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jblitzar
/
github-python-corpus

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img_trg, example_number)
# store image identifiers & caption
formated_caption, ref_identifier, trg_identifier = data_loader.dataset.get_triplet_info(example_number)
directory = os.path.join(args.heatmap_dir, args.exp_name, str(example_number))
with open(os.path.join(directory, "metadata.txt"), "a") as f:
f.write(f"{example_number}*{formated_caption}*{ref_identifier}*{trg_identifier}\n")
# Clean
del img_src, txt, txt_len, img_trg, ret_caps, data_id
# Iterate
itr += 1
def generate_heatmap_from_single_data(args, model, img_src, txt, txt_len,
img_trg, example_number):
"""
Generate and save heatmaps for a given data example.
Input:
example_number: index of the current data example in a dataloader.
"""
if torch.cuda.is_available():
img_src, img_trg, txt, txt_len = img_src.cuda(), img_trg.cuda(), txt.cuda(), txt_len.cuda()
img_src = img_src.requires_grad_(True)
img_trg = img_trg.requires_grad_(True)
# Forward pass, during which intermediate results are stored in model.hold_results
_ = model.forward_save_intermediary(img_src, img_trg, txt, txt_len) # output scores
# Generate heatmaps for each score, EM & IS
heatmap_from_score(args, model, example_number,
"IS", "A_IS_r", "A_IS_t", img_trg,
img_src=img_src, r_is_involved=True)
heatmap_from_score(args, model, example_number,
"EM", "Tr_m", "A_EM_t", img_trg)
def heatmap_from_score(args, model, example_number, s_name,
query_contrib_name, t_contrib_name,
img_trg, img_src=None, r_is_involved=False):
"""
Generate and save heatmaps for a given data example and score.
The studied score (indicated by s_name) results from the dot product of two
known subresults, one that is query-related (query_contrib_name), and the
other that is target-related (t_contrib_name).
Input:
example_number: index of the current data example in a dataloader.
s_name: score name (EM|IS)
query_contrib_name: name of the query-related subresult that contributes
to the score to study (Tr_m|A_IS_r)
t_contrib_name: name of the target-related subresult that contributes to
the score to study (A_EM_t|A_IS_t)
r_is_involved: whether the reference image is involved in the score.
"""
r_heatmap_tmp = None, None
t_heatmap_tmp = None, None
# get images activations
if r_is_involved:
r_activation = model.hold_results["r_activation"] # size (batch_size, channels, 7, 7)
t_activation = model.hold_results["t_activation"] # size (batch_size, channels, 7, 7)
# find the coeffs that contribute the most to the score
query_contrib = model.hold_results[query_contrib_name]
t_contrib = model.hold_results[t_contrib_name]
main_coeffs = get_main_coeffs(query_contrib, t_contrib)
# produce one heatmap per main coeff for each score
for main_coeff in main_coeffs:
# extract the contribution of the selected output coeff
score_contrib = (query_contrib*t_contrib)[:,main_coeff]
# get pooled gradients across the channels for the given output coeff
if r_is_involved:
r_weights = get_weights(model, score_contrib, r_activation) # size (batch_size, channels)
t_weights = get_weights(model, score_contrib, t_activation) # size (batch_size, channels)
# weight the channels of the activation map with the pooled gradients,
# and add this weighted activation map to the total heatmap, accounting for
# the contribution of the selected output coeff, if only one heatmap is required ;
# otherwise, save the current heatmap
if r_is_involved:
r_heatmap_tmp = (r_activation * r_weights.view(args.batch_size, -1, 1, 1)).sum(dim=1).detach().cpu() # size (batch_size, 7, 7)
t_heatmap_tmp = (t_activation * t_weights.view(args.batch_size, -1, 1, 1)).sum(dim=1).detach().cpu() # size (batch_size, 7, 7)
save_heatmaps(args,
example_number,
f"{s_name}_coeff_{main_coeff}",
round(score_contrib[0].item(), 4),
t_heatmap_tmp,
img_trg,
r_heatmap_tmp,
img_src,