local variable 'step' referenced before assignment", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mUnboundLocalError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[13], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhistory\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/deeplay/callbacks/history.py:133\u001b[0m, in \u001b[0;36mLogHistory.plot\u001b[0;34m(self, yscale, *args, **kwargs)\u001b[0m\n\u001b[1;32m 122\u001b[0m ax\u001b[38;5;241m.\u001b[39mplot(\n\u001b[1;32m 123\u001b[0m step,\n\u001b[1;32m 124\u001b[0m history[train_key][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalue\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 127\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mmarker_kwargs,\n\u001b[1;32m 128\u001b[0m )\n\u001b[1;32m 130\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m val_key \u001b[38;5;129;01min\u001b[39;00m history:\n\u001b[1;32m 131\u001b[0m marker_kwargs \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 132\u001b[0m \u001b[38;5;28mdict\u001b[39m(marker\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124md\u001b[39m\u001b[38;5;124m\"\u001b[39m, markerfacecolor\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwhite\u001b[39m\u001b[38;5;124m\"\u001b[39m, markeredgewidth\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1.5\u001b[39m)\n\u001b[0;32m--> 133\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[43mstep\u001b[49m) \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m20\u001b[39m\n\u001b[1;32m 134\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m {}\n\u001b[1;32m 135\u001b[0m )\n\u001b[1;32m 136\u001b[0m ax\u001b[38;5;241m.\u001b[39mplot(\n\u001b[1;32m 137\u001b[0m history[val_key][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstep\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[1;32m 138\u001b[0m history[val_key][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalue\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 142\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mmarker_kwargs,\n\u001b[1;32m 143\u001b[0m )\n\u001b[1;32m 145\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_title(\n\u001b[1;32m 146\u001b[0m key\u001b[38;5;241m.\u001b[39mreplace(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m)\u001b[38;5;241m.\u001b[39mreplace(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mepoch\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m)\u001b[38;5;241m.\u001b[39mstrip()\u001b[38;5;241m.\u001b[39mcapitalize()\n\u001b[1;32m 147\u001b[0m )\n", + "\u001b[0;31mUnboundLocalError\u001b[0m: local variable 'step' referenced before assignment" + ] + }, + { + "data": { + "image/png": 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Inductive bias refers to the set of assumptions a model makes about the data it trains on to generalize from the training data to unseen data effectively. For instance, CNNs inherently assume spatial hierarchies in images, leveraging local patterns (e.g., edges and textures in early layers) and composing them into higher-order features (like objects) in deeper layers. This architectural innatency towards capturing spatial locality and translation invariance directly supports learning from image data.\n", + "\n", + "Due to the absence of these biases, ViT requires a substantial amount of data to learn effectively. It treats the image as a sequence of patches and learns to interpret these without the innate advantage of assuming spatial correlations, relying instead on the data to dictate these relationships. Consequently, ViT models need larger datasets to achieve their impressive performance, as they must learn from scratch what CNNs get for free through their architecture.\n", + "\n", + "The CIFAR-10 dataset, containing only 60,000 images, is relatively small compared to the massive datasets where ViT models excel. Although 60,000 might seem substantial, it pales in comparison to the vast datasets like ImageNet with over 14 million images, which have been instrumental in training ViTs to their state-of-the-art performance.\n", + "\n", + "[CutMix](https://arxiv.org/abs/1905.04899) is a technique designed to address this challenge, among others. It works by forcing the network to learn local relationships more efficiently, effectively injecting the network with sufficient pathways to learn spatial insights without relying on vast amounts of data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### **Implementing CutMix**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "CutMix is a data augmentation strategy that seeks to enhance the model's ability to understand and integrate local features within images, thereby improving its generalization capabilities. Originating from the idea of [Mixup](https://arxiv.org/abs/1710.09412), where two images are blended together by averaging their pixels, CutMix takes this concept further by combining patches from two different images rather than mixing their entire content. This technique not only enriches the dataset but also introduces a more challenging and nuanced learning task for the model.\n", + "\n", + "In a typical CutMix augmentation, a patch from one image is cut and pasted onto another image, and the labels are mixed proportionally to the area of the patches. This process creates a new training example that is a composite of features and labels from two distinct images. For the model, the task then becomes to predict a mixed label, reflecting the composition of the input image. The primary advantage of this method is its ability to force the model to focus on less dominant features of the image, promoting a deeper and more robust learning of spatial relationships and feature representations." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following class `CutMix` implements the CutMix augmentation." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "\n", + "class CutMix(object):\n", + " def __init__(self, size, beta):\n", + " self.size = size\n", + " self.beta = beta\n", + "\n", + " def __call__(self, batch):\n", + " img, label = batch\n", + " rand_img, rand_label = self._shuffle_minibatch(batch)\n", + " lam = np.random.beta(self.beta, self.beta)\n", + "\n", + " # Clone images to ensure the original batch is not modified\n", + " augmented_img = img.clone()\n", + "\n", + " r_x = np.random.uniform(0, self.size)\n", + " r_y = np.random.uniform(0, self.size)\n", + " r_w = self.size * np.sqrt(1 - lam)\n", + " r_h = self.size * np.sqrt(1 - lam)\n", + "\n", + " x1 = np.clip(int(r_x - r_w // 2), a_min=0, a_max=self.size)\n", + " x2 = np.clip(int(r_x + r_w // 2), a_min=0, a_max=self.size)\n", + " y1 = np.clip(int(r_y - r_h // 2), a_min=0, a_max=self.size)\n", + " y2 = np.clip(int(r_y + r_h // 2), a_min=0, a_max=self.size)\n", + "\n", + " # Replace parts of the cloned images with parts from the randomized images\n", + " augmented_img[:, :, y1:y2, x1:x2] = rand_img[:, :, y1:y2, x1:x2]\n", + "\n", + " lam = 1 - ((x2 - x1) * (y2 - y1) / (self.size**2))\n", + " \n", + " return augmented_img, label, rand_label, lam\n", + "\n", + " def _shuffle_minibatch(self, batch):\n", + " img, label = batch\n", + " rand_idx = torch.randperm(img.size(0))\n", + " rand_img = img[rand_idx]\n", + " rand_label = label[rand_idx]\n", + " return rand_img, rand_label" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's visualize the CutMix augmentation by applying it to a random pair of images from the training dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import random\n", + "from itertools import chain\n", + "\n", + "\n", + "def create_batch(samples):\n", + " images, labels = zip(*samples)\n", + " return torch.stack(images), torch.tensor(labels)\n", + "\n", + "\n", + "def normalize_image(image):\n", + " mean = torch.tensor([0.49139968, 0.48215841, 0.44653091]).view(3, 1, 1)\n", + " std = torch.tensor([0.24703223, 0.24348513, 0.26158784]).view(3, 1, 1)\n", + " return image * std + mean\n", + "\n", + "\n", + "samples = random.choices(train_dataset, k=2) # (1)\n", + "batch = create_batch(samples) # (2)\n", + "\n", + "augmented_img, *_, lam = CutMix(size=32, beta=1.0)(batch) # (3)\n", + "\n", + "fig, axs = plt.subplots(1, 4, figsize=(12, 3)) # (4)\n", + "titles = [\"Original Image 1\", \"Original Image 2\", \"CutMix Image 1\", \"CutMix Image 2\"]\n", + "\n", + "for i, (img, title) in enumerate(zip(chain(batch[0], augmented_img), titles)):\n", + " axs[i].imshow(normalize_image(img).permute(1, 2, 0))\n", + " axs[i].set_title(f\"{title}\\nλ={lam:.2f}\" if \"CutMix\" in title else title)\n", + " axs[i].axis(\"off\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This code snippet **(1)** selects two random images from the dataset `train_dataset`. Next, **(2)** the `create_batch` function is used to create a batch of images and labels from the selected images. **(3)** The `CutMix` class is then applied to the batch to generate the augmented images and labels. Finally, **(4)** the original and augmented images are displayed side by side for comparison.\n", + "\n", + "*Please note that due to possibility of the shuffle operation resulting in the original order, there's a chance that the images might appear identical in certain instances. If this occurs, consider re-running the operation.*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### **Training with CutMix**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To train the model with CutMix, we define the `CutMixClassifier` class, which extends the `Classifier` to incorporate the CutMix augmentation." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "import torch.optim as optim\n", + "import warmup_scheduler\n", + "\n", + "\n", + "class CutMixClassifier(Classifier):\n", + " def __init__(self, model, size=32, beta=1.0, **kwargs):\n", + " super().__init__(model, **kwargs)\n", + "\n", + " self.cutmix = CutMix(size=size, beta=beta)\n", + "\n", + " def training_step(self, batch, batch_idx):\n", + "\n", + " augmented_img, labels, rand_labels, lam = self.cutmix(batch) # (1)\n", + "\n", + " output = self.model(augmented_img) # (2)\n", + " loss = self.loss(output, labels) * lam + (1 - lam) * self.loss( # (3)\n", + " output, rand_labels\n", + " )\n", + "\n", + " self.log(\n", + " \"train_loss\", loss, on_step=True, on_epoch=True, prog_bar=True, logger=True\n", + " )\n", + " self.log_metrics(\n", + " \"train\",\n", + " output,\n", + " labels,\n", + " on_step=True,\n", + " on_epoch=True,\n", + " prog_bar=True,\n", + " logger=True,\n", + " )\n", + " return loss\n", + "\n", + " def configure_optimizers(self):\n", + " optimizer = super().configure_optimizers()\n", + "\n", + " base_scheduler = optim.lr_scheduler.CosineAnnealingLR( # (4)\n", + " optimizer, T_max=200, eta_min=1e-5\n", + " )\n", + " scheduler = warmup_scheduler.GradualWarmupScheduler( # (5)\n", + " optimizer, multiplier=1.0, total_epoch=5, after_scheduler=base_scheduler\n", + " )\n", + "\n", + " return {\n", + " \"optimizer\": optimizer,\n", + " \"lr_scheduler\": {\n", + " \"scheduler\": scheduler,\n", + " \"interval\": \"epoch\",\n", + " },\n", + " }" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "During training, **(1)** `CutMixClassifier` first applies CutMix augmentation to the input batch, creating augmented images with their corresponding labels and random labels alongside the mixing coefficient `lam`. **(2)** The model processes the augmented images, and **(3)** the loss is calculated using a combination of the original and random labels weighted by `lam` and `1-lam`, respectively, to reflect the proportion of each image in the augmented mix. \n", + "\n", + "To further enhance the performance of the model, `CutMixClassifier` **(4)** sets up a learning rate schedule starting with a `CosineAnnealingLR` scheduler to adjust the learning rate following a cosine curve, and then **(5)** employs a warm-up phase using `GradualWarmupScheduler`, which gradually increases the learning rate over the first five epochs to the initial learning rate before transitioning to the cosine annealing schedule. This approach optimizes the learning process, improving training stability and model performance." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we proceed to train the model using the `CutMixClassifier` class. \n", + "\n", + "The model is trained for 700 epochs to allow sufficient time for the model to learn from the augmented data and improve its performance." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "model[..., \"attention#-1\"].log_output(\"attention_output\") # (1)\n", + "\n", + "classifier = CutMixClassifier(\n", + " model=model,\n", + " optimizer=Adam(lr=1e-3, weight_decay=5e-5, betas=(0.9, 0.999)),\n", + " num_classes=10,\n", + ").create()\n", + "\n", + "checkpoint_callback = ModelCheckpoint(\n", + " monitor=\"valMulticlassAccuracy\",\n", + " mode=\"max\",\n", + " dirpath=\"checkpoint_cutmix_classifier\",\n", + " filename=\"cifar10-{epoch:02d}-{valMulticlassAccuracy:.2f}\",\n", + " auto_insert_metric_name=False,\n", + ")\n", + "\n", + "trainer = Trainer(max_epochs=700, callbacks=[checkpoint_callback])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**(1)** the `log_output` method is used to log the output of the attention layer in the last transformer block. This will allow us to visualize the attention maps generated by the model during inference for interpretability purposes." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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+       "┃    Name           Type              Params  Mode  ┃\n",
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+       "│ 0 │ loss          │ CrossEntropyLoss │      0 │ train │\n",
+       "│ 1 │ train_metrics │ MetricCollection │      0 │ train │\n",
+       "│ 2 │ val_metrics   │ MetricCollection │      0 │ train │\n",
+       "│ 3 │ test_metrics  │ MetricCollection │      0 │ train │\n",
+       "│ 4 │ model         │ ViT              │  6.4 M │ train │\n",
+       "│ 5 │ optimizer     │ Adam             │      0 │ train │\n",
+       "└───┴───────────────┴──────────────────┴────────┴───────┘\n",
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Trainable params: 6.4 M                                                                                            \n",
+       "Non-trainable params: 0                                                                                            \n",
+       "Total params: 6.4 M                                                                                                \n",
+       "Total estimated model params size (MB): 25                                                                         \n",
+       "Modules in train mode: 174                                                                                         \n",
+       "Modules in eval mode: 0                                                                                            \n",
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ckpt_path,\n\u001b[1;32m 571\u001b[0m model_provided\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m 572\u001b[0m model_connected\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlightning_module \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 573\u001b[0m )\n\u001b[0;32m--> 574\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mckpt_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mckpt_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 576\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mstopped\n", + "File 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\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/lightning/pytorch/loops/evaluation_loop.py:113\u001b[0m, in \u001b[0;36m_EvaluationLoop.run\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 112\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m []\n\u001b[0;32m--> 113\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreset\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 114\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mon_run_start()\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/lightning/pytorch/loops/evaluation_loop.py:231\u001b[0m, in \u001b[0;36m_EvaluationLoop.reset\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 230\u001b[0m data_fetcher\u001b[38;5;241m.\u001b[39msetup(combined_loader)\n\u001b[0;32m--> 231\u001b[0m \u001b[38;5;28;43miter\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mdata_fetcher\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# creates the iterator inside the fetcher\u001b[39;00m\n\u001b[1;32m 233\u001b[0m \u001b[38;5;66;03m# add the previous `fetched` value to properly track `is_last_batch` with no prefetching\u001b[39;00m\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/lightning/pytorch/loops/fetchers.py:104\u001b[0m, in \u001b[0;36m_PrefetchDataFetcher.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 102\u001b[0m \u001b[38;5;129m@override\u001b[39m\n\u001b[1;32m 103\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__iter__\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_PrefetchDataFetcher\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m--> 104\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__iter__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 105\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlength \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 106\u001b[0m \u001b[38;5;66;03m# ignore pre-fetching, it's not necessary\u001b[39;00m\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/lightning/pytorch/loops/fetchers.py:51\u001b[0m, in \u001b[0;36m_DataFetcher.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 49\u001b[0m \u001b[38;5;129m@override\u001b[39m\n\u001b[1;32m 50\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__iter__\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_DataFetcher\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m---> 51\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39miterator \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43miter\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcombined_loader\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 52\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreset()\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/lightning/pytorch/utilities/combined_loader.py:351\u001b[0m, in \u001b[0;36mCombinedLoader.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 350\u001b[0m iterator \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mflattened, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_limits)\n\u001b[0;32m--> 351\u001b[0m \u001b[38;5;28;43miter\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 352\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_iterator \u001b[38;5;241m=\u001b[39m iterator\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/lightning/pytorch/utilities/combined_loader.py:155\u001b[0m, in \u001b[0;36m_Sequential.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 154\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_idx \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m--> 155\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_load_current_iterator\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 156\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/lightning/pytorch/utilities/combined_loader.py:173\u001b[0m, in \u001b[0;36m_Sequential._load_current_iterator\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 172\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_iterator_idx \u001b[38;5;241m<\u001b[39m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39miterables):\n\u001b[0;32m--> 173\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39miterators \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28;43miter\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miterables\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_iterator_idx\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m]\n\u001b[1;32m 174\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 175\u001b[0m \u001b[38;5;66;03m# No more iterables to step through, return an empty list\u001b[39;00m\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/torch/utils/data/dataloader.py:439\u001b[0m, in \u001b[0;36mDataLoader.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 438\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 439\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_iterator\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/torch/utils/data/dataloader.py:387\u001b[0m, in \u001b[0;36mDataLoader._get_iterator\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 386\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcheck_worker_number_rationality()\n\u001b[0;32m--> 387\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_MultiProcessingDataLoaderIter\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/torch/utils/data/dataloader.py:1040\u001b[0m, in \u001b[0;36m_MultiProcessingDataLoaderIter.__init__\u001b[0;34m(self, loader)\u001b[0m\n\u001b[1;32m 1034\u001b[0m \u001b[38;5;66;03m# NB: Process.start() actually take some time as it needs to\u001b[39;00m\n\u001b[1;32m 1035\u001b[0m \u001b[38;5;66;03m# start a process and pass the arguments over via a pipe.\u001b[39;00m\n\u001b[1;32m 1036\u001b[0m \u001b[38;5;66;03m# Therefore, we only add a worker to self._workers list after\u001b[39;00m\n\u001b[1;32m 1037\u001b[0m \u001b[38;5;66;03m# it started, so that we do not call .join() if program dies\u001b[39;00m\n\u001b[1;32m 1038\u001b[0m \u001b[38;5;66;03m# before it starts, and __del__ tries to join but will get:\u001b[39;00m\n\u001b[1;32m 1039\u001b[0m \u001b[38;5;66;03m# AssertionError: can only join a started process.\u001b[39;00m\n\u001b[0;32m-> 1040\u001b[0m \u001b[43mw\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstart\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1041\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_index_queues\u001b[38;5;241m.\u001b[39mappend(index_queue)\n", + "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/multiprocessing/process.py:121\u001b[0m, in \u001b[0;36mBaseProcess.start\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 120\u001b[0m _cleanup()\n\u001b[0;32m--> 121\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_popen \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_Popen\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 122\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sentinel \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_popen\u001b[38;5;241m.\u001b[39msentinel\n", + "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/multiprocessing/context.py:224\u001b[0m, in \u001b[0;36mProcess._Popen\u001b[0;34m(process_obj)\u001b[0m\n\u001b[1;32m 222\u001b[0m \u001b[38;5;129m@staticmethod\u001b[39m\n\u001b[1;32m 223\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_Popen\u001b[39m(process_obj):\n\u001b[0;32m--> 224\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_default_context\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_context\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mProcess\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_Popen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprocess_obj\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/multiprocessing/context.py:288\u001b[0m, in \u001b[0;36mSpawnProcess._Popen\u001b[0;34m(process_obj)\u001b[0m\n\u001b[1;32m 287\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpopen_spawn_posix\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Popen\n\u001b[0;32m--> 288\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mPopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprocess_obj\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/multiprocessing/popen_spawn_posix.py:32\u001b[0m, in \u001b[0;36mPopen.__init__\u001b[0;34m(self, process_obj)\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fds \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m---> 32\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mprocess_obj\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/multiprocessing/popen_fork.py:19\u001b[0m, in \u001b[0;36mPopen.__init__\u001b[0;34m(self, process_obj)\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfinalizer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m---> 19\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_launch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprocess_obj\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/multiprocessing/popen_spawn_posix.py:47\u001b[0m, in \u001b[0;36mPopen._launch\u001b[0;34m(self, process_obj)\u001b[0m\n\u001b[1;32m 46\u001b[0m reduction\u001b[38;5;241m.\u001b[39mdump(prep_data, fp)\n\u001b[0;32m---> 47\u001b[0m \u001b[43mreduction\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdump\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprocess_obj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfp\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 48\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n", + "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/multiprocessing/reduction.py:60\u001b[0m, in \u001b[0;36mdump\u001b[0;34m(obj, file, protocol)\u001b[0m\n\u001b[1;32m 59\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m'''Replacement for pickle.dump() using ForkingPickler.'''\u001b[39;00m\n\u001b[0;32m---> 60\u001b[0m \u001b[43mForkingPickler\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprotocol\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdump\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/numpy/core/__init__.py:145\u001b[0m, in \u001b[0;36m_DType_reduce\u001b[0;34m(DType)\u001b[0m\n\u001b[1;32m 142\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(dtype(scalar_type))\n\u001b[0;32m--> 145\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_DType_reduce\u001b[39m(DType):\n\u001b[1;32m 146\u001b[0m \u001b[38;5;66;03m# As types/classes, most DTypes can simply be pickled by their name:\u001b[39;00m\n\u001b[1;32m 147\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m DType\u001b[38;5;241m.\u001b[39m_legacy \u001b[38;5;129;01mor\u001b[39;00m DType\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__module__\u001b[39m \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnumpy.dtypes\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: ", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[23], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mclassifier\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_dataloader\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval_dataloader\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/lightning/pytorch/trainer/trainer.py:538\u001b[0m, in \u001b[0;36mTrainer.fit\u001b[0;34m(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)\u001b[0m\n\u001b[1;32m 536\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mstatus \u001b[38;5;241m=\u001b[39m TrainerStatus\u001b[38;5;241m.\u001b[39mRUNNING\n\u001b[1;32m 537\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtraining \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m--> 538\u001b[0m \u001b[43mcall\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_and_handle_interrupt\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 539\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit_impl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_dataloaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval_dataloaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdatamodule\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mckpt_path\u001b[49m\n\u001b[1;32m 540\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/lightning/pytorch/trainer/call.py:64\u001b[0m, in \u001b[0;36m_call_and_handle_interrupt\u001b[0;34m(trainer, trainer_fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(launcher, _SubprocessScriptLauncher):\n\u001b[1;32m 63\u001b[0m launcher\u001b[38;5;241m.\u001b[39mkill(_get_sigkill_signal())\n\u001b[0;32m---> 64\u001b[0m \u001b[43mexit\u001b[49m(\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 66\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exception:\n\u001b[1;32m 67\u001b[0m _interrupt(trainer, exception)\n", + "\u001b[0;31mNameError\u001b[0m: name 'exit' is not defined" + ] + } + ], + "source": [ + "trainer.fit(classifier, train_dataloader, val_dataloader)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that the model has achieved a validation accuracy of approximately 90%, which is a significant improvement over the previous accuracy of 76%." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "ename": "IndexError", + "evalue": "list index out of range", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[24], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m best_model_path \u001b[38;5;241m=\u001b[39m glob\u001b[38;5;241m.\u001b[39mglob(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcheckpoint_cutmix_classifier\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m*.ckpt\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[0;32m----> 2\u001b[0m best_model_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msorted\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mbest_model_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgetmtime\u001b[49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[1;32m 4\u001b[0m checkpoint \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mload(best_model_path)\n\u001b[1;32m 5\u001b[0m classifier\u001b[38;5;241m.\u001b[39mload_state_dict(checkpoint[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstate_dict\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n", + "\u001b[0;31mIndexError\u001b[0m: list index out of range" + ] + } + ], + "source": [ + "best_model_path = glob.glob(os.path.join(\"checkpoint_cutmix_classifier\", \"*.ckpt\"))\n", + "best_model_path = sorted(best_model_path, key=os.path.getmtime)[-1]\n", + "\n", + "checkpoint = torch.load(best_model_path)\n", + "classifier.load_state_dict(checkpoint[\"state_dict\"])\n", + "\n", + "trainer.test(classifier, test_loader)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### **Visualizing attention maps**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's visualize the model's performance by plotting test images, attention maps and predicted labels." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from skimage.transform import resize\n", + "\n", + "\n", + "samples = random.choices(test_dataset, k=4)\n", + "images, labels = create_batch(samples)\n", + "\n", + "classifier.model.eval()\n", + "output = classifier(images)\n", + "preds = torch.argmax(output, dim=1)\n", + "\n", + "attention_maps = classifier.logs[\"attention_output\"][1][:, 0, 1:].reshape(4, 8, 8)\n", + "\n", + "\n", + "fig, axs = plt.subplots(2, 4, figsize=(12, 6))\n", + "for i, (img, label, pred, attention_map) in enumerate(zip(images, labels, preds, attention_maps)):\n", + " img = normalize_image(img).detach().permute(1, 2, 0)\n", + " \n", + " axs[0, i].imshow(img)\n", + " axs[0, i].set_title(f\"Label: {test_dataset.classes[label]}\\nPred: {test_dataset.classes[pred]}\")\n", + " axs[0, i].axis(\"off\")\n", + "\n", + " resized_attention_map = resize(attention_map.detach().numpy(), (32, 32), anti_aliasing=True)\n", + " axs[1, i].imshow(img)\n", + " axs[1, i].imshow(resized_attention_map, cmap=\"hot\", alpha=0.5)\n", + " axs[1, i].set_title(\"Attention Map\")\n", + " axs[1, i].axis(\"off\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### **Using a pre-trained ViT model**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model achieved a 90% accuracy, which is comparable to the performance levels of CNNs on the CIFAR-10 dataset. However, as the original ViT paper states, this model achieves its best performance when trained on large-scale datasets like ImageNet, which contains millions of images. In such cases, the model can leverage its capacity to learn from vast amounts of data and generalize effectively, outperforming CNNs significantly.\n", + "\n", + "To demonstrate this, we will use a pre-trained ViT model that has already learned rich representations from a diverse range of images in the ImageNet dataset. This model can perform well on a variety of tasks. \n", + "\n", + "The following loads the pre-trained ViT model and wrap it in a Torch Module that appends a classification head." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "798fd9dba92949d6a1668cb3782b39ea", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "config.json: 0%| | 0.00/502 [00:00┏━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━┓\n", + "┃ Name Type Params Mode ┃\n", + "┡━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━┩\n", + "│ 0 │ loss │ CrossEntropyLoss │ 0 │ train │\n", + "│ 1 │ train_metrics │ MetricCollection │ 0 │ train │\n", + "│ 2 │ val_metrics │ MetricCollection │ 0 │ train │\n", + "│ 3 │ test_metrics │ MetricCollection │ 0 │ train │\n", + "│ 4 │ model │ PretrainedViTModel │ 86.4 M │ train │\n", + "│ 5 │ optimizer │ Adam │ 0 │ train │\n", + "└───┴───────────────┴────────────────────┴────────┴───────┘\n", + "\n" + ], + "text/plain": [ + "┏━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━┓\n", + "┃\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mName \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mType \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mParams\u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mMode \u001b[0m\u001b[1;35m \u001b[0m┃\n", + "┡━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━┩\n", + "│\u001b[2m \u001b[0m\u001b[2m0\u001b[0m\u001b[2m \u001b[0m│ loss │ CrossEntropyLoss │ 0 │ train │\n", + "│\u001b[2m \u001b[0m\u001b[2m1\u001b[0m\u001b[2m \u001b[0m│ train_metrics │ MetricCollection │ 0 │ train │\n", + "│\u001b[2m \u001b[0m\u001b[2m2\u001b[0m\u001b[2m \u001b[0m│ val_metrics │ MetricCollection │ 0 │ train │\n", + "│\u001b[2m \u001b[0m\u001b[2m3\u001b[0m\u001b[2m \u001b[0m│ test_metrics │ MetricCollection │ 0 │ train │\n", + "│\u001b[2m \u001b[0m\u001b[2m4\u001b[0m\u001b[2m \u001b[0m│ model │ PretrainedViTModel │ 86.4 M │ train │\n", + "│\u001b[2m \u001b[0m\u001b[2m5\u001b[0m\u001b[2m \u001b[0m│ optimizer │ Adam │ 0 │ train │\n", + "└───┴───────────────┴────────────────────┴────────┴───────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
Trainable params: 86.4 M                                                                                           \n",
+       "Non-trainable params: 0                                                                                            \n",
+       "Total params: 86.4 M                                                                                               \n",
+       "Total estimated model params size (MB): 345                                                                        \n",
+       "Modules in train mode: 10                                                                                          \n",
+       "Modules in eval mode: 227                                                                                          \n",
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\u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtrainer_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 49\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m _TunerExitException:\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/lightning/pytorch/trainer/trainer.py:574\u001b[0m, in \u001b[0;36mTrainer._fit_impl\u001b[0;34m(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)\u001b[0m\n\u001b[1;32m 568\u001b[0m ckpt_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_checkpoint_connector\u001b[38;5;241m.\u001b[39m_select_ckpt_path(\n\u001b[1;32m 569\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mfn,\n\u001b[1;32m 570\u001b[0m ckpt_path,\n\u001b[1;32m 571\u001b[0m model_provided\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m 572\u001b[0m model_connected\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlightning_module \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 573\u001b[0m )\n\u001b[0;32m--> 574\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mckpt_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mckpt_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 576\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mstopped\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/lightning/pytorch/trainer/trainer.py:981\u001b[0m, in \u001b[0;36mTrainer._run\u001b[0;34m(self, model, ckpt_path)\u001b[0m\n\u001b[1;32m 978\u001b[0m \u001b[38;5;66;03m# ----------------------------\u001b[39;00m\n\u001b[1;32m 979\u001b[0m \u001b[38;5;66;03m# RUN THE TRAINER\u001b[39;00m\n\u001b[1;32m 980\u001b[0m \u001b[38;5;66;03m# ----------------------------\u001b[39;00m\n\u001b[0;32m--> 981\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run_stage\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 983\u001b[0m \u001b[38;5;66;03m# ----------------------------\u001b[39;00m\n\u001b[1;32m 984\u001b[0m \u001b[38;5;66;03m# POST-Training CLEAN UP\u001b[39;00m\n\u001b[1;32m 985\u001b[0m \u001b[38;5;66;03m# ----------------------------\u001b[39;00m\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/lightning/pytorch/trainer/trainer.py:1025\u001b[0m, in \u001b[0;36mTrainer._run_stage\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1024\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mautograd\u001b[38;5;241m.\u001b[39mset_detect_anomaly(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_detect_anomaly):\n\u001b[0;32m-> 1025\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_loop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1026\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/lightning/pytorch/loops/fit_loop.py:205\u001b[0m, in \u001b[0;36m_FitLoop.run\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 204\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mon_advance_start()\n\u001b[0;32m--> 205\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madvance\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 206\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mon_advance_end()\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/lightning/pytorch/loops/fit_loop.py:363\u001b[0m, in \u001b[0;36m_FitLoop.advance\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 362\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_data_fetcher \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 363\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mepoch_loop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_data_fetcher\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/lightning/pytorch/loops/training_epoch_loop.py:140\u001b[0m, in \u001b[0;36m_TrainingEpochLoop.run\u001b[0;34m(self, data_fetcher)\u001b[0m\n\u001b[1;32m 139\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 140\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madvance\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata_fetcher\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 141\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mon_advance_end(data_fetcher)\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/lightning/pytorch/loops/training_epoch_loop.py:250\u001b[0m, in \u001b[0;36m_TrainingEpochLoop.advance\u001b[0;34m(self, data_fetcher)\u001b[0m\n\u001b[1;32m 248\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mlightning_module\u001b[38;5;241m.\u001b[39mautomatic_optimization:\n\u001b[1;32m 249\u001b[0m \u001b[38;5;66;03m# in automatic optimization, there can only be one optimizer\u001b[39;00m\n\u001b[0;32m--> 250\u001b[0m batch_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautomatic_optimization\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptimizers\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 251\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/lightning/pytorch/loops/optimization/automatic.py:190\u001b[0m, in \u001b[0;36m_AutomaticOptimization.run\u001b[0;34m(self, optimizer, batch_idx, kwargs)\u001b[0m\n\u001b[1;32m 185\u001b[0m \u001b[38;5;66;03m# ------------------------------\u001b[39;00m\n\u001b[1;32m 186\u001b[0m \u001b[38;5;66;03m# BACKWARD PASS\u001b[39;00m\n\u001b[1;32m 187\u001b[0m \u001b[38;5;66;03m# ------------------------------\u001b[39;00m\n\u001b[1;32m 188\u001b[0m \u001b[38;5;66;03m# gradient update with accumulated gradients\u001b[39;00m\n\u001b[1;32m 189\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 190\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_optimizer_step\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclosure\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 192\u001b[0m result \u001b[38;5;241m=\u001b[39m closure\u001b[38;5;241m.\u001b[39mconsume_result()\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/lightning/pytorch/loops/optimization/automatic.py:268\u001b[0m, in \u001b[0;36m_AutomaticOptimization._optimizer_step\u001b[0;34m(self, batch_idx, train_step_and_backward_closure)\u001b[0m\n\u001b[1;32m 267\u001b[0m \u001b[38;5;66;03m# model hook\u001b[39;00m\n\u001b[0;32m--> 268\u001b[0m \u001b[43mcall\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_lightning_module_hook\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 269\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrainer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 270\u001b[0m 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\u001b[0;36m_call_lightning_module_hook\u001b[0;34m(trainer, hook_name, pl_module, *args, **kwargs)\u001b[0m\n\u001b[1;32m 166\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mprofile(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[LightningModule]\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpl_module\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mhook_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 167\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 169\u001b[0m \u001b[38;5;66;03m# restore current_fx when nested context\u001b[39;00m\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/lightning/pytorch/core/module.py:1306\u001b[0m, in \u001b[0;36mLightningModule.optimizer_step\u001b[0;34m(self, epoch, batch_idx, optimizer, optimizer_closure)\u001b[0m\n\u001b[1;32m 1282\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"Override this method to adjust the default way the :class:`~lightning.pytorch.trainer.trainer.Trainer` calls\u001b[39;00m\n\u001b[1;32m 1283\u001b[0m \u001b[38;5;124;03mthe optimizer.\u001b[39;00m\n\u001b[1;32m 1284\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1304\u001b[0m \n\u001b[1;32m 1305\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m-> 1306\u001b[0m \u001b[43moptimizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43mclosure\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptimizer_closure\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/lightning/pytorch/core/optimizer.py:153\u001b[0m, in \u001b[0;36mLightningOptimizer.step\u001b[0;34m(self, closure, **kwargs)\u001b[0m\n\u001b[1;32m 152\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_strategy \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 153\u001b[0m step_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_strategy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptimizer_step\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_optimizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclosure\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 155\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_on_after_step()\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/lightning/pytorch/strategies/strategy.py:238\u001b[0m, in \u001b[0;36mStrategy.optimizer_step\u001b[0;34m(self, optimizer, closure, model, **kwargs)\u001b[0m\n\u001b[1;32m 237\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(model, pl\u001b[38;5;241m.\u001b[39mLightningModule)\n\u001b[0;32m--> 238\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprecision_plugin\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptimizer_step\u001b[49m\u001b[43m(\u001b[49m\u001b[43moptimizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclosure\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclosure\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/lightning/pytorch/plugins/precision/precision.py:122\u001b[0m, in \u001b[0;36mPrecision.optimizer_step\u001b[0;34m(self, optimizer, model, closure, **kwargs)\u001b[0m\n\u001b[1;32m 121\u001b[0m closure \u001b[38;5;241m=\u001b[39m partial(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_wrap_closure, model, optimizer, closure)\n\u001b[0;32m--> 122\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43moptimizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43mclosure\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclosure\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/torch/optim/optimizer.py:385\u001b[0m, in \u001b[0;36mOptimizer.profile_hook_step..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 381\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m 382\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must return None or a tuple of (new_args, new_kwargs), but got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresult\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 383\u001b[0m )\n\u001b[0;32m--> 385\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 386\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_optimizer_step_code()\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/torch/optim/optimizer.py:76\u001b[0m, in \u001b[0;36m_use_grad_for_differentiable.._use_grad\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 75\u001b[0m torch\u001b[38;5;241m.\u001b[39m_dynamo\u001b[38;5;241m.\u001b[39mgraph_break()\n\u001b[0;32m---> 76\u001b[0m ret \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 77\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/torch/optim/adam.py:146\u001b[0m, in \u001b[0;36mAdam.step\u001b[0;34m(self, closure)\u001b[0m\n\u001b[1;32m 145\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39menable_grad():\n\u001b[0;32m--> 146\u001b[0m loss \u001b[38;5;241m=\u001b[39m \u001b[43mclosure\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 148\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m group \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparam_groups:\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/lightning/pytorch/plugins/precision/precision.py:108\u001b[0m, in \u001b[0;36mPrecision._wrap_closure\u001b[0;34m(self, model, optimizer, closure)\u001b[0m\n\u001b[1;32m 101\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"This double-closure allows makes sure the ``closure`` is executed before the ``on_before_optimizer_step``\u001b[39;00m\n\u001b[1;32m 102\u001b[0m \u001b[38;5;124;03mhook is called.\u001b[39;00m\n\u001b[1;32m 103\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 106\u001b[0m \n\u001b[1;32m 107\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m--> 108\u001b[0m closure_result \u001b[38;5;241m=\u001b[39m \u001b[43mclosure\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 109\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_after_closure(model, optimizer)\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/lightning/pytorch/loops/optimization/automatic.py:144\u001b[0m, in \u001b[0;36mClosure.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 142\u001b[0m \u001b[38;5;129m@override\u001b[39m\n\u001b[1;32m 143\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs: Any, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Optional[Tensor]:\n\u001b[0;32m--> 144\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclosure\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 145\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_result\u001b[38;5;241m.\u001b[39mloss\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/torch/utils/_contextlib.py:115\u001b[0m, in \u001b[0;36mcontext_decorator..decorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m ctx_factory():\n\u001b[0;32m--> 115\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/lightning/pytorch/loops/optimization/automatic.py:138\u001b[0m, in \u001b[0;36mClosure.closure\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 137\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_fn \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m step_output\u001b[38;5;241m.\u001b[39mclosure_loss \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 138\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_backward_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstep_output\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclosure_loss\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 140\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m step_output\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/lightning/pytorch/loops/optimization/automatic.py:239\u001b[0m, in \u001b[0;36m_AutomaticOptimization._make_backward_fn..backward_fn\u001b[0;34m(loss)\u001b[0m\n\u001b[1;32m 238\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mbackward_fn\u001b[39m(loss: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 239\u001b[0m \u001b[43mcall\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_strategy_hook\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrainer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mbackward\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mloss\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptimizer\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/lightning/pytorch/trainer/call.py:319\u001b[0m, in \u001b[0;36m_call_strategy_hook\u001b[0;34m(trainer, hook_name, *args, **kwargs)\u001b[0m\n\u001b[1;32m 318\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mprofile(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[Strategy]\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtrainer\u001b[38;5;241m.\u001b[39mstrategy\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mhook_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 319\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 321\u001b[0m \u001b[38;5;66;03m# restore current_fx when nested context\u001b[39;00m\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/lightning/pytorch/strategies/strategy.py:212\u001b[0m, in \u001b[0;36mStrategy.backward\u001b[0;34m(self, closure_loss, optimizer, *args, **kwargs)\u001b[0m\n\u001b[1;32m 210\u001b[0m closure_loss \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprecision_plugin\u001b[38;5;241m.\u001b[39mpre_backward(closure_loss, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlightning_module)\n\u001b[0;32m--> 212\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprecision_plugin\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\u001b[43mclosure_loss\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlightning_module\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptimizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 214\u001b[0m closure_loss \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprecision_plugin\u001b[38;5;241m.\u001b[39mpost_backward(closure_loss, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlightning_module)\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/lightning/pytorch/plugins/precision/precision.py:72\u001b[0m, in \u001b[0;36mPrecision.backward\u001b[0;34m(self, tensor, model, optimizer, *args, **kwargs)\u001b[0m\n\u001b[1;32m 61\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"Performs the actual backpropagation.\u001b[39;00m\n\u001b[1;32m 62\u001b[0m \n\u001b[1;32m 63\u001b[0m \u001b[38;5;124;03mArgs:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 70\u001b[0m \n\u001b[1;32m 71\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m---> 72\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtensor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/lightning/pytorch/core/module.py:1101\u001b[0m, in \u001b[0;36mLightningModule.backward\u001b[0;34m(self, loss, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1100\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1101\u001b[0m \u001b[43mloss\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/torch/_tensor.py:522\u001b[0m, in \u001b[0;36mTensor.backward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m 513\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(\n\u001b[1;32m 514\u001b[0m Tensor\u001b[38;5;241m.\u001b[39mbackward,\n\u001b[1;32m 515\u001b[0m (\u001b[38;5;28mself\u001b[39m,),\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 520\u001b[0m inputs\u001b[38;5;241m=\u001b[39minputs,\n\u001b[1;32m 521\u001b[0m )\n\u001b[0;32m--> 522\u001b[0m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautograd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 523\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgradient\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs\u001b[49m\n\u001b[1;32m 524\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/torch/autograd/__init__.py:266\u001b[0m, in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m 263\u001b[0m \u001b[38;5;66;03m# The reason we repeat the same comment below is that\u001b[39;00m\n\u001b[1;32m 264\u001b[0m \u001b[38;5;66;03m# some Python versions print out the first line of a multi-line function\u001b[39;00m\n\u001b[1;32m 265\u001b[0m \u001b[38;5;66;03m# calls in the traceback and some print out the last line\u001b[39;00m\n\u001b[0;32m--> 266\u001b[0m \u001b[43mVariable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execution_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_backward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Calls into the C++ engine to run the backward pass\u001b[39;49;00m\n\u001b[1;32m 267\u001b[0m \u001b[43m \u001b[49m\u001b[43mtensors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 268\u001b[0m \u001b[43m \u001b[49m\u001b[43mgrad_tensors_\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 269\u001b[0m \u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 270\u001b[0m \u001b[43m 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3\u001b[0m optimizer\u001b[38;5;241m=\u001b[39mAdam(lr\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2e-5\u001b[39m),\n\u001b[1;32m 4\u001b[0m num_classes\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m10\u001b[39m,\n\u001b[1;32m 5\u001b[0m )\u001b[38;5;241m.\u001b[39mcreate()\n\u001b[1;32m 7\u001b[0m trainer \u001b[38;5;241m=\u001b[39m Trainer(max_epochs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m, accelerator\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcpu\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m----> 9\u001b[0m \u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mclassifier\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_dataloader\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval_dataloader\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/lightning/pytorch/trainer/trainer.py:538\u001b[0m, in \u001b[0;36mTrainer.fit\u001b[0;34m(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)\u001b[0m\n\u001b[1;32m 536\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mstatus \u001b[38;5;241m=\u001b[39m TrainerStatus\u001b[38;5;241m.\u001b[39mRUNNING\n\u001b[1;32m 537\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtraining \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m--> 538\u001b[0m \u001b[43mcall\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_and_handle_interrupt\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 539\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit_impl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_dataloaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval_dataloaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdatamodule\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mckpt_path\u001b[49m\n\u001b[1;32m 540\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/lightning/pytorch/trainer/call.py:64\u001b[0m, in \u001b[0;36m_call_and_handle_interrupt\u001b[0;34m(trainer, trainer_fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(launcher, _SubprocessScriptLauncher):\n\u001b[1;32m 63\u001b[0m launcher\u001b[38;5;241m.\u001b[39mkill(_get_sigkill_signal())\n\u001b[0;32m---> 64\u001b[0m \u001b[43mexit\u001b[49m(\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 66\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exception:\n\u001b[1;32m 67\u001b[0m _interrupt(trainer, exception)\n", + "\u001b[0;31mNameError\u001b[0m: name 'exit' is not defined" + ] + } + ], + "source": [ + "classifier = Classifier(\n", + " model=pretrained_model,\n", + " optimizer=Adam(lr=2e-5),\n", + " num_classes=10,\n", + ").create()\n", + "\n", + "trainer = Trainer(max_epochs=2)\n", + "\n", + "trainer.fit(classifier, train_dataloader, val_dataloader)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After only two epochs, the pre-trained ViT model achieves an accuracy of approximately 98% on the CIFAR-10 dataset, outperforming the performance of the model trained from scratch." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "4a67bb5aaf2a478f8ad668d9bb4fdab1", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Output()" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/841602/Documents/GitHub/Environments/deeplay_env/lib/python3.10/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'test_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=15` in the `DataLoader` to improve performance.\n" + ] + } + ], + "source": [ + "trainer.test(classifier, test_dataloader)" + ] + } + ], + "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": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Ch08_Transformers/ec08_B_ViT/ViT.ipynb b/Ch08_Transformers/ec08_B_ViT/ViT.ipynb deleted file mode 100644 index 3f3ae6b81..000000000 --- a/Ch08_Transformers/ec08_B_ViT/ViT.ipynb +++ /dev/null @@ -1,1349 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### **Introduction to Vision Transformer (ViT)**\n", - "\n", - "The [Vision Transformer](https://arxiv.org/abs/2010.11929) (ViT) represents an innovative breakthrough in how machines interpret images. Developed by Google Research, the ViT draws inspiration from the transformative impact of transformer models in Natural Language Processing (NLP). Traditionally, Convolutional Neural Networks (CNNs) dominated image analysis tasks. However, the introduction of ViT marked a paradigm shift, demonstrating that a model primarily designed for text could excel in visual domains as well. The ViT model has not only outperformed existing CNN-based methods across various image classification benchmarks but also shown remarkable scalability and generalization capabilities. Its success has led to its adoption in several leading-edge applications, including image segmentation in [Segment Anything](https://arxiv.org/abs/2304.02643) (SAM) and multimodal learning in [Contrastive Language-Image Pre-training](https://arxiv.org/pdf/2103.00020.pdf) (CLIP).\n", - "\n", - "This notebook provides an introduction to the Vision Transformer (ViT) model, explaining its architecture and how it can be implemented using PyTorch for image classification tasks.\n", - "\n", - "#### **CIFAR-10 Dataset**\n", - "\n", - "The CIFAR-10 dataset will serve as our primary playground for experimenting with ViT.\n", - "\n", - "Comprising 60,000 images, each 32x32 pixels and in full color, CIFAR-10 is categorized into 10 distinct classes, with each class containing 6,000 images. The dataset is divided into a training set of 50,000 images and a testing set of 10,000 images.\n", - "\n", - "Use the following code snippet to download and prepare the data:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Files already downloaded and verified\n", - "Files already downloaded and verified\n" - ] - } - ], - "source": [ - "from torchvision import transforms, datasets\n", - "\n", - "train_dataset = datasets.CIFAR10(\n", - " root=\"./data\", train=True, download=True, transform=transforms.ToTensor(), # (1)\n", - ")\n", - "test_dataset = datasets.CIFAR10(\n", - " root=\"./data\", train=False, download=True, transform=transforms.ToTensor() # (2)\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This script initializes the CIFAR-10 dataset for training and testing purposes using PyTorch's `datasets.CIFAR10` class for seamless data handling. \n", - "\n", - "**(1)** To initiate the training dataset, the script specifies the root directory for storing the data, sets the `train` parameter to `True`, and enables the download option. Additionally, it uses the `transforms.ToTensor()` method to convert the images into PyTorch tensors.\n", - "\n", - "**(2)** To initialize the testing dataset, similar parameters are used except for the `train` parameter, which is set to `False`." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's visualize images from the CIFAR-10 dataset by defining the function `plot_class_examples`." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "\n", - "def plot_class_examples(dataset, n_images=5):\n", - " \"\"\"\n", - " Plot the first n_images for each class in the dataset.\n", - " \"\"\"\n", - " classes = dataset.classes # (1)\n", - " indices = {c: [] for c in range(len(classes))} # (2)\n", - "\n", - " # Populate indices\n", - " for i, (_, label) in enumerate(dataset): # (3)\n", - " if all(len(v) == n_images for v in indices.values()): # (4)\n", - " break \n", - " if len(indices[label]) < n_images: # (5)\n", - " indices[label].append(i) # (6)\n", - "\n", - " # Plotting\n", - " fig, axes = plt.subplots(len(classes), n_images, figsize=(8, 16), squeeze=False) # (7)\n", - " for i, (c, idx) in enumerate(indices.items()): # (8)\n", - " for j, index in enumerate(idx): # (9)\n", - " img = dataset[index][0].permute(1, 2, 0) # (10)\n", - " ax = axes[i, j]\n", - " ax.imshow(img)\n", - " ax.set(xticks=[], yticks=[])\n", - " if j == 0:\n", - " ax.set_ylabel(classes[c], size=\"large\")\n", - "\n", - " plt.subplots_adjust(wspace=0.05, hspace=0.05)\n", - " plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This function displays the first `n_images` examples for each class from the dataset.\n", - "\n", - "**(1)** The process starts by extracting the classes from the dataset and **(2)** initializing a dictionary to hold indices of images for each class. **(3-4)** Then, as it iterates over the dataset, the function checks whether enough images have been collected for each class. **(5-6)** If a class still needs more images, the current index is added to its list of indices. **(7)** Once the desired number of images for each class is reached, the function prepares a matplotlib figure with a grid of subplots. The subplots are arranged to have one row per class and one column per image. This ensures that the layout supports the visualization of all selected images. \n", - "\n", - "**(8-10)** For each class, the function iterates over its collected image indices, retrieves each image, permutes it for correct display formatting, and plots them in the corresponding subplot cell." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_class_examples(train_dataset, 5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### **Preparing the data**\n", - "\n", - "Preparing the data involves more than just loading it; we also need to ensure that our model trains effectively and generalizes well to unseen data. To this end, the CIFAR-10 dataset, initially comprising only training and testing subsets, requires further partitioning to create a separate validation set.\n", - "\n", - "For this purpose, we implement the `split_train_val` function, which splits the training set into training and validation subsets given a specified validation split ratio. This split allows us to independently evaluate the model's performance and make adjustments as needed without compromising the integrity of the test dataset.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "import torch\n", - "from torch.utils.data import Subset\n", - "\n", - "import copy\n", - "\n", - "def split_train_val(dataset, val_split=0.2):\n", - " \"\"\"\n", - " Splits a dataset into training and validation sets.\n", - " \"\"\"\n", - "\n", - " # Generate random indices\n", - " indices = torch.randperm(len(dataset)).tolist() # (1)\n", - "\n", - " # Calculate split index\n", - " split = int(len(dataset) * val_split) # (2)\n", - "\n", - " # Split indices\n", - " train_indices, val_indices = indices[split:], indices[:split] # (3)\n", - "\n", - " # Create dataset subsets\n", - " train_subset = Subset(dataset, train_indices) # (4)\n", - " val_subset = Subset(copy.deepcopy(dataset), val_indices) # (5)\n", - "\n", - " return train_subset, val_subset" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `split_train_val` function takes a dataset and a validation split ratio as inputs, and returns the training and validation subsets. The function performs the following steps:\n", - "\n", - "**(1)** It generates random indices for the dataset.\n", - "\n", - "**(2)** It calculates the split index based on the validation split ratio.\n", - "\n", - "**(3)** It splits the indices into training and validation indices.\n", - "\n", - "**(4)** It creates the training subset using the `Subset` class, which allows us to create a subset of the dataset using the specified indices.\n", - "\n", - "**(5)** It creates the validation subset using the same method as step 4. To ensure that the training and validation subsets are independent of each other, the function uses the `copy.deepcopy` function." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For this example, we will use a validation split ratio of 0.2, which means that 20% of the training data will be reserved for validation." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "train_dataset, val_dataset = split_train_val(train_dataset, val_split=0.2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To enhance the model's ability to generalize and prevent overfitting, we will apply various data augmentation techniques to the training dataset. " - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "train_transform = transforms.Compose(\n", - " [\n", - " transforms.RandomCrop(32, padding=4), # (1)\n", - " transforms.RandomHorizontalFlip(), # (2) \n", - " transforms.ToTensor(),\n", - " transforms.Normalize(\n", - " [0.49139968, 0.48215841, 0.44653091], [0.24703223, 0.24348513, 0.26158784] # (3)\n", - " ),\n", - " ]\n", - ")\n", - "train_dataset.dataset.transform = train_transform" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "These include **(1)** random cropping and padding, **(2)** horizontal flipping, and **(3)** normalization of the image tensors using the CIFAR-10 training set mean and standard deviation values for each channel." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For the validation dataset, however, we'll restrict the preprocessing to just conversion to tensors and normalization, avoiding augmentation to maintain the validity of the evaluation process." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "val_transform = transforms.Compose(\n", - " [\n", - " transforms.ToTensor(),\n", - " transforms.Normalize(\n", - " [0.49139968, 0.48215841, 0.44653091], [0.24703223, 0.24348513, 0.26158784]\n", - " ),\n", - " ]\n", - ")\n", - "val_dataset.dataset.transform = val_transform" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### **Defining data loaders**" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we will define data loaders that will feed the training, validation, and test data to the model during the training and evaluation processes." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "from torch.utils.data import DataLoader\n", - "\n", - "train_dataloader = DataLoader(train_dataset, batch_size=128, shuffle=True, num_workers=4)\n", - "val_dataloader = DataLoader(val_dataset, batch_size=128, shuffle=False, num_workers=4)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### **Building a Vision Transformer model**" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Having prepared our dataset, we define the Vision Transformer (ViT) model. \n", - "\n", - "**Figure 1** illustrates the ViT architecture. It is built around four pivotal components that work together to process and classify images.\n", - "\n", - "Firstly, **Image Patch Embeddings** play an essential role by slicing the input image into fixed-size, non-overlapping patches. These patches are linearly embedded, creating a series of flattened embeddings that serve as the input sequence for the transformer encoder (**Figure 1a**). To capture global information about the image, an additional learnable class token is integrated with these patch embeddings (**Figure 1b**). This class token, while not directly corresponding to any individual patch, symbolizes the collective image and its interactions with the embeddings, providing a holistic view for classification.\n", - "\n", - "Secondly, the model incorporates **Positional Encodings** to permeate the patch embeddings with spatial context (**Figure 1b**). Unlike sin/cos encoding, these positional encodings are initially randomized and subsequently refined through the learning process, ensuring the model comprehends the layout and position of each patch within the image.\n", - "\n", - "The **Transformer Encoder**, the third component, is the heart of the model (**Figure 1c**). It comprises several layers of transformer blocks that each feature a multi-head self-attention mechanism and a feedforward neural network. This structure allows the model to focus on different parts of the image simultaneously, facilitating a deeper understanding of the visual content.\n", - "\n", - "The **Classification Head** or **Dense Top** is the last component of ViT (**Figure 1d**). It takes the class token output from the transformer encoder and feeds it through a feedforward neural network to predict the image's class label.\n", - "\n", - "\n", - "

\n", - " \n", - "
\n", - " Figure 1. Vision Transformer (ViT) Architecture\n", - "

" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us now instantiate the ViT model" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "from deeplay import ViT \n", - "\n", - "model = ViT(\n", - " in_channels=3, # (1)\n", - " image_size=32, # (2)\n", - " patch_size=4, # (3)\n", - " hidden_features=[384,] * 7, # (4)\n", - " out_features=10, # (5)\n", - " num_heads=12, # (6)\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The Deeplay `ViT` class is responsible for managing the ViT (Vision Transformer) model architecture. The class constructor initializes the model components, including the patch embeddings, positional encodings, transformer encoder, and classification head. `ViT` accepts the following parameters:\n", - "\n", - "**(1)** `in_channels`: This parameter defines the number of input channels in the image. For the CIFAR-10 dataset, the number of channels is 3 (RGB).\n", - "\n", - "**(2)** `image_size`: This parameter defines the size of the input image, which is 32x32 for the CIFAR-10 dataset.\n", - "\n", - "**(3)** `patch_size`: This parameter defines the size of the image patches. By dividing the image into smaller patches, the model can analyze the image in segments and learn local features before integrating them for global understanding. This parameter is set to 4, resulting in a total of 64 patches of size 4x4.\n", - "\n", - "**(4)** `hidden_dim`: This parameter defines the dimension of the hidden layers in the transformer encoder. Seven transformer blocks (or layers) are defined in the model, each with a hidden size of 384 channels.\n", - "\n", - "**(5)** `out_channels`: This parameter defines the number of classes in the dataset, which is 10 for CIFAR-10.\n", - "\n", - "**(6)** `num_heads`: This parameter defines the number of attention heads in the multi-head self-attention mechanism. In this case, it is set to 12.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ViT(\n", - " (patch_embedder): Patchify(\n", - " (layer): Layer[Conv2d](in_channels=3, out_channels=384, kernel_size=4, stride=4)\n", - " (dropout): Layer[Dropout](p=0)\n", - " )\n", - " (positional_embedder): PositionalEmbedding(\n", - " (dropout): Layer[Dropout](p=0)\n", - " )\n", - " (transformer_encoder): TransformerEncoderLayer(\n", - " (blocks): LayerList(\n", - " (0-6): 7 x SequentialBlock(\n", - " (multihead): LayerDropoutSkipNormalization(\n", - " (normalization): Layer[LayerNorm](normalized_shape=384)\n", - " (layer): MultiheadSelfAttention(\n", - " (projection): Layer[Identity]()\n", - " (attention): Layer[MultiheadAttention](embed_dim=384, num_heads=12, batch_first=True)\n", - " )\n", - " (dropout): Layer[Dropout](p=0)\n", - " (skip): Add()\n", - " )\n", - " (feed_forward): LayerDropoutSkipNormalization(\n", - " (normalization): Layer[LayerNorm](normalized_shape=384)\n", - " (layer): MultiLayerPerceptron(\n", - " (blocks): LayerList(\n", - " (0): LayerActivationNormalizationDropout(\n", - " (layer): Layer[Linear](in_features=384, out_features=384)\n", - " (activation): Layer[GELU]()\n", - " (normalization): Layer[Identity](num_features=384)\n", - " (dropout): Layer[Dropout](p=0)\n", - " )\n", - " (1): LayerActivationNormalizationDropout(\n", - " (layer): Layer[Linear](in_features=384, out_features=384)\n", - " (activation): Layer[GELU]()\n", - " (normalization): Layer[Identity](num_features=384)\n", - " (dropout): Layer[Identity]()\n", - " )\n", - " )\n", - " )\n", - " (dropout): Layer[Dropout](p=0)\n", - " (skip): Add()\n", - " )\n", - " )\n", - " )\n", - " )\n", - " (dense_top): MultiLayerPerceptron(\n", - " (blocks): LayerList(\n", - " (0): LayerActivationNormalizationDropout(\n", - " (normalization): Layer[Identity](num_features=192)\n", - " (layer): Layer[Linear](in_features=384, out_features=192)\n", - " (activation): Layer[GELU]()\n", - " (dropout): Layer[Dropout](p=0)\n", - " )\n", - " (1): LayerActivationNormalizationDropout(\n", - " (normalization): Layer[Identity](num_features=96)\n", - " (layer): Layer[Linear](in_features=192, out_features=96)\n", - " (activation): Layer[GELU]()\n", - " (dropout): Layer[Dropout](p=0)\n", - " )\n", - " (2): LayerActivationNormalizationDropout(\n", - " (normalization): Layer[Identity](num_features=10)\n", - " (layer): Layer[Linear](in_features=96, out_features=10)\n", - " (activation): Layer[Identity]()\n", - " (dropout): Layer[Identity]()\n", - " )\n", - " )\n", - " )\n", - ")\n" - ] - } - ], - "source": [ - "print(model)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### **Training the model**\n", - "\n", - "The following create a `Trainer` object to train the model." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will use the Adam optimizer with a learning rate of `1e-3` and weight decay of `1e-5`, and `CrossEntropyLoss` as the loss function." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "from deeplay import Classifier, Adam, Trainer\n", - "\n", - "from lightning.pytorch.callbacks import ModelCheckpoint\n", - "\n", - "classifier = Classifier(\n", - " model=model,\n", - " optimizer=Adam(\n", - " lr=1e-3,\n", - " weight_decay=5e-5,\n", - " betas=(0.9, 0.999),\n", - " ),\n", - " num_classes=10,\n", - ").create()\n", - "\n", - "checkpoint_callback = ModelCheckpoint( # (2)\n", - " monitor=\"valMulticlassAccuracy\",\n", - " mode=\"max\",\n", - " dirpath=\"checkpoint_classifier\",\n", - " filename=\"cifar10-{epoch:02d}-{valMulticlassAccuracy:.2f}\",\n", - " auto_insert_metric_name=False,\n", - ")\n", - "\n", - "trainer = Trainer(max_epochs=100, callbacks=[checkpoint_callback])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "trainer.fit(classifier, train_dataloader, val_dataloader)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Use the following code snippet to visualize the training and validation loss and accuracy." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(
,\n", - " array([,\n", - " ],\n", - " dtype=object))" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n" - ], - "text/plain": [ - "\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "[{'test_loss_epoch': 0.711128830909729,\n", - " 'testMulticlassAccuracy_epoch': 0.7612000107765198}]" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import glob\n", - "import os\n", - "\n", - "best_model_path = glob.glob(os.path.join(\"checkpoint_classifier\", \"*.ckpt\"))\n", - "best_model_path = sorted(best_model_path, key=os.path.getmtime)[-1]\n", - "\n", - "checkpoint = torch.load(best_model_path)\n", - "classifier.load_state_dict(checkpoint[\"state_dict\"])\n", - "\n", - "test_dataset.transform = val_transform\n", - "test_loader = DataLoader(test_dataset, batch_size=128, shuffle=False)\n", - "\n", - "trainer.test(classifier, test_loader)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "However, the ViT model can be further fine-tuned to improve its performance." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### **Improving the model**" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Compared to CNNs, ViT models lack strong inductive biases. Inductive bias refers to the set of assumptions a model makes about the data it trains on to generalize from the training data to unseen data effectively. For instance, CNNs inherently assume spatial hierarchies in images, leveraging local patterns (e.g., edges and textures in early layers) and composing them into higher-order features (like objects) in deeper layers. This architectural innatency towards capturing spatial locality and translation invariance directly supports learning from image data.\n", - "\n", - "Due to the absence of these biases, ViT requires a substantial amount of data to learn effectively. It treats the image as a sequence of patches and learns to interpret these without the innate advantage of assuming spatial correlations, relying instead on the data to dictate these relationships. Consequently, ViT models need larger datasets to achieve their impressive performance, as they must learn from scratch what CNNs get for free through their architecture.\n", - "\n", - "The CIFAR-10 dataset, containing only 60,000 images, is relatively small compared to the massive datasets where ViT models excel. Although 60,000 might seem substantial, it pales in comparison to the vast datasets like ImageNet with over 14 million images, which have been instrumental in training ViTs to their state-of-the-art performance.\n", - "\n", - "[CutMix](https://arxiv.org/abs/1905.04899) is a technique designed to address this challenge, among others. It works by forcing the network to learn local relationships more efficiently, effectively injecting the network with sufficient pathways to learn spatial insights without relying on vast amounts of data." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### **Implementing CutMix**" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "CutMix is a data augmentation strategy that seeks to enhance the model's ability to understand and integrate local features within images, thereby improving its generalization capabilities. Originating from the idea of [Mixup](https://arxiv.org/abs/1710.09412), where two images are blended together by averaging their pixels, CutMix takes this concept further by combining patches from two different images rather than mixing their entire content. This technique not only enriches the dataset but also introduces a more challenging and nuanced learning task for the model.\n", - "\n", - "In a typical CutMix augmentation, a patch from one image is cut and pasted onto another image, and the labels are mixed proportionally to the area of the patches. This process creates a new training example that is a composite of features and labels from two distinct images. For the model, the task then becomes to predict a mixed label, reflecting the composition of the input image. The primary advantage of this method is its ability to force the model to focus on less dominant features of the image, promoting a deeper and more robust learning of spatial relationships and feature representations." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The following class `CutMix` implements the CutMix augmentation." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "\n", - "class CutMix(object):\n", - " def __init__(self, size, beta):\n", - " self.size = size\n", - " self.beta = beta\n", - "\n", - " def __call__(self, batch):\n", - " img, label = batch\n", - " rand_img, rand_label = self._shuffle_minibatch(batch)\n", - " lam = np.random.beta(self.beta, self.beta)\n", - "\n", - " # Clone images to ensure the original batch is not modified\n", - " augmented_img = img.clone()\n", - "\n", - " r_x = np.random.uniform(0, self.size)\n", - " r_y = np.random.uniform(0, self.size)\n", - " r_w = self.size * np.sqrt(1 - lam)\n", - " r_h = self.size * np.sqrt(1 - lam)\n", - "\n", - " x1 = np.clip(int(r_x - r_w // 2), a_min=0, a_max=self.size)\n", - " x2 = np.clip(int(r_x + r_w // 2), a_min=0, a_max=self.size)\n", - " y1 = np.clip(int(r_y - r_h // 2), a_min=0, a_max=self.size)\n", - " y2 = np.clip(int(r_y + r_h // 2), a_min=0, a_max=self.size)\n", - "\n", - " # Replace parts of the cloned images with parts from the randomized images\n", - " augmented_img[:, :, y1:y2, x1:x2] = rand_img[:, :, y1:y2, x1:x2]\n", - "\n", - " lam = 1 - ((x2 - x1) * (y2 - y1) / (self.size**2))\n", - " \n", - " return augmented_img, label, rand_label, lam\n", - "\n", - " def _shuffle_minibatch(self, batch):\n", - " img, label = batch\n", - " rand_idx = torch.randperm(img.size(0))\n", - " rand_img = img[rand_idx]\n", - " rand_label = label[rand_idx]\n", - " return rand_img, rand_label" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's visualize the CutMix augmentation by applying it to a random pair of images from the training dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import random\n", - "from itertools import chain\n", - "\n", - "\n", - "def create_batch(samples):\n", - " images, labels = zip(*samples)\n", - " return torch.stack(images), torch.tensor(labels)\n", - "\n", - "\n", - "def normalize_image(image):\n", - " mean = torch.tensor([0.49139968, 0.48215841, 0.44653091]).view(3, 1, 1)\n", - " std = torch.tensor([0.24703223, 0.24348513, 0.26158784]).view(3, 1, 1)\n", - " return image * std + mean\n", - "\n", - "\n", - "samples = random.choices(train_dataset, k=2) # (1)\n", - "batch = create_batch(samples) # (2)\n", - "\n", - "augmented_img, *_, lam = CutMix(size=32, beta=1.0)(batch) # (3)\n", - "\n", - "fig, axs = plt.subplots(1, 4, figsize=(12, 3)) # (4)\n", - "titles = [\"Original Image 1\", \"Original Image 2\", \"CutMix Image 1\", \"CutMix Image 2\"]\n", - "\n", - "for i, (img, title) in enumerate(zip(chain(batch[0], augmented_img), titles)):\n", - " axs[i].imshow(normalize_image(img).permute(1, 2, 0))\n", - " axs[i].set_title(f\"{title}\\nλ={lam:.2f}\" if \"CutMix\" in title else title)\n", - " axs[i].axis(\"off\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This code snippet **(1)** selects two random images from the dataset `train_dataset`. Next, **(2)** the `create_batch` function is used to create a batch of images and labels from the selected images. **(3)** The `CutMix` class is then applied to the batch to generate the augmented images and labels. Finally, **(4)** the original and augmented images are displayed side by side for comparison.\n", - "\n", - "*Please note that due to possibility of the shuffle operation resulting in the original order, there's a chance that the images might appear identical in certain instances. If this occurs, consider re-running the operation.*" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### **Training with CutMix**" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To train the model with CutMix, we define the `CutMixClassifier` class, which extends the `Classifier` to incorporate the CutMix augmentation." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "import torch.optim as optim\n", - "import warmup_scheduler\n", - "\n", - "\n", - "class CutMixClassifier(Classifier):\n", - " def __init__(self, model, size=32, beta=1.0, **kwargs):\n", - " super().__init__(model, **kwargs)\n", - "\n", - " self.cutmix = CutMix(size=size, beta=beta)\n", - "\n", - " def training_step(self, batch, batch_idx):\n", - "\n", - " augmented_img, labels, rand_labels, lam = self.cutmix(batch) # (1)\n", - "\n", - " output = self.model(augmented_img) # (2)\n", - " loss = self.loss(output, labels) * lam + (1 - lam) * self.loss( # (3)\n", - " output, rand_labels\n", - " )\n", - "\n", - " self.log(\n", - " \"train_loss\", loss, on_step=True, on_epoch=True, prog_bar=True, logger=True\n", - " )\n", - " self.log_metrics(\n", - " \"train\",\n", - " output,\n", - " labels,\n", - " on_step=True,\n", - " on_epoch=True,\n", - " prog_bar=True,\n", - " logger=True,\n", - " )\n", - " return loss\n", - "\n", - " def configure_optimizers(self):\n", - " optimizer = super().configure_optimizers()\n", - "\n", - " base_scheduler = optim.lr_scheduler.CosineAnnealingLR( # (4)\n", - " optimizer, T_max=200, eta_min=1e-5\n", - " )\n", - " scheduler = warmup_scheduler.GradualWarmupScheduler( # (5)\n", - " optimizer, multiplier=1.0, total_epoch=5, after_scheduler=base_scheduler\n", - " )\n", - "\n", - " return {\n", - " \"optimizer\": optimizer,\n", - " \"lr_scheduler\": {\n", - " \"scheduler\": scheduler,\n", - " \"interval\": \"epoch\",\n", - " },\n", - " }" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "During training, **(1)** `CutMixClassifier` first applies CutMix augmentation to the input batch, creating augmented images with their corresponding labels and random labels alongside the mixing coefficient `lam`. **(2)** The model processes the augmented images, and **(3)** the loss is calculated using a combination of the original and random labels weighted by `lam` and `1-lam`, respectively, to reflect the proportion of each image in the augmented mix. \n", - "\n", - "To further enhance the performance of the model, `CutMixClassifier` **(4)** sets up a learning rate schedule starting with a `CosineAnnealingLR` scheduler to adjust the learning rate following a cosine curve, and then **(5)** employs a warm-up phase using `GradualWarmupScheduler`, which gradually increases the learning rate over the first five epochs to the initial learning rate before transitioning to the cosine annealing schedule. This approach optimizes the learning process, improving training stability and model performance." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we proceed to train the model using the `CutMixClassifier` class. \n", - "\n", - "The model is trained for 700 epochs to allow sufficient time for the model to learn from the augmented data and improve its performance." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "model[..., \"attention#-1\"].log_output(\"attention_output\") # (1)\n", - "\n", - "classifier = CutMixClassifier(\n", - " model=model,\n", - " optimizer=Adam(lr=1e-3, weight_decay=5e-5, betas=(0.9, 0.999)),\n", - " num_classes=10,\n", - ").create()\n", - "\n", - "checkpoint_callback = ModelCheckpoint(\n", - " monitor=\"valMulticlassAccuracy\",\n", - " mode=\"max\",\n", - " dirpath=\"checkpoint_cutmix_classifier\",\n", - " filename=\"cifar10-{epoch:02d}-{valMulticlassAccuracy:.2f}\",\n", - " auto_insert_metric_name=False,\n", - ")\n", - "\n", - "trainer = Trainer(max_epochs=700, callbacks=[checkpoint_callback])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**(1)** the `log_output` method is used to log the output of the attention layer in the last transformer block. This will allow us to visualize the attention maps generated by the model during inference for interpretability purposes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "trainer.fit(classifier, train_dataloader, val_dataloader)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that the model has achieved a validation accuracy of approximately 90%, which is a significant improvement over the previous accuracy of 76%." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "89e5390871c1464d88b72701f2d21ea1", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Output()" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
-       "│ testMulticlassAccuracy_epoch       0.8977000117301941      │\n",
-       "│       test_loss_epoch              0.3408765196800232      │\n",
-       "└──────────────────────────────┴──────────────────────────────┘\n",
-       "
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"cell_type": "code", - "execution_count": 91, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from skimage.transform import resize\n", - "\n", - "\n", - "samples = random.choices(test_dataset, k=4)\n", - "images, labels = create_batch(samples)\n", - "\n", - "classifier.model.eval()\n", - "output = classifier(images)\n", - "preds = torch.argmax(output, dim=1)\n", - "\n", - "attention_maps = classifier.logs[\"attention_output\"][1][:, 0, 1:].reshape(4, 8, 8)\n", - "\n", - "\n", - "fig, axs = plt.subplots(2, 4, figsize=(12, 6))\n", - "for i, (img, label, pred, attention_map) in enumerate(zip(images, labels, preds, attention_maps)):\n", - " img = normalize_image(img).detach().permute(1, 2, 0)\n", - " \n", - " axs[0, i].imshow(img)\n", - " axs[0, i].set_title(f\"Label: {test_dataset.classes[label]}\\nPred: {test_dataset.classes[pred]}\")\n", - " axs[0, i].axis(\"off\")\n", - "\n", - " resized_attention_map = resize(attention_map.detach().numpy(), (32, 32), anti_aliasing=True)\n", - " axs[1, i].imshow(img)\n", - " axs[1, i].imshow(resized_attention_map, cmap=\"hot\", alpha=0.5)\n", - " axs[1, i].set_title(\"Attention Map\")\n", - " axs[1, i].axis(\"off\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### **Using a pre-trained ViT model**" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The model achieved a 90% accuracy, which is comparable to the performance levels of CNNs on the CIFAR-10 dataset. However, as the original ViT paper states, this model achieves its best performance when trained on large-scale datasets like ImageNet, which contains millions of images. In such cases, the model can leverage its capacity to learn from vast amounts of data and generalize effectively, outperforming CNNs significantly.\n", - "\n", - "To demonstrate this, we will use a pre-trained ViT model that has already learned rich representations from a diverse range of images in the ImageNet dataset. This model can perform well on a variety of tasks. \n", - "\n", - "The following loads the pre-trained ViT model and wrap it in a Torch Module that appends a classification head." - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": {}, - "outputs": [], - "source": [ - "import torch.nn as nn\n", - "\n", - "from transformers import ViTModel\n", - "\n", - "class PretrainedViTModel(nn.Module):\n", - " def __init__(self, output_channels, pretrained_model=\"google/vit-base-patch16-224-in21k\"):\n", - " super().__init__()\n", - "\n", - " self.backbone = ViTModel.from_pretrained(pretrained_model)\n", - "\n", - " hidden_features = self.backbone.config.hidden_size\n", - " self.classifier = nn.Linear(hidden_features, output_channels)\n", - " \n", - "\n", - " def forward(self, x):\n", - " x = self.backbone(x).last_hidden_state[:, 0]\n", - " return self.classifier(x)\n", - " \n", - "pretrained_model = PretrainedViTModel(output_channels=10)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Images from ImageNet are typically larger than those in CIFAR-10, so we need to resize the images to 224x224 before feeding them to the model. We also normalize the images using the mean and standard deviation values of the ImageNet dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "metadata": {}, - "outputs": [], - "source": [ - "import torchvision.transforms as transforms\n", - "\n", - "from transformers import ViTImageProcessor\n", - "\n", - "\n", - "processor = ViTImageProcessor.from_pretrained(\"google/vit-base-patch16-224-in21k\")\n", - "\n", - "image_mean, image_std = processor.image_mean, processor.image_std\n", - "size = processor.size[\"height\"]\n", - "\n", - "train_transforms = [\n", - " transforms.Resize((size, size)),\n", - " transforms.RandomCrop(size, padding=4), \n", - " transforms.RandomHorizontalFlip(),\n", - " transforms.ToTensor(),\n", - " transforms.Normalize(image_mean, image_std), \n", - "]\n", - "\n", - "val_transforms = [\n", - " transforms.Resize((size, size)),\n", - " transforms.ToTensor(),\n", - " transforms.Normalize(image_mean, image_std),\n", - "]\n", - "\n", - "train_dataset.dataset.transform = transforms.Compose(train_transforms)\n", - "val_dataset.dataset.transform = transforms.Compose(val_transforms)\n", - "test_dataset.transform = transforms.Compose(val_transforms)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will redefine the data loaders to accommodate the resizing and normalization of the images." - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "metadata": {}, - "outputs": [], - "source": [ - "train_dataloader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n", - "val_dataloader = DataLoader(val_dataset, batch_size=32, shuffle=False)\n", - "test_dataloader = DataLoader(test_dataset, batch_size=32, shuffle=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "and train the model using the `Classifier` class." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "classifier = Classifier(\n", - " model=pretrained_model,\n", - " optimizer=Adam(lr=2e-5),\n", - " num_classes=10,\n", - ").create()\n", - "\n", - "trainer = Trainer(max_epochs=2)\n", - "\n", - "trainer.fit(classifier, train_dataloader, val_dataloader)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "After only two epochs, the pre-trained ViT model achieves an accuracy of approximately 98% on the CIFAR-10 dataset, outperforming the performance of the model trained from scratch." - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "72bec59e141040289c6706d7974fbb35", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Output()" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/conda/lib/python3.8/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:441: The 'test_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=63` in the `DataLoader` to improve performance.\n" - ] - }, - { - "data": { - "text/html": [ - "
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-       "│ testMulticlassAccuracy_epoch       0.9825000166893005      │\n",
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